diff --git a/lib/python3.11/site-packages/_distutils_hack/__init__.py b/lib/python3.11/site-packages/_distutils_hack/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94f71b99ecefd27e6cf3fd06fc3c0eef5fd73910 --- /dev/null +++ b/lib/python3.11/site-packages/_distutils_hack/__init__.py @@ -0,0 +1,239 @@ +# don't import any costly modules +import os +import sys + +report_url = ( + "https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml" +) + + +def warn_distutils_present(): + if 'distutils' not in sys.modules: + return + import warnings + + warnings.warn( + "Distutils was imported before Setuptools, but importing Setuptools " + "also replaces the `distutils` module in `sys.modules`. This may lead " + "to undesirable behaviors or errors. To avoid these issues, avoid " + "using distutils directly, ensure that setuptools is installed in the " + "traditional way (e.g. not an editable install), and/or make sure " + "that setuptools is always imported before distutils." + ) + + +def clear_distutils(): + if 'distutils' not in sys.modules: + return + import warnings + + warnings.warn( + "Setuptools is replacing distutils. Support for replacing " + "an already imported distutils is deprecated. In the future, " + "this condition will fail. " + f"Register concerns at {report_url}" + ) + mods = [ + name + for name in sys.modules + if name == "distutils" or name.startswith("distutils.") + ] + for name in mods: + del sys.modules[name] + + +def enabled(): + """ + Allow selection of distutils by environment variable. + """ + which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local') + if which == 'stdlib': + import warnings + + warnings.warn( + "Reliance on distutils from stdlib is deprecated. Users " + "must rely on setuptools to provide the distutils module. " + "Avoid importing distutils or import setuptools first, " + "and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. " + f"Register concerns at {report_url}" + ) + return which == 'local' + + +def ensure_local_distutils(): + import importlib + + clear_distutils() + + # With the DistutilsMetaFinder in place, + # perform an import to cause distutils to be + # loaded from setuptools._distutils. Ref #2906. + with shim(): + importlib.import_module('distutils') + + # check that submodules load as expected + core = importlib.import_module('distutils.core') + assert '_distutils' in core.__file__, core.__file__ + assert 'setuptools._distutils.log' not in sys.modules + + +def do_override(): + """ + Ensure that the local copy of distutils is preferred over stdlib. + + See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401 + for more motivation. + """ + if enabled(): + warn_distutils_present() + ensure_local_distutils() + + +class _TrivialRe: + def __init__(self, *patterns) -> None: + self._patterns = patterns + + def match(self, string): + return all(pat in string for pat in self._patterns) + + +class DistutilsMetaFinder: + def find_spec(self, fullname, path, target=None): + # optimization: only consider top level modules and those + # found in the CPython test suite. + if path is not None and not fullname.startswith('test.'): + return None + + method_name = 'spec_for_{fullname}'.format(**locals()) + method = getattr(self, method_name, lambda: None) + return method() + + def spec_for_distutils(self): + if self.is_cpython(): + return None + + import importlib + import importlib.abc + import importlib.util + + try: + mod = importlib.import_module('setuptools._distutils') + except Exception: + # There are a couple of cases where setuptools._distutils + # may not be present: + # - An older Setuptools without a local distutils is + # taking precedence. Ref #2957. + # - Path manipulation during sitecustomize removes + # setuptools from the path but only after the hook + # has been loaded. Ref #2980. + # In either case, fall back to stdlib behavior. + return None + + class DistutilsLoader(importlib.abc.Loader): + def create_module(self, spec): + mod.__name__ = 'distutils' + return mod + + def exec_module(self, module): + pass + + return importlib.util.spec_from_loader( + 'distutils', DistutilsLoader(), origin=mod.__file__ + ) + + @staticmethod + def is_cpython(): + """ + Suppress supplying distutils for CPython (build and tests). + Ref #2965 and #3007. + """ + return os.path.isfile('pybuilddir.txt') + + def spec_for_pip(self): + """ + Ensure stdlib distutils when running under pip. + See pypa/pip#8761 for rationale. + """ + if sys.version_info >= (3, 12) or self.pip_imported_during_build(): + return + clear_distutils() + self.spec_for_distutils = lambda: None + + @classmethod + def pip_imported_during_build(cls): + """ + Detect if pip is being imported in a build script. Ref #2355. + """ + import traceback + + return any( + cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None) + ) + + @staticmethod + def frame_file_is_setup(frame): + """ + Return True if the indicated frame suggests a setup.py file. + """ + # some frames may not have __file__ (#2940) + return frame.f_globals.get('__file__', '').endswith('setup.py') + + def spec_for_sensitive_tests(self): + """ + Ensure stdlib distutils when running select tests under CPython. + + python/cpython#91169 + """ + clear_distutils() + self.spec_for_distutils = lambda: None + + sensitive_tests = ( + [ + 'test.test_distutils', + 'test.test_peg_generator', + 'test.test_importlib', + ] + if sys.version_info < (3, 10) + else [ + 'test.test_distutils', + ] + ) + + +for name in DistutilsMetaFinder.sensitive_tests: + setattr( + DistutilsMetaFinder, + f'spec_for_{name}', + DistutilsMetaFinder.spec_for_sensitive_tests, + ) + + +DISTUTILS_FINDER = DistutilsMetaFinder() + + +def add_shim(): + DISTUTILS_FINDER in sys.meta_path or insert_shim() + + +class shim: + def __enter__(self) -> None: + insert_shim() + + def __exit__(self, exc: object, value: object, tb: object) -> None: + _remove_shim() + + +def insert_shim(): + sys.meta_path.insert(0, DISTUTILS_FINDER) + + +def _remove_shim(): + try: + sys.meta_path.remove(DISTUTILS_FINDER) + except ValueError: + pass + + +if sys.version_info < (3, 12): + # DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632) + remove_shim = _remove_shim diff --git a/lib/python3.11/site-packages/_distutils_hack/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/_distutils_hack/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..526dabf1f6b6bd2342ad19ce61a4b82a1aa87603 Binary files /dev/null and b/lib/python3.11/site-packages/_distutils_hack/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/_distutils_hack/__pycache__/override.cpython-311.pyc b/lib/python3.11/site-packages/_distutils_hack/__pycache__/override.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc235e38daa761da067eaf5c85e43b7cbc370a20 Binary files /dev/null and b/lib/python3.11/site-packages/_distutils_hack/__pycache__/override.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/_distutils_hack/override.py b/lib/python3.11/site-packages/_distutils_hack/override.py new file mode 100644 index 0000000000000000000000000000000000000000..2cc433a4a55e3b41fa31089918fb62096092f89f --- /dev/null +++ b/lib/python3.11/site-packages/_distutils_hack/override.py @@ -0,0 +1 @@ +__import__('_distutils_hack').do_override() diff --git a/lib/python3.11/site-packages/pkg_resources/__init__.py b/lib/python3.11/site-packages/pkg_resources/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0c70593955a5687d49d9120fac0cf5ea13e85cf7 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/__init__.py @@ -0,0 +1,3715 @@ +""" +Package resource API +-------------------- + +A resource is a logical file contained within a package, or a logical +subdirectory thereof. The package resource API expects resource names +to have their path parts separated with ``/``, *not* whatever the local +path separator is. Do not use os.path operations to manipulate resource +names being passed into the API. + +The package resource API is designed to work with normal filesystem packages, +.egg files, and unpacked .egg files. It can also work in a limited way with +.zip files and with custom PEP 302 loaders that support the ``get_data()`` +method. + +This module is deprecated. Users are directed to :mod:`importlib.resources`, +:mod:`importlib.metadata` and :pypi:`packaging` instead. +""" + +from __future__ import annotations + +import sys + +if sys.version_info < (3, 9): # noqa: UP036 # Check for unsupported versions + raise RuntimeError("Python 3.9 or later is required") + +import _imp +import collections +import email.parser +import errno +import functools +import importlib +import importlib.abc +import importlib.machinery +import inspect +import io +import ntpath +import operator +import os +import pkgutil +import platform +import plistlib +import posixpath +import re +import stat +import tempfile +import textwrap +import time +import types +import warnings +import zipfile +import zipimport +from collections.abc import Iterable, Iterator, Mapping, MutableSequence +from pkgutil import get_importer +from typing import ( + TYPE_CHECKING, + Any, + BinaryIO, + Callable, + Literal, + NamedTuple, + NoReturn, + Protocol, + TypeVar, + Union, + overload, +) + +sys.path.extend(((vendor_path := os.path.join(os.path.dirname(os.path.dirname(__file__)), 'setuptools', '_vendor')) not in sys.path) * [vendor_path]) # fmt: skip +# workaround for #4476 +sys.modules.pop('backports', None) + +# capture these to bypass sandboxing +from os import open as os_open, utime # isort: skip +from os.path import isdir, split # isort: skip + +try: + from os import mkdir, rename, unlink + + WRITE_SUPPORT = True +except ImportError: + # no write support, probably under GAE + WRITE_SUPPORT = False + +import packaging.markers +import packaging.requirements +import packaging.specifiers +import packaging.utils +import packaging.version +from jaraco.text import drop_comment, join_continuation, yield_lines +from platformdirs import user_cache_dir as _user_cache_dir + +if TYPE_CHECKING: + from _typeshed import BytesPath, StrOrBytesPath, StrPath + from _typeshed.importlib import LoaderProtocol + from typing_extensions import Self, TypeAlias + +warnings.warn( + "pkg_resources is deprecated as an API. " + "See https://setuptools.pypa.io/en/latest/pkg_resources.html. " + "The pkg_resources package is slated for removal as early as " + "2025-11-30. Refrain from using this package or pin to " + "Setuptools<81.", + UserWarning, + stacklevel=2, +) + +_T = TypeVar("_T") +_DistributionT = TypeVar("_DistributionT", bound="Distribution") +# Type aliases +_NestedStr: TypeAlias = Union[str, Iterable[Union[str, Iterable["_NestedStr"]]]] +_StrictInstallerType: TypeAlias = Callable[["Requirement"], "_DistributionT"] +_InstallerType: TypeAlias = Callable[["Requirement"], Union["Distribution", None]] +_PkgReqType: TypeAlias = Union[str, "Requirement"] +_EPDistType: TypeAlias = Union["Distribution", _PkgReqType] +_MetadataType: TypeAlias = Union["IResourceProvider", None] +_ResolvedEntryPoint: TypeAlias = Any # Can be any attribute in the module +_ResourceStream: TypeAlias = Any # TODO / Incomplete: A readable file-like object +# Any object works, but let's indicate we expect something like a module (optionally has __loader__ or __file__) +_ModuleLike: TypeAlias = Union[object, types.ModuleType] +# Any: Should be _ModuleLike but we end up with issues where _ModuleLike doesn't have _ZipLoaderModule's __loader__ +_ProviderFactoryType: TypeAlias = Callable[[Any], "IResourceProvider"] +_DistFinderType: TypeAlias = Callable[[_T, str, bool], Iterable["Distribution"]] +_NSHandlerType: TypeAlias = Callable[[_T, str, str, types.ModuleType], Union[str, None]] +_AdapterT = TypeVar( + "_AdapterT", _DistFinderType[Any], _ProviderFactoryType, _NSHandlerType[Any] +) + + +class _ZipLoaderModule(Protocol): + __loader__: zipimport.zipimporter + + +_PEP440_FALLBACK = re.compile( + r"^v?(?P(?:[0-9]+!)?[0-9]+(?:\.[0-9]+)*)", re.IGNORECASE +) + + +class PEP440Warning(RuntimeWarning): + """ + Used when there is an issue with a version or specifier not complying with + PEP 440. + """ + + +parse_version = packaging.version.Version + +_state_vars: dict[str, str] = {} + + +def _declare_state(vartype: str, varname: str, initial_value: _T) -> _T: + _state_vars[varname] = vartype + return initial_value + + +def __getstate__() -> dict[str, Any]: + state = {} + g = globals() + for k, v in _state_vars.items(): + state[k] = g['_sget_' + v](g[k]) + return state + + +def __setstate__(state: dict[str, Any]) -> dict[str, Any]: + g = globals() + for k, v in state.items(): + g['_sset_' + _state_vars[k]](k, g[k], v) + return state + + +def _sget_dict(val): + return val.copy() + + +def _sset_dict(key, ob, state) -> None: + ob.clear() + ob.update(state) + + +def _sget_object(val): + return val.__getstate__() + + +def _sset_object(key, ob, state) -> None: + ob.__setstate__(state) + + +_sget_none = _sset_none = lambda *args: None + + +def get_supported_platform(): + """Return this platform's maximum compatible version. + + distutils.util.get_platform() normally reports the minimum version + of macOS that would be required to *use* extensions produced by + distutils. But what we want when checking compatibility is to know the + version of macOS that we are *running*. To allow usage of packages that + explicitly require a newer version of macOS, we must also know the + current version of the OS. + + If this condition occurs for any other platform with a version in its + platform strings, this function should be extended accordingly. + """ + plat = get_build_platform() + m = macosVersionString.match(plat) + if m is not None and sys.platform == "darwin": + try: + major_minor = '.'.join(_macos_vers()[:2]) + build = m.group(3) + plat = f'macosx-{major_minor}-{build}' + except ValueError: + # not macOS + pass + return plat + + +__all__ = [ + # Basic resource access and distribution/entry point discovery + 'require', + 'run_script', + 'get_provider', + 'get_distribution', + 'load_entry_point', + 'get_entry_map', + 'get_entry_info', + 'iter_entry_points', + 'resource_string', + 'resource_stream', + 'resource_filename', + 'resource_listdir', + 'resource_exists', + 'resource_isdir', + # Environmental control + 'declare_namespace', + 'working_set', + 'add_activation_listener', + 'find_distributions', + 'set_extraction_path', + 'cleanup_resources', + 'get_default_cache', + # Primary implementation classes + 'Environment', + 'WorkingSet', + 'ResourceManager', + 'Distribution', + 'Requirement', + 'EntryPoint', + # Exceptions + 'ResolutionError', + 'VersionConflict', + 'DistributionNotFound', + 'UnknownExtra', + 'ExtractionError', + # Warnings + 'PEP440Warning', + # Parsing functions and string utilities + 'parse_requirements', + 'parse_version', + 'safe_name', + 'safe_version', + 'get_platform', + 'compatible_platforms', + 'yield_lines', + 'split_sections', + 'safe_extra', + 'to_filename', + 'invalid_marker', + 'evaluate_marker', + # filesystem utilities + 'ensure_directory', + 'normalize_path', + # Distribution "precedence" constants + 'EGG_DIST', + 'BINARY_DIST', + 'SOURCE_DIST', + 'CHECKOUT_DIST', + 'DEVELOP_DIST', + # "Provider" interfaces, implementations, and registration/lookup APIs + 'IMetadataProvider', + 'IResourceProvider', + 'FileMetadata', + 'PathMetadata', + 'EggMetadata', + 'EmptyProvider', + 'empty_provider', + 'NullProvider', + 'EggProvider', + 'DefaultProvider', + 'ZipProvider', + 'register_finder', + 'register_namespace_handler', + 'register_loader_type', + 'fixup_namespace_packages', + 'get_importer', + # Warnings + 'PkgResourcesDeprecationWarning', + # Deprecated/backward compatibility only + 'run_main', + 'AvailableDistributions', +] + + +class ResolutionError(Exception): + """Abstract base for dependency resolution errors""" + + def __repr__(self) -> str: + return self.__class__.__name__ + repr(self.args) + + +class VersionConflict(ResolutionError): + """ + An already-installed version conflicts with the requested version. + + Should be initialized with the installed Distribution and the requested + Requirement. + """ + + _template = "{self.dist} is installed but {self.req} is required" + + @property + def dist(self) -> Distribution: + return self.args[0] + + @property + def req(self) -> Requirement: + return self.args[1] + + def report(self): + return self._template.format(**locals()) + + def with_context( + self, required_by: set[Distribution | str] + ) -> Self | ContextualVersionConflict: + """ + If required_by is non-empty, return a version of self that is a + ContextualVersionConflict. + """ + if not required_by: + return self + args = self.args + (required_by,) + return ContextualVersionConflict(*args) + + +class ContextualVersionConflict(VersionConflict): + """ + A VersionConflict that accepts a third parameter, the set of the + requirements that required the installed Distribution. + """ + + _template = VersionConflict._template + ' by {self.required_by}' + + @property + def required_by(self) -> set[str]: + return self.args[2] + + +class DistributionNotFound(ResolutionError): + """A requested distribution was not found""" + + _template = ( + "The '{self.req}' distribution was not found " + "and is required by {self.requirers_str}" + ) + + @property + def req(self) -> Requirement: + return self.args[0] + + @property + def requirers(self) -> set[str] | None: + return self.args[1] + + @property + def requirers_str(self): + if not self.requirers: + return 'the application' + return ', '.join(self.requirers) + + def report(self): + return self._template.format(**locals()) + + def __str__(self) -> str: + return self.report() + + +class UnknownExtra(ResolutionError): + """Distribution doesn't have an "extra feature" of the given name""" + + +_provider_factories: dict[type[_ModuleLike], _ProviderFactoryType] = {} + +PY_MAJOR = f'{sys.version_info.major}.{sys.version_info.minor}' +EGG_DIST = 3 +BINARY_DIST = 2 +SOURCE_DIST = 1 +CHECKOUT_DIST = 0 +DEVELOP_DIST = -1 + + +def register_loader_type( + loader_type: type[_ModuleLike], provider_factory: _ProviderFactoryType +) -> None: + """Register `provider_factory` to make providers for `loader_type` + + `loader_type` is the type or class of a PEP 302 ``module.__loader__``, + and `provider_factory` is a function that, passed a *module* object, + returns an ``IResourceProvider`` for that module. + """ + _provider_factories[loader_type] = provider_factory + + +@overload +def get_provider(moduleOrReq: str) -> IResourceProvider: ... +@overload +def get_provider(moduleOrReq: Requirement) -> Distribution: ... +def get_provider(moduleOrReq: str | Requirement) -> IResourceProvider | Distribution: + """Return an IResourceProvider for the named module or requirement""" + if isinstance(moduleOrReq, Requirement): + return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0] + try: + module = sys.modules[moduleOrReq] + except KeyError: + __import__(moduleOrReq) + module = sys.modules[moduleOrReq] + loader = getattr(module, '__loader__', None) + return _find_adapter(_provider_factories, loader)(module) + + +@functools.cache +def _macos_vers(): + version = platform.mac_ver()[0] + # fallback for MacPorts + if version == '': + plist = '/System/Library/CoreServices/SystemVersion.plist' + if os.path.exists(plist): + with open(plist, 'rb') as fh: + plist_content = plistlib.load(fh) + if 'ProductVersion' in plist_content: + version = plist_content['ProductVersion'] + return version.split('.') + + +def _macos_arch(machine): + return {'PowerPC': 'ppc', 'Power_Macintosh': 'ppc'}.get(machine, machine) + + +def get_build_platform(): + """Return this platform's string for platform-specific distributions""" + from sysconfig import get_platform + + plat = get_platform() + if sys.platform == "darwin" and not plat.startswith('macosx-'): + try: + version = _macos_vers() + machine = _macos_arch(os.uname()[4].replace(" ", "_")) + return f"macosx-{version[0]}.{version[1]}-{machine}" + except ValueError: + # if someone is running a non-Mac darwin system, this will fall + # through to the default implementation + pass + return plat + + +macosVersionString = re.compile(r"macosx-(\d+)\.(\d+)-(.*)") +darwinVersionString = re.compile(r"darwin-(\d+)\.(\d+)\.(\d+)-(.*)") +# XXX backward compat +get_platform = get_build_platform + + +def compatible_platforms(provided: str | None, required: str | None) -> bool: + """Can code for the `provided` platform run on the `required` platform? + + Returns true if either platform is ``None``, or the platforms are equal. + + XXX Needs compatibility checks for Linux and other unixy OSes. + """ + if provided is None or required is None or provided == required: + # easy case + return True + + # macOS special cases + reqMac = macosVersionString.match(required) + if reqMac: + provMac = macosVersionString.match(provided) + + # is this a Mac package? + if not provMac: + # this is backwards compatibility for packages built before + # setuptools 0.6. All packages built after this point will + # use the new macOS designation. + provDarwin = darwinVersionString.match(provided) + if provDarwin: + dversion = int(provDarwin.group(1)) + macosversion = f"{reqMac.group(1)}.{reqMac.group(2)}" + if ( + dversion == 7 + and macosversion >= "10.3" + or dversion == 8 + and macosversion >= "10.4" + ): + return True + # egg isn't macOS or legacy darwin + return False + + # are they the same major version and machine type? + if provMac.group(1) != reqMac.group(1) or provMac.group(3) != reqMac.group(3): + return False + + # is the required OS major update >= the provided one? + if int(provMac.group(2)) > int(reqMac.group(2)): + return False + + return True + + # XXX Linux and other platforms' special cases should go here + return False + + +@overload +def get_distribution(dist: _DistributionT) -> _DistributionT: ... +@overload +def get_distribution(dist: _PkgReqType) -> Distribution: ... +def get_distribution(dist: Distribution | _PkgReqType) -> Distribution: + """Return a current distribution object for a Requirement or string""" + if isinstance(dist, str): + dist = Requirement.parse(dist) + if isinstance(dist, Requirement): + dist = get_provider(dist) + if not isinstance(dist, Distribution): + raise TypeError("Expected str, Requirement, or Distribution", dist) + return dist + + +def load_entry_point(dist: _EPDistType, group: str, name: str) -> _ResolvedEntryPoint: + """Return `name` entry point of `group` for `dist` or raise ImportError""" + return get_distribution(dist).load_entry_point(group, name) + + +@overload +def get_entry_map( + dist: _EPDistType, group: None = None +) -> dict[str, dict[str, EntryPoint]]: ... +@overload +def get_entry_map(dist: _EPDistType, group: str) -> dict[str, EntryPoint]: ... +def get_entry_map(dist: _EPDistType, group: str | None = None): + """Return the entry point map for `group`, or the full entry map""" + return get_distribution(dist).get_entry_map(group) + + +def get_entry_info(dist: _EPDistType, group: str, name: str) -> EntryPoint | None: + """Return the EntryPoint object for `group`+`name`, or ``None``""" + return get_distribution(dist).get_entry_info(group, name) + + +class IMetadataProvider(Protocol): + def has_metadata(self, name: str) -> bool: + """Does the package's distribution contain the named metadata?""" + ... + + def get_metadata(self, name: str) -> str: + """The named metadata resource as a string""" + ... + + def get_metadata_lines(self, name: str) -> Iterator[str]: + """Yield named metadata resource as list of non-blank non-comment lines + + Leading and trailing whitespace is stripped from each line, and lines + with ``#`` as the first non-blank character are omitted.""" + ... + + def metadata_isdir(self, name: str) -> bool: + """Is the named metadata a directory? (like ``os.path.isdir()``)""" + ... + + def metadata_listdir(self, name: str) -> list[str]: + """List of metadata names in the directory (like ``os.listdir()``)""" + ... + + def run_script(self, script_name: str, namespace: dict[str, Any]) -> None: + """Execute the named script in the supplied namespace dictionary""" + ... + + +class IResourceProvider(IMetadataProvider, Protocol): + """An object that provides access to package resources""" + + def get_resource_filename( + self, manager: ResourceManager, resource_name: str + ) -> str: + """Return a true filesystem path for `resource_name` + + `manager` must be a ``ResourceManager``""" + ... + + def get_resource_stream( + self, manager: ResourceManager, resource_name: str + ) -> _ResourceStream: + """Return a readable file-like object for `resource_name` + + `manager` must be a ``ResourceManager``""" + ... + + def get_resource_string( + self, manager: ResourceManager, resource_name: str + ) -> bytes: + """Return the contents of `resource_name` as :obj:`bytes` + + `manager` must be a ``ResourceManager``""" + ... + + def has_resource(self, resource_name: str) -> bool: + """Does the package contain the named resource?""" + ... + + def resource_isdir(self, resource_name: str) -> bool: + """Is the named resource a directory? (like ``os.path.isdir()``)""" + ... + + def resource_listdir(self, resource_name: str) -> list[str]: + """List of resource names in the directory (like ``os.listdir()``)""" + ... + + +class WorkingSet: + """A collection of active distributions on sys.path (or a similar list)""" + + def __init__(self, entries: Iterable[str] | None = None) -> None: + """Create working set from list of path entries (default=sys.path)""" + self.entries: list[str] = [] + self.entry_keys: dict[str | None, list[str]] = {} + self.by_key: dict[str, Distribution] = {} + self.normalized_to_canonical_keys: dict[str, str] = {} + self.callbacks: list[Callable[[Distribution], object]] = [] + + if entries is None: + entries = sys.path + + for entry in entries: + self.add_entry(entry) + + @classmethod + def _build_master(cls): + """ + Prepare the master working set. + """ + ws = cls() + try: + from __main__ import __requires__ + except ImportError: + # The main program does not list any requirements + return ws + + # ensure the requirements are met + try: + ws.require(__requires__) + except VersionConflict: + return cls._build_from_requirements(__requires__) + + return ws + + @classmethod + def _build_from_requirements(cls, req_spec): + """ + Build a working set from a requirement spec. Rewrites sys.path. + """ + # try it without defaults already on sys.path + # by starting with an empty path + ws = cls([]) + reqs = parse_requirements(req_spec) + dists = ws.resolve(reqs, Environment()) + for dist in dists: + ws.add(dist) + + # add any missing entries from sys.path + for entry in sys.path: + if entry not in ws.entries: + ws.add_entry(entry) + + # then copy back to sys.path + sys.path[:] = ws.entries + return ws + + def add_entry(self, entry: str) -> None: + """Add a path item to ``.entries``, finding any distributions on it + + ``find_distributions(entry, True)`` is used to find distributions + corresponding to the path entry, and they are added. `entry` is + always appended to ``.entries``, even if it is already present. + (This is because ``sys.path`` can contain the same value more than + once, and the ``.entries`` of the ``sys.path`` WorkingSet should always + equal ``sys.path``.) + """ + self.entry_keys.setdefault(entry, []) + self.entries.append(entry) + for dist in find_distributions(entry, True): + self.add(dist, entry, False) + + def __contains__(self, dist: Distribution) -> bool: + """True if `dist` is the active distribution for its project""" + return self.by_key.get(dist.key) == dist + + def find(self, req: Requirement) -> Distribution | None: + """Find a distribution matching requirement `req` + + If there is an active distribution for the requested project, this + returns it as long as it meets the version requirement specified by + `req`. But, if there is an active distribution for the project and it + does *not* meet the `req` requirement, ``VersionConflict`` is raised. + If there is no active distribution for the requested project, ``None`` + is returned. + """ + dist: Distribution | None = None + + candidates = ( + req.key, + self.normalized_to_canonical_keys.get(req.key), + safe_name(req.key).replace(".", "-"), + ) + + for candidate in filter(None, candidates): + dist = self.by_key.get(candidate) + if dist: + req.key = candidate + break + + if dist is not None and dist not in req: + # XXX add more info + raise VersionConflict(dist, req) + return dist + + def iter_entry_points( + self, group: str, name: str | None = None + ) -> Iterator[EntryPoint]: + """Yield entry point objects from `group` matching `name` + + If `name` is None, yields all entry points in `group` from all + distributions in the working set, otherwise only ones matching + both `group` and `name` are yielded (in distribution order). + """ + return ( + entry + for dist in self + for entry in dist.get_entry_map(group).values() + if name is None or name == entry.name + ) + + def run_script(self, requires: str, script_name: str) -> None: + """Locate distribution for `requires` and run `script_name` script""" + ns = sys._getframe(1).f_globals + name = ns['__name__'] + ns.clear() + ns['__name__'] = name + self.require(requires)[0].run_script(script_name, ns) + + def __iter__(self) -> Iterator[Distribution]: + """Yield distributions for non-duplicate projects in the working set + + The yield order is the order in which the items' path entries were + added to the working set. + """ + seen = set() + for item in self.entries: + if item not in self.entry_keys: + # workaround a cache issue + continue + + for key in self.entry_keys[item]: + if key not in seen: + seen.add(key) + yield self.by_key[key] + + def add( + self, + dist: Distribution, + entry: str | None = None, + insert: bool = True, + replace: bool = False, + ) -> None: + """Add `dist` to working set, associated with `entry` + + If `entry` is unspecified, it defaults to the ``.location`` of `dist`. + On exit from this routine, `entry` is added to the end of the working + set's ``.entries`` (if it wasn't already present). + + `dist` is only added to the working set if it's for a project that + doesn't already have a distribution in the set, unless `replace=True`. + If it's added, any callbacks registered with the ``subscribe()`` method + will be called. + """ + if insert: + dist.insert_on(self.entries, entry, replace=replace) + + if entry is None: + entry = dist.location + keys = self.entry_keys.setdefault(entry, []) + keys2 = self.entry_keys.setdefault(dist.location, []) + if not replace and dist.key in self.by_key: + # ignore hidden distros + return + + self.by_key[dist.key] = dist + normalized_name = packaging.utils.canonicalize_name(dist.key) + self.normalized_to_canonical_keys[normalized_name] = dist.key + if dist.key not in keys: + keys.append(dist.key) + if dist.key not in keys2: + keys2.append(dist.key) + self._added_new(dist) + + @overload + def resolve( + self, + requirements: Iterable[Requirement], + env: Environment | None, + installer: _StrictInstallerType[_DistributionT], + replace_conflicting: bool = False, + extras: tuple[str, ...] | None = None, + ) -> list[_DistributionT]: ... + @overload + def resolve( + self, + requirements: Iterable[Requirement], + env: Environment | None = None, + *, + installer: _StrictInstallerType[_DistributionT], + replace_conflicting: bool = False, + extras: tuple[str, ...] | None = None, + ) -> list[_DistributionT]: ... + @overload + def resolve( + self, + requirements: Iterable[Requirement], + env: Environment | None = None, + installer: _InstallerType | None = None, + replace_conflicting: bool = False, + extras: tuple[str, ...] | None = None, + ) -> list[Distribution]: ... + def resolve( + self, + requirements: Iterable[Requirement], + env: Environment | None = None, + installer: _InstallerType | None | _StrictInstallerType[_DistributionT] = None, + replace_conflicting: bool = False, + extras: tuple[str, ...] | None = None, + ) -> list[Distribution] | list[_DistributionT]: + """List all distributions needed to (recursively) meet `requirements` + + `requirements` must be a sequence of ``Requirement`` objects. `env`, + if supplied, should be an ``Environment`` instance. If + not supplied, it defaults to all distributions available within any + entry or distribution in the working set. `installer`, if supplied, + will be invoked with each requirement that cannot be met by an + already-installed distribution; it should return a ``Distribution`` or + ``None``. + + Unless `replace_conflicting=True`, raises a VersionConflict exception + if + any requirements are found on the path that have the correct name but + the wrong version. Otherwise, if an `installer` is supplied it will be + invoked to obtain the correct version of the requirement and activate + it. + + `extras` is a list of the extras to be used with these requirements. + This is important because extra requirements may look like `my_req; + extra = "my_extra"`, which would otherwise be interpreted as a purely + optional requirement. Instead, we want to be able to assert that these + requirements are truly required. + """ + + # set up the stack + requirements = list(requirements)[::-1] + # set of processed requirements + processed = set() + # key -> dist + best: dict[str, Distribution] = {} + to_activate: list[Distribution] = [] + + req_extras = _ReqExtras() + + # Mapping of requirement to set of distributions that required it; + # useful for reporting info about conflicts. + required_by = collections.defaultdict[Requirement, set[str]](set) + + while requirements: + # process dependencies breadth-first + req = requirements.pop(0) + if req in processed: + # Ignore cyclic or redundant dependencies + continue + + if not req_extras.markers_pass(req, extras): + continue + + dist = self._resolve_dist( + req, best, replace_conflicting, env, installer, required_by, to_activate + ) + + # push the new requirements onto the stack + new_requirements = dist.requires(req.extras)[::-1] + requirements.extend(new_requirements) + + # Register the new requirements needed by req + for new_requirement in new_requirements: + required_by[new_requirement].add(req.project_name) + req_extras[new_requirement] = req.extras + + processed.add(req) + + # return list of distros to activate + return to_activate + + def _resolve_dist( + self, req, best, replace_conflicting, env, installer, required_by, to_activate + ) -> Distribution: + dist = best.get(req.key) + if dist is None: + # Find the best distribution and add it to the map + dist = self.by_key.get(req.key) + if dist is None or (dist not in req and replace_conflicting): + ws = self + if env is None: + if dist is None: + env = Environment(self.entries) + else: + # Use an empty environment and workingset to avoid + # any further conflicts with the conflicting + # distribution + env = Environment([]) + ws = WorkingSet([]) + dist = best[req.key] = env.best_match( + req, ws, installer, replace_conflicting=replace_conflicting + ) + if dist is None: + requirers = required_by.get(req, None) + raise DistributionNotFound(req, requirers) + to_activate.append(dist) + if dist not in req: + # Oops, the "best" so far conflicts with a dependency + dependent_req = required_by[req] + raise VersionConflict(dist, req).with_context(dependent_req) + return dist + + @overload + def find_plugins( + self, + plugin_env: Environment, + full_env: Environment | None, + installer: _StrictInstallerType[_DistributionT], + fallback: bool = True, + ) -> tuple[list[_DistributionT], dict[Distribution, Exception]]: ... + @overload + def find_plugins( + self, + plugin_env: Environment, + full_env: Environment | None = None, + *, + installer: _StrictInstallerType[_DistributionT], + fallback: bool = True, + ) -> tuple[list[_DistributionT], dict[Distribution, Exception]]: ... + @overload + def find_plugins( + self, + plugin_env: Environment, + full_env: Environment | None = None, + installer: _InstallerType | None = None, + fallback: bool = True, + ) -> tuple[list[Distribution], dict[Distribution, Exception]]: ... + def find_plugins( + self, + plugin_env: Environment, + full_env: Environment | None = None, + installer: _InstallerType | None | _StrictInstallerType[_DistributionT] = None, + fallback: bool = True, + ) -> tuple[ + list[Distribution] | list[_DistributionT], + dict[Distribution, Exception], + ]: + """Find all activatable distributions in `plugin_env` + + Example usage:: + + distributions, errors = working_set.find_plugins( + Environment(plugin_dirlist) + ) + # add plugins+libs to sys.path + map(working_set.add, distributions) + # display errors + print('Could not load', errors) + + The `plugin_env` should be an ``Environment`` instance that contains + only distributions that are in the project's "plugin directory" or + directories. The `full_env`, if supplied, should be an ``Environment`` + contains all currently-available distributions. If `full_env` is not + supplied, one is created automatically from the ``WorkingSet`` this + method is called on, which will typically mean that every directory on + ``sys.path`` will be scanned for distributions. + + `installer` is a standard installer callback as used by the + ``resolve()`` method. The `fallback` flag indicates whether we should + attempt to resolve older versions of a plugin if the newest version + cannot be resolved. + + This method returns a 2-tuple: (`distributions`, `error_info`), where + `distributions` is a list of the distributions found in `plugin_env` + that were loadable, along with any other distributions that are needed + to resolve their dependencies. `error_info` is a dictionary mapping + unloadable plugin distributions to an exception instance describing the + error that occurred. Usually this will be a ``DistributionNotFound`` or + ``VersionConflict`` instance. + """ + + plugin_projects = list(plugin_env) + # scan project names in alphabetic order + plugin_projects.sort() + + error_info: dict[Distribution, Exception] = {} + distributions: dict[Distribution, Exception | None] = {} + + if full_env is None: + env = Environment(self.entries) + env += plugin_env + else: + env = full_env + plugin_env + + shadow_set = self.__class__([]) + # put all our entries in shadow_set + list(map(shadow_set.add, self)) + + for project_name in plugin_projects: + for dist in plugin_env[project_name]: + req = [dist.as_requirement()] + + try: + resolvees = shadow_set.resolve(req, env, installer) + + except ResolutionError as v: + # save error info + error_info[dist] = v + if fallback: + # try the next older version of project + continue + else: + # give up on this project, keep going + break + + else: + list(map(shadow_set.add, resolvees)) + distributions.update(dict.fromkeys(resolvees)) + + # success, no need to try any more versions of this project + break + + sorted_distributions = list(distributions) + sorted_distributions.sort() + + return sorted_distributions, error_info + + def require(self, *requirements: _NestedStr) -> list[Distribution]: + """Ensure that distributions matching `requirements` are activated + + `requirements` must be a string or a (possibly-nested) sequence + thereof, specifying the distributions and versions required. The + return value is a sequence of the distributions that needed to be + activated to fulfill the requirements; all relevant distributions are + included, even if they were already activated in this working set. + """ + needed = self.resolve(parse_requirements(requirements)) + + for dist in needed: + self.add(dist) + + return needed + + def subscribe( + self, callback: Callable[[Distribution], object], existing: bool = True + ) -> None: + """Invoke `callback` for all distributions + + If `existing=True` (default), + call on all existing ones, as well. + """ + if callback in self.callbacks: + return + self.callbacks.append(callback) + if not existing: + return + for dist in self: + callback(dist) + + def _added_new(self, dist) -> None: + for callback in self.callbacks: + callback(dist) + + def __getstate__( + self, + ) -> tuple[ + list[str], + dict[str | None, list[str]], + dict[str, Distribution], + dict[str, str], + list[Callable[[Distribution], object]], + ]: + return ( + self.entries[:], + self.entry_keys.copy(), + self.by_key.copy(), + self.normalized_to_canonical_keys.copy(), + self.callbacks[:], + ) + + def __setstate__(self, e_k_b_n_c) -> None: + entries, keys, by_key, normalized_to_canonical_keys, callbacks = e_k_b_n_c + self.entries = entries[:] + self.entry_keys = keys.copy() + self.by_key = by_key.copy() + self.normalized_to_canonical_keys = normalized_to_canonical_keys.copy() + self.callbacks = callbacks[:] + + +class _ReqExtras(dict["Requirement", tuple[str, ...]]): + """ + Map each requirement to the extras that demanded it. + """ + + def markers_pass(self, req: Requirement, extras: tuple[str, ...] | None = None): + """ + Evaluate markers for req against each extra that + demanded it. + + Return False if the req has a marker and fails + evaluation. Otherwise, return True. + """ + return not req.marker or any( + req.marker.evaluate({'extra': extra}) + for extra in self.get(req, ()) + (extras or ("",)) + ) + + +class Environment: + """Searchable snapshot of distributions on a search path""" + + def __init__( + self, + search_path: Iterable[str] | None = None, + platform: str | None = get_supported_platform(), + python: str | None = PY_MAJOR, + ) -> None: + """Snapshot distributions available on a search path + + Any distributions found on `search_path` are added to the environment. + `search_path` should be a sequence of ``sys.path`` items. If not + supplied, ``sys.path`` is used. + + `platform` is an optional string specifying the name of the platform + that platform-specific distributions must be compatible with. If + unspecified, it defaults to the current platform. `python` is an + optional string naming the desired version of Python (e.g. ``'3.6'``); + it defaults to the current version. + + You may explicitly set `platform` (and/or `python`) to ``None`` if you + wish to map *all* distributions, not just those compatible with the + running platform or Python version. + """ + self._distmap: dict[str, list[Distribution]] = {} + self.platform = platform + self.python = python + self.scan(search_path) + + def can_add(self, dist: Distribution) -> bool: + """Is distribution `dist` acceptable for this environment? + + The distribution must match the platform and python version + requirements specified when this environment was created, or False + is returned. + """ + py_compat = ( + self.python is None + or dist.py_version is None + or dist.py_version == self.python + ) + return py_compat and compatible_platforms(dist.platform, self.platform) + + def remove(self, dist: Distribution) -> None: + """Remove `dist` from the environment""" + self._distmap[dist.key].remove(dist) + + def scan(self, search_path: Iterable[str] | None = None) -> None: + """Scan `search_path` for distributions usable in this environment + + Any distributions found are added to the environment. + `search_path` should be a sequence of ``sys.path`` items. If not + supplied, ``sys.path`` is used. Only distributions conforming to + the platform/python version defined at initialization are added. + """ + if search_path is None: + search_path = sys.path + + for item in search_path: + for dist in find_distributions(item): + self.add(dist) + + def __getitem__(self, project_name: str) -> list[Distribution]: + """Return a newest-to-oldest list of distributions for `project_name` + + Uses case-insensitive `project_name` comparison, assuming all the + project's distributions use their project's name converted to all + lowercase as their key. + + """ + distribution_key = project_name.lower() + return self._distmap.get(distribution_key, []) + + def add(self, dist: Distribution) -> None: + """Add `dist` if we ``can_add()`` it and it has not already been added""" + if self.can_add(dist) and dist.has_version(): + dists = self._distmap.setdefault(dist.key, []) + if dist not in dists: + dists.append(dist) + dists.sort(key=operator.attrgetter('hashcmp'), reverse=True) + + @overload + def best_match( + self, + req: Requirement, + working_set: WorkingSet, + installer: _StrictInstallerType[_DistributionT], + replace_conflicting: bool = False, + ) -> _DistributionT: ... + @overload + def best_match( + self, + req: Requirement, + working_set: WorkingSet, + installer: _InstallerType | None = None, + replace_conflicting: bool = False, + ) -> Distribution | None: ... + def best_match( + self, + req: Requirement, + working_set: WorkingSet, + installer: _InstallerType | None | _StrictInstallerType[_DistributionT] = None, + replace_conflicting: bool = False, + ) -> Distribution | None: + """Find distribution best matching `req` and usable on `working_set` + + This calls the ``find(req)`` method of the `working_set` to see if a + suitable distribution is already active. (This may raise + ``VersionConflict`` if an unsuitable version of the project is already + active in the specified `working_set`.) If a suitable distribution + isn't active, this method returns the newest distribution in the + environment that meets the ``Requirement`` in `req`. If no suitable + distribution is found, and `installer` is supplied, then the result of + calling the environment's ``obtain(req, installer)`` method will be + returned. + """ + try: + dist = working_set.find(req) + except VersionConflict: + if not replace_conflicting: + raise + dist = None + if dist is not None: + return dist + for dist in self[req.key]: + if dist in req: + return dist + # try to download/install + return self.obtain(req, installer) + + @overload + def obtain( + self, + requirement: Requirement, + installer: _StrictInstallerType[_DistributionT], + ) -> _DistributionT: ... + @overload + def obtain( + self, + requirement: Requirement, + installer: Callable[[Requirement], None] | None = None, + ) -> None: ... + @overload + def obtain( + self, + requirement: Requirement, + installer: _InstallerType | None = None, + ) -> Distribution | None: ... + def obtain( + self, + requirement: Requirement, + installer: Callable[[Requirement], None] + | _InstallerType + | None + | _StrictInstallerType[_DistributionT] = None, + ) -> Distribution | None: + """Obtain a distribution matching `requirement` (e.g. via download) + + Obtain a distro that matches requirement (e.g. via download). In the + base ``Environment`` class, this routine just returns + ``installer(requirement)``, unless `installer` is None, in which case + None is returned instead. This method is a hook that allows subclasses + to attempt other ways of obtaining a distribution before falling back + to the `installer` argument.""" + return installer(requirement) if installer else None + + def __iter__(self) -> Iterator[str]: + """Yield the unique project names of the available distributions""" + for key in self._distmap.keys(): + if self[key]: + yield key + + def __iadd__(self, other: Distribution | Environment) -> Self: + """In-place addition of a distribution or environment""" + if isinstance(other, Distribution): + self.add(other) + elif isinstance(other, Environment): + for project in other: + for dist in other[project]: + self.add(dist) + else: + raise TypeError(f"Can't add {other!r} to environment") + return self + + def __add__(self, other: Distribution | Environment) -> Self: + """Add an environment or distribution to an environment""" + new = self.__class__([], platform=None, python=None) + for env in self, other: + new += env + return new + + +# XXX backward compatibility +AvailableDistributions = Environment + + +class ExtractionError(RuntimeError): + """An error occurred extracting a resource + + The following attributes are available from instances of this exception: + + manager + The resource manager that raised this exception + + cache_path + The base directory for resource extraction + + original_error + The exception instance that caused extraction to fail + """ + + manager: ResourceManager + cache_path: str + original_error: BaseException | None + + +class ResourceManager: + """Manage resource extraction and packages""" + + extraction_path: str | None = None + + def __init__(self) -> None: + # acts like a set + self.cached_files: dict[str, Literal[True]] = {} + + def resource_exists( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> bool: + """Does the named resource exist?""" + return get_provider(package_or_requirement).has_resource(resource_name) + + def resource_isdir( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> bool: + """Is the named resource an existing directory?""" + return get_provider(package_or_requirement).resource_isdir(resource_name) + + def resource_filename( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> str: + """Return a true filesystem path for specified resource""" + return get_provider(package_or_requirement).get_resource_filename( + self, resource_name + ) + + def resource_stream( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> _ResourceStream: + """Return a readable file-like object for specified resource""" + return get_provider(package_or_requirement).get_resource_stream( + self, resource_name + ) + + def resource_string( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> bytes: + """Return specified resource as :obj:`bytes`""" + return get_provider(package_or_requirement).get_resource_string( + self, resource_name + ) + + def resource_listdir( + self, package_or_requirement: _PkgReqType, resource_name: str + ) -> list[str]: + """List the contents of the named resource directory""" + return get_provider(package_or_requirement).resource_listdir(resource_name) + + def extraction_error(self) -> NoReturn: + """Give an error message for problems extracting file(s)""" + + old_exc = sys.exc_info()[1] + cache_path = self.extraction_path or get_default_cache() + + tmpl = textwrap.dedent( + """ + Can't extract file(s) to egg cache + + The following error occurred while trying to extract file(s) + to the Python egg cache: + + {old_exc} + + The Python egg cache directory is currently set to: + + {cache_path} + + Perhaps your account does not have write access to this directory? + You can change the cache directory by setting the PYTHON_EGG_CACHE + environment variable to point to an accessible directory. + """ + ).lstrip() + err = ExtractionError(tmpl.format(**locals())) + err.manager = self + err.cache_path = cache_path + err.original_error = old_exc + raise err + + def get_cache_path(self, archive_name: str, names: Iterable[StrPath] = ()) -> str: + """Return absolute location in cache for `archive_name` and `names` + + The parent directory of the resulting path will be created if it does + not already exist. `archive_name` should be the base filename of the + enclosing egg (which may not be the name of the enclosing zipfile!), + including its ".egg" extension. `names`, if provided, should be a + sequence of path name parts "under" the egg's extraction location. + + This method should only be called by resource providers that need to + obtain an extraction location, and only for names they intend to + extract, as it tracks the generated names for possible cleanup later. + """ + extract_path = self.extraction_path or get_default_cache() + target_path = os.path.join(extract_path, archive_name + '-tmp', *names) + try: + _bypass_ensure_directory(target_path) + except Exception: + self.extraction_error() + + self._warn_unsafe_extraction_path(extract_path) + + self.cached_files[target_path] = True + return target_path + + @staticmethod + def _warn_unsafe_extraction_path(path) -> None: + """ + If the default extraction path is overridden and set to an insecure + location, such as /tmp, it opens up an opportunity for an attacker to + replace an extracted file with an unauthorized payload. Warn the user + if a known insecure location is used. + + See Distribute #375 for more details. + """ + if os.name == 'nt' and not path.startswith(os.environ['windir']): + # On Windows, permissions are generally restrictive by default + # and temp directories are not writable by other users, so + # bypass the warning. + return + mode = os.stat(path).st_mode + if mode & stat.S_IWOTH or mode & stat.S_IWGRP: + msg = ( + "Extraction path is writable by group/others " + "and vulnerable to attack when " + "used with get_resource_filename ({path}). " + "Consider a more secure " + "location (set with .set_extraction_path or the " + "PYTHON_EGG_CACHE environment variable)." + ).format(**locals()) + warnings.warn(msg, UserWarning) + + def postprocess(self, tempname: StrOrBytesPath, filename: StrOrBytesPath) -> None: + """Perform any platform-specific postprocessing of `tempname` + + This is where Mac header rewrites should be done; other platforms don't + have anything special they should do. + + Resource providers should call this method ONLY after successfully + extracting a compressed resource. They must NOT call it on resources + that are already in the filesystem. + + `tempname` is the current (temporary) name of the file, and `filename` + is the name it will be renamed to by the caller after this routine + returns. + """ + + if os.name == 'posix': + # Make the resource executable + mode = ((os.stat(tempname).st_mode) | 0o555) & 0o7777 + os.chmod(tempname, mode) + + def set_extraction_path(self, path: str) -> None: + """Set the base path where resources will be extracted to, if needed. + + If you do not call this routine before any extractions take place, the + path defaults to the return value of ``get_default_cache()``. (Which + is based on the ``PYTHON_EGG_CACHE`` environment variable, with various + platform-specific fallbacks. See that routine's documentation for more + details.) + + Resources are extracted to subdirectories of this path based upon + information given by the ``IResourceProvider``. You may set this to a + temporary directory, but then you must call ``cleanup_resources()`` to + delete the extracted files when done. There is no guarantee that + ``cleanup_resources()`` will be able to remove all extracted files. + + (Note: you may not change the extraction path for a given resource + manager once resources have been extracted, unless you first call + ``cleanup_resources()``.) + """ + if self.cached_files: + raise ValueError("Can't change extraction path, files already extracted") + + self.extraction_path = path + + def cleanup_resources(self, force: bool = False) -> list[str]: + """ + Delete all extracted resource files and directories, returning a list + of the file and directory names that could not be successfully removed. + This function does not have any concurrency protection, so it should + generally only be called when the extraction path is a temporary + directory exclusive to a single process. This method is not + automatically called; you must call it explicitly or register it as an + ``atexit`` function if you wish to ensure cleanup of a temporary + directory used for extractions. + """ + # XXX + return [] + + +def get_default_cache() -> str: + """ + Return the ``PYTHON_EGG_CACHE`` environment variable + or a platform-relevant user cache dir for an app + named "Python-Eggs". + """ + return os.environ.get('PYTHON_EGG_CACHE') or _user_cache_dir(appname='Python-Eggs') + + +def safe_name(name: str) -> str: + """Convert an arbitrary string to a standard distribution name + + Any runs of non-alphanumeric/. characters are replaced with a single '-'. + """ + return re.sub('[^A-Za-z0-9.]+', '-', name) + + +def safe_version(version: str) -> str: + """ + Convert an arbitrary string to a standard version string + """ + try: + # normalize the version + return str(packaging.version.Version(version)) + except packaging.version.InvalidVersion: + version = version.replace(' ', '.') + return re.sub('[^A-Za-z0-9.]+', '-', version) + + +def _forgiving_version(version) -> str: + """Fallback when ``safe_version`` is not safe enough + >>> parse_version(_forgiving_version('0.23ubuntu1')) + + >>> parse_version(_forgiving_version('0.23-')) + + >>> parse_version(_forgiving_version('0.-_')) + + >>> parse_version(_forgiving_version('42.+?1')) + + >>> parse_version(_forgiving_version('hello world')) + + """ + version = version.replace(' ', '.') + match = _PEP440_FALLBACK.search(version) + if match: + safe = match["safe"] + rest = version[len(safe) :] + else: + safe = "0" + rest = version + local = f"sanitized.{_safe_segment(rest)}".strip(".") + return f"{safe}.dev0+{local}" + + +def _safe_segment(segment): + """Convert an arbitrary string into a safe segment""" + segment = re.sub('[^A-Za-z0-9.]+', '-', segment) + segment = re.sub('-[^A-Za-z0-9]+', '-', segment) + return re.sub(r'\.[^A-Za-z0-9]+', '.', segment).strip(".-") + + +def safe_extra(extra: str) -> str: + """Convert an arbitrary string to a standard 'extra' name + + Any runs of non-alphanumeric characters are replaced with a single '_', + and the result is always lowercased. + """ + return re.sub('[^A-Za-z0-9.-]+', '_', extra).lower() + + +def to_filename(name: str) -> str: + """Convert a project or version name to its filename-escaped form + + Any '-' characters are currently replaced with '_'. + """ + return name.replace('-', '_') + + +def invalid_marker(text: str) -> SyntaxError | Literal[False]: + """ + Validate text as a PEP 508 environment marker; return an exception + if invalid or False otherwise. + """ + try: + evaluate_marker(text) + except SyntaxError as e: + e.filename = None + e.lineno = None + return e + return False + + +def evaluate_marker(text: str, extra: str | None = None) -> bool: + """ + Evaluate a PEP 508 environment marker. + Return a boolean indicating the marker result in this environment. + Raise SyntaxError if marker is invalid. + + This implementation uses the 'pyparsing' module. + """ + try: + marker = packaging.markers.Marker(text) + return marker.evaluate() + except packaging.markers.InvalidMarker as e: + raise SyntaxError(e) from e + + +class NullProvider: + """Try to implement resources and metadata for arbitrary PEP 302 loaders""" + + egg_name: str | None = None + egg_info: str | None = None + loader: LoaderProtocol | None = None + + def __init__(self, module: _ModuleLike) -> None: + self.loader = getattr(module, '__loader__', None) + self.module_path = os.path.dirname(getattr(module, '__file__', '')) + + def get_resource_filename( + self, manager: ResourceManager, resource_name: str + ) -> str: + return self._fn(self.module_path, resource_name) + + def get_resource_stream( + self, manager: ResourceManager, resource_name: str + ) -> BinaryIO: + return io.BytesIO(self.get_resource_string(manager, resource_name)) + + def get_resource_string( + self, manager: ResourceManager, resource_name: str + ) -> bytes: + return self._get(self._fn(self.module_path, resource_name)) + + def has_resource(self, resource_name: str) -> bool: + return self._has(self._fn(self.module_path, resource_name)) + + def _get_metadata_path(self, name): + return self._fn(self.egg_info, name) + + def has_metadata(self, name: str) -> bool: + if not self.egg_info: + return False + + path = self._get_metadata_path(name) + return self._has(path) + + def get_metadata(self, name: str) -> str: + if not self.egg_info: + return "" + path = self._get_metadata_path(name) + value = self._get(path) + try: + return value.decode('utf-8') + except UnicodeDecodeError as exc: + # Include the path in the error message to simplify + # troubleshooting, and without changing the exception type. + exc.reason += f' in {name} file at path: {path}' + raise + + def get_metadata_lines(self, name: str) -> Iterator[str]: + return yield_lines(self.get_metadata(name)) + + def resource_isdir(self, resource_name: str) -> bool: + return self._isdir(self._fn(self.module_path, resource_name)) + + def metadata_isdir(self, name: str) -> bool: + return bool(self.egg_info and self._isdir(self._fn(self.egg_info, name))) + + def resource_listdir(self, resource_name: str) -> list[str]: + return self._listdir(self._fn(self.module_path, resource_name)) + + def metadata_listdir(self, name: str) -> list[str]: + if self.egg_info: + return self._listdir(self._fn(self.egg_info, name)) + return [] + + def run_script(self, script_name: str, namespace: dict[str, Any]) -> None: + script = 'scripts/' + script_name + if not self.has_metadata(script): + raise ResolutionError( + "Script {script!r} not found in metadata at {self.egg_info!r}".format( + **locals() + ), + ) + + script_text = self.get_metadata(script).replace('\r\n', '\n') + script_text = script_text.replace('\r', '\n') + script_filename = self._fn(self.egg_info, script) + namespace['__file__'] = script_filename + if os.path.exists(script_filename): + source = _read_utf8_with_fallback(script_filename) + code = compile(source, script_filename, 'exec') + exec(code, namespace, namespace) + else: + from linecache import cache + + cache[script_filename] = ( + len(script_text), + 0, + script_text.split('\n'), + script_filename, + ) + script_code = compile(script_text, script_filename, 'exec') + exec(script_code, namespace, namespace) + + def _has(self, path) -> bool: + raise NotImplementedError( + "Can't perform this operation for unregistered loader type" + ) + + def _isdir(self, path) -> bool: + raise NotImplementedError( + "Can't perform this operation for unregistered loader type" + ) + + def _listdir(self, path) -> list[str]: + raise NotImplementedError( + "Can't perform this operation for unregistered loader type" + ) + + def _fn(self, base: str | None, resource_name: str): + if base is None: + raise TypeError( + "`base` parameter in `_fn` is `None`. Either override this method or check the parameter first." + ) + self._validate_resource_path(resource_name) + if resource_name: + return os.path.join(base, *resource_name.split('/')) + return base + + @staticmethod + def _validate_resource_path(path) -> None: + """ + Validate the resource paths according to the docs. + https://setuptools.pypa.io/en/latest/pkg_resources.html#basic-resource-access + + >>> warned = getfixture('recwarn') + >>> warnings.simplefilter('always') + >>> vrp = NullProvider._validate_resource_path + >>> vrp('foo/bar.txt') + >>> bool(warned) + False + >>> vrp('../foo/bar.txt') + >>> bool(warned) + True + >>> warned.clear() + >>> vrp('/foo/bar.txt') + >>> bool(warned) + True + >>> vrp('foo/../../bar.txt') + >>> bool(warned) + True + >>> warned.clear() + >>> vrp('foo/f../bar.txt') + >>> bool(warned) + False + + Windows path separators are straight-up disallowed. + >>> vrp(r'\\foo/bar.txt') + Traceback (most recent call last): + ... + ValueError: Use of .. or absolute path in a resource path \ +is not allowed. + + >>> vrp(r'C:\\foo/bar.txt') + Traceback (most recent call last): + ... + ValueError: Use of .. or absolute path in a resource path \ +is not allowed. + + Blank values are allowed + + >>> vrp('') + >>> bool(warned) + False + + Non-string values are not. + + >>> vrp(None) + Traceback (most recent call last): + ... + AttributeError: ... + """ + invalid = ( + os.path.pardir in path.split(posixpath.sep) + or posixpath.isabs(path) + or ntpath.isabs(path) + or path.startswith("\\") + ) + if not invalid: + return + + msg = "Use of .. or absolute path in a resource path is not allowed." + + # Aggressively disallow Windows absolute paths + if (path.startswith("\\") or ntpath.isabs(path)) and not posixpath.isabs(path): + raise ValueError(msg) + + # for compatibility, warn; in future + # raise ValueError(msg) + issue_warning( + msg[:-1] + " and will raise exceptions in a future release.", + DeprecationWarning, + ) + + def _get(self, path) -> bytes: + if hasattr(self.loader, 'get_data') and self.loader: + # Already checked get_data exists + return self.loader.get_data(path) # type: ignore[attr-defined] + raise NotImplementedError( + "Can't perform this operation for loaders without 'get_data()'" + ) + + +register_loader_type(object, NullProvider) + + +def _parents(path): + """ + yield all parents of path including path + """ + last = None + while path != last: + yield path + last = path + path, _ = os.path.split(path) + + +class EggProvider(NullProvider): + """Provider based on a virtual filesystem""" + + def __init__(self, module: _ModuleLike) -> None: + super().__init__(module) + self._setup_prefix() + + def _setup_prefix(self): + # Assume that metadata may be nested inside a "basket" + # of multiple eggs and use module_path instead of .archive. + eggs = filter(_is_egg_path, _parents(self.module_path)) + egg = next(eggs, None) + egg and self._set_egg(egg) + + def _set_egg(self, path: str) -> None: + self.egg_name = os.path.basename(path) + self.egg_info = os.path.join(path, 'EGG-INFO') + self.egg_root = path + + +class DefaultProvider(EggProvider): + """Provides access to package resources in the filesystem""" + + def _has(self, path) -> bool: + return os.path.exists(path) + + def _isdir(self, path) -> bool: + return os.path.isdir(path) + + def _listdir(self, path): + return os.listdir(path) + + def get_resource_stream( + self, manager: object, resource_name: str + ) -> io.BufferedReader: + return open(self._fn(self.module_path, resource_name), 'rb') + + def _get(self, path) -> bytes: + with open(path, 'rb') as stream: + return stream.read() + + @classmethod + def _register(cls) -> None: + loader_names = ( + 'SourceFileLoader', + 'SourcelessFileLoader', + ) + for name in loader_names: + loader_cls = getattr(importlib.machinery, name, type(None)) + register_loader_type(loader_cls, cls) + + +DefaultProvider._register() + + +class EmptyProvider(NullProvider): + """Provider that returns nothing for all requests""" + + # A special case, we don't want all Providers inheriting from NullProvider to have a potentially None module_path + module_path: str | None = None # type: ignore[assignment] + + _isdir = _has = lambda self, path: False + + def _get(self, path) -> bytes: + return b'' + + def _listdir(self, path): + return [] + + def __init__(self) -> None: + pass + + +empty_provider = EmptyProvider() + + +class ZipManifests(dict[str, "MemoizedZipManifests.manifest_mod"]): + """ + zip manifest builder + """ + + # `path` could be `StrPath | IO[bytes]` but that violates the LSP for `MemoizedZipManifests.load` + @classmethod + def build(cls, path: str) -> dict[str, zipfile.ZipInfo]: + """ + Build a dictionary similar to the zipimport directory + caches, except instead of tuples, store ZipInfo objects. + + Use a platform-specific path separator (os.sep) for the path keys + for compatibility with pypy on Windows. + """ + with zipfile.ZipFile(path) as zfile: + items = ( + ( + name.replace('/', os.sep), + zfile.getinfo(name), + ) + for name in zfile.namelist() + ) + return dict(items) + + load = build + + +class MemoizedZipManifests(ZipManifests): + """ + Memoized zipfile manifests. + """ + + class manifest_mod(NamedTuple): + manifest: dict[str, zipfile.ZipInfo] + mtime: float + + def load(self, path: str) -> dict[str, zipfile.ZipInfo]: # type: ignore[override] # ZipManifests.load is a classmethod + """ + Load a manifest at path or return a suitable manifest already loaded. + """ + path = os.path.normpath(path) + mtime = os.stat(path).st_mtime + + if path not in self or self[path].mtime != mtime: + manifest = self.build(path) + self[path] = self.manifest_mod(manifest, mtime) + + return self[path].manifest + + +class ZipProvider(EggProvider): + """Resource support for zips and eggs""" + + eagers: list[str] | None = None + _zip_manifests = MemoizedZipManifests() + # ZipProvider's loader should always be a zipimporter or equivalent + loader: zipimport.zipimporter + + def __init__(self, module: _ZipLoaderModule) -> None: + super().__init__(module) + self.zip_pre = self.loader.archive + os.sep + + def _zipinfo_name(self, fspath): + # Convert a virtual filename (full path to file) into a zipfile subpath + # usable with the zipimport directory cache for our target archive + fspath = fspath.rstrip(os.sep) + if fspath == self.loader.archive: + return '' + if fspath.startswith(self.zip_pre): + return fspath[len(self.zip_pre) :] + raise AssertionError(f"{fspath} is not a subpath of {self.zip_pre}") + + def _parts(self, zip_path): + # Convert a zipfile subpath into an egg-relative path part list. + # pseudo-fs path + fspath = self.zip_pre + zip_path + if fspath.startswith(self.egg_root + os.sep): + return fspath[len(self.egg_root) + 1 :].split(os.sep) + raise AssertionError(f"{fspath} is not a subpath of {self.egg_root}") + + @property + def zipinfo(self): + return self._zip_manifests.load(self.loader.archive) + + def get_resource_filename( + self, manager: ResourceManager, resource_name: str + ) -> str: + if not self.egg_name: + raise NotImplementedError( + "resource_filename() only supported for .egg, not .zip" + ) + # no need to lock for extraction, since we use temp names + zip_path = self._resource_to_zip(resource_name) + eagers = self._get_eager_resources() + if '/'.join(self._parts(zip_path)) in eagers: + for name in eagers: + self._extract_resource(manager, self._eager_to_zip(name)) + return self._extract_resource(manager, zip_path) + + @staticmethod + def _get_date_and_size(zip_stat): + size = zip_stat.file_size + # ymdhms+wday, yday, dst + date_time = zip_stat.date_time + (0, 0, -1) + # 1980 offset already done + timestamp = time.mktime(date_time) + return timestamp, size + + # FIXME: 'ZipProvider._extract_resource' is too complex (12) + def _extract_resource(self, manager: ResourceManager, zip_path) -> str: # noqa: C901 + if zip_path in self._index(): + for name in self._index()[zip_path]: + last = self._extract_resource(manager, os.path.join(zip_path, name)) + # return the extracted directory name + return os.path.dirname(last) + + timestamp, _size = self._get_date_and_size(self.zipinfo[zip_path]) + + if not WRITE_SUPPORT: + raise OSError( + '"os.rename" and "os.unlink" are not supported on this platform' + ) + try: + if not self.egg_name: + raise OSError( + '"egg_name" is empty. This likely means no egg could be found from the "module_path".' + ) + real_path = manager.get_cache_path(self.egg_name, self._parts(zip_path)) + + if self._is_current(real_path, zip_path): + return real_path + + outf, tmpnam = _mkstemp( + ".$extract", + dir=os.path.dirname(real_path), + ) + os.write(outf, self.loader.get_data(zip_path)) + os.close(outf) + utime(tmpnam, (timestamp, timestamp)) + manager.postprocess(tmpnam, real_path) + + try: + rename(tmpnam, real_path) + + except OSError: + if os.path.isfile(real_path): + if self._is_current(real_path, zip_path): + # the file became current since it was checked above, + # so proceed. + return real_path + # Windows, del old file and retry + elif os.name == 'nt': + unlink(real_path) + rename(tmpnam, real_path) + return real_path + raise + + except OSError: + # report a user-friendly error + manager.extraction_error() + + return real_path + + def _is_current(self, file_path, zip_path): + """ + Return True if the file_path is current for this zip_path + """ + timestamp, size = self._get_date_and_size(self.zipinfo[zip_path]) + if not os.path.isfile(file_path): + return False + stat = os.stat(file_path) + if stat.st_size != size or stat.st_mtime != timestamp: + return False + # check that the contents match + zip_contents = self.loader.get_data(zip_path) + with open(file_path, 'rb') as f: + file_contents = f.read() + return zip_contents == file_contents + + def _get_eager_resources(self): + if self.eagers is None: + eagers = [] + for name in ('native_libs.txt', 'eager_resources.txt'): + if self.has_metadata(name): + eagers.extend(self.get_metadata_lines(name)) + self.eagers = eagers + return self.eagers + + def _index(self): + try: + return self._dirindex + except AttributeError: + ind = {} + for path in self.zipinfo: + parts = path.split(os.sep) + while parts: + parent = os.sep.join(parts[:-1]) + if parent in ind: + ind[parent].append(parts[-1]) + break + else: + ind[parent] = [parts.pop()] + self._dirindex = ind + return ind + + def _has(self, fspath) -> bool: + zip_path = self._zipinfo_name(fspath) + return zip_path in self.zipinfo or zip_path in self._index() + + def _isdir(self, fspath) -> bool: + return self._zipinfo_name(fspath) in self._index() + + def _listdir(self, fspath): + return list(self._index().get(self._zipinfo_name(fspath), ())) + + def _eager_to_zip(self, resource_name: str): + return self._zipinfo_name(self._fn(self.egg_root, resource_name)) + + def _resource_to_zip(self, resource_name: str): + return self._zipinfo_name(self._fn(self.module_path, resource_name)) + + +register_loader_type(zipimport.zipimporter, ZipProvider) + + +class FileMetadata(EmptyProvider): + """Metadata handler for standalone PKG-INFO files + + Usage:: + + metadata = FileMetadata("/path/to/PKG-INFO") + + This provider rejects all data and metadata requests except for PKG-INFO, + which is treated as existing, and will be the contents of the file at + the provided location. + """ + + def __init__(self, path: StrPath) -> None: + self.path = path + + def _get_metadata_path(self, name): + return self.path + + def has_metadata(self, name: str) -> bool: + return name == 'PKG-INFO' and os.path.isfile(self.path) + + def get_metadata(self, name: str) -> str: + if name != 'PKG-INFO': + raise KeyError("No metadata except PKG-INFO is available") + + with open(self.path, encoding='utf-8', errors="replace") as f: + metadata = f.read() + self._warn_on_replacement(metadata) + return metadata + + def _warn_on_replacement(self, metadata) -> None: + replacement_char = '�' + if replacement_char in metadata: + tmpl = "{self.path} could not be properly decoded in UTF-8" + msg = tmpl.format(**locals()) + warnings.warn(msg) + + def get_metadata_lines(self, name: str) -> Iterator[str]: + return yield_lines(self.get_metadata(name)) + + +class PathMetadata(DefaultProvider): + """Metadata provider for egg directories + + Usage:: + + # Development eggs: + + egg_info = "/path/to/PackageName.egg-info" + base_dir = os.path.dirname(egg_info) + metadata = PathMetadata(base_dir, egg_info) + dist_name = os.path.splitext(os.path.basename(egg_info))[0] + dist = Distribution(basedir, project_name=dist_name, metadata=metadata) + + # Unpacked egg directories: + + egg_path = "/path/to/PackageName-ver-pyver-etc.egg" + metadata = PathMetadata(egg_path, os.path.join(egg_path,'EGG-INFO')) + dist = Distribution.from_filename(egg_path, metadata=metadata) + """ + + def __init__(self, path: str, egg_info: str) -> None: + self.module_path = path + self.egg_info = egg_info + + +class EggMetadata(ZipProvider): + """Metadata provider for .egg files""" + + def __init__(self, importer: zipimport.zipimporter) -> None: + """Create a metadata provider from a zipimporter""" + + self.zip_pre = importer.archive + os.sep + self.loader = importer + if importer.prefix: + self.module_path = os.path.join(importer.archive, importer.prefix) + else: + self.module_path = importer.archive + self._setup_prefix() + + +_distribution_finders: dict[type, _DistFinderType[Any]] = _declare_state( + 'dict', '_distribution_finders', {} +) + + +def register_finder( + importer_type: type[_T], distribution_finder: _DistFinderType[_T] +) -> None: + """Register `distribution_finder` to find distributions in sys.path items + + `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item + handler), and `distribution_finder` is a callable that, passed a path + item and the importer instance, yields ``Distribution`` instances found on + that path item. See ``pkg_resources.find_on_path`` for an example.""" + _distribution_finders[importer_type] = distribution_finder + + +def find_distributions(path_item: str, only: bool = False) -> Iterable[Distribution]: + """Yield distributions accessible via `path_item`""" + importer = get_importer(path_item) + finder = _find_adapter(_distribution_finders, importer) + return finder(importer, path_item, only) + + +def find_eggs_in_zip( + importer: zipimport.zipimporter, path_item: str, only: bool = False +) -> Iterator[Distribution]: + """ + Find eggs in zip files; possibly multiple nested eggs. + """ + if importer.archive.endswith('.whl'): + # wheels are not supported with this finder + # they don't have PKG-INFO metadata, and won't ever contain eggs + return + metadata = EggMetadata(importer) + if metadata.has_metadata('PKG-INFO'): + yield Distribution.from_filename(path_item, metadata=metadata) + if only: + # don't yield nested distros + return + for subitem in metadata.resource_listdir(''): + if _is_egg_path(subitem): + subpath = os.path.join(path_item, subitem) + dists = find_eggs_in_zip(zipimport.zipimporter(subpath), subpath) + yield from dists + elif subitem.lower().endswith(('.dist-info', '.egg-info')): + subpath = os.path.join(path_item, subitem) + submeta = EggMetadata(zipimport.zipimporter(subpath)) + submeta.egg_info = subpath + yield Distribution.from_location(path_item, subitem, submeta) + + +register_finder(zipimport.zipimporter, find_eggs_in_zip) + + +def find_nothing( + importer: object | None, path_item: str | None, only: bool | None = False +): + return () + + +register_finder(object, find_nothing) + + +def find_on_path(importer: object | None, path_item, only=False): + """Yield distributions accessible on a sys.path directory""" + path_item = _normalize_cached(path_item) + + if _is_unpacked_egg(path_item): + yield Distribution.from_filename( + path_item, + metadata=PathMetadata(path_item, os.path.join(path_item, 'EGG-INFO')), + ) + return + + entries = (os.path.join(path_item, child) for child in safe_listdir(path_item)) + + # scan for .egg and .egg-info in directory + for entry in sorted(entries): + fullpath = os.path.join(path_item, entry) + factory = dist_factory(path_item, entry, only) + yield from factory(fullpath) + + +def dist_factory(path_item, entry, only): + """Return a dist_factory for the given entry.""" + lower = entry.lower() + is_egg_info = lower.endswith('.egg-info') + is_dist_info = lower.endswith('.dist-info') and os.path.isdir( + os.path.join(path_item, entry) + ) + is_meta = is_egg_info or is_dist_info + return ( + distributions_from_metadata + if is_meta + else find_distributions + if not only and _is_egg_path(entry) + else resolve_egg_link + if not only and lower.endswith('.egg-link') + else NoDists() + ) + + +class NoDists: + """ + >>> bool(NoDists()) + False + + >>> list(NoDists()('anything')) + [] + """ + + def __bool__(self) -> Literal[False]: + return False + + def __call__(self, fullpath: object): + return iter(()) + + +def safe_listdir(path: StrOrBytesPath): + """ + Attempt to list contents of path, but suppress some exceptions. + """ + try: + return os.listdir(path) + except (PermissionError, NotADirectoryError): + pass + except OSError as e: + # Ignore the directory if does not exist, not a directory or + # permission denied + if e.errno not in (errno.ENOTDIR, errno.EACCES, errno.ENOENT): + raise + return () + + +def distributions_from_metadata(path: str): + root = os.path.dirname(path) + if os.path.isdir(path): + if len(os.listdir(path)) == 0: + # empty metadata dir; skip + return + metadata: _MetadataType = PathMetadata(root, path) + else: + metadata = FileMetadata(path) + entry = os.path.basename(path) + yield Distribution.from_location( + root, + entry, + metadata, + precedence=DEVELOP_DIST, + ) + + +def non_empty_lines(path): + """ + Yield non-empty lines from file at path + """ + for line in _read_utf8_with_fallback(path).splitlines(): + line = line.strip() + if line: + yield line + + +def resolve_egg_link(path): + """ + Given a path to an .egg-link, resolve distributions + present in the referenced path. + """ + referenced_paths = non_empty_lines(path) + resolved_paths = ( + os.path.join(os.path.dirname(path), ref) for ref in referenced_paths + ) + dist_groups = map(find_distributions, resolved_paths) + return next(dist_groups, ()) + + +if hasattr(pkgutil, 'ImpImporter'): + register_finder(pkgutil.ImpImporter, find_on_path) + +register_finder(importlib.machinery.FileFinder, find_on_path) + +_namespace_handlers: dict[type, _NSHandlerType[Any]] = _declare_state( + 'dict', '_namespace_handlers', {} +) +_namespace_packages: dict[str | None, list[str]] = _declare_state( + 'dict', '_namespace_packages', {} +) + + +def register_namespace_handler( + importer_type: type[_T], namespace_handler: _NSHandlerType[_T] +) -> None: + """Register `namespace_handler` to declare namespace packages + + `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item + handler), and `namespace_handler` is a callable like this:: + + def namespace_handler(importer, path_entry, moduleName, module): + # return a path_entry to use for child packages + + Namespace handlers are only called if the importer object has already + agreed that it can handle the relevant path item, and they should only + return a subpath if the module __path__ does not already contain an + equivalent subpath. For an example namespace handler, see + ``pkg_resources.file_ns_handler``. + """ + _namespace_handlers[importer_type] = namespace_handler + + +def _handle_ns(packageName, path_item): + """Ensure that named package includes a subpath of path_item (if needed)""" + + importer = get_importer(path_item) + if importer is None: + return None + + # use find_spec (PEP 451) and fall-back to find_module (PEP 302) + try: + spec = importer.find_spec(packageName) + except AttributeError: + # capture warnings due to #1111 + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + loader = importer.find_module(packageName) + else: + loader = spec.loader if spec else None + + if loader is None: + return None + module = sys.modules.get(packageName) + if module is None: + module = sys.modules[packageName] = types.ModuleType(packageName) + module.__path__ = [] + _set_parent_ns(packageName) + elif not hasattr(module, '__path__'): + raise TypeError("Not a package:", packageName) + handler = _find_adapter(_namespace_handlers, importer) + subpath = handler(importer, path_item, packageName, module) + if subpath is not None: + path = module.__path__ + path.append(subpath) + importlib.import_module(packageName) + _rebuild_mod_path(path, packageName, module) + return subpath + + +def _rebuild_mod_path(orig_path, package_name, module: types.ModuleType) -> None: + """ + Rebuild module.__path__ ensuring that all entries are ordered + corresponding to their sys.path order + """ + sys_path = [_normalize_cached(p) for p in sys.path] + + def safe_sys_path_index(entry): + """ + Workaround for #520 and #513. + """ + try: + return sys_path.index(entry) + except ValueError: + return float('inf') + + def position_in_sys_path(path): + """ + Return the ordinal of the path based on its position in sys.path + """ + path_parts = path.split(os.sep) + module_parts = package_name.count('.') + 1 + parts = path_parts[:-module_parts] + return safe_sys_path_index(_normalize_cached(os.sep.join(parts))) + + new_path = sorted(orig_path, key=position_in_sys_path) + new_path = [_normalize_cached(p) for p in new_path] + + if isinstance(module.__path__, list): + module.__path__[:] = new_path + else: + module.__path__ = new_path + + +def declare_namespace(packageName: str) -> None: + """Declare that package 'packageName' is a namespace package""" + + msg = ( + f"Deprecated call to `pkg_resources.declare_namespace({packageName!r})`.\n" + "Implementing implicit namespace packages (as specified in PEP 420) " + "is preferred to `pkg_resources.declare_namespace`. " + "See https://setuptools.pypa.io/en/latest/references/" + "keywords.html#keyword-namespace-packages" + ) + warnings.warn(msg, DeprecationWarning, stacklevel=2) + + _imp.acquire_lock() + try: + if packageName in _namespace_packages: + return + + path: MutableSequence[str] = sys.path + parent, _, _ = packageName.rpartition('.') + + if parent: + declare_namespace(parent) + if parent not in _namespace_packages: + __import__(parent) + try: + path = sys.modules[parent].__path__ + except AttributeError as e: + raise TypeError("Not a package:", parent) from e + + # Track what packages are namespaces, so when new path items are added, + # they can be updated + _namespace_packages.setdefault(parent or None, []).append(packageName) + _namespace_packages.setdefault(packageName, []) + + for path_item in path: + # Ensure all the parent's path items are reflected in the child, + # if they apply + _handle_ns(packageName, path_item) + + finally: + _imp.release_lock() + + +def fixup_namespace_packages(path_item: str, parent: str | None = None) -> None: + """Ensure that previously-declared namespace packages include path_item""" + _imp.acquire_lock() + try: + for package in _namespace_packages.get(parent, ()): + subpath = _handle_ns(package, path_item) + if subpath: + fixup_namespace_packages(subpath, package) + finally: + _imp.release_lock() + + +def file_ns_handler( + importer: object, + path_item: StrPath, + packageName: str, + module: types.ModuleType, +): + """Compute an ns-package subpath for a filesystem or zipfile importer""" + + subpath = os.path.join(path_item, packageName.split('.')[-1]) + normalized = _normalize_cached(subpath) + for item in module.__path__: + if _normalize_cached(item) == normalized: + break + else: + # Only return the path if it's not already there + return subpath + + +if hasattr(pkgutil, 'ImpImporter'): + register_namespace_handler(pkgutil.ImpImporter, file_ns_handler) + +register_namespace_handler(zipimport.zipimporter, file_ns_handler) +register_namespace_handler(importlib.machinery.FileFinder, file_ns_handler) + + +def null_ns_handler( + importer: object, + path_item: str | None, + packageName: str | None, + module: _ModuleLike | None, +) -> None: + return None + + +register_namespace_handler(object, null_ns_handler) + + +@overload +def normalize_path(filename: StrPath) -> str: ... +@overload +def normalize_path(filename: BytesPath) -> bytes: ... +def normalize_path(filename: StrOrBytesPath) -> str | bytes: + """Normalize a file/dir name for comparison purposes""" + return os.path.normcase(os.path.realpath(os.path.normpath(_cygwin_patch(filename)))) + + +def _cygwin_patch(filename: StrOrBytesPath): # pragma: nocover + """ + Contrary to POSIX 2008, on Cygwin, getcwd (3) contains + symlink components. Using + os.path.abspath() works around this limitation. A fix in os.getcwd() + would probably better, in Cygwin even more so, except + that this seems to be by design... + """ + return os.path.abspath(filename) if sys.platform == 'cygwin' else filename + + +if TYPE_CHECKING: + # https://github.com/python/mypy/issues/16261 + # https://github.com/python/typeshed/issues/6347 + @overload + def _normalize_cached(filename: StrPath) -> str: ... + @overload + def _normalize_cached(filename: BytesPath) -> bytes: ... + def _normalize_cached(filename: StrOrBytesPath) -> str | bytes: ... + +else: + + @functools.cache + def _normalize_cached(filename): + return normalize_path(filename) + + +def _is_egg_path(path): + """ + Determine if given path appears to be an egg. + """ + return _is_zip_egg(path) or _is_unpacked_egg(path) + + +def _is_zip_egg(path): + return ( + path.lower().endswith('.egg') + and os.path.isfile(path) + and zipfile.is_zipfile(path) + ) + + +def _is_unpacked_egg(path): + """ + Determine if given path appears to be an unpacked egg. + """ + return path.lower().endswith('.egg') and os.path.isfile( + os.path.join(path, 'EGG-INFO', 'PKG-INFO') + ) + + +def _set_parent_ns(packageName) -> None: + parts = packageName.split('.') + name = parts.pop() + if parts: + parent = '.'.join(parts) + setattr(sys.modules[parent], name, sys.modules[packageName]) + + +MODULE = re.compile(r"\w+(\.\w+)*$").match +EGG_NAME = re.compile( + r""" + (?P[^-]+) ( + -(?P[^-]+) ( + -py(?P[^-]+) ( + -(?P.+) + )? + )? + )? + """, + re.VERBOSE | re.IGNORECASE, +).match + + +class EntryPoint: + """Object representing an advertised importable object""" + + def __init__( + self, + name: str, + module_name: str, + attrs: Iterable[str] = (), + extras: Iterable[str] = (), + dist: Distribution | None = None, + ) -> None: + if not MODULE(module_name): + raise ValueError("Invalid module name", module_name) + self.name = name + self.module_name = module_name + self.attrs = tuple(attrs) + self.extras = tuple(extras) + self.dist = dist + + def __str__(self) -> str: + s = f"{self.name} = {self.module_name}" + if self.attrs: + s += ':' + '.'.join(self.attrs) + if self.extras: + extras = ','.join(self.extras) + s += f' [{extras}]' + return s + + def __repr__(self) -> str: + return f"EntryPoint.parse({str(self)!r})" + + @overload + def load( + self, + require: Literal[True] = True, + env: Environment | None = None, + installer: _InstallerType | None = None, + ) -> _ResolvedEntryPoint: ... + @overload + def load( + self, + require: Literal[False], + *args: Any, + **kwargs: Any, + ) -> _ResolvedEntryPoint: ... + def load( + self, + require: bool = True, + *args: Environment | _InstallerType | None, + **kwargs: Environment | _InstallerType | None, + ) -> _ResolvedEntryPoint: + """ + Require packages for this EntryPoint, then resolve it. + """ + if not require or args or kwargs: + warnings.warn( + "Parameters to load are deprecated. Call .resolve and " + ".require separately.", + PkgResourcesDeprecationWarning, + stacklevel=2, + ) + if require: + # We could pass `env` and `installer` directly, + # but keeping `*args` and `**kwargs` for backwards compatibility + self.require(*args, **kwargs) # type: ignore[arg-type] + return self.resolve() + + def resolve(self) -> _ResolvedEntryPoint: + """ + Resolve the entry point from its module and attrs. + """ + module = __import__(self.module_name, fromlist=['__name__'], level=0) + try: + return functools.reduce(getattr, self.attrs, module) + except AttributeError as exc: + raise ImportError(str(exc)) from exc + + def require( + self, + env: Environment | None = None, + installer: _InstallerType | None = None, + ) -> None: + if not self.dist: + error_cls = UnknownExtra if self.extras else AttributeError + raise error_cls("Can't require() without a distribution", self) + + # Get the requirements for this entry point with all its extras and + # then resolve them. We have to pass `extras` along when resolving so + # that the working set knows what extras we want. Otherwise, for + # dist-info distributions, the working set will assume that the + # requirements for that extra are purely optional and skip over them. + reqs = self.dist.requires(self.extras) + items = working_set.resolve(reqs, env, installer, extras=self.extras) + list(map(working_set.add, items)) + + pattern = re.compile( + r'\s*' + r'(?P.+?)\s*' + r'=\s*' + r'(?P[\w.]+)\s*' + r'(:\s*(?P[\w.]+))?\s*' + r'(?P\[.*\])?\s*$' + ) + + @classmethod + def parse(cls, src: str, dist: Distribution | None = None) -> Self: + """Parse a single entry point from string `src` + + Entry point syntax follows the form:: + + name = some.module:some.attr [extra1, extra2] + + The entry name and module name are required, but the ``:attrs`` and + ``[extras]`` parts are optional + """ + m = cls.pattern.match(src) + if not m: + msg = "EntryPoint must be in 'name=module:attrs [extras]' format" + raise ValueError(msg, src) + res = m.groupdict() + extras = cls._parse_extras(res['extras']) + attrs = res['attr'].split('.') if res['attr'] else () + return cls(res['name'], res['module'], attrs, extras, dist) + + @classmethod + def _parse_extras(cls, extras_spec): + if not extras_spec: + return () + req = Requirement.parse('x' + extras_spec) + if req.specs: + raise ValueError + return req.extras + + @classmethod + def parse_group( + cls, + group: str, + lines: _NestedStr, + dist: Distribution | None = None, + ) -> dict[str, Self]: + """Parse an entry point group""" + if not MODULE(group): + raise ValueError("Invalid group name", group) + this: dict[str, Self] = {} + for line in yield_lines(lines): + ep = cls.parse(line, dist) + if ep.name in this: + raise ValueError("Duplicate entry point", group, ep.name) + this[ep.name] = ep + return this + + @classmethod + def parse_map( + cls, + data: str | Iterable[str] | dict[str, str | Iterable[str]], + dist: Distribution | None = None, + ) -> dict[str, dict[str, Self]]: + """Parse a map of entry point groups""" + _data: Iterable[tuple[str | None, str | Iterable[str]]] + if isinstance(data, dict): + _data = data.items() + else: + _data = split_sections(data) + maps: dict[str, dict[str, Self]] = {} + for group, lines in _data: + if group is None: + if not lines: + continue + raise ValueError("Entry points must be listed in groups") + group = group.strip() + if group in maps: + raise ValueError("Duplicate group name", group) + maps[group] = cls.parse_group(group, lines, dist) + return maps + + +def _version_from_file(lines): + """ + Given an iterable of lines from a Metadata file, return + the value of the Version field, if present, or None otherwise. + """ + + def is_version_line(line): + return line.lower().startswith('version:') + + version_lines = filter(is_version_line, lines) + line = next(iter(version_lines), '') + _, _, value = line.partition(':') + return safe_version(value.strip()) or None + + +class Distribution: + """Wrap an actual or potential sys.path entry w/metadata""" + + PKG_INFO = 'PKG-INFO' + + def __init__( + self, + location: str | None = None, + metadata: _MetadataType = None, + project_name: str | None = None, + version: str | None = None, + py_version: str | None = PY_MAJOR, + platform: str | None = None, + precedence: int = EGG_DIST, + ) -> None: + self.project_name = safe_name(project_name or 'Unknown') + if version is not None: + self._version = safe_version(version) + self.py_version = py_version + self.platform = platform + self.location = location + self.precedence = precedence + self._provider = metadata or empty_provider + + @classmethod + def from_location( + cls, + location: str, + basename: StrPath, + metadata: _MetadataType = None, + **kw: int, # We could set `precedence` explicitly, but keeping this as `**kw` for full backwards and subclassing compatibility + ) -> Distribution: + project_name, version, py_version, platform = [None] * 4 + basename, ext = os.path.splitext(basename) + if ext.lower() in _distributionImpl: + cls = _distributionImpl[ext.lower()] + + match = EGG_NAME(basename) + if match: + project_name, version, py_version, platform = match.group( + 'name', 'ver', 'pyver', 'plat' + ) + return cls( + location, + metadata, + project_name=project_name, + version=version, + py_version=py_version, + platform=platform, + **kw, + )._reload_version() + + def _reload_version(self): + return self + + @property + def hashcmp(self): + return ( + self._forgiving_parsed_version, + self.precedence, + self.key, + self.location, + self.py_version or '', + self.platform or '', + ) + + def __hash__(self) -> int: + return hash(self.hashcmp) + + def __lt__(self, other: Distribution) -> bool: + return self.hashcmp < other.hashcmp + + def __le__(self, other: Distribution) -> bool: + return self.hashcmp <= other.hashcmp + + def __gt__(self, other: Distribution) -> bool: + return self.hashcmp > other.hashcmp + + def __ge__(self, other: Distribution) -> bool: + return self.hashcmp >= other.hashcmp + + def __eq__(self, other: object) -> bool: + if not isinstance(other, self.__class__): + # It's not a Distribution, so they are not equal + return False + return self.hashcmp == other.hashcmp + + def __ne__(self, other: object) -> bool: + return not self == other + + # These properties have to be lazy so that we don't have to load any + # metadata until/unless it's actually needed. (i.e., some distributions + # may not know their name or version without loading PKG-INFO) + + @property + def key(self): + try: + return self._key + except AttributeError: + self._key = key = self.project_name.lower() + return key + + @property + def parsed_version(self): + if not hasattr(self, "_parsed_version"): + try: + self._parsed_version = parse_version(self.version) + except packaging.version.InvalidVersion as ex: + info = f"(package: {self.project_name})" + if hasattr(ex, "add_note"): + ex.add_note(info) # PEP 678 + raise + raise packaging.version.InvalidVersion(f"{str(ex)} {info}") from None + + return self._parsed_version + + @property + def _forgiving_parsed_version(self): + try: + return self.parsed_version + except packaging.version.InvalidVersion as ex: + self._parsed_version = parse_version(_forgiving_version(self.version)) + + notes = "\n".join(getattr(ex, "__notes__", [])) # PEP 678 + msg = f"""!!\n\n + ************************************************************************* + {str(ex)}\n{notes} + + This is a long overdue deprecation. + For the time being, `pkg_resources` will use `{self._parsed_version}` + as a replacement to avoid breaking existing environments, + but no future compatibility is guaranteed. + + If you maintain package {self.project_name} you should implement + the relevant changes to adequate the project to PEP 440 immediately. + ************************************************************************* + \n\n!! + """ + warnings.warn(msg, DeprecationWarning) + + return self._parsed_version + + @property + def version(self): + try: + return self._version + except AttributeError as e: + version = self._get_version() + if version is None: + path = self._get_metadata_path_for_display(self.PKG_INFO) + msg = f"Missing 'Version:' header and/or {self.PKG_INFO} file at path: {path}" + raise ValueError(msg, self) from e + + return version + + @property + def _dep_map(self): + """ + A map of extra to its list of (direct) requirements + for this distribution, including the null extra. + """ + try: + return self.__dep_map + except AttributeError: + self.__dep_map = self._filter_extras(self._build_dep_map()) + return self.__dep_map + + @staticmethod + def _filter_extras( + dm: dict[str | None, list[Requirement]], + ) -> dict[str | None, list[Requirement]]: + """ + Given a mapping of extras to dependencies, strip off + environment markers and filter out any dependencies + not matching the markers. + """ + for extra in list(filter(None, dm)): + new_extra: str | None = extra + reqs = dm.pop(extra) + new_extra, _, marker = extra.partition(':') + fails_marker = marker and ( + invalid_marker(marker) or not evaluate_marker(marker) + ) + if fails_marker: + reqs = [] + new_extra = safe_extra(new_extra) or None + + dm.setdefault(new_extra, []).extend(reqs) + return dm + + def _build_dep_map(self): + dm = {} + for name in 'requires.txt', 'depends.txt': + for extra, reqs in split_sections(self._get_metadata(name)): + dm.setdefault(extra, []).extend(parse_requirements(reqs)) + return dm + + def requires(self, extras: Iterable[str] = ()) -> list[Requirement]: + """List of Requirements needed for this distro if `extras` are used""" + dm = self._dep_map + deps: list[Requirement] = [] + deps.extend(dm.get(None, ())) + for ext in extras: + try: + deps.extend(dm[safe_extra(ext)]) + except KeyError as e: + raise UnknownExtra(f"{self} has no such extra feature {ext!r}") from e + return deps + + def _get_metadata_path_for_display(self, name): + """ + Return the path to the given metadata file, if available. + """ + try: + # We need to access _get_metadata_path() on the provider object + # directly rather than through this class's __getattr__() + # since _get_metadata_path() is marked private. + path = self._provider._get_metadata_path(name) + + # Handle exceptions e.g. in case the distribution's metadata + # provider doesn't support _get_metadata_path(). + except Exception: + return '[could not detect]' + + return path + + def _get_metadata(self, name): + if self.has_metadata(name): + yield from self.get_metadata_lines(name) + + def _get_version(self): + lines = self._get_metadata(self.PKG_INFO) + return _version_from_file(lines) + + def activate(self, path: list[str] | None = None, replace: bool = False) -> None: + """Ensure distribution is importable on `path` (default=sys.path)""" + if path is None: + path = sys.path + self.insert_on(path, replace=replace) + if path is sys.path and self.location is not None: + fixup_namespace_packages(self.location) + for pkg in self._get_metadata('namespace_packages.txt'): + if pkg in sys.modules: + declare_namespace(pkg) + + def egg_name(self): + """Return what this distribution's standard .egg filename should be""" + filename = f"{to_filename(self.project_name)}-{to_filename(self.version)}-py{self.py_version or PY_MAJOR}" + + if self.platform: + filename += '-' + self.platform + return filename + + def __repr__(self) -> str: + if self.location: + return f"{self} ({self.location})" + else: + return str(self) + + def __str__(self) -> str: + try: + version = getattr(self, 'version', None) + except ValueError: + version = None + version = version or "[unknown version]" + return f"{self.project_name} {version}" + + def __getattr__(self, attr: str): + """Delegate all unrecognized public attributes to .metadata provider""" + if attr.startswith('_'): + raise AttributeError(attr) + return getattr(self._provider, attr) + + def __dir__(self): + return list( + set(super().__dir__()) + | set(attr for attr in self._provider.__dir__() if not attr.startswith('_')) + ) + + @classmethod + def from_filename( + cls, + filename: StrPath, + metadata: _MetadataType = None, + **kw: int, # We could set `precedence` explicitly, but keeping this as `**kw` for full backwards and subclassing compatibility + ) -> Distribution: + return cls.from_location( + _normalize_cached(filename), os.path.basename(filename), metadata, **kw + ) + + def as_requirement(self): + """Return a ``Requirement`` that matches this distribution exactly""" + if isinstance(self.parsed_version, packaging.version.Version): + spec = f"{self.project_name}=={self.parsed_version}" + else: + spec = f"{self.project_name}==={self.parsed_version}" + + return Requirement.parse(spec) + + def load_entry_point(self, group: str, name: str) -> _ResolvedEntryPoint: + """Return the `name` entry point of `group` or raise ImportError""" + ep = self.get_entry_info(group, name) + if ep is None: + raise ImportError(f"Entry point {(group, name)!r} not found") + return ep.load() + + @overload + def get_entry_map(self, group: None = None) -> dict[str, dict[str, EntryPoint]]: ... + @overload + def get_entry_map(self, group: str) -> dict[str, EntryPoint]: ... + def get_entry_map(self, group: str | None = None): + """Return the entry point map for `group`, or the full entry map""" + if not hasattr(self, "_ep_map"): + self._ep_map = EntryPoint.parse_map( + self._get_metadata('entry_points.txt'), self + ) + if group is not None: + return self._ep_map.get(group, {}) + return self._ep_map + + def get_entry_info(self, group: str, name: str) -> EntryPoint | None: + """Return the EntryPoint object for `group`+`name`, or ``None``""" + return self.get_entry_map(group).get(name) + + # FIXME: 'Distribution.insert_on' is too complex (13) + def insert_on( # noqa: C901 + self, + path: list[str], + loc=None, + replace: bool = False, + ) -> None: + """Ensure self.location is on path + + If replace=False (default): + - If location is already in path anywhere, do nothing. + - Else: + - If it's an egg and its parent directory is on path, + insert just ahead of the parent. + - Else: add to the end of path. + If replace=True: + - If location is already on path anywhere (not eggs) + or higher priority than its parent (eggs) + do nothing. + - Else: + - If it's an egg and its parent directory is on path, + insert just ahead of the parent, + removing any lower-priority entries. + - Else: add it to the front of path. + """ + + loc = loc or self.location + if not loc: + return + + nloc = _normalize_cached(loc) + bdir = os.path.dirname(nloc) + npath = [(p and _normalize_cached(p) or p) for p in path] + + for p, item in enumerate(npath): + if item == nloc: + if replace: + break + else: + # don't modify path (even removing duplicates) if + # found and not replace + return + elif item == bdir and self.precedence == EGG_DIST: + # if it's an .egg, give it precedence over its directory + # UNLESS it's already been added to sys.path and replace=False + if (not replace) and nloc in npath[p:]: + return + if path is sys.path: + self.check_version_conflict() + path.insert(p, loc) + npath.insert(p, nloc) + break + else: + if path is sys.path: + self.check_version_conflict() + if replace: + path.insert(0, loc) + else: + path.append(loc) + return + + # p is the spot where we found or inserted loc; now remove duplicates + while True: + try: + np = npath.index(nloc, p + 1) + except ValueError: + break + else: + del npath[np], path[np] + # ha! + p = np + + return + + def check_version_conflict(self): + if self.key == 'setuptools': + # ignore the inevitable setuptools self-conflicts :( + return + + nsp = dict.fromkeys(self._get_metadata('namespace_packages.txt')) + loc = normalize_path(self.location) + for modname in self._get_metadata('top_level.txt'): + if ( + modname not in sys.modules + or modname in nsp + or modname in _namespace_packages + ): + continue + if modname in ('pkg_resources', 'setuptools', 'site'): + continue + fn = getattr(sys.modules[modname], '__file__', None) + if fn and ( + normalize_path(fn).startswith(loc) or fn.startswith(self.location) + ): + continue + issue_warning( + f"Module {modname} was already imported from {fn}, " + f"but {self.location} is being added to sys.path", + ) + + def has_version(self) -> bool: + try: + self.version + except ValueError: + issue_warning("Unbuilt egg for " + repr(self)) + return False + except SystemError: + # TODO: remove this except clause when python/cpython#103632 is fixed. + return False + return True + + def clone(self, **kw: str | int | IResourceProvider | None) -> Self: + """Copy this distribution, substituting in any changed keyword args""" + names = 'project_name version py_version platform location precedence' + for attr in names.split(): + kw.setdefault(attr, getattr(self, attr, None)) + kw.setdefault('metadata', self._provider) + # Unsafely unpacking. But keeping **kw for backwards and subclassing compatibility + return self.__class__(**kw) # type:ignore[arg-type] + + @property + def extras(self): + return [dep for dep in self._dep_map if dep] + + +class EggInfoDistribution(Distribution): + def _reload_version(self): + """ + Packages installed by distutils (e.g. numpy or scipy), + which uses an old safe_version, and so + their version numbers can get mangled when + converted to filenames (e.g., 1.11.0.dev0+2329eae to + 1.11.0.dev0_2329eae). These distributions will not be + parsed properly + downstream by Distribution and safe_version, so + take an extra step and try to get the version number from + the metadata file itself instead of the filename. + """ + md_version = self._get_version() + if md_version: + self._version = md_version + return self + + +class DistInfoDistribution(Distribution): + """ + Wrap an actual or potential sys.path entry + w/metadata, .dist-info style. + """ + + PKG_INFO = 'METADATA' + EQEQ = re.compile(r"([\(,])\s*(\d.*?)\s*([,\)])") + + @property + def _parsed_pkg_info(self): + """Parse and cache metadata""" + try: + return self._pkg_info + except AttributeError: + metadata = self.get_metadata(self.PKG_INFO) + self._pkg_info = email.parser.Parser().parsestr(metadata) + return self._pkg_info + + @property + def _dep_map(self): + try: + return self.__dep_map + except AttributeError: + self.__dep_map = self._compute_dependencies() + return self.__dep_map + + def _compute_dependencies(self) -> dict[str | None, list[Requirement]]: + """Recompute this distribution's dependencies.""" + self.__dep_map: dict[str | None, list[Requirement]] = {None: []} + + reqs: list[Requirement] = [] + # Including any condition expressions + for req in self._parsed_pkg_info.get_all('Requires-Dist') or []: + reqs.extend(parse_requirements(req)) + + def reqs_for_extra(extra): + for req in reqs: + if not req.marker or req.marker.evaluate({'extra': extra}): + yield req + + common = types.MappingProxyType(dict.fromkeys(reqs_for_extra(None))) + self.__dep_map[None].extend(common) + + for extra in self._parsed_pkg_info.get_all('Provides-Extra') or []: + s_extra = safe_extra(extra.strip()) + self.__dep_map[s_extra] = [ + r for r in reqs_for_extra(extra) if r not in common + ] + + return self.__dep_map + + +_distributionImpl = { + '.egg': Distribution, + '.egg-info': EggInfoDistribution, + '.dist-info': DistInfoDistribution, +} + + +def issue_warning(*args, **kw): + level = 1 + g = globals() + try: + # find the first stack frame that is *not* code in + # the pkg_resources module, to use for the warning + while sys._getframe(level).f_globals is g: + level += 1 + except ValueError: + pass + warnings.warn(stacklevel=level + 1, *args, **kw) + + +def parse_requirements(strs: _NestedStr) -> map[Requirement]: + """ + Yield ``Requirement`` objects for each specification in `strs`. + + `strs` must be a string, or a (possibly-nested) iterable thereof. + """ + return map(Requirement, join_continuation(map(drop_comment, yield_lines(strs)))) + + +class RequirementParseError(packaging.requirements.InvalidRequirement): + "Compatibility wrapper for InvalidRequirement" + + +class Requirement(packaging.requirements.Requirement): + # prefer variable length tuple to set (as found in + # packaging.requirements.Requirement) + extras: tuple[str, ...] # type: ignore[assignment] + + def __init__(self, requirement_string: str) -> None: + """DO NOT CALL THIS UNDOCUMENTED METHOD; use Requirement.parse()!""" + super().__init__(requirement_string) + self.unsafe_name = self.name + project_name = safe_name(self.name) + self.project_name, self.key = project_name, project_name.lower() + self.specs = [(spec.operator, spec.version) for spec in self.specifier] + self.extras = tuple(map(safe_extra, self.extras)) + self.hashCmp = ( + self.key, + self.url, + self.specifier, + frozenset(self.extras), + str(self.marker) if self.marker else None, + ) + self.__hash = hash(self.hashCmp) + + def __eq__(self, other: object) -> bool: + return isinstance(other, Requirement) and self.hashCmp == other.hashCmp + + def __ne__(self, other: object) -> bool: + return not self == other + + def __contains__( + self, item: Distribution | packaging.specifiers.UnparsedVersion + ) -> bool: + if isinstance(item, Distribution): + if item.key != self.key: + return False + + version = item.version + else: + version = item + + # Allow prereleases always in order to match the previous behavior of + # this method. In the future this should be smarter and follow PEP 440 + # more accurately. + return self.specifier.contains( + version, + prereleases=True, + ) + + def __hash__(self) -> int: + return self.__hash + + def __repr__(self) -> str: + return f"Requirement.parse({str(self)!r})" + + @staticmethod + def parse(s: str | Iterable[str]) -> Requirement: + (req,) = parse_requirements(s) + return req + + +def _always_object(classes): + """ + Ensure object appears in the mro even + for old-style classes. + """ + if object not in classes: + return classes + (object,) + return classes + + +def _find_adapter(registry: Mapping[type, _AdapterT], ob: object) -> _AdapterT: + """Return an adapter factory for `ob` from `registry`""" + types = _always_object(inspect.getmro(getattr(ob, '__class__', type(ob)))) + for t in types: + if t in registry: + return registry[t] + # _find_adapter would previously return None, and immediately be called. + # So we're raising a TypeError to keep backward compatibility if anyone depended on that behaviour. + raise TypeError(f"Could not find adapter for {registry} and {ob}") + + +def ensure_directory(path: StrOrBytesPath) -> None: + """Ensure that the parent directory of `path` exists""" + dirname = os.path.dirname(path) + os.makedirs(dirname, exist_ok=True) + + +def _bypass_ensure_directory(path) -> None: + """Sandbox-bypassing version of ensure_directory()""" + if not WRITE_SUPPORT: + raise OSError('"os.mkdir" not supported on this platform.') + dirname, filename = split(path) + if dirname and filename and not isdir(dirname): + _bypass_ensure_directory(dirname) + try: + mkdir(dirname, 0o755) + except FileExistsError: + pass + + +def split_sections(s: _NestedStr) -> Iterator[tuple[str | None, list[str]]]: + """Split a string or iterable thereof into (section, content) pairs + + Each ``section`` is a stripped version of the section header ("[section]") + and each ``content`` is a list of stripped lines excluding blank lines and + comment-only lines. If there are any such lines before the first section + header, they're returned in a first ``section`` of ``None``. + """ + section = None + content: list[str] = [] + for line in yield_lines(s): + if line.startswith("["): + if line.endswith("]"): + if section or content: + yield section, content + section = line[1:-1].strip() + content = [] + else: + raise ValueError("Invalid section heading", line) + else: + content.append(line) + + # wrap up last segment + yield section, content + + +def _mkstemp(*args, **kw): + old_open = os.open + try: + # temporarily bypass sandboxing + os.open = os_open + return tempfile.mkstemp(*args, **kw) + finally: + # and then put it back + os.open = old_open + + +# Silence the PEP440Warning by default, so that end users don't get hit by it +# randomly just because they use pkg_resources. We want to append the rule +# because we want earlier uses of filterwarnings to take precedence over this +# one. +warnings.filterwarnings("ignore", category=PEP440Warning, append=True) + + +class PkgResourcesDeprecationWarning(Warning): + """ + Base class for warning about deprecations in ``pkg_resources`` + + This class is not derived from ``DeprecationWarning``, and as such is + visible by default. + """ + + +# Ported from ``setuptools`` to avoid introducing an import inter-dependency: +_LOCALE_ENCODING = "locale" if sys.version_info >= (3, 10) else None + + +# This must go before calls to `_call_aside`. See https://github.com/pypa/setuptools/pull/4422 +def _read_utf8_with_fallback(file: str, fallback_encoding=_LOCALE_ENCODING) -> str: + """See setuptools.unicode_utils._read_utf8_with_fallback""" + try: + with open(file, "r", encoding="utf-8") as f: + return f.read() + except UnicodeDecodeError: # pragma: no cover + msg = f"""\ + ******************************************************************************** + `encoding="utf-8"` fails with {file!r}, trying `encoding={fallback_encoding!r}`. + + This fallback behaviour is considered **deprecated** and future versions of + `setuptools/pkg_resources` may not implement it. + + Please encode {file!r} with "utf-8" to ensure future builds will succeed. + + If this file was produced by `setuptools` itself, cleaning up the cached files + and re-building/re-installing the package with a newer version of `setuptools` + (e.g. by updating `build-system.requires` in its `pyproject.toml`) + might solve the problem. + ******************************************************************************** + """ + # TODO: Add a deadline? + # See comment in setuptools.unicode_utils._Utf8EncodingNeeded + warnings.warn(msg, PkgResourcesDeprecationWarning, stacklevel=2) + with open(file, "r", encoding=fallback_encoding) as f: + return f.read() + + +# from jaraco.functools 1.3 +def _call_aside(f, *args, **kwargs): + f(*args, **kwargs) + return f + + +@_call_aside +def _initialize(g=globals()) -> None: + "Set up global resource manager (deliberately not state-saved)" + manager = ResourceManager() + g['_manager'] = manager + g.update( + (name, getattr(manager, name)) + for name in dir(manager) + if not name.startswith('_') + ) + + +@_call_aside +def _initialize_master_working_set() -> None: + """ + Prepare the master working set and make the ``require()`` + API available. + + This function has explicit effects on the global state + of pkg_resources. It is intended to be invoked once at + the initialization of this module. + + Invocation by other packages is unsupported and done + at their own risk. + """ + working_set = _declare_state('object', 'working_set', WorkingSet._build_master()) + + require = working_set.require + iter_entry_points = working_set.iter_entry_points + add_activation_listener = working_set.subscribe + run_script = working_set.run_script + # backward compatibility + run_main = run_script + # Activate all distributions already on sys.path with replace=False and + # ensure that all distributions added to the working set in the future + # (e.g. by calling ``require()``) will get activated as well, + # with higher priority (replace=True). + tuple(dist.activate(replace=False) for dist in working_set) + add_activation_listener( + lambda dist: dist.activate(replace=True), + existing=False, + ) + working_set.entries = [] + # match order + list(map(working_set.add_entry, sys.path)) + globals().update(locals()) + + +if TYPE_CHECKING: + # All of these are set by the @_call_aside methods above + __resource_manager = ResourceManager() # Won't exist at runtime + resource_exists = __resource_manager.resource_exists + resource_isdir = __resource_manager.resource_isdir + resource_filename = __resource_manager.resource_filename + resource_stream = __resource_manager.resource_stream + resource_string = __resource_manager.resource_string + resource_listdir = __resource_manager.resource_listdir + set_extraction_path = __resource_manager.set_extraction_path + cleanup_resources = __resource_manager.cleanup_resources + + working_set = WorkingSet() + require = working_set.require + iter_entry_points = working_set.iter_entry_points + add_activation_listener = working_set.subscribe + run_script = working_set.run_script + run_main = run_script diff --git a/lib/python3.11/site-packages/pkg_resources/api_tests.txt b/lib/python3.11/site-packages/pkg_resources/api_tests.txt new file mode 100644 index 0000000000000000000000000000000000000000..d72b85aa375b3a19cb3d36ccb1dd8d205c425938 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/api_tests.txt @@ -0,0 +1,424 @@ +Pluggable Distributions of Python Software +========================================== + +Distributions +------------- + +A "Distribution" is a collection of files that represent a "Release" of a +"Project" as of a particular point in time, denoted by a +"Version":: + + >>> import sys, pkg_resources + >>> from pkg_resources import Distribution + >>> Distribution(project_name="Foo", version="1.2") + Foo 1.2 + +Distributions have a location, which can be a filename, URL, or really anything +else you care to use:: + + >>> dist = Distribution( + ... location="http://example.com/something", + ... project_name="Bar", version="0.9" + ... ) + + >>> dist + Bar 0.9 (http://example.com/something) + + +Distributions have various introspectable attributes:: + + >>> dist.location + 'http://example.com/something' + + >>> dist.project_name + 'Bar' + + >>> dist.version + '0.9' + + >>> dist.py_version == '{}.{}'.format(*sys.version_info) + True + + >>> print(dist.platform) + None + +Including various computed attributes:: + + >>> from pkg_resources import parse_version + >>> dist.parsed_version == parse_version(dist.version) + True + + >>> dist.key # case-insensitive form of the project name + 'bar' + +Distributions are compared (and hashed) by version first:: + + >>> Distribution(version='1.0') == Distribution(version='1.0') + True + >>> Distribution(version='1.0') == Distribution(version='1.1') + False + >>> Distribution(version='1.0') < Distribution(version='1.1') + True + +but also by project name (case-insensitive), platform, Python version, +location, etc.:: + + >>> Distribution(project_name="Foo",version="1.0") == \ + ... Distribution(project_name="Foo",version="1.0") + True + + >>> Distribution(project_name="Foo",version="1.0") == \ + ... Distribution(project_name="foo",version="1.0") + True + + >>> Distribution(project_name="Foo",version="1.0") == \ + ... Distribution(project_name="Foo",version="1.1") + False + + >>> Distribution(project_name="Foo",py_version="2.3",version="1.0") == \ + ... Distribution(project_name="Foo",py_version="2.4",version="1.0") + False + + >>> Distribution(location="spam",version="1.0") == \ + ... Distribution(location="spam",version="1.0") + True + + >>> Distribution(location="spam",version="1.0") == \ + ... Distribution(location="baz",version="1.0") + False + + + +Hash and compare distribution by prio/plat + +Get version from metadata +provider capabilities +egg_name() +as_requirement() +from_location, from_filename (w/path normalization) + +Releases may have zero or more "Requirements", which indicate +what releases of another project the release requires in order to +function. A Requirement names the other project, expresses some criteria +as to what releases of that project are acceptable, and lists any "Extras" +that the requiring release may need from that project. (An Extra is an +optional feature of a Release, that can only be used if its additional +Requirements are satisfied.) + + + +The Working Set +--------------- + +A collection of active distributions is called a Working Set. Note that a +Working Set can contain any importable distribution, not just pluggable ones. +For example, the Python standard library is an importable distribution that +will usually be part of the Working Set, even though it is not pluggable. +Similarly, when you are doing development work on a project, the files you are +editing are also a Distribution. (And, with a little attention to the +directory names used, and including some additional metadata, such a +"development distribution" can be made pluggable as well.) + + >>> from pkg_resources import WorkingSet + +A working set's entries are the sys.path entries that correspond to the active +distributions. By default, the working set's entries are the items on +``sys.path``:: + + >>> ws = WorkingSet() + >>> ws.entries == sys.path + True + +But you can also create an empty working set explicitly, and add distributions +to it:: + + >>> ws = WorkingSet([]) + >>> ws.add(dist) + >>> ws.entries + ['http://example.com/something'] + >>> dist in ws + True + >>> Distribution('foo',version="") in ws + False + +And you can iterate over its distributions:: + + >>> list(ws) + [Bar 0.9 (http://example.com/something)] + +Adding the same distribution more than once is a no-op:: + + >>> ws.add(dist) + >>> list(ws) + [Bar 0.9 (http://example.com/something)] + +For that matter, adding multiple distributions for the same project also does +nothing, because a working set can only hold one active distribution per +project -- the first one added to it:: + + >>> ws.add( + ... Distribution( + ... 'http://example.com/something', project_name="Bar", + ... version="7.2" + ... ) + ... ) + >>> list(ws) + [Bar 0.9 (http://example.com/something)] + +You can append a path entry to a working set using ``add_entry()``:: + + >>> ws.entries + ['http://example.com/something'] + >>> ws.add_entry(pkg_resources.__file__) + >>> ws.entries + ['http://example.com/something', '...pkg_resources...'] + +Multiple additions result in multiple entries, even if the entry is already in +the working set (because ``sys.path`` can contain the same entry more than +once):: + + >>> ws.add_entry(pkg_resources.__file__) + >>> ws.entries + ['...example.com...', '...pkg_resources...', '...pkg_resources...'] + +And you can specify the path entry a distribution was found under, using the +optional second parameter to ``add()``:: + + >>> ws = WorkingSet([]) + >>> ws.add(dist,"foo") + >>> ws.entries + ['foo'] + +But even if a distribution is found under multiple path entries, it still only +shows up once when iterating the working set: + + >>> ws.add_entry(ws.entries[0]) + >>> list(ws) + [Bar 0.9 (http://example.com/something)] + +You can ask a WorkingSet to ``find()`` a distribution matching a requirement:: + + >>> from pkg_resources import Requirement + >>> print(ws.find(Requirement.parse("Foo==1.0"))) # no match, return None + None + + >>> ws.find(Requirement.parse("Bar==0.9")) # match, return distribution + Bar 0.9 (http://example.com/something) + +Note that asking for a conflicting version of a distribution already in a +working set triggers a ``pkg_resources.VersionConflict`` error: + + >>> try: + ... ws.find(Requirement.parse("Bar==1.0")) + ... except pkg_resources.VersionConflict as exc: + ... print(str(exc)) + ... else: + ... raise AssertionError("VersionConflict was not raised") + (Bar 0.9 (http://example.com/something), Requirement.parse('Bar==1.0')) + +You can subscribe a callback function to receive notifications whenever a new +distribution is added to a working set. The callback is immediately invoked +once for each existing distribution in the working set, and then is called +again for new distributions added thereafter:: + + >>> def added(dist): print("Added %s" % dist) + >>> ws.subscribe(added) + Added Bar 0.9 + >>> foo12 = Distribution(project_name="Foo", version="1.2", location="f12") + >>> ws.add(foo12) + Added Foo 1.2 + +Note, however, that only the first distribution added for a given project name +will trigger a callback, even during the initial ``subscribe()`` callback:: + + >>> foo14 = Distribution(project_name="Foo", version="1.4", location="f14") + >>> ws.add(foo14) # no callback, because Foo 1.2 is already active + + >>> ws = WorkingSet([]) + >>> ws.add(foo12) + >>> ws.add(foo14) + >>> ws.subscribe(added) + Added Foo 1.2 + +And adding a callback more than once has no effect, either:: + + >>> ws.subscribe(added) # no callbacks + + # and no double-callbacks on subsequent additions, either + >>> just_a_test = Distribution(project_name="JustATest", version="0.99") + >>> ws.add(just_a_test) + Added JustATest 0.99 + + +Finding Plugins +--------------- + +``WorkingSet`` objects can be used to figure out what plugins in an +``Environment`` can be loaded without any resolution errors:: + + >>> from pkg_resources import Environment + + >>> plugins = Environment([]) # normally, a list of plugin directories + >>> plugins.add(foo12) + >>> plugins.add(foo14) + >>> plugins.add(just_a_test) + +In the simplest case, we just get the newest version of each distribution in +the plugin environment:: + + >>> ws = WorkingSet([]) + >>> ws.find_plugins(plugins) + ([JustATest 0.99, Foo 1.4 (f14)], {}) + +But if there's a problem with a version conflict or missing requirements, the +method falls back to older versions, and the error info dict will contain an +exception instance for each unloadable plugin:: + + >>> ws.add(foo12) # this will conflict with Foo 1.4 + >>> ws.find_plugins(plugins) + ([JustATest 0.99, Foo 1.2 (f12)], {Foo 1.4 (f14): VersionConflict(...)}) + +But if you disallow fallbacks, the failed plugin will be skipped instead of +trying older versions:: + + >>> ws.find_plugins(plugins, fallback=False) + ([JustATest 0.99], {Foo 1.4 (f14): VersionConflict(...)}) + + + +Platform Compatibility Rules +---------------------------- + +On the Mac, there are potential compatibility issues for modules compiled +on newer versions of macOS than what the user is running. Additionally, +macOS will soon have two platforms to contend with: Intel and PowerPC. + +Basic equality works as on other platforms:: + + >>> from pkg_resources import compatible_platforms as cp + >>> reqd = 'macosx-10.4-ppc' + >>> cp(reqd, reqd) + True + >>> cp("win32", reqd) + False + +Distributions made on other machine types are not compatible:: + + >>> cp("macosx-10.4-i386", reqd) + False + +Distributions made on earlier versions of the OS are compatible, as +long as they are from the same top-level version. The patchlevel version +number does not matter:: + + >>> cp("macosx-10.4-ppc", reqd) + True + >>> cp("macosx-10.3-ppc", reqd) + True + >>> cp("macosx-10.5-ppc", reqd) + False + >>> cp("macosx-9.5-ppc", reqd) + False + +Backwards compatibility for packages made via earlier versions of +setuptools is provided as well:: + + >>> cp("darwin-8.2.0-Power_Macintosh", reqd) + True + >>> cp("darwin-7.2.0-Power_Macintosh", reqd) + True + >>> cp("darwin-8.2.0-Power_Macintosh", "macosx-10.3-ppc") + False + + +Environment Markers +------------------- + + >>> from pkg_resources import invalid_marker as im, evaluate_marker as em + >>> import os + + >>> print(im("sys_platform")) + Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in + sys_platform + ^ + + >>> print(im("sys_platform==")) + Expected a marker variable or quoted string + sys_platform== + ^ + + >>> print(im("sys_platform=='win32'")) + False + + >>> print(im("sys=='x'")) + Expected a marker variable or quoted string + sys=='x' + ^ + + >>> print(im("(extra)")) + Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in + (extra) + ^ + + >>> print(im("(extra")) + Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in + (extra + ^ + + >>> print(im("os.open('foo')=='y'")) + Expected a marker variable or quoted string + os.open('foo')=='y' + ^ + + >>> print(im("'x'=='y' and os.open('foo')=='y'")) # no short-circuit! + Expected a marker variable or quoted string + 'x'=='y' and os.open('foo')=='y' + ^ + + >>> print(im("'x'=='x' or os.open('foo')=='y'")) # no short-circuit! + Expected a marker variable or quoted string + 'x'=='x' or os.open('foo')=='y' + ^ + + >>> print(im("r'x'=='x'")) + Expected a marker variable or quoted string + r'x'=='x' + ^ + + >>> print(im("'''x'''=='x'")) + Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, ===, in, not in + '''x'''=='x' + ^ + + >>> print(im('"""x"""=="x"')) + Expected marker operator, one of <=, <, !=, ==, >=, >, ~=, 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b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package-source/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package-source/setup.py b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package-source/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..ce9080640c0cf4a5b42274ac0c91278d6df17643 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package-source/setup.py @@ -0,0 +1,7 @@ +import setuptools + +setuptools.setup( + name="my-test-package", + version="1.0", + zip_safe=True, +) diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/PKG-INFO b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..7328e3f7d18d31422d13b4fd63a7ef746956b7db --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/PKG-INFO @@ -0,0 +1,10 @@ +Metadata-Version: 1.0 +Name: my-test-package +Version: 1.0 +Summary: UNKNOWN +Home-page: UNKNOWN +Author: UNKNOWN +Author-email: UNKNOWN +License: UNKNOWN +Description: UNKNOWN +Platform: UNKNOWN diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/SOURCES.txt b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c4ee1676d4fec9f10f3c0f58c2d992d97975b85 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/SOURCES.txt @@ -0,0 +1,7 @@ +setup.cfg +setup.py +my_test_package.egg-info/PKG-INFO +my_test_package.egg-info/SOURCES.txt +my_test_package.egg-info/dependency_links.txt +my_test_package.egg-info/top_level.txt +my_test_package.egg-info/zip-safe \ No newline at end of file diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/dependency_links.txt b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/top_level.txt b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/top_level.txt @@ -0,0 +1 @@ + diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/zip-safe b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/zip-safe new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_unpacked-egg/my_test_package-1.0-py3.7.egg/EGG-INFO/zip-safe @@ -0,0 +1 @@ + diff --git a/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_zipped-egg/my_test_package-1.0-py3.7.egg b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_zipped-egg/my_test_package-1.0-py3.7.egg new file mode 100644 index 0000000000000000000000000000000000000000..5115b8957da8213d02b718649c8702ac88bf0e72 Binary files /dev/null and b/lib/python3.11/site-packages/pkg_resources/tests/data/my-test-package_zipped-egg/my_test_package-1.0-py3.7.egg differ diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_find_distributions.py b/lib/python3.11/site-packages/pkg_resources/tests/test_find_distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..301b36d6cdbf128f839db473aa6b191eba694937 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_find_distributions.py @@ -0,0 +1,56 @@ +import shutil +from pathlib import Path + +import pytest + +import pkg_resources + +TESTS_DATA_DIR = Path(__file__).parent / 'data' + + +class TestFindDistributions: + @pytest.fixture + def target_dir(self, tmpdir): + target_dir = tmpdir.mkdir('target') + # place a .egg named directory in the target that is not an egg: + target_dir.mkdir('not.an.egg') + return target_dir + + def test_non_egg_dir_named_egg(self, target_dir): + dists = pkg_resources.find_distributions(str(target_dir)) + assert not list(dists) + + def test_standalone_egg_directory(self, target_dir): + shutil.copytree( + TESTS_DATA_DIR / 'my-test-package_unpacked-egg', + target_dir, + dirs_exist_ok=True, + ) + dists = pkg_resources.find_distributions(str(target_dir)) + assert [dist.project_name for dist in dists] == ['my-test-package'] + dists = pkg_resources.find_distributions(str(target_dir), only=True) + assert not list(dists) + + def test_zipped_egg(self, target_dir): + shutil.copytree( + TESTS_DATA_DIR / 'my-test-package_zipped-egg', + target_dir, + dirs_exist_ok=True, + ) + dists = pkg_resources.find_distributions(str(target_dir)) + assert [dist.project_name for dist in dists] == ['my-test-package'] + dists = pkg_resources.find_distributions(str(target_dir), only=True) + assert not list(dists) + + def test_zipped_sdist_one_level_removed(self, target_dir): + shutil.copytree( + TESTS_DATA_DIR / 'my-test-package-zip', target_dir, dirs_exist_ok=True + ) + dists = pkg_resources.find_distributions( + str(target_dir / "my-test-package.zip") + ) + assert [dist.project_name for dist in dists] == ['my-test-package'] + dists = pkg_resources.find_distributions( + str(target_dir / "my-test-package.zip"), only=True + ) + assert not list(dists) diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_integration_zope_interface.py b/lib/python3.11/site-packages/pkg_resources/tests/test_integration_zope_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..4e37c3401b50c9f7387647303990887c7ce670f5 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_integration_zope_interface.py @@ -0,0 +1,54 @@ +import platform +from inspect import cleandoc + +import jaraco.path +import pytest + +pytestmark = pytest.mark.integration + + +# For the sake of simplicity this test uses fixtures defined in +# `setuptools.test.fixtures`, +# and it also exercise conditions considered deprecated... +# So if needed this test can be deleted. +@pytest.mark.skipif( + platform.system() != "Linux", + reason="only demonstrated to fail on Linux in #4399", +) +def test_interop_pkg_resources_iter_entry_points(tmp_path, venv): + """ + Importing pkg_resources.iter_entry_points on console_scripts + seems to cause trouble with zope-interface, when deprecates installation method + is used. See #4399. + """ + project = { + "pkg": { + "foo.py": cleandoc( + """ + from pkg_resources import iter_entry_points + + def bar(): + print("Print me if you can") + """ + ), + "setup.py": cleandoc( + """ + from setuptools import setup, find_packages + + setup( + install_requires=["zope-interface==6.4.post2"], + entry_points={ + "console_scripts": [ + "foo=foo:bar", + ], + }, + ) + """ + ), + } + } + jaraco.path.build(project, prefix=tmp_path) + cmd = ["pip", "install", "-e", ".", "--no-use-pep517"] + venv.run(cmd, cwd=tmp_path / "pkg") # Needs this version of pkg_resources installed + out = venv.run(["foo"]) + assert "Print me if you can" in out diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_markers.py b/lib/python3.11/site-packages/pkg_resources/tests/test_markers.py new file mode 100644 index 0000000000000000000000000000000000000000..9306d5b3483e8913b4a04d702091fb5efbc3f444 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_markers.py @@ -0,0 +1,8 @@ +from unittest import mock + +from pkg_resources import evaluate_marker + + +@mock.patch('platform.python_version', return_value='2.7.10') +def test_ordering(python_version_mock): + assert evaluate_marker("python_full_version > '2.7.3'") is True diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_pkg_resources.py b/lib/python3.11/site-packages/pkg_resources/tests/test_pkg_resources.py new file mode 100644 index 0000000000000000000000000000000000000000..cfc9b16c0f187175a0fa504f0b07968c0551333b --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_pkg_resources.py @@ -0,0 +1,485 @@ +from __future__ import annotations + +import builtins +import datetime +import inspect +import os +import plistlib +import stat +import subprocess +import sys +import tempfile +import zipfile +from unittest import mock + +import pytest + +import pkg_resources +from pkg_resources import DistInfoDistribution, Distribution, EggInfoDistribution + +import distutils.command.install_egg_info +import distutils.dist + + +class EggRemover(str): + def __call__(self): + if self in sys.path: + sys.path.remove(self) + if os.path.exists(self): + os.remove(self) + + +class TestZipProvider: + finalizers: list[EggRemover] = [] + + ref_time = datetime.datetime(2013, 5, 12, 13, 25, 0) + "A reference time for a file modification" + + @classmethod + def setup_class(cls): + "create a zip egg and add it to sys.path" + egg = tempfile.NamedTemporaryFile(suffix='.egg', delete=False) + zip_egg = zipfile.ZipFile(egg, 'w') + zip_info = zipfile.ZipInfo() + zip_info.filename = 'mod.py' + zip_info.date_time = cls.ref_time.timetuple() + zip_egg.writestr(zip_info, 'x = 3\n') + zip_info = zipfile.ZipInfo() + zip_info.filename = 'data.dat' + zip_info.date_time = cls.ref_time.timetuple() + zip_egg.writestr(zip_info, 'hello, world!') + zip_info = zipfile.ZipInfo() + zip_info.filename = 'subdir/mod2.py' + zip_info.date_time = cls.ref_time.timetuple() + zip_egg.writestr(zip_info, 'x = 6\n') + zip_info = zipfile.ZipInfo() + zip_info.filename = 'subdir/data2.dat' + zip_info.date_time = cls.ref_time.timetuple() + zip_egg.writestr(zip_info, 'goodbye, world!') + zip_egg.close() + egg.close() + + sys.path.append(egg.name) + subdir = os.path.join(egg.name, 'subdir') + sys.path.append(subdir) + cls.finalizers.append(EggRemover(subdir)) + cls.finalizers.append(EggRemover(egg.name)) + + @classmethod + def teardown_class(cls): + for finalizer in cls.finalizers: + finalizer() + + def test_resource_listdir(self): + import mod # pyright: ignore[reportMissingImports] # Temporary package for test + + zp = pkg_resources.ZipProvider(mod) + + expected_root = ['data.dat', 'mod.py', 'subdir'] + assert sorted(zp.resource_listdir('')) == expected_root + + expected_subdir = ['data2.dat', 'mod2.py'] + assert sorted(zp.resource_listdir('subdir')) == expected_subdir + assert sorted(zp.resource_listdir('subdir/')) == expected_subdir + + assert zp.resource_listdir('nonexistent') == [] + assert zp.resource_listdir('nonexistent/') == [] + + import mod2 # pyright: ignore[reportMissingImports] # Temporary package for test + + zp2 = pkg_resources.ZipProvider(mod2) + + assert sorted(zp2.resource_listdir('')) == expected_subdir + + assert zp2.resource_listdir('subdir') == [] + assert zp2.resource_listdir('subdir/') == [] + + def test_resource_filename_rewrites_on_change(self): + """ + If a previous call to get_resource_filename has saved the file, but + the file has been subsequently mutated with different file of the + same size and modification time, it should not be overwritten on a + subsequent call to get_resource_filename. + """ + import mod # pyright: ignore[reportMissingImports] # Temporary package for test + + manager = pkg_resources.ResourceManager() + zp = pkg_resources.ZipProvider(mod) + filename = zp.get_resource_filename(manager, 'data.dat') + actual = datetime.datetime.fromtimestamp(os.stat(filename).st_mtime) + assert actual == self.ref_time + f = open(filename, 'w', encoding="utf-8") + f.write('hello, world?') + f.close() + ts = self.ref_time.timestamp() + os.utime(filename, (ts, ts)) + filename = zp.get_resource_filename(manager, 'data.dat') + with open(filename, encoding="utf-8") as f: + assert f.read() == 'hello, world!' + manager.cleanup_resources() + + +class TestResourceManager: + def test_get_cache_path(self): + mgr = pkg_resources.ResourceManager() + path = mgr.get_cache_path('foo') + type_ = str(type(path)) + message = "Unexpected type from get_cache_path: " + type_ + assert isinstance(path, str), message + + def test_get_cache_path_race(self, tmpdir): + # Patch to os.path.isdir to create a race condition + def patched_isdir(dirname, unpatched_isdir=pkg_resources.isdir): + patched_isdir.dirnames.append(dirname) + + was_dir = unpatched_isdir(dirname) + if not was_dir: + os.makedirs(dirname) + return was_dir + + patched_isdir.dirnames = [] + + # Get a cache path with a "race condition" + mgr = pkg_resources.ResourceManager() + mgr.set_extraction_path(str(tmpdir)) + + archive_name = os.sep.join(('foo', 'bar', 'baz')) + with mock.patch.object(pkg_resources, 'isdir', new=patched_isdir): + mgr.get_cache_path(archive_name) + + # Because this test relies on the implementation details of this + # function, these assertions are a sentinel to ensure that the + # test suite will not fail silently if the implementation changes. + called_dirnames = patched_isdir.dirnames + assert len(called_dirnames) == 2 + assert called_dirnames[0].split(os.sep)[-2:] == ['foo', 'bar'] + assert called_dirnames[1].split(os.sep)[-1:] == ['foo'] + + """ + Tests to ensure that pkg_resources runs independently from setuptools. + """ + + def test_setuptools_not_imported(self): + """ + In a separate Python environment, import pkg_resources and assert + that action doesn't cause setuptools to be imported. + """ + lines = ( + 'import pkg_resources', + 'import sys', + ('assert "setuptools" not in sys.modules, "setuptools was imported"'), + ) + cmd = [sys.executable, '-c', '; '.join(lines)] + subprocess.check_call(cmd) + + +def make_test_distribution(metadata_path, metadata): + """ + Make a test Distribution object, and return it. + + :param metadata_path: the path to the metadata file that should be + created. This should be inside a distribution directory that should + also be created. For example, an argument value might end with + ".dist-info/METADATA". + :param metadata: the desired contents of the metadata file, as bytes. + """ + dist_dir = os.path.dirname(metadata_path) + os.mkdir(dist_dir) + with open(metadata_path, 'wb') as f: + f.write(metadata) + dists = list(pkg_resources.distributions_from_metadata(dist_dir)) + (dist,) = dists + + return dist + + +def test_get_metadata__bad_utf8(tmpdir): + """ + Test a metadata file with bytes that can't be decoded as utf-8. + """ + filename = 'METADATA' + # Convert the tmpdir LocalPath object to a string before joining. + metadata_path = os.path.join(str(tmpdir), 'foo.dist-info', filename) + # Encode a non-ascii string with the wrong encoding (not utf-8). + metadata = 'née'.encode('iso-8859-1') + dist = make_test_distribution(metadata_path, metadata=metadata) + + with pytest.raises(UnicodeDecodeError) as excinfo: + dist.get_metadata(filename) + + exc = excinfo.value + actual = str(exc) + expected = ( + # The error message starts with "'utf-8' codec ..." However, the + # spelling of "utf-8" can vary (e.g. "utf8") so we don't include it + "codec can't decode byte 0xe9 in position 1: " + 'invalid continuation byte in METADATA file at path: ' + ) + assert expected in actual, f'actual: {actual}' + assert actual.endswith(metadata_path), f'actual: {actual}' + + +def make_distribution_no_version(tmpdir, basename): + """ + Create a distribution directory with no file containing the version. + """ + dist_dir = tmpdir / basename + dist_dir.ensure_dir() + # Make the directory non-empty so distributions_from_metadata() + # will detect it and yield it. + dist_dir.join('temp.txt').ensure() + + dists = list(pkg_resources.distributions_from_metadata(dist_dir)) + assert len(dists) == 1 + (dist,) = dists + + return dist, dist_dir + + +@pytest.mark.parametrize( + ("suffix", "expected_filename", "expected_dist_type"), + [ + ('egg-info', 'PKG-INFO', EggInfoDistribution), + ('dist-info', 'METADATA', DistInfoDistribution), + ], +) +@pytest.mark.xfail( + sys.version_info[:2] == (3, 12) and sys.version_info.releaselevel != 'final', + reason="https://github.com/python/cpython/issues/103632", +) +def test_distribution_version_missing( + tmpdir, suffix, expected_filename, expected_dist_type +): + """ + Test Distribution.version when the "Version" header is missing. + """ + basename = f'foo.{suffix}' + dist, dist_dir = make_distribution_no_version(tmpdir, basename) + + expected_text = ( + f"Missing 'Version:' header and/or {expected_filename} file at path: " + ) + metadata_path = os.path.join(dist_dir, expected_filename) + + # Now check the exception raised when the "version" attribute is accessed. + with pytest.raises(ValueError) as excinfo: + dist.version + + err = str(excinfo.value) + # Include a string expression after the assert so the full strings + # will be visible for inspection on failure. + assert expected_text in err, str((expected_text, err)) + + # Also check the args passed to the ValueError. + msg, dist = excinfo.value.args + assert expected_text in msg + # Check that the message portion contains the path. + assert metadata_path in msg, str((metadata_path, msg)) + assert type(dist) is expected_dist_type + + +@pytest.mark.xfail( + sys.version_info[:2] == (3, 12) and sys.version_info.releaselevel != 'final', + reason="https://github.com/python/cpython/issues/103632", +) +def test_distribution_version_missing_undetected_path(): + """ + Test Distribution.version when the "Version" header is missing and + the path can't be detected. + """ + # Create a Distribution object with no metadata argument, which results + # in an empty metadata provider. + dist = Distribution('/foo') + with pytest.raises(ValueError) as excinfo: + dist.version + + msg, dist = excinfo.value.args + expected = ( + "Missing 'Version:' header and/or PKG-INFO file at path: [could not detect]" + ) + assert msg == expected + + +@pytest.mark.parametrize('only', [False, True]) +def test_dist_info_is_not_dir(tmp_path, only): + """Test path containing a file with dist-info extension.""" + dist_info = tmp_path / 'foobar.dist-info' + dist_info.touch() + assert not pkg_resources.dist_factory(str(tmp_path), str(dist_info), only) + + +def test_macos_vers_fallback(monkeypatch, tmp_path): + """Regression test for pkg_resources._macos_vers""" + orig_open = builtins.open + + # Pretend we need to use the plist file + monkeypatch.setattr('platform.mac_ver', mock.Mock(return_value=('', (), ''))) + + # Create fake content for the fake plist file + with open(tmp_path / 'fake.plist', 'wb') as fake_file: + plistlib.dump({"ProductVersion": "11.4"}, fake_file) + + # Pretend the fake file exists + monkeypatch.setattr('os.path.exists', mock.Mock(return_value=True)) + + def fake_open(file, *args, **kwargs): + return orig_open(tmp_path / 'fake.plist', *args, **kwargs) + + # Ensure that the _macos_vers works correctly + with mock.patch('builtins.open', mock.Mock(side_effect=fake_open)) as m: + pkg_resources._macos_vers.cache_clear() + assert pkg_resources._macos_vers() == ["11", "4"] + pkg_resources._macos_vers.cache_clear() + + m.assert_called() + + +class TestDeepVersionLookupDistutils: + @pytest.fixture + def env(self, tmpdir): + """ + Create a package environment, similar to a virtualenv, + in which packages are installed. + """ + + class Environment(str): + pass + + env = Environment(tmpdir) + tmpdir.chmod(stat.S_IRWXU) + subs = 'home', 'lib', 'scripts', 'data', 'egg-base' + env.paths = dict((dirname, str(tmpdir / dirname)) for dirname in subs) + list(map(os.mkdir, env.paths.values())) + return env + + def create_foo_pkg(self, env, version): + """ + Create a foo package installed (distutils-style) to env.paths['lib'] + as version. + """ + ld = "This package has unicode metadata! ❄" + attrs = dict(name='foo', version=version, long_description=ld) + dist = distutils.dist.Distribution(attrs) + iei_cmd = distutils.command.install_egg_info.install_egg_info(dist) + iei_cmd.initialize_options() + iei_cmd.install_dir = env.paths['lib'] + iei_cmd.finalize_options() + iei_cmd.run() + + def test_version_resolved_from_egg_info(self, env): + version = '1.11.0.dev0+2329eae' + self.create_foo_pkg(env, version) + + # this requirement parsing will raise a VersionConflict unless the + # .egg-info file is parsed (see #419 on BitBucket) + req = pkg_resources.Requirement.parse('foo>=1.9') + dist = pkg_resources.WorkingSet([env.paths['lib']]).find(req) + assert dist.version == version + + @pytest.mark.parametrize( + ("unnormalized", "normalized"), + [ + ('foo', 'foo'), + ('foo/', 'foo'), + ('foo/bar', 'foo/bar'), + ('foo/bar/', 'foo/bar'), + ], + ) + def test_normalize_path_trailing_sep(self, unnormalized, normalized): + """Ensure the trailing slash is cleaned for path comparison. + + See pypa/setuptools#1519. + """ + result_from_unnormalized = pkg_resources.normalize_path(unnormalized) + result_from_normalized = pkg_resources.normalize_path(normalized) + assert result_from_unnormalized == result_from_normalized + + @pytest.mark.skipif( + os.path.normcase('A') != os.path.normcase('a'), + reason='Testing case-insensitive filesystems.', + ) + @pytest.mark.parametrize( + ("unnormalized", "normalized"), + [ + ('MiXeD/CasE', 'mixed/case'), + ], + ) + def test_normalize_path_normcase(self, unnormalized, normalized): + """Ensure mixed case is normalized on case-insensitive filesystems.""" + result_from_unnormalized = pkg_resources.normalize_path(unnormalized) + result_from_normalized = pkg_resources.normalize_path(normalized) + assert result_from_unnormalized == result_from_normalized + + @pytest.mark.skipif( + os.path.sep != '\\', + reason='Testing systems using backslashes as path separators.', + ) + @pytest.mark.parametrize( + ("unnormalized", "expected"), + [ + ('forward/slash', 'forward\\slash'), + ('forward/slash/', 'forward\\slash'), + ('backward\\slash\\', 'backward\\slash'), + ], + ) + def test_normalize_path_backslash_sep(self, unnormalized, expected): + """Ensure path seps are cleaned on backslash path sep systems.""" + result = pkg_resources.normalize_path(unnormalized) + assert result.endswith(expected) + + +class TestWorkdirRequire: + def fake_site_packages(self, tmp_path, monkeypatch, dist_files): + site_packages = tmp_path / "site-packages" + site_packages.mkdir() + for file, content in self.FILES.items(): + path = site_packages / file + path.parent.mkdir(exist_ok=True, parents=True) + path.write_text(inspect.cleandoc(content), encoding="utf-8") + + monkeypatch.setattr(sys, "path", [site_packages]) + return os.fspath(site_packages) + + FILES = { + "pkg1_mod-1.2.3.dist-info/METADATA": """ + Metadata-Version: 2.4 + Name: pkg1.mod + Version: 1.2.3 + """, + "pkg2.mod-0.42.dist-info/METADATA": """ + Metadata-Version: 2.1 + Name: pkg2.mod + Version: 0.42 + """, + "pkg3_mod.egg-info/PKG-INFO": """ + Name: pkg3.mod + Version: 1.2.3.4 + """, + "pkg4.mod.egg-info/PKG-INFO": """ + Name: pkg4.mod + Version: 0.42.1 + """, + } + + @pytest.mark.parametrize( + ("version", "requirement"), + [ + ("1.2.3", "pkg1.mod>=1"), + ("0.42", "pkg2.mod>=0.4"), + ("1.2.3.4", "pkg3.mod<=2"), + ("0.42.1", "pkg4.mod>0.2,<1"), + ], + ) + def test_require_non_normalised_name( + self, tmp_path, monkeypatch, version, requirement + ): + # https://github.com/pypa/setuptools/issues/4853 + site_packages = self.fake_site_packages(tmp_path, monkeypatch, self.FILES) + ws = pkg_resources.WorkingSet([site_packages]) + + for req in [requirement, requirement.replace(".", "-")]: + [dist] = ws.require(req) + assert dist.version == version + assert os.path.samefile( + os.path.commonpath([dist.location, site_packages]), site_packages + ) diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_resources.py b/lib/python3.11/site-packages/pkg_resources/tests/test_resources.py new file mode 100644 index 0000000000000000000000000000000000000000..70436c08812df9a36d4fe5d40fb0b3bc07f02db9 --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_resources.py @@ -0,0 +1,869 @@ +import itertools +import os +import platform +import string +import sys + +import pytest +from packaging.specifiers import SpecifierSet + +import pkg_resources +from pkg_resources import ( + Distribution, + EntryPoint, + Requirement, + VersionConflict, + WorkingSet, + parse_requirements, + parse_version, + safe_name, + safe_version, +) + + +# from Python 3.6 docs. Available from itertools on Python 3.10 +def pairwise(iterable): + "s -> (s0,s1), (s1,s2), (s2, s3), ..." + a, b = itertools.tee(iterable) + next(b, None) + return zip(a, b) + + +class Metadata(pkg_resources.EmptyProvider): + """Mock object to return metadata as if from an on-disk distribution""" + + def __init__(self, *pairs) -> None: + self.metadata = dict(pairs) + + def has_metadata(self, name) -> bool: + return name in self.metadata + + def get_metadata(self, name): + return self.metadata[name] + + def get_metadata_lines(self, name): + return pkg_resources.yield_lines(self.get_metadata(name)) + + +dist_from_fn = pkg_resources.Distribution.from_filename + + +class TestDistro: + def testCollection(self): + # empty path should produce no distributions + ad = pkg_resources.Environment([], platform=None, python=None) + assert list(ad) == [] + assert ad['FooPkg'] == [] + ad.add(dist_from_fn("FooPkg-1.3_1.egg")) + ad.add(dist_from_fn("FooPkg-1.4-py2.4-win32.egg")) + ad.add(dist_from_fn("FooPkg-1.2-py2.4.egg")) + + # Name is in there now + assert ad['FooPkg'] + # But only 1 package + assert list(ad) == ['foopkg'] + + # Distributions sort by version + expected = ['1.4', '1.3-1', '1.2'] + assert [dist.version for dist in ad['FooPkg']] == expected + + # Removing a distribution leaves sequence alone + ad.remove(ad['FooPkg'][1]) + assert [dist.version for dist in ad['FooPkg']] == ['1.4', '1.2'] + + # And inserting adds them in order + ad.add(dist_from_fn("FooPkg-1.9.egg")) + assert [dist.version for dist in ad['FooPkg']] == ['1.9', '1.4', '1.2'] + + ws = WorkingSet([]) + foo12 = dist_from_fn("FooPkg-1.2-py2.4.egg") + foo14 = dist_from_fn("FooPkg-1.4-py2.4-win32.egg") + (req,) = parse_requirements("FooPkg>=1.3") + + # Nominal case: no distros on path, should yield all applicable + assert ad.best_match(req, ws).version == '1.9' + # If a matching distro is already installed, should return only that + ws.add(foo14) + assert ad.best_match(req, ws).version == '1.4' + + # If the first matching distro is unsuitable, it's a version conflict + ws = WorkingSet([]) + ws.add(foo12) + ws.add(foo14) + with pytest.raises(VersionConflict): + ad.best_match(req, ws) + + # If more than one match on the path, the first one takes precedence + ws = WorkingSet([]) + ws.add(foo14) + ws.add(foo12) + ws.add(foo14) + assert ad.best_match(req, ws).version == '1.4' + + def checkFooPkg(self, d): + assert d.project_name == "FooPkg" + assert d.key == "foopkg" + assert d.version == "1.3.post1" + assert d.py_version == "2.4" + assert d.platform == "win32" + assert d.parsed_version == parse_version("1.3-1") + + def testDistroBasics(self): + d = Distribution( + "/some/path", + project_name="FooPkg", + version="1.3-1", + py_version="2.4", + platform="win32", + ) + self.checkFooPkg(d) + + d = Distribution("/some/path") + assert d.py_version == f'{sys.version_info.major}.{sys.version_info.minor}' + assert d.platform is None + + def testDistroParse(self): + d = dist_from_fn("FooPkg-1.3.post1-py2.4-win32.egg") + self.checkFooPkg(d) + d = dist_from_fn("FooPkg-1.3.post1-py2.4-win32.egg-info") + self.checkFooPkg(d) + + def testDistroMetadata(self): + d = Distribution( + "/some/path", + project_name="FooPkg", + py_version="2.4", + platform="win32", + metadata=Metadata(('PKG-INFO', "Metadata-Version: 1.0\nVersion: 1.3-1\n")), + ) + self.checkFooPkg(d) + + def distRequires(self, txt): + return Distribution("/foo", metadata=Metadata(('depends.txt', txt))) + + def checkRequires(self, dist, txt, extras=()): + assert list(dist.requires(extras)) == list(parse_requirements(txt)) + + def testDistroDependsSimple(self): + for v in "Twisted>=1.5", "Twisted>=1.5\nZConfig>=2.0": + self.checkRequires(self.distRequires(v), v) + + needs_object_dir = pytest.mark.skipif( + not hasattr(object, '__dir__'), + reason='object.__dir__ necessary for self.__dir__ implementation', + ) + + def test_distribution_dir(self): + d = pkg_resources.Distribution() + dir(d) + + @needs_object_dir + def test_distribution_dir_includes_provider_dir(self): + d = pkg_resources.Distribution() + before = d.__dir__() + assert 'test_attr' not in before + d._provider.test_attr = None + after = d.__dir__() + assert len(after) == len(before) + 1 + assert 'test_attr' in after + + @needs_object_dir + def test_distribution_dir_ignores_provider_dir_leading_underscore(self): + d = pkg_resources.Distribution() + before = d.__dir__() + assert '_test_attr' not in before + d._provider._test_attr = None + after = d.__dir__() + assert len(after) == len(before) + assert '_test_attr' not in after + + def testResolve(self): + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + # Resolving no requirements -> nothing to install + assert list(ws.resolve([], ad)) == [] + # Request something not in the collection -> DistributionNotFound + with pytest.raises(pkg_resources.DistributionNotFound): + ws.resolve(parse_requirements("Foo"), ad) + + Foo = Distribution.from_filename( + "/foo_dir/Foo-1.2.egg", + metadata=Metadata(('depends.txt', "[bar]\nBaz>=2.0")), + ) + ad.add(Foo) + ad.add(Distribution.from_filename("Foo-0.9.egg")) + + # Request thing(s) that are available -> list to activate + for i in range(3): + targets = list(ws.resolve(parse_requirements("Foo"), ad)) + assert targets == [Foo] + list(map(ws.add, targets)) + with pytest.raises(VersionConflict): + ws.resolve(parse_requirements("Foo==0.9"), ad) + ws = WorkingSet([]) # reset + + # Request an extra that causes an unresolved dependency for "Baz" + with pytest.raises(pkg_resources.DistributionNotFound): + ws.resolve(parse_requirements("Foo[bar]"), ad) + Baz = Distribution.from_filename( + "/foo_dir/Baz-2.1.egg", metadata=Metadata(('depends.txt', "Foo")) + ) + ad.add(Baz) + + # Activation list now includes resolved dependency + assert list(ws.resolve(parse_requirements("Foo[bar]"), ad)) == [Foo, Baz] + # Requests for conflicting versions produce VersionConflict + with pytest.raises(VersionConflict) as vc: + ws.resolve(parse_requirements("Foo==1.2\nFoo!=1.2"), ad) + + msg = 'Foo 0.9 is installed but Foo==1.2 is required' + assert vc.value.report() == msg + + def test_environment_marker_evaluation_negative(self): + """Environment markers are evaluated at resolution time.""" + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + res = ws.resolve(parse_requirements("Foo;python_version<'2'"), ad) + assert list(res) == [] + + def test_environment_marker_evaluation_positive(self): + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + Foo = Distribution.from_filename("/foo_dir/Foo-1.2.dist-info") + ad.add(Foo) + res = ws.resolve(parse_requirements("Foo;python_version>='2'"), ad) + assert list(res) == [Foo] + + def test_environment_marker_evaluation_called(self): + """ + If one package foo requires bar without any extras, + markers should pass for bar without extras. + """ + (parent_req,) = parse_requirements("foo") + (req,) = parse_requirements("bar;python_version>='2'") + req_extras = pkg_resources._ReqExtras({req: parent_req.extras}) + assert req_extras.markers_pass(req) + + (parent_req,) = parse_requirements("foo[]") + (req,) = parse_requirements("bar;python_version>='2'") + req_extras = pkg_resources._ReqExtras({req: parent_req.extras}) + assert req_extras.markers_pass(req) + + def test_marker_evaluation_with_extras(self): + """Extras are also evaluated as markers at resolution time.""" + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + Foo = Distribution.from_filename( + "/foo_dir/Foo-1.2.dist-info", + metadata=Metadata(( + "METADATA", + "Provides-Extra: baz\nRequires-Dist: quux; extra=='baz'", + )), + ) + ad.add(Foo) + assert list(ws.resolve(parse_requirements("Foo"), ad)) == [Foo] + quux = Distribution.from_filename("/foo_dir/quux-1.0.dist-info") + ad.add(quux) + res = list(ws.resolve(parse_requirements("Foo[baz]"), ad)) + assert res == [Foo, quux] + + def test_marker_evaluation_with_extras_normlized(self): + """Extras are also evaluated as markers at resolution time.""" + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + Foo = Distribution.from_filename( + "/foo_dir/Foo-1.2.dist-info", + metadata=Metadata(( + "METADATA", + "Provides-Extra: baz-lightyear\n" + "Requires-Dist: quux; extra=='baz-lightyear'", + )), + ) + ad.add(Foo) + assert list(ws.resolve(parse_requirements("Foo"), ad)) == [Foo] + quux = Distribution.from_filename("/foo_dir/quux-1.0.dist-info") + ad.add(quux) + res = list(ws.resolve(parse_requirements("Foo[baz-lightyear]"), ad)) + assert res == [Foo, quux] + + def test_marker_evaluation_with_multiple_extras(self): + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + Foo = Distribution.from_filename( + "/foo_dir/Foo-1.2.dist-info", + metadata=Metadata(( + "METADATA", + "Provides-Extra: baz\n" + "Requires-Dist: quux; extra=='baz'\n" + "Provides-Extra: bar\n" + "Requires-Dist: fred; extra=='bar'\n", + )), + ) + ad.add(Foo) + quux = Distribution.from_filename("/foo_dir/quux-1.0.dist-info") + ad.add(quux) + fred = Distribution.from_filename("/foo_dir/fred-0.1.dist-info") + ad.add(fred) + res = list(ws.resolve(parse_requirements("Foo[baz,bar]"), ad)) + assert sorted(res) == [fred, quux, Foo] + + def test_marker_evaluation_with_extras_loop(self): + ad = pkg_resources.Environment([]) + ws = WorkingSet([]) + a = Distribution.from_filename( + "/foo_dir/a-0.2.dist-info", + metadata=Metadata(("METADATA", "Requires-Dist: c[a]")), + ) + b = Distribution.from_filename( + "/foo_dir/b-0.3.dist-info", + metadata=Metadata(("METADATA", "Requires-Dist: c[b]")), + ) + c = Distribution.from_filename( + "/foo_dir/c-1.0.dist-info", + metadata=Metadata(( + "METADATA", + "Provides-Extra: a\n" + "Requires-Dist: b;extra=='a'\n" + "Provides-Extra: b\n" + "Requires-Dist: foo;extra=='b'", + )), + ) + foo = Distribution.from_filename("/foo_dir/foo-0.1.dist-info") + for dist in (a, b, c, foo): + ad.add(dist) + res = list(ws.resolve(parse_requirements("a"), ad)) + assert res == [a, c, b, foo] + + @pytest.mark.xfail( + sys.version_info[:2] == (3, 12) and sys.version_info.releaselevel != 'final', + reason="https://github.com/python/cpython/issues/103632", + ) + def testDistroDependsOptions(self): + d = self.distRequires( + """ + Twisted>=1.5 + [docgen] + ZConfig>=2.0 + docutils>=0.3 + [fastcgi] + fcgiapp>=0.1""" + ) + self.checkRequires(d, "Twisted>=1.5") + self.checkRequires( + d, "Twisted>=1.5 ZConfig>=2.0 docutils>=0.3".split(), ["docgen"] + ) + self.checkRequires(d, "Twisted>=1.5 fcgiapp>=0.1".split(), ["fastcgi"]) + self.checkRequires( + d, + "Twisted>=1.5 ZConfig>=2.0 docutils>=0.3 fcgiapp>=0.1".split(), + ["docgen", "fastcgi"], + ) + self.checkRequires( + d, + "Twisted>=1.5 fcgiapp>=0.1 ZConfig>=2.0 docutils>=0.3".split(), + ["fastcgi", "docgen"], + ) + with pytest.raises(pkg_resources.UnknownExtra): + d.requires(["foo"]) + + +class TestWorkingSet: + def test_find_conflicting(self): + ws = WorkingSet([]) + Foo = Distribution.from_filename("/foo_dir/Foo-1.2.egg") + ws.add(Foo) + + # create a requirement that conflicts with Foo 1.2 + req = next(parse_requirements("Foo<1.2")) + + with pytest.raises(VersionConflict) as vc: + ws.find(req) + + msg = 'Foo 1.2 is installed but Foo<1.2 is required' + assert vc.value.report() == msg + + def test_resolve_conflicts_with_prior(self): + """ + A ContextualVersionConflict should be raised when a requirement + conflicts with a prior requirement for a different package. + """ + # Create installation where Foo depends on Baz 1.0 and Bar depends on + # Baz 2.0. + ws = WorkingSet([]) + md = Metadata(('depends.txt', "Baz==1.0")) + Foo = Distribution.from_filename("/foo_dir/Foo-1.0.egg", metadata=md) + ws.add(Foo) + md = Metadata(('depends.txt', "Baz==2.0")) + Bar = Distribution.from_filename("/foo_dir/Bar-1.0.egg", metadata=md) + ws.add(Bar) + Baz = Distribution.from_filename("/foo_dir/Baz-1.0.egg") + ws.add(Baz) + Baz = Distribution.from_filename("/foo_dir/Baz-2.0.egg") + ws.add(Baz) + + with pytest.raises(VersionConflict) as vc: + ws.resolve(parse_requirements("Foo\nBar\n")) + + msg = "Baz 1.0 is installed but Baz==2.0 is required by " + msg += repr(set(['Bar'])) + assert vc.value.report() == msg + + +class TestEntryPoints: + def assertfields(self, ep): + assert ep.name == "foo" + assert ep.module_name == "pkg_resources.tests.test_resources" + assert ep.attrs == ("TestEntryPoints",) + assert ep.extras == ("x",) + assert ep.load() is TestEntryPoints + expect = "foo = pkg_resources.tests.test_resources:TestEntryPoints [x]" + assert str(ep) == expect + + def setup_method(self, method): + self.dist = Distribution.from_filename( + "FooPkg-1.2-py2.4.egg", metadata=Metadata(('requires.txt', '[x]')) + ) + + def testBasics(self): + ep = EntryPoint( + "foo", + "pkg_resources.tests.test_resources", + ["TestEntryPoints"], + ["x"], + self.dist, + ) + self.assertfields(ep) + + def testParse(self): + s = "foo = pkg_resources.tests.test_resources:TestEntryPoints [x]" + ep = EntryPoint.parse(s, self.dist) + self.assertfields(ep) + + ep = EntryPoint.parse("bar baz= spammity[PING]") + assert ep.name == "bar baz" + assert ep.module_name == "spammity" + assert ep.attrs == () + assert ep.extras == ("ping",) + + ep = EntryPoint.parse(" fizzly = wocka:foo") + assert ep.name == "fizzly" + assert ep.module_name == "wocka" + assert ep.attrs == ("foo",) + assert ep.extras == () + + # plus in the name + spec = "html+mako = mako.ext.pygmentplugin:MakoHtmlLexer" + ep = EntryPoint.parse(spec) + assert ep.name == 'html+mako' + + reject_specs = "foo", "x=a:b:c", "q=x/na", "fez=pish:tush-z", "x=f[a]>2" + + @pytest.mark.parametrize("reject_spec", reject_specs) + def test_reject_spec(self, reject_spec): + with pytest.raises(ValueError): + EntryPoint.parse(reject_spec) + + def test_printable_name(self): + """ + Allow any printable character in the name. + """ + # Create a name with all printable characters; strip the whitespace. + name = string.printable.strip() + spec = "{name} = module:attr".format(**locals()) + ep = EntryPoint.parse(spec) + assert ep.name == name + + def checkSubMap(self, m): + assert len(m) == len(self.submap_expect) + for key, ep in self.submap_expect.items(): + assert m.get(key).name == ep.name + assert m.get(key).module_name == ep.module_name + assert sorted(m.get(key).attrs) == sorted(ep.attrs) + assert sorted(m.get(key).extras) == sorted(ep.extras) + + submap_expect = dict( + feature1=EntryPoint('feature1', 'somemodule', ['somefunction']), + feature2=EntryPoint( + 'feature2', 'another.module', ['SomeClass'], ['extra1', 'extra2'] + ), + feature3=EntryPoint('feature3', 'this.module', extras=['something']), + ) + submap_str = """ + # define features for blah blah + feature1 = somemodule:somefunction + feature2 = another.module:SomeClass [extra1,extra2] + feature3 = this.module [something] + """ + + def testParseList(self): + self.checkSubMap(EntryPoint.parse_group("xyz", self.submap_str)) + with pytest.raises(ValueError): + EntryPoint.parse_group("x a", "foo=bar") + with pytest.raises(ValueError): + EntryPoint.parse_group("x", ["foo=baz", "foo=bar"]) + + def testParseMap(self): + m = EntryPoint.parse_map({'xyz': self.submap_str}) + self.checkSubMap(m['xyz']) + assert list(m.keys()) == ['xyz'] + m = EntryPoint.parse_map("[xyz]\n" + self.submap_str) + self.checkSubMap(m['xyz']) + assert list(m.keys()) == ['xyz'] + with pytest.raises(ValueError): + EntryPoint.parse_map(["[xyz]", "[xyz]"]) + with pytest.raises(ValueError): + EntryPoint.parse_map(self.submap_str) + + def testDeprecationWarnings(self): + ep = EntryPoint( + "foo", "pkg_resources.tests.test_resources", ["TestEntryPoints"], ["x"] + ) + with pytest.warns(pkg_resources.PkgResourcesDeprecationWarning): + ep.load(require=False) + + +class TestRequirements: + def testBasics(self): + r = Requirement.parse("Twisted>=1.2") + assert str(r) == "Twisted>=1.2" + assert repr(r) == "Requirement.parse('Twisted>=1.2')" + assert r == Requirement("Twisted>=1.2") + assert r == Requirement("twisTed>=1.2") + assert r != Requirement("Twisted>=2.0") + assert r != Requirement("Zope>=1.2") + assert r != Requirement("Zope>=3.0") + assert r != Requirement("Twisted[extras]>=1.2") + + def testOrdering(self): + r1 = Requirement("Twisted==1.2c1,>=1.2") + r2 = Requirement("Twisted>=1.2,==1.2c1") + assert r1 == r2 + assert str(r1) == str(r2) + assert str(r2) == "Twisted==1.2c1,>=1.2" + assert Requirement("Twisted") != Requirement( + "Twisted @ https://localhost/twisted.zip" + ) + + def testBasicContains(self): + r = Requirement("Twisted>=1.2") + foo_dist = Distribution.from_filename("FooPkg-1.3_1.egg") + twist11 = Distribution.from_filename("Twisted-1.1.egg") + twist12 = Distribution.from_filename("Twisted-1.2.egg") + assert parse_version('1.2') in r + assert parse_version('1.1') not in r + assert '1.2' in r + assert '1.1' not in r + assert foo_dist not in r + assert twist11 not in r + assert twist12 in r + + def testOptionsAndHashing(self): + r1 = Requirement.parse("Twisted[foo,bar]>=1.2") + r2 = Requirement.parse("Twisted[bar,FOO]>=1.2") + assert r1 == r2 + assert set(r1.extras) == set(("foo", "bar")) + assert set(r2.extras) == set(("foo", "bar")) + assert hash(r1) == hash(r2) + assert hash(r1) == hash(( + "twisted", + None, + SpecifierSet(">=1.2"), + frozenset(["foo", "bar"]), + None, + )) + assert hash( + Requirement.parse("Twisted @ https://localhost/twisted.zip") + ) == hash(( + "twisted", + "https://localhost/twisted.zip", + SpecifierSet(), + frozenset(), + None, + )) + + def testVersionEquality(self): + r1 = Requirement.parse("foo==0.3a2") + r2 = Requirement.parse("foo!=0.3a4") + d = Distribution.from_filename + + assert d("foo-0.3a4.egg") not in r1 + assert d("foo-0.3a1.egg") not in r1 + assert d("foo-0.3a4.egg") not in r2 + + assert d("foo-0.3a2.egg") in r1 + assert d("foo-0.3a2.egg") in r2 + assert d("foo-0.3a3.egg") in r2 + assert d("foo-0.3a5.egg") in r2 + + def testSetuptoolsProjectName(self): + """ + The setuptools project should implement the setuptools package. + """ + + assert Requirement.parse('setuptools').project_name == 'setuptools' + # setuptools 0.7 and higher means setuptools. + assert Requirement.parse('setuptools == 0.7').project_name == 'setuptools' + assert Requirement.parse('setuptools == 0.7a1').project_name == 'setuptools' + assert Requirement.parse('setuptools >= 0.7').project_name == 'setuptools' + + +class TestParsing: + def testEmptyParse(self): + assert list(parse_requirements('')) == [] + + def testYielding(self): + for inp, out in [ + ([], []), + ('x', ['x']), + ([[]], []), + (' x\n y', ['x', 'y']), + (['x\n\n', 'y'], ['x', 'y']), + ]: + assert list(pkg_resources.yield_lines(inp)) == out + + def testSplitting(self): + sample = """ + x + [Y] + z + + a + [b ] + # foo + c + [ d] + [q] + v + """ + assert list(pkg_resources.split_sections(sample)) == [ + (None, ["x"]), + ("Y", ["z", "a"]), + ("b", ["c"]), + ("d", []), + ("q", ["v"]), + ] + with pytest.raises(ValueError): + list(pkg_resources.split_sections("[foo")) + + def testSafeName(self): + assert safe_name("adns-python") == "adns-python" + assert safe_name("WSGI Utils") == "WSGI-Utils" + assert safe_name("WSGI Utils") == "WSGI-Utils" + assert safe_name("Money$$$Maker") == "Money-Maker" + assert safe_name("peak.web") != "peak-web" + + def testSafeVersion(self): + assert safe_version("1.2-1") == "1.2.post1" + assert safe_version("1.2 alpha") == "1.2.alpha" + assert safe_version("2.3.4 20050521") == "2.3.4.20050521" + assert safe_version("Money$$$Maker") == "Money-Maker" + assert safe_version("peak.web") == "peak.web" + + def testSimpleRequirements(self): + assert list(parse_requirements('Twis-Ted>=1.2-1')) == [ + Requirement('Twis-Ted>=1.2-1') + ] + assert list(parse_requirements('Twisted >=1.2, \\ # more\n<2.0')) == [ + Requirement('Twisted>=1.2,<2.0') + ] + assert Requirement.parse("FooBar==1.99a3") == Requirement("FooBar==1.99a3") + with pytest.raises(ValueError): + Requirement.parse(">=2.3") + with pytest.raises(ValueError): + Requirement.parse("x\\") + with pytest.raises(ValueError): + Requirement.parse("x==2 q") + with pytest.raises(ValueError): + Requirement.parse("X==1\nY==2") + with pytest.raises(ValueError): + Requirement.parse("#") + + def test_requirements_with_markers(self): + assert Requirement.parse("foobar;os_name=='a'") == Requirement.parse( + "foobar;os_name=='a'" + ) + assert Requirement.parse( + "name==1.1;python_version=='2.7'" + ) != Requirement.parse("name==1.1;python_version=='3.6'") + assert Requirement.parse( + "name==1.0;python_version=='2.7'" + ) != Requirement.parse("name==1.2;python_version=='2.7'") + assert Requirement.parse( + "name[foo]==1.0;python_version=='3.6'" + ) != Requirement.parse("name[foo,bar]==1.0;python_version=='3.6'") + + def test_local_version(self): + parse_requirements('foo==1.0+org1') + + def test_spaces_between_multiple_versions(self): + parse_requirements('foo>=1.0, <3') + parse_requirements('foo >= 1.0, < 3') + + @pytest.mark.parametrize( + ("lower", "upper"), + [ + ('1.2-rc1', '1.2rc1'), + ('0.4', '0.4.0'), + ('0.4.0.0', '0.4.0'), + ('0.4.0-0', '0.4-0'), + ('0post1', '0.0post1'), + ('0pre1', '0.0c1'), + ('0.0.0preview1', '0c1'), + ('0.0c1', '0-rc1'), + ('1.2a1', '1.2.a.1'), + ('1.2.a', '1.2a'), + ], + ) + def testVersionEquality(self, lower, upper): + assert parse_version(lower) == parse_version(upper) + + torture = """ + 0.80.1-3 0.80.1-2 0.80.1-1 0.79.9999+0.80.0pre4-1 + 0.79.9999+0.80.0pre2-3 0.79.9999+0.80.0pre2-2 + 0.77.2-1 0.77.1-1 0.77.0-1 + """ + + @pytest.mark.parametrize( + ("lower", "upper"), + [ + ('2.1', '2.1.1'), + ('2a1', '2b0'), + ('2a1', '2.1'), + ('2.3a1', '2.3'), + ('2.1-1', '2.1-2'), + ('2.1-1', '2.1.1'), + ('2.1', '2.1post4'), + ('2.1a0-20040501', '2.1'), + ('1.1', '02.1'), + ('3.2', '3.2.post0'), + ('3.2post1', '3.2post2'), + ('0.4', '4.0'), + ('0.0.4', '0.4.0'), + ('0post1', '0.4post1'), + ('2.1.0-rc1', '2.1.0'), + ('2.1dev', '2.1a0'), + ] + + list(pairwise(reversed(torture.split()))), + ) + def testVersionOrdering(self, lower, upper): + assert parse_version(lower) < parse_version(upper) + + def testVersionHashable(self): + """ + Ensure that our versions stay hashable even though we've subclassed + them and added some shim code to them. + """ + assert hash(parse_version("1.0")) == hash(parse_version("1.0")) + + +class TestNamespaces: + ns_str = "__import__('pkg_resources').declare_namespace(__name__)\n" + + @pytest.fixture + def symlinked_tmpdir(self, tmpdir): + """ + Where available, return the tempdir as a symlink, + which as revealed in #231 is more fragile than + a natural tempdir. + """ + if not hasattr(os, 'symlink'): + yield str(tmpdir) + return + + link_name = str(tmpdir) + '-linked' + os.symlink(str(tmpdir), link_name) + try: + yield type(tmpdir)(link_name) + finally: + os.unlink(link_name) + + @pytest.fixture(autouse=True) + def patched_path(self, tmpdir): + """ + Patch sys.path to include the 'site-pkgs' dir. Also + restore pkg_resources._namespace_packages to its + former state. + """ + saved_ns_pkgs = pkg_resources._namespace_packages.copy() + saved_sys_path = sys.path[:] + site_pkgs = tmpdir.mkdir('site-pkgs') + sys.path.append(str(site_pkgs)) + try: + yield + finally: + pkg_resources._namespace_packages = saved_ns_pkgs + sys.path = saved_sys_path + + issue591 = pytest.mark.xfail(platform.system() == 'Windows', reason="#591") + + @issue591 + def test_two_levels_deep(self, symlinked_tmpdir): + """ + Test nested namespace packages + Create namespace packages in the following tree : + site-packages-1/pkg1/pkg2 + site-packages-2/pkg1/pkg2 + Check both are in the _namespace_packages dict and that their __path__ + is correct + """ + real_tmpdir = symlinked_tmpdir.realpath() + tmpdir = symlinked_tmpdir + sys.path.append(str(tmpdir / 'site-pkgs2')) + site_dirs = tmpdir / 'site-pkgs', tmpdir / 'site-pkgs2' + for site in site_dirs: + pkg1 = site / 'pkg1' + pkg2 = pkg1 / 'pkg2' + pkg2.ensure_dir() + (pkg1 / '__init__.py').write_text(self.ns_str, encoding='utf-8') + (pkg2 / '__init__.py').write_text(self.ns_str, encoding='utf-8') + with pytest.warns(DeprecationWarning, match="pkg_resources.declare_namespace"): + import pkg1 # pyright: ignore[reportMissingImports] # Temporary package for test + assert "pkg1" in pkg_resources._namespace_packages + # attempt to import pkg2 from site-pkgs2 + with pytest.warns(DeprecationWarning, match="pkg_resources.declare_namespace"): + import pkg1.pkg2 # pyright: ignore[reportMissingImports] # Temporary package for test + # check the _namespace_packages dict + assert "pkg1.pkg2" in pkg_resources._namespace_packages + assert pkg_resources._namespace_packages["pkg1"] == ["pkg1.pkg2"] + # check the __path__ attribute contains both paths + expected = [ + str(real_tmpdir / "site-pkgs" / "pkg1" / "pkg2"), + str(real_tmpdir / "site-pkgs2" / "pkg1" / "pkg2"), + ] + assert pkg1.pkg2.__path__ == expected + + @issue591 + def test_path_order(self, symlinked_tmpdir): + """ + Test that if multiple versions of the same namespace package subpackage + are on different sys.path entries, that only the one earliest on + sys.path is imported, and that the namespace package's __path__ is in + the correct order. + + Regression test for https://github.com/pypa/setuptools/issues/207 + """ + + tmpdir = symlinked_tmpdir + site_dirs = ( + tmpdir / "site-pkgs", + tmpdir / "site-pkgs2", + tmpdir / "site-pkgs3", + ) + + vers_str = "__version__ = %r" + + for number, site in enumerate(site_dirs, 1): + if number > 1: + sys.path.append(str(site)) + nspkg = site / 'nspkg' + subpkg = nspkg / 'subpkg' + subpkg.ensure_dir() + (nspkg / '__init__.py').write_text(self.ns_str, encoding='utf-8') + (subpkg / '__init__.py').write_text(vers_str % number, encoding='utf-8') + + with pytest.warns(DeprecationWarning, match="pkg_resources.declare_namespace"): + import nspkg # pyright: ignore[reportMissingImports] # Temporary package for test + import nspkg.subpkg # pyright: ignore[reportMissingImports] # Temporary package for test + expected = [str(site.realpath() / 'nspkg') for site in site_dirs] + assert nspkg.__path__ == expected + assert nspkg.subpkg.__version__ == 1 diff --git a/lib/python3.11/site-packages/pkg_resources/tests/test_working_set.py b/lib/python3.11/site-packages/pkg_resources/tests/test_working_set.py new file mode 100644 index 0000000000000000000000000000000000000000..ed20c59dd3bb100b69f323e3ef66bd037ac3804c --- /dev/null +++ b/lib/python3.11/site-packages/pkg_resources/tests/test_working_set.py @@ -0,0 +1,505 @@ +import functools +import inspect +import re +import textwrap + +import pytest + +import pkg_resources + +from .test_resources import Metadata + + +def strip_comments(s): + return '\n'.join( + line + for line in s.split('\n') + if line.strip() and not line.strip().startswith('#') + ) + + +def parse_distributions(s): + """ + Parse a series of distribution specs of the form: + {project_name}-{version} + [optional, indented requirements specification] + + Example: + + foo-0.2 + bar-1.0 + foo>=3.0 + [feature] + baz + + yield 2 distributions: + - project_name=foo, version=0.2 + - project_name=bar, version=1.0, + requires=['foo>=3.0', 'baz; extra=="feature"'] + """ + s = s.strip() + for spec in re.split(r'\n(?=[^\s])', s): + if not spec: + continue + fields = spec.split('\n', 1) + assert 1 <= len(fields) <= 2 + name, version = fields.pop(0).rsplit('-', 1) + if fields: + requires = textwrap.dedent(fields.pop(0)) + metadata = Metadata(('requires.txt', requires)) + else: + metadata = None + dist = pkg_resources.Distribution( + project_name=name, version=version, metadata=metadata + ) + yield dist + + +class FakeInstaller: + def __init__(self, installable_dists) -> None: + self._installable_dists = installable_dists + + def __call__(self, req): + return next( + iter(filter(lambda dist: dist in req, self._installable_dists)), None + ) + + +def parametrize_test_working_set_resolve(*test_list): + idlist = [] + argvalues = [] + for test in test_list: + ( + name, + installed_dists, + installable_dists, + requirements, + expected1, + expected2, + ) = ( + strip_comments(s.lstrip()) + for s in textwrap.dedent(test).lstrip().split('\n\n', 5) + ) + installed_dists = list(parse_distributions(installed_dists)) + installable_dists = list(parse_distributions(installable_dists)) + requirements = list(pkg_resources.parse_requirements(requirements)) + for id_, replace_conflicting, expected in ( + (name, False, expected1), + (name + '_replace_conflicting', True, expected2), + ): + idlist.append(id_) + expected = strip_comments(expected.strip()) + if re.match(r'\w+$', expected): + expected = getattr(pkg_resources, expected) + assert issubclass(expected, Exception) + else: + expected = list(parse_distributions(expected)) + argvalues.append( + pytest.param( + installed_dists, + installable_dists, + requirements, + replace_conflicting, + expected, + ) + ) + return pytest.mark.parametrize( + ( + "installed_dists", + "installable_dists", + "requirements", + "replace_conflicting", + "resolved_dists_or_exception", + ), + argvalues, + ids=idlist, + ) + + +@parametrize_test_working_set_resolve( + """ + # id + noop + + # installed + + # installable + + # wanted + + # resolved + + # resolved [replace conflicting] + """, + """ + # id + already_installed + + # installed + foo-3.0 + + # installable + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + foo-3.0 + + # resolved [replace conflicting] + foo-3.0 + """, + """ + # id + installable_not_installed + + # installed + + # installable + foo-3.0 + foo-4.0 + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + foo-3.0 + + # resolved [replace conflicting] + foo-3.0 + """, + """ + # id + not_installable + + # installed + + # installable + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + DistributionNotFound + + # resolved [replace conflicting] + DistributionNotFound + """, + """ + # id + no_matching_version + + # installed + + # installable + foo-3.1 + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + DistributionNotFound + + # resolved [replace conflicting] + DistributionNotFound + """, + """ + # id + installable_with_installed_conflict + + # installed + foo-3.1 + + # installable + foo-3.5 + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + VersionConflict + + # resolved [replace conflicting] + foo-3.5 + """, + """ + # id + not_installable_with_installed_conflict + + # installed + foo-3.1 + + # installable + + # wanted + foo>=2.1,!=3.1,<4 + + # resolved + VersionConflict + + # resolved [replace conflicting] + DistributionNotFound + """, + """ + # id + installed_with_installed_require + + # installed + foo-3.9 + baz-0.1 + foo>=2.1,!=3.1,<4 + + # installable + + # wanted + baz + + # resolved + foo-3.9 + baz-0.1 + + # resolved [replace conflicting] + foo-3.9 + baz-0.1 + """, + """ + # id + installed_with_conflicting_installed_require + + # installed + foo-5 + baz-0.1 + foo>=2.1,!=3.1,<4 + + # installable + + # wanted + baz + + # resolved + VersionConflict + + # resolved [replace conflicting] + DistributionNotFound + """, + """ + # id + installed_with_installable_conflicting_require + + # installed + foo-5 + baz-0.1 + foo>=2.1,!=3.1,<4 + + # installable + foo-2.9 + + # wanted + baz + + # resolved + VersionConflict + + # resolved [replace conflicting] + baz-0.1 + foo-2.9 + """, + """ + # id + installed_with_installable_require + + # installed + baz-0.1 + foo>=2.1,!=3.1,<4 + + # installable + foo-3.9 + + # wanted + baz + + # resolved + foo-3.9 + baz-0.1 + + # resolved [replace conflicting] + foo-3.9 + baz-0.1 + """, + """ + # id + installable_with_installed_require + + # installed + foo-3.9 + + # installable + baz-0.1 + foo>=2.1,!=3.1,<4 + + # wanted + baz + + # resolved + foo-3.9 + baz-0.1 + + # resolved [replace conflicting] + foo-3.9 + baz-0.1 + """, + """ + # id + installable_with_installable_require + + # installed + + # installable + foo-3.9 + baz-0.1 + foo>=2.1,!=3.1,<4 + + # wanted + baz + + # resolved + foo-3.9 + baz-0.1 + + # resolved [replace conflicting] + foo-3.9 + baz-0.1 + """, + """ + # id + installable_with_conflicting_installable_require + + # installed + foo-5 + + # installable + foo-2.9 + baz-0.1 + foo>=2.1,!=3.1,<4 + + # wanted + baz + + # resolved + VersionConflict + + # resolved [replace conflicting] + baz-0.1 + foo-2.9 + """, + """ + # id + conflicting_installables + + # installed + + # installable + foo-2.9 + foo-5.0 + + # wanted + foo>=2.1,!=3.1,<4 + foo>=4 + + # resolved + VersionConflict + + # resolved [replace conflicting] + VersionConflict + """, + """ + # id + installables_with_conflicting_requires + + # installed + + # installable + foo-2.9 + dep==1.0 + baz-5.0 + dep==2.0 + dep-1.0 + dep-2.0 + + # wanted + foo + baz + + # resolved + VersionConflict + + # resolved [replace conflicting] + VersionConflict + """, + """ + # id + installables_with_conflicting_nested_requires + + # installed + + # installable + foo-2.9 + dep1 + dep1-1.0 + subdep<1.0 + baz-5.0 + dep2 + dep2-1.0 + subdep>1.0 + subdep-0.9 + subdep-1.1 + + # wanted + foo + baz + + # resolved + VersionConflict + + # resolved [replace conflicting] + VersionConflict + """, + """ + # id + wanted_normalized_name_installed_canonical + + # installed + foo.bar-3.6 + + # installable + + # wanted + foo-bar==3.6 + + # resolved + foo.bar-3.6 + + # resolved [replace conflicting] + foo.bar-3.6 + """, +) +def test_working_set_resolve( + installed_dists, + installable_dists, + requirements, + replace_conflicting, + resolved_dists_or_exception, +): + ws = pkg_resources.WorkingSet([]) + list(map(ws.add, installed_dists)) + resolve_call = functools.partial( + ws.resolve, + requirements, + installer=FakeInstaller(installable_dists), + replace_conflicting=replace_conflicting, + ) + if inspect.isclass(resolved_dists_or_exception): + with pytest.raises(resolved_dists_or_exception): + resolve_call() + else: + assert sorted(resolve_call()) == sorted(resolved_dists_or_exception) diff --git a/lib/python3.11/site-packages/setuptools/__init__.py b/lib/python3.11/site-packages/setuptools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..87ca47bbc9fd4d1e5083d4dedb7a6c86f43c73fc --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/__init__.py @@ -0,0 +1,256 @@ +"""Extensions to the 'distutils' for large or complex distributions""" +# mypy: disable_error_code=override +# Command.reinitialize_command has an extra **kw param that distutils doesn't have +# Can't disable on the exact line because distutils doesn't exists on Python 3.12 +# and mypy isn't aware of distutils_hack, causing distutils.core.Command to be Any, +# and a [unused-ignore] to be raised on 3.12+ + +from __future__ import annotations + +import functools +import os +import sys +from abc import abstractmethod +from collections.abc import Mapping +from typing import TYPE_CHECKING, TypeVar, overload + +sys.path.extend(((vendor_path := os.path.join(os.path.dirname(os.path.dirname(__file__)), 'setuptools', '_vendor')) not in sys.path) * [vendor_path]) # fmt: skip +# workaround for #4476 +sys.modules.pop('backports', None) + +import _distutils_hack.override # noqa: F401 + +from . import logging, monkey +from .depends import Require +from .discovery import PackageFinder, PEP420PackageFinder +from .dist import Distribution +from .extension import Extension +from .version import __version__ as __version__ +from .warnings import SetuptoolsDeprecationWarning + +import distutils.core + +__all__ = [ + 'setup', + 'Distribution', + 'Command', + 'Extension', + 'Require', + 'SetuptoolsDeprecationWarning', + 'find_packages', + 'find_namespace_packages', +] + +_CommandT = TypeVar("_CommandT", bound="_Command") + +bootstrap_install_from = None + +find_packages = PackageFinder.find +find_namespace_packages = PEP420PackageFinder.find + + +def _install_setup_requires(attrs): + # Note: do not use `setuptools.Distribution` directly, as + # our PEP 517 backend patch `distutils.core.Distribution`. + class MinimalDistribution(distutils.core.Distribution): + """ + A minimal version of a distribution for supporting the + fetch_build_eggs interface. + """ + + def __init__(self, attrs: Mapping[str, object]) -> None: + _incl = 'dependency_links', 'setup_requires' + filtered = {k: attrs[k] for k in set(_incl) & set(attrs)} + super().__init__(filtered) + # Prevent accidentally triggering discovery with incomplete set of attrs + self.set_defaults._disable() + + def _get_project_config_files(self, filenames=None): + """Ignore ``pyproject.toml``, they are not related to setup_requires""" + try: + cfg, _toml = super()._split_standard_project_metadata(filenames) + except Exception: + return filenames, () + return cfg, () + + def finalize_options(self): + """ + Disable finalize_options to avoid building the working set. + Ref #2158. + """ + + dist = MinimalDistribution(attrs) + + # Honor setup.cfg's options. + dist.parse_config_files(ignore_option_errors=True) + if dist.setup_requires: + _fetch_build_eggs(dist) + + +def _fetch_build_eggs(dist: Distribution): + try: + dist.fetch_build_eggs(dist.setup_requires) + except Exception as ex: + msg = """ + It is possible a package already installed in your system + contains an version that is invalid according to PEP 440. + You can try `pip install --use-pep517` as a workaround for this problem, + or rely on a new virtual environment. + + If the problem refers to a package that is not installed yet, + please contact that package's maintainers or distributors. + """ + if "InvalidVersion" in ex.__class__.__name__: + if hasattr(ex, "add_note"): + ex.add_note(msg) # PEP 678 + else: + dist.announce(f"\n{msg}\n") + raise + + +def setup(**attrs) -> Distribution: + logging.configure() + # Make sure we have any requirements needed to interpret 'attrs'. + _install_setup_requires(attrs) + # Override return type of distutils.core.Distribution with setuptools.dist.Distribution + # (implicitly implemented via `setuptools.monkey.patch_all`). + return distutils.core.setup(**attrs) # type: ignore[return-value] + + +setup.__doc__ = distutils.core.setup.__doc__ + +if TYPE_CHECKING: + # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962 + from distutils.core import Command as _Command +else: + _Command = monkey.get_unpatched(distutils.core.Command) + + +class Command(_Command): + """ + Setuptools internal actions are organized using a *command design pattern*. + This means that each action (or group of closely related actions) executed during + the build should be implemented as a ``Command`` subclass. + + These commands are abstractions and do not necessarily correspond to a command that + can (or should) be executed via a terminal, in a CLI fashion (although historically + they would). + + When creating a new command from scratch, custom defined classes **SHOULD** inherit + from ``setuptools.Command`` and implement a few mandatory methods. + Between these mandatory methods, are listed: + :meth:`initialize_options`, :meth:`finalize_options` and :meth:`run`. + + A useful analogy for command classes is to think of them as subroutines with local + variables called "options". The options are "declared" in :meth:`initialize_options` + and "defined" (given their final values, aka "finalized") in :meth:`finalize_options`, + both of which must be defined by every command class. The "body" of the subroutine, + (where it does all the work) is the :meth:`run` method. + Between :meth:`initialize_options` and :meth:`finalize_options`, ``setuptools`` may set + the values for options/attributes based on user's input (or circumstance), + which means that the implementation should be careful to not overwrite values in + :meth:`finalize_options` unless necessary. + + Please note that other commands (or other parts of setuptools) may also overwrite + the values of the command's options/attributes multiple times during the build + process. + Therefore it is important to consistently implement :meth:`initialize_options` and + :meth:`finalize_options`. For example, all derived attributes (or attributes that + depend on the value of other attributes) **SHOULD** be recomputed in + :meth:`finalize_options`. + + When overwriting existing commands, custom defined classes **MUST** abide by the + same APIs implemented by the original class. They also **SHOULD** inherit from the + original class. + """ + + command_consumes_arguments = False + distribution: Distribution # override distutils.dist.Distribution with setuptools.dist.Distribution + + dry_run = False # type: ignore[assignment] # pyright: ignore[reportAssignmentType] (until #4689; see #4872) + """ + For compatibility with vendored bdist_wheel. + https://github.com/pypa/setuptools/pull/4872/files#r1986395142 + """ + + def __init__(self, dist: Distribution, **kw) -> None: + """ + Construct the command for dist, updating + vars(self) with any keyword parameters. + """ + super().__init__(dist) + vars(self).update(kw) + + @overload + def reinitialize_command( + self, command: str, reinit_subcommands: bool = False, **kw + ) -> Command: ... # override distutils.cmd.Command with setuptools.Command + @overload + def reinitialize_command( + self, command: _CommandT, reinit_subcommands: bool = False, **kw + ) -> _CommandT: ... + def reinitialize_command( + self, command: str | _Command, reinit_subcommands: bool = False, **kw + ) -> Command | _Command: + cmd = _Command.reinitialize_command(self, command, reinit_subcommands) + vars(cmd).update(kw) + return cmd # pyright: ignore[reportReturnType] # pypa/distutils#307 + + @abstractmethod + def initialize_options(self) -> None: + """ + Set or (reset) all options/attributes/caches used by the command + to their default values. Note that these values may be overwritten during + the build. + """ + raise NotImplementedError + + @abstractmethod + def finalize_options(self) -> None: + """ + Set final values for all options/attributes used by the command. + Most of the time, each option/attribute/cache should only be set if it does not + have any value yet (e.g. ``if self.attr is None: self.attr = val``). + """ + raise NotImplementedError + + @abstractmethod + def run(self) -> None: + """ + Execute the actions intended by the command. + (Side effects **SHOULD** only take place when :meth:`run` is executed, + for example, creating new files or writing to the terminal output). + """ + raise NotImplementedError + + +def _find_all_simple(path): + """ + Find all files under 'path' + """ + results = ( + os.path.join(base, file) + for base, dirs, files in os.walk(path, followlinks=True) + for file in files + ) + return filter(os.path.isfile, results) + + +def findall(dir=os.curdir): + """ + Find all files under 'dir' and return the list of full filenames. + Unless dir is '.', return full filenames with dir prepended. + """ + files = _find_all_simple(dir) + if dir == os.curdir: + make_rel = functools.partial(os.path.relpath, start=dir) + files = map(make_rel, files) + return list(files) + + +class sic(str): + """Treat this string as-is (https://en.wikipedia.org/wiki/Sic)""" + + +# Apply monkey patches +monkey.patch_all() diff --git a/lib/python3.11/site-packages/setuptools/_core_metadata.py b/lib/python3.11/site-packages/setuptools/_core_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..a52d5cf755cdd6a502e752c2f7a3afa3b25897d5 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_core_metadata.py @@ -0,0 +1,337 @@ +""" +Handling of Core Metadata for Python packages (including reading and writing). + +See: https://packaging.python.org/en/latest/specifications/core-metadata/ +""" + +from __future__ import annotations + +import os +import stat +import textwrap +from email import message_from_file +from email.message import Message +from tempfile import NamedTemporaryFile + +from packaging.markers import Marker +from packaging.requirements import Requirement +from packaging.utils import canonicalize_name, canonicalize_version +from packaging.version import Version + +from . import _normalization, _reqs +from ._static import is_static +from .warnings import SetuptoolsDeprecationWarning + +from distutils.util import rfc822_escape + + +def get_metadata_version(self): + mv = getattr(self, 'metadata_version', None) + if mv is None: + mv = Version('2.4') + self.metadata_version = mv + return mv + + +def rfc822_unescape(content: str) -> str: + """Reverse RFC-822 escaping by removing leading whitespaces from content.""" + lines = content.splitlines() + if len(lines) == 1: + return lines[0].lstrip() + return '\n'.join((lines[0].lstrip(), textwrap.dedent('\n'.join(lines[1:])))) + + +def _read_field_from_msg(msg: Message, field: str) -> str | None: + """Read Message header field.""" + value = msg[field] + if value == 'UNKNOWN': + return None + return value + + +def _read_field_unescaped_from_msg(msg: Message, field: str) -> str | None: + """Read Message header field and apply rfc822_unescape.""" + value = _read_field_from_msg(msg, field) + if value is None: + return value + return rfc822_unescape(value) + + +def _read_list_from_msg(msg: Message, field: str) -> list[str] | None: + """Read Message header field and return all results as list.""" + values = msg.get_all(field, None) + if values == []: + return None + return values + + +def _read_payload_from_msg(msg: Message) -> str | None: + value = str(msg.get_payload()).strip() + if value == 'UNKNOWN' or not value: + return None + return value + + +def read_pkg_file(self, file): + """Reads the metadata values from a file object.""" + msg = message_from_file(file) + + self.metadata_version = Version(msg['metadata-version']) + self.name = _read_field_from_msg(msg, 'name') + self.version = _read_field_from_msg(msg, 'version') + self.description = _read_field_from_msg(msg, 'summary') + # we are filling author only. + self.author = _read_field_from_msg(msg, 'author') + self.maintainer = None + self.author_email = _read_field_from_msg(msg, 'author-email') + self.maintainer_email = None + self.url = _read_field_from_msg(msg, 'home-page') + self.download_url = _read_field_from_msg(msg, 'download-url') + self.license = _read_field_unescaped_from_msg(msg, 'license') + self.license_expression = _read_field_unescaped_from_msg(msg, 'license-expression') + + self.long_description = _read_field_unescaped_from_msg(msg, 'description') + if self.long_description is None and self.metadata_version >= Version('2.1'): + self.long_description = _read_payload_from_msg(msg) + self.description = _read_field_from_msg(msg, 'summary') + + if 'keywords' in msg: + self.keywords = _read_field_from_msg(msg, 'keywords').split(',') + + self.platforms = _read_list_from_msg(msg, 'platform') + self.classifiers = _read_list_from_msg(msg, 'classifier') + + # PEP 314 - these fields only exist in 1.1 + if self.metadata_version == Version('1.1'): + self.requires = _read_list_from_msg(msg, 'requires') + self.provides = _read_list_from_msg(msg, 'provides') + self.obsoletes = _read_list_from_msg(msg, 'obsoletes') + else: + self.requires = None + self.provides = None + self.obsoletes = None + + self.license_files = _read_list_from_msg(msg, 'license-file') + + +def single_line(val): + """ + Quick and dirty validation for Summary pypa/setuptools#1390. + """ + if '\n' in val: + # TODO: Replace with `raise ValueError("newlines not allowed")` + # after reviewing #2893. + msg = "newlines are not allowed in `summary` and will break in the future" + SetuptoolsDeprecationWarning.emit("Invalid config.", msg) + # due_date is undefined. Controversial change, there was a lot of push back. + val = val.strip().split('\n')[0] + return val + + +def write_pkg_info(self, base_dir): + """Write the PKG-INFO file into the release tree.""" + temp = "" + final = os.path.join(base_dir, 'PKG-INFO') + try: + # Use a temporary file while writing to avoid race conditions + # (e.g. `importlib.metadata` reading `.egg-info/PKG-INFO`): + with NamedTemporaryFile("w", encoding="utf-8", dir=base_dir, delete=False) as f: + temp = f.name + self.write_pkg_file(f) + permissions = stat.S_IMODE(os.lstat(temp).st_mode) + os.chmod(temp, permissions | stat.S_IRGRP | stat.S_IROTH) + os.replace(temp, final) # atomic operation. + finally: + if temp and os.path.exists(temp): + os.remove(temp) + + +# Based on Python 3.5 version +def write_pkg_file(self, file): # noqa: C901 # is too complex (14) # FIXME + """Write the PKG-INFO format data to a file object.""" + version = self.get_metadata_version() + + def write_field(key, value): + file.write(f"{key}: {value}\n") + + write_field('Metadata-Version', str(version)) + write_field('Name', self.get_name()) + write_field('Version', self.get_version()) + + summary = self.get_description() + if summary: + write_field('Summary', single_line(summary)) + + optional_fields = ( + ('Home-page', 'url'), + ('Download-URL', 'download_url'), + ('Author', 'author'), + ('Author-email', 'author_email'), + ('Maintainer', 'maintainer'), + ('Maintainer-email', 'maintainer_email'), + ) + + for field, attr in optional_fields: + attr_val = getattr(self, attr, None) + if attr_val is not None: + write_field(field, attr_val) + + if license_expression := self.license_expression: + write_field('License-Expression', license_expression) + elif license := self.get_license(): + write_field('License', rfc822_escape(license)) + + for label, url in self.project_urls.items(): + write_field('Project-URL', f'{label}, {url}') + + keywords = ','.join(self.get_keywords()) + if keywords: + write_field('Keywords', keywords) + + platforms = self.get_platforms() or [] + for platform in platforms: + write_field('Platform', platform) + + self._write_list(file, 'Classifier', self.get_classifiers()) + + # PEP 314 + self._write_list(file, 'Requires', self.get_requires()) + self._write_list(file, 'Provides', self.get_provides()) + self._write_list(file, 'Obsoletes', self.get_obsoletes()) + + # Setuptools specific for PEP 345 + if hasattr(self, 'python_requires'): + write_field('Requires-Python', self.python_requires) + + # PEP 566 + if self.long_description_content_type: + write_field('Description-Content-Type', self.long_description_content_type) + + safe_license_files = map(_safe_license_file, self.license_files or []) + self._write_list(file, 'License-File', safe_license_files) + _write_requirements(self, file) + + for field, attr in _POSSIBLE_DYNAMIC_FIELDS.items(): + if (val := getattr(self, attr, None)) and not is_static(val): + write_field('Dynamic', field) + + long_description = self.get_long_description() + if long_description: + file.write(f"\n{long_description}") + if not long_description.endswith("\n"): + file.write("\n") + + +def _write_requirements(self, file): + for req in _reqs.parse(self.install_requires): + file.write(f"Requires-Dist: {req}\n") + + processed_extras = {} + for augmented_extra, reqs in self.extras_require.items(): + # Historically, setuptools allows "augmented extras": `:` + unsafe_extra, _, condition = augmented_extra.partition(":") + unsafe_extra = unsafe_extra.strip() + extra = _normalization.safe_extra(unsafe_extra) + + if extra: + _write_provides_extra(file, processed_extras, extra, unsafe_extra) + for req in _reqs.parse_strings(reqs): + r = _include_extra(req, extra, condition.strip()) + file.write(f"Requires-Dist: {r}\n") + + return processed_extras + + +def _include_extra(req: str, extra: str, condition: str) -> Requirement: + r = Requirement(req) # create a fresh object that can be modified + parts = ( + f"({r.marker})" if r.marker else None, + f"({condition})" if condition else None, + f"extra == {extra!r}" if extra else None, + ) + r.marker = Marker(" and ".join(x for x in parts if x)) + return r + + +def _write_provides_extra(file, processed_extras, safe, unsafe): + previous = processed_extras.get(safe) + if previous == unsafe: + SetuptoolsDeprecationWarning.emit( + 'Ambiguity during "extra" normalization for dependencies.', + f""" + {previous!r} and {unsafe!r} normalize to the same value:\n + {safe!r}\n + In future versions, setuptools might halt the build process. + """, + see_url="https://peps.python.org/pep-0685/", + ) + else: + processed_extras[safe] = unsafe + file.write(f"Provides-Extra: {safe}\n") + + +# from pypa/distutils#244; needed only until that logic is always available +def get_fullname(self): + return _distribution_fullname(self.get_name(), self.get_version()) + + +def _distribution_fullname(name: str, version: str) -> str: + """ + >>> _distribution_fullname('setup.tools', '1.0-2') + 'setup_tools-1.0.post2' + >>> _distribution_fullname('setup-tools', '1.2post2') + 'setup_tools-1.2.post2' + >>> _distribution_fullname('setup-tools', '1.0-r2') + 'setup_tools-1.0.post2' + >>> _distribution_fullname('setup.tools', '1.0.post') + 'setup_tools-1.0.post0' + >>> _distribution_fullname('setup.tools', '1.0+ubuntu-1') + 'setup_tools-1.0+ubuntu.1' + """ + return "{}-{}".format( + canonicalize_name(name).replace('-', '_'), + canonicalize_version(version, strip_trailing_zero=False), + ) + + +def _safe_license_file(file): + # XXX: Do we need this after the deprecation discussed in #4892, #4896?? + normalized = os.path.normpath(file).replace(os.sep, "/") + if "../" in normalized: + return os.path.basename(normalized) # Temporarily restore pre PEP639 behaviour + return normalized + + +_POSSIBLE_DYNAMIC_FIELDS = { + # Core Metadata Field x related Distribution attribute + "author": "author", + "author-email": "author_email", + "classifier": "classifiers", + "description": "long_description", + "description-content-type": "long_description_content_type", + "download-url": "download_url", + "home-page": "url", + "keywords": "keywords", + "license": "license", + # XXX: License-File is complicated because the user gives globs that are expanded + # during the build. Without special handling it is likely always + # marked as Dynamic, which is an acceptable outcome according to: + # https://github.com/pypa/setuptools/issues/4629#issuecomment-2331233677 + "license-file": "license_files", + "license-expression": "license_expression", # PEP 639 + "maintainer": "maintainer", + "maintainer-email": "maintainer_email", + "obsoletes": "obsoletes", + # "obsoletes-dist": "obsoletes_dist", # NOT USED + "platform": "platforms", + "project-url": "project_urls", + "provides": "provides", + # "provides-dist": "provides_dist", # NOT USED + "provides-extra": "extras_require", + "requires": "requires", + "requires-dist": "install_requires", + # "requires-external": "requires_external", # NOT USED + "requires-python": "python_requires", + "summary": "description", + # "supported-platform": "supported_platforms", # NOT USED +} diff --git a/lib/python3.11/site-packages/setuptools/_discovery.py b/lib/python3.11/site-packages/setuptools/_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b4a0ee0351d69de5f0ab7d65bbe6958e398916 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_discovery.py @@ -0,0 +1,33 @@ +import functools +import operator + +import packaging.requirements + + +# from coherent.build.discovery +def extras_from_dep(dep): + try: + markers = packaging.requirements.Requirement(dep).marker._markers + except AttributeError: + markers = () + return set( + marker[2].value + for marker in markers + if isinstance(marker, tuple) and marker[0].value == 'extra' + ) + + +def extras_from_deps(deps): + """ + >>> extras_from_deps(['requests']) + set() + >>> extras_from_deps(['pytest; extra == "test"']) + {'test'} + >>> sorted(extras_from_deps([ + ... 'requests', + ... 'pytest; extra == "test"', + ... 'pytest-cov; extra == "test"', + ... 'sphinx; extra=="doc"'])) + ['doc', 'test'] + """ + return functools.reduce(operator.or_, map(extras_from_dep, deps), set()) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/__init__.py b/lib/python3.11/site-packages/setuptools/_distutils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e374d5c5604d0eeed555e68b7523bfac72fd9101 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/__init__.py @@ -0,0 +1,14 @@ +import importlib +import sys + +__version__, _, _ = sys.version.partition(' ') + + +try: + # Allow Debian and pkgsrc (only) to customize system + # behavior. 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0000000000000000000000000000000000000000..7ea51614654a2d86131dc68e6f162df602e02419 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/__pycache__/debug.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/_log.py b/lib/python3.11/site-packages/setuptools/_distutils/_log.py new file mode 100644 index 0000000000000000000000000000000000000000..0148f157ff3bf8bd728c82f49cd3d1cd9cb5a43e --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/_log.py @@ -0,0 +1,3 @@ +import logging + +log = logging.getLogger() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/_macos_compat.py b/lib/python3.11/site-packages/setuptools/_distutils/_macos_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..76ecb96abe478313ce7d09342b2643bf4a765331 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/_macos_compat.py @@ -0,0 +1,12 @@ +import importlib +import sys + + +def bypass_compiler_fixup(cmd, args): + return cmd + + +if sys.platform == 'darwin': + compiler_fixup = importlib.import_module('_osx_support').compiler_fixup +else: + compiler_fixup = bypass_compiler_fixup diff --git a/lib/python3.11/site-packages/setuptools/_distutils/_modified.py b/lib/python3.11/site-packages/setuptools/_distutils/_modified.py new file mode 100644 index 0000000000000000000000000000000000000000..f64cab7d61ae20dd0c425b8843b068f39faa6961 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/_modified.py @@ -0,0 +1,95 @@ +"""Timestamp comparison of files and groups of files.""" + +from __future__ import annotations + +import functools +import os.path +from collections.abc import Callable, Iterable +from typing import Literal, TypeVar + +from jaraco.functools import splat + +from .compat.py39 import zip_strict +from .errors import DistutilsFileError + +_SourcesT = TypeVar( + "_SourcesT", bound="str | bytes | os.PathLike[str] | os.PathLike[bytes]" +) +_TargetsT = TypeVar( + "_TargetsT", bound="str | bytes | os.PathLike[str] | os.PathLike[bytes]" +) + + +def _newer(source, target): + return not os.path.exists(target) or ( + os.path.getmtime(source) > os.path.getmtime(target) + ) + + +def newer( + source: str | bytes | os.PathLike[str] | os.PathLike[bytes], + target: str | bytes | os.PathLike[str] | os.PathLike[bytes], +) -> bool: + """ + Is source modified more recently than target. + + Returns True if 'source' is modified more recently than + 'target' or if 'target' does not exist. + + Raises DistutilsFileError if 'source' does not exist. + """ + if not os.path.exists(source): + raise DistutilsFileError(f"file {os.path.abspath(source)!r} does not exist") + + return _newer(source, target) + + +def newer_pairwise( + sources: Iterable[_SourcesT], + targets: Iterable[_TargetsT], + newer: Callable[[_SourcesT, _TargetsT], bool] = newer, +) -> tuple[list[_SourcesT], list[_TargetsT]]: + """ + Filter filenames where sources are newer than targets. + + Walk two filename iterables in parallel, testing if each source is newer + than its corresponding target. Returns a pair of lists (sources, + targets) where source is newer than target, according to the semantics + of 'newer()'. + """ + newer_pairs = filter(splat(newer), zip_strict(sources, targets)) + return tuple(map(list, zip(*newer_pairs))) or ([], []) + + +def newer_group( + sources: Iterable[str | bytes | os.PathLike[str] | os.PathLike[bytes]], + target: str | bytes | os.PathLike[str] | os.PathLike[bytes], + missing: Literal["error", "ignore", "newer"] = "error", +) -> bool: + """ + Is target out-of-date with respect to any file in sources. + + Return True if 'target' is out-of-date with respect to any file + listed in 'sources'. In other words, if 'target' exists and is newer + than every file in 'sources', return False; otherwise return True. + ``missing`` controls how to handle a missing source file: + + - error (default): allow the ``stat()`` call to fail. + - ignore: silently disregard any missing source files. + - newer: treat missing source files as "target out of date". This + mode is handy in "dry-run" mode: it will pretend to carry out + commands that wouldn't work because inputs are missing, but + that doesn't matter because dry-run won't run the commands. + """ + + def missing_as_newer(source): + return missing == 'newer' and not os.path.exists(source) + + ignored = os.path.exists if missing == 'ignore' else None + return not os.path.exists(target) or any( + missing_as_newer(source) or _newer(source, target) + for source in filter(ignored, sources) + ) + + +newer_pairwise_group = functools.partial(newer_pairwise, newer=newer_group) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/_msvccompiler.py b/lib/python3.11/site-packages/setuptools/_distutils/_msvccompiler.py new file mode 100644 index 0000000000000000000000000000000000000000..d07c86ef8e41fc31357c989133d51e9618c3e43d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/_msvccompiler.py @@ -0,0 +1,16 @@ +import warnings + +from .compilers.C import msvc + +__all__ = ["MSVCCompiler"] + +MSVCCompiler = msvc.Compiler + + +def __getattr__(name): + if name == '_get_vc_env': + warnings.warn( + "_get_vc_env is private; find an alternative (pypa/distutils#340)" + ) + return msvc._get_vc_env + raise AttributeError(name) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/archive_util.py b/lib/python3.11/site-packages/setuptools/_distutils/archive_util.py new file mode 100644 index 0000000000000000000000000000000000000000..4a7fb9c9e7a0c4a0ca2a3ab608ffab6acf10bb3e --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/archive_util.py @@ -0,0 +1,284 @@ +"""distutils.archive_util + +Utility functions for creating archive files (tarballs, zip files, +that sort of thing).""" + +from __future__ import annotations + +import os +from typing import Literal, overload + +try: + import zipfile +except ImportError: + zipfile = None + + +from ._log import log +from .dir_util import mkpath +from .errors import DistutilsExecError +from .spawn import spawn + +try: + from pwd import getpwnam +except ImportError: + getpwnam = None + +try: + from grp import getgrnam +except ImportError: + getgrnam = None + + +def _get_gid(name): + """Returns a gid, given a group name.""" + if getgrnam is None or name is None: + return None + try: + result = getgrnam(name) + except KeyError: + result = None + if result is not None: + return result[2] + return None + + +def _get_uid(name): + """Returns an uid, given a user name.""" + if getpwnam is None or name is None: + return None + try: + result = getpwnam(name) + except KeyError: + result = None + if result is not None: + return result[2] + return None + + +def make_tarball( + base_name: str, + base_dir: str | os.PathLike[str], + compress: Literal["gzip", "bzip2", "xz"] | None = "gzip", + verbose: bool = False, + owner: str | None = None, + group: str | None = None, +) -> str: + """Create a (possibly compressed) tar file from all the files under + 'base_dir'. + + 'compress' must be "gzip" (the default), "bzip2", "xz", or None. + + 'owner' and 'group' can be used to define an owner and a group for the + archive that is being built. If not provided, the current owner and group + will be used. + + The output tar file will be named 'base_dir' + ".tar", possibly plus + the appropriate compression extension (".gz", ".bz2", ".xz" or ".Z"). + + Returns the output filename. + """ + tar_compression = { + 'gzip': 'gz', + 'bzip2': 'bz2', + 'xz': 'xz', + None: '', + } + compress_ext = {'gzip': '.gz', 'bzip2': '.bz2', 'xz': '.xz'} + + # flags for compression program, each element of list will be an argument + if compress is not None and compress not in compress_ext.keys(): + raise ValueError( + "bad value for 'compress': must be None, 'gzip', 'bzip2', 'xz'" + ) + + archive_name = base_name + '.tar' + archive_name += compress_ext.get(compress, '') + + mkpath(os.path.dirname(archive_name)) + + # creating the tarball + import tarfile # late import so Python build itself doesn't break + + log.info('Creating tar archive') + + uid = _get_uid(owner) + gid = _get_gid(group) + + def _set_uid_gid(tarinfo): + if gid is not None: + tarinfo.gid = gid + tarinfo.gname = group + if uid is not None: + tarinfo.uid = uid + tarinfo.uname = owner + return tarinfo + + tar = tarfile.open(archive_name, f'w|{tar_compression[compress]}') + try: + tar.add(base_dir, filter=_set_uid_gid) + finally: + tar.close() + + return archive_name + + +def make_zipfile( + base_name: str, + base_dir: str | os.PathLike[str], + verbose: bool = False, +) -> str: + """Create a zip file from all the files under 'base_dir'. + + The output zip file will be named 'base_name' + ".zip". Uses either the + "zipfile" Python module (if available) or the InfoZIP "zip" utility + (if installed and found on the default search path). If neither tool is + available, raises DistutilsExecError. Returns the name of the output zip + file. + """ + zip_filename = base_name + ".zip" + mkpath(os.path.dirname(zip_filename)) + + # If zipfile module is not available, try spawning an external + # 'zip' command. + if zipfile is None: + if verbose: + zipoptions = "-r" + else: + zipoptions = "-rq" + + try: + spawn(["zip", zipoptions, zip_filename, base_dir]) + except DistutilsExecError: + # XXX really should distinguish between "couldn't find + # external 'zip' command" and "zip failed". + raise DistutilsExecError( + f"unable to create zip file '{zip_filename}': " + "could neither import the 'zipfile' module nor " + "find a standalone zip utility" + ) + + else: + log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir) + + try: + zip = zipfile.ZipFile(zip_filename, "w", compression=zipfile.ZIP_DEFLATED) + except RuntimeError: + zip = zipfile.ZipFile(zip_filename, "w", compression=zipfile.ZIP_STORED) + + with zip: + if base_dir != os.curdir: + path = os.path.normpath(os.path.join(base_dir, '')) + zip.write(path, path) + log.info("adding '%s'", path) + for dirpath, dirnames, filenames in os.walk(base_dir): + for name in dirnames: + path = os.path.normpath(os.path.join(dirpath, name, '')) + zip.write(path, path) + log.info("adding '%s'", path) + for name in filenames: + path = os.path.normpath(os.path.join(dirpath, name)) + if os.path.isfile(path): + zip.write(path, path) + log.info("adding '%s'", path) + + return zip_filename + + +ARCHIVE_FORMATS = { + 'gztar': (make_tarball, [('compress', 'gzip')], "gzip'ed tar-file"), + 'bztar': (make_tarball, [('compress', 'bzip2')], "bzip2'ed tar-file"), + 'xztar': (make_tarball, [('compress', 'xz')], "xz'ed tar-file"), + 'ztar': (make_tarball, [('compress', 'compress')], "compressed tar file"), + 'tar': (make_tarball, [('compress', None)], "uncompressed tar file"), + 'zip': (make_zipfile, [], "ZIP file"), +} + + +def check_archive_formats(formats): + """Returns the first format from the 'format' list that is unknown. + + If all formats are known, returns None + """ + for format in formats: + if format not in ARCHIVE_FORMATS: + return format + return None + + +@overload +def make_archive( + base_name: str, + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None, + base_dir: str | None = None, + verbose: bool = False, + owner: str | None = None, + group: str | None = None, +) -> str: ... +@overload +def make_archive( + base_name: str | os.PathLike[str], + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes], + base_dir: str | None = None, + verbose: bool = False, + owner: str | None = None, + group: str | None = None, +) -> str: ... +def make_archive( + base_name: str | os.PathLike[str], + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None, + base_dir: str | None = None, + verbose: bool = False, + owner: str | None = None, + group: str | None = None, +) -> str: + """Create an archive file (eg. zip or tar). + + 'base_name' is the name of the file to create, minus any format-specific + extension; 'format' is the archive format: one of "zip", "tar", "gztar", + "bztar", "xztar", or "ztar". + + 'root_dir' is a directory that will be the root directory of the + archive; ie. we typically chdir into 'root_dir' before creating the + archive. 'base_dir' is the directory where we start archiving from; + ie. 'base_dir' will be the common prefix of all files and + directories in the archive. 'root_dir' and 'base_dir' both default + to the current directory. Returns the name of the archive file. + + 'owner' and 'group' are used when creating a tar archive. By default, + uses the current owner and group. + """ + save_cwd = os.getcwd() + if root_dir is not None: + log.debug("changing into '%s'", root_dir) + base_name = os.path.abspath(base_name) + os.chdir(root_dir) + + if base_dir is None: + base_dir = os.curdir + + kwargs: dict[str, bool | None] = {} + + try: + format_info = ARCHIVE_FORMATS[format] + except KeyError: + raise ValueError(f"unknown archive format '{format}'") + + func = format_info[0] + kwargs.update(format_info[1]) + + if format != 'zip': + kwargs['owner'] = owner + kwargs['group'] = group + + try: + filename = func(base_name, base_dir, **kwargs) + finally: + if root_dir is not None: + log.debug("changing back to '%s'", save_cwd) + os.chdir(save_cwd) + + return filename diff --git a/lib/python3.11/site-packages/setuptools/_distutils/ccompiler.py b/lib/python3.11/site-packages/setuptools/_distutils/ccompiler.py new file mode 100644 index 0000000000000000000000000000000000000000..58bc6a55e24fd2b0b170e4499c6095e934313827 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/ccompiler.py @@ -0,0 +1,26 @@ +from .compat.numpy import ( # noqa: F401 + _default_compilers, + compiler_class, +) +from .compilers.C import base +from .compilers.C.base import ( + gen_lib_options, + gen_preprocess_options, + get_default_compiler, + new_compiler, + show_compilers, +) +from .compilers.C.errors import CompileError, LinkError + +__all__ = [ + 'CompileError', + 'LinkError', + 'gen_lib_options', + 'gen_preprocess_options', + 'get_default_compiler', + 'new_compiler', + 'show_compilers', +] + + +CCompiler = base.Compiler diff --git a/lib/python3.11/site-packages/setuptools/_distutils/cmd.py b/lib/python3.11/site-packages/setuptools/_distutils/cmd.py new file mode 100644 index 0000000000000000000000000000000000000000..530cc108d4743ed26fdf0dd7a8eef204d309b5de --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/cmd.py @@ -0,0 +1,535 @@ +"""distutils.cmd + +Provides the Command class, the base class for the command classes +in the distutils.command package. +""" + +from __future__ import annotations + +import logging +import os +import re +import sys +from abc import abstractmethod +from collections.abc import Callable, MutableSequence +from typing import TYPE_CHECKING, Any, ClassVar, TypeVar, overload + +from . import _modified, archive_util, dir_util, file_util, util +from ._log import log +from .errors import DistutilsOptionError + +if TYPE_CHECKING: + # type-only import because of mutual dependence between these classes + from distutils.dist import Distribution + + from typing_extensions import TypeVarTuple, Unpack + + _Ts = TypeVarTuple("_Ts") + +_StrPathT = TypeVar("_StrPathT", bound="str | os.PathLike[str]") +_BytesPathT = TypeVar("_BytesPathT", bound="bytes | os.PathLike[bytes]") +_CommandT = TypeVar("_CommandT", bound="Command") + + +class Command: + """Abstract base class for defining command classes, the "worker bees" + of the Distutils. A useful analogy for command classes is to think of + them as subroutines with local variables called "options". The options + are "declared" in 'initialize_options()' and "defined" (given their + final values, aka "finalized") in 'finalize_options()', both of which + must be defined by every command class. The distinction between the + two is necessary because option values might come from the outside + world (command line, config file, ...), and any options dependent on + other options must be computed *after* these outside influences have + been processed -- hence 'finalize_options()'. The "body" of the + subroutine, where it does all its work based on the values of its + options, is the 'run()' method, which must also be implemented by every + command class. + """ + + # 'sub_commands' formalizes the notion of a "family" of commands, + # eg. "install" as the parent with sub-commands "install_lib", + # "install_headers", etc. The parent of a family of commands + # defines 'sub_commands' as a class attribute; it's a list of + # (command_name : string, predicate : unbound_method | string | None) + # tuples, where 'predicate' is a method of the parent command that + # determines whether the corresponding command is applicable in the + # current situation. (Eg. we "install_headers" is only applicable if + # we have any C header files to install.) If 'predicate' is None, + # that command is always applicable. + # + # 'sub_commands' is usually defined at the *end* of a class, because + # predicates can be unbound methods, so they must already have been + # defined. The canonical example is the "install" command. + sub_commands: ClassVar[ # Any to work around variance issues + list[tuple[str, Callable[[Any], bool] | None]] + ] = [] + + user_options: ClassVar[ + # Specifying both because list is invariant. Avoids mypy override assignment issues + list[tuple[str, str, str]] | list[tuple[str, str | None, str]] + ] = [] + + # -- Creation/initialization methods ------------------------------- + + def __init__(self, dist: Distribution) -> None: + """Create and initialize a new Command object. Most importantly, + invokes the 'initialize_options()' method, which is the real + initializer and depends on the actual command being + instantiated. + """ + # late import because of mutual dependence between these classes + from distutils.dist import Distribution + + if not isinstance(dist, Distribution): + raise TypeError("dist must be a Distribution instance") + if self.__class__ is Command: + raise RuntimeError("Command is an abstract class") + + self.distribution = dist + self.initialize_options() + + # Per-command versions of the global flags, so that the user can + # customize Distutils' behaviour command-by-command and let some + # commands fall back on the Distribution's behaviour. None means + # "not defined, check self.distribution's copy". + + # verbose is largely ignored, but needs to be set for + # backwards compatibility (I think)? + self.verbose = dist.verbose + + # Some commands define a 'self.force' option to ignore file + # timestamps, but methods defined *here* assume that + # 'self.force' exists for all commands. So define it here + # just to be safe. + self.force = None + + # The 'help' flag is just used for command-line parsing, so + # none of that complicated bureaucracy is needed. + self.help = False + + # 'finalized' records whether or not 'finalize_options()' has been + # called. 'finalize_options()' itself should not pay attention to + # this flag: it is the business of 'ensure_finalized()', which + # always calls 'finalize_options()', to respect/update it. + self.finalized = False + + def ensure_finalized(self) -> None: + if not self.finalized: + self.finalize_options() + self.finalized = True + + # Subclasses must define: + # initialize_options() + # provide default values for all options; may be customized by + # setup script, by options from config file(s), or by command-line + # options + # finalize_options() + # decide on the final values for all options; this is called + # after all possible intervention from the outside world + # (command-line, option file, etc.) has been processed + # run() + # run the command: do whatever it is we're here to do, + # controlled by the command's various option values + + @abstractmethod + def initialize_options(self) -> None: + """Set default values for all the options that this command + supports. Note that these defaults may be overridden by other + commands, by the setup script, by config files, or by the + command-line. Thus, this is not the place to code dependencies + between options; generally, 'initialize_options()' implementations + are just a bunch of "self.foo = None" assignments. + + This method must be implemented by all command classes. + """ + raise RuntimeError( + f"abstract method -- subclass {self.__class__} must override" + ) + + @abstractmethod + def finalize_options(self) -> None: + """Set final values for all the options that this command supports. + This is always called as late as possible, ie. after any option + assignments from the command-line or from other commands have been + done. Thus, this is the place to code option dependencies: if + 'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as + long as 'foo' still has the same value it was assigned in + 'initialize_options()'. + + This method must be implemented by all command classes. + """ + raise RuntimeError( + f"abstract method -- subclass {self.__class__} must override" + ) + + def dump_options(self, header=None, indent=""): + from distutils.fancy_getopt import longopt_xlate + + if header is None: + header = f"command options for '{self.get_command_name()}':" + self.announce(indent + header, level=logging.INFO) + indent = indent + " " + for option, _, _ in self.user_options: + option = option.translate(longopt_xlate) + if option[-1] == "=": + option = option[:-1] + value = getattr(self, option) + self.announce(indent + f"{option} = {value}", level=logging.INFO) + + @abstractmethod + def run(self) -> None: + """A command's raison d'etre: carry out the action it exists to + perform, controlled by the options initialized in + 'initialize_options()', customized by other commands, the setup + script, the command-line, and config files, and finalized in + 'finalize_options()'. All terminal output and filesystem + interaction should be done by 'run()'. + + This method must be implemented by all command classes. + """ + raise RuntimeError( + f"abstract method -- subclass {self.__class__} must override" + ) + + def announce(self, msg: object, level: int = logging.DEBUG) -> None: + log.log(level, msg) + + def debug_print(self, msg: object) -> None: + """Print 'msg' to stdout if the global DEBUG (taken from the + DISTUTILS_DEBUG environment variable) flag is true. + """ + from distutils.debug import DEBUG + + if DEBUG: + print(msg) + sys.stdout.flush() + + # -- Option validation methods ------------------------------------- + # (these are very handy in writing the 'finalize_options()' method) + # + # NB. the general philosophy here is to ensure that a particular option + # value meets certain type and value constraints. If not, we try to + # force it into conformance (eg. if we expect a list but have a string, + # split the string on comma and/or whitespace). If we can't force the + # option into conformance, raise DistutilsOptionError. Thus, command + # classes need do nothing more than (eg.) + # self.ensure_string_list('foo') + # and they can be guaranteed that thereafter, self.foo will be + # a list of strings. + + def _ensure_stringlike(self, option, what, default=None): + val = getattr(self, option) + if val is None: + setattr(self, option, default) + return default + elif not isinstance(val, str): + raise DistutilsOptionError(f"'{option}' must be a {what} (got `{val}`)") + return val + + def ensure_string(self, option: str, default: str | None = None) -> None: + """Ensure that 'option' is a string; if not defined, set it to + 'default'. + """ + self._ensure_stringlike(option, "string", default) + + def ensure_string_list(self, option: str) -> None: + r"""Ensure that 'option' is a list of strings. If 'option' is + currently a string, we split it either on /,\s*/ or /\s+/, so + "foo bar baz", "foo,bar,baz", and "foo, bar baz" all become + ["foo", "bar", "baz"]. + """ + val = getattr(self, option) + if val is None: + return + elif isinstance(val, str): + setattr(self, option, re.split(r',\s*|\s+', val)) + else: + if isinstance(val, list): + ok = all(isinstance(v, str) for v in val) + else: + ok = False + if not ok: + raise DistutilsOptionError( + f"'{option}' must be a list of strings (got {val!r})" + ) + + def _ensure_tested_string(self, option, tester, what, error_fmt, default=None): + val = self._ensure_stringlike(option, what, default) + if val is not None and not tester(val): + raise DistutilsOptionError( + ("error in '%s' option: " + error_fmt) % (option, val) + ) + + def ensure_filename(self, option: str) -> None: + """Ensure that 'option' is the name of an existing file.""" + self._ensure_tested_string( + option, os.path.isfile, "filename", "'%s' does not exist or is not a file" + ) + + def ensure_dirname(self, option: str) -> None: + self._ensure_tested_string( + option, + os.path.isdir, + "directory name", + "'%s' does not exist or is not a directory", + ) + + # -- Convenience methods for commands ------------------------------ + + def get_command_name(self) -> str: + if hasattr(self, 'command_name'): + return self.command_name + else: + return self.__class__.__name__ + + def set_undefined_options( + self, src_cmd: str, *option_pairs: tuple[str, str] + ) -> None: + """Set the values of any "undefined" options from corresponding + option values in some other command object. "Undefined" here means + "is None", which is the convention used to indicate that an option + has not been changed between 'initialize_options()' and + 'finalize_options()'. Usually called from 'finalize_options()' for + options that depend on some other command rather than another + option of the same command. 'src_cmd' is the other command from + which option values will be taken (a command object will be created + for it if necessary); the remaining arguments are + '(src_option,dst_option)' tuples which mean "take the value of + 'src_option' in the 'src_cmd' command object, and copy it to + 'dst_option' in the current command object". + """ + # Option_pairs: list of (src_option, dst_option) tuples + src_cmd_obj = self.distribution.get_command_obj(src_cmd) + src_cmd_obj.ensure_finalized() + for src_option, dst_option in option_pairs: + if getattr(self, dst_option) is None: + setattr(self, dst_option, getattr(src_cmd_obj, src_option)) + + # NOTE: Because distutils is private to Setuptools and not all commands are exposed here, + # not every possible command is enumerated in the signature. + def get_finalized_command(self, command: str, create: bool = True) -> Command: + """Wrapper around Distribution's 'get_command_obj()' method: find + (create if necessary and 'create' is true) the command object for + 'command', call its 'ensure_finalized()' method, and return the + finalized command object. + """ + cmd_obj = self.distribution.get_command_obj(command, create) + cmd_obj.ensure_finalized() + return cmd_obj + + # XXX rename to 'get_reinitialized_command()'? (should do the + # same in dist.py, if so) + @overload + def reinitialize_command( + self, command: str, reinit_subcommands: bool = False + ) -> Command: ... + @overload + def reinitialize_command( + self, command: _CommandT, reinit_subcommands: bool = False + ) -> _CommandT: ... + def reinitialize_command( + self, command: str | Command, reinit_subcommands=False + ) -> Command: + return self.distribution.reinitialize_command(command, reinit_subcommands) + + def run_command(self, command: str) -> None: + """Run some other command: uses the 'run_command()' method of + Distribution, which creates and finalizes the command object if + necessary and then invokes its 'run()' method. + """ + self.distribution.run_command(command) + + def get_sub_commands(self) -> list[str]: + """Determine the sub-commands that are relevant in the current + distribution (ie., that need to be run). This is based on the + 'sub_commands' class attribute: each tuple in that list may include + a method that we call to determine if the subcommand needs to be + run for the current distribution. Return a list of command names. + """ + commands = [] + for cmd_name, method in self.sub_commands: + if method is None or method(self): + commands.append(cmd_name) + return commands + + # -- External world manipulation ----------------------------------- + + def warn(self, msg: object) -> None: + log.warning("warning: %s: %s\n", self.get_command_name(), msg) + + def execute( + self, + func: Callable[[Unpack[_Ts]], object], + args: tuple[Unpack[_Ts]], + msg: object = None, + level: int = 1, + ) -> None: + util.execute(func, args, msg) + + def mkpath(self, name: str, mode: int = 0o777) -> None: + dir_util.mkpath(name, mode) + + @overload + def copy_file( + self, + infile: str | os.PathLike[str], + outfile: _StrPathT, + preserve_mode: bool = True, + preserve_times: bool = True, + link: str | None = None, + level: int = 1, + ) -> tuple[_StrPathT | str, bool]: ... + @overload + def copy_file( + self, + infile: bytes | os.PathLike[bytes], + outfile: _BytesPathT, + preserve_mode: bool = True, + preserve_times: bool = True, + link: str | None = None, + level: int = 1, + ) -> tuple[_BytesPathT | bytes, bool]: ... + def copy_file( + self, + infile: str | os.PathLike[str] | bytes | os.PathLike[bytes], + outfile: str | os.PathLike[str] | bytes | os.PathLike[bytes], + preserve_mode: bool = True, + preserve_times: bool = True, + link: str | None = None, + level: int = 1, + ) -> tuple[str | os.PathLike[str] | bytes | os.PathLike[bytes], bool]: + """Copy a file respecting verbose, dry-run and force flags. (The + former two default to whatever is in the Distribution object, and + the latter defaults to false for commands that don't define it.)""" + return file_util.copy_file( + infile, + outfile, + preserve_mode, + preserve_times, + not self.force, + link, + ) + + def copy_tree( + self, + infile: str | os.PathLike[str], + outfile: str, + preserve_mode: bool = True, + preserve_times: bool = True, + preserve_symlinks: bool = False, + level: int = 1, + ) -> list[str]: + """Copy an entire directory tree respecting verbose, dry-run, + and force flags. + """ + return dir_util.copy_tree( + infile, + outfile, + preserve_mode, + preserve_times, + preserve_symlinks, + not self.force, + ) + + @overload + def move_file( + self, src: str | os.PathLike[str], dst: _StrPathT, level: int = 1 + ) -> _StrPathT | str: ... + @overload + def move_file( + self, src: bytes | os.PathLike[bytes], dst: _BytesPathT, level: int = 1 + ) -> _BytesPathT | bytes: ... + def move_file( + self, + src: str | os.PathLike[str] | bytes | os.PathLike[bytes], + dst: str | os.PathLike[str] | bytes | os.PathLike[bytes], + level: int = 1, + ) -> str | os.PathLike[str] | bytes | os.PathLike[bytes]: + """Move a file respecting dry-run flag.""" + return file_util.move_file(src, dst) + + def spawn( + self, cmd: MutableSequence[str], search_path: bool = True, level: int = 1 + ) -> None: + """Spawn an external command respecting dry-run flag.""" + from distutils.spawn import spawn + + spawn(cmd, search_path) + + @overload + def make_archive( + self, + base_name: str, + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None, + base_dir: str | None = None, + owner: str | None = None, + group: str | None = None, + ) -> str: ... + @overload + def make_archive( + self, + base_name: str | os.PathLike[str], + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes], + base_dir: str | None = None, + owner: str | None = None, + group: str | None = None, + ) -> str: ... + def make_archive( + self, + base_name: str | os.PathLike[str], + format: str, + root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None, + base_dir: str | None = None, + owner: str | None = None, + group: str | None = None, + ) -> str: + return archive_util.make_archive( + base_name, + format, + root_dir, + base_dir, + owner=owner, + group=group, + ) + + def make_file( + self, + infiles: str | list[str] | tuple[str, ...], + outfile: str | os.PathLike[str] | bytes | os.PathLike[bytes], + func: Callable[[Unpack[_Ts]], object], + args: tuple[Unpack[_Ts]], + exec_msg: object = None, + skip_msg: object = None, + level: int = 1, + ) -> None: + """Special case of 'execute()' for operations that process one or + more input files and generate one output file. Works just like + 'execute()', except the operation is skipped and a different + message printed if 'outfile' already exists and is newer than all + files listed in 'infiles'. If the command defined 'self.force', + and it is true, then the command is unconditionally run -- does no + timestamp checks. + """ + if skip_msg is None: + skip_msg = f"skipping {outfile} (inputs unchanged)" + + # Allow 'infiles' to be a single string + if isinstance(infiles, str): + infiles = (infiles,) + elif not isinstance(infiles, (list, tuple)): + raise TypeError("'infiles' must be a string, or a list or tuple of strings") + + if exec_msg is None: + exec_msg = "generating {} from {}".format(outfile, ', '.join(infiles)) + + # If 'outfile' must be regenerated (either because it doesn't + # exist, is out-of-date, or the 'force' flag is true) then + # perform the action that presumably regenerates it + if self.force or _modified.newer_group(infiles, outfile): + self.execute(func, args, exec_msg, level) + # Otherwise, print the "skip" message + else: + log.debug(skip_msg) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/__init__.py b/lib/python3.11/site-packages/setuptools/_distutils/command/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0f8a1692ba96f5e03167e7307c1c2ff5776b74d6 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/__init__.py @@ -0,0 +1,23 @@ +"""distutils.command + +Package containing implementation of all the standard Distutils +commands.""" + +__all__ = [ + 'build', + 'build_py', + 'build_ext', + 'build_clib', + 'build_scripts', + 'clean', + 'install', + 'install_lib', + 'install_headers', + 'install_scripts', + 'install_data', + 'sdist', + 'bdist', + 'bdist_dumb', + 'bdist_rpm', + 'check', +] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/command/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ddd3386e9f2ae7bb36b68776b5e59675b9e67ac2 Binary files /dev/null and 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Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/command/__pycache__/sdist.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/_framework_compat.py b/lib/python3.11/site-packages/setuptools/_distutils/command/_framework_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..00d34bc7d8c85126dbae716ebfa48e35b0833222 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/_framework_compat.py @@ -0,0 +1,54 @@ +""" +Backward compatibility for homebrew builds on macOS. +""" + +import functools +import os +import subprocess +import sys +import sysconfig + + +@functools.lru_cache +def enabled(): + """ + Only enabled for Python 3.9 framework homebrew builds + except ensurepip and venv. + """ + PY39 = (3, 9) < sys.version_info < (3, 10) + framework = sys.platform == 'darwin' and sys._framework + homebrew = "Cellar" in sysconfig.get_config_var('projectbase') + venv = sys.prefix != sys.base_prefix + ensurepip = os.environ.get("ENSUREPIP_OPTIONS") + return PY39 and framework and homebrew and not venv and not ensurepip + + +schemes = dict( + osx_framework_library=dict( + stdlib='{installed_base}/{platlibdir}/python{py_version_short}', + platstdlib='{platbase}/{platlibdir}/python{py_version_short}', + purelib='{homebrew_prefix}/lib/python{py_version_short}/site-packages', + platlib='{homebrew_prefix}/{platlibdir}/python{py_version_short}/site-packages', + include='{installed_base}/include/python{py_version_short}{abiflags}', + platinclude='{installed_platbase}/include/python{py_version_short}{abiflags}', + scripts='{homebrew_prefix}/bin', + data='{homebrew_prefix}', + ) +) + + +@functools.lru_cache +def vars(): + if not enabled(): + return {} + homebrew_prefix = subprocess.check_output(['brew', '--prefix'], text=True).strip() + return locals() + + +def scheme(name): + """ + Override the selected scheme for posix_prefix. + """ + if not enabled() or not name.endswith('_prefix'): + return name + return 'osx_framework_library' diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/bdist.py b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist.py new file mode 100644 index 0000000000000000000000000000000000000000..07811aab27ad7fcd2303883024243e2f52c2bbcb --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist.py @@ -0,0 +1,167 @@ +"""distutils.command.bdist + +Implements the Distutils 'bdist' command (create a built [binary] +distribution).""" + +from __future__ import annotations + +import os +import warnings +from collections.abc import Callable +from typing import TYPE_CHECKING, ClassVar + +from ..core import Command +from ..errors import DistutilsOptionError, DistutilsPlatformError +from ..util import get_platform + +if TYPE_CHECKING: + from typing_extensions import deprecated +else: + + def deprecated(message): + return lambda fn: fn + + +def show_formats(): + """Print list of available formats (arguments to "--format" option).""" + from ..fancy_getopt import FancyGetopt + + formats = [ + ("formats=" + format, None, bdist.format_commands[format][1]) + for format in bdist.format_commands + ] + pretty_printer = FancyGetopt(formats) + pretty_printer.print_help("List of available distribution formats:") + + +class ListCompat(dict[str, tuple[str, str]]): + # adapter to allow for Setuptools compatibility in format_commands + @deprecated("format_commands is now a dict. append is deprecated.") + def append(self, item: object) -> None: + warnings.warn( + "format_commands is now a dict. append is deprecated.", + DeprecationWarning, + stacklevel=2, + ) + + +class bdist(Command): + description = "create a built (binary) distribution" + + user_options = [ + ('bdist-base=', 'b', "temporary directory for creating built distributions"), + ( + 'plat-name=', + 'p', + "platform name to embed in generated filenames " + f"[default: {get_platform()}]", + ), + ('formats=', None, "formats for distribution (comma-separated list)"), + ( + 'dist-dir=', + 'd', + "directory to put final built distributions in [default: dist]", + ), + ('skip-build', None, "skip rebuilding everything (for testing/debugging)"), + ( + 'owner=', + 'u', + "Owner name used when creating a tar file [default: current user]", + ), + ( + 'group=', + 'g', + "Group name used when creating a tar file [default: current group]", + ), + ] + + boolean_options: ClassVar[list[str]] = ['skip-build'] + + help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [ + ('help-formats', None, "lists available distribution formats", show_formats), + ] + + # The following commands do not take a format option from bdist + no_format_option: ClassVar[tuple[str, ...]] = ('bdist_rpm',) + + # This won't do in reality: will need to distinguish RPM-ish Linux, + # Debian-ish Linux, Solaris, FreeBSD, ..., Windows, Mac OS. + default_format: ClassVar[dict[str, str]] = {'posix': 'gztar', 'nt': 'zip'} + + # Define commands in preferred order for the --help-formats option + format_commands = ListCompat({ + 'rpm': ('bdist_rpm', "RPM distribution"), + 'gztar': ('bdist_dumb', "gzip'ed tar file"), + 'bztar': ('bdist_dumb', "bzip2'ed tar file"), + 'xztar': ('bdist_dumb', "xz'ed tar file"), + 'ztar': ('bdist_dumb', "compressed tar file"), + 'tar': ('bdist_dumb', "tar file"), + 'zip': ('bdist_dumb', "ZIP file"), + }) + + # for compatibility until consumers only reference format_commands + format_command = format_commands + + def initialize_options(self): + self.bdist_base = None + self.plat_name = None + self.formats = None + self.dist_dir = None + self.skip_build = False + self.group = None + self.owner = None + + def finalize_options(self) -> None: + # have to finalize 'plat_name' before 'bdist_base' + if self.plat_name is None: + if self.skip_build: + self.plat_name = get_platform() + else: + self.plat_name = self.get_finalized_command('build').plat_name + + # 'bdist_base' -- parent of per-built-distribution-format + # temporary directories (eg. we'll probably have + # "build/bdist./dumb", "build/bdist./rpm", etc.) + if self.bdist_base is None: + build_base = self.get_finalized_command('build').build_base + self.bdist_base = os.path.join(build_base, 'bdist.' + self.plat_name) + + self.ensure_string_list('formats') + if self.formats is None: + try: + self.formats = [self.default_format[os.name]] + except KeyError: + raise DistutilsPlatformError( + "don't know how to create built distributions " + f"on platform {os.name}" + ) + + if self.dist_dir is None: + self.dist_dir = "dist" + + def run(self) -> None: + # Figure out which sub-commands we need to run. + commands = [] + for format in self.formats: + try: + commands.append(self.format_commands[format][0]) + except KeyError: + raise DistutilsOptionError(f"invalid format '{format}'") + + # Reinitialize and run each command. + for i in range(len(self.formats)): + cmd_name = commands[i] + sub_cmd = self.reinitialize_command(cmd_name) + if cmd_name not in self.no_format_option: + sub_cmd.format = self.formats[i] + + # passing the owner and group names for tar archiving + if cmd_name == 'bdist_dumb': + sub_cmd.owner = self.owner + sub_cmd.group = self.group + + # If we're going to need to run this command again, tell it to + # keep its temporary files around so subsequent runs go faster. + if cmd_name in commands[i + 1 :]: + sub_cmd.keep_temp = True + self.run_command(cmd_name) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_dumb.py b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_dumb.py new file mode 100644 index 0000000000000000000000000000000000000000..c49ef253b47cd0791aae1fc65c005c668cb1e97b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_dumb.py @@ -0,0 +1,141 @@ +"""distutils.command.bdist_dumb + +Implements the Distutils 'bdist_dumb' command (create a "dumb" built +distribution -- i.e., just an archive to be unpacked under $prefix or +$exec_prefix).""" + +import os +from distutils._log import log +from typing import ClassVar + +from ..core import Command +from ..dir_util import ensure_relative, remove_tree +from ..errors import DistutilsPlatformError +from ..sysconfig import get_python_version +from ..util import get_platform + + +class bdist_dumb(Command): + description = "create a \"dumb\" built distribution" + + user_options = [ + ('bdist-dir=', 'd', "temporary directory for creating the distribution"), + ( + 'plat-name=', + 'p', + "platform name to embed in generated filenames " + f"[default: {get_platform()}]", + ), + ( + 'format=', + 'f', + "archive format to create (tar, gztar, bztar, xztar, ztar, zip)", + ), + ( + 'keep-temp', + 'k', + "keep the pseudo-installation tree around after creating the distribution archive", + ), + ('dist-dir=', 'd', "directory to put final built distributions in"), + ('skip-build', None, "skip rebuilding everything (for testing/debugging)"), + ( + 'relative', + None, + "build the archive using relative paths [default: false]", + ), + ( + 'owner=', + 'u', + "Owner name used when creating a tar file [default: current user]", + ), + ( + 'group=', + 'g', + "Group name used when creating a tar file [default: current group]", + ), + ] + + boolean_options: ClassVar[list[str]] = ['keep-temp', 'skip-build', 'relative'] + + default_format = {'posix': 'gztar', 'nt': 'zip'} + + def initialize_options(self): + self.bdist_dir = None + self.plat_name = None + self.format = None + self.keep_temp = False + self.dist_dir = None + self.skip_build = None + self.relative = False + self.owner = None + self.group = None + + def finalize_options(self): + if self.bdist_dir is None: + bdist_base = self.get_finalized_command('bdist').bdist_base + self.bdist_dir = os.path.join(bdist_base, 'dumb') + + if self.format is None: + try: + self.format = self.default_format[os.name] + except KeyError: + raise DistutilsPlatformError( + "don't know how to create dumb built distributions " + f"on platform {os.name}" + ) + + self.set_undefined_options( + 'bdist', + ('dist_dir', 'dist_dir'), + ('plat_name', 'plat_name'), + ('skip_build', 'skip_build'), + ) + + def run(self): + if not self.skip_build: + self.run_command('build') + + install = self.reinitialize_command('install', reinit_subcommands=True) + install.root = self.bdist_dir + install.skip_build = self.skip_build + install.warn_dir = False + + log.info("installing to %s", self.bdist_dir) + self.run_command('install') + + # And make an archive relative to the root of the + # pseudo-installation tree. + archive_basename = f"{self.distribution.get_fullname()}.{self.plat_name}" + + pseudoinstall_root = os.path.join(self.dist_dir, archive_basename) + if not self.relative: + archive_root = self.bdist_dir + else: + if self.distribution.has_ext_modules() and ( + install.install_base != install.install_platbase + ): + raise DistutilsPlatformError( + "can't make a dumb built distribution where " + f"base and platbase are different ({install.install_base!r}, {install.install_platbase!r})" + ) + else: + archive_root = os.path.join( + self.bdist_dir, ensure_relative(install.install_base) + ) + + # Make the archive + filename = self.make_archive( + pseudoinstall_root, + self.format, + root_dir=archive_root, + owner=self.owner, + group=self.group, + ) + if self.distribution.has_ext_modules(): + pyversion = get_python_version() + else: + pyversion = 'any' + self.distribution.dist_files.append(('bdist_dumb', pyversion, filename)) + + if not self.keep_temp: + remove_tree(self.bdist_dir) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_rpm.py b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_rpm.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa5f99dda095af76d8ffac33e1dd8bfd6aad931 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/bdist_rpm.py @@ -0,0 +1,597 @@ +"""distutils.command.bdist_rpm + +Implements the Distutils 'bdist_rpm' command (create RPM source and binary +distributions).""" + +import os +import subprocess +import sys +from distutils._log import log +from typing import ClassVar + +from ..core import Command +from ..debug import DEBUG +from ..errors import ( + DistutilsExecError, + DistutilsFileError, + DistutilsOptionError, + DistutilsPlatformError, +) +from ..file_util import write_file +from ..sysconfig import get_python_version + + +class bdist_rpm(Command): + description = "create an RPM distribution" + + user_options = [ + ('bdist-base=', None, "base directory for creating built distributions"), + ( + 'rpm-base=', + None, + "base directory for creating RPMs (defaults to \"rpm\" under " + "--bdist-base; must be specified for RPM 2)", + ), + ( + 'dist-dir=', + 'd', + "directory to put final RPM files in (and .spec files if --spec-only)", + ), + ( + 'python=', + None, + "path to Python interpreter to hard-code in the .spec file " + "[default: \"python\"]", + ), + ( + 'fix-python', + None, + "hard-code the exact path to the current Python interpreter in " + "the .spec file", + ), + ('spec-only', None, "only regenerate spec file"), + ('source-only', None, "only generate source RPM"), + ('binary-only', None, "only generate binary RPM"), + ('use-bzip2', None, "use bzip2 instead of gzip to create source distribution"), + # More meta-data: too RPM-specific to put in the setup script, + # but needs to go in the .spec file -- so we make these options + # to "bdist_rpm". The idea is that packagers would put this + # info in setup.cfg, although they are of course free to + # supply it on the command line. + ( + 'distribution-name=', + None, + "name of the (Linux) distribution to which this " + "RPM applies (*not* the name of the module distribution!)", + ), + ('group=', None, "package classification [default: \"Development/Libraries\"]"), + ('release=', None, "RPM release number"), + ('serial=', None, "RPM serial number"), + ( + 'vendor=', + None, + "RPM \"vendor\" (eg. \"Joe Blow \") " + "[default: maintainer or author from setup script]", + ), + ( + 'packager=', + None, + "RPM packager (eg. \"Jane Doe \") [default: vendor]", + ), + ('doc-files=', None, "list of documentation files (space or comma-separated)"), + ('changelog=', None, "RPM changelog"), + ('icon=', None, "name of icon file"), + ('provides=', None, "capabilities provided by this package"), + ('requires=', None, "capabilities required by this package"), + ('conflicts=', None, "capabilities which conflict with this package"), + ('build-requires=', None, "capabilities required to build this package"), + ('obsoletes=', None, "capabilities made obsolete by this package"), + ('no-autoreq', None, "do not automatically calculate dependencies"), + # Actions to take when building RPM + ('keep-temp', 'k', "don't clean up RPM build directory"), + ('no-keep-temp', None, "clean up RPM build directory [default]"), + ( + 'use-rpm-opt-flags', + None, + "compile with RPM_OPT_FLAGS when building from source RPM", + ), + ('no-rpm-opt-flags', None, "do not pass any RPM CFLAGS to compiler"), + ('rpm3-mode', None, "RPM 3 compatibility mode (default)"), + ('rpm2-mode', None, "RPM 2 compatibility mode"), + # Add the hooks necessary for specifying custom scripts + ('prep-script=', None, "Specify a script for the PREP phase of RPM building"), + ('build-script=', None, "Specify a script for the BUILD phase of RPM building"), + ( + 'pre-install=', + None, + "Specify a script for the pre-INSTALL phase of RPM building", + ), + ( + 'install-script=', + None, + "Specify a script for the INSTALL phase of RPM building", + ), + ( + 'post-install=', + None, + "Specify a script for the post-INSTALL phase of RPM building", + ), + ( + 'pre-uninstall=', + None, + "Specify a script for the pre-UNINSTALL phase of RPM building", + ), + ( + 'post-uninstall=', + None, + "Specify a script for the post-UNINSTALL phase of RPM building", + ), + ('clean-script=', None, "Specify a script for the CLEAN phase of RPM building"), + ( + 'verify-script=', + None, + "Specify a script for the VERIFY phase of the RPM build", + ), + # Allow a packager to explicitly force an architecture + ('force-arch=', None, "Force an architecture onto the RPM build process"), + ('quiet', 'q', "Run the INSTALL phase of RPM building in quiet mode"), + ] + + boolean_options: ClassVar[list[str]] = [ + 'keep-temp', + 'use-rpm-opt-flags', + 'rpm3-mode', + 'no-autoreq', + 'quiet', + ] + + negative_opt: ClassVar[dict[str, str]] = { + 'no-keep-temp': 'keep-temp', + 'no-rpm-opt-flags': 'use-rpm-opt-flags', + 'rpm2-mode': 'rpm3-mode', + } + + def initialize_options(self): + self.bdist_base = None + self.rpm_base = None + self.dist_dir = None + self.python = None + self.fix_python = None + self.spec_only = None + self.binary_only = None + self.source_only = None + self.use_bzip2 = None + + self.distribution_name = None + self.group = None + self.release = None + self.serial = None + self.vendor = None + self.packager = None + self.doc_files = None + self.changelog = None + self.icon = None + + self.prep_script = None + self.build_script = None + self.install_script = None + self.clean_script = None + self.verify_script = None + self.pre_install = None + self.post_install = None + self.pre_uninstall = None + self.post_uninstall = None + self.prep = None + self.provides = None + self.requires = None + self.conflicts = None + self.build_requires = None + self.obsoletes = None + + self.keep_temp = False + self.use_rpm_opt_flags = True + self.rpm3_mode = True + self.no_autoreq = False + + self.force_arch = None + self.quiet = False + + def finalize_options(self) -> None: + self.set_undefined_options('bdist', ('bdist_base', 'bdist_base')) + if self.rpm_base is None: + if not self.rpm3_mode: + raise DistutilsOptionError("you must specify --rpm-base in RPM 2 mode") + self.rpm_base = os.path.join(self.bdist_base, "rpm") + + if self.python is None: + if self.fix_python: + self.python = sys.executable + else: + self.python = "python3" + elif self.fix_python: + raise DistutilsOptionError( + "--python and --fix-python are mutually exclusive options" + ) + + if os.name != 'posix': + raise DistutilsPlatformError( + f"don't know how to create RPM distributions on platform {os.name}" + ) + if self.binary_only and self.source_only: + raise DistutilsOptionError( + "cannot supply both '--source-only' and '--binary-only'" + ) + + # don't pass CFLAGS to pure python distributions + if not self.distribution.has_ext_modules(): + self.use_rpm_opt_flags = False + + self.set_undefined_options('bdist', ('dist_dir', 'dist_dir')) + self.finalize_package_data() + + def finalize_package_data(self) -> None: + self.ensure_string('group', "Development/Libraries") + self.ensure_string( + 'vendor', + f"{self.distribution.get_contact()} <{self.distribution.get_contact_email()}>", + ) + self.ensure_string('packager') + self.ensure_string_list('doc_files') + if isinstance(self.doc_files, list): + for readme in ('README', 'README.txt'): + if os.path.exists(readme) and readme not in self.doc_files: + self.doc_files.append(readme) + + self.ensure_string('release', "1") + self.ensure_string('serial') # should it be an int? + + self.ensure_string('distribution_name') + + self.ensure_string('changelog') + # Format changelog correctly + self.changelog = self._format_changelog(self.changelog) + + self.ensure_filename('icon') + + self.ensure_filename('prep_script') + self.ensure_filename('build_script') + self.ensure_filename('install_script') + self.ensure_filename('clean_script') + self.ensure_filename('verify_script') + self.ensure_filename('pre_install') + self.ensure_filename('post_install') + self.ensure_filename('pre_uninstall') + self.ensure_filename('post_uninstall') + + # XXX don't forget we punted on summaries and descriptions -- they + # should be handled here eventually! + + # Now *this* is some meta-data that belongs in the setup script... + self.ensure_string_list('provides') + self.ensure_string_list('requires') + self.ensure_string_list('conflicts') + self.ensure_string_list('build_requires') + self.ensure_string_list('obsoletes') + + self.ensure_string('force_arch') + + def run(self) -> None: # noqa: C901 + if DEBUG: + print("before _get_package_data():") + print("vendor =", self.vendor) + print("packager =", self.packager) + print("doc_files =", self.doc_files) + print("changelog =", self.changelog) + + # make directories + if self.spec_only: + spec_dir = self.dist_dir + self.mkpath(spec_dir) + else: + rpm_dir = {} + for d in ('SOURCES', 'SPECS', 'BUILD', 'RPMS', 'SRPMS'): + rpm_dir[d] = os.path.join(self.rpm_base, d) + self.mkpath(rpm_dir[d]) + spec_dir = rpm_dir['SPECS'] + + # Spec file goes into 'dist_dir' if '--spec-only specified', + # build/rpm. otherwise. + spec_path = os.path.join(spec_dir, f"{self.distribution.get_name()}.spec") + self.execute( + write_file, (spec_path, self._make_spec_file()), f"writing '{spec_path}'" + ) + + if self.spec_only: # stop if requested + return + + # Make a source distribution and copy to SOURCES directory with + # optional icon. + saved_dist_files = self.distribution.dist_files[:] + sdist = self.reinitialize_command('sdist') + if self.use_bzip2: + sdist.formats = ['bztar'] + else: + sdist.formats = ['gztar'] + self.run_command('sdist') + self.distribution.dist_files = saved_dist_files + + source = sdist.get_archive_files()[0] + source_dir = rpm_dir['SOURCES'] + self.copy_file(source, source_dir) + + if self.icon: + if os.path.exists(self.icon): + self.copy_file(self.icon, source_dir) + else: + raise DistutilsFileError(f"icon file '{self.icon}' does not exist") + + # build package + log.info("building RPMs") + rpm_cmd = ['rpmbuild'] + + if self.source_only: # what kind of RPMs? + rpm_cmd.append('-bs') + elif self.binary_only: + rpm_cmd.append('-bb') + else: + rpm_cmd.append('-ba') + rpm_cmd.extend(['--define', f'__python {self.python}']) + if self.rpm3_mode: + rpm_cmd.extend(['--define', f'_topdir {os.path.abspath(self.rpm_base)}']) + if not self.keep_temp: + rpm_cmd.append('--clean') + + if self.quiet: + rpm_cmd.append('--quiet') + + rpm_cmd.append(spec_path) + # Determine the binary rpm names that should be built out of this spec + # file + # Note that some of these may not be really built (if the file + # list is empty) + nvr_string = "%{name}-%{version}-%{release}" + src_rpm = nvr_string + ".src.rpm" + non_src_rpm = "%{arch}/" + nvr_string + ".%{arch}.rpm" + q_cmd = rf"rpm -q --qf '{src_rpm} {non_src_rpm}\n' --specfile '{spec_path}'" + + out = os.popen(q_cmd) + try: + binary_rpms = [] + source_rpm = None + while True: + line = out.readline() + if not line: + break + ell = line.strip().split() + assert len(ell) == 2 + binary_rpms.append(ell[1]) + # The source rpm is named after the first entry in the spec file + if source_rpm is None: + source_rpm = ell[0] + + status = out.close() + if status: + raise DistutilsExecError(f"Failed to execute: {q_cmd!r}") + + finally: + out.close() + + self.spawn(rpm_cmd) + + if self.distribution.has_ext_modules(): + pyversion = get_python_version() + else: + pyversion = 'any' + + if not self.binary_only: + srpm = os.path.join(rpm_dir['SRPMS'], source_rpm) + assert os.path.exists(srpm) + self.move_file(srpm, self.dist_dir) + filename = os.path.join(self.dist_dir, source_rpm) + self.distribution.dist_files.append(('bdist_rpm', pyversion, filename)) + + if not self.source_only: + for rpm in binary_rpms: + rpm = os.path.join(rpm_dir['RPMS'], rpm) + if os.path.exists(rpm): + self.move_file(rpm, self.dist_dir) + filename = os.path.join(self.dist_dir, os.path.basename(rpm)) + self.distribution.dist_files.append(( + 'bdist_rpm', + pyversion, + filename, + )) + + def _dist_path(self, path): + return os.path.join(self.dist_dir, os.path.basename(path)) + + def _make_spec_file(self): # noqa: C901 + """Generate the text of an RPM spec file and return it as a + list of strings (one per line). + """ + # definitions and headers + spec_file = [ + '%define name ' + self.distribution.get_name(), + '%define version ' + self.distribution.get_version().replace('-', '_'), + '%define unmangled_version ' + self.distribution.get_version(), + '%define release ' + self.release.replace('-', '_'), + '', + 'Summary: ' + (self.distribution.get_description() or "UNKNOWN"), + ] + + # Workaround for #14443 which affects some RPM based systems such as + # RHEL6 (and probably derivatives) + vendor_hook = subprocess.getoutput('rpm --eval %{__os_install_post}') + # Generate a potential replacement value for __os_install_post (whilst + # normalizing the whitespace to simplify the test for whether the + # invocation of brp-python-bytecompile passes in __python): + vendor_hook = '\n'.join([ + f' {line.strip()} \\' for line in vendor_hook.splitlines() + ]) + problem = "brp-python-bytecompile \\\n" + fixed = "brp-python-bytecompile %{__python} \\\n" + fixed_hook = vendor_hook.replace(problem, fixed) + if fixed_hook != vendor_hook: + spec_file.append('# Workaround for https://bugs.python.org/issue14443') + spec_file.append('%define __os_install_post ' + fixed_hook + '\n') + + # put locale summaries into spec file + # XXX not supported for now (hard to put a dictionary + # in a config file -- arg!) + # for locale in self.summaries.keys(): + # spec_file.append('Summary(%s): %s' % (locale, + # self.summaries[locale])) + + spec_file.extend([ + 'Name: %{name}', + 'Version: %{version}', + 'Release: %{release}', + ]) + + # XXX yuck! this filename is available from the "sdist" command, + # but only after it has run: and we create the spec file before + # running "sdist", in case of --spec-only. + if self.use_bzip2: + spec_file.append('Source0: %{name}-%{unmangled_version}.tar.bz2') + else: + spec_file.append('Source0: %{name}-%{unmangled_version}.tar.gz') + + spec_file.extend([ + 'License: ' + (self.distribution.get_license() or "UNKNOWN"), + 'Group: ' + self.group, + 'BuildRoot: %{_tmppath}/%{name}-%{version}-%{release}-buildroot', + 'Prefix: %{_prefix}', + ]) + + if not self.force_arch: + # noarch if no extension modules + if not self.distribution.has_ext_modules(): + spec_file.append('BuildArch: noarch') + else: + spec_file.append(f'BuildArch: {self.force_arch}') + + for field in ( + 'Vendor', + 'Packager', + 'Provides', + 'Requires', + 'Conflicts', + 'Obsoletes', + ): + val = getattr(self, field.lower()) + if isinstance(val, list): + spec_file.append('{}: {}'.format(field, ' '.join(val))) + elif val is not None: + spec_file.append(f'{field}: {val}') + + if self.distribution.get_url(): + spec_file.append('Url: ' + self.distribution.get_url()) + + if self.distribution_name: + spec_file.append('Distribution: ' + self.distribution_name) + + if self.build_requires: + spec_file.append('BuildRequires: ' + ' '.join(self.build_requires)) + + if self.icon: + spec_file.append('Icon: ' + os.path.basename(self.icon)) + + if self.no_autoreq: + spec_file.append('AutoReq: 0') + + spec_file.extend([ + '', + '%description', + self.distribution.get_long_description() or "", + ]) + + # put locale descriptions into spec file + # XXX again, suppressed because config file syntax doesn't + # easily support this ;-( + # for locale in self.descriptions.keys(): + # spec_file.extend([ + # '', + # '%description -l ' + locale, + # self.descriptions[locale], + # ]) + + # rpm scripts + # figure out default build script + def_setup_call = f"{self.python} {os.path.basename(sys.argv[0])}" + def_build = f"{def_setup_call} build" + if self.use_rpm_opt_flags: + def_build = 'env CFLAGS="$RPM_OPT_FLAGS" ' + def_build + + # insert contents of files + + # XXX this is kind of misleading: user-supplied options are files + # that we open and interpolate into the spec file, but the defaults + # are just text that we drop in as-is. Hmmm. + + install_cmd = f'{def_setup_call} install -O1 --root=$RPM_BUILD_ROOT --record=INSTALLED_FILES' + + script_options = [ + ('prep', 'prep_script', "%setup -n %{name}-%{unmangled_version}"), + ('build', 'build_script', def_build), + ('install', 'install_script', install_cmd), + ('clean', 'clean_script', "rm -rf $RPM_BUILD_ROOT"), + ('verifyscript', 'verify_script', None), + ('pre', 'pre_install', None), + ('post', 'post_install', None), + ('preun', 'pre_uninstall', None), + ('postun', 'post_uninstall', None), + ] + + for rpm_opt, attr, default in script_options: + # Insert contents of file referred to, if no file is referred to + # use 'default' as contents of script + val = getattr(self, attr) + if val or default: + spec_file.extend([ + '', + '%' + rpm_opt, + ]) + if val: + with open(val) as f: + spec_file.extend(f.read().split('\n')) + else: + spec_file.append(default) + + # files section + spec_file.extend([ + '', + '%files -f INSTALLED_FILES', + '%defattr(-,root,root)', + ]) + + if self.doc_files: + spec_file.append('%doc ' + ' '.join(self.doc_files)) + + if self.changelog: + spec_file.extend([ + '', + '%changelog', + ]) + spec_file.extend(self.changelog) + + return spec_file + + def _format_changelog(self, changelog): + """Format the changelog correctly and convert it to a list of strings""" + if not changelog: + return changelog + new_changelog = [] + for line in changelog.strip().split('\n'): + line = line.strip() + if line[0] == '*': + new_changelog.extend(['', line]) + elif line[0] == '-': + new_changelog.append(line) + else: + new_changelog.append(' ' + line) + + # strip trailing newline inserted by first changelog entry + if not new_changelog[0]: + del new_changelog[0] + + return new_changelog diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/build.py b/lib/python3.11/site-packages/setuptools/_distutils/command/build.py new file mode 100644 index 0000000000000000000000000000000000000000..6a8303a954ea705c547e2014dc6b51d9d987a532 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/build.py @@ -0,0 +1,156 @@ +"""distutils.command.build + +Implements the Distutils 'build' command.""" + +from __future__ import annotations + +import os +import sys +import sysconfig +from collections.abc import Callable +from typing import ClassVar + +from ..ccompiler import show_compilers +from ..core import Command +from ..errors import DistutilsOptionError +from ..util import get_platform + + +class build(Command): + description = "build everything needed to install" + + user_options = [ + ('build-base=', 'b', "base directory for build library"), + ('build-purelib=', None, "build directory for platform-neutral distributions"), + ('build-platlib=', None, "build directory for platform-specific distributions"), + ( + 'build-lib=', + None, + "build directory for all distribution (defaults to either build-purelib or build-platlib", + ), + ('build-scripts=', None, "build directory for scripts"), + ('build-temp=', 't', "temporary build directory"), + ( + 'plat-name=', + 'p', + f"platform name to build for, if supported [default: {get_platform()}]", + ), + ('compiler=', 'c', "specify the compiler type"), + ('parallel=', 'j', "number of parallel build jobs"), + ('debug', 'g', "compile extensions and libraries with debugging information"), + ('force', 'f', "forcibly build everything (ignore file timestamps)"), + ('executable=', 'e', "specify final destination interpreter path (build.py)"), + ] + + boolean_options: ClassVar[list[str]] = ['debug', 'force'] + + help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [ + ('help-compiler', None, "list available compilers", show_compilers), + ] + + def initialize_options(self): + self.build_base = 'build' + # these are decided only after 'build_base' has its final value + # (unless overridden by the user or client) + self.build_purelib = None + self.build_platlib = None + self.build_lib = None + self.build_temp = None + self.build_scripts = None + self.compiler = None + self.plat_name = None + self.debug = None + self.force = False + self.executable = None + self.parallel = None + + def finalize_options(self) -> None: # noqa: C901 + if self.plat_name is None: + self.plat_name = get_platform() + else: + # plat-name only supported for windows (other platforms are + # supported via ./configure flags, if at all). Avoid misleading + # other platforms. + if os.name != 'nt': + raise DistutilsOptionError( + "--plat-name only supported on Windows (try " + "using './configure --help' on your platform)" + ) + + plat_specifier = f".{self.plat_name}-{sys.implementation.cache_tag}" + + # Python 3.13+ with --disable-gil shouldn't share build directories + if sysconfig.get_config_var('Py_GIL_DISABLED'): + plat_specifier += 't' + + # Make it so Python 2.x and Python 2.x with --with-pydebug don't + # share the same build directories. Doing so confuses the build + # process for C modules + if hasattr(sys, 'gettotalrefcount'): + plat_specifier += '-pydebug' + + # 'build_purelib' and 'build_platlib' just default to 'lib' and + # 'lib.' under the base build directory. We only use one of + # them for a given distribution, though -- + if self.build_purelib is None: + self.build_purelib = os.path.join(self.build_base, 'lib') + if self.build_platlib is None: + self.build_platlib = os.path.join(self.build_base, 'lib' + plat_specifier) + + # 'build_lib' is the actual directory that we will use for this + # particular module distribution -- if user didn't supply it, pick + # one of 'build_purelib' or 'build_platlib'. + if self.build_lib is None: + if self.distribution.has_ext_modules(): + self.build_lib = self.build_platlib + else: + self.build_lib = self.build_purelib + + # 'build_temp' -- temporary directory for compiler turds, + # "build/temp." + if self.build_temp is None: + self.build_temp = os.path.join(self.build_base, 'temp' + plat_specifier) + if self.build_scripts is None: + self.build_scripts = os.path.join( + self.build_base, + f'scripts-{sys.version_info.major}.{sys.version_info.minor}', + ) + + if self.executable is None and sys.executable: + self.executable = os.path.normpath(sys.executable) + + if isinstance(self.parallel, str): + try: + self.parallel = int(self.parallel) + except ValueError: + raise DistutilsOptionError("parallel should be an integer") + + def run(self) -> None: + # Run all relevant sub-commands. This will be some subset of: + # - build_py - pure Python modules + # - build_clib - standalone C libraries + # - build_ext - Python extensions + # - build_scripts - (Python) scripts + for cmd_name in self.get_sub_commands(): + self.run_command(cmd_name) + + # -- Predicates for the sub-command list --------------------------- + + def has_pure_modules(self): + return self.distribution.has_pure_modules() + + def has_c_libraries(self): + return self.distribution.has_c_libraries() + + def has_ext_modules(self): + return self.distribution.has_ext_modules() + + def has_scripts(self): + return self.distribution.has_scripts() + + sub_commands = [ + ('build_py', has_pure_modules), + ('build_clib', has_c_libraries), + ('build_ext', has_ext_modules), + ('build_scripts', has_scripts), + ] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/build_clib.py b/lib/python3.11/site-packages/setuptools/_distutils/command/build_clib.py new file mode 100644 index 0000000000000000000000000000000000000000..8de5df239c793ac74265d5aac6b113c99b1934fd --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/build_clib.py @@ -0,0 +1,199 @@ +"""distutils.command.build_clib + +Implements the Distutils 'build_clib' command, to build a C/C++ library +that is included in the module distribution and needed by an extension +module.""" + +# XXX this module has *lots* of code ripped-off quite transparently from +# build_ext.py -- not surprisingly really, as the work required to build +# a static library from a collection of C source files is not really all +# that different from what's required to build a shared object file from +# a collection of C source files. Nevertheless, I haven't done the +# necessary refactoring to account for the overlap in code between the +# two modules, mainly because a number of subtle details changed in the +# cut 'n paste. Sigh. +from __future__ import annotations + +import os +from collections.abc import Callable +from distutils._log import log +from typing import ClassVar + +from ..ccompiler import new_compiler, show_compilers +from ..core import Command +from ..errors import DistutilsSetupError +from ..sysconfig import customize_compiler + + +class build_clib(Command): + description = "build C/C++ libraries used by Python extensions" + + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ('build-clib=', 'b', "directory to build C/C++ libraries to"), + ('build-temp=', 't', "directory to put temporary build by-products"), + ('debug', 'g', "compile with debugging information"), + ('force', 'f', "forcibly build everything (ignore file timestamps)"), + ('compiler=', 'c', "specify the compiler type"), + ] + + boolean_options: ClassVar[list[str]] = ['debug', 'force'] + + help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [ + ('help-compiler', None, "list available compilers", show_compilers), + ] + + def initialize_options(self): + self.build_clib = None + self.build_temp = None + + # List of libraries to build + self.libraries = None + + # Compilation options for all libraries + self.include_dirs = None + self.define = None + self.undef = None + self.debug = None + self.force = False + self.compiler = None + + def finalize_options(self) -> None: + # This might be confusing: both build-clib and build-temp default + # to build-temp as defined by the "build" command. This is because + # I think that C libraries are really just temporary build + # by-products, at least from the point of view of building Python + # extensions -- but I want to keep my options open. + self.set_undefined_options( + 'build', + ('build_temp', 'build_clib'), + ('build_temp', 'build_temp'), + ('compiler', 'compiler'), + ('debug', 'debug'), + ('force', 'force'), + ) + + self.libraries = self.distribution.libraries + if self.libraries: + self.check_library_list(self.libraries) + + if self.include_dirs is None: + self.include_dirs = self.distribution.include_dirs or [] + if isinstance(self.include_dirs, str): + self.include_dirs = self.include_dirs.split(os.pathsep) + + # XXX same as for build_ext -- what about 'self.define' and + # 'self.undef' ? + + def run(self) -> None: + if not self.libraries: + return + + self.compiler = new_compiler(compiler=self.compiler, force=self.force) + customize_compiler(self.compiler) + + if self.include_dirs is not None: + self.compiler.set_include_dirs(self.include_dirs) + if self.define is not None: + # 'define' option is a list of (name,value) tuples + for name, value in self.define: + self.compiler.define_macro(name, value) + if self.undef is not None: + for macro in self.undef: + self.compiler.undefine_macro(macro) + + self.build_libraries(self.libraries) + + def check_library_list(self, libraries) -> None: + """Ensure that the list of libraries is valid. + + `library` is presumably provided as a command option 'libraries'. + This method checks that it is a list of 2-tuples, where the tuples + are (library_name, build_info_dict). + + Raise DistutilsSetupError if the structure is invalid anywhere; + just returns otherwise. + """ + if not isinstance(libraries, list): + raise DistutilsSetupError("'libraries' option must be a list of tuples") + + for lib in libraries: + if not isinstance(lib, tuple) and len(lib) != 2: + raise DistutilsSetupError("each element of 'libraries' must a 2-tuple") + + name, build_info = lib + + if not isinstance(name, str): + raise DistutilsSetupError( + "first element of each tuple in 'libraries' " + "must be a string (the library name)" + ) + + if '/' in name or (os.sep != '/' and os.sep in name): + raise DistutilsSetupError( + f"bad library name '{lib[0]}': may not contain directory separators" + ) + + if not isinstance(build_info, dict): + raise DistutilsSetupError( + "second element of each tuple in 'libraries' " + "must be a dictionary (build info)" + ) + + def get_library_names(self): + # Assume the library list is valid -- 'check_library_list()' is + # called from 'finalize_options()', so it should be! + if not self.libraries: + return None + + lib_names = [] + for lib_name, _build_info in self.libraries: + lib_names.append(lib_name) + return lib_names + + def get_source_files(self): + self.check_library_list(self.libraries) + filenames = [] + for lib_name, build_info in self.libraries: + sources = build_info.get('sources') + if sources is None or not isinstance(sources, (list, tuple)): + raise DistutilsSetupError( + f"in 'libraries' option (library '{lib_name}'), " + "'sources' must be present and must be " + "a list of source filenames" + ) + + filenames.extend(sources) + return filenames + + def build_libraries(self, libraries) -> None: + for lib_name, build_info in libraries: + sources = build_info.get('sources') + if sources is None or not isinstance(sources, (list, tuple)): + raise DistutilsSetupError( + f"in 'libraries' option (library '{lib_name}'), " + "'sources' must be present and must be " + "a list of source filenames" + ) + sources = list(sources) + + log.info("building '%s' library", lib_name) + + # First, compile the source code to object files in the library + # directory. (This should probably change to putting object + # files in a temporary build directory.) + macros = build_info.get('macros') + include_dirs = build_info.get('include_dirs') + objects = self.compiler.compile( + sources, + output_dir=self.build_temp, + macros=macros, + include_dirs=include_dirs, + debug=self.debug, + ) + + # Now "link" the object files together into a static library. + # (On Unix at least, this isn't really linking -- it just + # builds an archive. Whatever.) + self.compiler.create_static_lib( + objects, lib_name, output_dir=self.build_clib, debug=self.debug + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py b/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py new file mode 100644 index 0000000000000000000000000000000000000000..df623d7e0ef2f667bfce329283256513621eacf7 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/build_ext.py @@ -0,0 +1,811 @@ +"""distutils.command.build_ext + +Implements the Distutils 'build_ext' command, for building extension +modules (currently limited to C extensions, should accommodate C++ +extensions ASAP).""" + +from __future__ import annotations + +import contextlib +import os +import re +import sys +from collections.abc import Callable +from distutils._log import log +from site import USER_BASE +from typing import ClassVar + +from .._modified import newer_group +from ..ccompiler import new_compiler, show_compilers +from ..core import Command +from ..errors import ( + CCompilerError, + CompileError, + DistutilsError, + DistutilsOptionError, + DistutilsPlatformError, + DistutilsSetupError, +) +from ..extension import Extension +from ..sysconfig import customize_compiler, get_config_h_filename, get_python_version +from ..util import get_platform, is_freethreaded, is_mingw + +# An extension name is just a dot-separated list of Python NAMEs (ie. +# the same as a fully-qualified module name). +extension_name_re = re.compile(r'^[a-zA-Z_][a-zA-Z_0-9]*(\.[a-zA-Z_][a-zA-Z_0-9]*)*$') + + +class build_ext(Command): + description = "build C/C++ extensions (compile/link to build directory)" + + # XXX thoughts on how to deal with complex command-line options like + # these, i.e. how to make it so fancy_getopt can suck them off the + # command line and make it look like setup.py defined the appropriate + # lists of tuples of what-have-you. + # - each command needs a callback to process its command-line options + # - Command.__init__() needs access to its share of the whole + # command line (must ultimately come from + # Distribution.parse_command_line()) + # - it then calls the current command class' option-parsing + # callback to deal with weird options like -D, which have to + # parse the option text and churn out some custom data + # structure + # - that data structure (in this case, a list of 2-tuples) + # will then be present in the command object by the time + # we get to finalize_options() (i.e. the constructor + # takes care of both command-line and client options + # in between initialize_options() and finalize_options()) + + sep_by = f" (separated by '{os.pathsep}')" + user_options = [ + ('build-lib=', 'b', "directory for compiled extension modules"), + ('build-temp=', 't', "directory for temporary files (build by-products)"), + ( + 'plat-name=', + 'p', + "platform name to cross-compile for, if supported " + f"[default: {get_platform()}]", + ), + ( + 'inplace', + 'i', + "ignore build-lib and put compiled extensions into the source " + "directory alongside your pure Python modules", + ), + ( + 'include-dirs=', + 'I', + "list of directories to search for header files" + sep_by, + ), + ('define=', 'D', "C preprocessor macros to define"), + ('undef=', 'U', "C preprocessor macros to undefine"), + ('libraries=', 'l', "external C libraries to link with"), + ( + 'library-dirs=', + 'L', + "directories to search for external C libraries" + sep_by, + ), + ('rpath=', 'R', "directories to search for shared C libraries at runtime"), + ('link-objects=', 'O', "extra explicit link objects to include in the link"), + ('debug', 'g', "compile/link with debugging information"), + ('force', 'f', "forcibly build everything (ignore file timestamps)"), + ('compiler=', 'c', "specify the compiler type"), + ('parallel=', 'j', "number of parallel build jobs"), + ('swig-cpp', None, "make SWIG create C++ files (default is C)"), + ('swig-opts=', None, "list of SWIG command line options"), + ('swig=', None, "path to the SWIG executable"), + ('user', None, "add user include, library and rpath"), + ] + + boolean_options: ClassVar[list[str]] = [ + 'inplace', + 'debug', + 'force', + 'swig-cpp', + 'user', + ] + + help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [ + ('help-compiler', None, "list available compilers", show_compilers), + ] + + def initialize_options(self): + self.extensions = None + self.build_lib = None + self.plat_name = None + self.build_temp = None + self.inplace = False + self.package = None + + self.include_dirs = None + self.define = None + self.undef = None + self.libraries = None + self.library_dirs = None + self.rpath = None + self.link_objects = None + self.debug = None + self.force = None + self.compiler = None + self.swig = None + self.swig_cpp = None + self.swig_opts = None + self.user = None + self.parallel = None + + @staticmethod + def _python_lib_dir(sysconfig): + """ + Resolve Python's library directory for building extensions + that rely on a shared Python library. + + See python/cpython#44264 and python/cpython#48686 + """ + if not sysconfig.get_config_var('Py_ENABLE_SHARED'): + return + + if sysconfig.python_build: + yield '.' + return + + if sys.platform == 'zos': + # On z/OS, a user is not required to install Python to + # a predetermined path, but can use Python portably + installed_dir = sysconfig.get_config_var('base') + lib_dir = sysconfig.get_config_var('platlibdir') + yield os.path.join(installed_dir, lib_dir) + else: + # building third party extensions + yield sysconfig.get_config_var('LIBDIR') + + def finalize_options(self) -> None: # noqa: C901 + from distutils import sysconfig + + self.set_undefined_options( + 'build', + ('build_lib', 'build_lib'), + ('build_temp', 'build_temp'), + ('compiler', 'compiler'), + ('debug', 'debug'), + ('force', 'force'), + ('parallel', 'parallel'), + ('plat_name', 'plat_name'), + ) + + if self.package is None: + self.package = self.distribution.ext_package + + self.extensions = self.distribution.ext_modules + + # Make sure Python's include directories (for Python.h, pyconfig.h, + # etc.) are in the include search path. + py_include = sysconfig.get_python_inc() + plat_py_include = sysconfig.get_python_inc(plat_specific=True) + if self.include_dirs is None: + self.include_dirs = self.distribution.include_dirs or [] + if isinstance(self.include_dirs, str): + self.include_dirs = self.include_dirs.split(os.pathsep) + + # If in a virtualenv, add its include directory + # Issue 16116 + if sys.exec_prefix != sys.base_exec_prefix: + self.include_dirs.append(os.path.join(sys.exec_prefix, 'include')) + + # Put the Python "system" include dir at the end, so that + # any local include dirs take precedence. + self.include_dirs.extend(py_include.split(os.path.pathsep)) + if plat_py_include != py_include: + self.include_dirs.extend(plat_py_include.split(os.path.pathsep)) + + self.ensure_string_list('libraries') + self.ensure_string_list('link_objects') + + # Life is easier if we're not forever checking for None, so + # simplify these options to empty lists if unset + if self.libraries is None: + self.libraries = [] + if self.library_dirs is None: + self.library_dirs = [] + elif isinstance(self.library_dirs, str): + self.library_dirs = self.library_dirs.split(os.pathsep) + + if self.rpath is None: + self.rpath = [] + elif isinstance(self.rpath, str): + self.rpath = self.rpath.split(os.pathsep) + + # for extensions under windows use different directories + # for Release and Debug builds. + # also Python's library directory must be appended to library_dirs + if os.name == 'nt' and not is_mingw(): + # the 'libs' directory is for binary installs - we assume that + # must be the *native* platform. But we don't really support + # cross-compiling via a binary install anyway, so we let it go. + self.library_dirs.append(os.path.join(sys.exec_prefix, 'libs')) + if sys.base_exec_prefix != sys.prefix: # Issue 16116 + self.library_dirs.append(os.path.join(sys.base_exec_prefix, 'libs')) + if self.debug: + self.build_temp = os.path.join(self.build_temp, "Debug") + else: + self.build_temp = os.path.join(self.build_temp, "Release") + + # Append the source distribution include and library directories, + # this allows distutils on windows to work in the source tree + self.include_dirs.append(os.path.dirname(get_config_h_filename())) + self.library_dirs.append(sys.base_exec_prefix) + + # Use the .lib files for the correct architecture + if self.plat_name == 'win32': + suffix = 'win32' + else: + # win-amd64 + suffix = self.plat_name[4:] + new_lib = os.path.join(sys.exec_prefix, 'PCbuild') + if suffix: + new_lib = os.path.join(new_lib, suffix) + self.library_dirs.append(new_lib) + + # For extensions under Cygwin, Python's library directory must be + # appended to library_dirs + if sys.platform[:6] == 'cygwin': + if not sysconfig.python_build: + # building third party extensions + self.library_dirs.append( + os.path.join( + sys.prefix, "lib", "python" + get_python_version(), "config" + ) + ) + else: + # building python standard extensions + self.library_dirs.append('.') + + self.library_dirs.extend(self._python_lib_dir(sysconfig)) + + # The argument parsing will result in self.define being a string, but + # it has to be a list of 2-tuples. All the preprocessor symbols + # specified by the 'define' option will be set to '1'. Multiple + # symbols can be separated with commas. + + if self.define: + defines = self.define.split(',') + self.define = [(symbol, '1') for symbol in defines] + + # The option for macros to undefine is also a string from the + # option parsing, but has to be a list. Multiple symbols can also + # be separated with commas here. + if self.undef: + self.undef = self.undef.split(',') + + if self.swig_opts is None: + self.swig_opts = [] + else: + self.swig_opts = self.swig_opts.split(' ') + + # Finally add the user include and library directories if requested + if self.user: + user_include = os.path.join(USER_BASE, "include") + user_lib = os.path.join(USER_BASE, "lib") + if os.path.isdir(user_include): + self.include_dirs.append(user_include) + if os.path.isdir(user_lib): + self.library_dirs.append(user_lib) + self.rpath.append(user_lib) + + if isinstance(self.parallel, str): + try: + self.parallel = int(self.parallel) + except ValueError: + raise DistutilsOptionError("parallel should be an integer") + + def run(self) -> None: # noqa: C901 + # 'self.extensions', as supplied by setup.py, is a list of + # Extension instances. See the documentation for Extension (in + # distutils.extension) for details. + # + # For backwards compatibility with Distutils 0.8.2 and earlier, we + # also allow the 'extensions' list to be a list of tuples: + # (ext_name, build_info) + # where build_info is a dictionary containing everything that + # Extension instances do except the name, with a few things being + # differently named. We convert these 2-tuples to Extension + # instances as needed. + + if not self.extensions: + return + + # If we were asked to build any C/C++ libraries, make sure that the + # directory where we put them is in the library search path for + # linking extensions. + if self.distribution.has_c_libraries(): + build_clib = self.get_finalized_command('build_clib') + self.libraries.extend(build_clib.get_library_names() or []) + self.library_dirs.append(build_clib.build_clib) + + # Setup the CCompiler object that we'll use to do all the + # compiling and linking + self.compiler = new_compiler( + compiler=self.compiler, + verbose=self.verbose, + force=self.force, + ) + customize_compiler(self.compiler) + # If we are cross-compiling, init the compiler now (if we are not + # cross-compiling, init would not hurt, but people may rely on + # late initialization of compiler even if they shouldn't...) + if os.name == 'nt' and self.plat_name != get_platform(): + self.compiler.initialize(self.plat_name) + + # The official Windows free threaded Python installer doesn't set + # Py_GIL_DISABLED because its pyconfig.h is shared with the + # default build, so define it here (pypa/setuptools#4662). + if os.name == 'nt' and is_freethreaded(): + self.compiler.define_macro('Py_GIL_DISABLED', '1') + + # And make sure that any compile/link-related options (which might + # come from the command-line or from the setup script) are set in + # that CCompiler object -- that way, they automatically apply to + # all compiling and linking done here. + if self.include_dirs is not None: + self.compiler.set_include_dirs(self.include_dirs) + if self.define is not None: + # 'define' option is a list of (name,value) tuples + for name, value in self.define: + self.compiler.define_macro(name, value) + if self.undef is not None: + for macro in self.undef: + self.compiler.undefine_macro(macro) + if self.libraries is not None: + self.compiler.set_libraries(self.libraries) + if self.library_dirs is not None: + self.compiler.set_library_dirs(self.library_dirs) + if self.rpath is not None: + self.compiler.set_runtime_library_dirs(self.rpath) + if self.link_objects is not None: + self.compiler.set_link_objects(self.link_objects) + + # Now actually compile and link everything. + self.build_extensions() + + def check_extensions_list(self, extensions) -> None: # noqa: C901 + """Ensure that the list of extensions (presumably provided as a + command option 'extensions') is valid, i.e. it is a list of + Extension objects. We also support the old-style list of 2-tuples, + where the tuples are (ext_name, build_info), which are converted to + Extension instances here. + + Raise DistutilsSetupError if the structure is invalid anywhere; + just returns otherwise. + """ + if not isinstance(extensions, list): + raise DistutilsSetupError( + "'ext_modules' option must be a list of Extension instances" + ) + + for i, ext in enumerate(extensions): + if isinstance(ext, Extension): + continue # OK! (assume type-checking done + # by Extension constructor) + + if not isinstance(ext, tuple) or len(ext) != 2: + raise DistutilsSetupError( + "each element of 'ext_modules' option must be an " + "Extension instance or 2-tuple" + ) + + ext_name, build_info = ext + + log.warning( + "old-style (ext_name, build_info) tuple found in " + "ext_modules for extension '%s' " + "-- please convert to Extension instance", + ext_name, + ) + + if not (isinstance(ext_name, str) and extension_name_re.match(ext_name)): + raise DistutilsSetupError( + "first element of each tuple in 'ext_modules' " + "must be the extension name (a string)" + ) + + if not isinstance(build_info, dict): + raise DistutilsSetupError( + "second element of each tuple in 'ext_modules' " + "must be a dictionary (build info)" + ) + + # OK, the (ext_name, build_info) dict is type-safe: convert it + # to an Extension instance. + ext = Extension(ext_name, build_info['sources']) + + # Easy stuff: one-to-one mapping from dict elements to + # instance attributes. + for key in ( + 'include_dirs', + 'library_dirs', + 'libraries', + 'extra_objects', + 'extra_compile_args', + 'extra_link_args', + ): + val = build_info.get(key) + if val is not None: + setattr(ext, key, val) + + # Medium-easy stuff: same syntax/semantics, different names. + ext.runtime_library_dirs = build_info.get('rpath') + if 'def_file' in build_info: + log.warning("'def_file' element of build info dict no longer supported") + + # Non-trivial stuff: 'macros' split into 'define_macros' + # and 'undef_macros'. + macros = build_info.get('macros') + if macros: + ext.define_macros = [] + ext.undef_macros = [] + for macro in macros: + if not (isinstance(macro, tuple) and len(macro) in (1, 2)): + raise DistutilsSetupError( + "'macros' element of build info dict must be 1- or 2-tuple" + ) + if len(macro) == 1: + ext.undef_macros.append(macro[0]) + elif len(macro) == 2: + ext.define_macros.append(macro) + + extensions[i] = ext + + def get_source_files(self): + self.check_extensions_list(self.extensions) + filenames = [] + + # Wouldn't it be neat if we knew the names of header files too... + for ext in self.extensions: + filenames.extend(ext.sources) + return filenames + + def get_outputs(self): + # Sanity check the 'extensions' list -- can't assume this is being + # done in the same run as a 'build_extensions()' call (in fact, we + # can probably assume that it *isn't*!). + self.check_extensions_list(self.extensions) + + # And build the list of output (built) filenames. Note that this + # ignores the 'inplace' flag, and assumes everything goes in the + # "build" tree. + return [self.get_ext_fullpath(ext.name) for ext in self.extensions] + + def build_extensions(self) -> None: + # First, sanity-check the 'extensions' list + self.check_extensions_list(self.extensions) + if self.parallel: + self._build_extensions_parallel() + else: + self._build_extensions_serial() + + def _build_extensions_parallel(self): + workers = self.parallel + if self.parallel is True: + workers = os.cpu_count() # may return None + try: + from concurrent.futures import ThreadPoolExecutor + except ImportError: + workers = None + + if workers is None: + self._build_extensions_serial() + return + + with ThreadPoolExecutor(max_workers=workers) as executor: + futures = [ + executor.submit(self.build_extension, ext) for ext in self.extensions + ] + for ext, fut in zip(self.extensions, futures): + with self._filter_build_errors(ext): + fut.result() + + def _build_extensions_serial(self): + for ext in self.extensions: + with self._filter_build_errors(ext): + self.build_extension(ext) + + @contextlib.contextmanager + def _filter_build_errors(self, ext): + try: + yield + except (CCompilerError, DistutilsError, CompileError) as e: + if not ext.optional: + raise + self.warn(f'building extension "{ext.name}" failed: {e}') + + def build_extension(self, ext) -> None: + sources = ext.sources + if sources is None or not isinstance(sources, (list, tuple)): + raise DistutilsSetupError( + f"in 'ext_modules' option (extension '{ext.name}'), " + "'sources' must be present and must be " + "a list of source filenames" + ) + # sort to make the resulting .so file build reproducible + sources = sorted(sources) + + ext_path = self.get_ext_fullpath(ext.name) + depends = sources + ext.depends + if not (self.force or newer_group(depends, ext_path, 'newer')): + log.debug("skipping '%s' extension (up-to-date)", ext.name) + return + else: + log.info("building '%s' extension", ext.name) + + # First, scan the sources for SWIG definition files (.i), run + # SWIG on 'em to create .c files, and modify the sources list + # accordingly. + sources = self.swig_sources(sources, ext) + + # Next, compile the source code to object files. + + # XXX not honouring 'define_macros' or 'undef_macros' -- the + # CCompiler API needs to change to accommodate this, and I + # want to do one thing at a time! + + # Two possible sources for extra compiler arguments: + # - 'extra_compile_args' in Extension object + # - CFLAGS environment variable (not particularly + # elegant, but people seem to expect it and I + # guess it's useful) + # The environment variable should take precedence, and + # any sensible compiler will give precedence to later + # command line args. Hence we combine them in order: + extra_args = ext.extra_compile_args or [] + + macros = ext.define_macros[:] + for undef in ext.undef_macros: + macros.append((undef,)) + + objects = self.compiler.compile( + sources, + output_dir=self.build_temp, + macros=macros, + include_dirs=ext.include_dirs, + debug=self.debug, + extra_postargs=extra_args, + depends=ext.depends, + ) + + # XXX outdated variable, kept here in case third-part code + # needs it. + self._built_objects = objects[:] + + # Now link the object files together into a "shared object" -- + # of course, first we have to figure out all the other things + # that go into the mix. + if ext.extra_objects: + objects.extend(ext.extra_objects) + extra_args = ext.extra_link_args or [] + + # Detect target language, if not provided + language = ext.language or self.compiler.detect_language(sources) + + self.compiler.link_shared_object( + objects, + ext_path, + libraries=self.get_libraries(ext), + library_dirs=ext.library_dirs, + runtime_library_dirs=ext.runtime_library_dirs, + extra_postargs=extra_args, + export_symbols=self.get_export_symbols(ext), + debug=self.debug, + build_temp=self.build_temp, + target_lang=language, + ) + + def swig_sources(self, sources, extension): + """Walk the list of source files in 'sources', looking for SWIG + interface (.i) files. Run SWIG on all that are found, and + return a modified 'sources' list with SWIG source files replaced + by the generated C (or C++) files. + """ + new_sources = [] + swig_sources = [] + swig_targets = {} + + # XXX this drops generated C/C++ files into the source tree, which + # is fine for developers who want to distribute the generated + # source -- but there should be an option to put SWIG output in + # the temp dir. + + if self.swig_cpp: + log.warning("--swig-cpp is deprecated - use --swig-opts=-c++") + + if ( + self.swig_cpp + or ('-c++' in self.swig_opts) + or ('-c++' in extension.swig_opts) + ): + target_ext = '.cpp' + else: + target_ext = '.c' + + for source in sources: + (base, ext) = os.path.splitext(source) + if ext == ".i": # SWIG interface file + new_sources.append(base + '_wrap' + target_ext) + swig_sources.append(source) + swig_targets[source] = new_sources[-1] + else: + new_sources.append(source) + + if not swig_sources: + return new_sources + + swig = self.swig or self.find_swig() + swig_cmd = [swig, "-python"] + swig_cmd.extend(self.swig_opts) + if self.swig_cpp: + swig_cmd.append("-c++") + + # Do not override commandline arguments + if not self.swig_opts: + swig_cmd.extend(extension.swig_opts) + + for source in swig_sources: + target = swig_targets[source] + log.info("swigging %s to %s", source, target) + self.spawn(swig_cmd + ["-o", target, source]) + + return new_sources + + def find_swig(self): + """Return the name of the SWIG executable. On Unix, this is + just "swig" -- it should be in the PATH. Tries a bit harder on + Windows. + """ + if os.name == "posix": + return "swig" + elif os.name == "nt": + # Look for SWIG in its standard installation directory on + # Windows (or so I presume!). If we find it there, great; + # if not, act like Unix and assume it's in the PATH. + for vers in ("1.3", "1.2", "1.1"): + fn = os.path.join(f"c:\\swig{vers}", "swig.exe") + if os.path.isfile(fn): + return fn + else: + return "swig.exe" + else: + raise DistutilsPlatformError( + f"I don't know how to find (much less run) SWIG on platform '{os.name}'" + ) + + # -- Name generators ----------------------------------------------- + # (extension names, filenames, whatever) + def get_ext_fullpath(self, ext_name: str) -> str: + """Returns the path of the filename for a given extension. + + The file is located in `build_lib` or directly in the package + (inplace option). + """ + fullname = self.get_ext_fullname(ext_name) + modpath = fullname.split('.') + filename = self.get_ext_filename(modpath[-1]) + + if not self.inplace: + # no further work needed + # returning : + # build_dir/package/path/filename + filename = os.path.join(*modpath[:-1] + [filename]) + return os.path.join(self.build_lib, filename) + + # the inplace option requires to find the package directory + # using the build_py command for that + package = '.'.join(modpath[0:-1]) + build_py = self.get_finalized_command('build_py') + package_dir = os.path.abspath(build_py.get_package_dir(package)) + + # returning + # package_dir/filename + return os.path.join(package_dir, filename) + + def get_ext_fullname(self, ext_name: str) -> str: + """Returns the fullname of a given extension name. + + Adds the `package.` prefix""" + if self.package is None: + return ext_name + else: + return self.package + '.' + ext_name + + def get_ext_filename(self, ext_name: str) -> str: + r"""Convert the name of an extension (eg. "foo.bar") into the name + of the file from which it will be loaded (eg. "foo/bar.so", or + "foo\bar.pyd"). + """ + from ..sysconfig import get_config_var + + ext_path = ext_name.split('.') + ext_suffix = get_config_var('EXT_SUFFIX') + return os.path.join(*ext_path) + ext_suffix + + def get_export_symbols(self, ext: Extension) -> list[str]: + """Return the list of symbols that a shared extension has to + export. This either uses 'ext.export_symbols' or, if it's not + provided, "PyInit_" + module_name. Only relevant on Windows, where + the .pyd file (DLL) must export the module "PyInit_" function. + """ + name = self._get_module_name_for_symbol(ext) + try: + # Unicode module name support as defined in PEP-489 + # https://peps.python.org/pep-0489/#export-hook-name + name.encode('ascii') + except UnicodeEncodeError: + suffix = 'U_' + name.encode('punycode').replace(b'-', b'_').decode('ascii') + else: + suffix = "_" + name + + initfunc_name = "PyInit" + suffix + if initfunc_name not in ext.export_symbols: + ext.export_symbols.append(initfunc_name) + return ext.export_symbols + + def _get_module_name_for_symbol(self, ext): + # Package name should be used for `__init__` modules + # https://github.com/python/cpython/issues/80074 + # https://github.com/pypa/setuptools/issues/4826 + parts = ext.name.split(".") + if parts[-1] == "__init__" and len(parts) >= 2: + return parts[-2] + return parts[-1] + + def get_libraries(self, ext: Extension) -> list[str]: # noqa: C901 + """Return the list of libraries to link against when building a + shared extension. On most platforms, this is just 'ext.libraries'; + on Windows, we add the Python library (eg. python20.dll). + """ + # The python library is always needed on Windows. For MSVC, this + # is redundant, since the library is mentioned in a pragma in + # pyconfig.h that MSVC groks. The other Windows compilers all seem + # to need it mentioned explicitly, though, so that's what we do. + # Append '_d' to the python import library on debug builds. + if sys.platform == "win32" and not is_mingw(): + from .._msvccompiler import MSVCCompiler + + if not isinstance(self.compiler, MSVCCompiler): + template = "python%d%d" + if self.debug: + template = template + '_d' + pythonlib = template % ( + sys.hexversion >> 24, + (sys.hexversion >> 16) & 0xFF, + ) + # don't extend ext.libraries, it may be shared with other + # extensions, it is a reference to the original list + return ext.libraries + [pythonlib] + else: + # On Android only the main executable and LD_PRELOADs are considered + # to be RTLD_GLOBAL, all the dependencies of the main executable + # remain RTLD_LOCAL and so the shared libraries must be linked with + # libpython when python is built with a shared python library (issue + # bpo-21536). + # On Cygwin (and if required, other POSIX-like platforms based on + # Windows like MinGW) it is simply necessary that all symbols in + # shared libraries are resolved at link time. + from ..sysconfig import get_config_var + + link_libpython = False + if get_config_var('Py_ENABLE_SHARED'): + # A native build on an Android device or on Cygwin + if hasattr(sys, 'getandroidapilevel'): + link_libpython = True + elif sys.platform == 'cygwin' or is_mingw(): + link_libpython = True + elif '_PYTHON_HOST_PLATFORM' in os.environ: + # We are cross-compiling for one of the relevant platforms + if get_config_var('ANDROID_API_LEVEL') != 0: + link_libpython = True + elif get_config_var('MACHDEP') == 'cygwin': + link_libpython = True + + if link_libpython: + ldversion = get_config_var('LDVERSION') + return ext.libraries + ['python' + ldversion] + + return ext.libraries diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/build_py.py b/lib/python3.11/site-packages/setuptools/_distutils/command/build_py.py new file mode 100644 index 0000000000000000000000000000000000000000..d6ec7d10d2a807e02f1856fcacf488057ec69081 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/build_py.py @@ -0,0 +1,404 @@ +"""distutils.command.build_py + +Implements the Distutils 'build_py' command.""" + +import glob +import importlib.util +import os +import sys +from distutils._log import log +from typing import ClassVar + +from ..core import Command +from ..errors import DistutilsFileError, DistutilsOptionError +from ..util import convert_path + + +class build_py(Command): + description = "\"build\" pure Python modules (copy to build directory)" + + user_options = [ + ('build-lib=', 'd', "directory to \"build\" (copy) to"), + ('compile', 'c', "compile .py to .pyc"), + ('no-compile', None, "don't compile .py files [default]"), + ( + 'optimize=', + 'O', + "also compile with optimization: -O1 for \"python -O\", " + "-O2 for \"python -OO\", and -O0 to disable [default: -O0]", + ), + ('force', 'f', "forcibly build everything (ignore file timestamps)"), + ] + + boolean_options: ClassVar[list[str]] = ['compile', 'force'] + negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'} + + def initialize_options(self): + self.build_lib = None + self.py_modules = None + self.package = None + self.package_data = None + self.package_dir = None + self.compile = False + self.optimize = 0 + self.force = None + + def finalize_options(self) -> None: + self.set_undefined_options( + 'build', ('build_lib', 'build_lib'), ('force', 'force') + ) + + # Get the distribution options that are aliases for build_py + # options -- list of packages and list of modules. + self.packages = self.distribution.packages + self.py_modules = self.distribution.py_modules + self.package_data = self.distribution.package_data + self.package_dir = {} + if self.distribution.package_dir: + for name, path in self.distribution.package_dir.items(): + self.package_dir[name] = convert_path(path) + self.data_files = self.get_data_files() + + # Ick, copied straight from install_lib.py (fancy_getopt needs a + # type system! Hell, *everything* needs a type system!!!) + if not isinstance(self.optimize, int): + try: + self.optimize = int(self.optimize) + assert 0 <= self.optimize <= 2 + except (ValueError, AssertionError): + raise DistutilsOptionError("optimize must be 0, 1, or 2") + + def run(self) -> None: + # XXX copy_file by default preserves atime and mtime. IMHO this is + # the right thing to do, but perhaps it should be an option -- in + # particular, a site administrator might want installed files to + # reflect the time of installation rather than the last + # modification time before the installed release. + + # XXX copy_file by default preserves mode, which appears to be the + # wrong thing to do: if a file is read-only in the working + # directory, we want it to be installed read/write so that the next + # installation of the same module distribution can overwrite it + # without problems. (This might be a Unix-specific issue.) Thus + # we turn off 'preserve_mode' when copying to the build directory, + # since the build directory is supposed to be exactly what the + # installation will look like (ie. we preserve mode when + # installing). + + # Two options control which modules will be installed: 'packages' + # and 'py_modules'. The former lets us work with whole packages, not + # specifying individual modules at all; the latter is for + # specifying modules one-at-a-time. + + if self.py_modules: + self.build_modules() + if self.packages: + self.build_packages() + self.build_package_data() + + self.byte_compile(self.get_outputs(include_bytecode=False)) + + def get_data_files(self): + """Generate list of '(package,src_dir,build_dir,filenames)' tuples""" + data = [] + if not self.packages: + return data + for package in self.packages: + # Locate package source directory + src_dir = self.get_package_dir(package) + + # Compute package build directory + build_dir = os.path.join(*([self.build_lib] + package.split('.'))) + + # Length of path to strip from found files + plen = 0 + if src_dir: + plen = len(src_dir) + 1 + + # Strip directory from globbed filenames + filenames = [file[plen:] for file in self.find_data_files(package, src_dir)] + data.append((package, src_dir, build_dir, filenames)) + return data + + def find_data_files(self, package, src_dir): + """Return filenames for package's data files in 'src_dir'""" + globs = self.package_data.get('', []) + self.package_data.get(package, []) + files = [] + for pattern in globs: + # Each pattern has to be converted to a platform-specific path + filelist = glob.glob( + os.path.join(glob.escape(src_dir), convert_path(pattern)) + ) + # Files that match more than one pattern are only added once + files.extend([ + fn for fn in filelist if fn not in files and os.path.isfile(fn) + ]) + return files + + def build_package_data(self) -> None: + """Copy data files into build directory""" + for _package, src_dir, build_dir, filenames in self.data_files: + for filename in filenames: + target = os.path.join(build_dir, filename) + self.mkpath(os.path.dirname(target)) + self.copy_file( + os.path.join(src_dir, filename), target, preserve_mode=False + ) + + def get_package_dir(self, package): + """Return the directory, relative to the top of the source + distribution, where package 'package' should be found + (at least according to the 'package_dir' option, if any).""" + path = package.split('.') + + if not self.package_dir: + if path: + return os.path.join(*path) + else: + return '' + else: + tail = [] + while path: + try: + pdir = self.package_dir['.'.join(path)] + except KeyError: + tail.insert(0, path[-1]) + del path[-1] + else: + tail.insert(0, pdir) + return os.path.join(*tail) + else: + # Oops, got all the way through 'path' without finding a + # match in package_dir. If package_dir defines a directory + # for the root (nameless) package, then fallback on it; + # otherwise, we might as well have not consulted + # package_dir at all, as we just use the directory implied + # by 'tail' (which should be the same as the original value + # of 'path' at this point). + pdir = self.package_dir.get('') + if pdir is not None: + tail.insert(0, pdir) + + if tail: + return os.path.join(*tail) + else: + return '' + + def check_package(self, package, package_dir): + # Empty dir name means current directory, which we can probably + # assume exists. Also, os.path.exists and isdir don't know about + # my "empty string means current dir" convention, so we have to + # circumvent them. + if package_dir != "": + if not os.path.exists(package_dir): + raise DistutilsFileError( + f"package directory '{package_dir}' does not exist" + ) + if not os.path.isdir(package_dir): + raise DistutilsFileError( + f"supposed package directory '{package_dir}' exists, " + "but is not a directory" + ) + + # Directories without __init__.py are namespace packages (PEP 420). + if package: + init_py = os.path.join(package_dir, "__init__.py") + if os.path.isfile(init_py): + return init_py + + # Either not in a package at all (__init__.py not expected), or + # __init__.py doesn't exist -- so don't return the filename. + return None + + def check_module(self, module, module_file): + if not os.path.isfile(module_file): + log.warning("file %s (for module %s) not found", module_file, module) + return False + else: + return True + + def find_package_modules(self, package, package_dir): + self.check_package(package, package_dir) + module_files = glob.glob(os.path.join(glob.escape(package_dir), "*.py")) + modules = [] + setup_script = os.path.abspath(self.distribution.script_name) + + for f in module_files: + abs_f = os.path.abspath(f) + if abs_f != setup_script: + module = os.path.splitext(os.path.basename(f))[0] + modules.append((package, module, f)) + else: + self.debug_print(f"excluding {setup_script}") + return modules + + def find_modules(self): + """Finds individually-specified Python modules, ie. those listed by + module name in 'self.py_modules'. Returns a list of tuples (package, + module_base, filename): 'package' is a tuple of the path through + package-space to the module; 'module_base' is the bare (no + packages, no dots) module name, and 'filename' is the path to the + ".py" file (relative to the distribution root) that implements the + module. + """ + # Map package names to tuples of useful info about the package: + # (package_dir, checked) + # package_dir - the directory where we'll find source files for + # this package + # checked - true if we have checked that the package directory + # is valid (exists, contains __init__.py, ... ?) + packages = {} + + # List of (package, module, filename) tuples to return + modules = [] + + # We treat modules-in-packages almost the same as toplevel modules, + # just the "package" for a toplevel is empty (either an empty + # string or empty list, depending on context). Differences: + # - don't check for __init__.py in directory for empty package + for module in self.py_modules: + path = module.split('.') + package = '.'.join(path[0:-1]) + module_base = path[-1] + + try: + (package_dir, checked) = packages[package] + except KeyError: + package_dir = self.get_package_dir(package) + checked = False + + if not checked: + init_py = self.check_package(package, package_dir) + packages[package] = (package_dir, 1) + if init_py: + modules.append((package, "__init__", init_py)) + + # XXX perhaps we should also check for just .pyc files + # (so greedy closed-source bastards can distribute Python + # modules too) + module_file = os.path.join(package_dir, module_base + ".py") + if not self.check_module(module, module_file): + continue + + modules.append((package, module_base, module_file)) + + return modules + + def find_all_modules(self): + """Compute the list of all modules that will be built, whether + they are specified one-module-at-a-time ('self.py_modules') or + by whole packages ('self.packages'). Return a list of tuples + (package, module, module_file), just like 'find_modules()' and + 'find_package_modules()' do.""" + modules = [] + if self.py_modules: + modules.extend(self.find_modules()) + if self.packages: + for package in self.packages: + package_dir = self.get_package_dir(package) + m = self.find_package_modules(package, package_dir) + modules.extend(m) + return modules + + def get_source_files(self): + return [module[-1] for module in self.find_all_modules()] + + def get_module_outfile(self, build_dir, package, module): + outfile_path = [build_dir] + list(package) + [module + ".py"] + return os.path.join(*outfile_path) + + def get_outputs(self, include_bytecode: bool = True) -> list[str]: + modules = self.find_all_modules() + outputs = [] + for package, module, _module_file in modules: + package = package.split('.') + filename = self.get_module_outfile(self.build_lib, package, module) + outputs.append(filename) + if include_bytecode: + if self.compile: + outputs.append( + importlib.util.cache_from_source(filename, optimization='') + ) + if self.optimize > 0: + outputs.append( + importlib.util.cache_from_source( + filename, optimization=self.optimize + ) + ) + + outputs += [ + os.path.join(build_dir, filename) + for package, src_dir, build_dir, filenames in self.data_files + for filename in filenames + ] + + return outputs + + def build_module(self, module, module_file, package): + if isinstance(package, str): + package = package.split('.') + elif not isinstance(package, (list, tuple)): + raise TypeError( + "'package' must be a string (dot-separated), list, or tuple" + ) + + # Now put the module source file into the "build" area -- this is + # easy, we just copy it somewhere under self.build_lib (the build + # directory for Python source). + outfile = self.get_module_outfile(self.build_lib, package, module) + dir = os.path.dirname(outfile) + self.mkpath(dir) + return self.copy_file(module_file, outfile, preserve_mode=False) + + def build_modules(self) -> None: + modules = self.find_modules() + for package, module, module_file in modules: + # Now "build" the module -- ie. copy the source file to + # self.build_lib (the build directory for Python source). + # (Actually, it gets copied to the directory for this package + # under self.build_lib.) + self.build_module(module, module_file, package) + + def build_packages(self) -> None: + for package in self.packages: + # Get list of (package, module, module_file) tuples based on + # scanning the package directory. 'package' is only included + # in the tuple so that 'find_modules()' and + # 'find_package_tuples()' have a consistent interface; it's + # ignored here (apart from a sanity check). Also, 'module' is + # the *unqualified* module name (ie. no dots, no package -- we + # already know its package!), and 'module_file' is the path to + # the .py file, relative to the current directory + # (ie. including 'package_dir'). + package_dir = self.get_package_dir(package) + modules = self.find_package_modules(package, package_dir) + + # Now loop over the modules we found, "building" each one (just + # copy it to self.build_lib). + for package_, module, module_file in modules: + assert package == package_ + self.build_module(module, module_file, package) + + def byte_compile(self, files) -> None: + if sys.dont_write_bytecode: + self.warn('byte-compiling is disabled, skipping.') + return + + from ..util import byte_compile + + prefix = self.build_lib + if prefix[-1] != os.sep: + prefix = prefix + os.sep + + # XXX this code is essentially the same as the 'byte_compile() + # method of the "install_lib" command, except for the determination + # of the 'prefix' string. Hmmm. + if self.compile: + byte_compile(files, optimize=0, force=self.force, prefix=prefix) + if self.optimize > 0: + byte_compile( + files, + optimize=self.optimize, + force=self.force, + prefix=prefix, + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/build_scripts.py b/lib/python3.11/site-packages/setuptools/_distutils/command/build_scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..0b551d11be0200b4e9c620d1af387ff7c2752096 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/build_scripts.py @@ -0,0 +1,150 @@ +"""distutils.command.build_scripts + +Implements the Distutils 'build_scripts' command.""" + +import os +import re +import tokenize +from distutils._log import log +from stat import ST_MODE +from typing import ClassVar + +from .._modified import newer +from ..core import Command +from ..util import convert_path + +shebang_pattern = re.compile('^#!.*python[0-9.]*([ \t].*)?$') +""" +Pattern matching a Python interpreter indicated in first line of a script. +""" + +# for Setuptools compatibility +first_line_re = shebang_pattern + + +class build_scripts(Command): + description = "\"build\" scripts (copy and fixup #! line)" + + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ('build-dir=', 'd', "directory to \"build\" (copy) to"), + ('force', 'f', "forcibly build everything (ignore file timestamps"), + ('executable=', 'e', "specify final destination interpreter path"), + ] + + boolean_options: ClassVar[list[str]] = ['force'] + + def initialize_options(self): + self.build_dir = None + self.scripts = None + self.force = None + self.executable = None + + def finalize_options(self): + self.set_undefined_options( + 'build', + ('build_scripts', 'build_dir'), + ('force', 'force'), + ('executable', 'executable'), + ) + self.scripts = self.distribution.scripts + + def get_source_files(self): + return self.scripts + + def run(self): + if not self.scripts: + return + self.copy_scripts() + + def copy_scripts(self): + """ + Copy each script listed in ``self.scripts``. + + If a script is marked as a Python script (first line matches + 'shebang_pattern', i.e. starts with ``#!`` and contains + "python"), then adjust in the copy the first line to refer to + the current Python interpreter. + """ + self.mkpath(self.build_dir) + outfiles = [] + updated_files = [] + for script in self.scripts: + self._copy_script(script, outfiles, updated_files) + + self._change_modes(outfiles) + + return outfiles, updated_files + + def _copy_script(self, script, outfiles, updated_files): + shebang_match = None + script = convert_path(script) + outfile = os.path.join(self.build_dir, os.path.basename(script)) + outfiles.append(outfile) + + if not self.force and not newer(script, outfile): + log.debug("not copying %s (up-to-date)", script) + return + + # Always open the file, but ignore failures in dry-run mode + # in order to attempt to copy directly. + f = tokenize.open(script) + + first_line = f.readline() + if not first_line: + self.warn(f"{script} is an empty file (skipping)") + return + + shebang_match = shebang_pattern.match(first_line) + + updated_files.append(outfile) + if shebang_match: + log.info("copying and adjusting %s -> %s", script, self.build_dir) + post_interp = shebang_match.group(1) or '' + shebang = "#!" + self.executable + post_interp + "\n" + self._validate_shebang(shebang, f.encoding) + with open(outfile, "w", encoding=f.encoding) as outf: + outf.write(shebang) + outf.writelines(f.readlines()) + if f: + f.close() + else: + if f: + f.close() + self.copy_file(script, outfile) + + def _change_modes(self, outfiles): + if os.name != 'posix': + return + + for file in outfiles: + self._change_mode(file) + + def _change_mode(self, file): + oldmode = os.stat(file)[ST_MODE] & 0o7777 + newmode = (oldmode | 0o555) & 0o7777 + if newmode != oldmode: + log.info("changing mode of %s from %o to %o", file, oldmode, newmode) + os.chmod(file, newmode) + + @staticmethod + def _validate_shebang(shebang, encoding): + # Python parser starts to read a script using UTF-8 until + # it gets a #coding:xxx cookie. The shebang has to be the + # first line of a file, the #coding:xxx cookie cannot be + # written before. So the shebang has to be encodable to + # UTF-8. + try: + shebang.encode('utf-8') + except UnicodeEncodeError: + raise ValueError(f"The shebang ({shebang!r}) is not encodable to utf-8") + + # If the script is encoded to a custom encoding (use a + # #coding:xxx cookie), the shebang has to be encodable to + # the script encoding too. + try: + shebang.encode(encoding) + except UnicodeEncodeError: + raise ValueError( + f"The shebang ({shebang!r}) is not encodable " + f"to the script encoding ({encoding})" + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/check.py b/lib/python3.11/site-packages/setuptools/_distutils/command/check.py new file mode 100644 index 0000000000000000000000000000000000000000..58a823dd3904cf105ed432e458a45d94948fbb4c --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/check.py @@ -0,0 +1,152 @@ +"""distutils.command.check + +Implements the Distutils 'check' command. +""" + +import contextlib +from typing import ClassVar + +from ..core import Command +from ..errors import DistutilsSetupError + +with contextlib.suppress(ImportError): + import docutils.frontend + import docutils.nodes + import docutils.parsers.rst + import docutils.utils + + class SilentReporter(docutils.utils.Reporter): + def __init__( + self, + source, + report_level, + halt_level, + stream=None, + debug=False, + encoding='ascii', + error_handler='replace', + ): + self.messages = [] + super().__init__( + source, report_level, halt_level, stream, debug, encoding, error_handler + ) + + def system_message(self, level, message, *children, **kwargs): + self.messages.append((level, message, children, kwargs)) + return docutils.nodes.system_message( + message, *children, level=level, type=self.levels[level], **kwargs + ) + + +class check(Command): + """This command checks the meta-data of the package.""" + + description = "perform some checks on the package" + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ('metadata', 'm', 'Verify meta-data'), + ( + 'restructuredtext', + 'r', + 'Checks if long string meta-data syntax are reStructuredText-compliant', + ), + ('strict', 's', 'Will exit with an error if a check fails'), + ] + + boolean_options: ClassVar[list[str]] = ['metadata', 'restructuredtext', 'strict'] + + def initialize_options(self): + """Sets default values for options.""" + self.restructuredtext = False + self.metadata = 1 + self.strict = False + self._warnings = 0 + + def finalize_options(self): + pass + + def warn(self, msg): + """Counts the number of warnings that occurs.""" + self._warnings += 1 + return Command.warn(self, msg) + + def run(self): + """Runs the command.""" + # perform the various tests + if self.metadata: + self.check_metadata() + if self.restructuredtext: + if 'docutils' in globals(): + try: + self.check_restructuredtext() + except TypeError as exc: + raise DistutilsSetupError(str(exc)) + elif self.strict: + raise DistutilsSetupError('The docutils package is needed.') + + # let's raise an error in strict mode, if we have at least + # one warning + if self.strict and self._warnings > 0: + raise DistutilsSetupError('Please correct your package.') + + def check_metadata(self): + """Ensures that all required elements of meta-data are supplied. + + Required fields: + name, version + + Warns if any are missing. + """ + metadata = self.distribution.metadata + + missing = [ + attr for attr in ('name', 'version') if not getattr(metadata, attr, None) + ] + + if missing: + self.warn("missing required meta-data: {}".format(', '.join(missing))) + + def check_restructuredtext(self): + """Checks if the long string fields are reST-compliant.""" + data = self.distribution.get_long_description() + for warning in self._check_rst_data(data): + line = warning[-1].get('line') + if line is None: + warning = warning[1] + else: + warning = f'{warning[1]} (line {line})' + self.warn(warning) + + def _check_rst_data(self, data): + """Returns warnings when the provided data doesn't compile.""" + # the include and csv_table directives need this to be a path + source_path = self.distribution.script_name or 'setup.py' + parser = docutils.parsers.rst.Parser() + settings = docutils.frontend.OptionParser( + components=(docutils.parsers.rst.Parser,) + ).get_default_values() + settings.tab_width = 4 + settings.pep_references = None + settings.rfc_references = None + reporter = SilentReporter( + source_path, + settings.report_level, + settings.halt_level, + stream=settings.warning_stream, + debug=settings.debug, + encoding=settings.error_encoding, + error_handler=settings.error_encoding_error_handler, + ) + + document = docutils.nodes.document(settings, reporter, source=source_path) + document.note_source(source_path, -1) + try: + parser.parse(data, document) + except (AttributeError, TypeError) as e: + reporter.messages.append(( + -1, + f'Could not finish the parsing: {e}.', + '', + {}, + )) + + return reporter.messages diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py b/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py new file mode 100644 index 0000000000000000000000000000000000000000..fc1711be8ef51e2bcd5fe2742588f95a65bcd153 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/clean.py @@ -0,0 +1,76 @@ +"""distutils.command.clean + +Implements the Distutils 'clean' command.""" + +# contributed by Bastian Kleineidam , added 2000-03-18 + +import os +from distutils._log import log +from typing import ClassVar + +from ..core import Command +from ..dir_util import remove_tree + + +class clean(Command): + description = "clean up temporary files from 'build' command" + user_options = [ + ('build-base=', 'b', "base build directory [default: 'build.build-base']"), + ( + 'build-lib=', + None, + "build directory for all modules [default: 'build.build-lib']", + ), + ('build-temp=', 't', "temporary build directory [default: 'build.build-temp']"), + ( + 'build-scripts=', + None, + "build directory for scripts [default: 'build.build-scripts']", + ), + ('bdist-base=', None, "temporary directory for built distributions"), + ('all', 'a', "remove all build output, not just temporary by-products"), + ] + + boolean_options: ClassVar[list[str]] = ['all'] + + def initialize_options(self): + self.build_base = None + self.build_lib = None + self.build_temp = None + self.build_scripts = None + self.bdist_base = None + self.all = None + + def finalize_options(self): + self.set_undefined_options( + 'build', + ('build_base', 'build_base'), + ('build_lib', 'build_lib'), + ('build_scripts', 'build_scripts'), + ('build_temp', 'build_temp'), + ) + self.set_undefined_options('bdist', ('bdist_base', 'bdist_base')) + + def run(self): + # remove the build/temp. directory (unless it's already + # gone) + if os.path.exists(self.build_temp): + remove_tree(self.build_temp) + else: + log.debug("'%s' does not exist -- can't clean it", self.build_temp) + + if self.all: + # remove build directories + for directory in (self.build_lib, self.bdist_base, self.build_scripts): + if os.path.exists(directory): + remove_tree(directory) + else: + log.warning("'%s' does not exist -- can't clean it", directory) + + # just for the heck of it, try to remove the base build directory: + # we might have emptied it right now, but if not we don't care + try: + os.rmdir(self.build_base) + log.info("removing '%s'", self.build_base) + except OSError: + pass diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/config.py b/lib/python3.11/site-packages/setuptools/_distutils/command/config.py new file mode 100644 index 0000000000000000000000000000000000000000..9818418f2675125631e7d9d1ad74c30487e9dd5b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/config.py @@ -0,0 +1,348 @@ +"""distutils.command.config + +Implements the Distutils 'config' command, a (mostly) empty command class +that exists mainly to be sub-classed by specific module distributions and +applications. The idea is that while every "config" command is different, +at least they're all named the same, and users always see "config" in the +list of standard commands. Also, this is a good place to put common +configure-like tasks: "try to compile this C code", or "figure out where +this header file lives". +""" + +from __future__ import annotations + +import os +import pathlib +import re +from collections.abc import Sequence +from distutils._log import log + +from ..ccompiler import CCompiler, CompileError, LinkError, new_compiler +from ..core import Command +from ..errors import DistutilsExecError +from ..sysconfig import customize_compiler + +LANG_EXT = {"c": ".c", "c++": ".cxx"} + + +class config(Command): + description = "prepare to build" + + user_options = [ + ('compiler=', None, "specify the compiler type"), + ('cc=', None, "specify the compiler executable"), + ('include-dirs=', 'I', "list of directories to search for header files"), + ('define=', 'D', "C preprocessor macros to define"), + ('undef=', 'U', "C preprocessor macros to undefine"), + ('libraries=', 'l', "external C libraries to link with"), + ('library-dirs=', 'L', "directories to search for external C libraries"), + ('noisy', None, "show every action (compile, link, run, ...) taken"), + ( + 'dump-source', + None, + "dump generated source files before attempting to compile them", + ), + ] + + # The three standard command methods: since the "config" command + # does nothing by default, these are empty. + + def initialize_options(self): + self.compiler = None + self.cc = None + self.include_dirs = None + self.libraries = None + self.library_dirs = None + + # maximal output for now + self.noisy = 1 + self.dump_source = 1 + + # list of temporary files generated along-the-way that we have + # to clean at some point + self.temp_files = [] + + def finalize_options(self): + if self.include_dirs is None: + self.include_dirs = self.distribution.include_dirs or [] + elif isinstance(self.include_dirs, str): + self.include_dirs = self.include_dirs.split(os.pathsep) + + if self.libraries is None: + self.libraries = [] + elif isinstance(self.libraries, str): + self.libraries = [self.libraries] + + if self.library_dirs is None: + self.library_dirs = [] + elif isinstance(self.library_dirs, str): + self.library_dirs = self.library_dirs.split(os.pathsep) + + def run(self): + pass + + # Utility methods for actual "config" commands. The interfaces are + # loosely based on Autoconf macros of similar names. Sub-classes + # may use these freely. + + def _check_compiler(self): + """Check that 'self.compiler' really is a CCompiler object; + if not, make it one. + """ + if not isinstance(self.compiler, CCompiler): + self.compiler = new_compiler(compiler=self.compiler, force=True) + customize_compiler(self.compiler) + if self.include_dirs: + self.compiler.set_include_dirs(self.include_dirs) + if self.libraries: + self.compiler.set_libraries(self.libraries) + if self.library_dirs: + self.compiler.set_library_dirs(self.library_dirs) + + def _gen_temp_sourcefile(self, body, headers, lang): + filename = "_configtest" + LANG_EXT[lang] + with open(filename, "w", encoding='utf-8') as file: + if headers: + for header in headers: + file.write(f"#include <{header}>\n") + file.write("\n") + file.write(body) + if body[-1] != "\n": + file.write("\n") + return filename + + def _preprocess(self, body, headers, include_dirs, lang): + src = self._gen_temp_sourcefile(body, headers, lang) + out = "_configtest.i" + self.temp_files.extend([src, out]) + self.compiler.preprocess(src, out, include_dirs=include_dirs) + return (src, out) + + def _compile(self, body, headers, include_dirs, lang): + src = self._gen_temp_sourcefile(body, headers, lang) + if self.dump_source: + dump_file(src, f"compiling '{src}':") + (obj,) = self.compiler.object_filenames([src]) + self.temp_files.extend([src, obj]) + self.compiler.compile([src], include_dirs=include_dirs) + return (src, obj) + + def _link(self, body, headers, include_dirs, libraries, library_dirs, lang): + (src, obj) = self._compile(body, headers, include_dirs, lang) + prog = os.path.splitext(os.path.basename(src))[0] + self.compiler.link_executable( + [obj], + prog, + libraries=libraries, + library_dirs=library_dirs, + target_lang=lang, + ) + + if self.compiler.exe_extension is not None: + prog = prog + self.compiler.exe_extension + self.temp_files.append(prog) + + return (src, obj, prog) + + def _clean(self, *filenames): + if not filenames: + filenames = self.temp_files + self.temp_files = [] + log.info("removing: %s", ' '.join(filenames)) + for filename in filenames: + try: + os.remove(filename) + except OSError: + pass + + # XXX need access to the header search path and maybe default macros. + + def try_cpp(self, body=None, headers=None, include_dirs=None, lang="c"): + """Construct a source file from 'body' (a string containing lines + of C/C++ code) and 'headers' (a list of header files to include) + and run it through the preprocessor. Return true if the + preprocessor succeeded, false if there were any errors. + ('body' probably isn't of much use, but what the heck.) + """ + self._check_compiler() + ok = True + try: + self._preprocess(body, headers, include_dirs, lang) + except CompileError: + ok = False + + self._clean() + return ok + + def search_cpp(self, pattern, body=None, headers=None, include_dirs=None, lang="c"): + """Construct a source file (just like 'try_cpp()'), run it through + the preprocessor, and return true if any line of the output matches + 'pattern'. 'pattern' should either be a compiled regex object or a + string containing a regex. If both 'body' and 'headers' are None, + preprocesses an empty file -- which can be useful to determine the + symbols the preprocessor and compiler set by default. + """ + self._check_compiler() + src, out = self._preprocess(body, headers, include_dirs, lang) + + if isinstance(pattern, str): + pattern = re.compile(pattern) + + with open(out, encoding='utf-8') as file: + match = any(pattern.search(line) for line in file) + + self._clean() + return match + + def try_compile(self, body, headers=None, include_dirs=None, lang="c"): + """Try to compile a source file built from 'body' and 'headers'. + Return true on success, false otherwise. + """ + self._check_compiler() + try: + self._compile(body, headers, include_dirs, lang) + ok = True + except CompileError: + ok = False + + log.info(ok and "success!" or "failure.") + self._clean() + return ok + + def try_link( + self, + body, + headers=None, + include_dirs=None, + libraries=None, + library_dirs=None, + lang="c", + ): + """Try to compile and link a source file, built from 'body' and + 'headers', to executable form. Return true on success, false + otherwise. + """ + self._check_compiler() + try: + self._link(body, headers, include_dirs, libraries, library_dirs, lang) + ok = True + except (CompileError, LinkError): + ok = False + + log.info(ok and "success!" or "failure.") + self._clean() + return ok + + def try_run( + self, + body, + headers=None, + include_dirs=None, + libraries=None, + library_dirs=None, + lang="c", + ): + """Try to compile, link to an executable, and run a program + built from 'body' and 'headers'. Return true on success, false + otherwise. + """ + self._check_compiler() + try: + src, obj, exe = self._link( + body, headers, include_dirs, libraries, library_dirs, lang + ) + self.spawn([exe]) + ok = True + except (CompileError, LinkError, DistutilsExecError): + ok = False + + log.info(ok and "success!" or "failure.") + self._clean() + return ok + + # -- High-level methods -------------------------------------------- + # (these are the ones that are actually likely to be useful + # when implementing a real-world config command!) + + def check_func( + self, + func, + headers=None, + include_dirs=None, + libraries=None, + library_dirs=None, + decl=False, + call=False, + ): + """Determine if function 'func' is available by constructing a + source file that refers to 'func', and compiles and links it. + If everything succeeds, returns true; otherwise returns false. + + The constructed source file starts out by including the header + files listed in 'headers'. If 'decl' is true, it then declares + 'func' (as "int func()"); you probably shouldn't supply 'headers' + and set 'decl' true in the same call, or you might get errors about + a conflicting declarations for 'func'. Finally, the constructed + 'main()' function either references 'func' or (if 'call' is true) + calls it. 'libraries' and 'library_dirs' are used when + linking. + """ + self._check_compiler() + body = [] + if decl: + body.append(f"int {func} ();") + body.append("int main () {") + if call: + body.append(f" {func}();") + else: + body.append(f" {func};") + body.append("}") + body = "\n".join(body) + "\n" + + return self.try_link(body, headers, include_dirs, libraries, library_dirs) + + def check_lib( + self, + library, + library_dirs=None, + headers=None, + include_dirs=None, + other_libraries: Sequence[str] = [], + ): + """Determine if 'library' is available to be linked against, + without actually checking that any particular symbols are provided + by it. 'headers' will be used in constructing the source file to + be compiled, but the only effect of this is to check if all the + header files listed are available. Any libraries listed in + 'other_libraries' will be included in the link, in case 'library' + has symbols that depend on other libraries. + """ + self._check_compiler() + return self.try_link( + "int main (void) { }", + headers, + include_dirs, + [library] + list(other_libraries), + library_dirs, + ) + + def check_header(self, header, include_dirs=None, library_dirs=None, lang="c"): + """Determine if the system header file named by 'header_file' + exists and can be found by the preprocessor; return true if so, + false otherwise. + """ + return self.try_cpp( + body="/* No body */", headers=[header], include_dirs=include_dirs + ) + + +def dump_file(filename, head=None): + """Dumps a file content into log.info. + + If head is not None, will be dumped before the file content. + """ + if head is None: + log.info('%s', filename) + else: + log.info(head) + log.info(pathlib.Path(filename).read_text(encoding='utf-8')) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install.py new file mode 100644 index 0000000000000000000000000000000000000000..dc17e56a8070b26f0e3d09c82250e006f1aed826 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install.py @@ -0,0 +1,805 @@ +"""distutils.command.install + +Implements the Distutils 'install' command.""" + +from __future__ import annotations + +import collections +import contextlib +import itertools +import os +import sys +import sysconfig +from distutils._log import log +from site import USER_BASE, USER_SITE +from typing import ClassVar + +from ..core import Command +from ..debug import DEBUG +from ..errors import DistutilsOptionError, DistutilsPlatformError +from ..file_util import write_file +from ..sysconfig import get_config_vars +from ..util import change_root, convert_path, get_platform, subst_vars +from . import _framework_compat as fw + +HAS_USER_SITE = True + +WINDOWS_SCHEME = { + 'purelib': '{base}/Lib/site-packages', + 'platlib': '{base}/Lib/site-packages', + 'headers': '{base}/Include/{dist_name}', + 'scripts': '{base}/Scripts', + 'data': '{base}', +} + +INSTALL_SCHEMES = { + 'posix_prefix': { + 'purelib': '{base}/lib/{implementation_lower}{py_version_short}/site-packages', + 'platlib': '{platbase}/{platlibdir}/{implementation_lower}' + '{py_version_short}/site-packages', + 'headers': '{base}/include/{implementation_lower}' + '{py_version_short}{abiflags}/{dist_name}', + 'scripts': '{base}/bin', + 'data': '{base}', + }, + 'posix_home': { + 'purelib': '{base}/lib/{implementation_lower}', + 'platlib': '{base}/{platlibdir}/{implementation_lower}', + 'headers': '{base}/include/{implementation_lower}/{dist_name}', + 'scripts': '{base}/bin', + 'data': '{base}', + }, + 'nt': WINDOWS_SCHEME, + 'pypy': { + 'purelib': '{base}/site-packages', + 'platlib': '{base}/site-packages', + 'headers': '{base}/include/{dist_name}', + 'scripts': '{base}/bin', + 'data': '{base}', + }, + 'pypy_nt': { + 'purelib': '{base}/site-packages', + 'platlib': '{base}/site-packages', + 'headers': '{base}/include/{dist_name}', + 'scripts': '{base}/Scripts', + 'data': '{base}', + }, +} + +# user site schemes +if HAS_USER_SITE: + INSTALL_SCHEMES['nt_user'] = { + 'purelib': '{usersite}', + 'platlib': '{usersite}', + 'headers': '{userbase}/{implementation}{py_version_nodot_plat}' + '/Include/{dist_name}', + 'scripts': '{userbase}/{implementation}{py_version_nodot_plat}/Scripts', + 'data': '{userbase}', + } + + INSTALL_SCHEMES['posix_user'] = { + 'purelib': '{usersite}', + 'platlib': '{usersite}', + 'headers': '{userbase}/include/{implementation_lower}' + '{py_version_short}{abiflags}/{dist_name}', + 'scripts': '{userbase}/bin', + 'data': '{userbase}', + } + + +INSTALL_SCHEMES.update(fw.schemes) + + +# The keys to an installation scheme; if any new types of files are to be +# installed, be sure to add an entry to every installation scheme above, +# and to SCHEME_KEYS here. +SCHEME_KEYS = ('purelib', 'platlib', 'headers', 'scripts', 'data') + + +def _load_sysconfig_schemes(): + with contextlib.suppress(AttributeError): + return { + scheme: sysconfig.get_paths(scheme, expand=False) + for scheme in sysconfig.get_scheme_names() + } + + +def _load_schemes(): + """ + Extend default schemes with schemes from sysconfig. + """ + + sysconfig_schemes = _load_sysconfig_schemes() or {} + + return { + scheme: { + **INSTALL_SCHEMES.get(scheme, {}), + **sysconfig_schemes.get(scheme, {}), + } + for scheme in set(itertools.chain(INSTALL_SCHEMES, sysconfig_schemes)) + } + + +def _get_implementation(): + if hasattr(sys, 'pypy_version_info'): + return 'PyPy' + else: + return 'Python' + + +def _select_scheme(ob, name): + scheme = _inject_headers(name, _load_scheme(_resolve_scheme(name))) + vars(ob).update(_remove_set(ob, _scheme_attrs(scheme))) + + +def _remove_set(ob, attrs): + """ + Include only attrs that are None in ob. + """ + return {key: value for key, value in attrs.items() if getattr(ob, key) is None} + + +def _resolve_scheme(name): + os_name, sep, key = name.partition('_') + try: + resolved = sysconfig.get_preferred_scheme(key) + except Exception: + resolved = fw.scheme(name) + return resolved + + +def _load_scheme(name): + return _load_schemes()[name] + + +def _inject_headers(name, scheme): + """ + Given a scheme name and the resolved scheme, + if the scheme does not include headers, resolve + the fallback scheme for the name and use headers + from it. pypa/distutils#88 + """ + # Bypass the preferred scheme, which may not + # have defined headers. + fallback = _load_scheme(name) + scheme.setdefault('headers', fallback['headers']) + return scheme + + +def _scheme_attrs(scheme): + """Resolve install directories by applying the install schemes.""" + return {f'install_{key}': scheme[key] for key in SCHEME_KEYS} + + +class install(Command): + description = "install everything from build directory" + + user_options = [ + # Select installation scheme and set base director(y|ies) + ('prefix=', None, "installation prefix"), + ('exec-prefix=', None, "(Unix only) prefix for platform-specific files"), + ('home=', None, "(Unix only) home directory to install under"), + # Or, just set the base director(y|ies) + ( + 'install-base=', + None, + "base installation directory (instead of --prefix or --home)", + ), + ( + 'install-platbase=', + None, + "base installation directory for platform-specific files (instead of --exec-prefix or --home)", + ), + ('root=', None, "install everything relative to this alternate root directory"), + # Or, explicitly set the installation scheme + ( + 'install-purelib=', + None, + "installation directory for pure Python module distributions", + ), + ( + 'install-platlib=', + None, + "installation directory for non-pure module distributions", + ), + ( + 'install-lib=', + None, + "installation directory for all module distributions (overrides --install-purelib and --install-platlib)", + ), + ('install-headers=', None, "installation directory for C/C++ headers"), + ('install-scripts=', None, "installation directory for Python scripts"), + ('install-data=', None, "installation directory for data files"), + # Byte-compilation options -- see install_lib.py for details, as + # these are duplicated from there (but only install_lib does + # anything with them). + ('compile', 'c', "compile .py to .pyc [default]"), + ('no-compile', None, "don't compile .py files"), + ( + 'optimize=', + 'O', + "also compile with optimization: -O1 for \"python -O\", " + "-O2 for \"python -OO\", and -O0 to disable [default: -O0]", + ), + # Miscellaneous control options + ('force', 'f', "force installation (overwrite any existing files)"), + ('skip-build', None, "skip rebuilding everything (for testing/debugging)"), + # Where to install documentation (eventually!) + # ('doc-format=', None, "format of documentation to generate"), + # ('install-man=', None, "directory for Unix man pages"), + # ('install-html=', None, "directory for HTML documentation"), + # ('install-info=', None, "directory for GNU info files"), + ('record=', None, "filename in which to record list of installed files"), + ] + + boolean_options: ClassVar[list[str]] = ['compile', 'force', 'skip-build'] + + if HAS_USER_SITE: + user_options.append(( + 'user', + None, + f"install in user site-package '{USER_SITE}'", + )) + boolean_options.append('user') + + negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'} + + def initialize_options(self) -> None: + """Initializes options.""" + # High-level options: these select both an installation base + # and scheme. + self.prefix: str | None = None + self.exec_prefix: str | None = None + self.home: str | None = None + self.user = False + + # These select only the installation base; it's up to the user to + # specify the installation scheme (currently, that means supplying + # the --install-{platlib,purelib,scripts,data} options). + self.install_base = None + self.install_platbase = None + self.root: str | None = None + + # These options are the actual installation directories; if not + # supplied by the user, they are filled in using the installation + # scheme implied by prefix/exec-prefix/home and the contents of + # that installation scheme. + self.install_purelib = None # for pure module distributions + self.install_platlib = None # non-pure (dists w/ extensions) + self.install_headers = None # for C/C++ headers + self.install_lib: str | None = None # set to either purelib or platlib + self.install_scripts = None + self.install_data = None + self.install_userbase = USER_BASE + self.install_usersite = USER_SITE + + self.compile = None + self.optimize = None + + # Deprecated + # These two are for putting non-packagized distributions into their + # own directory and creating a .pth file if it makes sense. + # 'extra_path' comes from the setup file; 'install_path_file' can + # be turned off if it makes no sense to install a .pth file. (But + # better to install it uselessly than to guess wrong and not + # install it when it's necessary and would be used!) Currently, + # 'install_path_file' is always true unless some outsider meddles + # with it. + self.extra_path = None + self.install_path_file = True + + # 'force' forces installation, even if target files are not + # out-of-date. 'skip_build' skips running the "build" command, + # handy if you know it's not necessary. 'warn_dir' (which is *not* + # a user option, it's just there so the bdist_* commands can turn + # it off) determines whether we warn about installing to a + # directory not in sys.path. + self.force = False + self.skip_build = False + self.warn_dir = True + + # These are only here as a conduit from the 'build' command to the + # 'install_*' commands that do the real work. ('build_base' isn't + # actually used anywhere, but it might be useful in future.) They + # are not user options, because if the user told the install + # command where the build directory is, that wouldn't affect the + # build command. + self.build_base = None + self.build_lib = None + + # Not defined yet because we don't know anything about + # documentation yet. + # self.install_man = None + # self.install_html = None + # self.install_info = None + + self.record = None + + # -- Option finalizing methods ------------------------------------- + # (This is rather more involved than for most commands, + # because this is where the policy for installing third- + # party Python modules on various platforms given a wide + # array of user input is decided. Yes, it's quite complex!) + + def finalize_options(self) -> None: # noqa: C901 + """Finalizes options.""" + # This method (and its helpers, like 'finalize_unix()', + # 'finalize_other()', and 'select_scheme()') is where the default + # installation directories for modules, extension modules, and + # anything else we care to install from a Python module + # distribution. Thus, this code makes a pretty important policy + # statement about how third-party stuff is added to a Python + # installation! Note that the actual work of installation is done + # by the relatively simple 'install_*' commands; they just take + # their orders from the installation directory options determined + # here. + + # Check for errors/inconsistencies in the options; first, stuff + # that's wrong on any platform. + + if (self.prefix or self.exec_prefix or self.home) and ( + self.install_base or self.install_platbase + ): + raise DistutilsOptionError( + "must supply either prefix/exec-prefix/home or install-base/install-platbase -- not both" + ) + + if self.home and (self.prefix or self.exec_prefix): + raise DistutilsOptionError( + "must supply either home or prefix/exec-prefix -- not both" + ) + + if self.user and ( + self.prefix + or self.exec_prefix + or self.home + or self.install_base + or self.install_platbase + ): + raise DistutilsOptionError( + "can't combine user with prefix, " + "exec_prefix/home, or install_(plat)base" + ) + + # Next, stuff that's wrong (or dubious) only on certain platforms. + if os.name != "posix": + if self.exec_prefix: + self.warn("exec-prefix option ignored on this platform") + self.exec_prefix = None + + # Now the interesting logic -- so interesting that we farm it out + # to other methods. The goal of these methods is to set the final + # values for the install_{lib,scripts,data,...} options, using as + # input a heady brew of prefix, exec_prefix, home, install_base, + # install_platbase, user-supplied versions of + # install_{purelib,platlib,lib,scripts,data,...}, and the + # install schemes. Phew! + + self.dump_dirs("pre-finalize_{unix,other}") + + if os.name == 'posix': + self.finalize_unix() + else: + self.finalize_other() + + self.dump_dirs("post-finalize_{unix,other}()") + + # Expand configuration variables, tilde, etc. in self.install_base + # and self.install_platbase -- that way, we can use $base or + # $platbase in the other installation directories and not worry + # about needing recursive variable expansion (shudder). + + py_version = sys.version.split()[0] + (prefix, exec_prefix) = get_config_vars('prefix', 'exec_prefix') + try: + abiflags = sys.abiflags + except AttributeError: + # sys.abiflags may not be defined on all platforms. + abiflags = '' + local_vars = { + 'dist_name': self.distribution.get_name(), + 'dist_version': self.distribution.get_version(), + 'dist_fullname': self.distribution.get_fullname(), + 'py_version': py_version, + 'py_version_short': f'{sys.version_info.major}.{sys.version_info.minor}', + 'py_version_nodot': f'{sys.version_info.major}{sys.version_info.minor}', + 'sys_prefix': prefix, + 'prefix': prefix, + 'sys_exec_prefix': exec_prefix, + 'exec_prefix': exec_prefix, + 'abiflags': abiflags, + 'platlibdir': getattr(sys, 'platlibdir', 'lib'), + 'implementation_lower': _get_implementation().lower(), + 'implementation': _get_implementation(), + } + + # vars for compatibility on older Pythons + compat_vars = dict( + # Python 3.9 and earlier + py_version_nodot_plat=getattr(sys, 'winver', '').replace('.', ''), + ) + + if HAS_USER_SITE: + local_vars['userbase'] = self.install_userbase + local_vars['usersite'] = self.install_usersite + + self.config_vars = collections.ChainMap( + local_vars, + sysconfig.get_config_vars(), + compat_vars, + fw.vars(), + ) + + self.expand_basedirs() + + self.dump_dirs("post-expand_basedirs()") + + # Now define config vars for the base directories so we can expand + # everything else. + local_vars['base'] = self.install_base + local_vars['platbase'] = self.install_platbase + + if DEBUG: + from pprint import pprint + + print("config vars:") + pprint(dict(self.config_vars)) + + # Expand "~" and configuration variables in the installation + # directories. + self.expand_dirs() + + self.dump_dirs("post-expand_dirs()") + + # Create directories in the home dir: + if self.user: + self.create_home_path() + + # Pick the actual directory to install all modules to: either + # install_purelib or install_platlib, depending on whether this + # module distribution is pure or not. Of course, if the user + # already specified install_lib, use their selection. + if self.install_lib is None: + if self.distribution.has_ext_modules(): # has extensions: non-pure + self.install_lib = self.install_platlib + else: + self.install_lib = self.install_purelib + + # Convert directories from Unix /-separated syntax to the local + # convention. + self.convert_paths( + 'lib', + 'purelib', + 'platlib', + 'scripts', + 'data', + 'headers', + 'userbase', + 'usersite', + ) + + # Deprecated + # Well, we're not actually fully completely finalized yet: we still + # have to deal with 'extra_path', which is the hack for allowing + # non-packagized module distributions (hello, Numerical Python!) to + # get their own directories. + self.handle_extra_path() + self.install_libbase = self.install_lib # needed for .pth file + self.install_lib = os.path.join(self.install_lib, self.extra_dirs) + + # If a new root directory was supplied, make all the installation + # dirs relative to it. + if self.root is not None: + self.change_roots( + 'libbase', 'lib', 'purelib', 'platlib', 'scripts', 'data', 'headers' + ) + + self.dump_dirs("after prepending root") + + # Find out the build directories, ie. where to install from. + self.set_undefined_options( + 'build', ('build_base', 'build_base'), ('build_lib', 'build_lib') + ) + + # Punt on doc directories for now -- after all, we're punting on + # documentation completely! + + def dump_dirs(self, msg) -> None: + """Dumps the list of user options.""" + if not DEBUG: + return + from ..fancy_getopt import longopt_xlate + + log.debug(msg + ":") + for opt in self.user_options: + opt_name = opt[0] + if opt_name[-1] == "=": + opt_name = opt_name[0:-1] + if opt_name in self.negative_opt: + opt_name = self.negative_opt[opt_name] + opt_name = opt_name.translate(longopt_xlate) + val = not getattr(self, opt_name) + else: + opt_name = opt_name.translate(longopt_xlate) + val = getattr(self, opt_name) + log.debug(" %s: %s", opt_name, val) + + def finalize_unix(self) -> None: + """Finalizes options for posix platforms.""" + if self.install_base is not None or self.install_platbase is not None: + incomplete_scheme = ( + ( + self.install_lib is None + and self.install_purelib is None + and self.install_platlib is None + ) + or self.install_headers is None + or self.install_scripts is None + or self.install_data is None + ) + if incomplete_scheme: + raise DistutilsOptionError( + "install-base or install-platbase supplied, but " + "installation scheme is incomplete" + ) + return + + if self.user: + if self.install_userbase is None: + raise DistutilsPlatformError("User base directory is not specified") + self.install_base = self.install_platbase = self.install_userbase + self.select_scheme("posix_user") + elif self.home is not None: + self.install_base = self.install_platbase = self.home + self.select_scheme("posix_home") + else: + if self.prefix is None: + if self.exec_prefix is not None: + raise DistutilsOptionError( + "must not supply exec-prefix without prefix" + ) + + # Allow Fedora to add components to the prefix + _prefix_addition = getattr(sysconfig, '_prefix_addition', "") + + self.prefix = os.path.normpath(sys.prefix) + _prefix_addition + self.exec_prefix = os.path.normpath(sys.exec_prefix) + _prefix_addition + + else: + if self.exec_prefix is None: + self.exec_prefix = self.prefix + + self.install_base = self.prefix + self.install_platbase = self.exec_prefix + self.select_scheme("posix_prefix") + + def finalize_other(self) -> None: + """Finalizes options for non-posix platforms""" + if self.user: + if self.install_userbase is None: + raise DistutilsPlatformError("User base directory is not specified") + self.install_base = self.install_platbase = self.install_userbase + self.select_scheme(os.name + "_user") + elif self.home is not None: + self.install_base = self.install_platbase = self.home + self.select_scheme("posix_home") + else: + if self.prefix is None: + self.prefix = os.path.normpath(sys.prefix) + + self.install_base = self.install_platbase = self.prefix + try: + self.select_scheme(os.name) + except KeyError: + raise DistutilsPlatformError( + f"I don't know how to install stuff on '{os.name}'" + ) + + def select_scheme(self, name) -> None: + _select_scheme(self, name) + + def _expand_attrs(self, attrs): + for attr in attrs: + val = getattr(self, attr) + if val is not None: + if os.name in ('posix', 'nt'): + val = os.path.expanduser(val) + val = subst_vars(val, self.config_vars) + setattr(self, attr, val) + + def expand_basedirs(self) -> None: + """Calls `os.path.expanduser` on install_base, install_platbase and + root.""" + self._expand_attrs(['install_base', 'install_platbase', 'root']) + + def expand_dirs(self) -> None: + """Calls `os.path.expanduser` on install dirs.""" + self._expand_attrs([ + 'install_purelib', + 'install_platlib', + 'install_lib', + 'install_headers', + 'install_scripts', + 'install_data', + ]) + + def convert_paths(self, *names) -> None: + """Call `convert_path` over `names`.""" + for name in names: + attr = "install_" + name + setattr(self, attr, convert_path(getattr(self, attr))) + + def handle_extra_path(self) -> None: + """Set `path_file` and `extra_dirs` using `extra_path`.""" + if self.extra_path is None: + self.extra_path = self.distribution.extra_path + + if self.extra_path is not None: + log.warning( + "Distribution option extra_path is deprecated. " + "See issue27919 for details." + ) + if isinstance(self.extra_path, str): + self.extra_path = self.extra_path.split(',') + + if len(self.extra_path) == 1: + path_file = extra_dirs = self.extra_path[0] + elif len(self.extra_path) == 2: + path_file, extra_dirs = self.extra_path + else: + raise DistutilsOptionError( + "'extra_path' option must be a list, tuple, or " + "comma-separated string with 1 or 2 elements" + ) + + # convert to local form in case Unix notation used (as it + # should be in setup scripts) + extra_dirs = convert_path(extra_dirs) + else: + path_file = None + extra_dirs = '' + + # XXX should we warn if path_file and not extra_dirs? (in which + # case the path file would be harmless but pointless) + self.path_file = path_file + self.extra_dirs = extra_dirs + + def change_roots(self, *names) -> None: + """Change the install directories pointed by name using root.""" + for name in names: + attr = "install_" + name + setattr(self, attr, change_root(self.root, getattr(self, attr))) + + def create_home_path(self) -> None: + """Create directories under ~.""" + if not self.user: + return + home = convert_path(os.path.expanduser("~")) + for path in self.config_vars.values(): + if str(path).startswith(home) and not os.path.isdir(path): + self.debug_print(f"os.makedirs('{path}', 0o700)") + os.makedirs(path, 0o700) + + # -- Command execution methods ------------------------------------- + + def run(self): + """Runs the command.""" + # Obviously have to build before we can install + if not self.skip_build: + self.run_command('build') + # If we built for any other platform, we can't install. + build_plat = self.distribution.get_command_obj('build').plat_name + # check warn_dir - it is a clue that the 'install' is happening + # internally, and not to sys.path, so we don't check the platform + # matches what we are running. + if self.warn_dir and build_plat != get_platform(): + raise DistutilsPlatformError("Can't install when cross-compiling") + + # Run all sub-commands (at least those that need to be run) + for cmd_name in self.get_sub_commands(): + self.run_command(cmd_name) + + if self.path_file: + self.create_path_file() + + # write list of installed files, if requested. + if self.record: + outputs = self.get_outputs() + if self.root: # strip any package prefix + root_len = len(self.root) + for counter in range(len(outputs)): + outputs[counter] = outputs[counter][root_len:] + self.execute( + write_file, + (self.record, outputs), + f"writing list of installed files to '{self.record}'", + ) + + sys_path = map(os.path.normpath, sys.path) + sys_path = map(os.path.normcase, sys_path) + install_lib = os.path.normcase(os.path.normpath(self.install_lib)) + if ( + self.warn_dir + and not (self.path_file and self.install_path_file) + and install_lib not in sys_path + ): + log.debug( + ( + "modules installed to '%s', which is not in " + "Python's module search path (sys.path) -- " + "you'll have to change the search path yourself" + ), + self.install_lib, + ) + + def create_path_file(self): + """Creates the .pth file""" + filename = os.path.join(self.install_libbase, self.path_file + ".pth") + if self.install_path_file: + self.execute( + write_file, (filename, [self.extra_dirs]), f"creating {filename}" + ) + else: + self.warn(f"path file '{filename}' not created") + + # -- Reporting methods --------------------------------------------- + + def get_outputs(self): + """Assembles the outputs of all the sub-commands.""" + outputs = [] + for cmd_name in self.get_sub_commands(): + cmd = self.get_finalized_command(cmd_name) + # Add the contents of cmd.get_outputs(), ensuring + # that outputs doesn't contain duplicate entries + for filename in cmd.get_outputs(): + if filename not in outputs: + outputs.append(filename) + + if self.path_file and self.install_path_file: + outputs.append(os.path.join(self.install_libbase, self.path_file + ".pth")) + + return outputs + + def get_inputs(self): + """Returns the inputs of all the sub-commands""" + # XXX gee, this looks familiar ;-( + inputs = [] + for cmd_name in self.get_sub_commands(): + cmd = self.get_finalized_command(cmd_name) + inputs.extend(cmd.get_inputs()) + + return inputs + + # -- Predicates for sub-command list ------------------------------- + + def has_lib(self): + """Returns true if the current distribution has any Python + modules to install.""" + return ( + self.distribution.has_pure_modules() or self.distribution.has_ext_modules() + ) + + def has_headers(self): + """Returns true if the current distribution has any headers to + install.""" + return self.distribution.has_headers() + + def has_scripts(self): + """Returns true if the current distribution has any scripts to. + install.""" + return self.distribution.has_scripts() + + def has_data(self): + """Returns true if the current distribution has any data to. + install.""" + return self.distribution.has_data_files() + + # 'sub_commands': a list of commands this command might have to run to + # get its work done. See cmd.py for more info. + sub_commands = [ + ('install_lib', has_lib), + ('install_headers', has_headers), + ('install_scripts', has_scripts), + ('install_data', has_data), + ('install_egg_info', lambda self: True), + ] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install_data.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install_data.py new file mode 100644 index 0000000000000000000000000000000000000000..4ad186e8ec61b92967ea1c12cdca51d00e82dd15 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install_data.py @@ -0,0 +1,94 @@ +"""distutils.command.install_data + +Implements the Distutils 'install_data' command, for installing +platform-independent data files.""" + +# contributed by Bastian Kleineidam + +from __future__ import annotations + +import functools +import os +from collections.abc import Iterable +from typing import ClassVar + +from ..core import Command +from ..util import change_root, convert_path + + +class install_data(Command): + description = "install data files" + + user_options = [ + ( + 'install-dir=', + 'd', + "base directory for installing data files [default: installation base dir]", + ), + ('root=', None, "install everything relative to this alternate root directory"), + ('force', 'f', "force installation (overwrite existing files)"), + ] + + boolean_options: ClassVar[list[str]] = ['force'] + + def initialize_options(self): + self.install_dir = None + self.outfiles = [] + self.root = None + self.force = False + self.data_files = self.distribution.data_files + self.warn_dir = True + + def finalize_options(self) -> None: + self.set_undefined_options( + 'install', + ('install_data', 'install_dir'), + ('root', 'root'), + ('force', 'force'), + ) + + def run(self) -> None: + self.mkpath(self.install_dir) + for f in self.data_files: + self._copy(f) + + @functools.singledispatchmethod + def _copy(self, f: tuple[str | os.PathLike, Iterable[str | os.PathLike]]): + # it's a tuple with path to install to and a list of files + dir = convert_path(f[0]) + if not os.path.isabs(dir): + dir = os.path.join(self.install_dir, dir) + elif self.root: + dir = change_root(self.root, dir) + self.mkpath(dir) + + if f[1] == []: + # If there are no files listed, the user must be + # trying to create an empty directory, so add the + # directory to the list of output files. + self.outfiles.append(dir) + else: + # Copy files, adding them to the list of output files. + for data in f[1]: + data = convert_path(data) + (out, _) = self.copy_file(data, dir) + self.outfiles.append(out) + + @_copy.register(str) + @_copy.register(os.PathLike) + def _(self, f: str | os.PathLike): + # it's a simple file, so copy it + f = convert_path(f) + if self.warn_dir: + self.warn( + "setup script did not provide a directory for " + f"'{f}' -- installing right in '{self.install_dir}'" + ) + (out, _) = self.copy_file(f, self.install_dir) + self.outfiles.append(out) + + def get_inputs(self): + return self.data_files or [] + + def get_outputs(self): + return self.outfiles diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install_egg_info.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install_egg_info.py new file mode 100644 index 0000000000000000000000000000000000000000..6840b34ec2b5e2a3f9514cda5e940bc6cd90c731 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install_egg_info.py @@ -0,0 +1,90 @@ +""" +distutils.command.install_egg_info + +Implements the Distutils 'install_egg_info' command, for installing +a package's PKG-INFO metadata. +""" + +import os +import re +import sys +from typing import ClassVar + +from .. import dir_util +from .._log import log +from ..cmd import Command + + +class install_egg_info(Command): + """Install an .egg-info file for the package""" + + description = "Install package's PKG-INFO metadata as an .egg-info file" + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ('install-dir=', 'd', "directory to install to"), + ] + + def initialize_options(self): + self.install_dir = None + + @property + def basename(self): + """ + Allow basename to be overridden by child class. + Ref pypa/distutils#2. + """ + name = to_filename(safe_name(self.distribution.get_name())) + version = to_filename(safe_version(self.distribution.get_version())) + return f"{name}-{version}-py{sys.version_info.major}.{sys.version_info.minor}.egg-info" + + def finalize_options(self): + self.set_undefined_options('install_lib', ('install_dir', 'install_dir')) + self.target = os.path.join(self.install_dir, self.basename) + self.outputs = [self.target] + + def run(self): + target = self.target + if os.path.isdir(target) and not os.path.islink(target): + dir_util.remove_tree(target) + elif os.path.exists(target): + self.execute(os.unlink, (self.target,), "Removing " + target) + elif not os.path.isdir(self.install_dir): + self.execute( + os.makedirs, (self.install_dir,), "Creating " + self.install_dir + ) + log.info("Writing %s", target) + with open(target, 'w', encoding='UTF-8') as f: + self.distribution.metadata.write_pkg_file(f) + + def get_outputs(self): + return self.outputs + + +# The following routines are taken from setuptools' pkg_resources module and +# can be replaced by importing them from pkg_resources once it is included +# in the stdlib. + + +def safe_name(name): + """Convert an arbitrary string to a standard distribution name + + Any runs of non-alphanumeric/. characters are replaced with a single '-'. + """ + return re.sub('[^A-Za-z0-9.]+', '-', name) + + +def safe_version(version): + """Convert an arbitrary string to a standard version string + + Spaces become dots, and all other non-alphanumeric characters become + dashes, with runs of multiple dashes condensed to a single dash. + """ + version = version.replace(' ', '.') + return re.sub('[^A-Za-z0-9.]+', '-', version) + + +def to_filename(name): + """Convert a project or version name to its filename-escaped form + + Any '-' characters are currently replaced with '_'. + """ + return name.replace('-', '_') diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install_headers.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install_headers.py new file mode 100644 index 0000000000000000000000000000000000000000..97af1371efcc73423e9b91d7b3ced684ff936377 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install_headers.py @@ -0,0 +1,46 @@ +"""distutils.command.install_headers + +Implements the Distutils 'install_headers' command, to install C/C++ header +files to the Python include directory.""" + +from typing import ClassVar + +from ..core import Command + + +# XXX force is never used +class install_headers(Command): + description = "install C/C++ header files" + + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ('install-dir=', 'd', "directory to install header files to"), + ('force', 'f', "force installation (overwrite existing files)"), + ] + + boolean_options: ClassVar[list[str]] = ['force'] + + def initialize_options(self): + self.install_dir = None + self.force = False + self.outfiles = [] + + def finalize_options(self): + self.set_undefined_options( + 'install', ('install_headers', 'install_dir'), ('force', 'force') + ) + + def run(self): + headers = self.distribution.headers + if not headers: + return + + self.mkpath(self.install_dir) + for header in headers: + (out, _) = self.copy_file(header, self.install_dir) + self.outfiles.append(out) + + def get_inputs(self): + return self.distribution.headers or [] + + def get_outputs(self): + return self.outfiles diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py new file mode 100644 index 0000000000000000000000000000000000000000..9909a61762bd658f837057f07fa1d1200fe4e6ac --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py @@ -0,0 +1,236 @@ +"""distutils.command.install_lib + +Implements the Distutils 'install_lib' command +(install all Python modules).""" + +from __future__ import annotations + +import importlib.util +import os +import sys +from typing import Any, ClassVar + +from ..core import Command +from ..errors import DistutilsOptionError + +# Extension for Python source files. +PYTHON_SOURCE_EXTENSION = ".py" + + +class install_lib(Command): + description = "install all Python modules (extensions and pure Python)" + + # The byte-compilation options are a tad confusing. Here are the + # possible scenarios: + # 1) no compilation at all (--no-compile --no-optimize) + # 2) compile .pyc only (--compile --no-optimize; default) + # 3) compile .pyc and "opt-1" .pyc (--compile --optimize) + # 4) compile "opt-1" .pyc only (--no-compile --optimize) + # 5) compile .pyc and "opt-2" .pyc (--compile --optimize-more) + # 6) compile "opt-2" .pyc only (--no-compile --optimize-more) + # + # The UI for this is two options, 'compile' and 'optimize'. + # 'compile' is strictly boolean, and only decides whether to + # generate .pyc files. 'optimize' is three-way (0, 1, or 2), and + # decides both whether to generate .pyc files and what level of + # optimization to use. + + user_options = [ + ('install-dir=', 'd', "directory to install to"), + ('build-dir=', 'b', "build directory (where to install from)"), + ('force', 'f', "force installation (overwrite existing files)"), + ('compile', 'c', "compile .py to .pyc [default]"), + ('no-compile', None, "don't compile .py files"), + ( + 'optimize=', + 'O', + "also compile with optimization: -O1 for \"python -O\", " + "-O2 for \"python -OO\", and -O0 to disable [default: -O0]", + ), + ('skip-build', None, "skip the build steps"), + ] + + boolean_options: ClassVar[list[str]] = ['force', 'compile', 'skip-build'] + negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'} + + def initialize_options(self): + # let the 'install' command dictate our installation directory + self.install_dir = None + self.build_dir = None + self.force = False + self.compile = None + self.optimize = None + self.skip_build = None + + def finalize_options(self) -> None: + # Get all the information we need to install pure Python modules + # from the umbrella 'install' command -- build (source) directory, + # install (target) directory, and whether to compile .py files. + self.set_undefined_options( + 'install', + ('build_lib', 'build_dir'), + ('install_lib', 'install_dir'), + ('force', 'force'), + ('compile', 'compile'), + ('optimize', 'optimize'), + ('skip_build', 'skip_build'), + ) + + if self.compile is None: + self.compile = True + if self.optimize is None: + self.optimize = False + + if not isinstance(self.optimize, int): + try: + self.optimize = int(self.optimize) + except ValueError: + pass + if self.optimize not in (0, 1, 2): + raise DistutilsOptionError("optimize must be 0, 1, or 2") + + def run(self) -> None: + # Make sure we have built everything we need first + self.build() + + # Install everything: simply dump the entire contents of the build + # directory to the installation directory (that's the beauty of + # having a build directory!) + outfiles = self.install() + + # (Optionally) compile .py to .pyc + if outfiles is not None and self.distribution.has_pure_modules(): + self.byte_compile(outfiles) + + # -- Top-level worker functions ------------------------------------ + # (called from 'run()') + + def build(self) -> None: + if not self.skip_build: + if self.distribution.has_pure_modules(): + self.run_command('build_py') + if self.distribution.has_ext_modules(): + self.run_command('build_ext') + + # Any: https://typing.readthedocs.io/en/latest/guides/writing_stubs.html#the-any-trick + def install(self) -> list[str] | Any: + if os.path.isdir(self.build_dir): + outfiles = self.copy_tree(self.build_dir, self.install_dir) + else: + self.warn( + f"'{self.build_dir}' does not exist -- no Python modules to install" + ) + return + return outfiles + + def byte_compile(self, files) -> None: + if sys.dont_write_bytecode: + self.warn('byte-compiling is disabled, skipping.') + return + + from ..util import byte_compile + + # Get the "--root" directory supplied to the "install" command, + # and use it as a prefix to strip off the purported filename + # encoded in bytecode files. This is far from complete, but it + # should at least generate usable bytecode in RPM distributions. + install_root = self.get_finalized_command('install').root + + if self.compile: + byte_compile( + files, + optimize=0, + force=self.force, + prefix=install_root, + ) + if self.optimize > 0: + byte_compile( + files, + optimize=self.optimize, + force=self.force, + prefix=install_root, + verbose=self.verbose, + ) + + # -- Utility methods ----------------------------------------------- + + def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir): + if not has_any: + return [] + + build_cmd = self.get_finalized_command(build_cmd) + build_files = build_cmd.get_outputs() + build_dir = getattr(build_cmd, cmd_option) + + prefix_len = len(build_dir) + len(os.sep) + outputs = [os.path.join(output_dir, file[prefix_len:]) for file in build_files] + + return outputs + + def _bytecode_filenames(self, py_filenames): + bytecode_files = [] + for py_file in py_filenames: + # Since build_py handles package data installation, the + # list of outputs can contain more than just .py files. + # Make sure we only report bytecode for the .py files. + ext = os.path.splitext(os.path.normcase(py_file))[1] + if ext != PYTHON_SOURCE_EXTENSION: + continue + if self.compile: + bytecode_files.append( + importlib.util.cache_from_source(py_file, optimization='') + ) + if self.optimize > 0: + bytecode_files.append( + importlib.util.cache_from_source( + py_file, optimization=self.optimize + ) + ) + + return bytecode_files + + # -- External interface -------------------------------------------- + # (called by outsiders) + + def get_outputs(self): + """Return the list of files that would be installed if this command + were actually run. Not affected by the "dry-run" flag or whether + modules have actually been built yet. + """ + pure_outputs = self._mutate_outputs( + self.distribution.has_pure_modules(), + 'build_py', + 'build_lib', + self.install_dir, + ) + if self.compile: + bytecode_outputs = self._bytecode_filenames(pure_outputs) + else: + bytecode_outputs = [] + + ext_outputs = self._mutate_outputs( + self.distribution.has_ext_modules(), + 'build_ext', + 'build_lib', + self.install_dir, + ) + + return pure_outputs + bytecode_outputs + ext_outputs + + def get_inputs(self): + """Get the list of files that are input to this command, ie. the + files that get installed as they are named in the build tree. + The files in this list correspond one-to-one to the output + filenames returned by 'get_outputs()'. + """ + inputs = [] + + if self.distribution.has_pure_modules(): + build_py = self.get_finalized_command('build_py') + inputs.extend(build_py.get_outputs()) + + if self.distribution.has_ext_modules(): + build_ext = self.get_finalized_command('build_ext') + inputs.extend(build_ext.get_outputs()) + + return inputs diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/install_scripts.py b/lib/python3.11/site-packages/setuptools/_distutils/command/install_scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..33d235d976e39db426aaff628b8b9bb5b832dcb6 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/install_scripts.py @@ -0,0 +1,59 @@ +"""distutils.command.install_scripts + +Implements the Distutils 'install_scripts' command, for installing +Python scripts.""" + +# contributed by Bastian Kleineidam + +import os +from distutils._log import log +from stat import ST_MODE +from typing import ClassVar + +from ..core import Command + + +class install_scripts(Command): + description = "install scripts (Python or otherwise)" + + user_options = [ + ('install-dir=', 'd', "directory to install scripts to"), + ('build-dir=', 'b', "build directory (where to install from)"), + ('force', 'f', "force installation (overwrite existing files)"), + ('skip-build', None, "skip the build steps"), + ] + + boolean_options: ClassVar[list[str]] = ['force', 'skip-build'] + + def initialize_options(self): + self.install_dir = None + self.force = False + self.build_dir = None + self.skip_build = None + + def finalize_options(self) -> None: + self.set_undefined_options('build', ('build_scripts', 'build_dir')) + self.set_undefined_options( + 'install', + ('install_scripts', 'install_dir'), + ('force', 'force'), + ('skip_build', 'skip_build'), + ) + + def run(self) -> None: + if not self.skip_build: + self.run_command('build_scripts') + self.outfiles = self.copy_tree(self.build_dir, self.install_dir) + if os.name == 'posix': + # Set the executable bits (owner, group, and world) on + # all the scripts we just installed. + for file in self.get_outputs(): + mode = ((os.stat(file)[ST_MODE]) | 0o555) & 0o7777 + log.info("changing mode of %s to %o", file, mode) + os.chmod(file, mode) + + def get_inputs(self): + return self.distribution.scripts or [] + + def get_outputs(self): + return self.outfiles or [] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/command/sdist.py b/lib/python3.11/site-packages/setuptools/_distutils/command/sdist.py new file mode 100644 index 0000000000000000000000000000000000000000..e64cc82902ac292fa391a219ff8eae117b252fe7 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/command/sdist.py @@ -0,0 +1,521 @@ +"""distutils.command.sdist + +Implements the Distutils 'sdist' command (create a source distribution).""" + +from __future__ import annotations + +import os +import sys +from collections.abc import Callable +from distutils import archive_util, dir_util, file_util +from distutils._log import log +from glob import glob +from itertools import filterfalse +from typing import ClassVar + +from ..core import Command +from ..errors import DistutilsOptionError, DistutilsTemplateError +from ..filelist import FileList +from ..text_file import TextFile +from ..util import convert_path + + +def show_formats(): + """Print all possible values for the 'formats' option (used by + the "--help-formats" command-line option). + """ + from ..archive_util import ARCHIVE_FORMATS + from ..fancy_getopt import FancyGetopt + + formats = sorted( + ("formats=" + format, None, ARCHIVE_FORMATS[format][2]) + for format in ARCHIVE_FORMATS.keys() + ) + FancyGetopt(formats).print_help("List of available source distribution formats:") + + +class sdist(Command): + description = "create a source distribution (tarball, zip file, etc.)" + + def checking_metadata(self) -> bool: + """Callable used for the check sub-command. + + Placed here so user_options can view it""" + return self.metadata_check + + user_options = [ + ('template=', 't', "name of manifest template file [default: MANIFEST.in]"), + ('manifest=', 'm', "name of manifest file [default: MANIFEST]"), + ( + 'use-defaults', + None, + "include the default file set in the manifest " + "[default; disable with --no-defaults]", + ), + ('no-defaults', None, "don't include the default file set"), + ( + 'prune', + None, + "specifically exclude files/directories that should not be " + "distributed (build tree, RCS/CVS dirs, etc.) " + "[default; disable with --no-prune]", + ), + ('no-prune', None, "don't automatically exclude anything"), + ( + 'manifest-only', + 'o', + "just regenerate the manifest and then stop (implies --force-manifest)", + ), + ( + 'force-manifest', + 'f', + "forcibly regenerate the manifest and carry on as usual. " + "Deprecated: now the manifest is always regenerated.", + ), + ('formats=', None, "formats for source distribution (comma-separated list)"), + ( + 'keep-temp', + 'k', + "keep the distribution tree around after creating " + "archive file(s)", + ), + ( + 'dist-dir=', + 'd', + "directory to put the source distribution archive(s) in [default: dist]", + ), + ( + 'metadata-check', + None, + "Ensure that all required elements of meta-data " + "are supplied. Warn if any missing. [default]", + ), + ( + 'owner=', + 'u', + "Owner name used when creating a tar file [default: current user]", + ), + ( + 'group=', + 'g', + "Group name used when creating a tar file [default: current group]", + ), + ] + + boolean_options: ClassVar[list[str]] = [ + 'use-defaults', + 'prune', + 'manifest-only', + 'force-manifest', + 'keep-temp', + 'metadata-check', + ] + + help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [ + ('help-formats', None, "list available distribution formats", show_formats), + ] + + negative_opt: ClassVar[dict[str, str]] = { + 'no-defaults': 'use-defaults', + 'no-prune': 'prune', + } + + sub_commands = [('check', checking_metadata)] + + READMES: ClassVar[tuple[str, ...]] = ('README', 'README.txt', 'README.rst') + + def initialize_options(self): + # 'template' and 'manifest' are, respectively, the names of + # the manifest template and manifest file. + self.template = None + self.manifest = None + + # 'use_defaults': if true, we will include the default file set + # in the manifest + self.use_defaults = True + self.prune = True + + self.manifest_only = False + self.force_manifest = False + + self.formats = ['gztar'] + self.keep_temp = False + self.dist_dir = None + + self.archive_files = None + self.metadata_check = True + self.owner = None + self.group = None + + def finalize_options(self) -> None: + if self.manifest is None: + self.manifest = "MANIFEST" + if self.template is None: + self.template = "MANIFEST.in" + + self.ensure_string_list('formats') + + bad_format = archive_util.check_archive_formats(self.formats) + if bad_format: + raise DistutilsOptionError(f"unknown archive format '{bad_format}'") + + if self.dist_dir is None: + self.dist_dir = "dist" + + def run(self) -> None: + # 'filelist' contains the list of files that will make up the + # manifest + self.filelist = FileList() + + # Run sub commands + for cmd_name in self.get_sub_commands(): + self.run_command(cmd_name) + + # Do whatever it takes to get the list of files to process + # (process the manifest template, read an existing manifest, + # whatever). File list is accumulated in 'self.filelist'. + self.get_file_list() + + # If user just wanted us to regenerate the manifest, stop now. + if self.manifest_only: + return + + # Otherwise, go ahead and create the source distribution tarball, + # or zipfile, or whatever. + self.make_distribution() + + def get_file_list(self) -> None: + """Figure out the list of files to include in the source + distribution, and put it in 'self.filelist'. This might involve + reading the manifest template (and writing the manifest), or just + reading the manifest, or just using the default file set -- it all + depends on the user's options. + """ + # new behavior when using a template: + # the file list is recalculated every time because + # even if MANIFEST.in or setup.py are not changed + # the user might have added some files in the tree that + # need to be included. + # + # This makes --force the default and only behavior with templates. + template_exists = os.path.isfile(self.template) + if not template_exists and self._manifest_is_not_generated(): + self.read_manifest() + self.filelist.sort() + self.filelist.remove_duplicates() + return + + if not template_exists: + self.warn( + ("manifest template '%s' does not exist " + "(using default file list)") + % self.template + ) + self.filelist.findall() + + if self.use_defaults: + self.add_defaults() + + if template_exists: + self.read_template() + + if self.prune: + self.prune_file_list() + + self.filelist.sort() + self.filelist.remove_duplicates() + self.write_manifest() + + def add_defaults(self) -> None: + """Add all the default files to self.filelist: + - README or README.txt + - setup.py + - tests/test*.py and test/test*.py + - all pure Python modules mentioned in setup script + - all files pointed by package_data (build_py) + - all files defined in data_files. + - all files defined as scripts. + - all C sources listed as part of extensions or C libraries + in the setup script (doesn't catch C headers!) + Warns if (README or README.txt) or setup.py are missing; everything + else is optional. + """ + self._add_defaults_standards() + self._add_defaults_optional() + self._add_defaults_python() + self._add_defaults_data_files() + self._add_defaults_ext() + self._add_defaults_c_libs() + self._add_defaults_scripts() + + @staticmethod + def _cs_path_exists(fspath): + """ + Case-sensitive path existence check + + >>> sdist._cs_path_exists(__file__) + True + >>> sdist._cs_path_exists(__file__.upper()) + False + """ + if not os.path.exists(fspath): + return False + # make absolute so we always have a directory + abspath = os.path.abspath(fspath) + directory, filename = os.path.split(abspath) + return filename in os.listdir(directory) + + def _add_defaults_standards(self): + standards = [self.READMES, self.distribution.script_name] + for fn in standards: + if isinstance(fn, tuple): + alts = fn + got_it = False + for fn in alts: + if self._cs_path_exists(fn): + got_it = True + self.filelist.append(fn) + break + + if not got_it: + self.warn( + "standard file not found: should have one of " + ', '.join(alts) + ) + else: + if self._cs_path_exists(fn): + self.filelist.append(fn) + else: + self.warn(f"standard file '{fn}' not found") + + def _add_defaults_optional(self): + optional = ['tests/test*.py', 'test/test*.py', 'setup.cfg'] + for pattern in optional: + files = filter(os.path.isfile, glob(pattern)) + self.filelist.extend(files) + + def _add_defaults_python(self): + # build_py is used to get: + # - python modules + # - files defined in package_data + build_py = self.get_finalized_command('build_py') + + # getting python files + if self.distribution.has_pure_modules(): + self.filelist.extend(build_py.get_source_files()) + + # getting package_data files + # (computed in build_py.data_files by build_py.finalize_options) + for _pkg, src_dir, _build_dir, filenames in build_py.data_files: + for filename in filenames: + self.filelist.append(os.path.join(src_dir, filename)) + + def _add_defaults_data_files(self): + # getting distribution.data_files + if self.distribution.has_data_files(): + for item in self.distribution.data_files: + if isinstance(item, str): + # plain file + item = convert_path(item) + if os.path.isfile(item): + self.filelist.append(item) + else: + # a (dirname, filenames) tuple + dirname, filenames = item + for f in filenames: + f = convert_path(f) + if os.path.isfile(f): + self.filelist.append(f) + + def _add_defaults_ext(self): + if self.distribution.has_ext_modules(): + build_ext = self.get_finalized_command('build_ext') + self.filelist.extend(build_ext.get_source_files()) + + def _add_defaults_c_libs(self): + if self.distribution.has_c_libraries(): + build_clib = self.get_finalized_command('build_clib') + self.filelist.extend(build_clib.get_source_files()) + + def _add_defaults_scripts(self): + if self.distribution.has_scripts(): + build_scripts = self.get_finalized_command('build_scripts') + self.filelist.extend(build_scripts.get_source_files()) + + def read_template(self) -> None: + """Read and parse manifest template file named by self.template. + + (usually "MANIFEST.in") The parsing and processing is done by + 'self.filelist', which updates itself accordingly. + """ + log.info("reading manifest template '%s'", self.template) + template = TextFile( + self.template, + strip_comments=True, + skip_blanks=True, + join_lines=True, + lstrip_ws=True, + rstrip_ws=True, + collapse_join=True, + ) + + try: + while True: + line = template.readline() + if line is None: # end of file + break + + try: + self.filelist.process_template_line(line) + # the call above can raise a DistutilsTemplateError for + # malformed lines, or a ValueError from the lower-level + # convert_path function + except (DistutilsTemplateError, ValueError) as msg: + self.warn( + f"{template.filename}, line {int(template.current_line)}: {msg}" + ) + finally: + template.close() + + def prune_file_list(self) -> None: + """Prune off branches that might slip into the file list as created + by 'read_template()', but really don't belong there: + * the build tree (typically "build") + * the release tree itself (only an issue if we ran "sdist" + previously with --keep-temp, or it aborted) + * any RCS, CVS, .svn, .hg, .git, .bzr, _darcs directories + """ + build = self.get_finalized_command('build') + base_dir = self.distribution.get_fullname() + + self.filelist.exclude_pattern(None, prefix=os.fspath(build.build_base)) + self.filelist.exclude_pattern(None, prefix=base_dir) + + if sys.platform == 'win32': + seps = r'/|\\' + else: + seps = '/' + + vcs_dirs = ['RCS', 'CVS', r'\.svn', r'\.hg', r'\.git', r'\.bzr', '_darcs'] + vcs_ptrn = r'(^|{})({})({}).*'.format(seps, '|'.join(vcs_dirs), seps) + self.filelist.exclude_pattern(vcs_ptrn, is_regex=True) + + def write_manifest(self) -> None: + """Write the file list in 'self.filelist' (presumably as filled in + by 'add_defaults()' and 'read_template()') to the manifest file + named by 'self.manifest'. + """ + if self._manifest_is_not_generated(): + log.info( + f"not writing to manually maintained manifest file '{self.manifest}'" + ) + return + + content = self.filelist.files[:] + content.insert(0, '# file GENERATED by distutils, do NOT edit') + self.execute( + file_util.write_file, + (self.manifest, content), + f"writing manifest file '{self.manifest}'", + ) + + def _manifest_is_not_generated(self): + # check for special comment used in 3.1.3 and higher + if not os.path.isfile(self.manifest): + return False + + with open(self.manifest, encoding='utf-8') as fp: + first_line = next(fp) + return first_line != '# file GENERATED by distutils, do NOT edit\n' + + def read_manifest(self) -> None: + """Read the manifest file (named by 'self.manifest') and use it to + fill in 'self.filelist', the list of files to include in the source + distribution. + """ + log.info("reading manifest file '%s'", self.manifest) + with open(self.manifest, encoding='utf-8') as lines: + self.filelist.extend( + # ignore comments and blank lines + filter(None, filterfalse(is_comment, map(str.strip, lines))) + ) + + def make_release_tree(self, base_dir, files) -> None: + """Create the directory tree that will become the source + distribution archive. All directories implied by the filenames in + 'files' are created under 'base_dir', and then we hard link or copy + (if hard linking is unavailable) those files into place. + Essentially, this duplicates the developer's source tree, but in a + directory named after the distribution, containing only the files + to be distributed. + """ + # Create all the directories under 'base_dir' necessary to + # put 'files' there; the 'mkpath()' is just so we don't die + # if the manifest happens to be empty. + self.mkpath(base_dir) + dir_util.create_tree(base_dir, files) + + # And walk over the list of files, either making a hard link (if + # os.link exists) to each one that doesn't already exist in its + # corresponding location under 'base_dir', or copying each file + # that's out-of-date in 'base_dir'. (Usually, all files will be + # out-of-date, because by default we blow away 'base_dir' when + # we're done making the distribution archives.) + + if hasattr(os, 'link'): # can make hard links on this system + link = 'hard' + msg = f"making hard links in {base_dir}..." + else: # nope, have to copy + link = None + msg = f"copying files to {base_dir}..." + + if not files: + log.warning("no files to distribute -- empty manifest?") + else: + log.info(msg) + for file in files: + if not os.path.isfile(file): + log.warning("'%s' not a regular file -- skipping", file) + else: + dest = os.path.join(base_dir, file) + self.copy_file(file, dest, link=link) + + self.distribution.metadata.write_pkg_info(base_dir) + + def make_distribution(self) -> None: + """Create the source distribution(s). First, we create the release + tree with 'make_release_tree()'; then, we create all required + archive files (according to 'self.formats') from the release tree. + Finally, we clean up by blowing away the release tree (unless + 'self.keep_temp' is true). The list of archive files created is + stored so it can be retrieved later by 'get_archive_files()'. + """ + # Don't warn about missing meta-data here -- should be (and is!) + # done elsewhere. + base_dir = self.distribution.get_fullname() + base_name = os.path.join(self.dist_dir, base_dir) + + self.make_release_tree(base_dir, self.filelist.files) + archive_files = [] # remember names of files we create + # tar archive must be created last to avoid overwrite and remove + if 'tar' in self.formats: + self.formats.append(self.formats.pop(self.formats.index('tar'))) + + for fmt in self.formats: + file = self.make_archive( + base_name, fmt, base_dir=base_dir, owner=self.owner, group=self.group + ) + archive_files.append(file) + self.distribution.dist_files.append(('sdist', '', file)) + + self.archive_files = archive_files + + if not self.keep_temp: + dir_util.remove_tree(base_dir) + + def get_archive_files(self): + """Return the list of archive files created when the command + was run, or None if the command hasn't run yet. + """ + return self.archive_files + + +def is_comment(line: str) -> bool: + return line.startswith('#') diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/__init__.py b/lib/python3.11/site-packages/setuptools/_distutils/compat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2c43729b09ea5e5bf9d8c0708e63c69b5114a279 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compat/__init__.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from collections.abc import Iterable +from typing import TypeVar + +_IterableT = TypeVar("_IterableT", bound="Iterable[str]") + + +def consolidate_linker_args(args: _IterableT) -> _IterableT | str: + """ + Ensure the return value is a string for backward compatibility. + + Retain until at least 2025-04-31. See pypa/distutils#246 + """ + + if not all(arg.startswith('-Wl,') for arg in args): + return args + return '-Wl,' + ','.join(arg.removeprefix('-Wl,') for arg in args) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c484ee3e6964b47ac93e368a644a8d1d3ed0a726 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/numpy.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/numpy.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5261c4dc321af182ef4d2072acbb6b791bfc22de Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/numpy.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/py39.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/py39.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..79e60918c1dd44305c83695c3c7dc26e8f82297b Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compat/__pycache__/py39.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/numpy.py b/lib/python3.11/site-packages/setuptools/_distutils/compat/numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..73eca7acb103b7c7ca8e7a9f7f2d772a64aa344b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compat/numpy.py @@ -0,0 +1,2 @@ +# required for older numpy versions on Pythons prior to 3.12; see pypa/setuptools#4876 +from ..compilers.C.base import _default_compilers, compiler_class # noqa: F401 diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compat/py39.py b/lib/python3.11/site-packages/setuptools/_distutils/compat/py39.py new file mode 100644 index 0000000000000000000000000000000000000000..1b436d76586cbcf38164548f3fc849ce1a3ff600 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compat/py39.py @@ -0,0 +1,66 @@ +import functools +import itertools +import platform +import sys + + +def add_ext_suffix_39(vars): + """ + Ensure vars contains 'EXT_SUFFIX'. pypa/distutils#130 + """ + import _imp + + ext_suffix = _imp.extension_suffixes()[0] + vars.update( + EXT_SUFFIX=ext_suffix, + # sysconfig sets SO to match EXT_SUFFIX, so maintain + # that expectation. + # https://github.com/python/cpython/blob/785cc6770588de087d09e89a69110af2542be208/Lib/sysconfig.py#L671-L673 + SO=ext_suffix, + ) + + +needs_ext_suffix = sys.version_info < (3, 10) and platform.system() == 'Windows' +add_ext_suffix = add_ext_suffix_39 if needs_ext_suffix else lambda vars: None + + +# from more_itertools +class UnequalIterablesError(ValueError): + def __init__(self, details=None): + msg = 'Iterables have different lengths' + if details is not None: + msg += (': index 0 has length {}; index {} has length {}').format(*details) + + super().__init__(msg) + + +# from more_itertools +def _zip_equal_generator(iterables): + _marker = object() + for combo in itertools.zip_longest(*iterables, fillvalue=_marker): + for val in combo: + if val is _marker: + raise UnequalIterablesError() + yield combo + + +# from more_itertools +def _zip_equal(*iterables): + # Check whether the iterables are all the same size. + try: + first_size = len(iterables[0]) + for i, it in enumerate(iterables[1:], 1): + size = len(it) + if size != first_size: + raise UnequalIterablesError(details=(first_size, i, size)) + # All sizes are equal, we can use the built-in zip. + return zip(*iterables) + # If any one of the iterables didn't have a length, start reading + # them until one runs out. + except TypeError: + return _zip_equal_generator(iterables) + + +zip_strict = ( + _zip_equal if sys.version_info < (3, 10) else functools.partial(zip, strict=True) +) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/base.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/base.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..49935ee963b8dbead617efce62b887547a37633c Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/base.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/cygwin.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/cygwin.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..12a8c2efc1d279be0903c814e6901e6d516b3e21 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/cygwin.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/errors.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/errors.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..63444f370af37a54a9d918f92f0e5666d58b521f Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/errors.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/msvc.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/msvc.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f5a5d4d0f51e45fd09fe40c1267443b8d3346748 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/msvc.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/unix.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/unix.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d7c4c0f8ceea52d59e55a299b8bec9c89e558229 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/unix.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/zos.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/zos.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29146346d5693f11b9dded9b455c5c5b26a6454c Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/__pycache__/zos.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/base.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/base.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6b5458ef2b1d0c936cbfd9aaea19f5e41bbea9 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/base.py @@ -0,0 +1,1386 @@ +"""distutils.ccompiler + +Contains Compiler, an abstract base class that defines the interface +for the Distutils compiler abstraction model.""" + +from __future__ import annotations + +import os +import pathlib +import re +import sys +import warnings +from collections.abc import Callable, Iterable, MutableSequence, Sequence +from typing import ( + TYPE_CHECKING, + ClassVar, + Literal, + TypeVar, + Union, + overload, +) + +from more_itertools import always_iterable + +from ..._log import log +from ..._modified import newer_group +from ...dir_util import mkpath +from ...errors import ( + DistutilsModuleError, + DistutilsPlatformError, +) +from ...file_util import move_file +from ...spawn import spawn +from ...util import execute, is_mingw, split_quoted +from .errors import ( + CompileError, + LinkError, + UnknownFileType, +) + +if TYPE_CHECKING: + from typing_extensions import TypeAlias, TypeVarTuple, Unpack + + _Ts = TypeVarTuple("_Ts") + +_Macro: TypeAlias = Union[tuple[str], tuple[str, Union[str, None]]] +_StrPathT = TypeVar("_StrPathT", bound="str | os.PathLike[str]") +_BytesPathT = TypeVar("_BytesPathT", bound="bytes | os.PathLike[bytes]") + + +class Compiler: + """Abstract base class to define the interface that must be implemented + by real compiler classes. Also has some utility methods used by + several compiler classes. + + The basic idea behind a compiler abstraction class is that each + instance can be used for all the compile/link steps in building a + single project. Thus, attributes common to all of those compile and + link steps -- include directories, macros to define, libraries to link + against, etc. -- are attributes of the compiler instance. To allow for + variability in how individual files are treated, most of those + attributes may be varied on a per-compilation or per-link basis. + """ + + # 'compiler_type' is a class attribute that identifies this class. It + # keeps code that wants to know what kind of compiler it's dealing with + # from having to import all possible compiler classes just to do an + # 'isinstance'. In concrete CCompiler subclasses, 'compiler_type' + # should really, really be one of the keys of the 'compiler_class' + # dictionary (see below -- used by the 'new_compiler()' factory + # function) -- authors of new compiler interface classes are + # responsible for updating 'compiler_class'! + compiler_type: ClassVar[str] = None # type: ignore[assignment] + + # XXX things not handled by this compiler abstraction model: + # * client can't provide additional options for a compiler, + # e.g. warning, optimization, debugging flags. Perhaps this + # should be the domain of concrete compiler abstraction classes + # (UnixCCompiler, MSVCCompiler, etc.) -- or perhaps the base + # class should have methods for the common ones. + # * can't completely override the include or library searchg + # path, ie. no "cc -I -Idir1 -Idir2" or "cc -L -Ldir1 -Ldir2". + # I'm not sure how widely supported this is even by Unix + # compilers, much less on other platforms. And I'm even less + # sure how useful it is; maybe for cross-compiling, but + # support for that is a ways off. (And anyways, cross + # compilers probably have a dedicated binary with the + # right paths compiled in. I hope.) + # * can't do really freaky things with the library list/library + # dirs, e.g. "-Ldir1 -lfoo -Ldir2 -lfoo" to link against + # different versions of libfoo.a in different locations. I + # think this is useless without the ability to null out the + # library search path anyways. + + executables: ClassVar[dict] + + # Subclasses that rely on the standard filename generation methods + # implemented below should override these; see the comment near + # those methods ('object_filenames()' et. al.) for details: + src_extensions: ClassVar[list[str] | None] = None + obj_extension: ClassVar[str | None] = None + static_lib_extension: ClassVar[str | None] = None + shared_lib_extension: ClassVar[str | None] = None + static_lib_format: ClassVar[str | None] = None # format string + shared_lib_format: ClassVar[str | None] = None # prob. same as static_lib_format + exe_extension: ClassVar[str | None] = None + + # Default language settings. language_map is used to detect a source + # file or Extension target language, checking source filenames. + # language_order is used to detect the language precedence, when deciding + # what language to use when mixing source types. For example, if some + # extension has two files with ".c" extension, and one with ".cpp", it + # is still linked as c++. + language_map: ClassVar[dict[str, str]] = { + ".c": "c", + ".cc": "c++", + ".cpp": "c++", + ".cxx": "c++", + ".m": "objc", + } + language_order: ClassVar[list[str]] = ["c++", "objc", "c"] + + include_dirs: list[str] = [] + """ + include dirs specific to this compiler class + """ + + library_dirs: list[str] = [] + """ + library dirs specific to this compiler class + """ + + def __init__(self, verbose: bool = False, force: bool = False) -> None: + self.force = force + self.verbose = verbose + + # 'output_dir': a common output directory for object, library, + # shared object, and shared library files + self.output_dir: str | None = None + + # 'macros': a list of macro definitions (or undefinitions). A + # macro definition is a 2-tuple (name, value), where the value is + # either a string or None (no explicit value). A macro + # undefinition is a 1-tuple (name,). + self.macros: list[_Macro] = [] + + # 'include_dirs': a list of directories to search for include files + self.include_dirs = [] + + # 'libraries': a list of libraries to include in any link + # (library names, not filenames: eg. "foo" not "libfoo.a") + self.libraries: list[str] = [] + + # 'library_dirs': a list of directories to search for libraries + self.library_dirs = [] + + # 'runtime_library_dirs': a list of directories to search for + # shared libraries/objects at runtime + self.runtime_library_dirs: list[str] = [] + + # 'objects': a list of object files (or similar, such as explicitly + # named library files) to include on any link + self.objects: list[str] = [] + + for key in self.executables.keys(): + self.set_executable(key, self.executables[key]) + + def set_executables(self, **kwargs: str) -> None: + """Define the executables (and options for them) that will be run + to perform the various stages of compilation. The exact set of + executables that may be specified here depends on the compiler + class (via the 'executables' class attribute), but most will have: + compiler the C/C++ compiler + linker_so linker used to create shared objects and libraries + linker_exe linker used to create binary executables + archiver static library creator + + On platforms with a command-line (Unix, DOS/Windows), each of these + is a string that will be split into executable name and (optional) + list of arguments. (Splitting the string is done similarly to how + Unix shells operate: words are delimited by spaces, but quotes and + backslashes can override this. See + 'distutils.util.split_quoted()'.) + """ + + # Note that some CCompiler implementation classes will define class + # attributes 'cpp', 'cc', etc. with hard-coded executable names; + # this is appropriate when a compiler class is for exactly one + # compiler/OS combination (eg. MSVCCompiler). Other compiler + # classes (UnixCCompiler, in particular) are driven by information + # discovered at run-time, since there are many different ways to do + # basically the same things with Unix C compilers. + + for key in kwargs: + if key not in self.executables: + raise ValueError( + f"unknown executable '{key}' for class {self.__class__.__name__}" + ) + self.set_executable(key, kwargs[key]) + + def set_executable(self, key, value): + if isinstance(value, str): + setattr(self, key, split_quoted(value)) + else: + setattr(self, key, value) + + def _find_macro(self, name): + i = 0 + for defn in self.macros: + if defn[0] == name: + return i + i += 1 + return None + + def _check_macro_definitions(self, definitions): + """Ensure that every element of 'definitions' is valid.""" + for defn in definitions: + self._check_macro_definition(*defn) + + def _check_macro_definition(self, defn): + """ + Raise a TypeError if defn is not valid. + + A valid definition is either a (name, value) 2-tuple or a (name,) tuple. + """ + if not isinstance(defn, tuple) or not self._is_valid_macro(*defn): + raise TypeError( + f"invalid macro definition '{defn}': " + "must be tuple (string,), (string, string), or (string, None)" + ) + + @staticmethod + def _is_valid_macro(name, value=None): + """ + A valid macro is a ``name : str`` and a ``value : str | None``. + + >>> Compiler._is_valid_macro('foo', None) + True + """ + return isinstance(name, str) and isinstance(value, (str, type(None))) + + # -- Bookkeeping methods ------------------------------------------- + + def define_macro(self, name: str, value: str | None = None) -> None: + """Define a preprocessor macro for all compilations driven by this + compiler object. The optional parameter 'value' should be a + string; if it is not supplied, then the macro will be defined + without an explicit value and the exact outcome depends on the + compiler used (XXX true? does ANSI say anything about this?) + """ + # Delete from the list of macro definitions/undefinitions if + # already there (so that this one will take precedence). + i = self._find_macro(name) + if i is not None: + del self.macros[i] + + self.macros.append((name, value)) + + def undefine_macro(self, name: str) -> None: + """Undefine a preprocessor macro for all compilations driven by + this compiler object. If the same macro is defined by + 'define_macro()' and undefined by 'undefine_macro()' the last call + takes precedence (including multiple redefinitions or + undefinitions). If the macro is redefined/undefined on a + per-compilation basis (ie. in the call to 'compile()'), then that + takes precedence. + """ + # Delete from the list of macro definitions/undefinitions if + # already there (so that this one will take precedence). + i = self._find_macro(name) + if i is not None: + del self.macros[i] + + undefn = (name,) + self.macros.append(undefn) + + def add_include_dir(self, dir: str) -> None: + """Add 'dir' to the list of directories that will be searched for + header files. The compiler is instructed to search directories in + the order in which they are supplied by successive calls to + 'add_include_dir()'. + """ + self.include_dirs.append(dir) + + def set_include_dirs(self, dirs: list[str]) -> None: + """Set the list of directories that will be searched to 'dirs' (a + list of strings). Overrides any preceding calls to + 'add_include_dir()'; subsequence calls to 'add_include_dir()' add + to the list passed to 'set_include_dirs()'. This does not affect + any list of standard include directories that the compiler may + search by default. + """ + self.include_dirs = dirs[:] + + def add_library(self, libname: str) -> None: + """Add 'libname' to the list of libraries that will be included in + all links driven by this compiler object. Note that 'libname' + should *not* be the name of a file containing a library, but the + name of the library itself: the actual filename will be inferred by + the linker, the compiler, or the compiler class (depending on the + platform). + + The linker will be instructed to link against libraries in the + order they were supplied to 'add_library()' and/or + 'set_libraries()'. It is perfectly valid to duplicate library + names; the linker will be instructed to link against libraries as + many times as they are mentioned. + """ + self.libraries.append(libname) + + def set_libraries(self, libnames: list[str]) -> None: + """Set the list of libraries to be included in all links driven by + this compiler object to 'libnames' (a list of strings). This does + not affect any standard system libraries that the linker may + include by default. + """ + self.libraries = libnames[:] + + def add_library_dir(self, dir: str) -> None: + """Add 'dir' to the list of directories that will be searched for + libraries specified to 'add_library()' and 'set_libraries()'. The + linker will be instructed to search for libraries in the order they + are supplied to 'add_library_dir()' and/or 'set_library_dirs()'. + """ + self.library_dirs.append(dir) + + def set_library_dirs(self, dirs: list[str]) -> None: + """Set the list of library search directories to 'dirs' (a list of + strings). This does not affect any standard library search path + that the linker may search by default. + """ + self.library_dirs = dirs[:] + + def add_runtime_library_dir(self, dir: str) -> None: + """Add 'dir' to the list of directories that will be searched for + shared libraries at runtime. + """ + self.runtime_library_dirs.append(dir) + + def set_runtime_library_dirs(self, dirs: list[str]) -> None: + """Set the list of directories to search for shared libraries at + runtime to 'dirs' (a list of strings). This does not affect any + standard search path that the runtime linker may search by + default. + """ + self.runtime_library_dirs = dirs[:] + + def add_link_object(self, object: str) -> None: + """Add 'object' to the list of object files (or analogues, such as + explicitly named library files or the output of "resource + compilers") to be included in every link driven by this compiler + object. + """ + self.objects.append(object) + + def set_link_objects(self, objects: list[str]) -> None: + """Set the list of object files (or analogues) to be included in + every link to 'objects'. This does not affect any standard object + files that the linker may include by default (such as system + libraries). + """ + self.objects = objects[:] + + # -- Private utility methods -------------------------------------- + # (here for the convenience of subclasses) + + # Helper method to prep compiler in subclass compile() methods + + def _setup_compile( + self, + outdir: str | None, + macros: list[_Macro] | None, + incdirs: list[str] | tuple[str, ...] | None, + sources, + depends, + extra, + ): + """Process arguments and decide which source files to compile.""" + outdir, macros, incdirs = self._fix_compile_args(outdir, macros, incdirs) + + if extra is None: + extra = [] + + # Get the list of expected output (object) files + objects = self.object_filenames(sources, strip_dir=False, output_dir=outdir) + assert len(objects) == len(sources) + + pp_opts = gen_preprocess_options(macros, incdirs) + + build = {} + for i in range(len(sources)): + src = sources[i] + obj = objects[i] + ext = os.path.splitext(src)[1] + self.mkpath(os.path.dirname(obj)) + build[obj] = (src, ext) + + return macros, objects, extra, pp_opts, build + + def _get_cc_args(self, pp_opts, debug, before): + # works for unixccompiler, cygwinccompiler + cc_args = pp_opts + ['-c'] + if debug: + cc_args[:0] = ['-g'] + if before: + cc_args[:0] = before + return cc_args + + def _fix_compile_args( + self, + output_dir: str | None, + macros: list[_Macro] | None, + include_dirs: list[str] | tuple[str, ...] | None, + ) -> tuple[str, list[_Macro], list[str]]: + """Typecheck and fix-up some of the arguments to the 'compile()' + method, and return fixed-up values. Specifically: if 'output_dir' + is None, replaces it with 'self.output_dir'; ensures that 'macros' + is a list, and augments it with 'self.macros'; ensures that + 'include_dirs' is a list, and augments it with 'self.include_dirs'. + Guarantees that the returned values are of the correct type, + i.e. for 'output_dir' either string or None, and for 'macros' and + 'include_dirs' either list or None. + """ + if output_dir is None: + output_dir = self.output_dir + elif not isinstance(output_dir, str): + raise TypeError("'output_dir' must be a string or None") + + if macros is None: + macros = list(self.macros) + elif isinstance(macros, list): + macros = macros + (self.macros or []) + else: + raise TypeError("'macros' (if supplied) must be a list of tuples") + + if include_dirs is None: + include_dirs = list(self.include_dirs) + elif isinstance(include_dirs, (list, tuple)): + include_dirs = list(include_dirs) + (self.include_dirs or []) + else: + raise TypeError("'include_dirs' (if supplied) must be a list of strings") + + # add include dirs for class + include_dirs += self.__class__.include_dirs + + return output_dir, macros, include_dirs + + def _prep_compile(self, sources, output_dir, depends=None): + """Decide which source files must be recompiled. + + Determine the list of object files corresponding to 'sources', + and figure out which ones really need to be recompiled. + Return a list of all object files and a dictionary telling + which source files can be skipped. + """ + # Get the list of expected output (object) files + objects = self.object_filenames(sources, output_dir=output_dir) + assert len(objects) == len(sources) + + # Return an empty dict for the "which source files can be skipped" + # return value to preserve API compatibility. + return objects, {} + + def _fix_object_args( + self, objects: list[str] | tuple[str, ...], output_dir: str | None + ) -> tuple[list[str], str]: + """Typecheck and fix up some arguments supplied to various methods. + Specifically: ensure that 'objects' is a list; if output_dir is + None, replace with self.output_dir. Return fixed versions of + 'objects' and 'output_dir'. + """ + if not isinstance(objects, (list, tuple)): + raise TypeError("'objects' must be a list or tuple of strings") + objects = list(objects) + + if output_dir is None: + output_dir = self.output_dir + elif not isinstance(output_dir, str): + raise TypeError("'output_dir' must be a string or None") + + return (objects, output_dir) + + def _fix_lib_args( + self, + libraries: list[str] | tuple[str, ...] | None, + library_dirs: list[str] | tuple[str, ...] | None, + runtime_library_dirs: list[str] | tuple[str, ...] | None, + ) -> tuple[list[str], list[str], list[str]]: + """Typecheck and fix up some of the arguments supplied to the + 'link_*' methods. Specifically: ensure that all arguments are + lists, and augment them with their permanent versions + (eg. 'self.libraries' augments 'libraries'). Return a tuple with + fixed versions of all arguments. + """ + if libraries is None: + libraries = list(self.libraries) + elif isinstance(libraries, (list, tuple)): + libraries = list(libraries) + (self.libraries or []) + else: + raise TypeError("'libraries' (if supplied) must be a list of strings") + + if library_dirs is None: + library_dirs = list(self.library_dirs) + elif isinstance(library_dirs, (list, tuple)): + library_dirs = list(library_dirs) + (self.library_dirs or []) + else: + raise TypeError("'library_dirs' (if supplied) must be a list of strings") + + # add library dirs for class + library_dirs += self.__class__.library_dirs + + if runtime_library_dirs is None: + runtime_library_dirs = list(self.runtime_library_dirs) + elif isinstance(runtime_library_dirs, (list, tuple)): + runtime_library_dirs = list(runtime_library_dirs) + ( + self.runtime_library_dirs or [] + ) + else: + raise TypeError( + "'runtime_library_dirs' (if supplied) must be a list of strings" + ) + + return (libraries, library_dirs, runtime_library_dirs) + + def _need_link(self, objects, output_file): + """Return true if we need to relink the files listed in 'objects' + to recreate 'output_file'. + """ + if self.force: + return True + newer = newer_group(objects, output_file) + return newer + + def detect_language(self, sources: str | list[str]) -> str | None: + """Detect the language of a given file, or list of files. Uses + language_map, and language_order to do the job. + """ + if not isinstance(sources, list): + sources = [sources] + lang = None + index = len(self.language_order) + for source in sources: + base, ext = os.path.splitext(source) + extlang = self.language_map.get(ext) + try: + extindex = self.language_order.index(extlang) + if extindex < index: + lang = extlang + index = extindex + except ValueError: + pass + return lang + + # -- Worker methods ------------------------------------------------ + # (must be implemented by subclasses) + + def preprocess( + self, + source: str | os.PathLike[str], + output_file: str | os.PathLike[str] | None = None, + macros: list[_Macro] | None = None, + include_dirs: list[str] | tuple[str, ...] | None = None, + extra_preargs: list[str] | None = None, + extra_postargs: Iterable[str] | None = None, + ): + """Preprocess a single C/C++ source file, named in 'source'. + Output will be written to file named 'output_file', or stdout if + 'output_file' not supplied. 'macros' is a list of macro + definitions as for 'compile()', which will augment the macros set + with 'define_macro()' and 'undefine_macro()'. 'include_dirs' is a + list of directory names that will be added to the default list. + + Raises PreprocessError on failure. + """ + pass + + def compile( + self, + sources: Sequence[str | os.PathLike[str]], + output_dir: str | None = None, + macros: list[_Macro] | None = None, + include_dirs: list[str] | tuple[str, ...] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: list[str] | None = None, + depends: list[str] | tuple[str, ...] | None = None, + ) -> list[str]: + """Compile one or more source files. + + 'sources' must be a list of filenames, most likely C/C++ + files, but in reality anything that can be handled by a + particular compiler and compiler class (eg. MSVCCompiler can + handle resource files in 'sources'). Return a list of object + filenames, one per source filename in 'sources'. Depending on + the implementation, not all source files will necessarily be + compiled, but all corresponding object filenames will be + returned. + + If 'output_dir' is given, object files will be put under it, while + retaining their original path component. That is, "foo/bar.c" + normally compiles to "foo/bar.o" (for a Unix implementation); if + 'output_dir' is "build", then it would compile to + "build/foo/bar.o". + + 'macros', if given, must be a list of macro definitions. A macro + definition is either a (name, value) 2-tuple or a (name,) 1-tuple. + The former defines a macro; if the value is None, the macro is + defined without an explicit value. The 1-tuple case undefines a + macro. Later definitions/redefinitions/ undefinitions take + precedence. + + 'include_dirs', if given, must be a list of strings, the + directories to add to the default include file search path for this + compilation only. + + 'debug' is a boolean; if true, the compiler will be instructed to + output debug symbols in (or alongside) the object file(s). + + 'extra_preargs' and 'extra_postargs' are implementation- dependent. + On platforms that have the notion of a command-line (e.g. Unix, + DOS/Windows), they are most likely lists of strings: extra + command-line arguments to prepend/append to the compiler command + line. On other platforms, consult the implementation class + documentation. In any event, they are intended as an escape hatch + for those occasions when the abstract compiler framework doesn't + cut the mustard. + + 'depends', if given, is a list of filenames that all targets + depend on. If a source file is older than any file in + depends, then the source file will be recompiled. This + supports dependency tracking, but only at a coarse + granularity. + + Raises CompileError on failure. + """ + # A concrete compiler class can either override this method + # entirely or implement _compile(). + macros, objects, extra_postargs, pp_opts, build = self._setup_compile( + output_dir, macros, include_dirs, sources, depends, extra_postargs + ) + cc_args = self._get_cc_args(pp_opts, debug, extra_preargs) + + for obj in objects: + try: + src, ext = build[obj] + except KeyError: + continue + self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts) + + # Return *all* object filenames, not just the ones we just built. + return objects + + def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts): + """Compile 'src' to product 'obj'.""" + # A concrete compiler class that does not override compile() + # should implement _compile(). + pass + + def create_static_lib( + self, + objects: list[str] | tuple[str, ...], + output_libname: str, + output_dir: str | None = None, + debug: bool = False, + target_lang: str | None = None, + ) -> None: + """Link a bunch of stuff together to create a static library file. + The "bunch of stuff" consists of the list of object files supplied + as 'objects', the extra object files supplied to + 'add_link_object()' and/or 'set_link_objects()', the libraries + supplied to 'add_library()' and/or 'set_libraries()', and the + libraries supplied as 'libraries' (if any). + + 'output_libname' should be a library name, not a filename; the + filename will be inferred from the library name. 'output_dir' is + the directory where the library file will be put. + + 'debug' is a boolean; if true, debugging information will be + included in the library (note that on most platforms, it is the + compile step where this matters: the 'debug' flag is included here + just for consistency). + + 'target_lang' is the target language for which the given objects + are being compiled. This allows specific linkage time treatment of + certain languages. + + Raises LibError on failure. + """ + pass + + # values for target_desc parameter in link() + SHARED_OBJECT = "shared_object" + SHARED_LIBRARY = "shared_library" + EXECUTABLE = "executable" + + def link( + self, + target_desc: str, + objects: list[str] | tuple[str, ...], + output_filename: str, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + export_symbols: Iterable[str] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: list[str] | None = None, + build_temp: str | os.PathLike[str] | None = None, + target_lang: str | None = None, + ): + """Link a bunch of stuff together to create an executable or + shared library file. + + The "bunch of stuff" consists of the list of object files supplied + as 'objects'. 'output_filename' should be a filename. If + 'output_dir' is supplied, 'output_filename' is relative to it + (i.e. 'output_filename' can provide directory components if + needed). + + 'libraries' is a list of libraries to link against. These are + library names, not filenames, since they're translated into + filenames in a platform-specific way (eg. "foo" becomes "libfoo.a" + on Unix and "foo.lib" on DOS/Windows). However, they can include a + directory component, which means the linker will look in that + specific directory rather than searching all the normal locations. + + 'library_dirs', if supplied, should be a list of directories to + search for libraries that were specified as bare library names + (ie. no directory component). These are on top of the system + default and those supplied to 'add_library_dir()' and/or + 'set_library_dirs()'. 'runtime_library_dirs' is a list of + directories that will be embedded into the shared library and used + to search for other shared libraries that *it* depends on at + run-time. (This may only be relevant on Unix.) + + 'export_symbols' is a list of symbols that the shared library will + export. (This appears to be relevant only on Windows.) + + 'debug' is as for 'compile()' and 'create_static_lib()', with the + slight distinction that it actually matters on most platforms (as + opposed to 'create_static_lib()', which includes a 'debug' flag + mostly for form's sake). + + 'extra_preargs' and 'extra_postargs' are as for 'compile()' (except + of course that they supply command-line arguments for the + particular linker being used). + + 'target_lang' is the target language for which the given objects + are being compiled. This allows specific linkage time treatment of + certain languages. + + Raises LinkError on failure. + """ + raise NotImplementedError + + # Old 'link_*()' methods, rewritten to use the new 'link()' method. + + def link_shared_lib( + self, + objects: list[str] | tuple[str, ...], + output_libname: str, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + export_symbols: Iterable[str] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: list[str] | None = None, + build_temp: str | os.PathLike[str] | None = None, + target_lang: str | None = None, + ): + self.link( + Compiler.SHARED_LIBRARY, + objects, + self.library_filename(output_libname, lib_type='shared'), + output_dir, + libraries, + library_dirs, + runtime_library_dirs, + export_symbols, + debug, + extra_preargs, + extra_postargs, + build_temp, + target_lang, + ) + + def link_shared_object( + self, + objects: list[str] | tuple[str, ...], + output_filename: str, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + export_symbols: Iterable[str] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: list[str] | None = None, + build_temp: str | os.PathLike[str] | None = None, + target_lang: str | None = None, + ): + self.link( + Compiler.SHARED_OBJECT, + objects, + output_filename, + output_dir, + libraries, + library_dirs, + runtime_library_dirs, + export_symbols, + debug, + extra_preargs, + extra_postargs, + build_temp, + target_lang, + ) + + def link_executable( + self, + objects: list[str] | tuple[str, ...], + output_progname: str, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: list[str] | None = None, + target_lang: str | None = None, + ): + self.link( + Compiler.EXECUTABLE, + objects, + self.executable_filename(output_progname), + output_dir, + libraries, + library_dirs, + runtime_library_dirs, + None, + debug, + extra_preargs, + extra_postargs, + None, + target_lang, + ) + + # -- Miscellaneous methods ----------------------------------------- + # These are all used by the 'gen_lib_options() function; there is + # no appropriate default implementation so subclasses should + # implement all of these. + + def library_dir_option(self, dir: str) -> str: + """Return the compiler option to add 'dir' to the list of + directories searched for libraries. + """ + raise NotImplementedError + + def runtime_library_dir_option(self, dir: str) -> str: + """Return the compiler option to add 'dir' to the list of + directories searched for runtime libraries. + """ + raise NotImplementedError + + def library_option(self, lib: str) -> str: + """Return the compiler option to add 'lib' to the list of libraries + linked into the shared library or executable. + """ + raise NotImplementedError + + def has_function( # noqa: C901 + self, + funcname: str, + includes: Iterable[str] | None = None, + include_dirs: list[str] | tuple[str, ...] | None = None, + libraries: list[str] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + ) -> bool: + """Return a boolean indicating whether funcname is provided as + a symbol on the current platform. The optional arguments can + be used to augment the compilation environment. + + The libraries argument is a list of flags to be passed to the + linker to make additional symbol definitions available for + linking. + + The includes and include_dirs arguments are deprecated. + Usually, supplying include files with function declarations + will cause function detection to fail even in cases where the + symbol is available for linking. + + """ + # this can't be included at module scope because it tries to + # import math which might not be available at that point - maybe + # the necessary logic should just be inlined? + import tempfile + + if includes is None: + includes = [] + else: + warnings.warn("includes is deprecated", DeprecationWarning) + if include_dirs is None: + include_dirs = [] + else: + warnings.warn("include_dirs is deprecated", DeprecationWarning) + if libraries is None: + libraries = [] + if library_dirs is None: + library_dirs = [] + fd, fname = tempfile.mkstemp(".c", funcname, text=True) + with os.fdopen(fd, "w", encoding='utf-8') as f: + for incl in includes: + f.write(f"""#include "{incl}"\n""") + if not includes: + # Use "char func(void);" as the prototype to follow + # what autoconf does. This prototype does not match + # any well-known function the compiler might recognize + # as a builtin, so this ends up as a true link test. + # Without a fake prototype, the test would need to + # know the exact argument types, and the has_function + # interface does not provide that level of information. + f.write( + f"""\ +#ifdef __cplusplus +extern "C" +#endif +char {funcname}(void); +""" + ) + f.write( + f"""\ +int main (int argc, char **argv) {{ + {funcname}(); + return 0; +}} +""" + ) + + try: + objects = self.compile([fname], include_dirs=include_dirs) + except CompileError: + return False + finally: + os.remove(fname) + + try: + self.link_executable( + objects, "a.out", libraries=libraries, library_dirs=library_dirs + ) + except (LinkError, TypeError): + return False + else: + os.remove( + self.executable_filename("a.out", output_dir=self.output_dir or '') + ) + finally: + for fn in objects: + os.remove(fn) + return True + + def find_library_file( + self, dirs: Iterable[str], lib: str, debug: bool = False + ) -> str | None: + """Search the specified list of directories for a static or shared + library file 'lib' and return the full path to that file. If + 'debug' true, look for a debugging version (if that makes sense on + the current platform). Return None if 'lib' wasn't found in any of + the specified directories. + """ + raise NotImplementedError + + # -- Filename generation methods ----------------------------------- + + # The default implementation of the filename generating methods are + # prejudiced towards the Unix/DOS/Windows view of the world: + # * object files are named by replacing the source file extension + # (eg. .c/.cpp -> .o/.obj) + # * library files (shared or static) are named by plugging the + # library name and extension into a format string, eg. + # "lib%s.%s" % (lib_name, ".a") for Unix static libraries + # * executables are named by appending an extension (possibly + # empty) to the program name: eg. progname + ".exe" for + # Windows + # + # To reduce redundant code, these methods expect to find + # several attributes in the current object (presumably defined + # as class attributes): + # * src_extensions - + # list of C/C++ source file extensions, eg. ['.c', '.cpp'] + # * obj_extension - + # object file extension, eg. '.o' or '.obj' + # * static_lib_extension - + # extension for static library files, eg. '.a' or '.lib' + # * shared_lib_extension - + # extension for shared library/object files, eg. '.so', '.dll' + # * static_lib_format - + # format string for generating static library filenames, + # eg. 'lib%s.%s' or '%s.%s' + # * shared_lib_format + # format string for generating shared library filenames + # (probably same as static_lib_format, since the extension + # is one of the intended parameters to the format string) + # * exe_extension - + # extension for executable files, eg. '' or '.exe' + + def object_filenames( + self, + source_filenames: Iterable[str | os.PathLike[str]], + strip_dir: bool = False, + output_dir: str | os.PathLike[str] | None = '', + ) -> list[str]: + if output_dir is None: + output_dir = '' + return list( + self._make_out_path(output_dir, strip_dir, src_name) + for src_name in source_filenames + ) + + @property + def out_extensions(self): + return dict.fromkeys(self.src_extensions, self.obj_extension) + + def _make_out_path(self, output_dir, strip_dir, src_name): + return self._make_out_path_exts( + output_dir, strip_dir, src_name, self.out_extensions + ) + + @classmethod + def _make_out_path_exts(cls, output_dir, strip_dir, src_name, extensions): + r""" + >>> exts = {'.c': '.o'} + >>> Compiler._make_out_path_exts('.', False, '/foo/bar.c', exts).replace('\\', '/') + './foo/bar.o' + >>> Compiler._make_out_path_exts('.', True, '/foo/bar.c', exts).replace('\\', '/') + './bar.o' + """ + src = pathlib.PurePath(src_name) + # Ensure base is relative to honor output_dir (python/cpython#37775). + base = cls._make_relative(src) + try: + new_ext = extensions[src.suffix] + except LookupError: + raise UnknownFileType(f"unknown file type '{src.suffix}' (from '{src}')") + if strip_dir: + base = pathlib.PurePath(base.name) + return os.path.join(output_dir, base.with_suffix(new_ext)) + + @staticmethod + def _make_relative(base: pathlib.Path): + return base.relative_to(base.anchor) + + @overload + def shared_object_filename( + self, + basename: str, + strip_dir: Literal[False] = False, + output_dir: str | os.PathLike[str] = "", + ) -> str: ... + @overload + def shared_object_filename( + self, + basename: str | os.PathLike[str], + strip_dir: Literal[True], + output_dir: str | os.PathLike[str] = "", + ) -> str: ... + def shared_object_filename( + self, + basename: str | os.PathLike[str], + strip_dir: bool = False, + output_dir: str | os.PathLike[str] = '', + ) -> str: + assert output_dir is not None + if strip_dir: + basename = os.path.basename(basename) + return os.path.join(output_dir, basename + self.shared_lib_extension) + + @overload + def executable_filename( + self, + basename: str, + strip_dir: Literal[False] = False, + output_dir: str | os.PathLike[str] = "", + ) -> str: ... + @overload + def executable_filename( + self, + basename: str | os.PathLike[str], + strip_dir: Literal[True], + output_dir: str | os.PathLike[str] = "", + ) -> str: ... + def executable_filename( + self, + basename: str | os.PathLike[str], + strip_dir: bool = False, + output_dir: str | os.PathLike[str] = '', + ) -> str: + assert output_dir is not None + if strip_dir: + basename = os.path.basename(basename) + return os.path.join(output_dir, basename + (self.exe_extension or '')) + + def library_filename( + self, + libname: str, + lib_type: str = "static", + strip_dir: bool = False, + output_dir: str | os.PathLike[str] = "", # or 'shared' + ): + assert output_dir is not None + expected = '"static", "shared", "dylib", "xcode_stub"' + if lib_type not in eval(expected): + raise ValueError(f"'lib_type' must be {expected}") + fmt = getattr(self, lib_type + "_lib_format") + ext = getattr(self, lib_type + "_lib_extension") + + dir, base = os.path.split(libname) + filename = fmt % (base, ext) + if strip_dir: + dir = '' + + return os.path.join(output_dir, dir, filename) + + # -- Utility methods ----------------------------------------------- + + def announce(self, msg: object, level: int = 1) -> None: + log.debug(msg) + + def debug_print(self, msg: object) -> None: + from distutils.debug import DEBUG + + if DEBUG: + print(msg) + + def warn(self, msg: object) -> None: + sys.stderr.write(f"warning: {msg}\n") + + def execute( + self, + func: Callable[[Unpack[_Ts]], object], + args: tuple[Unpack[_Ts]], + msg: object = None, + level: int = 1, + ) -> None: + execute(func, args, msg) + + def spawn( + self, cmd: MutableSequence[bytes | str | os.PathLike[str]], **kwargs + ) -> None: + spawn(cmd, **kwargs) + + @overload + def move_file( + self, src: str | os.PathLike[str], dst: _StrPathT + ) -> _StrPathT | str: ... + @overload + def move_file( + self, src: bytes | os.PathLike[bytes], dst: _BytesPathT + ) -> _BytesPathT | bytes: ... + def move_file( + self, + src: str | os.PathLike[str] | bytes | os.PathLike[bytes], + dst: str | os.PathLike[str] | bytes | os.PathLike[bytes], + ) -> str | os.PathLike[str] | bytes | os.PathLike[bytes]: + return move_file(src, dst) + + def mkpath(self, name, mode=0o777): + mkpath(name, mode) + + +# Map a sys.platform/os.name ('posix', 'nt') to the default compiler +# type for that platform. Keys are interpreted as re match +# patterns. Order is important; platform mappings are preferred over +# OS names. +_default_compilers = ( + # Platform string mappings + # on a cygwin built python we can use gcc like an ordinary UNIXish + # compiler + ('cygwin.*', 'unix'), + ('zos', 'zos'), + # OS name mappings + ('posix', 'unix'), + ('nt', 'msvc'), +) + + +def get_default_compiler(osname: str | None = None, platform: str | None = None) -> str: + """Determine the default compiler to use for the given platform. + + osname should be one of the standard Python OS names (i.e. the + ones returned by os.name) and platform the common value + returned by sys.platform for the platform in question. + + The default values are os.name and sys.platform in case the + parameters are not given. + """ + if osname is None: + osname = os.name + if platform is None: + platform = sys.platform + # Mingw is a special case where sys.platform is 'win32' but we + # want to use the 'mingw32' compiler, so check it first + if is_mingw(): + return 'mingw32' + for pattern, compiler in _default_compilers: + if ( + re.match(pattern, platform) is not None + or re.match(pattern, osname) is not None + ): + return compiler + # Default to Unix compiler + return 'unix' + + +# Map compiler types to (module_name, class_name) pairs -- ie. where to +# find the code that implements an interface to this compiler. (The module +# is assumed to be in the 'distutils' package.) +compiler_class = { + 'unix': ('unixccompiler', 'UnixCCompiler', "standard UNIX-style compiler"), + 'msvc': ('_msvccompiler', 'MSVCCompiler', "Microsoft Visual C++"), + 'cygwin': ( + 'cygwinccompiler', + 'CygwinCCompiler', + "Cygwin port of GNU C Compiler for Win32", + ), + 'mingw32': ( + 'cygwinccompiler', + 'Mingw32CCompiler', + "Mingw32 port of GNU C Compiler for Win32", + ), + 'bcpp': ('bcppcompiler', 'BCPPCompiler', "Borland C++ Compiler"), + 'zos': ('zosccompiler', 'zOSCCompiler', 'IBM XL C/C++ Compilers'), +} + + +def show_compilers() -> None: + """Print list of available compilers (used by the "--help-compiler" + options to "build", "build_ext", "build_clib"). + """ + # XXX this "knows" that the compiler option it's describing is + # "--compiler", which just happens to be the case for the three + # commands that use it. + from distutils.fancy_getopt import FancyGetopt + + compilers = sorted( + ("compiler=" + compiler, None, compiler_class[compiler][2]) + for compiler in compiler_class.keys() + ) + pretty_printer = FancyGetopt(compilers) + pretty_printer.print_help("List of available compilers:") + + +def new_compiler( + plat: str | None = None, + compiler: str | None = None, + verbose: bool = False, + force: bool = False, +) -> Compiler: + """Generate an instance of some CCompiler subclass for the supplied + platform/compiler combination. 'plat' defaults to 'os.name' + (eg. 'posix', 'nt'), and 'compiler' defaults to the default compiler + for that platform. Currently only 'posix' and 'nt' are supported, and + the default compilers are "traditional Unix interface" (UnixCCompiler + class) and Visual C++ (MSVCCompiler class). Note that it's perfectly + possible to ask for a Unix compiler object under Windows, and a + Microsoft compiler object under Unix -- if you supply a value for + 'compiler', 'plat' is ignored. + """ + if plat is None: + plat = os.name + + try: + if compiler is None: + compiler = get_default_compiler(plat) + + (module_name, class_name, long_description) = compiler_class[compiler] + except KeyError: + msg = f"don't know how to compile C/C++ code on platform '{plat}'" + if compiler is not None: + msg = msg + f" with '{compiler}' compiler" + raise DistutilsPlatformError(msg) + + try: + module_name = "distutils." + module_name + __import__(module_name) + module = sys.modules[module_name] + klass = vars(module)[class_name] + except ImportError: + raise DistutilsModuleError( + f"can't compile C/C++ code: unable to load module '{module_name}'" + ) + except KeyError: + raise DistutilsModuleError( + f"can't compile C/C++ code: unable to find class '{class_name}' " + f"in module '{module_name}'" + ) + + # XXX The None is necessary to preserve backwards compatibility + # with classes that expect verbose to be the first positional + # argument. + return klass(None, force=force) + + +def gen_preprocess_options( + macros: Iterable[_Macro], include_dirs: Iterable[str] +) -> list[str]: + """Generate C pre-processor options (-D, -U, -I) as used by at least + two types of compilers: the typical Unix compiler and Visual C++. + 'macros' is the usual thing, a list of 1- or 2-tuples, where (name,) + means undefine (-U) macro 'name', and (name,value) means define (-D) + macro 'name' to 'value'. 'include_dirs' is just a list of directory + names to be added to the header file search path (-I). Returns a list + of command-line options suitable for either Unix compilers or Visual + C++. + """ + # XXX it would be nice (mainly aesthetic, and so we don't generate + # stupid-looking command lines) to go over 'macros' and eliminate + # redundant definitions/undefinitions (ie. ensure that only the + # latest mention of a particular macro winds up on the command + # line). I don't think it's essential, though, since most (all?) + # Unix C compilers only pay attention to the latest -D or -U + # mention of a macro on their command line. Similar situation for + # 'include_dirs'. I'm punting on both for now. Anyways, weeding out + # redundancies like this should probably be the province of + # CCompiler, since the data structures used are inherited from it + # and therefore common to all CCompiler classes. + pp_opts = [] + for macro in macros: + if not (isinstance(macro, tuple) and 1 <= len(macro) <= 2): + raise TypeError( + f"bad macro definition '{macro}': " + "each element of 'macros' list must be a 1- or 2-tuple" + ) + + if len(macro) == 1: # undefine this macro + pp_opts.append(f"-U{macro[0]}") + elif len(macro) == 2: + if macro[1] is None: # define with no explicit value + pp_opts.append(f"-D{macro[0]}") + else: + # XXX *don't* need to be clever about quoting the + # macro value here, because we're going to avoid the + # shell at all costs when we spawn the command! + pp_opts.append("-D{}={}".format(*macro)) + + pp_opts.extend(f"-I{dir}" for dir in include_dirs) + return pp_opts + + +def gen_lib_options( + compiler: Compiler, + library_dirs: Iterable[str], + runtime_library_dirs: Iterable[str], + libraries: Iterable[str], +) -> list[str]: + """Generate linker options for searching library directories and + linking with specific libraries. 'libraries' and 'library_dirs' are, + respectively, lists of library names (not filenames!) and search + directories. Returns a list of command-line options suitable for use + with some compiler (depending on the two format strings passed in). + """ + lib_opts = [compiler.library_dir_option(dir) for dir in library_dirs] + + for dir in runtime_library_dirs: + lib_opts.extend(always_iterable(compiler.runtime_library_dir_option(dir))) + + # XXX it's important that we *not* remove redundant library mentions! + # sometimes you really do have to say "-lfoo -lbar -lfoo" in order to + # resolve all symbols. I just hope we never have to say "-lfoo obj.o + # -lbar" to get things to work -- that's certainly a possibility, but a + # pretty nasty way to arrange your C code. + + for lib in libraries: + (lib_dir, lib_name) = os.path.split(lib) + if lib_dir: + lib_file = compiler.find_library_file([lib_dir], lib_name) + if lib_file: + lib_opts.append(lib_file) + else: + compiler.warn( + f"no library file corresponding to '{lib}' found (skipping)" + ) + else: + lib_opts.append(compiler.library_option(lib)) + return lib_opts diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/cygwin.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/cygwin.py new file mode 100644 index 0000000000000000000000000000000000000000..f349e9decd083ad60a9b9d1181b57e1bbcce2c98 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/cygwin.py @@ -0,0 +1,340 @@ +"""distutils.cygwinccompiler + +Provides the CygwinCCompiler class, a subclass of UnixCCompiler that +handles the Cygwin port of the GNU C compiler to Windows. It also contains +the Mingw32CCompiler class which handles the mingw32 port of GCC (same as +cygwin in no-cygwin mode). +""" + +import copy +import os +import pathlib +import shlex +import sys +import warnings +from subprocess import check_output + +from ...errors import ( + DistutilsExecError, + DistutilsPlatformError, +) +from ...file_util import write_file +from ...sysconfig import get_config_vars +from ...version import LooseVersion, suppress_known_deprecation +from . import unix +from .errors import ( + CompileError, + Error, +) + + +def get_msvcr(): + """No longer needed, but kept for backward compatibility.""" + return [] + + +_runtime_library_dirs_msg = ( + "Unable to set runtime library search path on Windows, " + "usually indicated by `runtime_library_dirs` parameter to Extension" +) + + +class Compiler(unix.Compiler): + """Handles the Cygwin port of the GNU C compiler to Windows.""" + + compiler_type = 'cygwin' + obj_extension = ".o" + static_lib_extension = ".a" + shared_lib_extension = ".dll.a" + dylib_lib_extension = ".dll" + static_lib_format = "lib%s%s" + shared_lib_format = "lib%s%s" + dylib_lib_format = "cyg%s%s" + exe_extension = ".exe" + + def __init__(self, verbose=False, force=False): + super().__init__(verbose, force=force) + + status, details = check_config_h() + self.debug_print(f"Python's GCC status: {status} (details: {details})") + if status is not CONFIG_H_OK: + self.warn( + "Python's pyconfig.h doesn't seem to support your compiler. " + f"Reason: {details}. " + "Compiling may fail because of undefined preprocessor macros." + ) + + self.cc, self.cxx = get_config_vars('CC', 'CXX') + + # Override 'CC' and 'CXX' environment variables for + # building using MINGW compiler for MSVC python. + self.cc = os.environ.get('CC', self.cc or 'gcc') + self.cxx = os.environ.get('CXX', self.cxx or 'g++') + + self.linker_dll = self.cc + self.linker_dll_cxx = self.cxx + shared_option = "-shared" + + self.set_executables( + compiler=f'{self.cc} -mcygwin -O -Wall', + compiler_so=f'{self.cc} -mcygwin -mdll -O -Wall', + compiler_cxx=f'{self.cxx} -mcygwin -O -Wall', + compiler_so_cxx=f'{self.cxx} -mcygwin -mdll -O -Wall', + linker_exe=f'{self.cc} -mcygwin', + linker_so=f'{self.linker_dll} -mcygwin {shared_option}', + linker_exe_cxx=f'{self.cxx} -mcygwin', + linker_so_cxx=f'{self.linker_dll_cxx} -mcygwin {shared_option}', + ) + + self.dll_libraries = get_msvcr() + + @property + def gcc_version(self): + # Older numpy depended on this existing to check for ancient + # gcc versions. This doesn't make much sense with clang etc so + # just hardcode to something recent. + # https://github.com/numpy/numpy/pull/20333 + warnings.warn( + "gcc_version attribute of CygwinCCompiler is deprecated. " + "Instead of returning actual gcc version a fixed value 11.2.0 is returned.", + DeprecationWarning, + stacklevel=2, + ) + with suppress_known_deprecation(): + return LooseVersion("11.2.0") + + def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts): + """Compiles the source by spawning GCC and windres if needed.""" + if ext in ('.rc', '.res'): + # gcc needs '.res' and '.rc' compiled to object files !!! + try: + self.spawn(["windres", "-i", src, "-o", obj]) + except DistutilsExecError as msg: + raise CompileError(msg) + else: # for other files use the C-compiler + try: + if self.detect_language(src) == 'c++': + self.spawn( + self.compiler_so_cxx + + cc_args + + [src, '-o', obj] + + extra_postargs + ) + else: + self.spawn( + self.compiler_so + cc_args + [src, '-o', obj] + extra_postargs + ) + except DistutilsExecError as msg: + raise CompileError(msg) + + def link( + self, + target_desc, + objects, + output_filename, + output_dir=None, + libraries=None, + library_dirs=None, + runtime_library_dirs=None, + export_symbols=None, + debug=False, + extra_preargs=None, + extra_postargs=None, + build_temp=None, + target_lang=None, + ): + """Link the objects.""" + # use separate copies, so we can modify the lists + extra_preargs = copy.copy(extra_preargs or []) + libraries = copy.copy(libraries or []) + objects = copy.copy(objects or []) + + if runtime_library_dirs: + self.warn(_runtime_library_dirs_msg) + + # Additional libraries + libraries.extend(self.dll_libraries) + + # handle export symbols by creating a def-file + # with executables this only works with gcc/ld as linker + if (export_symbols is not None) and ( + target_desc != self.EXECUTABLE or self.linker_dll == "gcc" + ): + # (The linker doesn't do anything if output is up-to-date. + # So it would probably better to check if we really need this, + # but for this we had to insert some unchanged parts of + # UnixCCompiler, and this is not what we want.) + + # we want to put some files in the same directory as the + # object files are, build_temp doesn't help much + # where are the object files + temp_dir = os.path.dirname(objects[0]) + # name of dll to give the helper files the same base name + (dll_name, dll_extension) = os.path.splitext( + os.path.basename(output_filename) + ) + + # generate the filenames for these files + def_file = os.path.join(temp_dir, dll_name + ".def") + + # Generate .def file + contents = [f"LIBRARY {os.path.basename(output_filename)}", "EXPORTS"] + contents.extend(export_symbols) + self.execute(write_file, (def_file, contents), f"writing {def_file}") + + # next add options for def-file + + # for gcc/ld the def-file is specified as any object files + objects.append(def_file) + + # end: if ((export_symbols is not None) and + # (target_desc != self.EXECUTABLE or self.linker_dll == "gcc")): + + # who wants symbols and a many times larger output file + # should explicitly switch the debug mode on + # otherwise we let ld strip the output file + # (On my machine: 10KiB < stripped_file < ??100KiB + # unstripped_file = stripped_file + XXX KiB + # ( XXX=254 for a typical python extension)) + if not debug: + extra_preargs.append("-s") + + super().link( + target_desc, + objects, + output_filename, + output_dir, + libraries, + library_dirs, + runtime_library_dirs, + None, # export_symbols, we do this in our def-file + debug, + extra_preargs, + extra_postargs, + build_temp, + target_lang, + ) + + def runtime_library_dir_option(self, dir): + # cygwin doesn't support rpath. While in theory we could error + # out like MSVC does, code might expect it to work like on Unix, so + # just warn and hope for the best. + self.warn(_runtime_library_dirs_msg) + return [] + + # -- Miscellaneous methods ----------------------------------------- + + def _make_out_path(self, output_dir, strip_dir, src_name): + # use normcase to make sure '.rc' is really '.rc' and not '.RC' + norm_src_name = os.path.normcase(src_name) + return super()._make_out_path(output_dir, strip_dir, norm_src_name) + + @property + def out_extensions(self): + """ + Add support for rc and res files. + """ + return { + **super().out_extensions, + **{ext: ext + self.obj_extension for ext in ('.res', '.rc')}, + } + + +# the same as cygwin plus some additional parameters +class MinGW32Compiler(Compiler): + """Handles the Mingw32 port of the GNU C compiler to Windows.""" + + compiler_type = 'mingw32' + + def __init__(self, verbose=False, force=False): + super().__init__(verbose, force) + + shared_option = "-shared" + + if is_cygwincc(self.cc): + raise Error('Cygwin gcc cannot be used with --compiler=mingw32') + + self.set_executables( + compiler=f'{self.cc} -O -Wall', + compiler_so=f'{self.cc} -shared -O -Wall', + compiler_so_cxx=f'{self.cxx} -shared -O -Wall', + compiler_cxx=f'{self.cxx} -O -Wall', + linker_exe=f'{self.cc}', + linker_so=f'{self.linker_dll} {shared_option}', + linker_exe_cxx=f'{self.cxx}', + linker_so_cxx=f'{self.linker_dll_cxx} {shared_option}', + ) + + def runtime_library_dir_option(self, dir): + raise DistutilsPlatformError(_runtime_library_dirs_msg) + + +# Because these compilers aren't configured in Python's pyconfig.h file by +# default, we should at least warn the user if he is using an unmodified +# version. + +CONFIG_H_OK = "ok" +CONFIG_H_NOTOK = "not ok" +CONFIG_H_UNCERTAIN = "uncertain" + + +def check_config_h(): + """Check if the current Python installation appears amenable to building + extensions with GCC. + + Returns a tuple (status, details), where 'status' is one of the following + constants: + + - CONFIG_H_OK: all is well, go ahead and compile + - CONFIG_H_NOTOK: doesn't look good + - CONFIG_H_UNCERTAIN: not sure -- unable to read pyconfig.h + + 'details' is a human-readable string explaining the situation. + + Note there are two ways to conclude "OK": either 'sys.version' contains + the string "GCC" (implying that this Python was built with GCC), or the + installed "pyconfig.h" contains the string "__GNUC__". + """ + + # XXX since this function also checks sys.version, it's not strictly a + # "pyconfig.h" check -- should probably be renamed... + + from distutils import sysconfig + + # if sys.version contains GCC then python was compiled with GCC, and the + # pyconfig.h file should be OK + if "GCC" in sys.version: + return CONFIG_H_OK, "sys.version mentions 'GCC'" + + # Clang would also work + if "Clang" in sys.version: + return CONFIG_H_OK, "sys.version mentions 'Clang'" + + # let's see if __GNUC__ is mentioned in python.h + fn = sysconfig.get_config_h_filename() + try: + config_h = pathlib.Path(fn).read_text(encoding='utf-8') + except OSError as exc: + return (CONFIG_H_UNCERTAIN, f"couldn't read '{fn}': {exc.strerror}") + else: + substring = '__GNUC__' + if substring in config_h: + code = CONFIG_H_OK + mention_inflected = 'mentions' + else: + code = CONFIG_H_NOTOK + mention_inflected = 'does not mention' + return code, f"{fn!r} {mention_inflected} {substring!r}" + + +def is_cygwincc(cc): + """Try to determine if the compiler that would be used is from cygwin.""" + out_string = check_output(shlex.split(cc) + ['-dumpmachine']) + return out_string.strip().endswith(b'cygwin') + + +get_versions = None +""" +A stand-in for the previous get_versions() function to prevent failures +when monkeypatched. See pypa/setuptools#2969. +""" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/errors.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..01328592b2dad80f4d16c55cd9db9829b3c650f1 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/errors.py @@ -0,0 +1,24 @@ +class Error(Exception): + """Some compile/link operation failed.""" + + +class PreprocessError(Error): + """Failure to preprocess one or more C/C++ files.""" + + +class CompileError(Error): + """Failure to compile one or more C/C++ source files.""" + + +class LibError(Error): + """Failure to create a static library from one or more C/C++ object + files.""" + + +class LinkError(Error): + """Failure to link one or more C/C++ object files into an executable + or shared library file.""" + + +class UnknownFileType(Error): + """Attempt to process an unknown file type.""" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/msvc.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/msvc.py new file mode 100644 index 0000000000000000000000000000000000000000..1f50fa50880e02ea7adf64e94a234af25f8284c7 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/msvc.py @@ -0,0 +1,614 @@ +"""distutils._msvccompiler + +Contains MSVCCompiler, an implementation of the abstract CCompiler class +for Microsoft Visual Studio 2015. + +This module requires VS 2015 or later. +""" + +# Written by Perry Stoll +# hacked by Robin Becker and Thomas Heller to do a better job of +# finding DevStudio (through the registry) +# ported to VS 2005 and VS 2008 by Christian Heimes +# ported to VS 2015 by Steve Dower +from __future__ import annotations + +import contextlib +import os +import subprocess +import unittest.mock as mock +import warnings +from collections.abc import Iterable + +with contextlib.suppress(ImportError): + import winreg + +from itertools import count + +from ..._log import log +from ...errors import ( + DistutilsExecError, + DistutilsPlatformError, +) +from ...util import get_host_platform, get_platform +from . import base +from .base import gen_lib_options +from .errors import ( + CompileError, + LibError, + LinkError, +) + + +def _find_vc2015(): + try: + key = winreg.OpenKeyEx( + winreg.HKEY_LOCAL_MACHINE, + r"Software\Microsoft\VisualStudio\SxS\VC7", + access=winreg.KEY_READ | winreg.KEY_WOW64_32KEY, + ) + except OSError: + log.debug("Visual C++ is not registered") + return None, None + + best_version = 0 + best_dir = None + with key: + for i in count(): + try: + v, vc_dir, vt = winreg.EnumValue(key, i) + except OSError: + break + if v and vt == winreg.REG_SZ and os.path.isdir(vc_dir): + try: + version = int(float(v)) + except (ValueError, TypeError): + continue + if version >= 14 and version > best_version: + best_version, best_dir = version, vc_dir + return best_version, best_dir + + +def _find_vc2017(): + """Returns "15, path" based on the result of invoking vswhere.exe + If no install is found, returns "None, None" + + The version is returned to avoid unnecessarily changing the function + result. It may be ignored when the path is not None. + + If vswhere.exe is not available, by definition, VS 2017 is not + installed. + """ + root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles") + if not root: + return None, None + + variant = 'arm64' if get_platform() == 'win-arm64' else 'x86.x64' + suitable_components = ( + f"Microsoft.VisualStudio.Component.VC.Tools.{variant}", + "Microsoft.VisualStudio.Workload.WDExpress", + ) + + for component in suitable_components: + # Workaround for `-requiresAny` (only available on VS 2017 > 15.6) + with contextlib.suppress( + subprocess.CalledProcessError, OSError, UnicodeDecodeError + ): + path = ( + subprocess.check_output([ + os.path.join( + root, "Microsoft Visual Studio", "Installer", "vswhere.exe" + ), + "-latest", + "-prerelease", + "-requires", + component, + "-property", + "installationPath", + "-products", + "*", + ]) + .decode(encoding="mbcs", errors="strict") + .strip() + ) + + path = os.path.join(path, "VC", "Auxiliary", "Build") + if os.path.isdir(path): + return 15, path + + return None, None # no suitable component found + + +PLAT_SPEC_TO_RUNTIME = { + 'x86': 'x86', + 'x86_amd64': 'x64', + 'x86_arm': 'arm', + 'x86_arm64': 'arm64', +} + + +def _find_vcvarsall(plat_spec): + # bpo-38597: Removed vcruntime return value + _, best_dir = _find_vc2017() + + if not best_dir: + best_version, best_dir = _find_vc2015() + + if not best_dir: + log.debug("No suitable Visual C++ version found") + return None, None + + vcvarsall = os.path.join(best_dir, "vcvarsall.bat") + if not os.path.isfile(vcvarsall): + log.debug("%s cannot be found", vcvarsall) + return None, None + + return vcvarsall, None + + +def _get_vc_env(plat_spec): + if os.getenv("DISTUTILS_USE_SDK"): + return {key.lower(): value for key, value in os.environ.items()} + + vcvarsall, _ = _find_vcvarsall(plat_spec) + if not vcvarsall: + raise DistutilsPlatformError( + 'Microsoft Visual C++ 14.0 or greater is required. ' + 'Get it with "Microsoft C++ Build Tools": ' + 'https://visualstudio.microsoft.com/visual-cpp-build-tools/' + ) + + try: + out = subprocess.check_output( + f'cmd /u /c "{vcvarsall}" {plat_spec} && set', + stderr=subprocess.STDOUT, + ).decode('utf-16le', errors='replace') + except subprocess.CalledProcessError as exc: + log.error(exc.output) + raise DistutilsPlatformError(f"Error executing {exc.cmd}") + + env = { + key.lower(): value + for key, _, value in (line.partition('=') for line in out.splitlines()) + if key and value + } + + return env + + +def _find_exe(exe, paths=None): + """Return path to an MSVC executable program. + + Tries to find the program in several places: first, one of the + MSVC program search paths from the registry; next, the directories + in the PATH environment variable. If any of those work, return an + absolute path that is known to exist. If none of them work, just + return the original program name, 'exe'. + """ + if not paths: + paths = os.getenv('path').split(os.pathsep) + for p in paths: + fn = os.path.join(os.path.abspath(p), exe) + if os.path.isfile(fn): + return fn + return exe + + +_vcvars_names = { + 'win32': 'x86', + 'win-amd64': 'amd64', + 'win-arm32': 'arm', + 'win-arm64': 'arm64', +} + + +def _get_vcvars_spec(host_platform, platform): + """ + Given a host platform and platform, determine the spec for vcvarsall. + + Uses the native MSVC host if the host platform would need expensive + emulation for x86. + + >>> _get_vcvars_spec('win-arm64', 'win32') + 'arm64_x86' + >>> _get_vcvars_spec('win-arm64', 'win-amd64') + 'arm64_amd64' + + Otherwise, always cross-compile from x86 to work with the + lighter-weight MSVC installs that do not include native 64-bit tools. + + >>> _get_vcvars_spec('win32', 'win32') + 'x86' + >>> _get_vcvars_spec('win-arm32', 'win-arm32') + 'x86_arm' + >>> _get_vcvars_spec('win-amd64', 'win-arm64') + 'x86_arm64' + """ + if host_platform != 'win-arm64': + host_platform = 'win32' + vc_hp = _vcvars_names[host_platform] + vc_plat = _vcvars_names[platform] + return vc_hp if vc_hp == vc_plat else f'{vc_hp}_{vc_plat}' + + +class Compiler(base.Compiler): + """Concrete class that implements an interface to Microsoft Visual C++, + as defined by the CCompiler abstract class.""" + + compiler_type = 'msvc' + + # Just set this so CCompiler's constructor doesn't barf. We currently + # don't use the 'set_executables()' bureaucracy provided by CCompiler, + # as it really isn't necessary for this sort of single-compiler class. + # Would be nice to have a consistent interface with UnixCCompiler, + # though, so it's worth thinking about. + executables = {} + + # Private class data (need to distinguish C from C++ source for compiler) + _c_extensions = ['.c'] + _cpp_extensions = ['.cc', '.cpp', '.cxx'] + _rc_extensions = ['.rc'] + _mc_extensions = ['.mc'] + + # Needed for the filename generation methods provided by the + # base class, CCompiler. + src_extensions = _c_extensions + _cpp_extensions + _rc_extensions + _mc_extensions + res_extension = '.res' + obj_extension = '.obj' + static_lib_extension = '.lib' + shared_lib_extension = '.dll' + static_lib_format = shared_lib_format = '%s%s' + exe_extension = '.exe' + + def __init__(self, verbose=False, force=False) -> None: + super().__init__(verbose, force=force) + # target platform (.plat_name is consistent with 'bdist') + self.plat_name = None + self.initialized = False + + @classmethod + def _configure(cls, vc_env): + """ + Set class-level include/lib dirs. + """ + cls.include_dirs = cls._parse_path(vc_env.get('include', '')) + cls.library_dirs = cls._parse_path(vc_env.get('lib', '')) + + @staticmethod + def _parse_path(val): + return [dir.rstrip(os.sep) for dir in val.split(os.pathsep) if dir] + + def initialize(self, plat_name: str | None = None) -> None: + # multi-init means we would need to check platform same each time... + assert not self.initialized, "don't init multiple times" + if plat_name is None: + plat_name = get_platform() + # sanity check for platforms to prevent obscure errors later. + if plat_name not in _vcvars_names: + raise DistutilsPlatformError( + f"--plat-name must be one of {tuple(_vcvars_names)}" + ) + + plat_spec = _get_vcvars_spec(get_host_platform(), plat_name) + + vc_env = _get_vc_env(plat_spec) + if not vc_env: + raise DistutilsPlatformError( + "Unable to find a compatible Visual Studio installation." + ) + self._configure(vc_env) + + self._paths = vc_env.get('path', '') + paths = self._paths.split(os.pathsep) + self.cc = _find_exe("cl.exe", paths) + self.linker = _find_exe("link.exe", paths) + self.lib = _find_exe("lib.exe", paths) + self.rc = _find_exe("rc.exe", paths) # resource compiler + self.mc = _find_exe("mc.exe", paths) # message compiler + self.mt = _find_exe("mt.exe", paths) # message compiler + + self.preprocess_options = None + # bpo-38597: Always compile with dynamic linking + # Future releases of Python 3.x will include all past + # versions of vcruntime*.dll for compatibility. + self.compile_options = ['/nologo', '/O2', '/W3', '/GL', '/DNDEBUG', '/MD'] + + self.compile_options_debug = [ + '/nologo', + '/Od', + '/MDd', + '/Zi', + '/W3', + '/D_DEBUG', + ] + + ldflags = ['/nologo', '/INCREMENTAL:NO', '/LTCG'] + + ldflags_debug = ['/nologo', '/INCREMENTAL:NO', '/LTCG', '/DEBUG:FULL'] + + self.ldflags_exe = [*ldflags, '/MANIFEST:EMBED,ID=1'] + self.ldflags_exe_debug = [*ldflags_debug, '/MANIFEST:EMBED,ID=1'] + self.ldflags_shared = [ + *ldflags, + '/DLL', + '/MANIFEST:EMBED,ID=2', + '/MANIFESTUAC:NO', + ] + self.ldflags_shared_debug = [ + *ldflags_debug, + '/DLL', + '/MANIFEST:EMBED,ID=2', + '/MANIFESTUAC:NO', + ] + self.ldflags_static = [*ldflags] + self.ldflags_static_debug = [*ldflags_debug] + + self._ldflags = { + (base.Compiler.EXECUTABLE, None): self.ldflags_exe, + (base.Compiler.EXECUTABLE, False): self.ldflags_exe, + (base.Compiler.EXECUTABLE, True): self.ldflags_exe_debug, + (base.Compiler.SHARED_OBJECT, None): self.ldflags_shared, + (base.Compiler.SHARED_OBJECT, False): self.ldflags_shared, + (base.Compiler.SHARED_OBJECT, True): self.ldflags_shared_debug, + (base.Compiler.SHARED_LIBRARY, None): self.ldflags_static, + (base.Compiler.SHARED_LIBRARY, False): self.ldflags_static, + (base.Compiler.SHARED_LIBRARY, True): self.ldflags_static_debug, + } + + self.initialized = True + + # -- Worker methods ------------------------------------------------ + + @property + def out_extensions(self) -> dict[str, str]: + return { + **super().out_extensions, + **{ + ext: self.res_extension + for ext in self._rc_extensions + self._mc_extensions + }, + } + + def compile( # noqa: C901 + self, + sources, + output_dir=None, + macros=None, + include_dirs=None, + debug=False, + extra_preargs=None, + extra_postargs=None, + depends=None, + ): + if not self.initialized: + self.initialize() + compile_info = self._setup_compile( + output_dir, macros, include_dirs, sources, depends, extra_postargs + ) + macros, objects, extra_postargs, pp_opts, build = compile_info + + compile_opts = extra_preargs or [] + compile_opts.append('/c') + if debug: + compile_opts.extend(self.compile_options_debug) + else: + compile_opts.extend(self.compile_options) + + add_cpp_opts = False + + for obj in objects: + try: + src, ext = build[obj] + except KeyError: + continue + if debug: + # pass the full pathname to MSVC in debug mode, + # this allows the debugger to find the source file + # without asking the user to browse for it + src = os.path.abspath(src) + + if ext in self._c_extensions: + input_opt = f"/Tc{src}" + elif ext in self._cpp_extensions: + input_opt = f"/Tp{src}" + add_cpp_opts = True + elif ext in self._rc_extensions: + # compile .RC to .RES file + input_opt = src + output_opt = "/fo" + obj + try: + self.spawn([self.rc] + pp_opts + [output_opt, input_opt]) + except DistutilsExecError as msg: + raise CompileError(msg) + continue + elif ext in self._mc_extensions: + # Compile .MC to .RC file to .RES file. + # * '-h dir' specifies the directory for the + # generated include file + # * '-r dir' specifies the target directory of the + # generated RC file and the binary message resource + # it includes + # + # For now (since there are no options to change this), + # we use the source-directory for the include file and + # the build directory for the RC file and message + # resources. This works at least for win32all. + h_dir = os.path.dirname(src) + rc_dir = os.path.dirname(obj) + try: + # first compile .MC to .RC and .H file + self.spawn([self.mc, '-h', h_dir, '-r', rc_dir, src]) + base, _ = os.path.splitext(os.path.basename(src)) + rc_file = os.path.join(rc_dir, base + '.rc') + # then compile .RC to .RES file + self.spawn([self.rc, "/fo" + obj, rc_file]) + + except DistutilsExecError as msg: + raise CompileError(msg) + continue + else: + # how to handle this file? + raise CompileError(f"Don't know how to compile {src} to {obj}") + + args = [self.cc] + compile_opts + pp_opts + if add_cpp_opts: + args.append('/EHsc') + args.extend((input_opt, "/Fo" + obj)) + args.extend(extra_postargs) + + try: + self.spawn(args) + except DistutilsExecError as msg: + raise CompileError(msg) + + return objects + + def create_static_lib( + self, + objects: list[str] | tuple[str, ...], + output_libname: str, + output_dir: str | None = None, + debug: bool = False, + target_lang: str | None = None, + ) -> None: + if not self.initialized: + self.initialize() + objects, output_dir = self._fix_object_args(objects, output_dir) + output_filename = self.library_filename(output_libname, output_dir=output_dir) + + if self._need_link(objects, output_filename): + lib_args = objects + ['/OUT:' + output_filename] + if debug: + pass # XXX what goes here? + try: + log.debug('Executing "%s" %s', self.lib, ' '.join(lib_args)) + self.spawn([self.lib] + lib_args) + except DistutilsExecError as msg: + raise LibError(msg) + else: + log.debug("skipping %s (up-to-date)", output_filename) + + def link( + self, + target_desc: str, + objects: list[str] | tuple[str, ...], + output_filename: str, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + export_symbols: Iterable[str] | None = None, + debug: bool = False, + extra_preargs: list[str] | None = None, + extra_postargs: Iterable[str] | None = None, + build_temp: str | os.PathLike[str] | None = None, + target_lang: str | None = None, + ) -> None: + if not self.initialized: + self.initialize() + objects, output_dir = self._fix_object_args(objects, output_dir) + fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs) + libraries, library_dirs, runtime_library_dirs = fixed_args + + if runtime_library_dirs: + self.warn( + "I don't know what to do with 'runtime_library_dirs': " + + str(runtime_library_dirs) + ) + + lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries) + if output_dir is not None: + output_filename = os.path.join(output_dir, output_filename) + + if self._need_link(objects, output_filename): + ldflags = self._ldflags[target_desc, debug] + + export_opts = ["/EXPORT:" + sym for sym in (export_symbols or [])] + + ld_args = ( + ldflags + lib_opts + export_opts + objects + ['/OUT:' + output_filename] + ) + + # The MSVC linker generates .lib and .exp files, which cannot be + # suppressed by any linker switches. The .lib files may even be + # needed! Make sure they are generated in the temporary build + # directory. Since they have different names for debug and release + # builds, they can go into the same directory. + build_temp = os.path.dirname(objects[0]) + if export_symbols is not None: + (dll_name, dll_ext) = os.path.splitext( + os.path.basename(output_filename) + ) + implib_file = os.path.join(build_temp, self.library_filename(dll_name)) + ld_args.append('/IMPLIB:' + implib_file) + + if extra_preargs: + ld_args[:0] = extra_preargs + if extra_postargs: + ld_args.extend(extra_postargs) + + output_dir = os.path.dirname(os.path.abspath(output_filename)) + self.mkpath(output_dir) + try: + log.debug('Executing "%s" %s', self.linker, ' '.join(ld_args)) + self.spawn([self.linker] + ld_args) + except DistutilsExecError as msg: + raise LinkError(msg) + else: + log.debug("skipping %s (up-to-date)", output_filename) + + def spawn(self, cmd): + env = dict(os.environ, PATH=self._paths) + with self._fallback_spawn(cmd, env) as fallback: + return super().spawn(cmd, env=env) + return fallback.value + + @contextlib.contextmanager + def _fallback_spawn(self, cmd, env): + """ + Discovered in pypa/distutils#15, some tools monkeypatch the compiler, + so the 'env' kwarg causes a TypeError. Detect this condition and + restore the legacy, unsafe behavior. + """ + bag = type('Bag', (), {})() + try: + yield bag + except TypeError as exc: + if "unexpected keyword argument 'env'" not in str(exc): + raise + else: + return + warnings.warn("Fallback spawn triggered. Please update distutils monkeypatch.") + with mock.patch.dict('os.environ', env): + bag.value = super().spawn(cmd) + + # -- Miscellaneous methods ----------------------------------------- + # These are all used by the 'gen_lib_options() function, in + # ccompiler.py. + + def library_dir_option(self, dir): + return "/LIBPATH:" + dir + + def runtime_library_dir_option(self, dir): + raise DistutilsPlatformError( + "don't know how to set runtime library search path for MSVC" + ) + + def library_option(self, lib): + return self.library_filename(lib) + + def find_library_file(self, dirs, lib, debug=False): + # Prefer a debugging library if found (and requested), but deal + # with it if we don't have one. + if debug: + try_names = [lib + "_d", lib] + else: + try_names = [lib] + for dir in dirs: + for name in try_names: + libfile = os.path.join(dir, self.library_filename(name)) + if os.path.isfile(libfile): + return libfile + else: + # Oops, didn't find it in *any* of 'dirs' + return None diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_base.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_base.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..87b0dcf4684ab493357b1a4594f52e6a3b3b1430 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_base.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_cygwin.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_cygwin.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d93a408e6c41fe965b16af435d24b88781b4d2b7 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_cygwin.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_mingw.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_mingw.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b206266fd700ff0d30ae29e7f6c044fa35ac1f2b Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_mingw.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_msvc.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_msvc.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..410551cb2c37fcd6198c8b01a959ece05413af75 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_msvc.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_unix.cpython-311.pyc b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_unix.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ccf257c44c7a10fbe7d2e6bcfbc1d8b6f669ba3 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/__pycache__/test_unix.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_base.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..a762e2b649d7977ef42f4d35854eab26bd54e9e7 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_base.py @@ -0,0 +1,83 @@ +import platform +import sysconfig +import textwrap + +import pytest + +from .. import base + +pytestmark = pytest.mark.usefixtures('suppress_path_mangle') + + +@pytest.fixture +def c_file(tmp_path): + c_file = tmp_path / 'foo.c' + gen_headers = ('Python.h',) + is_windows = platform.system() == "Windows" + plat_headers = ('windows.h',) * is_windows + all_headers = gen_headers + plat_headers + headers = '\n'.join(f'#include <{header}>\n' for header in all_headers) + payload = ( + textwrap.dedent( + """ + #headers + void PyInit_foo(void) {} + """ + ) + .lstrip() + .replace('#headers', headers) + ) + c_file.write_text(payload, encoding='utf-8') + return c_file + + +def test_set_include_dirs(c_file): + """ + Extensions should build even if set_include_dirs is invoked. + In particular, compiler-specific paths should not be overridden. + """ + compiler = base.new_compiler() + python = sysconfig.get_paths()['include'] + compiler.set_include_dirs([python]) + compiler.compile([c_file]) + + # do it again, setting include dirs after any initialization + compiler.set_include_dirs([python]) + compiler.compile([c_file]) + + +def test_has_function_prototype(): + # Issue https://github.com/pypa/setuptools/issues/3648 + # Test prototype-generating behavior. + + compiler = base.new_compiler() + + # Every C implementation should have these. + assert compiler.has_function('abort') + assert compiler.has_function('exit') + with pytest.deprecated_call(match='includes is deprecated'): + # abort() is a valid expression with the prototype. + assert compiler.has_function('abort', includes=['stdlib.h']) + with pytest.deprecated_call(match='includes is deprecated'): + # But exit() is not valid with the actual prototype in scope. + assert not compiler.has_function('exit', includes=['stdlib.h']) + # And setuptools_does_not_exist is not declared or defined at all. + assert not compiler.has_function('setuptools_does_not_exist') + with pytest.deprecated_call(match='includes is deprecated'): + assert not compiler.has_function( + 'setuptools_does_not_exist', includes=['stdio.h'] + ) + + +def test_include_dirs_after_multiple_compile_calls(c_file): + """ + Calling compile multiple times should not change the include dirs + (regression test for setuptools issue #3591). + """ + compiler = base.new_compiler() + python = sysconfig.get_paths()['include'] + compiler.set_include_dirs([python]) + compiler.compile([c_file]) + assert compiler.include_dirs == [python] + compiler.compile([c_file]) + assert compiler.include_dirs == [python] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py new file mode 100644 index 0000000000000000000000000000000000000000..9adf6b8ebf165b4fe92a52d8a14aed21aebba539 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py @@ -0,0 +1,76 @@ +"""Tests for distutils.cygwinccompiler.""" + +import os +import sys +from distutils import sysconfig +from distutils.tests import support + +import pytest + +from .. import cygwin + + +@pytest.fixture(autouse=True) +def stuff(request, monkeypatch, distutils_managed_tempdir): + self = request.instance + self.python_h = os.path.join(self.mkdtemp(), 'python.h') + monkeypatch.setattr(sysconfig, 'get_config_h_filename', self._get_config_h_filename) + monkeypatch.setattr(sys, 'version', sys.version) + + +class TestCygwinCCompiler(support.TempdirManager): + def _get_config_h_filename(self): + return self.python_h + + @pytest.mark.skipif('sys.platform != "cygwin"') + @pytest.mark.skipif('not os.path.exists("/usr/lib/libbash.dll.a")') + def test_find_library_file(self): + from distutils.cygwinccompiler import CygwinCCompiler + + compiler = CygwinCCompiler() + link_name = "bash" + linkable_file = compiler.find_library_file(["/usr/lib"], link_name) + assert linkable_file is not None + assert os.path.exists(linkable_file) + assert linkable_file == f"/usr/lib/lib{link_name:s}.dll.a" + + @pytest.mark.skipif('sys.platform != "cygwin"') + def test_runtime_library_dir_option(self): + from distutils.cygwinccompiler import CygwinCCompiler + + compiler = CygwinCCompiler() + assert compiler.runtime_library_dir_option('/foo') == [] + + def test_check_config_h(self): + # check_config_h looks for "GCC" in sys.version first + # returns CONFIG_H_OK if found + sys.version = ( + '2.6.1 (r261:67515, Dec 6 2008, 16:42:21) \n[GCC ' + '4.0.1 (Apple Computer, Inc. build 5370)]' + ) + + assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_OK + + # then it tries to see if it can find "__GNUC__" in pyconfig.h + sys.version = 'something without the *CC word' + + # if the file doesn't exist it returns CONFIG_H_UNCERTAIN + assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_UNCERTAIN + + # if it exists but does not contain __GNUC__, it returns CONFIG_H_NOTOK + self.write_file(self.python_h, 'xxx') + assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_NOTOK + + # and CONFIG_H_OK if __GNUC__ is found + self.write_file(self.python_h, 'xxx __GNUC__ xxx') + assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_OK + + def test_get_msvcr(self): + assert cygwin.get_msvcr() == [] + + @pytest.mark.skipif('sys.platform != "cygwin"') + def test_dll_libraries_not_none(self): + from distutils.cygwinccompiler import CygwinCCompiler + + compiler = CygwinCCompiler() + assert compiler.dll_libraries is not None diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py new file mode 100644 index 0000000000000000000000000000000000000000..dc45687a917808edbf692ced18b0c99183d21e2d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py @@ -0,0 +1,48 @@ +from distutils import sysconfig +from distutils.errors import DistutilsPlatformError +from distutils.util import is_mingw, split_quoted + +import pytest + +from .. import cygwin, errors + + +class TestMinGW32Compiler: + @pytest.mark.skipif(not is_mingw(), reason='not on mingw') + def test_compiler_type(self): + compiler = cygwin.MinGW32Compiler() + assert compiler.compiler_type == 'mingw32' + + @pytest.mark.skipif(not is_mingw(), reason='not on mingw') + def test_set_executables(self, monkeypatch): + monkeypatch.setenv('CC', 'cc') + monkeypatch.setenv('CXX', 'c++') + + compiler = cygwin.MinGW32Compiler() + + assert compiler.compiler == split_quoted('cc -O -Wall') + assert compiler.compiler_so == split_quoted('cc -shared -O -Wall') + assert compiler.compiler_cxx == split_quoted('c++ -O -Wall') + assert compiler.linker_exe == split_quoted('cc') + assert compiler.linker_so == split_quoted('cc -shared') + + @pytest.mark.skipif(not is_mingw(), reason='not on mingw') + def test_runtime_library_dir_option(self): + compiler = cygwin.MinGW32Compiler() + with pytest.raises(DistutilsPlatformError): + compiler.runtime_library_dir_option('/usr/lib') + + @pytest.mark.skipif(not is_mingw(), reason='not on mingw') + def test_cygwincc_error(self, monkeypatch): + monkeypatch.setattr(cygwin, 'is_cygwincc', lambda _: True) + + with pytest.raises(errors.Error): + cygwin.MinGW32Compiler() + + @pytest.mark.skipif('sys.platform == "cygwin"') + def test_customize_compiler_with_msvc_python(self): + # In case we have an MSVC Python build, but still want to use + # MinGW32Compiler, then customize_compiler() shouldn't fail at least. + # https://github.com/pypa/setuptools/issues/4456 + compiler = cygwin.MinGW32Compiler() + sysconfig.customize_compiler(compiler) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py new file mode 100644 index 0000000000000000000000000000000000000000..eca831996adcb07f05560581fe4285cdb29392d3 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py @@ -0,0 +1,136 @@ +import os +import sys +import sysconfig +import threading +import unittest.mock as mock +from distutils.errors import DistutilsPlatformError +from distutils.tests import support +from distutils.util import get_platform + +import pytest + +from .. import msvc + +needs_winreg = pytest.mark.skipif('not hasattr(msvc, "winreg")') + + +class Testmsvccompiler(support.TempdirManager): + def test_no_compiler(self, monkeypatch): + # makes sure query_vcvarsall raises + # a DistutilsPlatformError if the compiler + # is not found + def _find_vcvarsall(plat_spec): + return None, None + + monkeypatch.setattr(msvc, '_find_vcvarsall', _find_vcvarsall) + + with pytest.raises(DistutilsPlatformError): + msvc._get_vc_env( + 'wont find this version', + ) + + @pytest.mark.skipif( + not sysconfig.get_platform().startswith("win"), + reason="Only run test for non-mingw Windows platforms", + ) + @pytest.mark.parametrize( + "plat_name, expected", + [ + ("win-arm64", "win-arm64"), + ("win-amd64", "win-amd64"), + (None, get_platform()), + ], + ) + def test_cross_platform_compilation_paths(self, monkeypatch, plat_name, expected): + """ + Ensure a specified target platform is passed to _get_vcvars_spec. + """ + compiler = msvc.Compiler() + + def _get_vcvars_spec(host_platform, platform): + assert platform == expected + + monkeypatch.setattr(msvc, '_get_vcvars_spec', _get_vcvars_spec) + compiler.initialize(plat_name) + + @needs_winreg + def test_get_vc_env_unicode(self): + test_var = 'ṰḖṤṪ┅ṼẨṜ' + test_value = '₃⁴₅' + + # Ensure we don't early exit from _get_vc_env + old_distutils_use_sdk = os.environ.pop('DISTUTILS_USE_SDK', None) + os.environ[test_var] = test_value + try: + env = msvc._get_vc_env('x86') + assert test_var.lower() in env + assert test_value == env[test_var.lower()] + finally: + os.environ.pop(test_var) + if old_distutils_use_sdk: + os.environ['DISTUTILS_USE_SDK'] = old_distutils_use_sdk + + @needs_winreg + @pytest.mark.parametrize('ver', (2015, 2017)) + def test_get_vc(self, ver): + # This function cannot be mocked, so pass if VC is found + # and skip otherwise. + lookup = getattr(msvc, f'_find_vc{ver}') + expected_version = {2015: 14, 2017: 15}[ver] + version, path = lookup() + if not version: + pytest.skip(f"VS {ver} is not installed") + assert version >= expected_version + assert os.path.isdir(path) + + +class CheckThread(threading.Thread): + exc_info = None + + def run(self): + try: + super().run() + except Exception: + self.exc_info = sys.exc_info() + + def __bool__(self): + return not self.exc_info + + +class TestSpawn: + def test_concurrent_safe(self): + """ + Concurrent calls to spawn should have consistent results. + """ + compiler = msvc.Compiler() + compiler._paths = "expected" + inner_cmd = 'import os; assert os.environ["PATH"] == "expected"' + command = [sys.executable, '-c', inner_cmd] + + threads = [ + CheckThread(target=compiler.spawn, args=[command]) for n in range(100) + ] + for thread in threads: + thread.start() + for thread in threads: + thread.join() + assert all(threads) + + def test_concurrent_safe_fallback(self): + """ + If CCompiler.spawn has been monkey-patched without support + for an env, it should still execute. + """ + from distutils import ccompiler + + compiler = msvc.Compiler() + compiler._paths = "expected" + + def CCompiler_spawn(self, cmd): + "A spawn without an env argument." + assert os.environ["PATH"] == "expected" + + with mock.patch.object(ccompiler.CCompiler, 'spawn', CCompiler_spawn): + compiler.spawn(["n/a"]) + + assert os.environ.get("PATH") != "expected" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py new file mode 100644 index 0000000000000000000000000000000000000000..35b6b0e050c654bd9eff711ec3b94bea9adff2af --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py @@ -0,0 +1,413 @@ +"""Tests for distutils.unixccompiler.""" + +import os +import sys +import unittest.mock as mock +from distutils import sysconfig +from distutils.compat import consolidate_linker_args +from distutils.errors import DistutilsPlatformError +from distutils.tests import support +from distutils.tests.compat.py39 import EnvironmentVarGuard +from distutils.util import _clear_cached_macosx_ver + +import pytest + +from .. import unix + + +@pytest.fixture(autouse=True) +def save_values(monkeypatch): + monkeypatch.setattr(sys, 'platform', sys.platform) + monkeypatch.setattr(sysconfig, 'get_config_var', sysconfig.get_config_var) + monkeypatch.setattr(sysconfig, 'get_config_vars', sysconfig.get_config_vars) + + +@pytest.fixture(autouse=True) +def compiler_wrapper(request): + class CompilerWrapper(unix.Compiler): + def rpath_foo(self): + return self.runtime_library_dir_option('/foo') + + request.instance.cc = CompilerWrapper() + + +class TestUnixCCompiler(support.TempdirManager): + @pytest.mark.skipif('platform.system == "Windows"') + def test_runtime_libdir_option(self): # noqa: C901 + # Issue #5900; GitHub Issue #37 + # + # Ensure RUNPATH is added to extension modules with RPATH if + # GNU ld is used + + # darwin + sys.platform = 'darwin' + darwin_ver_var = 'MACOSX_DEPLOYMENT_TARGET' + darwin_rpath_flag = '-Wl,-rpath,/foo' + darwin_lib_flag = '-L/foo' + + # (macOS version from syscfg, macOS version from env var) -> flag + # Version value of None generates two tests: as None and as empty string + # Expected flag value of None means an mismatch exception is expected + darwin_test_cases = [ + ((None, None), darwin_lib_flag), + ((None, '11'), darwin_rpath_flag), + (('10', None), darwin_lib_flag), + (('10.3', None), darwin_lib_flag), + (('10.3.1', None), darwin_lib_flag), + (('10.5', None), darwin_rpath_flag), + (('10.5.1', None), darwin_rpath_flag), + (('10.3', '10.3'), darwin_lib_flag), + (('10.3', '10.5'), darwin_rpath_flag), + (('10.5', '10.3'), darwin_lib_flag), + (('10.5', '11'), darwin_rpath_flag), + (('10.4', '10'), None), + ] + + def make_darwin_gcv(syscfg_macosx_ver): + def gcv(var): + if var == darwin_ver_var: + return syscfg_macosx_ver + return "xxx" + + return gcv + + def do_darwin_test(syscfg_macosx_ver, env_macosx_ver, expected_flag): + env = os.environ + msg = f"macOS version = (sysconfig={syscfg_macosx_ver!r}, env={env_macosx_ver!r})" + + # Save + old_gcv = sysconfig.get_config_var + old_env_macosx_ver = env.get(darwin_ver_var) + + # Setup environment + _clear_cached_macosx_ver() + sysconfig.get_config_var = make_darwin_gcv(syscfg_macosx_ver) + if env_macosx_ver is not None: + env[darwin_ver_var] = env_macosx_ver + elif darwin_ver_var in env: + env.pop(darwin_ver_var) + + # Run the test + if expected_flag is not None: + assert self.cc.rpath_foo() == expected_flag, msg + else: + with pytest.raises( + DistutilsPlatformError, match=darwin_ver_var + r' mismatch' + ): + self.cc.rpath_foo() + + # Restore + if old_env_macosx_ver is not None: + env[darwin_ver_var] = old_env_macosx_ver + elif darwin_ver_var in env: + env.pop(darwin_ver_var) + sysconfig.get_config_var = old_gcv + _clear_cached_macosx_ver() + + for macosx_vers, expected_flag in darwin_test_cases: + syscfg_macosx_ver, env_macosx_ver = macosx_vers + do_darwin_test(syscfg_macosx_ver, env_macosx_ver, expected_flag) + # Bonus test cases with None interpreted as empty string + if syscfg_macosx_ver is None: + do_darwin_test("", env_macosx_ver, expected_flag) + if env_macosx_ver is None: + do_darwin_test(syscfg_macosx_ver, "", expected_flag) + if syscfg_macosx_ver is None and env_macosx_ver is None: + do_darwin_test("", "", expected_flag) + + old_gcv = sysconfig.get_config_var + + # hp-ux + sys.platform = 'hp-ux' + + def gcv(v): + return 'xxx' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == ['+s', '-L/foo'] + + def gcv(v): + return 'gcc' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == ['-Wl,+s', '-L/foo'] + + def gcv(v): + return 'g++' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == ['-Wl,+s', '-L/foo'] + + sysconfig.get_config_var = old_gcv + + # GCC GNULD + sys.platform = 'bar' + + def gcv(v): + if v == 'CC': + return 'gcc' + elif v == 'GNULD': + return 'yes' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == consolidate_linker_args([ + '-Wl,--enable-new-dtags', + '-Wl,-rpath,/foo', + ]) + + def gcv(v): + if v == 'CC': + return 'gcc -pthread -B /bar' + elif v == 'GNULD': + return 'yes' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == consolidate_linker_args([ + '-Wl,--enable-new-dtags', + '-Wl,-rpath,/foo', + ]) + + # GCC non-GNULD + sys.platform = 'bar' + + def gcv(v): + if v == 'CC': + return 'gcc' + elif v == 'GNULD': + return 'no' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == '-Wl,-R/foo' + + # GCC GNULD with fully qualified configuration prefix + # see #7617 + sys.platform = 'bar' + + def gcv(v): + if v == 'CC': + return 'x86_64-pc-linux-gnu-gcc-4.4.2' + elif v == 'GNULD': + return 'yes' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == consolidate_linker_args([ + '-Wl,--enable-new-dtags', + '-Wl,-rpath,/foo', + ]) + + # non-GCC GNULD + sys.platform = 'bar' + + def gcv(v): + if v == 'CC': + return 'cc' + elif v == 'GNULD': + return 'yes' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == consolidate_linker_args([ + '-Wl,--enable-new-dtags', + '-Wl,-rpath,/foo', + ]) + + # non-GCC non-GNULD + sys.platform = 'bar' + + def gcv(v): + if v == 'CC': + return 'cc' + elif v == 'GNULD': + return 'no' + + sysconfig.get_config_var = gcv + assert self.cc.rpath_foo() == '-Wl,-R/foo' + + @pytest.mark.skipif('platform.system == "Windows"') + def test_cc_overrides_ldshared(self): + # Issue #18080: + # ensure that setting CC env variable also changes default linker + def gcv(v): + if v == 'LDSHARED': + return 'gcc-4.2 -bundle -undefined dynamic_lookup ' + return 'gcc-4.2' + + def gcvs(*args, _orig=sysconfig.get_config_vars): + if args: + return list(map(sysconfig.get_config_var, args)) + return _orig() + + sysconfig.get_config_var = gcv + sysconfig.get_config_vars = gcvs + with EnvironmentVarGuard() as env: + env['CC'] = 'my_cc' + del env['LDSHARED'] + sysconfig.customize_compiler(self.cc) + assert self.cc.linker_so[0] == 'my_cc' + + @pytest.mark.skipif('platform.system == "Windows"') + def test_cxx_commands_used_are_correct(self): + def gcv(v): + if v == 'LDSHARED': + return 'ccache gcc-4.2 -bundle -undefined dynamic_lookup' + elif v == 'LDCXXSHARED': + return 'ccache g++-4.2 -bundle -undefined dynamic_lookup' + elif v == 'CXX': + return 'ccache g++-4.2' + elif v == 'CC': + return 'ccache gcc-4.2' + return '' + + def gcvs(*args, _orig=sysconfig.get_config_vars): + if args: + return list(map(sysconfig.get_config_var, args)) + return _orig() # pragma: no cover + + sysconfig.get_config_var = gcv + sysconfig.get_config_vars = gcvs + with ( + mock.patch.object(self.cc, 'spawn', return_value=None) as mock_spawn, + mock.patch.object(self.cc, '_need_link', return_value=True), + mock.patch.object(self.cc, 'mkpath', return_value=None), + EnvironmentVarGuard() as env, + ): + # override environment overrides in case they're specified by CI + del env['CXX'] + del env['LDCXXSHARED'] + + sysconfig.customize_compiler(self.cc) + assert self.cc.linker_so_cxx[0:2] == ['ccache', 'g++-4.2'] + assert self.cc.linker_exe_cxx[0:2] == ['ccache', 'g++-4.2'] + self.cc.link(None, [], 'a.out', target_lang='c++') + call_args = mock_spawn.call_args[0][0] + expected = ['ccache', 'g++-4.2', '-bundle', '-undefined', 'dynamic_lookup'] + assert call_args[:5] == expected + + self.cc.link_executable([], 'a.out', target_lang='c++') + call_args = mock_spawn.call_args[0][0] + expected = ['ccache', 'g++-4.2', '-o', self.cc.executable_filename('a.out')] + assert call_args[:4] == expected + + env['LDCXXSHARED'] = 'wrapper g++-4.2 -bundle -undefined dynamic_lookup' + env['CXX'] = 'wrapper g++-4.2' + sysconfig.customize_compiler(self.cc) + assert self.cc.linker_so_cxx[0:2] == ['wrapper', 'g++-4.2'] + assert self.cc.linker_exe_cxx[0:2] == ['wrapper', 'g++-4.2'] + self.cc.link(None, [], 'a.out', target_lang='c++') + call_args = mock_spawn.call_args[0][0] + expected = ['wrapper', 'g++-4.2', '-bundle', '-undefined', 'dynamic_lookup'] + assert call_args[:5] == expected + + self.cc.link_executable([], 'a.out', target_lang='c++') + call_args = mock_spawn.call_args[0][0] + expected = [ + 'wrapper', + 'g++-4.2', + '-o', + self.cc.executable_filename('a.out'), + ] + assert call_args[:4] == expected + + @pytest.mark.skipif('platform.system == "Windows"') + @pytest.mark.usefixtures('disable_macos_customization') + def test_cc_overrides_ldshared_for_cxx_correctly(self): + """ + Ensure that setting CC env variable also changes default linker + correctly when building C++ extensions. + + pypa/distutils#126 + """ + + def gcv(v): + if v == 'LDSHARED': + return 'gcc-4.2 -bundle -undefined dynamic_lookup ' + elif v == 'LDCXXSHARED': + return 'g++-4.2 -bundle -undefined dynamic_lookup ' + elif v == 'CXX': + return 'g++-4.2' + elif v == 'CC': + return 'gcc-4.2' + return '' + + def gcvs(*args, _orig=sysconfig.get_config_vars): + if args: + return list(map(sysconfig.get_config_var, args)) + return _orig() + + sysconfig.get_config_var = gcv + sysconfig.get_config_vars = gcvs + with ( + mock.patch.object(self.cc, 'spawn', return_value=None) as mock_spawn, + mock.patch.object(self.cc, '_need_link', return_value=True), + mock.patch.object(self.cc, 'mkpath', return_value=None), + EnvironmentVarGuard() as env, + ): + env['CC'] = 'ccache my_cc' + env['CXX'] = 'my_cxx' + del env['LDSHARED'] + sysconfig.customize_compiler(self.cc) + assert self.cc.linker_so[0:2] == ['ccache', 'my_cc'] + self.cc.link(None, [], 'a.out', target_lang='c++') + call_args = mock_spawn.call_args[0][0] + expected = ['my_cxx', '-bundle', '-undefined', 'dynamic_lookup'] + assert call_args[:4] == expected + + @pytest.mark.skipif('platform.system == "Windows"') + def test_explicit_ldshared(self): + # Issue #18080: + # ensure that setting CC env variable does not change + # explicit LDSHARED setting for linker + def gcv(v): + if v == 'LDSHARED': + return 'gcc-4.2 -bundle -undefined dynamic_lookup ' + return 'gcc-4.2' + + def gcvs(*args, _orig=sysconfig.get_config_vars): + if args: + return list(map(sysconfig.get_config_var, args)) + return _orig() + + sysconfig.get_config_var = gcv + sysconfig.get_config_vars = gcvs + with EnvironmentVarGuard() as env: + env['CC'] = 'my_cc' + env['LDSHARED'] = 'my_ld -bundle -dynamic' + sysconfig.customize_compiler(self.cc) + assert self.cc.linker_so[0] == 'my_ld' + + def test_has_function(self): + # Issue https://github.com/pypa/distutils/issues/64: + # ensure that setting output_dir does not raise + # FileNotFoundError: [Errno 2] No such file or directory: 'a.out' + self.cc.output_dir = 'scratch' + os.chdir(self.mkdtemp()) + self.cc.has_function('abort') + + def test_find_library_file(self, monkeypatch): + compiler = unix.Compiler() + compiler._library_root = lambda dir: dir + monkeypatch.setattr(os.path, 'exists', lambda d: 'existing' in d) + + libname = 'libabc.dylib' if sys.platform != 'cygwin' else 'cygabc.dll' + dirs = ('/foo/bar/missing', '/foo/bar/existing') + assert ( + compiler.find_library_file(dirs, 'abc').replace('\\', '/') + == f'/foo/bar/existing/{libname}' + ) + assert ( + compiler.find_library_file(reversed(dirs), 'abc').replace('\\', '/') + == f'/foo/bar/existing/{libname}' + ) + + monkeypatch.setattr( + os.path, + 'exists', + lambda d: 'existing' in d and '.a' in d and '.dll.a' not in d, + ) + assert ( + compiler.find_library_file(dirs, 'abc').replace('\\', '/') + == '/foo/bar/existing/libabc.a' + ) + assert ( + compiler.find_library_file(reversed(dirs), 'abc').replace('\\', '/') + == '/foo/bar/existing/libabc.a' + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/unix.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/unix.py new file mode 100644 index 0000000000000000000000000000000000000000..1231b32d20fc0524ea50f6a70d7e017211c69e9b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/unix.py @@ -0,0 +1,422 @@ +"""distutils.unixccompiler + +Contains the UnixCCompiler class, a subclass of CCompiler that handles +the "typical" Unix-style command-line C compiler: + * macros defined with -Dname[=value] + * macros undefined with -Uname + * include search directories specified with -Idir + * libraries specified with -lllib + * library search directories specified with -Ldir + * compile handled by 'cc' (or similar) executable with -c option: + compiles .c to .o + * link static library handled by 'ar' command (possibly with 'ranlib') + * link shared library handled by 'cc -shared' +""" + +from __future__ import annotations + +import itertools +import os +import re +import shlex +import sys +from collections.abc import Iterable + +from ... import sysconfig +from ..._log import log +from ..._macos_compat import compiler_fixup +from ..._modified import newer +from ...compat import consolidate_linker_args +from ...errors import DistutilsExecError +from . import base +from .base import _Macro, gen_lib_options, gen_preprocess_options +from .errors import ( + CompileError, + LibError, + LinkError, +) + +# XXX Things not currently handled: +# * optimization/debug/warning flags; we just use whatever's in Python's +# Makefile and live with it. Is this adequate? If not, we might +# have to have a bunch of subclasses GNUCCompiler, SGICCompiler, +# SunCCompiler, and I suspect down that road lies madness. +# * even if we don't know a warning flag from an optimization flag, +# we need some way for outsiders to feed preprocessor/compiler/linker +# flags in to us -- eg. a sysadmin might want to mandate certain flags +# via a site config file, or a user might want to set something for +# compiling this module distribution only via the setup.py command +# line, whatever. As long as these options come from something on the +# current system, they can be as system-dependent as they like, and we +# should just happily stuff them into the preprocessor/compiler/linker +# options and carry on. + + +def _split_env(cmd): + """ + For macOS, split command into 'env' portion (if any) + and the rest of the linker command. + + >>> _split_env(['a', 'b', 'c']) + ([], ['a', 'b', 'c']) + >>> _split_env(['/usr/bin/env', 'A=3', 'gcc']) + (['/usr/bin/env', 'A=3'], ['gcc']) + """ + pivot = 0 + if os.path.basename(cmd[0]) == "env": + pivot = 1 + while '=' in cmd[pivot]: + pivot += 1 + return cmd[:pivot], cmd[pivot:] + + +def _split_aix(cmd): + """ + AIX platforms prefix the compiler with the ld_so_aix + script, so split that from the linker command. + + >>> _split_aix(['a', 'b', 'c']) + ([], ['a', 'b', 'c']) + >>> _split_aix(['/bin/foo/ld_so_aix', 'gcc']) + (['/bin/foo/ld_so_aix'], ['gcc']) + """ + pivot = os.path.basename(cmd[0]) == 'ld_so_aix' + return cmd[:pivot], cmd[pivot:] + + +def _linker_params(linker_cmd, compiler_cmd): + """ + The linker command usually begins with the compiler + command (possibly multiple elements), followed by zero or more + params for shared library building. + + If the LDSHARED env variable overrides the linker command, + however, the commands may not match. + + Return the best guess of the linker parameters by stripping + the linker command. If the compiler command does not + match the linker command, assume the linker command is + just the first element. + + >>> _linker_params('gcc foo bar'.split(), ['gcc']) + ['foo', 'bar'] + >>> _linker_params('gcc foo bar'.split(), ['other']) + ['foo', 'bar'] + >>> _linker_params('ccache gcc foo bar'.split(), 'ccache gcc'.split()) + ['foo', 'bar'] + >>> _linker_params(['gcc'], ['gcc']) + [] + """ + c_len = len(compiler_cmd) + pivot = c_len if linker_cmd[:c_len] == compiler_cmd else 1 + return linker_cmd[pivot:] + + +class Compiler(base.Compiler): + compiler_type = 'unix' + + # These are used by CCompiler in two places: the constructor sets + # instance attributes 'preprocessor', 'compiler', etc. from them, and + # 'set_executable()' allows any of these to be set. The defaults here + # are pretty generic; they will probably have to be set by an outsider + # (eg. using information discovered by the sysconfig about building + # Python extensions). + executables = { + 'preprocessor': None, + 'compiler': ["cc"], + 'compiler_so': ["cc"], + 'compiler_cxx': ["c++"], + 'compiler_so_cxx': ["c++"], + 'linker_so': ["cc", "-shared"], + 'linker_so_cxx': ["c++", "-shared"], + 'linker_exe': ["cc"], + 'linker_exe_cxx': ["c++", "-shared"], + 'archiver': ["ar", "-cr"], + 'ranlib': None, + } + + if sys.platform[:6] == "darwin": + executables['ranlib'] = ["ranlib"] + + # Needed for the filename generation methods provided by the base + # class, CCompiler. NB. whoever instantiates/uses a particular + # UnixCCompiler instance should set 'shared_lib_ext' -- we set a + # reasonable common default here, but it's not necessarily used on all + # Unices! + + src_extensions = [".c", ".C", ".cc", ".cxx", ".cpp", ".m"] + obj_extension = ".o" + static_lib_extension = ".a" + shared_lib_extension = ".so" + dylib_lib_extension = ".dylib" + xcode_stub_lib_extension = ".tbd" + static_lib_format = shared_lib_format = dylib_lib_format = "lib%s%s" + xcode_stub_lib_format = dylib_lib_format + if sys.platform == "cygwin": + exe_extension = ".exe" + shared_lib_extension = ".dll.a" + dylib_lib_extension = ".dll" + dylib_lib_format = "cyg%s%s" + + def _fix_lib_args(self, libraries, library_dirs, runtime_library_dirs): + """Remove standard library path from rpath""" + libraries, library_dirs, runtime_library_dirs = super()._fix_lib_args( + libraries, library_dirs, runtime_library_dirs + ) + libdir = sysconfig.get_config_var('LIBDIR') + if ( + runtime_library_dirs + and libdir.startswith("/usr/lib") + and (libdir in runtime_library_dirs) + ): + runtime_library_dirs.remove(libdir) + return libraries, library_dirs, runtime_library_dirs + + def preprocess( + self, + source: str | os.PathLike[str], + output_file: str | os.PathLike[str] | None = None, + macros: list[_Macro] | None = None, + include_dirs: list[str] | tuple[str, ...] | None = None, + extra_preargs: list[str] | None = None, + extra_postargs: Iterable[str] | None = None, + ): + fixed_args = self._fix_compile_args(None, macros, include_dirs) + ignore, macros, include_dirs = fixed_args + pp_opts = gen_preprocess_options(macros, include_dirs) + pp_args = self.preprocessor + pp_opts + if output_file: + pp_args.extend(['-o', output_file]) + if extra_preargs: + pp_args[:0] = extra_preargs + if extra_postargs: + pp_args.extend(extra_postargs) + pp_args.append(source) + + # reasons to preprocess: + # - force is indicated + # - output is directed to stdout + # - source file is newer than the target + preprocess = self.force or output_file is None or newer(source, output_file) + if not preprocess: + return + + if output_file: + self.mkpath(os.path.dirname(output_file)) + + try: + self.spawn(pp_args) + except DistutilsExecError as msg: + raise CompileError(msg) + + def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts): + compiler_so = compiler_fixup(self.compiler_so, cc_args + extra_postargs) + compiler_so_cxx = compiler_fixup(self.compiler_so_cxx, cc_args + extra_postargs) + try: + if self.detect_language(src) == 'c++': + self.spawn( + compiler_so_cxx + cc_args + [src, '-o', obj] + extra_postargs + ) + else: + self.spawn(compiler_so + cc_args + [src, '-o', obj] + extra_postargs) + except DistutilsExecError as msg: + raise CompileError(msg) + + def create_static_lib( + self, objects, output_libname, output_dir=None, debug=False, target_lang=None + ): + objects, output_dir = self._fix_object_args(objects, output_dir) + + output_filename = self.library_filename(output_libname, output_dir=output_dir) + + if self._need_link(objects, output_filename): + self.mkpath(os.path.dirname(output_filename)) + self.spawn(self.archiver + [output_filename] + objects + self.objects) + + # Not many Unices required ranlib anymore -- SunOS 4.x is, I + # think the only major Unix that does. Maybe we need some + # platform intelligence here to skip ranlib if it's not + # needed -- or maybe Python's configure script took care of + # it for us, hence the check for leading colon. + if self.ranlib: + try: + self.spawn(self.ranlib + [output_filename]) + except DistutilsExecError as msg: + raise LibError(msg) + else: + log.debug("skipping %s (up-to-date)", output_filename) + + def link( + self, + target_desc, + objects: list[str] | tuple[str, ...], + output_filename, + output_dir: str | None = None, + libraries: list[str] | tuple[str, ...] | None = None, + library_dirs: list[str] | tuple[str, ...] | None = None, + runtime_library_dirs: list[str] | tuple[str, ...] | None = None, + export_symbols=None, + debug=False, + extra_preargs=None, + extra_postargs=None, + build_temp=None, + target_lang=None, + ): + objects, output_dir = self._fix_object_args(objects, output_dir) + fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs) + libraries, library_dirs, runtime_library_dirs = fixed_args + + lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries) + if not isinstance(output_dir, (str, type(None))): + raise TypeError("'output_dir' must be a string or None") + if output_dir is not None: + output_filename = os.path.join(output_dir, output_filename) + + if self._need_link(objects, output_filename): + ld_args = objects + self.objects + lib_opts + ['-o', output_filename] + if debug: + ld_args[:0] = ['-g'] + if extra_preargs: + ld_args[:0] = extra_preargs + if extra_postargs: + ld_args.extend(extra_postargs) + self.mkpath(os.path.dirname(output_filename)) + try: + # Select a linker based on context: linker_exe when + # building an executable or linker_so (with shared options) + # when building a shared library. + building_exe = target_desc == base.Compiler.EXECUTABLE + target_cxx = target_lang == "c++" + linker = ( + (self.linker_exe_cxx if target_cxx else self.linker_exe) + if building_exe + else (self.linker_so_cxx if target_cxx else self.linker_so) + )[:] + + if target_cxx and self.compiler_cxx: + env, linker_ne = _split_env(linker) + aix, linker_na = _split_aix(linker_ne) + _, compiler_cxx_ne = _split_env(self.compiler_cxx) + _, linker_exe_ne = _split_env(self.linker_exe_cxx) + + params = _linker_params(linker_na, linker_exe_ne) + linker = env + aix + compiler_cxx_ne + params + + linker = compiler_fixup(linker, ld_args) + + self.spawn(linker + ld_args) + except DistutilsExecError as msg: + raise LinkError(msg) + else: + log.debug("skipping %s (up-to-date)", output_filename) + + # -- Miscellaneous methods ----------------------------------------- + # These are all used by the 'gen_lib_options() function, in + # ccompiler.py. + + def library_dir_option(self, dir): + return "-L" + dir + + def _is_gcc(self): + cc_var = sysconfig.get_config_var("CC") + compiler = os.path.basename(shlex.split(cc_var)[0]) + return "gcc" in compiler or "g++" in compiler + + def runtime_library_dir_option(self, dir: str) -> str | list[str]: # type: ignore[override] # Fixed in pypa/distutils#339 + # XXX Hackish, at the very least. See Python bug #445902: + # https://bugs.python.org/issue445902 + # Linkers on different platforms need different options to + # specify that directories need to be added to the list of + # directories searched for dependencies when a dynamic library + # is sought. GCC on GNU systems (Linux, FreeBSD, ...) has to + # be told to pass the -R option through to the linker, whereas + # other compilers and gcc on other systems just know this. + # Other compilers may need something slightly different. At + # this time, there's no way to determine this information from + # the configuration data stored in the Python installation, so + # we use this hack. + if sys.platform[:6] == "darwin": + from distutils.util import get_macosx_target_ver, split_version + + macosx_target_ver = get_macosx_target_ver() + if macosx_target_ver and split_version(macosx_target_ver) >= [10, 5]: + return "-Wl,-rpath," + dir + else: # no support for -rpath on earlier macOS versions + return "-L" + dir + elif sys.platform[:7] == "freebsd": + return "-Wl,-rpath=" + dir + elif sys.platform[:5] == "hp-ux": + return [ + "-Wl,+s" if self._is_gcc() else "+s", + "-L" + dir, + ] + + # For all compilers, `-Wl` is the presumed way to pass a + # compiler option to the linker + if sysconfig.get_config_var("GNULD") == "yes": + return consolidate_linker_args([ + # Force RUNPATH instead of RPATH + "-Wl,--enable-new-dtags", + "-Wl,-rpath," + dir, + ]) + else: + return "-Wl,-R" + dir + + def library_option(self, lib): + return "-l" + lib + + @staticmethod + def _library_root(dir): + """ + macOS users can specify an alternate SDK using'-isysroot'. + Calculate the SDK root if it is specified. + + Note that, as of Xcode 7, Apple SDKs may contain textual stub + libraries with .tbd extensions rather than the normal .dylib + shared libraries installed in /. The Apple compiler tool + chain handles this transparently but it can cause problems + for programs that are being built with an SDK and searching + for specific libraries. Callers of find_library_file need to + keep in mind that the base filename of the returned SDK library + file might have a different extension from that of the library + file installed on the running system, for example: + /Applications/Xcode.app/Contents/Developer/Platforms/ + MacOSX.platform/Developer/SDKs/MacOSX10.11.sdk/ + usr/lib/libedit.tbd + vs + /usr/lib/libedit.dylib + """ + cflags = sysconfig.get_config_var('CFLAGS') + match = re.search(r'-isysroot\s*(\S+)', cflags) + + apply_root = ( + sys.platform == 'darwin' + and match + and ( + dir.startswith('/System/') + or (dir.startswith('/usr/') and not dir.startswith('/usr/local/')) + ) + ) + + return os.path.join(match.group(1), dir[1:]) if apply_root else dir + + def find_library_file(self, dirs, lib, debug=False): + """ + Second-guess the linker with not much hard + data to go on: GCC seems to prefer the shared library, so + assume that *all* Unix C compilers do, + ignoring even GCC's "-static" option. + """ + lib_names = ( + self.library_filename(lib, lib_type=type) + for type in 'dylib xcode_stub shared static'.split() + ) + + roots = map(self._library_root, dirs) + + searched = itertools.starmap(os.path.join, itertools.product(roots, lib_names)) + + found = filter(os.path.exists, searched) + + # Return None if it could not be found in any dir. + return next(found, None) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/zos.py b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/zos.py new file mode 100644 index 0000000000000000000000000000000000000000..681c63166be8dfea8ec3f621586b901220abe1c5 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/compilers/C/zos.py @@ -0,0 +1,230 @@ +"""distutils.zosccompiler + +Contains the selection of the c & c++ compilers on z/OS. There are several +different c compilers on z/OS, all of them are optional, so the correct +one needs to be chosen based on the users input. This is compatible with +the following compilers: + +IBM C/C++ For Open Enterprise Languages on z/OS 2.0 +IBM Open XL C/C++ 1.1 for z/OS +IBM XL C/C++ V2.4.1 for z/OS 2.4 and 2.5 +IBM z/OS XL C/C++ +""" + +import os + +from ... import sysconfig +from ...errors import DistutilsExecError +from . import unix +from .errors import CompileError + +_cc_args = { + 'ibm-openxl': [ + '-m64', + '-fvisibility=default', + '-fzos-le-char-mode=ascii', + '-fno-short-enums', + ], + 'ibm-xlclang': [ + '-q64', + '-qexportall', + '-qascii', + '-qstrict', + '-qnocsect', + '-Wa,asa,goff', + '-Wa,xplink', + '-qgonumber', + '-qenum=int', + '-Wc,DLL', + ], + 'ibm-xlc': [ + '-q64', + '-qexportall', + '-qascii', + '-qstrict', + '-qnocsect', + '-Wa,asa,goff', + '-Wa,xplink', + '-qgonumber', + '-qenum=int', + '-Wc,DLL', + '-qlanglvl=extc99', + ], +} + +_cxx_args = { + 'ibm-openxl': [ + '-m64', + '-fvisibility=default', + '-fzos-le-char-mode=ascii', + '-fno-short-enums', + ], + 'ibm-xlclang': [ + '-q64', + '-qexportall', + '-qascii', + '-qstrict', + '-qnocsect', + '-Wa,asa,goff', + '-Wa,xplink', + '-qgonumber', + '-qenum=int', + '-Wc,DLL', + ], + 'ibm-xlc': [ + '-q64', + '-qexportall', + '-qascii', + '-qstrict', + '-qnocsect', + '-Wa,asa,goff', + '-Wa,xplink', + '-qgonumber', + '-qenum=int', + '-Wc,DLL', + '-qlanglvl=extended0x', + ], +} + +_asm_args = { + 'ibm-openxl': ['-fasm', '-fno-integrated-as', '-Wa,--ASA', '-Wa,--GOFF'], + 'ibm-xlclang': [], + 'ibm-xlc': [], +} + +_ld_args = { + 'ibm-openxl': [], + 'ibm-xlclang': ['-Wl,dll', '-q64'], + 'ibm-xlc': ['-Wl,dll', '-q64'], +} + + +# Python on z/OS is built with no compiler specific options in it's CFLAGS. +# But each compiler requires it's own specific options to build successfully, +# though some of the options are common between them +class Compiler(unix.Compiler): + src_extensions = ['.c', '.C', '.cc', '.cxx', '.cpp', '.m', '.s'] + _cpp_extensions = ['.cc', '.cpp', '.cxx', '.C'] + _asm_extensions = ['.s'] + + def _get_zos_compiler_name(self): + zos_compiler_names = [ + os.path.basename(binary) + for envvar in ('CC', 'CXX', 'LDSHARED') + if (binary := os.environ.get(envvar, None)) + ] + if len(zos_compiler_names) == 0: + return 'ibm-openxl' + + zos_compilers = {} + for compiler in ( + 'ibm-clang', + 'ibm-clang64', + 'ibm-clang++', + 'ibm-clang++64', + 'clang', + 'clang++', + 'clang-14', + ): + zos_compilers[compiler] = 'ibm-openxl' + + for compiler in ('xlclang', 'xlclang++', 'njsc', 'njsc++'): + zos_compilers[compiler] = 'ibm-xlclang' + + for compiler in ('xlc', 'xlC', 'xlc++'): + zos_compilers[compiler] = 'ibm-xlc' + + return zos_compilers.get(zos_compiler_names[0], 'ibm-openxl') + + def __init__(self, verbose=False, force=False): + super().__init__(verbose, force=force) + self.zos_compiler = self._get_zos_compiler_name() + sysconfig.customize_compiler(self) + + def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts): + local_args = [] + if ext in self._cpp_extensions: + compiler = self.compiler_cxx + local_args.extend(_cxx_args[self.zos_compiler]) + elif ext in self._asm_extensions: + compiler = self.compiler_so + local_args.extend(_cc_args[self.zos_compiler]) + local_args.extend(_asm_args[self.zos_compiler]) + else: + compiler = self.compiler_so + local_args.extend(_cc_args[self.zos_compiler]) + local_args.extend(cc_args) + + try: + self.spawn(compiler + local_args + [src, '-o', obj] + extra_postargs) + except DistutilsExecError as msg: + raise CompileError(msg) + + def runtime_library_dir_option(self, dir): + return '-L' + dir + + def link( + self, + target_desc, + objects, + output_filename, + output_dir=None, + libraries=None, + library_dirs=None, + runtime_library_dirs=None, + export_symbols=None, + debug=False, + extra_preargs=None, + extra_postargs=None, + build_temp=None, + target_lang=None, + ): + # For a built module to use functions from cpython, it needs to use Pythons + # side deck file. The side deck is located beside the libpython3.xx.so + ldversion = sysconfig.get_config_var('LDVERSION') + if sysconfig.python_build: + side_deck_path = os.path.join( + sysconfig.get_config_var('abs_builddir'), + f'libpython{ldversion}.x', + ) + else: + side_deck_path = os.path.join( + sysconfig.get_config_var('installed_base'), + sysconfig.get_config_var('platlibdir'), + f'libpython{ldversion}.x', + ) + + if os.path.exists(side_deck_path): + if extra_postargs: + extra_postargs.append(side_deck_path) + else: + extra_postargs = [side_deck_path] + + # Check and replace libraries included side deck files + if runtime_library_dirs: + for dir in runtime_library_dirs: + for library in libraries[:]: + library_side_deck = os.path.join(dir, f'{library}.x') + if os.path.exists(library_side_deck): + libraries.remove(library) + extra_postargs.append(library_side_deck) + break + + # Any required ld args for the given compiler + extra_postargs.extend(_ld_args[self.zos_compiler]) + + super().link( + target_desc, + objects, + output_filename, + output_dir, + libraries, + library_dirs, + runtime_library_dirs, + export_symbols, + debug, + extra_preargs, + extra_postargs, + build_temp, + target_lang, + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/core.py b/lib/python3.11/site-packages/setuptools/_distutils/core.py new file mode 100644 index 0000000000000000000000000000000000000000..bd62546bdd9d35dcc46e0b46a46b0c61a43fd0ba --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/core.py @@ -0,0 +1,289 @@ +"""distutils.core + +The only module that needs to be imported to use the Distutils; provides +the 'setup' function (which is to be called from the setup script). Also +indirectly provides the Distribution and Command classes, although they are +really defined in distutils.dist and distutils.cmd. +""" + +from __future__ import annotations + +import os +import sys +import tokenize +from collections.abc import Iterable + +from .cmd import Command +from .debug import DEBUG + +# Mainly import these so setup scripts can "from distutils.core import" them. +from .dist import Distribution +from .errors import ( + CCompilerError, + DistutilsArgError, + DistutilsError, + DistutilsSetupError, +) +from .extension import Extension + +__all__ = ['Distribution', 'Command', 'Extension', 'setup'] + +# This is a barebones help message generated displayed when the user +# runs the setup script with no arguments at all. More useful help +# is generated with various --help options: global help, list commands, +# and per-command help. +USAGE = """\ +usage: %(script)s [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...] + or: %(script)s --help [cmd1 cmd2 ...] + or: %(script)s --help-commands + or: %(script)s cmd --help +""" + + +def gen_usage(script_name): + script = os.path.basename(script_name) + return USAGE % locals() + + +# Some mild magic to control the behaviour of 'setup()' from 'run_setup()'. +_setup_stop_after = None +_setup_distribution = None + +# Legal keyword arguments for the setup() function +setup_keywords = ( + 'distclass', + 'script_name', + 'script_args', + 'options', + 'name', + 'version', + 'author', + 'author_email', + 'maintainer', + 'maintainer_email', + 'url', + 'license', + 'description', + 'long_description', + 'keywords', + 'platforms', + 'classifiers', + 'download_url', + 'requires', + 'provides', + 'obsoletes', +) + +# Legal keyword arguments for the Extension constructor +extension_keywords = ( + 'name', + 'sources', + 'include_dirs', + 'define_macros', + 'undef_macros', + 'library_dirs', + 'libraries', + 'runtime_library_dirs', + 'extra_objects', + 'extra_compile_args', + 'extra_link_args', + 'swig_opts', + 'export_symbols', + 'depends', + 'language', +) + + +def setup(**attrs): # noqa: C901 + """The gateway to the Distutils: do everything your setup script needs + to do, in a highly flexible and user-driven way. Briefly: create a + Distribution instance; find and parse config files; parse the command + line; run each Distutils command found there, customized by the options + supplied to 'setup()' (as keyword arguments), in config files, and on + the command line. + + The Distribution instance might be an instance of a class supplied via + the 'distclass' keyword argument to 'setup'; if no such class is + supplied, then the Distribution class (in dist.py) is instantiated. + All other arguments to 'setup' (except for 'cmdclass') are used to set + attributes of the Distribution instance. + + The 'cmdclass' argument, if supplied, is a dictionary mapping command + names to command classes. Each command encountered on the command line + will be turned into a command class, which is in turn instantiated; any + class found in 'cmdclass' is used in place of the default, which is + (for command 'foo_bar') class 'foo_bar' in module + 'distutils.command.foo_bar'. The command class must provide a + 'user_options' attribute which is a list of option specifiers for + 'distutils.fancy_getopt'. Any command-line options between the current + and the next command are used to set attributes of the current command + object. + + When the entire command-line has been successfully parsed, calls the + 'run()' method on each command object in turn. This method will be + driven entirely by the Distribution object (which each command object + has a reference to, thanks to its constructor), and the + command-specific options that became attributes of each command + object. + """ + + global _setup_stop_after, _setup_distribution + + # Determine the distribution class -- either caller-supplied or + # our Distribution (see below). + klass = attrs.get('distclass') + if klass: + attrs.pop('distclass') + else: + klass = Distribution + + if 'script_name' not in attrs: + attrs['script_name'] = os.path.basename(sys.argv[0]) + if 'script_args' not in attrs: + attrs['script_args'] = sys.argv[1:] + + # Create the Distribution instance, using the remaining arguments + # (ie. everything except distclass) to initialize it + try: + _setup_distribution = dist = klass(attrs) + except DistutilsSetupError as msg: + if 'name' not in attrs: + raise SystemExit(f"error in setup command: {msg}") + else: + raise SystemExit("error in {} setup command: {}".format(attrs['name'], msg)) + + if _setup_stop_after == "init": + return dist + + # Find and parse the config file(s): they will override options from + # the setup script, but be overridden by the command line. + dist.parse_config_files() + + if DEBUG: + print("options (after parsing config files):") + dist.dump_option_dicts() + + if _setup_stop_after == "config": + return dist + + # Parse the command line and override config files; any + # command-line errors are the end user's fault, so turn them into + # SystemExit to suppress tracebacks. + try: + ok = dist.parse_command_line() + except DistutilsArgError as msg: + raise SystemExit(gen_usage(dist.script_name) + f"\nerror: {msg}") + + if DEBUG: + print("options (after parsing command line):") + dist.dump_option_dicts() + + if _setup_stop_after == "commandline": + return dist + + # And finally, run all the commands found on the command line. + if ok: + return run_commands(dist) + + return dist + + +# setup () + + +def run_commands(dist): + """Given a Distribution object run all the commands, + raising ``SystemExit`` errors in the case of failure. + + This function assumes that either ``sys.argv`` or ``dist.script_args`` + is already set accordingly. + """ + try: + dist.run_commands() + except KeyboardInterrupt: + raise SystemExit("interrupted") + except OSError as exc: + if DEBUG: + sys.stderr.write(f"error: {exc}\n") + raise + else: + raise SystemExit(f"error: {exc}") + + except (DistutilsError, CCompilerError) as msg: + if DEBUG: + raise + else: + raise SystemExit("error: " + str(msg)) + + return dist + + +def run_setup(script_name, script_args: Iterable[str] | None = None, stop_after="run"): + """Run a setup script in a somewhat controlled environment, and + return the Distribution instance that drives things. This is useful + if you need to find out the distribution meta-data (passed as + keyword args from 'script' to 'setup()', or the contents of the + config files or command-line. + + 'script_name' is a file that will be read and run with 'exec()'; + 'sys.argv[0]' will be replaced with 'script' for the duration of the + call. 'script_args' is a list of strings; if supplied, + 'sys.argv[1:]' will be replaced by 'script_args' for the duration of + the call. + + 'stop_after' tells 'setup()' when to stop processing; possible + values: + init + stop after the Distribution instance has been created and + populated with the keyword arguments to 'setup()' + config + stop after config files have been parsed (and their data + stored in the Distribution instance) + commandline + stop after the command-line ('sys.argv[1:]' or 'script_args') + have been parsed (and the data stored in the Distribution) + run [default] + stop after all commands have been run (the same as if 'setup()' + had been called in the usual way + + Returns the Distribution instance, which provides all information + used to drive the Distutils. + """ + if stop_after not in ('init', 'config', 'commandline', 'run'): + raise ValueError(f"invalid value for 'stop_after': {stop_after!r}") + + global _setup_stop_after, _setup_distribution + _setup_stop_after = stop_after + + save_argv = sys.argv.copy() + g = {'__file__': script_name, '__name__': '__main__'} + try: + try: + sys.argv[0] = script_name + if script_args is not None: + sys.argv[1:] = script_args + # tokenize.open supports automatic encoding detection + with tokenize.open(script_name) as f: + code = f.read().replace(r'\r\n', r'\n') + exec(code, g) + finally: + sys.argv = save_argv + _setup_stop_after = None + except SystemExit: + # Hmm, should we do something if exiting with a non-zero code + # (ie. error)? + pass + + if _setup_distribution is None: + raise RuntimeError( + "'distutils.core.setup()' was never called -- " + f"perhaps '{script_name}' is not a Distutils setup script?" + ) + + # I wonder if the setup script's namespace -- g and l -- would be of + # any interest to callers? + # print "_setup_distribution:", _setup_distribution + return _setup_distribution + + +# run_setup () diff --git a/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py b/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py new file mode 100644 index 0000000000000000000000000000000000000000..de89e3cd8402c9b7048c9e456ff67601934f972b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/cygwinccompiler.py @@ -0,0 +1,31 @@ +from .compilers.C import cygwin +from .compilers.C.cygwin import ( + CONFIG_H_NOTOK, + CONFIG_H_OK, + CONFIG_H_UNCERTAIN, + check_config_h, + get_msvcr, + is_cygwincc, +) + +__all__ = [ + 'CONFIG_H_NOTOK', + 'CONFIG_H_OK', + 'CONFIG_H_UNCERTAIN', + 'CygwinCCompiler', + 'Mingw32CCompiler', + 'check_config_h', + 'get_msvcr', + 'is_cygwincc', +] + + +CygwinCCompiler = cygwin.Compiler +Mingw32CCompiler = cygwin.MinGW32Compiler + + +get_versions = None +""" +A stand-in for the previous get_versions() function to prevent failures +when monkeypatched. See pypa/setuptools#2969. +""" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/debug.py b/lib/python3.11/site-packages/setuptools/_distutils/debug.py new file mode 100644 index 0000000000000000000000000000000000000000..daf1660f0d821143e388d37532a39ddfd2ca0347 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/debug.py @@ -0,0 +1,5 @@ +import os + +# If DISTUTILS_DEBUG is anything other than the empty string, we run in +# debug mode. +DEBUG = os.environ.get('DISTUTILS_DEBUG') diff --git a/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py b/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py new file mode 100644 index 0000000000000000000000000000000000000000..09a8a2e126c8bb009dbbe61c5c4bd5e358744996 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/dep_util.py @@ -0,0 +1,14 @@ +import warnings + +from . import _modified + + +def __getattr__(name): + if name not in ['newer', 'newer_group', 'newer_pairwise']: + raise AttributeError(name) + warnings.warn( + "dep_util is Deprecated. Use functions from setuptools instead.", + DeprecationWarning, + stacklevel=2, + ) + return getattr(_modified, name) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/dir_util.py b/lib/python3.11/site-packages/setuptools/_distutils/dir_util.py new file mode 100644 index 0000000000000000000000000000000000000000..61b4575aa0318029c103f40af179cab20ed179b2 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/dir_util.py @@ -0,0 +1,232 @@ +"""distutils.dir_util + +Utility functions for manipulating directories and directory trees.""" + +import functools +import itertools +import os +import pathlib + +from . import file_util +from ._log import log +from .errors import DistutilsFileError, DistutilsInternalError + + +class SkipRepeatAbsolutePaths(set): + """ + Cache for mkpath. + + In addition to cheapening redundant calls, eliminates redundant + "creating /foo/bar/baz" messages in dry-run mode. + """ + + def __init__(self): + SkipRepeatAbsolutePaths.instance = self + + @classmethod + def clear(cls): + super(cls, cls.instance).clear() + + def wrap(self, func): + @functools.wraps(func) + def wrapper(path, *args, **kwargs): + if path.absolute() in self: + return + result = func(path, *args, **kwargs) + self.add(path.absolute()) + return result + + return wrapper + + +# Python 3.8 compatibility +wrapper = SkipRepeatAbsolutePaths().wrap + + +@functools.singledispatch +@wrapper +def mkpath(name: pathlib.Path, mode=0o777, verbose=True) -> None: + """Create a directory and any missing ancestor directories. + + If the directory already exists (or if 'name' is the empty string, which + means the current directory, which of course exists), then do nothing. + Raise DistutilsFileError if unable to create some directory along the way + (eg. some sub-path exists, but is a file rather than a directory). + If 'verbose' is true, log the directory created. + """ + if verbose and not name.is_dir(): + log.info("creating %s", name) + + try: + name.mkdir(mode=mode, parents=True, exist_ok=True) + except OSError as exc: + raise DistutilsFileError(f"could not create '{name}': {exc.args[-1]}") + + +@mkpath.register +def _(name: str, *args, **kwargs): + return mkpath(pathlib.Path(name), *args, **kwargs) + + +@mkpath.register +def _(name: None, *args, **kwargs): + """ + Detect a common bug -- name is None. + """ + raise DistutilsInternalError(f"mkpath: 'name' must be a string (got {name!r})") + + +def create_tree(base_dir, files, mode=0o777, verbose=True): + """Create all the empty directories under 'base_dir' needed to put 'files' + there. + + 'base_dir' is just the name of a directory which doesn't necessarily + exist yet; 'files' is a list of filenames to be interpreted relative to + 'base_dir'. 'base_dir' + the directory portion of every file in 'files' + will be created if it doesn't already exist. 'mode' and 'verbose' + flags are as for 'mkpath()'. + """ + # First get the list of directories to create + need_dir = set(os.path.join(base_dir, os.path.dirname(file)) for file in files) + + # Now create them + for dir in sorted(need_dir): + mkpath(dir, mode, verbose=verbose) + + +def copy_tree( + src, + dst, + preserve_mode=True, + preserve_times=True, + preserve_symlinks=False, + update=False, + verbose=True, +): + """Copy an entire directory tree 'src' to a new location 'dst'. + + Both 'src' and 'dst' must be directory names. If 'src' is not a + directory, raise DistutilsFileError. If 'dst' does not exist, it is + created with 'mkpath()'. The end result of the copy is that every + file in 'src' is copied to 'dst', and directories under 'src' are + recursively copied to 'dst'. Return the list of files that were + copied or might have been copied, using their output name. The + return value is unaffected by 'update': it is simply + the list of all files under 'src', with the names changed to be + under 'dst'. + + 'preserve_mode' and 'preserve_times' are the same as for + 'copy_file'; note that they only apply to regular files, not to + directories. If 'preserve_symlinks' is true, symlinks will be + copied as symlinks (on platforms that support them!); otherwise + (the default), the destination of the symlink will be copied. + 'update' and 'verbose' are the same as for 'copy_file'. + """ + if not os.path.isdir(src): + raise DistutilsFileError(f"cannot copy tree '{src}': not a directory") + try: + names = os.listdir(src) + except OSError as e: + raise DistutilsFileError(f"error listing files in '{src}': {e.strerror}") + + mkpath(dst, verbose=verbose) + + copy_one = functools.partial( + _copy_one, + src=src, + dst=dst, + preserve_symlinks=preserve_symlinks, + verbose=verbose, + preserve_mode=preserve_mode, + preserve_times=preserve_times, + update=update, + ) + return list(itertools.chain.from_iterable(map(copy_one, names))) + + +def _copy_one( + name, + *, + src, + dst, + preserve_symlinks, + verbose, + preserve_mode, + preserve_times, + update, +): + src_name = os.path.join(src, name) + dst_name = os.path.join(dst, name) + + if name.startswith('.nfs'): + # skip NFS rename files + return + + if preserve_symlinks and os.path.islink(src_name): + link_dest = os.readlink(src_name) + if verbose >= 1: + log.info("linking %s -> %s", dst_name, link_dest) + os.symlink(link_dest, dst_name) + yield dst_name + + elif os.path.isdir(src_name): + yield from copy_tree( + src_name, + dst_name, + preserve_mode, + preserve_times, + preserve_symlinks, + update, + verbose=verbose, + ) + else: + file_util.copy_file( + src_name, + dst_name, + preserve_mode, + preserve_times, + update, + verbose=verbose, + ) + yield dst_name + + +def _build_cmdtuple(path, cmdtuples): + """Helper for remove_tree().""" + for f in os.listdir(path): + real_f = os.path.join(path, f) + if os.path.isdir(real_f) and not os.path.islink(real_f): + _build_cmdtuple(real_f, cmdtuples) + else: + cmdtuples.append((os.remove, real_f)) + cmdtuples.append((os.rmdir, path)) + + +def remove_tree(directory, verbose=True): + """Recursively remove an entire directory tree. + + Any errors are ignored (apart from being reported to stdout if 'verbose' + is true). + """ + if verbose >= 1: + log.info("removing '%s' (and everything under it)", directory) + cmdtuples = [] + _build_cmdtuple(directory, cmdtuples) + for cmd in cmdtuples: + try: + cmd[0](cmd[1]) + # Clear the cache + SkipRepeatAbsolutePaths.clear() + except OSError as exc: + log.warning("error removing %s: %s", directory, exc) + + +def ensure_relative(path): + """Take the full path 'path', and make it a relative path. + + This is useful to make 'path' the second argument to os.path.join(). + """ + drive, path = os.path.splitdrive(path) + if path[0:1] == os.sep: + path = drive + path[1:] + return path diff --git a/lib/python3.11/site-packages/setuptools/_distutils/dist.py b/lib/python3.11/site-packages/setuptools/_distutils/dist.py new file mode 100644 index 0000000000000000000000000000000000000000..e04e487b58bd75b5b50ccb005c2c3de4cf5038af --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/dist.py @@ -0,0 +1,1384 @@ +"""distutils.dist + +Provides the Distribution class, which represents the module distribution +being built/installed/distributed. +""" + +from __future__ import annotations + +import contextlib +import logging +import os +import pathlib +import re +import sys +import warnings +from collections.abc import Iterable, MutableMapping +from email import message_from_file +from typing import ( + IO, + TYPE_CHECKING, + Any, + ClassVar, + Literal, + TypeVar, + Union, + overload, +) + +from packaging.utils import canonicalize_name, canonicalize_version + +from ._log import log +from .debug import DEBUG +from .errors import ( + DistutilsArgError, + DistutilsClassError, + DistutilsModuleError, + DistutilsOptionError, +) +from .fancy_getopt import FancyGetopt, translate_longopt +from .util import check_environ, rfc822_escape, strtobool + +if TYPE_CHECKING: + from _typeshed import SupportsWrite + from typing_extensions import TypeAlias + + # type-only import because of mutual dependence between these modules + from .cmd import Command + +_CommandT = TypeVar("_CommandT", bound="Command") +_OptionsList: TypeAlias = list[ + Union[tuple[str, Union[str, None], str, int], tuple[str, Union[str, None], str]] +] + + +# Regex to define acceptable Distutils command names. This is not *quite* +# the same as a Python NAME -- I don't allow leading underscores. The fact +# that they're very similar is no coincidence; the default naming scheme is +# to look for a Python module named after the command. +command_re = re.compile(r'^[a-zA-Z]([a-zA-Z0-9_]*)$') + + +def _ensure_list(value: str | Iterable[str], fieldname) -> str | list[str]: + if isinstance(value, str): + # a string containing comma separated values is okay. It will + # be converted to a list by Distribution.finalize_options(). + pass + elif not isinstance(value, list): + # passing a tuple or an iterator perhaps, warn and convert + typename = type(value).__name__ + msg = "Warning: '{fieldname}' should be a list, got type '{typename}'" + msg = msg.format(**locals()) + log.warning(msg) + value = list(value) + return value + + +class Distribution: + """The core of the Distutils. Most of the work hiding behind 'setup' + is really done within a Distribution instance, which farms the work out + to the Distutils commands specified on the command line. + + Setup scripts will almost never instantiate Distribution directly, + unless the 'setup()' function is totally inadequate to their needs. + However, it is conceivable that a setup script might wish to subclass + Distribution for some specialized purpose, and then pass the subclass + to 'setup()' as the 'distclass' keyword argument. If so, it is + necessary to respect the expectations that 'setup' has of Distribution. + See the code for 'setup()', in core.py, for details. + """ + + # 'global_options' describes the command-line options that may be + # supplied to the setup script prior to any actual commands. + # Eg. "./setup.py -n" or "./setup.py --quiet" both take advantage of + # these global options. This list should be kept to a bare minimum, + # since every global option is also valid as a command option -- and we + # don't want to pollute the commands with too many options that they + # have minimal control over. + # The fourth entry for verbose means that it can be repeated. + global_options: ClassVar[_OptionsList] = [ + ('verbose', 'v', "run verbosely (default)", 1), + ('quiet', 'q', "run quietly (turns verbosity off)"), + ('help', 'h', "show detailed help message"), + ('no-user-cfg', None, 'ignore pydistutils.cfg in your home directory'), + ] + + # 'common_usage' is a short (2-3 line) string describing the common + # usage of the setup script. + common_usage: ClassVar[str] = """\ +Common commands: (see '--help-commands' for more) + + setup.py build will build the package underneath 'build/' + setup.py install will install the package +""" + + # options that are not propagated to the commands + display_options: ClassVar[_OptionsList] = [ + ('help-commands', None, "list all available commands"), + ('name', None, "print package name"), + ('version', 'V', "print package version"), + ('fullname', None, "print -"), + ('author', None, "print the author's name"), + ('author-email', None, "print the author's email address"), + ('maintainer', None, "print the maintainer's name"), + ('maintainer-email', None, "print the maintainer's email address"), + ('contact', None, "print the maintainer's name if known, else the author's"), + ( + 'contact-email', + None, + "print the maintainer's email address if known, else the author's", + ), + ('url', None, "print the URL for this package"), + ('license', None, "print the license of the package"), + ('licence', None, "alias for --license"), + ('description', None, "print the package description"), + ('long-description', None, "print the long package description"), + ('platforms', None, "print the list of platforms"), + ('classifiers', None, "print the list of classifiers"), + ('keywords', None, "print the list of keywords"), + ('provides', None, "print the list of packages/modules provided"), + ('requires', None, "print the list of packages/modules required"), + ('obsoletes', None, "print the list of packages/modules made obsolete"), + ] + display_option_names: ClassVar[list[str]] = [ + translate_longopt(x[0]) for x in display_options + ] + + # negative options are options that exclude other options + negative_opt: ClassVar[dict[str, str]] = {'quiet': 'verbose'} + + # -- Creation/initialization methods ------------------------------- + + # Can't Unpack a TypedDict with optional properties, so using Any instead + def __init__(self, attrs: MutableMapping[str, Any] | None = None) -> None: # noqa: C901 + """Construct a new Distribution instance: initialize all the + attributes of a Distribution, and then use 'attrs' (a dictionary + mapping attribute names to values) to assign some of those + attributes their "real" values. (Any attributes not mentioned in + 'attrs' will be assigned to some null value: 0, None, an empty list + or dictionary, etc.) Most importantly, initialize the + 'command_obj' attribute to the empty dictionary; this will be + filled in with real command objects by 'parse_command_line()'. + """ + + # Default values for our command-line options + self.verbose = True + self.help = False + for attr in self.display_option_names: + setattr(self, attr, False) + + # Store the distribution meta-data (name, version, author, and so + # forth) in a separate object -- we're getting to have enough + # information here (and enough command-line options) that it's + # worth it. Also delegate 'get_XXX()' methods to the 'metadata' + # object in a sneaky and underhanded (but efficient!) way. + self.metadata = DistributionMetadata() + for basename in self.metadata._METHOD_BASENAMES: + method_name = "get_" + basename + setattr(self, method_name, getattr(self.metadata, method_name)) + + # 'cmdclass' maps command names to class objects, so we + # can 1) quickly figure out which class to instantiate when + # we need to create a new command object, and 2) have a way + # for the setup script to override command classes + self.cmdclass: dict[str, type[Command]] = {} + + # 'command_packages' is a list of packages in which commands + # are searched for. The factory for command 'foo' is expected + # to be named 'foo' in the module 'foo' in one of the packages + # named here. This list is searched from the left; an error + # is raised if no named package provides the command being + # searched for. (Always access using get_command_packages().) + self.command_packages: str | list[str] | None = None + + # 'script_name' and 'script_args' are usually set to sys.argv[0] + # and sys.argv[1:], but they can be overridden when the caller is + # not necessarily a setup script run from the command-line. + self.script_name: str | os.PathLike[str] | None = None + self.script_args: list[str] | None = None + + # 'command_options' is where we store command options between + # parsing them (from config files, the command-line, etc.) and when + # they are actually needed -- ie. when the command in question is + # instantiated. It is a dictionary of dictionaries of 2-tuples: + # command_options = { command_name : { option : (source, value) } } + self.command_options: dict[str, dict[str, tuple[str, str]]] = {} + + # 'dist_files' is the list of (command, pyversion, file) that + # have been created by any dist commands run so far. This is + # filled regardless of whether the run is dry or not. pyversion + # gives sysconfig.get_python_version() if the dist file is + # specific to a Python version, 'any' if it is good for all + # Python versions on the target platform, and '' for a source + # file. pyversion should not be used to specify minimum or + # maximum required Python versions; use the metainfo for that + # instead. + self.dist_files: list[tuple[str, str, str]] = [] + + # These options are really the business of various commands, rather + # than of the Distribution itself. We provide aliases for them in + # Distribution as a convenience to the developer. + self.packages = None + self.package_data: dict[str, list[str]] = {} + self.package_dir = None + self.py_modules = None + self.libraries = None + self.headers = None + self.ext_modules = None + self.ext_package = None + self.include_dirs = None + self.extra_path = None + self.scripts = None + self.data_files = None + self.password = '' + + # And now initialize bookkeeping stuff that can't be supplied by + # the caller at all. 'command_obj' maps command names to + # Command instances -- that's how we enforce that every command + # class is a singleton. + self.command_obj: dict[str, Command] = {} + + # 'have_run' maps command names to boolean values; it keeps track + # of whether we have actually run a particular command, to make it + # cheap to "run" a command whenever we think we might need to -- if + # it's already been done, no need for expensive filesystem + # operations, we just check the 'have_run' dictionary and carry on. + # It's only safe to query 'have_run' for a command class that has + # been instantiated -- a false value will be inserted when the + # command object is created, and replaced with a true value when + # the command is successfully run. Thus it's probably best to use + # '.get()' rather than a straight lookup. + self.have_run: dict[str, bool] = {} + + # Now we'll use the attrs dictionary (ultimately, keyword args from + # the setup script) to possibly override any or all of these + # distribution options. + + if attrs: + # Pull out the set of command options and work on them + # specifically. Note that this order guarantees that aliased + # command options will override any supplied redundantly + # through the general options dictionary. + options = attrs.get('options') + if options is not None: + del attrs['options'] + for command, cmd_options in options.items(): + opt_dict = self.get_option_dict(command) + for opt, val in cmd_options.items(): + opt_dict[opt] = ("setup script", val) + + if 'licence' in attrs: + attrs['license'] = attrs['licence'] + del attrs['licence'] + msg = "'licence' distribution option is deprecated; use 'license'" + warnings.warn(msg) + + # Now work on the rest of the attributes. Any attribute that's + # not already defined is invalid! + for key, val in attrs.items(): + if hasattr(self.metadata, "set_" + key): + getattr(self.metadata, "set_" + key)(val) + elif hasattr(self.metadata, key): + setattr(self.metadata, key, val) + elif hasattr(self, key): + setattr(self, key, val) + else: + msg = f"Unknown distribution option: {key!r}" + warnings.warn(msg) + + # no-user-cfg is handled before other command line args + # because other args override the config files, and this + # one is needed before we can load the config files. + # If attrs['script_args'] wasn't passed, assume false. + # + # This also make sure we just look at the global options + self.want_user_cfg = True + + if self.script_args is not None: + # Coerce any possible iterable from attrs into a list + self.script_args = list(self.script_args) + for arg in self.script_args: + if not arg.startswith('-'): + break + if arg == '--no-user-cfg': + self.want_user_cfg = False + break + + self.finalize_options() + + def get_option_dict(self, command): + """Get the option dictionary for a given command. If that + command's option dictionary hasn't been created yet, then create it + and return the new dictionary; otherwise, return the existing + option dictionary. + """ + dict = self.command_options.get(command) + if dict is None: + dict = self.command_options[command] = {} + return dict + + def dump_option_dicts(self, header=None, commands=None, indent: str = "") -> None: + from pprint import pformat + + if commands is None: # dump all command option dicts + commands = sorted(self.command_options.keys()) + + if header is not None: + self.announce(indent + header) + indent = indent + " " + + if not commands: + self.announce(indent + "no commands known yet") + return + + for cmd_name in commands: + opt_dict = self.command_options.get(cmd_name) + if opt_dict is None: + self.announce(indent + f"no option dict for '{cmd_name}' command") + else: + self.announce(indent + f"option dict for '{cmd_name}' command:") + out = pformat(opt_dict) + for line in out.split('\n'): + self.announce(indent + " " + line) + + # -- Config file finding/parsing methods --------------------------- + + def find_config_files(self): + """Find as many configuration files as should be processed for this + platform, and return a list of filenames in the order in which they + should be parsed. The filenames returned are guaranteed to exist + (modulo nasty race conditions). + + There are multiple possible config files: + - distutils.cfg in the Distutils installation directory (i.e. + where the top-level Distutils __inst__.py file lives) + - a file in the user's home directory named .pydistutils.cfg + on Unix and pydistutils.cfg on Windows/Mac; may be disabled + with the ``--no-user-cfg`` option + - setup.cfg in the current directory + - a file named by an environment variable + """ + check_environ() + files = [str(path) for path in self._gen_paths() if os.path.isfile(path)] + + if DEBUG: + self.announce("using config files: {}".format(', '.join(files))) + + return files + + def _gen_paths(self): + # The system-wide Distutils config file + sys_dir = pathlib.Path(sys.modules['distutils'].__file__).parent + yield sys_dir / "distutils.cfg" + + # The per-user config file + prefix = '.' * (os.name == 'posix') + filename = prefix + 'pydistutils.cfg' + if self.want_user_cfg: + with contextlib.suppress(RuntimeError): + yield pathlib.Path('~').expanduser() / filename + + # All platforms support local setup.cfg + yield pathlib.Path('setup.cfg') + + # Additional config indicated in the environment + with contextlib.suppress(TypeError): + yield pathlib.Path(os.getenv("DIST_EXTRA_CONFIG")) + + def parse_config_files(self, filenames=None): # noqa: C901 + from configparser import ConfigParser + + # Ignore install directory options if we have a venv + if sys.prefix != sys.base_prefix: + ignore_options = [ + 'install-base', + 'install-platbase', + 'install-lib', + 'install-platlib', + 'install-purelib', + 'install-headers', + 'install-scripts', + 'install-data', + 'prefix', + 'exec-prefix', + 'home', + 'user', + 'root', + ] + else: + ignore_options = [] + + ignore_options = frozenset(ignore_options) + + if filenames is None: + filenames = self.find_config_files() + + if DEBUG: + self.announce("Distribution.parse_config_files():") + + parser = ConfigParser() + for filename in filenames: + if DEBUG: + self.announce(f" reading {filename}") + parser.read(filename, encoding='utf-8') + for section in parser.sections(): + options = parser.options(section) + opt_dict = self.get_option_dict(section) + + for opt in options: + if opt != '__name__' and opt not in ignore_options: + val = parser.get(section, opt) + opt = opt.replace('-', '_') + opt_dict[opt] = (filename, val) + + # Make the ConfigParser forget everything (so we retain + # the original filenames that options come from) + parser.__init__() + + # If there was a "global" section in the config file, use it + # to set Distribution options. + + if 'global' in self.command_options: + for opt, (_src, val) in self.command_options['global'].items(): + alias = self.negative_opt.get(opt) + try: + if alias: + setattr(self, alias, not strtobool(val)) + elif opt in ('verbose',): # ugh! + setattr(self, opt, strtobool(val)) + else: + setattr(self, opt, val) + except ValueError as msg: + raise DistutilsOptionError(msg) + + # -- Command-line parsing methods ---------------------------------- + + def parse_command_line(self): + """Parse the setup script's command line, taken from the + 'script_args' instance attribute (which defaults to 'sys.argv[1:]' + -- see 'setup()' in core.py). This list is first processed for + "global options" -- options that set attributes of the Distribution + instance. Then, it is alternately scanned for Distutils commands + and options for that command. Each new command terminates the + options for the previous command. The allowed options for a + command are determined by the 'user_options' attribute of the + command class -- thus, we have to be able to load command classes + in order to parse the command line. Any error in that 'options' + attribute raises DistutilsGetoptError; any error on the + command-line raises DistutilsArgError. If no Distutils commands + were found on the command line, raises DistutilsArgError. Return + true if command-line was successfully parsed and we should carry + on with executing commands; false if no errors but we shouldn't + execute commands (currently, this only happens if user asks for + help). + """ + # + # We now have enough information to show the Macintosh dialog + # that allows the user to interactively specify the "command line". + # + toplevel_options = self._get_toplevel_options() + + # We have to parse the command line a bit at a time -- global + # options, then the first command, then its options, and so on -- + # because each command will be handled by a different class, and + # the options that are valid for a particular class aren't known + # until we have loaded the command class, which doesn't happen + # until we know what the command is. + + self.commands = [] + parser = FancyGetopt(toplevel_options + self.display_options) + parser.set_negative_aliases(self.negative_opt) + parser.set_aliases({'licence': 'license'}) + args = parser.getopt(args=self.script_args, object=self) + option_order = parser.get_option_order() + logging.getLogger().setLevel(logging.WARN - 10 * self.verbose) + + # for display options we return immediately + if self.handle_display_options(option_order): + return + while args: + args = self._parse_command_opts(parser, args) + if args is None: # user asked for help (and got it) + return + + # Handle the cases of --help as a "global" option, ie. + # "setup.py --help" and "setup.py --help command ...". For the + # former, we show global options (--verbose, --dry-run, etc.) + # and display-only options (--name, --version, etc.); for the + # latter, we omit the display-only options and show help for + # each command listed on the command line. + if self.help: + self._show_help( + parser, display_options=len(self.commands) == 0, commands=self.commands + ) + return + + # Oops, no commands found -- an end-user error + if not self.commands: + raise DistutilsArgError("no commands supplied") + + # All is well: return true + return True + + def _get_toplevel_options(self): + """Return the non-display options recognized at the top level. + + This includes options that are recognized *only* at the top + level as well as options recognized for commands. + """ + return self.global_options + [ + ( + "command-packages=", + None, + "list of packages that provide distutils commands", + ), + ] + + def _parse_command_opts(self, parser, args): # noqa: C901 + """Parse the command-line options for a single command. + 'parser' must be a FancyGetopt instance; 'args' must be the list + of arguments, starting with the current command (whose options + we are about to parse). Returns a new version of 'args' with + the next command at the front of the list; will be the empty + list if there are no more commands on the command line. Returns + None if the user asked for help on this command. + """ + # late import because of mutual dependence between these modules + from distutils.cmd import Command + + # Pull the current command from the head of the command line + command = args[0] + if not command_re.match(command): + raise SystemExit(f"invalid command name '{command}'") + self.commands.append(command) + + # Dig up the command class that implements this command, so we + # 1) know that it's a valid command, and 2) know which options + # it takes. + try: + cmd_class = self.get_command_class(command) + except DistutilsModuleError as msg: + raise DistutilsArgError(msg) + + # Require that the command class be derived from Command -- want + # to be sure that the basic "command" interface is implemented. + if not issubclass(cmd_class, Command): + raise DistutilsClassError( + f"command class {cmd_class} must subclass Command" + ) + + # Also make sure that the command object provides a list of its + # known options. + if not ( + hasattr(cmd_class, 'user_options') + and isinstance(cmd_class.user_options, list) + ): + msg = ( + "command class %s must provide " + "'user_options' attribute (a list of tuples)" + ) + raise DistutilsClassError(msg % cmd_class) + + # If the command class has a list of negative alias options, + # merge it in with the global negative aliases. + negative_opt = self.negative_opt + if hasattr(cmd_class, 'negative_opt'): + negative_opt = negative_opt.copy() + negative_opt.update(cmd_class.negative_opt) + + # Check for help_options in command class. They have a different + # format (tuple of four) so we need to preprocess them here. + if hasattr(cmd_class, 'help_options') and isinstance( + cmd_class.help_options, list + ): + help_options = fix_help_options(cmd_class.help_options) + else: + help_options = [] + + # All commands support the global options too, just by adding + # in 'global_options'. + parser.set_option_table( + self.global_options + cmd_class.user_options + help_options + ) + parser.set_negative_aliases(negative_opt) + (args, opts) = parser.getopt(args[1:]) + if hasattr(opts, 'help') and opts.help: + self._show_help(parser, display_options=False, commands=[cmd_class]) + return + + if hasattr(cmd_class, 'help_options') and isinstance( + cmd_class.help_options, list + ): + help_option_found = 0 + for help_option, _short, _desc, func in cmd_class.help_options: + if hasattr(opts, parser.get_attr_name(help_option)): + help_option_found = 1 + if callable(func): + func() + else: + raise DistutilsClassError( + f"invalid help function {func!r} for help option '{help_option}': " + "must be a callable object (function, etc.)" + ) + + if help_option_found: + return + + # Put the options from the command-line into their official + # holding pen, the 'command_options' dictionary. + opt_dict = self.get_option_dict(command) + for name, value in vars(opts).items(): + opt_dict[name] = ("command line", value) + + return args + + def finalize_options(self) -> None: + """Set final values for all the options on the Distribution + instance, analogous to the .finalize_options() method of Command + objects. + """ + for attr in ('keywords', 'platforms'): + value = getattr(self.metadata, attr) + if value is None: + continue + if isinstance(value, str): + value = [elm.strip() for elm in value.split(',')] + setattr(self.metadata, attr, value) + + def _show_help( + self, parser, global_options=True, display_options=True, commands: Iterable = () + ): + """Show help for the setup script command-line in the form of + several lists of command-line options. 'parser' should be a + FancyGetopt instance; do not expect it to be returned in the + same state, as its option table will be reset to make it + generate the correct help text. + + If 'global_options' is true, lists the global options: + --verbose, --dry-run, etc. If 'display_options' is true, lists + the "display-only" options: --name, --version, etc. Finally, + lists per-command help for every command name or command class + in 'commands'. + """ + # late import because of mutual dependence between these modules + from distutils.cmd import Command + from distutils.core import gen_usage + + if global_options: + if display_options: + options = self._get_toplevel_options() + else: + options = self.global_options + parser.set_option_table(options) + parser.print_help(self.common_usage + "\nGlobal options:") + print() + + if display_options: + parser.set_option_table(self.display_options) + parser.print_help( + "Information display options (just display information, ignore any commands)" + ) + print() + + for command in commands: + if isinstance(command, type) and issubclass(command, Command): + klass = command + else: + klass = self.get_command_class(command) + if hasattr(klass, 'help_options') and isinstance(klass.help_options, list): + parser.set_option_table( + klass.user_options + fix_help_options(klass.help_options) + ) + else: + parser.set_option_table(klass.user_options) + parser.print_help(f"Options for '{klass.__name__}' command:") + print() + + print(gen_usage(self.script_name)) + + def handle_display_options(self, option_order): + """If there were any non-global "display-only" options + (--help-commands or the metadata display options) on the command + line, display the requested info and return true; else return + false. + """ + from distutils.core import gen_usage + + # User just wants a list of commands -- we'll print it out and stop + # processing now (ie. if they ran "setup --help-commands foo bar", + # we ignore "foo bar"). + if self.help_commands: + self.print_commands() + print() + print(gen_usage(self.script_name)) + return 1 + + # If user supplied any of the "display metadata" options, then + # display that metadata in the order in which the user supplied the + # metadata options. + any_display_options = 0 + is_display_option = set() + for option in self.display_options: + is_display_option.add(option[0]) + + for opt, val in option_order: + if val and opt in is_display_option: + opt = translate_longopt(opt) + value = getattr(self.metadata, "get_" + opt)() + if opt in ('keywords', 'platforms'): + print(','.join(value)) + elif opt in ('classifiers', 'provides', 'requires', 'obsoletes'): + print('\n'.join(value)) + else: + print(value) + any_display_options = 1 + + return any_display_options + + def print_command_list(self, commands, header, max_length) -> None: + """Print a subset of the list of all commands -- used by + 'print_commands()'. + """ + print(header + ":") + + for cmd in commands: + klass = self.cmdclass.get(cmd) + if not klass: + klass = self.get_command_class(cmd) + try: + description = klass.description + except AttributeError: + description = "(no description available)" + + print(f" {cmd:<{max_length}} {description}") + + def print_commands(self) -> None: + """Print out a help message listing all available commands with a + description of each. The list is divided into "standard commands" + (listed in distutils.command.__all__) and "extra commands" + (mentioned in self.cmdclass, but not a standard command). The + descriptions come from the command class attribute + 'description'. + """ + import distutils.command + + std_commands = distutils.command.__all__ + is_std = set(std_commands) + + extra_commands = [cmd for cmd in self.cmdclass.keys() if cmd not in is_std] + + max_length = 0 + for cmd in std_commands + extra_commands: + if len(cmd) > max_length: + max_length = len(cmd) + + self.print_command_list(std_commands, "Standard commands", max_length) + if extra_commands: + print() + self.print_command_list(extra_commands, "Extra commands", max_length) + + def get_command_list(self): + """Get a list of (command, description) tuples. + The list is divided into "standard commands" (listed in + distutils.command.__all__) and "extra commands" (mentioned in + self.cmdclass, but not a standard command). The descriptions come + from the command class attribute 'description'. + """ + # Currently this is only used on Mac OS, for the Mac-only GUI + # Distutils interface (by Jack Jansen) + import distutils.command + + std_commands = distutils.command.__all__ + is_std = set(std_commands) + + extra_commands = [cmd for cmd in self.cmdclass.keys() if cmd not in is_std] + + rv = [] + for cmd in std_commands + extra_commands: + klass = self.cmdclass.get(cmd) + if not klass: + klass = self.get_command_class(cmd) + try: + description = klass.description + except AttributeError: + description = "(no description available)" + rv.append((cmd, description)) + return rv + + # -- Command class/object methods ---------------------------------- + + def get_command_packages(self): + """Return a list of packages from which commands are loaded.""" + pkgs = self.command_packages + if not isinstance(pkgs, list): + if pkgs is None: + pkgs = '' + pkgs = [pkg.strip() for pkg in pkgs.split(',') if pkg != ''] + if "distutils.command" not in pkgs: + pkgs.insert(0, "distutils.command") + self.command_packages = pkgs + return pkgs + + def get_command_class(self, command: str) -> type[Command]: + """Return the class that implements the Distutils command named by + 'command'. First we check the 'cmdclass' dictionary; if the + command is mentioned there, we fetch the class object from the + dictionary and return it. Otherwise we load the command module + ("distutils.command." + command) and fetch the command class from + the module. The loaded class is also stored in 'cmdclass' + to speed future calls to 'get_command_class()'. + + Raises DistutilsModuleError if the expected module could not be + found, or if that module does not define the expected class. + """ + klass = self.cmdclass.get(command) + if klass: + return klass + + for pkgname in self.get_command_packages(): + module_name = f"{pkgname}.{command}" + klass_name = command + + try: + __import__(module_name) + module = sys.modules[module_name] + except ImportError: + continue + + try: + klass = getattr(module, klass_name) + except AttributeError: + raise DistutilsModuleError( + f"invalid command '{command}' (no class '{klass_name}' in module '{module_name}')" + ) + + self.cmdclass[command] = klass + return klass + + raise DistutilsModuleError(f"invalid command '{command}'") + + @overload + def get_command_obj( + self, command: str, create: Literal[True] = True + ) -> Command: ... + @overload + def get_command_obj( + self, command: str, create: Literal[False] + ) -> Command | None: ... + def get_command_obj(self, command: str, create: bool = True) -> Command | None: + """Return the command object for 'command'. Normally this object + is cached on a previous call to 'get_command_obj()'; if no command + object for 'command' is in the cache, then we either create and + return it (if 'create' is true) or return None. + """ + cmd_obj = self.command_obj.get(command) + if not cmd_obj and create: + if DEBUG: + self.announce( + "Distribution.get_command_obj(): " + f"creating '{command}' command object" + ) + + klass = self.get_command_class(command) + cmd_obj = self.command_obj[command] = klass(self) + self.have_run[command] = False + + # Set any options that were supplied in config files + # or on the command line. (NB. support for error + # reporting is lame here: any errors aren't reported + # until 'finalize_options()' is called, which means + # we won't report the source of the error.) + options = self.command_options.get(command) + if options: + self._set_command_options(cmd_obj, options) + + return cmd_obj + + def _set_command_options(self, command_obj, option_dict=None): # noqa: C901 + """Set the options for 'command_obj' from 'option_dict'. Basically + this means copying elements of a dictionary ('option_dict') to + attributes of an instance ('command'). + + 'command_obj' must be a Command instance. If 'option_dict' is not + supplied, uses the standard option dictionary for this command + (from 'self.command_options'). + """ + command_name = command_obj.get_command_name() + if option_dict is None: + option_dict = self.get_option_dict(command_name) + + if DEBUG: + self.announce(f" setting options for '{command_name}' command:") + for option, (source, value) in option_dict.items(): + if DEBUG: + self.announce(f" {option} = {value} (from {source})") + try: + bool_opts = [translate_longopt(o) for o in command_obj.boolean_options] + except AttributeError: + bool_opts = [] + try: + neg_opt = command_obj.negative_opt + except AttributeError: + neg_opt = {} + + try: + is_string = isinstance(value, str) + if option in neg_opt and is_string: + setattr(command_obj, neg_opt[option], not strtobool(value)) + elif option in bool_opts and is_string: + setattr(command_obj, option, strtobool(value)) + elif hasattr(command_obj, option): + setattr(command_obj, option, value) + else: + raise DistutilsOptionError( + f"error in {source}: command '{command_name}' has no such option '{option}'" + ) + except ValueError as msg: + raise DistutilsOptionError(msg) + + @overload + def reinitialize_command( + self, command: str, reinit_subcommands: bool = False + ) -> Command: ... + @overload + def reinitialize_command( + self, command: _CommandT, reinit_subcommands: bool = False + ) -> _CommandT: ... + def reinitialize_command( + self, command: str | Command, reinit_subcommands=False + ) -> Command: + """Reinitializes a command to the state it was in when first + returned by 'get_command_obj()': ie., initialized but not yet + finalized. This provides the opportunity to sneak option + values in programmatically, overriding or supplementing + user-supplied values from the config files and command line. + You'll have to re-finalize the command object (by calling + 'finalize_options()' or 'ensure_finalized()') before using it for + real. + + 'command' should be a command name (string) or command object. If + 'reinit_subcommands' is true, also reinitializes the command's + sub-commands, as declared by the 'sub_commands' class attribute (if + it has one). See the "install" command for an example. Only + reinitializes the sub-commands that actually matter, ie. those + whose test predicates return true. + + Returns the reinitialized command object. + """ + from distutils.cmd import Command + + if not isinstance(command, Command): + command_name = command + command = self.get_command_obj(command_name) + else: + command_name = command.get_command_name() + + if not command.finalized: + return command + command.initialize_options() + command.finalized = False + self.have_run[command_name] = False + self._set_command_options(command) + + if reinit_subcommands: + for sub in command.get_sub_commands(): + self.reinitialize_command(sub, reinit_subcommands) + + return command + + # -- Methods that operate on the Distribution ---------------------- + + def announce(self, msg, level: int = logging.INFO) -> None: + log.log(level, msg) + + def run_commands(self) -> None: + """Run each command that was seen on the setup script command line. + Uses the list of commands found and cache of command objects + created by 'get_command_obj()'. + """ + for cmd in self.commands: + self.run_command(cmd) + + # -- Methods that operate on its Commands -------------------------- + + def run_command(self, command: str) -> None: + """Do whatever it takes to run a command (including nothing at all, + if the command has already been run). Specifically: if we have + already created and run the command named by 'command', return + silently without doing anything. If the command named by 'command' + doesn't even have a command object yet, create one. Then invoke + 'run()' on that command object (or an existing one). + """ + # Already been here, done that? then return silently. + if self.have_run.get(command): + return + + log.info("running %s", command) + cmd_obj = self.get_command_obj(command) + cmd_obj.ensure_finalized() + cmd_obj.run() + self.have_run[command] = True + + # -- Distribution query methods ------------------------------------ + + def has_pure_modules(self) -> bool: + return len(self.packages or self.py_modules or []) > 0 + + def has_ext_modules(self) -> bool: + return self.ext_modules and len(self.ext_modules) > 0 + + def has_c_libraries(self) -> bool: + return self.libraries and len(self.libraries) > 0 + + def has_modules(self) -> bool: + return self.has_pure_modules() or self.has_ext_modules() + + def has_headers(self) -> bool: + return self.headers and len(self.headers) > 0 + + def has_scripts(self) -> bool: + return self.scripts and len(self.scripts) > 0 + + def has_data_files(self) -> bool: + return self.data_files and len(self.data_files) > 0 + + def is_pure(self) -> bool: + return ( + self.has_pure_modules() + and not self.has_ext_modules() + and not self.has_c_libraries() + ) + + # -- Metadata query methods ---------------------------------------- + + # If you're looking for 'get_name()', 'get_version()', and so forth, + # they are defined in a sneaky way: the constructor binds self.get_XXX + # to self.metadata.get_XXX. The actual code is in the + # DistributionMetadata class, below. + if TYPE_CHECKING: + # Unfortunately this means we need to specify them manually or not expose statically + def _(self) -> None: + self.get_name = self.metadata.get_name + self.get_version = self.metadata.get_version + self.get_fullname = self.metadata.get_fullname + self.get_author = self.metadata.get_author + self.get_author_email = self.metadata.get_author_email + self.get_maintainer = self.metadata.get_maintainer + self.get_maintainer_email = self.metadata.get_maintainer_email + self.get_contact = self.metadata.get_contact + self.get_contact_email = self.metadata.get_contact_email + self.get_url = self.metadata.get_url + self.get_license = self.metadata.get_license + self.get_licence = self.metadata.get_licence + self.get_description = self.metadata.get_description + self.get_long_description = self.metadata.get_long_description + self.get_keywords = self.metadata.get_keywords + self.get_platforms = self.metadata.get_platforms + self.get_classifiers = self.metadata.get_classifiers + self.get_download_url = self.metadata.get_download_url + self.get_requires = self.metadata.get_requires + self.get_provides = self.metadata.get_provides + self.get_obsoletes = self.metadata.get_obsoletes + + # Default attributes generated in __init__ from self.display_option_names + help_commands: bool + name: str | Literal[False] + version: str | Literal[False] + fullname: str | Literal[False] + author: str | Literal[False] + author_email: str | Literal[False] + maintainer: str | Literal[False] + maintainer_email: str | Literal[False] + contact: str | Literal[False] + contact_email: str | Literal[False] + url: str | Literal[False] + license: str | Literal[False] + licence: str | Literal[False] + description: str | Literal[False] + long_description: str | Literal[False] + platforms: str | list[str] | Literal[False] + classifiers: str | list[str] | Literal[False] + keywords: str | list[str] | Literal[False] + provides: list[str] | Literal[False] + requires: list[str] | Literal[False] + obsoletes: list[str] | Literal[False] + + +class DistributionMetadata: + """Dummy class to hold the distribution meta-data: name, version, + author, and so forth. + """ + + _METHOD_BASENAMES = ( + "name", + "version", + "author", + "author_email", + "maintainer", + "maintainer_email", + "url", + "license", + "description", + "long_description", + "keywords", + "platforms", + "fullname", + "contact", + "contact_email", + "classifiers", + "download_url", + # PEP 314 + "provides", + "requires", + "obsoletes", + ) + + def __init__( + self, path: str | bytes | os.PathLike[str] | os.PathLike[bytes] | None = None + ) -> None: + if path is not None: + self.read_pkg_file(open(path)) + else: + self.name: str | None = None + self.version: str | None = None + self.author: str | None = None + self.author_email: str | None = None + self.maintainer: str | None = None + self.maintainer_email: str | None = None + self.url: str | None = None + self.license: str | None = None + self.description: str | None = None + self.long_description: str | None = None + self.keywords: str | list[str] | None = None + self.platforms: str | list[str] | None = None + self.classifiers: str | list[str] | None = None + self.download_url: str | None = None + # PEP 314 + self.provides: str | list[str] | None = None + self.requires: str | list[str] | None = None + self.obsoletes: str | list[str] | None = None + + def read_pkg_file(self, file: IO[str]) -> None: + """Reads the metadata values from a file object.""" + msg = message_from_file(file) + + def _read_field(name: str) -> str | None: + value = msg[name] + if value and value != "UNKNOWN": + return value + return None + + def _read_list(name): + values = msg.get_all(name, None) + if values == []: + return None + return values + + metadata_version = msg['metadata-version'] + self.name = _read_field('name') + self.version = _read_field('version') + self.description = _read_field('summary') + # we are filling author only. + self.author = _read_field('author') + self.maintainer = None + self.author_email = _read_field('author-email') + self.maintainer_email = None + self.url = _read_field('home-page') + self.license = _read_field('license') + + if 'download-url' in msg: + self.download_url = _read_field('download-url') + else: + self.download_url = None + + self.long_description = _read_field('description') + self.description = _read_field('summary') + + if 'keywords' in msg: + self.keywords = _read_field('keywords').split(',') + + self.platforms = _read_list('platform') + self.classifiers = _read_list('classifier') + + # PEP 314 - these fields only exist in 1.1 + if metadata_version == '1.1': + self.requires = _read_list('requires') + self.provides = _read_list('provides') + self.obsoletes = _read_list('obsoletes') + else: + self.requires = None + self.provides = None + self.obsoletes = None + + def write_pkg_info(self, base_dir: str | os.PathLike[str]) -> None: + """Write the PKG-INFO file into the release tree.""" + with open( + os.path.join(base_dir, 'PKG-INFO'), 'w', encoding='UTF-8' + ) as pkg_info: + self.write_pkg_file(pkg_info) + + def write_pkg_file(self, file: SupportsWrite[str]) -> None: + """Write the PKG-INFO format data to a file object.""" + version = '1.0' + if ( + self.provides + or self.requires + or self.obsoletes + or self.classifiers + or self.download_url + ): + version = '1.1' + + # required fields + file.write(f'Metadata-Version: {version}\n') + file.write(f'Name: {self.get_name()}\n') + file.write(f'Version: {self.get_version()}\n') + + def maybe_write(header, val): + if val: + file.write(f"{header}: {val}\n") + + # optional fields + maybe_write("Summary", self.get_description()) + maybe_write("Home-page", self.get_url()) + maybe_write("Author", self.get_contact()) + maybe_write("Author-email", self.get_contact_email()) + maybe_write("License", self.get_license()) + maybe_write("Download-URL", self.download_url) + maybe_write("Description", rfc822_escape(self.get_long_description() or "")) + maybe_write("Keywords", ",".join(self.get_keywords())) + + self._write_list(file, 'Platform', self.get_platforms()) + self._write_list(file, 'Classifier', self.get_classifiers()) + + # PEP 314 + self._write_list(file, 'Requires', self.get_requires()) + self._write_list(file, 'Provides', self.get_provides()) + self._write_list(file, 'Obsoletes', self.get_obsoletes()) + + def _write_list(self, file, name, values): + values = values or [] + for value in values: + file.write(f'{name}: {value}\n') + + # -- Metadata query methods ---------------------------------------- + + def get_name(self) -> str: + return self.name or "UNKNOWN" + + def get_version(self) -> str: + return self.version or "0.0.0" + + def get_fullname(self) -> str: + return self._fullname(self.get_name(), self.get_version()) + + @staticmethod + def _fullname(name: str, version: str) -> str: + """ + >>> DistributionMetadata._fullname('setup.tools', '1.0-2') + 'setup_tools-1.0.post2' + >>> DistributionMetadata._fullname('setup-tools', '1.2post2') + 'setup_tools-1.2.post2' + >>> DistributionMetadata._fullname('setup-tools', '1.0-r2') + 'setup_tools-1.0.post2' + >>> DistributionMetadata._fullname('setup.tools', '1.0.post') + 'setup_tools-1.0.post0' + >>> DistributionMetadata._fullname('setup.tools', '1.0+ubuntu-1') + 'setup_tools-1.0+ubuntu.1' + """ + return "{}-{}".format( + canonicalize_name(name).replace('-', '_'), + canonicalize_version(version, strip_trailing_zero=False), + ) + + def get_author(self) -> str | None: + return self.author + + def get_author_email(self) -> str | None: + return self.author_email + + def get_maintainer(self) -> str | None: + return self.maintainer + + def get_maintainer_email(self) -> str | None: + return self.maintainer_email + + def get_contact(self) -> str | None: + return self.maintainer or self.author + + def get_contact_email(self) -> str | None: + return self.maintainer_email or self.author_email + + def get_url(self) -> str | None: + return self.url + + def get_license(self) -> str | None: + return self.license + + get_licence = get_license + + def get_description(self) -> str | None: + return self.description + + def get_long_description(self) -> str | None: + return self.long_description + + def get_keywords(self) -> str | list[str]: + return self.keywords or [] + + def set_keywords(self, value: str | Iterable[str]) -> None: + self.keywords = _ensure_list(value, 'keywords') + + def get_platforms(self) -> str | list[str] | None: + return self.platforms + + def set_platforms(self, value: str | Iterable[str]) -> None: + self.platforms = _ensure_list(value, 'platforms') + + def get_classifiers(self) -> str | list[str]: + return self.classifiers or [] + + def set_classifiers(self, value: str | Iterable[str]) -> None: + self.classifiers = _ensure_list(value, 'classifiers') + + def get_download_url(self) -> str | None: + return self.download_url + + # PEP 314 + def get_requires(self) -> str | list[str]: + return self.requires or [] + + def set_requires(self, value: Iterable[str]) -> None: + import distutils.versionpredicate + + for v in value: + distutils.versionpredicate.VersionPredicate(v) + self.requires = list(value) + + def get_provides(self) -> str | list[str]: + return self.provides or [] + + def set_provides(self, value: Iterable[str]) -> None: + value = [v.strip() for v in value] + for v in value: + import distutils.versionpredicate + + distutils.versionpredicate.split_provision(v) + self.provides = value + + def get_obsoletes(self) -> str | list[str]: + return self.obsoletes or [] + + def set_obsoletes(self, value: Iterable[str]) -> None: + import distutils.versionpredicate + + for v in value: + distutils.versionpredicate.VersionPredicate(v) + self.obsoletes = list(value) + + +def fix_help_options(options): + """Convert a 4-tuple 'help_options' list as found in various command + classes to the 3-tuple form required by FancyGetopt. + """ + return [opt[0:3] for opt in options] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/errors.py b/lib/python3.11/site-packages/setuptools/_distutils/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..409d21faa2caeb8a53fed10e4266f7cccc764d23 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/errors.py @@ -0,0 +1,108 @@ +""" +Exceptions used by the Distutils modules. + +Distutils modules may raise these or standard exceptions, +including :exc:`SystemExit`. +""" + +# compiler exceptions aliased for compatibility +from .compilers.C.errors import CompileError as CompileError +from .compilers.C.errors import Error as _Error +from .compilers.C.errors import LibError as LibError +from .compilers.C.errors import LinkError as LinkError +from .compilers.C.errors import PreprocessError as PreprocessError +from .compilers.C.errors import UnknownFileType as _UnknownFileType + +CCompilerError = _Error +UnknownFileError = _UnknownFileType + + +class DistutilsError(Exception): + """The root of all Distutils evil.""" + + pass + + +class DistutilsModuleError(DistutilsError): + """Unable to load an expected module, or to find an expected class + within some module (in particular, command modules and classes).""" + + pass + + +class DistutilsClassError(DistutilsError): + """Some command class (or possibly distribution class, if anyone + feels a need to subclass Distribution) is found not to be holding + up its end of the bargain, ie. implementing some part of the + "command "interface.""" + + pass + + +class DistutilsGetoptError(DistutilsError): + """The option table provided to 'fancy_getopt()' is bogus.""" + + pass + + +class DistutilsArgError(DistutilsError): + """Raised by fancy_getopt in response to getopt.error -- ie. an + error in the command line usage.""" + + pass + + +class DistutilsFileError(DistutilsError): + """Any problems in the filesystem: expected file not found, etc. + Typically this is for problems that we detect before OSError + could be raised.""" + + pass + + +class DistutilsOptionError(DistutilsError): + """Syntactic/semantic errors in command options, such as use of + mutually conflicting options, or inconsistent options, + badly-spelled values, etc. No distinction is made between option + values originating in the setup script, the command line, config + files, or what-have-you -- but if we *know* something originated in + the setup script, we'll raise DistutilsSetupError instead.""" + + pass + + +class DistutilsSetupError(DistutilsError): + """For errors that can be definitely blamed on the setup script, + such as invalid keyword arguments to 'setup()'.""" + + pass + + +class DistutilsPlatformError(DistutilsError): + """We don't know how to do something on the current platform (but + we do know how to do it on some platform) -- eg. trying to compile + C files on a platform not supported by a CCompiler subclass.""" + + pass + + +class DistutilsExecError(DistutilsError): + """Any problems executing an external program (such as the C + compiler, when compiling C files).""" + + pass + + +class DistutilsInternalError(DistutilsError): + """Internal inconsistencies or impossibilities (obviously, this + should never be seen if the code is working!).""" + + pass + + +class DistutilsTemplateError(DistutilsError): + """Syntax error in a file list template.""" + + +class DistutilsByteCompileError(DistutilsError): + """Byte compile error.""" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/extension.py b/lib/python3.11/site-packages/setuptools/_distutils/extension.py new file mode 100644 index 0000000000000000000000000000000000000000..f51411266e4047275280d883b75e7103af21faef --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/extension.py @@ -0,0 +1,258 @@ +"""distutils.extension + +Provides the Extension class, used to describe C/C++ extension +modules in setup scripts.""" + +from __future__ import annotations + +import os +import warnings +from collections.abc import Iterable + +# This class is really only used by the "build_ext" command, so it might +# make sense to put it in distutils.command.build_ext. However, that +# module is already big enough, and I want to make this class a bit more +# complex to simplify some common cases ("foo" module in "foo.c") and do +# better error-checking ("foo.c" actually exists). +# +# Also, putting this in build_ext.py means every setup script would have to +# import that large-ish module (indirectly, through distutils.core) in +# order to do anything. + + +class Extension: + """Just a collection of attributes that describes an extension + module and everything needed to build it (hopefully in a portable + way, but there are hooks that let you be as unportable as you need). + + Instance attributes: + name : string + the full name of the extension, including any packages -- ie. + *not* a filename or pathname, but Python dotted name + sources : Iterable[string | os.PathLike] + iterable of source filenames (except strings, which could be misinterpreted + as a single filename), relative to the distribution root (where the setup + script lives), in Unix form (slash-separated) for portability. Can be any + non-string iterable (list, tuple, set, etc.) containing strings or + PathLike objects. Source files may be C, C++, SWIG (.i), platform-specific + resource files, or whatever else is recognized by the "build_ext" command + as source for a Python extension. + include_dirs : [string] + list of directories to search for C/C++ header files (in Unix + form for portability) + define_macros : [(name : string, value : string|None)] + list of macros to define; each macro is defined using a 2-tuple, + where 'value' is either the string to define it to or None to + define it without a particular value (equivalent of "#define + FOO" in source or -DFOO on Unix C compiler command line) + undef_macros : [string] + list of macros to undefine explicitly + library_dirs : [string] + list of directories to search for C/C++ libraries at link time + libraries : [string] + list of library names (not filenames or paths) to link against + runtime_library_dirs : [string] + list of directories to search for C/C++ libraries at run time + (for shared extensions, this is when the extension is loaded) + extra_objects : [string] + list of extra files to link with (eg. object files not implied + by 'sources', static library that must be explicitly specified, + binary resource files, etc.) + extra_compile_args : [string] + any extra platform- and compiler-specific information to use + when compiling the source files in 'sources'. For platforms and + compilers where "command line" makes sense, this is typically a + list of command-line arguments, but for other platforms it could + be anything. + extra_link_args : [string] + any extra platform- and compiler-specific information to use + when linking object files together to create the extension (or + to create a new static Python interpreter). Similar + interpretation as for 'extra_compile_args'. + export_symbols : [string] + list of symbols to be exported from a shared extension. Not + used on all platforms, and not generally necessary for Python + extensions, which typically export exactly one symbol: "init" + + extension_name. + swig_opts : [string] + any extra options to pass to SWIG if a source file has the .i + extension. + depends : [string] + list of files that the extension depends on + language : string + extension language (i.e. "c", "c++", "objc"). Will be detected + from the source extensions if not provided. + optional : boolean + specifies that a build failure in the extension should not abort the + build process, but simply not install the failing extension. + """ + + # When adding arguments to this constructor, be sure to update + # setup_keywords in core.py. + def __init__( + self, + name: str, + sources: Iterable[str | os.PathLike[str]], + include_dirs: list[str] | None = None, + define_macros: list[tuple[str, str | None]] | None = None, + undef_macros: list[str] | None = None, + library_dirs: list[str] | None = None, + libraries: list[str] | None = None, + runtime_library_dirs: list[str] | None = None, + extra_objects: list[str] | None = None, + extra_compile_args: list[str] | None = None, + extra_link_args: list[str] | None = None, + export_symbols: list[str] | None = None, + swig_opts: list[str] | None = None, + depends: list[str] | None = None, + language: str | None = None, + optional: bool | None = None, + **kw, # To catch unknown keywords + ): + if not isinstance(name, str): + raise TypeError("'name' must be a string") + + # handle the string case first; since strings are iterable, disallow them + if isinstance(sources, str): + raise TypeError( + "'sources' must be an iterable of strings or PathLike objects, not a string" + ) + + # now we check if it's iterable and contains valid types + try: + self.sources = list(map(os.fspath, sources)) + except TypeError: + raise TypeError( + "'sources' must be an iterable of strings or PathLike objects" + ) + + self.name = name + self.include_dirs = include_dirs or [] + self.define_macros = define_macros or [] + self.undef_macros = undef_macros or [] + self.library_dirs = library_dirs or [] + self.libraries = libraries or [] + self.runtime_library_dirs = runtime_library_dirs or [] + self.extra_objects = extra_objects or [] + self.extra_compile_args = extra_compile_args or [] + self.extra_link_args = extra_link_args or [] + self.export_symbols = export_symbols or [] + self.swig_opts = swig_opts or [] + self.depends = depends or [] + self.language = language + self.optional = optional + + # If there are unknown keyword options, warn about them + if len(kw) > 0: + options = [repr(option) for option in kw] + options = ', '.join(sorted(options)) + msg = f"Unknown Extension options: {options}" + warnings.warn(msg) + + def __repr__(self): + return f'<{self.__class__.__module__}.{self.__class__.__qualname__}({self.name!r}) at {id(self):#x}>' + + +def read_setup_file(filename): # noqa: C901 + """Reads a Setup file and returns Extension instances.""" + from distutils.sysconfig import _variable_rx, expand_makefile_vars, parse_makefile + from distutils.text_file import TextFile + from distutils.util import split_quoted + + # First pass over the file to gather "VAR = VALUE" assignments. + vars = parse_makefile(filename) + + # Second pass to gobble up the real content: lines of the form + # ... [ ...] [ ...] [ ...] + file = TextFile( + filename, + strip_comments=True, + skip_blanks=True, + join_lines=True, + lstrip_ws=True, + rstrip_ws=True, + ) + try: + extensions = [] + + while True: + line = file.readline() + if line is None: # eof + break + if _variable_rx.match(line): # VAR=VALUE, handled in first pass + continue + + if line[0] == line[-1] == "*": + file.warn(f"'{line}' lines not handled yet") + continue + + line = expand_makefile_vars(line, vars) + words = split_quoted(line) + + # NB. this parses a slightly different syntax than the old + # makesetup script: here, there must be exactly one extension per + # line, and it must be the first word of the line. I have no idea + # why the old syntax supported multiple extensions per line, as + # they all wind up being the same. + + module = words[0] + ext = Extension(module, []) + append_next_word = None + + for word in words[1:]: + if append_next_word is not None: + append_next_word.append(word) + append_next_word = None + continue + + suffix = os.path.splitext(word)[1] + switch = word[0:2] + value = word[2:] + + if suffix in (".c", ".cc", ".cpp", ".cxx", ".c++", ".m", ".mm"): + # hmm, should we do something about C vs. C++ sources? + # or leave it up to the CCompiler implementation to + # worry about? + ext.sources.append(word) + elif switch == "-I": + ext.include_dirs.append(value) + elif switch == "-D": + equals = value.find("=") + if equals == -1: # bare "-DFOO" -- no value + ext.define_macros.append((value, None)) + else: # "-DFOO=blah" + ext.define_macros.append((value[0:equals], value[equals + 2 :])) + elif switch == "-U": + ext.undef_macros.append(value) + elif switch == "-C": # only here 'cause makesetup has it! + ext.extra_compile_args.append(word) + elif switch == "-l": + ext.libraries.append(value) + elif switch == "-L": + ext.library_dirs.append(value) + elif switch == "-R": + ext.runtime_library_dirs.append(value) + elif word == "-rpath": + append_next_word = ext.runtime_library_dirs + elif word == "-Xlinker": + append_next_word = ext.extra_link_args + elif word == "-Xcompiler": + append_next_word = ext.extra_compile_args + elif switch == "-u": + ext.extra_link_args.append(word) + if not value: + append_next_word = ext.extra_link_args + elif suffix in (".a", ".so", ".sl", ".o", ".dylib"): + # NB. a really faithful emulation of makesetup would + # append a .o file to extra_objects only if it + # had a slash in it; otherwise, it would s/.o/.c/ + # and append it to sources. Hmmmm. + ext.extra_objects.append(word) + else: + file.warn(f"unrecognized argument '{word}'") + + extensions.append(ext) + finally: + file.close() + + return extensions diff --git a/lib/python3.11/site-packages/setuptools/_distutils/fancy_getopt.py b/lib/python3.11/site-packages/setuptools/_distutils/fancy_getopt.py new file mode 100644 index 0000000000000000000000000000000000000000..1a1d3a05da544b98ff84d03ecbee1185b2eb45f7 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/fancy_getopt.py @@ -0,0 +1,471 @@ +"""distutils.fancy_getopt + +Wrapper around the standard getopt module that provides the following +additional features: + * short and long options are tied together + * options have help strings, so fancy_getopt could potentially + create a complete usage summary + * options set attributes of a passed-in object +""" + +from __future__ import annotations + +import getopt +import re +import string +import sys +from collections.abc import Sequence +from typing import Any + +from .errors import DistutilsArgError, DistutilsGetoptError + +# Much like command_re in distutils.core, this is close to but not quite +# the same as a Python NAME -- except, in the spirit of most GNU +# utilities, we use '-' in place of '_'. (The spirit of LISP lives on!) +# The similarities to NAME are again not a coincidence... +longopt_pat = r'[a-zA-Z](?:[a-zA-Z0-9-]*)' +longopt_re = re.compile(rf'^{longopt_pat}$') + +# For recognizing "negative alias" options, eg. "quiet=!verbose" +neg_alias_re = re.compile(f"^({longopt_pat})=!({longopt_pat})$") + +# This is used to translate long options to legitimate Python identifiers +# (for use as attributes of some object). +longopt_xlate = str.maketrans('-', '_') + + +class FancyGetopt: + """Wrapper around the standard 'getopt()' module that provides some + handy extra functionality: + * short and long options are tied together + * options have help strings, and help text can be assembled + from them + * options set attributes of a passed-in object + * boolean options can have "negative aliases" -- eg. if + --quiet is the "negative alias" of --verbose, then "--quiet" + on the command line sets 'verbose' to false + """ + + def __init__(self, option_table=None): + # The option table is (currently) a list of tuples. The + # tuples may have 3 or four values: + # (long_option, short_option, help_string [, repeatable]) + # if an option takes an argument, its long_option should have '=' + # appended; short_option should just be a single character, no ':' + # in any case. If a long_option doesn't have a corresponding + # short_option, short_option should be None. All option tuples + # must have long options. + self.option_table = option_table + + # 'option_index' maps long option names to entries in the option + # table (ie. those 3-tuples). + self.option_index = {} + if self.option_table: + self._build_index() + + # 'alias' records (duh) alias options; {'foo': 'bar'} means + # --foo is an alias for --bar + self.alias = {} + + # 'negative_alias' keeps track of options that are the boolean + # opposite of some other option + self.negative_alias = {} + + # These keep track of the information in the option table. We + # don't actually populate these structures until we're ready to + # parse the command-line, since the 'option_table' passed in here + # isn't necessarily the final word. + self.short_opts = [] + self.long_opts = [] + self.short2long = {} + self.attr_name = {} + self.takes_arg = {} + + # And 'option_order' is filled up in 'getopt()'; it records the + # original order of options (and their values) on the command-line, + # but expands short options, converts aliases, etc. + self.option_order = [] + + def _build_index(self): + self.option_index.clear() + for option in self.option_table: + self.option_index[option[0]] = option + + def set_option_table(self, option_table): + self.option_table = option_table + self._build_index() + + def add_option(self, long_option, short_option=None, help_string=None): + if long_option in self.option_index: + raise DistutilsGetoptError( + f"option conflict: already an option '{long_option}'" + ) + else: + option = (long_option, short_option, help_string) + self.option_table.append(option) + self.option_index[long_option] = option + + def has_option(self, long_option): + """Return true if the option table for this parser has an + option with long name 'long_option'.""" + return long_option in self.option_index + + def get_attr_name(self, long_option): + """Translate long option name 'long_option' to the form it + has as an attribute of some object: ie., translate hyphens + to underscores.""" + return long_option.translate(longopt_xlate) + + def _check_alias_dict(self, aliases, what): + assert isinstance(aliases, dict) + for alias, opt in aliases.items(): + if alias not in self.option_index: + raise DistutilsGetoptError( + f"invalid {what} '{alias}': option '{alias}' not defined" + ) + if opt not in self.option_index: + raise DistutilsGetoptError( + f"invalid {what} '{alias}': aliased option '{opt}' not defined" + ) + + def set_aliases(self, alias): + """Set the aliases for this option parser.""" + self._check_alias_dict(alias, "alias") + self.alias = alias + + def set_negative_aliases(self, negative_alias): + """Set the negative aliases for this option parser. + 'negative_alias' should be a dictionary mapping option names to + option names, both the key and value must already be defined + in the option table.""" + self._check_alias_dict(negative_alias, "negative alias") + self.negative_alias = negative_alias + + def _grok_option_table(self): # noqa: C901 + """Populate the various data structures that keep tabs on the + option table. Called by 'getopt()' before it can do anything + worthwhile. + """ + self.long_opts = [] + self.short_opts = [] + self.short2long.clear() + self.repeat = {} + + for option in self.option_table: + if len(option) == 3: + long, short, help = option + repeat = 0 + elif len(option) == 4: + long, short, help, repeat = option + else: + # the option table is part of the code, so simply + # assert that it is correct + raise ValueError(f"invalid option tuple: {option!r}") + + # Type- and value-check the option names + if not isinstance(long, str) or len(long) < 2: + raise DistutilsGetoptError( + f"invalid long option '{long}': must be a string of length >= 2" + ) + + if not ((short is None) or (isinstance(short, str) and len(short) == 1)): + raise DistutilsGetoptError( + f"invalid short option '{short}': must a single character or None" + ) + + self.repeat[long] = repeat + self.long_opts.append(long) + + if long[-1] == '=': # option takes an argument? + if short: + short = short + ':' + long = long[0:-1] + self.takes_arg[long] = True + else: + # Is option is a "negative alias" for some other option (eg. + # "quiet" == "!verbose")? + alias_to = self.negative_alias.get(long) + if alias_to is not None: + if self.takes_arg[alias_to]: + raise DistutilsGetoptError( + f"invalid negative alias '{long}': " + f"aliased option '{alias_to}' takes a value" + ) + + self.long_opts[-1] = long # XXX redundant?! + self.takes_arg[long] = False + + # If this is an alias option, make sure its "takes arg" flag is + # the same as the option it's aliased to. + alias_to = self.alias.get(long) + if alias_to is not None: + if self.takes_arg[long] != self.takes_arg[alias_to]: + raise DistutilsGetoptError( + f"invalid alias '{long}': inconsistent with " + f"aliased option '{alias_to}' (one of them takes a value, " + "the other doesn't" + ) + + # Now enforce some bondage on the long option name, so we can + # later translate it to an attribute name on some object. Have + # to do this a bit late to make sure we've removed any trailing + # '='. + if not longopt_re.match(long): + raise DistutilsGetoptError( + f"invalid long option name '{long}' " + "(must be letters, numbers, hyphens only" + ) + + self.attr_name[long] = self.get_attr_name(long) + if short: + self.short_opts.append(short) + self.short2long[short[0]] = long + + def getopt(self, args: Sequence[str] | None = None, object=None): # noqa: C901 + """Parse command-line options in args. Store as attributes on object. + + If 'args' is None or not supplied, uses 'sys.argv[1:]'. If + 'object' is None or not supplied, creates a new OptionDummy + object, stores option values there, and returns a tuple (args, + object). If 'object' is supplied, it is modified in place and + 'getopt()' just returns 'args'; in both cases, the returned + 'args' is a modified copy of the passed-in 'args' list, which + is left untouched. + """ + if args is None: + args = sys.argv[1:] + if object is None: + object = OptionDummy() + created_object = True + else: + created_object = False + + self._grok_option_table() + + short_opts = ' '.join(self.short_opts) + try: + opts, args = getopt.getopt(args, short_opts, self.long_opts) + except getopt.error as msg: + raise DistutilsArgError(msg) + + for opt, val in opts: + if len(opt) == 2 and opt[0] == '-': # it's a short option + opt = self.short2long[opt[1]] + else: + assert len(opt) > 2 and opt[:2] == '--' + opt = opt[2:] + + alias = self.alias.get(opt) + if alias: + opt = alias + + if not self.takes_arg[opt]: # boolean option? + assert val == '', "boolean option can't have value" + alias = self.negative_alias.get(opt) + if alias: + opt = alias + val = 0 + else: + val = 1 + + attr = self.attr_name[opt] + # The only repeating option at the moment is 'verbose'. + # It has a negative option -q quiet, which should set verbose = False. + if val and self.repeat.get(attr) is not None: + val = getattr(object, attr, 0) + 1 + setattr(object, attr, val) + self.option_order.append((opt, val)) + + # for opts + if created_object: + return args, object + else: + return args + + def get_option_order(self): + """Returns the list of (option, value) tuples processed by the + previous run of 'getopt()'. Raises RuntimeError if + 'getopt()' hasn't been called yet. + """ + if self.option_order is None: + raise RuntimeError("'getopt()' hasn't been called yet") + else: + return self.option_order + + def generate_help(self, header=None): # noqa: C901 + """Generate help text (a list of strings, one per suggested line of + output) from the option table for this FancyGetopt object. + """ + # Blithely assume the option table is good: probably wouldn't call + # 'generate_help()' unless you've already called 'getopt()'. + + # First pass: determine maximum length of long option names + max_opt = 0 + for option in self.option_table: + long = option[0] + short = option[1] + ell = len(long) + if long[-1] == '=': + ell = ell - 1 + if short is not None: + ell = ell + 5 # " (-x)" where short == 'x' + if ell > max_opt: + max_opt = ell + + opt_width = max_opt + 2 + 2 + 2 # room for indent + dashes + gutter + + # Typical help block looks like this: + # --foo controls foonabulation + # Help block for longest option looks like this: + # --flimflam set the flim-flam level + # and with wrapped text: + # --flimflam set the flim-flam level (must be between + # 0 and 100, except on Tuesdays) + # Options with short names will have the short name shown (but + # it doesn't contribute to max_opt): + # --foo (-f) controls foonabulation + # If adding the short option would make the left column too wide, + # we push the explanation off to the next line + # --flimflam (-l) + # set the flim-flam level + # Important parameters: + # - 2 spaces before option block start lines + # - 2 dashes for each long option name + # - min. 2 spaces between option and explanation (gutter) + # - 5 characters (incl. space) for short option name + + # Now generate lines of help text. (If 80 columns were good enough + # for Jesus, then 78 columns are good enough for me!) + line_width = 78 + text_width = line_width - opt_width + big_indent = ' ' * opt_width + if header: + lines = [header] + else: + lines = ['Option summary:'] + + for option in self.option_table: + long, short, help = option[:3] + text = wrap_text(help, text_width) + if long[-1] == '=': + long = long[0:-1] + + # Case 1: no short option at all (makes life easy) + if short is None: + if text: + lines.append(f" --{long:<{max_opt}} {text[0]}") + else: + lines.append(f" --{long:<{max_opt}}") + + # Case 2: we have a short option, so we have to include it + # just after the long option + else: + opt_names = f"{long} (-{short})" + if text: + lines.append(f" --{opt_names:<{max_opt}} {text[0]}") + else: + lines.append(f" --{opt_names:<{max_opt}}") + + for ell in text[1:]: + lines.append(big_indent + ell) + return lines + + def print_help(self, header=None, file=None): + if file is None: + file = sys.stdout + for line in self.generate_help(header): + file.write(line + "\n") + + +def fancy_getopt(options, negative_opt, object, args: Sequence[str] | None): + parser = FancyGetopt(options) + parser.set_negative_aliases(negative_opt) + return parser.getopt(args, object) + + +WS_TRANS = {ord(_wschar): ' ' for _wschar in string.whitespace} + + +def wrap_text(text, width): + """wrap_text(text : string, width : int) -> [string] + + Split 'text' into multiple lines of no more than 'width' characters + each, and return the list of strings that results. + """ + if text is None: + return [] + if len(text) <= width: + return [text] + + text = text.expandtabs() + text = text.translate(WS_TRANS) + chunks = re.split(r'( +|-+)', text) + chunks = [ch for ch in chunks if ch] # ' - ' results in empty strings + lines = [] + + while chunks: + cur_line = [] # list of chunks (to-be-joined) + cur_len = 0 # length of current line + + while chunks: + ell = len(chunks[0]) + if cur_len + ell <= width: # can squeeze (at least) this chunk in + cur_line.append(chunks[0]) + del chunks[0] + cur_len = cur_len + ell + else: # this line is full + # drop last chunk if all space + if cur_line and cur_line[-1][0] == ' ': + del cur_line[-1] + break + + if chunks: # any chunks left to process? + # if the current line is still empty, then we had a single + # chunk that's too big too fit on a line -- so we break + # down and break it up at the line width + if cur_len == 0: + cur_line.append(chunks[0][0:width]) + chunks[0] = chunks[0][width:] + + # all-whitespace chunks at the end of a line can be discarded + # (and we know from the re.split above that if a chunk has + # *any* whitespace, it is *all* whitespace) + if chunks[0][0] == ' ': + del chunks[0] + + # and store this line in the list-of-all-lines -- as a single + # string, of course! + lines.append(''.join(cur_line)) + + return lines + + +def translate_longopt(opt): + """Convert a long option name to a valid Python identifier by + changing "-" to "_". + """ + return opt.translate(longopt_xlate) + + +class OptionDummy: + """Dummy class just used as a place to hold command-line option + values as instance attributes.""" + + def __init__(self, options: Sequence[Any] = []): + """Create a new OptionDummy instance. The attributes listed in + 'options' will be initialized to None.""" + for opt in options: + setattr(self, opt, None) + + +if __name__ == "__main__": + text = """\ +Tra-la-la, supercalifragilisticexpialidocious. +How *do* you spell that odd word, anyways? +(Someone ask Mary -- she'll know [or she'll +say, "How should I know?"].)""" + + for w in (10, 20, 30, 40): + print(f"width: {w}") + print("\n".join(wrap_text(text, w))) + print() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/file_util.py b/lib/python3.11/site-packages/setuptools/_distutils/file_util.py new file mode 100644 index 0000000000000000000000000000000000000000..a9724b1779703543aaacc7b4a5872b68911d2144 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/file_util.py @@ -0,0 +1,228 @@ +"""distutils.file_util + +Utility functions for operating on single files. +""" + +import os + +from ._log import log +from .errors import DistutilsFileError + +# for generating verbose output in 'copy_file()' +_copy_action = {None: 'copying', 'hard': 'hard linking', 'sym': 'symbolically linking'} + + +def _copy_file_contents(src, dst, buffer_size=16 * 1024): # noqa: C901 + """Copy the file 'src' to 'dst'; both must be filenames. Any error + opening either file, reading from 'src', or writing to 'dst', raises + DistutilsFileError. Data is read/written in chunks of 'buffer_size' + bytes (default 16k). No attempt is made to handle anything apart from + regular files. + """ + # Stolen from shutil module in the standard library, but with + # custom error-handling added. + fsrc = None + fdst = None + try: + try: + fsrc = open(src, 'rb') + except OSError as e: + raise DistutilsFileError(f"could not open '{src}': {e.strerror}") + + if os.path.exists(dst): + try: + os.unlink(dst) + except OSError as e: + raise DistutilsFileError(f"could not delete '{dst}': {e.strerror}") + + try: + fdst = open(dst, 'wb') + except OSError as e: + raise DistutilsFileError(f"could not create '{dst}': {e.strerror}") + + while True: + try: + buf = fsrc.read(buffer_size) + except OSError as e: + raise DistutilsFileError(f"could not read from '{src}': {e.strerror}") + + if not buf: + break + + try: + fdst.write(buf) + except OSError as e: + raise DistutilsFileError(f"could not write to '{dst}': {e.strerror}") + finally: + if fdst: + fdst.close() + if fsrc: + fsrc.close() + + +def copy_file( # noqa: C901 + src, + dst, + preserve_mode=True, + preserve_times=True, + update=False, + link=None, + verbose=True, +): + """Copy a file 'src' to 'dst'. If 'dst' is a directory, then 'src' is + copied there with the same name; otherwise, it must be a filename. (If + the file exists, it will be ruthlessly clobbered.) If 'preserve_mode' + is true (the default), the file's mode (type and permission bits, or + whatever is analogous on the current platform) is copied. If + 'preserve_times' is true (the default), the last-modified and + last-access times are copied as well. If 'update' is true, 'src' will + only be copied if 'dst' does not exist, or if 'dst' does exist but is + older than 'src'. + + 'link' allows you to make hard links (os.link) or symbolic links + (os.symlink) instead of copying: set it to "hard" or "sym"; if it is + None (the default), files are copied. Don't set 'link' on systems that + don't support it: 'copy_file()' doesn't check if hard or symbolic + linking is available. If hardlink fails, falls back to + _copy_file_contents(). + + Under Mac OS, uses the native file copy function in macostools; on + other systems, uses '_copy_file_contents()' to copy file contents. + + Return a tuple (dest_name, copied): 'dest_name' is the actual name of + the output file, and 'copied' is true if the file was copied. + """ + # XXX if the destination file already exists, we clobber it if + # copying, but blow up if linking. Hmmm. And I don't know what + # macostools.copyfile() does. Should definitely be consistent, and + # should probably blow up if destination exists and we would be + # changing it (ie. it's not already a hard/soft link to src OR + # (not update) and (src newer than dst). + + from distutils._modified import newer + from stat import S_IMODE, ST_ATIME, ST_MODE, ST_MTIME + + if not os.path.isfile(src): + raise DistutilsFileError( + f"can't copy '{src}': doesn't exist or not a regular file" + ) + + if os.path.isdir(dst): + dir = dst + dst = os.path.join(dst, os.path.basename(src)) + else: + dir = os.path.dirname(dst) + + if update and not newer(src, dst): + if verbose >= 1: + log.debug("not copying %s (output up-to-date)", src) + return (dst, False) + + try: + action = _copy_action[link] + except KeyError: + raise ValueError(f"invalid value '{link}' for 'link' argument") + + if verbose >= 1: + if os.path.basename(dst) == os.path.basename(src): + log.info("%s %s -> %s", action, src, dir) + else: + log.info("%s %s -> %s", action, src, dst) + + # If linking (hard or symbolic), use the appropriate system call + # (Unix only, of course, but that's the caller's responsibility) + if link == 'hard': + if not (os.path.exists(dst) and os.path.samefile(src, dst)): + try: + os.link(src, dst) + except OSError: + # If hard linking fails, fall back on copying file + # (some special filesystems don't support hard linking + # even under Unix, see issue #8876). + pass + else: + return (dst, True) + elif link == 'sym': + if not (os.path.exists(dst) and os.path.samefile(src, dst)): + os.symlink(src, dst) + return (dst, True) + + # Otherwise (non-Mac, not linking), copy the file contents and + # (optionally) copy the times and mode. + _copy_file_contents(src, dst) + if preserve_mode or preserve_times: + st = os.stat(src) + + # According to David Ascher , utime() should be done + # before chmod() (at least under NT). + if preserve_times: + os.utime(dst, (st[ST_ATIME], st[ST_MTIME])) + if preserve_mode: + os.chmod(dst, S_IMODE(st[ST_MODE])) + + return (dst, True) + + +# XXX I suspect this is Unix-specific -- need porting help! +def move_file(src, dst, verbose=True): # noqa: C901 + """Move a file 'src' to 'dst'. If 'dst' is a directory, the file will + be moved into it with the same name; otherwise, 'src' is just renamed + to 'dst'. Return the new full name of the file. + + Handles cross-device moves on Unix using 'copy_file()'. What about + other systems??? + """ + import errno + from os.path import basename, dirname, exists, isdir, isfile + + if verbose >= 1: + log.info("moving %s -> %s", src, dst) + + if not isfile(src): + raise DistutilsFileError(f"can't move '{src}': not a regular file") + + if isdir(dst): + dst = os.path.join(dst, basename(src)) + elif exists(dst): + raise DistutilsFileError( + f"can't move '{src}': destination '{dst}' already exists" + ) + + if not isdir(dirname(dst)): + raise DistutilsFileError( + f"can't move '{src}': destination '{dst}' not a valid path" + ) + + copy_it = False + try: + os.rename(src, dst) + except OSError as e: + (num, msg) = e.args + if num == errno.EXDEV: + copy_it = True + else: + raise DistutilsFileError(f"couldn't move '{src}' to '{dst}': {msg}") + + if copy_it: + copy_file(src, dst, verbose=verbose) + try: + os.unlink(src) + except OSError as e: + (num, msg) = e.args + try: + os.unlink(dst) + except OSError: + pass + raise DistutilsFileError( + f"couldn't move '{src}' to '{dst}' by copy/delete: " + f"delete '{src}' failed: {msg}" + ) + return dst + + +def write_file(filename, contents): + """Create a file with the specified name and write 'contents' (a + sequence of strings without line terminators) to it. + """ + with open(filename, 'w', encoding='utf-8') as f: + f.writelines(line + '\n' for line in contents) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/filelist.py b/lib/python3.11/site-packages/setuptools/_distutils/filelist.py new file mode 100644 index 0000000000000000000000000000000000000000..70dc0fdebc3f4200245aa1c6280ab39861dcf8b0 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/filelist.py @@ -0,0 +1,431 @@ +"""distutils.filelist + +Provides the FileList class, used for poking about the filesystem +and building lists of files. +""" + +from __future__ import annotations + +import fnmatch +import functools +import os +import re +from collections.abc import Iterable +from typing import Literal, overload + +from ._log import log +from .errors import DistutilsInternalError, DistutilsTemplateError +from .util import convert_path + + +class FileList: + """A list of files built by on exploring the filesystem and filtered by + applying various patterns to what we find there. + + Instance attributes: + dir + directory from which files will be taken -- only used if + 'allfiles' not supplied to constructor + files + list of filenames currently being built/filtered/manipulated + allfiles + complete list of files under consideration (ie. without any + filtering applied) + """ + + def __init__(self, warn: object = None, debug_print: object = None) -> None: + # ignore argument to FileList, but keep them for backwards + # compatibility + self.allfiles: Iterable[str] | None = None + self.files: list[str] = [] + + def set_allfiles(self, allfiles: Iterable[str]) -> None: + self.allfiles = allfiles + + def findall(self, dir: str | os.PathLike[str] = os.curdir) -> None: + self.allfiles = findall(dir) + + def debug_print(self, msg: object) -> None: + """Print 'msg' to stdout if the global DEBUG (taken from the + DISTUTILS_DEBUG environment variable) flag is true. + """ + from distutils.debug import DEBUG + + if DEBUG: + print(msg) + + # Collection methods + + def append(self, item: str) -> None: + self.files.append(item) + + def extend(self, items: Iterable[str]) -> None: + self.files.extend(items) + + def sort(self) -> None: + # Not a strict lexical sort! + sortable_files = sorted(map(os.path.split, self.files)) + self.files = [] + for sort_tuple in sortable_files: + self.files.append(os.path.join(*sort_tuple)) + + # Other miscellaneous utility methods + + def remove_duplicates(self) -> None: + # Assumes list has been sorted! + for i in range(len(self.files) - 1, 0, -1): + if self.files[i] == self.files[i - 1]: + del self.files[i] + + # "File template" methods + + def _parse_template_line(self, line): + words = line.split() + action = words[0] + + patterns = dir = dir_pattern = None + + if action in ('include', 'exclude', 'global-include', 'global-exclude'): + if len(words) < 2: + raise DistutilsTemplateError( + f"'{action}' expects ..." + ) + patterns = [convert_path(w) for w in words[1:]] + elif action in ('recursive-include', 'recursive-exclude'): + if len(words) < 3: + raise DistutilsTemplateError( + f"'{action}' expects ..." + ) + dir = convert_path(words[1]) + patterns = [convert_path(w) for w in words[2:]] + elif action in ('graft', 'prune'): + if len(words) != 2: + raise DistutilsTemplateError( + f"'{action}' expects a single " + ) + dir_pattern = convert_path(words[1]) + else: + raise DistutilsTemplateError(f"unknown action '{action}'") + + return (action, patterns, dir, dir_pattern) + + def process_template_line(self, line: str) -> None: # noqa: C901 + # Parse the line: split it up, make sure the right number of words + # is there, and return the relevant words. 'action' is always + # defined: it's the first word of the line. Which of the other + # three are defined depends on the action; it'll be either + # patterns, (dir and patterns), or (dir_pattern). + (action, patterns, dir, dir_pattern) = self._parse_template_line(line) + + # OK, now we know that the action is valid and we have the + # right number of words on the line for that action -- so we + # can proceed with minimal error-checking. + if action == 'include': + self.debug_print("include " + ' '.join(patterns)) + for pattern in patterns: + if not self.include_pattern(pattern, anchor=True): + log.warning("warning: no files found matching '%s'", pattern) + + elif action == 'exclude': + self.debug_print("exclude " + ' '.join(patterns)) + for pattern in patterns: + if not self.exclude_pattern(pattern, anchor=True): + log.warning( + "warning: no previously-included files found matching '%s'", + pattern, + ) + + elif action == 'global-include': + self.debug_print("global-include " + ' '.join(patterns)) + for pattern in patterns: + if not self.include_pattern(pattern, anchor=False): + log.warning( + ( + "warning: no files found matching '%s' " + "anywhere in distribution" + ), + pattern, + ) + + elif action == 'global-exclude': + self.debug_print("global-exclude " + ' '.join(patterns)) + for pattern in patterns: + if not self.exclude_pattern(pattern, anchor=False): + log.warning( + ( + "warning: no previously-included files matching " + "'%s' found anywhere in distribution" + ), + pattern, + ) + + elif action == 'recursive-include': + self.debug_print("recursive-include {} {}".format(dir, ' '.join(patterns))) + for pattern in patterns: + if not self.include_pattern(pattern, prefix=dir): + msg = "warning: no files found matching '%s' under directory '%s'" + log.warning(msg, pattern, dir) + + elif action == 'recursive-exclude': + self.debug_print("recursive-exclude {} {}".format(dir, ' '.join(patterns))) + for pattern in patterns: + if not self.exclude_pattern(pattern, prefix=dir): + log.warning( + ( + "warning: no previously-included files matching " + "'%s' found under directory '%s'" + ), + pattern, + dir, + ) + + elif action == 'graft': + self.debug_print("graft " + dir_pattern) + if not self.include_pattern(None, prefix=dir_pattern): + log.warning("warning: no directories found matching '%s'", dir_pattern) + + elif action == 'prune': + self.debug_print("prune " + dir_pattern) + if not self.exclude_pattern(None, prefix=dir_pattern): + log.warning( + ("no previously-included directories found matching '%s'"), + dir_pattern, + ) + else: + raise DistutilsInternalError( + f"this cannot happen: invalid action '{action}'" + ) + + # Filtering/selection methods + @overload + def include_pattern( + self, + pattern: str, + anchor: bool = True, + prefix: str | None = None, + is_regex: Literal[False] = False, + ) -> bool: ... + @overload + def include_pattern( + self, + pattern: str | re.Pattern[str], + anchor: bool = True, + prefix: str | None = None, + *, + is_regex: Literal[True], + ) -> bool: ... + @overload + def include_pattern( + self, + pattern: str | re.Pattern[str], + anchor: bool, + prefix: str | None, + is_regex: Literal[True], + ) -> bool: ... + def include_pattern( + self, + pattern: str | re.Pattern, + anchor: bool = True, + prefix: str | None = None, + is_regex: bool = False, + ) -> bool: + """Select strings (presumably filenames) from 'self.files' that + match 'pattern', a Unix-style wildcard (glob) pattern. Patterns + are not quite the same as implemented by the 'fnmatch' module: '*' + and '?' match non-special characters, where "special" is platform- + dependent: slash on Unix; colon, slash, and backslash on + DOS/Windows; and colon on Mac OS. + + If 'anchor' is true (the default), then the pattern match is more + stringent: "*.py" will match "foo.py" but not "foo/bar.py". If + 'anchor' is false, both of these will match. + + If 'prefix' is supplied, then only filenames starting with 'prefix' + (itself a pattern) and ending with 'pattern', with anything in between + them, will match. 'anchor' is ignored in this case. + + If 'is_regex' is true, 'anchor' and 'prefix' are ignored, and + 'pattern' is assumed to be either a string containing a regex or a + regex object -- no translation is done, the regex is just compiled + and used as-is. + + Selected strings will be added to self.files. + + Return True if files are found, False otherwise. + """ + # XXX docstring lying about what the special chars are? + files_found = False + pattern_re = translate_pattern(pattern, anchor, prefix, is_regex) + self.debug_print(f"include_pattern: applying regex r'{pattern_re.pattern}'") + + # delayed loading of allfiles list + if self.allfiles is None: + self.findall() + + for name in self.allfiles: + if pattern_re.search(name): + self.debug_print(" adding " + name) + self.files.append(name) + files_found = True + return files_found + + @overload + def exclude_pattern( + self, + pattern: str, + anchor: bool = True, + prefix: str | None = None, + is_regex: Literal[False] = False, + ) -> bool: ... + @overload + def exclude_pattern( + self, + pattern: str | re.Pattern[str], + anchor: bool = True, + prefix: str | None = None, + *, + is_regex: Literal[True], + ) -> bool: ... + @overload + def exclude_pattern( + self, + pattern: str | re.Pattern[str], + anchor: bool, + prefix: str | None, + is_regex: Literal[True], + ) -> bool: ... + def exclude_pattern( + self, + pattern: str | re.Pattern, + anchor: bool = True, + prefix: str | None = None, + is_regex: bool = False, + ) -> bool: + """Remove strings (presumably filenames) from 'files' that match + 'pattern'. Other parameters are the same as for + 'include_pattern()', above. + The list 'self.files' is modified in place. + Return True if files are found, False otherwise. + """ + files_found = False + pattern_re = translate_pattern(pattern, anchor, prefix, is_regex) + self.debug_print(f"exclude_pattern: applying regex r'{pattern_re.pattern}'") + for i in range(len(self.files) - 1, -1, -1): + if pattern_re.search(self.files[i]): + self.debug_print(" removing " + self.files[i]) + del self.files[i] + files_found = True + return files_found + + +# Utility functions + + +def _find_all_simple(path): + """ + Find all files under 'path' + """ + all_unique = _UniqueDirs.filter(os.walk(path, followlinks=True)) + results = ( + os.path.join(base, file) for base, dirs, files in all_unique for file in files + ) + return filter(os.path.isfile, results) + + +class _UniqueDirs(set): + """ + Exclude previously-seen dirs from walk results, + avoiding infinite recursion. + Ref https://bugs.python.org/issue44497. + """ + + def __call__(self, walk_item): + """ + Given an item from an os.walk result, determine + if the item represents a unique dir for this instance + and if not, prevent further traversal. + """ + base, dirs, files = walk_item + stat = os.stat(base) + candidate = stat.st_dev, stat.st_ino + found = candidate in self + if found: + del dirs[:] + self.add(candidate) + return not found + + @classmethod + def filter(cls, items): + return filter(cls(), items) + + +def findall(dir: str | os.PathLike[str] = os.curdir): + """ + Find all files under 'dir' and return the list of full filenames. + Unless dir is '.', return full filenames with dir prepended. + """ + files = _find_all_simple(dir) + if dir == os.curdir: + make_rel = functools.partial(os.path.relpath, start=dir) + files = map(make_rel, files) + return list(files) + + +def glob_to_re(pattern): + """Translate a shell-like glob pattern to a regular expression; return + a string containing the regex. Differs from 'fnmatch.translate()' in + that '*' does not match "special characters" (which are + platform-specific). + """ + pattern_re = fnmatch.translate(pattern) + + # '?' and '*' in the glob pattern become '.' and '.*' in the RE, which + # IMHO is wrong -- '?' and '*' aren't supposed to match slash in Unix, + # and by extension they shouldn't match such "special characters" under + # any OS. So change all non-escaped dots in the RE to match any + # character except the special characters (currently: just os.sep). + sep = os.sep + if os.sep == '\\': + # we're using a regex to manipulate a regex, so we need + # to escape the backslash twice + sep = r'\\\\' + escaped = rf'\1[^{sep}]' + pattern_re = re.sub(r'((?= 2: + set_threshold(logging.DEBUG) + + +class Log(logging.Logger): + """distutils.log.Log is deprecated, please use an alternative from `logging`.""" + + def __init__(self, threshold=WARN): + warnings.warn(Log.__doc__) # avoid DeprecationWarning to ensure warn is shown + super().__init__(__name__, level=threshold) + + @property + def threshold(self): + return self.level + + @threshold.setter + def threshold(self, level): + self.setLevel(level) + + warn = logging.Logger.warning diff --git a/lib/python3.11/site-packages/setuptools/_distutils/spawn.py b/lib/python3.11/site-packages/setuptools/_distutils/spawn.py new file mode 100644 index 0000000000000000000000000000000000000000..e47f1d19272cdd5fb8daa67eff3e52c8c435cfde --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/spawn.py @@ -0,0 +1,130 @@ +"""distutils.spawn + +Provides the 'spawn()' function, a front-end to various platform- +specific functions for launching another program in a sub-process. +""" + +from __future__ import annotations + +import os +import platform +import shutil +import subprocess +import sys +import warnings +from collections.abc import Mapping, MutableSequence +from typing import TYPE_CHECKING, TypeVar, overload + +from ._log import log +from .debug import DEBUG +from .errors import DistutilsExecError + +if TYPE_CHECKING: + from subprocess import _ENV + + +_MappingT = TypeVar("_MappingT", bound=Mapping) + + +def _debug(cmd): + """ + Render a subprocess command differently depending on DEBUG. + """ + return cmd if DEBUG else cmd[0] + + +def _inject_macos_ver(env: _MappingT | None) -> _MappingT | dict[str, str | int] | None: + if platform.system() != 'Darwin': + return env + + from .util import MACOSX_VERSION_VAR, get_macosx_target_ver + + target_ver = get_macosx_target_ver() + update = {MACOSX_VERSION_VAR: target_ver} if target_ver else {} + return {**_resolve(env), **update} + + +@overload +def _resolve(env: None) -> os._Environ[str]: ... +@overload +def _resolve(env: _MappingT) -> _MappingT: ... +def _resolve(env: _MappingT | None) -> _MappingT | os._Environ[str]: + return os.environ if env is None else env + + +def spawn( + cmd: MutableSequence[bytes | str | os.PathLike[str]], + search_path: bool = True, + verbose: bool = False, + env: _ENV | None = None, +) -> None: + """Run another program, specified as a command list 'cmd', in a new process. + + 'cmd' is just the argument list for the new process, ie. + cmd[0] is the program to run and cmd[1:] are the rest of its arguments. + There is no way to run a program with a name different from that of its + executable. + + If 'search_path' is true (the default), the system's executable + search path will be used to find the program; otherwise, cmd[0] + must be the exact path to the executable. + + Raise DistutilsExecError if running the program fails in any way; just + return on success. + """ + log.info(subprocess.list2cmdline(cmd)) + + if search_path: + executable = shutil.which(cmd[0]) + if executable is not None: + cmd[0] = executable + + try: + subprocess.check_call(cmd, env=_inject_macos_ver(env)) + except OSError as exc: + raise DistutilsExecError( + f"command {_debug(cmd)!r} failed: {exc.args[-1]}" + ) from exc + except subprocess.CalledProcessError as err: + raise DistutilsExecError( + f"command {_debug(cmd)!r} failed with exit code {err.returncode}" + ) from err + + +def find_executable(executable: str, path: str | None = None) -> str | None: + """Tries to find 'executable' in the directories listed in 'path'. + + A string listing directories separated by 'os.pathsep'; defaults to + os.environ['PATH']. Returns the complete filename or None if not found. + """ + warnings.warn( + 'Use shutil.which instead of find_executable', DeprecationWarning, stacklevel=2 + ) + _, ext = os.path.splitext(executable) + if (sys.platform == 'win32') and (ext != '.exe'): + executable = executable + '.exe' + + if os.path.isfile(executable): + return executable + + if path is None: + path = os.environ.get('PATH', None) + # bpo-35755: Don't fall through if PATH is the empty string + if path is None: + try: + path = os.confstr("CS_PATH") + except (AttributeError, ValueError): + # os.confstr() or CS_PATH is not available + path = os.defpath + + # PATH='' doesn't match, whereas PATH=':' looks in the current directory + if not path: + return None + + paths = path.split(os.pathsep) + for p in paths: + f = os.path.join(p, executable) + if os.path.isfile(f): + # the file exists, we have a shot at spawn working + return f + return None diff --git a/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py b/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py new file mode 100644 index 0000000000000000000000000000000000000000..7ddc869ab5603d691a8863988da89ec84689de84 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/sysconfig.py @@ -0,0 +1,598 @@ +"""Provide access to Python's configuration information. The specific +configuration variables available depend heavily on the platform and +configuration. The values may be retrieved using +get_config_var(name), and the list of variables is available via +get_config_vars().keys(). Additional convenience functions are also +available. + +Written by: Fred L. Drake, Jr. +Email: +""" + +from __future__ import annotations + +import functools +import os +import pathlib +import re +import sys +import sysconfig +from typing import TYPE_CHECKING, Literal, overload + +from jaraco.functools import pass_none + +from .ccompiler import CCompiler +from .compat import py39 +from .errors import DistutilsPlatformError +from .util import is_mingw + +if TYPE_CHECKING: + from typing_extensions import deprecated +else: + + def deprecated(message): + return lambda fn: fn + + +IS_PYPY = '__pypy__' in sys.builtin_module_names + +# These are needed in a couple of spots, so just compute them once. +PREFIX = os.path.normpath(sys.prefix) +EXEC_PREFIX = os.path.normpath(sys.exec_prefix) +BASE_PREFIX = os.path.normpath(sys.base_prefix) +BASE_EXEC_PREFIX = os.path.normpath(sys.base_exec_prefix) + +# Path to the base directory of the project. On Windows the binary may +# live in project/PCbuild/win32 or project/PCbuild/amd64. +# set for cross builds +if "_PYTHON_PROJECT_BASE" in os.environ: + project_base = os.path.abspath(os.environ["_PYTHON_PROJECT_BASE"]) +else: + if sys.executable: + project_base = os.path.dirname(os.path.abspath(sys.executable)) + else: + # sys.executable can be empty if argv[0] has been changed and Python is + # unable to retrieve the real program name + project_base = os.getcwd() + + +def _is_python_source_dir(d): + """ + Return True if the target directory appears to point to an + un-installed Python. + """ + modules = pathlib.Path(d).joinpath('Modules') + return any(modules.joinpath(fn).is_file() for fn in ('Setup', 'Setup.local')) + + +_sys_home = getattr(sys, '_home', None) + + +def _is_parent(dir_a, dir_b): + """ + Return True if a is a parent of b. + """ + return os.path.normcase(dir_a).startswith(os.path.normcase(dir_b)) + + +if os.name == 'nt': + + @pass_none + def _fix_pcbuild(d): + # In a venv, sys._home will be inside BASE_PREFIX rather than PREFIX. + prefixes = PREFIX, BASE_PREFIX + matched = ( + prefix + for prefix in prefixes + if _is_parent(d, os.path.join(prefix, "PCbuild")) + ) + return next(matched, d) + + project_base = _fix_pcbuild(project_base) + _sys_home = _fix_pcbuild(_sys_home) + + +def _python_build(): + if _sys_home: + return _is_python_source_dir(_sys_home) + return _is_python_source_dir(project_base) + + +python_build = _python_build() + + +# Calculate the build qualifier flags if they are defined. Adding the flags +# to the include and lib directories only makes sense for an installation, not +# an in-source build. +build_flags = '' +try: + if not python_build: + build_flags = sys.abiflags +except AttributeError: + # It's not a configure-based build, so the sys module doesn't have + # this attribute, which is fine. + pass + + +def get_python_version(): + """Return a string containing the major and minor Python version, + leaving off the patchlevel. Sample return values could be '1.5' + or '2.2'. + """ + return f'{sys.version_info.major}.{sys.version_info.minor}' + + +def get_python_inc(plat_specific: bool = False, prefix: str | None = None) -> str: + """Return the directory containing installed Python header files. + + If 'plat_specific' is false (the default), this is the path to the + non-platform-specific header files, i.e. Python.h and so on; + otherwise, this is the path to platform-specific header files + (namely pyconfig.h). + + If 'prefix' is supplied, use it instead of sys.base_prefix or + sys.base_exec_prefix -- i.e., ignore 'plat_specific'. + """ + default_prefix = BASE_EXEC_PREFIX if plat_specific else BASE_PREFIX + resolved_prefix = prefix if prefix is not None else default_prefix + # MinGW imitates posix like layout, but os.name != posix + os_name = "posix" if is_mingw() else os.name + try: + getter = globals()[f'_get_python_inc_{os_name}'] + except KeyError: + raise DistutilsPlatformError( + "I don't know where Python installs its C header files " + f"on platform '{os.name}'" + ) + return getter(resolved_prefix, prefix, plat_specific) + + +@pass_none +def _extant(path): + """ + Replace path with None if it doesn't exist. + """ + return path if os.path.exists(path) else None + + +def _get_python_inc_posix(prefix, spec_prefix, plat_specific): + return ( + _get_python_inc_posix_python(plat_specific) + or _extant(_get_python_inc_from_config(plat_specific, spec_prefix)) + or _get_python_inc_posix_prefix(prefix) + ) + + +def _get_python_inc_posix_python(plat_specific): + """ + Assume the executable is in the build directory. The + pyconfig.h file should be in the same directory. Since + the build directory may not be the source directory, + use "srcdir" from the makefile to find the "Include" + directory. + """ + if not python_build: + return + if plat_specific: + return _sys_home or project_base + incdir = os.path.join(get_config_var('srcdir'), 'Include') + return os.path.normpath(incdir) + + +def _get_python_inc_from_config(plat_specific, spec_prefix): + """ + If no prefix was explicitly specified, provide the include + directory from the config vars. Useful when + cross-compiling, since the config vars may come from + the host + platform Python installation, while the current Python + executable is from the build platform installation. + + >>> monkeypatch = getfixture('monkeypatch') + >>> gpifc = _get_python_inc_from_config + >>> monkeypatch.setitem(gpifc.__globals__, 'get_config_var', str.lower) + >>> gpifc(False, '/usr/bin/') + >>> gpifc(False, '') + >>> gpifc(False, None) + 'includepy' + >>> gpifc(True, None) + 'confincludepy' + """ + if spec_prefix is None: + return get_config_var('CONF' * plat_specific + 'INCLUDEPY') + + +def _get_python_inc_posix_prefix(prefix): + implementation = 'pypy' if IS_PYPY else 'python' + python_dir = implementation + get_python_version() + build_flags + return os.path.join(prefix, "include", python_dir) + + +def _get_python_inc_nt(prefix, spec_prefix, plat_specific): + if python_build: + # Include both include dirs to ensure we can find pyconfig.h + return ( + os.path.join(prefix, "include") + + os.path.pathsep + + os.path.dirname(sysconfig.get_config_h_filename()) + ) + return os.path.join(prefix, "include") + + +# allow this behavior to be monkey-patched. Ref pypa/distutils#2. +def _posix_lib(standard_lib, libpython, early_prefix, prefix): + if standard_lib: + return libpython + else: + return os.path.join(libpython, "site-packages") + + +def get_python_lib( + plat_specific: bool = False, standard_lib: bool = False, prefix: str | None = None +) -> str: + """Return the directory containing the Python library (standard or + site additions). + + If 'plat_specific' is true, return the directory containing + platform-specific modules, i.e. any module from a non-pure-Python + module distribution; otherwise, return the platform-shared library + directory. If 'standard_lib' is true, return the directory + containing standard Python library modules; otherwise, return the + directory for site-specific modules. + + If 'prefix' is supplied, use it instead of sys.base_prefix or + sys.base_exec_prefix -- i.e., ignore 'plat_specific'. + """ + + early_prefix = prefix + + if prefix is None: + if standard_lib: + prefix = plat_specific and BASE_EXEC_PREFIX or BASE_PREFIX + else: + prefix = plat_specific and EXEC_PREFIX or PREFIX + + if os.name == "posix" or is_mingw(): + if plat_specific or standard_lib: + # Platform-specific modules (any module from a non-pure-Python + # module distribution) or standard Python library modules. + libdir = getattr(sys, "platlibdir", "lib") + else: + # Pure Python + libdir = "lib" + implementation = 'pypy' if IS_PYPY else 'python' + libpython = os.path.join(prefix, libdir, implementation + get_python_version()) + return _posix_lib(standard_lib, libpython, early_prefix, prefix) + elif os.name == "nt": + if standard_lib: + return os.path.join(prefix, "Lib") + else: + return os.path.join(prefix, "Lib", "site-packages") + else: + raise DistutilsPlatformError( + f"I don't know where Python installs its library on platform '{os.name}'" + ) + + +@functools.lru_cache +def _customize_macos(): + """ + Perform first-time customization of compiler-related + config vars on macOS. Use after a compiler is known + to be needed. This customization exists primarily to support Pythons + from binary installers. The kind and paths to build tools on + the user system may vary significantly from the system + that Python itself was built on. Also the user OS + version and build tools may not support the same set + of CPU architectures for universal builds. + """ + + sys.platform == "darwin" and __import__('_osx_support').customize_compiler( + get_config_vars() + ) + + +def customize_compiler(compiler: CCompiler) -> None: + """Do any platform-specific customization of a CCompiler instance. + + Mainly needed on Unix, so we can plug in the information that + varies across Unices and is stored in Python's Makefile. + """ + if compiler.compiler_type in ["unix", "cygwin"] or ( + compiler.compiler_type == "mingw32" and is_mingw() + ): + _customize_macos() + + ( + cc, + cxx, + cflags, + ccshared, + ldshared, + ldcxxshared, + shlib_suffix, + ar, + ar_flags, + ) = get_config_vars( + 'CC', + 'CXX', + 'CFLAGS', + 'CCSHARED', + 'LDSHARED', + 'LDCXXSHARED', + 'SHLIB_SUFFIX', + 'AR', + 'ARFLAGS', + ) + + cxxflags = cflags + + if 'CC' in os.environ: + newcc = os.environ['CC'] + if 'LDSHARED' not in os.environ and ldshared.startswith(cc): + # If CC is overridden, use that as the default + # command for LDSHARED as well + ldshared = newcc + ldshared[len(cc) :] + cc = newcc + cxx = os.environ.get('CXX', cxx) + ldshared = os.environ.get('LDSHARED', ldshared) + ldcxxshared = os.environ.get('LDCXXSHARED', ldcxxshared) + cpp = os.environ.get( + 'CPP', + cc + " -E", # not always + ) + + ldshared = _add_flags(ldshared, 'LD') + ldcxxshared = _add_flags(ldcxxshared, 'LD') + cflags = os.environ.get('CFLAGS', cflags) + ldshared = _add_flags(ldshared, 'C') + cxxflags = os.environ.get('CXXFLAGS', cxxflags) + ldcxxshared = _add_flags(ldcxxshared, 'CXX') + cpp = _add_flags(cpp, 'CPP') + cflags = _add_flags(cflags, 'CPP') + cxxflags = _add_flags(cxxflags, 'CPP') + ldshared = _add_flags(ldshared, 'CPP') + ldcxxshared = _add_flags(ldcxxshared, 'CPP') + + ar = os.environ.get('AR', ar) + + archiver = ar + ' ' + os.environ.get('ARFLAGS', ar_flags) + cc_cmd = cc + ' ' + cflags + cxx_cmd = cxx + ' ' + cxxflags + + compiler.set_executables( + preprocessor=cpp, + compiler=cc_cmd, + compiler_so=cc_cmd + ' ' + ccshared, + compiler_cxx=cxx_cmd, + compiler_so_cxx=cxx_cmd + ' ' + ccshared, + linker_so=ldshared, + linker_so_cxx=ldcxxshared, + linker_exe=cc, + linker_exe_cxx=cxx, + archiver=archiver, + ) + + if 'RANLIB' in os.environ and compiler.executables.get('ranlib', None): + compiler.set_executables(ranlib=os.environ['RANLIB']) + + compiler.shared_lib_extension = shlib_suffix + + +def get_config_h_filename() -> str: + """Return full pathname of installed pyconfig.h file.""" + return sysconfig.get_config_h_filename() + + +def get_makefile_filename() -> str: + """Return full pathname of installed Makefile from the Python build.""" + return sysconfig.get_makefile_filename() + + +def parse_config_h(fp, g=None): + """Parse a config.h-style file. + + A dictionary containing name/value pairs is returned. If an + optional dictionary is passed in as the second argument, it is + used instead of a new dictionary. + """ + return sysconfig.parse_config_h(fp, vars=g) + + +# Regexes needed for parsing Makefile (and similar syntaxes, +# like old-style Setup files). +_variable_rx = re.compile(r"([a-zA-Z][a-zA-Z0-9_]+)\s*=\s*(.*)") +_findvar1_rx = re.compile(r"\$\(([A-Za-z][A-Za-z0-9_]*)\)") +_findvar2_rx = re.compile(r"\${([A-Za-z][A-Za-z0-9_]*)}") + + +def parse_makefile(fn, g=None): # noqa: C901 + """Parse a Makefile-style file. + + A dictionary containing name/value pairs is returned. If an + optional dictionary is passed in as the second argument, it is + used instead of a new dictionary. + """ + from distutils.text_file import TextFile + + fp = TextFile( + fn, + strip_comments=True, + skip_blanks=True, + join_lines=True, + errors="surrogateescape", + ) + + if g is None: + g = {} + done = {} + notdone = {} + + while True: + line = fp.readline() + if line is None: # eof + break + m = _variable_rx.match(line) + if m: + n, v = m.group(1, 2) + v = v.strip() + # `$$' is a literal `$' in make + tmpv = v.replace('$$', '') + + if "$" in tmpv: + notdone[n] = v + else: + try: + v = int(v) + except ValueError: + # insert literal `$' + done[n] = v.replace('$$', '$') + else: + done[n] = v + + # Variables with a 'PY_' prefix in the makefile. These need to + # be made available without that prefix through sysconfig. + # Special care is needed to ensure that variable expansion works, even + # if the expansion uses the name without a prefix. + renamed_variables = ('CFLAGS', 'LDFLAGS', 'CPPFLAGS') + + # do variable interpolation here + while notdone: + for name in list(notdone): + value = notdone[name] + m = _findvar1_rx.search(value) or _findvar2_rx.search(value) + if m: + n = m.group(1) + found = True + if n in done: + item = str(done[n]) + elif n in notdone: + # get it on a subsequent round + found = False + elif n in os.environ: + # do it like make: fall back to environment + item = os.environ[n] + + elif n in renamed_variables: + if name.startswith('PY_') and name[3:] in renamed_variables: + item = "" + + elif 'PY_' + n in notdone: + found = False + + else: + item = str(done['PY_' + n]) + else: + done[n] = item = "" + if found: + after = value[m.end() :] + value = value[: m.start()] + item + after + if "$" in after: + notdone[name] = value + else: + try: + value = int(value) + except ValueError: + done[name] = value.strip() + else: + done[name] = value + del notdone[name] + + if name.startswith('PY_') and name[3:] in renamed_variables: + name = name[3:] + if name not in done: + done[name] = value + else: + # bogus variable reference; just drop it since we can't deal + del notdone[name] + + fp.close() + + # strip spurious spaces + for k, v in done.items(): + if isinstance(v, str): + done[k] = v.strip() + + # save the results in the global dictionary + g.update(done) + return g + + +def expand_makefile_vars(s, vars): + """Expand Makefile-style variables -- "${foo}" or "$(foo)" -- in + 'string' according to 'vars' (a dictionary mapping variable names to + values). Variables not present in 'vars' are silently expanded to the + empty string. The variable values in 'vars' should not contain further + variable expansions; if 'vars' is the output of 'parse_makefile()', + you're fine. Returns a variable-expanded version of 's'. + """ + + # This algorithm does multiple expansion, so if vars['foo'] contains + # "${bar}", it will expand ${foo} to ${bar}, and then expand + # ${bar}... and so forth. This is fine as long as 'vars' comes from + # 'parse_makefile()', which takes care of such expansions eagerly, + # according to make's variable expansion semantics. + + while True: + m = _findvar1_rx.search(s) or _findvar2_rx.search(s) + if m: + (beg, end) = m.span() + s = s[0:beg] + vars.get(m.group(1)) + s[end:] + else: + break + return s + + +_config_vars = None + + +@overload +def get_config_vars() -> dict[str, str | int]: ... +@overload +def get_config_vars(arg: str, /, *args: str) -> list[str | int]: ... +def get_config_vars(*args: str) -> list[str | int] | dict[str, str | int]: + """With no arguments, return a dictionary of all configuration + variables relevant for the current platform. Generally this includes + everything needed to build extensions and install both pure modules and + extensions. On Unix, this means every variable defined in Python's + installed Makefile; on Windows it's a much smaller set. + + With arguments, return a list of values that result from looking up + each argument in the configuration variable dictionary. + """ + global _config_vars + if _config_vars is None: + _config_vars = sysconfig.get_config_vars().copy() + py39.add_ext_suffix(_config_vars) + + return [_config_vars.get(name) for name in args] if args else _config_vars + + +@overload +@deprecated( + "SO is deprecated, use EXT_SUFFIX. Support will be removed when this module is synchronized with stdlib Python 3.11" +) +def get_config_var(name: Literal["SO"]) -> int | str | None: ... +@overload +def get_config_var(name: str) -> int | str | None: ... +def get_config_var(name: str) -> int | str | None: + """Return the value of a single variable using the dictionary + returned by 'get_config_vars()'. Equivalent to + get_config_vars().get(name) + """ + if name == 'SO': + import warnings + + warnings.warn('SO is deprecated, use EXT_SUFFIX', DeprecationWarning, 2) + return get_config_vars().get(name) + + +@pass_none +def _add_flags(value: str, type: str) -> str: + """ + Add any flags from the environment for the given type. + + type is the prefix to FLAGS in the environment key (e.g. "C" for "CFLAGS"). + """ + flags = os.environ.get(f'{type}FLAGS') + return f'{value} {flags}' if flags else value diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/__init__.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5a8ab061009f1cd87a53ce51eff3bc9e9a632273 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/__init__.py @@ -0,0 +1,42 @@ +""" +Test suite for distutils. + +Tests for the command classes in the distutils.command package are +included in distutils.tests as well, instead of using a separate +distutils.command.tests package, since command identification is done +by import rather than matching pre-defined names. +""" + +import shutil +from collections.abc import Sequence + + +def missing_compiler_executable(cmd_names: Sequence[str] = []): # pragma: no cover + """Check if the compiler components used to build the interpreter exist. + + Check for the existence of the compiler executables whose names are listed + in 'cmd_names' or all the compiler executables when 'cmd_names' is empty + and return the first missing executable or None when none is found + missing. + + """ + from distutils import ccompiler, errors, sysconfig + + compiler = ccompiler.new_compiler() + sysconfig.customize_compiler(compiler) + if compiler.compiler_type == "msvc": + # MSVC has no executables, so check whether initialization succeeds + try: + compiler.initialize() + except errors.DistutilsPlatformError: + return "msvc" + for name in 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+ from test.support import ( + CleanImport as CleanImport, + ) + from test.support import ( + DirsOnSysPath as DirsOnSysPath, + ) + from test.support import ( + EnvironmentVarGuard as EnvironmentVarGuard, + ) + from test.support import ( + rmtree as rmtree, + ) + from test.support import ( + skip_unless_symlink as skip_unless_symlink, + ) + from test.support import ( + unlink as unlink, + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/support.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/support.py new file mode 100644 index 0000000000000000000000000000000000000000..9cd2b8a9eedbb9f11a3ed793af57b3290c5febef --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/support.py @@ -0,0 +1,134 @@ +"""Support code for distutils test cases.""" + +import itertools +import os +import pathlib +import shutil +import sys +import sysconfig +import tempfile +from distutils.core import Distribution + +import pytest +from more_itertools import always_iterable + + +@pytest.mark.usefixtures('distutils_managed_tempdir') +class TempdirManager: + """ + Mix-in class that handles temporary directories for test cases. + """ + + def mkdtemp(self): + """Create a temporary directory that will be cleaned up. + + Returns the path of the directory. + """ + d = tempfile.mkdtemp() + self.tempdirs.append(d) + return d + + def write_file(self, path, content='xxx'): + """Writes a file in the given path. + + path can be a string or a sequence. + """ + pathlib.Path(*always_iterable(path)).write_text(content, encoding='utf-8') + + def create_dist(self, pkg_name='foo', **kw): + """Will generate a test environment. + + This function creates: + - a Distribution instance using keywords + - a temporary directory with a package structure + + It returns the package directory and the distribution + instance. + """ + tmp_dir = self.mkdtemp() + pkg_dir = os.path.join(tmp_dir, pkg_name) + os.mkdir(pkg_dir) + dist = Distribution(attrs=kw) + + return pkg_dir, dist + + +class DummyCommand: + """Class to store options for retrieval via set_undefined_options().""" + + def __init__(self, **kwargs): + vars(self).update(kwargs) + + def ensure_finalized(self): + pass + + +def copy_xxmodule_c(directory): + """Helper for tests that need the xxmodule.c source file. + + Example use: + + def test_compile(self): + copy_xxmodule_c(self.tmpdir) + self.assertIn('xxmodule.c', os.listdir(self.tmpdir)) + + If the source file can be found, it will be copied to *directory*. If not, + the test will be skipped. Errors during copy are not caught. + """ + shutil.copy(_get_xxmodule_path(), os.path.join(directory, 'xxmodule.c')) + + +def _get_xxmodule_path(): + source_name = 'xxmodule.c' if sys.version_info > (3, 9) else 'xxmodule-3.8.c' + return os.path.join(os.path.dirname(__file__), source_name) + + +def fixup_build_ext(cmd): + """Function needed to make build_ext tests pass. + + When Python was built with --enable-shared on Unix, -L. is not enough to + find libpython.so, because regrtest runs in a tempdir, not in the + source directory where the .so lives. + + When Python was built with in debug mode on Windows, build_ext commands + need their debug attribute set, and it is not done automatically for + some reason. + + This function handles both of these things. Example use: + + cmd = build_ext(dist) + support.fixup_build_ext(cmd) + cmd.ensure_finalized() + + Unlike most other Unix platforms, Mac OS X embeds absolute paths + to shared libraries into executables, so the fixup is not needed there. + """ + if os.name == 'nt': + cmd.debug = sys.executable.endswith('_d.exe') + elif sysconfig.get_config_var('Py_ENABLE_SHARED'): + # To further add to the shared builds fun on Unix, we can't just add + # library_dirs to the Extension() instance because that doesn't get + # plumbed through to the final compiler command. + runshared = sysconfig.get_config_var('RUNSHARED') + if runshared is None: + cmd.library_dirs = ['.'] + else: + if sys.platform == 'darwin': + cmd.library_dirs = [] + else: + name, equals, value = runshared.partition('=') + cmd.library_dirs = [d for d in value.split(os.pathsep) if d] + + +def combine_markers(cls): + """ + pytest will honor markers as found on the class, but when + markers are on multiple subclasses, only one appears. Use + this decorator to combine those markers. + """ + cls.pytestmark = [ + mark + for base in itertools.chain([cls], cls.__bases__) + for mark in getattr(base, 'pytestmark', []) + ] + return cls diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_archive_util.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_archive_util.py new file mode 100644 index 0000000000000000000000000000000000000000..a9cc5eb00968ed9d5d77cac60ef543827c669539 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_archive_util.py @@ -0,0 +1,342 @@ +"""Tests for distutils.archive_util.""" + +import functools +import operator +import os +import pathlib +import sys +import tarfile +from distutils import archive_util +from distutils.archive_util import ( + ARCHIVE_FORMATS, + check_archive_formats, + make_archive, + make_tarball, + make_zipfile, +) +from distutils.spawn import spawn +from distutils.tests import support +from os.path import splitdrive + +import path +import pytest +from test.support import patch + +from .unix_compat import UID_0_SUPPORT, grp, pwd, require_uid_0, require_unix_id + + +def can_fs_encode(filename): + """ + Return True if the filename can be saved in the file system. + """ + if os.path.supports_unicode_filenames: + return True + try: + filename.encode(sys.getfilesystemencoding()) + except UnicodeEncodeError: + return False + return True + + +def all_equal(values): + return functools.reduce(operator.eq, values) + + +def same_drive(*paths): + return all_equal(pathlib.Path(path).drive for path in paths) + + +class ArchiveUtilTestCase(support.TempdirManager): + @pytest.mark.usefixtures('needs_zlib') + def test_make_tarball(self, name='archive'): + # creating something to tar + tmpdir = self._create_files() + self._make_tarball(tmpdir, name, '.tar.gz') + # trying an uncompressed one + self._make_tarball(tmpdir, name, '.tar', compress=None) + + @pytest.mark.usefixtures('needs_zlib') + def test_make_tarball_gzip(self): + tmpdir = self._create_files() + self._make_tarball(tmpdir, 'archive', '.tar.gz', compress='gzip') + + def test_make_tarball_bzip2(self): + pytest.importorskip('bz2') + tmpdir = self._create_files() + self._make_tarball(tmpdir, 'archive', '.tar.bz2', compress='bzip2') + + def test_make_tarball_xz(self): + pytest.importorskip('lzma') + tmpdir = self._create_files() + self._make_tarball(tmpdir, 'archive', '.tar.xz', compress='xz') + + @pytest.mark.skipif("not can_fs_encode('årchiv')") + def test_make_tarball_latin1(self): + """ + Mirror test_make_tarball, except filename contains latin characters. + """ + self.test_make_tarball('årchiv') # note this isn't a real word + + @pytest.mark.skipif("not can_fs_encode('のアーカイブ')") + def test_make_tarball_extended(self): + """ + Mirror test_make_tarball, except filename contains extended + characters outside the latin charset. + """ + self.test_make_tarball('のアーカイブ') # japanese for archive + + def _make_tarball(self, tmpdir, target_name, suffix, **kwargs): + tmpdir2 = self.mkdtemp() + if same_drive(tmpdir, tmpdir2): + pytest.skip("source and target should be on same drive") + + base_name = os.path.join(tmpdir2, target_name) + + # working with relative paths to avoid tar warnings + with path.Path(tmpdir): + make_tarball(splitdrive(base_name)[1], 'dist', **kwargs) + + # check if the compressed tarball was created + tarball = base_name + suffix + assert os.path.exists(tarball) + assert self._tarinfo(tarball) == self._created_files + + def _tarinfo(self, path): + tar = tarfile.open(path) + try: + names = tar.getnames() + names.sort() + return names + finally: + tar.close() + + _zip_created_files = [ + 'dist/', + 'dist/file1', + 'dist/file2', + 'dist/sub/', + 'dist/sub/file3', + 'dist/sub2/', + ] + _created_files = [p.rstrip('/') for p in _zip_created_files] + + def _create_files(self): + # creating something to tar + tmpdir = self.mkdtemp() + dist = os.path.join(tmpdir, 'dist') + os.mkdir(dist) + self.write_file([dist, 'file1'], 'xxx') + self.write_file([dist, 'file2'], 'xxx') + os.mkdir(os.path.join(dist, 'sub')) + self.write_file([dist, 'sub', 'file3'], 'xxx') + os.mkdir(os.path.join(dist, 'sub2')) + return tmpdir + + @pytest.mark.usefixtures('needs_zlib') + @pytest.mark.skipif("not (shutil.which('tar') and shutil.which('gzip'))") + def test_tarfile_vs_tar(self): + tmpdir = self._create_files() + tmpdir2 = self.mkdtemp() + base_name = os.path.join(tmpdir2, 'archive') + old_dir = os.getcwd() + os.chdir(tmpdir) + try: + make_tarball(base_name, 'dist') + finally: + os.chdir(old_dir) + + # check if the compressed tarball was created + tarball = base_name + '.tar.gz' + assert os.path.exists(tarball) + + # now create another tarball using `tar` + tarball2 = os.path.join(tmpdir, 'archive2.tar.gz') + tar_cmd = ['tar', '-cf', 'archive2.tar', 'dist'] + gzip_cmd = ['gzip', '-f', '-9', 'archive2.tar'] + old_dir = os.getcwd() + os.chdir(tmpdir) + try: + spawn(tar_cmd) + spawn(gzip_cmd) + finally: + os.chdir(old_dir) + + assert os.path.exists(tarball2) + # let's compare both tarballs + assert self._tarinfo(tarball) == self._created_files + assert self._tarinfo(tarball2) == self._created_files + + # trying an uncompressed one + base_name = os.path.join(tmpdir2, 'archive') + old_dir = os.getcwd() + os.chdir(tmpdir) + try: + make_tarball(base_name, 'dist', compress=None) + finally: + os.chdir(old_dir) + tarball = base_name + '.tar' + assert os.path.exists(tarball) + + @pytest.mark.usefixtures('needs_zlib') + def test_make_zipfile(self): + zipfile = pytest.importorskip('zipfile') + # creating something to tar + tmpdir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + with path.Path(tmpdir): + make_zipfile(base_name, 'dist') + + # check if the compressed tarball was created + tarball = base_name + '.zip' + assert os.path.exists(tarball) + with zipfile.ZipFile(tarball) as zf: + assert sorted(zf.namelist()) == self._zip_created_files + + def test_make_zipfile_no_zlib(self): + zipfile = pytest.importorskip('zipfile') + patch(self, archive_util.zipfile, 'zlib', None) # force zlib ImportError + + called = [] + zipfile_class = zipfile.ZipFile + + def fake_zipfile(*a, **kw): + if kw.get('compression', None) == zipfile.ZIP_STORED: + called.append((a, kw)) + return zipfile_class(*a, **kw) + + patch(self, archive_util.zipfile, 'ZipFile', fake_zipfile) + + # create something to tar and compress + tmpdir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + with path.Path(tmpdir): + make_zipfile(base_name, 'dist') + + tarball = base_name + '.zip' + assert called == [((tarball, "w"), {'compression': zipfile.ZIP_STORED})] + assert os.path.exists(tarball) + with zipfile.ZipFile(tarball) as zf: + assert sorted(zf.namelist()) == self._zip_created_files + + def test_check_archive_formats(self): + assert check_archive_formats(['gztar', 'xxx', 'zip']) == 'xxx' + assert ( + check_archive_formats(['gztar', 'bztar', 'xztar', 'ztar', 'tar', 'zip']) + is None + ) + + def test_make_archive(self): + tmpdir = self.mkdtemp() + base_name = os.path.join(tmpdir, 'archive') + with pytest.raises(ValueError): + make_archive(base_name, 'xxx') + + def test_make_archive_cwd(self): + current_dir = os.getcwd() + + def _breaks(*args, **kw): + raise RuntimeError() + + ARCHIVE_FORMATS['xxx'] = (_breaks, [], 'xxx file') + try: + try: + make_archive('xxx', 'xxx', root_dir=self.mkdtemp()) + except Exception: + pass + assert os.getcwd() == current_dir + finally: + ARCHIVE_FORMATS.pop('xxx') + + def test_make_archive_tar(self): + base_dir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + res = make_archive(base_name, 'tar', base_dir, 'dist') + assert os.path.exists(res) + assert os.path.basename(res) == 'archive.tar' + assert self._tarinfo(res) == self._created_files + + @pytest.mark.usefixtures('needs_zlib') + def test_make_archive_gztar(self): + base_dir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + res = make_archive(base_name, 'gztar', base_dir, 'dist') + assert os.path.exists(res) + assert os.path.basename(res) == 'archive.tar.gz' + assert self._tarinfo(res) == self._created_files + + def test_make_archive_bztar(self): + pytest.importorskip('bz2') + base_dir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + res = make_archive(base_name, 'bztar', base_dir, 'dist') + assert os.path.exists(res) + assert os.path.basename(res) == 'archive.tar.bz2' + assert self._tarinfo(res) == self._created_files + + def test_make_archive_xztar(self): + pytest.importorskip('lzma') + base_dir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + res = make_archive(base_name, 'xztar', base_dir, 'dist') + assert os.path.exists(res) + assert os.path.basename(res) == 'archive.tar.xz' + assert self._tarinfo(res) == self._created_files + + def test_make_archive_owner_group(self): + # testing make_archive with owner and group, with various combinations + # this works even if there's not gid/uid support + if UID_0_SUPPORT: + group = grp.getgrgid(0)[0] + owner = pwd.getpwuid(0)[0] + else: + group = owner = 'root' + + base_dir = self._create_files() + root_dir = self.mkdtemp() + base_name = os.path.join(self.mkdtemp(), 'archive') + res = make_archive( + base_name, 'zip', root_dir, base_dir, owner=owner, group=group + ) + assert os.path.exists(res) + + res = make_archive(base_name, 'zip', root_dir, base_dir) + assert os.path.exists(res) + + res = make_archive( + base_name, 'tar', root_dir, base_dir, owner=owner, group=group + ) + assert os.path.exists(res) + + res = make_archive( + base_name, 'tar', root_dir, base_dir, owner='kjhkjhkjg', group='oihohoh' + ) + assert os.path.exists(res) + + @pytest.mark.usefixtures('needs_zlib') + @require_unix_id + @require_uid_0 + def test_tarfile_root_owner(self): + tmpdir = self._create_files() + base_name = os.path.join(self.mkdtemp(), 'archive') + old_dir = os.getcwd() + os.chdir(tmpdir) + group = grp.getgrgid(0)[0] + owner = pwd.getpwuid(0)[0] + try: + archive_name = make_tarball( + base_name, 'dist', compress=None, owner=owner, group=group + ) + finally: + os.chdir(old_dir) + + # check if the compressed tarball was created + assert os.path.exists(archive_name) + + # now checks the rights + archive = tarfile.open(archive_name) + try: + for member in archive.getmembers(): + assert member.uid == 0 + assert member.gid == 0 + finally: + archive.close() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist.py new file mode 100644 index 0000000000000000000000000000000000000000..d5696fc3dcd8081335606dc92badd73a69ceac69 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist.py @@ -0,0 +1,47 @@ +"""Tests for distutils.command.bdist.""" + +from distutils.command.bdist import bdist +from distutils.tests import support + + +class TestBuild(support.TempdirManager): + def test_formats(self): + # let's create a command and make sure + # we can set the format + dist = self.create_dist()[1] + cmd = bdist(dist) + cmd.formats = ['gztar'] + cmd.ensure_finalized() + assert cmd.formats == ['gztar'] + + # what formats does bdist offer? + formats = [ + 'bztar', + 'gztar', + 'rpm', + 'tar', + 'xztar', + 'zip', + 'ztar', + ] + found = sorted(cmd.format_commands) + assert found == formats + + def test_skip_build(self): + # bug #10946: bdist --skip-build should trickle down to subcommands + dist = self.create_dist()[1] + cmd = bdist(dist) + cmd.skip_build = True + cmd.ensure_finalized() + dist.command_obj['bdist'] = cmd + + names = [ + 'bdist_dumb', + ] # bdist_rpm does not support --skip-build + + for name in names: + subcmd = cmd.get_finalized_command(name) + if getattr(subcmd, '_unsupported', False): + # command is not supported on this build + continue + assert subcmd.skip_build, f'{name} should take --skip-build from bdist' diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py new file mode 100644 index 0000000000000000000000000000000000000000..1fc51d244a78058329c56a618b092e466715e565 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py @@ -0,0 +1,78 @@ +"""Tests for distutils.command.bdist_dumb.""" + +import os +import sys +import zipfile +from distutils.command.bdist_dumb import bdist_dumb +from distutils.core import Distribution +from distutils.tests import support + +import pytest + +SETUP_PY = """\ +from distutils.core import setup +import foo + +setup(name='foo', version='0.1', py_modules=['foo'], + url='xxx', author='xxx', author_email='xxx') + +""" + + +@support.combine_markers +@pytest.mark.usefixtures('save_env') +@pytest.mark.usefixtures('save_argv') +@pytest.mark.usefixtures('save_cwd') +class TestBuildDumb( + support.TempdirManager, +): + @pytest.mark.usefixtures('needs_zlib') + def test_simple_built(self): + # let's create a simple package + tmp_dir = self.mkdtemp() + pkg_dir = os.path.join(tmp_dir, 'foo') + os.mkdir(pkg_dir) + self.write_file((pkg_dir, 'setup.py'), SETUP_PY) + self.write_file((pkg_dir, 'foo.py'), '#') + self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py') + self.write_file((pkg_dir, 'README'), '') + + dist = Distribution({ + 'name': 'foo', + 'version': '0.1', + 'py_modules': ['foo'], + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + }) + dist.script_name = 'setup.py' + os.chdir(pkg_dir) + + sys.argv = ['setup.py'] + cmd = bdist_dumb(dist) + + # so the output is the same no matter + # what is the platform + cmd.format = 'zip' + + cmd.ensure_finalized() + cmd.run() + + # see what we have + dist_created = os.listdir(os.path.join(pkg_dir, 'dist')) + base = f"{dist.get_fullname()}.{cmd.plat_name}.zip" + + assert dist_created == [base] + + # now let's check what we have in the zip file + fp = zipfile.ZipFile(os.path.join('dist', base)) + try: + contents = fp.namelist() + finally: + fp.close() + + contents = sorted(filter(None, map(os.path.basename, contents))) + wanted = ['foo-0.1-py{}.{}.egg-info'.format(*sys.version_info[:2]), 'foo.py'] + if not sys.dont_write_bytecode: + wanted.append(f'foo.{sys.implementation.cache_tag}.pyc') + assert contents == sorted(wanted) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py new file mode 100644 index 0000000000000000000000000000000000000000..75051430e224c8908e22601133a40f5f82ee455a --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py @@ -0,0 +1,127 @@ +"""Tests for distutils.command.bdist_rpm.""" + +import os +import shutil # noqa: F401 +import sys +from distutils.command.bdist_rpm import bdist_rpm +from distutils.core import Distribution +from distutils.tests import support + +import pytest +from test.support import requires_zlib + +SETUP_PY = """\ +from distutils.core import setup +import foo + +setup(name='foo', version='0.1', py_modules=['foo'], + url='xxx', author='xxx', author_email='xxx') + +""" + + +@pytest.fixture(autouse=True) +def sys_executable_encodable(): + try: + sys.executable.encode('UTF-8') + except UnicodeEncodeError: + pytest.skip("sys.executable is not encodable to UTF-8") + + +mac_woes = pytest.mark.skipif( + "not sys.platform.startswith('linux')", + reason='spurious sdtout/stderr output under macOS', +) + + +@pytest.mark.usefixtures('save_env') +@pytest.mark.usefixtures('save_argv') +@pytest.mark.usefixtures('save_cwd') +class TestBuildRpm( + support.TempdirManager, +): + @mac_woes + @requires_zlib() + @pytest.mark.skipif("not shutil.which('rpm')") + @pytest.mark.skipif("not shutil.which('rpmbuild')") + def test_quiet(self): + # let's create a package + tmp_dir = self.mkdtemp() + os.environ['HOME'] = tmp_dir # to confine dir '.rpmdb' creation + pkg_dir = os.path.join(tmp_dir, 'foo') + os.mkdir(pkg_dir) + self.write_file((pkg_dir, 'setup.py'), SETUP_PY) + self.write_file((pkg_dir, 'foo.py'), '#') + self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py') + self.write_file((pkg_dir, 'README'), '') + + dist = Distribution({ + 'name': 'foo', + 'version': '0.1', + 'py_modules': ['foo'], + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + }) + dist.script_name = 'setup.py' + os.chdir(pkg_dir) + + sys.argv = ['setup.py'] + cmd = bdist_rpm(dist) + cmd.fix_python = True + + # running in quiet mode + cmd.quiet = True + cmd.ensure_finalized() + cmd.run() + + dist_created = os.listdir(os.path.join(pkg_dir, 'dist')) + assert 'foo-0.1-1.noarch.rpm' in dist_created + + # bug #2945: upload ignores bdist_rpm files + assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.src.rpm') in dist.dist_files + assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.noarch.rpm') in dist.dist_files + + @mac_woes + @requires_zlib() + # https://bugs.python.org/issue1533164 + @pytest.mark.skipif("not shutil.which('rpm')") + @pytest.mark.skipif("not shutil.which('rpmbuild')") + def test_no_optimize_flag(self): + # let's create a package that breaks bdist_rpm + tmp_dir = self.mkdtemp() + os.environ['HOME'] = tmp_dir # to confine dir '.rpmdb' creation + pkg_dir = os.path.join(tmp_dir, 'foo') + os.mkdir(pkg_dir) + self.write_file((pkg_dir, 'setup.py'), SETUP_PY) + self.write_file((pkg_dir, 'foo.py'), '#') + self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py') + self.write_file((pkg_dir, 'README'), '') + + dist = Distribution({ + 'name': 'foo', + 'version': '0.1', + 'py_modules': ['foo'], + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + }) + dist.script_name = 'setup.py' + os.chdir(pkg_dir) + + sys.argv = ['setup.py'] + cmd = bdist_rpm(dist) + cmd.fix_python = True + + cmd.quiet = True + cmd.ensure_finalized() + cmd.run() + + dist_created = os.listdir(os.path.join(pkg_dir, 'dist')) + assert 'foo-0.1-1.noarch.rpm' in dist_created + + # bug #2945: upload ignores bdist_rpm files + assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.src.rpm') in dist.dist_files + assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.noarch.rpm') in dist.dist_files + + os.remove(os.path.join(pkg_dir, 'dist', 'foo-0.1-1.noarch.rpm')) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build.py new file mode 100644 index 0000000000000000000000000000000000000000..f7fe69acd1ad39cf40d277afd6409648b18df146 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build.py @@ -0,0 +1,49 @@ +"""Tests for distutils.command.build.""" + +import os +import sys +from distutils.command.build import build +from distutils.tests import support +from sysconfig import get_config_var, get_platform + + +class TestBuild(support.TempdirManager): + def test_finalize_options(self): + pkg_dir, dist = self.create_dist() + cmd = build(dist) + cmd.finalize_options() + + # if not specified, plat_name gets the current platform + assert cmd.plat_name == get_platform() + + # build_purelib is build + lib + wanted = os.path.join(cmd.build_base, 'lib') + assert cmd.build_purelib == wanted + + # build_platlib is 'build/lib.platform-cache_tag[-pydebug]' + # examples: + # build/lib.macosx-10.3-i386-cpython39 + plat_spec = f'.{cmd.plat_name}-{sys.implementation.cache_tag}' + if get_config_var('Py_GIL_DISABLED'): + plat_spec += 't' + if hasattr(sys, 'gettotalrefcount'): + assert cmd.build_platlib.endswith('-pydebug') + plat_spec += '-pydebug' + wanted = os.path.join(cmd.build_base, 'lib' + plat_spec) + assert cmd.build_platlib == wanted + + # by default, build_lib = build_purelib + assert cmd.build_lib == cmd.build_purelib + + # build_temp is build/temp. + wanted = os.path.join(cmd.build_base, 'temp' + plat_spec) + assert cmd.build_temp == wanted + + # build_scripts is build/scripts-x.x + wanted = os.path.join( + cmd.build_base, f'scripts-{sys.version_info.major}.{sys.version_info.minor}' + ) + assert cmd.build_scripts == wanted + + # executable is os.path.normpath(sys.executable) + assert cmd.executable == os.path.normpath(sys.executable) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_clib.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_clib.py new file mode 100644 index 0000000000000000000000000000000000000000..f76f26bcea04ebeaeba20d3c65881198f841c9b5 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_clib.py @@ -0,0 +1,134 @@ +"""Tests for distutils.command.build_clib.""" + +import os +from distutils.command.build_clib import build_clib +from distutils.errors import DistutilsSetupError +from distutils.tests import missing_compiler_executable, support + +import pytest + + +class TestBuildCLib(support.TempdirManager): + def test_check_library_dist(self): + pkg_dir, dist = self.create_dist() + cmd = build_clib(dist) + + # 'libraries' option must be a list + with pytest.raises(DistutilsSetupError): + cmd.check_library_list('foo') + + # each element of 'libraries' must a 2-tuple + with pytest.raises(DistutilsSetupError): + cmd.check_library_list(['foo1', 'foo2']) + + # first element of each tuple in 'libraries' + # must be a string (the library name) + with pytest.raises(DistutilsSetupError): + cmd.check_library_list([(1, 'foo1'), ('name', 'foo2')]) + + # library name may not contain directory separators + with pytest.raises(DistutilsSetupError): + cmd.check_library_list( + [('name', 'foo1'), ('another/name', 'foo2')], + ) + + # second element of each tuple must be a dictionary (build info) + with pytest.raises(DistutilsSetupError): + cmd.check_library_list( + [('name', {}), ('another', 'foo2')], + ) + + # those work + libs = [('name', {}), ('name', {'ok': 'good'})] + cmd.check_library_list(libs) + + def test_get_source_files(self): + pkg_dir, dist = self.create_dist() + cmd = build_clib(dist) + + # "in 'libraries' option 'sources' must be present and must be + # a list of source filenames + cmd.libraries = [('name', {})] + with pytest.raises(DistutilsSetupError): + cmd.get_source_files() + + cmd.libraries = [('name', {'sources': 1})] + with pytest.raises(DistutilsSetupError): + cmd.get_source_files() + + cmd.libraries = [('name', {'sources': ['a', 'b']})] + assert cmd.get_source_files() == ['a', 'b'] + + cmd.libraries = [('name', {'sources': ('a', 'b')})] + assert cmd.get_source_files() == ['a', 'b'] + + cmd.libraries = [ + ('name', {'sources': ('a', 'b')}), + ('name2', {'sources': ['c', 'd']}), + ] + assert cmd.get_source_files() == ['a', 'b', 'c', 'd'] + + def test_build_libraries(self): + pkg_dir, dist = self.create_dist() + cmd = build_clib(dist) + + class FakeCompiler: + def compile(*args, **kw): + pass + + create_static_lib = compile + + cmd.compiler = FakeCompiler() + + # build_libraries is also doing a bit of typo checking + lib = [('name', {'sources': 'notvalid'})] + with pytest.raises(DistutilsSetupError): + cmd.build_libraries(lib) + + lib = [('name', {'sources': list()})] + cmd.build_libraries(lib) + + lib = [('name', {'sources': tuple()})] + cmd.build_libraries(lib) + + def test_finalize_options(self): + pkg_dir, dist = self.create_dist() + cmd = build_clib(dist) + + cmd.include_dirs = 'one-dir' + cmd.finalize_options() + assert cmd.include_dirs == ['one-dir'] + + cmd.include_dirs = None + cmd.finalize_options() + assert cmd.include_dirs == [] + + cmd.distribution.libraries = 'WONTWORK' + with pytest.raises(DistutilsSetupError): + cmd.finalize_options() + + @pytest.mark.skipif('platform.system() == "Windows"') + def test_run(self): + pkg_dir, dist = self.create_dist() + cmd = build_clib(dist) + + foo_c = os.path.join(pkg_dir, 'foo.c') + self.write_file(foo_c, 'int main(void) { return 1;}\n') + cmd.libraries = [('foo', {'sources': [foo_c]})] + + build_temp = os.path.join(pkg_dir, 'build') + os.mkdir(build_temp) + cmd.build_temp = build_temp + cmd.build_clib = build_temp + + # Before we run the command, we want to make sure + # all commands are present on the system. + ccmd = missing_compiler_executable() + if ccmd is not None: + self.skipTest(f'The {ccmd!r} command is not found') + + # this should work + cmd.run() + + # let's check the result + assert 'libfoo.a' in os.listdir(build_temp) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_ext.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_ext.py new file mode 100644 index 0000000000000000000000000000000000000000..dab0507f3d3ec104fb4acfe5b15f92661ff33a37 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_ext.py @@ -0,0 +1,628 @@ +import contextlib +import glob +import importlib +import os.path +import platform +import re +import shutil +import site +import subprocess +import sys +import tempfile +import textwrap +import time +from distutils import sysconfig +from distutils.command.build_ext import build_ext +from distutils.core import Distribution +from distutils.errors import ( + CompileError, + DistutilsPlatformError, + DistutilsSetupError, + UnknownFileError, +) +from distutils.extension import Extension +from distutils.tests import missing_compiler_executable +from distutils.tests.support import TempdirManager, copy_xxmodule_c, fixup_build_ext +from io import StringIO + +import jaraco.path +import path +import pytest +from test import support + +from .compat import py39 as import_helper + + +@pytest.fixture() +def user_site_dir(request): + self = request.instance + self.tmp_dir = self.mkdtemp() + self.tmp_path = path.Path(self.tmp_dir) + from distutils.command import build_ext + + orig_user_base = site.USER_BASE + + site.USER_BASE = self.mkdtemp() + build_ext.USER_BASE = site.USER_BASE + + # bpo-30132: On Windows, a .pdb file may be created in the current + # working directory. Create a temporary working directory to cleanup + # everything at the end of the test. + with self.tmp_path: + yield + + site.USER_BASE = orig_user_base + build_ext.USER_BASE = orig_user_base + + if sys.platform == 'cygwin': + time.sleep(1) + + +@contextlib.contextmanager +def safe_extension_import(name, path): + with import_helper.CleanImport(name): + with extension_redirect(name, path) as new_path: + with import_helper.DirsOnSysPath(new_path): + yield + + +@contextlib.contextmanager +def extension_redirect(mod, path): + """ + Tests will fail to tear down an extension module if it's been imported. + + Before importing, copy the file to a temporary directory that won't + be cleaned up. Yield the new path. + """ + if platform.system() != "Windows" and sys.platform != "cygwin": + yield path + return + with import_helper.DirsOnSysPath(path): + spec = importlib.util.find_spec(mod) + filename = os.path.basename(spec.origin) + trash_dir = tempfile.mkdtemp(prefix='deleteme') + dest = os.path.join(trash_dir, os.path.basename(filename)) + shutil.copy(spec.origin, dest) + yield trash_dir + # TODO: can the file be scheduled for deletion? + + +@pytest.mark.usefixtures('user_site_dir') +class TestBuildExt(TempdirManager): + def build_ext(self, *args, **kwargs): + return build_ext(*args, **kwargs) + + @pytest.mark.parametrize("copy_so", [False]) + def test_build_ext(self, copy_so): + missing_compiler_executable() + copy_xxmodule_c(self.tmp_dir) + xx_c = os.path.join(self.tmp_dir, 'xxmodule.c') + xx_ext = Extension('xx', [xx_c]) + if sys.platform != "win32": + if not copy_so: + xx_ext = Extension( + 'xx', + [xx_c], + library_dirs=['/usr/lib'], + libraries=['z'], + runtime_library_dirs=['/usr/lib'], + ) + elif sys.platform == 'linux': + libz_so = { + os.path.realpath(name) for name in glob.iglob('/usr/lib*/libz.so*') + } + libz_so = sorted(libz_so, key=lambda lib_path: len(lib_path)) + shutil.copyfile(libz_so[-1], '/tmp/libxx_z.so') + + xx_ext = Extension( + 'xx', + [xx_c], + library_dirs=['/tmp'], + libraries=['xx_z'], + runtime_library_dirs=['/tmp'], + ) + dist = Distribution({'name': 'xx', 'ext_modules': [xx_ext]}) + dist.package_dir = self.tmp_dir + cmd = self.build_ext(dist) + fixup_build_ext(cmd) + cmd.build_lib = self.tmp_dir + cmd.build_temp = self.tmp_dir + + old_stdout = sys.stdout + if not support.verbose: + # silence compiler output + sys.stdout = StringIO() + try: + cmd.ensure_finalized() + cmd.run() + finally: + sys.stdout = old_stdout + + with safe_extension_import('xx', self.tmp_dir): + self._test_xx(copy_so) + + if sys.platform == 'linux' and copy_so: + os.unlink('/tmp/libxx_z.so') + + @staticmethod + def _test_xx(copy_so): + import xx # type: ignore[import-not-found] # Module generated for tests + + for attr in ('error', 'foo', 'new', 'roj'): + assert hasattr(xx, attr) + + assert xx.foo(2, 5) == 7 + assert xx.foo(13, 15) == 28 + assert xx.new().demo() is None + if support.HAVE_DOCSTRINGS: + doc = 'This is a template module just for instruction.' + assert xx.__doc__ == doc + assert isinstance(xx.Null(), xx.Null) + assert isinstance(xx.Str(), xx.Str) + + if sys.platform == 'linux': + so_headers = subprocess.check_output( + ["readelf", "-d", xx.__file__], universal_newlines=True + ) + import pprint + + pprint.pprint(so_headers) + rpaths = [ + rpath + for line in so_headers.split("\n") + if "RPATH" in line or "RUNPATH" in line + for rpath in line.split()[2][1:-1].split(":") + ] + if not copy_so: + pprint.pprint(rpaths) + # Linked against a library in /usr/lib{,64} + assert "/usr/lib" not in rpaths and "/usr/lib64" not in rpaths + else: + # Linked against a library in /tmp + assert "/tmp" in rpaths + # The import is the real test here + + def test_solaris_enable_shared(self): + dist = Distribution({'name': 'xx'}) + cmd = self.build_ext(dist) + old = sys.platform + + sys.platform = 'sunos' # fooling finalize_options + from distutils.sysconfig import _config_vars + + old_var = _config_vars.get('Py_ENABLE_SHARED') + _config_vars['Py_ENABLE_SHARED'] = True + try: + cmd.ensure_finalized() + finally: + sys.platform = old + if old_var is None: + del _config_vars['Py_ENABLE_SHARED'] + else: + _config_vars['Py_ENABLE_SHARED'] = old_var + + # make sure we get some library dirs under solaris + assert len(cmd.library_dirs) > 0 + + def test_user_site(self): + import site + + dist = Distribution({'name': 'xx'}) + cmd = self.build_ext(dist) + + # making sure the user option is there + options = [name for name, short, label in cmd.user_options] + assert 'user' in options + + # setting a value + cmd.user = True + + # setting user based lib and include + lib = os.path.join(site.USER_BASE, 'lib') + incl = os.path.join(site.USER_BASE, 'include') + os.mkdir(lib) + os.mkdir(incl) + + # let's run finalize + cmd.ensure_finalized() + + # see if include_dirs and library_dirs + # were set + assert lib in cmd.library_dirs + assert lib in cmd.rpath + assert incl in cmd.include_dirs + + def test_optional_extension(self): + # this extension will fail, but let's ignore this failure + # with the optional argument. + modules = [Extension('foo', ['xxx'], optional=False)] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.ensure_finalized() + with pytest.raises((UnknownFileError, CompileError)): + cmd.run() # should raise an error + + modules = [Extension('foo', ['xxx'], optional=True)] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.ensure_finalized() + cmd.run() # should pass + + def test_finalize_options(self): + # Make sure Python's include directories (for Python.h, pyconfig.h, + # etc.) are in the include search path. + modules = [Extension('foo', ['xxx'], optional=False)] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.finalize_options() + + py_include = sysconfig.get_python_inc() + for p in py_include.split(os.path.pathsep): + assert p in cmd.include_dirs + + plat_py_include = sysconfig.get_python_inc(plat_specific=True) + for p in plat_py_include.split(os.path.pathsep): + assert p in cmd.include_dirs + + # make sure cmd.libraries is turned into a list + # if it's a string + cmd = self.build_ext(dist) + cmd.libraries = 'my_lib, other_lib lastlib' + cmd.finalize_options() + assert cmd.libraries == ['my_lib', 'other_lib', 'lastlib'] + + # make sure cmd.library_dirs is turned into a list + # if it's a string + cmd = self.build_ext(dist) + cmd.library_dirs = f'my_lib_dir{os.pathsep}other_lib_dir' + cmd.finalize_options() + assert 'my_lib_dir' in cmd.library_dirs + assert 'other_lib_dir' in cmd.library_dirs + + # make sure rpath is turned into a list + # if it's a string + cmd = self.build_ext(dist) + cmd.rpath = f'one{os.pathsep}two' + cmd.finalize_options() + assert cmd.rpath == ['one', 'two'] + + # make sure cmd.link_objects is turned into a list + # if it's a string + cmd = build_ext(dist) + cmd.link_objects = 'one two,three' + cmd.finalize_options() + assert cmd.link_objects == ['one', 'two', 'three'] + + # XXX more tests to perform for win32 + + # make sure define is turned into 2-tuples + # strings if they are ','-separated strings + cmd = self.build_ext(dist) + cmd.define = 'one,two' + cmd.finalize_options() + assert cmd.define == [('one', '1'), ('two', '1')] + + # make sure undef is turned into a list of + # strings if they are ','-separated strings + cmd = self.build_ext(dist) + cmd.undef = 'one,two' + cmd.finalize_options() + assert cmd.undef == ['one', 'two'] + + # make sure swig_opts is turned into a list + cmd = self.build_ext(dist) + cmd.swig_opts = None + cmd.finalize_options() + assert cmd.swig_opts == [] + + cmd = self.build_ext(dist) + cmd.swig_opts = '1 2' + cmd.finalize_options() + assert cmd.swig_opts == ['1', '2'] + + def test_check_extensions_list(self): + dist = Distribution() + cmd = self.build_ext(dist) + cmd.finalize_options() + + # 'extensions' option must be a list of Extension instances + with pytest.raises(DistutilsSetupError): + cmd.check_extensions_list('foo') + + # each element of 'ext_modules' option must be an + # Extension instance or 2-tuple + exts = [('bar', 'foo', 'bar'), 'foo'] + with pytest.raises(DistutilsSetupError): + cmd.check_extensions_list(exts) + + # first element of each tuple in 'ext_modules' + # must be the extension name (a string) and match + # a python dotted-separated name + exts = [('foo-bar', '')] + with pytest.raises(DistutilsSetupError): + cmd.check_extensions_list(exts) + + # second element of each tuple in 'ext_modules' + # must be a dictionary (build info) + exts = [('foo.bar', '')] + with pytest.raises(DistutilsSetupError): + cmd.check_extensions_list(exts) + + # ok this one should pass + exts = [('foo.bar', {'sources': [''], 'libraries': 'foo', 'some': 'bar'})] + cmd.check_extensions_list(exts) + ext = exts[0] + assert isinstance(ext, Extension) + + # check_extensions_list adds in ext the values passed + # when they are in ('include_dirs', 'library_dirs', 'libraries' + # 'extra_objects', 'extra_compile_args', 'extra_link_args') + assert ext.libraries == 'foo' + assert not hasattr(ext, 'some') + + # 'macros' element of build info dict must be 1- or 2-tuple + exts = [ + ( + 'foo.bar', + { + 'sources': [''], + 'libraries': 'foo', + 'some': 'bar', + 'macros': [('1', '2', '3'), 'foo'], + }, + ) + ] + with pytest.raises(DistutilsSetupError): + cmd.check_extensions_list(exts) + + exts[0][1]['macros'] = [('1', '2'), ('3',)] + cmd.check_extensions_list(exts) + assert exts[0].undef_macros == ['3'] + assert exts[0].define_macros == [('1', '2')] + + def test_get_source_files(self): + modules = [Extension('foo', ['xxx'], optional=False)] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.ensure_finalized() + assert cmd.get_source_files() == ['xxx'] + + def test_unicode_module_names(self): + modules = [ + Extension('foo', ['aaa'], optional=False), + Extension('föö', ['uuu'], optional=False), + ] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.ensure_finalized() + assert re.search(r'foo(_d)?\..*', cmd.get_ext_filename(modules[0].name)) + assert re.search(r'föö(_d)?\..*', cmd.get_ext_filename(modules[1].name)) + assert cmd.get_export_symbols(modules[0]) == ['PyInit_foo'] + assert cmd.get_export_symbols(modules[1]) == ['PyInitU_f_1gaa'] + + def test_export_symbols__init__(self): + # https://github.com/python/cpython/issues/80074 + # https://github.com/pypa/setuptools/issues/4826 + modules = [ + Extension('foo.__init__', ['aaa']), + Extension('föö.__init__', ['uuu']), + ] + dist = Distribution({'name': 'xx', 'ext_modules': modules}) + cmd = self.build_ext(dist) + cmd.ensure_finalized() + assert cmd.get_export_symbols(modules[0]) == ['PyInit_foo'] + assert cmd.get_export_symbols(modules[1]) == ['PyInitU_f_1gaa'] + + def test_compiler_option(self): + # cmd.compiler is an option and + # should not be overridden by a compiler instance + # when the command is run + dist = Distribution() + cmd = self.build_ext(dist) + cmd.compiler = 'unix' + cmd.ensure_finalized() + cmd.run() + assert cmd.compiler == 'unix' + + def test_get_outputs(self): + missing_compiler_executable() + tmp_dir = self.mkdtemp() + c_file = os.path.join(tmp_dir, 'foo.c') + self.write_file(c_file, 'void PyInit_foo(void) {}\n') + ext = Extension('foo', [c_file], optional=False) + dist = Distribution({'name': 'xx', 'ext_modules': [ext]}) + cmd = self.build_ext(dist) + fixup_build_ext(cmd) + cmd.ensure_finalized() + assert len(cmd.get_outputs()) == 1 + + cmd.build_lib = os.path.join(self.tmp_dir, 'build') + cmd.build_temp = os.path.join(self.tmp_dir, 'tempt') + + # issue #5977 : distutils build_ext.get_outputs + # returns wrong result with --inplace + other_tmp_dir = os.path.realpath(self.mkdtemp()) + old_wd = os.getcwd() + os.chdir(other_tmp_dir) + try: + cmd.inplace = True + cmd.run() + so_file = cmd.get_outputs()[0] + finally: + os.chdir(old_wd) + assert os.path.exists(so_file) + ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') + assert so_file.endswith(ext_suffix) + so_dir = os.path.dirname(so_file) + assert so_dir == other_tmp_dir + + cmd.inplace = False + cmd.compiler = None + cmd.run() + so_file = cmd.get_outputs()[0] + assert os.path.exists(so_file) + assert so_file.endswith(ext_suffix) + so_dir = os.path.dirname(so_file) + assert so_dir == cmd.build_lib + + # inplace = False, cmd.package = 'bar' + build_py = cmd.get_finalized_command('build_py') + build_py.package_dir = {'': 'bar'} + path = cmd.get_ext_fullpath('foo') + # checking that the last directory is the build_dir + path = os.path.split(path)[0] + assert path == cmd.build_lib + + # inplace = True, cmd.package = 'bar' + cmd.inplace = True + other_tmp_dir = os.path.realpath(self.mkdtemp()) + old_wd = os.getcwd() + os.chdir(other_tmp_dir) + try: + path = cmd.get_ext_fullpath('foo') + finally: + os.chdir(old_wd) + # checking that the last directory is bar + path = os.path.split(path)[0] + lastdir = os.path.split(path)[-1] + assert lastdir == 'bar' + + def test_ext_fullpath(self): + ext = sysconfig.get_config_var('EXT_SUFFIX') + # building lxml.etree inplace + # etree_c = os.path.join(self.tmp_dir, 'lxml.etree.c') + # etree_ext = Extension('lxml.etree', [etree_c]) + # dist = Distribution({'name': 'lxml', 'ext_modules': [etree_ext]}) + dist = Distribution() + cmd = self.build_ext(dist) + cmd.inplace = True + cmd.distribution.package_dir = {'': 'src'} + cmd.distribution.packages = ['lxml', 'lxml.html'] + curdir = os.getcwd() + wanted = os.path.join(curdir, 'src', 'lxml', 'etree' + ext) + path = cmd.get_ext_fullpath('lxml.etree') + assert wanted == path + + # building lxml.etree not inplace + cmd.inplace = False + cmd.build_lib = os.path.join(curdir, 'tmpdir') + wanted = os.path.join(curdir, 'tmpdir', 'lxml', 'etree' + ext) + path = cmd.get_ext_fullpath('lxml.etree') + assert wanted == path + + # building twisted.runner.portmap not inplace + build_py = cmd.get_finalized_command('build_py') + build_py.package_dir = {} + cmd.distribution.packages = ['twisted', 'twisted.runner.portmap'] + path = cmd.get_ext_fullpath('twisted.runner.portmap') + wanted = os.path.join(curdir, 'tmpdir', 'twisted', 'runner', 'portmap' + ext) + assert wanted == path + + # building twisted.runner.portmap inplace + cmd.inplace = True + path = cmd.get_ext_fullpath('twisted.runner.portmap') + wanted = os.path.join(curdir, 'twisted', 'runner', 'portmap' + ext) + assert wanted == path + + @pytest.mark.skipif('platform.system() != "Darwin"') + @pytest.mark.usefixtures('save_env') + def test_deployment_target_default(self): + # Issue 9516: Test that, in the absence of the environment variable, + # an extension module is compiled with the same deployment target as + # the interpreter. + self._try_compile_deployment_target('==', None) + + @pytest.mark.skipif('platform.system() != "Darwin"') + @pytest.mark.usefixtures('save_env') + def test_deployment_target_too_low(self): + # Issue 9516: Test that an extension module is not allowed to be + # compiled with a deployment target less than that of the interpreter. + with pytest.raises(DistutilsPlatformError): + self._try_compile_deployment_target('>', '10.1') + + @pytest.mark.skipif('platform.system() != "Darwin"') + @pytest.mark.usefixtures('save_env') + def test_deployment_target_higher_ok(self): # pragma: no cover + # Issue 9516: Test that an extension module can be compiled with a + # deployment target higher than that of the interpreter: the ext + # module may depend on some newer OS feature. + deptarget = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET') + if deptarget: + # increment the minor version number (i.e. 10.6 -> 10.7) + deptarget = [int(x) for x in deptarget.split('.')] + deptarget[-1] += 1 + deptarget = '.'.join(str(i) for i in deptarget) + self._try_compile_deployment_target('<', deptarget) + + def _try_compile_deployment_target(self, operator, target): # pragma: no cover + if target is None: + if os.environ.get('MACOSX_DEPLOYMENT_TARGET'): + del os.environ['MACOSX_DEPLOYMENT_TARGET'] + else: + os.environ['MACOSX_DEPLOYMENT_TARGET'] = target + + jaraco.path.build( + { + 'deptargetmodule.c': textwrap.dedent(f"""\ + #include + + int dummy; + + #if TARGET {operator} MAC_OS_X_VERSION_MIN_REQUIRED + #else + #error "Unexpected target" + #endif + + """), + }, + self.tmp_path, + ) + + # get the deployment target that the interpreter was built with + target = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET') + target = tuple(map(int, target.split('.')[0:2])) + # format the target value as defined in the Apple + # Availability Macros. We can't use the macro names since + # at least one value we test with will not exist yet. + if target[:2] < (10, 10): + # for 10.1 through 10.9.x -> "10n0" + tmpl = '{:02}{:01}0' + else: + # for 10.10 and beyond -> "10nn00" + if len(target) >= 2: + tmpl = '{:02}{:02}00' + else: + # 11 and later can have no minor version (11 instead of 11.0) + tmpl = '{:02}0000' + target = tmpl.format(*target) + deptarget_ext = Extension( + 'deptarget', + [self.tmp_path / 'deptargetmodule.c'], + extra_compile_args=[f'-DTARGET={target}'], + ) + dist = Distribution({'name': 'deptarget', 'ext_modules': [deptarget_ext]}) + dist.package_dir = self.tmp_dir + cmd = self.build_ext(dist) + cmd.build_lib = self.tmp_dir + cmd.build_temp = self.tmp_dir + + try: + old_stdout = sys.stdout + if not support.verbose: + # silence compiler output + sys.stdout = StringIO() + try: + cmd.ensure_finalized() + cmd.run() + finally: + sys.stdout = old_stdout + + except CompileError: + self.fail("Wrong deployment target during compilation") + + +class TestParallelBuildExt(TestBuildExt): + def build_ext(self, *args, **kwargs): + build_ext = super().build_ext(*args, **kwargs) + build_ext.parallel = True + return build_ext diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_py.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_py.py new file mode 100644 index 0000000000000000000000000000000000000000..b316ed43a11836210caeb7177bd8890a1f61d1bb --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_py.py @@ -0,0 +1,196 @@ +"""Tests for distutils.command.build_py.""" + +import os +import sys +from distutils.command.build_py import build_py +from distutils.core import Distribution +from distutils.errors import DistutilsFileError +from distutils.tests import support + +import jaraco.path +import pytest + + +@support.combine_markers +class TestBuildPy(support.TempdirManager): + def test_package_data(self): + sources = self.mkdtemp() + jaraco.path.build( + { + '__init__.py': "# Pretend this is a package.", + 'README.txt': 'Info about this package', + }, + sources, + ) + + destination = self.mkdtemp() + + dist = Distribution({"packages": ["pkg"], "package_dir": {"pkg": sources}}) + # script_name need not exist, it just need to be initialized + dist.script_name = os.path.join(sources, "setup.py") + dist.command_obj["build"] = support.DummyCommand( + force=False, build_lib=destination + ) + dist.packages = ["pkg"] + dist.package_data = {"pkg": ["README.txt"]} + dist.package_dir = {"pkg": sources} + + cmd = build_py(dist) + cmd.compile = True + cmd.ensure_finalized() + assert cmd.package_data == dist.package_data + + cmd.run() + + # This makes sure the list of outputs includes byte-compiled + # files for Python modules but not for package data files + # (there shouldn't *be* byte-code files for those!). + assert len(cmd.get_outputs()) == 3 + pkgdest = os.path.join(destination, "pkg") + files = os.listdir(pkgdest) + pycache_dir = os.path.join(pkgdest, "__pycache__") + assert "__init__.py" in files + assert "README.txt" in files + if sys.dont_write_bytecode: + assert not os.path.exists(pycache_dir) + else: + pyc_files = os.listdir(pycache_dir) + assert f"__init__.{sys.implementation.cache_tag}.pyc" in pyc_files + + def test_empty_package_dir(self): + # See bugs #1668596/#1720897 + sources = self.mkdtemp() + jaraco.path.build({'__init__.py': '', 'doc': {'testfile': ''}}, sources) + + os.chdir(sources) + dist = Distribution({ + "packages": ["pkg"], + "package_dir": {"pkg": ""}, + "package_data": {"pkg": ["doc/*"]}, + }) + # script_name need not exist, it just need to be initialized + dist.script_name = os.path.join(sources, "setup.py") + dist.script_args = ["build"] + dist.parse_command_line() + + try: + dist.run_commands() + except DistutilsFileError: + self.fail("failed package_data test when package_dir is ''") + + @pytest.mark.skipif('sys.dont_write_bytecode') + def test_byte_compile(self): + project_dir, dist = self.create_dist(py_modules=['boiledeggs']) + os.chdir(project_dir) + self.write_file('boiledeggs.py', 'import antigravity') + cmd = build_py(dist) + cmd.compile = True + cmd.build_lib = 'here' + cmd.finalize_options() + cmd.run() + + found = os.listdir(cmd.build_lib) + assert sorted(found) == ['__pycache__', 'boiledeggs.py'] + found = os.listdir(os.path.join(cmd.build_lib, '__pycache__')) + assert found == [f'boiledeggs.{sys.implementation.cache_tag}.pyc'] + + @pytest.mark.skipif('sys.dont_write_bytecode') + def test_byte_compile_optimized(self): + project_dir, dist = self.create_dist(py_modules=['boiledeggs']) + os.chdir(project_dir) + self.write_file('boiledeggs.py', 'import antigravity') + cmd = build_py(dist) + cmd.compile = False + cmd.optimize = 1 + cmd.build_lib = 'here' + cmd.finalize_options() + cmd.run() + + found = os.listdir(cmd.build_lib) + assert sorted(found) == ['__pycache__', 'boiledeggs.py'] + found = os.listdir(os.path.join(cmd.build_lib, '__pycache__')) + expect = f'boiledeggs.{sys.implementation.cache_tag}.opt-1.pyc' + assert sorted(found) == [expect] + + def test_dir_in_package_data(self): + """ + A directory in package_data should not be added to the filelist. + """ + # See bug 19286 + sources = self.mkdtemp() + jaraco.path.build( + { + 'pkg': { + '__init__.py': '', + 'doc': { + 'testfile': '', + # create a directory that could be incorrectly detected as a file + 'otherdir': {}, + }, + } + }, + sources, + ) + + os.chdir(sources) + dist = Distribution({"packages": ["pkg"], "package_data": {"pkg": ["doc/*"]}}) + # script_name need not exist, it just need to be initialized + dist.script_name = os.path.join(sources, "setup.py") + dist.script_args = ["build"] + dist.parse_command_line() + + try: + dist.run_commands() + except DistutilsFileError: + self.fail("failed package_data when data dir includes a dir") + + def test_dont_write_bytecode(self, caplog): + # makes sure byte_compile is not used + dist = self.create_dist()[1] + cmd = build_py(dist) + cmd.compile = True + cmd.optimize = 1 + + old_dont_write_bytecode = sys.dont_write_bytecode + sys.dont_write_bytecode = True + try: + cmd.byte_compile([]) + finally: + sys.dont_write_bytecode = old_dont_write_bytecode + + assert 'byte-compiling is disabled' in caplog.records[0].message + + def test_namespace_package_does_not_warn(self, caplog): + """ + Originally distutils implementation did not account for PEP 420 + and included warns for package directories that did not contain + ``__init__.py`` files. + After the acceptance of PEP 420, these warnings don't make more sense + so we want to ensure there are not displayed to not confuse the users. + """ + # Create a fake project structure with a package namespace: + tmp = self.mkdtemp() + jaraco.path.build({'ns': {'pkg': {'module.py': ''}}}, tmp) + os.chdir(tmp) + + # Configure the package: + attrs = { + "name": "ns.pkg", + "packages": ["ns", "ns.pkg"], + "script_name": "setup.py", + } + dist = Distribution(attrs) + + # Run code paths that would trigger the trap: + cmd = dist.get_command_obj("build_py") + cmd.finalize_options() + modules = cmd.find_all_modules() + assert len(modules) == 1 + module_path = modules[0][-1] + assert module_path.replace(os.sep, "/") == "ns/pkg/module.py" + + cmd.run() + + assert not any( + "package init file" in msg and "not found" in msg for msg in caplog.messages + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_scripts.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..3582f691ef578a419555c6ffbba6a619310517a6 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_build_scripts.py @@ -0,0 +1,96 @@ +"""Tests for distutils.command.build_scripts.""" + +import os +import textwrap +from distutils import sysconfig +from distutils.command.build_scripts import build_scripts +from distutils.core import Distribution +from distutils.tests import support + +import jaraco.path + + +class TestBuildScripts(support.TempdirManager): + def test_default_settings(self): + cmd = self.get_build_scripts_cmd("/foo/bar", []) + assert not cmd.force + assert cmd.build_dir is None + + cmd.finalize_options() + + assert cmd.force + assert cmd.build_dir == "/foo/bar" + + def test_build(self): + source = self.mkdtemp() + target = self.mkdtemp() + expected = self.write_sample_scripts(source) + + cmd = self.get_build_scripts_cmd( + target, [os.path.join(source, fn) for fn in expected] + ) + cmd.finalize_options() + cmd.run() + + built = os.listdir(target) + for name in expected: + assert name in built + + def get_build_scripts_cmd(self, target, scripts): + import sys + + dist = Distribution() + dist.scripts = scripts + dist.command_obj["build"] = support.DummyCommand( + build_scripts=target, force=True, executable=sys.executable + ) + return build_scripts(dist) + + @staticmethod + def write_sample_scripts(dir): + spec = { + 'script1.py': textwrap.dedent(""" + #! /usr/bin/env python2.3 + # bogus script w/ Python sh-bang + pass + """).lstrip(), + 'script2.py': textwrap.dedent(""" + #!/usr/bin/python + # bogus script w/ Python sh-bang + pass + """).lstrip(), + 'shell.sh': textwrap.dedent(""" + #!/bin/sh + # bogus shell script w/ sh-bang + exit 0 + """).lstrip(), + } + jaraco.path.build(spec, dir) + return list(spec) + + def test_version_int(self): + source = self.mkdtemp() + target = self.mkdtemp() + expected = self.write_sample_scripts(source) + + cmd = self.get_build_scripts_cmd( + target, [os.path.join(source, fn) for fn in expected] + ) + cmd.finalize_options() + + # https://bugs.python.org/issue4524 + # + # On linux-g++-32 with command line `./configure --enable-ipv6 + # --with-suffix=3`, python is compiled okay but the build scripts + # failed when writing the name of the executable + old = sysconfig.get_config_vars().get('VERSION') + sysconfig._config_vars['VERSION'] = 4 + try: + cmd.run() + finally: + if old is not None: + sysconfig._config_vars['VERSION'] = old + + built = os.listdir(target) + for name in expected: + assert name in built diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_check.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_check.py new file mode 100644 index 0000000000000000000000000000000000000000..b672b1f972b46f399eb5b2ef263da2548d52ef89 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_check.py @@ -0,0 +1,194 @@ +"""Tests for distutils.command.check.""" + +import os +import textwrap +from distutils.command.check import check +from distutils.errors import DistutilsSetupError +from distutils.tests import support + +import pytest + +try: + import pygments +except ImportError: + pygments = None + + +HERE = os.path.dirname(__file__) + + +@support.combine_markers +class TestCheck(support.TempdirManager): + def _run(self, metadata=None, cwd=None, **options): + if metadata is None: + metadata = {} + if cwd is not None: + old_dir = os.getcwd() + os.chdir(cwd) + pkg_info, dist = self.create_dist(**metadata) + cmd = check(dist) + cmd.initialize_options() + for name, value in options.items(): + setattr(cmd, name, value) + cmd.ensure_finalized() + cmd.run() + if cwd is not None: + os.chdir(old_dir) + return cmd + + def test_check_metadata(self): + # let's run the command with no metadata at all + # by default, check is checking the metadata + # should have some warnings + cmd = self._run() + assert cmd._warnings == 1 + + # now let's add the required fields + # and run it again, to make sure we don't get + # any warning anymore + metadata = { + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + 'name': 'xxx', + 'version': 'xxx', + } + cmd = self._run(metadata) + assert cmd._warnings == 0 + + # now with the strict mode, we should + # get an error if there are missing metadata + with pytest.raises(DistutilsSetupError): + self._run({}, **{'strict': 1}) + + # and of course, no error when all metadata are present + cmd = self._run(metadata, strict=True) + assert cmd._warnings == 0 + + # now a test with non-ASCII characters + metadata = { + 'url': 'xxx', + 'author': '\u00c9ric', + 'author_email': 'xxx', + 'name': 'xxx', + 'version': 'xxx', + 'description': 'Something about esszet \u00df', + 'long_description': 'More things about esszet \u00df', + } + cmd = self._run(metadata) + assert cmd._warnings == 0 + + def test_check_author_maintainer(self): + for kind in ("author", "maintainer"): + # ensure no warning when author_email or maintainer_email is given + # (the spec allows these fields to take the form "Name ") + metadata = { + 'url': 'xxx', + kind + '_email': 'Name ', + 'name': 'xxx', + 'version': 'xxx', + } + cmd = self._run(metadata) + assert cmd._warnings == 0 + + # the check should not warn if only email is given + metadata[kind + '_email'] = 'name@email.com' + cmd = self._run(metadata) + assert cmd._warnings == 0 + + # the check should not warn if only the name is given + metadata[kind] = "Name" + del metadata[kind + '_email'] + cmd = self._run(metadata) + assert cmd._warnings == 0 + + def test_check_document(self): + pytest.importorskip('docutils') + pkg_info, dist = self.create_dist() + cmd = check(dist) + + # let's see if it detects broken rest + broken_rest = 'title\n===\n\ntest' + msgs = cmd._check_rst_data(broken_rest) + assert len(msgs) == 1 + + # and non-broken rest + rest = 'title\n=====\n\ntest' + msgs = cmd._check_rst_data(rest) + assert len(msgs) == 0 + + def test_check_restructuredtext(self): + pytest.importorskip('docutils') + # let's see if it detects broken rest in long_description + broken_rest = 'title\n===\n\ntest' + pkg_info, dist = self.create_dist(long_description=broken_rest) + cmd = check(dist) + cmd.check_restructuredtext() + assert cmd._warnings == 1 + + # let's see if we have an error with strict=True + metadata = { + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + 'name': 'xxx', + 'version': 'xxx', + 'long_description': broken_rest, + } + with pytest.raises(DistutilsSetupError): + self._run(metadata, **{'strict': 1, 'restructuredtext': 1}) + + # and non-broken rest, including a non-ASCII character to test #12114 + metadata['long_description'] = 'title\n=====\n\ntest \u00df' + cmd = self._run(metadata, strict=True, restructuredtext=True) + assert cmd._warnings == 0 + + # check that includes work to test #31292 + metadata['long_description'] = 'title\n=====\n\n.. include:: includetest.rst' + cmd = self._run(metadata, cwd=HERE, strict=True, restructuredtext=True) + assert cmd._warnings == 0 + + def test_check_restructuredtext_with_syntax_highlight(self): + pytest.importorskip('docutils') + # Don't fail if there is a `code` or `code-block` directive + + example_rst_docs = [ + textwrap.dedent( + """\ + Here's some code: + + .. code:: python + + def foo(): + pass + """ + ), + textwrap.dedent( + """\ + Here's some code: + + .. code-block:: python + + def foo(): + pass + """ + ), + ] + + for rest_with_code in example_rst_docs: + pkg_info, dist = self.create_dist(long_description=rest_with_code) + cmd = check(dist) + cmd.check_restructuredtext() + msgs = cmd._check_rst_data(rest_with_code) + if pygments is not None: + assert len(msgs) == 0 + else: + assert len(msgs) == 1 + assert ( + str(msgs[0][1]) + == 'Cannot analyze code. Pygments package not found.' + ) + + def test_check_all(self): + with pytest.raises(DistutilsSetupError): + self._run({}, **{'strict': 1, 'restructuredtext': 1}) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_clean.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_clean.py new file mode 100644 index 0000000000000000000000000000000000000000..cc78f30f34b323845b977b9147c2153390774bd3 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_clean.py @@ -0,0 +1,45 @@ +"""Tests for distutils.command.clean.""" + +import os +from distutils.command.clean import clean +from distutils.tests import support + + +class TestClean(support.TempdirManager): + def test_simple_run(self): + pkg_dir, dist = self.create_dist() + cmd = clean(dist) + + # let's add some elements clean should remove + dirs = [ + (d, os.path.join(pkg_dir, d)) + for d in ( + 'build_temp', + 'build_lib', + 'bdist_base', + 'build_scripts', + 'build_base', + ) + ] + + for name, path in dirs: + os.mkdir(path) + setattr(cmd, name, path) + if name == 'build_base': + continue + for f in ('one', 'two', 'three'): + self.write_file(os.path.join(path, f)) + + # let's run the command + cmd.all = 1 + cmd.ensure_finalized() + cmd.run() + + # make sure the files where removed + for _name, path in dirs: + assert not os.path.exists(path), f'{path} was not removed' + + # let's run the command again (should spit warnings but succeed) + cmd.all = 1 + cmd.ensure_finalized() + cmd.run() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_cmd.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_cmd.py new file mode 100644 index 0000000000000000000000000000000000000000..76e8f5989b3b102a32239436ff3b65f26e09e029 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_cmd.py @@ -0,0 +1,107 @@ +"""Tests for distutils.cmd.""" + +import os +from distutils import debug +from distutils.cmd import Command +from distutils.dist import Distribution +from distutils.errors import DistutilsOptionError + +import pytest + + +class MyCmd(Command): + def initialize_options(self): + pass + + +@pytest.fixture +def cmd(request): + return MyCmd(Distribution()) + + +class TestCommand: + def test_ensure_string_list(self, cmd): + cmd.not_string_list = ['one', 2, 'three'] + cmd.yes_string_list = ['one', 'two', 'three'] + cmd.not_string_list2 = object() + cmd.yes_string_list2 = 'ok' + cmd.ensure_string_list('yes_string_list') + cmd.ensure_string_list('yes_string_list2') + + with pytest.raises(DistutilsOptionError): + cmd.ensure_string_list('not_string_list') + + with pytest.raises(DistutilsOptionError): + cmd.ensure_string_list('not_string_list2') + + cmd.option1 = 'ok,dok' + cmd.ensure_string_list('option1') + assert cmd.option1 == ['ok', 'dok'] + + cmd.option2 = ['xxx', 'www'] + cmd.ensure_string_list('option2') + + cmd.option3 = ['ok', 2] + with pytest.raises(DistutilsOptionError): + cmd.ensure_string_list('option3') + + def test_make_file(self, cmd): + # making sure it raises when infiles is not a string or a list/tuple + with pytest.raises(TypeError): + cmd.make_file(infiles=True, outfile='', func='func', args=()) + + # making sure execute gets called properly + def _execute(func, args, exec_msg, level): + assert exec_msg == 'generating out from in' + + cmd.force = True + cmd.execute = _execute + cmd.make_file(infiles='in', outfile='out', func='func', args=()) + + def test_dump_options(self, cmd): + msgs = [] + + def _announce(msg, level): + msgs.append(msg) + + cmd.announce = _announce + cmd.option1 = 1 + cmd.option2 = 1 + cmd.user_options = [('option1', '', ''), ('option2', '', '')] + cmd.dump_options() + + wanted = ["command options for 'MyCmd':", ' option1 = 1', ' option2 = 1'] + assert msgs == wanted + + def test_ensure_string(self, cmd): + cmd.option1 = 'ok' + cmd.ensure_string('option1') + + cmd.option2 = None + cmd.ensure_string('option2', 'xxx') + assert hasattr(cmd, 'option2') + + cmd.option3 = 1 + with pytest.raises(DistutilsOptionError): + cmd.ensure_string('option3') + + def test_ensure_filename(self, cmd): + cmd.option1 = __file__ + cmd.ensure_filename('option1') + cmd.option2 = 'xxx' + with pytest.raises(DistutilsOptionError): + cmd.ensure_filename('option2') + + def test_ensure_dirname(self, cmd): + cmd.option1 = os.path.dirname(__file__) or os.curdir + cmd.ensure_dirname('option1') + cmd.option2 = 'xxx' + with pytest.raises(DistutilsOptionError): + cmd.ensure_dirname('option2') + + def test_debug_print(self, cmd, capsys, monkeypatch): + cmd.debug_print('xxx') + assert capsys.readouterr().out == '' + monkeypatch.setattr(debug, 'DEBUG', True) + cmd.debug_print('xxx') + assert capsys.readouterr().out == 'xxx\n' diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_config_cmd.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_config_cmd.py new file mode 100644 index 0000000000000000000000000000000000000000..ebee2ef93f0bc9d422984e2e4872665b1be3a1de --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_config_cmd.py @@ -0,0 +1,87 @@ +"""Tests for distutils.command.config.""" + +import os +import sys +from distutils._log import log +from distutils.command.config import config, dump_file +from distutils.tests import missing_compiler_executable, support + +import more_itertools +import path +import pytest + + +@pytest.fixture(autouse=True) +def info_log(request, monkeypatch): + self = request.instance + self._logs = [] + monkeypatch.setattr(log, 'info', self._info) + + +@support.combine_markers +class TestConfig(support.TempdirManager): + def _info(self, msg, *args): + for line in msg.splitlines(): + self._logs.append(line) + + def test_dump_file(self): + this_file = path.Path(__file__).with_suffix('.py') + with this_file.open(encoding='utf-8') as f: + numlines = more_itertools.ilen(f) + + dump_file(this_file, 'I am the header') + assert len(self._logs) == numlines + 1 + + @pytest.mark.skipif('platform.system() == "Windows"') + def test_search_cpp(self): + cmd = missing_compiler_executable(['preprocessor']) + if cmd is not None: + self.skipTest(f'The {cmd!r} command is not found') + pkg_dir, dist = self.create_dist() + cmd = config(dist) + cmd._check_compiler() + compiler = cmd.compiler + if sys.platform[:3] == "aix" and "xlc" in compiler.preprocessor[0].lower(): + self.skipTest( + 'xlc: The -E option overrides the -P, -o, and -qsyntaxonly options' + ) + + # simple pattern searches + match = cmd.search_cpp(pattern='xxx', body='/* xxx */') + assert match == 0 + + match = cmd.search_cpp(pattern='_configtest', body='/* xxx */') + assert match == 1 + + def test_finalize_options(self): + # finalize_options does a bit of transformation + # on options + pkg_dir, dist = self.create_dist() + cmd = config(dist) + cmd.include_dirs = f'one{os.pathsep}two' + cmd.libraries = 'one' + cmd.library_dirs = f'three{os.pathsep}four' + cmd.ensure_finalized() + + assert cmd.include_dirs == ['one', 'two'] + assert cmd.libraries == ['one'] + assert cmd.library_dirs == ['three', 'four'] + + def test_clean(self): + # _clean removes files + tmp_dir = self.mkdtemp() + f1 = os.path.join(tmp_dir, 'one') + f2 = os.path.join(tmp_dir, 'two') + + self.write_file(f1, 'xxx') + self.write_file(f2, 'xxx') + + for f in (f1, f2): + assert os.path.exists(f) + + pkg_dir, dist = self.create_dist() + cmd = config(dist) + cmd._clean(f1, f2) + + for f in (f1, f2): + assert not os.path.exists(f) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_core.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_core.py new file mode 100644 index 0000000000000000000000000000000000000000..bad3fb7e8318d3ee73ecabe20ed98c279f606a77 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_core.py @@ -0,0 +1,130 @@ +"""Tests for distutils.core.""" + +import distutils.core +import io +import os +import sys +from distutils.dist import Distribution + +import pytest + +# setup script that uses __file__ +setup_using___file__ = """\ + +__file__ + +from distutils.core import setup +setup() +""" + +setup_prints_cwd = """\ + +import os +print(os.getcwd()) + +from distutils.core import setup +setup() +""" + +setup_does_nothing = """\ +from distutils.core import setup +setup() +""" + + +setup_defines_subclass = """\ +from distutils.core import setup +from distutils.command.install import install as _install + +class install(_install): + sub_commands = _install.sub_commands + ['cmd'] + +setup(cmdclass={'install': install}) +""" + +setup_within_if_main = """\ +from distutils.core import setup + +def main(): + return setup(name="setup_within_if_main") + +if __name__ == "__main__": + main() +""" + + +@pytest.fixture(autouse=True) +def save_stdout(monkeypatch): + monkeypatch.setattr(sys, 'stdout', sys.stdout) + + +@pytest.fixture +def temp_file(tmp_path): + return tmp_path / 'file' + + +@pytest.mark.usefixtures('save_env') +@pytest.mark.usefixtures('save_argv') +class TestCore: + def test_run_setup_provides_file(self, temp_file): + # Make sure the script can use __file__; if that's missing, the test + # setup.py script will raise NameError. + temp_file.write_text(setup_using___file__, encoding='utf-8') + distutils.core.run_setup(temp_file) + + def test_run_setup_preserves_sys_argv(self, temp_file): + # Make sure run_setup does not clobber sys.argv + argv_copy = sys.argv.copy() + temp_file.write_text(setup_does_nothing, encoding='utf-8') + distutils.core.run_setup(temp_file) + assert sys.argv == argv_copy + + def test_run_setup_defines_subclass(self, temp_file): + # Make sure the script can use __file__; if that's missing, the test + # setup.py script will raise NameError. + temp_file.write_text(setup_defines_subclass, encoding='utf-8') + dist = distutils.core.run_setup(temp_file) + install = dist.get_command_obj('install') + assert 'cmd' in install.sub_commands + + def test_run_setup_uses_current_dir(self, tmp_path): + """ + Test that the setup script is run with the current directory + as its own current directory. + """ + sys.stdout = io.StringIO() + cwd = os.getcwd() + + # Create a directory and write the setup.py file there: + setup_py = tmp_path / 'setup.py' + setup_py.write_text(setup_prints_cwd, encoding='utf-8') + distutils.core.run_setup(setup_py) + + output = sys.stdout.getvalue() + if output.endswith("\n"): + output = output[:-1] + assert cwd == output + + def test_run_setup_within_if_main(self, temp_file): + temp_file.write_text(setup_within_if_main, encoding='utf-8') + dist = distutils.core.run_setup(temp_file, stop_after="config") + assert isinstance(dist, Distribution) + assert dist.get_name() == "setup_within_if_main" + + def test_run_commands(self, temp_file): + sys.argv = ['setup.py', 'build'] + temp_file.write_text(setup_within_if_main, encoding='utf-8') + dist = distutils.core.run_setup(temp_file, stop_after="commandline") + assert 'build' not in dist.have_run + distutils.core.run_commands(dist) + assert 'build' in dist.have_run + + def test_debug_mode(self, capsys, monkeypatch): + # this covers the code called when DEBUG is set + sys.argv = ['setup.py', '--name'] + distutils.core.setup(name='bar') + assert capsys.readouterr().out == 'bar\n' + monkeypatch.setattr(distutils.core, 'DEBUG', True) + distutils.core.setup(name='bar') + wanted = "options (after parsing config files):\n" + assert capsys.readouterr().out.startswith(wanted) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dir_util.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dir_util.py new file mode 100644 index 0000000000000000000000000000000000000000..326cb346145ec7e3a1dd0384c33d4125131feaae --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dir_util.py @@ -0,0 +1,139 @@ +"""Tests for distutils.dir_util.""" + +import os +import pathlib +import stat +import sys +import unittest.mock as mock +from distutils import dir_util, errors +from distutils.dir_util import ( + copy_tree, + create_tree, + ensure_relative, + mkpath, + remove_tree, +) +from distutils.tests import support + +import jaraco.path +import path +import pytest + + +@pytest.fixture(autouse=True) +def stuff(request, monkeypatch, distutils_managed_tempdir): + self = request.instance + tmp_dir = self.mkdtemp() + self.root_target = os.path.join(tmp_dir, 'deep') + self.target = os.path.join(self.root_target, 'here') + self.target2 = os.path.join(tmp_dir, 'deep2') + + +class TestDirUtil(support.TempdirManager): + def test_mkpath_remove_tree_verbosity(self, caplog): + mkpath(self.target, verbose=False) + assert not caplog.records + remove_tree(self.root_target, verbose=False) + + mkpath(self.target, verbose=True) + wanted = [f'creating {self.target}'] + assert caplog.messages == wanted + caplog.clear() + + remove_tree(self.root_target, verbose=True) + wanted = [f"removing '{self.root_target}' (and everything under it)"] + assert caplog.messages == wanted + + @pytest.mark.skipif("platform.system() == 'Windows'") + def test_mkpath_with_custom_mode(self): + # Get and set the current umask value for testing mode bits. + umask = os.umask(0o002) + os.umask(umask) + mkpath(self.target, 0o700) + assert stat.S_IMODE(os.stat(self.target).st_mode) == 0o700 & ~umask + mkpath(self.target2, 0o555) + assert stat.S_IMODE(os.stat(self.target2).st_mode) == 0o555 & ~umask + + def test_create_tree_verbosity(self, caplog): + create_tree(self.root_target, ['one', 'two', 'three'], verbose=False) + assert caplog.messages == [] + remove_tree(self.root_target, verbose=False) + + wanted = [f'creating {self.root_target}'] + create_tree(self.root_target, ['one', 'two', 'three'], verbose=True) + assert caplog.messages == wanted + + remove_tree(self.root_target, verbose=False) + + def test_copy_tree_verbosity(self, caplog): + mkpath(self.target, verbose=False) + + copy_tree(self.target, self.target2, verbose=False) + assert caplog.messages == [] + + remove_tree(self.root_target, verbose=False) + + mkpath(self.target, verbose=False) + a_file = path.Path(self.target) / 'ok.txt' + jaraco.path.build({'ok.txt': 'some content'}, self.target) + + wanted = [f'copying {a_file} -> {self.target2}'] + copy_tree(self.target, self.target2, verbose=True) + assert caplog.messages == wanted + + remove_tree(self.root_target, verbose=False) + remove_tree(self.target2, verbose=False) + + def test_copy_tree_skips_nfs_temp_files(self): + mkpath(self.target, verbose=False) + + jaraco.path.build({'ok.txt': 'some content', '.nfs123abc': ''}, self.target) + + copy_tree(self.target, self.target2) + assert os.listdir(self.target2) == ['ok.txt'] + + remove_tree(self.root_target, verbose=False) + remove_tree(self.target2, verbose=False) + + def test_ensure_relative(self): + if os.sep == '/': + assert ensure_relative('/home/foo') == 'home/foo' + assert ensure_relative('some/path') == 'some/path' + else: # \\ + assert ensure_relative('c:\\home\\foo') == 'c:home\\foo' + assert ensure_relative('home\\foo') == 'home\\foo' + + def test_copy_tree_exception_in_listdir(self): + """ + An exception in listdir should raise a DistutilsFileError + """ + with ( + mock.patch("os.listdir", side_effect=OSError()), + pytest.raises(errors.DistutilsFileError), + ): + src = self.tempdirs[-1] + dir_util.copy_tree(src, None) + + def test_mkpath_exception_uncached(self, monkeypatch, tmp_path): + """ + Caching should not remember failed attempts. + + pypa/distutils#304 + """ + + class FailPath(pathlib.Path): + def mkdir(self, *args, **kwargs): + raise OSError("Failed to create directory") + + if sys.version_info < (3, 12): + _flavour = pathlib.Path()._flavour + + target = tmp_path / 'foodir' + + with pytest.raises(errors.DistutilsFileError): + mkpath(FailPath(target)) + + assert not target.exists() + + mkpath(target) + assert target.exists() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dist.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dist.py new file mode 100644 index 0000000000000000000000000000000000000000..2c5beebe64eb9d747088cf117082a546330b29f3 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_dist.py @@ -0,0 +1,552 @@ +"""Tests for distutils.dist.""" + +import email +import email.generator +import email.policy +import functools +import io +import os +import sys +import textwrap +import unittest.mock as mock +import warnings +from distutils.cmd import Command +from distutils.dist import Distribution, fix_help_options +from distutils.tests import support +from typing import ClassVar + +import jaraco.path +import pytest + +pydistutils_cfg = '.' * (os.name == 'posix') + 'pydistutils.cfg' + + +class test_dist(Command): + """Sample distutils extension command.""" + + user_options: ClassVar[list[tuple[str, str, str]]] = [ + ("sample-option=", "S", "help text"), + ] + + def initialize_options(self): + self.sample_option = None + + +class TestDistribution(Distribution): + """Distribution subclasses that avoids the default search for + configuration files. + + The ._config_files attribute must be set before + .parse_config_files() is called. + """ + + def find_config_files(self): + return self._config_files + + +@pytest.fixture +def clear_argv(): + del sys.argv[1:] + + +@support.combine_markers +@pytest.mark.usefixtures('save_env') +@pytest.mark.usefixtures('save_argv') +class TestDistributionBehavior(support.TempdirManager): + def create_distribution(self, configfiles=()): + d = TestDistribution() + d._config_files = configfiles + d.parse_config_files() + d.parse_command_line() + return d + + def test_command_packages_unspecified(self, clear_argv): + sys.argv.append("build") + d = self.create_distribution() + assert d.get_command_packages() == ["distutils.command"] + + def test_command_packages_cmdline(self, clear_argv): + from distutils.tests.test_dist import test_dist + + sys.argv.extend([ + "--command-packages", + "foo.bar,distutils.tests", + "test_dist", + "-Ssometext", + ]) + d = self.create_distribution() + # let's actually try to load our test command: + assert d.get_command_packages() == [ + "distutils.command", + "foo.bar", + "distutils.tests", + ] + cmd = d.get_command_obj("test_dist") + assert isinstance(cmd, test_dist) + assert cmd.sample_option == "sometext" + + @pytest.mark.skipif( + 'distutils' not in Distribution.parse_config_files.__module__, + reason='Cannot test when virtualenv has monkey-patched Distribution', + ) + def test_venv_install_options(self, tmp_path, clear_argv): + sys.argv.append("install") + file = str(tmp_path / 'file') + + fakepath = '/somedir' + + jaraco.path.build({ + file: f""" + [install] + install-base = {fakepath} + install-platbase = {fakepath} + install-lib = {fakepath} + install-platlib = {fakepath} + install-purelib = {fakepath} + install-headers = {fakepath} + install-scripts = {fakepath} + install-data = {fakepath} + prefix = {fakepath} + exec-prefix = {fakepath} + home = {fakepath} + user = {fakepath} + root = {fakepath} + """, + }) + + # Base case: Not in a Virtual Environment + with mock.patch.multiple(sys, prefix='/a', base_prefix='/a'): + d = self.create_distribution([file]) + + option_tuple = (file, fakepath) + + result_dict = { + 'install_base': option_tuple, + 'install_platbase': option_tuple, + 'install_lib': option_tuple, + 'install_platlib': option_tuple, + 'install_purelib': option_tuple, + 'install_headers': option_tuple, + 'install_scripts': option_tuple, + 'install_data': option_tuple, + 'prefix': option_tuple, + 'exec_prefix': option_tuple, + 'home': option_tuple, + 'user': option_tuple, + 'root': option_tuple, + } + + assert sorted(d.command_options.get('install').keys()) == sorted( + result_dict.keys() + ) + + for key, value in d.command_options.get('install').items(): + assert value == result_dict[key] + + # Test case: In a Virtual Environment + with mock.patch.multiple(sys, prefix='/a', base_prefix='/b'): + d = self.create_distribution([file]) + + for key in result_dict.keys(): + assert key not in d.command_options.get('install', {}) + + def test_command_packages_configfile(self, tmp_path, clear_argv): + sys.argv.append("build") + file = str(tmp_path / "file") + jaraco.path.build({ + file: """ + [global] + command_packages = foo.bar, splat + """, + }) + + d = self.create_distribution([file]) + assert d.get_command_packages() == ["distutils.command", "foo.bar", "splat"] + + # ensure command line overrides config: + sys.argv[1:] = ["--command-packages", "spork", "build"] + d = self.create_distribution([file]) + assert d.get_command_packages() == ["distutils.command", "spork"] + + # Setting --command-packages to '' should cause the default to + # be used even if a config file specified something else: + sys.argv[1:] = ["--command-packages", "", "build"] + d = self.create_distribution([file]) + assert d.get_command_packages() == ["distutils.command"] + + def test_empty_options(self, request): + # an empty options dictionary should not stay in the + # list of attributes + + # catching warnings + warns = [] + + def _warn(msg): + warns.append(msg) + + request.addfinalizer( + functools.partial(setattr, warnings, 'warn', warnings.warn) + ) + warnings.warn = _warn + dist = Distribution( + attrs={ + 'author': 'xxx', + 'name': 'xxx', + 'version': 'xxx', + 'url': 'xxxx', + 'options': {}, + } + ) + + assert len(warns) == 0 + assert 'options' not in dir(dist) + + def test_finalize_options(self): + attrs = {'keywords': 'one,two', 'platforms': 'one,two'} + + dist = Distribution(attrs=attrs) + dist.finalize_options() + + # finalize_option splits platforms and keywords + assert dist.metadata.platforms == ['one', 'two'] + assert dist.metadata.keywords == ['one', 'two'] + + attrs = {'keywords': 'foo bar', 'platforms': 'foo bar'} + dist = Distribution(attrs=attrs) + dist.finalize_options() + assert dist.metadata.platforms == ['foo bar'] + assert dist.metadata.keywords == ['foo bar'] + + def test_get_command_packages(self): + dist = Distribution() + assert dist.command_packages is None + cmds = dist.get_command_packages() + assert cmds == ['distutils.command'] + assert dist.command_packages == ['distutils.command'] + + dist.command_packages = 'one,two' + cmds = dist.get_command_packages() + assert cmds == ['distutils.command', 'one', 'two'] + + def test_announce(self): + # make sure the level is known + dist = Distribution() + with pytest.raises(TypeError): + dist.announce('ok', level='ok2') + + def test_find_config_files_disable(self, temp_home): + # Ticket #1180: Allow user to disable their home config file. + jaraco.path.build({pydistutils_cfg: '[distutils]\n'}, temp_home) + + d = Distribution() + all_files = d.find_config_files() + + d = Distribution(attrs={'script_args': ['--no-user-cfg']}) + files = d.find_config_files() + + # make sure --no-user-cfg disables the user cfg file + assert len(all_files) - 1 == len(files) + + def test_script_args_list_coercion(self): + d = Distribution(attrs={'script_args': ('build', '--no-user-cfg')}) + + # make sure script_args is a list even if it started as a different iterable + assert d.script_args == ['build', '--no-user-cfg'] + + @pytest.mark.skipif( + 'platform.system() == "Windows"', + reason='Windows does not honor chmod 000', + ) + def test_find_config_files_permission_error(self, fake_home): + """ + Finding config files should not fail when directory is inaccessible. + """ + fake_home.joinpath(pydistutils_cfg).write_text('', encoding='utf-8') + fake_home.chmod(0o000) + Distribution().find_config_files() + + +@pytest.mark.usefixtures('save_env') +@pytest.mark.usefixtures('save_argv') +class TestMetadata(support.TempdirManager): + def format_metadata(self, dist): + sio = io.StringIO() + dist.metadata.write_pkg_file(sio) + return sio.getvalue() + + def test_simple_metadata(self): + attrs = {"name": "package", "version": "1.0"} + dist = Distribution(attrs) + meta = self.format_metadata(dist) + assert "Metadata-Version: 1.0" in meta + assert "provides:" not in meta.lower() + assert "requires:" not in meta.lower() + assert "obsoletes:" not in meta.lower() + + def test_provides(self): + attrs = { + "name": "package", + "version": "1.0", + "provides": ["package", "package.sub"], + } + dist = Distribution(attrs) + assert dist.metadata.get_provides() == ["package", "package.sub"] + assert dist.get_provides() == ["package", "package.sub"] + meta = self.format_metadata(dist) + assert "Metadata-Version: 1.1" in meta + assert "requires:" not in meta.lower() + assert "obsoletes:" not in meta.lower() + + def test_provides_illegal(self): + with pytest.raises(ValueError): + Distribution( + {"name": "package", "version": "1.0", "provides": ["my.pkg (splat)"]}, + ) + + def test_requires(self): + attrs = { + "name": "package", + "version": "1.0", + "requires": ["other", "another (==1.0)"], + } + dist = Distribution(attrs) + assert dist.metadata.get_requires() == ["other", "another (==1.0)"] + assert dist.get_requires() == ["other", "another (==1.0)"] + meta = self.format_metadata(dist) + assert "Metadata-Version: 1.1" in meta + assert "provides:" not in meta.lower() + assert "Requires: other" in meta + assert "Requires: another (==1.0)" in meta + assert "obsoletes:" not in meta.lower() + + def test_requires_illegal(self): + with pytest.raises(ValueError): + Distribution( + {"name": "package", "version": "1.0", "requires": ["my.pkg (splat)"]}, + ) + + def test_requires_to_list(self): + attrs = {"name": "package", "requires": iter(["other"])} + dist = Distribution(attrs) + assert isinstance(dist.metadata.requires, list) + + def test_obsoletes(self): + attrs = { + "name": "package", + "version": "1.0", + "obsoletes": ["other", "another (<1.0)"], + } + dist = Distribution(attrs) + assert dist.metadata.get_obsoletes() == ["other", "another (<1.0)"] + assert dist.get_obsoletes() == ["other", "another (<1.0)"] + meta = self.format_metadata(dist) + assert "Metadata-Version: 1.1" in meta + assert "provides:" not in meta.lower() + assert "requires:" not in meta.lower() + assert "Obsoletes: other" in meta + assert "Obsoletes: another (<1.0)" in meta + + def test_obsoletes_illegal(self): + with pytest.raises(ValueError): + Distribution( + {"name": "package", "version": "1.0", "obsoletes": ["my.pkg (splat)"]}, + ) + + def test_obsoletes_to_list(self): + attrs = {"name": "package", "obsoletes": iter(["other"])} + dist = Distribution(attrs) + assert isinstance(dist.metadata.obsoletes, list) + + def test_classifier(self): + attrs = { + 'name': 'Boa', + 'version': '3.0', + 'classifiers': ['Programming Language :: Python :: 3'], + } + dist = Distribution(attrs) + assert dist.get_classifiers() == ['Programming Language :: Python :: 3'] + meta = self.format_metadata(dist) + assert 'Metadata-Version: 1.1' in meta + + def test_classifier_invalid_type(self, caplog): + attrs = { + 'name': 'Boa', + 'version': '3.0', + 'classifiers': ('Programming Language :: Python :: 3',), + } + d = Distribution(attrs) + # should have warning about passing a non-list + assert 'should be a list' in caplog.messages[0] + # should be converted to a list + assert isinstance(d.metadata.classifiers, list) + assert d.metadata.classifiers == list(attrs['classifiers']) + + def test_keywords(self): + attrs = { + 'name': 'Monty', + 'version': '1.0', + 'keywords': ['spam', 'eggs', 'life of brian'], + } + dist = Distribution(attrs) + assert dist.get_keywords() == ['spam', 'eggs', 'life of brian'] + + def test_keywords_invalid_type(self, caplog): + attrs = { + 'name': 'Monty', + 'version': '1.0', + 'keywords': ('spam', 'eggs', 'life of brian'), + } + d = Distribution(attrs) + # should have warning about passing a non-list + assert 'should be a list' in caplog.messages[0] + # should be converted to a list + assert isinstance(d.metadata.keywords, list) + assert d.metadata.keywords == list(attrs['keywords']) + + def test_platforms(self): + attrs = { + 'name': 'Monty', + 'version': '1.0', + 'platforms': ['GNU/Linux', 'Some Evil Platform'], + } + dist = Distribution(attrs) + assert dist.get_platforms() == ['GNU/Linux', 'Some Evil Platform'] + + def test_platforms_invalid_types(self, caplog): + attrs = { + 'name': 'Monty', + 'version': '1.0', + 'platforms': ('GNU/Linux', 'Some Evil Platform'), + } + d = Distribution(attrs) + # should have warning about passing a non-list + assert 'should be a list' in caplog.messages[0] + # should be converted to a list + assert isinstance(d.metadata.platforms, list) + assert d.metadata.platforms == list(attrs['platforms']) + + def test_download_url(self): + attrs = { + 'name': 'Boa', + 'version': '3.0', + 'download_url': 'http://example.org/boa', + } + dist = Distribution(attrs) + meta = self.format_metadata(dist) + assert 'Metadata-Version: 1.1' in meta + + def test_long_description(self): + long_desc = textwrap.dedent( + """\ + example:: + We start here + and continue here + and end here.""" + ) + attrs = {"name": "package", "version": "1.0", "long_description": long_desc} + + dist = Distribution(attrs) + meta = self.format_metadata(dist) + meta = meta.replace('\n' + 8 * ' ', '\n') + assert long_desc in meta + + def test_custom_pydistutils(self, temp_home): + """ + pydistutils.cfg is found + """ + jaraco.path.build({pydistutils_cfg: ''}, temp_home) + config_path = temp_home / pydistutils_cfg + + assert str(config_path) in Distribution().find_config_files() + + def test_extra_pydistutils(self, monkeypatch, tmp_path): + jaraco.path.build({'overrides.cfg': ''}, tmp_path) + filename = tmp_path / 'overrides.cfg' + monkeypatch.setenv('DIST_EXTRA_CONFIG', str(filename)) + assert str(filename) in Distribution().find_config_files() + + def test_fix_help_options(self): + help_tuples = [('a', 'b', 'c', 'd'), (1, 2, 3, 4)] + fancy_options = fix_help_options(help_tuples) + assert fancy_options[0] == ('a', 'b', 'c') + assert fancy_options[1] == (1, 2, 3) + + def test_show_help(self, request, capsys): + # smoke test, just makes sure some help is displayed + dist = Distribution() + sys.argv = [] + dist.help = True + dist.script_name = 'setup.py' + dist.parse_command_line() + + output = [ + line for line in capsys.readouterr().out.split('\n') if line.strip() != '' + ] + assert output + + def test_read_metadata(self): + attrs = { + "name": "package", + "version": "1.0", + "long_description": "desc", + "description": "xxx", + "download_url": "http://example.com", + "keywords": ['one', 'two'], + "requires": ['foo'], + } + + dist = Distribution(attrs) + metadata = dist.metadata + + # write it then reloads it + PKG_INFO = io.StringIO() + metadata.write_pkg_file(PKG_INFO) + PKG_INFO.seek(0) + metadata.read_pkg_file(PKG_INFO) + + assert metadata.name == "package" + assert metadata.version == "1.0" + assert metadata.description == "xxx" + assert metadata.download_url == 'http://example.com' + assert metadata.keywords == ['one', 'two'] + assert metadata.platforms is None + assert metadata.obsoletes is None + assert metadata.requires == ['foo'] + + def test_round_trip_through_email_generator(self): + """ + In pypa/setuptools#4033, it was shown that once PKG-INFO is + re-generated using ``email.generator.Generator``, some control + characters might cause problems. + """ + # Given a PKG-INFO file ... + attrs = { + "name": "package", + "version": "1.0", + "long_description": "hello\x0b\nworld\n", + } + dist = Distribution(attrs) + metadata = dist.metadata + + with io.StringIO() as buffer: + metadata.write_pkg_file(buffer) + msg = buffer.getvalue() + + # ... when it is read and re-written using stdlib's email library, + orig = email.message_from_string(msg) + policy = email.policy.EmailPolicy( + utf8=True, + mangle_from_=False, + max_line_length=0, + ) + with io.StringIO() as buffer: + email.generator.Generator(buffer, policy=policy).flatten(orig) + + buffer.seek(0) + regen = email.message_from_file(buffer) + + # ... then it should be the same as the original + # (except for the specific line break characters) + orig_desc = set(orig["Description"].splitlines()) + regen_desc = set(regen["Description"].splitlines()) + assert regen_desc == orig_desc diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_extension.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..5e8e768223ea40d7a629111db181a946cf8ca5b0 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_extension.py @@ -0,0 +1,117 @@ +"""Tests for distutils.extension.""" + +import os +import pathlib +import warnings +from distutils.extension import Extension, read_setup_file + +import pytest +from test.support.warnings_helper import check_warnings + + +class TestExtension: + def test_read_setup_file(self): + # trying to read a Setup file + # (sample extracted from the PyGame project) + setup = os.path.join(os.path.dirname(__file__), 'Setup.sample') + + exts = read_setup_file(setup) + names = [ext.name for ext in exts] + names.sort() + + # here are the extensions read_setup_file should have created + # out of the file + wanted = [ + '_arraysurfarray', + '_camera', + '_numericsndarray', + '_numericsurfarray', + 'base', + 'bufferproxy', + 'cdrom', + 'color', + 'constants', + 'display', + 'draw', + 'event', + 'fastevent', + 'font', + 'gfxdraw', + 'image', + 'imageext', + 'joystick', + 'key', + 'mask', + 'mixer', + 'mixer_music', + 'mouse', + 'movie', + 'overlay', + 'pixelarray', + 'pypm', + 'rect', + 'rwobject', + 'scrap', + 'surface', + 'surflock', + 'time', + 'transform', + ] + + assert names == wanted + + def test_extension_init(self): + # the first argument, which is the name, must be a string + with pytest.raises(TypeError): + Extension(1, []) + ext = Extension('name', []) + assert ext.name == 'name' + + # the second argument, which is the list of files, must + # be an iterable of strings or PathLike objects, and not a string + with pytest.raises(TypeError): + Extension('name', 'file') + with pytest.raises(TypeError): + Extension('name', ['file', 1]) + ext = Extension('name', ['file1', 'file2']) + assert ext.sources == ['file1', 'file2'] + ext = Extension('name', [pathlib.Path('file1'), pathlib.Path('file2')]) + assert ext.sources == ['file1', 'file2'] + + # any non-string iterable of strings or PathLike objects should work + ext = Extension('name', ('file1', 'file2')) # tuple + assert ext.sources == ['file1', 'file2'] + ext = Extension('name', {'file1', 'file2'}) # set + assert sorted(ext.sources) == ['file1', 'file2'] + ext = Extension('name', iter(['file1', 'file2'])) # iterator + assert ext.sources == ['file1', 'file2'] + ext = Extension('name', [pathlib.Path('file1'), 'file2']) # mixed types + assert ext.sources == ['file1', 'file2'] + + # others arguments have defaults + for attr in ( + 'include_dirs', + 'define_macros', + 'undef_macros', + 'library_dirs', + 'libraries', + 'runtime_library_dirs', + 'extra_objects', + 'extra_compile_args', + 'extra_link_args', + 'export_symbols', + 'swig_opts', + 'depends', + ): + assert getattr(ext, attr) == [] + + assert ext.language is None + assert ext.optional is None + + # if there are unknown keyword options, warn about them + with check_warnings() as w: + warnings.simplefilter('always') + ext = Extension('name', ['file1', 'file2'], chic=True) + + assert len(w.warnings) == 1 + assert str(w.warnings[0].message) == "Unknown Extension options: 'chic'" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_file_util.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_file_util.py new file mode 100644 index 0000000000000000000000000000000000000000..a75d4a0317dc717c24f67127370aa87089eeba1a --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_file_util.py @@ -0,0 +1,95 @@ +"""Tests for distutils.file_util.""" + +import errno +import os +import unittest.mock as mock +from distutils.errors import DistutilsFileError +from distutils.file_util import copy_file, move_file + +import jaraco.path +import pytest + + +@pytest.fixture(autouse=True) +def stuff(request, tmp_path): + self = request.instance + self.source = tmp_path / 'f1' + self.target = tmp_path / 'f2' + self.target_dir = tmp_path / 'd1' + + +class TestFileUtil: + def test_move_file_verbosity(self, caplog): + jaraco.path.build({self.source: 'some content'}) + + move_file(self.source, self.target, verbose=False) + assert not caplog.messages + + # back to original state + move_file(self.target, self.source, verbose=False) + + move_file(self.source, self.target, verbose=True) + wanted = [f'moving {self.source} -> {self.target}'] + assert caplog.messages == wanted + + # back to original state + move_file(self.target, self.source, verbose=False) + + caplog.clear() + # now the target is a dir + os.mkdir(self.target_dir) + move_file(self.source, self.target_dir, verbose=True) + wanted = [f'moving {self.source} -> {self.target_dir}'] + assert caplog.messages == wanted + + def test_move_file_exception_unpacking_rename(self): + # see issue 22182 + with ( + mock.patch("os.rename", side_effect=OSError("wrong", 1)), + pytest.raises(DistutilsFileError), + ): + jaraco.path.build({self.source: 'spam eggs'}) + move_file(self.source, self.target, verbose=False) + + def test_move_file_exception_unpacking_unlink(self): + # see issue 22182 + with ( + mock.patch("os.rename", side_effect=OSError(errno.EXDEV, "wrong")), + mock.patch("os.unlink", side_effect=OSError("wrong", 1)), + pytest.raises(DistutilsFileError), + ): + jaraco.path.build({self.source: 'spam eggs'}) + move_file(self.source, self.target, verbose=False) + + def test_copy_file_hard_link(self): + jaraco.path.build({self.source: 'some content'}) + # Check first that copy_file() will not fall back on copying the file + # instead of creating the hard link. + try: + os.link(self.source, self.target) + except OSError as e: + self.skipTest(f'os.link: {e}') + else: + self.target.unlink() + st = os.stat(self.source) + copy_file(self.source, self.target, link='hard') + st2 = os.stat(self.source) + st3 = os.stat(self.target) + assert os.path.samestat(st, st2), (st, st2) + assert os.path.samestat(st2, st3), (st2, st3) + assert self.source.read_text(encoding='utf-8') == 'some content' + + def test_copy_file_hard_link_failure(self): + # If hard linking fails, copy_file() falls back on copying file + # (some special filesystems don't support hard linking even under + # Unix, see issue #8876). + jaraco.path.build({self.source: 'some content'}) + st = os.stat(self.source) + with mock.patch("os.link", side_effect=OSError(0, "linking unsupported")): + copy_file(self.source, self.target, link='hard') + st2 = os.stat(self.source) + st3 = os.stat(self.target) + assert os.path.samestat(st, st2), (st, st2) + assert not os.path.samestat(st2, st3), (st2, st3) + for fn in (self.source, self.target): + assert fn.read_text(encoding='utf-8') == 'some content' diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_filelist.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_filelist.py new file mode 100644 index 0000000000000000000000000000000000000000..130e6fb53b0bd9670bdcfe6d4bc7fcf8fd90416e --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_filelist.py @@ -0,0 +1,336 @@ +"""Tests for distutils.filelist.""" + +import logging +import os +import re +from distutils import debug, filelist +from distutils.errors import DistutilsTemplateError +from distutils.filelist import FileList, glob_to_re, translate_pattern + +import jaraco.path +import pytest + +from .compat import py39 as os_helper + +MANIFEST_IN = """\ +include ok +include xo +exclude xo +include foo.tmp +include buildout.cfg +global-include *.x +global-include *.txt +global-exclude *.tmp +recursive-include f *.oo +recursive-exclude global *.x +graft dir +prune dir3 +""" + + +def make_local_path(s): + """Converts '/' in a string to os.sep""" + return s.replace('/', os.sep) + + +class TestFileList: + def assertNoWarnings(self, caplog): + warnings = [rec for rec in caplog.records if rec.levelno == logging.WARNING] + assert not warnings + caplog.clear() + + def assertWarnings(self, caplog): + warnings = [rec for rec in caplog.records if rec.levelno == logging.WARNING] + assert warnings + caplog.clear() + + def test_glob_to_re(self): + sep = os.sep + if os.sep == '\\': + sep = re.escape(os.sep) + + for glob, regex in ( + # simple cases + ('foo*', r'(?s:foo[^%(sep)s]*)\Z'), + ('foo?', r'(?s:foo[^%(sep)s])\Z'), + ('foo??', r'(?s:foo[^%(sep)s][^%(sep)s])\Z'), + # special cases + (r'foo\\*', r'(?s:foo\\\\[^%(sep)s]*)\Z'), + (r'foo\\\*', r'(?s:foo\\\\\\[^%(sep)s]*)\Z'), + ('foo????', r'(?s:foo[^%(sep)s][^%(sep)s][^%(sep)s][^%(sep)s])\Z'), + (r'foo\\??', r'(?s:foo\\\\[^%(sep)s][^%(sep)s])\Z'), + ): + regex = regex % {'sep': sep} + assert glob_to_re(glob) == regex + + def test_process_template_line(self): + # testing all MANIFEST.in template patterns + file_list = FileList() + mlp = make_local_path + + # simulated file list + file_list.allfiles = [ + 'foo.tmp', + 'ok', + 'xo', + 'four.txt', + 'buildout.cfg', + # filelist does not filter out VCS directories, + # it's sdist that does + mlp('.hg/last-message.txt'), + mlp('global/one.txt'), + mlp('global/two.txt'), + mlp('global/files.x'), + mlp('global/here.tmp'), + mlp('f/o/f.oo'), + mlp('dir/graft-one'), + mlp('dir/dir2/graft2'), + mlp('dir3/ok'), + mlp('dir3/sub/ok.txt'), + ] + + for line in MANIFEST_IN.split('\n'): + if line.strip() == '': + continue + file_list.process_template_line(line) + + wanted = [ + 'ok', + 'buildout.cfg', + 'four.txt', + mlp('.hg/last-message.txt'), + mlp('global/one.txt'), + mlp('global/two.txt'), + mlp('f/o/f.oo'), + mlp('dir/graft-one'), + mlp('dir/dir2/graft2'), + ] + + assert file_list.files == wanted + + def test_debug_print(self, capsys, monkeypatch): + file_list = FileList() + file_list.debug_print('xxx') + assert capsys.readouterr().out == '' + + monkeypatch.setattr(debug, 'DEBUG', True) + file_list.debug_print('xxx') + assert capsys.readouterr().out == 'xxx\n' + + def test_set_allfiles(self): + file_list = FileList() + files = ['a', 'b', 'c'] + file_list.set_allfiles(files) + assert file_list.allfiles == files + + def test_remove_duplicates(self): + file_list = FileList() + file_list.files = ['a', 'b', 'a', 'g', 'c', 'g'] + # files must be sorted beforehand (sdist does it) + file_list.sort() + file_list.remove_duplicates() + assert file_list.files == ['a', 'b', 'c', 'g'] + + def test_translate_pattern(self): + # not regex + assert hasattr(translate_pattern('a', anchor=True, is_regex=False), 'search') + + # is a regex + regex = re.compile('a') + assert translate_pattern(regex, anchor=True, is_regex=True) == regex + + # plain string flagged as regex + assert hasattr(translate_pattern('a', anchor=True, is_regex=True), 'search') + + # glob support + assert translate_pattern('*.py', anchor=True, is_regex=False).search( + 'filelist.py' + ) + + def test_exclude_pattern(self): + # return False if no match + file_list = FileList() + assert not file_list.exclude_pattern('*.py') + + # return True if files match + file_list = FileList() + file_list.files = ['a.py', 'b.py'] + assert file_list.exclude_pattern('*.py') + + # test excludes + file_list = FileList() + file_list.files = ['a.py', 'a.txt'] + file_list.exclude_pattern('*.py') + assert file_list.files == ['a.txt'] + + def test_include_pattern(self): + # return False if no match + file_list = FileList() + file_list.set_allfiles([]) + assert not file_list.include_pattern('*.py') + + # return True if files match + file_list = FileList() + file_list.set_allfiles(['a.py', 'b.txt']) + assert file_list.include_pattern('*.py') + + # test * matches all files + file_list = FileList() + assert file_list.allfiles is None + file_list.set_allfiles(['a.py', 'b.txt']) + file_list.include_pattern('*') + assert file_list.allfiles == ['a.py', 'b.txt'] + + def test_process_template(self, caplog): + mlp = make_local_path + # invalid lines + file_list = FileList() + for action in ( + 'include', + 'exclude', + 'global-include', + 'global-exclude', + 'recursive-include', + 'recursive-exclude', + 'graft', + 'prune', + 'blarg', + ): + with pytest.raises(DistutilsTemplateError): + file_list.process_template_line(action) + + # include + file_list = FileList() + file_list.set_allfiles(['a.py', 'b.txt', mlp('d/c.py')]) + + file_list.process_template_line('include *.py') + assert file_list.files == ['a.py'] + self.assertNoWarnings(caplog) + + file_list.process_template_line('include *.rb') + assert file_list.files == ['a.py'] + self.assertWarnings(caplog) + + # exclude + file_list = FileList() + file_list.files = ['a.py', 'b.txt', mlp('d/c.py')] + + file_list.process_template_line('exclude *.py') + assert file_list.files == ['b.txt', mlp('d/c.py')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('exclude *.rb') + assert file_list.files == ['b.txt', mlp('d/c.py')] + self.assertWarnings(caplog) + + # global-include + file_list = FileList() + file_list.set_allfiles(['a.py', 'b.txt', mlp('d/c.py')]) + + file_list.process_template_line('global-include *.py') + assert file_list.files == ['a.py', mlp('d/c.py')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('global-include *.rb') + assert file_list.files == ['a.py', mlp('d/c.py')] + self.assertWarnings(caplog) + + # global-exclude + file_list = FileList() + file_list.files = ['a.py', 'b.txt', mlp('d/c.py')] + + file_list.process_template_line('global-exclude *.py') + assert file_list.files == ['b.txt'] + self.assertNoWarnings(caplog) + + file_list.process_template_line('global-exclude *.rb') + assert file_list.files == ['b.txt'] + self.assertWarnings(caplog) + + # recursive-include + file_list = FileList() + file_list.set_allfiles(['a.py', mlp('d/b.py'), mlp('d/c.txt'), mlp('d/d/e.py')]) + + file_list.process_template_line('recursive-include d *.py') + assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('recursive-include e *.py') + assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')] + self.assertWarnings(caplog) + + # recursive-exclude + file_list = FileList() + file_list.files = ['a.py', mlp('d/b.py'), mlp('d/c.txt'), mlp('d/d/e.py')] + + file_list.process_template_line('recursive-exclude d *.py') + assert file_list.files == ['a.py', mlp('d/c.txt')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('recursive-exclude e *.py') + assert file_list.files == ['a.py', mlp('d/c.txt')] + self.assertWarnings(caplog) + + # graft + file_list = FileList() + file_list.set_allfiles(['a.py', mlp('d/b.py'), mlp('d/d/e.py'), mlp('f/f.py')]) + + file_list.process_template_line('graft d') + assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('graft e') + assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')] + self.assertWarnings(caplog) + + # prune + file_list = FileList() + file_list.files = ['a.py', mlp('d/b.py'), mlp('d/d/e.py'), mlp('f/f.py')] + + file_list.process_template_line('prune d') + assert file_list.files == ['a.py', mlp('f/f.py')] + self.assertNoWarnings(caplog) + + file_list.process_template_line('prune e') + assert file_list.files == ['a.py', mlp('f/f.py')] + self.assertWarnings(caplog) + + +class TestFindAll: + @os_helper.skip_unless_symlink + def test_missing_symlink(self, temp_cwd): + os.symlink('foo', 'bar') + assert filelist.findall() == [] + + def test_basic_discovery(self, temp_cwd): + """ + When findall is called with no parameters or with + '.' as the parameter, the dot should be omitted from + the results. + """ + jaraco.path.build({'foo': {'file1.txt': ''}, 'bar': {'file2.txt': ''}}) + file1 = os.path.join('foo', 'file1.txt') + file2 = os.path.join('bar', 'file2.txt') + expected = [file2, file1] + assert sorted(filelist.findall()) == expected + + def test_non_local_discovery(self, tmp_path): + """ + When findall is called with another path, the full + path name should be returned. + """ + jaraco.path.build({'file1.txt': ''}, tmp_path) + expected = [str(tmp_path / 'file1.txt')] + assert filelist.findall(tmp_path) == expected + + @os_helper.skip_unless_symlink + def test_symlink_loop(self, tmp_path): + jaraco.path.build( + { + 'link-to-parent': jaraco.path.Symlink('.'), + 'somefile': '', + }, + tmp_path, + ) + files = filelist.findall(tmp_path) + assert len(files) == 1 diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ffb2e668ac5a042c5a2889d7b51b5755b73157 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install.py @@ -0,0 +1,245 @@ +"""Tests for distutils.command.install.""" + +import logging +import os +import pathlib +import site +import sys +from distutils import sysconfig +from distutils.command import install as install_module +from distutils.command.build_ext import build_ext +from distutils.command.install import INSTALL_SCHEMES, install +from distutils.core import Distribution +from distutils.errors import DistutilsOptionError +from distutils.extension import Extension +from distutils.tests import missing_compiler_executable, support +from distutils.util import is_mingw + +import pytest + + +def _make_ext_name(modname): + return modname + sysconfig.get_config_var('EXT_SUFFIX') + + +@support.combine_markers +@pytest.mark.usefixtures('save_env') +class TestInstall( + support.TempdirManager, +): + @pytest.mark.xfail( + 'platform.system() == "Windows" and sys.version_info > (3, 11)', + reason="pypa/distutils#148", + ) + def test_home_installation_scheme(self): + # This ensure two things: + # - that --home generates the desired set of directory names + # - test --home is supported on all platforms + builddir = self.mkdtemp() + destination = os.path.join(builddir, "installation") + + dist = Distribution({"name": "foopkg"}) + # script_name need not exist, it just need to be initialized + dist.script_name = os.path.join(builddir, "setup.py") + dist.command_obj["build"] = support.DummyCommand( + build_base=builddir, + build_lib=os.path.join(builddir, "lib"), + ) + + cmd = install(dist) + cmd.home = destination + cmd.ensure_finalized() + + assert cmd.install_base == destination + assert cmd.install_platbase == destination + + def check_path(got, expected): + got = os.path.normpath(got) + expected = os.path.normpath(expected) + assert got == expected + + impl_name = sys.implementation.name.replace("cpython", "python") + libdir = os.path.join(destination, "lib", impl_name) + check_path(cmd.install_lib, libdir) + _platlibdir = getattr(sys, "platlibdir", "lib") + platlibdir = os.path.join(destination, _platlibdir, impl_name) + check_path(cmd.install_platlib, platlibdir) + check_path(cmd.install_purelib, libdir) + check_path( + cmd.install_headers, + os.path.join(destination, "include", impl_name, "foopkg"), + ) + check_path(cmd.install_scripts, os.path.join(destination, "bin")) + check_path(cmd.install_data, destination) + + def test_user_site(self, monkeypatch): + # test install with --user + # preparing the environment for the test + self.tmpdir = self.mkdtemp() + orig_site = site.USER_SITE + orig_base = site.USER_BASE + monkeypatch.setattr(site, 'USER_BASE', os.path.join(self.tmpdir, 'B')) + monkeypatch.setattr(site, 'USER_SITE', os.path.join(self.tmpdir, 'S')) + monkeypatch.setattr(install_module, 'USER_BASE', site.USER_BASE) + monkeypatch.setattr(install_module, 'USER_SITE', site.USER_SITE) + + def _expanduser(path): + if path.startswith('~'): + return os.path.normpath(self.tmpdir + path[1:]) + return path + + monkeypatch.setattr(os.path, 'expanduser', _expanduser) + + for key in ('nt_user', 'posix_user'): + assert key in INSTALL_SCHEMES + + dist = Distribution({'name': 'xx'}) + cmd = install(dist) + + # making sure the user option is there + options = [name for name, short, label in cmd.user_options] + assert 'user' in options + + # setting a value + cmd.user = True + + # user base and site shouldn't be created yet + assert not os.path.exists(site.USER_BASE) + assert not os.path.exists(site.USER_SITE) + + # let's run finalize + cmd.ensure_finalized() + + # now they should + assert os.path.exists(site.USER_BASE) + assert os.path.exists(site.USER_SITE) + + assert 'userbase' in cmd.config_vars + assert 'usersite' in cmd.config_vars + + actual_headers = os.path.relpath(cmd.install_headers, site.USER_BASE) + if os.name == 'nt' and not is_mingw(): + site_path = os.path.relpath(os.path.dirname(orig_site), orig_base) + include = os.path.join(site_path, 'Include') + else: + include = sysconfig.get_python_inc(0, '') + expect_headers = os.path.join(include, 'xx') + + assert os.path.normcase(actual_headers) == os.path.normcase(expect_headers) + + def test_handle_extra_path(self): + dist = Distribution({'name': 'xx', 'extra_path': 'path,dirs'}) + cmd = install(dist) + + # two elements + cmd.handle_extra_path() + assert cmd.extra_path == ['path', 'dirs'] + assert cmd.extra_dirs == 'dirs' + assert cmd.path_file == 'path' + + # one element + cmd.extra_path = ['path'] + cmd.handle_extra_path() + assert cmd.extra_path == ['path'] + assert cmd.extra_dirs == 'path' + assert cmd.path_file == 'path' + + # none + dist.extra_path = cmd.extra_path = None + cmd.handle_extra_path() + assert cmd.extra_path is None + assert cmd.extra_dirs == '' + assert cmd.path_file is None + + # three elements (no way !) + cmd.extra_path = 'path,dirs,again' + with pytest.raises(DistutilsOptionError): + cmd.handle_extra_path() + + def test_finalize_options(self): + dist = Distribution({'name': 'xx'}) + cmd = install(dist) + + # must supply either prefix/exec-prefix/home or + # install-base/install-platbase -- not both + cmd.prefix = 'prefix' + cmd.install_base = 'base' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + + # must supply either home or prefix/exec-prefix -- not both + cmd.install_base = None + cmd.home = 'home' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + + # can't combine user with prefix/exec_prefix/home or + # install_(plat)base + cmd.prefix = None + cmd.user = 'user' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + + def test_record(self): + install_dir = self.mkdtemp() + project_dir, dist = self.create_dist(py_modules=['hello'], scripts=['sayhi']) + os.chdir(project_dir) + self.write_file('hello.py', "def main(): print('o hai')") + self.write_file('sayhi', 'from hello import main; main()') + + cmd = install(dist) + dist.command_obj['install'] = cmd + cmd.root = install_dir + cmd.record = os.path.join(project_dir, 'filelist') + cmd.ensure_finalized() + cmd.run() + + content = pathlib.Path(cmd.record).read_text(encoding='utf-8') + + found = [pathlib.Path(line).name for line in content.splitlines()] + expected = [ + 'hello.py', + f'hello.{sys.implementation.cache_tag}.pyc', + 'sayhi', + 'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]), + ] + assert found == expected + + def test_record_extensions(self): + cmd = missing_compiler_executable() + if cmd is not None: + pytest.skip(f'The {cmd!r} command is not found') + install_dir = self.mkdtemp() + project_dir, dist = self.create_dist( + ext_modules=[Extension('xx', ['xxmodule.c'])] + ) + os.chdir(project_dir) + support.copy_xxmodule_c(project_dir) + + buildextcmd = build_ext(dist) + support.fixup_build_ext(buildextcmd) + buildextcmd.ensure_finalized() + + cmd = install(dist) + dist.command_obj['install'] = cmd + dist.command_obj['build_ext'] = buildextcmd + cmd.root = install_dir + cmd.record = os.path.join(project_dir, 'filelist') + cmd.ensure_finalized() + cmd.run() + + content = pathlib.Path(cmd.record).read_text(encoding='utf-8') + + found = [pathlib.Path(line).name for line in content.splitlines()] + expected = [ + _make_ext_name('xx'), + 'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]), + ] + assert found == expected + + def test_debug_mode(self, caplog, monkeypatch): + # this covers the code called when DEBUG is set + monkeypatch.setattr(install_module, 'DEBUG', True) + caplog.set_level(logging.DEBUG) + self.test_record() + assert any(rec for rec in caplog.records if rec.levelno == logging.DEBUG) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_data.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_data.py new file mode 100644 index 0000000000000000000000000000000000000000..c800f86c645d86fcb11aa3bef1513325ef163132 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_data.py @@ -0,0 +1,74 @@ +"""Tests for distutils.command.install_data.""" + +import os +import pathlib +from distutils.command.install_data import install_data +from distutils.tests import support + +import pytest + + +@pytest.mark.usefixtures('save_env') +class TestInstallData( + support.TempdirManager, +): + def test_simple_run(self): + pkg_dir, dist = self.create_dist() + cmd = install_data(dist) + cmd.install_dir = inst = os.path.join(pkg_dir, 'inst') + + # data_files can contain + # - simple files + # - a Path object + # - a tuple with a path, and a list of file + one = os.path.join(pkg_dir, 'one') + self.write_file(one, 'xxx') + inst2 = os.path.join(pkg_dir, 'inst2') + two = os.path.join(pkg_dir, 'two') + self.write_file(two, 'xxx') + three = pathlib.Path(pkg_dir) / 'three' + self.write_file(three, 'xxx') + + cmd.data_files = [one, (inst2, [two]), three] + assert cmd.get_inputs() == [one, (inst2, [two]), three] + + # let's run the command + cmd.ensure_finalized() + cmd.run() + + # let's check the result + assert len(cmd.get_outputs()) == 3 + rthree = os.path.split(one)[-1] + assert os.path.exists(os.path.join(inst, rthree)) + rtwo = os.path.split(two)[-1] + assert os.path.exists(os.path.join(inst2, rtwo)) + rone = os.path.split(one)[-1] + assert os.path.exists(os.path.join(inst, rone)) + cmd.outfiles = [] + + # let's try with warn_dir one + cmd.warn_dir = True + cmd.ensure_finalized() + cmd.run() + + # let's check the result + assert len(cmd.get_outputs()) == 3 + assert os.path.exists(os.path.join(inst, rthree)) + assert os.path.exists(os.path.join(inst2, rtwo)) + assert os.path.exists(os.path.join(inst, rone)) + cmd.outfiles = [] + + # now using root and empty dir + cmd.root = os.path.join(pkg_dir, 'root') + inst5 = os.path.join(pkg_dir, 'inst5') + four = os.path.join(cmd.install_dir, 'four') + self.write_file(four, 'xx') + cmd.data_files = [one, (inst2, [two]), three, ('inst5', [four]), (inst5, [])] + cmd.ensure_finalized() + cmd.run() + + # let's check the result + assert len(cmd.get_outputs()) == 5 + assert os.path.exists(os.path.join(inst, rthree)) + assert os.path.exists(os.path.join(inst2, rtwo)) + assert os.path.exists(os.path.join(inst, rone)) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_headers.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_headers.py new file mode 100644 index 0000000000000000000000000000000000000000..2c74f06b976f3c3ea977667a5c16c0e7f201a558 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_headers.py @@ -0,0 +1,33 @@ +"""Tests for distutils.command.install_headers.""" + +import os +from distutils.command.install_headers import install_headers +from distutils.tests import support + +import pytest + + +@pytest.mark.usefixtures('save_env') +class TestInstallHeaders( + support.TempdirManager, +): + def test_simple_run(self): + # we have two headers + header_list = self.mkdtemp() + header1 = os.path.join(header_list, 'header1') + header2 = os.path.join(header_list, 'header2') + self.write_file(header1) + self.write_file(header2) + headers = [header1, header2] + + pkg_dir, dist = self.create_dist(headers=headers) + cmd = install_headers(dist) + assert cmd.get_inputs() == headers + + # let's run the command + cmd.install_dir = os.path.join(pkg_dir, 'inst') + cmd.ensure_finalized() + cmd.run() + + # let's check the results + assert len(cmd.get_outputs()) == 2 diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_lib.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_lib.py new file mode 100644 index 0000000000000000000000000000000000000000..f685a579560ea70bceea221de0e54485975ce180 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_lib.py @@ -0,0 +1,110 @@ +"""Tests for distutils.command.install_data.""" + +import importlib.util +import os +import sys +from distutils.command.install_lib import install_lib +from distutils.errors import DistutilsOptionError +from distutils.extension import Extension +from distutils.tests import support + +import pytest + + +@support.combine_markers +@pytest.mark.usefixtures('save_env') +class TestInstallLib( + support.TempdirManager, +): + def test_finalize_options(self): + dist = self.create_dist()[1] + cmd = install_lib(dist) + + cmd.finalize_options() + assert cmd.compile == 1 + assert cmd.optimize == 0 + + # optimize must be 0, 1, or 2 + cmd.optimize = 'foo' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + cmd.optimize = '4' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + + cmd.optimize = '2' + cmd.finalize_options() + assert cmd.optimize == 2 + + @pytest.mark.skipif('sys.dont_write_bytecode') + def test_byte_compile(self): + project_dir, dist = self.create_dist() + os.chdir(project_dir) + cmd = install_lib(dist) + cmd.compile = cmd.optimize = 1 + + f = os.path.join(project_dir, 'foo.py') + self.write_file(f, '# python file') + cmd.byte_compile([f]) + pyc_file = importlib.util.cache_from_source('foo.py', optimization='') + pyc_opt_file = importlib.util.cache_from_source( + 'foo.py', optimization=cmd.optimize + ) + assert os.path.exists(pyc_file) + assert os.path.exists(pyc_opt_file) + + def test_get_outputs(self): + project_dir, dist = self.create_dist() + os.chdir(project_dir) + os.mkdir('spam') + cmd = install_lib(dist) + + # setting up a dist environment + cmd.compile = cmd.optimize = 1 + cmd.install_dir = self.mkdtemp() + f = os.path.join(project_dir, 'spam', '__init__.py') + self.write_file(f, '# python package') + cmd.distribution.ext_modules = [Extension('foo', ['xxx'])] + cmd.distribution.packages = ['spam'] + cmd.distribution.script_name = 'setup.py' + + # get_outputs should return 4 elements: spam/__init__.py and .pyc, + # foo.import-tag-abiflags.so / foo.pyd + outputs = cmd.get_outputs() + assert len(outputs) == 4, outputs + + def test_get_inputs(self): + project_dir, dist = self.create_dist() + os.chdir(project_dir) + os.mkdir('spam') + cmd = install_lib(dist) + + # setting up a dist environment + cmd.compile = cmd.optimize = 1 + cmd.install_dir = self.mkdtemp() + f = os.path.join(project_dir, 'spam', '__init__.py') + self.write_file(f, '# python package') + cmd.distribution.ext_modules = [Extension('foo', ['xxx'])] + cmd.distribution.packages = ['spam'] + cmd.distribution.script_name = 'setup.py' + + # get_inputs should return 2 elements: spam/__init__.py and + # foo.import-tag-abiflags.so / foo.pyd + inputs = cmd.get_inputs() + assert len(inputs) == 2, inputs + + def test_dont_write_bytecode(self, caplog): + # makes sure byte_compile is not used + dist = self.create_dist()[1] + cmd = install_lib(dist) + cmd.compile = True + cmd.optimize = 1 + + old_dont_write_bytecode = sys.dont_write_bytecode + sys.dont_write_bytecode = True + try: + cmd.byte_compile([]) + finally: + sys.dont_write_bytecode = old_dont_write_bytecode + + assert 'byte-compiling is disabled' in caplog.messages[0] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_scripts.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..868b1c22521f23a5dd398e4636c94330d4c238ac --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_install_scripts.py @@ -0,0 +1,52 @@ +"""Tests for distutils.command.install_scripts.""" + +import os +from distutils.command.install_scripts import install_scripts +from distutils.core import Distribution +from distutils.tests import support + +from . import test_build_scripts + + +class TestInstallScripts(support.TempdirManager): + def test_default_settings(self): + dist = Distribution() + dist.command_obj["build"] = support.DummyCommand(build_scripts="/foo/bar") + dist.command_obj["install"] = support.DummyCommand( + install_scripts="/splat/funk", + force=True, + skip_build=True, + ) + cmd = install_scripts(dist) + assert not cmd.force + assert not cmd.skip_build + assert cmd.build_dir is None + assert cmd.install_dir is None + + cmd.finalize_options() + + assert cmd.force + assert cmd.skip_build + assert cmd.build_dir == "/foo/bar" + assert cmd.install_dir == "/splat/funk" + + def test_installation(self): + source = self.mkdtemp() + + expected = test_build_scripts.TestBuildScripts.write_sample_scripts(source) + + target = self.mkdtemp() + dist = Distribution() + dist.command_obj["build"] = support.DummyCommand(build_scripts=source) + dist.command_obj["install"] = support.DummyCommand( + install_scripts=target, + force=True, + skip_build=True, + ) + cmd = install_scripts(dist) + cmd.finalize_options() + cmd.run() + + installed = os.listdir(target) + for name in expected: + assert name in installed diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_log.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_log.py new file mode 100644 index 0000000000000000000000000000000000000000..d67779fc9fa0f58a010929d5a456890c6dde3376 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_log.py @@ -0,0 +1,12 @@ +"""Tests for distutils.log""" + +import logging +from distutils._log import log + + +class TestLog: + def test_non_ascii(self, caplog): + caplog.set_level(logging.DEBUG) + log.debug('Dεbug\tMėssãge') + log.fatal('Fαtal\tÈrrōr') + assert caplog.messages == ['Dεbug\tMėssãge', 'Fαtal\tÈrrōr'] diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_modified.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_modified.py new file mode 100644 index 0000000000000000000000000000000000000000..e35cec2d6f1a8d27619e33c253f0a83f0f72a75d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_modified.py @@ -0,0 +1,126 @@ +"""Tests for distutils._modified.""" + +import os +import types +from distutils._modified import newer, newer_group, newer_pairwise, newer_pairwise_group +from distutils.errors import DistutilsFileError +from distutils.tests import support + +import pytest + + +class TestDepUtil(support.TempdirManager): + def test_newer(self): + tmpdir = self.mkdtemp() + new_file = os.path.join(tmpdir, 'new') + old_file = os.path.abspath(__file__) + + # Raise DistutilsFileError if 'new_file' does not exist. + with pytest.raises(DistutilsFileError): + newer(new_file, old_file) + + # Return true if 'new_file' exists and is more recently modified than + # 'old_file', or if 'new_file' exists and 'old_file' doesn't. + self.write_file(new_file) + assert newer(new_file, 'I_dont_exist') + assert newer(new_file, old_file) + + # Return false if both exist and 'old_file' is the same age or younger + # than 'new_file'. + assert not newer(old_file, new_file) + + def _setup_1234(self): + tmpdir = self.mkdtemp() + sources = os.path.join(tmpdir, 'sources') + targets = os.path.join(tmpdir, 'targets') + os.mkdir(sources) + os.mkdir(targets) + one = os.path.join(sources, 'one') + two = os.path.join(sources, 'two') + three = os.path.abspath(__file__) # I am the old file + four = os.path.join(targets, 'four') + self.write_file(one) + self.write_file(two) + self.write_file(four) + return one, two, three, four + + def test_newer_pairwise(self): + one, two, three, four = self._setup_1234() + + assert newer_pairwise([one, two], [three, four]) == ([one], [three]) + + def test_newer_pairwise_mismatch(self): + one, two, three, four = self._setup_1234() + + with pytest.raises(ValueError): + newer_pairwise([one], [three, four]) + + with pytest.raises(ValueError): + newer_pairwise([one, two], [three]) + + def test_newer_pairwise_empty(self): + assert newer_pairwise([], []) == ([], []) + + def test_newer_pairwise_fresh(self): + one, two, three, four = self._setup_1234() + + assert newer_pairwise([one, three], [two, four]) == ([], []) + + def test_newer_group(self): + tmpdir = self.mkdtemp() + sources = os.path.join(tmpdir, 'sources') + os.mkdir(sources) + one = os.path.join(sources, 'one') + two = os.path.join(sources, 'two') + three = os.path.join(sources, 'three') + old_file = os.path.abspath(__file__) + + # return true if 'old_file' is out-of-date with respect to any file + # listed in 'sources'. + self.write_file(one) + self.write_file(two) + self.write_file(three) + assert newer_group([one, two, three], old_file) + assert not newer_group([one, two, old_file], three) + + # missing handling + os.remove(one) + with pytest.raises(OSError): + newer_group([one, two, old_file], three) + + assert not newer_group([one, two, old_file], three, missing='ignore') + + assert newer_group([one, two, old_file], three, missing='newer') + + +@pytest.fixture +def groups_target(tmp_path): + """ + Set up some older sources, a target, and newer sources. + + Returns a simple namespace with these values. + """ + filenames = ['older.c', 'older.h', 'target.o', 'newer.c', 'newer.h'] + paths = [tmp_path / name for name in filenames] + + for mtime, path in enumerate(paths): + path.write_text('', encoding='utf-8') + + # make sure modification times are sequential + os.utime(path, (mtime, mtime)) + + return types.SimpleNamespace(older=paths[:2], target=paths[2], newer=paths[3:]) + + +def test_newer_pairwise_group(groups_target): + older = newer_pairwise_group([groups_target.older], [groups_target.target]) + newer = newer_pairwise_group([groups_target.newer], [groups_target.target]) + assert older == ([], []) + assert newer == ([groups_target.newer], [groups_target.target]) + + +def test_newer_group_no_sources_no_target(tmp_path): + """ + Consider no sources and no target "newer". + """ + assert newer_group([], str(tmp_path / 'does-not-exist')) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sdist.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sdist.py new file mode 100644 index 0000000000000000000000000000000000000000..6b1a376b262162aa7c8d3d989709c05b38563d6c --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sdist.py @@ -0,0 +1,470 @@ +"""Tests for distutils.command.sdist.""" + +import os +import pathlib +import shutil # noqa: F401 +import tarfile +import zipfile +from distutils.archive_util import ARCHIVE_FORMATS +from distutils.command.sdist import sdist, show_formats +from distutils.core import Distribution +from distutils.errors import DistutilsOptionError +from distutils.filelist import FileList +from os.path import join +from textwrap import dedent + +import jaraco.path +import path +import pytest +from more_itertools import ilen + +from . import support +from .unix_compat import grp, pwd, require_uid_0, require_unix_id + +SETUP_PY = """ +from distutils.core import setup +import somecode + +setup(name='fake') +""" + +MANIFEST = """\ +# file GENERATED by distutils, do NOT edit +README +buildout.cfg +inroot.txt +setup.py +data%(sep)sdata.dt +scripts%(sep)sscript.py +some%(sep)sfile.txt +some%(sep)sother_file.txt +somecode%(sep)s__init__.py +somecode%(sep)sdoc.dat +somecode%(sep)sdoc.txt +""" + + +@pytest.fixture(autouse=True) +def project_dir(request, distutils_managed_tempdir): + self = request.instance + self.tmp_dir = self.mkdtemp() + jaraco.path.build( + { + 'somecode': { + '__init__.py': '#', + }, + 'README': 'xxx', + 'setup.py': SETUP_PY, + }, + self.tmp_dir, + ) + with path.Path(self.tmp_dir): + yield + + +def clean_lines(filepath): + with pathlib.Path(filepath).open(encoding='utf-8') as f: + yield from filter(None, map(str.strip, f)) + + +class TestSDist(support.TempdirManager): + def get_cmd(self, metadata=None): + """Returns a cmd""" + if metadata is None: + metadata = { + 'name': 'ns.fake--pkg', + 'version': '1.0', + 'url': 'xxx', + 'author': 'xxx', + 'author_email': 'xxx', + } + dist = Distribution(metadata) + dist.script_name = 'setup.py' + dist.packages = ['somecode'] + dist.include_package_data = True + cmd = sdist(dist) + cmd.dist_dir = 'dist' + return dist, cmd + + @pytest.mark.usefixtures('needs_zlib') + def test_prune_file_list(self): + # this test creates a project with some VCS dirs and an NFS rename + # file, then launches sdist to check they get pruned on all systems + + # creating VCS directories with some files in them + os.mkdir(join(self.tmp_dir, 'somecode', '.svn')) + self.write_file((self.tmp_dir, 'somecode', '.svn', 'ok.py'), 'xxx') + + os.mkdir(join(self.tmp_dir, 'somecode', '.hg')) + self.write_file((self.tmp_dir, 'somecode', '.hg', 'ok'), 'xxx') + + os.mkdir(join(self.tmp_dir, 'somecode', '.git')) + self.write_file((self.tmp_dir, 'somecode', '.git', 'ok'), 'xxx') + + self.write_file((self.tmp_dir, 'somecode', '.nfs0001'), 'xxx') + + # now building a sdist + dist, cmd = self.get_cmd() + + # zip is available universally + # (tar might not be installed under win32) + cmd.formats = ['zip'] + + cmd.ensure_finalized() + cmd.run() + + # now let's check what we have + dist_folder = join(self.tmp_dir, 'dist') + files = os.listdir(dist_folder) + assert files == ['ns_fake_pkg-1.0.zip'] + + zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip')) + try: + content = zip_file.namelist() + finally: + zip_file.close() + + # making sure everything has been pruned correctly + expected = [ + '', + 'PKG-INFO', + 'README', + 'setup.py', + 'somecode/', + 'somecode/__init__.py', + ] + assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected] + + @pytest.mark.usefixtures('needs_zlib') + @pytest.mark.skipif("not shutil.which('tar')") + @pytest.mark.skipif("not shutil.which('gzip')") + def test_make_distribution(self): + # now building a sdist + dist, cmd = self.get_cmd() + + # creating a gztar then a tar + cmd.formats = ['gztar', 'tar'] + cmd.ensure_finalized() + cmd.run() + + # making sure we have two files + dist_folder = join(self.tmp_dir, 'dist') + result = os.listdir(dist_folder) + result.sort() + assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz'] + + os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar')) + os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar.gz')) + + # now trying a tar then a gztar + cmd.formats = ['tar', 'gztar'] + + cmd.ensure_finalized() + cmd.run() + + result = os.listdir(dist_folder) + result.sort() + assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz'] + + @pytest.mark.usefixtures('needs_zlib') + def test_add_defaults(self): + # https://bugs.python.org/issue2279 + + # add_default should also include + # data_files and package_data + dist, cmd = self.get_cmd() + + # filling data_files by pointing files + # in package_data + dist.package_data = {'': ['*.cfg', '*.dat'], 'somecode': ['*.txt']} + self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#') + self.write_file((self.tmp_dir, 'somecode', 'doc.dat'), '#') + + # adding some data in data_files + data_dir = join(self.tmp_dir, 'data') + os.mkdir(data_dir) + self.write_file((data_dir, 'data.dt'), '#') + some_dir = join(self.tmp_dir, 'some') + os.mkdir(some_dir) + # make sure VCS directories are pruned (#14004) + hg_dir = join(self.tmp_dir, '.hg') + os.mkdir(hg_dir) + self.write_file((hg_dir, 'last-message.txt'), '#') + # a buggy regex used to prevent this from working on windows (#6884) + self.write_file((self.tmp_dir, 'buildout.cfg'), '#') + self.write_file((self.tmp_dir, 'inroot.txt'), '#') + self.write_file((some_dir, 'file.txt'), '#') + self.write_file((some_dir, 'other_file.txt'), '#') + + dist.data_files = [ + ('data', ['data/data.dt', 'buildout.cfg', 'inroot.txt', 'notexisting']), + 'some/file.txt', + 'some/other_file.txt', + ] + + # adding a script + script_dir = join(self.tmp_dir, 'scripts') + os.mkdir(script_dir) + self.write_file((script_dir, 'script.py'), '#') + dist.scripts = [join('scripts', 'script.py')] + + cmd.formats = ['zip'] + cmd.use_defaults = True + + cmd.ensure_finalized() + cmd.run() + + # now let's check what we have + dist_folder = join(self.tmp_dir, 'dist') + files = os.listdir(dist_folder) + assert files == ['ns_fake_pkg-1.0.zip'] + + zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip')) + try: + content = zip_file.namelist() + finally: + zip_file.close() + + # making sure everything was added + expected = [ + '', + 'PKG-INFO', + 'README', + 'buildout.cfg', + 'data/', + 'data/data.dt', + 'inroot.txt', + 'scripts/', + 'scripts/script.py', + 'setup.py', + 'some/', + 'some/file.txt', + 'some/other_file.txt', + 'somecode/', + 'somecode/__init__.py', + 'somecode/doc.dat', + 'somecode/doc.txt', + ] + assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected] + + # checking the MANIFEST + manifest = pathlib.Path(self.tmp_dir, 'MANIFEST').read_text(encoding='utf-8') + assert manifest == MANIFEST % {'sep': os.sep} + + @staticmethod + def warnings(messages, prefix='warning: '): + return [msg for msg in messages if msg.startswith(prefix)] + + @pytest.mark.usefixtures('needs_zlib') + def test_metadata_check_option(self, caplog): + # testing the `medata-check` option + dist, cmd = self.get_cmd(metadata={}) + + # this should raise some warnings ! + # with the `check` subcommand + cmd.ensure_finalized() + cmd.run() + assert len(self.warnings(caplog.messages, 'warning: check: ')) == 1 + + # trying with a complete set of metadata + caplog.clear() + dist, cmd = self.get_cmd() + cmd.ensure_finalized() + cmd.metadata_check = False + cmd.run() + assert len(self.warnings(caplog.messages, 'warning: check: ')) == 0 + + def test_show_formats(self, capsys): + show_formats() + + # the output should be a header line + one line per format + num_formats = len(ARCHIVE_FORMATS.keys()) + output = [ + line + for line in capsys.readouterr().out.split('\n') + if line.strip().startswith('--formats=') + ] + assert len(output) == num_formats + + def test_finalize_options(self): + dist, cmd = self.get_cmd() + cmd.finalize_options() + + # default options set by finalize + assert cmd.manifest == 'MANIFEST' + assert cmd.template == 'MANIFEST.in' + assert cmd.dist_dir == 'dist' + + # formats has to be a string splitable on (' ', ',') or + # a stringlist + cmd.formats = 1 + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + cmd.formats = ['zip'] + cmd.finalize_options() + + # formats has to be known + cmd.formats = 'supazipa' + with pytest.raises(DistutilsOptionError): + cmd.finalize_options() + + # the following tests make sure there is a nice error message instead + # of a traceback when parsing an invalid manifest template + + def _check_template(self, content, caplog): + dist, cmd = self.get_cmd() + os.chdir(self.tmp_dir) + self.write_file('MANIFEST.in', content) + cmd.ensure_finalized() + cmd.filelist = FileList() + cmd.read_template() + assert len(self.warnings(caplog.messages)) == 1 + + def test_invalid_template_unknown_command(self, caplog): + self._check_template('taunt knights *', caplog) + + def test_invalid_template_wrong_arguments(self, caplog): + # this manifest command takes one argument + self._check_template('prune', caplog) + + @pytest.mark.skipif("platform.system() != 'Windows'") + def test_invalid_template_wrong_path(self, caplog): + # on Windows, trailing slashes are not allowed + # this used to crash instead of raising a warning: #8286 + self._check_template('include examples/', caplog) + + @pytest.mark.usefixtures('needs_zlib') + def test_get_file_list(self): + # make sure MANIFEST is recalculated + dist, cmd = self.get_cmd() + + # filling data_files by pointing files in package_data + dist.package_data = {'somecode': ['*.txt']} + self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#') + cmd.formats = ['gztar'] + cmd.ensure_finalized() + cmd.run() + + assert ilen(clean_lines(cmd.manifest)) == 5 + + # adding a file + self.write_file((self.tmp_dir, 'somecode', 'doc2.txt'), '#') + + # make sure build_py is reinitialized, like a fresh run + build_py = dist.get_command_obj('build_py') + build_py.finalized = False + build_py.ensure_finalized() + + cmd.run() + + manifest2 = list(clean_lines(cmd.manifest)) + + # do we have the new file in MANIFEST ? + assert len(manifest2) == 6 + assert 'doc2.txt' in manifest2[-1] + + @pytest.mark.usefixtures('needs_zlib') + def test_manifest_marker(self): + # check that autogenerated MANIFESTs have a marker + dist, cmd = self.get_cmd() + cmd.ensure_finalized() + cmd.run() + + assert ( + next(clean_lines(cmd.manifest)) + == '# file GENERATED by distutils, do NOT edit' + ) + + @pytest.mark.usefixtures('needs_zlib') + def test_manifest_comments(self): + # make sure comments don't cause exceptions or wrong includes + contents = dedent( + """\ + # bad.py + #bad.py + good.py + """ + ) + dist, cmd = self.get_cmd() + cmd.ensure_finalized() + self.write_file((self.tmp_dir, cmd.manifest), contents) + self.write_file((self.tmp_dir, 'good.py'), '# pick me!') + self.write_file((self.tmp_dir, 'bad.py'), "# don't pick me!") + self.write_file((self.tmp_dir, '#bad.py'), "# don't pick me!") + cmd.run() + assert cmd.filelist.files == ['good.py'] + + @pytest.mark.usefixtures('needs_zlib') + def test_manual_manifest(self): + # check that a MANIFEST without a marker is left alone + dist, cmd = self.get_cmd() + cmd.formats = ['gztar'] + cmd.ensure_finalized() + self.write_file((self.tmp_dir, cmd.manifest), 'README.manual') + self.write_file( + (self.tmp_dir, 'README.manual'), + 'This project maintains its MANIFEST file itself.', + ) + cmd.run() + assert cmd.filelist.files == ['README.manual'] + + assert list(clean_lines(cmd.manifest)) == ['README.manual'] + + archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz') + archive = tarfile.open(archive_name) + try: + filenames = [tarinfo.name for tarinfo in archive] + finally: + archive.close() + assert sorted(filenames) == [ + 'ns_fake_pkg-1.0', + 'ns_fake_pkg-1.0/PKG-INFO', + 'ns_fake_pkg-1.0/README.manual', + ] + + @pytest.mark.usefixtures('needs_zlib') + @require_unix_id + @require_uid_0 + @pytest.mark.skipif("not shutil.which('tar')") + @pytest.mark.skipif("not shutil.which('gzip')") + def test_make_distribution_owner_group(self): + # now building a sdist + dist, cmd = self.get_cmd() + + # creating a gztar and specifying the owner+group + cmd.formats = ['gztar'] + cmd.owner = pwd.getpwuid(0)[0] + cmd.group = grp.getgrgid(0)[0] + cmd.ensure_finalized() + cmd.run() + + # making sure we have the good rights + archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz') + archive = tarfile.open(archive_name) + try: + for member in archive.getmembers(): + assert member.uid == 0 + assert member.gid == 0 + finally: + archive.close() + + # building a sdist again + dist, cmd = self.get_cmd() + + # creating a gztar + cmd.formats = ['gztar'] + cmd.ensure_finalized() + cmd.run() + + # making sure we have the good rights + archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz') + archive = tarfile.open(archive_name) + + # note that we are not testing the group ownership here + # because, depending on the platforms and the container + # rights (see #7408) + try: + for member in archive.getmembers(): + assert member.uid == os.getuid() + finally: + archive.close() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_spawn.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_spawn.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9fc926f6ffc41673630fbf5775e6f4ad638b18 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_spawn.py @@ -0,0 +1,141 @@ +"""Tests for distutils.spawn.""" + +import os +import stat +import sys +import unittest.mock as mock +from distutils.errors import DistutilsExecError +from distutils.spawn import find_executable, spawn +from distutils.tests import support + +import path +import pytest +from test.support import unix_shell + +from .compat import py39 as os_helper + + +class TestSpawn(support.TempdirManager): + @pytest.mark.skipif("os.name not in ('nt', 'posix')") + def test_spawn(self): + tmpdir = self.mkdtemp() + + # creating something executable + # through the shell that returns 1 + if sys.platform != 'win32': + exe = os.path.join(tmpdir, 'foo.sh') + self.write_file(exe, f'#!{unix_shell}\nexit 1') + else: + exe = os.path.join(tmpdir, 'foo.bat') + self.write_file(exe, 'exit 1') + + os.chmod(exe, 0o777) + with pytest.raises(DistutilsExecError): + spawn([exe]) + + # now something that works + if sys.platform != 'win32': + exe = os.path.join(tmpdir, 'foo.sh') + self.write_file(exe, f'#!{unix_shell}\nexit 0') + else: + exe = os.path.join(tmpdir, 'foo.bat') + self.write_file(exe, 'exit 0') + + os.chmod(exe, 0o777) + spawn([exe]) # should work without any error + + def test_find_executable(self, tmp_path): + program_path = self._make_executable(tmp_path, '.exe') + program = program_path.name + program_noeext = program_path.with_suffix('').name + filename = str(program_path) + tmp_dir = path.Path(tmp_path) + + # test path parameter + rv = find_executable(program, path=tmp_dir) + assert rv == filename + + if sys.platform == 'win32': + # test without ".exe" extension + rv = find_executable(program_noeext, path=tmp_dir) + assert rv == filename + + # test find in the current directory + with tmp_dir: + rv = find_executable(program) + assert rv == program + + # test non-existent program + dont_exist_program = "dontexist_" + program + rv = find_executable(dont_exist_program, path=tmp_dir) + assert rv is None + + # PATH='': no match, except in the current directory + with os_helper.EnvironmentVarGuard() as env: + env['PATH'] = '' + with ( + mock.patch( + 'distutils.spawn.os.confstr', return_value=tmp_dir, create=True + ), + mock.patch('distutils.spawn.os.defpath', tmp_dir), + ): + rv = find_executable(program) + assert rv is None + + # look in current directory + with tmp_dir: + rv = find_executable(program) + assert rv == program + + # PATH=':': explicitly looks in the current directory + with os_helper.EnvironmentVarGuard() as env: + env['PATH'] = os.pathsep + with ( + mock.patch('distutils.spawn.os.confstr', return_value='', create=True), + mock.patch('distutils.spawn.os.defpath', ''), + ): + rv = find_executable(program) + assert rv is None + + # look in current directory + with tmp_dir: + rv = find_executable(program) + assert rv == program + + # missing PATH: test os.confstr("CS_PATH") and os.defpath + with os_helper.EnvironmentVarGuard() as env: + env.pop('PATH', None) + + # without confstr + with ( + mock.patch( + 'distutils.spawn.os.confstr', side_effect=ValueError, create=True + ), + mock.patch('distutils.spawn.os.defpath', tmp_dir), + ): + rv = find_executable(program) + assert rv == filename + + # with confstr + with ( + mock.patch( + 'distutils.spawn.os.confstr', return_value=tmp_dir, create=True + ), + mock.patch('distutils.spawn.os.defpath', ''), + ): + rv = find_executable(program) + assert rv == filename + + @staticmethod + def _make_executable(tmp_path, ext): + # Give the temporary program a suffix regardless of platform. + # It's needed on Windows and not harmful on others. + program = tmp_path.joinpath('program').with_suffix(ext) + program.write_text("", encoding='utf-8') + program.chmod(stat.S_IXUSR) + return program + + def test_spawn_missing_exe(self): + with pytest.raises(DistutilsExecError) as ctx: + spawn(['does-not-exist']) + assert "command 'does-not-exist' failed" in str(ctx.value) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sysconfig.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sysconfig.py new file mode 100644 index 0000000000000000000000000000000000000000..43d77c23fad426be237b9f91a6d98d7c36233d84 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_sysconfig.py @@ -0,0 +1,319 @@ +"""Tests for distutils.sysconfig.""" + +import contextlib +import distutils +import os +import pathlib +import subprocess +import sys +from distutils import sysconfig +from distutils.ccompiler import new_compiler # noqa: F401 +from distutils.unixccompiler import UnixCCompiler + +import jaraco.envs +import path +import pytest +from jaraco.text import trim +from test.support import swap_item + + +def _gen_makefile(root, contents): + jaraco.path.build({'Makefile': trim(contents)}, root) + return root / 'Makefile' + + +@pytest.mark.usefixtures('save_env') +class TestSysconfig: + def test_get_config_h_filename(self): + config_h = sysconfig.get_config_h_filename() + assert os.path.isfile(config_h) + + @pytest.mark.skipif("platform.system() == 'Windows'") + @pytest.mark.skipif("sys.implementation.name != 'cpython'") + def test_get_makefile_filename(self): + makefile = sysconfig.get_makefile_filename() + assert os.path.isfile(makefile) + + def test_get_python_lib(self, tmp_path): + assert sysconfig.get_python_lib() != sysconfig.get_python_lib(prefix=tmp_path) + + def test_get_config_vars(self): + cvars = sysconfig.get_config_vars() + assert isinstance(cvars, dict) + assert cvars + + @pytest.mark.skipif('sysconfig.IS_PYPY') + @pytest.mark.skipif('sysconfig.python_build') + @pytest.mark.xfail('platform.system() == "Windows"') + def test_srcdir_simple(self): + # See #15364. + srcdir = pathlib.Path(sysconfig.get_config_var('srcdir')) + + assert srcdir.absolute() + assert srcdir.is_dir() + + makefile = pathlib.Path(sysconfig.get_makefile_filename()) + assert makefile.parent.samefile(srcdir) + + @pytest.mark.skipif('sysconfig.IS_PYPY') + @pytest.mark.skipif('not sysconfig.python_build') + def test_srcdir_python_build(self): + # See #15364. + srcdir = pathlib.Path(sysconfig.get_config_var('srcdir')) + + # The python executable has not been installed so srcdir + # should be a full source checkout. + Python_h = srcdir.joinpath('Include', 'Python.h') + assert Python_h.is_file() + assert sysconfig._is_python_source_dir(srcdir) + assert sysconfig._is_python_source_dir(str(srcdir)) + + def test_srcdir_independent_of_cwd(self): + """ + srcdir should be independent of the current working directory + """ + # See #15364. + srcdir = sysconfig.get_config_var('srcdir') + with path.Path('..'): + srcdir2 = sysconfig.get_config_var('srcdir') + assert srcdir == srcdir2 + + def customize_compiler(self): + # make sure AR gets caught + class compiler: + compiler_type = 'unix' + executables = UnixCCompiler.executables + + def __init__(self): + self.exes = {} + + def set_executables(self, **kw): + for k, v in kw.items(): + self.exes[k] = v + + sysconfig_vars = { + 'AR': 'sc_ar', + 'CC': 'sc_cc', + 'CXX': 'sc_cxx', + 'ARFLAGS': '--sc-arflags', + 'CFLAGS': '--sc-cflags', + 'CCSHARED': '--sc-ccshared', + 'LDSHARED': 'sc_ldshared', + 'SHLIB_SUFFIX': 'sc_shutil_suffix', + } + + comp = compiler() + with contextlib.ExitStack() as cm: + for key, value in sysconfig_vars.items(): + cm.enter_context(swap_item(sysconfig._config_vars, key, value)) + sysconfig.customize_compiler(comp) + + return comp + + @pytest.mark.skipif("not isinstance(new_compiler(), UnixCCompiler)") + @pytest.mark.usefixtures('disable_macos_customization') + def test_customize_compiler(self): + # Make sure that sysconfig._config_vars is initialized + sysconfig.get_config_vars() + + os.environ['AR'] = 'env_ar' + os.environ['CC'] = 'env_cc' + os.environ['CPP'] = 'env_cpp' + os.environ['CXX'] = 'env_cxx --env-cxx-flags' + os.environ['LDSHARED'] = 'env_ldshared' + os.environ['LDFLAGS'] = '--env-ldflags' + os.environ['ARFLAGS'] = '--env-arflags' + os.environ['CFLAGS'] = '--env-cflags' + os.environ['CPPFLAGS'] = '--env-cppflags' + os.environ['RANLIB'] = 'env_ranlib' + + comp = self.customize_compiler() + assert comp.exes['archiver'] == 'env_ar --env-arflags' + assert comp.exes['preprocessor'] == 'env_cpp --env-cppflags' + assert comp.exes['compiler'] == 'env_cc --env-cflags --env-cppflags' + assert comp.exes['compiler_so'] == ( + 'env_cc --env-cflags --env-cppflags --sc-ccshared' + ) + assert ( + comp.exes['compiler_cxx'] + == 'env_cxx --env-cxx-flags --sc-cflags --env-cppflags' + ) + assert comp.exes['linker_exe'] == 'env_cc' + assert comp.exes['linker_so'] == ( + 'env_ldshared --env-ldflags --env-cflags --env-cppflags' + ) + assert comp.shared_lib_extension == 'sc_shutil_suffix' + + if sys.platform == "darwin": + assert comp.exes['ranlib'] == 'env_ranlib' + else: + assert 'ranlib' not in comp.exes + + del os.environ['AR'] + del os.environ['CC'] + del os.environ['CPP'] + del os.environ['CXX'] + del os.environ['LDSHARED'] + del os.environ['LDFLAGS'] + del os.environ['ARFLAGS'] + del os.environ['CFLAGS'] + del os.environ['CPPFLAGS'] + del os.environ['RANLIB'] + + comp = self.customize_compiler() + assert comp.exes['archiver'] == 'sc_ar --sc-arflags' + assert comp.exes['preprocessor'] == 'sc_cc -E' + assert comp.exes['compiler'] == 'sc_cc --sc-cflags' + assert comp.exes['compiler_so'] == 'sc_cc --sc-cflags --sc-ccshared' + assert comp.exes['compiler_cxx'] == 'sc_cxx --sc-cflags' + assert comp.exes['linker_exe'] == 'sc_cc' + assert comp.exes['linker_so'] == 'sc_ldshared' + assert comp.shared_lib_extension == 'sc_shutil_suffix' + assert 'ranlib' not in comp.exes + + def test_parse_makefile_base(self, tmp_path): + makefile = _gen_makefile( + tmp_path, + """ + CONFIG_ARGS= '--arg1=optarg1' 'ENV=LIB' + VAR=$OTHER + OTHER=foo + """, + ) + d = sysconfig.parse_makefile(makefile) + assert d == {'CONFIG_ARGS': "'--arg1=optarg1' 'ENV=LIB'", 'OTHER': 'foo'} + + def test_parse_makefile_literal_dollar(self, tmp_path): + makefile = _gen_makefile( + tmp_path, + """ + CONFIG_ARGS= '--arg1=optarg1' 'ENV=\\$$LIB' + VAR=$OTHER + OTHER=foo + """, + ) + d = sysconfig.parse_makefile(makefile) + assert d == {'CONFIG_ARGS': r"'--arg1=optarg1' 'ENV=\$LIB'", 'OTHER': 'foo'} + + def test_sysconfig_module(self): + import sysconfig as global_sysconfig + + assert global_sysconfig.get_config_var('CFLAGS') == sysconfig.get_config_var( + 'CFLAGS' + ) + assert global_sysconfig.get_config_var('LDFLAGS') == sysconfig.get_config_var( + 'LDFLAGS' + ) + + # On macOS, binary installers support extension module building on + # various levels of the operating system with differing Xcode + # configurations, requiring customization of some of the + # compiler configuration directives to suit the environment on + # the installed machine. Some of these customizations may require + # running external programs and are thus deferred until needed by + # the first extension module build. Only + # the Distutils version of sysconfig is used for extension module + # builds, which happens earlier in the Distutils tests. This may + # cause the following tests to fail since no tests have caused + # the global version of sysconfig to call the customization yet. + # The solution for now is to simply skip this test in this case. + # The longer-term solution is to only have one version of sysconfig. + @pytest.mark.skipif("sysconfig.get_config_var('CUSTOMIZED_OSX_COMPILER')") + def test_sysconfig_compiler_vars(self): + import sysconfig as global_sysconfig + + if sysconfig.get_config_var('CUSTOMIZED_OSX_COMPILER'): + pytest.skip('compiler flags customized') + assert global_sysconfig.get_config_var('LDSHARED') == sysconfig.get_config_var( + 'LDSHARED' + ) + assert global_sysconfig.get_config_var('CC') == sysconfig.get_config_var('CC') + + @pytest.mark.skipif("not sysconfig.get_config_var('EXT_SUFFIX')") + def test_SO_deprecation(self): + with pytest.warns(DeprecationWarning): + sysconfig.get_config_var('SO') + + def test_customize_compiler_before_get_config_vars(self, tmp_path): + # Issue #21923: test that a Distribution compiler + # instance can be called without an explicit call to + # get_config_vars(). + jaraco.path.build( + { + 'file': trim(""" + from distutils.core import Distribution + config = Distribution().get_command_obj('config') + # try_compile may pass or it may fail if no compiler + # is found but it should not raise an exception. + rc = config.try_compile('int x;') + """) + }, + tmp_path, + ) + p = subprocess.Popen( + [sys.executable, tmp_path / 'file'], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + universal_newlines=True, + encoding='utf-8', + ) + outs, errs = p.communicate() + assert 0 == p.returncode, "Subprocess failed: " + outs + + def test_parse_config_h(self): + config_h = sysconfig.get_config_h_filename() + input = {} + with open(config_h, encoding="utf-8") as f: + result = sysconfig.parse_config_h(f, g=input) + assert input is result + with open(config_h, encoding="utf-8") as f: + result = sysconfig.parse_config_h(f) + assert isinstance(result, dict) + + @pytest.mark.skipif("platform.system() != 'Windows'") + @pytest.mark.skipif("sys.implementation.name != 'cpython'") + def test_win_ext_suffix(self): + assert sysconfig.get_config_var("EXT_SUFFIX").endswith(".pyd") + assert sysconfig.get_config_var("EXT_SUFFIX") != ".pyd" + + @pytest.mark.skipif("platform.system() != 'Windows'") + @pytest.mark.skipif("sys.implementation.name != 'cpython'") + @pytest.mark.skipif( + '\\PCbuild\\'.casefold() not in sys.executable.casefold(), + reason='Need sys.executable to be in a source tree', + ) + def test_win_build_venv_from_source_tree(self, tmp_path): + """Ensure distutils.sysconfig detects venvs from source tree builds.""" + env = jaraco.envs.VEnv() + env.create_opts = env.clean_opts + env.root = tmp_path + env.ensure_env() + cmd = [ + env.exe(), + "-c", + "import distutils.sysconfig; print(distutils.sysconfig.python_build)", + ] + distutils_path = os.path.dirname(os.path.dirname(distutils.__file__)) + out = subprocess.check_output( + cmd, env={**os.environ, "PYTHONPATH": distutils_path} + ) + assert out == "True" + + def test_get_python_inc_missing_config_dir(self, monkeypatch): + """ + In portable Python installations, the sysconfig will be broken, + pointing to the directories where the installation was built and + not where it currently is. In this case, ensure that the missing + directory isn't used for get_python_inc. + + See pypa/distutils#178. + """ + + def override(name): + if name == 'INCLUDEPY': + return '/does-not-exist' + return sysconfig.get_config_var(name) + + monkeypatch.setattr(sysconfig, 'get_config_var', override) + + assert os.path.exists(sysconfig.get_python_inc()) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_text_file.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_text_file.py new file mode 100644 index 0000000000000000000000000000000000000000..f51115656135cd3f348a45d4a14f97eeee47fb98 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_text_file.py @@ -0,0 +1,127 @@ +"""Tests for distutils.text_file.""" + +from distutils.tests import support +from distutils.text_file import TextFile + +import jaraco.path +import path + +TEST_DATA = """# test file + +line 3 \\ +# intervening comment + continues on next line +""" + + +class TestTextFile(support.TempdirManager): + def test_class(self): + # old tests moved from text_file.__main__ + # so they are really called by the buildbots + + # result 1: no fancy options + result1 = [ + '# test file\n', + '\n', + 'line 3 \\\n', + '# intervening comment\n', + ' continues on next line\n', + ] + + # result 2: just strip comments + result2 = ["\n", "line 3 \\\n", " continues on next line\n"] + + # result 3: just strip blank lines + result3 = [ + "# test file\n", + "line 3 \\\n", + "# intervening comment\n", + " continues on next line\n", + ] + + # result 4: default, strip comments, blank lines, + # and trailing whitespace + result4 = ["line 3 \\", " continues on next line"] + + # result 5: strip comments and blanks, plus join lines (but don't + # "collapse" joined lines + result5 = ["line 3 continues on next line"] + + # result 6: strip comments and blanks, plus join lines (and + # "collapse" joined lines + result6 = ["line 3 continues on next line"] + + def test_input(count, description, file, expected_result): + result = file.readlines() + assert result == expected_result + + tmp_path = path.Path(self.mkdtemp()) + filename = tmp_path / 'test.txt' + jaraco.path.build({filename.name: TEST_DATA}, tmp_path) + + in_file = TextFile( + filename, + strip_comments=False, + skip_blanks=False, + lstrip_ws=False, + rstrip_ws=False, + ) + try: + test_input(1, "no processing", in_file, result1) + finally: + in_file.close() + + in_file = TextFile( + filename, + strip_comments=True, + skip_blanks=False, + lstrip_ws=False, + rstrip_ws=False, + ) + try: + test_input(2, "strip comments", in_file, result2) + finally: + in_file.close() + + in_file = TextFile( + filename, + strip_comments=False, + skip_blanks=True, + lstrip_ws=False, + rstrip_ws=False, + ) + try: + test_input(3, "strip blanks", in_file, result3) + finally: + in_file.close() + + in_file = TextFile(filename) + try: + test_input(4, "default processing", in_file, result4) + finally: + in_file.close() + + in_file = TextFile( + filename, + strip_comments=True, + skip_blanks=True, + join_lines=True, + rstrip_ws=True, + ) + try: + test_input(5, "join lines without collapsing", in_file, result5) + finally: + in_file.close() + + in_file = TextFile( + filename, + strip_comments=True, + skip_blanks=True, + join_lines=True, + rstrip_ws=True, + collapse_join=True, + ) + try: + test_input(6, "join lines with collapsing", in_file, result6) + finally: + in_file.close() diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_util.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_util.py new file mode 100644 index 0000000000000000000000000000000000000000..00c9743ed0a2664039060ef149d30f165bf2f465 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_util.py @@ -0,0 +1,243 @@ +"""Tests for distutils.util.""" + +import email +import email.generator +import email.policy +import io +import os +import pathlib +import sys +import sysconfig as stdlib_sysconfig +import unittest.mock as mock +from copy import copy +from distutils import sysconfig, util +from distutils.errors import DistutilsByteCompileError, DistutilsPlatformError +from distutils.util import ( + byte_compile, + change_root, + check_environ, + convert_path, + get_host_platform, + get_platform, + grok_environment_error, + rfc822_escape, + split_quoted, + strtobool, +) + +import pytest + + +@pytest.fixture(autouse=True) +def environment(monkeypatch): + monkeypatch.setattr(os, 'name', os.name) + monkeypatch.setattr(sys, 'platform', sys.platform) + monkeypatch.setattr(sys, 'version', sys.version) + monkeypatch.setattr(os, 'sep', os.sep) + monkeypatch.setattr(os.path, 'join', os.path.join) + monkeypatch.setattr(os.path, 'isabs', os.path.isabs) + monkeypatch.setattr(os.path, 'splitdrive', os.path.splitdrive) + monkeypatch.setattr(sysconfig, '_config_vars', copy(sysconfig._config_vars)) + + +@pytest.mark.usefixtures('save_env') +class TestUtil: + def test_get_host_platform(self): + with mock.patch('os.name', 'nt'): + with mock.patch('sys.version', '... [... (ARM64)]'): + assert get_host_platform() == 'win-arm64' + with mock.patch('sys.version', '... [... (ARM)]'): + assert get_host_platform() == 'win-arm32' + + with mock.patch('sys.version_info', (3, 9, 0, 'final', 0)): + assert get_host_platform() == stdlib_sysconfig.get_platform() + + def test_get_platform(self): + with mock.patch('os.name', 'nt'): + with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'x86'}): + assert get_platform() == 'win32' + with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'x64'}): + assert get_platform() == 'win-amd64' + with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'arm'}): + assert get_platform() == 'win-arm32' + with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'arm64'}): + assert get_platform() == 'win-arm64' + + def test_convert_path(self): + expected = os.sep.join(('', 'home', 'to', 'my', 'stuff')) + assert convert_path('/home/to/my/stuff') == expected + assert convert_path(pathlib.Path('/home/to/my/stuff')) == expected + assert convert_path('.') == os.curdir + + def test_change_root(self): + # linux/mac + os.name = 'posix' + + def _isabs(path): + return path[0] == '/' + + os.path.isabs = _isabs + + def _join(*path): + return '/'.join(path) + + os.path.join = _join + + assert change_root('/root', '/old/its/here') == '/root/old/its/here' + assert change_root('/root', 'its/here') == '/root/its/here' + + # windows + os.name = 'nt' + os.sep = '\\' + + def _isabs(path): + return path.startswith('c:\\') + + os.path.isabs = _isabs + + def _splitdrive(path): + if path.startswith('c:'): + return ('', path.replace('c:', '')) + return ('', path) + + os.path.splitdrive = _splitdrive + + def _join(*path): + return '\\'.join(path) + + os.path.join = _join + + assert ( + change_root('c:\\root', 'c:\\old\\its\\here') == 'c:\\root\\old\\its\\here' + ) + assert change_root('c:\\root', 'its\\here') == 'c:\\root\\its\\here' + + # BugsBunny os (it's a great os) + os.name = 'BugsBunny' + with pytest.raises(DistutilsPlatformError): + change_root('c:\\root', 'its\\here') + + # XXX platforms to be covered: mac + + def test_check_environ(self): + util.check_environ.cache_clear() + os.environ.pop('HOME', None) + + check_environ() + + assert os.environ['PLAT'] == get_platform() + + @pytest.mark.skipif("os.name != 'posix'") + def test_check_environ_getpwuid(self): + util.check_environ.cache_clear() + os.environ.pop('HOME', None) + + import pwd + + # only set pw_dir field, other fields are not used + result = pwd.struct_passwd(( + None, + None, + None, + None, + None, + '/home/distutils', + None, + )) + with mock.patch.object(pwd, 'getpwuid', return_value=result): + check_environ() + assert os.environ['HOME'] == '/home/distutils' + + util.check_environ.cache_clear() + os.environ.pop('HOME', None) + + # bpo-10496: Catch pwd.getpwuid() error + with mock.patch.object(pwd, 'getpwuid', side_effect=KeyError): + check_environ() + assert 'HOME' not in os.environ + + def test_split_quoted(self): + assert split_quoted('""one"" "two" \'three\' \\four') == [ + 'one', + 'two', + 'three', + 'four', + ] + + def test_strtobool(self): + yes = ('y', 'Y', 'yes', 'True', 't', 'true', 'True', 'On', 'on', '1') + no = ('n', 'no', 'f', 'false', 'off', '0', 'Off', 'No', 'N') + + for y in yes: + assert strtobool(y) + + for n in no: + assert not strtobool(n) + + indent = 8 * ' ' + + @pytest.mark.parametrize( + "given,wanted", + [ + # 0x0b, 0x0c, ..., etc are also considered a line break by Python + ("hello\x0b\nworld\n", f"hello\x0b{indent}\n{indent}world\n{indent}"), + ("hello\x1eworld", f"hello\x1e{indent}world"), + ("", ""), + ( + "I am a\npoor\nlonesome\nheader\n", + f"I am a\n{indent}poor\n{indent}lonesome\n{indent}header\n{indent}", + ), + ], + ) + def test_rfc822_escape(self, given, wanted): + """ + We want to ensure a multi-line header parses correctly. + + For interoperability, the escaped value should also "round-trip" over + `email.generator.Generator.flatten` and `email.message_from_*` + (see pypa/setuptools#4033). + + The main issue is that internally `email.policy.EmailPolicy` uses + `splitlines` which will split on some control chars. If all the new lines + are not prefixed with spaces, the parser will interrupt reading + the current header and produce an incomplete value, while + incorrectly interpreting the rest of the headers as part of the payload. + """ + res = rfc822_escape(given) + + policy = email.policy.EmailPolicy( + utf8=True, + mangle_from_=False, + max_line_length=0, + ) + with io.StringIO() as buffer: + raw = f"header: {res}\nother-header: 42\n\npayload\n" + orig = email.message_from_string(raw) + email.generator.Generator(buffer, policy=policy).flatten(orig) + buffer.seek(0) + regen = email.message_from_file(buffer) + + for msg in (orig, regen): + assert msg.get_payload() == "payload\n" + assert msg["other-header"] == "42" + # Generator may replace control chars with `\n` + assert set(msg["header"].splitlines()) == set(res.splitlines()) + + assert res == wanted + + def test_dont_write_bytecode(self): + # makes sure byte_compile raise a DistutilsError + # if sys.dont_write_bytecode is True + old_dont_write_bytecode = sys.dont_write_bytecode + sys.dont_write_bytecode = True + try: + with pytest.raises(DistutilsByteCompileError): + byte_compile([]) + finally: + sys.dont_write_bytecode = old_dont_write_bytecode + + def test_grok_environment_error(self): + # test obsolete function to ensure backward compat (#4931) + exc = OSError("Unable to find batch file") + msg = grok_environment_error(exc) + assert msg == "error: Unable to find batch file" diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_version.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_version.py new file mode 100644 index 0000000000000000000000000000000000000000..b68f0977249aa23aafbf31cf74a0e4127f777d05 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_version.py @@ -0,0 +1,80 @@ +"""Tests for distutils.version.""" + +import distutils +from distutils.version import LooseVersion, StrictVersion + +import pytest + + +@pytest.fixture(autouse=True) +def suppress_deprecation(): + with distutils.version.suppress_known_deprecation(): + yield + + +class TestVersion: + def test_prerelease(self): + version = StrictVersion('1.2.3a1') + assert version.version == (1, 2, 3) + assert version.prerelease == ('a', 1) + assert str(version) == '1.2.3a1' + + version = StrictVersion('1.2.0') + assert str(version) == '1.2' + + def test_cmp_strict(self): + versions = ( + ('1.5.1', '1.5.2b2', -1), + ('161', '3.10a', ValueError), + ('8.02', '8.02', 0), + ('3.4j', '1996.07.12', ValueError), + ('3.2.pl0', '3.1.1.6', ValueError), + ('2g6', '11g', ValueError), + ('0.9', '2.2', -1), + ('1.2.1', '1.2', 1), + ('1.1', '1.2.2', -1), + ('1.2', '1.1', 1), + ('1.2.1', '1.2.2', -1), + ('1.2.2', '1.2', 1), + ('1.2', '1.2.2', -1), + ('0.4.0', '0.4', 0), + ('1.13++', '5.5.kw', ValueError), + ) + + for v1, v2, wanted in versions: + try: + res = StrictVersion(v1)._cmp(StrictVersion(v2)) + except ValueError: + if wanted is ValueError: + continue + else: + raise AssertionError(f"cmp({v1}, {v2}) shouldn't raise ValueError") + assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}' + res = StrictVersion(v1)._cmp(v2) + assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}' + res = StrictVersion(v1)._cmp(object()) + assert res is NotImplemented, ( + f'cmp({v1}, {v2}) should be NotImplemented, got {res}' + ) + + def test_cmp(self): + versions = ( + ('1.5.1', '1.5.2b2', -1), + ('161', '3.10a', 1), + ('8.02', '8.02', 0), + ('3.4j', '1996.07.12', -1), + ('3.2.pl0', '3.1.1.6', 1), + ('2g6', '11g', -1), + ('0.960923', '2.2beta29', -1), + ('1.13++', '5.5.kw', -1), + ) + + for v1, v2, wanted in versions: + res = LooseVersion(v1)._cmp(LooseVersion(v2)) + assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}' + res = LooseVersion(v1)._cmp(v2) + assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}' + res = LooseVersion(v1)._cmp(object()) + assert res is NotImplemented, ( + f'cmp({v1}, {v2}) should be NotImplemented, got {res}' + ) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/test_versionpredicate.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/test_versionpredicate.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/setuptools/_distutils/tests/unix_compat.py b/lib/python3.11/site-packages/setuptools/_distutils/tests/unix_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d9ee45ccc639794d29ba0a5fd24f781ee5ecf5 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/tests/unix_compat.py @@ -0,0 +1,17 @@ +import sys + +try: + import grp + import pwd +except ImportError: + grp = pwd = None + +import pytest + +UNIX_ID_SUPPORT = grp and pwd +UID_0_SUPPORT = UNIX_ID_SUPPORT and sys.platform != "cygwin" + +require_unix_id = pytest.mark.skipif( + not UNIX_ID_SUPPORT, reason="Requires grp and pwd support" +) +require_uid_0 = pytest.mark.skipif(not UID_0_SUPPORT, reason="Requires UID 0 support") diff --git a/lib/python3.11/site-packages/setuptools/_distutils/text_file.py b/lib/python3.11/site-packages/setuptools/_distutils/text_file.py new file mode 100644 index 0000000000000000000000000000000000000000..89d9048d5902950516c3541ad212bff7b6cb2045 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/text_file.py @@ -0,0 +1,286 @@ +"""text_file + +provides the TextFile class, which gives an interface to text files +that (optionally) takes care of stripping comments, ignoring blank +lines, and joining lines with backslashes.""" + +import sys + + +class TextFile: + """Provides a file-like object that takes care of all the things you + commonly want to do when processing a text file that has some + line-by-line syntax: strip comments (as long as "#" is your + comment character), skip blank lines, join adjacent lines by + escaping the newline (ie. backslash at end of line), strip + leading and/or trailing whitespace. All of these are optional + and independently controllable. + + Provides a 'warn()' method so you can generate warning messages that + report physical line number, even if the logical line in question + spans multiple physical lines. Also provides 'unreadline()' for + implementing line-at-a-time lookahead. + + Constructor is called as: + + TextFile (filename=None, file=None, **options) + + It bombs (RuntimeError) if both 'filename' and 'file' are None; + 'filename' should be a string, and 'file' a file object (or + something that provides 'readline()' and 'close()' methods). It is + recommended that you supply at least 'filename', so that TextFile + can include it in warning messages. If 'file' is not supplied, + TextFile creates its own using 'io.open()'. + + The options are all boolean, and affect the value returned by + 'readline()': + strip_comments [default: true] + strip from "#" to end-of-line, as well as any whitespace + leading up to the "#" -- unless it is escaped by a backslash + lstrip_ws [default: false] + strip leading whitespace from each line before returning it + rstrip_ws [default: true] + strip trailing whitespace (including line terminator!) from + each line before returning it + skip_blanks [default: true} + skip lines that are empty *after* stripping comments and + whitespace. (If both lstrip_ws and rstrip_ws are false, + then some lines may consist of solely whitespace: these will + *not* be skipped, even if 'skip_blanks' is true.) + join_lines [default: false] + if a backslash is the last non-newline character on a line + after stripping comments and whitespace, join the following line + to it to form one "logical line"; if N consecutive lines end + with a backslash, then N+1 physical lines will be joined to + form one logical line. + collapse_join [default: false] + strip leading whitespace from lines that are joined to their + predecessor; only matters if (join_lines and not lstrip_ws) + errors [default: 'strict'] + error handler used to decode the file content + + Note that since 'rstrip_ws' can strip the trailing newline, the + semantics of 'readline()' must differ from those of the builtin file + object's 'readline()' method! In particular, 'readline()' returns + None for end-of-file: an empty string might just be a blank line (or + an all-whitespace line), if 'rstrip_ws' is true but 'skip_blanks' is + not.""" + + default_options = { + 'strip_comments': 1, + 'skip_blanks': 1, + 'lstrip_ws': 0, + 'rstrip_ws': 1, + 'join_lines': 0, + 'collapse_join': 0, + 'errors': 'strict', + } + + def __init__(self, filename=None, file=None, **options): + """Construct a new TextFile object. At least one of 'filename' + (a string) and 'file' (a file-like object) must be supplied. + They keyword argument options are described above and affect + the values returned by 'readline()'.""" + if filename is None and file is None: + raise RuntimeError( + "you must supply either or both of 'filename' and 'file'" + ) + + # set values for all options -- either from client option hash + # or fallback to default_options + for opt in self.default_options.keys(): + if opt in options: + setattr(self, opt, options[opt]) + else: + setattr(self, opt, self.default_options[opt]) + + # sanity check client option hash + for opt in options.keys(): + if opt not in self.default_options: + raise KeyError(f"invalid TextFile option '{opt}'") + + if file is None: + self.open(filename) + else: + self.filename = filename + self.file = file + self.current_line = 0 # assuming that file is at BOF! + + # 'linebuf' is a stack of lines that will be emptied before we + # actually read from the file; it's only populated by an + # 'unreadline()' operation + self.linebuf = [] + + def open(self, filename): + """Open a new file named 'filename'. This overrides both the + 'filename' and 'file' arguments to the constructor.""" + self.filename = filename + self.file = open(self.filename, errors=self.errors, encoding='utf-8') + self.current_line = 0 + + def close(self): + """Close the current file and forget everything we know about it + (filename, current line number).""" + file = self.file + self.file = None + self.filename = None + self.current_line = None + file.close() + + def gen_error(self, msg, line=None): + outmsg = [] + if line is None: + line = self.current_line + outmsg.append(self.filename + ", ") + if isinstance(line, (list, tuple)): + outmsg.append("lines {}-{}: ".format(*line)) + else: + outmsg.append(f"line {int(line)}: ") + outmsg.append(str(msg)) + return "".join(outmsg) + + def error(self, msg, line=None): + raise ValueError("error: " + self.gen_error(msg, line)) + + def warn(self, msg, line=None): + """Print (to stderr) a warning message tied to the current logical + line in the current file. If the current logical line in the + file spans multiple physical lines, the warning refers to the + whole range, eg. "lines 3-5". If 'line' supplied, it overrides + the current line number; it may be a list or tuple to indicate a + range of physical lines, or an integer for a single physical + line.""" + sys.stderr.write("warning: " + self.gen_error(msg, line) + "\n") + + def readline(self): # noqa: C901 + """Read and return a single logical line from the current file (or + from an internal buffer if lines have previously been "unread" + with 'unreadline()'). If the 'join_lines' option is true, this + may involve reading multiple physical lines concatenated into a + single string. Updates the current line number, so calling + 'warn()' after 'readline()' emits a warning about the physical + line(s) just read. Returns None on end-of-file, since the empty + string can occur if 'rstrip_ws' is true but 'strip_blanks' is + not.""" + # If any "unread" lines waiting in 'linebuf', return the top + # one. (We don't actually buffer read-ahead data -- lines only + # get put in 'linebuf' if the client explicitly does an + # 'unreadline()'. + if self.linebuf: + line = self.linebuf[-1] + del self.linebuf[-1] + return line + + buildup_line = '' + + while True: + # read the line, make it None if EOF + line = self.file.readline() + if line == '': + line = None + + if self.strip_comments and line: + # Look for the first "#" in the line. If none, never + # mind. If we find one and it's the first character, or + # is not preceded by "\", then it starts a comment -- + # strip the comment, strip whitespace before it, and + # carry on. Otherwise, it's just an escaped "#", so + # unescape it (and any other escaped "#"'s that might be + # lurking in there) and otherwise leave the line alone. + + pos = line.find("#") + if pos == -1: # no "#" -- no comments + pass + + # It's definitely a comment -- either "#" is the first + # character, or it's elsewhere and unescaped. + elif pos == 0 or line[pos - 1] != "\\": + # Have to preserve the trailing newline, because it's + # the job of a later step (rstrip_ws) to remove it -- + # and if rstrip_ws is false, we'd better preserve it! + # (NB. this means that if the final line is all comment + # and has no trailing newline, we will think that it's + # EOF; I think that's OK.) + eol = (line[-1] == '\n') and '\n' or '' + line = line[0:pos] + eol + + # If all that's left is whitespace, then skip line + # *now*, before we try to join it to 'buildup_line' -- + # that way constructs like + # hello \\ + # # comment that should be ignored + # there + # result in "hello there". + if line.strip() == "": + continue + else: # it's an escaped "#" + line = line.replace("\\#", "#") + + # did previous line end with a backslash? then accumulate + if self.join_lines and buildup_line: + # oops: end of file + if line is None: + self.warn("continuation line immediately precedes end-of-file") + return buildup_line + + if self.collapse_join: + line = line.lstrip() + line = buildup_line + line + + # careful: pay attention to line number when incrementing it + if isinstance(self.current_line, list): + self.current_line[1] = self.current_line[1] + 1 + else: + self.current_line = [self.current_line, self.current_line + 1] + # just an ordinary line, read it as usual + else: + if line is None: # eof + return None + + # still have to be careful about incrementing the line number! + if isinstance(self.current_line, list): + self.current_line = self.current_line[1] + 1 + else: + self.current_line = self.current_line + 1 + + # strip whitespace however the client wants (leading and + # trailing, or one or the other, or neither) + if self.lstrip_ws and self.rstrip_ws: + line = line.strip() + elif self.lstrip_ws: + line = line.lstrip() + elif self.rstrip_ws: + line = line.rstrip() + + # blank line (whether we rstrip'ed or not)? skip to next line + # if appropriate + if line in ('', '\n') and self.skip_blanks: + continue + + if self.join_lines: + if line[-1] == '\\': + buildup_line = line[:-1] + continue + + if line[-2:] == '\\\n': + buildup_line = line[0:-2] + '\n' + continue + + # well, I guess there's some actual content there: return it + return line + + def readlines(self): + """Read and return the list of all logical lines remaining in the + current file.""" + lines = [] + while True: + line = self.readline() + if line is None: + return lines + lines.append(line) + + def unreadline(self, line): + """Push 'line' (a string) onto an internal buffer that will be + checked by future 'readline()' calls. Handy for implementing + a parser with line-at-a-time lookahead.""" + self.linebuf.append(line) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/unixccompiler.py b/lib/python3.11/site-packages/setuptools/_distutils/unixccompiler.py new file mode 100644 index 0000000000000000000000000000000000000000..20b8ce6b9bba21bd709b77249bdcae5b4119e9ca --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/unixccompiler.py @@ -0,0 +1,9 @@ +import importlib + +from .compilers.C import unix + +UnixCCompiler = unix.Compiler + +# ensure import of unixccompiler implies ccompiler imported +# (pypa/setuptools#4871) +importlib.import_module('distutils.ccompiler') diff --git a/lib/python3.11/site-packages/setuptools/_distutils/util.py b/lib/python3.11/site-packages/setuptools/_distutils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..47bb5af5e4b9c78c8b7e3657594562115750db8a --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/util.py @@ -0,0 +1,506 @@ +"""distutils.util + +Miscellaneous utility functions -- anything that doesn't fit into +one of the other *util.py modules. +""" + +from __future__ import annotations + +import functools +import importlib.util +import os +import pathlib +import re +import string +import subprocess +import sys +import sysconfig +import tempfile +from collections.abc import Callable, Iterable, Mapping +from typing import TYPE_CHECKING, AnyStr + +from jaraco.functools import pass_none + +from ._log import log +from ._modified import newer +from .errors import DistutilsByteCompileError, DistutilsPlatformError +from .spawn import spawn + +if TYPE_CHECKING: + from typing_extensions import TypeVarTuple, Unpack + + _Ts = TypeVarTuple("_Ts") + + +def get_host_platform() -> str: + """ + Return a string that identifies the current platform. Use this + function to distinguish platform-specific build directories and + platform-specific built distributions. + """ + + # This function initially exposed platforms as defined in Python 3.9 + # even with older Python versions when distutils was split out. + # Now it delegates to stdlib sysconfig. + + return sysconfig.get_platform() + + +def get_platform() -> str: + if os.name == 'nt': + TARGET_TO_PLAT = { + 'x86': 'win32', + 'x64': 'win-amd64', + 'arm': 'win-arm32', + 'arm64': 'win-arm64', + } + target = os.environ.get('VSCMD_ARG_TGT_ARCH') + return TARGET_TO_PLAT.get(target) or get_host_platform() + return get_host_platform() + + +if sys.platform == 'darwin': + _syscfg_macosx_ver = None # cache the version pulled from sysconfig +MACOSX_VERSION_VAR = 'MACOSX_DEPLOYMENT_TARGET' + + +def _clear_cached_macosx_ver(): + """For testing only. Do not call.""" + global _syscfg_macosx_ver + _syscfg_macosx_ver = None + + +def get_macosx_target_ver_from_syscfg(): + """Get the version of macOS latched in the Python interpreter configuration. + Returns the version as a string or None if can't obtain one. Cached.""" + global _syscfg_macosx_ver + if _syscfg_macosx_ver is None: + from distutils import sysconfig + + ver = sysconfig.get_config_var(MACOSX_VERSION_VAR) or '' + if ver: + _syscfg_macosx_ver = ver + return _syscfg_macosx_ver + + +def get_macosx_target_ver(): + """Return the version of macOS for which we are building. + + The target version defaults to the version in sysconfig latched at time + the Python interpreter was built, unless overridden by an environment + variable. If neither source has a value, then None is returned""" + + syscfg_ver = get_macosx_target_ver_from_syscfg() + env_ver = os.environ.get(MACOSX_VERSION_VAR) + + if env_ver: + # Validate overridden version against sysconfig version, if have both. + # Ensure that the deployment target of the build process is not less + # than 10.3 if the interpreter was built for 10.3 or later. This + # ensures extension modules are built with correct compatibility + # values, specifically LDSHARED which can use + # '-undefined dynamic_lookup' which only works on >= 10.3. + if ( + syscfg_ver + and split_version(syscfg_ver) >= [10, 3] + and split_version(env_ver) < [10, 3] + ): + my_msg = ( + '$' + MACOSX_VERSION_VAR + ' mismatch: ' + f'now "{env_ver}" but "{syscfg_ver}" during configure; ' + 'must use 10.3 or later' + ) + raise DistutilsPlatformError(my_msg) + return env_ver + return syscfg_ver + + +def split_version(s: str) -> list[int]: + """Convert a dot-separated string into a list of numbers for comparisons""" + return [int(n) for n in s.split('.')] + + +@pass_none +def convert_path(pathname: str | os.PathLike[str]) -> str: + r""" + Allow for pathlib.Path inputs, coax to a native path string. + + If None is passed, will just pass it through as + Setuptools relies on this behavior. + + >>> convert_path(None) is None + True + + Removes empty paths. + + >>> convert_path('foo/./bar').replace('\\', '/') + 'foo/bar' + """ + return os.fspath(pathlib.PurePath(pathname)) + + +def change_root( + new_root: AnyStr | os.PathLike[AnyStr], pathname: AnyStr | os.PathLike[AnyStr] +) -> AnyStr: + """Return 'pathname' with 'new_root' prepended. If 'pathname' is + relative, this is equivalent to "os.path.join(new_root,pathname)". + Otherwise, it requires making 'pathname' relative and then joining the + two, which is tricky on DOS/Windows and Mac OS. + """ + if os.name == 'posix': + if not os.path.isabs(pathname): + return os.path.join(new_root, pathname) + else: + return os.path.join(new_root, pathname[1:]) + + elif os.name == 'nt': + (drive, path) = os.path.splitdrive(pathname) + if path[0] == os.sep: + path = path[1:] + return os.path.join(new_root, path) + + raise DistutilsPlatformError(f"nothing known about platform '{os.name}'") + + +@functools.lru_cache +def check_environ() -> None: + """Ensure that 'os.environ' has all the environment variables we + guarantee that users can use in config files, command-line options, + etc. Currently this includes: + HOME - user's home directory (Unix only) + PLAT - description of the current platform, including hardware + and OS (see 'get_platform()') + """ + if os.name == 'posix' and 'HOME' not in os.environ: + try: + import pwd + + os.environ['HOME'] = pwd.getpwuid(os.getuid())[5] + except (ImportError, KeyError): + # bpo-10496: if the current user identifier doesn't exist in the + # password database, do nothing + pass + + if 'PLAT' not in os.environ: + os.environ['PLAT'] = get_platform() + + +def subst_vars(s, local_vars: Mapping[str, object]) -> str: + """ + Perform variable substitution on 'string'. + Variables are indicated by format-style braces ("{var}"). + Variable is substituted by the value found in the 'local_vars' + dictionary or in 'os.environ' if it's not in 'local_vars'. + 'os.environ' is first checked/augmented to guarantee that it contains + certain values: see 'check_environ()'. Raise ValueError for any + variables not found in either 'local_vars' or 'os.environ'. + """ + check_environ() + lookup = dict(os.environ) + lookup.update((name, str(value)) for name, value in local_vars.items()) + try: + return _subst_compat(s).format_map(lookup) + except KeyError as var: + raise ValueError(f"invalid variable {var}") + + +def _subst_compat(s): + """ + Replace shell/Perl-style variable substitution with + format-style. For compatibility. + """ + + def _subst(match): + return f'{{{match.group(1)}}}' + + repl = re.sub(r'\$([a-zA-Z_][a-zA-Z_0-9]*)', _subst, s) + if repl != s: + import warnings + + warnings.warn( + "shell/Perl-style substitutions are deprecated", + DeprecationWarning, + ) + return repl + + +def grok_environment_error(exc: object, prefix: str = "error: ") -> str: + # Function kept for backward compatibility. + # Used to try clever things with EnvironmentErrors, + # but nowadays str(exception) produces good messages. + return prefix + str(exc) + + +# Needed by 'split_quoted()' +_wordchars_re = _squote_re = _dquote_re = None + + +def _init_regex(): + global _wordchars_re, _squote_re, _dquote_re + _wordchars_re = re.compile(rf'[^\\\'\"{string.whitespace} ]*') + _squote_re = re.compile(r"'(?:[^'\\]|\\.)*'") + _dquote_re = re.compile(r'"(?:[^"\\]|\\.)*"') + + +def split_quoted(s: str) -> list[str]: + """Split a string up according to Unix shell-like rules for quotes and + backslashes. In short: words are delimited by spaces, as long as those + spaces are not escaped by a backslash, or inside a quoted string. + Single and double quotes are equivalent, and the quote characters can + be backslash-escaped. The backslash is stripped from any two-character + escape sequence, leaving only the escaped character. The quote + characters are stripped from any quoted string. Returns a list of + words. + """ + + # This is a nice algorithm for splitting up a single string, since it + # doesn't require character-by-character examination. It was a little + # bit of a brain-bender to get it working right, though... + if _wordchars_re is None: + _init_regex() + + s = s.strip() + words = [] + pos = 0 + + while s: + m = _wordchars_re.match(s, pos) + end = m.end() + if end == len(s): + words.append(s[:end]) + break + + if s[end] in string.whitespace: + # unescaped, unquoted whitespace: now + # we definitely have a word delimiter + words.append(s[:end]) + s = s[end:].lstrip() + pos = 0 + + elif s[end] == '\\': + # preserve whatever is being escaped; + # will become part of the current word + s = s[:end] + s[end + 1 :] + pos = end + 1 + + else: + if s[end] == "'": # slurp singly-quoted string + m = _squote_re.match(s, end) + elif s[end] == '"': # slurp doubly-quoted string + m = _dquote_re.match(s, end) + else: + raise RuntimeError(f"this can't happen (bad char '{s[end]}')") + + if m is None: + raise ValueError(f"bad string (mismatched {s[end]} quotes?)") + + (beg, end) = m.span() + s = s[:beg] + s[beg + 1 : end - 1] + s[end:] + pos = m.end() - 2 + + if pos >= len(s): + words.append(s) + break + + return words + + +# split_quoted () + + +def execute( + func: Callable[[Unpack[_Ts]], object], + args: tuple[Unpack[_Ts]], + msg: object = None, + verbose: bool = False, +) -> None: + """ + Perform some action that affects the outside world (e.g. by + writing to the filesystem). Was previously used to deal with + "dry run" operations, but now runs unconditionally. + """ + if msg is None: + msg = f"{func.__name__}{args!r}" + if msg[-2:] == ',)': # correct for singleton tuple + msg = msg[0:-2] + ')' + + log.info(msg) + func(*args) + + +def strtobool(val: str) -> bool: + """Convert a string representation of truth to true (1) or false (0). + + True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values + are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if + 'val' is anything else. + """ + val = val.lower() + if val in ('y', 'yes', 't', 'true', 'on', '1'): + return True + elif val in ('n', 'no', 'f', 'false', 'off', '0'): + return False + else: + raise ValueError(f"invalid truth value {val!r}") + + +def byte_compile( # noqa: C901 + py_files: Iterable[str], + optimize: int = 0, + force: bool = False, + prefix: str | None = None, + base_dir: str | None = None, + verbose: bool = True, + direct: bool | None = None, +) -> None: + """Byte-compile a collection of Python source files to .pyc + files in a __pycache__ subdirectory. 'py_files' is a list + of files to compile; any files that don't end in ".py" are silently + skipped. 'optimize' must be one of the following: + 0 - don't optimize + 1 - normal optimization (like "python -O") + 2 - extra optimization (like "python -OO") + If 'force' is true, all files are recompiled regardless of + timestamps. + + The source filename encoded in each bytecode file defaults to the + filenames listed in 'py_files'; you can modify these with 'prefix' and + 'basedir'. 'prefix' is a string that will be stripped off of each + source filename, and 'base_dir' is a directory name that will be + prepended (after 'prefix' is stripped). You can supply either or both + (or neither) of 'prefix' and 'base_dir', as you wish. + + Byte-compilation is either done directly in this interpreter process + with the standard py_compile module, or indirectly by writing a + temporary script and executing it. Normally, you should let + 'byte_compile()' figure out to use direct compilation or not (see + the source for details). The 'direct' flag is used by the script + generated in indirect mode; unless you know what you're doing, leave + it set to None. + """ + + # nothing is done if sys.dont_write_bytecode is True + if sys.dont_write_bytecode: + raise DistutilsByteCompileError('byte-compiling is disabled.') + + # First, if the caller didn't force us into direct or indirect mode, + # figure out which mode we should be in. We take a conservative + # approach: choose direct mode *only* if the current interpreter is + # in debug mode and optimize is 0. If we're not in debug mode (-O + # or -OO), we don't know which level of optimization this + # interpreter is running with, so we can't do direct + # byte-compilation and be certain that it's the right thing. Thus, + # always compile indirectly if the current interpreter is in either + # optimize mode, or if either optimization level was requested by + # the caller. + if direct is None: + direct = __debug__ and optimize == 0 + + # "Indirect" byte-compilation: write a temporary script and then + # run it with the appropriate flags. + if not direct: + (script_fd, script_name) = tempfile.mkstemp(".py") + log.info("writing byte-compilation script '%s'", script_name) + script = os.fdopen(script_fd, "w", encoding='utf-8') + + with script: + script.write( + """\ +from distutils.util import byte_compile +files = [ +""" + ) + + # XXX would be nice to write absolute filenames, just for + # safety's sake (script should be more robust in the face of + # chdir'ing before running it). But this requires abspath'ing + # 'prefix' as well, and that breaks the hack in build_lib's + # 'byte_compile()' method that carefully tacks on a trailing + # slash (os.sep really) to make sure the prefix here is "just + # right". This whole prefix business is rather delicate -- the + # problem is that it's really a directory, but I'm treating it + # as a dumb string, so trailing slashes and so forth matter. + + script.write(",\n".join(map(repr, py_files)) + "]\n") + script.write( + f""" +byte_compile(files, optimize={optimize!r}, force={force!r}, + prefix={prefix!r}, base_dir={base_dir!r}, + verbose={verbose!r}, + direct=True) +""" + ) + + cmd = [sys.executable] + cmd.extend(subprocess._optim_args_from_interpreter_flags()) + cmd.append(script_name) + spawn(cmd) + execute(os.remove, (script_name,), f"removing {script_name}") + + # "Direct" byte-compilation: use the py_compile module to compile + # right here, right now. Note that the script generated in indirect + # mode simply calls 'byte_compile()' in direct mode, a weird sort of + # cross-process recursion. Hey, it works! + else: + from py_compile import compile + + for file in py_files: + if file[-3:] != ".py": + # This lets us be lazy and not filter filenames in + # the "install_lib" command. + continue + + # Terminology from the py_compile module: + # cfile - byte-compiled file + # dfile - purported source filename (same as 'file' by default) + if optimize >= 0: + opt = '' if optimize == 0 else optimize + cfile = importlib.util.cache_from_source(file, optimization=opt) + else: + cfile = importlib.util.cache_from_source(file) + dfile = file + if prefix: + if file[: len(prefix)] != prefix: + raise ValueError( + f"invalid prefix: filename {file!r} doesn't start with {prefix!r}" + ) + dfile = dfile[len(prefix) :] + if base_dir: + dfile = os.path.join(base_dir, dfile) + + cfile_base = os.path.basename(cfile) + if direct: + if force or newer(file, cfile): + log.info("byte-compiling %s to %s", file, cfile_base) + compile(file, cfile, dfile) + else: + log.debug("skipping byte-compilation of %s to %s", file, cfile_base) + + +def rfc822_escape(header: str) -> str: + """Return a version of the string escaped for inclusion in an + RFC-822 header, by ensuring there are 8 spaces space after each newline. + """ + indent = 8 * " " + lines = header.splitlines(keepends=True) + + # Emulate the behaviour of `str.split` + # (the terminal line break in `splitlines` does not result in an extra line): + ends_in_newline = lines and lines[-1].splitlines()[0] != lines[-1] + suffix = indent if ends_in_newline else "" + + return indent.join(lines) + suffix + + +def is_mingw() -> bool: + """Returns True if the current platform is mingw. + + Python compiled with Mingw-w64 has sys.platform == 'win32' and + get_platform() starts with 'mingw'. + """ + return sys.platform == 'win32' and get_platform().startswith('mingw') + + +def is_freethreaded(): + """Return True if the Python interpreter is built with free threading support.""" + return bool(sysconfig.get_config_var('Py_GIL_DISABLED')) diff --git a/lib/python3.11/site-packages/setuptools/_distutils/version.py b/lib/python3.11/site-packages/setuptools/_distutils/version.py new file mode 100644 index 0000000000000000000000000000000000000000..2223ee9c8cab2495034a57d4585625feb81a8330 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/version.py @@ -0,0 +1,348 @@ +# +# distutils/version.py +# +# Implements multiple version numbering conventions for the +# Python Module Distribution Utilities. +# +# $Id$ +# + +"""Provides classes to represent module version numbers (one class for +each style of version numbering). There are currently two such classes +implemented: StrictVersion and LooseVersion. + +Every version number class implements the following interface: + * the 'parse' method takes a string and parses it to some internal + representation; if the string is an invalid version number, + 'parse' raises a ValueError exception + * the class constructor takes an optional string argument which, + if supplied, is passed to 'parse' + * __str__ reconstructs the string that was passed to 'parse' (or + an equivalent string -- ie. one that will generate an equivalent + version number instance) + * __repr__ generates Python code to recreate the version number instance + * _cmp compares the current instance with either another instance + of the same class or a string (which will be parsed to an instance + of the same class, thus must follow the same rules) +""" + +import contextlib +import re +import warnings + + +@contextlib.contextmanager +def suppress_known_deprecation(): + with warnings.catch_warnings(record=True) as ctx: + warnings.filterwarnings( + action='default', + category=DeprecationWarning, + message="distutils Version classes are deprecated.", + ) + yield ctx + + +class Version: + """Abstract base class for version numbering classes. Just provides + constructor (__init__) and reproducer (__repr__), because those + seem to be the same for all version numbering classes; and route + rich comparisons to _cmp. + """ + + def __init__(self, vstring=None): + if vstring: + self.parse(vstring) + warnings.warn( + "distutils Version classes are deprecated. Use packaging.version instead.", + DeprecationWarning, + stacklevel=2, + ) + + def __repr__(self): + return f"{self.__class__.__name__} ('{self}')" + + def __eq__(self, other): + c = self._cmp(other) + if c is NotImplemented: + return c + return c == 0 + + def __lt__(self, other): + c = self._cmp(other) + if c is NotImplemented: + return c + return c < 0 + + def __le__(self, other): + c = self._cmp(other) + if c is NotImplemented: + return c + return c <= 0 + + def __gt__(self, other): + c = self._cmp(other) + if c is NotImplemented: + return c + return c > 0 + + def __ge__(self, other): + c = self._cmp(other) + if c is NotImplemented: + return c + return c >= 0 + + +# Interface for version-number classes -- must be implemented +# by the following classes (the concrete ones -- Version should +# be treated as an abstract class). +# __init__ (string) - create and take same action as 'parse' +# (string parameter is optional) +# parse (string) - convert a string representation to whatever +# internal representation is appropriate for +# this style of version numbering +# __str__ (self) - convert back to a string; should be very similar +# (if not identical to) the string supplied to parse +# __repr__ (self) - generate Python code to recreate +# the instance +# _cmp (self, other) - compare two version numbers ('other' may +# be an unparsed version string, or another +# instance of your version class) + + +class StrictVersion(Version): + """Version numbering for anal retentives and software idealists. + Implements the standard interface for version number classes as + described above. A version number consists of two or three + dot-separated numeric components, with an optional "pre-release" tag + on the end. The pre-release tag consists of the letter 'a' or 'b' + followed by a number. If the numeric components of two version + numbers are equal, then one with a pre-release tag will always + be deemed earlier (lesser) than one without. + + The following are valid version numbers (shown in the order that + would be obtained by sorting according to the supplied cmp function): + + 0.4 0.4.0 (these two are equivalent) + 0.4.1 + 0.5a1 + 0.5b3 + 0.5 + 0.9.6 + 1.0 + 1.0.4a3 + 1.0.4b1 + 1.0.4 + + The following are examples of invalid version numbers: + + 1 + 2.7.2.2 + 1.3.a4 + 1.3pl1 + 1.3c4 + + The rationale for this version numbering system will be explained + in the distutils documentation. + """ + + version_re = re.compile( + r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII + ) + + def parse(self, vstring): + match = self.version_re.match(vstring) + if not match: + raise ValueError(f"invalid version number '{vstring}'") + + (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6) + + if patch: + self.version = tuple(map(int, [major, minor, patch])) + else: + self.version = tuple(map(int, [major, minor])) + (0,) + + if prerelease: + self.prerelease = (prerelease[0], int(prerelease_num)) + else: + self.prerelease = None + + def __str__(self): + if self.version[2] == 0: + vstring = '.'.join(map(str, self.version[0:2])) + else: + vstring = '.'.join(map(str, self.version)) + + if self.prerelease: + vstring = vstring + self.prerelease[0] + str(self.prerelease[1]) + + return vstring + + def _cmp(self, other): + if isinstance(other, str): + with suppress_known_deprecation(): + other = StrictVersion(other) + elif not isinstance(other, StrictVersion): + return NotImplemented + + if self.version == other.version: + # versions match; pre-release drives the comparison + return self._cmp_prerelease(other) + + return -1 if self.version < other.version else 1 + + def _cmp_prerelease(self, other): + """ + case 1: self has prerelease, other doesn't; other is greater + case 2: self doesn't have prerelease, other does: self is greater + case 3: both or neither have prerelease: compare them! + """ + if self.prerelease and not other.prerelease: + return -1 + elif not self.prerelease and other.prerelease: + return 1 + + if self.prerelease == other.prerelease: + return 0 + elif self.prerelease < other.prerelease: + return -1 + else: + return 1 + + +# end class StrictVersion + + +# The rules according to Greg Stein: +# 1) a version number has 1 or more numbers separated by a period or by +# sequences of letters. If only periods, then these are compared +# left-to-right to determine an ordering. +# 2) sequences of letters are part of the tuple for comparison and are +# compared lexicographically +# 3) recognize the numeric components may have leading zeroes +# +# The LooseVersion class below implements these rules: a version number +# string is split up into a tuple of integer and string components, and +# comparison is a simple tuple comparison. This means that version +# numbers behave in a predictable and obvious way, but a way that might +# not necessarily be how people *want* version numbers to behave. There +# wouldn't be a problem if people could stick to purely numeric version +# numbers: just split on period and compare the numbers as tuples. +# However, people insist on putting letters into their version numbers; +# the most common purpose seems to be: +# - indicating a "pre-release" version +# ('alpha', 'beta', 'a', 'b', 'pre', 'p') +# - indicating a post-release patch ('p', 'pl', 'patch') +# but of course this can't cover all version number schemes, and there's +# no way to know what a programmer means without asking him. +# +# The problem is what to do with letters (and other non-numeric +# characters) in a version number. The current implementation does the +# obvious and predictable thing: keep them as strings and compare +# lexically within a tuple comparison. This has the desired effect if +# an appended letter sequence implies something "post-release": +# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002". +# +# However, if letters in a version number imply a pre-release version, +# the "obvious" thing isn't correct. Eg. you would expect that +# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison +# implemented here, this just isn't so. +# +# Two possible solutions come to mind. The first is to tie the +# comparison algorithm to a particular set of semantic rules, as has +# been done in the StrictVersion class above. This works great as long +# as everyone can go along with bondage and discipline. Hopefully a +# (large) subset of Python module programmers will agree that the +# particular flavour of bondage and discipline provided by StrictVersion +# provides enough benefit to be worth using, and will submit their +# version numbering scheme to its domination. The free-thinking +# anarchists in the lot will never give in, though, and something needs +# to be done to accommodate them. +# +# Perhaps a "moderately strict" version class could be implemented that +# lets almost anything slide (syntactically), and makes some heuristic +# assumptions about non-digits in version number strings. This could +# sink into special-case-hell, though; if I was as talented and +# idiosyncratic as Larry Wall, I'd go ahead and implement a class that +# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is +# just as happy dealing with things like "2g6" and "1.13++". I don't +# think I'm smart enough to do it right though. +# +# In any case, I've coded the test suite for this module (see +# ../test/test_version.py) specifically to fail on things like comparing +# "1.2a2" and "1.2". That's not because the *code* is doing anything +# wrong, it's because the simple, obvious design doesn't match my +# complicated, hairy expectations for real-world version numbers. It +# would be a snap to fix the test suite to say, "Yep, LooseVersion does +# the Right Thing" (ie. the code matches the conception). But I'd rather +# have a conception that matches common notions about version numbers. + + +class LooseVersion(Version): + """Version numbering for anarchists and software realists. + Implements the standard interface for version number classes as + described above. A version number consists of a series of numbers, + separated by either periods or strings of letters. When comparing + version numbers, the numeric components will be compared + numerically, and the alphabetic components lexically. The following + are all valid version numbers, in no particular order: + + 1.5.1 + 1.5.2b2 + 161 + 3.10a + 8.02 + 3.4j + 1996.07.12 + 3.2.pl0 + 3.1.1.6 + 2g6 + 11g + 0.960923 + 2.2beta29 + 1.13++ + 5.5.kw + 2.0b1pl0 + + In fact, there is no such thing as an invalid version number under + this scheme; the rules for comparison are simple and predictable, + but may not always give the results you want (for some definition + of "want"). + """ + + component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE) + + def parse(self, vstring): + # I've given up on thinking I can reconstruct the version string + # from the parsed tuple -- so I just store the string here for + # use by __str__ + self.vstring = vstring + components = [x for x in self.component_re.split(vstring) if x and x != '.'] + for i, obj in enumerate(components): + try: + components[i] = int(obj) + except ValueError: + pass + + self.version = components + + def __str__(self): + return self.vstring + + def __repr__(self): + return f"LooseVersion ('{self}')" + + def _cmp(self, other): + if isinstance(other, str): + other = LooseVersion(other) + elif not isinstance(other, LooseVersion): + return NotImplemented + + if self.version == other.version: + return 0 + if self.version < other.version: + return -1 + if self.version > other.version: + return 1 + + +# end class LooseVersion diff --git a/lib/python3.11/site-packages/setuptools/_distutils/versionpredicate.py b/lib/python3.11/site-packages/setuptools/_distutils/versionpredicate.py new file mode 100644 index 0000000000000000000000000000000000000000..fe31b0ed8ef843080ea64df9f58a398d317434ad --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/versionpredicate.py @@ -0,0 +1,175 @@ +"""Module for parsing and testing package version predicate strings.""" + +import operator +import re + +from . import version + +re_validPackage = re.compile(r"(?i)^\s*([a-z_]\w*(?:\.[a-z_]\w*)*)(.*)", re.ASCII) +# (package) (rest) + +re_paren = re.compile(r"^\s*\((.*)\)\s*$") # (list) inside of parentheses +re_splitComparison = re.compile(r"^\s*(<=|>=|<|>|!=|==)\s*([^\s,]+)\s*$") +# (comp) (version) + + +def splitUp(pred): + """Parse a single version comparison. + + Return (comparison string, StrictVersion) + """ + res = re_splitComparison.match(pred) + if not res: + raise ValueError(f"bad package restriction syntax: {pred!r}") + comp, verStr = res.groups() + with version.suppress_known_deprecation(): + other = version.StrictVersion(verStr) + return (comp, other) + + +compmap = { + "<": operator.lt, + "<=": operator.le, + "==": operator.eq, + ">": operator.gt, + ">=": operator.ge, + "!=": operator.ne, +} + + +class VersionPredicate: + """Parse and test package version predicates. + + >>> v = VersionPredicate('pyepat.abc (>1.0, <3333.3a1, !=1555.1b3)') + + The `name` attribute provides the full dotted name that is given:: + + >>> v.name + 'pyepat.abc' + + The str() of a `VersionPredicate` provides a normalized + human-readable version of the expression:: + + >>> print(v) + pyepat.abc (> 1.0, < 3333.3a1, != 1555.1b3) + + The `satisfied_by()` method can be used to determine with a given + version number is included in the set described by the version + restrictions:: + + >>> v.satisfied_by('1.1') + True + >>> v.satisfied_by('1.4') + True + >>> v.satisfied_by('1.0') + False + >>> v.satisfied_by('4444.4') + False + >>> v.satisfied_by('1555.1b3') + False + + `VersionPredicate` is flexible in accepting extra whitespace:: + + >>> v = VersionPredicate(' pat( == 0.1 ) ') + >>> v.name + 'pat' + >>> v.satisfied_by('0.1') + True + >>> v.satisfied_by('0.2') + False + + If any version numbers passed in do not conform to the + restrictions of `StrictVersion`, a `ValueError` is raised:: + + >>> v = VersionPredicate('p1.p2.p3.p4(>=1.0, <=1.3a1, !=1.2zb3)') + Traceback (most recent call last): + ... + ValueError: invalid version number '1.2zb3' + + It the module or package name given does not conform to what's + allowed as a legal module or package name, `ValueError` is + raised:: + + >>> v = VersionPredicate('foo-bar') + Traceback (most recent call last): + ... + ValueError: expected parenthesized list: '-bar' + + >>> v = VersionPredicate('foo bar (12.21)') + Traceback (most recent call last): + ... + ValueError: expected parenthesized list: 'bar (12.21)' + + """ + + def __init__(self, versionPredicateStr): + """Parse a version predicate string.""" + # Fields: + # name: package name + # pred: list of (comparison string, StrictVersion) + + versionPredicateStr = versionPredicateStr.strip() + if not versionPredicateStr: + raise ValueError("empty package restriction") + match = re_validPackage.match(versionPredicateStr) + if not match: + raise ValueError(f"bad package name in {versionPredicateStr!r}") + self.name, paren = match.groups() + paren = paren.strip() + if paren: + match = re_paren.match(paren) + if not match: + raise ValueError(f"expected parenthesized list: {paren!r}") + str = match.groups()[0] + self.pred = [splitUp(aPred) for aPred in str.split(",")] + if not self.pred: + raise ValueError(f"empty parenthesized list in {versionPredicateStr!r}") + else: + self.pred = [] + + def __str__(self): + if self.pred: + seq = [cond + " " + str(ver) for cond, ver in self.pred] + return self.name + " (" + ", ".join(seq) + ")" + else: + return self.name + + def satisfied_by(self, version): + """True if version is compatible with all the predicates in self. + The parameter version must be acceptable to the StrictVersion + constructor. It may be either a string or StrictVersion. + """ + for cond, ver in self.pred: + if not compmap[cond](version, ver): + return False + return True + + +_provision_rx = None + + +def split_provision(value): + """Return the name and optional version number of a provision. + + The version number, if given, will be returned as a `StrictVersion` + instance, otherwise it will be `None`. + + >>> split_provision('mypkg') + ('mypkg', None) + >>> split_provision(' mypkg( 1.2 ) ') + ('mypkg', StrictVersion ('1.2')) + """ + global _provision_rx + if _provision_rx is None: + _provision_rx = re.compile( + r"([a-zA-Z_]\w*(?:\.[a-zA-Z_]\w*)*)(?:\s*\(\s*([^)\s]+)\s*\))?$", re.ASCII + ) + value = value.strip() + m = _provision_rx.match(value) + if not m: + raise ValueError(f"illegal provides specification: {value!r}") + ver = m.group(2) or None + if ver: + with version.suppress_known_deprecation(): + ver = version.StrictVersion(ver) + return m.group(1), ver diff --git a/lib/python3.11/site-packages/setuptools/_distutils/zosccompiler.py b/lib/python3.11/site-packages/setuptools/_distutils/zosccompiler.py new file mode 100644 index 0000000000000000000000000000000000000000..e49630ac6ed8cad62c4d2ff8361758519e6dc83b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_distutils/zosccompiler.py @@ -0,0 +1,3 @@ +from .compilers.C import zos + +zOSCCompiler = zos.Compiler diff --git a/lib/python3.11/site-packages/setuptools/_entry_points.py b/lib/python3.11/site-packages/setuptools/_entry_points.py new file mode 100644 index 0000000000000000000000000000000000000000..cd5dd2c8ac99783a2edb617278627ba170a9872b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_entry_points.py @@ -0,0 +1,94 @@ +import functools +import itertools +import operator + +from jaraco.functools import pass_none +from jaraco.text import yield_lines +from more_itertools import consume + +from ._importlib import metadata +from ._itertools import ensure_unique +from .errors import OptionError + + +def ensure_valid(ep): + """ + Exercise one of the dynamic properties to trigger + the pattern match. + + This function is deprecated in favor of importlib_metadata 8.7 and + Python 3.14 importlib.metadata, which validates entry points on + construction. + """ + try: + ep.extras + except (AttributeError, AssertionError) as ex: + # Why both? See https://github.com/python/importlib_metadata/issues/488 + msg = ( + f"Problems to parse {ep}.\nPlease ensure entry-point follows the spec: " + "https://packaging.python.org/en/latest/specifications/entry-points/" + ) + raise OptionError(msg) from ex + + +def load_group(value, group): + """ + Given a value of an entry point or series of entry points, + return each as an EntryPoint. + """ + # normalize to a single sequence of lines + lines = yield_lines(value) + text = f'[{group}]\n' + '\n'.join(lines) + return metadata.EntryPoints._from_text(text) + + +def by_group_and_name(ep): + return ep.group, ep.name + + +def validate(eps: metadata.EntryPoints): + """ + Ensure entry points are unique by group and name and validate each. + """ + consume(map(ensure_valid, ensure_unique(eps, key=by_group_and_name))) + return eps + + +@functools.singledispatch +def load(eps): + """ + Given a Distribution.entry_points, produce EntryPoints. + """ + groups = itertools.chain.from_iterable( + load_group(value, group) for group, value in eps.items() + ) + return validate(metadata.EntryPoints(groups)) + + +@load.register(str) +def _(eps): + r""" + >>> ep, = load('[console_scripts]\nfoo=bar') + >>> ep.group + 'console_scripts' + >>> ep.name + 'foo' + >>> ep.value + 'bar' + """ + return validate(metadata.EntryPoints(metadata.EntryPoints._from_text(eps))) + + +load.register(type(None), lambda x: x) + + +@pass_none +def render(eps: metadata.EntryPoints): + by_group = operator.attrgetter('group') + groups = itertools.groupby(sorted(eps, key=by_group), by_group) + + return '\n'.join(f'[{group}]\n{render_items(items)}\n' for group, items in groups) + + +def render_items(eps): + return '\n'.join(f'{ep.name} = {ep.value}' for ep in sorted(eps)) diff --git a/lib/python3.11/site-packages/setuptools/_imp.py b/lib/python3.11/site-packages/setuptools/_imp.py new file mode 100644 index 0000000000000000000000000000000000000000..f1d9f29218987d4f830f2d57aca9e3f74d00a095 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_imp.py @@ -0,0 +1,87 @@ +""" +Re-implementation of find_module and get_frozen_object +from the deprecated imp module. +""" + +import importlib.machinery +import importlib.util +import os +import tokenize +from importlib.util import module_from_spec + +PY_SOURCE = 1 +PY_COMPILED = 2 +C_EXTENSION = 3 +C_BUILTIN = 6 +PY_FROZEN = 7 + + +def find_spec(module, paths): + finder = ( + importlib.machinery.PathFinder().find_spec + if isinstance(paths, list) + else importlib.util.find_spec + ) + return finder(module, paths) + + +def find_module(module, paths=None): + """Just like 'imp.find_module()', but with package support""" + spec = find_spec(module, paths) + if spec is None: + raise ImportError(f"Can't find {module}") + if not spec.has_location and hasattr(spec, 'submodule_search_locations'): + spec = importlib.util.spec_from_loader('__init__.py', spec.loader) + + kind = -1 + file = None + static = isinstance(spec.loader, type) + if ( + spec.origin == 'frozen' + or static + and issubclass(spec.loader, importlib.machinery.FrozenImporter) + ): + kind = PY_FROZEN + path = None # imp compabilty + suffix = mode = '' # imp compatibility + elif ( + spec.origin == 'built-in' + or static + and issubclass(spec.loader, importlib.machinery.BuiltinImporter) + ): + kind = C_BUILTIN + path = None # imp compabilty + suffix = mode = '' # imp compatibility + elif spec.has_location: + path = spec.origin + suffix = os.path.splitext(path)[1] + mode = 'r' if suffix in importlib.machinery.SOURCE_SUFFIXES else 'rb' + + if suffix in importlib.machinery.SOURCE_SUFFIXES: + kind = PY_SOURCE + file = tokenize.open(path) + elif suffix in importlib.machinery.BYTECODE_SUFFIXES: + kind = PY_COMPILED + file = open(path, 'rb') + elif suffix in importlib.machinery.EXTENSION_SUFFIXES: + kind = C_EXTENSION + + else: + path = None + suffix = mode = '' + + return file, path, (suffix, mode, kind) + + +def get_frozen_object(module, paths=None): + spec = find_spec(module, paths) + if not spec: + raise ImportError(f"Can't find {module}") + return spec.loader.get_code(module) + + +def get_module(module, paths, info): + spec = find_spec(module, paths) + if not spec: + raise ImportError(f"Can't find {module}") + return module_from_spec(spec) diff --git a/lib/python3.11/site-packages/setuptools/_importlib.py b/lib/python3.11/site-packages/setuptools/_importlib.py new file mode 100644 index 0000000000000000000000000000000000000000..ce0fd52653b56c9c2cb2b2c7bfb35e3ec3c61408 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_importlib.py @@ -0,0 +1,9 @@ +import sys + +if sys.version_info < (3, 10): + import importlib_metadata as metadata # pragma: no cover +else: + import importlib.metadata as metadata # noqa: F401 + + +import importlib.resources as resources # noqa: F401 diff --git a/lib/python3.11/site-packages/setuptools/_itertools.py b/lib/python3.11/site-packages/setuptools/_itertools.py new file mode 100644 index 0000000000000000000000000000000000000000..d6ca841353ce39ac4361013f5c8160d69028d0d8 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_itertools.py @@ -0,0 +1,23 @@ +from more_itertools import consume # noqa: F401 + + +# copied from jaraco.itertools 6.1 +def ensure_unique(iterable, key=lambda x: x): + """ + Wrap an iterable to raise a ValueError if non-unique values are encountered. + + >>> list(ensure_unique('abc')) + ['a', 'b', 'c'] + >>> consume(ensure_unique('abca')) + Traceback (most recent call last): + ... + ValueError: Duplicate element 'a' encountered. + """ + seen = set() + seen_add = seen.add + for element in iterable: + k = key(element) + if k in seen: + raise ValueError(f"Duplicate element {element!r} encountered.") + seen_add(k) + yield element diff --git a/lib/python3.11/site-packages/setuptools/_normalization.py b/lib/python3.11/site-packages/setuptools/_normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..6b8d4ddbf900d648e30d61d96d9b9993c7c57663 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_normalization.py @@ -0,0 +1,180 @@ +""" +Helpers for normalization as expected in wheel/sdist/module file names +and core metadata +""" + +import re +from typing import TYPE_CHECKING + +import packaging + +# https://packaging.python.org/en/latest/specifications/core-metadata/#name +_VALID_NAME = re.compile(r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.IGNORECASE) +_UNSAFE_NAME_CHARS = re.compile(r"[^A-Z0-9._-]+", re.IGNORECASE) +_NON_ALPHANUMERIC = re.compile(r"[^A-Z0-9]+", re.IGNORECASE) +_PEP440_FALLBACK = re.compile( + r"^v?(?P(?:[0-9]+!)?[0-9]+(?:\.[0-9]+)*)", re.IGNORECASE +) + + +def safe_identifier(name: str) -> str: + """Make a string safe to be used as Python identifier. + >>> safe_identifier("12abc") + '_12abc' + >>> safe_identifier("__editable__.myns.pkg-78.9.3_local") + '__editable___myns_pkg_78_9_3_local' + """ + safe = re.sub(r'\W|^(?=\d)', '_', name) + assert safe.isidentifier() + return safe + + +def safe_name(component: str) -> str: + """Escape a component used as a project name according to Core Metadata. + >>> safe_name("hello world") + 'hello-world' + >>> safe_name("hello?world") + 'hello-world' + >>> safe_name("hello_world") + 'hello_world' + """ + return _UNSAFE_NAME_CHARS.sub("-", component) + + +def safe_version(version: str) -> str: + """Convert an arbitrary string into a valid version string. + Can still raise an ``InvalidVersion`` exception. + To avoid exceptions use ``best_effort_version``. + >>> safe_version("1988 12 25") + '1988.12.25' + >>> safe_version("v0.2.1") + '0.2.1' + >>> safe_version("v0.2?beta") + '0.2b0' + >>> safe_version("v0.2 beta") + '0.2b0' + >>> safe_version("ubuntu lts") + Traceback (most recent call last): + ... + packaging.version.InvalidVersion: Invalid version: 'ubuntu.lts' + """ + v = version.replace(' ', '.') + try: + return str(packaging.version.Version(v)) + except packaging.version.InvalidVersion: + attempt = _UNSAFE_NAME_CHARS.sub("-", v) + return str(packaging.version.Version(attempt)) + + +def best_effort_version(version: str) -> str: + """Convert an arbitrary string into a version-like string. + Fallback when ``safe_version`` is not safe enough. + >>> best_effort_version("v0.2 beta") + '0.2b0' + >>> best_effort_version("ubuntu lts") + '0.dev0+sanitized.ubuntu.lts' + >>> best_effort_version("0.23ubuntu1") + '0.23.dev0+sanitized.ubuntu1' + >>> best_effort_version("0.23-") + '0.23.dev0+sanitized' + >>> best_effort_version("0.-_") + '0.dev0+sanitized' + >>> best_effort_version("42.+?1") + '42.dev0+sanitized.1' + """ + try: + return safe_version(version) + except packaging.version.InvalidVersion: + v = version.replace(' ', '.') + match = _PEP440_FALLBACK.search(v) + if match: + safe = match["safe"] + rest = v[len(safe) :] + else: + safe = "0" + rest = version + safe_rest = _NON_ALPHANUMERIC.sub(".", rest).strip(".") + local = f"sanitized.{safe_rest}".strip(".") + return safe_version(f"{safe}.dev0+{local}") + + +def safe_extra(extra: str) -> str: + """Normalize extra name according to PEP 685 + >>> safe_extra("_FrIeNdLy-._.-bArD") + 'friendly-bard' + >>> safe_extra("FrIeNdLy-._.-bArD__._-") + 'friendly-bard' + """ + return _NON_ALPHANUMERIC.sub("-", extra).strip("-").lower() + + +def filename_component(value: str) -> str: + """Normalize each component of a filename (e.g. distribution/version part of wheel) + Note: ``value`` needs to be already normalized. + >>> filename_component("my-pkg") + 'my_pkg' + """ + return value.replace("-", "_").strip("_") + + +def filename_component_broken(value: str) -> str: + """ + Produce the incorrect filename component for compatibility. + + See pypa/setuptools#4167 for detailed analysis. + + TODO: replace this with filename_component after pip 24 is + nearly-ubiquitous. + + >>> filename_component_broken('foo_bar-baz') + 'foo-bar-baz' + """ + return value.replace('_', '-') + + +def safer_name(value: str) -> str: + """Like ``safe_name`` but can be used as filename component for wheel""" + # See bdist_wheel.safer_name + return ( + # Per https://packaging.python.org/en/latest/specifications/name-normalization/#name-normalization + re + .sub(r"[-_.]+", "-", safe_name(value)) + .lower() + # Per https://packaging.python.org/en/latest/specifications/binary-distribution-format/#escaping-and-unicode + .replace("-", "_") + ) + + +def safer_best_effort_version(value: str) -> str: + """Like ``best_effort_version`` but can be used as filename component for wheel""" + # See bdist_wheel.safer_verion + # TODO: Replace with only safe_version in the future (no need for best effort) + return filename_component(best_effort_version(value)) + + +def _missing_canonicalize_license_expression(expression: str) -> str: + """ + Defer import error to affect only users that actually use it + https://github.com/pypa/setuptools/issues/4894 + >>> _missing_canonicalize_license_expression("a OR b") + Traceback (most recent call last): + ... + ImportError: ...Cannot import `packaging.licenses`... + """ + raise ImportError( + "Cannot import `packaging.licenses`." + """ + Setuptools>=77.0.0 requires "packaging>=24.2" to work properly. + Please make sure you have a suitable version installed. + """ + ) + + +try: + from packaging.licenses import ( + canonicalize_license_expression as _canonicalize_license_expression, + ) +except ImportError: # pragma: nocover + if not TYPE_CHECKING: + # XXX: pyright is still upset even with # pyright: ignore[reportAssignmentType] + _canonicalize_license_expression = _missing_canonicalize_license_expression diff --git a/lib/python3.11/site-packages/setuptools/_path.py b/lib/python3.11/site-packages/setuptools/_path.py new file mode 100644 index 0000000000000000000000000000000000000000..2b78022934d89599e642a10f861b382947031be9 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_path.py @@ -0,0 +1,93 @@ +from __future__ import annotations + +import contextlib +import os +import sys +from typing import TYPE_CHECKING, TypeVar, Union + +from more_itertools import unique_everseen + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + +StrPath: TypeAlias = Union[str, os.PathLike[str]] # Same as _typeshed.StrPath +StrPathT = TypeVar("StrPathT", bound=Union[str, os.PathLike[str]]) + + +def ensure_directory(path): + """Ensure that the parent directory of `path` exists""" + dirname = os.path.dirname(path) + os.makedirs(dirname, exist_ok=True) + + +def same_path(p1: StrPath, p2: StrPath) -> bool: + """Differs from os.path.samefile because it does not require paths to exist. + Purely string based (no comparison between i-nodes). + >>> same_path("a/b", "./a/b") + True + >>> same_path("a/b", "a/./b") + True + >>> same_path("a/b", "././a/b") + True + >>> same_path("a/b", "./a/b/c/..") + True + >>> same_path("a/b", "../a/b/c") + False + >>> same_path("a", "a/b") + False + """ + return normpath(p1) == normpath(p2) + + +def _cygwin_patch(filename: StrPath): # pragma: nocover + """ + Contrary to POSIX 2008, on Cygwin, getcwd (3) contains + symlink components. Using + os.path.abspath() works around this limitation. A fix in os.getcwd() + would probably better, in Cygwin even more so, except + that this seems to be by design... + """ + return os.path.abspath(filename) if sys.platform == 'cygwin' else filename + + +def normpath(filename: StrPath) -> str: + """Normalize a file/dir name for comparison purposes.""" + return os.path.normcase(os.path.realpath(os.path.normpath(_cygwin_patch(filename)))) + + +@contextlib.contextmanager +def paths_on_pythonpath(paths): + """ + Add the indicated paths to the head of the PYTHONPATH environment + variable so that subprocesses will also see the packages at + these paths. + + Do this in a context that restores the value on exit. + + >>> getfixture('monkeypatch').setenv('PYTHONPATH', 'anything') + >>> with paths_on_pythonpath(['foo', 'bar']): + ... assert 'foo' in os.environ['PYTHONPATH'] + ... assert 'anything' in os.environ['PYTHONPATH'] + >>> os.environ['PYTHONPATH'] + 'anything' + + >>> getfixture('monkeypatch').delenv('PYTHONPATH') + >>> with paths_on_pythonpath(['foo', 'bar']): + ... assert 'foo' in os.environ['PYTHONPATH'] + >>> os.environ.get('PYTHONPATH') + """ + nothing = object() + orig_pythonpath = os.environ.get('PYTHONPATH', nothing) + current_pythonpath = os.environ.get('PYTHONPATH', '') + try: + prefix = os.pathsep.join(unique_everseen(paths)) + to_join = filter(None, [prefix, current_pythonpath]) + new_path = os.pathsep.join(to_join) + if new_path: + os.environ['PYTHONPATH'] = new_path + yield + finally: + if orig_pythonpath is nothing: + os.environ.pop('PYTHONPATH', None) + else: + os.environ['PYTHONPATH'] = orig_pythonpath diff --git a/lib/python3.11/site-packages/setuptools/_reqs.py b/lib/python3.11/site-packages/setuptools/_reqs.py new file mode 100644 index 0000000000000000000000000000000000000000..7be56cbf35d4a7cbeacdca1c18599433d7fb2f7f --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_reqs.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +from collections.abc import Iterable, Iterator +from functools import lru_cache +from typing import TYPE_CHECKING, Callable, TypeVar, Union, overload + +import jaraco.text as text +from packaging.requirements import Requirement + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + +_T = TypeVar("_T") +_StrOrIter: TypeAlias = Union[str, Iterable[str]] + + +parse_req: Callable[[str], Requirement] = lru_cache()(Requirement) +# Setuptools parses the same requirement many times +# (e.g. first for validation than for normalisation), +# so it might be worth to cache. + + +def parse_strings(strs: _StrOrIter) -> Iterator[str]: + """ + Yield requirement strings for each specification in `strs`. + + `strs` must be a string, or a (possibly-nested) iterable thereof. + """ + return text.join_continuation(map(text.drop_comment, text.yield_lines(strs))) + + +# These overloads are only needed because of a mypy false-positive, pyright gets it right +# https://github.com/python/mypy/issues/3737 +@overload +def parse(strs: _StrOrIter) -> Iterator[Requirement]: ... +@overload +def parse(strs: _StrOrIter, parser: Callable[[str], _T]) -> Iterator[_T]: ... +def parse(strs: _StrOrIter, parser: Callable[[str], _T] = parse_req) -> Iterator[_T]: # type: ignore[assignment] + """ + Parse requirements. + """ + return map(parser, parse_strings(strs)) diff --git a/lib/python3.11/site-packages/setuptools/_scripts.py b/lib/python3.11/site-packages/setuptools/_scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..88bf02f927ba7cb47731fc0984f4b4f135fbdd45 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_scripts.py @@ -0,0 +1,361 @@ +from __future__ import annotations + +import os +import re +import shlex +import shutil +import struct +import subprocess +import sys +import textwrap +from collections.abc import Iterable +from typing import TYPE_CHECKING, TypedDict + +from ._importlib import metadata, resources + +if TYPE_CHECKING: + from typing_extensions import Self + +from .warnings import SetuptoolsWarning + +from distutils.command.build_scripts import first_line_re +from distutils.util import get_platform + + +class _SplitArgs(TypedDict, total=False): + comments: bool + posix: bool + + +class CommandSpec(list): + """ + A command spec for a #! header, specified as a list of arguments akin to + those passed to Popen. + """ + + options: list[str] = [] + split_args = _SplitArgs() + + @classmethod + def best(cls): + """ + Choose the best CommandSpec class based on environmental conditions. + """ + return cls + + @classmethod + def _sys_executable(cls): + _default = os.path.normpath(sys.executable) + return os.environ.get('__PYVENV_LAUNCHER__', _default) + + @classmethod + def from_param(cls, param: Self | str | Iterable[str] | None) -> Self: + """ + Construct a CommandSpec from a parameter to build_scripts, which may + be None. + """ + if isinstance(param, cls): + return param + if isinstance(param, str): + return cls.from_string(param) + if isinstance(param, Iterable): + return cls(param) + if param is None: + return cls.from_environment() + raise TypeError(f"Argument has an unsupported type {type(param)}") + + @classmethod + def from_environment(cls): + return cls([cls._sys_executable()]) + + @classmethod + def from_string(cls, string: str) -> Self: + """ + Construct a command spec from a simple string representing a command + line parseable by shlex.split. + """ + items = shlex.split(string, **cls.split_args) + return cls(items) + + def install_options(self, script_text: str): + self.options = shlex.split(self._extract_options(script_text)) + cmdline = subprocess.list2cmdline(self) + if not isascii(cmdline): + self.options[:0] = ['-x'] + + @staticmethod + def _extract_options(orig_script): + """ + Extract any options from the first line of the script. + """ + first = (orig_script + '\n').splitlines()[0] + match = _first_line_re().match(first) + options = match.group(1) or '' if match else '' + return options.strip() + + def as_header(self): + return self._render(self + list(self.options)) + + @staticmethod + def _strip_quotes(item): + _QUOTES = '"\'' + for q in _QUOTES: + if item.startswith(q) and item.endswith(q): + return item[1:-1] + return item + + @staticmethod + def _render(items): + cmdline = subprocess.list2cmdline( + CommandSpec._strip_quotes(item.strip()) for item in items + ) + return '#!' + cmdline + '\n' + + +class WindowsCommandSpec(CommandSpec): + split_args = _SplitArgs(posix=False) + + +class ScriptWriter: + """ + Encapsulates behavior around writing entry point scripts for console and + gui apps. + """ + + template = textwrap.dedent( + r""" + # EASY-INSTALL-ENTRY-SCRIPT: %(spec)r,%(group)r,%(name)r + import re + import sys + + # for compatibility with easy_install; see #2198 + __requires__ = %(spec)r + + try: + from importlib.metadata import distribution + except ImportError: + try: + from importlib_metadata import distribution + except ImportError: + from pkg_resources import load_entry_point + + + def importlib_load_entry_point(spec, group, name): + dist_name, _, _ = spec.partition('==') + matches = ( + entry_point + for entry_point in distribution(dist_name).entry_points + if entry_point.group == group and entry_point.name == name + ) + return next(matches).load() + + + globals().setdefault('load_entry_point', importlib_load_entry_point) + + + if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) + sys.exit(load_entry_point(%(spec)r, %(group)r, %(name)r)()) + """ + ).lstrip() + + command_spec_class = CommandSpec + + @classmethod + def get_args(cls, dist, header=None): + """ + Yield write_script() argument tuples for a distribution's + console_scripts and gui_scripts entry points. + """ + + # If distribution is not an importlib.metadata.Distribution, assume + # it's a pkg_resources.Distribution and transform it. + if not hasattr(dist, 'entry_points'): + SetuptoolsWarning.emit("Unsupported distribution encountered.") + dist = metadata.Distribution.at(dist.egg_info) + + if header is None: + header = cls.get_header() + spec = f'{dist.name}=={dist.version}' + for type_ in 'console', 'gui': + group = f'{type_}_scripts' + for ep in dist.entry_points.select(group=group): + name = ep.name + cls._ensure_safe_name(ep.name) + script_text = cls.template % locals() + args = cls._get_script_args(type_, ep.name, header, script_text) + yield from args + + @staticmethod + def _ensure_safe_name(name): + """ + Prevent paths in *_scripts entry point names. + """ + has_path_sep = re.search(r'[\\/]', name) + if has_path_sep: + raise ValueError("Path separators not allowed in script names") + + @classmethod + def best(cls): + """ + Select the best ScriptWriter for this environment. + """ + if sys.platform == 'win32' or (os.name == 'java' and os._name == 'nt'): + return WindowsScriptWriter.best() + else: + return cls + + @classmethod + def _get_script_args(cls, type_, name, header, script_text): + # Simply write the stub with no extension. + yield (name, header + script_text) + + @classmethod + def get_header( + cls, + script_text: str = "", + executable: str | CommandSpec | Iterable[str] | None = None, + ) -> str: + """Create a #! line, getting options (if any) from script_text""" + cmd = cls.command_spec_class.best().from_param(executable) + cmd.install_options(script_text) + return cmd.as_header() + + +class WindowsScriptWriter(ScriptWriter): + command_spec_class = WindowsCommandSpec + + @classmethod + def best(cls): + """ + Select the best ScriptWriter suitable for Windows + """ + writer_lookup = dict( + executable=WindowsExecutableLauncherWriter, + natural=cls, + ) + # for compatibility, use the executable launcher by default + launcher = os.environ.get('SETUPTOOLS_LAUNCHER', 'executable') + return writer_lookup[launcher] + + @classmethod + def _get_script_args(cls, type_, name, header, script_text): + "For Windows, add a .py extension" + ext = dict(console='.pya', gui='.pyw')[type_] + if ext not in os.environ['PATHEXT'].lower().split(';'): + msg = ( + "{ext} not listed in PATHEXT; scripts will not be " + "recognized as executables." + ).format(**locals()) + SetuptoolsWarning.emit(msg) + old = ['.pya', '.py', '-script.py', '.pyc', '.pyo', '.pyw', '.exe'] + old.remove(ext) + header = cls._adjust_header(type_, header) + blockers = [name + x for x in old] + yield name + ext, header + script_text, 't', blockers + + @classmethod + def _adjust_header(cls, type_, orig_header): + """ + Make sure 'pythonw' is used for gui and 'python' is used for + console (regardless of what sys.executable is). + """ + pattern = 'pythonw.exe' + repl = 'python.exe' + if type_ == 'gui': + pattern, repl = repl, pattern + pattern_ob = re.compile(re.escape(pattern), re.IGNORECASE) + new_header = pattern_ob.sub(string=orig_header, repl=repl) + return new_header if cls._use_header(new_header) else orig_header + + @staticmethod + def _use_header(new_header): + """ + Should _adjust_header use the replaced header? + + On non-windows systems, always use. On + Windows systems, only use the replaced header if it resolves + to an executable on the system. + """ + clean_header = new_header[2:-1].strip('"') + return sys.platform != 'win32' or shutil.which(clean_header) + + +class WindowsExecutableLauncherWriter(WindowsScriptWriter): + @classmethod + def _get_script_args(cls, type_, name, header, script_text): + """ + For Windows, add a .py extension and an .exe launcher + """ + if type_ == 'gui': + launcher_type = 'gui' + ext = '-script.pyw' + old = ['.pyw'] + else: + launcher_type = 'cli' + ext = '-script.py' + old = ['.py', '.pyc', '.pyo'] + hdr = cls._adjust_header(type_, header) + blockers = [name + x for x in old] + yield (name + ext, hdr + script_text, 't', blockers) + yield ( + name + '.exe', + get_win_launcher(launcher_type), + 'b', # write in binary mode + ) + if not is_64bit(): + # install a manifest for the launcher to prevent Windows + # from detecting it as an installer (which it will for + # launchers like easy_install.exe). Consider only + # adding a manifest for launchers detected as installers. + # See Distribute #143 for details. + m_name = name + '.exe.manifest' + yield (m_name, load_launcher_manifest(name), 't') + + +def get_win_launcher(type): + """ + Load the Windows launcher (executable) suitable for launching a script. + + `type` should be either 'cli' or 'gui' + + Returns the executable as a byte string. + """ + launcher_fn = f'{type}.exe' + if is_64bit(): + if get_platform() == "win-arm64": + launcher_fn = launcher_fn.replace(".", "-arm64.") + else: + launcher_fn = launcher_fn.replace(".", "-64.") + else: + launcher_fn = launcher_fn.replace(".", "-32.") + return resources.files('setuptools').joinpath(launcher_fn).read_bytes() + + +def load_launcher_manifest(name): + res = resources.files(__name__).joinpath('launcher manifest.xml') + return res.read_text(encoding='utf-8') % vars() + + +def _first_line_re(): + """ + Return a regular expression based on first_line_re suitable for matching + strings. + """ + if isinstance(first_line_re.pattern, str): + return first_line_re + + # first_line_re in Python >=3.1.4 and >=3.2.1 is a bytes pattern. + return re.compile(first_line_re.pattern.decode()) + + +def is_64bit(): + return struct.calcsize("P") == 8 + + +def isascii(s): + try: + s.encode('ascii') + except UnicodeError: + return False + return True diff --git a/lib/python3.11/site-packages/setuptools/_shutil.py b/lib/python3.11/site-packages/setuptools/_shutil.py new file mode 100644 index 0000000000000000000000000000000000000000..660459a1102eed0da597ebd7c1a38cb4eee99791 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_shutil.py @@ -0,0 +1,59 @@ +"""Convenience layer on top of stdlib's shutil and os""" + +import os +import stat +from typing import Callable, TypeVar + +from .compat import py311 + +from distutils import log + +try: + from os import chmod # pyright: ignore[reportAssignmentType] + # Losing type-safety w/ pyright, but that's ok +except ImportError: # pragma: no cover + # Jython compatibility + def chmod(*args: object, **kwargs: object) -> None: # type: ignore[misc] # Mypy reuses the imported definition anyway + pass + + +_T = TypeVar("_T") + + +def attempt_chmod_verbose(path, mode): + log.debug("changing mode of %s to %o", path, mode) + try: + chmod(path, mode) + except OSError as e: # pragma: no cover + log.debug("chmod failed: %s", e) + + +# Must match shutil._OnExcCallback +def _auto_chmod( + func: Callable[..., _T], arg: str, exc: BaseException +) -> _T: # pragma: no cover + """shutils onexc callback to automatically call chmod for certain functions.""" + # Only retry for scenarios known to have an issue + if func in [os.unlink, os.remove] and os.name == 'nt': + attempt_chmod_verbose(arg, stat.S_IWRITE) + return func(arg) + raise exc + + +def rmtree(path, ignore_errors=False, onexc=_auto_chmod): + """ + Similar to ``shutil.rmtree`` but automatically executes ``chmod`` + for well know Windows failure scenarios. + """ + return py311.shutil_rmtree(path, ignore_errors, onexc) + + +def rmdir(path, **opts): + if os.path.isdir(path): + rmtree(path, **opts) + + +def current_umask(): + tmp = os.umask(0o022) + os.umask(tmp) + return tmp diff --git a/lib/python3.11/site-packages/setuptools/_static.py b/lib/python3.11/site-packages/setuptools/_static.py new file mode 100644 index 0000000000000000000000000000000000000000..af35862cf8b759a0e60110ce9e92bfdb1b49bc5f --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/_static.py @@ -0,0 +1,188 @@ +from functools import wraps +from typing import TypeVar + +import packaging.specifiers + +from .warnings import SetuptoolsDeprecationWarning + + +class Static: + """ + Wrapper for built-in object types that are allow setuptools to identify + static core metadata (in opposition to ``Dynamic``, as defined :pep:`643`). + + The trick is to mark values with :class:`Static` when they come from + ``pyproject.toml`` or ``setup.cfg``, so if any plugin overwrite the value + with a built-in, setuptools will be able to recognise the change. + + We inherit from built-in classes, so that we don't need to change the existing + code base to deal with the new types. + We also should strive for immutability objects to avoid changes after the + initial parsing. + """ + + _mutated_: bool = False # TODO: Remove after deprecation warning is solved + + +def _prevent_modification(target: type, method: str, copying: str) -> None: + """ + Because setuptools is very flexible we cannot fully prevent + plugins and user customizations from modifying static values that were + parsed from config files. + But we can attempt to block "in-place" mutations and identify when they + were done. + """ + fn = getattr(target, method, None) + if fn is None: + return + + @wraps(fn) + def _replacement(self: Static, *args, **kwargs): + # TODO: After deprecation period raise NotImplementedError instead of warning + # which obviated the existence and checks of the `_mutated_` attribute. + self._mutated_ = True + SetuptoolsDeprecationWarning.emit( + "Direct modification of value will be disallowed", + f""" + In an effort to implement PEP 643, direct/in-place changes of static values + that come from configuration files are deprecated. + If you need to modify this value, please first create a copy with {copying} + and make sure conform to all relevant standards when overriding setuptools + functionality (https://packaging.python.org/en/latest/specifications/). + """, + due_date=(2025, 10, 10), # Initially introduced in 2024-09-06 + ) + return fn(self, *args, **kwargs) + + _replacement.__doc__ = "" # otherwise doctest may fail. + setattr(target, method, _replacement) + + +class Str(str, Static): + pass + + +class Tuple(tuple, Static): + pass + + +class List(list, Static): + """ + :meta private: + >>> x = List([1, 2, 3]) + >>> is_static(x) + True + >>> x += [0] # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + SetuptoolsDeprecationWarning: Direct modification ... + >>> is_static(x) # no longer static after modification + False + >>> y = list(x) + >>> y.clear() + >>> y + [] + >>> y == x + False + >>> is_static(List(y)) + True + """ + + +# Make `List` immutable-ish +# (certain places of setuptools/distutils issue a warn if we use tuple instead of list) +for _method in ( + '__delitem__', + '__iadd__', + '__setitem__', + 'append', + 'clear', + 'extend', + 'insert', + 'remove', + 'reverse', + 'pop', +): + _prevent_modification(List, _method, "`list(value)`") + + +class Dict(dict, Static): + """ + :meta private: + >>> x = Dict({'a': 1, 'b': 2}) + >>> is_static(x) + True + >>> x['c'] = 0 # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + SetuptoolsDeprecationWarning: Direct modification ... + >>> x._mutated_ + True + >>> is_static(x) # no longer static after modification + False + >>> y = dict(x) + >>> y.popitem() + ('b', 2) + >>> y == x + False + >>> is_static(Dict(y)) + True + """ + + +# Make `Dict` immutable-ish (we cannot inherit from types.MappingProxyType): +for _method in ( + '__delitem__', + '__ior__', + '__setitem__', + 'clear', + 'pop', + 'popitem', + 'setdefault', + 'update', +): + _prevent_modification(Dict, _method, "`dict(value)`") + + +class SpecifierSet(packaging.specifiers.SpecifierSet, Static): + """Not exactly a built-in type but useful for ``requires-python``""" + + +T = TypeVar("T") + + +def noop(value: T) -> T: + """ + >>> noop(42) + 42 + """ + return value + + +_CONVERSIONS = {str: Str, tuple: Tuple, list: List, dict: Dict} + + +def attempt_conversion(value: T) -> T: + """ + >>> is_static(attempt_conversion("hello")) + True + >>> is_static(object()) + False + """ + return _CONVERSIONS.get(type(value), noop)(value) # type: ignore[call-overload] + + +def is_static(value: object) -> bool: + """ + >>> is_static(a := Dict({'a': 1})) + True + >>> is_static(dict(a)) + False + >>> is_static(b := List([1, 2, 3])) + True + >>> is_static(list(b)) + False + """ + return isinstance(value, Static) and not value._mutated_ + + +EMPTY_LIST = List() +EMPTY_DICT = Dict() diff --git a/lib/python3.11/site-packages/setuptools/archive_util.py b/lib/python3.11/site-packages/setuptools/archive_util.py new file mode 100644 index 0000000000000000000000000000000000000000..1a02010bb2af2be0487730d6a32080877b9ac220 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/archive_util.py @@ -0,0 +1,219 @@ +"""Utilities for extracting common archive formats""" + +import contextlib +import os +import posixpath +import shutil +import tarfile +import zipfile + +from ._path import ensure_directory + +from distutils.errors import DistutilsError + +__all__ = [ + "unpack_archive", + "unpack_zipfile", + "unpack_tarfile", + "default_filter", + "UnrecognizedFormat", + "extraction_drivers", + "unpack_directory", +] + + +class UnrecognizedFormat(DistutilsError): + """Couldn't recognize the archive type""" + + +def default_filter(src, dst): + """The default progress/filter callback; returns True for all files""" + return dst + + +def unpack_archive( + filename, extract_dir, progress_filter=default_filter, drivers=None +) -> None: + """Unpack `filename` to `extract_dir`, or raise ``UnrecognizedFormat`` + + `progress_filter` is a function taking two arguments: a source path + internal to the archive ('/'-separated), and a filesystem path where it + will be extracted. The callback must return the desired extract path + (which may be the same as the one passed in), or else ``None`` to skip + that file or directory. The callback can thus be used to report on the + progress of the extraction, as well as to filter the items extracted or + alter their extraction paths. + + `drivers`, if supplied, must be a non-empty sequence of functions with the + same signature as this function (minus the `drivers` argument), that raise + ``UnrecognizedFormat`` if they do not support extracting the designated + archive type. The `drivers` are tried in sequence until one is found that + does not raise an error, or until all are exhausted (in which case + ``UnrecognizedFormat`` is raised). If you do not supply a sequence of + drivers, the module's ``extraction_drivers`` constant will be used, which + means that ``unpack_zipfile`` and ``unpack_tarfile`` will be tried, in that + order. + """ + for driver in drivers or extraction_drivers: + try: + driver(filename, extract_dir, progress_filter) + except UnrecognizedFormat: + continue + else: + return + else: + raise UnrecognizedFormat(f"Not a recognized archive type: {filename}") + + +def unpack_directory(filename, extract_dir, progress_filter=default_filter) -> None: + """ "Unpack" a directory, using the same interface as for archives + + Raises ``UnrecognizedFormat`` if `filename` is not a directory + """ + if not os.path.isdir(filename): + raise UnrecognizedFormat(f"{filename} is not a directory") + + paths = { + filename: ('', extract_dir), + } + for base, dirs, files in os.walk(filename): + src, dst = paths[base] + for d in dirs: + paths[os.path.join(base, d)] = src + d + '/', os.path.join(dst, d) + for f in files: + target = os.path.join(dst, f) + target = progress_filter(src + f, target) + if not target: + # skip non-files + continue + ensure_directory(target) + f = os.path.join(base, f) + shutil.copyfile(f, target) + shutil.copystat(f, target) + + +def unpack_zipfile(filename, extract_dir, progress_filter=default_filter) -> None: + """Unpack zip `filename` to `extract_dir` + + Raises ``UnrecognizedFormat`` if `filename` is not a zipfile (as determined + by ``zipfile.is_zipfile()``). See ``unpack_archive()`` for an explanation + of the `progress_filter` argument. + """ + + if not zipfile.is_zipfile(filename): + raise UnrecognizedFormat(f"{filename} is not a zip file") + + with zipfile.ZipFile(filename) as z: + _unpack_zipfile_obj(z, extract_dir, progress_filter) + + +def _unpack_zipfile_obj(zipfile_obj, extract_dir, progress_filter=default_filter): + """Internal/private API used by other parts of setuptools. + Similar to ``unpack_zipfile``, but receives an already opened :obj:`zipfile.ZipFile` + object instead of a filename. + """ + for info in zipfile_obj.infolist(): + name = info.filename + + # don't extract absolute paths or ones with .. in them + if name.startswith('/') or '..' in name.split('/'): + continue + + target = os.path.join(extract_dir, *name.split('/')) + target = progress_filter(name, target) + if not target: + continue + if name.endswith('/'): + # directory + ensure_directory(target) + else: + # file + ensure_directory(target) + data = zipfile_obj.read(info.filename) + with open(target, 'wb') as f: + f.write(data) + unix_attributes = info.external_attr >> 16 + if unix_attributes: + os.chmod(target, unix_attributes) + + +def _resolve_tar_file_or_dir(tar_obj, tar_member_obj): + """Resolve any links and extract link targets as normal files.""" + while tar_member_obj is not None and ( + tar_member_obj.islnk() or tar_member_obj.issym() + ): + linkpath = tar_member_obj.linkname + if tar_member_obj.issym(): + base = posixpath.dirname(tar_member_obj.name) + linkpath = posixpath.join(base, linkpath) + linkpath = posixpath.normpath(linkpath) + tar_member_obj = tar_obj._getmember(linkpath) + + is_file_or_dir = tar_member_obj is not None and ( + tar_member_obj.isfile() or tar_member_obj.isdir() + ) + if is_file_or_dir: + return tar_member_obj + + raise LookupError('Got unknown file type') + + +def _iter_open_tar(tar_obj, extract_dir, progress_filter): + """Emit member-destination pairs from a tar archive.""" + # don't do any chowning! + tar_obj.chown = lambda *args: None + + with contextlib.closing(tar_obj): + for member in tar_obj: + name = member.name + # don't extract absolute paths or ones with .. in them + if name.startswith('/') or '..' in name.split('/'): + continue + + prelim_dst = os.path.join(extract_dir, *name.split('/')) + + try: + member = _resolve_tar_file_or_dir(tar_obj, member) + except LookupError: + continue + + final_dst = progress_filter(name, prelim_dst) + if not final_dst: + continue + + if final_dst.endswith(os.sep): + final_dst = final_dst[:-1] + + yield member, final_dst + + +def unpack_tarfile(filename, extract_dir, progress_filter=default_filter) -> bool: + """Unpack tar/tar.gz/tar.bz2 `filename` to `extract_dir` + + Raises ``UnrecognizedFormat`` if `filename` is not a tarfile (as determined + by ``tarfile.open()``). See ``unpack_archive()`` for an explanation + of the `progress_filter` argument. + """ + try: + tarobj = tarfile.open(filename) + except tarfile.TarError as e: + raise UnrecognizedFormat( + f"{filename} is not a compressed or uncompressed tar file" + ) from e + + for member, final_dst in _iter_open_tar( + tarobj, + extract_dir, + progress_filter, + ): + try: + # XXX Ugh + tarobj._extract_member(member, final_dst) + except tarfile.ExtractError: + # chown/chmod/mkfifo/mknode/makedev failed + pass + + return True + + +extraction_drivers = unpack_directory, unpack_zipfile, unpack_tarfile diff --git a/lib/python3.11/site-packages/setuptools/build_meta.py b/lib/python3.11/site-packages/setuptools/build_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..1d93b6193be40883b113265aac44ccd775075a0d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/build_meta.py @@ -0,0 +1,556 @@ +"""A PEP 517 interface to setuptools + +Previously, when a user or a command line tool (let's call it a "frontend") +needed to make a request of setuptools to take a certain action, for +example, generating a list of installation requirements, the frontend +would call "setup.py egg_info" or "setup.py bdist_wheel" on the command line. + +PEP 517 defines a different method of interfacing with setuptools. Rather +than calling "setup.py" directly, the frontend should: + + 1. Set the current directory to the directory with a setup.py file + 2. Import this module into a safe python interpreter (one in which + setuptools can potentially set global variables or crash hard). + 3. Call one of the functions defined in PEP 517. + +What each function does is defined in PEP 517. However, here is a "casual" +definition of the functions (this definition should not be relied on for +bug reports or API stability): + + - `build_wheel`: build a wheel in the folder and return the basename + - `get_requires_for_build_wheel`: get the `setup_requires` to build + - `prepare_metadata_for_build_wheel`: get the `install_requires` + - `build_sdist`: build an sdist in the folder and return the basename + - `get_requires_for_build_sdist`: get the `setup_requires` to build + +Again, this is not a formal definition! Just a "taste" of the module. +""" + +from __future__ import annotations + +import contextlib +import io +import os +import shlex +import shutil +import sys +import tempfile +import tokenize +import warnings +from collections.abc import Iterable, Iterator, Mapping +from pathlib import Path +from typing import TYPE_CHECKING, NoReturn, Union + +import setuptools + +from . import errors +from ._path import StrPath, same_path +from ._reqs import parse_strings +from .warnings import SetuptoolsDeprecationWarning + +import distutils +from distutils.util import strtobool + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + +__all__ = [ + 'get_requires_for_build_sdist', + 'get_requires_for_build_wheel', + 'prepare_metadata_for_build_wheel', + 'build_wheel', + 'build_sdist', + 'get_requires_for_build_editable', + 'prepare_metadata_for_build_editable', + 'build_editable', + '__legacy__', + 'SetupRequirementsError', +] + + +class SetupRequirementsError(BaseException): + def __init__(self, specifiers) -> None: + self.specifiers = specifiers + + +class Distribution(setuptools.dist.Distribution): + def fetch_build_eggs(self, specifiers) -> NoReturn: + specifier_list = list(parse_strings(specifiers)) + + raise SetupRequirementsError(specifier_list) + + @classmethod + @contextlib.contextmanager + def patch(cls) -> Iterator[None]: + """ + Replace + distutils.dist.Distribution with this class + for the duration of this context. + """ + orig = distutils.core.Distribution + distutils.core.Distribution = cls # type: ignore[misc] # monkeypatching + try: + yield + finally: + distutils.core.Distribution = orig # type: ignore[misc] # monkeypatching + + +@contextlib.contextmanager +def no_install_setup_requires(): + """Temporarily disable installing setup_requires + + Under PEP 517, the backend reports build dependencies to the frontend, + and the frontend is responsible for ensuring they're installed. + So setuptools (acting as a backend) should not try to install them. + """ + orig = setuptools._install_setup_requires + setuptools._install_setup_requires = lambda attrs: None + try: + yield + finally: + setuptools._install_setup_requires = orig + + +def _get_immediate_subdirectories(a_dir): + return [ + name for name in os.listdir(a_dir) if os.path.isdir(os.path.join(a_dir, name)) + ] + + +def _file_with_extension(directory: StrPath, extension: str | tuple[str, ...]): + matching = (f for f in os.listdir(directory) if f.endswith(extension)) + try: + (file,) = matching + except ValueError: + raise ValueError( + 'No distribution was found. Ensure that `setup.py` ' + 'is not empty and that it calls `setup()`.' + ) from None + return file + + +def _open_setup_script(setup_script): + if not os.path.exists(setup_script): + # Supply a default setup.py + return io.StringIO("from setuptools import setup; setup()") + + return tokenize.open(setup_script) + + +@contextlib.contextmanager +def suppress_known_deprecation(): + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', 'setup.py install is deprecated') + yield + + +_ConfigSettings: TypeAlias = Union[Mapping[str, Union[str, list[str], None]], None] +""" +Currently the user can run:: + + pip install -e . --config-settings key=value + python -m build -C--key=value -C key=value + +- pip will pass both key and value as strings and overwriting repeated keys + (pypa/pip#11059). +- build will accumulate values associated with repeated keys in a list. + It will also accept keys with no associated value. + This means that an option passed by build can be ``str | list[str] | None``. +- PEP 517 specifies that ``config_settings`` is an optional dict. +""" + + +class _ConfigSettingsTranslator: + """Translate ``config_settings`` into distutils-style command arguments. + Only a limited number of options is currently supported. + """ + + # See pypa/setuptools#1928 pypa/setuptools#2491 + + def _get_config(self, key: str, config_settings: _ConfigSettings) -> list[str]: + """ + Get the value of a specific key in ``config_settings`` as a list of strings. + + >>> fn = _ConfigSettingsTranslator()._get_config + >>> fn("--global-option", None) + [] + >>> fn("--global-option", {}) + [] + >>> fn("--global-option", {'--global-option': 'foo'}) + ['foo'] + >>> fn("--global-option", {'--global-option': ['foo']}) + ['foo'] + >>> fn("--global-option", {'--global-option': 'foo'}) + ['foo'] + >>> fn("--global-option", {'--global-option': 'foo bar'}) + ['foo', 'bar'] + """ + cfg = config_settings or {} + opts = cfg.get(key) or [] + return shlex.split(opts) if isinstance(opts, str) else opts + + def _global_args(self, config_settings: _ConfigSettings) -> Iterator[str]: + """ + Let the user specify ``verbose`` or ``quiet`` + escape hatch via + ``--global-option``. + Note: ``-v``, ``-vv``, ``-vvv`` have similar effects in setuptools, + so we just have to cover the basic scenario ``-v``. + + >>> fn = _ConfigSettingsTranslator()._global_args + >>> list(fn(None)) + [] + >>> list(fn({"verbose": "False"})) + ['-q'] + >>> list(fn({"verbose": "1"})) + ['-v'] + >>> list(fn({"--verbose": None})) + ['-v'] + >>> list(fn({"verbose": "true", "--global-option": "-q --no-user-cfg"})) + ['-v', '-q', '--no-user-cfg'] + >>> list(fn({"--quiet": None})) + ['-q'] + """ + cfg = config_settings or {} + falsey = {"false", "no", "0", "off"} + if "verbose" in cfg or "--verbose" in cfg: + level = str(cfg.get("verbose") or cfg.get("--verbose") or "1") + yield ("-q" if level.lower() in falsey else "-v") + if "quiet" in cfg or "--quiet" in cfg: + level = str(cfg.get("quiet") or cfg.get("--quiet") or "1") + yield ("-v" if level.lower() in falsey else "-q") + + yield from self._get_config("--global-option", config_settings) + + def __dist_info_args(self, config_settings: _ConfigSettings) -> Iterator[str]: + """ + The ``dist_info`` command accepts ``tag-date`` and ``tag-build``. + + .. warning:: + We cannot use this yet as it requires the ``sdist`` and ``bdist_wheel`` + commands run in ``build_sdist`` and ``build_wheel`` to reuse the egg-info + directory created in ``prepare_metadata_for_build_wheel``. + + >>> fn = _ConfigSettingsTranslator()._ConfigSettingsTranslator__dist_info_args + >>> list(fn(None)) + [] + >>> list(fn({"tag-date": "False"})) + ['--no-date'] + >>> list(fn({"tag-date": None})) + ['--no-date'] + >>> list(fn({"tag-date": "true", "tag-build": ".a"})) + ['--tag-date', '--tag-build', '.a'] + """ + cfg = config_settings or {} + if "tag-date" in cfg: + val = strtobool(str(cfg["tag-date"] or "false")) + yield ("--tag-date" if val else "--no-date") + if "tag-build" in cfg: + yield from ["--tag-build", str(cfg["tag-build"])] + + def _editable_args(self, config_settings: _ConfigSettings) -> Iterator[str]: + """ + The ``editable_wheel`` command accepts ``editable-mode=strict``. + + >>> fn = _ConfigSettingsTranslator()._editable_args + >>> list(fn(None)) + [] + >>> list(fn({"editable-mode": "strict"})) + ['--mode', 'strict'] + """ + cfg = config_settings or {} + mode = cfg.get("editable-mode") or cfg.get("editable_mode") + if not mode: + return + yield from ["--mode", str(mode)] + + def _arbitrary_args(self, config_settings: _ConfigSettings) -> Iterator[str]: + """ + Users may expect to pass arbitrary lists of arguments to a command + via "--global-option" (example provided in PEP 517 of a "escape hatch"). + + >>> fn = _ConfigSettingsTranslator()._arbitrary_args + >>> list(fn(None)) + [] + >>> list(fn({})) + [] + >>> list(fn({'--build-option': 'foo'})) + ['foo'] + >>> list(fn({'--build-option': ['foo']})) + ['foo'] + >>> list(fn({'--build-option': 'foo'})) + ['foo'] + >>> list(fn({'--build-option': 'foo bar'})) + ['foo', 'bar'] + >>> list(fn({'--global-option': 'foo'})) + [] + """ + yield from self._get_config("--build-option", config_settings) + + +class _BuildMetaBackend(_ConfigSettingsTranslator): + def _get_build_requires( + self, config_settings: _ConfigSettings, requirements: list[str] + ): + sys.argv = [ + *sys.argv[:1], + *self._global_args(config_settings), + "egg_info", + ] + try: + with Distribution.patch(): + self.run_setup() + except SetupRequirementsError as e: + requirements += e.specifiers + + return requirements + + def run_setup(self, setup_script: str = 'setup.py') -> None: + # Note that we can reuse our build directory between calls + # Correctness comes first, then optimization later + __file__ = os.path.abspath(setup_script) + __name__ = '__main__' + + with _open_setup_script(__file__) as f: + code = f.read().replace(r'\r\n', r'\n') + + try: + exec(code, locals()) + except SystemExit as e: + if e.code: + raise + # We ignore exit code indicating success + SetuptoolsDeprecationWarning.emit( + "Running `setup.py` directly as CLI tool is deprecated.", + "Please avoid using `sys.exit(0)` or similar statements " + "that don't fit in the paradigm of a configuration file.", + see_url="https://blog.ganssle.io/articles/2021/10/" + "setup-py-deprecated.html", + ) + + def get_requires_for_build_wheel( + self, config_settings: _ConfigSettings = None + ) -> list[str]: + return self._get_build_requires(config_settings, requirements=[]) + + def get_requires_for_build_sdist( + self, config_settings: _ConfigSettings = None + ) -> list[str]: + return self._get_build_requires(config_settings, requirements=[]) + + def _bubble_up_info_directory( + self, metadata_directory: StrPath, suffix: str + ) -> str: + """ + PEP 517 requires that the .dist-info directory be placed in the + metadata_directory. To comply, we MUST copy the directory to the root. + + Returns the basename of the info directory, e.g. `proj-0.0.0.dist-info`. + """ + info_dir = self._find_info_directory(metadata_directory, suffix) + if not same_path(info_dir.parent, metadata_directory): + shutil.move(str(info_dir), metadata_directory) + # PEP 517 allow other files and dirs to exist in metadata_directory + return info_dir.name + + def _find_info_directory(self, metadata_directory: StrPath, suffix: str) -> Path: + for parent, dirs, _ in os.walk(metadata_directory): + candidates = [f for f in dirs if f.endswith(suffix)] + + if len(candidates) != 0 or len(dirs) != 1: + assert len(candidates) == 1, ( + f"Exactly one {suffix} should have been produced, but found {len(candidates)}: {candidates}" + ) + return Path(parent, candidates[0]) + + msg = f"No {suffix} directory found in {metadata_directory}" + raise errors.InternalError(msg) + + def prepare_metadata_for_build_wheel( + self, metadata_directory: StrPath, config_settings: _ConfigSettings = None + ) -> str: + sys.argv = [ + *sys.argv[:1], + *self._global_args(config_settings), + "dist_info", + "--output-dir", + str(metadata_directory), + "--keep-egg-info", + ] + with no_install_setup_requires(): + self.run_setup() + + self._bubble_up_info_directory(metadata_directory, ".egg-info") + return self._bubble_up_info_directory(metadata_directory, ".dist-info") + + def _build_with_temp_dir( + self, + setup_command: Iterable[str], + result_extension: str | tuple[str, ...], + result_directory: StrPath, + config_settings: _ConfigSettings, + arbitrary_args: Iterable[str] = (), + ): + result_directory = os.path.abspath(result_directory) + + # Build in a temporary directory, then copy to the target. + os.makedirs(result_directory, exist_ok=True) + + with tempfile.TemporaryDirectory( + prefix=".tmp-", dir=result_directory + ) as tmp_dist_dir: + sys.argv = [ + *sys.argv[:1], + *self._global_args(config_settings), + *setup_command, + "--dist-dir", + tmp_dist_dir, + *arbitrary_args, + ] + with no_install_setup_requires(): + self.run_setup() + + result_basename = _file_with_extension(tmp_dist_dir, result_extension) + result_path = os.path.join(result_directory, result_basename) + if os.path.exists(result_path): + # os.rename will fail overwriting on non-Unix. + os.remove(result_path) + os.rename(os.path.join(tmp_dist_dir, result_basename), result_path) + + return result_basename + + def build_wheel( + self, + wheel_directory: StrPath, + config_settings: _ConfigSettings = None, + metadata_directory: StrPath | None = None, + ) -> str: + def _build(cmd: list[str]): + with suppress_known_deprecation(): + return self._build_with_temp_dir( + cmd, + '.whl', + wheel_directory, + config_settings, + self._arbitrary_args(config_settings), + ) + + if metadata_directory is None: + return _build(['bdist_wheel']) + + try: + return _build(['bdist_wheel', '--dist-info-dir', str(metadata_directory)]) + except SystemExit as ex: # pragma: nocover + # pypa/setuptools#4683 + if "--dist-info-dir not recognized" not in str(ex): + raise + _IncompatibleBdistWheel.emit() + return _build(['bdist_wheel']) + + def build_sdist( + self, sdist_directory: StrPath, config_settings: _ConfigSettings = None + ) -> str: + return self._build_with_temp_dir( + ['sdist', '--formats', 'gztar'], '.tar.gz', sdist_directory, config_settings + ) + + def _get_dist_info_dir(self, metadata_directory: StrPath | None) -> str | None: + if not metadata_directory: + return None + dist_info_candidates = list(Path(metadata_directory).glob("*.dist-info")) + assert len(dist_info_candidates) <= 1 + return str(dist_info_candidates[0]) if dist_info_candidates else None + + def build_editable( + self, + wheel_directory: StrPath, + config_settings: _ConfigSettings = None, + metadata_directory: StrPath | None = None, + ) -> str: + # XXX can or should we hide our editable_wheel command normally? + info_dir = self._get_dist_info_dir(metadata_directory) + opts = ["--dist-info-dir", info_dir] if info_dir else [] + cmd = ["editable_wheel", *opts, *self._editable_args(config_settings)] + with suppress_known_deprecation(): + return self._build_with_temp_dir( + cmd, ".whl", wheel_directory, config_settings + ) + + def get_requires_for_build_editable( + self, config_settings: _ConfigSettings = None + ) -> list[str]: + return self.get_requires_for_build_wheel(config_settings) + + def prepare_metadata_for_build_editable( + self, metadata_directory: StrPath, config_settings: _ConfigSettings = None + ) -> str: + return self.prepare_metadata_for_build_wheel( + metadata_directory, config_settings + ) + + +class _BuildMetaLegacyBackend(_BuildMetaBackend): + """Compatibility backend for setuptools + + This is a version of setuptools.build_meta that endeavors + to maintain backwards + compatibility with pre-PEP 517 modes of invocation. It + exists as a temporary + bridge between the old packaging mechanism and the new + packaging mechanism, + and will eventually be removed. + """ + + def run_setup(self, setup_script: str = 'setup.py') -> None: + # In order to maintain compatibility with scripts assuming that + # the setup.py script is in a directory on the PYTHONPATH, inject + # '' into sys.path. (pypa/setuptools#1642) + sys_path = list(sys.path) # Save the original path + + script_dir = os.path.dirname(os.path.abspath(setup_script)) + if script_dir not in sys.path: + sys.path.insert(0, script_dir) + + # Some setup.py scripts (e.g. in pygame and numpy) use sys.argv[0] to + # get the directory of the source code. They expect it to refer to the + # setup.py script. + sys_argv_0 = sys.argv[0] + sys.argv[0] = setup_script + + try: + super().run_setup(setup_script=setup_script) + finally: + # While PEP 517 frontends should be calling each hook in a fresh + # subprocess according to the standard (and thus it should not be + # strictly necessary to restore the old sys.path), we'll restore + # the original path so that the path manipulation does not persist + # within the hook after run_setup is called. + sys.path[:] = sys_path + sys.argv[0] = sys_argv_0 + + +class _IncompatibleBdistWheel(SetuptoolsDeprecationWarning): + _SUMMARY = "wheel.bdist_wheel is deprecated, please import it from setuptools" + _DETAILS = """ + Ensure that any custom bdist_wheel implementation is a subclass of + setuptools.command.bdist_wheel.bdist_wheel. + """ + _DUE_DATE = (2025, 10, 15) + # Initially introduced in 2024/10/15, but maybe too disruptive to be enforced? + _SEE_URL = "https://github.com/pypa/wheel/pull/631" + + +# The primary backend +_BACKEND = _BuildMetaBackend() + +get_requires_for_build_wheel = _BACKEND.get_requires_for_build_wheel +get_requires_for_build_sdist = _BACKEND.get_requires_for_build_sdist +prepare_metadata_for_build_wheel = _BACKEND.prepare_metadata_for_build_wheel +build_wheel = _BACKEND.build_wheel +build_sdist = _BACKEND.build_sdist +get_requires_for_build_editable = _BACKEND.get_requires_for_build_editable +prepare_metadata_for_build_editable = _BACKEND.prepare_metadata_for_build_editable +build_editable = _BACKEND.build_editable + + +# The legacy backend +__legacy__ = _BuildMetaLegacyBackend() diff --git a/lib/python3.11/site-packages/setuptools/cli-32.exe b/lib/python3.11/site-packages/setuptools/cli-32.exe new file mode 100644 index 0000000000000000000000000000000000000000..65c3cd99cc7433f271a5b9387abdd1ddb949d1a6 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/cli-32.exe differ diff --git a/lib/python3.11/site-packages/setuptools/cli-64.exe b/lib/python3.11/site-packages/setuptools/cli-64.exe new file mode 100644 index 0000000000000000000000000000000000000000..3ea50eebfe3f0113b231a318cc1ad6e238afd60d Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/cli-64.exe differ diff --git a/lib/python3.11/site-packages/setuptools/cli-arm64.exe b/lib/python3.11/site-packages/setuptools/cli-arm64.exe new file mode 100644 index 0000000000000000000000000000000000000000..da96455a07a0bad4cde5dc5626544325f82c722b Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/cli-arm64.exe differ diff --git a/lib/python3.11/site-packages/setuptools/cli.exe b/lib/python3.11/site-packages/setuptools/cli.exe new file mode 100644 index 0000000000000000000000000000000000000000..65c3cd99cc7433f271a5b9387abdd1ddb949d1a6 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/cli.exe differ diff --git a/lib/python3.11/site-packages/setuptools/depends.py b/lib/python3.11/site-packages/setuptools/depends.py new file mode 100644 index 0000000000000000000000000000000000000000..e5223b79561c36d9b6c45ead78288098e1cb0f1d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/depends.py @@ -0,0 +1,185 @@ +from __future__ import annotations + +import contextlib +import dis +import marshal +import sys +from types import CodeType +from typing import Any, Literal, TypeVar + +from packaging.version import Version + +from . import _imp +from ._imp import PY_COMPILED, PY_FROZEN, PY_SOURCE, find_module + +_T = TypeVar("_T") + +__all__ = ['Require', 'find_module'] + + +class Require: + """A prerequisite to building or installing a distribution""" + + def __init__( + self, + name, + requested_version, + module, + homepage: str = '', + attribute=None, + format=None, + ) -> None: + if format is None and requested_version is not None: + format = Version + + if format is not None: + requested_version = format(requested_version) + if attribute is None: + attribute = '__version__' + + self.__dict__.update(locals()) + del self.self + + def full_name(self): + """Return full package/distribution name, w/version""" + if self.requested_version is not None: + return f'{self.name}-{self.requested_version}' + return self.name + + def version_ok(self, version): + """Is 'version' sufficiently up-to-date?""" + return ( + self.attribute is None + or self.format is None + or str(version) != "unknown" + and self.format(version) >= self.requested_version + ) + + def get_version( + self, paths=None, default: _T | Literal["unknown"] = "unknown" + ) -> _T | Literal["unknown"] | None | Any: + """Get version number of installed module, 'None', or 'default' + + Search 'paths' for module. If not found, return 'None'. If found, + return the extracted version attribute, or 'default' if no version + attribute was specified, or the value cannot be determined without + importing the module. The version is formatted according to the + requirement's version format (if any), unless it is 'None' or the + supplied 'default'. + """ + + if self.attribute is None: + try: + f, _p, _i = find_module(self.module, paths) + except ImportError: + return None + if f: + f.close() + return default + + v = get_module_constant(self.module, self.attribute, default, paths) + + if v is not None and v is not default and self.format is not None: + return self.format(v) + + return v + + def is_present(self, paths=None): + """Return true if dependency is present on 'paths'""" + return self.get_version(paths) is not None + + def is_current(self, paths=None): + """Return true if dependency is present and up-to-date on 'paths'""" + version = self.get_version(paths) + if version is None: + return False + return self.version_ok(str(version)) + + +def maybe_close(f): + @contextlib.contextmanager + def empty(): + yield + return + + if not f: + return empty() + + return contextlib.closing(f) + + +# Some objects are not available on some platforms. +# XXX it'd be better to test assertions about bytecode instead. +if not sys.platform.startswith('java') and sys.platform != 'cli': + + def get_module_constant( + module, symbol, default: _T | int = -1, paths=None + ) -> _T | int | None | Any: + """Find 'module' by searching 'paths', and extract 'symbol' + + Return 'None' if 'module' does not exist on 'paths', or it does not define + 'symbol'. If the module defines 'symbol' as a constant, return the + constant. Otherwise, return 'default'.""" + + try: + f, path, (_suffix, _mode, kind) = info = find_module(module, paths) + except ImportError: + # Module doesn't exist + return None + + with maybe_close(f): + if kind == PY_COMPILED: + f.read(8) # skip magic & date + code = marshal.load(f) + elif kind == PY_FROZEN: + code = _imp.get_frozen_object(module, paths) + elif kind == PY_SOURCE: + code = compile(f.read(), path, 'exec') + else: + # Not something we can parse; we'll have to import it. :( + imported = _imp.get_module(module, paths, info) + return getattr(imported, symbol, None) + + return extract_constant(code, symbol, default) + + def extract_constant( + code: CodeType, symbol: str, default: _T | int = -1 + ) -> _T | int | None | Any: + """Extract the constant value of 'symbol' from 'code' + + If the name 'symbol' is bound to a constant value by the Python code + object 'code', return that value. If 'symbol' is bound to an expression, + return 'default'. Otherwise, return 'None'. + + Return value is based on the first assignment to 'symbol'. 'symbol' must + be a global, or at least a non-"fast" local in the code block. That is, + only 'STORE_NAME' and 'STORE_GLOBAL' opcodes are checked, and 'symbol' + must be present in 'code.co_names'. + """ + if symbol not in code.co_names: + # name's not there, can't possibly be an assignment + return None + + name_idx = list(code.co_names).index(symbol) + + STORE_NAME = dis.opmap['STORE_NAME'] + STORE_GLOBAL = dis.opmap['STORE_GLOBAL'] + LOAD_CONST = dis.opmap['LOAD_CONST'] + + const = default + + for byte_code in dis.Bytecode(code): + op = byte_code.opcode + arg = byte_code.arg + + if op == LOAD_CONST: + assert arg is not None + const = code.co_consts[arg] + elif arg == name_idx and (op == STORE_NAME or op == STORE_GLOBAL): + return const + else: + const = default + + return None + + __all__ += ['get_module_constant', 'extract_constant'] diff --git a/lib/python3.11/site-packages/setuptools/discovery.py b/lib/python3.11/site-packages/setuptools/discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..296d3193ab429f64bc5f3bfdcad7ee6e1050dc6d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/discovery.py @@ -0,0 +1,614 @@ +"""Automatic discovery of Python modules and packages (for inclusion in the +distribution) and other config values. + +For the purposes of this module, the following nomenclature is used: + +- "src-layout": a directory representing a Python project that contains a "src" + folder. Everything under the "src" folder is meant to be included in the + distribution when packaging the project. Example:: + + . + ├── tox.ini + ├── pyproject.toml + └── src/ + └── mypkg/ + ├── __init__.py + ├── mymodule.py + └── my_data_file.txt + +- "flat-layout": a Python project that does not use "src-layout" but instead + have a directory under the project root for each package:: + + . + ├── tox.ini + ├── pyproject.toml + └── mypkg/ + ├── __init__.py + ├── mymodule.py + └── my_data_file.txt + +- "single-module": a project that contains a single Python script direct under + the project root (no directory used):: + + . + ├── tox.ini + ├── pyproject.toml + └── mymodule.py + +""" + +from __future__ import annotations + +import itertools +import os +from collections.abc import Iterable, Iterator, Mapping +from fnmatch import fnmatchcase +from glob import glob +from pathlib import Path +from typing import TYPE_CHECKING, ClassVar + +import _distutils_hack.override # noqa: F401 + +from ._path import StrPath + +from distutils import log +from distutils.util import convert_path + +if TYPE_CHECKING: + from setuptools import Distribution + +chain_iter = itertools.chain.from_iterable + + +def _valid_name(path: StrPath) -> bool: + # Ignore invalid names that cannot be imported directly + return os.path.basename(path).isidentifier() + + +class _Filter: + """ + Given a list of patterns, create a callable that will be true only if + the input matches at least one of the patterns. + """ + + def __init__(self, *patterns: str) -> None: + self._patterns = dict.fromkeys(patterns) + + def __call__(self, item: str) -> bool: + return any(fnmatchcase(item, pat) for pat in self._patterns) + + def __contains__(self, item: str) -> bool: + return item in self._patterns + + +class _Finder: + """Base class that exposes functionality for module/package finders""" + + ALWAYS_EXCLUDE: ClassVar[tuple[str, ...]] = () + DEFAULT_EXCLUDE: ClassVar[tuple[str, ...]] = () + + @classmethod + def find( + cls, + where: StrPath = '.', + exclude: Iterable[str] = (), + include: Iterable[str] = ('*',), + ) -> list[str]: + """Return a list of all Python items (packages or modules, depending on + the finder implementation) found within directory ``where``. + + ``where`` is the root directory which will be searched. + It should be supplied as a "cross-platform" (i.e. URL-style) path; + it will be converted to the appropriate local path syntax. + + ``exclude`` is a sequence of names to exclude; ``*`` can be used + as a wildcard in the names. + When finding packages, ``foo.*`` will exclude all subpackages of ``foo`` + (but not ``foo`` itself). + + ``include`` is a sequence of names to include. + If it's specified, only the named items will be included. + If it's not specified, all found items will be included. + ``include`` can contain shell style wildcard patterns just like + ``exclude``. + """ + + exclude = exclude or cls.DEFAULT_EXCLUDE + return list( + cls._find_iter( + convert_path(str(where)), + _Filter(*cls.ALWAYS_EXCLUDE, *exclude), + _Filter(*include), + ) + ) + + @classmethod + def _find_iter( + cls, where: StrPath, exclude: _Filter, include: _Filter + ) -> Iterator[str]: + raise NotImplementedError + + +class PackageFinder(_Finder): + """ + Generate a list of all Python packages found within a directory + """ + + ALWAYS_EXCLUDE = ("ez_setup", "*__pycache__") + + @classmethod + def _find_iter( + cls, where: StrPath, exclude: _Filter, include: _Filter + ) -> Iterator[str]: + """ + All the packages found in 'where' that pass the 'include' filter, but + not the 'exclude' filter. + """ + for root, dirs, files in os.walk(str(where), followlinks=True): + # Copy dirs to iterate over it, then empty dirs. + all_dirs = dirs[:] + dirs[:] = [] + + for dir in all_dirs: + full_path = os.path.join(root, dir) + rel_path = os.path.relpath(full_path, where) + package = rel_path.replace(os.path.sep, '.') + + # Skip directory trees that are not valid packages + if '.' in dir or not cls._looks_like_package(full_path, package): + continue + + # Should this package be included? + if include(package) and not exclude(package): + yield package + + # Early pruning if there is nothing else to be scanned + if f"{package}*" in exclude or f"{package}.*" in exclude: + continue + + # Keep searching subdirectories, as there may be more packages + # down there, even if the parent was excluded. + dirs.append(dir) + + @staticmethod + def _looks_like_package(path: StrPath, _package_name: str) -> bool: + """Does a directory look like a package?""" + return os.path.isfile(os.path.join(path, '__init__.py')) + + +class PEP420PackageFinder(PackageFinder): + @staticmethod + def _looks_like_package(_path: StrPath, _package_name: str) -> bool: + return True + + +class ModuleFinder(_Finder): + """Find isolated Python modules. + This function will **not** recurse subdirectories. + """ + + @classmethod + def _find_iter( + cls, where: StrPath, exclude: _Filter, include: _Filter + ) -> Iterator[str]: + for file in glob(os.path.join(where, "*.py")): + module, _ext = os.path.splitext(os.path.basename(file)) + + if not cls._looks_like_module(module): + continue + + if include(module) and not exclude(module): + yield module + + _looks_like_module = staticmethod(_valid_name) + + +# We have to be extra careful in the case of flat layout to not include files +# and directories not meant for distribution (e.g. tool-related) + + +class FlatLayoutPackageFinder(PEP420PackageFinder): + _EXCLUDE = ( + "ci", + "bin", + "debian", + "doc", + "docs", + "documentation", + "manpages", + "news", + "newsfragments", + "changelog", + "test", + "tests", + "unit_test", + "unit_tests", + "example", + "examples", + "scripts", + "tools", + "util", + "utils", + "python", + "build", + "dist", + "venv", + "env", + "requirements", + # ---- Task runners / Build tools ---- + "tasks", # invoke + "fabfile", # fabric + "site_scons", # SCons + # ---- Other tools ---- + "benchmark", + "benchmarks", + "exercise", + "exercises", + "htmlcov", # Coverage.py + # ---- Hidden directories/Private packages ---- + "[._]*", + ) + + DEFAULT_EXCLUDE = tuple(chain_iter((p, f"{p}.*") for p in _EXCLUDE)) + """Reserved package names""" + + @staticmethod + def _looks_like_package(_path: StrPath, package_name: str) -> bool: + names = package_name.split('.') + # Consider PEP 561 + root_pkg_is_valid = names[0].isidentifier() or names[0].endswith("-stubs") + return root_pkg_is_valid and all(name.isidentifier() for name in names[1:]) + + +class FlatLayoutModuleFinder(ModuleFinder): + DEFAULT_EXCLUDE = ( + "setup", + "conftest", + "test", + "tests", + "example", + "examples", + "build", + # ---- Task runners ---- + "toxfile", + "noxfile", + "pavement", + "dodo", + "tasks", + "fabfile", + # ---- Other tools ---- + "[Ss][Cc]onstruct", # SCons + "conanfile", # Connan: C/C++ build tool + "manage", # Django + "benchmark", + "benchmarks", + "exercise", + "exercises", + # ---- Hidden files/Private modules ---- + "[._]*", + ) + """Reserved top-level module names""" + + +def _find_packages_within(root_pkg: str, pkg_dir: StrPath) -> list[str]: + nested = PEP420PackageFinder.find(pkg_dir) + return [root_pkg] + [".".join((root_pkg, n)) for n in nested] + + +class ConfigDiscovery: + """Fill-in metadata and options that can be automatically derived + (from other metadata/options, the file system or conventions) + """ + + def __init__(self, distribution: Distribution) -> None: + self.dist = distribution + self._called = False + self._disabled = False + self._skip_ext_modules = False + + def _disable(self): + """Internal API to disable automatic discovery""" + self._disabled = True + + def _ignore_ext_modules(self): + """Internal API to disregard ext_modules. + + Normally auto-discovery would not be triggered if ``ext_modules`` are set + (this is done for backward compatibility with existing packages relying on + ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function + to ignore given ``ext_modules`` and proceed with the auto-discovery if + ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml + metadata). + """ + self._skip_ext_modules = True + + @property + def _root_dir(self) -> StrPath: + # The best is to wait until `src_root` is set in dist, before using _root_dir. + return self.dist.src_root or os.curdir + + @property + def _package_dir(self) -> dict[str, str]: + if self.dist.package_dir is None: + return {} + return self.dist.package_dir + + def __call__( + self, force: bool = False, name: bool = True, ignore_ext_modules: bool = False + ) -> None: + """Automatically discover missing configuration fields + and modifies the given ``distribution`` object in-place. + + Note that by default this will only have an effect the first time the + ``ConfigDiscovery`` object is called. + + To repeatedly invoke automatic discovery (e.g. when the project + directory changes), please use ``force=True`` (or create a new + ``ConfigDiscovery`` instance). + """ + if force is False and (self._called or self._disabled): + # Avoid overhead of multiple calls + return + + self._analyse_package_layout(ignore_ext_modules) + if name: + self.analyse_name() # depends on ``packages`` and ``py_modules`` + + self._called = True + + def _explicitly_specified(self, ignore_ext_modules: bool) -> bool: + """``True`` if the user has specified some form of package/module listing""" + ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules + ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules) + return ( + self.dist.packages is not None + or self.dist.py_modules is not None + or ext_modules + or hasattr(self.dist, "configuration") + and self.dist.configuration + # ^ Some projects use numpy.distutils.misc_util.Configuration + ) + + def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool: + if self._explicitly_specified(ignore_ext_modules): + # For backward compatibility, just try to find modules/packages + # when nothing is given + return True + + log.debug( + "No `packages` or `py_modules` configuration, performing " + "automatic discovery." + ) + + return ( + self._analyse_explicit_layout() + or self._analyse_src_layout() + # flat-layout is the trickiest for discovery so it should be last + or self._analyse_flat_layout() + ) + + def _analyse_explicit_layout(self) -> bool: + """The user can explicitly give a package layout via ``package_dir``""" + package_dir = self._package_dir.copy() # don't modify directly + package_dir.pop("", None) # This falls under the "src-layout" umbrella + root_dir = self._root_dir + + if not package_dir: + return False + + log.debug(f"`explicit-layout` detected -- analysing {package_dir}") + pkgs = chain_iter( + _find_packages_within(pkg, os.path.join(root_dir, parent_dir)) + for pkg, parent_dir in package_dir.items() + ) + self.dist.packages = list(pkgs) + log.debug(f"discovered packages -- {self.dist.packages}") + return True + + def _analyse_src_layout(self) -> bool: + """Try to find all packages or modules under the ``src`` directory + (or anything pointed by ``package_dir[""]``). + + The "src-layout" is relatively safe for automatic discovery. + We assume that everything within is meant to be included in the + distribution. + + If ``package_dir[""]`` is not given, but the ``src`` directory exists, + this function will set ``package_dir[""] = "src"``. + """ + package_dir = self._package_dir + src_dir = os.path.join(self._root_dir, package_dir.get("", "src")) + if not os.path.isdir(src_dir): + return False + + log.debug(f"`src-layout` detected -- analysing {src_dir}") + package_dir.setdefault("", os.path.basename(src_dir)) + self.dist.package_dir = package_dir # persist eventual modifications + self.dist.packages = PEP420PackageFinder.find(src_dir) + self.dist.py_modules = ModuleFinder.find(src_dir) + log.debug(f"discovered packages -- {self.dist.packages}") + log.debug(f"discovered py_modules -- {self.dist.py_modules}") + return True + + def _analyse_flat_layout(self) -> bool: + """Try to find all packages and modules under the project root. + + Since the ``flat-layout`` is more dangerous in terms of accidentally including + extra files/directories, this function is more conservative and will raise an + error if multiple packages or modules are found. + + This assumes that multi-package dists are uncommon and refuse to support that + use case in order to be able to prevent unintended errors. + """ + log.debug(f"`flat-layout` detected -- analysing {self._root_dir}") + return self._analyse_flat_packages() or self._analyse_flat_modules() + + def _analyse_flat_packages(self) -> bool: + self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir) + top_level = remove_nested_packages(remove_stubs(self.dist.packages)) + log.debug(f"discovered packages -- {self.dist.packages}") + self._ensure_no_accidental_inclusion(top_level, "packages") + return bool(top_level) + + def _analyse_flat_modules(self) -> bool: + self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir) + log.debug(f"discovered py_modules -- {self.dist.py_modules}") + self._ensure_no_accidental_inclusion(self.dist.py_modules, "modules") + return bool(self.dist.py_modules) + + def _ensure_no_accidental_inclusion(self, detected: list[str], kind: str): + if len(detected) > 1: + from inspect import cleandoc + + from setuptools.errors import PackageDiscoveryError + + msg = f"""Multiple top-level {kind} discovered in a flat-layout: {detected}. + + To avoid accidental inclusion of unwanted files or directories, + setuptools will not proceed with this build. + + If you are trying to create a single distribution with multiple {kind} + on purpose, you should not rely on automatic discovery. + Instead, consider the following options: + + 1. set up custom discovery (`find` directive with `include` or `exclude`) + 2. use a `src-layout` + 3. explicitly set `py_modules` or `packages` with a list of names + + To find more information, look for "package discovery" on setuptools docs. + """ + raise PackageDiscoveryError(cleandoc(msg)) + + def analyse_name(self) -> None: + """The packages/modules are the essential contribution of the author. + Therefore the name of the distribution can be derived from them. + """ + if self.dist.metadata.name or self.dist.name: + # get_name() is not reliable (can return "UNKNOWN") + return + + log.debug("No `name` configuration, performing automatic discovery") + + name = ( + self._find_name_single_package_or_module() + or self._find_name_from_packages() + ) + if name: + self.dist.metadata.name = name + + def _find_name_single_package_or_module(self) -> str | None: + """Exactly one module or package""" + for field in ('packages', 'py_modules'): + items = getattr(self.dist, field, None) or [] + if items and len(items) == 1: + log.debug(f"Single module/package detected, name: {items[0]}") + return items[0] + + return None + + def _find_name_from_packages(self) -> str | None: + """Try to find the root package that is not a PEP 420 namespace""" + if not self.dist.packages: + return None + + packages = remove_stubs(sorted(self.dist.packages, key=len)) + package_dir = self.dist.package_dir or {} + + parent_pkg = find_parent_package(packages, package_dir, self._root_dir) + if parent_pkg: + log.debug(f"Common parent package detected, name: {parent_pkg}") + return parent_pkg + + log.warn("No parent package detected, impossible to derive `name`") + return None + + +def remove_nested_packages(packages: list[str]) -> list[str]: + """Remove nested packages from a list of packages. + + >>> remove_nested_packages(["a", "a.b1", "a.b2", "a.b1.c1"]) + ['a'] + >>> remove_nested_packages(["a", "b", "c.d", "c.d.e.f", "g.h", "a.a1"]) + ['a', 'b', 'c.d', 'g.h'] + """ + pkgs = sorted(packages, key=len) + top_level = pkgs[:] + size = len(pkgs) + for i, name in enumerate(reversed(pkgs)): + if any(name.startswith(f"{other}.") for other in top_level): + top_level.pop(size - i - 1) + + return top_level + + +def remove_stubs(packages: list[str]) -> list[str]: + """Remove type stubs (:pep:`561`) from a list of packages. + + >>> remove_stubs(["a", "a.b", "a-stubs", "a-stubs.b.c", "b", "c-stubs"]) + ['a', 'a.b', 'b'] + """ + return [pkg for pkg in packages if not pkg.split(".")[0].endswith("-stubs")] + + +def find_parent_package( + packages: list[str], package_dir: Mapping[str, str], root_dir: StrPath +) -> str | None: + """Find the parent package that is not a namespace.""" + packages = sorted(packages, key=len) + common_ancestors = [] + for i, name in enumerate(packages): + if not all(n.startswith(f"{name}.") for n in packages[i + 1 :]): + # Since packages are sorted by length, this condition is able + # to find a list of all common ancestors. + # When there is divergence (e.g. multiple root packages) + # the list will be empty + break + common_ancestors.append(name) + + for name in common_ancestors: + pkg_path = find_package_path(name, package_dir, root_dir) + init = os.path.join(pkg_path, "__init__.py") + if os.path.isfile(init): + return name + + return None + + +def find_package_path( + name: str, package_dir: Mapping[str, str], root_dir: StrPath +) -> str: + """Given a package name, return the path where it should be found on + disk, considering the ``package_dir`` option. + + >>> path = find_package_path("my.pkg", {"": "root/is/nested"}, ".") + >>> path.replace(os.sep, "/") + './root/is/nested/my/pkg' + + >>> path = find_package_path("my.pkg", {"my": "root/is/nested"}, ".") + >>> path.replace(os.sep, "/") + './root/is/nested/pkg' + + >>> path = find_package_path("my.pkg", {"my.pkg": "root/is/nested"}, ".") + >>> path.replace(os.sep, "/") + './root/is/nested' + + >>> path = find_package_path("other.pkg", {"my.pkg": "root/is/nested"}, ".") + >>> path.replace(os.sep, "/") + './other/pkg' + """ + parts = name.split(".") + for i in range(len(parts), 0, -1): + # Look backwards, the most specific package_dir first + partial_name = ".".join(parts[:i]) + if partial_name in package_dir: + parent = package_dir[partial_name] + return os.path.join(root_dir, parent, *parts[i:]) + + parent = package_dir.get("") or "" + return os.path.join(root_dir, *parent.split("/"), *parts) + + +def construct_package_dir(packages: list[str], package_path: StrPath) -> dict[str, str]: + parent_pkgs = remove_nested_packages(packages) + prefix = Path(package_path).parts + return {pkg: "/".join([*prefix, *pkg.split(".")]) for pkg in parent_pkgs} diff --git a/lib/python3.11/site-packages/setuptools/dist.py b/lib/python3.11/site-packages/setuptools/dist.py new file mode 100644 index 0000000000000000000000000000000000000000..a224b3ee44cdbfb5fa76b2075c934b2f04c89924 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/dist.py @@ -0,0 +1,1124 @@ +from __future__ import annotations + +import functools +import io +import itertools +import numbers +import os +import re +import sys +from collections.abc import Iterable, Iterator, MutableMapping, Sequence +from glob import glob +from pathlib import Path +from typing import TYPE_CHECKING, Any, Union + +from more_itertools import partition, unique_everseen +from packaging.markers import InvalidMarker, Marker +from packaging.specifiers import InvalidSpecifier, SpecifierSet +from packaging.version import Version + +from . import ( + _entry_points, + _reqs, + _static, + command as _, # noqa: F401 # imported for side-effects +) +from ._importlib import metadata +from ._normalization import _canonicalize_license_expression +from ._path import StrPath +from ._reqs import _StrOrIter +from .config import pyprojecttoml, setupcfg +from .discovery import ConfigDiscovery +from .errors import InvalidConfigError +from .monkey import get_unpatched +from .warnings import InformationOnly, SetuptoolsDeprecationWarning + +import distutils.cmd +import distutils.command +import distutils.core +import distutils.dist +import distutils.log +from distutils.debug import DEBUG +from distutils.errors import DistutilsOptionError, DistutilsSetupError +from distutils.fancy_getopt import translate_longopt +from distutils.util import strtobool + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + + +__all__ = ['Distribution'] + +_sequence = tuple, list +""" +:meta private: + +Supported iterable types that are known to be: +- ordered (which `set` isn't) +- not match a str (which `Sequence[str]` does) +- not imply a nested type (like `dict`) +for use with `isinstance`. +""" +_Sequence: TypeAlias = Union[tuple[str, ...], list[str]] +# This is how stringifying _Sequence would look in Python 3.10 +_sequence_type_repr = "tuple[str, ...] | list[str]" +_OrderedStrSequence: TypeAlias = Union[str, dict[str, Any], Sequence[str]] +""" +:meta private: +Avoid single-use iterable. Disallow sets. +A poor approximation of an OrderedSequence (dict doesn't match a Sequence). +""" + + +def __getattr__(name: str) -> Any: # pragma: no cover + if name == "sequence": + SetuptoolsDeprecationWarning.emit( + "`setuptools.dist.sequence` is an internal implementation detail.", + "Please define your own `sequence = tuple, list` instead.", + due_date=(2025, 8, 28), # Originally added on 2024-08-27 + ) + return _sequence + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +def check_importable(dist, attr, value): + try: + ep = metadata.EntryPoint(value=value, name=None, group=None) + assert not ep.extras + except (TypeError, ValueError, AttributeError, AssertionError) as e: + raise DistutilsSetupError( + f"{attr!r} must be importable 'module:attrs' string (got {value!r})" + ) from e + + +def assert_string_list(dist, attr: str, value: _Sequence) -> None: + """Verify that value is a string list""" + try: + # verify that value is a list or tuple to exclude unordered + # or single-use iterables + assert isinstance(value, _sequence) + # verify that elements of value are strings + assert ''.join(value) != value + except (TypeError, ValueError, AttributeError, AssertionError) as e: + raise DistutilsSetupError( + f"{attr!r} must be of type <{_sequence_type_repr}> (got {value!r})" + ) from e + + +def check_nsp(dist, attr, value): + """Verify that namespace packages are valid""" + ns_packages = value + assert_string_list(dist, attr, ns_packages) + for nsp in ns_packages: + if not dist.has_contents_for(nsp): + raise DistutilsSetupError( + f"Distribution contains no modules or packages for namespace package {nsp!r}" + ) + parent, _sep, _child = nsp.rpartition('.') + if parent and parent not in ns_packages: + distutils.log.warn( + "WARNING: %r is declared as a package namespace, but %r" + " is not: please correct this in setup.py", + nsp, + parent, + ) + SetuptoolsDeprecationWarning.emit( + "The namespace_packages parameter is deprecated.", + "Please replace its usage with implicit namespaces (PEP 420).", + see_docs="references/keywords.html#keyword-namespace-packages", + # TODO: define due_date, it may break old packages that are no longer + # maintained (e.g. sphinxcontrib extensions) when installed from source. + # Warning officially introduced in May 2022, however the deprecation + # was mentioned much earlier in the docs (May 2020, see #2149). + ) + + +def check_extras(dist, attr, value): + """Verify that extras_require mapping is valid""" + try: + list(itertools.starmap(_check_extra, value.items())) + except (TypeError, ValueError, AttributeError) as e: + raise DistutilsSetupError( + "'extras_require' must be a dictionary whose values are " + "strings or lists of strings containing valid project/version " + "requirement specifiers." + ) from e + + +def _check_extra(extra, reqs): + _name, _sep, marker = extra.partition(':') + try: + _check_marker(marker) + except InvalidMarker: + msg = f"Invalid environment marker: {marker} ({extra!r})" + raise DistutilsSetupError(msg) from None + list(_reqs.parse(reqs)) + + +def _check_marker(marker): + if not marker: + return + m = Marker(marker) + m.evaluate() + + +def assert_bool(dist, attr, value): + """Verify that value is True, False, 0, or 1""" + if bool(value) != value: + raise DistutilsSetupError(f"{attr!r} must be a boolean value (got {value!r})") + + +def invalid_unless_false(dist, attr, value): + if not value: + DistDeprecationWarning.emit(f"{attr} is ignored.") + # TODO: should there be a `due_date` here? + return + raise DistutilsSetupError(f"{attr} is invalid.") + + +def check_requirements(dist, attr: str, value: _OrderedStrSequence) -> None: + """Verify that install_requires is a valid requirements list""" + try: + list(_reqs.parse(value)) + if isinstance(value, set): + raise TypeError("Unordered types are not allowed") + except (TypeError, ValueError) as error: + msg = ( + f"{attr!r} must be a string or iterable of strings " + f"containing valid project/version requirement specifiers; {error}" + ) + raise DistutilsSetupError(msg) from error + + +def check_specifier(dist, attr, value): + """Verify that value is a valid version specifier""" + try: + SpecifierSet(value) + except (InvalidSpecifier, AttributeError) as error: + msg = f"{attr!r} must be a string containing valid version specifiers; {error}" + raise DistutilsSetupError(msg) from error + + +def check_entry_points(dist, attr, value): + """Verify that entry_points map is parseable""" + try: + _entry_points.load(value) + except Exception as e: + raise DistutilsSetupError(e) from e + + +def check_package_data(dist, attr, value): + """Verify that value is a dictionary of package names to glob lists""" + if not isinstance(value, dict): + raise DistutilsSetupError( + f"{attr!r} must be a dictionary mapping package names to lists of " + "string wildcard patterns" + ) + for k, v in value.items(): + if not isinstance(k, str): + raise DistutilsSetupError( + f"keys of {attr!r} dict must be strings (got {k!r})" + ) + assert_string_list(dist, f'values of {attr!r} dict', v) + + +def check_packages(dist, attr, value): + for pkgname in value: + if not re.match(r'\w+(\.\w+)*', pkgname): + distutils.log.warn( + "WARNING: %r not a valid package name; please use only " + ".-separated package names in setup.py", + pkgname, + ) + + +if TYPE_CHECKING: + # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962 + from distutils.core import Distribution as _Distribution +else: + _Distribution = get_unpatched(distutils.core.Distribution) + + +class Distribution(_Distribution): + """Distribution with support for tests and package data + + This is an enhanced version of 'distutils.dist.Distribution' that + effectively adds the following new optional keyword arguments to 'setup()': + + 'install_requires' -- a string or sequence of strings specifying project + versions that the distribution requires when installed, in the format + used by 'pkg_resources.require()'. They will be installed + automatically when the package is installed. If you wish to use + packages that are not available in PyPI, or want to give your users an + alternate download location, you can add a 'find_links' option to the + '[easy_install]' section of your project's 'setup.cfg' file, and then + setuptools will scan the listed web pages for links that satisfy the + requirements. + + 'extras_require' -- a dictionary mapping names of optional "extras" to the + additional requirement(s) that using those extras incurs. For example, + this:: + + extras_require = dict(reST = ["docutils>=0.3", "reSTedit"]) + + indicates that the distribution can optionally provide an extra + capability called "reST", but it can only be used if docutils and + reSTedit are installed. If the user installs your package using + EasyInstall and requests one of your extras, the corresponding + additional requirements will be installed if needed. + + 'package_data' -- a dictionary mapping package names to lists of filenames + or globs to use to find data files contained in the named packages. + If the dictionary has filenames or globs listed under '""' (the empty + string), those names will be searched for in every package, in addition + to any names for the specific package. Data files found using these + names/globs will be installed along with the package, in the same + location as the package. Note that globs are allowed to reference + the contents of non-package subdirectories, as long as you use '/' as + a path separator. (Globs are automatically converted to + platform-specific paths at runtime.) + + In addition to these new keywords, this class also has several new methods + for manipulating the distribution's contents. For example, the 'include()' + and 'exclude()' methods can be thought of as in-place add and subtract + commands that add or remove packages, modules, extensions, and so on from + the distribution. + """ + + _DISTUTILS_UNSUPPORTED_METADATA = { + 'long_description_content_type': lambda: None, + 'project_urls': dict, + 'provides_extras': dict, # behaves like an ordered set + 'license_expression': lambda: None, + 'license_file': lambda: None, + 'license_files': lambda: None, + 'install_requires': list, + 'extras_require': dict, + } + + # Used by build_py, editable_wheel and install_lib commands for legacy namespaces + namespace_packages: list[str] #: :meta private: DEPRECATED + + # Any: Dynamic assignment results in Incompatible types in assignment + def __init__(self, attrs: MutableMapping[str, Any] | None = None) -> None: + have_package_data = hasattr(self, "package_data") + if not have_package_data: + self.package_data: dict[str, list[str]] = {} + attrs = attrs or {} + self.dist_files: list[tuple[str, str, str]] = [] + self.include_package_data: bool | None = None + self.exclude_package_data: dict[str, list[str]] | None = None + # Filter-out setuptools' specific options. + self.src_root: str | None = attrs.pop("src_root", None) + self.dependency_links: list[str] = attrs.pop('dependency_links', []) + self.setup_requires: list[str] = attrs.pop('setup_requires', []) + for ep in metadata.entry_points(group='distutils.setup_keywords'): + vars(self).setdefault(ep.name, None) + + metadata_only = set(self._DISTUTILS_UNSUPPORTED_METADATA) + metadata_only -= {"install_requires", "extras_require"} + dist_attrs = {k: v for k, v in attrs.items() if k not in metadata_only} + _Distribution.__init__(self, dist_attrs) + + # Private API (setuptools-use only, not restricted to Distribution) + # Stores files that are referenced by the configuration and need to be in the + # sdist (e.g. `version = file: VERSION.txt`) + self._referenced_files = set[str]() + + self.set_defaults = ConfigDiscovery(self) + + self._set_metadata_defaults(attrs) + + self.metadata.version = self._normalize_version(self.metadata.version) + self._finalize_requires() + + def _validate_metadata(self): + required = {"name"} + provided = { + key + for key in vars(self.metadata) + if getattr(self.metadata, key, None) is not None + } + missing = required - provided + + if missing: + msg = f"Required package metadata is missing: {missing}" + raise DistutilsSetupError(msg) + + def _set_metadata_defaults(self, attrs): + """ + Fill-in missing metadata fields not supported by distutils. + Some fields may have been set by other tools (e.g. pbr). + Those fields (vars(self.metadata)) take precedence to + supplied attrs. + """ + for option, default in self._DISTUTILS_UNSUPPORTED_METADATA.items(): + vars(self.metadata).setdefault(option, attrs.get(option, default())) + + @staticmethod + def _normalize_version(version): + from . import sic + + if isinstance(version, numbers.Number): + # Some people apparently take "version number" too literally :) + version = str(version) + elif isinstance(version, sic) or version is None: + return version + + normalized = str(Version(version)) + if version != normalized: + InformationOnly.emit(f"Normalizing '{version}' to '{normalized}'") + return normalized + return version + + def _finalize_requires(self): + """ + Set `metadata.python_requires` and fix environment markers + in `install_requires` and `extras_require`. + """ + if getattr(self, 'python_requires', None): + self.metadata.python_requires = self.python_requires + + self._normalize_requires() + self.metadata.install_requires = self.install_requires + self.metadata.extras_require = self.extras_require + + if self.extras_require: + for extra in self.extras_require.keys(): + # Setuptools allows a weird ": syntax for extras + extra = extra.split(':')[0] + if extra: + self.metadata.provides_extras.setdefault(extra) + + def _normalize_requires(self): + """Make sure requirement-related attributes exist and are normalized""" + install_requires = getattr(self, "install_requires", None) or [] + extras_require = getattr(self, "extras_require", None) or {} + + # Preserve the "static"-ness of values parsed from config files + list_ = _static.List if _static.is_static(install_requires) else list + self.install_requires = list_(map(str, _reqs.parse(install_requires))) + + dict_ = _static.Dict if _static.is_static(extras_require) else dict + self.extras_require = dict_( + (k, list(map(str, _reqs.parse(v or [])))) for k, v in extras_require.items() + ) + + def _finalize_license_expression(self) -> None: + """ + Normalize license and license_expression. + >>> dist = Distribution({"license_expression": _static.Str("mit aNd gpl-3.0-OR-later")}) + >>> _static.is_static(dist.metadata.license_expression) + True + >>> dist._finalize_license_expression() + >>> _static.is_static(dist.metadata.license_expression) # preserve "static-ness" + True + >>> print(dist.metadata.license_expression) + MIT AND GPL-3.0-or-later + """ + classifiers = self.metadata.get_classifiers() + license_classifiers = [cl for cl in classifiers if cl.startswith("License :: ")] + + license_expr = self.metadata.license_expression + if license_expr: + str_ = _static.Str if _static.is_static(license_expr) else str + normalized = str_(_canonicalize_license_expression(license_expr)) + if license_expr != normalized: + InformationOnly.emit(f"Normalizing '{license_expr}' to '{normalized}'") + self.metadata.license_expression = normalized + if license_classifiers: + raise InvalidConfigError( + "License classifiers have been superseded by license expressions " + "(see https://peps.python.org/pep-0639/). Please remove:\n\n" + + "\n".join(license_classifiers), + ) + elif license_classifiers: + pypa_guides = "guides/writing-pyproject-toml/#license" + SetuptoolsDeprecationWarning.emit( + "License classifiers are deprecated.", + "Please consider removing the following classifiers in favor of a " + "SPDX license expression:\n\n" + "\n".join(license_classifiers), + see_url=f"https://packaging.python.org/en/latest/{pypa_guides}", + # Warning introduced on 2025-02-17 + # TODO: Should we add a due date? It may affect old/unmaintained + # packages in the ecosystem and cause problems... + ) + + def _finalize_license_files(self) -> None: + """Compute names of all license files which should be included.""" + license_files: list[str] | None = self.metadata.license_files + patterns = license_files or [] + + license_file: str | None = self.metadata.license_file + if license_file and license_file not in patterns: + patterns.append(license_file) + + if license_files is None and license_file is None: + # Default patterns match the ones wheel uses + # See https://wheel.readthedocs.io/en/stable/user_guide.html + # -> 'Including license files in the generated wheel file' + patterns = ['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*'] + files = self._expand_patterns(patterns, enforce_match=False) + else: # Patterns explicitly given by the user + files = self._expand_patterns(patterns, enforce_match=True) + + self.metadata.license_files = list(unique_everseen(files)) + + @classmethod + def _expand_patterns( + cls, patterns: list[str], enforce_match: bool = True + ) -> Iterator[str]: + """ + >>> getfixture('sample_project_cwd') + >>> list(Distribution._expand_patterns(['LICENSE.txt'])) + ['LICENSE.txt'] + >>> list(Distribution._expand_patterns(['pyproject.toml', 'LIC*'])) + ['pyproject.toml', 'LICENSE.txt'] + >>> list(Distribution._expand_patterns(['src/**/*.dat'])) + ['src/sample/package_data.dat'] + """ + return ( + path.replace(os.sep, "/") + for pattern in patterns + for path in sorted(cls._find_pattern(pattern, enforce_match)) + if not path.endswith('~') and os.path.isfile(path) + ) + + @staticmethod + def _find_pattern(pattern: str, enforce_match: bool = True) -> list[str]: + r""" + >>> getfixture('sample_project_cwd') + >>> Distribution._find_pattern("LICENSE.txt") + ['LICENSE.txt'] + >>> Distribution._find_pattern("/LICENSE.MIT") + Traceback (most recent call last): + ... + setuptools.errors.InvalidConfigError: Pattern '/LICENSE.MIT' should be relative... + >>> Distribution._find_pattern("../LICENSE.MIT") + Traceback (most recent call last): + ... + setuptools.warnings.SetuptoolsDeprecationWarning: ...Pattern '../LICENSE.MIT' cannot contain '..'... + >>> Distribution._find_pattern("LICEN{CSE*") + Traceback (most recent call last): + ... + setuptools.warnings.SetuptoolsDeprecationWarning: ...Pattern 'LICEN{CSE*' contains invalid characters... + """ + pypa_guides = "specifications/glob-patterns/" + if ".." in pattern: + SetuptoolsDeprecationWarning.emit( + f"Pattern {pattern!r} cannot contain '..'", + """ + Please ensure the files specified are contained by the root + of the Python package (normally marked by `pyproject.toml`). + """, + see_url=f"https://packaging.python.org/en/latest/{pypa_guides}", + due_date=(2027, 2, 18), # Introduced in 2025-03-20 + # Replace with InvalidConfigError after deprecation + ) + if pattern.startswith((os.sep, "/")) or ":\\" in pattern: + raise InvalidConfigError( + f"Pattern {pattern!r} should be relative and must not start with '/'" + ) + if re.match(r'^[\w\-\.\/\*\?\[\]]+$', pattern) is None: + SetuptoolsDeprecationWarning.emit( + "Please provide a valid glob pattern.", + "Pattern {pattern!r} contains invalid characters.", + pattern=pattern, + see_url=f"https://packaging.python.org/en/latest/{pypa_guides}", + due_date=(2027, 2, 18), # Introduced in 2025-02-20 + ) + + found = glob(pattern, recursive=True) + + if enforce_match and not found: + SetuptoolsDeprecationWarning.emit( + "Cannot find any files for the given pattern.", + "Pattern {pattern!r} did not match any files.", + pattern=pattern, + due_date=(2027, 2, 18), # Introduced in 2025-02-20 + # PEP 639 requires us to error, but as a transition period + # we will only issue a warning to give people time to prepare. + # After the transition, this should raise an InvalidConfigError. + ) + return found + + # FIXME: 'Distribution._parse_config_files' is too complex (14) + def _parse_config_files(self, filenames=None): # noqa: C901 + """ + Adapted from distutils.dist.Distribution.parse_config_files, + this method provides the same functionality in subtly-improved + ways. + """ + from configparser import ConfigParser + + # Ignore install directory options if we have a venv + ignore_options = ( + [] + if sys.prefix == sys.base_prefix + else [ + 'install-base', + 'install-platbase', + 'install-lib', + 'install-platlib', + 'install-purelib', + 'install-headers', + 'install-scripts', + 'install-data', + 'prefix', + 'exec-prefix', + 'home', + 'user', + 'root', + ] + ) + + ignore_options = frozenset(ignore_options) + + if filenames is None: + filenames = self.find_config_files() + + if DEBUG: + self.announce("Distribution.parse_config_files():") + + parser = ConfigParser() + parser.optionxform = str + for filename in filenames: + with open(filename, encoding='utf-8') as reader: + if DEBUG: + self.announce(" reading {filename}".format(**locals())) + parser.read_file(reader) + for section in parser.sections(): + options = parser.options(section) + opt_dict = self.get_option_dict(section) + + for opt in options: + if opt == '__name__' or opt in ignore_options: + continue + + val = parser.get(section, opt) + opt = self._enforce_underscore(opt, section) + opt = self._enforce_option_lowercase(opt, section) + opt_dict[opt] = (filename, val) + + # Make the ConfigParser forget everything (so we retain + # the original filenames that options come from) + parser.__init__() + + if 'global' not in self.command_options: + return + + # If there was a "global" section in the config file, use it + # to set Distribution options. + + for opt, (src, val) in self.command_options['global'].items(): + alias = self.negative_opt.get(opt) + if alias: + val = not strtobool(val) + elif opt == 'verbose': + val = strtobool(val) + + try: + setattr(self, alias or opt, val) + except ValueError as e: + raise DistutilsOptionError(e) from e + + def _enforce_underscore(self, opt: str, section: str) -> str: + if "-" not in opt or self._skip_setupcfg_normalization(section): + return opt + + underscore_opt = opt.replace('-', '_') + affected = f"(Affected: {self.metadata.name})." if self.metadata.name else "" + SetuptoolsDeprecationWarning.emit( + f"Invalid dash-separated key {opt!r} in {section!r} (setup.cfg), " + f"please use the underscore name {underscore_opt!r} instead.", + f""" + Usage of dash-separated {opt!r} will not be supported in future + versions. Please use the underscore name {underscore_opt!r} instead. + {affected} + + Available configuration options are listed in: + https://setuptools.pypa.io/en/latest/userguide/declarative_config.html + """, + see_url="https://github.com/pypa/setuptools/discussions/5011", + due_date=(2026, 3, 3), + # Warning initially introduced in 3 Mar 2021 + ) + return underscore_opt + + def _enforce_option_lowercase(self, opt: str, section: str) -> str: + if opt.islower() or self._skip_setupcfg_normalization(section): + return opt + + lowercase_opt = opt.lower() + affected = f"(Affected: {self.metadata.name})." if self.metadata.name else "" + SetuptoolsDeprecationWarning.emit( + f"Invalid uppercase key {opt!r} in {section!r} (setup.cfg), " + f"please use lowercase {lowercase_opt!r} instead.", + f""" + Usage of uppercase key {opt!r} in {section!r} will not be supported in + future versions. Please use lowercase {lowercase_opt!r} instead. + {affected} + + Available configuration options are listed in: + https://setuptools.pypa.io/en/latest/userguide/declarative_config.html + """, + see_url="https://github.com/pypa/setuptools/discussions/5011", + due_date=(2026, 3, 3), + # Warning initially introduced in 6 Mar 2021 + ) + return lowercase_opt + + def _skip_setupcfg_normalization(self, section: str) -> bool: + skip = ( + 'options.extras_require', + 'options.data_files', + 'options.entry_points', + 'options.package_data', + 'options.exclude_package_data', + ) + return section in skip or not self._is_setuptools_section(section) + + def _is_setuptools_section(self, section: str) -> bool: + return ( + section == "metadata" + or section.startswith("options") + or section in _setuptools_commands() + ) + + # FIXME: 'Distribution._set_command_options' is too complex (14) + def _set_command_options(self, command_obj, option_dict=None): # noqa: C901 + """ + Set the options for 'command_obj' from 'option_dict'. Basically + this means copying elements of a dictionary ('option_dict') to + attributes of an instance ('command'). + + 'command_obj' must be a Command instance. If 'option_dict' is not + supplied, uses the standard option dictionary for this command + (from 'self.command_options'). + + (Adopted from distutils.dist.Distribution._set_command_options) + """ + command_name = command_obj.get_command_name() + if option_dict is None: + option_dict = self.get_option_dict(command_name) + + if DEBUG: + self.announce(f" setting options for '{command_name}' command:") + for option, (source, value) in option_dict.items(): + if DEBUG: + self.announce(f" {option} = {value} (from {source})") + try: + bool_opts = [translate_longopt(o) for o in command_obj.boolean_options] + except AttributeError: + bool_opts = [] + try: + neg_opt = command_obj.negative_opt + except AttributeError: + neg_opt = {} + + try: + is_string = isinstance(value, str) + if option in neg_opt and is_string: + setattr(command_obj, neg_opt[option], not strtobool(value)) + elif option in bool_opts and is_string: + setattr(command_obj, option, strtobool(value)) + elif hasattr(command_obj, option): + setattr(command_obj, option, value) + else: + raise DistutilsOptionError( + f"error in {source}: command '{command_name}' has no such option '{option}'" + ) + except ValueError as e: + raise DistutilsOptionError(e) from e + + def _get_project_config_files(self, filenames: Iterable[StrPath] | None): + """Add default file and split between INI and TOML""" + tomlfiles = [] + standard_project_metadata = Path(self.src_root or os.curdir, "pyproject.toml") + if filenames is not None: + parts = partition(lambda f: Path(f).suffix == ".toml", filenames) + filenames = list(parts[0]) # 1st element => predicate is False + tomlfiles = list(parts[1]) # 2nd element => predicate is True + elif standard_project_metadata.exists(): + tomlfiles = [standard_project_metadata] + return filenames, tomlfiles + + def parse_config_files( + self, + filenames: Iterable[StrPath] | None = None, + ignore_option_errors: bool = False, + ) -> None: + """Parses configuration files from various levels + and loads configuration. + """ + inifiles, tomlfiles = self._get_project_config_files(filenames) + + self._parse_config_files(filenames=inifiles) + + setupcfg.parse_configuration( + self, self.command_options, ignore_option_errors=ignore_option_errors + ) + for filename in tomlfiles: + pyprojecttoml.apply_configuration(self, filename, ignore_option_errors) + + self._finalize_requires() + self._finalize_license_expression() + self._finalize_license_files() + + def fetch_build_eggs(self, requires: _StrOrIter) -> list[metadata.Distribution]: + """Resolve pre-setup requirements""" + from .installer import _fetch_build_eggs + + return _fetch_build_eggs(self, requires) + + def finalize_options(self) -> None: + """ + Allow plugins to apply arbitrary operations to the + distribution. Each hook may optionally define a 'order' + to influence the order of execution. Smaller numbers + go first and the default is 0. + """ + group = 'setuptools.finalize_distribution_options' + + def by_order(hook): + return getattr(hook, 'order', 0) + + defined = metadata.entry_points(group=group) + filtered = itertools.filterfalse(self._removed, defined) + loaded = map(lambda e: e.load(), filtered) + for ep in sorted(loaded, key=by_order): + ep(self) + + @staticmethod + def _removed(ep): + """ + When removing an entry point, if metadata is loaded + from an older version of Setuptools, that removed + entry point will attempt to be loaded and will fail. + See #2765 for more details. + """ + removed = { + # removed 2021-09-05 + '2to3_doctests', + } + return ep.name in removed + + def _finalize_setup_keywords(self): + for ep in metadata.entry_points(group='distutils.setup_keywords'): + value = getattr(self, ep.name, None) + if value is not None: + ep.load()(self, ep.name, value) + + def get_egg_cache_dir(self) -> str: + from . import windows_support + + egg_cache_dir = os.path.join(os.curdir, '.eggs') + if not os.path.exists(egg_cache_dir): + os.mkdir(egg_cache_dir) + windows_support.hide_file(egg_cache_dir) + readme_txt_filename = os.path.join(egg_cache_dir, 'README.txt') + with open(readme_txt_filename, 'w', encoding="utf-8") as f: + f.write( + 'This directory contains eggs that were downloaded ' + 'by setuptools to build, test, and run plug-ins.\n\n' + ) + f.write( + 'This directory caches those eggs to prevent ' + 'repeated downloads.\n\n' + ) + f.write('However, it is safe to delete this directory.\n\n') + + return egg_cache_dir + + def fetch_build_egg(self, req): + """Fetch an egg needed for building""" + from .installer import fetch_build_egg + + return fetch_build_egg(self, req) + + def get_command_class(self, command: str) -> type[distutils.cmd.Command]: # type: ignore[override] # Not doing complex overrides yet + """Pluggable version of get_command_class()""" + if command in self.cmdclass: + return self.cmdclass[command] + + # Special case bdist_wheel so it's never loaded from "wheel" + if command == 'bdist_wheel': + from .command.bdist_wheel import bdist_wheel + + return bdist_wheel + + eps = metadata.entry_points(group='distutils.commands', name=command) + for ep in eps: + self.cmdclass[command] = cmdclass = ep.load() + return cmdclass + else: + return _Distribution.get_command_class(self, command) + + def print_commands(self): + for ep in metadata.entry_points(group='distutils.commands'): + if ep.name not in self.cmdclass: + cmdclass = ep.load() + self.cmdclass[ep.name] = cmdclass + return _Distribution.print_commands(self) + + def get_command_list(self): + for ep in metadata.entry_points(group='distutils.commands'): + if ep.name not in self.cmdclass: + cmdclass = ep.load() + self.cmdclass[ep.name] = cmdclass + return _Distribution.get_command_list(self) + + def include(self, **attrs) -> None: + """Add items to distribution that are named in keyword arguments + + For example, 'dist.include(py_modules=["x"])' would add 'x' to + the distribution's 'py_modules' attribute, if it was not already + there. + + Currently, this method only supports inclusion for attributes that are + lists or tuples. If you need to add support for adding to other + attributes in this or a subclass, you can add an '_include_X' method, + where 'X' is the name of the attribute. The method will be called with + the value passed to 'include()'. So, 'dist.include(foo={"bar":"baz"})' + will try to call 'dist._include_foo({"bar":"baz"})', which can then + handle whatever special inclusion logic is needed. + """ + for k, v in attrs.items(): + include = getattr(self, '_include_' + k, None) + if include: + include(v) + else: + self._include_misc(k, v) + + def exclude_package(self, package: str) -> None: + """Remove packages, modules, and extensions in named package""" + + pfx = package + '.' + if self.packages: + self.packages = [ + p for p in self.packages if p != package and not p.startswith(pfx) + ] + + if self.py_modules: + self.py_modules = [ + p for p in self.py_modules if p != package and not p.startswith(pfx) + ] + + if self.ext_modules: + self.ext_modules = [ + p + for p in self.ext_modules + if p.name != package and not p.name.startswith(pfx) + ] + + def has_contents_for(self, package: str) -> bool: + """Return true if 'exclude_package(package)' would do something""" + + pfx = package + '.' + + for p in self.iter_distribution_names(): + if p == package or p.startswith(pfx): + return True + + return False + + def _exclude_misc(self, name: str, value: _Sequence) -> None: + """Handle 'exclude()' for list/tuple attrs without a special handler""" + if not isinstance(value, _sequence): + raise DistutilsSetupError( + f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})" + ) + try: + old = getattr(self, name) + except AttributeError as e: + raise DistutilsSetupError(f"{name}: No such distribution setting") from e + if old is not None and not isinstance(old, _sequence): + raise DistutilsSetupError( + name + ": this setting cannot be changed via include/exclude" + ) + elif old: + setattr(self, name, [item for item in old if item not in value]) + + def _include_misc(self, name: str, value: _Sequence) -> None: + """Handle 'include()' for list/tuple attrs without a special handler""" + + if not isinstance(value, _sequence): + raise DistutilsSetupError( + f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})" + ) + try: + old = getattr(self, name) + except AttributeError as e: + raise DistutilsSetupError(f"{name}: No such distribution setting") from e + if old is None: + setattr(self, name, value) + elif not isinstance(old, _sequence): + raise DistutilsSetupError( + name + ": this setting cannot be changed via include/exclude" + ) + else: + new = [item for item in value if item not in old] + setattr(self, name, list(old) + new) + + def exclude(self, **attrs) -> None: + """Remove items from distribution that are named in keyword arguments + + For example, 'dist.exclude(py_modules=["x"])' would remove 'x' from + the distribution's 'py_modules' attribute. Excluding packages uses + the 'exclude_package()' method, so all of the package's contained + packages, modules, and extensions are also excluded. + + Currently, this method only supports exclusion from attributes that are + lists or tuples. If you need to add support for excluding from other + attributes in this or a subclass, you can add an '_exclude_X' method, + where 'X' is the name of the attribute. The method will be called with + the value passed to 'exclude()'. So, 'dist.exclude(foo={"bar":"baz"})' + will try to call 'dist._exclude_foo({"bar":"baz"})', which can then + handle whatever special exclusion logic is needed. + """ + for k, v in attrs.items(): + exclude = getattr(self, '_exclude_' + k, None) + if exclude: + exclude(v) + else: + self._exclude_misc(k, v) + + def _exclude_packages(self, packages: _Sequence) -> None: + if not isinstance(packages, _sequence): + raise DistutilsSetupError( + f"packages: setting must be of type <{_sequence_type_repr}> (got {packages!r})" + ) + list(map(self.exclude_package, packages)) + + def _parse_command_opts(self, parser, args): + # Remove --with-X/--without-X options when processing command args + self.global_options = self.__class__.global_options + self.negative_opt = self.__class__.negative_opt + + # First, expand any aliases + command = args[0] + aliases = self.get_option_dict('aliases') + while command in aliases: + _src, alias = aliases[command] + del aliases[command] # ensure each alias can expand only once! + import shlex + + args[:1] = shlex.split(alias, True) + command = args[0] + + nargs = _Distribution._parse_command_opts(self, parser, args) + + # Handle commands that want to consume all remaining arguments + cmd_class = self.get_command_class(command) + if getattr(cmd_class, 'command_consumes_arguments', None): + self.get_option_dict(command)['args'] = ("command line", nargs) + if nargs is not None: + return [] + + return nargs + + def get_cmdline_options(self) -> dict[str, dict[str, str | None]]: + """Return a '{cmd: {opt:val}}' map of all command-line options + + Option names are all long, but do not include the leading '--', and + contain dashes rather than underscores. If the option doesn't take + an argument (e.g. '--quiet'), the 'val' is 'None'. + + Note that options provided by config files are intentionally excluded. + """ + + d: dict[str, dict[str, str | None]] = {} + + for cmd, opts in self.command_options.items(): + val: str | None + for opt, (src, val) in opts.items(): + if src != "command line": + continue + + opt = opt.replace('_', '-') + + if val == 0: + cmdobj = self.get_command_obj(cmd) + neg_opt = self.negative_opt.copy() + neg_opt.update(getattr(cmdobj, 'negative_opt', {})) + for neg, pos in neg_opt.items(): + if pos == opt: + opt = neg + val = None + break + else: + raise AssertionError("Shouldn't be able to get here") + + elif val == 1: + val = None + + d.setdefault(cmd, {})[opt] = val + + return d + + def iter_distribution_names(self) -> Iterator[str]: + """Yield all packages, modules, and extension names in distribution""" + + yield from self.packages or () + + yield from self.py_modules or () + + for ext in self.ext_modules or (): + if isinstance(ext, tuple): + name, _buildinfo = ext + else: + name = ext.name + name = name.removesuffix('module') + yield name + + def handle_display_options(self, option_order): + """If there were any non-global "display-only" options + (--help-commands or the metadata display options) on the command + line, display the requested info and return true; else return + false. + """ + import sys + + if self.help_commands: + return _Distribution.handle_display_options(self, option_order) + + # Stdout may be StringIO (e.g. in tests) + if not isinstance(sys.stdout, io.TextIOWrapper): + return _Distribution.handle_display_options(self, option_order) + + # Don't wrap stdout if utf-8 is already the encoding. Provides + # workaround for #334. + if sys.stdout.encoding.lower() in ('utf-8', 'utf8'): + return _Distribution.handle_display_options(self, option_order) + + # Print metadata in UTF-8 no matter the platform + encoding = sys.stdout.encoding + sys.stdout.reconfigure(encoding='utf-8') + try: + return _Distribution.handle_display_options(self, option_order) + finally: + sys.stdout.reconfigure(encoding=encoding) + + def run_command(self, command) -> None: + self.set_defaults() + # Postpone defaults until all explicit configuration is considered + # (setup() args, config files, command line and plugins) + + super().run_command(command) + + +@functools.cache +def _setuptools_commands() -> set[str]: + try: + # Use older API for importlib.metadata compatibility + entry_points = metadata.distribution('setuptools').entry_points + eps: Iterable[str] = (ep.name for ep in entry_points) + except metadata.PackageNotFoundError: + # during bootstrapping, distribution doesn't exist + eps = [] + return {*distutils.command.__all__, *eps} + + +class DistDeprecationWarning(SetuptoolsDeprecationWarning): + """Class for warning about deprecations in dist in + setuptools. Not ignored by default, unlike DeprecationWarning.""" diff --git a/lib/python3.11/site-packages/setuptools/errors.py b/lib/python3.11/site-packages/setuptools/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..990ecbf4e2f18eb188addc9e0466152a20193a90 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/errors.py @@ -0,0 +1,67 @@ +"""setuptools.errors + +Provides exceptions used by setuptools modules. +""" + +from __future__ import annotations + +from distutils import errors as _distutils_errors + +# Re-export errors from distutils to facilitate the migration to PEP632 + +ByteCompileError = _distutils_errors.DistutilsByteCompileError +CCompilerError = _distutils_errors.CCompilerError +ClassError = _distutils_errors.DistutilsClassError +CompileError = _distutils_errors.CompileError +ExecError = _distutils_errors.DistutilsExecError +FileError = _distutils_errors.DistutilsFileError +InternalError = _distutils_errors.DistutilsInternalError +LibError = _distutils_errors.LibError +LinkError = _distutils_errors.LinkError +ModuleError = _distutils_errors.DistutilsModuleError +OptionError = _distutils_errors.DistutilsOptionError +PlatformError = _distutils_errors.DistutilsPlatformError +PreprocessError = _distutils_errors.PreprocessError +SetupError = _distutils_errors.DistutilsSetupError +TemplateError = _distutils_errors.DistutilsTemplateError +UnknownFileError = _distutils_errors.UnknownFileError + +# The root error class in the hierarchy +BaseError = _distutils_errors.DistutilsError + + +class InvalidConfigError(OptionError): # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+ + """Error used for invalid configurations.""" + + +class RemovedConfigError(OptionError): # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+ + """Error used for configurations that were deprecated and removed.""" + + +class RemovedCommandError(BaseError, RuntimeError): # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+ + """Error used for commands that have been removed in setuptools. + + Since ``setuptools`` is built on ``distutils``, simply removing a command + from ``setuptools`` will make the behavior fall back to ``distutils``; this + error is raised if a command exists in ``distutils`` but has been actively + removed in ``setuptools``. + """ + + +class PackageDiscoveryError(BaseError, RuntimeError): # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+ + """Impossible to perform automatic discovery of packages and/or modules. + + The current project layout or given discovery options can lead to problems when + scanning the project directory. + + Setuptools might also refuse to complete auto-discovery if an error prone condition + is detected (e.g. when a project is organised as a flat-layout but contains + multiple directories that can be taken as top-level packages inside a single + distribution [*]_). In these situations the users are encouraged to be explicit + about which packages to include or to make the discovery parameters more specific. + + .. [*] Since multi-package distributions are uncommon it is very likely that the + developers did not intend for all the directories to be packaged, and are just + leaving auxiliary code in the repository top-level, such as maintenance-related + scripts. + """ diff --git a/lib/python3.11/site-packages/setuptools/extension.py b/lib/python3.11/site-packages/setuptools/extension.py new file mode 100644 index 0000000000000000000000000000000000000000..3e63cbe12aefc3b52a5d1ca2fe3e1ca2e172e29d --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/extension.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +import functools +import re +from collections.abc import Iterable +from typing import TYPE_CHECKING + +from setuptools._path import StrPath + +from .monkey import get_unpatched + +import distutils.core +import distutils.errors +import distutils.extension + + +def _have_cython() -> bool: + """ + Return True if Cython can be imported. + """ + cython_impl = 'Cython.Distutils.build_ext' + try: + # from (cython_impl) import build_ext + __import__(cython_impl, fromlist=['build_ext']).build_ext + except Exception: + return False + return True + + +# for compatibility +have_pyrex = _have_cython +if TYPE_CHECKING: + # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962 + from distutils.core import Extension as _Extension +else: + _Extension = get_unpatched(distutils.core.Extension) + + +class Extension(_Extension): + """ + Describes a single extension module. + + This means that all source files will be compiled into a single binary file + ``.`` (with ```` derived from ``name`` and + ```` defined by one of the values in + ``importlib.machinery.EXTENSION_SUFFIXES``). + + In the case ``.pyx`` files are passed as ``sources and`` ``Cython`` is **not** + installed in the build environment, ``setuptools`` may also try to look for the + equivalent ``.cpp`` or ``.c`` files. + + :arg str name: + the full name of the extension, including any packages -- ie. + *not* a filename or pathname, but Python dotted name + + :arg Iterable[str | os.PathLike[str]] sources: + iterable of source filenames, (except strings, which could be misinterpreted + as a single filename), relative to the distribution root + (where the setup script lives), in Unix form (slash-separated) + for portability. Source files may be C, C++, SWIG (.i), + platform-specific resource files, or whatever else is recognized + by the "build_ext" command as source for a Python extension. + + :keyword list[str] include_dirs: + list of directories to search for C/C++ header files (in Unix + form for portability) + + :keyword list[tuple[str, str|None]] define_macros: + list of macros to define; each macro is defined using a 2-tuple: + the first item corresponding to the name of the macro and the second + item either a string with its value or None to + define it without a particular value (equivalent of "#define + FOO" in source or -DFOO on Unix C compiler command line) + + :keyword list[str] undef_macros: + list of macros to undefine explicitly + + :keyword list[str] library_dirs: + list of directories to search for C/C++ libraries at link time + + :keyword list[str] libraries: + list of library names (not filenames or paths) to link against + + :keyword list[str] runtime_library_dirs: + list of directories to search for C/C++ libraries at run time + (for shared extensions, this is when the extension is loaded). + Setting this will cause an exception during build on Windows + platforms. + + :keyword list[str] extra_objects: + list of extra files to link with (eg. object files not implied + by 'sources', static library that must be explicitly specified, + binary resource files, etc.) + + :keyword list[str] extra_compile_args: + any extra platform- and compiler-specific information to use + when compiling the source files in 'sources'. For platforms and + compilers where "command line" makes sense, this is typically a + list of command-line arguments, but for other platforms it could + be anything. + + :keyword list[str] extra_link_args: + any extra platform- and compiler-specific information to use + when linking object files together to create the extension (or + to create a new static Python interpreter). Similar + interpretation as for 'extra_compile_args'. + + :keyword list[str] export_symbols: + list of symbols to be exported from a shared extension. Not + used on all platforms, and not generally necessary for Python + extensions, which typically export exactly one symbol: "init" + + extension_name. + + :keyword list[str] swig_opts: + any extra options to pass to SWIG if a source file has the .i + extension. + + :keyword list[str] depends: + list of files that the extension depends on + + :keyword str language: + extension language (i.e. "c", "c++", "objc"). Will be detected + from the source extensions if not provided. + + :keyword bool optional: + specifies that a build failure in the extension should not abort the + build process, but simply not install the failing extension. + + :keyword bool py_limited_api: + opt-in flag for the usage of :doc:`Python's limited API `. + + :raises setuptools.errors.PlatformError: if ``runtime_library_dirs`` is + specified on Windows. (since v63) + """ + + # These 4 are set and used in setuptools/command/build_ext.py + # The lack of a default value and risk of `AttributeError` is purposeful + # to avoid people forgetting to call finalize_options if they modify the extension list. + # See example/rationale in https://github.com/pypa/setuptools/issues/4529. + _full_name: str #: Private API, internal use only. + _links_to_dynamic: bool #: Private API, internal use only. + _needs_stub: bool #: Private API, internal use only. + _file_name: str #: Private API, internal use only. + + def __init__( + self, + name: str, + sources: Iterable[StrPath], + *args, + py_limited_api: bool = False, + **kw, + ) -> None: + # The *args is needed for compatibility as calls may use positional + # arguments. py_limited_api may be set only via keyword. + self.py_limited_api = py_limited_api + super().__init__( + name, + sources, # type: ignore[arg-type] # Vendored version of setuptools supports PathLike + *args, + **kw, + ) + + def _convert_pyx_sources_to_lang(self): + """ + Replace sources with .pyx extensions to sources with the target + language extension. This mechanism allows language authors to supply + pre-converted sources but to prefer the .pyx sources. + """ + if _have_cython(): + # the build has Cython, so allow it to compile the .pyx files + return + lang = self.language or '' + target_ext = '.cpp' if lang.lower() == 'c++' else '.c' + sub = functools.partial(re.sub, '.pyx$', target_ext) + self.sources = list(map(sub, self.sources)) + + +class Library(Extension): + """Just like a regular Extension, but built as a library instead""" diff --git a/lib/python3.11/site-packages/setuptools/glob.py b/lib/python3.11/site-packages/setuptools/glob.py new file mode 100644 index 0000000000000000000000000000000000000000..1dfff2cd50ff87b8cef9d936f1fc9d4a2478b136 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/glob.py @@ -0,0 +1,185 @@ +""" +Filename globbing utility. Mostly a copy of `glob` from Python 3.5. + +Changes include: + * `yield from` and PEP3102 `*` removed. + * Hidden files are not ignored. +""" + +from __future__ import annotations + +import fnmatch +import os +import re +from collections.abc import Iterable, Iterator +from typing import TYPE_CHECKING, AnyStr, overload + +if TYPE_CHECKING: + from _typeshed import BytesPath, StrOrBytesPath, StrPath + +__all__ = ["glob", "iglob", "escape"] + + +def glob(pathname: AnyStr, recursive: bool = False) -> list[AnyStr]: + """Return a list of paths matching a pathname pattern. + + The pattern may contain simple shell-style wildcards a la + fnmatch. However, unlike fnmatch, filenames starting with a + dot are special cases that are not matched by '*' and '?' + patterns. + + If recursive is true, the pattern '**' will match any files and + zero or more directories and subdirectories. + """ + return list(iglob(pathname, recursive=recursive)) + + +def iglob(pathname: AnyStr, recursive: bool = False) -> Iterator[AnyStr]: + """Return an iterator which yields the paths matching a pathname pattern. + + The pattern may contain simple shell-style wildcards a la + fnmatch. However, unlike fnmatch, filenames starting with a + dot are special cases that are not matched by '*' and '?' + patterns. + + If recursive is true, the pattern '**' will match any files and + zero or more directories and subdirectories. + """ + it = _iglob(pathname, recursive) + if recursive and _isrecursive(pathname): + s = next(it) # skip empty string + assert not s + return it + + +def _iglob(pathname: AnyStr, recursive: bool) -> Iterator[AnyStr]: + dirname, basename = os.path.split(pathname) + glob_in_dir = glob2 if recursive and _isrecursive(basename) else glob1 + + if not has_magic(pathname): + if basename: + if os.path.lexists(pathname): + yield pathname + else: + # Patterns ending with a slash should match only directories + if os.path.isdir(dirname): + yield pathname + return + + if not dirname: + yield from glob_in_dir(dirname, basename) + return + # `os.path.split()` returns the argument itself as a dirname if it is a + # drive or UNC path. Prevent an infinite recursion if a drive or UNC path + # contains magic characters (i.e. r'\\?\C:'). + if dirname != pathname and has_magic(dirname): + dirs: Iterable[AnyStr] = _iglob(dirname, recursive) + else: + dirs = [dirname] + if not has_magic(basename): + glob_in_dir = glob0 + for dirname in dirs: + for name in glob_in_dir(dirname, basename): + yield os.path.join(dirname, name) + + +# These 2 helper functions non-recursively glob inside a literal directory. +# They return a list of basenames. `glob1` accepts a pattern while `glob0` +# takes a literal basename (so it only has to check for its existence). + + +@overload +def glob1(dirname: StrPath, pattern: str) -> list[str]: ... +@overload +def glob1(dirname: BytesPath, pattern: bytes) -> list[bytes]: ... +def glob1(dirname: StrOrBytesPath, pattern: str | bytes) -> list[str] | list[bytes]: + if not dirname: + if isinstance(pattern, bytes): + dirname = os.curdir.encode('ASCII') + else: + dirname = os.curdir + try: + names = os.listdir(dirname) + except OSError: + return [] + # mypy false-positives: str or bytes type possibility is always kept in sync + return fnmatch.filter(names, pattern) # type: ignore[type-var, return-value] + + +def glob0(dirname, basename): + if not basename: + # `os.path.split()` returns an empty basename for paths ending with a + # directory separator. 'q*x/' should match only directories. + if os.path.isdir(dirname): + return [basename] + else: + if os.path.lexists(os.path.join(dirname, basename)): + return [basename] + return [] + + +# This helper function recursively yields relative pathnames inside a literal +# directory. + + +@overload +def glob2(dirname: StrPath, pattern: str) -> Iterator[str]: ... +@overload +def glob2(dirname: BytesPath, pattern: bytes) -> Iterator[bytes]: ... +def glob2(dirname: StrOrBytesPath, pattern: str | bytes) -> Iterator[str | bytes]: + assert _isrecursive(pattern) + yield pattern[:0] + yield from _rlistdir(dirname) + + +# Recursively yields relative pathnames inside a literal directory. +@overload +def _rlistdir(dirname: StrPath) -> Iterator[str]: ... +@overload +def _rlistdir(dirname: BytesPath) -> Iterator[bytes]: ... +def _rlistdir(dirname: StrOrBytesPath) -> Iterator[str | bytes]: + if not dirname: + if isinstance(dirname, bytes): + dirname = os.curdir.encode('ASCII') + else: + dirname = os.curdir + try: + names = os.listdir(dirname) + except OSError: + return + for x in names: + yield x + # mypy false-positives: str or bytes type possibility is always kept in sync + path = os.path.join(dirname, x) if dirname else x # type: ignore[arg-type] + for y in _rlistdir(path): + yield os.path.join(x, y) # type: ignore[arg-type] + + +magic_check = re.compile('([*?[])') +magic_check_bytes = re.compile(b'([*?[])') + + +def has_magic(s: str | bytes) -> bool: + if isinstance(s, bytes): + return magic_check_bytes.search(s) is not None + else: + return magic_check.search(s) is not None + + +def _isrecursive(pattern: str | bytes) -> bool: + if isinstance(pattern, bytes): + return pattern == b'**' + else: + return pattern == '**' + + +def escape(pathname): + """Escape all special characters.""" + # Escaping is done by wrapping any of "*?[" between square brackets. + # Metacharacters do not work in the drive part and shouldn't be escaped. + drive, pathname = os.path.splitdrive(pathname) + if isinstance(pathname, bytes): + pathname = magic_check_bytes.sub(rb'[\1]', pathname) + else: + pathname = magic_check.sub(r'[\1]', pathname) + return drive + pathname diff --git a/lib/python3.11/site-packages/setuptools/gui-32.exe b/lib/python3.11/site-packages/setuptools/gui-32.exe new file mode 100644 index 0000000000000000000000000000000000000000..1eb430c6d614a5daea4139badc09c222a4b0e72a Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/gui-32.exe differ diff --git a/lib/python3.11/site-packages/setuptools/gui-64.exe b/lib/python3.11/site-packages/setuptools/gui-64.exe new file mode 100644 index 0000000000000000000000000000000000000000..031cb77c17ba8d8a983448268851d612e05e80d1 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/gui-64.exe differ diff --git a/lib/python3.11/site-packages/setuptools/gui-arm64.exe b/lib/python3.11/site-packages/setuptools/gui-arm64.exe new file mode 100644 index 0000000000000000000000000000000000000000..1e00ffacb182c2af206e5dd9d9fbc41d236da0d1 Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/gui-arm64.exe differ diff --git a/lib/python3.11/site-packages/setuptools/gui.exe b/lib/python3.11/site-packages/setuptools/gui.exe new file mode 100644 index 0000000000000000000000000000000000000000..1eb430c6d614a5daea4139badc09c222a4b0e72a Binary files /dev/null and b/lib/python3.11/site-packages/setuptools/gui.exe differ diff --git a/lib/python3.11/site-packages/setuptools/installer.py b/lib/python3.11/site-packages/setuptools/installer.py new file mode 100644 index 0000000000000000000000000000000000000000..36a8b092279780d730fd0a3cc20c1c76bd72c8a0 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/installer.py @@ -0,0 +1,155 @@ +from __future__ import annotations + +import glob +import itertools +import os +import subprocess +import sys +import tempfile + +import packaging.requirements +import packaging.utils + +from . import _reqs +from ._importlib import metadata +from .warnings import SetuptoolsDeprecationWarning +from .wheel import Wheel + +from distutils import log +from distutils.errors import DistutilsError + + +def _fixup_find_links(find_links): + """Ensure find-links option end-up being a list of strings.""" + if isinstance(find_links, str): + return find_links.split() + assert isinstance(find_links, (tuple, list)) + return find_links + + +def fetch_build_egg(dist, req) -> metadata.Distribution | metadata.PathDistribution: + """Fetch an egg needed for building. + + Use pip/wheel to fetch/build a wheel.""" + _DeprecatedInstaller.emit() + _warn_wheel_not_available(dist) + return _fetch_build_egg_no_warn(dist, req) + + +def _present(req): + return any(_dist_matches_req(dist, req) for dist in metadata.distributions()) + + +def _fetch_build_eggs(dist, requires: _reqs._StrOrIter) -> list[metadata.Distribution]: + _DeprecatedInstaller.emit(stacklevel=3) + _warn_wheel_not_available(dist) + + parsed_reqs = _reqs.parse(requires) + + missing_reqs = itertools.filterfalse(_present, parsed_reqs) + + needed_reqs = ( + req for req in missing_reqs if not req.marker or req.marker.evaluate() + ) + resolved_dists = [_fetch_build_egg_no_warn(dist, req) for req in needed_reqs] + for dist in resolved_dists: + # dist.locate_file('') is the directory containing EGG-INFO, where the importabl + # contents can be found. + sys.path.insert(0, str(dist.locate_file(''))) + return resolved_dists + + +def _dist_matches_req(egg_dist, req): + return ( + packaging.utils.canonicalize_name(egg_dist.name) + == packaging.utils.canonicalize_name(req.name) + and egg_dist.version in req.specifier + ) + + +def _fetch_build_egg_no_warn(dist, req): # noqa: C901 # is too complex (16) # FIXME + # Ignore environment markers; if supplied, it is required. + req = strip_marker(req) + # Take easy_install options into account, but do not override relevant + # pip environment variables (like PIP_INDEX_URL or PIP_QUIET); they'll + # take precedence. + opts = dist.get_option_dict('easy_install') + if 'allow_hosts' in opts: + raise DistutilsError( + 'the `allow-hosts` option is not supported ' + 'when using pip to install requirements.' + ) + quiet = 'PIP_QUIET' not in os.environ and 'PIP_VERBOSE' not in os.environ + if 'PIP_INDEX_URL' in os.environ: + index_url = None + elif 'index_url' in opts: + index_url = opts['index_url'][1] + else: + index_url = None + find_links = ( + _fixup_find_links(opts['find_links'][1])[:] if 'find_links' in opts else [] + ) + if dist.dependency_links: + find_links.extend(dist.dependency_links) + eggs_dir = os.path.realpath(dist.get_egg_cache_dir()) + cached_dists = metadata.Distribution.discover(path=glob.glob(f'{eggs_dir}/*.egg')) + for egg_dist in cached_dists: + if _dist_matches_req(egg_dist, req): + return egg_dist + with tempfile.TemporaryDirectory() as tmpdir: + cmd = [ + sys.executable, + '-m', + 'pip', + '--disable-pip-version-check', + 'wheel', + '--no-deps', + '-w', + tmpdir, + ] + if quiet: + cmd.append('--quiet') + if index_url is not None: + cmd.extend(('--index-url', index_url)) + for link in find_links or []: + cmd.extend(('--find-links', link)) + # If requirement is a PEP 508 direct URL, directly pass + # the URL to pip, as `req @ url` does not work on the + # command line. + cmd.append(req.url or str(req)) + try: + subprocess.check_call(cmd) + except subprocess.CalledProcessError as e: + raise DistutilsError(str(e)) from e + wheel = Wheel(glob.glob(os.path.join(tmpdir, '*.whl'))[0]) + dist_location = os.path.join(eggs_dir, wheel.egg_name()) + wheel.install_as_egg(dist_location) + return metadata.Distribution.at(dist_location + '/EGG-INFO') + + +def strip_marker(req) -> packaging.requirements.Requirement: + """ + Return a new requirement without the environment marker to avoid + calling pip with something like `babel; extra == "i18n"`, which + would always be ignored. + """ + # create a copy to avoid mutating the input + req = packaging.requirements.Requirement(str(req)) + req.marker = None + return req + + +def _warn_wheel_not_available(dist): + try: + metadata.distribution('wheel') + except metadata.PackageNotFoundError: + dist.announce('WARNING: The wheel package is not available.', log.WARN) + + +class _DeprecatedInstaller(SetuptoolsDeprecationWarning): + _SUMMARY = "setuptools.installer and fetch_build_eggs are deprecated." + _DETAILS = """ + Requirements should be satisfied by a PEP 517 installer. + If you are using pip, you can try `pip install --use-pep517`. + """ + _DUE_DATE = 2025, 10, 31 diff --git a/lib/python3.11/site-packages/setuptools/launch.py b/lib/python3.11/site-packages/setuptools/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..0d162647d55777d7afa1bf1e44a6c200a3f82419 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/launch.py @@ -0,0 +1,36 @@ +""" +Launch the Python script on the command line after +setuptools is bootstrapped via import. +""" + +# Note that setuptools gets imported implicitly by the +# invocation of this script using python -m setuptools.launch + +import sys +import tokenize + + +def run() -> None: + """ + Run the script in sys.argv[1] as if it had + been invoked naturally. + """ + __builtins__ + script_name = sys.argv[1] + namespace = dict( + __file__=script_name, + __name__='__main__', + __doc__=None, + ) + sys.argv[:] = sys.argv[1:] + + open_ = getattr(tokenize, 'open', open) + with open_(script_name) as fid: + script = fid.read() + norm_script = script.replace('\\r\\n', '\\n') + code = compile(norm_script, script_name, 'exec') + exec(code, namespace) + + +if __name__ == '__main__': + run() diff --git a/lib/python3.11/site-packages/setuptools/logging.py b/lib/python3.11/site-packages/setuptools/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..532da899f7dc02f9fea9a44c429086b98fe043d8 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/logging.py @@ -0,0 +1,40 @@ +import inspect +import logging +import sys + +from . import monkey + +import distutils.log + + +def _not_warning(record): + return record.levelno < logging.WARNING + + +def configure() -> None: + """ + Configure logging to emit warning and above to stderr + and everything else to stdout. This behavior is provided + for compatibility with distutils.log but may change in + the future. + """ + err_handler = logging.StreamHandler() + err_handler.setLevel(logging.WARNING) + out_handler = logging.StreamHandler(sys.stdout) + out_handler.addFilter(_not_warning) + handlers = err_handler, out_handler + logging.basicConfig( + format="{message}", style='{', handlers=handlers, level=logging.DEBUG + ) + if inspect.ismodule(distutils.dist.log): + monkey.patch_func(set_threshold, distutils.log, 'set_threshold') + # For some reason `distutils.log` module is getting cached in `distutils.dist` + # and then loaded again when patched, + # implying: id(distutils.log) != id(distutils.dist.log). + # Make sure the same module object is used everywhere: + distutils.dist.log = distutils.log + + +def set_threshold(level: int) -> int: + logging.root.setLevel(level * 10) + return set_threshold.unpatched(level) diff --git a/lib/python3.11/site-packages/setuptools/modified.py b/lib/python3.11/site-packages/setuptools/modified.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba02fab68734e1e96fd50d7c4b6ffb1442717fb --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/modified.py @@ -0,0 +1,18 @@ +try: + # Ensure a DistutilsError raised by these methods is the same as distutils.errors.DistutilsError + from distutils._modified import ( + newer, + newer_group, + newer_pairwise, + newer_pairwise_group, + ) +except ImportError: + # fallback for SETUPTOOLS_USE_DISTUTILS=stdlib, because _modified never existed in stdlib + from ._distutils._modified import ( + newer, + newer_group, + newer_pairwise, + newer_pairwise_group, + ) + +__all__ = ['newer', 'newer_pairwise', 'newer_group', 'newer_pairwise_group'] diff --git a/lib/python3.11/site-packages/setuptools/monkey.py b/lib/python3.11/site-packages/setuptools/monkey.py new file mode 100644 index 0000000000000000000000000000000000000000..24bb8180f960a2cd62f352a41241e107e9521750 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/monkey.py @@ -0,0 +1,126 @@ +""" +Monkey patching of distutils. +""" + +from __future__ import annotations + +import inspect +import platform +import sys +import types +from typing import TypeVar, cast, overload + +import distutils.filelist + +_T = TypeVar("_T") +_UnpatchT = TypeVar("_UnpatchT", type, types.FunctionType) + + +__all__: list[str] = [] +""" +Everything is private. Contact the project team +if you think you need this functionality. +""" + + +def _get_mro(cls): + """ + Returns the bases classes for cls sorted by the MRO. + + Works around an issue on Jython where inspect.getmro will not return all + base classes if multiple classes share the same name. Instead, this + function will return a tuple containing the class itself, and the contents + of cls.__bases__. See https://github.com/pypa/setuptools/issues/1024. + """ + if platform.python_implementation() == "Jython": + return (cls,) + cls.__bases__ + return inspect.getmro(cls) + + +@overload +def get_unpatched(item: _UnpatchT) -> _UnpatchT: ... +@overload +def get_unpatched(item: object) -> None: ... +def get_unpatched( + item: type | types.FunctionType | object, +) -> type | types.FunctionType | None: + if isinstance(item, type): + return get_unpatched_class(item) + if isinstance(item, types.FunctionType): + return get_unpatched_function(item) + return None + + +def get_unpatched_class(cls: type[_T]) -> type[_T]: + """Protect against re-patching the distutils if reloaded + + Also ensures that no other distutils extension monkeypatched the distutils + first. + """ + external_bases = ( + cast(type[_T], cls) + for cls in _get_mro(cls) + if not cls.__module__.startswith('setuptools') + ) + base = next(external_bases) + if not base.__module__.startswith('distutils'): + msg = f"distutils has already been patched by {cls!r}" + raise AssertionError(msg) + return base + + +def patch_all() -> None: + import setuptools + + # we can't patch distutils.cmd, alas + distutils.core.Command = setuptools.Command # type: ignore[misc,assignment] # monkeypatching + + _patch_distribution_metadata() + + # Install Distribution throughout the distutils + for module in distutils.dist, distutils.core, distutils.cmd: + module.Distribution = setuptools.dist.Distribution + + # Install the patched Extension + distutils.core.Extension = setuptools.extension.Extension # type: ignore[misc,assignment] # monkeypatching + distutils.extension.Extension = setuptools.extension.Extension # type: ignore[misc,assignment] # monkeypatching + if 'distutils.command.build_ext' in sys.modules: + sys.modules[ + 'distutils.command.build_ext' + ].Extension = setuptools.extension.Extension + + +def _patch_distribution_metadata(): + from . import _core_metadata + + """Patch write_pkg_file and read_pkg_file for higher metadata standards""" + for attr in ( + 'write_pkg_info', + 'write_pkg_file', + 'read_pkg_file', + 'get_metadata_version', + 'get_fullname', + ): + new_val = getattr(_core_metadata, attr) + setattr(distutils.dist.DistributionMetadata, attr, new_val) + + +def patch_func(replacement, target_mod, func_name) -> None: + """ + Patch func_name in target_mod with replacement + + Important - original must be resolved by name to avoid + patching an already patched function. + """ + original = getattr(target_mod, func_name) + + # set the 'unpatched' attribute on the replacement to + # point to the original. + vars(replacement).setdefault('unpatched', original) + + # replace the function in the original module + setattr(target_mod, func_name, replacement) + + +def get_unpatched_function(candidate): + return candidate.unpatched diff --git a/lib/python3.11/site-packages/setuptools/msvc.py b/lib/python3.11/site-packages/setuptools/msvc.py new file mode 100644 index 0000000000000000000000000000000000000000..f506c8222dc1994dfd155f3b2c2404c62fc50659 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/msvc.py @@ -0,0 +1,1557 @@ +""" +Environment info about Microsoft Compilers. + +>>> getfixture('windows_only') +>>> ei = EnvironmentInfo('amd64') +""" + +from __future__ import annotations + +import contextlib +import itertools +import json +import os +import os.path +import platform +from typing import TYPE_CHECKING, TypedDict, overload + +from more_itertools import unique_everseen + +from ._path import StrPath +from .compat import py310 + +import distutils.errors + +if TYPE_CHECKING: + from typing_extensions import LiteralString, NotRequired + +# https://github.com/python/mypy/issues/8166 +if not TYPE_CHECKING and platform.system() == 'Windows': + import winreg + from os import environ +else: + # Mock winreg and environ so the module can be imported on this platform. + + class winreg: + HKEY_USERS = None + HKEY_CURRENT_USER = None + HKEY_LOCAL_MACHINE = None + HKEY_CLASSES_ROOT = None + + environ: dict[str, str] = dict() + + +class PlatformInfo: + """ + Current and Target Architectures information. + + Parameters + ---------- + arch: str + Target architecture. + """ + + current_cpu = environ.get('processor_architecture', '').lower() + + def __init__(self, arch: str) -> None: + self.arch = arch.lower().replace('x64', 'amd64') + + @property + def target_cpu(self) -> str: + """ + Return Target CPU architecture. + + Return + ------ + str + Target CPU + """ + return self.arch[self.arch.find('_') + 1 :] + + def target_is_x86(self) -> bool: + """ + Return True if target CPU is x86 32 bits.. + + Return + ------ + bool + CPU is x86 32 bits + """ + return self.target_cpu == 'x86' + + def current_is_x86(self) -> bool: + """ + Return True if current CPU is x86 32 bits.. + + Return + ------ + bool + CPU is x86 32 bits + """ + return self.current_cpu == 'x86' + + def current_dir(self, hidex86=False, x64=False) -> str: + """ + Current platform specific subfolder. + + Parameters + ---------- + hidex86: bool + return '' and not '\x86' if architecture is x86. + x64: bool + return '\x64' and not '\amd64' if architecture is amd64. + + Return + ------ + str + subfolder: '\target', or '' (see hidex86 parameter) + """ + return ( + '' + if (self.current_cpu == 'x86' and hidex86) + else r'\x64' + if (self.current_cpu == 'amd64' and x64) + else rf'\{self.current_cpu}' + ) + + def target_dir(self, hidex86=False, x64=False) -> str: + r""" + Target platform specific subfolder. + + Parameters + ---------- + hidex86: bool + return '' and not '\x86' if architecture is x86. + x64: bool + return '\x64' and not '\amd64' if architecture is amd64. + + Return + ------ + str + subfolder: '\current', or '' (see hidex86 parameter) + """ + return ( + '' + if (self.target_cpu == 'x86' and hidex86) + else r'\x64' + if (self.target_cpu == 'amd64' and x64) + else rf'\{self.target_cpu}' + ) + + def cross_dir(self, forcex86=False) -> str: + r""" + Cross platform specific subfolder. + + Parameters + ---------- + forcex86: bool + Use 'x86' as current architecture even if current architecture is + not x86. + + Return + ------ + str + subfolder: '' if target architecture is current architecture, + '\current_target' if not. + """ + current = 'x86' if forcex86 else self.current_cpu + return ( + '' + if self.target_cpu == current + else self.target_dir().replace('\\', f'\\{current}_') + ) + + +class RegistryInfo: + """ + Microsoft Visual Studio related registry information. + + Parameters + ---------- + platform_info: PlatformInfo + "PlatformInfo" instance. + """ + + HKEYS = ( + winreg.HKEY_USERS, + winreg.HKEY_CURRENT_USER, + winreg.HKEY_LOCAL_MACHINE, + winreg.HKEY_CLASSES_ROOT, + ) + + def __init__(self, platform_info: PlatformInfo) -> None: + self.pi = platform_info + + @property + def visualstudio(self) -> LiteralString: + """ + Microsoft Visual Studio root registry key. + + Return + ------ + str + Registry key + """ + return 'VisualStudio' + + @property + def sxs(self) -> LiteralString: + """ + Microsoft Visual Studio SxS registry key. + + Return + ------ + str + Registry key + """ + return os.path.join(self.visualstudio, 'SxS') + + @property + def vc(self) -> LiteralString: + """ + Microsoft Visual C++ VC7 registry key. + + Return + ------ + str + Registry key + """ + return os.path.join(self.sxs, 'VC7') + + @property + def vs(self) -> LiteralString: + """ + Microsoft Visual Studio VS7 registry key. + + Return + ------ + str + Registry key + """ + return os.path.join(self.sxs, 'VS7') + + @property + def vc_for_python(self) -> LiteralString: + """ + Microsoft Visual C++ for Python registry key. + + Return + ------ + str + Registry key + """ + return r'DevDiv\VCForPython' + + @property + def microsoft_sdk(self) -> LiteralString: + """ + Microsoft SDK registry key. + + Return + ------ + str + Registry key + """ + return 'Microsoft SDKs' + + @property + def windows_sdk(self) -> LiteralString: + """ + Microsoft Windows/Platform SDK registry key. + + Return + ------ + str + Registry key + """ + return os.path.join(self.microsoft_sdk, 'Windows') + + @property + def netfx_sdk(self) -> LiteralString: + """ + Microsoft .NET Framework SDK registry key. + + Return + ------ + str + Registry key + """ + return os.path.join(self.microsoft_sdk, 'NETFXSDK') + + @property + def windows_kits_roots(self) -> LiteralString: + """ + Microsoft Windows Kits Roots registry key. + + Return + ------ + str + Registry key + """ + return r'Windows Kits\Installed Roots' + + @overload + def microsoft(self, key: LiteralString, x86: bool = False) -> LiteralString: ... + @overload + def microsoft(self, key: str, x86: bool = False) -> str: ... # type: ignore[misc] + def microsoft(self, key: str, x86: bool = False) -> str: + """ + Return key in Microsoft software registry. + + Parameters + ---------- + key: str + Registry key path where look. + x86: bool + Force x86 software registry. + + Return + ------ + str + Registry key + """ + node64 = '' if self.pi.current_is_x86() or x86 else 'Wow6432Node' + return os.path.join('Software', node64, 'Microsoft', key) + + def lookup(self, key: str, name: str) -> str | None: + """ + Look for values in registry in Microsoft software registry. + + Parameters + ---------- + key: str + Registry key path where look. + name: str + Value name to find. + + Return + ------ + str | None + value + """ + key_read = winreg.KEY_READ + openkey = winreg.OpenKey + closekey = winreg.CloseKey + ms = self.microsoft + for hkey in self.HKEYS: + bkey = None + try: + bkey = openkey(hkey, ms(key), 0, key_read) + except OSError: + if not self.pi.current_is_x86(): + try: + bkey = openkey(hkey, ms(key, True), 0, key_read) + except OSError: + continue + else: + continue + try: + return winreg.QueryValueEx(bkey, name)[0] + except OSError: + pass + finally: + if bkey: + closekey(bkey) + return None + + +class SystemInfo: + """ + Microsoft Windows and Visual Studio related system information. + + Parameters + ---------- + registry_info: RegistryInfo + "RegistryInfo" instance. + vc_ver: float + Required Microsoft Visual C++ version. + """ + + # Variables and properties in this class use originals CamelCase variables + # names from Microsoft source files for more easy comparison. + WinDir = environ.get('WinDir', '') + ProgramFiles = environ.get('ProgramFiles', '') + ProgramFilesx86 = environ.get('ProgramFiles(x86)', ProgramFiles) + + def __init__( + self, registry_info: RegistryInfo, vc_ver: float | None = None + ) -> None: + self.ri = registry_info + self.pi = self.ri.pi + + self.known_vs_paths = self.find_programdata_vs_vers() + + # Except for VS15+, VC version is aligned with VS version + self.vs_ver = self.vc_ver = vc_ver or self._find_latest_available_vs_ver() + + def _find_latest_available_vs_ver(self): + """ + Find the latest VC version + + Return + ------ + float + version + """ + reg_vc_vers = self.find_reg_vs_vers() + + if not (reg_vc_vers or self.known_vs_paths): + raise distutils.errors.DistutilsPlatformError( + 'No Microsoft Visual C++ version found' + ) + + vc_vers = set(reg_vc_vers) + vc_vers.update(self.known_vs_paths) + return max(vc_vers) + + def find_reg_vs_vers(self) -> list[float]: + """ + Find Microsoft Visual Studio versions available in registry. + + Return + ------ + list of float + Versions + """ + ms = self.ri.microsoft + vckeys = (self.ri.vc, self.ri.vc_for_python, self.ri.vs) + vs_vers = [] + for hkey, key in itertools.product(self.ri.HKEYS, vckeys): + try: + bkey = winreg.OpenKey(hkey, ms(key), 0, winreg.KEY_READ) + except OSError: + continue + with bkey: + subkeys, values, _ = winreg.QueryInfoKey(bkey) + for i in range(values): + with contextlib.suppress(ValueError): + ver = float(winreg.EnumValue(bkey, i)[0]) + if ver not in vs_vers: + vs_vers.append(ver) + for i in range(subkeys): + with contextlib.suppress(ValueError): + ver = float(winreg.EnumKey(bkey, i)) + if ver not in vs_vers: + vs_vers.append(ver) + return sorted(vs_vers) + + def find_programdata_vs_vers(self) -> dict[float, str]: + r""" + Find Visual studio 2017+ versions from information in + "C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances". + + Return + ------ + dict + float version as key, path as value. + """ + vs_versions: dict[float, str] = {} + instances_dir = r'C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances' + + try: + hashed_names = os.listdir(instances_dir) + + except OSError: + # Directory not exists with all Visual Studio versions + return vs_versions + + for name in hashed_names: + try: + # Get VS installation path from "state.json" file + state_path = os.path.join(instances_dir, name, 'state.json') + with open(state_path, 'rt', encoding='utf-8') as state_file: + state = json.load(state_file) + vs_path = state['installationPath'] + + # Raises OSError if this VS installation does not contain VC + os.listdir(os.path.join(vs_path, r'VC\Tools\MSVC')) + + # Store version and path + vs_versions[self._as_float_version(state['installationVersion'])] = ( + vs_path + ) + + except (OSError, KeyError): + # Skip if "state.json" file is missing or bad format + continue + + return vs_versions + + @staticmethod + def _as_float_version(version): + """ + Return a string version as a simplified float version (major.minor) + + Parameters + ---------- + version: str + Version. + + Return + ------ + float + version + """ + return float('.'.join(version.split('.')[:2])) + + @property + def VSInstallDir(self) -> str: + """ + Microsoft Visual Studio directory. + + Return + ------ + str + path + """ + # Default path + default = os.path.join( + self.ProgramFilesx86, f'Microsoft Visual Studio {self.vs_ver:0.1f}' + ) + + # Try to get path from registry, if fail use default path + return self.ri.lookup(self.ri.vs, f'{self.vs_ver:0.1f}') or default + + @property + def VCInstallDir(self) -> str: + """ + Microsoft Visual C++ directory. + + Return + ------ + str + path + """ + path = self._guess_vc() or self._guess_vc_legacy() + + if not os.path.isdir(path): + msg = 'Microsoft Visual C++ directory not found' + raise distutils.errors.DistutilsPlatformError(msg) + + return path + + def _guess_vc(self): + """ + Locate Visual C++ for VS2017+. + + Return + ------ + str + path + """ + if self.vs_ver <= 14.0: + return '' + + try: + # First search in known VS paths + vs_dir = self.known_vs_paths[self.vs_ver] + except KeyError: + # Else, search with path from registry + vs_dir = self.VSInstallDir + + guess_vc = os.path.join(vs_dir, r'VC\Tools\MSVC') + + # Subdir with VC exact version as name + try: + # Update the VC version with real one instead of VS version + vc_ver = os.listdir(guess_vc)[-1] + self.vc_ver = self._as_float_version(vc_ver) + return os.path.join(guess_vc, vc_ver) + except (OSError, IndexError): + return '' + + def _guess_vc_legacy(self): + """ + Locate Visual C++ for versions prior to 2017. + + Return + ------ + str + path + """ + default = os.path.join( + self.ProgramFilesx86, + rf'Microsoft Visual Studio {self.vs_ver:0.1f}\VC', + ) + + # Try to get "VC++ for Python" path from registry as default path + reg_path = os.path.join(self.ri.vc_for_python, f'{self.vs_ver:0.1f}') + python_vc = self.ri.lookup(reg_path, 'installdir') + default_vc = os.path.join(python_vc, 'VC') if python_vc else default + + # Try to get path from registry, if fail use default path + return self.ri.lookup(self.ri.vc, f'{self.vs_ver:0.1f}') or default_vc + + @property + def WindowsSdkVersion(self) -> tuple[LiteralString, ...]: + """ + Microsoft Windows SDK versions for specified MSVC++ version. + + Return + ------ + tuple of str + versions + """ + if self.vs_ver <= 9.0: + return '7.0', '6.1', '6.0a' + elif self.vs_ver == 10.0: + return '7.1', '7.0a' + elif self.vs_ver == 11.0: + return '8.0', '8.0a' + elif self.vs_ver == 12.0: + return '8.1', '8.1a' + elif self.vs_ver >= 14.0: + return '10.0', '8.1' + return () + + @property + def WindowsSdkLastVersion(self) -> str: + """ + Microsoft Windows SDK last version. + + Return + ------ + str + version + """ + return self._use_last_dir_name(os.path.join(self.WindowsSdkDir, 'lib')) + + @property + def WindowsSdkDir(self) -> str: # noqa: C901 # is too complex (12) # FIXME + """ + Microsoft Windows SDK directory. + + Return + ------ + str + path + """ + sdkdir: str | None = '' + for ver in self.WindowsSdkVersion: + # Try to get it from registry + loc = os.path.join(self.ri.windows_sdk, f'v{ver}') + sdkdir = self.ri.lookup(loc, 'installationfolder') + if sdkdir: + break + if not sdkdir or not os.path.isdir(sdkdir): + # Try to get "VC++ for Python" version from registry + path = os.path.join(self.ri.vc_for_python, f'{self.vc_ver:0.1f}') + install_base = self.ri.lookup(path, 'installdir') + if install_base: + sdkdir = os.path.join(install_base, 'WinSDK') + if not sdkdir or not os.path.isdir(sdkdir): + # If fail, use default new path + for ver in self.WindowsSdkVersion: + intver = ver[: ver.rfind('.')] + path = rf'Microsoft SDKs\Windows Kits\{intver}' + d = os.path.join(self.ProgramFiles, path) + if os.path.isdir(d): + sdkdir = d + if not sdkdir or not os.path.isdir(sdkdir): + # If fail, use default old path + for ver in self.WindowsSdkVersion: + path = rf'Microsoft SDKs\Windows\v{ver}' + d = os.path.join(self.ProgramFiles, path) + if os.path.isdir(d): + sdkdir = d + if not sdkdir: + # If fail, use Platform SDK + sdkdir = os.path.join(self.VCInstallDir, 'PlatformSDK') + return sdkdir + + @property + def WindowsSDKExecutablePath(self) -> str | None: + """ + Microsoft Windows SDK executable directory. + + Return + ------ + str | None + path + """ + # Find WinSDK NetFx Tools registry dir name + if self.vs_ver <= 11.0: + netfxver = 35 + arch = '' + else: + netfxver = 40 + hidex86 = True if self.vs_ver <= 12.0 else False + arch = self.pi.current_dir(x64=True, hidex86=hidex86).replace('\\', '-') + fx = f'WinSDK-NetFx{netfxver}Tools{arch}' + + # list all possibles registry paths + regpaths = [] + if self.vs_ver >= 14.0: + for ver in self.NetFxSdkVersion: + regpaths += [os.path.join(self.ri.netfx_sdk, ver, fx)] + + for ver in self.WindowsSdkVersion: + regpaths += [os.path.join(self.ri.windows_sdk, f'v{ver}A', fx)] + + # Return installation folder from the more recent path + for path in regpaths: + execpath = self.ri.lookup(path, 'installationfolder') + if execpath: + return execpath + + return None + + @property + def FSharpInstallDir(self) -> str: + """ + Microsoft Visual F# directory. + + Return + ------ + str + path + """ + path = os.path.join(self.ri.visualstudio, rf'{self.vs_ver:0.1f}\Setup\F#') + return self.ri.lookup(path, 'productdir') or '' + + @property + def UniversalCRTSdkDir(self) -> str | None: + """ + Microsoft Universal CRT SDK directory. + + Return + ------ + str | None + path + """ + # Set Kit Roots versions for specified MSVC++ version + vers = ('10', '81') if self.vs_ver >= 14.0 else () + + # Find path of the more recent Kit + for ver in vers: + sdkdir = self.ri.lookup(self.ri.windows_kits_roots, f'kitsroot{ver}') + if sdkdir: + return sdkdir + + return None + + @property + def UniversalCRTSdkLastVersion(self) -> str: + """ + Microsoft Universal C Runtime SDK last version. + + Return + ------ + str + version + """ + try: + return self._use_last_dir_name(os.path.join(self.UniversalCRTSdkDir, 'lib')) # type: ignore[arg-type] # Expected TypeError + except TypeError as ex: + py310.add_note(ex, "Cannot find UniversalCRTSdkDir") + raise + + @property + def NetFxSdkVersion(self) -> tuple[LiteralString, ...]: + """ + Microsoft .NET Framework SDK versions. + + Return + ------ + tuple of str + versions + """ + # Set FxSdk versions for specified VS version + return ( + ('4.7.2', '4.7.1', '4.7', '4.6.2', '4.6.1', '4.6', '4.5.2', '4.5.1', '4.5') + if self.vs_ver >= 14.0 + else () + ) + + @property + def NetFxSdkDir(self) -> str | None: + """ + Microsoft .NET Framework SDK directory. + + Return + ------ + str | None + path + """ + sdkdir: str | None = '' + for ver in self.NetFxSdkVersion: + loc = os.path.join(self.ri.netfx_sdk, ver) + sdkdir = self.ri.lookup(loc, 'kitsinstallationfolder') + if sdkdir: + break + return sdkdir + + @property + def FrameworkDir32(self) -> str: + """ + Microsoft .NET Framework 32bit directory. + + Return + ------ + str + path + """ + # Default path + guess_fw = os.path.join(self.WinDir, r'Microsoft.NET\Framework') + + # Try to get path from registry, if fail use default path + return self.ri.lookup(self.ri.vc, 'frameworkdir32') or guess_fw + + @property + def FrameworkDir64(self) -> str: + """ + Microsoft .NET Framework 64bit directory. + + Return + ------ + str + path + """ + # Default path + guess_fw = os.path.join(self.WinDir, r'Microsoft.NET\Framework64') + + # Try to get path from registry, if fail use default path + return self.ri.lookup(self.ri.vc, 'frameworkdir64') or guess_fw + + @property + def FrameworkVersion32(self) -> tuple[str, ...]: + """ + Microsoft .NET Framework 32bit versions. + + Return + ------ + tuple of str + versions + """ + return self._find_dot_net_versions(32) + + @property + def FrameworkVersion64(self) -> tuple[str, ...]: + """ + Microsoft .NET Framework 64bit versions. + + Return + ------ + tuple of str + versions + """ + return self._find_dot_net_versions(64) + + def _find_dot_net_versions(self, bits) -> tuple[str, ...]: + """ + Find Microsoft .NET Framework versions. + + Parameters + ---------- + bits: int + Platform number of bits: 32 or 64. + + Return + ------ + tuple of str + versions + """ + # Find actual .NET version in registry + reg_ver = self.ri.lookup(self.ri.vc, f'frameworkver{bits}') + dot_net_dir = getattr(self, f'FrameworkDir{bits}') + ver = reg_ver or self._use_last_dir_name(dot_net_dir, 'v') or '' + + # Set .NET versions for specified MSVC++ version + if self.vs_ver >= 12.0: + return ver, 'v4.0' + elif self.vs_ver >= 10.0: + return 'v4.0.30319' if ver.lower()[:2] != 'v4' else ver, 'v3.5' + elif self.vs_ver == 9.0: + return 'v3.5', 'v2.0.50727' + elif self.vs_ver == 8.0: + return 'v3.0', 'v2.0.50727' + return () + + @staticmethod + def _use_last_dir_name(path: StrPath, prefix: str = '') -> str: + """ + Return name of the last dir in path or '' if no dir found. + + Parameters + ---------- + path: StrPath + Use dirs in this path + prefix: str + Use only dirs starting by this prefix + + Return + ------ + str + name + """ + matching_dirs = ( + dir_name + for dir_name in reversed(os.listdir(path)) + if os.path.isdir(os.path.join(path, dir_name)) + and dir_name.startswith(prefix) + ) + return next(matching_dirs, '') + + +class _EnvironmentDict(TypedDict): + include: str + lib: str + libpath: str + path: str + py_vcruntime_redist: NotRequired[str | None] + + +class EnvironmentInfo: + """ + Return environment variables for specified Microsoft Visual C++ version + and platform : Lib, Include, Path and libpath. + + This function is compatible with Microsoft Visual C++ 9.0 to 14.X. + + Script created by analysing Microsoft environment configuration files like + "vcvars[...].bat", "SetEnv.Cmd", "vcbuildtools.bat", ... + + Parameters + ---------- + arch: str + Target architecture. + vc_ver: float + Required Microsoft Visual C++ version. If not set, autodetect the last + version. + vc_min_ver: float + Minimum Microsoft Visual C++ version. + """ + + # Variables and properties in this class use originals CamelCase variables + # names from Microsoft source files for more easy comparison. + + def __init__(self, arch, vc_ver=None, vc_min_ver=0) -> None: + self.pi = PlatformInfo(arch) + self.ri = RegistryInfo(self.pi) + self.si = SystemInfo(self.ri, vc_ver) + + if self.vc_ver < vc_min_ver: + err = 'No suitable Microsoft Visual C++ version found' + raise distutils.errors.DistutilsPlatformError(err) + + @property + def vs_ver(self): + """ + Microsoft Visual Studio. + + Return + ------ + float + version + """ + return self.si.vs_ver + + @property + def vc_ver(self): + """ + Microsoft Visual C++ version. + + Return + ------ + float + version + """ + return self.si.vc_ver + + @property + def VSTools(self): + """ + Microsoft Visual Studio Tools. + + Return + ------ + list of str + paths + """ + paths = [r'Common7\IDE', r'Common7\Tools'] + + if self.vs_ver >= 14.0: + arch_subdir = self.pi.current_dir(hidex86=True, x64=True) + paths += [r'Common7\IDE\CommonExtensions\Microsoft\TestWindow'] + paths += [r'Team Tools\Performance Tools'] + paths += [rf'Team Tools\Performance Tools{arch_subdir}'] + + return [os.path.join(self.si.VSInstallDir, path) for path in paths] + + @property + def VCIncludes(self): + """ + Microsoft Visual C++ & Microsoft Foundation Class Includes. + + Return + ------ + list of str + paths + """ + return [ + os.path.join(self.si.VCInstallDir, 'Include'), + os.path.join(self.si.VCInstallDir, r'ATLMFC\Include'), + ] + + @property + def VCLibraries(self): + """ + Microsoft Visual C++ & Microsoft Foundation Class Libraries. + + Return + ------ + list of str + paths + """ + if self.vs_ver >= 15.0: + arch_subdir = self.pi.target_dir(x64=True) + else: + arch_subdir = self.pi.target_dir(hidex86=True) + paths = [f'Lib{arch_subdir}', rf'ATLMFC\Lib{arch_subdir}'] + + if self.vs_ver >= 14.0: + paths += [rf'Lib\store{arch_subdir}'] + + return [os.path.join(self.si.VCInstallDir, path) for path in paths] + + @property + def VCStoreRefs(self): + """ + Microsoft Visual C++ store references Libraries. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 14.0: + return [] + return [os.path.join(self.si.VCInstallDir, r'Lib\store\references')] + + @property + def VCTools(self): + """ + Microsoft Visual C++ Tools. + + Return + ------ + list of str + paths + + When host CPU is ARM, the tools should be found for ARM. + + >>> getfixture('windows_only') + >>> mp = getfixture('monkeypatch') + >>> mp.setattr(PlatformInfo, 'current_cpu', 'arm64') + >>> ei = EnvironmentInfo(arch='irrelevant') + >>> paths = ei.VCTools + >>> any('HostARM64' in path for path in paths) + True + """ + si = self.si + tools = [os.path.join(si.VCInstallDir, 'VCPackages')] + + forcex86 = True if self.vs_ver <= 10.0 else False + arch_subdir = self.pi.cross_dir(forcex86) + if arch_subdir: + tools += [os.path.join(si.VCInstallDir, f'Bin{arch_subdir}')] + + if self.vs_ver == 14.0: + path = f'Bin{self.pi.current_dir(hidex86=True)}' + tools += [os.path.join(si.VCInstallDir, path)] + + elif self.vs_ver >= 15.0: + host_id = self.pi.current_cpu.replace('amd64', 'x64').upper() + host_dir = os.path.join('bin', f'Host{host_id}%s') + tools += [ + os.path.join(si.VCInstallDir, host_dir % self.pi.target_dir(x64=True)) + ] + + if self.pi.current_cpu != self.pi.target_cpu: + tools += [ + os.path.join( + si.VCInstallDir, host_dir % self.pi.current_dir(x64=True) + ) + ] + + else: + tools += [os.path.join(si.VCInstallDir, 'Bin')] + + return tools + + @property + def OSLibraries(self): + """ + Microsoft Windows SDK Libraries. + + Return + ------ + list of str + paths + """ + if self.vs_ver <= 10.0: + arch_subdir = self.pi.target_dir(hidex86=True, x64=True) + return [os.path.join(self.si.WindowsSdkDir, f'Lib{arch_subdir}')] + + else: + arch_subdir = self.pi.target_dir(x64=True) + lib = os.path.join(self.si.WindowsSdkDir, 'lib') + libver = self._sdk_subdir + return [os.path.join(lib, f'{libver}um{arch_subdir}')] + + @property + def OSIncludes(self): + """ + Microsoft Windows SDK Include. + + Return + ------ + list of str + paths + """ + include = os.path.join(self.si.WindowsSdkDir, 'include') + + if self.vs_ver <= 10.0: + return [include, os.path.join(include, 'gl')] + + else: + if self.vs_ver >= 14.0: + sdkver = self._sdk_subdir + else: + sdkver = '' + return [ + os.path.join(include, f'{sdkver}shared'), + os.path.join(include, f'{sdkver}um'), + os.path.join(include, f'{sdkver}winrt'), + ] + + @property + def OSLibpath(self): + """ + Microsoft Windows SDK Libraries Paths. + + Return + ------ + list of str + paths + """ + ref = os.path.join(self.si.WindowsSdkDir, 'References') + libpath = [] + + if self.vs_ver <= 9.0: + libpath += self.OSLibraries + + if self.vs_ver >= 11.0: + libpath += [os.path.join(ref, r'CommonConfiguration\Neutral')] + + if self.vs_ver >= 14.0: + libpath += [ + ref, + os.path.join(self.si.WindowsSdkDir, 'UnionMetadata'), + os.path.join(ref, 'Windows.Foundation.UniversalApiContract', '1.0.0.0'), + os.path.join(ref, 'Windows.Foundation.FoundationContract', '1.0.0.0'), + os.path.join( + ref, 'Windows.Networking.Connectivity.WwanContract', '1.0.0.0' + ), + os.path.join( + self.si.WindowsSdkDir, + 'ExtensionSDKs', + 'Microsoft.VCLibs', + f'{self.vs_ver:0.1f}', + 'References', + 'CommonConfiguration', + 'neutral', + ), + ] + return libpath + + @property + def SdkTools(self): + """ + Microsoft Windows SDK Tools. + + Return + ------ + list of str + paths + """ + return list(self._sdk_tools()) + + def _sdk_tools(self): + """ + Microsoft Windows SDK Tools paths generator. + + Return + ------ + generator of str + paths + """ + if self.vs_ver < 15.0: + bin_dir = 'Bin' if self.vs_ver <= 11.0 else r'Bin\x86' + yield os.path.join(self.si.WindowsSdkDir, bin_dir) + + if not self.pi.current_is_x86(): + arch_subdir = self.pi.current_dir(x64=True) + path = f'Bin{arch_subdir}' + yield os.path.join(self.si.WindowsSdkDir, path) + + if self.vs_ver in (10.0, 11.0): + if self.pi.target_is_x86(): + arch_subdir = '' + else: + arch_subdir = self.pi.current_dir(hidex86=True, x64=True) + path = rf'Bin\NETFX 4.0 Tools{arch_subdir}' + yield os.path.join(self.si.WindowsSdkDir, path) + + elif self.vs_ver >= 15.0: + path = os.path.join(self.si.WindowsSdkDir, 'Bin') + arch_subdir = self.pi.current_dir(x64=True) + sdkver = self.si.WindowsSdkLastVersion + yield os.path.join(path, f'{sdkver}{arch_subdir}') + + if self.si.WindowsSDKExecutablePath: + yield self.si.WindowsSDKExecutablePath + + @property + def _sdk_subdir(self) -> str: + """ + Microsoft Windows SDK version subdir. + + Return + ------ + str + subdir + """ + ucrtver = self.si.WindowsSdkLastVersion + return (f'{ucrtver}\\') if ucrtver else '' + + @property + def SdkSetup(self): + """ + Microsoft Windows SDK Setup. + + Return + ------ + list of str + paths + """ + if self.vs_ver > 9.0: + return [] + + return [os.path.join(self.si.WindowsSdkDir, 'Setup')] + + @property + def FxTools(self): + """ + Microsoft .NET Framework Tools. + + Return + ------ + list of str + paths + """ + pi = self.pi + si = self.si + + if self.vs_ver <= 10.0: + include32 = True + include64 = not pi.target_is_x86() and not pi.current_is_x86() + else: + include32 = pi.target_is_x86() or pi.current_is_x86() + include64 = pi.current_cpu == 'amd64' or pi.target_cpu == 'amd64' + + tools = [] + if include32: + tools += [ + os.path.join(si.FrameworkDir32, ver) for ver in si.FrameworkVersion32 + ] + if include64: + tools += [ + os.path.join(si.FrameworkDir64, ver) for ver in si.FrameworkVersion64 + ] + return tools + + @property + def NetFxSDKLibraries(self): + """ + Microsoft .Net Framework SDK Libraries. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 14.0 or not self.si.NetFxSdkDir: + return [] + + arch_subdir = self.pi.target_dir(x64=True) + return [os.path.join(self.si.NetFxSdkDir, rf'lib\um{arch_subdir}')] + + @property + def NetFxSDKIncludes(self): + """ + Microsoft .Net Framework SDK Includes. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 14.0 or not self.si.NetFxSdkDir: + return [] + + return [os.path.join(self.si.NetFxSdkDir, r'include\um')] + + @property + def VsTDb(self): + """ + Microsoft Visual Studio Team System Database. + + Return + ------ + list of str + paths + """ + return [os.path.join(self.si.VSInstallDir, r'VSTSDB\Deploy')] + + @property + def MSBuild(self): + """ + Microsoft Build Engine. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 12.0: + return [] + elif self.vs_ver < 15.0: + base_path = self.si.ProgramFilesx86 + arch_subdir = self.pi.current_dir(hidex86=True) + else: + base_path = self.si.VSInstallDir + arch_subdir = '' + + path = rf'MSBuild\{self.vs_ver:0.1f}\bin{arch_subdir}' + build = [os.path.join(base_path, path)] + + if self.vs_ver >= 15.0: + # Add Roslyn C# & Visual Basic Compiler + build += [os.path.join(base_path, path, 'Roslyn')] + + return build + + @property + def HTMLHelpWorkshop(self): + """ + Microsoft HTML Help Workshop. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 11.0: + return [] + + return [os.path.join(self.si.ProgramFilesx86, 'HTML Help Workshop')] + + @property + def UCRTLibraries(self) -> list[str]: + """ + Microsoft Universal C Runtime SDK Libraries. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 14.0: + return [] + + arch_subdir = self.pi.target_dir(x64=True) + try: + lib = os.path.join(self.si.UniversalCRTSdkDir, 'lib') # type: ignore[arg-type] # Expected TypeError + except TypeError as ex: + py310.add_note(ex, "Cannot find UniversalCRTSdkDir") + raise + ucrtver = self._ucrt_subdir + return [os.path.join(lib, f'{ucrtver}ucrt{arch_subdir}')] + + @property + def UCRTIncludes(self) -> list[str]: + """ + Microsoft Universal C Runtime SDK Include. + + Return + ------ + list of str + paths + """ + if self.vs_ver < 14.0: + return [] + + try: + include = os.path.join(self.si.UniversalCRTSdkDir, 'include') # type: ignore[arg-type] # Expected TypeError + except TypeError as ex: + py310.add_note(ex, "Cannot find UniversalCRTSdkDir") + raise + return [os.path.join(include, f'{self._ucrt_subdir}ucrt')] + + @property + def _ucrt_subdir(self) -> str: + """ + Microsoft Universal C Runtime SDK version subdir. + + Return + ------ + str + subdir + """ + ucrtver = self.si.UniversalCRTSdkLastVersion + return (f'{ucrtver}\\') if ucrtver else '' + + @property + def FSharp(self): + """ + Microsoft Visual F#. + + Return + ------ + list of str + paths + """ + if 11.0 > self.vs_ver > 12.0: + return [] + + return [self.si.FSharpInstallDir] + + @property + def VCRuntimeRedist(self) -> str | None: + """ + Microsoft Visual C++ runtime redistributable dll. + + Returns the first suitable path found or None. + """ + vcruntime = f'vcruntime{self.vc_ver}0.dll' + arch_subdir = self.pi.target_dir(x64=True).strip('\\') + + # Installation prefixes candidates + prefixes = [] + tools_path = self.si.VCInstallDir + redist_path = os.path.dirname(tools_path.replace(r'\Tools', r'\Redist')) + if os.path.isdir(redist_path): + # Redist version may not be exactly the same as tools + redist_path = os.path.join(redist_path, os.listdir(redist_path)[-1]) + prefixes += [redist_path, os.path.join(redist_path, 'onecore')] + + prefixes += [os.path.join(tools_path, 'redist')] # VS14 legacy path + + # CRT directory + crt_dirs = ( + f'Microsoft.VC{self.vc_ver * 10}.CRT', + # Sometime store in directory with VS version instead of VC + f'Microsoft.VC{int(self.vs_ver) * 10}.CRT', + ) + + # vcruntime path + candidate_paths = ( + os.path.join(prefix, arch_subdir, crt_dir, vcruntime) + for (prefix, crt_dir) in itertools.product(prefixes, crt_dirs) + ) + return next(filter(os.path.isfile, candidate_paths), None) # type: ignore[arg-type] #python/mypy#12682 + + def return_env(self, exists: bool = True) -> _EnvironmentDict: + """ + Return environment dict. + + Parameters + ---------- + exists: bool + It True, only return existing paths. + + Return + ------ + dict + environment + """ + env = _EnvironmentDict( + include=self._build_paths( + 'include', + [ + self.VCIncludes, + self.OSIncludes, + self.UCRTIncludes, + self.NetFxSDKIncludes, + ], + exists, + ), + lib=self._build_paths( + 'lib', + [ + self.VCLibraries, + self.OSLibraries, + self.FxTools, + self.UCRTLibraries, + self.NetFxSDKLibraries, + ], + exists, + ), + libpath=self._build_paths( + 'libpath', + [self.VCLibraries, self.FxTools, self.VCStoreRefs, self.OSLibpath], + exists, + ), + path=self._build_paths( + 'path', + [ + self.VCTools, + self.VSTools, + self.VsTDb, + self.SdkTools, + self.SdkSetup, + self.FxTools, + self.MSBuild, + self.HTMLHelpWorkshop, + self.FSharp, + ], + exists, + ), + ) + if self.vs_ver >= 14 and self.VCRuntimeRedist: + env['py_vcruntime_redist'] = self.VCRuntimeRedist + return env + + def _build_paths(self, name, spec_path_lists, exists): + """ + Given an environment variable name and specified paths, + return a pathsep-separated string of paths containing + unique, extant, directories from those paths and from + the environment variable. Raise an error if no paths + are resolved. + + Parameters + ---------- + name: str + Environment variable name + spec_path_lists: list of str + Paths + exists: bool + It True, only return existing paths. + + Return + ------ + str + Pathsep-separated paths + """ + # flatten spec_path_lists + spec_paths = itertools.chain.from_iterable(spec_path_lists) + env_paths = environ.get(name, '').split(os.pathsep) + paths = itertools.chain(spec_paths, env_paths) + extant_paths = list(filter(os.path.isdir, paths)) if exists else paths + if not extant_paths: + msg = f"{name.upper()} environment variable is empty" + raise distutils.errors.DistutilsPlatformError(msg) + unique_paths = unique_everseen(extant_paths) + return os.pathsep.join(unique_paths) diff --git a/lib/python3.11/site-packages/setuptools/namespaces.py b/lib/python3.11/site-packages/setuptools/namespaces.py new file mode 100644 index 0000000000000000000000000000000000000000..7760f07fb468605d4caff5a2f816b7a425fcd70b --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/namespaces.py @@ -0,0 +1,101 @@ +import itertools +import os + +from .compat import py312 + +from distutils import log + +flatten = itertools.chain.from_iterable + + +class Installer: + nspkg_ext = '-nspkg.pth' + + def install_namespaces(self) -> None: + nsp = self._get_all_ns_packages() + if not nsp: + return + filename = self._get_nspkg_file() + self.outputs.append(filename) + log.info("Installing %s", filename) + lines = map(self._gen_nspkg_line, nsp) + + with open(filename, 'wt', encoding=py312.PTH_ENCODING) as f: + # Python<3.13 requires encoding="locale" instead of "utf-8" + # See: python/cpython#77102 + f.writelines(lines) + + def uninstall_namespaces(self) -> None: + filename = self._get_nspkg_file() + if not os.path.exists(filename): + return + log.info("Removing %s", filename) + os.remove(filename) + + def _get_nspkg_file(self): + filename, _ = os.path.splitext(self._get_target()) + return filename + self.nspkg_ext + + def _get_target(self): + return self.target + + _nspkg_tmpl = ( + "import sys, types, os", + "p = os.path.join(%(root)s, *%(pth)r)", + "importlib = __import__('importlib.util')", + "__import__('importlib.machinery')", + ( + "m = " + "sys.modules.setdefault(%(pkg)r, " + "importlib.util.module_from_spec(" + "importlib.machinery.PathFinder.find_spec(%(pkg)r, " + "[os.path.dirname(p)])))" + ), + ("m = m or sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"), + "mp = (m or []) and m.__dict__.setdefault('__path__',[])", + "(p not in mp) and mp.append(p)", + ) + "lines for the namespace installer" + + _nspkg_tmpl_multi = ('m and setattr(sys.modules[%(parent)r], %(child)r, m)',) + "additional line(s) when a parent package is indicated" + + def _get_root(self): + return "sys._getframe(1).f_locals['sitedir']" + + def _gen_nspkg_line(self, pkg): + pth = tuple(pkg.split('.')) + root = self._get_root() + tmpl_lines = self._nspkg_tmpl + parent, sep, child = pkg.rpartition('.') + if parent: + tmpl_lines += self._nspkg_tmpl_multi + return ';'.join(tmpl_lines) % locals() + '\n' + + def _get_all_ns_packages(self): + """Return sorted list of all package namespaces""" + pkgs = self.distribution.namespace_packages or [] + return sorted(set(flatten(map(self._pkg_names, pkgs)))) + + @staticmethod + def _pkg_names(pkg): + """ + Given a namespace package, yield the components of that + package. + + >>> names = Installer._pkg_names('a.b.c') + >>> set(names) == set(['a', 'a.b', 'a.b.c']) + True + """ + parts = pkg.split('.') + while parts: + yield '.'.join(parts) + parts.pop() + + +class DevelopInstaller(Installer): + def _get_root(self): + return repr(str(self.egg_path)) + + def _get_target(self): + return self.egg_link diff --git a/lib/python3.11/site-packages/setuptools/script (dev).tmpl b/lib/python3.11/site-packages/setuptools/script (dev).tmpl new file mode 100644 index 0000000000000000000000000000000000000000..39a24b04888e79df51e2237577b303a2f901be63 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/script (dev).tmpl @@ -0,0 +1,6 @@ +# EASY-INSTALL-DEV-SCRIPT: %(spec)r,%(script_name)r +__requires__ = %(spec)r +__import__('pkg_resources').require(%(spec)r) +__file__ = %(dev_path)r +with open(__file__) as f: + exec(compile(f.read(), __file__, 'exec')) diff --git a/lib/python3.11/site-packages/setuptools/script.tmpl b/lib/python3.11/site-packages/setuptools/script.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..ff5efbcab3b58063dd84787181c26a95fb663d94 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/script.tmpl @@ -0,0 +1,3 @@ +# EASY-INSTALL-SCRIPT: %(spec)r,%(script_name)r +__requires__ = %(spec)r +__import__('pkg_resources').run_script(%(spec)r, %(script_name)r) diff --git a/lib/python3.11/site-packages/setuptools/unicode_utils.py b/lib/python3.11/site-packages/setuptools/unicode_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f502f5b089619eafd28e2c7a61967e34e16920e5 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/unicode_utils.py @@ -0,0 +1,102 @@ +import sys +import unicodedata +from configparser import RawConfigParser + +from .compat import py39 +from .warnings import SetuptoolsDeprecationWarning + + +# HFS Plus uses decomposed UTF-8 +def decompose(path): + if isinstance(path, str): + return unicodedata.normalize('NFD', path) + try: + path = path.decode('utf-8') + path = unicodedata.normalize('NFD', path) + path = path.encode('utf-8') + except UnicodeError: + pass # Not UTF-8 + return path + + +def filesys_decode(path): + """ + Ensure that the given path is decoded, + ``None`` when no expected encoding works + """ + + if isinstance(path, str): + return path + + fs_enc = sys.getfilesystemencoding() or 'utf-8' + candidates = fs_enc, 'utf-8' + + for enc in candidates: + try: + return path.decode(enc) + except UnicodeDecodeError: + continue + + return None + + +def try_encode(string, enc): + "turn unicode encoding into a functional routine" + try: + return string.encode(enc) + except UnicodeEncodeError: + return None + + +def _read_utf8_with_fallback(file: str, fallback_encoding=py39.LOCALE_ENCODING) -> str: + """ + First try to read the file with UTF-8, if there is an error fallback to a + different encoding ("locale" by default). Returns the content of the file. + Also useful when reading files that might have been produced by an older version of + setuptools. + """ + try: + with open(file, "r", encoding="utf-8") as f: + return f.read() + except UnicodeDecodeError: # pragma: no cover + _Utf8EncodingNeeded.emit(file=file, fallback_encoding=fallback_encoding) + with open(file, "r", encoding=fallback_encoding) as f: + return f.read() + + +def _cfg_read_utf8_with_fallback( + cfg: RawConfigParser, file: str, fallback_encoding=py39.LOCALE_ENCODING +) -> None: + """Same idea as :func:`_read_utf8_with_fallback`, but for the + :meth:`RawConfigParser.read` method. + + This method may call ``cfg.clear()``. + """ + try: + cfg.read(file, encoding="utf-8") + except UnicodeDecodeError: # pragma: no cover + _Utf8EncodingNeeded.emit(file=file, fallback_encoding=fallback_encoding) + cfg.clear() + cfg.read(file, encoding=fallback_encoding) + + +class _Utf8EncodingNeeded(SetuptoolsDeprecationWarning): + _SUMMARY = """ + `encoding="utf-8"` fails with {file!r}, trying `encoding={fallback_encoding!r}`. + """ + + _DETAILS = """ + Fallback behavior for UTF-8 is considered **deprecated** and future versions of + `setuptools` may not implement it. + + Please encode {file!r} with "utf-8" to ensure future builds will succeed. + + If this file was produced by `setuptools` itself, cleaning up the cached files + and re-building/re-installing the package with a newer version of `setuptools` + (e.g. by updating `build-system.requires` in its `pyproject.toml`) + might solve the problem. + """ + # TODO: Add a deadline? + # Will we be able to remove this? + # The question comes to mind mainly because of sdists that have been produced + # by old versions of setuptools and published to PyPI... diff --git a/lib/python3.11/site-packages/setuptools/version.py b/lib/python3.11/site-packages/setuptools/version.py new file mode 100644 index 0000000000000000000000000000000000000000..ec253c414474677d3a5977511cfe901bfb786740 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/version.py @@ -0,0 +1,6 @@ +from ._importlib import metadata + +try: + __version__ = metadata.version('setuptools') or '0.dev0+unknown' +except Exception: + __version__ = '0.dev0+unknown' diff --git a/lib/python3.11/site-packages/setuptools/warnings.py b/lib/python3.11/site-packages/setuptools/warnings.py new file mode 100644 index 0000000000000000000000000000000000000000..96467787c237846bfbacf2d44eb833be0a88b633 --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/warnings.py @@ -0,0 +1,110 @@ +"""Provide basic warnings used by setuptools modules. + +Using custom classes (other than ``UserWarning``) allow users to set +``PYTHONWARNINGS`` filters to run tests and prepare for upcoming changes in +setuptools. +""" + +from __future__ import annotations + +import os +import warnings +from datetime import date +from inspect import cleandoc +from textwrap import indent +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + +_DueDate: TypeAlias = tuple[int, int, int] # time tuple +_INDENT = 8 * " " +_TEMPLATE = f"""{80 * '*'}\n{{details}}\n{80 * '*'}""" + + +class SetuptoolsWarning(UserWarning): + """Base class in ``setuptools`` warning hierarchy.""" + + @classmethod + def emit( + cls, + summary: str | None = None, + details: str | None = None, + due_date: _DueDate | None = None, + see_docs: str | None = None, + see_url: str | None = None, + stacklevel: int = 2, + **kwargs, + ) -> None: + """Private: reserved for ``setuptools`` internal use only""" + # Default values: + summary_ = summary or getattr(cls, "_SUMMARY", None) or "" + details_ = details or getattr(cls, "_DETAILS", None) or "" + due_date = due_date or getattr(cls, "_DUE_DATE", None) + docs_ref = see_docs or getattr(cls, "_SEE_DOCS", None) + docs_url = docs_ref and f"https://setuptools.pypa.io/en/latest/{docs_ref}" + see_url = see_url or getattr(cls, "_SEE_URL", None) + due = date(*due_date) if due_date else None + + text = cls._format(summary_, details_, due, see_url or docs_url, kwargs) + if due and due < date.today() and _should_enforce(): + raise cls(text) + warnings.warn(text, cls, stacklevel=stacklevel + 1) + + @classmethod + def _format( + cls, + summary: str, + details: str, + due_date: date | None = None, + see_url: str | None = None, + format_args: dict | None = None, + ) -> str: + """Private: reserved for ``setuptools`` internal use only""" + today = date.today() + summary = cleandoc(summary).format_map(format_args or {}) + possible_parts = [ + cleandoc(details).format_map(format_args or {}), + ( + f"\nBy {due_date:%Y-%b-%d}, you need to update your project and remove " + "deprecated calls\nor your builds will no longer be supported." + if due_date and due_date > today + else None + ), + ( + "\nThis deprecation is overdue, please update your project and remove " + "deprecated\ncalls to avoid build errors in the future." + if due_date and due_date < today + else None + ), + (f"\nSee {see_url} for details." if see_url else None), + ] + parts = [x for x in possible_parts if x] + if parts: + body = indent(_TEMPLATE.format(details="\n".join(parts)), _INDENT) + return "\n".join([summary, "!!\n", body, "\n!!"]) + return summary + + +class InformationOnly(SetuptoolsWarning): + """Currently there is no clear way of displaying messages to the users + that use the setuptools backend directly via ``pip``. + The only thing that might work is a warning, although it is not the + most appropriate tool for the job... + + See pypa/packaging-problems#558. + """ + + +class SetuptoolsDeprecationWarning(SetuptoolsWarning): + """ + Base class for warning deprecations in ``setuptools`` + + This class is not derived from ``DeprecationWarning``, and as such is + visible by default. + """ + + +def _should_enforce(): + enforce = os.getenv("SETUPTOOLS_ENFORCE_DEPRECATION", "false").lower() + return enforce in ("true", "on", "ok", "1") diff --git a/lib/python3.11/site-packages/setuptools/wheel.py b/lib/python3.11/site-packages/setuptools/wheel.py new file mode 100644 index 0000000000000000000000000000000000000000..93663031541eba1b459f44bd81bc418c9e40decc --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/wheel.py @@ -0,0 +1,262 @@ +"""Wheels support.""" + +import contextlib +import email +import functools +import itertools +import os +import posixpath +import re +import zipfile +from collections.abc import Iterator + +from packaging.requirements import Requirement +from packaging.tags import sys_tags +from packaging.utils import canonicalize_name +from packaging.version import Version as parse_version + +import setuptools +from setuptools.archive_util import _unpack_zipfile_obj +from setuptools.command.egg_info import _egg_basename, write_requirements + +from ._discovery import extras_from_deps +from ._importlib import metadata +from .unicode_utils import _read_utf8_with_fallback + +from distutils.util import get_platform + +WHEEL_NAME = re.compile( + r"""^(?P.+?)-(?P\d.*?) + ((-(?P\d.*?))?-(?P.+?)-(?P.+?)-(?P.+?) + )\.whl$""", + re.VERBOSE, +).match + +NAMESPACE_PACKAGE_INIT = "__import__('pkg_resources').declare_namespace(__name__)\n" + + +@functools.cache +def _get_supported_tags(): + # We calculate the supported tags only once, otherwise calling + # this method on thousands of wheels takes seconds instead of + # milliseconds. + return {(t.interpreter, t.abi, t.platform) for t in sys_tags()} + + +def unpack(src_dir, dst_dir) -> None: + """Move everything under `src_dir` to `dst_dir`, and delete the former.""" + for dirpath, dirnames, filenames in os.walk(src_dir): + subdir = os.path.relpath(dirpath, src_dir) + for f in filenames: + src = os.path.join(dirpath, f) + dst = os.path.join(dst_dir, subdir, f) + os.renames(src, dst) + for n, d in reversed(list(enumerate(dirnames))): + src = os.path.join(dirpath, d) + dst = os.path.join(dst_dir, subdir, d) + if not os.path.exists(dst): + # Directory does not exist in destination, + # rename it and prune it from os.walk list. + os.renames(src, dst) + del dirnames[n] + # Cleanup. + for dirpath, dirnames, filenames in os.walk(src_dir, topdown=True): + assert not filenames + os.rmdir(dirpath) + + +@contextlib.contextmanager +def disable_info_traces() -> Iterator[None]: + """ + Temporarily disable info traces. + """ + from distutils import log + + saved = log.set_threshold(log.WARN) + try: + yield + finally: + log.set_threshold(saved) + + +class Wheel: + def __init__(self, filename) -> None: + match = WHEEL_NAME(os.path.basename(filename)) + if match is None: + raise ValueError(f'invalid wheel name: {filename!r}') + self.filename = filename + for k, v in match.groupdict().items(): + setattr(self, k, v) + + def tags(self): + """List tags (py_version, abi, platform) supported by this wheel.""" + return itertools.product( + self.py_version.split('.'), + self.abi.split('.'), + self.platform.split('.'), + ) + + def is_compatible(self): + """Is the wheel compatible with the current platform?""" + return next((True for t in self.tags() if t in _get_supported_tags()), False) + + def egg_name(self): + return ( + _egg_basename( + self.project_name, + self.version, + platform=(None if self.platform == 'any' else get_platform()), + ) + + ".egg" + ) + + def get_dist_info(self, zf): + # find the correct name of the .dist-info dir in the wheel file + for member in zf.namelist(): + dirname = posixpath.dirname(member) + if dirname.endswith('.dist-info') and canonicalize_name(dirname).startswith( + canonicalize_name(self.project_name) + ): + return dirname + raise ValueError("unsupported wheel format. .dist-info not found") + + def install_as_egg(self, destination_eggdir) -> None: + """Install wheel as an egg directory.""" + with zipfile.ZipFile(self.filename) as zf: + self._install_as_egg(destination_eggdir, zf) + + def _install_as_egg(self, destination_eggdir, zf): + dist_basename = f'{self.project_name}-{self.version}' + dist_info = self.get_dist_info(zf) + dist_data = f'{dist_basename}.data' + egg_info = os.path.join(destination_eggdir, 'EGG-INFO') + + self._convert_metadata(zf, destination_eggdir, dist_info, egg_info) + self._move_data_entries(destination_eggdir, dist_data) + self._fix_namespace_packages(egg_info, destination_eggdir) + + @staticmethod + def _convert_metadata(zf, destination_eggdir, dist_info, egg_info): + def get_metadata(name): + with zf.open(posixpath.join(dist_info, name)) as fp: + value = fp.read().decode('utf-8') + return email.parser.Parser().parsestr(value) + + wheel_metadata = get_metadata('WHEEL') + # Check wheel format version is supported. + wheel_version = parse_version(wheel_metadata.get('Wheel-Version')) + wheel_v1 = parse_version('1.0') <= wheel_version < parse_version('2.0dev0') + if not wheel_v1: + raise ValueError(f'unsupported wheel format version: {wheel_version}') + # Extract to target directory. + _unpack_zipfile_obj(zf, destination_eggdir) + dist_info = os.path.join(destination_eggdir, dist_info) + install_requires, extras_require = Wheel._convert_requires( + destination_eggdir, dist_info + ) + os.rename(dist_info, egg_info) + os.rename( + os.path.join(egg_info, 'METADATA'), + os.path.join(egg_info, 'PKG-INFO'), + ) + setup_dist = setuptools.Distribution( + attrs=dict( + install_requires=install_requires, + extras_require=extras_require, + ), + ) + with disable_info_traces(): + write_requirements( + setup_dist.get_command_obj('egg_info'), + None, + os.path.join(egg_info, 'requires.txt'), + ) + + @staticmethod + def _convert_requires(destination_eggdir, dist_info): + md = metadata.Distribution.at(dist_info).metadata + deps = md.get_all('Requires-Dist') or [] + reqs = list(map(Requirement, deps)) + + extras = extras_from_deps(deps) + + # Note: Evaluate and strip markers now, + # as it's difficult to convert back from the syntax: + # foobar; "linux" in sys_platform and extra == 'test' + def raw_req(req): + req = Requirement(str(req)) + req.marker = None + return str(req) + + def eval(req, **env): + return not req.marker or req.marker.evaluate(env) + + def for_extra(req): + try: + markers = req.marker._markers + except AttributeError: + markers = () + return set( + marker[2].value + for marker in markers + if isinstance(marker, tuple) and marker[0].value == 'extra' + ) + + install_requires = list( + map(raw_req, filter(eval, itertools.filterfalse(for_extra, reqs))) + ) + extras_require = { + extra: list( + map( + raw_req, + (req for req in reqs if for_extra(req) and eval(req, extra=extra)), + ) + ) + for extra in extras + } + return install_requires, extras_require + + @staticmethod + def _move_data_entries(destination_eggdir, dist_data): + """Move data entries to their correct location.""" + dist_data = os.path.join(destination_eggdir, dist_data) + dist_data_scripts = os.path.join(dist_data, 'scripts') + if os.path.exists(dist_data_scripts): + egg_info_scripts = os.path.join(destination_eggdir, 'EGG-INFO', 'scripts') + os.mkdir(egg_info_scripts) + for entry in os.listdir(dist_data_scripts): + # Remove bytecode, as it's not properly handled + # during easy_install scripts install phase. + if entry.endswith('.pyc'): + os.unlink(os.path.join(dist_data_scripts, entry)) + else: + os.rename( + os.path.join(dist_data_scripts, entry), + os.path.join(egg_info_scripts, entry), + ) + os.rmdir(dist_data_scripts) + for subdir in filter( + os.path.exists, + ( + os.path.join(dist_data, d) + for d in ('data', 'headers', 'purelib', 'platlib') + ), + ): + unpack(subdir, destination_eggdir) + if os.path.exists(dist_data): + os.rmdir(dist_data) + + @staticmethod + def _fix_namespace_packages(egg_info, destination_eggdir): + namespace_packages = os.path.join(egg_info, 'namespace_packages.txt') + if os.path.exists(namespace_packages): + namespace_packages = _read_utf8_with_fallback(namespace_packages).split() + + for mod in namespace_packages: + mod_dir = os.path.join(destination_eggdir, *mod.split('.')) + mod_init = os.path.join(mod_dir, '__init__.py') + if not os.path.exists(mod_dir): + os.mkdir(mod_dir) + if not os.path.exists(mod_init): + with open(mod_init, 'w', encoding="utf-8") as fp: + fp.write(NAMESPACE_PACKAGE_INIT) diff --git a/lib/python3.11/site-packages/setuptools/windows_support.py b/lib/python3.11/site-packages/setuptools/windows_support.py new file mode 100644 index 0000000000000000000000000000000000000000..7a2b53a291409c66851961a559eb4d69be0f4acc --- /dev/null +++ b/lib/python3.11/site-packages/setuptools/windows_support.py @@ -0,0 +1,30 @@ +import platform + + +def windows_only(func): + if platform.system() != 'Windows': + return lambda *args, **kwargs: None + return func + + +@windows_only +def hide_file(path: str) -> None: + """ + Set the hidden attribute on a file or directory. + + From https://stackoverflow.com/questions/19622133/ + + `path` must be text. + """ + import ctypes + import ctypes.wintypes + + 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0000000000000000000000000000000000000000..164e6d168414a11039f3b63885760ad08b81ae99 --- /dev/null +++ b/lib/python3.11/site-packages/torch/testing/_internal/test_module/no_future_div.py @@ -0,0 +1,11 @@ +# mypy: ignore-errors + +import torch # noqa: F401 + + +def div_int_nofuture(): + return 1 / 2 + + +def div_float_nofuture(): + return 3.14 / 0.125 diff --git a/lib/python3.11/site-packages/torch/utils/__init__.py b/lib/python3.11/site-packages/torch/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..23188bba9b8003e9dc44d099ac1ab6099ffff199 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/__init__.py @@ -0,0 +1,113 @@ +# mypy: allow-untyped-defs + +import copyreg +import os.path as _osp +import weakref + +import torch +from torch.utils import ( + backcompat as backcompat, + collect_env as collect_env, + data as data, + deterministic as deterministic, + hooks as hooks, +) +from torch.utils.backend_registration import ( + generate_methods_for_privateuse1_backend, + rename_privateuse1_backend, +) +from torch.utils.cpp_backtrace import get_cpp_backtrace +from torch.utils.throughput_benchmark import ThroughputBenchmark + + +def set_module(obj, mod): + """ + Set the module attribute on a python object for a given object for nicer printing + """ + if not isinstance(mod, str): + raise TypeError("The mod argument should be a string") + obj.__module__ = mod + + +if torch._running_with_deploy(): + # not valid inside torch_deploy interpreter, no paths exists for frozen modules + cmake_prefix_path = None +else: + cmake_prefix_path = _osp.join( + _osp.dirname(_osp.dirname(__file__)), "share", "cmake" + ) + + +def swap_tensors(t1, t2): + """ + This function swaps the content of the two Tensor objects. + At a high level, this will make t1 have the content of t2 while preserving + its identity. + + This will not work if t1 and t2 have different slots. + """ + # Ensure there are no weakrefs + if weakref.getweakrefs(t1): + raise RuntimeError("Cannot swap t1 because it has weakref associated with it") + if weakref.getweakrefs(t2): + raise RuntimeError("Cannot swap t2 because it has weakref associated with it") + t1_slots = set(copyreg._slotnames(t1.__class__)) # type: ignore[attr-defined] + t2_slots = set(copyreg._slotnames(t2.__class__)) # type: ignore[attr-defined] + if t1_slots != t2_slots: + raise RuntimeError("Cannot swap t1 and t2 if they have different slots") + + def swap_attr(name): + tmp = getattr(t1, name) + setattr(t1, name, (getattr(t2, name))) + setattr(t2, name, tmp) + + def error_pre_hook(grad_outputs): + raise RuntimeError( + "Trying to execute AccumulateGrad node that was poisoned by swap_tensors " + "this can happen when you try to run backward on a tensor that was swapped. " + "For a module m with `torch.__future__.set_swap_module_params_on_conversion(True)` " + "you should not change the device or dtype of the module (e.g. `m.cpu()` or `m.half()`) " + "between running forward and backward. To resolve this, please only change the " + "device/dtype before running forward (or after both forward and backward)." + ) + + def check_use_count(t, name="t1"): + use_count = t._use_count() + error_str = ( + f"Expected use_count of {name} to be 1 or 2 with an AccumulateGrad node but got {use_count} " + f"make sure you are not holding references to the tensor in other places." + ) + if use_count > 1: + if use_count == 2 and t.is_leaf: + accum_grad_node = torch.autograd.graph.get_gradient_edge(t).node + # Make sure that the accumulate_grad node was not lazy_init-ed by get_gradient_edge + if t._use_count() == 2: + accum_grad_node.register_prehook(error_pre_hook) + else: + raise RuntimeError(error_str) + else: + raise RuntimeError(error_str) + + check_use_count(t1, "t1") + check_use_count(t2, "t2") + + # Swap the types + # Note that this will fail if there are mismatched slots + swap_attr("__class__") + + # Swap the dynamic attributes + swap_attr("__dict__") + + # Swap the slots + for slot in t1_slots: + if hasattr(t1, slot) and hasattr(t2, slot): + swap_attr(slot) + elif hasattr(t1, slot): + setattr(t2, slot, (getattr(t1, slot))) + delattr(t1, slot) + elif hasattr(t2, slot): + setattr(t1, slot, (getattr(t2, slot))) + delattr(t2, slot) + + # Swap the at::Tensor they point to + torch._C._swap_tensor_impl(t1, t2) diff --git a/lib/python3.11/site-packages/torch/utils/_backport_slots.py b/lib/python3.11/site-packages/torch/utils/_backport_slots.py new file mode 100644 index 0000000000000000000000000000000000000000..dcafb32877f34ca51aa8748b2c23b15e487c9b26 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_backport_slots.py @@ -0,0 +1,114 @@ +# This code is backported from python 3.10 dataclasses. Once 3.10 becomes the +# minimum supported we should use dataclass(slots=True) instead. + +from __future__ import annotations + +import dataclasses +import itertools +from typing import Generator, List, Type, TYPE_CHECKING, TypeVar + + +if TYPE_CHECKING: + from _typeshed import DataclassInstance + + +__all__ = ["dataclass_slots"] + +_T = TypeVar("_T", bound="DataclassInstance") + + +def dataclass_slots(cls: Type[_T]) -> Type[DataclassInstance]: + assert dataclasses.is_dataclass(cls), "Can only be used on dataclasses." + + def _get_slots(cls: Type[DataclassInstance]) -> Generator[str, None, None]: + slots = cls.__dict__.get("__slots__") + # `__dictoffset__` and `__weakrefoffset__` can tell us whether + # the base type has dict/weakref slots, in a way that works correctly + # for both Python classes and C extension types. Extension types + # don't use `__slots__` for slot creation + if slots is None: + slots = [] + if getattr(cls, "__weakrefoffset__", -1) != 0: + slots.append("__weakref__") + if getattr(cls, "__dictrefoffset__", -1) != 0: + slots.append("__dict__") + yield from slots + elif isinstance(slots, str): + yield slots + # Slots may be any iterable, but we cannot handle an iterator + # because it will already be (partially) consumed. + elif not hasattr(cls, "__next__"): + yield from slots + else: + raise TypeError(f"Slots of '{cls.__name__}' cannot be determined") + + def _add_slots( + cls: Type[DataclassInstance], is_frozen: bool, weakref_slot: bool + ) -> Type[DataclassInstance]: + # Need to create a new class, since we can't set __slots__ + # after a class has been created. + + # Make sure __slots__ isn't already set. + if "__slots__" in cls.__dict__: + raise TypeError(f"{cls.__name__} already specifies __slots__") + + # Create a new dict for our new class. + cls_dict = dict(cls.__dict__) + field_names = tuple(f.name for f in dataclasses.fields(cls)) + # Make sure slots don't overlap with those in base classes. + inherited_slots = set( + itertools.chain.from_iterable(map(_get_slots, cls.__mro__[1:-1])) + ) + # The slots for our class. Remove slots from our base classes. Add + # '__weakref__' if weakref_slot was given, unless it is already present. + cls_dict["__slots__"] = tuple( + itertools.filterfalse( + inherited_slots.__contains__, + itertools.chain( + # gh-93521: '__weakref__' also needs to be filtered out if + # already present in inherited_slots + field_names, + ("__weakref__",) if weakref_slot else (), + ), + ), + ) + + for field_name in field_names: + # Remove our attributes, if present. They'll still be + # available in _MARKER. + cls_dict.pop(field_name, None) + + # Remove __dict__ itself. + cls_dict.pop("__dict__", None) + + # Clear existing `__weakref__` descriptor, it belongs to a previous type: + cls_dict.pop("__weakref__", None) # gh-102069 + + # And finally create the class. + qualname = getattr(cls, "__qualname__", None) + cls = type(cls.__name__, cls.__bases__, cls_dict) + if qualname is not None: + cls.__qualname__ = qualname + + def _dataclass_getstate(self: _T) -> object: + fields = dataclasses.fields(self) + return [getattr(self, f.name) for f in fields] + + def _dataclass_setstate(self: _T, state: List[object]) -> None: + fields = dataclasses.fields(self) + for field, value in zip(fields, state): + # use setattr because dataclass may be frozen + object.__setattr__(self, field.name, value) + + if is_frozen: + # Need this for pickling frozen classes with slots. + if "__getstate__" not in cls_dict: + cls.__getstate__ = _dataclass_getstate # type: ignore[method-assign, assignment] + if "__setstate__" not in cls_dict: + cls.__setstate__ = _dataclass_setstate # type: ignore[attr-defined] + + return cls + + params = getattr(cls, dataclasses._PARAMS) # type: ignore[attr-defined] + weakref_slot = getattr(params, "weakref_slot", False) + return _add_slots(cls, params.frozen, weakref_slot) diff --git a/lib/python3.11/site-packages/torch/utils/_config_module.py b/lib/python3.11/site-packages/torch/utils/_config_module.py new file mode 100644 index 0000000000000000000000000000000000000000..ee7197afda2345b3905bed437b840a09c5acc407 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_config_module.py @@ -0,0 +1,595 @@ +import contextlib +import copy +import hashlib +import inspect +import io +import os +import pickle +import sys +import tokenize +import unittest +import warnings +from dataclasses import dataclass +from types import FunctionType, ModuleType +from typing import Any, Callable, Dict, List, NoReturn, Optional, Set, Union +from typing_extensions import deprecated +from unittest import mock + +from torch._utils_internal import justknobs_check + + +@dataclass +class Config: + """Represents a config with richer behaviour than just a default value. + :: + i.e. + foo = Config(justknob="//foo:bar", default=False) + install_config_module(...) + + This configs must be installed with install_config_module to be used + + Precedence Order: + env_name_force: If set, this environment variable overrides everything + user_override: If a user sets a value (i.e. foo.bar=True), that + has precedence over everything after this. + env_name_default: If set, this environment variable will override everything + after this. + justknob: If this pytorch installation supports justknobs, that will + override defaults, but will not override the user_override precendence. + default: This value is the lowest precendance, and will be used if nothing is + set. + + Environment Variables: + These are interpreted to be either "0" or "1" to represent true and false. + + Arguments: + justknob: the name of the feature / JK. In OSS this is unused. + default: is the value to default this knob to in OSS. + env_name_force: The environment variable to read that is a FORCE + environment variable. I.e. it overrides everything + env_name_default: The environment variable to read that changes the + default behaviour. I.e. user overrides take preference. + """ + + default: Any = True + justknob: Optional[str] = None + env_name_default: Optional[str] = None + env_name_force: Optional[str] = None + value_type: Optional[type] = None + + def __init__( + self, + default: Any = True, + justknob: Optional[str] = None, + env_name_default: Optional[str] = None, + env_name_force: Optional[str] = None, + value_type: Optional[type] = None, + ): + # python 3.9 does not support kw_only on the dataclass :(. + self.default = default + self.justknob = justknob + self.env_name_default = env_name_default + self.env_name_force = env_name_force + self.value_type = value_type + if self.justknob is not None: + assert isinstance( + self.default, bool + ), f"justknobs only support booleans, {self.default} is not a boolean" + + +# Types saved/loaded in configs +CONFIG_TYPES = (int, float, bool, type(None), str, list, set, tuple, dict) + + +def _read_env_variable(name: str) -> Optional[bool]: + value = os.environ.get(name) + if value == "1": + return True + if value == "0": + return False + return None + + +def install_config_module(module: ModuleType) -> None: + """ + Converts a module-level config into a `ConfigModule()`. + + See _config_typing.pyi for instructions on how to get the converted module to typecheck. + """ + + class ConfigModuleInstance(ConfigModule): + # __annotations__ is written to by Sphinx autodoc + _bypass_keys = set({"_is_dirty", "_hash_digest", "__annotations__"}) + + def visit( + source: Union[ModuleType, type], + dest: Union[ModuleType, SubConfigProxy], + prefix: str, + ) -> None: + """Walk the module structure and move everything to module._config""" + if sys.version_info[:2] < (3, 10): + type_hints = getattr(source, "__annotations__", {}) + else: + type_hints = inspect.get_annotations(source) + for key, value in list(source.__dict__.items()): + if ( + key.startswith("__") + or isinstance(value, (ModuleType, FunctionType)) + or (hasattr(value, "__module__") and value.__module__ == "typing") + # Handle from torch.utils._config_module import Config + or (isinstance(value, type) and issubclass(value, Config)) + ): + continue + + name = f"{prefix}{key}" + if isinstance(value, CONFIG_TYPES): + annotated_type = type_hints.get(key, None) + config[name] = _ConfigEntry( + Config(default=value, value_type=annotated_type) + ) + if dest is module: + delattr(module, key) + elif isinstance(value, Config): + config[name] = _ConfigEntry(value) + + if dest is module: + delattr(module, key) + elif isinstance(value, type): + assert value.__module__ == module.__name__ + # a subconfig with `class Blah:` syntax + proxy = SubConfigProxy(module, f"{name}.") + visit(value, proxy, f"{name}.") + if dest is module: + setattr(dest, key, proxy) + else: + dest.__dict__[key] = proxy + else: + raise AssertionError(f"Unhandled config {key}={value} ({type(value)})") + + config: Dict[str, _ConfigEntry] = {} + + compile_ignored_keys = get_assignments_with_compile_ignored_comments(module) + + visit(module, module, "") + module._config = config # type: ignore[attr-defined] + module._compile_ignored_keys = compile_ignored_keys # type: ignore[attr-defined] + module.__class__ = ConfigModuleInstance + module._is_dirty = True # type: ignore[attr-defined] + module._hash_digest = None # type: ignore[attr-defined] + + +COMPILE_IGNORED_MARKER = "@compile_ignored" + + +# Gets all the keys (i.e. assignments) with a @compile_ignored comment +def get_assignments_with_compile_ignored_comments(module: ModuleType) -> Set[str]: + source_code = inspect.getsource(module) + assignments = set() + + # Tokenize the source code to retrieve comments + tokens = tokenize.tokenize(io.BytesIO(source_code.encode("utf-8")).readline) + current_comment = "", -1 + prev_name = "" + + for token in tokens: + if token.type == tokenize.COMMENT: + prev_name = "" + maybe_current = token.string.strip() + if COMPILE_IGNORED_MARKER in maybe_current: + assert current_comment == ( + "", + -1, + ), f"unconsumed {COMPILE_IGNORED_MARKER}" + current_comment = maybe_current, token.start[0] + elif token.type == tokenize.NAME: + # Only accept the first name token, to handle if you have + # something like foo: Bar = ... + if not prev_name: + prev_name = token.string + elif token.type == tokenize.OP and token.string == "=": + # Check if the current assignment follows a comment + # with COMPILE_IGNORED_MARKER + if ( + COMPILE_IGNORED_MARKER in current_comment[0] + and current_comment[1] == token.start[0] - 1 + ): + assignments.add(prev_name) + current_comment = "", -1 # reset + prev_name = "" + assert current_comment == ("", -1), f"unconsumed {COMPILE_IGNORED_MARKER}" + return assignments + + +_UNSET_SENTINEL = object() + + +@dataclass +class _ConfigEntry: + # The default value specified in the configuration + default: Any + # The type of the configuration value + value_type: type + # The value specified by the user when they overrode the configuration + # _UNSET_SENTINEL indicates the value is not set. + user_override: Any = _UNSET_SENTINEL + # The justknob to check for this config + justknob: Optional[str] = None + # environment variables are read at install time + env_value_force: Any = _UNSET_SENTINEL + env_value_default: Any = _UNSET_SENTINEL + # Used to work arounds bad assumptions in unittest.mock.patch + # The code to blame is + # https://github.com/python/cpython/blob/94a7a4e22fb8f567090514785c69e65298acca42/Lib/unittest/mock.py#L1637 + # Essentially, mock.patch requires, that if __dict__ isn't accessible + # (which it isn't), that after delattr is called on the object, the + # object must throw when hasattr is called. Otherwise, it doesn't call + # setattr again. + # Technically we'll have an intermediate state of hiding the config while + # mock.patch is unpatching itself, but it calls setattr after the delete + # call so the final state is correct. It's just very unintuitive. + # upstream bug - python/cpython#126886 + hide: bool = False + + def __init__(self, config: Config): + self.default = config.default + self.value_type = ( + config.value_type if config.value_type is not None else type(self.default) + ) + self.justknob = config.justknob + if config.env_name_default is not None: + if (env_value := _read_env_variable(config.env_name_default)) is not None: + self.env_value_default = env_value + if config.env_name_force is not None: + if (env_value := _read_env_variable(config.env_name_force)) is not None: + self.env_value_force = env_value + + +class ConfigModule(ModuleType): + # NOTE: This should be kept in sync with _config_typing.pyi. + + # The actual configuration settings. E.g., torch._dynamo.config.debug + # would live as "debug" in the key, and torch._inductor.config.triton.cudagraphs + # maps as "triton.cudagraphs". See discussion on the class for meaning of various sub items + _config: Dict[str, _ConfigEntry] + _bypass_keys: Set[str] + _compile_ignored_keys: Set[str] + _is_dirty: bool + _hash_digest: Optional[bytes] + + def __init__(self) -> None: + raise NotImplementedError( + f"use {__name__}.install_config_module(sys.modules[__name__])" + ) + + def __setattr__(self, name: str, value: object) -> None: + if name in self._bypass_keys: + super().__setattr__(name, value) + elif name not in self._config: + raise AttributeError(f"{self.__name__}.{name} does not exist") + else: + self._config[name].user_override = value + self._is_dirty = True + self._config[name].hide = False + + def __getattr__(self, name: str) -> Any: + try: + config = self._config[name] + + if config.hide: + raise AttributeError(f"{self.__name__}.{name} does not exist") + + if config.env_value_force is not _UNSET_SENTINEL: + return config.env_value_force + + if config.user_override is not _UNSET_SENTINEL: + return config.user_override + + if config.env_value_default is not _UNSET_SENTINEL: + return config.env_value_default + + if config.justknob is not None: + # JK only supports bools and ints + return justknobs_check(name=config.justknob, default=config.default) + + # Note that reference types can still be modified, so we + # copy them to user_overrides in case the user overrides + # them + if isinstance(config.default, (list, set, dict)): + config.user_override = copy.deepcopy(config.default) + return config.user_override + return config.default + + except KeyError as e: + # make hasattr() work properly + raise AttributeError(f"{self.__name__}.{name} does not exist") from e + + def __delattr__(self, name: str) -> None: + self._is_dirty = True + # must support delete because unittest.mock.patch deletes + # then recreate things + self._config[name].user_override = _UNSET_SENTINEL + self._config[name].hide = True + + def _is_default(self, name: str) -> bool: + return self._config[name].user_override is _UNSET_SENTINEL + + def _get_dict( + self, + ignored_keys: Optional[List[str]] = None, + ignored_prefixes: Optional[List[str]] = None, + skip_default: bool = False, + ) -> Dict[str, Any]: + """Export a dictionary of current configuration keys and values. + + This function is design to provide a single point which handles + accessing config options and exporting them into a dictionary. + This is used by a number of different user facing export methods + which all have slightly different semantics re: how and what to + skip. + + Arguments: + ignored_keys are keys that should not be exported. + ignored_prefixes are prefixes that if a key matches should + not be exported + skip_default does two things. One if a key has not been modified + it skips it. The other is it modified the logging behaviour + to match what codegen already did for modified skipped keys + """ + config: Dict[str, Any] = {} + for key in self._config: + if ignored_keys and key in ignored_keys: + if skip_default and not self._is_default(key): + warnings.warn( + f"Skipping serialization of {key} value {getattr(self, key)}" + ) + continue + if ignored_prefixes: + if any(key.startswith(prefix) for prefix in ignored_prefixes): + continue + if skip_default and self._is_default(key): + continue + config[key] = copy.deepcopy(getattr(self, key)) + return config + + def get_type(self, config_name: str) -> type: + return self._config[config_name].value_type + + def save_config(self) -> bytes: + """Convert config to a pickled blob""" + ignored_keys = getattr(self, "_save_config_ignore", []) + return pickle.dumps( + self._get_dict(ignored_keys=ignored_keys), + protocol=2, + ) + + def save_config_portable(self) -> Dict[str, Any]: + """Convert config to portable format""" + prefixes = ["_"] + prefixes.extend(getattr(self, "_cache_config_ignore_prefix", [])) + return self._get_dict(ignored_prefixes=prefixes) + + def codegen_config(self) -> str: + """Convert config to Python statements that replicate current config. + This does NOT include config settings that are at default values. + """ + lines = [] + mod = self.__name__ + for k, v in self._get_dict( + ignored_keys=getattr(self, "_save_config_ignore", []), skip_default=True + ).items(): + lines.append(f"{mod}.{k} = {v!r}") + return "\n".join(lines) + + def get_hash(self) -> bytes: + """Hashes the configs that are not compile_ignored""" + if self._is_dirty or self._hash_digest is None: + dict_to_hash = self._get_dict(ignored_keys=list(self._compile_ignored_keys)) + string_to_hash = repr(sorted(dict_to_hash.items())) + self._hash_digest = hashlib.md5(string_to_hash.encode("utf-8")).digest() + self._is_dirty = False + return self._hash_digest + + @deprecated( + "`config.to_dict()` has been deprecated. It no longer changes the underlying config." + " use `config.get_config_copy()` instead if you just want a copy of the config, or " + "config.load_config if you need mutable access", + category=FutureWarning, + ) + def to_dict(self) -> Dict[str, Any]: + return self.get_config_copy() + + @deprecated( + "`config.shallow_copy_dict()` has been deprecated. It no longer changes the underlying config." + " use `config.get_config_copy()` instead if you just want a copy of the config, or " + "config.load_config if you need mutable access", + category=FutureWarning, + ) + def shallow_copy_dict(self) -> Dict[str, Any]: + return self.get_config_copy() + + def load_config(self, maybe_pickled_config: Union[bytes, Dict[str, Any]]) -> None: + """Restore from a prior call to save_config() or shallow_copy_dict()""" + if not isinstance(maybe_pickled_config, dict): + config = pickle.loads(maybe_pickled_config) + else: + config = maybe_pickled_config + for k, v in config.items(): + if k in self._config: + setattr(self, k, v) + else: + warnings.warn( + f"key {k} with value {v} is not understood by this config" + ) + + def get_config_copy(self) -> Dict[str, Any]: + return self._get_dict() + + def patch( + self, + arg1: Optional[Union[str, Dict[str, Any]]] = None, + arg2: Any = None, + **kwargs: Dict[str, Any], + ) -> "ContextDecorator": + """ + Decorator and/or context manager to make temporary changes to a config. + + As a decorator: + + @config.patch("name", val) + @config.patch(name1=val1, name2=val2) + @config.patch({"name1": val1, "name2", val2}) + def foo(...): + ... + + As a context manager: + + with config.patch("name", val): + ... + """ + changes: Dict[str, Any] + if arg1 is not None: + if arg2 is not None: + assert isinstance(arg1, str) + # patch("key", True) syntax + changes = {arg1: arg2} + else: + assert isinstance(arg1, dict) + # patch({"key": True}) syntax + changes = arg1 + assert not kwargs + else: + # patch(key=True) syntax + changes = kwargs + assert arg2 is None + assert isinstance(changes, dict), f"expected `dict` got {type(changes)}" + prior: Dict[str, Any] = {} + config = self + + class ConfigPatch(ContextDecorator): + def __enter__(self) -> None: + assert not prior + for key in changes.keys(): + # KeyError on invalid entry + prior[key] = config.__getattr__(key) + for k, v in changes.items(): + config.__setattr__(k, v) + + def __exit__(self, exc_type, exc_val, exc_tb): # type: ignore[no-untyped-def] + for k, v in prior.items(): + config.__setattr__(k, v) + prior.clear() + + return ConfigPatch() + + def _make_closure_patcher(self, **changes: Dict[str, Any]) -> Any: + """ + A lower-overhead version of patch() for things on the critical path. + + Usage: + + # do this off the critical path + change_fn = config.make_closure_patcher(foo=True) + + ... + + revert = change_fn() + try: + ... + finally: + revert() + + """ + config = self._config + + def change() -> Callable[[], None]: + prior = {k: config[k].user_override for k in changes} + for k, v in changes.items(): + self._config[k].user_override = v + + def revert() -> None: + for k, v in prior.items(): + self._config[k].user_override = v + + return revert + + return change + + +class ContextDecorator(contextlib.ContextDecorator): + """ + Same as contextlib.ContextDecorator, but with support for + `unittest.TestCase` + """ + + def __enter__(self) -> None: + raise NotImplementedError("NYI") + + def __exit__(self, exc_type, exc_val, exc_tb) -> NoReturn: # type: ignore[no-untyped-def] + raise NotImplementedError("NYI") + + def __call__(self, func: Callable[[Any], Any]) -> Any: + if isinstance(func, type) and issubclass(func, unittest.TestCase): + + class _TestCase(func): # type: ignore[valid-type, misc] + @classmethod + def setUpClass(cls) -> None: + self.__enter__() + try: + super().setUpClass() + except Exception: + self.__exit__(None, None, None) + raise + + @classmethod + def tearDownClass(cls) -> None: + try: + super().tearDownClass() + finally: + self.__exit__(None, None, None) + + _TestCase.__name__ = func.__name__ + _TestCase.__qualname__ = func.__qualname__ + _TestCase.__module__ = func.__module__ + + return _TestCase + + return super().__call__(func) + + +class SubConfigProxy: + """ + Shim to redirect to main config. + `config.triton.cudagraphs` maps to _config["triton.cudagraphs"] + """ + + def __init__(self, config: object, prefix: str): + # `super().__setattr__` to bypass custom `__setattr__` + super().__setattr__("_config", config) + super().__setattr__("_prefix", prefix) + + def __setattr__(self, name: str, value: object) -> None: + return self._config.__setattr__(self._prefix + name, value) + + def __getattr__(self, name: str) -> Any: + return self._config.__getattr__(self._prefix + name) + + def __delattr__(self, name: str) -> None: + return self._config.__delattr__(self._prefix + name) + + +def patch_object(obj: object, name: str, value: object) -> object: + """ + Workaround `mock.patch.object` issue with ConfigModule + """ + if isinstance(obj, ConfigModule): + return obj.patch(name, value) + return mock.patch.object(obj, name, value) + + +def get_tristate_env(name: str, default: Any = None) -> Optional[bool]: + value = os.environ.get(name) + if value == "1": + return True + if value == "0": + return False + return default diff --git a/lib/python3.11/site-packages/torch/utils/_content_store.py b/lib/python3.11/site-packages/torch/utils/_content_store.py new file mode 100644 index 0000000000000000000000000000000000000000..deb3ba9008c06a46f1c3c43c7ea8ae34e19f73e8 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_content_store.py @@ -0,0 +1,238 @@ +# mypy: allow-untyped-defs +# This module provides a FAST (on GPU) content addressable store for storages +# (and tensors on top of them) with VERY WEAK portability guarantees (e.g., +# don't expect CPU/CUDA to address to the same hash, don't expect it to be +# portable across devices) that is NOT cryptographically secure. In return, +# we are able to hash 40G of tensor data on GPU in less than a second, +# compared to running SHA-1 in CPU which would a minute or so. The primary +# use case is for efficiently snapshotting intermediate tensor data for +# offline debugging, but it's been put in this module in case you think of +# another use case for it. The hash function could be replaced with a +# straight reimplementation of SHA-1, which would give us much stronger +# portability guarantees. +# +# WARNING: THERE IS NO BC/FC GUARANTEE FOR THIS FORMAT! If you need to format +# shift the result, consider packing it into a single torch.save object +# with traditional view sharing. +# +# Because of the weak portability guarantees, you can only write to the +# content store from a single process; we don't provide any capability +# of "reopening" a content store to add more things to it. But we don't +# assume that you can keep all of the tensors you want to add to the store +# in memory at once, because you probably can't! Nor do we assume that +# you know a priori whether or not two storages can be deduplicated or not. +# +# Note: only storages are content-addressed; tensors are name addressed +# +# Note: our padding strategy means that [1, 0] and [1] int16 tensors would +# map to the same (padded) storage. We think this will be immaterial for most +# users. + +import ctypes +import functools +import hashlib +import os.path +import struct +from collections import defaultdict +from typing import Dict, Optional, Set + +import torch +import torch._prims as prims +import torch._utils +import torch.nn.functional as F +from torch._C import default_generator +from torch.multiprocessing.reductions import StorageWeakRef + + +def lazy_compile(**compile_kwargs): + """Lazily wrap a function with torch.compile on the first call + + This avoids eagerly importing dynamo. + """ + + def decorate_fn(fn): + @functools.wraps(fn) + def compile_hook(*args, **kwargs): + compiled_fn = torch.compile(fn, **compile_kwargs) + globals()[fn.__name__] = functools.wraps(fn)(compiled_fn) + return compiled_fn(*args, **kwargs) + + return compile_hook + + return decorate_fn + + +# Use of torch.compile is mandatory for (1) good memory usage +# and (2) xor_sum implementation. This is our first instance of +# using PT2 to implement a kernel in PyTorch; if we get AOT capabilities +# it would be good to apply it here. +@lazy_compile(dynamic=True) +def hash_storage_kernel(x): + # The randint calls are carefully written to hit things we + # have lowerings for in inductor. Lack of unsigned 32-bit integer + # is a pain. + a = torch.randint( + -(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32 + ).abs() + a = ((a % (2**31 - 1)) + 1).long() + b = ( + torch.randint(-(2**31), 2**31, x.shape, device=x.device, dtype=torch.int32) + .abs() + .long() + ) + # This is a standard shift-multiply universal hash family + # plus xor sum hash, using Philox to generate random numbers. + # Our Philox RNG is not deterministic across devices so + # don't use this for stable hashing. + # + # This assumes fixed length so you're also obligated to bucket + # by the length of tensor as well + return prims.xor_sum((a * x + b).int(), [0]) + + +# Returns a hex digest of the data in the storage. Guaranteed to be +# SHA-1 if stable_hash=True, otherwise it will consistent for a single +# process run but not necessarily across processes. +def hash_storage(storage: torch.UntypedStorage, *, stable_hash: bool = False) -> str: + import torch._dynamo + from torch._dynamo.utils import is_compile_supported + + device_type = storage.device.type + if stable_hash or not is_compile_supported(device_type): + cpu_storage = storage.cpu() + # TODO: make storage support buffer protocol so this isn't + # necessary + buf = (ctypes.c_byte * cpu_storage.nbytes()).from_address( + cpu_storage.data_ptr() + ) + sha1 = hashlib.sha1() + sha1.update(buf) + return sha1.hexdigest() + + # TODO: factor this into a random utility + if device_type == "cpu": + generator = default_generator + elif device_type == "cuda": + import torch.cuda + + generator = torch.cuda.default_generators[storage.device.index] + else: + raise AssertionError(f"unhandled device type {device_type}") + state = generator.get_state() + try: + generator.manual_seed(0) + x = torch.empty(0, dtype=torch.uint8, device=storage.device).set_(storage) # type: ignore[call-overload] + # The dtype-casting view cannot be compiled, and so the + # padding/reshaping also needs to be done externally even + # though it could be profitably fused + pad = -x.numel() % 4 + if pad > 0: + x = F.pad(x, (0, pad), "constant", 0) + x = x.view(torch.int32) + # We run the 32-bit hash five times with differing parameters to + # reduce chance of collision + ITER = 5 + cs = [hash_storage_kernel(x).item() for _ in range(ITER)] + return struct.pack(">" + "i" * ITER, *cs).hex() + finally: + generator.set_state(state) + + +class ContentStoreWriter: + # Structure: + # storages/ + # 00/ + # 0000..00 + # tensors/ + # name + def __init__(self, loc: str, stable_hash: bool = False) -> None: + self.loc: str = loc + self.seen_storage_hashes: Set[str] = set() + self.stable_hash = stable_hash + + # TODO: offer some sort of non-blocking API to speed things up + def write_storage(self, storage: torch.UntypedStorage) -> str: + h = hash_storage(storage, stable_hash=self.stable_hash) + if h in self.seen_storage_hashes: + return h + # TODO: consider not using torch.save for this; we don't actually + # need any metadata for the storage + subfolder = os.path.join(self.loc, "storages") + os.makedirs(subfolder, exist_ok=True) + target = os.path.join(subfolder, h) + if os.path.exists(target): + return h + torch.save(storage, target) + self.seen_storage_hashes.add(h) + return h + + def compute_tensor_metadata(self, t: torch.Tensor, h=None): + if h is None: + h = hash_storage(t.untyped_storage(), stable_hash=self.stable_hash) + return ( + t.dtype, + h, + t.storage_offset(), + tuple(t.shape), + t.stride(), + torch._utils.get_tensor_metadata(t), + ) + + def write_tensor(self, name: str, t: torch.Tensor) -> None: + storage = t.untyped_storage() + h = self.write_storage(storage) + # TODO: Support more advanced snapshotting of requires_grad/grad/etc + d, f = os.path.split(name) + payload = self.compute_tensor_metadata(t, h=h) + subfolder = os.path.join(self.loc, "tensors", d) + os.makedirs(subfolder, exist_ok=True) + torch.save(payload, os.path.join(subfolder, f)) + + +class ContentStoreReader: + def __init__(self, loc: str, *, cache=True) -> None: + self.loc = loc + self.storage_cache: Optional[ + Dict[Optional[torch.device], Dict[str, StorageWeakRef]] + ] = None + if cache: + self.storage_cache = defaultdict(dict) + + def read_storage(self, h: str, *, device=None) -> torch.UntypedStorage: + if device is not None: + device = torch.device(device) + ws = ( + self.storage_cache[device].get(h) + if self.storage_cache is not None + else None + ) + s: Optional[torch.UntypedStorage] + if ws is not None: + s = torch.UntypedStorage._new_with_weak_ptr(ws.cdata) + if s is not None: + return s + s = torch.load( + os.path.join(self.loc, "storages", h), + weights_only=True, + map_location=device, + )._untyped_storage + assert s is not None + if self.storage_cache is not None: + self.storage_cache[device][h] = StorageWeakRef(s) + return s + + def read_tensor_metadata(self, name: str): + fn = os.path.join(self.loc, "tensors", name) + if not os.path.exists(fn): + raise FileNotFoundError(fn) + return torch.load(fn, weights_only=True) + + def read_tensor(self, name: str, *, device=None) -> torch.Tensor: + dtype, h, storage_offset, size, stride, metadata = self.read_tensor_metadata( + name + ) + storage = self.read_storage(h, device=device) + t = torch.tensor([], dtype=dtype, device=storage.device) + t.set_(storage, storage_offset, size, stride) + torch._utils.set_tensor_metadata(t, metadata) + return t diff --git a/lib/python3.11/site-packages/torch/utils/_contextlib.py b/lib/python3.11/site-packages/torch/utils/_contextlib.py new file mode 100644 index 0000000000000000000000000000000000000000..4f1b991438c0783ff3b1d7e7ab50bc628cb5cf2c --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_contextlib.py @@ -0,0 +1,157 @@ +# mypy: allow-untyped-defs +# Extra utilities for working with context managers that should have been +# in the standard library but are not + +import functools +import inspect +import warnings +import sys +from typing import Any, Callable, TypeVar, cast + +# Used for annotating the decorator usage of _DecoratorContextManager (e.g., +# 'no_grad' and 'enable_grad'). +# See https://mypy.readthedocs.io/en/latest/generics.html#declaring-decorators +FuncType = Callable[..., Any] +F = TypeVar('F', bound=FuncType) + + +def _wrap_generator(ctx_factory, func): + """ + Wrap each generator invocation with the context manager factory. + + The input should be a function that returns a context manager, + not a context manager itself, to handle one-shot context managers. + """ + @functools.wraps(func) + def generator_context(*args, **kwargs): + gen = func(*args, **kwargs) + + # Generators are suspended and unsuspended at `yield`, hence we + # make sure the grad mode is properly set every time the execution + # flow returns into the wrapped generator and restored when it + # returns through our `yield` to our caller (see PR #49017). + try: + # Issuing `None` to a generator fires it up + with ctx_factory(): + response = gen.send(None) + + while True: + try: + # Forward the response to our caller and get its next request + request = yield response + + except GeneratorExit: + # Inform the still active generator about its imminent closure + with ctx_factory(): + gen.close() + raise + + except BaseException: + # Propagate the exception thrown at us by the caller + with ctx_factory(): + response = gen.throw(*sys.exc_info()) + + else: + # Pass the last request to the generator and get its response + with ctx_factory(): + response = gen.send(request) + + # We let the exceptions raised above by the generator's `.throw` or + # `.send` methods bubble up to our caller, except for StopIteration + except StopIteration as e: + # The generator informed us that it is done: take whatever its + # returned value (if any) was and indicate that we're done too + # by returning it (see docs for python's return-statement). + return e.value + + return generator_context + + +def context_decorator(ctx, func): + """ + Like contextlib.ContextDecorator. + + But with the following differences: + 1. Is done by wrapping, rather than inheritance, so it works with context + managers that are implemented from C and thus cannot easily inherit from + Python classes + 2. Wraps generators in the intuitive way (c.f. https://bugs.python.org/issue37743) + 3. Errors out if you try to wrap a class, because it is ambiguous whether + or not you intended to wrap only the constructor + + The input argument can either be a context manager (in which case it must + be a multi-shot context manager that can be directly invoked multiple times) + or a callable that produces a context manager. + """ + assert not (callable(ctx) and hasattr(ctx, '__enter__')), ( + f"Passed in {ctx} is both callable and also a valid context manager " + "(has __enter__), making it ambiguous which interface to use. If you " + "intended to pass a context manager factory, rewrite your call as " + "context_decorator(lambda: ctx()); if you intended to pass a context " + "manager directly, rewrite your call as context_decorator(lambda: ctx)" + ) + + if not callable(ctx): + def ctx_factory(): + return ctx + else: + ctx_factory = ctx + + if inspect.isclass(func): + raise RuntimeError( + "Cannot decorate classes; it is ambiguous whether or not only the " + "constructor or all methods should have the context manager applied; " + "additionally, decorating a class at definition-site will prevent " + "use of the identifier as a conventional type. " + "To specify which methods to decorate, decorate each of them " + "individually." + ) + + if inspect.isgeneratorfunction(func): + return _wrap_generator(ctx_factory, func) + + @functools.wraps(func) + def decorate_context(*args, **kwargs): + with ctx_factory(): + return func(*args, **kwargs) + + return decorate_context + + +class _DecoratorContextManager: + """Allow a context manager to be used as a decorator.""" + + def __call__(self, orig_func: F) -> F: + if inspect.isclass(orig_func): + warnings.warn( + "Decorating classes is deprecated and will be disabled in " + "future versions. You should only decorate functions or methods. " + "To preserve the current behavior of class decoration, you can " + "directly decorate the `__init__` method and nothing else.", + FutureWarning, + stacklevel=2, + ) + func = cast(F, lambda *args, **kwargs: orig_func(*args, **kwargs)) + else: + func = orig_func + + return cast(F, context_decorator(self.clone, func)) + + def __enter__(self) -> None: + raise NotImplementedError + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + raise NotImplementedError + + def clone(self): + # override this method if your children class takes __init__ parameters + return self.__class__() + + +class _NoParamDecoratorContextManager(_DecoratorContextManager): + """Allow a context manager to be used as a decorator without parentheses.""" + + def __new__(cls, orig_func=None): + if orig_func is None: + return super().__new__(cls) + return cls()(orig_func) diff --git a/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py b/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py new file mode 100644 index 0000000000000000000000000000000000000000..a12acca1ca11bf37f0f009d18d11cffd25300045 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_cpp_extension_versioner.py @@ -0,0 +1,59 @@ +# mypy: allow-untyped-defs +import collections + + +Entry = collections.namedtuple('Entry', 'version, hash') + + +def update_hash(seed, value): + # Good old boost::hash_combine + # https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html + return seed ^ (hash(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2)) + + +def hash_source_files(hash_value, source_files): + for filename in source_files: + with open(filename, 'rb') as file: + hash_value = update_hash(hash_value, file.read()) + return hash_value + + +def hash_build_arguments(hash_value, build_arguments): + for group in build_arguments: + if group: + for argument in group: + hash_value = update_hash(hash_value, argument) + return hash_value + + +class ExtensionVersioner: + def __init__(self): + self.entries = {} + + def get_version(self, name): + entry = self.entries.get(name) + return None if entry is None else entry.version + + def bump_version_if_changed(self, + name, + source_files, + build_arguments, + build_directory, + with_cuda, + is_python_module, + is_standalone): + hash_value = 0 + hash_value = hash_source_files(hash_value, source_files) + hash_value = hash_build_arguments(hash_value, build_arguments) + hash_value = update_hash(hash_value, build_directory) + hash_value = update_hash(hash_value, with_cuda) + hash_value = update_hash(hash_value, is_python_module) + hash_value = update_hash(hash_value, is_standalone) + + entry = self.entries.get(name) + if entry is None: + self.entries[name] = entry = Entry(0, hash_value) + elif hash_value != entry.hash: + self.entries[name] = entry = Entry(entry.version + 1, hash_value) + + return entry.version diff --git a/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py b/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py new file mode 100644 index 0000000000000000000000000000000000000000..83c35d9f5c5b12ad0593105f97ae9e8e53f7c3b2 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_cxx_pytree.py @@ -0,0 +1,1041 @@ +""" +Contains utility functions for working with nested python data structures. + +A *pytree* is Python nested data structure. It is a tree in the sense that +nodes are Python collections (e.g., list, tuple, dict) and the leaves are +Python values. Furthermore, a pytree should not contain reference cycles. + +pytrees are useful for working with nested collections of Tensors. For example, +one can use `tree_map` to map a function over all Tensors inside some nested +collection of Tensors and `tree_leaves` to get a flat list of all Tensors +inside some nested collection. pytrees are helpful for implementing nested +collection support for PyTorch APIs. +""" + +import functools +import sys +import types +from typing import ( + Any, + Callable, + Iterable, + List, + Optional, + overload, + Tuple, + Type, + TypeVar, + Union, +) +from typing_extensions import deprecated + +import optree +from optree import PyTreeSpec as TreeSpec # direct import for type annotations + +import torch.utils._pytree as python_pytree +from torch.utils._pytree import KeyEntry as KeyEntry + + +__all__ = [ + "PyTree", + "Context", + "FlattenFunc", + "UnflattenFunc", + "DumpableContext", + "ToDumpableContextFn", + "FromDumpableContextFn", + "TreeSpec", + "LeafSpec", + "keystr", + "key_get", + "register_pytree_node", + "tree_flatten", + "tree_flatten_with_path", + "tree_unflatten", + "tree_iter", + "tree_leaves", + "tree_leaves_with_path", + "tree_structure", + "tree_map", + "tree_map_with_path", + "tree_map_", + "tree_map_only", + "tree_map_only_", + "tree_all", + "tree_any", + "tree_all_only", + "tree_any_only", + "treespec_dumps", + "treespec_loads", + "treespec_pprint", +] + + +T = TypeVar("T") +S = TypeVar("S") +U = TypeVar("U") +R = TypeVar("R") + + +Context = Any +PyTree = Any +FlattenFunc = Callable[[PyTree], Tuple[List[Any], Context]] +UnflattenFunc = Callable[[Iterable[Any], Context], PyTree] +OpTreeUnflattenFunc = Callable[[Context, Iterable[Any]], PyTree] +DumpableContext = Any # Any json dumpable text +ToDumpableContextFn = Callable[[Context], DumpableContext] +FromDumpableContextFn = Callable[[DumpableContext], Context] +KeyPath = Tuple[KeyEntry, ...] +FlattenWithKeysFunc = Callable[[PyTree], Tuple[List[Tuple[KeyEntry, Any]], Any]] + + +def _reverse_args(func: UnflattenFunc) -> OpTreeUnflattenFunc: + @functools.wraps(func) + def wrapped(*args: Any, **kwargs: Any) -> Any: + return func(*reversed(args), **kwargs) + + return wrapped + + +def register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, + flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None, +) -> None: + """Register a container-like type as pytree node. + + Args: + cls (type): A Python type to treat as an internal pytree node. + flatten_fn (callable): A function to be used during flattening, taking an instance of + ``cls`` and returning a pair, with (1) an iterable for the children to be flattened + recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be + passed to the ``unflatten_fn``. + unflatten_fn (callable): A function taking two arguments: the auxiliary data that was + returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. + The function should return an instance of ``cls``. + serialized_type_name (str, optional): A keyword argument used to specify the fully + qualified name used when serializing the tree spec. + to_dumpable_context (callable, optional): An optional keyword argument to custom specify how + to convert the context of the pytree to a custom json dumpable representation. This is + used for json serialization, which is being used in :mod:`torch.export` right now. + from_dumpable_context (callable, optional): An optional keyword argument to custom specify + how to convert the custom json dumpable representation of the context back to the + original context. This is used for json deserialization, which is being used in + :mod:`torch.export` right now. + + Example:: + + >>> # xdoctest: +SKIP + >>> # Registry a Python type with lambda functions + >>> register_pytree_node( + ... set, + ... lambda s: (sorted(s), None, None), + ... lambda children, _: set(children), + ... ) + """ + if flatten_with_keys_fn is not None: + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + _private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + ) + + python_pytree._private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + ) + + +@deprecated( + "`torch.utils._cxx_pytree._register_pytree_node` is deprecated. " + "Please use `torch.utils._cxx_pytree.register_pytree_node` instead.", + category=FutureWarning, +) +def _register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, +) -> None: + """Register a container-like type as pytree node for the C++ pytree only. + + The ``namespace`` argument is used to avoid collisions that occur when different libraries + register the same Python type with different behaviors. It is recommended to add a unique prefix + to the namespace to avoid conflicts with other libraries. Namespaces can also be used to specify + the same class in different namespaces for different use cases. + + .. warning:: + For safety reasons, a ``namespace`` must be specified while registering a custom type. It is + used to isolate the behavior of flattening and unflattening a pytree node type. This is to + prevent accidental collisions between different libraries that may register the same type. + + Args: + cls (type): A Python type to treat as an internal pytree node. + flatten_fn (callable): A function to be used during flattening, taking an instance of + ``cls`` and returning a pair, with (1) an iterable for the children to be flattened + recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be + passed to the ``unflatten_fn``. + unflatten_fn (callable): A function taking two arguments: the auxiliary data that was + returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. + The function should return an instance of ``cls``. + serialized_type_name (str, optional): A keyword argument used to specify the fully + qualified name used when serializing the tree spec. + to_dumpable_context (callable, optional): An optional keyword argument to custom specify how + to convert the context of the pytree to a custom json dumpable representation. This is + used for json serialization, which is being used in :mod:`torch.export` right now. + from_dumpable_context (callable, optional): An optional keyword argument to custom specify + how to convert the custom json dumpable representation of the context back to the + original context. This is used for json deserialization, which is being used in + :mod:`torch.export` right now. + """ + + _private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + ) + + +def _private_register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, +) -> None: + """This is an internal function that is used to register a pytree node type + for the C++ pytree only. End-users should use :func:`register_pytree_node` + instead. + """ + # TODO(XuehaiPan): remove this condition when we make Python pytree out-of-box support + # PyStructSequence types + if not optree.is_structseq_class(cls): + optree.register_pytree_node( + cls, + flatten_fn, + _reverse_args(unflatten_fn), + namespace="torch", + ) + + +def tree_is_leaf( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + """Check if a pytree is a leaf. + + >>> tree_is_leaf(1) + True + >>> tree_is_leaf(None) + True + >>> tree_is_leaf([1, 2, 3]) + False + >>> tree_is_leaf((1, 2, 3), is_leaf=lambda x: isinstance(x, tuple)) + True + >>> tree_is_leaf({'a': 1, 'b': 2, 'c': 3}) + False + >>> tree_is_leaf({'a': 1, 'b': 2, 'c': None}) + False + + Args: + tree (pytree): A pytree to check if it is a leaf node. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A boolean indicating if the pytree is a leaf node. + """ + return optree.tree_is_leaf( + tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_flatten( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Tuple[List[Any], TreeSpec]: + """Flatten a pytree. + + See also :func:`tree_unflatten`. + + The flattening order (i.e., the order of elements in the output list) is deterministic, + corresponding to a left-to-right depth-first tree traversal. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_flatten(tree) + ([1, 2, 3, 4, None, 5], PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf)) + >>> tree_flatten(1) + ([1], PyTreeSpec(*, NoneIsLeaf)) + >>> tree_flatten(None) + ([None], PyTreeSpec(*, NoneIsLeaf)) + + For unordered dictionaries, :class:`dict` and :class:`collections.defaultdict`, the order is + dependent on the **sorted** keys in the dictionary. Please use :class:`collections.OrderedDict` + if you want to keep the keys in the insertion order. + + >>> from collections import OrderedDict + >>> tree = OrderedDict([('b', (2, [3, 4])), ('a', 1), ('c', None), ('d', 5)]) + >>> tree_flatten(tree) + ([2, 3, 4, 1, None, 5], PyTreeSpec(OrderedDict({'b': (*, [*, *]), 'a': *, 'c': *, 'd': *}), NoneIsLeaf)) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A pair ``(leaves, treespec)`` where the first element is a list of leaf values and the + second element is a treespec representing the structure of the pytree. + """ + return optree.tree_flatten( # type: ignore[return-value] + tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree: + """Reconstruct a pytree from the treespec and the leaves. + + The inverse of :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> leaves, treespec = tree_flatten(tree) + >>> tree == tree_unflatten(leaves, treespec) + True + + Args: + leaves (iterable): The list of leaves to use for reconstruction. The list must match the + number of leaves of the treespec. + treespec (TreeSpec): The treespec to reconstruct. + + Returns: + The reconstructed pytree, containing the ``leaves`` placed in the structure described by + ``treespec``. + """ + if not isinstance(treespec, TreeSpec): + raise TypeError( + f"tree_unflatten(values, spec): Expected `spec` to be instance of " + f"TreeSpec but got item of type {type(treespec)}." + ) + return optree.tree_unflatten(treespec, leaves) # type: ignore[arg-type] + + +def tree_iter( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Iterable[Any]: + """Get an iterator over the leaves of a pytree. + + See also :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> list(tree_iter(tree)) + [1, 2, 3, 4, None, 5] + >>> list(tree_iter(1)) + [1] + >>> list(tree_iter(None)) + [None] + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + An iterator over the leaf values. + """ + return optree.tree_iter( + tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_leaves( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> List[Any]: + """Get the leaves of a pytree. + + See also :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_leaves(tree) + [1, 2, 3, 4, None, 5] + >>> tree_leaves(1) + [1] + >>> tree_leaves(None) + [None] + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A list of leaf values. + """ + return optree.tree_leaves( + tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_structure( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> TreeSpec: + """Get the treespec for a pytree. + + See also :func:`tree_flatten`. + + >>> tree = {'b': (2, [3, 4]), 'a': 1, 'c': None, 'd': 5} + >>> tree_structure(tree) + PyTreeSpec({'a': *, 'b': (*, [*, *]), 'c': *, 'd': *}, NoneIsLeaf) + >>> tree_structure(1) + PyTreeSpec(*, NoneIsLeaf) + >>> tree_structure(None) + PyTreeSpec(*, NoneIsLeaf) + + Args: + tree (pytree): A pytree to flatten. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A treespec object representing the structure of the pytree. + """ + return optree.tree_structure( # type: ignore[return-value] + tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_map( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Map a multi-input function over pytree args to produce a new pytree. + + See also :func:`tree_map_`. + + >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)}) + {'x': 8, 'y': (43, 65)} + >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': False, 'y': (False, False), 'z': True} + + If multiple inputs are given, the structure of the tree is taken from the first input; + subsequent inputs need only have ``tree`` as a prefix: + + >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]]) + [[5, 7, 9], [6, 1, 2]] + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs`` + is the tuple of values at corresponding nodes in ``rests``. + """ + return optree.tree_map( + func, + tree, + *rests, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +def tree_map_( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree. + + See also :func:`tree_map`. + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + The original ``tree`` with the value at each leaf is given by the side-effect of function + ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf + in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``. + """ + return optree.tree_map_( + func, + tree, + *rests, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +Type2 = Tuple[Type[T], Type[S]] +Type3 = Tuple[Type[T], Type[S], Type[U]] +if sys.version_info >= (3, 10): + TypeAny = Union[Type[Any], Tuple[Type[Any], ...], types.UnionType] +else: + TypeAny = Union[Type[Any], Tuple[Type[Any], ...]] + +Fn2 = Callable[[Union[T, S]], R] +Fn3 = Callable[[Union[T, S, U]], R] +Fn = Callable[[T], R] +FnAny = Callable[[Any], R] + +MapOnlyFn = Callable[[T], Callable[[Any], Any]] + + +# These specializations help with type inference on the lambda passed to this +# function +@overload +def map_only(__type_or_types_or_pred: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Type[T]) -> MapOnlyFn[Fn[T, Any]]: + ... + + +# This specialization is needed for the implementations below that call +@overload +def map_only(__type_or_types_or_pred: TypeAny) -> MapOnlyFn[FnAny[Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Callable[[Any], bool]) -> MapOnlyFn[FnAny[Any]]: + ... + + +def map_only( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]] +) -> MapOnlyFn[FnAny[Any]]: + """ + Suppose you are writing a tree_map over tensors, leaving everything + else unchanged. Ordinarily you would have to write: + + def go(t): + if isinstance(t, Tensor): + return ... + else: + return t + + With this function, you only need to write: + + @map_only(Tensor) + def go(t): + return ... + + You can also directly use 'tree_map_only' + """ + if isinstance(__type_or_types_or_pred, (type, tuple)) or ( + sys.version_info >= (3, 10) + and isinstance(__type_or_types_or_pred, types.UnionType) + ): + + def pred(x: Any) -> bool: + return isinstance(x, __type_or_types_or_pred) # type: ignore[arg-type] + + elif callable(__type_or_types_or_pred): + pred = __type_or_types_or_pred # type: ignore[assignment] + else: + raise TypeError("Argument must be a type, a tuple of types, or a callable.") + + def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]: + @functools.wraps(func) + def wrapped(x: T) -> Any: + if pred(x): + return func(x) + return x + + return wrapped + + return wrapper + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type[T], + func: Fn[T, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type2[T, S], + func: Fn2[T, S, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type3[T, S, U], + func: Fn3[T, S, U, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Callable[[Any], bool], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +def tree_map_only( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + return tree_map(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf) + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type[T], + func: Fn[T, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type2[T, S], + func: Fn2[T, S, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type3[T, S, U], + func: Fn3[T, S, U, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Callable[[Any], bool], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +def tree_map_only_( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + return tree_map_(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf) + + +def tree_all( + pred: Callable[[Any], bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return all(map(pred, flat_args)) + + +def tree_any( + pred: Callable[[Any], bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return any(map(pred, flat_args)) + + +@overload +def tree_all_only( + __type_or_types: Type[T], + pred: Fn[T, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_all_only( + __type_or_types: Type2[T, S], + pred: Fn2[T, S, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_all_only( + __type_or_types: Type3[T, S, U], + pred: Fn3[T, S, U, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +def tree_all_only( + __type_or_types: TypeAny, + pred: FnAny[bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return all(pred(x) for x in flat_args if isinstance(x, __type_or_types)) + + +@overload +def tree_any_only( + __type_or_types: Type[T], + pred: Fn[T, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_any_only( + __type_or_types: Type2[T, S], + pred: Fn2[T, S, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_any_only( + __type_or_types: Type3[T, S, U], + pred: Fn3[T, S, U, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +def tree_any_only( + __type_or_types: TypeAny, + pred: FnAny[bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return any(pred(x) for x in flat_args if isinstance(x, __type_or_types)) + + +def broadcast_prefix( + prefix_tree: PyTree, + full_tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> List[Any]: + """Return a list of broadcasted leaves in ``prefix_tree`` to match the number of leaves in ``full_tree``. + + If a ``prefix_tree`` is a prefix of a ``full_tree``, this means the ``full_tree`` can be + constructed by replacing the leaves of ``prefix_tree`` with appropriate **subtrees**. + + This function returns a list of leaves with the same size as ``full_tree``. The leaves are + replicated from ``prefix_tree``. The number of replicas is determined by the corresponding + subtree in ``full_tree``. + + >>> broadcast_prefix(1, [1, 2, 3]) + [1, 1, 1] + >>> broadcast_prefix([1, 2, 3], [1, 2, 3]) + [1, 2, 3] + >>> broadcast_prefix([1, 2, 3], [1, 2, 3, 4]) + Traceback (most recent call last): + ... + ValueError: list arity mismatch; expected: 3, got: 4; list: [1, 2, 3, 4]. + >>> broadcast_prefix([1, 2, 3], [1, 2, (3, 4)]) + [1, 2, 3, 3] + >>> broadcast_prefix([1, 2, 3], [1, 2, {'a': 3, 'b': 4, 'c': (None, 5)}]) + [1, 2, 3, 3, 3, 3] + + Args: + prefix_tree (pytree): A pytree with the same structure as a prefix of ``full_tree``. + full_tree (pytree): A pytree with the same structure as a suffix of ``prefix_tree``. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A list of leaves in ``prefix_tree`` broadcasted to match the number of leaves in ``full_tree``. + """ + return optree.broadcast_prefix( + prefix_tree, + full_tree, + is_leaf=is_leaf, + none_is_leaf=True, + namespace="torch", + ) + + +# Broadcasts a pytree to the provided TreeSpec and returns the flattened +# values. If this is not possible, then this function returns None. +# +# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]), +# would return [0, 0]. This is useful for part of the vmap implementation: +# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be +# broadcastable to the tree structure of `inputs` and we use +# _broadcast_to_and_flatten to check this. +def _broadcast_to_and_flatten( + tree: PyTree, + treespec: TreeSpec, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Optional[List[Any]]: + assert isinstance(treespec, TreeSpec) + full_tree = tree_unflatten([0] * treespec.num_leaves, treespec) + try: + return broadcast_prefix(tree, full_tree, is_leaf=is_leaf) + except ValueError: + return None + + +def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str: + """Serialize a treespec to a JSON string.""" + if not isinstance(treespec, TreeSpec): + raise TypeError( + f"treespec_dumps(spec): Expected `spec` to be instance of " + f"TreeSpec but got item of type {type(treespec)}." + ) + + dummy_tree = tree_unflatten([0] * treespec.num_leaves, treespec) + orig_treespec = python_pytree.tree_structure(dummy_tree) + return python_pytree.treespec_dumps(orig_treespec, protocol=protocol) + + +def treespec_loads(serialized: str) -> TreeSpec: + """Deserialize a treespec from a JSON string.""" + orig_treespec = python_pytree.treespec_loads(serialized) + dummy_tree = python_pytree.tree_unflatten( + [0] * orig_treespec.num_leaves, + orig_treespec, + ) + treespec = tree_structure(dummy_tree) + return treespec + + +class _DummyLeaf: + def __repr__(self) -> str: + return "*" + + +def treespec_pprint(treespec: TreeSpec) -> str: + dummy_tree = tree_unflatten( + [_DummyLeaf() for _ in range(treespec.num_leaves)], + treespec, + ) + return repr(dummy_tree) + + +class LeafSpecMeta(type(TreeSpec)): # type: ignore[misc] + def __instancecheck__(self, instance: object) -> bool: + return isinstance(instance, TreeSpec) and instance.is_leaf() + + +class LeafSpec(TreeSpec, metaclass=LeafSpecMeta): + def __new__(cls) -> "LeafSpec": + return optree.treespec_leaf(none_is_leaf=True) # type: ignore[return-value] + + +def tree_flatten_with_path( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Tuple[List[Tuple[KeyPath, Any]], TreeSpec]: + """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path. + + Args: + tree: a pytree to flatten. If it contains a custom type, that type must be + registered with an appropriate `tree_flatten_with_path_fn` when registered + with :func:`register_pytree_node`. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + Returns: + A tuple where the first element is a list of (key path, leaf) pairs, and the + second element is a :class:`TreeSpec` representing the structure of the flattened + tree. + """ + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + +def tree_leaves_with_path( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> List[Tuple[KeyPath, Any]]: + """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path. + + Args: + tree: a pytree. If it contains a custom type, that type must be + registered with an appropriate `tree_flatten_with_path_fn` when registered + with :func:`register_pytree_node`. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + Returns: + A list of (key path, leaf) pairs. + """ + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + +def tree_map_with_path( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Like :func:`tree_map`, but the provided callable takes an additional key path argument. + + Args: + func: A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. The first positional argument + to ``func`` is the key path of the leaf in question. The second + positional argument is the value of the leaf. + tree: A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests: A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the + corresponding leaf in ``tree``, ``x`` is the value at that leaf, and + ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + +def keystr(kp: KeyPath) -> str: + """Given a key path, return a pretty-printed representation.""" + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + +def key_get(obj: Any, kp: KeyPath) -> Any: + """Given an object and a key path, return the value at the key path.""" + raise NotImplementedError("KeyPaths are not yet supported in cxx_pytree.") + + +with python_pytree._NODE_REGISTRY_LOCK: + python_pytree._cxx_pytree_imported = True + args, kwargs = (), {} # type: ignore[var-annotated] + for args, kwargs in python_pytree._cxx_pytree_pending_imports: + _private_register_pytree_node(*args, **kwargs) + python_pytree._cxx_pytree_pending_imports.clear() + del args, kwargs diff --git a/lib/python3.11/site-packages/torch/utils/_device.py b/lib/python3.11/site-packages/torch/utils/_device.py new file mode 100644 index 0000000000000000000000000000000000000000..d7903fc3b465a362e050997c6aa30db4fcfd6ab4 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_device.py @@ -0,0 +1,117 @@ +# mypy: allow-untyped-defs +from typing import Optional +import torch +from torch.overrides import TorchFunctionMode, _pop_mode, _push_mode +from torch.utils._contextlib import context_decorator +from torch._C import _len_torch_function_stack +import functools + +CURRENT_DEVICE: Optional[torch.device] = None + +@functools.lru_cache(1) +def _device_constructors(): + return { + # standard ones + torch.empty, + torch.empty_permuted, + torch.empty_strided, + torch.empty_quantized, + torch.ones, + torch.arange, + torch.bartlett_window, + torch.blackman_window, + torch.eye, + torch.fft.fftfreq, + torch.fft.rfftfreq, + torch.full, + torch.fill, + torch.hamming_window, + torch.hann_window, + torch.kaiser_window, + torch.linspace, + torch.logspace, + torch.nested.nested_tensor, + # This function doesn't actually take a device argument + # torch.normal, + torch.ones, + torch.rand, + torch.randn, + torch.randint, + torch.randperm, + torch.range, + torch.sparse_coo_tensor, + torch.sparse_compressed_tensor, + torch.sparse_csr_tensor, + torch.sparse_csc_tensor, + torch.sparse_bsr_tensor, + torch.sparse_bsc_tensor, + torch.tril_indices, + torch.triu_indices, + torch.vander, + torch.zeros, + torch.asarray, + # weird ones + torch.tensor, + torch.as_tensor, + torch.scalar_tensor, + torch.asarray, + } + +# NB: This is directly called from C++ in torch/csrc/Device.cpp +class DeviceContext(TorchFunctionMode): + def __init__(self, device): + self.device = torch.device(device) + + def __enter__(self): + global CURRENT_DEVICE + self.old_device = CURRENT_DEVICE + CURRENT_DEVICE = self.device + # We need to put the device at the bottom of the stack + # If we set default device within a function mode context + # exiting that context mode will pop the device function mode off + # of the stack incorrectly + cur_stack = [_pop_mode() for _ in range(_len_torch_function_stack())] + + _push_mode(self) + + for mode in reversed(cur_stack): + _push_mode(mode) + + + def __exit__(self, exc_type, exc_val, exc_tb): + global CURRENT_DEVICE + CURRENT_DEVICE = self.old_device + cur_stack = [] + # Invariant: there should only be one DeviceContext on the stack at any time + # (At the bottom), pop all mdoes until we hit the bottom, assert it's a DeviceContext + # or else someone else has popped it! + for _ in range(_len_torch_function_stack() - 1): + mode = _pop_mode() + assert not isinstance(mode, DeviceContext) + cur_stack.append(mode) + + if _len_torch_function_stack() > 0: + mode = _pop_mode() + assert isinstance(mode, DeviceContext) + + for mode in reversed(cur_stack): + _push_mode(mode) + + def __torch_function__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + if func in _device_constructors() and kwargs.get('device') is None: + kwargs['device'] = self.device + return func(*args, **kwargs) + +# NB: This is directly called from C++ in torch/csrc/Device.cpp +def device_decorator(device, func): + return context_decorator(lambda: device, func) + +def set_device(device): + """ + Set the default device inside of the wrapped function by decorating it with this function. + + If you would like to use this as a context manager, use device as a + context manager directly, e.g., ``with torch.device(device)``. + """ + return lambda func: device_decorator(torch.device(device), func) diff --git a/lib/python3.11/site-packages/torch/utils/_exposed_in.py b/lib/python3.11/site-packages/torch/utils/_exposed_in.py new file mode 100644 index 0000000000000000000000000000000000000000..a0963b0e4e6aeb8c31d8f87ff6fa1a91ed5730cc --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_exposed_in.py @@ -0,0 +1,20 @@ +from typing import Callable, TypeVar + + +F = TypeVar("F") + + +# Allows one to expose an API in a private submodule publicly as per the definition +# in PyTorch's public api policy. +# +# It is a temporary solution while we figure out if it should be the long-term solution +# or if we should amend PyTorch's public api policy. The concern is that this approach +# may not be very robust because it's not clear what __module__ is used for. +# However, both numpy and jax overwrite the __module__ attribute of their APIs +# without problem, so it seems fine. +def exposed_in(module: str) -> Callable[[F], F]: + def wrapper(fn: F) -> F: + fn.__module__ = module + return fn + + return wrapper diff --git a/lib/python3.11/site-packages/torch/utils/_foreach_utils.py b/lib/python3.11/site-packages/torch/utils/_foreach_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c3100d41b6c0f55dcff986a18e3bb0c2d417e111 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_foreach_utils.py @@ -0,0 +1,44 @@ +from typing import List, Dict, Tuple, Optional + +import torch +from torch import Tensor +from torch.autograd.grad_mode import no_grad +from typing_extensions import TypeAlias + +def _get_foreach_kernels_supported_devices() -> List[str]: + r"""Return the device type list that supports foreach kernels.""" + return ["cuda", "xpu", torch._C._get_privateuse1_backend_name()] + +def _get_fused_kernels_supported_devices() -> List[str]: + r"""Return the device type list that supports fused kernels in optimizer.""" + return ["mps", "cuda", "xpu", "cpu", torch._C._get_privateuse1_backend_name()] + +TensorListList: TypeAlias = List[List[Optional[Tensor]]] +Indices: TypeAlias = List[int] +_foreach_supported_types = [torch.Tensor] + + +# This util function splits tensors into groups by device and dtype, which is useful before sending +# tensors off to a foreach implementation, which requires tensors to be on one device and dtype. +# If tensorlistlist contains more than one tensorlist, the following assumptions are made BUT NOT verified: +# - tensorlists CAN be None +# - all tensors in the first specified list cannot be None +# - given an index i, all specified tensorlist[i]s match in dtype and device +# with_indices (bool, optional): whether to track previous indices as the last list per dictionary entry. +# It comes in handy if there are Nones or literals in the tensorlists that are getting scattered out. +# Whereas mutating a tensor in the resulting split-up tensorlists WILL propagate changes back to the +# original input tensorlists, changing up Nones/literals WILL NOT propagate, and manual propagation +# may be necessary. Check out torch/optim/sgd.py for an example. +@no_grad() +def _group_tensors_by_device_and_dtype( + tensorlistlist: TensorListList, + with_indices: bool = False, +) -> Dict[Tuple[torch.device, torch.dtype], Tuple[TensorListList, Indices]]: + return torch._C._group_tensors_by_device_and_dtype(tensorlistlist, with_indices) + +def _device_has_foreach_support(device: torch.device) -> bool: + return device.type in (_get_foreach_kernels_supported_devices() + ["cpu"]) and not torch.jit.is_scripting() + + +def _has_foreach_support(tensors: List[Tensor], device: torch.device) -> bool: + return _device_has_foreach_support(device) and all(t is None or type(t) in _foreach_supported_types for t in tensors) diff --git a/lib/python3.11/site-packages/torch/utils/_freeze.py b/lib/python3.11/site-packages/torch/utils/_freeze.py new file mode 100644 index 0000000000000000000000000000000000000000..60bdbf8b056ec9cacc06bb25e3775ec3780cc02a --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_freeze.py @@ -0,0 +1,290 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +""" +Freeze Python packages. + +Freezing makes it possible to ship arbitrary Python modules as part of a C++ +library. The Python source of the module is compiled to bytecode and written +to `.c` files, to be imported by Python's built-in FrozenImporter. + +In a normal Python installation, FrozenImporter is only used to bootstrap the +initialization of the import machinery. Python's importers are defined in +Python (see `_bootstrap.py` and `_bootstrap_external.py`) but need to be +retrieved before any importers are available. Freezing the module bytecode +resolves this circular dependency. + +This script will freeze the Python standard library. It produces two things: +- Bytecode files: A set of `.c` that define C variables containing Python bytecode. +- Main file: A `main.c` file listing all of these modules in the right form to be + consumed by FrozenImporter. + +The library that wishes to these modules make them available to the local +Python instance by extending `PyImport_FrozenModules` appropriately (see +https://docs.python.org/3/c-api/import.html#c.PyImport_FrozenModules). +""" + +import argparse +import functools +import itertools +import marshal +import os +import types +from dataclasses import dataclass +from pathlib import Path +from typing import List + + +PATH_MARKER = "" +MAIN_INCLUDES = """#include + +""" + +MAIN_PREFIX_TEMPLATE = """ +// Compiled standard library modules. These should be appended to the existing +// `PyImport_FrozenModules` that ships with CPython. +struct _frozen {}[] = {{ +""" + +FAKE_PREFIX = MAIN_PREFIX_TEMPLATE.format("_PyImport_FrozenModules") + +MAIN_SUFFIX = """\ + {0, 0, 0} /* sentinel */ +}; +""" + +# Exclude some standard library modules to: +# 1. Slim down the final frozen lib. +# 2. Remove functionality we don't want to support. +DENY_LIST = [ + # Interface to unix databases + "dbm", + # ncurses bindings (terminal interfaces) + "curses", + # Tcl/Tk GUI + "tkinter", + "tkinter", + # Tests for the standard library + "test", + "tests", + "idle_test", + "__phello__.foo.py", + # importlib frozen modules. These are already baked into CPython. + "_bootstrap.py", + "_bootstrap_external.py", +] + +NUM_BYTECODE_FILES = 5 + + +def indent_msg(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + args[0].indent += 1 + ret = fn(*args, **kwargs) + args[0].indent -= 1 + return ret + + return wrapper + + +@dataclass +class FrozenModule: + # The fully qualified module name, e.g. 'foo.bar.baz' + module_name: str + # The name of the C variable that holds the bytecode, e.g. 'M_foo__bar__baz' + c_name: str + # The size of the C variable. Negative if this module is a package. + size: int + # The frozen bytecode + bytecode: bytes + + +class Freezer: + def __init__(self, verbose: bool): + self.frozen_modules: List[FrozenModule] = [] + self.indent: int = 0 + self.verbose: bool = verbose + + def msg(self, path: Path, code: str): + if not self.verbose: + return + # P: package dir + # F: python file + # S: skipped (not a package dir) + # X: skipped (deny-listed) + # N: skipped (not a python file) + print(" " * self.indent, end="") + print(f"{code} {path}") + + def write_bytecode(self, install_root): + """ + Write the `.c` files containing the frozen bytecode. + + Shared frozen modules evenly across the files. + """ + bytecode_file_names = [f"bytecode_{i}.c" for i in range(NUM_BYTECODE_FILES)] + bytecode_files = [ + open(os.path.join(install_root, name), "w") for name in bytecode_file_names + ] + it = itertools.cycle(bytecode_files) + for m in self.frozen_modules: + self.write_frozen(m, next(it)) + + for f in bytecode_files: + f.close() + + def write_main(self, install_root, oss, symbol_name): + """Write the `main.c` file containing a table enumerating all the frozen modules.""" + with open(os.path.join(install_root, "main.c"), "w") as outfp: + outfp.write(MAIN_INCLUDES) + for m in self.frozen_modules: + outfp.write(f"extern unsigned char {m.c_name}[];\n") + + outfp.write(MAIN_PREFIX_TEMPLATE.format(symbol_name)) + for m in self.frozen_modules: + outfp.write(f'\t{{"{m.module_name}", {m.c_name}, {m.size}}},\n') + outfp.write(MAIN_SUFFIX) + if oss: + outfp.write(FAKE_PREFIX) + outfp.write(MAIN_SUFFIX) + + def write_frozen(self, m: FrozenModule, outfp): + """Write a single frozen module's bytecode out to a C variable.""" + outfp.write(f"unsigned char {m.c_name}[] = {{") + for i in range(0, len(m.bytecode), 16): + outfp.write("\n\t") + for c in bytes(m.bytecode[i : i + 16]): + outfp.write("%d," % c) + outfp.write("\n};\n") + + def compile_path(self, path: Path, top_package_path: Path): + """Entry point for compiling a Path object.""" + if path.is_dir(): + self.compile_package(path, top_package_path) + else: + self.compile_file(path, top_package_path) + + @indent_msg + def compile_package(self, path: Path, top_package_path: Path): + """Compile all the files within a Python package dir.""" + assert path.is_dir() + if path.name in DENY_LIST: + self.msg(path, "X") + return + + # Python packages are directories that have __init__.py in them. + is_package_dir = any(child.name == "__init__.py" for child in path.iterdir()) + if not is_package_dir: + self.msg(path, "S") + return + + self.msg(path, "P") + # Recursively compile all children in this dir + for child in path.iterdir(): + self.compile_path(child, top_package_path) + + def get_module_qualname(self, file_path: Path, top_package_path: Path) -> List[str]: + # `path` looks like 'Lib/foo/bar/baz.py' + + # chop off 'Lib/' to get something that represents a Python module hierarchy. + # e.g. 'foo/bar/baz.py', which maps to 'foo.bar.baz' + normalized_path = file_path.relative_to(top_package_path.parent) + + if normalized_path.name == "__init__.py": + # Special handling for `__init__.py`. In this case, this file + # specifies that the containing directory should be treated as a package. + # For 'foo/bar/baz/__init__.py': + # - The module name is 'baz' + module_basename = normalized_path.parent.name + # - The parent is foo.bar (need to shave off the 'baz') + module_parent = normalized_path.parent.parent.parts + else: + module_basename = normalized_path.stem + module_parent = normalized_path.parent.parts + return list(module_parent) + [module_basename] + + def compile_string(self, file_content: str) -> types.CodeType: + # instead of passing in the real build time path to 'compile', we + # pass in a marker instead. This prevents the build time path being + # leaked to runtime. That path may not be available at runtime. + # Setting the path to a mark make sure it's a hard error rather + # than a flaky error when inspect module tries to retrieve python source + # code during torchscripting. + path_marker = PATH_MARKER + return compile(file_content, path_marker, "exec") + + @indent_msg + def compile_file(self, path: Path, top_package_path: Path): + """ + Compile a Python source file to frozen bytecode. + + Append the result to `self.frozen_modules`. + """ + assert path.is_file() + if path.suffix != ".py": + self.msg(path, "N") + return + + if path.name in DENY_LIST: + self.msg(path, "X") + return + + self.msg(path, "F") + module_qualname = self.get_module_qualname(path, top_package_path) + module_mangled_name = "__".join(module_qualname) + c_name = "M_" + module_mangled_name + + with open(path) as src_file: + co = self.compile_string(src_file.read()) + + bytecode = marshal.dumps(co) + size = len(bytecode) + if path.name == "__init__.py": + # Python packages are signified by negative size. + size = -size + self.frozen_modules.append( + FrozenModule(".".join(module_qualname), c_name, size, bytecode) + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Compile py source") + parser.add_argument("paths", nargs="*", help="Paths to freeze.") + parser.add_argument("--verbose", action="store_true", help="Print debug logs") + parser.add_argument( + "--install-dir", "--install_dir", help="Root directory for all output files" + ) + parser.add_argument( + "--oss", + action="store_true", + help="If it's OSS build, add a fake _PyImport_FrozenModules", + ) + parser.add_argument( + "--symbol-name", + "--symbol_name", + help="The name of the frozen module array symbol to generate", + default="_PyImport_FrozenModules_torch", + ) + + args = parser.parse_args() + + f = Freezer(args.verbose) + + for p in args.paths: + path = Path(p) + if path.is_dir() and not Path.exists(path / "__init__.py"): + # this 'top level path p' is a standard directory containing modules, + # not a module itself + # each 'mod' could be a dir containing __init__.py or .py file + # NB: sorted to make sure this is deterministic + for mod in sorted(path.glob("*")): + f.compile_path(mod, mod) + else: + f.compile_path(path, path) + + f.write_bytecode(args.install_dir) + f.write_main(args.install_dir, args.oss, args.symbol_name) + + +if __name__ == "__main__": + main() # pragma: no cover diff --git a/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py b/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py new file mode 100644 index 0000000000000000000000000000000000000000..0367980509c2c43d8645959e707ceb3530fa8777 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_get_clean_triton.py @@ -0,0 +1,152 @@ +# mypy: allow-untyped-defs +import argparse +import os +import re +from pathlib import Path +from typing import Dict, List + + +def remove_triton_function_declaration(source_code: str) -> str: + remove_head = re.sub(r"(\n.+\s\'\'\'\n)", "\n", source_code) + remove_tail = re.sub(r"(\'\'\'\,.+)", "\n", remove_head) + return remove_tail + + +def remove_async_compile(source_code: str) -> str: + remove_top_level = str.replace(source_code, "async_compile = AsyncCompile()", "") + remove_compile = str.replace(remove_top_level, "async_compile.wait(globals())", "") + remove_del = str.replace(remove_compile, "del async_compile", "") + return remove_del + + +def rename_kernels(source_code: str) -> str: + pattern = r"(\w+)\s*=\s*async_compile\.triton\('triton_',\s" + triton_kernel_decl = "def triton_" + matches = [ + (match.end(), match.group(1)) + for match in re.finditer(pattern, source_code, re.DOTALL) + ] + + # Starting from the last match to avoid issues with shifting indices after replacements + for end_index, captured_string in reversed(matches): + # Find the index of the next "B" after the current match + index_of_B = source_code.find(triton_kernel_decl, end_index) + if index_of_B != -1: + # Replace the triton_kernel_decl with the captured string + source_code = ( + source_code[:index_of_B] + + f"def {captured_string}" + + source_code[index_of_B + len(triton_kernel_decl) :] + ) + else: + # If triton_kernel_decl is not found after the current match, continue to the next + continue + + return source_code + + +def merge_params(original_params: List[str], new_params: List[str]) -> List[str]: + assert len(new_params) >= len(original_params) + for idx in range(len(new_params)): + if new_params[idx] == "T": + new_params[idx] = original_params[idx] + return new_params + + +def add_launch_params(original: str, kernel_to_params: Dict[str, str]) -> str: + # Regex to match the function call in the original string + pattern = r"(\w+)\.run\((.*), grid=(.*\)), [^)]*\)" + + def replace(match) -> str: + # Extract parts from the regex match + func_name = match.group(1) + params = match.group(2) + grid = match.group(3) + new_params = kernel_to_params[func_name] + new_params = merge_params(params.split(", "), new_params.split(", ")) + + # Format the new function call + new_string = f"{func_name}[{grid}]({', '.join(new_params)})" + return new_string + + transformed = re.sub(pattern, replace, original) + + remove_inductor_wrappers = re.sub( + r"@triton_heuristics[^@]*@triton.jit", + r"@triton.jit", + transformed, + flags=re.DOTALL, + ) + + return remove_inductor_wrappers + + +def process_file(input_filename: str, output_filename: str) -> str: + with open(input_filename) as file: + source_code = file.read() + + transformed_code = source_code + if "def triton_(" in source_code: + raise RuntimeError( + "Need to run original Pytorch code generating kernels with TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1" + ) + # transformed_code = rename_kernels(transformed_code) + transformed_code = remove_triton_function_declaration(transformed_code) + transformed_code = remove_async_compile(transformed_code) + + launch_params_filename = f"{input_filename}.launch_params" + if not os.path.exists(launch_params_filename): + raise RuntimeError( + f"Missing {launch_params_filename}. Run `TORCHINDUCTOR_DUMP_LAUNCH_PARAMS=1 python {input_filename} first." + ) + + with open(launch_params_filename) as f: + launch_params_meta = f.readlines() + + split_params = [i.split("|") for i in launch_params_meta] + strip_params = [[a.strip(), b.strip()] for a, b in split_params] + kernel_to_args: Dict[str, str] = dict(strip_params) + transformed_code = add_launch_params(transformed_code, kernel_to_args) + + with open(output_filename, "w") as file: + file.write(transformed_code) + return transformed_code + + +def get_clean_triton( + input_path: Path, output_path: Path = Path("triton_only_repro.py") +): + """Run experiments and output results to file + + Args: + input_path (Optional[Path]): Path to inductor generated output codede + output_path (Optional[Path]): Path to write out the new python file + """ + return process_file(str(input_path), str(output_path)) + + +if __name__ == "__main__": + """Sample usage: + # Running sweep + python inputcode.py + """ + parser = argparse.ArgumentParser( + description="Clean Inductor generated code to remove Inductor dependencies" + ) + + # Add the arguments + parser.add_argument( + "input_path", type=Path, help="Path to inductor generated output code" + ) + parser.add_argument( + "--output_path", + type=Path, + default=Path("triton_only_repro.py"), + help="Path to write out the clean triton output", + ) + + # Parse the arguments + args = parser.parse_args() + + # Call the function with parsed arguments + result = get_clean_triton(args.input_path, args.output_path) diff --git a/lib/python3.11/site-packages/torch/utils/_import_utils.py b/lib/python3.11/site-packages/torch/utils/_import_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1102fa8a019dadee99bfa579a4fe643dc00d1a41 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_import_utils.py @@ -0,0 +1,43 @@ +# mypy: allow-untyped-defs +import functools +import importlib.util + +import torch + + +def _check_module_exists(name: str) -> bool: + r"""Returns if a top-level module with :attr:`name` exists *without** + importing it. This is generally safer than try-catch block around a + `import X`. It avoids third party libraries breaking assumptions of some of + our tests, e.g., setting multiprocessing start method when imported + (see librosa/#747, torchvision/#544). + """ + try: + spec = importlib.util.find_spec(name) + return spec is not None + except ImportError: + return False + + +@functools.lru_cache +def dill_available(): + return ( + _check_module_exists("dill") + # dill fails to import under torchdeploy + and not torch._running_with_deploy() + ) + + +@functools.lru_cache +def import_dill(): + if not dill_available(): + return None + + import dill + + # XXX: By default, dill writes the Pickler dispatch table to inject its + # own logic there. This globally affects the behavior of the standard library + # pickler for any user who transitively depends on this module! + # Undo this extension to avoid altering the behavior of the pickler globally. + dill.extend(use_dill=False) + return dill diff --git a/lib/python3.11/site-packages/torch/utils/_mode_utils.py b/lib/python3.11/site-packages/torch/utils/_mode_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..91c0e07b3d9345094e532beb4b94c1ff3c4b26a6 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_mode_utils.py @@ -0,0 +1,11 @@ +# mypy: allow-untyped-defs +import torch +from typing import TypeVar + +T = TypeVar('T') + +# returns if all are the same mode +def all_same_mode(modes): + return all(tuple(mode == modes[0] for mode in modes)) + +no_dispatch = torch._C._DisableTorchDispatch diff --git a/lib/python3.11/site-packages/torch/utils/_ordered_set.py b/lib/python3.11/site-packages/torch/utils/_ordered_set.py new file mode 100644 index 0000000000000000000000000000000000000000..3e076deca79b8321f01c4d6cccc1720b70f0b7fa --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_ordered_set.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +from collections.abc import MutableSet, Set as AbstractSet +from typing import ( + Any, + cast, + Dict, + Generic, + Iterable, + Iterator, + List, + Optional, + Tuple, + Type, + TypeVar, +) + + +T = TypeVar("T") +T_co = TypeVar("T_co", covariant=True) + +__all__ = ["OrderedSet"] + + +# Using Generic[T] bc py38 does not support type parameterized MutableSet +class OrderedSet(MutableSet, Generic[T]): + """ + Insertion ordered set, similar to OrderedDict. + """ + + __slots__ = ("_dict",) + + def __init__(self, iterable: Optional[Iterable[T]] = None): + self._dict = dict.fromkeys(iterable, None) if iterable is not None else {} + + @staticmethod + def _from_dict(dict_inp: Dict[T, None]) -> OrderedSet[T]: + s: OrderedSet[T] = OrderedSet() + s._dict = dict_inp + return s + + # + # Required overriden abstract methods + # + def __contains__(self, elem: object) -> bool: + return elem in self._dict + + def __iter__(self) -> Iterator[T]: + return iter(self._dict) + + def __len__(self) -> int: + return len(self._dict) + + def add(self, elem: T) -> None: + self._dict[elem] = None + + def discard(self, elem: T) -> None: + self._dict.pop(elem, None) + + def clear(self) -> None: + # overridden because MutableSet impl is slow + self._dict.clear() + + # Unimplemented set() methods in _collections_abc.MutableSet + + @classmethod + def _wrap_iter_in_set(cls, other: Any) -> Any: + """ + Wrap non-Set Iterables in OrderedSets + + Some of the magic methods are more strict on input types than + the public apis, so we need to wrap inputs in sets. + """ + + if not isinstance(other, AbstractSet) and isinstance(other, Iterable): + return cls(other) + else: + return other + + def pop(self) -> T: + if not self: + raise KeyError("pop from an empty set") + return self._dict.popitem()[0] + + def copy(self) -> OrderedSet[T]: + return OrderedSet._from_dict(self._dict.copy()) + + def difference(self, *others: Iterable[T]) -> OrderedSet[T]: + res = self.copy() + res.difference_update(*others) + return res + + def difference_update(self, *others: Iterable[T]) -> None: + for other in others: + self -= other # type: ignore[operator, arg-type] + + def update(self, *others: Iterable[T]) -> None: + for other in others: + self |= other # type: ignore[operator, arg-type] + + def intersection(self, *others: Iterable[T]) -> OrderedSet[T]: + res = self.copy() + for other in others: + if other is not self: + res &= other # type: ignore[operator, arg-type] + return res + + def intersection_update(self, *others: Iterable[T]) -> None: + for other in others: + self &= other # type: ignore[operator, arg-type] + + def issubset(self, other: Iterable[T]) -> bool: + return self <= self._wrap_iter_in_set(other) + + def issuperset(self, other: Iterable[T]) -> bool: + return self >= self._wrap_iter_in_set(other) + + def symmetric_difference(self, other: Iterable[T]) -> OrderedSet[T]: + return self ^ other # type: ignore[operator, arg-type] + + def symmetric_difference_update(self, other: Iterable[T]) -> None: + self ^= other # type: ignore[operator, arg-type] + + def union(self, *others: Iterable[T]) -> OrderedSet[T]: + res = self.copy() + for other in others: + if other is self: + continue + res |= other # type: ignore[operator, arg-type] + return res + + # Specify here for correct type inference, otherwise would + # return AbstractSet[T] + def __sub__(self, other: AbstractSet[T_co]) -> OrderedSet[T]: + # following cpython set impl optimization + if isinstance(other, OrderedSet) and (len(self) * 4) > len(other): + out = self.copy() + out -= other + return out + return cast(OrderedSet[T], super().__sub__(other)) + + def __ior__(self, other: Iterable[T]) -> OrderedSet[T]: # type: ignore[misc, override] # noqa: PYI034 + if isinstance(other, OrderedSet): + self._dict.update(other._dict) + return self + return super().__ior__(other) # type: ignore[arg-type] + + def __eq__(self, other: AbstractSet[T]) -> bool: # type: ignore[misc, override] + if isinstance(other, OrderedSet): + return self._dict == other._dict + return super().__eq__(other) # type: ignore[arg-type] + + def __ne__(self, other: AbstractSet[T]) -> bool: # type: ignore[misc, override] + if isinstance(other, OrderedSet): + return self._dict != other._dict + return super().__ne__(other) # type: ignore[arg-type] + + def __or__(self, other: AbstractSet[T_co]) -> OrderedSet[T]: + return cast(OrderedSet[T], super().__or__(other)) + + def __and__(self, other: AbstractSet[T_co]) -> OrderedSet[T]: + # MutableSet impl will iterate over other, iter over smaller of two sets + if isinstance(other, OrderedSet) and len(self) < len(other): + return other & self + return cast(OrderedSet[T], super().__and__(other)) + + def __xor__(self, other: AbstractSet[T_co]) -> OrderedSet[T]: + return cast(OrderedSet[T], super().__xor__(other)) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({list(self)})" + + def __getstate__(self) -> List[T]: + return list(self._dict.keys()) + + def __setstate__(self, state: List[T]) -> None: + self._dict = dict.fromkeys(state, None) + + def __reduce__(self) -> Tuple[Type[OrderedSet[T]], Tuple[List[T]]]: + return (OrderedSet, (list(self),)) diff --git a/lib/python3.11/site-packages/torch/utils/_python_dispatch.py b/lib/python3.11/site-packages/torch/utils/_python_dispatch.py new file mode 100644 index 0000000000000000000000000000000000000000..1f0308941b0b7d1599a08bf1dc8715682de5b923 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_python_dispatch.py @@ -0,0 +1,711 @@ +# mypy: allow-untyped-defs +import contextlib + +import warnings +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Set, Union, Protocol, Tuple, Sequence, overload, Deque, Type +from typing_extensions import TypeIs +from collections import deque + +import torch +import torchgen +import torchgen.model +from torch._C import ( + _get_dispatch_stack_at, + _len_torch_dispatch_stack, + _pop_torch_dispatch_stack, + _push_on_torch_dispatch_stack, + DispatchKey, +) + + +# TODO: Limitations and things about enable_torch_dispatch_mode we should fix before exposing it: +# - We need a better user-facing api for _DisableTorchDispatch that +# is able to selectively disable __torch_dispatch__ of a particular class. +# - It doesn't work with the tensor constructors (torch.tensor, torch.Tensor) +# - Better name (see https://github.com/pytorch/pytorch/pull/63496#discussion_r694091694) + +_is_in_torch_dispatch_mode = False +_is_in_non_infra_torch_dispatch_mode = False + +def is_in_torch_dispatch_mode(include_infra_modes=True) -> bool: + return _is_in_torch_dispatch_mode if include_infra_modes else _is_in_non_infra_torch_dispatch_mode + + +class TorchDispatchMode: + """ + A ``TorchDispatchMode`` allows you to override the meaning of all + ``__torch_dispatch__`` overrideable functions within a dynamic scope, + without having to actually create a tensor subclass or manually + monkey-patch functions in the PyTorch API. Some common situations + where you should use a mode: + + * You want to override the meaning of factory functions, or other + functions that do not otherwise take a tensor as an argument + (these cannot be overridden with tensor subclasses). + + * You want to override the behavior of all functions without needing + to wrap your inputs in tensor subclasses; e.g., if you are just + interested in logging intermediate computations. + + * You want to control the order of execution of various tensor + subclasses explicitly, rather than implicitly via the return of + ``NotImplemented``. + + Independent subclasses of :class:`TorchDispatchMode` are compositional: + modes can be pushed onto a stack using ``with MyMode():``. + When you call functions in the PyTorch API inside your + ``__torch_dispatch__`` implementation, by default, they will forward on to + the next mode on the mode stack. If you want recursively call back into + your current ``__torch_dispatch__`` implementation, either explicitly + invoke ``self.__torch_dispatch__(...)``, or use the context manager + ``__torch_dispatch__(self)`` to make PyTorch + API self-referential (beware of infinite loops, in this case!) + """ + + def __init__(self, _dispatch_key=None): + if _dispatch_key is not None: + assert isinstance(_dispatch_key, torch._C.DispatchKey) + self.__dict__["_dispatch_key"] = _dispatch_key + + self.old_dispatch_mode_flags: Deque[bool] = deque() + self.old_non_infra_dispatch_mode_flags: Deque[bool] = deque() + + def _lazy_init_old_dispatch_mode_flags(self): + if not hasattr(self, "old_dispatch_mode_flags"): + self.old_dispatch_mode_flags: Deque[bool] = deque() # type: ignore[no-redef] + + if not hasattr(self, "old_non_infra_dispatch_mode_flags"): + self.old_non_infra_dispatch_mode_flags: Deque[bool] = deque() # type: ignore[no-redef] + + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + raise NotImplementedError + + def __enter__(self): + global _is_in_torch_dispatch_mode + global _is_in_non_infra_torch_dispatch_mode + # Previously, there wasn't any state in this class' constructor + # super calls were added to existing modes, but for any new modes + # this will replicate the previous behavior of not strictly needing + # to call super().__init__() + self._lazy_init_old_dispatch_mode_flags() + self.old_dispatch_mode_flags.append(_is_in_torch_dispatch_mode) + _is_in_torch_dispatch_mode = True + self.old_non_infra_dispatch_mode_flags.append(_is_in_non_infra_torch_dispatch_mode) + _is_in_non_infra_torch_dispatch_mode = _is_in_non_infra_torch_dispatch_mode or not self.is_infra_mode() + _push_mode(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + mb_dk_or_mode_key = self.__dict__.get("_dispatch_key", None) + if mb_dk_or_mode_key is None: + # Today, mode keys are not used at all in the per-dispatch-key-mode logic (for pre-dispatch) + # We should probably revisit this. + mb_dk_or_mode_key = self.__dict__.get("_mode_key", None) + global _is_in_torch_dispatch_mode + _is_in_torch_dispatch_mode = self.old_dispatch_mode_flags.pop() + global _is_in_non_infra_torch_dispatch_mode + _is_in_non_infra_torch_dispatch_mode = self.old_non_infra_dispatch_mode_flags.pop() + _pop_mode(mb_dk_or_mode_key) + + @classmethod + def push(cls, *args, **kwargs): + warnings.warn( + "`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`" + ) + instance = cls(*args, **kwargs) + return instance + + @classmethod + def is_infra_mode(cls): + return False + + + +def _get_current_dispatch_mode(): + stack_len = _len_torch_dispatch_stack() + # Return a user mode on the stack if there are any + if stack_len > 0: + return _get_dispatch_stack_at(stack_len - 1) + return None + + +def _detect_infra_mode(key): + assert key in [torch._C._TorchDispatchModeKey.FUNCTIONAL, torch._C._TorchDispatchModeKey.PROXY] + from torch._ops import _get_dispatch_mode_pre_dispatch + + pre_dispatch_mode = _get_dispatch_mode_pre_dispatch( + key + ) + post_dispatch_mode = torch._C._get_dispatch_mode( + key + ) + + assert (pre_dispatch_mode is None) or ( + post_dispatch_mode is None + ) + + if pre_dispatch_mode is None: + return post_dispatch_mode + + return pre_dispatch_mode + + +def _unset_infra_mode(key): + from torch._ops import _get_dispatch_mode_pre_dispatch, unset_mode_pre_dispatch + + pre_dispatch_mode = _get_dispatch_mode_pre_dispatch(key) + post_dispatch_mode = torch._C._get_dispatch_mode(key) + if pre_dispatch_mode and post_dispatch_mode: + raise AssertionError( + "Can't have active infra mode on both pre and post dispatch mode stack" + ) + + if pre_dispatch_mode: + mode = unset_mode_pre_dispatch(key) + return mode + if post_dispatch_mode: + return torch._C._unset_dispatch_mode(key) + + +def _disable_infra_mode(key): + assert key in ( + torch._C._TorchDispatchModeKey.FUNCTIONAL, + torch._C._TorchDispatchModeKey.PROXY, + ) + mode_unset = _unset_infra_mode(key) + try: + yield mode_unset + finally: + if mode_unset is not None: + _push_mode(mode_unset) + + +def _get_current_dispatch_mode_stack(): + stack_len = _len_torch_dispatch_stack() + return [_get_dispatch_stack_at(i) for i in range(stack_len)] + + +def _push_mode(mode: TorchDispatchMode): + k = mode._dispatch_key if hasattr(mode, "_dispatch_key") else None + assert k is None or k == torch._C.DispatchKey.PreDispatch + if k is None: + _push_on_torch_dispatch_stack(mode) + return + + from torch._ops import _set_mode_pre_dispatch, get_cached_ops + + # See Note [Not Caching Per-Dispatch-Key Mode Handlers] + # Clear the cache of every op that has been used so far, for this particular key. + ks = torch._C._functionality_to_backend_keys(k) + for op in get_cached_ops(): + for key in ks: + op._uncache_dispatch(key) + _set_mode_pre_dispatch(mode) + + +def _pop_mode(k: Optional[Union[DispatchKey, torch._C._TorchDispatchModeKey]] = None): + if k == torch._C.DispatchKey.PreDispatch: # type: ignore[attr-defined] + from torch._ops import _pop_mode_from_pre_dispatch + + return _pop_mode_from_pre_dispatch() + + if k is None or isinstance(k, torch._C._TorchDispatchModeKey): + return _pop_torch_dispatch_stack(k) + + +@contextlib.contextmanager +def _pop_mode_temporarily(k: Optional[DispatchKey] = None): + old = _pop_mode(k) + try: + yield old + finally: + _push_mode(old) + + +@contextlib.contextmanager +def _disable_current_modes(): + from torch._ops import ( + _len_torch_dispatch_stack_pre_dispatch, + _pop_mode_from_pre_dispatch, + ) + from torch._subclasses.functional_tensor import FunctionalTensorMode + from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode + from torch._subclasses.schema_check_mode import SchemaCheckMode + + mode_len_pre_dispatch = _len_torch_dispatch_stack_pre_dispatch() + old_pre_dispatch_modes = [ + _pop_mode_from_pre_dispatch() for _ in range(mode_len_pre_dispatch) + ] + + has_proxy_mode_in_pre_dispatch = False + has_functional_mode_in_pre_dispatch = False + has_schema_check_mode_in_pre_dispatch = False + + for i in old_pre_dispatch_modes: + if isinstance(i, ProxyTorchDispatchMode): + has_proxy_mode_in_pre_dispatch = True + if isinstance(i, FunctionalTensorMode): + has_functional_mode_in_pre_dispatch = True + if isinstance(i, SchemaCheckMode): + has_schema_check_mode_in_pre_dispatch = True + + mode_len = _len_torch_dispatch_stack() + old_modes = [_pop_mode() for _ in range(mode_len)] + + for old in old_modes: + if ( + isinstance(old, FunctionalTensorMode) + and has_functional_mode_in_pre_dispatch + ): + raise AssertionError( + "Can't have FunctionalMode available both in PreDispatch and Python Key" + ) + if isinstance(old, ProxyTorchDispatchMode) and has_proxy_mode_in_pre_dispatch: + raise AssertionError( + "Can't have ProxyTorchDispatchMode available both in PreDispatch and Python Key" + ) + if ( + isinstance(old, SchemaCheckMode) + and has_schema_check_mode_in_pre_dispatch + ): + raise AssertionError( + "Can't have SchemaCheckMode available both in PreDispatch and Python Key" + ) + + # Manually disable proxy and fake modes, if any are active + try: + yield old_pre_dispatch_modes + old_modes + finally: + for mode in reversed(old_modes): + _push_mode(mode) + for mode in reversed(old_pre_dispatch_modes): + _push_mode(mode) + + +class BaseTorchDispatchMode(TorchDispatchMode): + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + if kwargs is None: + kwargs = {} + return func(*args, **kwargs) + + +# Subtypes which have __tensor_flatten__ and __tensor_unflatten__. +class TensorWithFlatten(Protocol): + def __tensor_flatten__(self) -> Tuple[Sequence[str], object]: + ... + + @staticmethod + def __tensor_unflatten__(inner_tensors: int, flatten_spec: int, outer_size: int, outer_stride: int) -> torch.Tensor: + ... + + # It would be really nice to be able to say that the return of + # is_traceable_wrapper_subclass() is Intersection[torch.Tensor, + # TensorWithFlatten] - but that doesn't exist. + + shape: torch._C.Size + + @overload + def stride(self, dim: None = None) -> Tuple[int, ...]: + ... + + @overload + def stride(self, dim: int) -> int: + ... + + @overload + def size(self, dim: None = None) -> Tuple[int, ...]: + ... + + @overload + def size(self, dim: int) -> int: + ... + + def storage_offset(self) -> int: + ... + + def dim(self) -> int: + ... + + @overload + def to( + self, + dtype: torch.types._dtype, + non_blocking: bool = False, + copy: bool = False, + *, + memory_format: Optional[torch.memory_format] = None + ) -> torch.Tensor: + ... + + @overload + def to( + self, + device: Optional["torch._prims_common.DeviceLikeType"] = None, + dtype: Optional[torch.types._dtype] = None, + non_blocking: bool = False, + copy: bool = False, + *, + memory_format: Optional[torch.memory_format] = None + ) -> torch.Tensor: + ... + + @overload + def to( + self, + other: torch.Tensor, + non_blocking: bool = False, + copy: bool = False, + *, + memory_format: Optional[torch.memory_format] = None + ) -> torch.Tensor: + ... + + + + +def is_traceable_wrapper_subclass(t: object) -> TypeIs[TensorWithFlatten]: + """ + Returns whether or not a tensor subclass that implements __torch_dispatch__ + is 'traceable' with torch.compile. + In order for a tensor subclass to support TorchDispatchMode-style tracing in PT2, + It must implement two magic methods: __tensor_flatten__ and __tensor_unflatten__. + It is also expected to obey some restrictions around traceability and aliasing: + * The subclass's __torch_dispatch__() implementation should desugar into pytorch + dispatcher operations that can be traced into a graph. + * The subclass should use return_and_correct_aliasing(). This is needed today to make + sure that torch.compile does the right thing in a few cases around input mutation + and output aliasing. + + Expected magic method signatures: + attrs, ctx = t.__tensor_flatten__() + attrs: list of attribute name strings for inner tensors + ctx: dict containing any other subclass-specific metadata needed for unflattening + + t = MySubClass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride) + inner_tensors: dict mapping attribute name -> tensor for each inner tensor + ctx: dict with subclass metadata in the form that __tensor_flatten__() produces + outer_size: expected (possibly symbolic) size that the returned subclass + instance should have. Note that this arg is useful for certain subclasses + that require the shape info to be constructed. In most cases, this arg can be + safely ignored. + outer_stride: expected (possibly symbolic) stride that the returned subclass + instance should have. Note that this arg is useful for certain subclasses + that require the stride info to be constructed. In most cases, this arg can be + safely ignored. + """ + is_subclass = isinstance(t, torch.Tensor) and type(t) != torch.Tensor + return ( + is_subclass + and hasattr(t, "__tensor_flatten__") + and hasattr(t, "__tensor_unflatten__") + ) + +def is_traceable_wrapper_subclass_type(t: Type) -> TypeIs[Type[TensorWithFlatten]]: + """Same as above, but takes a type argument instead of an instance.""" + return (issubclass(t, torch.Tensor) and t != torch.Tensor + and hasattr(t, "__tensor_flatten__") and hasattr(t, "__tensor_unflatten__")) + + +def transform_subclass(t, callback, outer_size=None, outer_stride=None): + """ + Given a traceable, wrapper tensor subclass ``t`` that implements + ``__torch_dispatch__`` and holds some inner tensors, + and a callback of type ``Callable[[str, torch.Tensor], torch.Tensor]``, + `transform_subclass` will construct a fresh instance of the wrapper tensor subclass. + It will do so by grabbing each inner tensor attribute from the wrapper, + passing them into ``callback`` to get a transformed tensor, + and putting each transformed tensor into the fresh tensor subclass instance. + + Note: this function will not handle ensuring that the fresh subclass + gets the same (autograd, and aliasing) metadata as the original tensor. + This is generally handled in other subsystems like AOTAutograd. + """ + outer_size = outer_size if outer_size is not None else t.size() + outer_stride = outer_stride if outer_stride is not None else t.stride() + + attrs, ctx = t.__tensor_flatten__() + transformed_tensors_dict = {} + for attr in attrs: + transformed_tensors_dict[attr] = callback(attr, getattr(t, attr)) + sub = type(t).__tensor_unflatten__( + transformed_tensors_dict, ctx, outer_size, outer_stride + ) + + # NB: Purposefully guard here to simplify the inner / outer symbols. + # Using sym_eq() for symbolic comparison can result in an expression that's too + # difficult to guard on, so we use == here. + assert sub.shape == outer_size, ( + f"Expected return value from {type(t)}__tensor_unflatten__() to have " + f"shape equal to {outer_size}, but got: {sub.shape}" + ) + assert sub.stride() == outer_stride, ( + f"Expected return value from {type(t)}__tensor_unflatten__() to have " + f"stride equal to {outer_stride}, but got: {sub.stride()}" + ) + + return sub + + +def _correct_storage_aliasing(func, schema_info, args, outs): + """ + Given: an OpOverload, a SchemaInfo (cached information from torchgen about schema), + and the inputs/outputs to the OpOverload, + this function checks to see if func is a view operator + (by checking if any of the outputs in the op's schema + are immutable aliases of inputs). + If so, this function manually aliases the storage of the output tensor + with its corresponding input tensor alias. + It does this by unsafely overwriting the storage field of the output tensor + to be the same storage as the input. + """ + assert isinstance(func, torch._ops.OpOverload) + assert isinstance(args, tuple) + assert isinstance(outs, (list, tuple)) + + def alias_non_inplace_storage(arg, ret): + # This is hopefully a reasonable assert: + # subclasses that rely on this API for output aliasing + # should always return wrapper tensor subclasses for us to manually alias. + # in theory if a subclass that needs this API wants to sometimes return + # plain tensors, we could remove the assert and just not perform the aliasing, + # but it seems safer to learn more about this case first. + if is_traceable_wrapper_subclass(arg) or is_traceable_wrapper_subclass(ret): + ret_list = ret if isinstance(ret, list) else [ret] + for r in ret_list: + assert type(arg) == type( + r + ), f"""Called {str(func)} with input of type {type(arg)} +and output of type {type(ret)}. But expected types to match.""" + # Need to call a non-dispatcher helper, because we explicitly do **not** + # want our subclass to intercept the set_() call. + # instead, our subclass should directly have its storage swapped out. + # we **explicitly** don't want to reset the sizes on ret, if the storage implies a size change. + # Why? + # The purpose of this API is *not* to change the size/strides of our output- we assume it's already correct. + # We just want to "fix up" the storage aliasing, without modifying or output's metadata. + # Example: out = inp.expand(inp.shape[0], inp.shape[0]) + # This requires swapping the storage of out to be the same as inp, + # but we do *not* want it to change the sizes/strides that were compute for out. + + if isinstance(ret, list): + for r in ret: + torch._functionalize_unsafe_set(r, arg) + else: + assert isinstance(ret, torch.Tensor), f"type: {type(ret)}" + torch._functionalize_unsafe_set(ret, arg) + + def is_read_only_alias_match(arg, ret): + shared_aliases = arg.alias_set & ret.alias_set + return len(shared_aliases) > 0 and not arg.is_write + + num_args = len(func._schema.arguments) + num_returns = len(func._schema.returns) + for arg_idx in range(num_args): + for return_idx in range(num_returns): + if is_read_only_alias_match( + schema_info.args[arg_idx], schema_info.outs[return_idx] + ): + alias_non_inplace_storage(args[arg_idx], outs[return_idx]) + + +# This abstracts over the fact that in return_and_correct_aliasing, +# we sometimes use torchgen schema parsing (for aten ops, since torchscript's schema parsing is sometimes buggy), +# and sometimes use torchscript schema parsing (for custom ops, for which torchgen parsing is untested). +@dataclass +class AliasInfo: + alias_set: Set[str] + is_write: bool + name: Optional[str] + + +@dataclass +class SchemaInfo: + args: List[AliasInfo] + outs: List[AliasInfo] + + +# Can't import torch._ops.OpOverload due to circular reference +parsed_schema_map: Dict[Any, SchemaInfo] = {} + + +# Given an OpOverload, returns schema information on it. +# This is cached for efficiency, since it can involve running torchgen +def get_alias_info(func) -> SchemaInfo: + if func in parsed_schema_map: + return parsed_schema_map[func] + # For ATen ops: use torchgen (since torchscript parser doesn't handle alias annotations + # properly for some ops that output tensorlists) + if func.namespace == "aten": + torchgen_schema_str = str(func._schema) + assert torchgen_schema_str.startswith("aten::") + # remove the aten:: namespace, which is added by the torchscript parser, + # and torchgen doesn't know how to handle + torchgen_schema_str = torchgen_schema_str[6:] + import re + + # the torchscript parser ends up converting int[2]=1 into int[2]=[1, 1], + # which torchgen chokes on. + torchgen_schema_str = re.sub(r"=\[[0, ]+\]", "=0", torchgen_schema_str) + torchgen_schema_str = re.sub(r"=\[[1, ]+\]", "=1", torchgen_schema_str) + # for aten::rot90 / aten:fft_* + torchgen_schema_str = re.sub(r"=\[(-?[0-9]+), (-?[0-9]+)\]", r"=[\1,\2]", torchgen_schema_str) + torchgen_schema = torchgen.model.FunctionSchema.parse(torchgen_schema_str) + arg_schemas = [ + AliasInfo( + alias_set=( + set() if a.annotation is None else set(a.annotation.alias_set) + ), + is_write=a.annotation is not None and a.annotation.is_write, + name=a.name, + ) + for a in torchgen_schema.arguments.flat_all + ] + out_schemas = [ + AliasInfo( + alias_set=( + set() if a.annotation is None else set(a.annotation.alias_set) + ), + is_write=a.annotation is not None and a.annotation.is_write, + name=a.name, + ) + for a in torchgen_schema.returns + ] + else: + # For non-aten ops, torchgen is untested so we rely on torchscript schema parsing + arg_schemas = [ + AliasInfo( + alias_set=( + set() if a.alias_info is None else set(a.alias_info.before_set) + ), + is_write=a.alias_info is not None and a.alias_info.is_write, + name=a.name, + ) + for a in func._schema.arguments + ] + out_schemas = [ + AliasInfo( + alias_set=( + set() if a.alias_info is None else set(a.alias_info.before_set) + ), + is_write=a.alias_info is not None and a.alias_info.is_write, + name=a.name, + ) + for a in func._schema.returns + ] + schema_info = SchemaInfo(args=arg_schemas, outs=out_schemas) + parsed_schema_map[func] = schema_info + return schema_info + + +def return_and_correct_aliasing(func, args, kwargs, out): + """ + This function should be used by wrapper tensor ``__torch_dispatch__`` subclasses + that would like to work with torch.compile. It ensures that the subclass + properly implements the aliasing behavior of every op, + which is needed for correctness in AOTAutograd. + This function will handle: + + * When we see a view op, we will alias the storages of any + input and output tensor subclasses + + * When we see an inplace or out= op, we will directly + return the corresponding input tensor, instead of returning + a (potentially) fresh output tensor. + """ + + # Caching here because torchgen parsing is definitely not fast, and this function is called + # once for every op in the graph during functionalization. + schema_info = get_alias_info(func) + + def get_write_alias(x): + if len(x.alias_set) == 0: + return None + alias_set = list(x.alias_set) + # torchscript allows for complicated alias sets, but our dispatcher ops only really involve simple aliasing + assert len(alias_set) == 1 + if x.is_write: + return alias_set[0] + return None + + def get_arg_from_alias(output_alias, schema_info, args, kwargs): + new_args, new_kwargs = torch.fx.operator_schemas.normalize_function( # type: ignore[misc] + func, args=args, kwargs=kwargs + ) + + arg_indices = [ + i for i, a in enumerate(schema_info.args) if output_alias in a.alias_set + ] + # For any dispatcher op with an output alias, we expect it to map to exactly one alias in the schema's input arguments. + assert len(arg_indices) == 1 + idx = arg_indices[0] + arg_info = schema_info.args[idx] + if arg_info.name is not None and arg_info.name in new_kwargs: + return new_kwargs[arg_info.name] + return new_args[idx] + + # Fix up the storages of any outs so that they point to the same storage as the input, + # if func is a view op. + _correct_storage_aliasing( + func, schema_info, args, (out,) if not isinstance(out, tuple) else out + ) + + # For inplace_view ops in particular, we'll try hard to make sure that the wrapper subclass's + # metadata is set correctly. + if torch.Tag.inplace_view in func.tags: + # no_dispatch() to make sure that we secretly change the metadata on the wrapper, + # but don't end up dispatching the op anywhere else. + mutated_args = [ + x + for i, x in enumerate(args) + if get_write_alias(schema_info.args[i]) is not None + ] + # Assumption: we have a very small number of inplace_view ops that follow a strict schema: + # there is only a single argument that gets its metadata mutated. + assert len(mutated_args) == 1 + # This check exists because we generally *do* want to update the metadata of any wrapper subclasses, + # but FunctionalTensor is special: it overrides all size/stride calls to plumb to the inner tensor. + # so we don't actually need to update the metadata (and attempting to do so causes errors) + from torch._subclasses.functional_tensor import FunctionalTensor + + if not isinstance(mutated_args[0], FunctionalTensor): + with torch.utils._mode_utils.no_dispatch(): + # See Note: [Fake Tensor Dispatch Keys] + # we're borrowing the way it modifies dispatch key TLS. + meta_in_tls = torch._C._meta_in_tls_dispatch_include() + torch._C._set_meta_in_tls_dispatch_include(True) + try: + func(*args, **kwargs) + finally: + torch._C._set_meta_in_tls_dispatch_include(meta_in_tls) + + # Next: we need to make sure to return inputs directly, if the output is a mutable alias (e.g. add_()). + + # simple case: none of our outputs have mutable aliases, so we can return the output as-is + if not any(get_write_alias(r) is not None for r in schema_info.outs): + return out + + # simplifying assumption: we don't have **any** ops with return types like "-> (Tensor(a!), Tensor)" + if not all(get_write_alias(r) is not None for r in schema_info.outs): + raise RuntimeError("Unsupported schema: " + str(func._schema)) + + if len(func._schema.returns) == 1: + return get_arg_from_alias( + get_write_alias(schema_info.outs[0]), schema_info, args, kwargs + ) + + # In the multi-return case, all aten ops return a tuple / list, so cast accordingly. + outs_to_return = type(out)( + [ + ( + get_arg_from_alias( + get_write_alias(schema_info.outs[i]), schema_info, args, kwargs + ) + if get_write_alias(r) is not None + else o + ) + for ((i, r), o) in zip(enumerate(schema_info.outs), out) + ] + ) + return outs_to_return diff --git a/lib/python3.11/site-packages/torch/utils/_pytree.py b/lib/python3.11/site-packages/torch/utils/_pytree.py new file mode 100644 index 0000000000000000000000000000000000000000..298c339fb7591ca3bdf3c45bfa05925a842911a0 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_pytree.py @@ -0,0 +1,1647 @@ +""" +Contains utility functions for working with nested python data structures. + +A *pytree* is Python nested data structure. It is a tree in the sense that +nodes are Python collections (e.g., list, tuple, dict) and the leaves are +Python values. Furthermore, a pytree should not contain reference cycles. + +pytrees are useful for working with nested collections of Tensors. For example, +one can use `tree_map` to map a function over all Tensors inside some nested +collection of Tensors and `tree_leaves` to get a flat list of all Tensors +inside some nested collection. pytrees are helpful for implementing nested +collection support for PyTorch APIs. + +This pytree implementation is not very performant due to Python overhead +To improve the performance we can move parts of the implementation to C++. +""" + +import dataclasses +import functools +import importlib +import importlib.metadata +import json +import sys +import threading +import types +import warnings +from collections import defaultdict, deque, namedtuple, OrderedDict +from enum import Enum +from typing import ( + Any, + Callable, + cast, + DefaultDict, + Deque, + Dict, + FrozenSet, + Generic, + Hashable, + Iterable, + List, + Mapping, + NamedTuple, + Optional, + OrderedDict as GenericOrderedDict, + overload, + Protocol, + Sequence, + Tuple, + Type, + TypeVar, + Union, +) +from typing_extensions import deprecated + + +__all__ = [ + "PyTree", + "Context", + "FlattenFunc", + "UnflattenFunc", + "DumpableContext", + "ToDumpableContextFn", + "FromDumpableContextFn", + "TreeSpec", + "LeafSpec", + "keystr", + "key_get", + "register_pytree_node", + "tree_flatten", + "tree_flatten_with_path", + "tree_unflatten", + "tree_iter", + "tree_leaves", + "tree_leaves_with_path", + "tree_structure", + "tree_map", + "tree_map_with_path", + "tree_map_", + "tree_map_only", + "tree_map_only_", + "tree_all", + "tree_any", + "tree_all_only", + "tree_any_only", + "treespec_dumps", + "treespec_loads", + "treespec_pprint", +] + + +T = TypeVar("T") +S = TypeVar("S") +U = TypeVar("U") +R = TypeVar("R") + + +DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL = 1 +NO_SERIALIZED_TYPE_NAME_FOUND = "NO_SERIALIZED_TYPE_NAME_FOUND" + + +class KeyEntry(Protocol): + def __hash__(self) -> int: + ... + + def __eq__(self, other: object) -> bool: + ... + + def __str__(self) -> str: + ... + + def get(self, parent: Any) -> Any: + ... + + +class EnumEncoder(json.JSONEncoder): + def default(self, obj: object) -> str: + if isinstance(obj, Enum): + return obj.value # type: ignore[no-any-return] + return super().default(obj) # type: ignore[no-any-return] + + +Context = Any +PyTree = Any +FlattenFunc = Callable[[PyTree], Tuple[List[Any], Context]] +UnflattenFunc = Callable[[Iterable[Any], Context], PyTree] +DumpableContext = Any # Any json dumpable text +ToDumpableContextFn = Callable[[Context], DumpableContext] +FromDumpableContextFn = Callable[[DumpableContext], Context] +ToStrFunc = Callable[["TreeSpec", List[str]], str] +MaybeFromStrFunc = Callable[[str], Optional[Tuple[Any, Context, str]]] +KeyPath = Tuple[KeyEntry, ...] +FlattenWithKeysFunc = Callable[[PyTree], Tuple[List[Tuple[KeyEntry, Any]], Any]] + + +# A NodeDef holds two callables: +# - flatten_fn should take the collection and return a flat list of values. +# It can also return some context that is used in reconstructing the +# collection. +# - unflatten_fn should take a flat list of values and some context +# (returned by flatten_fn). It returns the collection by reconstructing +# it from the list and the context. +# - flatten_with_keys_fn, which is a callable that takes a +# pytree and returns a list of (keypath, value) pairs and a context. +class NodeDef(NamedTuple): + type: Type[Any] + flatten_fn: FlattenFunc + unflatten_fn: UnflattenFunc + flatten_with_keys_fn: Optional[FlattenWithKeysFunc] + + +_NODE_REGISTRY_LOCK = threading.Lock() +SUPPORTED_NODES: Dict[Type[Any], NodeDef] = {} + + +# _SerializeNodeDef holds the following: +# - typ: the type of the node (e.g., "Dict", "List", etc) +# - serialized_type_name: the fully qualified name of the type, e.g. "collections.OrderedDict" +# - to_dumpable_context takes a TreeSpec, and returns a serialized string format of the +# context, and the version number +# - from_dumpable_context takes in a string representation of the context, and the +# version, and returns the deserialized context +class _SerializeNodeDef(NamedTuple): + typ: Type[Any] + serialized_type_name: str + to_dumpable_context: Optional[ToDumpableContextFn] + from_dumpable_context: Optional[FromDumpableContextFn] + + +SUPPORTED_SERIALIZED_TYPES: Dict[Type[Any], _SerializeNodeDef] = {} +SERIALIZED_TYPE_TO_PYTHON_TYPE: Dict[str, Type[Any]] = {} + +# NB: we try really hard to not import _cxx_pytree (which depends on optree) +# as much as possible. This is for isolation: a user who is not using C++ pytree +# shouldn't pay for it, and it helps makes things like cpython upgrades easier. +try: + _optree_version = importlib.metadata.version("optree") +except importlib.metadata.PackageNotFoundError: + _cxx_pytree_dynamo_traceable = _cxx_pytree_exists = False +else: + _cxx_pytree_exists = True + from torch._vendor.packaging.version import Version + + _cxx_pytree_dynamo_traceable = Version(_optree_version) >= Version("0.13.0") + if not _cxx_pytree_dynamo_traceable: + warnings.warn( + "optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. " + "C++ pytree support is disabled. " + "Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.", + FutureWarning, + ) + + del Version + +_cxx_pytree_imported = False +_cxx_pytree_pending_imports: List[Any] = [] + + +def register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, + flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None, +) -> None: + """Register a container-like type as pytree node. + + Args: + cls: the type to register + flatten_fn: A callable that takes a pytree and returns a flattened + representation of the pytree and additional context to represent the + flattened pytree. + unflatten_fn: A callable that takes a flattened version of the pytree, + additional context, and returns an unflattened pytree. + serialized_type_name: A keyword argument used to specify the fully qualified + name used when serializing the tree spec. + to_dumpable_context: An optional keyword argument to custom specify how + to convert the context of the pytree to a custom json dumpable + representation. This is used for json serialization, which is being + used in torch.export right now. + from_dumpable_context: An optional keyword argument to custom specify how + to convert the custom json dumpable representation of the context + back to the original context. This is used for json deserialization, + which is being used in torch.export right now. + flatten_with_keys_fn: An optional keyword argument to specify how to + access each pytree leaf's keypath when flattening and tree-mapping. + Like ``flatten_fn``, but in place of a List[leaf], it should return + a List[(keypath, leaf)]. + """ + with _NODE_REGISTRY_LOCK: + if cls in SUPPORTED_NODES: + raise ValueError(f"{cls} is already registered as pytree node.") + + _private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + flatten_with_keys_fn=flatten_with_keys_fn, + ) + + if not _cxx_pytree_exists: + return + + if _cxx_pytree_imported: + from . import _cxx_pytree as cxx + + cxx._private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + ) + else: + args = (cls, flatten_fn, unflatten_fn) + kwargs = { + "serialized_type_name": serialized_type_name, + "to_dumpable_context": to_dumpable_context, + "from_dumpable_context": from_dumpable_context, + } + _cxx_pytree_pending_imports.append((args, kwargs)) + + +def _register_namedtuple( + cls: Type[Any], + *, + serialized_type_name: str, +) -> None: + """ + Registers a namedtuple as a valid pytree node. By default namedtuples are + valid pytree nodes, but they are not serializable. This API provides the + argument `serialized_type_name` which allows these namedtuples to be + serialized. + + Args: + cls: the dataclass type to register + serialized_type_name: The serialized name for the dataclass. This is + required if you want to serialize the pytree TreeSpec containing this + namedtuple. + """ + _private_register_pytree_node( + cls, + _namedtuple_flatten, + _namedtuple_unflatten, + serialized_type_name=serialized_type_name, + to_dumpable_context=_namedtuple_serialize, + from_dumpable_context=_namedtuple_deserialize, + flatten_with_keys_fn=_namedtuple_flatten_with_keys, + ) + + +@deprecated( + "`torch.utils._pytree._register_pytree_node` is deprecated. " + "Please use `torch.utils._pytree.register_pytree_node` instead.", + category=FutureWarning, +) +def _register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + to_str_fn: Optional[ToStrFunc] = None, # deprecated + maybe_from_str_fn: Optional[MaybeFromStrFunc] = None, # deprecated + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, + flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None, +) -> None: + """Register a container-like type as pytree node for the Python pytree only. + + Args: + cls: the type to register + flatten_fn: A callable that takes a pytree and returns a flattened + representation of the pytree and additional context to represent the + flattened pytree. + unflatten_fn: A callable that takes a flattened version of the pytree, + additional context, and returns an unflattened pytree. + serialized_type_name: A keyword argument used to specify the fully qualified + name used when serializing the tree spec. + to_dumpable_context: An optional keyword argument to custom specify how + to convert the context of the pytree to a custom json dumpable + representation. This is used for json serialization, which is being + used in torch.export right now. + from_dumpable_context: An optional keyword argument to custom specify how + to convert the custom json dumpable representation of the context + back to the original context. This is used for json deserialization, + which is being used in torch.export right now. + flatten_with_keys_fn: An optional keyword argument to specify how to + access each pytree leaf's keypath when flattening and tree-mapping. + Like ``flatten_fn``, but in place of a List[leaf], it should return + a List[(keypath, leaf)]. + """ + if to_str_fn is not None or maybe_from_str_fn is not None: + warnings.warn( + "`to_str_fn` and `maybe_from_str_fn` is deprecated. " + "Please use `to_dumpable_context` and `from_dumpable_context` instead.", + FutureWarning, + stacklevel=2, + ) + + _private_register_pytree_node( + cls, + flatten_fn, + unflatten_fn, + serialized_type_name=serialized_type_name, + to_dumpable_context=to_dumpable_context, + from_dumpable_context=from_dumpable_context, + flatten_with_keys_fn=flatten_with_keys_fn, + ) + + +def _private_register_pytree_node( + cls: Type[Any], + flatten_fn: FlattenFunc, + unflatten_fn: UnflattenFunc, + *, + serialized_type_name: Optional[str] = None, + to_dumpable_context: Optional[ToDumpableContextFn] = None, + from_dumpable_context: Optional[FromDumpableContextFn] = None, + flatten_with_keys_fn: Optional[FlattenWithKeysFunc] = None, +) -> None: + """This is an internal function that is used to register a pytree node type + for the Python pytree only. End-users should use :func:`register_pytree_node` + instead. + """ + with _NODE_REGISTRY_LOCK: + if cls in SUPPORTED_NODES: + # TODO: change this warning to an error after OSS/internal stabilize + warnings.warn( + f"{cls} is already registered as pytree node. " + "Overwriting the previous registration.", + ) + + node_def = NodeDef(cls, flatten_fn, unflatten_fn, flatten_with_keys_fn) + SUPPORTED_NODES[cls] = node_def + + if (to_dumpable_context is None) ^ (from_dumpable_context is None): + raise ValueError( + f"Both to_dumpable_context and from_dumpable_context for {cls} must " + "be None or registered." + ) + + if serialized_type_name is None: + serialized_type_name = NO_SERIALIZED_TYPE_NAME_FOUND + + serialize_node_def = _SerializeNodeDef( + cls, + serialized_type_name, + to_dumpable_context, + from_dumpable_context, + ) + SUPPORTED_SERIALIZED_TYPES[cls] = serialize_node_def + SERIALIZED_TYPE_TO_PYTHON_TYPE[serialized_type_name] = cls + + +@dataclasses.dataclass(frozen=True) +class SequenceKey(Generic[T]): + idx: int + + def __str__(self) -> str: + return f"[{self.idx!r}]" + + def get(self, sequence: Sequence[T]) -> T: + return sequence[self.idx] + + +K = TypeVar("K", bound=Hashable) + + +@dataclasses.dataclass(frozen=True) +class MappingKey(Generic[K, T]): + key: K + + def __str__(self) -> str: + return f"[{self.key!r}]" + + def get(self, mapping: Mapping[K, T]) -> T: + return mapping[self.key] + + +@dataclasses.dataclass(frozen=True) +class GetAttrKey: + name: str + + def __str__(self) -> str: + return f".{self.name}" + + def get(self, obj: Any) -> Any: + return getattr(obj, self.name) + + +def _tuple_flatten(d: Tuple[Any, ...]) -> Tuple[List[Any], Context]: + return list(d), None + + +def _tuple_flatten_with_keys( + d: Tuple[Any, ...] +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _tuple_flatten(d) + return [(SequenceKey(i), v) for i, v in enumerate(values)], context + + +def _tuple_unflatten(values: Iterable[Any], context: Context) -> Tuple[Any, ...]: + return tuple(values) + + +def _list_flatten(d: List[Any]) -> Tuple[List[Any], Context]: + return d, None + + +def _list_flatten_with_keys(d: List[Any]) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _list_flatten(d) + return [(SequenceKey(i), v) for i, v in enumerate(values)], context + + +def _list_unflatten(values: Iterable[Any], context: Context) -> List[Any]: + return list(values) + + +def _dict_flatten(d: Dict[Any, Any]) -> Tuple[List[Any], Context]: + return list(d.values()), list(d.keys()) + + +def _dict_flatten_with_keys( + d: Dict[Any, Any] +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _dict_flatten(d) + return [(MappingKey(k), v) for k, v in zip(context, values)], context + + +def _dict_unflatten(values: Iterable[Any], context: Context) -> Dict[Any, Any]: + return dict(zip(context, values)) + + +def _namedtuple_flatten(d: NamedTuple) -> Tuple[List[Any], Context]: + return list(d), type(d) + + +def _namedtuple_flatten_with_keys( + d: NamedTuple, +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _namedtuple_flatten(d) + return ( + [(GetAttrKey(field), v) for field, v in zip(context._fields, values)], + context, + ) + + +def _namedtuple_unflatten(values: Iterable[Any], context: Context) -> NamedTuple: + return cast(NamedTuple, context(*values)) + + +def _namedtuple_serialize(context: Context) -> DumpableContext: + if context not in SUPPORTED_SERIALIZED_TYPES: + raise NotImplementedError( + f"Can't serialize TreeSpec of namedtuple class {context} because we " + "didn't register a serializated_type_name. Please register using " + "`_register_namedtuple`." + ) + + serialize_node_def = SUPPORTED_SERIALIZED_TYPES[context] + serialized_type_name = serialize_node_def.serialized_type_name + + if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND: + raise NotImplementedError( + f"Can't serialize TreeSpec of namedtuple class {context} because we " + "couldn't find a serializated_type_name. Please register using " + "`_register_namedtuple`." + ) + return serialized_type_name + + +def _namedtuple_deserialize(dumpable_context: DumpableContext) -> Context: + if dumpable_context not in SERIALIZED_TYPE_TO_PYTHON_TYPE: + raise NotImplementedError( + f"Can't deserialize TreeSpec of namedtuple class {dumpable_context} " + "because we couldn't find a serializated name." + ) + + typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[dumpable_context] + return typ + + +def _ordereddict_flatten(d: GenericOrderedDict[Any, Any]) -> Tuple[List[Any], Context]: + return list(d.values()), list(d.keys()) + + +def _ordereddict_flatten_with_keys( + d: GenericOrderedDict[Any, Any] +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _ordereddict_flatten(d) + return [(MappingKey(k), v) for k, v in zip(context, values)], context + + +def _ordereddict_unflatten( + values: Iterable[Any], + context: Context, +) -> GenericOrderedDict[Any, Any]: + return OrderedDict((key, value) for key, value in zip(context, values)) + + +_odict_flatten = _ordereddict_flatten +_odict_unflatten = _ordereddict_unflatten + + +def _defaultdict_flatten(d: DefaultDict[Any, Any]) -> Tuple[List[Any], Context]: + values, dict_context = _dict_flatten(d) + return values, [d.default_factory, dict_context] + + +def _defaultdict_flatten_with_keys( + d: DefaultDict[Any, Any] +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _defaultdict_flatten(d) + _, dict_context = context + return [(MappingKey(k), v) for k, v in zip(dict_context, values)], context + + +def _defaultdict_unflatten( + values: Iterable[Any], + context: Context, +) -> DefaultDict[Any, Any]: + default_factory, dict_context = context + return defaultdict(default_factory, _dict_unflatten(values, dict_context)) + + +def _defaultdict_serialize(context: Context) -> DumpableContext: + default_factory, dict_context = context + json_defaultdict = { + "default_factory_module": default_factory.__module__, + "default_factory_name": default_factory.__qualname__, + "dict_context": dict_context, + } + return json_defaultdict + + +def _defaultdict_deserialize(dumpable_context: DumpableContext) -> Context: + assert isinstance(dumpable_context, dict) + assert set(dumpable_context) == { + "default_factory_module", + "default_factory_name", + "dict_context", + } + + default_factory_module = dumpable_context["default_factory_module"] + default_factory_name = dumpable_context["default_factory_name"] + assert isinstance(default_factory_module, str) + assert isinstance(default_factory_name, str) + module = importlib.import_module(default_factory_module) + default_factory = getattr(module, default_factory_name) + + dict_context = dumpable_context["dict_context"] + return [default_factory, dict_context] + + +def _deque_flatten(d: Deque[Any]) -> Tuple[List[Any], Context]: + return list(d), d.maxlen + + +def _deque_flatten_with_keys( + d: Deque[Any], +) -> Tuple[List[Tuple[KeyEntry, Any]], Context]: + values, context = _deque_flatten(d) + return [(SequenceKey(i), v) for i, v in enumerate(values)], context + + +def _deque_unflatten(values: Iterable[Any], context: Context) -> Deque[Any]: + return deque(values, maxlen=context) + + +_private_register_pytree_node( + tuple, + _tuple_flatten, + _tuple_unflatten, + serialized_type_name="builtins.tuple", + flatten_with_keys_fn=_tuple_flatten_with_keys, +) +_private_register_pytree_node( + list, + _list_flatten, + _list_unflatten, + serialized_type_name="builtins.list", + flatten_with_keys_fn=_list_flatten_with_keys, +) +_private_register_pytree_node( + dict, + _dict_flatten, + _dict_unflatten, + serialized_type_name="builtins.dict", + flatten_with_keys_fn=_dict_flatten_with_keys, +) +_private_register_pytree_node( + namedtuple, # type: ignore[arg-type] + _namedtuple_flatten, + _namedtuple_unflatten, + serialized_type_name="collections.namedtuple", + to_dumpable_context=_namedtuple_serialize, + from_dumpable_context=_namedtuple_deserialize, + flatten_with_keys_fn=_namedtuple_flatten_with_keys, +) +_private_register_pytree_node( + OrderedDict, + _ordereddict_flatten, + _ordereddict_unflatten, + serialized_type_name="collections.OrderedDict", + flatten_with_keys_fn=_ordereddict_flatten_with_keys, +) +_private_register_pytree_node( + defaultdict, + _defaultdict_flatten, + _defaultdict_unflatten, + serialized_type_name="collections.defaultdict", + to_dumpable_context=_defaultdict_serialize, + from_dumpable_context=_defaultdict_deserialize, + flatten_with_keys_fn=_defaultdict_flatten_with_keys, +) +_private_register_pytree_node( + deque, + _deque_flatten, + _deque_unflatten, + serialized_type_name="collections.deque", + flatten_with_keys_fn=_deque_flatten_with_keys, +) + + +STANDARD_DICT_TYPES: FrozenSet[type] = frozenset( + {dict, OrderedDict, defaultdict}, +) +BUILTIN_TYPES: FrozenSet[type] = frozenset( + {tuple, list, dict, namedtuple, OrderedDict, defaultdict, deque}, # type: ignore[arg-type] +) + + +# h/t https://stackoverflow.com/questions/2166818/how-to-check-if-an-object-is-an-instance-of-a-namedtuple +def _is_namedtuple_instance(tree: Any) -> bool: + typ = type(tree) + bases = typ.__bases__ + if len(bases) != 1 or bases[0] != tuple: + return False + fields = getattr(typ, "_fields", None) + if not isinstance(fields, tuple): + return False + return all(type(entry) == str for entry in fields) + + +def _get_node_type(tree: Any) -> Any: + if _is_namedtuple_instance(tree): + return namedtuple + return type(tree) + + +# A leaf is defined as anything that is not a Node. +def _is_leaf(tree: PyTree, is_leaf: Optional[Callable[[PyTree], bool]] = None) -> bool: + return (is_leaf is not None and is_leaf(tree)) or _get_node_type( + tree + ) not in SUPPORTED_NODES + + +# A TreeSpec represents the structure of a pytree. It holds: +# "type": the type of root Node of the pytree +# context: some context that is useful in unflattening the pytree +# children_specs: specs for each child of the root Node +# num_leaves: the number of leaves +@dataclasses.dataclass(init=True, frozen=True, eq=True, repr=False) +class TreeSpec: + type: Any + context: Context + children_specs: List["TreeSpec"] + + num_nodes: int = dataclasses.field(init=False) + num_leaves: int = dataclasses.field(init=False) + num_children: int = dataclasses.field(init=False) + + def __post_init__(self) -> None: + num_nodes = sum((spec.num_nodes for spec in self.children_specs), start=1) + num_leaves = sum(spec.num_leaves for spec in self.children_specs) + num_children = len(self.children_specs) + object.__setattr__(self, "num_nodes", num_nodes) + object.__setattr__(self, "num_leaves", num_leaves) + object.__setattr__(self, "num_children", num_children) + + def __repr__(self, indent: int = 0) -> str: + repr_prefix: str = f"TreeSpec({self.type.__name__}, {self.context}, [" + children_specs_str: str = "" + if self.num_children > 0: + indent += 2 + children_specs_str += self.children_specs[0].__repr__(indent) + children_specs_str += "," if self.num_children > 1 else "" + children_specs_str += ",".join( + [ + "\n" + " " * indent + child.__repr__(indent) + for child in self.children_specs[1:] + ] + ) + repr_suffix: str = f"{children_specs_str}])" + return repr_prefix + repr_suffix + + def is_leaf(self) -> bool: + return self.num_nodes == 1 and self.num_leaves == 1 + + def _flatten_up_to_helper(self, tree: PyTree, subtrees: List[PyTree]) -> None: + if self.is_leaf(): + subtrees.append(tree) + return + + node_type = _get_node_type(tree) + if self.type not in BUILTIN_TYPES: + # Always require custom node types to match exactly + if node_type != self.type: + raise ValueError( + f"Type mismatch; " + f"expected {self.type!r}, but got {node_type!r}.", + ) + flatten_fn = SUPPORTED_NODES[node_type].flatten_fn + child_pytrees, context = flatten_fn(tree) + if len(child_pytrees) != self.num_children: + raise ValueError( + f"Node arity mismatch; " + f"expected {self.num_children}, but got {len(child_pytrees)}.", + ) + if context != self.context: + raise ValueError( + f"Node context mismatch for custom node type {self.type!r}.", + ) + else: + # For builtin dictionary types, we allow some flexibility + # Otherwise, we require exact matches + both_standard_dict = ( + self.type in STANDARD_DICT_TYPES and node_type in STANDARD_DICT_TYPES + ) + if node_type != self.type and not both_standard_dict: + raise ValueError( + f"Node type mismatch; " + f"expected {self.type!r}, but got {node_type!r}.", + ) + if len(tree) != self.num_children: + raise ValueError( + f"Node arity mismatch; " + f"expected {self.num_children}, but got {len(tree)}.", + ) + + if both_standard_dict: # dictionary types are compatible with each other + dict_context = ( + self.context + if self.type is not defaultdict + # ignore mismatch of `default_factory` for defaultdict + else self.context[1] + ) + expected_keys = dict_context + got_key_set = set(tree) + expected_key_set = set(expected_keys) + if got_key_set != expected_key_set: + missing_keys = expected_key_set.difference(got_key_set) + extra_keys = got_key_set.difference(expected_key_set) + message = "" + if missing_keys: + message += f"; missing key(s): {missing_keys}" + if extra_keys: + message += f"; extra key(s): {extra_keys}" + raise ValueError(f"Node keys mismatch{message}.") + child_pytrees = [tree[key] for key in expected_keys] + else: + flatten_fn = SUPPORTED_NODES[node_type].flatten_fn + child_pytrees, context = flatten_fn(tree) + if ( + context != self.context + and self.type is not deque # ignore mismatch of `maxlen` for deque + ): + raise ValueError( + f"Node context mismatch for node type {self.type!r}; " + f"expected {self.context!r}, but got {context!r}.", # namedtuple type mismatch + ) + + for child_pytree, child_spec in zip(child_pytrees, self.children_specs): + child_spec._flatten_up_to_helper(child_pytree, subtrees) + + def flatten_up_to(self, tree: PyTree) -> List[PyTree]: + subtrees: List[PyTree] = [] + self._flatten_up_to_helper(tree, subtrees) + return subtrees + + def unflatten(self, leaves: Iterable[Any]) -> PyTree: + if not isinstance(leaves, (list, tuple)): + leaves = list(leaves) + if len(leaves) != self.num_leaves: + raise ValueError( + f"treespec.unflatten(leaves): `leaves` has length {len(leaves)} " + f"but the spec refers to a pytree that holds {self.num_leaves} " + f"items ({self}).", + ) + if self.is_leaf(): + return leaves[0] + + unflatten_fn = SUPPORTED_NODES[self.type].unflatten_fn + + # Recursively unflatten the children + start = 0 + end = 0 + child_pytrees = [] + for child_spec in self.children_specs: + end += child_spec.num_leaves + child_pytrees.append(child_spec.unflatten(leaves[start:end])) + start = end + + return unflatten_fn(child_pytrees, self.context) + + +class LeafSpec(TreeSpec): + def __init__(self) -> None: + super().__init__(None, None, []) + + def __post_init__(self) -> None: + object.__setattr__(self, "num_nodes", 1) + object.__setattr__(self, "num_leaves", 1) + object.__setattr__(self, "num_children", 0) + + def __repr__(self, indent: int = 0) -> str: + return "*" + + +# All leaves are equivalent, so represent with a single object to save on +# object construction time +_LEAF_SPEC = LeafSpec() + + +def _tree_flatten_helper( + tree: PyTree, + leaves: List[Any], + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> TreeSpec: + if _is_leaf(tree, is_leaf=is_leaf): + leaves.append(tree) + return _LEAF_SPEC + + node_type = _get_node_type(tree) + flatten_fn = SUPPORTED_NODES[node_type].flatten_fn + child_pytrees, context = flatten_fn(tree) + + # Recursively flatten the children + children_specs = [ + _tree_flatten_helper(child, leaves, is_leaf=is_leaf) for child in child_pytrees + ] + + return TreeSpec(node_type, context, children_specs) + + +def tree_flatten( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Tuple[List[Any], TreeSpec]: + """Flattens a pytree into a list of values and a TreeSpec that can be used + to reconstruct the pytree. + """ + leaves: List[Any] = [] + spec = _tree_flatten_helper(tree, leaves, is_leaf=is_leaf) + return leaves, spec + + +def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree: + """Given a list of values and a TreeSpec, builds a pytree. + This is the inverse operation of `tree_flatten`. + """ + if not isinstance(treespec, TreeSpec): + raise TypeError( + f"tree_unflatten(leaves, treespec): Expected `treespec` to be " + f"instance of TreeSpec but got item of type {type(treespec)}.", + ) + return treespec.unflatten(leaves) + + +def tree_iter( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Iterable[Any]: + """Get an iterator over the leaves of a pytree.""" + if _is_leaf(tree, is_leaf=is_leaf): + yield tree + else: + node_type = _get_node_type(tree) + flatten_fn = SUPPORTED_NODES[node_type].flatten_fn + child_pytrees, _ = flatten_fn(tree) + + # Recursively flatten the children + for child in child_pytrees: + yield from tree_iter(child, is_leaf=is_leaf) + + +def tree_leaves( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> List[Any]: + """Get a list of leaves of a pytree.""" + return list(tree_iter(tree, is_leaf=is_leaf)) + + +def tree_structure( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> TreeSpec: + """Get the TreeSpec for a pytree.""" + return tree_flatten(tree, is_leaf=is_leaf)[1] + + +def tree_map( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Map a multi-input function over pytree args to produce a new pytree. + + See also :func:`tree_map_`. + + >>> tree_map(lambda x: x + 1, {'x': 7, 'y': (42, 64)}) + {'x': 8, 'y': (43, 65)} + >>> tree_map(lambda x: x is None, {'x': 7, 'y': (42, 64), 'z': None}) + {'x': False, 'y': (False, False), 'z': True} + + If multiple inputs are given, the structure of the tree is taken from the first input; + subsequent inputs need only have ``tree`` as a prefix: + + >>> tree_map(lambda x, y: [x] + y, [5, 6], [[7, 9], [1, 2]]) + [[5, 7, 9], [6, 1, 2]] + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(x, *xs)`` where ``x`` is the value at the corresponding leaf in ``tree`` and ``xs`` + is the tuple of values at corresponding nodes in ``rests``. + """ + leaves, treespec = tree_flatten(tree, is_leaf=is_leaf) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + return treespec.unflatten(map(func, *flat_args)) + + +def tree_map_( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Like :func:`tree_map`, but do an inplace call on each leaf and return the original tree. + + See also :func:`tree_map`. + + Args: + func (callable): A function that takes ``1 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. + tree (pytree): A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests (tuple of pytree): A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf (callable, optional): An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns: + The original ``tree`` with the value at each leaf is given by the side-effect of function + ``func(x, *xs)`` (not the return value) where ``x`` is the value at the corresponding leaf + in ``tree`` and ``xs`` is the tuple of values at values at corresponding nodes in ``rests``. + """ + leaves, treespec = tree_flatten(tree, is_leaf=is_leaf) + flat_args = [leaves] + [treespec.flatten_up_to(r) for r in rests] + deque(map(func, *flat_args), maxlen=0) # consume and exhaust the iterable + return tree + + +Type2 = Tuple[Type[T], Type[S]] +Type3 = Tuple[Type[T], Type[S], Type[U]] +if sys.version_info >= (3, 10): + TypeAny = Union[Type[Any], Tuple[Type[Any], ...], types.UnionType] +else: + TypeAny = Union[Type[Any], Tuple[Type[Any], ...]] + +Fn2 = Callable[[Union[T, S]], R] +Fn3 = Callable[[Union[T, S, U]], R] +Fn = Callable[[T], R] +FnAny = Callable[[Any], R] + +MapOnlyFn = Callable[[T], Callable[[Any], Any]] + + +# These specializations help with type inference on the lambda passed to this +# function +@overload +def map_only(__type_or_types_or_pred: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Type[T]) -> MapOnlyFn[Fn[T, Any]]: + ... + + +# This specialization is needed for the implementations below that call +@overload +def map_only(__type_or_types_or_pred: TypeAny) -> MapOnlyFn[FnAny[Any]]: + ... + + +@overload +def map_only(__type_or_types_or_pred: Callable[[Any], bool]) -> MapOnlyFn[FnAny[Any]]: + ... + + +def map_only( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]] +) -> MapOnlyFn[FnAny[Any]]: + """ + Suppose you are writing a tree_map over tensors, leaving everything + else unchanged. Ordinarily you would have to write: + + def go(t): + if isinstance(t, Tensor): + return ... + else: + return t + + With this function, you only need to write: + + @map_only(Tensor) + def go(t): + return ... + + You can also directly use 'tree_map_only' + """ + if isinstance(__type_or_types_or_pred, (type, tuple)) or ( + sys.version_info >= (3, 10) + and isinstance(__type_or_types_or_pred, types.UnionType) + ): + + def pred(x: Any) -> bool: + return isinstance(x, __type_or_types_or_pred) # type: ignore[arg-type] + + elif callable(__type_or_types_or_pred): + pred = __type_or_types_or_pred # type: ignore[assignment] + else: + raise TypeError("Argument must be a type, a tuple of types, or a callable.") + + def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]: + @functools.wraps(func) + def wrapped(x: T) -> Any: + if pred(x): + return func(x) + return x + + return wrapped + + return wrapper + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type[T], + func: Fn[T, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type2[T, S], + func: Fn2[T, S, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Type3[T, S, U], + func: Fn3[T, S, U, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only( + __type_or_types_or_pred: Callable[[Any], bool], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +def tree_map_only( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + return tree_map(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf) + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type[T], + func: Fn[T, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type2[T, S], + func: Fn2[T, S, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Type3[T, S, U], + func: Fn3[T, S, U, Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +@overload +def tree_map_only_( + __type_or_types_or_pred: Callable[[Any], bool], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + ... + + +def tree_map_only_( + __type_or_types_or_pred: Union[TypeAny, Callable[[Any], bool]], + func: FnAny[Any], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + return tree_map_(map_only(__type_or_types_or_pred)(func), tree, is_leaf=is_leaf) + + +def tree_all( + pred: Callable[[Any], bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return all(map(pred, flat_args)) + + +def tree_any( + pred: Callable[[Any], bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return any(map(pred, flat_args)) + + +@overload +def tree_all_only( + __type_or_types: Type[T], + pred: Fn[T, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_all_only( + __type_or_types: Type2[T, S], + pred: Fn2[T, S, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_all_only( + __type_or_types: Type3[T, S, U], + pred: Fn3[T, S, U, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +def tree_all_only( + __type_or_types: TypeAny, + pred: FnAny[bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return all(pred(x) for x in flat_args if isinstance(x, __type_or_types)) + + +@overload +def tree_any_only( + __type_or_types: Type[T], + pred: Fn[T, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_any_only( + __type_or_types: Type2[T, S], + pred: Fn2[T, S, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +@overload +def tree_any_only( + __type_or_types: Type3[T, S, U], + pred: Fn3[T, S, U, bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + ... + + +def tree_any_only( + __type_or_types: TypeAny, + pred: FnAny[bool], + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> bool: + flat_args = tree_iter(tree, is_leaf=is_leaf) + return any(pred(x) for x in flat_args if isinstance(x, __type_or_types)) + + +# Broadcasts a pytree to the provided TreeSpec and returns the flattened +# values. If this is not possible, then this function returns None. +# +# For example, given pytree=0 and spec=TreeSpec(list, None, [LeafSpec(), LeafSpec()]), +# would return [0, 0]. This is useful for part of the vmap implementation: +# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be +# broadcastable to the tree structure of `inputs` and we use +# _broadcast_to_and_flatten to check this. +def _broadcast_to_and_flatten( + tree: PyTree, + treespec: TreeSpec, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Optional[List[Any]]: + assert isinstance(treespec, TreeSpec) + + if _is_leaf(tree, is_leaf=is_leaf): + return [tree] * treespec.num_leaves + if treespec.is_leaf(): + return None + node_type = _get_node_type(tree) + if node_type != treespec.type: + return None + + flatten_fn = SUPPORTED_NODES[node_type].flatten_fn + child_pytrees, ctx = flatten_fn(tree) + + # Check if the Node is different from the spec + if len(child_pytrees) != treespec.num_children or ctx != treespec.context: + return None + + # Recursively flatten the children + result: List[Any] = [] + for child, child_spec in zip(child_pytrees, treespec.children_specs): + flat = _broadcast_to_and_flatten(child, child_spec, is_leaf=is_leaf) + if flat is not None: + result += flat + else: + return None + + return result + + +@dataclasses.dataclass +class _TreeSpecSchema: + """ + _TreeSpecSchema is the schema used to serialize the TreeSpec + It contains the following fields: + - type: A string name of the type. null for the case of a LeafSpec. + - context: Any format which is json dumpable + - children_spec: A list of children serialized specs. + """ + + type: Optional[str] + context: DumpableContext + children_spec: List["_TreeSpecSchema"] + + +class _ProtocolFn(NamedTuple): + treespec_to_json: Callable[[TreeSpec], DumpableContext] + json_to_treespec: Callable[[DumpableContext], TreeSpec] + + +_SUPPORTED_PROTOCOLS: Dict[int, _ProtocolFn] = {} + + +def _treespec_to_json(treespec: TreeSpec) -> _TreeSpecSchema: + if treespec.is_leaf(): + return _TreeSpecSchema(None, None, []) + + if treespec.type not in SUPPORTED_SERIALIZED_TYPES: + raise NotImplementedError( + f"Serializing {treespec.type} in pytree is not registered.", + ) + + serialize_node_def = SUPPORTED_SERIALIZED_TYPES[treespec.type] + + serialized_type_name = serialize_node_def.serialized_type_name + + if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND: + raise NotImplementedError( + f"No registered serialization name for {treespec.type} found. " + "Please update your _register_pytree_node call with a `serialized_type_name` kwarg." + ) + + if serialize_node_def.to_dumpable_context is None: + try: + serialized_context = json.dumps(treespec.context, cls=EnumEncoder) + except TypeError as e: + raise TypeError( + "Unable to serialize context. " + "Please make the context json dump-able, or register a " + "custom serializer using _register_pytree_node." + ) from e + else: + serialized_context = serialize_node_def.to_dumpable_context(treespec.context) + + child_schemas = [_treespec_to_json(child) for child in treespec.children_specs] + + return _TreeSpecSchema(serialized_type_name, serialized_context, child_schemas) + + +def _json_to_treespec(json_schema: DumpableContext) -> TreeSpec: + if ( + json_schema["type"] is None + and json_schema["context"] is None + and len(json_schema["children_spec"]) == 0 + ): + return _LEAF_SPEC + + if json_schema["type"] not in SERIALIZED_TYPE_TO_PYTHON_TYPE: + raise NotImplementedError( + f'Deserializing {json_schema["type"]} in pytree is not registered.', + ) + + typ = SERIALIZED_TYPE_TO_PYTHON_TYPE[json_schema["type"]] + serialize_node_def = SUPPORTED_SERIALIZED_TYPES[typ] + + if serialize_node_def.from_dumpable_context is None: + try: + context = json.loads(json_schema["context"]) + except TypeError as ex: + raise TypeError( + "Unable to deserialize context. " + "Please make the context json load-able, or register a " + "custom serializer using _register_pytree_node.", + ) from ex + else: + context = serialize_node_def.from_dumpable_context(json_schema["context"]) + + children_specs = [ + _json_to_treespec(child_string) for child_string in json_schema["children_spec"] + ] + + return TreeSpec(typ, context, children_specs) + + +_SUPPORTED_PROTOCOLS[1] = _ProtocolFn(_treespec_to_json, _json_to_treespec) + + +def treespec_dumps(treespec: TreeSpec, protocol: Optional[int] = None) -> str: + if not isinstance(treespec, TreeSpec): + raise TypeError( + f"treespec_dumps(treespec, protocol): Expected `treespec` to be instance of " + f"TreeSpec but got item of type {type(treespec)}.", + ) + + if protocol is None: + protocol = DEFAULT_TREESPEC_SERIALIZATION_PROTOCOL + + if protocol in _SUPPORTED_PROTOCOLS: + json_spec = _SUPPORTED_PROTOCOLS[protocol].treespec_to_json(treespec) + else: + raise ValueError( + f"Unknown protocol {protocol}. " + f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}", + ) + + str_spec = json.dumps((protocol, dataclasses.asdict(json_spec))) + return str_spec + + +def treespec_loads(serialized: str) -> TreeSpec: + protocol, json_schema = json.loads(serialized) + + if protocol in _SUPPORTED_PROTOCOLS: + return _SUPPORTED_PROTOCOLS[protocol].json_to_treespec(json_schema) + raise ValueError( + f"Unknown protocol {protocol}. " + f"Available protocols: {list(_SUPPORTED_PROTOCOLS.keys())}", + ) + + +class _DummyLeaf: + def __repr__(self) -> str: + return "*" + + +def treespec_pprint(treespec: TreeSpec) -> str: + dummy_tree = tree_unflatten( + [_DummyLeaf() for _ in range(treespec.num_leaves)], + treespec, + ) + return repr(dummy_tree) + + +# TODO(angelayi): remove this function after OSS/internal stabilize +@deprecated( + "`pytree_to_str` is deprecated. Please use `treespec_dumps` instead.", + category=FutureWarning, +) +def pytree_to_str(treespec: TreeSpec) -> str: + return treespec_dumps(treespec) + + +# TODO(angelayi): remove this function after OSS/internal stabilize +@deprecated( + "`str_to_pytree` is deprecated. Please use `treespec_loads` instead.", + category=FutureWarning, +) +def str_to_pytree(json: str) -> TreeSpec: + return treespec_loads(json) + + +def arg_tree_leaves(*args: PyTree, **kwargs: PyTree) -> List[Any]: + """Get a flat list of arguments to this function + + A slightly faster version of tree_leaves((args, kwargs)) + """ + leaves: List[Any] = [] + for a in args: + leaves.extend(tree_iter(a)) + for a in kwargs.values(): + leaves.extend(tree_iter(a)) + return leaves + + +def tree_flatten_with_path( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Tuple[List[Tuple[KeyPath, Any]], TreeSpec]: + """Flattens a pytree like :func:`tree_flatten`, but also returns each leaf's key path. + + Args: + tree: a pytree to flatten. If it contains a custom type, that type must be + registered with an appropriate `tree_flatten_with_path_fn` when registered + with :func:`register_pytree_node`. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + Returns: + A tuple where the first element is a list of (key path, leaf) pairs, and the + second element is a :class:`TreeSpec` representing the structure of the flattened + tree. + """ + _, treespec = tree_flatten(tree, is_leaf) + return list(_generate_key_paths((), tree, is_leaf)), treespec + + +def tree_leaves_with_path( + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> List[Tuple[KeyPath, Any]]: + """Gets the leaves of a pytree like ``tree_leaves`` and returns each leaf's key path. + + Args: + tree: a pytree. If it contains a custom type, that type must be + registered with an appropriate `tree_flatten_with_path_fn` when registered + with :func:`register_pytree_node`. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + Returns: + A list of (key path, leaf) pairs. + """ + return list(_generate_key_paths((), tree, is_leaf)) + + +def _generate_key_paths( + key_path: KeyPath, + tree: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> Iterable[Tuple[KeyPath, Any]]: + if is_leaf and is_leaf(tree): + yield key_path, tree + return + + node_type = _get_node_type(tree) + handler = SUPPORTED_NODES.get(node_type) + if not handler: + # This is a leaf + yield key_path, tree + return + + flatten_with_keys = handler.flatten_with_keys_fn + if flatten_with_keys: + key_children, _ = flatten_with_keys(tree) + for k, c in key_children: + yield from _generate_key_paths((*key_path, k), c, is_leaf) + else: + # We registered this pytree but didn't add a flatten_with_keys_fn, complain. + raise ValueError( + f"Did not find a flatten_with_keys_fn for type: {node_type}. " + "Please pass a flatten_with_keys_fn argument to register_pytree_node." + ) + + +def tree_map_with_path( + func: Callable[..., Any], + tree: PyTree, + *rests: PyTree, + is_leaf: Optional[Callable[[PyTree], bool]] = None, +) -> PyTree: + """Like :func:`tree_map`, but the provided callable takes an additional key path argument. + + Args: + func: A function that takes ``2 + len(rests)`` arguments, to be applied at the + corresponding leaves of the pytrees. The first positional argument + to ``func`` is the key path of the leaf in question. The second + positional argument is the value of the leaf. + tree: A pytree to be mapped over, with each leaf providing the first positional + argument to function ``func``. + rests: A tuple of pytrees, each of which has the same structure as + ``tree`` or has ``tree`` as a prefix. + is_leaf: An extra leaf predicate function that will be called at each + flattening step. The function should have a single argument with signature + ``is_leaf(node) -> bool``. If it returns :data:`True`, the whole subtree being treated + as a leaf. Otherwise, the default pytree registry will be used to determine a node is a + leaf or not. If the function is not specified, the default pytree registry will be used. + + Returns + A new pytree with the same structure as ``tree`` but with the value at each leaf given by + ``func(keypath, x, *xs)`` where ``keypath`` is the key path at the + corresponding leaf in ``tree``, ``x`` is the value at that leaf, and + ``xs`` is the tuple of values at corresponding nodes in ``rests``. + """ + keypath_leaves, treespec = tree_flatten_with_path(tree, is_leaf) + keypath_leaves = list(zip(*keypath_leaves)) + all_keypath_leaves = keypath_leaves + [treespec.flatten_up_to(r) for r in rests] + return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves)) + + +def keystr(kp: KeyPath) -> str: + """Given a key path, return a pretty-printed representation.""" + return "".join([str(k) for k in kp]) + + +def key_get(obj: Any, kp: KeyPath) -> Any: + """Given an object and a key path, return the value at the key path.""" + for k in kp: + obj = k.get(obj) + return obj diff --git a/lib/python3.11/site-packages/torch/utils/_stats.py b/lib/python3.11/site-packages/torch/utils/_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..c11cbd5df270a1224041eb80ff69cf39799106ab --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_stats.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +# NOTE! PLEASE KEEP THIS FILE *FREE* OF TORCH DEPS! IT SHOULD BE IMPORTABLE ANYWHERE. +# IF YOU FEEL AN OVERWHELMING URGE TO ADD A TORCH DEP, MAKE A TRAMPOLINE FILE A LA torch._dynamo.utils +# AND SCRUB AWAY TORCH NOTIONS THERE. +import collections +import functools +from typing import OrderedDict + +simple_call_counter: OrderedDict[str, int] = collections.OrderedDict() + +def count_label(label): + prev = simple_call_counter.setdefault(label, 0) + simple_call_counter[label] = prev + 1 + +def count(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + if fn.__qualname__ not in simple_call_counter: + simple_call_counter[fn.__qualname__] = 0 + simple_call_counter[fn.__qualname__] = simple_call_counter[fn.__qualname__] + 1 + return fn(*args, **kwargs) + return wrapper diff --git a/lib/python3.11/site-packages/torch/utils/_strobelight/__init__.py b/lib/python3.11/site-packages/torch/utils/_strobelight/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..35aabaaa8d97a1fa4902918fea6a0c32404a5d9f Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/cli_function_profiler.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/cli_function_profiler.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3919ab0aa9ab7211b0c1754803b1d5e071363c8 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/_strobelight/__pycache__/cli_function_profiler.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/_strobelight/cli_function_profiler.py b/lib/python3.11/site-packages/torch/utils/_strobelight/cli_function_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..c2e4ae679a941bb7a5f0b7d8112726e9465a8f57 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_strobelight/cli_function_profiler.py @@ -0,0 +1,302 @@ +# mypy: disallow-untyped-defs + +import functools +import logging +import os +import re +import subprocess +import time +from threading import Lock +from typing import Any, List, Optional, Sequence + + +logger = logging.getLogger("strobelight_function_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + + +class StrobelightCLIProfilerError(Exception): + """ + Raised when an error happens during strobelight profiling + """ + + +def _pid_namespace_link(pid: Optional[int] = None) -> str: + """Returns the link to the process's namespace, example: pid:[4026531836]""" + PID_NAMESPACE_PATH = "/proc/{}/ns/pid" + pid = pid or os.getpid() + return os.readlink(PID_NAMESPACE_PATH.format(pid)) + + +def _pid_namespace(pid: Optional[int] = None) -> int: + """Returns the process's namespace id""" + pid = pid or os.getpid() + link = _pid_namespace_link(pid) + return int(link[link.find("[") + 1 : -1]) + + +def _command_to_string(command: Sequence[str]) -> str: + return " ".join(command) + + +class StrobelightCLIFunctionProfiler: + """ + Note: this is a meta only tool. + + StrobelightCLIFunctionProfiler can be used to profile a python function and + generate a strobelight link with the results. It works on meta servers but + does not requries an fbcode target. + When stop_at_error is false(default), error during profiling does not prevent + the work function from running. + + Check function_profiler_example.py for an example. + """ + + # This lock is used to make sure only one thread is running the profiler at any point. + _lock = Lock() + + def __init__( + self, + *, + stop_at_error: bool = False, + max_profile_duration_sec: int = 60 * 10, + sample_each: float = 1e7, # sample each sample_each cycles. + run_user_name: str = "pytorch-strobelight-ondemand", + timeout_wait_for_running_sec: int = 60, + timeout_wait_for_finished_sec: int = 60, + recorded_env_variables: Optional[List[str]] = None, + sample_tags: Optional[List[str]] = None, + stack_max_len: int = 127, + async_stack_max_len: int = 127, + ): + self.stop_at_error = stop_at_error + self.max_profile_duration_sec = max_profile_duration_sec + self.sample_each = sample_each + self.run_user_name = run_user_name + self.timeout_wait_for_running_sec = timeout_wait_for_running_sec + self.timeout_wait_for_finished_sec = timeout_wait_for_finished_sec + # Results of the most recent run. + # Tracks the strobelight run id of the most recent run + self.current_run_id: Optional[int] = None + self.sample_tags = sample_tags + + def _run_async(self) -> None: + processId = os.getpid() + namespace = _pid_namespace(processId) + command = [ + "strobeclient", + "run", + "--profiler", + "pyperf", + "--event", + "cycles", + "--async", + "--sample-interval", + f"{int(self.sample_each)}", + "--duration-ms", + f"{int(self.max_profile_duration_sec * 1000)}", + "--pid", + f"{namespace}:{processId}", + ] + + if self.sample_tags: + command.append("--sample-tags") + command.append(",".join(self.sample_tags)) + + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in run_async:{output}" + ) + + if match := re.search(r"INFO Run Id: (-?\d+)", output): + self.current_run_id = int(match.group(1)) + return + + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, unexpected result {output}" + ) + + def _wait_for_running(self, counter: int = 0) -> None: + if counter > 20: + raise StrobelightCLIProfilerError( + "wait_for_running called more than 20 times" + ) + + command = ["strobeclient", "getRunStatus", "--run-id", f"{self.current_run_id}"] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in wait_for_running:{output}" + ) + + if match := re.search("Profile run status: (.*)", output): + current_status = match.group(1) + if current_status == "RUNNING": + return + elif current_status == "PREPARING": + time.sleep(10) + self._wait_for_running(counter + 1) + return + else: + raise StrobelightCLIProfilerError(f"unexpected {current_status} phase") + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _stop_run(self) -> None: + command = ["strobeclient", "stopRun", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, return code is not 0 :{output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Success!"): + return + else: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, got {current_status} result" + ) + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _get_results(self) -> None: + command = ["strobeclient", "getRunStatus", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, return code is not 0 : {output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Profile run status: PROCESSING"): + time.sleep(10) + self._get_results() + return + elif not current_status.__contains__("Profile run finished with SUCCESS"): + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, unexpected response {output}" + ) + + for item in re.findall( + r"(Total samples(.*)|GraphProfiler(.*)|Icicle view \(python stack\)(.*))", + output, + ): + logger.info(item[0]) + + def _stop_strobelight_no_throw( + self, + collect_results: bool, + ) -> None: + try: + # call stop run + self._stop_run() + logger.info("strobelight profiling stopped") + + logger.debug("collection stopped") + + if not collect_results: + return + + self._get_results() + except Exception: + logger.warning("error during stop_strobelight", exc_info=True) + + # Return true if strobelight started and is running. Never throw. + def _start_strobelight(self) -> bool: + strobelight_started = False + try: + self._run_async() + strobelight_started = True + logger.info("strobelight run id is: %s", self.current_run_id) + self._wait_for_running() + logger.info("strobelight profiling running") + return True + + except Exception: + logger.warning("error during start_strobelight:", exc_info=True) + if strobelight_started: + self._stop_strobelight_no_throw(collect_results=False) + return False + + def profile(self, work_function: Any, *args: Any, **kwargs: Any) -> Any: + self.current_run_id = None + + if locked := StrobelightCLIFunctionProfiler._lock.acquire(False): + if not locked: + if self.stop_at_error: + raise StrobelightCLIProfilerError("concurrent runs not supported") + + logger.warning("concurrent runs not supported") + return work_function(*args, **kwargs) + + started = self._start_strobelight() + if not started: + if self.stop_at_error: + StrobelightCLIFunctionProfiler._lock.release() + raise StrobelightCLIProfilerError( + "failed to start strobelight profiling" + ) + result = work_function(*args, **kwargs) + StrobelightCLIFunctionProfiler._lock.release() + return result + + try: + logger.debug("collection started") + result = work_function(*args, **kwargs) + self._stop_strobelight_no_throw(collect_results=True) + StrobelightCLIFunctionProfiler._lock.release() + return result + except Exception as error: + logger.warning("work function throw exception", exc_info=True) + self._stop_strobelight_no_throw(collect_results=False) + StrobelightCLIFunctionProfiler._lock.release() + raise error + + +# A function decorator that wraps profile, if no profiler is provided one with +# default args is created. A function can be annotated as: +# @strobelight() +# @strobelight(profiler = StrobelightFunctionProfiler(stop_at_error=True,..)) +# @strobelight(stop_at_error=True,...) +def strobelight( + profiler: Optional[StrobelightCLIFunctionProfiler] = None, **kwargs: Any +) -> Any: + if not profiler: + profiler = StrobelightCLIFunctionProfiler(**kwargs) + + def strobelight_inner(work_function: Any) -> Any: + @functools.wraps(work_function) + def wrapper_function(*args: Any, **kwargs: Any) -> Any: + return profiler.profile(work_function, *args, **kwargs) + + return wrapper_function + + return strobelight_inner diff --git a/lib/python3.11/site-packages/torch/utils/_sympy/__init__.py b/lib/python3.11/site-packages/torch/utils/_sympy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/torch/utils/_thunk.py b/lib/python3.11/site-packages/torch/utils/_thunk.py new file mode 100644 index 0000000000000000000000000000000000000000..08cf6efc96fcffef154e8421905f570d1fdcda66 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_thunk.py @@ -0,0 +1,28 @@ +from typing import Callable, Generic, Optional, TypeVar + + +R = TypeVar("R") + + +class Thunk(Generic[R]): + """ + A simple lazy evaluation implementation that lets you delay + execution of a function. It properly handles releasing the + function once it is forced. + """ + + f: Optional[Callable[[], R]] + r: Optional[R] + + __slots__ = ["f", "r"] + + def __init__(self, f: Callable[[], R]): + self.f = f + self.r = None + + def force(self) -> R: + if self.f is None: + return self.r # type: ignore[return-value] + self.r = self.f() + self.f = None + return self.r diff --git a/lib/python3.11/site-packages/torch/utils/_traceback.py b/lib/python3.11/site-packages/torch/utils/_traceback.py new file mode 100644 index 0000000000000000000000000000000000000000..2a7aa1b56d43b16383d7d1f509ffa244e2143559 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_traceback.py @@ -0,0 +1,255 @@ +# mypy: allow-untyped-defs +from types import TracebackType +from typing import List, Optional +import tempfile +import traceback +import contextlib +import inspect +import os.path + +# This file contains utilities for ensuring dynamically compile()'d +# code fragments display their line numbers in backtraces. +# +# The constraints: +# +# - We don't have control over the user exception printer (in particular, +# we cannot assume the linecache trick will work, c.f. +# https://stackoverflow.com/q/50515651/23845 ) +# +# - We don't want to create temporary files every time we compile() +# some code; file creation should happen lazily only at exception +# time. Arguably, you *should* be willing to write out your +# generated Python code to file system, but in some situations +# (esp. library code) it would violate user expectation to write +# to the file system, so we try to avoid it. In particular, we'd +# like to keep the files around, so users can open up the files +# mentioned in the trace; if the file is invisible, we want to +# avoid clogging up the filesystem. +# +# If this is not a constraint for you, there is a substantially simpler +# way to implement the functionality in this PR: instead of using +# eval/exec directly, just always write a Python file to filesystem +# and compile that. +# +# - You have control over a context where the compiled code will get +# executed, so that we can interpose while the stack is unwinding +# (otherwise, we have no way to interpose on the exception printing +# process.) +# +# There are two things you have to do to make use of the utilities here: +# +# - When you compile your source code, you must save its string source +# in its f_globals under the magic name "__compile_source__" +# +# - Before running the compiled code, enter the +# report_compile_source_on_error() context manager. + +@contextlib.contextmanager +def report_compile_source_on_error(): + try: + yield + except Exception as exc: + tb = exc.__traceback__ + + # Walk the traceback, looking for frames that have + # source attached + stack = [] + while tb is not None: + filename = tb.tb_frame.f_code.co_filename + source = tb.tb_frame.f_globals.get("__compile_source__") + + if filename == "" and source is not None: + # What black magic are we doing here? Intuitively, what + # we would like to do is overwrite the co_filename on any + # frames that were generated from exec/eval so that they + # point to a temporary file that has the actual line + # information, so Python's default error printer can print + # useful line information on it. + # + # Writing out the temporary file is easy. But overwriting + # co_filename is not! You can't modify the code object + # associated with a frame. You can, however, reconstruct + # a traceback with entirely new frames from scratch, so that's + # what we do. But there's another problem, which is how to + # make the frame? + # + # The black magic is we make a frankenstein frame and code + # object which resembles the original frame/code enough so + # that it will print properly under traceback and the default + # error printer, but IT IS NOT THE ORIGINAL FRAME (you + # couldn't, e.g., execute its code with different variables + # and expect it to work.) + + # Don't delete the temporary file so the user can inspect it + # TODO: This creates a temporary file for every frame, but we + # technically only need one per distinct __compile_source__ + with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix=".py") as f: + f.write(source) + # Create a frame. Python doesn't let you construct + # FrameType directly, so just make one with compile + frame = tb.tb_frame + code = compile('__inspect_currentframe()', f.name, 'eval') + code = code.replace(co_name=frame.f_code.co_name) + # Python 3.11 only + if hasattr(frame.f_code, 'co_linetable'): + # We can't copy ALL of the metadata over, because you + # can cause Python to segfault this way. What exactly + # do we need? We need enough information for + # traceback to be able to print the exception + # correctly. Code reading Lib/traceback.py reveals + # that traceback calls code.co_positions() in order to + # get the augmented line/col numbers. Objects/codeobject.c, + # specifically _PyCode_InitAddressRange, reveals that + # this iterator is initialized from co_linetable and + # co_firstfileno. So copy these we must! + code = code.replace( # type: ignore[call-arg] + co_linetable=frame.f_code.co_linetable, # type: ignore[attr-defined] + co_firstlineno=frame.f_code.co_firstlineno, # type: ignore[attr-defined] + ) + fake_frame = eval( + code, + frame.f_globals, + { + **frame.f_locals, + '__inspect_currentframe': inspect.currentframe + } + ) + fake_tb = TracebackType( + None, fake_frame, tb.tb_lasti, tb.tb_lineno + ) + stack.append(fake_tb) + else: + stack.append(tb) + + tb = tb.tb_next + + # Reconstruct the linked list + tb_next = None + for tb in reversed(stack): + tb.tb_next = tb_next + tb_next = tb + + raise exc.with_traceback(tb_next) # noqa: B904 + +def shorten_filename(fn, *, base=None): + """Shorten a source filepath, with the assumption that torch/ subdirectories don't need to be shown to user.""" + if base is None: + base = os.path.dirname(os.path.dirname(__file__)) + # Truncate torch/foo.py to foo.py + try: + prefix = os.path.commonpath([fn, base]) + except ValueError: + return fn + else: + return fn[len(prefix) + 1:] + +def format_frame(frame, *, base=None, line=False): + """ + Format a FrameSummary in a short way, without printing full absolute path or code. + + The idea is the result fits on a single line. + """ + extra_line = "" + if line: + extra_line = f"{frame.line} # " + return f"{extra_line}{shorten_filename(frame.filename, base=base)}:{frame.lineno} in {frame.name}" + +def format_traceback_short(tb): + """Format a TracebackType in a short way, printing only the inner-most frame.""" + return format_frame(traceback.extract_tb(tb)[-1]) + +class CapturedTraceback: + __slots__ = ['tb', 'skip'] + + def __init__(self, tb, skip=0): + self.tb = tb + self.skip = skip + + def cleanup(self): + self.tb = None + + def summary(self): + import torch._C._profiler + + if self.tb is None: + # TODO: Maybe indicate that the traceback was elided? + return traceback.StackSummary() + + return _extract_symbolized_tb( + torch._C._profiler.symbolize_tracebacks([self.tb])[0], + self.skip + ) + + def __getstate__(self): + return (None, { + 'tb': None, # TB is not pickleable + 'skip': self.skip, + }) + + @staticmethod + def extract(*, script=False, cpp=False, skip=0): + """ + Like traceback.extract_stack(), but faster (approximately 20x faster); it + is fast enough that you can unconditionally log stacks this way as part of + normal execution. It returns a torch._C._profiler.CapturedTraceback + object that must be formatted specially with format_captured_tb. + + By default, this only reports Python backtraces (like extract_stack). You + can set the script/cpp kwargs to also turn on TorchScript/C++ trace + reporting. + """ + import torch._C._profiler + + if script or cpp: + assert skip == 0, "skip with script/cpp NYI" + + return CapturedTraceback( + torch._C._profiler.gather_traceback(python=True, script=script, cpp=cpp), + # Elide extract() frame if we don't have script/cpp frames. If + # we do have those frames, it doesn't work so force zero. + 0 if script or cpp else skip + 1 + ) + + def format(self): + """ + Formats a single torch._C._profiler.CapturedTraceback into a list of + strings equivalent to the output of traceback.format_list. Note that if + pass it CapturedTraceback with C++ traces, it is better not to use this + function and use the batch formatting API format_captured_tbs to amortize + the cost of symbolization + """ + return traceback.format_list(self.summary()) + + @staticmethod + def format_all(tbs): + """ + Bulk version of CapturedTraceback.format. Returns a list of list of strings. + """ + import torch._C._profiler + + # Directly populate tracebacks that already have cached summaries + rs: List[Optional[List[str]]] = [] + delayed_idxs = [] + for i, tb in enumerate(tbs): + if tb.tb is None: + rs.append([]) + else: + rs.append(None) + delayed_idxs.append(i) + + torch._C._profiler.symbolize_tracebacks([tbs[i].tb for i in delayed_idxs]) + for i in delayed_idxs: + rs[i] = traceback.format_list(tbs[i].summary()) + + return rs + + +def _extract_symbolized_tb(tb, skip): + """ + Given a symbolized traceback from symbolize_tracebacks, return a StackSummary object of + pre-processed stack trace entries. + """ + stack = traceback.StackSummary() + for f in reversed(tb[skip:]): + stack.append(traceback.FrameSummary(f['filename'], f['line'], f['name'])) + return stack diff --git a/lib/python3.11/site-packages/torch/utils/_triton.py b/lib/python3.11/site-packages/torch/utils/_triton.py new file mode 100644 index 0000000000000000000000000000000000000000..1609a3fe77c87085ef9b9917c5f4f6b70f03e600 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_triton.py @@ -0,0 +1,135 @@ +# mypy: allow-untyped-defs +import functools +import hashlib + + +@functools.lru_cache(None) +def has_triton_package() -> bool: + try: + from triton.compiler.compiler import triton_key + + return triton_key is not None + except ImportError: + return False + except RuntimeError: + return False + + +@functools.lru_cache(None) +def has_triton_tma(): + if has_triton_package(): + import torch + + if ( + torch.cuda.is_available() + and torch.cuda.get_device_capability() >= (9, 0) + and not torch.version.hip + ): + try: + from triton.tools.experimental_descriptor import ( # noqa: F401 + create_1d_tma_descriptor, + create_2d_tma_descriptor, + ) + + return True + except ImportError: + pass + + return False + + +@functools.lru_cache(None) +def has_triton_tma_device(): + if has_triton_package(): + import torch + + if ( + torch.cuda.is_available() + and torch.cuda.get_device_capability() >= (9, 0) + and not torch.version.hip + ): + try: + from triton.language.extra.cuda import ( # noqa: F401 + experimental_device_tensormap_create1d, + experimental_device_tensormap_create2d, + ) + + return True + except ImportError: + pass + + return False + + +@functools.lru_cache(None) +def has_triton() -> bool: + if not has_triton_package(): + return False + + from torch._dynamo.device_interface import get_interface_for_device + + def cuda_extra_check(device_interface): + return device_interface.Worker.get_device_properties().major >= 7 + + def cpu_extra_check(device_interface): + import triton.backends + + return "cpu" in triton.backends.backends + + def _return_true(device_interface): + return True + + triton_supported_devices = { + "cuda": cuda_extra_check, + "xpu": _return_true, + "cpu": cpu_extra_check, + } + + def is_device_compatible_with_triton(): + for device, extra_check in triton_supported_devices.items(): + device_interface = get_interface_for_device(device) + if device_interface.is_available() and extra_check(device_interface): + return True + return False + + return is_device_compatible_with_triton() + + +@functools.lru_cache(None) +def triton_backend(): + from triton.compiler.compiler import make_backend + from triton.runtime.driver import driver + + target = driver.active.get_current_target() + return make_backend(target) + + +@functools.lru_cache(None) +def triton_hash_with_backend(): + from triton.compiler.compiler import triton_key + + backend = triton_backend() + key = f"{triton_key()}-{backend.hash()}" + + # Hash is upper case so that it can't contain any Python keywords. + return hashlib.sha256(key.encode("utf-8")).hexdigest().upper() + + +def dtype_to_string(dtype): + if dtype.name.startswith("fp"): + suffix = "float" + dtype.name[2:] + elif dtype.name.startswith("bf"): + suffix = "bfloat" + dtype.name[2:] + else: + suffix = dtype.name + return "triton.language." + suffix + + +def patch_triton_dtype_repr(): + import triton + + # Hack to get triton dtype repr to produce an evaluatable expression + # triton.language.float32 emits triton.language.fp32 which does not + # exist + # REMOVE when https://github.com/openai/triton/pull/3342 lands + triton.language.dtype.__repr__ = lambda self: dtype_to_string(self) diff --git a/lib/python3.11/site-packages/torch/utils/_typing_utils.py b/lib/python3.11/site-packages/torch/utils/_typing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ffb6b383e4e6b9506baa803ab5ac6613dfc9b387 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_typing_utils.py @@ -0,0 +1,14 @@ +"""Miscellaneous utilities to aid with typing.""" + +from typing import Optional, TypeVar + + +# Helper to turn Optional[T] into T when we know None either isn't +# possible or should trigger an exception. +T = TypeVar("T") + + +def not_none(obj: Optional[T]) -> T: + if obj is None: + raise TypeError("Invariant encountered: value was None when it should not be") + return obj diff --git a/lib/python3.11/site-packages/torch/utils/_zip.py b/lib/python3.11/site-packages/torch/utils/_zip.py new file mode 100644 index 0000000000000000000000000000000000000000..c7dd6445fabe98209d3294e933a3f1baca71bd64 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/_zip.py @@ -0,0 +1,86 @@ +# mypy: allow-untyped-defs +import argparse +import glob +import os +from pathlib import Path +from zipfile import ZipFile + +# Exclude some standard library modules to: +# 1. Slim down the final zipped file size +# 2. Remove functionality we don't want to support. +DENY_LIST = [ + # Interface to unix databases + "dbm", + # ncurses bindings (terminal interfaces) + "curses", + # Tcl/Tk GUI + "tkinter", + "tkinter", + # Tests for the standard library + "test", + "tests", + "idle_test", + "__phello__.foo.py", + # importlib frozen modules. These are already baked into CPython. + "_bootstrap.py", + "_bootstrap_external.py", +] + +strip_file_dir = "" + + +def remove_prefix(text, prefix): + if text.startswith(prefix): + return text[len(prefix) :] + return text + + +def write_to_zip(file_path, strip_file_path, zf, prepend_str=""): + stripped_file_path = prepend_str + remove_prefix(file_path, strip_file_dir + "/") + path = Path(stripped_file_path) + if path.name in DENY_LIST: + return + zf.write(file_path, stripped_file_path) + + +def main() -> None: + global strip_file_dir + parser = argparse.ArgumentParser(description="Zip py source") + parser.add_argument("paths", nargs="*", help="Paths to zip.") + parser.add_argument( + "--install-dir", "--install_dir", help="Root directory for all output files" + ) + parser.add_argument( + "--strip-dir", + "--strip_dir", + help="The absolute directory we want to remove from zip", + ) + parser.add_argument( + "--prepend-str", + "--prepend_str", + help="A string to prepend onto all paths of a file in the zip", + default="", + ) + parser.add_argument("--zip-name", "--zip_name", help="Output zip name") + + args = parser.parse_args() + + zip_file_name = args.install_dir + "/" + args.zip_name + strip_file_dir = args.strip_dir + prepend_str = args.prepend_str + zf = ZipFile(zip_file_name, mode="w") + + for p in sorted(args.paths): + if os.path.isdir(p): + files = glob.glob(p + "/**/*.py", recursive=True) + for file_path in sorted(files): + # strip the absolute path + write_to_zip( + file_path, strip_file_dir + "/", zf, prepend_str=prepend_str + ) + else: + write_to_zip(p, strip_file_dir + "/", zf, prepend_str=prepend_str) + + +if __name__ == "__main__": + main() # pragma: no cover diff --git a/lib/python3.11/site-packages/torch/utils/backend_registration.py b/lib/python3.11/site-packages/torch/utils/backend_registration.py new file mode 100644 index 0000000000000000000000000000000000000000..6f3444116f3a118541d650670f35d8542f40427b --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/backend_registration.py @@ -0,0 +1,382 @@ +# mypy: allow-untyped-defs +import torch +from torch.overrides import ( + handle_torch_function, + has_torch_function_unary, +) +from torch._C import _rename_privateuse1_backend, _get_privateuse1_backend_name +from typing import List, Optional, Union + +__all__ = ["rename_privateuse1_backend", "generate_methods_for_privateuse1_backend"] + +# TODO: Should use `torch._C._get_privateuse1_backend_name()` to get +# renamed-backend name for `privateuse1`, but the func will cause an +# error with torch.jit.script, so we use the global variable named +# `_privateuse1_backend_name`. +_privateuse1_backend_name = "privateuseone" + +def rename_privateuse1_backend(backend_name: str) -> None: + r""" + Rename the privateuse1 backend device to make it more convenient to use as a device name within PyTorch APIs. + + The steps are: + + (1) (In C++) implement kernels for various torch operations, and register them + to the PrivateUse1 dispatch key. + (2) (In python) call torch.utils.rename_privateuse1_backend("foo") + + You can now use "foo" as an ordinary device string in python. + + Note: this API can only be called once per process. Attempting to change + the external backend after it's already been set will result in an error. + + Note(AMP): If you want to support AMP on your device, you can register a custom backend module. + The backend must register a custom backend module with ``torch._register_device_module("foo", BackendModule)``. + BackendModule needs to have the following API's: + + (1) ``get_amp_supported_dtype() -> List[torch.dtype]`` + get the supported dtypes on your "foo" device in AMP, maybe the "foo" device supports one more dtype. + + Note(random): If you want to support to set seed for your device, BackendModule needs to have the following API's: + + (1) ``_is_in_bad_fork() -> bool`` + Return ``True`` if now it is in bad_fork, else return ``False``. + + (2) ``manual_seed_all(seed int) -> None`` + Sets the seed for generating random numbers for your devices. + + (3) ``device_count() -> int`` + Returns the number of "foo"s available. + + (4) ``get_rng_state(device: Union[int, str, torch.device] = 'foo') -> Tensor`` + Returns a list of ByteTensor representing the random number states of all devices. + + (5) ``set_rng_state(new_state: Tensor, device: Union[int, str, torch.device] = 'foo') -> None`` + Sets the random number generator state of the specified "foo" device. + + And there are some common funcs: + + (1) ``is_available() -> bool`` + Returns a bool indicating if "foo" is currently available. + + (2) ``current_device() -> int`` + Returns the index of a currently selected device. + + For more details, see https://pytorch.org/tutorials/advanced/extend_dispatcher.html#get-a-dispatch-key-for-your-backend + For an existing example, see https://github.com/bdhirsh/pytorch_open_registration_example + + Example:: + + >>> # xdoctest: +SKIP("failing") + >>> torch.utils.rename_privateuse1_backend("foo") + # This will work, assuming that you've implemented the right C++ kernels + # to implement torch.ones. + >>> a = torch.ones(2, device="foo") + + """ + _rename_privateuse1_backend(backend_name) + global _privateuse1_backend_name + _privateuse1_backend_name = backend_name + +def _check_register_once(module, attr): + if hasattr(module, attr): + raise RuntimeError(f"The custom device module of {module} has already been registered with {attr}") + + +def _normalization_device(custom_backend_name: str, device: Optional[Union[int, str, torch.device]] = None) -> int: + def _get_current_device_index(): + _get_device_index = "current_device" + if hasattr(torch, custom_backend_name) and \ + hasattr(getattr(torch, custom_backend_name), _get_device_index): + return getattr(getattr(torch, custom_backend_name), _get_device_index)() + else: + # The default device index is 0. + return 0 + + if device is None: + return _get_current_device_index() + # if isinstance(device, str), this means that the parameter passed in is in the string format "foo:0" + # convert str object to torch.device object, and then process it uniformly + elif isinstance(device, str): + device = torch.device(device) + + # variable devcie can only be torch.device type or int type + if isinstance(device, torch.device): + if device.type != custom_backend_name: + raise RuntimeError(f"Invalid device, must be {custom_backend_name} device") + elif device.index is None: + device_idx = _get_current_device_index() + else: + device_idx = device.index + # if isinstance(device, int), we can take the index number directly + else: + device_idx = device + return device_idx + + +def _generate_tensor_methods_for_privateuse1_backend(custom_backend_name: str) -> None: + @property # type: ignore[misc] + def wrap_tensor_backend(self: torch.Tensor) -> bool: + if has_torch_function_unary(self): + # TODO mypy doesn't support @property, see: https://github.com/python/mypy/issues/6185 + return handle_torch_function(wrap_tensor_backend.__get__, (self,), self) # type: ignore[attr-defined] + return self.device.type == custom_backend_name + + _check_register_once(torch.Tensor, f'is_{custom_backend_name}') + wrap_tensor_backend.fget.__name__ = f'is_{custom_backend_name}' # type: ignore[attr-defined] + setattr(torch.Tensor, f'is_{custom_backend_name}', wrap_tensor_backend) + + def wrap_tensor_to(self: torch.Tensor, device: Optional[Union[int, torch.device]] = None, non_blocking=False, + **kwargs) -> torch.Tensor: + r"""Perform Tensor device conversion. Call the to operator implementation. + + .. note:: + If the ``self`` Tensor already + has the correct :class:`torch.device`, then ``self`` is returned. + Otherwise, the returned tensor is a copy of ``self`` with the desired :class:`torch.device`. + + Args: + device (int, optional): if specified, all parameters will be copied to that device + non_blocking (bool): If ``True`` and the source is in pinned memory, + the copy will be asynchronous with respect to the host. Otherwise, + the argument has no effect. + **kwargs (dict): For compatibility, may contain the key ``memory_format`` argument. + """ + if has_torch_function_unary(self): + return handle_torch_function(wrap_tensor_to, (self,), self, device=device, non_blocking=False, **kwargs) + device_idx = _normalization_device(custom_backend_name, device) + return self.to(device=torch.device(f'{custom_backend_name}:{device_idx}'), non_blocking=non_blocking, **kwargs) + + _check_register_once(torch.Tensor, custom_backend_name) + wrap_tensor_to.__name__ = custom_backend_name + setattr(torch.Tensor, custom_backend_name, wrap_tensor_to) + + +def _generate_module_methods_for_privateuse1_backend(custom_backend_name: str) -> None: + # Generate Module attributes and methods depends on Tensor methods, + # so we need to check whether Tensor methods is already registered. + if not hasattr(torch.Tensor, custom_backend_name): + raise RuntimeError( + f"Can not automatically generate {custom_backend_name}() method for torch.nn.Module." + f"Because torch.Tensor doesn't has the method {custom_backend_name}()." + f"For this error, you can try setting for_tensor=True.") + + def wrap_module_to(self: torch.nn.modules.module.T, + device: Optional[Union[int, torch.device]] = None) -> torch.nn.modules.module.T: + r"""Move all model parameters and buffers to the custom device. + + This also makes associated parameters and buffers different objects. So + it should be called before constructing optimizer if the module will + live on device while being optimized. + + .. note:: + This method modifies the module in-place. + + Args: + device (int, optional): if specified, all parameters will be copied to that device + """ + return self._apply(lambda t: getattr(t, custom_backend_name)(device)) + + _check_register_once(torch.nn.Module, custom_backend_name) + setattr(torch.nn.Module, custom_backend_name, wrap_module_to) + +def _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name: str) -> None: + # Generate PackedSequence Module attributes and methods depends on Tensor methods, + # so we need to check whether Tensor methods is already registered. + if not hasattr(torch.Tensor, f'is_{custom_backend_name}') or \ + not hasattr(torch.Tensor, custom_backend_name): + raise RuntimeError( + f"Can not automatically generate is_{custom_backend_name}() or " + f"{custom_backend_name}() method for torch.nn.utils.rnn.PackedSequence." + f"Because torch.Tensor doesn't has the method is_{custom_backend_name}()" + f"or {custom_backend_name}()." + f"For this error, you can try setting for_tensor=True.") + + @property # type: ignore[misc] + def wrap_tensor_backend(self: torch.nn.utils.rnn.PackedSequence) -> bool: + return self.data.device.type == custom_backend_name + + _check_register_once(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}') + setattr(torch.nn.utils.rnn.PackedSequence, f'is_{custom_backend_name}', wrap_tensor_backend) + + def wrap_module_to(self: torch.nn.utils.rnn.PackedSequence, + *args, **kwargs) -> torch.nn.utils.rnn.PackedSequence: + r"""Move all model parameters and buffers to the custom device. + + This also makes associated parameters and buffers different objects. So + it should be called before constructing optimizer if the module will + live on device while being optimized. + + .. note:: + This method modifies the module in-place. + + Args: + device (int, optional): if specified, all parameters will be copied to that device + """ + ex = torch.tensor((), dtype=self.data.dtype, device=self.data.device).to(*args, **kwargs) + if ex.device.type == custom_backend_name: + return self.to(*args, **kwargs) + kwargs.update({'device': custom_backend_name}) + return self.to(*args, **kwargs) + + _check_register_once(torch.nn.utils.rnn.PackedSequence, custom_backend_name) + setattr(torch.nn.utils.rnn.PackedSequence, custom_backend_name, wrap_module_to) + +def _generate_storage_methods_for_privateuse1_backend(custom_backend_name: str, + unsupported_dtype: Optional[List[torch.dtype]] = None) -> None: + # Attribute is registered in the _StorageBase class + # and UntypedStorage obtains through inheritance. + @property # type: ignore[misc] + def wrap_storage_backend(self: torch.storage._StorageBase) -> bool: + r"""Return the internal :class:`torch.UntypedStorage`.""" + return self.device.type == custom_backend_name + + _check_register_once(torch.storage._StorageBase, f'is_{custom_backend_name}') + setattr(torch.storage._StorageBase, f'is_{custom_backend_name}', wrap_storage_backend) + + def wrap_storage_to(self, device=None, non_blocking=False): + r"""Return a copy of this object in custom device memory. + + If this object is already in device memory and on the correct device, then + no copy is performed and the original object is returned. + + Args: + device (int): The destination device id. Defaults to the current device. + non_blocking (bool): If ``True`` and the source is in pinned memory, + the copy will be asynchronous with respect to the host. Otherwise, + the argument has no effect. + """ + # There should be a judgment related to storage device and a judgment related to storage type, + # but it depends on the extended function, so this part is temporarily omitted in the automatic generation. + device_idx = _normalization_device(custom_backend_name, device) + + if getattr(self, f'is_{custom_backend_name}'): + # storage has already on expected device. + if self.get_device() == device_idx: + return self + # For sparse storage, custom need to extend the implementation by themselves. + if self.is_sparse: + raise RuntimeError(f"Can not support a sparse storage move to {custom_backend_name} backend") + # create untyped_storage and copy data + untyped_storage = torch.UntypedStorage( + self.size(), device=torch.device(f'{custom_backend_name}:{device_idx}') + ) + untyped_storage.copy_(self, non_blocking) + return untyped_storage + + _check_register_once(torch.storage._StorageBase, custom_backend_name) + setattr(torch.storage._StorageBase, custom_backend_name, wrap_storage_to) + + # Register the corresponding attribute for the TypedStorage class. + # When the TypedStorage class is removed, the registration is also removed. + + @property # type: ignore[misc] + def wrap_typed_storage_backend(self: torch.storage.TypedStorage) -> bool: + torch.storage._warn_typed_storage_removal() + return self._untyped_storage.device.type == custom_backend_name + + _check_register_once(torch.TypedStorage, f'is_{custom_backend_name}') + setattr(torch.storage.TypedStorage, f'is_{custom_backend_name}', wrap_typed_storage_backend) + + def wrap_typed_storage_to(self: torch.storage.TypedStorage, + device=None, non_blocking=False, **kwargs) -> torch.storage.TypedStorage: + torch.storage._warn_typed_storage_removal() + if unsupported_dtype and self.dtype in unsupported_dtype: + raise RuntimeError(f"Cannot create {custom_backend_name} storage " + f"as {self.dtype} dtype is not supported by this backend") + custom_backend_storage: torch.UntypedStorage = getattr( + self._untyped_storage, custom_backend_name)(device, non_blocking, **kwargs) + return self._new_wrapped_storage(custom_backend_storage) + + _check_register_once(torch.TypedStorage, custom_backend_name) + setattr(torch.TypedStorage, custom_backend_name, wrap_typed_storage_to) + + +def generate_methods_for_privateuse1_backend(for_tensor: bool = True, for_module: bool = True, + for_packed_sequence: bool = True, + for_storage: bool = False, + unsupported_dtype: Optional[List[torch.dtype]] = None) -> None: + r""" + Automatically generate attributes and methods for the custom backend after rename privateuse1 backend. + + In the default scenario, storage-related methods will not be generated automatically. + + When you implement kernels for various torch operations, and register them to the PrivateUse1 dispatch key. + And call the function torch.rename_privateuse1_backend("foo") to rename your backend name. + At this point, you can easily register specific methods and attributes by calling this function. + Just like torch.Tensor.foo(), torch.Tensor.is_foo, torch.Storage.foo(), torch.Storage.is_foo. + + Note: We recommend you use generic functions (check devices are equal or to(device=)). + We provide these methods for convenience only and they will be "monkey patched" onto the objects + and so will not be properly typed. For Storage methods generate, if you need to support sparse data storage, + you need to extend the implementation yourself. + + Args: + for_tensor (bool): whether register related methods for torch.Tensor class. + for_module (bool): whether register related methods for torch.nn.Module class. + for_storage (bool): whether register related methods for torch.Storage class. + unsupported_dtype (List[torch.dtype]): takes effect only when the storage method needs to be generated, + indicating that the storage does not support the torch.dtype type. + + Example:: + + >>> # xdoctest: +SKIP("failing") + >>> torch.utils.rename_privateuse1_backend("foo") + >>> torch.utils.generate_methods_for_privateuse1_backend() + # Then automatically generate backend-related attributes and methods. + >>> a = torch.tensor(2).foo() + >>> a.is_foo + >>> hasattr(torch.nn.Module, 'foo') + """ + custom_backend_name = _get_privateuse1_backend_name() + + if for_tensor: + _generate_tensor_methods_for_privateuse1_backend(custom_backend_name) + + if for_module: + _generate_module_methods_for_privateuse1_backend(custom_backend_name) + + if for_storage: + _generate_storage_methods_for_privateuse1_backend(custom_backend_name, unsupported_dtype) + + if for_packed_sequence: + _generate_packed_sequence_methods_for_privateuse1_backend(custom_backend_name) + +def _get_custom_mod_func(func_name: str): + r""" + Return the func named `func_name` defined in custom device module. If not defined, + return `None`. And the func is registered with `torch.utils.rename_privateuse1_backend('foo')` + and `torch._register_device_module('foo', BackendModule)`. + If the custom device module or the func is not defined, it will give warning or error message. + Args: + func_name (str): return the callable func named func_name defined in custom device module. + Example:: + class DummyfooModule: + @staticmethod + def is_available(): + return True + @staticmethod + def func_name(*args, **kwargs): + .... + torch.utils.rename_privateuse1_backend("foo") + torch._register_device_module("foo", DummyfooModule) + foo_is_available_func = torch.utils.backend_registration._get_custom_mod_func("is_available") + if foo_is_available_func: + foo_is_available = foo_is_available_func() + func_ = torch.utils.backend_registration._get_custom_mod_func("func_name") + if func_: + result = func_(*args, **kwargs) + Attention: This function is not meant to be used directly by users, which is why + it is marked as private. It is a convenience function for backend implementers to + more easily call the hooks into their backend extensions. + """ + assert isinstance(func_name, str), f"func_name must be `str`, but got `{type(func_name)}`." + backend_name = _get_privateuse1_backend_name() + custom_device_mod = getattr(torch, backend_name, None) # type: ignore[arg-type] + function = getattr(custom_device_mod, func_name, None) # type: ignore[arg-type] + if custom_device_mod is None or function is None: + message = f'Try to call torch.{backend_name}.{func_name}. The backend must register a custom backend ' + message += f"module with `torch._register_device_module('{backend_name}', BackendModule)`. And " + message += f"BackendModule needs to have the following API's:\n `{func_name}(*args, **kwargs)`. \n" + raise RuntimeError(message) + return function diff --git a/lib/python3.11/site-packages/torch/utils/bundled_inputs.py b/lib/python3.11/site-packages/torch/utils/bundled_inputs.py new file mode 100644 index 0000000000000000000000000000000000000000..21fa4e50396da2f79e26963a00f2a79dd3c9d521 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/bundled_inputs.py @@ -0,0 +1,469 @@ +#!/usr/bin/env python3 +# mypy: allow-untyped-defs +from typing import Any, TypeVar, Optional, Tuple, List, NamedTuple, Union, Sequence, Dict, Callable +import textwrap +import torch +from torch._C import TupleType, ListType +from torch.jit._recursive import wrap_cpp_module + + +T = TypeVar("T") + +MAX_RAW_TENSOR_SIZE = 16 + +class InflatableArg(NamedTuple): + """Helper type for bundled inputs. + + 'value' is the compressed/deflated input that is stored in the model. Value + must be of the same type as the argument to the function that it is a deflated + input for. + + 'fmt' is a formatable code string that is executed to inflate the compressed data into + the appropriate input. It can use 'value' as an input to the format str. It must result + in a value of the same type as 'value'. + + 'fmt_fn' is a formatable function code string that is executed to inflate the compressed + data into the appropriate input. It must result in a value of the same type as 'value'. + The function name should be the formatable part of the string. + + Note: Only top level InflatableArgs can be inflated. i.e. you cannot place + an inflatable arg inside of some other structure. You should instead create + an inflatable arg such that the fmt code string returns the full structure + of your input. + """ + + value: Any + fmt: str = "{}" + fmt_fn: str = "" + + +def bundle_inputs( + model: torch.jit.ScriptModule, + inputs: Union[Optional[Sequence[Tuple[Any, ...]]], Dict[Callable, Optional[Sequence[Tuple[Any, ...]]]]], + info: Optional[Union[List[str], Dict[Callable, List[str]]]] = None, + *, + _receive_inflate_expr: Optional[List[str]] = None, +) -> torch.jit.ScriptModule: + """Create and return a copy of the specified model with inputs attached. + + The original model is not mutated or changed in any way. + + Models with bundled inputs can be invoked in a uniform manner by + benchmarking and code coverage tools. + + If inputs is passed in as a list then the inputs will be bundled for 'forward'. + If inputs is instead passed in as a map then all the methods specified in the map + will have their corresponding inputs bundled. Info should match watchever type is + chosen for the inputs. + + The returned model will support the following methods: + + `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]` + Returns a list of tuples suitable for passing to the model like + `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)` + + `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]` + Returns a dictionary mapping function names to a metadata dictionary. + This nested dictionary maps preset strings like: + 'get_inputs_function_name' -> the name of a function attribute in this model that can be + run to get back a list of inputs corresponding to that function. + 'info' -> the user provided extra information about the bundled inputs + + If forward has bundled inputs then these following functions will also be defined on the returned module: + + `get_all_bundled_inputs() -> List[Tuple[Any, ...]]` + Returns a list of tuples suitable for passing to the model like + `for inp in model.get_all_bundled_inputs(): model(*inp)` + + `get_num_bundled_inputs() -> int` + Equivalent to `len(model.get_all_bundled_inputs())`, + but slightly easier to call from C++. + + Inputs can be specified in one of two ways: + + - The model can define `_generate_bundled_inputs_for_`. + If the user chooses this method inputs[] should map to None + + - The `inputs` argument to this function can be a dictionary mapping functions to a + list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_. + Alternatively if only bundling inputs for forward the map can be omitted and a singular list of inputs + can be provided instead. + + The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a + list of inputs, the inner tuple is the list of args that together make up one input. + For inputs of functions that take one arg, this will be a tuple of length one. The Any, ... + is the actual data that makes up the args, e.g. a tensor. + + Info is an optional parameter that maps functions to a list of strings providing extra information about that + function's bundled inputs. Alternatively if only bundling inputs for forward the map can be omitted and + a singular list of information can be provided instead. This could be descriptions, expected outputs, etc. + - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']} + + This function will attempt to optimize arguments so that (e.g.) + arguments like `torch.zeros(1000)` will be represented compactly. + Only top-level arguments will be optimized. + Tensors in lists or tuples will not. + """ + if not isinstance(model, torch.jit.ScriptModule): + raise Exception("Only ScriptModule is supported.") # noqa: TRY002 + + ignored_methods, ignored_attrs = _get_bundled_inputs_attributes_and_methods(model) + clone = torch._C._hack_do_not_use_clone_module_with_class( # type: ignore[attr-defined] + model._c, + ignored_methods, + ignored_attrs, + ) + + # The above cloning function returns a torch._C.scriptmodule and we need a torch.jit.scriptmodule. + # Fortunately theres a function in _recursive that does exactly that conversion. + cloned_module = wrap_cpp_module(clone) + if isinstance(inputs, dict): + assert isinstance(info, dict) or info is None + augment_many_model_functions_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info) + else: + assert isinstance(info, list) or info is None + augment_model_with_bundled_inputs(cloned_module, inputs, _receive_inflate_expr, info) + return cloned_module + +def augment_model_with_bundled_inputs( + model: torch.jit.ScriptModule, + inputs: Optional[Sequence[Tuple[Any, ...]]] = None, + _receive_inflate_expr: Optional[List[str]] = None, # For debugging. + info: Optional[List[str]] = None, # Optional argument to provide info about forward or its inputs + skip_size_check=False, +) -> None: + """Add bundled sample inputs to a model for the forward function. + + Models with bundled inputs can be invoked in a uniform manner by + benchmarking and code coverage tools. + + Augmented models will support the following methods: + + `get_all_bundled_inputs() -> List[Tuple[Any, ...]]` + Returns a list of tuples suitable for passing to the model like + `for inp in model.get_all_bundled_inputs(): model(*inp)` + + `get_num_bundled_inputs() -> int` + Equivalent to `len(model.get_all_bundled_inputs())`, + but slightly easier to call from C++. + + `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]` + Returns a dictionary mapping function names to a metadata dictionary. + This nested dictionary maps preset strings like: + 'get_inputs_function_name' -> the name of a function attribute in this model that can be + run to get back a list of inputs corresponding to that function. + 'info' -> the user provided extra information about the bundled inputs + + Inputs can be specified in one of two ways: + + - The model can define `_generate_bundled_inputs_for_forward`. + If the user chooses this method inputs should be None + + - `inputs` is a list of inputs of form List[Tuple[Any, ...]]. A list of tuples where the elements + of each tuple are the args that make up one input. + """ + if not isinstance(model, torch.jit.ScriptModule): + raise Exception("Only ScriptModule is supported.") # noqa: TRY002 + + forward: Callable = model.forward + + # Sometimes forward won't have a name attached so just in case + if not hasattr(forward, "__name__"): + forward.__name__ = 'forward' + augment_many_model_functions_with_bundled_inputs( + model, + inputs={forward : inputs}, + _receive_inflate_expr=_receive_inflate_expr, + info={forward : info} if info else None, + skip_size_check=skip_size_check, + ) + + +def augment_many_model_functions_with_bundled_inputs( + model: torch.jit.ScriptModule, + inputs: Dict[Callable, Optional[Sequence[Tuple[Any, ...]]]], + _receive_inflate_expr: Optional[List[str]] = None, # For debugging. + info: Optional[Dict[Callable, List[str]]] = None, # Optional argument to provide info about the function or its inputs + skip_size_check=False, +) -> None: + """Add bundled sample inputs to a model for an arbitrary list of public functions. + + Models with bundled inputs can be invoked in a uniform manner by + benchmarking and code coverage tools. + + Augmented models will support the following methods: + + `get_all_bundled_inputs_for_() -> List[Tuple[Any, ...]]` + Returns a list of tuples suitable for passing to the model like + `for inp in model.get_all_bundled_inputs_for_foo(): model.foo(*inp)` + + `get_bundled_inputs_functions_and_info() -> Dict[str, Dict[str: List[str]]]` + Returns a dictionary mapping function names to a metadata dictionary. + This nested dictionary maps preset strings like: + 'get_inputs_function_name' -> the name of a function attribute in this model that can be + run to get back a list of inputs corresponding to that function. + 'info' -> the user provided extra information about the bundled inputs + + If forward has bundled inputs then these following functions are also defined: + + `get_all_bundled_inputs() -> List[Tuple[Any, ...]]` + Returns a list of tuples suitable for passing to the model like + `for inp in model.get_all_bundled_inputs(): model(*inp)` + + `get_num_bundled_inputs() -> int` + Equivalent to `len(model.get_all_bundled_inputs())`, + but slightly easier to call from C++. + + Inputs can be specified in one of two ways: + + - The model can define `_generate_bundled_inputs_for_`. + If the user chooses this method inputs[] should map to None + + - The `inputs` argument to this function can be a dictionary mapping functions to a + list of inputs, of the same form that will be returned by get_all_bundled_inputs_for_. + The type of the inputs is List[Tuple[Any, ...]]. The outer list corresponds with a + list of inputs, the inner tuple is the list of args that together make up one input. + For inputs of functions that take one arg, this will be a tuple of length one. The Any, ... + is the actual data that makes up the args, e.g. a tensor. + + Info is an optional parameter that maps functions to a list of strings providing extra information about that + function's bundled inputs. This could be descriptions, expected outputs, etc. + - Ex: info={model.forward : ['man eating icecream', 'an airplane', 'a dog']} + + This function will attempt to optimize arguments so that (e.g.) + arguments like `torch.zeros(1000)` will be represented compactly. + Only top-level arguments will be optimized. + Tensors in lists or tuples will not. + """ + if not isinstance(model, torch.jit.ScriptModule): + raise Exception("Only ScriptModule is supported.") # noqa: TRY002 + + if not inputs: + raise Exception("Please provide inputs for at least 1 function") # noqa: TRY002 + + if hasattr(model, "get_all_bundled_inputs") or hasattr(model, "get_bundled_inputs_functions_and_info"): + raise Exception( # noqa: TRY002 + "Models can only be augmented with bundled inputs once. " + "This Model seems to have already been augmented with " + "bundled inputs. Please start afresh with one that " + "doesn't have bundled inputs.", + ) + + get_bundled_inputs_functions_and_info_template = "" + + for function, input_list in inputs.items(): + if hasattr(function, "__name__"): + function_name = function.__name__ + else: + if hasattr(function, "name"): + function_name = function.name # type: ignore[attr-defined] + else: + raise Exception( # noqa: TRY002 + 'At least one of your functions has no attribute name please ensure all have one. m.foo.name = "foo"') + + + if input_list is not None and not isinstance(input_list, Sequence): + raise TypeError(f"Error inputs for function {function_name} is not a Sequence") + + function_arg_types = [arg.type for arg in function.schema.arguments[1:]] # type: ignore[attr-defined] + deflated_inputs_type: ListType = ListType(TupleType(function_arg_types)) + model._c._register_attribute(f"_bundled_inputs_deflated_{function_name}", deflated_inputs_type, []) + + if hasattr(model, "_generate_bundled_inputs_for_" + function_name): + if input_list is not None: + raise Exception( # noqa: TRY002 + f"inputs[{function_name}] is not None, but _generate_bundled_inputs_for_{function_name} is already defined" + ) + # Model author already defined _generate_bundled_inputs_for_. + elif input_list is None or len(input_list) == 0: + raise Exception( # noqa: TRY002 + f"inputs for {function_name} must be specified if " + f"_generate_bundled_inputs_for_{function_name} is not already defined" + ) + else: + # Iterate over the inputs and args in each input. + # Accumulate `deflated_inputs` as (possibly) compressed values + # and `parts` to be joined into the expression that unpacks them. + deflated_inputs = [] + parts = [] + for inp_idx, args in enumerate(input_list): + if not isinstance(args, Tuple) and not isinstance(args, List): # type: ignore[arg-type] + raise TypeError( + f"Error bundled input for function {function_name} idx: {inp_idx} is not a Tuple or a List" + ) + deflated_args = [] + parts.append("(") + for arg_idx, arg in enumerate(args): + inflate_helper_fn_name = _get_inflate_helper_fn_name(arg_idx, inp_idx, function_name) + deflated, inflater, helper_definition = _inflate_expr( + arg, + f"deflated[{inp_idx}][{arg_idx}]", + inflate_helper_fn_name, + skip_size_check=skip_size_check, + ) + deflated_args.append(deflated) + parts.append(f" {inflater},") + if helper_definition: + model.define(textwrap.dedent(helper_definition)) + deflated_inputs.append(tuple(deflated_args)) + parts.append("),") + parts.append("") + expr = "\n".join(parts) + + # Back-channel return this expr for debugging. + if _receive_inflate_expr is not None: + _receive_inflate_expr.append(expr) + setattr(model, f"_bundled_inputs_deflated_{function_name}", deflated_inputs) + definition = textwrap.dedent(""" + def _generate_bundled_inputs_for_{name}(self): + deflated = self._bundled_inputs_deflated_{name} + return [ + {expr} + ] + """).format(expr=expr, name=function_name) + model.define(definition) + + # Define get_all_bundled_inputs_for_ that caches the generated inputs. + model.define(textwrap.dedent(""" + def get_all_bundled_inputs_for_{name}(self): + all_inputs = self._generate_bundled_inputs_for_{name}() + assert all_inputs is not None + return all_inputs + """).format(name=function_name)) + + # Add to the high level helper methods + inputs_info = repr(info[function]) if info and function in info else '[]' + get_bundled_inputs_functions_and_info_template += f""" + temp_dict : Dict[str,List[str]] = {{}} + info: List[str] = {inputs_info} + + temp_dict['info'] = info + temp_dict['get_inputs_function_name'] = ['get_all_bundled_inputs_for_{function_name}'] + all_inputs['{function_name}'] = temp_dict + """ + + # To ensure backwards compatibility and a streamlined api for forward these wrappers are provided + if function_name == 'forward': + model.define(textwrap.dedent(""" + def get_all_bundled_inputs(self): + return self.get_all_bundled_inputs_for_forward() + """)) + model.define(textwrap.dedent(""" + def get_num_bundled_inputs(self): + return len(self.get_all_bundled_inputs_for_forward()) + """)) + + # Define some high level helper methods that act on all bundled inputs + model.define(textwrap.dedent(f""" + def get_bundled_inputs_functions_and_info(self): + all_inputs : Dict[str, Dict[str,List[str]]] = {{}} + {get_bundled_inputs_functions_and_info_template} + return all_inputs + """)) + +def _inflate_expr( + arg: T, ref: str, inflate_helper_fn_name: str, skip_size_check: bool = False +) -> Tuple[Union[T, torch.Tensor], str, Optional[str]]: + # Allow custom inflation expressions any object. + # For example, calling custom image-decoding ops. + # Or just use "{}" as the format string to ignore size limits. + if isinstance(arg, InflatableArg): + if arg.fmt_fn: + if arg.fmt not in ["{}", ""]: + raise Exception( # noqa: TRY002 + f"Bundled input argument at position '{ref}' has " + f"both arg.fmt_fn => \n{arg.fmt_fn} " + f"\n and arg.fmt => {arg.fmt}. " + "Please choose `arg.fmt` if the deflater is straightforward or " + "`arg.fmt_fn` if you need a function." + ) + + helper_definition = arg.fmt_fn.format(inflate_helper_fn_name) + expr = f"self.{inflate_helper_fn_name}({ref})" + + return arg.value, expr, helper_definition + else: + return arg.value, arg.fmt.format(ref), None + + if isinstance(arg, torch.Tensor): + # Small-storage tensors can just be saved directly. + if arg._typed_storage().size() <= MAX_RAW_TENSOR_SIZE or skip_size_check: + return arg, ref, None + # Small contiguous tensors can be cloned to have small storage. + # TODO: Should we do this even for non-contiguous tensors? + if arg.is_contiguous() and arg.numel() <= MAX_RAW_TENSOR_SIZE: + return arg.clone(), ref, None + # Example inputs commonly come from torch.zeros, torch.ones, or torch.full. + # These can be represented compactly. + for fmt in [torch.contiguous_format, torch.channels_last]: + if arg.is_contiguous(memory_format=fmt) and (arg == arg.flatten()[0]).all().item(): + return (arg.flatten()[0].clone().expand(*arg.size()), + f"{ref}.contiguous(memory_format={fmt})", None) + # Prevent big tensors from being bundled by default. + # TODO: Provide more useful diagnostics. + raise Exception( # noqa: TRY002 + f"Bundled input argument at position '{ref}' is " + f"a tensor with storage size {arg._typed_storage().size()}. " + f"You probably don't want to bundle this as an input. " + ) + else: + return arg, ref, None + +def _get_bundled_inputs_attributes_and_methods(script_module: torch.jit.ScriptModule) -> Tuple[List[str], List[str]]: + methods: List[str] = [] + attributes: List[str] = [] + + # Has bundled inputs for forward + if hasattr(script_module, 'get_all_bundled_inputs'): + methods.append('get_all_bundled_inputs') + methods.append('get_num_bundled_inputs') + methods.append('run_on_bundled_input') + + if hasattr(script_module, 'get_bundled_inputs_functions_and_info'): + methods.append('get_bundled_inputs_functions_and_info') + all_info = script_module.get_bundled_inputs_functions_and_info() + for function_name in all_info: + methods.append("get_all_bundled_inputs_for_" + function_name) + methods.append("_generate_bundled_inputs_for_" + function_name) + attributes.append("_bundled_inputs_deflated_" + function_name) + + bundled_inputs_fn = getattr( + script_module, + f"get_all_bundled_inputs_for_{function_name}" + ) + num_bundled_inputs: int = len(bundled_inputs_fn()) + + # Check inflate helper functions for each function, argument and bundled input + func = getattr(script_module, function_name) + for arg_idx in range(len(func.schema.arguments) - 1): + for input_idx in range(num_bundled_inputs): + helper_fn_name = _get_inflate_helper_fn_name( + arg_idx=arg_idx, + input_idx=input_idx, + function_name=function_name + ) + # if the arg has an InflatableArg with fmt_fn, add the helper function name + if hasattr(script_module, helper_fn_name): + methods.append(helper_fn_name) + + return (methods, attributes) + + +def _get_inflate_helper_fn_name( + arg_idx: int, + input_idx: int, + function_name: str, +) -> str: + return f"_inflate_helper_for_{function_name}_input_{input_idx}_arg_{arg_idx}" + + + +def bundle_randn(*size, dtype=None): + """Generate a tensor that will be inflated with torch.randn.""" + stub = torch.zeros(1, dtype=dtype).expand(*size) + return InflatableArg(value=stub, fmt="torch.randn_like({})") + + +def bundle_large_tensor(t): + """Wrap a tensor to allow bundling regardless of size.""" + return InflatableArg(value=t, fmt="{}") diff --git a/lib/python3.11/site-packages/torch/utils/checkpoint.py b/lib/python3.11/site-packages/torch/utils/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..e431ef9b0abf78d7448f5aacab333fe461a78be5 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/checkpoint.py @@ -0,0 +1,1549 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import contextlib +import platform +import uuid +import warnings +import weakref +from collections import defaultdict +from typing import * # noqa: F403 +import enum +from weakref import ReferenceType + +import torch +import torch.fx.traceback as fx_traceback +from torch._functorch._aot_autograd.functional_utils import is_fun +from torch.utils._pytree import tree_map +from torch.testing._internal.logging_tensor import capture_logs, LoggingTensorMode +from torch.utils._python_dispatch import TorchDispatchMode + +__all__ = [ + "checkpoint", + "checkpoint_sequential", + "CheckpointError", + "CheckpointFunction", + "check_backward_validity", + "detach_variable", + "get_device_states", + "set_device_states", + "noop_context_fn", + "set_checkpoint_early_stop", + "DefaultDeviceType", + "set_checkpoint_debug_enabled", + "CheckpointPolicy", + "SelectiveCheckpointContext", + "create_selective_checkpoint_contexts", + "SAC_IGNORED_OPS", +] + +_DEFAULT_DETERMINISM_MODE = "default" + +_checkpoint_debug_enabled: Optional[bool] = None + + +@contextlib.contextmanager +def set_checkpoint_debug_enabled(enabled: Optional[bool]): + """ + Context manager that sets whether checkpoint should print additional debug + information when running. See the ``debug`` flag for + :func:`~torch.utils.checkpoint.checkpoint` for more information. Note that + when set, this context manager overrides the value of ``debug`` passed to + checkpoint. To defer to the local setting, pass ``None`` to this context. + + Args: + enabled (bool): Whether checkpoint should print debug information. + Default is 'None'. + """ + global _checkpoint_debug_enabled + try: + prev = _checkpoint_debug_enabled + _checkpoint_debug_enabled = enabled + yield + finally: + _checkpoint_debug_enabled = prev + + +def detach_variable(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]: + if isinstance(inputs, tuple): + out = [] + for inp in inputs: + if not isinstance(inp, torch.Tensor): + out.append(inp) + continue + + x = inp.detach() + x.requires_grad = inp.requires_grad + out.append(x) + return tuple(out) + else: + raise RuntimeError( + "Only tuple of tensors is supported. Got Unsupported input type: ", + type(inputs).__name__, + ) + + +def check_backward_validity(inputs: Iterable[Any]) -> None: + if not any(inp.requires_grad for inp in inputs if isinstance(inp, torch.Tensor)): + warnings.warn( + "None of the inputs have requires_grad=True. Gradients will be None" + ) + + +def _get_device_module(device="cuda"): + if device == "meta": + return torch.device("meta") + device_module = getattr(torch, device) + return device_module + + +class DefaultDeviceType: + r""" + A class that manages the default device type for checkpointing. + + If no non-CPU tensors are present, the default device type will + be used. The default value is 'cuda'. The device type is used in + the checkpointing process when determining which device states + to save and restore for recomputation. + """ + + _default_device_type = "cuda" + + @staticmethod + def set_device_type(device: str = "cuda"): + """ + Set the default device type for checkpointing. + + Args: + device (str): The device type to be set as default. Default is 'cuda'. + """ + DefaultDeviceType._default_device_type = device + + @staticmethod + def get_device_type() -> str: + """ + Get the current default device type for checkpointing. + + Returns: + str: The current default device type. + """ + return DefaultDeviceType._default_device_type + + +def _infer_device_type(*args): + device_types = [] + + def add_device_types(arg): + nonlocal device_types + if isinstance(arg, torch.Tensor) and not arg.device.type == "cpu": + device_types.append(arg.device.type) + tree_map(add_device_types, args) + + device_types_set = set(device_types) + if len(device_types_set) > 1: + warnings.warn( + "Tensor arguments, excluding CPU tensors, are detected on at least two types of devices. " + "Device state will only be saved for devices of a single device type, and the remaining " + "devices will be ignored. Consequently, if any checkpointed functions involve randomness, " + "this may result in incorrect gradients. (Note that if CUDA devices are among the devices " + "detected, it will be prioritized; otherwise, the first device encountered will be selected.)" + f"\nDevice types: {sorted(device_types_set)} first device type: {device_types[0]}" + ) + if len(device_types) == 0: + return DefaultDeviceType.get_device_type() + elif "cuda" in device_types_set: + return "cuda" + else: + return device_types[0] + + +# We can't know if the run_fn will internally move some args to different devices, +# which would require logic to preserve rng states for those devices as well. +# We could paranoically stash and restore ALL the rng states for all visible devices, +# but that seems very wasteful for most cases. Compromise: Stash the RNG state for +# the device of all Tensor args. +# +# To consider: maybe get_device_states and set_device_states should reside in torch/random.py? +def get_device_states(*args) -> Tuple[List[int], List[torch.Tensor]]: + # This will not error out if "arg" is a CPU tensor or a non-tensor type because + # the conditionals short-circuit. + fwd_device_ids = [] + + def add_device_ids(arg): + nonlocal fwd_device_ids + if isinstance(arg, torch.Tensor) and arg.device.type not in {"cpu", "meta"}: + fwd_device_ids.append(arg.get_device()) + tree_map(add_device_ids, args) + + fwd_device_states = [] + device_module = _get_device_module(_infer_device_type(*args)) + for device_id in fwd_device_ids: + with device_module.device(device_id): + fwd_device_states.append(device_module.get_rng_state()) + + return fwd_device_ids, fwd_device_states + + +def set_device_states(devices, states, *, device_type=None) -> None: + """Sets random number generator states for the specified devices. + + Args: + devices: Device ids to set states for. + states: States to set. + device_type: ``device_type`` of the devices to set states for. Default + is the device returned by a call to ``DefaultDeviceType.get_device_type()``, + which is ``cuda`` if not changed by calling ``DefaultDeviceType::set_device_type()``. + """ + if device_type is None: + device_type = DefaultDeviceType.get_device_type() + if device_type == "meta": + return + device_module = _get_device_module(device_type) + for device, state in zip(devices, states): + with device_module.device(device): + device_module.set_rng_state(state) + + +def _get_autocast_kwargs(device_type="cuda"): + if torch.amp.is_autocast_available(device_type): + device_autocast_kwargs = { + "enabled": torch.is_autocast_enabled(device_type), + "dtype": torch.get_autocast_dtype(device_type), + "cache_enabled": torch.is_autocast_cache_enabled(), + } + else: + device_autocast_kwargs = None + + cpu_autocast_kwargs = { + "enabled": torch.is_autocast_enabled('cpu'), + "dtype": torch.get_autocast_dtype('cpu'), + "cache_enabled": torch.is_autocast_cache_enabled(), + } + + return device_autocast_kwargs, cpu_autocast_kwargs + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, preserve_rng_state, *args): + check_backward_validity(args) + ctx.run_function = run_function + ctx.preserve_rng_state = preserve_rng_state + # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu. + ctx.device_type = _infer_device_type(*args) + ctx.device_autocast_kwargs, ctx.cpu_autocast_kwargs = _get_autocast_kwargs( + ctx.device_type + ) + if preserve_rng_state: + ctx.fwd_cpu_state = torch.get_rng_state() + # Don't eagerly initialize the cuda context by accident. + # (If the user intends that the context is initialized later, within their + # run_function, we SHOULD actually stash the cuda state here. Unfortunately, + # we have no way to anticipate this will happen before we run the function.) + ctx.had_device_in_fwd = False + device_module = _get_device_module(ctx.device_type) + if getattr(device_module, "_initialized", False): + ctx.had_device_in_fwd = True + ctx.fwd_devices, ctx.fwd_device_states = get_device_states(*args) + + # Save non-tensor inputs in ctx, keep a placeholder None for tensors + # to be filled out during the backward. + ctx.inputs = [] + ctx.tensor_indices = [] + tensor_inputs = [] + for i, arg in enumerate(args): + if torch.is_tensor(arg): + tensor_inputs.append(arg) + ctx.tensor_indices.append(i) + ctx.inputs.append(None) + else: + ctx.inputs.append(arg) + + ctx.save_for_backward(*tensor_inputs) + + with torch.no_grad(): + outputs = run_function(*args) + return outputs + + @staticmethod + def backward(ctx, *args): + if not torch.autograd._is_checkpoint_valid(): + raise RuntimeError( + "When use_reentrant=True, torch.utils.checkpoint is incompatible" + " with .grad() or passing an `inputs` parameter to .backward()." + " To resolve this error, you can either set use_reentrant=False," + " or call .backward() without passing the `inputs` argument." + ) + # Copy the list to avoid modifying original list. + inputs = list(ctx.inputs) + tensor_indices = ctx.tensor_indices + tensors = ctx.saved_tensors + + # Fill in inputs with appropriate saved tensors. + for i, idx in enumerate(tensor_indices): + inputs[idx] = tensors[i] + + # Stash the surrounding rng state, and mimic the state that was + # present at this time during forward. Restore the surrounding state + # when we're done. + rng_devices = [] + if ctx.preserve_rng_state and ctx.had_device_in_fwd: + rng_devices = ctx.fwd_devices + with torch.random.fork_rng( + devices=rng_devices, enabled=ctx.preserve_rng_state, device_type=ctx.device_type + ): + if ctx.preserve_rng_state: + torch.set_rng_state(ctx.fwd_cpu_state) + if ctx.had_device_in_fwd: + set_device_states(ctx.fwd_devices, ctx.fwd_device_states, device_type=ctx.device_type) + detached_inputs = detach_variable(tuple(inputs)) + + device_autocast_ctx = torch.amp.autocast( + device_type=ctx.device_type, **ctx.device_autocast_kwargs + ) if torch.amp.is_autocast_available(ctx.device_type) else contextlib.nullcontext() + with torch.enable_grad(), device_autocast_ctx, torch.amp.autocast("cpu", **ctx.cpu_autocast_kwargs): # type: ignore[attr-defined] + outputs = ctx.run_function(*detached_inputs) + + if isinstance(outputs, torch.Tensor): + outputs = (outputs,) + + # run backward() with only tensor that requires grad + outputs_with_grad = [] + args_with_grad = [] + for i in range(len(outputs)): + if torch.is_tensor(outputs[i]) and outputs[i].requires_grad: + outputs_with_grad.append(outputs[i]) + args_with_grad.append(args[i]) + if len(outputs_with_grad) == 0: + raise RuntimeError( + "none of output has requires_grad=True," + " this checkpoint() is not necessary" + ) + torch.autograd.backward(outputs_with_grad, args_with_grad) + grads = tuple( + inp.grad if isinstance(inp, torch.Tensor) else None + for inp in detached_inputs + ) + + return (None, None) + grads + + +def noop_context_fn(): + return contextlib.nullcontext(), contextlib.nullcontext() + +# TorchDynamo does not step inside utils.checkpoint function. The flow +# looks likes this +# 1) TorchDynamo tries to wrap utils.checkpoint in a HigherOrderOp by +# speculatively checking if the forward function is safe to trace. +# 2) If yes, then Dynamo-generated Fx graph has the wrapped higher +# order op. As a result, TorchDynamo does not look inside utils.checkpoint. +# 3) If not, then TorchDynamo falls back to eager by performing a graph +# break. And here, the following disable wrapper ensures that +# TorchDynamo does not trigger again on the frames created by +# utils.checkpoint innards. +@torch._disable_dynamo +def checkpoint( + function, + *args, + use_reentrant: Optional[bool] = None, + context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn, + determinism_check: str = _DEFAULT_DETERMINISM_MODE, + debug: bool = False, + **kwargs +): + r"""Checkpoint a model or part of the model. + + Activation checkpointing is a technique that trades compute for memory. + Instead of keeping tensors needed for backward alive until they are used in + gradient computation during backward, forward computation in checkpointed + regions omits saving tensors for backward and recomputes them during the + backward pass. Activation checkpointing can be applied to any part of a + model. + + There are currently two checkpointing implementations available, determined + by the :attr:`use_reentrant` parameter. It is recommended that you use + ``use_reentrant=False``. Please refer the note below for a discussion of + their differences. + + .. warning:: + + If the :attr:`function` invocation during the backward pass differs + from the forward pass, e.g., due to a global variable, the checkpointed + version may not be equivalent, potentially causing an + error being raised or leading to silently incorrect gradients. + + .. warning:: + + The ``use_reentrant`` parameter should be passed explicitly. In version + 2.4 we will raise an exception if ``use_reentrant`` is not passed. + If you are using the ``use_reentrant=True`` variant, please refer to the + note below for important considerations and potential limitations. + + .. note:: + + The reentrant variant of checkpoint (``use_reentrant=True``) and + the non-reentrant variant of checkpoint (``use_reentrant=False``) + differ in the following ways: + + * Non-reentrant checkpoint stops recomputation as soon as all needed + intermediate activations have been recomputed. This feature is enabled + by default, but can be disabled with :func:`set_checkpoint_early_stop`. + Reentrant checkpoint always recomputes :attr:`function` in its + entirety during the backward pass. + + * The reentrant variant does not record the autograd graph during the + forward pass, as it runs with the forward pass under + :func:`torch.no_grad`. The non-reentrant version does record the + autograd graph, allowing one to perform backward on the graph within + checkpointed regions. + + * The reentrant checkpoint only supports the + :func:`torch.autograd.backward` API for the backward pass without its + `inputs` argument, while the non-reentrant version supports all ways + of performing the backward pass. + + * At least one input and output must have ``requires_grad=True`` for the + reentrant variant. If this condition is unmet, the checkpointed part + of the model will not have gradients. The non-reentrant version does + not have this requirement. + + * The reentrant version does not consider tensors in nested structures + (e.g., custom objects, lists, dicts, etc) as participating in + autograd, while the non-reentrant version does. + + * The reentrant checkpoint does not support checkpointed regions with + detached tensors from the computational graph, whereas the + non-reentrant version does. For the reentrant variant, if the + checkpointed segment contains tensors detached using ``detach()`` or + with :func:`torch.no_grad`, the backward pass will raise an error. + This is because ``checkpoint`` makes all the outputs require gradients + and this causes issues when a tensor is defined to have no gradient in + the model. To avoid this, detach the tensors outside of the + ``checkpoint`` function. + + Args: + function: describes what to run in the forward pass of the model or + part of the model. It should also know how to handle the inputs + passed as the tuple. For example, in LSTM, if user passes + ``(activation, hidden)``, :attr:`function` should correctly use the + first input as ``activation`` and the second input as ``hidden`` + preserve_rng_state(bool, optional): Omit stashing and restoring + the RNG state during each checkpoint. Note that under torch.compile, + this flag doesn't take effect and we always preserve RNG state. + Default: ``True`` + use_reentrant(bool): + specify whether to use the activation checkpoint variant that + requires reentrant autograd. This parameter should be passed + explicitly. In version 2.5 we will raise an exception if + ``use_reentrant`` is not passed. If ``use_reentrant=False``, + ``checkpoint`` will use an implementation that does not require + reentrant autograd. This allows ``checkpoint`` to support additional + functionality, such as working as expected with + ``torch.autograd.grad`` and support for keyword arguments input into + the checkpointed function. + context_fn(Callable, optional): A callable returning a tuple of two + context managers. The function and its recomputation will be run + under the first and second context managers respectively. + This argument is only supported if ``use_reentrant=False``. + determinism_check(str, optional): A string specifying the determinism + check to perform. By default it is set to ``"default"`` which + compares the shapes, dtypes, and devices of the recomputed tensors + against those the saved tensors. To turn off this check, specify + ``"none"``. Currently these are the only two supported values. + Please open an issue if you would like to see more determinism + checks. This argument is only supported if ``use_reentrant=False``, + if ``use_reentrant=True``, the determinism check is always disabled. + debug(bool, optional): If ``True``, error messages will also include + a trace of the operators ran during the original forward computation + as well as the recomputation. This argument is only supported if + ``use_reentrant=False``. + args: tuple containing inputs to the :attr:`function` + + Returns: + Output of running :attr:`function` on :attr:`*args` + """ + if use_reentrant is None: + warnings.warn( + "torch.utils.checkpoint: the use_reentrant parameter should be " + "passed explicitly. In version 2.5 we will raise an exception " + "if use_reentrant is not passed. use_reentrant=False is " + "recommended, but if you need to preserve the current default " + "behavior, you can pass use_reentrant=True. Refer to docs for more " + "details on the differences between the two variants.", + stacklevel=2 + ) + use_reentrant = True + + # Hack to mix *args with **kwargs in a python 2.7-compliant way + preserve = kwargs.pop("preserve_rng_state", True) + if kwargs and use_reentrant: + raise ValueError( + "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs) + ) + + if use_reentrant: + if context_fn is not noop_context_fn or debug is not False: + raise ValueError( + "Passing `context_fn` or `debug` is only supported when " + "use_reentrant=False." + ) + return CheckpointFunction.apply(function, preserve, *args) + else: + gen = _checkpoint_without_reentrant_generator( + function, preserve, context_fn, determinism_check, debug, *args, **kwargs + ) + # Runs pre-forward logic + next(gen) + ret = function(*args, **kwargs) + # Runs post-forward logic + try: + next(gen) + except StopIteration: + return ret + + +def checkpoint_sequential(functions, segments, input, use_reentrant=None, **kwargs): + r"""Checkpoint a sequential model to save memory. + + Sequential models execute a list of modules/functions in order + (sequentially). Therefore, we can divide such a model in various segments + and checkpoint each segment. All segments except the last will not store + the intermediate activations. The inputs of each checkpointed segment will + be saved for re-running the segment in the backward pass. + + .. warning:: + The ``use_reentrant`` parameter should be passed explicitly. In version + 2.4 we will raise an exception if ``use_reentrant`` is not passed. + If you are using the ``use_reentrant=True` variant, please see + :func:`~torch.utils.checkpoint.checkpoint` for + the important considerations and limitations of this variant. It is + recommended that you use ``use_reentrant=False``. + + .. warning: + Since PyTorch 1.4, it allows only one Tensor as the input and + intermediate outputs, just like :class:`torch.nn.Sequential`. + + Args: + functions: A :class:`torch.nn.Sequential` or the list of modules or + functions (comprising the model) to run sequentially. + segments: Number of chunks to create in the model + input: A Tensor that is input to :attr:`functions` + preserve_rng_state(bool, optional): Omit stashing and restoring + the RNG state during each checkpoint. + Default: ``True`` + use_reentrant(bool): + specify whether to use the activation checkpoint variant that + requires reentrant autograd. This parameter should be passed + explicitly. In version 2.5 we will raise an exception if + ``use_reentrant`` is not passed. If ``use_reentrant=False``, + ``checkpoint`` will use an implementation that does not require + reentrant autograd. This allows ``checkpoint`` to support additional + functionality, such as working as expected with + ``torch.autograd.grad`` and support for keyword arguments input into + the checkpointed function. + + Returns: + Output of running :attr:`functions` sequentially on :attr:`*inputs` + + Example: + >>> # xdoctest: +SKIP("stub") + >>> model = nn.Sequential(...) + >>> input_var = checkpoint_sequential(model, chunks, input_var) + """ + if use_reentrant is None: + warnings.warn( + "torch.utils.checkpoint.checkpoint_sequential: the use_reentrant " + "parameter should be passed explicitly. " + "In version 2.5 we will raise an exception if use_reentrant " + "is not passed. use_reentrant=False is " + "recommended, but if you need to preserve the current default " + "behavior, you can pass use_reentrant=True. Refer to docs for more " + "details on the differences between the two variants." + ) + use_reentrant = True + + # Hack for keyword-only parameter in a python 2.7-compliant way + preserve = kwargs.pop("preserve_rng_state", True) + if kwargs: + raise ValueError( + "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs) + ) + + def run_function(start, end, functions): + def forward(input): + for j in range(start, end + 1): + input = functions[j](input) + return input + + return forward + + if isinstance(functions, torch.nn.Sequential): + functions = list(functions.children()) + + segment_size = len(functions) // segments + # the last chunk has to be non-volatile + end = -1 + for start in range(0, segment_size * (segments - 1), segment_size): + end = start + segment_size - 1 + input = checkpoint( + run_function(start, end, functions), + input, + use_reentrant=use_reentrant, + preserve_rng_state=preserve, + ) + return run_function(end + 1, len(functions) - 1, functions)(input) + + +def _internal_assert(cond): + if not cond: + raise AssertionError( + "Something went unexpectedly wrong in activation checkpoint. " + "Please report this bug by filing an issue to PyTorch." + ) + + +# NOTE [ Nestable Checkpoint ] +# +# The semantics of nested checkpoint can be defined by two basic rules. +# Following the two rules leads to an important implication that is central +# to motivating the design. +# +# Rule 1. Saved tensors are managed by inner-most checkpoint only and hidden +# from any outer layers of checkpoint. +# +# Rule 2. The inputs of inner checkpoints are treated as tensors saved to its +# parent checkpoint. +# +# Implication: To recompute any given saved tensor, we need to recompute all of +# the checkpoints wrapping it. +# +# Why is this implied? To unpack a saved tensor X during backward we need to +# recompute the inner-most checkpoint (#1), and in order to recompute that +# checkpoint I need to have its inputs, which are managed by that checkpoint's +# parent (#2), which thus also needs to be recomputed first. Continue this line +# of reasoning and we realize that in order to unpack X, all checkpoints that +# were active at the time X was saved need to be recomputed. (unless we have +# already done so in that backward for some other saved tensor). +# +# In practice, we use a noop autograd Function to save inputs as saved tensors. +# During unpack calling ctx.saved_tensor triggers the parent checkpoint to +# recompute. +# +# Rule 3. We should start recomputation as if there are no checkpoints currently +# active. Checkpoints encountered during recomputation are still +# respected. +# +# When we start recomputation, we push the saved variable hook meant for +# recomputation on the stack. See examples in Rule 6 for more context. +# +# * * * * +# +# Beyond the basic semantics specific to nested checkpoint, we impose several +# more constraints that may apply to checkpointing in general. +# +# Rule 4. Lifetime of recomputed tensors +# +# Recomputed tensors are considered specific to particular invocations +# of backward and are always cleared immediately as they are unpacked +# Particularly, we require this to happen even if retain_graph=True. +# +# [ Implementation details of Rule 4 ] +# +# If we were okay with recomputed tensors staying alive after backward is run +# with retain_graph=True, we would store recomputed variables as the values of a +# WeakKeyDictionary and pack strong references to the keys, so that as we +# backward, those packed keys would be cleared as long as retain_graph=False. +# Clearing the packed key clears the corresponding entry in the WKD. +# +# If we wish recomputed variables to be immediately cleared as we unpack them in +# the retain_graph=True case, we cannot rely on the packed keys to be cleared by +# backward automatically. Instead of packing the strong reference to the key +# directly, we pack a container object, which we manually clear as we unpack. +# +# An important detail is that if a second backward happens, the second +# recomputation needs to reset the container with a newly created key. +# +# Rule 5. Stop recomputation as soon as we've recomputed the saved tensors we +# know we need. +# +# [ Implementation details of Rule 5 ] +# +# During recomputation, raise an exception if the number of recomputed tensors +# matches the number of tensors that we expected to recompute. We wrap the +# recomputation call with a try-catch to catch this specific exception. See +# Rule #6 below for some examples. +# +# Rule 6. We support doing backward inside checkpoint context +# +# [ retain_graph is True] +# +# def fn(x): +# y = x.sin() +# z = y.cos() +# gx, = torch.autograd.grad(z, x, retains_grad=True) +# return gx, z +# +# out = checkpoint(fn)(inp) +# out.backward() +# +# Because z is saved by cos while checkpoint is enabled, it would not be +# actually saved, and so the .grad() call inside must trigger a recomputation. +# +# During recomputation the "inner pack hook" has two responsibilities: +# +# 1) As usual, populating the WeakKeyDictionary storing recomputed tensors +# 2) Pack the actual tensor (detached) so that one may perform backward on the +# recomputed graph. The tensors saved to this graph will live until the end +# of recomputation, or die earlier if someone performs backward with +# retain_graph=False. +# +# More generally performing backward on the recomputed graph occurs in the +# following cases: +# - If backward is performed inside forward, +# - During the original forward IF early-stop is disabled +# - During the original backward +# - If there are multiple .grad()/.backward() calls, we would perform backward +# on the recomputed graph even if early-stop is enabled (see the example below) +# +# [ retain_graph is False ] +# +# The example below shows what happens if during recomputation we find that some +# of the tensors we are trying to recompute have already been cleared. +# +# Spoiler: we don't do anything special, we just skip over them! +# +# def fn(x): +# y = x.sin() # (1) +# z = y.cos() # (2) +# gx, = torch.autograd.grad(z, x) # (3) +# return x.cos() * gx # (4) +# +# out = checkpoint(fn)(inp) +# out.backward() # (5) +# +# 1, 2. Don't save x and y since we are inside a checkpoint. +# 3. Trigger a recompute of fn since x and y weren't saved. +# And depending on whether early stop is enabled, either stop at (2) or +# continue running the function. +# Because we are running backward with retain_graph=False, we clear x and y's +# holders. +# 4. Don't save x since we are inside a checkpoint. +# 5. Calling backward triggers another recompute of fn. During recompute, we see +# that x and y have already been cleared in the original graph as indicated +# by holder=None. We skip over them. We still save x at (4) (since its holder +# is still alive.) + +_enable_checkpoint_early_stop = True + + +@contextlib.contextmanager +def set_checkpoint_early_stop(enable: bool): + """Context manager that sets whether checkpoint should stop recomputation early. + + By default, non-reentrant checkpoint stops recomputation as soon as it + has computed all needed Tensors. This context manager can be used to disable + that feature if it is problematic for your specific application. + + This context manager only needs to be active when forward is run. It does + not need to be active during backward. + + Example:: + + >>> # xdoctest: +SKIP(failing) + >>> message = "saved tensors default hooks are disabled" + >>> with set_checkpoint_early_stop(False): + ... # Any checkpoint under this context manager will respect this + ... # context manager, even if its backward is performed outside. + ... out = checkpoint(fn, inputs) + ... + >>> out.backward() + """ + global _enable_checkpoint_early_stop + try: + prev = _enable_checkpoint_early_stop + _enable_checkpoint_early_stop = enable + yield + finally: + _enable_checkpoint_early_stop = prev + + +class _Handle: + pass + + +class _Holder: + def __init__(self): + self.handles: Dict[int, Optional[_Handle]] = {} + + +class _NoopSaveInputs(torch.autograd.Function): + @staticmethod + def forward(*args): + return torch.empty((0,)) + + @staticmethod + def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None: + # Only tensors can be saved with ctx.save_for_backward, everything else + # is captured by get_args, which is saved directly on ctx + tensor_indices, tensors = zip( + *[(i, o) for i, o in enumerate(inputs) if isinstance(o, torch.Tensor)] + ) + idx2saved_idx = {b: a for a, b in enumerate(tensor_indices)} + # args but with tensors replaced with None as placeholders + args = [None if isinstance(o, torch.Tensor) else o for o in inputs] + + def get_args(saved_tensors): + # restore the placeholders with the original tensors grabbed from + # ctx.saved_tensors (which may be saved on a parent checkpoint if + # this checkpoint is nested, and that would trigger a recursive + # unpack!) + ret = [ + saved_tensors[idx2saved_idx[i]] if i in tensor_indices else o + for i, o in enumerate(args) + ] + # grab the tail since we also saved the dummy to avoid having to explicitly + # handle the case where there are no tensor inputs + return ret[1:] + + ctx.get_args = get_args + ctx.save_for_backward(*tensors) + + @staticmethod + def backward(ctx, *grad_outputs): + raise AssertionError("Did not expect to backward on this graph") + + +class _CheckpointFrame: + def __init__(self, recompute_fn, early_stop, unpack_error_cb, metadata_fn): + self.recompute_fn = recompute_fn + self.input_saver = None + self.weak_holders: List[ReferenceType] = [] + # We store this as a weakkeydictionary so that in the case of a partial + # backward, the entries in the dict are cleared alongside the Holder + # which will be removed when the SavedVariable is cleared. + self.recomputed: DefaultDict[ + int, weakref.WeakKeyDictionary[_Handle, torch.Tensor] + ] = defaultdict(weakref.WeakKeyDictionary) + # We need both recomp_counter and recomputed since they can diverge + # https://github.com/pytorch/pytorch/pull/90105#discussion_r1135889885 + self.recomp_counter: DefaultDict[int, int] = defaultdict(int) + self.is_recomputed: DefaultDict[int, bool] = defaultdict(bool) + + # See Rule 5 + self.early_stop = early_stop + + # Debugging + self.metadata_fn = metadata_fn + self.unpack_error_cb = unpack_error_cb + self.x_metadatas = [] + self.forward_completed = False + self.ignore_saved_mismatch = False + + def check_recomputed_tensors_match(self, gid): + if self.ignore_saved_mismatch: + # TODO: we can probably make this check stricter by checking that + # the metadata of the first tensors still match. + return + # NOTE [ Error handling for checkpoint ] + # + # At a high level, we need to check that the tensors saved + # during original forward matches tensors saved during recompute + # This means handling 3 cases: + # + # 1. During recompute, more tensors were saved. + # + # Usually this is hidden due to the StopRecomputationError + # but if early stop is not enabled, or we would have errored + # anyway because there aren't enough weak_holders. But we + # do want to have a nice error. See the _recomputation_hook + # for details. + if not len(self.weak_holders) == self.recomp_counter[gid]: + # 2. During recompute, fewer tensors were saved + # + # We know that everytime we save something do original forward + # we append to weak_holder, and every time we save a tensor + # during recompute we increment recompute_counter. + raise CheckpointError( + "torch.utils.checkpoint: A different number of tensors was saved " + "during the original forward and recomputation.\n" + f"Number of tensors saved during forward: {len(self.weak_holders)}\n" + f"Number of tensors saved during recomputation: {self.recomp_counter[gid]}" + ) + + # 3. During recompute, the same tensors were saved, but they + # have different metadata + nb_meta_different = [] + for idx, weak_holder in enumerate(self.weak_holders): + holder = weak_holder() + if holder is None: + continue + # We've seen all holders since we iterate over them in order + # For every holder that is still alive now, it must've been + # alive when we saw it during recompute, therefore, the + # gid must be set. + _internal_assert(gid in holder.handles) + # We know this is the first unpack, so it couldn't have been set + # to None yet. + _internal_assert(holder.handles[gid] is not None) + # We always set these together in the recomputation hook + _internal_assert(holder.handles[gid] in self.recomputed[gid]) + # see pack hook, x_metadata is 1:1 with weak_holders. + x_meta = self.x_metadatas[idx] + recomputed_x = self.recomputed[gid][holder.handles[gid]] + if x_meta != self.metadata_fn(recomputed_x): + nb_meta_different.append((idx, x_meta, self.metadata_fn(recomputed_x))) + + if len(nb_meta_different) > 0: + mismatched_tensors = "" + for idx, x_meta, recomputed_meta in nb_meta_different: + mismatched_tensors += ( + f"tensor at position {idx}:\n" + f"saved metadata: {x_meta}\n" + f"recomputed metadata: {recomputed_meta}\n" + ) + raise CheckpointError( + "torch.utils.checkpoint: Recomputed values for the following tensors " + "have different metadata than during the forward pass.\n" + f"{mismatched_tensors}" + ) + + +_checkpoint_error_template = """ \ +An error happened while unpacking tensors; dumping logs of latest computation +because you passed `debug=True` to `torch.utils.checkpoint.checkpoint()`. +Scroll all the way down for guidance on how to navigate these logs. + ++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ +| 1. Stack traces of the operators that ran in the original forward | ++------------------------------------------------------------------------------+ + +{forward_traces} ++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ +| 2. Stack traces of the operators that ran during recomputation | ++------------------------------------------------------------------------------+ + +{recompute_traces} ++~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ +| 3. Log of operators in the original forward and recomputation | ++------------------------------------------------------------------------------+ +(Scroll up to correlate stack traces with each operation listed below. This + helps identify their source in the code.) + +IMPORTANT: Differences in "detach" calls between the original forward and the + recomputation are expected. They are introduced by the checkpointing + mechanism and can be ignored. + +Operations executed during the original forward: + +{forward_ops} + +Operations executed during recomputation: + +{recompute_ops} + ++------------------------------------------------------------------------------+ + ERROR: Detected non-determinism while running activation checkpointing + + You are seeing this error because you passed `debug=True` to checkpoint and + tensors to be saved during the original forward and differ between those saved + during recomputation. This can happen if different operators were ran in the + original forward and in the recomputation. + + To identify where the mismatch may be coming from, you can do the following: + + 1) Compare the operators ran during original forward and recomputation to + see where they differ. These operators are printed above in the order they + were executed. + + 2) Review the stack trace for each operator to locate its invocation source. + Each operator's stack trace is printed in their execution order. + + Note that the logs can be quite long. Here's how they are structured: + (Tip: you can Ctrl-f for these headers) + + 1. Stack traces of the operators that ran in the original forward + 2. Stack traces of the operators that ran during recomputation + 3. Log of operators in the original forward and recomputation + 4. Error message <--- You are here +-------------------------------------------------------------------------------- +""" + +class CheckpointError(RuntimeError): + pass + + +def _get_debug_context_and_cb() -> Tuple[Callable[[], Any], Callable[[CheckpointError], None]]: + # This function returns the context_fn and error_cb to be used by the + # checkpointing mechanism. error_cb is invoked when an error is detected + # during unpack. + + # record_context_cpp is not support on non-linux non-x86_64 platforms + cpp_tb = platform.machine() == 'x86_64' and platform.system() == 'Linux' + + class CaptureLogs: + def __init__(self): + self.logs = None + self.tbs = None + + def get_context_manager(self): + @contextlib.contextmanager + def logging_mode(): + with LoggingTensorMode(), \ + capture_logs(True, python_tb=True, script_tb=True, cpp_tb=cpp_tb) as logs_and_tb: + self.logs, self.tbs = logs_and_tb + yield logs_and_tb + return logging_mode() + + capture_logs_fwd = CaptureLogs() + capture_logs_recompute = CaptureLogs() + + def unpack_error_cb(e: CheckpointError): + def get_str_tb(label, capture_logs): + out = "" + total_len = len(capture_logs.logs) + for i, (log, tb) in enumerate(zip(capture_logs.logs, capture_logs.tbs)): + out += f"{log} ({i + 1} of {total_len} in {label})\n\n" + found_torch_dispatch = False + for line in tb: + # Start printing stack trace only after __torch_dispatch__ is found + is_torch_dispatch = line['name'] == '__torch_dispatch__' + if not found_torch_dispatch and not is_torch_dispatch: + continue + elif is_torch_dispatch: + found_torch_dispatch = True + continue + out += f"{line['filename']}:{line['line']}:{line['name']}\n" + out += "\n\n" + return out + assert capture_logs_fwd.logs is not None + assert capture_logs_recompute.logs is not None + raise CheckpointError( + _checkpoint_error_template.format( + forward_traces=get_str_tb("original", capture_logs_fwd), + recompute_traces=get_str_tb("recompute", capture_logs_recompute), + forward_ops="\n".join(capture_logs_fwd.logs), + recompute_ops="\n".join(capture_logs_recompute.logs) + ) + ) from e + + def context_fn(): + return capture_logs_fwd.get_context_manager(), capture_logs_recompute.get_context_manager() + + return context_fn, unpack_error_cb + +def _default_meta_extractor(x: torch.Tensor) -> Dict[str, Any]: + # These properties are fast to check, easy to understand + return { + "shape": x.shape, + "dtype": x.dtype, + "device": x.device + } + +_allowed_determinism_checks_to_fns: Dict[str, Callable[[torch.Tensor], Any]] = { + _DEFAULT_DETERMINISM_MODE: _default_meta_extractor, + "none": lambda _: None, +} + +# See Rule 5 +class _StopRecomputationError(Exception): + pass + + +class _recomputation_hook(torch.autograd.graph.saved_tensors_hooks): + def __init__(self, target_frame_ref: ReferenceType, gid: int): + def pack_hook(x): + x = x.detach() if x.requires_grad else x + target_frame = target_frame_ref() + assert target_frame is not None # appease mypy + recomp_idx = target_frame.recomp_counter[gid] + target_frame.recomp_counter[gid] += 1 + + if recomp_idx >= len(target_frame.weak_holders): + assert not target_frame.early_stop + if not target_frame.forward_completed: + # We run into this case when early stop is not enabled and do + # grad within checkpoint. + # We need to set this flag, so we don't error out later when + # we check if the number of tensors saved during forward and + # recomputation match. + target_frame.ignore_saved_mismatch = True + return x + raise CheckpointError( + "torch.utils.checkpoint: trying to save more tensors during " + "recomputation than during the original forward pass." + ) + + holder = target_frame.weak_holders[recomp_idx]() + + # This holder may have been cleared because someone may have called + # backward within forward. If so, we don't need to save. + if holder is not None: + _internal_assert(holder.handles.get(gid, None) is None) + holder.handles[gid] = _Handle() + target_frame.recomputed[gid][holder.handles[gid]] = x + + if target_frame.early_stop and target_frame.recomp_counter[gid] == len( + target_frame.weak_holders + ): + raise _StopRecomputationError + # See Rule 6: [ retain_graph is True ] above + return x + + def unpack_hook(x): + # See Rule 6: [ retain_graph is True ] above for an example of when + # the graph created during recomputation could be backwarded. + return x + + super().__init__(pack_hook, unpack_hook) + + +class _checkpoint_hook(torch.autograd.graph.saved_tensors_hooks): + def __init__(self, frame): + def pack_hook(x): + # See Rule 4 above + holder = _Holder() + frame.weak_holders.append(weakref.ref(holder)) + # Save metadata to detect non-determinism + if frame.metadata_fn is not None: + with torch.no_grad(): + frame.x_metadatas.append(frame.metadata_fn(x)) + return holder + + def unpack_hook(holder): + gid = torch._C._current_graph_task_id() + if gid == -1: + # generate a temporary id if we trigger unpack outside of a backward call + gid = int(uuid.uuid4()) + + if not frame.is_recomputed[gid]: + ctx = frame.input_saver.grad_fn + args = ctx.get_args(ctx.saved_tensors) + + try: + with _recomputation_hook( + weakref.ref(frame), gid + ), torch.autograd.enable_grad(): + frame.recompute_fn(*args) + except _StopRecomputationError: + pass + frame.is_recomputed[gid] = True + frame.check_recomputed_tensors_match(gid) + + _internal_assert(gid in holder.handles) + + if holder.handles[gid] is None: + raise CheckpointError( + "torch.utils.checkpoint: Unpack is being triggered for a tensor that was already " + "unpacked once. If you are calling ctx.saved_tensors in backward, make sure to do " + "so only once. Otherwise please open an issue with details on your use case." + ) + _internal_assert(holder.handles[gid] in frame.recomputed[gid]) + ret = frame.recomputed[gid][holder.handles[gid]] + holder.handles[gid] = None + return ret + + if frame.unpack_error_cb is not None: + def unpack_hook_with_error_cb(holder): + try: + return unpack_hook(holder) + except CheckpointError as e: + frame.unpack_error_cb(e) + super().__init__(pack_hook, unpack_hook_with_error_cb) + else: + super().__init__(pack_hook, unpack_hook) + + +def _is_compiling(func, args, kwargs): + # Check if we are under AOTAutograd tracing + # There should probably be a better way to do this... + # TODO: unify _is_compiling across all compile stacks + for arg in args: + if isinstance(arg, torch.Tensor) and is_fun(arg): + return True + return False + + +class _VersionWrapper: + # Check that cached tensors are not mutated. + def __init__(self, val): + self.val: Union[torch.Tensor, Any] = val + self.version: Optional[int] = val._version if isinstance(val, torch.Tensor) else None + + def get_val(self, allow_cache_entry_mutation): + if self.version is not None and not allow_cache_entry_mutation: + if self.val._version != self.version: + # Can we give user a stack trace of where the mutation happened? + raise RuntimeError( + "Tensor cached during selective activation checkpoint has been mutated" + ) + return self.val + + +def _maybe_detach(x, any_ret_has_alias_info): + # We detach for two separate reasons: + # - For view ops, we need to ensure that when the tensor is returned from + # CachedDispatchMode, as_view sees that the AutogradMeta is nullptr + # - Avoid reference cycles + # For case 1, it is not enough to check whether x has differentiable dtype + # because non-differentiable dtype can have non-nullptr AutogradMeta, e.g. + # when the tensor is a view. + if isinstance(x, torch.Tensor) and (x.is_floating_point() or x.is_complex() or any_ret_has_alias_info): + with torch._C._SetExcludeDispatchKeyGuard(torch._C.DispatchKey.ADInplaceOrView, False): + # Ensure that view performed beneath autograd properly propagates + # version counter. TODO: Use reentrant_dispatch instead of + # manually manipulating dispatch keys. Using reentrant_dispatch + # would respect inference_mode, though that is not relevant for + # this case. + x = x.detach() + return x + + +class SelectiveCheckpointContext: + """ + Context passed to policy function during selective checkpointing. + + This class is used to pass relevant metadata to the policy function during + selective checkpointing. The metadata includes whether the current invocation + of the policy function is during recomputation or not. + + Example: + >>> # xdoctest: +SKIP(stub) + >>> + >>> def policy_fn(ctx, op, *args, **kwargs): + >>> print(ctx.is_recompute) + >>> + >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) + >>> + >>> out = torch.utils.checkpoint.checkpoint( + >>> fn, x, y, + >>> use_reentrant=False, + >>> context_fn=context_fn, + >>> ) + """ + def __init__(self, *, is_recompute): + self.is_recompute = is_recompute + + +class CheckpointPolicy(enum.Enum): + """ + Enum for specifying the policy for checkpointing during backpropagation. + + The following policies are supported: + + - ``{MUST,PREFER}_SAVE``: The operation's output will be saved during the forward + pass and will not be recomputed during the backward pass + - ``{MUST,PREFER}_RECOMPUTE``: The operation's output will not be saved during the + forward pass and will be recomputed during the backward pass + + Use ``MUST_*`` over ``PREFER_*`` to indicate that the policy should not be overridden + by other subsystems like `torch.compile`. + + .. note:: + A policy function that always returns ``PREFER_RECOMPUTE`` is + equivalent to vanilla checkpointing. + + A policy function that returns ``PREFER_SAVE`` every op is + NOT equivalent to not using checkpointing. Using such a policy would + save additional tensors not limited to ones that are actually needed for + gradient computation. + """ + MUST_SAVE = 0 + PREFER_SAVE = 1 + MUST_RECOMPUTE = 2 + PREFER_RECOMPUTE = 3 + + +def _policy_from_bool(b): + # For backward compatability + return CheckpointPolicy.MUST_SAVE if b else CheckpointPolicy.PREFER_RECOMPUTE + + +SAC_IGNORED_OPS = { + # AC inserts different number of detach during forward and recompute. + torch.ops.aten.detach.default, + # AC's determinism check invokes additional metadata ops during forward. + # With subclasses involved, these metadata ops become dispatchable, this + # can result in incorrectness if these ops are selected cached. + torch.ops.prim.device.default, +} | set(torch._subclasses.functional_tensor.FunctionalTensor.metadata_fns) + + +class _CachingTorchDispatchMode(TorchDispatchMode): + # Used together with _CachedTorchDispatchMode to implement SAC. + def __init__(self, policy_fn, storage): + self.policy_fn = policy_fn + self.storage = storage + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + if func in SAC_IGNORED_OPS: + return func(*args, **kwargs) + + kwargs = {} if kwargs is None else kwargs + policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=False), + func, *args, **kwargs) + if isinstance(policy, bool): + policy = _policy_from_bool(policy) + + is_compiling = _is_compiling(func, args, kwargs) + + if is_compiling: + # Overwrite each node's "recompute" tag to add in the user annotation. + fx_traceback.current_meta["recompute"] = policy + + out = func(*args, **kwargs) + + any_ret_has_alias_info = any(ret.alias_info is not None for ret in func._schema.returns) + + if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling: + self.storage[func].append(tree_map(lambda x: _VersionWrapper(_maybe_detach(x, any_ret_has_alias_info)), out)) + return out + +class _CachedTorchDispatchMode(TorchDispatchMode): + # Used together with _CachedTorchDispatchMode to implement SAC. + def __init__(self, policy_fn, storage, allow_cache_entry_mutation): + self.policy_fn = policy_fn + self.storage = storage + self.allow_cache_entry_mutation = allow_cache_entry_mutation + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + if func in SAC_IGNORED_OPS: + return func(*args, **kwargs) + + kwargs = {} if kwargs is None else kwargs + policy = self.policy_fn(SelectiveCheckpointContext(is_recompute=True), + func, *args, **kwargs) + if isinstance(policy, bool): + policy = _policy_from_bool(policy) + + is_compiling = _is_compiling(func, args, kwargs) + + if policy in (CheckpointPolicy.MUST_SAVE, CheckpointPolicy.PREFER_SAVE) or is_compiling: + storage = self.storage.get(func) + if storage is None: + raise RuntimeError(f"{func} encountered during backward, but not found in storage") + if len(storage) == 0: + raise RuntimeError( + "Trying to backward an extra time. You are only allowed to backward once " + "on any region computed under selective activation checkpoint." + ) + out = tree_map(lambda x: x.get_val(self.allow_cache_entry_mutation), storage.pop(0)) + else: + out = func(*args, **kwargs) + return out + + +def create_selective_checkpoint_contexts(policy_fn_or_list, allow_cache_entry_mutation=False): + """ + Helper to avoid recomputing certain ops during activation checkpointing. + + Use this with `torch.utils.checkpoint.checkpoint` to control which + operations are recomputed during the backward pass. + + Args: + policy_fn_or_list (Callable or List): + - If a policy function is provided, it should accept a + :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and + kwargs to the op, and return a :class:`CheckpointPolicy` enum value + indicating whether the execution of the op should be recomputed or not. + - If a list of operations is provided, it is equivalent to a policy + returning `CheckpointPolicy.MUST_SAVE` for the specified + operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other + operations. + allow_cache_entry_mutation (bool, optional): By default, an error is + raised if any tensors cached by selective activation checkpoint are + mutated in order to ensure correctness. If set to `True`, this check + is disabled. + Returns: + A tuple of two context managers. + + Example: + >>> # xdoctest: +REQUIRES(LINUX) + >>> import functools + >>> + >>> x = torch.rand(10, 10, requires_grad=True) + >>> y = torch.rand(10, 10, requires_grad=True) + >>> + >>> ops_to_save = [ + >>> torch.ops.aten.mm.default, + >>> ] + >>> + >>> def policy_fn(ctx, op, *args, **kwargs): + >>> if op in ops_to_save: + >>> return CheckpointPolicy.MUST_SAVE + >>> else: + >>> return CheckpointPolicy.PREFER_RECOMPUTE + >>> + >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) + >>> + >>> # or equivalently + >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) + >>> + >>> def fn(x, y): + >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y + >>> + >>> out = torch.utils.checkpoint.checkpoint( + >>> fn, x, y, + >>> use_reentrant=False, + >>> context_fn=context_fn, + >>> ) + """ + # NB: If grad_mode is disabled, checkpoint would not run forward under + # context_fn anyway, so proceed as usual. + if isinstance(policy_fn_or_list, list): + for op in policy_fn_or_list: + if not isinstance(op, torch._ops.OpOverload): + _extra_msg = ( + "Please update the OpOverloadPacket to a specific OpOverload." + "For example, if you have `torch.ops.aten.mm`, change it to `torch.ops.aten.mm.default`." + ) if isinstance(op, torch._ops.OpOverloadPacket) else "" + raise ValueError( + f"Expected op in `op_list` to be an OpOverload but got: {op} " + f"of type {type(op)}. {_extra_msg}" + ) + + def policy_fn(ctx, op, *args, **kwargs): + if op in policy_fn_or_list: + return CheckpointPolicy.MUST_SAVE + else: + return CheckpointPolicy.PREFER_RECOMPUTE + elif callable(policy_fn_or_list): + policy_fn = policy_fn_or_list + else: + raise TypeError("policy_fn_or_list must be either a function or a list of ops.") + + storage: Dict[Any, List[Any]] = defaultdict(list) + return ( + _CachingTorchDispatchMode(policy_fn, storage), + _CachedTorchDispatchMode(policy_fn, storage, allow_cache_entry_mutation), + ) + +# NB: this helper wraps fn before calling checkpoint_impl. kwargs and +# saving/restoring of global state is handled here. + +def _checkpoint_without_reentrant_generator( + fn, + preserve_rng_state=True, + context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn, + determinism_check: str = _DEFAULT_DETERMINISM_MODE, + debug: bool = False, + *args, + **kwargs +): + """Checkpointing without reentrant autograd. + + Args: + fn: describes what to run in the forward pass of the model or + part of the model. It should also know how to handle the inputs + passed as the tuple. For example, in LSTM, if user passes + ``(activation, hidden)``, :attr:`function` should correctly use the + first input as ``activation`` and the second input as ``hidden`` + preserve_rng_state(bool, optional): Omit stashing and restoring + the RNG state during each checkpoint. + Default: ``True`` + context_fn(Callable, optional): A callable returning a tuple of two + context managers. The function and its recomputation will be run + under the first and second context managers respectively. + determinism_check(str, optional): A string specifying the determinism + check to perform. By default it is set to ``"default"`` which + compares the shapes, dtypes, and devices of the recomputed tensors + against those the saved tensors. To turn off this check, specify + ``"none"``. Currently these are the only two supported values. + Please open an issue if you would like to see more determinism + checks. + debug(bool, optional): If ``True``, error messages will also include + a trace of the operators ran during the original forward computation + as well as the recomputation. + *args: Arguments to pass in to the given ``function``. + **kwargs: Keyword arguments to pass into the given ``function``. + """ + unpack_error_cb = None + + if _checkpoint_debug_enabled if _checkpoint_debug_enabled is not None else debug: + if context_fn != noop_context_fn: + raise ValueError( + "debug=True is incompatible with non-default context_fn" + ) + context_fn, unpack_error_cb = _get_debug_context_and_cb() + + if determinism_check in _allowed_determinism_checks_to_fns: + metadata_fn = _allowed_determinism_checks_to_fns[determinism_check] + else: + raise ValueError( + f"determinism_check should be one of {list(_allowed_determinism_checks_to_fns.keys())}, " + f"but got {determinism_check}" + ) + + device_type = _infer_device_type(*args) + device_module = _get_device_module(device_type) + forward_context, recompute_context = context_fn() + if _is_compiling(fn, args, kwargs) and context_fn != noop_context_fn: + assert ( + isinstance(forward_context, TorchDispatchMode) and + isinstance(recompute_context, TorchDispatchMode) + ), \ + "In torch.compile mode, `context_fn` arg passed to `torch.utils.checkpoint` " + \ + "must generate a tuple of two `TorchDispatchMode`s." + # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu. + device_autocast_kwargs, cpu_autocast_kwargs = _get_autocast_kwargs(device_type=device_type) + + if preserve_rng_state: + fwd_cpu_state = torch.get_rng_state() + # Don't eagerly initialize the cuda context by accident. + # (If the user intends that the context is initialized later, within their + # run_function, we SHOULD actually stash the cuda state here. Unfortunately, + # we have no way to anticipate this will happen before we run the function. + # If they do so, we raise an error.) + had_device_in_fwd = False + if getattr(device_module, "_initialized", False): + had_device_in_fwd = True + fwd_devices, fwd_device_states = get_device_states(*args) + + def recompute_fn(*inputs): + kwargs, *args = inputs + # This will be called later during recomputation. This wrapping enables + # the necessary global state to be captured. + rng_devices = [] + if preserve_rng_state and had_device_in_fwd: + rng_devices = fwd_devices + with torch.random.fork_rng( + devices=rng_devices, enabled=preserve_rng_state, device_type=device_type + ): + if preserve_rng_state: + torch.set_rng_state(fwd_cpu_state) + if had_device_in_fwd: + set_device_states(fwd_devices, fwd_device_states, device_type=device_type) + + device_autocast_ctx = torch.amp.autocast( + device_type=device_type, **device_autocast_kwargs + ) if torch.amp.is_autocast_available(device_type) else contextlib.nullcontext() + with device_autocast_ctx, torch.amp.autocast("cpu", **cpu_autocast_kwargs), recompute_context: # type: ignore[attr-defined] + fn(*args, **kwargs) + + new_frame = _CheckpointFrame( + recompute_fn, + _enable_checkpoint_early_stop, + unpack_error_cb, + metadata_fn + ) + dummy = torch.empty((0,), requires_grad=True) + new_frame.input_saver = _NoopSaveInputs.apply(dummy, kwargs, *args) + + # When ambient grad_mode is False + if new_frame.input_saver.grad_fn is None: + yield + return + + with _checkpoint_hook(new_frame), forward_context: + yield + new_frame.forward_completed = True + + if getattr(device_module, "_initialized", False) and \ + preserve_rng_state and not had_device_in_fwd: # type: ignore[possibly-undefined] + # Device was not initialized before running the forward, so we didn't + # stash the device state. + raise RuntimeError( + "PyTorch's device state was initialized in the forward pass " + "of a Checkpoint, which is not allowed. Please open an issue " + "if you need this feature." + ) + + return diff --git a/lib/python3.11/site-packages/torch/utils/collect_env.py b/lib/python3.11/site-packages/torch/utils/collect_env.py new file mode 100644 index 0000000000000000000000000000000000000000..ce397d6090ead9d7d6051187c98dc4b3e76658c5 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/collect_env.py @@ -0,0 +1,692 @@ +# mypy: allow-untyped-defs + +# Unlike the rest of the PyTorch this file must be python2 compliant. +# This script outputs relevant system environment info +# Run it with `python collect_env.py` or `python -m torch.utils.collect_env` +import datetime +import json +import locale +import re +import subprocess +import sys +import os +from collections import namedtuple + + +try: + import torch + TORCH_AVAILABLE = True +except (ImportError, NameError, AttributeError, OSError): + TORCH_AVAILABLE = False + +# System Environment Information +SystemEnv = namedtuple('SystemEnv', [ + 'torch_version', + 'is_debug_build', + 'cuda_compiled_version', + 'gcc_version', + 'clang_version', + 'cmake_version', + 'os', + 'libc_version', + 'python_version', + 'python_platform', + 'is_cuda_available', + 'cuda_runtime_version', + 'cuda_module_loading', + 'nvidia_driver_version', + 'nvidia_gpu_models', + 'cudnn_version', + 'pip_version', # 'pip' or 'pip3' + 'pip_packages', + 'conda_packages', + 'hip_compiled_version', + 'hip_runtime_version', + 'miopen_runtime_version', + 'caching_allocator_config', + 'is_xnnpack_available', + 'cpu_info', +]) + +COMMON_PATTERNS = [ + "torch", + "numpy", + "triton", + "optree", +] + +NVIDIA_PATTERNS = [ + "cuda-cudart", + "cuda-cupti", + "cuda-libraries", + "cuda-opencl", + "cuda-nvrtc", + "cuda-runtime", + "cublas", + "cudnn", + "cufft", + "curand", + "cusolver", + "cusparse", + "nccl", + "nvjitlink", + "nvtx", +] + +CONDA_PATTERNS = [ + "cudatoolkit", + "soumith", + "mkl", + "magma", +] + +PIP_PATTERNS = [ + "mypy", + "flake8", + "onnx", +] + + +def run(command): + """Return (return-code, stdout, stderr).""" + shell = True if type(command) is str else False + p = subprocess.Popen(command, stdout=subprocess.PIPE, + stderr=subprocess.PIPE, shell=shell) + raw_output, raw_err = p.communicate() + rc = p.returncode + if get_platform() == 'win32': + enc = 'oem' + else: + enc = locale.getpreferredencoding() + output = raw_output.decode(enc) + err = raw_err.decode(enc) + return rc, output.strip(), err.strip() + + +def run_and_read_all(run_lambda, command): + """Run command using run_lambda; reads and returns entire output if rc is 0.""" + rc, out, _ = run_lambda(command) + if rc != 0: + return None + return out + + +def run_and_parse_first_match(run_lambda, command, regex): + """Run command using run_lambda, returns the first regex match if it exists.""" + rc, out, _ = run_lambda(command) + if rc != 0: + return None + match = re.search(regex, out) + if match is None: + return None + return match.group(1) + +def run_and_return_first_line(run_lambda, command): + """Run command using run_lambda and returns first line if output is not empty.""" + rc, out, _ = run_lambda(command) + if rc != 0: + return None + return out.split('\n')[0] + + +def get_conda_packages(run_lambda, patterns=None): + if patterns is None: + patterns = CONDA_PATTERNS + COMMON_PATTERNS + NVIDIA_PATTERNS + conda = os.environ.get('CONDA_EXE', 'conda') + out = run_and_read_all(run_lambda, "{} list".format(conda)) + if out is None: + return out + + return "\n".join( + line + for line in out.splitlines() + if not line.startswith("#") + and any(name in line for name in patterns) + ) + +def get_gcc_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'gcc --version', r'gcc (.*)') + +def get_clang_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'clang --version', r'clang version (.*)') + + +def get_cmake_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'cmake --version', r'cmake (.*)') + + +def get_nvidia_driver_version(run_lambda): + if get_platform() == 'darwin': + cmd = 'kextstat | grep -i cuda' + return run_and_parse_first_match(run_lambda, cmd, + r'com[.]nvidia[.]CUDA [(](.*?)[)]') + smi = get_nvidia_smi() + return run_and_parse_first_match(run_lambda, smi, r'Driver Version: (.*?) ') + + +def get_gpu_info(run_lambda): + if get_platform() == 'darwin' or (TORCH_AVAILABLE and hasattr(torch.version, 'hip') and torch.version.hip is not None): + if TORCH_AVAILABLE and torch.cuda.is_available(): + if torch.version.hip is not None: + prop = torch.cuda.get_device_properties(0) + if hasattr(prop, "gcnArchName"): + gcnArch = " ({})".format(prop.gcnArchName) + else: + gcnArch = "NoGCNArchNameOnOldPyTorch" + else: + gcnArch = "" + return torch.cuda.get_device_name(None) + gcnArch + return None + smi = get_nvidia_smi() + uuid_regex = re.compile(r' \(UUID: .+?\)') + rc, out, _ = run_lambda(smi + ' -L') + if rc != 0: + return None + # Anonymize GPUs by removing their UUID + return re.sub(uuid_regex, '', out) + + +def get_running_cuda_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'nvcc --version', r'release .+ V(.*)') + + +def get_cudnn_version(run_lambda): + """Return a list of libcudnn.so; it's hard to tell which one is being used.""" + if get_platform() == 'win32': + system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows') + cuda_path = os.environ.get('CUDA_PATH', "%CUDA_PATH%") + where_cmd = os.path.join(system_root, 'System32', 'where') + cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path) + elif get_platform() == 'darwin': + # CUDA libraries and drivers can be found in /usr/local/cuda/. See + # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install + # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac + # Use CUDNN_LIBRARY when cudnn library is installed elsewhere. + cudnn_cmd = 'ls /usr/local/cuda/lib/libcudnn*' + else: + cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev' + rc, out, _ = run_lambda(cudnn_cmd) + # find will return 1 if there are permission errors or if not found + if len(out) == 0 or (rc != 1 and rc != 0): + l = os.environ.get('CUDNN_LIBRARY') + if l is not None and os.path.isfile(l): + return os.path.realpath(l) + return None + files_set = set() + for fn in out.split('\n'): + fn = os.path.realpath(fn) # eliminate symbolic links + if os.path.isfile(fn): + files_set.add(fn) + if not files_set: + return None + # Alphabetize the result because the order is non-deterministic otherwise + files = sorted(files_set) + if len(files) == 1: + return files[0] + result = '\n'.join(files) + return 'Probably one of the following:\n{}'.format(result) + + +def get_nvidia_smi(): + # Note: nvidia-smi is currently available only on Windows and Linux + smi = 'nvidia-smi' + if get_platform() == 'win32': + system_root = os.environ.get('SYSTEMROOT', 'C:\\Windows') + program_files_root = os.environ.get('PROGRAMFILES', 'C:\\Program Files') + legacy_path = os.path.join(program_files_root, 'NVIDIA Corporation', 'NVSMI', smi) + new_path = os.path.join(system_root, 'System32', smi) + smis = [new_path, legacy_path] + for candidate_smi in smis: + if os.path.exists(candidate_smi): + smi = '"{}"'.format(candidate_smi) + break + return smi + + +# example outputs of CPU infos +# * linux +# Architecture: x86_64 +# CPU op-mode(s): 32-bit, 64-bit +# Address sizes: 46 bits physical, 48 bits virtual +# Byte Order: Little Endian +# CPU(s): 128 +# On-line CPU(s) list: 0-127 +# Vendor ID: GenuineIntel +# Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz +# CPU family: 6 +# Model: 106 +# Thread(s) per core: 2 +# Core(s) per socket: 32 +# Socket(s): 2 +# Stepping: 6 +# BogoMIPS: 5799.78 +# Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr +# sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl +# xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 +# pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand +# hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced +# fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap +# avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 +# xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq +# avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities +# Virtualization features: +# Hypervisor vendor: KVM +# Virtualization type: full +# Caches (sum of all): +# L1d: 3 MiB (64 instances) +# L1i: 2 MiB (64 instances) +# L2: 80 MiB (64 instances) +# L3: 108 MiB (2 instances) +# NUMA: +# NUMA node(s): 2 +# NUMA node0 CPU(s): 0-31,64-95 +# NUMA node1 CPU(s): 32-63,96-127 +# Vulnerabilities: +# Itlb multihit: Not affected +# L1tf: Not affected +# Mds: Not affected +# Meltdown: Not affected +# Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown +# Retbleed: Not affected +# Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp +# Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization +# Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence +# Srbds: Not affected +# Tsx async abort: Not affected +# * win32 +# Architecture=9 +# CurrentClockSpeed=2900 +# DeviceID=CPU0 +# Family=179 +# L2CacheSize=40960 +# L2CacheSpeed= +# Manufacturer=GenuineIntel +# MaxClockSpeed=2900 +# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz +# ProcessorType=3 +# Revision=27142 +# +# Architecture=9 +# CurrentClockSpeed=2900 +# DeviceID=CPU1 +# Family=179 +# L2CacheSize=40960 +# L2CacheSpeed= +# Manufacturer=GenuineIntel +# MaxClockSpeed=2900 +# Name=Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz +# ProcessorType=3 +# Revision=27142 + +def get_cpu_info(run_lambda): + rc, out, err = 0, '', '' + if get_platform() == 'linux': + rc, out, err = run_lambda('lscpu') + elif get_platform() == 'win32': + rc, out, err = run_lambda( + 'powershell.exe "gwmi -Class Win32_Processor | Select-Object -Property Name,Manufacturer,Family,\ + Architecture,ProcessorType,DeviceID,CurrentClockSpeed,MaxClockSpeed,L2CacheSize,L2CacheSpeed,Revision\ + | ConvertTo-Json"' + ) + if rc == 0: + lst = [] + try: + obj = json.loads(out) + if type(obj) is list: + for o in obj: + lst.append("----------------------") + lst.extend([f"{k}: {v}" for (k, v) in o.items()]) + else: + lst.extend([f"{k}: {v}" for (k, v) in obj.items()]) + except ValueError as e: + lst.append(out) + lst.append(str(e)) + out = "\n".join(lst) + elif get_platform() == 'darwin': + rc, out, err = run_lambda("sysctl -n machdep.cpu.brand_string") + cpu_info = 'None' + if rc == 0: + cpu_info = out + else: + cpu_info = err + return cpu_info + + +def get_platform(): + if sys.platform.startswith('linux'): + return 'linux' + elif sys.platform.startswith('win32'): + return 'win32' + elif sys.platform.startswith('cygwin'): + return 'cygwin' + elif sys.platform.startswith('darwin'): + return 'darwin' + else: + return sys.platform + + +def get_mac_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'sw_vers -productVersion', r'(.*)') + + +def get_windows_version(run_lambda): + ret = run_and_read_all( + run_lambda, + 'powershell.exe "gwmi -Class Win32_OperatingSystem | Select-Object -Property Caption,\ + OSArchitecture,Version | ConvertTo-Json"', + ) + try: + obj = json.loads(ret) + ret = f'{obj["Caption"]} ({obj["Version"]} {obj["OSArchitecture"]})' + except ValueError as e: + ret += f"\n{str(e)}" + return ret + + +def get_lsb_version(run_lambda): + return run_and_parse_first_match(run_lambda, 'lsb_release -a', r'Description:\t(.*)') + + +def check_release_file(run_lambda): + return run_and_parse_first_match(run_lambda, 'cat /etc/*-release', + r'PRETTY_NAME="(.*)"') + + +def get_os(run_lambda): + from platform import machine + platform = get_platform() + + if platform == 'win32' or platform == 'cygwin': + return get_windows_version(run_lambda) + + if platform == 'darwin': + version = get_mac_version(run_lambda) + if version is None: + return None + return 'macOS {} ({})'.format(version, machine()) + + if platform == 'linux': + # Ubuntu/Debian based + desc = get_lsb_version(run_lambda) + if desc is not None: + return '{} ({})'.format(desc, machine()) + + # Try reading /etc/*-release + desc = check_release_file(run_lambda) + if desc is not None: + return '{} ({})'.format(desc, machine()) + + return '{} ({})'.format(platform, machine()) + + # Unknown platform + return platform + + +def get_python_platform(): + import platform + return platform.platform() + + +def get_libc_version(): + import platform + if get_platform() != 'linux': + return 'N/A' + return '-'.join(platform.libc_ver()) + + +def get_pip_packages(run_lambda, patterns=None): + """Return `pip list` output. Note: will also find conda-installed pytorch and numpy packages.""" + if patterns is None: + patterns = PIP_PATTERNS + COMMON_PATTERNS + NVIDIA_PATTERNS + + pip_version = 'pip3' if sys.version[0] == '3' else 'pip' + + os.environ['PIP_DISABLE_PIP_VERSION_CHECK'] = '1' + # People generally have pip as `pip` or `pip3` + # But here it is invoked as `python -mpip` + out = run_and_read_all(run_lambda, [sys.executable, '-mpip', 'list', '--format=freeze']) + filtered_out = '\n'.join( + line + for line in out.splitlines() + if any(name in line for name in patterns) + ) + + return pip_version, filtered_out + + +def get_cachingallocator_config(): + ca_config = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', '') + return ca_config + + +def get_cuda_module_loading_config(): + if TORCH_AVAILABLE and torch.cuda.is_available(): + torch.cuda.init() + config = os.environ.get('CUDA_MODULE_LOADING', '') + return config + else: + return "N/A" + + +def is_xnnpack_available(): + if TORCH_AVAILABLE: + import torch.backends.xnnpack + return str(torch.backends.xnnpack.enabled) # type: ignore[attr-defined] + else: + return "N/A" + +def get_env_info(): + """ + Collects environment information to aid in debugging. + + The returned environment information contains details on torch version, is debug build + or not, cuda compiled version, gcc version, clang version, cmake version, operating + system, libc version, python version, python platform, CUDA availability, CUDA + runtime version, CUDA module loading config, GPU model and configuration, Nvidia + driver version, cuDNN version, pip version and versions of relevant pip and + conda packages, HIP runtime version, MIOpen runtime version, + Caching allocator config, XNNPACK availability and CPU information. + + Returns: + SystemEnv (namedtuple): A tuple containining various environment details + and system information. + """ + run_lambda = run + pip_version, pip_list_output = get_pip_packages(run_lambda) + + if TORCH_AVAILABLE: + version_str = torch.__version__ + debug_mode_str = str(torch.version.debug) + cuda_available_str = str(torch.cuda.is_available()) + cuda_version_str = torch.version.cuda + if not hasattr(torch.version, 'hip') or torch.version.hip is None: # cuda version + hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A' + else: # HIP version + def get_version_or_na(cfg, prefix): + _lst = [s.rsplit(None, 1)[-1] for s in cfg if prefix in s] + return _lst[0] if _lst else 'N/A' + + cfg = torch._C._show_config().split('\n') + hip_runtime_version = get_version_or_na(cfg, 'HIP Runtime') + miopen_runtime_version = get_version_or_na(cfg, 'MIOpen') + cuda_version_str = 'N/A' + hip_compiled_version = torch.version.hip + else: + version_str = debug_mode_str = cuda_available_str = cuda_version_str = 'N/A' + hip_compiled_version = hip_runtime_version = miopen_runtime_version = 'N/A' + + sys_version = sys.version.replace("\n", " ") + + conda_packages = get_conda_packages(run_lambda) + + return SystemEnv( + torch_version=version_str, + is_debug_build=debug_mode_str, + python_version='{} ({}-bit runtime)'.format(sys_version, sys.maxsize.bit_length() + 1), + python_platform=get_python_platform(), + is_cuda_available=cuda_available_str, + cuda_compiled_version=cuda_version_str, + cuda_runtime_version=get_running_cuda_version(run_lambda), + cuda_module_loading=get_cuda_module_loading_config(), + nvidia_gpu_models=get_gpu_info(run_lambda), + nvidia_driver_version=get_nvidia_driver_version(run_lambda), + cudnn_version=get_cudnn_version(run_lambda), + hip_compiled_version=hip_compiled_version, + hip_runtime_version=hip_runtime_version, + miopen_runtime_version=miopen_runtime_version, + pip_version=pip_version, + pip_packages=pip_list_output, + conda_packages=conda_packages, + os=get_os(run_lambda), + libc_version=get_libc_version(), + gcc_version=get_gcc_version(run_lambda), + clang_version=get_clang_version(run_lambda), + cmake_version=get_cmake_version(run_lambda), + caching_allocator_config=get_cachingallocator_config(), + is_xnnpack_available=is_xnnpack_available(), + cpu_info=get_cpu_info(run_lambda), + ) + +env_info_fmt = """ +PyTorch version: {torch_version} +Is debug build: {is_debug_build} +CUDA used to build PyTorch: {cuda_compiled_version} +ROCM used to build PyTorch: {hip_compiled_version} + +OS: {os} +GCC version: {gcc_version} +Clang version: {clang_version} +CMake version: {cmake_version} +Libc version: {libc_version} + +Python version: {python_version} +Python platform: {python_platform} +Is CUDA available: {is_cuda_available} +CUDA runtime version: {cuda_runtime_version} +CUDA_MODULE_LOADING set to: {cuda_module_loading} +GPU models and configuration: {nvidia_gpu_models} +Nvidia driver version: {nvidia_driver_version} +cuDNN version: {cudnn_version} +HIP runtime version: {hip_runtime_version} +MIOpen runtime version: {miopen_runtime_version} +Is XNNPACK available: {is_xnnpack_available} + +CPU: +{cpu_info} + +Versions of relevant libraries: +{pip_packages} +{conda_packages} +""".strip() + + +def pretty_str(envinfo): + def replace_nones(dct, replacement='Could not collect'): + for key in dct.keys(): + if dct[key] is not None: + continue + dct[key] = replacement + return dct + + def replace_bools(dct, true='Yes', false='No'): + for key in dct.keys(): + if dct[key] is True: + dct[key] = true + elif dct[key] is False: + dct[key] = false + return dct + + def prepend(text, tag='[prepend]'): + lines = text.split('\n') + updated_lines = [tag + line for line in lines] + return '\n'.join(updated_lines) + + def replace_if_empty(text, replacement='No relevant packages'): + if text is not None and len(text) == 0: + return replacement + return text + + def maybe_start_on_next_line(string): + # If `string` is multiline, prepend a \n to it. + if string is not None and len(string.split('\n')) > 1: + return '\n{}\n'.format(string) + return string + + mutable_dict = envinfo._asdict() + + # If nvidia_gpu_models is multiline, start on the next line + mutable_dict['nvidia_gpu_models'] = \ + maybe_start_on_next_line(envinfo.nvidia_gpu_models) + + # If the machine doesn't have CUDA, report some fields as 'No CUDA' + dynamic_cuda_fields = [ + 'cuda_runtime_version', + 'nvidia_gpu_models', + 'nvidia_driver_version', + ] + all_cuda_fields = dynamic_cuda_fields + ['cudnn_version'] + all_dynamic_cuda_fields_missing = all( + mutable_dict[field] is None for field in dynamic_cuda_fields) + if TORCH_AVAILABLE and not torch.cuda.is_available() and all_dynamic_cuda_fields_missing: + for field in all_cuda_fields: + mutable_dict[field] = 'No CUDA' + if envinfo.cuda_compiled_version is None: + mutable_dict['cuda_compiled_version'] = 'None' + + # Replace True with Yes, False with No + mutable_dict = replace_bools(mutable_dict) + + # Replace all None objects with 'Could not collect' + mutable_dict = replace_nones(mutable_dict) + + # If either of these are '', replace with 'No relevant packages' + mutable_dict['pip_packages'] = replace_if_empty(mutable_dict['pip_packages']) + mutable_dict['conda_packages'] = replace_if_empty(mutable_dict['conda_packages']) + + # Tag conda and pip packages with a prefix + # If they were previously None, they'll show up as ie '[conda] Could not collect' + if mutable_dict['pip_packages']: + mutable_dict['pip_packages'] = prepend(mutable_dict['pip_packages'], + '[{}] '.format(envinfo.pip_version)) + if mutable_dict['conda_packages']: + mutable_dict['conda_packages'] = prepend(mutable_dict['conda_packages'], + '[conda] ') + mutable_dict['cpu_info'] = envinfo.cpu_info + return env_info_fmt.format(**mutable_dict) + + +def get_pretty_env_info(): + """ + Returns a pretty string of environment information. + + This function retrieves environment information by calling the `get_env_info` function + and then formats the information into a human-readable string. The retrieved environment + information is listed in the document of `get_env_info`. + This function is used in `python collect_env.py` that should be executed when reporting a bug. + + Returns: + str: A pretty string of the environment information. + """ + return pretty_str(get_env_info()) + + +def main(): + print("Collecting environment information...") + output = get_pretty_env_info() + print(output) + + if TORCH_AVAILABLE and hasattr(torch, 'utils') and hasattr(torch.utils, '_crash_handler'): + minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR + if sys.platform == "linux" and os.path.exists(minidump_dir): + dumps = [os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)] + latest = max(dumps, key=os.path.getctime) + ctime = os.path.getctime(latest) + creation_time = datetime.datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S') + msg = "\n*** Detected a minidump at {} created on {}, ".format(latest, creation_time) + \ + "if this is related to your bug please include it when you file a report ***" + print(msg, file=sys.stderr) + + + +if __name__ == '__main__': + main() diff --git a/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py b/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py new file mode 100644 index 0000000000000000000000000000000000000000..af4a7fcb63e263038255359c946a6a0d4a21dbd0 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/cpp_backtrace.py @@ -0,0 +1,12 @@ +# mypy: allow-untyped-defs +from torch._C import _get_cpp_backtrace + +def get_cpp_backtrace(frames_to_skip=0, maximum_number_of_frames=64) -> str: + r""" + Return a string containing the C++ stack trace of the current thread. + + Args: + frames_to_skip (int): the number of frames to skip from the top of the stack + maximum_number_of_frames (int): the maximum number of frames to return + """ + return _get_cpp_backtrace(frames_to_skip, maximum_number_of_frames) diff --git a/lib/python3.11/site-packages/torch/utils/cpp_extension.py b/lib/python3.11/site-packages/torch/utils/cpp_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a70dcc06edcdb07f86a692944ebd7716c243a2 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/cpp_extension.py @@ -0,0 +1,2534 @@ +# mypy: allow-untyped-defs +import copy +import glob +import importlib +import importlib.abc +import os +import platform +import re +import shlex +import shutil +import setuptools +import subprocess +import sys +import sysconfig +import warnings +import collections +from pathlib import Path +import errno + +import torch +import torch._appdirs +from .file_baton import FileBaton +from ._cpp_extension_versioner import ExtensionVersioner +from .hipify import hipify_python +from .hipify.hipify_python import GeneratedFileCleaner +from typing import Dict, List, Optional, Union, Tuple +from torch.torch_version import TorchVersion, Version + +from setuptools.command.build_ext import build_ext + +IS_WINDOWS = sys.platform == 'win32' +IS_MACOS = sys.platform.startswith('darwin') +IS_LINUX = sys.platform.startswith('linux') +LIB_EXT = '.pyd' if IS_WINDOWS else '.so' +EXEC_EXT = '.exe' if IS_WINDOWS else '' +CLIB_PREFIX = '' if IS_WINDOWS else 'lib' +CLIB_EXT = '.dll' if IS_WINDOWS else '.so' +SHARED_FLAG = '/DLL' if IS_WINDOWS else '-shared' + +_HERE = os.path.abspath(__file__) +_TORCH_PATH = os.path.dirname(os.path.dirname(_HERE)) +TORCH_LIB_PATH = os.path.join(_TORCH_PATH, 'lib') + + +SUBPROCESS_DECODE_ARGS = ('oem',) if IS_WINDOWS else () +MINIMUM_GCC_VERSION = (5, 0, 0) +MINIMUM_MSVC_VERSION = (19, 0, 24215) + +VersionRange = Tuple[Tuple[int, ...], Tuple[int, ...]] +VersionMap = Dict[str, VersionRange] +# The following values were taken from the following GitHub gist that +# summarizes the minimum valid major versions of g++/clang++ for each supported +# CUDA version: https://gist.github.com/ax3l/9489132 +# Or from include/crt/host_config.h in the CUDA SDK +# The second value is the exclusive(!) upper bound, i.e. min <= version < max +CUDA_GCC_VERSIONS: VersionMap = { + '11.0': (MINIMUM_GCC_VERSION, (10, 0)), + '11.1': (MINIMUM_GCC_VERSION, (11, 0)), + '11.2': (MINIMUM_GCC_VERSION, (11, 0)), + '11.3': (MINIMUM_GCC_VERSION, (11, 0)), + '11.4': ((6, 0, 0), (12, 0)), + '11.5': ((6, 0, 0), (12, 0)), + '11.6': ((6, 0, 0), (12, 0)), + '11.7': ((6, 0, 0), (12, 0)), +} + +MINIMUM_CLANG_VERSION = (3, 3, 0) +CUDA_CLANG_VERSIONS: VersionMap = { + '11.1': (MINIMUM_CLANG_VERSION, (11, 0)), + '11.2': (MINIMUM_CLANG_VERSION, (12, 0)), + '11.3': (MINIMUM_CLANG_VERSION, (12, 0)), + '11.4': (MINIMUM_CLANG_VERSION, (13, 0)), + '11.5': (MINIMUM_CLANG_VERSION, (13, 0)), + '11.6': (MINIMUM_CLANG_VERSION, (14, 0)), + '11.7': (MINIMUM_CLANG_VERSION, (14, 0)), +} + +__all__ = ["get_default_build_root", "check_compiler_ok_for_platform", "get_compiler_abi_compatibility_and_version", "BuildExtension", + "CppExtension", "CUDAExtension", "include_paths", "library_paths", "load", "load_inline", "is_ninja_available", + "verify_ninja_availability", "remove_extension_h_precompiler_headers", "get_cxx_compiler", "check_compiler_is_gcc"] +# Taken directly from python stdlib < 3.9 +# See https://github.com/pytorch/pytorch/issues/48617 +def _nt_quote_args(args: Optional[List[str]]) -> List[str]: + """Quote command-line arguments for DOS/Windows conventions. + + Just wraps every argument which contains blanks in double quotes, and + returns a new argument list. + """ + # Cover None-type + if not args: + return [] + return [f'"{arg}"' if ' ' in arg else arg for arg in args] + +def _find_cuda_home() -> Optional[str]: + """Find the CUDA install path.""" + # Guess #1 + cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH') + if cuda_home is None: + # Guess #2 + nvcc_path = shutil.which("nvcc") + if nvcc_path is not None: + cuda_home = os.path.dirname(os.path.dirname(nvcc_path)) + else: + # Guess #3 + if IS_WINDOWS: + cuda_homes = glob.glob( + 'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*') + if len(cuda_homes) == 0: + cuda_home = '' + else: + cuda_home = cuda_homes[0] + else: + cuda_home = '/usr/local/cuda' + if not os.path.exists(cuda_home): + cuda_home = None + if cuda_home and not torch.cuda.is_available(): + print(f"No CUDA runtime is found, using CUDA_HOME='{cuda_home}'", + file=sys.stderr) + return cuda_home + +def _find_rocm_home() -> Optional[str]: + """Find the ROCm install path.""" + # Guess #1 + rocm_home = os.environ.get('ROCM_HOME') or os.environ.get('ROCM_PATH') + if rocm_home is None: + # Guess #2 + hipcc_path = shutil.which('hipcc') + if hipcc_path is not None: + rocm_home = os.path.dirname(os.path.dirname( + os.path.realpath(hipcc_path))) + # can be either /hip/bin/hipcc or /bin/hipcc + if os.path.basename(rocm_home) == 'hip': + rocm_home = os.path.dirname(rocm_home) + else: + # Guess #3 + fallback_path = '/opt/rocm' + if os.path.exists(fallback_path): + rocm_home = fallback_path + if rocm_home and torch.version.hip is None: + print(f"No ROCm runtime is found, using ROCM_HOME='{rocm_home}'", + file=sys.stderr) + return rocm_home + +def _find_sycl_home() -> Optional[str]: + """Find the OneAPI install path.""" + # Guess #1 + sycl_home = os.environ.get('ONEAPI_ROOT') + if sycl_home is None: + # Guess #2 + icpx_path = shutil.which('icpx') + if icpx_path is not None: + sycl_home = os.path.dirname(os.path.dirname( + os.path.realpath(icpx_path))) + + if sycl_home and not torch.xpu.is_available(): + print(f"No XPU runtime is found, using ONEAPI_ROOT='{sycl_home}'", + file=sys.stderr) + return sycl_home + +def _join_rocm_home(*paths) -> str: + """ + Join paths with ROCM_HOME, or raises an error if it ROCM_HOME is not set. + + This is basically a lazy way of raising an error for missing $ROCM_HOME + only once we need to get any ROCm-specific path. + """ + if ROCM_HOME is None: + raise OSError('ROCM_HOME environment variable is not set. ' + 'Please set it to your ROCm install root.') + elif IS_WINDOWS: + raise OSError('Building PyTorch extensions using ' + 'ROCm and Windows is not supported.') + return os.path.join(ROCM_HOME, *paths) + +def _join_sycl_home(*paths) -> str: + """ + Join paths with SYCL_HOME, or raises an error if it SYCL_HOME is not set. + + This is basically a lazy way of raising an error for missing $SYCL_HOME + only once we need to get any SYCL-specific path. + """ + if SYCL_HOME is None: + raise OSError('SYCL_HOME environment variable is not set. ' + 'Please set it to your OneAPI install root.') + + return os.path.join(SYCL_HOME, *paths) + + + +ABI_INCOMPATIBILITY_WARNING = ''' + + !! WARNING !! + +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +Your compiler ({}) may be ABI-incompatible with PyTorch! +Please use a compiler that is ABI-compatible with GCC 5.0 and above. +See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html. + +See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6 +for instructions on how to install GCC 5 or higher. +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + + !! WARNING !! +''' +WRONG_COMPILER_WARNING = ''' + + !! WARNING !! + +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +Your compiler ({user_compiler}) is not compatible with the compiler Pytorch was +built with for this platform, which is {pytorch_compiler} on {platform}. Please +use {pytorch_compiler} to to compile your extension. Alternatively, you may +compile PyTorch from source using {user_compiler}, and then you can also use +{user_compiler} to compile your extension. + +See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help +with compiling PyTorch from source. +!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + + !! WARNING !! +''' +CUDA_MISMATCH_MESSAGE = ''' +The detected CUDA version ({0}) mismatches the version that was used to compile +PyTorch ({1}). Please make sure to use the same CUDA versions. +''' +CUDA_MISMATCH_WARN = "The detected CUDA version ({0}) has a minor version mismatch with the version that was used to compile PyTorch ({1}). Most likely this shouldn't be a problem." +CUDA_NOT_FOUND_MESSAGE = ''' +CUDA was not found on the system, please set the CUDA_HOME or the CUDA_PATH +environment variable or add NVCC to your system PATH. The extension compilation will fail. +''' +ROCM_HOME = _find_rocm_home() +HIP_HOME = _join_rocm_home('hip') if ROCM_HOME else None +IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False +ROCM_VERSION = None +if torch.version.hip is not None: + ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split('.')[:2]) + +CUDA_HOME = _find_cuda_home() if torch.cuda._is_compiled() else None +CUDNN_HOME = os.environ.get('CUDNN_HOME') or os.environ.get('CUDNN_PATH') +SYCL_HOME = _find_sycl_home() if torch.xpu._is_compiled() else None + +# PyTorch releases have the version pattern major.minor.patch, whereas when +# PyTorch is built from source, we append the git commit hash, which gives +# it the below pattern. +BUILT_FROM_SOURCE_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\w+\+\w+') + +COMMON_MSVC_FLAGS = ['/MD', '/wd4819', '/wd4251', '/wd4244', '/wd4267', '/wd4275', '/wd4018', '/wd4190', '/wd4624', '/wd4067', '/wd4068', '/EHsc'] + +MSVC_IGNORE_CUDAFE_WARNINGS = [ + 'base_class_has_different_dll_interface', + 'field_without_dll_interface', + 'dll_interface_conflict_none_assumed', + 'dll_interface_conflict_dllexport_assumed' +] + +COMMON_NVCC_FLAGS = [ + '-D__CUDA_NO_HALF_OPERATORS__', + '-D__CUDA_NO_HALF_CONVERSIONS__', + '-D__CUDA_NO_BFLOAT16_CONVERSIONS__', + '-D__CUDA_NO_HALF2_OPERATORS__', + '--expt-relaxed-constexpr' +] + +COMMON_HIP_FLAGS = [ + '-fPIC', + '-D__HIP_PLATFORM_AMD__=1', + '-DUSE_ROCM=1', + '-DHIPBLAS_V2', +] + +COMMON_HIPCC_FLAGS = [ + '-DCUDA_HAS_FP16=1', + '-D__HIP_NO_HALF_OPERATORS__=1', + '-D__HIP_NO_HALF_CONVERSIONS__=1', +] + +JIT_EXTENSION_VERSIONER = ExtensionVersioner() + +PLAT_TO_VCVARS = { + 'win32' : 'x86', + 'win-amd64' : 'x86_amd64', +} + +def get_cxx_compiler(): + if IS_WINDOWS: + compiler = os.environ.get('CXX', 'cl') + else: + compiler = os.environ.get('CXX', 'c++') + return compiler + +def _is_binary_build() -> bool: + return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__) + + +def _accepted_compilers_for_platform() -> List[str]: + # gnu-c++ and gnu-cc are the conda gcc compilers + return ['clang++', 'clang'] if IS_MACOS else ['g++', 'gcc', 'gnu-c++', 'gnu-cc', 'clang++', 'clang'] + +def _maybe_write(filename, new_content): + r''' + Equivalent to writing the content into the file but will not touch the file + if it already had the right content (to avoid triggering recompile). + ''' + if os.path.exists(filename): + with open(filename) as f: + content = f.read() + + if content == new_content: + # The file already contains the right thing! + return + + with open(filename, 'w') as source_file: + source_file.write(new_content) + +def get_default_build_root() -> str: + """ + Return the path to the root folder under which extensions will built. + + For each extension module built, there will be one folder underneath the + folder returned by this function. For example, if ``p`` is the path + returned by this function and ``ext`` the name of an extension, the build + folder for the extension will be ``p/ext``. + + This directory is **user-specific** so that multiple users on the same + machine won't meet permission issues. + """ + return os.path.realpath(torch._appdirs.user_cache_dir(appname='torch_extensions')) + + +def check_compiler_ok_for_platform(compiler: str) -> bool: + """ + Verify that the compiler is the expected one for the current platform. + + Args: + compiler (str): The compiler executable to check. + + Returns: + True if the compiler is gcc/g++ on Linux or clang/clang++ on macOS, + and always True for Windows. + """ + if IS_WINDOWS: + return True + compiler_path = shutil.which(compiler) + if compiler_path is None: + return False + # Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'. + compiler_path = os.path.realpath(compiler_path) + # Check the compiler name + if any(name in compiler_path for name in _accepted_compilers_for_platform()): + return True + # If compiler wrapper is used try to infer the actual compiler by invoking it with -v flag + env = os.environ.copy() + env['LC_ALL'] = 'C' # Don't localize output + version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS) + if IS_LINUX: + # Check for 'gcc' or 'g++' for sccache wrapper + pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE) + results = re.findall(pattern, version_string) + if len(results) != 1: + # Clang is also a supported compiler on Linux + # Though on Ubuntu it's sometimes called "Ubuntu clang version" + return 'clang version' in version_string + compiler_path = os.path.realpath(results[0].strip()) + # On RHEL/CentOS c++ is a gcc compiler wrapper + if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string: + return True + return any(name in compiler_path for name in _accepted_compilers_for_platform()) + if IS_MACOS: + # Check for 'clang' or 'clang++' + return version_string.startswith("Apple clang") + return False + + +def get_compiler_abi_compatibility_and_version(compiler) -> Tuple[bool, TorchVersion]: + """ + Determine if the given compiler is ABI-compatible with PyTorch alongside its version. + + Args: + compiler (str): The compiler executable name to check (e.g. ``g++``). + Must be executable in a shell process. + + Returns: + A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch, + followed by a `TorchVersion` string that contains the compiler version separated by dots. + """ + if not _is_binary_build(): + return (True, TorchVersion('0.0.0')) + if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']: + return (True, TorchVersion('0.0.0')) + + # First check if the compiler is one of the expected ones for the particular platform. + if not check_compiler_ok_for_platform(compiler): + warnings.warn(WRONG_COMPILER_WARNING.format( + user_compiler=compiler, + pytorch_compiler=_accepted_compilers_for_platform()[0], + platform=sys.platform)) + return (False, TorchVersion('0.0.0')) + + if IS_MACOS: + # There is no particular minimum version we need for clang, so we're good here. + return (True, TorchVersion('0.0.0')) + try: + if IS_LINUX: + minimum_required_version = MINIMUM_GCC_VERSION + versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion']) + version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).strip().split('.') + else: + minimum_required_version = MINIMUM_MSVC_VERSION + compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT) + match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode(*SUBPROCESS_DECODE_ARGS).strip()) + version = ['0', '0', '0'] if match is None else list(match.groups()) + except Exception: + _, error, _ = sys.exc_info() + warnings.warn(f'Error checking compiler version for {compiler}: {error}') + return (False, TorchVersion('0.0.0')) + + if tuple(map(int, version)) >= minimum_required_version: + return (True, TorchVersion('.'.join(version))) + + compiler = f'{compiler} {".".join(version)}' + warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler)) + + return (False, TorchVersion('.'.join(version))) + + +def _check_cuda_version(compiler_name: str, compiler_version: TorchVersion) -> None: + if not CUDA_HOME: + raise RuntimeError(CUDA_NOT_FOUND_MESSAGE) + + nvcc = os.path.join(CUDA_HOME, 'bin', 'nvcc') + cuda_version_str = subprocess.check_output([nvcc, '--version']).strip().decode(*SUBPROCESS_DECODE_ARGS) + cuda_version = re.search(r'release (\d+[.]\d+)', cuda_version_str) + if cuda_version is None: + return + + cuda_str_version = cuda_version.group(1) + cuda_ver = Version(cuda_str_version) + if torch.version.cuda is None: + return + + torch_cuda_version = Version(torch.version.cuda) + if cuda_ver != torch_cuda_version: + # major/minor attributes are only available in setuptools>=49.4.0 + if getattr(cuda_ver, "major", None) is None: + raise ValueError("setuptools>=49.4.0 is required") + if cuda_ver.major != torch_cuda_version.major: + raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda)) + warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda)) + + if not (sys.platform.startswith('linux') and + os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') not in ['ON', '1', 'YES', 'TRUE', 'Y'] and + _is_binary_build()): + return + + cuda_compiler_bounds: VersionMap = CUDA_CLANG_VERSIONS if compiler_name.startswith('clang') else CUDA_GCC_VERSIONS + + if cuda_str_version not in cuda_compiler_bounds: + warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}') + else: + min_compiler_version, max_excl_compiler_version = cuda_compiler_bounds[cuda_str_version] + # Special case for 11.4.0, which has lower compiler bounds than 11.4.1 + if "V11.4.48" in cuda_version_str and cuda_compiler_bounds == CUDA_GCC_VERSIONS: + max_excl_compiler_version = (11, 0) + min_compiler_version_str = '.'.join(map(str, min_compiler_version)) + max_excl_compiler_version_str = '.'.join(map(str, max_excl_compiler_version)) + + version_bound_str = f'>={min_compiler_version_str}, <{max_excl_compiler_version_str}' + + if compiler_version < TorchVersion(min_compiler_version_str): + raise RuntimeError( + f'The current installed version of {compiler_name} ({compiler_version}) is less ' + f'than the minimum required version by CUDA {cuda_str_version} ({min_compiler_version_str}). ' + f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).' + ) + if compiler_version >= TorchVersion(max_excl_compiler_version_str): + raise RuntimeError( + f'The current installed version of {compiler_name} ({compiler_version}) is greater ' + f'than the maximum required version by CUDA {cuda_str_version}. ' + f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).' + ) + + +class BuildExtension(build_ext): + """ + A custom :mod:`setuptools` build extension . + + This :class:`setuptools.build_ext` subclass takes care of passing the + minimum required compiler flags (e.g. ``-std=c++17``) as well as mixed + C++/CUDA compilation (and support for CUDA files in general). + + When using :class:`BuildExtension`, it is allowed to supply a dictionary + for ``extra_compile_args`` (rather than the usual list) that maps from + languages (``cxx`` or ``nvcc``) to a list of additional compiler flags to + supply to the compiler. This makes it possible to supply different flags to + the C++ and CUDA compiler during mixed compilation. + + ``use_ninja`` (bool): If ``use_ninja`` is ``True`` (default), then we + attempt to build using the Ninja backend. Ninja greatly speeds up + compilation compared to the standard ``setuptools.build_ext``. + Fallbacks to the standard distutils backend if Ninja is not available. + + .. note:: + By default, the Ninja backend uses #CPUS + 2 workers to build the + extension. This may use up too many resources on some systems. One + can control the number of workers by setting the `MAX_JOBS` environment + variable to a non-negative number. + """ + + @classmethod + def with_options(cls, **options): + """Return a subclass with alternative constructor that extends any original keyword arguments to the original constructor with the given options.""" + class cls_with_options(cls): # type: ignore[misc, valid-type] + def __init__(self, *args, **kwargs): + kwargs.update(options) + super().__init__(*args, **kwargs) + + return cls_with_options + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False) + + self.use_ninja = kwargs.get('use_ninja', True) + if self.use_ninja: + # Test if we can use ninja. Fallback otherwise. + msg = ('Attempted to use ninja as the BuildExtension backend but ' + '{}. Falling back to using the slow distutils backend.') + if not is_ninja_available(): + warnings.warn(msg.format('we could not find ninja.')) + self.use_ninja = False + + def finalize_options(self) -> None: + super().finalize_options() + if self.use_ninja: + self.force = True + + def build_extensions(self) -> None: + compiler_name, compiler_version = self._check_abi() + + cuda_ext = False + extension_iter = iter(self.extensions) + extension = next(extension_iter, None) + while not cuda_ext and extension: + for source in extension.sources: + _, ext = os.path.splitext(source) + if ext == '.cu': + cuda_ext = True + break + extension = next(extension_iter, None) + + if cuda_ext and not IS_HIP_EXTENSION: + _check_cuda_version(compiler_name, compiler_version) + + for extension in self.extensions: + # Ensure at least an empty list of flags for 'cxx' and 'nvcc' when + # extra_compile_args is a dict. Otherwise, default torch flags do + # not get passed. Necessary when only one of 'cxx' and 'nvcc' is + # passed to extra_compile_args in CUDAExtension, i.e. + # CUDAExtension(..., extra_compile_args={'cxx': [...]}) + # or + # CUDAExtension(..., extra_compile_args={'nvcc': [...]}) + if isinstance(extension.extra_compile_args, dict): + for ext in ['cxx', 'nvcc']: + if ext not in extension.extra_compile_args: + extension.extra_compile_args[ext] = [] + + self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H') + # See note [Pybind11 ABI constants] + for name in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]: + val = getattr(torch._C, f"_PYBIND11_{name}") + if val is not None and not IS_WINDOWS: + self._add_compile_flag(extension, f'-DPYBIND11_{name}="{val}"') + self._define_torch_extension_name(extension) + self._add_gnu_cpp_abi_flag(extension) + + if 'nvcc_dlink' in extension.extra_compile_args: + assert self.use_ninja, f"With dlink=True, ninja is required to build cuda extension {extension.name}." + + # Register .cu, .cuh, .hip, and .mm as valid source extensions. + self.compiler.src_extensions += ['.cu', '.cuh', '.hip'] + if torch.backends.mps.is_built(): + self.compiler.src_extensions += ['.mm'] + # Save the original _compile method for later. + if self.compiler.compiler_type == 'msvc': + self.compiler._cpp_extensions += ['.cu', '.cuh'] + original_compile = self.compiler.compile + original_spawn = self.compiler.spawn + else: + original_compile = self.compiler._compile + + def append_std17_if_no_std_present(cflags) -> None: + # NVCC does not allow multiple -std to be passed, so we avoid + # overriding the option if the user explicitly passed it. + cpp_format_prefix = '/{}:' if self.compiler.compiler_type == 'msvc' else '-{}=' + cpp_flag_prefix = cpp_format_prefix.format('std') + cpp_flag = cpp_flag_prefix + 'c++17' + if not any(flag.startswith(cpp_flag_prefix) for flag in cflags): + cflags.append(cpp_flag) + + def unix_cuda_flags(cflags): + cflags = (COMMON_NVCC_FLAGS + + ['--compiler-options', "'-fPIC'"] + + cflags + _get_cuda_arch_flags(cflags)) + + # NVCC does not allow multiple -ccbin/--compiler-bindir to be passed, so we avoid + # overriding the option if the user explicitly passed it. + _ccbin = os.getenv("CC") + if ( + _ccbin is not None + and not any(flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags) + ): + cflags.extend(['-ccbin', _ccbin]) + + return cflags + + def convert_to_absolute_paths_inplace(paths): + # Helper function. See Note [Absolute include_dirs] + if paths is not None: + for i in range(len(paths)): + if not os.path.isabs(paths[i]): + paths[i] = os.path.abspath(paths[i]) + + def unix_wrap_single_compile(obj, src, ext, cc_args, extra_postargs, pp_opts) -> None: + # Copy before we make any modifications. + cflags = copy.deepcopy(extra_postargs) + try: + original_compiler = self.compiler.compiler_so + if _is_cuda_file(src): + nvcc = [_join_rocm_home('bin', 'hipcc') if IS_HIP_EXTENSION else _join_cuda_home('bin', 'nvcc')] + self.compiler.set_executable('compiler_so', nvcc) + if isinstance(cflags, dict): + cflags = cflags['nvcc'] + if IS_HIP_EXTENSION: + cflags = COMMON_HIPCC_FLAGS + cflags + _get_rocm_arch_flags(cflags) + else: + cflags = unix_cuda_flags(cflags) + elif isinstance(cflags, dict): + cflags = cflags['cxx'] + if IS_HIP_EXTENSION: + cflags = COMMON_HIP_FLAGS + cflags + append_std17_if_no_std_present(cflags) + + original_compile(obj, src, ext, cc_args, cflags, pp_opts) + finally: + # Put the original compiler back in place. + self.compiler.set_executable('compiler_so', original_compiler) + + def unix_wrap_ninja_compile(sources, + output_dir=None, + macros=None, + include_dirs=None, + debug=0, + extra_preargs=None, + extra_postargs=None, + depends=None): + r"""Compiles sources by outputting a ninja file and running it.""" + # NB: I copied some lines from self.compiler (which is an instance + # of distutils.UnixCCompiler). See the following link. + # https://github.com/python/cpython/blob/f03a8f8d5001963ad5b5b28dbd95497e9cc15596/Lib/distutils/ccompiler.py#L564-L567 + # This can be fragile, but a lot of other repos also do this + # (see https://github.com/search?q=_setup_compile&type=Code) + # so it is probably OK; we'll also get CI signal if/when + # we update our python version (which is when distutils can be + # upgraded) + + # Use absolute path for output_dir so that the object file paths + # (`objects`) get generated with absolute paths. + output_dir = os.path.abspath(output_dir) + + # See Note [Absolute include_dirs] + convert_to_absolute_paths_inplace(self.compiler.include_dirs) + + _, objects, extra_postargs, pp_opts, _ = \ + self.compiler._setup_compile(output_dir, macros, + include_dirs, sources, + depends, extra_postargs) + common_cflags = self.compiler._get_cc_args(pp_opts, debug, extra_preargs) + extra_cc_cflags = self.compiler.compiler_so[1:] + with_cuda = any(map(_is_cuda_file, sources)) + + # extra_postargs can be either: + # - a dict mapping cxx/nvcc to extra flags + # - a list of extra flags. + if isinstance(extra_postargs, dict): + post_cflags = extra_postargs['cxx'] + else: + post_cflags = list(extra_postargs) + if IS_HIP_EXTENSION: + post_cflags = COMMON_HIP_FLAGS + post_cflags + append_std17_if_no_std_present(post_cflags) + + cuda_post_cflags = None + cuda_cflags = None + if with_cuda: + cuda_cflags = common_cflags + if isinstance(extra_postargs, dict): + cuda_post_cflags = extra_postargs['nvcc'] + else: + cuda_post_cflags = list(extra_postargs) + if IS_HIP_EXTENSION: + cuda_post_cflags = cuda_post_cflags + _get_rocm_arch_flags(cuda_post_cflags) + cuda_post_cflags = COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS + cuda_post_cflags + else: + cuda_post_cflags = unix_cuda_flags(cuda_post_cflags) + append_std17_if_no_std_present(cuda_post_cflags) + cuda_cflags = [shlex.quote(f) for f in cuda_cflags] + cuda_post_cflags = [shlex.quote(f) for f in cuda_post_cflags] + + if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs: + cuda_dlink_post_cflags = unix_cuda_flags(extra_postargs['nvcc_dlink']) + else: + cuda_dlink_post_cflags = None + _write_ninja_file_and_compile_objects( + sources=sources, + objects=objects, + cflags=[shlex.quote(f) for f in extra_cc_cflags + common_cflags], + post_cflags=[shlex.quote(f) for f in post_cflags], + cuda_cflags=cuda_cflags, + cuda_post_cflags=cuda_post_cflags, + cuda_dlink_post_cflags=cuda_dlink_post_cflags, + build_directory=output_dir, + verbose=True, + with_cuda=with_cuda) + + # Return *all* object filenames, not just the ones we just built. + return objects + + def win_cuda_flags(cflags): + return (COMMON_NVCC_FLAGS + + cflags + _get_cuda_arch_flags(cflags)) + + def win_wrap_single_compile(sources, + output_dir=None, + macros=None, + include_dirs=None, + debug=0, + extra_preargs=None, + extra_postargs=None, + depends=None): + + self.cflags = copy.deepcopy(extra_postargs) + extra_postargs = None + + def spawn(cmd): + # Using regex to match src, obj and include files + src_regex = re.compile('/T(p|c)(.*)') + src_list = [ + m.group(2) for m in (src_regex.match(elem) for elem in cmd) + if m + ] + + obj_regex = re.compile('/Fo(.*)') + obj_list = [ + m.group(1) for m in (obj_regex.match(elem) for elem in cmd) + if m + ] + + include_regex = re.compile(r'((\-|\/)I.*)') + include_list = [ + m.group(1) + for m in (include_regex.match(elem) for elem in cmd) if m + ] + + if len(src_list) >= 1 and len(obj_list) >= 1: + src = src_list[0] + obj = obj_list[0] + if _is_cuda_file(src): + nvcc = _join_cuda_home('bin', 'nvcc') + if isinstance(self.cflags, dict): + cflags = self.cflags['nvcc'] + elif isinstance(self.cflags, list): + cflags = self.cflags + else: + cflags = [] + + cflags = win_cuda_flags(cflags) + ['-std=c++17', '--use-local-env'] + for flag in COMMON_MSVC_FLAGS: + cflags = ['-Xcompiler', flag] + cflags + for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS: + cflags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cflags + cmd = [nvcc, '-c', src, '-o', obj] + include_list + cflags + elif isinstance(self.cflags, dict): + cflags = COMMON_MSVC_FLAGS + self.cflags['cxx'] + append_std17_if_no_std_present(cflags) + cmd += cflags + elif isinstance(self.cflags, list): + cflags = COMMON_MSVC_FLAGS + self.cflags + append_std17_if_no_std_present(cflags) + cmd += cflags + + return original_spawn(cmd) + + try: + self.compiler.spawn = spawn + return original_compile(sources, output_dir, macros, + include_dirs, debug, extra_preargs, + extra_postargs, depends) + finally: + self.compiler.spawn = original_spawn + + def win_wrap_ninja_compile(sources, + output_dir=None, + macros=None, + include_dirs=None, + debug=0, + extra_preargs=None, + extra_postargs=None, + depends=None): + + if not self.compiler.initialized: + self.compiler.initialize() + output_dir = os.path.abspath(output_dir) + + # Note [Absolute include_dirs] + # Convert relative path in self.compiler.include_dirs to absolute path if any. + # For ninja build, the build location is not local, but instead, the build happens + # in a script-created build folder. Thus, relative paths lose their correctness. + # To be consistent with jit extension, we allow user to enter relative include_dirs + # in setuptools.setup, and we convert the relative path to absolute path here. + convert_to_absolute_paths_inplace(self.compiler.include_dirs) + + _, objects, extra_postargs, pp_opts, _ = \ + self.compiler._setup_compile(output_dir, macros, + include_dirs, sources, + depends, extra_postargs) + common_cflags = extra_preargs or [] + cflags = [] + if debug: + cflags.extend(self.compiler.compile_options_debug) + else: + cflags.extend(self.compiler.compile_options) + common_cflags.extend(COMMON_MSVC_FLAGS) + cflags = cflags + common_cflags + pp_opts + with_cuda = any(map(_is_cuda_file, sources)) + + # extra_postargs can be either: + # - a dict mapping cxx/nvcc to extra flags + # - a list of extra flags. + if isinstance(extra_postargs, dict): + post_cflags = extra_postargs['cxx'] + else: + post_cflags = list(extra_postargs) + append_std17_if_no_std_present(post_cflags) + + cuda_post_cflags = None + cuda_cflags = None + if with_cuda: + cuda_cflags = ['-std=c++17', '--use-local-env'] + for common_cflag in common_cflags: + cuda_cflags.append('-Xcompiler') + cuda_cflags.append(common_cflag) + for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS: + cuda_cflags.append('-Xcudafe') + cuda_cflags.append('--diag_suppress=' + ignore_warning) + cuda_cflags.extend(pp_opts) + if isinstance(extra_postargs, dict): + cuda_post_cflags = extra_postargs['nvcc'] + else: + cuda_post_cflags = list(extra_postargs) + cuda_post_cflags = win_cuda_flags(cuda_post_cflags) + + cflags = _nt_quote_args(cflags) + post_cflags = _nt_quote_args(post_cflags) + if with_cuda: + cuda_cflags = _nt_quote_args(cuda_cflags) + cuda_post_cflags = _nt_quote_args(cuda_post_cflags) + if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs: + cuda_dlink_post_cflags = win_cuda_flags(extra_postargs['nvcc_dlink']) + else: + cuda_dlink_post_cflags = None + + _write_ninja_file_and_compile_objects( + sources=sources, + objects=objects, + cflags=cflags, + post_cflags=post_cflags, + cuda_cflags=cuda_cflags, + cuda_post_cflags=cuda_post_cflags, + cuda_dlink_post_cflags=cuda_dlink_post_cflags, + build_directory=output_dir, + verbose=True, + with_cuda=with_cuda) + + # Return *all* object filenames, not just the ones we just built. + return objects + + # Monkey-patch the _compile or compile method. + # https://github.com/python/cpython/blob/dc0284ee8f7a270b6005467f26d8e5773d76e959/Lib/distutils/ccompiler.py#L511 + if self.compiler.compiler_type == 'msvc': + if self.use_ninja: + self.compiler.compile = win_wrap_ninja_compile + else: + self.compiler.compile = win_wrap_single_compile + else: + if self.use_ninja: + self.compiler.compile = unix_wrap_ninja_compile + else: + self.compiler._compile = unix_wrap_single_compile + + build_ext.build_extensions(self) + + def get_ext_filename(self, ext_name): + # Get the original shared library name. For Python 3, this name will be + # suffixed with ".so", where will be something like + # cpython-37m-x86_64-linux-gnu. + ext_filename = super().get_ext_filename(ext_name) + # If `no_python_abi_suffix` is `True`, we omit the Python 3 ABI + # component. This makes building shared libraries with setuptools that + # aren't Python modules nicer. + if self.no_python_abi_suffix: + # The parts will be e.g. ["my_extension", "cpython-37m-x86_64-linux-gnu", "so"]. + ext_filename_parts = ext_filename.split('.') + # Omit the second to last element. + without_abi = ext_filename_parts[:-2] + ext_filename_parts[-1:] + ext_filename = '.'.join(without_abi) + return ext_filename + + def _check_abi(self) -> Tuple[str, TorchVersion]: + # On some platforms, like Windows, compiler_cxx is not available. + if hasattr(self.compiler, 'compiler_cxx'): + compiler = self.compiler.compiler_cxx[0] + else: + compiler = get_cxx_compiler() + _, version = get_compiler_abi_compatibility_and_version(compiler) + # Warn user if VC env is activated but `DISTUILS_USE_SDK` is not set. + if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' in os.environ and 'DISTUTILS_USE_SDK' not in os.environ: + msg = ('It seems that the VC environment is activated but DISTUTILS_USE_SDK is not set.' + 'This may lead to multiple activations of the VC env.' + 'Please set `DISTUTILS_USE_SDK=1` and try again.') + raise UserWarning(msg) + return compiler, version + + def _add_compile_flag(self, extension, flag): + extension.extra_compile_args = copy.deepcopy(extension.extra_compile_args) + if isinstance(extension.extra_compile_args, dict): + for args in extension.extra_compile_args.values(): + args.append(flag) + else: + extension.extra_compile_args.append(flag) + + def _define_torch_extension_name(self, extension): + # pybind11 doesn't support dots in the names + # so in order to support extensions in the packages + # like torch._C, we take the last part of the string + # as the library name + names = extension.name.split('.') + name = names[-1] + define = f'-DTORCH_EXTENSION_NAME={name}' + self._add_compile_flag(extension, define) + + def _add_gnu_cpp_abi_flag(self, extension): + # use the same CXX ABI as what PyTorch was compiled with + self._add_compile_flag(extension, '-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))) + + +def CppExtension(name, sources, *args, **kwargs): + """ + Create a :class:`setuptools.Extension` for C++. + + Convenience method that creates a :class:`setuptools.Extension` with the + bare minimum (but often sufficient) arguments to build a C++ extension. + + All arguments are forwarded to the :class:`setuptools.Extension` + constructor. Full list arguments can be found at + https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference + + .. note:: + The PyTorch python API (as provided in libtorch_python) cannot be built + with the flag ``py_limited_api=True``. When this flag is passed, it is + the user's responsibility in their library to not use APIs from + libtorch_python (in particular pytorch/python bindings) and to only use + APIs from libtorch (aten objects, operators and the dispatcher). For + example, to give access to custom ops from python, the library should + register the ops through the dispatcher. + + Example: + >>> # xdoctest: +SKIP + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) + >>> from setuptools import setup + >>> from torch.utils.cpp_extension import BuildExtension, CppExtension + >>> setup( + ... name='extension', + ... ext_modules=[ + ... CppExtension( + ... name='extension', + ... sources=['extension.cpp'], + ... extra_compile_args=['-g'], + ... extra_link_args=['-Wl,--no-as-needed', '-lm']) + ... ], + ... cmdclass={ + ... 'build_ext': BuildExtension + ... }) + """ + include_dirs = kwargs.get('include_dirs', []) + include_dirs += include_paths() + kwargs['include_dirs'] = include_dirs + + library_dirs = kwargs.get('library_dirs', []) + library_dirs += library_paths() + kwargs['library_dirs'] = library_dirs + + libraries = kwargs.get('libraries', []) + libraries.append('c10') + libraries.append('torch') + libraries.append('torch_cpu') + if not kwargs.get('py_limited_api', False): + # torch_python uses more than the python limited api + libraries.append('torch_python') + if IS_WINDOWS and platform.machine().lower() != "arm64": + libraries.append("sleef") + + kwargs['libraries'] = libraries + + kwargs['language'] = 'c++' + return setuptools.Extension(name, sources, *args, **kwargs) + + +def CUDAExtension(name, sources, *args, **kwargs): + """ + Create a :class:`setuptools.Extension` for CUDA/C++. + + Convenience method that creates a :class:`setuptools.Extension` with the + bare minimum (but often sufficient) arguments to build a CUDA/C++ + extension. This includes the CUDA include path, library path and runtime + library. + + All arguments are forwarded to the :class:`setuptools.Extension` + constructor. Full list arguments can be found at + https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference + + .. note:: + The PyTorch python API (as provided in libtorch_python) cannot be built + with the flag ``py_limited_api=True``. When this flag is passed, it is + the user's responsibility in their library to not use APIs from + libtorch_python (in particular pytorch/python bindings) and to only use + APIs from libtorch (aten objects, operators and the dispatcher). For + example, to give access to custom ops from python, the library should + register the ops through the dispatcher. + + Example: + >>> # xdoctest: +SKIP + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) + >>> from setuptools import setup + >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension + >>> setup( + ... name='cuda_extension', + ... ext_modules=[ + ... CUDAExtension( + ... name='cuda_extension', + ... sources=['extension.cpp', 'extension_kernel.cu'], + ... extra_compile_args={'cxx': ['-g'], + ... 'nvcc': ['-O2']}, + ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) + ... ], + ... cmdclass={ + ... 'build_ext': BuildExtension + ... }) + + Compute capabilities: + + By default the extension will be compiled to run on all archs of the cards visible during the + building process of the extension, plus PTX. If down the road a new card is installed the + extension may need to be recompiled. If a visible card has a compute capability (CC) that's + newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch + will make nvcc fall back to building kernels with the newest version of PTX your nvcc does + support (see below for details on PTX). + + You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which + CCs you want the extension to support: + + ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` + ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` + + The +PTX option causes extension kernel binaries to include PTX instructions for the specified + CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= + the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with + CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to + provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on + those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better + off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, + "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but + "8.0 8.6" would be better. + + Note that while it's possible to include all supported archs, the more archs get included the + slower the building process will be, as it will build a separate kernel image for each arch. + + Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. + To workaround the issue, move python binding logic to pure C++ file. + + Example use: + #include + at::Tensor SigmoidAlphaBlendForwardCuda(....) + + Instead of: + #include + torch::Tensor SigmoidAlphaBlendForwardCuda(...) + + Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 + Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 + + Relocatable device code linking: + + If you want to reference device symbols across compilation units (across object files), + the object files need to be built with `relocatable device code` (-rdc=true or -dc). + An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. + `Relocatable device code` is less optimized so it needs to be used only on object files that need it. + Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step + helps reduce the protentional perf degradation of `-rdc`. + Note that it needs to be used at both steps to be useful. + + If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. + There is also a case where `-dlink` is used without `-rdc`: + when an extension is linked against a static lib containing rdc-compiled objects + like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). + + Note: Ninja is required to build a CUDA Extension with RDC linking. + + Example: + >>> # xdoctest: +SKIP + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) + >>> CUDAExtension( + ... name='cuda_extension', + ... sources=['extension.cpp', 'extension_kernel.cu'], + ... dlink=True, + ... dlink_libraries=["dlink_lib"], + ... extra_compile_args={'cxx': ['-g'], + ... 'nvcc': ['-O2', '-rdc=true']}) + """ + library_dirs = kwargs.get('library_dirs', []) + library_dirs += library_paths(device_type="cuda") + kwargs['library_dirs'] = library_dirs + + libraries = kwargs.get('libraries', []) + libraries.append('c10') + libraries.append('torch') + libraries.append('torch_cpu') + if not kwargs.get('py_limited_api', False): + # torch_python uses more than the python limited api + libraries.append('torch_python') + if IS_HIP_EXTENSION: + libraries.append('amdhip64') + libraries.append('c10_hip') + libraries.append('torch_hip') + else: + libraries.append('cudart') + libraries.append('c10_cuda') + libraries.append('torch_cuda') + kwargs['libraries'] = libraries + + include_dirs = kwargs.get('include_dirs', []) + + if IS_HIP_EXTENSION: + build_dir = os.getcwd() + hipify_result = hipify_python.hipify( + project_directory=build_dir, + output_directory=build_dir, + header_include_dirs=include_dirs, + includes=[os.path.join(build_dir, '*')], # limit scope to build_dir only + extra_files=[os.path.abspath(s) for s in sources], + show_detailed=True, + is_pytorch_extension=True, + hipify_extra_files_only=True, # don't hipify everything in includes path + ) + + hipified_sources = set() + for source in sources: + s_abs = os.path.abspath(source) + hipified_s_abs = (hipify_result[s_abs].hipified_path if (s_abs in hipify_result and + hipify_result[s_abs].hipified_path is not None) else s_abs) + # setup() arguments must *always* be /-separated paths relative to the setup.py directory, + # *never* absolute paths + hipified_sources.add(os.path.relpath(hipified_s_abs, build_dir)) + + sources = list(hipified_sources) + + include_dirs += include_paths(device_type="cuda") + kwargs['include_dirs'] = include_dirs + + kwargs['language'] = 'c++' + + dlink_libraries = kwargs.get('dlink_libraries', []) + dlink = kwargs.get('dlink', False) or dlink_libraries + if dlink: + extra_compile_args = kwargs.get('extra_compile_args', {}) + + extra_compile_args_dlink = extra_compile_args.get('nvcc_dlink', []) + extra_compile_args_dlink += ['-dlink'] + extra_compile_args_dlink += [f'-L{x}' for x in library_dirs] + extra_compile_args_dlink += [f'-l{x}' for x in dlink_libraries] + + if (torch.version.cuda is not None) and TorchVersion(torch.version.cuda) >= '11.2': + extra_compile_args_dlink += ['-dlto'] # Device Link Time Optimization started from cuda 11.2 + + extra_compile_args['nvcc_dlink'] = extra_compile_args_dlink + + kwargs['extra_compile_args'] = extra_compile_args + + return setuptools.Extension(name, sources, *args, **kwargs) + + +def include_paths(device_type: str = "cpu") -> List[str]: + """ + Get the include paths required to build a C++ or CUDA or SYCL extension. + + Args: + device_type: Defaults to "cpu". + Returns: + A list of include path strings. + """ + lib_include = os.path.join(_TORCH_PATH, 'include') + paths = [ + lib_include, + # Remove this once torch/torch.h is officially no longer supported for C++ extensions. + os.path.join(lib_include, 'torch', 'csrc', 'api', 'include'), + # Some internal (old) Torch headers don't properly prefix their includes, + # so we need to pass -Itorch/lib/include/TH as well. + os.path.join(lib_include, 'TH'), + os.path.join(lib_include, 'THC') + ] + if device_type == "cuda" and IS_HIP_EXTENSION: + paths.append(os.path.join(lib_include, 'THH')) + paths.append(_join_rocm_home('include')) + elif device_type == "cuda": + cuda_home_include = _join_cuda_home('include') + # if we have the Debian/Ubuntu packages for cuda, we get /usr as cuda home. + # but gcc doesn't like having /usr/include passed explicitly + if cuda_home_include != '/usr/include': + paths.append(cuda_home_include) + + # Support CUDA_INC_PATH env variable supported by CMake files + if (cuda_inc_path := os.environ.get("CUDA_INC_PATH", None)) and \ + cuda_inc_path != '/usr/include': + paths.append(cuda_inc_path) + if CUDNN_HOME is not None: + paths.append(os.path.join(CUDNN_HOME, 'include')) + elif device_type == "xpu": + paths.append(_join_sycl_home('include')) + return paths + + +def library_paths(device_type: str = "cpu") -> List[str]: + """ + Get the library paths required to build a C++ or CUDA extension. + + Args: + device_type: Defaults to "cpu". + + Returns: + A list of library path strings. + """ + # We need to link against libtorch.so + paths = [TORCH_LIB_PATH] + + if device_type == "cuda" and IS_HIP_EXTENSION: + lib_dir = 'lib' + paths.append(_join_rocm_home(lib_dir)) + if HIP_HOME is not None: + paths.append(os.path.join(HIP_HOME, 'lib')) + elif device_type == "cuda": + if IS_WINDOWS: + lib_dir = os.path.join('lib', 'x64') + else: + lib_dir = 'lib64' + if (not os.path.exists(_join_cuda_home(lib_dir)) and + os.path.exists(_join_cuda_home('lib'))): + # 64-bit CUDA may be installed in 'lib' (see e.g. gh-16955) + # Note that it's also possible both don't exist (see + # _find_cuda_home) - in that case we stay with 'lib64'. + lib_dir = 'lib' + + paths.append(_join_cuda_home(lib_dir)) + if CUDNN_HOME is not None: + paths.append(os.path.join(CUDNN_HOME, lib_dir)) + elif device_type == "xpu": + if IS_WINDOWS: + lib_dir = os.path.join('lib', 'x64') + else: + lib_dir = 'lib64' + if (not os.path.exists(_join_sycl_home(lib_dir)) and + os.path.exists(_join_sycl_home('lib'))): + lib_dir = 'lib' + + paths.append(_join_sycl_home(lib_dir)) + + return paths + + +def load(name, + sources: Union[str, List[str]], + extra_cflags=None, + extra_cuda_cflags=None, + extra_ldflags=None, + extra_include_paths=None, + build_directory=None, + verbose=False, + with_cuda: Optional[bool] = None, + is_python_module=True, + is_standalone=False, + keep_intermediates=True): + """ + Load a PyTorch C++ extension just-in-time (JIT). + + To load an extension, a Ninja build file is emitted, which is used to + compile the given sources into a dynamic library. This library is + subsequently loaded into the current Python process as a module and + returned from this function, ready for use. + + By default, the directory to which the build file is emitted and the + resulting library compiled to is ``/torch_extensions/``, where + ```` is the temporary folder on the current platform and ```` + the name of the extension. This location can be overridden in two ways. + First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it + replaces ``/torch_extensions`` and all extensions will be compiled + into subfolders of this directory. Second, if the ``build_directory`` + argument to this function is supplied, it overrides the entire path, i.e. + the library will be compiled into that folder directly. + + To compile the sources, the default system compiler (``c++``) is used, + which can be overridden by setting the ``CXX`` environment variable. To pass + additional arguments to the compilation process, ``extra_cflags`` or + ``extra_ldflags`` can be provided. For example, to compile your extension + with optimizations, pass ``extra_cflags=['-O3']``. You can also use + ``extra_cflags`` to pass further include directories. + + CUDA support with mixed compilation is provided. Simply pass CUDA source + files (``.cu`` or ``.cuh``) along with other sources. Such files will be + detected and compiled with nvcc rather than the C++ compiler. This includes + passing the CUDA lib64 directory as a library directory, and linking + ``cudart``. You can pass additional flags to nvcc via + ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various + heuristics for finding the CUDA install directory are used, which usually + work fine. If not, setting the ``CUDA_HOME`` environment variable is the + safest option. + + Args: + name: The name of the extension to build. This MUST be the same as the + name of the pybind11 module! + sources: A list of relative or absolute paths to C++ source files. + extra_cflags: optional list of compiler flags to forward to the build. + extra_cuda_cflags: optional list of compiler flags to forward to nvcc + when building CUDA sources. + extra_ldflags: optional list of linker flags to forward to the build. + extra_include_paths: optional list of include directories to forward + to the build. + build_directory: optional path to use as build workspace. + verbose: If ``True``, turns on verbose logging of load steps. + with_cuda: Determines whether CUDA headers and libraries are added to + the build. If set to ``None`` (default), this value is + automatically determined based on the existence of ``.cu`` or + ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers + and libraries to be included. + is_python_module: If ``True`` (default), imports the produced shared + library as a Python module. If ``False``, behavior depends on + ``is_standalone``. + is_standalone: If ``False`` (default) loads the constructed extension + into the process as a plain dynamic library. If ``True``, build a + standalone executable. + + Returns: + If ``is_python_module`` is ``True``: + Returns the loaded PyTorch extension as a Python module. + + If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: + Returns nothing. (The shared library is loaded into the process as + a side effect.) + + If ``is_standalone`` is ``True``. + Return the path to the executable. (On Windows, TORCH_LIB_PATH is + added to the PATH environment variable as a side effect.) + + Example: + >>> # xdoctest: +SKIP + >>> from torch.utils.cpp_extension import load + >>> module = load( + ... name='extension', + ... sources=['extension.cpp', 'extension_kernel.cu'], + ... extra_cflags=['-O2'], + ... verbose=True) + """ + return _jit_compile( + name, + [sources] if isinstance(sources, str) else sources, + extra_cflags, + extra_cuda_cflags, + extra_ldflags, + extra_include_paths, + build_directory or _get_build_directory(name, verbose), + verbose, + with_cuda, + is_python_module, + is_standalone, + keep_intermediates=keep_intermediates) + +def _get_pybind11_abi_build_flags(): + # Note [Pybind11 ABI constants] + # + # Pybind11 before 2.4 used to build an ABI strings using the following pattern: + # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_BUILD_TYPE}__" + # Since 2.4 compier type, stdlib and build abi parameters are also encoded like this: + # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_COMPILER_TYPE}{PYBIND11_STDLIB}{PYBIND11_BUILD_ABI}{PYBIND11_BUILD_TYPE}__" + # + # This was done in order to further narrow down the chances of compiler ABI incompatibility + # that can cause a hard to debug segfaults. + # For PyTorch extensions we want to relax those restrictions and pass compiler, stdlib and abi properties + # captured during PyTorch native library compilation in torch/csrc/Module.cpp + # + # Note that these flags don't have side effects even if the PyTorch extension does not + # require nor use pybind, so we do not do anything differently for them in the py_limited_api + # case. + + abi_cflags = [] + for pname in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]: + pval = getattr(torch._C, f"_PYBIND11_{pname}") + if pval is not None and not IS_WINDOWS: + abi_cflags.append(f'-DPYBIND11_{pname}=\\"{pval}\\"') + return abi_cflags + +def _get_glibcxx_abi_build_flags(): + glibcxx_abi_cflags = ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))] + return glibcxx_abi_cflags + +def check_compiler_is_gcc(compiler): + if not IS_LINUX: + return False + + env = os.environ.copy() + env['LC_ALL'] = 'C' # Don't localize output + try: + version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS) + except Exception: + try: + version_string = subprocess.check_output([compiler, '--version'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS) + except Exception: + return False + # Check for 'gcc' or 'g++' for sccache wrapper + pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE) + results = re.findall(pattern, version_string) + if len(results) != 1: + return False + compiler_path = os.path.realpath(results[0].strip()) + # On RHEL/CentOS c++ is a gcc compiler wrapper + if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string: + return True + return False + +def _check_and_build_extension_h_precompiler_headers( + extra_cflags, + extra_include_paths, + is_standalone=False): + r''' + Precompiled Headers(PCH) can pre-build the same headers and reduce build time for pytorch load_inline modules. + GCC offical manual: https://gcc.gnu.org/onlinedocs/gcc-4.0.4/gcc/Precompiled-Headers.html + PCH only works when built pch file(header.h.gch) and build target have the same build parameters. So, We need + add a signature file to record PCH file parameters. If the build parameters(signature) changed, it should rebuild + PCH file. + + Note: + 1. Windows and MacOS have different PCH mechanism. We only support Linux currently. + 2. It only works on GCC/G++. + ''' + if not IS_LINUX: + return + + compiler = get_cxx_compiler() + + b_is_gcc = check_compiler_is_gcc(compiler) + if b_is_gcc is False: + return + + head_file = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h') + head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch') + head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign') + + def listToString(s): + # initialize an empty string + string = "" + if s is None: + return string + + # traverse in the string + for element in s: + string += (element + ' ') + # return string + return string + + def format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags, torch_include_dirs, extra_cflags, extra_include_paths): + return re.sub( + r"[ \n]+", + " ", + f""" + {compiler} -x c++-header {head_file} -o {head_file_pch} {torch_include_dirs} {extra_include_paths} {extra_cflags} {common_cflags} + """, + ).strip() + + def command_to_signature(cmd): + signature = cmd.replace(' ', '_') + return signature + + def check_pch_signature_in_file(file_path, signature): + b_exist = os.path.isfile(file_path) + if b_exist is False: + return False + + with open(file_path) as file: + # read all content of a file + content = file.read() + # check if string present in a file + return signature == content + + def _create_if_not_exist(path_dir): + if not os.path.exists(path_dir): + try: + Path(path_dir).mkdir(parents=True, exist_ok=True) + except OSError as exc: # Guard against race condition + if exc.errno != errno.EEXIST: + raise RuntimeError(f"Fail to create path {path_dir}") from exc + + def write_pch_signature_to_file(file_path, pch_sign): + _create_if_not_exist(os.path.dirname(file_path)) + with open(file_path, "w") as f: + f.write(pch_sign) + f.close() + + def build_precompile_header(pch_cmd): + try: + subprocess.check_output(pch_cmd, shell=True, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"Compile PreCompile Header fail, command: {pch_cmd}") from e + + extra_cflags_str = listToString(extra_cflags) + extra_include_paths_str = " ".join( + [f"-I{include}" for include in extra_include_paths] if extra_include_paths else [] + ) + + lib_include = os.path.join(_TORCH_PATH, 'include') + torch_include_dirs = [ + f"-I {lib_include}", + # Python.h + "-I {}".format(sysconfig.get_path("include")), + # torch/all.h + "-I {}".format(os.path.join(lib_include, 'torch', 'csrc', 'api', 'include')), + ] + + torch_include_dirs_str = listToString(torch_include_dirs) + + common_cflags = [] + if not is_standalone: + common_cflags += ['-DTORCH_API_INCLUDE_EXTENSION_H'] + + common_cflags += ['-std=c++17', '-fPIC'] + common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()] + common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()] + common_cflags_str = listToString(common_cflags) + + pch_cmd = format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags_str, torch_include_dirs_str, extra_cflags_str, extra_include_paths_str) + pch_sign = command_to_signature(pch_cmd) + + if os.path.isfile(head_file_pch) is not True: + build_precompile_header(pch_cmd) + write_pch_signature_to_file(head_file_signature, pch_sign) + else: + b_same_sign = check_pch_signature_in_file(head_file_signature, pch_sign) + if b_same_sign is False: + build_precompile_header(pch_cmd) + write_pch_signature_to_file(head_file_signature, pch_sign) + +def remove_extension_h_precompiler_headers(): + def _remove_if_file_exists(path_file): + if os.path.exists(path_file): + os.remove(path_file) + + head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch') + head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign') + + _remove_if_file_exists(head_file_pch) + _remove_if_file_exists(head_file_signature) + +def load_inline(name, + cpp_sources, + cuda_sources=None, + functions=None, + extra_cflags=None, + extra_cuda_cflags=None, + extra_ldflags=None, + extra_include_paths=None, + build_directory=None, + verbose=False, + with_cuda=None, + is_python_module=True, + with_pytorch_error_handling=True, + keep_intermediates=True, + use_pch=False): + r''' + Load a PyTorch C++ extension just-in-time (JIT) from string sources. + + This function behaves exactly like :func:`load`, but takes its sources as + strings rather than filenames. These strings are stored to files in the + build directory, after which the behavior of :func:`load_inline` is + identical to :func:`load`. + + See `the + tests `_ + for good examples of using this function. + + Sources may omit two required parts of a typical non-inline C++ extension: + the necessary header includes, as well as the (pybind11) binding code. More + precisely, strings passed to ``cpp_sources`` are first concatenated into a + single ``.cpp`` file. This file is then prepended with ``#include + ``. + + Furthermore, if the ``functions`` argument is supplied, bindings will be + automatically generated for each function specified. ``functions`` can + either be a list of function names, or a dictionary mapping from function + names to docstrings. If a list is given, the name of each function is used + as its docstring. + + The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` + file and prepended with ``torch/types.h``, ``cuda.h`` and + ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled + separately, but ultimately linked into a single library. Note that no + bindings are generated for functions in ``cuda_sources`` per se. To bind + to a CUDA kernel, you must create a C++ function that calls it, and either + declare or define this C++ function in one of the ``cpp_sources`` (and + include its name in ``functions``). + + See :func:`load` for a description of arguments omitted below. + + Args: + cpp_sources: A string, or list of strings, containing C++ source code. + cuda_sources: A string, or list of strings, containing CUDA source code. + functions: A list of function names for which to generate function + bindings. If a dictionary is given, it should map function names to + docstrings (which are otherwise just the function names). + with_cuda: Determines whether CUDA headers and libraries are added to + the build. If set to ``None`` (default), this value is + automatically determined based on whether ``cuda_sources`` is + provided. Set it to ``True`` to force CUDA headers + and libraries to be included. + with_pytorch_error_handling: Determines whether pytorch error and + warning macros are handled by pytorch instead of pybind. To do + this, each function ``foo`` is called via an intermediary ``_safe_foo`` + function. This redirection might cause issues in obscure cases + of cpp. This flag should be set to ``False`` when this redirect + causes issues. + + Example: + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) + >>> from torch.utils.cpp_extension import load_inline + >>> source = """ + at::Tensor sin_add(at::Tensor x, at::Tensor y) { + return x.sin() + y.sin(); + } + """ + >>> module = load_inline(name='inline_extension', + ... cpp_sources=[source], + ... functions=['sin_add']) + + .. note:: + Since load_inline will just-in-time compile the source code, please ensure + that you have the right toolchains installed in the runtime. For example, + when loading C++, make sure a C++ compiler is available. If you're loading + a CUDA extension, you will need to additionally install the corresponding CUDA + toolkit (nvcc and any other dependencies your code has). Compiling toolchains + are not included when you install torch and must be additionally installed. + + During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build + the extension. This may use up too many resources on some systems. One + can control the number of workers by setting the `MAX_JOBS` environment + variable to a non-negative number. + ''' + build_directory = build_directory or _get_build_directory(name, verbose) + + if isinstance(cpp_sources, str): + cpp_sources = [cpp_sources] + cuda_sources = cuda_sources or [] + if isinstance(cuda_sources, str): + cuda_sources = [cuda_sources] + + cpp_sources.insert(0, '#include ') + + if use_pch is True: + # Using PreCompile Header('torch/extension.h') to reduce compile time. + _check_and_build_extension_h_precompiler_headers(extra_cflags, extra_include_paths) + else: + remove_extension_h_precompiler_headers() + + # If `functions` is supplied, we create the pybind11 bindings for the user. + # Here, `functions` is (or becomes, after some processing) a map from + # function names to function docstrings. + if functions is not None: + module_def = [] + module_def.append('PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {') + if isinstance(functions, str): + functions = [functions] + if isinstance(functions, list): + # Make the function docstring the same as the function name. + functions = {f: f for f in functions} + elif not isinstance(functions, dict): + raise ValueError(f"Expected 'functions' to be a list or dict, but was {type(functions)}") + for function_name, docstring in functions.items(): + if with_pytorch_error_handling: + module_def.append(f'm.def("{function_name}", torch::wrap_pybind_function({function_name}), "{docstring}");') + else: + module_def.append(f'm.def("{function_name}", {function_name}, "{docstring}");') + module_def.append('}') + cpp_sources += module_def + + cpp_source_path = os.path.join(build_directory, 'main.cpp') + _maybe_write(cpp_source_path, "\n".join(cpp_sources)) + + sources = [cpp_source_path] + + if cuda_sources: + cuda_sources.insert(0, '#include ') + cuda_sources.insert(1, '#include ') + cuda_sources.insert(2, '#include ') + + cuda_source_path = os.path.join(build_directory, 'cuda.cu') + _maybe_write(cuda_source_path, "\n".join(cuda_sources)) + + sources.append(cuda_source_path) + + return _jit_compile( + name, + sources, + extra_cflags, + extra_cuda_cflags, + extra_ldflags, + extra_include_paths, + build_directory, + verbose, + with_cuda, + is_python_module, + is_standalone=False, + keep_intermediates=keep_intermediates) + + +def _jit_compile(name, + sources, + extra_cflags, + extra_cuda_cflags, + extra_ldflags, + extra_include_paths, + build_directory: str, + verbose: bool, + with_cuda: Optional[bool], + is_python_module, + is_standalone, + keep_intermediates=True) -> None: + if is_python_module and is_standalone: + raise ValueError("`is_python_module` and `is_standalone` are mutually exclusive.") + + if with_cuda is None: + with_cuda = any(map(_is_cuda_file, sources)) + with_cudnn = any('cudnn' in f for f in extra_ldflags or []) + old_version = JIT_EXTENSION_VERSIONER.get_version(name) + version = JIT_EXTENSION_VERSIONER.bump_version_if_changed( + name, + sources, + build_arguments=[extra_cflags, extra_cuda_cflags, extra_ldflags, extra_include_paths], + build_directory=build_directory, + with_cuda=with_cuda, + is_python_module=is_python_module, + is_standalone=is_standalone, + ) + if version > 0: + if version != old_version and verbose: + print(f'The input conditions for extension module {name} have changed. ' + + f'Bumping to version {version} and re-building as {name}_v{version}...', + file=sys.stderr) + name = f'{name}_v{version}' + + baton = FileBaton(os.path.join(build_directory, 'lock')) + if baton.try_acquire(): + try: + if version != old_version: + with GeneratedFileCleaner(keep_intermediates=keep_intermediates) as clean_ctx: + if IS_HIP_EXTENSION and (with_cuda or with_cudnn): + hipify_result = hipify_python.hipify( + project_directory=build_directory, + output_directory=build_directory, + header_include_dirs=(extra_include_paths if extra_include_paths is not None else []), + extra_files=[os.path.abspath(s) for s in sources], + ignores=[_join_rocm_home('*'), os.path.join(_TORCH_PATH, '*')], # no need to hipify ROCm or PyTorch headers + show_detailed=verbose, + show_progress=verbose, + is_pytorch_extension=True, + clean_ctx=clean_ctx + ) + + hipified_sources = set() + for source in sources: + s_abs = os.path.abspath(source) + hipified_sources.add(hipify_result[s_abs].hipified_path if s_abs in hipify_result else s_abs) + + sources = list(hipified_sources) + + _write_ninja_file_and_build_library( + name=name, + sources=sources, + extra_cflags=extra_cflags or [], + extra_cuda_cflags=extra_cuda_cflags or [], + extra_ldflags=extra_ldflags or [], + extra_include_paths=extra_include_paths or [], + build_directory=build_directory, + verbose=verbose, + with_cuda=with_cuda, + is_standalone=is_standalone) + elif verbose: + print('No modifications detected for re-loaded extension ' + f'module {name}, skipping build step...', file=sys.stderr) + finally: + baton.release() + else: + baton.wait() + + if verbose: + print(f'Loading extension module {name}...', file=sys.stderr) + + if is_standalone: + return _get_exec_path(name, build_directory) + + return _import_module_from_library(name, build_directory, is_python_module) + + +def _write_ninja_file_and_compile_objects( + sources: List[str], + objects, + cflags, + post_cflags, + cuda_cflags, + cuda_post_cflags, + cuda_dlink_post_cflags, + build_directory: str, + verbose: bool, + with_cuda: Optional[bool]) -> None: + verify_ninja_availability() + + compiler = get_cxx_compiler() + + get_compiler_abi_compatibility_and_version(compiler) + if with_cuda is None: + with_cuda = any(map(_is_cuda_file, sources)) + build_file_path = os.path.join(build_directory, 'build.ninja') + if verbose: + print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr) + + # Create build_directory if it does not exist + if not os.path.exists(build_directory): + if verbose: + print(f'Creating directory {build_directory}...', file=sys.stderr) + # This is like mkdir -p, i.e. will also create parent directories. + os.makedirs(build_directory, exist_ok=True) + + _write_ninja_file( + path=build_file_path, + cflags=cflags, + post_cflags=post_cflags, + cuda_cflags=cuda_cflags, + cuda_post_cflags=cuda_post_cflags, + cuda_dlink_post_cflags=cuda_dlink_post_cflags, + sources=sources, + objects=objects, + ldflags=None, + library_target=None, + with_cuda=with_cuda) + if verbose: + print('Compiling objects...', file=sys.stderr) + _run_ninja_build( + build_directory, + verbose, + # It would be better if we could tell users the name of the extension + # that failed to build but there isn't a good way to get it here. + error_prefix='Error compiling objects for extension') + + +def _write_ninja_file_and_build_library( + name, + sources: List[str], + extra_cflags, + extra_cuda_cflags, + extra_ldflags, + extra_include_paths, + build_directory: str, + verbose: bool, + with_cuda: Optional[bool], + is_standalone: bool = False) -> None: + verify_ninja_availability() + + compiler = get_cxx_compiler() + + get_compiler_abi_compatibility_and_version(compiler) + if with_cuda is None: + with_cuda = any(map(_is_cuda_file, sources)) + extra_ldflags = _prepare_ldflags( + extra_ldflags or [], + with_cuda, + verbose, + is_standalone) + build_file_path = os.path.join(build_directory, 'build.ninja') + if verbose: + print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr) + + # Create build_directory if it does not exist + if not os.path.exists(build_directory): + if verbose: + print(f'Creating directory {build_directory}...', file=sys.stderr) + # This is like mkdir -p, i.e. will also create parent directories. + os.makedirs(build_directory, exist_ok=True) + + # NOTE: Emitting a new ninja build file does not cause re-compilation if + # the sources did not change, so it's ok to re-emit (and it's fast). + _write_ninja_file_to_build_library( + path=build_file_path, + name=name, + sources=sources, + extra_cflags=extra_cflags or [], + extra_cuda_cflags=extra_cuda_cflags or [], + extra_ldflags=extra_ldflags or [], + extra_include_paths=extra_include_paths or [], + with_cuda=with_cuda, + is_standalone=is_standalone) + + if verbose: + print(f'Building extension module {name}...', file=sys.stderr) + _run_ninja_build( + build_directory, + verbose, + error_prefix=f"Error building extension '{name}'") + + +def is_ninja_available(): + """Return ``True`` if the `ninja `_ build system is available on the system, ``False`` otherwise.""" + try: + subprocess.check_output('ninja --version'.split()) + except Exception: + return False + else: + return True + + +def verify_ninja_availability(): + """Raise ``RuntimeError`` if `ninja `_ build system is not available on the system, does nothing otherwise.""" + if not is_ninja_available(): + raise RuntimeError("Ninja is required to load C++ extensions") + + +def _prepare_ldflags(extra_ldflags, with_cuda, verbose, is_standalone): + if IS_WINDOWS: + python_lib_path = os.path.join(sys.base_exec_prefix, 'libs') + + extra_ldflags.append('c10.lib') + if with_cuda: + extra_ldflags.append('c10_cuda.lib') + extra_ldflags.append('torch_cpu.lib') + if with_cuda: + extra_ldflags.append('torch_cuda.lib') + # /INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it. + # Related issue: https://github.com/pytorch/pytorch/issues/31611 + extra_ldflags.append('-INCLUDE:?warp_size@cuda@at@@YAHXZ') + extra_ldflags.append('torch.lib') + extra_ldflags.append(f'/LIBPATH:{TORCH_LIB_PATH}') + if not is_standalone: + extra_ldflags.append('torch_python.lib') + extra_ldflags.append(f'/LIBPATH:{python_lib_path}') + + else: + extra_ldflags.append(f'-L{TORCH_LIB_PATH}') + extra_ldflags.append('-lc10') + if with_cuda: + extra_ldflags.append('-lc10_hip' if IS_HIP_EXTENSION else '-lc10_cuda') + extra_ldflags.append('-ltorch_cpu') + if with_cuda: + extra_ldflags.append('-ltorch_hip' if IS_HIP_EXTENSION else '-ltorch_cuda') + extra_ldflags.append('-ltorch') + if not is_standalone: + extra_ldflags.append('-ltorch_python') + + if is_standalone: + extra_ldflags.append(f"-Wl,-rpath,{TORCH_LIB_PATH}") + + if with_cuda: + if verbose: + print('Detected CUDA files, patching ldflags', file=sys.stderr) + if IS_WINDOWS: + extra_ldflags.append(f'/LIBPATH:{_join_cuda_home("lib", "x64")}') + extra_ldflags.append('cudart.lib') + if CUDNN_HOME is not None: + extra_ldflags.append(f'/LIBPATH:{os.path.join(CUDNN_HOME, "lib", "x64")}') + elif not IS_HIP_EXTENSION: + extra_lib_dir = "lib64" + if (not os.path.exists(_join_cuda_home(extra_lib_dir)) and + os.path.exists(_join_cuda_home("lib"))): + # 64-bit CUDA may be installed in "lib" + # Note that it's also possible both don't exist (see _find_cuda_home) - in that case we stay with "lib64" + extra_lib_dir = "lib" + extra_ldflags.append(f'-L{_join_cuda_home(extra_lib_dir)}') + extra_ldflags.append('-lcudart') + if CUDNN_HOME is not None: + extra_ldflags.append(f'-L{os.path.join(CUDNN_HOME, "lib64")}') + elif IS_HIP_EXTENSION: + extra_ldflags.append(f'-L{_join_rocm_home("lib")}') + extra_ldflags.append('-lamdhip64') + return extra_ldflags + + +def _get_cuda_arch_flags(cflags: Optional[List[str]] = None) -> List[str]: + """ + Determine CUDA arch flags to use. + + For an arch, say "6.1", the added compile flag will be + ``-gencode=arch=compute_61,code=sm_61``. + For an added "+PTX", an additional + ``-gencode=arch=compute_xx,code=compute_xx`` is added. + + See select_compute_arch.cmake for corresponding named and supported arches + when building with CMake. + """ + # If cflags is given, there may already be user-provided arch flags in it + # (from `extra_compile_args`) + if cflags is not None: + for flag in cflags: + if 'TORCH_EXTENSION_NAME' in flag: + continue + if 'arch' in flag: + return [] + + # Note: keep combined names ("arch1+arch2") above single names, otherwise + # string replacement may not do the right thing + named_arches = collections.OrderedDict([ + ('Kepler+Tesla', '3.7'), + ('Kepler', '3.5+PTX'), + ('Maxwell+Tegra', '5.3'), + ('Maxwell', '5.0;5.2+PTX'), + ('Pascal', '6.0;6.1+PTX'), + ('Volta+Tegra', '7.2'), + ('Volta', '7.0+PTX'), + ('Turing', '7.5+PTX'), + ('Ampere+Tegra', '8.7'), + ('Ampere', '8.0;8.6+PTX'), + ('Ada', '8.9+PTX'), + ('Hopper', '9.0+PTX'), + ('Blackwell', '10.0+PTX'), + ]) + + supported_arches = ['3.5', '3.7', '5.0', '5.2', '5.3', '6.0', '6.1', '6.2', + '7.0', '7.2', '7.5', '8.0', '8.6', '8.7', '8.9', '9.0', '9.0a', + '10.0'] + valid_arch_strings = supported_arches + [s + "+PTX" for s in supported_arches] + + # The default is sm_30 for CUDA 9.x and 10.x + # First check for an env var (same as used by the main setup.py) + # Can be one or more architectures, e.g. "6.1" or "3.5;5.2;6.0;6.1;7.0+PTX" + # See cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake + _arch_list = os.environ.get('TORCH_CUDA_ARCH_LIST', None) + + # If not given, determine what's best for the GPU / CUDA version that can be found + if not _arch_list: + warnings.warn( + "TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n" + "If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].") + arch_list = [] + # the assumption is that the extension should run on any of the currently visible cards, + # which could be of different types - therefore all archs for visible cards should be included + for i in range(torch.cuda.device_count()): + capability = torch.cuda.get_device_capability(i) + supported_sm = [int(arch.split('_')[1]) + for arch in torch.cuda.get_arch_list() if 'sm_' in arch] + max_supported_sm = max((sm // 10, sm % 10) for sm in supported_sm) + # Capability of the device may be higher than what's supported by the user's + # NVCC, causing compilation error. User's NVCC is expected to match the one + # used to build pytorch, so we use the maximum supported capability of pytorch + # to clamp the capability. + capability = min(max_supported_sm, capability) + arch = f'{capability[0]}.{capability[1]}' + if arch not in arch_list: + arch_list.append(arch) + arch_list = sorted(arch_list) + arch_list[-1] += '+PTX' + else: + # Deal with lists that are ' ' separated (only deal with ';' after) + _arch_list = _arch_list.replace(' ', ';') + # Expand named arches + for named_arch, archval in named_arches.items(): + _arch_list = _arch_list.replace(named_arch, archval) + + arch_list = _arch_list.split(';') + + flags = [] + for arch in arch_list: + if arch not in valid_arch_strings: + raise ValueError(f"Unknown CUDA arch ({arch}) or GPU not supported") + else: + # Handle both single and double-digit architecture versions + version = arch.split('+')[0] # Remove "+PTX" if present + major, minor = version.split('.') + num = f"{major}{minor}" + flags.append(f'-gencode=arch=compute_{num},code=sm_{num}') + if arch.endswith('+PTX'): + flags.append(f'-gencode=arch=compute_{num},code=compute_{num}') + + return sorted(set(flags)) + + +def _get_rocm_arch_flags(cflags: Optional[List[str]] = None) -> List[str]: + # If cflags is given, there may already be user-provided arch flags in it + # (from `extra_compile_args`) + if cflags is not None: + for flag in cflags: + if 'amdgpu-target' in flag or 'offload-arch' in flag: + return ['-fno-gpu-rdc'] + # Use same defaults as used for building PyTorch + # Allow env var to override, just like during initial cmake build. + _archs = os.environ.get('PYTORCH_ROCM_ARCH', None) + if not _archs: + archFlags = torch._C._cuda_getArchFlags() + if archFlags: + archs = archFlags.split() + else: + archs = [] + else: + archs = _archs.replace(' ', ';').split(';') + flags = [f'--offload-arch={arch}' for arch in archs] + flags += ['-fno-gpu-rdc'] + return flags + +def _get_build_directory(name: str, verbose: bool) -> str: + root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR') + if root_extensions_directory is None: + root_extensions_directory = get_default_build_root() + cu_str = ('cpu' if torch.version.cuda is None else + f'cu{torch.version.cuda.replace(".", "")}') + python_version = f'py{sys.version_info.major}{sys.version_info.minor}{getattr(sys, "abiflags", "")}' + build_folder = f'{python_version}_{cu_str}' + + root_extensions_directory = os.path.join( + root_extensions_directory, build_folder) + + if verbose: + print(f'Using {root_extensions_directory} as PyTorch extensions root...', file=sys.stderr) + + build_directory = os.path.join(root_extensions_directory, name) + if not os.path.exists(build_directory): + if verbose: + print(f'Creating extension directory {build_directory}...', file=sys.stderr) + # This is like mkdir -p, i.e. will also create parent directories. + os.makedirs(build_directory, exist_ok=True) + + return build_directory + + +def _get_num_workers(verbose: bool) -> Optional[int]: + max_jobs = os.environ.get('MAX_JOBS') + if max_jobs is not None and max_jobs.isdigit(): + if verbose: + print(f'Using envvar MAX_JOBS ({max_jobs}) as the number of workers...', + file=sys.stderr) + return int(max_jobs) + if verbose: + print('Allowing ninja to set a default number of workers... ' + '(overridable by setting the environment variable MAX_JOBS=N)', + file=sys.stderr) + return None + + +def _get_vc_env(vc_arch: str) -> dict[str, str]: + try: + from setuptools import distutils + return distutils._msvccompiler._get_vc_env(vc_arch) + except AttributeError: + from setuptools._distutils import _msvccompiler + return _msvccompiler._get_vc_env(vc_arch) + + +def _run_ninja_build(build_directory: str, verbose: bool, error_prefix: str) -> None: + command = ['ninja', '-v'] + num_workers = _get_num_workers(verbose) + if num_workers is not None: + command.extend(['-j', str(num_workers)]) + env = os.environ.copy() + # Try to activate the vc env for the users + if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' not in env: + from setuptools import distutils + + plat_name = distutils.util.get_platform() + plat_spec = PLAT_TO_VCVARS[plat_name] + vc_env = {k.upper(): v for k, v in _get_vc_env(plat_spec).items()} + for k, v in env.items(): + uk = k.upper() + if uk not in vc_env: + vc_env[uk] = v + env = vc_env + try: + sys.stdout.flush() + sys.stderr.flush() + # Warning: don't pass stdout=None to subprocess.run to get output. + # subprocess.run assumes that sys.__stdout__ has not been modified and + # attempts to write to it by default. However, when we call _run_ninja_build + # from ahead-of-time cpp extensions, the following happens: + # 1) If the stdout encoding is not utf-8, setuptools detachs __stdout__. + # https://github.com/pypa/setuptools/blob/7e97def47723303fafabe48b22168bbc11bb4821/setuptools/dist.py#L1110 + # (it probably shouldn't do this) + # 2) subprocess.run (on POSIX, with no stdout override) relies on + # __stdout__ not being detached: + # https://github.com/python/cpython/blob/c352e6c7446c894b13643f538db312092b351789/Lib/subprocess.py#L1214 + # To work around this, we pass in the fileno directly and hope that + # it is valid. + stdout_fileno = 1 + subprocess.run( + command, + stdout=stdout_fileno if verbose else subprocess.PIPE, + stderr=subprocess.STDOUT, + cwd=build_directory, + check=True, + env=env) + except subprocess.CalledProcessError as e: + # Python 2 and 3 compatible way of getting the error object. + _, error, _ = sys.exc_info() + # error.output contains the stdout and stderr of the build attempt. + message = error_prefix + # `error` is a CalledProcessError (which has an `output`) attribute, but + # mypy thinks it's Optional[BaseException] and doesn't narrow + if hasattr(error, 'output') and error.output: # type: ignore[union-attr] + message += f": {error.output.decode(*SUBPROCESS_DECODE_ARGS)}" # type: ignore[union-attr] + raise RuntimeError(message) from e + + +def _get_exec_path(module_name, path): + if IS_WINDOWS and TORCH_LIB_PATH not in os.getenv('PATH', '').split(';'): + torch_lib_in_path = any( + os.path.exists(p) and os.path.samefile(p, TORCH_LIB_PATH) + for p in os.getenv('PATH', '').split(';') + ) + if not torch_lib_in_path: + os.environ['PATH'] = f"{TORCH_LIB_PATH};{os.getenv('PATH', '')}" + return os.path.join(path, f'{module_name}{EXEC_EXT}') + + +def _import_module_from_library(module_name, path, is_python_module): + filepath = os.path.join(path, f"{module_name}{LIB_EXT}") + if is_python_module: + # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path + spec = importlib.util.spec_from_file_location(module_name, filepath) + assert spec is not None + module = importlib.util.module_from_spec(spec) + assert isinstance(spec.loader, importlib.abc.Loader) + spec.loader.exec_module(module) + return module + else: + torch.ops.load_library(filepath) + return filepath + + +def _write_ninja_file_to_build_library(path, + name, + sources, + extra_cflags, + extra_cuda_cflags, + extra_ldflags, + extra_include_paths, + with_cuda, + is_standalone) -> None: + extra_cflags = [flag.strip() for flag in extra_cflags] + extra_cuda_cflags = [flag.strip() for flag in extra_cuda_cflags] + extra_ldflags = [flag.strip() for flag in extra_ldflags] + extra_include_paths = [flag.strip() for flag in extra_include_paths] + + # Turn into absolute paths so we can emit them into the ninja build + # file wherever it is. + user_includes = [os.path.abspath(file) for file in extra_include_paths] + + # include_paths() gives us the location of torch/extension.h + # TODO generalize with_cuda as specific device type. + if with_cuda: + system_includes = include_paths("cuda") + else: + system_includes = include_paths("cpu") + # sysconfig.get_path('include') gives us the location of Python.h + # Explicitly specify 'posix_prefix' scheme on non-Windows platforms to workaround error on some MacOS + # installations where default `get_path` points to non-existing `/Library/Python/M.m/include` folder + python_include_path = sysconfig.get_path('include', scheme='nt' if IS_WINDOWS else 'posix_prefix') + if python_include_path is not None: + system_includes.append(python_include_path) + + common_cflags = [] + if not is_standalone: + common_cflags.append(f'-DTORCH_EXTENSION_NAME={name}') + common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H') + + common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()] + + # Windows does not understand `-isystem` and quotes flags later. + if IS_WINDOWS: + common_cflags += [f'-I{include}' for include in user_includes + system_includes] + else: + common_cflags += [f'-I{shlex.quote(include)}' for include in user_includes] + common_cflags += [f'-isystem {shlex.quote(include)}' for include in system_includes] + + common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()] + + if IS_WINDOWS: + cflags = common_cflags + COMMON_MSVC_FLAGS + ['/std:c++17'] + extra_cflags + cflags = _nt_quote_args(cflags) + else: + cflags = common_cflags + ['-fPIC', '-std=c++17'] + extra_cflags + + if with_cuda and IS_HIP_EXTENSION: + cuda_flags = ['-DWITH_HIP'] + cflags + COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS + cuda_flags += extra_cuda_cflags + cuda_flags += _get_rocm_arch_flags(cuda_flags) + elif with_cuda: + cuda_flags = common_cflags + COMMON_NVCC_FLAGS + _get_cuda_arch_flags() + if IS_WINDOWS: + for flag in COMMON_MSVC_FLAGS: + cuda_flags = ['-Xcompiler', flag] + cuda_flags + for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS: + cuda_flags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cuda_flags + cuda_flags = cuda_flags + ['-std=c++17'] + cuda_flags = _nt_quote_args(cuda_flags) + cuda_flags += _nt_quote_args(extra_cuda_cflags) + else: + cuda_flags += ['--compiler-options', "'-fPIC'"] + cuda_flags += extra_cuda_cflags + if not any(flag.startswith('-std=') for flag in cuda_flags): + cuda_flags.append('-std=c++17') + cc_env = os.getenv("CC") + if cc_env is not None: + cuda_flags = ['-ccbin', cc_env] + cuda_flags + else: + cuda_flags = None + + def object_file_path(source_file: str) -> str: + # '/path/to/file.cpp' -> 'file' + file_name = os.path.splitext(os.path.basename(source_file))[0] + if _is_cuda_file(source_file) and with_cuda: + # Use a different object filename in case a C++ and CUDA file have + # the same filename but different extension (.cpp vs. .cu). + target = f'{file_name}.cuda.o' + else: + target = f'{file_name}.o' + return target + + objects = [object_file_path(src) for src in sources] + ldflags = ([] if is_standalone else [SHARED_FLAG]) + extra_ldflags + + # The darwin linker needs explicit consent to ignore unresolved symbols. + if IS_MACOS: + ldflags.append('-undefined dynamic_lookup') + elif IS_WINDOWS: + ldflags = _nt_quote_args(ldflags) + + ext = EXEC_EXT if is_standalone else LIB_EXT + library_target = f'{name}{ext}' + + _write_ninja_file( + path=path, + cflags=cflags, + post_cflags=None, + cuda_cflags=cuda_flags, + cuda_post_cflags=None, + cuda_dlink_post_cflags=None, + sources=sources, + objects=objects, + ldflags=ldflags, + library_target=library_target, + with_cuda=with_cuda) + + +def _write_ninja_file(path, + cflags, + post_cflags, + cuda_cflags, + cuda_post_cflags, + cuda_dlink_post_cflags, + sources, + objects, + ldflags, + library_target, + with_cuda) -> None: + r"""Write a ninja file that does the desired compiling and linking. + + `path`: Where to write this file + `cflags`: list of flags to pass to $cxx. Can be None. + `post_cflags`: list of flags to append to the $cxx invocation. Can be None. + `cuda_cflags`: list of flags to pass to $nvcc. Can be None. + `cuda_postflags`: list of flags to append to the $nvcc invocation. Can be None. + `sources`: list of paths to source files + `objects`: list of desired paths to objects, one per source. + `ldflags`: list of flags to pass to linker. Can be None. + `library_target`: Name of the output library. Can be None; in that case, + we do no linking. + `with_cuda`: If we should be compiling with CUDA. + """ + def sanitize_flags(flags): + if flags is None: + return [] + else: + return [flag.strip() for flag in flags] + + cflags = sanitize_flags(cflags) + post_cflags = sanitize_flags(post_cflags) + cuda_cflags = sanitize_flags(cuda_cflags) + cuda_post_cflags = sanitize_flags(cuda_post_cflags) + cuda_dlink_post_cflags = sanitize_flags(cuda_dlink_post_cflags) + ldflags = sanitize_flags(ldflags) + + # Sanity checks... + assert len(sources) == len(objects) + assert len(sources) > 0 + + compiler = get_cxx_compiler() + + # Version 1.3 is required for the `deps` directive. + config = ['ninja_required_version = 1.3'] + config.append(f'cxx = {compiler}') + if with_cuda or cuda_dlink_post_cflags: + if "PYTORCH_NVCC" in os.environ: + nvcc = os.getenv("PYTORCH_NVCC") # user can set nvcc compiler with ccache using the environment variable here + else: + if IS_HIP_EXTENSION: + nvcc = _join_rocm_home('bin', 'hipcc') + else: + nvcc = _join_cuda_home('bin', 'nvcc') + config.append(f'nvcc = {nvcc}') + + if IS_HIP_EXTENSION: + post_cflags = COMMON_HIP_FLAGS + post_cflags + flags = [f'cflags = {" ".join(cflags)}'] + flags.append(f'post_cflags = {" ".join(post_cflags)}') + if with_cuda: + flags.append(f'cuda_cflags = {" ".join(cuda_cflags)}') + flags.append(f'cuda_post_cflags = {" ".join(cuda_post_cflags)}') + flags.append(f'cuda_dlink_post_cflags = {" ".join(cuda_dlink_post_cflags)}') + flags.append(f'ldflags = {" ".join(ldflags)}') + + # Turn into absolute paths so we can emit them into the ninja build + # file wherever it is. + sources = [os.path.abspath(file) for file in sources] + + # See https://ninja-build.org/build.ninja.html for reference. + compile_rule = ['rule compile'] + if IS_WINDOWS: + compile_rule.append( + ' command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags') + compile_rule.append(' deps = msvc') + else: + compile_rule.append( + ' command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags') + compile_rule.append(' depfile = $out.d') + compile_rule.append(' deps = gcc') + + if with_cuda: + cuda_compile_rule = ['rule cuda_compile'] + nvcc_gendeps = '' + # --generate-dependencies-with-compile is not supported by ROCm + # Nvcc flag `--generate-dependencies-with-compile` is not supported by sccache, which may increase build time. + if torch.version.cuda is not None and os.getenv('TORCH_EXTENSION_SKIP_NVCC_GEN_DEPENDENCIES', '0') != '1': + cuda_compile_rule.append(' depfile = $out.d') + cuda_compile_rule.append(' deps = gcc') + # Note: non-system deps with nvcc are only supported + # on Linux so use --generate-dependencies-with-compile + # to make this work on Windows too. + nvcc_gendeps = '--generate-dependencies-with-compile --dependency-output $out.d' + cuda_compile_rule.append( + f' command = $nvcc {nvcc_gendeps} $cuda_cflags -c $in -o $out $cuda_post_cflags') + + # Emit one build rule per source to enable incremental build. + build = [] + for source_file, object_file in zip(sources, objects): + is_cuda_source = _is_cuda_file(source_file) and with_cuda + rule = 'cuda_compile' if is_cuda_source else 'compile' + if IS_WINDOWS: + source_file = source_file.replace(':', '$:') + object_file = object_file.replace(':', '$:') + source_file = source_file.replace(" ", "$ ") + object_file = object_file.replace(" ", "$ ") + build.append(f'build {object_file}: {rule} {source_file}') + + if cuda_dlink_post_cflags: + devlink_out = os.path.join(os.path.dirname(objects[0]), 'dlink.o') + devlink_rule = ['rule cuda_devlink'] + devlink_rule.append(' command = $nvcc $in -o $out $cuda_dlink_post_cflags') + devlink = [f'build {devlink_out}: cuda_devlink {" ".join(objects)}'] + objects += [devlink_out] + else: + devlink_rule, devlink = [], [] + + if library_target is not None: + link_rule = ['rule link'] + if IS_WINDOWS: + cl_paths = subprocess.check_output(['where', + 'cl']).decode(*SUBPROCESS_DECODE_ARGS).split('\r\n') + if len(cl_paths) >= 1: + cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:') + else: + raise RuntimeError("MSVC is required to load C++ extensions") + link_rule.append(f' command = "{cl_path}/link.exe" $in /nologo $ldflags /out:$out') + else: + link_rule.append(' command = $cxx $in $ldflags -o $out') + + link = [f'build {library_target}: link {" ".join(objects)}'] + + default = [f'default {library_target}'] + else: + link_rule, link, default = [], [], [] + + # 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+ Deque, + Iterator, + List, + Literal, + Optional, + Sized, + Tuple, + TypeVar, +) + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes._hook_iterator import _SnapshotState +from torch.utils.data.datapipes.datapipe import IterDataPipe +from torch.utils.data.datapipes.utils.common import _check_unpickable_fn, StreamWrapper + + +__all__ = [ + "ConcaterIterDataPipe", + "DemultiplexerIterDataPipe", + "ForkerIterDataPipe", + "MultiplexerIterDataPipe", + "ZipperIterDataPipe", +] + + +_T_co = TypeVar("_T_co", covariant=True) + + +@functional_datapipe("concat") +class ConcaterIterDataPipe(IterDataPipe): + r""" + Concatenates multiple Iterable DataPipes (functional name: ``concat``). + + The resulting DataPipe will yield all the elements from the first input DataPipe, before yielding from the subsequent ones. + + Args: + datapipes: Iterable DataPipes being concatenated + + Example: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> import random + >>> from torchdata.datapipes.iter import IterableWrapper + >>> dp1 = IterableWrapper(range(3)) + >>> dp2 = IterableWrapper(range(5)) + >>> list(dp1.concat(dp2)) + [0, 1, 2, 0, 1, 2, 3, 4] + """ + + datapipes: Tuple[IterDataPipe] + + def __init__(self, *datapipes: IterDataPipe): + if len(datapipes) == 0: + raise ValueError("Expected at least one DataPipe, but got nothing") + if not all(isinstance(dp, IterDataPipe) for dp in datapipes): + raise TypeError("Expected all inputs to be `IterDataPipe`") + self.datapipes = datapipes # type: ignore[assignment] + + def __iter__(self) -> Iterator: + for dp in self.datapipes: + yield from dp + + def __len__(self) -> int: + if all(isinstance(dp, Sized) for dp in self.datapipes): + return sum(len(dp) for dp in self.datapipes) + else: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") + + +@functional_datapipe("fork") +class ForkerIterDataPipe(IterDataPipe): + r""" + Creates multiple instances of the same Iterable DataPipe (functional name: ``fork``). + + Args: + datapipe: Iterable DataPipe being copied + num_instances: number of instances of the datapipe to create + buffer_size: this restricts how far ahead the leading child DataPipe + can read relative to the slowest child DataPipe. + Defaults to ``1000``. Use ``-1`` for the unlimited buffer. + copy: copy strategy to use for items yielded by each branch. Supported + options are ``None`` for no copying, ``"shallow"`` for shallow object + copies, and ``"deep"`` for deep object copies. Defaults to ``None``. + + Note: + All branches of the forked pipeline return the identical object unless + the copy parameter is supplied. If the object is mutable or contains + mutable objects, changing them in one branch will affect all others. + + Example: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> from torchdata.datapipes.iter import IterableWrapper + >>> source_dp = IterableWrapper(range(5)) + >>> dp1, dp2 = source_dp.fork(num_instances=2) + >>> list(dp1) + [0, 1, 2, 3, 4] + >>> list(dp2) + [0, 1, 2, 3, 4] + """ + + def __new__( + cls, + datapipe: IterDataPipe, + num_instances: int, + buffer_size: int = 1000, + copy: Optional[Literal["shallow", "deep"]] = None, + ): + if num_instances < 1: + raise ValueError( + f"Expected `num_instances` larger than 0, but {num_instances} is found" + ) + if num_instances == 1: + return datapipe + container = _ForkerIterDataPipe(datapipe, num_instances, buffer_size, copy) # type: ignore[abstract] + return [_ChildDataPipe(container, i) for i in range(num_instances)] + + +class _ContainerTemplate(ABC): + r"""Abstract class for container ``DataPipes``. The followings are three required methods.""" + + @abstractmethod + def get_next_element_by_instance(self, instance_id: int): + ... + + @abstractmethod + def is_every_instance_exhausted(self) -> bool: + ... + + @abstractmethod + def reset(self) -> None: + ... + + @abstractmethod + def get_length_by_instance(self, instance_id: int): + r"""Raise TypeError if it's not supposed to be implemented to support `list(datapipe)`.""" + + +def _no_op(x): + return x + + +class _ForkerIterDataPipe(IterDataPipe, _ContainerTemplate): + r""" + Container to hold instance-specific information on behalf of ForkerIterDataPipe. + + It tracks the state of its child DataPipes, maintains the buffer, and yields the next value + as requested by the child DataPipes. + """ + + def __init__( + self, + datapipe: IterDataPipe, + num_instances: int, + buffer_size: int = 1000, + copy: Optional[Literal["shallow", "deep"]] = None, + ): + self.main_datapipe = datapipe + self._datapipe_iterator: Optional[Iterator[Any]] = None + self.num_instances = num_instances + self.buffer: Deque = deque() + self.buffer_size = buffer_size + if self.buffer_size < 0: + warnings.warn( + "Unlimited buffer size is set for `fork`, " + "please be aware of OOM at random places", + UserWarning, + ) + if copy is None: + self.copy_fn = _no_op + elif copy == "shallow": + self.copy_fn = copymodule.copy + elif copy == "deep": + self.copy_fn = copymodule.deepcopy + else: + raise ValueError( + f"Unknown copy method `{copy}` requested, choose one of None, `shallow` or `deep`." + ) + + self.child_pointers: List[int] = [ + 0 + ] * num_instances # Indicate the indices of the next element to get + self.slowest_ptr = 0 # The index to read by the slowest child + self.leading_ptr = 0 # The index to read by the fastest child + self.end_ptr: Optional[int] = None # The index to stop child + self._child_stop: List[bool] = [True for _ in range(num_instances)] + + def __len__(self): + return len(self.main_datapipe) + + def get_next_element_by_instance(self, instance_id: int): + if self._datapipe_iterator is None and self._child_stop[instance_id]: + self._datapipe_iterator = iter(self.main_datapipe) + self._snapshot_state = _SnapshotState.Iterating + for i in range(self.num_instances): + self._child_stop[i] = False + try: + while not self._child_stop[instance_id]: + self.child_pointers[instance_id] += 1 + if ( + self.end_ptr is not None + and self.child_pointers[instance_id] == self.end_ptr + ): + self._child_stop[instance_id] = True + break + # Use buffer + if self.buffer and self.child_pointers[instance_id] <= self.leading_ptr: + idx = self.child_pointers[instance_id] - self.slowest_ptr - 1 + return_val = self.buffer[idx] + else: # Retrieve one element from main datapipe + self.leading_ptr = self.child_pointers[instance_id] + try: + return_val = next(self._datapipe_iterator) # type: ignore[arg-type] + self.buffer.append(return_val) + except StopIteration: + self._child_stop[instance_id] = True + self._datapipe_iterator = None + self.end_ptr = self.leading_ptr + continue + if self.child_pointers[instance_id] == self.slowest_ptr + 1: + new_min = min( + self.child_pointers + ) # Can optimize by avoiding the call to min() + if self.slowest_ptr < new_min: + self.slowest_ptr = new_min + self.buffer.popleft() + if ( + self.buffer_size >= 0 + and self.leading_ptr > self.buffer_size + self.slowest_ptr + ): + raise BufferError( + "ForkerIterDataPipe buffer overflow," + + f"buffer size {self.buffer_size} is insufficient." + ) + + yield self.copy_fn(return_val) # type: ignore[possibly-undefined] + finally: + self._child_stop[instance_id] = True + # Cleanup _datapipe_iterator for the case that fork exits earlier + if all(self._child_stop): + self._datapipe_iterator = None + self._cleanup() + + def is_every_instance_exhausted(self) -> bool: + return self.end_ptr is not None and all(self._child_stop) + + def get_length_by_instance(self, instance_id: int) -> int: + return len(self.main_datapipe) + + def reset(self) -> None: + self._datapipe_iterator = None + self.buffer = deque() + self.child_pointers = [0] * self.num_instances + self.slowest_ptr = 0 + self.leading_ptr = 0 + self.end_ptr = None + self._child_stop = [True for _ in range(self.num_instances)] + + def __getstate__(self): + state = ( + self.main_datapipe, + self.num_instances, + self.buffer_size, + self.copy_fn, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) + if IterDataPipe.getstate_hook is not None: + return IterDataPipe.getstate_hook(state) + return state + + def __setstate__(self, state): + ( + self.main_datapipe, + self.num_instances, + self.buffer_size, + self.copy_fn, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) = state + self._datapipe_iterator = None + self.buffer = deque() + self.child_pointers = [0] * self.num_instances + self.slowest_ptr = 0 + self.leading_ptr = 0 + self.end_ptr = None + self._child_stop = [True for _ in range(self.num_instances)] + + def _cleanup(self): + while self.buffer: + d = self.buffer.popleft() + StreamWrapper.close_streams(d) + + def __del__(self): + self._cleanup() + + +class _ChildDataPipe(IterDataPipe): + r""" + Iterable Datapipe that is a child of a main DataPipe. + + The instance of this class will pass its instance_id to get the next value from its main DataPipe. + + Note: + ChildDataPipe, like all other IterDataPipe, follows the single iterator per IterDataPipe constraint. + Since ChildDataPipes share a common buffer, when an iterator is created for one of the ChildDataPipes, + the previous iterators for all ChildDataPipes must be invalidated, with the exception when a ChildDataPipe + hasn't had an iterator created from it since the last invalidation. See the example below. + + Example: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> # Singler Iterator per IteraDataPipe Invalidation + >>> from torchdata.datapipes.iter import IterableWrapper + >>> source_dp = IterableWrapper(range(10)) + >>> cdp1, cdp2 = source_dp.fork(num_instances=2) + >>> it1, it2 = iter(cdp1), iter(cdp2) + >>> it3 = iter(cdp1) + >>> # The line above invalidates `it1` and `it2`, and resets `ForkerIterDataPipe`. + >>> it4 = iter(cdp2) + >>> # The line above doesn't invalidate `it3`, because an iterator for `cdp2` hasn't been created since + >>> # the last invalidation. + + Args: + main_datapipe: Main DataPipe with a method 'get_next_element_by_instance(instance_id)' + instance_id: integer identifier of this instance + """ + + _is_child_datapipe: bool = True + + def __init__(self, main_datapipe: IterDataPipe, instance_id: int): + assert isinstance(main_datapipe, _ContainerTemplate) + + self.main_datapipe: IterDataPipe = main_datapipe + self.instance_id = instance_id + + def __iter__(self): + # Note that the logic behind setting iterator ID and `reset` are handled within `hook_iterator` + # We want to separate the code for reset and yield, so that 'reset' executes before __next__ is called + return self.main_datapipe.get_next_element_by_instance(self.instance_id) + + def __len__(self): + return self.main_datapipe.get_length_by_instance(self.instance_id) + + # This method is called by `hook_iterator` in `_typing.py`. + def _set_main_datapipe_valid_iterator_id(self) -> int: + r""" + Update the valid iterator ID for both this DataPipe object and `main_datapipe`. + + `main_datapipe.reset()` is called when the ID is incremented to a new generation. + """ + # 1. First time any child iterator is created + if self.main_datapipe._valid_iterator_id is None: + self.main_datapipe._valid_iterator_id = 0 # type: ignore[attr-defined] + # 2. This instance was already in the same generation as `main_datapipe`, + # we need to increment the ID further by 1 + elif self.main_datapipe._valid_iterator_id == self._valid_iterator_id: # type: ignore[has-type] + self.main_datapipe._valid_iterator_id += 1 # type: ignore[attr-defined] + # Whenever a new generation of iterator is created, the `main_datapipe` must reset + if not self.main_datapipe.is_every_instance_exhausted(): + warnings.warn( + "Some child DataPipes are not exhausted when __iter__ is called. We are resetting " + "the buffer and each child DataPipe will read from the start again.", + UserWarning, + ) + self.main_datapipe.reset() + # 3. Otherwise, the iterator is behind the others, so it will just need to catch up by setting + # the instance's iterator to match that of `main_datapipe` + self._valid_iterator_id = self.main_datapipe._valid_iterator_id + return self._valid_iterator_id + + # This method is called by `hook_iterator` in `_typing.py`. + def _check_valid_iterator_id(self, iterator_id) -> bool: + r"""Check the valid iterator ID against that of DataPipe object and that of `main_datapipe`.""" + return ( + iterator_id == self._valid_iterator_id + and iterator_id == self.main_datapipe._valid_iterator_id + ) + + +@functional_datapipe("demux") +class DemultiplexerIterDataPipe(IterDataPipe): + r""" + Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). + + A list of the child DataPipes is returned from this operation. + + Args: + datapipe: Iterable DataPipe being filtered + num_instances: number of instances of the DataPipe to create + classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` + drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` + buffer_size: this defines the maximum number of inputs that the buffer can hold across all child + DataPipes while waiting for their values to be yielded. + Defaults to ``1000``. Use ``-1`` for the unlimited buffer. + + Examples: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> from torchdata.datapipes.iter import IterableWrapper + >>> def odd_or_even(n): + ... return n % 2 + >>> source_dp = IterableWrapper(range(5)) + >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) + >>> list(dp1) + [0, 2, 4] + >>> list(dp2) + [1, 3] + >>> # It can also filter out any element that gets `None` from the `classifier_fn` + >>> def odd_or_even_no_zero(n): + ... return n % 2 if n != 0 else None + >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True) + >>> list(dp1) + [2, 4] + >>> list(dp2) + [1, 3] + """ + + def __new__( + cls, + datapipe: IterDataPipe, + num_instances: int, + classifier_fn: Callable[[_T_co], Optional[int]], + drop_none: bool = False, + buffer_size: int = 1000, + ): + if num_instances < 1: + raise ValueError( + f"Expected `num_instances` larger than 0, but {num_instances} is found" + ) + + _check_unpickable_fn(classifier_fn) + + # When num_instances == 1, demux can be replaced by filter, + # but keep it as Demultiplexer for the sake of consistency + # like throwing Error when classification result is out of o range + container = _DemultiplexerIterDataPipe(datapipe, num_instances, classifier_fn, drop_none, buffer_size) # type: ignore[abstract] + return [_ChildDataPipe(container, i) for i in range(num_instances)] + + +class _DemultiplexerIterDataPipe(IterDataPipe, _ContainerTemplate): + r""" + Container to hold instance-specific information on behalf of DemultiplexerIterDataPipe. + + It tracks the state of its child DataPipes, maintains the buffer, classifies and yields the next correct value + as requested by the child DataPipes. + """ + + def __init__( + self, + datapipe: IterDataPipe[_T_co], + num_instances: int, + classifier_fn: Callable[[_T_co], Optional[int]], + drop_none: bool, + buffer_size: int, + ): + self.main_datapipe = datapipe + self._datapipe_iterator: Optional[Iterator[Any]] = None + self.num_instances = num_instances + self.buffer_size = buffer_size + if self.buffer_size < 0: + warnings.warn( + "Unlimited buffer size is set for `demux`, " + "please be aware of OOM at random places", + UserWarning, + ) + self.current_buffer_usage = 0 + self.child_buffers: List[Deque[_T_co]] = [deque() for _ in range(num_instances)] + self.classifier_fn = classifier_fn + self.drop_none = drop_none + self.main_datapipe_exhausted = False + self._child_stop: List[bool] = [True for _ in range(num_instances)] + + def _find_next(self, instance_id: int) -> _T_co: # type: ignore[type-var] + while True: + if self.main_datapipe_exhausted or self._child_stop[instance_id]: + raise StopIteration + if self._datapipe_iterator is None: + raise ValueError( + "_datapipe_iterator has not been set, likely because this private method is called directly " + "without invoking get_next_element_by_instance() first." + ) + value = next(self._datapipe_iterator) + classification = self.classifier_fn(value) + if classification is None and self.drop_none: + StreamWrapper.close_streams(value) + continue + if ( + classification is None + or classification >= self.num_instances + or classification < 0 + ): + raise ValueError( + f"Output of the classification fn should be between 0 and {self.num_instances - 1}. " + + f"{classification} is returned." + ) + if classification == instance_id: + return value + self.child_buffers[classification].append(value) + self.current_buffer_usage += 1 + if self.buffer_size >= 0 and self.current_buffer_usage > self.buffer_size: + raise BufferError( + f"DemultiplexerIterDataPipe buffer overflow, buffer size {self.buffer_size} is insufficient." + ) + + def get_next_element_by_instance(self, instance_id: int): + if self._datapipe_iterator is None and self._child_stop[instance_id]: + self._datapipe_iterator = iter(self.main_datapipe) + self._snapshot_state = ( + _SnapshotState.Iterating + ) # This is necessary for the DataPipe to reset properly. + self.main_datapipe_exhausted = False + for i in range(self.num_instances): + self._child_stop[i] = False + + try: + while not self._child_stop[instance_id]: + if self.child_buffers[instance_id]: + self.current_buffer_usage -= 1 + yield self.child_buffers[instance_id].popleft() + else: + try: + yield self._find_next(instance_id) + except StopIteration: + self._child_stop[instance_id] = True + self.main_datapipe_exhausted = True + self._datapipe_iterator = None + finally: + self._child_stop[instance_id] = True + # Cleanup _datapipe_iterator for the case that demux exits earlier + if all(self._child_stop): + self._datapipe_iterator = None + if self.child_buffers[instance_id]: + self._cleanup(instance_id) + + def is_every_instance_exhausted(self) -> bool: + return self.main_datapipe_exhausted and all(self._child_stop) + + def get_length_by_instance(self, instance_id: int) -> int: + raise TypeError + + def reset(self) -> None: + self._datapipe_iterator = None + self.current_buffer_usage = 0 + self.child_buffers = [deque() for _ in range(self.num_instances)] + self._child_stop = [True for _ in range(self.num_instances)] + self.main_datapipe_exhausted = False + + def __getstate__(self): + state = ( + self.main_datapipe, + self.num_instances, + self.buffer_size, + self.classifier_fn, + self.drop_none, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) + if IterDataPipe.getstate_hook is not None: + return IterDataPipe.getstate_hook(state) + return state + + def __setstate__(self, state): + ( + self.main_datapipe, + self.num_instances, + self.buffer_size, + self.classifier_fn, + self.drop_none, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) = state + self._datapipe_iterator = None + self.current_buffer_usage = 0 + self.child_buffers = [deque() for _ in range(self.num_instances)] + self._child_stop = [True for _ in range(self.num_instances)] + self.main_datapipe_exhausted = False + + def _cleanup(self, instance_id: Optional[int] = None): + ids = ( + range(self.num_instances) + if instance_id is None + else [ + instance_id, + ] + ) + for i in ids: + q = self.child_buffers[i] + while q: + d = q.popleft() + StreamWrapper.close_streams(d) + + def __del__(self): + self._cleanup() + + +@functional_datapipe("mux") +class MultiplexerIterDataPipe(IterDataPipe): + r""" + Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). + + As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, + and so on. It ends when the shortest input DataPipe is exhausted. + + Args: + datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted + + Example: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> from torchdata.datapipes.iter import IterableWrapper + >>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25)) + >>> list(dp1.mux(dp2, dp3)) + [0, 10, 20, 1, 11, 21, 2, 12, 22] + """ + + def __init__(self, *datapipes): + self.datapipes = datapipes + self.buffer: List = ( + [] + ) # Store values to be yielded only when every iterator provides one + + def __iter__(self): + iterators = [iter(x) for x in self.datapipes] + while len(iterators): + for it in iterators: + try: + value = next(it) + self.buffer.append(value) + except StopIteration: + self.buffer.clear() + return + yield from self.buffer + self.buffer.clear() + + def __len__(self): + if all(isinstance(dp, Sized) for dp in self.datapipes): + return min(len(dp) for dp in self.datapipes) * len(self.datapipes) + else: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") + + def reset(self) -> None: + self.buffer = [] + + def __getstate__(self): + state = ( + self.datapipes, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) + if IterDataPipe.getstate_hook is not None: + return IterDataPipe.getstate_hook(state) + return state + + def __setstate__(self, state): + ( + self.datapipes, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) = state + self.buffer = [] + + def __del__(self): + self.buffer.clear() + + +@functional_datapipe("zip") +class ZipperIterDataPipe(IterDataPipe[Tuple[_T_co]]): + r""" + Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). + + The output is stopped as soon as the shortest input DataPipe is exhausted. + + Args: + *datapipes: Iterable DataPipes being aggregated + + Example: + >>> # xdoctest: +REQUIRES(module:torchdata) + >>> from torchdata.datapipes.iter import IterableWrapper + >>> dp1, dp2, dp3 = IterableWrapper(range(5)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25)) + >>> list(dp1.zip(dp2, dp3)) + [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] + """ + + datapipes: Tuple[IterDataPipe] + + def __init__(self, *datapipes: IterDataPipe): + if not all(isinstance(dp, IterDataPipe) for dp in datapipes): + raise TypeError( + "All inputs are required to be `IterDataPipe` " "for `ZipIterDataPipe`." + ) + super().__init__() + self.datapipes = datapipes # type: ignore[assignment] + + def __iter__(self) -> Iterator[Tuple[_T_co]]: + iterators = [iter(datapipe) for datapipe in self.datapipes] + yield from zip(*iterators) + + def __len__(self) -> int: + if all(isinstance(dp, Sized) for dp in self.datapipes): + return min(len(dp) for dp in self.datapipes) + else: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/filelister.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/filelister.py new file mode 100644 index 0000000000000000000000000000000000000000..032433aafe810858217270320f61564b8f277e65 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/filelister.py @@ -0,0 +1,68 @@ +# mypy: allow-untyped-defs +from typing import Iterator, List, Sequence, Union + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import IterDataPipe +from torch.utils.data.datapipes.iter.utils import IterableWrapperIterDataPipe +from torch.utils.data.datapipes.utils.common import get_file_pathnames_from_root + + +__all__ = ["FileListerIterDataPipe"] + + +@functional_datapipe("list_files") +class FileListerIterDataPipe(IterDataPipe[str]): + r""" + Given path(s) to the root directory, yields file pathname(s) (path + filename) of files within the root directory. + + Multiple root directories can be provided (functional name: ``list_files``). + + Args: + root: Root directory or a sequence of root directories + masks: Unix style filter string or string list for filtering file name(s) + recursive: Whether to return pathname from nested directories or not + abspath: Whether to return relative pathname or absolute pathname + non_deterministic: Whether to return pathname in sorted order or not. + If ``False``, the results yielded from each root directory will be sorted + length: Nominal length of the datapipe + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import FileLister + >>> dp = FileLister(root=".", recursive=True) + >>> list(dp) + ['example.py', './data/data.tar'] + """ + + def __init__( + self, + root: Union[str, Sequence[str], IterDataPipe] = ".", + masks: Union[str, List[str]] = "", + *, + recursive: bool = False, + abspath: bool = False, + non_deterministic: bool = False, + length: int = -1, + ) -> None: + super().__init__() + if isinstance(root, str): + root = [root] + if not isinstance(root, IterDataPipe): + root = IterableWrapperIterDataPipe(root) + self.datapipe: IterDataPipe = root + self.masks: Union[str, List[str]] = masks + self.recursive: bool = recursive + self.abspath: bool = abspath + self.non_deterministic: bool = non_deterministic + self.length: int = length + + def __iter__(self) -> Iterator[str]: + for path in self.datapipe: + yield from get_file_pathnames_from_root( + path, self.masks, self.recursive, self.abspath, self.non_deterministic + ) + + def __len__(self): + if self.length == -1: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") + return self.length diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/grouping.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/grouping.py new file mode 100644 index 0000000000000000000000000000000000000000..604ee680aeb99739e268839ca271e1d6a7be054d --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/grouping.py @@ -0,0 +1,331 @@ +# mypy: allow-untyped-defs +import warnings +from collections import defaultdict +from typing import ( + Any, + Callable, + DefaultDict, + Iterator, + List, + Optional, + Sized, + Type, + TypeVar, +) + +import torch.utils.data.datapipes.iter.sharding +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import DataChunk, IterDataPipe +from torch.utils.data.datapipes.utils.common import _check_unpickable_fn + + +__all__ = [ + "BatcherIterDataPipe", + "GrouperIterDataPipe", + "UnBatcherIterDataPipe", +] + + +_T_co = TypeVar("_T_co", covariant=True) + + +def __getattr__(name: str): + if name in ["SHARDING_PRIORITIES", "ShardingFilterIterDataPipe"]: + warnings.warn( + f"`{name}` from `torch.utils.data.datapipes.iter.grouping` is going to be removed in PyTorch 2.1" + f"Please use `{name}` from the `torch.utils.data.datapipes.iter.sharding`", + category=FutureWarning, + stacklevel=2, + ) + + return getattr(torch.utils.data.datapipes.iter.sharding, name) + + raise AttributeError(f"module {__name__} has no attribute {name}") + + +@functional_datapipe("batch") +class BatcherIterDataPipe(IterDataPipe[DataChunk]): + r""" + Creates mini-batches of data (functional name: ``batch``). + + An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``, or ``length % batch_size`` for the + last batch if ``drop_last`` is set to ``False``. + + Args: + datapipe: Iterable DataPipe being batched + batch_size: The size of each batch + drop_last: Option to drop the last batch if it's not full + wrapper_class: wrapper to apply onto each batch (type ``List``) before yielding, + defaults to ``DataChunk`` + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper + >>> dp = IterableWrapper(range(10)) + >>> dp = dp.batch(batch_size=3, drop_last=True) + >>> list(dp) + [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + """ + + datapipe: IterDataPipe + batch_size: int + drop_last: bool + + def __init__( + self, + datapipe: IterDataPipe, + batch_size: int, + drop_last: bool = False, + wrapper_class: Type[DataChunk] = DataChunk, + ) -> None: + assert batch_size > 0, "Batch size is required to be larger than 0!" + super().__init__() + self.datapipe = datapipe + self.batch_size = batch_size + self.drop_last = drop_last + self.wrapper_class = wrapper_class + + def __iter__(self) -> Iterator[DataChunk]: + batch: List = [] + for x in self.datapipe: + batch.append(x) + if len(batch) == self.batch_size: + yield self.wrapper_class(batch) + batch = [] + if len(batch) > 0: + if not self.drop_last: + yield self.wrapper_class(batch) + + def __len__(self) -> int: + if isinstance(self.datapipe, Sized): + if self.drop_last: + return len(self.datapipe) // self.batch_size + else: + return (len(self.datapipe) + self.batch_size - 1) // self.batch_size + else: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") + + +@functional_datapipe("unbatch") +class UnBatcherIterDataPipe(IterDataPipe): + r""" + Undos batching of data (functional name: ``unbatch``). + + In other words, it flattens the data up to the specified level within a batched DataPipe. + + Args: + datapipe: Iterable DataPipe being un-batched + unbatch_level: Defaults to ``1`` (only flattening the top level). If set to ``2``, + it will flatten the top two levels, and ``-1`` will flatten the entire DataPipe. + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper + >>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]]) + >>> dp1 = source_dp.unbatch() + >>> list(dp1) + [[0, 1], [2], [3, 4], [5], [6]] + >>> dp2 = source_dp.unbatch(unbatch_level=2) + >>> list(dp2) + [0, 1, 2, 3, 4, 5, 6] + """ + + def __init__(self, datapipe: IterDataPipe, unbatch_level: int = 1): + self.datapipe = datapipe + self.unbatch_level = unbatch_level + + def __iter__(self): + for element in self.datapipe: + yield from self._dive(element, unbatch_level=self.unbatch_level) + + def _dive(self, element, unbatch_level): + if unbatch_level < -1: + raise ValueError("unbatch_level must be -1 or >= 0") + if unbatch_level == -1: + if isinstance(element, (list, DataChunk)): + for item in element: + yield from self._dive(item, unbatch_level=-1) + else: + yield element + elif unbatch_level == 0: + yield element + else: + if isinstance(element, (list, DataChunk)): + for item in element: + yield from self._dive(item, unbatch_level=unbatch_level - 1) + else: + raise IndexError( + f"unbatch_level {self.unbatch_level} exceeds the depth of the DataPipe" + ) + + +@functional_datapipe("groupby") +class GrouperIterDataPipe(IterDataPipe[DataChunk]): + r""" + Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. + + (functional name: ``groupby``). + + The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group + will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, + the DataPipe will yield the largest batch with the same key, provided that its size is larger + than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. + + After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity + will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. + + Args: + datapipe: Iterable datapipe to be grouped + group_key_fn: Function used to generate group key from the data of the source datapipe + keep_key: Option to yield the matching key along with the items in a tuple, + resulting in `(key, [items])` otherwise returning [items] + buffer_size: The size of buffer for ungrouped data + group_size: The max size of each group, a batch is yielded as soon as it reaches this size + guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full + drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer + when the buffer is full + + Example: + >>> import os + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper + >>> def group_fn(file): + ... return os.path.basename(file).split(".")[0] + >>> source_dp = IterableWrapper(["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"]) + >>> dp0 = source_dp.groupby(group_key_fn=group_fn) + >>> list(dp0) + [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] + >>> # A group is yielded as soon as its size equals to `group_size` + >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) + >>> list(dp1) + [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] + >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` + >>> dp2 = source_dp.groupby(group_key_fn=group_fn, buffer_size=3, group_size=3, guaranteed_group_size=2) + >>> list(dp2) + [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] + """ + + def __init__( + self, + datapipe: IterDataPipe[_T_co], + group_key_fn: Callable[[_T_co], Any], + *, + keep_key: bool = False, + buffer_size: int = 10000, + group_size: Optional[int] = None, + guaranteed_group_size: Optional[int] = None, + drop_remaining: bool = False, + ): + _check_unpickable_fn(group_key_fn) + self.datapipe = datapipe + self.group_key_fn = group_key_fn + + self.keep_key = keep_key + self.max_buffer_size = buffer_size + self.buffer_elements: DefaultDict[Any, List] = defaultdict(list) + self.curr_buffer_size = 0 + self.group_size = group_size + self.guaranteed_group_size = None + if group_size is not None and buffer_size is not None: + assert 0 < group_size <= buffer_size + self.guaranteed_group_size = group_size + if guaranteed_group_size is not None: + assert group_size is not None and 0 < guaranteed_group_size <= group_size + self.guaranteed_group_size = guaranteed_group_size + self.drop_remaining = drop_remaining + self.wrapper_class = DataChunk + + def _remove_biggest_key(self): + biggest_key = None + biggest_size = 0 + result_to_yield = None + for findkey in self.buffer_elements.keys(): + if len(self.buffer_elements[findkey]) > biggest_size: + biggest_size = len(self.buffer_elements[findkey]) + biggest_key = findkey + + if ( + self.guaranteed_group_size is not None + and biggest_size < self.guaranteed_group_size + and not self.drop_remaining + ): + raise RuntimeError( + "Failed to group items", str(self.buffer_elements[biggest_key]) + ) + + if ( + self.guaranteed_group_size is None + or biggest_size >= self.guaranteed_group_size + ): + result_to_yield = self.buffer_elements[biggest_key] + + self.curr_buffer_size -= biggest_size + del self.buffer_elements[biggest_key] + + return result_to_yield + + def __iter__(self): + for x in self.datapipe: + key = self.group_key_fn(x) + + self.buffer_elements[key].append(x) + self.curr_buffer_size += 1 + + if self.group_size is not None and self.group_size == len( + self.buffer_elements[key] + ): + result: DataChunk[Any] = self.wrapper_class(self.buffer_elements[key]) + yield (key, result) if self.keep_key else result + self.curr_buffer_size -= len(self.buffer_elements[key]) + del self.buffer_elements[key] + + if self.curr_buffer_size == self.max_buffer_size: + result_to_yield = self._remove_biggest_key() + if result_to_yield is not None: + result = self.wrapper_class(result_to_yield) + yield (key, result) if self.keep_key else result + + for key in tuple(self.buffer_elements.keys()): + result = self.wrapper_class(self.buffer_elements.pop(key)) + self.curr_buffer_size -= len(result) + yield (key, result) if self.keep_key else result + + def reset(self) -> None: + self.curr_buffer_size = 0 + self.buffer_elements = defaultdict(list) + + def __getstate__(self): + state = ( + self.datapipe, + self.group_key_fn, + self.keep_key, + self.max_buffer_size, + self.group_size, + self.guaranteed_group_size, + self.drop_remaining, + self.wrapper_class, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) + if IterDataPipe.getstate_hook is not None: + return IterDataPipe.getstate_hook(state) + return state + + def __setstate__(self, state): + ( + self.datapipe, + self.group_key_fn, + self.keep_key, + self.max_buffer_size, + self.group_size, + self.guaranteed_group_size, + self.drop_remaining, + self.wrapper_class, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) = state + self.curr_buffer_size = 0 + self.buffer_elements = defaultdict(list) + + def __del__(self): + self.buffer_elements.clear() diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py new file mode 100644 index 0000000000000000000000000000000000000000..320ee3c29cedda4f683e9356eae0c4c683e2d38f --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/routeddecoder.py @@ -0,0 +1,69 @@ +from io import BufferedIOBase +from typing import Any, Callable, Iterable, Iterator, Sized, Tuple + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import IterDataPipe +from torch.utils.data.datapipes.utils.common import _deprecation_warning +from torch.utils.data.datapipes.utils.decoder import ( + basichandlers as decoder_basichandlers, + Decoder, + extension_extract_fn, + imagehandler as decoder_imagehandler, +) + + +__all__ = ["RoutedDecoderIterDataPipe"] + + +@functional_datapipe("routed_decode") +class RoutedDecoderIterDataPipe(IterDataPipe[Tuple[str, Any]]): + r""" + Decodes binary streams from input DataPipe, yields pathname and decoded data in a tuple. + + (functional name: ``routed_decode``) + + Args: + datapipe: Iterable datapipe that provides pathname and binary stream in tuples + handlers: Optional user defined decoder handlers. If ``None``, basic and image decoder + handlers will be set as default. If multiple handles are provided, the priority + order follows the order of handlers (the first handler has the top priority) + key_fn: Function for decoder to extract key from pathname to dispatch handlers. + Default is set to extract file extension from pathname + + Note: + When ``key_fn`` is specified returning anything other than extension, the default + handler will not work and users need to specify custom handler. Custom handler + could use regex to determine the eligibility to handle data. + """ + + def __init__( + self, + datapipe: Iterable[Tuple[str, BufferedIOBase]], + *handlers: Callable, + key_fn: Callable = extension_extract_fn, + ) -> None: + super().__init__() + self.datapipe: Iterable[Tuple[str, BufferedIOBase]] = datapipe + if not handlers: + handlers = (decoder_basichandlers, decoder_imagehandler("torch")) + self.decoder = Decoder(*handlers, key_fn=key_fn) + _deprecation_warning( + type(self).__name__, + deprecation_version="1.12", + removal_version="1.13", + old_functional_name="routed_decode", + ) + + def add_handler(self, *handler: Callable) -> None: + self.decoder.add_handler(*handler) + + def __iter__(self) -> Iterator[Tuple[str, Any]]: + for data in self.datapipe: + pathname = data[0] + result = self.decoder(data) + yield (pathname, result[pathname]) + + def __len__(self) -> int: + if isinstance(self.datapipe, Sized): + return len(self.datapipe) + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/selecting.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/selecting.py new file mode 100644 index 0000000000000000000000000000000000000000..5d579209a8fff5a746ba686e5d7580fcf488b263 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/selecting.py @@ -0,0 +1,101 @@ +# mypy: allow-untyped-defs +from typing import Callable, Iterator, Tuple, TypeVar + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper +from torch.utils.data.datapipes.datapipe import IterDataPipe +from torch.utils.data.datapipes.utils.common import ( + _check_unpickable_fn, + StreamWrapper, + validate_input_col, +) + + +__all__ = ["FilterIterDataPipe"] + + +_T = TypeVar("_T") +_T_co = TypeVar("_T_co", covariant=True) + + +@functional_datapipe("filter") +class FilterIterDataPipe(IterDataPipe[_T_co]): + r""" + Filters out elements from the source datapipe according to input ``filter_fn`` (functional name: ``filter``). + + Args: + datapipe: Iterable DataPipe being filtered + filter_fn: Customized function mapping an element to a boolean. + input_col: Index or indices of data which ``filter_fn`` is applied, such as: + + - ``None`` as default to apply ``filter_fn`` to the data directly. + - Integer(s) is used for list/tuple. + - Key(s) is used for dict. + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper + >>> def is_even(n): + ... return n % 2 == 0 + >>> dp = IterableWrapper(range(5)) + >>> filter_dp = dp.filter(filter_fn=is_even) + >>> list(filter_dp) + [0, 2, 4] + """ + + datapipe: IterDataPipe[_T_co] + filter_fn: Callable + + def __init__( + self, + datapipe: IterDataPipe[_T_co], + filter_fn: Callable, + input_col=None, + ) -> None: + super().__init__() + self.datapipe = datapipe + + _check_unpickable_fn(filter_fn) + self.filter_fn = filter_fn # type: ignore[assignment] + + self.input_col = input_col + validate_input_col(filter_fn, input_col) + + def _apply_filter_fn(self, data) -> bool: + if self.input_col is None: + return self.filter_fn(data) + elif isinstance(self.input_col, (list, tuple)): + args = tuple(data[col] for col in self.input_col) + return self.filter_fn(*args) + else: + return self.filter_fn(data[self.input_col]) + + def __iter__(self) -> Iterator[_T_co]: + for data in self.datapipe: + condition, filtered = self._returnIfTrue(data) + if condition: + yield filtered + else: + StreamWrapper.close_streams(data) + + def _returnIfTrue(self, data: _T) -> Tuple[bool, _T]: + condition = self._apply_filter_fn(data) + + if df_wrapper.is_column(condition): + # We are operating on DataFrames filter here + result = [] + for idx, mask in enumerate(df_wrapper.iterate(condition)): + if mask: + result.append(df_wrapper.get_item(data, idx)) + if len(result): + return True, df_wrapper.concat(result) + else: + return False, None # type: ignore[return-value] + + if not isinstance(condition, bool): + raise ValueError( + "Boolean output is required for `filter_fn` of FilterIterDataPipe, got", + type(condition), + ) + + return condition, data diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/sharding.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/sharding.py new file mode 100644 index 0000000000000000000000000000000000000000..f5ca3f06b246e4f85de85a2dc228dd1b3c563216 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/sharding.py @@ -0,0 +1,101 @@ +# mypy: allow-untyped-defs +from enum import IntEnum +from typing import Dict, Sized, Tuple + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import IterDataPipe + + +__all__ = [ + "SHARDING_PRIORITIES", + "ShardingFilterIterDataPipe", +] + + +class SHARDING_PRIORITIES(IntEnum): + DEFAULT = 1 + DISTRIBUTED = 2 + MULTIPROCESSING = 3 + + +class _ShardingIterDataPipe(IterDataPipe): + def apply_sharding( + self, + num_of_instances: int, + instance_id: int, + sharding_group: SHARDING_PRIORITIES, + ): + raise NotImplementedError + + +@functional_datapipe("sharding_filter") +class ShardingFilterIterDataPipe(_ShardingIterDataPipe): + r""" + Wrapper that allows DataPipe to be sharded (functional name: ``sharding_filter``). + + After ``apply_sharding`` is called, each instance of the DataPipe (on different workers) will have every `n`-th element of the + original DataPipe, where `n` equals to the number of instances. + + Args: + source_datapipe: Iterable DataPipe that will be sharded + """ + + def __init__(self, source_datapipe: IterDataPipe, sharding_group_filter=None): + self.source_datapipe = source_datapipe + self.sharding_group_filter = sharding_group_filter + self.groups: Dict[int, Tuple[int, int]] = {} + self.num_of_instances = 1 + self.instance_id = 0 + self._update_num_of_instances() + + def apply_sharding( + self, num_of_instances, instance_id, sharding_group=SHARDING_PRIORITIES.DEFAULT + ): + if instance_id >= num_of_instances: + raise ValueError( + f"instance_id({instance_id}) should be smaller than num_of_instances({num_of_instances})" + ) + if sharding_group == SHARDING_PRIORITIES.DEFAULT: + if len(self.groups) and SHARDING_PRIORITIES.DEFAULT not in self.groups: + raise RuntimeError( + "ShardingFilter cannot mix DEFAULT and non DEFAULT groups" + ) + else: + if SHARDING_PRIORITIES.DEFAULT in self.groups: + raise RuntimeError( + "ShardingFilter cannot mix DEFAULT and non DEFAULT groups" + ) + self.groups[sharding_group] = (num_of_instances, instance_id) + self._update_num_of_instances() + + def _update_num_of_instances(self): + sorted_sharding_groups = [ + self.groups[key] + for key in sorted(self.groups.keys()) + if self.sharding_group_filter is None or key == self.sharding_group_filter + ] + + sorted_sharding_groups.reverse() + + self.num_of_instances = 1 + self.instance_id = 0 + + for group_num_of_instances, group_instance_id in sorted_sharding_groups: + self.instance_id += self.num_of_instances * group_instance_id + self.num_of_instances *= group_num_of_instances + + def __iter__(self): + for i, item in enumerate(self.source_datapipe): + if i % self.num_of_instances == self.instance_id: + yield item + + def __len__(self): + if isinstance(self.source_datapipe, Sized): + return len(self.source_datapipe) // self.num_of_instances + ( + 1 + if ( + self.instance_id < len(self.source_datapipe) % self.num_of_instances + ) + else 0 + ) + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/streamreader.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/streamreader.py new file mode 100644 index 0000000000000000000000000000000000000000..0b20f248c65be6f21873e499886a07101f776caa --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/streamreader.py @@ -0,0 +1,43 @@ +# mypy: allow-untyped-defs +from typing import Tuple + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import IterDataPipe + + +__all__ = ["StreamReaderIterDataPipe"] + + +@functional_datapipe("read_from_stream") +class StreamReaderIterDataPipe(IterDataPipe[Tuple[str, bytes]]): + r""" + Given IO streams and their label names, yield bytes with label name as tuple. + + (functional name: ``read_from_stream``). + + Args: + datapipe: Iterable DataPipe provides label/URL and byte stream + chunk: Number of bytes to be read from stream per iteration. + If ``None``, all bytes will be read until the EOF. + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper, StreamReader + >>> from io import StringIO + >>> dp = IterableWrapper([("alphabet", StringIO("abcde"))]) + >>> list(StreamReader(dp, chunk=1)) + [('alphabet', 'a'), ('alphabet', 'b'), ('alphabet', 'c'), ('alphabet', 'd'), ('alphabet', 'e')] + """ + + def __init__(self, datapipe, chunk=None): + self.datapipe = datapipe + self.chunk = chunk + + def __iter__(self): + for furl, stream in self.datapipe: + while True: + d = stream.read(self.chunk) + if not d: + stream.close() + break + yield (furl, d) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/utils.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0728746af23ddd7e628dbcdd0218af44aceb1ec9 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/iter/utils.py @@ -0,0 +1,54 @@ +# mypy: allow-untyped-defs +import copy +import warnings + +from torch.utils.data.datapipes.datapipe import IterDataPipe + + +__all__ = ["IterableWrapperIterDataPipe"] + + +class IterableWrapperIterDataPipe(IterDataPipe): + r""" + Wraps an iterable object to create an IterDataPipe. + + Args: + iterable: Iterable object to be wrapped into an IterDataPipe + deepcopy: Option to deepcopy input iterable object for each + iterator. The copy is made when the first element is read in ``iter()``. + + .. note:: + If ``deepcopy`` is explicitly set to ``False``, users should ensure + that the data pipeline doesn't contain any in-place operations over + the iterable instance to prevent data inconsistency across iterations. + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.iter import IterableWrapper + >>> dp = IterableWrapper(range(10)) + >>> list(dp) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + """ + + def __init__(self, iterable, deepcopy=True): + self.iterable = iterable + self.deepcopy = deepcopy + + def __iter__(self): + source_data = self.iterable + if self.deepcopy: + try: + source_data = copy.deepcopy(self.iterable) + # For the case that data cannot be deep-copied, + # all in-place operations will affect iterable variable. + # When this DataPipe is iterated second time, it will + # yield modified items. + except TypeError: + warnings.warn( + "The input iterable can not be deepcopied, " + "please be aware of in-place modification would affect source data." + ) + yield from source_data + + def __len__(self): + return len(self.iterable) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__init__.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7fa8932dd6fcafa2d807c591755852e74d7fdc51 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__init__.py @@ -0,0 +1,19 @@ +# Functional DataPipe +from torch.utils.data.datapipes.map.callable import MapperMapDataPipe as Mapper +from torch.utils.data.datapipes.map.combinatorics import ( + ShufflerIterDataPipe as Shuffler, +) +from torch.utils.data.datapipes.map.combining import ( + ConcaterMapDataPipe as Concater, + ZipperMapDataPipe as Zipper, +) +from torch.utils.data.datapipes.map.grouping import BatcherMapDataPipe as Batcher +from torch.utils.data.datapipes.map.utils import ( + SequenceWrapperMapDataPipe as SequenceWrapper, +) + + +__all__ = ["Batcher", "Concater", "Mapper", "SequenceWrapper", "Shuffler", "Zipper"] + +# Please keep this list sorted +assert __all__ == sorted(__all__) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cd83981a17eacdc5c46d78e94c4826e4f4d30f0c Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__pycache__/callable.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/__pycache__/callable.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8a3e98378c228f6472b0fb51166929bc0745df6e Binary files 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differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/callable.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/callable.py new file mode 100644 index 0000000000000000000000000000000000000000..cee08b7a8c8d1578befd1fb5c75647293a9c3db6 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/callable.py @@ -0,0 +1,65 @@ +# mypy: allow-untyped-defs +from typing import Callable, TypeVar + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import MapDataPipe +from torch.utils.data.datapipes.utils.common import _check_unpickable_fn + + +__all__ = ["MapperMapDataPipe", "default_fn"] + + +_T_co = TypeVar("_T_co", covariant=True) + + +# Default function to return each item directly +# In order to keep datapipe picklable, eliminates the usage +# of python lambda function +def default_fn(data): + return data + + +@functional_datapipe("map") +class MapperMapDataPipe(MapDataPipe[_T_co]): + r""" + Apply the input function over each item from the source DataPipe (functional name: ``map``). + + The function can be any regular Python function or partial object. Lambda + function is not recommended as it is not supported by pickle. + + Args: + datapipe: Source MapDataPipe + fn: Function being applied to each item + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper, Mapper + >>> def add_one(x): + ... return x + 1 + >>> dp = SequenceWrapper(range(10)) + >>> map_dp_1 = dp.map(add_one) + >>> list(map_dp_1) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> map_dp_2 = Mapper(dp, lambda x: x + 1) + >>> list(map_dp_2) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + """ + + datapipe: MapDataPipe + fn: Callable + + def __init__( + self, + datapipe: MapDataPipe, + fn: Callable = default_fn, + ) -> None: + super().__init__() + self.datapipe = datapipe + _check_unpickable_fn(fn) + self.fn = fn # type: ignore[assignment] + + def __len__(self) -> int: + return len(self.datapipe) + + def __getitem__(self, index) -> _T_co: + return self.fn(self.datapipe[index]) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combinatorics.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combinatorics.py new file mode 100644 index 0000000000000000000000000000000000000000..29c6dd1566006bbf27a8866977e89327ad2321d3 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combinatorics.py @@ -0,0 +1,129 @@ +# mypy: allow-untyped-defs +import random +from typing import Iterator, List, Optional, TypeVar + +import torch +from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe + + +__all__ = ["ShufflerIterDataPipe"] + + +_T_co = TypeVar("_T_co", covariant=True) + + +# @functional_datapipe('shuffle') +class ShufflerIterDataPipe(IterDataPipe[_T_co]): + r""" + Shuffle the input MapDataPipe via its indices (functional name: ``shuffle``). + + When it is used with :class:`~torch.utils.data.DataLoader`, the methods to + set up random seed are different based on :attr:`num_workers`. + + For single-process mode (:attr:`num_workers == 0`), the random seed is set before + the :class:`~torch.utils.data.DataLoader` in the main process. For multi-process + mode (:attr:`num_worker > 0`), ``worker_init_fn`` is used to set up a random seed + for each worker process. + + Args: + datapipe: MapDataPipe being shuffled + indices: a list of indices of the MapDataPipe. If not provided, we assume it uses 0-based indexing + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper + >>> dp = SequenceWrapper(range(10)) + >>> shuffle_dp = dp.shuffle().set_seed(0) + >>> list(shuffle_dp) + [7, 8, 1, 5, 3, 4, 2, 0, 9, 6] + >>> list(shuffle_dp) + [6, 1, 9, 5, 2, 4, 7, 3, 8, 0] + >>> # Reset seed for Shuffler + >>> shuffle_dp = shuffle_dp.set_seed(0) + >>> list(shuffle_dp) + [7, 8, 1, 5, 3, 4, 2, 0, 9, 6] + + Note: + Even thought this ``shuffle`` operation takes a ``MapDataPipe`` as the input, it would return an + ``IterDataPipe`` rather than a ``MapDataPipe``, because ``MapDataPipe`` should be non-sensitive to + the order of data order for the sake of random reads, but ``IterDataPipe`` depends on the order + of data during data-processing. + """ + + datapipe: MapDataPipe[_T_co] + _enabled: bool + _seed: Optional[int] + _rng: random.Random + + def __init__( + self, + datapipe: MapDataPipe[_T_co], + *, + indices: Optional[List] = None, + ) -> None: + super().__init__() + self.datapipe = datapipe + self.indices = list(range(len(datapipe))) if indices is None else indices + self._enabled = True + self._seed = None + self._rng = random.Random() + self._shuffled_indices: List = self.indices + + def set_shuffle(self, shuffle=True): + self._enabled = shuffle + return self + + def set_seed(self, seed: int): + self._seed = seed + return self + + def __iter__(self) -> Iterator[_T_co]: + if not self._enabled: + for idx in self.indices: + yield self.datapipe[idx] + else: + while self._shuffled_indices: + idx = self._shuffled_indices.pop() + yield self.datapipe[idx] + + def reset(self) -> None: + if self._enabled and self._seed is None: + self._seed = int(torch.empty((), dtype=torch.int64).random_().item()) + self._rng.seed(self._seed) + self._seed = None + self._shuffled_indices = self._rng.sample(self.indices, len(self.indices)) + + def __len__(self) -> int: + return len(self.datapipe) + + def __getstate__(self): + state = ( + self.datapipe, + self.indices, + self._enabled, + self._seed, + self._rng.getstate(), + self._shuffled_indices, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) + if IterDataPipe.getstate_hook is not None: + return IterDataPipe.getstate_hook(state) + return state + + def __setstate__(self, state): + ( + self.datapipe, + self.indices, + self._enabled, + self._seed, + rng_state, + self._shuffled_indices, + self._valid_iterator_id, + self._number_of_samples_yielded, + ) = state + self._rng = random.Random() + self._rng.setstate(rng_state) + + +MapDataPipe.register_datapipe_as_function("shuffle", ShufflerIterDataPipe) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combining.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combining.py new file mode 100644 index 0000000000000000000000000000000000000000..8ec6be0d1cd80549023e4e4a678b488456322da4 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/combining.py @@ -0,0 +1,104 @@ +# mypy: allow-untyped-defs +from typing import Sized, Tuple, TypeVar + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import MapDataPipe + + +__all__ = ["ConcaterMapDataPipe", "ZipperMapDataPipe"] + +_T_co = TypeVar("_T_co", covariant=True) + + +@functional_datapipe("concat") +class ConcaterMapDataPipe(MapDataPipe): + r""" + Concatenate multiple Map DataPipes (functional name: ``concat``). + + The new index of is the cumulative sum of source DataPipes. + For example, if there are 2 source DataPipes both with length 5, + index 0 to 4 of the resulting `ConcatMapDataPipe` would refer to + elements of the first DataPipe, and 5 to 9 would refer to elements + of the second DataPipe. + + Args: + datapipes: Map DataPipes being concatenated + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper + >>> dp1 = SequenceWrapper(range(3)) + >>> dp2 = SequenceWrapper(range(3)) + >>> concat_dp = dp1.concat(dp2) + >>> list(concat_dp) + [0, 1, 2, 0, 1, 2] + """ + + datapipes: Tuple[MapDataPipe] + + def __init__(self, *datapipes: MapDataPipe): + if len(datapipes) == 0: + raise ValueError("Expected at least one DataPipe, but got nothing") + if not all(isinstance(dp, MapDataPipe) for dp in datapipes): + raise TypeError("Expected all inputs to be `MapDataPipe`") + if not all(isinstance(dp, Sized) for dp in datapipes): + raise TypeError("Expected all inputs to be `Sized`") + self.datapipes = datapipes # type: ignore[assignment] + + def __getitem__(self, index) -> _T_co: # type: ignore[type-var] + offset = 0 + for dp in self.datapipes: + if index - offset < len(dp): + return dp[index - offset] + else: + offset += len(dp) + raise IndexError(f"Index {index} is out of range.") + + def __len__(self) -> int: + return sum(len(dp) for dp in self.datapipes) + + +@functional_datapipe("zip") +class ZipperMapDataPipe(MapDataPipe[Tuple[_T_co, ...]]): + r""" + Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). + + This MataPipe is out of bound as soon as the shortest input DataPipe is exhausted. + + Args: + *datapipes: Map DataPipes being aggregated + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper + >>> dp1 = SequenceWrapper(range(3)) + >>> dp2 = SequenceWrapper(range(10, 13)) + >>> zip_dp = dp1.zip(dp2) + >>> list(zip_dp) + [(0, 10), (1, 11), (2, 12)] + """ + + datapipes: Tuple[MapDataPipe[_T_co], ...] + + def __init__(self, *datapipes: MapDataPipe[_T_co]) -> None: + if len(datapipes) == 0: + raise ValueError("Expected at least one DataPipe, but got nothing") + if not all(isinstance(dp, MapDataPipe) for dp in datapipes): + raise TypeError("Expected all inputs to be `MapDataPipe`") + if not all(isinstance(dp, Sized) for dp in datapipes): + raise TypeError("Expected all inputs to be `Sized`") + self.datapipes = datapipes + + def __getitem__(self, index) -> Tuple[_T_co, ...]: + res = [] + for dp in self.datapipes: + try: + res.append(dp[index]) + except IndexError as e: + raise IndexError( + f"Index {index} is out of range for one of the input MapDataPipes {dp}." + ) from e + return tuple(res) + + def __len__(self) -> int: + return min(len(dp) for dp in self.datapipes) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/grouping.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/grouping.py new file mode 100644 index 0000000000000000000000000000000000000000..33aeaf344549929814c17d5a0fb0292034a9da44 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/grouping.py @@ -0,0 +1,73 @@ +# mypy: allow-untyped-defs +from typing import List, Sized, Type, TypeVar + +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import DataChunk, MapDataPipe + + +__all__ = ["BatcherMapDataPipe"] + + +_T = TypeVar("_T") + + +@functional_datapipe("batch") +class BatcherMapDataPipe(MapDataPipe[DataChunk]): + r""" + Create mini-batches of data (functional name: ``batch``). + + An outer dimension will be added as ``batch_size`` if ``drop_last`` is set to ``True``, + or ``length % batch_size`` for the last batch if ``drop_last`` is set to ``False``. + + Args: + datapipe: Iterable DataPipe being batched + batch_size: The size of each batch + drop_last: Option to drop the last batch if it's not full + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper + >>> dp = SequenceWrapper(range(10)) + >>> batch_dp = dp.batch(batch_size=2) + >>> list(batch_dp) + [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]] + """ + + datapipe: MapDataPipe + batch_size: int + drop_last: bool + + def __init__( + self, + datapipe: MapDataPipe[_T], + batch_size: int, + drop_last: bool = False, + wrapper_class: Type[DataChunk] = DataChunk, + ) -> None: + assert batch_size > 0, "Batch size is required to be larger than 0!" + super().__init__() + self.datapipe = datapipe + self.batch_size = batch_size + self.drop_last = drop_last + self.wrapper_class = wrapper_class + + def __getitem__(self, index) -> DataChunk: + batch: List = [] + indices = range(index * self.batch_size, (index + 1) * self.batch_size) + try: + batch.extend(self.datapipe[i] for i in indices) + return self.wrapper_class(batch) + except IndexError as e: + if not self.drop_last and len(batch) > 0: + return self.wrapper_class(batch) + else: + raise IndexError(f"Index {index} is out of bound.") from e + + def __len__(self) -> int: + if isinstance(self.datapipe, Sized): + if self.drop_last: + return len(self.datapipe) // self.batch_size + else: + return (len(self.datapipe) + self.batch_size - 1) // self.batch_size + else: + raise TypeError(f"{type(self).__name__} instance doesn't have valid length") diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/map/utils.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..38e8b8ff56fe440f341f1b1d402f5aaf5446c445 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/map/utils.py @@ -0,0 +1,53 @@ +# mypy: allow-untyped-defs +import copy +import warnings + +from torch.utils.data.datapipes.datapipe import MapDataPipe + + +__all__ = ["SequenceWrapperMapDataPipe"] + + +class SequenceWrapperMapDataPipe(MapDataPipe): + r""" + Wraps a sequence object into a MapDataPipe. + + Args: + sequence: Sequence object to be wrapped into an MapDataPipe + deepcopy: Option to deepcopy input sequence object + + .. note:: + If ``deepcopy`` is set to False explicitly, users should ensure + that data pipeline doesn't contain any in-place operations over + the iterable instance, in order to prevent data inconsistency + across iterations. + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper + >>> dp = SequenceWrapper(range(10)) + >>> list(dp) + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + >>> dp = SequenceWrapper({'a': 100, 'b': 200, 'c': 300, 'd': 400}) + >>> dp['a'] + 100 + """ + + def __init__(self, sequence, deepcopy=True): + if deepcopy: + try: + self.sequence = copy.deepcopy(sequence) + except TypeError: + warnings.warn( + "The input sequence can not be deepcopied, " + "please be aware of in-place modification would affect source data" + ) + self.sequence = sequence + else: + self.sequence = sequence + + def __getitem__(self, index): + return self.sequence[index] + + def __len__(self): + return len(self.sequence) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__init__.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bc055adc5b10e6dd9928abb83e551c06b85a99a4 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/common.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/common.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ceb2349687034e61df78637083d073d09da3a7b3 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/common.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/decoder.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/decoder.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1e2dff6969748dba04baf3b752cd3bc2d88c7111 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/decoder.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/snapshot.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/snapshot.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e2dc8575f88292745244ae47afa401018900a23c Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/__pycache__/snapshot.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/common.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..f608c15e5a1fcdc45f9c15704b89afca776de1b5 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/common.py @@ -0,0 +1,411 @@ +# mypy: allow-untyped-defs +import fnmatch +import functools +import inspect +import os +import warnings +from io import IOBase +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union + +from torch.utils._import_utils import dill_available + + +__all__ = [ + "validate_input_col", + "StreamWrapper", + "get_file_binaries_from_pathnames", + "get_file_pathnames_from_root", + "match_masks", + "validate_pathname_binary_tuple", +] + + +# BC for torchdata +DILL_AVAILABLE = dill_available() + + +def validate_input_col(fn: Callable, input_col: Optional[Union[int, tuple, list]]): + """ + Check that function used in a callable datapipe works with the input column. + + This simply ensures that the number of positional arguments matches the size + of the input column. The function must not contain any non-default + keyword-only arguments. + + Examples: + >>> # xdoctest: +SKIP("Failing on some CI machines") + >>> def f(a, b, *, c=1): + >>> return a + b + c + >>> def f_def(a, b=1, *, c=1): + >>> return a + b + c + >>> assert validate_input_col(f, [1, 2]) + >>> assert validate_input_col(f_def, 1) + >>> assert validate_input_col(f_def, [1, 2]) + + Notes: + If the function contains variable positional (`inspect.VAR_POSITIONAL`) arguments, + for example, f(a, *args), the validator will accept any size of input column + greater than or equal to the number of positional arguments. + (in this case, 1). + + Args: + fn: The function to check. + input_col: The input column to check. + + Raises: + ValueError: If the function is not compatible with the input column. + """ + try: + sig = inspect.signature(fn) + except ( + ValueError + ): # Signature cannot be inspected, likely it is a built-in fn or written in C + return + if isinstance(input_col, (list, tuple)): + input_col_size = len(input_col) + else: + input_col_size = 1 + + pos = [] + var_positional = False + non_default_kw_only = [] + + for p in sig.parameters.values(): + if p.kind in ( + inspect.Parameter.POSITIONAL_ONLY, + inspect.Parameter.POSITIONAL_OR_KEYWORD, + ): + pos.append(p) + elif p.kind is inspect.Parameter.VAR_POSITIONAL: + var_positional = True + elif p.kind is inspect.Parameter.KEYWORD_ONLY: + if p.default is p.empty: + non_default_kw_only.append(p) + else: + continue + + if isinstance(fn, functools.partial): + fn_name = getattr(fn.func, "__name__", repr(fn.func)) + else: + fn_name = getattr(fn, "__name__", repr(fn)) + + if len(non_default_kw_only) > 0: + raise ValueError( + f"The function {fn_name} takes {len(non_default_kw_only)} " + f"non-default keyword-only parameters, which is not allowed." + ) + + if len(sig.parameters) < input_col_size: + if not var_positional: + raise ValueError( + f"The function {fn_name} takes {len(sig.parameters)} " + f"parameters, but {input_col_size} are required." + ) + else: + if len(pos) > input_col_size: + if any(p.default is p.empty for p in pos[input_col_size:]): + raise ValueError( + f"The function {fn_name} takes {len(pos)} " + f"positional parameters, but {input_col_size} are required." + ) + elif len(pos) < input_col_size: + if not var_positional: + raise ValueError( + f"The function {fn_name} takes {len(pos)} " + f"positional parameters, but {input_col_size} are required." + ) + + +def _is_local_fn(fn): + # Functions or Methods + if hasattr(fn, "__code__"): + return fn.__code__.co_flags & inspect.CO_NESTED + # Callable Objects + else: + if hasattr(fn, "__qualname__"): + return "" in fn.__qualname__ + fn_type = type(fn) + if hasattr(fn_type, "__qualname__"): + return "" in fn_type.__qualname__ + return False + + +def _check_unpickable_fn(fn: Callable): + """ + Check function is pickable or not. + + If it is a lambda or local function, a UserWarning will be raised. If it's not a callable function, a TypeError will be raised. + """ + if not callable(fn): + raise TypeError(f"A callable function is expected, but {type(fn)} is provided.") + + # Extract function from partial object + # Nested partial function is automatically expanded as a single partial object + if isinstance(fn, functools.partial): + fn = fn.func + + # Local function + if _is_local_fn(fn) and not dill_available(): + warnings.warn( + "Local function is not supported by pickle, please use " + "regular python function or functools.partial instead." + ) + return + + # Lambda function + if hasattr(fn, "__name__") and fn.__name__ == "" and not dill_available(): + warnings.warn( + "Lambda function is not supported by pickle, please use " + "regular python function or functools.partial instead." + ) + return + + +def match_masks(name: str, masks: Union[str, List[str]]) -> bool: + # empty mask matches any input name + if not masks: + return True + + if isinstance(masks, str): + return fnmatch.fnmatch(name, masks) + + for mask in masks: + if fnmatch.fnmatch(name, mask): + return True + return False + + +def get_file_pathnames_from_root( + root: str, + masks: Union[str, List[str]], + recursive: bool = False, + abspath: bool = False, + non_deterministic: bool = False, +) -> Iterable[str]: + # print out an error message and raise the error out + def onerror(err: OSError): + warnings.warn(err.filename + " : " + err.strerror) + raise err + + if os.path.isfile(root): + path = root + if abspath: + path = os.path.abspath(path) + fname = os.path.basename(path) + if match_masks(fname, masks): + yield path + else: + for path, dirs, files in os.walk(root, onerror=onerror): + if abspath: + path = os.path.abspath(path) + if not non_deterministic: + files.sort() + for f in files: + if match_masks(f, masks): + yield os.path.join(path, f) + if not recursive: + break + if not non_deterministic: + # Note that this is in-place modifying the internal list from `os.walk` + # This only works because `os.walk` doesn't shallow copy before turn + # https://github.com/python/cpython/blob/f4c03484da59049eb62a9bf7777b963e2267d187/Lib/os.py#L407 + dirs.sort() + + +def get_file_binaries_from_pathnames( + pathnames: Iterable, mode: str, encoding: Optional[str] = None +): + if not isinstance(pathnames, Iterable): + pathnames = [ + pathnames, + ] + + if mode in ("b", "t"): + mode = "r" + mode + + for pathname in pathnames: + if not isinstance(pathname, str): + raise TypeError( + f"Expected string type for pathname, but got {type(pathname)}" + ) + yield pathname, StreamWrapper(open(pathname, mode, encoding=encoding)) + + +def validate_pathname_binary_tuple(data: Tuple[str, IOBase]): + if not isinstance(data, tuple): + raise TypeError( + f"pathname binary data should be tuple type, but it is type {type(data)}" + ) + if len(data) != 2: + raise TypeError( + f"pathname binary stream tuple length should be 2, but got {len(data)}" + ) + if not isinstance(data[0], str): + raise TypeError( + f"pathname within the tuple should have string type pathname, but it is type {type(data[0])}" + ) + if not isinstance(data[1], IOBase) and not isinstance(data[1], StreamWrapper): + raise TypeError( + f"binary stream within the tuple should have IOBase or" + f"its subclasses as type, but it is type {type(data[1])}" + ) + + +# Deprecated function names and its corresponding DataPipe type and kwargs for the `_deprecation_warning` function +_iter_deprecated_functional_names: Dict[str, Dict] = {} +_map_deprecated_functional_names: Dict[str, Dict] = {} + + +def _deprecation_warning( + old_class_name: str, + *, + deprecation_version: str, + removal_version: str, + old_functional_name: str = "", + old_argument_name: str = "", + new_class_name: str = "", + new_functional_name: str = "", + new_argument_name: str = "", + deprecate_functional_name_only: bool = False, +) -> None: + if new_functional_name and not old_functional_name: + raise ValueError( + "Old functional API needs to be specified for the deprecation warning." + ) + if new_argument_name and not old_argument_name: + raise ValueError( + "Old argument name needs to be specified for the deprecation warning." + ) + + if old_functional_name and old_argument_name: + raise ValueError( + "Deprecating warning for functional API and argument should be separated." + ) + + msg = f"`{old_class_name}()`" + if deprecate_functional_name_only and old_functional_name: + msg = f"{msg}'s functional API `.{old_functional_name}()` is" + elif old_functional_name: + msg = f"{msg} and its functional API `.{old_functional_name}()` are" + elif old_argument_name: + msg = f"The argument `{old_argument_name}` of {msg} is" + else: + msg = f"{msg} is" + msg = ( + f"{msg} deprecated since {deprecation_version} and will be removed in {removal_version}." + f"\nSee https://github.com/pytorch/data/issues/163 for details." + ) + + if new_class_name or new_functional_name: + msg = f"{msg}\nPlease use" + if new_class_name: + msg = f"{msg} `{new_class_name}()`" + if new_class_name and new_functional_name: + msg = f"{msg} or" + if new_functional_name: + msg = f"{msg} `.{new_functional_name}()`" + msg = f"{msg} instead." + + if new_argument_name: + msg = f"{msg}\nPlease use `{old_class_name}({new_argument_name}=)` instead." + + warnings.warn(msg, FutureWarning) + + +class StreamWrapper: + """ + StreamWrapper is introduced to wrap file handler generated by DataPipe operation like `FileOpener`. + + StreamWrapper would guarantee the wrapped file handler is closed when it's out of scope. + """ + + session_streams: Dict[Any, int] = {} + debug_unclosed_streams: bool = False + + def __init__(self, file_obj, parent_stream=None, name=None): + self.file_obj = file_obj + self.child_counter = 0 + self.parent_stream = parent_stream + self.close_on_last_child = False + self.name = name + self.closed = False + if parent_stream is not None: + if not isinstance(parent_stream, StreamWrapper): + raise RuntimeError( + f"Parent stream should be StreamWrapper, {type(parent_stream)} was given" + ) + parent_stream.child_counter += 1 + self.parent_stream = parent_stream + if StreamWrapper.debug_unclosed_streams: + StreamWrapper.session_streams[self] = 1 + + @classmethod + def close_streams(cls, v, depth=0): + """Traverse structure and attempts to close all found StreamWrappers on best effort basis.""" + if depth > 10: + return + if isinstance(v, StreamWrapper): + v.close() + else: + # Traverse only simple structures + if isinstance(v, dict): + for vv in v.values(): + cls.close_streams(vv, depth=depth + 1) + elif isinstance(v, (list, tuple)): + for vv in v: + cls.close_streams(vv, depth=depth + 1) + + def __getattr__(self, name): + file_obj = self.__dict__["file_obj"] + return getattr(file_obj, name) + + def close(self, *args, **kwargs): + if self.closed: + return + if StreamWrapper.debug_unclosed_streams: + del StreamWrapper.session_streams[self] + if hasattr(self, "parent_stream") and self.parent_stream is not None: + self.parent_stream.child_counter -= 1 + if ( + not self.parent_stream.child_counter + and self.parent_stream.close_on_last_child + ): + self.parent_stream.close() + try: + self.file_obj.close(*args, **kwargs) + except AttributeError: + pass + self.closed = True + + def autoclose(self): + """Automatically close stream when all child streams are closed or if there are none.""" + self.close_on_last_child = True + if self.child_counter == 0: + self.close() + + def __dir__(self): + attrs = list(self.__dict__.keys()) + list(StreamWrapper.__dict__.keys()) + attrs += dir(self.file_obj) + return list(set(attrs)) + + def __del__(self): + if not self.closed: + self.close() + + def __iter__(self): + yield from self.file_obj + + def __next__(self): + return next(self.file_obj) + + def __repr__(self): + if self.name is None: + return f"StreamWrapper<{self.file_obj!r}>" + else: + return f"StreamWrapper<{self.name},{self.file_obj!r}>" + + def __getstate__(self): + return self.file_obj + + def __setstate__(self, obj): + self.file_obj = obj diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/decoder.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..fc32ae6dc223f726300aac25b65404181e4a95f6 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/decoder.py @@ -0,0 +1,378 @@ +# mypy: allow-untyped-defs +# This file takes partial of the implementation from NVIDIA's webdataset at here: +# https://github.com/tmbdev/webdataset/blob/master/webdataset/autodecode.py + +import io +import json +import os.path +import pickle +import tempfile + +import torch +from torch.utils.data.datapipes.utils.common import StreamWrapper + + +__all__ = [ + "Decoder", + "ImageHandler", + "MatHandler", + "audiohandler", + "basichandlers", + "extension_extract_fn", + "handle_extension", + "imagehandler", + "mathandler", + "videohandler", +] + + +################################################################ +# handle basic datatypes +################################################################ +def basichandlers(extension: str, data): + """Transforms raw data (byte stream) into python objects. + + Looks at the extension and loads the data into a python object supporting + the corresponding extension. + + Args: + extension (str): The file extension + data (byte stream): Data to load into a python object. + + Returns: + object: The data loaded into a corresponding python object + supporting the extension. + + Example: + >>> import pickle + >>> data = pickle.dumps('some data') + >>> new_data = basichandlers('pickle', data) + >>> new_data + some data + + The transformation of data for extensions are: + - txt, text, transcript: utf-8 decoded data of str format + - cls, cls2, class, count, index, inx, id: int + - json, jsn: json loaded data + - pickle, pyd: pickle loaded data + - pt: torch loaded data + """ + + if extension in "txt text transcript": + return data.decode("utf-8") + + if extension in "cls cls2 class count index inx id".split(): + try: + return int(data) + except ValueError: + return None + + if extension in "json jsn": + return json.loads(data) + + if extension in "pyd pickle".split(): + return pickle.loads(data) + + if extension in "pt".split(): + stream = io.BytesIO(data) + return torch.load(stream) + + # if extension in "ten tb".split(): + # from . import tenbin + # return tenbin.decode_buffer(data) + + # if extension in "mp msgpack msg".split(): + # import msgpack + # return msgpack.unpackb(data) + + return None + + +################################################################ +# handle images +################################################################ +imagespecs = { + "l8": ("numpy", "uint8", "l"), + "rgb8": ("numpy", "uint8", "rgb"), + "rgba8": ("numpy", "uint8", "rgba"), + "l": ("numpy", "float", "l"), + "rgb": ("numpy", "float", "rgb"), + "rgba": ("numpy", "float", "rgba"), + "torchl8": ("torch", "uint8", "l"), + "torchrgb8": ("torch", "uint8", "rgb"), + "torchrgba8": ("torch", "uint8", "rgba"), + "torchl": ("torch", "float", "l"), + "torchrgb": ("torch", "float", "rgb"), + "torch": ("torch", "float", "rgb"), + "torchrgba": ("torch", "float", "rgba"), + "pill": ("pil", None, "l"), + "pil": ("pil", None, "rgb"), + "pilrgb": ("pil", None, "rgb"), + "pilrgba": ("pil", None, "rgba"), +} + + +def handle_extension(extensions, f): + """ + Return a decoder handler function for the list of extensions. + + Extensions can be a space separated list of extensions. + Extensions can contain dots, in which case the corresponding number + of extension components must be present in the key given to f. + Comparisons are case insensitive. + Examples: + handle_extension("jpg jpeg", my_decode_jpg) # invoked for any file.jpg + handle_extension("seg.jpg", special_case_jpg) # invoked only for file.seg.jpg + """ + extensions = extensions.lower().split() + + def g(key, data): + extension = key.lower().split(".") + + for target in extensions: + target = target.split(".") + if len(target) > len(extension): + continue + + if extension[-len(target) :] == target: + return f(data) + return None + + return g + + +class ImageHandler: + """ + Decode image data using the given `imagespec`. + + The `imagespec` specifies whether the image is decoded + to numpy/torch/pi, decoded to uint8/float, and decoded + to l/rgb/rgba: + + - l8: numpy uint8 l + - rgb8: numpy uint8 rgb + - rgba8: numpy uint8 rgba + - l: numpy float l + - rgb: numpy float rgb + - rgba: numpy float rgba + - torchl8: torch uint8 l + - torchrgb8: torch uint8 rgb + - torchrgba8: torch uint8 rgba + - torchl: torch float l + - torchrgb: torch float rgb + - torch: torch float rgb + - torchrgba: torch float rgba + - pill: pil None l + - pil: pil None rgb + - pilrgb: pil None rgb + - pilrgba: pil None rgba + """ + + def __init__(self, imagespec): + assert imagespec in list( + imagespecs.keys() + ), f"unknown image specification: {imagespec}" + self.imagespec = imagespec.lower() + + def __call__(self, extension, data): + if extension.lower() not in "jpg jpeg png ppm pgm pbm pnm".split(): + return None + + try: + import numpy as np + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "Package `numpy` is required to be installed for default image decoder." + "Please use `pip install numpy` to install the package" + ) from e + + try: + import PIL.Image + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "Package `PIL` is required to be installed for default image decoder." + "Please use `pip install Pillow` to install the package" + ) from e + + imagespec = self.imagespec + atype, etype, mode = imagespecs[imagespec] + + with io.BytesIO(data) as stream: + img = PIL.Image.open(stream) + img.load() + img = img.convert(mode.upper()) + if atype == "pil": + return img + elif atype == "numpy": + result = np.asarray(img) + assert ( + result.dtype == np.uint8 + ), f"numpy image array should be type uint8, but got {result.dtype}" + if etype == "uint8": + return result + else: + return result.astype("f") / 255.0 + elif atype == "torch": + result = np.asarray(img) + assert ( + result.dtype == np.uint8 + ), f"numpy image array should be type uint8, but got {result.dtype}" + + if etype == "uint8": + result = np.array(result.transpose(2, 0, 1)) + return torch.tensor(result) + else: + result = np.array(result.transpose(2, 0, 1)) + return torch.tensor(result) / 255.0 + return None + + +def imagehandler(imagespec): + return ImageHandler(imagespec) + + +################################################################ +# torch video +################################################################ +def videohandler(extension, data): + if extension not in "mp4 ogv mjpeg avi mov h264 mpg webm wmv".split(): + return None + + try: + import torchvision.io + except ImportError as e: + raise ModuleNotFoundError( + "Package `torchvision` is required to be installed for default video file loader." + "Please use `pip install torchvision` or `conda install torchvision -c pytorch`" + "to install the package" + ) from e + + with tempfile.TemporaryDirectory() as dirname: + fname = os.path.join(dirname, f"file.{extension}") + with open(fname, "wb") as stream: + stream.write(data) + return torchvision.io.read_video(fname) + + +################################################################ +# torchaudio +################################################################ +def audiohandler(extension, data): + if extension not in ["flac", "mp3", "sox", "wav", "m4a", "ogg", "wma"]: + return None + + try: + import torchaudio # type: ignore[import] + except ImportError as e: + raise ModuleNotFoundError( + "Package `torchaudio` is required to be installed for default audio file loader." + "Please use `pip install torchaudio` or `conda install torchaudio -c pytorch`" + "to install the package" + ) from e + + with tempfile.TemporaryDirectory() as dirname: + fname = os.path.join(dirname, f"file.{extension}") + with open(fname, "wb") as stream: + stream.write(data) + return torchaudio.load(fname) + + +################################################################ +# mat +################################################################ +class MatHandler: + def __init__(self, **loadmat_kwargs) -> None: + try: + import scipy.io as sio + except ImportError as e: + raise ModuleNotFoundError( + "Package `scipy` is required to be installed for mat file." + "Please use `pip install scipy` or `conda install scipy`" + "to install the package" + ) from e + self.sio = sio + self.loadmat_kwargs = loadmat_kwargs + + def __call__(self, extension, data): + if extension != "mat": + return None + with io.BytesIO(data) as stream: + return self.sio.loadmat(stream, **self.loadmat_kwargs) + + +def mathandler(**loadmat_kwargs): + return MatHandler(**loadmat_kwargs) + + +################################################################ +# a sample decoder +################################################################ +# Extract extension from pathname +def extension_extract_fn(pathname): + ext = os.path.splitext(pathname)[1] + # Remove dot + if ext: + ext = ext[1:] + return ext + + +class Decoder: + """ + Decode key/data sets using a list of handlers. + + For each key/data item, this iterates through the list of + handlers until some handler returns something other than None. + """ + + def __init__(self, *handler, key_fn=extension_extract_fn): + self.handlers = list(handler) if handler else [] + self.key_fn = key_fn + + # Insert new handler from the beginning of handlers list to make sure the new + # handler having the highest priority + def add_handler(self, *handler): + if not handler: + return + self.handlers = list(handler) + self.handlers + + @staticmethod + def _is_stream_handle(data): + obj_to_check = data.file_obj if isinstance(data, StreamWrapper) else data + return isinstance(obj_to_check, (io.BufferedIOBase, io.RawIOBase)) + + def decode1(self, key, data): + if not data: + return data + + # if data is a stream handle, we need to read all the content before decoding + if Decoder._is_stream_handle(data): + ds = data + # The behavior of .read can differ between streams (e.g. HTTPResponse), hence this is used instead + data = b"".join(data) + ds.close() + + for f in self.handlers: + result = f(key, data) + if result is not None: + return result + return data + + def decode(self, data): + result = {} + # single data tuple(pathname, data stream) + if isinstance(data, tuple): + data = [data] + + if data is not None: + for k, v in data: + # TODO: xinyu, figure out why Nvidia do this? + if k[0] == "_": + if isinstance(v, bytes): + v = v.decode("utf-8") + result[k] = v + continue + result[k] = self.decode1(self.key_fn(k), v) + return result + + def __call__(self, data): + return self.decode(data) diff --git a/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/snapshot.py b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/snapshot.py new file mode 100644 index 0000000000000000000000000000000000000000..d120025a934ec2456306c59dd70a6f5d348e16b4 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/data/datapipes/utils/snapshot.py @@ -0,0 +1,64 @@ +# mypy: allow-untyped-defs +from torch.utils.data.datapipes._hook_iterator import _SnapshotState +from torch.utils.data.datapipes.datapipe import IterDataPipe +from torch.utils.data.graph_settings import apply_random_seed + + +# TODO: Caveats +# 1. Caller (either the ReadingService or DataLoader) must pass in the initial RNG +# 2. `in_batch_shuffle` and `bucketbatch` are not compatible with this because they currently +# lack the option to `set_seed`. +def _simple_graph_snapshot_restoration( + datapipe: IterDataPipe, n_iterations: int, rng=None +) -> None: + r""" + Fast-forward the given DataPipe and its parents by ``n_iterations``, re-doing computations to restore a snapshot. + + For instance, applying this function to the final DataPipe of a graph will restore the snapshot + (via fast-forward) every DataPipe within the graph. + + After you deserialize a DataPipe, you can use its `_number_of_samples_yielded` attribute as the input + to this function to forward the DataPipe. + + A DataPipe cannot be restored twice in a row unless there is an iteration started between the restoration + attempts. + + Note: + This is the simplest but least efficient way to fast-forward a DataPipe. Usage of other fast-forwarding + methods (custom ones if necessary) are recommended. + + Args: + datapipe: IterDataPipe to be fast-forwarded + n_iterations: number of iterations to fast-forward + rng: ``Optional[torch.Generator]``. If not ``None``, this RNG will be used for shuffling. The generator + should be in its `initial` state as it was first passed into ``DataLoader`` or ``ReadingService``. + """ + if datapipe._snapshot_state == _SnapshotState.Restored: + raise RuntimeError( + "Snapshot restoration cannot be applied. You can only restore simple snapshot to the graph " + "if your graph has not been restored." + ) + + # For this snapshot restoration function, we want the DataPipe to be at its initial state prior to + # simple fast-forwarding. Therefore, we need to call `reset` twice, because if `SnapshotState` is `Restored`, + # the first reset will not actually reset. + datapipe.reset() # This ensures `SnapshotState` is `Iterating` by this point, even if it was `Restored`. + apply_random_seed(datapipe, rng) + + remainder = n_iterations + it = iter(datapipe) # This always reset the DataPipe if it hasn't already. + while remainder > 0: + try: + next(it) + remainder -= 1 + except StopIteration as e: + raise RuntimeError( + f"Fast-forward {datapipe} by {n_iterations} iterations " + "exceeds the number of samples available." + ) from e + datapipe._fast_forward_iterator = it + # While the DataPipe has `_fast_forward_iterator`, `next()` will get result from there instead of elsewhere. + + # This will prevent the DataPipe from resetting in the `iter()` call + # If another DataPipe is consuming it, it won't have to start over again + datapipe._snapshot_state = _SnapshotState.Restored diff --git a/lib/python3.11/site-packages/torch/utils/deterministic.py b/lib/python3.11/site-packages/torch/utils/deterministic.py new file mode 100644 index 0000000000000000000000000000000000000000..a055c43be531a5c65c4f29f6c8165104e98e5ca0 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/deterministic.py @@ -0,0 +1,22 @@ +# mypy: allow-untyped-defs +import sys +import types + +import torch + + +class _Deterministic(types.ModuleType): + @property + def fill_uninitialized_memory(self): + """ + Whether to fill uninitialized memory with a known value when + :meth:`torch.use_deterministic_algorithms()` is set to ``True``. + """ + return torch._C._get_deterministic_fill_uninitialized_memory() + + @fill_uninitialized_memory.setter + def fill_uninitialized_memory(self, mode): + return torch._C._set_deterministic_fill_uninitialized_memory(mode) + + +sys.modules[__name__].__class__ = _Deterministic diff --git a/lib/python3.11/site-packages/torch/utils/dlpack.py b/lib/python3.11/site-packages/torch/utils/dlpack.py new file mode 100644 index 0000000000000000000000000000000000000000..6bfa4b9f85bd6fc8bb8524926210b1e931e2bd50 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/dlpack.py @@ -0,0 +1,121 @@ +from typing import Any + +import torch +import enum + +from torch._C import _from_dlpack +from torch._C import _to_dlpack as to_dlpack + + +class DLDeviceType(enum.IntEnum): + # Enums as in DLPack specification (aten/src/ATen/dlpack.h) + kDLCPU = 1, + kDLGPU = 2, + kDLCPUPinned = 3, + kDLOpenCL = 4, + kDLVulkan = 7, + kDLMetal = 8, + kDLVPI = 9, + kDLROCM = 10, + kDLExtDev = 12, + kDLOneAPI = 14, + + +torch._C._add_docstr(to_dlpack, r"""to_dlpack(tensor) -> PyCapsule + +Returns an opaque object (a "DLPack capsule") representing the tensor. + +.. note:: + ``to_dlpack`` is a legacy DLPack interface. The capsule it returns + cannot be used for anything in Python other than use it as input to + ``from_dlpack``. The more idiomatic use of DLPack is to call + ``from_dlpack`` directly on the tensor object - this works when that + object has a ``__dlpack__`` method, which PyTorch and most other + libraries indeed have now. + +.. warning:: + Only call ``from_dlpack`` once per capsule produced with ``to_dlpack``. + Behavior when a capsule is consumed multiple times is undefined. + +Args: + tensor: a tensor to be exported + +The DLPack capsule shares the tensor's memory. +""") + + +# TODO: add a typing.Protocol to be able to tell Mypy that only objects with +# __dlpack__ and __dlpack_device__ methods are accepted. +def from_dlpack(ext_tensor: Any) -> 'torch.Tensor': + """from_dlpack(ext_tensor) -> Tensor + + Converts a tensor from an external library into a ``torch.Tensor``. + + The returned PyTorch tensor will share the memory with the input tensor + (which may have come from another library). Note that in-place operations + will therefore also affect the data of the input tensor. This may lead to + unexpected issues (e.g., other libraries may have read-only flags or + immutable data structures), so the user should only do this if they know + for sure that this is fine. + + Args: + ext_tensor (object with ``__dlpack__`` attribute, or a DLPack capsule): + The tensor or DLPack capsule to convert. + + If ``ext_tensor`` is a tensor (or ndarray) object, it must support + the ``__dlpack__`` protocol (i.e., have a ``ext_tensor.__dlpack__`` + method). Otherwise ``ext_tensor`` may be a DLPack capsule, which is + an opaque ``PyCapsule`` instance, typically produced by a + ``to_dlpack`` function or method. + + Examples:: + + >>> import torch.utils.dlpack + >>> t = torch.arange(4) + + # Convert a tensor directly (supported in PyTorch >= 1.10) + >>> t2 = torch.from_dlpack(t) + >>> t2[:2] = -1 # show that memory is shared + >>> t2 + tensor([-1, -1, 2, 3]) + >>> t + tensor([-1, -1, 2, 3]) + + # The old-style DLPack usage, with an intermediate capsule object + >>> capsule = torch.utils.dlpack.to_dlpack(t) + >>> capsule + + >>> t3 = torch.from_dlpack(capsule) + >>> t3 + tensor([-1, -1, 2, 3]) + >>> t3[0] = -9 # now we're sharing memory between 3 tensors + >>> t3 + tensor([-9, -1, 2, 3]) + >>> t2 + tensor([-9, -1, 2, 3]) + >>> t + tensor([-9, -1, 2, 3]) + + """ + if hasattr(ext_tensor, '__dlpack__'): + device = ext_tensor.__dlpack_device__() + # device is either CUDA or ROCm, we need to pass the current + # stream + if device[0] in (DLDeviceType.kDLGPU, DLDeviceType.kDLROCM): + stream = torch.cuda.current_stream(f'cuda:{device[1]}') + # cuda_stream is the pointer to the stream and it is a public + # attribute, but it is not documented + # The array API specify that the default legacy stream must be passed + # with a value of 1 for CUDA + # https://data-apis.org/array-api/latest/API_specification/array_object.html?dlpack-self-stream-none#dlpack-self-stream-none + is_cuda = device[0] == DLDeviceType.kDLGPU + # Since pytorch is not using PTDS by default, lets directly pass + # the legacy stream + stream_ptr = 1 if is_cuda and stream.cuda_stream == 0 else stream.cuda_stream + dlpack = ext_tensor.__dlpack__(stream=stream_ptr) + else: + dlpack = ext_tensor.__dlpack__() + else: + # Old versions just call the converter + dlpack = ext_tensor + return _from_dlpack(dlpack) diff --git a/lib/python3.11/site-packages/torch/utils/file_baton.py b/lib/python3.11/site-packages/torch/utils/file_baton.py new file mode 100644 index 0000000000000000000000000000000000000000..77ee5091b3f727a8ec56bf6dde10f7fbc2e3352f --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/file_baton.py @@ -0,0 +1,50 @@ +# mypy: allow-untyped-defs +import os +import time + + +class FileBaton: + """A primitive, file-based synchronization utility.""" + + def __init__(self, lock_file_path, wait_seconds=0.1): + """ + Create a new :class:`FileBaton`. + + Args: + lock_file_path: The path to the file used for locking. + wait_seconds: The seconds to periodically sleep (spin) when + calling ``wait()``. + """ + self.lock_file_path = lock_file_path + self.wait_seconds = wait_seconds + self.fd = None + + def try_acquire(self): + """ + Try to atomically create a file under exclusive access. + + Returns: + True if the file could be created, else False. + """ + try: + self.fd = os.open(self.lock_file_path, os.O_CREAT | os.O_EXCL) + return True + except FileExistsError: + return False + + def wait(self): + """ + Periodically sleeps for a certain amount until the baton is released. + + The amount of time slept depends on the ``wait_seconds`` parameter + passed to the constructor. + """ + while os.path.exists(self.lock_file_path): + time.sleep(self.wait_seconds) + + def release(self): + """Release the baton and removes its file.""" + if self.fd is not None: + os.close(self.fd) + + os.remove(self.lock_file_path) diff --git a/lib/python3.11/site-packages/torch/utils/flop_counter.py b/lib/python3.11/site-packages/torch/utils/flop_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..c9c0ef2e422ce05d47932a4480db80e85888e58a --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/flop_counter.py @@ -0,0 +1,773 @@ +# mypy: allow-untyped-defs +# mypy: allow-untyped-decorators +import torch +from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten +from .module_tracker import ModuleTracker +from typing import List, Any, Dict, Optional, Union, Tuple, Iterator +from collections import defaultdict +from torch.utils._python_dispatch import TorchDispatchMode +from math import prod +from functools import wraps +import warnings + + + +__all__ = ["FlopCounterMode", "register_flop_formula"] + +aten = torch.ops.aten + +def get_shape(i): + if isinstance(i, torch.Tensor): + return i.shape + return i + +flop_registry: Dict[Any, Any] = {} + +def shape_wrapper(f): + @wraps(f) + def nf(*args, out_val=None, **kwargs): + args, kwargs, out_shape = tree_map(get_shape, (args, kwargs, out_val)) + return f(*args, out_shape=out_shape, **kwargs) + return nf + +def register_flop_formula(targets, get_raw=False): + def register_fun(flop_formula): + if not get_raw: + flop_formula = shape_wrapper(flop_formula) + + def register(target): + if not isinstance(target, torch._ops.OpOverloadPacket): + raise ValueError( + f"register_flop_formula(targets): expected each target to be " + f"OpOverloadPacket (i.e. torch.ops.mylib.foo), got " + f"{target} which is of type {type(target)}") + if target in flop_registry: + raise RuntimeError(f"duplicate registrations for {target}") + flop_registry[target] = flop_formula + + # To handle allowing multiple aten_ops at once + torch.utils._pytree.tree_map_(register, targets) + + return flop_formula + + return register_fun + +@register_flop_formula(aten.mm) +def mm_flop(a_shape, b_shape, *args, out_shape=None, **kwargs) -> int: + """Count flops for matmul.""" + # Inputs should be a list of length 2. + # Inputs contains the shapes of two matrices. + m, k = a_shape + k2, n = b_shape + assert k == k2 + # NB(chilli): Should be 2 * k - 1 technically for FLOPs. + return m * n * 2 * k + +@register_flop_formula(aten.addmm) +def addmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int: + """Count flops for addmm.""" + return mm_flop(a_shape, b_shape) + +@register_flop_formula(aten.bmm) +def bmm_flop(a_shape, b_shape, out_shape=None, **kwargs) -> int: + """Count flops for the bmm operation.""" + # Inputs should be a list of length 2. + # Inputs contains the shapes of two tensor. + b, m, k = a_shape + b2, k2, n = b_shape + assert b == b2 + assert k == k2 + # NB(chilli): Should be 2 * k - 1 technically for FLOPs. + flop = b * m * n * 2 * k + return flop + +@register_flop_formula(aten.baddbmm) +def baddbmm_flop(self_shape, a_shape, b_shape, out_shape=None, **kwargs) -> int: + """Count flops for the baddbmm operation.""" + # Inputs should be a list of length 3. + # Inputs contains the shapes of three tensors. + return bmm_flop(a_shape, b_shape) + + +def conv_flop_count( + x_shape: List[int], + w_shape: List[int], + out_shape: List[int], + transposed: bool = False, +) -> int: + """Count flops for convolution. + + Note only multiplication is + counted. Computation for bias are ignored. + Flops for a transposed convolution are calculated as + flops = (x_shape[2:] * prod(w_shape) * batch_size). + Args: + x_shape (list(int)): The input shape before convolution. + w_shape (list(int)): The filter shape. + out_shape (list(int)): The output shape after convolution. + transposed (bool): is the convolution transposed + Returns: + int: the number of flops + """ + + batch_size = x_shape[0] + conv_shape = (x_shape if transposed else out_shape)[2:] + c_out, c_in, *filter_size = w_shape + + """ + General idea here is that for a regular conv, for each point in the output + spatial dimension we convolve the filter with something (hence + `prod(conv_shape) * prod(filter_size)` ops). Then, this gets multiplied by + 1. batch_size, 2. the cross product of input and weight channels. + + For the transpose, it's not each point in the *output* spatial dimension but + each point in the *input* spatial dimension. + """ + # NB(chilli): I don't think this properly accounts for padding :think: + # NB(chilli): Should be 2 * c_in - 1 technically for FLOPs. + flop = prod(conv_shape) * prod(filter_size) * batch_size * c_out * c_in * 2 + return flop + +@register_flop_formula([aten.convolution, aten._convolution]) +def conv_flop(x_shape, w_shape, _bias, _stride, _padding, _dilation, transposed, *args, out_shape=None, **kwargs) -> int: + """Count flops for convolution.""" + return conv_flop_count(x_shape, w_shape, out_shape, transposed=transposed) + + +@register_flop_formula(aten.convolution_backward) +def conv_backward_flop( + grad_out_shape, + x_shape, + w_shape, + _bias, + _stride, + _padding, + _dilation, + transposed, + _output_padding, + _groups, + output_mask, + out_shape) -> int: + + def t(shape): + return [shape[1], shape[0]] + list(shape[2:]) + flop_count = 0 + + """ + Let's say we have a regular 1D conv + {A, B, C} [inp] + {i, j} [weight] + => (conv) + {Ai + Bj, Bi + Cj} [out] + + And as a reminder, the transposed conv of the above is + => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out] + + For the backwards of conv, we now have + {D, E} [grad_out] + {A, B, C} [inp] + {i, j} [weight] + + # grad_inp as conv_transpose(grad_out, weight) + Let's first compute grad_inp. To do so, we can simply look at all the + multiplications that each element of inp is involved in. For example, A is + only involved in the first element of the output (and thus only depends upon + D in grad_out), and C is only involved in the last element of the output + (and thus only depends upon E in grad_out) + + {Di, Dj + Ei, Ej} [grad_inp] + + Note that this corresponds to the below conv_transpose. This gives us the + output_mask[0] branch, which is grad_inp. + + {D, E} [inp (grad_out)] + {i, j} [weight] + => (conv_transpose) + {Di, Dj + Ei, Ej} [out (grad_inp)] + + I leave the fact that grad_inp for a transposed conv is just conv(grad_out, + weight) as an exercise for the reader. + + # grad_weight as conv(inp, grad_out) + To compute grad_weight, we again look at the terms in the output, which as + a reminder is: + => {Ai + Bj, Bi + Cj} [out] + => {D, E} [grad_out] + If we manually compute the gradient for the weights, we see it's + {AD + BE, BD + CE} [grad_weight] + + This corresponds to the below conv + {A, B, C} [inp] + {D, E} [weight (grad_out)] + => (conv) + {AD + BE, BD + CE} [out (grad_weight)] + + # grad_weight of transposed conv as conv(grad_out, inp) + As a reminder, the terms of the output of a transposed conv are: + => {Ai, Aj + Bi, Bj + Ci, Cj} [transposed conv out] + => {D, E, F, G} [grad_out] + + Manually computing the gradient for the weights, we see it's + {AD + BE + CF, AE + BF + CG} [grad_weight] + + This corresponds to the below conv + {D, E, F, G} [inp (grad_out)] + {A, B, C} [weight (inp)] + => (conv) + {AD + BE + CF, AE + BF + CG} [out (grad_weight)] + + For the full backwards formula, there are also some details involving + transpose of the batch/channel dimensions and groups, but I skip those for + the sake of brevity (and they're pretty similar to matmul backwards) + + Check [conv backwards decomposition as conv forwards] + """ + # grad_inp as conv_transpose(grad_out, weight) + if output_mask[0]: + grad_input_shape = get_shape(out_shape[0]) + flop_count += conv_flop_count(grad_out_shape, w_shape, grad_input_shape, not transposed) + + if output_mask[1]: + grad_weight_shape = get_shape(out_shape[1]) + if transposed: + # grad_weight of transposed conv as conv(grad_out, inp) + flop_count += conv_flop_count(t(grad_out_shape), t(x_shape), t(grad_weight_shape), transposed=False) + else: + # grad_weight as conv(inp, grad_out) + flop_count += conv_flop_count(t(x_shape), t(grad_out_shape), t(grad_weight_shape), transposed=False) + + return flop_count + +def sdpa_flop_count(query_shape, key_shape, value_shape): + """ + Count flops for self-attention. + + NB: We can assume that value_shape == key_shape + """ + b, h, s_q, d_q = query_shape + _b2, _h2, s_k, _d2 = key_shape + _b3, _h3, _s3, d_v = value_shape + assert b == _b2 == _b3 and h == _h2 == _h3 and d_q == _d2 and s_k == _s3 and d_q == _d2 + total_flops = 0 + # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k] + total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k)) + # scores: [b, h, s_q, s_k] @ v: [b, h, s_k, d_v] -> out: [b, h, s_q, d_v] + total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_v)) + return total_flops + + +@register_flop_formula([aten._scaled_dot_product_efficient_attention, + aten._scaled_dot_product_flash_attention, + aten._scaled_dot_product_cudnn_attention]) +def sdpa_flop(query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int: + """Count flops for self-attention.""" + # NB: We aren't accounting for causal attention here + return sdpa_flop_count(query_shape, key_shape, value_shape) + + +def _offsets_to_lengths(offsets, max_len): + """ + If the offsets tensor is fake, then we don't know the actual lengths. + In that case, we can just assume the worst case; each batch has max length. + """ + from torch._subclasses.fake_tensor import FakeTensor + from torch._subclasses.functional_tensor import FunctionalTensor + if not isinstance(offsets, (FakeTensor, FunctionalTensor)): + return offsets.diff().tolist() + return [max_len] * (offsets.size(0) - 1) + + +def _unpack_flash_attention_nested_shapes( + *, + query, + key, + value, + grad_out=None, + cum_seq_q, + cum_seq_k, + max_q, + max_k, +) -> Iterator[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...], Optional[Tuple[int, ...]]]]: + """ + Given inputs to a flash_attention_(forward|backward) kernel, this will handle behavior for + NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for + each batch element. + + In the case that this isn't a NestedTensor kernel, then it just yields the original shapes. + """ + if cum_seq_q is not None: + # This means we should be dealing with a Nested Jagged Tensor query. + # The inputs will have shape (sum(sequence len), heads, dimension) + # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension) + # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension) + # So the flops calculation in this case is an overestimate of the actual flops. + assert len(key.shape) == 3 + assert len(value.shape) == 3 + assert grad_out is None or grad_out.shape == query.shape + _, h_q, d_q = query.shape + _, h_k, d_k = key.shape + _, h_v, d_v = value.shape + assert cum_seq_q is not None + assert cum_seq_k is not None + assert cum_seq_q.shape == cum_seq_k.shape + seq_q_lengths = _offsets_to_lengths(cum_seq_q, max_q) + seq_k_lengths = _offsets_to_lengths(cum_seq_k, max_k) + for (seq_q_len, seq_k_len) in zip(seq_q_lengths, seq_k_lengths): + new_query_shape = (1, h_q, seq_q_len, d_q) + new_key_shape = (1, h_k, seq_k_len, d_k) + new_value_shape = (1, h_v, seq_k_len, d_v) + new_grad_out_shape = new_query_shape if grad_out is not None else None + yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape + return + + yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None + + +def _unpack_efficient_attention_nested_shapes( + *, + query, + key, + value, + grad_out=None, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, +) -> Iterator[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...], Optional[Tuple[int, ...]]]]: + """ + Given inputs to a efficient_attention_(forward|backward) kernel, this will handle behavior for + NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for + each batch element. + + In the case that this isn't a NestedTensor kernel, then it just yields the original shapes. + """ + if cu_seqlens_q is not None: + # Unlike flash_attention_forward, we get a 4D tensor instead of a 3D tensor for efficient attention. + # + # This means we should be dealing with a Nested Jagged Tensor query. + # The inputs will have shape (sum(sequence len), heads, dimension) + # In comparison, non-Nested inputs have shape (batch, heads, sequence len, dimension) + # To deal with this, we convert to a shape of (batch, heads, max_seq_len, dimension) + # So the flops calculation in this case is an overestimate of the actual flops. + assert len(key.shape) == 4 + assert len(value.shape) == 4 + assert grad_out is None or grad_out.shape == query.shape + _, _, h_q, d_q = query.shape + _, _, h_k, d_k = key.shape + _, _, h_v, d_v = value.shape + assert cu_seqlens_q is not None + assert cu_seqlens_k is not None + assert cu_seqlens_q.shape == cu_seqlens_k.shape + seqlens_q = _offsets_to_lengths(cu_seqlens_q, max_seqlen_q) + seqlens_k = _offsets_to_lengths(cu_seqlens_k, max_seqlen_k) + for len_q, len_k in zip(seqlens_q, seqlens_k): + new_query_shape = (1, h_q, len_q, d_q) + new_key_shape = (1, h_k, len_k, d_k) + new_value_shape = (1, h_v, len_k, d_v) + new_grad_out_shape = new_query_shape if grad_out is not None else None + yield new_query_shape, new_key_shape, new_value_shape, new_grad_out_shape + return + + yield query.shape, key.shape, value.shape, grad_out.shape if grad_out is not None else None + + +@register_flop_formula(aten._flash_attention_forward, get_raw=True) +def _flash_attention_forward_flop( + query, + key, + value, + cum_seq_q, + cum_seq_k, + max_q, + max_k, + *args, + out_shape=None, + **kwargs +) -> int: + """Count flops for self-attention.""" + # NB: We aren't accounting for causal attention here + # in case this is a nested tensor, we unpack the individual batch elements + # and then sum the flops per batch element + sizes = _unpack_flash_attention_nested_shapes( + query=query, + key=key, + value=value, + cum_seq_q=cum_seq_q, + cum_seq_k=cum_seq_k, + max_q=max_q, + max_k=max_k, + ) + return sum( + sdpa_flop_count(query_shape, key_shape, value_shape) + for query_shape, key_shape, value_shape, _ in sizes + ) + + +@register_flop_formula(aten._efficient_attention_forward, get_raw=True) +def _efficient_attention_forward_flop( + query, + key, + value, + bias, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + *args, + **kwargs +) -> int: + """Count flops for self-attention.""" + # NB: We aren't accounting for causal attention here + # in case this is a nested tensor, we unpack the individual batch elements + # and then sum the flops per batch element + sizes = _unpack_efficient_attention_nested_shapes( + query=query, + key=key, + value=value, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + ) + return sum( + sdpa_flop_count(query_shape, key_shape, value_shape) + for query_shape, key_shape, value_shape, _ in sizes + ) + + +def sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape): + total_flops = 0 + b, h, s_q, d_q = query_shape + _b2, _h2, s_k, _d2 = key_shape + _b3, _h3, _s3, d_v = value_shape + _b4, _h4, _s4, _d4 = grad_out_shape + assert b == _b2 == _b3 == _b4 and h == _h2 == _h3 == _h4 and d_q == _d2 + assert d_v == _d4 and s_k == _s3 and s_q == _s4 + total_flops = 0 + # Step 1: We recompute the scores matrix. + # q: [b, h, s_q, d_q] @ k: [b, h, d_q, s_k] -> scores: [b, h, s_q, s_k] + total_flops += bmm_flop((b * h, s_q, d_q), (b * h, d_q, s_k)) + + # Step 2: We propagate the gradients through the score @ v operation. + # gradOut: [b, h, s_q, d_v] @ v: [b, h, d_v, s_k] -> gradScores: [b, h, s_q, s_k] + total_flops += bmm_flop((b * h, s_q, d_v), (b * h, d_v, s_k)) + # scores: [b, h, s_k, s_q] @ gradOut: [b, h, s_q, d_v] -> gradV: [b, h, s_k, d_v] + total_flops += bmm_flop((b * h, s_k, s_q), (b * h, s_q, d_v)) + + # Step 3: We propagate th gradients through the k @ v operation + # gradScores: [b, h, s_q, s_k] @ k: [b, h, s_k, d_q] -> gradQ: [b, h, s_q, d_q] + total_flops += bmm_flop((b * h, s_q, s_k), (b * h, s_k, d_q)) + # q: [b, h, d_q, s_q] @ gradScores: [b, h, s_q, s_k] -> gradK: [b, h, d_q, s_k] + total_flops += bmm_flop((b * h, d_q, s_q), (b * h, s_q, s_k)) + return total_flops + + +@register_flop_formula([aten._scaled_dot_product_efficient_attention_backward, + aten._scaled_dot_product_flash_attention_backward, + aten._scaled_dot_product_cudnn_attention_backward]) +def sdpa_backward_flop(grad_out_shape, query_shape, key_shape, value_shape, *args, out_shape=None, **kwargs) -> int: + """Count flops for self-attention backward.""" + return sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape) + +@register_flop_formula(aten._flash_attention_backward, get_raw=True) +def _flash_attention_backward_flop( + grad_out, + query, + key, + value, + out, # named _out_shape to avoid kwarg collision with out_shape created in wrapper + logsumexp, + cum_seq_q, + cum_seq_k, + max_q, + max_k, + *args, + **kwargs, +) -> int: + # in case this is a nested tensor, we unpack the individual batch elements + # and then sum the flops per batch element + shapes = _unpack_flash_attention_nested_shapes( + query=query, + key=key, + value=value, + grad_out=grad_out, + cum_seq_q=cum_seq_q, + cum_seq_k=cum_seq_k, + max_q=max_q, + max_k=max_k, + ) + return sum( + sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape) + for query_shape, key_shape, value_shape, grad_out_shape in shapes + ) + + +@register_flop_formula(aten._efficient_attention_backward, get_raw=True) +def _efficient_attention_backward_flop( + grad_out, + query, + key, + value, + bias, + out, # named _out to avoid kwarg collision with out created in wrapper + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + *args, + **kwargs, +) -> int: + # in case this is a nested tensor, we unpack the individual batch elements + # and then sum the flops per batch element + shapes = _unpack_efficient_attention_nested_shapes( + query=query, + key=key, + value=value, + grad_out=grad_out, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + ) + return sum( + sdpa_backward_flop_count(grad_out_shape, query_shape, key_shape, value_shape) + for query_shape, key_shape, value_shape, grad_out_shape in shapes + ) + + +flop_registry = { + aten.mm: mm_flop, + aten.addmm: addmm_flop, + aten.bmm: bmm_flop, + aten.baddbmm: baddbmm_flop, + aten.convolution: conv_flop, + aten._convolution: conv_flop, + aten.convolution_backward: conv_backward_flop, + aten._scaled_dot_product_efficient_attention: sdpa_flop, + aten._scaled_dot_product_flash_attention: sdpa_flop, + aten._scaled_dot_product_cudnn_attention: sdpa_flop, + aten._scaled_dot_product_efficient_attention_backward: sdpa_backward_flop, + aten._scaled_dot_product_flash_attention_backward: sdpa_backward_flop, + aten._scaled_dot_product_cudnn_attention_backward: sdpa_backward_flop, + aten._flash_attention_forward: _flash_attention_forward_flop, + aten._efficient_attention_forward: _efficient_attention_forward_flop, + aten._flash_attention_backward: _flash_attention_backward_flop, + aten._efficient_attention_backward: _efficient_attention_backward_flop, +} + +def normalize_tuple(x): + if not isinstance(x, tuple): + return (x,) + return x + + +# Define the suffixes for different orders of magnitude +suffixes = ["", "K", "M", "B", "T"] +# Thanks BingChat! +def get_suffix_str(number): + # Find the index of the appropriate suffix based on the number of digits + # with some additional overflow. + # i.e. 1.01B should be displayed as 1001M, not 1.001B + index = max(0, min(len(suffixes) - 1, (len(str(number)) - 2) // 3)) + return suffixes[index] + +def convert_num_with_suffix(number, suffix): + index = suffixes.index(suffix) + # Divide the number by 1000^index and format it to two decimal places + value = f"{number / 1000 ** index:.3f}" + # Return the value and the suffix as a string + return value + suffixes[index] + +def convert_to_percent_str(num, denom): + if denom == 0: + return "0%" + return f"{num / denom:.2%}" + +def _pytreeify_preserve_structure(f): + @wraps(f) + def nf(args): + flat_args, spec = tree_flatten(args) + out = f(*flat_args) + return tree_unflatten(out, spec) + + return nf + + +class FlopCounterMode: + """ + ``FlopCounterMode`` is a context manager that counts the number of flops within its context. + + It does this using a ``TorchDispatchMode``. + + It also supports hierarchical output by passing a module (or list of + modules) to FlopCounterMode on construction. If you do not need hierarchical + output, you do not need to use it with a module. + + Example usage + + .. code-block:: python + + mod = ... + with FlopCounterMode(mod) as flop_counter: + mod.sum().backward() + + """ + + def __init__( + self, + mods: Optional[Union[torch.nn.Module, List[torch.nn.Module]]] = None, + depth: int = 2, + display: bool = True, + custom_mapping: Optional[Dict[Any, Any]] = None): + super().__init__() + self.flop_counts: Dict[str, Dict[Any, int]] = defaultdict(lambda: defaultdict(int)) + self.depth = depth + self.display = display + self.mode: Optional[_FlopCounterMode] = None + if custom_mapping is None: + custom_mapping = {} + if mods is not None: + warnings.warn("mods argument is not needed anymore, you can stop passing it", stacklevel=2) + self.flop_registry = { + **flop_registry, + **{k: v if getattr(v, "_get_raw", False) else shape_wrapper(v) for k, v in custom_mapping.items()} + } + self.mod_tracker = ModuleTracker() + + def get_total_flops(self) -> int: + return sum(self.flop_counts['Global'].values()) + + def get_flop_counts(self) -> Dict[str, Dict[Any, int]]: + """Return the flop counts as a dictionary of dictionaries. + + The outer + dictionary is keyed by module name, and the inner dictionary is keyed by + operation name. + + Returns: + Dict[str, Dict[Any, int]]: The flop counts as a dictionary. + """ + return {k: dict(v) for k, v in self.flop_counts.items()} + + def get_table(self, depth=None): + if depth is None: + depth = self.depth + if depth is None: + depth = 999999 + + import tabulate + tabulate.PRESERVE_WHITESPACE = True + header = ["Module", "FLOP", "% Total"] + values = [] + global_flops = self.get_total_flops() + global_suffix = get_suffix_str(global_flops) + is_global_subsumed = False + + def process_mod(mod_name, depth): + nonlocal is_global_subsumed + + total_flops = sum(self.flop_counts[mod_name].values()) + + is_global_subsumed |= total_flops >= global_flops + + padding = " " * depth + values = [] + values.append([ + padding + mod_name, + convert_num_with_suffix(total_flops, global_suffix), + convert_to_percent_str(total_flops, global_flops) + ]) + for k, v in self.flop_counts[mod_name].items(): + values.append([ + padding + " - " + str(k), + convert_num_with_suffix(v, global_suffix), + convert_to_percent_str(v, global_flops) + ]) + return values + + for mod in sorted(self.flop_counts.keys()): + if mod == 'Global': + continue + mod_depth = mod.count(".") + 1 + if mod_depth > depth: + continue + + cur_values = process_mod(mod, mod_depth - 1) + values.extend(cur_values) + + # We do a bit of messing around here to only output the "Global" value + # if there are any FLOPs in there that aren't already fully contained by + # a module. + if 'Global' in self.flop_counts and not is_global_subsumed: + for value in values: + value[0] = " " + value[0] + + values = process_mod('Global', 0) + values + + if len(values) == 0: + values = [["Global", "0", "0%"]] + + return tabulate.tabulate(values, headers=header, colalign=("left", "right", "right")) + + # NB: This context manager is NOT reentrant + def __enter__(self): + self.flop_counts.clear() + self.mod_tracker.__enter__() + self.mode = _FlopCounterMode(self) + self.mode.__enter__() + return self + + def __exit__(self, *args): + assert self.mode is not None + b = self.mode.__exit__(*args) + self.mode = None # break cycles + self.mod_tracker.__exit__() + if self.display: + print(self.get_table(self.depth)) + return b + + def _count_flops(self, func_packet, out, args, kwargs): + if func_packet in self.flop_registry: + flop_count_func = self.flop_registry[func_packet] + flop_count = flop_count_func(*args, **kwargs, out_val=out) # type: ignore[operator] + for par in set(self.mod_tracker.parents): + self.flop_counts[par][func_packet] += flop_count + + return out + + +class _FlopCounterMode(TorchDispatchMode): + def __init__(self, counter: FlopCounterMode): + self.counter = counter + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + kwargs = kwargs if kwargs else {} + + # Skip ops from non-standard dispatch_sizes_strides_policy such as NJT + if func in {torch.ops.aten.is_contiguous.default, + torch.ops.aten.is_contiguous.memory_format, + torch.ops.aten.is_strides_like_format.default, + torch.ops.aten.is_non_overlapping_and_dense.default, + torch.ops.aten.size.default, + torch.ops.aten.sym_size.default, + torch.ops.aten.stride.default, + torch.ops.aten.sym_stride.default, + torch.ops.aten.storage_offset.default, + torch.ops.aten.sym_storage_offset.default, + torch.ops.aten.numel.default, + torch.ops.aten.sym_numel.default, + torch.ops.aten.dim.default, + torch.ops.prim.layout.default}: + + return NotImplemented + + # If we don't have func in flop_registry, see if it can decompose + if func not in self.counter.flop_registry and func is not torch.ops.prim.device.default: + with self: + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return r + + # no further decomposition; execute & count flops + out = func(*args, **kwargs) + return self.counter._count_flops(func._overloadpacket, out, args, kwargs) diff --git a/lib/python3.11/site-packages/torch/utils/hipify/__init__.py b/lib/python3.11/site-packages/torch/utils/hipify/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..58f3ace6c03d093337c9fa417ccbe8bc267b6c69 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hipify/__init__.py @@ -0,0 +1 @@ +from .version import __version__ diff --git a/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4a11f63b3a0c2d292d81a3300a1bbc8292d25a59 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/constants.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/constants.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..280450966433ed20af21b97a4dbe12e30de3f373 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/constants.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/hipify_python.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/hipify_python.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4412754f46c560d659de3d5fd7a59dbce38e6578 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/hipify_python.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/version.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/version.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e90e89d35fd486318693c105ecc88a13118b0ae0 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/hipify/__pycache__/version.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/hipify/constants.py b/lib/python3.11/site-packages/torch/utils/hipify/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..a9053b261ad44d1ef8b8cbdf3a27da0306d92f36 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hipify/constants.py @@ -0,0 +1,62 @@ +"""Constants for annotations in the mapping. + +The constants defined here are used to annotate the mapping tuples in cuda_to_hip_mappings.py. +They are based on +https://github.com/ROCm/HIPIFY/blob/master/src/Statistics.h +and fall in three categories: 1) type of mapping, 2) API of mapping, 3) unsupported +mapping. +""" + +CONV_VERSION = 0, +CONV_INIT = 1 +CONV_DEVICE = 2 +CONV_MEM = 3 +CONV_KERN = 4 +CONV_COORD_FUNC = 5 +CONV_MATH_FUNC = 6 +CONV_DEVICE_FUNC = 7 +CONV_SPECIAL_FUNC = 8 +CONV_STREAM = 9 +CONV_EVENT = 10 +CONV_OCCUPANCY = 11 +CONV_CONTEXT = 12 +CONV_PEER = 13 +CONV_MODULE = 14 +CONV_CACHE = 15 +CONV_EXEC = 16 +CONV_ERROR = 17 +CONV_DEF = 18 +CONV_TEX = 19 +CONV_GL = 20 +CONV_GRAPHICS = 21 +CONV_SURFACE = 22 +CONV_JIT = 23 +CONV_D3D9 = 24 +CONV_D3D10 = 25 +CONV_D3D11 = 26 +CONV_VDPAU = 27 +CONV_EGL = 28 +CONV_THREAD = 29 +CONV_OTHER = 30 +CONV_INCLUDE = 31 +CONV_INCLUDE_CUDA_MAIN_H = 32 +CONV_TYPE = 33 +CONV_LITERAL = 34 +CONV_NUMERIC_LITERAL = 35 +CONV_LAST = 36 + +API_DRIVER = 37 +API_RUNTIME = 38 +API_BLAS = 39 +API_SPECIAL = 40 +API_RAND = 41 +API_LAST = 42 +API_FFT = 43 +API_RTC = 44 +API_ROCTX = 45 + +HIP_UNSUPPORTED = 46 +API_PYTORCH = 1337 +API_CAFFE2 = 1338 +API_C10 = 1339 +API_ROCMSMI = 1340 diff --git a/lib/python3.11/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py b/lib/python3.11/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py new file mode 100644 index 0000000000000000000000000000000000000000..bb9257079a7989fe00c011be740c42283ce8ca90 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hipify/cuda_to_hip_mappings.py @@ -0,0 +1,8738 @@ +import collections +import os + +from .constants import (API_BLAS, API_C10, API_CAFFE2, API_DRIVER, API_FFT, + API_PYTORCH, API_RAND, API_ROCTX, API_RTC, API_RUNTIME, + API_SPECIAL, API_ROCMSMI, CONV_CACHE, CONV_CONTEXT, CONV_D3D9, + CONV_D3D10, CONV_D3D11, CONV_DEF, CONV_DEVICE, + CONV_DEVICE_FUNC, CONV_EGL, CONV_ERROR, CONV_EVENT, + CONV_EXEC, CONV_GL, CONV_GRAPHICS, CONV_INCLUDE, + CONV_INCLUDE_CUDA_MAIN_H, CONV_INIT, CONV_JIT, + CONV_MATH_FUNC, CONV_MEM, CONV_MODULE, + CONV_NUMERIC_LITERAL, CONV_OCCUPANCY, CONV_OTHER, + CONV_PEER, CONV_SPECIAL_FUNC, CONV_STREAM, + CONV_SURFACE, CONV_TEX, CONV_THREAD, CONV_TYPE, + CONV_VDPAU, CONV_VERSION, HIP_UNSUPPORTED) + +""" Mapping of CUDA functions, include files, constants, and types to ROCm/HIP equivalents +This closely follows the implementation in hipify-clang +https://github.com/ROCm-Developer-Tools/HIP/blob/master/hipify-clang/src/CUDA2HipMap.cpp +and its structure. +There are different maps for fundamental names, include files, identifies, sparse, and +PyTorch specific translations. +Each of the entries in these maps translates a CUDA string to a tuple containing the +ROCm/HIP string, a type and API annotation and - optionally - an annotation if it is not +supported in ROCm/HIP yet. +""" + +_IS_FBCODE = os.environ.get("IS_FBCODE", "0") == "1" + +# FBCODE compiles against rccl sources instead of an installed rccl package. +# The header location is src/rccl.h versus rccl/rccl.h, respectively. +_RCCL_HEADER = "" if _IS_FBCODE else "" + +# List of math functions that should be replaced inside device code only. +MATH_TRANSPILATIONS = collections.OrderedDict( + [ + ("std::max", ("::max")), + ("std::min", ("::min")), + ("std::ceil", ("::ceil")), + ("std::floor", ("::floor")), + ("std::exp", ("::exp")), + ("std::log", ("::log")), + ("std::pow", ("::pow")), + ("std::fabs", ("::fabs")), + ("std::fmod", ("::fmod")), + ("std::remainder", ("::remainder")), + ("std::frexp", ("::frexp")), + ] +) + +CUDA_TYPE_NAME_MAP = collections.OrderedDict( + [ + ("CUresult", ("hipError_t", CONV_TYPE, API_DRIVER)), + ("cudaError_t", ("hipError_t", CONV_TYPE, API_RUNTIME)), + ("cudaError", ("hipError_t", CONV_TYPE, API_RUNTIME)), + ( + "CUDA_ARRAY3D_DESCRIPTOR", + ("HIP_ARRAY3D_DESCRIPTOR", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUDA_ARRAY_DESCRIPTOR", ("HIP_ARRAY_DESCRIPTOR", CONV_TYPE, API_DRIVER)), + ("CUDA_MEMCPY2D", ("hip_Memcpy2D", CONV_TYPE, API_DRIVER)), + ("CUDA_MEMCPY3D", ("HIP_MEMCPY3D", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUDA_MEMCPY3D_PEER", + ("HIP_MEMCPY3D_PEER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_POINTER_ATTRIBUTE_P2P_TOKENS", + ( + "HIP_POINTER_ATTRIBUTE_P2P_TOKENS", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CUDA_RESOURCE_DESC", + ("HIP_RESOURCE_DESC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_RESOURCE_VIEW_DESC", + ("HIP_RESOURCE_VIEW_DESC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUipcEventHandle", + ("hipIpcEventHandle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUipcMemHandle", ("hipIpcMemHandle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ("CUaddress_mode", ("hipAddress_mode", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUarray_cubemap_face", + ("hipArray_cubemap_face", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUarray_format", ("hipArray_format", CONV_TYPE, API_DRIVER)), + ("CUcomputemode", ("hipComputemode", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ("CUmem_advise", ("hipMemAdvise", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUmem_range_attribute", + ("hipMemRangeAttribute", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUctx_flags", ("hipCctx_flags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ("CUdevice", ("hipDevice_t", CONV_TYPE, API_DRIVER)), + ("CUdevice_attribute_enum", ("hipDeviceAttribute_t", CONV_TYPE, API_DRIVER)), + ("CUdevice_attribute", ("hipDeviceAttribute_t", CONV_TYPE, API_DRIVER)), + ("CUpointer_attribute", ("hipPointer_attribute", CONV_TYPE, API_DRIVER)), + ("CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL", ("HIP_POINTER_ATTRIBUTE_DEVICE_ORDINAL", CONV_TYPE, API_DRIVER)), + ("CU_POINTER_ATTRIBUTE_BUFFER_ID", ("HIP_POINTER_ATTRIBUTE_BUFFER_ID", CONV_TYPE, API_DRIVER)), + ("CUdeviceptr", ("hipDeviceptr_t", CONV_TYPE, API_DRIVER)), + ("CUarray_st", ("hipArray", CONV_TYPE, API_DRIVER)), + ("CUarray", ("hipArray *", CONV_TYPE, API_DRIVER)), + ("CUdevprop_st", ("hipDeviceProp_t", CONV_TYPE, API_DRIVER)), + ("CUdevprop", ("hipDeviceProp_t", CONV_TYPE, API_DRIVER)), + ("CUfunction", ("hipFunction_t", CONV_TYPE, API_DRIVER)), + ( + "CUgraphicsResource", + ("hipGraphicsResource_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUmipmappedArray", + ("hipMipmappedArray_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUfunction_attribute", + ("hipFuncAttribute_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUfunction_attribute_enum", + ("hipFuncAttribute_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUgraphicsMapResourceFlags", + ("hipGraphicsMapFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUgraphicsMapResourceFlags_enum", + ("hipGraphicsMapFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUgraphicsRegisterFlags", + ("hipGraphicsRegisterFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUgraphicsRegisterFlags_enum", + ("hipGraphicsRegisterFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUoccupancy_flags", + ("hipOccupancyFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUoccupancy_flags_enum", + ("hipOccupancyFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUfunc_cache_enum", ("hipFuncCache", CONV_TYPE, API_DRIVER)), + ("CUfunc_cache", ("hipFuncCache", CONV_TYPE, API_DRIVER)), + ("CUipcMem_flags", ("hipIpcMemFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUipcMem_flags_enum", + ("hipIpcMemFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUjit_cacheMode", ("hipJitCacheMode", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUjit_cacheMode_enum", + ("hipJitCacheMode", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUjit_fallback", ("hipJitFallback", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUjit_fallback_enum", + ("hipJitFallback", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUjit_option", ("hipJitOption", CONV_JIT, API_DRIVER)), + ("CUjit_option_enum", ("hipJitOption", CONV_JIT, API_DRIVER)), + ("CUjit_target", ("hipJitTarget", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)), + ("CUjit_target_enum", ("hipJitTarget", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)), + ("CUjitInputType", ("hipJitInputType", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUjitInputType_enum", + ("hipJitInputType", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUlimit", ("hipLimit_t", CONV_TYPE, API_DRIVER)), + ("CUlimit_enum", ("hipLimit_t", CONV_TYPE, API_DRIVER)), + ( + "CUmemAttach_flags", + ("hipMemAttachFlags_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUmemAttach_flags_enum", + ("hipMemAttachFlags_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUmemorytype", ("hipMemType_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ("CUmemorytype_enum", ("hipMemType_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ("CUresourcetype", ("hipResourceType", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUresourcetype_enum", + ("hipResourceType", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUresourceViewFormat", ("hipResourceViewFormat", CONV_TEX, API_DRIVER)), + ("CUresourceViewFormat_enum", ("hipResourceViewFormat", CONV_TEX, API_DRIVER)), + ("CUsharedconfig", ("hipSharedMemConfig", CONV_TYPE, API_DRIVER)), + ("CUsharedconfig_enum", ("hipSharedMemConfig", CONV_TYPE, API_DRIVER)), + ("CUcontext", ("hipCtx_t", CONV_TYPE, API_DRIVER)), + ("CUmodule", ("hipModule_t", CONV_TYPE, API_DRIVER)), + ("CUstream", ("hipStream_t", CONV_TYPE, API_DRIVER)), + ("CUstream_st", ("ihipStream_t", CONV_TYPE, API_DRIVER)), + ("CUstreamCallback", ("hipStreamCallback_t", CONV_TYPE, API_DRIVER)), + ("CUsurfObject", ("hipSurfaceObject", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUsurfref", + ("hipSurfaceReference_t", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUtexObject", ("hipTextureObject_t", CONV_TYPE, API_DRIVER)), + ("CUtexref", ("textureReference", CONV_TYPE, API_DRIVER)), + ("CUstream_flags", ("hipStreamFlags", CONV_TYPE, API_DRIVER)), + ( + "CUstreamWaitValue_flags", + ("hipStreamWaitValueFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUstreamWriteValue_flags", + ("hipStreamWriteValueFlags", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUstreamBatchMemOpType", + ("hipStreamBatchMemOpType", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUdevice_P2PAttribute", + ("hipDeviceP2PAttribute", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUevent", ("hipEvent_t", CONV_TYPE, API_DRIVER)), + ("CUevent_st", ("ihipEvent_t", CONV_TYPE, API_DRIVER)), + ("CUevent_flags", ("hipEventFlags", CONV_EVENT, API_DRIVER, HIP_UNSUPPORTED)), + ("CUfilter_mode", ("hipTextureFilterMode", CONV_TEX, API_DRIVER)), + ("CUGLDeviceList", ("hipGLDeviceList", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ("CUGLmap_flags", ("hipGLMapFlags", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUd3d9DeviceList", + ("hipD3D9DeviceList", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d9map_flags", + ("hipD3D9MapFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d9register_flags", + ("hipD3D9RegisterFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d10DeviceList", + ("hipd3d10DeviceList", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d10map_flags", + ("hipD3D10MapFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d10register_flags", + ("hipD3D10RegisterFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUd3d11DeviceList", + ("hipd3d11DeviceList", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUeglStreamConnection_st", + ("hipEglStreamConnection", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUeglStreamConnection", + ("hipEglStreamConnection", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "libraryPropertyType_t", + ("hipLibraryPropertyType_t", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "libraryPropertyType", + ("hipLibraryPropertyType_t", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaStreamCallback_t", ("hipStreamCallback_t", CONV_TYPE, API_RUNTIME)), + ("cudaArray", ("hipArray", CONV_MEM, API_RUNTIME)), + ("cudaArray_t", ("hipArray_t", CONV_MEM, API_RUNTIME)), + ("cudaArray_const_t", ("hipArray_const_t", CONV_MEM, API_RUNTIME)), + ("cudaMipmappedArray_t", ("hipMipmappedArray_t", CONV_MEM, API_RUNTIME)), + ( + "cudaMipmappedArray_const_t", + ("hipMipmappedArray_const_t", CONV_MEM, API_RUNTIME), + ), + ("cudaArrayDefault", ("hipArrayDefault", CONV_MEM, API_RUNTIME)), + ("cudaArrayLayered", ("hipArrayLayered", CONV_MEM, API_RUNTIME)), + ( + "cudaArraySurfaceLoadStore", + ("hipArraySurfaceLoadStore", CONV_MEM, API_RUNTIME), + ), + ("cudaArrayCubemap", ("hipArrayCubemap", CONV_MEM, API_RUNTIME)), + ("cudaArrayTextureGather", ("hipArrayTextureGather", CONV_MEM, API_RUNTIME)), + ("cudaMemoryAdvise", ("hipMemoryAdvise", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaMemRangeAttribute", + ("hipMemRangeAttribute", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemcpyKind", ("hipMemcpyKind", CONV_MEM, API_RUNTIME)), + ("cudaMemoryType", ("hipMemoryType", CONV_MEM, API_RUNTIME)), + ("cudaExtent", ("hipExtent", CONV_MEM, API_RUNTIME)), + ("cudaPitchedPtr", ("hipPitchedPtr", CONV_MEM, API_RUNTIME)), + ("cudaPos", ("hipPos", CONV_MEM, API_RUNTIME)), + ("cudaEvent_t", ("hipEvent_t", CONV_TYPE, API_RUNTIME)), + ("cudaStream_t", ("hipStream_t", CONV_TYPE, API_RUNTIME)), + ("cudaPointerAttributes", ("hipPointerAttribute_t", CONV_TYPE, API_RUNTIME)), + ("cudaDeviceAttr", ("hipDeviceAttribute_t", CONV_TYPE, API_RUNTIME)), + ("cudaDeviceProp", ("hipDeviceProp_t", CONV_TYPE, API_RUNTIME)), + ( + "cudaDeviceP2PAttr", + ("hipDeviceP2PAttribute", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaComputeMode", + ("hipComputeMode", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaFuncCache", ("hipFuncCache_t", CONV_CACHE, API_RUNTIME)), + ( + "cudaFuncAttributes", + ("hipFuncAttributes", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaSharedMemConfig", ("hipSharedMemConfig", CONV_TYPE, API_RUNTIME)), + ("cudaLimit", ("hipLimit_t", CONV_TYPE, API_RUNTIME)), + ("cudaOutputMode", ("hipOutputMode", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED)), + ("cudaTextureReadMode", ("hipTextureReadMode", CONV_TEX, API_RUNTIME)), + ("cudaTextureFilterMode", ("hipTextureFilterMode", CONV_TEX, API_RUNTIME)), + ("cudaChannelFormatKind", ("hipChannelFormatKind", CONV_TEX, API_RUNTIME)), + ("cudaChannelFormatDesc", ("hipChannelFormatDesc", CONV_TEX, API_RUNTIME)), + ("cudaResourceDesc", ("hipResourceDesc", CONV_TEX, API_RUNTIME)), + ("cudaResourceViewDesc", ("hipResourceViewDesc", CONV_TEX, API_RUNTIME)), + ("cudaTextureDesc", ("hipTextureDesc", CONV_TEX, API_RUNTIME)), + ( + "surfaceReference", + ("hipSurfaceReference", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaTextureObject_t", ("hipTextureObject_t", CONV_TEX, API_RUNTIME)), + ("cudaResourceType", ("hipResourceType", CONV_TEX, API_RUNTIME)), + ("cudaResourceViewFormat", ("hipResourceViewFormat", CONV_TEX, API_RUNTIME)), + ("cudaTextureAddressMode", ("hipTextureAddressMode", CONV_TEX, API_RUNTIME)), + ( + "cudaSurfaceBoundaryMode", + ("hipSurfaceBoundaryMode", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaSurfaceFormatMode", + ("hipSurfaceFormatMode", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaTextureType1D", ("hipTextureType1D", CONV_TEX, API_RUNTIME)), + ("cudaTextureType2D", ("hipTextureType2D", CONV_TEX, API_RUNTIME)), + ("cudaTextureType3D", ("hipTextureType3D", CONV_TEX, API_RUNTIME)), + ("cudaTextureTypeCubemap", ("hipTextureTypeCubemap", CONV_TEX, API_RUNTIME)), + ( + "cudaTextureType1DLayered", + ("hipTextureType1DLayered", CONV_TEX, API_RUNTIME), + ), + ( + "cudaTextureType2DLayered", + ("hipTextureType2DLayered", CONV_TEX, API_RUNTIME), + ), + ( + "cudaTextureTypeCubemapLayered", + ("hipTextureTypeCubemapLayered", CONV_TEX, API_RUNTIME), + ), + ("cudaIpcEventHandle_t", ("hipIpcEventHandle_t", CONV_TYPE, API_RUNTIME)), + ("cudaIpcEventHandle_st", ("hipIpcEventHandle_t", CONV_TYPE, API_RUNTIME)), + ("cudaIpcMemHandle_t", ("hipIpcMemHandle_t", CONV_TYPE, API_RUNTIME)), + ("cudaIpcMemHandle_st", ("hipIpcMemHandle_t", CONV_TYPE, API_RUNTIME)), + ( + "cudaGraphicsCubeFace", + ("hipGraphicsCubeFace", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsMapFlags", + ("hipGraphicsMapFlags", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsRegisterFlags", + ("hipGraphicsRegisterFlags", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLDeviceList", + ("hipGLDeviceList", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaGLMapFlags", ("hipGLMapFlags", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaD3D9DeviceList", + ("hipD3D9DeviceList", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9MapFlags", + ("hipD3D9MapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9RegisterFlags", + ("hipD3D9RegisterFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10DeviceList", + ("hipd3d10DeviceList", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10MapFlags", + ("hipD3D10MapFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10RegisterFlags", + ("hipD3D10RegisterFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D11DeviceList", + ("hipd3d11DeviceList", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaEglStreamConnection", + ("hipEglStreamConnection", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cublasHandle_t", ("hipblasHandle_t", CONV_TYPE, API_BLAS)), + ("cublasOperation_t", ("hipblasOperation_t", CONV_TYPE, API_BLAS)), + ("cublasStatus_t", ("hipblasStatus_t", CONV_TYPE, API_BLAS)), + ("cublasFillMode_t", ("hipblasFillMode_t", CONV_TYPE, API_BLAS)), + ("cublasDiagType_t", ("hipblasDiagType_t", CONV_TYPE, API_BLAS)), + ("cublasSideMode_t", ("hipblasSideMode_t", CONV_TYPE, API_BLAS)), + ("cublasPointerMode_t", ("hipblasPointerMode_t", CONV_TYPE, API_BLAS)), + ("cublasGemmAlgo_t", ("hipblasGemmAlgo_t", CONV_TYPE, API_BLAS)), + ( + "cublasAtomicsMode_t", + ("hipblasAtomicsMode_t", CONV_TYPE, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDataType_t", + ("hipblasDatatype_t", CONV_TYPE, API_BLAS, HIP_UNSUPPORTED), + ), + ("curandStatus", ("hiprandStatus_t", CONV_TYPE, API_RAND)), + ("curandStatus_t", ("hiprandStatus_t", CONV_TYPE, API_RAND)), + ("curandRngType", ("hiprandRngType_t", CONV_TYPE, API_RAND)), + ("curandRngType_t", ("hiprandRngType_t", CONV_TYPE, API_RAND)), + ("curandGenerator_st", ("hiprandGenerator_st", CONV_TYPE, API_RAND)), + ("curandGenerator_t", ("hiprandGenerator_t", CONV_TYPE, API_RAND)), + ( + "curandDirectionVectorSet", + ("hiprandDirectionVectorSet_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDirectionVectorSet_t", + ("hiprandDirectionVectorSet_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ("curandOrdering", ("hiprandOrdering_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)), + ( + "curandOrdering_t", + ("hiprandOrdering_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistribution_st", + ("hiprandDistribution_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2V_st", + ("hiprandDistribution_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistribution_t", + ("hiprandDistribution_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2V_t", + ("hiprandDistribution_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistributionShift_st", + ("hiprandDistributionShift_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistributionShift_t", + ("hiprandDistributionShift_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistributionM2Shift_st", + ("hiprandDistributionM2Shift_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDistributionM2Shift_t", + ("hiprandDistributionM2Shift_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2_st", + ("hiprandHistogramM2_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2_t", + ("hiprandHistogramM2_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2K_st", + ("hiprandHistogramM2K_st", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandHistogramM2K_t", + ("hiprandHistogramM2K_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandDiscreteDistribution_st", + ("hiprandDiscreteDistribution_st", CONV_TYPE, API_RAND), + ), + ( + "curandDiscreteDistribution_t", + ("hiprandDiscreteDistribution_t", CONV_TYPE, API_RAND), + ), + ("curandMethod", ("hiprandMethod_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)), + ("curandMethod_t", ("hiprandMethod_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED)), + ( + "curandDirectionVectors32_t", + ("hiprandDirectionVectors32_t", CONV_TYPE, API_RAND), + ), + ( + "curandDirectionVectors64_t", + ("hiprandDirectionVectors64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ("curandStateMtgp32_t", ("hiprandStateMtgp32_t", CONV_TYPE, API_RAND)), + ("curandStateMtgp32", ("hiprandStateMtgp32_t", CONV_TYPE, API_RAND)), + ( + "curandStateScrambledSobol64_t", + ("hiprandStateScrambledSobol64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandStateSobol64_t", + ("hiprandStateSobol64_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandStateScrambledSobol32_t", + ("hiprandStateScrambledSobol32_t", CONV_TYPE, API_RAND, HIP_UNSUPPORTED), + ), + ("curandStateSobol32_t", ("hiprandStateSobol32_t", CONV_TYPE, API_RAND)), + ("curandStateMRG32k3a_t", ("hiprandStateMRG32k3a_t", CONV_TYPE, API_RAND)), + ( + "curandStatePhilox4_32_10_t", + ("hiprandStatePhilox4_32_10_t", CONV_TYPE, API_RAND), + ), + ("curandStateXORWOW_t", ("hiprandStateXORWOW_t", CONV_TYPE, API_RAND)), + ("curandState_t", ("hiprandState_t", CONV_TYPE, API_RAND)), + ("curandState", ("hiprandState_t", CONV_TYPE, API_RAND)), + ("CUuuid", ("hipUUID", CONV_TYPE, API_RUNTIME)), + ("cudaGraph_t", ("hipGraph_t", CONV_TYPE, API_RAND)), + ("cudaGraphExec_t", ("hipGraphExec_t", CONV_TYPE, API_RAND)), + ("__nv_bfloat16", ("__hip_bfloat16", CONV_TYPE, API_RUNTIME)), + ("__nv_bfloat162", ("__hip_bfloat162", CONV_TYPE, API_RUNTIME)), + ] +) + +CUDA_INCLUDE_MAP = collections.OrderedDict( + [ + # since pytorch uses "\b{pattern}\b" as the actual re pattern, + # patterns listed here have to begin and end with alnum chars + ( + "include " to differentiate + ("", (_RCCL_HEADER, CONV_INCLUDE, API_RUNTIME)), + ("nvrtc.h", ("hip/hiprtc.h", CONV_INCLUDE, API_RTC)), + ("thrust/system/cuda", ("thrust/system/hip", CONV_INCLUDE, API_BLAS)), + ("cub/util_allocator.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/block/block_reduce.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/block/block_raking_layout.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/cub.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/config.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/util_ptx.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/util_type.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/device/device_run_length_encode.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/block/block_load.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/block/block_store.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/block/block_scan.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/device/device_radix_sort.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/device/device_reduce.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/device/device_scan.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("cub/device/device_select.cuh", ("hipcub/hipcub.hpp", CONV_INCLUDE, API_BLAS)), + ("nvtx3/nvtx3.hpp", ("roctracer/roctx.h", CONV_INCLUDE, API_ROCTX)), + ("nvToolsExt.h", ("roctracer/roctx.h", CONV_INCLUDE, API_ROCTX)), + ("nvml.h", ("rocm_smi/rocm_smi.h", CONV_INCLUDE, API_ROCMSMI)), + ] +) + +CUDA_IDENTIFIER_MAP = collections.OrderedDict( + [ + ("__CUDACC__", ("__HIPCC__", CONV_DEF, API_RUNTIME)), + ( + "CUDA_ERROR_INVALID_CONTEXT", + ("hipErrorInvalidContext", CONV_TYPE, API_DRIVER), + ), + ( + "CUDA_ERROR_CONTEXT_ALREADY_CURRENT", + ("hipErrorContextAlreadyCurrent", CONV_TYPE, API_DRIVER), + ), + ( + "CUDA_ERROR_ARRAY_IS_MAPPED", + ("hipErrorArrayIsMapped", CONV_TYPE, API_DRIVER), + ), + ("CUDA_ERROR_ALREADY_MAPPED", ("hipErrorAlreadyMapped", CONV_TYPE, API_DRIVER)), + ( + "CUDA_ERROR_ALREADY_ACQUIRED", + ("hipErrorAlreadyAcquired", CONV_TYPE, API_DRIVER), + ), + ("CUDA_ERROR_NOT_MAPPED", ("hipErrorNotMapped", CONV_TYPE, API_DRIVER)), + ( + "CUDA_ERROR_NOT_MAPPED_AS_ARRAY", + ("hipErrorNotMappedAsArray", CONV_TYPE, API_DRIVER), + ), + ( + "CUDA_ERROR_NOT_MAPPED_AS_POINTER", + ("hipErrorNotMappedAsPointer", CONV_TYPE, API_DRIVER), + ), + ( + "CUDA_ERROR_CONTEXT_ALREADY_IN_USE", + ("hipErrorContextAlreadyInUse", CONV_TYPE, API_DRIVER), + ), + ("CUDA_ERROR_INVALID_SOURCE", ("hipErrorInvalidSource", CONV_TYPE, API_DRIVER)), + ("CUDA_ERROR_FILE_NOT_FOUND", ("hipErrorFileNotFound", CONV_TYPE, API_DRIVER)), + ("CUDA_ERROR_NOT_FOUND", ("hipErrorNotFound", CONV_TYPE, API_DRIVER)), + ( + "CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING", + ( + "hipErrorLaunchIncompatibleTexturing", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE", + ("hipErrorPrimaryContextActive", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_CONTEXT_IS_DESTROYED", + ("hipErrorContextIsDestroyed", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_NOT_PERMITTED", + ("hipErrorNotPermitted", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_NOT_SUPPORTED", + ("hipErrorNotSupported", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorMissingConfiguration", + ("hipErrorMissingConfiguration", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorPriorLaunchFailure", + ("hipErrorPriorLaunchFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidDeviceFunction", + ("hipErrorInvalidDeviceFunction", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidConfiguration", + ("hipErrorInvalidConfiguration", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidPitchValue", + ("hipErrorInvalidPitchValue", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidSymbol", + ("hipErrorInvalidSymbol", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidHostPointer", + ("hipErrorInvalidHostPointer", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidDevicePointer", + ("hipErrorInvalidDevicePointer", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaErrorInvalidTexture", + ("hipErrorInvalidTexture", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidTextureBinding", + ("hipErrorInvalidTextureBinding", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidChannelDescriptor", + ( + "hipErrorInvalidChannelDescriptor", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaErrorInvalidMemcpyDirection", + ("hipErrorInvalidMemcpyDirection", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorAddressOfConstant", + ("hipErrorAddressOfConstant", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorTextureFetchFailed", + ("hipErrorTextureFetchFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorTextureNotBound", + ("hipErrorTextureNotBound", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorSynchronizationError", + ("hipErrorSynchronizationError", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidFilterSetting", + ("hipErrorInvalidFilterSetting", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidNormSetting", + ("hipErrorInvalidNormSetting", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorMixedDeviceExecution", + ("hipErrorMixedDeviceExecution", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorNotYetImplemented", + ("hipErrorNotYetImplemented", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorMemoryValueTooLarge", + ("hipErrorMemoryValueTooLarge", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInsufficientDriver", + ("hipErrorInsufficientDriver", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorSetOnActiveProcess", + ("hipErrorSetOnActiveProcess", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidSurface", + ("hipErrorInvalidSurface", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorDuplicateVariableName", + ("hipErrorDuplicateVariableName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorDuplicateTextureName", + ("hipErrorDuplicateTextureName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorDuplicateSurfaceName", + ("hipErrorDuplicateSurfaceName", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorDevicesUnavailable", + ("hipErrorDevicesUnavailable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorIncompatibleDriverContext", + ( + "hipErrorIncompatibleDriverContext", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaErrorDeviceAlreadyInUse", + ("hipErrorDeviceAlreadyInUse", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorLaunchMaxDepthExceeded", + ("hipErrorLaunchMaxDepthExceeded", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorLaunchFileScopedTex", + ("hipErrorLaunchFileScopedTex", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorLaunchFileScopedSurf", + ("hipErrorLaunchFileScopedSurf", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorSyncDepthExceeded", + ("hipErrorSyncDepthExceeded", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorLaunchPendingCountExceeded", + ( + "hipErrorLaunchPendingCountExceeded", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaErrorNotPermitted", + ("hipErrorNotPermitted", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorNotSupported", + ("hipErrorNotSupported", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorStartupFailure", + ("hipErrorStartupFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaErrorApiFailureBase", + ("hipErrorApiFailureBase", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("CUDA_SUCCESS", ("hipSuccess", CONV_TYPE, API_DRIVER)), + ("cudaSuccess", ("hipSuccess", CONV_TYPE, API_RUNTIME)), + ("CUDA_ERROR_INVALID_VALUE", ("hipErrorInvalidValue", CONV_TYPE, API_DRIVER)), + ("cudaErrorInvalidValue", ("hipErrorInvalidValue", CONV_TYPE, API_RUNTIME)), + ( + "CUDA_ERROR_OUT_OF_MEMORY", + ("hipErrorMemoryAllocation", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorMemoryAllocation", + ("hipErrorMemoryAllocation", CONV_TYPE, API_RUNTIME), + ), + ( + "CUDA_ERROR_NOT_INITIALIZED", + ("hipErrorNotInitialized", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorInitializationError", + ("hipErrorInitializationError", CONV_TYPE, API_RUNTIME), + ), + ("CUDA_ERROR_DEINITIALIZED", ("hipErrorDeinitialized", CONV_TYPE, API_DRIVER)), + ( + "cudaErrorCudartUnloading", + ("hipErrorDeinitialized", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PROFILER_DISABLED", + ("hipErrorProfilerDisabled", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorProfilerDisabled", + ("hipErrorProfilerDisabled", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PROFILER_NOT_INITIALIZED", + ("hipErrorProfilerNotInitialized", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorProfilerNotInitialized", + ("hipErrorProfilerNotInitialized", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PROFILER_ALREADY_STARTED", + ("hipErrorProfilerAlreadyStarted", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorProfilerAlreadyStarted", + ("hipErrorProfilerAlreadyStarted", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PROFILER_ALREADY_STOPPED", + ("hipErrorProfilerAlreadyStopped", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorProfilerAlreadyStopped", + ("hipErrorProfilerAlreadyStopped", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("CUDA_ERROR_NO_DEVICE", ("hipErrorNoDevice", CONV_TYPE, API_DRIVER)), + ("cudaErrorNoDevice", ("hipErrorNoDevice", CONV_TYPE, API_RUNTIME)), + ("CUDA_ERROR_INVALID_DEVICE", ("hipErrorInvalidDevice", CONV_TYPE, API_DRIVER)), + ("cudaErrorInvalidDevice", ("hipErrorInvalidDevice", CONV_TYPE, API_RUNTIME)), + ("CUDA_ERROR_INVALID_IMAGE", ("hipErrorInvalidImage", CONV_TYPE, API_DRIVER)), + ( + "cudaErrorInvalidKernelImage", + ("hipErrorInvalidImage", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("CUDA_ERROR_MAP_FAILED", ("hipErrorMapFailed", CONV_TYPE, API_DRIVER)), + ( + "cudaErrorMapBufferObjectFailed", + ("hipErrorMapFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("CUDA_ERROR_UNMAP_FAILED", ("hipErrorUnmapFailed", CONV_TYPE, API_DRIVER)), + ( + "cudaErrorUnmapBufferObjectFailed", + ("hipErrorUnmapFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_NO_BINARY_FOR_GPU", + ("hipErrorNoBinaryForGpu", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorNoKernelImageForDevice", + ("hipErrorNoBinaryForGpu", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_ECC_UNCORRECTABLE", + ("hipErrorECCNotCorrectable", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorECCUncorrectable", + ("hipErrorECCNotCorrectable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_UNSUPPORTED_LIMIT", + ("hipErrorUnsupportedLimit", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorUnsupportedLimit", + ("hipErrorUnsupportedLimit", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PEER_ACCESS_UNSUPPORTED", + ("hipErrorPeerAccessUnsupported", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorPeerAccessUnsupported", + ("hipErrorPeerAccessUnsupported", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_INVALID_PTX", + ("hipErrorInvalidKernelFile", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorInvalidPtx", + ("hipErrorInvalidKernelFile", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_INVALID_GRAPHICS_CONTEXT", + ("hipErrorInvalidGraphicsContext", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorInvalidGraphicsContext", + ("hipErrorInvalidGraphicsContext", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_NVLINK_UNCORRECTABLE", + ("hipErrorNvlinkUncorrectable", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorNvlinkUncorrectable", + ("hipErrorNvlinkUncorrectable", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND", + ("hipErrorSharedObjectSymbolNotFound", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorSharedObjectSymbolNotFound", + ( + "hipErrorSharedObjectSymbolNotFound", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "CUDA_ERROR_SHARED_OBJECT_INIT_FAILED", + ("hipErrorSharedObjectInitFailed", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorSharedObjectInitFailed", + ("hipErrorSharedObjectInitFailed", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_OPERATING_SYSTEM", + ("hipErrorOperatingSystem", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorOperatingSystem", + ("hipErrorOperatingSystem", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_INVALID_HANDLE", + ("hipErrorInvalidResourceHandle", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorInvalidResourceHandle", + ("hipErrorInvalidResourceHandle", CONV_TYPE, API_RUNTIME), + ), + ("CUDA_ERROR_NOT_READY", ("hipErrorNotReady", CONV_TYPE, API_DRIVER)), + ("cudaErrorNotReady", ("hipErrorNotReady", CONV_TYPE, API_RUNTIME)), + ( + "CUDA_ERROR_ILLEGAL_ADDRESS", + ("hipErrorIllegalAddress", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorIllegalAddress", + ("hipErrorIllegalAddress", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES", + ("hipErrorLaunchOutOfResources", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorLaunchOutOfResources", + ("hipErrorLaunchOutOfResources", CONV_TYPE, API_RUNTIME), + ), + ("CUDA_ERROR_LAUNCH_TIMEOUT", ("hipErrorLaunchTimeOut", CONV_TYPE, API_DRIVER)), + ( + "cudaErrorLaunchTimeout", + ("hipErrorLaunchTimeOut", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED", + ("hipErrorPeerAccessAlreadyEnabled", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorPeerAccessAlreadyEnabled", + ("hipErrorPeerAccessAlreadyEnabled", CONV_TYPE, API_RUNTIME), + ), + ( + "CUDA_ERROR_PEER_ACCESS_NOT_ENABLED", + ("hipErrorPeerAccessNotEnabled", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorPeerAccessNotEnabled", + ("hipErrorPeerAccessNotEnabled", CONV_TYPE, API_RUNTIME), + ), + ( + "CUDA_ERROR_ASSERT", + ("hipErrorAssert", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorAssert", + ("hipErrorAssert", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_TOO_MANY_PEERS", + ("hipErrorTooManyPeers", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorTooManyPeers", + ("hipErrorTooManyPeers", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED", + ("hipErrorHostMemoryAlreadyRegistered", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorHostMemoryAlreadyRegistered", + ("hipErrorHostMemoryAlreadyRegistered", CONV_TYPE, API_RUNTIME), + ), + ( + "CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED", + ("hipErrorHostMemoryNotRegistered", CONV_TYPE, API_DRIVER), + ), + ( + "cudaErrorHostMemoryNotRegistered", + ("hipErrorHostMemoryNotRegistered", CONV_TYPE, API_RUNTIME), + ), + ( + "CUDA_ERROR_HARDWARE_STACK_ERROR", + ("hipErrorHardwareStackError", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorHardwareStackError", + ("hipErrorHardwareStackError", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_ILLEGAL_INSTRUCTION", + ("hipErrorIllegalInstruction", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorIllegalInstruction", + ("hipErrorIllegalInstruction", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_MISALIGNED_ADDRESS", + ("hipErrorMisalignedAddress", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorMisalignedAddress", + ("hipErrorMisalignedAddress", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_INVALID_ADDRESS_SPACE", + ("hipErrorInvalidAddressSpace", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidAddressSpace", + ("hipErrorInvalidAddressSpace", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_INVALID_PC", + ("hipErrorInvalidPc", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorInvalidPc", + ("hipErrorInvalidPc", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_LAUNCH_FAILED", + ("hipErrorLaunchFailure", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cudaErrorLaunchFailure", + ("hipErrorLaunchFailure", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "CUDA_ERROR_UNKNOWN", + ("hipErrorUnknown", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cudaErrorUnknown", ("hipErrorUnknown", CONV_TYPE, API_RUNTIME)), + ( + "CU_TR_ADDRESS_MODE_WRAP", + ("HIP_TR_ADDRESS_MODE_WRAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TR_ADDRESS_MODE_CLAMP", + ("HIP_TR_ADDRESS_MODE_CLAMP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TR_ADDRESS_MODE_MIRROR", + ("HIP_TR_ADDRESS_MODE_MIRROR", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TR_ADDRESS_MODE_BORDER", + ("HIP_TR_ADDRESS_MODE_BORDER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_POSITIVE_X", + ("HIP_CUBEMAP_FACE_POSITIVE_X", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_NEGATIVE_X", + ("HIP_CUBEMAP_FACE_NEGATIVE_X", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_POSITIVE_Y", + ("HIP_CUBEMAP_FACE_POSITIVE_Y", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_NEGATIVE_Y", + ("HIP_CUBEMAP_FACE_NEGATIVE_Y", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_POSITIVE_Z", + ("HIP_CUBEMAP_FACE_POSITIVE_Z", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CUBEMAP_FACE_NEGATIVE_Z", + ("HIP_CUBEMAP_FACE_NEGATIVE_Z", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_AD_FORMAT_UNSIGNED_INT8", + ("HIP_AD_FORMAT_UNSIGNED_INT8", CONV_TYPE, API_DRIVER), + ), + ( + "CU_AD_FORMAT_UNSIGNED_INT16", + ("HIP_AD_FORMAT_UNSIGNED_INT16", CONV_TYPE, API_DRIVER), + ), + ( + "CU_AD_FORMAT_UNSIGNED_INT32", + ("HIP_AD_FORMAT_UNSIGNED_INT32", CONV_TYPE, API_DRIVER), + ), + ( + "CU_AD_FORMAT_SIGNED_INT8", + ("HIP_AD_FORMAT_SIGNED_INT8", CONV_TYPE, API_DRIVER), + ), + ( + "CU_AD_FORMAT_SIGNED_INT16", + ("HIP_AD_FORMAT_SIGNED_INT16", CONV_TYPE, API_DRIVER), + ), + ( + "CU_AD_FORMAT_SIGNED_INT32", + ("HIP_AD_FORMAT_SIGNED_INT32", CONV_TYPE, API_DRIVER), + ), + ("CU_AD_FORMAT_HALF", ("HIP_AD_FORMAT_HALF", CONV_TYPE, API_DRIVER)), + ("CU_AD_FORMAT_FLOAT", ("HIP_AD_FORMAT_FLOAT", CONV_TYPE, API_DRIVER)), + ( + "CU_COMPUTEMODE_DEFAULT", + ("hipComputeModeDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_COMPUTEMODE_EXCLUSIVE", + ("hipComputeModeExclusive", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_COMPUTEMODE_PROHIBITED", + ("hipComputeModeProhibited", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_COMPUTEMODE_EXCLUSIVE_PROCESS", + ("hipComputeModeExclusiveProcess", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ADVISE_SET_READ_MOSTLY", + ("hipMemAdviseSetReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ADVISE_UNSET_READ_MOSTLY", + ("hipMemAdviseUnsetReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ADVISE_SET_PREFERRED_LOCATION", + ( + "hipMemAdviseSetPreferredLocation", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION", + ( + "hipMemAdviseUnsetPreferredLocation", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_MEM_ADVISE_SET_ACCESSED_BY", + ("hipMemAdviseSetAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ADVISE_UNSET_ACCESSED_BY", + ("hipMemAdviseUnsetAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY", + ("hipMemRangeAttributeReadMostly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION", + ( + "hipMemRangeAttributePreferredLocation", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY", + ("hipMemRangeAttributeAccessedBy", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION", + ( + "hipMemRangeAttributeLastPrefetchLocation", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_CTX_SCHED_AUTO", + ("HIP_CTX_SCHED_AUTO", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_SCHED_SPIN", + ("HIP_CTX_SCHED_SPIN", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_SCHED_YIELD", + ("HIP_CTX_SCHED_YIELD", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_SCHED_BLOCKING_SYNC", + ("HIP_CTX_SCHED_BLOCKING_SYNC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_BLOCKING_SYNC", + ("HIP_CTX_BLOCKING_SYNC", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_SCHED_MASK", + ("HIP_CTX_SCHED_MASK", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_MAP_HOST", + ("HIP_CTX_MAP_HOST", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_LMEM_RESIZE_TO_MAX", + ("HIP_CTX_LMEM_RESIZE_TO_MAX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_CTX_FLAGS_MASK", + ("HIP_CTX_FLAGS_MASK", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_LAUNCH_PARAM_BUFFER_POINTER", + ("HIP_LAUNCH_PARAM_BUFFER_POINTER", CONV_TYPE, API_DRIVER), + ), + ( + "CU_LAUNCH_PARAM_BUFFER_SIZE", + ("HIP_LAUNCH_PARAM_BUFFER_SIZE", CONV_TYPE, API_DRIVER), + ), + ("CU_LAUNCH_PARAM_END", ("HIP_LAUNCH_PARAM_END", CONV_TYPE, API_DRIVER)), + ( + "CU_IPC_HANDLE_SIZE", + ("HIP_IPC_HANDLE_SIZE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTALLOC_DEVICEMAP", + ("HIP_MEMHOSTALLOC_DEVICEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTALLOC_PORTABLE", + ("HIP_MEMHOSTALLOC_PORTABLE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTALLOC_WRITECOMBINED", + ("HIP_MEMHOSTALLOC_WRITECOMBINED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTREGISTER_DEVICEMAP", + ("HIP_MEMHOSTREGISTER_DEVICEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTREGISTER_IOMEMORY", + ("HIP_MEMHOSTREGISTER_IOMEMORY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMHOSTREGISTER_PORTABLE", + ("HIP_MEMHOSTREGISTER_PORTABLE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_PARAM_TR_DEFAULT", + ("HIP_PARAM_TR_DEFAULT", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_LEGACY", + ("HIP_STREAM_LEGACY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_PER_THREAD", + ("HIP_STREAM_PER_THREAD", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TRSA_OVERRIDE_FORMAT", + ("HIP_TRSA_OVERRIDE_FORMAT", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TRSF_NORMALIZED_COORDINATES", + ("HIP_TRSF_NORMALIZED_COORDINATES", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TRSF_READ_AS_INTEGER", + ("HIP_TRSF_READ_AS_INTEGER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CU_TRSF_SRGB", ("HIP_TRSF_SRGB", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CUDA_ARRAY3D_2DARRAY", + ("HIP_ARRAY3D_LAYERED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ARRAY3D_CUBEMAP", + ("HIP_ARRAY3D_CUBEMAP", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ARRAY3D_DEPTH_TEXTURE", + ("HIP_ARRAY3D_DEPTH_TEXTURE", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ARRAY3D_LAYERED", + ("HIP_ARRAY3D_LAYERED", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ARRAY3D_SURFACE_LDST", + ("HIP_ARRAY3D_SURFACE_LDST", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CUDA_ARRAY3D_TEXTURE_GATHER", + ("HIP_ARRAY3D_TEXTURE_GATHER", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK", + ( + "hipDeviceAttributeMaxThreadsPerBlock", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X", + ("hipDeviceAttributeMaxBlockDimX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y", + ("hipDeviceAttributeMaxBlockDimY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z", + ("hipDeviceAttributeMaxBlockDimZ", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X", + ("hipDeviceAttributeMaxGridDimX", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y", + ("hipDeviceAttributeMaxGridDimY", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z", + ("hipDeviceAttributeMaxGridDimZ", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK", + ( + "hipDeviceAttributeMaxSharedMemoryPerBlock", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK", + ( + "hipDeviceAttributeMaxSharedMemoryPerBlock", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY", + ( + "hipDeviceAttributeTotalConstantMemory", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_WARP_SIZE", + ("hipDeviceAttributeWarpSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_PITCH", + ("hipDeviceAttributeMaxPitch", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK", + ( + "hipDeviceAttributeMaxRegistersPerBlock", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK", + ( + "hipDeviceAttributeMaxRegistersPerBlock", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_CLOCK_RATE", + ("hipDeviceAttributeClockRate", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT", + ( + "hipDeviceAttributeTextureAlignment", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_GPU_OVERLAP", + ( + "hipDeviceAttributeAsyncEngineCount", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT", + ( + "hipDeviceAttributeMultiprocessorCount", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT", + ( + "hipDeviceAttributeKernelExecTimeout", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_INTEGRATED", + ("hipDeviceAttributeIntegrated", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY", + ( + "hipDeviceAttributeCanMapHostMemory", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_COMPUTE_MODE", + ("hipDeviceAttributeComputeMode", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH", + ( + "hipDeviceAttributeMaxTexture1DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH", + ( + "hipDeviceAttributeMaxTexture3DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT", + ( + "hipDeviceAttributeMaxTexture3DHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH", + ( + "hipDeviceAttributeMaxTexture3DDepth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DLayeredHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxTexture2DLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DLayeredHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES", + ( + "hipDeviceAttributeMaxTexture2DLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT", + ( + "hipDeviceAttributeSurfaceAlignment", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS", + ("hipDeviceAttributeConcurrentKernels", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_ECC_ENABLED", + ("hipDeviceAttributeEccEnabled", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_PCI_BUS_ID", + ("hipDeviceAttributePciBusId", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID", + ("hipDeviceAttributePciDeviceId", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_TCC_DRIVER", + ("hipDeviceAttributeTccDriver", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE", + ( + "hipDeviceAttributeMemoryClockRate", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH", + ("hipDeviceAttributeMemoryBusWidth", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE", + ("hipDeviceAttributeL2CacheSize", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR", + ("hipDeviceAttributeMaxThreadsPerMultiProcessor", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT", + ( + "hipDeviceAttributeAsyncEngineCount", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING", + ( + "hipDeviceAttributeUnifiedAddressing", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxTexture1DLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxTexture1DLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER", + ( + "hipDeviceAttributeCanTex2DGather", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DGatherWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DGatherHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE", + ( + "hipDeviceAttributeMaxTexture3DWidthAlternate", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE", + ( + "hipDeviceAttributeMaxTexture3DHeightAlternate", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE", + ( + "hipDeviceAttributeMaxTexture3DDepthAlternate", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID", + ("hipDeviceAttributePciDomainId", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT", + ( + "hipDeviceAttributeTexturePitchAlignment", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH", + ( + "hipDeviceAttributeMaxTextureCubemapWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxTextureCubemapLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxTextureCubemapLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH", + ( + "hipDeviceAttributeMaxSurface1DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH", + ( + "hipDeviceAttributeMaxSurface2DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT", + ( + "hipDeviceAttributeMaxSurface2DHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH", + ( + "hipDeviceAttributeMaxSurface3DWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT", + ( + "hipDeviceAttributeMaxSurface3DHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH", + ( + "hipDeviceAttributeMaxSurface3DDepth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxSurface1DLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxSurface1DLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxSurface2DLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT", + ( + "hipDeviceAttributeMaxSurface2DLayeredHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxSurface2DLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH", + ( + "hipDeviceAttributeMaxSurfaceCubemapWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH", + ( + "hipDeviceAttributeMaxSurfaceCubemapLayeredWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS", + ( + "hipDeviceAttributeMaxSurfaceCubemapLayeredLayers", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH", + ( + "hipDeviceAttributeMaxTexture1DLinearWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DLinearWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DLinearHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH", + ( + "hipDeviceAttributeMaxTexture2DLinearPitch", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH", + ( + "hipDeviceAttributeMaxTexture2DMipmappedWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT", + ( + "hipDeviceAttributeMaxTexture2DMipmappedHeight", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR", + ("hipDeviceAttributeComputeCapabilityMajor", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR", + ("hipDeviceAttributeComputeCapabilityMinor", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH", + ( + "hipDeviceAttributeMaxTexture1DMipmappedWidth", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED", + ( + "hipDeviceAttributeStreamPrioritiesSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED", + ( + "hipDeviceAttributeGlobalL1CacheSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED", + ( + "hipDeviceAttributeLocalL1CacheSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR", + ( + "hipDeviceAttributeMaxSharedMemoryPerMultiprocessor", + CONV_TYPE, + API_DRIVER, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR", + ( + "hipDeviceAttributeMaxRegistersPerMultiprocessor", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY", + ("hipDeviceAttributeManagedMemory", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD", + ("hipDeviceAttributeIsMultiGpuBoard", CONV_TYPE, API_DRIVER), + ), + ( + "CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID", + ( + "hipDeviceAttributeMultiGpuBoardGroupId", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED", + ( + "hipDeviceAttributeHostNativeAtomicSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO", + ( + "hipDeviceAttributeSingleToDoublePrecisionPerfRatio", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS", + ( + "hipDeviceAttributePageableMemoryAccess", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS", + ( + "hipDeviceAttributeConcurrentManagedAccess", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED", + ( + "hipDeviceAttributeComputePreemptionSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM", + ( + "hipDeviceAttributeCanUseHostPointerForRegisteredMem", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_ATTRIBUTE_MAX", + ("hipDeviceAttributeMax", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_CONTEXT", + ("hipPointerAttributeContext", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_MEMORY_TYPE", + ("hipPointerAttributeMemoryType", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_DEVICE_POINTER", + ( + "hipPointerAttributeDevicePointer", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_POINTER_ATTRIBUTE_HOST_POINTER", + ("hipPointerAttributeHostPointer", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_P2P_TOKENS", + ("hipPointerAttributeP2pTokens", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_SYNC_MEMOPS", + ("hipPointerAttributeSyncMemops", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_BUFFER_ID", + ("hipPointerAttributeBufferId", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_POINTER_ATTRIBUTE_IS_MANAGED", + ("hipPointerAttributeIsManaged", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK", + ( + "hipFuncAttributeMaxThreadsPerBlocks", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES", + ("hipFuncAttributeSharedSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES", + ("hipFuncAttributeMaxDynamicSharedMemorySize", CONV_TYPE, API_RUNTIME), + ), + ( + "CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES", + ("hipFuncAttributeConstSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES", + ("hipFuncAttributeLocalSizeBytes", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_NUM_REGS", + ("hipFuncAttributeNumRegs", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_PTX_VERSION", + ("hipFuncAttributePtxVersion", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_BINARY_VERSION", + ("hipFuncAttributeBinaryVersion", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_CACHE_MODE_CA", + ("hipFuncAttributeCacheModeCA", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_FUNC_ATTRIBUTE_MAX", + ("hipFuncAttributeMax", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE", + ("hipGraphicsMapFlagsNone", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY", + ("hipGraphicsMapFlagsReadOnly", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD", + ("hipGraphicsMapFlagsWriteDiscard", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GRAPHICS_REGISTER_FLAGS_NONE", + ("hipGraphicsRegisterFlagsNone", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY", + ( + "hipGraphicsRegisterFlagsReadOnly", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD", + ( + "hipGraphicsRegisterFlagsWriteDiscard", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST", + ( + "hipGraphicsRegisterFlagsSurfaceLoadStore", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER", + ( + "hipGraphicsRegisterFlagsTextureGather", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_OCCUPANCY_DEFAULT", + ("hipOccupancyDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE", + ( + "hipOccupancyDisableCachingOverride", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_FUNC_CACHE_PREFER_NONE", + ("hipFuncCachePreferNone", CONV_CACHE, API_DRIVER), + ), + ( + "CU_FUNC_CACHE_PREFER_SHARED", + ("hipFuncCachePreferShared", CONV_CACHE, API_DRIVER), + ), + ("CU_FUNC_CACHE_PREFER_L1", ("hipFuncCachePreferL1", CONV_CACHE, API_DRIVER)), + ( + "CU_FUNC_CACHE_PREFER_EQUAL", + ("hipFuncCachePreferEqual", CONV_CACHE, API_DRIVER), + ), + ( + "CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS", + ("hipIpcMemLazyEnablePeerAccess", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CUDA_IPC_HANDLE_SIZE", ("HIP_IPC_HANDLE_SIZE", CONV_TYPE, API_DRIVER)), + ( + "CU_JIT_CACHE_OPTION_NONE", + ("hipJitCacheModeOptionNone", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_CACHE_OPTION_CG", + ("hipJitCacheModeOptionCG", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_CACHE_OPTION_CA", + ("hipJitCacheModeOptionCA", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_PREFER_PTX", + ("hipJitFallbackPreferPtx", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_PREFER_BINARY", + ("hipJitFallbackPreferBinary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CU_JIT_MAX_REGISTERS", ("hipJitOptionMaxRegisters", CONV_JIT, API_DRIVER)), + ( + "CU_JIT_THREADS_PER_BLOCK", + ("hipJitOptionThreadsPerBlock", CONV_JIT, API_DRIVER), + ), + ("CU_JIT_WALL_TIME", ("hipJitOptionWallTime", CONV_JIT, API_DRIVER)), + ("CU_JIT_INFO_LOG_BUFFER", ("hipJitOptionInfoLogBuffer", CONV_JIT, API_DRIVER)), + ( + "CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES", + ("hipJitOptionInfoLogBufferSizeBytes", CONV_JIT, API_DRIVER), + ), + ( + "CU_JIT_ERROR_LOG_BUFFER", + ("hipJitOptionErrorLogBuffer", CONV_JIT, API_DRIVER), + ), + ( + "CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES", + ("hipJitOptionErrorLogBufferSizeBytes", CONV_JIT, API_DRIVER), + ), + ( + "CU_JIT_OPTIMIZATION_LEVEL", + ("hipJitOptionOptimizationLevel", CONV_JIT, API_DRIVER), + ), + ( + "CU_JIT_TARGET_FROM_CUCONTEXT", + ("hipJitOptionTargetFromContext", CONV_JIT, API_DRIVER), + ), + ("CU_JIT_TARGET", ("hipJitOptionTarget", CONV_JIT, API_DRIVER)), + ( + "CU_JIT_FALLBACK_STRATEGY", + ("hipJitOptionFallbackStrategy", CONV_JIT, API_DRIVER), + ), + ( + "CU_JIT_GENERATE_DEBUG_INFO", + ("hipJitOptionGenerateDebugInfo", CONV_JIT, API_DRIVER), + ), + ("CU_JIT_LOG_VERBOSE", ("hipJitOptionLogVerbose", CONV_JIT, API_DRIVER)), + ( + "CU_JIT_GENERATE_LINE_INFO", + ("hipJitOptionGenerateLineInfo", CONV_JIT, API_DRIVER), + ), + ("CU_JIT_CACHE_MODE", ("hipJitOptionCacheMode", CONV_JIT, API_DRIVER)), + ("CU_JIT_NEW_SM3X_OPT", ("hipJitOptionSm3xOpt", CONV_JIT, API_DRIVER)), + ("CU_JIT_FAST_COMPILE", ("hipJitOptionFastCompile", CONV_JIT, API_DRIVER)), + ("CU_JIT_NUM_OPTIONS", ("hipJitOptionNumOptions", CONV_JIT, API_DRIVER)), + ( + "CU_TARGET_COMPUTE_10", + ("hipJitTargetCompute10", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_11", + ("hipJitTargetCompute11", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_12", + ("hipJitTargetCompute12", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_13", + ("hipJitTargetCompute13", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_20", + ("hipJitTargetCompute20", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_21", + ("hipJitTargetCompute21", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_30", + ("hipJitTargetCompute30", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_32", + ("hipJitTargetCompute32", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_35", + ("hipJitTargetCompute35", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_37", + ("hipJitTargetCompute37", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_50", + ("hipJitTargetCompute50", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_52", + ("hipJitTargetCompute52", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_53", + ("hipJitTargetCompute53", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_60", + ("hipJitTargetCompute60", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_61", + ("hipJitTargetCompute61", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_TARGET_COMPUTE_62", + ("hipJitTargetCompute62", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_INPUT_CUBIN", + ("hipJitInputTypeBin", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_INPUT_PTX", + ("hipJitInputTypePtx", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_INPUT_FATBINARY", + ("hipJitInputTypeFatBinary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_INPUT_OBJECT", + ("hipJitInputTypeObject", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_INPUT_LIBRARY", + ("hipJitInputTypeLibrary", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_JIT_NUM_INPUT_TYPES", + ("hipJitInputTypeNumInputTypes", CONV_JIT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_LIMIT_STACK_SIZE", + ("hipLimitStackSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_LIMIT_PRINTF_FIFO_SIZE", + ("hipLimitPrintfFifoSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_LIMIT_MALLOC_HEAP_SIZE", + ("hipLimitMallocHeapSize", CONV_TYPE, API_DRIVER), + ), + ( + "CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH", + ("hipLimitDevRuntimeSyncDepth", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT", + ( + "hipLimitDevRuntimePendingLaunchCount", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_LIMIT_STACK_SIZE", + ("hipLimitStackSize", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ATTACH_GLOBAL", + ("hipMemAttachGlobal", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ATTACH_HOST", + ("hipMemAttachHost", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEM_ATTACH_SINGLE", + ("hipMemAttachSingle", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMORYTYPE_HOST", + ("hipMemTypeHost", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMORYTYPE_DEVICE", + ("hipMemTypeDevice", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMORYTYPE_ARRAY", + ("hipMemTypeArray", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_MEMORYTYPE_UNIFIED", + ("hipMemTypeUnified", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_RESOURCE_TYPE_ARRAY", + ("hipResourceTypeArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_RESOURCE_TYPE_MIPMAPPED_ARRAY", + ("hipResourceTypeMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_RESOURCE_TYPE_LINEAR", + ("hipResourceTypeLinear", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_RESOURCE_TYPE_PITCH2D", + ("hipResourceTypePitch2D", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CU_RES_VIEW_FORMAT_NONE", ("hipResViewFormatNone", CONV_TEX, API_DRIVER)), + ( + "CU_RES_VIEW_FORMAT_UINT_1X8", + ("hipResViewFormatUnsignedChar1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_2X8", + ("hipResViewFormatUnsignedChar2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_4X8", + ("hipResViewFormatUnsignedChar4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_1X8", + ("hipResViewFormatSignedChar1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_2X8", + ("hipResViewFormatSignedChar2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_4X8", + ("hipResViewFormatSignedChar4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_1X16", + ("hipResViewFormatUnsignedShort1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_2X16", + ("hipResViewFormatUnsignedShort2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_4X16", + ("hipResViewFormatUnsignedShort4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_1X16", + ("hipResViewFormatSignedShort1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_2X16", + ("hipResViewFormatSignedShort2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_4X16", + ("hipResViewFormatSignedShort4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_1X32", + ("hipResViewFormatUnsignedInt1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_2X32", + ("hipResViewFormatUnsignedInt2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UINT_4X32", + ("hipResViewFormatUnsignedInt4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_1X32", + ("hipResViewFormatSignedInt1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_2X32", + ("hipResViewFormatSignedInt2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SINT_4X32", + ("hipResViewFormatSignedInt4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_1X16", + ("hipResViewFormatHalf1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_2X16", + ("hipResViewFormatHalf2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_4X16", + ("hipResViewFormatHalf4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_1X32", + ("hipResViewFormatFloat1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_2X32", + ("hipResViewFormatFloat2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_FLOAT_4X32", + ("hipResViewFormatFloat4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC1", + ("hipResViewFormatUnsignedBlockCompressed1", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC2", + ("hipResViewFormatUnsignedBlockCompressed2", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC3", + ("hipResViewFormatUnsignedBlockCompressed3", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC4", + ("hipResViewFormatUnsignedBlockCompressed4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SIGNED_BC4", + ("hipResViewFormatSignedBlockCompressed4", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC5", + ("hipResViewFormatUnsignedBlockCompressed5", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SIGNED_BC5", + ("hipResViewFormatSignedBlockCompressed5", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC6H", + ("hipResViewFormatUnsignedBlockCompressed6H", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_SIGNED_BC6H", + ("hipResViewFormatSignedBlockCompressed6H", CONV_TEX, API_DRIVER), + ), + ( + "CU_RES_VIEW_FORMAT_UNSIGNED_BC7", + ("hipResViewFormatUnsignedBlockCompressed7", CONV_TEX, API_DRIVER), + ), + ( + "CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE", + ("hipSharedMemBankSizeDefault", CONV_TYPE, API_DRIVER), + ), + ( + "CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE", + ("hipSharedMemBankSizeFourByte", CONV_TYPE, API_DRIVER), + ), + ( + "CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE", + ("hipSharedMemBankSizeEightByte", CONV_TYPE, API_DRIVER), + ), + ("CU_STREAM_DEFAULT", ("hipStreamDefault", CONV_TYPE, API_DRIVER)), + ("CU_STREAM_NON_BLOCKING", ("hipStreamNonBlocking", CONV_TYPE, API_DRIVER)), + ( + "CU_STREAM_WAIT_VALUE_GEQ", + ("hipStreamWaitValueGeq", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_WAIT_VALUE_EQ", + ("hipStreamWaitValueEq", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_WAIT_VALUE_AND", + ("hipStreamWaitValueAnd", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_WAIT_VALUE_FLUSH", + ("hipStreamWaitValueFlush", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_WRITE_VALUE_DEFAULT", + ("hipStreamWriteValueDefault", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER", + ( + "hipStreamWriteValueNoMemoryBarrier", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_STREAM_MEM_OP_WAIT_VALUE_32", + ("hipStreamBatchMemOpWaitValue32", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_MEM_OP_WRITE_VALUE_32", + ("hipStreamBatchMemOpWriteValue32", CONV_TYPE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES", + ( + "hipStreamBatchMemOpFlushRemoteWrites", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGetErrorName", + ("hipGetErrorName", CONV_ERROR, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGetErrorString", + ("hipDrvGetErrorString", CONV_ERROR, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuInit", ("hipInit", CONV_INIT, API_DRIVER)), + ("cuDriverGetVersion", ("hipDriverGetVersion", CONV_VERSION, API_DRIVER)), + ("cuCtxCreate", ("hipCtxCreate", CONV_CONTEXT, API_DRIVER)), + ("cuCtxCreate_v2", ("hipCtxCreate", CONV_CONTEXT, API_DRIVER)), + ("cuCtxDestroy", ("hipCtxDestroy", CONV_CONTEXT, API_DRIVER)), + ("cuCtxDestroy_v2", ("hipCtxDestroy", CONV_CONTEXT, API_DRIVER)), + ("cuCtxGetApiVersion", ("hipCtxGetApiVersion", CONV_CONTEXT, API_DRIVER)), + ("cuCtxGetCacheConfig", ("hipCtxGetCacheConfig", CONV_CONTEXT, API_DRIVER)), + ("cuCtxGetCurrent", ("hipCtxGetCurrent", CONV_CONTEXT, API_DRIVER)), + ("cuCtxGetDevice", ("hipCtxGetDevice", CONV_CONTEXT, API_DRIVER)), + ("cuCtxGetFlags", ("hipCtxGetFlags", CONV_CONTEXT, API_DRIVER)), + ("cuDeviceGetUuid", ("hipDeviceGetUuid", CONV_CONTEXT, API_DRIVER)), + ( + "cuCtxGetLimit", + ("hipCtxGetLimit", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuCtxGetSharedMemConfig", + ("hipCtxGetSharedMemConfig", CONV_CONTEXT, API_DRIVER), + ), + ( + "cuCtxGetStreamPriorityRange", + ("hipCtxGetStreamPriorityRange", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuCtxPopCurrent_v2", ("hipCtxPopCurrent", CONV_CONTEXT, API_DRIVER)), + ("cuCtxPushCurrent_v2", ("hipCtxPushCurrent", CONV_CONTEXT, API_DRIVER)), + ("cuCtxSetCacheConfig", ("hipCtxSetCacheConfig", CONV_CONTEXT, API_DRIVER)), + ("cuCtxSetCurrent", ("hipCtxSetCurrent", CONV_CONTEXT, API_DRIVER)), + ( + "cuCtxSetLimit", + ("hipCtxSetLimit", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuCtxSetSharedMemConfig", + ("hipCtxSetSharedMemConfig", CONV_CONTEXT, API_DRIVER), + ), + ("cuCtxSynchronize", ("hipCtxSynchronize", CONV_CONTEXT, API_DRIVER)), + ("cuCtxAttach", ("hipCtxAttach", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED)), + ("cuCtxDetach", ("hipCtxDetach", CONV_CONTEXT, API_DRIVER, HIP_UNSUPPORTED)), + ("cuCtxEnablePeerAccess", ("hipCtxEnablePeerAccess", CONV_PEER, API_DRIVER)), + ("cuCtxDisablePeerAccess", ("hipCtxDisablePeerAccess", CONV_PEER, API_DRIVER)), + ("cuDeviceCanAccessPeer", ("hipDeviceCanAccessPeer", CONV_PEER, API_DRIVER)), + ( + "cuDeviceGetP2PAttribute", + ("hipDeviceGetP2PAttribute", CONV_PEER, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuDevicePrimaryCtxGetState", + ("hipDevicePrimaryCtxGetState", CONV_CONTEXT, API_DRIVER), + ), + ( + "cuDevicePrimaryCtxRelease", + ("hipDevicePrimaryCtxRelease", CONV_CONTEXT, API_DRIVER), + ), + ( + "cuDevicePrimaryCtxReset", + ("hipDevicePrimaryCtxReset", CONV_CONTEXT, API_DRIVER), + ), + ( + "cuDevicePrimaryCtxRetain", + ("hipDevicePrimaryCtxRetain", CONV_CONTEXT, API_DRIVER), + ), + ( + "cuDevicePrimaryCtxSetFlags", + ("hipDevicePrimaryCtxSetFlags", CONV_CONTEXT, API_DRIVER), + ), + ("cuDeviceGet", ("hipDeviceGet", CONV_DEVICE, API_DRIVER)), + ("cuDeviceGetName", ("hipDeviceGetName", CONV_DEVICE, API_DRIVER)), + ("cuDeviceGetCount", ("hipGetDeviceCount", CONV_DEVICE, API_DRIVER)), + ("cuDeviceGetAttribute", ("hipDeviceGetAttribute", CONV_DEVICE, API_DRIVER)), + ("cuDeviceGetPCIBusId", ("hipDeviceGetPCIBusId", CONV_DEVICE, API_DRIVER)), + ("cuDeviceGetByPCIBusId", ("hipDeviceGetByPCIBusId", CONV_DEVICE, API_DRIVER)), + ("cuDeviceTotalMem_v2", ("hipDeviceTotalMem", CONV_DEVICE, API_DRIVER)), + ( + "cuDeviceComputeCapability", + ("hipDeviceComputeCapability", CONV_DEVICE, API_DRIVER), + ), + ("cuDeviceGetProperties", ("hipGetDeviceProperties", CONV_DEVICE, API_DRIVER)), + ("cuLinkAddData", ("hipLinkAddData", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ("cuLinkAddFile", ("hipLinkAddFile", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuLinkComplete", + ("hipLinkComplete", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuLinkCreate", ("hipLinkCreate", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ("cuLinkDestroy", ("hipLinkDestroy", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ("cuModuleGetFunction", ("hipModuleGetFunction", CONV_MODULE, API_DRIVER)), + ("cuModuleGetGlobal_v2", ("hipModuleGetGlobal", CONV_MODULE, API_DRIVER)), + ( + "cuModuleGetSurfRef", + ("hipModuleGetSurfRef", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuModuleGetTexRef", ("hipModuleGetTexRef", CONV_MODULE, API_DRIVER)), + ("cuModuleLoad", ("hipModuleLoad", CONV_MODULE, API_DRIVER)), + ("cuModuleLoadData", ("hipModuleLoadData", CONV_MODULE, API_DRIVER)), + ("cuModuleLoadDataEx", ("hipModuleLoadDataEx", CONV_MODULE, API_DRIVER)), + ( + "cuModuleLoadFatBinary", + ("hipModuleLoadFatBinary", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuModuleUnload", ("hipModuleUnload", CONV_MODULE, API_DRIVER)), + ( + "CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK", + ( + "hipDeviceP2PAttributePerformanceRank", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED", + ( + "hipDeviceP2PAttributeAccessSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED", + ( + "hipDeviceP2PAttributeNativeAtomicSupported", + CONV_TYPE, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ("CU_EVENT_DEFAULT", ("hipEventDefault", CONV_EVENT, API_DRIVER)), + ("CU_EVENT_BLOCKING_SYNC", ("hipEventBlockingSync", CONV_EVENT, API_DRIVER)), + ("CU_EVENT_DISABLE_TIMING", ("hipEventDisableTiming", CONV_EVENT, API_DRIVER)), + ("CU_EVENT_INTERPROCESS", ("hipEventInterprocess", CONV_EVENT, API_DRIVER)), + ("cuEventCreate", ("hipEventCreate", CONV_EVENT, API_DRIVER)), + ("cuEventDestroy", ("hipEventDestroy", CONV_EVENT, API_DRIVER)), + ("cuEventDestroy_v2", ("hipEventDestroy", CONV_EVENT, API_DRIVER)), + ("cuEventElapsedTime", ("hipEventElapsedTime", CONV_EVENT, API_DRIVER)), + ("cuEventQuery", ("hipEventQuery", CONV_EVENT, API_DRIVER)), + ("cuEventRecord", ("hipEventRecord", CONV_EVENT, API_DRIVER)), + ("cuEventSynchronize", ("hipEventSynchronize", CONV_EVENT, API_DRIVER)), + ("cuFuncSetAttribute", ("hipFuncSetAttribute", CONV_EVENT, API_DRIVER)), + ( + "cuFuncGetAttribute", + ("hipFuncGetAttribute", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuFuncSetCacheConfig", ("hipFuncSetCacheConfig", CONV_MODULE, API_DRIVER)), + ( + "cuFuncSetSharedMemConfig", + ("hipFuncSetSharedMemConfig", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuLaunchKernel", ("hipModuleLaunchKernel", CONV_MODULE, API_DRIVER)), + ( + "cuFuncSetBlockShape", + ("hipFuncSetBlockShape", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuFuncSetSharedSize", + ("hipFuncSetSharedSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuLaunch", ("hipLaunch", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ("cuLaunchGrid", ("hipLaunchGrid", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuLaunchGridAsync", + ("hipLaunchGridAsync", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuParamSetf", ("hipParamSetf", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ("cuParamSeti", ("hipParamSeti", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuParamSetSize", + ("hipParamSetSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuParamSetSize", + ("hipParamSetSize", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuParamSetv", ("hipParamSetv", CONV_MODULE, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuOccupancyMaxActiveBlocksPerMultiprocessor", + ( + "hipModuleOccupancyMaxActiveBlocksPerMultiprocessor", + CONV_OCCUPANCY, + API_DRIVER, + ), + ), + ( + "cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", + ( + "hipModuleOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", + CONV_OCCUPANCY, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuOccupancyMaxPotentialBlockSize", + ("hipModuleOccupancyMaxPotentialBlockSize", CONV_OCCUPANCY, API_DRIVER), + ), + ( + "cuOccupancyMaxPotentialBlockSizeWithFlags", + ( + "hipModuleOccupancyMaxPotentialBlockSizeWithFlags", + CONV_OCCUPANCY, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ("cuStreamAddCallback", ("hipStreamAddCallback", CONV_STREAM, API_DRIVER)), + ( + "cuStreamAttachMemAsync", + ("hipStreamAttachMemAsync", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuStreamCreate", + ("hipStreamCreate__", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuStreamCreateWithPriority", + ("hipStreamCreateWithPriority", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuStreamDestroy", ("hipStreamDestroy", CONV_STREAM, API_DRIVER)), + ("cuStreamDestroy_v2", ("hipStreamDestroy", CONV_STREAM, API_DRIVER)), + ("cuStreamGetFlags", ("hipStreamGetFlags", CONV_STREAM, API_DRIVER)), + ( + "cuStreamGetPriority", + ("hipStreamGetPriority", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuStreamQuery", ("hipStreamQuery", CONV_STREAM, API_DRIVER)), + ("cuStreamSynchronize", ("hipStreamSynchronize", CONV_STREAM, API_DRIVER)), + ("cuStreamWaitEvent", ("hipStreamWaitEvent", CONV_STREAM, API_DRIVER)), + ( + "cuStreamWaitValue32", + ("hipStreamWaitValue32", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuStreamWriteValue32", + ("hipStreamWriteValue32", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuStreamBatchMemOp", + ("hipStreamBatchMemOp", CONV_STREAM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuArray3DCreate", ("hipArray3DCreate", CONV_MEM, API_DRIVER)), + ( + "cuArray3DGetDescriptor", + ("hipArray3DGetDescriptor", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuArrayCreate", ("hipArrayCreate", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuArrayDestroy", ("hipArrayDestroy", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuArrayGetDescriptor", + ("hipArrayGetDescriptor", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuIpcCloseMemHandle", + ("hipIpcCloseMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuIpcGetEventHandle", + ("hipIpcGetEventHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuIpcGetMemHandle", + ("hipIpcGetMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuIpcOpenEventHandle", + ("hipIpcOpenEventHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuIpcOpenMemHandle", + ("hipIpcOpenMemHandle", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemAlloc_v2", ("hipMalloc", CONV_MEM, API_DRIVER)), + ("cuMemAllocHost", ("hipMemAllocHost", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemAllocManaged", + ("hipMemAllocManaged", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemAllocPitch", + ("hipMemAllocPitch__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpy", ("hipMemcpy__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemcpy2D", ("hipMemcpy2D__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemcpy2DAsync", + ("hipMemcpy2DAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemcpy2DUnaligned", + ("hipMemcpy2DUnaligned", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpy3D", ("hipMemcpy3D__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemcpy3DAsync", + ("hipMemcpy3DAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemcpy3DPeer", + ("hipMemcpy3DPeer__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemcpy3DPeerAsync", + ("hipMemcpy3DPeerAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpyAsync", ("hipMemcpyAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemcpyAtoA", ("hipMemcpyAtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemcpyAtoD", ("hipMemcpyAtoD", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemcpyAtoH", ("hipMemcpyAtoH", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemcpyAtoHAsync", + ("hipMemcpyAtoHAsync", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpyDtoA", ("hipMemcpyDtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemcpyDtoD_v2", ("hipMemcpyDtoD", CONV_MEM, API_DRIVER)), + ("cuMemcpyDtoDAsync_v2", ("hipMemcpyDtoDAsync", CONV_MEM, API_DRIVER)), + ("cuMemcpyDtoH_v2", ("hipMemcpyDtoH", CONV_MEM, API_DRIVER)), + ("cuMemcpyDtoHAsync_v2", ("hipMemcpyDtoHAsync", CONV_MEM, API_DRIVER)), + ("cuMemcpyHtoA", ("hipMemcpyHtoA", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemcpyHtoAAsync", + ("hipMemcpyHtoAAsync", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpyHtoD_v2", ("hipMemcpyHtoD", CONV_MEM, API_DRIVER)), + ("cuMemcpyHtoDAsync_v2", ("hipMemcpyHtoDAsync", CONV_MEM, API_DRIVER)), + ( + "cuMemcpyPeerAsync", + ("hipMemcpyPeerAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemcpyPeer", ("hipMemcpyPeer__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ("cuMemFree", ("hipFree", CONV_MEM, API_DRIVER)), + ("cuMemFree_v2", ("hipFree", CONV_MEM, API_DRIVER)), + ("cuMemFreeHost", ("hipHostFree", CONV_MEM, API_DRIVER)), + ( + "cuMemGetAddressRange", + ("hipMemGetAddressRange", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemGetInfo_v2", ("hipMemGetInfo", CONV_MEM, API_DRIVER)), + ("cuMemHostAlloc", ("hipHostMalloc", CONV_MEM, API_DRIVER)), + ( + "cuMemHostGetDevicePointer", + ("hipMemHostGetDevicePointer", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemHostGetFlags", + ("hipMemHostGetFlags", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemHostRegister_v2", ("hipHostRegister", CONV_MEM, API_DRIVER)), + ("cuMemHostUnregister", ("hipHostUnregister", CONV_MEM, API_DRIVER)), + ("cuMemsetD16_v2", ("hipMemsetD16", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemsetD16Async", + ("hipMemsetD16Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemsetD2D16_v2", ("hipMemsetD2D16", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemsetD2D16Async", + ("hipMemsetD2D16Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemsetD2D32_v2", ("hipMemsetD2D32", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemsetD2D32Async", + ("hipMemsetD2D32Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemsetD2D8_v2", ("hipMemsetD2D8", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemsetD2D8Async", + ("hipMemsetD2D8Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemsetD32_v2", ("hipMemset", CONV_MEM, API_DRIVER)), + ("cuMemsetD32Async", ("hipMemsetAsync", CONV_MEM, API_DRIVER)), + ("cuMemsetD8_v2", ("hipMemsetD8", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemsetD8Async", + ("hipMemsetD8Async", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMipmappedArrayCreate", + ("hipMipmappedArrayCreate", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMipmappedArrayDestroy", + ("hipMipmappedArrayDestroy", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMipmappedArrayGetLevel", + ("hipMipmappedArrayGetLevel", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemPrefetchAsync", + ("hipMemPrefetchAsync__", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuMemAdvise", ("hipMemAdvise", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuMemRangeGetAttribute", + ("hipMemRangeGetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemRangeGetAttributes", + ("hipMemRangeGetAttributes", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuPointerGetAttribute", + ("hipPointerGetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuMemGetAddressRange_v2", + ("hipMemGetAddressRange", CONV_MEM, API_DRIVER), + ), + ( + "cuPointerGetAttributes", + ("hipPointerGetAttributes", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuPointerSetAttribute", + ("hipPointerSetAttribute", CONV_MEM, API_DRIVER, HIP_UNSUPPORTED), + ), + ("CU_TR_FILTER_MODE_POINT", ("hipFilterModePoint", CONV_TEX, API_DRIVER)), + ( + "CU_TR_FILTER_MODE_LINEAR", + ("hipFilterModeLinear", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetAddress", + ("hipTexRefGetAddress", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetAddressMode", + ("hipTexRefGetAddressMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetArray", + ("hipTexRefGetArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetBorderColor", + ("hipTexRefGetBorderColor", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetFilterMode", + ("hipTexRefGetFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetFlags", + ("hipTexRefGetFlags", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetFormat", + ("hipTexRefGetFormat", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetMaxAnisotropy", + ("hipTexRefGetMaxAnisotropy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetMipmapFilterMode", + ("hipTexRefGetMipmapFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetMipmapLevelBias", + ("hipTexRefGetMipmapLevelBias", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetMipmapLevelClamp", + ("hipTexRefGetMipmapLevelClamp", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefGetMipmappedArray", + ("hipTexRefGetMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetAddress", + ("hipTexRefSetAddress", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetAddress2D", + ("hipTexRefSetAddress2D", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuTexRefSetAddressMode", ("hipTexRefSetAddressMode", CONV_TEX, API_DRIVER)), + ("cuTexRefSetArray", ("hipTexRefSetArray", CONV_TEX, API_DRIVER)), + ( + "cuTexRefSetBorderColor", + ("hipTexRefSetBorderColor", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuTexRefSetFilterMode", ("hipTexRefSetFilterMode", CONV_TEX, API_DRIVER)), + ("cuTexRefSetFlags", ("hipTexRefSetFlags", CONV_TEX, API_DRIVER)), + ("cuTexRefSetFormat", ("hipTexRefSetFormat", CONV_TEX, API_DRIVER)), + ( + "cuTexRefSetMaxAnisotropy", + ("hipTexRefSetMaxAnisotropy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetMipmapFilterMode", + ("hipTexRefSetMipmapFilterMode", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetMipmapLevelBias", + ("hipTexRefSetMipmapLevelBias", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetMipmapLevelClamp", + ("hipTexRefSetMipmapLevelClamp", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexRefSetMipmappedArray", + ("hipTexRefSetMipmappedArray", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuTexRefCreate", ("hipTexRefCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuTexRefDestroy", + ("hipTexRefDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuSurfRefGetArray", + ("hipSurfRefGetArray", CONV_SURFACE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuSurfRefSetArray", + ("hipSurfRefSetArray", CONV_SURFACE, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexObjectCreate", + ("hipTexObjectCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexObjectDestroy", + ("hipTexObjectDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexObjectGetResourceDesc", + ("hipTexObjectGetResourceDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexObjectGetResourceViewDesc", + ("hipTexObjectGetResourceViewDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuTexObjectGetTextureDesc", + ("hipTexObjectGetTextureDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuSurfObjectCreate", + ("hipSurfObjectCreate", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuSurfObjectDestroy", + ("hipSurfObjectDestroy", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuSurfObjectGetResourceDesc", + ("hipSurfObjectGetResourceDesc", CONV_TEX, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsMapResources", + ("hipGraphicsMapResources", CONV_GRAPHICS, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsResourceGetMappedMipmappedArray", + ( + "hipGraphicsResourceGetMappedMipmappedArray", + CONV_GRAPHICS, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGraphicsResourceGetMappedPointer", + ( + "hipGraphicsResourceGetMappedPointer", + CONV_GRAPHICS, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGraphicsResourceSetMapFlags", + ( + "hipGraphicsResourceSetMapFlags", + CONV_GRAPHICS, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGraphicsSubResourceGetMappedArray", + ( + "hipGraphicsSubResourceGetMappedArray", + CONV_GRAPHICS, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGraphicsUnmapResources", + ("hipGraphicsUnmapResources", CONV_GRAPHICS, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsUnregisterResource", + ( + "hipGraphicsUnregisterResource", + CONV_GRAPHICS, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuProfilerInitialize", + ("hipProfilerInitialize", CONV_OTHER, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuProfilerStart", ("hipProfilerStart", CONV_OTHER, API_DRIVER)), + ("cuProfilerStop", ("hipProfilerStop", CONV_OTHER, API_DRIVER)), + ( + "CU_GL_DEVICE_LIST_ALL", + ("HIP_GL_DEVICE_LIST_ALL", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GL_DEVICE_LIST_CURRENT_FRAME", + ("HIP_GL_DEVICE_LIST_CURRENT_FRAME", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GL_DEVICE_LIST_NEXT_FRAME", + ("HIP_GL_DEVICE_LIST_NEXT_FRAME", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuGLGetDevices", ("hipGLGetDevices", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuGraphicsGLRegisterBuffer", + ("hipGraphicsGLRegisterBuffer", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsGLRegisterImage", + ("hipGraphicsGLRegisterImage", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ("cuWGLGetDevice", ("hipWGLGetDevice", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ( + "CU_GL_MAP_RESOURCE_FLAGS_NONE", + ("HIP_GL_MAP_RESOURCE_FLAGS_NONE", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_GL_MAP_RESOURCE_FLAGS_READ_ONLY", + ( + "HIP_GL_MAP_RESOURCE_FLAGS_READ_ONLY", + CONV_GL, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD", + ( + "HIP_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD", + CONV_GL, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ("cuGLCtxCreate", ("hipGLCtxCreate", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ("cuGLInit", ("hipGLInit", CONV_GL, API_DRIVER, HIP_UNSUPPORTED)), + ( + "cuGLMapBufferObject", + ("hipGLMapBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLMapBufferObjectAsync", + ("hipGLMapBufferObjectAsync", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLRegisterBufferObject", + ("hipGLRegisterBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLSetBufferObjectMapFlags", + ("hipGLSetBufferObjectMapFlags", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLUnmapBufferObject", + ("hipGLUnmapBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLUnmapBufferObjectAsync", + ("hipGLUnmapBufferObjectAsync", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGLUnregisterBufferObject", + ("hipGLUnregisterBufferObject", CONV_GL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D9_DEVICE_LIST_ALL", + ("HIP_D3D9_DEVICE_LIST_ALL", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D9_DEVICE_LIST_CURRENT_FRAME", + ( + "HIP_D3D9_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D9, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D9_DEVICE_LIST_NEXT_FRAME", + ("HIP_D3D9_DEVICE_LIST_NEXT_FRAME", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9CtxCreate", + ("hipD3D9CtxCreate", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9CtxCreateOnDevice", + ("hipD3D9CtxCreateOnDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9GetDevice", + ("hipD3D9GetDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9GetDevices", + ("hipD3D9GetDevices", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9GetDirect3DDevice", + ("hipD3D9GetDirect3DDevice", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsD3D9RegisterResource", + ("hipGraphicsD3D9RegisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D9_MAPRESOURCE_FLAGS_NONE", + ("HIP_D3D9_MAPRESOURCE_FLAGS_NONE", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D9_MAPRESOURCE_FLAGS_READONLY", + ( + "HIP_D3D9_MAPRESOURCE_FLAGS_READONLY", + CONV_D3D9, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD", + ( + "HIP_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD", + CONV_D3D9, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D9_REGISTER_FLAGS_NONE", + ("HIP_D3D9_REGISTER_FLAGS_NONE", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D9_REGISTER_FLAGS_ARRAY", + ("HIP_D3D9_REGISTER_FLAGS_ARRAY", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9MapResources", + ("hipD3D9MapResources", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9RegisterResource", + ("hipD3D9RegisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9ResourceGetMappedArray", + ("hipD3D9ResourceGetMappedArray", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9ResourceGetMappedPitch", + ("hipD3D9ResourceGetMappedPitch", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9ResourceGetMappedPointer", + ("hipD3D9ResourceGetMappedPointer", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9ResourceGetMappedSize", + ("hipD3D9ResourceGetMappedSize", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9ResourceGetSurfaceDimensions", + ( + "hipD3D9ResourceGetSurfaceDimensions", + CONV_D3D9, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD3D9ResourceSetMapFlags", + ("hipD3D9ResourceSetMapFlags", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9UnmapResources", + ("hipD3D9UnmapResources", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D9UnregisterResource", + ("hipD3D9UnregisterResource", CONV_D3D9, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D10_DEVICE_LIST_ALL", + ("HIP_D3D10_DEVICE_LIST_ALL", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D10_DEVICE_LIST_CURRENT_FRAME", + ( + "HIP_D3D10_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D10_DEVICE_LIST_NEXT_FRAME", + ( + "HIP_D3D10_DEVICE_LIST_NEXT_FRAME", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD3D10GetDevice", + ("hipD3D10GetDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10GetDevices", + ("hipD3D10GetDevices", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsD3D10RegisterResource", + ( + "hipGraphicsD3D10RegisterResource", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D10_MAPRESOURCE_FLAGS_NONE", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_NONE", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D10_MAPRESOURCE_FLAGS_READONLY", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_READONLY", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D10_REGISTER_FLAGS_NONE", + ("HIP_D3D10_REGISTER_FLAGS_NONE", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D10_REGISTER_FLAGS_ARRAY", + ("HIP_D3D10_REGISTER_FLAGS_ARRAY", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10CtxCreate", + ("hipD3D10CtxCreate", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10CtxCreateOnDevice", + ("hipD3D10CtxCreateOnDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10GetDirect3DDevice", + ("hipD3D10GetDirect3DDevice", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10MapResources", + ("hipD3D10MapResources", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10RegisterResource", + ("hipD3D10RegisterResource", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10ResourceGetMappedArray", + ("hipD3D10ResourceGetMappedArray", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10ResourceGetMappedPitch", + ("hipD3D10ResourceGetMappedPitch", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10ResourceGetMappedPointer", + ( + "hipD3D10ResourceGetMappedPointer", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD3D10ResourceGetMappedSize", + ("hipD3D10ResourceGetMappedSize", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10ResourceGetSurfaceDimensions", + ( + "hipD3D10ResourceGetSurfaceDimensions", + CONV_D3D10, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD310ResourceSetMapFlags", + ("hipD3D10ResourceSetMapFlags", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10UnmapResources", + ("hipD3D10UnmapResources", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D10UnregisterResource", + ("hipD3D10UnregisterResource", CONV_D3D10, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D11_DEVICE_LIST_ALL", + ("HIP_D3D11_DEVICE_LIST_ALL", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "CU_D3D11_DEVICE_LIST_CURRENT_FRAME", + ( + "HIP_D3D11_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D11, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "CU_D3D11_DEVICE_LIST_NEXT_FRAME", + ( + "HIP_D3D11_DEVICE_LIST_NEXT_FRAME", + CONV_D3D11, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD3D11GetDevice", + ("hipD3D11GetDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D11GetDevices", + ("hipD3D11GetDevices", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsD3D11RegisterResource", + ( + "hipGraphicsD3D11RegisterResource", + CONV_D3D11, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuD3D11CtxCreate", + ("hipD3D11CtxCreate", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D11CtxCreateOnDevice", + ("hipD3D11CtxCreateOnDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuD3D11GetDirect3DDevice", + ("hipD3D11GetDirect3DDevice", CONV_D3D11, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsVDPAURegisterOutputSurface", + ( + "hipGraphicsVDPAURegisterOutputSurface", + CONV_VDPAU, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuGraphicsVDPAURegisterVideoSurface", + ( + "hipGraphicsVDPAURegisterVideoSurface", + CONV_VDPAU, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuVDPAUGetDevice", + ("hipVDPAUGetDevice", CONV_VDPAU, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuVDPAUCtxCreate", + ("hipVDPAUCtxCreate", CONV_VDPAU, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamConsumerAcquireFrame", + ("hipEGLStreamConsumerAcquireFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamConsumerConnect", + ("hipEGLStreamConsumerConnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamConsumerConnectWithFlags", + ( + "hipEGLStreamConsumerConnectWithFlags", + CONV_EGL, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ( + "cuEGLStreamConsumerDisconnect", + ("hipEGLStreamConsumerDisconnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamConsumerReleaseFrame", + ("hipEGLStreamConsumerReleaseFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamProducerConnect", + ("hipEGLStreamProducerConnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamProducerDisconnect", + ("hipEGLStreamProducerDisconnect", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamProducerPresentFrame", + ("hipEGLStreamProducerPresentFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuEGLStreamProducerReturnFrame", + ("hipEGLStreamProducerReturnFrame", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsEGLRegisterImage", + ("hipGraphicsEGLRegisterImage", CONV_EGL, API_DRIVER, HIP_UNSUPPORTED), + ), + ( + "cuGraphicsResourceGetMappedEglFrame", + ( + "hipGraphicsResourceGetMappedEglFrame", + CONV_EGL, + API_DRIVER, + HIP_UNSUPPORTED, + ), + ), + ("cudaDataType_t", ("hipDataType", CONV_TYPE, API_RUNTIME)), + ("cudaDataType", ("hipDataType", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_32F", ("HIP_R_32F", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_64F", ("HIP_R_64F", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_16F", ("HIP_R_16F", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_8I", ("HIP_R_8I", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_32F", ("HIP_C_32F", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_64F", ("HIP_C_64F", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_16F", ("HIP_C_16F", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_8I", ("HIP_C_8I", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_8U", ("HIP_R_8U", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_8U", ("HIP_C_8U", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_32I", ("HIP_R_32I", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_32I", ("HIP_C_32I", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_32U", ("HIP_R_32U", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_32U", ("HIP_C_32U", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_16BF", ("HIP_R_16BF", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_16BF", ("HIP_C_16BF", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_4I", ("HIP_R_4I", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_4I", ("HIP_C_4I", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_4U", ("HIP_R_4U", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_4U", ("HIP_C_4U", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_16I", ("HIP_R_16I", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_16I", ("HIP_C_16I", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_16U", ("HIP_R_16U", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_16U", ("HIP_C_16U", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_64I", ("HIP_R_64I", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_64I", ("HIP_C_64I", CONV_TYPE, API_RUNTIME)), + ("CUDA_R_64U", ("HIP_R_64U", CONV_TYPE, API_RUNTIME)), + ("CUDA_C_64U", ("HIP_C_64U", CONV_TYPE, API_RUNTIME)), + ( + "MAJOR_VERSION", + ("hipLibraryMajorVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "MINOR_VERSION", + ("hipLibraryMinorVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "PATCH_LEVEL", + ("hipLibraryPatchVersion", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAttachGlobal", + ("hipMemAttachGlobal", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAttachHost", + ("hipMemAttachHost", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAttachSingle", + ("hipMemAttachSingle", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaOccupancyDefault", + ("hipOccupancyDefault", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaOccupancyDisableCachingOverride", + ( + "hipOccupancyDisableCachingOverride", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cudaGetLastError", ("hipGetLastError", CONV_ERROR, API_RUNTIME)), + ("cudaPeekAtLastError", ("hipPeekAtLastError", CONV_ERROR, API_RUNTIME)), + ("cudaGetErrorName", ("hipGetErrorName", CONV_ERROR, API_RUNTIME)), + ("cudaGetErrorString", ("hipGetErrorString", CONV_ERROR, API_RUNTIME)), + ("cudaMemcpy3DParms", ("hipMemcpy3DParms", CONV_MEM, API_RUNTIME)), + ( + "cudaMemcpy3DPeerParms", + ("hipMemcpy3DPeerParms", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemcpy", ("hipMemcpy", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyToArray", ("hipMemcpyToArray", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyToSymbol", ("hipMemcpyToSymbol", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyToSymbolAsync", ("hipMemcpyToSymbolAsync", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyAsync", ("hipMemcpyAsync", CONV_MEM, API_RUNTIME)), + ("cudaMemcpy2D", ("hipMemcpy2D", CONV_MEM, API_RUNTIME)), + ("cudaMemcpy2DAsync", ("hipMemcpy2DAsync", CONV_MEM, API_RUNTIME)), + ("cudaMemcpy2DToArray", ("hipMemcpy2DToArray", CONV_MEM, API_RUNTIME)), + ( + "cudaMemcpy2DArrayToArray", + ("hipMemcpy2DArrayToArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpy2DFromArray", + ("hipMemcpy2DFromArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpy2DFromArrayAsync", + ("hipMemcpy2DFromArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpy2DToArrayAsync", + ("hipMemcpy2DToArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemcpy3D", ("hipMemcpy3D", CONV_MEM, API_RUNTIME)), + ( + "cudaMemcpy3DAsync", + ("hipMemcpy3DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpy3DPeer", + ("hipMemcpy3DPeer", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpy3DPeerAsync", + ("hipMemcpy3DPeerAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpyArrayToArray", + ("hipMemcpyArrayToArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemcpyFromArrayAsync", + ("hipMemcpyFromArrayAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemcpyFromSymbol", ("hipMemcpyFromSymbol", CONV_MEM, API_RUNTIME)), + ( + "cudaMemcpyFromSymbolAsync", + ("hipMemcpyFromSymbolAsync", CONV_MEM, API_RUNTIME), + ), + ("cudaMemAdvise", ("hipMemAdvise", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaMemRangeGetAttribute", + ("hipMemRangeGetAttribute", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemRangeGetAttributes", + ("hipMemRangeGetAttributes", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAdviseSetReadMostly", + ("hipMemAdviseSetReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAdviseUnsetReadMostly", + ("hipMemAdviseUnsetReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAdviseSetPreferredLocation", + ( + "hipMemAdviseSetPreferredLocation", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaMemAdviseUnsetPreferredLocation", + ( + "hipMemAdviseUnsetPreferredLocation", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaMemAdviseSetAccessedBy", + ("hipMemAdviseSetAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemAdviseUnsetAccessedBy", + ("hipMemAdviseUnsetAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemRangeAttributeReadMostly", + ("hipMemRangeAttributeReadMostly", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemRangeAttributePreferredLocation", + ( + "hipMemRangeAttributePreferredLocation", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaMemRangeAttributeAccessedBy", + ("hipMemRangeAttributeAccessedBy", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemRangeAttributeLastPrefetchLocation", + ( + "hipMemRangeAttributeLastPrefetchLocation", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cudaMemcpyHostToHost", ("hipMemcpyHostToHost", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyHostToDevice", ("hipMemcpyHostToDevice", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyDeviceToHost", ("hipMemcpyDeviceToHost", CONV_MEM, API_RUNTIME)), + ( + "cudaMemcpyDeviceToDevice", + ("hipMemcpyDeviceToDevice", CONV_MEM, API_RUNTIME), + ), + ("cudaMemcpyDefault", ("hipMemcpyDefault", CONV_MEM, API_RUNTIME)), + ("cudaMemset", ("hipMemset", CONV_MEM, API_RUNTIME)), + ("cudaMemsetAsync", ("hipMemsetAsync", CONV_MEM, API_RUNTIME)), + ("cudaMemset2D", ("hipMemset2D", CONV_MEM, API_RUNTIME)), + ( + "cudaMemset2DAsync", + ("hipMemset2DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemset3D", ("hipMemset3D", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaMemset3DAsync", + ("hipMemset3DAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMemGetInfo", ("hipMemGetInfo", CONV_MEM, API_RUNTIME)), + ( + "cudaArrayGetInfo", + ("hipArrayGetInfo", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaFreeMipmappedArray", + ("hipFreeMipmappedArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetMipmappedArrayLevel", + ("hipGetMipmappedArrayLevel", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetSymbolAddress", + ("hipGetSymbolAddress", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetSymbolSize", + ("hipGetSymbolSize", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMemPrefetchAsync", + ("hipMemPrefetchAsync", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMallocHost", ("hipHostMalloc", CONV_MEM, API_RUNTIME)), + ("cudaMallocArray", ("hipMallocArray", CONV_MEM, API_RUNTIME)), + ("cudaMalloc", ("hipMalloc", CONV_MEM, API_RUNTIME)), + ("cudaMalloc3D", ("hipMalloc3D", CONV_MEM, API_RUNTIME)), + ("cudaMalloc3DArray", ("hipMalloc3DArray", CONV_MEM, API_RUNTIME)), + ( + "cudaMallocManaged", + ("hipMallocManaged", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaMallocMipmappedArray", + ("hipMallocMipmappedArray", CONV_MEM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaMallocPitch", ("hipMallocPitch", CONV_MEM, API_RUNTIME)), + ("cudaFreeHost", ("hipHostFree", CONV_MEM, API_RUNTIME)), + ("cudaFreeArray", ("hipFreeArray", CONV_MEM, API_RUNTIME)), + ("cudaFree", ("hipFree", CONV_MEM, API_RUNTIME)), + ("cudaHostRegister", ("hipHostRegister", CONV_MEM, API_RUNTIME)), + ("cudaHostUnregister", ("hipHostUnregister", CONV_MEM, API_RUNTIME)), + ("cudaHostAlloc", ("hipHostMalloc", CONV_MEM, API_RUNTIME)), + ("cudaMemoryTypeHost", ("hipMemoryTypeHost", CONV_MEM, API_RUNTIME)), + ("cudaMemoryTypeDevice", ("hipMemoryTypeDevice", CONV_MEM, API_RUNTIME)), + ("make_cudaExtent", ("make_hipExtent", CONV_MEM, API_RUNTIME)), + ("make_cudaPitchedPtr", ("make_hipPitchedPtr", CONV_MEM, API_RUNTIME)), + ("make_cudaPos", ("make_hipPos", CONV_MEM, API_RUNTIME)), + ("cudaHostAllocDefault", ("hipHostMallocDefault", CONV_MEM, API_RUNTIME)), + ("cudaHostAllocPortable", ("hipHostMallocPortable", CONV_MEM, API_RUNTIME)), + ("cudaHostAllocMapped", ("hipHostMallocMapped", CONV_MEM, API_RUNTIME)), + ( + "cudaHostAllocWriteCombined", + ("hipHostMallocWriteCombined", CONV_MEM, API_RUNTIME), + ), + ("cudaHostGetFlags", ("hipHostGetFlags", CONV_MEM, API_RUNTIME)), + ("cudaHostRegisterDefault", ("hipHostRegisterDefault", CONV_MEM, API_RUNTIME)), + ( + "cudaHostRegisterPortable", + ("hipHostRegisterPortable", CONV_MEM, API_RUNTIME), + ), + ("cudaHostRegisterMapped", ("hipHostRegisterMapped", CONV_MEM, API_RUNTIME)), + ( + "cudaHostRegisterIoMemory", + ("hipHostRegisterIoMemory", CONV_MEM, API_RUNTIME), + ), + # ("warpSize", ("hipWarpSize", CONV_SPECIAL_FUNC, API_RUNTIME), (HIP actually uses warpSize...)), + ("cudaEventCreate", ("hipEventCreate", CONV_EVENT, API_RUNTIME)), + ( + "cudaEventCreateWithFlags", + ("hipEventCreateWithFlags", CONV_EVENT, API_RUNTIME), + ), + ("cudaEventDestroy", ("hipEventDestroy", CONV_EVENT, API_RUNTIME)), + ("cudaEventRecord", ("hipEventRecord", CONV_EVENT, API_RUNTIME)), + ("cudaEventElapsedTime", ("hipEventElapsedTime", CONV_EVENT, API_RUNTIME)), + ("cudaEventSynchronize", ("hipEventSynchronize", CONV_EVENT, API_RUNTIME)), + ("cudaEventQuery", ("hipEventQuery", CONV_EVENT, API_RUNTIME)), + ("cudaEventDefault", ("hipEventDefault", CONV_EVENT, API_RUNTIME)), + ("cudaEventBlockingSync", ("hipEventBlockingSync", CONV_EVENT, API_RUNTIME)), + ("cudaEventDisableTiming", ("hipEventDisableTiming", CONV_EVENT, API_RUNTIME)), + ("cudaEventInterprocess", ("hipEventInterprocess", CONV_EVENT, API_RUNTIME)), + ("cudaStreamCreate", ("hipStreamCreate", CONV_STREAM, API_RUNTIME)), + ( + "cudaStreamCreateWithFlags", + ("hipStreamCreateWithFlags", CONV_STREAM, API_RUNTIME), + ), + ( + "cudaStreamCreateWithPriority", + ("hipStreamCreateWithPriority", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaStreamDestroy", ("hipStreamDestroy", CONV_STREAM, API_RUNTIME)), + ("cudaStreamWaitEvent", ("hipStreamWaitEvent", CONV_STREAM, API_RUNTIME)), + ("cudaStreamSynchronize", ("hipStreamSynchronize", CONV_STREAM, API_RUNTIME)), + ("cudaStreamGetFlags", ("hipStreamGetFlags", CONV_STREAM, API_RUNTIME)), + ("cudaStreamQuery", ("hipStreamQuery", CONV_STREAM, API_RUNTIME)), + ("cudaStreamAddCallback", ("hipStreamAddCallback", CONV_STREAM, API_RUNTIME)), + ( + "cudaStreamAttachMemAsync", + ("hipStreamAttachMemAsync", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaStreamGetPriority", + ("hipStreamGetPriority", CONV_STREAM, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaCpuDeviceId", ("hipCpuDeviceId", CONV_TYPE, API_RUNTIME)), + ("cudaStreamDefault", ("hipStreamDefault", CONV_TYPE, API_RUNTIME)), + ("cudaStreamNonBlocking", ("hipStreamNonBlocking", CONV_TYPE, API_RUNTIME)), + ("cudaStreamGetCaptureInfo", ("hipStreamGetCaptureInfo", CONV_TYPE, API_RUNTIME)), + ("cudaStreamGetCaptureInfo_v2", ("hipStreamGetCaptureInfo_v2", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureStatus", ("hipStreamCaptureStatus", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureStatusActive", ("hipStreamCaptureStatusActive", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureMode", ("hipStreamCaptureMode", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureModeGlobal", ("hipStreamCaptureModeGlobal", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureModeRelaxed", ("hipStreamCaptureModeRelaxed", CONV_TYPE, API_RUNTIME)), + ("cudaStreamCaptureModeThreadLocal", ("hipStreamCaptureModeThreadLocal", CONV_TYPE, API_RUNTIME)), + ("cudaStreamBeginCapture", ("hipStreamBeginCapture", CONV_TYPE, API_RUNTIME)), + ("cudaStreamEndCapture", ("hipStreamEndCapture", CONV_TYPE, API_RUNTIME)), + ("cudaGraphInstantiate", ("hipGraphInstantiate", CONV_TYPE, API_RUNTIME)), + ("cudaGraphInstantiateWithFlags", ("hipGraphInstantiateWithFlags", CONV_TYPE, API_RUNTIME)), + ("cudaGraphInstantiateFlagAutoFreeOnLaunch", ("hipGraphInstantiateFlagAutoFreeOnLaunch", CONV_TYPE, API_RUNTIME)), + ("cudaGraphDestroy", ("hipGraphDestroy", CONV_TYPE, API_RUNTIME)), + ("cudaGraphExecDestroy", ("hipGraphExecDestroy", CONV_TYPE, API_RUNTIME)), + ("cudaGraphLaunch", ("hipGraphLaunch", CONV_TYPE, API_RUNTIME)), + ("cudaGraphGetNodes", ("hipGraphGetNodes", CONV_TYPE, API_RUNTIME)), + ("cudaGraphDebugDotPrint", ("hipGraphDebugDotPrint", CONV_TYPE, API_RUNTIME)), + ("cudaGraphDebugDotFlagsVerbose", ("hipGraphDebugDotFlagsVerbose", CONV_NUMERIC_LITERAL, API_RUNTIME)), + ("cudaGraphRetainUserObject", ("hipGraphRetainUserObject", CONV_TYPE, API_RUNTIME)), + ("cudaGraphUserObjectMove", ("hipGraphUserObjectMove", CONV_TYPE, API_RUNTIME)), + ("cudaUserObject_t", ("hipUserObject_t", CONV_TYPE, API_RUNTIME)), + ("cudaUserObjectCreate", ("hipUserObjectCreate", CONV_TYPE, API_RUNTIME)), + ("cudaUserObjectNoDestructorSync", ("hipUserObjectNoDestructorSync", CONV_TYPE, API_RUNTIME)), + ("cudaThreadExchangeStreamCaptureMode", ("hipThreadExchangeStreamCaptureMode", CONV_TYPE, API_RUNTIME)), + ("cudaStreamIsCapturing", ("hipStreamIsCapturing", CONV_TYPE, API_RUNTIME)), + ("cudaDeviceSynchronize", ("hipDeviceSynchronize", CONV_DEVICE, API_RUNTIME)), + ("cudaDeviceReset", ("hipDeviceReset", CONV_DEVICE, API_RUNTIME)), + ("cudaSetDevice", ("hipSetDevice", CONV_DEVICE, API_RUNTIME)), + ("cudaGetDevice", ("hipGetDevice", CONV_DEVICE, API_RUNTIME)), + ("cudaGetDeviceCount", ("hipGetDeviceCount", CONV_DEVICE, API_RUNTIME)), + ("cudaChooseDevice", ("hipChooseDevice", CONV_DEVICE, API_RUNTIME)), + ("cudaThreadExit", ("hipDeviceReset", CONV_THREAD, API_RUNTIME)), + ( + "cudaThreadGetCacheConfig", + ("hipDeviceGetCacheConfig", CONV_THREAD, API_RUNTIME), + ), + ( + "cudaThreadGetLimit", + ("hipThreadGetLimit", CONV_THREAD, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaThreadSetCacheConfig", + ("hipDeviceSetCacheConfig", CONV_THREAD, API_RUNTIME), + ), + ( + "cudaThreadSetLimit", + ("hipThreadSetLimit", CONV_THREAD, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaThreadSynchronize", ("hipDeviceSynchronize", CONV_THREAD, API_RUNTIME)), + ("cudaDeviceGetAttribute", ("hipDeviceGetAttribute", CONV_DEVICE, API_RUNTIME)), + ( + "cudaDevAttrMaxThreadsPerBlock", + ("hipDeviceAttributeMaxThreadsPerBlock", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxBlockDimX", + ("hipDeviceAttributeMaxBlockDimX", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxBlockDimY", + ("hipDeviceAttributeMaxBlockDimY", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxBlockDimZ", + ("hipDeviceAttributeMaxBlockDimZ", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxGridDimX", + ("hipDeviceAttributeMaxGridDimX", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxGridDimY", + ("hipDeviceAttributeMaxGridDimY", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxGridDimZ", + ("hipDeviceAttributeMaxGridDimZ", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxSharedMemoryPerBlock", + ("hipDeviceAttributeMaxSharedMemoryPerBlock", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxSharedMemoryPerBlockOptin", + ("hipDeviceAttributeMaxSharedMemoryPerBlock", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrTotalConstantMemory", + ("hipDeviceAttributeTotalConstantMemory", CONV_TYPE, API_RUNTIME), + ), + ("cudaDevAttrWarpSize", ("hipDeviceAttributeWarpSize", CONV_TYPE, API_RUNTIME)), + ( + "cudaDevAttrMaxPitch", + ("hipDeviceAttributeMaxPitch", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevAttrMaxRegistersPerBlock", + ("hipDeviceAttributeMaxRegistersPerBlock", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrClockRate", + ("hipDeviceAttributeClockRate", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrTextureAlignment", + ( + "hipDeviceAttributeTextureAlignment", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrGpuOverlap", + ("hipDeviceAttributeGpuOverlap", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevAttrMultiProcessorCount", + ("hipDeviceAttributeMultiprocessorCount", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrKernelExecTimeout", + ( + "hipDeviceAttributeKernelExecTimeout", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrIntegrated", + ("hipDeviceAttributeIntegrated", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevAttrCanMapHostMemory", + ( + "hipDeviceAttributeCanMapHostMemory", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrComputeMode", + ("hipDeviceAttributeComputeMode", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxTexture1DWidth", + ( + "hipDeviceAttributeMaxTexture1DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DWidth", + ( + "hipDeviceAttributeMaxTexture2DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DHeight", + ( + "hipDeviceAttributeMaxTexture2DHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DWidth", + ( + "hipDeviceAttributeMaxTexture3DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DHeight", + ( + "hipDeviceAttributeMaxTexture3DHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DDepth", + ( + "hipDeviceAttributeMaxTexture3DDepth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLayeredWidth", + ( + "hipDeviceAttributeMaxTexture2DLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLayeredHeight", + ( + "hipDeviceAttributeMaxTexture2DLayeredHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLayeredLayers", + ( + "hipDeviceAttributeMaxTexture2DLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrSurfaceAlignment", + ( + "hipDeviceAttributeSurfaceAlignment", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrConcurrentKernels", + ("hipDeviceAttributeConcurrentKernels", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrEccEnabled", + ("hipDeviceAttributeEccEnabled", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaDevAttrPciBusId", ("hipDeviceAttributePciBusId", CONV_TYPE, API_RUNTIME)), + ( + "cudaDevAttrPciDeviceId", + ("hipDeviceAttributePciDeviceId", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrTccDriver", + ("hipDeviceAttributeTccDriver", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevAttrMemoryClockRate", + ("hipDeviceAttributeMemoryClockRate", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrGlobalMemoryBusWidth", + ("hipDeviceAttributeMemoryBusWidth", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrL2CacheSize", + ("hipDeviceAttributeL2CacheSize", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxThreadsPerMultiProcessor", + ("hipDeviceAttributeMaxThreadsPerMultiProcessor", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrAsyncEngineCount", + ( + "hipDeviceAttributeAsyncEngineCount", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrUnifiedAddressing", + ( + "hipDeviceAttributeUnifiedAddressing", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture1DLayeredWidth", + ( + "hipDeviceAttributeMaxTexture1DLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture1DLayeredLayers", + ( + "hipDeviceAttributeMaxTexture1DLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DGatherWidth", + ( + "hipDeviceAttributeMaxTexture2DGatherWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DGatherHeight", + ( + "hipDeviceAttributeMaxTexture2DGatherHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DWidthAlt", + ( + "hipDeviceAttributeMaxTexture3DWidthAlternate", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DHeightAlt", + ( + "hipDeviceAttributeMaxTexture3DHeightAlternate", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture3DDepthAlt", + ( + "hipDeviceAttributeMaxTexture3DDepthAlternate", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrPciDomainId", + ("hipDeviceAttributePciDomainId", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevAttrTexturePitchAlignment", + ( + "hipDeviceAttributeTexturePitchAlignment", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTextureCubemapWidth", + ( + "hipDeviceAttributeMaxTextureCubemapWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTextureCubemapLayeredWidth", + ( + "hipDeviceAttributeMaxTextureCubemapLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTextureCubemapLayeredLayers", + ( + "hipDeviceAttributeMaxTextureCubemapLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface1DWidth", + ( + "hipDeviceAttributeMaxSurface1DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface2DWidth", + ( + "hipDeviceAttributeMaxSurface2DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface2DHeight", + ( + "hipDeviceAttributeMaxSurface2DHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface3DWidth", + ( + "hipDeviceAttributeMaxSurface3DWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface3DHeight", + ( + "hipDeviceAttributeMaxSurface3DHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface3DDepth", + ( + "hipDeviceAttributeMaxSurface3DDepth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface1DLayeredWidth", + ( + "hipDeviceAttributeMaxSurface1DLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface1DLayeredLayers", + ( + "hipDeviceAttributeMaxSurface1DLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface2DLayeredWidth", + ( + "hipDeviceAttributeMaxSurface2DLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface2DLayeredHeight", + ( + "hipDeviceAttributeMaxSurface2DLayeredHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurface2DLayeredLayers", + ( + "hipDeviceAttributeMaxSurface2DLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurfaceCubemapWidth", + ( + "hipDeviceAttributeMaxSurfaceCubemapWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurfaceCubemapLayeredWidth", + ( + "hipDeviceAttributeMaxSurfaceCubemapLayeredWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSurfaceCubemapLayeredLayers", + ( + "hipDeviceAttributeMaxSurfaceCubemapLayeredLayers", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture1DLinearWidth", + ( + "hipDeviceAttributeMaxTexture1DLinearWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLinearWidth", + ( + "hipDeviceAttributeMaxTexture2DLinearWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLinearHeight", + ( + "hipDeviceAttributeMaxTexture2DLinearHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DLinearPitch", + ( + "hipDeviceAttributeMaxTexture2DLinearPitch", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DMipmappedWidth", + ( + "hipDeviceAttributeMaxTexture2DMipmappedWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxTexture2DMipmappedHeight", + ( + "hipDeviceAttributeMaxTexture2DMipmappedHeight", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrComputeCapabilityMajor", + ("hipDeviceAttributeComputeCapabilityMajor", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrComputeCapabilityMinor", + ("hipDeviceAttributeComputeCapabilityMinor", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMaxTexture1DMipmappedWidth", + ( + "hipDeviceAttributeMaxTexture1DMipmappedWidth", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrStreamPrioritiesSupported", + ( + "hipDeviceAttributeStreamPrioritiesSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrGlobalL1CacheSupported", + ( + "hipDeviceAttributeGlobalL1CacheSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrLocalL1CacheSupported", + ( + "hipDeviceAttributeLocalL1CacheSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrMaxSharedMemoryPerMultiprocessor", + ( + "hipDeviceAttributeMaxSharedMemoryPerMultiprocessor", + CONV_TYPE, + API_RUNTIME, + ), + ), + ( + "cudaDevAttrMaxRegistersPerMultiprocessor", + ( + "hipDeviceAttributeMaxRegistersPerMultiprocessor", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrManagedMemory", + ( + "hipDeviceAttributeManagedMemory", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrIsMultiGpuBoard", + ("hipDeviceAttributeIsMultiGpuBoard", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDevAttrMultiGpuBoardGroupID", + ( + "hipDeviceAttributeMultiGpuBoardGroupID", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrHostNativeAtomicSupported", + ( + "hipDeviceAttributeHostNativeAtomicSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrSingleToDoublePrecisionPerfRatio", + ( + "hipDeviceAttributeSingleToDoublePrecisionPerfRatio", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrPageableMemoryAccess", + ( + "hipDeviceAttributePageableMemoryAccess", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrConcurrentManagedAccess", + ( + "hipDeviceAttributeConcurrentManagedAccess", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrComputePreemptionSupported", + ( + "hipDeviceAttributeComputePreemptionSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevAttrCanUseHostPointerForRegisteredMem", + ( + "hipDeviceAttributeCanUseHostPointerForRegisteredMem", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaPointerGetAttributes", + ("hipPointerGetAttributes", CONV_MEM, API_RUNTIME), + ), + ( + "cudaHostGetDevicePointer", + ("hipHostGetDevicePointer", CONV_MEM, API_RUNTIME), + ), + ( + "cudaGetDeviceProperties", + ("hipGetDeviceProperties", CONV_DEVICE, API_RUNTIME), + ), + ("cudaDeviceGetPCIBusId", ("hipDeviceGetPCIBusId", CONV_DEVICE, API_RUNTIME)), + ( + "cudaDeviceGetByPCIBusId", + ("hipDeviceGetByPCIBusId", CONV_DEVICE, API_RUNTIME), + ), + ( + "cudaDeviceGetStreamPriorityRange", + ( + "hipDeviceGetStreamPriorityRange", + CONV_DEVICE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaSetValidDevices", + ("hipSetValidDevices", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDevP2PAttrPerformanceRank", + ( + "hipDeviceP2PAttributePerformanceRank", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevP2PAttrAccessSupported", + ( + "hipDeviceP2PAttributeAccessSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDevP2PAttrNativeAtomicSupported", + ( + "hipDeviceP2PAttributeNativeAtomicSupported", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaDeviceGetP2PAttribute", + ("hipDeviceGetP2PAttribute", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaComputeModeDefault", + ("hipComputeModeDefault", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaComputeModeExclusive", + ("hipComputeModeExclusive", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaComputeModeProhibited", + ("hipComputeModeProhibited", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaComputeModeExclusiveProcess", + ("hipComputeModeExclusiveProcess", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetDeviceFlags", + ("hipGetDeviceFlags", CONV_DEVICE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaSetDeviceFlags", ("hipSetDeviceFlags", CONV_DEVICE, API_RUNTIME)), + ("cudaDeviceScheduleAuto", ("hipDeviceScheduleAuto", CONV_TYPE, API_RUNTIME)), + ("cudaDeviceScheduleSpin", ("hipDeviceScheduleSpin", CONV_TYPE, API_RUNTIME)), + ("cudaDeviceScheduleYield", ("hipDeviceScheduleYield", CONV_TYPE, API_RUNTIME)), + ( + "cudaDeviceBlockingSync", + ("hipDeviceScheduleBlockingSync", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDeviceScheduleBlockingSync", + ("hipDeviceScheduleBlockingSync", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDeviceScheduleMask", + ("hipDeviceScheduleMask", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaDeviceMapHost", ("hipDeviceMapHost", CONV_TYPE, API_RUNTIME)), + ( + "cudaDeviceLmemResizeToMax", + ("hipDeviceLmemResizeToMax", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaDeviceMask", ("hipDeviceMask", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaDeviceSetCacheConfig", + ("hipDeviceSetCacheConfig", CONV_CACHE, API_RUNTIME), + ), + ( + "cudaDeviceGetCacheConfig", + ("hipDeviceGetCacheConfig", CONV_CACHE, API_RUNTIME), + ), + ( + "cudaFuncAttributes", + ("hipFuncAttributes", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaFuncAttributeMaxDynamicSharedMemorySize", + ("hipFuncAttributeMaxDynamicSharedMemorySize", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaFuncAttributePreferredSharedMemoryCarveout", + ("hipFuncAttributePreferredSharedMemoryCarveout", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaFuncSetAttribute", + ("hipFuncSetAttribute", CONV_EXEC, API_RUNTIME), + ), + ("cudaFuncSetCacheConfig", ("hipFuncSetCacheConfig", CONV_CACHE, API_RUNTIME)), + ( + "cudaFuncCachePreferNone", + ("hipFuncCachePreferNone", CONV_CACHE, API_RUNTIME), + ), + ( + "cudaFuncCachePreferShared", + ("hipFuncCachePreferShared", CONV_CACHE, API_RUNTIME), + ), + ("cudaFuncCachePreferL1", ("hipFuncCachePreferL1", CONV_CACHE, API_RUNTIME)), + ( + "cudaFuncCachePreferEqual", + ("hipFuncCachePreferEqual", CONV_CACHE, API_RUNTIME), + ), + ( + "cudaFuncGetAttributes", + ("hipFuncGetAttributes", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaFuncSetSharedMemConfig", + ("hipFuncSetSharedMemConfig", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetParameterBuffer", + ("hipGetParameterBuffer", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaSetDoubleForDevice", + ("hipSetDoubleForDevice", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaSetDoubleForHost", + ("hipSetDoubleForHost", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaConfigureCall", + ("hipConfigureCall", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaLaunch", ("hipLaunch", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaLaunchCooperativeKernel", + ("hipLaunchCooperativeKernel", CONV_EXEC, API_RUNTIME), + ), + ("cudaLaunchHostFunc", ("hipLaunchHostFunc", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED)), + ( + "cudaSetupArgument", + ("hipSetupArgument", CONV_EXEC, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaDriverGetVersion", ("hipDriverGetVersion", CONV_VERSION, API_RUNTIME)), + ( + "cudaRuntimeGetVersion", + ("hipRuntimeGetVersion", CONV_VERSION, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaOccupancyMaxPotentialBlockSize", + ("hipOccupancyMaxPotentialBlockSize", CONV_OCCUPANCY, API_RUNTIME), + ), + ( + "cudaOccupancyMaxPotentialBlockSizeWithFlags", + ( + "hipOccupancyMaxPotentialBlockSizeWithFlags", + CONV_OCCUPANCY, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaOccupancyMaxActiveBlocksPerMultiprocessor", + ( + "hipOccupancyMaxActiveBlocksPerMultiprocessor", + CONV_OCCUPANCY, + API_RUNTIME, + ), + ), + ( + "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", + ( + "hipOccupancyMaxActiveBlocksPerMultiprocessorWithFlags", + CONV_OCCUPANCY, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaOccupancyMaxPotentialBlockSizeVariableSMem", + ( + "hipOccupancyMaxPotentialBlockSizeVariableSMem", + CONV_OCCUPANCY, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags", + ( + "hipOccupancyMaxPotentialBlockSizeVariableSMemWithFlags", + CONV_OCCUPANCY, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cudaDeviceCanAccessPeer", ("hipDeviceCanAccessPeer", CONV_PEER, API_RUNTIME)), + ( + "cudaDeviceDisablePeerAccess", + ("hipDeviceDisablePeerAccess", CONV_PEER, API_RUNTIME), + ), + ( + "cudaDeviceEnablePeerAccess", + ("hipDeviceEnablePeerAccess", CONV_PEER, API_RUNTIME), + ), + ("cudaMemcpyPeerAsync", ("hipMemcpyPeerAsync", CONV_MEM, API_RUNTIME)), + ("cudaMemcpyPeer", ("hipMemcpyPeer", CONV_MEM, API_RUNTIME)), + ( + "cudaIpcMemLazyEnablePeerAccess", + ("hipIpcMemLazyEnablePeerAccess", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaDeviceSetSharedMemConfig", + ("hipDeviceSetSharedMemConfig", CONV_DEVICE, API_RUNTIME), + ), + ( + "cudaDeviceGetSharedMemConfig", + ("hipDeviceGetSharedMemConfig", CONV_DEVICE, API_RUNTIME), + ), + ( + "cudaSharedMemBankSizeDefault", + ("hipSharedMemBankSizeDefault", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaSharedMemBankSizeFourByte", + ("hipSharedMemBankSizeFourByte", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaSharedMemBankSizeEightByte", + ("hipSharedMemBankSizeEightByte", CONV_TYPE, API_RUNTIME), + ), + ( + "cudaLimitStackSize", + ("hipLimitStackSize", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaLimitPrintfFifoSize", + ("hipLimitPrintfFifoSize", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaLimitMallocHeapSize", ("hipLimitMallocHeapSize", CONV_TYPE, API_RUNTIME)), + ( + "cudaLimitDevRuntimeSyncDepth", + ("hipLimitDevRuntimeSyncDepth", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaLimitDevRuntimePendingLaunchCount", + ( + "hipLimitDevRuntimePendingLaunchCount", + CONV_TYPE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cudaDeviceGetLimit", ("hipDeviceGetLimit", CONV_DEVICE, API_RUNTIME)), + ( + "cudaProfilerInitialize", + ("hipProfilerInitialize", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaProfilerStart", ("hipProfilerStart", CONV_OTHER, API_RUNTIME)), + ("cudaProfilerStop", ("hipProfilerStop", CONV_OTHER, API_RUNTIME)), + ( + "cudaKeyValuePair", + ("hipKeyValuePair", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED), + ), + ("cudaCSV", ("hipCSV", CONV_OTHER, API_RUNTIME, HIP_UNSUPPORTED)), + ("cudaReadModeElementType", ("hipReadModeElementType", CONV_TEX, API_RUNTIME)), + ( + "cudaReadModeNormalizedFloat", + ("hipReadModeNormalizedFloat", CONV_TEX, API_RUNTIME), + ), + ("cudaFilterModePoint", ("hipFilterModePoint", CONV_TEX, API_RUNTIME)), + ("cudaFilterModeLinear", ("hipFilterModeLinear", CONV_TEX, API_RUNTIME)), + ("cudaBindTexture", ("hipBindTexture", CONV_TEX, API_RUNTIME)), + ("cudaUnbindTexture", ("hipUnbindTexture", CONV_TEX, API_RUNTIME)), + ("cudaBindTexture2D", ("hipBindTexture2D", CONV_TEX, API_RUNTIME)), + ("cudaBindTextureToArray", ("hipBindTextureToArray", CONV_TEX, API_RUNTIME)), + ( + "cudaBindTextureToMipmappedArray", + ("hipBindTextureToMipmappedArray", CONV_TEX, API_RUNTIME), + ), + ( + "cudaGetTextureAlignmentOffset", + ("hipGetTextureAlignmentOffset", CONV_TEX, API_RUNTIME), + ), + ("cudaGetTextureReference", ("hipGetTextureReference", CONV_TEX, API_RUNTIME)), + ( + "cudaChannelFormatKindSigned", + ("hipChannelFormatKindSigned", CONV_TEX, API_RUNTIME), + ), + ( + "cudaChannelFormatKindUnsigned", + ("hipChannelFormatKindUnsigned", CONV_TEX, API_RUNTIME), + ), + ( + "cudaChannelFormatKindFloat", + ("hipChannelFormatKindFloat", CONV_TEX, API_RUNTIME), + ), + ( + "cudaChannelFormatKindNone", + ("hipChannelFormatKindNone", CONV_TEX, API_RUNTIME), + ), + ("cudaCreateChannelDesc", ("hipCreateChannelDesc", CONV_TEX, API_RUNTIME)), + ("cudaGetChannelDesc", ("hipGetChannelDesc", CONV_TEX, API_RUNTIME)), + ("cudaResourceTypeArray", ("hipResourceTypeArray", CONV_TEX, API_RUNTIME)), + ( + "cudaResourceTypeMipmappedArray", + ("hipResourceTypeMipmappedArray", CONV_TEX, API_RUNTIME), + ), + ("cudaResourceTypeLinear", ("hipResourceTypeLinear", CONV_TEX, API_RUNTIME)), + ("cudaResourceTypePitch2D", ("hipResourceTypePitch2D", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatNone", ("hipResViewFormatNone", CONV_TEX, API_RUNTIME)), + ( + "cudaResViewFormatUnsignedChar1", + ("hipResViewFormatUnsignedChar1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedChar2", + ("hipResViewFormatUnsignedChar2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedChar4", + ("hipResViewFormatUnsignedChar4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedChar1", + ("hipResViewFormatSignedChar1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedChar2", + ("hipResViewFormatSignedChar2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedChar4", + ("hipResViewFormatSignedChar4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedShort1", + ("hipResViewFormatUnsignedShort1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedShort2", + ("hipResViewFormatUnsignedShort2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedShort4", + ("hipResViewFormatUnsignedShort4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedShort1", + ("hipResViewFormatSignedShort1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedShort2", + ("hipResViewFormatSignedShort2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedShort4", + ("hipResViewFormatSignedShort4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedInt1", + ("hipResViewFormatUnsignedInt1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedInt2", + ("hipResViewFormatUnsignedInt2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedInt4", + ("hipResViewFormatUnsignedInt4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedInt1", + ("hipResViewFormatSignedInt1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedInt2", + ("hipResViewFormatSignedInt2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedInt4", + ("hipResViewFormatSignedInt4", CONV_TEX, API_RUNTIME), + ), + ("cudaResViewFormatHalf1", ("hipResViewFormatHalf1", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatHalf2", ("hipResViewFormatHalf2", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatHalf4", ("hipResViewFormatHalf4", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatFloat1", ("hipResViewFormatFloat1", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatFloat2", ("hipResViewFormatFloat2", CONV_TEX, API_RUNTIME)), + ("cudaResViewFormatFloat4", ("hipResViewFormatFloat4", CONV_TEX, API_RUNTIME)), + ( + "cudaResViewFormatUnsignedBlockCompressed1", + ("hipResViewFormatUnsignedBlockCompressed1", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed2", + ("hipResViewFormatUnsignedBlockCompressed2", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed3", + ("hipResViewFormatUnsignedBlockCompressed3", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed4", + ("hipResViewFormatUnsignedBlockCompressed4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedBlockCompressed4", + ("hipResViewFormatSignedBlockCompressed4", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed5", + ("hipResViewFormatUnsignedBlockCompressed5", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedBlockCompressed5", + ("hipResViewFormatSignedBlockCompressed5", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed6H", + ("hipResViewFormatUnsignedBlockCompressed6H", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatSignedBlockCompressed6H", + ("hipResViewFormatSignedBlockCompressed6H", CONV_TEX, API_RUNTIME), + ), + ( + "cudaResViewFormatUnsignedBlockCompressed7", + ("hipResViewFormatUnsignedBlockCompressed7", CONV_TEX, API_RUNTIME), + ), + ("cudaAddressModeWrap", ("hipAddressModeWrap", CONV_TEX, API_RUNTIME)), + ("cudaAddressModeClamp", ("hipAddressModeClamp", CONV_TEX, API_RUNTIME)), + ("cudaAddressModeMirror", ("hipAddressModeMirror", CONV_TEX, API_RUNTIME)), + ("cudaAddressModeBorder", ("hipAddressModeBorder", CONV_TEX, API_RUNTIME)), + ("cudaCreateTextureObject", ("hipCreateTextureObject", CONV_TEX, API_RUNTIME)), + ( + "cudaDestroyTextureObject", + ("hipDestroyTextureObject", CONV_TEX, API_RUNTIME), + ), + ( + "cudaGetTextureObjectResourceDesc", + ("hipGetTextureObjectResourceDesc", CONV_TEX, API_RUNTIME), + ), + ( + "cudaGetTextureObjectResourceViewDesc", + ("hipGetTextureObjectResourceViewDesc", CONV_TEX, API_RUNTIME), + ), + ( + "cudaGetTextureObjectTextureDesc", + ("hipGetTextureObjectTextureDesc", CONV_TEX, API_RUNTIME), + ), + ( + "cudaBindSurfaceToArray", + ("hipBindSurfaceToArray", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetSurfaceReference", + ("hipGetSurfaceReference", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaBoundaryModeZero", + ("hipBoundaryModeZero", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaBoundaryModeClamp", + ("hipBoundaryModeClamp", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaBoundaryModeTrap", + ("hipBoundaryModeTrap", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaFormatModeForced", + ("hipFormatModeForced", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaFormatModeAuto", + ("hipFormatModeAuto", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaCreateSurfaceObject", + ("hipCreateSurfaceObject", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaDestroySurfaceObject", + ("hipDestroySurfaceObject", CONV_SURFACE, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGetSurfaceObjectResourceDesc", + ( + "hipGetSurfaceObjectResourceDesc", + CONV_SURFACE, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cudaIpcCloseMemHandle", ("hipIpcCloseMemHandle", CONV_DEVICE, API_RUNTIME)), + ("cudaIpcGetEventHandle", ("hipIpcGetEventHandle", CONV_DEVICE, API_RUNTIME)), + ("cudaIpcGetMemHandle", ("hipIpcGetMemHandle", CONV_DEVICE, API_RUNTIME)), + ("cudaIpcOpenEventHandle", ("hipIpcOpenEventHandle", CONV_DEVICE, API_RUNTIME)), + ("cudaIpcOpenMemHandle", ("hipIpcOpenMemHandle", CONV_DEVICE, API_RUNTIME)), + ( + "cudaGLGetDevices", + ("hipGLGetDevices", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsGLRegisterBuffer", + ("hipGraphicsGLRegisterBuffer", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsGLRegisterImage", + ("hipGraphicsGLRegisterImage", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaWGLGetDevice", + ("hipWGLGetDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsMapResources", + ("hipGraphicsMapResources", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsResourceGetMappedMipmappedArray", + ( + "hipGraphicsResourceGetMappedMipmappedArray", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsResourceGetMappedPointer", + ( + "hipGraphicsResourceGetMappedPointer", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsResourceSetMapFlags", + ( + "hipGraphicsResourceSetMapFlags", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsSubResourceGetMappedArray", + ( + "hipGraphicsSubResourceGetMappedArray", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsUnmapResources", + ("hipGraphicsUnmapResources", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsUnregisterResource", + ( + "hipGraphicsUnregisterResource", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFacePositiveX", + ( + "hipGraphicsCubeFacePositiveX", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFaceNegativeX", + ( + "hipGraphicsCubeFaceNegativeX", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFacePositiveY", + ( + "hipGraphicsCubeFacePositiveY", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFaceNegativeY", + ( + "hipGraphicsCubeFaceNegativeY", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFacePositiveZ", + ( + "hipGraphicsCubeFacePositiveZ", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsCubeFaceNegativeZ", + ( + "hipGraphicsCubeFaceNegativeZ", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsMapFlagsNone", + ("hipGraphicsMapFlagsNone", CONV_GRAPHICS, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsMapFlagsReadOnly", + ( + "hipGraphicsMapFlagsReadOnly", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsMapFlagsWriteDiscard", + ( + "hipGraphicsMapFlagsWriteDiscard", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsRegisterFlagsNone", + ( + "hipGraphicsRegisterFlagsNone", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsRegisterFlagsReadOnly", + ( + "hipGraphicsRegisterFlagsReadOnly", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsRegisterFlagsWriteDiscard", + ( + "hipGraphicsRegisterFlagsWriteDiscard", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsRegisterFlagsSurfaceLoadStore", + ( + "hipGraphicsRegisterFlagsSurfaceLoadStore", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsRegisterFlagsTextureGather", + ( + "hipGraphicsRegisterFlagsTextureGather", + CONV_GRAPHICS, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGLDeviceListAll", + ("HIP_GL_DEVICE_LIST_ALL", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLDeviceListCurrentFrame", + ("HIP_GL_DEVICE_LIST_CURRENT_FRAME", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLDeviceListNextFrame", + ("HIP_GL_DEVICE_LIST_NEXT_FRAME", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLGetDevices", + ("hipGLGetDevices", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsGLRegisterBuffer", + ("hipGraphicsGLRegisterBuffer", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsGLRegisterImage", + ("hipGraphicsGLRegisterImage", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaWGLGetDevice", + ("hipWGLGetDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLMapFlagsNone", + ("HIP_GL_MAP_RESOURCE_FLAGS_NONE", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLMapFlagsReadOnly", + ( + "HIP_GL_MAP_RESOURCE_FLAGS_READ_ONLY", + CONV_GL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGLMapFlagsWriteDiscard", + ( + "HIP_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD", + CONV_GL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGLMapBufferObject", + ("hipGLMapBufferObject__", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLMapBufferObjectAsync", + ("hipGLMapBufferObjectAsync__", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLRegisterBufferObject", + ("hipGLRegisterBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLSetBufferObjectMapFlags", + ("hipGLSetBufferObjectMapFlags", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLSetGLDevice", + ("hipGLSetGLDevice", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLUnmapBufferObject", + ("hipGLUnmapBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLUnmapBufferObjectAsync", + ("hipGLUnmapBufferObjectAsync", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGLUnregisterBufferObject", + ("hipGLUnregisterBufferObject", CONV_GL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9DeviceListAll", + ("HIP_D3D9_DEVICE_LIST_ALL", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9DeviceListCurrentFrame", + ( + "HIP_D3D9_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9DeviceListNextFrame", + ( + "HIP_D3D9_DEVICE_LIST_NEXT_FRAME", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9GetDevice", + ("hipD3D9GetDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9GetDevices", + ("hipD3D9GetDevices", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9GetDirect3DDevice", + ("hipD3D9GetDirect3DDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9SetDirect3DDevice", + ("hipD3D9SetDirect3DDevice", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsD3D9RegisterResource", + ( + "hipGraphicsD3D9RegisterResource", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9MapFlags", + ("hipD3D9MapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9MapFlagsNone", + ( + "HIP_D3D9_MAPRESOURCE_FLAGS_NONE", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9MapFlagsReadOnly", + ( + "HIP_D3D9_MAPRESOURCE_FLAGS_READONLY", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9MapFlagsWriteDiscard", + ( + "HIP_D3D9_MAPRESOURCE_FLAGS_WRITEDISCARD", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9RegisterFlagsNone", + ("HIP_D3D9_REGISTER_FLAGS_NONE", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9RegisterFlagsArray", + ("HIP_D3D9_REGISTER_FLAGS_ARRAY", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9MapResources", + ("hipD3D9MapResources", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9RegisterResource", + ("hipD3D9RegisterResource", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9ResourceGetMappedArray", + ("hipD3D9ResourceGetMappedArray", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9ResourceGetMappedPitch", + ("hipD3D9ResourceGetMappedPitch", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9ResourceGetMappedPointer", + ( + "hipD3D9ResourceGetMappedPointer", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9ResourceGetMappedSize", + ("hipD3D9ResourceGetMappedSize", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9ResourceGetSurfaceDimensions", + ( + "hipD3D9ResourceGetSurfaceDimensions", + CONV_D3D9, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D9ResourceSetMapFlags", + ("hipD3D9ResourceSetMapFlags", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9UnmapResources", + ("hipD3D9UnmapResources", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D9UnregisterResource", + ("hipD3D9UnregisterResource", CONV_D3D9, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10DeviceListAll", + ("HIP_D3D10_DEVICE_LIST_ALL", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10DeviceListCurrentFrame", + ( + "HIP_D3D10_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10DeviceListNextFrame", + ( + "HIP_D3D10_DEVICE_LIST_NEXT_FRAME", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10GetDevice", + ("hipD3D10GetDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10GetDevices", + ("hipD3D10GetDevices", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsD3D10RegisterResource", + ( + "hipGraphicsD3D10RegisterResource", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10MapFlagsNone", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_NONE", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10MapFlagsReadOnly", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_READONLY", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10MapFlagsWriteDiscard", + ( + "HIP_D3D10_MAPRESOURCE_FLAGS_WRITEDISCARD", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10RegisterFlagsNone", + ("HIP_D3D10_REGISTER_FLAGS_NONE", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10RegisterFlagsArray", + ( + "HIP_D3D10_REGISTER_FLAGS_ARRAY", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10GetDirect3DDevice", + ("hipD3D10GetDirect3DDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10MapResources", + ("hipD3D10MapResources", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10RegisterResource", + ("hipD3D10RegisterResource", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10ResourceGetMappedArray", + ( + "hipD3D10ResourceGetMappedArray", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10ResourceGetMappedPitch", + ( + "hipD3D10ResourceGetMappedPitch", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10ResourceGetMappedPointer", + ( + "hipD3D10ResourceGetMappedPointer", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10ResourceGetMappedSize", + ("hipD3D10ResourceGetMappedSize", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10ResourceGetSurfaceDimensions", + ( + "hipD3D10ResourceGetSurfaceDimensions", + CONV_D3D10, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D10ResourceSetMapFlags", + ("hipD3D10ResourceSetMapFlags", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10SetDirect3DDevice", + ("hipD3D10SetDirect3DDevice", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10UnmapResources", + ("hipD3D10UnmapResources", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D10UnregisterResource", + ("hipD3D10UnregisterResource", CONV_D3D10, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D11DeviceListAll", + ("HIP_D3D11_DEVICE_LIST_ALL", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D11DeviceListCurrentFrame", + ( + "HIP_D3D11_DEVICE_LIST_CURRENT_FRAME", + CONV_D3D11, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D11DeviceListNextFrame", + ( + "HIP_D3D11_DEVICE_LIST_NEXT_FRAME", + CONV_D3D11, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D11GetDevice", + ("hipD3D11GetDevice", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D11GetDevices", + ("hipD3D11GetDevices", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsD3D11RegisterResource", + ( + "hipGraphicsD3D11RegisterResource", + CONV_D3D11, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaD3D11GetDevice", + ("hipD3D11GetDevice", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaD3D11GetDevices", + ("hipD3D11GetDevices", CONV_D3D11, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsD3D11RegisterResource", + ( + "hipGraphicsD3D11RegisterResource", + CONV_D3D11, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsVDPAURegisterOutputSurface", + ( + "hipGraphicsVDPAURegisterOutputSurface", + CONV_VDPAU, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaGraphicsVDPAURegisterVideoSurface", + ( + "hipGraphicsVDPAURegisterVideoSurface", + CONV_VDPAU, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaVDPAUGetDevice", + ("hipVDPAUGetDevice", CONV_VDPAU, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaVDPAUSetVDPAUDevice", + ("hipVDPAUSetDevice", CONV_VDPAU, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaEGLStreamConsumerAcquireFrame", + ( + "hipEGLStreamConsumerAcquireFrame", + CONV_EGL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaEGLStreamConsumerConnect", + ("hipEGLStreamConsumerConnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaEGLStreamConsumerConnectWithFlags", + ( + "hipEGLStreamConsumerConnectWithFlags", + CONV_EGL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaEGLStreamConsumerReleaseFrame", + ( + "hipEGLStreamConsumerReleaseFrame", + CONV_EGL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaEGLStreamProducerConnect", + ("hipEGLStreamProducerConnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaEGLStreamProducerDisconnect", + ("hipEGLStreamProducerDisconnect", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaEGLStreamProducerPresentFrame", + ( + "hipEGLStreamProducerPresentFrame", + CONV_EGL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ( + "cudaEGLStreamProducerReturnFrame", + ("hipEGLStreamProducerReturnFrame", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsEGLRegisterImage", + ("hipGraphicsEGLRegisterImage", CONV_EGL, API_RUNTIME, HIP_UNSUPPORTED), + ), + ( + "cudaGraphicsResourceGetMappedEglFrame", + ( + "hipGraphicsResourceGetMappedEglFrame", + CONV_EGL, + API_RUNTIME, + HIP_UNSUPPORTED, + ), + ), + ("cublasInit", ("hipblasInit", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasShutdown", + ("hipblasShutdown", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGetVersion", + ("hipblasGetVersion", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGetError", + ("hipblasGetError", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasAlloc", ("hipblasAlloc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasFree", ("hipblasFree", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasSetKernelStream", + ("hipblasSetKernelStream", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGetAtomicsMode", + ("hipblasGetAtomicsMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSetAtomicsMode", + ("hipblasSetAtomicsMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGetMathMode", + ("hipblasGetMathMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSetMathMode", + ("hipblasSetMathMode", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("CUBLAS_OP_N", ("HIPBLAS_OP_N", CONV_NUMERIC_LITERAL, API_BLAS)), + ( + "CUBLAS_OP_T", + ("HIPBLAS_OP_T", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_OP_C", + ("HIPBLAS_OP_C", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_SUCCESS", + ("HIPBLAS_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_NOT_INITIALIZED", + ("HIPBLAS_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_ALLOC_FAILED", + ("HIPBLAS_STATUS_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_INVALID_VALUE", + ("HIPBLAS_STATUS_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_MAPPING_ERROR", + ("HIPBLAS_STATUS_MAPPING_ERROR", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_EXECUTION_FAILED", + ("HIPBLAS_STATUS_EXECUTION_FAILED", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_INTERNAL_ERROR", + ("HIPBLAS_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_NOT_SUPPORTED", + ("HIPBLAS_STATUS_NOT_SUPPORTED", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_STATUS_ARCH_MISMATCH", + ("HIPBLAS_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_FILL_MODE_LOWER", + ("HIPBLAS_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_FILL_MODE_UPPER", + ("HIPBLAS_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_DIAG_NON_UNIT", + ("HIPBLAS_DIAG_NON_UNIT", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ("CUBLAS_DIAG_UNIT", ("HIPBLAS_DIAG_UNIT", CONV_NUMERIC_LITERAL, API_BLAS)), + ("CUBLAS_SIDE_LEFT", ("HIPBLAS_SIDE_LEFT", CONV_NUMERIC_LITERAL, API_BLAS)), + ("CUBLAS_SIDE_RIGHT", ("HIPBLAS_SIDE_RIGHT", CONV_NUMERIC_LITERAL, API_BLAS)), + ( + "CUBLAS_POINTER_MODE_HOST", + ("HIPBLAS_POINTER_MODE_HOST", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_POINTER_MODE_DEVICE", + ("HIPBLAS_POINTER_MODE_DEVICE", CONV_NUMERIC_LITERAL, API_BLAS), + ), + ( + "CUBLAS_ATOMICS_NOT_ALLOWED", + ( + "HIPBLAS_ATOMICS_NOT_ALLOWED", + CONV_NUMERIC_LITERAL, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "CUBLAS_ATOMICS_ALLOWED", + ( + "HIPBLAS_ATOMICS_ALLOWED", + CONV_NUMERIC_LITERAL, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "CUBLAS_DATA_FLOAT", + ( + "HIPBLAS_DATA_FLOAT", + CONV_NUMERIC_LITERAL, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "CUBLAS_DATA_DOUBLE", + ( + "HIPBLAS_DATA_DOUBLE", + CONV_NUMERIC_LITERAL, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "CUBLAS_DATA_HALF", + ("HIPBLAS_DATA_HALF", CONV_NUMERIC_LITERAL, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "CUBLAS_DATA_INT8", + ("HIPBLAS_DATA_INT8", CONV_NUMERIC_LITERAL, API_BLAS, HIP_UNSUPPORTED), + ), + ("CUBLAS_GEMM_DEFAULT", ("HIPBLAS_GEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_BLAS)), + ("CUBLAS_GEMM_DEFAULT_TENSOR_OP", ("HIPBLAS_GEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_BLAS)), + ("cublasCreate", ("hipblasCreate", CONV_MATH_FUNC, API_BLAS)), + ("cublasDestroy", ("hipblasDestroy", CONV_MATH_FUNC, API_BLAS)), + ("cublasSetVector", ("hipblasSetVector", CONV_MATH_FUNC, API_BLAS)), + ("cublasGetVector", ("hipblasGetVector", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSetVectorAsync", + ("hipblasSetVectorAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGetVectorAsync", + ("hipblasGetVectorAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSetMatrix", ("hipblasSetMatrix", CONV_MATH_FUNC, API_BLAS)), + ("cublasGetMatrix", ("hipblasGetMatrix", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasGetMatrixAsync", + ("hipblasGetMatrixAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSetMatrixAsync", + ("hipblasSetMatrixAsync", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasXerbla", ("hipblasXerbla", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSnrm2", ("hipblasSnrm2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDnrm2", ("hipblasDnrm2", CONV_MATH_FUNC, API_BLAS)), + ("cublasScnrm2", ("hipblasScnrm2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDznrm2", ("hipblasDznrm2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasNrm2Ex", + ("hipblasNrm2Ex", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSdot", ("hipblasSdot", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSdotBatched", + ("hipblasSdotBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDdot", ("hipblasDdot", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasDdotBatched", + ("hipblasDdotBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasCdotu", ("hipblasCdotu", CONV_MATH_FUNC, API_BLAS)), + ("cublasCdotc", ("hipblasCdotc", CONV_MATH_FUNC, API_BLAS)), + ("cublasZdotu", ("hipblasZdotu", CONV_MATH_FUNC, API_BLAS)), + ("cublasZdotc", ("hipblasZdotc", CONV_MATH_FUNC, API_BLAS)), + ("cublasSscal", ("hipblasSscal", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSscalBatched", + ("hipblasSscalBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDscal", ("hipblasDscal", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasDscalBatched", + ("hipblasDscalBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasCscal", ("hipblasCscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsscal", ("hipblasCsscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZscal", ("hipblasZscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZdscal", ("hipblasZdscal", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSaxpy", ("hipblasSaxpy", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSaxpyBatched", + ("hipblasSaxpyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDaxpy", ("hipblasDaxpy", CONV_MATH_FUNC, API_BLAS)), + ("cublasCaxpy", ("hipblasCaxpy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZaxpy", ("hipblasZaxpy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasScopy", ("hipblasScopy", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasScopyBatched", + ("hipblasScopyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDcopy", ("hipblasDcopy", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasDcopyBatched", + ("hipblasDcopyBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasCcopy", ("hipblasCcopy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZcopy", ("hipblasZcopy", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSswap", ("hipblasSswap", CONV_MATH_FUNC, API_BLAS)), + ("cublasDswap", ("hipblasDswap", CONV_MATH_FUNC, API_BLAS)), + ("cublasCswap", ("hipblasCswap", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZswap", ("hipblasZswap", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasIsamax", ("hipblasIsamax", CONV_MATH_FUNC, API_BLAS)), + ("cublasIdamax", ("hipblasIdamax", CONV_MATH_FUNC, API_BLAS)), + ("cublasIcamax", ("hipblasIcamax", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasIzamax", ("hipblasIzamax", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasIsamin", ("hipblasIsamin", CONV_MATH_FUNC, API_BLAS)), + ("cublasIdamin", ("hipblasIdamin", CONV_MATH_FUNC, API_BLAS)), + ("cublasIcamin", ("hipblasIcamin", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasIzamin", ("hipblasIzamin", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSasum", ("hipblasSasum", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSasumBatched", + ("hipblasSasumBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDasum", ("hipblasDasum", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasDasumBatched", + ("hipblasDasumBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasScasum", ("hipblasScasum", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDzasum", ("hipblasDzasum", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSrot", ("hipblasSrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDrot", ("hipblasDrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCrot", ("hipblasCrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsrot", ("hipblasCsrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZrot", ("hipblasZrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZdrot", ("hipblasZdrot", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSrotg", ("hipblasSrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDrotg", ("hipblasDrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCrotg", ("hipblasCrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZrotg", ("hipblasZrotg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSrotm", ("hipblasSrotm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDrotm", ("hipblasDrotm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSrotmg", ("hipblasSrotmg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDrotmg", ("hipblasDrotmg", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSgemv", ("hipblasSgemv", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasSgemvBatched", + ("hipblasSgemvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDgemv", ("hipblasDgemv", CONV_MATH_FUNC, API_BLAS)), + ("cublasCgemv", ("hipblasCgemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZgemv", ("hipblasZgemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSgbmv", ("hipblasSgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDgbmv", ("hipblasDgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCgbmv", ("hipblasCgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZgbmv", ("hipblasZgbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStrmv", ("hipblasStrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtrmv", ("hipblasDtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtrmv", ("hipblasCtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtrmv", ("hipblasZtrmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStbmv", ("hipblasStbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtbmv", ("hipblasDtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtbmv", ("hipblasCtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtbmv", ("hipblasZtbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStpmv", ("hipblasStpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtpmv", ("hipblasDtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtpmv", ("hipblasCtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtpmv", ("hipblasZtpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStrsv", ("hipblasStrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtrsv", ("hipblasDtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtrsv", ("hipblasCtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtrsv", ("hipblasZtrsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStpsv", ("hipblasStpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtpsv", ("hipblasDtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtpsv", ("hipblasCtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtpsv", ("hipblasZtpsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStbsv", ("hipblasStbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtbsv", ("hipblasDtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtbsv", ("hipblasCtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtbsv", ("hipblasZtbsv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsymv", ("hipblasSsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsymv", ("hipblasDsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsymv", ("hipblasCsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsymv", ("hipblasZsymv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChemv", ("hipblasChemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhemv", ("hipblasZhemv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsbmv", ("hipblasSsbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsbmv", ("hipblasDsbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChbmv", ("hipblasChbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhbmv", ("hipblasZhbmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSspmv", ("hipblasSspmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDspmv", ("hipblasDspmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChpmv", ("hipblasChpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhpmv", ("hipblasZhpmv", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSger", ("hipblasSger", CONV_MATH_FUNC, API_BLAS)), + ("cublasDger", ("hipblasDger", CONV_MATH_FUNC, API_BLAS)), + ("cublasCgeru", ("hipblasCgeru", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCgerc", ("hipblasCgerc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZgeru", ("hipblasZgeru", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZgerc", ("hipblasZgerc", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsyr", ("hipblasSsyr", CONV_MATH_FUNC, API_BLAS)), + ("cublasDsyr", ("hipblasDsyr", CONV_MATH_FUNC, API_BLAS)), + ("cublasCher", ("hipblasCher", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZher", ("hipblasZher", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSspr", ("hipblasSspr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDspr", ("hipblasDspr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChpr", ("hipblasChpr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhpr", ("hipblasZhpr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsyr2", ("hipblasSsyr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsyr2", ("hipblasDsyr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCher2", ("hipblasCher2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZher2", ("hipblasZher2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSspr2", ("hipblasSspr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDspr2", ("hipblasDspr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChpr2", ("hipblasChpr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhpr2", ("hipblasZhpr2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasSgemmBatched", + ("hipblasSgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgemmBatched", + ("hipblasDgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasHgemmBatched", + ("hipblasHgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgemmStridedBatched", + ("hipblasSgemmStridedBatched", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasDgemmStridedBatched", + ("hipblasDgemmStridedBatched", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasHgemmStridedBatched", + ("hipblasHgemmStridedBatched", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasCgemmBatched", + ("hipblasCgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgemm3mBatched", + ("hipblasCgemm3mBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgemmBatched", + ("hipblasZgemmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgemmStridedBatched", + ( + "hipblasCgemmStridedBatched", + CONV_MATH_FUNC, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "cublasCgemm3mStridedBatched", + ( + "hipblasCgemm3mStridedBatched", + CONV_MATH_FUNC, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "cublasZgemmStridedBatched", + ( + "hipblasZgemmStridedBatched", + CONV_MATH_FUNC, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ( + "cublasHgemmStridedBatched", + ( + "hipblasHgemmStridedBatched", + CONV_MATH_FUNC, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ("cublasSgemm", ("hipblasSgemm", CONV_MATH_FUNC, API_BLAS)), + ("cublasDgemm", ("hipblasDgemm", CONV_MATH_FUNC, API_BLAS)), + ("cublasCgemm", ("hipblasCgemm", CONV_MATH_FUNC, API_BLAS)), + ("cublasZgemm", ("hipblasZgemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasHgemm", ("hipblasHgemm", CONV_MATH_FUNC, API_BLAS)), + ("cublasSsyrk", ("hipblasSsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsyrk", ("hipblasDsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsyrk", ("hipblasCsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsyrk", ("hipblasZsyrk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCherk", ("hipblasCherk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZherk", ("hipblasZherk", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsyr2k", ("hipblasSsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsyr2k", ("hipblasDsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsyr2k", ("hipblasCsyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsyr2k", ("hipblasZyr2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsyrkx", ("hipblasSsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsyrkx", ("hipblasDsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsyrkx", ("hipblasCsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsyrkx", ("hipblasZsyrkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCher2k", ("hipblasCher2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZher2k", ("hipblasZher2k", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCherkx", ("hipblasCherkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZherkx", ("hipblasZherkx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSsymm", ("hipblasSsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsymm", ("hipblasDsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsymm", ("hipblasCsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsymm", ("hipblasZsymm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChemm", ("hipblasChemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhemm", ("hipblasZhemm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStrsm", ("hipblasStrsm", CONV_MATH_FUNC, API_BLAS)), + ("cublasDtrsm", ("hipblasDtrsm", CONV_MATH_FUNC, API_BLAS)), + ("cublasCtrsm", ("hipblasCtrsm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtrsm", ("hipblasZtrsm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasStrsmBatched", + ("hipblasStrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrsmBatched", + ("hipblasDtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrsmBatched", + ("hipblasCtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrsmBatched", + ("hipblasZtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasStrmm", ("hipblasStrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtrmm", ("hipblasDtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtrmm", ("hipblasCtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtrmm", ("hipblasZtrmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSgeam", ("hipblasSgeam", CONV_MATH_FUNC, API_BLAS)), + ("cublasDgeam", ("hipblasDgeam", CONV_MATH_FUNC, API_BLAS)), + ("cublasCgeam", ("hipblasCgeam", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZgeam", ("hipblasZgeam", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasSgetrfBatched", + ("hipblasSgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgetrfBatched", + ("hipblasDgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgetrfBatched", + ("hipblasCgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgetrfBatched", + ("hipblasZgetrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgetriBatched", + ("hipblasSgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgetriBatched", + ("hipblasDgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgetriBatched", + ("hipblasCgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgetriBatched", + ("hipblasZgetriBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgetrsBatched", + ("hipblasSgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgetrsBatched", + ("hipblasDgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgetrsBatched", + ("hipblasCgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgetrsBatched", + ("hipblasZgetrsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStrsmBatched", + ("hipblasStrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrsmBatched", + ("hipblasDtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrsmBatched", + ("hipblasCtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrsmBatched", + ("hipblasZtrsmBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSmatinvBatched", + ("hipblasSmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDmatinvBatched", + ("hipblasDmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCmatinvBatched", + ("hipblasCmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZmatinvBatched", + ("hipblasZmatinvBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgeqrfBatched", + ("hipblasSgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgeqrfBatched", + ("hipblasDgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgeqrfBatched", + ("hipblasCgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgeqrfBatched", + ("hipblasZgeqrfBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgelsBatched", + ("hipblasSgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgelsBatched", + ("hipblasDgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgelsBatched", + ("hipblasCgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgelsBatched", + ("hipblasZgelsBatched", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSdgmm", ("hipblasSdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDdgmm", ("hipblasDdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCdgmm", ("hipblasCdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZdgmm", ("hipblasZdgmm", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStpttr", ("hipblasStpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtpttr", ("hipblasDtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtpttr", ("hipblasCtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtpttr", ("hipblasZtpttr", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasStrttp", ("hipblasStrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDtrttp", ("hipblasDtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCtrttp", ("hipblasCtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZtrttp", ("hipblasZtrttp", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCreate_v2", ("hipblasCreate_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDestroy_v2", ("hipblasDestroy_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasGetVersion_v2", + ("hipblasGetVersion_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSetWorkspace", ("hipblasSetWorkspace", CONV_MATH_FUNC, API_BLAS)), + ("cublasSetStream", ("hipblasSetStream", CONV_MATH_FUNC, API_BLAS)), + ("cublasGetStream", ("hipblasGetStream", CONV_MATH_FUNC, API_BLAS)), + ("cublasSetStream_v2", ("hipblasSetStream_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasGetStream_v2", ("hipblasGetStream_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasGetPointerMode", + ("hipblasGetPointerMode", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasSetPointerMode", + ("hipblasSetPointerMode", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasGetPointerMode_v2", + ("hipblasGetPointerMode_v2", CONV_MATH_FUNC, API_BLAS), + ), + ( + "cublasSetPointerMode_v2", + ("hipblasSetPointerMode_v2", CONV_MATH_FUNC, API_BLAS), + ), + ("cublasSgemv_v2", ("hipblasSgemv_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDgemv_v2", ("hipblasDgemv_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCgemv_v2", + ("hipblasCgemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgemv_v2", + ("hipblasZgemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgbmv_v2", + ("hipblasSgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDgbmv_v2", + ("hipblasDgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgbmv_v2", + ("hipblasCgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgbmv_v2", + ("hipblasZgbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStrmv_v2", + ("hipblasStrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrmv_v2", + ("hipblasDtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrmv_v2", + ("hipblasCtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrmv_v2", + ("hipblasZtrmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStbmv_v2", + ("hipblasStbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtbmv_v2", + ("hipblasDtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtbmv_v2", + ("hipblasCtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtbmv_v2", + ("hipblasZtbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStpmv_v2", + ("hipblasStpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtpmv_v2", + ("hipblasDtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtpmv_v2", + ("hipblasCtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtpmv_v2", + ("hipblasZtpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStrsv_v2", + ("hipblasStrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrsv_v2", + ("hipblasDtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrsv_v2", + ("hipblasCtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrsv_v2", + ("hipblasZtrsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStpsv_v2", + ("hipblasStpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtpsv_v2", + ("hipblasDtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtpsv_v2", + ("hipblasCtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtpsv_v2", + ("hipblasZtpsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStbsv_v2", + ("hipblasStbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtbsv_v2", + ("hipblasDtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtbsv_v2", + ("hipblasCtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtbsv_v2", + ("hipblasZtbsv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSsymv_v2", + ("hipblasSsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsymv_v2", + ("hipblasDsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsymv_v2", + ("hipblasCsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZsymv_v2", + ("hipblasZsymv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasChemv_v2", + ("hipblasChemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZhemv_v2", + ("hipblasZhemv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSsbmv_v2", + ("hipblasSsbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsbmv_v2", + ("hipblasDsbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasChbmv_v2", + ("hipblasChbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZhbmv_v2", + ("hipblasZhbmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSspmv_v2", + ("hipblasSspmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDspmv_v2", + ("hipblasDspmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasChpmv_v2", + ("hipblasChpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZhpmv_v2", + ("hipblasZhpmv_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSger_v2", ("hipblasSger_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDger_v2", ("hipblasDger_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCgeru_v2", + ("hipblasCgeru_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgerc_v2", + ("hipblasCergc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgeru_v2", + ("hipblasZgeru_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgerc_v2", + ("hipblasZgerc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSsyr_v2", ("hipblasSsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDsyr_v2", ("hipblasDsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCsyr_v2", ("hipblasCsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZsyr_v2", ("hipblasZsyr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCher_v2", ("hipblasCher_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZher_v2", ("hipblasZher_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSspr_v2", ("hipblasSspr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDspr_v2", ("hipblasDspr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasChpr_v2", ("hipblasChpr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasZhpr_v2", ("hipblasZhpr_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasSsyr2_v2", + ("hipblasSsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsyr2_v2", + ("hipblasDsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsyr2_v2", + ("hipblasCsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZsyr2_v2", + ("hipblasZsyr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCher2_v2", + ("hipblasCher2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZher2_v2", + ("hipblasZher2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSspr2_v2", + ("hipblasSspr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDspr2_v2", + ("hipblasDspr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasChpr2_v2", + ("hipblasChpr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZhpr2_v2", + ("hipblasZhpr2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSgemm_v2", ("hipblasSgemm_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDgemm_v2", ("hipblasDgemm_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCgemm_v2", + ("hipblasCgemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgemm3m", + ("hipblasCgemm3m", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgemm3mEx", + ("hipblasCgemm3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgemm_v2", + ("hipblasZgemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZgemm3m", + ("hipblasZgemm3m", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSgemmEx", + ("hipblasSgemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasGemmEx", ("hipblasGemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasGemmBatchedEx", + ("hipblasGemmBatchedEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasGemmStridedBatchedEx", + ("hipblasGemmStridedBatchedEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCgemmEx", + ("hipblasCgemmEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasUint8gemmBias", + ("hipblasUint8gemmBias", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSsyrk_v2", + ("hipblasSsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsyrk_v2", + ("hipblasDsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsyrk_v2", + ("hipblasCsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZsyrk_v2", + ("hipblasZsyrk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsyrkEx", + ("hipblasCsyrkEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsyrk3mEx", + ("hipblasCsyrk3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCherk_v2", + ("hipblasCherk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCherkEx", + ("hipblasCherkEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCherk3mEx", + ("hipblasCherk3mEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZherk_v2", + ("hipblasZherk_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSsyr2k_v2", + ("hipblasSsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsyr2k_v2", + ("hipblasDsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsyr2k_v2", + ("hipblasCsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZsyr2k_v2", + ("hipblasZsyr2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCher2k_v2", + ("hipblasCher2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZher2k_v2", + ("hipblasZher2k_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSsymm_v2", + ("hipblasSsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDsymm_v2", + ("hipblasDsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsymm_v2", + ("hipblasCsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZsymm_v2", + ("hipblasZsymm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasChemm_v2", + ("hipblasChemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZhemm_v2", + ("hipblasZhemm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStrsm_v2", + ("hipblasStrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrsm_v2", + ("hipblasDtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrsm_v2", + ("hipblasCtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrsm_v2", + ("hipblasZtrsm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasStrmm_v2", + ("hipblasStrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDtrmm_v2", + ("hipblasDtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCtrmm_v2", + ("hipblasCtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZtrmm_v2", + ("hipblasZtrmm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSnrm2_v2", ("hipblasSnrm2_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDnrm2_v2", ("hipblasDnrm2_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasScnrm2_v2", + ("hipblasScnrm2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDznrm2_v2", + ("hipblasDznrm2_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasDotEx", ("hipblasDotEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDotcEx", ("hipblasDotcEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSdot_v2", ("hipblasSdot_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDdot_v2", ("hipblasDdot_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCdotu_v2", + ("hipblasCdotu_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCdotc_v2", + ("hipblasCdotc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZdotu_v2", + ("hipblasZdotu_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZdotc_v2", + ("hipblasZdotc_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasScalEx", ("hipblasScalEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSscal_v2", ("hipblasSscal_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDscal_v2", ("hipblasDscal_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCscal_v2", + ("hipblasCscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCsscal_v2", + ("hipblasCsscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZscal_v2", + ("hipblasZcsal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZdscal_v2", + ("hipblasZdscal_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasAxpyEx", ("hipblasAxpyEx", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasSaxpy_v2", ("hipblasSaxpy_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDaxpy_v2", ("hipblasDaxpy_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCaxpy_v2", + ("hipblasCaxpy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZaxpy_v2", + ("hipblasZaxpy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasScopy_v2", ("hipblasScopy_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDcopy_v2", ("hipblasDcopy_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCcopy_v2", + ("hipblasCcopy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZcopy_v2", + ("hipblasZcopy_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSswap_v2", ("hipblasSswap_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDswap_v2", ("hipblasDswap_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasCswap_v2", + ("hipblasCswap_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZswap_v2", + ("hipblasZswap_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasIsamax_v2", ("hipblasIsamax_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasIdamax_v2", ("hipblasIdamax_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasIcamax_v2", + ("hipblasIcamax_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasIzamax_v2", + ("hipblasIzamax_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasIsamin_v2", ("hipblasIsamin_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasIdamin_v2", ("hipblasIdamin_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasIcamin_v2", + ("hipblasIcamin_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasIzamin_v2", + ("hipblasIzamin_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSasum_v2", ("hipblasSasum_v2", CONV_MATH_FUNC, API_BLAS)), + ("cublasDasum_v2", ("hipblasDasum_v2", CONV_MATH_FUNC, API_BLAS)), + ( + "cublasScasum_v2", + ("hipblasScasum_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDzasum_v2", + ("hipblasDzasum_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasSrot_v2", ("hipblasSrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasDrot_v2", ("hipblasDrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ("cublasCrot_v2", ("hipblasCrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasCsrot_v2", + ("hipblasCsrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ("cublasZrot_v2", ("hipblasZrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED)), + ( + "cublasZdrot_v2", + ("hipblasZdrot_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSrotg_v2", + ("hipblasSrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDrotg_v2", + ("hipblasDrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasCrotg_v2", + ("hipblasCrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasZrotg_v2", + ("hipblasZrotg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSrotm_v2", + ("hipblasSrotm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDrotm_v2", + ("hipblasDrotm_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasSrotmg_v2", + ("hipblasSrotmg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasDrotmg_v2", + ("hipblasDrotmg_v2", CONV_MATH_FUNC, API_BLAS, HIP_UNSUPPORTED), + ), + ( + "cublasComputeType_t", + ("hipblasComputeType_t", CONV_MATH_FUNC, API_BLAS) + ), + ( + "CUBLAS_COMPUTE_32I", + ("HIPBLAS_COMPUTE_32I", CONV_MATH_FUNC, API_BLAS) + ), + ( + "CUBLAS_COMPUTE_32F", + ("HIPBLAS_COMPUTE_32F", CONV_MATH_FUNC, API_BLAS) + ), + ( + "CUBLAS_COMPUTE_64F", + ("HIPBLAS_COMPUTE_64F", CONV_MATH_FUNC, API_BLAS) + ), + ("cublasLtEpilogue_t", ("hipblasLtEpilogue_t", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_DEFAULT", ("HIPBLASLT_EPILOGUE_DEFAULT", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_RELU", ("HIPBLASLT_EPILOGUE_RELU", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_BIAS", ("HIPBLASLT_EPILOGUE_BIAS", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_RELU_BIAS", ("HIPBLASLT_EPILOGUE_RELU_BIAS", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_GELU", ("HIPBLASLT_EPILOGUE_GELU", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_EPILOGUE_GELU_BIAS", ("HIPBLASLT_EPILOGUE_GELU_BIAS", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtHandle_t", ("hipblasLtHandle_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDesc_t", ("hipblasLtMatmulDesc_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDescOpaque_t", ("hipblasLtMatmulDescOpaque_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDescAttributes_t", ("hipblasLtMatmulDescAttributes_t", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_TRANSA", ("HIPBLASLT_MATMUL_DESC_TRANSA", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_TRANSB", ("HIPBLASLT_MATMUL_DESC_TRANSB", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_EPILOGUE", ("HIPBLASLT_MATMUL_DESC_EPILOGUE", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_BIAS_POINTER", ("HIPBLASLT_MATMUL_DESC_BIAS_POINTER", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_A_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_B_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_D_SCALE_POINTER", ("HIPBLASLT_MATMUL_DESC_D_SCALE_POINTER", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_AMAX_D_POINTER", ("HIPBLASLT_MATMUL_DESC_AMAX_D_POINTER", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE", ("HIPBLASLT_MATMUL_DESC_BIAS_DATA_TYPE", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayout_t", ("hipblasLtMatrixLayout_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayoutOpaque_t", ("hipblasLtMatrixLayoutOpaque_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayoutAttribute_t", ("hipblasLtMatrixLayoutAttribute_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayoutCreate", ("hipblasLtMatrixLayoutCreate", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayoutDestroy", ("hipblasLtMatrixLayoutDestroy", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatrixLayoutSetAttribute", ("hipblasLtMatrixLayoutSetAttribute", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT", ("HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET", ("HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreference_t", ("hipblasLtMatmulPreference_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreferenceOpaque_t", ("hipblasLtMatmulPreferenceOpaque_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreferenceAttributes_t", ("hipblasLtMatmulPreferenceAttributes_t", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_PREF_SEARCH_MODE", ("HIPBLASLT_MATMUL_PREF_SEARCH_MODE", CONV_MATH_FUNC, API_BLAS)), + ("CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES", ("HIPBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulAlgo_t", ("hipblasLtMatmulAlgo_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulHeuristicResult_t", ("hipblasLtMatmulHeuristicResult_t", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtCreate", ("hipblasLtCreate", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtDestroy", ("hipblasLtDestroy", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDescCreate", ("hipblasLtMatmulDescCreate", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDescDestroy", ("hipblasLtMatmulDescDestroy", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulDescSetAttribute", ("hipblasLtMatmulDescSetAttribute", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreferenceCreate", ("hipblasLtMatmulPreferenceCreate", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreferenceDestroy", ("hipblasLtMatmulPreferenceDestroy", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulPreferenceSetAttribute", ("hipblasLtMatmulPreferenceSetAttribute", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmulAlgoGetHeuristic", ("hipblasLtMatmulAlgoGetHeuristic", CONV_MATH_FUNC, API_BLAS)), + ("cublasLtMatmul", ("hipblasLtMatmul", CONV_MATH_FUNC, API_BLAS)), + ( + "CURAND_STATUS_SUCCESS", + ("HIPRAND_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_VERSION_MISMATCH", + ("HIPRAND_STATUS_VERSION_MISMATCH", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_NOT_INITIALIZED", + ("HIPRAND_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_ALLOCATION_FAILED", + ("HIPRAND_STATUS_ALLOCATION_FAILED", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_TYPE_ERROR", + ("HIPRAND_STATUS_TYPE_ERROR", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_OUT_OF_RANGE", + ("HIPRAND_STATUS_OUT_OF_RANGE", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_LENGTH_NOT_MULTIPLE", + ("HIPRAND_STATUS_LENGTH_NOT_MULTIPLE", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED", + ( + "HIPRAND_STATUS_DOUBLE_PRECISION_REQUIRED", + CONV_NUMERIC_LITERAL, + API_RAND, + ), + ), + ( + "CURAND_STATUS_LAUNCH_FAILURE", + ("HIPRAND_STATUS_LAUNCH_FAILURE", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_PREEXISTING_FAILURE", + ("HIPRAND_STATUS_PREEXISTING_FAILURE", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_INITIALIZATION_FAILED", + ("HIPRAND_STATUS_INITIALIZATION_FAILED", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_ARCH_MISMATCH", + ("HIPRAND_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_STATUS_INTERNAL_ERROR", + ("HIPRAND_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_RAND), + ), + ("CURAND_RNG_TEST", ("HIPRAND_RNG_TEST", CONV_NUMERIC_LITERAL, API_RAND)), + ( + "mtgp32dc_params_fast_11213", + ("mtgp32dc_params_fast_11213", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_DEFAULT", + ("HIPRAND_RNG_PSEUDO_DEFAULT", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_XORWOW", + ("HIPRAND_RNG_PSEUDO_XORWOW", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_MRG32K3A", + ("HIPRAND_RNG_PSEUDO_MRG32K3A", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_MTGP32", + ("HIPRAND_RNG_PSEUDO_MTGP32", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_MT19937", + ("HIPRAND_RNG_PSEUDO_MT19937", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_PSEUDO_PHILOX4_32_10", + ("HIPRAND_RNG_PSEUDO_PHILOX4_32_10", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_QUASI_DEFAULT", + ("HIPRAND_RNG_QUASI_DEFAULT", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_QUASI_SOBOL32", + ("HIPRAND_RNG_QUASI_SOBOL32", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_QUASI_SCRAMBLED_SOBOL32", + ("HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_QUASI_SOBOL64", + ("HIPRAND_RNG_QUASI_SOBOL64", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "CURAND_RNG_QUASI_SCRAMBLED_SOBOL64", + ("HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64", CONV_NUMERIC_LITERAL, API_RAND), + ), + ( + "curand_ORDERING_PSEUDO_BEST", + ( + "HIPRAND_ORDERING_PSEUDO_BEST", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_ORDERING_PSEUDO_DEFAULT", + ( + "HIPRAND_ORDERING_PSEUDO_DEFAULT", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_ORDERING_PSEUDO_SEEDED", + ( + "HIPRAND_ORDERING_PSEUDO_SEEDED", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_ORDERING_QUASI_DEFAULT", + ( + "HIPRAND_ORDERING_QUASI_DEFAULT", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_DIRECTION_VECTORS_32_JOEKUO6", + ( + "HIPRAND_DIRECTION_VECTORS_32_JOEKUO6", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6", + ( + "HIPRAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_DIRECTION_VECTORS_64_JOEKUO6", + ( + "HIPRAND_DIRECTION_VECTORS_64_JOEKUO6", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6", + ( + "HIPRAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6", + CONV_NUMERIC_LITERAL, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_CHOOSE_BEST", + ("HIPRAND_CHOOSE_BEST", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_ITR", + ("HIPRAND_ITR", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_KNUTH", + ("HIPRAND_KNUTH", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_HITR", + ("HIPRAND_HITR", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ("curand_M1", ("HIPRAND_M1", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED)), + ("curand_M2", ("HIPRAND_M2", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED)), + ( + "curand_BINARY_SEARCH", + ("HIPRAND_BINARY_SEARCH", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_DISCRETE_GAUSS", + ("HIPRAND_DISCRETE_GAUSS", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_REJECTION", + ("HIPRAND_REJECTION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_DEVICE_API", + ("HIPRAND_DEVICE_API", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_FAST_REJECTION", + ("HIPRAND_FAST_REJECTION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_3RD", + ("HIPRAND_3RD", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_DEFINITION", + ("HIPRAND_DEFINITION", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_POISSON", + ("HIPRAND_POISSON", CONV_NUMERIC_LITERAL, API_RAND, HIP_UNSUPPORTED), + ), + ("curandCreateGenerator", ("hiprandCreateGenerator", CONV_MATH_FUNC, API_RAND)), + ( + "curandCreateGeneratorHost", + ("hiprandCreateGeneratorHost", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandCreatePoissonDistribution", + ("hiprandCreatePoissonDistribution", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandDestroyDistribution", + ("hiprandDestroyDistribution", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandDestroyGenerator", + ("hiprandDestroyGenerator", CONV_MATH_FUNC, API_RAND), + ), + ("curandGenerate", ("hiprandGenerate", CONV_MATH_FUNC, API_RAND)), + ( + "curandGenerateLogNormal", + ("hiprandGenerateLogNormal", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandGenerateLogNormalDouble", + ("hiprandGenerateLogNormalDouble", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandGenerateLongLong", + ("hiprandGenerateLongLong", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ("curandGenerateNormal", ("hiprandGenerateNormal", CONV_MATH_FUNC, API_RAND)), + ( + "curandGenerateNormalDouble", + ("hiprandGenerateNormalDouble", CONV_MATH_FUNC, API_RAND), + ), + ("curandGeneratePoisson", ("hiprandGeneratePoisson", CONV_MATH_FUNC, API_RAND)), + ("curandGenerateSeeds", ("hiprandGenerateSeeds", CONV_MATH_FUNC, API_RAND)), + ("curandGenerateUniform", ("hiprandGenerateUniform", CONV_MATH_FUNC, API_RAND)), + ( + "curandGenerateUniformDouble", + ("hiprandGenerateUniformDouble", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandGetDirectionVectors32", + ("hiprandGetDirectionVectors32", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandGetDirectionVectors64", + ("hiprandGetDirectionVectors64", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandGetProperty", + ("hiprandGetProperty", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandGetScrambleConstants32", + ( + "hiprandGetScrambleConstants32", + CONV_MATH_FUNC, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curandGetScrambleConstants64", + ( + "hiprandGetScrambleConstants64", + CONV_MATH_FUNC, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ("curandGetVersion", ("hiprandGetVersion", CONV_MATH_FUNC, API_RAND)), + ( + "curandSetGeneratorOffset", + ("hiprandSetGeneratorOffset", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandSetGeneratorOrdering", + ("hiprandSetGeneratorOrdering", CONV_MATH_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curandSetPseudoRandomGeneratorSeed", + ("hiprandSetPseudoRandomGeneratorSeed", CONV_MATH_FUNC, API_RAND), + ), + ( + "curandSetQuasiRandomGeneratorDimensions", + ("hiprandSetQuasiRandomGeneratorDimensions", CONV_MATH_FUNC, API_RAND), + ), + ("curandSetStream", ("hiprandSetStream", CONV_MATH_FUNC, API_RAND)), + ("curand", ("hiprand", CONV_DEVICE_FUNC, API_RAND)), + ("curand4", ("hiprand4", CONV_DEVICE_FUNC, API_RAND)), + ("curand_init", ("hiprand_init", CONV_DEVICE_FUNC, API_RAND)), + ("curand_log_normal", ("hiprand_log_normal", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_log_normal_double", + ("hiprand_log_normal_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_log_normal2", ("hiprand_log_normal2", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_log_normal2_double", + ("hiprand_log_normal2_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_log_normal4", ("hiprand_log_normal4", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_log_normal4_double", + ("hiprand_log_normal4_double", CONV_DEVICE_FUNC, API_RAND), + ), + ( + "curand_mtgp32_single", + ("hiprand_mtgp32_single", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ( + "curand_mtgp32_single_specific", + ( + "hiprand_mtgp32_single_specific", + CONV_DEVICE_FUNC, + API_RAND, + HIP_UNSUPPORTED, + ), + ), + ( + "curand_mtgp32_specific", + ("hiprand_mtgp32_specific", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ("curand_normal", ("hiprand_normal", CONV_DEVICE_FUNC, API_RAND)), + ( + "curandMakeMTGP32Constants", + ("hiprandMakeMTGP32Constants", CONV_DEVICE_FUNC, API_RAND), + ), + ( + "curandMakeMTGP32KernelState", + ("hiprandMakeMTGP32KernelState", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_normal_double", ("hiprand_normal_double", CONV_DEVICE_FUNC, API_RAND)), + ("curand_normal2", ("hiprand_normal2", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_normal2_double", + ("hiprand_normal2_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_normal4", ("hiprand_normal4", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_normal4_double", + ("hiprand_normal4_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_uniform", ("hiprand_uniform", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_uniform_double", + ("hiprand_uniform_double", CONV_DEVICE_FUNC, API_RAND), + ), + ( + "curand_uniform2_double", + ("hiprand_uniform2_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_uniform4", ("hiprand_uniform4", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_uniform4_double", + ("hiprand_uniform4_double", CONV_DEVICE_FUNC, API_RAND), + ), + ("curand_discrete", ("hiprand_discrete", CONV_DEVICE_FUNC, API_RAND)), + ("curand_discrete4", ("hiprand_discrete4", CONV_DEVICE_FUNC, API_RAND)), + ("curand_poisson", ("hiprand_poisson", CONV_DEVICE_FUNC, API_RAND)), + ("curand_poisson4", ("hiprand_poisson4", CONV_DEVICE_FUNC, API_RAND)), + ( + "curand_Philox4x32_10", + ("hiprand_Philox4x32_10", CONV_DEVICE_FUNC, API_RAND, HIP_UNSUPPORTED), + ), + ("mtgp32_kernel_params", ("mtgp32_kernel_params_t", CONV_MATH_FUNC, API_RAND)), + ("CUFFT_FORWARD", ("HIPFFT_FORWARD", CONV_NUMERIC_LITERAL, API_BLAS)), + ("CUFFT_INVERSE", ("HIPFFT_BACKWARD", CONV_NUMERIC_LITERAL, API_BLAS)), + ( + "CUFFT_COMPATIBILITY_DEFAULT", + ( + "HIPFFT_COMPATIBILITY_DEFAULT", + CONV_NUMERIC_LITERAL, + API_BLAS, + HIP_UNSUPPORTED, + ), + ), + ("cuComplex", ("hipComplex", CONV_TYPE, API_BLAS)), + ("cuDoubleComplex", ("hipDoubleComplex", CONV_TYPE, API_BLAS)), + ("cufftResult_t", ("hipfftResult_t", CONV_TYPE, API_FFT)), + ("cufftResult", ("hipfftResult", CONV_TYPE, API_FFT)), + ("CUFFT_SUCCESS", ("HIPFFT_SUCCESS", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_INVALID_PLAN", ("HIPFFT_INVALID_PLAN", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_ALLOC_FAILED", ("HIPFFT_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_INVALID_TYPE", ("HIPFFT_INVALID_TYPE", CONV_NUMERIC_LITERAL, API_FFT)), + ( + "CUFFT_INVALID_VALUE", + ("HIPFFT_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_FFT), + ), + ( + "CUFFT_INTERNAL_ERROR", + ("HIPFFT_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_FFT), + ), + ("CUFFT_EXEC_FAILED", ("HIPFFT_EXEC_FAILED", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_SETUP_FAILED", ("HIPFFT_SETUP_FAILED", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_INVALID_SIZE", ("HIPFFT_INVALID_SIZE", CONV_NUMERIC_LITERAL, API_FFT)), + ( + "CUFFT_UNALIGNED_DATA", + ("HIPFFT_UNALIGNED_DATA", CONV_NUMERIC_LITERAL, API_FFT), + ), + ( + "CUFFT_INCOMPLETE_PARAMETER_LIST", + ("HIPFFT_INCOMPLETE_PARAMETER_LIST", CONV_NUMERIC_LITERAL, API_FFT), + ), + ( + "CUFFT_INVALID_DEVICE", + ("HIPFFT_INVALID_DEVICE", CONV_NUMERIC_LITERAL, API_FFT), + ), + ("CUFFT_PARSE_ERROR", ("HIPFFT_PARSE_ERROR", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_NO_WORKSPACE", ("HIPFFT_NO_WORKSPACE", CONV_NUMERIC_LITERAL, API_FFT)), + ( + "CUFFT_NOT_IMPLEMENTED", + ("HIPFFT_NOT_IMPLEMENTED", CONV_NUMERIC_LITERAL, API_FFT), + ), + ( + "CUFFT_LICENSE_ERROR", + ("HIPFFT_LICENSE_ERROR", CONV_NUMERIC_LITERAL, API_FFT, HIP_UNSUPPORTED), + ), + ( + "CUFFT_NOT_SUPPORTED", + ("HIPFFT_NOT_SUPPORTED", CONV_NUMERIC_LITERAL, API_FFT), + ), + ("cufftType_t", ("hipfftType_t", CONV_TYPE, API_FFT)), + ("cufftType", ("hipfftType", CONV_TYPE, API_FFT)), + ("CUFFT_R2C", ("HIPFFT_R2C", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_C2R", ("HIPFFT_C2R", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_C2C", ("HIPFFT_C2C", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_D2Z", ("HIPFFT_D2Z", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_Z2D", ("HIPFFT_Z2D", CONV_NUMERIC_LITERAL, API_FFT)), + ("CUFFT_Z2Z", ("HIPFFT_Z2Z", CONV_NUMERIC_LITERAL, API_FFT)), + ( + "cufftCompatibility_t", + ("hipfftCompatibility_t", CONV_TYPE, API_FFT, HIP_UNSUPPORTED), + ), + ( + "cufftCompatibility", + ("hipfftCompatibility", CONV_TYPE, API_FFT, HIP_UNSUPPORTED), + ), + ( + "CUFFT_COMPATIBILITY_FFTW_PADDING", + ( + "HIPFFT_COMPATIBILITY_FFTW_PADDING", + CONV_NUMERIC_LITERAL, + API_FFT, + HIP_UNSUPPORTED, + ), + ), + ("cufftReal", ("hipfftReal", CONV_TYPE, API_FFT)), + ("cufftDoubleReal", ("hipfftDoubleReal", CONV_TYPE, API_FFT)), + ("cufftComplex", ("hipfftComplex", CONV_TYPE, API_FFT)), + ("cufftDoubleComplex", ("hipfftDoubleComplex", CONV_TYPE, API_FFT)), + ("cufftHandle", ("hipfftHandle", CONV_TYPE, API_FFT)), + ("cufftPlan1d", ("hipfftPlan1d", CONV_MATH_FUNC, API_FFT)), + ("cufftPlan2d", ("hipfftPlan2d", CONV_MATH_FUNC, API_FFT)), + ("cufftPlan3d", ("hipfftPlan3d", CONV_MATH_FUNC, API_FFT)), + ("cufftPlanMany", ("hipfftPlanMany", CONV_MATH_FUNC, API_FFT)), + ("cufftMakePlan1d", ("hipfftMakePlan1d", CONV_MATH_FUNC, API_FFT)), + ("cufftMakePlan2d", ("hipfftMakePlan2d", CONV_MATH_FUNC, API_FFT)), + ("cufftMakePlan3d", ("hipfftMakePlan3d", CONV_MATH_FUNC, API_FFT)), + ("cufftMakePlanMany", ("hipfftMakePlanMany", CONV_MATH_FUNC, API_FFT)), + ("cufftMakePlanMany64", ("hipfftMakePlanMany64", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSizeMany64", ("hipfftGetSizeMany64", CONV_MATH_FUNC, API_FFT)), + ("cufftEstimate1d", ("hipfftEstimate1d", CONV_MATH_FUNC, API_FFT)), + ("cufftEstimate2d", ("hipfftEstimate2d", CONV_MATH_FUNC, API_FFT)), + ("cufftEstimate3d", ("hipfftEstimate3d", CONV_MATH_FUNC, API_FFT)), + ("cufftEstimateMany", ("hipfftEstimateMany", CONV_MATH_FUNC, API_FFT)), + ("cufftCreate", ("hipfftCreate", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSize1d", ("hipfftGetSize1d", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSize2d", ("hipfftGetSize2d", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSize3d", ("hipfftGetSize3d", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSizeMany", ("hipfftGetSizeMany", CONV_MATH_FUNC, API_FFT)), + ("cufftGetSize", ("hipfftGetSize", CONV_MATH_FUNC, API_FFT)), + ("cufftSetWorkArea", ("hipfftSetWorkArea", CONV_MATH_FUNC, API_FFT)), + ( + "cufftSetAutoAllocation", + ("hipfftSetAutoAllocation", CONV_MATH_FUNC, API_FFT), + ), + ("cufftXtExec", ("hipfftXtExec", CONV_MATH_FUNC, API_FFT)), + ("cufftXtMakePlanMany", ("hipfftXtMakePlanMany", CONV_MATH_FUNC, API_FFT)), + ("cufftExecC2C", ("hipfftExecC2C", CONV_MATH_FUNC, API_FFT)), + ("cufftExecR2C", ("hipfftExecR2C", CONV_MATH_FUNC, API_FFT)), + ("cufftExecC2R", ("hipfftExecC2R", CONV_MATH_FUNC, API_FFT)), + ("cufftExecZ2Z", ("hipfftExecZ2Z", CONV_MATH_FUNC, API_FFT)), + ("cufftExecD2Z", ("hipfftExecD2Z", CONV_MATH_FUNC, API_FFT)), + ("cufftExecZ2D", ("hipfftExecZ2D", CONV_MATH_FUNC, API_FFT)), + ("cufftSetStream", ("hipfftSetStream", CONV_MATH_FUNC, API_FFT)), + ("cufftDestroy", ("hipfftDestroy", CONV_MATH_FUNC, API_FFT)), + ("cufftGetVersion", ("hipfftGetVersion", CONV_MATH_FUNC, API_FFT)), + ( + "cufftGetProperty", + ("hipfftGetProperty", CONV_MATH_FUNC, API_FFT, HIP_UNSUPPORTED), + ), + ("nvrtcResult", ("hiprtcResult", CONV_TYPE, API_RTC)), + ("NVRTC_SUCCESS", ("HIPRTC_SUCCESS", CONV_TYPE, API_RTC)), + ( + "NVRTC_ERROR_OUT_OF_MEMORY", + ("HIPRTC_ERROR_OUT_OF_MEMORY", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_PROGRAM_CREATION_FAILURE", + ("HIPRTC_ERROR_PROGRAM_CREATION_FAILURE", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_INVALID_INPUT", + ("HIPRTC_ERROR_INVALID_INPUT", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_INVALID_PROGRAM", + ("HIPRTC_ERROR_INVALID_PROGRAM", CONV_TYPE, API_RTC), + ), + ("NVRTC_ERROR_COMPILATION", ("HIPRTC_ERROR_COMPILATION", CONV_TYPE, API_RTC)), + ( + "NVRTC_ERROR_BUILTIN_OPERATION_FAILURE", + ("HIPRTC_ERROR_BUILTIN_OPERATION_FAILURE", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION", + ("HIPRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_NAME_EXPRESSION_NOT_VALID", + ("HIPRTC_ERROR_NAME_EXPRESSION_NOT_VALID", CONV_TYPE, API_RTC), + ), + ( + "NVRTC_ERROR_INTERNAL_ERROR", + ("HIPRTC_ERROR_INTERNAL_ERROR", CONV_TYPE, API_RTC), + ), + ("nvrtcGetErrorString", ("hiprtcGetErrorString", CONV_JIT, API_RTC)), + ("nvrtcVersion", ("hiprtcVersion", CONV_JIT, API_RTC)), + ("nvrtcProgram", ("hiprtcProgram", CONV_TYPE, API_RTC)), + ("nvrtcAddNameExpression", ("hiprtcAddNameExpression", CONV_JIT, API_RTC)), + ("nvrtcCompileProgram", ("hiprtcCompileProgram", CONV_JIT, API_RTC)), + ("nvrtcCreateProgram", ("hiprtcCreateProgram", CONV_JIT, API_RTC)), + ("nvrtcDestroyProgram", ("hiprtcDestroyProgram", CONV_JIT, API_RTC)), + ("nvrtcGetLoweredName", ("hiprtcGetLoweredName", CONV_JIT, API_RTC)), + ("nvrtcGetProgramLog", ("hiprtcGetProgramLog", CONV_JIT, API_RTC)), + ("nvrtcGetProgramLogSize", ("hiprtcGetProgramLogSize", CONV_JIT, API_RTC)), + ("nvrtcGetPTX", ("hiprtcGetCode", CONV_JIT, API_RTC)), + ("nvrtcGetPTXSize", ("hiprtcGetCodeSize", CONV_JIT, API_RTC)), + ("thrust::cuda", ("thrust::hip", CONV_MATH_FUNC, API_BLAS)), + ( + "cudaCpuDeviceId", + ("hipCpuDeviceId", CONV_TYPE, API_RUNTIME, HIP_UNSUPPORTED), + ), + # The caffe2 directory does a string match; pytorch does a word-boundary match. + # Patterns such as 'cub::' will not match for pytorch. + # We list all current uses of cub symbols for this reason. + ("cub::", ("hipcub::", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::ArgMax", ("hipcub::ArgMax", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::ArgMin", ("hipcub::ArgMin", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BLOCK_SCAN_WARP_SCANS", ("hipcub::BLOCK_SCAN_WARP_SCANS", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BLOCK_REDUCE_WARP_REDUCTIONS", ("hipcub::BLOCK_REDUCE_WARP_REDUCTIONS", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BLOCK_STORE_WARP_TRANSPOSE", ("hipcub::BLOCK_STORE_WARP_TRANSPOSE", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BLOCK_LOAD_DIRECT", ("hipcub::BLOCK_LOAD_DIRECT", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BLOCK_STORE_DIRECT", ("hipcub::BLOCK_STORE_DIRECT", CONV_SPECIAL_FUNC, API_RUNTIME)), + ( + "cub::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY", + ("hipcub::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY", CONV_SPECIAL_FUNC, API_RUNTIME) + ), + ("cub::BlockReduce", ("hipcub::BlockReduce", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BlockScan", ("hipcub::BlockScan", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BlockLoad", ("hipcub::BlockLoad", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BlockStore", ("hipcub::BlockStore", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BlockRakingLayout", ("hipcub::BlockRakingLayout", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::BlockRadixSort", ("hipcub::BlockRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::Uninitialized", ("hipcub::Uninitialized", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::RowMajorTid", ("hipcub::RowMajorTid", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::CachingDeviceAllocator", ("hipcub::CachingDeviceAllocator", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::CountingInputIterator", ("hipcub::CountingInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceRadixSort", ("hipcub::DeviceRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceReduce", ("hipcub::DeviceReduce", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceRunLengthEncode", ("hipcub::DeviceRunLengthEncode", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceScan", ("hipcub::DeviceScan", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceSegmentedRadixSort", ("hipcub::DeviceSegmentedRadixSort", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceSegmentedReduce", ("hipcub::DeviceSegmentedReduce", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::DeviceSelect", ("hipcub::DeviceSelect", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::FpLimits", ("hipcub::FpLimits", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::KeyValuePair", ("hipcub::KeyValuePair", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::Max", ("hipcub::Max", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::Min", ("hipcub::Min", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::Sum", ("hipcub::Sum", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::Log2", ("hipcub::Log2", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::LaneId", ("hipcub::LaneId", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::WarpMask", ("hipcub::WarpMask", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::ShuffleIndex", ("hipcub::ShuffleIndex", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::ShuffleDown", ("hipcub::ShuffleDown", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::ArgIndexInputIterator", ("hipcub::ArgIndexInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::TransformInputIterator", ("hipcub::TransformInputIterator", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::WarpReduce", ("hipcub::WarpReduce", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("cub::CTA_SYNC", ("hipcub::CTA_SYNC", CONV_SPECIAL_FUNC, API_RUNTIME)), + ("nvtxMark", ("roctxMark", CONV_OTHER, API_ROCTX)), + ("nvtxMarkA", ("roctxMarkA", CONV_OTHER, API_ROCTX)), + ("nvtxRangePushA", ("roctxRangePushA", CONV_OTHER, API_ROCTX)), + ("nvtxRangePop", ("roctxRangePop", CONV_OTHER, API_ROCTX)), + ("nvtxRangeStartA", ("roctxRangeStartA", CONV_OTHER, API_ROCTX)), + ("nvtxRangeEnd", ("roctxRangeStop", CONV_OTHER, API_ROCTX)), + ("nvtxRangeId_t", ("int", CONV_OTHER, API_ROCTX)), + ("nvmlReturn_t", ("rsmi_status_t", CONV_OTHER, API_ROCMSMI)), + ("NVML_SUCCESS", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)), + ("NVML_P2P_CAPS_INDEX_READ", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)), + ("NVML_P2P_STATUS_OK", ("RSMI_STATUS_SUCCESS", CONV_OTHER, API_ROCMSMI)), + ("NVML_ERROR_INSUFFICIENT_SIZE", ("RSMI_STATUS_INSUFFICIENT_SIZE", CONV_OTHER, API_ROCMSMI)), + ("nvmlDevice_t", ("uint32_t", CONV_OTHER, API_ROCMSMI)), + ("nvmlGpuP2PStatus_t", ("bool", CONV_OTHER, API_ROCMSMI)), + ("nvmlProcessInfo_t", ("rsmi_process_info_t", CONV_OTHER, API_ROCMSMI)), + ("nvmlGpuP2PCapsIndex_t", ("uint32_t", CONV_OTHER, API_ROCMSMI)), + ] +) + +CUDA_SPECIAL_MAP = collections.OrderedDict( + [ + # SPARSE + ("cusparseStatus_t", ("hipsparseStatus_t", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseHandle_t", ("hipsparseHandle_t", CONV_MATH_FUNC, API_SPECIAL)), + ("cuComplex", ("hipComplex", CONV_TYPE, API_SPECIAL)), + ("cuDoubleComplex", ("hipDoubleComplex", CONV_TYPE, API_SPECIAL)), + ( + "CUSPARSE_POINTER_MODE_HOST", + ("HIPSPARSE_POINTER_MODE_HOST", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ("cusparseOperation_t", ("hipsparseOperation_t", CONV_TYPE, API_SPECIAL)), + ( + "cusparseCreateMatDescr", + ("hipsparseCreateMatDescr", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseCreate", ("hipsparseCreate", CONV_MATH_FUNC, API_SPECIAL)), + ( + "cusparseDestroyMatDescr", + ("hipsparseDestroyMatDescr", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseDestroy", ("hipsparseDestroy", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseXcoo2csr", ("hipsparseXcoo2csr", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseMatDescr_t", ("hipsparseMatDescr_t", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDiagType_t", ("hipsparseDiagType_t", CONV_TYPE, API_SPECIAL)), + ("CUSPARSE_DIAG_TYPE_UNIT", ("HIPSPARSE_DIAG_TYPE_UNIT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_DIAG_TYPE_NON_UNIT", ("HIPSPARSE_DIAG_TYPE_NON_UNIT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("cusparseSetMatDiagType", ("hipsparseSetMatDiagType", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseFillMode_t", ("hipsparseFillMode_t", CONV_TYPE, API_SPECIAL)), + ("CUSPARSE_FILL_MODE_UPPER", ("HIPSPARSE_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_FILL_MODE_LOWER", ("HIPSPARSE_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("cusparseSetMatFillMode", ("hipsparseSetMatFillMode", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDirection_t", ("hipsparseDirection_t", CONV_TYPE, API_SPECIAL)), + ("CUSPARSE_DIRECTION_ROW", ("HIPSPARSE_DIRECTION_ROW", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_DIRECTION_COLUMN", ("HIPSPARSE_DIRECTION_COLUMN", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("cusparseSolvePolicy_t", ("hipsparseSolvePolicy_t", CONV_TYPE, API_SPECIAL)), + ("CUSPARSE_SOLVE_POLICY_NO_LEVEL", ("HIPSPARSE_SOLVE_POLICY_NO_LEVEL", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SOLVE_POLICY_USE_LEVEL", ("HIPSPARSE_SOLVE_POLICY_USE_LEVEL", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("cusparseCreateBsrsv2Info", ("hipsparseCreateBsrsv2Info", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateBsrsm2Info", ("hipsparseCreateBsrsm2Info", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDestroyBsrsv2Info", ("hipsparseDestroyBsrsv2Info", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDestroyBsrsm2Info", ("hipsparseDestroyBsrsm2Info", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrmm", ("hipsparseSbsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrmm", ("hipsparseDbsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrmm", ("hipsparseCbsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrmm", ("hipsparseZbsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrmv", ("hipsparseSbsrmv", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrmv", ("hipsparseDbsrmv", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrmv", ("hipsparseCbsrmv", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrmv", ("hipsparseZbsrmv", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsv2_bufferSize", ("hipsparseSbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsv2_bufferSize", ("hipsparseDbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsv2_bufferSize", ("hipsparseCbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsv2_bufferSize", ("hipsparseZbsrsv2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsv2_analysis", ("hipsparseSbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsv2_analysis", ("hipsparseDbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsv2_analysis", ("hipsparseCbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsv2_analysis", ("hipsparseZbsrsv2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsv2_solve", ("hipsparseSbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsv2_solve", ("hipsparseDbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsv2_solve", ("hipsparseCbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsv2_solve", ("hipsparseZbsrsv2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsm2_bufferSize", ("hipsparseSbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsm2_bufferSize", ("hipsparseDbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsm2_bufferSize", ("hipsparseCbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsm2_bufferSize", ("hipsparseZbsrsm2_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsm2_analysis", ("hipsparseSbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsm2_analysis", ("hipsparseDbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsm2_analysis", ("hipsparseCbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsm2_analysis", ("hipsparseZbsrsm2_analysis", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSbsrsm2_solve", ("hipsparseSbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDbsrsm2_solve", ("hipsparseDbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCbsrsm2_solve", ("hipsparseCbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZbsrsm2_solve", ("hipsparseZbsrsm2_solve", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrmm2", ("hipsparseScsrmm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrmm2", ("hipsparseDcsrmm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCcsrmm2", ("hipsparseCcsrmm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZcsrmm2", ("hipsparseZcsrmm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrmm", ("hipsparseScsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrmm", ("hipsparseDcsrmm", CONV_MATH_FUNC, API_SPECIAL)), + ( + "cusparseXcsrsort_bufferSizeExt", + ("hipsparseXcsrsort_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseCreateCsrgemm2Info", ("hipsparseCreateCsrgemm2Info", CONV_MATH_FUNC, API_SPECIAL)), + ( + "cusparseDestroyCsrgemm2Info", + ("hipsparseDestroyCsrgemm2Info", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseXcsrgemm2Nnz", ("hipsparseXcsrgemm2Nnz", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrgemm2_bufferSizeExt", ("hipsparseDcsrgemm2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrgemm2_bufferSizeExt", ("hipsparseScsrgemm2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrgemm2", ("hipsparseDcsrgemm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrgemm2", ("hipsparseScsrgemm2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSetPointerMode", ("hipsparseSetPointerMode", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseXcsrgeam2Nnz", ("hipsparseXcsrgeam2Nnz", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrgeam2_bufferSizeExt", ("hipsparseScsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrgeam2_bufferSizeExt", ("hipsparseDcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCcsrgeam2_bufferSizeExt", ("hipsparseCcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZcsrgeam2_bufferSizeExt", ("hipsparseZcsrgeam2_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseScsrgeam2", ("hipsparseScsrgeam2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDcsrgeam2", ("hipsparseDcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCcsrgeam2", ("hipsparseCcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseZcsrgeam2", ("hipsparseZcsrgeam2", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseXcsrsort", ("hipsparseXcsrsort", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseXbsrsm2_zeroPivot", ("hipsparseXbsrsm2_zeroPivot", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseXbsrsv2_zeroPivot", ("hipsparseXbsrsv2_zeroPivot", CONV_MATH_FUNC, API_SPECIAL)), + ( + "cusparseXcoosort_bufferSizeExt", + ("hipsparseXcoosort_bufferSizeExt", CONV_MATH_FUNC, API_SPECIAL), + ), + ( + "cusparseXcoosortByRow", + ("hipsparseXcoosortByRow", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseSetStream", ("hipsparseSetStream", CONV_MATH_FUNC, API_SPECIAL)), + ( + "cusparseCreateIdentityPermutation", + ("hipsparseCreateIdentityPermutation", CONV_MATH_FUNC, API_SPECIAL), + ), + ( + "cusparseSetMatIndexBase", + ("hipsparseSetMatIndexBase", CONV_MATH_FUNC, API_SPECIAL), + ), + ("cusparseSetMatType", ("hipsparseSetMatType", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMV", ("hipsparseSpMV", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMV_bufferSize", ("hipsparseSpMV_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMM", ("hipsparseSpMM", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMM_bufferSize", ("hipsparseSpMM_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateDnMat", ("hipsparseCreateDnMat", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDnMatSetStridedBatch", ("hipsparseDnMatSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCsrSetStridedBatch", ("hipsparseCsrSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateDnVec", ("hipsparseCreateDnVec", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateCsr", ("hipsparseCreateCsr", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDestroyDnMat", ("hipsparseDestroyDnMat", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDestroyDnVec", ("hipsparseDestroyDnVec", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDestroySpMat", ("hipsparseDestroySpMat", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpGEMM_destroyDescr", ("hipsparseSpGEMM_destroyDescr", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateCoo", ("hipsparseCreateCoo", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCreateCsr", ("hipsparseCreateCsr", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpGEMM_createDescr", ("hipsparseSpGEMM_createDescr", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseDnMatSetStridedBatch", ("hipsparseDnMatSetStridedBatch", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpGEMM_copy", ("hipsparseSpGEMM_copy", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSDDMM_bufferSize", ("hipsparseSDDMM_bufferSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSDDMM_preprocess", ("hipsparseSDDMM_preprocess", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSDDMM", ("hipsparseSDDMM", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpGEMM_compute", ("hipsparseSpGEMM_compute", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpGEMM_workEstimation", ("hipsparseSpGEMM_workEstimation", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMatGetSize", ("hipsparseSpMatGetSize", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseCsrSetPointers", ("hipsparseCsrSetPointers", CONV_MATH_FUNC, API_SPECIAL)), + ("cusparseSpMVAlg_t", ("hipsparseSpMVAlg_t", CONV_TYPE, API_SPECIAL)), + ("cusparseSpMMAlg_t", ("hipsparseSpMMAlg_t", CONV_TYPE, API_SPECIAL)), + ("cusparseIndexType_t", ("hipsparseIndexType_t", CONV_TYPE, API_SPECIAL)), + # Unsupported ("cusparseMatDescr", ("hipsparseMatDescr", CONV_TYPE, API_SPECIAL)), + # Unsupported ("cusparseDnMatDescr", ("hipsparseDnMatDescr", CONV_TYPE, API_SPECIAL)), + # Unsupported ("cusparseDnVecDescr", ("hipsparseDnVecDescr", CONV_TYPE, API_SPECIAL)), + # Unsupported ("cusparseSpMatDescr", ("hipsparseSpMatDescr", CONV_TYPE, API_SPECIAL)), + # Unsupported ("cusparseSpGEMMDescr", ("hipsparseSpGEMMDescr", CONV_TYPE, API_SPECIAL)), + ("cusparseDnMatDescr_t", ("hipsparseDnMatDescr_t", CONV_TYPE, API_SPECIAL)), + ("cusparseDnVecDescr_t", ("hipsparseDnVecDescr_t", CONV_TYPE, API_SPECIAL)), + ("cusparseSpMatDescr_t", ("hipsparseSpMatDescr_t", CONV_TYPE, API_SPECIAL)), + ("cusparseSpGEMMDescr_t", ("hipsparseSpGEMMDescr_t", CONV_TYPE, API_SPECIAL)), + ("CUSPARSE_INDEX_32I", ("HIPSPARSE_INDEX_32I", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_INDEX_64I", ("HIPSPARSE_INDEX_64I", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_ORDER_COL", ("HIPSPARSE_ORDER_COLUMN", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_MV_ALG_DEFAULT", ("HIPSPARSE_MV_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_MM_ALG_DEFAULT", ("HIPSPARSE_MM_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SPMM_COO_ALG1", ("HIPSPARSE_SPMM_COO_ALG1", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SPMM_COO_ALG2", ("HIPSPARSE_SPMM_COO_ALG2", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_COOMV_ALG", ("HIPSPARSE_COOMV_ALG", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SPMM_CSR_ALG1", ("HIPSPARSE_CSRMM_ALG1", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SPGEMM_DEFAULT", ("HIPSPARSE_SPGEMM_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSPARSE_SDDMM_ALG_DEFAULT", ("HIPSPARSE_SDDMM_ALG_DEFAULT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ( + "CUSPARSE_STATUS_SUCCESS", + ("HIPSPARSE_STATUS_SUCCESS", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_NOT_INITIALIZED", + ("HIPSPARSE_STATUS_NOT_INITIALIZED", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_ALLOC_FAILED", + ("HIPSPARSE_STATUS_ALLOC_FAILED", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_INVALID_VALUE", + ("HIPSPARSE_STATUS_INVALID_VALUE", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_MAPPING_ERROR", + ("HIPSPARSE_STATUS_MAPPING_ERROR", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_EXECUTION_FAILED", + ("HIPSPARSE_STATUS_EXECUTION_FAILED", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_INTERNAL_ERROR", + ("HIPSPARSE_STATUS_INTERNAL_ERROR", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED", + ( + "HIPSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED", + CONV_NUMERIC_LITERAL, + API_SPECIAL, + ), + ), + ( + "CUSPARSE_STATUS_ARCH_MISMATCH", + ("HIPSPARSE_STATUS_ARCH_MISMATCH", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_STATUS_ZERO_PIVOT", + ("HIPSPARSE_STATUS_ZERO_PIVOT", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_OPERATION_TRANSPOSE", + ("HIPSPARSE_OPERATION_TRANSPOSE", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_OPERATION_NON_TRANSPOSE", + ("HIPSPARSE_OPERATION_NON_TRANSPOSE", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_OPERATION_CONJUGATE_TRANSPOSE", + ( + "HIPSPARSE_OPERATION_CONJUGATE_TRANSPOSE", + CONV_NUMERIC_LITERAL, + API_SPECIAL, + ), + ), + ( + "CUSPARSE_INDEX_BASE_ZERO", + ("HIPSPARSE_INDEX_BASE_ZERO", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_INDEX_BASE_ONE", + ("HIPSPARSE_INDEX_BASE_ONE", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUSPARSE_MATRIX_TYPE_GENERAL", + ("HIPSPARSE_MATRIX_TYPE_GENERAL", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + # SOLVER + ("cublasOperation_t", ("hipsolverOperation_t", CONV_TYPE, API_SPECIAL)), + ("CUBLAS_OP_N", ("HIPSOLVER_OP_N", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ( + "CUBLAS_OP_T", + ("HIPSOLVER_OP_T", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUBLAS_OP_C", + ("HIPSOLVER_OP_C", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ("cublasFillMode_t", ("hipsolverFillMode_t", CONV_TYPE, API_SPECIAL)), + ( + "CUBLAS_FILL_MODE_LOWER", + ("HIPSOLVER_FILL_MODE_LOWER", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ( + "CUBLAS_FILL_MODE_UPPER", + ("HIPSOLVER_FILL_MODE_UPPER", CONV_NUMERIC_LITERAL, API_SPECIAL), + ), + ("cublasSideMode_t", ("hipsolverSideMode_t", CONV_TYPE, API_SPECIAL)), + ("CUBLAS_SIDE_LEFT", ("HIPSOLVER_SIDE_LEFT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUBLAS_SIDE_RIGHT", ("HIPSOLVER_SIDE_RIGHT", CONV_NUMERIC_LITERAL, API_SPECIAL)), + + ("cusolverEigMode_t", ("hipsolverEigMode_t", CONV_TYPE, API_SPECIAL)), + ("CUSOLVER_EIG_MODE_VECTOR", ("HIPSOLVER_EIG_MODE_VECTOR", CONV_NUMERIC_LITERAL, API_SPECIAL)), + ("CUSOLVER_EIG_MODE_NOVECTOR", ("HIPSOLVER_EIG_MODE_NOVECTOR", CONV_NUMERIC_LITERAL, API_SPECIAL)), + + ("syevjInfo_t", ("hipsolverSyevjInfo_t", CONV_TYPE, API_SPECIAL)), + ("cusolverDnCreateSyevjInfo", ("hipsolverDnCreateSyevjInfo", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnXsyevjSetSortEig", ("hipsolverDnXsyevjSetSortEig", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnDestroySyevjInfo", ("hipsolverDnDestroySyevjInfo", CONV_MATH_FUNC, API_SPECIAL)), + + ("gesvdjInfo_t", ("hipsolverGesvdjInfo_t", CONV_TYPE, API_SPECIAL)), + ("cusolverDnCreateGesvdjInfo", ("hipsolverDnCreateGesvdjInfo", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnXgesvdjSetSortEig", ("hipsolverDnXgesvdjSetSortEig", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnDestroyGesvdjInfo", ("hipsolverDnDestroyGesvdjInfo", CONV_MATH_FUNC, API_SPECIAL)), + + ("cusolverDnHandle_t", ("hipsolverDnHandle_t", CONV_TYPE, API_SPECIAL)), + ("cusolverDnCreate", ("hipsolverDnCreate", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnSetStream", ("hipsolverDnSetStream", CONV_MATH_FUNC, API_SPECIAL)), + ("cusolverDnDestroy", ("hipsolverDnDestroy", CONV_MATH_FUNC, API_SPECIAL)), + + # from aten/src/ATen/native/hip/linalg/HIPSolver.cpp + ('cusolverDnParams_t', ('hipsolverDnParams_t', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgeqrf', ('hipsolverDnCgeqrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgeqrf_bufferSize', ('hipsolverDnCgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvd', ('hipsolverDnCgesvd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvd_bufferSize', ('hipsolverDnCgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvdj', ('hipsolverDnCgesvdj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvdjBatched', ('hipsolverDnCgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvdjBatched_bufferSize', ('hipsolverDnCgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvdj_bufferSize', ('hipsolverDnCgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgetrf', ('hipsolverDnCgetrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgetrf_bufferSize', ('hipsolverDnCgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgetrs', ('hipsolverDnCgetrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevd', ('hipsolverDnCheevd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevd_bufferSize', ('hipsolverDnCheevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevj', ('hipsolverDnCheevj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevjBatched', ('hipsolverDnCheevjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevjBatched_bufferSize', ('hipsolverDnCheevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCheevj_bufferSize', ('hipsolverDnCheevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCpotrf', ('hipsolverDnCpotrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCpotrfBatched', ('hipsolverDnCpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCpotrf_bufferSize', ('hipsolverDnCpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCpotrs', ('hipsolverDnCpotrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCpotrsBatched', ('hipsolverDnCpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCungqr', ('hipsolverDnCungqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCungqr_bufferSize', ('hipsolverDnCungqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCunmqr', ('hipsolverDnCunmqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCunmqr_bufferSize', ('hipsolverDnCunmqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgeqrf', ('hipsolverDnDgeqrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgeqrf_bufferSize', ('hipsolverDnDgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvd', ('hipsolverDnDgesvd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvd_bufferSize', ('hipsolverDnDgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvdj', ('hipsolverDnDgesvdj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvdjBatched', ('hipsolverDnDgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvdjBatched_bufferSize', ('hipsolverDnDgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvdj_bufferSize', ('hipsolverDnDgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgetrf', ('hipsolverDnDgetrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgetrf_bufferSize', ('hipsolverDnDgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgetrs', ('hipsolverDnDgetrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDorgqr', ('hipsolverDnDorgqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDorgqr_bufferSize', ('hipsolverDnDorgqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDormqr', ('hipsolverDnDormqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDormqr_bufferSize', ('hipsolverDnDormqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDpotrf', ('hipsolverDnDpotrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDpotrfBatched', ('hipsolverDnDpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDpotrf_bufferSize', ('hipsolverDnDpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDpotrs', ('hipsolverDnDpotrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDpotrsBatched', ('hipsolverDnDpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevd', ('hipsolverDnDsyevd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevd_bufferSize', ('hipsolverDnDsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevj', ('hipsolverDnDsyevj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevjBatched', ('hipsolverDnDsyevjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevjBatched_bufferSize', ('hipsolverDnDsyevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsyevj_bufferSize', ('hipsolverDnDsyevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgeqrf', ('hipsolverDnSgeqrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgeqrf_bufferSize', ('hipsolverDnSgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvd', ('hipsolverDnSgesvd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvd_bufferSize', ('hipsolverDnSgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvdj', ('hipsolverDnSgesvdj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvdjBatched', ('hipsolverDnSgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvdjBatched_bufferSize', ('hipsolverDnSgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgesvdj_bufferSize', ('hipsolverDnSgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgetrf', ('hipsolverDnSgetrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgetrf_bufferSize', ('hipsolverDnSgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSgetrs', ('hipsolverDnSgetrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSorgqr', ('hipsolverDnSorgqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSorgqr_bufferSize', ('hipsolverDnSorgqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSormqr', ('hipsolverDnSormqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSormqr_bufferSize', ('hipsolverDnSormqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSpotrf', ('hipsolverDnSpotrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSpotrfBatched', ('hipsolverDnSpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSpotrf_bufferSize', ('hipsolverDnSpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSpotrs', ('hipsolverDnSpotrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSpotrsBatched', ('hipsolverDnSpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevd', ('hipsolverDnSsyevd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevd_bufferSize', ('hipsolverDnSsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevj', ('hipsolverDnSsyevj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevjBatched', ('hipsolverDnSsyevjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevjBatched_bufferSize', ('hipsolverDnSsyevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsyevj_bufferSize', ('hipsolverDnSsyevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXgeqrf', ('hipsolverDnXgeqrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXgeqrf_bufferSize', ('hipsolverDnXgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXpotrf', ('hipsolverDnXpotrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXpotrf_bufferSize', ('hipsolverDnXpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXpotrs', ('hipsolverDnXpotrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXsyevd', ('hipsolverDnXsyevd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXsyevd_bufferSize', ('hipsolverDnXsyevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgeqrf', ('hipsolverDnZgeqrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgeqrf_bufferSize', ('hipsolverDnZgeqrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvd', ('hipsolverDnZgesvd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvd_bufferSize', ('hipsolverDnZgesvd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvdj', ('hipsolverDnZgesvdj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvdjBatched', ('hipsolverDnZgesvdjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvdjBatched_bufferSize', ('hipsolverDnZgesvdjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvdj_bufferSize', ('hipsolverDnZgesvdj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgetrf', ('hipsolverDnZgetrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgetrf_bufferSize', ('hipsolverDnZgetrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgetrs', ('hipsolverDnZgetrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevd', ('hipsolverDnZheevd', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevd_bufferSize', ('hipsolverDnZheevd_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevj', ('hipsolverDnZheevj', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevjBatched', ('hipsolverDnZheevjBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevjBatched_bufferSize', ('hipsolverDnZheevjBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZheevj_bufferSize', ('hipsolverDnZheevj_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZpotrf', ('hipsolverDnZpotrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZpotrfBatched', ('hipsolverDnZpotrfBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZpotrf_bufferSize', ('hipsolverDnZpotrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZpotrs', ('hipsolverDnZpotrs', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZpotrsBatched', ('hipsolverDnZpotrsBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZungqr', ('hipsolverDnZungqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZungqr_bufferSize', ('hipsolverDnZungqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZunmqr', ('hipsolverDnZunmqr', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZunmqr_bufferSize', ('hipsolverDnZunmqr_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + + # sytrf + ('cusolverDnDsytrf_bufferSize', ('hipsolverDnDsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsytrf_bufferSize', ('hipsolverDnSsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZsytrf_bufferSize', ('hipsolverDnZsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCsytrf_bufferSize', ('hipsolverDnCsytrf_bufferSize', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDsytrf', ('hipsolverDnDsytrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnSsytrf', ('hipsolverDnSsytrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZsytrf', ('hipsolverDnZsytrf', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCsytrf', ('hipsolverDnCsytrf', CONV_MATH_FUNC, API_SPECIAL)), + + # gesdva strided + ( + 'cusolverDnSgesvdaStridedBatched_bufferSize', + ('hipsolverDnSgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL) + ), + ( + 'cusolverDnDgesvdaStridedBatched_bufferSize', + ('hipsolverDnDgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL) + ), + ( + 'cusolverDnCgesvdaStridedBatched_bufferSize', + ('hipsolverDnCgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL) + ), + ( + 'cusolverDnZgesvdaStridedBatched_bufferSize', + ('hipsolverDnZgesvdaStridedBatched_bufferSize', CONV_MATH_FUNC, API_SPECIAL) + ), + ('cusolverDnSgesvdaStridedBatched', ('hipsolverDnSgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnDgesvdaStridedBatched', ('hipsolverDnDgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnCgesvdaStridedBatched', ('hipsolverDnCgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnZgesvdaStridedBatched', ('hipsolverDnZgesvdaStridedBatched', CONV_MATH_FUNC, API_SPECIAL)), + + # gesvdj SetXXX + ('cusolverDnXgesvdjSetTolerance', ('hipsolverDnXgesvdjSetTolerance', CONV_MATH_FUNC, API_SPECIAL)), + ('cusolverDnXgesvdjSetMaxSweeps', ('hipsolverDnXgesvdjSetMaxSweeps', CONV_MATH_FUNC, API_SPECIAL)), + ] +) + +PYTORCH_SPECIFIC_MAPPINGS = collections.OrderedDict( + [ + ("USE_CUDA", ("USE_ROCM", API_PYTORCH)), + ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_PYTORCH)), + ("cudaHostAllocator", ("hipHostAllocator", API_PYTORCH)), + ("cudaDeviceAllocator", ("hipDeviceAllocator", API_PYTORCH)), + ("define MAX_NUM_BLOCKS 200", ("define MAX_NUM_BLOCKS 64", API_PYTORCH)), + ("cuda::CUDAGuard", ("hip::HIPGuardMasqueradingAsCUDA", API_PYTORCH)), + ("CUDAGuard", ("HIPGuardMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::OptionalCUDAGuard", + ("hip::OptionalHIPGuardMasqueradingAsCUDA", API_PYTORCH), + ), + ("OptionalCUDAGuard", ("OptionalHIPGuardMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::CUDAStreamGuard", + ("hip::HIPStreamGuardMasqueradingAsCUDA", API_PYTORCH), + ), + ("CUDAStreamGuard", ("HIPStreamGuardMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::OptionalCUDAStreamGuard", + ("hip::OptionalHIPStreamGuardMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "OptionalCUDAStreamGuard", + ("OptionalHIPStreamGuardMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "cuda::CUDAMultiStreamGuard", + ("hip::HIPMultiStreamGuardMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "CUDAMultiStreamGuard", + ("HIPMultiStreamGuardMasqueradingAsCUDA", API_PYTORCH), + ), + # Only get needs to be transformed this way; all the other ones can go + # straight to the normal versions hip::HIPCachingAllocator + ( + "cuda::CUDACachingAllocator::get", + ("hip::HIPCachingAllocatorMasqueradingAsCUDA::get", API_PYTORCH), + ), + ( + "CUDACachingAllocator::get", + ("HIPCachingAllocatorMasqueradingAsCUDA::get", API_PYTORCH), + ), + ( + "cuda::CUDACachingAllocator::recordStream", + ( + "hip::HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA", + API_PYTORCH, + ), + ), + ( + "CUDACachingAllocator::recordStream", + ( + "HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA", + API_PYTORCH, + ), + ), + ( + "cuda::CUDAAllocator::recordStream", + ( + "hip::HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA", + API_PYTORCH, + ), + ), + ( + "CUDAAllocator::recordStream", + ( + "HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA", + API_PYTORCH, + ), + ), + ("cuda::CUDAStream", ("hip::HIPStreamMasqueradingAsCUDA", API_PYTORCH)), + ("CUDAStream", ("HIPStreamMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::getStreamFromPool", + ("hip::getStreamFromPoolMasqueradingAsCUDA", API_PYTORCH), + ), + ("getStreamFromPool", ("getStreamFromPoolMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::getDefaultCUDAStream", + ("hip::getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "cuda::getStreamFromExternal", + ("hip::getStreamFromExternalMasqueradingAsCUDA", API_PYTORCH), + ), + ("getStreamFromExternal", ("getStreamFromExternalMasqueradingAsCUDA", API_PYTORCH)), + ( + "cuda::getDefaultCUDAStream", + ("hip::getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "getDefaultCUDAStream", + ("getDefaultHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "cuda::getCurrentCUDAStream", + ("hip::getCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "getCurrentCUDAStream", + ("getCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "cuda::setCurrentCUDAStream", + ("hip::setCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "setCurrentCUDAStream", + ("setCurrentHIPStreamMasqueradingAsCUDA", API_PYTORCH), + ), + ( + "ATen/cudnn/Handle.h", + ("ATen/miopen/Handle.h", API_PYTORCH), + ), + # TODO: Undo this special-case; see the header for motivation behind this + # hack. It's VERY important this is only applied to PyTorch HIPify. + ( + "c10/cuda/CUDAGuard.h", + ("ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h", API_PYTORCH), + ), + ( + "c10/cuda/CUDACachingAllocator.h", + ("ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.h", API_PYTORCH), + ), + ( + "c10/cuda/CUDAStream.h", + ("ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h", API_PYTORCH), + ), + ("gloo/cuda.h", ("gloo/hip.h", API_PYTORCH)), + ( + "gloo/cuda_allreduce_halving_doubling.h", + ("gloo/hip_allreduce_halving_doubling.h", API_PYTORCH), + ), + ( + "gloo/cuda_allreduce_halving_doubling_pipelined.h", + ("gloo/hip_allreduce_halving_doubling_pipelined.h", API_PYTORCH), + ), + ("gloo/cuda_allreduce_ring.h", ("gloo/hip_allreduce_ring.h", API_PYTORCH)), + ( + "gloo/cuda_broadcast_one_to_all.h", + ("gloo/hip_broadcast_one_to_all.h", API_PYTORCH), + ), + ( + "gloo::CudaAllreduceHalvingDoublingPipelined", + ("gloo::HipAllreduceHalvingDoublingPipelined", API_PYTORCH), + ), + ("gloo::CudaBroadcastOneToAll", ("gloo::HipBroadcastOneToAll", API_PYTORCH)), + ("gloo::CudaHostWorkspace", ("gloo::HipHostWorkspace", API_PYTORCH)), + ("gloo::CudaDeviceWorkspace", ("gloo::HipDeviceWorkspace", API_PYTORCH)), + ("CUDNN_RNN_RELU", ("miopenRNNRELU", API_PYTORCH)), + ("CUDNN_RNN_TANH", ("miopenRNNTANH", API_PYTORCH)), + ("CUDNN_LSTM", ("miopenLSTM", API_PYTORCH)), + ("CUDNN_GRU", ("miopenGRU", API_PYTORCH)), + ("cudnnRNNMode_t", ("miopenRNNMode_t", API_PYTORCH)), + ("magma_queue_create_from_cuda", ("magma_queue_create_from_hip", API_PYTORCH)), + ] +) + +CAFFE2_SPECIFIC_MAPPINGS = collections.OrderedDict( + [ + ("PYTORCH_NO_CUDA_MEMORY_CACHING", ("PYTORCH_NO_CUDA_MEMORY_CACHING", API_CAFFE2)), + ("cuda_stream", ("hip_stream", API_CAFFE2)), + # if the header is a native hip folder (under hip directory), + # there is no need to add a hip path to it; the trie in hipify script + # takes this mapping order to forbid further replacement + ("/hip/", ("/hip/", API_CAFFE2)), + ("/context_gpu", ("/hip/context_gpu", API_CAFFE2)), + ("/common_gpu", ("/hip/common_gpu", API_CAFFE2)), + ("/cuda_nccl_gpu", ("/hip/hip_nccl_gpu", API_CAFFE2)), + ("/mixed_utils", ("/hip/mixed_utils", API_CAFFE2)), + ("/operator_fallback_gpu", ("/hip/operator_fallback_gpu", API_CAFFE2)), + ( + "/spatial_batch_norm_op_impl", + ("/hip/spatial_batch_norm_op_impl", API_CAFFE2), + ), + ( + "/recurrent_network_executor_gpu", + ("/hip/recurrent_network_executor_gpu", API_CAFFE2), + ), + ( + "/generate_proposals_op_util_nms_gpu", + ("/hip/generate_proposals_op_util_nms_gpu", API_CAFFE2), + ), + ("/max_pool_with_index_gpu", ("/hip/max_pool_with_index_gpu", API_CAFFE2)), + ("/THCCachingAllocator_gpu", ("/hip/THCCachingAllocator_gpu", API_CAFFE2)), + ("/top_k_heap_selection", ("/hip/top_k_heap_selection", API_CAFFE2)), + ("/top_k_radix_selection", ("/hip/top_k_radix_selection", API_CAFFE2)), + ("/GpuAtomics", ("/hip/GpuAtomics", API_CAFFE2)), + ("/GpuDefs", ("/hip/GpuDefs", API_CAFFE2)), + ("/GpuScanUtils", ("/hip/GpuScanUtils", API_CAFFE2)), + ("/GpuBitonicSort", ("/hip/GpuBitonicSort", API_CAFFE2)), + ("/math/reduce.cuh", ("/math/hip/reduce.cuh", API_CAFFE2)), + ("/sgd/adagrad_fused_op_gpu.cuh", ("/sgd/hip/adagrad_fused_op_gpu.cuh", API_CAFFE2)), + ("/operators/segment_reduction_op_gpu.cuh", ("/operators/hip/segment_reduction_op_gpu.cuh", API_CAFFE2)), + ("/gather_op.cuh", ("/hip/gather_op.cuh", API_CAFFE2)), + ("caffe2/core/common_cudnn.h", ("caffe2/core/hip/common_miopen.h", API_CAFFE2)), + ("REGISTER_CUDA_OPERATOR", ("REGISTER_HIP_OPERATOR", API_CAFFE2)), + ("CUDA_1D_KERNEL_LOOP", ("HIP_1D_KERNEL_LOOP", API_CAFFE2)), + ("CUDAContext", ("HIPContext", API_CAFFE2)), + ("CAFFE_CUDA_NUM_THREADS", ("CAFFE_HIP_NUM_THREADS", API_CAFFE2)), + ("HasCudaGPU", ("HasHipGPU", API_CAFFE2)), + ("__expf", ("expf", API_CAFFE2)), + ("CUBLAS_ENFORCE", ("HIPBLAS_ENFORCE", API_CAFFE2)), + ("CUBLAS_CHECK", ("HIPBLAS_CHECK", API_CAFFE2)), + ("cublas_handle", ("hipblas_handle", API_CAFFE2)), + ("CURAND_ENFORCE", ("HIPRAND_ENFORCE", API_CAFFE2)), + ("CURAND_CHECK", ("HIPRAND_CHECK", API_CAFFE2)), + ("curandGenerateUniform", ("hiprandGenerateUniform", API_CAFFE2)), + ("curand_generator", ("hiprand_generator", API_CAFFE2)), + ("CaffeCudaGetDevice", ("CaffeHipGetDevice", API_CAFFE2)), + # do not rename CUDA_KERNEL_ASSERT, lazyInitCUDA in caffe2 sources + # the ordered dict guarantees this pattern will match first, before "CUDA" + ("CUDA_KERNEL_ASSERT", ("CUDA_KERNEL_ASSERT", API_CAFFE2)), + ("lazyInitCUDA", ("lazyInitCUDA", API_CAFFE2)), + ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_CAFFE2)), + ("CUDA", ("HIP", API_CAFFE2)), + ("Cuda", ("Hip", API_CAFFE2)), + ("cuda_", ("hip_", API_CAFFE2)), + ("_cuda", ("_hip", API_CAFFE2)), + ("CUDNN", ("MIOPEN", API_CAFFE2)), + ("CuDNN", ("MIOPEN", API_CAFFE2)), + ("cudnn", ("miopen", API_CAFFE2)), + ("namespace cuda", ("namespace hip", API_CAFFE2)), + ("cuda::CUDAGuard", ("hip::HIPGuard", API_CAFFE2)), + ("cuda::OptionalCUDAGuard", ("hip::OptionalHIPGuard", API_CAFFE2)), + ("cuda::CUDAStreamGuard", ("hip::HIPStreamGuard", API_CAFFE2)), + ("cuda::OptionalCUDAStreamGuard", ("hip::OptionalHIPStreamGuard", API_CAFFE2)), + ("c10/cuda/CUDAGuard.h", ("c10/hip/HIPGuard.h", API_CAFFE2)), + ("gloo/cuda", ("gloo/hip", API_CAFFE2)), + ] +) + +# We must tread very carefully here. Blanket conversions like are done +# in CAFFE2_SPECIFIC_MAPPINGS are not presently supported on PyTorch, +# because a regex for CUDA will also match a filename like CUDAGuard.h, +# but the HIPIFY script doesn't presently move the file and so the substitution +# will be invalid. Instead, we specifically list out every identifier +# and file from c10/cuda which may be used externally, and do substitutions this +# way. +# +# NB: if you want a transformation to ONLY apply to the c10/ directory, +# put it as API_CAFFE2 +C10_MAPPINGS = collections.OrderedDict( + [ + ("CUDA_VERSION", ("TORCH_HIP_VERSION", API_PYTORCH)), + ("CUDA_LAUNCH_BLOCKING=1", ("AMD_SERIALIZE_KERNEL=3", API_C10)), + ("CUDA_LAUNCH_BLOCKING", ("AMD_SERIALIZE_KERNEL", API_C10)), + ("cuda::compat::", ("hip::compat::", API_C10)), + ("c10/cuda/CUDAAlgorithm.h", ("c10/hip/HIPAlgorithm.h", API_C10)), + ("c10/cuda/CUDADeviceAssertion.h", ("c10/hip/HIPDeviceAssertion.h", API_C10)), + ("c10/cuda/CUDADeviceAssertionHost.h", ("c10/hip/HIPDeviceAssertionHost.h", API_C10)), + ("c10/cuda/CUDAException.h", ("c10/hip/HIPException.h", API_C10)), + ("c10/cuda/CUDAMacros.h", ("c10/hip/HIPMacros.h", API_C10)), + ("c10/cuda/CUDAMathCompat.h", ("c10/hip/HIPMathCompat.h", API_C10)), + ("c10/cuda/CUDAFunctions.h", ("c10/hip/HIPFunctions.h", API_C10)), + ("c10/cuda/CUDAMiscFunctions.h", ("c10/hip/HIPMiscFunctions.h", API_C10)), + ("c10/cuda/CUDAStream.h", ("c10/hip/HIPStream.h", API_C10)), + ("c10/cuda/CUDAGraphsC10Utils.h", ("c10/hip/HIPGraphsC10Utils.h", API_C10)), + ("c10/cuda/CUDAAllocatorConfig.h", ("c10/hip/HIPAllocatorConfig.h", API_C10)), + ("c10/cuda/CUDACachingAllocator.h", ("c10/hip/HIPCachingAllocator.h", API_C10)), + ("c10/cuda/impl/CUDATest.h", ("c10/hip/impl/HIPTest.h", API_C10)), + ("c10/cuda/impl/CUDAGuardImpl.h", ("c10/hip/impl/HIPGuardImpl.h", API_C10)), + ( + "c10/cuda/impl/cuda_cmake_macros.h", + ("c10/hip/impl/hip_cmake_macros.h", API_C10), + ), + ("C10_CUDA_CHECK", ("C10_HIP_CHECK", API_C10)), + ("C10_CUDA_CHECK_WARN", ("C10_HIP_CHECK_WARN", API_C10)), + ("C10_CUDA_ERROR_HANDLED", ("C10_HIP_ERROR_HANDLED", API_C10)), + ("C10_CUDA_IGNORE_ERROR", ("C10_HIP_IGNORE_ERROR", API_C10)), + ("C10_CUDA_CLEAR_ERROR", ("C10_HIP_CLEAR_ERROR", API_C10)), + ("c10::cuda", ("c10::hip", API_C10)), + ("cuda::CUDAStream", ("hip::HIPStream", API_C10)), + ("CUDAStream", ("HIPStream", API_C10)), + # This substitution is not permissible, because there's another copy of this + # function in torch/cuda.h + # ("cuda::device_count", ("hip::device_count", API_C10)), + ("cuda::current_device", ("hip::current_device", API_C10)), + ("cuda::set_device", ("hip::set_device", API_C10)), + ("cuda::device_synchronize", ("hip::device_synchronize", API_C10)), + ("cuda::getStreamFromPool", ("hip::getStreamFromPool", API_C10)), + ("getStreamFromPool", ("getStreamFromPool", API_C10)), + ("cuda::getDefaultCUDAStream", ("hip::getDefaultHIPStream", API_C10)), + ("getDefaultCUDAStream", ("getDefaultHIPStream", API_C10)), + ("cuda::getCurrentCUDAStream", ("hip::getCurrentHIPStream", API_C10)), + ("getCurrentCUDAStream", ("getCurrentHIPStream", API_C10)), + ("cuda::get_cuda_check_prefix", ("hip::get_cuda_check_prefix", API_C10)), + ("cuda::setCurrentCUDAStream", ("hip::setCurrentHIPStream", API_C10)), + ("setCurrentCUDAStream", ("setCurrentHIPStream", API_C10)), + ("cuda::CUDACachingAllocator", ("hip::HIPCachingAllocator", API_C10)), + ("CUDACachingAllocator", ("HIPCachingAllocator", API_C10)), + ("cuda::CUDAAllocatorConfig", ("hip::HIPAllocatorConfig", API_C10)), + ("CUDAAllocatorConfig", ("HIPAllocatorConfig", API_C10)), + ("pinned_use_cuda_host_register", ("pinned_use_hip_host_register", API_C10)), + ("c10::cuda::CUDAAllocator", ("c10::hip::HIPAllocator", API_C10)), + ("cuda::CUDAAllocator", ("hip::HIPAllocator", API_C10)), + ("CUDAStreamCaptureModeGuard", ("HIPStreamCaptureModeGuard", API_C10)), + ("cuda::CUDAStreamCaptureModeGuard", ("cuda::HIPStreamCaptureModeGuard", API_C10)), + ("CUDAAllocator", ("HIPAllocator", API_C10)), + ("C10_CUDA_KERNEL_LAUNCH_CHECK", ("C10_HIP_KERNEL_LAUNCH_CHECK", API_C10)) + ] +) + +# NB: C10 mappings are more specific than Caffe2 mappings, so run them +# first +CUDA_TO_HIP_MAPPINGS = [ + CUDA_IDENTIFIER_MAP, + CUDA_TYPE_NAME_MAP, + CUDA_INCLUDE_MAP, + CUDA_SPECIAL_MAP, + C10_MAPPINGS, + PYTORCH_SPECIFIC_MAPPINGS, + CAFFE2_SPECIFIC_MAPPINGS, +] diff --git a/lib/python3.11/site-packages/torch/utils/hipify/hipify_python.py b/lib/python3.11/site-packages/torch/utils/hipify/hipify_python.py new file mode 100644 index 0000000000000000000000000000000000000000..008aabe82157065e570776aa45bc52263b00fd86 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hipify/hipify_python.py @@ -0,0 +1,1175 @@ +#!/usr/bin/env python3 +# mypy: allow-untyped-defs +""" The Python Hipify script. +## +# Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved. +# 2017-2018 Advanced Micro Devices, Inc. and +# Facebook Inc. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +""" +import argparse +import fnmatch +import re +import shutil +import sys +import os + +from . import constants +from .cuda_to_hip_mappings import CUDA_TO_HIP_MAPPINGS +from .cuda_to_hip_mappings import MATH_TRANSPILATIONS + +from typing import Dict, List, Iterator, Optional +from collections.abc import Mapping, Iterable +from enum import Enum +import functools +import hashlib + +class CurrentState(Enum): + INITIALIZED = 1 + DONE = 2 + +class HipifyResult: + def __init__(self, current_state, hipified_path): + self.current_state = current_state + self.hipified_path = hipified_path + self.status = "" + + def __str__(self): + return (f"HipifyResult:: current_state: {self.current_state}, hipified_path : {self.hipified_path}, status: {self.status}") + +HipifyFinalResult = Dict[str, HipifyResult] +HIPIFY_C_BREADCRUMB = "// !!! This is a file automatically generated by hipify!!!\n" +HIPIFY_FINAL_RESULT: HipifyFinalResult = {} + +# Hardcode the PyTorch template map +"""This dictionary provides the mapping from PyTorch kernel template types +to their actual types.""" +PYTORCH_TEMPLATE_MAP = {"Dtype": "scalar_t", "T": "scalar_t"} + +__all__ = ['InputError', 'openf', 'bcolors', 'GeneratedFileCleaner', 'match_extensions', 'matched_files_iter', + 'preprocess_file_and_save_result', 'compute_stats', 'add_dim3', 'processKernelLaunches', 'find_closure_group', + 'find_bracket_group', 'find_parentheses_group', 'replace_math_functions', 'hip_header_magic', 'replace_extern_shared', + 'get_hip_file_path', 'is_out_of_place', 'is_pytorch_file', 'is_cusparse_file', 'is_special_file', 'is_caffe2_gpu_file', + 'is_caffe2_gpu_file', 'Trie', 'preprocessor', 'file_specific_replacement', 'file_add_header', + 'fix_static_global_kernels', 'extract_arguments', 'str2bool', 'CurrentState', 'HipifyResult', 'hipify'] + + +class InputError(Exception): + # Exception raised for errors in the input. + + def __init__(self, message): + super().__init__(message) + self.message = message + + def __str__(self): + return f"Input error: {self.message}" + + +def openf(filename, mode): + return open(filename, mode, errors='ignore') + + +# Color coding for printing +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + + +# To the programmer, the output of hipify most likely are intermediates. +# This class allows users of hipify to ask for a cleanup by running the +# hipify and compilation in a with instantiating this context manager class +# with keep_intermediates=False. +# The main usecase is the cpp_extensions, specifically the load method. +# It is a good idea to keep intermediates (in case of errors or to +# not recompile unchanged files), but in cases where you don't want to +# keep them (e.g. in the CI), this can be used to remove files. +class GeneratedFileCleaner: + """Context Manager to clean up generated files""" + def __init__(self, keep_intermediates=False): + self.keep_intermediates = keep_intermediates + self.files_to_clean = set() + self.dirs_to_clean = [] + + def __enter__(self): + return self + + def open(self, fn, *args, **kwargs): + if not os.path.exists(fn): + self.files_to_clean.add(os.path.abspath(fn)) + return open(fn, *args, **kwargs) + + def makedirs(self, dn, exist_ok=False): + parent, n = os.path.split(dn) + if not n: + parent, n = os.path.split(parent) + if parent and n and not os.path.exists(parent): + self.makedirs(parent, exist_ok=True) + if not os.path.isdir(dn) or not exist_ok: + os.mkdir(dn) + self.dirs_to_clean.append(os.path.abspath(dn)) + + def __exit__(self, type, value, traceback): + if not self.keep_intermediates: + for f in self.files_to_clean: + os.unlink(f) + for d in self.dirs_to_clean[::-1]: + os.rmdir(d) + + +# Follow UNIX convention for paths to use '/' instead of '\\' on Windows +def _to_unix_path(path: str) -> str: + return path.replace(os.sep, '/') + +def match_extensions(filename: str, extensions: Iterable) -> bool: + """Helper method to see if filename ends with certain extension""" + return any(filename.endswith(e) for e in extensions) + + +def _fnmatch(filepath, patterns): + return any(fnmatch.fnmatch(filepath, pattern) for pattern in patterns) + + +def matched_files_iter( + root_path: str, + includes: Iterable = (), + ignores: Iterable = (), + extensions: Iterable = (), + out_of_place_only: bool = False, + is_pytorch_extension: bool = False) -> Iterator[str]: + + exact_matches = set(includes) + + # This is a very rough heuristic; really, we want to avoid scanning + # any file which is not checked into source control, but this script + # needs to work even if you're in a Git or Hg checkout, so easier to + # just block the biggest time sinks that won't matter in the + # end. + for (abs_dirpath, dirs, filenames) in os.walk(root_path, topdown=True): + rel_dirpath = os.path.relpath(abs_dirpath, root_path) + if rel_dirpath == '.': + # Blah blah blah O(n) blah blah + if ".git" in dirs: + dirs.remove(".git") + if "build" in dirs: + dirs.remove("build") + if "third_party" in dirs: + dirs.remove("third_party") + dirs.append("third_party/nvfuser") + for filename in filenames: + filepath = _to_unix_path(os.path.join(abs_dirpath, filename)) + rel_filepath = _to_unix_path(os.path.join(rel_dirpath, filename)) + # We respect extensions, UNLESS you wrote the entire + # filename verbatim, in which case we always accept it + if ( + _fnmatch(filepath, includes) + and (not _fnmatch(filepath, ignores)) + and (match_extensions(filepath, extensions) or filepath in exact_matches) + ): + if not is_pytorch_extension: # for pytorch extensions, consider all files + if not is_pytorch_file(rel_filepath) and not is_caffe2_gpu_file(rel_filepath): + continue + if out_of_place_only and not is_out_of_place(rel_filepath): + continue + yield filepath + + +def preprocess_file_and_save_result( + output_directory: str, + filepath: str, + all_files: Iterable, + header_include_dirs: Iterable, + stats: Dict[str, List], + hip_clang_launch: bool, + is_pytorch_extension: bool, + clean_ctx: GeneratedFileCleaner, + show_progress: bool) -> None: + fin_path = os.path.abspath(os.path.join(output_directory, filepath)) + hipify_result = HipifyResult(current_state=CurrentState.INITIALIZED, hipified_path=fin_path) + HIPIFY_FINAL_RESULT[fin_path] = hipify_result + result = preprocessor(output_directory, filepath, all_files, header_include_dirs, stats, + hip_clang_launch, is_pytorch_extension, clean_ctx, show_progress) + + # Show what happened + if show_progress and "ignored" not in result.status: + print( + fin_path, "->", + result.hipified_path, result.status, flush=True) + + HIPIFY_FINAL_RESULT[fin_path] = result + + +def compute_stats(stats): + unsupported_calls = {cuda_call for (cuda_call, _filepath) in stats["unsupported_calls"]} + + # Print the number of unsupported calls + print(f"Total number of unsupported CUDA function calls: {len(unsupported_calls):d}") + + # Print the list of unsupported calls + print(", ".join(unsupported_calls)) + + # Print the number of kernel launches + print(f"\nTotal number of replaced kernel launches: {len(stats['kernel_launches']):d}") + + +def add_dim3(kernel_string, cuda_kernel): + '''adds dim3() to the second and third arguments in the kernel launch''' + count = 0 + closure = 0 + kernel_string = kernel_string.replace("<<<", "").replace(">>>", "") + arg_locs: List[Dict[str, int]] = [{} for _ in range(2)] + arg_locs[count]['start'] = 0 + for ind, c in enumerate(kernel_string): + if count > 1: + break + if c == "(": + closure += 1 + elif c == ")": + closure -= 1 + if (c == "," or ind == len(kernel_string) - 1) and closure == 0: + arg_locs[count]['end'] = ind + (c != ",") + count += 1 + if count < 2: + arg_locs[count]['start'] = ind + 1 + + first_arg_raw = kernel_string[arg_locs[0]['start']:arg_locs[0]['end'] + 1] + second_arg_raw = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']] + + first_arg_clean = kernel_string[arg_locs[0]['start']:arg_locs[0]['end']].replace("\n", "").strip(" ") + second_arg_clean = kernel_string[arg_locs[1]['start']:arg_locs[1]['end']].replace("\n", "").strip(" ") + + first_arg_dim3 = f"dim3({first_arg_clean})" + second_arg_dim3 = f"dim3({second_arg_clean})" + + first_arg_raw_dim3 = first_arg_raw.replace(first_arg_clean, first_arg_dim3) + second_arg_raw_dim3 = second_arg_raw.replace(second_arg_clean, second_arg_dim3) + cuda_kernel = cuda_kernel.replace(first_arg_raw + second_arg_raw, first_arg_raw_dim3 + second_arg_raw_dim3) + return cuda_kernel + + +RE_KERNEL_LAUNCH = re.compile(r'([ ]+)(detail?)::[ ]+\\\n[ ]+') + + +def processKernelLaunches(string, stats): + """ Replace the CUDA style Kernel launches with the HIP style kernel launches.""" + # Concat the namespace with the kernel names. (Find cleaner way of doing this later). + string = RE_KERNEL_LAUNCH.sub(lambda inp: f"{inp.group(1)}{inp.group(2)}::", string) + + def grab_method_and_template(in_kernel): + # The positions for relevant kernel components. + pos = { + "kernel_launch": {"start": in_kernel["start"], "end": in_kernel["end"]}, + "kernel_name": {"start": -1, "end": -1}, + "template": {"start": -1, "end": -1} + } + + # Count for balancing template + count = {"<>": 0} + + # Status for whether we are parsing a certain item. + START = 0 + AT_TEMPLATE = 1 + AFTER_TEMPLATE = 2 + AT_KERNEL_NAME = 3 + + status = START + + # Parse the string character by character + for i in range(pos["kernel_launch"]["start"] - 1, -1, -1): + char = string[i] + + # Handle Templating Arguments + if status in (START, AT_TEMPLATE): + if char == ">": + if status == START: + status = AT_TEMPLATE + pos["template"]["end"] = i + count["<>"] += 1 + + if char == "<": + count["<>"] -= 1 + if count["<>"] == 0 and (status == AT_TEMPLATE): + pos["template"]["start"] = i + status = AFTER_TEMPLATE + + # Handle Kernel Name + if status != AT_TEMPLATE: + if string[i].isalnum() or string[i] in {'(', ')', '_', ':', '#'}: + if status != AT_KERNEL_NAME: + status = AT_KERNEL_NAME + pos["kernel_name"]["end"] = i + + # Case: Kernel name starts the string. + if i == 0: + pos["kernel_name"]["start"] = 0 + + # Finished + return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])] + + else: + # Potential ending point if we're already traversing a kernel's name. + if status == AT_KERNEL_NAME: + pos["kernel_name"]["start"] = i + + # Finished + return [(pos["kernel_name"]), (pos["template"]), (pos["kernel_launch"])] + + def find_kernel_bounds(string): + """Finds the starting and ending points for all kernel launches in the string.""" + kernel_end = 0 + kernel_positions = [] + + # Continue until we cannot find any more kernels anymore. + while string.find("<<<", kernel_end) != -1: + # Get kernel starting position (starting from the previous ending point) + kernel_start = string.find("<<<", kernel_end) + + # Get kernel ending position (adjust end point past the >>>) + kernel_end = string.find(">>>", kernel_start) + 3 + if kernel_end <= 0: + raise InputError("no kernel end found") + + # Add to list of traversed kernels + kernel_positions.append({"start": kernel_start, "end": kernel_end, + "group": string[kernel_start: kernel_end]}) + + return kernel_positions + + # Replace comments and string literals from the code so that find_kernel_bounds does not + # wrongly capture kernels in comments and string literals. + # This function replaces them with "x" to keep positions. + def mask_comments(string): + in_comment = '' + prev_c = '' + new_string = '' + for c in string: + if in_comment == '': + # Outside comments + if c == '/' and prev_c == '/': + in_comment = '//' + elif c == '*' and prev_c == '/': + in_comment = '/*' + elif c == '"' and prev_c != '\\' and prev_c != "'": + in_comment = '"' + elif in_comment == '//': + # In // xxx + if c == '\r' or c == '\n': + in_comment = '' + elif in_comment == '/*': + # In /* xxx */ + if c == '/' and prev_c == '*': + in_comment = '' + elif in_comment == '"': + # In "" + if c == '"' and prev_c != '\\': + in_comment = '' + prev_c = c + if in_comment == '': + new_string += c + else: + new_string += 'x' + return new_string + + # Grab positional ranges of all kernel launches + get_kernel_positions = list(find_kernel_bounds(mask_comments(string))) + output_string = string + + # Replace each CUDA kernel with a HIP kernel. + for kernel in get_kernel_positions: + # Get kernel components + params = grab_method_and_template(kernel) + + # Find parenthesis after kernel launch + parenthesis = string.find("(", kernel["end"]) + + # Extract cuda kernel + cuda_kernel = string[params[0]["start"]:parenthesis + 1] + kernel_string = string[kernel['start']:kernel['end']] + end_param_index = 0 if params[1]['end'] == -1 else 1 + kernel_name_with_template = string[params[0]['start']:params[end_param_index]['end'] + 1] + cuda_kernel_dim3 = add_dim3(kernel_string, cuda_kernel) + # Keep number of kernel launch params consistent (grid dims, group dims, stream, dynamic shared size) + num_klp = len(extract_arguments(0, kernel["group"].replace("<<<", "(").replace(">>>", ")"))) + + hip_kernel = "hipLaunchKernelGGL(" + cuda_kernel_dim3[0:-1].replace( + ">>>", ", 0" * (4 - num_klp) + ">>>").replace("<<<", ", ").replace( + ">>>", ", ").replace(kernel_name_with_template, "(" + kernel_name_with_template + ")") + + # Replace cuda kernel with hip kernel + output_string = output_string.replace(cuda_kernel, hip_kernel) + + # Update the statistics + stats["kernel_launches"].append(hip_kernel) + + return output_string + + +def find_closure_group(input_string, start, group): + """Generalization for finding a balancing closure group + + if group = ["(", ")"], then finds the first balanced parentheses. + if group = ["{", "}"], then finds the first balanced bracket. + + Given an input string, a starting position in the input string, and the group type, + find_closure_group returns the positions of group[0] and group[1] as a tuple. + + Example: + >>> find_closure_group("(hi)", 0, ["(", ")"]) + (0, 3) + """ + + inside_parenthesis = False + parens = 0 + pos = start + p_start, p_end = -1, -1 + + while pos < len(input_string): + if input_string[pos] == group[0]: + if inside_parenthesis is False: + inside_parenthesis = True + parens = 1 + p_start = pos + else: + parens += 1 + elif input_string[pos] == group[1] and inside_parenthesis: + parens -= 1 + + if parens == 0: + p_end = pos + return p_start, p_end + + pos += 1 + return None, None + + +def find_bracket_group(input_string, start): + """Finds the first balanced parantheses.""" + return find_closure_group(input_string, start, group=["{", "}"]) + + +def find_parentheses_group(input_string, start): + """Finds the first balanced bracket.""" + return find_closure_group(input_string, start, group=["(", ")"]) + + +RE_ASSERT = re.compile(r"\bassert[ ]*\(") + + +def replace_math_functions(input_string): + """FIXME: Temporarily replace std:: invocations of math functions + with non-std:: versions to prevent linker errors NOTE: This + can lead to correctness issues when running tests, since the + correct version of the math function (exp/expf) might not get + called. Plan is to remove this function once HIP supports + std:: math function calls inside device code + + """ + output_string = input_string + for func in MATH_TRANSPILATIONS: + output_string = output_string.replace(fr'{func}(', f'{MATH_TRANSPILATIONS[func]}(') + + return output_string + + +RE_SYNCTHREADS = re.compile(r":?:?\b(__syncthreads)\b(\w*\()") + + +def hip_header_magic(input_string): + """If the file makes kernel builtin calls and does not include the cuda_runtime.h header, + then automatically add an #include to match the "magic" includes provided by NVCC. + TODO: + Update logic to ignore cases where the cuda_runtime.h is included by another file. + """ + + # Copy the input. + output_string = input_string + + # Check if one of the following headers is already included. + headers = ["hip/hip_runtime.h", "hip/hip_runtime_api.h"] + if any(re.search(fr'#include ("{ext}"|<{ext}>)', output_string) for ext in headers): + return output_string + + # Rough logic to detect if we're inside device code + hasDeviceLogic: int + hasDeviceLogic = "hipLaunchKernelGGL" in output_string + hasDeviceLogic += "__global__" in output_string + hasDeviceLogic += "__shared__" in output_string + hasDeviceLogic += RE_SYNCTHREADS.search(output_string) is not None + + # If device logic found, provide the necessary header. + if hasDeviceLogic: + output_string = '#include "hip/hip_runtime.h"\n' + input_string + + return output_string + + +RE_EXTERN_SHARED = re.compile(r"extern\s+([\w\(\)]+)?\s*__shared__\s+([\w:<>\s]+)\s+(\w+)\s*\[\s*\]\s*;") + + +def replace_extern_shared(input_string): + """Match extern __shared__ type foo[]; syntax and use HIP_DYNAMIC_SHARED() MACRO instead. + https://github.com/ROCm-Developer-Tools/HIP/blob/master/docs/markdown/hip_kernel_language.md#__shared__ + Example: + "extern __shared__ char smemChar[];" => "HIP_DYNAMIC_SHARED( char, smemChar)" + "extern __shared__ unsigned char smem[];" => "HIP_DYNAMIC_SHARED( unsigned char, my_smem)" + """ + output_string = input_string + output_string = RE_EXTERN_SHARED.sub( + lambda inp: f"HIP_DYNAMIC_SHARED({inp.group(1) or ''} {inp.group(2)}, {inp.group(3)})", output_string) + + return output_string + + +def get_hip_file_path(rel_filepath, is_pytorch_extension=False): + """ + Returns the new name of the hipified file + """ + # At the moment, some PyTorch source files are HIPified in place. The predicate + # is_out_of_place tells us if this is the case or not. + assert not os.path.isabs(rel_filepath) + if not is_pytorch_extension and not is_out_of_place(rel_filepath): + return rel_filepath + + dirpath, filename = os.path.split(rel_filepath) + root, ext = os.path.splitext(filename) + + # Here's the plan: + # + # In general, we need to disambiguate the HIPified filename so that + # it gets a different name from the original filename, so + # that we don't overwrite the original file + # + # There's a lot of different naming conventions across PyTorch + # and Caffe2, but the general recipe is to convert occurrences + # of cuda/gpu to hip, and add hip if there are no occurrences + # of cuda/gpu anywhere. + # + # Concretely, we do the following: + # + # - If there is a directory component named "cuda", replace + # it with "hip", AND + # + # - If the file name contains "CUDA", replace it with "HIP", AND + # + # - ALWAYS replace '.cu' with '.hip', because those files + # contain CUDA kernels that needs to be hipified and processed with + # hip compiler + # + # - If we are not hipifying a PyTorch extension, and the parent + # directory name did not change as a result of the above + # transformations, insert "hip" in the file path + # as the direct parent folder of the file + # + # - If we are hipifying a PyTorch extension, and the parent directory + # name as well as the filename (incl. extension) did not change as + # a result of the above transformations, insert "_hip" in the filename + # + # This isn't set in stone; we might adjust this to support other + # naming conventions. + + if ext == '.cu': + ext = '.hip' + + orig_filename = filename + orig_dirpath = dirpath + + dirpath = dirpath.replace('cuda', 'hip') + dirpath = dirpath.replace('CUDA', 'HIP') + dirpath = dirpath.replace('THC', 'THH') + + root = root.replace('cuda', 'hip') + root = root.replace('CUDA', 'HIP') + # Special case to handle caffe2/core/THCCachingAllocator + if dirpath != "caffe2/core": + root = root.replace('THC', 'THH') + + if not is_pytorch_extension and dirpath == orig_dirpath: + dirpath = os.path.join(dirpath, 'hip') + + if is_pytorch_extension and dirpath == orig_dirpath and (root + ext) == orig_filename: + root = root + "_hip" + + return os.path.join(dirpath, root + ext) + + +def is_out_of_place(rel_filepath): + assert not os.path.isabs(rel_filepath) + if rel_filepath.startswith("torch/"): + return False + if rel_filepath.startswith("third_party/nvfuser/"): + return False + if rel_filepath.startswith("tools/autograd/templates/"): + return False + return True + + +# Keep this synchronized with includes/ignores in build_amd.py +def is_pytorch_file(rel_filepath): + assert not os.path.isabs(rel_filepath) + if rel_filepath.startswith("aten/"): + if rel_filepath.startswith("aten/src/ATen/core/"): + return False + return True + if rel_filepath.startswith("torch/"): + return True + if rel_filepath.startswith("third_party/nvfuser/"): + return True + if rel_filepath.startswith("tools/autograd/templates/"): + return True + return False + + +def is_cusparse_file(rel_filepath): + if is_pytorch_file(rel_filepath): + return "sparse" in rel_filepath.lower() + return False + + +def is_special_file(rel_filepath): + if is_pytorch_file(rel_filepath): + if "sparse" in rel_filepath.lower(): + return True + elif "linalg" in rel_filepath.lower(): + if "batchlinearalgebralibblas" in rel_filepath.lower(): + return False # don't use "special" mappings for this specific linalg cublas file + return True + return False + +def is_caffe2_gpu_file(rel_filepath): + assert not os.path.isabs(rel_filepath) + if rel_filepath.startswith("c10/cuda"): + return True + filename = os.path.basename(rel_filepath) + _, ext = os.path.splitext(filename) + return ('gpu' in filename or ext in ['.cu', '.cuh']) and ('cudnn' not in filename) + +class TrieNode: + """A Trie node whose children are represented as a directory of char: TrieNode. + A special char '' represents end of word + """ + + def __init__(self): + self.children = {} + +class Trie: + """Creates a Trie out of a list of words. The trie can be exported to a Regex pattern. + The corresponding Regex should match much faster than a simple Regex union.""" + + def __init__(self): + """Initialize the trie with an empty root node.""" + self.root = TrieNode() + self._hash = hashlib.md5() + self._digest = self._hash.digest() + + def add(self, word): + """Add a word to the Trie. """ + self._hash.update(word.encode()) + self._digest = self._hash.digest() + node = self.root + + for char in word: + node.children.setdefault(char, TrieNode()) + node = node.children[char] + node.children[''] = True # Mark the end of the word + + def dump(self): + """Return the root node of Trie. """ + return self.root + + def quote(self, char): + """ Escape a char for regex. """ + return re.escape(char) + + def search(self, word): + """Search whether word is present in the Trie. + Returns True if yes, else return False""" + node = self.root + for char in word: + if char in node.children: + node = node.children[char] + else: + return False + + # make sure to check the end-of-word marker present + return '' in node.children + + @functools.lru_cache # noqa: B019 + def _pattern(self, root, digest): + """Convert a Trie into a regular expression pattern + + Memoized on the hash digest of the trie, which is built incrementally + during add(). + """ + node = root + + if "" in node.children and len(node.children.keys()) == 1: + return None + + alt = [] # store alternative patterns + cc = [] # store char to char classes + q = 0 # for node representing the end of word + for char in sorted(node.children.keys()): + if isinstance(node.children[char], TrieNode): + try: + recurse = self._pattern(node.children[char], self._digest) + alt.append(self.quote(char) + recurse) + except Exception: + cc.append(self.quote(char)) + else: + q = 1 + cconly = not len(alt) > 0 + + if len(cc) > 0: + if len(cc) == 1: + alt.append(cc[0]) + else: + alt.append('[' + ''.join(cc) + ']') + + if len(alt) == 1: + result = alt[0] + else: + result = "(?:" + "|".join(alt) + ")" + + if q: + if cconly: + result += "?" + else: + result = f"(?:{result})?" + return result + + def pattern(self): + """Export the Trie to a regex pattern.""" + return self._pattern(self.root, self._digest) + + def export_to_regex(self): + """Export the Trie to a regex pattern.""" + return self._pattern(self.root, self._digest) + +CAFFE2_TRIE = Trie() +CAFFE2_MAP = {} +PYTORCH_TRIE = Trie() +PYTORCH_MAP: Dict[str, object] = {} + +# In PyTorch, we map cuBLAS->rocBLAS and cuSPARSE->hipSPARSE. Note the prefix, roc versus hip. +# The 'hip' APIs offer a more direct CUDA-friendly mapping, but calling rocBLAS directly has better performance. +# Unfortunately, the roc* types and hip* types differ, i.e., rocblas_float_complex versus hipComplex. +# In the case of SPARSE, we must use the hip types for complex instead of the roc types, +# but the pytorch mappings assume roc. Therefore, we create a new SPARSE mapping that has a higher priority. +# Its mappings will trigger first, and only when a miss occurs will the lower-priority pytorch mapping take place. +# When a file contains "sparse" in the filename, a mapping marked with API_SPARSE is preferred over other choices. +# Similarly, "linalg" files require rocBLAS -> hipSOLVER so they also need special handling. +PYTORCH_SPECIAL_MAP = {} + +for mapping in CUDA_TO_HIP_MAPPINGS: + assert isinstance(mapping, Mapping) + for src, value in mapping.items(): + dst = value[0] + meta_data = value[1:] + if constants.API_CAFFE2 not in meta_data: + PYTORCH_TRIE.add(src) + # if src is already in PYTORCH_MAP and dst belongs to API_SPECIAL + # do not overwrite PYTORCH_MAP, store dst separately + if constants.API_SPECIAL in meta_data and PYTORCH_MAP.get(src, ""): + PYTORCH_SPECIAL_MAP[src] = dst + else: + PYTORCH_MAP[src] = dst + if constants.API_PYTORCH not in meta_data and constants.API_SPECIAL not in meta_data: + CAFFE2_TRIE.add(src) + CAFFE2_MAP[src] = dst +RE_CAFFE2_PREPROCESSOR = re.compile(CAFFE2_TRIE.export_to_regex()) +RE_PYTORCH_PREPROCESSOR = re.compile(fr'(?<=\W)({PYTORCH_TRIE.export_to_regex()})(?=\W)') + +RE_QUOTE_HEADER = re.compile(r'#include "([^"]+)"') +RE_ANGLE_HEADER = re.compile(r'#include <([^>]+)>') +RE_THC_GENERIC_FILE = re.compile(r'#define THC_GENERIC_FILE "([^"]+)"') +RE_CU_SUFFIX = re.compile(r'\.cu\b') # be careful not to pick up .cuh + +""" +Returns a HipifyResult object with the following details: + "hipified_path" : absolute path of hipified source file + "status" : "ok" if hipified file was written out + "skipped" if an identical hipified file already existed or hipified file couldn't be written out + "ignored" if the source file was a hipified file itself or not meant to be hipified + "current_state" : CurrentState.INITIALIZED if source file is first ready to be hipified + CurrentState.DONE if source file is done with hipification process +""" + + +def preprocessor( + output_directory: str, + filepath: str, + all_files: Iterable, + header_include_dirs: Iterable, + stats: Dict[str, List], + hip_clang_launch: bool, + is_pytorch_extension: bool, + clean_ctx: GeneratedFileCleaner, + show_progress: bool) -> HipifyResult: + """ Executes the CUDA -> HIP conversion on the specified file. """ + fin_path = os.path.abspath(os.path.join(output_directory, filepath)) + hipify_result = HIPIFY_FINAL_RESULT[fin_path] + if filepath not in all_files: + hipify_result.hipified_path = None + hipify_result.status = "[ignored, not to be hipified]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + + rel_filepath = _to_unix_path(os.path.relpath(filepath, output_directory)) + + with open(fin_path, encoding='utf-8') as fin: + if fin.readline() == HIPIFY_C_BREADCRUMB: + hipify_result.hipified_path = None + hipify_result.status = "[ignored, input is hipified output]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + fin.seek(0) + output_source = fin.read() + + orig_output_source = output_source + + # get_hip_file_path needs a relative path to work correctly + fout_path = os.path.abspath(os.path.join(output_directory, get_hip_file_path(rel_filepath, is_pytorch_extension))) + if not os.path.exists(os.path.dirname(fout_path)): + clean_ctx.makedirs(os.path.dirname(fout_path)) + + # unsupported_calls statistics reporting is broken atm + def pt_repl(m): + return PYTORCH_MAP[m.group(0)] + + def pt_special_repl(m): + # checks SPECIAL map first, and if a miss occurs, falls back to pytorch mappings + return PYTORCH_SPECIAL_MAP.get(m.group(0), pt_repl(m)) + + + if is_pytorch_extension: + output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source) + else: + if is_special_file(rel_filepath): + output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_special_repl, output_source) + elif is_pytorch_file(rel_filepath): + output_source = RE_PYTORCH_PREPROCESSOR.sub(pt_repl, output_source) + else: + def c2_repl(m): + return CAFFE2_MAP[m.group(0)] + output_source = RE_CAFFE2_PREPROCESSOR.sub(c2_repl, output_source) + + # Header rewrites + def mk_repl(templ, include_current_dir=True): + def repl(m): + f = m.group(1) + filename = os.path.basename(f) + if ( + f.startswith(("ATen/cuda", + "ATen/native/cuda", + "ATen/native/nested/cuda", + "ATen/native/quantized/cuda", + "ATen/native/sparse/cuda", + "ATen/native/transformers/cuda", + "THC/")) or + (f.startswith("THC") and not f.startswith("THCP")) + ): + return templ.format(get_hip_file_path(m.group(1), is_pytorch_extension)) + # if filename is one of the files being hipified for this extension + if (is_pytorch_extension and any(s.endswith(filename) for s in all_files)): + header_dir = None + header_filepath = None + # If include_current_dir True, look first in same dir as the including source file + if include_current_dir: + header_dir_to_check = os.path.dirname(fin_path) + header_path_to_check = os.path.abspath(os.path.join(header_dir_to_check, f)) + if os.path.exists(header_path_to_check): + header_dir = header_dir_to_check + header_filepath = header_path_to_check + # If not found, look in include dirs one by one and first match wins + if header_filepath is None: + for header_include_dir in header_include_dirs: + header_dir_to_check = os.path.join(output_directory, header_include_dir) + header_path_to_check = os.path.abspath(os.path.join(header_dir_to_check, f)) + if os.path.exists(header_path_to_check): + header_dir = header_dir_to_check + header_filepath = header_path_to_check + # If header file not found, keep as is + if header_filepath is None: + return m.group(0) + # Hipify header file first if needed + if header_filepath not in HIPIFY_FINAL_RESULT: + preprocess_file_and_save_result(output_directory, + header_filepath, + all_files, header_include_dirs, stats, hip_clang_launch, + is_pytorch_extension, clean_ctx, show_progress) + elif header_filepath in HIPIFY_FINAL_RESULT: + header_result = HIPIFY_FINAL_RESULT[header_filepath] + if header_result.current_state == CurrentState.INITIALIZED: + # get_hip_file_path needs a relative path to work correctly + header_rel_path = os.path.relpath(header_filepath, output_directory) + header_fout_path = os.path.abspath(os.path.join(output_directory, + get_hip_file_path(header_rel_path, is_pytorch_extension))) + header_result.hipified_path = header_fout_path + HIPIFY_FINAL_RESULT[header_filepath] = header_result + return templ.format(os.path.relpath(header_fout_path if header_fout_path is not None + else header_filepath, header_dir)) + hipified_header_filepath = HIPIFY_FINAL_RESULT[header_filepath].hipified_path + return templ.format(os.path.relpath(hipified_header_filepath if hipified_header_filepath is not None + else header_filepath, header_dir)) + + return m.group(0) + return repl + output_source = RE_QUOTE_HEADER.sub(mk_repl('#include "{0}"', True), output_source) + output_source = RE_ANGLE_HEADER.sub(mk_repl('#include <{0}>', False), output_source) + output_source = RE_THC_GENERIC_FILE.sub(mk_repl('#define THC_GENERIC_FILE "{0}"'), output_source) + + # CMakeLists.txt rewrites + if filepath.endswith('CMakeLists.txt'): + output_source = output_source.replace('CUDA', 'HIP') + output_source = output_source.replace('THC', 'THH') + output_source = RE_CU_SUFFIX.sub('.hip', output_source) + + # Perform Kernel Launch Replacements + if not hip_clang_launch: + output_source = processKernelLaunches(output_source, stats) + + # Replace std:: with non-std:: versions + if (filepath.endswith((".cu", ".cuh"))) and "PowKernel" not in filepath: + output_source = replace_math_functions(output_source) + + # Include header if device code is contained. + output_source = hip_header_magic(output_source) + + # Replace the extern __shared__ + # NOTE: No longer needed after transition from hcc to hipclang. + # output_source = replace_extern_shared(output_source) + + # Don't write out identical hipified files for extensions if dirpath has not changed + if ( + is_pytorch_extension + and orig_output_source == output_source + and os.path.dirname(fin_path) == os.path.dirname(fout_path) + ): + hipify_result.hipified_path = fin_path + hipify_result.status = "[skipped, no changes]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + + # Add hipify breadcrumb for C-style files to avoid re-hipification + if fin_path != fout_path and match_extensions(fin_path, (".cu", ".cuh", ".c", ".cc", ".cpp", ".h", ".hpp")): + output_source = HIPIFY_C_BREADCRUMB + output_source + + do_write = True + if os.path.exists(fout_path): + with open(fout_path, encoding='utf-8') as fout_old: + do_write = fout_old.read() != output_source + if do_write: + try: + with clean_ctx.open(fout_path, 'w', encoding='utf-8') as fout: + fout.write(output_source) + hipify_result.hipified_path = fout_path + hipify_result.status = "[ok]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + except PermissionError as e: + print(f'{bcolors.WARNING}Failed to save {fout_path} with "{e.strerror}", leaving {fin_path} unchanged.{bcolors.ENDC}', + file=sys.stderr) + hipify_result.hipified_path = fin_path + hipify_result.status = "[skipped, no permissions]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + else: + hipify_result.hipified_path = fout_path + hipify_result.status = "[skipped, already hipified]" + hipify_result.current_state = CurrentState.DONE + return hipify_result + +def file_specific_replacement(filepath, search_string, replace_string, strict=False): + with openf(filepath, "r+") as f: + contents = f.read() + if strict: + contents = re.sub(fr'\b({re.escape(search_string)})\b', lambda x: replace_string, contents) + else: + contents = contents.replace(search_string, replace_string) + f.seek(0) + f.write(contents) + f.truncate() + + +def file_add_header(filepath, header): + with openf(filepath, "r+") as f: + contents = f.read() + if header[0] != "<" and header[-1] != ">": + header = f'"{header}"' + contents = (f'#include {header} \n') + contents + f.seek(0) + f.write(contents) + f.truncate() + + +def fix_static_global_kernels(in_txt): + """Static global kernels in HIP results in a compilation error.""" + in_txt = in_txt.replace(" __global__ static", "__global__") + return in_txt + + +RE_INCLUDE = re.compile(r"#include .*\n") + + +def extract_arguments(start, string): + """ Return the list of arguments in the upcoming function parameter closure. + Example: + string (input): '(blocks, threads, 0, THCState_getCurrentStream(state))' + arguments (output): + '[{'start': 1, 'end': 7}, + {'start': 8, 'end': 16}, + {'start': 17, 'end': 19}, + {'start': 20, 'end': 53}]' + """ + + arguments = [] + closures = { + "<": 0, + "(": 0 + } + current_position = start + argument_start_pos = current_position + 1 + + # Search for final parenthesis + while current_position < len(string): + if string[current_position] == "(": + closures["("] += 1 + elif string[current_position] == ")": + closures["("] -= 1 + elif string[current_position] == "<": + closures["<"] += 1 + elif string[current_position] == ">" and string[current_position - 1] != "-" and closures["<"] > 0: + closures["<"] -= 1 + + # Finished all arguments + if closures["("] == 0 and closures["<"] == 0: + # Add final argument + arguments.append({"start": argument_start_pos, "end": current_position}) + break + + # Finished current argument + if closures["("] == 1 and closures["<"] == 0 and string[current_position] == ",": + arguments.append({"start": argument_start_pos, "end": current_position}) + argument_start_pos = current_position + 1 + + current_position += 1 + + return arguments + + +def str2bool(v): + """ArgumentParser doesn't support type=bool. Thus, this helper method will convert + from possible string types to True / False.""" + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + + +def hipify( + project_directory: str, + show_detailed: bool = False, + extensions: Iterable = (".cu", ".cuh", ".c", ".cc", ".cpp", ".h", ".in", ".hpp"), + header_extensions: Iterable = (".cuh", ".h", ".hpp"), + output_directory: str = "", + header_include_dirs: Iterable = (), + includes: Iterable = ('*',), + extra_files: Iterable = (), + out_of_place_only: bool = False, + ignores: Iterable = (), + show_progress: bool = True, + hip_clang_launch: bool = False, + is_pytorch_extension: bool = False, + hipify_extra_files_only: bool = False, + clean_ctx: Optional[GeneratedFileCleaner] = None +) -> HipifyFinalResult: + if project_directory == "": + project_directory = os.getcwd() + + # Verify the project directory exists. + if not os.path.exists(project_directory): + print("The project folder specified does not exist.") + sys.exit(1) + + # If no output directory, provide a default one. + if not output_directory: + project_directory.rstrip("/") + output_directory = project_directory + "_amd" + + if project_directory != output_directory: + includes = [include.replace(project_directory, output_directory) for include in includes] + ignores = [ignore.replace(project_directory, output_directory) for ignore in ignores] + + # Copy from project directory to output directory if not done already. + if not os.path.exists(output_directory): + shutil.copytree(project_directory, output_directory) + + includes = list(map(_to_unix_path, includes)) + ignores = list(map(_to_unix_path, ignores)) + + all_files = list(matched_files_iter(output_directory, includes=includes, + ignores=ignores, extensions=extensions, + out_of_place_only=out_of_place_only, + is_pytorch_extension=is_pytorch_extension)) + all_files_set = set(all_files) + for f in extra_files: + if not os.path.isabs(f): + f = os.path.join(output_directory, f) + if f not in all_files_set: + all_files.append(f) + + # List all files in header_include_paths to ensure they are hipified + from pathlib import Path + for header_include_dir in header_include_dirs: + if os.path.isabs(header_include_dir): + header_include_dir_path = Path(header_include_dir) + else: + header_include_dir_path = Path(os.path.join(output_directory, header_include_dir)) + all_files.extend( + str(path) for path in header_include_dir_path.rglob('*') if path.is_file() + and _fnmatch(str(path), includes) + and (not _fnmatch(str(path), ignores)) + and match_extensions(path.name, header_extensions) + ) + + if clean_ctx is None: + clean_ctx = GeneratedFileCleaner(keep_intermediates=True) + + # Preprocessing statistics. + stats: Dict[str, List] = {"unsupported_calls": [], "kernel_launches": []} + + for filepath in (all_files if not hipify_extra_files_only else extra_files): + preprocess_file_and_save_result(output_directory, filepath, all_files, header_include_dirs, + stats, hip_clang_launch, is_pytorch_extension, clean_ctx, show_progress) + + print(bcolors.OKGREEN + "Successfully preprocessed all matching files." + bcolors.ENDC, file=sys.stderr) + + # Show detailed summary + if show_detailed: + compute_stats(stats) + + return HIPIFY_FINAL_RESULT diff --git a/lib/python3.11/site-packages/torch/utils/hipify/version.py b/lib/python3.11/site-packages/torch/utils/hipify/version.py new file mode 100644 index 0000000000000000000000000000000000000000..1f356cc57bfa00a3b251402604c54702fb414c96 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hipify/version.py @@ -0,0 +1 @@ +__version__ = '1.0.0' diff --git a/lib/python3.11/site-packages/torch/utils/hooks.py b/lib/python3.11/site-packages/torch/utils/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..f01c65ddb0efbc2594988bb35a8a91ac386a5a5f --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/hooks.py @@ -0,0 +1,251 @@ +# mypy: allow-untyped-defs +import torch +from collections import OrderedDict +import weakref +import warnings +from typing import Any, Tuple + +__all__ = ["RemovableHandle", "unserializable_hook", "warn_if_has_hooks", "BackwardHook"] + +class RemovableHandle: + r""" + A handle which provides the capability to remove a hook. + + Args: + hooks_dict (dict): A dictionary of hooks, indexed by hook ``id``. + extra_dict (Union[dict, List[dict]]): An additional dictionary or list of + dictionaries whose keys will be deleted when the same keys are + removed from ``hooks_dict``. + """ + + id: int + next_id: int = 0 + + def __init__(self, hooks_dict: Any, *, extra_dict: Any = None) -> None: + self.hooks_dict_ref = weakref.ref(hooks_dict) + self.id = RemovableHandle.next_id + RemovableHandle.next_id += 1 + + self.extra_dict_ref: Tuple = () + if isinstance(extra_dict, dict): + self.extra_dict_ref = (weakref.ref(extra_dict),) + elif isinstance(extra_dict, list): + self.extra_dict_ref = tuple(weakref.ref(d) for d in extra_dict) + + def remove(self) -> None: + hooks_dict = self.hooks_dict_ref() + if hooks_dict is not None and self.id in hooks_dict: + del hooks_dict[self.id] + + for ref in self.extra_dict_ref: + extra_dict = ref() + if extra_dict is not None and self.id in extra_dict: + del extra_dict[self.id] + + def __getstate__(self): + if self.extra_dict_ref is None: + return (self.hooks_dict_ref(), self.id) + else: + return (self.hooks_dict_ref(), self.id, tuple(ref() for ref in self.extra_dict_ref)) + + def __setstate__(self, state) -> None: + if state[0] is None: + # create a dead reference + self.hooks_dict_ref = weakref.ref(OrderedDict()) + else: + self.hooks_dict_ref = weakref.ref(state[0]) + self.id = state[1] + RemovableHandle.next_id = max(RemovableHandle.next_id, self.id + 1) + + if len(state) < 3 or state[2] is None: + self.extra_dict_ref = () + else: + self.extra_dict_ref = tuple(weakref.ref(d) for d in state[2]) + + def __enter__(self) -> "RemovableHandle": + return self + + def __exit__(self, type: Any, value: Any, tb: Any) -> None: + self.remove() + + +def unserializable_hook(f): + """ + Mark a function as an unserializable hook with this decorator. + + This suppresses warnings that would otherwise arise if you attempt + to serialize a tensor that has a hook. + """ + f.__torch_unserializable__ = True + return f + + +def warn_if_has_hooks(tensor): + if tensor._backward_hooks: + for k in tensor._backward_hooks: + hook = tensor._backward_hooks[k] + if not hasattr(hook, "__torch_unserializable__"): + warnings.warn(f"backward hook {repr(hook)} on tensor will not be " + "serialized. If this is expected, you can " + "decorate the function with @torch.utils.hooks.unserializable_hook " + "to suppress this warning") + +class BackwardHook: + """ + A wrapper class to implement nn.Module backward hooks. + + It handles: + - Ignoring non-Tensor inputs and replacing them by None before calling the user hook + - Generating the proper Node to capture a set of Tensor's gradients + - Linking the gradients captures for the outputs with the gradients captured for the input + - Calling the user hook once both output and input gradients are available + """ + + def __init__(self, module, user_hooks, user_pre_hooks): + self.user_hooks = user_hooks + self.user_pre_hooks = user_pre_hooks + self.module = module + + self.grad_outputs = None + self.n_outputs = -1 + self.output_tensors_index = None + self.n_inputs = -1 + self.input_tensors_index = None + + def _pack_with_none(self, indices, values, size): + res = [None] * size + for idx, val in zip(indices, values): + res[idx] = val + + return tuple(res) + + def _unpack_none(self, indices, values): + res = [values[idx] for idx in indices] + + return tuple(res) + + def _set_user_hook(self, grad_fn): + def hook(grad_input, _): + if self.grad_outputs is None: + # This happens because the gradient in your nn.Module flows to + # the Module's input without " passing through the Module's + # output, e.g. when you're doing double backward. + return + res = self._pack_with_none(self.input_tensors_index, grad_input, self.n_inputs) + + for hook in self.user_hooks: + out = hook(self.module, res, self.grad_outputs) + + if out is None: + continue + + if len(out) != len(res): + raise RuntimeError("Backward hook returned an invalid number of grad_input, " + f"got {len(out)}, but expected {len(res)}") + + res = out + + self.grad_outputs = None + + return self._unpack_none(self.input_tensors_index, res) + + grad_fn.register_hook(hook) + + def _apply_on_tensors(self, fn, args): + # Can be used to apply the given function to the tensors contained in the + # args. Will return updated args and the tensors indices + tensors_idx = [] + tensors = [] + + requires_grad = False + for i, arg in enumerate(args): + if isinstance(arg, torch.Tensor): + tensors_idx.append(i) + tensors.append(arg) + requires_grad |= arg.requires_grad + + if not (requires_grad and torch.is_grad_enabled()): + return args, None + + new_tensors = torch.nn.modules._functions.BackwardHookFunction.apply(*tensors) + if len(new_tensors) == 0: + raise RuntimeError("Cannot set Module backward hook for a Module with no input Tensors.") + + grad_fns = [t.grad_fn for t in new_tensors if t.grad_fn is not None and t.grad_fn.name() == "BackwardHookFunctionBackward"] + if len(grad_fns) == 0: + raise RuntimeError("Error while setting up backward hooks. Please open " + "an issue with a code sample to reproduce this.") + + fn(grad_fns[0]) + + arg_list = list(args) + for idx, val in zip(tensors_idx, new_tensors): + arg_list[idx] = val + + if type(args) is tuple: + out = tuple(arg_list) + else: + out = type(args)(*arg_list) + return out, tensors_idx + + def setup_input_hook(self, args): + def fn(grad_fn): + self._set_user_hook(grad_fn) + + res, input_idx = self._apply_on_tensors(fn, args) + self.n_inputs = len(args) + self.input_tensors_index = input_idx + return res + + def setup_output_hook(self, args): + def fn(grad_fn): + def hook(_, grad_output): + self.grad_outputs = self._pack_with_none(self.output_tensors_index, + grad_output, + self.n_outputs) + + if self.user_pre_hooks: + expected_len = len(self.grad_outputs) + for user_pre_hook in self.user_pre_hooks: + hook_grad_outputs = user_pre_hook(self.module, self.grad_outputs) + if hook_grad_outputs is None: + continue + + actual_len = len(hook_grad_outputs) + if actual_len != expected_len: + raise RuntimeError("Backward pre hook returned an invalid number of grad_output, " + f"got {actual_len}, but expected {expected_len}") + self.grad_outputs = hook_grad_outputs + + # We need to be able to clear self.grad_outputs but also return it + local_grad_outputs = self.grad_outputs + + # Special case if no input required gradients, this hook should call the user + # hook directly + if self.input_tensors_index is None: + grad_inputs = self._pack_with_none([], [], self.n_inputs) + for user_hook in self.user_hooks: + res = user_hook(self.module, grad_inputs, self.grad_outputs) + if res is not None and not (isinstance(res, tuple) and all(el is None for el in res)): + raise RuntimeError("Backward hook for Modules where no input requires " + "gradient should always return None or None for all gradients.") + self.grad_outputs = None + + if local_grad_outputs is not None: + assert self.output_tensors_index is not None # mypy + return tuple(local_grad_outputs[i] for i in self.output_tensors_index) + + grad_fn.register_hook(hook) + + is_tuple = True + if not isinstance(args, tuple): + args = (args,) + is_tuple = False + + res, output_idx = self._apply_on_tensors(fn, args) + self.n_outputs = len(args) + self.output_tensors_index = output_idx + + if not is_tuple: + res = res[0] + return res diff --git a/lib/python3.11/site-packages/torch/utils/jit/__init__.py b/lib/python3.11/site-packages/torch/utils/jit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/torch/utils/jit/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/jit/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2bfc785a792d8265b6f0d794b10e77f320196a76 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/jit/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/jit/__pycache__/log_extract.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/jit/__pycache__/log_extract.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb704bd9daf3917566781b97dacd461122584a4f Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/jit/__pycache__/log_extract.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/jit/log_extract.py b/lib/python3.11/site-packages/torch/utils/jit/log_extract.py new file mode 100644 index 0000000000000000000000000000000000000000..88ffe7bc5926ddf5c893dd16149f8ab3bbf64b56 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/jit/log_extract.py @@ -0,0 +1,113 @@ +# mypy: allow-untyped-defs +from contextlib import contextmanager +from typing import Any, List, Tuple, cast +import random +import torch +import time +from torch.utils.benchmark import Timer + +def extract_ir(filename: str) -> List[str]: + BEGIN = "" + END = "" + pfx = None + graphs = [] + with open(filename) as f: + split_strs = f.read().split(BEGIN) + for i, split_str in enumerate(split_strs): + if i == 0: + continue + end_loc = split_str.find(END) + if end_loc == -1: + continue + s = split_str[:end_loc] + pfx = split_strs[i - 1].splitlines()[-1] + lines = [x[len(pfx):] for x in s.splitlines(keepends=True)] + graphs.append(''.join(lines)) + + return graphs + + +def make_tensor_from_type(inp_type: torch._C.TensorType): + size = inp_type.sizes() + stride = inp_type.strides() + device = inp_type.device() + dtype = inp_type.dtype() + assert size is not None + assert stride is not None + assert device is not None + assert dtype is not None + return torch.empty_strided(size=size, stride=stride, device=device, dtype=dtype) + +def load_graph_and_inputs(ir: str) -> Tuple[Any, List[Any]]: + graph = torch._C.parse_ir(ir, parse_tensor_constants=True) + graph.makeMultiOutputIntoTuple() + inputs = [] + for inp in graph.inputs(): + if isinstance(inp.type(), torch._C.FloatType): + inputs.append(random.uniform(.1, 100)) + elif isinstance(inp.type(), torch._C.IntType): + inputs.append(random.randint(1, 100)) + elif isinstance(inp.type(), torch._C.TensorType): + tensorType = cast(torch._C.TensorType, inp.type()) + inputs.append(make_tensor_from_type(tensorType)) + elif isinstance(inp.type(), torch._C.BoolType): + inputs.append(random.randint(0, 1) == 1) + else: + raise NotImplementedError(f"A default value is not implemented for type {inp.type()}") + + func = torch._C._create_function_from_graph("forward", graph) + torch._C._jit_pass_erase_shape_information(func.graph) + return (func, inputs) + +def time_cuda(fn, inputs, test_runs): + t = Timer(stmt="fn(*inputs)", globals={"fn": fn, "inputs" : inputs}) + times = t.blocked_autorange() + return times.median * 1000 # time in ms + +def time_cpu(fn, inputs, test_runs): + s = time.perf_counter() + for _ in range(test_runs): + fn(*inputs) + e = time.perf_counter() + return (e - s) / test_runs * 1000 # time in ms + +def run_test(ir, inputs, *, warmup_runs=10, test_runs=20) -> float: + graph, _ = load_graph_and_inputs(ir) + for _ in range(warmup_runs): + graph(*inputs) + + is_cpu = None + for input in inputs: + if isinstance(input, torch.Tensor): + is_cpu = input.device.type == "cpu" + break + assert is_cpu is not None + + out = time_cpu(graph, inputs, test_runs) if is_cpu else time_cuda(graph, inputs, test_runs) + return out + +@contextmanager +def no_fuser(*args, **kwargs): + old_optimize = torch._C._get_graph_executor_optimize(False) + try: + yield + finally: + torch._C._get_graph_executor_optimize(old_optimize) + +def run_baseline_no_fusion(ir, inputs) -> float: + with no_fuser(): + return run_test(ir, inputs) + + +def run_nnc(ir, inputs, dynamic) -> float: + try: + strat = [("DYNAMIC", 10)] if dynamic else [("STATIC", 10)] + old_strat = torch.jit.set_fusion_strategy(strat) + with torch.jit.fuser("fuser1"): + return run_test(ir, inputs) + finally: + torch.jit.set_fusion_strategy(old_strat) + +def run_nvfuser(ir, inputs) -> float: + with torch.jit.fuser("fuser2"): + return run_test(ir, inputs) diff --git a/lib/python3.11/site-packages/torch/utils/mkldnn.py b/lib/python3.11/site-packages/torch/utils/mkldnn.py new file mode 100644 index 0000000000000000000000000000000000000000..06ca96d2de9a9729b8250d5c333d74d2352af861 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/mkldnn.py @@ -0,0 +1,234 @@ +# mypy: allow-untyped-defs +import torch + + +class MkldnnLinear(torch.jit.ScriptModule): + def __init__(self, dense_module, dtype): + super().__init__() + self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype)) + if dense_module.bias is not None: + # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy, + # we use fp32 dtype. + self.register_buffer('bias', dense_module.bias.to_mkldnn()) + else: + # TODO: Remove this once ScriptModule supports registering None buffer + self.register_buffer( + 'bias', + torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn()) + + @torch.jit.script_method + def __getstate__(self): + return (self.weight.to_dense(), self.bias.to_dense(), self.training) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = state[0].to_mkldnn() + self.bias = state[1].to_mkldnn() + self.training = state[2] + + @torch.jit.script_method + def forward(self, x): + x_mkldnn = x if x.is_mkldnn else x.to_mkldnn() + y_mkldnn = torch._C._nn.mkldnn_linear(x_mkldnn, self.weight, self.bias) + y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense() + return y + + +class _MkldnnConvNd(torch.jit.ScriptModule): + """Common base of MkldnnConv1d and MkldnnConv2d.""" + + __constants__ = ['stride', 'padding', 'dilation', 'groups'] + + def __init__(self, dense_module): + super().__init__() + + self.stride = dense_module.stride + self.padding = dense_module.padding + self.dilation = dense_module.dilation + self.groups = dense_module.groups + + if dense_module.bias is not None: + self.register_buffer('bias', dense_module.bias.to_mkldnn()) + else: + # Bias can be fp32 or bf16 for OneDNN bf16 path, but for good accuracy, + # we use fp32 dtype. + # TODO: Remove this once ScriptModule supports registering None buffer + self.register_buffer( + 'bias', + torch.zeros([dense_module.weight.size(0)], dtype=torch.float).to_mkldnn()) + + @torch.jit.script_method + def __getstate__(self): + return (self.weight.to_dense(), self.bias.to_dense(), self.training) + + @torch.jit.script_method + def forward(self, x): + return torch.mkldnn_convolution( + x, + self.weight, + self.bias, + self.padding, + self.stride, + self.dilation, + self.groups) + + +class MkldnnConv1d(_MkldnnConvNd): + def __init__(self, dense_module, dtype): + super().__init__(dense_module) + + self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype)) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = state[0].to_mkldnn() + self.bias = state[1].to_mkldnn() + self.training = state[2] + + +class MkldnnConv2d(_MkldnnConvNd): + def __init__(self, dense_module, dtype): + super().__init__(dense_module) + + self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv2d_weight( + dense_module.weight.to_mkldnn(dtype), + self.padding, + self.stride, + self.dilation, + self.groups)) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = torch._C._nn.mkldnn_reorder_conv2d_weight( + state[0].to_mkldnn(), + self.padding, + self.stride, + self.dilation, + self.groups) + self.bias = state[1].to_mkldnn() + self.training = state[2] + +class MkldnnConv3d(_MkldnnConvNd): + def __init__(self, dense_module, dtype): + super().__init__(dense_module) + + self.register_buffer('weight', torch._C._nn.mkldnn_reorder_conv3d_weight( + dense_module.weight.to_mkldnn(dtype), + self.padding, + self.stride, + self.dilation, + self.groups)) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = torch._C._nn.mkldnn_reorder_conv3d_weight( + state[0].to_mkldnn(), + self.padding, + self.stride, + self.dilation, + self.groups) + self.bias = state[1].to_mkldnn() + self.training = state[2] + + +class MkldnnBatchNorm(torch.jit.ScriptModule): + __constants__ = ['exponential_average_factor', 'eps'] + + def __init__(self, dense_module): + super().__init__() + + assert not dense_module.training + assert dense_module.track_running_stats + assert dense_module.affine + + if dense_module.momentum is None: + self.exponential_average_factor = 0.0 + else: + self.exponential_average_factor = dense_module.momentum + self.eps = dense_module.eps + + self.register_buffer('weight', dense_module.weight.to_mkldnn()) + self.register_buffer('bias', dense_module.bias.to_mkldnn()) + self.register_buffer('running_mean', dense_module.running_mean.to_mkldnn()) + self.register_buffer('running_var', dense_module.running_var.to_mkldnn()) + + @torch.jit.script_method + def __getstate__(self): + weight = self.weight.to_dense() + bias = self.bias.to_dense() + running_mean = self.running_mean.to_dense() + running_var = self.running_var.to_dense() + return (weight, bias, running_mean, running_var, self.training) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = state[0].to_mkldnn() + self.bias = state[1].to_mkldnn() + self.running_mean = state[2].to_mkldnn() + self.running_var = state[3].to_mkldnn() + self.training = state[4] + + @torch.jit.script_method + def forward(self, x): + return torch.batch_norm( + x, + self.weight, + self.bias, + self.running_mean, + self.running_var, + False, # training + self.exponential_average_factor, + self.eps, + False, # cuda_enabled + ) + +class MkldnnPrelu(torch.jit.ScriptModule): + def __init__(self, dense_module, dtype): + super().__init__() + self.register_buffer('weight', dense_module.weight.to_mkldnn(dtype)) + + @torch.jit.script_method + def __getstate__(self): + return (self.weight.to_dense(), self.training) + + @torch.jit.script_method + def __setstate__(self, state): + self.weight = state[0].to_mkldnn() + self.training = state[1] + + @torch.jit.script_method + def forward(self, x): + x_mkldnn = x if x.is_mkldnn else x.to_mkldnn() + y_mkldnn = torch.prelu(x_mkldnn, self.weight) + y = y_mkldnn if x.is_mkldnn else y_mkldnn.to_dense() + return y + +def to_mkldnn(module, dtype=torch.float): + assert dtype in [torch.float, torch.bfloat16, torch.half], \ + "MKLDNN only support float, bfloat16, and half path now" + + def m_fn(m, d): + if isinstance(m, torch.nn.Linear): + return MkldnnLinear(m, d) + elif isinstance(m, torch.nn.Conv1d): + return MkldnnConv1d(m, d) + elif isinstance(m, torch.nn.Conv2d): + return MkldnnConv2d(m, d) + elif isinstance(m, torch.nn.Conv3d): + return MkldnnConv3d(m, d) + elif isinstance(m, (torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)): + # For batchnorm bf16 path, OneDNN requires weight and bias need fp32 dtype. + # so it doesn't need dtype argument. + return MkldnnBatchNorm(m) + elif isinstance(m, torch.nn.PReLU): + return MkldnnPrelu(m, d) + else: + return m + + def m_fn_rec(m, d): + new_m = m_fn(m, d) + for name, sub_m in m.named_children(): + setattr(new_m, name, m_fn_rec(sub_m, d)) + return new_m + + return m_fn_rec(module, dtype) diff --git a/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py b/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..faaf48283e66520ae62daf6ec894f93a6e3b4185 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/mobile_optimizer.py @@ -0,0 +1,135 @@ +# mypy: allow-untyped-defs +"""This module contains utility method for mobile model optimization and lint.""" + +import torch +from enum import Enum +from torch._C import _MobileOptimizerType as MobileOptimizerType +from typing import Optional, Set, List, AnyStr + +class LintCode(Enum): + BUNDLED_INPUT = 1 + REQUIRES_GRAD = 2 + DROPOUT = 3 + BATCHNORM = 4 + +def optimize_for_mobile( + script_module: torch.jit.ScriptModule, + optimization_blocklist: Optional[Set[MobileOptimizerType]] = None, + preserved_methods: Optional[List[AnyStr]] = None, + backend: str = 'CPU') -> torch.jit.RecursiveScriptModule: + """ + Optimize a torch script module for mobile deployment. + + Args: + script_module: An instance of torch script module with type of ScriptModule. + optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed, + optimization method will run all the optimizer pass; otherwise, optimizer + method will run the optimization pass that is not included inside optimization_blocklist. + preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked + backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal'). + Returns: + A new optimized torch script module + """ + if not isinstance(script_module, torch.jit.ScriptModule): + raise TypeError( + f'Got {type(script_module)}, but ScriptModule is expected.') + + if optimization_blocklist is None: + optimization_blocklist = set() + + if preserved_methods is None: + preserved_methods = [] + + # Convert potential byte arrays into strings (if there is any) to pass type checking + # Here we use a new name as assigning it back to preserved_methods will invoke + # mypy errors (i.e. List[AnyStr] = List[str]) + preserved_methods_str: List[str] = [str(method) for method in preserved_methods] + + bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str) + if all(hasattr(script_module, method) for method in bundled_inputs_attributes): + preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes)) + + non_exist_methods = [method for method in preserved_methods_str if not hasattr(script_module, method)] + if non_exist_methods: + raise AttributeError( + f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}") + + backend = backend.lower() + if backend == 'cpu': + optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile( + script_module._c, + optimization_blocklist, + preserved_methods_str) + elif backend == 'vulkan': + optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile( + script_module._c, + optimization_blocklist, + preserved_methods_str) + elif backend == 'metal': + optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str) + else: + raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'") + + return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module) + + +def generate_mobile_module_lints(script_module: torch.jit.ScriptModule): + """ + Generate a list of lints for a given torch script module. + + Args: + script_module: An instance of torch script module with type of ScriptModule. + + Returns: + lint_map: A list of dictionary that contains modules lints + """ + if not isinstance(script_module, torch.jit.ScriptModule): + raise TypeError( + f'Got {type(script_module)}, but ScriptModule is expected.') + + lint_list = [] + + if not hasattr(script_module, "_generate_bundled_inputs_for_forward"): + lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs " + "before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."}) + + for name, param in script_module.named_parameters(): + if param.requires_grad: + lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, " + "please set torch.no_grad() to reduce memory usage and improve computation speed during " + "inference phase."}) + + op_names = torch.jit.export_opnames(script_module) + for op_name in op_names: + if "dropout" in op_name: + lint_list.append({"name": LintCode.DROPOUT.name, + "message": f"Operator {op_name} exists, remember to call eval() before " + "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout " + "operator."}) + if "batch_norm" in op_name: + lint_list.append({"name": LintCode.BATCHNORM.name, + "message": f"Operator {op_name} exists, remember to call eval() before " + "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm " + "operator."}) + + return lint_list + +def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: List[str]) -> List[str]: + + bundled_inputs_attributes = [] + # Has bundled inputs for forward + if hasattr(script_module, 'get_all_bundled_inputs'): + bundled_inputs_attributes.append('get_all_bundled_inputs') + bundled_inputs_attributes.append('get_num_bundled_inputs') + + # Bundled inputs in module after the change that introduced bundled inputs for multiple functions + if hasattr(script_module, 'get_bundled_inputs_functions_and_info'): + bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info') + all_info = script_module.get_bundled_inputs_functions_and_info() + for function_name in all_info: + if function_name not in preserved_methods: + bundled_inputs_attributes.append(function_name) + bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name) + bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name) + + return bundled_inputs_attributes diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/__init__.py b/lib/python3.11/site-packages/torch/utils/model_dump/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b4c79a8824c03fbb2a630feac310137c5b4d1e4d --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_dump/__init__.py @@ -0,0 +1,411 @@ +#!/usr/bin/env python3 +# mypy: allow-untyped-defs +""" +model_dump: a one-stop shop for TorchScript model inspection. + +The goal of this tool is to provide a simple way to extract lots of +useful information from a TorchScript model and make it easy for humans +to consume. It (mostly) replaces zipinfo, common uses of show_pickle, +and various ad-hoc analysis notebooks. + +The tool extracts information from the model and serializes it as JSON. +That JSON can then be rendered by an HTML+JS page, either by +loading the JSON over HTTP or producing a fully self-contained page +with all of the code and data burned-in. +""" + +# Maintainer notes follow. +""" +The implementation strategy has tension between 3 goals: +- Small file size. +- Fully self-contained. +- Easy, modern JS environment. +Using Preact and HTM achieves 1 and 2 with a decent result for 3. +However, the models I tested with result in ~1MB JSON output, +so even using something heavier like full React might be tolerable +if the build process can be worked out. + +One principle I have followed that I think is very beneficial +is to keep the JSON data as close as possible to the model +and do most of the rendering logic on the client. +This makes for easier development (just refresh, usually), +allows for more laziness and dynamism, and lets us add more +views of the same data without bloating the HTML file. + +Currently, this code doesn't actually load the model or even +depend on any part of PyTorch. I don't know if that's an important +feature to maintain, but it's probably worth preserving the ability +to run at least basic analysis on models that cannot be loaded. + +I think the easiest way to develop this code is to cd into model_dump and +run "python -m http.server", then load http://localhost:8000/skeleton.html +in the browser. In another terminal, run +"python -m torch.utils.model_dump --style=json FILE > \ + torch/utils/model_dump/model_info.json" +every time you update the Python code or model. +When you update JS, just refresh. + +Possible improvements: + - Fix various TODO comments in this file and the JS. + - Make the HTML much less janky, especially the auxiliary data panel. + - Make the auxiliary data panel start small, expand when + data is available, and have a button to clear/contract. + - Clean up the JS. There's a lot of copypasta because + I don't really know how to use Preact. + - Make the HTML render and work nicely inside a Jupyter notebook. + - Add the ability for JS to choose the URL to load the JSON based + on the page URL (query or hash). That way we could publish the + inlined skeleton once and have it load various JSON blobs. + - Add a button to expand all expandable sections so ctrl-F works well. + - Add hyperlinking from data to code, and code to code. + - Add hyperlinking from debug info to Diffusion. + - Make small tensor contents available. + - Do something nice for quantized models + (they probably don't work at all right now). +""" + +import argparse +import io +import json +import os +import pickle +import pprint +import re +import sys +import urllib.parse +import zipfile +from pathlib import Path +from typing import Dict +import warnings + +import torch.utils.show_pickle + + +DEFAULT_EXTRA_FILE_SIZE_LIMIT = 16 * 1024 + +__all__ = ['get_storage_info', 'hierarchical_pickle', 'get_model_info', 'get_inline_skeleton', + 'burn_in_info', 'get_info_and_burn_skeleton'] + +def get_storage_info(storage): + assert isinstance(storage, torch.utils.show_pickle.FakeObject) + assert storage.module == "pers" + assert storage.name == "obj" + assert storage.state is None + assert isinstance(storage.args, tuple) + assert len(storage.args) == 1 + sa = storage.args[0] + assert isinstance(sa, tuple) + assert len(sa) == 5 + assert sa[0] == "storage" + assert isinstance(sa[1], torch.utils.show_pickle.FakeClass) + assert sa[1].module == "torch" + assert sa[1].name.endswith("Storage") + storage_info = [sa[1].name.replace("Storage", "")] + list(sa[2:]) + return storage_info + + +def hierarchical_pickle(data): + if isinstance(data, (bool, int, float, str, type(None))): + return data + if isinstance(data, list): + return [hierarchical_pickle(d) for d in data] + if isinstance(data, tuple): + return { + "__tuple_values__": hierarchical_pickle(list(data)), + } + if isinstance(data, dict): + return { + "__is_dict__": True, + "keys": hierarchical_pickle(list(data.keys())), + "values": hierarchical_pickle(list(data.values())), + } + if isinstance(data, torch.utils.show_pickle.FakeObject): + typename = f"{data.module}.{data.name}" + if ( + typename.startswith(('__torch__.', 'torch.jit.LoweredWrapper.', 'torch.jit.LoweredModule.')) + ): + assert data.args == () + return { + "__module_type__": typename, + "state": hierarchical_pickle(data.state), + } + if typename == "torch._utils._rebuild_tensor_v2": + assert data.state is None + storage, offset, size, stride, requires_grad, *_ = data.args + storage_info = get_storage_info(storage) + return {"__tensor_v2__": [storage_info, offset, size, stride, requires_grad]} + if typename == "torch._utils._rebuild_qtensor": + assert data.state is None + storage, offset, size, stride, quantizer, requires_grad, *_ = data.args + storage_info = get_storage_info(storage) + assert isinstance(quantizer, tuple) + assert isinstance(quantizer[0], torch.utils.show_pickle.FakeClass) + assert quantizer[0].module == "torch" + if quantizer[0].name == "per_tensor_affine": + assert len(quantizer) == 3 + assert isinstance(quantizer[1], float) + assert isinstance(quantizer[2], int) + quantizer_extra = list(quantizer[1:3]) + else: + quantizer_extra = [] + quantizer_json = [quantizer[0].name] + quantizer_extra + return {"__qtensor__": [storage_info, offset, size, stride, quantizer_json, requires_grad]} + if typename == "torch.jit._pickle.restore_type_tag": + assert data.state is None + obj, typ = data.args + assert isinstance(typ, str) + return hierarchical_pickle(obj) + if re.fullmatch(r"torch\.jit\._pickle\.build_[a-z]+list", typename): + assert data.state is None + ls, = data.args + assert isinstance(ls, list) + return hierarchical_pickle(ls) + if typename == "torch.device": + assert data.state is None + name, = data.args + assert isinstance(name, str) + # Just forget that it was a device and return the name. + return name + if typename == "builtin.UnicodeDecodeError": + assert data.state is None + msg, = data.args + assert isinstance(msg, str) + # Hack: Pretend this is a module so we don't need custom serialization. + # Hack: Wrap the message in a tuple so it looks like a nice state object. + # TODO: Undo at least that second hack. We should support string states. + return { + "__module_type__": typename, + "state": hierarchical_pickle((msg,)), + } + raise Exception(f"Can't prepare fake object of type for JS: {typename}") # noqa: TRY002 + raise Exception(f"Can't prepare data of type for JS: {type(data)}") # noqa: TRY002 + + +def get_model_info( + path_or_file, + title=None, + extra_file_size_limit=DEFAULT_EXTRA_FILE_SIZE_LIMIT): + """Get JSON-friendly information about a model. + + The result is suitable for being saved as model_info.json, + or passed to burn_in_info. + """ + + if isinstance(path_or_file, os.PathLike): + default_title = os.fspath(path_or_file) + file_size = path_or_file.stat().st_size # type: ignore[attr-defined] + elif isinstance(path_or_file, str): + default_title = path_or_file + file_size = Path(path_or_file).stat().st_size + else: + default_title = "buffer" + path_or_file.seek(0, io.SEEK_END) + file_size = path_or_file.tell() + path_or_file.seek(0) + + title = title or default_title + + with zipfile.ZipFile(path_or_file) as zf: + path_prefix = None + zip_files = [] + for zi in zf.infolist(): + prefix = re.sub("/.*", "", zi.filename) + if path_prefix is None: + path_prefix = prefix + elif prefix != path_prefix: + raise Exception(f"Mismatched prefixes: {path_prefix} != {prefix}") # noqa: TRY002 + zip_files.append(dict( + filename=zi.filename, + compression=zi.compress_type, + compressed_size=zi.compress_size, + file_size=zi.file_size, + )) + + assert path_prefix is not None + version = zf.read(path_prefix + "/version").decode("utf-8").strip() + + def get_pickle(name): + assert path_prefix is not None + with zf.open(path_prefix + f"/{name}.pkl") as handle: + raw = torch.utils.show_pickle.DumpUnpickler(handle, catch_invalid_utf8=True).load() + return hierarchical_pickle(raw) + + model_data = get_pickle("data") + constants = get_pickle("constants") + + # Intern strings that are likely to be re-used. + # Pickle automatically detects shared structure, + # so re-used strings are stored efficiently. + # However, JSON has no way of representing this, + # so we have to do it manually. + interned_strings : Dict[str, int] = {} + + def ist(s): + if s not in interned_strings: + interned_strings[s] = len(interned_strings) + return interned_strings[s] + + code_files = {} + for zi in zf.infolist(): + if not zi.filename.endswith(".py"): + continue + with zf.open(zi) as handle: + raw_code = handle.read() + with zf.open(zi.filename + ".debug_pkl") as handle: + raw_debug = handle.read() + + # Parse debug info and add begin/end markers if not present + # to ensure that we cover the entire source code. + debug_info_t = pickle.loads(raw_debug) + text_table = None + + if (len(debug_info_t) == 3 and + isinstance(debug_info_t[0], str) and + debug_info_t[0] == 'FORMAT_WITH_STRING_TABLE'): + _, text_table, content = debug_info_t + + def parse_new_format(line): + # (0, (('', '', 0), 0, 0)) + num, ((text_indexes, fname_idx, offset), start, end), tag = line + text = ''.join(text_table[x] for x in text_indexes) # type: ignore[index] + fname = text_table[fname_idx] # type: ignore[index] + return num, ((text, fname, offset), start, end), tag + + debug_info_t = map(parse_new_format, content) + + debug_info = list(debug_info_t) + if not debug_info: + debug_info.append((0, (('', '', 0), 0, 0))) + if debug_info[-1][0] != len(raw_code): + debug_info.append((len(raw_code), (('', '', 0), 0, 0))) + + code_parts = [] + for di, di_next in zip(debug_info, debug_info[1:]): + start, source_range, *_ = di + end = di_next[0] + assert end > start + source, s_start, s_end = source_range + s_text, s_file, s_line = source + # TODO: Handle this case better. TorchScript ranges are in bytes, + # but JS doesn't really handle byte strings. + # if bytes and chars are not equivalent for this string, + # zero out the ranges so we don't highlight the wrong thing. + if len(s_text) != len(s_text.encode("utf-8")): + s_start = 0 + s_end = 0 + text = raw_code[start:end] + code_parts.append([text.decode("utf-8"), ist(s_file), s_line, ist(s_text), s_start, s_end]) + code_files[zi.filename] = code_parts + + extra_files_json_pattern = re.compile(re.escape(path_prefix) + "/extra/.*\\.json") + extra_files_jsons = {} + for zi in zf.infolist(): + if not extra_files_json_pattern.fullmatch(zi.filename): + continue + if zi.file_size > extra_file_size_limit: + continue + with zf.open(zi) as handle: + try: + json_content = json.load(handle) + extra_files_jsons[zi.filename] = json_content + except json.JSONDecodeError: + extra_files_jsons[zi.filename] = "INVALID JSON" + + always_render_pickles = { + "bytecode.pkl", + } + extra_pickles = {} + for zi in zf.infolist(): + if not zi.filename.endswith(".pkl"): + continue + with zf.open(zi) as handle: + # TODO: handle errors here and just ignore the file? + # NOTE: For a lot of these files (like bytecode), + # we could get away with just unpickling, but this should be safer. + obj = torch.utils.show_pickle.DumpUnpickler(handle, catch_invalid_utf8=True).load() + buf = io.StringIO() + pprint.pprint(obj, buf) + contents = buf.getvalue() + # Checked the rendered length instead of the file size + # because pickles with shared structure can explode in size during rendering. + if os.path.basename(zi.filename) not in always_render_pickles and \ + len(contents) > extra_file_size_limit: + continue + extra_pickles[zi.filename] = contents + + return {"model": dict( + title=title, + file_size=file_size, + version=version, + zip_files=zip_files, + interned_strings=list(interned_strings), + code_files=code_files, + model_data=model_data, + constants=constants, + extra_files_jsons=extra_files_jsons, + extra_pickles=extra_pickles, + )} + + +def get_inline_skeleton(): + """Get a fully-inlined skeleton of the frontend. + + The returned HTML page has no external network dependencies for code. + It can load model_info.json over HTTP, or be passed to burn_in_info. + """ + + import importlib.resources + + skeleton = importlib.resources.read_text(__package__, "skeleton.html") + js_code = importlib.resources.read_text(__package__, "code.js") + for js_module in ["preact", "htm"]: + js_lib = importlib.resources.read_binary(__package__, f"{js_module}.mjs") + js_url = "data:application/javascript," + urllib.parse.quote(js_lib) + js_code = js_code.replace(f"https://unpkg.com/{js_module}?module", js_url) + skeleton = skeleton.replace(' src="./code.js">', ">\n" + js_code) + return skeleton + + +def burn_in_info(skeleton, info): + """Burn model info into the HTML skeleton. + + The result will render the hard-coded model info and + have no external network dependencies for code or data. + """ + + # Note that Python's json serializer does not escape slashes in strings. + # Since we're inlining this JSON directly into a script tag, a string + # containing "" would end the script prematurely and + # mess up our page. Unconditionally escape fixes that. + return skeleton.replace( + "BURNED_IN_MODEL_INFO = null", + "BURNED_IN_MODEL_INFO = " + json.dumps(info, sort_keys=True).replace("/", "\\/")) + + +def get_info_and_burn_skeleton(path_or_bytesio, **kwargs): + model_info = get_model_info(path_or_bytesio, **kwargs) + skeleton = get_inline_skeleton() + page = burn_in_info(skeleton, model_info) + return page + + +def main(argv, *, stdout=None): + warnings.warn("torch.utils.model_dump is deprecated and will be removed in a future PyTorch release.") + parser = argparse.ArgumentParser() + parser.add_argument("--style", choices=["json", "html"]) + parser.add_argument("--title") + parser.add_argument("model") + args = parser.parse_args(argv[1:]) + + info = get_model_info(args.model, title=args.title) + + output = stdout or sys.stdout + + if args.style == "json": + output.write(json.dumps(info, sort_keys=True) + "\n") + elif args.style == "html": + skeleton = get_inline_skeleton() + page = burn_in_info(skeleton, info) + output.write(page) + else: + raise Exception("Invalid style") # noqa: TRY002 diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/__main__.py b/lib/python3.11/site-packages/torch/utils/model_dump/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d4bdac389bb1f270d74efb6c876258d46077110 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_dump/__main__.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +import sys +from . import main + +sys.exit(main(sys.argv)) diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4550b322d07007c0c522db161e762b6ba3b89dc5 Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__main__.cpython-311.pyc b/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__main__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..782155269dc7ab31f41181a8f6e86bd766968f3f Binary files /dev/null and b/lib/python3.11/site-packages/torch/utils/model_dump/__pycache__/__main__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/code.js b/lib/python3.11/site-packages/torch/utils/model_dump/code.js new file mode 100644 index 0000000000000000000000000000000000000000..173ddfb639d847159ee4fdf46691404bf1bbb7a3 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_dump/code.js @@ -0,0 +1,689 @@ +import { h, Component, render } from 'https://unpkg.com/preact?module'; +import htm from 'https://unpkg.com/htm?module'; + +const html = htm.bind(h); + +const BURNED_IN_MODEL_INFO = null; + +// https://stackoverflow.com/a/20732091 +function humanFileSize(size) { + if (size == 0) { return "0 B"; } + var i = Math.floor( Math.log(size) / Math.log(1024) ); + return (size / Math.pow(1024, i)).toFixed(2) * 1 + ' ' + ['B', 'kB', 'MB', 'GB', 'TB'][i]; +} + +function caret(down) { + return down ? "\u25BE" : "\u25B8"; +} + +class Blamer { + constructor() { + this.blame_on_click = false; + this.aux_content_pane = null; + } + + setAuxContentPane(pane) { + this.aux_content_pane = pane; + } + + readyBlame() { + this.blame_on_click = true; + } + + maybeBlame(arg) { + if (!this.blame_on_click) { + return; + } + this.blame_on_click = false; + if (!this.aux_content_pane) { + return; + } + this.aux_content_pane.doBlame(arg); + } +} + +let blame = new Blamer(); + +class Hider extends Component { + constructor() { + super(); + this.state = { shown: null }; + } + + componentDidMount() { + this.setState({ shown: this.props.shown === "true" }); + } + + render({name, children}, {shown}) { + let my_caret = html` this.click()} >${caret(shown)}`; + return html`
+

${my_caret} ${name}

+
${shown ? this.props.children : []}
`; + } + + click() { + this.setState({shown: !this.state.shown}); + } +} + +function ModelSizeSection({model: {file_size, zip_files}}) { + let store_size = 0; + let compr_size = 0; + for (const zi of zip_files) { + if (zi.compression === 0) { + // TODO: Maybe check that compressed_size === file_size. + store_size += zi.compressed_size; + } else { + compr_size += zi.compressed_size; + } + } + let zip_overhead = file_size - store_size - compr_size; + // TODO: Better formatting. Right-align this. + return html` + <${Hider} name="Model Size" shown=true> + 
.
+      Model size: ${file_size} (${humanFileSize(file_size)})
+      Stored files: ${store_size} (${humanFileSize(store_size)})
+      Compressed files: ${compr_size} (${humanFileSize(compr_size)})
+      Zip overhead: ${zip_overhead} (${humanFileSize(zip_overhead)})
+
`; +} + +function StructuredDataSection({name, data, shown}) { + return html` + <${Hider} name=${name} shown=${shown}> +
+ <${StructuredData} data=${data} indent="" prefix=""/> +
`; +} + +class StructuredData extends Component { + constructor() { + super(); + this.state = { shown: false }; + + this.INLINE_TYPES = new Set(["boolean", "number", "string"]) + this.IGNORED_STATE_KEYS = new Set(["training", "_is_full_backward_hook"]) + } + + click() { + this.setState({shown: !this.state.shown}); + } + + expando(data) { + if (data === null || this.INLINE_TYPES.has(typeof(data))) { + return false; + } + if (typeof(data) != "object") { + throw new Error("Not an object"); + } + if (Array.isArray(data)) { + // TODO: Maybe show simple lists and tuples on one line. + return true; + } + if (data.__tuple_values__) { + // TODO: Maybe show simple lists and tuples on one line. + return true; + } + if (data.__is_dict__) { + // TODO: Maybe show simple (empty?) dicts on one line. + return true; + } + if (data.__module_type__) { + return true; + } + if (data.__tensor_v2__) { + return false; + } + if (data.__qtensor__) { + return false; + } + throw new Error("Can't handle data type.", data); + } + + renderHeadline(data) { + if (data === null) { + return "None"; + } + if (typeof(data) == "boolean") { + const sd = String(data); + return sd.charAt(0).toUpperCase() + sd.slice(1); + } + if (typeof(data) == "number") { + return JSON.stringify(data); + } + if (typeof(data) == "string") { + return JSON.stringify(data); + } + if (typeof(data) != "object") { + throw new Error("Not an object"); + } + if (Array.isArray(data)) { + return "list(["; + } + if (data.__tuple_values__) { + return "tuple(("; + } + if (data.__is_dict__) { + return "dict({"; + } + if (data.__module_type__) { + return data.__module_type__ + "()"; + } + if (data.__tensor_v2__) { + const [storage, offset, size, stride, grad] = data.__tensor_v2__; + const [dtype, key, device, numel] = storage; + return this.renderTensor( + "tensor", dtype, key, device, numel, offset, size, stride, grad, []); + } + if (data.__qtensor__) { + const [storage, offset, size, stride, quantizer, grad] = data.__qtensor__; + const [dtype, key, device, numel] = storage; + let extra_parts = []; + if (quantizer[0] == "per_tensor_affine") { + extra_parts.push(`scale=${quantizer[1]}`); + extra_parts.push(`zero_point=${quantizer[2]}`); + } else { + extra_parts.push(`quantizer=${quantizer[0]}`); + } + return this.renderTensor( + "qtensor", dtype, key, device, numel, offset, size, stride, grad, extra_parts); + } + throw new Error("Can't handle data type.", data); + } + + renderTensor( + prefix, + dtype, + storage_key, + device, + storage_numel, + offset, + size, + stride, + grad, + extra_parts) { + let parts = [ + "(" + size.join(",") + ")", + dtype, + ]; + parts.push(...extra_parts); + if (device != "cpu") { + parts.push(device); + } + if (grad) { + parts.push("grad"); + } + // TODO: Check stride and indicate if the tensor is channels-last or non-contiguous + // TODO: Check size, stride, offset, and numel and indicate if + // the tensor doesn't use all data in storage. + // TODO: Maybe show key? + void(offset); + void(stride); + void(storage_key); + void(storage_numel); + return prefix + "(" + parts.join(", ") + ")"; + } + + renderBody(indent, data) { + if (data === null || this.INLINE_TYPES.has(typeof(data))) { + throw "Should not reach here." + } + if (typeof(data) != "object") { + throw new Error("Not an object"); + } + if (Array.isArray(data)) { + let new_indent = indent + "\u00A0\u00A0"; + let parts = []; + for (let idx = 0; idx < data.length; idx++) { + // Does it make sense to put explicit index numbers here? + parts.push(html`
<${StructuredData} prefix=${idx + ": "} indent=${new_indent} data=${data[idx]} />`); + } + return parts; + } + if (data.__tuple_values__) { + // Handled the same as lists. + return this.renderBody(indent, data.__tuple_values__); + } + if (data.__is_dict__) { + let new_indent = indent + "\u00A0\u00A0"; + let parts = []; + for (let idx = 0; idx < data.keys.length; idx++) { + if (typeof(data.keys[idx]) != "string") { + parts.push(html`
${new_indent}Non-string key`); + } else { + parts.push(html`
<${StructuredData} prefix=${data.keys[idx] + ": "} indent=${new_indent} data=${data.values[idx]} />`); + } + } + return parts; + } + if (data.__module_type__) { + const mstate = data.state; + if (mstate === null || typeof(mstate) != "object") { + throw new Error("Bad module state"); + } + let new_indent = indent + "\u00A0\u00A0"; + let parts = []; + if (mstate.__is_dict__) { + // TODO: Less copy/paste between this and normal dicts. + for (let idx = 0; idx < mstate.keys.length; idx++) { + if (typeof(mstate.keys[idx]) != "string") { + parts.push(html`
${new_indent}Non-string key`); + } else if (this.IGNORED_STATE_KEYS.has(mstate.keys[idx])) { + // Do nothing. + } else { + parts.push(html`
<${StructuredData} prefix=${mstate.keys[idx] + ": "} indent=${new_indent} data=${mstate.values[idx]} />`); + } + } + } else if (mstate.__tuple_values__) { + parts.push(html`
<${StructuredData} prefix="" indent=${new_indent} data=${mstate} />`); + } else if (mstate.__module_type__) { + // We normally wouldn't have the state of a module be another module, + // but we use "modules" to encode special values (like Unicode decode + // errors) that might be valid states. Just go with it. + parts.push(html`
<${StructuredData} prefix="" indent=${new_indent} data=${mstate} />`); + } else { + throw new Error("Bad module state"); + } + return parts; + } + if (data.__tensor_v2__) { + throw "Should not reach here." + } + if (data.__qtensor__) { + throw "Should not reach here." + } + throw new Error("Can't handle data type.", data); + } + + render({data, indent, prefix}, {shown}) { + const exp = this.expando(data) ? html` this.click()} >${caret(shown)} ` : ""; + const headline = this.renderHeadline(data); + const body = shown ? this.renderBody(indent, data) : ""; + return html`${indent}${exp}${prefix}${headline}${body}`; + } +} + +function ZipContentsSection({model: {zip_files}}) { + // TODO: Add human-readable sizes? + // TODO: Add sorting options? + // TODO: Add hierarchical collapsible tree? + return html` + <${Hider} name="Zip Contents" shown=false> + + + + + + + + + + + ${zip_files.map(zf => html` + + + + + `)} + +
ModeSizeCompressedName
${{0: "store", 8: "deflate"}[zf.compression] || zf.compression}${zf.file_size}${zf.compressed_size}${zf.filename}
`; +} + +function CodeSection({model: {code_files}}) { + return html` + <${Hider} name="Code" shown=false> +
+ ${Object.entries(code_files).map(([fn, code]) => html`<${OneCodeSection} + filename=${fn} code=${code} />`)} +
`; +} + +class OneCodeSection extends Component { + constructor() { + super(); + this.state = { shown: false }; + } + + click() { + const shown = !this.state.shown; + this.setState({shown: shown}); + } + + render({filename, code}, {shown}) { + const header = html` +

+ this.click()} >${caret(shown)} + ${filename}

+ `; + if (!shown) { + return header; + } + return html` + ${header} + 
${code.map(c => this.renderBlock(c))}
+ `; + } + + renderBlock([text, ist_file, line, ist_s_text, s_start, s_end]) { + return html` blame.maybeBlame({ist_file, line, ist_s_text, s_start, s_end})} + >${text}`; + } +} + +function ExtraJsonSection({files}) { + return html` + <${Hider} name="Extra files (JSON)" shown=false> +
+

Use "Log Raw Model Info" for hierarchical view in browser console.

+ ${Object.entries(files).map(([fn, json]) => html`<${OneJsonSection} + filename=${fn} json=${json} />`)} +
`; +} + +class OneJsonSection extends Component { + constructor() { + super(); + this.state = { shown: false }; + } + + click() { + const shown = !this.state.shown; + this.setState({shown: shown}); + } + + render({filename, json}, {shown}) { + const header = html` +

+ this.click()} >${caret(shown)} + ${filename}

+ `; + if (!shown) { + return header; + } + return html` + ${header} + 
${JSON.stringify(json, null, 2)}
+ `; + } +} + +function ExtraPicklesSection({files}) { + return html` + <${Hider} name="Extra Pickles" shown=false> +
+ ${Object.entries(files).map(([fn, content]) => html`<${OnePickleSection} + filename=${fn} content=${content} />`)} +
`; +} + +class OnePickleSection extends Component { + constructor() { + super(); + this.state = { shown: false }; + } + + click() { + const shown = !this.state.shown; + this.setState({shown: shown}); + } + + render({filename, content}, {shown}) { + const header = html` +

+ this.click()} >${caret(shown)} + ${filename}

+ `; + if (!shown) { + return header; + } + return html` + ${header} + 
${content}
+ `; + } +} + +function assertStorageAreEqual(key, lhs, rhs) { + if (lhs.length !== rhs.length || + !lhs.every((val, idx) => val === rhs[idx])) { + throw new Error("Storage mismatch for key '" + key + "'"); + } +} + +function computeTensorMemory(numel, dtype) { + const sizes = { + "Byte": 1, + "Char": 1, + "Short": 2, + "Int": 4, + "Long": 8, + "Half": 2, + "Float": 4, + "Double": 8, + "ComplexHalf": 4, + "ComplexFloat": 8, + "ComplexDouble": 16, + "Bool": 1, + "QInt8": 1, + "QUInt8": 1, + "QInt32": 4, + "BFloat16": 2, + }; + let dtsize = sizes[dtype]; + if (!dtsize) { + throw new Error("Unrecognized dtype: " + dtype); + } + return numel * dtsize; +} + +// TODO: Maybe track by dtype as well. +// TODO: Maybe distinguish between visible size and storage size. +function getTensorStorages(data) { + if (data === null) { + return new Map(); + } + if (typeof(data) == "boolean") { + return new Map(); + } + if (typeof(data) == "number") { + return new Map(); + } + if (typeof(data) == "string") { + return new Map(); + } + if (typeof(data) != "object") { + throw new Error("Not an object"); + } + if (Array.isArray(data)) { + let result = new Map(); + for (const item of data) { + const tensors = getTensorStorages(item); + for (const [key, storage] of tensors.entries()) { + if (!result.has(key)) { + result.set(key, storage); + } else { + const old_storage = result.get(key); + assertStorageAreEqual(key, old_storage, storage); + } + } + } + return result; + } + if (data.__tuple_values__) { + return getTensorStorages(data.__tuple_values__); + } + if (data.__is_dict__) { + return getTensorStorages(data.values); + } + if (data.__module_type__) { + return getTensorStorages(data.state); + } + if (data.__tensor_v2__) { + const [storage, offset, size, stride, grad] = data.__tensor_v2__; + const [dtype, key, device, numel] = storage; + return new Map([[key, storage]]); + } + if (data.__qtensor__) { + const [storage, offset, size, stride, quantizer, grad] = data.__qtensor__; + const [dtype, key, device, numel] = storage; + return new Map([[key, storage]]); + } + throw new Error("Can't handle data type.", data); +} + +function getTensorMemoryByDevice(pickles) { + let all_tensors = []; + for (const [name, pickle] of pickles) { + const tensors = getTensorStorages(pickle); + all_tensors.push(...tensors.values()); + } + let result = {}; + for (const storage of all_tensors.values()) { + const [dtype, key, device, numel] = storage; + const size = computeTensorMemory(numel, dtype); + result[device] = (result[device] || 0) + size; + } + return result; +} + +// Make this a separate component so it is rendered lazily. +class OpenTensorMemorySection extends Component { + render({model: {model_data, constants}}) { + let sizes = getTensorMemoryByDevice(new Map([ + ["data", model_data], + ["constants", constants], + ])); + return html` + + + + + + + + + + ${Object.entries(sizes).map(([dev, size]) => html` + + + + `)} + +
DeviceBytesHuman
${dev}${size}${humanFileSize(size)}
`; + } +} + +function TensorMemorySection({model}) { + return html` + <${Hider} name="Tensor Memory" shown=false> + <${OpenTensorMemorySection} model=${model} />`; +} + +class AuxContentPane extends Component { + constructor() { + super(); + this.state = { + blame_info: null, + }; + } + + doBlame(arg) { + this.setState({...this.state, blame_info: arg}); + } + + render({model: {interned_strings}}, {blame_info}) { + let blame_content = ""; + if (blame_info) { + const {ist_file, line, ist_s_text, s_start, s_end} = blame_info; + let s_text = interned_strings[ist_s_text]; + if (s_start != 0 || s_end != s_text.length) { + let prefix = s_text.slice(0, s_start); + let main = s_text.slice(s_start, s_end); + let suffix = s_text.slice(s_end); + s_text = html`${prefix}${main}${suffix}`; + } + blame_content = html` +

${interned_strings[ist_file]}:${line}

+ 
${s_start}:${s_end}
+ 
${s_text}

+ `; + } + return html` + +
+ ${blame_content} + `; + } +} + +class App extends Component { + constructor() { + super(); + this.state = { + err: false, + model: null, + }; + } + + componentDidMount() { + const app = this; + if (BURNED_IN_MODEL_INFO !== null) { + app.setState({model: BURNED_IN_MODEL_INFO}); + } else { + fetch("./model_info.json").then(function(response) { + if (!response.ok) { + throw new Error("Response not ok."); + } + return response.json(); + }).then(function(body) { + app.setState({model: body}); + }).catch(function(error) { + console.log("Top-level error: ", error); + }); + } + } + + componentDidCatch(error) { + void(error); + this.setState({...this.state, err: true}); + } + + render(_, {err}) { + if (this.state.model === null) { + return html`

Loading...

`; + } + + const model = this.state.model.model; + + let error_msg = ""; + if (err) { + error_msg = html`

An error occurred. Check console

`; + } + + return html` + ${error_msg} +
+

TorchScript Model (version ${model.version}): ${model.title}

+ + <${ModelSizeSection} model=${model}/> + <${StructuredDataSection} name="Model Data" data=${model.model_data} shown=true/> + <${StructuredDataSection} name="Constants" data=${model.constants} shown=false/> + <${ZipContentsSection} model=${model}/> + <${CodeSection} model=${model}/> + <${ExtraJsonSection} files=${model.extra_files_jsons}/> + <${ExtraPicklesSection} files=${model.extra_pickles}/> + <${TensorMemorySection} model=${model}/> +
+
+ <${AuxContentPane} + err=${this.state.error} + model=${model} + ref=${(p) => blame.setAuxContentPane(p)}/> +
+ `; + } +} + +render(h(App), document.body); diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/htm.mjs b/lib/python3.11/site-packages/torch/utils/model_dump/htm.mjs new file mode 100644 index 0000000000000000000000000000000000000000..06f25a13d8021ff4f43de442bbf0279f24735d6c --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_dump/htm.mjs @@ -0,0 +1,2 @@ +// HTM, Apache License +var n=function(t,s,r,e){var u;s[0]=0;for(var h=1;h=5&&((e||!n&&5===r)&&(h.push(r,0,e,s),r=6),n&&(h.push(r,n,0,s),r=6)),e=""},a=0;a"===t?(r=1,e=""):e=t+e[0]:u?t===u?u="":e+=t:'"'===t||"'"===t?u=t:">"===t?(p(),r=1):r&&("="===t?(r=5,s=e,e=""):"/"===t&&(r<5||">"===n[a][l+1])?(p(),3===r&&(h=h[0]),r=h,(h=h[0]).push(2,0,r),r=0):" "===t||"\t"===t||"\n"===t||"\r"===t?(p(),r=2):e+=t),3===r&&"!--"===e&&(r=4,h=h[0])}return p(),h}(s)),r),arguments,[])).length>1?r:r[0]} diff --git a/lib/python3.11/site-packages/torch/utils/model_dump/preact.mjs b/lib/python3.11/site-packages/torch/utils/model_dump/preact.mjs new file mode 100644 index 0000000000000000000000000000000000000000..8c85bd948c6772ca8d40fc8d6fab6a220d55a1ef --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_dump/preact.mjs @@ -0,0 +1,2 @@ +// Preact, MIT License +var n,l,u,i,t,o,r={},f=[],e=/acit|ex(?:s|g|n|p|$)|rph|grid|ows|mnc|ntw|ine[ch]|zoo|^ord|itera/i;function c(e,n){for(var t in n)e[t]=n[t];return e}function s(e){var n=e.parentNode;n&&n.removeChild(e)}function a(e,n,t){var _,l,o,r=arguments,i={};for(o in n)"key"==o?_=n[o]:"ref"==o?l=n[o]:i[o]=n[o];if(arguments.length>3)for(t=[t],o=3;o0?v(m.type,m.props,m.key,null,m.__v):m)){if(m.__=t,m.__b=t.__b+1,null===(h=P[p])||h&&m.key==h.key&&m.type===h.type)P[p]=void 0;else for(a=0;a3)for(t=[t],o=3;o + + + TorchScript Model + + + + + + + + diff --git a/lib/python3.11/site-packages/torch/utils/model_zoo.py b/lib/python3.11/site-packages/torch/utils/model_zoo.py new file mode 100644 index 0000000000000000000000000000000000000000..e0c6004e23ea806a2c83e12cd2998e0279e0b16f --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/model_zoo.py @@ -0,0 +1,2 @@ +# torchvision imports tqdm from here. +from torch.hub import tqdm, load_state_dict_from_url as load_url # noqa: F401 diff --git a/lib/python3.11/site-packages/torch/utils/module_tracker.py b/lib/python3.11/site-packages/torch/utils/module_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f337c96a9ef036d7c40771aa35ae13f2300f80 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/module_tracker.py @@ -0,0 +1,159 @@ +# mypy: allow-untyped-defs +import logging +import weakref +from typing import List, Set, TYPE_CHECKING + +import torch +from torch.autograd.graph import register_multi_grad_hook +from torch.nn.modules.module import ( + register_module_forward_hook, + register_module_forward_pre_hook, +) +from torch.utils._pytree import tree_flatten + + +if TYPE_CHECKING: + from torch.utils.hooks import RemovableHandle + + +logger = logging.getLogger(__name__) + + +__all__ = ["ModuleTracker"] + + +class ModuleTracker: + """ + ``ModuleTracker`` is a context manager that tracks the nn.Module hierarchy during execution + so that other system can query which Module is currently being executed (or its backward is being + executed). + + You can access the ``parents`` attribute on this context manager to get the set of all the + Modules currently being executed via their fqn (fully qualified name, also used as the key within + the state_dict). + You can access the ``is_bw`` attribute to know if you are currently running in backward or not. + + Note that ``parents`` is never empty and always contains the "Global" key. The ``is_bw`` flag + will remain ``True`` after the forward until another Module is executed. If you need it to be + more accurate, please submit an issue requesting this. Adding a map from fqn to the module instance + is possible but not done yet, please submit an issue requesting this if you need it. + + Example usage + + .. code-block:: python + + mod = torch.nn.Linear(2, 2) + + with ModuleTracker() as tracker: + # Access anything during the forward pass + def my_linear(m1, m2, bias): + print(f"Current modules: {tracker.parents}") + return torch.mm(m1, m2.t()) + bias + torch.nn.functional.linear = my_linear + + mod(torch.rand(2, 2)) + + """ + + parents: Set[str] + """ + A Set containing the fqn for each module currently running their forward + """ + + def __init__(self) -> None: + self.parents = {"Global"} + self._known_modules: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary() + self._seen_modules: weakref.WeakSet = weakref.WeakSet() + self._has_callback = False + self._hooks: List[RemovableHandle] = [] + + def _maybe_set_engine_callback(self): + # This assumes no concurrent calls to backward + if self._has_callback: + return + + def callback(): + self.parents = {"Global"} + self._has_callback = False + + torch.autograd.Variable._execution_engine.queue_callback(callback) + self._has_callback = True + + @property + def is_bw(self): + """ + A boolean marking if this is currently running during the backward pass or not + """ + return torch._C._current_graph_task_id() != -1 + + def _get_mod_name(self, mod): + if mod not in self._known_modules: + self._known_modules[mod] = type(mod).__name__ + mod_name = self._known_modules[mod] + if mod not in self._seen_modules: + for name, submod in mod.named_children(): + self._known_modules[submod] = f"{mod_name}.{name}" + self._get_mod_name(submod) + self._seen_modules.add(mod) + return mod_name + + def _get_append_fn(self, name, is_bw): + def fn(*args): + if is_bw: + self._maybe_set_engine_callback() + if name in self.parents: + logger.info( + "The module hierarchy tracking seems to be broken as this Module was already entered. %s during %s", + name, + "backward" if is_bw else "forward", + ) + self.parents.add(name) + + return fn + + def _get_pop_fn(self, name, is_bw): + def fn(*args): + if name in self.parents: + self.parents.remove(name) + else: + logger.info( + "The Module hierarchy tracking is confused as we're exiting a Module that was never entered. %s during %s", + name, + "backward" if is_bw else "forward", + ) + + return fn + + def _fw_pre_hook(self, mod, input): + name = self._get_mod_name(mod) + self._get_append_fn(name, False)() + + args, _ = tree_flatten(input) + tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad] + if tensors: + self._hooks.append( + register_multi_grad_hook(tensors, self._get_pop_fn(name, True)) + ) + + def _fw_post_hook(self, mod, input, output): + name = self._get_mod_name(mod) + self._get_pop_fn(name, False)() + + args, _ = tree_flatten(output) + tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad] + if tensors: + self._hooks.append( + register_multi_grad_hook(tensors, self._get_append_fn(name, True)) + ) + + def __enter__(self): + self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook) + self._fw_post_handle = register_module_forward_hook(self._fw_post_hook) + return self + + def __exit__(self, *args): + self._fw_pre_handle.remove() + self._fw_post_handle.remove() + for hook in self._hooks: + hook.remove() + self._hooks.clear() diff --git a/lib/python3.11/site-packages/torch/utils/show_pickle.py b/lib/python3.11/site-packages/torch/utils/show_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..66549fac267326899db82c843e6cde2835c0257d --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/show_pickle.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +# mypy: allow-untyped-defs +import sys +import pickle +import struct +import pprint +import zipfile +import fnmatch +from typing import Any, IO, BinaryIO, Union + +__all__ = ["FakeObject", "FakeClass", "DumpUnpickler", "main"] + +class FakeObject: + def __init__(self, module, name, args): + self.module = module + self.name = name + self.args = args + # NOTE: We don't distinguish between state never set and state set to None. + self.state = None + + def __repr__(self): + state_str = "" if self.state is None else f"(state={self.state!r})" + return f"{self.module}.{self.name}{self.args!r}{state_str}" + + def __setstate__(self, state): + self.state = state + + @staticmethod + def pp_format(printer, obj, stream, indent, allowance, context, level): + if not obj.args and obj.state is None: + stream.write(repr(obj)) + return + if obj.state is None: + stream.write(f"{obj.module}.{obj.name}") + printer._format(obj.args, stream, indent + 1, allowance + 1, context, level) + return + if not obj.args: + stream.write(f"{obj.module}.{obj.name}()(state=\n") + indent += printer._indent_per_level + stream.write(" " * indent) + printer._format(obj.state, stream, indent, allowance + 1, context, level + 1) + stream.write(")") + return + raise Exception("Need to implement") # noqa: TRY002 + + +class FakeClass: + def __init__(self, module, name): + self.module = module + self.name = name + self.__new__ = self.fake_new # type: ignore[assignment] + + def __repr__(self): + return f"{self.module}.{self.name}" + + def __call__(self, *args): + return FakeObject(self.module, self.name, args) + + def fake_new(self, *args): + return FakeObject(self.module, self.name, args[1:]) + + +class DumpUnpickler(pickle._Unpickler): # type: ignore[name-defined] + def __init__( + self, + file, + *, + catch_invalid_utf8=False, + **kwargs): + super().__init__(file, **kwargs) + self.catch_invalid_utf8 = catch_invalid_utf8 + + def find_class(self, module, name): + return FakeClass(module, name) + + def persistent_load(self, pid): + return FakeObject("pers", "obj", (pid,)) + + dispatch = dict(pickle._Unpickler.dispatch) # type: ignore[attr-defined] + + # Custom objects in TorchScript are able to return invalid UTF-8 strings + # from their pickle (__getstate__) functions. Install a custom loader + # for strings that catches the decode exception and replaces it with + # a sentinel object. + def load_binunicode(self): + strlen, = struct.unpack(" sys.maxsize: + raise Exception("String too long.") # noqa: TRY002 + str_bytes = self.read(strlen) # type: ignore[attr-defined] + obj: Any + try: + obj = str(str_bytes, "utf-8", "surrogatepass") + except UnicodeDecodeError as exn: + if not self.catch_invalid_utf8: + raise + obj = FakeObject("builtin", "UnicodeDecodeError", (str(exn),)) + self.append(obj) # type: ignore[attr-defined] + dispatch[pickle.BINUNICODE[0]] = load_binunicode # type: ignore[assignment] + + @classmethod + def dump(cls, in_stream, out_stream): + value = cls(in_stream).load() + pprint.pprint(value, stream=out_stream) + return value + + +def main(argv, output_stream=None): + if len(argv) != 2: + # Don't spam stderr if not using stdout. + if output_stream is not None: + raise Exception("Pass argv of length 2.") # noqa: TRY002 + sys.stderr.write("usage: show_pickle PICKLE_FILE\n") + sys.stderr.write(" PICKLE_FILE can be any of:\n") + sys.stderr.write(" path to a pickle file\n") + sys.stderr.write(" file.zip@member.pkl\n") + sys.stderr.write(" file.zip@*/pattern.*\n") + sys.stderr.write(" (shell glob pattern for members)\n") + sys.stderr.write(" (only first match will be shown)\n") + return 2 + + fname = argv[1] + handle: Union[IO[bytes], BinaryIO] + if "@" not in fname: + with open(fname, "rb") as handle: + DumpUnpickler.dump(handle, output_stream) + else: + zfname, mname = fname.split("@", 1) + with zipfile.ZipFile(zfname) as zf: + if "*" not in mname: + with zf.open(mname) as handle: + DumpUnpickler.dump(handle, output_stream) + else: + found = False + for info in zf.infolist(): + if fnmatch.fnmatch(info.filename, mname): + with zf.open(info) as handle: + DumpUnpickler.dump(handle, output_stream) + found = True + break + if not found: + raise Exception(f"Could not find member matching {mname} in {zfname}") # noqa: TRY002 + + +if __name__ == "__main__": + # This hack works on every version of Python I've tested. + # I've tested on the following versions: + # 3.7.4 + if True: + pprint.PrettyPrinter._dispatch[FakeObject.__repr__] = FakeObject.pp_format # type: ignore[attr-defined] + + sys.exit(main(sys.argv)) diff --git a/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py b/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cca0fb95146039f6dcaedf23485219904b8ee570 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/tensorboard/__init__.py @@ -0,0 +1,13 @@ +import tensorboard +from torch._vendor.packaging.version import Version + +if not hasattr(tensorboard, "__version__") or Version( + tensorboard.__version__ +) < Version("1.15"): + raise ImportError("TensorBoard logging requires TensorBoard version 1.15 or above") + +del Version +del tensorboard + +from .writer import FileWriter, SummaryWriter # noqa: F401 +from tensorboard.summary.writer.record_writer import RecordWriter # noqa: F401 diff --git a/lib/python3.11/site-packages/torch/utils/tensorboard/_utils.py b/lib/python3.11/site-packages/torch/utils/tensorboard/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8acaf1696cb1f253fb4307e91e1fa088278334f0 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/tensorboard/_utils.py @@ -0,0 +1,127 @@ +# mypy: allow-untyped-defs +import numpy as np +import numpy.typing as npt + + +# Functions for converting +def figure_to_image(figures, close=True): + """Render matplotlib figure to numpy format. + + Note that this requires the ``matplotlib`` package. + + Args: + figures (matplotlib.pyplot.figure or list of figures): figure or a list of figures + close (bool): Flag to automatically close the figure + + Returns: + numpy.array: image in [CHW] order + """ + import matplotlib.pyplot as plt + import matplotlib.backends.backend_agg as plt_backend_agg + + def render_to_rgb(figure): + canvas = plt_backend_agg.FigureCanvasAgg(figure) + canvas.draw() + data: npt.NDArray = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8) + w, h = figure.canvas.get_width_height() + image_hwc = data.reshape([h, w, 4])[:, :, 0:3] + image_chw = np.moveaxis(image_hwc, source=2, destination=0) + if close: + plt.close(figure) + return image_chw + + if isinstance(figures, list): + images = [render_to_rgb(figure) for figure in figures] + return np.stack(images) + else: + image = render_to_rgb(figures) + return image + + +def _prepare_video(V): + """ + Convert a 5D tensor into 4D tensor. + + Convesrion is done from [batchsize, time(frame), channel(color), height, width] (5D tensor) + to [time(frame), new_width, new_height, channel] (4D tensor). + + A batch of images are spreaded to a grid, which forms a frame. + e.g. Video with batchsize 16 will have a 4x4 grid. + """ + b, t, c, h, w = V.shape + + if V.dtype == np.uint8: + V = np.float32(V) / 255.0 + + def is_power2(num): + return num != 0 and ((num & (num - 1)) == 0) + + # pad to nearest power of 2, all at once + if not is_power2(V.shape[0]): + len_addition = int(2 ** V.shape[0].bit_length() - V.shape[0]) + V = np.concatenate((V, np.zeros(shape=(len_addition, t, c, h, w))), axis=0) + + n_rows = 2 ** ((b.bit_length() - 1) // 2) + n_cols = V.shape[0] // n_rows + + V = np.reshape(V, newshape=(n_rows, n_cols, t, c, h, w)) + V = np.transpose(V, axes=(2, 0, 4, 1, 5, 3)) + V = np.reshape(V, newshape=(t, n_rows * h, n_cols * w, c)) + + return V + + +def make_grid(I, ncols=8): + # I: N1HW or N3HW + assert isinstance(I, np.ndarray), "plugin error, should pass numpy array here" + if I.shape[1] == 1: + I = np.concatenate([I, I, I], 1) + assert I.ndim == 4 and I.shape[1] == 3 + nimg = I.shape[0] + H = I.shape[2] + W = I.shape[3] + ncols = min(nimg, ncols) + nrows = int(np.ceil(float(nimg) / ncols)) + canvas = np.zeros((3, H * nrows, W * ncols), dtype=I.dtype) + i = 0 + for y in range(nrows): + for x in range(ncols): + if i >= nimg: + break + canvas[:, y * H : (y + 1) * H, x * W : (x + 1) * W] = I[i] + i = i + 1 + return canvas + + # if modality == 'IMG': + # if x.dtype == np.uint8: + # x = x.astype(np.float32) / 255.0 + + +def convert_to_HWC(tensor, input_format): # tensor: numpy array + assert len(set(input_format)) == len( + input_format + ), f"You can not use the same dimension shordhand twice. input_format: {input_format}" + assert len(tensor.shape) == len( + input_format + ), f"size of input tensor and input format are different. \ + tensor shape: {tensor.shape}, input_format: {input_format}" + input_format = input_format.upper() + + if len(input_format) == 4: + index = [input_format.find(c) for c in "NCHW"] + tensor_NCHW = tensor.transpose(index) + tensor_CHW = make_grid(tensor_NCHW) + return tensor_CHW.transpose(1, 2, 0) + + if len(input_format) == 3: + index = [input_format.find(c) for c in "HWC"] + tensor_HWC = tensor.transpose(index) + if tensor_HWC.shape[2] == 1: + tensor_HWC = np.concatenate([tensor_HWC, tensor_HWC, tensor_HWC], 2) + return tensor_HWC + + if len(input_format) == 2: + index = [input_format.find(c) for c in "HW"] + tensor = tensor.transpose(index) + tensor = np.stack([tensor, tensor, tensor], 2) + return tensor diff --git a/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py b/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..2778b37b5a78dfc51d0b6211d7eff320678ee752 --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py @@ -0,0 +1,160 @@ +# mypy: allow-untyped-defs + +import torch._C + + +def format_time(time_us=None, time_ms=None, time_s=None): + """Define time formatting.""" + assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1 + + US_IN_SECOND = 1e6 + US_IN_MS = 1e3 + + if time_us is None: + if time_ms is not None: + time_us = time_ms * US_IN_MS + elif time_s is not None: + time_us = time_s * US_IN_SECOND + else: + raise AssertionError("Shouldn't reach here :)") + + if time_us >= US_IN_SECOND: + return f'{time_us / US_IN_SECOND:.3f}s' + if time_us >= US_IN_MS: + return f'{time_us / US_IN_MS:.3f}ms' + return f'{time_us:.3f}us' + + +class ExecutionStats: + def __init__(self, c_stats, benchmark_config): + self._c_stats = c_stats + self.benchmark_config = benchmark_config + + @property + def latency_avg_ms(self): + return self._c_stats.latency_avg_ms + + @property + def num_iters(self): + return self._c_stats.num_iters + + @property + def iters_per_second(self): + """Return total number of iterations per second across all calling threads.""" + return self.num_iters / self.total_time_seconds + + @property + def total_time_seconds(self): + return self.num_iters * ( + self.latency_avg_ms / 1000.0) / self.benchmark_config.num_calling_threads + + def __str__(self): + return '\n'.join([ + "Average latency per example: " + format_time(time_ms=self.latency_avg_ms), + f"Total number of iterations: {self.num_iters}", + f"Total number of iterations per second (across all threads): {self.iters_per_second:.2f}", + "Total time: " + format_time(time_s=self.total_time_seconds) + ]) + + +class ThroughputBenchmark: + """ + This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. + + This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible + for executing a PyTorch module (nn.Module or ScriptModule) under an inference + server like load. It can emulate multiple calling threads to a single module + provided. In the future we plan to enhance this component to support inter and + intra-op parallelism as well as multiple models running in a single process. + + Please note that even though nn.Module is supported, it might incur an overhead + from the need to hold GIL every time we execute Python code or pass around + inputs as Python objects. As soon as you have a ScriptModule version of your + model for inference deployment it is better to switch to using it in this + benchmark. + + Example:: + + >>> # xdoctest: +SKIP("undefined vars") + >>> from torch.utils import ThroughputBenchmark + >>> bench = ThroughputBenchmark(my_module) + >>> # Pre-populate benchmark's data set with the inputs + >>> for input in inputs: + ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule + ... bench.add_input(input[0], x2=input[1]) + >>> # Inputs supplied above are randomly used during the execution + >>> stats = bench.benchmark( + ... num_calling_threads=4, + ... num_warmup_iters = 100, + ... num_iters = 1000, + ... ) + >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) + >>> print("Number of iterations: {}".format(stats.num_iters)) + """ + + def __init__(self, module): + if isinstance(module, torch.jit.ScriptModule): + self._benchmark = torch._C.ThroughputBenchmark(module._c) + else: + self._benchmark = torch._C.ThroughputBenchmark(module) + + def run_once(self, *args, **kwargs): + """ + Given input id (input_idx) run benchmark once and return prediction. + + This is useful for testing that benchmark actually runs the module you + want it to run. input_idx here is an index into inputs array populated + by calling add_input() method. + """ + return self._benchmark.run_once(*args, **kwargs) + + def add_input(self, *args, **kwargs): + """ + Store a single input to a module into the benchmark memory and keep it there. + + During the benchmark execution every thread is going to pick up a + random input from the all the inputs ever supplied to the benchmark via + this function. + """ + self._benchmark.add_input(*args, **kwargs) + + def benchmark( + self, + num_calling_threads=1, + num_warmup_iters=10, + num_iters=100, + profiler_output_path=""): + """ + Run a benchmark on the module. + + Args: + num_warmup_iters (int): Warmup iters are used to make sure we run a module + a few times before actually measuring things. This way we avoid cold + caches and any other similar problems. This is the number of warmup + iterations for each of the thread in separate + + num_iters (int): Number of iterations the benchmark should run with. + This number is separate from the warmup iterations. Also the number is + shared across all the threads. Once the num_iters iterations across all + the threads is reached, we will stop execution. Though total number of + iterations might be slightly larger. Which is reported as + stats.num_iters where stats is the result of this function + + profiler_output_path (str): Location to save Autograd Profiler trace. + If not empty, Autograd Profiler will be enabled for the main benchmark + execution (but not the warmup phase). The full trace will be saved + into the file path provided by this argument + + + This function returns BenchmarkExecutionStats object which is defined via pybind11. + It currently has two fields: + - num_iters - number of actual iterations the benchmark have made + - avg_latency_ms - average time it took to infer on one input example in milliseconds + """ + config = torch._C.BenchmarkConfig() + config.num_calling_threads = num_calling_threads + config.num_warmup_iters = num_warmup_iters + config.num_iters = num_iters + config.profiler_output_path = profiler_output_path + c_stats = self._benchmark.benchmark(config) + return ExecutionStats(c_stats, config) diff --git a/lib/python3.11/site-packages/torch/utils/weak.py b/lib/python3.11/site-packages/torch/utils/weak.py new file mode 100644 index 0000000000000000000000000000000000000000..f729ff06489ffed372b3641f00b35255a5ca6f5d --- /dev/null +++ b/lib/python3.11/site-packages/torch/utils/weak.py @@ -0,0 +1,322 @@ +# mypy: allow-untyped-defs +from __future__ import annotations + +import weakref +from weakref import ref +from _weakrefset import _IterationGuard # type: ignore[attr-defined] +from collections.abc import MutableMapping, Mapping +from torch import Tensor +import collections.abc as _collections_abc + + +WeakRef = ref + + +__all__ = ['TensorWeakRef', 'WeakIdRef', 'WeakIdKeyDictionary', 'WeakTensorKeyDictionary'] + + +# This file defines a variant of WeakKeyDictionary that overrides the hashing +# behavior of the key to use object identity, rather than the builtin +# __eq__/__hash__ functions. This is useful for Tensor weak keys, as their +# __eq__ implementation return a Tensor (elementwise equality), which means +# you can't use them directly with the WeakKeyDictionary in standard library. +# +# Our implementation strategy is to create a wrapper weak key object, which we +# use as a key in a stock Python dictionary. This is similar to how weakref +# implements WeakKeyDictionary, but instead of using weakref.ref as the +# wrapper, we use a custom wrapper that has different __eq__ and __hash__ +# behavior. Note that we subsequently store this weak key directly in an +# ORDINARY dictionary, since the newly constructed WeakIdKey's only use would +# be a dictionary so it would have no strong references. Ensuring that +# only live WeakIdKeys are in the map is handled by putting finalizers on the +# original key object. + + +# It is simpler to implement this with composition, but if we want to +# directly reuse the callback mechanism on weakref, we need the weakref +# and the key to be exactly the same object. Reusing the callback mechanism +# minimizes the divergence between our implementation and Lib/weakref.py +# +# NB: Prefer using this when working with weakrefs of Tensors; e.g., do +# WeakIdRef(tensor) rather than weakref.ref(tensor); it handles a number of +# easy to get wrong cases transparently for you. +class WeakIdRef(weakref.ref): + __slots__ = ['_id'] + + def __init__(self, key, callback=None): + # Unlike stock weakref, which preserves hash semantics of the + # original object but lazily defers hash calls until the first + # time the user attempts to hash the weakref, we can eagerly + # cache the id of the key as we know this is definitely the hash + # method + self._id = id(key) + super().__init__(key, callback) # type: ignore[call-arg] + + def __call__(self): + r = super().__call__() + # Special logic for Tensor PyObject resurrection + if hasattr(r, '_fix_weakref'): + r._fix_weakref() # type: ignore[union-attr] + return r + + def __hash__(self): + return self._id + + def __eq__(self, other): + # An attractive but wrong alternate implementation is to only test if + # the stored _ids match. This can lead to an ABA problem if you have: + # + # a1 = A() + # w1 = WeakIdRef(a1) + # del a1 + # a2 = A() # suppose it gets the same ID as a1 + # w2 = WeakIdRef(a2) + # print(w1 == w2) + # + # This should be False, as a1 and a2 are unrelated (and a1 is + # dead anyway) + a = self() + b = other() + if a is not None and b is not None: + return a is b + return self is other + +# This is the same as WeakIdRef but equality is checked using hash() rather than id. +# This will be equivalent to the one above except for classes where hash is not their id. +class _WeakHashRef(weakref.ref): + __slots__ = ['_id'] + + def __init__(self, key, callback=None): + # Unlike stock weakref, which preserves hash semantics of the + # original object but lazily defers hash calls until the first + # time the user attempts to hash the weakref, we can eagerly + # cache the id of the key as we know this is definitely the hash + # method + self._id = hash(key) + super().__init__(key, callback) # type: ignore[call-arg] + + def __call__(self): + r = super().__call__() + # Special logic for Tensor PyObject resurrection + if hasattr(r, '_fix_weakref'): + r._fix_weakref() # type: ignore[union-attr] + return r + + def __hash__(self): + return self._id + + def __eq__(self, other): + # Use hash equality to determine ref equality. + # ScriptObject implements __hash__ to return the wrapped IValue's id, so + # this is equivalent to doing an identity comparison. + a = self() + b = other() + if a is not None and b is not None: + return hash(a) == hash(b) + return self is other + +# This is directly adapted from cpython/Lib/weakref.py +class WeakIdKeyDictionary(MutableMapping): + def __init__(self, dict=None, ref_type=WeakIdRef): # CHANGED + self.data = {} + + self.ref_type = ref_type # CHANGED + + def remove(k, selfref=ref(self)): + self = selfref() + if self is not None: + if self._iterating: + self._pending_removals.append(k) + else: + try: + del self.data[k] + except KeyError: + pass + self._remove = remove + # A list of dead weakrefs (keys to be removed) + self._pending_removals = [] + self._iterating = set() + self._dirty_len = False + if dict is not None: + self.update(dict) + + def _commit_removals(self): + # NOTE: We don't need to call this method before mutating the dict, + # because a dead weakref never compares equal to a live weakref, + # even if they happened to refer to equal objects. + # However, it means keys may already have been removed. + pop = self._pending_removals.pop + d = self.data + while True: + try: + key = pop() + except IndexError: + return + + try: + del d[key] + except KeyError: + pass + + def _scrub_removals(self): + d = self.data + self._pending_removals = [k for k in self._pending_removals if k in d] + self._dirty_len = False + + def __delitem__(self, key): + self._dirty_len = True + del self.data[self.ref_type(key)] # CHANGED + + def __getitem__(self, key): + return self.data[self.ref_type(key)] # CHANGED + + def __len__(self): + if self._dirty_len and self._pending_removals: + # self._pending_removals may still contain keys which were + # explicitly removed, we have to scrub them (see issue #21173). + self._scrub_removals() + return len(self.data) - len(self._pending_removals) + + def __repr__(self): + return f"<{self.__class__.__name__} at {id(self):#x}>" + + def __setitem__(self, key, value): + self.data[self.ref_type(key, self._remove)] = value # CHANGED + + def copy(self): + new = WeakIdKeyDictionary() + with _IterationGuard(self): + for key, value in self.data.items(): + o = key() + if o is not None: + new[o] = value + return new + + __copy__ = copy + + def __deepcopy__(self, memo): + from copy import deepcopy + new = self.__class__() + with _IterationGuard(self): + for key, value in self.data.items(): + o = key() + if o is not None: + new[o] = deepcopy(value, memo) + return new + + def get(self, key, default=None): + return self.data.get(self.ref_type(key), default) # CHANGED + + def __contains__(self, key): + try: + wr = self.ref_type(key) # CHANGED + except TypeError: + return False + return wr in self.data + + def items(self): + with _IterationGuard(self): + for wr, value in self.data.items(): + key = wr() + if key is not None: + yield key, value + + def keys(self): + with _IterationGuard(self): + for wr in self.data: + obj = wr() + if obj is not None: + yield obj + + __iter__ = keys + + def values(self): + with _IterationGuard(self): + for wr, value in self.data.items(): + if wr() is not None: + yield value + + def keyrefs(self): + """Return a list of weak references to the keys. + + The references are not guaranteed to be 'live' at the time + they are used, so the result of calling the references needs + to be checked before being used. This can be used to avoid + creating references that will cause the garbage collector to + keep the keys around longer than needed. + + """ + return list(self.data) + + def popitem(self): + self._dirty_len = True + while True: + key, value = self.data.popitem() + o = key() + if o is not None: + return o, value + + def pop(self, key, *args): + self._dirty_len = True + return self.data.pop(self.ref_type(key), *args) # CHANGED + + def setdefault(self, key, default=None): + return self.data.setdefault(self.ref_type(key, self._remove), default) # CHANGED + + def update(self, dict=None, **kwargs): # type: ignore[override] + d = self.data + if dict is not None: + if not hasattr(dict, "items"): + dict = type({})(dict) + for key, value in dict.items(): + d[self.ref_type(key, self._remove)] = value # CHANGED + if len(kwargs): + self.update(kwargs) + + def __ior__(self, other): + self.update(other) + return self + + def __or__(self, other): + if isinstance(other, _collections_abc.Mapping): + c = self.copy() + c.update(other) + return c + return NotImplemented + + def __ror__(self, other): + if isinstance(other, _collections_abc.Mapping): + c = self.__class__() + c.update(other) + c.update(self) + return c + return NotImplemented + + # Default Mapping equality will tests keys for equality, but + # we want to test ids for equality + def __eq__(self, other): + if not isinstance(other, Mapping): + return NotImplemented + return {id(k): v for k, v in self.items()} == {id(k): v for k, v in other.items()} + +# Convenience alias +WeakTensorKeyDictionary = WeakIdKeyDictionary + + +class TensorWeakRef: + """Wrapper around a weak ref of a Tensor that handles the _fix_weakref() call required when unwrapping a Tensor weakref.""" + + ref: WeakRef[Tensor] + + def __init__(self, tensor: Tensor): + assert isinstance(tensor, Tensor) + self.ref = weakref.ref(tensor) + + def __call__(self): + out = self.ref() + if out is None: + return out + assert isinstance(out, Tensor) + # TODO, add _fix_weakref type binding + out._fix_weakref() # type: ignore[attr-defined] + return out diff --git a/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/INSTALLER b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/LICENSE b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1bec23eaf1dd562ae3d3216420b1b1bbfbd39cbc --- /dev/null +++ b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/LICENSE @@ -0,0 +1,25 @@ +BSD 2-Clause License + +Copyright (c) 2017 Facebook Inc. (Soumith Chintala), +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/METADATA b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..02015046dd92b6b974a83eda13ac9b08cbed2948 --- /dev/null +++ b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/METADATA @@ -0,0 +1,124 @@ +Metadata-Version: 2.2 +Name: torchaudio +Version: 2.6.0+cu124 +Summary: An audio package for PyTorch +Home-page: https://github.com/pytorch/audio +Author: Soumith Chintala, David Pollack, Sean Naren, Peter Goldsborough, Moto Hira, Caroline Chen, Jeff Hwang, Zhaoheng Ni, Xiaohui Zhang +Author-email: soumith@pytorch.org +Maintainer: Moto Hira, Caroline Chen, Jeff Hwang, Zhaoheng Ni, Xiaohui Zhang +Maintainer-email: moto@meta.com +Classifier: Environment :: Plugins +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: MacOS :: MacOS X +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX +Classifier: Programming Language :: C++ +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Topic :: Multimedia :: Sound/Audio +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: torch==2.6.0 +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: description-content-type +Dynamic: home-page +Dynamic: maintainer +Dynamic: maintainer-email +Dynamic: requires-dist +Dynamic: summary + +torchaudio: an audio library for PyTorch +======================================== + +[![Documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchaudio%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/audio/main/) +[![Anaconda Badge](https://anaconda.org/pytorch/torchaudio/badges/downloads.svg)](https://anaconda.org/pytorch/torchaudio) +[![Anaconda-Server Badge](https://anaconda.org/pytorch/torchaudio/badges/platforms.svg)](https://anaconda.org/pytorch/torchaudio) + +![TorchAudio Logo](docs/source/_static/img/logo.png) + +The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to +the audio domain. By supporting PyTorch, torchaudio follows the same philosophy +of providing strong GPU acceleration, having a focus on trainable features through +the autograd system, and having consistent style (tensor names and dimension names). +Therefore, it is primarily a machine learning library and not a general signal +processing library. The benefits of PyTorch can be seen in torchaudio through +having all the computations be through PyTorch operations which makes it easy +to use and feel like a natural extension. + +- [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/main/) + - Load a variety of audio formats, such as `wav`, `mp3`, `ogg`, `flac`, `opus`, `sphere`, into a torch Tensor using SoX + - [Kaldi (ark/scp)](http://pytorch.org/audio/main/kaldi_io.html) +- [Dataloaders for common audio datasets](http://pytorch.org/audio/main/datasets.html) +- Audio and speech processing functions + - [forced_align](https://pytorch.org/audio/main/generated/torchaudio.functional.forced_align.html) +- Common audio transforms + - [Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, MuLawDecoding, Resample](http://pytorch.org/audio/main/transforms.html) +- Compliance interfaces: Run code using PyTorch that align with other libraries + - [Kaldi: spectrogram, fbank, mfcc](https://pytorch.org/audio/main/compliance.kaldi.html) + +Installation +------------ + +Please refer to https://pytorch.org/audio/main/installation.html for installation and build process of TorchAudio. + + +API Reference +------------- + +API Reference is located here: http://pytorch.org/audio/main/ + +Contributing Guidelines +----------------------- + +Please refer to [CONTRIBUTING.md](./CONTRIBUTING.md) + +Citation +-------- + +If you find this package useful, please cite as: + +```bibtex +@article{yang2021torchaudio, + title={TorchAudio: Building Blocks for Audio and Speech Processing}, + author={Yao-Yuan Yang and Moto Hira and Zhaoheng Ni and Anjali Chourdia and Artyom Astafurov and Caroline Chen and Ching-Feng Yeh and Christian Puhrsch and David Pollack and Dmitriy Genzel and Donny Greenberg and Edward Z. Yang and Jason Lian and Jay Mahadeokar and Jeff Hwang and Ji Chen and Peter Goldsborough and Prabhat Roy and Sean Narenthiran and Shinji Watanabe and Soumith Chintala and Vincent Quenneville-Bélair and Yangyang Shi}, + journal={arXiv preprint arXiv:2110.15018}, + year={2021} +} +``` + +```bibtex +@misc{hwang2023torchaudio, + title={TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch}, + author={Jeff Hwang and Moto Hira and Caroline Chen and Xiaohui Zhang and Zhaoheng Ni and Guangzhi Sun and Pingchuan Ma and Ruizhe Huang and Vineel Pratap and Yuekai Zhang and Anurag Kumar and Chin-Yun Yu and Chuang Zhu and Chunxi Liu and Jacob Kahn and Mirco Ravanelli and Peng Sun and Shinji Watanabe and Yangyang Shi and Yumeng Tao and Robin Scheibler and Samuele Cornell and Sean Kim and Stavros Petridis}, + year={2023}, + eprint={2310.17864}, + archivePrefix={arXiv}, + primaryClass={eess.AS} +} +``` + +Disclaimer on Datasets +---------------------- + +This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. + +If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community! + +Pre-trained Model License +------------------------- + +The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case. + +For instance, SquimSubjective model is released under the Creative Commons Attribution Non Commercial 4.0 International (CC-BY-NC 4.0) license. See [the link](https://zenodo.org/record/4660670#.ZBtWPOxuerN) for additional details. + +Other pre-trained models that have different license are noted in documentation. 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0000000000000000000000000000000000000000..a7abacc82330687991f190efff67068c61b49b83 --- /dev/null +++ b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: setuptools (75.8.0) +Root-Is-Purelib: false +Tag: cp311-cp311-linux_x86_64 + diff --git a/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/top_level.txt b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f448fc64e7113394edf208556101c579616cc18 --- /dev/null +++ b/lib/python3.11/site-packages/torchaudio-2.6.0+cu124.dist-info/top_level.txt @@ -0,0 +1,2 @@ +torchaudio +torio diff --git a/lib/python3.11/site-packages/torchvision/__init__.py b/lib/python3.11/site-packages/torchvision/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d06156c25f1dfd34e9f01529e5a6b4bbeda7b42 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/__init__.py @@ -0,0 +1,105 @@ +import os +import warnings +from modulefinder import Module + +import torch + +# Don't re-order these, we need to load the _C extension (done when importing +# .extensions) before entering _meta_registrations. +from .extension import _HAS_OPS # usort:skip +from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip + +try: + from .version import __version__ # noqa: F401 +except ImportError: + pass + + +# Check if torchvision is being imported within the root folder +if not _HAS_OPS and os.path.dirname(os.path.realpath(__file__)) == os.path.join( + os.path.realpath(os.getcwd()), "torchvision" +): + message = ( + "You are importing torchvision within its own root folder ({}). " + "This is not expected to work and may give errors. Please exit the " + "torchvision project source and relaunch your python interpreter." + ) + warnings.warn(message.format(os.getcwd())) + +_image_backend = "PIL" + +_video_backend = "pyav" + + +def set_image_backend(backend): + """ + Specifies the package used to load images. + + Args: + backend (string): Name of the image backend. one of {'PIL', 'accimage'}. + The :mod:`accimage` package uses the Intel IPP library. It is + generally faster than PIL, but does not support as many operations. + """ + global _image_backend + if backend not in ["PIL", "accimage"]: + raise ValueError(f"Invalid backend '{backend}'. Options are 'PIL' and 'accimage'") + _image_backend = backend + + +def get_image_backend(): + """ + Gets the name of the package used to load images + """ + return _image_backend + + +def set_video_backend(backend): + """ + Specifies the package used to decode videos. + + Args: + backend (string): Name of the video backend. one of {'pyav', 'video_reader'}. + The :mod:`pyav` package uses the 3rd party PyAv library. It is a Pythonic + binding for the FFmpeg libraries. + The :mod:`video_reader` package includes a native C++ implementation on + top of FFMPEG libraries, and a python API of TorchScript custom operator. + It generally decodes faster than :mod:`pyav`, but is perhaps less robust. + + .. note:: + Building with FFMPEG is disabled by default in the latest `main`. If you want to use the 'video_reader' + backend, please compile torchvision from source. + """ + global _video_backend + if backend not in ["pyav", "video_reader", "cuda"]: + raise ValueError("Invalid video backend '%s'. Options are 'pyav', 'video_reader' and 'cuda'" % backend) + if backend == "video_reader" and not io._HAS_CPU_VIDEO_DECODER: + # TODO: better messages + message = "video_reader video backend is not available. Please compile torchvision from source and try again" + raise RuntimeError(message) + elif backend == "cuda" and not io._HAS_GPU_VIDEO_DECODER: + # TODO: better messages + message = "cuda video backend is not available." + raise RuntimeError(message) + else: + _video_backend = backend + + +def get_video_backend(): + """ + Returns the currently active video backend used to decode videos. + + Returns: + str: Name of the video backend. one of {'pyav', 'video_reader'}. + """ + + return _video_backend + + +def _is_tracing(): + return torch._C._get_tracing_state() + + +def disable_beta_transforms_warning(): + # Noop, only exists to avoid breaking existing code. + # See https://github.com/pytorch/vision/issues/7896 + pass diff --git a/lib/python3.11/site-packages/torchvision/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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b/lib/python3.11/site-packages/torchvision/_internally_replaced_utils.py @@ -0,0 +1,51 @@ +import importlib.machinery +import os + +from torch.hub import _get_torch_home + + +_HOME = os.path.join(_get_torch_home(), "datasets", "vision") +_USE_SHARDED_DATASETS = False +IN_FBCODE = False + + +def _download_file_from_remote_location(fpath: str, url: str) -> None: + pass + + +def _is_remote_location_available() -> bool: + return False + + +try: + from torch.hub import load_state_dict_from_url # noqa: 401 +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url # noqa: 401 + + +def _get_extension_path(lib_name): + + lib_dir = os.path.dirname(__file__) + if os.name == "nt": + # Register the main torchvision library location on the default DLL path + import ctypes + + kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True) + with_load_library_flags = hasattr(kernel32, "AddDllDirectory") + prev_error_mode = kernel32.SetErrorMode(0x0001) + + if with_load_library_flags: + kernel32.AddDllDirectory.restype = ctypes.c_void_p + + os.add_dll_directory(lib_dir) + + kernel32.SetErrorMode(prev_error_mode) + + loader_details = (importlib.machinery.ExtensionFileLoader, importlib.machinery.EXTENSION_SUFFIXES) + + extfinder = importlib.machinery.FileFinder(lib_dir, loader_details) + ext_specs = extfinder.find_spec(lib_name) + if ext_specs is None: + raise ImportError + + return ext_specs.origin diff --git a/lib/python3.11/site-packages/torchvision/_meta_registrations.py b/lib/python3.11/site-packages/torchvision/_meta_registrations.py new file mode 100644 index 0000000000000000000000000000000000000000..f75bfb77a7f25a1842509de595f109f232994574 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/_meta_registrations.py @@ -0,0 +1,225 @@ +import functools + +import torch +import torch._custom_ops +import torch.library + +# Ensure that torch.ops.torchvision is visible +import torchvision.extension # noqa: F401 + + +@functools.lru_cache(None) +def get_meta_lib(): + return torch.library.Library("torchvision", "IMPL", "Meta") + + +def register_meta(op_name, overload_name="default"): + def wrapper(fn): + if torchvision.extension._has_ops(): + get_meta_lib().impl(getattr(getattr(torch.ops.torchvision, op_name), overload_name), fn) + return fn + + return wrapper + + +@register_meta("roi_align") +def meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned): + torch._check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]") + torch._check( + input.dtype == rois.dtype, + lambda: ( + "Expected tensor for input to have the same type as tensor for rois; " + f"but type {input.dtype} does not equal {rois.dtype}" + ), + ) + num_rois = rois.size(0) + channels = input.size(1) + return input.new_empty((num_rois, channels, pooled_height, pooled_width)) + + +@register_meta("_roi_align_backward") +def meta_roi_align_backward( + grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned +): + torch._check( + grad.dtype == rois.dtype, + lambda: ( + "Expected tensor for grad to have the same type as tensor for rois; " + f"but type {grad.dtype} does not equal {rois.dtype}" + ), + ) + return grad.new_empty((batch_size, channels, height, width)) + + +@register_meta("ps_roi_align") +def meta_ps_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio): + torch._check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]") + torch._check( + input.dtype == rois.dtype, + lambda: ( + "Expected tensor for input to have the same type as tensor for rois; " + f"but type {input.dtype} does not equal {rois.dtype}" + ), + ) + channels = input.size(1) + torch._check( + channels % (pooled_height * pooled_width) == 0, + "input channels must be a multiple of pooling height * pooling width", + ) + + num_rois = rois.size(0) + out_size = (num_rois, channels // (pooled_height * pooled_width), pooled_height, pooled_width) + return input.new_empty(out_size), torch.empty(out_size, dtype=torch.int32, device="meta") + + +@register_meta("_ps_roi_align_backward") +def meta_ps_roi_align_backward( + grad, + rois, + channel_mapping, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio, + batch_size, + channels, + height, + width, +): + torch._check( + grad.dtype == rois.dtype, + lambda: ( + "Expected tensor for grad to have the same type as tensor for rois; " + f"but type {grad.dtype} does not equal {rois.dtype}" + ), + ) + return grad.new_empty((batch_size, channels, height, width)) + + +@register_meta("roi_pool") +def meta_roi_pool(input, rois, spatial_scale, pooled_height, pooled_width): + torch._check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]") + torch._check( + input.dtype == rois.dtype, + lambda: ( + "Expected tensor for input to have the same type as tensor for rois; " + f"but type {input.dtype} does not equal {rois.dtype}" + ), + ) + num_rois = rois.size(0) + channels = input.size(1) + out_size = (num_rois, channels, pooled_height, pooled_width) + return input.new_empty(out_size), torch.empty(out_size, device="meta", dtype=torch.int32) + + +@register_meta("_roi_pool_backward") +def meta_roi_pool_backward( + grad, rois, argmax, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width +): + torch._check( + grad.dtype == rois.dtype, + lambda: ( + "Expected tensor for grad to have the same type as tensor for rois; " + f"but type {grad.dtype} does not equal {rois.dtype}" + ), + ) + return grad.new_empty((batch_size, channels, height, width)) + + +@register_meta("ps_roi_pool") +def meta_ps_roi_pool(input, rois, spatial_scale, pooled_height, pooled_width): + torch._check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]") + torch._check( + input.dtype == rois.dtype, + lambda: ( + "Expected tensor for input to have the same type as tensor for rois; " + f"but type {input.dtype} does not equal {rois.dtype}" + ), + ) + channels = input.size(1) + torch._check( + channels % (pooled_height * pooled_width) == 0, + "input channels must be a multiple of pooling height * pooling width", + ) + num_rois = rois.size(0) + out_size = (num_rois, channels // (pooled_height * pooled_width), pooled_height, pooled_width) + return input.new_empty(out_size), torch.empty(out_size, device="meta", dtype=torch.int32) + + +@register_meta("_ps_roi_pool_backward") +def meta_ps_roi_pool_backward( + grad, rois, channel_mapping, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width +): + torch._check( + grad.dtype == rois.dtype, + lambda: ( + "Expected tensor for grad to have the same type as tensor for rois; " + f"but type {grad.dtype} does not equal {rois.dtype}" + ), + ) + return grad.new_empty((batch_size, channels, height, width)) + + +@torch.library.register_fake("torchvision::nms") +def meta_nms(dets, scores, iou_threshold): + torch._check(dets.dim() == 2, lambda: f"boxes should be a 2d tensor, got {dets.dim()}D") + torch._check(dets.size(1) == 4, lambda: f"boxes should have 4 elements in dimension 1, got {dets.size(1)}") + torch._check(scores.dim() == 1, lambda: f"scores should be a 1d tensor, got {scores.dim()}") + torch._check( + dets.size(0) == scores.size(0), + lambda: f"boxes and scores should have same number of elements in dimension 0, got {dets.size(0)} and {scores.size(0)}", + ) + ctx = torch._custom_ops.get_ctx() + num_to_keep = ctx.create_unbacked_symint() + return dets.new_empty(num_to_keep, dtype=torch.long) + + +@register_meta("deform_conv2d") +def meta_deform_conv2d( + input, + weight, + offset, + mask, + bias, + stride_h, + stride_w, + pad_h, + pad_w, + dil_h, + dil_w, + n_weight_grps, + n_offset_grps, + use_mask, +): + + out_height, out_width = offset.shape[-2:] + out_channels = weight.shape[0] + batch_size = input.shape[0] + return input.new_empty((batch_size, out_channels, out_height, out_width)) + + +@register_meta("_deform_conv2d_backward") +def meta_deform_conv2d_backward( + grad, + input, + weight, + offset, + mask, + bias, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + groups, + offset_groups, + use_mask, +): + + grad_input = input.new_empty(input.shape) + grad_weight = weight.new_empty(weight.shape) + grad_offset = offset.new_empty(offset.shape) + grad_mask = mask.new_empty(mask.shape) + grad_bias = bias.new_empty(bias.shape) + return grad_input, grad_weight, grad_offset, grad_mask, grad_bias diff --git a/lib/python3.11/site-packages/torchvision/_utils.py b/lib/python3.11/site-packages/torchvision/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b739ef0966e9b6fac4574f3d6f04051799f75a16 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/_utils.py @@ -0,0 +1,32 @@ +import enum +from typing import Sequence, Type, TypeVar + +T = TypeVar("T", bound=enum.Enum) + + +class StrEnumMeta(enum.EnumMeta): + auto = enum.auto + + def from_str(self: Type[T], member: str) -> T: # type: ignore[misc] + try: + return self[member] + except KeyError: + # TODO: use `add_suggestion` from torchvision.prototype.utils._internal to improve the error message as + # soon as it is migrated. + raise ValueError(f"Unknown value '{member}' for {self.__name__}.") from None + + +class StrEnum(enum.Enum, metaclass=StrEnumMeta): + pass + + +def sequence_to_str(seq: Sequence, separate_last: str = "") -> str: + if not seq: + return "" + if len(seq) == 1: + return f"'{seq[0]}'" + + head = "'" + "', '".join([str(item) for item in seq[:-1]]) + "'" + tail = f"{'' if separate_last and len(seq) == 2 else ','} {separate_last}'{seq[-1]}'" + + return head + tail diff --git a/lib/python3.11/site-packages/torchvision/datasets/__init__.py b/lib/python3.11/site-packages/torchvision/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4005b4a90729c9fe1b811f7388bd8453998d2322 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/__init__.py @@ -0,0 +1,147 @@ +from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel +from ._stereo_matching import ( + CarlaStereo, + CREStereo, + ETH3DStereo, + FallingThingsStereo, + InStereo2k, + Kitti2012Stereo, + Kitti2015Stereo, + Middlebury2014Stereo, + SceneFlowStereo, + SintelStereo, +) +from .caltech import Caltech101, Caltech256 +from .celeba import CelebA +from .cifar import CIFAR10, CIFAR100 +from .cityscapes import Cityscapes +from .clevr import CLEVRClassification +from .coco import CocoCaptions, CocoDetection +from .country211 import Country211 +from .dtd import DTD +from .eurosat import EuroSAT +from .fakedata import FakeData +from .fer2013 import FER2013 +from .fgvc_aircraft import FGVCAircraft +from .flickr import Flickr30k, Flickr8k +from .flowers102 import Flowers102 +from .folder import DatasetFolder, ImageFolder +from .food101 import Food101 +from .gtsrb import GTSRB +from .hmdb51 import HMDB51 +from .imagenet import ImageNet +from .imagenette import Imagenette +from .inaturalist import INaturalist +from .kinetics import Kinetics +from .kitti import Kitti +from .lfw import LFWPairs, LFWPeople +from .lsun import LSUN, LSUNClass +from .mnist import EMNIST, FashionMNIST, KMNIST, MNIST, QMNIST +from .moving_mnist import MovingMNIST +from .omniglot import Omniglot +from .oxford_iiit_pet import OxfordIIITPet +from .pcam import PCAM +from .phototour import PhotoTour +from .places365 import Places365 +from .rendered_sst2 import RenderedSST2 +from .sbd import SBDataset +from .sbu import SBU +from .semeion import SEMEION +from .stanford_cars import StanfordCars +from .stl10 import STL10 +from .sun397 import SUN397 +from .svhn import SVHN +from .ucf101 import UCF101 +from .usps import USPS +from .vision import VisionDataset +from .voc import VOCDetection, VOCSegmentation +from .widerface import WIDERFace + +__all__ = ( + "LSUN", + "LSUNClass", + "ImageFolder", + "DatasetFolder", + "FakeData", + "CocoCaptions", + "CocoDetection", + "CIFAR10", + "CIFAR100", + "EMNIST", + "FashionMNIST", + "QMNIST", + "MNIST", + "KMNIST", + "MovingMNIST", + "StanfordCars", + "STL10", + "SUN397", + "SVHN", + "PhotoTour", + "SEMEION", + "Omniglot", + "SBU", + "Flickr8k", + "Flickr30k", + "Flowers102", + "VOCSegmentation", + "VOCDetection", + "Cityscapes", + "ImageNet", + "Caltech101", + "Caltech256", + "CelebA", + "WIDERFace", + "SBDataset", + "VisionDataset", + "USPS", + "Kinetics", + "HMDB51", + "UCF101", + "Places365", + "Kitti", + "INaturalist", + "LFWPeople", + "LFWPairs", + "KittiFlow", + "Sintel", + "FlyingChairs", + "FlyingThings3D", + "HD1K", + "Food101", + "DTD", + "FER2013", + "GTSRB", + "CLEVRClassification", + "OxfordIIITPet", + "PCAM", + "Country211", + "FGVCAircraft", + "EuroSAT", + "RenderedSST2", + "Kitti2012Stereo", + "Kitti2015Stereo", + "CarlaStereo", + "Middlebury2014Stereo", + "CREStereo", + "FallingThingsStereo", + "SceneFlowStereo", + "SintelStereo", + "InStereo2k", + "ETH3DStereo", + "wrap_dataset_for_transforms_v2", + "Imagenette", +) + + +# We override current module's attributes to handle the import: +# from torchvision.datasets import wrap_dataset_for_transforms_v2 +# without a cyclic error. +# Ref: https://peps.python.org/pep-0562/ +def __getattr__(name): + if name in ("wrap_dataset_for_transforms_v2",): + from torchvision.tv_tensors._dataset_wrapper import wrap_dataset_for_transforms_v2 + + return wrap_dataset_for_transforms_v2 + + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/lib/python3.11/site-packages/torchvision/datasets/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/datasets/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..df63ad1cef2f9e0e01d01235a205576c26471079 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/datasets/__pycache__/__init__.cpython-311.pyc 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/dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/_optical_flow.py @@ -0,0 +1,490 @@ +import itertools +import os +from abc import ABC, abstractmethod +from glob import glob +from pathlib import Path +from typing import Callable, List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL import Image + +from ..io.image import decode_png, read_file +from .utils import _read_pfm, verify_str_arg +from .vision import VisionDataset + +T1 = Tuple[Image.Image, Image.Image, Optional[np.ndarray], Optional[np.ndarray]] +T2 = Tuple[Image.Image, Image.Image, Optional[np.ndarray]] + + +__all__ = ( + "KittiFlow", + "Sintel", + "FlyingThings3D", + "FlyingChairs", + "HD1K", +) + + +class FlowDataset(ABC, VisionDataset): + # Some datasets like Kitti have a built-in valid_flow_mask, indicating which flow values are valid + # For those we return (img1, img2, flow, valid_flow_mask), and for the rest we return (img1, img2, flow), + # and it's up to whatever consumes the dataset to decide what valid_flow_mask should be. + _has_builtin_flow_mask = False + + def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None: + + super().__init__(root=root) + self.transforms = transforms + + self._flow_list: List[str] = [] + self._image_list: List[List[str]] = [] + + def _read_img(self, file_name: str) -> Image.Image: + img = Image.open(file_name) + if img.mode != "RGB": + img = img.convert("RGB") # type: ignore[assignment] + return img + + @abstractmethod + def _read_flow(self, file_name: str): + # Return the flow or a tuple with the flow and the valid_flow_mask if _has_builtin_flow_mask is True + pass + + def __getitem__(self, index: int) -> Union[T1, T2]: + + img1 = self._read_img(self._image_list[index][0]) + img2 = self._read_img(self._image_list[index][1]) + + if self._flow_list: # it will be empty for some dataset when split="test" + flow = self._read_flow(self._flow_list[index]) + if self._has_builtin_flow_mask: + flow, valid_flow_mask = flow + else: + valid_flow_mask = None + else: + flow = valid_flow_mask = None + + if self.transforms is not None: + img1, img2, flow, valid_flow_mask = self.transforms(img1, img2, flow, valid_flow_mask) + + if self._has_builtin_flow_mask or valid_flow_mask is not None: + # The `or valid_flow_mask is not None` part is here because the mask can be generated within a transform + return img1, img2, flow, valid_flow_mask + else: + return img1, img2, flow + + def __len__(self) -> int: + return len(self._image_list) + + def __rmul__(self, v: int) -> torch.utils.data.ConcatDataset: + return torch.utils.data.ConcatDataset([self] * v) + + +class Sintel(FlowDataset): + """`Sintel `_ Dataset for optical flow. + + The dataset is expected to have the following structure: :: + + root + Sintel + testing + clean + scene_1 + scene_2 + ... + final + scene_1 + scene_2 + ... + training + clean + scene_1 + scene_2 + ... + final + scene_1 + scene_2 + ... + flow + scene_1 + scene_2 + ... + + Args: + root (str or ``pathlib.Path``): Root directory of the Sintel Dataset. + split (string, optional): The dataset split, either "train" (default) or "test" + pass_name (string, optional): The pass to use, either "clean" (default), "final", or "both". See link above for + details on the different passes. + transforms (callable, optional): A function/transform that takes in + ``img1, img2, flow, valid_flow_mask`` and returns a transformed version. + ``valid_flow_mask`` is expected for consistency with other datasets which + return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`. + """ + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + pass_name: str = "clean", + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root=root, transforms=transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both")) + passes = ["clean", "final"] if pass_name == "both" else [pass_name] + + root = Path(root) / "Sintel" + flow_root = root / "training" / "flow" + + for pass_name in passes: + split_dir = "training" if split == "train" else split + image_root = root / split_dir / pass_name + for scene in os.listdir(image_root): + image_list = sorted(glob(str(image_root / scene / "*.png"))) + for i in range(len(image_list) - 1): + self._image_list += [[image_list[i], image_list[i + 1]]] + + if split == "train": + self._flow_list += sorted(glob(str(flow_root / scene / "*.flo"))) + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img1, img2, flow)``. + The flow is a numpy array of shape (2, H, W) and the images are PIL images. + ``flow`` is None if ``split="test"``. + If a valid flow mask is generated within the ``transforms`` parameter, + a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned. + """ + return super().__getitem__(index) + + def _read_flow(self, file_name: str) -> np.ndarray: + return _read_flo(file_name) + + +class KittiFlow(FlowDataset): + """`KITTI `__ dataset for optical flow (2015). + + The dataset is expected to have the following structure: :: + + root + KittiFlow + testing + image_2 + training + image_2 + flow_occ + + Args: + root (str or ``pathlib.Path``): Root directory of the KittiFlow Dataset. + split (string, optional): The dataset split, either "train" (default) or "test" + transforms (callable, optional): A function/transform that takes in + ``img1, img2, flow, valid_flow_mask`` and returns a transformed version. + """ + + _has_builtin_flow_mask = True + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root=root, transforms=transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + + root = Path(root) / "KittiFlow" / (split + "ing") + images1 = sorted(glob(str(root / "image_2" / "*_10.png"))) + images2 = sorted(glob(str(root / "image_2" / "*_11.png"))) + + if not images1 or not images2: + raise FileNotFoundError( + "Could not find the Kitti flow images. Please make sure the directory structure is correct." + ) + + for img1, img2 in zip(images1, images2): + self._image_list += [[img1, img2]] + + if split == "train": + self._flow_list = sorted(glob(str(root / "flow_occ" / "*_10.png"))) + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` + where ``valid_flow_mask`` is a numpy boolean mask of shape (H, W) + indicating which flow values are valid. The flow is a numpy array of + shape (2, H, W) and the images are PIL images. ``flow`` and ``valid_flow_mask`` are None if + ``split="test"``. + """ + return super().__getitem__(index) + + def _read_flow(self, file_name: str) -> Tuple[np.ndarray, np.ndarray]: + return _read_16bits_png_with_flow_and_valid_mask(file_name) + + +class FlyingChairs(FlowDataset): + """`FlyingChairs `_ Dataset for optical flow. + + You will also need to download the FlyingChairs_train_val.txt file from the dataset page. + + The dataset is expected to have the following structure: :: + + root + FlyingChairs + data + 00001_flow.flo + 00001_img1.ppm + 00001_img2.ppm + ... + FlyingChairs_train_val.txt + + + Args: + root (str or ``pathlib.Path``): Root directory of the FlyingChairs Dataset. + split (string, optional): The dataset split, either "train" (default) or "val" + transforms (callable, optional): A function/transform that takes in + ``img1, img2, flow, valid_flow_mask`` and returns a transformed version. + ``valid_flow_mask`` is expected for consistency with other datasets which + return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`. + """ + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root=root, transforms=transforms) + + verify_str_arg(split, "split", valid_values=("train", "val")) + + root = Path(root) / "FlyingChairs" + images = sorted(glob(str(root / "data" / "*.ppm"))) + flows = sorted(glob(str(root / "data" / "*.flo"))) + + split_file_name = "FlyingChairs_train_val.txt" + + if not os.path.exists(root / split_file_name): + raise FileNotFoundError( + "The FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring)." + ) + + split_list = np.loadtxt(str(root / split_file_name), dtype=np.int32) + for i in range(len(flows)): + split_id = split_list[i] + if (split == "train" and split_id == 1) or (split == "val" and split_id == 2): + self._flow_list += [flows[i]] + self._image_list += [[images[2 * i], images[2 * i + 1]]] + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img1, img2, flow)``. + The flow is a numpy array of shape (2, H, W) and the images are PIL images. + ``flow`` is None if ``split="val"``. + If a valid flow mask is generated within the ``transforms`` parameter, + a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned. + """ + return super().__getitem__(index) + + def _read_flow(self, file_name: str) -> np.ndarray: + return _read_flo(file_name) + + +class FlyingThings3D(FlowDataset): + """`FlyingThings3D `_ dataset for optical flow. + + The dataset is expected to have the following structure: :: + + root + FlyingThings3D + frames_cleanpass + TEST + TRAIN + frames_finalpass + TEST + TRAIN + optical_flow + TEST + TRAIN + + Args: + root (str or ``pathlib.Path``): Root directory of the intel FlyingThings3D Dataset. + split (string, optional): The dataset split, either "train" (default) or "test" + pass_name (string, optional): The pass to use, either "clean" (default) or "final" or "both". See link above for + details on the different passes. + camera (string, optional): Which camera to return images from. Can be either "left" (default) or "right" or "both". + transforms (callable, optional): A function/transform that takes in + ``img1, img2, flow, valid_flow_mask`` and returns a transformed version. + ``valid_flow_mask`` is expected for consistency with other datasets which + return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`. + """ + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + pass_name: str = "clean", + camera: str = "left", + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root=root, transforms=transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + split = split.upper() + + verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both")) + passes = { + "clean": ["frames_cleanpass"], + "final": ["frames_finalpass"], + "both": ["frames_cleanpass", "frames_finalpass"], + }[pass_name] + + verify_str_arg(camera, "camera", valid_values=("left", "right", "both")) + cameras = ["left", "right"] if camera == "both" else [camera] + + root = Path(root) / "FlyingThings3D" + + directions = ("into_future", "into_past") + for pass_name, camera, direction in itertools.product(passes, cameras, directions): + image_dirs = sorted(glob(str(root / pass_name / split / "*/*"))) + image_dirs = sorted(Path(image_dir) / camera for image_dir in image_dirs) + + flow_dirs = sorted(glob(str(root / "optical_flow" / split / "*/*"))) + flow_dirs = sorted(Path(flow_dir) / direction / camera for flow_dir in flow_dirs) + + if not image_dirs or not flow_dirs: + raise FileNotFoundError( + "Could not find the FlyingThings3D flow images. " + "Please make sure the directory structure is correct." + ) + + for image_dir, flow_dir in zip(image_dirs, flow_dirs): + images = sorted(glob(str(image_dir / "*.png"))) + flows = sorted(glob(str(flow_dir / "*.pfm"))) + for i in range(len(flows) - 1): + if direction == "into_future": + self._image_list += [[images[i], images[i + 1]]] + self._flow_list += [flows[i]] + elif direction == "into_past": + self._image_list += [[images[i + 1], images[i]]] + self._flow_list += [flows[i + 1]] + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img1, img2, flow)``. + The flow is a numpy array of shape (2, H, W) and the images are PIL images. + ``flow`` is None if ``split="test"``. + If a valid flow mask is generated within the ``transforms`` parameter, + a 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` is returned. + """ + return super().__getitem__(index) + + def _read_flow(self, file_name: str) -> np.ndarray: + return _read_pfm(file_name) + + +class HD1K(FlowDataset): + """`HD1K `__ dataset for optical flow. + + The dataset is expected to have the following structure: :: + + root + hd1k + hd1k_challenge + image_2 + hd1k_flow_gt + flow_occ + hd1k_input + image_2 + + Args: + root (str or ``pathlib.Path``): Root directory of the HD1K Dataset. + split (string, optional): The dataset split, either "train" (default) or "test" + transforms (callable, optional): A function/transform that takes in + ``img1, img2, flow, valid_flow_mask`` and returns a transformed version. + """ + + _has_builtin_flow_mask = True + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root=root, transforms=transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + + root = Path(root) / "hd1k" + if split == "train": + # There are 36 "sequences" and we don't want seq i to overlap with seq i + 1, so we need this for loop + for seq_idx in range(36): + flows = sorted(glob(str(root / "hd1k_flow_gt" / "flow_occ" / f"{seq_idx:06d}_*.png"))) + images = sorted(glob(str(root / "hd1k_input" / "image_2" / f"{seq_idx:06d}_*.png"))) + for i in range(len(flows) - 1): + self._flow_list += [flows[i]] + self._image_list += [[images[i], images[i + 1]]] + else: + images1 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*10.png"))) + images2 = sorted(glob(str(root / "hd1k_challenge" / "image_2" / "*11.png"))) + for image1, image2 in zip(images1, images2): + self._image_list += [[image1, image2]] + + if not self._image_list: + raise FileNotFoundError( + "Could not find the HD1K images. Please make sure the directory structure is correct." + ) + + def _read_flow(self, file_name: str) -> Tuple[np.ndarray, np.ndarray]: + return _read_16bits_png_with_flow_and_valid_mask(file_name) + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img1, img2, flow, valid_flow_mask)`` where ``valid_flow_mask`` + is a numpy boolean mask of shape (H, W) + indicating which flow values are valid. The flow is a numpy array of + shape (2, H, W) and the images are PIL images. ``flow`` and ``valid_flow_mask`` are None if + ``split="test"``. + """ + return super().__getitem__(index) + + +def _read_flo(file_name: str) -> np.ndarray: + """Read .flo file in Middlebury format""" + # Code adapted from: + # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy + # Everything needs to be in little Endian according to + # https://vision.middlebury.edu/flow/code/flow-code/README.txt + with open(file_name, "rb") as f: + magic = np.fromfile(f, "c", count=4).tobytes() + if magic != b"PIEH": + raise ValueError("Magic number incorrect. Invalid .flo file") + + w = int(np.fromfile(f, " Tuple[np.ndarray, np.ndarray]: + + flow_and_valid = decode_png(read_file(file_name)).to(torch.float32) + flow, valid_flow_mask = flow_and_valid[:2, :, :], flow_and_valid[2, :, :] + flow = (flow - 2**15) / 64 # This conversion is explained somewhere on the kitti archive + valid_flow_mask = valid_flow_mask.bool() + + # For consistency with other datasets, we convert to numpy + return flow.numpy(), valid_flow_mask.numpy() diff --git a/lib/python3.11/site-packages/torchvision/datasets/_stereo_matching.py b/lib/python3.11/site-packages/torchvision/datasets/_stereo_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..09961211cc2b2eecb59ac3878baf60f3c36bd320 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/_stereo_matching.py @@ -0,0 +1,1223 @@ +import functools +import json +import os +import random +import shutil +from abc import ABC, abstractmethod +from glob import glob +from pathlib import Path +from typing import Callable, cast, List, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import _read_pfm, download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + +T1 = Tuple[Image.Image, Image.Image, Optional[np.ndarray], np.ndarray] +T2 = Tuple[Image.Image, Image.Image, Optional[np.ndarray]] + +__all__ = () + +_read_pfm_file = functools.partial(_read_pfm, slice_channels=1) + + +class StereoMatchingDataset(ABC, VisionDataset): + """Base interface for Stereo matching datasets""" + + _has_built_in_disparity_mask = False + + def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None: + """ + Args: + root(str): Root directory of the dataset. + transforms(callable, optional): A function/transform that takes in Tuples of + (images, disparities, valid_masks) and returns a transformed version of each of them. + images is a Tuple of (``PIL.Image``, ``PIL.Image``) + disparities is a Tuple of (``np.ndarray``, ``np.ndarray``) with shape (1, H, W) + valid_masks is a Tuple of (``np.ndarray``, ``np.ndarray``) with shape (H, W) + In some cases, when a dataset does not provide disparities, the ``disparities`` and + ``valid_masks`` can be Tuples containing None values. + For training splits generally the datasets provide a minimal guarantee of + images: (``PIL.Image``, ``PIL.Image``) + disparities: (``np.ndarray``, ``None``) with shape (1, H, W) + Optionally, based on the dataset, it can return a ``mask`` as well: + valid_masks: (``np.ndarray | None``, ``None``) with shape (H, W) + For some test splits, the datasets provides outputs that look like: + imgaes: (``PIL.Image``, ``PIL.Image``) + disparities: (``None``, ``None``) + Optionally, based on the dataset, it can return a ``mask`` as well: + valid_masks: (``None``, ``None``) + """ + super().__init__(root=root) + self.transforms = transforms + + self._images = [] # type: ignore + self._disparities = [] # type: ignore + + def _read_img(self, file_path: Union[str, Path]) -> Image.Image: + img = Image.open(file_path) + if img.mode != "RGB": + img = img.convert("RGB") # type: ignore [assignment] + return img + + def _scan_pairs( + self, + paths_left_pattern: str, + paths_right_pattern: Optional[str] = None, + ) -> List[Tuple[str, Optional[str]]]: + + left_paths = list(sorted(glob(paths_left_pattern))) + + right_paths: List[Union[None, str]] + if paths_right_pattern: + right_paths = list(sorted(glob(paths_right_pattern))) + else: + right_paths = list(None for _ in left_paths) + + if not left_paths: + raise FileNotFoundError(f"Could not find any files matching the patterns: {paths_left_pattern}") + + if not right_paths: + raise FileNotFoundError(f"Could not find any files matching the patterns: {paths_right_pattern}") + + if len(left_paths) != len(right_paths): + raise ValueError( + f"Found {len(left_paths)} left files but {len(right_paths)} right files using:\n " + f"left pattern: {paths_left_pattern}\n" + f"right pattern: {paths_right_pattern}\n" + ) + + paths = list((left, right) for left, right in zip(left_paths, right_paths)) + return paths + + @abstractmethod + def _read_disparity(self, file_path: str) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]: + # function that returns a disparity map and an occlusion map + pass + + def __getitem__(self, index: int) -> Union[T1, T2]: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3 or 4-tuple with ``(img_left, img_right, disparity, Optional[valid_mask])`` where ``valid_mask`` + can be a numpy boolean mask of shape (H, W) if the dataset provides a file + indicating which disparity pixels are valid. The disparity is a numpy array of + shape (1, H, W) and the images are PIL images. ``disparity`` is None for + datasets on which for ``split="test"`` the authors did not provide annotations. + """ + img_left = self._read_img(self._images[index][0]) + img_right = self._read_img(self._images[index][1]) + + dsp_map_left, valid_mask_left = self._read_disparity(self._disparities[index][0]) + dsp_map_right, valid_mask_right = self._read_disparity(self._disparities[index][1]) + + imgs = (img_left, img_right) + dsp_maps = (dsp_map_left, dsp_map_right) + valid_masks = (valid_mask_left, valid_mask_right) + + if self.transforms is not None: + ( + imgs, + dsp_maps, + valid_masks, + ) = self.transforms(imgs, dsp_maps, valid_masks) + + if self._has_built_in_disparity_mask or valid_masks[0] is not None: + return imgs[0], imgs[1], dsp_maps[0], cast(np.ndarray, valid_masks[0]) + else: + return imgs[0], imgs[1], dsp_maps[0] + + def __len__(self) -> int: + return len(self._images) + + +class CarlaStereo(StereoMatchingDataset): + """ + Carla simulator data linked in the `CREStereo github repo `_. + + The dataset is expected to have the following structure: :: + + root + carla-highres + trainingF + scene1 + img0.png + img1.png + disp0GT.pfm + disp1GT.pfm + calib.txt + scene2 + img0.png + img1.png + disp0GT.pfm + disp1GT.pfm + calib.txt + ... + + Args: + root (str or ``pathlib.Path``): Root directory where `carla-highres` is located. + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + root = Path(root) / "carla-highres" + + left_image_pattern = str(root / "trainingF" / "*" / "im0.png") + right_image_pattern = str(root / "trainingF" / "*" / "im1.png") + imgs = self._scan_pairs(left_image_pattern, right_image_pattern) + self._images = imgs + + left_disparity_pattern = str(root / "trainingF" / "*" / "disp0GT.pfm") + right_disparity_pattern = str(root / "trainingF" / "*" / "disp1GT.pfm") + disparities = self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + self._disparities = disparities + + def _read_disparity(self, file_path: str) -> Tuple[np.ndarray, None]: + disparity_map = _read_pfm_file(file_path) + disparity_map = np.abs(disparity_map) # ensure that the disparity is positive + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img_left, img_right, disparity)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + If a ``valid_mask`` is generated within the ``transforms`` parameter, + a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned. + """ + return cast(T1, super().__getitem__(index)) + + +class Kitti2012Stereo(StereoMatchingDataset): + """ + KITTI dataset from the `2012 stereo evaluation benchmark `_. + Uses the RGB images for consistency with KITTI 2015. + + The dataset is expected to have the following structure: :: + + root + Kitti2012 + testing + colored_0 + 1_10.png + 2_10.png + ... + colored_1 + 1_10.png + 2_10.png + ... + training + colored_0 + 1_10.png + 2_10.png + ... + colored_1 + 1_10.png + 2_10.png + ... + disp_noc + 1.png + 2.png + ... + calib + + Args: + root (str or ``pathlib.Path``): Root directory where `Kitti2012` is located. + split (string, optional): The dataset split of scenes, either "train" (default) or "test". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + _has_built_in_disparity_mask = True + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + + root = Path(root) / "Kitti2012" / (split + "ing") + + left_img_pattern = str(root / "colored_0" / "*_10.png") + right_img_pattern = str(root / "colored_1" / "*_10.png") + self._images = self._scan_pairs(left_img_pattern, right_img_pattern) + + if split == "train": + disparity_pattern = str(root / "disp_noc" / "*.png") + self._disparities = self._scan_pairs(disparity_pattern, None) + else: + self._disparities = list((None, None) for _ in self._images) + + def _read_disparity(self, file_path: str) -> Tuple[Optional[np.ndarray], None]: + # test split has no disparity maps + if file_path is None: + return None, None + + disparity_map = np.asarray(Image.open(file_path)) / 256.0 + # unsqueeze the disparity map into (C, H, W) format + disparity_map = disparity_map[None, :, :] + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + ``valid_mask`` is implicitly ``None`` if the ``transforms`` parameter does not + generate a valid mask. + Both ``disparity`` and ``valid_mask`` are ``None`` if the dataset split is test. + """ + return cast(T1, super().__getitem__(index)) + + +class Kitti2015Stereo(StereoMatchingDataset): + """ + KITTI dataset from the `2015 stereo evaluation benchmark `_. + + The dataset is expected to have the following structure: :: + + root + Kitti2015 + testing + image_2 + img1.png + img2.png + ... + image_3 + img1.png + img2.png + ... + training + image_2 + img1.png + img2.png + ... + image_3 + img1.png + img2.png + ... + disp_occ_0 + img1.png + img2.png + ... + disp_occ_1 + img1.png + img2.png + ... + calib + + Args: + root (str or ``pathlib.Path``): Root directory where `Kitti2015` is located. + split (string, optional): The dataset split of scenes, either "train" (default) or "test". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + _has_built_in_disparity_mask = True + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + + root = Path(root) / "Kitti2015" / (split + "ing") + left_img_pattern = str(root / "image_2" / "*.png") + right_img_pattern = str(root / "image_3" / "*.png") + self._images = self._scan_pairs(left_img_pattern, right_img_pattern) + + if split == "train": + left_disparity_pattern = str(root / "disp_occ_0" / "*.png") + right_disparity_pattern = str(root / "disp_occ_1" / "*.png") + self._disparities = self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + else: + self._disparities = list((None, None) for _ in self._images) + + def _read_disparity(self, file_path: str) -> Tuple[Optional[np.ndarray], None]: + # test split has no disparity maps + if file_path is None: + return None, None + + disparity_map = np.asarray(Image.open(file_path)) / 256.0 + # unsqueeze the disparity map into (C, H, W) format + disparity_map = disparity_map[None, :, :] + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + ``valid_mask`` is implicitly ``None`` if the ``transforms`` parameter does not + generate a valid mask. + Both ``disparity`` and ``valid_mask`` are ``None`` if the dataset split is test. + """ + return cast(T1, super().__getitem__(index)) + + +class Middlebury2014Stereo(StereoMatchingDataset): + """Publicly available scenes from the Middlebury dataset `2014 version `. + + The dataset mostly follows the original format, without containing the ambient subdirectories. : :: + + root + Middlebury2014 + train + scene1-{perfect,imperfect} + calib.txt + im{0,1}.png + im1E.png + im1L.png + disp{0,1}.pfm + disp{0,1}-n.png + disp{0,1}-sd.pfm + disp{0,1}y.pfm + scene2-{perfect,imperfect} + calib.txt + im{0,1}.png + im1E.png + im1L.png + disp{0,1}.pfm + disp{0,1}-n.png + disp{0,1}-sd.pfm + disp{0,1}y.pfm + ... + additional + scene1-{perfect,imperfect} + calib.txt + im{0,1}.png + im1E.png + im1L.png + disp{0,1}.pfm + disp{0,1}-n.png + disp{0,1}-sd.pfm + disp{0,1}y.pfm + ... + test + scene1 + calib.txt + im{0,1}.png + scene2 + calib.txt + im{0,1}.png + ... + + Args: + root (str or ``pathlib.Path``): Root directory of the Middleburry 2014 Dataset. + split (string, optional): The dataset split of scenes, either "train" (default), "test", or "additional" + use_ambient_views (boolean, optional): Whether to use different expose or lightning views when possible. + The dataset samples with equal probability between ``[im1.png, im1E.png, im1L.png]``. + calibration (string, optional): Whether or not to use the calibrated (default) or uncalibrated scenes. + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + download (boolean, optional): Whether or not to download the dataset in the ``root`` directory. + """ + + splits = { + "train": [ + "Adirondack", + "Jadeplant", + "Motorcycle", + "Piano", + "Pipes", + "Playroom", + "Playtable", + "Recycle", + "Shelves", + "Vintage", + ], + "additional": [ + "Backpack", + "Bicycle1", + "Cable", + "Classroom1", + "Couch", + "Flowers", + "Mask", + "Shopvac", + "Sticks", + "Storage", + "Sword1", + "Sword2", + "Umbrella", + ], + "test": [ + "Plants", + "Classroom2E", + "Classroom2", + "Australia", + "DjembeL", + "CrusadeP", + "Crusade", + "Hoops", + "Bicycle2", + "Staircase", + "Newkuba", + "AustraliaP", + "Djembe", + "Livingroom", + "Computer", + ], + } + + _has_built_in_disparity_mask = True + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + calibration: Optional[str] = "perfect", + use_ambient_views: bool = False, + transforms: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transforms) + + verify_str_arg(split, "split", valid_values=("train", "test", "additional")) + self.split = split + + if calibration: + verify_str_arg(calibration, "calibration", valid_values=("perfect", "imperfect", "both", None)) # type: ignore + if split == "test": + raise ValueError("Split 'test' has only no calibration settings, please set `calibration=None`.") + else: + if split != "test": + raise ValueError( + f"Split '{split}' has calibration settings, however None was provided as an argument." + f"\nSetting calibration to 'perfect' for split '{split}'. Available calibration settings are: 'perfect', 'imperfect', 'both'.", + ) + + if download: + self._download_dataset(root) + + root = Path(root) / "Middlebury2014" + + if not os.path.exists(root / split): + raise FileNotFoundError(f"The {split} directory was not found in the provided root directory") + + split_scenes = self.splits[split] + # check that the provided root folder contains the scene splits + if not any( + # using startswith to account for perfect / imperfect calibrartion + scene.startswith(s) + for scene in os.listdir(root / split) + for s in split_scenes + ): + raise FileNotFoundError(f"Provided root folder does not contain any scenes from the {split} split.") + + calibrartion_suffixes = { + None: [""], + "perfect": ["-perfect"], + "imperfect": ["-imperfect"], + "both": ["-perfect", "-imperfect"], + }[calibration] + + for calibration_suffix in calibrartion_suffixes: + scene_pattern = "*" + calibration_suffix + left_img_pattern = str(root / split / scene_pattern / "im0.png") + right_img_pattern = str(root / split / scene_pattern / "im1.png") + self._images += self._scan_pairs(left_img_pattern, right_img_pattern) + + if split == "test": + self._disparities = list((None, None) for _ in self._images) + else: + left_dispartity_pattern = str(root / split / scene_pattern / "disp0.pfm") + right_dispartity_pattern = str(root / split / scene_pattern / "disp1.pfm") + self._disparities += self._scan_pairs(left_dispartity_pattern, right_dispartity_pattern) + + self.use_ambient_views = use_ambient_views + + def _read_img(self, file_path: Union[str, Path]) -> Image.Image: + """ + Function that reads either the original right image or an augmented view when ``use_ambient_views`` is True. + When ``use_ambient_views`` is True, the dataset will return at random one of ``[im1.png, im1E.png, im1L.png]`` + as the right image. + """ + ambient_file_paths: List[Union[str, Path]] # make mypy happy + + if not isinstance(file_path, Path): + file_path = Path(file_path) + + if file_path.name == "im1.png" and self.use_ambient_views: + base_path = file_path.parent + # initialize sampleable container + ambient_file_paths = list(base_path / view_name for view_name in ["im1E.png", "im1L.png"]) + # double check that we're not going to try to read from an invalid file path + ambient_file_paths = list(filter(lambda p: os.path.exists(p), ambient_file_paths)) + # keep the original image as an option as well for uniform sampling between base views + ambient_file_paths.append(file_path) + file_path = random.choice(ambient_file_paths) # type: ignore + return super()._read_img(file_path) + + def _read_disparity(self, file_path: str) -> Union[Tuple[None, None], Tuple[np.ndarray, np.ndarray]]: + # test split has not disparity maps + if file_path is None: + return None, None + + disparity_map = _read_pfm_file(file_path) + disparity_map = np.abs(disparity_map) # ensure that the disparity is positive + disparity_map[disparity_map == np.inf] = 0 # remove infinite disparities + valid_mask = (disparity_map > 0).squeeze(0) # mask out invalid disparities + return disparity_map, valid_mask + + def _download_dataset(self, root: Union[str, Path]) -> None: + base_url = "https://vision.middlebury.edu/stereo/data/scenes2014/zip" + # train and additional splits have 2 different calibration settings + root = Path(root) / "Middlebury2014" + split_name = self.split + + if split_name != "test": + for split_scene in self.splits[split_name]: + split_root = root / split_name + for calibration in ["perfect", "imperfect"]: + scene_name = f"{split_scene}-{calibration}" + scene_url = f"{base_url}/{scene_name}.zip" + # download the scene only if it doesn't exist + if not (split_root / scene_name).exists(): + download_and_extract_archive( + url=scene_url, + filename=f"{scene_name}.zip", + download_root=str(split_root), + remove_finished=True, + ) + else: + os.makedirs(root / "test") + if any(s not in os.listdir(root / "test") for s in self.splits["test"]): + # test split is downloaded from a different location + test_set_url = "https://vision.middlebury.edu/stereo/submit3/zip/MiddEval3-data-F.zip" + # the unzip is going to produce a directory MiddEval3 with two subdirectories trainingF and testF + # we want to move the contents from testF into the directory + download_and_extract_archive(url=test_set_url, download_root=str(root), remove_finished=True) + for scene_dir, scene_names, _ in os.walk(str(root / "MiddEval3/testF")): + for scene in scene_names: + scene_dst_dir = root / "test" + scene_src_dir = Path(scene_dir) / scene + os.makedirs(scene_dst_dir, exist_ok=True) + shutil.move(str(scene_src_dir), str(scene_dst_dir)) + + # cleanup MiddEval3 directory + shutil.rmtree(str(root / "MiddEval3")) + + def __getitem__(self, index: int) -> T2: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + ``valid_mask`` is implicitly ``None`` for `split=test`. + """ + return cast(T2, super().__getitem__(index)) + + +class CREStereo(StereoMatchingDataset): + """Synthetic dataset used in training the `CREStereo `_ architecture. + Dataset details on the official paper `repo `_. + + The dataset is expected to have the following structure: :: + + root + CREStereo + tree + img1_left.jpg + img1_right.jpg + img1_left.disp.jpg + img1_right.disp.jpg + img2_left.jpg + img2_right.jpg + img2_left.disp.jpg + img2_right.disp.jpg + ... + shapenet + img1_left.jpg + img1_right.jpg + img1_left.disp.jpg + img1_right.disp.jpg + ... + reflective + img1_left.jpg + img1_right.jpg + img1_left.disp.jpg + img1_right.disp.jpg + ... + hole + img1_left.jpg + img1_right.jpg + img1_left.disp.jpg + img1_right.disp.jpg + ... + + Args: + root (str): Root directory of the dataset. + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + _has_built_in_disparity_mask = True + + def __init__( + self, + root: Union[str, Path], + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root, transforms) + + root = Path(root) / "CREStereo" + + dirs = ["shapenet", "reflective", "tree", "hole"] + + for s in dirs: + left_image_pattern = str(root / s / "*_left.jpg") + right_image_pattern = str(root / s / "*_right.jpg") + imgs = self._scan_pairs(left_image_pattern, right_image_pattern) + self._images += imgs + + left_disparity_pattern = str(root / s / "*_left.disp.png") + right_disparity_pattern = str(root / s / "*_right.disp.png") + disparities = self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + self._disparities += disparities + + def _read_disparity(self, file_path: str) -> Tuple[np.ndarray, None]: + disparity_map = np.asarray(Image.open(file_path), dtype=np.float32) + # unsqueeze the disparity map into (C, H, W) format + disparity_map = disparity_map[None, :, :] / 32.0 + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + ``valid_mask`` is implicitly ``None`` if the ``transforms`` parameter does not + generate a valid mask. + """ + return cast(T1, super().__getitem__(index)) + + +class FallingThingsStereo(StereoMatchingDataset): + """`FallingThings `_ dataset. + + The dataset is expected to have the following structure: :: + + root + FallingThings + single + dir1 + scene1 + _object_settings.json + _camera_settings.json + image1.left.depth.png + image1.right.depth.png + image1.left.jpg + image1.right.jpg + image2.left.depth.png + image2.right.depth.png + image2.left.jpg + image2.right + ... + scene2 + ... + mixed + scene1 + _object_settings.json + _camera_settings.json + image1.left.depth.png + image1.right.depth.png + image1.left.jpg + image1.right.jpg + image2.left.depth.png + image2.right.depth.png + image2.left.jpg + image2.right + ... + scene2 + ... + + Args: + root (str or ``pathlib.Path``): Root directory where FallingThings is located. + variant (string): Which variant to use. Either "single", "mixed", or "both". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + def __init__(self, root: Union[str, Path], variant: str = "single", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + root = Path(root) / "FallingThings" + + verify_str_arg(variant, "variant", valid_values=("single", "mixed", "both")) + + variants = { + "single": ["single"], + "mixed": ["mixed"], + "both": ["single", "mixed"], + }[variant] + + split_prefix = { + "single": Path("*") / "*", + "mixed": Path("*"), + } + + for s in variants: + left_img_pattern = str(root / s / split_prefix[s] / "*.left.jpg") + right_img_pattern = str(root / s / split_prefix[s] / "*.right.jpg") + self._images += self._scan_pairs(left_img_pattern, right_img_pattern) + + left_disparity_pattern = str(root / s / split_prefix[s] / "*.left.depth.png") + right_disparity_pattern = str(root / s / split_prefix[s] / "*.right.depth.png") + self._disparities += self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + + def _read_disparity(self, file_path: str) -> Tuple[np.ndarray, None]: + # (H, W) image + depth = np.asarray(Image.open(file_path)) + # as per https://research.nvidia.com/sites/default/files/pubs/2018-06_Falling-Things/readme_0.txt + # in order to extract disparity from depth maps + camera_settings_path = Path(file_path).parent / "_camera_settings.json" + with open(camera_settings_path, "r") as f: + # inverse of depth-from-disparity equation: depth = (baseline * focal) / (disparity * pixel_constant) + intrinsics = json.load(f) + focal = intrinsics["camera_settings"][0]["intrinsic_settings"]["fx"] + baseline, pixel_constant = 6, 100 # pixel constant is inverted + disparity_map = (baseline * focal * pixel_constant) / depth.astype(np.float32) + # unsqueeze disparity to (C, H, W) + disparity_map = disparity_map[None, :, :] + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img_left, img_right, disparity)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + If a ``valid_mask`` is generated within the ``transforms`` parameter, + a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned. + """ + return cast(T1, super().__getitem__(index)) + + +class SceneFlowStereo(StereoMatchingDataset): + """Dataset interface for `Scene Flow `_ datasets. + This interface provides access to the `FlyingThings3D, `Monkaa` and `Driving` datasets. + + The dataset is expected to have the following structure: :: + + root + SceneFlow + Monkaa + frames_cleanpass + scene1 + left + img1.png + img2.png + right + img1.png + img2.png + scene2 + left + img1.png + img2.png + right + img1.png + img2.png + frames_finalpass + scene1 + left + img1.png + img2.png + right + img1.png + img2.png + ... + ... + disparity + scene1 + left + img1.pfm + img2.pfm + right + img1.pfm + img2.pfm + FlyingThings3D + ... + ... + + Args: + root (str or ``pathlib.Path``): Root directory where SceneFlow is located. + variant (string): Which dataset variant to user, "FlyingThings3D" (default), "Monkaa" or "Driving". + pass_name (string): Which pass to use, "clean" (default), "final" or "both". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + + """ + + def __init__( + self, + root: Union[str, Path], + variant: str = "FlyingThings3D", + pass_name: str = "clean", + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root, transforms) + + root = Path(root) / "SceneFlow" + + verify_str_arg(variant, "variant", valid_values=("FlyingThings3D", "Driving", "Monkaa")) + verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both")) + + passes = { + "clean": ["frames_cleanpass"], + "final": ["frames_finalpass"], + "both": ["frames_cleanpass", "frames_finalpass"], + }[pass_name] + + root = root / variant + + prefix_directories = { + "Monkaa": Path("*"), + "FlyingThings3D": Path("*") / "*" / "*", + "Driving": Path("*") / "*" / "*", + } + + for p in passes: + left_image_pattern = str(root / p / prefix_directories[variant] / "left" / "*.png") + right_image_pattern = str(root / p / prefix_directories[variant] / "right" / "*.png") + self._images += self._scan_pairs(left_image_pattern, right_image_pattern) + + left_disparity_pattern = str(root / "disparity" / prefix_directories[variant] / "left" / "*.pfm") + right_disparity_pattern = str(root / "disparity" / prefix_directories[variant] / "right" / "*.pfm") + self._disparities += self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + + def _read_disparity(self, file_path: str) -> Tuple[np.ndarray, None]: + disparity_map = _read_pfm_file(file_path) + disparity_map = np.abs(disparity_map) # ensure that the disparity is positive + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img_left, img_right, disparity)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + If a ``valid_mask`` is generated within the ``transforms`` parameter, + a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned. + """ + return cast(T1, super().__getitem__(index)) + + +class SintelStereo(StereoMatchingDataset): + """Sintel `Stereo Dataset `_. + + The dataset is expected to have the following structure: :: + + root + Sintel + training + final_left + scene1 + img1.png + img2.png + ... + ... + final_right + scene2 + img1.png + img2.png + ... + ... + disparities + scene1 + img1.png + img2.png + ... + ... + occlusions + scene1 + img1.png + img2.png + ... + ... + outofframe + scene1 + img1.png + img2.png + ... + ... + + Args: + root (str or ``pathlib.Path``): Root directory where Sintel Stereo is located. + pass_name (string): The name of the pass to use, either "final", "clean" or "both". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + _has_built_in_disparity_mask = True + + def __init__(self, root: Union[str, Path], pass_name: str = "final", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + verify_str_arg(pass_name, "pass_name", valid_values=("final", "clean", "both")) + + root = Path(root) / "Sintel" + pass_names = { + "final": ["final"], + "clean": ["clean"], + "both": ["final", "clean"], + }[pass_name] + + for p in pass_names: + left_img_pattern = str(root / "training" / f"{p}_left" / "*" / "*.png") + right_img_pattern = str(root / "training" / f"{p}_right" / "*" / "*.png") + self._images += self._scan_pairs(left_img_pattern, right_img_pattern) + + disparity_pattern = str(root / "training" / "disparities" / "*" / "*.png") + self._disparities += self._scan_pairs(disparity_pattern, None) + + def _get_occlussion_mask_paths(self, file_path: str) -> Tuple[str, str]: + # helper function to get the occlusion mask paths + # a path will look like .../.../.../training/disparities/scene1/img1.png + # we want to get something like .../.../.../training/occlusions/scene1/img1.png + fpath = Path(file_path) + basename = fpath.name + scenedir = fpath.parent + # the parent of the scenedir is actually the disparity dir + sampledir = scenedir.parent.parent + + occlusion_path = str(sampledir / "occlusions" / scenedir.name / basename) + outofframe_path = str(sampledir / "outofframe" / scenedir.name / basename) + + if not os.path.exists(occlusion_path): + raise FileNotFoundError(f"Occlusion mask {occlusion_path} does not exist") + + if not os.path.exists(outofframe_path): + raise FileNotFoundError(f"Out of frame mask {outofframe_path} does not exist") + + return occlusion_path, outofframe_path + + def _read_disparity(self, file_path: str) -> Union[Tuple[None, None], Tuple[np.ndarray, np.ndarray]]: + if file_path is None: + return None, None + + # disparity decoding as per Sintel instructions in the README provided with the dataset + disparity_map = np.asarray(Image.open(file_path), dtype=np.float32) + r, g, b = np.split(disparity_map, 3, axis=-1) + disparity_map = r * 4 + g / (2**6) + b / (2**14) + # reshape into (C, H, W) format + disparity_map = np.transpose(disparity_map, (2, 0, 1)) + # find the appropriate file paths + occlued_mask_path, out_of_frame_mask_path = self._get_occlussion_mask_paths(file_path) + # occlusion masks + valid_mask = np.asarray(Image.open(occlued_mask_path)) == 0 + # out of frame masks + off_mask = np.asarray(Image.open(out_of_frame_mask_path)) == 0 + # combine the masks together + valid_mask = np.logical_and(off_mask, valid_mask) + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T2: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images whilst + the valid_mask is a numpy array of shape (H, W). + """ + return cast(T2, super().__getitem__(index)) + + +class InStereo2k(StereoMatchingDataset): + """`InStereo2k `_ dataset. + + The dataset is expected to have the following structure: :: + + root + InStereo2k + train + scene1 + left.png + right.png + left_disp.png + right_disp.png + ... + scene2 + ... + test + scene1 + left.png + right.png + left_disp.png + right_disp.png + ... + scene2 + ... + + Args: + root (str or ``pathlib.Path``): Root directory where InStereo2k is located. + split (string): Either "train" or "test". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + root = Path(root) / "InStereo2k" / split + + verify_str_arg(split, "split", valid_values=("train", "test")) + + left_img_pattern = str(root / "*" / "left.png") + right_img_pattern = str(root / "*" / "right.png") + self._images = self._scan_pairs(left_img_pattern, right_img_pattern) + + left_disparity_pattern = str(root / "*" / "left_disp.png") + right_disparity_pattern = str(root / "*" / "right_disp.png") + self._disparities = self._scan_pairs(left_disparity_pattern, right_disparity_pattern) + + def _read_disparity(self, file_path: str) -> Tuple[np.ndarray, None]: + disparity_map = np.asarray(Image.open(file_path), dtype=np.float32) + # unsqueeze disparity to (C, H, W) + disparity_map = disparity_map[None, :, :] / 1024.0 + valid_mask = None + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T1: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 3-tuple with ``(img_left, img_right, disparity)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + If a ``valid_mask`` is generated within the ``transforms`` parameter, + a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned. + """ + return cast(T1, super().__getitem__(index)) + + +class ETH3DStereo(StereoMatchingDataset): + """ETH3D `Low-Res Two-View `_ dataset. + + The dataset is expected to have the following structure: :: + + root + ETH3D + two_view_training + scene1 + im1.png + im0.png + images.txt + cameras.txt + calib.txt + scene2 + im1.png + im0.png + images.txt + cameras.txt + calib.txt + ... + two_view_training_gt + scene1 + disp0GT.pfm + mask0nocc.png + scene2 + disp0GT.pfm + mask0nocc.png + ... + two_view_testing + scene1 + im1.png + im0.png + images.txt + cameras.txt + calib.txt + scene2 + im1.png + im0.png + images.txt + cameras.txt + calib.txt + ... + + Args: + root (str or ``pathlib.Path``): Root directory of the ETH3D Dataset. + split (string, optional): The dataset split of scenes, either "train" (default) or "test". + transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version. + """ + + _has_built_in_disparity_mask = True + + def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None: + super().__init__(root, transforms) + + verify_str_arg(split, "split", valid_values=("train", "test")) + + root = Path(root) / "ETH3D" + + img_dir = "two_view_training" if split == "train" else "two_view_test" + anot_dir = "two_view_training_gt" + + left_img_pattern = str(root / img_dir / "*" / "im0.png") + right_img_pattern = str(root / img_dir / "*" / "im1.png") + self._images = self._scan_pairs(left_img_pattern, right_img_pattern) + + if split == "test": + self._disparities = list((None, None) for _ in self._images) + else: + disparity_pattern = str(root / anot_dir / "*" / "disp0GT.pfm") + self._disparities = self._scan_pairs(disparity_pattern, None) + + def _read_disparity(self, file_path: str) -> Union[Tuple[None, None], Tuple[np.ndarray, np.ndarray]]: + # test split has no disparity maps + if file_path is None: + return None, None + + disparity_map = _read_pfm_file(file_path) + disparity_map = np.abs(disparity_map) # ensure that the disparity is positive + mask_path = Path(file_path).parent / "mask0nocc.png" + valid_mask = Image.open(mask_path) + valid_mask = np.asarray(valid_mask).astype(bool) + return disparity_map, valid_mask + + def __getitem__(self, index: int) -> T2: + """Return example at given index. + + Args: + index(int): The index of the example to retrieve + + Returns: + tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)``. + The disparity is a numpy array of shape (1, H, W) and the images are PIL images. + ``valid_mask`` is implicitly ``None`` if the ``transforms`` parameter does not + generate a valid mask. + Both ``disparity`` and ``valid_mask`` are ``None`` if the dataset split is test. + """ + return cast(T2, super().__getitem__(index)) diff --git a/lib/python3.11/site-packages/torchvision/datasets/caltech.py b/lib/python3.11/site-packages/torchvision/datasets/caltech.py new file mode 100644 index 0000000000000000000000000000000000000000..b152c425c77c78e24eb6f17772fc54ed31329e6e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/caltech.py @@ -0,0 +1,240 @@ +import os +import os.path +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class Caltech101(VisionDataset): + """`Caltech 101 `_ Dataset. + + .. warning:: + + This class needs `scipy `_ to load target files from `.mat` format. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``caltech101`` exists or will be saved to if download is set to True. + target_type (string or list, optional): Type of target to use, ``category`` or + ``annotation``. Can also be a list to output a tuple with all specified + target types. ``category`` represents the target class, and + ``annotation`` is a list of points from a hand-generated outline. + Defaults to ``category``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + .. warning:: + + To download the dataset `gdown `_ is required. + """ + + def __init__( + self, + root: Union[str, Path], + target_type: Union[List[str], str] = "category", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform) + os.makedirs(self.root, exist_ok=True) + if isinstance(target_type, str): + target_type = [target_type] + self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation")) for t in target_type] + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories"))) + self.categories.remove("BACKGROUND_Google") # this is not a real class + + # For some reason, the category names in "101_ObjectCategories" and + # "Annotations" do not always match. This is a manual map between the + # two. Defaults to using same name, since most names are fine. + name_map = { + "Faces": "Faces_2", + "Faces_easy": "Faces_3", + "Motorbikes": "Motorbikes_16", + "airplanes": "Airplanes_Side_2", + } + self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories)) + + self.index: List[int] = [] + self.y = [] + for (i, c) in enumerate(self.categories): + n = len(os.listdir(os.path.join(self.root, "101_ObjectCategories", c))) + self.index.extend(range(1, n + 1)) + self.y.extend(n * [i]) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where the type of target specified by target_type. + """ + import scipy.io + + img = Image.open( + os.path.join( + self.root, + "101_ObjectCategories", + self.categories[self.y[index]], + f"image_{self.index[index]:04d}.jpg", + ) + ) + + target: Any = [] + for t in self.target_type: + if t == "category": + target.append(self.y[index]) + elif t == "annotation": + data = scipy.io.loadmat( + os.path.join( + self.root, + "Annotations", + self.annotation_categories[self.y[index]], + f"annotation_{self.index[index]:04d}.mat", + ) + ) + target.append(data["obj_contour"]) + target = tuple(target) if len(target) > 1 else target[0] + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def _check_integrity(self) -> bool: + # can be more robust and check hash of files + return os.path.exists(os.path.join(self.root, "101_ObjectCategories")) + + def __len__(self) -> int: + return len(self.index) + + def download(self) -> None: + if self._check_integrity(): + return + + download_and_extract_archive( + "https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp", + self.root, + filename="101_ObjectCategories.tar.gz", + md5="b224c7392d521a49829488ab0f1120d9", + ) + download_and_extract_archive( + "https://drive.google.com/file/d/175kQy3UsZ0wUEHZjqkUDdNVssr7bgh_m", + self.root, + filename="Annotations.tar", + md5="6f83eeb1f24d99cab4eb377263132c91", + ) + + def extra_repr(self) -> str: + return "Target type: {target_type}".format(**self.__dict__) + + +class Caltech256(VisionDataset): + """`Caltech 256 `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``caltech256`` exists or will be saved to if download is set to True. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + def __init__( + self, + root: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform) + os.makedirs(self.root, exist_ok=True) + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories"))) + self.index: List[int] = [] + self.y = [] + for (i, c) in enumerate(self.categories): + n = len( + [ + item + for item in os.listdir(os.path.join(self.root, "256_ObjectCategories", c)) + if item.endswith(".jpg") + ] + ) + self.index.extend(range(1, n + 1)) + self.y.extend(n * [i]) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img = Image.open( + os.path.join( + self.root, + "256_ObjectCategories", + self.categories[self.y[index]], + f"{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg", + ) + ) + + target = self.y[index] + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def _check_integrity(self) -> bool: + # can be more robust and check hash of files + return os.path.exists(os.path.join(self.root, "256_ObjectCategories")) + + def __len__(self) -> int: + return len(self.index) + + def download(self) -> None: + if self._check_integrity(): + return + + download_and_extract_archive( + "https://drive.google.com/file/d/1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK", + self.root, + filename="256_ObjectCategories.tar", + md5="67b4f42ca05d46448c6bb8ecd2220f6d", + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/celeba.py b/lib/python3.11/site-packages/torchvision/datasets/celeba.py new file mode 100644 index 0000000000000000000000000000000000000000..c15120af5a55a88ba215ae255b643e42577b51a4 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/celeba.py @@ -0,0 +1,193 @@ +import csv +import os +from collections import namedtuple +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +import PIL +import torch + +from .utils import check_integrity, download_file_from_google_drive, extract_archive, verify_str_arg +from .vision import VisionDataset + +CSV = namedtuple("CSV", ["header", "index", "data"]) + + +class CelebA(VisionDataset): + """`Large-scale CelebFaces Attributes (CelebA) Dataset `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + split (string): One of {'train', 'valid', 'test', 'all'}. + Accordingly dataset is selected. + target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``, + or ``landmarks``. Can also be a list to output a tuple with all specified target types. + The targets represent: + + - ``attr`` (Tensor shape=(40,) dtype=int): binary (0, 1) labels for attributes + - ``identity`` (int): label for each person (data points with the same identity are the same person) + - ``bbox`` (Tensor shape=(4,) dtype=int): bounding box (x, y, width, height) + - ``landmarks`` (Tensor shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x, + righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y) + + Defaults to ``attr``. If empty, ``None`` will be returned as target. + + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + .. warning:: + + To download the dataset `gdown `_ is required. + """ + + base_folder = "celeba" + # There currently does not appear to be an easy way to extract 7z in python (without introducing additional + # dependencies). The "in-the-wild" (not aligned+cropped) images are only in 7z, so they are not available + # right now. + file_list = [ + # File ID MD5 Hash Filename + ("0B7EVK8r0v71pZjFTYXZWM3FlRnM", "00d2c5bc6d35e252742224ab0c1e8fcb", "img_align_celeba.zip"), + # ("0B7EVK8r0v71pbWNEUjJKdDQ3dGc","b6cd7e93bc7a96c2dc33f819aa3ac651", "img_align_celeba_png.7z"), + # ("0B7EVK8r0v71peklHb0pGdDl6R28", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_celeba.7z"), + ("0B7EVK8r0v71pblRyaVFSWGxPY0U", "75e246fa4810816ffd6ee81facbd244c", "list_attr_celeba.txt"), + ("1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS", "32bd1bd63d3c78cd57e08160ec5ed1e2", "identity_CelebA.txt"), + ("0B7EVK8r0v71pbThiMVRxWXZ4dU0", "00566efa6fedff7a56946cd1c10f1c16", "list_bbox_celeba.txt"), + ("0B7EVK8r0v71pd0FJY3Blby1HUTQ", "cc24ecafdb5b50baae59b03474781f8c", "list_landmarks_align_celeba.txt"), + # ("0B7EVK8r0v71pTzJIdlJWdHczRlU", "063ee6ddb681f96bc9ca28c6febb9d1a", "list_landmarks_celeba.txt"), + ("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"), + ] + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + target_type: Union[List[str], str] = "attr", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.split = split + if isinstance(target_type, list): + self.target_type = target_type + else: + self.target_type = [target_type] + + if not self.target_type and self.target_transform is not None: + raise RuntimeError("target_transform is specified but target_type is empty") + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + split_map = { + "train": 0, + "valid": 1, + "test": 2, + "all": None, + } + split_ = split_map[verify_str_arg(split.lower(), "split", ("train", "valid", "test", "all"))] + splits = self._load_csv("list_eval_partition.txt") + identity = self._load_csv("identity_CelebA.txt") + bbox = self._load_csv("list_bbox_celeba.txt", header=1) + landmarks_align = self._load_csv("list_landmarks_align_celeba.txt", header=1) + attr = self._load_csv("list_attr_celeba.txt", header=1) + + mask = slice(None) if split_ is None else (splits.data == split_).squeeze() + + if mask == slice(None): # if split == "all" + self.filename = splits.index + else: + self.filename = [splits.index[i] for i in torch.squeeze(torch.nonzero(mask))] # type: ignore[arg-type] + self.identity = identity.data[mask] + self.bbox = bbox.data[mask] + self.landmarks_align = landmarks_align.data[mask] + self.attr = attr.data[mask] + # map from {-1, 1} to {0, 1} + self.attr = torch.div(self.attr + 1, 2, rounding_mode="floor") + self.attr_names = attr.header + + def _load_csv( + self, + filename: str, + header: Optional[int] = None, + ) -> CSV: + with open(os.path.join(self.root, self.base_folder, filename)) as csv_file: + data = list(csv.reader(csv_file, delimiter=" ", skipinitialspace=True)) + + if header is not None: + headers = data[header] + data = data[header + 1 :] + else: + headers = [] + + indices = [row[0] for row in data] + data = [row[1:] for row in data] + data_int = [list(map(int, i)) for i in data] + + return CSV(headers, indices, torch.tensor(data_int)) + + def _check_integrity(self) -> bool: + for (_, md5, filename) in self.file_list: + fpath = os.path.join(self.root, self.base_folder, filename) + _, ext = os.path.splitext(filename) + # Allow original archive to be deleted (zip and 7z) + # Only need the extracted images + if ext not in [".zip", ".7z"] and not check_integrity(fpath, md5): + return False + + # Should check a hash of the images + return os.path.isdir(os.path.join(self.root, self.base_folder, "img_align_celeba")) + + def download(self) -> None: + if self._check_integrity(): + return + + for (file_id, md5, filename) in self.file_list: + download_file_from_google_drive(file_id, os.path.join(self.root, self.base_folder), filename, md5) + + extract_archive(os.path.join(self.root, self.base_folder, "img_align_celeba.zip")) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index])) + + target: Any = [] + for t in self.target_type: + if t == "attr": + target.append(self.attr[index, :]) + elif t == "identity": + target.append(self.identity[index, 0]) + elif t == "bbox": + target.append(self.bbox[index, :]) + elif t == "landmarks": + target.append(self.landmarks_align[index, :]) + else: + # TODO: refactor with utils.verify_str_arg + raise ValueError(f'Target type "{t}" is not recognized.') + + if self.transform is not None: + X = self.transform(X) + + if target: + target = tuple(target) if len(target) > 1 else target[0] + + if self.target_transform is not None: + target = self.target_transform(target) + else: + target = None + + return X, target + + def __len__(self) -> int: + return len(self.attr) + + def extra_repr(self) -> str: + lines = ["Target type: {target_type}", "Split: {split}"] + return "\n".join(lines).format(**self.__dict__) diff --git a/lib/python3.11/site-packages/torchvision/datasets/cifar.py b/lib/python3.11/site-packages/torchvision/datasets/cifar.py new file mode 100644 index 0000000000000000000000000000000000000000..0f425d76c5763d3bc98c7c3cbb6b9241b6385622 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/cifar.py @@ -0,0 +1,167 @@ +import os.path +import pickle +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import check_integrity, download_and_extract_archive +from .vision import VisionDataset + + +class CIFAR10(VisionDataset): + """`CIFAR10 `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``cifar-10-batches-py`` exists or will be saved to if download is set to True. + train (bool, optional): If True, creates dataset from training set, otherwise + creates from test set. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + base_folder = "cifar-10-batches-py" + url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" + filename = "cifar-10-python.tar.gz" + tgz_md5 = "c58f30108f718f92721af3b95e74349a" + train_list = [ + ["data_batch_1", "c99cafc152244af753f735de768cd75f"], + ["data_batch_2", "d4bba439e000b95fd0a9bffe97cbabec"], + ["data_batch_3", "54ebc095f3ab1f0389bbae665268c751"], + ["data_batch_4", "634d18415352ddfa80567beed471001a"], + ["data_batch_5", "482c414d41f54cd18b22e5b47cb7c3cb"], + ] + + test_list = [ + ["test_batch", "40351d587109b95175f43aff81a1287e"], + ] + meta = { + "filename": "batches.meta", + "key": "label_names", + "md5": "5ff9c542aee3614f3951f8cda6e48888", + } + + def __init__( + self, + root: Union[str, Path], + train: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + + super().__init__(root, transform=transform, target_transform=target_transform) + + self.train = train # training set or test set + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + if self.train: + downloaded_list = self.train_list + else: + downloaded_list = self.test_list + + self.data: Any = [] + self.targets = [] + + # now load the picked numpy arrays + for file_name, checksum in downloaded_list: + file_path = os.path.join(self.root, self.base_folder, file_name) + with open(file_path, "rb") as f: + entry = pickle.load(f, encoding="latin1") + self.data.append(entry["data"]) + if "labels" in entry: + self.targets.extend(entry["labels"]) + else: + self.targets.extend(entry["fine_labels"]) + + self.data = np.vstack(self.data).reshape(-1, 3, 32, 32) + self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC + + self._load_meta() + + def _load_meta(self) -> None: + path = os.path.join(self.root, self.base_folder, self.meta["filename"]) + if not check_integrity(path, self.meta["md5"]): + raise RuntimeError("Dataset metadata file not found or corrupted. You can use download=True to download it") + with open(path, "rb") as infile: + data = pickle.load(infile, encoding="latin1") + self.classes = data[self.meta["key"]] + self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)} + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], self.targets[index] + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(img) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) + + def _check_integrity(self) -> bool: + for filename, md5 in self.train_list + self.test_list: + fpath = os.path.join(self.root, self.base_folder, filename) + if not check_integrity(fpath, md5): + return False + return True + + def download(self) -> None: + if self._check_integrity(): + return + download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5) + + def extra_repr(self) -> str: + split = "Train" if self.train is True else "Test" + return f"Split: {split}" + + +class CIFAR100(CIFAR10): + """`CIFAR100 `_ Dataset. + + This is a subclass of the `CIFAR10` Dataset. + """ + + base_folder = "cifar-100-python" + url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" + filename = "cifar-100-python.tar.gz" + tgz_md5 = "eb9058c3a382ffc7106e4002c42a8d85" + train_list = [ + ["train", "16019d7e3df5f24257cddd939b257f8d"], + ] + + test_list = [ + ["test", "f0ef6b0ae62326f3e7ffdfab6717acfc"], + ] + meta = { + "filename": "meta", + "key": "fine_label_names", + "md5": "7973b15100ade9c7d40fb424638fde48", + } diff --git a/lib/python3.11/site-packages/torchvision/datasets/cityscapes.py b/lib/python3.11/site-packages/torchvision/datasets/cityscapes.py new file mode 100644 index 0000000000000000000000000000000000000000..97a47c07bebba250fd598c8b48a95a8e1af290fd --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/cityscapes.py @@ -0,0 +1,222 @@ +import json +import os +from collections import namedtuple +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import extract_archive, iterable_to_str, verify_str_arg +from .vision import VisionDataset + + +class Cityscapes(VisionDataset): + """`Cityscapes `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory ``leftImg8bit`` + and ``gtFine`` or ``gtCoarse`` are located. + split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="fine" + otherwise ``train``, ``train_extra`` or ``val`` + mode (string, optional): The quality mode to use, ``fine`` or ``coarse`` + target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon`` + or ``color``. Can also be a list to output a tuple with all specified target types. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + + Examples: + + Get semantic segmentation target + + .. code-block:: python + + dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', + target_type='semantic') + + img, smnt = dataset[0] + + Get multiple targets + + .. code-block:: python + + dataset = Cityscapes('./data/cityscapes', split='train', mode='fine', + target_type=['instance', 'color', 'polygon']) + + img, (inst, col, poly) = dataset[0] + + Validate on the "coarse" set + + .. code-block:: python + + dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse', + target_type='semantic') + + img, smnt = dataset[0] + """ + + # Based on https://github.com/mcordts/cityscapesScripts + CityscapesClass = namedtuple( + "CityscapesClass", + ["name", "id", "train_id", "category", "category_id", "has_instances", "ignore_in_eval", "color"], + ) + + classes = [ + CityscapesClass("unlabeled", 0, 255, "void", 0, False, True, (0, 0, 0)), + CityscapesClass("ego vehicle", 1, 255, "void", 0, False, True, (0, 0, 0)), + CityscapesClass("rectification border", 2, 255, "void", 0, False, True, (0, 0, 0)), + CityscapesClass("out of roi", 3, 255, "void", 0, False, True, (0, 0, 0)), + CityscapesClass("static", 4, 255, "void", 0, False, True, (0, 0, 0)), + CityscapesClass("dynamic", 5, 255, "void", 0, False, True, (111, 74, 0)), + CityscapesClass("ground", 6, 255, "void", 0, False, True, (81, 0, 81)), + CityscapesClass("road", 7, 0, "flat", 1, False, False, (128, 64, 128)), + CityscapesClass("sidewalk", 8, 1, "flat", 1, False, False, (244, 35, 232)), + CityscapesClass("parking", 9, 255, "flat", 1, False, True, (250, 170, 160)), + CityscapesClass("rail track", 10, 255, "flat", 1, False, True, (230, 150, 140)), + CityscapesClass("building", 11, 2, "construction", 2, False, False, (70, 70, 70)), + CityscapesClass("wall", 12, 3, "construction", 2, False, False, (102, 102, 156)), + CityscapesClass("fence", 13, 4, "construction", 2, False, False, (190, 153, 153)), + CityscapesClass("guard rail", 14, 255, "construction", 2, False, True, (180, 165, 180)), + CityscapesClass("bridge", 15, 255, "construction", 2, False, True, (150, 100, 100)), + CityscapesClass("tunnel", 16, 255, "construction", 2, False, True, (150, 120, 90)), + CityscapesClass("pole", 17, 5, "object", 3, False, False, (153, 153, 153)), + CityscapesClass("polegroup", 18, 255, "object", 3, False, True, (153, 153, 153)), + CityscapesClass("traffic light", 19, 6, "object", 3, False, False, (250, 170, 30)), + CityscapesClass("traffic sign", 20, 7, "object", 3, False, False, (220, 220, 0)), + CityscapesClass("vegetation", 21, 8, "nature", 4, False, False, (107, 142, 35)), + CityscapesClass("terrain", 22, 9, "nature", 4, False, False, (152, 251, 152)), + CityscapesClass("sky", 23, 10, "sky", 5, False, False, (70, 130, 180)), + CityscapesClass("person", 24, 11, "human", 6, True, False, (220, 20, 60)), + CityscapesClass("rider", 25, 12, "human", 6, True, False, (255, 0, 0)), + CityscapesClass("car", 26, 13, "vehicle", 7, True, False, (0, 0, 142)), + CityscapesClass("truck", 27, 14, "vehicle", 7, True, False, (0, 0, 70)), + CityscapesClass("bus", 28, 15, "vehicle", 7, True, False, (0, 60, 100)), + CityscapesClass("caravan", 29, 255, "vehicle", 7, True, True, (0, 0, 90)), + CityscapesClass("trailer", 30, 255, "vehicle", 7, True, True, (0, 0, 110)), + CityscapesClass("train", 31, 16, "vehicle", 7, True, False, (0, 80, 100)), + CityscapesClass("motorcycle", 32, 17, "vehicle", 7, True, False, (0, 0, 230)), + CityscapesClass("bicycle", 33, 18, "vehicle", 7, True, False, (119, 11, 32)), + CityscapesClass("license plate", -1, -1, "vehicle", 7, False, True, (0, 0, 142)), + ] + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + mode: str = "fine", + target_type: Union[List[str], str] = "instance", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root, transforms, transform, target_transform) + self.mode = "gtFine" if mode == "fine" else "gtCoarse" + self.images_dir = os.path.join(self.root, "leftImg8bit", split) + self.targets_dir = os.path.join(self.root, self.mode, split) + self.target_type = target_type + self.split = split + self.images = [] + self.targets = [] + + verify_str_arg(mode, "mode", ("fine", "coarse")) + if mode == "fine": + valid_modes = ("train", "test", "val") + else: + valid_modes = ("train", "train_extra", "val") + msg = "Unknown value '{}' for argument split if mode is '{}'. Valid values are {{{}}}." + msg = msg.format(split, mode, iterable_to_str(valid_modes)) + verify_str_arg(split, "split", valid_modes, msg) + + if not isinstance(target_type, list): + self.target_type = [target_type] + [ + verify_str_arg(value, "target_type", ("instance", "semantic", "polygon", "color")) + for value in self.target_type + ] + + if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir): + + if split == "train_extra": + image_dir_zip = os.path.join(self.root, "leftImg8bit_trainextra.zip") + else: + image_dir_zip = os.path.join(self.root, "leftImg8bit_trainvaltest.zip") + + if self.mode == "gtFine": + target_dir_zip = os.path.join(self.root, f"{self.mode}_trainvaltest.zip") + elif self.mode == "gtCoarse": + target_dir_zip = os.path.join(self.root, f"{self.mode}.zip") + + if os.path.isfile(image_dir_zip) and os.path.isfile(target_dir_zip): + extract_archive(from_path=image_dir_zip, to_path=self.root) + extract_archive(from_path=target_dir_zip, to_path=self.root) + else: + raise RuntimeError( + "Dataset not found or incomplete. Please make sure all required folders for the" + ' specified "split" and "mode" are inside the "root" directory' + ) + + for city in os.listdir(self.images_dir): + img_dir = os.path.join(self.images_dir, city) + target_dir = os.path.join(self.targets_dir, city) + for file_name in os.listdir(img_dir): + target_types = [] + for t in self.target_type: + target_name = "{}_{}".format( + file_name.split("_leftImg8bit")[0], self._get_target_suffix(self.mode, t) + ) + target_types.append(os.path.join(target_dir, target_name)) + + self.images.append(os.path.join(img_dir, file_name)) + self.targets.append(target_types) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + Returns: + tuple: (image, target) where target is a tuple of all target types if target_type is a list with more + than one item. Otherwise, target is a json object if target_type="polygon", else the image segmentation. + """ + + image = Image.open(self.images[index]).convert("RGB") + + targets: Any = [] + for i, t in enumerate(self.target_type): + if t == "polygon": + target = self._load_json(self.targets[index][i]) + else: + target = Image.open(self.targets[index][i]) # type: ignore[assignment] + + targets.append(target) + + target = tuple(targets) if len(targets) > 1 else targets[0] # type: ignore[assignment] + + if self.transforms is not None: + image, target = self.transforms(image, target) + + return image, target + + def __len__(self) -> int: + return len(self.images) + + def extra_repr(self) -> str: + lines = ["Split: {split}", "Mode: {mode}", "Type: {target_type}"] + return "\n".join(lines).format(**self.__dict__) + + def _load_json(self, path: str) -> Dict[str, Any]: + with open(path) as file: + data = json.load(file) + return data + + def _get_target_suffix(self, mode: str, target_type: str) -> str: + if target_type == "instance": + return f"{mode}_instanceIds.png" + elif target_type == "semantic": + return f"{mode}_labelIds.png" + elif target_type == "color": + return f"{mode}_color.png" + else: + return f"{mode}_polygons.json" diff --git a/lib/python3.11/site-packages/torchvision/datasets/clevr.py b/lib/python3.11/site-packages/torchvision/datasets/clevr.py new file mode 100644 index 0000000000000000000000000000000000000000..328eb7d79da70c3607b86ded512021f901119d1b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/clevr.py @@ -0,0 +1,88 @@ +import json +import pathlib +from typing import Any, Callable, List, Optional, Tuple, Union +from urllib.parse import urlparse + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class CLEVRClassification(VisionDataset): + """`CLEVR `_ classification dataset. + + The number of objects in a scene are used as label. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory ``root/clevr`` exists or will be saved to if download is + set to True. + split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in them target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If + dataset is already downloaded, it is not downloaded again. + """ + + _URL = "https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip" + _MD5 = "b11922020e72d0cd9154779b2d3d07d2" + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + self._split = verify_str_arg(split, "split", ("train", "val", "test")) + super().__init__(root, transform=transform, target_transform=target_transform) + self._base_folder = pathlib.Path(self.root) / "clevr" + self._data_folder = self._base_folder / pathlib.Path(urlparse(self._URL).path).stem + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self._image_files = sorted(self._data_folder.joinpath("images", self._split).glob("*")) + + self._labels: List[Optional[int]] + if self._split != "test": + with open(self._data_folder / "scenes" / f"CLEVR_{self._split}_scenes.json") as file: + content = json.load(file) + num_objects = {scene["image_filename"]: len(scene["objects"]) for scene in content["scenes"]} + self._labels = [num_objects[image_file.name] for image_file in self._image_files] + else: + self._labels = [None] * len(self._image_files) + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file = self._image_files[idx] + label = self._labels[idx] + + image = Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def _check_exists(self) -> bool: + return self._data_folder.exists() and self._data_folder.is_dir() + + def _download(self) -> None: + if self._check_exists(): + return + + download_and_extract_archive(self._URL, str(self._base_folder), md5=self._MD5) + + def extra_repr(self) -> str: + return f"split={self._split}" diff --git a/lib/python3.11/site-packages/torchvision/datasets/coco.py b/lib/python3.11/site-packages/torchvision/datasets/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b7be798b2457de56416f230f82a49373fbe941 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/coco.py @@ -0,0 +1,109 @@ +import os.path +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +from PIL import Image + +from .vision import VisionDataset + + +class CocoDetection(VisionDataset): + """`MS Coco Detection `_ Dataset. + + It requires the `COCO API to be installed `_. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + """ + + def __init__( + self, + root: Union[str, Path], + annFile: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + transforms: Optional[Callable] = None, + ) -> None: + super().__init__(root, transforms, transform, target_transform) + from pycocotools.coco import COCO + + self.coco = COCO(annFile) + self.ids = list(sorted(self.coco.imgs.keys())) + + def _load_image(self, id: int) -> Image.Image: + path = self.coco.loadImgs(id)[0]["file_name"] + return Image.open(os.path.join(self.root, path)).convert("RGB") + + def _load_target(self, id: int) -> List[Any]: + return self.coco.loadAnns(self.coco.getAnnIds(id)) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + + if not isinstance(index, int): + raise ValueError(f"Index must be of type integer, got {type(index)} instead.") + + id = self.ids[index] + image = self._load_image(id) + target = self._load_target(id) + + if self.transforms is not None: + image, target = self.transforms(image, target) + + return image, target + + def __len__(self) -> int: + return len(self.ids) + + +class CocoCaptions(CocoDetection): + """`MS Coco Captions `_ Dataset. + + It requires the `COCO API to be installed `_. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + + Example: + + .. code:: python + + import torchvision.datasets as dset + import torchvision.transforms as transforms + cap = dset.CocoCaptions(root = 'dir where images are', + annFile = 'json annotation file', + transform=transforms.PILToTensor()) + + print('Number of samples: ', len(cap)) + img, target = cap[3] # load 4th sample + + print("Image Size: ", img.size()) + print(target) + + Output: :: + + Number of samples: 82783 + Image Size: (3L, 427L, 640L) + [u'A plane emitting smoke stream flying over a mountain.', + u'A plane darts across a bright blue sky behind a mountain covered in snow', + u'A plane leaves a contrail above the snowy mountain top.', + u'A mountain that has a plane flying overheard in the distance.', + u'A mountain view with a plume of smoke in the background'] + + """ + + def _load_target(self, id: int) -> List[str]: + return [ann["caption"] for ann in super()._load_target(id)] diff --git a/lib/python3.11/site-packages/torchvision/datasets/country211.py b/lib/python3.11/site-packages/torchvision/datasets/country211.py new file mode 100644 index 0000000000000000000000000000000000000000..a0f82ee1226670ae274cc09dc0697dd4c51fb1c2 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/country211.py @@ -0,0 +1,58 @@ +from pathlib import Path +from typing import Callable, Optional, Union + +from .folder import ImageFolder +from .utils import download_and_extract_archive, verify_str_arg + + +class Country211(ImageFolder): + """`The Country211 Data Set `_ from OpenAI. + + This dataset was built by filtering the images from the YFCC100m dataset + that have GPS coordinate corresponding to a ISO-3166 country code. The + dataset is balanced by sampling 150 train images, 50 validation images, and + 100 test images for each country. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), ``"valid"`` and ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and puts it into + ``root/country211/``. If dataset is already downloaded, it is not downloaded again. + """ + + _URL = "https://openaipublic.azureedge.net/clip/data/country211.tgz" + _MD5 = "84988d7644798601126c29e9877aab6a" + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + self._split = verify_str_arg(split, "split", ("train", "valid", "test")) + + root = Path(root).expanduser() + self.root = str(root) + self._base_folder = root / "country211" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + super().__init__(str(self._base_folder / self._split), transform=transform, target_transform=target_transform) + self.root = str(root) + + def _check_exists(self) -> bool: + return self._base_folder.exists() and self._base_folder.is_dir() + + def _download(self) -> None: + if self._check_exists(): + return + download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/dtd.py b/lib/python3.11/site-packages/torchvision/datasets/dtd.py new file mode 100644 index 0000000000000000000000000000000000000000..71c556bd201b37b0622df050e8c9dadd5f32f4e0 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/dtd.py @@ -0,0 +1,100 @@ +import os +import pathlib +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class DTD(VisionDataset): + """`Describable Textures Dataset (DTD) `_. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``. + partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``. + + .. note:: + + The partition only changes which split each image belongs to. Thus, regardless of the selected + partition, combining all splits will result in all images. + + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. Default is False. + """ + + _URL = "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz" + _MD5 = "fff73e5086ae6bdbea199a49dfb8a4c1" + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + partition: int = 1, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + self._split = verify_str_arg(split, "split", ("train", "val", "test")) + if not isinstance(partition, int) and not (1 <= partition <= 10): + raise ValueError( + f"Parameter 'partition' should be an integer with `1 <= partition <= 10`, " + f"but got {partition} instead" + ) + self._partition = partition + + super().__init__(root, transform=transform, target_transform=target_transform) + self._base_folder = pathlib.Path(self.root) / type(self).__name__.lower() + self._data_folder = self._base_folder / "dtd" + self._meta_folder = self._data_folder / "labels" + self._images_folder = self._data_folder / "images" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + self._image_files = [] + classes = [] + with open(self._meta_folder / f"{self._split}{self._partition}.txt") as file: + for line in file: + cls, name = line.strip().split("/") + self._image_files.append(self._images_folder.joinpath(cls, name)) + classes.append(cls) + + self.classes = sorted(set(classes)) + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + self._labels = [self.class_to_idx[cls] for cls in classes] + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._image_files[idx], self._labels[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def extra_repr(self) -> str: + return f"split={self._split}, partition={self._partition}" + + def _check_exists(self) -> bool: + return os.path.exists(self._data_folder) and os.path.isdir(self._data_folder) + + def _download(self) -> None: + if self._check_exists(): + return + download_and_extract_archive(self._URL, download_root=str(self._base_folder), md5=self._MD5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/eurosat.py b/lib/python3.11/site-packages/torchvision/datasets/eurosat.py new file mode 100644 index 0000000000000000000000000000000000000000..c6571d2abab11d457f0c0b9ff910c7a58efbcf87 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/eurosat.py @@ -0,0 +1,62 @@ +import os +from pathlib import Path +from typing import Callable, Optional, Union + +from .folder import ImageFolder +from .utils import download_and_extract_archive + + +class EuroSAT(ImageFolder): + """RGB version of the `EuroSAT `_ Dataset. + + For the MS version of the dataset, see + `TorchGeo `__. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``root/eurosat`` exists. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. Default is False. + """ + + def __init__( + self, + root: Union[str, Path], + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + self.root = os.path.expanduser(root) + self._base_folder = os.path.join(self.root, "eurosat") + self._data_folder = os.path.join(self._base_folder, "2750") + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + super().__init__(self._data_folder, transform=transform, target_transform=target_transform) + self.root = os.path.expanduser(root) + + def __len__(self) -> int: + return len(self.samples) + + def _check_exists(self) -> bool: + return os.path.exists(self._data_folder) + + def download(self) -> None: + + if self._check_exists(): + return + + os.makedirs(self._base_folder, exist_ok=True) + download_and_extract_archive( + "https://huggingface.co/datasets/torchgeo/eurosat/resolve/c877bcd43f099cd0196738f714544e355477f3fd/EuroSAT.zip", + download_root=self._base_folder, + md5="c8fa014336c82ac7804f0398fcb19387", + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/fakedata.py b/lib/python3.11/site-packages/torchvision/datasets/fakedata.py new file mode 100644 index 0000000000000000000000000000000000000000..af26a8579e5d954bde9fc06966d3518b7ca76c1c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/fakedata.py @@ -0,0 +1,67 @@ +from typing import Any, Callable, Optional, Tuple + +import torch + +from .. import transforms +from .vision import VisionDataset + + +class FakeData(VisionDataset): + """A fake dataset that returns randomly generated images and returns them as PIL images + + Args: + size (int, optional): Size of the dataset. Default: 1000 images + image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224) + num_classes(int, optional): Number of classes in the dataset. Default: 10 + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + random_offset (int): Offsets the index-based random seed used to + generate each image. Default: 0 + + """ + + def __init__( + self, + size: int = 1000, + image_size: Tuple[int, int, int] = (3, 224, 224), + num_classes: int = 10, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + random_offset: int = 0, + ) -> None: + super().__init__(transform=transform, target_transform=target_transform) + self.size = size + self.num_classes = num_classes + self.image_size = image_size + self.random_offset = random_offset + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is class_index of the target class. + """ + # create random image that is consistent with the index id + if index >= len(self): + raise IndexError(f"{self.__class__.__name__} index out of range") + rng_state = torch.get_rng_state() + torch.manual_seed(index + self.random_offset) + img = torch.randn(*self.image_size) + target = torch.randint(0, self.num_classes, size=(1,), dtype=torch.long)[0] + torch.set_rng_state(rng_state) + + # convert to PIL Image + img = transforms.ToPILImage()(img) + if self.transform is not None: + img = self.transform(img) + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target.item() + + def __len__(self) -> int: + return self.size diff --git a/lib/python3.11/site-packages/torchvision/datasets/fer2013.py b/lib/python3.11/site-packages/torchvision/datasets/fer2013.py new file mode 100644 index 0000000000000000000000000000000000000000..3afda07846b6d18b1cd43c37f7bb8747b8b172b5 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/fer2013.py @@ -0,0 +1,120 @@ +import csv +import pathlib +from typing import Any, Callable, Optional, Tuple, Union + +import torch +from PIL import Image + +from .utils import check_integrity, verify_str_arg +from .vision import VisionDataset + + +class FER2013(VisionDataset): + """`FER2013 + `_ Dataset. + + .. note:: + This dataset can return test labels only if ``fer2013.csv`` OR + ``icml_face_data.csv`` are present in ``root/fer2013/``. If only + ``train.csv`` and ``test.csv`` are present, the test labels are set to + ``None``. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``root/fer2013`` exists. This directory may contain either + ``fer2013.csv``, ``icml_face_data.csv``, or both ``train.csv`` and + ``test.csv``. Precendence is given in that order, i.e. if + ``fer2013.csv`` is present then the rest of the files will be + ignored. All these (combinations of) files contain the same data and + are supported for convenience, but only ``fer2013.csv`` and + ``icml_face_data.csv`` are able to return non-None test labels. + split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + """ + + _RESOURCES = { + "train": ("train.csv", "3f0dfb3d3fd99c811a1299cb947e3131"), + "test": ("test.csv", "b02c2298636a634e8c2faabbf3ea9a23"), + # The fer2013.csv and icml_face_data.csv files contain both train and + # tests instances, and unlike test.csv they contain the labels for the + # test instances. We give these 2 files precedence over train.csv and + # test.csv. And yes, they both contain the same data, but with different + # column names (note the spaces) and ordering: + # $ head -n 1 fer2013.csv icml_face_data.csv train.csv test.csv + # ==> fer2013.csv <== + # emotion,pixels,Usage + # + # ==> icml_face_data.csv <== + # emotion, Usage, pixels + # + # ==> train.csv <== + # emotion,pixels + # + # ==> test.csv <== + # pixels + "fer": ("fer2013.csv", "f8428a1edbd21e88f42c73edd2a14f95"), + "icml": ("icml_face_data.csv", "b114b9e04e6949e5fe8b6a98b3892b1d"), + } + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + self._split = verify_str_arg(split, "split", ("train", "test")) + super().__init__(root, transform=transform, target_transform=target_transform) + + base_folder = pathlib.Path(self.root) / "fer2013" + use_fer_file = (base_folder / self._RESOURCES["fer"][0]).exists() + use_icml_file = not use_fer_file and (base_folder / self._RESOURCES["icml"][0]).exists() + file_name, md5 = self._RESOURCES["fer" if use_fer_file else "icml" if use_icml_file else self._split] + data_file = base_folder / file_name + if not check_integrity(str(data_file), md5=md5): + raise RuntimeError( + f"{file_name} not found in {base_folder} or corrupted. " + f"You can download it from " + f"https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge" + ) + + pixels_key = " pixels" if use_icml_file else "pixels" + usage_key = " Usage" if use_icml_file else "Usage" + + def get_img(row): + return torch.tensor([int(idx) for idx in row[pixels_key].split()], dtype=torch.uint8).reshape(48, 48) + + def get_label(row): + if use_fer_file or use_icml_file or self._split == "train": + return int(row["emotion"]) + else: + return None + + with open(data_file, "r", newline="") as file: + rows = (row for row in csv.DictReader(file)) + + if use_fer_file or use_icml_file: + valid_keys = ("Training",) if self._split == "train" else ("PublicTest", "PrivateTest") + rows = (row for row in rows if row[usage_key] in valid_keys) + + self._samples = [(get_img(row), get_label(row)) for row in rows] + + def __len__(self) -> int: + return len(self._samples) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_tensor, target = self._samples[idx] + image = Image.fromarray(image_tensor.numpy()) + + if self.transform is not None: + image = self.transform(image) + + if self.target_transform is not None: + target = self.target_transform(target) + + return image, target + + def extra_repr(self) -> str: + return f"split={self._split}" diff --git a/lib/python3.11/site-packages/torchvision/datasets/fgvc_aircraft.py b/lib/python3.11/site-packages/torchvision/datasets/fgvc_aircraft.py new file mode 100644 index 0000000000000000000000000000000000000000..bbf4e970a787556e634f6c2eeb64ed4cd706fa2b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/fgvc_aircraft.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import os +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class FGVCAircraft(VisionDataset): + """`FGVC Aircraft `_ Dataset. + + The dataset contains 10,000 images of aircraft, with 100 images for each of 100 + different aircraft model variants, most of which are airplanes. + Aircraft models are organized in a three-levels hierarchy. The three levels, from + finer to coarser, are: + + - ``variant``, e.g. Boeing 737-700. A variant collapses all the models that are visually + indistinguishable into one class. The dataset comprises 100 different variants. + - ``family``, e.g. Boeing 737. The dataset comprises 70 different families. + - ``manufacturer``, e.g. Boeing. The dataset comprises 30 different manufacturers. + + Args: + root (str or ``pathlib.Path``): Root directory of the FGVC Aircraft dataset. + split (string, optional): The dataset split, supports ``train``, ``val``, + ``trainval`` and ``test``. + annotation_level (str, optional): The annotation level, supports ``variant``, + ``family`` and ``manufacturer``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + _URL = "https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz" + + def __init__( + self, + root: Union[str, Path], + split: str = "trainval", + annotation_level: str = "variant", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self._split = verify_str_arg(split, "split", ("train", "val", "trainval", "test")) + self._annotation_level = verify_str_arg( + annotation_level, "annotation_level", ("variant", "family", "manufacturer") + ) + + self._data_path = os.path.join(self.root, "fgvc-aircraft-2013b") + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + annotation_file = os.path.join( + self._data_path, + "data", + { + "variant": "variants.txt", + "family": "families.txt", + "manufacturer": "manufacturers.txt", + }[self._annotation_level], + ) + with open(annotation_file, "r") as f: + self.classes = [line.strip() for line in f] + + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + + image_data_folder = os.path.join(self._data_path, "data", "images") + labels_file = os.path.join(self._data_path, "data", f"images_{self._annotation_level}_{self._split}.txt") + + self._image_files = [] + self._labels = [] + + with open(labels_file, "r") as f: + for line in f: + image_name, label_name = line.strip().split(" ", 1) + self._image_files.append(os.path.join(image_data_folder, f"{image_name}.jpg")) + self._labels.append(self.class_to_idx[label_name]) + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._image_files[idx], self._labels[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def _download(self) -> None: + """ + Download the FGVC Aircraft dataset archive and extract it under root. + """ + if self._check_exists(): + return + download_and_extract_archive(self._URL, self.root) + + def _check_exists(self) -> bool: + return os.path.exists(self._data_path) and os.path.isdir(self._data_path) diff --git a/lib/python3.11/site-packages/torchvision/datasets/flickr.py b/lib/python3.11/site-packages/torchvision/datasets/flickr.py new file mode 100644 index 0000000000000000000000000000000000000000..1021309db0540bcd32c7bf35668b4c43ce586a4d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/flickr.py @@ -0,0 +1,167 @@ +import glob +import os +from collections import defaultdict +from html.parser import HTMLParser +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from PIL import Image + +from .vision import VisionDataset + + +class Flickr8kParser(HTMLParser): + """Parser for extracting captions from the Flickr8k dataset web page.""" + + def __init__(self, root: Union[str, Path]) -> None: + super().__init__() + + self.root = root + + # Data structure to store captions + self.annotations: Dict[str, List[str]] = {} + + # State variables + self.in_table = False + self.current_tag: Optional[str] = None + self.current_img: Optional[str] = None + + def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None: + self.current_tag = tag + + if tag == "table": + self.in_table = True + + def handle_endtag(self, tag: str) -> None: + self.current_tag = None + + if tag == "table": + self.in_table = False + + def handle_data(self, data: str) -> None: + if self.in_table: + if data == "Image Not Found": + self.current_img = None + elif self.current_tag == "a": + img_id = data.split("/")[-2] + img_id = os.path.join(self.root, img_id + "_*.jpg") + img_id = glob.glob(img_id)[0] + self.current_img = img_id + self.annotations[img_id] = [] + elif self.current_tag == "li" and self.current_img: + img_id = self.current_img + self.annotations[img_id].append(data.strip()) + + +class Flickr8k(VisionDataset): + """`Flickr8k Entities `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + ann_file (string): Path to annotation file. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__( + self, + root: Union[str, Path], + ann_file: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.ann_file = os.path.expanduser(ann_file) + + # Read annotations and store in a dict + parser = Flickr8kParser(self.root) + with open(self.ann_file) as fh: + parser.feed(fh.read()) + self.annotations = parser.annotations + + self.ids = list(sorted(self.annotations.keys())) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is a list of captions for the image. + """ + img_id = self.ids[index] + + # Image + img = Image.open(img_id).convert("RGB") + if self.transform is not None: + img = self.transform(img) + + # Captions + target = self.annotations[img_id] + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.ids) + + +class Flickr30k(VisionDataset): + """`Flickr30k Entities `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + ann_file (string): Path to annotation file. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__( + self, + root: str, + ann_file: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.ann_file = os.path.expanduser(ann_file) + + # Read annotations and store in a dict + self.annotations = defaultdict(list) + with open(self.ann_file) as fh: + for line in fh: + img_id, caption = line.strip().split("\t") + self.annotations[img_id[:-2]].append(caption) + + self.ids = list(sorted(self.annotations.keys())) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is a list of captions for the image. + """ + img_id = self.ids[index] + + # Image + filename = os.path.join(self.root, img_id) + img = Image.open(filename).convert("RGB") + if self.transform is not None: + img = self.transform(img) + + # Captions + target = self.annotations[img_id] + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.ids) diff --git a/lib/python3.11/site-packages/torchvision/datasets/flowers102.py b/lib/python3.11/site-packages/torchvision/datasets/flowers102.py new file mode 100644 index 0000000000000000000000000000000000000000..07f403702f5f0e06f3d890713f693571190546c6 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/flowers102.py @@ -0,0 +1,114 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg +from .vision import VisionDataset + + +class Flowers102(VisionDataset): + """`Oxford 102 Flower `_ Dataset. + + .. warning:: + + This class needs `scipy `_ to load target files from `.mat` format. + + Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The + flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of + between 40 and 258 images. + + The images have large scale, pose and light variations. In addition, there are categories that + have large variations within the category, and several very similar categories. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a + transformed version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + _download_url_prefix = "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/" + _file_dict = { # filename, md5 + "image": ("102flowers.tgz", "52808999861908f626f3c1f4e79d11fa"), + "label": ("imagelabels.mat", "e0620be6f572b9609742df49c70aed4d"), + "setid": ("setid.mat", "a5357ecc9cb78c4bef273ce3793fc85c"), + } + _splits_map = {"train": "trnid", "val": "valid", "test": "tstid"} + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self._split = verify_str_arg(split, "split", ("train", "val", "test")) + self._base_folder = Path(self.root) / "flowers-102" + self._images_folder = self._base_folder / "jpg" + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + from scipy.io import loadmat + + set_ids = loadmat(self._base_folder / self._file_dict["setid"][0], squeeze_me=True) + image_ids = set_ids[self._splits_map[self._split]].tolist() + + labels = loadmat(self._base_folder / self._file_dict["label"][0], squeeze_me=True) + image_id_to_label = dict(enumerate((labels["labels"] - 1).tolist(), 1)) + + self._labels = [] + self._image_files = [] + for image_id in image_ids: + self._labels.append(image_id_to_label[image_id]) + self._image_files.append(self._images_folder / f"image_{image_id:05d}.jpg") + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._image_files[idx], self._labels[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def extra_repr(self) -> str: + return f"split={self._split}" + + def _check_integrity(self): + if not (self._images_folder.exists() and self._images_folder.is_dir()): + return False + + for id in ["label", "setid"]: + filename, md5 = self._file_dict[id] + if not check_integrity(str(self._base_folder / filename), md5): + return False + return True + + def download(self): + if self._check_integrity(): + return + download_and_extract_archive( + f"{self._download_url_prefix}{self._file_dict['image'][0]}", + str(self._base_folder), + md5=self._file_dict["image"][1], + ) + for id in ["label", "setid"]: + filename, md5 = self._file_dict[id] + download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/folder.py b/lib/python3.11/site-packages/torchvision/datasets/folder.py new file mode 100644 index 0000000000000000000000000000000000000000..8f2f65c7b615aa12ec4cdb2bd472d521a6880ad7 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/folder.py @@ -0,0 +1,337 @@ +import os +import os.path +from pathlib import Path +from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union + +from PIL import Image + +from .vision import VisionDataset + + +def has_file_allowed_extension(filename: str, extensions: Union[str, Tuple[str, ...]]) -> bool: + """Checks if a file is an allowed extension. + + Args: + filename (string): path to a file + extensions (tuple of strings): extensions to consider (lowercase) + + Returns: + bool: True if the filename ends with one of given extensions + """ + return filename.lower().endswith(extensions if isinstance(extensions, str) else tuple(extensions)) + + +def is_image_file(filename: str) -> bool: + """Checks if a file is an allowed image extension. + + Args: + filename (string): path to a file + + Returns: + bool: True if the filename ends with a known image extension + """ + return has_file_allowed_extension(filename, IMG_EXTENSIONS) + + +def find_classes(directory: Union[str, Path]) -> Tuple[List[str], Dict[str, int]]: + """Finds the class folders in a dataset. + + See :class:`DatasetFolder` for details. + """ + classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) + if not classes: + raise FileNotFoundError(f"Couldn't find any class folder in {directory}.") + + class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)} + return classes, class_to_idx + + +def make_dataset( + directory: Union[str, Path], + class_to_idx: Optional[Dict[str, int]] = None, + extensions: Optional[Union[str, Tuple[str, ...]]] = None, + is_valid_file: Optional[Callable[[str], bool]] = None, + allow_empty: bool = False, +) -> List[Tuple[str, int]]: + """Generates a list of samples of a form (path_to_sample, class). + + See :class:`DatasetFolder` for details. + + Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` function + by default. + """ + directory = os.path.expanduser(directory) + + if class_to_idx is None: + _, class_to_idx = find_classes(directory) + elif not class_to_idx: + raise ValueError("'class_to_index' must have at least one entry to collect any samples.") + + both_none = extensions is None and is_valid_file is None + both_something = extensions is not None and is_valid_file is not None + if both_none or both_something: + raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time") + + if extensions is not None: + + def is_valid_file(x: str) -> bool: + return has_file_allowed_extension(x, extensions) # type: ignore[arg-type] + + is_valid_file = cast(Callable[[str], bool], is_valid_file) + + instances = [] + available_classes = set() + for target_class in sorted(class_to_idx.keys()): + class_index = class_to_idx[target_class] + target_dir = os.path.join(directory, target_class) + if not os.path.isdir(target_dir): + continue + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + if is_valid_file(path): + item = path, class_index + instances.append(item) + + if target_class not in available_classes: + available_classes.add(target_class) + + empty_classes = set(class_to_idx.keys()) - available_classes + if empty_classes and not allow_empty: + msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. " + if extensions is not None: + msg += f"Supported extensions are: {extensions if isinstance(extensions, str) else ', '.join(extensions)}" + raise FileNotFoundError(msg) + + return instances + + +class DatasetFolder(VisionDataset): + """A generic data loader. + + This default directory structure can be customized by overriding the + :meth:`find_classes` method. + + Args: + root (str or ``pathlib.Path``): Root directory path. + loader (callable): A function to load a sample given its path. + extensions (tuple[string]): A list of allowed extensions. + both extensions and is_valid_file should not be passed. + transform (callable, optional): A function/transform that takes in + a sample and returns a transformed version. + E.g, ``transforms.RandomCrop`` for images. + target_transform (callable, optional): A function/transform that takes + in the target and transforms it. + is_valid_file (callable, optional): A function that takes path of a file + and check if the file is a valid file (used to check of corrupt files) + both extensions and is_valid_file should not be passed. + allow_empty(bool, optional): If True, empty folders are considered to be valid classes. + An error is raised on empty folders if False (default). + + Attributes: + classes (list): List of the class names sorted alphabetically. + class_to_idx (dict): Dict with items (class_name, class_index). + samples (list): List of (sample path, class_index) tuples + targets (list): The class_index value for each image in the dataset + """ + + def __init__( + self, + root: Union[str, Path], + loader: Callable[[str], Any], + extensions: Optional[Tuple[str, ...]] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + is_valid_file: Optional[Callable[[str], bool]] = None, + allow_empty: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + classes, class_to_idx = self.find_classes(self.root) + samples = self.make_dataset( + self.root, + class_to_idx=class_to_idx, + extensions=extensions, + is_valid_file=is_valid_file, + allow_empty=allow_empty, + ) + + self.loader = loader + self.extensions = extensions + + self.classes = classes + self.class_to_idx = class_to_idx + self.samples = samples + self.targets = [s[1] for s in samples] + + @staticmethod + def make_dataset( + directory: Union[str, Path], + class_to_idx: Dict[str, int], + extensions: Optional[Tuple[str, ...]] = None, + is_valid_file: Optional[Callable[[str], bool]] = None, + allow_empty: bool = False, + ) -> List[Tuple[str, int]]: + """Generates a list of samples of a form (path_to_sample, class). + + This can be overridden to e.g. read files from a compressed zip file instead of from the disk. + + Args: + directory (str): root dataset directory, corresponding to ``self.root``. + class_to_idx (Dict[str, int]): Dictionary mapping class name to class index. + extensions (optional): A list of allowed extensions. + Either extensions or is_valid_file should be passed. Defaults to None. + is_valid_file (optional): A function that takes path of a file + and checks if the file is a valid file + (used to check of corrupt files) both extensions and + is_valid_file should not be passed. Defaults to None. + allow_empty(bool, optional): If True, empty folders are considered to be valid classes. + An error is raised on empty folders if False (default). + + Raises: + ValueError: In case ``class_to_idx`` is empty. + ValueError: In case ``extensions`` and ``is_valid_file`` are None or both are not None. + FileNotFoundError: In case no valid file was found for any class. + + Returns: + List[Tuple[str, int]]: samples of a form (path_to_sample, class) + """ + if class_to_idx is None: + # prevent potential bug since make_dataset() would use the class_to_idx logic of the + # find_classes() function, instead of using that of the find_classes() method, which + # is potentially overridden and thus could have a different logic. + raise ValueError("The class_to_idx parameter cannot be None.") + return make_dataset( + directory, class_to_idx, extensions=extensions, is_valid_file=is_valid_file, allow_empty=allow_empty + ) + + def find_classes(self, directory: Union[str, Path]) -> Tuple[List[str], Dict[str, int]]: + """Find the class folders in a dataset structured as follows:: + + directory/ + ├── class_x + │ ├── xxx.ext + │ ├── xxy.ext + │ └── ... + │ └── xxz.ext + └── class_y + ├── 123.ext + ├── nsdf3.ext + └── ... + └── asd932_.ext + + This method can be overridden to only consider + a subset of classes, or to adapt to a different dataset directory structure. + + Args: + directory(str): Root directory path, corresponding to ``self.root`` + + Raises: + FileNotFoundError: If ``dir`` has no class folders. + + Returns: + (Tuple[List[str], Dict[str, int]]): List of all classes and dictionary mapping each class to an index. + """ + return find_classes(directory) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (sample, target) where target is class_index of the target class. + """ + path, target = self.samples[index] + sample = self.loader(path) + if self.transform is not None: + sample = self.transform(sample) + if self.target_transform is not None: + target = self.target_transform(target) + + return sample, target + + def __len__(self) -> int: + return len(self.samples) + + +IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp") + + +def pil_loader(path: str) -> Image.Image: + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + +# TODO: specify the return type +def accimage_loader(path: str) -> Any: + import accimage + + try: + return accimage.Image(path) + except OSError: + # Potentially a decoding problem, fall back to PIL.Image + return pil_loader(path) + + +def default_loader(path: str) -> Any: + from torchvision import get_image_backend + + if get_image_backend() == "accimage": + return accimage_loader(path) + else: + return pil_loader(path) + + +class ImageFolder(DatasetFolder): + """A generic data loader where the images are arranged in this way by default: :: + + root/dog/xxx.png + root/dog/xxy.png + root/dog/[...]/xxz.png + + root/cat/123.png + root/cat/nsdf3.png + root/cat/[...]/asd932_.png + + This class inherits from :class:`~torchvision.datasets.DatasetFolder` so + the same methods can be overridden to customize the dataset. + + Args: + root (str or ``pathlib.Path``): Root directory path. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + loader (callable, optional): A function to load an image given its path. + is_valid_file (callable, optional): A function that takes path of an Image file + and check if the file is a valid file (used to check of corrupt files) + allow_empty(bool, optional): If True, empty folders are considered to be valid classes. + An error is raised on empty folders if False (default). + + Attributes: + classes (list): List of the class names sorted alphabetically. + class_to_idx (dict): Dict with items (class_name, class_index). + imgs (list): List of (image path, class_index) tuples + """ + + def __init__( + self, + root: Union[str, Path], + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + loader: Callable[[str], Any] = default_loader, + is_valid_file: Optional[Callable[[str], bool]] = None, + allow_empty: bool = False, + ): + super().__init__( + root, + loader, + IMG_EXTENSIONS if is_valid_file is None else None, + transform=transform, + target_transform=target_transform, + is_valid_file=is_valid_file, + allow_empty=allow_empty, + ) + self.imgs = self.samples diff --git a/lib/python3.11/site-packages/torchvision/datasets/food101.py b/lib/python3.11/site-packages/torchvision/datasets/food101.py new file mode 100644 index 0000000000000000000000000000000000000000..f734787c1bf638867ad8f05bc52f953e00954060 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/food101.py @@ -0,0 +1,93 @@ +import json +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class Food101(VisionDataset): + """`The Food-101 Data Set `_. + + The Food-101 is a challenging data set of 101 food categories with 101,000 images. + For each class, 250 manually reviewed test images are provided as well as 750 training images. + On purpose, the training images were not cleaned, and thus still contain some amount of noise. + This comes mostly in the form of intense colors and sometimes wrong labels. All images were + rescaled to have a maximum side length of 512 pixels. + + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. Default is False. + """ + + _URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz" + _MD5 = "85eeb15f3717b99a5da872d97d918f87" + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self._split = verify_str_arg(split, "split", ("train", "test")) + self._base_folder = Path(self.root) / "food-101" + self._meta_folder = self._base_folder / "meta" + self._images_folder = self._base_folder / "images" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + self._labels = [] + self._image_files = [] + with open(self._meta_folder / f"{split}.json") as f: + metadata = json.loads(f.read()) + + self.classes = sorted(metadata.keys()) + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + + for class_label, im_rel_paths in metadata.items(): + self._labels += [self.class_to_idx[class_label]] * len(im_rel_paths) + self._image_files += [ + self._images_folder.joinpath(*f"{im_rel_path}.jpg".split("/")) for im_rel_path in im_rel_paths + ] + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._image_files[idx], self._labels[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def extra_repr(self) -> str: + return f"split={self._split}" + + def _check_exists(self) -> bool: + return all(folder.exists() and folder.is_dir() for folder in (self._meta_folder, self._images_folder)) + + def _download(self) -> None: + if self._check_exists(): + return + download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/gtsrb.py b/lib/python3.11/site-packages/torchvision/datasets/gtsrb.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d012c70b22fd8209534a01a51fa9978c705d00 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/gtsrb.py @@ -0,0 +1,103 @@ +import csv +import pathlib +from typing import Any, Callable, Optional, Tuple, Union + +import PIL + +from .folder import make_dataset +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class GTSRB(VisionDataset): + """`German Traffic Sign Recognition Benchmark (GTSRB) `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + + super().__init__(root, transform=transform, target_transform=target_transform) + + self._split = verify_str_arg(split, "split", ("train", "test")) + self._base_folder = pathlib.Path(root) / "gtsrb" + self._target_folder = ( + self._base_folder / "GTSRB" / ("Training" if self._split == "train" else "Final_Test/Images") + ) + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + if self._split == "train": + samples = make_dataset(str(self._target_folder), extensions=(".ppm",)) + else: + with open(self._base_folder / "GT-final_test.csv") as csv_file: + samples = [ + (str(self._target_folder / row["Filename"]), int(row["ClassId"])) + for row in csv.DictReader(csv_file, delimiter=";", skipinitialspace=True) + ] + + self._samples = samples + self.transform = transform + self.target_transform = target_transform + + def __len__(self) -> int: + return len(self._samples) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + + path, target = self._samples[index] + sample = PIL.Image.open(path).convert("RGB") + + if self.transform is not None: + sample = self.transform(sample) + + if self.target_transform is not None: + target = self.target_transform(target) + + return sample, target + + def _check_exists(self) -> bool: + return self._target_folder.is_dir() + + def download(self) -> None: + if self._check_exists(): + return + + base_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/" + + if self._split == "train": + download_and_extract_archive( + f"{base_url}GTSRB-Training_fixed.zip", + download_root=str(self._base_folder), + md5="513f3c79a4c5141765e10e952eaa2478", + ) + else: + download_and_extract_archive( + f"{base_url}GTSRB_Final_Test_Images.zip", + download_root=str(self._base_folder), + md5="c7e4e6327067d32654124b0fe9e82185", + ) + download_and_extract_archive( + f"{base_url}GTSRB_Final_Test_GT.zip", + download_root=str(self._base_folder), + md5="fe31e9c9270bbcd7b84b7f21a9d9d9e5", + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/hmdb51.py b/lib/python3.11/site-packages/torchvision/datasets/hmdb51.py new file mode 100644 index 0000000000000000000000000000000000000000..8377e40d57ca7c2b29f98704b67bd03793a40836 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/hmdb51.py @@ -0,0 +1,152 @@ +import glob +import os +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from torch import Tensor + +from .folder import find_classes, make_dataset +from .video_utils import VideoClips +from .vision import VisionDataset + + +class HMDB51(VisionDataset): + """ + `HMDB51 `_ + dataset. + + HMDB51 is an action recognition video dataset. + This dataset consider every video as a collection of video clips of fixed size, specified + by ``frames_per_clip``, where the step in frames between each clip is given by + ``step_between_clips``. + + To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5`` + and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two + elements will come from video 1, and the next three elements from video 2. + Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all + frames in a video might be present. + + Internally, it uses a VideoClips object to handle clip creation. + + Args: + root (str or ``pathlib.Path``): Root directory of the HMDB51 Dataset. + annotation_path (str): Path to the folder containing the split files. + frames_per_clip (int): Number of frames in a clip. + step_between_clips (int): Number of frames between each clip. + fold (int, optional): Which fold to use. Should be between 1 and 3. + train (bool, optional): If ``True``, creates a dataset from the train split, + otherwise from the ``test`` split. + transform (callable, optional): A function/transform that takes in a TxHxWxC video + and returns a transformed version. + output_format (str, optional): The format of the output video tensors (before transforms). + Can be either "THWC" (default) or "TCHW". + + Returns: + tuple: A 3-tuple with the following entries: + + - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames + - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels + and `L` is the number of points + - label (int): class of the video clip + """ + + data_url = "https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar" + splits = { + "url": "https://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar", + "md5": "15e67781e70dcfbdce2d7dbb9b3344b5", + } + TRAIN_TAG = 1 + TEST_TAG = 2 + + def __init__( + self, + root: Union[str, Path], + annotation_path: str, + frames_per_clip: int, + step_between_clips: int = 1, + frame_rate: Optional[int] = None, + fold: int = 1, + train: bool = True, + transform: Optional[Callable] = None, + _precomputed_metadata: Optional[Dict[str, Any]] = None, + num_workers: int = 1, + _video_width: int = 0, + _video_height: int = 0, + _video_min_dimension: int = 0, + _audio_samples: int = 0, + output_format: str = "THWC", + ) -> None: + super().__init__(root) + if fold not in (1, 2, 3): + raise ValueError(f"fold should be between 1 and 3, got {fold}") + + extensions = ("avi",) + self.classes, class_to_idx = find_classes(self.root) + self.samples = make_dataset( + self.root, + class_to_idx, + extensions, + ) + + video_paths = [path for (path, _) in self.samples] + video_clips = VideoClips( + video_paths, + frames_per_clip, + step_between_clips, + frame_rate, + _precomputed_metadata, + num_workers=num_workers, + _video_width=_video_width, + _video_height=_video_height, + _video_min_dimension=_video_min_dimension, + _audio_samples=_audio_samples, + output_format=output_format, + ) + # we bookkeep the full version of video clips because we want to be able + # to return the metadata of full version rather than the subset version of + # video clips + self.full_video_clips = video_clips + self.fold = fold + self.train = train + self.indices = self._select_fold(video_paths, annotation_path, fold, train) + self.video_clips = video_clips.subset(self.indices) + self.transform = transform + + @property + def metadata(self) -> Dict[str, Any]: + return self.full_video_clips.metadata + + def _select_fold(self, video_list: List[str], annotations_dir: str, fold: int, train: bool) -> List[int]: + target_tag = self.TRAIN_TAG if train else self.TEST_TAG + split_pattern_name = f"*test_split{fold}.txt" + split_pattern_path = os.path.join(annotations_dir, split_pattern_name) + annotation_paths = glob.glob(split_pattern_path) + selected_files = set() + for filepath in annotation_paths: + with open(filepath) as fid: + lines = fid.readlines() + for line in lines: + video_filename, tag_string = line.split() + tag = int(tag_string) + if tag == target_tag: + selected_files.add(video_filename) + + indices = [] + for video_index, video_path in enumerate(video_list): + if os.path.basename(video_path) in selected_files: + indices.append(video_index) + + return indices + + def __len__(self) -> int: + return self.video_clips.num_clips() + + def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: + video, audio, _, video_idx = self.video_clips.get_clip(idx) + sample_index = self.indices[video_idx] + _, class_index = self.samples[sample_index] + + if self.transform is not None: + video = self.transform(video) + + return video, audio, class_index diff --git a/lib/python3.11/site-packages/torchvision/datasets/imagenet.py b/lib/python3.11/site-packages/torchvision/datasets/imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..d7caf328d2bb1adc149548daf83ee49c2a8459c3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/imagenet.py @@ -0,0 +1,219 @@ +import os +import shutil +import tempfile +from contextlib import contextmanager +from pathlib import Path +from typing import Any, Dict, Iterator, List, Optional, Tuple, Union + +import torch + +from .folder import ImageFolder +from .utils import check_integrity, extract_archive, verify_str_arg + +ARCHIVE_META = { + "train": ("ILSVRC2012_img_train.tar", "1d675b47d978889d74fa0da5fadfb00e"), + "val": ("ILSVRC2012_img_val.tar", "29b22e2961454d5413ddabcf34fc5622"), + "devkit": ("ILSVRC2012_devkit_t12.tar.gz", "fa75699e90414af021442c21a62c3abf"), +} + +META_FILE = "meta.bin" + + +class ImageNet(ImageFolder): + """`ImageNet `_ 2012 Classification Dataset. + + .. note:: + Before using this class, it is required to download ImageNet 2012 dataset from + `here `_ and + place the files ``ILSVRC2012_devkit_t12.tar.gz`` and ``ILSVRC2012_img_train.tar`` + or ``ILSVRC2012_img_val.tar`` based on ``split`` in the root directory. + + Args: + root (str or ``pathlib.Path``): Root directory of the ImageNet Dataset. + split (string, optional): The dataset split, supports ``train``, or ``val``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + loader (callable, optional): A function to load an image given its path. + + Attributes: + classes (list): List of the class name tuples. + class_to_idx (dict): Dict with items (class_name, class_index). + wnids (list): List of the WordNet IDs. + wnid_to_idx (dict): Dict with items (wordnet_id, class_index). + imgs (list): List of (image path, class_index) tuples + targets (list): The class_index value for each image in the dataset + """ + + def __init__(self, root: Union[str, Path], split: str = "train", **kwargs: Any) -> None: + root = self.root = os.path.expanduser(root) + self.split = verify_str_arg(split, "split", ("train", "val")) + + self.parse_archives() + wnid_to_classes = load_meta_file(self.root)[0] + + super().__init__(self.split_folder, **kwargs) + self.root = root + + self.wnids = self.classes + self.wnid_to_idx = self.class_to_idx + self.classes = [wnid_to_classes[wnid] for wnid in self.wnids] + self.class_to_idx = {cls: idx for idx, clss in enumerate(self.classes) for cls in clss} + + def parse_archives(self) -> None: + if not check_integrity(os.path.join(self.root, META_FILE)): + parse_devkit_archive(self.root) + + if not os.path.isdir(self.split_folder): + if self.split == "train": + parse_train_archive(self.root) + elif self.split == "val": + parse_val_archive(self.root) + + @property + def split_folder(self) -> str: + return os.path.join(self.root, self.split) + + def extra_repr(self) -> str: + return "Split: {split}".format(**self.__dict__) + + +def load_meta_file(root: Union[str, Path], file: Optional[str] = None) -> Tuple[Dict[str, str], List[str]]: + if file is None: + file = META_FILE + file = os.path.join(root, file) + + if check_integrity(file): + return torch.load(file, weights_only=True) + else: + msg = ( + "The meta file {} is not present in the root directory or is corrupted. " + "This file is automatically created by the ImageNet dataset." + ) + raise RuntimeError(msg.format(file, root)) + + +def _verify_archive(root: Union[str, Path], file: str, md5: str) -> None: + if not check_integrity(os.path.join(root, file), md5): + msg = ( + "The archive {} is not present in the root directory or is corrupted. " + "You need to download it externally and place it in {}." + ) + raise RuntimeError(msg.format(file, root)) + + +def parse_devkit_archive(root: Union[str, Path], file: Optional[str] = None) -> None: + """Parse the devkit archive of the ImageNet2012 classification dataset and save + the meta information in a binary file. + + Args: + root (str or ``pathlib.Path``): Root directory containing the devkit archive + file (str, optional): Name of devkit archive. Defaults to + 'ILSVRC2012_devkit_t12.tar.gz' + """ + import scipy.io as sio + + def parse_meta_mat(devkit_root: str) -> Tuple[Dict[int, str], Dict[str, Tuple[str, ...]]]: + metafile = os.path.join(devkit_root, "data", "meta.mat") + meta = sio.loadmat(metafile, squeeze_me=True)["synsets"] + nums_children = list(zip(*meta))[4] + meta = [meta[idx] for idx, num_children in enumerate(nums_children) if num_children == 0] + idcs, wnids, classes = list(zip(*meta))[:3] + classes = [tuple(clss.split(", ")) for clss in classes] + idx_to_wnid = {idx: wnid for idx, wnid in zip(idcs, wnids)} + wnid_to_classes = {wnid: clss for wnid, clss in zip(wnids, classes)} + return idx_to_wnid, wnid_to_classes + + def parse_val_groundtruth_txt(devkit_root: str) -> List[int]: + file = os.path.join(devkit_root, "data", "ILSVRC2012_validation_ground_truth.txt") + with open(file) as txtfh: + val_idcs = txtfh.readlines() + return [int(val_idx) for val_idx in val_idcs] + + @contextmanager + def get_tmp_dir() -> Iterator[str]: + tmp_dir = tempfile.mkdtemp() + try: + yield tmp_dir + finally: + shutil.rmtree(tmp_dir) + + archive_meta = ARCHIVE_META["devkit"] + if file is None: + file = archive_meta[0] + md5 = archive_meta[1] + + _verify_archive(root, file, md5) + + with get_tmp_dir() as tmp_dir: + extract_archive(os.path.join(root, file), tmp_dir) + + devkit_root = os.path.join(tmp_dir, "ILSVRC2012_devkit_t12") + idx_to_wnid, wnid_to_classes = parse_meta_mat(devkit_root) + val_idcs = parse_val_groundtruth_txt(devkit_root) + val_wnids = [idx_to_wnid[idx] for idx in val_idcs] + + torch.save((wnid_to_classes, val_wnids), os.path.join(root, META_FILE)) + + +def parse_train_archive(root: Union[str, Path], file: Optional[str] = None, folder: str = "train") -> None: + """Parse the train images archive of the ImageNet2012 classification dataset and + prepare it for usage with the ImageNet dataset. + + Args: + root (str or ``pathlib.Path``): Root directory containing the train images archive + file (str, optional): Name of train images archive. Defaults to + 'ILSVRC2012_img_train.tar' + folder (str, optional): Optional name for train images folder. Defaults to + 'train' + """ + archive_meta = ARCHIVE_META["train"] + if file is None: + file = archive_meta[0] + md5 = archive_meta[1] + + _verify_archive(root, file, md5) + + train_root = os.path.join(root, folder) + extract_archive(os.path.join(root, file), train_root) + + archives = [os.path.join(train_root, archive) for archive in os.listdir(train_root)] + for archive in archives: + extract_archive(archive, os.path.splitext(archive)[0], remove_finished=True) + + +def parse_val_archive( + root: Union[str, Path], file: Optional[str] = None, wnids: Optional[List[str]] = None, folder: str = "val" +) -> None: + """Parse the validation images archive of the ImageNet2012 classification dataset + and prepare it for usage with the ImageNet dataset. + + Args: + root (str or ``pathlib.Path``): Root directory containing the validation images archive + file (str, optional): Name of validation images archive. Defaults to + 'ILSVRC2012_img_val.tar' + wnids (list, optional): List of WordNet IDs of the validation images. If None + is given, the IDs are loaded from the meta file in the root directory + folder (str, optional): Optional name for validation images folder. Defaults to + 'val' + """ + archive_meta = ARCHIVE_META["val"] + if file is None: + file = archive_meta[0] + md5 = archive_meta[1] + if wnids is None: + wnids = load_meta_file(root)[1] + + _verify_archive(root, file, md5) + + val_root = os.path.join(root, folder) + extract_archive(os.path.join(root, file), val_root) + + images = sorted(os.path.join(val_root, image) for image in os.listdir(val_root)) + + for wnid in set(wnids): + os.mkdir(os.path.join(val_root, wnid)) + + for wnid, img_file in zip(wnids, images): + shutil.move(img_file, os.path.join(val_root, wnid, os.path.basename(img_file))) diff --git a/lib/python3.11/site-packages/torchvision/datasets/imagenette.py b/lib/python3.11/site-packages/torchvision/datasets/imagenette.py new file mode 100644 index 0000000000000000000000000000000000000000..0b27f3b25e51b1f87ccb17cebf7f20a539c0a5bf --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/imagenette.py @@ -0,0 +1,101 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +from PIL import Image + +from .folder import find_classes, make_dataset +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class Imagenette(VisionDataset): + """`Imagenette `_ image classification dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of the Imagenette dataset. + split (string, optional): The dataset split. Supports ``"train"`` (default), and ``"val"``. + size (string, optional): The image size. Supports ``"full"`` (default), ``"320px"``, and ``"160px"``. + download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already + downloaded archives are not downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version, e.g. ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + + Attributes: + classes (list): List of the class name tuples. + class_to_idx (dict): Dict with items (class name, class index). + wnids (list): List of the WordNet IDs. + wnid_to_idx (dict): Dict with items (WordNet ID, class index). + """ + + _ARCHIVES = { + "full": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz", "fe2fc210e6bb7c5664d602c3cd71e612"), + "320px": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz", "3df6f0d01a2c9592104656642f5e78a3"), + "160px": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz", "e793b78cc4c9e9a4ccc0c1155377a412"), + } + _WNID_TO_CLASS = { + "n01440764": ("tench", "Tinca tinca"), + "n02102040": ("English springer", "English springer spaniel"), + "n02979186": ("cassette player",), + "n03000684": ("chain saw", "chainsaw"), + "n03028079": ("church", "church building"), + "n03394916": ("French horn", "horn"), + "n03417042": ("garbage truck", "dustcart"), + "n03425413": ("gas pump", "gasoline pump", "petrol pump", "island dispenser"), + "n03445777": ("golf ball",), + "n03888257": ("parachute", "chute"), + } + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + size: str = "full", + download=False, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + + self._split = verify_str_arg(split, "split", ["train", "val"]) + self._size = verify_str_arg(size, "size", ["full", "320px", "160px"]) + + self._url, self._md5 = self._ARCHIVES[self._size] + self._size_root = Path(self.root) / Path(self._url).stem + self._image_root = str(self._size_root / self._split) + + if download: + self._download() + elif not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it.") + + self.wnids, self.wnid_to_idx = find_classes(self._image_root) + self.classes = [self._WNID_TO_CLASS[wnid] for wnid in self.wnids] + self.class_to_idx = { + class_name: idx for wnid, idx in self.wnid_to_idx.items() for class_name in self._WNID_TO_CLASS[wnid] + } + self._samples = make_dataset(self._image_root, self.wnid_to_idx, extensions=".jpeg") + + def _check_exists(self) -> bool: + return self._size_root.exists() + + def _download(self): + if self._check_exists(): + return + + download_and_extract_archive(self._url, self.root, md5=self._md5) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + path, label = self._samples[idx] + image = Image.open(path).convert("RGB") + + if self.transform is not None: + image = self.transform(image) + + if self.target_transform is not None: + label = self.target_transform(label) + + return image, label + + def __len__(self) -> int: + return len(self._samples) diff --git a/lib/python3.11/site-packages/torchvision/datasets/inaturalist.py b/lib/python3.11/site-packages/torchvision/datasets/inaturalist.py new file mode 100644 index 0000000000000000000000000000000000000000..e041d41f4a2322580607123cd33e5c3e23ac46df --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/inaturalist.py @@ -0,0 +1,238 @@ +import os +import os.path +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + +CATEGORIES_2021 = ["kingdom", "phylum", "class", "order", "family", "genus"] + +DATASET_URLS = { + "2017": "https://ml-inat-competition-datasets.s3.amazonaws.com/2017/train_val_images.tar.gz", + "2018": "https://ml-inat-competition-datasets.s3.amazonaws.com/2018/train_val2018.tar.gz", + "2019": "https://ml-inat-competition-datasets.s3.amazonaws.com/2019/train_val2019.tar.gz", + "2021_train": "https://ml-inat-competition-datasets.s3.amazonaws.com/2021/train.tar.gz", + "2021_train_mini": "https://ml-inat-competition-datasets.s3.amazonaws.com/2021/train_mini.tar.gz", + "2021_valid": "https://ml-inat-competition-datasets.s3.amazonaws.com/2021/val.tar.gz", +} + +DATASET_MD5 = { + "2017": "7c784ea5e424efaec655bd392f87301f", + "2018": "b1c6952ce38f31868cc50ea72d066cc3", + "2019": "c60a6e2962c9b8ccbd458d12c8582644", + "2021_train": "e0526d53c7f7b2e3167b2b43bb2690ed", + "2021_train_mini": "db6ed8330e634445efc8fec83ae81442", + "2021_valid": "f6f6e0e242e3d4c9569ba56400938afc", +} + + +class INaturalist(VisionDataset): + """`iNaturalist `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where the image files are stored. + This class does not require/use annotation files. + version (string, optional): Which version of the dataset to download/use. One of + '2017', '2018', '2019', '2021_train', '2021_train_mini', '2021_valid'. + Default: `2021_train`. + target_type (string or list, optional): Type of target to use, for 2021 versions, one of: + + - ``full``: the full category (species) + - ``kingdom``: e.g. "Animalia" + - ``phylum``: e.g. "Arthropoda" + - ``class``: e.g. "Insecta" + - ``order``: e.g. "Coleoptera" + - ``family``: e.g. "Cleridae" + - ``genus``: e.g. "Trichodes" + + for 2017-2019 versions, one of: + + - ``full``: the full (numeric) category + - ``super``: the super category, e.g. "Amphibians" + + Can also be a list to output a tuple with all specified target types. + Defaults to ``full``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + def __init__( + self, + root: Union[str, Path], + version: str = "2021_train", + target_type: Union[List[str], str] = "full", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + self.version = verify_str_arg(version, "version", DATASET_URLS.keys()) + + super().__init__(os.path.join(root, version), transform=transform, target_transform=target_transform) + + os.makedirs(root, exist_ok=True) + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self.all_categories: List[str] = [] + + # map: category type -> name of category -> index + self.categories_index: Dict[str, Dict[str, int]] = {} + + # list indexed by category id, containing mapping from category type -> index + self.categories_map: List[Dict[str, int]] = [] + + if not isinstance(target_type, list): + target_type = [target_type] + if self.version[:4] == "2021": + self.target_type = [verify_str_arg(t, "target_type", ("full", *CATEGORIES_2021)) for t in target_type] + self._init_2021() + else: + self.target_type = [verify_str_arg(t, "target_type", ("full", "super")) for t in target_type] + self._init_pre2021() + + # index of all files: (full category id, filename) + self.index: List[Tuple[int, str]] = [] + + for dir_index, dir_name in enumerate(self.all_categories): + files = os.listdir(os.path.join(self.root, dir_name)) + for fname in files: + self.index.append((dir_index, fname)) + + def _init_2021(self) -> None: + """Initialize based on 2021 layout""" + + self.all_categories = sorted(os.listdir(self.root)) + + # map: category type -> name of category -> index + self.categories_index = {k: {} for k in CATEGORIES_2021} + + for dir_index, dir_name in enumerate(self.all_categories): + pieces = dir_name.split("_") + if len(pieces) != 8: + raise RuntimeError(f"Unexpected category name {dir_name}, wrong number of pieces") + if pieces[0] != f"{dir_index:05d}": + raise RuntimeError(f"Unexpected category id {pieces[0]}, expecting {dir_index:05d}") + cat_map = {} + for cat, name in zip(CATEGORIES_2021, pieces[1:7]): + if name in self.categories_index[cat]: + cat_id = self.categories_index[cat][name] + else: + cat_id = len(self.categories_index[cat]) + self.categories_index[cat][name] = cat_id + cat_map[cat] = cat_id + self.categories_map.append(cat_map) + + def _init_pre2021(self) -> None: + """Initialize based on 2017-2019 layout""" + + # map: category type -> name of category -> index + self.categories_index = {"super": {}} + + cat_index = 0 + super_categories = sorted(os.listdir(self.root)) + for sindex, scat in enumerate(super_categories): + self.categories_index["super"][scat] = sindex + subcategories = sorted(os.listdir(os.path.join(self.root, scat))) + for subcat in subcategories: + if self.version == "2017": + # this version does not use ids as directory names + subcat_i = cat_index + cat_index += 1 + else: + try: + subcat_i = int(subcat) + except ValueError: + raise RuntimeError(f"Unexpected non-numeric dir name: {subcat}") + if subcat_i >= len(self.categories_map): + old_len = len(self.categories_map) + self.categories_map.extend([{}] * (subcat_i - old_len + 1)) + self.all_categories.extend([""] * (subcat_i - old_len + 1)) + if self.categories_map[subcat_i]: + raise RuntimeError(f"Duplicate category {subcat}") + self.categories_map[subcat_i] = {"super": sindex} + self.all_categories[subcat_i] = os.path.join(scat, subcat) + + # validate the dictionary + for cindex, c in enumerate(self.categories_map): + if not c: + raise RuntimeError(f"Missing category {cindex}") + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where the type of target specified by target_type. + """ + + cat_id, fname = self.index[index] + img = Image.open(os.path.join(self.root, self.all_categories[cat_id], fname)) + + target: Any = [] + for t in self.target_type: + if t == "full": + target.append(cat_id) + else: + target.append(self.categories_map[cat_id][t]) + target = tuple(target) if len(target) > 1 else target[0] + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.index) + + def category_name(self, category_type: str, category_id: int) -> str: + """ + Args: + category_type(str): one of "full", "kingdom", "phylum", "class", "order", "family", "genus" or "super" + category_id(int): an index (class id) from this category + + Returns: + the name of the category + """ + if category_type == "full": + return self.all_categories[category_id] + else: + if category_type not in self.categories_index: + raise ValueError(f"Invalid category type '{category_type}'") + else: + for name, id in self.categories_index[category_type].items(): + if id == category_id: + return name + raise ValueError(f"Invalid category id {category_id} for {category_type}") + + def _check_exists(self) -> bool: + return os.path.exists(self.root) and len(os.listdir(self.root)) > 0 + + def download(self) -> None: + if self._check_exists(): + return + + base_root = os.path.dirname(self.root) + + download_and_extract_archive( + DATASET_URLS[self.version], base_root, filename=f"{self.version}.tgz", md5=DATASET_MD5[self.version] + ) + + orig_dir_name = os.path.join(base_root, os.path.basename(DATASET_URLS[self.version]).rstrip(".tar.gz")) + if not os.path.exists(orig_dir_name): + raise RuntimeError(f"Unable to find downloaded files at {orig_dir_name}") + os.rename(orig_dir_name, self.root) diff --git a/lib/python3.11/site-packages/torchvision/datasets/kinetics.py b/lib/python3.11/site-packages/torchvision/datasets/kinetics.py new file mode 100644 index 0000000000000000000000000000000000000000..773d9f68ca9f849dcf30ac8f368212107f6a3cae --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/kinetics.py @@ -0,0 +1,237 @@ +import csv +import os +import urllib +from functools import partial +from multiprocessing import Pool +from os import path +from pathlib import Path +from typing import Any, Callable, Dict, Optional, Tuple, Union + +from torch import Tensor + +from .folder import find_classes, make_dataset +from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg +from .video_utils import VideoClips +from .vision import VisionDataset + + +def _dl_wrap(tarpath: Union[str, Path], videopath: Union[str, Path], line: str) -> None: + download_and_extract_archive(line, tarpath, videopath) + + +class Kinetics(VisionDataset): + """`Generic Kinetics `_ + dataset. + + Kinetics-400/600/700 are action recognition video datasets. + This dataset consider every video as a collection of video clips of fixed size, specified + by ``frames_per_clip``, where the step in frames between each clip is given by + ``step_between_clips``. + + To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5`` + and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two + elements will come from video 1, and the next three elements from video 2. + Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all + frames in a video might be present. + + Args: + root (str or ``pathlib.Path``): Root directory of the Kinetics Dataset. + Directory should be structured as follows: + .. code:: + + root/ + ├── split + │ ├── class1 + │ │ ├── vid1.mp4 + │ │ ├── vid2.mp4 + │ │ ├── vid3.mp4 + │ │ ├── ... + │ ├── class2 + │ │ ├── vidx.mp4 + │ │ └── ... + + Note: split is appended automatically using the split argument. + frames_per_clip (int): number of frames in a clip + num_classes (int): select between Kinetics-400 (default), Kinetics-600, and Kinetics-700 + split (str): split of the dataset to consider; supports ``"train"`` (default) ``"val"`` ``"test"`` + frame_rate (float): If omitted, interpolate different frame rate for each clip. + step_between_clips (int): number of frames between each clip + transform (callable, optional): A function/transform that takes in a TxHxWxC video + and returns a transformed version. + download (bool): Download the official version of the dataset to root folder. + num_workers (int): Use multiple workers for VideoClips creation + num_download_workers (int): Use multiprocessing in order to speed up download. + output_format (str, optional): The format of the output video tensors (before transforms). + Can be either "THWC" or "TCHW" (default). + Note that in most other utils and datasets, the default is actually "THWC". + + Returns: + tuple: A 3-tuple with the following entries: + + - video (Tensor[T, C, H, W] or Tensor[T, H, W, C]): the `T` video frames in torch.uint8 tensor + - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels + and `L` is the number of points in torch.float tensor + - label (int): class of the video clip + + Raises: + RuntimeError: If ``download is True`` and the video archives are already extracted. + """ + + _TAR_URLS = { + "400": "https://s3.amazonaws.com/kinetics/400/{split}/k400_{split}_path.txt", + "600": "https://s3.amazonaws.com/kinetics/600/{split}/k600_{split}_path.txt", + "700": "https://s3.amazonaws.com/kinetics/700_2020/{split}/k700_2020_{split}_path.txt", + } + _ANNOTATION_URLS = { + "400": "https://s3.amazonaws.com/kinetics/400/annotations/{split}.csv", + "600": "https://s3.amazonaws.com/kinetics/600/annotations/{split}.csv", + "700": "https://s3.amazonaws.com/kinetics/700_2020/annotations/{split}.csv", + } + + def __init__( + self, + root: Union[str, Path], + frames_per_clip: int, + num_classes: str = "400", + split: str = "train", + frame_rate: Optional[int] = None, + step_between_clips: int = 1, + transform: Optional[Callable] = None, + extensions: Tuple[str, ...] = ("avi", "mp4"), + download: bool = False, + num_download_workers: int = 1, + num_workers: int = 1, + _precomputed_metadata: Optional[Dict[str, Any]] = None, + _video_width: int = 0, + _video_height: int = 0, + _video_min_dimension: int = 0, + _audio_samples: int = 0, + _audio_channels: int = 0, + _legacy: bool = False, + output_format: str = "TCHW", + ) -> None: + + # TODO: support test + self.num_classes = verify_str_arg(num_classes, arg="num_classes", valid_values=["400", "600", "700"]) + self.extensions = extensions + self.num_download_workers = num_download_workers + + self.root = root + self._legacy = _legacy + + if _legacy: + self.split_folder = root + self.split = "unknown" + output_format = "THWC" + if download: + raise ValueError("Cannot download the videos using legacy_structure.") + else: + self.split_folder = path.join(root, split) + self.split = verify_str_arg(split, arg="split", valid_values=["train", "val", "test"]) + + if download: + self.download_and_process_videos() + + super().__init__(self.root) + + self.classes, class_to_idx = find_classes(self.split_folder) + self.samples = make_dataset(self.split_folder, class_to_idx, extensions, is_valid_file=None) + video_list = [x[0] for x in self.samples] + self.video_clips = VideoClips( + video_list, + frames_per_clip, + step_between_clips, + frame_rate, + _precomputed_metadata, + num_workers=num_workers, + _video_width=_video_width, + _video_height=_video_height, + _video_min_dimension=_video_min_dimension, + _audio_samples=_audio_samples, + _audio_channels=_audio_channels, + output_format=output_format, + ) + self.transform = transform + + def download_and_process_videos(self) -> None: + """Downloads all the videos to the _root_ folder in the expected format.""" + self._download_videos() + self._make_ds_structure() + + def _download_videos(self) -> None: + """download tarballs containing the video to "tars" folder and extract them into the _split_ folder where + split is one of the official dataset splits. + + Raises: + RuntimeError: if download folder exists, break to prevent downloading entire dataset again. + """ + if path.exists(self.split_folder): + return + tar_path = path.join(self.root, "tars") + file_list_path = path.join(self.root, "files") + + split_url = self._TAR_URLS[self.num_classes].format(split=self.split) + split_url_filepath = path.join(file_list_path, path.basename(split_url)) + if not check_integrity(split_url_filepath): + download_url(split_url, file_list_path) + with open(split_url_filepath) as file: + list_video_urls = [urllib.parse.quote(line, safe="/,:") for line in file.read().splitlines()] + + if self.num_download_workers == 1: + for line in list_video_urls: + download_and_extract_archive(line, tar_path, self.split_folder) + else: + part = partial(_dl_wrap, tar_path, self.split_folder) + poolproc = Pool(self.num_download_workers) + poolproc.map(part, list_video_urls) + + def _make_ds_structure(self) -> None: + """move videos from + split_folder/ + ├── clip1.avi + ├── clip2.avi + + to the correct format as described below: + split_folder/ + ├── class1 + │ ├── clip1.avi + + """ + annotation_path = path.join(self.root, "annotations") + if not check_integrity(path.join(annotation_path, f"{self.split}.csv")): + download_url(self._ANNOTATION_URLS[self.num_classes].format(split=self.split), annotation_path) + annotations = path.join(annotation_path, f"{self.split}.csv") + + file_fmtstr = "{ytid}_{start:06}_{end:06}.mp4" + with open(annotations) as csvfile: + reader = csv.DictReader(csvfile) + for row in reader: + f = file_fmtstr.format( + ytid=row["youtube_id"], + start=int(row["time_start"]), + end=int(row["time_end"]), + ) + label = row["label"].replace(" ", "_").replace("'", "").replace("(", "").replace(")", "") + os.makedirs(path.join(self.split_folder, label), exist_ok=True) + downloaded_file = path.join(self.split_folder, f) + if path.isfile(downloaded_file): + os.replace( + downloaded_file, + path.join(self.split_folder, label, f), + ) + + @property + def metadata(self) -> Dict[str, Any]: + return self.video_clips.metadata + + def __len__(self) -> int: + return self.video_clips.num_clips() + + def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: + video, audio, info, video_idx = self.video_clips.get_clip(idx) + label = self.samples[video_idx][1] + + if self.transform is not None: + video = self.transform(video) + + return video, audio, label diff --git a/lib/python3.11/site-packages/torchvision/datasets/kitti.py b/lib/python3.11/site-packages/torchvision/datasets/kitti.py new file mode 100644 index 0000000000000000000000000000000000000000..69e603c76f22bb6387a4ab6e42bb4d92f49a0bee --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/kitti.py @@ -0,0 +1,158 @@ +import csv +import os +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import download_and_extract_archive +from .vision import VisionDataset + + +class Kitti(VisionDataset): + """`KITTI `_ Dataset. + + It corresponds to the "left color images of object" dataset, for object detection. + + Args: + root (str or ``pathlib.Path``): Root directory where images are downloaded to. + Expects the following folder structure if download=False: + + .. code:: + + + └── Kitti + └─ raw + ├── training + | ├── image_2 + | └── label_2 + └── testing + └── image_2 + train (bool, optional): Use ``train`` split if true, else ``test`` split. + Defaults to ``train``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.PILToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample + and its target as entry and returns a transformed version. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + data_url = "https://s3.eu-central-1.amazonaws.com/avg-kitti/" + resources = [ + "data_object_image_2.zip", + "data_object_label_2.zip", + ] + image_dir_name = "image_2" + labels_dir_name = "label_2" + + def __init__( + self, + root: Union[str, Path], + train: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + transforms: Optional[Callable] = None, + download: bool = False, + ): + super().__init__( + root, + transform=transform, + target_transform=target_transform, + transforms=transforms, + ) + self.images = [] + self.targets = [] + self.train = train + self._location = "training" if self.train else "testing" + + if download: + self.download() + if not self._check_exists(): + raise RuntimeError("Dataset not found. You may use download=True to download it.") + + image_dir = os.path.join(self._raw_folder, self._location, self.image_dir_name) + if self.train: + labels_dir = os.path.join(self._raw_folder, self._location, self.labels_dir_name) + for img_file in os.listdir(image_dir): + self.images.append(os.path.join(image_dir, img_file)) + if self.train: + self.targets.append(os.path.join(labels_dir, f"{img_file.split('.')[0]}.txt")) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """Get item at a given index. + + Args: + index (int): Index + Returns: + tuple: (image, target), where + target is a list of dictionaries with the following keys: + + - type: str + - truncated: float + - occluded: int + - alpha: float + - bbox: float[4] + - dimensions: float[3] + - locations: float[3] + - rotation_y: float + + """ + image = Image.open(self.images[index]) + target = self._parse_target(index) if self.train else None + if self.transforms: + image, target = self.transforms(image, target) + return image, target + + def _parse_target(self, index: int) -> List: + target = [] + with open(self.targets[index]) as inp: + content = csv.reader(inp, delimiter=" ") + for line in content: + target.append( + { + "type": line[0], + "truncated": float(line[1]), + "occluded": int(line[2]), + "alpha": float(line[3]), + "bbox": [float(x) for x in line[4:8]], + "dimensions": [float(x) for x in line[8:11]], + "location": [float(x) for x in line[11:14]], + "rotation_y": float(line[14]), + } + ) + return target + + def __len__(self) -> int: + return len(self.images) + + @property + def _raw_folder(self) -> str: + return os.path.join(self.root, self.__class__.__name__, "raw") + + def _check_exists(self) -> bool: + """Check if the data directory exists.""" + folders = [self.image_dir_name] + if self.train: + folders.append(self.labels_dir_name) + return all(os.path.isdir(os.path.join(self._raw_folder, self._location, fname)) for fname in folders) + + def download(self) -> None: + """Download the KITTI data if it doesn't exist already.""" + + if self._check_exists(): + return + + os.makedirs(self._raw_folder, exist_ok=True) + + # download files + for fname in self.resources: + download_and_extract_archive( + url=f"{self.data_url}{fname}", + download_root=self._raw_folder, + filename=fname, + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/lfw.py b/lib/python3.11/site-packages/torchvision/datasets/lfw.py new file mode 100644 index 0000000000000000000000000000000000000000..18374fc3c9bea77e6631ce23bd67d20a5642082c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/lfw.py @@ -0,0 +1,255 @@ +import os +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg +from .vision import VisionDataset + + +class _LFW(VisionDataset): + + base_folder = "lfw-py" + download_url_prefix = "http://vis-www.cs.umass.edu/lfw/" + + file_dict = { + "original": ("lfw", "lfw.tgz", "a17d05bd522c52d84eca14327a23d494"), + "funneled": ("lfw_funneled", "lfw-funneled.tgz", "1b42dfed7d15c9b2dd63d5e5840c86ad"), + "deepfunneled": ("lfw-deepfunneled", "lfw-deepfunneled.tgz", "68331da3eb755a505a502b5aacb3c201"), + } + checksums = { + "pairs.txt": "9f1ba174e4e1c508ff7cdf10ac338a7d", + "pairsDevTest.txt": "5132f7440eb68cf58910c8a45a2ac10b", + "pairsDevTrain.txt": "4f27cbf15b2da4a85c1907eb4181ad21", + "people.txt": "450f0863dd89e85e73936a6d71a3474b", + "peopleDevTest.txt": "e4bf5be0a43b5dcd9dc5ccfcb8fb19c5", + "peopleDevTrain.txt": "54eaac34beb6d042ed3a7d883e247a21", + "lfw-names.txt": "a6d0a479bd074669f656265a6e693f6d", + } + annot_file = {"10fold": "", "train": "DevTrain", "test": "DevTest"} + names = "lfw-names.txt" + + def __init__( + self, + root: Union[str, Path], + split: str, + image_set: str, + view: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(os.path.join(root, self.base_folder), transform=transform, target_transform=target_transform) + + self.image_set = verify_str_arg(image_set.lower(), "image_set", self.file_dict.keys()) + images_dir, self.filename, self.md5 = self.file_dict[self.image_set] + + self.view = verify_str_arg(view.lower(), "view", ["people", "pairs"]) + self.split = verify_str_arg(split.lower(), "split", ["10fold", "train", "test"]) + self.labels_file = f"{self.view}{self.annot_file[self.split]}.txt" + self.data: List[Any] = [] + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self.images_dir = os.path.join(self.root, images_dir) + + def _loader(self, path: str) -> Image.Image: + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + def _check_integrity(self) -> bool: + st1 = check_integrity(os.path.join(self.root, self.filename), self.md5) + st2 = check_integrity(os.path.join(self.root, self.labels_file), self.checksums[self.labels_file]) + if not st1 or not st2: + return False + if self.view == "people": + return check_integrity(os.path.join(self.root, self.names), self.checksums[self.names]) + return True + + def download(self) -> None: + if self._check_integrity(): + return + url = f"{self.download_url_prefix}{self.filename}" + download_and_extract_archive(url, self.root, filename=self.filename, md5=self.md5) + download_url(f"{self.download_url_prefix}{self.labels_file}", self.root) + if self.view == "people": + download_url(f"{self.download_url_prefix}{self.names}", self.root) + + def _get_path(self, identity: str, no: Union[int, str]) -> str: + return os.path.join(self.images_dir, identity, f"{identity}_{int(no):04d}.jpg") + + def extra_repr(self) -> str: + return f"Alignment: {self.image_set}\nSplit: {self.split}" + + def __len__(self) -> int: + return len(self.data) + + +class LFWPeople(_LFW): + """`LFW `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``lfw-py`` exists or will be saved to if download is set to True. + split (string, optional): The image split to use. Can be one of ``train``, ``test``, + ``10fold`` (default). + image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or + ``deepfunneled``. Defaults to ``funneled``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomRotation`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + def __init__( + self, + root: str, + split: str = "10fold", + image_set: str = "funneled", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, split, image_set, "people", transform, target_transform, download) + + self.class_to_idx = self._get_classes() + self.data, self.targets = self._get_people() + + def _get_people(self) -> Tuple[List[str], List[int]]: + data, targets = [], [] + with open(os.path.join(self.root, self.labels_file)) as f: + lines = f.readlines() + n_folds, s = (int(lines[0]), 1) if self.split == "10fold" else (1, 0) + + for fold in range(n_folds): + n_lines = int(lines[s]) + people = [line.strip().split("\t") for line in lines[s + 1 : s + n_lines + 1]] + s += n_lines + 1 + for i, (identity, num_imgs) in enumerate(people): + for num in range(1, int(num_imgs) + 1): + img = self._get_path(identity, num) + data.append(img) + targets.append(self.class_to_idx[identity]) + + return data, targets + + def _get_classes(self) -> Dict[str, int]: + with open(os.path.join(self.root, self.names)) as f: + lines = f.readlines() + names = [line.strip().split()[0] for line in lines] + class_to_idx = {name: i for i, name in enumerate(names)} + return class_to_idx + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the identity of the person. + """ + img = self._loader(self.data[index]) + target = self.targets[index] + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def extra_repr(self) -> str: + return super().extra_repr() + f"\nClasses (identities): {len(self.class_to_idx)}" + + +class LFWPairs(_LFW): + """`LFW `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``lfw-py`` exists or will be saved to if download is set to True. + split (string, optional): The image split to use. Can be one of ``train``, ``test``, + ``10fold``. Defaults to ``10fold``. + image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or + ``deepfunneled``. Defaults to ``funneled``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomRotation`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + def __init__( + self, + root: str, + split: str = "10fold", + image_set: str = "funneled", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, split, image_set, "pairs", transform, target_transform, download) + + self.pair_names, self.data, self.targets = self._get_pairs(self.images_dir) + + def _get_pairs(self, images_dir: str) -> Tuple[List[Tuple[str, str]], List[Tuple[str, str]], List[int]]: + pair_names, data, targets = [], [], [] + with open(os.path.join(self.root, self.labels_file)) as f: + lines = f.readlines() + if self.split == "10fold": + n_folds, n_pairs = lines[0].split("\t") + n_folds, n_pairs = int(n_folds), int(n_pairs) + else: + n_folds, n_pairs = 1, int(lines[0]) + s = 1 + + for fold in range(n_folds): + matched_pairs = [line.strip().split("\t") for line in lines[s : s + n_pairs]] + unmatched_pairs = [line.strip().split("\t") for line in lines[s + n_pairs : s + (2 * n_pairs)]] + s += 2 * n_pairs + for pair in matched_pairs: + img1, img2, same = self._get_path(pair[0], pair[1]), self._get_path(pair[0], pair[2]), 1 + pair_names.append((pair[0], pair[0])) + data.append((img1, img2)) + targets.append(same) + for pair in unmatched_pairs: + img1, img2, same = self._get_path(pair[0], pair[1]), self._get_path(pair[2], pair[3]), 0 + pair_names.append((pair[0], pair[2])) + data.append((img1, img2)) + targets.append(same) + + return pair_names, data, targets + + def __getitem__(self, index: int) -> Tuple[Any, Any, int]: + """ + Args: + index (int): Index + + Returns: + tuple: (image1, image2, target) where target is `0` for different indentities and `1` for same identities. + """ + img1, img2 = self.data[index] + img1, img2 = self._loader(img1), self._loader(img2) + target = self.targets[index] + + if self.transform is not None: + img1, img2 = self.transform(img1), self.transform(img2) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img1, img2, target diff --git a/lib/python3.11/site-packages/torchvision/datasets/lsun.py b/lib/python3.11/site-packages/torchvision/datasets/lsun.py new file mode 100644 index 0000000000000000000000000000000000000000..61d40eee221dc1656e00b4927c64de76129156f6 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/lsun.py @@ -0,0 +1,168 @@ +import io +import os.path +import pickle +import string +from collections.abc import Iterable +from pathlib import Path +from typing import Any, Callable, cast, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import iterable_to_str, verify_str_arg +from .vision import VisionDataset + + +class LSUNClass(VisionDataset): + def __init__( + self, root: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None + ) -> None: + import lmdb + + super().__init__(root, transform=transform, target_transform=target_transform) + + self.env = lmdb.open(root, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False) + with self.env.begin(write=False) as txn: + self.length = txn.stat()["entries"] + cache_file = "_cache_" + "".join(c for c in root if c in string.ascii_letters) + if os.path.isfile(cache_file): + self.keys = pickle.load(open(cache_file, "rb")) + else: + with self.env.begin(write=False) as txn: + self.keys = [key for key in txn.cursor().iternext(keys=True, values=False)] + pickle.dump(self.keys, open(cache_file, "wb")) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + img, target = None, None + env = self.env + with env.begin(write=False) as txn: + imgbuf = txn.get(self.keys[index]) + + buf = io.BytesIO() + buf.write(imgbuf) + buf.seek(0) + img = Image.open(buf).convert("RGB") + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return self.length + + +class LSUN(VisionDataset): + """`LSUN `_ dataset. + + You will need to install the ``lmdb`` package to use this dataset: run + ``pip install lmdb`` + + Args: + root (str or ``pathlib.Path``): Root directory for the database files. + classes (string or list): One of {'train', 'val', 'test'} or a list of + categories to load. e,g. ['bedroom_train', 'church_outdoor_train']. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__( + self, + root: Union[str, Path], + classes: Union[str, List[str]] = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.classes = self._verify_classes(classes) + + # for each class, create an LSUNClassDataset + self.dbs = [] + for c in self.classes: + self.dbs.append(LSUNClass(root=os.path.join(root, f"{c}_lmdb"), transform=transform)) + + self.indices = [] + count = 0 + for db in self.dbs: + count += len(db) + self.indices.append(count) + + self.length = count + + def _verify_classes(self, classes: Union[str, List[str]]) -> List[str]: + categories = [ + "bedroom", + "bridge", + "church_outdoor", + "classroom", + "conference_room", + "dining_room", + "kitchen", + "living_room", + "restaurant", + "tower", + ] + dset_opts = ["train", "val", "test"] + + try: + classes = cast(str, classes) + verify_str_arg(classes, "classes", dset_opts) + if classes == "test": + classes = [classes] + else: + classes = [c + "_" + classes for c in categories] + except ValueError: + if not isinstance(classes, Iterable): + msg = "Expected type str or Iterable for argument classes, but got type {}." + raise ValueError(msg.format(type(classes))) + + classes = list(classes) + msg_fmtstr_type = "Expected type str for elements in argument classes, but got type {}." + for c in classes: + verify_str_arg(c, custom_msg=msg_fmtstr_type.format(type(c))) + c_short = c.split("_") + category, dset_opt = "_".join(c_short[:-1]), c_short[-1] + + msg_fmtstr = "Unknown value '{}' for {}. Valid values are {{{}}}." + msg = msg_fmtstr.format(category, "LSUN class", iterable_to_str(categories)) + verify_str_arg(category, valid_values=categories, custom_msg=msg) + + msg = msg_fmtstr.format(dset_opt, "postfix", iterable_to_str(dset_opts)) + verify_str_arg(dset_opt, valid_values=dset_opts, custom_msg=msg) + + return classes + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + """ + target = 0 + sub = 0 + for ind in self.indices: + if index < ind: + break + target += 1 + sub = ind + + db = self.dbs[target] + index = index - sub + + if self.target_transform is not None: + target = self.target_transform(target) + + img, _ = db[index] + return img, target + + def __len__(self) -> int: + return self.length + + def extra_repr(self) -> str: + return "Classes: {classes}".format(**self.__dict__) diff --git a/lib/python3.11/site-packages/torchvision/datasets/mnist.py b/lib/python3.11/site-packages/torchvision/datasets/mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..fd1455535298440467e5750c981529c7de50e493 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/mnist.py @@ -0,0 +1,560 @@ +import codecs +import os +import os.path +import shutil +import string +import sys +import warnings +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from urllib.error import URLError + +import numpy as np +import torch +from PIL import Image + +from .utils import _flip_byte_order, check_integrity, download_and_extract_archive, extract_archive, verify_str_arg +from .vision import VisionDataset + + +class MNIST(VisionDataset): + """`MNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``MNIST/raw/train-images-idx3-ubyte`` + and ``MNIST/raw/t10k-images-idx3-ubyte`` exist. + train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``, + otherwise from ``t10k-images-idx3-ubyte``. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + mirrors = [ + "http://yann.lecun.com/exdb/mnist/", + "https://ossci-datasets.s3.amazonaws.com/mnist/", + ] + + resources = [ + ("train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"), + ("train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"), + ("t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"), + ("t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c"), + ] + + training_file = "training.pt" + test_file = "test.pt" + classes = [ + "0 - zero", + "1 - one", + "2 - two", + "3 - three", + "4 - four", + "5 - five", + "6 - six", + "7 - seven", + "8 - eight", + "9 - nine", + ] + + @property + def train_labels(self): + warnings.warn("train_labels has been renamed targets") + return self.targets + + @property + def test_labels(self): + warnings.warn("test_labels has been renamed targets") + return self.targets + + @property + def train_data(self): + warnings.warn("train_data has been renamed data") + return self.data + + @property + def test_data(self): + warnings.warn("test_data has been renamed data") + return self.data + + def __init__( + self, + root: Union[str, Path], + train: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.train = train # training set or test set + + if self._check_legacy_exist(): + self.data, self.targets = self._load_legacy_data() + return + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + self.data, self.targets = self._load_data() + + def _check_legacy_exist(self): + processed_folder_exists = os.path.exists(self.processed_folder) + if not processed_folder_exists: + return False + + return all( + check_integrity(os.path.join(self.processed_folder, file)) for file in (self.training_file, self.test_file) + ) + + def _load_legacy_data(self): + # This is for BC only. We no longer cache the data in a custom binary, but simply read from the raw data + # directly. + data_file = self.training_file if self.train else self.test_file + return torch.load(os.path.join(self.processed_folder, data_file), weights_only=True) + + def _load_data(self): + image_file = f"{'train' if self.train else 't10k'}-images-idx3-ubyte" + data = read_image_file(os.path.join(self.raw_folder, image_file)) + + label_file = f"{'train' if self.train else 't10k'}-labels-idx1-ubyte" + targets = read_label_file(os.path.join(self.raw_folder, label_file)) + + return data, targets + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], int(self.targets[index]) + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(img.numpy(), mode="L") + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) + + @property + def raw_folder(self) -> str: + return os.path.join(self.root, self.__class__.__name__, "raw") + + @property + def processed_folder(self) -> str: + return os.path.join(self.root, self.__class__.__name__, "processed") + + @property + def class_to_idx(self) -> Dict[str, int]: + return {_class: i for i, _class in enumerate(self.classes)} + + def _check_exists(self) -> bool: + return all( + check_integrity(os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0])) + for url, _ in self.resources + ) + + def download(self) -> None: + """Download the MNIST data if it doesn't exist already.""" + + if self._check_exists(): + return + + os.makedirs(self.raw_folder, exist_ok=True) + + # download files + for filename, md5 in self.resources: + errors = [] + for mirror in self.mirrors: + url = f"{mirror}{filename}" + try: + download_and_extract_archive(url, download_root=self.raw_folder, filename=filename, md5=md5) + except URLError as e: + errors.append(e) + continue + break + else: + s = f"Error downloading {filename}:\n" + for mirror, err in zip(self.mirrors, errors): + s += f"Tried {mirror}, got:\n{str(err)}\n" + raise RuntimeError(s) + + def extra_repr(self) -> str: + split = "Train" if self.train is True else "Test" + return f"Split: {split}" + + +class FashionMNIST(MNIST): + """`Fashion-MNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``FashionMNIST/raw/train-images-idx3-ubyte`` + and ``FashionMNIST/raw/t10k-images-idx3-ubyte`` exist. + train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``, + otherwise from ``t10k-images-idx3-ubyte``. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + mirrors = ["http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/"] + + resources = [ + ("train-images-idx3-ubyte.gz", "8d4fb7e6c68d591d4c3dfef9ec88bf0d"), + ("train-labels-idx1-ubyte.gz", "25c81989df183df01b3e8a0aad5dffbe"), + ("t10k-images-idx3-ubyte.gz", "bef4ecab320f06d8554ea6380940ec79"), + ("t10k-labels-idx1-ubyte.gz", "bb300cfdad3c16e7a12a480ee83cd310"), + ] + classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] + + +class KMNIST(MNIST): + """`Kuzushiji-MNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``KMNIST/raw/train-images-idx3-ubyte`` + and ``KMNIST/raw/t10k-images-idx3-ubyte`` exist. + train (bool, optional): If True, creates dataset from ``train-images-idx3-ubyte``, + otherwise from ``t10k-images-idx3-ubyte``. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + mirrors = ["http://codh.rois.ac.jp/kmnist/dataset/kmnist/"] + + resources = [ + ("train-images-idx3-ubyte.gz", "bdb82020997e1d708af4cf47b453dcf7"), + ("train-labels-idx1-ubyte.gz", "e144d726b3acfaa3e44228e80efcd344"), + ("t10k-images-idx3-ubyte.gz", "5c965bf0a639b31b8f53240b1b52f4d7"), + ("t10k-labels-idx1-ubyte.gz", "7320c461ea6c1c855c0b718fb2a4b134"), + ] + classes = ["o", "ki", "su", "tsu", "na", "ha", "ma", "ya", "re", "wo"] + + +class EMNIST(MNIST): + """`EMNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``EMNIST/raw/train-images-idx3-ubyte`` + and ``EMNIST/raw/t10k-images-idx3-ubyte`` exist. + split (string): The dataset has 6 different splits: ``byclass``, ``bymerge``, + ``balanced``, ``letters``, ``digits`` and ``mnist``. This argument specifies + which one to use. + train (bool, optional): If True, creates dataset from ``training.pt``, + otherwise from ``test.pt``. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + url = "https://biometrics.nist.gov/cs_links/EMNIST/gzip.zip" + md5 = "58c8d27c78d21e728a6bc7b3cc06412e" + splits = ("byclass", "bymerge", "balanced", "letters", "digits", "mnist") + # Merged Classes assumes Same structure for both uppercase and lowercase version + _merged_classes = {"c", "i", "j", "k", "l", "m", "o", "p", "s", "u", "v", "w", "x", "y", "z"} + _all_classes = set(string.digits + string.ascii_letters) + classes_split_dict = { + "byclass": sorted(list(_all_classes)), + "bymerge": sorted(list(_all_classes - _merged_classes)), + "balanced": sorted(list(_all_classes - _merged_classes)), + "letters": ["N/A"] + list(string.ascii_lowercase), + "digits": list(string.digits), + "mnist": list(string.digits), + } + + def __init__(self, root: Union[str, Path], split: str, **kwargs: Any) -> None: + self.split = verify_str_arg(split, "split", self.splits) + self.training_file = self._training_file(split) + self.test_file = self._test_file(split) + super().__init__(root, **kwargs) + self.classes = self.classes_split_dict[self.split] + + @staticmethod + def _training_file(split) -> str: + return f"training_{split}.pt" + + @staticmethod + def _test_file(split) -> str: + return f"test_{split}.pt" + + @property + def _file_prefix(self) -> str: + return f"emnist-{self.split}-{'train' if self.train else 'test'}" + + @property + def images_file(self) -> str: + return os.path.join(self.raw_folder, f"{self._file_prefix}-images-idx3-ubyte") + + @property + def labels_file(self) -> str: + return os.path.join(self.raw_folder, f"{self._file_prefix}-labels-idx1-ubyte") + + def _load_data(self): + return read_image_file(self.images_file), read_label_file(self.labels_file) + + def _check_exists(self) -> bool: + return all(check_integrity(file) for file in (self.images_file, self.labels_file)) + + def download(self) -> None: + """Download the EMNIST data if it doesn't exist already.""" + + if self._check_exists(): + return + + os.makedirs(self.raw_folder, exist_ok=True) + + download_and_extract_archive(self.url, download_root=self.raw_folder, md5=self.md5) + gzip_folder = os.path.join(self.raw_folder, "gzip") + for gzip_file in os.listdir(gzip_folder): + if gzip_file.endswith(".gz"): + extract_archive(os.path.join(gzip_folder, gzip_file), self.raw_folder) + shutil.rmtree(gzip_folder) + + +class QMNIST(MNIST): + """`QMNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset whose ``raw`` + subdir contains binary files of the datasets. + what (string,optional): Can be 'train', 'test', 'test10k', + 'test50k', or 'nist' for respectively the mnist compatible + training set, the 60k qmnist testing set, the 10k qmnist + examples that match the mnist testing set, the 50k + remaining qmnist testing examples, or all the nist + digits. The default is to select 'train' or 'test' + according to the compatibility argument 'train'. + compat (bool,optional): A boolean that says whether the target + for each example is class number (for compatibility with + the MNIST dataloader) or a torch vector containing the + full qmnist information. Default=True. + download (bool, optional): If True, downloads the dataset from + the internet and puts it in root directory. If dataset is + already downloaded, it is not downloaded again. + transform (callable, optional): A function/transform that + takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform + that takes in the target and transforms it. + train (bool,optional,compatibility): When argument 'what' is + not specified, this boolean decides whether to load the + training set or the testing set. Default: True. + """ + + subsets = {"train": "train", "test": "test", "test10k": "test", "test50k": "test", "nist": "nist"} + resources: Dict[str, List[Tuple[str, str]]] = { # type: ignore[assignment] + "train": [ + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-images-idx3-ubyte.gz", + "ed72d4157d28c017586c42bc6afe6370", + ), + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-labels-idx2-int.gz", + "0058f8dd561b90ffdd0f734c6a30e5e4", + ), + ], + "test": [ + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-images-idx3-ubyte.gz", + "1394631089c404de565df7b7aeaf9412", + ), + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-labels-idx2-int.gz", + "5b5b05890a5e13444e108efe57b788aa", + ), + ], + "nist": [ + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-images-idx3-ubyte.xz", + "7f124b3b8ab81486c9d8c2749c17f834", + ), + ( + "https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-labels-idx2-int.xz", + "5ed0e788978e45d4a8bd4b7caec3d79d", + ), + ], + } + classes = [ + "0 - zero", + "1 - one", + "2 - two", + "3 - three", + "4 - four", + "5 - five", + "6 - six", + "7 - seven", + "8 - eight", + "9 - nine", + ] + + def __init__( + self, root: Union[str, Path], what: Optional[str] = None, compat: bool = True, train: bool = True, **kwargs: Any + ) -> None: + if what is None: + what = "train" if train else "test" + self.what = verify_str_arg(what, "what", tuple(self.subsets.keys())) + self.compat = compat + self.data_file = what + ".pt" + self.training_file = self.data_file + self.test_file = self.data_file + super().__init__(root, train, **kwargs) + + @property + def images_file(self) -> str: + (url, _), _ = self.resources[self.subsets[self.what]] + return os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0]) + + @property + def labels_file(self) -> str: + _, (url, _) = self.resources[self.subsets[self.what]] + return os.path.join(self.raw_folder, os.path.splitext(os.path.basename(url))[0]) + + def _check_exists(self) -> bool: + return all(check_integrity(file) for file in (self.images_file, self.labels_file)) + + def _load_data(self): + data = read_sn3_pascalvincent_tensor(self.images_file) + if data.dtype != torch.uint8: + raise TypeError(f"data should be of dtype torch.uint8 instead of {data.dtype}") + if data.ndimension() != 3: + raise ValueError("data should have 3 dimensions instead of {data.ndimension()}") + + targets = read_sn3_pascalvincent_tensor(self.labels_file).long() + if targets.ndimension() != 2: + raise ValueError(f"targets should have 2 dimensions instead of {targets.ndimension()}") + + if self.what == "test10k": + data = data[0:10000, :, :].clone() + targets = targets[0:10000, :].clone() + elif self.what == "test50k": + data = data[10000:, :, :].clone() + targets = targets[10000:, :].clone() + + return data, targets + + def download(self) -> None: + """Download the QMNIST data if it doesn't exist already. + Note that we only download what has been asked for (argument 'what'). + """ + if self._check_exists(): + return + + os.makedirs(self.raw_folder, exist_ok=True) + split = self.resources[self.subsets[self.what]] + + for url, md5 in split: + download_and_extract_archive(url, self.raw_folder, md5=md5) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + # redefined to handle the compat flag + img, target = self.data[index], self.targets[index] + img = Image.fromarray(img.numpy(), mode="L") + if self.transform is not None: + img = self.transform(img) + if self.compat: + target = int(target[0]) + if self.target_transform is not None: + target = self.target_transform(target) + return img, target + + def extra_repr(self) -> str: + return f"Split: {self.what}" + + +def get_int(b: bytes) -> int: + return int(codecs.encode(b, "hex"), 16) + + +SN3_PASCALVINCENT_TYPEMAP = { + 8: torch.uint8, + 9: torch.int8, + 11: torch.int16, + 12: torch.int32, + 13: torch.float32, + 14: torch.float64, +} + + +def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tensor: + """Read a SN3 file in "Pascal Vincent" format (Lush file 'libidx/idx-io.lsh'). + Argument may be a filename, compressed filename, or file object. + """ + # read + with open(path, "rb") as f: + data = f.read() + + # parse + if sys.byteorder == "little": + magic = get_int(data[0:4]) + nd = magic % 256 + ty = magic // 256 + else: + nd = get_int(data[0:1]) + ty = get_int(data[1:2]) + get_int(data[2:3]) * 256 + get_int(data[3:4]) * 256 * 256 + + assert 1 <= nd <= 3 + assert 8 <= ty <= 14 + torch_type = SN3_PASCALVINCENT_TYPEMAP[ty] + s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)] + + if sys.byteorder == "big": + for i in range(len(s)): + s[i] = int.from_bytes(s[i].to_bytes(4, byteorder="little"), byteorder="big", signed=False) + + parsed = torch.frombuffer(bytearray(data), dtype=torch_type, offset=(4 * (nd + 1))) + + # The MNIST format uses the big endian byte order, while `torch.frombuffer` uses whatever the system uses. In case + # that is little endian and the dtype has more than one byte, we need to flip them. + if sys.byteorder == "little" and parsed.element_size() > 1: + parsed = _flip_byte_order(parsed) + + assert parsed.shape[0] == np.prod(s) or not strict + return parsed.view(*s) + + +def read_label_file(path: str) -> torch.Tensor: + x = read_sn3_pascalvincent_tensor(path, strict=False) + if x.dtype != torch.uint8: + raise TypeError(f"x should be of dtype torch.uint8 instead of {x.dtype}") + if x.ndimension() != 1: + raise ValueError(f"x should have 1 dimension instead of {x.ndimension()}") + return x.long() + + +def read_image_file(path: str) -> torch.Tensor: + x = read_sn3_pascalvincent_tensor(path, strict=False) + if x.dtype != torch.uint8: + raise TypeError(f"x should be of dtype torch.uint8 instead of {x.dtype}") + if x.ndimension() != 3: + raise ValueError(f"x should have 3 dimension instead of {x.ndimension()}") + return x diff --git a/lib/python3.11/site-packages/torchvision/datasets/moving_mnist.py b/lib/python3.11/site-packages/torchvision/datasets/moving_mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..48715de4e8dea86fec71eb09ea98f3ff1b9d6fda --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/moving_mnist.py @@ -0,0 +1,94 @@ +import os.path +from pathlib import Path +from typing import Callable, Optional, Union + +import numpy as np +import torch +from torchvision.datasets.utils import download_url, verify_str_arg +from torchvision.datasets.vision import VisionDataset + + +class MovingMNIST(VisionDataset): + """`MovingMNIST `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where ``MovingMNIST/mnist_test_seq.npy`` exists. + split (string, optional): The dataset split, supports ``None`` (default), ``"train"`` and ``"test"``. + If ``split=None``, the full data is returned. + split_ratio (int, optional): The split ratio of number of frames. If ``split="train"``, the first split + frames ``data[:, :split_ratio]`` is returned. If ``split="test"``, the last split frames ``data[:, split_ratio:]`` + is returned. If ``split=None``, this parameter is ignored and the all frames data is returned. + transform (callable, optional): A function/transform that takes in a torch Tensor + and returns a transformed version. E.g, ``transforms.RandomCrop`` + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + _URL = "http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy" + + def __init__( + self, + root: Union[str, Path], + split: Optional[str] = None, + split_ratio: int = 10, + download: bool = False, + transform: Optional[Callable] = None, + ) -> None: + super().__init__(root, transform=transform) + + self._base_folder = os.path.join(self.root, self.__class__.__name__) + self._filename = self._URL.split("/")[-1] + + if split is not None: + verify_str_arg(split, "split", ("train", "test")) + self.split = split + + if not isinstance(split_ratio, int): + raise TypeError(f"`split_ratio` should be an integer, but got {type(split_ratio)}") + elif not (1 <= split_ratio <= 19): + raise ValueError(f"`split_ratio` should be `1 <= split_ratio <= 19`, but got {split_ratio} instead.") + self.split_ratio = split_ratio + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it.") + + data = torch.from_numpy(np.load(os.path.join(self._base_folder, self._filename))) + if self.split == "train": + data = data[: self.split_ratio] + elif self.split == "test": + data = data[self.split_ratio :] + self.data = data.transpose(0, 1).unsqueeze(2).contiguous() + + def __getitem__(self, idx: int) -> torch.Tensor: + """ + Args: + idx (int): Index + Returns: + torch.Tensor: Video frames (torch Tensor[T, C, H, W]). The `T` is the number of frames. + """ + data = self.data[idx] + if self.transform is not None: + data = self.transform(data) + + return data + + def __len__(self) -> int: + return len(self.data) + + def _check_exists(self) -> bool: + return os.path.exists(os.path.join(self._base_folder, self._filename)) + + def download(self) -> None: + if self._check_exists(): + return + + download_url( + url=self._URL, + root=self._base_folder, + filename=self._filename, + md5="be083ec986bfe91a449d63653c411eb2", + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/omniglot.py b/lib/python3.11/site-packages/torchvision/datasets/omniglot.py new file mode 100644 index 0000000000000000000000000000000000000000..c3434a7245616178b5a0aab9efe6643cc33bff14 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/omniglot.py @@ -0,0 +1,102 @@ +from os.path import join +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +from PIL import Image + +from .utils import check_integrity, download_and_extract_archive, list_dir, list_files +from .vision import VisionDataset + + +class Omniglot(VisionDataset): + """`Omniglot `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``omniglot-py`` exists. + background (bool, optional): If True, creates dataset from the "background" set, otherwise + creates from the "evaluation" set. This terminology is defined by the authors. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset zip files from the internet and + puts it in root directory. If the zip files are already downloaded, they are not + downloaded again. + """ + + folder = "omniglot-py" + download_url_prefix = "https://raw.githubusercontent.com/brendenlake/omniglot/master/python" + zips_md5 = { + "images_background": "68d2efa1b9178cc56df9314c21c6e718", + "images_evaluation": "6b91aef0f799c5bb55b94e3f2daec811", + } + + def __init__( + self, + root: Union[str, Path], + background: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(join(root, self.folder), transform=transform, target_transform=target_transform) + self.background = background + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + self.target_folder = join(self.root, self._get_target_folder()) + self._alphabets = list_dir(self.target_folder) + self._characters: List[str] = sum( + ([join(a, c) for c in list_dir(join(self.target_folder, a))] for a in self._alphabets), [] + ) + self._character_images = [ + [(image, idx) for image in list_files(join(self.target_folder, character), ".png")] + for idx, character in enumerate(self._characters) + ] + self._flat_character_images: List[Tuple[str, int]] = sum(self._character_images, []) + + def __len__(self) -> int: + return len(self._flat_character_images) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target character class. + """ + image_name, character_class = self._flat_character_images[index] + image_path = join(self.target_folder, self._characters[character_class], image_name) + image = Image.open(image_path, mode="r").convert("L") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + character_class = self.target_transform(character_class) + + return image, character_class + + def _check_integrity(self) -> bool: + zip_filename = self._get_target_folder() + if not check_integrity(join(self.root, zip_filename + ".zip"), self.zips_md5[zip_filename]): + return False + return True + + def download(self) -> None: + if self._check_integrity(): + return + + filename = self._get_target_folder() + zip_filename = filename + ".zip" + url = self.download_url_prefix + "/" + zip_filename + download_and_extract_archive(url, self.root, filename=zip_filename, md5=self.zips_md5[filename]) + + def _get_target_folder(self) -> str: + return "images_background" if self.background else "images_evaluation" diff --git a/lib/python3.11/site-packages/torchvision/datasets/oxford_iiit_pet.py b/lib/python3.11/site-packages/torchvision/datasets/oxford_iiit_pet.py new file mode 100644 index 0000000000000000000000000000000000000000..1d6d990fdf90106ef765d9ff47d1b760ce26d5d0 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/oxford_iiit_pet.py @@ -0,0 +1,132 @@ +import os +import os.path +import pathlib +from typing import Any, Callable, Optional, Sequence, Tuple, Union + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class OxfordIIITPet(VisionDataset): + """`Oxford-IIIT Pet Dataset `_. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"trainval"`` (default) or ``"test"``. + target_types (string, sequence of strings, optional): Types of target to use. Can be ``category`` (default) or + ``segmentation``. Can also be a list to output a tuple with all specified target types. The types represent: + + - ``category`` (int): Label for one of the 37 pet categories. + - ``binary-category`` (int): Binary label for cat or dog. + - ``segmentation`` (PIL image): Segmentation trimap of the image. + + If empty, ``None`` will be returned as target. + + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and puts it into + ``root/oxford-iiit-pet``. If dataset is already downloaded, it is not downloaded again. + """ + + _RESOURCES = ( + ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz", "5c4f3ee8e5d25df40f4fd59a7f44e54c"), + ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz", "95a8c909bbe2e81eed6a22bccdf3f68f"), + ) + _VALID_TARGET_TYPES = ("category", "binary-category", "segmentation") + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "trainval", + target_types: Union[Sequence[str], str] = "category", + transforms: Optional[Callable] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ): + self._split = verify_str_arg(split, "split", ("trainval", "test")) + if isinstance(target_types, str): + target_types = [target_types] + self._target_types = [ + verify_str_arg(target_type, "target_types", self._VALID_TARGET_TYPES) for target_type in target_types + ] + + super().__init__(root, transforms=transforms, transform=transform, target_transform=target_transform) + self._base_folder = pathlib.Path(self.root) / "oxford-iiit-pet" + self._images_folder = self._base_folder / "images" + self._anns_folder = self._base_folder / "annotations" + self._segs_folder = self._anns_folder / "trimaps" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + image_ids = [] + self._labels = [] + self._bin_labels = [] + with open(self._anns_folder / f"{self._split}.txt") as file: + for line in file: + image_id, label, bin_label, _ = line.strip().split() + image_ids.append(image_id) + self._labels.append(int(label) - 1) + self._bin_labels.append(int(bin_label) - 1) + + self.bin_classes = ["Cat", "Dog"] + self.classes = [ + " ".join(part.title() for part in raw_cls.split("_")) + for raw_cls, _ in sorted( + {(image_id.rsplit("_", 1)[0], label) for image_id, label in zip(image_ids, self._labels)}, + key=lambda image_id_and_label: image_id_and_label[1], + ) + ] + self.bin_class_to_idx = dict(zip(self.bin_classes, range(len(self.bin_classes)))) + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + + self._images = [self._images_folder / f"{image_id}.jpg" for image_id in image_ids] + self._segs = [self._segs_folder / f"{image_id}.png" for image_id in image_ids] + + def __len__(self) -> int: + return len(self._images) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image = Image.open(self._images[idx]).convert("RGB") + + target: Any = [] + for target_type in self._target_types: + if target_type == "category": + target.append(self._labels[idx]) + elif target_type == "binary-category": + target.append(self._bin_labels[idx]) + else: # target_type == "segmentation" + target.append(Image.open(self._segs[idx])) + + if not target: + target = None + elif len(target) == 1: + target = target[0] + else: + target = tuple(target) + + if self.transforms: + image, target = self.transforms(image, target) + + return image, target + + def _check_exists(self) -> bool: + for folder in (self._images_folder, self._anns_folder): + if not (os.path.exists(folder) and os.path.isdir(folder)): + return False + else: + return True + + def _download(self) -> None: + if self._check_exists(): + return + + for url, md5 in self._RESOURCES: + download_and_extract_archive(url, download_root=str(self._base_folder), md5=md5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/pcam.py b/lib/python3.11/site-packages/torchvision/datasets/pcam.py new file mode 100644 index 0000000000000000000000000000000000000000..8849e0ea39dd1bfb76920bc8afc7f6ced597cda4 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/pcam.py @@ -0,0 +1,134 @@ +import pathlib +from typing import Any, Callable, Optional, Tuple, Union + +from PIL import Image + +from .utils import _decompress, download_file_from_google_drive, verify_str_arg +from .vision import VisionDataset + + +class PCAM(VisionDataset): + """`PCAM Dataset `_. + + The PatchCamelyon dataset is a binary classification dataset with 327,680 + color images (96px x 96px), extracted from histopathologic scans of lymph node + sections. Each image is annotated with a binary label indicating presence of + metastatic tissue. + + This dataset requires the ``h5py`` package which you can install with ``pip install h5py``. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), ``"test"`` or ``"val"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and puts it into ``root/pcam``. If + dataset is already downloaded, it is not downloaded again. + + .. warning:: + + To download the dataset `gdown `_ is required. + """ + + _FILES = { + "train": { + "images": ( + "camelyonpatch_level_2_split_train_x.h5", # Data file name + "1Ka0XfEMiwgCYPdTI-vv6eUElOBnKFKQ2", # Google Drive ID + "1571f514728f59376b705fc836ff4b63", # md5 hash + ), + "targets": ( + "camelyonpatch_level_2_split_train_y.h5", + "1269yhu3pZDP8UYFQs-NYs3FPwuK-nGSG", + "35c2d7259d906cfc8143347bb8e05be7", + ), + }, + "test": { + "images": ( + "camelyonpatch_level_2_split_test_x.h5", + "1qV65ZqZvWzuIVthK8eVDhIwrbnsJdbg_", + "d8c2d60d490dbd479f8199bdfa0cf6ec", + ), + "targets": ( + "camelyonpatch_level_2_split_test_y.h5", + "17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP", + "60a7035772fbdb7f34eb86d4420cf66a", + ), + }, + "val": { + "images": ( + "camelyonpatch_level_2_split_valid_x.h5", + "1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3", + "d5b63470df7cfa627aeec8b9dc0c066e", + ), + "targets": ( + "camelyonpatch_level_2_split_valid_y.h5", + "1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO", + "2b85f58b927af9964a4c15b8f7e8f179", + ), + }, + } + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ): + try: + import h5py + + self.h5py = h5py + except ImportError: + raise RuntimeError( + "h5py is not found. This dataset needs to have h5py installed: please run pip install h5py" + ) + + self._split = verify_str_arg(split, "split", ("train", "test", "val")) + + super().__init__(root, transform=transform, target_transform=target_transform) + self._base_folder = pathlib.Path(self.root) / "pcam" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + def __len__(self) -> int: + images_file = self._FILES[self._split]["images"][0] + with self.h5py.File(self._base_folder / images_file) as images_data: + return images_data["x"].shape[0] + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + images_file = self._FILES[self._split]["images"][0] + with self.h5py.File(self._base_folder / images_file) as images_data: + image = Image.fromarray(images_data["x"][idx]).convert("RGB") + + targets_file = self._FILES[self._split]["targets"][0] + with self.h5py.File(self._base_folder / targets_file) as targets_data: + target = int(targets_data["y"][idx, 0, 0, 0]) # shape is [num_images, 1, 1, 1] + + if self.transform: + image = self.transform(image) + if self.target_transform: + target = self.target_transform(target) + + return image, target + + def _check_exists(self) -> bool: + images_file = self._FILES[self._split]["images"][0] + targets_file = self._FILES[self._split]["targets"][0] + return all(self._base_folder.joinpath(h5_file).exists() for h5_file in (images_file, targets_file)) + + def _download(self) -> None: + if self._check_exists(): + return + + for file_name, file_id, md5 in self._FILES[self._split].values(): + archive_name = file_name + ".gz" + download_file_from_google_drive(file_id, str(self._base_folder), filename=archive_name, md5=md5) + _decompress(str(self._base_folder / archive_name)) diff --git a/lib/python3.11/site-packages/torchvision/datasets/phototour.py b/lib/python3.11/site-packages/torchvision/datasets/phototour.py new file mode 100644 index 0000000000000000000000000000000000000000..9511f0626b40da10bb4d60158ea9091676fd150d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/phototour.py @@ -0,0 +1,230 @@ +import os +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL import Image + +from .utils import download_url +from .vision import VisionDataset + + +class PhotoTour(VisionDataset): + """`Multi-view Stereo Correspondence `_ Dataset. + + .. note:: + + We only provide the newer version of the dataset, since the authors state that it + + is more suitable for training descriptors based on difference of Gaussian, or Harris corners, as the + patches are centred on real interest point detections, rather than being projections of 3D points as is the + case in the old dataset. + + The original dataset is available under http://phototour.cs.washington.edu/patches/default.htm. + + + Args: + root (str or ``pathlib.Path``): Root directory where images are. + name (string): Name of the dataset to load. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + urls = { + "notredame_harris": [ + "http://matthewalunbrown.com/patchdata/notredame_harris.zip", + "notredame_harris.zip", + "69f8c90f78e171349abdf0307afefe4d", + ], + "yosemite_harris": [ + "http://matthewalunbrown.com/patchdata/yosemite_harris.zip", + "yosemite_harris.zip", + "a73253d1c6fbd3ba2613c45065c00d46", + ], + "liberty_harris": [ + "http://matthewalunbrown.com/patchdata/liberty_harris.zip", + "liberty_harris.zip", + "c731fcfb3abb4091110d0ae8c7ba182c", + ], + "notredame": [ + "http://icvl.ee.ic.ac.uk/vbalnt/notredame.zip", + "notredame.zip", + "509eda8535847b8c0a90bbb210c83484", + ], + "yosemite": ["http://icvl.ee.ic.ac.uk/vbalnt/yosemite.zip", "yosemite.zip", "533b2e8eb7ede31be40abc317b2fd4f0"], + "liberty": ["http://icvl.ee.ic.ac.uk/vbalnt/liberty.zip", "liberty.zip", "fdd9152f138ea5ef2091746689176414"], + } + means = { + "notredame": 0.4854, + "yosemite": 0.4844, + "liberty": 0.4437, + "notredame_harris": 0.4854, + "yosemite_harris": 0.4844, + "liberty_harris": 0.4437, + } + stds = { + "notredame": 0.1864, + "yosemite": 0.1818, + "liberty": 0.2019, + "notredame_harris": 0.1864, + "yosemite_harris": 0.1818, + "liberty_harris": 0.2019, + } + lens = { + "notredame": 468159, + "yosemite": 633587, + "liberty": 450092, + "liberty_harris": 379587, + "yosemite_harris": 450912, + "notredame_harris": 325295, + } + image_ext = "bmp" + info_file = "info.txt" + matches_files = "m50_100000_100000_0.txt" + + def __init__( + self, + root: Union[str, Path], + name: str, + train: bool = True, + transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform) + self.name = name + self.data_dir = os.path.join(self.root, name) + self.data_down = os.path.join(self.root, f"{name}.zip") + self.data_file = os.path.join(self.root, f"{name}.pt") + + self.train = train + self.mean = self.means[name] + self.std = self.stds[name] + + if download: + self.download() + + if not self._check_datafile_exists(): + self.cache() + + # load the serialized data + self.data, self.labels, self.matches = torch.load(self.data_file, weights_only=True) + + def __getitem__(self, index: int) -> Union[torch.Tensor, Tuple[Any, Any, torch.Tensor]]: + """ + Args: + index (int): Index + + Returns: + tuple: (data1, data2, matches) + """ + if self.train: + data = self.data[index] + if self.transform is not None: + data = self.transform(data) + return data + m = self.matches[index] + data1, data2 = self.data[m[0]], self.data[m[1]] + if self.transform is not None: + data1 = self.transform(data1) + data2 = self.transform(data2) + return data1, data2, m[2] + + def __len__(self) -> int: + return len(self.data if self.train else self.matches) + + def _check_datafile_exists(self) -> bool: + return os.path.exists(self.data_file) + + def _check_downloaded(self) -> bool: + return os.path.exists(self.data_dir) + + def download(self) -> None: + if self._check_datafile_exists(): + return + + if not self._check_downloaded(): + # download files + url = self.urls[self.name][0] + filename = self.urls[self.name][1] + md5 = self.urls[self.name][2] + fpath = os.path.join(self.root, filename) + + download_url(url, self.root, filename, md5) + + import zipfile + + with zipfile.ZipFile(fpath, "r") as z: + z.extractall(self.data_dir) + + os.unlink(fpath) + + def cache(self) -> None: + # process and save as torch files + + dataset = ( + read_image_file(self.data_dir, self.image_ext, self.lens[self.name]), + read_info_file(self.data_dir, self.info_file), + read_matches_files(self.data_dir, self.matches_files), + ) + + with open(self.data_file, "wb") as f: + torch.save(dataset, f) + + def extra_repr(self) -> str: + split = "Train" if self.train is True else "Test" + return f"Split: {split}" + + +def read_image_file(data_dir: str, image_ext: str, n: int) -> torch.Tensor: + """Return a Tensor containing the patches""" + + def PIL2array(_img: Image.Image) -> np.ndarray: + """Convert PIL image type to numpy 2D array""" + return np.array(_img.getdata(), dtype=np.uint8).reshape(64, 64) + + def find_files(_data_dir: str, _image_ext: str) -> List[str]: + """Return a list with the file names of the images containing the patches""" + files = [] + # find those files with the specified extension + for file_dir in os.listdir(_data_dir): + if file_dir.endswith(_image_ext): + files.append(os.path.join(_data_dir, file_dir)) + return sorted(files) # sort files in ascend order to keep relations + + patches = [] + list_files = find_files(data_dir, image_ext) + + for fpath in list_files: + img = Image.open(fpath) + for y in range(0, img.height, 64): + for x in range(0, img.width, 64): + patch = img.crop((x, y, x + 64, y + 64)) + patches.append(PIL2array(patch)) + return torch.ByteTensor(np.array(patches[:n])) + + +def read_info_file(data_dir: str, info_file: str) -> torch.Tensor: + """Return a Tensor containing the list of labels + Read the file and keep only the ID of the 3D point. + """ + with open(os.path.join(data_dir, info_file)) as f: + labels = [int(line.split()[0]) for line in f] + return torch.LongTensor(labels) + + +def read_matches_files(data_dir: str, matches_file: str) -> torch.Tensor: + """Return a Tensor containing the ground truth matches + Read the file and keep only 3D point ID. + Matches are represented with a 1, non matches with a 0. + """ + matches = [] + with open(os.path.join(data_dir, matches_file)) as f: + for line in f: + line_split = line.split() + matches.append([int(line_split[0]), int(line_split[3]), int(line_split[1] == line_split[4])]) + return torch.LongTensor(matches) diff --git a/lib/python3.11/site-packages/torchvision/datasets/places365.py b/lib/python3.11/site-packages/torchvision/datasets/places365.py new file mode 100644 index 0000000000000000000000000000000000000000..a120e0e217a246f660bda0d05aa97e34fce8ea74 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/places365.py @@ -0,0 +1,168 @@ +import os +from os import path +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from urllib.parse import urljoin + +from .folder import default_loader +from .utils import check_integrity, download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class Places365(VisionDataset): + r"""`Places365 `_ classification dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of the Places365 dataset. + split (string, optional): The dataset split. Can be one of ``train-standard`` (default), ``train-challenge``, + ``val``. + small (bool, optional): If ``True``, uses the small images, i.e. resized to 256 x 256 pixels, instead of the + high resolution ones. + download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already + downloaded archives are not downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + loader (callable, optional): A function to load an image given its path. + + Attributes: + classes (list): List of the class names. + class_to_idx (dict): Dict with items (class_name, class_index). + imgs (list): List of (image path, class_index) tuples + targets (list): The class_index value for each image in the dataset + + Raises: + RuntimeError: If ``download is False`` and the meta files, i.e. the devkit, are not present or corrupted. + RuntimeError: If ``download is True`` and the image archive is already extracted. + """ + _SPLITS = ("train-standard", "train-challenge", "val") + _BASE_URL = "http://data.csail.mit.edu/places/places365/" + # {variant: (archive, md5)} + _DEVKIT_META = { + "standard": ("filelist_places365-standard.tar", "35a0585fee1fa656440f3ab298f8479c"), + "challenge": ("filelist_places365-challenge.tar", "70a8307e459c3de41690a7c76c931734"), + } + # (file, md5) + _CATEGORIES_META = ("categories_places365.txt", "06c963b85866bd0649f97cb43dd16673") + # {split: (file, md5)} + _FILE_LIST_META = { + "train-standard": ("places365_train_standard.txt", "30f37515461640559006b8329efbed1a"), + "train-challenge": ("places365_train_challenge.txt", "b2931dc997b8c33c27e7329c073a6b57"), + "val": ("places365_val.txt", "e9f2fd57bfd9d07630173f4e8708e4b1"), + } + # {(split, small): (file, md5)} + _IMAGES_META = { + ("train-standard", False): ("train_large_places365standard.tar", "67e186b496a84c929568076ed01a8aa1"), + ("train-challenge", False): ("train_large_places365challenge.tar", "605f18e68e510c82b958664ea134545f"), + ("val", False): ("val_large.tar", "9b71c4993ad89d2d8bcbdc4aef38042f"), + ("train-standard", True): ("train_256_places365standard.tar", "53ca1c756c3d1e7809517cc47c5561c5"), + ("train-challenge", True): ("train_256_places365challenge.tar", "741915038a5e3471ec7332404dfb64ef"), + ("val", True): ("val_256.tar", "e27b17d8d44f4af9a78502beb927f808"), + } + + def __init__( + self, + root: Union[str, Path], + split: str = "train-standard", + small: bool = False, + download: bool = False, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + loader: Callable[[str], Any] = default_loader, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + + self.split = self._verify_split(split) + self.small = small + self.loader = loader + + self.classes, self.class_to_idx = self.load_categories(download) + self.imgs, self.targets = self.load_file_list(download) + + if download: + self.download_images() + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + file, target = self.imgs[index] + image = self.loader(file) + + if self.transforms is not None: + image, target = self.transforms(image, target) + + return image, target + + def __len__(self) -> int: + return len(self.imgs) + + @property + def variant(self) -> str: + return "challenge" if "challenge" in self.split else "standard" + + @property + def images_dir(self) -> str: + size = "256" if self.small else "large" + if self.split.startswith("train"): + dir = f"data_{size}_{self.variant}" + else: + dir = f"{self.split}_{size}" + return path.join(self.root, dir) + + def load_categories(self, download: bool = True) -> Tuple[List[str], Dict[str, int]]: + def process(line: str) -> Tuple[str, int]: + cls, idx = line.split() + return cls, int(idx) + + file, md5 = self._CATEGORIES_META + file = path.join(self.root, file) + if not self._check_integrity(file, md5, download): + self.download_devkit() + + with open(file) as fh: + class_to_idx = dict(process(line) for line in fh) + + return sorted(class_to_idx.keys()), class_to_idx + + def load_file_list(self, download: bool = True) -> Tuple[List[Tuple[str, int]], List[int]]: + def process(line: str, sep="/") -> Tuple[str, int]: + image, idx = line.split() + return path.join(self.images_dir, image.lstrip(sep).replace(sep, os.sep)), int(idx) + + file, md5 = self._FILE_LIST_META[self.split] + file = path.join(self.root, file) + if not self._check_integrity(file, md5, download): + self.download_devkit() + + with open(file) as fh: + images = [process(line) for line in fh] + + _, targets = zip(*images) + return images, list(targets) + + def download_devkit(self) -> None: + file, md5 = self._DEVKIT_META[self.variant] + download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5) + + def download_images(self) -> None: + if path.exists(self.images_dir): + return + + file, md5 = self._IMAGES_META[(self.split, self.small)] + download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5) + + if self.split.startswith("train"): + os.rename(self.images_dir.rsplit("_", 1)[0], self.images_dir) + + def extra_repr(self) -> str: + return "\n".join(("Split: {split}", "Small: {small}")).format(**self.__dict__) + + def _verify_split(self, split: str) -> str: + return verify_str_arg(split, "split", self._SPLITS) + + def _check_integrity(self, file: str, md5: str, download: bool) -> bool: + integrity = check_integrity(file, md5=md5) + if not integrity and not download: + raise RuntimeError( + f"The file {file} does not exist or is corrupted. You can set download=True to download it." + ) + return integrity diff --git a/lib/python3.11/site-packages/torchvision/datasets/rendered_sst2.py b/lib/python3.11/site-packages/torchvision/datasets/rendered_sst2.py new file mode 100644 index 0000000000000000000000000000000000000000..48b0ddfc4fb3394505c1125e67cc026b3fd14fc8 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/rendered_sst2.py @@ -0,0 +1,86 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .folder import make_dataset +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class RenderedSST2(VisionDataset): + """`The Rendered SST2 Dataset `_. + + Rendered SST2 is an image classification dataset used to evaluate the models capability on optical + character recognition. This dataset was generated by rendering sentences in the Standford Sentiment + Treebank v2 dataset. + + This dataset contains two classes (positive and negative) and is divided in three splits: a train + split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images + (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative). + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"train"`` (default), `"val"` and ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. Default is False. + """ + + _URL = "https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz" + _MD5 = "2384d08e9dcfa4bd55b324e610496ee5" + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self._split = verify_str_arg(split, "split", ("train", "val", "test")) + self._split_to_folder = {"train": "train", "val": "valid", "test": "test"} + self._base_folder = Path(self.root) / "rendered-sst2" + self.classes = ["negative", "positive"] + self.class_to_idx = {"negative": 0, "positive": 1} + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + self._samples = make_dataset(str(self._base_folder / self._split_to_folder[self._split]), extensions=("png",)) + + def __len__(self) -> int: + return len(self._samples) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._samples[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def extra_repr(self) -> str: + return f"split={self._split}" + + def _check_exists(self) -> bool: + for class_label in set(self.classes): + if not (self._base_folder / self._split_to_folder[self._split] / class_label).is_dir(): + return False + return True + + def _download(self) -> None: + if self._check_exists(): + return + download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/samplers/__init__.py b/lib/python3.11/site-packages/torchvision/datasets/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..58b2d2abd936d885221174d194a633a8e413935f --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/samplers/__init__.py @@ -0,0 +1,3 @@ +from .clip_sampler import DistributedSampler, RandomClipSampler, UniformClipSampler + +__all__ = ("DistributedSampler", "UniformClipSampler", "RandomClipSampler") diff --git a/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..196a20de8f0bf811e21088016123c89b0ab94026 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/clip_sampler.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/clip_sampler.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e809fe62159a27711d6fb607b7c146bb5c2bf533 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/datasets/samplers/__pycache__/clip_sampler.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/datasets/samplers/clip_sampler.py b/lib/python3.11/site-packages/torchvision/datasets/samplers/clip_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..026c3d75d3b8acd5d0240d8e537e608a92cddfdb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/samplers/clip_sampler.py @@ -0,0 +1,172 @@ +import math +from typing import cast, Iterator, List, Optional, Sized, Union + +import torch +import torch.distributed as dist +from torch.utils.data import Sampler +from torchvision.datasets.video_utils import VideoClips + + +class DistributedSampler(Sampler): + """ + Extension of DistributedSampler, as discussed in + https://github.com/pytorch/pytorch/issues/23430 + + Example: + dataset: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] + num_replicas: 4 + shuffle: False + + when group_size = 1 + RANK | shard_dataset + ========================= + rank_0 | [0, 4, 8, 12] + rank_1 | [1, 5, 9, 13] + rank_2 | [2, 6, 10, 0] + rank_3 | [3, 7, 11, 1] + + when group_size = 2 + + RANK | shard_dataset + ========================= + rank_0 | [0, 1, 8, 9] + rank_1 | [2, 3, 10, 11] + rank_2 | [4, 5, 12, 13] + rank_3 | [6, 7, 0, 1] + + """ + + def __init__( + self, + dataset: Sized, + num_replicas: Optional[int] = None, + rank: Optional[int] = None, + shuffle: bool = False, + group_size: int = 1, + ) -> None: + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + if len(dataset) % group_size != 0: + raise ValueError( + f"dataset length must be a multiplier of group size dataset length: {len(dataset)}, group size: {group_size}" + ) + self.dataset = dataset + self.group_size = group_size + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + dataset_group_length = len(dataset) // group_size + self.num_group_samples = int(math.ceil(dataset_group_length * 1.0 / self.num_replicas)) + self.num_samples = self.num_group_samples * group_size + self.total_size = self.num_samples * self.num_replicas + self.shuffle = shuffle + + def __iter__(self) -> Iterator[int]: + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + indices: Union[torch.Tensor, List[int]] + if self.shuffle: + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = list(range(len(self.dataset))) + + # add extra samples to make it evenly divisible + indices += indices[: (self.total_size - len(indices))] + assert len(indices) == self.total_size + + total_group_size = self.total_size // self.group_size + indices = torch.reshape(torch.LongTensor(indices), (total_group_size, self.group_size)) + + # subsample + indices = indices[self.rank : total_group_size : self.num_replicas, :] + indices = torch.reshape(indices, (-1,)).tolist() + assert len(indices) == self.num_samples + + if isinstance(self.dataset, Sampler): + orig_indices = list(iter(self.dataset)) + indices = [orig_indices[i] for i in indices] + + return iter(indices) + + def __len__(self) -> int: + return self.num_samples + + def set_epoch(self, epoch: int) -> None: + self.epoch = epoch + + +class UniformClipSampler(Sampler): + """ + Sample `num_video_clips_per_video` clips for each video, equally spaced. + When number of unique clips in the video is fewer than num_video_clips_per_video, + repeat the clips until `num_video_clips_per_video` clips are collected + + Args: + video_clips (VideoClips): video clips to sample from + num_clips_per_video (int): number of clips to be sampled per video + """ + + def __init__(self, video_clips: VideoClips, num_clips_per_video: int) -> None: + if not isinstance(video_clips, VideoClips): + raise TypeError(f"Expected video_clips to be an instance of VideoClips, got {type(video_clips)}") + self.video_clips = video_clips + self.num_clips_per_video = num_clips_per_video + + def __iter__(self) -> Iterator[int]: + idxs = [] + s = 0 + # select num_clips_per_video for each video, uniformly spaced + for c in self.video_clips.clips: + length = len(c) + if length == 0: + # corner case where video decoding fails + continue + + sampled = torch.linspace(s, s + length - 1, steps=self.num_clips_per_video).floor().to(torch.int64) + s += length + idxs.append(sampled) + return iter(cast(List[int], torch.cat(idxs).tolist())) + + def __len__(self) -> int: + return sum(self.num_clips_per_video for c in self.video_clips.clips if len(c) > 0) + + +class RandomClipSampler(Sampler): + """ + Samples at most `max_video_clips_per_video` clips for each video randomly + + Args: + video_clips (VideoClips): video clips to sample from + max_clips_per_video (int): maximum number of clips to be sampled per video + """ + + def __init__(self, video_clips: VideoClips, max_clips_per_video: int) -> None: + if not isinstance(video_clips, VideoClips): + raise TypeError(f"Expected video_clips to be an instance of VideoClips, got {type(video_clips)}") + self.video_clips = video_clips + self.max_clips_per_video = max_clips_per_video + + def __iter__(self) -> Iterator[int]: + idxs = [] + s = 0 + # select at most max_clips_per_video for each video, randomly + for c in self.video_clips.clips: + length = len(c) + size = min(length, self.max_clips_per_video) + sampled = torch.randperm(length)[:size] + s + s += length + idxs.append(sampled) + idxs_ = torch.cat(idxs) + # shuffle all clips randomly + perm = torch.randperm(len(idxs_)) + return iter(idxs_[perm].tolist()) + + def __len__(self) -> int: + return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips) diff --git a/lib/python3.11/site-packages/torchvision/datasets/sbd.py b/lib/python3.11/site-packages/torchvision/datasets/sbd.py new file mode 100644 index 0000000000000000000000000000000000000000..4b9ccb75eb9bc6c5c2e9af6fbb4b5868e8488923 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/sbd.py @@ -0,0 +1,126 @@ +import os +import shutil +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import download_and_extract_archive, download_url, verify_str_arg +from .vision import VisionDataset + + +class SBDataset(VisionDataset): + """`Semantic Boundaries Dataset `_ + + The SBD currently contains annotations from 11355 images taken from the PASCAL VOC 2011 dataset. + + .. note :: + + Please note that the train and val splits included with this dataset are different from + the splits in the PASCAL VOC dataset. In particular some "train" images might be part of + VOC2012 val. + If you are interested in testing on VOC 2012 val, then use `image_set='train_noval'`, + which excludes all val images. + + .. warning:: + + This class needs `scipy `_ to load target files from `.mat` format. + + Args: + root (str or ``pathlib.Path``): Root directory of the Semantic Boundaries Dataset + image_set (string, optional): Select the image_set to use, ``train``, ``val`` or ``train_noval``. + Image set ``train_noval`` excludes VOC 2012 val images. + mode (string, optional): Select target type. Possible values 'boundaries' or 'segmentation'. + In case of 'boundaries', the target is an array of shape `[num_classes, H, W]`, + where `num_classes=20`. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. Input sample is PIL image and target is a numpy array + if `mode='boundaries'` or PIL image if `mode='segmentation'`. + """ + + url = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz" + md5 = "82b4d87ceb2ed10f6038a1cba92111cb" + filename = "benchmark.tgz" + + voc_train_url = "https://www.cs.cornell.edu/~bharathh/train_noval.txt" + voc_split_filename = "train_noval.txt" + voc_split_md5 = "79bff800c5f0b1ec6b21080a3c066722" + + def __init__( + self, + root: Union[str, Path], + image_set: str = "train", + mode: str = "boundaries", + download: bool = False, + transforms: Optional[Callable] = None, + ) -> None: + + try: + from scipy.io import loadmat + + self._loadmat = loadmat + except ImportError: + raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy") + + super().__init__(root, transforms) + self.image_set = verify_str_arg(image_set, "image_set", ("train", "val", "train_noval")) + self.mode = verify_str_arg(mode, "mode", ("segmentation", "boundaries")) + self.num_classes = 20 + + sbd_root = self.root + image_dir = os.path.join(sbd_root, "img") + mask_dir = os.path.join(sbd_root, "cls") + + if download: + download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5) + extracted_ds_root = os.path.join(self.root, "benchmark_RELEASE", "dataset") + for f in ["cls", "img", "inst", "train.txt", "val.txt"]: + old_path = os.path.join(extracted_ds_root, f) + shutil.move(old_path, sbd_root) + if self.image_set == "train_noval": + # Note: this is failing as of June 2024 https://github.com/pytorch/vision/issues/8471 + download_url(self.voc_train_url, sbd_root, self.voc_split_filename, self.voc_split_md5) + + if not os.path.isdir(sbd_root): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + split_f = os.path.join(sbd_root, image_set.rstrip("\n") + ".txt") + + with open(os.path.join(split_f)) as fh: + file_names = [x.strip() for x in fh.readlines()] + + self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names] + self.masks = [os.path.join(mask_dir, x + ".mat") for x in file_names] + + self._get_target = self._get_segmentation_target if self.mode == "segmentation" else self._get_boundaries_target + + def _get_segmentation_target(self, filepath: str) -> Image.Image: + mat = self._loadmat(filepath) + return Image.fromarray(mat["GTcls"][0]["Segmentation"][0]) + + def _get_boundaries_target(self, filepath: str) -> np.ndarray: + mat = self._loadmat(filepath) + return np.concatenate( + [np.expand_dims(mat["GTcls"][0]["Boundaries"][0][i][0].toarray(), axis=0) for i in range(self.num_classes)], + axis=0, + ) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + img = Image.open(self.images[index]).convert("RGB") + target = self._get_target(self.masks[index]) + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + def __len__(self) -> int: + return len(self.images) + + def extra_repr(self) -> str: + lines = ["Image set: {image_set}", "Mode: {mode}"] + return "\n".join(lines).format(**self.__dict__) diff --git a/lib/python3.11/site-packages/torchvision/datasets/sbu.py b/lib/python3.11/site-packages/torchvision/datasets/sbu.py new file mode 100644 index 0000000000000000000000000000000000000000..b5f46101e07ed146f4a2c6392cf2df119413d50f --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/sbu.py @@ -0,0 +1,109 @@ +import os +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +from PIL import Image + +from .utils import check_integrity, download_and_extract_archive, download_url +from .vision import VisionDataset + + +class SBU(VisionDataset): + """`SBU Captioned Photo `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where tarball + ``SBUCaptionedPhotoDataset.tar.gz`` exists. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + url = "https://www.cs.rice.edu/~vo9/sbucaptions/SBUCaptionedPhotoDataset.tar.gz" + filename = "SBUCaptionedPhotoDataset.tar.gz" + md5_checksum = "9aec147b3488753cf758b4d493422285" + + def __init__( + self, + root: Union[str, Path], + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = True, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + # Read the caption for each photo + self.photos = [] + self.captions = [] + + file1 = os.path.join(self.root, "dataset", "SBU_captioned_photo_dataset_urls.txt") + file2 = os.path.join(self.root, "dataset", "SBU_captioned_photo_dataset_captions.txt") + + for line1, line2 in zip(open(file1), open(file2)): + url = line1.rstrip() + photo = os.path.basename(url) + filename = os.path.join(self.root, "dataset", photo) + if os.path.exists(filename): + caption = line2.rstrip() + self.photos.append(photo) + self.captions.append(caption) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is a caption for the photo. + """ + filename = os.path.join(self.root, "dataset", self.photos[index]) + img = Image.open(filename).convert("RGB") + if self.transform is not None: + img = self.transform(img) + + target = self.captions[index] + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + """The number of photos in the dataset.""" + return len(self.photos) + + def _check_integrity(self) -> bool: + """Check the md5 checksum of the downloaded tarball.""" + root = self.root + fpath = os.path.join(root, self.filename) + if not check_integrity(fpath, self.md5_checksum): + return False + return True + + def download(self) -> None: + """Download and extract the tarball, and download each individual photo.""" + + if self._check_integrity(): + return + + download_and_extract_archive(self.url, self.root, self.root, self.filename, self.md5_checksum) + + # Download individual photos + with open(os.path.join(self.root, "dataset", "SBU_captioned_photo_dataset_urls.txt")) as fh: + for line in fh: + url = line.rstrip() + try: + download_url(url, os.path.join(self.root, "dataset")) + except OSError: + # The images point to public images on Flickr. + # Note: Images might be removed by users at anytime. + pass diff --git a/lib/python3.11/site-packages/torchvision/datasets/semeion.py b/lib/python3.11/site-packages/torchvision/datasets/semeion.py new file mode 100644 index 0000000000000000000000000000000000000000..71485b14dcf99dc7b5064c77582c5657a7d1571d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/semeion.py @@ -0,0 +1,91 @@ +import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import check_integrity, download_url +from .vision import VisionDataset + + +class SEMEION(VisionDataset): + r"""`SEMEION `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``semeion.py`` exists. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + url = "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data" + filename = "semeion.data" + md5_checksum = "cb545d371d2ce14ec121470795a77432" + + def __init__( + self, + root: Union[str, Path], + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = True, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + fp = os.path.join(self.root, self.filename) + data = np.loadtxt(fp) + # convert value to 8 bit unsigned integer + # color (white #255) the pixels + self.data = (data[:, :256] * 255).astype("uint8") + self.data = np.reshape(self.data, (-1, 16, 16)) + self.labels = np.nonzero(data[:, 256:])[1] + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], int(self.labels[index]) + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(img, mode="L") + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) + + def _check_integrity(self) -> bool: + root = self.root + fpath = os.path.join(root, self.filename) + if not check_integrity(fpath, self.md5_checksum): + return False + return True + + def download(self) -> None: + if self._check_integrity(): + return + + root = self.root + download_url(self.url, root, self.filename, self.md5_checksum) diff --git a/lib/python3.11/site-packages/torchvision/datasets/stanford_cars.py b/lib/python3.11/site-packages/torchvision/datasets/stanford_cars.py new file mode 100644 index 0000000000000000000000000000000000000000..6264de82eb7a567d10572d5d733011dd84e08d5b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/stanford_cars.py @@ -0,0 +1,114 @@ +import pathlib +from typing import Any, Callable, Optional, Tuple, Union + +from PIL import Image + +from .utils import verify_str_arg +from .vision import VisionDataset + + +class StanfordCars(VisionDataset): + """Stanford Cars Dataset + + The Cars dataset contains 16,185 images of 196 classes of cars. The data is + split into 8,144 training images and 8,041 testing images, where each class + has been split roughly in a 50-50 split + + The original URL is https://ai.stanford.edu/~jkrause/cars/car_dataset.html, but it is broken. + Follow the instructions in ``download`` argument to obtain and use the dataset offline. + + .. note:: + + This class needs `scipy `_ to load target files from `.mat` format. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset + split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): This parameter exists for backward compatibility but it does not + download the dataset, since the original URL is not available anymore. The dataset + seems to be available on Kaggle so you can try to manually download and configure it using + `these instructions `_, + or use an integrated + `dataset on Kaggle `_. + In both cases, first download and configure the dataset locally, and use the dataset with + ``"download=False"``. + """ + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + + try: + import scipy.io as sio + except ImportError: + raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy") + + super().__init__(root, transform=transform, target_transform=target_transform) + + self._split = verify_str_arg(split, "split", ("train", "test")) + self._base_folder = pathlib.Path(root) / "stanford_cars" + devkit = self._base_folder / "devkit" + + if self._split == "train": + self._annotations_mat_path = devkit / "cars_train_annos.mat" + self._images_base_path = self._base_folder / "cars_train" + else: + self._annotations_mat_path = self._base_folder / "cars_test_annos_withlabels.mat" + self._images_base_path = self._base_folder / "cars_test" + + if download: + self.download() + + if not self._check_exists(): + raise RuntimeError( + "Dataset not found. Try to manually download following the instructions in " + "https://github.com/pytorch/vision/issues/7545#issuecomment-1631441616." + ) + + self._samples = [ + ( + str(self._images_base_path / annotation["fname"]), + annotation["class"] - 1, # Original target mapping starts from 1, hence -1 + ) + for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"] + ] + + self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist() + self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)} + + def __len__(self) -> int: + return len(self._samples) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + """Returns pil_image and class_id for given index""" + image_path, target = self._samples[idx] + pil_image = Image.open(image_path).convert("RGB") + + if self.transform is not None: + pil_image = self.transform(pil_image) + if self.target_transform is not None: + target = self.target_transform(target) + return pil_image, target + + def _check_exists(self) -> bool: + if not (self._base_folder / "devkit").is_dir(): + return False + + return self._annotations_mat_path.exists() and self._images_base_path.is_dir() + + def download(self): + raise ValueError( + "The original URL is broken so the StanfordCars dataset is not available for automatic " + "download anymore. You can try to download it manually following " + "https://github.com/pytorch/vision/issues/7545#issuecomment-1631441616, " + "and set download=False to avoid this error." + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/stl10.py b/lib/python3.11/site-packages/torchvision/datasets/stl10.py new file mode 100644 index 0000000000000000000000000000000000000000..39c058e497d4d950d57b9b027907b45f0bc46a58 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/stl10.py @@ -0,0 +1,174 @@ +import os.path +from pathlib import Path +from typing import Any, Callable, cast, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import check_integrity, download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class STL10(VisionDataset): + """`STL10 `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset where directory + ``stl10_binary`` exists. + split (string): One of {'train', 'test', 'unlabeled', 'train+unlabeled'}. + Accordingly, dataset is selected. + folds (int, optional): One of {0-9} or None. + For training, loads one of the 10 pre-defined folds of 1k samples for the + standard evaluation procedure. If no value is passed, loads the 5k samples. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + base_folder = "stl10_binary" + url = "http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz" + filename = "stl10_binary.tar.gz" + tgz_md5 = "91f7769df0f17e558f3565bffb0c7dfb" + class_names_file = "class_names.txt" + folds_list_file = "fold_indices.txt" + train_list = [ + ["train_X.bin", "918c2871b30a85fa023e0c44e0bee87f"], + ["train_y.bin", "5a34089d4802c674881badbb80307741"], + ["unlabeled_X.bin", "5242ba1fed5e4be9e1e742405eb56ca4"], + ] + + test_list = [["test_X.bin", "7f263ba9f9e0b06b93213547f721ac82"], ["test_y.bin", "36f9794fa4beb8a2c72628de14fa638e"]] + splits = ("train", "train+unlabeled", "unlabeled", "test") + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + folds: Optional[int] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.split = verify_str_arg(split, "split", self.splits) + self.folds = self._verify_folds(folds) + + if download: + self.download() + elif not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + # now load the picked numpy arrays + self.labels: Optional[np.ndarray] + if self.split == "train": + self.data, self.labels = self.__loadfile(self.train_list[0][0], self.train_list[1][0]) + self.labels = cast(np.ndarray, self.labels) + self.__load_folds(folds) + + elif self.split == "train+unlabeled": + self.data, self.labels = self.__loadfile(self.train_list[0][0], self.train_list[1][0]) + self.labels = cast(np.ndarray, self.labels) + self.__load_folds(folds) + unlabeled_data, _ = self.__loadfile(self.train_list[2][0]) + self.data = np.concatenate((self.data, unlabeled_data)) + self.labels = np.concatenate((self.labels, np.asarray([-1] * unlabeled_data.shape[0]))) + + elif self.split == "unlabeled": + self.data, _ = self.__loadfile(self.train_list[2][0]) + self.labels = np.asarray([-1] * self.data.shape[0]) + else: # self.split == 'test': + self.data, self.labels = self.__loadfile(self.test_list[0][0], self.test_list[1][0]) + + class_file = os.path.join(self.root, self.base_folder, self.class_names_file) + if os.path.isfile(class_file): + with open(class_file) as f: + self.classes = f.read().splitlines() + + def _verify_folds(self, folds: Optional[int]) -> Optional[int]: + if folds is None: + return folds + elif isinstance(folds, int): + if folds in range(10): + return folds + msg = "Value for argument folds should be in the range [0, 10), but got {}." + raise ValueError(msg.format(folds)) + else: + msg = "Expected type None or int for argument folds, but got type {}." + raise ValueError(msg.format(type(folds))) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + target: Optional[int] + if self.labels is not None: + img, target = self.data[index], int(self.labels[index]) + else: + img, target = self.data[index], None + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(np.transpose(img, (1, 2, 0))) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return self.data.shape[0] + + def __loadfile(self, data_file: str, labels_file: Optional[str] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]: + labels = None + if labels_file: + path_to_labels = os.path.join(self.root, self.base_folder, labels_file) + with open(path_to_labels, "rb") as f: + labels = np.fromfile(f, dtype=np.uint8) - 1 # 0-based + + path_to_data = os.path.join(self.root, self.base_folder, data_file) + with open(path_to_data, "rb") as f: + # read whole file in uint8 chunks + everything = np.fromfile(f, dtype=np.uint8) + images = np.reshape(everything, (-1, 3, 96, 96)) + images = np.transpose(images, (0, 1, 3, 2)) + + return images, labels + + def _check_integrity(self) -> bool: + for filename, md5 in self.train_list + self.test_list: + fpath = os.path.join(self.root, self.base_folder, filename) + if not check_integrity(fpath, md5): + return False + return True + + def download(self) -> None: + if self._check_integrity(): + return + download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5) + self._check_integrity() + + def extra_repr(self) -> str: + return "Split: {split}".format(**self.__dict__) + + def __load_folds(self, folds: Optional[int]) -> None: + # loads one of the folds if specified + if folds is None: + return + path_to_folds = os.path.join(self.root, self.base_folder, self.folds_list_file) + with open(path_to_folds) as f: + str_idx = f.read().splitlines()[folds] + list_idx = np.fromstring(str_idx, dtype=np.int64, sep=" ") + self.data = self.data[list_idx, :, :, :] + if self.labels is not None: + self.labels = self.labels[list_idx] diff --git a/lib/python3.11/site-packages/torchvision/datasets/sun397.py b/lib/python3.11/site-packages/torchvision/datasets/sun397.py new file mode 100644 index 0000000000000000000000000000000000000000..4db0a3cf237376f9a59e06f770f90554abfd87e2 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/sun397.py @@ -0,0 +1,76 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import PIL.Image + +from .utils import download_and_extract_archive +from .vision import VisionDataset + + +class SUN397(VisionDataset): + """`The SUN397 Data Set `_. + + The SUN397 or Scene UNderstanding (SUN) is a dataset for scene recognition consisting of + 397 categories with 108'754 images. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + """ + + _DATASET_URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz" + _DATASET_MD5 = "8ca2778205c41d23104230ba66911c7a" + + def __init__( + self, + root: Union[str, Path], + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self._data_dir = Path(self.root) / "SUN397" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + with open(self._data_dir / "ClassName.txt") as f: + self.classes = [c[3:].strip() for c in f] + + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + self._image_files = list(self._data_dir.rglob("sun_*.jpg")) + + self._labels = [ + self.class_to_idx["/".join(path.relative_to(self._data_dir).parts[1:-1])] for path in self._image_files + ] + + def __len__(self) -> int: + return len(self._image_files) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image_file, label = self._image_files[idx], self._labels[idx] + image = PIL.Image.open(image_file).convert("RGB") + + if self.transform: + image = self.transform(image) + + if self.target_transform: + label = self.target_transform(label) + + return image, label + + def _check_exists(self) -> bool: + return self._data_dir.is_dir() + + def _download(self) -> None: + if self._check_exists(): + return + download_and_extract_archive(self._DATASET_URL, download_root=self.root, md5=self._DATASET_MD5) diff --git a/lib/python3.11/site-packages/torchvision/datasets/svhn.py b/lib/python3.11/site-packages/torchvision/datasets/svhn.py new file mode 100644 index 0000000000000000000000000000000000000000..5d20d7db7e3ccc1d77c5949830cf7b610d88355f --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/svhn.py @@ -0,0 +1,130 @@ +import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import check_integrity, download_url, verify_str_arg +from .vision import VisionDataset + + +class SVHN(VisionDataset): + """`SVHN `_ Dataset. + Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset, + we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which + expect the class labels to be in the range `[0, C-1]` + + .. warning:: + + This class needs `scipy `_ to load data from `.mat` format. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset where the data is stored. + split (string): One of {'train', 'test', 'extra'}. + Accordingly dataset is selected. 'extra' is Extra training set. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + split_list = { + "train": [ + "http://ufldl.stanford.edu/housenumbers/train_32x32.mat", + "train_32x32.mat", + "e26dedcc434d2e4c54c9b2d4a06d8373", + ], + "test": [ + "http://ufldl.stanford.edu/housenumbers/test_32x32.mat", + "test_32x32.mat", + "eb5a983be6a315427106f1b164d9cef3", + ], + "extra": [ + "http://ufldl.stanford.edu/housenumbers/extra_32x32.mat", + "extra_32x32.mat", + "a93ce644f1a588dc4d68dda5feec44a7", + ], + } + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.split = verify_str_arg(split, "split", tuple(self.split_list.keys())) + self.url = self.split_list[split][0] + self.filename = self.split_list[split][1] + self.file_md5 = self.split_list[split][2] + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + # import here rather than at top of file because this is + # an optional dependency for torchvision + import scipy.io as sio + + # reading(loading) mat file as array + loaded_mat = sio.loadmat(os.path.join(self.root, self.filename)) + + self.data = loaded_mat["X"] + # loading from the .mat file gives an np.ndarray of type np.uint8 + # converting to np.int64, so that we have a LongTensor after + # the conversion from the numpy array + # the squeeze is needed to obtain a 1D tensor + self.labels = loaded_mat["y"].astype(np.int64).squeeze() + + # the svhn dataset assigns the class label "10" to the digit 0 + # this makes it inconsistent with several loss functions + # which expect the class labels to be in the range [0, C-1] + np.place(self.labels, self.labels == 10, 0) + self.data = np.transpose(self.data, (3, 2, 0, 1)) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], int(self.labels[index]) + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(np.transpose(img, (1, 2, 0))) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) + + def _check_integrity(self) -> bool: + root = self.root + md5 = self.split_list[self.split][2] + fpath = os.path.join(root, self.filename) + return check_integrity(fpath, md5) + + def download(self) -> None: + md5 = self.split_list[self.split][2] + download_url(self.url, self.root, self.filename, md5) + + def extra_repr(self) -> str: + return "Split: {split}".format(**self.__dict__) diff --git a/lib/python3.11/site-packages/torchvision/datasets/ucf101.py b/lib/python3.11/site-packages/torchvision/datasets/ucf101.py new file mode 100644 index 0000000000000000000000000000000000000000..935f8ad41c78e6086444b72ed1f5ae1f6de34bad --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/ucf101.py @@ -0,0 +1,131 @@ +import os +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +from torch import Tensor + +from .folder import find_classes, make_dataset +from .video_utils import VideoClips +from .vision import VisionDataset + + +class UCF101(VisionDataset): + """ + `UCF101 `_ dataset. + + UCF101 is an action recognition video dataset. + This dataset consider every video as a collection of video clips of fixed size, specified + by ``frames_per_clip``, where the step in frames between each clip is given by + ``step_between_clips``. The dataset itself can be downloaded from the dataset website; + annotations that ``annotation_path`` should be pointing to can be downloaded from `here + `_. + + To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5`` + and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two + elements will come from video 1, and the next three elements from video 2. + Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all + frames in a video might be present. + + Internally, it uses a VideoClips object to handle clip creation. + + Args: + root (str or ``pathlib.Path``): Root directory of the UCF101 Dataset. + annotation_path (str): path to the folder containing the split files; + see docstring above for download instructions of these files + frames_per_clip (int): number of frames in a clip. + step_between_clips (int, optional): number of frames between each clip. + fold (int, optional): which fold to use. Should be between 1 and 3. + train (bool, optional): if ``True``, creates a dataset from the train split, + otherwise from the ``test`` split. + transform (callable, optional): A function/transform that takes in a TxHxWxC video + and returns a transformed version. + output_format (str, optional): The format of the output video tensors (before transforms). + Can be either "THWC" (default) or "TCHW". + + Returns: + tuple: A 3-tuple with the following entries: + + - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames + - audio(Tensor[K, L]): the audio frames, where `K` is the number of channels + and `L` is the number of points + - label (int): class of the video clip + """ + + def __init__( + self, + root: Union[str, Path], + annotation_path: str, + frames_per_clip: int, + step_between_clips: int = 1, + frame_rate: Optional[int] = None, + fold: int = 1, + train: bool = True, + transform: Optional[Callable] = None, + _precomputed_metadata: Optional[Dict[str, Any]] = None, + num_workers: int = 1, + _video_width: int = 0, + _video_height: int = 0, + _video_min_dimension: int = 0, + _audio_samples: int = 0, + output_format: str = "THWC", + ) -> None: + super().__init__(root) + if not 1 <= fold <= 3: + raise ValueError(f"fold should be between 1 and 3, got {fold}") + + extensions = ("avi",) + self.fold = fold + self.train = train + + self.classes, class_to_idx = find_classes(self.root) + self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None) + video_list = [x[0] for x in self.samples] + video_clips = VideoClips( + video_list, + frames_per_clip, + step_between_clips, + frame_rate, + _precomputed_metadata, + num_workers=num_workers, + _video_width=_video_width, + _video_height=_video_height, + _video_min_dimension=_video_min_dimension, + _audio_samples=_audio_samples, + output_format=output_format, + ) + # we bookkeep the full version of video clips because we want to be able + # to return the metadata of full version rather than the subset version of + # video clips + self.full_video_clips = video_clips + self.indices = self._select_fold(video_list, annotation_path, fold, train) + self.video_clips = video_clips.subset(self.indices) + self.transform = transform + + @property + def metadata(self) -> Dict[str, Any]: + return self.full_video_clips.metadata + + def _select_fold(self, video_list: List[str], annotation_path: str, fold: int, train: bool) -> List[int]: + name = "train" if train else "test" + name = f"{name}list{fold:02d}.txt" + f = os.path.join(annotation_path, name) + selected_files = set() + with open(f) as fid: + data = fid.readlines() + data = [x.strip().split(" ")[0] for x in data] + data = [os.path.join(self.root, *x.split("/")) for x in data] + selected_files.update(data) + indices = [i for i in range(len(video_list)) if video_list[i] in selected_files] + return indices + + def __len__(self) -> int: + return self.video_clips.num_clips() + + def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]: + video, audio, info, video_idx = self.video_clips.get_clip(idx) + label = self.samples[self.indices[video_idx]][1] + + if self.transform is not None: + video = self.transform(video) + + return video, audio, label diff --git a/lib/python3.11/site-packages/torchvision/datasets/usps.py b/lib/python3.11/site-packages/torchvision/datasets/usps.py new file mode 100644 index 0000000000000000000000000000000000000000..9c681e79f6c3dc8fb6567f90b741ac7839298931 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/usps.py @@ -0,0 +1,96 @@ +import os +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import download_url +from .vision import VisionDataset + + +class USPS(VisionDataset): + """`USPS `_ Dataset. + The data-format is : [label [index:value ]*256 \\n] * num_lines, where ``label`` lies in ``[1, 10]``. + The value for each pixel lies in ``[-1, 1]``. Here we transform the ``label`` into ``[0, 9]`` + and make pixel values in ``[0, 255]``. + + Args: + root (str or ``pathlib.Path``): Root directory of dataset to store``USPS`` data files. + train (bool, optional): If True, creates dataset from ``usps.bz2``, + otherwise from ``usps.t.bz2``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + split_list = { + "train": [ + "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.bz2", + "usps.bz2", + "ec16c51db3855ca6c91edd34d0e9b197", + ], + "test": [ + "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.t.bz2", + "usps.t.bz2", + "8ea070ee2aca1ac39742fdd1ef5ed118", + ], + } + + def __init__( + self, + root: Union[str, Path], + train: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + split = "train" if train else "test" + url, filename, checksum = self.split_list[split] + full_path = os.path.join(self.root, filename) + + if download and not os.path.exists(full_path): + download_url(url, self.root, filename, md5=checksum) + + import bz2 + + with bz2.open(full_path) as fp: + raw_data = [line.decode().split() for line in fp.readlines()] + tmp_list = [[x.split(":")[-1] for x in data[1:]] for data in raw_data] + imgs = np.asarray(tmp_list, dtype=np.float32).reshape((-1, 16, 16)) + imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8) + targets = [int(d[0]) - 1 for d in raw_data] + + self.data = imgs + self.targets = targets + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], int(self.targets[index]) + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(img, mode="L") + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) diff --git a/lib/python3.11/site-packages/torchvision/datasets/utils.py b/lib/python3.11/site-packages/torchvision/datasets/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8bf310896c7efbdb5693156860afbf9977ebf4bb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/utils.py @@ -0,0 +1,471 @@ +import bz2 +import gzip +import hashlib +import lzma +import os +import os.path +import pathlib +import re +import sys +import tarfile +import urllib +import urllib.error +import urllib.request +import zipfile +from typing import Any, Callable, Dict, IO, Iterable, List, Optional, Tuple, TypeVar, Union +from urllib.parse import urlparse + +import numpy as np +import torch +from torch.utils.model_zoo import tqdm + +from .._internally_replaced_utils import _download_file_from_remote_location, _is_remote_location_available + +USER_AGENT = "pytorch/vision" + + +def _urlretrieve(url: str, filename: Union[str, pathlib.Path], chunk_size: int = 1024 * 32) -> None: + with urllib.request.urlopen(urllib.request.Request(url, headers={"User-Agent": USER_AGENT})) as response: + with open(filename, "wb") as fh, tqdm(total=response.length, unit="B", unit_scale=True) as pbar: + while chunk := response.read(chunk_size): + fh.write(chunk) + pbar.update(len(chunk)) + + +def calculate_md5(fpath: Union[str, pathlib.Path], chunk_size: int = 1024 * 1024) -> str: + # Setting the `usedforsecurity` flag does not change anything about the functionality, but indicates that we are + # not using the MD5 checksum for cryptography. This enables its usage in restricted environments like FIPS. Without + # it torchvision.datasets is unusable in these environments since we perform a MD5 check everywhere. + if sys.version_info >= (3, 9): + md5 = hashlib.md5(usedforsecurity=False) + else: + md5 = hashlib.md5() + with open(fpath, "rb") as f: + while chunk := f.read(chunk_size): + md5.update(chunk) + return md5.hexdigest() + + +def check_md5(fpath: Union[str, pathlib.Path], md5: str, **kwargs: Any) -> bool: + return md5 == calculate_md5(fpath, **kwargs) + + +def check_integrity(fpath: Union[str, pathlib.Path], md5: Optional[str] = None) -> bool: + if not os.path.isfile(fpath): + return False + if md5 is None: + return True + return check_md5(fpath, md5) + + +def _get_redirect_url(url: str, max_hops: int = 3) -> str: + initial_url = url + headers = {"Method": "HEAD", "User-Agent": USER_AGENT} + + for _ in range(max_hops + 1): + with urllib.request.urlopen(urllib.request.Request(url, headers=headers)) as response: + if response.url == url or response.url is None: + return url + + url = response.url + else: + raise RecursionError( + f"Request to {initial_url} exceeded {max_hops} redirects. The last redirect points to {url}." + ) + + +def _get_google_drive_file_id(url: str) -> Optional[str]: + parts = urlparse(url) + + if re.match(r"(drive|docs)[.]google[.]com", parts.netloc) is None: + return None + + match = re.match(r"/file/d/(?P[^/]*)", parts.path) + if match is None: + return None + + return match.group("id") + + +def download_url( + url: str, + root: Union[str, pathlib.Path], + filename: Optional[Union[str, pathlib.Path]] = None, + md5: Optional[str] = None, + max_redirect_hops: int = 3, +) -> None: + """Download a file from a url and place it in root. + + Args: + url (str): URL to download file from + root (str): Directory to place downloaded file in + filename (str, optional): Name to save the file under. If None, use the basename of the URL + md5 (str, optional): MD5 checksum of the download. If None, do not check + max_redirect_hops (int, optional): Maximum number of redirect hops allowed + """ + root = os.path.expanduser(root) + if not filename: + filename = os.path.basename(url) + fpath = os.fspath(os.path.join(root, filename)) + + os.makedirs(root, exist_ok=True) + + # check if file is already present locally + if check_integrity(fpath, md5): + return + + if _is_remote_location_available(): + _download_file_from_remote_location(fpath, url) + else: + # expand redirect chain if needed + url = _get_redirect_url(url, max_hops=max_redirect_hops) + + # check if file is located on Google Drive + file_id = _get_google_drive_file_id(url) + if file_id is not None: + return download_file_from_google_drive(file_id, root, filename, md5) + + # download the file + try: + _urlretrieve(url, fpath) + except (urllib.error.URLError, OSError) as e: # type: ignore[attr-defined] + if url[:5] == "https": + url = url.replace("https:", "http:") + _urlretrieve(url, fpath) + else: + raise e + + # check integrity of downloaded file + if not check_integrity(fpath, md5): + raise RuntimeError("File not found or corrupted.") + + +def list_dir(root: Union[str, pathlib.Path], prefix: bool = False) -> List[str]: + """List all directories at a given root + + Args: + root (str): Path to directory whose folders need to be listed + prefix (bool, optional): If true, prepends the path to each result, otherwise + only returns the name of the directories found + """ + root = os.path.expanduser(root) + directories = [p for p in os.listdir(root) if os.path.isdir(os.path.join(root, p))] + if prefix is True: + directories = [os.path.join(root, d) for d in directories] + return directories + + +def list_files(root: Union[str, pathlib.Path], suffix: str, prefix: bool = False) -> List[str]: + """List all files ending with a suffix at a given root + + Args: + root (str): Path to directory whose folders need to be listed + suffix (str or tuple): Suffix of the files to match, e.g. '.png' or ('.jpg', '.png'). + It uses the Python "str.endswith" method and is passed directly + prefix (bool, optional): If true, prepends the path to each result, otherwise + only returns the name of the files found + """ + root = os.path.expanduser(root) + files = [p for p in os.listdir(root) if os.path.isfile(os.path.join(root, p)) and p.endswith(suffix)] + if prefix is True: + files = [os.path.join(root, d) for d in files] + return files + + +def download_file_from_google_drive( + file_id: str, + root: Union[str, pathlib.Path], + filename: Optional[Union[str, pathlib.Path]] = None, + md5: Optional[str] = None, +): + """Download a Google Drive file from and place it in root. + + Args: + file_id (str): id of file to be downloaded + root (str): Directory to place downloaded file in + filename (str, optional): Name to save the file under. If None, use the id of the file. + md5 (str, optional): MD5 checksum of the download. If None, do not check + """ + try: + import gdown + except ModuleNotFoundError: + raise RuntimeError( + "To download files from GDrive, 'gdown' is required. You can install it with 'pip install gdown'." + ) + + root = os.path.expanduser(root) + if not filename: + filename = file_id + fpath = os.fspath(os.path.join(root, filename)) + + os.makedirs(root, exist_ok=True) + + if check_integrity(fpath, md5): + return + + gdown.download(id=file_id, output=fpath, quiet=False, user_agent=USER_AGENT) + + if not check_integrity(fpath, md5): + raise RuntimeError("File not found or corrupted.") + + +def _extract_tar( + from_path: Union[str, pathlib.Path], to_path: Union[str, pathlib.Path], compression: Optional[str] +) -> None: + with tarfile.open(from_path, f"r:{compression[1:]}" if compression else "r") as tar: + tar.extractall(to_path) + + +_ZIP_COMPRESSION_MAP: Dict[str, int] = { + ".bz2": zipfile.ZIP_BZIP2, + ".xz": zipfile.ZIP_LZMA, +} + + +def _extract_zip( + from_path: Union[str, pathlib.Path], to_path: Union[str, pathlib.Path], compression: Optional[str] +) -> None: + with zipfile.ZipFile( + from_path, "r", compression=_ZIP_COMPRESSION_MAP[compression] if compression else zipfile.ZIP_STORED + ) as zip: + zip.extractall(to_path) + + +_ARCHIVE_EXTRACTORS: Dict[str, Callable[[Union[str, pathlib.Path], Union[str, pathlib.Path], Optional[str]], None]] = { + ".tar": _extract_tar, + ".zip": _extract_zip, +} +_COMPRESSED_FILE_OPENERS: Dict[str, Callable[..., IO]] = { + ".bz2": bz2.open, + ".gz": gzip.open, + ".xz": lzma.open, +} +_FILE_TYPE_ALIASES: Dict[str, Tuple[Optional[str], Optional[str]]] = { + ".tbz": (".tar", ".bz2"), + ".tbz2": (".tar", ".bz2"), + ".tgz": (".tar", ".gz"), +} + + +def _detect_file_type(file: Union[str, pathlib.Path]) -> Tuple[str, Optional[str], Optional[str]]: + """Detect the archive type and/or compression of a file. + + Args: + file (str): the filename + + Returns: + (tuple): tuple of suffix, archive type, and compression + + Raises: + RuntimeError: if file has no suffix or suffix is not supported + """ + suffixes = pathlib.Path(file).suffixes + if not suffixes: + raise RuntimeError( + f"File '{file}' has no suffixes that could be used to detect the archive type and compression." + ) + suffix = suffixes[-1] + + # check if the suffix is a known alias + if suffix in _FILE_TYPE_ALIASES: + return (suffix, *_FILE_TYPE_ALIASES[suffix]) + + # check if the suffix is an archive type + if suffix in _ARCHIVE_EXTRACTORS: + return suffix, suffix, None + + # check if the suffix is a compression + if suffix in _COMPRESSED_FILE_OPENERS: + # check for suffix hierarchy + if len(suffixes) > 1: + suffix2 = suffixes[-2] + + # check if the suffix2 is an archive type + if suffix2 in _ARCHIVE_EXTRACTORS: + return suffix2 + suffix, suffix2, suffix + + return suffix, None, suffix + + valid_suffixes = sorted(set(_FILE_TYPE_ALIASES) | set(_ARCHIVE_EXTRACTORS) | set(_COMPRESSED_FILE_OPENERS)) + raise RuntimeError(f"Unknown compression or archive type: '{suffix}'.\nKnown suffixes are: '{valid_suffixes}'.") + + +def _decompress( + from_path: Union[str, pathlib.Path], + to_path: Optional[Union[str, pathlib.Path]] = None, + remove_finished: bool = False, +) -> pathlib.Path: + r"""Decompress a file. + + The compression is automatically detected from the file name. + + Args: + from_path (str): Path to the file to be decompressed. + to_path (str): Path to the decompressed file. If omitted, ``from_path`` without compression extension is used. + remove_finished (bool): If ``True``, remove the file after the extraction. + + Returns: + (str): Path to the decompressed file. + """ + suffix, archive_type, compression = _detect_file_type(from_path) + if not compression: + raise RuntimeError(f"Couldn't detect a compression from suffix {suffix}.") + + if to_path is None: + to_path = pathlib.Path(os.fspath(from_path).replace(suffix, archive_type if archive_type is not None else "")) + + # We don't need to check for a missing key here, since this was already done in _detect_file_type() + compressed_file_opener = _COMPRESSED_FILE_OPENERS[compression] + + with compressed_file_opener(from_path, "rb") as rfh, open(to_path, "wb") as wfh: + wfh.write(rfh.read()) + + if remove_finished: + os.remove(from_path) + + return pathlib.Path(to_path) + + +def extract_archive( + from_path: Union[str, pathlib.Path], + to_path: Optional[Union[str, pathlib.Path]] = None, + remove_finished: bool = False, +) -> Union[str, pathlib.Path]: + """Extract an archive. + + The archive type and a possible compression is automatically detected from the file name. If the file is compressed + but not an archive the call is dispatched to :func:`decompress`. + + Args: + from_path (str): Path to the file to be extracted. + to_path (str): Path to the directory the file will be extracted to. If omitted, the directory of the file is + used. + remove_finished (bool): If ``True``, remove the file after the extraction. + + Returns: + (str): Path to the directory the file was extracted to. + """ + + def path_or_str(ret_path: pathlib.Path) -> Union[str, pathlib.Path]: + if isinstance(from_path, str): + return os.fspath(ret_path) + else: + return ret_path + + if to_path is None: + to_path = os.path.dirname(from_path) + + suffix, archive_type, compression = _detect_file_type(from_path) + if not archive_type: + ret_path = _decompress( + from_path, + os.path.join(to_path, os.path.basename(from_path).replace(suffix, "")), + remove_finished=remove_finished, + ) + return path_or_str(ret_path) + + # We don't need to check for a missing key here, since this was already done in _detect_file_type() + extractor = _ARCHIVE_EXTRACTORS[archive_type] + + extractor(from_path, to_path, compression) + if remove_finished: + os.remove(from_path) + + return path_or_str(pathlib.Path(to_path)) + + +def download_and_extract_archive( + url: str, + download_root: Union[str, pathlib.Path], + extract_root: Optional[Union[str, pathlib.Path]] = None, + filename: Optional[Union[str, pathlib.Path]] = None, + md5: Optional[str] = None, + remove_finished: bool = False, +) -> None: + download_root = os.path.expanduser(download_root) + if extract_root is None: + extract_root = download_root + if not filename: + filename = os.path.basename(url) + + download_url(url, download_root, filename, md5) + + archive = os.path.join(download_root, filename) + extract_archive(archive, extract_root, remove_finished) + + +def iterable_to_str(iterable: Iterable) -> str: + return "'" + "', '".join([str(item) for item in iterable]) + "'" + + +T = TypeVar("T", str, bytes) + + +def verify_str_arg( + value: T, + arg: Optional[str] = None, + valid_values: Optional[Iterable[T]] = None, + custom_msg: Optional[str] = None, +) -> T: + if not isinstance(value, str): + if arg is None: + msg = "Expected type str, but got type {type}." + else: + msg = "Expected type str for argument {arg}, but got type {type}." + msg = msg.format(type=type(value), arg=arg) + raise ValueError(msg) + + if valid_values is None: + return value + + if value not in valid_values: + if custom_msg is not None: + msg = custom_msg + else: + msg = "Unknown value '{value}' for argument {arg}. Valid values are {{{valid_values}}}." + msg = msg.format(value=value, arg=arg, valid_values=iterable_to_str(valid_values)) + raise ValueError(msg) + + return value + + +def _read_pfm(file_name: Union[str, pathlib.Path], slice_channels: int = 2) -> np.ndarray: + """Read file in .pfm format. Might contain either 1 or 3 channels of data. + + Args: + file_name (str): Path to the file. + slice_channels (int): Number of channels to slice out of the file. + Useful for reading different data formats stored in .pfm files: Optical Flows, Stereo Disparity Maps, etc. + """ + + with open(file_name, "rb") as f: + header = f.readline().rstrip() + if header not in [b"PF", b"Pf"]: + raise ValueError("Invalid PFM file") + + dim_match = re.match(rb"^(\d+)\s(\d+)\s$", f.readline()) + if not dim_match: + raise Exception("Malformed PFM header.") + w, h = (int(dim) for dim in dim_match.groups()) + + scale = float(f.readline().rstrip()) + if scale < 0: # little-endian + endian = "<" + scale = -scale + else: + endian = ">" # big-endian + + data = np.fromfile(f, dtype=endian + "f") + + pfm_channels = 3 if header == b"PF" else 1 + + data = data.reshape(h, w, pfm_channels).transpose(2, 0, 1) + data = np.flip(data, axis=1) # flip on h dimension + data = data[:slice_channels, :, :] + return data.astype(np.float32) + + +def _flip_byte_order(t: torch.Tensor) -> torch.Tensor: + return ( + t.contiguous().view(torch.uint8).view(*t.shape, t.element_size()).flip(-1).view(*t.shape[:-1], -1).view(t.dtype) + ) diff --git a/lib/python3.11/site-packages/torchvision/datasets/video_utils.py b/lib/python3.11/site-packages/torchvision/datasets/video_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a412bc5841cca891e6808215a6bd7d658c69e0f3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/video_utils.py @@ -0,0 +1,419 @@ +import bisect +import math +import warnings +from fractions import Fraction +from typing import Any, Callable, cast, Dict, List, Optional, Tuple, TypeVar, Union + +import torch +from torchvision.io import _probe_video_from_file, _read_video_from_file, read_video, read_video_timestamps + +from .utils import tqdm + +T = TypeVar("T") + + +def pts_convert(pts: int, timebase_from: Fraction, timebase_to: Fraction, round_func: Callable = math.floor) -> int: + """convert pts between different time bases + Args: + pts: presentation timestamp, float + timebase_from: original timebase. Fraction + timebase_to: new timebase. Fraction + round_func: rounding function. + """ + new_pts = Fraction(pts, 1) * timebase_from / timebase_to + return round_func(new_pts) + + +def unfold(tensor: torch.Tensor, size: int, step: int, dilation: int = 1) -> torch.Tensor: + """ + similar to tensor.unfold, but with the dilation + and specialized for 1d tensors + + Returns all consecutive windows of `size` elements, with + `step` between windows. The distance between each element + in a window is given by `dilation`. + """ + if tensor.dim() != 1: + raise ValueError(f"tensor should have 1 dimension instead of {tensor.dim()}") + o_stride = tensor.stride(0) + numel = tensor.numel() + new_stride = (step * o_stride, dilation * o_stride) + new_size = ((numel - (dilation * (size - 1) + 1)) // step + 1, size) + if new_size[0] < 1: + new_size = (0, size) + return torch.as_strided(tensor, new_size, new_stride) + + +class _VideoTimestampsDataset: + """ + Dataset used to parallelize the reading of the timestamps + of a list of videos, given their paths in the filesystem. + + Used in VideoClips and defined at top level, so it can be + pickled when forking. + """ + + def __init__(self, video_paths: List[str]) -> None: + self.video_paths = video_paths + + def __len__(self) -> int: + return len(self.video_paths) + + def __getitem__(self, idx: int) -> Tuple[List[int], Optional[float]]: + return read_video_timestamps(self.video_paths[idx]) + + +def _collate_fn(x: T) -> T: + """ + Dummy collate function to be used with _VideoTimestampsDataset + """ + return x + + +class VideoClips: + """ + Given a list of video files, computes all consecutive subvideos of size + `clip_length_in_frames`, where the distance between each subvideo in the + same video is defined by `frames_between_clips`. + If `frame_rate` is specified, it will also resample all the videos to have + the same frame rate, and the clips will refer to this frame rate. + + Creating this instance the first time is time-consuming, as it needs to + decode all the videos in `video_paths`. It is recommended that you + cache the results after instantiation of the class. + + Recreating the clips for different clip lengths is fast, and can be done + with the `compute_clips` method. + + Args: + video_paths (List[str]): paths to the video files + clip_length_in_frames (int): size of a clip in number of frames + frames_between_clips (int): step (in frames) between each clip + frame_rate (float, optional): if specified, it will resample the video + so that it has `frame_rate`, and then the clips will be defined + on the resampled video + num_workers (int): how many subprocesses to use for data loading. + 0 means that the data will be loaded in the main process. (default: 0) + output_format (str): The format of the output video tensors. Can be either "THWC" (default) or "TCHW". + """ + + def __init__( + self, + video_paths: List[str], + clip_length_in_frames: int = 16, + frames_between_clips: int = 1, + frame_rate: Optional[float] = None, + _precomputed_metadata: Optional[Dict[str, Any]] = None, + num_workers: int = 0, + _video_width: int = 0, + _video_height: int = 0, + _video_min_dimension: int = 0, + _video_max_dimension: int = 0, + _audio_samples: int = 0, + _audio_channels: int = 0, + output_format: str = "THWC", + ) -> None: + + self.video_paths = video_paths + self.num_workers = num_workers + + # these options are not valid for pyav backend + self._video_width = _video_width + self._video_height = _video_height + self._video_min_dimension = _video_min_dimension + self._video_max_dimension = _video_max_dimension + self._audio_samples = _audio_samples + self._audio_channels = _audio_channels + self.output_format = output_format.upper() + if self.output_format not in ("THWC", "TCHW"): + raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.") + + if _precomputed_metadata is None: + self._compute_frame_pts() + else: + self._init_from_metadata(_precomputed_metadata) + self.compute_clips(clip_length_in_frames, frames_between_clips, frame_rate) + + def _compute_frame_pts(self) -> None: + self.video_pts = [] # len = num_videos. Each entry is a tensor of shape (num_frames_in_video,) + self.video_fps: List[float] = [] # len = num_videos + + # strategy: use a DataLoader to parallelize read_video_timestamps + # so need to create a dummy dataset first + import torch.utils.data + + dl: torch.utils.data.DataLoader = torch.utils.data.DataLoader( + _VideoTimestampsDataset(self.video_paths), # type: ignore[arg-type] + batch_size=16, + num_workers=self.num_workers, + collate_fn=_collate_fn, + ) + + with tqdm(total=len(dl)) as pbar: + for batch in dl: + pbar.update(1) + batch_pts, batch_fps = list(zip(*batch)) + # we need to specify dtype=torch.long because for empty list, + # torch.as_tensor will use torch.float as default dtype. This + # happens when decoding fails and no pts is returned in the list. + batch_pts = [torch.as_tensor(pts, dtype=torch.long) for pts in batch_pts] + self.video_pts.extend(batch_pts) + self.video_fps.extend(batch_fps) + + def _init_from_metadata(self, metadata: Dict[str, Any]) -> None: + self.video_paths = metadata["video_paths"] + assert len(self.video_paths) == len(metadata["video_pts"]) + self.video_pts = metadata["video_pts"] + assert len(self.video_paths) == len(metadata["video_fps"]) + self.video_fps = metadata["video_fps"] + + @property + def metadata(self) -> Dict[str, Any]: + _metadata = { + "video_paths": self.video_paths, + "video_pts": self.video_pts, + "video_fps": self.video_fps, + } + return _metadata + + def subset(self, indices: List[int]) -> "VideoClips": + video_paths = [self.video_paths[i] for i in indices] + video_pts = [self.video_pts[i] for i in indices] + video_fps = [self.video_fps[i] for i in indices] + metadata = { + "video_paths": video_paths, + "video_pts": video_pts, + "video_fps": video_fps, + } + return type(self)( + video_paths, + clip_length_in_frames=self.num_frames, + frames_between_clips=self.step, + frame_rate=self.frame_rate, + _precomputed_metadata=metadata, + num_workers=self.num_workers, + _video_width=self._video_width, + _video_height=self._video_height, + _video_min_dimension=self._video_min_dimension, + _video_max_dimension=self._video_max_dimension, + _audio_samples=self._audio_samples, + _audio_channels=self._audio_channels, + output_format=self.output_format, + ) + + @staticmethod + def compute_clips_for_video( + video_pts: torch.Tensor, num_frames: int, step: int, fps: Optional[float], frame_rate: Optional[float] = None + ) -> Tuple[torch.Tensor, Union[List[slice], torch.Tensor]]: + if fps is None: + # if for some reason the video doesn't have fps (because doesn't have a video stream) + # set the fps to 1. The value doesn't matter, because video_pts is empty anyway + fps = 1 + if frame_rate is None: + frame_rate = fps + total_frames = len(video_pts) * frame_rate / fps + _idxs = VideoClips._resample_video_idx(int(math.floor(total_frames)), fps, frame_rate) + video_pts = video_pts[_idxs] + clips = unfold(video_pts, num_frames, step) + if not clips.numel(): + warnings.warn( + "There aren't enough frames in the current video to get a clip for the given clip length and " + "frames between clips. The video (and potentially others) will be skipped." + ) + idxs: Union[List[slice], torch.Tensor] + if isinstance(_idxs, slice): + idxs = [_idxs] * len(clips) + else: + idxs = unfold(_idxs, num_frames, step) + return clips, idxs + + def compute_clips(self, num_frames: int, step: int, frame_rate: Optional[float] = None) -> None: + """ + Compute all consecutive sequences of clips from video_pts. + Always returns clips of size `num_frames`, meaning that the + last few frames in a video can potentially be dropped. + + Args: + num_frames (int): number of frames for the clip + step (int): distance between two clips + frame_rate (int, optional): The frame rate + """ + self.num_frames = num_frames + self.step = step + self.frame_rate = frame_rate + self.clips = [] + self.resampling_idxs = [] + for video_pts, fps in zip(self.video_pts, self.video_fps): + clips, idxs = self.compute_clips_for_video(video_pts, num_frames, step, fps, frame_rate) + self.clips.append(clips) + self.resampling_idxs.append(idxs) + clip_lengths = torch.as_tensor([len(v) for v in self.clips]) + self.cumulative_sizes = clip_lengths.cumsum(0).tolist() + + def __len__(self) -> int: + return self.num_clips() + + def num_videos(self) -> int: + return len(self.video_paths) + + def num_clips(self) -> int: + """ + Number of subclips that are available in the video list. + """ + return self.cumulative_sizes[-1] + + def get_clip_location(self, idx: int) -> Tuple[int, int]: + """ + Converts a flattened representation of the indices into a video_idx, clip_idx + representation. + """ + video_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if video_idx == 0: + clip_idx = idx + else: + clip_idx = idx - self.cumulative_sizes[video_idx - 1] + return video_idx, clip_idx + + @staticmethod + def _resample_video_idx(num_frames: int, original_fps: float, new_fps: float) -> Union[slice, torch.Tensor]: + step = original_fps / new_fps + if step.is_integer(): + # optimization: if step is integer, don't need to perform + # advanced indexing + step = int(step) + return slice(None, None, step) + idxs = torch.arange(num_frames, dtype=torch.float32) * step + idxs = idxs.floor().to(torch.int64) + return idxs + + def get_clip(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any], int]: + """ + Gets a subclip from a list of videos. + + Args: + idx (int): index of the subclip. Must be between 0 and num_clips(). + + Returns: + video (Tensor) + audio (Tensor) + info (Dict) + video_idx (int): index of the video in `video_paths` + """ + if idx >= self.num_clips(): + raise IndexError(f"Index {idx} out of range ({self.num_clips()} number of clips)") + video_idx, clip_idx = self.get_clip_location(idx) + video_path = self.video_paths[video_idx] + clip_pts = self.clips[video_idx][clip_idx] + + from torchvision import get_video_backend + + backend = get_video_backend() + + if backend == "pyav": + # check for invalid options + if self._video_width != 0: + raise ValueError("pyav backend doesn't support _video_width != 0") + if self._video_height != 0: + raise ValueError("pyav backend doesn't support _video_height != 0") + if self._video_min_dimension != 0: + raise ValueError("pyav backend doesn't support _video_min_dimension != 0") + if self._video_max_dimension != 0: + raise ValueError("pyav backend doesn't support _video_max_dimension != 0") + if self._audio_samples != 0: + raise ValueError("pyav backend doesn't support _audio_samples != 0") + + if backend == "pyav": + start_pts = clip_pts[0].item() + end_pts = clip_pts[-1].item() + video, audio, info = read_video(video_path, start_pts, end_pts) + else: + _info = _probe_video_from_file(video_path) + video_fps = _info.video_fps + audio_fps = None + + video_start_pts = cast(int, clip_pts[0].item()) + video_end_pts = cast(int, clip_pts[-1].item()) + + audio_start_pts, audio_end_pts = 0, -1 + audio_timebase = Fraction(0, 1) + video_timebase = Fraction(_info.video_timebase.numerator, _info.video_timebase.denominator) + if _info.has_audio: + audio_timebase = Fraction(_info.audio_timebase.numerator, _info.audio_timebase.denominator) + audio_start_pts = pts_convert(video_start_pts, video_timebase, audio_timebase, math.floor) + audio_end_pts = pts_convert(video_end_pts, video_timebase, audio_timebase, math.ceil) + audio_fps = _info.audio_sample_rate + video, audio, _ = _read_video_from_file( + video_path, + video_width=self._video_width, + video_height=self._video_height, + video_min_dimension=self._video_min_dimension, + video_max_dimension=self._video_max_dimension, + video_pts_range=(video_start_pts, video_end_pts), + video_timebase=video_timebase, + audio_samples=self._audio_samples, + audio_channels=self._audio_channels, + audio_pts_range=(audio_start_pts, audio_end_pts), + audio_timebase=audio_timebase, + ) + + info = {"video_fps": video_fps} + if audio_fps is not None: + info["audio_fps"] = audio_fps + + if self.frame_rate is not None: + resampling_idx = self.resampling_idxs[video_idx][clip_idx] + if isinstance(resampling_idx, torch.Tensor): + resampling_idx = resampling_idx - resampling_idx[0] + video = video[resampling_idx] + info["video_fps"] = self.frame_rate + assert len(video) == self.num_frames, f"{video.shape} x {self.num_frames}" + + if self.output_format == "TCHW": + # [T,H,W,C] --> [T,C,H,W] + video = video.permute(0, 3, 1, 2) + + return video, audio, info, video_idx + + def __getstate__(self) -> Dict[str, Any]: + video_pts_sizes = [len(v) for v in self.video_pts] + # To be back-compatible, we convert data to dtype torch.long as needed + # because for empty list, in legacy implementation, torch.as_tensor will + # use torch.float as default dtype. This happens when decoding fails and + # no pts is returned in the list. + video_pts = [x.to(torch.int64) for x in self.video_pts] + # video_pts can be an empty list if no frames have been decoded + if video_pts: + video_pts = torch.cat(video_pts) # type: ignore[assignment] + # avoid bug in https://github.com/pytorch/pytorch/issues/32351 + # TODO: Revert it once the bug is fixed. + video_pts = video_pts.numpy() # type: ignore[attr-defined] + + # make a copy of the fields of self + d = self.__dict__.copy() + d["video_pts_sizes"] = video_pts_sizes + d["video_pts"] = video_pts + # delete the following attributes to reduce the size of dictionary. They + # will be re-computed in "__setstate__()" + del d["clips"] + del d["resampling_idxs"] + del d["cumulative_sizes"] + + # for backwards-compatibility + d["_version"] = 2 + return d + + def __setstate__(self, d: Dict[str, Any]) -> None: + # for backwards-compatibility + if "_version" not in d: + self.__dict__ = d + return + + video_pts = torch.as_tensor(d["video_pts"], dtype=torch.int64) + video_pts = torch.split(video_pts, d["video_pts_sizes"], dim=0) + # don't need this info anymore + del d["video_pts_sizes"] + + d["video_pts"] = video_pts + self.__dict__ = d + # recompute attributes "clips", "resampling_idxs" and other derivative ones + self.compute_clips(self.num_frames, self.step, self.frame_rate) diff --git a/lib/python3.11/site-packages/torchvision/datasets/vision.py b/lib/python3.11/site-packages/torchvision/datasets/vision.py new file mode 100644 index 0000000000000000000000000000000000000000..e524c67e263abd9ec9ed5b86aa55203e1f3ba144 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/vision.py @@ -0,0 +1,111 @@ +import os +from pathlib import Path +from typing import Any, Callable, List, Optional, Tuple, Union + +import torch.utils.data as data + +from ..utils import _log_api_usage_once + + +class VisionDataset(data.Dataset): + """ + Base Class For making datasets which are compatible with torchvision. + It is necessary to override the ``__getitem__`` and ``__len__`` method. + + Args: + root (string, optional): Root directory of dataset. Only used for `__repr__`. + transforms (callable, optional): A function/transforms that takes in + an image and a label and returns the transformed versions of both. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + + .. note:: + + :attr:`transforms` and the combination of :attr:`transform` and :attr:`target_transform` are mutually exclusive. + """ + + _repr_indent = 4 + + def __init__( + self, + root: Union[str, Path] = None, # type: ignore[assignment] + transforms: Optional[Callable] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + _log_api_usage_once(self) + if isinstance(root, str): + root = os.path.expanduser(root) + self.root = root + + has_transforms = transforms is not None + has_separate_transform = transform is not None or target_transform is not None + if has_transforms and has_separate_transform: + raise ValueError("Only transforms or transform/target_transform can be passed as argument") + + # for backwards-compatibility + self.transform = transform + self.target_transform = target_transform + + if has_separate_transform: + transforms = StandardTransform(transform, target_transform) + self.transforms = transforms + + def __getitem__(self, index: int) -> Any: + """ + Args: + index (int): Index + + Returns: + (Any): Sample and meta data, optionally transformed by the respective transforms. + """ + raise NotImplementedError + + def __len__(self) -> int: + raise NotImplementedError + + def __repr__(self) -> str: + head = "Dataset " + self.__class__.__name__ + body = [f"Number of datapoints: {self.__len__()}"] + if self.root is not None: + body.append(f"Root location: {self.root}") + body += self.extra_repr().splitlines() + if hasattr(self, "transforms") and self.transforms is not None: + body += [repr(self.transforms)] + lines = [head] + [" " * self._repr_indent + line for line in body] + return "\n".join(lines) + + def _format_transform_repr(self, transform: Callable, head: str) -> List[str]: + lines = transform.__repr__().splitlines() + return [f"{head}{lines[0]}"] + ["{}{}".format(" " * len(head), line) for line in lines[1:]] + + def extra_repr(self) -> str: + return "" + + +class StandardTransform: + def __init__(self, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None) -> None: + self.transform = transform + self.target_transform = target_transform + + def __call__(self, input: Any, target: Any) -> Tuple[Any, Any]: + if self.transform is not None: + input = self.transform(input) + if self.target_transform is not None: + target = self.target_transform(target) + return input, target + + def _format_transform_repr(self, transform: Callable, head: str) -> List[str]: + lines = transform.__repr__().splitlines() + return [f"{head}{lines[0]}"] + ["{}{}".format(" " * len(head), line) for line in lines[1:]] + + def __repr__(self) -> str: + body = [self.__class__.__name__] + if self.transform is not None: + body += self._format_transform_repr(self.transform, "Transform: ") + if self.target_transform is not None: + body += self._format_transform_repr(self.target_transform, "Target transform: ") + + return "\n".join(body) diff --git a/lib/python3.11/site-packages/torchvision/datasets/voc.py b/lib/python3.11/site-packages/torchvision/datasets/voc.py new file mode 100644 index 0000000000000000000000000000000000000000..0f0e84c84fa159e942b6da20fb9b2f651816ac06 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/voc.py @@ -0,0 +1,224 @@ +import collections +import os +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from xml.etree.ElementTree import Element as ET_Element + +try: + from defusedxml.ElementTree import parse as ET_parse +except ImportError: + from xml.etree.ElementTree import parse as ET_parse + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + +DATASET_YEAR_DICT = { + "2012": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar", + "filename": "VOCtrainval_11-May-2012.tar", + "md5": "6cd6e144f989b92b3379bac3b3de84fd", + "base_dir": os.path.join("VOCdevkit", "VOC2012"), + }, + "2011": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar", + "filename": "VOCtrainval_25-May-2011.tar", + "md5": "6c3384ef61512963050cb5d687e5bf1e", + "base_dir": os.path.join("TrainVal", "VOCdevkit", "VOC2011"), + }, + "2010": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar", + "filename": "VOCtrainval_03-May-2010.tar", + "md5": "da459979d0c395079b5c75ee67908abb", + "base_dir": os.path.join("VOCdevkit", "VOC2010"), + }, + "2009": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2009/VOCtrainval_11-May-2009.tar", + "filename": "VOCtrainval_11-May-2009.tar", + "md5": "a3e00b113cfcfebf17e343f59da3caa1", + "base_dir": os.path.join("VOCdevkit", "VOC2009"), + }, + "2008": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2008/VOCtrainval_14-Jul-2008.tar", + "filename": "VOCtrainval_11-May-2012.tar", + "md5": "2629fa636546599198acfcfbfcf1904a", + "base_dir": os.path.join("VOCdevkit", "VOC2008"), + }, + "2007": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar", + "filename": "VOCtrainval_06-Nov-2007.tar", + "md5": "c52e279531787c972589f7e41ab4ae64", + "base_dir": os.path.join("VOCdevkit", "VOC2007"), + }, + "2007-test": { + "url": "http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar", + "filename": "VOCtest_06-Nov-2007.tar", + "md5": "b6e924de25625d8de591ea690078ad9f", + "base_dir": os.path.join("VOCdevkit", "VOC2007"), + }, +} + + +class _VOCBase(VisionDataset): + _SPLITS_DIR: str + _TARGET_DIR: str + _TARGET_FILE_EXT: str + + def __init__( + self, + root: Union[str, Path], + year: str = "2012", + image_set: str = "train", + download: bool = False, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + transforms: Optional[Callable] = None, + ): + super().__init__(root, transforms, transform, target_transform) + + self.year = verify_str_arg(year, "year", valid_values=[str(yr) for yr in range(2007, 2013)]) + + valid_image_sets = ["train", "trainval", "val"] + if year == "2007": + valid_image_sets.append("test") + self.image_set = verify_str_arg(image_set, "image_set", valid_image_sets) + + key = "2007-test" if year == "2007" and image_set == "test" else year + dataset_year_dict = DATASET_YEAR_DICT[key] + + self.url = dataset_year_dict["url"] + self.filename = dataset_year_dict["filename"] + self.md5 = dataset_year_dict["md5"] + + base_dir = dataset_year_dict["base_dir"] + voc_root = os.path.join(self.root, base_dir) + + if download: + download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5) + + if not os.path.isdir(voc_root): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + splits_dir = os.path.join(voc_root, "ImageSets", self._SPLITS_DIR) + split_f = os.path.join(splits_dir, image_set.rstrip("\n") + ".txt") + with open(os.path.join(split_f)) as f: + file_names = [x.strip() for x in f.readlines()] + + image_dir = os.path.join(voc_root, "JPEGImages") + self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names] + + target_dir = os.path.join(voc_root, self._TARGET_DIR) + self.targets = [os.path.join(target_dir, x + self._TARGET_FILE_EXT) for x in file_names] + + assert len(self.images) == len(self.targets) + + def __len__(self) -> int: + return len(self.images) + + +class VOCSegmentation(_VOCBase): + """`Pascal VOC `_ Segmentation Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of the VOC Dataset. + year (string, optional): The dataset year, supports years ``"2007"`` to ``"2012"``. + image_set (string, optional): Select the image_set to use, ``"train"``, ``"trainval"`` or ``"val"``. If + ``year=="2007"``, can also be ``"test"``. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + """ + + _SPLITS_DIR = "Segmentation" + _TARGET_DIR = "SegmentationClass" + _TARGET_FILE_EXT = ".png" + + @property + def masks(self) -> List[str]: + return self.targets + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is the image segmentation. + """ + img = Image.open(self.images[index]).convert("RGB") + target = Image.open(self.masks[index]) + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + +class VOCDetection(_VOCBase): + """`Pascal VOC `_ Detection Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory of the VOC Dataset. + year (string, optional): The dataset year, supports years ``"2007"`` to ``"2012"``. + image_set (string, optional): Select the image_set to use, ``"train"``, ``"trainval"`` or ``"val"``. If + ``year=="2007"``, can also be ``"test"``. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + (default: alphabetic indexing of VOC's 20 classes). + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, required): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + """ + + _SPLITS_DIR = "Main" + _TARGET_DIR = "Annotations" + _TARGET_FILE_EXT = ".xml" + + @property + def annotations(self) -> List[str]: + return self.targets + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is a dictionary of the XML tree. + """ + img = Image.open(self.images[index]).convert("RGB") + target = self.parse_voc_xml(ET_parse(self.annotations[index]).getroot()) + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + @staticmethod + def parse_voc_xml(node: ET_Element) -> Dict[str, Any]: + voc_dict: Dict[str, Any] = {} + children = list(node) + if children: + def_dic: Dict[str, Any] = collections.defaultdict(list) + for dc in map(VOCDetection.parse_voc_xml, children): + for ind, v in dc.items(): + def_dic[ind].append(v) + if node.tag == "annotation": + def_dic["object"] = [def_dic["object"]] + voc_dict = {node.tag: {ind: v[0] if len(v) == 1 else v for ind, v in def_dic.items()}} + if node.text: + text = node.text.strip() + if not children: + voc_dict[node.tag] = text + return voc_dict diff --git a/lib/python3.11/site-packages/torchvision/datasets/widerface.py b/lib/python3.11/site-packages/torchvision/datasets/widerface.py new file mode 100644 index 0000000000000000000000000000000000000000..b451ebe25b9c1999ae53e3df40e55e009d64942c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/datasets/widerface.py @@ -0,0 +1,196 @@ +import os +from os.path import abspath, expanduser +from pathlib import Path + +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +from PIL import Image + +from .utils import download_and_extract_archive, download_file_from_google_drive, extract_archive, verify_str_arg +from .vision import VisionDataset + + +class WIDERFace(VisionDataset): + """`WIDERFace `_ Dataset. + + Args: + root (str or ``pathlib.Path``): Root directory where images and annotations are downloaded to. + Expects the following folder structure if download=False: + + .. code:: + + + └── widerface + ├── wider_face_split ('wider_face_split.zip' if compressed) + ├── WIDER_train ('WIDER_train.zip' if compressed) + ├── WIDER_val ('WIDER_val.zip' if compressed) + └── WIDER_test ('WIDER_test.zip' if compressed) + split (string): The dataset split to use. One of {``train``, ``val``, ``test``}. + Defaults to ``train``. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + .. warning:: + + To download the dataset `gdown `_ is required. + + """ + + BASE_FOLDER = "widerface" + FILE_LIST = [ + # File ID MD5 Hash Filename + ("15hGDLhsx8bLgLcIRD5DhYt5iBxnjNF1M", "3fedf70df600953d25982bcd13d91ba2", "WIDER_train.zip"), + ("1GUCogbp16PMGa39thoMMeWxp7Rp5oM8Q", "dfa7d7e790efa35df3788964cf0bbaea", "WIDER_val.zip"), + ("1HIfDbVEWKmsYKJZm4lchTBDLW5N7dY5T", "e5d8f4248ed24c334bbd12f49c29dd40", "WIDER_test.zip"), + ] + ANNOTATIONS_FILE = ( + "http://shuoyang1213.me/WIDERFACE/support/bbx_annotation/wider_face_split.zip", + "0e3767bcf0e326556d407bf5bff5d27c", + "wider_face_split.zip", + ) + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__( + root=os.path.join(root, self.BASE_FOLDER), transform=transform, target_transform=target_transform + ) + # check arguments + self.split = verify_str_arg(split, "split", ("train", "val", "test")) + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download and prepare it") + + self.img_info: List[Dict[str, Union[str, Dict[str, torch.Tensor]]]] = [] + if self.split in ("train", "val"): + self.parse_train_val_annotations_file() + else: + self.parse_test_annotations_file() + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is a dict of annotations for all faces in the image. + target=None for the test split. + """ + + # stay consistent with other datasets and return a PIL Image + img = Image.open(self.img_info[index]["img_path"]) # type: ignore[arg-type] + + if self.transform is not None: + img = self.transform(img) + + target = None if self.split == "test" else self.img_info[index]["annotations"] + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.img_info) + + def extra_repr(self) -> str: + lines = ["Split: {split}"] + return "\n".join(lines).format(**self.__dict__) + + def parse_train_val_annotations_file(self) -> None: + filename = "wider_face_train_bbx_gt.txt" if self.split == "train" else "wider_face_val_bbx_gt.txt" + filepath = os.path.join(self.root, "wider_face_split", filename) + + with open(filepath) as f: + lines = f.readlines() + file_name_line, num_boxes_line, box_annotation_line = True, False, False + num_boxes, box_counter = 0, 0 + labels = [] + for line in lines: + line = line.rstrip() + if file_name_line: + img_path = os.path.join(self.root, "WIDER_" + self.split, "images", line) + img_path = abspath(expanduser(img_path)) + file_name_line = False + num_boxes_line = True + elif num_boxes_line: + num_boxes = int(line) + num_boxes_line = False + box_annotation_line = True + elif box_annotation_line: + box_counter += 1 + line_split = line.split(" ") + line_values = [int(x) for x in line_split] + labels.append(line_values) + if box_counter >= num_boxes: + box_annotation_line = False + file_name_line = True + labels_tensor = torch.tensor(labels) + self.img_info.append( + { + "img_path": img_path, + "annotations": { + "bbox": labels_tensor[:, 0:4].clone(), # x, y, width, height + "blur": labels_tensor[:, 4].clone(), + "expression": labels_tensor[:, 5].clone(), + "illumination": labels_tensor[:, 6].clone(), + "occlusion": labels_tensor[:, 7].clone(), + "pose": labels_tensor[:, 8].clone(), + "invalid": labels_tensor[:, 9].clone(), + }, + } + ) + box_counter = 0 + labels.clear() + else: + raise RuntimeError(f"Error parsing annotation file {filepath}") + + def parse_test_annotations_file(self) -> None: + filepath = os.path.join(self.root, "wider_face_split", "wider_face_test_filelist.txt") + filepath = abspath(expanduser(filepath)) + with open(filepath) as f: + lines = f.readlines() + for line in lines: + line = line.rstrip() + img_path = os.path.join(self.root, "WIDER_test", "images", line) + img_path = abspath(expanduser(img_path)) + self.img_info.append({"img_path": img_path}) + + def _check_integrity(self) -> bool: + # Allow original archive to be deleted (zip). Only need the extracted images + all_files = self.FILE_LIST.copy() + all_files.append(self.ANNOTATIONS_FILE) + for (_, md5, filename) in all_files: + file, ext = os.path.splitext(filename) + extracted_dir = os.path.join(self.root, file) + if not os.path.exists(extracted_dir): + return False + return True + + def download(self) -> None: + if self._check_integrity(): + return + + # download and extract image data + for (file_id, md5, filename) in self.FILE_LIST: + download_file_from_google_drive(file_id, self.root, filename, md5) + filepath = os.path.join(self.root, filename) + extract_archive(filepath) + + # download and extract annotation files + download_and_extract_archive( + url=self.ANNOTATIONS_FILE[0], download_root=self.root, md5=self.ANNOTATIONS_FILE[1] + ) diff --git a/lib/python3.11/site-packages/torchvision/extension.py b/lib/python3.11/site-packages/torchvision/extension.py new file mode 100644 index 0000000000000000000000000000000000000000..67801056e88b44d40bc2d382d62c389bf4ef039e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/extension.py @@ -0,0 +1,92 @@ +import os +import sys + +import torch + +from ._internally_replaced_utils import _get_extension_path + + +_HAS_OPS = False + + +def _has_ops(): + return False + + +try: + # On Windows Python-3.8.x has `os.add_dll_directory` call, + # which is called to configure dll search path. + # To find cuda related dlls we need to make sure the + # conda environment/bin path is configured Please take a look: + # https://stackoverflow.com/questions/59330863/cant-import-dll-module-in-python + # Please note: if some path can't be added using add_dll_directory we simply ignore this path + if os.name == "nt" and sys.version_info < (3, 9): + env_path = os.environ["PATH"] + path_arr = env_path.split(";") + for path in path_arr: + if os.path.exists(path): + try: + os.add_dll_directory(path) # type: ignore[attr-defined] + except Exception: + pass + + lib_path = _get_extension_path("_C") + torch.ops.load_library(lib_path) + _HAS_OPS = True + + def _has_ops(): # noqa: F811 + return True + +except (ImportError, OSError): + pass + + +def _assert_has_ops(): + if not _has_ops(): + raise RuntimeError( + "Couldn't load custom C++ ops. This can happen if your PyTorch and " + "torchvision versions are incompatible, or if you had errors while compiling " + "torchvision from source. For further information on the compatible versions, check " + "https://github.com/pytorch/vision#installation for the compatibility matrix. " + "Please check your PyTorch version with torch.__version__ and your torchvision " + "version with torchvision.__version__ and verify if they are compatible, and if not " + "please reinstall torchvision so that it matches your PyTorch install." + ) + + +def _check_cuda_version(): + """ + Make sure that CUDA versions match between the pytorch install and torchvision install + """ + if not _HAS_OPS: + return -1 + from torch.version import cuda as torch_version_cuda + + _version = torch.ops.torchvision._cuda_version() + if _version != -1 and torch_version_cuda is not None: + tv_version = str(_version) + if int(tv_version) < 10000: + tv_major = int(tv_version[0]) + tv_minor = int(tv_version[2]) + else: + tv_major = int(tv_version[0:2]) + tv_minor = int(tv_version[3]) + t_version = torch_version_cuda.split(".") + t_major = int(t_version[0]) + t_minor = int(t_version[1]) + if t_major != tv_major: + raise RuntimeError( + "Detected that PyTorch and torchvision were compiled with different CUDA major versions. " + f"PyTorch has CUDA Version={t_major}.{t_minor} and torchvision has " + f"CUDA Version={tv_major}.{tv_minor}. " + "Please reinstall the torchvision that matches your PyTorch install." + ) + return _version + + +def _load_library(lib_name): + lib_path = _get_extension_path(lib_name) + torch.ops.load_library(lib_path) + + +_check_cuda_version() diff --git a/lib/python3.11/site-packages/torchvision/io/__init__.py b/lib/python3.11/site-packages/torchvision/io/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..03bd5d23cb2cf8e3acb67b7567e3ad9ef8061874 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/__init__.py @@ -0,0 +1,73 @@ +try: + from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER +except ModuleNotFoundError: + _HAS_GPU_VIDEO_DECODER = False + +from ._video_opt import ( + _HAS_CPU_VIDEO_DECODER, + _HAS_VIDEO_OPT, + _probe_video_from_file, + _probe_video_from_memory, + _read_video_from_file, + _read_video_from_memory, + _read_video_timestamps_from_file, + _read_video_timestamps_from_memory, + Timebase, + VideoMetaData, +) +from .image import ( + decode_avif, + decode_gif, + decode_heic, + decode_image, + decode_jpeg, + decode_png, + decode_webp, + encode_jpeg, + encode_png, + ImageReadMode, + read_file, + read_image, + write_file, + write_jpeg, + write_png, +) +from .video import read_video, read_video_timestamps, write_video +from .video_reader import VideoReader + + +__all__ = [ + "write_video", + "read_video", + "read_video_timestamps", + "_read_video_from_file", + "_read_video_timestamps_from_file", + "_probe_video_from_file", + "_read_video_from_memory", + "_read_video_timestamps_from_memory", + "_probe_video_from_memory", + "_HAS_CPU_VIDEO_DECODER", + "_HAS_VIDEO_OPT", + "_HAS_GPU_VIDEO_DECODER", + "_read_video_clip_from_memory", + "_read_video_meta_data", + "VideoMetaData", + "Timebase", + "ImageReadMode", + "decode_image", + "decode_jpeg", + "decode_png", + "decode_avif", + "decode_heic", + "decode_webp", + "decode_gif", + "encode_jpeg", + "encode_png", + "read_file", + "read_image", + "write_file", + "write_jpeg", + "write_png", + "Video", + "VideoReader", +] diff --git a/lib/python3.11/site-packages/torchvision/io/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/io/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..27a2ea59450db2e8fa7fab0b978b25e7d1eabfbf Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/io/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/io/__pycache__/_load_gpu_decoder.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/io/__pycache__/_load_gpu_decoder.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5e9c2f5f84731af083e95020d676624f0eca8eff Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/io/__pycache__/_load_gpu_decoder.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/io/__pycache__/_video_opt.cpython-311.pyc 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files /dev/null and b/lib/python3.11/site-packages/torchvision/io/__pycache__/video.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/io/__pycache__/video_reader.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/io/__pycache__/video_reader.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fd213c22073efa018d5de7db61c8ef66121dcdcd Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/io/__pycache__/video_reader.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/io/_load_gpu_decoder.py b/lib/python3.11/site-packages/torchvision/io/_load_gpu_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd40c545d8201b67290e27bf74ce115774dace1 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/_load_gpu_decoder.py @@ -0,0 +1,8 @@ +from ..extension import _load_library + + +try: + _load_library("gpu_decoder") + _HAS_GPU_VIDEO_DECODER = True +except (ImportError, OSError): + _HAS_GPU_VIDEO_DECODER = False diff --git a/lib/python3.11/site-packages/torchvision/io/_video_opt.py b/lib/python3.11/site-packages/torchvision/io/_video_opt.py new file mode 100644 index 0000000000000000000000000000000000000000..69af045e7731882c949f9181f2f7e6d507edc4a1 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/_video_opt.py @@ -0,0 +1,513 @@ +import math +import warnings +from fractions import Fraction +from typing import Dict, List, Optional, Tuple, Union + +import torch + +from ..extension import _load_library + + +try: + _load_library("video_reader") + _HAS_CPU_VIDEO_DECODER = True +except (ImportError, OSError): + _HAS_CPU_VIDEO_DECODER = False + +_HAS_VIDEO_OPT = _HAS_CPU_VIDEO_DECODER # For BC +default_timebase = Fraction(0, 1) + + +# simple class for torch scripting +# the complex Fraction class from fractions module is not scriptable +class Timebase: + __annotations__ = {"numerator": int, "denominator": int} + __slots__ = ["numerator", "denominator"] + + def __init__( + self, + numerator: int, + denominator: int, + ) -> None: + self.numerator = numerator + self.denominator = denominator + + +class VideoMetaData: + __annotations__ = { + "has_video": bool, + "video_timebase": Timebase, + "video_duration": float, + "video_fps": float, + "has_audio": bool, + "audio_timebase": Timebase, + "audio_duration": float, + "audio_sample_rate": float, + } + __slots__ = [ + "has_video", + "video_timebase", + "video_duration", + "video_fps", + "has_audio", + "audio_timebase", + "audio_duration", + "audio_sample_rate", + ] + + def __init__(self) -> None: + self.has_video = False + self.video_timebase = Timebase(0, 1) + self.video_duration = 0.0 + self.video_fps = 0.0 + self.has_audio = False + self.audio_timebase = Timebase(0, 1) + self.audio_duration = 0.0 + self.audio_sample_rate = 0.0 + + +def _validate_pts(pts_range: Tuple[int, int]) -> None: + + if pts_range[0] > pts_range[1] > 0: + raise ValueError( + f"Start pts should not be smaller than end pts, got start pts: {pts_range[0]} and end pts: {pts_range[1]}" + ) + + +def _fill_info( + vtimebase: torch.Tensor, + vfps: torch.Tensor, + vduration: torch.Tensor, + atimebase: torch.Tensor, + asample_rate: torch.Tensor, + aduration: torch.Tensor, +) -> VideoMetaData: + """ + Build update VideoMetaData struct with info about the video + """ + meta = VideoMetaData() + if vtimebase.numel() > 0: + meta.video_timebase = Timebase(int(vtimebase[0].item()), int(vtimebase[1].item())) + timebase = vtimebase[0].item() / float(vtimebase[1].item()) + if vduration.numel() > 0: + meta.has_video = True + meta.video_duration = float(vduration.item()) * timebase + if vfps.numel() > 0: + meta.video_fps = float(vfps.item()) + if atimebase.numel() > 0: + meta.audio_timebase = Timebase(int(atimebase[0].item()), int(atimebase[1].item())) + timebase = atimebase[0].item() / float(atimebase[1].item()) + if aduration.numel() > 0: + meta.has_audio = True + meta.audio_duration = float(aduration.item()) * timebase + if asample_rate.numel() > 0: + meta.audio_sample_rate = float(asample_rate.item()) + + return meta + + +def _align_audio_frames( + aframes: torch.Tensor, aframe_pts: torch.Tensor, audio_pts_range: Tuple[int, int] +) -> torch.Tensor: + start, end = aframe_pts[0], aframe_pts[-1] + num_samples = aframes.size(0) + step_per_aframe = float(end - start + 1) / float(num_samples) + s_idx = 0 + e_idx = num_samples + if start < audio_pts_range[0]: + s_idx = int((audio_pts_range[0] - start) / step_per_aframe) + if audio_pts_range[1] != -1 and end > audio_pts_range[1]: + e_idx = int((audio_pts_range[1] - end) / step_per_aframe) + return aframes[s_idx:e_idx, :] + + +def _read_video_from_file( + filename: str, + seek_frame_margin: float = 0.25, + read_video_stream: bool = True, + video_width: int = 0, + video_height: int = 0, + video_min_dimension: int = 0, + video_max_dimension: int = 0, + video_pts_range: Tuple[int, int] = (0, -1), + video_timebase: Fraction = default_timebase, + read_audio_stream: bool = True, + audio_samples: int = 0, + audio_channels: int = 0, + audio_pts_range: Tuple[int, int] = (0, -1), + audio_timebase: Fraction = default_timebase, +) -> Tuple[torch.Tensor, torch.Tensor, VideoMetaData]: + """ + Reads a video from a file, returning both the video frames and the audio frames + + Args: + filename (str): path to the video file + seek_frame_margin (double, optional): seeking frame in the stream is imprecise. Thus, + when video_start_pts is specified, we seek the pts earlier by seek_frame_margin seconds + read_video_stream (int, optional): whether read video stream. If yes, set to 1. Otherwise, 0 + video_width/video_height/video_min_dimension/video_max_dimension (int): together decide + the size of decoded frames: + + - When video_width = 0, video_height = 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the original frame resolution + - When video_width = 0, video_height = 0, video_min_dimension != 0, + and video_max_dimension = 0, keep the aspect ratio and resize the + frame so that shorter edge size is video_min_dimension + - When video_width = 0, video_height = 0, video_min_dimension = 0, + and video_max_dimension != 0, keep the aspect ratio and resize + the frame so that longer edge size is video_max_dimension + - When video_width = 0, video_height = 0, video_min_dimension != 0, + and video_max_dimension != 0, resize the frame so that shorter + edge size is video_min_dimension, and longer edge size is + video_max_dimension. The aspect ratio may not be preserved + - When video_width = 0, video_height != 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the aspect ratio and resize + the frame so that frame video_height is $video_height + - When video_width != 0, video_height == 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the aspect ratio and resize + the frame so that frame video_width is $video_width + - When video_width != 0, video_height != 0, video_min_dimension = 0, + and video_max_dimension = 0, resize the frame so that frame + video_width and video_height are set to $video_width and + $video_height, respectively + video_pts_range (list(int), optional): the start and end presentation timestamp of video stream + video_timebase (Fraction, optional): a Fraction rational number which denotes timebase in video stream + read_audio_stream (int, optional): whether read audio stream. If yes, set to 1. Otherwise, 0 + audio_samples (int, optional): audio sampling rate + audio_channels (int optional): audio channels + audio_pts_range (list(int), optional): the start and end presentation timestamp of audio stream + audio_timebase (Fraction, optional): a Fraction rational number which denotes time base in audio stream + + Returns + vframes (Tensor[T, H, W, C]): the `T` video frames + aframes (Tensor[L, K]): the audio frames, where `L` is the number of points and + `K` is the number of audio_channels + info (Dict): metadata for the video and audio. Can contain the fields video_fps (float) + and audio_fps (int) + """ + _validate_pts(video_pts_range) + _validate_pts(audio_pts_range) + + result = torch.ops.video_reader.read_video_from_file( + filename, + seek_frame_margin, + 0, # getPtsOnly + read_video_stream, + video_width, + video_height, + video_min_dimension, + video_max_dimension, + video_pts_range[0], + video_pts_range[1], + video_timebase.numerator, + video_timebase.denominator, + read_audio_stream, + audio_samples, + audio_channels, + audio_pts_range[0], + audio_pts_range[1], + audio_timebase.numerator, + audio_timebase.denominator, + ) + vframes, _vframe_pts, vtimebase, vfps, vduration, aframes, aframe_pts, atimebase, asample_rate, aduration = result + info = _fill_info(vtimebase, vfps, vduration, atimebase, asample_rate, aduration) + if aframes.numel() > 0: + # when audio stream is found + aframes = _align_audio_frames(aframes, aframe_pts, audio_pts_range) + return vframes, aframes, info + + +def _read_video_timestamps_from_file(filename: str) -> Tuple[List[int], List[int], VideoMetaData]: + """ + Decode all video- and audio frames in the video. Only pts + (presentation timestamp) is returned. The actual frame pixel data is not + copied. Thus, it is much faster than read_video(...) + """ + result = torch.ops.video_reader.read_video_from_file( + filename, + 0, # seek_frame_margin + 1, # getPtsOnly + 1, # read_video_stream + 0, # video_width + 0, # video_height + 0, # video_min_dimension + 0, # video_max_dimension + 0, # video_start_pts + -1, # video_end_pts + 0, # video_timebase_num + 1, # video_timebase_den + 1, # read_audio_stream + 0, # audio_samples + 0, # audio_channels + 0, # audio_start_pts + -1, # audio_end_pts + 0, # audio_timebase_num + 1, # audio_timebase_den + ) + _vframes, vframe_pts, vtimebase, vfps, vduration, _aframes, aframe_pts, atimebase, asample_rate, aduration = result + info = _fill_info(vtimebase, vfps, vduration, atimebase, asample_rate, aduration) + + vframe_pts = vframe_pts.numpy().tolist() + aframe_pts = aframe_pts.numpy().tolist() + return vframe_pts, aframe_pts, info + + +def _probe_video_from_file(filename: str) -> VideoMetaData: + """ + Probe a video file and return VideoMetaData with info about the video + """ + result = torch.ops.video_reader.probe_video_from_file(filename) + vtimebase, vfps, vduration, atimebase, asample_rate, aduration = result + info = _fill_info(vtimebase, vfps, vduration, atimebase, asample_rate, aduration) + return info + + +def _read_video_from_memory( + video_data: torch.Tensor, + seek_frame_margin: float = 0.25, + read_video_stream: int = 1, + video_width: int = 0, + video_height: int = 0, + video_min_dimension: int = 0, + video_max_dimension: int = 0, + video_pts_range: Tuple[int, int] = (0, -1), + video_timebase_numerator: int = 0, + video_timebase_denominator: int = 1, + read_audio_stream: int = 1, + audio_samples: int = 0, + audio_channels: int = 0, + audio_pts_range: Tuple[int, int] = (0, -1), + audio_timebase_numerator: int = 0, + audio_timebase_denominator: int = 1, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Reads a video from memory, returning both the video frames as the audio frames + This function is torchscriptable. + + Args: + video_data (data type could be 1) torch.Tensor, dtype=torch.int8 or 2) python bytes): + compressed video content stored in either 1) torch.Tensor 2) python bytes + seek_frame_margin (double, optional): seeking frame in the stream is imprecise. + Thus, when video_start_pts is specified, we seek the pts earlier by seek_frame_margin seconds + read_video_stream (int, optional): whether read video stream. If yes, set to 1. Otherwise, 0 + video_width/video_height/video_min_dimension/video_max_dimension (int): together decide + the size of decoded frames: + + - When video_width = 0, video_height = 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the original frame resolution + - When video_width = 0, video_height = 0, video_min_dimension != 0, + and video_max_dimension = 0, keep the aspect ratio and resize the + frame so that shorter edge size is video_min_dimension + - When video_width = 0, video_height = 0, video_min_dimension = 0, + and video_max_dimension != 0, keep the aspect ratio and resize + the frame so that longer edge size is video_max_dimension + - When video_width = 0, video_height = 0, video_min_dimension != 0, + and video_max_dimension != 0, resize the frame so that shorter + edge size is video_min_dimension, and longer edge size is + video_max_dimension. The aspect ratio may not be preserved + - When video_width = 0, video_height != 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the aspect ratio and resize + the frame so that frame video_height is $video_height + - When video_width != 0, video_height == 0, video_min_dimension = 0, + and video_max_dimension = 0, keep the aspect ratio and resize + the frame so that frame video_width is $video_width + - When video_width != 0, video_height != 0, video_min_dimension = 0, + and video_max_dimension = 0, resize the frame so that frame + video_width and video_height are set to $video_width and + $video_height, respectively + video_pts_range (list(int), optional): the start and end presentation timestamp of video stream + video_timebase_numerator / video_timebase_denominator (float, optional): a rational + number which denotes timebase in video stream + read_audio_stream (int, optional): whether read audio stream. If yes, set to 1. Otherwise, 0 + audio_samples (int, optional): audio sampling rate + audio_channels (int optional): audio audio_channels + audio_pts_range (list(int), optional): the start and end presentation timestamp of audio stream + audio_timebase_numerator / audio_timebase_denominator (float, optional): + a rational number which denotes time base in audio stream + + Returns: + vframes (Tensor[T, H, W, C]): the `T` video frames + aframes (Tensor[L, K]): the audio frames, where `L` is the number of points and + `K` is the number of channels + """ + + _validate_pts(video_pts_range) + _validate_pts(audio_pts_range) + + if not isinstance(video_data, torch.Tensor): + with warnings.catch_warnings(): + # Ignore the warning because we actually don't modify the buffer in this function + warnings.filterwarnings("ignore", message="The given buffer is not writable") + video_data = torch.frombuffer(video_data, dtype=torch.uint8) + + result = torch.ops.video_reader.read_video_from_memory( + video_data, + seek_frame_margin, + 0, # getPtsOnly + read_video_stream, + video_width, + video_height, + video_min_dimension, + video_max_dimension, + video_pts_range[0], + video_pts_range[1], + video_timebase_numerator, + video_timebase_denominator, + read_audio_stream, + audio_samples, + audio_channels, + audio_pts_range[0], + audio_pts_range[1], + audio_timebase_numerator, + audio_timebase_denominator, + ) + + vframes, _vframe_pts, vtimebase, vfps, vduration, aframes, aframe_pts, atimebase, asample_rate, aduration = result + + if aframes.numel() > 0: + # when audio stream is found + aframes = _align_audio_frames(aframes, aframe_pts, audio_pts_range) + + return vframes, aframes + + +def _read_video_timestamps_from_memory( + video_data: torch.Tensor, +) -> Tuple[List[int], List[int], VideoMetaData]: + """ + Decode all frames in the video. Only pts (presentation timestamp) is returned. + The actual frame pixel data is not copied. Thus, read_video_timestamps(...) + is much faster than read_video(...) + """ + if not isinstance(video_data, torch.Tensor): + with warnings.catch_warnings(): + # Ignore the warning because we actually don't modify the buffer in this function + warnings.filterwarnings("ignore", message="The given buffer is not writable") + video_data = torch.frombuffer(video_data, dtype=torch.uint8) + result = torch.ops.video_reader.read_video_from_memory( + video_data, + 0, # seek_frame_margin + 1, # getPtsOnly + 1, # read_video_stream + 0, # video_width + 0, # video_height + 0, # video_min_dimension + 0, # video_max_dimension + 0, # video_start_pts + -1, # video_end_pts + 0, # video_timebase_num + 1, # video_timebase_den + 1, # read_audio_stream + 0, # audio_samples + 0, # audio_channels + 0, # audio_start_pts + -1, # audio_end_pts + 0, # audio_timebase_num + 1, # audio_timebase_den + ) + _vframes, vframe_pts, vtimebase, vfps, vduration, _aframes, aframe_pts, atimebase, asample_rate, aduration = result + info = _fill_info(vtimebase, vfps, vduration, atimebase, asample_rate, aduration) + + vframe_pts = vframe_pts.numpy().tolist() + aframe_pts = aframe_pts.numpy().tolist() + return vframe_pts, aframe_pts, info + + +def _probe_video_from_memory( + video_data: torch.Tensor, +) -> VideoMetaData: + """ + Probe a video in memory and return VideoMetaData with info about the video + This function is torchscriptable + """ + if not isinstance(video_data, torch.Tensor): + with warnings.catch_warnings(): + # Ignore the warning because we actually don't modify the buffer in this function + warnings.filterwarnings("ignore", message="The given buffer is not writable") + video_data = torch.frombuffer(video_data, dtype=torch.uint8) + result = torch.ops.video_reader.probe_video_from_memory(video_data) + vtimebase, vfps, vduration, atimebase, asample_rate, aduration = result + info = _fill_info(vtimebase, vfps, vduration, atimebase, asample_rate, aduration) + return info + + +def _read_video( + filename: str, + start_pts: Union[float, Fraction] = 0, + end_pts: Optional[Union[float, Fraction]] = None, + pts_unit: str = "pts", +) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, float]]: + if end_pts is None: + end_pts = float("inf") + + if pts_unit == "pts": + warnings.warn( + "The pts_unit 'pts' gives wrong results and will be removed in a " + + "follow-up version. Please use pts_unit 'sec'." + ) + + info = _probe_video_from_file(filename) + + has_video = info.has_video + has_audio = info.has_audio + + def get_pts(time_base): + start_offset = start_pts + end_offset = end_pts + if pts_unit == "sec": + start_offset = int(math.floor(start_pts * (1 / time_base))) + if end_offset != float("inf"): + end_offset = int(math.ceil(end_pts * (1 / time_base))) + if end_offset == float("inf"): + end_offset = -1 + return start_offset, end_offset + + video_pts_range = (0, -1) + video_timebase = default_timebase + if has_video: + video_timebase = Fraction(info.video_timebase.numerator, info.video_timebase.denominator) + video_pts_range = get_pts(video_timebase) + + audio_pts_range = (0, -1) + audio_timebase = default_timebase + if has_audio: + audio_timebase = Fraction(info.audio_timebase.numerator, info.audio_timebase.denominator) + audio_pts_range = get_pts(audio_timebase) + + vframes, aframes, info = _read_video_from_file( + filename, + read_video_stream=True, + video_pts_range=video_pts_range, + video_timebase=video_timebase, + read_audio_stream=True, + audio_pts_range=audio_pts_range, + audio_timebase=audio_timebase, + ) + _info = {} + if has_video: + _info["video_fps"] = info.video_fps + if has_audio: + _info["audio_fps"] = info.audio_sample_rate + + return vframes, aframes, _info + + +def _read_video_timestamps( + filename: str, pts_unit: str = "pts" +) -> Tuple[Union[List[int], List[Fraction]], Optional[float]]: + if pts_unit == "pts": + warnings.warn( + "The pts_unit 'pts' gives wrong results and will be removed in a " + + "follow-up version. Please use pts_unit 'sec'." + ) + + pts: Union[List[int], List[Fraction]] + pts, _, info = _read_video_timestamps_from_file(filename) + + if pts_unit == "sec": + video_time_base = Fraction(info.video_timebase.numerator, info.video_timebase.denominator) + pts = [x * video_time_base for x in pts] + + video_fps = info.video_fps if info.has_video else None + + return pts, video_fps diff --git a/lib/python3.11/site-packages/torchvision/io/image.py b/lib/python3.11/site-packages/torchvision/io/image.py new file mode 100644 index 0000000000000000000000000000000000000000..023898f33c6d7b82a7161daee8dcf367e694df62 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/image.py @@ -0,0 +1,511 @@ +from enum import Enum +from typing import List, Union +from warnings import warn + +import torch + +from ..extension import _load_library +from ..utils import _log_api_usage_once + + +try: + _load_library("image") +except (ImportError, OSError) as e: + warn( + f"Failed to load image Python extension: '{e}'" + f"If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. " + f"Otherwise, there might be something wrong with your environment. " + f"Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?" + ) + + +class ImageReadMode(Enum): + """Allow automatic conversion to RGB, RGBA, etc while decoding. + + .. note:: + + You don't need to use this struct, you can just pass strings to all + ``mode`` parameters, e.g. ``mode="RGB"``. + + The different available modes are the following. + + - UNCHANGED: loads the image as-is + - RGB: converts to RGB + - RGBA: converts to RGB with transparency (also aliased as RGB_ALPHA) + - GRAY: converts to grayscale + - GRAY_ALPHA: converts to grayscale with transparency + + .. note:: + + Some decoders won't support all possible values, e.g. GRAY and + GRAY_ALPHA are only supported for PNG and JPEG images. + """ + + UNCHANGED = 0 + GRAY = 1 + GRAY_ALPHA = 2 + RGB = 3 + RGB_ALPHA = 4 + RGBA = RGB_ALPHA # Alias for convenience + + +def read_file(path: str) -> torch.Tensor: + """ + Return the bytes contents of a file as a uint8 1D Tensor. + + Args: + path (str or ``pathlib.Path``): the path to the file to be read + + Returns: + data (Tensor) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(read_file) + data = torch.ops.image.read_file(str(path)) + return data + + +def write_file(filename: str, data: torch.Tensor) -> None: + """ + Write the content of an uint8 1D tensor to a file. + + Args: + filename (str or ``pathlib.Path``): the path to the file to be written + data (Tensor): the contents to be written to the output file + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(write_file) + torch.ops.image.write_file(str(filename), data) + + +def decode_png( + input: torch.Tensor, + mode: ImageReadMode = ImageReadMode.UNCHANGED, + apply_exif_orientation: bool = False, +) -> torch.Tensor: + """ + Decodes a PNG image into a 3 dimensional RGB or grayscale Tensor. + + The values of the output tensor are in uint8 in [0, 255] for most cases. If + the image is a 16-bit png, then the output tensor is uint16 in [0, 65535] + (supported from torchvision ``0.21``). Since uint16 support is limited in + pytorch, we recommend calling + :func:`torchvision.transforms.v2.functional.to_dtype()` with ``scale=True`` + after this function to convert the decoded image into a uint8 or float + tensor. + + Args: + input (Tensor[1]): a one dimensional uint8 tensor containing + the raw bytes of the PNG image. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor. + Default: False. + + Returns: + output (Tensor[image_channels, image_height, image_width]) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(decode_png) + if isinstance(mode, str): + mode = ImageReadMode[mode.upper()] + output = torch.ops.image.decode_png(input, mode.value, apply_exif_orientation) + return output + + +def encode_png(input: torch.Tensor, compression_level: int = 6) -> torch.Tensor: + """ + Takes an input tensor in CHW layout and returns a buffer with the contents + of its corresponding PNG file. + + Args: + input (Tensor[channels, image_height, image_width]): int8 image tensor of + ``c`` channels, where ``c`` must 3 or 1. + compression_level (int): Compression factor for the resulting file, it must be a number + between 0 and 9. Default: 6 + + Returns: + Tensor[1]: A one dimensional int8 tensor that contains the raw bytes of the + PNG file. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(encode_png) + output = torch.ops.image.encode_png(input, compression_level) + return output + + +def write_png(input: torch.Tensor, filename: str, compression_level: int = 6): + """ + Takes an input tensor in CHW layout (or HW in the case of grayscale images) + and saves it in a PNG file. + + Args: + input (Tensor[channels, image_height, image_width]): int8 image tensor of + ``c`` channels, where ``c`` must be 1 or 3. + filename (str or ``pathlib.Path``): Path to save the image. + compression_level (int): Compression factor for the resulting file, it must be a number + between 0 and 9. Default: 6 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(write_png) + output = encode_png(input, compression_level) + write_file(filename, output) + + +def decode_jpeg( + input: Union[torch.Tensor, List[torch.Tensor]], + mode: ImageReadMode = ImageReadMode.UNCHANGED, + device: Union[str, torch.device] = "cpu", + apply_exif_orientation: bool = False, +) -> Union[torch.Tensor, List[torch.Tensor]]: + """Decode JPEG image(s) into 3D RGB or grayscale Tensor(s), on CPU or CUDA. + + The values of the output tensor are uint8 between 0 and 255. + + .. note:: + When using a CUDA device, passing a list of tensors is more efficient than repeated individual calls to ``decode_jpeg``. + When using CPU the performance is equivalent. + The CUDA version of this function has explicitly been designed with thread-safety in mind. + This function does not return partial results in case of an error. + + Args: + input (Tensor[1] or list[Tensor[1]]): a (list of) one dimensional uint8 tensor(s) containing + the raw bytes of the JPEG image. The tensor(s) must be on CPU, + regardless of the ``device`` parameter. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + device (str or torch.device): The device on which the decoded image will + be stored. If a cuda device is specified, the image will be decoded + with `nvjpeg `_. This is only + supported for CUDA version >= 10.1 + + .. betastatus:: device parameter + + .. warning:: + There is a memory leak in the nvjpeg library for CUDA versions < 11.6. + Make sure to rely on CUDA 11.6 or above before using ``device="cuda"``. + apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor. + Default: False. Only implemented for JPEG format on CPU. + + Returns: + output (Tensor[image_channels, image_height, image_width] or list[Tensor[image_channels, image_height, image_width]]): + The values of the output tensor(s) are uint8 between 0 and 255. + ``output.device`` will be set to the specified ``device`` + + + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(decode_jpeg) + if isinstance(device, str): + device = torch.device(device) + if isinstance(mode, str): + mode = ImageReadMode[mode.upper()] + + if isinstance(input, list): + if len(input) == 0: + raise ValueError("Input list must contain at least one element") + if not all(isinstance(t, torch.Tensor) for t in input): + raise ValueError("All elements of the input list must be tensors.") + if not all(t.device.type == "cpu" for t in input): + raise ValueError("Input list must contain tensors on CPU.") + if device.type == "cuda": + return torch.ops.image.decode_jpegs_cuda(input, mode.value, device) + else: + return [torch.ops.image.decode_jpeg(img, mode.value, apply_exif_orientation) for img in input] + + else: # input is tensor + if input.device.type != "cpu": + raise ValueError("Input tensor must be a CPU tensor") + if device.type == "cuda": + return torch.ops.image.decode_jpegs_cuda([input], mode.value, device)[0] + else: + return torch.ops.image.decode_jpeg(input, mode.value, apply_exif_orientation) + + +def encode_jpeg( + input: Union[torch.Tensor, List[torch.Tensor]], quality: int = 75 +) -> Union[torch.Tensor, List[torch.Tensor]]: + """Encode RGB tensor(s) into raw encoded jpeg bytes, on CPU or CUDA. + + .. note:: + Passing a list of CUDA tensors is more efficient than repeated individual calls to ``encode_jpeg``. + For CPU tensors the performance is equivalent. + + Args: + input (Tensor[channels, image_height, image_width] or List[Tensor[channels, image_height, image_width]]): + (list of) uint8 image tensor(s) of ``c`` channels, where ``c`` must be 1 or 3 + quality (int): Quality of the resulting JPEG file(s). Must be a number between + 1 and 100. Default: 75 + + Returns: + output (Tensor[1] or list[Tensor[1]]): A (list of) one dimensional uint8 tensor(s) that contain the raw bytes of the JPEG file. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(encode_jpeg) + if quality < 1 or quality > 100: + raise ValueError("Image quality should be a positive number between 1 and 100") + if isinstance(input, list): + if not input: + raise ValueError("encode_jpeg requires at least one input tensor when a list is passed") + if input[0].device.type == "cuda": + return torch.ops.image.encode_jpegs_cuda(input, quality) + else: + return [torch.ops.image.encode_jpeg(image, quality) for image in input] + else: # single input tensor + if input.device.type == "cuda": + return torch.ops.image.encode_jpegs_cuda([input], quality)[0] + else: + return torch.ops.image.encode_jpeg(input, quality) + + +def write_jpeg(input: torch.Tensor, filename: str, quality: int = 75): + """ + Takes an input tensor in CHW layout and saves it in a JPEG file. + + Args: + input (Tensor[channels, image_height, image_width]): int8 image tensor of ``c`` + channels, where ``c`` must be 1 or 3. + filename (str or ``pathlib.Path``): Path to save the image. + quality (int): Quality of the resulting JPEG file, it must be a number + between 1 and 100. Default: 75 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(write_jpeg) + output = encode_jpeg(input, quality) + assert isinstance(output, torch.Tensor) # Needed for torchscript + write_file(filename, output) + + +def decode_image( + input: Union[torch.Tensor, str], + mode: ImageReadMode = ImageReadMode.UNCHANGED, + apply_exif_orientation: bool = False, +) -> torch.Tensor: + """Decode an image into a uint8 tensor, from a path or from raw encoded bytes. + + Currently supported image formats are jpeg, png, gif and webp. + + The values of the output tensor are in uint8 in [0, 255] for most cases. + + If the image is a 16-bit png, then the output tensor is uint16 in [0, 65535] + (supported from torchvision ``0.21``). Since uint16 support is limited in + pytorch, we recommend calling + :func:`torchvision.transforms.v2.functional.to_dtype()` with ``scale=True`` + after this function to convert the decoded image into a uint8 or float + tensor. + + .. note:: + + ``decode_image()`` doesn't work yet on AVIF or HEIC images. For these + formats, directly call :func:`~torchvision.io.decode_avif` or + :func:`~torchvision.io.decode_heic`. + + Args: + input (Tensor or str or ``pathlib.Path``): The image to decode. If a + tensor is passed, it must be one dimensional uint8 tensor containing + the raw bytes of the image. Otherwise, this must be a path to the image file. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor. + Only applies to JPEG and PNG images. Default: False. + + Returns: + output (Tensor[image_channels, image_height, image_width]) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(decode_image) + if not isinstance(input, torch.Tensor): + input = read_file(str(input)) + if isinstance(mode, str): + mode = ImageReadMode[mode.upper()] + output = torch.ops.image.decode_image(input, mode.value, apply_exif_orientation) + return output + + +def read_image( + path: str, + mode: ImageReadMode = ImageReadMode.UNCHANGED, + apply_exif_orientation: bool = False, +) -> torch.Tensor: + """[OBSOLETE] Use :func:`~torchvision.io.decode_image` instead.""" + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(read_image) + data = read_file(path) + return decode_image(data, mode, apply_exif_orientation=apply_exif_orientation) + + +def decode_gif(input: torch.Tensor) -> torch.Tensor: + """ + Decode a GIF image into a 3 or 4 dimensional RGB Tensor. + + The values of the output tensor are uint8 between 0 and 255. + The output tensor has shape ``(C, H, W)`` if there is only one image in the + GIF, and ``(N, C, H, W)`` if there are ``N`` images. + + Args: + input (Tensor[1]): a one dimensional contiguous uint8 tensor containing + the raw bytes of the GIF image. + + Returns: + output (Tensor[image_channels, image_height, image_width] or Tensor[num_images, image_channels, image_height, image_width]) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(decode_gif) + return torch.ops.image.decode_gif(input) + + +def decode_webp( + input: torch.Tensor, + mode: ImageReadMode = ImageReadMode.UNCHANGED, +) -> torch.Tensor: + """ + Decode a WEBP image into a 3 dimensional RGB[A] Tensor. + + The values of the output tensor are uint8 between 0 and 255. + + Args: + input (Tensor[1]): a one dimensional contiguous uint8 tensor containing + the raw bytes of the WEBP image. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + + Returns: + Decoded image (Tensor[image_channels, image_height, image_width]) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(decode_webp) + if isinstance(mode, str): + mode = ImageReadMode[mode.upper()] + return torch.ops.image.decode_webp(input, mode.value) + + +# TODO_AVIF_HEIC: Better support for torchscript. Scripting decode_avif of +# decode_heic currently fails, mainly because of the logic +# _load_extra_decoders_once() (using global variables, try/except statements, +# etc.). +# The ops (torch.ops.extra_decoders_ns.decode_*) are otherwise torchscript-able, +# and users who need torchscript can always just wrap those. + +# TODO_AVIF_HEIC: decode_image() should work for those. The key technical issue +# we have here is that the format detection logic of decode_image() is +# implemented in torchvision, and torchvision has zero knowledge of +# torchvision-extra-decoders, so we cannot call the AVIF/HEIC C++ decoders +# (those in torchvision-extra-decoders) from there. +# A trivial check that could be done within torchvision would be to check the +# file extension, if a path was passed. We could also just implement the +# AVIF/HEIC detection logic in Python as a fallback, if the file detection +# didn't find any format. In any case: properly determining whether a file is +# HEIC is far from trivial, and relying on libmagic would probably be best + + +_EXTRA_DECODERS_ALREADY_LOADED = False + + +def _load_extra_decoders_once(): + global _EXTRA_DECODERS_ALREADY_LOADED + if _EXTRA_DECODERS_ALREADY_LOADED: + return + + try: + import torchvision_extra_decoders + + # torchvision-extra-decoders only supports linux for now. BUT, users on + # e.g. MacOS can still install it: they will get the pure-python + # 0.0.0.dev version: + # https://pypi.org/project/torchvision-extra-decoders/0.0.0.dev0, which + # is a dummy version that was created to reserve the namespace on PyPI. + # We have to check that expose_extra_decoders() exists for those users, + # so we can properly error on non-Linux archs. + assert hasattr(torchvision_extra_decoders, "expose_extra_decoders") + except (AssertionError, ImportError) as e: + raise RuntimeError( + "In order to enable the AVIF and HEIC decoding capabilities of " + "torchvision, you need to `pip install torchvision-extra-decoders`. " + "Just install the package, you don't need to update your code. " + "This is only supported on Linux, and this feature is still in BETA stage. " + "Please let us know of any issue: https://github.com/pytorch/vision/issues/new/choose. " + "Note that `torchvision-extra-decoders` is released under the LGPL license. " + ) from e + + # This will expose torch.ops.extra_decoders_ns.decode_avif and torch.ops.extra_decoders_ns.decode_heic + torchvision_extra_decoders.expose_extra_decoders() + + _EXTRA_DECODERS_ALREADY_LOADED = True + + +def decode_avif(input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHANGED) -> torch.Tensor: + """Decode an AVIF image into a 3 dimensional RGB[A] Tensor. + + .. warning:: + In order to enable the AVIF decoding capabilities of torchvision, you + first need to run ``pip install torchvision-extra-decoders``. Just + install the package, you don't need to update your code. This is only + supported on Linux, and this feature is still in BETA stage. Please let + us know of any issue: + https://github.com/pytorch/vision/issues/new/choose. Note that + `torchvision-extra-decoders + `_ is + released under the LGPL license. + + The values of the output tensor are in uint8 in [0, 255] for most images. If + the image has a bit-depth of more than 8, then the output tensor is uint16 + in [0, 65535]. Since uint16 support is limited in pytorch, we recommend + calling :func:`torchvision.transforms.v2.functional.to_dtype()` with + ``scale=True`` after this function to convert the decoded image into a uint8 + or float tensor. + + Args: + input (Tensor[1]): a one dimensional contiguous uint8 tensor containing + the raw bytes of the AVIF image. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + + Returns: + Decoded image (Tensor[image_channels, image_height, image_width]) + """ + _load_extra_decoders_once() + if input.dtype != torch.uint8: + raise RuntimeError(f"Input tensor must have uint8 data type, got {input.dtype}") + return torch.ops.extra_decoders_ns.decode_avif(input, mode.value) + + +def decode_heic(input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHANGED) -> torch.Tensor: + """Decode an HEIC image into a 3 dimensional RGB[A] Tensor. + + .. warning:: + In order to enable the AVIF decoding capabilities of torchvision, you + first need to run ``pip install torchvision-extra-decoders``. Just + install the package, you don't need to update your code. This is only + supported on Linux, and this feature is still in BETA stage. Please let + us know of any issue: + https://github.com/pytorch/vision/issues/new/choose. Note that + `torchvision-extra-decoders + `_ is + released under the LGPL license. + + The values of the output tensor are in uint8 in [0, 255] for most images. If + the image has a bit-depth of more than 8, then the output tensor is uint16 + in [0, 65535]. Since uint16 support is limited in pytorch, we recommend + calling :func:`torchvision.transforms.v2.functional.to_dtype()` with + ``scale=True`` after this function to convert the decoded image into a uint8 + or float tensor. + + Args: + input (Tensor[1]): a one dimensional contiguous uint8 tensor containing + the raw bytes of the HEIC image. + mode (str or ImageReadMode): The mode to convert the image to, e.g. "RGB". + Default is "UNCHANGED". See :class:`~torchvision.io.ImageReadMode` + for available modes. + + Returns: + Decoded image (Tensor[image_channels, image_height, image_width]) + """ + _load_extra_decoders_once() + if input.dtype != torch.uint8: + raise RuntimeError(f"Input tensor must have uint8 data type, got {input.dtype}") + return torch.ops.extra_decoders_ns.decode_heic(input, mode.value) diff --git a/lib/python3.11/site-packages/torchvision/io/video.py b/lib/python3.11/site-packages/torchvision/io/video.py new file mode 100644 index 0000000000000000000000000000000000000000..2e3dbed65a279f0746a0fd38a0ae98d870938653 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/video.py @@ -0,0 +1,466 @@ +import gc +import math +import os +import re +import warnings +from fractions import Fraction +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch + +from ..utils import _log_api_usage_once +from . import _video_opt + +try: + import av + + av.logging.set_level(av.logging.ERROR) + if not hasattr(av.video.frame.VideoFrame, "pict_type"): + av = ImportError( + """\ +Your version of PyAV is too old for the necessary video operations in torchvision. +If you are on Python 3.5, you will have to build from source (the conda-forge +packages are not up-to-date). See +https://github.com/mikeboers/PyAV#installation for instructions on how to +install PyAV on your system. +""" + ) + try: + FFmpegError = av.FFmpegError # from av 14 https://github.com/PyAV-Org/PyAV/blob/main/CHANGELOG.rst + except AttributeError: + FFmpegError = av.AVError +except ImportError: + av = ImportError( + """\ +PyAV is not installed, and is necessary for the video operations in torchvision. +See https://github.com/mikeboers/PyAV#installation for instructions on how to +install PyAV on your system. +""" + ) + + +def _check_av_available() -> None: + if isinstance(av, Exception): + raise av + + +def _av_available() -> bool: + return not isinstance(av, Exception) + + +# PyAV has some reference cycles +_CALLED_TIMES = 0 +_GC_COLLECTION_INTERVAL = 10 + + +def write_video( + filename: str, + video_array: torch.Tensor, + fps: float, + video_codec: str = "libx264", + options: Optional[Dict[str, Any]] = None, + audio_array: Optional[torch.Tensor] = None, + audio_fps: Optional[float] = None, + audio_codec: Optional[str] = None, + audio_options: Optional[Dict[str, Any]] = None, +) -> None: + """ + Writes a 4d tensor in [T, H, W, C] format in a video file. + + This function relies on PyAV (therefore, ultimately FFmpeg) to encode + videos, you can get more fine-grained control by referring to the other + options at your disposal within `the FFMpeg wiki + `_. + + .. warning:: + + In the near future, we intend to centralize PyTorch's video decoding + capabilities within the `torchcodec + `_ project. We encourage you to + try it out and share your feedback, as the torchvision video decoders + will eventually be deprecated. + + Args: + filename (str): path where the video will be saved + video_array (Tensor[T, H, W, C]): tensor containing the individual frames, + as a uint8 tensor in [T, H, W, C] format + fps (Number): video frames per second + video_codec (str): the name of the video codec, i.e. "libx264", "h264", etc. + options (Dict): dictionary containing options to be passed into the PyAV video stream. + The list of options is codec-dependent and can all + be found from `the FFMpeg wiki `_. + audio_array (Tensor[C, N]): tensor containing the audio, where C is the number of channels + and N is the number of samples + audio_fps (Number): audio sample rate, typically 44100 or 48000 + audio_codec (str): the name of the audio codec, i.e. "mp3", "aac", etc. + audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream. + The list of options is codec-dependent and can all + be found from `the FFMpeg wiki `_. + + Examples:: + >>> # Creating libx264 video with CRF 17, for visually lossless footage: + >>> + >>> from torchvision.io import write_video + >>> # 1000 frames of 100x100, 3-channel image. + >>> vid = torch.randn(1000, 100, 100, 3, dtype = torch.uint8) + >>> write_video("video.mp4", options = {"crf": "17"}) + + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(write_video) + _check_av_available() + video_array = torch.as_tensor(video_array, dtype=torch.uint8).numpy(force=True) + + # PyAV does not support floating point numbers with decimal point + # and will throw OverflowException in case this is not the case + if isinstance(fps, float): + fps = np.round(fps) + + with av.open(filename, mode="w") as container: + stream = container.add_stream(video_codec, rate=fps) + stream.width = video_array.shape[2] + stream.height = video_array.shape[1] + stream.pix_fmt = "yuv420p" if video_codec != "libx264rgb" else "rgb24" + stream.options = options or {} + + if audio_array is not None: + audio_format_dtypes = { + "dbl": " 1 else "mono" + audio_sample_fmt = container.streams.audio[0].format.name + + format_dtype = np.dtype(audio_format_dtypes[audio_sample_fmt]) + audio_array = torch.as_tensor(audio_array).numpy(force=True).astype(format_dtype) + + frame = av.AudioFrame.from_ndarray(audio_array, format=audio_sample_fmt, layout=audio_layout) + + frame.sample_rate = audio_fps + + for packet in a_stream.encode(frame): + container.mux(packet) + + for packet in a_stream.encode(): + container.mux(packet) + + for img in video_array: + frame = av.VideoFrame.from_ndarray(img, format="rgb24") + try: + frame.pict_type = "NONE" + except TypeError: + from av.video.frame import PictureType # noqa + + frame.pict_type = PictureType.NONE + + for packet in stream.encode(frame): + container.mux(packet) + + # Flush stream + for packet in stream.encode(): + container.mux(packet) + + +def _read_from_stream( + container: "av.container.Container", + start_offset: float, + end_offset: float, + pts_unit: str, + stream: "av.stream.Stream", + stream_name: Dict[str, Optional[Union[int, Tuple[int, ...], List[int]]]], +) -> List["av.frame.Frame"]: + global _CALLED_TIMES, _GC_COLLECTION_INTERVAL + _CALLED_TIMES += 1 + if _CALLED_TIMES % _GC_COLLECTION_INTERVAL == _GC_COLLECTION_INTERVAL - 1: + gc.collect() + + if pts_unit == "sec": + # TODO: we should change all of this from ground up to simply take + # sec and convert to MS in C++ + start_offset = int(math.floor(start_offset * (1 / stream.time_base))) + if end_offset != float("inf"): + end_offset = int(math.ceil(end_offset * (1 / stream.time_base))) + else: + warnings.warn("The pts_unit 'pts' gives wrong results. Please use pts_unit 'sec'.") + + frames = {} + should_buffer = True + max_buffer_size = 5 + if stream.type == "video": + # DivX-style packed B-frames can have out-of-order pts (2 frames in a single pkt) + # so need to buffer some extra frames to sort everything + # properly + extradata = stream.codec_context.extradata + # overly complicated way of finding if `divx_packed` is set, following + # https://github.com/FFmpeg/FFmpeg/commit/d5a21172283572af587b3d939eba0091484d3263 + if extradata and b"DivX" in extradata: + # can't use regex directly because of some weird characters sometimes... + pos = extradata.find(b"DivX") + d = extradata[pos:] + o = re.search(rb"DivX(\d+)Build(\d+)(\w)", d) + if o is None: + o = re.search(rb"DivX(\d+)b(\d+)(\w)", d) + if o is not None: + should_buffer = o.group(3) == b"p" + seek_offset = start_offset + # some files don't seek to the right location, so better be safe here + seek_offset = max(seek_offset - 1, 0) + if should_buffer: + # FIXME this is kind of a hack, but we will jump to the previous keyframe + # so this will be safe + seek_offset = max(seek_offset - max_buffer_size, 0) + try: + # TODO check if stream needs to always be the video stream here or not + container.seek(seek_offset, any_frame=False, backward=True, stream=stream) + except FFmpegError: + # TODO add some warnings in this case + # print("Corrupted file?", container.name) + return [] + buffer_count = 0 + try: + for _idx, frame in enumerate(container.decode(**stream_name)): + frames[frame.pts] = frame + if frame.pts >= end_offset: + if should_buffer and buffer_count < max_buffer_size: + buffer_count += 1 + continue + break + except FFmpegError: + # TODO add a warning + pass + # ensure that the results are sorted wrt the pts + result = [frames[i] for i in sorted(frames) if start_offset <= frames[i].pts <= end_offset] + if len(frames) > 0 and start_offset > 0 and start_offset not in frames: + # if there is no frame that exactly matches the pts of start_offset + # add the last frame smaller than start_offset, to guarantee that + # we will have all the necessary data. This is most useful for audio + preceding_frames = [i for i in frames if i < start_offset] + if len(preceding_frames) > 0: + first_frame_pts = max(preceding_frames) + result.insert(0, frames[first_frame_pts]) + return result + + +def _align_audio_frames( + aframes: torch.Tensor, audio_frames: List["av.frame.Frame"], ref_start: int, ref_end: float +) -> torch.Tensor: + start, end = audio_frames[0].pts, audio_frames[-1].pts + total_aframes = aframes.shape[1] + step_per_aframe = (end - start + 1) / total_aframes + s_idx = 0 + e_idx = total_aframes + if start < ref_start: + s_idx = int((ref_start - start) / step_per_aframe) + if end > ref_end: + e_idx = int((ref_end - end) / step_per_aframe) + return aframes[:, s_idx:e_idx] + + +def read_video( + filename: str, + start_pts: Union[float, Fraction] = 0, + end_pts: Optional[Union[float, Fraction]] = None, + pts_unit: str = "pts", + output_format: str = "THWC", +) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]: + """ + Reads a video from a file, returning both the video frames and the audio frames + + .. warning:: + + In the near future, we intend to centralize PyTorch's video decoding + capabilities within the `torchcodec + `_ project. We encourage you to + try it out and share your feedback, as the torchvision video decoders + will eventually be deprecated. + + Args: + filename (str): path to the video file. If using the pyav backend, this can be whatever ``av.open`` accepts. + start_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional): + The start presentation time of the video + end_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional): + The end presentation time + pts_unit (str, optional): unit in which start_pts and end_pts values will be interpreted, + either 'pts' or 'sec'. Defaults to 'pts'. + output_format (str, optional): The format of the output video tensors. Can be either "THWC" (default) or "TCHW". + + Returns: + vframes (Tensor[T, H, W, C] or Tensor[T, C, H, W]): the `T` video frames + aframes (Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points + info (Dict): metadata for the video and audio. Can contain the fields video_fps (float) and audio_fps (int) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(read_video) + + output_format = output_format.upper() + if output_format not in ("THWC", "TCHW"): + raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.") + + from torchvision import get_video_backend + + if get_video_backend() != "pyav": + if not os.path.exists(filename): + raise RuntimeError(f"File not found: {filename}") + vframes, aframes, info = _video_opt._read_video(filename, start_pts, end_pts, pts_unit) + else: + _check_av_available() + + if end_pts is None: + end_pts = float("inf") + + if end_pts < start_pts: + raise ValueError( + f"end_pts should be larger than start_pts, got start_pts={start_pts} and end_pts={end_pts}" + ) + + info = {} + video_frames = [] + audio_frames = [] + audio_timebase = _video_opt.default_timebase + + try: + with av.open(filename, metadata_errors="ignore") as container: + if container.streams.audio: + audio_timebase = container.streams.audio[0].time_base + if container.streams.video: + video_frames = _read_from_stream( + container, + start_pts, + end_pts, + pts_unit, + container.streams.video[0], + {"video": 0}, + ) + video_fps = container.streams.video[0].average_rate + # guard against potentially corrupted files + if video_fps is not None: + info["video_fps"] = float(video_fps) + + if container.streams.audio: + audio_frames = _read_from_stream( + container, + start_pts, + end_pts, + pts_unit, + container.streams.audio[0], + {"audio": 0}, + ) + info["audio_fps"] = container.streams.audio[0].rate + + except FFmpegError: + # TODO raise a warning? + pass + + vframes_list = [frame.to_rgb().to_ndarray() for frame in video_frames] + aframes_list = [frame.to_ndarray() for frame in audio_frames] + + if vframes_list: + vframes = torch.as_tensor(np.stack(vframes_list)) + else: + vframes = torch.empty((0, 1, 1, 3), dtype=torch.uint8) + + if aframes_list: + aframes = np.concatenate(aframes_list, 1) + aframes = torch.as_tensor(aframes) + if pts_unit == "sec": + start_pts = int(math.floor(start_pts * (1 / audio_timebase))) + if end_pts != float("inf"): + end_pts = int(math.ceil(end_pts * (1 / audio_timebase))) + aframes = _align_audio_frames(aframes, audio_frames, start_pts, end_pts) + else: + aframes = torch.empty((1, 0), dtype=torch.float32) + + if output_format == "TCHW": + # [T,H,W,C] --> [T,C,H,W] + vframes = vframes.permute(0, 3, 1, 2) + + return vframes, aframes, info + + +def _can_read_timestamps_from_packets(container: "av.container.Container") -> bool: + extradata = container.streams[0].codec_context.extradata + if extradata is None: + return False + if b"Lavc" in extradata: + return True + return False + + +def _decode_video_timestamps(container: "av.container.Container") -> List[int]: + if _can_read_timestamps_from_packets(container): + # fast path + return [x.pts for x in container.demux(video=0) if x.pts is not None] + else: + return [x.pts for x in container.decode(video=0) if x.pts is not None] + + +def read_video_timestamps(filename: str, pts_unit: str = "pts") -> Tuple[List[int], Optional[float]]: + """ + List the video frames timestamps. + + .. warning:: + + In the near future, we intend to centralize PyTorch's video decoding + capabilities within the `torchcodec + `_ project. We encourage you to + try it out and share your feedback, as the torchvision video decoders + will eventually be deprecated. + + Note that the function decodes the whole video frame-by-frame. + + Args: + filename (str): path to the video file + pts_unit (str, optional): unit in which timestamp values will be returned + either 'pts' or 'sec'. Defaults to 'pts'. + + Returns: + pts (List[int] if pts_unit = 'pts', List[Fraction] if pts_unit = 'sec'): + presentation timestamps for each one of the frames in the video. + video_fps (float, optional): the frame rate for the video + + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(read_video_timestamps) + from torchvision import get_video_backend + + if get_video_backend() != "pyav": + return _video_opt._read_video_timestamps(filename, pts_unit) + + _check_av_available() + + video_fps = None + pts = [] + + try: + with av.open(filename, metadata_errors="ignore") as container: + if container.streams.video: + video_stream = container.streams.video[0] + video_time_base = video_stream.time_base + try: + pts = _decode_video_timestamps(container) + except FFmpegError: + warnings.warn(f"Failed decoding frames for file {filename}") + video_fps = float(video_stream.average_rate) + except FFmpegError as e: + msg = f"Failed to open container for {filename}; Caught error: {e}" + warnings.warn(msg, RuntimeWarning) + + pts.sort() + + if pts_unit == "sec": + pts = [x * video_time_base for x in pts] + + return pts, video_fps diff --git a/lib/python3.11/site-packages/torchvision/io/video_reader.py b/lib/python3.11/site-packages/torchvision/io/video_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..cf319fe288e1e1a67cc94cb798ab8a9212671101 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/io/video_reader.py @@ -0,0 +1,294 @@ +import io +import warnings + +from typing import Any, Dict, Iterator + +import torch + +from ..utils import _log_api_usage_once + +from ._video_opt import _HAS_CPU_VIDEO_DECODER + +if _HAS_CPU_VIDEO_DECODER: + + def _has_video_opt() -> bool: + return True + +else: + + def _has_video_opt() -> bool: + return False + + +try: + import av + + av.logging.set_level(av.logging.ERROR) + if not hasattr(av.video.frame.VideoFrame, "pict_type"): + av = ImportError( + """\ +Your version of PyAV is too old for the necessary video operations in torchvision. +If you are on Python 3.5, you will have to build from source (the conda-forge +packages are not up-to-date). See +https://github.com/mikeboers/PyAV#installation for instructions on how to +install PyAV on your system. +""" + ) +except ImportError: + av = ImportError( + """\ +PyAV is not installed, and is necessary for the video operations in torchvision. +See https://github.com/mikeboers/PyAV#installation for instructions on how to +install PyAV on your system. +""" + ) + + +class VideoReader: + """ + Fine-grained video-reading API. + Supports frame-by-frame reading of various streams from a single video + container. Much like previous video_reader API it supports the following + backends: video_reader, pyav, and cuda. + Backends can be set via `torchvision.set_video_backend` function. + + .. warning:: + + In the near future, we intend to centralize PyTorch's video decoding + capabilities within the `torchcodec + `_ project. We encourage you to + try it out and share your feedback, as the torchvision video decoders + will eventually be deprecated. + + .. betastatus:: VideoReader class + + Example: + The following examples creates a :mod:`VideoReader` object, seeks into 2s + point, and returns a single frame:: + + import torchvision + video_path = "path_to_a_test_video" + reader = torchvision.io.VideoReader(video_path, "video") + reader.seek(2.0) + frame = next(reader) + + :mod:`VideoReader` implements the iterable API, which makes it suitable to + using it in conjunction with :mod:`itertools` for more advanced reading. + As such, we can use a :mod:`VideoReader` instance inside for loops:: + + reader.seek(2) + for frame in reader: + frames.append(frame['data']) + # additionally, `seek` implements a fluent API, so we can do + for frame in reader.seek(2): + frames.append(frame['data']) + + With :mod:`itertools`, we can read all frames between 2 and 5 seconds with the + following code:: + + for frame in itertools.takewhile(lambda x: x['pts'] <= 5, reader.seek(2)): + frames.append(frame['data']) + + and similarly, reading 10 frames after the 2s timestamp can be achieved + as follows:: + + for frame in itertools.islice(reader.seek(2), 10): + frames.append(frame['data']) + + .. note:: + + Each stream descriptor consists of two parts: stream type (e.g. 'video') and + a unique stream id (which are determined by the video encoding). + In this way, if the video container contains multiple + streams of the same type, users can access the one they want. + If only stream type is passed, the decoder auto-detects first stream of that type. + + Args: + src (string, bytes object, or tensor): The media source. + If string-type, it must be a file path supported by FFMPEG. + If bytes, should be an in-memory representation of a file supported by FFMPEG. + If Tensor, it is interpreted internally as byte buffer. + It must be one-dimensional, of type ``torch.uint8``. + + stream (string, optional): descriptor of the required stream, followed by the stream id, + in the format ``{stream_type}:{stream_id}``. Defaults to ``"video:0"``. + Currently available options include ``['video', 'audio']`` + + num_threads (int, optional): number of threads used by the codec to decode video. + Default value (0) enables multithreading with codec-dependent heuristic. The performance + will depend on the version of FFMPEG codecs supported. + """ + + def __init__( + self, + src: str, + stream: str = "video", + num_threads: int = 0, + ) -> None: + _log_api_usage_once(self) + from .. import get_video_backend + + self.backend = get_video_backend() + if isinstance(src, str): + if not src: + raise ValueError("src cannot be empty") + elif isinstance(src, bytes): + if self.backend in ["cuda"]: + raise RuntimeError( + "VideoReader cannot be initialized from bytes object when using cuda or pyav backend." + ) + elif self.backend == "pyav": + src = io.BytesIO(src) + else: + with warnings.catch_warnings(): + # Ignore the warning because we actually don't modify the buffer in this function + warnings.filterwarnings("ignore", message="The given buffer is not writable") + src = torch.frombuffer(src, dtype=torch.uint8) + elif isinstance(src, torch.Tensor): + if self.backend in ["cuda", "pyav"]: + raise RuntimeError( + "VideoReader cannot be initialized from Tensor object when using cuda or pyav backend." + ) + else: + raise ValueError(f"src must be either string, Tensor or bytes object. Got {type(src)}") + + if self.backend == "cuda": + device = torch.device("cuda") + self._c = torch.classes.torchvision.GPUDecoder(src, device) + + elif self.backend == "video_reader": + if isinstance(src, str): + self._c = torch.classes.torchvision.Video(src, stream, num_threads) + elif isinstance(src, torch.Tensor): + self._c = torch.classes.torchvision.Video("", "", 0) + self._c.init_from_memory(src, stream, num_threads) + + elif self.backend == "pyav": + self.container = av.open(src, metadata_errors="ignore") + # TODO: load metadata + stream_type = stream.split(":")[0] + stream_id = 0 if len(stream.split(":")) == 1 else int(stream.split(":")[1]) + self.pyav_stream = {stream_type: stream_id} + self._c = self.container.decode(**self.pyav_stream) + + # TODO: add extradata exception + + else: + raise RuntimeError("Unknown video backend: {}".format(self.backend)) + + def __next__(self) -> Dict[str, Any]: + """Decodes and returns the next frame of the current stream. + Frames are encoded as a dict with mandatory + data and pts fields, where data is a tensor, and pts is a + presentation timestamp of the frame expressed in seconds + as a float. + + Returns: + (dict): a dictionary and containing decoded frame (``data``) + and corresponding timestamp (``pts``) in seconds + + """ + if self.backend == "cuda": + frame = self._c.next() + if frame.numel() == 0: + raise StopIteration + return {"data": frame, "pts": None} + elif self.backend == "video_reader": + frame, pts = self._c.next() + else: + try: + frame = next(self._c) + pts = float(frame.pts * frame.time_base) + if "video" in self.pyav_stream: + frame = torch.as_tensor(frame.to_rgb().to_ndarray()).permute(2, 0, 1) + elif "audio" in self.pyav_stream: + frame = torch.as_tensor(frame.to_ndarray()).permute(1, 0) + else: + frame = None + except av.error.EOFError: + raise StopIteration + + if frame.numel() == 0: + raise StopIteration + + return {"data": frame, "pts": pts} + + def __iter__(self) -> Iterator[Dict[str, Any]]: + return self + + def seek(self, time_s: float, keyframes_only: bool = False) -> "VideoReader": + """Seek within current stream. + + Args: + time_s (float): seek time in seconds + keyframes_only (bool): allow to seek only to keyframes + + .. note:: + Current implementation is the so-called precise seek. This + means following seek, call to :mod:`next()` will return the + frame with the exact timestamp if it exists or + the first frame with timestamp larger than ``time_s``. + """ + if self.backend in ["cuda", "video_reader"]: + self._c.seek(time_s, keyframes_only) + else: + # handle special case as pyav doesn't catch it + if time_s < 0: + time_s = 0 + temp_str = self.container.streams.get(**self.pyav_stream)[0] + offset = int(round(time_s / temp_str.time_base)) + if not keyframes_only: + warnings.warn("Accurate seek is not implemented for pyav backend") + self.container.seek(offset, backward=True, any_frame=False, stream=temp_str) + self._c = self.container.decode(**self.pyav_stream) + return self + + def get_metadata(self) -> Dict[str, Any]: + """Returns video metadata + + Returns: + (dict): dictionary containing duration and frame rate for every stream + """ + if self.backend == "pyav": + metadata = {} # type: Dict[str, Any] + for stream in self.container.streams: + if stream.type not in metadata: + if stream.type == "video": + rate_n = "fps" + else: + rate_n = "framerate" + metadata[stream.type] = {rate_n: [], "duration": []} + + rate = getattr(stream, "average_rate", None) or stream.sample_rate + + metadata[stream.type]["duration"].append(float(stream.duration * stream.time_base)) + metadata[stream.type][rate_n].append(float(rate)) + return metadata + return self._c.get_metadata() + + def set_current_stream(self, stream: str) -> bool: + """Set current stream. + Explicitly define the stream we are operating on. + + Args: + stream (string): descriptor of the required stream. Defaults to ``"video:0"`` + Currently available stream types include ``['video', 'audio']``. + Each descriptor consists of two parts: stream type (e.g. 'video') and + a unique stream id (which are determined by video encoding). + In this way, if the video container contains multiple + streams of the same type, users can access the one they want. + If only stream type is passed, the decoder auto-detects first stream + of that type and returns it. + + Returns: + (bool): True on success, False otherwise + """ + if self.backend == "cuda": + warnings.warn("GPU decoding only works with video stream.") + if self.backend == "pyav": + stream_type = stream.split(":")[0] + stream_id = 0 if len(stream.split(":")) == 1 else int(stream.split(":")[1]) + self.pyav_stream = {stream_type: stream_id} + self._c = self.container.decode(**self.pyav_stream) + return True + return self._c.set_current_stream(stream) diff --git a/lib/python3.11/site-packages/torchvision/models/__init__.py b/lib/python3.11/site-packages/torchvision/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6ea0a1f7178b6ca03776d58c17411a8ff483f8b2 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/__init__.py @@ -0,0 +1,23 @@ +from .alexnet import * +from .convnext import * +from .densenet import * +from .efficientnet import * +from .googlenet import * +from .inception import * +from .mnasnet import * +from .mobilenet import * +from .regnet import * +from .resnet import * +from .shufflenetv2 import * +from .squeezenet import * +from .vgg import * +from .vision_transformer import * +from .swin_transformer import * +from .maxvit import * +from . import detection, optical_flow, quantization, segmentation, video + +# The Weights and WeightsEnum are developer-facing utils that we make public for +# downstream libs like torchgeo https://github.com/pytorch/vision/issues/7094 +# TODO: we could / should document them publicly, but it's not clear where, 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"get_model", "get_model_builder", "get_model_weights", "get_weight", "list_models"] + + +@dataclass +class Weights: + """ + This class is used to group important attributes associated with the pre-trained weights. + + Args: + url (str): The location where we find the weights. + transforms (Callable): A callable that constructs the preprocessing method (or validation preset transforms) + needed to use the model. The reason we attach a constructor method rather than an already constructed + object is because the specific object might have memory and thus we want to delay initialization until + needed. + meta (Dict[str, Any]): Stores meta-data related to the weights of the model and its configuration. These can be + informative attributes (for example the number of parameters/flops, recipe link/methods used in training + etc), configuration parameters (for example the `num_classes`) needed to construct the model or important + meta-data (for example the `classes` of a classification model) needed to use the model. + """ + + url: str + transforms: Callable + meta: Dict[str, Any] + + def __eq__(self, other: Any) -> bool: + # We need this custom implementation for correct deep-copy and deserialization behavior. + # TL;DR: After the definition of an enum, creating a new instance, i.e. by deep-copying or deserializing it, + # involves an equality check against the defined members. Unfortunately, the `transforms` attribute is often + # defined with `functools.partial` and `fn = partial(...); assert deepcopy(fn) != fn`. Without custom handling + # for it, the check against the defined members would fail and effectively prevent the weights from being + # deep-copied or deserialized. + # See https://github.com/pytorch/vision/pull/7107 for details. + if not isinstance(other, Weights): + return NotImplemented + + if self.url != other.url: + return False + + if self.meta != other.meta: + return False + + if isinstance(self.transforms, partial) and isinstance(other.transforms, partial): + return ( + self.transforms.func == other.transforms.func + and self.transforms.args == other.transforms.args + and self.transforms.keywords == other.transforms.keywords + ) + else: + return self.transforms == other.transforms + + +class WeightsEnum(Enum): + """ + This class is the parent class of all model weights. Each model building method receives an optional `weights` + parameter with its associated pre-trained weights. It inherits from `Enum` and its values should be of type + `Weights`. + + Args: + value (Weights): The data class entry with the weight information. + """ + + @classmethod + def verify(cls, obj: Any) -> Any: + if obj is not None: + if type(obj) is str: + obj = cls[obj.replace(cls.__name__ + ".", "")] + elif not isinstance(obj, cls): + raise TypeError( + f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}." + ) + return obj + + def get_state_dict(self, *args: Any, **kwargs: Any) -> Mapping[str, Any]: + return load_state_dict_from_url(self.url, *args, **kwargs) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}.{self._name_}" + + @property + def url(self): + return self.value.url + + @property + def transforms(self): + return self.value.transforms + + @property + def meta(self): + return self.value.meta + + +def get_weight(name: str) -> WeightsEnum: + """ + Gets the weights enum value by its full name. Example: "ResNet50_Weights.IMAGENET1K_V1" + + Args: + name (str): The name of the weight enum entry. + + Returns: + WeightsEnum: The requested weight enum. + """ + try: + enum_name, value_name = name.split(".") + except ValueError: + raise ValueError(f"Invalid weight name provided: '{name}'.") + + base_module_name = ".".join(sys.modules[__name__].__name__.split(".")[:-1]) + base_module = importlib.import_module(base_module_name) + model_modules = [base_module] + [ + x[1] + for x in inspect.getmembers(base_module, inspect.ismodule) + if x[1].__file__.endswith("__init__.py") # type: ignore[union-attr] + ] + + weights_enum = None + for m in model_modules: + potential_class = m.__dict__.get(enum_name, None) + if potential_class is not None and issubclass(potential_class, WeightsEnum): + weights_enum = potential_class + break + + if weights_enum is None: + raise ValueError(f"The weight enum '{enum_name}' for the specific method couldn't be retrieved.") + + return weights_enum[value_name] + + +def get_model_weights(name: Union[Callable, str]) -> Type[WeightsEnum]: + """ + Returns the weights enum class associated to the given model. + + Args: + name (callable or str): The model builder function or the name under which it is registered. + + Returns: + weights_enum (WeightsEnum): The weights enum class associated with the model. + """ + model = get_model_builder(name) if isinstance(name, str) else name + return _get_enum_from_fn(model) + + +def _get_enum_from_fn(fn: Callable) -> Type[WeightsEnum]: + """ + Internal method that gets the weight enum of a specific model builder method. + + Args: + fn (Callable): The builder method used to create the model. + Returns: + WeightsEnum: The requested weight enum. + """ + sig = signature(fn) + if "weights" not in sig.parameters: + raise ValueError("The method is missing the 'weights' argument.") + + ann = signature(fn).parameters["weights"].annotation + weights_enum = None + if isinstance(ann, type) and issubclass(ann, WeightsEnum): + weights_enum = ann + else: + # handle cases like Union[Optional, T] + # TODO: Replace ann.__args__ with typing.get_args(ann) after python >= 3.8 + for t in ann.__args__: # type: ignore[union-attr] + if isinstance(t, type) and issubclass(t, WeightsEnum): + weights_enum = t + break + + if weights_enum is None: + raise ValueError( + "The WeightsEnum class for the specific method couldn't be retrieved. Make sure the typing info is correct." + ) + + return weights_enum + + +M = TypeVar("M", bound=nn.Module) + +BUILTIN_MODELS = {} + + +def register_model(name: Optional[str] = None) -> Callable[[Callable[..., M]], Callable[..., M]]: + def wrapper(fn: Callable[..., M]) -> Callable[..., M]: + key = name if name is not None else fn.__name__ + if key in BUILTIN_MODELS: + raise ValueError(f"An entry is already registered under the name '{key}'.") + BUILTIN_MODELS[key] = fn + return fn + + return wrapper + + +def list_models( + module: Optional[ModuleType] = None, + include: Union[Iterable[str], str, None] = None, + exclude: Union[Iterable[str], str, None] = None, +) -> List[str]: + """ + Returns a list with the names of registered models. + + Args: + module (ModuleType, optional): The module from which we want to extract the available models. + include (str or Iterable[str], optional): Filter(s) for including the models from the set of all models. + Filters are passed to `fnmatch `__ to match Unix shell-style + wildcards. In case of many filters, the results is the union of individual filters. + exclude (str or Iterable[str], optional): Filter(s) applied after include_filters to remove models. + Filter are passed to `fnmatch `__ to match Unix shell-style + wildcards. In case of many filters, the results is removal of all the models that match any individual filter. + + Returns: + models (list): A list with the names of available models. + """ + all_models = { + k for k, v in BUILTIN_MODELS.items() if module is None or v.__module__.rsplit(".", 1)[0] == module.__name__ + } + if include: + models: Set[str] = set() + if isinstance(include, str): + include = [include] + for include_filter in include: + models = models | set(fnmatch.filter(all_models, include_filter)) + else: + models = all_models + + if exclude: + if isinstance(exclude, str): + exclude = [exclude] + for exclude_filter in exclude: + models = models - set(fnmatch.filter(all_models, exclude_filter)) + return sorted(models) + + +def get_model_builder(name: str) -> Callable[..., nn.Module]: + """ + Gets the model name and returns the model builder method. + + Args: + name (str): The name under which the model is registered. + + Returns: + fn (Callable): The model builder method. + """ + name = name.lower() + try: + fn = BUILTIN_MODELS[name] + except KeyError: + raise ValueError(f"Unknown model {name}") + return fn + + +def get_model(name: str, **config: Any) -> nn.Module: + """ + Gets the model name and configuration and returns an instantiated model. + + Args: + name (str): The name under which the model is registered. + **config (Any): parameters passed to the model builder method. + + Returns: + model (nn.Module): The initialized model. + """ + fn = get_model_builder(name) + return fn(**config) diff --git a/lib/python3.11/site-packages/torchvision/models/_meta.py b/lib/python3.11/site-packages/torchvision/models/_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..e66f411c287e0f456448315ba4fd0bfcce281d2b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/_meta.py @@ -0,0 +1,1554 @@ +""" +This file is part of the private API. Please do not refer to any variables defined here directly as they will be +removed on future versions without warning. +""" + +# This will eventually be replaced with a call at torchvision.datasets.info("imagenet").categories +_IMAGENET_CATEGORIES = [ + "tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead", + "electric ray", + "stingray", + "cock", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "water ouzel", + "kite", + "bald eagle", + "vulture", + "great grey owl", + "European fire salamander", + "common newt", + "eft", + "spotted salamander", + "axolotl", + "bullfrog", + "tree frog", + "tailed frog", + "loggerhead", + "leatherback turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "common iguana", + "American chameleon", + "whiptail", + "agama", + "frilled lizard", + "alligator lizard", + "Gila monster", + "green lizard", + "African chameleon", + "Komodo dragon", + "African crocodile", + "American alligator", + "triceratops", + "thunder snake", + "ringneck snake", + "hognose snake", + "green snake", + "king snake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "rock python", + "Indian cobra", + "green mamba", + "sea snake", + "horned viper", + "diamondback", + "sidewinder", + "trilobite", + "harvestman", + "scorpion", + "black and gold garden spider", + "barn spider", + "garden spider", + "black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie chicken", + "peacock", + "quail", + "partridge", + "African grey", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "drake", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "American egret", + "bittern", + "crane bird", + "limpkin", + "European gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "red-backed sandpiper", + "redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese spaniel", + "Maltese dog", + "Pekinese", + "Shih-Tzu", + "Blenheim spaniel", + "papillon", + "toy terrier", + "Rhodesian ridgeback", + "Afghan hound", + "basset", + "beagle", + "bloodhound", + "bluetick", + "black-and-tan coonhound", + "Walker hound", + "English foxhound", + "redbone", + "borzoi", + "Irish wolfhound", + "Italian greyhound", + "whippet", + "Ibizan hound", + "Norwegian elkhound", + "otterhound", + "Saluki", + "Scottish deerhound", + "Weimaraner", + "Staffordshire bullterrier", + "American Staffordshire terrier", + "Bedlington terrier", + "Border terrier", + "Kerry blue terrier", + "Irish terrier", + "Norfolk terrier", + "Norwich terrier", + "Yorkshire terrier", + "wire-haired fox terrier", + "Lakeland terrier", + "Sealyham terrier", + "Airedale", + "cairn", + "Australian terrier", + "Dandie Dinmont", + "Boston bull", + "miniature schnauzer", + "giant schnauzer", + "standard schnauzer", + "Scotch terrier", + "Tibetan terrier", + "silky terrier", + "soft-coated wheaten terrier", + "West Highland white terrier", + "Lhasa", + "flat-coated retriever", + "curly-coated retriever", + "golden retriever", + "Labrador retriever", + "Chesapeake Bay retriever", + "German short-haired pointer", + "vizsla", + "English setter", + "Irish setter", + "Gordon setter", + "Brittany spaniel", + "clumber", + "English springer", + "Welsh springer spaniel", + "cocker spaniel", + "Sussex spaniel", + "Irish water spaniel", + "kuvasz", + "schipperke", + "groenendael", + "malinois", + "briard", + "kelpie", + "komondor", + "Old English sheepdog", + "Shetland sheepdog", + "collie", + "Border collie", + "Bouvier des Flandres", + "Rottweiler", + "German shepherd", + "Doberman", + "miniature pinscher", + "Greater Swiss Mountain dog", + "Bernese mountain dog", + "Appenzeller", + "EntleBucher", + "boxer", + "bull mastiff", + "Tibetan mastiff", + "French bulldog", + "Great Dane", + "Saint Bernard", + "Eskimo dog", + "malamute", + "Siberian husky", + "dalmatian", + "affenpinscher", + "basenji", + "pug", + "Leonberg", + "Newfoundland", + "Great Pyrenees", + "Samoyed", + "Pomeranian", + "chow", + "keeshond", + "Brabancon griffon", + "Pembroke", + "Cardigan", + "toy poodle", + "miniature poodle", + "standard poodle", + "Mexican hairless", + "timber wolf", + "white wolf", + "red wolf", + "coyote", + "dingo", + "dhole", + "African hunting dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian cat", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "ice bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "long-horned beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket", + "walking stick", + "cockroach", + "mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "admiral", + "ringlet", + "monarch", + "cabbage butterfly", + "sulphur butterfly", + "lycaenid", + "starfish", + "sea urchin", + "sea cucumber", + "wood rabbit", + "hare", + "Angora", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "sorrel", + "zebra", + "hog", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram", + "bighorn", + "ibex", + "hartebeest", + "impala", + "gazelle", + "Arabian camel", + "llama", + "weasel", + "mink", + "polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas", + "baboon", + "macaque", + "langur", + "colobus", + "proboscis monkey", + "marmoset", + "capuchin", + "howler monkey", + "titi", + "spider monkey", + "squirrel monkey", + "Madagascar cat", + "indri", + "Indian elephant", + "African elephant", + "lesser panda", + "giant panda", + "barracouta", + "eel", + "coho", + "rock beauty", + "anemone fish", + "sturgeon", + "gar", + "lionfish", + "puffer", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibian", + "analog clock", + "apiary", + "apron", + "ashcan", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint", + "Band Aid", + "banjo", + "bannister", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "barrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "bathing cap", + "bath towel", + "bathtub", + "beach wagon", + "beacon", + "beaker", + "bearskin", + "beer bottle", + "beer glass", + "bell cote", + "bib", + "bicycle-built-for-two", + "bikini", + "binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsled", + "bolo tie", + "bonnet", + "bookcase", + "bookshop", + "bottlecap", + "bow", + "bow tie", + "brass", + "brassiere", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "bullet train", + "butcher shop", + "cab", + "caldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "carpenter's kit", + "carton", + "car wheel", + "cash machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "cellular telephone", + "chain", + "chainlink fence", + "chain mail", + "chain saw", + "chest", + "chiffonier", + "chime", + "china cabinet", + "Christmas stocking", + "church", + "cinema", + "cleaver", + "cliff dwelling", + "cloak", + "clog", + "cocktail shaker", + "coffee mug", + "coffeepot", + "coil", + "combination lock", + "computer keyboard", + "confectionery", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "crane", + "crash helmet", + "crate", + "crib", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishrag", + "dishwasher", + "disk brake", + "dock", + "dogsled", + "dome", + "doormat", + "drilling platform", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso maker", + "face powder", + "feather boa", + "file", + "fireboat", + "fire engine", + "fire screen", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gasmask", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golfcart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "grille", + "grocery store", + "guillotine", + "hair slide", + "hair spray", + "half track", + "hammer", + "hamper", + "hand blower", + "hand-held computer", + "handkerchief", + "hard disc", + "harmonica", + "harp", + "harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoopskirt", + "horizontal bar", + "horse cart", + "hourglass", + "iPod", + "iron", + "jack-o'-lantern", + "jean", + "jeep", + "jersey", + "jigsaw puzzle", + "jinrikisha", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop", + "lawn mower", + "lens cap", + "letter opener", + "library", + "lifeboat", + "lighter", + "limousine", + "liner", + "lipstick", + "Loafer", + "lotion", + "loudspeaker", + "loupe", + "lumbermill", + "magnetic compass", + "mailbag", + "mailbox", + "maillot", + "maillot tank suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "matchstick", + "maypole", + "maze", + "measuring cup", + "medicine chest", + "megalith", + "microphone", + "microwave", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "Model T", + "modem", + "monastery", + "monitor", + "moped", + "mortar", + "mortarboard", + "mosque", + "mosquito net", + "motor scooter", + "mountain bike", + "mountain tent", + "mouse", + "mousetrap", + "moving van", + "muzzle", + "nail", + "neck brace", + "necklace", + "nipple", + "notebook", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "organ", + "oscilloscope", + "overskirt", + "oxcart", + "oxygen mask", + "packet", + "paddle", + "paddlewheel", + "padlock", + "paintbrush", + "pajama", + "palace", + "panpipe", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "passenger car", + "patio", + "pay-phone", + "pedestal", + "pencil box", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "pick", + "pickelhaube", + "picket fence", + "pickup", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate", + "pitcher", + "plane", + "planetarium", + "plastic bag", + "plate rack", + "plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "pool table", + "pop bottle", + "pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "projectile", + "projector", + "puck", + "punching bag", + "purse", + "quill", + "quilt", + "racer", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "rubber eraser", + "rugby ball", + "rule", + "running shoe", + "safe", + "safety pin", + "saltshaker", + "sandal", + "sarong", + "sax", + "scabbard", + "scale", + "school bus", + "schooner", + "scoreboard", + "screen", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe shop", + "shoji", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar dish", + "sombrero", + "soup bowl", + "space bar", + "space heater", + "space shuttle", + "spatula", + "speedboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "steel arch bridge", + "steel drum", + "stethoscope", + "stole", + "stone wall", + "stopwatch", + "stove", + "strainer", + "streetcar", + "stretcher", + "studio couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglass", + "sunglasses", + "sunscreen", + "suspension bridge", + "swab", + "sweatshirt", + "swimming trunks", + "swing", + "switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy", + "television", + "tennis ball", + "thatch", + "theater curtain", + "thimble", + "thresher", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toyshop", + "tractor", + "trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright", + "vacuum", + "vase", + "vault", + "velvet", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "warplane", + "washbasin", + "washer", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "wing", + "wok", + "wooden spoon", + "wool", + "worm fence", + "wreck", + "yawl", + "yurt", + "web site", + "comic book", + "crossword puzzle", + "street sign", + "traffic light", + "book jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "ice lolly", + "French loaf", + "bagel", + "pretzel", + "cheeseburger", + "hotdog", + "mashed potato", + "head cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "custard apple", + "pomegranate", + "hay", + "carbonara", + "chocolate sauce", + "dough", + "meat loaf", + "pizza", + "potpie", + "burrito", + "red wine", + "espresso", + "cup", + "eggnog", + "alp", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeside", + "promontory", + "sandbar", + "seashore", + "valley", + "volcano", + "ballplayer", + "groom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "hip", + "buckeye", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn", + "earthstar", + "hen-of-the-woods", + "bolete", + "ear", + "toilet tissue", +] + +# To be replaced with torchvision.datasets.info("coco").categories +_COCO_CATEGORIES = [ + "__background__", + "person", + "bicycle", + "car", + "motorcycle", + "airplane", + "bus", + "train", + "truck", + "boat", + "traffic light", + "fire hydrant", + "N/A", + "stop sign", + "parking meter", + "bench", + "bird", + "cat", + "dog", + "horse", + "sheep", + "cow", + "elephant", + "bear", + "zebra", + "giraffe", + "N/A", + "backpack", + "umbrella", + "N/A", + "N/A", + "handbag", + "tie", + "suitcase", + "frisbee", + "skis", + "snowboard", + "sports ball", + "kite", + "baseball bat", + "baseball glove", + "skateboard", + "surfboard", + "tennis racket", + "bottle", + "N/A", + "wine glass", + "cup", + "fork", + "knife", + "spoon", + "bowl", + "banana", + "apple", + "sandwich", + "orange", + "broccoli", + "carrot", + "hot dog", + "pizza", + "donut", + "cake", + "chair", + "couch", + "potted plant", + "bed", + "N/A", + "dining table", + "N/A", + "N/A", + "toilet", + "N/A", + "tv", + "laptop", + "mouse", + "remote", + "keyboard", + "cell phone", + "microwave", + "oven", + "toaster", + "sink", + "refrigerator", + "N/A", + "book", + "clock", + "vase", + "scissors", + "teddy bear", + "hair drier", + "toothbrush", +] + +# To be replaced with torchvision.datasets.info("coco_kp") +_COCO_PERSON_CATEGORIES = ["no person", "person"] +_COCO_PERSON_KEYPOINT_NAMES = [ + "nose", + "left_eye", + "right_eye", + "left_ear", + "right_ear", + "left_shoulder", + "right_shoulder", + "left_elbow", + "right_elbow", + "left_wrist", + "right_wrist", + "left_hip", + "right_hip", + "left_knee", + "right_knee", + "left_ankle", + "right_ankle", +] + +# To be replaced with torchvision.datasets.info("voc").categories +_VOC_CATEGORIES = [ + "__background__", + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "diningtable", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", +] + +# To be replaced with torchvision.datasets.info("kinetics400").categories +_KINETICS400_CATEGORIES = [ + "abseiling", + "air drumming", + "answering questions", + "applauding", + "applying cream", + "archery", + "arm wrestling", + "arranging flowers", + "assembling computer", + "auctioning", + "baby waking up", + "baking cookies", + "balloon blowing", + "bandaging", + "barbequing", + "bartending", + "beatboxing", + "bee keeping", + "belly dancing", + "bench pressing", + "bending back", + "bending metal", + "biking through snow", + "blasting sand", + "blowing glass", + "blowing leaves", + "blowing nose", + "blowing out candles", + "bobsledding", + "bookbinding", + "bouncing on trampoline", + "bowling", + "braiding hair", + "breading or breadcrumbing", + "breakdancing", + "brush painting", + "brushing hair", + "brushing teeth", + "building cabinet", + "building shed", + "bungee jumping", + "busking", + "canoeing or kayaking", + "capoeira", + "carrying baby", + "cartwheeling", + "carving pumpkin", + "catching fish", + "catching or throwing baseball", + "catching or throwing frisbee", + "catching or throwing softball", + "celebrating", + "changing oil", + "changing wheel", + "checking tires", + "cheerleading", + "chopping wood", + "clapping", + "clay pottery making", + "clean and jerk", + "cleaning floor", + "cleaning gutters", + "cleaning pool", + "cleaning shoes", + "cleaning toilet", + "cleaning windows", + "climbing a rope", + "climbing ladder", + "climbing tree", + "contact juggling", + "cooking chicken", + "cooking egg", + "cooking on campfire", + "cooking sausages", + "counting money", + "country line dancing", + "cracking neck", + "crawling baby", + "crossing river", + "crying", + "curling hair", + "cutting nails", + "cutting pineapple", + "cutting watermelon", + "dancing ballet", + "dancing charleston", + "dancing gangnam style", + "dancing macarena", + "deadlifting", + "decorating the christmas tree", + "digging", + "dining", + "disc golfing", + "diving cliff", + "dodgeball", + "doing aerobics", + "doing laundry", + "doing nails", + "drawing", + "dribbling basketball", + "drinking", + "drinking beer", + "drinking shots", + "driving car", + "driving tractor", + "drop kicking", + "drumming fingers", + "dunking basketball", + "dying hair", + "eating burger", + "eating cake", + "eating carrots", + "eating chips", + "eating doughnuts", + "eating hotdog", + "eating ice cream", + "eating spaghetti", + "eating watermelon", + "egg hunting", + "exercising arm", + "exercising with an exercise ball", + "extinguishing fire", + "faceplanting", + "feeding birds", + "feeding fish", + "feeding goats", + "filling eyebrows", + "finger snapping", + "fixing hair", + "flipping pancake", + "flying kite", + "folding clothes", + "folding napkins", + "folding paper", + "front raises", + "frying vegetables", + "garbage collecting", + "gargling", + "getting a haircut", + "getting a tattoo", + "giving or receiving award", + "golf chipping", + "golf driving", + "golf putting", + "grinding meat", + "grooming dog", + "grooming horse", + "gymnastics tumbling", + "hammer throw", + "headbanging", + "headbutting", + "high jump", + "high kick", + "hitting baseball", + "hockey stop", + "holding snake", + "hopscotch", + "hoverboarding", + "hugging", + "hula hooping", + "hurdling", + "hurling (sport)", + "ice climbing", + "ice fishing", + "ice skating", + "ironing", + "javelin throw", + "jetskiing", + "jogging", + "juggling balls", + "juggling fire", + "juggling soccer ball", + "jumping into pool", + "jumpstyle dancing", + "kicking field goal", + "kicking soccer ball", + "kissing", + "kitesurfing", + "knitting", + "krumping", + "laughing", + "laying bricks", + "long jump", + "lunge", + "making a cake", + "making a sandwich", + "making bed", + "making jewelry", + "making pizza", + "making snowman", + "making sushi", + "making tea", + "marching", + "massaging back", + "massaging feet", + "massaging legs", + "massaging person's head", + "milking cow", + "mopping floor", + "motorcycling", + "moving furniture", + "mowing lawn", + "news anchoring", + "opening bottle", + "opening present", + "paragliding", + "parasailing", + "parkour", + "passing American football (in game)", + "passing American football (not in game)", + "peeling apples", + "peeling potatoes", + "petting animal (not cat)", + "petting cat", + "picking fruit", + "planting trees", + "plastering", + "playing accordion", + "playing badminton", + "playing bagpipes", + "playing basketball", + "playing bass guitar", + "playing cards", + "playing cello", + "playing chess", + "playing clarinet", + "playing controller", + "playing cricket", + "playing cymbals", + "playing didgeridoo", + "playing drums", + "playing flute", + "playing guitar", + "playing harmonica", + "playing harp", + "playing ice hockey", + "playing keyboard", + "playing kickball", + "playing monopoly", + "playing organ", + "playing paintball", + "playing piano", + "playing poker", + "playing recorder", + "playing saxophone", + "playing squash or racquetball", + "playing tennis", + "playing trombone", + "playing trumpet", + "playing ukulele", + "playing violin", + "playing volleyball", + "playing xylophone", + "pole vault", + "presenting weather forecast", + "pull ups", + "pumping fist", + "pumping gas", + "punching bag", + "punching person (boxing)", + "push up", + "pushing car", + "pushing cart", + "pushing wheelchair", + "reading book", + "reading newspaper", + "recording music", + "riding a bike", + "riding camel", + "riding elephant", + "riding mechanical bull", + "riding mountain bike", + "riding mule", + "riding or walking with horse", + "riding scooter", + "riding unicycle", + "ripping paper", + "robot dancing", + "rock climbing", + "rock scissors paper", + "roller skating", + "running on treadmill", + "sailing", + "salsa dancing", + "sanding floor", + "scrambling eggs", + "scuba diving", + "setting table", + "shaking hands", + "shaking head", + "sharpening knives", + "sharpening pencil", + "shaving head", + "shaving legs", + "shearing sheep", + "shining shoes", + "shooting basketball", + "shooting goal (soccer)", + "shot put", + "shoveling snow", + "shredding paper", + "shuffling cards", + "side kick", + "sign language interpreting", + "singing", + "situp", + "skateboarding", + "ski jumping", + "skiing (not slalom or crosscountry)", + "skiing crosscountry", + "skiing slalom", + "skipping rope", + "skydiving", + "slacklining", + "slapping", + "sled dog racing", + "smoking", + "smoking hookah", + "snatch weight lifting", + "sneezing", + "sniffing", + "snorkeling", + "snowboarding", + "snowkiting", + "snowmobiling", + "somersaulting", + "spinning poi", + "spray painting", + "spraying", + "springboard diving", + "squat", + "sticking tongue out", + "stomping grapes", + "stretching arm", + "stretching leg", + "strumming guitar", + "surfing crowd", + "surfing water", + "sweeping floor", + "swimming backstroke", + "swimming breast stroke", + "swimming butterfly stroke", + "swing dancing", + "swinging legs", + "swinging on something", + "sword fighting", + "tai chi", + "taking a shower", + "tango dancing", + "tap dancing", + "tapping guitar", + "tapping pen", + "tasting beer", + "tasting food", + "testifying", + "texting", + "throwing axe", + "throwing ball", + "throwing discus", + "tickling", + "tobogganing", + "tossing coin", + "tossing salad", + "training dog", + "trapezing", + "trimming or shaving beard", + "trimming trees", + "triple jump", + "tying bow tie", + "tying knot (not on a tie)", + "tying tie", + "unboxing", + "unloading truck", + "using computer", + "using remote controller (not gaming)", + "using segway", + "vault", + "waiting in line", + "walking the dog", + "washing dishes", + "washing feet", + "washing hair", + "washing hands", + "water skiing", + "water sliding", + "watering plants", + "waxing back", + "waxing chest", + "waxing eyebrows", + "waxing legs", + "weaving basket", + "welding", + "whistling", + "windsurfing", + "wrapping present", + "wrestling", + "writing", + "yawning", + "yoga", + "zumba", +] diff --git a/lib/python3.11/site-packages/torchvision/models/_utils.py b/lib/python3.11/site-packages/torchvision/models/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d59a6220b91f0f2fe85b6bf01948ee5a506cd82a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/_utils.py @@ -0,0 +1,256 @@ +import functools +import inspect +import warnings +from collections import OrderedDict +from typing import Any, Callable, Dict, Optional, Tuple, TypeVar, Union + +from torch import nn + +from .._utils import sequence_to_str +from ._api import WeightsEnum + + +class IntermediateLayerGetter(nn.ModuleDict): + """ + Module wrapper that returns intermediate layers from a model + + It has a strong assumption that the modules have been registered + into the model in the same order as they are used. + This means that one should **not** reuse the same nn.Module + twice in the forward if you want this to work. + + Additionally, it is only able to query submodules that are directly + assigned to the model. So if `model` is passed, `model.feature1` can + be returned, but not `model.feature1.layer2`. + + Args: + model (nn.Module): model on which we will extract the features + return_layers (Dict[name, new_name]): a dict containing the names + of the modules for which the activations will be returned as + the key of the dict, and the value of the dict is the name + of the returned activation (which the user can specify). + + Examples:: + + >>> m = torchvision.models.resnet18(weights=ResNet18_Weights.DEFAULT) + >>> # extract layer1 and layer3, giving as names `feat1` and feat2` + >>> new_m = torchvision.models._utils.IntermediateLayerGetter(m, + >>> {'layer1': 'feat1', 'layer3': 'feat2'}) + >>> out = new_m(torch.rand(1, 3, 224, 224)) + >>> print([(k, v.shape) for k, v in out.items()]) + >>> [('feat1', torch.Size([1, 64, 56, 56])), + >>> ('feat2', torch.Size([1, 256, 14, 14]))] + """ + + _version = 2 + __annotations__ = { + "return_layers": Dict[str, str], + } + + def __init__(self, model: nn.Module, return_layers: Dict[str, str]) -> None: + if not set(return_layers).issubset([name for name, _ in model.named_children()]): + raise ValueError("return_layers are not present in model") + orig_return_layers = return_layers + return_layers = {str(k): str(v) for k, v in return_layers.items()} + layers = OrderedDict() + for name, module in model.named_children(): + layers[name] = module + if name in return_layers: + del return_layers[name] + if not return_layers: + break + + super().__init__(layers) + self.return_layers = orig_return_layers + + def forward(self, x): + out = OrderedDict() + for name, module in self.items(): + x = module(x) + if name in self.return_layers: + out_name = self.return_layers[name] + out[out_name] = x + return out + + +def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int: + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +D = TypeVar("D") + + +def kwonly_to_pos_or_kw(fn: Callable[..., D]) -> Callable[..., D]: + """Decorates a function that uses keyword only parameters to also allow them being passed as positionals. + + For example, consider the use case of changing the signature of ``old_fn`` into the one from ``new_fn``: + + .. code:: + + def old_fn(foo, bar, baz=None): + ... + + def new_fn(foo, *, bar, baz=None): + ... + + Calling ``old_fn("foo", "bar, "baz")`` was valid, but the same call is no longer valid with ``new_fn``. To keep BC + and at the same time warn the user of the deprecation, this decorator can be used: + + .. code:: + + @kwonly_to_pos_or_kw + def new_fn(foo, *, bar, baz=None): + ... + + new_fn("foo", "bar, "baz") + """ + params = inspect.signature(fn).parameters + + try: + keyword_only_start_idx = next( + idx for idx, param in enumerate(params.values()) if param.kind == param.KEYWORD_ONLY + ) + except StopIteration: + raise TypeError(f"Found no keyword-only parameter on function '{fn.__name__}'") from None + + keyword_only_params = tuple(inspect.signature(fn).parameters)[keyword_only_start_idx:] + + @functools.wraps(fn) + def wrapper(*args: Any, **kwargs: Any) -> D: + args, keyword_only_args = args[:keyword_only_start_idx], args[keyword_only_start_idx:] + if keyword_only_args: + keyword_only_kwargs = dict(zip(keyword_only_params, keyword_only_args)) + warnings.warn( + f"Using {sequence_to_str(tuple(keyword_only_kwargs.keys()), separate_last='and ')} as positional " + f"parameter(s) is deprecated since 0.13 and may be removed in the future. Please use keyword parameter(s) " + f"instead." + ) + kwargs.update(keyword_only_kwargs) + + return fn(*args, **kwargs) + + return wrapper + + +W = TypeVar("W", bound=WeightsEnum) +M = TypeVar("M", bound=nn.Module) +V = TypeVar("V") + + +def handle_legacy_interface(**weights: Tuple[str, Union[Optional[W], Callable[[Dict[str, Any]], Optional[W]]]]): + """Decorates a model builder with the new interface to make it compatible with the old. + + In particular this handles two things: + + 1. Allows positional parameters again, but emits a deprecation warning in case they are used. See + :func:`torchvision.prototype.utils._internal.kwonly_to_pos_or_kw` for details. + 2. Handles the default value change from ``pretrained=False`` to ``weights=None`` and ``pretrained=True`` to + ``weights=Weights`` and emits a deprecation warning with instructions for the new interface. + + Args: + **weights (Tuple[str, Union[Optional[W], Callable[[Dict[str, Any]], Optional[W]]]]): Deprecated parameter + name and default value for the legacy ``pretrained=True``. The default value can be a callable in which + case it will be called with a dictionary of the keyword arguments. The only key that is guaranteed to be in + the dictionary is the deprecated parameter name passed as first element in the tuple. All other parameters + should be accessed with :meth:`~dict.get`. + """ + + def outer_wrapper(builder: Callable[..., M]) -> Callable[..., M]: + @kwonly_to_pos_or_kw + @functools.wraps(builder) + def inner_wrapper(*args: Any, **kwargs: Any) -> M: + for weights_param, (pretrained_param, default) in weights.items(): # type: ignore[union-attr] + # If neither the weights nor the pretrained parameter as passed, or the weights argument already use + # the new style arguments, there is nothing to do. Note that we cannot use `None` as sentinel for the + # weight argument, since it is a valid value. + sentinel = object() + weights_arg = kwargs.get(weights_param, sentinel) + if ( + (weights_param not in kwargs and pretrained_param not in kwargs) + or isinstance(weights_arg, WeightsEnum) + or (isinstance(weights_arg, str) and weights_arg != "legacy") + or weights_arg is None + ): + continue + + # If the pretrained parameter was passed as positional argument, it is now mapped to + # `kwargs[weights_param]`. This happens because the @kwonly_to_pos_or_kw decorator uses the current + # signature to infer the names of positionally passed arguments and thus has no knowledge that there + # used to be a pretrained parameter. + pretrained_positional = weights_arg is not sentinel + if pretrained_positional: + # We put the pretrained argument under its legacy name in the keyword argument dictionary to have + # unified access to the value if the default value is a callable. + kwargs[pretrained_param] = pretrained_arg = kwargs.pop(weights_param) + else: + pretrained_arg = kwargs[pretrained_param] + + if pretrained_arg: + default_weights_arg = default(kwargs) if callable(default) else default + if not isinstance(default_weights_arg, WeightsEnum): + raise ValueError(f"No weights available for model {builder.__name__}") + else: + default_weights_arg = None + + if not pretrained_positional: + warnings.warn( + f"The parameter '{pretrained_param}' is deprecated since 0.13 and may be removed in the future, " + f"please use '{weights_param}' instead." + ) + + msg = ( + f"Arguments other than a weight enum or `None` for '{weights_param}' are deprecated since 0.13 and " + f"may be removed in the future. " + f"The current behavior is equivalent to passing `{weights_param}={default_weights_arg}`." + ) + if pretrained_arg: + msg = ( + f"{msg} You can also use `{weights_param}={type(default_weights_arg).__name__}.DEFAULT` " + f"to get the most up-to-date weights." + ) + warnings.warn(msg) + + del kwargs[pretrained_param] + kwargs[weights_param] = default_weights_arg + + return builder(*args, **kwargs) + + return inner_wrapper + + return outer_wrapper + + +def _ovewrite_named_param(kwargs: Dict[str, Any], param: str, new_value: V) -> None: + if param in kwargs: + if kwargs[param] != new_value: + raise ValueError(f"The parameter '{param}' expected value {new_value} but got {kwargs[param]} instead.") + else: + kwargs[param] = new_value + + +def _ovewrite_value_param(param: str, actual: Optional[V], expected: V) -> V: + if actual is not None: + if actual != expected: + raise ValueError(f"The parameter '{param}' expected value {expected} but got {actual} instead.") + return expected + + +class _ModelURLs(dict): + def __getitem__(self, item): + warnings.warn( + "Accessing the model URLs via the internal dictionary of the module is deprecated since 0.13 and may " + "be removed in the future. Please access them via the appropriate Weights Enum instead." + ) + return super().__getitem__(item) diff --git a/lib/python3.11/site-packages/torchvision/models/alexnet.py b/lib/python3.11/site-packages/torchvision/models/alexnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f85acbeb2148d2aa8f289808e61aa61e2d68e2f9 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/alexnet.py @@ -0,0 +1,119 @@ +from functools import partial +from typing import Any, Optional + +import torch +import torch.nn as nn + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = ["AlexNet", "AlexNet_Weights", "alexnet"] + + +class AlexNet(nn.Module): + def __init__(self, num_classes: int = 1000, dropout: float = 0.5) -> None: + super().__init__() + _log_api_usage_once(self) + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(64, 192, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(192, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(384, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + ) + self.avgpool = nn.AdaptiveAvgPool2d((6, 6)) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout), + nn.Linear(256 * 6 * 6, 4096), + nn.ReLU(inplace=True), + nn.Dropout(p=dropout), + nn.Linear(4096, 4096), + nn.ReLU(inplace=True), + nn.Linear(4096, num_classes), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + +class AlexNet_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + "num_params": 61100840, + "min_size": (63, 63), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg", + "_metrics": { + "ImageNet-1K": { + "acc@1": 56.522, + "acc@5": 79.066, + } + }, + "_ops": 0.714, + "_file_size": 233.087, + "_docs": """ + These weights reproduce closely the results of the paper using a simplified training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", AlexNet_Weights.IMAGENET1K_V1)) +def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) -> AlexNet: + """AlexNet model architecture from `One weird trick for parallelizing convolutional neural networks `__. + + .. note:: + AlexNet was originally introduced in the `ImageNet Classification with + Deep Convolutional Neural Networks + `__ + paper. Our implementation is based instead on the "One weird trick" + paper above. + + Args: + weights (:class:`~torchvision.models.AlexNet_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.AlexNet_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.squeezenet.AlexNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.AlexNet_Weights + :members: + """ + + weights = AlexNet_Weights.verify(weights) + + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = AlexNet(**kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/convnext.py b/lib/python3.11/site-packages/torchvision/models/convnext.py new file mode 100644 index 0000000000000000000000000000000000000000..444ef3c219efa09d1c720f29db44a8acc8714bbc --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/convnext.py @@ -0,0 +1,414 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence + +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from ..ops.misc import Conv2dNormActivation, Permute +from ..ops.stochastic_depth import StochasticDepth +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "ConvNeXt", + "ConvNeXt_Tiny_Weights", + "ConvNeXt_Small_Weights", + "ConvNeXt_Base_Weights", + "ConvNeXt_Large_Weights", + "convnext_tiny", + "convnext_small", + "convnext_base", + "convnext_large", +] + + +class LayerNorm2d(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + x = x.permute(0, 2, 3, 1) + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.permute(0, 3, 1, 2) + return x + + +class CNBlock(nn.Module): + def __init__( + self, + dim, + layer_scale: float, + stochastic_depth_prob: float, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + self.block = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True), + Permute([0, 2, 3, 1]), + norm_layer(dim), + nn.Linear(in_features=dim, out_features=4 * dim, bias=True), + nn.GELU(), + nn.Linear(in_features=4 * dim, out_features=dim, bias=True), + Permute([0, 3, 1, 2]), + ) + self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + def forward(self, input: Tensor) -> Tensor: + result = self.layer_scale * self.block(input) + result = self.stochastic_depth(result) + result += input + return result + + +class CNBlockConfig: + # Stores information listed at Section 3 of the ConvNeXt paper + def __init__( + self, + input_channels: int, + out_channels: Optional[int], + num_layers: int, + ) -> None: + self.input_channels = input_channels + self.out_channels = out_channels + self.num_layers = num_layers + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "input_channels={input_channels}" + s += ", out_channels={out_channels}" + s += ", num_layers={num_layers}" + s += ")" + return s.format(**self.__dict__) + + +class ConvNeXt(nn.Module): + def __init__( + self, + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float = 0.0, + layer_scale: float = 1e-6, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if not block_setting: + raise ValueError("The block_setting should not be empty") + elif not (isinstance(block_setting, Sequence) and all([isinstance(s, CNBlockConfig) for s in block_setting])): + raise TypeError("The block_setting should be List[CNBlockConfig]") + + if block is None: + block = CNBlock + + if norm_layer is None: + norm_layer = partial(LayerNorm2d, eps=1e-6) + + layers: List[nn.Module] = [] + + # Stem + firstconv_output_channels = block_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, + firstconv_output_channels, + kernel_size=4, + stride=4, + padding=0, + norm_layer=norm_layer, + activation_layer=None, + bias=True, + ) + ) + + total_stage_blocks = sum(cnf.num_layers for cnf in block_setting) + stage_block_id = 0 + for cnf in block_setting: + # Bottlenecks + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + stage.append(block(cnf.input_channels, layer_scale, sd_prob)) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + if cnf.out_channels is not None: + # Downsampling + layers.append( + nn.Sequential( + norm_layer(cnf.input_channels), + nn.Conv2d(cnf.input_channels, cnf.out_channels, kernel_size=2, stride=2), + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + + lastblock = block_setting[-1] + lastconv_output_channels = ( + lastblock.out_channels if lastblock.out_channels is not None else lastblock.input_channels + ) + self.classifier = nn.Sequential( + norm_layer(lastconv_output_channels), nn.Flatten(1), nn.Linear(lastconv_output_channels, num_classes) + ) + + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _convnext( + block_setting: List[CNBlockConfig], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ConvNeXt: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext", + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, +} + + +class ConvNeXt_Tiny_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=236), + meta={ + **_COMMON_META, + "num_params": 28589128, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.520, + "acc@5": 96.146, + } + }, + "_ops": 4.456, + "_file_size": 109.119, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Small_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_small-0c510722.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=230), + meta={ + **_COMMON_META, + "num_params": 50223688, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.616, + "acc@5": 96.650, + } + }, + "_ops": 8.684, + "_file_size": 191.703, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Base_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_base-6075fbad.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88591464, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.062, + "acc@5": 96.870, + } + }, + "_ops": 15.355, + "_file_size": 338.064, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ConvNeXt_Large_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/convnext_large-ea097f82.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 197767336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.414, + "acc@5": 96.976, + } + }, + "_ops": 34.361, + "_file_size": 754.537, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1)) +def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Tiny model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights + :members: + """ + weights = ConvNeXt_Tiny_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 9), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1)) +def convnext_small( + *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Small model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Small_Weights + :members: + """ + weights = ConvNeXt_Small_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(96, 192, 3), + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 27), + CNBlockConfig(768, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Base_Weights.IMAGENET1K_V1)) +def convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt: + """ConvNeXt Base model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Base_Weights + :members: + """ + weights = ConvNeXt_Base_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, 512, 3), + CNBlockConfig(512, 1024, 27), + CNBlockConfig(1024, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ConvNeXt_Large_Weights.IMAGENET1K_V1)) +def convnext_large( + *, weights: Optional[ConvNeXt_Large_Weights] = None, progress: bool = True, **kwargs: Any +) -> ConvNeXt: + """ConvNeXt Large model architecture from the + `A ConvNet for the 2020s `_ paper. + + Args: + weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ConvNeXt_Large_Weights + :members: + """ + weights = ConvNeXt_Large_Weights.verify(weights) + + block_setting = [ + CNBlockConfig(192, 384, 3), + CNBlockConfig(384, 768, 3), + CNBlockConfig(768, 1536, 27), + CNBlockConfig(1536, None, 3), + ] + stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5) + return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/densenet.py b/lib/python3.11/site-packages/torchvision/models/densenet.py new file mode 100644 index 0000000000000000000000000000000000000000..c4c1bc9525a43fbba565dc3f03c6b7231e913fe5 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/densenet.py @@ -0,0 +1,448 @@ +import re +from collections import OrderedDict +from functools import partial +from typing import Any, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from torch import Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + +__all__ = [ + "DenseNet", + "DenseNet121_Weights", + "DenseNet161_Weights", + "DenseNet169_Weights", + "DenseNet201_Weights", + "densenet121", + "densenet161", + "densenet169", + "densenet201", +] + + +class _DenseLayer(nn.Module): + def __init__( + self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False + ) -> None: + super().__init__() + self.norm1 = nn.BatchNorm2d(num_input_features) + self.relu1 = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False) + + self.norm2 = nn.BatchNorm2d(bn_size * growth_rate) + self.relu2 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) + + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + @torch.jit.unused # noqa: T484 + def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: + def closure(*inputs): + return self.bn_function(inputs) + + return cp.checkpoint(closure, *input, use_reentrant=False) + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: List[Tensor]) -> Tensor: # noqa: F811 + pass + + @torch.jit._overload_method # noqa: F811 + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + pass + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False, + ) -> None: + super().__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.add_module("denselayer%d" % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int) -> None: + super().__init__() + self.norm = nn.BatchNorm2d(num_input_features) + self.relu = nn.ReLU(inplace=True) + self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False) + self.pool = nn.AvgPool2d(kernel_size=2, stride=2) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_. + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_. + """ + + def __init__( + self, + growth_rate: int = 32, + block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), + num_init_features: int = 64, + bn_size: int = 4, + drop_rate: float = 0, + num_classes: int = 1000, + memory_efficient: bool = False, + ) -> None: + + super().__init__() + _log_api_usage_once(self) + + # First convolution + self.features = nn.Sequential( + OrderedDict( + [ + ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), + ("norm0", nn.BatchNorm2d(num_init_features)), + ("relu0", nn.ReLU(inplace=True)), + ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ] + ) + ) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.features.add_module("denseblock%d" % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) + self.features.add_module("transition%d" % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module("norm5", nn.BatchNorm2d(num_features)) + + # Linear layer + self.classifier = nn.Linear(num_features, num_classes) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor) -> Tensor: + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + out = self.classifier(out) + return out + + +def _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) -> None: + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$" + ) + + state_dict = weights.get_state_dict(progress=progress, check_hash=True) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + growth_rate: int, + block_config: Tuple[int, int, int, int], + num_init_features: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> DenseNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + + if weights is not None: + _load_state_dict(model=model, weights=weights, progress=progress) + + return model + + +_COMMON_META = { + "min_size": (29, 29), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/pull/116", + "_docs": """These weights are ported from LuaTorch.""", +} + + +class DenseNet121_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/densenet121-a639ec97.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 7978856, + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.434, + "acc@5": 91.972, + } + }, + "_ops": 2.834, + "_file_size": 30.845, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class DenseNet161_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/densenet161-8d451a50.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 28681000, + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.138, + "acc@5": 93.560, + } + }, + "_ops": 7.728, + "_file_size": 110.369, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class DenseNet169_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/densenet169-b2777c0a.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 14149480, + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.600, + "acc@5": 92.806, + } + }, + "_ops": 3.36, + "_file_size": 54.708, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class DenseNet201_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/densenet201-c1103571.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 20013928, + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.896, + "acc@5": 93.370, + } + }, + "_ops": 4.291, + "_file_size": 77.373, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1)) +def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-121 model from + `Densely Connected Convolutional Networks `_. + + Args: + weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.DenseNet121_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.DenseNet121_Weights + :members: + """ + weights = DenseNet121_Weights.verify(weights) + + return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1)) +def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-161 model from + `Densely Connected Convolutional Networks `_. + + Args: + weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.DenseNet161_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.DenseNet161_Weights + :members: + """ + weights = DenseNet161_Weights.verify(weights) + + return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1)) +def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-169 model from + `Densely Connected Convolutional Networks `_. + + Args: + weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.DenseNet169_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.DenseNet169_Weights + :members: + """ + weights = DenseNet169_Weights.verify(weights) + + return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1)) +def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: + r"""Densenet-201 model from + `Densely Connected Convolutional Networks `_. + + Args: + weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.DenseNet201_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.DenseNet201_Weights + :members: + """ + weights = DenseNet201_Weights.verify(weights) + + return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/detection/__init__.py b/lib/python3.11/site-packages/torchvision/models/detection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4146651c737971cc5a883b6750f2ded3051bc8ea --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/__init__.py @@ -0,0 +1,7 @@ +from .faster_rcnn import * +from .fcos import * +from .keypoint_rcnn import * +from .mask_rcnn import * +from .retinanet import * +from .ssd import * +from .ssdlite import * diff --git a/lib/python3.11/site-packages/torchvision/models/detection/__pycache__/__init__.cpython-311.pyc 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index 0000000000000000000000000000000000000000..15b58e919b9694745ebfa4639722fc4d1dafa6a5 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/models/detection/__pycache__/transform.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/models/detection/_utils.py b/lib/python3.11/site-packages/torchvision/models/detection/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..559db858ac32f3b9f157aff3c22da83abece2a73 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/_utils.py @@ -0,0 +1,540 @@ +import math +from collections import OrderedDict +from typing import Dict, List, Optional, Tuple + +import torch +from torch import nn, Tensor +from torch.nn import functional as F +from torchvision.ops import complete_box_iou_loss, distance_box_iou_loss, FrozenBatchNorm2d, generalized_box_iou_loss + + +class BalancedPositiveNegativeSampler: + """ + This class samples batches, ensuring that they contain a fixed proportion of positives + """ + + def __init__(self, batch_size_per_image: int, positive_fraction: float) -> None: + """ + Args: + batch_size_per_image (int): number of elements to be selected per image + positive_fraction (float): percentage of positive elements per batch + """ + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + + def __call__(self, matched_idxs: List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]: + """ + Args: + matched_idxs: list of tensors containing -1, 0 or positive values. + Each tensor corresponds to a specific image. + -1 values are ignored, 0 are considered as negatives and > 0 as + positives. + + Returns: + pos_idx (list[tensor]) + neg_idx (list[tensor]) + + Returns two lists of binary masks for each image. + The first list contains the positive elements that were selected, + and the second list the negative example. + """ + pos_idx = [] + neg_idx = [] + for matched_idxs_per_image in matched_idxs: + positive = torch.where(matched_idxs_per_image >= 1)[0] + negative = torch.where(matched_idxs_per_image == 0)[0] + + num_pos = int(self.batch_size_per_image * self.positive_fraction) + # protect against not enough positive examples + num_pos = min(positive.numel(), num_pos) + num_neg = self.batch_size_per_image - num_pos + # protect against not enough negative examples + num_neg = min(negative.numel(), num_neg) + + # randomly select positive and negative examples + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx_per_image = positive[perm1] + neg_idx_per_image = negative[perm2] + + # create binary mask from indices + pos_idx_per_image_mask = torch.zeros_like(matched_idxs_per_image, dtype=torch.uint8) + neg_idx_per_image_mask = torch.zeros_like(matched_idxs_per_image, dtype=torch.uint8) + + pos_idx_per_image_mask[pos_idx_per_image] = 1 + neg_idx_per_image_mask[neg_idx_per_image] = 1 + + pos_idx.append(pos_idx_per_image_mask) + neg_idx.append(neg_idx_per_image_mask) + + return pos_idx, neg_idx + + +@torch.jit._script_if_tracing +def encode_boxes(reference_boxes: Tensor, proposals: Tensor, weights: Tensor) -> Tensor: + """ + Encode a set of proposals with respect to some + reference boxes + + Args: + reference_boxes (Tensor): reference boxes + proposals (Tensor): boxes to be encoded + weights (Tensor[4]): the weights for ``(x, y, w, h)`` + """ + + # perform some unpacking to make it JIT-fusion friendly + wx = weights[0] + wy = weights[1] + ww = weights[2] + wh = weights[3] + + proposals_x1 = proposals[:, 0].unsqueeze(1) + proposals_y1 = proposals[:, 1].unsqueeze(1) + proposals_x2 = proposals[:, 2].unsqueeze(1) + proposals_y2 = proposals[:, 3].unsqueeze(1) + + reference_boxes_x1 = reference_boxes[:, 0].unsqueeze(1) + reference_boxes_y1 = reference_boxes[:, 1].unsqueeze(1) + reference_boxes_x2 = reference_boxes[:, 2].unsqueeze(1) + reference_boxes_y2 = reference_boxes[:, 3].unsqueeze(1) + + # implementation starts here + ex_widths = proposals_x2 - proposals_x1 + ex_heights = proposals_y2 - proposals_y1 + ex_ctr_x = proposals_x1 + 0.5 * ex_widths + ex_ctr_y = proposals_y1 + 0.5 * ex_heights + + gt_widths = reference_boxes_x2 - reference_boxes_x1 + gt_heights = reference_boxes_y2 - reference_boxes_y1 + gt_ctr_x = reference_boxes_x1 + 0.5 * gt_widths + gt_ctr_y = reference_boxes_y1 + 0.5 * gt_heights + + targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = ww * torch.log(gt_widths / ex_widths) + targets_dh = wh * torch.log(gt_heights / ex_heights) + + targets = torch.cat((targets_dx, targets_dy, targets_dw, targets_dh), dim=1) + return targets + + +class BoxCoder: + """ + This class encodes and decodes a set of bounding boxes into + the representation used for training the regressors. + """ + + def __init__( + self, weights: Tuple[float, float, float, float], bbox_xform_clip: float = math.log(1000.0 / 16) + ) -> None: + """ + Args: + weights (4-element tuple) + bbox_xform_clip (float) + """ + self.weights = weights + self.bbox_xform_clip = bbox_xform_clip + + def encode(self, reference_boxes: List[Tensor], proposals: List[Tensor]) -> List[Tensor]: + boxes_per_image = [len(b) for b in reference_boxes] + reference_boxes = torch.cat(reference_boxes, dim=0) + proposals = torch.cat(proposals, dim=0) + targets = self.encode_single(reference_boxes, proposals) + return targets.split(boxes_per_image, 0) + + def encode_single(self, reference_boxes: Tensor, proposals: Tensor) -> Tensor: + """ + Encode a set of proposals with respect to some + reference boxes + + Args: + reference_boxes (Tensor): reference boxes + proposals (Tensor): boxes to be encoded + """ + dtype = reference_boxes.dtype + device = reference_boxes.device + weights = torch.as_tensor(self.weights, dtype=dtype, device=device) + targets = encode_boxes(reference_boxes, proposals, weights) + + return targets + + def decode(self, rel_codes: Tensor, boxes: List[Tensor]) -> Tensor: + torch._assert( + isinstance(boxes, (list, tuple)), + "This function expects boxes of type list or tuple.", + ) + torch._assert( + isinstance(rel_codes, torch.Tensor), + "This function expects rel_codes of type torch.Tensor.", + ) + boxes_per_image = [b.size(0) for b in boxes] + concat_boxes = torch.cat(boxes, dim=0) + box_sum = 0 + for val in boxes_per_image: + box_sum += val + if box_sum > 0: + rel_codes = rel_codes.reshape(box_sum, -1) + pred_boxes = self.decode_single(rel_codes, concat_boxes) + if box_sum > 0: + pred_boxes = pred_boxes.reshape(box_sum, -1, 4) + return pred_boxes + + def decode_single(self, rel_codes: Tensor, boxes: Tensor) -> Tensor: + """ + From a set of original boxes and encoded relative box offsets, + get the decoded boxes. + + Args: + rel_codes (Tensor): encoded boxes + boxes (Tensor): reference boxes. + """ + + boxes = boxes.to(rel_codes.dtype) + + widths = boxes[:, 2] - boxes[:, 0] + heights = boxes[:, 3] - boxes[:, 1] + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + wx, wy, ww, wh = self.weights + dx = rel_codes[:, 0::4] / wx + dy = rel_codes[:, 1::4] / wy + dw = rel_codes[:, 2::4] / ww + dh = rel_codes[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.bbox_xform_clip) + dh = torch.clamp(dh, max=self.bbox_xform_clip) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + # Distance from center to box's corner. + c_to_c_h = torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h + c_to_c_w = torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w + + pred_boxes1 = pred_ctr_x - c_to_c_w + pred_boxes2 = pred_ctr_y - c_to_c_h + pred_boxes3 = pred_ctr_x + c_to_c_w + pred_boxes4 = pred_ctr_y + c_to_c_h + pred_boxes = torch.stack((pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=2).flatten(1) + return pred_boxes + + +class BoxLinearCoder: + """ + The linear box-to-box transform defined in FCOS. The transformation is parameterized + by the distance from the center of (square) src box to 4 edges of the target box. + """ + + def __init__(self, normalize_by_size: bool = True) -> None: + """ + Args: + normalize_by_size (bool): normalize deltas by the size of src (anchor) boxes. + """ + self.normalize_by_size = normalize_by_size + + def encode(self, reference_boxes: Tensor, proposals: Tensor) -> Tensor: + """ + Encode a set of proposals with respect to some reference boxes + + Args: + reference_boxes (Tensor): reference boxes + proposals (Tensor): boxes to be encoded + + Returns: + Tensor: the encoded relative box offsets that can be used to + decode the boxes. + + """ + + # get the center of reference_boxes + reference_boxes_ctr_x = 0.5 * (reference_boxes[..., 0] + reference_boxes[..., 2]) + reference_boxes_ctr_y = 0.5 * (reference_boxes[..., 1] + reference_boxes[..., 3]) + + # get box regression transformation deltas + target_l = reference_boxes_ctr_x - proposals[..., 0] + target_t = reference_boxes_ctr_y - proposals[..., 1] + target_r = proposals[..., 2] - reference_boxes_ctr_x + target_b = proposals[..., 3] - reference_boxes_ctr_y + + targets = torch.stack((target_l, target_t, target_r, target_b), dim=-1) + + if self.normalize_by_size: + reference_boxes_w = reference_boxes[..., 2] - reference_boxes[..., 0] + reference_boxes_h = reference_boxes[..., 3] - reference_boxes[..., 1] + reference_boxes_size = torch.stack( + (reference_boxes_w, reference_boxes_h, reference_boxes_w, reference_boxes_h), dim=-1 + ) + targets = targets / reference_boxes_size + return targets + + def decode(self, rel_codes: Tensor, boxes: Tensor) -> Tensor: + + """ + From a set of original boxes and encoded relative box offsets, + get the decoded boxes. + + Args: + rel_codes (Tensor): encoded boxes + boxes (Tensor): reference boxes. + + Returns: + Tensor: the predicted boxes with the encoded relative box offsets. + + .. note:: + This method assumes that ``rel_codes`` and ``boxes`` have same size for 0th dimension. i.e. ``len(rel_codes) == len(boxes)``. + + """ + + boxes = boxes.to(dtype=rel_codes.dtype) + + ctr_x = 0.5 * (boxes[..., 0] + boxes[..., 2]) + ctr_y = 0.5 * (boxes[..., 1] + boxes[..., 3]) + + if self.normalize_by_size: + boxes_w = boxes[..., 2] - boxes[..., 0] + boxes_h = boxes[..., 3] - boxes[..., 1] + + list_box_size = torch.stack((boxes_w, boxes_h, boxes_w, boxes_h), dim=-1) + rel_codes = rel_codes * list_box_size + + pred_boxes1 = ctr_x - rel_codes[..., 0] + pred_boxes2 = ctr_y - rel_codes[..., 1] + pred_boxes3 = ctr_x + rel_codes[..., 2] + pred_boxes4 = ctr_y + rel_codes[..., 3] + + pred_boxes = torch.stack((pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=-1) + return pred_boxes + + +class Matcher: + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be assigned to zero or more predicted elements. + + Matching is based on the MxN match_quality_matrix, that characterizes how well + each (ground-truth, predicted)-pair match. For example, if the elements are + boxes, the matrix may contain box IoU overlap values. + + The matcher returns a tensor of size N containing the index of the ground-truth + element m that matches to prediction n. If there is no match, a negative value + is returned. + """ + + BELOW_LOW_THRESHOLD = -1 + BETWEEN_THRESHOLDS = -2 + + __annotations__ = { + "BELOW_LOW_THRESHOLD": int, + "BETWEEN_THRESHOLDS": int, + } + + def __init__(self, high_threshold: float, low_threshold: float, allow_low_quality_matches: bool = False) -> None: + """ + Args: + high_threshold (float): quality values greater than or equal to + this value are candidate matches. + low_threshold (float): a lower quality threshold used to stratify + matches into three levels: + 1) matches >= high_threshold + 2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold) + 3) BELOW_LOW_THRESHOLD matches in [0, low_threshold) + allow_low_quality_matches (bool): if True, produce additional matches + for predictions that have only low-quality match candidates. See + set_low_quality_matches_ for more details. + """ + self.BELOW_LOW_THRESHOLD = -1 + self.BETWEEN_THRESHOLDS = -2 + torch._assert(low_threshold <= high_threshold, "low_threshold should be <= high_threshold") + self.high_threshold = high_threshold + self.low_threshold = low_threshold + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix: Tensor) -> Tensor: + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted elements. + + Returns: + matches (Tensor[int64]): an N tensor where N[i] is a matched gt in + [0, M - 1] or a negative value indicating that prediction i could not + be matched. + """ + if match_quality_matrix.numel() == 0: + # empty targets or proposals not supported during training + if match_quality_matrix.shape[0] == 0: + raise ValueError("No ground-truth boxes available for one of the images during training") + else: + raise ValueError("No proposal boxes available for one of the images during training") + + # match_quality_matrix is M (gt) x N (predicted) + # Max over gt elements (dim 0) to find best gt candidate for each prediction + matched_vals, matches = match_quality_matrix.max(dim=0) + if self.allow_low_quality_matches: + all_matches = matches.clone() + else: + all_matches = None # type: ignore[assignment] + + # Assign candidate matches with low quality to negative (unassigned) values + below_low_threshold = matched_vals < self.low_threshold + between_thresholds = (matched_vals >= self.low_threshold) & (matched_vals < self.high_threshold) + matches[below_low_threshold] = self.BELOW_LOW_THRESHOLD + matches[between_thresholds] = self.BETWEEN_THRESHOLDS + + if self.allow_low_quality_matches: + if all_matches is None: + torch._assert(False, "all_matches should not be None") + else: + self.set_low_quality_matches_(matches, all_matches, match_quality_matrix) + + return matches + + def set_low_quality_matches_(self, matches: Tensor, all_matches: Tensor, match_quality_matrix: Tensor) -> None: + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth with which it has the highest + quality value. + """ + # For each gt, find the prediction with which it has the highest quality + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + # Find the highest quality match available, even if it is low, including ties + gt_pred_pairs_of_highest_quality = torch.where(match_quality_matrix == highest_quality_foreach_gt[:, None]) + # Example gt_pred_pairs_of_highest_quality: + # (tensor([0, 1, 1, 2, 2, 3, 3, 4, 5, 5]), + # tensor([39796, 32055, 32070, 39190, 40255, 40390, 41455, 45470, 45325, 46390])) + # Each element in the first tensor is a gt index, and each element in second tensor is a prediction index + # Note how gt items 1, 2, 3, and 5 each have two ties + + pred_inds_to_update = gt_pred_pairs_of_highest_quality[1] + matches[pred_inds_to_update] = all_matches[pred_inds_to_update] + + +class SSDMatcher(Matcher): + def __init__(self, threshold: float) -> None: + super().__init__(threshold, threshold, allow_low_quality_matches=False) + + def __call__(self, match_quality_matrix: Tensor) -> Tensor: + matches = super().__call__(match_quality_matrix) + + # For each gt, find the prediction with which it has the highest quality + _, highest_quality_pred_foreach_gt = match_quality_matrix.max(dim=1) + matches[highest_quality_pred_foreach_gt] = torch.arange( + highest_quality_pred_foreach_gt.size(0), dtype=torch.int64, device=highest_quality_pred_foreach_gt.device + ) + + return matches + + +def overwrite_eps(model: nn.Module, eps: float) -> None: + """ + This method overwrites the default eps values of all the + FrozenBatchNorm2d layers of the model with the provided value. + This is necessary to address the BC-breaking change introduced + by the bug-fix at pytorch/vision#2933. The overwrite is applied + only when the pretrained weights are loaded to maintain compatibility + with previous versions. + + Args: + model (nn.Module): The model on which we perform the overwrite. + eps (float): The new value of eps. + """ + for module in model.modules(): + if isinstance(module, FrozenBatchNorm2d): + module.eps = eps + + +def retrieve_out_channels(model: nn.Module, size: Tuple[int, int]) -> List[int]: + """ + This method retrieves the number of output channels of a specific model. + + Args: + model (nn.Module): The model for which we estimate the out_channels. + It should return a single Tensor or an OrderedDict[Tensor]. + size (Tuple[int, int]): The size (wxh) of the input. + + Returns: + out_channels (List[int]): A list of the output channels of the model. + """ + in_training = model.training + model.eval() + + with torch.no_grad(): + # Use dummy data to retrieve the feature map sizes to avoid hard-coding their values + device = next(model.parameters()).device + tmp_img = torch.zeros((1, 3, size[1], size[0]), device=device) + features = model(tmp_img) + if isinstance(features, torch.Tensor): + features = OrderedDict([("0", features)]) + out_channels = [x.size(1) for x in features.values()] + + if in_training: + model.train() + + return out_channels + + +@torch.jit.unused +def _fake_cast_onnx(v: Tensor) -> int: + return v # type: ignore[return-value] + + +def _topk_min(input: Tensor, orig_kval: int, axis: int) -> int: + """ + ONNX spec requires the k-value to be less than or equal to the number of inputs along + provided dim. Certain models use the number of elements along a particular axis instead of K + if K exceeds the number of elements along that axis. Previously, python's min() function was + used to determine whether to use the provided k-value or the specified dim axis value. + + However, in cases where the model is being exported in tracing mode, python min() is + static causing the model to be traced incorrectly and eventually fail at the topk node. + In order to avoid this situation, in tracing mode, torch.min() is used instead. + + Args: + input (Tensor): The original input tensor. + orig_kval (int): The provided k-value. + axis(int): Axis along which we retrieve the input size. + + Returns: + min_kval (int): Appropriately selected k-value. + """ + if not torch.jit.is_tracing(): + return min(orig_kval, input.size(axis)) + axis_dim_val = torch._shape_as_tensor(input)[axis].unsqueeze(0) + min_kval = torch.min(torch.cat((torch.tensor([orig_kval], dtype=axis_dim_val.dtype), axis_dim_val), 0)) + return _fake_cast_onnx(min_kval) + + +def _box_loss( + type: str, + box_coder: BoxCoder, + anchors_per_image: Tensor, + matched_gt_boxes_per_image: Tensor, + bbox_regression_per_image: Tensor, + cnf: Optional[Dict[str, float]] = None, +) -> Tensor: + torch._assert(type in ["l1", "smooth_l1", "ciou", "diou", "giou"], f"Unsupported loss: {type}") + + if type == "l1": + target_regression = box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image) + return F.l1_loss(bbox_regression_per_image, target_regression, reduction="sum") + elif type == "smooth_l1": + target_regression = box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image) + beta = cnf["beta"] if cnf is not None and "beta" in cnf else 1.0 + return F.smooth_l1_loss(bbox_regression_per_image, target_regression, reduction="sum", beta=beta) + else: + bbox_per_image = box_coder.decode_single(bbox_regression_per_image, anchors_per_image) + eps = cnf["eps"] if cnf is not None and "eps" in cnf else 1e-7 + if type == "ciou": + return complete_box_iou_loss(bbox_per_image, matched_gt_boxes_per_image, reduction="sum", eps=eps) + if type == "diou": + return distance_box_iou_loss(bbox_per_image, matched_gt_boxes_per_image, reduction="sum", eps=eps) + # otherwise giou + return generalized_box_iou_loss(bbox_per_image, matched_gt_boxes_per_image, reduction="sum", eps=eps) diff --git a/lib/python3.11/site-packages/torchvision/models/detection/anchor_utils.py b/lib/python3.11/site-packages/torchvision/models/detection/anchor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..253f6502a9b6344f5a3da239f2394179a256424e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/anchor_utils.py @@ -0,0 +1,268 @@ +import math +from typing import List, Optional + +import torch +from torch import nn, Tensor + +from .image_list import ImageList + + +class AnchorGenerator(nn.Module): + """ + Module that generates anchors for a set of feature maps and + image sizes. + + The module support computing anchors at multiple sizes and aspect ratios + per feature map. This module assumes aspect ratio = height / width for + each anchor. + + sizes and aspect_ratios should have the same number of elements, and it should + correspond to the number of feature maps. + + sizes[i] and aspect_ratios[i] can have an arbitrary number of elements, + and AnchorGenerator will output a set of sizes[i] * aspect_ratios[i] anchors + per spatial location for feature map i. + + Args: + sizes (Tuple[Tuple[int]]): + aspect_ratios (Tuple[Tuple[float]]): + """ + + __annotations__ = { + "cell_anchors": List[torch.Tensor], + } + + def __init__( + self, + sizes=((128, 256, 512),), + aspect_ratios=((0.5, 1.0, 2.0),), + ): + super().__init__() + + if not isinstance(sizes[0], (list, tuple)): + # TODO change this + sizes = tuple((s,) for s in sizes) + if not isinstance(aspect_ratios[0], (list, tuple)): + aspect_ratios = (aspect_ratios,) * len(sizes) + + self.sizes = sizes + self.aspect_ratios = aspect_ratios + self.cell_anchors = [ + self.generate_anchors(size, aspect_ratio) for size, aspect_ratio in zip(sizes, aspect_ratios) + ] + + # TODO: https://github.com/pytorch/pytorch/issues/26792 + # For every (aspect_ratios, scales) combination, output a zero-centered anchor with those values. + # (scales, aspect_ratios) are usually an element of zip(self.scales, self.aspect_ratios) + # This method assumes aspect ratio = height / width for an anchor. + def generate_anchors( + self, + scales: List[int], + aspect_ratios: List[float], + dtype: torch.dtype = torch.float32, + device: torch.device = torch.device("cpu"), + ) -> Tensor: + scales = torch.as_tensor(scales, dtype=dtype, device=device) + aspect_ratios = torch.as_tensor(aspect_ratios, dtype=dtype, device=device) + h_ratios = torch.sqrt(aspect_ratios) + w_ratios = 1 / h_ratios + + ws = (w_ratios[:, None] * scales[None, :]).view(-1) + hs = (h_ratios[:, None] * scales[None, :]).view(-1) + + base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2 + return base_anchors.round() + + def set_cell_anchors(self, dtype: torch.dtype, device: torch.device): + self.cell_anchors = [cell_anchor.to(dtype=dtype, device=device) for cell_anchor in self.cell_anchors] + + def num_anchors_per_location(self) -> List[int]: + return [len(s) * len(a) for s, a in zip(self.sizes, self.aspect_ratios)] + + # For every combination of (a, (g, s), i) in (self.cell_anchors, zip(grid_sizes, strides), 0:2), + # output g[i] anchors that are s[i] distance apart in direction i, with the same dimensions as a. + def grid_anchors(self, grid_sizes: List[List[int]], strides: List[List[Tensor]]) -> List[Tensor]: + anchors = [] + cell_anchors = self.cell_anchors + torch._assert(cell_anchors is not None, "cell_anchors should not be None") + torch._assert( + len(grid_sizes) == len(strides) == len(cell_anchors), + "Anchors should be Tuple[Tuple[int]] because each feature " + "map could potentially have different sizes and aspect ratios. " + "There needs to be a match between the number of " + "feature maps passed and the number of sizes / aspect ratios specified.", + ) + + for size, stride, base_anchors in zip(grid_sizes, strides, cell_anchors): + grid_height, grid_width = size + stride_height, stride_width = stride + device = base_anchors.device + + # For output anchor, compute [x_center, y_center, x_center, y_center] + shifts_x = torch.arange(0, grid_width, dtype=torch.int32, device=device) * stride_width + shifts_y = torch.arange(0, grid_height, dtype=torch.int32, device=device) * stride_height + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x, indexing="ij") + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) + + # For every (base anchor, output anchor) pair, + # offset each zero-centered base anchor by the center of the output anchor. + anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) + + return anchors + + def forward(self, image_list: ImageList, feature_maps: List[Tensor]) -> List[Tensor]: + grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps] + image_size = image_list.tensors.shape[-2:] + dtype, device = feature_maps[0].dtype, feature_maps[0].device + strides = [ + [ + torch.empty((), dtype=torch.int64, device=device).fill_(image_size[0] // g[0]), + torch.empty((), dtype=torch.int64, device=device).fill_(image_size[1] // g[1]), + ] + for g in grid_sizes + ] + self.set_cell_anchors(dtype, device) + anchors_over_all_feature_maps = self.grid_anchors(grid_sizes, strides) + anchors: List[List[torch.Tensor]] = [] + for _ in range(len(image_list.image_sizes)): + anchors_in_image = [anchors_per_feature_map for anchors_per_feature_map in anchors_over_all_feature_maps] + anchors.append(anchors_in_image) + anchors = [torch.cat(anchors_per_image) for anchors_per_image in anchors] + return anchors + + +class DefaultBoxGenerator(nn.Module): + """ + This module generates the default boxes of SSD for a set of feature maps and image sizes. + + Args: + aspect_ratios (List[List[int]]): A list with all the aspect ratios used in each feature map. + min_ratio (float): The minimum scale :math:`\text{s}_{\text{min}}` of the default boxes used in the estimation + of the scales of each feature map. It is used only if the ``scales`` parameter is not provided. + max_ratio (float): The maximum scale :math:`\text{s}_{\text{max}}` of the default boxes used in the estimation + of the scales of each feature map. It is used only if the ``scales`` parameter is not provided. + scales (List[float]], optional): The scales of the default boxes. If not provided it will be estimated using + the ``min_ratio`` and ``max_ratio`` parameters. + steps (List[int]], optional): It's a hyper-parameter that affects the tiling of default boxes. If not provided + it will be estimated from the data. + clip (bool): Whether the standardized values of default boxes should be clipped between 0 and 1. The clipping + is applied while the boxes are encoded in format ``(cx, cy, w, h)``. + """ + + def __init__( + self, + aspect_ratios: List[List[int]], + min_ratio: float = 0.15, + max_ratio: float = 0.9, + scales: Optional[List[float]] = None, + steps: Optional[List[int]] = None, + clip: bool = True, + ): + super().__init__() + if steps is not None and len(aspect_ratios) != len(steps): + raise ValueError("aspect_ratios and steps should have the same length") + self.aspect_ratios = aspect_ratios + self.steps = steps + self.clip = clip + num_outputs = len(aspect_ratios) + + # Estimation of default boxes scales + if scales is None: + if num_outputs > 1: + range_ratio = max_ratio - min_ratio + self.scales = [min_ratio + range_ratio * k / (num_outputs - 1.0) for k in range(num_outputs)] + self.scales.append(1.0) + else: + self.scales = [min_ratio, max_ratio] + else: + self.scales = scales + + self._wh_pairs = self._generate_wh_pairs(num_outputs) + + def _generate_wh_pairs( + self, num_outputs: int, dtype: torch.dtype = torch.float32, device: torch.device = torch.device("cpu") + ) -> List[Tensor]: + _wh_pairs: List[Tensor] = [] + for k in range(num_outputs): + # Adding the 2 default width-height pairs for aspect ratio 1 and scale s'k + s_k = self.scales[k] + s_prime_k = math.sqrt(self.scales[k] * self.scales[k + 1]) + wh_pairs = [[s_k, s_k], [s_prime_k, s_prime_k]] + + # Adding 2 pairs for each aspect ratio of the feature map k + for ar in self.aspect_ratios[k]: + sq_ar = math.sqrt(ar) + w = self.scales[k] * sq_ar + h = self.scales[k] / sq_ar + wh_pairs.extend([[w, h], [h, w]]) + + _wh_pairs.append(torch.as_tensor(wh_pairs, dtype=dtype, device=device)) + return _wh_pairs + + def num_anchors_per_location(self) -> List[int]: + # Estimate num of anchors based on aspect ratios: 2 default boxes + 2 * ratios of feaure map. + return [2 + 2 * len(r) for r in self.aspect_ratios] + + # Default Boxes calculation based on page 6 of SSD paper + def _grid_default_boxes( + self, grid_sizes: List[List[int]], image_size: List[int], dtype: torch.dtype = torch.float32 + ) -> Tensor: + default_boxes = [] + for k, f_k in enumerate(grid_sizes): + # Now add the default boxes for each width-height pair + if self.steps is not None: + x_f_k = image_size[1] / self.steps[k] + y_f_k = image_size[0] / self.steps[k] + else: + y_f_k, x_f_k = f_k + + shifts_x = ((torch.arange(0, f_k[1]) + 0.5) / x_f_k).to(dtype=dtype) + shifts_y = ((torch.arange(0, f_k[0]) + 0.5) / y_f_k).to(dtype=dtype) + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x, indexing="ij") + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + + shifts = torch.stack((shift_x, shift_y) * len(self._wh_pairs[k]), dim=-1).reshape(-1, 2) + # Clipping the default boxes while the boxes are encoded in format (cx, cy, w, h) + _wh_pair = self._wh_pairs[k].clamp(min=0, max=1) if self.clip else self._wh_pairs[k] + wh_pairs = _wh_pair.repeat((f_k[0] * f_k[1]), 1) + + default_box = torch.cat((shifts, wh_pairs), dim=1) + + default_boxes.append(default_box) + + return torch.cat(default_boxes, dim=0) + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"aspect_ratios={self.aspect_ratios}" + f", clip={self.clip}" + f", scales={self.scales}" + f", steps={self.steps}" + ")" + ) + return s + + def forward(self, image_list: ImageList, feature_maps: List[Tensor]) -> List[Tensor]: + grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps] + image_size = image_list.tensors.shape[-2:] + dtype, device = feature_maps[0].dtype, feature_maps[0].device + default_boxes = self._grid_default_boxes(grid_sizes, image_size, dtype=dtype) + default_boxes = default_boxes.to(device) + + dboxes = [] + x_y_size = torch.tensor([image_size[1], image_size[0]], device=default_boxes.device) + for _ in image_list.image_sizes: + dboxes_in_image = default_boxes + dboxes_in_image = torch.cat( + [ + (dboxes_in_image[:, :2] - 0.5 * dboxes_in_image[:, 2:]) * x_y_size, + (dboxes_in_image[:, :2] + 0.5 * dboxes_in_image[:, 2:]) * x_y_size, + ], + -1, + ) + dboxes.append(dboxes_in_image) + return dboxes diff --git a/lib/python3.11/site-packages/torchvision/models/detection/backbone_utils.py b/lib/python3.11/site-packages/torchvision/models/detection/backbone_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..668e6b31696eb949513d07878eada9d468dc99cd --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/backbone_utils.py @@ -0,0 +1,244 @@ +import warnings +from typing import Callable, Dict, List, Optional, Union + +from torch import nn, Tensor +from torchvision.ops import misc as misc_nn_ops +from torchvision.ops.feature_pyramid_network import ExtraFPNBlock, FeaturePyramidNetwork, LastLevelMaxPool + +from .. import mobilenet, resnet +from .._api import _get_enum_from_fn, WeightsEnum +from .._utils import handle_legacy_interface, IntermediateLayerGetter + + +class BackboneWithFPN(nn.Module): + """ + Adds a FPN on top of a model. + Internally, it uses torchvision.models._utils.IntermediateLayerGetter to + extract a submodel that returns the feature maps specified in return_layers. + The same limitations of IntermediateLayerGetter apply here. + Args: + backbone (nn.Module) + return_layers (Dict[name, new_name]): a dict containing the names + of the modules for which the activations will be returned as + the key of the dict, and the value of the dict is the name + of the returned activation (which the user can specify). + in_channels_list (List[int]): number of channels for each feature map + that is returned, in the order they are present in the OrderedDict + out_channels (int): number of channels in the FPN. + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + Attributes: + out_channels (int): the number of channels in the FPN + """ + + def __init__( + self, + backbone: nn.Module, + return_layers: Dict[str, str], + in_channels_list: List[int], + out_channels: int, + extra_blocks: Optional[ExtraFPNBlock] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + + if extra_blocks is None: + extra_blocks = LastLevelMaxPool() + + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.fpn = FeaturePyramidNetwork( + in_channels_list=in_channels_list, + out_channels=out_channels, + extra_blocks=extra_blocks, + norm_layer=norm_layer, + ) + self.out_channels = out_channels + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + x = self.body(x) + x = self.fpn(x) + return x + + +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: _get_enum_from_fn(resnet.__dict__[kwargs["backbone_name"]])["IMAGENET1K_V1"], + ), +) +def resnet_fpn_backbone( + *, + backbone_name: str, + weights: Optional[WeightsEnum], + norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d, + trainable_layers: int = 3, + returned_layers: Optional[List[int]] = None, + extra_blocks: Optional[ExtraFPNBlock] = None, +) -> BackboneWithFPN: + """ + Constructs a specified ResNet backbone with FPN on top. Freezes the specified number of layers in the backbone. + + Examples:: + + >>> import torch + >>> from torchvision.models import ResNet50_Weights + >>> from torchvision.models.detection.backbone_utils import resnet_fpn_backbone + >>> backbone = resnet_fpn_backbone(backbone_name='resnet50', weights=ResNet50_Weights.DEFAULT, trainable_layers=3) + >>> # get some dummy image + >>> x = torch.rand(1,3,64,64) + >>> # compute the output + >>> output = backbone(x) + >>> print([(k, v.shape) for k, v in output.items()]) + >>> # returns + >>> [('0', torch.Size([1, 256, 16, 16])), + >>> ('1', torch.Size([1, 256, 8, 8])), + >>> ('2', torch.Size([1, 256, 4, 4])), + >>> ('3', torch.Size([1, 256, 2, 2])), + >>> ('pool', torch.Size([1, 256, 1, 1]))] + + Args: + backbone_name (string): resnet architecture. Possible values are 'resnet18', 'resnet34', 'resnet50', + 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2' + weights (WeightsEnum, optional): The pretrained weights for the model + norm_layer (callable): it is recommended to use the default value. For details visit: + (https://github.com/facebookresearch/maskrcnn-benchmark/issues/267) + trainable_layers (int): number of trainable (not frozen) layers starting from final block. + Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. + returned_layers (list of int): The layers of the network to return. Each entry must be in ``[1, 4]``. + By default, all layers are returned. + extra_blocks (ExtraFPNBlock or None): if provided, extra operations will + be performed. It is expected to take the fpn features, the original + features and the names of the original features as input, and returns + a new list of feature maps and their corresponding names. By + default, a ``LastLevelMaxPool`` is used. + """ + backbone = resnet.__dict__[backbone_name](weights=weights, norm_layer=norm_layer) + return _resnet_fpn_extractor(backbone, trainable_layers, returned_layers, extra_blocks) + + +def _resnet_fpn_extractor( + backbone: resnet.ResNet, + trainable_layers: int, + returned_layers: Optional[List[int]] = None, + extra_blocks: Optional[ExtraFPNBlock] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, +) -> BackboneWithFPN: + + # select layers that won't be frozen + if trainable_layers < 0 or trainable_layers > 5: + raise ValueError(f"Trainable layers should be in the range [0,5], got {trainable_layers}") + layers_to_train = ["layer4", "layer3", "layer2", "layer1", "conv1"][:trainable_layers] + if trainable_layers == 5: + layers_to_train.append("bn1") + for name, parameter in backbone.named_parameters(): + if all([not name.startswith(layer) for layer in layers_to_train]): + parameter.requires_grad_(False) + + if extra_blocks is None: + extra_blocks = LastLevelMaxPool() + + if returned_layers is None: + returned_layers = [1, 2, 3, 4] + if min(returned_layers) <= 0 or max(returned_layers) >= 5: + raise ValueError(f"Each returned layer should be in the range [1,4]. Got {returned_layers}") + return_layers = {f"layer{k}": str(v) for v, k in enumerate(returned_layers)} + + in_channels_stage2 = backbone.inplanes // 8 + in_channels_list = [in_channels_stage2 * 2 ** (i - 1) for i in returned_layers] + out_channels = 256 + return BackboneWithFPN( + backbone, return_layers, in_channels_list, out_channels, extra_blocks=extra_blocks, norm_layer=norm_layer + ) + + +def _validate_trainable_layers( + is_trained: bool, + trainable_backbone_layers: Optional[int], + max_value: int, + default_value: int, +) -> int: + # don't freeze any layers if pretrained model or backbone is not used + if not is_trained: + if trainable_backbone_layers is not None: + warnings.warn( + "Changing trainable_backbone_layers has no effect if " + "neither pretrained nor pretrained_backbone have been set to True, " + f"falling back to trainable_backbone_layers={max_value} so that all layers are trainable" + ) + trainable_backbone_layers = max_value + + # by default freeze first blocks + if trainable_backbone_layers is None: + trainable_backbone_layers = default_value + if trainable_backbone_layers < 0 or trainable_backbone_layers > max_value: + raise ValueError( + f"Trainable backbone layers should be in the range [0,{max_value}], got {trainable_backbone_layers} " + ) + return trainable_backbone_layers + + +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: _get_enum_from_fn(mobilenet.__dict__[kwargs["backbone_name"]])["IMAGENET1K_V1"], + ), +) +def mobilenet_backbone( + *, + backbone_name: str, + weights: Optional[WeightsEnum], + fpn: bool, + norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d, + trainable_layers: int = 2, + returned_layers: Optional[List[int]] = None, + extra_blocks: Optional[ExtraFPNBlock] = None, +) -> nn.Module: + backbone = mobilenet.__dict__[backbone_name](weights=weights, norm_layer=norm_layer) + return _mobilenet_extractor(backbone, fpn, trainable_layers, returned_layers, extra_blocks) + + +def _mobilenet_extractor( + backbone: Union[mobilenet.MobileNetV2, mobilenet.MobileNetV3], + fpn: bool, + trainable_layers: int, + returned_layers: Optional[List[int]] = None, + extra_blocks: Optional[ExtraFPNBlock] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, +) -> nn.Module: + backbone = backbone.features + # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks. + # The first and last blocks are always included because they are the C0 (conv1) and Cn. + stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1] + num_stages = len(stage_indices) + + # find the index of the layer from which we won't freeze + if trainable_layers < 0 or trainable_layers > num_stages: + raise ValueError(f"Trainable layers should be in the range [0,{num_stages}], got {trainable_layers} ") + freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers] + + for b in backbone[:freeze_before]: + for parameter in b.parameters(): + parameter.requires_grad_(False) + + out_channels = 256 + if fpn: + if extra_blocks is None: + extra_blocks = LastLevelMaxPool() + + if returned_layers is None: + returned_layers = [num_stages - 2, num_stages - 1] + if min(returned_layers) < 0 or max(returned_layers) >= num_stages: + raise ValueError(f"Each returned layer should be in the range [0,{num_stages - 1}], got {returned_layers} ") + return_layers = {f"{stage_indices[k]}": str(v) for v, k in enumerate(returned_layers)} + + in_channels_list = [backbone[stage_indices[i]].out_channels for i in returned_layers] + return BackboneWithFPN( + backbone, return_layers, in_channels_list, out_channels, extra_blocks=extra_blocks, norm_layer=norm_layer + ) + else: + m = nn.Sequential( + backbone, + # depthwise linear combination of channels to reduce their size + nn.Conv2d(backbone[-1].out_channels, out_channels, 1), + ) + m.out_channels = out_channels # type: ignore[assignment] + return m diff --git a/lib/python3.11/site-packages/torchvision/models/detection/faster_rcnn.py b/lib/python3.11/site-packages/torchvision/models/detection/faster_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..18474ee84f4539cfec99d24534acb1e1e74a14b3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/faster_rcnn.py @@ -0,0 +1,846 @@ +from typing import Any, Callable, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from torch import nn +from torchvision.ops import MultiScaleRoIAlign + +from ...ops import misc as misc_nn_ops +from ...transforms._presets import ObjectDetection +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights +from ..resnet import resnet50, ResNet50_Weights +from ._utils import overwrite_eps +from .anchor_utils import AnchorGenerator +from .backbone_utils import _mobilenet_extractor, _resnet_fpn_extractor, _validate_trainable_layers +from .generalized_rcnn import GeneralizedRCNN +from .roi_heads import RoIHeads +from .rpn import RegionProposalNetwork, RPNHead +from .transform import GeneralizedRCNNTransform + + +__all__ = [ + "FasterRCNN", + "FasterRCNN_ResNet50_FPN_Weights", + "FasterRCNN_ResNet50_FPN_V2_Weights", + "FasterRCNN_MobileNet_V3_Large_FPN_Weights", + "FasterRCNN_MobileNet_V3_Large_320_FPN_Weights", + "fasterrcnn_resnet50_fpn", + "fasterrcnn_resnet50_fpn_v2", + "fasterrcnn_mobilenet_v3_large_fpn", + "fasterrcnn_mobilenet_v3_large_320_fpn", +] + + +def _default_anchorgen(): + anchor_sizes = ((32,), (64,), (128,), (256,), (512,)) + aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes) + return AnchorGenerator(anchor_sizes, aspect_ratios) + + +class FasterRCNN(GeneralizedRCNN): + """ + Implements Faster R-CNN. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses for both the RPN and the R-CNN. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows: + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each image + - scores (Tensor[N]): the scores or each prediction + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute, which indicates the number of output + channels that each feature map has (and it should be the same for all feature maps). + The backbone should return a single Tensor or and OrderedDict[Tensor]. + num_classes (int): number of output classes of the model (including the background). + If box_predictor is specified, num_classes should be None. + min_size (int): Images are rescaled before feeding them to the backbone: + we attempt to preserve the aspect ratio and scale the shorter edge + to ``min_size``. If the resulting longer edge exceeds ``max_size``, + then downscale so that the longer edge does not exceed ``max_size``. + This may result in the shorter edge beeing lower than ``min_size``. + max_size (int): See ``min_size``. + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. + rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN + rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training + rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing + rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training + rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing + rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals + rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training of the RPN. + rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be + considered as negative during training of the RPN. + rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN + for computing the loss + rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training + of the RPN + rpn_score_thresh (float): only return proposals with an objectness score greater than rpn_score_thresh + box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in + the locations indicated by the bounding boxes + box_head (nn.Module): module that takes the cropped feature maps as input + box_predictor (nn.Module): module that takes the output of box_head and returns the + classification logits and box regression deltas. + box_score_thresh (float): during inference, only return proposals with a classification score + greater than box_score_thresh + box_nms_thresh (float): NMS threshold for the prediction head. Used during inference + box_detections_per_img (int): maximum number of detections per image, for all classes. + box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be + considered as positive during training of the classification head + box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be + considered as negative during training of the classification head + box_batch_size_per_image (int): number of proposals that are sampled during training of the + classification head + box_positive_fraction (float): proportion of positive proposals in a mini-batch during training + of the classification head + bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the + bounding boxes + + Example:: + + >>> import torch + >>> import torchvision + >>> from torchvision.models.detection import FasterRCNN + >>> from torchvision.models.detection.rpn import AnchorGenerator + >>> # load a pre-trained model for classification and return + >>> # only the features + >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features + >>> # FasterRCNN needs to know the number of + >>> # output channels in a backbone. For mobilenet_v2, it's 1280, + >>> # so we need to add it here + >>> backbone.out_channels = 1280 + >>> + >>> # let's make the RPN generate 5 x 3 anchors per spatial + >>> # location, with 5 different sizes and 3 different aspect + >>> # ratios. We have a Tuple[Tuple[int]] because each feature + >>> # map could potentially have different sizes and + >>> # aspect ratios + >>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), + >>> aspect_ratios=((0.5, 1.0, 2.0),)) + >>> + >>> # let's define what are the feature maps that we will + >>> # use to perform the region of interest cropping, as well as + >>> # the size of the crop after rescaling. + >>> # if your backbone returns a Tensor, featmap_names is expected to + >>> # be ['0']. More generally, the backbone should return an + >>> # OrderedDict[Tensor], and in featmap_names you can choose which + >>> # feature maps to use. + >>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + >>> output_size=7, + >>> sampling_ratio=2) + >>> + >>> # put the pieces together inside a FasterRCNN model + >>> model = FasterRCNN(backbone, + >>> num_classes=2, + >>> rpn_anchor_generator=anchor_generator, + >>> box_roi_pool=roi_pooler) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + """ + + def __init__( + self, + backbone, + num_classes=None, + # transform parameters + min_size=800, + max_size=1333, + image_mean=None, + image_std=None, + # RPN parameters + rpn_anchor_generator=None, + rpn_head=None, + rpn_pre_nms_top_n_train=2000, + rpn_pre_nms_top_n_test=1000, + rpn_post_nms_top_n_train=2000, + rpn_post_nms_top_n_test=1000, + rpn_nms_thresh=0.7, + rpn_fg_iou_thresh=0.7, + rpn_bg_iou_thresh=0.3, + rpn_batch_size_per_image=256, + rpn_positive_fraction=0.5, + rpn_score_thresh=0.0, + # Box parameters + box_roi_pool=None, + box_head=None, + box_predictor=None, + box_score_thresh=0.05, + box_nms_thresh=0.5, + box_detections_per_img=100, + box_fg_iou_thresh=0.5, + box_bg_iou_thresh=0.5, + box_batch_size_per_image=512, + box_positive_fraction=0.25, + bbox_reg_weights=None, + **kwargs, + ): + + if not hasattr(backbone, "out_channels"): + raise ValueError( + "backbone should contain an attribute out_channels " + "specifying the number of output channels (assumed to be the " + "same for all the levels)" + ) + + if not isinstance(rpn_anchor_generator, (AnchorGenerator, type(None))): + raise TypeError( + f"rpn_anchor_generator should be of type AnchorGenerator or None instead of {type(rpn_anchor_generator)}" + ) + if not isinstance(box_roi_pool, (MultiScaleRoIAlign, type(None))): + raise TypeError( + f"box_roi_pool should be of type MultiScaleRoIAlign or None instead of {type(box_roi_pool)}" + ) + + if num_classes is not None: + if box_predictor is not None: + raise ValueError("num_classes should be None when box_predictor is specified") + else: + if box_predictor is None: + raise ValueError("num_classes should not be None when box_predictor is not specified") + + out_channels = backbone.out_channels + + if rpn_anchor_generator is None: + rpn_anchor_generator = _default_anchorgen() + if rpn_head is None: + rpn_head = RPNHead(out_channels, rpn_anchor_generator.num_anchors_per_location()[0]) + + rpn_pre_nms_top_n = dict(training=rpn_pre_nms_top_n_train, testing=rpn_pre_nms_top_n_test) + rpn_post_nms_top_n = dict(training=rpn_post_nms_top_n_train, testing=rpn_post_nms_top_n_test) + + rpn = RegionProposalNetwork( + rpn_anchor_generator, + rpn_head, + rpn_fg_iou_thresh, + rpn_bg_iou_thresh, + rpn_batch_size_per_image, + rpn_positive_fraction, + rpn_pre_nms_top_n, + rpn_post_nms_top_n, + rpn_nms_thresh, + score_thresh=rpn_score_thresh, + ) + + if box_roi_pool is None: + box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2) + + if box_head is None: + resolution = box_roi_pool.output_size[0] + representation_size = 1024 + box_head = TwoMLPHead(out_channels * resolution**2, representation_size) + + if box_predictor is None: + representation_size = 1024 + box_predictor = FastRCNNPredictor(representation_size, num_classes) + + roi_heads = RoIHeads( + # Box + box_roi_pool, + box_head, + box_predictor, + box_fg_iou_thresh, + box_bg_iou_thresh, + box_batch_size_per_image, + box_positive_fraction, + bbox_reg_weights, + box_score_thresh, + box_nms_thresh, + box_detections_per_img, + ) + + if image_mean is None: + image_mean = [0.485, 0.456, 0.406] + if image_std is None: + image_std = [0.229, 0.224, 0.225] + transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs) + + super().__init__(backbone, rpn, roi_heads, transform) + + +class TwoMLPHead(nn.Module): + """ + Standard heads for FPN-based models + + Args: + in_channels (int): number of input channels + representation_size (int): size of the intermediate representation + """ + + def __init__(self, in_channels, representation_size): + super().__init__() + + self.fc6 = nn.Linear(in_channels, representation_size) + self.fc7 = nn.Linear(representation_size, representation_size) + + def forward(self, x): + x = x.flatten(start_dim=1) + + x = F.relu(self.fc6(x)) + x = F.relu(self.fc7(x)) + + return x + + +class FastRCNNConvFCHead(nn.Sequential): + def __init__( + self, + input_size: Tuple[int, int, int], + conv_layers: List[int], + fc_layers: List[int], + norm_layer: Optional[Callable[..., nn.Module]] = None, + ): + """ + Args: + input_size (Tuple[int, int, int]): the input size in CHW format. + conv_layers (list): feature dimensions of each Convolution layer + fc_layers (list): feature dimensions of each FCN layer + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + """ + in_channels, in_height, in_width = input_size + + blocks = [] + previous_channels = in_channels + for current_channels in conv_layers: + blocks.append(misc_nn_ops.Conv2dNormActivation(previous_channels, current_channels, norm_layer=norm_layer)) + previous_channels = current_channels + blocks.append(nn.Flatten()) + previous_channels = previous_channels * in_height * in_width + for current_channels in fc_layers: + blocks.append(nn.Linear(previous_channels, current_channels)) + blocks.append(nn.ReLU(inplace=True)) + previous_channels = current_channels + + super().__init__(*blocks) + for layer in self.modules(): + if isinstance(layer, nn.Conv2d): + nn.init.kaiming_normal_(layer.weight, mode="fan_out", nonlinearity="relu") + if layer.bias is not None: + nn.init.zeros_(layer.bias) + + +class FastRCNNPredictor(nn.Module): + """ + Standard classification + bounding box regression layers + for Fast R-CNN. + + Args: + in_channels (int): number of input channels + num_classes (int): number of output classes (including background) + """ + + def __init__(self, in_channels, num_classes): + super().__init__() + self.cls_score = nn.Linear(in_channels, num_classes) + self.bbox_pred = nn.Linear(in_channels, num_classes * 4) + + def forward(self, x): + if x.dim() == 4: + torch._assert( + list(x.shape[2:]) == [1, 1], + f"x has the wrong shape, expecting the last two dimensions to be [1,1] instead of {list(x.shape[2:])}", + ) + x = x.flatten(start_dim=1) + scores = self.cls_score(x) + bbox_deltas = self.bbox_pred(x) + + return scores, bbox_deltas + + +_COMMON_META = { + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), +} + + +class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 41755286, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn", + "_metrics": { + "COCO-val2017": { + "box_map": 37.0, + } + }, + "_ops": 134.38, + "_file_size": 159.743, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +class FasterRCNN_ResNet50_FPN_V2_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 43712278, + "recipe": "https://github.com/pytorch/vision/pull/5763", + "_metrics": { + "COCO-val2017": { + "box_map": 46.7, + } + }, + "_ops": 280.371, + "_file_size": 167.104, + "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""", + }, + ) + DEFAULT = COCO_V1 + + +class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 19386354, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn", + "_metrics": { + "COCO-val2017": { + "box_map": 32.8, + } + }, + "_ops": 4.494, + "_file_size": 74.239, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 19386354, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn", + "_metrics": { + "COCO-val2017": { + "box_map": 22.8, + } + }, + "_ops": 0.719, + "_file_size": 74.239, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FasterRCNN_ResNet50_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def fasterrcnn_resnet50_fpn( + *, + weights: Optional[FasterRCNN_ResNet50_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> FasterRCNN: + """ + Faster R-CNN model with a ResNet-50-FPN backbone from the `Faster R-CNN: Towards Real-Time Object + Detection with Region Proposal Networks `__ + paper. + + .. betastatus:: detection module + + The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each + image, and should be in ``0-1`` range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and a targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the class label for each ground-truth box + + The model returns a ``Dict[Tensor]`` during training, containing the classification and regression + losses for both the RPN and the R-CNN. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as + follows, where ``N`` is the number of detections: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the predicted labels for each detection + - scores (``Tensor[N]``): the scores of each detection + + For more details on the output, you may refer to :ref:`instance_seg_output`. + + Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size. + + Example:: + + >>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT) + >>> # For training + >>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4) + >>> boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4] + >>> labels = torch.randint(1, 91, (4, 11)) + >>> images = list(image for image in images) + >>> targets = [] + >>> for i in range(len(images)): + >>> d = {} + >>> d['boxes'] = boxes[i] + >>> d['labels'] = labels[i] + >>> targets.append(d) + >>> output = model(images, targets) + >>> # For inference + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + >>> + >>> # optionally, if you want to export the model to ONNX: + >>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11) + + Args: + weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights + :members: + """ + weights = FasterRCNN_ResNet50_FPN_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers) + model = FasterRCNN(backbone, num_classes=num_classes, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1: + overwrite_eps(model, 0.0) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def fasterrcnn_resnet50_fpn_v2( + *, + weights: Optional[FasterRCNN_ResNet50_FPN_V2_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = None, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> FasterRCNN: + """ + Constructs an improved Faster R-CNN model with a ResNet-50-FPN backbone from `Benchmarking Detection + Transfer Learning with Vision Transformers `__ paper. + + .. betastatus:: detection module + + It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See + :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more + details. + + Args: + weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights + :members: + """ + weights = FasterRCNN_ResNet50_FPN_V2_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + + backbone = resnet50(weights=weights_backbone, progress=progress) + backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers, norm_layer=nn.BatchNorm2d) + rpn_anchor_generator = _default_anchorgen() + rpn_head = RPNHead(backbone.out_channels, rpn_anchor_generator.num_anchors_per_location()[0], conv_depth=2) + box_head = FastRCNNConvFCHead( + (backbone.out_channels, 7, 7), [256, 256, 256, 256], [1024], norm_layer=nn.BatchNorm2d + ) + model = FasterRCNN( + backbone, + num_classes=num_classes, + rpn_anchor_generator=rpn_anchor_generator, + rpn_head=rpn_head, + box_head=box_head, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +def _fasterrcnn_mobilenet_v3_large_fpn( + *, + weights: Optional[Union[FasterRCNN_MobileNet_V3_Large_FPN_Weights, FasterRCNN_MobileNet_V3_Large_320_FPN_Weights]], + progress: bool, + num_classes: Optional[int], + weights_backbone: Optional[MobileNet_V3_Large_Weights], + trainable_backbone_layers: Optional[int], + **kwargs: Any, +) -> FasterRCNN: + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 6, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = mobilenet_v3_large(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers) + anchor_sizes = ( + ( + 32, + 64, + 128, + 256, + 512, + ), + ) * 3 + aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes) + model = FasterRCNN( + backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios), **kwargs + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1), +) +def fasterrcnn_mobilenet_v3_large_320_fpn( + *, + weights: Optional[FasterRCNN_MobileNet_V3_Large_320_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> FasterRCNN: + """ + Low resolution Faster R-CNN model with a MobileNetV3-Large backbone tuned for mobile use cases. + + .. betastatus:: detection module + + It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See + :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more + details. + + Example:: + + >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights + :members: + """ + weights = FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.verify(weights) + weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone) + + defaults = { + "min_size": 320, + "max_size": 640, + "rpn_pre_nms_top_n_test": 150, + "rpn_post_nms_top_n_test": 150, + "rpn_score_thresh": 0.05, + } + + kwargs = {**defaults, **kwargs} + return _fasterrcnn_mobilenet_v3_large_fpn( + weights=weights, + progress=progress, + num_classes=num_classes, + weights_backbone=weights_backbone, + trainable_backbone_layers=trainable_backbone_layers, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1), +) +def fasterrcnn_mobilenet_v3_large_fpn( + *, + weights: Optional[FasterRCNN_MobileNet_V3_Large_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> FasterRCNN: + """ + Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone. + + .. betastatus:: detection module + + It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See + :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more + details. + + Example:: + + >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=FasterRCNN_MobileNet_V3_Large_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights + :members: + """ + weights = FasterRCNN_MobileNet_V3_Large_FPN_Weights.verify(weights) + weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone) + + defaults = { + "rpn_score_thresh": 0.05, + } + + kwargs = {**defaults, **kwargs} + return _fasterrcnn_mobilenet_v3_large_fpn( + weights=weights, + progress=progress, + num_classes=num_classes, + weights_backbone=weights_backbone, + trainable_backbone_layers=trainable_backbone_layers, + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/detection/fcos.py b/lib/python3.11/site-packages/torchvision/models/detection/fcos.py new file mode 100644 index 0000000000000000000000000000000000000000..a86ad2f424c32bd1cf951d474d3ef14bd1bddbb7 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/fcos.py @@ -0,0 +1,775 @@ +import math +import warnings +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Tuple + +import torch +from torch import nn, Tensor + +from ...ops import boxes as box_ops, generalized_box_iou_loss, misc as misc_nn_ops, sigmoid_focal_loss +from ...ops.feature_pyramid_network import LastLevelP6P7 +from ...transforms._presets import ObjectDetection +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..resnet import resnet50, ResNet50_Weights +from . import _utils as det_utils +from .anchor_utils import AnchorGenerator +from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers +from .transform import GeneralizedRCNNTransform + + +__all__ = [ + "FCOS", + "FCOS_ResNet50_FPN_Weights", + "fcos_resnet50_fpn", +] + + +class FCOSHead(nn.Module): + """ + A regression and classification head for use in FCOS. + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + num_classes (int): number of classes to be predicted + num_convs (Optional[int]): number of conv layer of head. Default: 4. + """ + + __annotations__ = { + "box_coder": det_utils.BoxLinearCoder, + } + + def __init__(self, in_channels: int, num_anchors: int, num_classes: int, num_convs: Optional[int] = 4) -> None: + super().__init__() + self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True) + self.classification_head = FCOSClassificationHead(in_channels, num_anchors, num_classes, num_convs) + self.regression_head = FCOSRegressionHead(in_channels, num_anchors, num_convs) + + def compute_loss( + self, + targets: List[Dict[str, Tensor]], + head_outputs: Dict[str, Tensor], + anchors: List[Tensor], + matched_idxs: List[Tensor], + ) -> Dict[str, Tensor]: + + cls_logits = head_outputs["cls_logits"] # [N, HWA, C] + bbox_regression = head_outputs["bbox_regression"] # [N, HWA, 4] + bbox_ctrness = head_outputs["bbox_ctrness"] # [N, HWA, 1] + + all_gt_classes_targets = [] + all_gt_boxes_targets = [] + for targets_per_image, matched_idxs_per_image in zip(targets, matched_idxs): + if len(targets_per_image["labels"]) == 0: + gt_classes_targets = targets_per_image["labels"].new_zeros((len(matched_idxs_per_image),)) + gt_boxes_targets = targets_per_image["boxes"].new_zeros((len(matched_idxs_per_image), 4)) + else: + gt_classes_targets = targets_per_image["labels"][matched_idxs_per_image.clip(min=0)] + gt_boxes_targets = targets_per_image["boxes"][matched_idxs_per_image.clip(min=0)] + gt_classes_targets[matched_idxs_per_image < 0] = -1 # background + all_gt_classes_targets.append(gt_classes_targets) + all_gt_boxes_targets.append(gt_boxes_targets) + + # List[Tensor] to Tensor conversion of `all_gt_boxes_target`, `all_gt_classes_targets` and `anchors` + all_gt_boxes_targets, all_gt_classes_targets, anchors = ( + torch.stack(all_gt_boxes_targets), + torch.stack(all_gt_classes_targets), + torch.stack(anchors), + ) + + # compute foregroud + foregroud_mask = all_gt_classes_targets >= 0 + num_foreground = foregroud_mask.sum().item() + + # classification loss + gt_classes_targets = torch.zeros_like(cls_logits) + gt_classes_targets[foregroud_mask, all_gt_classes_targets[foregroud_mask]] = 1.0 + loss_cls = sigmoid_focal_loss(cls_logits, gt_classes_targets, reduction="sum") + + # amp issue: pred_boxes need to convert float + pred_boxes = self.box_coder.decode(bbox_regression, anchors) + + # regression loss: GIoU loss + loss_bbox_reg = generalized_box_iou_loss( + pred_boxes[foregroud_mask], + all_gt_boxes_targets[foregroud_mask], + reduction="sum", + ) + + # ctrness loss + + bbox_reg_targets = self.box_coder.encode(anchors, all_gt_boxes_targets) + + if len(bbox_reg_targets) == 0: + gt_ctrness_targets = bbox_reg_targets.new_zeros(bbox_reg_targets.size()[:-1]) + else: + left_right = bbox_reg_targets[:, :, [0, 2]] + top_bottom = bbox_reg_targets[:, :, [1, 3]] + gt_ctrness_targets = torch.sqrt( + (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) + * (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) + ) + pred_centerness = bbox_ctrness.squeeze(dim=2) + loss_bbox_ctrness = nn.functional.binary_cross_entropy_with_logits( + pred_centerness[foregroud_mask], gt_ctrness_targets[foregroud_mask], reduction="sum" + ) + + return { + "classification": loss_cls / max(1, num_foreground), + "bbox_regression": loss_bbox_reg / max(1, num_foreground), + "bbox_ctrness": loss_bbox_ctrness / max(1, num_foreground), + } + + def forward(self, x: List[Tensor]) -> Dict[str, Tensor]: + cls_logits = self.classification_head(x) + bbox_regression, bbox_ctrness = self.regression_head(x) + return { + "cls_logits": cls_logits, + "bbox_regression": bbox_regression, + "bbox_ctrness": bbox_ctrness, + } + + +class FCOSClassificationHead(nn.Module): + """ + A classification head for use in FCOS. + + Args: + in_channels (int): number of channels of the input feature. + num_anchors (int): number of anchors to be predicted. + num_classes (int): number of classes to be predicted. + num_convs (Optional[int]): number of conv layer. Default: 4. + prior_probability (Optional[float]): probability of prior. Default: 0.01. + norm_layer: Module specifying the normalization layer to use. + """ + + def __init__( + self, + in_channels: int, + num_anchors: int, + num_classes: int, + num_convs: int = 4, + prior_probability: float = 0.01, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + + self.num_classes = num_classes + self.num_anchors = num_anchors + + if norm_layer is None: + norm_layer = partial(nn.GroupNorm, 32) + + conv = [] + for _ in range(num_convs): + conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) + conv.append(norm_layer(in_channels)) + conv.append(nn.ReLU()) + self.conv = nn.Sequential(*conv) + + for layer in self.conv.children(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, std=0.01) + torch.nn.init.constant_(layer.bias, 0) + + self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1) + torch.nn.init.normal_(self.cls_logits.weight, std=0.01) + torch.nn.init.constant_(self.cls_logits.bias, -math.log((1 - prior_probability) / prior_probability)) + + def forward(self, x: List[Tensor]) -> Tensor: + all_cls_logits = [] + + for features in x: + cls_logits = self.conv(features) + cls_logits = self.cls_logits(cls_logits) + + # Permute classification output from (N, A * K, H, W) to (N, HWA, K). + N, _, H, W = cls_logits.shape + cls_logits = cls_logits.view(N, -1, self.num_classes, H, W) + cls_logits = cls_logits.permute(0, 3, 4, 1, 2) + cls_logits = cls_logits.reshape(N, -1, self.num_classes) # Size=(N, HWA, 4) + + all_cls_logits.append(cls_logits) + + return torch.cat(all_cls_logits, dim=1) + + +class FCOSRegressionHead(nn.Module): + """ + A regression head for use in FCOS, which combines regression branch and center-ness branch. + This can obtain better performance. + + Reference: `FCOS: A simple and strong anchor-free object detector `_. + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + num_convs (Optional[int]): number of conv layer. Default: 4. + norm_layer: Module specifying the normalization layer to use. + """ + + def __init__( + self, + in_channels: int, + num_anchors: int, + num_convs: int = 4, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ): + super().__init__() + + if norm_layer is None: + norm_layer = partial(nn.GroupNorm, 32) + + conv = [] + for _ in range(num_convs): + conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) + conv.append(norm_layer(in_channels)) + conv.append(nn.ReLU()) + self.conv = nn.Sequential(*conv) + + self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) + self.bbox_ctrness = nn.Conv2d(in_channels, num_anchors * 1, kernel_size=3, stride=1, padding=1) + for layer in [self.bbox_reg, self.bbox_ctrness]: + torch.nn.init.normal_(layer.weight, std=0.01) + torch.nn.init.zeros_(layer.bias) + + for layer in self.conv.children(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, std=0.01) + torch.nn.init.zeros_(layer.bias) + + def forward(self, x: List[Tensor]) -> Tuple[Tensor, Tensor]: + all_bbox_regression = [] + all_bbox_ctrness = [] + + for features in x: + bbox_feature = self.conv(features) + bbox_regression = nn.functional.relu(self.bbox_reg(bbox_feature)) + bbox_ctrness = self.bbox_ctrness(bbox_feature) + + # permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4). + N, _, H, W = bbox_regression.shape + bbox_regression = bbox_regression.view(N, -1, 4, H, W) + bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2) + bbox_regression = bbox_regression.reshape(N, -1, 4) # Size=(N, HWA, 4) + all_bbox_regression.append(bbox_regression) + + # permute bbox ctrness output from (N, 1 * A, H, W) to (N, HWA, 1). + bbox_ctrness = bbox_ctrness.view(N, -1, 1, H, W) + bbox_ctrness = bbox_ctrness.permute(0, 3, 4, 1, 2) + bbox_ctrness = bbox_ctrness.reshape(N, -1, 1) + all_bbox_ctrness.append(bbox_ctrness) + + return torch.cat(all_bbox_regression, dim=1), torch.cat(all_bbox_ctrness, dim=1) + + +class FCOS(nn.Module): + """ + Implements FCOS. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + + The model returns a Dict[Tensor] during training, containing the classification, regression + and centerness losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows: + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each image + - scores (Tensor[N]): the scores for each prediction + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute, which indicates the number of output + channels that each feature map has (and it should be the same for all feature maps). + The backbone should return a single Tensor or an OrderedDict[Tensor]. + num_classes (int): number of output classes of the model (including the background). + min_size (int): Images are rescaled before feeding them to the backbone: + we attempt to preserve the aspect ratio and scale the shorter edge + to ``min_size``. If the resulting longer edge exceeds ``max_size``, + then downscale so that the longer edge does not exceed ``max_size``. + This may result in the shorter edge beeing lower than ``min_size``. + max_size (int): See ``min_size``. + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. For FCOS, only set one anchor for per position of each level, the width and height equal to + the stride of feature map, and set aspect ratio = 1.0, so the center of anchor is equivalent to the point + in FCOS paper. + head (nn.Module): Module run on top of the feature pyramid. + Defaults to a module containing a classification and regression module. + center_sampling_radius (int): radius of the "center" of a groundtruth box, + within which all anchor points are labeled positive. + score_thresh (float): Score threshold used for postprocessing the detections. + nms_thresh (float): NMS threshold used for postprocessing the detections. + detections_per_img (int): Number of best detections to keep after NMS. + topk_candidates (int): Number of best detections to keep before NMS. + + Example: + + >>> import torch + >>> import torchvision + >>> from torchvision.models.detection import FCOS + >>> from torchvision.models.detection.anchor_utils import AnchorGenerator + >>> # load a pre-trained model for classification and return + >>> # only the features + >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features + >>> # FCOS needs to know the number of + >>> # output channels in a backbone. For mobilenet_v2, it's 1280, + >>> # so we need to add it here + >>> backbone.out_channels = 1280 + >>> + >>> # let's make the network generate 5 x 3 anchors per spatial + >>> # location, with 5 different sizes and 3 different aspect + >>> # ratios. We have a Tuple[Tuple[int]] because each feature + >>> # map could potentially have different sizes and + >>> # aspect ratios + >>> anchor_generator = AnchorGenerator( + >>> sizes=((8,), (16,), (32,), (64,), (128,)), + >>> aspect_ratios=((1.0,),) + >>> ) + >>> + >>> # put the pieces together inside a FCOS model + >>> model = FCOS( + >>> backbone, + >>> num_classes=80, + >>> anchor_generator=anchor_generator, + >>> ) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + """ + + __annotations__ = { + "box_coder": det_utils.BoxLinearCoder, + } + + def __init__( + self, + backbone: nn.Module, + num_classes: int, + # transform parameters + min_size: int = 800, + max_size: int = 1333, + image_mean: Optional[List[float]] = None, + image_std: Optional[List[float]] = None, + # Anchor parameters + anchor_generator: Optional[AnchorGenerator] = None, + head: Optional[nn.Module] = None, + center_sampling_radius: float = 1.5, + score_thresh: float = 0.2, + nms_thresh: float = 0.6, + detections_per_img: int = 100, + topk_candidates: int = 1000, + **kwargs, + ): + super().__init__() + _log_api_usage_once(self) + + if not hasattr(backbone, "out_channels"): + raise ValueError( + "backbone should contain an attribute out_channels " + "specifying the number of output channels (assumed to be the " + "same for all the levels)" + ) + self.backbone = backbone + + if not isinstance(anchor_generator, (AnchorGenerator, type(None))): + raise TypeError( + f"anchor_generator should be of type AnchorGenerator or None, instead got {type(anchor_generator)}" + ) + + if anchor_generator is None: + anchor_sizes = ((8,), (16,), (32,), (64,), (128,)) # equal to strides of multi-level feature map + aspect_ratios = ((1.0,),) * len(anchor_sizes) # set only one anchor + anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios) + self.anchor_generator = anchor_generator + if self.anchor_generator.num_anchors_per_location()[0] != 1: + raise ValueError( + f"anchor_generator.num_anchors_per_location()[0] should be 1 instead of {anchor_generator.num_anchors_per_location()[0]}" + ) + + if head is None: + head = FCOSHead(backbone.out_channels, anchor_generator.num_anchors_per_location()[0], num_classes) + self.head = head + + self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True) + + if image_mean is None: + image_mean = [0.485, 0.456, 0.406] + if image_std is None: + image_std = [0.229, 0.224, 0.225] + self.transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs) + + self.center_sampling_radius = center_sampling_radius + self.score_thresh = score_thresh + self.nms_thresh = nms_thresh + self.detections_per_img = detections_per_img + self.topk_candidates = topk_candidates + + # used only on torchscript mode + self._has_warned = False + + @torch.jit.unused + def eager_outputs( + self, losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]] + ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: + if self.training: + return losses + + return detections + + def compute_loss( + self, + targets: List[Dict[str, Tensor]], + head_outputs: Dict[str, Tensor], + anchors: List[Tensor], + num_anchors_per_level: List[int], + ) -> Dict[str, Tensor]: + matched_idxs = [] + for anchors_per_image, targets_per_image in zip(anchors, targets): + if targets_per_image["boxes"].numel() == 0: + matched_idxs.append( + torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device) + ) + continue + + gt_boxes = targets_per_image["boxes"] + gt_centers = (gt_boxes[:, :2] + gt_boxes[:, 2:]) / 2 # Nx2 + anchor_centers = (anchors_per_image[:, :2] + anchors_per_image[:, 2:]) / 2 # N + anchor_sizes = anchors_per_image[:, 2] - anchors_per_image[:, 0] + # center sampling: anchor point must be close enough to gt center. + pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max( + dim=2 + ).values < self.center_sampling_radius * anchor_sizes[:, None] + # compute pairwise distance between N points and M boxes + x, y = anchor_centers.unsqueeze(dim=2).unbind(dim=1) # (N, 1) + x0, y0, x1, y1 = gt_boxes.unsqueeze(dim=0).unbind(dim=2) # (1, M) + pairwise_dist = torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2) # (N, M) + + # anchor point must be inside gt + pairwise_match &= pairwise_dist.min(dim=2).values > 0 + + # each anchor is only responsible for certain scale range. + lower_bound = anchor_sizes * 4 + lower_bound[: num_anchors_per_level[0]] = 0 + upper_bound = anchor_sizes * 8 + upper_bound[-num_anchors_per_level[-1] :] = float("inf") + pairwise_dist = pairwise_dist.max(dim=2).values + pairwise_match &= (pairwise_dist > lower_bound[:, None]) & (pairwise_dist < upper_bound[:, None]) + + # match the GT box with minimum area, if there are multiple GT matches + gt_areas = (gt_boxes[:, 2] - gt_boxes[:, 0]) * (gt_boxes[:, 3] - gt_boxes[:, 1]) # N + pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :]) + min_values, matched_idx = pairwise_match.max(dim=1) # R, per-anchor match + matched_idx[min_values < 1e-5] = -1 # unmatched anchors are assigned -1 + + matched_idxs.append(matched_idx) + + return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs) + + def postprocess_detections( + self, head_outputs: Dict[str, List[Tensor]], anchors: List[List[Tensor]], image_shapes: List[Tuple[int, int]] + ) -> List[Dict[str, Tensor]]: + class_logits = head_outputs["cls_logits"] + box_regression = head_outputs["bbox_regression"] + box_ctrness = head_outputs["bbox_ctrness"] + + num_images = len(image_shapes) + + detections: List[Dict[str, Tensor]] = [] + + for index in range(num_images): + box_regression_per_image = [br[index] for br in box_regression] + logits_per_image = [cl[index] for cl in class_logits] + box_ctrness_per_image = [bc[index] for bc in box_ctrness] + anchors_per_image, image_shape = anchors[index], image_shapes[index] + + image_boxes = [] + image_scores = [] + image_labels = [] + + for box_regression_per_level, logits_per_level, box_ctrness_per_level, anchors_per_level in zip( + box_regression_per_image, logits_per_image, box_ctrness_per_image, anchors_per_image + ): + num_classes = logits_per_level.shape[-1] + + # remove low scoring boxes + scores_per_level = torch.sqrt( + torch.sigmoid(logits_per_level) * torch.sigmoid(box_ctrness_per_level) + ).flatten() + keep_idxs = scores_per_level > self.score_thresh + scores_per_level = scores_per_level[keep_idxs] + topk_idxs = torch.where(keep_idxs)[0] + + # keep only topk scoring predictions + num_topk = det_utils._topk_min(topk_idxs, self.topk_candidates, 0) + scores_per_level, idxs = scores_per_level.topk(num_topk) + topk_idxs = topk_idxs[idxs] + + anchor_idxs = torch.div(topk_idxs, num_classes, rounding_mode="floor") + labels_per_level = topk_idxs % num_classes + + boxes_per_level = self.box_coder.decode( + box_regression_per_level[anchor_idxs], anchors_per_level[anchor_idxs] + ) + boxes_per_level = box_ops.clip_boxes_to_image(boxes_per_level, image_shape) + + image_boxes.append(boxes_per_level) + image_scores.append(scores_per_level) + image_labels.append(labels_per_level) + + image_boxes = torch.cat(image_boxes, dim=0) + image_scores = torch.cat(image_scores, dim=0) + image_labels = torch.cat(image_labels, dim=0) + + # non-maximum suppression + keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh) + keep = keep[: self.detections_per_img] + + detections.append( + { + "boxes": image_boxes[keep], + "scores": image_scores[keep], + "labels": image_labels[keep], + } + ) + + return detections + + def forward( + self, + images: List[Tensor], + targets: Optional[List[Dict[str, Tensor]]] = None, + ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: + """ + Args: + images (list[Tensor]): images to be processed + targets (list[Dict[Tensor]]): ground-truth boxes present in the image (optional) + + Returns: + result (list[BoxList] or dict[Tensor]): the output from the model. + During training, it returns a dict[Tensor] which contains the losses. + During testing, it returns list[BoxList] contains additional fields + like `scores`, `labels` and `mask` (for Mask R-CNN models). + """ + if self.training: + + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + for target in targets: + boxes = target["boxes"] + torch._assert(isinstance(boxes, torch.Tensor), "Expected target boxes to be of type Tensor.") + torch._assert( + len(boxes.shape) == 2 and boxes.shape[-1] == 4, + f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.", + ) + + original_image_sizes: List[Tuple[int, int]] = [] + for img in images: + val = img.shape[-2:] + torch._assert( + len(val) == 2, + f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}", + ) + original_image_sizes.append((val[0], val[1])) + + # transform the input + images, targets = self.transform(images, targets) + + # Check for degenerate boxes + if targets is not None: + for target_idx, target in enumerate(targets): + boxes = target["boxes"] + degenerate_boxes = boxes[:, 2:] <= boxes[:, :2] + if degenerate_boxes.any(): + # print the first degenerate box + bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] + degen_bb: List[float] = boxes[bb_idx].tolist() + torch._assert( + False, + f"All bounding boxes should have positive height and width. Found invalid box {degen_bb} for target at index {target_idx}.", + ) + + # get the features from the backbone + features = self.backbone(images.tensors) + if isinstance(features, torch.Tensor): + features = OrderedDict([("0", features)]) + + features = list(features.values()) + + # compute the fcos heads outputs using the features + head_outputs = self.head(features) + + # create the set of anchors + anchors = self.anchor_generator(images, features) + # recover level sizes + num_anchors_per_level = [x.size(2) * x.size(3) for x in features] + + losses = {} + detections: List[Dict[str, Tensor]] = [] + if self.training: + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + # compute the losses + losses = self.compute_loss(targets, head_outputs, anchors, num_anchors_per_level) + else: + # split outputs per level + split_head_outputs: Dict[str, List[Tensor]] = {} + for k in head_outputs: + split_head_outputs[k] = list(head_outputs[k].split(num_anchors_per_level, dim=1)) + split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors] + + # compute the detections + detections = self.postprocess_detections(split_head_outputs, split_anchors, images.image_sizes) + detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) + + if torch.jit.is_scripting(): + if not self._has_warned: + warnings.warn("FCOS always returns a (Losses, Detections) tuple in scripting") + self._has_warned = True + return losses, detections + return self.eager_outputs(losses, detections) + + +class FCOS_ResNet50_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth", + transforms=ObjectDetection, + meta={ + "num_params": 32269600, + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#fcos-resnet-50-fpn", + "_metrics": { + "COCO-val2017": { + "box_map": 39.2, + } + }, + "_ops": 128.207, + "_file_size": 123.608, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FCOS_ResNet50_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def fcos_resnet50_fpn( + *, + weights: Optional[FCOS_ResNet50_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> FCOS: + """ + Constructs a FCOS model with a ResNet-50-FPN backbone. + + .. betastatus:: detection module + + Reference: `FCOS: Fully Convolutional One-Stage Object Detection `_. + `FCOS: A simple and strong anchor-free object detector `_. + + The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each + image, and should be in ``0-1`` range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the class label for each ground-truth box + + The model returns a ``Dict[Tensor]`` during training, containing the classification and regression + losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as + follows, where ``N`` is the number of detections: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the predicted labels for each detection + - scores (``Tensor[N]``): the scores of each detection + + For more details on the output, you may refer to :ref:`instance_seg_output`. + + Example: + + >>> model = torchvision.models.detection.fcos_resnet50_fpn(weights=FCOS_ResNet50_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for + the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) resnet layers starting + from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. Default: None + **kwargs: parameters passed to the ``torchvision.models.detection.FCOS`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.FCOS_ResNet50_FPN_Weights + :members: + """ + weights = FCOS_ResNet50_FPN_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + backbone = _resnet_fpn_extractor( + backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256) + ) + model = FCOS(backbone, num_classes, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/generalized_rcnn.py b/lib/python3.11/site-packages/torchvision/models/detection/generalized_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..b481265077fb5a582402d81aeb3516ffca063653 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/generalized_rcnn.py @@ -0,0 +1,118 @@ +""" +Implements the Generalized R-CNN framework +""" + +import warnings +from collections import OrderedDict +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch import nn, Tensor + +from ...utils import _log_api_usage_once + + +class GeneralizedRCNN(nn.Module): + """ + Main class for Generalized R-CNN. + + Args: + backbone (nn.Module): + rpn (nn.Module): + roi_heads (nn.Module): takes the features + the proposals from the RPN and computes + detections / masks from it. + transform (nn.Module): performs the data transformation from the inputs to feed into + the model + """ + + def __init__(self, backbone: nn.Module, rpn: nn.Module, roi_heads: nn.Module, transform: nn.Module) -> None: + super().__init__() + _log_api_usage_once(self) + self.transform = transform + self.backbone = backbone + self.rpn = rpn + self.roi_heads = roi_heads + # used only on torchscript mode + self._has_warned = False + + @torch.jit.unused + def eager_outputs(self, losses, detections): + # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Union[Dict[str, Tensor], List[Dict[str, Tensor]]] + if self.training: + return losses + + return detections + + def forward(self, images, targets=None): + # type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] + """ + Args: + images (list[Tensor]): images to be processed + targets (list[Dict[str, Tensor]]): ground-truth boxes present in the image (optional) + + Returns: + result (list[BoxList] or dict[Tensor]): the output from the model. + During training, it returns a dict[Tensor] which contains the losses. + During testing, it returns list[BoxList] contains additional fields + like `scores`, `labels` and `mask` (for Mask R-CNN models). + + """ + if self.training: + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + for target in targets: + boxes = target["boxes"] + if isinstance(boxes, torch.Tensor): + torch._assert( + len(boxes.shape) == 2 and boxes.shape[-1] == 4, + f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.", + ) + else: + torch._assert(False, f"Expected target boxes to be of type Tensor, got {type(boxes)}.") + + original_image_sizes: List[Tuple[int, int]] = [] + for img in images: + val = img.shape[-2:] + torch._assert( + len(val) == 2, + f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}", + ) + original_image_sizes.append((val[0], val[1])) + + images, targets = self.transform(images, targets) + + # Check for degenerate boxes + # TODO: Move this to a function + if targets is not None: + for target_idx, target in enumerate(targets): + boxes = target["boxes"] + degenerate_boxes = boxes[:, 2:] <= boxes[:, :2] + if degenerate_boxes.any(): + # print the first degenerate box + bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] + degen_bb: List[float] = boxes[bb_idx].tolist() + torch._assert( + False, + "All bounding boxes should have positive height and width." + f" Found invalid box {degen_bb} for target at index {target_idx}.", + ) + + features = self.backbone(images.tensors) + if isinstance(features, torch.Tensor): + features = OrderedDict([("0", features)]) + proposals, proposal_losses = self.rpn(images, features, targets) + detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets) + detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) # type: ignore[operator] + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + + if torch.jit.is_scripting(): + if not self._has_warned: + warnings.warn("RCNN always returns a (Losses, Detections) tuple in scripting") + self._has_warned = True + return losses, detections + else: + return self.eager_outputs(losses, detections) diff --git a/lib/python3.11/site-packages/torchvision/models/detection/image_list.py b/lib/python3.11/site-packages/torchvision/models/detection/image_list.py new file mode 100644 index 0000000000000000000000000000000000000000..583866557e4c9ec178e7cc268272db3de1698e41 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/image_list.py @@ -0,0 +1,25 @@ +from typing import List, Tuple + +import torch +from torch import Tensor + + +class ImageList: + """ + Structure that holds a list of images (of possibly + varying sizes) as a single tensor. + This works by padding the images to the same size, + and storing in a field the original sizes of each image + + Args: + tensors (tensor): Tensor containing images. + image_sizes (list[tuple[int, int]]): List of Tuples each containing size of images. + """ + + def __init__(self, tensors: Tensor, image_sizes: List[Tuple[int, int]]) -> None: + self.tensors = tensors + self.image_sizes = image_sizes + + def to(self, device: torch.device) -> "ImageList": + cast_tensor = self.tensors.to(device) + return ImageList(cast_tensor, self.image_sizes) diff --git a/lib/python3.11/site-packages/torchvision/models/detection/keypoint_rcnn.py b/lib/python3.11/site-packages/torchvision/models/detection/keypoint_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7ff0ea433a681064a11a22c3e276e253997772 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/keypoint_rcnn.py @@ -0,0 +1,474 @@ +from typing import Any, Optional + +import torch +from torch import nn +from torchvision.ops import MultiScaleRoIAlign + +from ...ops import misc as misc_nn_ops +from ...transforms._presets import ObjectDetection +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..resnet import resnet50, ResNet50_Weights +from ._utils import overwrite_eps +from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers +from .faster_rcnn import FasterRCNN + + +__all__ = [ + "KeypointRCNN", + "KeypointRCNN_ResNet50_FPN_Weights", + "keypointrcnn_resnet50_fpn", +] + + +class KeypointRCNN(FasterRCNN): + """ + Implements Keypoint R-CNN. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + - keypoints (FloatTensor[N, K, 3]): the K keypoints location for each of the N instances, in the + format [x, y, visibility], where visibility=0 means that the keypoint is not visible. + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses for both the RPN and the R-CNN, and the keypoint loss. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each image + - scores (Tensor[N]): the scores or each prediction + - keypoints (FloatTensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format. + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute, which indicates the number of output + channels that each feature map has (and it should be the same for all feature maps). + The backbone should return a single Tensor or and OrderedDict[Tensor]. + num_classes (int): number of output classes of the model (including the background). + If box_predictor is specified, num_classes should be None. + min_size (int): Images are rescaled before feeding them to the backbone: + we attempt to preserve the aspect ratio and scale the shorter edge + to ``min_size``. If the resulting longer edge exceeds ``max_size``, + then downscale so that the longer edge does not exceed ``max_size``. + This may result in the shorter edge beeing lower than ``min_size``. + max_size (int): See ``min_size``. + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. + rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN + rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training + rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing + rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training + rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing + rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals + rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training of the RPN. + rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be + considered as negative during training of the RPN. + rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN + for computing the loss + rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training + of the RPN + rpn_score_thresh (float): only return proposals with an objectness score greater than rpn_score_thresh + box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in + the locations indicated by the bounding boxes + box_head (nn.Module): module that takes the cropped feature maps as input + box_predictor (nn.Module): module that takes the output of box_head and returns the + classification logits and box regression deltas. + box_score_thresh (float): during inference, only return proposals with a classification score + greater than box_score_thresh + box_nms_thresh (float): NMS threshold for the prediction head. Used during inference + box_detections_per_img (int): maximum number of detections per image, for all classes. + box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be + considered as positive during training of the classification head + box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be + considered as negative during training of the classification head + box_batch_size_per_image (int): number of proposals that are sampled during training of the + classification head + box_positive_fraction (float): proportion of positive proposals in a mini-batch during training + of the classification head + bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the + bounding boxes + keypoint_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in + the locations indicated by the bounding boxes, which will be used for the keypoint head. + keypoint_head (nn.Module): module that takes the cropped feature maps as input + keypoint_predictor (nn.Module): module that takes the output of the keypoint_head and returns the + heatmap logits + + Example:: + + >>> import torch + >>> import torchvision + >>> from torchvision.models.detection import KeypointRCNN + >>> from torchvision.models.detection.anchor_utils import AnchorGenerator + >>> + >>> # load a pre-trained model for classification and return + >>> # only the features + >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features + >>> # KeypointRCNN needs to know the number of + >>> # output channels in a backbone. For mobilenet_v2, it's 1280, + >>> # so we need to add it here + >>> backbone.out_channels = 1280 + >>> + >>> # let's make the RPN generate 5 x 3 anchors per spatial + >>> # location, with 5 different sizes and 3 different aspect + >>> # ratios. We have a Tuple[Tuple[int]] because each feature + >>> # map could potentially have different sizes and + >>> # aspect ratios + >>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), + >>> aspect_ratios=((0.5, 1.0, 2.0),)) + >>> + >>> # let's define what are the feature maps that we will + >>> # use to perform the region of interest cropping, as well as + >>> # the size of the crop after rescaling. + >>> # if your backbone returns a Tensor, featmap_names is expected to + >>> # be ['0']. More generally, the backbone should return an + >>> # OrderedDict[Tensor], and in featmap_names you can choose which + >>> # feature maps to use. + >>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + >>> output_size=7, + >>> sampling_ratio=2) + >>> + >>> keypoint_roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + >>> output_size=14, + >>> sampling_ratio=2) + >>> # put the pieces together inside a KeypointRCNN model + >>> model = KeypointRCNN(backbone, + >>> num_classes=2, + >>> rpn_anchor_generator=anchor_generator, + >>> box_roi_pool=roi_pooler, + >>> keypoint_roi_pool=keypoint_roi_pooler) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + """ + + def __init__( + self, + backbone, + num_classes=None, + # transform parameters + min_size=None, + max_size=1333, + image_mean=None, + image_std=None, + # RPN parameters + rpn_anchor_generator=None, + rpn_head=None, + rpn_pre_nms_top_n_train=2000, + rpn_pre_nms_top_n_test=1000, + rpn_post_nms_top_n_train=2000, + rpn_post_nms_top_n_test=1000, + rpn_nms_thresh=0.7, + rpn_fg_iou_thresh=0.7, + rpn_bg_iou_thresh=0.3, + rpn_batch_size_per_image=256, + rpn_positive_fraction=0.5, + rpn_score_thresh=0.0, + # Box parameters + box_roi_pool=None, + box_head=None, + box_predictor=None, + box_score_thresh=0.05, + box_nms_thresh=0.5, + box_detections_per_img=100, + box_fg_iou_thresh=0.5, + box_bg_iou_thresh=0.5, + box_batch_size_per_image=512, + box_positive_fraction=0.25, + bbox_reg_weights=None, + # keypoint parameters + keypoint_roi_pool=None, + keypoint_head=None, + keypoint_predictor=None, + num_keypoints=None, + **kwargs, + ): + + if not isinstance(keypoint_roi_pool, (MultiScaleRoIAlign, type(None))): + raise TypeError( + "keypoint_roi_pool should be of type MultiScaleRoIAlign or None instead of {type(keypoint_roi_pool)}" + ) + if min_size is None: + min_size = (640, 672, 704, 736, 768, 800) + + if num_keypoints is not None: + if keypoint_predictor is not None: + raise ValueError("num_keypoints should be None when keypoint_predictor is specified") + else: + num_keypoints = 17 + + out_channels = backbone.out_channels + + if keypoint_roi_pool is None: + keypoint_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=14, sampling_ratio=2) + + if keypoint_head is None: + keypoint_layers = tuple(512 for _ in range(8)) + keypoint_head = KeypointRCNNHeads(out_channels, keypoint_layers) + + if keypoint_predictor is None: + keypoint_dim_reduced = 512 # == keypoint_layers[-1] + keypoint_predictor = KeypointRCNNPredictor(keypoint_dim_reduced, num_keypoints) + + super().__init__( + backbone, + num_classes, + # transform parameters + min_size, + max_size, + image_mean, + image_std, + # RPN-specific parameters + rpn_anchor_generator, + rpn_head, + rpn_pre_nms_top_n_train, + rpn_pre_nms_top_n_test, + rpn_post_nms_top_n_train, + rpn_post_nms_top_n_test, + rpn_nms_thresh, + rpn_fg_iou_thresh, + rpn_bg_iou_thresh, + rpn_batch_size_per_image, + rpn_positive_fraction, + rpn_score_thresh, + # Box parameters + box_roi_pool, + box_head, + box_predictor, + box_score_thresh, + box_nms_thresh, + box_detections_per_img, + box_fg_iou_thresh, + box_bg_iou_thresh, + box_batch_size_per_image, + box_positive_fraction, + bbox_reg_weights, + **kwargs, + ) + + self.roi_heads.keypoint_roi_pool = keypoint_roi_pool + self.roi_heads.keypoint_head = keypoint_head + self.roi_heads.keypoint_predictor = keypoint_predictor + + +class KeypointRCNNHeads(nn.Sequential): + def __init__(self, in_channels, layers): + d = [] + next_feature = in_channels + for out_channels in layers: + d.append(nn.Conv2d(next_feature, out_channels, 3, stride=1, padding=1)) + d.append(nn.ReLU(inplace=True)) + next_feature = out_channels + super().__init__(*d) + for m in self.children(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(m.bias, 0) + + +class KeypointRCNNPredictor(nn.Module): + def __init__(self, in_channels, num_keypoints): + super().__init__() + input_features = in_channels + deconv_kernel = 4 + self.kps_score_lowres = nn.ConvTranspose2d( + input_features, + num_keypoints, + deconv_kernel, + stride=2, + padding=deconv_kernel // 2 - 1, + ) + nn.init.kaiming_normal_(self.kps_score_lowres.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(self.kps_score_lowres.bias, 0) + self.up_scale = 2 + self.out_channels = num_keypoints + + def forward(self, x): + x = self.kps_score_lowres(x) + return torch.nn.functional.interpolate( + x, scale_factor=float(self.up_scale), mode="bilinear", align_corners=False, recompute_scale_factor=False + ) + + +_COMMON_META = { + "categories": _COCO_PERSON_CATEGORIES, + "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES, + "min_size": (1, 1), +} + + +class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum): + COCO_LEGACY = Weights( + url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 59137258, + "recipe": "https://github.com/pytorch/vision/issues/1606", + "_metrics": { + "COCO-val2017": { + "box_map": 50.6, + "kp_map": 61.1, + } + }, + "_ops": 133.924, + "_file_size": 226.054, + "_docs": """ + These weights were produced by following a similar training recipe as on the paper but use a checkpoint + from an early epoch. + """, + }, + ) + COCO_V1 = Weights( + url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 59137258, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#keypoint-r-cnn", + "_metrics": { + "COCO-val2017": { + "box_map": 54.6, + "kp_map": 65.0, + } + }, + "_ops": 137.42, + "_file_size": 226.054, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: KeypointRCNN_ResNet50_FPN_Weights.COCO_LEGACY + if kwargs["pretrained"] == "legacy" + else KeypointRCNN_ResNet50_FPN_Weights.COCO_V1, + ), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def keypointrcnn_resnet50_fpn( + *, + weights: Optional[KeypointRCNN_ResNet50_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + num_keypoints: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> KeypointRCNN: + """ + Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone. + + .. betastatus:: detection module + + Reference: `Mask R-CNN `__. + + The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each + image, and should be in ``0-1`` range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the class label for each ground-truth box + - keypoints (``FloatTensor[N, K, 3]``): the ``K`` keypoints location for each of the ``N`` instances, in the + format ``[x, y, visibility]``, where ``visibility=0`` means that the keypoint is not visible. + + The model returns a ``Dict[Tensor]`` during training, containing the classification and regression + losses for both the RPN and the R-CNN, and the keypoint loss. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as + follows, where ``N`` is the number of detected instances: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the predicted labels for each instance + - scores (``Tensor[N]``): the scores or each instance + - keypoints (``FloatTensor[N, K, 3]``): the locations of the predicted keypoints, in ``[x, y, v]`` format. + + For more details on the output, you may refer to :ref:`instance_seg_output`. + + Keypoint R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size. + + Example:: + + >>> model = torchvision.models.detection.keypointrcnn_resnet50_fpn(weights=KeypointRCNN_ResNet50_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + >>> + >>> # optionally, if you want to export the model to ONNX: + >>> torch.onnx.export(model, x, "keypoint_rcnn.onnx", opset_version = 11) + + Args: + weights (:class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr + num_classes (int, optional): number of output classes of the model (including the background) + num_keypoints (int, optional): number of keypoints + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block. + Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is + passed (the default) this value is set to 3. + + .. autoclass:: torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights + :members: + """ + weights = KeypointRCNN_ResNet50_FPN_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + num_keypoints = _ovewrite_value_param("num_keypoints", num_keypoints, len(weights.meta["keypoint_names"])) + else: + if num_classes is None: + num_classes = 2 + if num_keypoints is None: + num_keypoints = 17 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers) + model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if weights == KeypointRCNN_ResNet50_FPN_Weights.COCO_V1: + overwrite_eps(model, 0.0) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/mask_rcnn.py b/lib/python3.11/site-packages/torchvision/models/detection/mask_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..cdabbfd26ca8bbefaefdb6fb8b098afac217b595 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/mask_rcnn.py @@ -0,0 +1,590 @@ +from collections import OrderedDict +from typing import Any, Callable, Optional + +from torch import nn +from torchvision.ops import MultiScaleRoIAlign + +from ...ops import misc as misc_nn_ops +from ...transforms._presets import ObjectDetection +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..resnet import resnet50, ResNet50_Weights +from ._utils import overwrite_eps +from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers +from .faster_rcnn import _default_anchorgen, FasterRCNN, FastRCNNConvFCHead, RPNHead + + +__all__ = [ + "MaskRCNN", + "MaskRCNN_ResNet50_FPN_Weights", + "MaskRCNN_ResNet50_FPN_V2_Weights", + "maskrcnn_resnet50_fpn", + "maskrcnn_resnet50_fpn_v2", +] + + +class MaskRCNN(FasterRCNN): + """ + Implements Mask R-CNN. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + - masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses for both the RPN and the R-CNN, and the mask loss. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows: + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each image + - scores (Tensor[N]): the scores or each prediction + - masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to + obtain the final segmentation masks, the soft masks can be thresholded, generally + with a value of 0.5 (mask >= 0.5) + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute, which indicates the number of output + channels that each feature map has (and it should be the same for all feature maps). + The backbone should return a single Tensor or and OrderedDict[Tensor]. + num_classes (int): number of output classes of the model (including the background). + If box_predictor is specified, num_classes should be None. + min_size (int): Images are rescaled before feeding them to the backbone: + we attempt to preserve the aspect ratio and scale the shorter edge + to ``min_size``. If the resulting longer edge exceeds ``max_size``, + then downscale so that the longer edge does not exceed ``max_size``. + This may result in the shorter edge beeing lower than ``min_size``. + max_size (int): See ``min_size``. + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. + rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN + rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training + rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing + rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training + rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing + rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals + rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training of the RPN. + rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be + considered as negative during training of the RPN. + rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN + for computing the loss + rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training + of the RPN + rpn_score_thresh (float): only return proposals with an objectness score greater than rpn_score_thresh + box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in + the locations indicated by the bounding boxes + box_head (nn.Module): module that takes the cropped feature maps as input + box_predictor (nn.Module): module that takes the output of box_head and returns the + classification logits and box regression deltas. + box_score_thresh (float): during inference, only return proposals with a classification score + greater than box_score_thresh + box_nms_thresh (float): NMS threshold for the prediction head. Used during inference + box_detections_per_img (int): maximum number of detections per image, for all classes. + box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be + considered as positive during training of the classification head + box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be + considered as negative during training of the classification head + box_batch_size_per_image (int): number of proposals that are sampled during training of the + classification head + box_positive_fraction (float): proportion of positive proposals in a mini-batch during training + of the classification head + bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the + bounding boxes + mask_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in + the locations indicated by the bounding boxes, which will be used for the mask head. + mask_head (nn.Module): module that takes the cropped feature maps as input + mask_predictor (nn.Module): module that takes the output of the mask_head and returns the + segmentation mask logits + + Example:: + + >>> import torch + >>> import torchvision + >>> from torchvision.models.detection import MaskRCNN + >>> from torchvision.models.detection.anchor_utils import AnchorGenerator + >>> + >>> # load a pre-trained model for classification and return + >>> # only the features + >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features + >>> # MaskRCNN needs to know the number of + >>> # output channels in a backbone. For mobilenet_v2, it's 1280 + >>> # so we need to add it here, + >>> backbone.out_channels = 1280 + >>> + >>> # let's make the RPN generate 5 x 3 anchors per spatial + >>> # location, with 5 different sizes and 3 different aspect + >>> # ratios. We have a Tuple[Tuple[int]] because each feature + >>> # map could potentially have different sizes and + >>> # aspect ratios + >>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), + >>> aspect_ratios=((0.5, 1.0, 2.0),)) + >>> + >>> # let's define what are the feature maps that we will + >>> # use to perform the region of interest cropping, as well as + >>> # the size of the crop after rescaling. + >>> # if your backbone returns a Tensor, featmap_names is expected to + >>> # be ['0']. More generally, the backbone should return an + >>> # OrderedDict[Tensor], and in featmap_names you can choose which + >>> # feature maps to use. + >>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + >>> output_size=7, + >>> sampling_ratio=2) + >>> + >>> mask_roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + >>> output_size=14, + >>> sampling_ratio=2) + >>> # put the pieces together inside a MaskRCNN model + >>> model = MaskRCNN(backbone, + >>> num_classes=2, + >>> rpn_anchor_generator=anchor_generator, + >>> box_roi_pool=roi_pooler, + >>> mask_roi_pool=mask_roi_pooler) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + """ + + def __init__( + self, + backbone, + num_classes=None, + # transform parameters + min_size=800, + max_size=1333, + image_mean=None, + image_std=None, + # RPN parameters + rpn_anchor_generator=None, + rpn_head=None, + rpn_pre_nms_top_n_train=2000, + rpn_pre_nms_top_n_test=1000, + rpn_post_nms_top_n_train=2000, + rpn_post_nms_top_n_test=1000, + rpn_nms_thresh=0.7, + rpn_fg_iou_thresh=0.7, + rpn_bg_iou_thresh=0.3, + rpn_batch_size_per_image=256, + rpn_positive_fraction=0.5, + rpn_score_thresh=0.0, + # Box parameters + box_roi_pool=None, + box_head=None, + box_predictor=None, + box_score_thresh=0.05, + box_nms_thresh=0.5, + box_detections_per_img=100, + box_fg_iou_thresh=0.5, + box_bg_iou_thresh=0.5, + box_batch_size_per_image=512, + box_positive_fraction=0.25, + bbox_reg_weights=None, + # Mask parameters + mask_roi_pool=None, + mask_head=None, + mask_predictor=None, + **kwargs, + ): + + if not isinstance(mask_roi_pool, (MultiScaleRoIAlign, type(None))): + raise TypeError( + f"mask_roi_pool should be of type MultiScaleRoIAlign or None instead of {type(mask_roi_pool)}" + ) + + if num_classes is not None: + if mask_predictor is not None: + raise ValueError("num_classes should be None when mask_predictor is specified") + + out_channels = backbone.out_channels + + if mask_roi_pool is None: + mask_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=14, sampling_ratio=2) + + if mask_head is None: + mask_layers = (256, 256, 256, 256) + mask_dilation = 1 + mask_head = MaskRCNNHeads(out_channels, mask_layers, mask_dilation) + + if mask_predictor is None: + mask_predictor_in_channels = 256 # == mask_layers[-1] + mask_dim_reduced = 256 + mask_predictor = MaskRCNNPredictor(mask_predictor_in_channels, mask_dim_reduced, num_classes) + + super().__init__( + backbone, + num_classes, + # transform parameters + min_size, + max_size, + image_mean, + image_std, + # RPN-specific parameters + rpn_anchor_generator, + rpn_head, + rpn_pre_nms_top_n_train, + rpn_pre_nms_top_n_test, + rpn_post_nms_top_n_train, + rpn_post_nms_top_n_test, + rpn_nms_thresh, + rpn_fg_iou_thresh, + rpn_bg_iou_thresh, + rpn_batch_size_per_image, + rpn_positive_fraction, + rpn_score_thresh, + # Box parameters + box_roi_pool, + box_head, + box_predictor, + box_score_thresh, + box_nms_thresh, + box_detections_per_img, + box_fg_iou_thresh, + box_bg_iou_thresh, + box_batch_size_per_image, + box_positive_fraction, + bbox_reg_weights, + **kwargs, + ) + + self.roi_heads.mask_roi_pool = mask_roi_pool + self.roi_heads.mask_head = mask_head + self.roi_heads.mask_predictor = mask_predictor + + +class MaskRCNNHeads(nn.Sequential): + _version = 2 + + def __init__(self, in_channels, layers, dilation, norm_layer: Optional[Callable[..., nn.Module]] = None): + """ + Args: + in_channels (int): number of input channels + layers (list): feature dimensions of each FCN layer + dilation (int): dilation rate of kernel + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + """ + blocks = [] + next_feature = in_channels + for layer_features in layers: + blocks.append( + misc_nn_ops.Conv2dNormActivation( + next_feature, + layer_features, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + ) + ) + next_feature = layer_features + + super().__init__(*blocks) + for layer in self.modules(): + if isinstance(layer, nn.Conv2d): + nn.init.kaiming_normal_(layer.weight, mode="fan_out", nonlinearity="relu") + if layer.bias is not None: + nn.init.zeros_(layer.bias) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + num_blocks = len(self) + for i in range(num_blocks): + for type in ["weight", "bias"]: + old_key = f"{prefix}mask_fcn{i+1}.{type}" + new_key = f"{prefix}{i}.0.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class MaskRCNNPredictor(nn.Sequential): + def __init__(self, in_channels, dim_reduced, num_classes): + super().__init__( + OrderedDict( + [ + ("conv5_mask", nn.ConvTranspose2d(in_channels, dim_reduced, 2, 2, 0)), + ("relu", nn.ReLU(inplace=True)), + ("mask_fcn_logits", nn.Conv2d(dim_reduced, num_classes, 1, 1, 0)), + ] + ) + ) + + for name, param in self.named_parameters(): + if "weight" in name: + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + # elif "bias" in name: + # nn.init.constant_(param, 0) + + +_COMMON_META = { + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), +} + + +class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 44401393, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#mask-r-cnn", + "_metrics": { + "COCO-val2017": { + "box_map": 37.9, + "mask_map": 34.6, + } + }, + "_ops": 134.38, + "_file_size": 169.84, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +class MaskRCNN_ResNet50_FPN_V2_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/maskrcnn_resnet50_fpn_v2_coco-73cbd019.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 46359409, + "recipe": "https://github.com/pytorch/vision/pull/5773", + "_metrics": { + "COCO-val2017": { + "box_map": 47.4, + "mask_map": 41.8, + } + }, + "_ops": 333.577, + "_file_size": 177.219, + "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", MaskRCNN_ResNet50_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def maskrcnn_resnet50_fpn( + *, + weights: Optional[MaskRCNN_ResNet50_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> MaskRCNN: + """Mask R-CNN model with a ResNet-50-FPN backbone from the `Mask R-CNN + `_ paper. + + .. betastatus:: detection module + + The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each + image, and should be in ``0-1`` range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the class label for each ground-truth box + - masks (``UInt8Tensor[N, H, W]``): the segmentation binary masks for each instance + + The model returns a ``Dict[Tensor]`` during training, containing the classification and regression + losses for both the RPN and the R-CNN, and the mask loss. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as + follows, where ``N`` is the number of detected instances: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the predicted labels for each instance + - scores (``Tensor[N]``): the scores or each instance + - masks (``UInt8Tensor[N, 1, H, W]``): the predicted masks for each instance, in ``0-1`` range. In order to + obtain the final segmentation masks, the soft masks can be thresholded, generally + with a value of 0.5 (``mask >= 0.5``) + + For more details on the output and on how to plot the masks, you may refer to :ref:`instance_seg_output`. + + Mask R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size. + + Example:: + + >>> model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + >>> + >>> # optionally, if you want to export the model to ONNX: + >>> torch.onnx.export(model, x, "mask_rcnn.onnx", opset_version = 11) + + Args: + weights (:class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.mask_rcnn.MaskRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.MaskRCNN_ResNet50_FPN_Weights + :members: + """ + weights = MaskRCNN_ResNet50_FPN_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers) + model = MaskRCNN(backbone, num_classes=num_classes, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if weights == MaskRCNN_ResNet50_FPN_Weights.COCO_V1: + overwrite_eps(model, 0.0) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", MaskRCNN_ResNet50_FPN_V2_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def maskrcnn_resnet50_fpn_v2( + *, + weights: Optional[MaskRCNN_ResNet50_FPN_V2_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = None, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> MaskRCNN: + """Improved Mask R-CNN model with a ResNet-50-FPN backbone from the `Benchmarking Detection Transfer + Learning with Vision Transformers `_ paper. + + .. betastatus:: detection module + + :func:`~torchvision.models.detection.maskrcnn_resnet50_fpn` for more details. + + Args: + weights (:class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_V2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.MaskRCNN_ResNet50_FPN_V2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from + final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are + trainable. If ``None`` is passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.mask_rcnn.MaskRCNN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.MaskRCNN_ResNet50_FPN_V2_Weights + :members: + """ + weights = MaskRCNN_ResNet50_FPN_V2_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + + backbone = resnet50(weights=weights_backbone, progress=progress) + backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers, norm_layer=nn.BatchNorm2d) + rpn_anchor_generator = _default_anchorgen() + rpn_head = RPNHead(backbone.out_channels, rpn_anchor_generator.num_anchors_per_location()[0], conv_depth=2) + box_head = FastRCNNConvFCHead( + (backbone.out_channels, 7, 7), [256, 256, 256, 256], [1024], norm_layer=nn.BatchNorm2d + ) + mask_head = MaskRCNNHeads(backbone.out_channels, [256, 256, 256, 256], 1, norm_layer=nn.BatchNorm2d) + model = MaskRCNN( + backbone, + num_classes=num_classes, + rpn_anchor_generator=rpn_anchor_generator, + rpn_head=rpn_head, + box_head=box_head, + mask_head=mask_head, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/retinanet.py b/lib/python3.11/site-packages/torchvision/models/detection/retinanet.py new file mode 100644 index 0000000000000000000000000000000000000000..a8cc7755014b6010965108a46c080f71b2d609db --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/retinanet.py @@ -0,0 +1,903 @@ +import math +import warnings +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Tuple + +import torch +from torch import nn, Tensor + +from ...ops import boxes as box_ops, misc as misc_nn_ops, sigmoid_focal_loss +from ...ops.feature_pyramid_network import LastLevelP6P7 +from ...transforms._presets import ObjectDetection +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..resnet import resnet50, ResNet50_Weights +from . import _utils as det_utils +from ._utils import _box_loss, overwrite_eps +from .anchor_utils import AnchorGenerator +from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers +from .transform import GeneralizedRCNNTransform + + +__all__ = [ + "RetinaNet", + "RetinaNet_ResNet50_FPN_Weights", + "RetinaNet_ResNet50_FPN_V2_Weights", + "retinanet_resnet50_fpn", + "retinanet_resnet50_fpn_v2", +] + + +def _sum(x: List[Tensor]) -> Tensor: + res = x[0] + for i in x[1:]: + res = res + i + return res + + +def _v1_to_v2_weights(state_dict, prefix): + for i in range(4): + for type in ["weight", "bias"]: + old_key = f"{prefix}conv.{2*i}.{type}" + new_key = f"{prefix}conv.{i}.0.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + +def _default_anchorgen(): + anchor_sizes = tuple((x, int(x * 2 ** (1.0 / 3)), int(x * 2 ** (2.0 / 3))) for x in [32, 64, 128, 256, 512]) + aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes) + anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios) + return anchor_generator + + +class RetinaNetHead(nn.Module): + """ + A regression and classification head for use in RetinaNet. + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + num_classes (int): number of classes to be predicted + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + """ + + def __init__(self, in_channels, num_anchors, num_classes, norm_layer: Optional[Callable[..., nn.Module]] = None): + super().__init__() + self.classification_head = RetinaNetClassificationHead( + in_channels, num_anchors, num_classes, norm_layer=norm_layer + ) + self.regression_head = RetinaNetRegressionHead(in_channels, num_anchors, norm_layer=norm_layer) + + def compute_loss(self, targets, head_outputs, anchors, matched_idxs): + # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor], List[Tensor]) -> Dict[str, Tensor] + return { + "classification": self.classification_head.compute_loss(targets, head_outputs, matched_idxs), + "bbox_regression": self.regression_head.compute_loss(targets, head_outputs, anchors, matched_idxs), + } + + def forward(self, x): + # type: (List[Tensor]) -> Dict[str, Tensor] + return {"cls_logits": self.classification_head(x), "bbox_regression": self.regression_head(x)} + + +class RetinaNetClassificationHead(nn.Module): + """ + A classification head for use in RetinaNet. + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + num_classes (int): number of classes to be predicted + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + """ + + _version = 2 + + def __init__( + self, + in_channels, + num_anchors, + num_classes, + prior_probability=0.01, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ): + super().__init__() + + conv = [] + for _ in range(4): + conv.append(misc_nn_ops.Conv2dNormActivation(in_channels, in_channels, norm_layer=norm_layer)) + self.conv = nn.Sequential(*conv) + + for layer in self.conv.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, std=0.01) + if layer.bias is not None: + torch.nn.init.constant_(layer.bias, 0) + + self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1) + torch.nn.init.normal_(self.cls_logits.weight, std=0.01) + torch.nn.init.constant_(self.cls_logits.bias, -math.log((1 - prior_probability) / prior_probability)) + + self.num_classes = num_classes + self.num_anchors = num_anchors + + # This is to fix using det_utils.Matcher.BETWEEN_THRESHOLDS in TorchScript. + # TorchScript doesn't support class attributes. + # https://github.com/pytorch/vision/pull/1697#issuecomment-630255584 + self.BETWEEN_THRESHOLDS = det_utils.Matcher.BETWEEN_THRESHOLDS + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + _v1_to_v2_weights(state_dict, prefix) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + def compute_loss(self, targets, head_outputs, matched_idxs): + # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor]) -> Tensor + losses = [] + + cls_logits = head_outputs["cls_logits"] + + for targets_per_image, cls_logits_per_image, matched_idxs_per_image in zip(targets, cls_logits, matched_idxs): + # determine only the foreground + foreground_idxs_per_image = matched_idxs_per_image >= 0 + num_foreground = foreground_idxs_per_image.sum() + + # create the target classification + gt_classes_target = torch.zeros_like(cls_logits_per_image) + gt_classes_target[ + foreground_idxs_per_image, + targets_per_image["labels"][matched_idxs_per_image[foreground_idxs_per_image]], + ] = 1.0 + + # find indices for which anchors should be ignored + valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS + + # compute the classification loss + losses.append( + sigmoid_focal_loss( + cls_logits_per_image[valid_idxs_per_image], + gt_classes_target[valid_idxs_per_image], + reduction="sum", + ) + / max(1, num_foreground) + ) + + return _sum(losses) / len(targets) + + def forward(self, x): + # type: (List[Tensor]) -> Tensor + all_cls_logits = [] + + for features in x: + cls_logits = self.conv(features) + cls_logits = self.cls_logits(cls_logits) + + # Permute classification output from (N, A * K, H, W) to (N, HWA, K). + N, _, H, W = cls_logits.shape + cls_logits = cls_logits.view(N, -1, self.num_classes, H, W) + cls_logits = cls_logits.permute(0, 3, 4, 1, 2) + cls_logits = cls_logits.reshape(N, -1, self.num_classes) # Size=(N, HWA, 4) + + all_cls_logits.append(cls_logits) + + return torch.cat(all_cls_logits, dim=1) + + +class RetinaNetRegressionHead(nn.Module): + """ + A regression head for use in RetinaNet. + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + """ + + _version = 2 + + __annotations__ = { + "box_coder": det_utils.BoxCoder, + } + + def __init__(self, in_channels, num_anchors, norm_layer: Optional[Callable[..., nn.Module]] = None): + super().__init__() + + conv = [] + for _ in range(4): + conv.append(misc_nn_ops.Conv2dNormActivation(in_channels, in_channels, norm_layer=norm_layer)) + self.conv = nn.Sequential(*conv) + + self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) + torch.nn.init.normal_(self.bbox_reg.weight, std=0.01) + torch.nn.init.zeros_(self.bbox_reg.bias) + + for layer in self.conv.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, std=0.01) + if layer.bias is not None: + torch.nn.init.zeros_(layer.bias) + + self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) + self._loss_type = "l1" + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + _v1_to_v2_weights(state_dict, prefix) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + def compute_loss(self, targets, head_outputs, anchors, matched_idxs): + # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor], List[Tensor]) -> Tensor + losses = [] + + bbox_regression = head_outputs["bbox_regression"] + + for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in zip( + targets, bbox_regression, anchors, matched_idxs + ): + # determine only the foreground indices, ignore the rest + foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0] + num_foreground = foreground_idxs_per_image.numel() + + # select only the foreground boxes + matched_gt_boxes_per_image = targets_per_image["boxes"][matched_idxs_per_image[foreground_idxs_per_image]] + bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :] + anchors_per_image = anchors_per_image[foreground_idxs_per_image, :] + + # compute the loss + losses.append( + _box_loss( + self._loss_type, + self.box_coder, + anchors_per_image, + matched_gt_boxes_per_image, + bbox_regression_per_image, + ) + / max(1, num_foreground) + ) + + return _sum(losses) / max(1, len(targets)) + + def forward(self, x): + # type: (List[Tensor]) -> Tensor + all_bbox_regression = [] + + for features in x: + bbox_regression = self.conv(features) + bbox_regression = self.bbox_reg(bbox_regression) + + # Permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4). + N, _, H, W = bbox_regression.shape + bbox_regression = bbox_regression.view(N, -1, 4, H, W) + bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2) + bbox_regression = bbox_regression.reshape(N, -1, 4) # Size=(N, HWA, 4) + + all_bbox_regression.append(bbox_regression) + + return torch.cat(all_bbox_regression, dim=1) + + +class RetinaNet(nn.Module): + """ + Implements RetinaNet. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows: + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each image + - scores (Tensor[N]): the scores for each prediction + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute, which indicates the number of output + channels that each feature map has (and it should be the same for all feature maps). + The backbone should return a single Tensor or an OrderedDict[Tensor]. + num_classes (int): number of output classes of the model (including the background). + min_size (int): Images are rescaled before feeding them to the backbone: + we attempt to preserve the aspect ratio and scale the shorter edge + to ``min_size``. If the resulting longer edge exceeds ``max_size``, + then downscale so that the longer edge does not exceed ``max_size``. + This may result in the shorter edge beeing lower than ``min_size``. + max_size (int): See ``min_size``. + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. + head (nn.Module): Module run on top of the feature pyramid. + Defaults to a module containing a classification and regression module. + score_thresh (float): Score threshold used for postprocessing the detections. + nms_thresh (float): NMS threshold used for postprocessing the detections. + detections_per_img (int): Number of best detections to keep after NMS. + fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training. + bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be + considered as negative during training. + topk_candidates (int): Number of best detections to keep before NMS. + + Example: + + >>> import torch + >>> import torchvision + >>> from torchvision.models.detection import RetinaNet + >>> from torchvision.models.detection.anchor_utils import AnchorGenerator + >>> # load a pre-trained model for classification and return + >>> # only the features + >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features + >>> # RetinaNet needs to know the number of + >>> # output channels in a backbone. For mobilenet_v2, it's 1280, + >>> # so we need to add it here + >>> backbone.out_channels = 1280 + >>> + >>> # let's make the network generate 5 x 3 anchors per spatial + >>> # location, with 5 different sizes and 3 different aspect + >>> # ratios. We have a Tuple[Tuple[int]] because each feature + >>> # map could potentially have different sizes and + >>> # aspect ratios + >>> anchor_generator = AnchorGenerator( + >>> sizes=((32, 64, 128, 256, 512),), + >>> aspect_ratios=((0.5, 1.0, 2.0),) + >>> ) + >>> + >>> # put the pieces together inside a RetinaNet model + >>> model = RetinaNet(backbone, + >>> num_classes=2, + >>> anchor_generator=anchor_generator) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + """ + + __annotations__ = { + "box_coder": det_utils.BoxCoder, + "proposal_matcher": det_utils.Matcher, + } + + def __init__( + self, + backbone, + num_classes, + # transform parameters + min_size=800, + max_size=1333, + image_mean=None, + image_std=None, + # Anchor parameters + anchor_generator=None, + head=None, + proposal_matcher=None, + score_thresh=0.05, + nms_thresh=0.5, + detections_per_img=300, + fg_iou_thresh=0.5, + bg_iou_thresh=0.4, + topk_candidates=1000, + **kwargs, + ): + super().__init__() + _log_api_usage_once(self) + + if not hasattr(backbone, "out_channels"): + raise ValueError( + "backbone should contain an attribute out_channels " + "specifying the number of output channels (assumed to be the " + "same for all the levels)" + ) + self.backbone = backbone + + if not isinstance(anchor_generator, (AnchorGenerator, type(None))): + raise TypeError( + f"anchor_generator should be of type AnchorGenerator or None instead of {type(anchor_generator)}" + ) + + if anchor_generator is None: + anchor_generator = _default_anchorgen() + self.anchor_generator = anchor_generator + + if head is None: + head = RetinaNetHead(backbone.out_channels, anchor_generator.num_anchors_per_location()[0], num_classes) + self.head = head + + if proposal_matcher is None: + proposal_matcher = det_utils.Matcher( + fg_iou_thresh, + bg_iou_thresh, + allow_low_quality_matches=True, + ) + self.proposal_matcher = proposal_matcher + + self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) + + if image_mean is None: + image_mean = [0.485, 0.456, 0.406] + if image_std is None: + image_std = [0.229, 0.224, 0.225] + self.transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs) + + self.score_thresh = score_thresh + self.nms_thresh = nms_thresh + self.detections_per_img = detections_per_img + self.topk_candidates = topk_candidates + + # used only on torchscript mode + self._has_warned = False + + @torch.jit.unused + def eager_outputs(self, losses, detections): + # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] + if self.training: + return losses + + return detections + + def compute_loss(self, targets, head_outputs, anchors): + # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor]) -> Dict[str, Tensor] + matched_idxs = [] + for anchors_per_image, targets_per_image in zip(anchors, targets): + if targets_per_image["boxes"].numel() == 0: + matched_idxs.append( + torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device) + ) + continue + + match_quality_matrix = box_ops.box_iou(targets_per_image["boxes"], anchors_per_image) + matched_idxs.append(self.proposal_matcher(match_quality_matrix)) + + return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs) + + def postprocess_detections(self, head_outputs, anchors, image_shapes): + # type: (Dict[str, List[Tensor]], List[List[Tensor]], List[Tuple[int, int]]) -> List[Dict[str, Tensor]] + class_logits = head_outputs["cls_logits"] + box_regression = head_outputs["bbox_regression"] + + num_images = len(image_shapes) + + detections: List[Dict[str, Tensor]] = [] + + for index in range(num_images): + box_regression_per_image = [br[index] for br in box_regression] + logits_per_image = [cl[index] for cl in class_logits] + anchors_per_image, image_shape = anchors[index], image_shapes[index] + + image_boxes = [] + image_scores = [] + image_labels = [] + + for box_regression_per_level, logits_per_level, anchors_per_level in zip( + box_regression_per_image, logits_per_image, anchors_per_image + ): + num_classes = logits_per_level.shape[-1] + + # remove low scoring boxes + scores_per_level = torch.sigmoid(logits_per_level).flatten() + keep_idxs = scores_per_level > self.score_thresh + scores_per_level = scores_per_level[keep_idxs] + topk_idxs = torch.where(keep_idxs)[0] + + # keep only topk scoring predictions + num_topk = det_utils._topk_min(topk_idxs, self.topk_candidates, 0) + scores_per_level, idxs = scores_per_level.topk(num_topk) + topk_idxs = topk_idxs[idxs] + + anchor_idxs = torch.div(topk_idxs, num_classes, rounding_mode="floor") + labels_per_level = topk_idxs % num_classes + + boxes_per_level = self.box_coder.decode_single( + box_regression_per_level[anchor_idxs], anchors_per_level[anchor_idxs] + ) + boxes_per_level = box_ops.clip_boxes_to_image(boxes_per_level, image_shape) + + image_boxes.append(boxes_per_level) + image_scores.append(scores_per_level) + image_labels.append(labels_per_level) + + image_boxes = torch.cat(image_boxes, dim=0) + image_scores = torch.cat(image_scores, dim=0) + image_labels = torch.cat(image_labels, dim=0) + + # non-maximum suppression + keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh) + keep = keep[: self.detections_per_img] + + detections.append( + { + "boxes": image_boxes[keep], + "scores": image_scores[keep], + "labels": image_labels[keep], + } + ) + + return detections + + def forward(self, images, targets=None): + # type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] + """ + Args: + images (list[Tensor]): images to be processed + targets (list[Dict[Tensor]]): ground-truth boxes present in the image (optional) + + Returns: + result (list[BoxList] or dict[Tensor]): the output from the model. + During training, it returns a dict[Tensor] which contains the losses. + During testing, it returns list[BoxList] contains additional fields + like `scores`, `labels` and `mask` (for Mask R-CNN models). + + """ + if self.training: + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + for target in targets: + boxes = target["boxes"] + torch._assert(isinstance(boxes, torch.Tensor), "Expected target boxes to be of type Tensor.") + torch._assert( + len(boxes.shape) == 2 and boxes.shape[-1] == 4, + "Expected target boxes to be a tensor of shape [N, 4].", + ) + + # get the original image sizes + original_image_sizes: List[Tuple[int, int]] = [] + for img in images: + val = img.shape[-2:] + torch._assert( + len(val) == 2, + f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}", + ) + original_image_sizes.append((val[0], val[1])) + + # transform the input + images, targets = self.transform(images, targets) + + # Check for degenerate boxes + # TODO: Move this to a function + if targets is not None: + for target_idx, target in enumerate(targets): + boxes = target["boxes"] + degenerate_boxes = boxes[:, 2:] <= boxes[:, :2] + if degenerate_boxes.any(): + # print the first degenerate box + bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] + degen_bb: List[float] = boxes[bb_idx].tolist() + torch._assert( + False, + "All bounding boxes should have positive height and width." + f" Found invalid box {degen_bb} for target at index {target_idx}.", + ) + + # get the features from the backbone + features = self.backbone(images.tensors) + if isinstance(features, torch.Tensor): + features = OrderedDict([("0", features)]) + + # TODO: Do we want a list or a dict? + features = list(features.values()) + + # compute the retinanet heads outputs using the features + head_outputs = self.head(features) + + # create the set of anchors + anchors = self.anchor_generator(images, features) + + losses = {} + detections: List[Dict[str, Tensor]] = [] + if self.training: + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + # compute the losses + losses = self.compute_loss(targets, head_outputs, anchors) + else: + # recover level sizes + num_anchors_per_level = [x.size(2) * x.size(3) for x in features] + HW = 0 + for v in num_anchors_per_level: + HW += v + HWA = head_outputs["cls_logits"].size(1) + A = HWA // HW + num_anchors_per_level = [hw * A for hw in num_anchors_per_level] + + # split outputs per level + split_head_outputs: Dict[str, List[Tensor]] = {} + for k in head_outputs: + split_head_outputs[k] = list(head_outputs[k].split(num_anchors_per_level, dim=1)) + split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors] + + # compute the detections + detections = self.postprocess_detections(split_head_outputs, split_anchors, images.image_sizes) + detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) + + if torch.jit.is_scripting(): + if not self._has_warned: + warnings.warn("RetinaNet always returns a (Losses, Detections) tuple in scripting") + self._has_warned = True + return losses, detections + return self.eager_outputs(losses, detections) + + +_COMMON_META = { + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), +} + + +class RetinaNet_ResNet50_FPN_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 34014999, + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet", + "_metrics": { + "COCO-val2017": { + "box_map": 36.4, + } + }, + "_ops": 151.54, + "_file_size": 130.267, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +class RetinaNet_ResNet50_FPN_V2_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth", + transforms=ObjectDetection, + meta={ + **_COMMON_META, + "num_params": 38198935, + "recipe": "https://github.com/pytorch/vision/pull/5756", + "_metrics": { + "COCO-val2017": { + "box_map": 41.5, + } + }, + "_ops": 152.238, + "_file_size": 146.037, + "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", RetinaNet_ResNet50_FPN_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def retinanet_resnet50_fpn( + *, + weights: Optional[RetinaNet_ResNet50_FPN_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> RetinaNet: + """ + Constructs a RetinaNet model with a ResNet-50-FPN backbone. + + .. betastatus:: detection module + + Reference: `Focal Loss for Dense Object Detection `_. + + The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each + image, and should be in ``0-1`` range. Different images can have different sizes. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the class label for each ground-truth box + + The model returns a ``Dict[Tensor]`` during training, containing the classification and regression + losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as + follows, where ``N`` is the number of detections: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (``Int64Tensor[N]``): the predicted labels for each detection + - scores (``Tensor[N]``): the scores of each detection + + For more details on the output, you may refer to :ref:`instance_seg_output`. + + Example:: + + >>> model = torchvision.models.detection.retinanet_resnet50_fpn(weights=RetinaNet_ResNet50_FPN_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for + the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block. + Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is + passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.RetinaNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights + :members: + """ + weights = RetinaNet_ResNet50_FPN_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d + + backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer) + # skip P2 because it generates too many anchors (according to their paper) + backbone = _resnet_fpn_extractor( + backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256) + ) + model = RetinaNet(backbone, num_classes, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if weights == RetinaNet_ResNet50_FPN_Weights.COCO_V1: + overwrite_eps(model, 0.0) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", RetinaNet_ResNet50_FPN_V2_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def retinanet_resnet50_fpn_v2( + *, + weights: Optional[RetinaNet_ResNet50_FPN_V2_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[ResNet50_Weights] = None, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> RetinaNet: + """ + Constructs an improved RetinaNet model with a ResNet-50-FPN backbone. + + .. betastatus:: detection module + + Reference: `Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection + `_. + + :func:`~torchvision.models.detection.retinanet_resnet50_fpn` for more details. + + Args: + weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for + the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block. + Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is + passed (the default) this value is set to 3. + **kwargs: parameters passed to the ``torchvision.models.detection.RetinaNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights + :members: + """ + weights = RetinaNet_ResNet50_FPN_V2_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + is_trained = weights is not None or weights_backbone is not None + trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3) + + backbone = resnet50(weights=weights_backbone, progress=progress) + backbone = _resnet_fpn_extractor( + backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(2048, 256) + ) + anchor_generator = _default_anchorgen() + head = RetinaNetHead( + backbone.out_channels, + anchor_generator.num_anchors_per_location()[0], + num_classes, + norm_layer=partial(nn.GroupNorm, 32), + ) + head.regression_head._loss_type = "giou" + model = RetinaNet(backbone, num_classes, anchor_generator=anchor_generator, head=head, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/roi_heads.py b/lib/python3.11/site-packages/torchvision/models/detection/roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..51b210cb6f368c1f4914ffe99287efef6057cba4 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/roi_heads.py @@ -0,0 +1,876 @@ +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn, Tensor +from torchvision.ops import boxes as box_ops, roi_align + +from . import _utils as det_utils + + +def fastrcnn_loss(class_logits, box_regression, labels, regression_targets): + # type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor] + """ + Computes the loss for Faster R-CNN. + + Args: + class_logits (Tensor) + box_regression (Tensor) + labels (list[BoxList]) + regression_targets (Tensor) + + Returns: + classification_loss (Tensor) + box_loss (Tensor) + """ + + labels = torch.cat(labels, dim=0) + regression_targets = torch.cat(regression_targets, dim=0) + + classification_loss = F.cross_entropy(class_logits, labels) + + # get indices that correspond to the regression targets for + # the corresponding ground truth labels, to be used with + # advanced indexing + sampled_pos_inds_subset = torch.where(labels > 0)[0] + labels_pos = labels[sampled_pos_inds_subset] + N, num_classes = class_logits.shape + box_regression = box_regression.reshape(N, box_regression.size(-1) // 4, 4) + + box_loss = F.smooth_l1_loss( + box_regression[sampled_pos_inds_subset, labels_pos], + regression_targets[sampled_pos_inds_subset], + beta=1 / 9, + reduction="sum", + ) + box_loss = box_loss / labels.numel() + + return classification_loss, box_loss + + +def maskrcnn_inference(x, labels): + # type: (Tensor, List[Tensor]) -> List[Tensor] + """ + From the results of the CNN, post process the masks + by taking the mask corresponding to the class with max + probability (which are of fixed size and directly output + by the CNN) and return the masks in the mask field of the BoxList. + + Args: + x (Tensor): the mask logits + labels (list[BoxList]): bounding boxes that are used as + reference, one for ech image + + Returns: + results (list[BoxList]): one BoxList for each image, containing + the extra field mask + """ + mask_prob = x.sigmoid() + + # select masks corresponding to the predicted classes + num_masks = x.shape[0] + boxes_per_image = [label.shape[0] for label in labels] + labels = torch.cat(labels) + index = torch.arange(num_masks, device=labels.device) + mask_prob = mask_prob[index, labels][:, None] + mask_prob = mask_prob.split(boxes_per_image, dim=0) + + return mask_prob + + +def project_masks_on_boxes(gt_masks, boxes, matched_idxs, M): + # type: (Tensor, Tensor, Tensor, int) -> Tensor + """ + Given segmentation masks and the bounding boxes corresponding + to the location of the masks in the image, this function + crops and resizes the masks in the position defined by the + boxes. This prepares the masks for them to be fed to the + loss computation as the targets. + """ + matched_idxs = matched_idxs.to(boxes) + rois = torch.cat([matched_idxs[:, None], boxes], dim=1) + gt_masks = gt_masks[:, None].to(rois) + return roi_align(gt_masks, rois, (M, M), 1.0)[:, 0] + + +def maskrcnn_loss(mask_logits, proposals, gt_masks, gt_labels, mask_matched_idxs): + # type: (Tensor, List[Tensor], List[Tensor], List[Tensor], List[Tensor]) -> Tensor + """ + Args: + proposals (list[BoxList]) + mask_logits (Tensor) + targets (list[BoxList]) + + Return: + mask_loss (Tensor): scalar tensor containing the loss + """ + + discretization_size = mask_logits.shape[-1] + labels = [gt_label[idxs] for gt_label, idxs in zip(gt_labels, mask_matched_idxs)] + mask_targets = [ + project_masks_on_boxes(m, p, i, discretization_size) for m, p, i in zip(gt_masks, proposals, mask_matched_idxs) + ] + + labels = torch.cat(labels, dim=0) + mask_targets = torch.cat(mask_targets, dim=0) + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it separately + if mask_targets.numel() == 0: + return mask_logits.sum() * 0 + + mask_loss = F.binary_cross_entropy_with_logits( + mask_logits[torch.arange(labels.shape[0], device=labels.device), labels], mask_targets + ) + return mask_loss + + +def keypoints_to_heatmap(keypoints, rois, heatmap_size): + # type: (Tensor, Tensor, int) -> Tuple[Tensor, Tensor] + offset_x = rois[:, 0] + offset_y = rois[:, 1] + scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) + scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) + + offset_x = offset_x[:, None] + offset_y = offset_y[:, None] + scale_x = scale_x[:, None] + scale_y = scale_y[:, None] + + x = keypoints[..., 0] + y = keypoints[..., 1] + + x_boundary_inds = x == rois[:, 2][:, None] + y_boundary_inds = y == rois[:, 3][:, None] + + x = (x - offset_x) * scale_x + x = x.floor().long() + y = (y - offset_y) * scale_y + y = y.floor().long() + + x[x_boundary_inds] = heatmap_size - 1 + y[y_boundary_inds] = heatmap_size - 1 + + valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) + vis = keypoints[..., 2] > 0 + valid = (valid_loc & vis).long() + + lin_ind = y * heatmap_size + x + heatmaps = lin_ind * valid + + return heatmaps, valid + + +def _onnx_heatmaps_to_keypoints( + maps, maps_i, roi_map_width, roi_map_height, widths_i, heights_i, offset_x_i, offset_y_i +): + num_keypoints = torch.scalar_tensor(maps.size(1), dtype=torch.int64) + + width_correction = widths_i / roi_map_width + height_correction = heights_i / roi_map_height + + roi_map = F.interpolate( + maps_i[:, None], size=(int(roi_map_height), int(roi_map_width)), mode="bicubic", align_corners=False + )[:, 0] + + w = torch.scalar_tensor(roi_map.size(2), dtype=torch.int64) + pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1) + + x_int = pos % w + y_int = (pos - x_int) // w + + x = (torch.tensor(0.5, dtype=torch.float32) + x_int.to(dtype=torch.float32)) * width_correction.to( + dtype=torch.float32 + ) + y = (torch.tensor(0.5, dtype=torch.float32) + y_int.to(dtype=torch.float32)) * height_correction.to( + dtype=torch.float32 + ) + + xy_preds_i_0 = x + offset_x_i.to(dtype=torch.float32) + xy_preds_i_1 = y + offset_y_i.to(dtype=torch.float32) + xy_preds_i_2 = torch.ones(xy_preds_i_1.shape, dtype=torch.float32) + xy_preds_i = torch.stack( + [ + xy_preds_i_0.to(dtype=torch.float32), + xy_preds_i_1.to(dtype=torch.float32), + xy_preds_i_2.to(dtype=torch.float32), + ], + 0, + ) + + # TODO: simplify when indexing without rank will be supported by ONNX + base = num_keypoints * num_keypoints + num_keypoints + 1 + ind = torch.arange(num_keypoints) + ind = ind.to(dtype=torch.int64) * base + end_scores_i = ( + roi_map.index_select(1, y_int.to(dtype=torch.int64)) + .index_select(2, x_int.to(dtype=torch.int64)) + .view(-1) + .index_select(0, ind.to(dtype=torch.int64)) + ) + + return xy_preds_i, end_scores_i + + +@torch.jit._script_if_tracing +def _onnx_heatmaps_to_keypoints_loop( + maps, rois, widths_ceil, heights_ceil, widths, heights, offset_x, offset_y, num_keypoints +): + xy_preds = torch.zeros((0, 3, int(num_keypoints)), dtype=torch.float32, device=maps.device) + end_scores = torch.zeros((0, int(num_keypoints)), dtype=torch.float32, device=maps.device) + + for i in range(int(rois.size(0))): + xy_preds_i, end_scores_i = _onnx_heatmaps_to_keypoints( + maps, maps[i], widths_ceil[i], heights_ceil[i], widths[i], heights[i], offset_x[i], offset_y[i] + ) + xy_preds = torch.cat((xy_preds.to(dtype=torch.float32), xy_preds_i.unsqueeze(0).to(dtype=torch.float32)), 0) + end_scores = torch.cat( + (end_scores.to(dtype=torch.float32), end_scores_i.to(dtype=torch.float32).unsqueeze(0)), 0 + ) + return xy_preds, end_scores + + +def heatmaps_to_keypoints(maps, rois): + """Extract predicted keypoint locations from heatmaps. Output has shape + (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob) + for each keypoint. + """ + # This function converts a discrete image coordinate in a HEATMAP_SIZE x + # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain + # consistency with keypoints_to_heatmap_labels by using the conversion from + # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a + # continuous coordinate. + offset_x = rois[:, 0] + offset_y = rois[:, 1] + + widths = rois[:, 2] - rois[:, 0] + heights = rois[:, 3] - rois[:, 1] + widths = widths.clamp(min=1) + heights = heights.clamp(min=1) + widths_ceil = widths.ceil() + heights_ceil = heights.ceil() + + num_keypoints = maps.shape[1] + + if torchvision._is_tracing(): + xy_preds, end_scores = _onnx_heatmaps_to_keypoints_loop( + maps, + rois, + widths_ceil, + heights_ceil, + widths, + heights, + offset_x, + offset_y, + torch.scalar_tensor(num_keypoints, dtype=torch.int64), + ) + return xy_preds.permute(0, 2, 1), end_scores + + xy_preds = torch.zeros((len(rois), 3, num_keypoints), dtype=torch.float32, device=maps.device) + end_scores = torch.zeros((len(rois), num_keypoints), dtype=torch.float32, device=maps.device) + for i in range(len(rois)): + roi_map_width = int(widths_ceil[i].item()) + roi_map_height = int(heights_ceil[i].item()) + width_correction = widths[i] / roi_map_width + height_correction = heights[i] / roi_map_height + roi_map = F.interpolate( + maps[i][:, None], size=(roi_map_height, roi_map_width), mode="bicubic", align_corners=False + )[:, 0] + # roi_map_probs = scores_to_probs(roi_map.copy()) + w = roi_map.shape[2] + pos = roi_map.reshape(num_keypoints, -1).argmax(dim=1) + + x_int = pos % w + y_int = torch.div(pos - x_int, w, rounding_mode="floor") + # assert (roi_map_probs[k, y_int, x_int] == + # roi_map_probs[k, :, :].max()) + x = (x_int.float() + 0.5) * width_correction + y = (y_int.float() + 0.5) * height_correction + xy_preds[i, 0, :] = x + offset_x[i] + xy_preds[i, 1, :] = y + offset_y[i] + xy_preds[i, 2, :] = 1 + end_scores[i, :] = roi_map[torch.arange(num_keypoints, device=roi_map.device), y_int, x_int] + + return xy_preds.permute(0, 2, 1), end_scores + + +def keypointrcnn_loss(keypoint_logits, proposals, gt_keypoints, keypoint_matched_idxs): + # type: (Tensor, List[Tensor], List[Tensor], List[Tensor]) -> Tensor + N, K, H, W = keypoint_logits.shape + if H != W: + raise ValueError( + f"keypoint_logits height and width (last two elements of shape) should be equal. Instead got H = {H} and W = {W}" + ) + discretization_size = H + heatmaps = [] + valid = [] + for proposals_per_image, gt_kp_in_image, midx in zip(proposals, gt_keypoints, keypoint_matched_idxs): + kp = gt_kp_in_image[midx] + heatmaps_per_image, valid_per_image = keypoints_to_heatmap(kp, proposals_per_image, discretization_size) + heatmaps.append(heatmaps_per_image.view(-1)) + valid.append(valid_per_image.view(-1)) + + keypoint_targets = torch.cat(heatmaps, dim=0) + valid = torch.cat(valid, dim=0).to(dtype=torch.uint8) + valid = torch.where(valid)[0] + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it sepaartely + if keypoint_targets.numel() == 0 or len(valid) == 0: + return keypoint_logits.sum() * 0 + + keypoint_logits = keypoint_logits.view(N * K, H * W) + + keypoint_loss = F.cross_entropy(keypoint_logits[valid], keypoint_targets[valid]) + return keypoint_loss + + +def keypointrcnn_inference(x, boxes): + # type: (Tensor, List[Tensor]) -> Tuple[List[Tensor], List[Tensor]] + kp_probs = [] + kp_scores = [] + + boxes_per_image = [box.size(0) for box in boxes] + x2 = x.split(boxes_per_image, dim=0) + + for xx, bb in zip(x2, boxes): + kp_prob, scores = heatmaps_to_keypoints(xx, bb) + kp_probs.append(kp_prob) + kp_scores.append(scores) + + return kp_probs, kp_scores + + +def _onnx_expand_boxes(boxes, scale): + # type: (Tensor, float) -> Tensor + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half = w_half.to(dtype=torch.float32) * scale + h_half = h_half.to(dtype=torch.float32) * scale + + boxes_exp0 = x_c - w_half + boxes_exp1 = y_c - h_half + boxes_exp2 = x_c + w_half + boxes_exp3 = y_c + h_half + boxes_exp = torch.stack((boxes_exp0, boxes_exp1, boxes_exp2, boxes_exp3), 1) + return boxes_exp + + +# the next two functions should be merged inside Masker +# but are kept here for the moment while we need them +# temporarily for paste_mask_in_image +def expand_boxes(boxes, scale): + # type: (Tensor, float) -> Tensor + if torchvision._is_tracing(): + return _onnx_expand_boxes(boxes, scale) + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half *= scale + h_half *= scale + + boxes_exp = torch.zeros_like(boxes) + boxes_exp[:, 0] = x_c - w_half + boxes_exp[:, 2] = x_c + w_half + boxes_exp[:, 1] = y_c - h_half + boxes_exp[:, 3] = y_c + h_half + return boxes_exp + + +@torch.jit.unused +def expand_masks_tracing_scale(M, padding): + # type: (int, int) -> float + return torch.tensor(M + 2 * padding).to(torch.float32) / torch.tensor(M).to(torch.float32) + + +def expand_masks(mask, padding): + # type: (Tensor, int) -> Tuple[Tensor, float] + M = mask.shape[-1] + if torch._C._get_tracing_state(): # could not import is_tracing(), not sure why + scale = expand_masks_tracing_scale(M, padding) + else: + scale = float(M + 2 * padding) / M + padded_mask = F.pad(mask, (padding,) * 4) + return padded_mask, scale + + +def paste_mask_in_image(mask, box, im_h, im_w): + # type: (Tensor, Tensor, int, int) -> Tensor + TO_REMOVE = 1 + w = int(box[2] - box[0] + TO_REMOVE) + h = int(box[3] - box[1] + TO_REMOVE) + w = max(w, 1) + h = max(h, 1) + + # Set shape to [batchxCxHxW] + mask = mask.expand((1, 1, -1, -1)) + + # Resize mask + mask = F.interpolate(mask, size=(h, w), mode="bilinear", align_corners=False) + mask = mask[0][0] + + im_mask = torch.zeros((im_h, im_w), dtype=mask.dtype, device=mask.device) + x_0 = max(box[0], 0) + x_1 = min(box[2] + 1, im_w) + y_0 = max(box[1], 0) + y_1 = min(box[3] + 1, im_h) + + im_mask[y_0:y_1, x_0:x_1] = mask[(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])] + return im_mask + + +def _onnx_paste_mask_in_image(mask, box, im_h, im_w): + one = torch.ones(1, dtype=torch.int64) + zero = torch.zeros(1, dtype=torch.int64) + + w = box[2] - box[0] + one + h = box[3] - box[1] + one + w = torch.max(torch.cat((w, one))) + h = torch.max(torch.cat((h, one))) + + # Set shape to [batchxCxHxW] + mask = mask.expand((1, 1, mask.size(0), mask.size(1))) + + # Resize mask + mask = F.interpolate(mask, size=(int(h), int(w)), mode="bilinear", align_corners=False) + mask = mask[0][0] + + x_0 = torch.max(torch.cat((box[0].unsqueeze(0), zero))) + x_1 = torch.min(torch.cat((box[2].unsqueeze(0) + one, im_w.unsqueeze(0)))) + y_0 = torch.max(torch.cat((box[1].unsqueeze(0), zero))) + y_1 = torch.min(torch.cat((box[3].unsqueeze(0) + one, im_h.unsqueeze(0)))) + + unpaded_im_mask = mask[(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])] + + # TODO : replace below with a dynamic padding when support is added in ONNX + + # pad y + zeros_y0 = torch.zeros(y_0, unpaded_im_mask.size(1)) + zeros_y1 = torch.zeros(im_h - y_1, unpaded_im_mask.size(1)) + concat_0 = torch.cat((zeros_y0, unpaded_im_mask.to(dtype=torch.float32), zeros_y1), 0)[0:im_h, :] + # pad x + zeros_x0 = torch.zeros(concat_0.size(0), x_0) + zeros_x1 = torch.zeros(concat_0.size(0), im_w - x_1) + im_mask = torch.cat((zeros_x0, concat_0, zeros_x1), 1)[:, :im_w] + return im_mask + + +@torch.jit._script_if_tracing +def _onnx_paste_masks_in_image_loop(masks, boxes, im_h, im_w): + res_append = torch.zeros(0, im_h, im_w) + for i in range(masks.size(0)): + mask_res = _onnx_paste_mask_in_image(masks[i][0], boxes[i], im_h, im_w) + mask_res = mask_res.unsqueeze(0) + res_append = torch.cat((res_append, mask_res)) + return res_append + + +def paste_masks_in_image(masks, boxes, img_shape, padding=1): + # type: (Tensor, Tensor, Tuple[int, int], int) -> Tensor + masks, scale = expand_masks(masks, padding=padding) + boxes = expand_boxes(boxes, scale).to(dtype=torch.int64) + im_h, im_w = img_shape + + if torchvision._is_tracing(): + return _onnx_paste_masks_in_image_loop( + masks, boxes, torch.scalar_tensor(im_h, dtype=torch.int64), torch.scalar_tensor(im_w, dtype=torch.int64) + )[:, None] + res = [paste_mask_in_image(m[0], b, im_h, im_w) for m, b in zip(masks, boxes)] + if len(res) > 0: + ret = torch.stack(res, dim=0)[:, None] + else: + ret = masks.new_empty((0, 1, im_h, im_w)) + return ret + + +class RoIHeads(nn.Module): + __annotations__ = { + "box_coder": det_utils.BoxCoder, + "proposal_matcher": det_utils.Matcher, + "fg_bg_sampler": det_utils.BalancedPositiveNegativeSampler, + } + + def __init__( + self, + box_roi_pool, + box_head, + box_predictor, + # Faster R-CNN training + fg_iou_thresh, + bg_iou_thresh, + batch_size_per_image, + positive_fraction, + bbox_reg_weights, + # Faster R-CNN inference + score_thresh, + nms_thresh, + detections_per_img, + # Mask + mask_roi_pool=None, + mask_head=None, + mask_predictor=None, + keypoint_roi_pool=None, + keypoint_head=None, + keypoint_predictor=None, + ): + super().__init__() + + self.box_similarity = box_ops.box_iou + # assign ground-truth boxes for each proposal + self.proposal_matcher = det_utils.Matcher(fg_iou_thresh, bg_iou_thresh, allow_low_quality_matches=False) + + self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(batch_size_per_image, positive_fraction) + + if bbox_reg_weights is None: + bbox_reg_weights = (10.0, 10.0, 5.0, 5.0) + self.box_coder = det_utils.BoxCoder(bbox_reg_weights) + + self.box_roi_pool = box_roi_pool + self.box_head = box_head + self.box_predictor = box_predictor + + self.score_thresh = score_thresh + self.nms_thresh = nms_thresh + self.detections_per_img = detections_per_img + + self.mask_roi_pool = mask_roi_pool + self.mask_head = mask_head + self.mask_predictor = mask_predictor + + self.keypoint_roi_pool = keypoint_roi_pool + self.keypoint_head = keypoint_head + self.keypoint_predictor = keypoint_predictor + + def has_mask(self): + if self.mask_roi_pool is None: + return False + if self.mask_head is None: + return False + if self.mask_predictor is None: + return False + return True + + def has_keypoint(self): + if self.keypoint_roi_pool is None: + return False + if self.keypoint_head is None: + return False + if self.keypoint_predictor is None: + return False + return True + + def assign_targets_to_proposals(self, proposals, gt_boxes, gt_labels): + # type: (List[Tensor], List[Tensor], List[Tensor]) -> Tuple[List[Tensor], List[Tensor]] + matched_idxs = [] + labels = [] + for proposals_in_image, gt_boxes_in_image, gt_labels_in_image in zip(proposals, gt_boxes, gt_labels): + + if gt_boxes_in_image.numel() == 0: + # Background image + device = proposals_in_image.device + clamped_matched_idxs_in_image = torch.zeros( + (proposals_in_image.shape[0],), dtype=torch.int64, device=device + ) + labels_in_image = torch.zeros((proposals_in_image.shape[0],), dtype=torch.int64, device=device) + else: + # set to self.box_similarity when https://github.com/pytorch/pytorch/issues/27495 lands + match_quality_matrix = box_ops.box_iou(gt_boxes_in_image, proposals_in_image) + matched_idxs_in_image = self.proposal_matcher(match_quality_matrix) + + clamped_matched_idxs_in_image = matched_idxs_in_image.clamp(min=0) + + labels_in_image = gt_labels_in_image[clamped_matched_idxs_in_image] + labels_in_image = labels_in_image.to(dtype=torch.int64) + + # Label background (below the low threshold) + bg_inds = matched_idxs_in_image == self.proposal_matcher.BELOW_LOW_THRESHOLD + labels_in_image[bg_inds] = 0 + + # Label ignore proposals (between low and high thresholds) + ignore_inds = matched_idxs_in_image == self.proposal_matcher.BETWEEN_THRESHOLDS + labels_in_image[ignore_inds] = -1 # -1 is ignored by sampler + + matched_idxs.append(clamped_matched_idxs_in_image) + labels.append(labels_in_image) + return matched_idxs, labels + + def subsample(self, labels): + # type: (List[Tensor]) -> List[Tensor] + sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) + sampled_inds = [] + for img_idx, (pos_inds_img, neg_inds_img) in enumerate(zip(sampled_pos_inds, sampled_neg_inds)): + img_sampled_inds = torch.where(pos_inds_img | neg_inds_img)[0] + sampled_inds.append(img_sampled_inds) + return sampled_inds + + def add_gt_proposals(self, proposals, gt_boxes): + # type: (List[Tensor], List[Tensor]) -> List[Tensor] + proposals = [torch.cat((proposal, gt_box)) for proposal, gt_box in zip(proposals, gt_boxes)] + + return proposals + + def check_targets(self, targets): + # type: (Optional[List[Dict[str, Tensor]]]) -> None + if targets is None: + raise ValueError("targets should not be None") + if not all(["boxes" in t for t in targets]): + raise ValueError("Every element of targets should have a boxes key") + if not all(["labels" in t for t in targets]): + raise ValueError("Every element of targets should have a labels key") + if self.has_mask(): + if not all(["masks" in t for t in targets]): + raise ValueError("Every element of targets should have a masks key") + + def select_training_samples( + self, + proposals, # type: List[Tensor] + targets, # type: Optional[List[Dict[str, Tensor]]] + ): + # type: (...) -> Tuple[List[Tensor], List[Tensor], List[Tensor], List[Tensor]] + self.check_targets(targets) + if targets is None: + raise ValueError("targets should not be None") + dtype = proposals[0].dtype + device = proposals[0].device + + gt_boxes = [t["boxes"].to(dtype) for t in targets] + gt_labels = [t["labels"] for t in targets] + + # append ground-truth bboxes to propos + proposals = self.add_gt_proposals(proposals, gt_boxes) + + # get matching gt indices for each proposal + matched_idxs, labels = self.assign_targets_to_proposals(proposals, gt_boxes, gt_labels) + # sample a fixed proportion of positive-negative proposals + sampled_inds = self.subsample(labels) + matched_gt_boxes = [] + num_images = len(proposals) + for img_id in range(num_images): + img_sampled_inds = sampled_inds[img_id] + proposals[img_id] = proposals[img_id][img_sampled_inds] + labels[img_id] = labels[img_id][img_sampled_inds] + matched_idxs[img_id] = matched_idxs[img_id][img_sampled_inds] + + gt_boxes_in_image = gt_boxes[img_id] + if gt_boxes_in_image.numel() == 0: + gt_boxes_in_image = torch.zeros((1, 4), dtype=dtype, device=device) + matched_gt_boxes.append(gt_boxes_in_image[matched_idxs[img_id]]) + + regression_targets = self.box_coder.encode(matched_gt_boxes, proposals) + return proposals, matched_idxs, labels, regression_targets + + def postprocess_detections( + self, + class_logits, # type: Tensor + box_regression, # type: Tensor + proposals, # type: List[Tensor] + image_shapes, # type: List[Tuple[int, int]] + ): + # type: (...) -> Tuple[List[Tensor], List[Tensor], List[Tensor]] + device = class_logits.device + num_classes = class_logits.shape[-1] + + boxes_per_image = [boxes_in_image.shape[0] for boxes_in_image in proposals] + pred_boxes = self.box_coder.decode(box_regression, proposals) + + pred_scores = F.softmax(class_logits, -1) + + pred_boxes_list = pred_boxes.split(boxes_per_image, 0) + pred_scores_list = pred_scores.split(boxes_per_image, 0) + + all_boxes = [] + all_scores = [] + all_labels = [] + for boxes, scores, image_shape in zip(pred_boxes_list, pred_scores_list, image_shapes): + boxes = box_ops.clip_boxes_to_image(boxes, image_shape) + + # create labels for each prediction + labels = torch.arange(num_classes, device=device) + labels = labels.view(1, -1).expand_as(scores) + + # remove predictions with the background label + boxes = boxes[:, 1:] + scores = scores[:, 1:] + labels = labels[:, 1:] + + # batch everything, by making every class prediction be a separate instance + boxes = boxes.reshape(-1, 4) + scores = scores.reshape(-1) + labels = labels.reshape(-1) + + # remove low scoring boxes + inds = torch.where(scores > self.score_thresh)[0] + boxes, scores, labels = boxes[inds], scores[inds], labels[inds] + + # remove empty boxes + keep = box_ops.remove_small_boxes(boxes, min_size=1e-2) + boxes, scores, labels = boxes[keep], scores[keep], labels[keep] + + # non-maximum suppression, independently done per class + keep = box_ops.batched_nms(boxes, scores, labels, self.nms_thresh) + # keep only topk scoring predictions + keep = keep[: self.detections_per_img] + boxes, scores, labels = boxes[keep], scores[keep], labels[keep] + + all_boxes.append(boxes) + all_scores.append(scores) + all_labels.append(labels) + + return all_boxes, all_scores, all_labels + + def forward( + self, + features, # type: Dict[str, Tensor] + proposals, # type: List[Tensor] + image_shapes, # type: List[Tuple[int, int]] + targets=None, # type: Optional[List[Dict[str, Tensor]]] + ): + # type: (...) -> Tuple[List[Dict[str, Tensor]], Dict[str, Tensor]] + """ + Args: + features (List[Tensor]) + proposals (List[Tensor[N, 4]]) + image_shapes (List[Tuple[H, W]]) + targets (List[Dict]) + """ + if targets is not None: + for t in targets: + # TODO: https://github.com/pytorch/pytorch/issues/26731 + floating_point_types = (torch.float, torch.double, torch.half) + if not t["boxes"].dtype in floating_point_types: + raise TypeError(f"target boxes must of float type, instead got {t['boxes'].dtype}") + if not t["labels"].dtype == torch.int64: + raise TypeError(f"target labels must of int64 type, instead got {t['labels'].dtype}") + if self.has_keypoint(): + if not t["keypoints"].dtype == torch.float32: + raise TypeError(f"target keypoints must of float type, instead got {t['keypoints'].dtype}") + + if self.training: + proposals, matched_idxs, labels, regression_targets = self.select_training_samples(proposals, targets) + else: + labels = None + regression_targets = None + matched_idxs = None + + box_features = self.box_roi_pool(features, proposals, image_shapes) + box_features = self.box_head(box_features) + class_logits, box_regression = self.box_predictor(box_features) + + result: List[Dict[str, torch.Tensor]] = [] + losses = {} + if self.training: + if labels is None: + raise ValueError("labels cannot be None") + if regression_targets is None: + raise ValueError("regression_targets cannot be None") + loss_classifier, loss_box_reg = fastrcnn_loss(class_logits, box_regression, labels, regression_targets) + losses = {"loss_classifier": loss_classifier, "loss_box_reg": loss_box_reg} + else: + boxes, scores, labels = self.postprocess_detections(class_logits, box_regression, proposals, image_shapes) + num_images = len(boxes) + for i in range(num_images): + result.append( + { + "boxes": boxes[i], + "labels": labels[i], + "scores": scores[i], + } + ) + + if self.has_mask(): + mask_proposals = [p["boxes"] for p in result] + if self.training: + if matched_idxs is None: + raise ValueError("if in training, matched_idxs should not be None") + + # during training, only focus on positive boxes + num_images = len(proposals) + mask_proposals = [] + pos_matched_idxs = [] + for img_id in range(num_images): + pos = torch.where(labels[img_id] > 0)[0] + mask_proposals.append(proposals[img_id][pos]) + pos_matched_idxs.append(matched_idxs[img_id][pos]) + else: + pos_matched_idxs = None + + if self.mask_roi_pool is not None: + mask_features = self.mask_roi_pool(features, mask_proposals, image_shapes) + mask_features = self.mask_head(mask_features) + mask_logits = self.mask_predictor(mask_features) + else: + raise Exception("Expected mask_roi_pool to be not None") + + loss_mask = {} + if self.training: + if targets is None or pos_matched_idxs is None or mask_logits is None: + raise ValueError("targets, pos_matched_idxs, mask_logits cannot be None when training") + + gt_masks = [t["masks"] for t in targets] + gt_labels = [t["labels"] for t in targets] + rcnn_loss_mask = maskrcnn_loss(mask_logits, mask_proposals, gt_masks, gt_labels, pos_matched_idxs) + loss_mask = {"loss_mask": rcnn_loss_mask} + else: + labels = [r["labels"] for r in result] + masks_probs = maskrcnn_inference(mask_logits, labels) + for mask_prob, r in zip(masks_probs, result): + r["masks"] = mask_prob + + losses.update(loss_mask) + + # keep none checks in if conditional so torchscript will conditionally + # compile each branch + if ( + self.keypoint_roi_pool is not None + and self.keypoint_head is not None + and self.keypoint_predictor is not None + ): + keypoint_proposals = [p["boxes"] for p in result] + if self.training: + # during training, only focus on positive boxes + num_images = len(proposals) + keypoint_proposals = [] + pos_matched_idxs = [] + if matched_idxs is None: + raise ValueError("if in trainning, matched_idxs should not be None") + + for img_id in range(num_images): + pos = torch.where(labels[img_id] > 0)[0] + keypoint_proposals.append(proposals[img_id][pos]) + pos_matched_idxs.append(matched_idxs[img_id][pos]) + else: + pos_matched_idxs = None + + keypoint_features = self.keypoint_roi_pool(features, keypoint_proposals, image_shapes) + keypoint_features = self.keypoint_head(keypoint_features) + keypoint_logits = self.keypoint_predictor(keypoint_features) + + loss_keypoint = {} + if self.training: + if targets is None or pos_matched_idxs is None: + raise ValueError("both targets and pos_matched_idxs should not be None when in training mode") + + gt_keypoints = [t["keypoints"] for t in targets] + rcnn_loss_keypoint = keypointrcnn_loss( + keypoint_logits, keypoint_proposals, gt_keypoints, pos_matched_idxs + ) + loss_keypoint = {"loss_keypoint": rcnn_loss_keypoint} + else: + if keypoint_logits is None or keypoint_proposals is None: + raise ValueError( + "both keypoint_logits and keypoint_proposals should not be None when not in training mode" + ) + + keypoints_probs, kp_scores = keypointrcnn_inference(keypoint_logits, keypoint_proposals) + for keypoint_prob, kps, r in zip(keypoints_probs, kp_scores, result): + r["keypoints"] = keypoint_prob + r["keypoints_scores"] = kps + losses.update(loss_keypoint) + + return result, losses diff --git a/lib/python3.11/site-packages/torchvision/models/detection/rpn.py b/lib/python3.11/site-packages/torchvision/models/detection/rpn.py new file mode 100644 index 0000000000000000000000000000000000000000..f103181e4c6cba48c1a3b4c97583c5fb6785a8c4 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/rpn.py @@ -0,0 +1,388 @@ +from typing import Dict, List, Optional, Tuple + +import torch +from torch import nn, Tensor +from torch.nn import functional as F +from torchvision.ops import boxes as box_ops, Conv2dNormActivation + +from . import _utils as det_utils + +# Import AnchorGenerator to keep compatibility. +from .anchor_utils import AnchorGenerator # noqa: 401 +from .image_list import ImageList + + +class RPNHead(nn.Module): + """ + Adds a simple RPN Head with classification and regression heads + + Args: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + conv_depth (int, optional): number of convolutions + """ + + _version = 2 + + def __init__(self, in_channels: int, num_anchors: int, conv_depth=1) -> None: + super().__init__() + convs = [] + for _ in range(conv_depth): + convs.append(Conv2dNormActivation(in_channels, in_channels, kernel_size=3, norm_layer=None)) + self.conv = nn.Sequential(*convs) + self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1) + self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1) + + for layer in self.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, std=0.01) # type: ignore[arg-type] + if layer.bias is not None: + torch.nn.init.constant_(layer.bias, 0) # type: ignore[arg-type] + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + for type in ["weight", "bias"]: + old_key = f"{prefix}conv.{type}" + new_key = f"{prefix}conv.0.0.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + def forward(self, x: List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]: + logits = [] + bbox_reg = [] + for feature in x: + t = self.conv(feature) + logits.append(self.cls_logits(t)) + bbox_reg.append(self.bbox_pred(t)) + return logits, bbox_reg + + +def permute_and_flatten(layer: Tensor, N: int, A: int, C: int, H: int, W: int) -> Tensor: + layer = layer.view(N, -1, C, H, W) + layer = layer.permute(0, 3, 4, 1, 2) + layer = layer.reshape(N, -1, C) + return layer + + +def concat_box_prediction_layers(box_cls: List[Tensor], box_regression: List[Tensor]) -> Tuple[Tensor, Tensor]: + box_cls_flattened = [] + box_regression_flattened = [] + # for each feature level, permute the outputs to make them be in the + # same format as the labels. Note that the labels are computed for + # all feature levels concatenated, so we keep the same representation + # for the objectness and the box_regression + for box_cls_per_level, box_regression_per_level in zip(box_cls, box_regression): + N, AxC, H, W = box_cls_per_level.shape + Ax4 = box_regression_per_level.shape[1] + A = Ax4 // 4 + C = AxC // A + box_cls_per_level = permute_and_flatten(box_cls_per_level, N, A, C, H, W) + box_cls_flattened.append(box_cls_per_level) + + box_regression_per_level = permute_and_flatten(box_regression_per_level, N, A, 4, H, W) + box_regression_flattened.append(box_regression_per_level) + # concatenate on the first dimension (representing the feature levels), to + # take into account the way the labels were generated (with all feature maps + # being concatenated as well) + box_cls = torch.cat(box_cls_flattened, dim=1).flatten(0, -2) + box_regression = torch.cat(box_regression_flattened, dim=1).reshape(-1, 4) + return box_cls, box_regression + + +class RegionProposalNetwork(torch.nn.Module): + """ + Implements Region Proposal Network (RPN). + + Args: + anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature + maps. + head (nn.Module): module that computes the objectness and regression deltas + fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training of the RPN. + bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be + considered as negative during training of the RPN. + batch_size_per_image (int): number of anchors that are sampled during training of the RPN + for computing the loss + positive_fraction (float): proportion of positive anchors in a mini-batch during training + of the RPN + pre_nms_top_n (Dict[str, int]): number of proposals to keep before applying NMS. It should + contain two fields: training and testing, to allow for different values depending + on training or evaluation + post_nms_top_n (Dict[str, int]): number of proposals to keep after applying NMS. It should + contain two fields: training and testing, to allow for different values depending + on training or evaluation + nms_thresh (float): NMS threshold used for postprocessing the RPN proposals + score_thresh (float): only return proposals with an objectness score greater than score_thresh + + """ + + __annotations__ = { + "box_coder": det_utils.BoxCoder, + "proposal_matcher": det_utils.Matcher, + "fg_bg_sampler": det_utils.BalancedPositiveNegativeSampler, + } + + def __init__( + self, + anchor_generator: AnchorGenerator, + head: nn.Module, + # Faster-RCNN Training + fg_iou_thresh: float, + bg_iou_thresh: float, + batch_size_per_image: int, + positive_fraction: float, + # Faster-RCNN Inference + pre_nms_top_n: Dict[str, int], + post_nms_top_n: Dict[str, int], + nms_thresh: float, + score_thresh: float = 0.0, + ) -> None: + super().__init__() + self.anchor_generator = anchor_generator + self.head = head + self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) + + # used during training + self.box_similarity = box_ops.box_iou + + self.proposal_matcher = det_utils.Matcher( + fg_iou_thresh, + bg_iou_thresh, + allow_low_quality_matches=True, + ) + + self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(batch_size_per_image, positive_fraction) + # used during testing + self._pre_nms_top_n = pre_nms_top_n + self._post_nms_top_n = post_nms_top_n + self.nms_thresh = nms_thresh + self.score_thresh = score_thresh + self.min_size = 1e-3 + + def pre_nms_top_n(self) -> int: + if self.training: + return self._pre_nms_top_n["training"] + return self._pre_nms_top_n["testing"] + + def post_nms_top_n(self) -> int: + if self.training: + return self._post_nms_top_n["training"] + return self._post_nms_top_n["testing"] + + def assign_targets_to_anchors( + self, anchors: List[Tensor], targets: List[Dict[str, Tensor]] + ) -> Tuple[List[Tensor], List[Tensor]]: + + labels = [] + matched_gt_boxes = [] + for anchors_per_image, targets_per_image in zip(anchors, targets): + gt_boxes = targets_per_image["boxes"] + + if gt_boxes.numel() == 0: + # Background image (negative example) + device = anchors_per_image.device + matched_gt_boxes_per_image = torch.zeros(anchors_per_image.shape, dtype=torch.float32, device=device) + labels_per_image = torch.zeros((anchors_per_image.shape[0],), dtype=torch.float32, device=device) + else: + match_quality_matrix = self.box_similarity(gt_boxes, anchors_per_image) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # get the targets corresponding GT for each proposal + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + matched_gt_boxes_per_image = gt_boxes[matched_idxs.clamp(min=0)] + + labels_per_image = matched_idxs >= 0 + labels_per_image = labels_per_image.to(dtype=torch.float32) + + # Background (negative examples) + bg_indices = matched_idxs == self.proposal_matcher.BELOW_LOW_THRESHOLD + labels_per_image[bg_indices] = 0.0 + + # discard indices that are between thresholds + inds_to_discard = matched_idxs == self.proposal_matcher.BETWEEN_THRESHOLDS + labels_per_image[inds_to_discard] = -1.0 + + labels.append(labels_per_image) + matched_gt_boxes.append(matched_gt_boxes_per_image) + return labels, matched_gt_boxes + + def _get_top_n_idx(self, objectness: Tensor, num_anchors_per_level: List[int]) -> Tensor: + r = [] + offset = 0 + for ob in objectness.split(num_anchors_per_level, 1): + num_anchors = ob.shape[1] + pre_nms_top_n = det_utils._topk_min(ob, self.pre_nms_top_n(), 1) + _, top_n_idx = ob.topk(pre_nms_top_n, dim=1) + r.append(top_n_idx + offset) + offset += num_anchors + return torch.cat(r, dim=1) + + def filter_proposals( + self, + proposals: Tensor, + objectness: Tensor, + image_shapes: List[Tuple[int, int]], + num_anchors_per_level: List[int], + ) -> Tuple[List[Tensor], List[Tensor]]: + + num_images = proposals.shape[0] + device = proposals.device + # do not backprop through objectness + objectness = objectness.detach() + objectness = objectness.reshape(num_images, -1) + + levels = [ + torch.full((n,), idx, dtype=torch.int64, device=device) for idx, n in enumerate(num_anchors_per_level) + ] + levels = torch.cat(levels, 0) + levels = levels.reshape(1, -1).expand_as(objectness) + + # select top_n boxes independently per level before applying nms + top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level) + + image_range = torch.arange(num_images, device=device) + batch_idx = image_range[:, None] + + objectness = objectness[batch_idx, top_n_idx] + levels = levels[batch_idx, top_n_idx] + proposals = proposals[batch_idx, top_n_idx] + + objectness_prob = torch.sigmoid(objectness) + + final_boxes = [] + final_scores = [] + for boxes, scores, lvl, img_shape in zip(proposals, objectness_prob, levels, image_shapes): + boxes = box_ops.clip_boxes_to_image(boxes, img_shape) + + # remove small boxes + keep = box_ops.remove_small_boxes(boxes, self.min_size) + boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep] + + # remove low scoring boxes + # use >= for Backwards compatibility + keep = torch.where(scores >= self.score_thresh)[0] + boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep] + + # non-maximum suppression, independently done per level + keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh) + + # keep only topk scoring predictions + keep = keep[: self.post_nms_top_n()] + boxes, scores = boxes[keep], scores[keep] + + final_boxes.append(boxes) + final_scores.append(scores) + return final_boxes, final_scores + + def compute_loss( + self, objectness: Tensor, pred_bbox_deltas: Tensor, labels: List[Tensor], regression_targets: List[Tensor] + ) -> Tuple[Tensor, Tensor]: + """ + Args: + objectness (Tensor) + pred_bbox_deltas (Tensor) + labels (List[Tensor]) + regression_targets (List[Tensor]) + + Returns: + objectness_loss (Tensor) + box_loss (Tensor) + """ + + sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) + sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0] + sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0] + + sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0) + + objectness = objectness.flatten() + + labels = torch.cat(labels, dim=0) + regression_targets = torch.cat(regression_targets, dim=0) + + box_loss = F.smooth_l1_loss( + pred_bbox_deltas[sampled_pos_inds], + regression_targets[sampled_pos_inds], + beta=1 / 9, + reduction="sum", + ) / (sampled_inds.numel()) + + objectness_loss = F.binary_cross_entropy_with_logits(objectness[sampled_inds], labels[sampled_inds]) + + return objectness_loss, box_loss + + def forward( + self, + images: ImageList, + features: Dict[str, Tensor], + targets: Optional[List[Dict[str, Tensor]]] = None, + ) -> Tuple[List[Tensor], Dict[str, Tensor]]: + + """ + Args: + images (ImageList): images for which we want to compute the predictions + features (Dict[str, Tensor]): features computed from the images that are + used for computing the predictions. Each tensor in the list + correspond to different feature levels + targets (List[Dict[str, Tensor]]): ground-truth boxes present in the image (optional). + If provided, each element in the dict should contain a field `boxes`, + with the locations of the ground-truth boxes. + + Returns: + boxes (List[Tensor]): the predicted boxes from the RPN, one Tensor per + image. + losses (Dict[str, Tensor]): the losses for the model during training. During + testing, it is an empty dict. + """ + # RPN uses all feature maps that are available + features = list(features.values()) + objectness, pred_bbox_deltas = self.head(features) + anchors = self.anchor_generator(images, features) + + num_images = len(anchors) + num_anchors_per_level_shape_tensors = [o[0].shape for o in objectness] + num_anchors_per_level = [s[0] * s[1] * s[2] for s in num_anchors_per_level_shape_tensors] + objectness, pred_bbox_deltas = concat_box_prediction_layers(objectness, pred_bbox_deltas) + # apply pred_bbox_deltas to anchors to obtain the decoded proposals + # note that we detach the deltas because Faster R-CNN do not backprop through + # the proposals + proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors) + proposals = proposals.view(num_images, -1, 4) + boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level) + + losses = {} + if self.training: + if targets is None: + raise ValueError("targets should not be None") + labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets) + regression_targets = self.box_coder.encode(matched_gt_boxes, anchors) + loss_objectness, loss_rpn_box_reg = self.compute_loss( + objectness, pred_bbox_deltas, labels, regression_targets + ) + losses = { + "loss_objectness": loss_objectness, + "loss_rpn_box_reg": loss_rpn_box_reg, + } + return boxes, losses diff --git a/lib/python3.11/site-packages/torchvision/models/detection/ssd.py b/lib/python3.11/site-packages/torchvision/models/detection/ssd.py new file mode 100644 index 0000000000000000000000000000000000000000..87062d2bc88a5bf17625e0530116aba22941c538 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/ssd.py @@ -0,0 +1,682 @@ +import warnings +from collections import OrderedDict +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ...ops import boxes as box_ops +from ...transforms._presets import ObjectDetection +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..vgg import VGG, vgg16, VGG16_Weights +from . import _utils as det_utils +from .anchor_utils import DefaultBoxGenerator +from .backbone_utils import _validate_trainable_layers +from .transform import GeneralizedRCNNTransform + + +__all__ = [ + "SSD300_VGG16_Weights", + "ssd300_vgg16", +] + + +class SSD300_VGG16_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth", + transforms=ObjectDetection, + meta={ + "num_params": 35641826, + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16", + "_metrics": { + "COCO-val2017": { + "box_map": 25.1, + } + }, + "_ops": 34.858, + "_file_size": 135.988, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +def _xavier_init(conv: nn.Module): + for layer in conv.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.xavier_uniform_(layer.weight) + if layer.bias is not None: + torch.nn.init.constant_(layer.bias, 0.0) + + +class SSDHead(nn.Module): + def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int): + super().__init__() + self.classification_head = SSDClassificationHead(in_channels, num_anchors, num_classes) + self.regression_head = SSDRegressionHead(in_channels, num_anchors) + + def forward(self, x: List[Tensor]) -> Dict[str, Tensor]: + return { + "bbox_regression": self.regression_head(x), + "cls_logits": self.classification_head(x), + } + + +class SSDScoringHead(nn.Module): + def __init__(self, module_list: nn.ModuleList, num_columns: int): + super().__init__() + self.module_list = module_list + self.num_columns = num_columns + + def _get_result_from_module_list(self, x: Tensor, idx: int) -> Tensor: + """ + This is equivalent to self.module_list[idx](x), + but torchscript doesn't support this yet + """ + num_blocks = len(self.module_list) + if idx < 0: + idx += num_blocks + out = x + for i, module in enumerate(self.module_list): + if i == idx: + out = module(x) + return out + + def forward(self, x: List[Tensor]) -> Tensor: + all_results = [] + + for i, features in enumerate(x): + results = self._get_result_from_module_list(features, i) + + # Permute output from (N, A * K, H, W) to (N, HWA, K). + N, _, H, W = results.shape + results = results.view(N, -1, self.num_columns, H, W) + results = results.permute(0, 3, 4, 1, 2) + results = results.reshape(N, -1, self.num_columns) # Size=(N, HWA, K) + + all_results.append(results) + + return torch.cat(all_results, dim=1) + + +class SSDClassificationHead(SSDScoringHead): + def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int): + cls_logits = nn.ModuleList() + for channels, anchors in zip(in_channels, num_anchors): + cls_logits.append(nn.Conv2d(channels, num_classes * anchors, kernel_size=3, padding=1)) + _xavier_init(cls_logits) + super().__init__(cls_logits, num_classes) + + +class SSDRegressionHead(SSDScoringHead): + def __init__(self, in_channels: List[int], num_anchors: List[int]): + bbox_reg = nn.ModuleList() + for channels, anchors in zip(in_channels, num_anchors): + bbox_reg.append(nn.Conv2d(channels, 4 * anchors, kernel_size=3, padding=1)) + _xavier_init(bbox_reg) + super().__init__(bbox_reg, 4) + + +class SSD(nn.Module): + """ + Implements SSD architecture from `"SSD: Single Shot MultiBox Detector" `_. + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes, but they will be resized + to a fixed size before passing it to the backbone. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows, where ``N`` is the number of detections: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each detection + - scores (Tensor[N]): the scores for each detection + + Args: + backbone (nn.Module): the network used to compute the features for the model. + It should contain an out_channels attribute with the list of the output channels of + each feature map. The backbone should return a single Tensor or an OrderedDict[Tensor]. + anchor_generator (DefaultBoxGenerator): module that generates the default boxes for a + set of feature maps. + size (Tuple[int, int]): the width and height to which images will be rescaled before feeding them + to the backbone. + num_classes (int): number of output classes of the model (including the background). + image_mean (Tuple[float, float, float]): mean values used for input normalization. + They are generally the mean values of the dataset on which the backbone has been trained + on + image_std (Tuple[float, float, float]): std values used for input normalization. + They are generally the std values of the dataset on which the backbone has been trained on + head (nn.Module, optional): Module run on top of the backbone features. Defaults to a module containing + a classification and regression module. + score_thresh (float): Score threshold used for postprocessing the detections. + nms_thresh (float): NMS threshold used for postprocessing the detections. + detections_per_img (int): Number of best detections to keep after NMS. + iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be + considered as positive during training. + topk_candidates (int): Number of best detections to keep before NMS. + positive_fraction (float): a number between 0 and 1 which indicates the proportion of positive + proposals used during the training of the classification head. It is used to estimate the negative to + positive ratio. + """ + + __annotations__ = { + "box_coder": det_utils.BoxCoder, + "proposal_matcher": det_utils.Matcher, + } + + def __init__( + self, + backbone: nn.Module, + anchor_generator: DefaultBoxGenerator, + size: Tuple[int, int], + num_classes: int, + image_mean: Optional[List[float]] = None, + image_std: Optional[List[float]] = None, + head: Optional[nn.Module] = None, + score_thresh: float = 0.01, + nms_thresh: float = 0.45, + detections_per_img: int = 200, + iou_thresh: float = 0.5, + topk_candidates: int = 400, + positive_fraction: float = 0.25, + **kwargs: Any, + ): + super().__init__() + _log_api_usage_once(self) + + self.backbone = backbone + + self.anchor_generator = anchor_generator + + self.box_coder = det_utils.BoxCoder(weights=(10.0, 10.0, 5.0, 5.0)) + + if head is None: + if hasattr(backbone, "out_channels"): + out_channels = backbone.out_channels + else: + out_channels = det_utils.retrieve_out_channels(backbone, size) + + if len(out_channels) != len(anchor_generator.aspect_ratios): + raise ValueError( + f"The length of the output channels from the backbone ({len(out_channels)}) do not match the length of the anchor generator aspect ratios ({len(anchor_generator.aspect_ratios)})" + ) + + num_anchors = self.anchor_generator.num_anchors_per_location() + head = SSDHead(out_channels, num_anchors, num_classes) + self.head = head + + self.proposal_matcher = det_utils.SSDMatcher(iou_thresh) + + if image_mean is None: + image_mean = [0.485, 0.456, 0.406] + if image_std is None: + image_std = [0.229, 0.224, 0.225] + self.transform = GeneralizedRCNNTransform( + min(size), max(size), image_mean, image_std, size_divisible=1, fixed_size=size, **kwargs + ) + + self.score_thresh = score_thresh + self.nms_thresh = nms_thresh + self.detections_per_img = detections_per_img + self.topk_candidates = topk_candidates + self.neg_to_pos_ratio = (1.0 - positive_fraction) / positive_fraction + + # used only on torchscript mode + self._has_warned = False + + @torch.jit.unused + def eager_outputs( + self, losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]] + ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: + if self.training: + return losses + + return detections + + def compute_loss( + self, + targets: List[Dict[str, Tensor]], + head_outputs: Dict[str, Tensor], + anchors: List[Tensor], + matched_idxs: List[Tensor], + ) -> Dict[str, Tensor]: + bbox_regression = head_outputs["bbox_regression"] + cls_logits = head_outputs["cls_logits"] + + # Match original targets with default boxes + num_foreground = 0 + bbox_loss = [] + cls_targets = [] + for ( + targets_per_image, + bbox_regression_per_image, + cls_logits_per_image, + anchors_per_image, + matched_idxs_per_image, + ) in zip(targets, bbox_regression, cls_logits, anchors, matched_idxs): + # produce the matching between boxes and targets + foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0] + foreground_matched_idxs_per_image = matched_idxs_per_image[foreground_idxs_per_image] + num_foreground += foreground_matched_idxs_per_image.numel() + + # Calculate regression loss + matched_gt_boxes_per_image = targets_per_image["boxes"][foreground_matched_idxs_per_image] + bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :] + anchors_per_image = anchors_per_image[foreground_idxs_per_image, :] + target_regression = self.box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image) + bbox_loss.append( + torch.nn.functional.smooth_l1_loss(bbox_regression_per_image, target_regression, reduction="sum") + ) + + # Estimate ground truth for class targets + gt_classes_target = torch.zeros( + (cls_logits_per_image.size(0),), + dtype=targets_per_image["labels"].dtype, + device=targets_per_image["labels"].device, + ) + gt_classes_target[foreground_idxs_per_image] = targets_per_image["labels"][ + foreground_matched_idxs_per_image + ] + cls_targets.append(gt_classes_target) + + bbox_loss = torch.stack(bbox_loss) + cls_targets = torch.stack(cls_targets) + + # Calculate classification loss + num_classes = cls_logits.size(-1) + cls_loss = F.cross_entropy(cls_logits.view(-1, num_classes), cls_targets.view(-1), reduction="none").view( + cls_targets.size() + ) + + # Hard Negative Sampling + foreground_idxs = cls_targets > 0 + num_negative = self.neg_to_pos_ratio * foreground_idxs.sum(1, keepdim=True) + # num_negative[num_negative < self.neg_to_pos_ratio] = self.neg_to_pos_ratio + negative_loss = cls_loss.clone() + negative_loss[foreground_idxs] = -float("inf") # use -inf to detect positive values that creeped in the sample + values, idx = negative_loss.sort(1, descending=True) + # background_idxs = torch.logical_and(idx.sort(1)[1] < num_negative, torch.isfinite(values)) + background_idxs = idx.sort(1)[1] < num_negative + + N = max(1, num_foreground) + return { + "bbox_regression": bbox_loss.sum() / N, + "classification": (cls_loss[foreground_idxs].sum() + cls_loss[background_idxs].sum()) / N, + } + + def forward( + self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None + ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: + if self.training: + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + for target in targets: + boxes = target["boxes"] + if isinstance(boxes, torch.Tensor): + torch._assert( + len(boxes.shape) == 2 and boxes.shape[-1] == 4, + f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.", + ) + else: + torch._assert(False, f"Expected target boxes to be of type Tensor, got {type(boxes)}.") + + # get the original image sizes + original_image_sizes: List[Tuple[int, int]] = [] + for img in images: + val = img.shape[-2:] + torch._assert( + len(val) == 2, + f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}", + ) + original_image_sizes.append((val[0], val[1])) + + # transform the input + images, targets = self.transform(images, targets) + + # Check for degenerate boxes + if targets is not None: + for target_idx, target in enumerate(targets): + boxes = target["boxes"] + degenerate_boxes = boxes[:, 2:] <= boxes[:, :2] + if degenerate_boxes.any(): + bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] + degen_bb: List[float] = boxes[bb_idx].tolist() + torch._assert( + False, + "All bounding boxes should have positive height and width." + f" Found invalid box {degen_bb} for target at index {target_idx}.", + ) + + # get the features from the backbone + features = self.backbone(images.tensors) + if isinstance(features, torch.Tensor): + features = OrderedDict([("0", features)]) + + features = list(features.values()) + + # compute the ssd heads outputs using the features + head_outputs = self.head(features) + + # create the set of anchors + anchors = self.anchor_generator(images, features) + + losses = {} + detections: List[Dict[str, Tensor]] = [] + if self.training: + matched_idxs = [] + if targets is None: + torch._assert(False, "targets should not be none when in training mode") + else: + for anchors_per_image, targets_per_image in zip(anchors, targets): + if targets_per_image["boxes"].numel() == 0: + matched_idxs.append( + torch.full( + (anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device + ) + ) + continue + + match_quality_matrix = box_ops.box_iou(targets_per_image["boxes"], anchors_per_image) + matched_idxs.append(self.proposal_matcher(match_quality_matrix)) + + losses = self.compute_loss(targets, head_outputs, anchors, matched_idxs) + else: + detections = self.postprocess_detections(head_outputs, anchors, images.image_sizes) + detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) + + if torch.jit.is_scripting(): + if not self._has_warned: + warnings.warn("SSD always returns a (Losses, Detections) tuple in scripting") + self._has_warned = True + return losses, detections + return self.eager_outputs(losses, detections) + + def postprocess_detections( + self, head_outputs: Dict[str, Tensor], image_anchors: List[Tensor], image_shapes: List[Tuple[int, int]] + ) -> List[Dict[str, Tensor]]: + bbox_regression = head_outputs["bbox_regression"] + pred_scores = F.softmax(head_outputs["cls_logits"], dim=-1) + + num_classes = pred_scores.size(-1) + device = pred_scores.device + + detections: List[Dict[str, Tensor]] = [] + + for boxes, scores, anchors, image_shape in zip(bbox_regression, pred_scores, image_anchors, image_shapes): + boxes = self.box_coder.decode_single(boxes, anchors) + boxes = box_ops.clip_boxes_to_image(boxes, image_shape) + + image_boxes = [] + image_scores = [] + image_labels = [] + for label in range(1, num_classes): + score = scores[:, label] + + keep_idxs = score > self.score_thresh + score = score[keep_idxs] + box = boxes[keep_idxs] + + # keep only topk scoring predictions + num_topk = det_utils._topk_min(score, self.topk_candidates, 0) + score, idxs = score.topk(num_topk) + box = box[idxs] + + image_boxes.append(box) + image_scores.append(score) + image_labels.append(torch.full_like(score, fill_value=label, dtype=torch.int64, device=device)) + + image_boxes = torch.cat(image_boxes, dim=0) + image_scores = torch.cat(image_scores, dim=0) + image_labels = torch.cat(image_labels, dim=0) + + # non-maximum suppression + keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh) + keep = keep[: self.detections_per_img] + + detections.append( + { + "boxes": image_boxes[keep], + "scores": image_scores[keep], + "labels": image_labels[keep], + } + ) + return detections + + +class SSDFeatureExtractorVGG(nn.Module): + def __init__(self, backbone: nn.Module, highres: bool): + super().__init__() + + _, _, maxpool3_pos, maxpool4_pos, _ = (i for i, layer in enumerate(backbone) if isinstance(layer, nn.MaxPool2d)) + + # Patch ceil_mode for maxpool3 to get the same WxH output sizes as the paper + backbone[maxpool3_pos].ceil_mode = True + + # parameters used for L2 regularization + rescaling + self.scale_weight = nn.Parameter(torch.ones(512) * 20) + + # Multiple Feature maps - page 4, Fig 2 of SSD paper + self.features = nn.Sequential(*backbone[:maxpool4_pos]) # until conv4_3 + + # SSD300 case - page 4, Fig 2 of SSD paper + extra = nn.ModuleList( + [ + nn.Sequential( + nn.Conv2d(1024, 256, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=2), # conv8_2 + nn.ReLU(inplace=True), + ), + nn.Sequential( + nn.Conv2d(512, 128, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2), # conv9_2 + nn.ReLU(inplace=True), + ), + nn.Sequential( + nn.Conv2d(256, 128, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(128, 256, kernel_size=3), # conv10_2 + nn.ReLU(inplace=True), + ), + nn.Sequential( + nn.Conv2d(256, 128, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(128, 256, kernel_size=3), # conv11_2 + nn.ReLU(inplace=True), + ), + ] + ) + if highres: + # Additional layers for the SSD512 case. See page 11, footernote 5. + extra.append( + nn.Sequential( + nn.Conv2d(256, 128, kernel_size=1), + nn.ReLU(inplace=True), + nn.Conv2d(128, 256, kernel_size=4), # conv12_2 + nn.ReLU(inplace=True), + ) + ) + _xavier_init(extra) + + fc = nn.Sequential( + nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=False), # add modified maxpool5 + nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6), # FC6 with atrous + nn.ReLU(inplace=True), + nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1), # FC7 + nn.ReLU(inplace=True), + ) + _xavier_init(fc) + extra.insert( + 0, + nn.Sequential( + *backbone[maxpool4_pos:-1], # until conv5_3, skip maxpool5 + fc, + ), + ) + self.extra = extra + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + # L2 regularization + Rescaling of 1st block's feature map + x = self.features(x) + rescaled = self.scale_weight.view(1, -1, 1, 1) * F.normalize(x) + output = [rescaled] + + # Calculating Feature maps for the rest blocks + for block in self.extra: + x = block(x) + output.append(x) + + return OrderedDict([(str(i), v) for i, v in enumerate(output)]) + + +def _vgg_extractor(backbone: VGG, highres: bool, trainable_layers: int): + backbone = backbone.features + # Gather the indices of maxpools. These are the locations of output blocks. + stage_indices = [0] + [i for i, b in enumerate(backbone) if isinstance(b, nn.MaxPool2d)][:-1] + num_stages = len(stage_indices) + + # find the index of the layer from which we won't freeze + torch._assert( + 0 <= trainable_layers <= num_stages, + f"trainable_layers should be in the range [0, {num_stages}]. Instead got {trainable_layers}", + ) + freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers] + + for b in backbone[:freeze_before]: + for parameter in b.parameters(): + parameter.requires_grad_(False) + + return SSDFeatureExtractorVGG(backbone, highres) + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", SSD300_VGG16_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", VGG16_Weights.IMAGENET1K_FEATURES), +) +def ssd300_vgg16( + *, + weights: Optional[SSD300_VGG16_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[VGG16_Weights] = VGG16_Weights.IMAGENET1K_FEATURES, + trainable_backbone_layers: Optional[int] = None, + **kwargs: Any, +) -> SSD: + """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector + `_ paper. + + .. betastatus:: detection module + + The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each + image, and should be in 0-1 range. Different images can have different sizes, but they will be resized + to a fixed size before passing it to the backbone. + + The behavior of the model changes depending on if it is in training or evaluation mode. + + During training, the model expects both the input tensors and targets (list of dictionary), + containing: + + - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the class label for each ground-truth box + + The model returns a Dict[Tensor] during training, containing the classification and regression + losses. + + During inference, the model requires only the input tensors, and returns the post-processed + predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as + follows, where ``N`` is the number of detections: + + - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with + ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``. + - labels (Int64Tensor[N]): the predicted labels for each detection + - scores (Tensor[N]): the scores for each detection + + Example: + + >>> model = torchvision.models.detection.ssd300_vgg16(weights=SSD300_VGG16_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.SSD300_VGG16_Weights`, optional): The pretrained + weights to use. See + :class:`~torchvision.models.detection.SSD300_VGG16_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr + Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + weights_backbone (:class:`~torchvision.models.VGG16_Weights`, optional): The pretrained weights for the + backbone + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block. + Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is + passed (the default) this value is set to 4. + **kwargs: parameters passed to the ``torchvision.models.detection.SSD`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.SSD300_VGG16_Weights + :members: + """ + weights = SSD300_VGG16_Weights.verify(weights) + weights_backbone = VGG16_Weights.verify(weights_backbone) + + if "size" in kwargs: + warnings.warn("The size of the model is already fixed; ignoring the parameter.") + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + trainable_backbone_layers = _validate_trainable_layers( + weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4 + ) + + # Use custom backbones more appropriate for SSD + backbone = vgg16(weights=weights_backbone, progress=progress) + backbone = _vgg_extractor(backbone, False, trainable_backbone_layers) + anchor_generator = DefaultBoxGenerator( + [[2], [2, 3], [2, 3], [2, 3], [2], [2]], + scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05], + steps=[8, 16, 32, 64, 100, 300], + ) + + defaults = { + # Rescale the input in a way compatible to the backbone + "image_mean": [0.48235, 0.45882, 0.40784], + "image_std": [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0], # undo the 0-1 scaling of toTensor + } + kwargs: Any = {**defaults, **kwargs} + model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/ssdlite.py b/lib/python3.11/site-packages/torchvision/models/detection/ssdlite.py new file mode 100644 index 0000000000000000000000000000000000000000..eda21bf941ef0d4a9051312ebdba6911c6760e8d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/ssdlite.py @@ -0,0 +1,331 @@ +import warnings +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Union + +import torch +from torch import nn, Tensor + +from ...ops.misc import Conv2dNormActivation +from ...transforms._presets import ObjectDetection +from ...utils import _log_api_usage_once +from .. import mobilenet +from .._api import register_model, Weights, WeightsEnum +from .._meta import _COCO_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface +from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights +from . import _utils as det_utils +from .anchor_utils import DefaultBoxGenerator +from .backbone_utils import _validate_trainable_layers +from .ssd import SSD, SSDScoringHead + + +__all__ = [ + "SSDLite320_MobileNet_V3_Large_Weights", + "ssdlite320_mobilenet_v3_large", +] + + +# Building blocks of SSDlite as described in section 6.2 of MobileNetV2 paper +def _prediction_block( + in_channels: int, out_channels: int, kernel_size: int, norm_layer: Callable[..., nn.Module] +) -> nn.Sequential: + return nn.Sequential( + # 3x3 depthwise with stride 1 and padding 1 + Conv2dNormActivation( + in_channels, + in_channels, + kernel_size=kernel_size, + groups=in_channels, + norm_layer=norm_layer, + activation_layer=nn.ReLU6, + ), + # 1x1 projetion to output channels + nn.Conv2d(in_channels, out_channels, 1), + ) + + +def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[..., nn.Module]) -> nn.Sequential: + activation = nn.ReLU6 + intermediate_channels = out_channels // 2 + return nn.Sequential( + # 1x1 projection to half output channels + Conv2dNormActivation( + in_channels, intermediate_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation + ), + # 3x3 depthwise with stride 2 and padding 1 + Conv2dNormActivation( + intermediate_channels, + intermediate_channels, + kernel_size=3, + stride=2, + groups=intermediate_channels, + norm_layer=norm_layer, + activation_layer=activation, + ), + # 1x1 projetion to output channels + Conv2dNormActivation( + intermediate_channels, out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation + ), + ) + + +def _normal_init(conv: nn.Module): + for layer in conv.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, mean=0.0, std=0.03) + if layer.bias is not None: + torch.nn.init.constant_(layer.bias, 0.0) + + +class SSDLiteHead(nn.Module): + def __init__( + self, in_channels: List[int], num_anchors: List[int], num_classes: int, norm_layer: Callable[..., nn.Module] + ): + super().__init__() + self.classification_head = SSDLiteClassificationHead(in_channels, num_anchors, num_classes, norm_layer) + self.regression_head = SSDLiteRegressionHead(in_channels, num_anchors, norm_layer) + + def forward(self, x: List[Tensor]) -> Dict[str, Tensor]: + return { + "bbox_regression": self.regression_head(x), + "cls_logits": self.classification_head(x), + } + + +class SSDLiteClassificationHead(SSDScoringHead): + def __init__( + self, in_channels: List[int], num_anchors: List[int], num_classes: int, norm_layer: Callable[..., nn.Module] + ): + cls_logits = nn.ModuleList() + for channels, anchors in zip(in_channels, num_anchors): + cls_logits.append(_prediction_block(channels, num_classes * anchors, 3, norm_layer)) + _normal_init(cls_logits) + super().__init__(cls_logits, num_classes) + + +class SSDLiteRegressionHead(SSDScoringHead): + def __init__(self, in_channels: List[int], num_anchors: List[int], norm_layer: Callable[..., nn.Module]): + bbox_reg = nn.ModuleList() + for channels, anchors in zip(in_channels, num_anchors): + bbox_reg.append(_prediction_block(channels, 4 * anchors, 3, norm_layer)) + _normal_init(bbox_reg) + super().__init__(bbox_reg, 4) + + +class SSDLiteFeatureExtractorMobileNet(nn.Module): + def __init__( + self, + backbone: nn.Module, + c4_pos: int, + norm_layer: Callable[..., nn.Module], + width_mult: float = 1.0, + min_depth: int = 16, + ): + super().__init__() + _log_api_usage_once(self) + + if backbone[c4_pos].use_res_connect: + raise ValueError("backbone[c4_pos].use_res_connect should be False") + + self.features = nn.Sequential( + # As described in section 6.3 of MobileNetV3 paper + nn.Sequential(*backbone[:c4_pos], backbone[c4_pos].block[0]), # from start until C4 expansion layer + nn.Sequential(backbone[c4_pos].block[1:], *backbone[c4_pos + 1 :]), # from C4 depthwise until end + ) + + get_depth = lambda d: max(min_depth, int(d * width_mult)) # noqa: E731 + extra = nn.ModuleList( + [ + _extra_block(backbone[-1].out_channels, get_depth(512), norm_layer), + _extra_block(get_depth(512), get_depth(256), norm_layer), + _extra_block(get_depth(256), get_depth(256), norm_layer), + _extra_block(get_depth(256), get_depth(128), norm_layer), + ] + ) + _normal_init(extra) + + self.extra = extra + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + # Get feature maps from backbone and extra. Can't be refactored due to JIT limitations. + output = [] + for block in self.features: + x = block(x) + output.append(x) + + for block in self.extra: + x = block(x) + output.append(x) + + return OrderedDict([(str(i), v) for i, v in enumerate(output)]) + + +def _mobilenet_extractor( + backbone: Union[mobilenet.MobileNetV2, mobilenet.MobileNetV3], + trainable_layers: int, + norm_layer: Callable[..., nn.Module], +): + backbone = backbone.features + # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks. + # The first and last blocks are always included because they are the C0 (conv1) and Cn. + stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1] + num_stages = len(stage_indices) + + # find the index of the layer from which we won't freeze + if not 0 <= trainable_layers <= num_stages: + raise ValueError("trainable_layers should be in the range [0, {num_stages}], instead got {trainable_layers}") + freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers] + + for b in backbone[:freeze_before]: + for parameter in b.parameters(): + parameter.requires_grad_(False) + + return SSDLiteFeatureExtractorMobileNet(backbone, stage_indices[-2], norm_layer) + + +class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum): + COCO_V1 = Weights( + url="https://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pth", + transforms=ObjectDetection, + meta={ + "num_params": 3440060, + "categories": _COCO_CATEGORIES, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-large", + "_metrics": { + "COCO-val2017": { + "box_map": 21.3, + } + }, + "_ops": 0.583, + "_file_size": 13.418, + "_docs": """These weights were produced by following a similar training recipe as on the paper.""", + }, + ) + DEFAULT = COCO_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", SSDLite320_MobileNet_V3_Large_Weights.COCO_V1), + weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1), +) +def ssdlite320_mobilenet_v3_large( + *, + weights: Optional[SSDLite320_MobileNet_V3_Large_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, + trainable_backbone_layers: Optional[int] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + **kwargs: Any, +) -> SSD: + """SSDlite model architecture with input size 320x320 and a MobileNetV3 Large backbone, as + described at `Searching for MobileNetV3 `__ and + `MobileNetV2: Inverted Residuals and Linear Bottlenecks `__. + + .. betastatus:: detection module + + See :func:`~torchvision.models.detection.ssd300_vgg16` for more details. + + Example: + + >>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(weights=SSDLite320_MobileNet_V3_Large_Weights.DEFAULT) + >>> model.eval() + >>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)] + >>> predictions = model(x) + + Args: + weights (:class:`~torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model + (including the background). + weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained + weights for the backbone. + trainable_backbone_layers (int, optional): number of trainable (not frozen) layers + starting from final block. Valid values are between 0 and 6, with 6 meaning all + backbone layers are trainable. If ``None`` is passed (the default) this value is + set to 6. + norm_layer (callable, optional): Module specifying the normalization layer to use. + **kwargs: parameters passed to the ``torchvision.models.detection.ssd.SSD`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights + :members: + """ + + weights = SSDLite320_MobileNet_V3_Large_Weights.verify(weights) + weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone) + + if "size" in kwargs: + warnings.warn("The size of the model is already fixed; ignoring the parameter.") + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 91 + + trainable_backbone_layers = _validate_trainable_layers( + weights is not None or weights_backbone is not None, trainable_backbone_layers, 6, 6 + ) + + # Enable reduced tail if no pretrained backbone is selected. See Table 6 of MobileNetV3 paper. + reduce_tail = weights_backbone is None + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.03) + + backbone = mobilenet_v3_large( + weights=weights_backbone, progress=progress, norm_layer=norm_layer, reduced_tail=reduce_tail, **kwargs + ) + if weights_backbone is None: + # Change the default initialization scheme if not pretrained + _normal_init(backbone) + backbone = _mobilenet_extractor( + backbone, + trainable_backbone_layers, + norm_layer, + ) + + size = (320, 320) + anchor_generator = DefaultBoxGenerator([[2, 3] for _ in range(6)], min_ratio=0.2, max_ratio=0.95) + out_channels = det_utils.retrieve_out_channels(backbone, size) + num_anchors = anchor_generator.num_anchors_per_location() + if len(out_channels) != len(anchor_generator.aspect_ratios): + raise ValueError( + f"The length of the output channels from the backbone {len(out_channels)} do not match the length of the anchor generator aspect ratios {len(anchor_generator.aspect_ratios)}" + ) + + defaults = { + "score_thresh": 0.001, + "nms_thresh": 0.55, + "detections_per_img": 300, + "topk_candidates": 300, + # Rescale the input in a way compatible to the backbone: + # The following mean/std rescale the data from [0, 1] to [-1, 1] + "image_mean": [0.5, 0.5, 0.5], + "image_std": [0.5, 0.5, 0.5], + } + kwargs: Any = {**defaults, **kwargs} + model = SSD( + backbone, + anchor_generator, + size, + num_classes, + head=SSDLiteHead(out_channels, num_anchors, num_classes, norm_layer), + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/detection/transform.py b/lib/python3.11/site-packages/torchvision/models/detection/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..9c569b0aafb0c5464815654c0f343d7fb927dc6c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/detection/transform.py @@ -0,0 +1,319 @@ +import math +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torchvision +from torch import nn, Tensor + +from .image_list import ImageList +from .roi_heads import paste_masks_in_image + + +@torch.jit.unused +def _get_shape_onnx(image: Tensor) -> Tensor: + from torch.onnx import operators + + return operators.shape_as_tensor(image)[-2:] + + +@torch.jit.unused +def _fake_cast_onnx(v: Tensor) -> float: + # ONNX requires a tensor but here we fake its type for JIT. + return v + + +def _resize_image_and_masks( + image: Tensor, + self_min_size: int, + self_max_size: int, + target: Optional[Dict[str, Tensor]] = None, + fixed_size: Optional[Tuple[int, int]] = None, +) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + if torchvision._is_tracing(): + im_shape = _get_shape_onnx(image) + elif torch.jit.is_scripting(): + im_shape = torch.tensor(image.shape[-2:]) + else: + im_shape = image.shape[-2:] + + size: Optional[List[int]] = None + scale_factor: Optional[float] = None + recompute_scale_factor: Optional[bool] = None + if fixed_size is not None: + size = [fixed_size[1], fixed_size[0]] + else: + if torch.jit.is_scripting() or torchvision._is_tracing(): + min_size = torch.min(im_shape).to(dtype=torch.float32) + max_size = torch.max(im_shape).to(dtype=torch.float32) + self_min_size_f = float(self_min_size) + self_max_size_f = float(self_max_size) + scale = torch.min(self_min_size_f / min_size, self_max_size_f / max_size) + + if torchvision._is_tracing(): + scale_factor = _fake_cast_onnx(scale) + else: + scale_factor = scale.item() + + else: + # Do it the normal way + min_size = min(im_shape) + max_size = max(im_shape) + scale_factor = min(self_min_size / min_size, self_max_size / max_size) + + recompute_scale_factor = True + + image = torch.nn.functional.interpolate( + image[None], + size=size, + scale_factor=scale_factor, + mode="bilinear", + recompute_scale_factor=recompute_scale_factor, + align_corners=False, + )[0] + + if target is None: + return image, target + + if "masks" in target: + mask = target["masks"] + mask = torch.nn.functional.interpolate( + mask[:, None].float(), size=size, scale_factor=scale_factor, recompute_scale_factor=recompute_scale_factor + )[:, 0].byte() + target["masks"] = mask + return image, target + + +class GeneralizedRCNNTransform(nn.Module): + """ + Performs input / target transformation before feeding the data to a GeneralizedRCNN + model. + + The transformations it performs are: + - input normalization (mean subtraction and std division) + - input / target resizing to match min_size / max_size + + It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets + """ + + def __init__( + self, + min_size: int, + max_size: int, + image_mean: List[float], + image_std: List[float], + size_divisible: int = 32, + fixed_size: Optional[Tuple[int, int]] = None, + **kwargs: Any, + ): + super().__init__() + if not isinstance(min_size, (list, tuple)): + min_size = (min_size,) + self.min_size = min_size + self.max_size = max_size + self.image_mean = image_mean + self.image_std = image_std + self.size_divisible = size_divisible + self.fixed_size = fixed_size + self._skip_resize = kwargs.pop("_skip_resize", False) + + def forward( + self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None + ) -> Tuple[ImageList, Optional[List[Dict[str, Tensor]]]]: + images = [img for img in images] + if targets is not None: + # make a copy of targets to avoid modifying it in-place + # once torchscript supports dict comprehension + # this can be simplified as follows + # targets = [{k: v for k,v in t.items()} for t in targets] + targets_copy: List[Dict[str, Tensor]] = [] + for t in targets: + data: Dict[str, Tensor] = {} + for k, v in t.items(): + data[k] = v + targets_copy.append(data) + targets = targets_copy + for i in range(len(images)): + image = images[i] + target_index = targets[i] if targets is not None else None + + if image.dim() != 3: + raise ValueError(f"images is expected to be a list of 3d tensors of shape [C, H, W], got {image.shape}") + image = self.normalize(image) + image, target_index = self.resize(image, target_index) + images[i] = image + if targets is not None and target_index is not None: + targets[i] = target_index + + image_sizes = [img.shape[-2:] for img in images] + images = self.batch_images(images, size_divisible=self.size_divisible) + image_sizes_list: List[Tuple[int, int]] = [] + for image_size in image_sizes: + torch._assert( + len(image_size) == 2, + f"Input tensors expected to have in the last two elements H and W, instead got {image_size}", + ) + image_sizes_list.append((image_size[0], image_size[1])) + + image_list = ImageList(images, image_sizes_list) + return image_list, targets + + def normalize(self, image: Tensor) -> Tensor: + if not image.is_floating_point(): + raise TypeError( + f"Expected input images to be of floating type (in range [0, 1]), " + f"but found type {image.dtype} instead" + ) + dtype, device = image.dtype, image.device + mean = torch.as_tensor(self.image_mean, dtype=dtype, device=device) + std = torch.as_tensor(self.image_std, dtype=dtype, device=device) + return (image - mean[:, None, None]) / std[:, None, None] + + def torch_choice(self, k: List[int]) -> int: + """ + Implements `random.choice` via torch ops, so it can be compiled with + TorchScript and we use PyTorch's RNG (not native RNG) + """ + index = int(torch.empty(1).uniform_(0.0, float(len(k))).item()) + return k[index] + + def resize( + self, + image: Tensor, + target: Optional[Dict[str, Tensor]] = None, + ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]: + h, w = image.shape[-2:] + if self.training: + if self._skip_resize: + return image, target + size = self.torch_choice(self.min_size) + else: + size = self.min_size[-1] + image, target = _resize_image_and_masks(image, size, self.max_size, target, self.fixed_size) + + if target is None: + return image, target + + bbox = target["boxes"] + bbox = resize_boxes(bbox, (h, w), image.shape[-2:]) + target["boxes"] = bbox + + if "keypoints" in target: + keypoints = target["keypoints"] + keypoints = resize_keypoints(keypoints, (h, w), image.shape[-2:]) + target["keypoints"] = keypoints + return image, target + + # _onnx_batch_images() is an implementation of + # batch_images() that is supported by ONNX tracing. + @torch.jit.unused + def _onnx_batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor: + max_size = [] + for i in range(images[0].dim()): + max_size_i = torch.max(torch.stack([img.shape[i] for img in images]).to(torch.float32)).to(torch.int64) + max_size.append(max_size_i) + stride = size_divisible + max_size[1] = (torch.ceil((max_size[1].to(torch.float32)) / stride) * stride).to(torch.int64) + max_size[2] = (torch.ceil((max_size[2].to(torch.float32)) / stride) * stride).to(torch.int64) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # which is not yet supported in onnx + padded_imgs = [] + for img in images: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + return torch.stack(padded_imgs) + + def max_by_axis(self, the_list: List[List[int]]) -> List[int]: + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + def batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor: + if torchvision._is_tracing(): + # batch_images() does not export well to ONNX + # call _onnx_batch_images() instead + return self._onnx_batch_images(images, size_divisible) + + max_size = self.max_by_axis([list(img.shape) for img in images]) + stride = float(size_divisible) + max_size = list(max_size) + max_size[1] = int(math.ceil(float(max_size[1]) / stride) * stride) + max_size[2] = int(math.ceil(float(max_size[2]) / stride) * stride) + + batch_shape = [len(images)] + max_size + batched_imgs = images[0].new_full(batch_shape, 0) + for i in range(batched_imgs.shape[0]): + img = images[i] + batched_imgs[i, : img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + + return batched_imgs + + def postprocess( + self, + result: List[Dict[str, Tensor]], + image_shapes: List[Tuple[int, int]], + original_image_sizes: List[Tuple[int, int]], + ) -> List[Dict[str, Tensor]]: + if self.training: + return result + for i, (pred, im_s, o_im_s) in enumerate(zip(result, image_shapes, original_image_sizes)): + boxes = pred["boxes"] + boxes = resize_boxes(boxes, im_s, o_im_s) + result[i]["boxes"] = boxes + if "masks" in pred: + masks = pred["masks"] + masks = paste_masks_in_image(masks, boxes, o_im_s) + result[i]["masks"] = masks + if "keypoints" in pred: + keypoints = pred["keypoints"] + keypoints = resize_keypoints(keypoints, im_s, o_im_s) + result[i]["keypoints"] = keypoints + return result + + def __repr__(self) -> str: + format_string = f"{self.__class__.__name__}(" + _indent = "\n " + format_string += f"{_indent}Normalize(mean={self.image_mean}, std={self.image_std})" + format_string += f"{_indent}Resize(min_size={self.min_size}, max_size={self.max_size}, mode='bilinear')" + format_string += "\n)" + return format_string + + +def resize_keypoints(keypoints: Tensor, original_size: List[int], new_size: List[int]) -> Tensor: + ratios = [ + torch.tensor(s, dtype=torch.float32, device=keypoints.device) + / torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device) + for s, s_orig in zip(new_size, original_size) + ] + ratio_h, ratio_w = ratios + resized_data = keypoints.clone() + if torch._C._get_tracing_state(): + resized_data_0 = resized_data[:, :, 0] * ratio_w + resized_data_1 = resized_data[:, :, 1] * ratio_h + resized_data = torch.stack((resized_data_0, resized_data_1, resized_data[:, :, 2]), dim=2) + else: + resized_data[..., 0] *= ratio_w + resized_data[..., 1] *= ratio_h + return resized_data + + +def resize_boxes(boxes: Tensor, original_size: List[int], new_size: List[int]) -> Tensor: + ratios = [ + torch.tensor(s, dtype=torch.float32, device=boxes.device) + / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device) + for s, s_orig in zip(new_size, original_size) + ] + ratio_height, ratio_width = ratios + xmin, ymin, xmax, ymax = boxes.unbind(1) + + xmin = xmin * ratio_width + xmax = xmax * ratio_width + ymin = ymin * ratio_height + ymax = ymax * ratio_height + return torch.stack((xmin, ymin, xmax, ymax), dim=1) diff --git a/lib/python3.11/site-packages/torchvision/models/efficientnet.py b/lib/python3.11/site-packages/torchvision/models/efficientnet.py new file mode 100644 index 0000000000000000000000000000000000000000..65f0b2fef44702f5c0db9c545c2ecb751a683a72 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/efficientnet.py @@ -0,0 +1,1131 @@ +import copy +import math +from dataclasses import dataclass +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union + +import torch +from torch import nn, Tensor +from torchvision.ops import StochasticDepth + +from ..ops.misc import Conv2dNormActivation, SqueezeExcitation +from ..transforms._presets import ImageClassification, InterpolationMode +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "EfficientNet", + "EfficientNet_B0_Weights", + "EfficientNet_B1_Weights", + "EfficientNet_B2_Weights", + "EfficientNet_B3_Weights", + "EfficientNet_B4_Weights", + "EfficientNet_B5_Weights", + "EfficientNet_B6_Weights", + "EfficientNet_B7_Weights", + "EfficientNet_V2_S_Weights", + "EfficientNet_V2_M_Weights", + "EfficientNet_V2_L_Weights", + "efficientnet_b0", + "efficientnet_b1", + "efficientnet_b2", + "efficientnet_b3", + "efficientnet_b4", + "efficientnet_b5", + "efficientnet_b6", + "efficientnet_b7", + "efficientnet_v2_s", + "efficientnet_v2_m", + "efficientnet_v2_l", +] + + +@dataclass +class _MBConvConfig: + expand_ratio: float + kernel: int + stride: int + input_channels: int + out_channels: int + num_layers: int + block: Callable[..., nn.Module] + + @staticmethod + def adjust_channels(channels: int, width_mult: float, min_value: Optional[int] = None) -> int: + return _make_divisible(channels * width_mult, 8, min_value) + + +class MBConvConfig(_MBConvConfig): + # Stores information listed at Table 1 of the EfficientNet paper & Table 4 of the EfficientNetV2 paper + def __init__( + self, + expand_ratio: float, + kernel: int, + stride: int, + input_channels: int, + out_channels: int, + num_layers: int, + width_mult: float = 1.0, + depth_mult: float = 1.0, + block: Optional[Callable[..., nn.Module]] = None, + ) -> None: + input_channels = self.adjust_channels(input_channels, width_mult) + out_channels = self.adjust_channels(out_channels, width_mult) + num_layers = self.adjust_depth(num_layers, depth_mult) + if block is None: + block = MBConv + super().__init__(expand_ratio, kernel, stride, input_channels, out_channels, num_layers, block) + + @staticmethod + def adjust_depth(num_layers: int, depth_mult: float): + return int(math.ceil(num_layers * depth_mult)) + + +class FusedMBConvConfig(_MBConvConfig): + # Stores information listed at Table 4 of the EfficientNetV2 paper + def __init__( + self, + expand_ratio: float, + kernel: int, + stride: int, + input_channels: int, + out_channels: int, + num_layers: int, + block: Optional[Callable[..., nn.Module]] = None, + ) -> None: + if block is None: + block = FusedMBConv + super().__init__(expand_ratio, kernel, stride, input_channels, out_channels, num_layers, block) + + +class MBConv(nn.Module): + def __init__( + self, + cnf: MBConvConfig, + stochastic_depth_prob: float, + norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = SqueezeExcitation, + ) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError("illegal stride value") + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + # expand + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + layers.append( + Conv2dNormActivation( + cnf.input_channels, + expanded_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # depthwise + layers.append( + Conv2dNormActivation( + expanded_channels, + expanded_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + groups=expanded_channels, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # squeeze and excitation + squeeze_channels = max(1, cnf.input_channels // 4) + layers.append(se_layer(expanded_channels, squeeze_channels, activation=partial(nn.SiLU, inplace=True))) + + # project + layers.append( + Conv2dNormActivation( + expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None + ) + ) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class FusedMBConv(nn.Module): + def __init__( + self, + cnf: FusedMBConvConfig, + stochastic_depth_prob: float, + norm_layer: Callable[..., nn.Module], + ) -> None: + super().__init__() + + if not (1 <= cnf.stride <= 2): + raise ValueError("illegal stride value") + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.SiLU + + expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio) + if expanded_channels != cnf.input_channels: + # fused expand + layers.append( + Conv2dNormActivation( + cnf.input_channels, + expanded_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # project + layers.append( + Conv2dNormActivation( + expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None + ) + ) + else: + layers.append( + Conv2dNormActivation( + cnf.input_channels, + cnf.out_channels, + kernel_size=cnf.kernel, + stride=cnf.stride, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + self.block = nn.Sequential(*layers) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.out_channels = cnf.out_channels + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result = self.stochastic_depth(result) + result += input + return result + + +class EfficientNet(nn.Module): + def __init__( + self, + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]], + dropout: float, + stochastic_depth_prob: float = 0.2, + num_classes: int = 1000, + norm_layer: Optional[Callable[..., nn.Module]] = None, + last_channel: Optional[int] = None, + ) -> None: + """ + EfficientNet V1 and V2 main class + + Args: + inverted_residual_setting (Sequence[Union[MBConvConfig, FusedMBConvConfig]]): Network structure + dropout (float): The droupout probability + stochastic_depth_prob (float): The stochastic depth probability + num_classes (int): Number of classes + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + last_channel (int): The number of channels on the penultimate layer + """ + super().__init__() + _log_api_usage_once(self) + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not ( + isinstance(inverted_residual_setting, Sequence) + and all([isinstance(s, _MBConvConfig) for s in inverted_residual_setting]) + ): + raise TypeError("The inverted_residual_setting should be List[MBConvConfig]") + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.SiLU + ) + ) + + # building inverted residual blocks + total_stage_blocks = sum(cnf.num_layers for cnf in inverted_residual_setting) + stage_block_id = 0 + for cnf in inverted_residual_setting: + stage: List[nn.Module] = [] + for _ in range(cnf.num_layers): + # copy to avoid modifications. shallow copy is enough + block_cnf = copy.copy(cnf) + + # overwrite info if not the first conv in the stage + if stage: + block_cnf.input_channels = block_cnf.out_channels + block_cnf.stride = 1 + + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / total_stage_blocks + + stage.append(block_cnf.block(block_cnf, sd_prob, norm_layer)) + stage_block_id += 1 + + layers.append(nn.Sequential(*stage)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = last_channel if last_channel is not None else 4 * lastconv_input_channels + layers.append( + Conv2dNormActivation( + lastconv_input_channels, + lastconv_output_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=nn.SiLU, + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout, inplace=True), + nn.Linear(lastconv_output_channels, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + init_range = 1.0 / math.sqrt(m.out_features) + nn.init.uniform_(m.weight, -init_range, init_range) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _efficientnet( + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]], + dropout: float, + last_channel: Optional[int], + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> EfficientNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = EfficientNet(inverted_residual_setting, dropout, last_channel=last_channel, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +def _efficientnet_conf( + arch: str, + **kwargs: Any, +) -> Tuple[Sequence[Union[MBConvConfig, FusedMBConvConfig]], Optional[int]]: + inverted_residual_setting: Sequence[Union[MBConvConfig, FusedMBConvConfig]] + if arch.startswith("efficientnet_b"): + bneck_conf = partial(MBConvConfig, width_mult=kwargs.pop("width_mult"), depth_mult=kwargs.pop("depth_mult")) + inverted_residual_setting = [ + bneck_conf(1, 3, 1, 32, 16, 1), + bneck_conf(6, 3, 2, 16, 24, 2), + bneck_conf(6, 5, 2, 24, 40, 2), + bneck_conf(6, 3, 2, 40, 80, 3), + bneck_conf(6, 5, 1, 80, 112, 3), + bneck_conf(6, 5, 2, 112, 192, 4), + bneck_conf(6, 3, 1, 192, 320, 1), + ] + last_channel = None + elif arch.startswith("efficientnet_v2_s"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 24, 24, 2), + FusedMBConvConfig(4, 3, 2, 24, 48, 4), + FusedMBConvConfig(4, 3, 2, 48, 64, 4), + MBConvConfig(4, 3, 2, 64, 128, 6), + MBConvConfig(6, 3, 1, 128, 160, 9), + MBConvConfig(6, 3, 2, 160, 256, 15), + ] + last_channel = 1280 + elif arch.startswith("efficientnet_v2_m"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 24, 24, 3), + FusedMBConvConfig(4, 3, 2, 24, 48, 5), + FusedMBConvConfig(4, 3, 2, 48, 80, 5), + MBConvConfig(4, 3, 2, 80, 160, 7), + MBConvConfig(6, 3, 1, 160, 176, 14), + MBConvConfig(6, 3, 2, 176, 304, 18), + MBConvConfig(6, 3, 1, 304, 512, 5), + ] + last_channel = 1280 + elif arch.startswith("efficientnet_v2_l"): + inverted_residual_setting = [ + FusedMBConvConfig(1, 3, 1, 32, 32, 4), + FusedMBConvConfig(4, 3, 2, 32, 64, 7), + FusedMBConvConfig(4, 3, 2, 64, 96, 7), + MBConvConfig(4, 3, 2, 96, 192, 10), + MBConvConfig(6, 3, 1, 192, 224, 19), + MBConvConfig(6, 3, 2, 224, 384, 25), + MBConvConfig(6, 3, 1, 384, 640, 7), + ] + last_channel = 1280 + else: + raise ValueError(f"Unsupported model type {arch}") + + return inverted_residual_setting, last_channel + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + + +_COMMON_META_V1 = { + **_COMMON_META, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1", +} + + +_COMMON_META_V2 = { + **_COMMON_META, + "min_size": (33, 33), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2", +} + + +class EfficientNet_B0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 5288548, + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.692, + "acc@5": 93.532, + } + }, + "_ops": 0.386, + "_file_size": 20.451, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B1_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b1_rwightman-bac287d4.pth", + transforms=partial( + ImageClassification, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 7794184, + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.642, + "acc@5": 94.186, + } + }, + "_ops": 0.687, + "_file_size": 30.134, + "_docs": """These weights are ported from the original paper.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth", + transforms=partial( + ImageClassification, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR + ), + meta={ + **_COMMON_META_V1, + "num_params": 7794184, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuning", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.838, + "acc@5": 94.934, + } + }, + "_ops": 0.687, + "_file_size": 30.136, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class EfficientNet_B2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pth", + transforms=partial( + ImageClassification, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 9109994, + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.608, + "acc@5": 95.310, + } + }, + "_ops": 1.088, + "_file_size": 35.174, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B3_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pth", + transforms=partial( + ImageClassification, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 12233232, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.008, + "acc@5": 96.054, + } + }, + "_ops": 1.827, + "_file_size": 47.184, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B4_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/rwightman/pytorch-image-models/ + url="https://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pth", + transforms=partial( + ImageClassification, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 19341616, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.384, + "acc@5": 96.594, + } + }, + "_ops": 4.394, + "_file_size": 74.489, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B5_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pth", + transforms=partial( + ImageClassification, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 30389784, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.444, + "acc@5": 96.628, + } + }, + "_ops": 10.266, + "_file_size": 116.864, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B6_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pth", + transforms=partial( + ImageClassification, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 43040704, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.008, + "acc@5": 96.916, + } + }, + "_ops": 19.068, + "_file_size": 165.362, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_B7_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/ + url="https://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pth", + transforms=partial( + ImageClassification, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META_V1, + "num_params": 66347960, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.122, + "acc@5": 96.908, + } + }, + "_ops": 37.746, + "_file_size": 254.675, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_S_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BILINEAR, + ), + meta={ + **_COMMON_META_V2, + "num_params": 21458488, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.228, + "acc@5": 96.878, + } + }, + "_ops": 8.366, + "_file_size": 82.704, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_M_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth", + transforms=partial( + ImageClassification, + crop_size=480, + resize_size=480, + interpolation=InterpolationMode.BILINEAR, + ), + meta={ + **_COMMON_META_V2, + "num_params": 54139356, + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.112, + "acc@5": 97.156, + } + }, + "_ops": 24.582, + "_file_size": 208.01, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class EfficientNet_V2_L_Weights(WeightsEnum): + # Weights ported from https://github.com/google/automl/tree/master/efficientnetv2 + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth", + transforms=partial( + ImageClassification, + crop_size=480, + resize_size=480, + interpolation=InterpolationMode.BICUBIC, + mean=(0.5, 0.5, 0.5), + std=(0.5, 0.5, 0.5), + ), + meta={ + **_COMMON_META_V2, + "num_params": 118515272, + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.808, + "acc@5": 97.788, + } + }, + "_ops": 56.08, + "_file_size": 454.573, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B0_Weights.IMAGENET1K_V1)) +def efficientnet_b0( + *, weights: Optional[EfficientNet_B0_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B0 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B0_Weights + :members: + """ + weights = EfficientNet_B0_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b0", width_mult=1.0, depth_mult=1.0) + return _efficientnet( + inverted_residual_setting, kwargs.pop("dropout", 0.2), last_channel, weights, progress, **kwargs + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B1_Weights.IMAGENET1K_V1)) +def efficientnet_b1( + *, weights: Optional[EfficientNet_B1_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B1 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B1_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B1_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B1_Weights + :members: + """ + weights = EfficientNet_B1_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b1", width_mult=1.0, depth_mult=1.1) + return _efficientnet( + inverted_residual_setting, kwargs.pop("dropout", 0.2), last_channel, weights, progress, **kwargs + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B2_Weights.IMAGENET1K_V1)) +def efficientnet_b2( + *, weights: Optional[EfficientNet_B2_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B2_Weights + :members: + """ + weights = EfficientNet_B2_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b2", width_mult=1.1, depth_mult=1.2) + return _efficientnet( + inverted_residual_setting, kwargs.pop("dropout", 0.3), last_channel, weights, progress, **kwargs + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B3_Weights.IMAGENET1K_V1)) +def efficientnet_b3( + *, weights: Optional[EfficientNet_B3_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B3 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B3_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B3_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B3_Weights + :members: + """ + weights = EfficientNet_B3_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b3", width_mult=1.2, depth_mult=1.4) + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.3), + last_channel, + weights, + progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B4_Weights.IMAGENET1K_V1)) +def efficientnet_b4( + *, weights: Optional[EfficientNet_B4_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B4_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B4_Weights + :members: + """ + weights = EfficientNet_B4_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b4", width_mult=1.4, depth_mult=1.8) + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.4), + last_channel, + weights, + progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B5_Weights.IMAGENET1K_V1)) +def efficientnet_b5( + *, weights: Optional[EfficientNet_B5_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B5 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B5_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B5_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B5_Weights + :members: + """ + weights = EfficientNet_B5_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b5", width_mult=1.6, depth_mult=2.2) + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.4), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B6_Weights.IMAGENET1K_V1)) +def efficientnet_b6( + *, weights: Optional[EfficientNet_B6_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B6_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B6_Weights + :members: + """ + weights = EfficientNet_B6_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b6", width_mult=1.8, depth_mult=2.6) + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.5), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_B7_Weights.IMAGENET1K_V1)) +def efficientnet_b7( + *, weights: Optional[EfficientNet_B7_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """EfficientNet B7 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional + Neural Networks `_ paper. + + Args: + weights (:class:`~torchvision.models.EfficientNet_B7_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_B7_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_B7_Weights + :members: + """ + weights = EfficientNet_B7_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_b7", width_mult=2.0, depth_mult=3.1) + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.5), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.01), + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_S_Weights.IMAGENET1K_V1)) +def efficientnet_v2_s( + *, weights: Optional[EfficientNet_V2_S_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-S architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights + :members: + """ + weights = EfficientNet_V2_S_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_s") + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.2), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_M_Weights.IMAGENET1K_V1)) +def efficientnet_v2_m( + *, weights: Optional[EfficientNet_V2_M_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-M architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_M_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights + :members: + """ + weights = EfficientNet_V2_M_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_m") + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.3), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_L_Weights.IMAGENET1K_V1)) +def efficientnet_v2_l( + *, weights: Optional[EfficientNet_V2_L_Weights] = None, progress: bool = True, **kwargs: Any +) -> EfficientNet: + """ + Constructs an EfficientNetV2-L architecture from + `EfficientNetV2: Smaller Models and Faster Training `_. + + Args: + weights (:class:`~torchvision.models.EfficientNet_V2_L_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.EfficientNet_V2_L_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.EfficientNet_V2_L_Weights + :members: + """ + weights = EfficientNet_V2_L_Weights.verify(weights) + + inverted_residual_setting, last_channel = _efficientnet_conf("efficientnet_v2_l") + return _efficientnet( + inverted_residual_setting, + kwargs.pop("dropout", 0.4), + last_channel, + weights, + progress, + norm_layer=partial(nn.BatchNorm2d, eps=1e-03), + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/feature_extraction.py b/lib/python3.11/site-packages/torchvision/models/feature_extraction.py new file mode 100644 index 0000000000000000000000000000000000000000..a20387a6b8936272f1eadc55e76451244dba83b5 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/feature_extraction.py @@ -0,0 +1,607 @@ +import copy +import inspect +import math +import re +import warnings +from collections import OrderedDict +from copy import deepcopy +from itertools import chain +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torchvision +from torch import fx, nn +from torch.fx.graph_module import _CodeOnlyModule, _copy_attr, _USER_PRESERVED_ATTRIBUTES_KEY + + +__all__ = ["create_feature_extractor", "get_graph_node_names"] + + +class LeafModuleAwareTracer(fx.Tracer): + """ + An fx.Tracer that allows the user to specify a set of leaf modules, i.e. + modules that are not to be traced through. The resulting graph ends up + having single nodes referencing calls to the leaf modules' forward methods. + """ + + def __init__(self, *args, **kwargs): + self.leaf_modules = {} + if "leaf_modules" in kwargs: + leaf_modules = kwargs.pop("leaf_modules") + self.leaf_modules = leaf_modules + super().__init__(*args, **kwargs) + + def is_leaf_module(self, m: nn.Module, module_qualname: str) -> bool: + if isinstance(m, tuple(self.leaf_modules)): + return True + return super().is_leaf_module(m, module_qualname) + + +class NodePathTracer(LeafModuleAwareTracer): + """ + NodePathTracer is an FX tracer that, for each operation, also records the + name of the Node from which the operation originated. A node name here is + a `.` separated path walking the hierarchy from top level module down to + leaf operation or leaf module. The name of the top level module is not + included as part of the node name. For example, if we trace a module whose + forward method applies a ReLU module, the name for that node will simply + be 'relu'. + + Some notes on the specifics: + - Nodes are recorded to `self.node_to_qualname` which is a dictionary + mapping a given Node object to its node name. + - Nodes are recorded in the order which they are executed during + tracing. + - When a duplicate node name is encountered, a suffix of the form + _{int} is added. The counter starts from 1. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # Track the qualified name of the Node being traced + self.current_module_qualname = "" + # A map from FX Node to the qualified name\# + # NOTE: This is loosely like the "qualified name" mentioned in the + # torch.fx docs https://pytorch.org/docs/stable/fx.html but adapted + # for the purposes of the torchvision feature extractor + self.node_to_qualname = OrderedDict() + + def call_module(self, m: torch.nn.Module, forward: Callable, args, kwargs): + """ + Override of `fx.Tracer.call_module` + This override: + 1) Stores away the qualified name of the caller for restoration later + 2) Adds the qualified name of the caller to + `current_module_qualname` for retrieval by `create_proxy` + 3) Once a leaf module is reached, calls `create_proxy` + 4) Restores the caller's qualified name into current_module_qualname + """ + old_qualname = self.current_module_qualname + try: + module_qualname = self.path_of_module(m) + self.current_module_qualname = module_qualname + if not self.is_leaf_module(m, module_qualname): + out = forward(*args, **kwargs) + return out + return self.create_proxy("call_module", module_qualname, args, kwargs) + finally: + self.current_module_qualname = old_qualname + + def create_proxy( + self, kind: str, target: fx.node.Target, args, kwargs, name=None, type_expr=None, *_ + ) -> fx.proxy.Proxy: + """ + Override of `Tracer.create_proxy`. This override intercepts the recording + of every operation and stores away the current traced module's qualified + name in `node_to_qualname` + """ + proxy = super().create_proxy(kind, target, args, kwargs, name, type_expr) + self.node_to_qualname[proxy.node] = self._get_node_qualname(self.current_module_qualname, proxy.node) + return proxy + + def _get_node_qualname(self, module_qualname: str, node: fx.node.Node) -> str: + node_qualname = module_qualname + + if node.op != "call_module": + # In this case module_qualname from torch.fx doesn't go all the + # way to the leaf function/op, so we need to append it + if len(node_qualname) > 0: + # Only append '.' if we are deeper than the top level module + node_qualname += "." + node_qualname += str(node) + + # Now we need to add an _{index} postfix on any repeated node names + # For modules we do this from scratch + # But for anything else, torch.fx already has a globally scoped + # _{index} postfix. But we want it locally (relative to direct parent) + # scoped. So first we need to undo the torch.fx postfix + if re.match(r".+_[0-9]+$", node_qualname) is not None: + node_qualname = node_qualname.rsplit("_", 1)[0] + + # ... and now we add on our own postfix + for existing_qualname in reversed(self.node_to_qualname.values()): + # Check to see if existing_qualname is of the form + # {node_qualname} or {node_qualname}_{int} + if re.match(rf"{node_qualname}(_[0-9]+)?$", existing_qualname) is not None: + postfix = existing_qualname.replace(node_qualname, "") + if len(postfix): + # existing_qualname is of the form {node_qualname}_{int} + next_index = int(postfix[1:]) + 1 + else: + # existing_qualname is of the form {node_qualname} + next_index = 1 + node_qualname += f"_{next_index}" + break + + return node_qualname + + +def _is_subseq(x, y): + """Check if y is a subsequence of x + https://stackoverflow.com/a/24017747/4391249 + """ + iter_x = iter(x) + return all(any(x_item == y_item for x_item in iter_x) for y_item in y) + + +def _warn_graph_differences(train_tracer: NodePathTracer, eval_tracer: NodePathTracer): + """ + Utility function for warning the user if there are differences between + the train graph nodes and the eval graph nodes. + """ + train_nodes = list(train_tracer.node_to_qualname.values()) + eval_nodes = list(eval_tracer.node_to_qualname.values()) + + if len(train_nodes) == len(eval_nodes) and all(t == e for t, e in zip(train_nodes, eval_nodes)): + return + + suggestion_msg = ( + "When choosing nodes for feature extraction, you may need to specify " + "output nodes for train and eval mode separately." + ) + + if _is_subseq(train_nodes, eval_nodes): + msg = ( + "NOTE: The nodes obtained by tracing the model in eval mode " + "are a subsequence of those obtained in train mode. " + ) + elif _is_subseq(eval_nodes, train_nodes): + msg = ( + "NOTE: The nodes obtained by tracing the model in train mode " + "are a subsequence of those obtained in eval mode. " + ) + else: + msg = "The nodes obtained by tracing the model in train mode are different to those obtained in eval mode. " + warnings.warn(msg + suggestion_msg) + + +def _get_leaf_modules_for_ops() -> List[type]: + members = inspect.getmembers(torchvision.ops) + result = [] + for _, obj in members: + if inspect.isclass(obj) and issubclass(obj, torch.nn.Module): + result.append(obj) + return result + + +def _set_default_tracer_kwargs(original_tr_kwargs: Optional[Dict[str, Any]]) -> Dict[str, Any]: + default_autowrap_modules = (math, torchvision.ops) + default_leaf_modules = _get_leaf_modules_for_ops() + result_tracer_kwargs = {} if original_tr_kwargs is None else original_tr_kwargs + result_tracer_kwargs["autowrap_modules"] = ( + tuple(set(result_tracer_kwargs["autowrap_modules"] + default_autowrap_modules)) + if "autowrap_modules" in result_tracer_kwargs + else default_autowrap_modules + ) + result_tracer_kwargs["leaf_modules"] = ( + list(set(result_tracer_kwargs["leaf_modules"] + default_leaf_modules)) + if "leaf_modules" in result_tracer_kwargs + else default_leaf_modules + ) + return result_tracer_kwargs + + +def get_graph_node_names( + model: nn.Module, + tracer_kwargs: Optional[Dict[str, Any]] = None, + suppress_diff_warning: bool = False, + concrete_args: Optional[Dict[str, Any]] = None, +) -> Tuple[List[str], List[str]]: + """ + Dev utility to return node names in order of execution. See note on node + names under :func:`create_feature_extractor`. Useful for seeing which node + names are available for feature extraction. There are two reasons that + node names can't easily be read directly from the code for a model: + + 1. Not all submodules are traced through. Modules from ``torch.nn`` all + fall within this category. + 2. Nodes representing the repeated application of the same operation + or leaf module get a ``_{counter}`` postfix. + + The model is traced twice: once in train mode, and once in eval mode. Both + sets of node names are returned. + + For more details on the node naming conventions used here, please see the + :ref:`relevant subheading ` in the + `documentation `_. + + Args: + model (nn.Module): model for which we'd like to print node names + tracer_kwargs (dict, optional): a dictionary of keyword arguments for + ``NodePathTracer`` (they are eventually passed onto + `torch.fx.Tracer `_). + By default, it will be set to wrap and make leaf nodes all torchvision ops: + {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),} + WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user + provided dictionary. + suppress_diff_warning (bool, optional): whether to suppress a warning + when there are discrepancies between the train and eval version of + the graph. Defaults to False. + concrete_args (Optional[Dict[str, any]]): Concrete arguments that should + not be treated as Proxies. According to the `Pytorch docs + `_, + this parameter's API may not be guaranteed. + + Returns: + tuple(list, list): a list of node names from tracing the model in + train mode, and another from tracing the model in eval mode. + + Examples:: + + >>> model = torchvision.models.resnet18() + >>> train_nodes, eval_nodes = get_graph_node_names(model) + """ + tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs) + is_training = model.training + train_tracer = NodePathTracer(**tracer_kwargs) + train_tracer.trace(model.train(), concrete_args=concrete_args) + eval_tracer = NodePathTracer(**tracer_kwargs) + eval_tracer.trace(model.eval(), concrete_args=concrete_args) + train_nodes = list(train_tracer.node_to_qualname.values()) + eval_nodes = list(eval_tracer.node_to_qualname.values()) + if not suppress_diff_warning: + _warn_graph_differences(train_tracer, eval_tracer) + # Restore training state + model.train(is_training) + return train_nodes, eval_nodes + + +class DualGraphModule(fx.GraphModule): + """ + A derivative of `fx.GraphModule`. Differs in the following ways: + - Requires a train and eval version of the underlying graph + - Copies submodules according to the nodes of both train and eval graphs. + - Calling train(mode) switches between train graph and eval graph. + """ + + def __init__( + self, root: torch.nn.Module, train_graph: fx.Graph, eval_graph: fx.Graph, class_name: str = "GraphModule" + ): + """ + Args: + root (nn.Module): module from which the copied module hierarchy is + built + train_graph (fx.Graph): the graph that should be used in train mode + eval_graph (fx.Graph): the graph that should be used in eval mode + """ + super(fx.GraphModule, self).__init__() + + self.__class__.__name__ = class_name + + self.train_graph = train_graph + self.eval_graph = eval_graph + + # Copy all get_attr and call_module ops (indicated by BOTH train and + # eval graphs) + for node in chain(iter(train_graph.nodes), iter(eval_graph.nodes)): + if node.op in ["get_attr", "call_module"]: + if not isinstance(node.target, str): + raise TypeError(f"node.target should be of type str instead of {type(node.target)}") + _copy_attr(root, self, node.target) + + # train mode by default + self.train() + self.graph = train_graph + + # (borrowed from fx.GraphModule): + # Store the Tracer class responsible for creating a Graph separately as part of the + # GraphModule state, except when the Tracer is defined in a local namespace. + # Locally defined Tracers are not pickleable. This is needed because torch.package will + # serialize a GraphModule without retaining the Graph, and needs to use the correct Tracer + # to re-create the Graph during deserialization. + if self.eval_graph._tracer_cls != self.train_graph._tracer_cls: + raise TypeError( + f"Train mode and eval mode should use the same tracer class. Instead got {self.eval_graph._tracer_cls} for eval vs {self.train_graph._tracer_cls} for train" + ) + self._tracer_cls = None + if self.graph._tracer_cls and "" not in self.graph._tracer_cls.__qualname__: + self._tracer_cls = self.graph._tracer_cls + + def train(self, mode=True): + """ + Swap out the graph depending on the selected training mode. + NOTE this should be safe when calling model.eval() because that just + calls this with mode == False. + """ + # NOTE: Only set self.graph if the current graph is not the desired + # one. This saves us from recompiling the graph where not necessary. + if mode and not self.training: + self.graph = self.train_graph + elif not mode and self.training: + self.graph = self.eval_graph + return super().train(mode=mode) + + def _deepcopy_init(self): + # See __deepcopy__ below + return DualGraphModule.__init__ + + def __deepcopy__(self, memo): + # Same as the base class' __deepcopy__ from pytorch, with minor + # modification to account for train_graph and eval_graph + # https://github.com/pytorch/pytorch/blob/f684dbd0026f98f8fa291cab74dbc4d61ba30580/torch/fx/graph_module.py#L875 + # + # This is using a bunch of private stuff from torch, so if that breaks, + # we'll likely have to remove this, along with the associated + # non-regression test. + res = type(self).__new__(type(self)) + memo[id(self)] = res + fake_mod = _CodeOnlyModule(copy.deepcopy(self.__dict__, memo)) + self._deepcopy_init()(res, fake_mod, fake_mod.__dict__["train_graph"], fake_mod.__dict__["eval_graph"]) + + extra_preserved_attrs = [ + "_state_dict_hooks", + "_load_state_dict_pre_hooks", + "_load_state_dict_post_hooks", + "_replace_hook", + "_create_node_hooks", + "_erase_node_hooks", + ] + for attr in extra_preserved_attrs: + if attr in self.__dict__: + setattr(res, attr, copy.deepcopy(self.__dict__[attr], memo)) + res.meta = copy.deepcopy(getattr(self, "meta", {}), memo) + if _USER_PRESERVED_ATTRIBUTES_KEY in res.meta: + for attr_name, attr in res.meta[_USER_PRESERVED_ATTRIBUTES_KEY].items(): + setattr(res, attr_name, attr) + return res + + +def create_feature_extractor( + model: nn.Module, + return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, + train_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, + eval_return_nodes: Optional[Union[List[str], Dict[str, str]]] = None, + tracer_kwargs: Optional[Dict[str, Any]] = None, + suppress_diff_warning: bool = False, + concrete_args: Optional[Dict[str, Any]] = None, +) -> fx.GraphModule: + """ + Creates a new graph module that returns intermediate nodes from a given + model as dictionary with user specified keys as strings, and the requested + outputs as values. This is achieved by re-writing the computation graph of + the model via FX to return the desired nodes as outputs. All unused nodes + are removed, together with their corresponding parameters. + + Desired output nodes must be specified as a ``.`` separated + path walking the module hierarchy from top level module down to leaf + operation or leaf module. For more details on the node naming conventions + used here, please see the :ref:`relevant subheading ` + in the `documentation `_. + + Not all models will be FX traceable, although with some massaging they can + be made to cooperate. Here's a (not exhaustive) list of tips: + + - If you don't need to trace through a particular, problematic + sub-module, turn it into a "leaf module" by passing a list of + ``leaf_modules`` as one of the ``tracer_kwargs`` (see example below). + It will not be traced through, but rather, the resulting graph will + hold a reference to that module's forward method. + - Likewise, you may turn functions into leaf functions by passing a + list of ``autowrap_functions`` as one of the ``tracer_kwargs`` (see + example below). + - Some inbuilt Python functions can be problematic. For instance, + ``int`` will raise an error during tracing. You may wrap them in your + own function and then pass that in ``autowrap_functions`` as one of + the ``tracer_kwargs``. + + For further information on FX see the + `torch.fx documentation `_. + + Args: + model (nn.Module): model on which we will extract the features + return_nodes (list or dict, optional): either a ``List`` or a ``Dict`` + containing the names (or partial names - see note above) + of the nodes for which the activations will be returned. If it is + a ``Dict``, the keys are the node names, and the values + are the user-specified keys for the graph module's returned + dictionary. If it is a ``List``, it is treated as a ``Dict`` mapping + node specification strings directly to output names. In the case + that ``train_return_nodes`` and ``eval_return_nodes`` are specified, + this should not be specified. + train_return_nodes (list or dict, optional): similar to + ``return_nodes``. This can be used if the return nodes + for train mode are different than those from eval mode. + If this is specified, ``eval_return_nodes`` must also be specified, + and ``return_nodes`` should not be specified. + eval_return_nodes (list or dict, optional): similar to + ``return_nodes``. This can be used if the return nodes + for train mode are different than those from eval mode. + If this is specified, ``train_return_nodes`` must also be specified, + and `return_nodes` should not be specified. + tracer_kwargs (dict, optional): a dictionary of keyword arguments for + ``NodePathTracer`` (which passes them onto it's parent class + `torch.fx.Tracer `_). + By default, it will be set to wrap and make leaf nodes all torchvision ops: + {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),} + WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user + provided dictionary. + suppress_diff_warning (bool, optional): whether to suppress a warning + when there are discrepancies between the train and eval version of + the graph. Defaults to False. + concrete_args (Optional[Dict[str, any]]): Concrete arguments that should + not be treated as Proxies. According to the `Pytorch docs + `_, + this parameter's API may not be guaranteed. + + Examples:: + + >>> # Feature extraction with resnet + >>> model = torchvision.models.resnet18() + >>> # extract layer1 and layer3, giving as names `feat1` and feat2` + >>> model = create_feature_extractor( + >>> model, {'layer1': 'feat1', 'layer3': 'feat2'}) + >>> out = model(torch.rand(1, 3, 224, 224)) + >>> print([(k, v.shape) for k, v in out.items()]) + >>> [('feat1', torch.Size([1, 64, 56, 56])), + >>> ('feat2', torch.Size([1, 256, 14, 14]))] + + >>> # Specifying leaf modules and leaf functions + >>> def leaf_function(x): + >>> # This would raise a TypeError if traced through + >>> return int(x) + >>> + >>> class LeafModule(torch.nn.Module): + >>> def forward(self, x): + >>> # This would raise a TypeError if traced through + >>> int(x.shape[0]) + >>> return torch.nn.functional.relu(x + 4) + >>> + >>> class MyModule(torch.nn.Module): + >>> def __init__(self): + >>> super().__init__() + >>> self.conv = torch.nn.Conv2d(3, 1, 3) + >>> self.leaf_module = LeafModule() + >>> + >>> def forward(self, x): + >>> leaf_function(x.shape[0]) + >>> x = self.conv(x) + >>> return self.leaf_module(x) + >>> + >>> model = create_feature_extractor( + >>> MyModule(), return_nodes=['leaf_module'], + >>> tracer_kwargs={'leaf_modules': [LeafModule], + >>> 'autowrap_functions': [leaf_function]}) + + """ + tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs) + is_training = model.training + + if all(arg is None for arg in [return_nodes, train_return_nodes, eval_return_nodes]): + + raise ValueError( + "Either `return_nodes` or `train_return_nodes` and `eval_return_nodes` together, should be specified" + ) + + if (train_return_nodes is None) ^ (eval_return_nodes is None): + raise ValueError( + "If any of `train_return_nodes` and `eval_return_nodes` are specified, then both should be specified" + ) + + if not ((return_nodes is None) ^ (train_return_nodes is None)): + raise ValueError("If `train_return_nodes` and `eval_return_nodes` are specified, then both should be specified") + + # Put *_return_nodes into Dict[str, str] format + def to_strdict(n) -> Dict[str, str]: + if isinstance(n, list): + return {str(i): str(i) for i in n} + return {str(k): str(v) for k, v in n.items()} + + if train_return_nodes is None: + return_nodes = to_strdict(return_nodes) + train_return_nodes = deepcopy(return_nodes) + eval_return_nodes = deepcopy(return_nodes) + else: + train_return_nodes = to_strdict(train_return_nodes) + eval_return_nodes = to_strdict(eval_return_nodes) + + # Repeat the tracing and graph rewriting for train and eval mode + tracers = {} + graphs = {} + mode_return_nodes: Dict[str, Dict[str, str]] = {"train": train_return_nodes, "eval": eval_return_nodes} + for mode in ["train", "eval"]: + if mode == "train": + model.train() + elif mode == "eval": + model.eval() + + # Instantiate our NodePathTracer and use that to trace the model + tracer = NodePathTracer(**tracer_kwargs) + graph = tracer.trace(model, concrete_args=concrete_args) + + name = model.__class__.__name__ if isinstance(model, nn.Module) else model.__name__ + graph_module = fx.GraphModule(tracer.root, graph, name) + + available_nodes = list(tracer.node_to_qualname.values()) + # FIXME We don't know if we should expect this to happen + if len(set(available_nodes)) != len(available_nodes): + raise ValueError( + "There are duplicate nodes! Please raise an issue https://github.com/pytorch/vision/issues" + ) + # Check that all outputs in return_nodes are present in the model + for query in mode_return_nodes[mode].keys(): + # To check if a query is available we need to check that at least + # one of the available names starts with it up to a . + if not any([re.match(rf"^{query}(\.|$)", n) is not None for n in available_nodes]): + raise ValueError( + f"node: '{query}' is not present in model. Hint: use " + "`get_graph_node_names` to make sure the " + "`return_nodes` you specified are present. It may even " + "be that you need to specify `train_return_nodes` and " + "`eval_return_nodes` separately." + ) + + # Remove existing output nodes (train mode) + orig_output_nodes = [] + for n in reversed(graph_module.graph.nodes): + if n.op == "output": + orig_output_nodes.append(n) + if not orig_output_nodes: + raise ValueError("No output nodes found in graph_module.graph.nodes") + + for n in orig_output_nodes: + graph_module.graph.erase_node(n) + + # Find nodes corresponding to return_nodes and make them into output_nodes + nodes = [n for n in graph_module.graph.nodes] + output_nodes = OrderedDict() + for n in reversed(nodes): + module_qualname = tracer.node_to_qualname.get(n) + if module_qualname is None: + # NOTE - Know cases where this happens: + # - Node representing creation of a tensor constant - probably + # not interesting as a return node + # - When packing outputs into a named tuple like in InceptionV3 + continue + for query in mode_return_nodes[mode]: + depth = query.count(".") + if ".".join(module_qualname.split(".")[: depth + 1]) == query: + output_nodes[mode_return_nodes[mode][query]] = n + mode_return_nodes[mode].pop(query) + break + output_nodes = OrderedDict(reversed(list(output_nodes.items()))) + + # And add them in the end of the graph + with graph_module.graph.inserting_after(nodes[-1]): + graph_module.graph.output(output_nodes) + + # Remove unused modules / parameters + graph_module.graph.eliminate_dead_code() + graph_module.recompile() + + # Keep track of the tracer and graph, so we can choose the main one + tracers[mode] = tracer + graphs[mode] = graph + + # Warn user if there are any discrepancies between the graphs of the + # train and eval modes + if not suppress_diff_warning: + _warn_graph_differences(tracers["train"], tracers["eval"]) + + # Build the final graph module + graph_module = DualGraphModule(model, graphs["train"], graphs["eval"], class_name=name) + + # Restore original training mode + model.train(is_training) + graph_module.train(is_training) + + return graph_module diff --git a/lib/python3.11/site-packages/torchvision/models/googlenet.py b/lib/python3.11/site-packages/torchvision/models/googlenet.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8ecbcd3b227c19d44f4d4fee532617c3e38ba3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/googlenet.py @@ -0,0 +1,345 @@ +import warnings +from collections import namedtuple +from functools import partial +from typing import Any, Callable, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNet_Weights", "googlenet"] + + +GoogLeNetOutputs = namedtuple("GoogLeNetOutputs", ["logits", "aux_logits2", "aux_logits1"]) +GoogLeNetOutputs.__annotations__ = {"logits": Tensor, "aux_logits2": Optional[Tensor], "aux_logits1": Optional[Tensor]} + +# Script annotations failed with _GoogleNetOutputs = namedtuple ... +# _GoogLeNetOutputs set here for backwards compat +_GoogLeNetOutputs = GoogLeNetOutputs + + +class GoogLeNet(nn.Module): + __constants__ = ["aux_logits", "transform_input"] + + def __init__( + self, + num_classes: int = 1000, + aux_logits: bool = True, + transform_input: bool = False, + init_weights: Optional[bool] = None, + blocks: Optional[List[Callable[..., nn.Module]]] = None, + dropout: float = 0.2, + dropout_aux: float = 0.7, + ) -> None: + super().__init__() + _log_api_usage_once(self) + if blocks is None: + blocks = [BasicConv2d, Inception, InceptionAux] + if init_weights is None: + warnings.warn( + "The default weight initialization of GoogleNet will be changed in future releases of " + "torchvision. If you wish to keep the old behavior (which leads to long initialization times" + " due to scipy/scipy#11299), please set init_weights=True.", + FutureWarning, + ) + init_weights = True + if len(blocks) != 3: + raise ValueError(f"blocks length should be 3 instead of {len(blocks)}") + conv_block = blocks[0] + inception_block = blocks[1] + inception_aux_block = blocks[2] + + self.aux_logits = aux_logits + self.transform_input = transform_input + + self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3) + self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + self.conv2 = conv_block(64, 64, kernel_size=1) + self.conv3 = conv_block(64, 192, kernel_size=3, padding=1) + self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + + self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32) + self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64) + self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + + self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64) + self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64) + self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64) + self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64) + self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128) + self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128) + self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128) + + if aux_logits: + self.aux1 = inception_aux_block(512, num_classes, dropout=dropout_aux) + self.aux2 = inception_aux_block(528, num_classes, dropout=dropout_aux) + else: + self.aux1 = None # type: ignore[assignment] + self.aux2 = None # type: ignore[assignment] + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(p=dropout) + self.fc = nn.Linear(1024, num_classes) + + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=0.01, a=-2, b=2) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _transform_input(self, x: Tensor) -> Tensor: + if self.transform_input: + x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 + x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 + x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 + x = torch.cat((x_ch0, x_ch1, x_ch2), 1) + return x + + def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + # N x 3 x 224 x 224 + x = self.conv1(x) + # N x 64 x 112 x 112 + x = self.maxpool1(x) + # N x 64 x 56 x 56 + x = self.conv2(x) + # N x 64 x 56 x 56 + x = self.conv3(x) + # N x 192 x 56 x 56 + x = self.maxpool2(x) + + # N x 192 x 28 x 28 + x = self.inception3a(x) + # N x 256 x 28 x 28 + x = self.inception3b(x) + # N x 480 x 28 x 28 + x = self.maxpool3(x) + # N x 480 x 14 x 14 + x = self.inception4a(x) + # N x 512 x 14 x 14 + aux1: Optional[Tensor] = None + if self.aux1 is not None: + if self.training: + aux1 = self.aux1(x) + + x = self.inception4b(x) + # N x 512 x 14 x 14 + x = self.inception4c(x) + # N x 512 x 14 x 14 + x = self.inception4d(x) + # N x 528 x 14 x 14 + aux2: Optional[Tensor] = None + if self.aux2 is not None: + if self.training: + aux2 = self.aux2(x) + + x = self.inception4e(x) + # N x 832 x 14 x 14 + x = self.maxpool4(x) + # N x 832 x 7 x 7 + x = self.inception5a(x) + # N x 832 x 7 x 7 + x = self.inception5b(x) + # N x 1024 x 7 x 7 + + x = self.avgpool(x) + # N x 1024 x 1 x 1 + x = torch.flatten(x, 1) + # N x 1024 + x = self.dropout(x) + x = self.fc(x) + # N x 1000 (num_classes) + return x, aux2, aux1 + + @torch.jit.unused + def eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs: + if self.training and self.aux_logits: + return _GoogLeNetOutputs(x, aux2, aux1) + else: + return x # type: ignore[return-value] + + def forward(self, x: Tensor) -> GoogLeNetOutputs: + x = self._transform_input(x) + x, aux2, aux1 = self._forward(x) + aux_defined = self.training and self.aux_logits + if torch.jit.is_scripting(): + if not aux_defined: + warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple") + return GoogLeNetOutputs(x, aux2, aux1) + else: + return self.eager_outputs(x, aux2, aux1) + + +class Inception(nn.Module): + def __init__( + self, + in_channels: int, + ch1x1: int, + ch3x3red: int, + ch3x3: int, + ch5x5red: int, + ch5x5: int, + pool_proj: int, + conv_block: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1) + + self.branch2 = nn.Sequential( + conv_block(in_channels, ch3x3red, kernel_size=1), conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1) + ) + + self.branch3 = nn.Sequential( + conv_block(in_channels, ch5x5red, kernel_size=1), + # Here, kernel_size=3 instead of kernel_size=5 is a known bug. + # Please see https://github.com/pytorch/vision/issues/906 for details. + conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1), + ) + + self.branch4 = nn.Sequential( + nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True), + conv_block(in_channels, pool_proj, kernel_size=1), + ) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch1 = self.branch1(x) + branch2 = self.branch2(x) + branch3 = self.branch3(x) + branch4 = self.branch4(x) + + outputs = [branch1, branch2, branch3, branch4] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionAux(nn.Module): + def __init__( + self, + in_channels: int, + num_classes: int, + conv_block: Optional[Callable[..., nn.Module]] = None, + dropout: float = 0.7, + ) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.conv = conv_block(in_channels, 128, kernel_size=1) + + self.fc1 = nn.Linear(2048, 1024) + self.fc2 = nn.Linear(1024, num_classes) + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x: Tensor) -> Tensor: + # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14 + x = F.adaptive_avg_pool2d(x, (4, 4)) + # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4 + x = self.conv(x) + # N x 128 x 4 x 4 + x = torch.flatten(x, 1) + # N x 2048 + x = F.relu(self.fc1(x), inplace=True) + # N x 1024 + x = self.dropout(x) + # N x 1024 + x = self.fc2(x) + # N x 1000 (num_classes) + + return x + + +class BasicConv2d(nn.Module): + def __init__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None: + super().__init__() + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001) + + def forward(self, x: Tensor) -> Tensor: + x = self.conv(x) + x = self.bn(x) + return F.relu(x, inplace=True) + + +class GoogLeNet_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/googlenet-1378be20.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + "num_params": 6624904, + "min_size": (15, 15), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#googlenet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.778, + "acc@5": 89.530, + } + }, + "_ops": 1.498, + "_file_size": 49.731, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", GoogLeNet_Weights.IMAGENET1K_V1)) +def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet: + """GoogLeNet (Inception v1) model architecture from + `Going Deeper with Convolutions `_. + + Args: + weights (:class:`~torchvision.models.GoogLeNet_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.GoogLeNet_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.GoogLeNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.GoogLeNet_Weights + :members: + """ + weights = GoogLeNet_Weights.verify(weights) + + original_aux_logits = kwargs.get("aux_logits", False) + if weights is not None: + if "transform_input" not in kwargs: + _ovewrite_named_param(kwargs, "transform_input", True) + _ovewrite_named_param(kwargs, "aux_logits", True) + _ovewrite_named_param(kwargs, "init_weights", False) + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = GoogLeNet(**kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if not original_aux_logits: + model.aux_logits = False + model.aux1 = None # type: ignore[assignment] + model.aux2 = None # type: ignore[assignment] + else: + warnings.warn( + "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them" + ) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/inception.py b/lib/python3.11/site-packages/torchvision/models/inception.py new file mode 100644 index 0000000000000000000000000000000000000000..447a7682d62c0e31570aaf1e32c2da55a0d697d1 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/inception.py @@ -0,0 +1,478 @@ +import warnings +from collections import namedtuple +from functools import partial +from typing import Any, Callable, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception_V3_Weights", "inception_v3"] + + +InceptionOutputs = namedtuple("InceptionOutputs", ["logits", "aux_logits"]) +InceptionOutputs.__annotations__ = {"logits": Tensor, "aux_logits": Optional[Tensor]} + +# Script annotations failed with _GoogleNetOutputs = namedtuple ... +# _InceptionOutputs set here for backwards compat +_InceptionOutputs = InceptionOutputs + + +class Inception3(nn.Module): + def __init__( + self, + num_classes: int = 1000, + aux_logits: bool = True, + transform_input: bool = False, + inception_blocks: Optional[List[Callable[..., nn.Module]]] = None, + init_weights: Optional[bool] = None, + dropout: float = 0.5, + ) -> None: + super().__init__() + _log_api_usage_once(self) + if inception_blocks is None: + inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, InceptionAux] + if init_weights is None: + warnings.warn( + "The default weight initialization of inception_v3 will be changed in future releases of " + "torchvision. If you wish to keep the old behavior (which leads to long initialization times" + " due to scipy/scipy#11299), please set init_weights=True.", + FutureWarning, + ) + init_weights = True + if len(inception_blocks) != 7: + raise ValueError(f"length of inception_blocks should be 7 instead of {len(inception_blocks)}") + conv_block = inception_blocks[0] + inception_a = inception_blocks[1] + inception_b = inception_blocks[2] + inception_c = inception_blocks[3] + inception_d = inception_blocks[4] + inception_e = inception_blocks[5] + inception_aux = inception_blocks[6] + + self.aux_logits = aux_logits + self.transform_input = transform_input + self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2) + self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3) + self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1) + self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2) + self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3) + self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2) + self.Mixed_5b = inception_a(192, pool_features=32) + self.Mixed_5c = inception_a(256, pool_features=64) + self.Mixed_5d = inception_a(288, pool_features=64) + self.Mixed_6a = inception_b(288) + self.Mixed_6b = inception_c(768, channels_7x7=128) + self.Mixed_6c = inception_c(768, channels_7x7=160) + self.Mixed_6d = inception_c(768, channels_7x7=160) + self.Mixed_6e = inception_c(768, channels_7x7=192) + self.AuxLogits: Optional[nn.Module] = None + if aux_logits: + self.AuxLogits = inception_aux(768, num_classes) + self.Mixed_7a = inception_d(768) + self.Mixed_7b = inception_e(1280) + self.Mixed_7c = inception_e(2048) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(p=dropout) + self.fc = nn.Linear(2048, num_classes) + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1 # type: ignore + torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=stddev, a=-2, b=2) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _transform_input(self, x: Tensor) -> Tensor: + if self.transform_input: + x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 + x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 + x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 + x = torch.cat((x_ch0, x_ch1, x_ch2), 1) + return x + + def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor]]: + # N x 3 x 299 x 299 + x = self.Conv2d_1a_3x3(x) + # N x 32 x 149 x 149 + x = self.Conv2d_2a_3x3(x) + # N x 32 x 147 x 147 + x = self.Conv2d_2b_3x3(x) + # N x 64 x 147 x 147 + x = self.maxpool1(x) + # N x 64 x 73 x 73 + x = self.Conv2d_3b_1x1(x) + # N x 80 x 73 x 73 + x = self.Conv2d_4a_3x3(x) + # N x 192 x 71 x 71 + x = self.maxpool2(x) + # N x 192 x 35 x 35 + x = self.Mixed_5b(x) + # N x 256 x 35 x 35 + x = self.Mixed_5c(x) + # N x 288 x 35 x 35 + x = self.Mixed_5d(x) + # N x 288 x 35 x 35 + x = self.Mixed_6a(x) + # N x 768 x 17 x 17 + x = self.Mixed_6b(x) + # N x 768 x 17 x 17 + x = self.Mixed_6c(x) + # N x 768 x 17 x 17 + x = self.Mixed_6d(x) + # N x 768 x 17 x 17 + x = self.Mixed_6e(x) + # N x 768 x 17 x 17 + aux: Optional[Tensor] = None + if self.AuxLogits is not None: + if self.training: + aux = self.AuxLogits(x) + # N x 768 x 17 x 17 + x = self.Mixed_7a(x) + # N x 1280 x 8 x 8 + x = self.Mixed_7b(x) + # N x 2048 x 8 x 8 + x = self.Mixed_7c(x) + # N x 2048 x 8 x 8 + # Adaptive average pooling + x = self.avgpool(x) + # N x 2048 x 1 x 1 + x = self.dropout(x) + # N x 2048 x 1 x 1 + x = torch.flatten(x, 1) + # N x 2048 + x = self.fc(x) + # N x 1000 (num_classes) + return x, aux + + @torch.jit.unused + def eager_outputs(self, x: Tensor, aux: Optional[Tensor]) -> InceptionOutputs: + if self.training and self.aux_logits: + return InceptionOutputs(x, aux) + else: + return x # type: ignore[return-value] + + def forward(self, x: Tensor) -> InceptionOutputs: + x = self._transform_input(x) + x, aux = self._forward(x) + aux_defined = self.training and self.aux_logits + if torch.jit.is_scripting(): + if not aux_defined: + warnings.warn("Scripted Inception3 always returns Inception3 Tuple") + return InceptionOutputs(x, aux) + else: + return self.eager_outputs(x, aux) + + +class InceptionA(nn.Module): + def __init__( + self, in_channels: int, pool_features: int, conv_block: Optional[Callable[..., nn.Module]] = None + ) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 64, kernel_size=1) + + self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1) + self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2) + + self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1) + + self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionB(nn.Module): + def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2) + + self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch3x3 = self.branch3x3(x) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + + outputs = [branch3x3, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionC(nn.Module): + def __init__( + self, in_channels: int, channels_7x7: int, conv_block: Optional[Callable[..., nn.Module]] = None + ) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 192, kernel_size=1) + + c7 = channels_7x7 + self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1) + self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0)) + + self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1) + self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3)) + + self.branch_pool = conv_block(in_channels, 192, kernel_size=1) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionD(nn.Module): + def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1) + self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2) + + self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1) + self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch3x3 = self.branch3x3_1(x) + branch3x3 = self.branch3x3_2(branch3x3) + + branch7x7x3 = self.branch7x7x3_1(x) + branch7x7x3 = self.branch7x7x3_2(branch7x7x3) + branch7x7x3 = self.branch7x7x3_3(branch7x7x3) + branch7x7x3 = self.branch7x7x3_4(branch7x7x3) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + outputs = [branch3x3, branch7x7x3, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionE(nn.Module): + def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 320, kernel_size=1) + + self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1) + self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1) + self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1) + self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch_pool = conv_block(in_channels, 192, kernel_size=1) + + def _forward(self, x: Tensor) -> List[Tensor]: + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionAux(nn.Module): + def __init__( + self, in_channels: int, num_classes: int, conv_block: Optional[Callable[..., nn.Module]] = None + ) -> None: + super().__init__() + if conv_block is None: + conv_block = BasicConv2d + self.conv0 = conv_block(in_channels, 128, kernel_size=1) + self.conv1 = conv_block(128, 768, kernel_size=5) + self.conv1.stddev = 0.01 # type: ignore[assignment] + self.fc = nn.Linear(768, num_classes) + self.fc.stddev = 0.001 # type: ignore[assignment] + + def forward(self, x: Tensor) -> Tensor: + # N x 768 x 17 x 17 + x = F.avg_pool2d(x, kernel_size=5, stride=3) + # N x 768 x 5 x 5 + x = self.conv0(x) + # N x 128 x 5 x 5 + x = self.conv1(x) + # N x 768 x 1 x 1 + # Adaptive average pooling + x = F.adaptive_avg_pool2d(x, (1, 1)) + # N x 768 x 1 x 1 + x = torch.flatten(x, 1) + # N x 768 + x = self.fc(x) + # N x 1000 + return x + + +class BasicConv2d(nn.Module): + def __init__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None: + super().__init__() + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001) + + def forward(self, x: Tensor) -> Tensor: + x = self.conv(x) + x = self.bn(x) + return F.relu(x, inplace=True) + + +class Inception_V3_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth", + transforms=partial(ImageClassification, crop_size=299, resize_size=342), + meta={ + "num_params": 27161264, + "min_size": (75, 75), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#inception-v3", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.294, + "acc@5": 93.450, + } + }, + "_ops": 5.713, + "_file_size": 103.903, + "_docs": """These weights are ported from the original paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Inception_V3_Weights.IMAGENET1K_V1)) +def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3: + """ + Inception v3 model architecture from + `Rethinking the Inception Architecture for Computer Vision `_. + + .. note:: + **Important**: In contrast to the other models the inception_v3 expects tensors with a size of + N x 3 x 299 x 299, so ensure your images are sized accordingly. + + Args: + weights (:class:`~torchvision.models.Inception_V3_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.Inception_V3_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.Inception3`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Inception_V3_Weights + :members: + """ + weights = Inception_V3_Weights.verify(weights) + + original_aux_logits = kwargs.get("aux_logits", True) + if weights is not None: + if "transform_input" not in kwargs: + _ovewrite_named_param(kwargs, "transform_input", True) + _ovewrite_named_param(kwargs, "aux_logits", True) + _ovewrite_named_param(kwargs, "init_weights", False) + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = Inception3(**kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if not original_aux_logits: + model.aux_logits = False + model.AuxLogits = None + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/maxvit.py b/lib/python3.11/site-packages/torchvision/models/maxvit.py new file mode 100644 index 0000000000000000000000000000000000000000..66f49772218d7ee2fe5c76d914be248ac6ae2c9d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/maxvit.py @@ -0,0 +1,833 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, Optional, Sequence, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn, Tensor +from torchvision.models._api import register_model, Weights, WeightsEnum +from torchvision.models._meta import _IMAGENET_CATEGORIES +from torchvision.models._utils import _ovewrite_named_param, handle_legacy_interface +from torchvision.ops.misc import Conv2dNormActivation, SqueezeExcitation +from torchvision.ops.stochastic_depth import StochasticDepth +from torchvision.transforms._presets import ImageClassification, InterpolationMode +from torchvision.utils import _log_api_usage_once + +__all__ = [ + "MaxVit", + "MaxVit_T_Weights", + "maxvit_t", +] + + +def _get_conv_output_shape(input_size: Tuple[int, int], kernel_size: int, stride: int, padding: int) -> Tuple[int, int]: + return ( + (input_size[0] - kernel_size + 2 * padding) // stride + 1, + (input_size[1] - kernel_size + 2 * padding) // stride + 1, + ) + + +def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List[Tuple[int, int]]: + """Util function to check that the input size is correct for a MaxVit configuration.""" + shapes = [] + block_input_shape = _get_conv_output_shape(input_size, 3, 2, 1) + for _ in range(n_blocks): + block_input_shape = _get_conv_output_shape(block_input_shape, 3, 2, 1) + shapes.append(block_input_shape) + return shapes + + +def _get_relative_position_index(height: int, width: int) -> torch.Tensor: + coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)], indexing="ij")) + coords_flat = torch.flatten(coords, 1) + relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += height - 1 + relative_coords[:, :, 1] += width - 1 + relative_coords[:, :, 0] *= 2 * width - 1 + return relative_coords.sum(-1) + + +class MBConv(nn.Module): + """MBConv: Mobile Inverted Residual Bottleneck. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + expansion_ratio (float): Expansion ratio in the bottleneck. + squeeze_ratio (float): Squeeze ratio in the SE Layer. + stride (int): Stride of the depthwise convolution. + activation_layer (Callable[..., nn.Module]): Activation function. + norm_layer (Callable[..., nn.Module]): Normalization function. + p_stochastic_dropout (float): Probability of stochastic depth. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + expansion_ratio: float, + squeeze_ratio: float, + stride: int, + activation_layer: Callable[..., nn.Module], + norm_layer: Callable[..., nn.Module], + p_stochastic_dropout: float = 0.0, + ) -> None: + super().__init__() + + proj: Sequence[nn.Module] + self.proj: nn.Module + + should_proj = stride != 1 or in_channels != out_channels + if should_proj: + proj = [nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=True)] + if stride == 2: + proj = [nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)] + proj # type: ignore + self.proj = nn.Sequential(*proj) + else: + self.proj = nn.Identity() # type: ignore + + mid_channels = int(out_channels * expansion_ratio) + sqz_channels = int(out_channels * squeeze_ratio) + + if p_stochastic_dropout: + self.stochastic_depth = StochasticDepth(p_stochastic_dropout, mode="row") # type: ignore + else: + self.stochastic_depth = nn.Identity() # type: ignore + + _layers = OrderedDict() + _layers["pre_norm"] = norm_layer(in_channels) + _layers["conv_a"] = Conv2dNormActivation( + in_channels, + mid_channels, + kernel_size=1, + stride=1, + padding=0, + activation_layer=activation_layer, + norm_layer=norm_layer, + inplace=None, + ) + _layers["conv_b"] = Conv2dNormActivation( + mid_channels, + mid_channels, + kernel_size=3, + stride=stride, + padding=1, + activation_layer=activation_layer, + norm_layer=norm_layer, + groups=mid_channels, + inplace=None, + ) + _layers["squeeze_excitation"] = SqueezeExcitation(mid_channels, sqz_channels, activation=nn.SiLU) + _layers["conv_c"] = nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, bias=True) + + self.layers = nn.Sequential(_layers) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Input tensor with expected layout of [B, C, H, W]. + Returns: + Tensor: Output tensor with expected layout of [B, C, H / stride, W / stride]. + """ + res = self.proj(x) + x = self.stochastic_depth(self.layers(x)) + return res + x + + +class RelativePositionalMultiHeadAttention(nn.Module): + """Relative Positional Multi-Head Attention. + + Args: + feat_dim (int): Number of input features. + head_dim (int): Number of features per head. + max_seq_len (int): Maximum sequence length. + """ + + def __init__( + self, + feat_dim: int, + head_dim: int, + max_seq_len: int, + ) -> None: + super().__init__() + + if feat_dim % head_dim != 0: + raise ValueError(f"feat_dim: {feat_dim} must be divisible by head_dim: {head_dim}") + + self.n_heads = feat_dim // head_dim + self.head_dim = head_dim + self.size = int(math.sqrt(max_seq_len)) + self.max_seq_len = max_seq_len + + self.to_qkv = nn.Linear(feat_dim, self.n_heads * self.head_dim * 3) + self.scale_factor = feat_dim**-0.5 + + self.merge = nn.Linear(self.head_dim * self.n_heads, feat_dim) + self.relative_position_bias_table = nn.parameter.Parameter( + torch.empty(((2 * self.size - 1) * (2 * self.size - 1), self.n_heads), dtype=torch.float32), + ) + + self.register_buffer("relative_position_index", _get_relative_position_index(self.size, self.size)) + # initialize with truncated normal the bias + torch.nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02) + + def get_relative_positional_bias(self) -> torch.Tensor: + bias_index = self.relative_position_index.view(-1) # type: ignore + relative_bias = self.relative_position_bias_table[bias_index].view(self.max_seq_len, self.max_seq_len, -1) # type: ignore + relative_bias = relative_bias.permute(2, 0, 1).contiguous() + return relative_bias.unsqueeze(0) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Input tensor with expected layout of [B, G, P, D]. + Returns: + Tensor: Output tensor with expected layout of [B, G, P, D]. + """ + B, G, P, D = x.shape + H, DH = self.n_heads, self.head_dim + + qkv = self.to_qkv(x) + q, k, v = torch.chunk(qkv, 3, dim=-1) + + q = q.reshape(B, G, P, H, DH).permute(0, 1, 3, 2, 4) + k = k.reshape(B, G, P, H, DH).permute(0, 1, 3, 2, 4) + v = v.reshape(B, G, P, H, DH).permute(0, 1, 3, 2, 4) + + k = k * self.scale_factor + dot_prod = torch.einsum("B G H I D, B G H J D -> B G H I J", q, k) + pos_bias = self.get_relative_positional_bias() + + dot_prod = F.softmax(dot_prod + pos_bias, dim=-1) + + out = torch.einsum("B G H I J, B G H J D -> B G H I D", dot_prod, v) + out = out.permute(0, 1, 3, 2, 4).reshape(B, G, P, D) + + out = self.merge(out) + return out + + +class SwapAxes(nn.Module): + """Permute the axes of a tensor.""" + + def __init__(self, a: int, b: int) -> None: + super().__init__() + self.a = a + self.b = b + + def forward(self, x: torch.Tensor) -> torch.Tensor: + res = torch.swapaxes(x, self.a, self.b) + return res + + +class WindowPartition(nn.Module): + """ + Partition the input tensor into non-overlapping windows. + """ + + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor, p: int) -> Tensor: + """ + Args: + x (Tensor): Input tensor with expected layout of [B, C, H, W]. + p (int): Number of partitions. + Returns: + Tensor: Output tensor with expected layout of [B, H/P, W/P, P*P, C]. + """ + B, C, H, W = x.shape + P = p + # chunk up H and W dimensions + x = x.reshape(B, C, H // P, P, W // P, P) + x = x.permute(0, 2, 4, 3, 5, 1) + # colapse P * P dimension + x = x.reshape(B, (H // P) * (W // P), P * P, C) + return x + + +class WindowDepartition(nn.Module): + """ + Departition the input tensor of non-overlapping windows into a feature volume of layout [B, C, H, W]. + """ + + def __init__(self) -> None: + super().__init__() + + def forward(self, x: Tensor, p: int, h_partitions: int, w_partitions: int) -> Tensor: + """ + Args: + x (Tensor): Input tensor with expected layout of [B, (H/P * W/P), P*P, C]. + p (int): Number of partitions. + h_partitions (int): Number of vertical partitions. + w_partitions (int): Number of horizontal partitions. + Returns: + Tensor: Output tensor with expected layout of [B, C, H, W]. + """ + B, G, PP, C = x.shape + P = p + HP, WP = h_partitions, w_partitions + # split P * P dimension into 2 P tile dimensionsa + x = x.reshape(B, HP, WP, P, P, C) + # permute into B, C, HP, P, WP, P + x = x.permute(0, 5, 1, 3, 2, 4) + # reshape into B, C, H, W + x = x.reshape(B, C, HP * P, WP * P) + return x + + +class PartitionAttentionLayer(nn.Module): + """ + Layer for partitioning the input tensor into non-overlapping windows and applying attention to each window. + + Args: + in_channels (int): Number of input channels. + head_dim (int): Dimension of each attention head. + partition_size (int): Size of the partitions. + partition_type (str): Type of partitioning to use. Can be either "grid" or "window". + grid_size (Tuple[int, int]): Size of the grid to partition the input tensor into. + mlp_ratio (int): Ratio of the feature size expansion in the MLP layer. + activation_layer (Callable[..., nn.Module]): Activation function to use. + norm_layer (Callable[..., nn.Module]): Normalization function to use. + attention_dropout (float): Dropout probability for the attention layer. + mlp_dropout (float): Dropout probability for the MLP layer. + p_stochastic_dropout (float): Probability of dropping out a partition. + """ + + def __init__( + self, + in_channels: int, + head_dim: int, + # partitioning parameters + partition_size: int, + partition_type: str, + # grid size needs to be known at initialization time + # because we need to know hamy relative offsets there are in the grid + grid_size: Tuple[int, int], + mlp_ratio: int, + activation_layer: Callable[..., nn.Module], + norm_layer: Callable[..., nn.Module], + attention_dropout: float, + mlp_dropout: float, + p_stochastic_dropout: float, + ) -> None: + super().__init__() + + self.n_heads = in_channels // head_dim + self.head_dim = head_dim + self.n_partitions = grid_size[0] // partition_size + self.partition_type = partition_type + self.grid_size = grid_size + + if partition_type not in ["grid", "window"]: + raise ValueError("partition_type must be either 'grid' or 'window'") + + if partition_type == "window": + self.p, self.g = partition_size, self.n_partitions + else: + self.p, self.g = self.n_partitions, partition_size + + self.partition_op = WindowPartition() + self.departition_op = WindowDepartition() + self.partition_swap = SwapAxes(-2, -3) if partition_type == "grid" else nn.Identity() + self.departition_swap = SwapAxes(-2, -3) if partition_type == "grid" else nn.Identity() + + self.attn_layer = nn.Sequential( + norm_layer(in_channels), + # it's always going to be partition_size ** 2 because + # of the axis swap in the case of grid partitioning + RelativePositionalMultiHeadAttention(in_channels, head_dim, partition_size**2), + nn.Dropout(attention_dropout), + ) + + # pre-normalization similar to transformer layers + self.mlp_layer = nn.Sequential( + nn.LayerNorm(in_channels), + nn.Linear(in_channels, in_channels * mlp_ratio), + activation_layer(), + nn.Linear(in_channels * mlp_ratio, in_channels), + nn.Dropout(mlp_dropout), + ) + + # layer scale factors + self.stochastic_dropout = StochasticDepth(p_stochastic_dropout, mode="row") + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Input tensor with expected layout of [B, C, H, W]. + Returns: + Tensor: Output tensor with expected layout of [B, C, H, W]. + """ + + # Undefined behavior if H or W are not divisible by p + # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766 + gh, gw = self.grid_size[0] // self.p, self.grid_size[1] // self.p + torch._assert( + self.grid_size[0] % self.p == 0 and self.grid_size[1] % self.p == 0, + "Grid size must be divisible by partition size. Got grid size of {} and partition size of {}".format( + self.grid_size, self.p + ), + ) + + x = self.partition_op(x, self.p) + x = self.partition_swap(x) + x = x + self.stochastic_dropout(self.attn_layer(x)) + x = x + self.stochastic_dropout(self.mlp_layer(x)) + x = self.departition_swap(x) + x = self.departition_op(x, self.p, gh, gw) + + return x + + +class MaxVitLayer(nn.Module): + """ + MaxVit layer consisting of a MBConv layer followed by a PartitionAttentionLayer with `window` and a PartitionAttentionLayer with `grid`. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + expansion_ratio (float): Expansion ratio in the bottleneck. + squeeze_ratio (float): Squeeze ratio in the SE Layer. + stride (int): Stride of the depthwise convolution. + activation_layer (Callable[..., nn.Module]): Activation function. + norm_layer (Callable[..., nn.Module]): Normalization function. + head_dim (int): Dimension of the attention heads. + mlp_ratio (int): Ratio of the MLP layer. + mlp_dropout (float): Dropout probability for the MLP layer. + attention_dropout (float): Dropout probability for the attention layer. + p_stochastic_dropout (float): Probability of stochastic depth. + partition_size (int): Size of the partitions. + grid_size (Tuple[int, int]): Size of the input feature grid. + """ + + def __init__( + self, + # conv parameters + in_channels: int, + out_channels: int, + squeeze_ratio: float, + expansion_ratio: float, + stride: int, + # conv + transformer parameters + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + # transformer parameters + head_dim: int, + mlp_ratio: int, + mlp_dropout: float, + attention_dropout: float, + p_stochastic_dropout: float, + # partitioning parameters + partition_size: int, + grid_size: Tuple[int, int], + ) -> None: + super().__init__() + + layers: OrderedDict = OrderedDict() + + # convolutional layer + layers["MBconv"] = MBConv( + in_channels=in_channels, + out_channels=out_channels, + expansion_ratio=expansion_ratio, + squeeze_ratio=squeeze_ratio, + stride=stride, + activation_layer=activation_layer, + norm_layer=norm_layer, + p_stochastic_dropout=p_stochastic_dropout, + ) + # attention layers, block -> grid + layers["window_attention"] = PartitionAttentionLayer( + in_channels=out_channels, + head_dim=head_dim, + partition_size=partition_size, + partition_type="window", + grid_size=grid_size, + mlp_ratio=mlp_ratio, + activation_layer=activation_layer, + norm_layer=nn.LayerNorm, + attention_dropout=attention_dropout, + mlp_dropout=mlp_dropout, + p_stochastic_dropout=p_stochastic_dropout, + ) + layers["grid_attention"] = PartitionAttentionLayer( + in_channels=out_channels, + head_dim=head_dim, + partition_size=partition_size, + partition_type="grid", + grid_size=grid_size, + mlp_ratio=mlp_ratio, + activation_layer=activation_layer, + norm_layer=nn.LayerNorm, + attention_dropout=attention_dropout, + mlp_dropout=mlp_dropout, + p_stochastic_dropout=p_stochastic_dropout, + ) + self.layers = nn.Sequential(layers) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Input tensor of shape (B, C, H, W). + Returns: + Tensor: Output tensor of shape (B, C, H, W). + """ + x = self.layers(x) + return x + + +class MaxVitBlock(nn.Module): + """ + A MaxVit block consisting of `n_layers` MaxVit layers. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + expansion_ratio (float): Expansion ratio in the bottleneck. + squeeze_ratio (float): Squeeze ratio in the SE Layer. + activation_layer (Callable[..., nn.Module]): Activation function. + norm_layer (Callable[..., nn.Module]): Normalization function. + head_dim (int): Dimension of the attention heads. + mlp_ratio (int): Ratio of the MLP layer. + mlp_dropout (float): Dropout probability for the MLP layer. + attention_dropout (float): Dropout probability for the attention layer. + p_stochastic_dropout (float): Probability of stochastic depth. + partition_size (int): Size of the partitions. + input_grid_size (Tuple[int, int]): Size of the input feature grid. + n_layers (int): Number of layers in the block. + p_stochastic (List[float]): List of probabilities for stochastic depth for each layer. + """ + + def __init__( + self, + # conv parameters + in_channels: int, + out_channels: int, + squeeze_ratio: float, + expansion_ratio: float, + # conv + transformer parameters + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + # transformer parameters + head_dim: int, + mlp_ratio: int, + mlp_dropout: float, + attention_dropout: float, + # partitioning parameters + partition_size: int, + input_grid_size: Tuple[int, int], + # number of layers + n_layers: int, + p_stochastic: List[float], + ) -> None: + super().__init__() + if not len(p_stochastic) == n_layers: + raise ValueError(f"p_stochastic must have length n_layers={n_layers}, got p_stochastic={p_stochastic}.") + + self.layers = nn.ModuleList() + # account for the first stride of the first layer + self.grid_size = _get_conv_output_shape(input_grid_size, kernel_size=3, stride=2, padding=1) + + for idx, p in enumerate(p_stochastic): + stride = 2 if idx == 0 else 1 + self.layers += [ + MaxVitLayer( + in_channels=in_channels if idx == 0 else out_channels, + out_channels=out_channels, + squeeze_ratio=squeeze_ratio, + expansion_ratio=expansion_ratio, + stride=stride, + norm_layer=norm_layer, + activation_layer=activation_layer, + head_dim=head_dim, + mlp_ratio=mlp_ratio, + mlp_dropout=mlp_dropout, + attention_dropout=attention_dropout, + partition_size=partition_size, + grid_size=self.grid_size, + p_stochastic_dropout=p, + ), + ] + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Input tensor of shape (B, C, H, W). + Returns: + Tensor: Output tensor of shape (B, C, H, W). + """ + for layer in self.layers: + x = layer(x) + return x + + +class MaxVit(nn.Module): + """ + Implements MaxVit Transformer from the `MaxViT: Multi-Axis Vision Transformer `_ paper. + Args: + input_size (Tuple[int, int]): Size of the input image. + stem_channels (int): Number of channels in the stem. + partition_size (int): Size of the partitions. + block_channels (List[int]): Number of channels in each block. + block_layers (List[int]): Number of layers in each block. + stochastic_depth_prob (float): Probability of stochastic depth. Expands to a list of probabilities for each layer that scales linearly to the specified value. + squeeze_ratio (float): Squeeze ratio in the SE Layer. Default: 0.25. + expansion_ratio (float): Expansion ratio in the MBConv bottleneck. Default: 4. + norm_layer (Callable[..., nn.Module]): Normalization function. Default: None (setting to None will produce a `BatchNorm2d(eps=1e-3, momentum=0.01)`). + activation_layer (Callable[..., nn.Module]): Activation function Default: nn.GELU. + head_dim (int): Dimension of the attention heads. + mlp_ratio (int): Expansion ratio of the MLP layer. Default: 4. + mlp_dropout (float): Dropout probability for the MLP layer. Default: 0.0. + attention_dropout (float): Dropout probability for the attention layer. Default: 0.0. + num_classes (int): Number of classes. Default: 1000. + """ + + def __init__( + self, + # input size parameters + input_size: Tuple[int, int], + # stem and task parameters + stem_channels: int, + # partitioning parameters + partition_size: int, + # block parameters + block_channels: List[int], + block_layers: List[int], + # attention head dimensions + head_dim: int, + stochastic_depth_prob: float, + # conv + transformer parameters + # norm_layer is applied only to the conv layers + # activation_layer is applied both to conv and transformer layers + norm_layer: Optional[Callable[..., nn.Module]] = None, + activation_layer: Callable[..., nn.Module] = nn.GELU, + # conv parameters + squeeze_ratio: float = 0.25, + expansion_ratio: float = 4, + # transformer parameters + mlp_ratio: int = 4, + mlp_dropout: float = 0.0, + attention_dropout: float = 0.0, + # task parameters + num_classes: int = 1000, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + input_channels = 3 + + # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L1029-L1030 + # for the exact parameters used in batchnorm + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01) + + # Make sure input size will be divisible by the partition size in all blocks + # Undefined behavior if H or W are not divisible by p + # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L766 + block_input_sizes = _make_block_input_shapes(input_size, len(block_channels)) + for idx, block_input_size in enumerate(block_input_sizes): + if block_input_size[0] % partition_size != 0 or block_input_size[1] % partition_size != 0: + raise ValueError( + f"Input size {block_input_size} of block {idx} is not divisible by partition size {partition_size}. " + f"Consider changing the partition size or the input size.\n" + f"Current configuration yields the following block input sizes: {block_input_sizes}." + ) + + # stem + self.stem = nn.Sequential( + Conv2dNormActivation( + input_channels, + stem_channels, + 3, + stride=2, + norm_layer=norm_layer, + activation_layer=activation_layer, + bias=False, + inplace=None, + ), + Conv2dNormActivation( + stem_channels, stem_channels, 3, stride=1, norm_layer=None, activation_layer=None, bias=True + ), + ) + + # account for stem stride + input_size = _get_conv_output_shape(input_size, kernel_size=3, stride=2, padding=1) + self.partition_size = partition_size + + # blocks + self.blocks = nn.ModuleList() + in_channels = [stem_channels] + block_channels[:-1] + out_channels = block_channels + + # precompute the stochastich depth probabilities from 0 to stochastic_depth_prob + # since we have N blocks with L layers, we will have N * L probabilities uniformly distributed + # over the range [0, stochastic_depth_prob] + p_stochastic = np.linspace(0, stochastic_depth_prob, sum(block_layers)).tolist() + + p_idx = 0 + for in_channel, out_channel, num_layers in zip(in_channels, out_channels, block_layers): + self.blocks.append( + MaxVitBlock( + in_channels=in_channel, + out_channels=out_channel, + squeeze_ratio=squeeze_ratio, + expansion_ratio=expansion_ratio, + norm_layer=norm_layer, + activation_layer=activation_layer, + head_dim=head_dim, + mlp_ratio=mlp_ratio, + mlp_dropout=mlp_dropout, + attention_dropout=attention_dropout, + partition_size=partition_size, + input_grid_size=input_size, + n_layers=num_layers, + p_stochastic=p_stochastic[p_idx : p_idx + num_layers], + ), + ) + input_size = self.blocks[-1].grid_size # type: ignore[assignment] + p_idx += num_layers + + # see https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L1137-L1158 + # for why there is Linear -> Tanh -> Linear + self.classifier = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Flatten(), + nn.LayerNorm(block_channels[-1]), + nn.Linear(block_channels[-1], block_channels[-1]), + nn.Tanh(), + nn.Linear(block_channels[-1], num_classes, bias=False), + ) + + self._init_weights() + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + for block in self.blocks: + x = block(x) + x = self.classifier(x) + return x + + def _init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + +def _maxvit( + # stem parameters + stem_channels: int, + # block parameters + block_channels: List[int], + block_layers: List[int], + stochastic_depth_prob: float, + # partitioning parameters + partition_size: int, + # transformer parameters + head_dim: int, + # Weights API + weights: Optional[WeightsEnum] = None, + progress: bool = False, + # kwargs, + **kwargs: Any, +) -> MaxVit: + + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "input_size", weights.meta["min_size"]) + + input_size = kwargs.pop("input_size", (224, 224)) + + model = MaxVit( + stem_channels=stem_channels, + block_channels=block_channels, + block_layers=block_layers, + stochastic_depth_prob=stochastic_depth_prob, + head_dim=head_dim, + partition_size=partition_size, + input_size=input_size, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +class MaxVit_T_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # URL empty until official release + url="https://download.pytorch.org/models/maxvit_t-bc5ab103.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=224, interpolation=InterpolationMode.BICUBIC + ), + meta={ + "categories": _IMAGENET_CATEGORIES, + "num_params": 30919624, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#maxvit", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.700, + "acc@5": 96.722, + } + }, + "_ops": 5.558, + "_file_size": 118.769, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe. + They were trained with a BatchNorm2D momentum of 0.99 instead of the more correct 0.01.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MaxVit_T_Weights.IMAGENET1K_V1)) +def maxvit_t(*, weights: Optional[MaxVit_T_Weights] = None, progress: bool = True, **kwargs: Any) -> MaxVit: + """ + Constructs a maxvit_t architecture from + `MaxViT: Multi-Axis Vision Transformer `_. + + Args: + weights (:class:`~torchvision.models.MaxVit_T_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MaxVit_T_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.maxvit.MaxVit`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MaxVit_T_Weights + :members: + """ + weights = MaxVit_T_Weights.verify(weights) + + return _maxvit( + stem_channels=64, + block_channels=[64, 128, 256, 512], + block_layers=[2, 2, 5, 2], + head_dim=32, + stochastic_depth_prob=0.2, + partition_size=7, + weights=weights, + progress=progress, + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/mnasnet.py b/lib/python3.11/site-packages/torchvision/models/mnasnet.py new file mode 100644 index 0000000000000000000000000000000000000000..5846111ab1c05b4ebca7ccf9240ac744267b859a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/mnasnet.py @@ -0,0 +1,434 @@ +import warnings +from functools import partial +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "MNASNet", + "MNASNet0_5_Weights", + "MNASNet0_75_Weights", + "MNASNet1_0_Weights", + "MNASNet1_3_Weights", + "mnasnet0_5", + "mnasnet0_75", + "mnasnet1_0", + "mnasnet1_3", +] + + +# Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is +# 1.0 - tensorflow. +_BN_MOMENTUM = 1 - 0.9997 + + +class _InvertedResidual(nn.Module): + def __init__( + self, in_ch: int, out_ch: int, kernel_size: int, stride: int, expansion_factor: int, bn_momentum: float = 0.1 + ) -> None: + super().__init__() + if stride not in [1, 2]: + raise ValueError(f"stride should be 1 or 2 instead of {stride}") + if kernel_size not in [3, 5]: + raise ValueError(f"kernel_size should be 3 or 5 instead of {kernel_size}") + mid_ch = in_ch * expansion_factor + self.apply_residual = in_ch == out_ch and stride == 1 + self.layers = nn.Sequential( + # Pointwise + nn.Conv2d(in_ch, mid_ch, 1, bias=False), + nn.BatchNorm2d(mid_ch, momentum=bn_momentum), + nn.ReLU(inplace=True), + # Depthwise + nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=kernel_size // 2, stride=stride, groups=mid_ch, bias=False), + nn.BatchNorm2d(mid_ch, momentum=bn_momentum), + nn.ReLU(inplace=True), + # Linear pointwise. Note that there's no activation. + nn.Conv2d(mid_ch, out_ch, 1, bias=False), + nn.BatchNorm2d(out_ch, momentum=bn_momentum), + ) + + def forward(self, input: Tensor) -> Tensor: + if self.apply_residual: + return self.layers(input) + input + else: + return self.layers(input) + + +def _stack( + in_ch: int, out_ch: int, kernel_size: int, stride: int, exp_factor: int, repeats: int, bn_momentum: float +) -> nn.Sequential: + """Creates a stack of inverted residuals.""" + if repeats < 1: + raise ValueError(f"repeats should be >= 1, instead got {repeats}") + # First one has no skip, because feature map size changes. + first = _InvertedResidual(in_ch, out_ch, kernel_size, stride, exp_factor, bn_momentum=bn_momentum) + remaining = [] + for _ in range(1, repeats): + remaining.append(_InvertedResidual(out_ch, out_ch, kernel_size, 1, exp_factor, bn_momentum=bn_momentum)) + return nn.Sequential(first, *remaining) + + +def _round_to_multiple_of(val: float, divisor: int, round_up_bias: float = 0.9) -> int: + """Asymmetric rounding to make `val` divisible by `divisor`. With default + bias, will round up, unless the number is no more than 10% greater than the + smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88.""" + if not 0.0 < round_up_bias < 1.0: + raise ValueError(f"round_up_bias should be greater than 0.0 and smaller than 1.0 instead of {round_up_bias}") + new_val = max(divisor, int(val + divisor / 2) // divisor * divisor) + return new_val if new_val >= round_up_bias * val else new_val + divisor + + +def _get_depths(alpha: float) -> List[int]: + """Scales tensor depths as in reference MobileNet code, prefers rounding up + rather than down.""" + depths = [32, 16, 24, 40, 80, 96, 192, 320] + return [_round_to_multiple_of(depth * alpha, 8) for depth in depths] + + +class MNASNet(torch.nn.Module): + """MNASNet, as described in https://arxiv.org/abs/1807.11626. This + implements the B1 variant of the model. + >>> model = MNASNet(1.0, num_classes=1000) + >>> x = torch.rand(1, 3, 224, 224) + >>> y = model(x) + >>> y.dim() + 2 + >>> y.nelement() + 1000 + """ + + # Version 2 adds depth scaling in the initial stages of the network. + _version = 2 + + def __init__(self, alpha: float, num_classes: int = 1000, dropout: float = 0.2) -> None: + super().__init__() + _log_api_usage_once(self) + if alpha <= 0.0: + raise ValueError(f"alpha should be greater than 0.0 instead of {alpha}") + self.alpha = alpha + self.num_classes = num_classes + depths = _get_depths(alpha) + layers = [ + # First layer: regular conv. + nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False), + nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM), + nn.ReLU(inplace=True), + # Depthwise separable, no skip. + nn.Conv2d(depths[0], depths[0], 3, padding=1, stride=1, groups=depths[0], bias=False), + nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM), + nn.ReLU(inplace=True), + nn.Conv2d(depths[0], depths[1], 1, padding=0, stride=1, bias=False), + nn.BatchNorm2d(depths[1], momentum=_BN_MOMENTUM), + # MNASNet blocks: stacks of inverted residuals. + _stack(depths[1], depths[2], 3, 2, 3, 3, _BN_MOMENTUM), + _stack(depths[2], depths[3], 5, 2, 3, 3, _BN_MOMENTUM), + _stack(depths[3], depths[4], 5, 2, 6, 3, _BN_MOMENTUM), + _stack(depths[4], depths[5], 3, 1, 6, 2, _BN_MOMENTUM), + _stack(depths[5], depths[6], 5, 2, 6, 4, _BN_MOMENTUM), + _stack(depths[6], depths[7], 3, 1, 6, 1, _BN_MOMENTUM), + # Final mapping to classifier input. + nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False), + nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM), + nn.ReLU(inplace=True), + ] + self.layers = nn.Sequential(*layers) + self.classifier = nn.Sequential(nn.Dropout(p=dropout, inplace=True), nn.Linear(1280, num_classes)) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.kaiming_uniform_(m.weight, mode="fan_out", nonlinearity="sigmoid") + nn.init.zeros_(m.bias) + + def forward(self, x: Tensor) -> Tensor: + x = self.layers(x) + # Equivalent to global avgpool and removing H and W dimensions. + x = x.mean([2, 3]) + return self.classifier(x) + + def _load_from_state_dict( + self, + state_dict: Dict, + prefix: str, + local_metadata: Dict, + strict: bool, + missing_keys: List[str], + unexpected_keys: List[str], + error_msgs: List[str], + ) -> None: + version = local_metadata.get("version", None) + if version not in [1, 2]: + raise ValueError(f"version shluld be set to 1 or 2 instead of {version}") + + if version == 1 and not self.alpha == 1.0: + # In the initial version of the model (v1), stem was fixed-size. + # All other layer configurations were the same. This will patch + # the model so that it's identical to v1. Model with alpha 1.0 is + # unaffected. + depths = _get_depths(self.alpha) + v1_stem = [ + nn.Conv2d(3, 32, 3, padding=1, stride=2, bias=False), + nn.BatchNorm2d(32, momentum=_BN_MOMENTUM), + nn.ReLU(inplace=True), + nn.Conv2d(32, 32, 3, padding=1, stride=1, groups=32, bias=False), + nn.BatchNorm2d(32, momentum=_BN_MOMENTUM), + nn.ReLU(inplace=True), + nn.Conv2d(32, 16, 1, padding=0, stride=1, bias=False), + nn.BatchNorm2d(16, momentum=_BN_MOMENTUM), + _stack(16, depths[2], 3, 2, 3, 3, _BN_MOMENTUM), + ] + for idx, layer in enumerate(v1_stem): + self.layers[idx] = layer + + # The model is now identical to v1, and must be saved as such. + self._version = 1 + warnings.warn( + "A new version of MNASNet model has been implemented. " + "Your checkpoint was saved using the previous version. " + "This checkpoint will load and work as before, but " + "you may want to upgrade by training a newer model or " + "transfer learning from an updated ImageNet checkpoint.", + UserWarning, + ) + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/1e100/mnasnet_trainer", +} + + +class MNASNet0_5_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 2218512, + "_metrics": { + "ImageNet-1K": { + "acc@1": 67.734, + "acc@5": 87.490, + } + }, + "_ops": 0.104, + "_file_size": 8.591, + "_docs": """These weights reproduce closely the results of the paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class MNASNet0_75_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mnasnet0_75-7090bc5f.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/6019", + "num_params": 3170208, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.180, + "acc@5": 90.496, + } + }, + "_ops": 0.215, + "_file_size": 12.303, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class MNASNet1_0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 4383312, + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.456, + "acc@5": 91.510, + } + }, + "_ops": 0.314, + "_file_size": 16.915, + "_docs": """These weights reproduce closely the results of the paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class MNASNet1_3_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mnasnet1_3-a4c69d6f.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/6019", + "num_params": 6282256, + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.506, + "acc@5": 93.522, + } + }, + "_ops": 0.526, + "_file_size": 24.246, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +def _mnasnet(alpha: float, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> MNASNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = MNASNet(alpha, **kwargs) + + if weights: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MNASNet0_5_Weights.IMAGENET1K_V1)) +def mnasnet0_5(*, weights: Optional[MNASNet0_5_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet: + """MNASNet with depth multiplier of 0.5 from + `MnasNet: Platform-Aware Neural Architecture Search for Mobile + `_ paper. + + Args: + weights (:class:`~torchvision.models.MNASNet0_5_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MNASNet0_5_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MNASNet0_5_Weights + :members: + """ + weights = MNASNet0_5_Weights.verify(weights) + + return _mnasnet(0.5, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MNASNet0_75_Weights.IMAGENET1K_V1)) +def mnasnet0_75(*, weights: Optional[MNASNet0_75_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet: + """MNASNet with depth multiplier of 0.75 from + `MnasNet: Platform-Aware Neural Architecture Search for Mobile + `_ paper. + + Args: + weights (:class:`~torchvision.models.MNASNet0_75_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MNASNet0_75_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MNASNet0_75_Weights + :members: + """ + weights = MNASNet0_75_Weights.verify(weights) + + return _mnasnet(0.75, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MNASNet1_0_Weights.IMAGENET1K_V1)) +def mnasnet1_0(*, weights: Optional[MNASNet1_0_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet: + """MNASNet with depth multiplier of 1.0 from + `MnasNet: Platform-Aware Neural Architecture Search for Mobile + `_ paper. + + Args: + weights (:class:`~torchvision.models.MNASNet1_0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MNASNet1_0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MNASNet1_0_Weights + :members: + """ + weights = MNASNet1_0_Weights.verify(weights) + + return _mnasnet(1.0, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MNASNet1_3_Weights.IMAGENET1K_V1)) +def mnasnet1_3(*, weights: Optional[MNASNet1_3_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet: + """MNASNet with depth multiplier of 1.3 from + `MnasNet: Platform-Aware Neural Architecture Search for Mobile + `_ paper. + + Args: + weights (:class:`~torchvision.models.MNASNet1_3_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MNASNet1_3_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mnasnet.MNASNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MNASNet1_3_Weights + :members: + """ + weights = MNASNet1_3_Weights.verify(weights) + + return _mnasnet(1.3, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/mobilenet.py b/lib/python3.11/site-packages/torchvision/models/mobilenet.py new file mode 100644 index 0000000000000000000000000000000000000000..0a270d14d3a4ad9eda62b68c2c01e9fdb710ef38 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/mobilenet.py @@ -0,0 +1,6 @@ +from .mobilenetv2 import * # noqa: F401, F403 +from .mobilenetv3 import * # noqa: F401, F403 +from .mobilenetv2 import __all__ as mv2_all +from .mobilenetv3 import __all__ as mv3_all + +__all__ = mv2_all + mv3_all diff --git a/lib/python3.11/site-packages/torchvision/models/mobilenetv2.py b/lib/python3.11/site-packages/torchvision/models/mobilenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..fbb6a4981d6dec73c563dfc845329a58eaa3e083 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/mobilenetv2.py @@ -0,0 +1,260 @@ +from functools import partial +from typing import Any, Callable, List, Optional + +import torch +from torch import nn, Tensor + +from ..ops.misc import Conv2dNormActivation +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface + + +__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"] + + +# necessary for backwards compatibility +class InvertedResidual(nn.Module): + def __init__( + self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super().__init__() + self.stride = stride + if stride not in [1, 2]: + raise ValueError(f"stride should be 1 or 2 instead of {stride}") + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + hidden_dim = int(round(inp * expand_ratio)) + self.use_res_connect = self.stride == 1 and inp == oup + + layers: List[nn.Module] = [] + if expand_ratio != 1: + # pw + layers.append( + Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6) + ) + layers.extend( + [ + # dw + Conv2dNormActivation( + hidden_dim, + hidden_dim, + stride=stride, + groups=hidden_dim, + norm_layer=norm_layer, + activation_layer=nn.ReLU6, + ), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + norm_layer(oup), + ] + ) + self.conv = nn.Sequential(*layers) + self.out_channels = oup + self._is_cn = stride > 1 + + def forward(self, x: Tensor) -> Tensor: + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV2(nn.Module): + def __init__( + self, + num_classes: int = 1000, + width_mult: float = 1.0, + inverted_residual_setting: Optional[List[List[int]]] = None, + round_nearest: int = 8, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + dropout: float = 0.2, + ) -> None: + """ + MobileNet V2 main class + + Args: + num_classes (int): Number of classes + width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount + inverted_residual_setting: Network structure + round_nearest (int): Round the number of channels in each layer to be a multiple of this number + Set to 1 to turn off rounding + block: Module specifying inverted residual building block for mobilenet + norm_layer: Module specifying the normalization layer to use + dropout (float): The droupout probability + + """ + super().__init__() + _log_api_usage_once(self) + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = nn.BatchNorm2d + + input_channel = 32 + last_channel = 1280 + + if inverted_residual_setting is None: + inverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + + # only check the first element, assuming user knows t,c,n,s are required + if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: + raise ValueError( + f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}" + ) + + # building first layer + input_channel = _make_divisible(input_channel * width_mult, round_nearest) + self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) + features: List[nn.Module] = [ + Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6) + ] + # building inverted residual blocks + for t, c, n, s in inverted_residual_setting: + output_channel = _make_divisible(c * width_mult, round_nearest) + for i in range(n): + stride = s if i == 0 else 1 + features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer)) + input_channel = output_channel + # building last several layers + features.append( + Conv2dNormActivation( + input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6 + ) + ) + # make it nn.Sequential + self.features = nn.Sequential(*features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(p=dropout), + nn.Linear(self.last_channel, num_classes), + ) + + # weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + # This exists since TorchScript doesn't support inheritance, so the superclass method + # (this one) needs to have a name other than `forward` that can be accessed in a subclass + x = self.features(x) + # Cannot use "squeeze" as batch-size can be 1 + x = nn.functional.adaptive_avg_pool2d(x, (1, 1)) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +_COMMON_META = { + "num_params": 3504872, + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, +} + + +class MobileNet_V2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2", + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.878, + "acc@5": 90.286, + } + }, + "_ops": 0.301, + "_file_size": 13.555, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning", + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.154, + "acc@5": 90.822, + } + }, + "_ops": 0.301, + "_file_size": 13.598, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1)) +def mobilenet_v2( + *, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any +) -> MobileNetV2: + """MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear + Bottlenecks `_ paper. + + Args: + weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MobileNet_V2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mobilenetv2.MobileNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MobileNet_V2_Weights + :members: + """ + weights = MobileNet_V2_Weights.verify(weights) + + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = MobileNetV2(**kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/mobilenetv3.py b/lib/python3.11/site-packages/torchvision/models/mobilenetv3.py new file mode 100644 index 0000000000000000000000000000000000000000..1041d4d149fb1c414991b79156733434a2268e4b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/mobilenetv3.py @@ -0,0 +1,423 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence + +import torch +from torch import nn, Tensor + +from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "MobileNetV3", + "MobileNet_V3_Large_Weights", + "MobileNet_V3_Small_Weights", + "mobilenet_v3_large", + "mobilenet_v3_small", +] + + +class InvertedResidualConfig: + # Stores information listed at Tables 1 and 2 of the MobileNetV3 paper + def __init__( + self, + input_channels: int, + kernel: int, + expanded_channels: int, + out_channels: int, + use_se: bool, + activation: str, + stride: int, + dilation: int, + width_mult: float, + ): + self.input_channels = self.adjust_channels(input_channels, width_mult) + self.kernel = kernel + self.expanded_channels = self.adjust_channels(expanded_channels, width_mult) + self.out_channels = self.adjust_channels(out_channels, width_mult) + self.use_se = use_se + self.use_hs = activation == "HS" + self.stride = stride + self.dilation = dilation + + @staticmethod + def adjust_channels(channels: int, width_mult: float): + return _make_divisible(channels * width_mult, 8) + + +class InvertedResidual(nn.Module): + # Implemented as described at section 5 of MobileNetV3 paper + def __init__( + self, + cnf: InvertedResidualConfig, + norm_layer: Callable[..., nn.Module], + se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid), + ): + super().__init__() + if not (1 <= cnf.stride <= 2): + raise ValueError("illegal stride value") + + self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels + + layers: List[nn.Module] = [] + activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU + + # expand + if cnf.expanded_channels != cnf.input_channels: + layers.append( + Conv2dNormActivation( + cnf.input_channels, + cnf.expanded_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + + # depthwise + stride = 1 if cnf.dilation > 1 else cnf.stride + layers.append( + Conv2dNormActivation( + cnf.expanded_channels, + cnf.expanded_channels, + kernel_size=cnf.kernel, + stride=stride, + dilation=cnf.dilation, + groups=cnf.expanded_channels, + norm_layer=norm_layer, + activation_layer=activation_layer, + ) + ) + if cnf.use_se: + squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8) + layers.append(se_layer(cnf.expanded_channels, squeeze_channels)) + + # project + layers.append( + Conv2dNormActivation( + cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None + ) + ) + + self.block = nn.Sequential(*layers) + self.out_channels = cnf.out_channels + self._is_cn = cnf.stride > 1 + + def forward(self, input: Tensor) -> Tensor: + result = self.block(input) + if self.use_res_connect: + result += input + return result + + +class MobileNetV3(nn.Module): + def __init__( + self, + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + num_classes: int = 1000, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + dropout: float = 0.2, + **kwargs: Any, + ) -> None: + """ + MobileNet V3 main class + + Args: + inverted_residual_setting (List[InvertedResidualConfig]): Network structure + last_channel (int): The number of channels on the penultimate layer + num_classes (int): Number of classes + block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet + norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use + dropout (float): The droupout probability + """ + super().__init__() + _log_api_usage_once(self) + + if not inverted_residual_setting: + raise ValueError("The inverted_residual_setting should not be empty") + elif not ( + isinstance(inverted_residual_setting, Sequence) + and all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting]) + ): + raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]") + + if block is None: + block = InvertedResidual + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01) + + layers: List[nn.Module] = [] + + # building first layer + firstconv_output_channels = inverted_residual_setting[0].input_channels + layers.append( + Conv2dNormActivation( + 3, + firstconv_output_channels, + kernel_size=3, + stride=2, + norm_layer=norm_layer, + activation_layer=nn.Hardswish, + ) + ) + + # building inverted residual blocks + for cnf in inverted_residual_setting: + layers.append(block(cnf, norm_layer)) + + # building last several layers + lastconv_input_channels = inverted_residual_setting[-1].out_channels + lastconv_output_channels = 6 * lastconv_input_channels + layers.append( + Conv2dNormActivation( + lastconv_input_channels, + lastconv_output_channels, + kernel_size=1, + norm_layer=norm_layer, + activation_layer=nn.Hardswish, + ) + ) + + self.features = nn.Sequential(*layers) + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Sequential( + nn.Linear(lastconv_output_channels, last_channel), + nn.Hardswish(inplace=True), + nn.Dropout(p=dropout, inplace=True), + nn.Linear(last_channel, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.zeros_(m.bias) + + def _forward_impl(self, x: Tensor) -> Tensor: + x = self.features(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.classifier(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _mobilenet_v3_conf( + arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, **kwargs: Any +): + reduce_divider = 2 if reduced_tail else 1 + dilation = 2 if dilated else 1 + + bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult) + adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult) + + if arch == "mobilenet_v3_large": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, False, "RE", 1, 1), + bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1 + bneck_conf(24, 3, 72, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2 + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 5, 120, 40, True, "RE", 1, 1), + bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3 + bneck_conf(80, 3, 200, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 184, 80, False, "HS", 1, 1), + bneck_conf(80, 3, 480, 112, True, "HS", 1, 1), + bneck_conf(112, 3, 672, 112, True, "HS", 1, 1), + bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1280 // reduce_divider) # C5 + elif arch == "mobilenet_v3_small": + inverted_residual_setting = [ + bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1 + bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2 + bneck_conf(24, 3, 88, 24, False, "RE", 1, 1), + bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3 + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 240, 40, True, "HS", 1, 1), + bneck_conf(40, 5, 120, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 144, 48, True, "HS", 1, 1), + bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4 + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation), + ] + last_channel = adjust_channels(1024 // reduce_divider) # C5 + else: + raise ValueError(f"Unsupported model type {arch}") + + return inverted_residual_setting, last_channel + + +def _mobilenet_v3( + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> MobileNetV3: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, +} + + +class MobileNet_V3_Large_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 5483032, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small", + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.042, + "acc@5": 91.340, + } + }, + "_ops": 0.217, + "_file_size": 21.114, + "_docs": """These weights were trained from scratch by using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 5483032, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.274, + "acc@5": 92.566, + } + }, + "_ops": 0.217, + "_file_size": 21.107, + "_docs": """ + These weights improve marginally upon the results of the original paper by using a modified version of + TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class MobileNet_V3_Small_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 2542856, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small", + "_metrics": { + "ImageNet-1K": { + "acc@1": 67.668, + "acc@5": 87.402, + } + }, + "_ops": 0.057, + "_file_size": 9.829, + "_docs": """ + These weights improve upon the results of the original paper by using a simple training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Large_Weights.IMAGENET1K_V1)) +def mobilenet_v3_large( + *, weights: Optional[MobileNet_V3_Large_Weights] = None, progress: bool = True, **kwargs: Any +) -> MobileNetV3: + """ + Constructs a large MobileNetV3 architecture from + `Searching for MobileNetV3 `__. + + Args: + weights (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MobileNet_V3_Large_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mobilenet.MobileNetV3`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights + :members: + """ + weights = MobileNet_V3_Large_Weights.verify(weights) + + inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs) + return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Small_Weights.IMAGENET1K_V1)) +def mobilenet_v3_small( + *, weights: Optional[MobileNet_V3_Small_Weights] = None, progress: bool = True, **kwargs: Any +) -> MobileNetV3: + """ + Constructs a small MobileNetV3 architecture from + `Searching for MobileNetV3 `__. + + Args: + weights (:class:`~torchvision.models.MobileNet_V3_Small_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.MobileNet_V3_Small_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.mobilenet.MobileNetV3`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.MobileNet_V3_Small_Weights + :members: + """ + weights = MobileNet_V3_Small_Weights.verify(weights) + + inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs) + return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/__init__.py b/lib/python3.11/site-packages/torchvision/models/optical_flow/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..89d2302f825ff0dbe25d02f6dc7c84d3c0790ad0 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/optical_flow/__init__.py @@ -0,0 +1 @@ +from .raft import * diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb916daed7f7b81bc8123032b824a03583b33f64 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/_utils.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/_utils.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..75975af1879a788ea1c0a8e7e59e7f4817176512 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/_utils.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/raft.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/raft.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2a17ecad96874c30a6b9605c4de4421d4f07e54f Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/models/optical_flow/__pycache__/raft.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/_utils.py b/lib/python3.11/site-packages/torchvision/models/optical_flow/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2454a27315d6e560dccb6ea2ce6083da03e256 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/optical_flow/_utils.py @@ -0,0 +1,48 @@ +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import Tensor + + +def grid_sample(img: Tensor, absolute_grid: Tensor, mode: str = "bilinear", align_corners: Optional[bool] = None): + """Same as torch's grid_sample, with absolute pixel coordinates instead of normalized coordinates.""" + h, w = img.shape[-2:] + + xgrid, ygrid = absolute_grid.split([1, 1], dim=-1) + xgrid = 2 * xgrid / (w - 1) - 1 + # Adding condition if h > 1 to enable this function be reused in raft-stereo + if h > 1: + ygrid = 2 * ygrid / (h - 1) - 1 + normalized_grid = torch.cat([xgrid, ygrid], dim=-1) + + return F.grid_sample(img, normalized_grid, mode=mode, align_corners=align_corners) + + +def make_coords_grid(batch_size: int, h: int, w: int, device: str = "cpu"): + device = torch.device(device) + coords = torch.meshgrid(torch.arange(h, device=device), torch.arange(w, device=device), indexing="ij") + coords = torch.stack(coords[::-1], dim=0).float() + return coords[None].repeat(batch_size, 1, 1, 1) + + +def upsample_flow(flow, up_mask: Optional[Tensor] = None, factor: int = 8): + """Upsample flow by the input factor (default 8). + + If up_mask is None we just interpolate. + If up_mask is specified, we upsample using a convex combination of its weights. See paper page 8 and appendix B. + Note that in appendix B the picture assumes a downsample factor of 4 instead of 8. + """ + batch_size, num_channels, h, w = flow.shape + new_h, new_w = h * factor, w * factor + + if up_mask is None: + return factor * F.interpolate(flow, size=(new_h, new_w), mode="bilinear", align_corners=True) + + up_mask = up_mask.view(batch_size, 1, 9, factor, factor, h, w) + up_mask = torch.softmax(up_mask, dim=2) # "convex" == weights sum to 1 + + upsampled_flow = F.unfold(factor * flow, kernel_size=3, padding=1).view(batch_size, num_channels, 9, 1, 1, h, w) + upsampled_flow = torch.sum(up_mask * upsampled_flow, dim=2) + + return upsampled_flow.permute(0, 1, 4, 2, 5, 3).reshape(batch_size, num_channels, new_h, new_w) diff --git a/lib/python3.11/site-packages/torchvision/models/optical_flow/raft.py b/lib/python3.11/site-packages/torchvision/models/optical_flow/raft.py new file mode 100644 index 0000000000000000000000000000000000000000..c294777ee6ffc0a9151f76f13bf2bde018580f9e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/optical_flow/raft.py @@ -0,0 +1,947 @@ +from typing import List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.nn.modules.batchnorm import BatchNorm2d +from torch.nn.modules.instancenorm import InstanceNorm2d +from torchvision.ops import Conv2dNormActivation + +from ...transforms._presets import OpticalFlow +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._utils import handle_legacy_interface +from ._utils import grid_sample, make_coords_grid, upsample_flow + + +__all__ = ( + "RAFT", + "raft_large", + "raft_small", + "Raft_Large_Weights", + "Raft_Small_Weights", +) + + +class ResidualBlock(nn.Module): + """Slightly modified Residual block with extra relu and biases.""" + + def __init__(self, in_channels, out_channels, *, norm_layer, stride=1, always_project: bool = False): + super().__init__() + + # Note regarding bias=True: + # Usually we can pass bias=False in conv layers followed by a norm layer. + # But in the RAFT training reference, the BatchNorm2d layers are only activated for the first dataset, + # and frozen for the rest of the training process (i.e. set as eval()). The bias term is thus still useful + # for the rest of the datasets. Technically, we could remove the bias for other norm layers like Instance norm + # because these aren't frozen, but we don't bother (also, we wouldn't be able to load the original weights). + self.convnormrelu1 = Conv2dNormActivation( + in_channels, out_channels, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True + ) + self.convnormrelu2 = Conv2dNormActivation( + out_channels, out_channels, norm_layer=norm_layer, kernel_size=3, bias=True + ) + + # make mypy happy + self.downsample: nn.Module + + if stride == 1 and not always_project: + self.downsample = nn.Identity() + else: + self.downsample = Conv2dNormActivation( + in_channels, + out_channels, + norm_layer=norm_layer, + kernel_size=1, + stride=stride, + bias=True, + activation_layer=None, + ) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + y = x + y = self.convnormrelu1(y) + y = self.convnormrelu2(y) + + x = self.downsample(x) + + return self.relu(x + y) + + +class BottleneckBlock(nn.Module): + """Slightly modified BottleNeck block (extra relu and biases)""" + + def __init__(self, in_channels, out_channels, *, norm_layer, stride=1): + super().__init__() + + # See note in ResidualBlock for the reason behind bias=True + self.convnormrelu1 = Conv2dNormActivation( + in_channels, out_channels // 4, norm_layer=norm_layer, kernel_size=1, bias=True + ) + self.convnormrelu2 = Conv2dNormActivation( + out_channels // 4, out_channels // 4, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True + ) + self.convnormrelu3 = Conv2dNormActivation( + out_channels // 4, out_channels, norm_layer=norm_layer, kernel_size=1, bias=True + ) + self.relu = nn.ReLU(inplace=True) + + if stride == 1: + self.downsample = nn.Identity() + else: + self.downsample = Conv2dNormActivation( + in_channels, + out_channels, + norm_layer=norm_layer, + kernel_size=1, + stride=stride, + bias=True, + activation_layer=None, + ) + + def forward(self, x): + y = x + y = self.convnormrelu1(y) + y = self.convnormrelu2(y) + y = self.convnormrelu3(y) + + x = self.downsample(x) + + return self.relu(x + y) + + +class FeatureEncoder(nn.Module): + """The feature encoder, used both as the actual feature encoder, and as the context encoder. + + It must downsample its input by 8. + """ + + def __init__( + self, *, block=ResidualBlock, layers=(64, 64, 96, 128, 256), strides=(2, 1, 2, 2), norm_layer=nn.BatchNorm2d + ): + super().__init__() + + if len(layers) != 5: + raise ValueError(f"The expected number of layers is 5, instead got {len(layers)}") + + # See note in ResidualBlock for the reason behind bias=True + self.convnormrelu = Conv2dNormActivation( + 3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=strides[0], bias=True + ) + + self.layer1 = self._make_2_blocks(block, layers[0], layers[1], norm_layer=norm_layer, first_stride=strides[1]) + self.layer2 = self._make_2_blocks(block, layers[1], layers[2], norm_layer=norm_layer, first_stride=strides[2]) + self.layer3 = self._make_2_blocks(block, layers[2], layers[3], norm_layer=norm_layer, first_stride=strides[3]) + + self.conv = nn.Conv2d(layers[3], layers[4], kernel_size=1) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + num_downsamples = len(list(filter(lambda s: s == 2, strides))) + self.output_dim = layers[-1] + self.downsample_factor = 2**num_downsamples + + def _make_2_blocks(self, block, in_channels, out_channels, norm_layer, first_stride): + block1 = block(in_channels, out_channels, norm_layer=norm_layer, stride=first_stride) + block2 = block(out_channels, out_channels, norm_layer=norm_layer, stride=1) + return nn.Sequential(block1, block2) + + def forward(self, x): + x = self.convnormrelu(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + + x = self.conv(x) + + return x + + +class MotionEncoder(nn.Module): + """The motion encoder, part of the update block. + + Takes the current predicted flow and the correlation features as input and returns an encoded version of these. + """ + + def __init__(self, *, in_channels_corr, corr_layers=(256, 192), flow_layers=(128, 64), out_channels=128): + super().__init__() + + if len(flow_layers) != 2: + raise ValueError(f"The expected number of flow_layers is 2, instead got {len(flow_layers)}") + if len(corr_layers) not in (1, 2): + raise ValueError(f"The number of corr_layers should be 1 or 2, instead got {len(corr_layers)}") + + self.convcorr1 = Conv2dNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1) + if len(corr_layers) == 2: + self.convcorr2 = Conv2dNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3) + else: + self.convcorr2 = nn.Identity() + + self.convflow1 = Conv2dNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7) + self.convflow2 = Conv2dNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3) + + # out_channels - 2 because we cat the flow (2 channels) at the end + self.conv = Conv2dNormActivation( + corr_layers[-1] + flow_layers[-1], out_channels - 2, norm_layer=None, kernel_size=3 + ) + + self.out_channels = out_channels + + def forward(self, flow, corr_features): + corr = self.convcorr1(corr_features) + corr = self.convcorr2(corr) + + flow_orig = flow + flow = self.convflow1(flow) + flow = self.convflow2(flow) + + corr_flow = torch.cat([corr, flow], dim=1) + corr_flow = self.conv(corr_flow) + return torch.cat([corr_flow, flow_orig], dim=1) + + +class ConvGRU(nn.Module): + """Convolutional Gru unit.""" + + def __init__(self, *, input_size, hidden_size, kernel_size, padding): + super().__init__() + self.convz = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding) + self.convr = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding) + self.convq = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding) + + def forward(self, h, x): + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz(hx)) + r = torch.sigmoid(self.convr(hx)) + q = torch.tanh(self.convq(torch.cat([r * h, x], dim=1))) + h = (1 - z) * h + z * q + return h + + +def _pass_through_h(h, _): + # Declared here for torchscript + return h + + +class RecurrentBlock(nn.Module): + """Recurrent block, part of the update block. + + Takes the current hidden state and the concatenation of (motion encoder output, context) as input. + Returns an updated hidden state. + """ + + def __init__(self, *, input_size, hidden_size, kernel_size=((1, 5), (5, 1)), padding=((0, 2), (2, 0))): + super().__init__() + + if len(kernel_size) != len(padding): + raise ValueError( + f"kernel_size should have the same length as padding, instead got len(kernel_size) = {len(kernel_size)} and len(padding) = {len(padding)}" + ) + if len(kernel_size) not in (1, 2): + raise ValueError(f"kernel_size should either 1 or 2, instead got {len(kernel_size)}") + + self.convgru1 = ConvGRU( + input_size=input_size, hidden_size=hidden_size, kernel_size=kernel_size[0], padding=padding[0] + ) + if len(kernel_size) == 2: + self.convgru2 = ConvGRU( + input_size=input_size, hidden_size=hidden_size, kernel_size=kernel_size[1], padding=padding[1] + ) + else: + self.convgru2 = _pass_through_h + + self.hidden_size = hidden_size + + def forward(self, h, x): + h = self.convgru1(h, x) + h = self.convgru2(h, x) + return h + + +class FlowHead(nn.Module): + """Flow head, part of the update block. + + Takes the hidden state of the recurrent unit as input, and outputs the predicted "delta flow". + """ + + def __init__(self, *, in_channels, hidden_size): + super().__init__() + self.conv1 = nn.Conv2d(in_channels, hidden_size, 3, padding=1) + self.conv2 = nn.Conv2d(hidden_size, 2, 3, padding=1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + return self.conv2(self.relu(self.conv1(x))) + + +class UpdateBlock(nn.Module): + """The update block which contains the motion encoder, the recurrent block, and the flow head. + + It must expose a ``hidden_state_size`` attribute which is the hidden state size of its recurrent block. + """ + + def __init__(self, *, motion_encoder, recurrent_block, flow_head): + super().__init__() + self.motion_encoder = motion_encoder + self.recurrent_block = recurrent_block + self.flow_head = flow_head + + self.hidden_state_size = recurrent_block.hidden_size + + def forward(self, hidden_state, context, corr_features, flow): + motion_features = self.motion_encoder(flow, corr_features) + x = torch.cat([context, motion_features], dim=1) + + hidden_state = self.recurrent_block(hidden_state, x) + delta_flow = self.flow_head(hidden_state) + return hidden_state, delta_flow + + +class MaskPredictor(nn.Module): + """Mask predictor to be used when upsampling the predicted flow. + + It takes the hidden state of the recurrent unit as input and outputs the mask. + This is not used in the raft-small model. + """ + + def __init__(self, *, in_channels, hidden_size, multiplier=0.25): + super().__init__() + self.convrelu = Conv2dNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3) + # 8 * 8 * 9 because the predicted flow is downsampled by 8, from the downsampling of the initial FeatureEncoder, + # and we interpolate with all 9 surrounding neighbors. See paper and appendix B. + self.conv = nn.Conv2d(hidden_size, 8 * 8 * 9, 1, padding=0) + + # In the original code, they use a factor of 0.25 to "downweight the gradients" of that branch. + # See e.g. https://github.com/princeton-vl/RAFT/issues/119#issuecomment-953950419 + # or https://github.com/princeton-vl/RAFT/issues/24. + # It doesn't seem to affect epe significantly and can likely be set to 1. + self.multiplier = multiplier + + def forward(self, x): + x = self.convrelu(x) + x = self.conv(x) + return self.multiplier * x + + +class CorrBlock(nn.Module): + """The correlation block. + + Creates a correlation pyramid with ``num_levels`` levels from the outputs of the feature encoder, + and then indexes from this pyramid to create correlation features. + The "indexing" of a given centroid pixel x' is done by concatenating its surrounding neighbors that + are within a ``radius``, according to the infinity norm (see paper section 3.2). + Note: typo in the paper, it should be infinity norm, not 1-norm. + """ + + def __init__(self, *, num_levels: int = 4, radius: int = 4): + super().__init__() + self.num_levels = num_levels + self.radius = radius + + self.corr_pyramid: List[Tensor] = [torch.tensor(0)] # useless, but torchscript is otherwise confused :') + + # The neighborhood of a centroid pixel x' is {x' + delta, ||delta||_inf <= radius} + # so it's a square surrounding x', and its sides have a length of 2 * radius + 1 + # The paper claims that it's ||.||_1 instead of ||.||_inf but it's a typo: + # https://github.com/princeton-vl/RAFT/issues/122 + self.out_channels = num_levels * (2 * radius + 1) ** 2 + + def build_pyramid(self, fmap1, fmap2): + """Build the correlation pyramid from two feature maps. + + The correlation volume is first computed as the dot product of each pair (pixel_in_fmap1, pixel_in_fmap2) + The last 2 dimensions of the correlation volume are then pooled num_levels times at different resolutions + to build the correlation pyramid. + """ + + if fmap1.shape != fmap2.shape: + raise ValueError( + f"Input feature maps should have the same shape, instead got {fmap1.shape} (fmap1.shape) != {fmap2.shape} (fmap2.shape)" + ) + + # Explaining min_fmap_size below: the fmaps are down-sampled (num_levels - 1) times by a factor of 2. + # The last corr_volume most have at least 2 values (hence the 2* factor), otherwise grid_sample() would + # produce nans in its output. + min_fmap_size = 2 * (2 ** (self.num_levels - 1)) + if any(fmap_size < min_fmap_size for fmap_size in fmap1.shape[-2:]): + raise ValueError( + "Feature maps are too small to be down-sampled by the correlation pyramid. " + f"H and W of feature maps should be at least {min_fmap_size}; got: {fmap1.shape[-2:]}. " + "Remember that input images to the model are downsampled by 8, so that means their " + f"dimensions should be at least 8 * {min_fmap_size} = {8 * min_fmap_size}." + ) + + corr_volume = self._compute_corr_volume(fmap1, fmap2) + + batch_size, h, w, num_channels, _, _ = corr_volume.shape # _, _ = h, w + corr_volume = corr_volume.reshape(batch_size * h * w, num_channels, h, w) + self.corr_pyramid = [corr_volume] + for _ in range(self.num_levels - 1): + corr_volume = F.avg_pool2d(corr_volume, kernel_size=2, stride=2) + self.corr_pyramid.append(corr_volume) + + def index_pyramid(self, centroids_coords): + """Return correlation features by indexing from the pyramid.""" + neighborhood_side_len = 2 * self.radius + 1 # see note in __init__ about out_channels + di = torch.linspace(-self.radius, self.radius, neighborhood_side_len) + dj = torch.linspace(-self.radius, self.radius, neighborhood_side_len) + delta = torch.stack(torch.meshgrid(di, dj, indexing="ij"), dim=-1).to(centroids_coords.device) + delta = delta.view(1, neighborhood_side_len, neighborhood_side_len, 2) + + batch_size, _, h, w = centroids_coords.shape # _ = 2 + centroids_coords = centroids_coords.permute(0, 2, 3, 1).reshape(batch_size * h * w, 1, 1, 2) + + indexed_pyramid = [] + for corr_volume in self.corr_pyramid: + sampling_coords = centroids_coords + delta # end shape is (batch_size * h * w, side_len, side_len, 2) + indexed_corr_volume = grid_sample(corr_volume, sampling_coords, align_corners=True, mode="bilinear").view( + batch_size, h, w, -1 + ) + indexed_pyramid.append(indexed_corr_volume) + centroids_coords = centroids_coords / 2 + + corr_features = torch.cat(indexed_pyramid, dim=-1).permute(0, 3, 1, 2).contiguous() + + expected_output_shape = (batch_size, self.out_channels, h, w) + if corr_features.shape != expected_output_shape: + raise ValueError( + f"Output shape of index pyramid is incorrect. Should be {expected_output_shape}, got {corr_features.shape}" + ) + + return corr_features + + def _compute_corr_volume(self, fmap1, fmap2): + batch_size, num_channels, h, w = fmap1.shape + fmap1 = fmap1.view(batch_size, num_channels, h * w) + fmap2 = fmap2.view(batch_size, num_channels, h * w) + + corr = torch.matmul(fmap1.transpose(1, 2), fmap2) + corr = corr.view(batch_size, h, w, 1, h, w) + return corr / torch.sqrt(torch.tensor(num_channels)) + + +class RAFT(nn.Module): + def __init__(self, *, feature_encoder, context_encoder, corr_block, update_block, mask_predictor=None): + """RAFT model from + `RAFT: Recurrent All Pairs Field Transforms for Optical Flow `_. + + args: + feature_encoder (nn.Module): The feature encoder. It must downsample the input by 8. + Its input is the concatenation of ``image1`` and ``image2``. + context_encoder (nn.Module): The context encoder. It must downsample the input by 8. + Its input is ``image1``. As in the original implementation, its output will be split into 2 parts: + + - one part will be used as the actual "context", passed to the recurrent unit of the ``update_block`` + - one part will be used to initialize the hidden state of the recurrent unit of + the ``update_block`` + + These 2 parts are split according to the ``hidden_state_size`` of the ``update_block``, so the output + of the ``context_encoder`` must be strictly greater than ``hidden_state_size``. + + corr_block (nn.Module): The correlation block, which creates a correlation pyramid from the output of the + ``feature_encoder``, and then indexes from this pyramid to create correlation features. It must expose + 2 methods: + + - a ``build_pyramid`` method that takes ``feature_map_1`` and ``feature_map_2`` as input (these are the + output of the ``feature_encoder``). + - a ``index_pyramid`` method that takes the coordinates of the centroid pixels as input, and returns + the correlation features. See paper section 3.2. + + It must expose an ``out_channels`` attribute. + + update_block (nn.Module): The update block, which contains the motion encoder, the recurrent unit, and the + flow head. It takes as input the hidden state of its recurrent unit, the context, the correlation + features, and the current predicted flow. It outputs an updated hidden state, and the ``delta_flow`` + prediction (see paper appendix A). It must expose a ``hidden_state_size`` attribute. + mask_predictor (nn.Module, optional): Predicts the mask that will be used to upsample the predicted flow. + The output channel must be 8 * 8 * 9 - see paper section 3.3, and Appendix B. + If ``None`` (default), the flow is upsampled using interpolation. + """ + super().__init__() + _log_api_usage_once(self) + + self.feature_encoder = feature_encoder + self.context_encoder = context_encoder + self.corr_block = corr_block + self.update_block = update_block + + self.mask_predictor = mask_predictor + + if not hasattr(self.update_block, "hidden_state_size"): + raise ValueError("The update_block parameter should expose a 'hidden_state_size' attribute.") + + def forward(self, image1, image2, num_flow_updates: int = 12): + + batch_size, _, h, w = image1.shape + if (h, w) != image2.shape[-2:]: + raise ValueError(f"input images should have the same shape, instead got ({h}, {w}) != {image2.shape[-2:]}") + if not (h % 8 == 0) and (w % 8 == 0): + raise ValueError(f"input image H and W should be divisible by 8, instead got {h} (h) and {w} (w)") + + fmaps = self.feature_encoder(torch.cat([image1, image2], dim=0)) + fmap1, fmap2 = torch.chunk(fmaps, chunks=2, dim=0) + if fmap1.shape[-2:] != (h // 8, w // 8): + raise ValueError("The feature encoder should downsample H and W by 8") + + self.corr_block.build_pyramid(fmap1, fmap2) + + context_out = self.context_encoder(image1) + if context_out.shape[-2:] != (h // 8, w // 8): + raise ValueError("The context encoder should downsample H and W by 8") + + # As in the original paper, the actual output of the context encoder is split in 2 parts: + # - one part is used to initialize the hidden state of the recurent units of the update block + # - the rest is the "actual" context. + hidden_state_size = self.update_block.hidden_state_size + out_channels_context = context_out.shape[1] - hidden_state_size + if out_channels_context <= 0: + raise ValueError( + f"The context encoder outputs {context_out.shape[1]} channels, but it should have at strictly more than hidden_state={hidden_state_size} channels" + ) + hidden_state, context = torch.split(context_out, [hidden_state_size, out_channels_context], dim=1) + hidden_state = torch.tanh(hidden_state) + context = F.relu(context) + + coords0 = make_coords_grid(batch_size, h // 8, w // 8).to(fmap1.device) + coords1 = make_coords_grid(batch_size, h // 8, w // 8).to(fmap1.device) + + flow_predictions = [] + for _ in range(num_flow_updates): + coords1 = coords1.detach() # Don't backpropagate gradients through this branch, see paper + corr_features = self.corr_block.index_pyramid(centroids_coords=coords1) + + flow = coords1 - coords0 + hidden_state, delta_flow = self.update_block(hidden_state, context, corr_features, flow) + + coords1 = coords1 + delta_flow + + up_mask = None if self.mask_predictor is None else self.mask_predictor(hidden_state) + upsampled_flow = upsample_flow(flow=(coords1 - coords0), up_mask=up_mask) + flow_predictions.append(upsampled_flow) + + return flow_predictions + + +_COMMON_META = { + "min_size": (128, 128), +} + + +class Raft_Large_Weights(WeightsEnum): + """The metrics reported here are as follows. + + ``epe`` is the "end-point-error" and indicates how far (in pixels) the + predicted flow is from its true value. This is averaged over all pixels + of all images. ``per_image_epe`` is similar, but the average is different: + the epe is first computed on each image independently, and then averaged + over all images. This corresponds to "Fl-epe" (sometimes written "F1-epe") + in the original paper, and it's only used on Kitti. ``fl-all`` is also a + Kitti-specific metric, defined by the author of the dataset and used for the + Kitti leaderboard. It corresponds to the average of pixels whose epe is + either <3px, or <5% of flow's 2-norm. + """ + + C_T_V1 = Weights( + # Weights ported from https://github.com/princeton-vl/RAFT + url="https://download.pytorch.org/models/raft_large_C_T_V1-22a6c225.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/princeton-vl/RAFT", + "_metrics": { + "Sintel-Train-Cleanpass": {"epe": 1.4411}, + "Sintel-Train-Finalpass": {"epe": 2.7894}, + "Kitti-Train": {"per_image_epe": 5.0172, "fl_all": 17.4506}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """These weights were ported from the original paper. They + are trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`.""", + }, + ) + + C_T_V2 = Weights( + url="https://download.pytorch.org/models/raft_large_C_T_V2-1bb1363a.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow", + "_metrics": { + "Sintel-Train-Cleanpass": {"epe": 1.3822}, + "Sintel-Train-Finalpass": {"epe": 2.7161}, + "Kitti-Train": {"per_image_epe": 4.5118, "fl_all": 16.0679}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """These weights were trained from scratch on + :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`.""", + }, + ) + + C_T_SKHT_V1 = Weights( + # Weights ported from https://github.com/princeton-vl/RAFT + url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V1-0b8c9e55.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/princeton-vl/RAFT", + "_metrics": { + "Sintel-Test-Cleanpass": {"epe": 1.94}, + "Sintel-Test-Finalpass": {"epe": 3.18}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """ + These weights were ported from the original paper. They are + trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D` and fine-tuned on + Sintel. The Sintel fine-tuning step is a combination of + :class:`~torchvision.datasets.Sintel`, + :class:`~torchvision.datasets.KittiFlow`, + :class:`~torchvision.datasets.HD1K`, and + :class:`~torchvision.datasets.FlyingThings3D` (clean pass). + """, + }, + ) + + C_T_SKHT_V2 = Weights( + url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V2-ff5fadd5.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow", + "_metrics": { + "Sintel-Test-Cleanpass": {"epe": 1.819}, + "Sintel-Test-Finalpass": {"epe": 3.067}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """ + These weights were trained from scratch. They are + pre-trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D` and then + fine-tuned on Sintel. The Sintel fine-tuning step is a + combination of :class:`~torchvision.datasets.Sintel`, + :class:`~torchvision.datasets.KittiFlow`, + :class:`~torchvision.datasets.HD1K`, and + :class:`~torchvision.datasets.FlyingThings3D` (clean pass). + """, + }, + ) + + C_T_SKHT_K_V1 = Weights( + # Weights ported from https://github.com/princeton-vl/RAFT + url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V1-4a6a5039.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/princeton-vl/RAFT", + "_metrics": { + "Kitti-Test": {"fl_all": 5.10}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """ + These weights were ported from the original paper. They are + pre-trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`, + fine-tuned on Sintel, and then fine-tuned on + :class:`~torchvision.datasets.KittiFlow`. The Sintel fine-tuning + step was described above. + """, + }, + ) + + C_T_SKHT_K_V2 = Weights( + url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V2-b5c70766.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 5257536, + "recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow", + "_metrics": { + "Kitti-Test": {"fl_all": 5.19}, + }, + "_ops": 211.007, + "_file_size": 20.129, + "_docs": """ + These weights were trained from scratch. They are + pre-trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`, + fine-tuned on Sintel, and then fine-tuned on + :class:`~torchvision.datasets.KittiFlow`. The Sintel fine-tuning + step was described above. + """, + }, + ) + + DEFAULT = C_T_SKHT_V2 + + +class Raft_Small_Weights(WeightsEnum): + """The metrics reported here are as follows. + + ``epe`` is the "end-point-error" and indicates how far (in pixels) the + predicted flow is from its true value. This is averaged over all pixels + of all images. ``per_image_epe`` is similar, but the average is different: + the epe is first computed on each image independently, and then averaged + over all images. This corresponds to "Fl-epe" (sometimes written "F1-epe") + in the original paper, and it's only used on Kitti. ``fl-all`` is also a + Kitti-specific metric, defined by the author of the dataset and used for the + Kitti leaderboard. It corresponds to the average of pixels whose epe is + either <3px, or <5% of flow's 2-norm. + """ + + C_T_V1 = Weights( + # Weights ported from https://github.com/princeton-vl/RAFT + url="https://download.pytorch.org/models/raft_small_C_T_V1-ad48884c.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 990162, + "recipe": "https://github.com/princeton-vl/RAFT", + "_metrics": { + "Sintel-Train-Cleanpass": {"epe": 2.1231}, + "Sintel-Train-Finalpass": {"epe": 3.2790}, + "Kitti-Train": {"per_image_epe": 7.6557, "fl_all": 25.2801}, + }, + "_ops": 47.655, + "_file_size": 3.821, + "_docs": """These weights were ported from the original paper. They + are trained on :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`.""", + }, + ) + C_T_V2 = Weights( + url="https://download.pytorch.org/models/raft_small_C_T_V2-01064c6d.pth", + transforms=OpticalFlow, + meta={ + **_COMMON_META, + "num_params": 990162, + "recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow", + "_metrics": { + "Sintel-Train-Cleanpass": {"epe": 1.9901}, + "Sintel-Train-Finalpass": {"epe": 3.2831}, + "Kitti-Train": {"per_image_epe": 7.5978, "fl_all": 25.2369}, + }, + "_ops": 47.655, + "_file_size": 3.821, + "_docs": """These weights were trained from scratch on + :class:`~torchvision.datasets.FlyingChairs` + + :class:`~torchvision.datasets.FlyingThings3D`.""", + }, + ) + + DEFAULT = C_T_V2 + + +def _raft( + *, + weights=None, + progress=False, + # Feature encoder + feature_encoder_layers, + feature_encoder_block, + feature_encoder_norm_layer, + # Context encoder + context_encoder_layers, + context_encoder_block, + context_encoder_norm_layer, + # Correlation block + corr_block_num_levels, + corr_block_radius, + # Motion encoder + motion_encoder_corr_layers, + motion_encoder_flow_layers, + motion_encoder_out_channels, + # Recurrent block + recurrent_block_hidden_state_size, + recurrent_block_kernel_size, + recurrent_block_padding, + # Flow Head + flow_head_hidden_size, + # Mask predictor + use_mask_predictor, + **kwargs, +): + feature_encoder = kwargs.pop("feature_encoder", None) or FeatureEncoder( + block=feature_encoder_block, layers=feature_encoder_layers, norm_layer=feature_encoder_norm_layer + ) + context_encoder = kwargs.pop("context_encoder", None) or FeatureEncoder( + block=context_encoder_block, layers=context_encoder_layers, norm_layer=context_encoder_norm_layer + ) + + corr_block = kwargs.pop("corr_block", None) or CorrBlock(num_levels=corr_block_num_levels, radius=corr_block_radius) + + update_block = kwargs.pop("update_block", None) + if update_block is None: + motion_encoder = MotionEncoder( + in_channels_corr=corr_block.out_channels, + corr_layers=motion_encoder_corr_layers, + flow_layers=motion_encoder_flow_layers, + out_channels=motion_encoder_out_channels, + ) + + # See comments in forward pass of RAFT class about why we split the output of the context encoder + out_channels_context = context_encoder_layers[-1] - recurrent_block_hidden_state_size + recurrent_block = RecurrentBlock( + input_size=motion_encoder.out_channels + out_channels_context, + hidden_size=recurrent_block_hidden_state_size, + kernel_size=recurrent_block_kernel_size, + padding=recurrent_block_padding, + ) + + flow_head = FlowHead(in_channels=recurrent_block_hidden_state_size, hidden_size=flow_head_hidden_size) + + update_block = UpdateBlock(motion_encoder=motion_encoder, recurrent_block=recurrent_block, flow_head=flow_head) + + mask_predictor = kwargs.pop("mask_predictor", None) + if mask_predictor is None and use_mask_predictor: + mask_predictor = MaskPredictor( + in_channels=recurrent_block_hidden_state_size, + hidden_size=256, + multiplier=0.25, # See comment in MaskPredictor about this + ) + + model = RAFT( + feature_encoder=feature_encoder, + context_encoder=context_encoder, + corr_block=corr_block, + update_block=update_block, + mask_predictor=mask_predictor, + **kwargs, # not really needed, all params should be consumed by now + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Raft_Large_Weights.C_T_SKHT_V2)) +def raft_large(*, weights: Optional[Raft_Large_Weights] = None, progress=True, **kwargs) -> RAFT: + """RAFT model from + `RAFT: Recurrent All Pairs Field Transforms for Optical Flow `_. + + Please see the example below for a tutorial on how to use this model. + + Args: + weights(:class:`~torchvision.models.optical_flow.Raft_Large_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.optical_flow.Raft_Large_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.optical_flow.RAFT`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.optical_flow.Raft_Large_Weights + :members: + """ + + weights = Raft_Large_Weights.verify(weights) + + return _raft( + weights=weights, + progress=progress, + # Feature encoder + feature_encoder_layers=(64, 64, 96, 128, 256), + feature_encoder_block=ResidualBlock, + feature_encoder_norm_layer=InstanceNorm2d, + # Context encoder + context_encoder_layers=(64, 64, 96, 128, 256), + context_encoder_block=ResidualBlock, + context_encoder_norm_layer=BatchNorm2d, + # Correlation block + corr_block_num_levels=4, + corr_block_radius=4, + # Motion encoder + motion_encoder_corr_layers=(256, 192), + motion_encoder_flow_layers=(128, 64), + motion_encoder_out_channels=128, + # Recurrent block + recurrent_block_hidden_state_size=128, + recurrent_block_kernel_size=((1, 5), (5, 1)), + recurrent_block_padding=((0, 2), (2, 0)), + # Flow head + flow_head_hidden_size=256, + # Mask predictor + use_mask_predictor=True, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Raft_Small_Weights.C_T_V2)) +def raft_small(*, weights: Optional[Raft_Small_Weights] = None, progress=True, **kwargs) -> RAFT: + """RAFT "small" model from + `RAFT: Recurrent All Pairs Field Transforms for Optical Flow `__. + + Please see the example below for a tutorial on how to use this model. + + Args: + weights(:class:`~torchvision.models.optical_flow.Raft_Small_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.optical_flow.Raft_Small_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.optical_flow.RAFT`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.optical_flow.Raft_Small_Weights + :members: + """ + weights = Raft_Small_Weights.verify(weights) + + return _raft( + weights=weights, + progress=progress, + # Feature encoder + feature_encoder_layers=(32, 32, 64, 96, 128), + feature_encoder_block=BottleneckBlock, + feature_encoder_norm_layer=InstanceNorm2d, + # Context encoder + context_encoder_layers=(32, 32, 64, 96, 160), + context_encoder_block=BottleneckBlock, + context_encoder_norm_layer=None, + # Correlation block + corr_block_num_levels=4, + corr_block_radius=3, + # Motion encoder + motion_encoder_corr_layers=(96,), + motion_encoder_flow_layers=(64, 32), + motion_encoder_out_channels=82, + # Recurrent block + recurrent_block_hidden_state_size=96, + recurrent_block_kernel_size=(3,), + recurrent_block_padding=(1,), + # Flow head + flow_head_hidden_size=128, + # Mask predictor + use_mask_predictor=False, + **kwargs, + ) 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b/lib/python3.11/site-packages/torchvision/models/quantization/googlenet.py new file mode 100644 index 0000000000000000000000000000000000000000..30ef3356ba13108b9bdc4c90a9ab4cb7f92e445a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/googlenet.py @@ -0,0 +1,210 @@ +import warnings +from functools import partial +from typing import Any, Optional, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn import functional as F + +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from ..googlenet import BasicConv2d, GoogLeNet, GoogLeNet_Weights, GoogLeNetOutputs, Inception, InceptionAux +from .utils import _fuse_modules, _replace_relu, quantize_model + + +__all__ = [ + "QuantizableGoogLeNet", + "GoogLeNet_QuantizedWeights", + "googlenet", +] + + +class QuantizableBasicConv2d(BasicConv2d): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.relu = nn.ReLU() + + def forward(self, x: Tensor) -> Tensor: + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + _fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True) + + +class QuantizableInception(Inception): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.cat = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.cat.cat(outputs, 1) + + +class QuantizableInceptionAux(InceptionAux): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.relu = nn.ReLU() + + def forward(self, x: Tensor) -> Tensor: + # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14 + x = F.adaptive_avg_pool2d(x, (4, 4)) + # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4 + x = self.conv(x) + # N x 128 x 4 x 4 + x = torch.flatten(x, 1) + # N x 2048 + x = self.relu(self.fc1(x)) + # N x 1024 + x = self.dropout(x) + # N x 1024 + x = self.fc2(x) + # N x 1000 (num_classes) + + return x + + +class QuantizableGoogLeNet(GoogLeNet): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__( # type: ignore[misc] + *args, blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], **kwargs + ) + self.quant = torch.ao.quantization.QuantStub() + self.dequant = torch.ao.quantization.DeQuantStub() + + def forward(self, x: Tensor) -> GoogLeNetOutputs: + x = self._transform_input(x) + x = self.quant(x) + x, aux1, aux2 = self._forward(x) + x = self.dequant(x) + aux_defined = self.training and self.aux_logits + if torch.jit.is_scripting(): + if not aux_defined: + warnings.warn("Scripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple") + return GoogLeNetOutputs(x, aux2, aux1) + else: + return self.eager_outputs(x, aux2, aux1) + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + r"""Fuse conv/bn/relu modules in googlenet model + + Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. + Model is modified in place. Note that this operation does not change numerics + and the model after modification is in floating point + """ + + for m in self.modules(): + if type(m) is QuantizableBasicConv2d: + m.fuse_model(is_qat) + + +class GoogLeNet_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/googlenet_fbgemm-c81f6644.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + "num_params": 6624904, + "min_size": (15, 15), + "categories": _IMAGENET_CATEGORIES, + "backend": "fbgemm", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models", + "unquantized": GoogLeNet_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.826, + "acc@5": 89.404, + } + }, + "_ops": 1.498, + "_file_size": 12.618, + "_docs": """ + These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized + weights listed below. + """, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +@register_model(name="quantized_googlenet") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else GoogLeNet_Weights.IMAGENET1K_V1, + ) +) +def googlenet( + *, + weights: Optional[Union[GoogLeNet_QuantizedWeights, GoogLeNet_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableGoogLeNet: + """GoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions `__. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` or :class:`~torchvision.models.GoogLeNet_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool, optional): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableGoogLeNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.GoogLeNet_Weights + :members: + :noindex: + """ + weights = (GoogLeNet_QuantizedWeights if quantize else GoogLeNet_Weights).verify(weights) + + original_aux_logits = kwargs.get("aux_logits", False) + if weights is not None: + if "transform_input" not in kwargs: + _ovewrite_named_param(kwargs, "transform_input", True) + _ovewrite_named_param(kwargs, "aux_logits", True) + _ovewrite_named_param(kwargs, "init_weights", False) + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "fbgemm") + + model = QuantizableGoogLeNet(**kwargs) + _replace_relu(model) + if quantize: + quantize_model(model, backend) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if not original_aux_logits: + model.aux_logits = False + model.aux1 = None # type: ignore[assignment] + model.aux2 = None # type: ignore[assignment] + else: + warnings.warn( + "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them" + ) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/inception.py b/lib/python3.11/site-packages/torchvision/models/quantization/inception.py new file mode 100644 index 0000000000000000000000000000000000000000..75c126697e99befd6ae7d3c1ee88fb8542e06d31 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/inception.py @@ -0,0 +1,273 @@ +import warnings +from functools import partial +from typing import Any, List, Optional, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torchvision.models import inception as inception_module +from torchvision.models.inception import Inception_V3_Weights, InceptionOutputs + +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from .utils import _fuse_modules, _replace_relu, quantize_model + + +__all__ = [ + "QuantizableInception3", + "Inception_V3_QuantizedWeights", + "inception_v3", +] + + +class QuantizableBasicConv2d(inception_module.BasicConv2d): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.relu = nn.ReLU() + + def forward(self, x: Tensor) -> Tensor: + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + _fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True) + + +class QuantizableInceptionA(inception_module.InceptionA): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.myop = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.myop.cat(outputs, 1) + + +class QuantizableInceptionB(inception_module.InceptionB): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.myop = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.myop.cat(outputs, 1) + + +class QuantizableInceptionC(inception_module.InceptionC): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.myop = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.myop.cat(outputs, 1) + + +class QuantizableInceptionD(inception_module.InceptionD): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.myop = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.myop.cat(outputs, 1) + + +class QuantizableInceptionE(inception_module.InceptionE): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + self.myop1 = nn.quantized.FloatFunctional() + self.myop2 = nn.quantized.FloatFunctional() + self.myop3 = nn.quantized.FloatFunctional() + + def _forward(self, x: Tensor) -> List[Tensor]: + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3)] + branch3x3 = self.myop1.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = self.myop2.cat(branch3x3dbl, 1) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x: Tensor) -> Tensor: + outputs = self._forward(x) + return self.myop3.cat(outputs, 1) + + +class QuantizableInceptionAux(inception_module.InceptionAux): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc] + + +class QuantizableInception3(inception_module.Inception3): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__( # type: ignore[misc] + *args, + inception_blocks=[ + QuantizableBasicConv2d, + QuantizableInceptionA, + QuantizableInceptionB, + QuantizableInceptionC, + QuantizableInceptionD, + QuantizableInceptionE, + QuantizableInceptionAux, + ], + **kwargs, + ) + self.quant = torch.ao.quantization.QuantStub() + self.dequant = torch.ao.quantization.DeQuantStub() + + def forward(self, x: Tensor) -> InceptionOutputs: + x = self._transform_input(x) + x = self.quant(x) + x, aux = self._forward(x) + x = self.dequant(x) + aux_defined = self.training and self.aux_logits + if torch.jit.is_scripting(): + if not aux_defined: + warnings.warn("Scripted QuantizableInception3 always returns QuantizableInception3 Tuple") + return InceptionOutputs(x, aux) + else: + return self.eager_outputs(x, aux) + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + r"""Fuse conv/bn/relu modules in inception model + + Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. + Model is modified in place. Note that this operation does not change numerics + and the model after modification is in floating point + """ + + for m in self.modules(): + if type(m) is QuantizableBasicConv2d: + m.fuse_model(is_qat) + + +class Inception_V3_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pth", + transforms=partial(ImageClassification, crop_size=299, resize_size=342), + meta={ + "num_params": 27161264, + "min_size": (75, 75), + "categories": _IMAGENET_CATEGORIES, + "backend": "fbgemm", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models", + "unquantized": Inception_V3_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.176, + "acc@5": 93.354, + } + }, + "_ops": 5.713, + "_file_size": 23.146, + "_docs": """ + These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized + weights listed below. + """, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +@register_model(name="quantized_inception_v3") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else Inception_V3_Weights.IMAGENET1K_V1, + ) +) +def inception_v3( + *, + weights: Optional[Union[Inception_V3_QuantizedWeights, Inception_V3_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableInception3: + r"""Inception v3 model architecture from + `Rethinking the Inception Architecture for Computer Vision `__. + + .. note:: + **Important**: In contrast to the other models the inception_v3 expects tensors with a size of + N x 3 x 299 x 299, so ensure your images are sized accordingly. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.Inception_V3_QuantizedWeights` or :class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained + weights for the model. See + :class:`~torchvision.models.quantization.Inception_V3_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. + Default is True. + quantize (bool, optional): If True, return a quantized version of the model. + Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableInception3`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.Inception_V3_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.Inception_V3_Weights + :members: + :noindex: + """ + weights = (Inception_V3_QuantizedWeights if quantize else Inception_V3_Weights).verify(weights) + + original_aux_logits = kwargs.get("aux_logits", False) + if weights is not None: + if "transform_input" not in kwargs: + _ovewrite_named_param(kwargs, "transform_input", True) + _ovewrite_named_param(kwargs, "aux_logits", True) + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "fbgemm") + + model = QuantizableInception3(**kwargs) + _replace_relu(model) + if quantize: + quantize_model(model, backend) + + if weights is not None: + if quantize and not original_aux_logits: + model.aux_logits = False + model.AuxLogits = None + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + if not quantize and not original_aux_logits: + model.aux_logits = False + model.AuxLogits = None + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/mobilenet.py b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenet.py new file mode 100644 index 0000000000000000000000000000000000000000..0a270d14d3a4ad9eda62b68c2c01e9fdb710ef38 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenet.py @@ -0,0 +1,6 @@ +from .mobilenetv2 import * # noqa: F401, F403 +from .mobilenetv3 import * # noqa: F401, F403 +from .mobilenetv2 import __all__ as mv2_all +from .mobilenetv3 import __all__ as mv3_all + +__all__ = mv2_all + mv3_all diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv2.py b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..4700bb4af931072f1aee3403c1e8c461ec33c76d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv2.py @@ -0,0 +1,154 @@ +from functools import partial +from typing import Any, Optional, Union + +from torch import nn, Tensor +from torch.ao.quantization import DeQuantStub, QuantStub +from torchvision.models.mobilenetv2 import InvertedResidual, MobileNet_V2_Weights, MobileNetV2 + +from ...ops.misc import Conv2dNormActivation +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from .utils import _fuse_modules, _replace_relu, quantize_model + + +__all__ = [ + "QuantizableMobileNetV2", + "MobileNet_V2_QuantizedWeights", + "mobilenet_v2", +] + + +class QuantizableInvertedResidual(InvertedResidual): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + if self.use_res_connect: + return self.skip_add.add(x, self.conv(x)) + else: + return self.conv(x) + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + for idx in range(len(self.conv)): + if type(self.conv[idx]) is nn.Conv2d: + _fuse_modules(self.conv, [str(idx), str(idx + 1)], is_qat, inplace=True) + + +class QuantizableMobileNetV2(MobileNetV2): + def __init__(self, *args: Any, **kwargs: Any) -> None: + """ + MobileNet V2 main class + + Args: + Inherits args from floating point MobileNetV2 + """ + super().__init__(*args, **kwargs) + self.quant = QuantStub() + self.dequant = DeQuantStub() + + def forward(self, x: Tensor) -> Tensor: + x = self.quant(x) + x = self._forward_impl(x) + x = self.dequant(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + for m in self.modules(): + if type(m) is Conv2dNormActivation: + _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True) + if type(m) is QuantizableInvertedResidual: + m.fuse_model(is_qat) + + +class MobileNet_V2_QuantizedWeights(WeightsEnum): + IMAGENET1K_QNNPACK_V1 = Weights( + url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + "num_params": 3504872, + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "backend": "qnnpack", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2", + "unquantized": MobileNet_V2_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.658, + "acc@5": 90.150, + } + }, + "_ops": 0.301, + "_file_size": 3.423, + "_docs": """ + These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized + weights listed below. + """, + }, + ) + DEFAULT = IMAGENET1K_QNNPACK_V1 + + +@register_model(name="quantized_mobilenet_v2") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1 + if kwargs.get("quantize", False) + else MobileNet_V2_Weights.IMAGENET1K_V1, + ) +) +def mobilenet_v2( + *, + weights: Optional[Union[MobileNet_V2_QuantizedWeights, MobileNet_V2_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableMobileNetV2: + """ + Constructs a MobileNetV2 architecture from + `MobileNetV2: Inverted Residuals and Linear Bottlenecks + `_. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + quantize (bool, optional): If True, returns a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights + :members: + .. autoclass:: torchvision.models.MobileNet_V2_Weights + :members: + :noindex: + """ + weights = (MobileNet_V2_QuantizedWeights if quantize else MobileNet_V2_Weights).verify(weights) + + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "qnnpack") + + model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs) + _replace_relu(model) + if quantize: + quantize_model(model, backend) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv3.py b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv3.py new file mode 100644 index 0000000000000000000000000000000000000000..f1fdcfec9570d35683efb10344e667d3f4487fce --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/mobilenetv3.py @@ -0,0 +1,237 @@ +from functools import partial +from typing import Any, List, Optional, Union + +import torch +from torch import nn, Tensor +from torch.ao.quantization import DeQuantStub, QuantStub + +from ...ops.misc import Conv2dNormActivation, SqueezeExcitation +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from ..mobilenetv3 import ( + _mobilenet_v3_conf, + InvertedResidual, + InvertedResidualConfig, + MobileNet_V3_Large_Weights, + MobileNetV3, +) +from .utils import _fuse_modules, _replace_relu + + +__all__ = [ + "QuantizableMobileNetV3", + "MobileNet_V3_Large_QuantizedWeights", + "mobilenet_v3_large", +] + + +class QuantizableSqueezeExcitation(SqueezeExcitation): + _version = 2 + + def __init__(self, *args: Any, **kwargs: Any) -> None: + kwargs["scale_activation"] = nn.Hardsigmoid + super().__init__(*args, **kwargs) + self.skip_mul = nn.quantized.FloatFunctional() + + def forward(self, input: Tensor) -> Tensor: + return self.skip_mul.mul(self._scale(input), input) + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + _fuse_modules(self, ["fc1", "activation"], is_qat, inplace=True) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if hasattr(self, "qconfig") and (version is None or version < 2): + default_state_dict = { + "scale_activation.activation_post_process.scale": torch.tensor([1.0]), + "scale_activation.activation_post_process.activation_post_process.scale": torch.tensor([1.0]), + "scale_activation.activation_post_process.zero_point": torch.tensor([0], dtype=torch.int32), + "scale_activation.activation_post_process.activation_post_process.zero_point": torch.tensor( + [0], dtype=torch.int32 + ), + "scale_activation.activation_post_process.fake_quant_enabled": torch.tensor([1]), + "scale_activation.activation_post_process.observer_enabled": torch.tensor([1]), + } + for k, v in default_state_dict.items(): + full_key = prefix + k + if full_key not in state_dict: + state_dict[full_key] = v + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class QuantizableInvertedResidual(InvertedResidual): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, se_layer=QuantizableSqueezeExcitation, **kwargs) # type: ignore[misc] + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + if self.use_res_connect: + return self.skip_add.add(x, self.block(x)) + else: + return self.block(x) + + +class QuantizableMobileNetV3(MobileNetV3): + def __init__(self, *args: Any, **kwargs: Any) -> None: + """ + MobileNet V3 main class + + Args: + Inherits args from floating point MobileNetV3 + """ + super().__init__(*args, **kwargs) + self.quant = QuantStub() + self.dequant = DeQuantStub() + + def forward(self, x: Tensor) -> Tensor: + x = self.quant(x) + x = self._forward_impl(x) + x = self.dequant(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + for m in self.modules(): + if type(m) is Conv2dNormActivation: + modules_to_fuse = ["0", "1"] + if len(m) == 3 and type(m[2]) is nn.ReLU: + modules_to_fuse.append("2") + _fuse_modules(m, modules_to_fuse, is_qat, inplace=True) + elif type(m) is QuantizableSqueezeExcitation: + m.fuse_model(is_qat) + + +def _mobilenet_v3_model( + inverted_residual_setting: List[InvertedResidualConfig], + last_channel: int, + weights: Optional[WeightsEnum], + progress: bool, + quantize: bool, + **kwargs: Any, +) -> QuantizableMobileNetV3: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "qnnpack") + + model = QuantizableMobileNetV3(inverted_residual_setting, last_channel, block=QuantizableInvertedResidual, **kwargs) + _replace_relu(model) + + if quantize: + # Instead of quantizing the model and then loading the quantized weights we take a different approach. + # We prepare the QAT model, load the QAT weights from training and then convert it. + # This is done to avoid extremely low accuracies observed on the specific model. This is rather a workaround + # for an unresolved bug on the eager quantization API detailed at: https://github.com/pytorch/vision/issues/5890 + model.fuse_model(is_qat=True) + model.qconfig = torch.ao.quantization.get_default_qat_qconfig(backend) + torch.ao.quantization.prepare_qat(model, inplace=True) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + if quantize: + torch.ao.quantization.convert(model, inplace=True) + model.eval() + + return model + + +class MobileNet_V3_Large_QuantizedWeights(WeightsEnum): + IMAGENET1K_QNNPACK_V1 = Weights( + url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + "num_params": 5483032, + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "backend": "qnnpack", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3", + "unquantized": MobileNet_V3_Large_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.004, + "acc@5": 90.858, + } + }, + "_ops": 0.217, + "_file_size": 21.554, + "_docs": """ + These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized + weights listed below. + """, + }, + ) + DEFAULT = IMAGENET1K_QNNPACK_V1 + + +@register_model(name="quantized_mobilenet_v3_large") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1 + if kwargs.get("quantize", False) + else MobileNet_V3_Large_Weights.IMAGENET1K_V1, + ) +) +def mobilenet_v3_large( + *, + weights: Optional[Union[MobileNet_V3_Large_QuantizedWeights, MobileNet_V3_Large_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableMobileNetV3: + """ + MobileNetV3 (Large) model from + `Searching for MobileNetV3 `_. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights + :members: + .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights + :members: + :noindex: + """ + weights = (MobileNet_V3_Large_QuantizedWeights if quantize else MobileNet_V3_Large_Weights).verify(weights) + + inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs) + return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/resnet.py b/lib/python3.11/site-packages/torchvision/models/quantization/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..39958a010fbd335709bc77a1aaf26c996584a398 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/resnet.py @@ -0,0 +1,484 @@ +from functools import partial +from typing import Any, List, Optional, Type, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torchvision.models.resnet import ( + BasicBlock, + Bottleneck, + ResNet, + ResNet18_Weights, + ResNet50_Weights, + ResNeXt101_32X8D_Weights, + ResNeXt101_64X4D_Weights, +) + +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from .utils import _fuse_modules, _replace_relu, quantize_model + + +__all__ = [ + "QuantizableResNet", + "ResNet18_QuantizedWeights", + "ResNet50_QuantizedWeights", + "ResNeXt101_32X8D_QuantizedWeights", + "ResNeXt101_64X4D_QuantizedWeights", + "resnet18", + "resnet50", + "resnext101_32x8d", + "resnext101_64x4d", +] + + +class QuantizableBasicBlock(BasicBlock): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.add_relu = torch.nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = self.add_relu.add_relu(out, identity) + + return out + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + _fuse_modules(self, [["conv1", "bn1", "relu"], ["conv2", "bn2"]], is_qat, inplace=True) + if self.downsample: + _fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True) + + +class QuantizableBottleneck(Bottleneck): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.skip_add_relu = nn.quantized.FloatFunctional() + self.relu1 = nn.ReLU(inplace=False) + self.relu2 = nn.ReLU(inplace=False) + + def forward(self, x: Tensor) -> Tensor: + identity = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu1(out) + out = self.conv2(out) + out = self.bn2(out) + out = self.relu2(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + out = self.skip_add_relu.add_relu(out, identity) + + return out + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + _fuse_modules( + self, [["conv1", "bn1", "relu1"], ["conv2", "bn2", "relu2"], ["conv3", "bn3"]], is_qat, inplace=True + ) + if self.downsample: + _fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True) + + +class QuantizableResNet(ResNet): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + self.quant = torch.ao.quantization.QuantStub() + self.dequant = torch.ao.quantization.DeQuantStub() + + def forward(self, x: Tensor) -> Tensor: + x = self.quant(x) + # Ensure scriptability + # super(QuantizableResNet,self).forward(x) + # is not scriptable + x = self._forward_impl(x) + x = self.dequant(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + r"""Fuse conv/bn/relu modules in resnet models + + Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization. + Model is modified in place. Note that this operation does not change numerics + and the model after modification is in floating point + """ + _fuse_modules(self, ["conv1", "bn1", "relu"], is_qat, inplace=True) + for m in self.modules(): + if type(m) is QuantizableBottleneck or type(m) is QuantizableBasicBlock: + m.fuse_model(is_qat) + + +def _resnet( + block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]], + layers: List[int], + weights: Optional[WeightsEnum], + progress: bool, + quantize: bool, + **kwargs: Any, +) -> QuantizableResNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "fbgemm") + + model = QuantizableResNet(block, layers, **kwargs) + _replace_relu(model) + if quantize: + quantize_model(model, backend) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "backend": "fbgemm", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models", + "_docs": """ + These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized + weights listed below. + """, +} + + +class ResNet18_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 11689512, + "unquantized": ResNet18_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.494, + "acc@5": 88.882, + } + }, + "_ops": 1.814, + "_file_size": 11.238, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +class ResNet50_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 25557032, + "unquantized": ResNet50_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.920, + "acc@5": 92.814, + } + }, + "_ops": 4.089, + "_file_size": 24.759, + }, + ) + IMAGENET1K_FBGEMM_V2 = Weights( + url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 25557032, + "unquantized": ResNet50_Weights.IMAGENET1K_V2, + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.282, + "acc@5": 94.976, + } + }, + "_ops": 4.089, + "_file_size": 24.953, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V2 + + +class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88791336, + "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.986, + "acc@5": 94.480, + } + }, + "_ops": 16.414, + "_file_size": 86.034, + }, + ) + IMAGENET1K_FBGEMM_V2 = Weights( + url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88791336, + "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.574, + "acc@5": 96.132, + } + }, + "_ops": 16.414, + "_file_size": 86.645, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V2 + + +class ResNeXt101_64X4D_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 83455272, + "recipe": "https://github.com/pytorch/vision/pull/5935", + "unquantized": ResNeXt101_64X4D_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.898, + "acc@5": 96.326, + } + }, + "_ops": 15.46, + "_file_size": 81.556, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +@register_model(name="quantized_resnet18") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ResNet18_Weights.IMAGENET1K_V1, + ) +) +def resnet18( + *, + weights: Optional[Union[ResNet18_QuantizedWeights, ResNet18_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableResNet: + """ResNet-18 model from + `Deep Residual Learning for Image Recognition `_ + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool, optional): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ResNet18_Weights + :members: + :noindex: + """ + weights = (ResNet18_QuantizedWeights if quantize else ResNet18_Weights).verify(weights) + + return _resnet(QuantizableBasicBlock, [2, 2, 2, 2], weights, progress, quantize, **kwargs) + + +@register_model(name="quantized_resnet50") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ResNet50_Weights.IMAGENET1K_V1, + ) +) +def resnet50( + *, + weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableResNet: + """ResNet-50 model from + `Deep Residual Learning for Image Recognition `_ + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool, optional): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ResNet50_Weights + :members: + :noindex: + """ + weights = (ResNet50_QuantizedWeights if quantize else ResNet50_Weights).verify(weights) + + return _resnet(QuantizableBottleneck, [3, 4, 6, 3], weights, progress, quantize, **kwargs) + + +@register_model(name="quantized_resnext101_32x8d") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ResNeXt101_32X8D_Weights.IMAGENET1K_V1, + ) +) +def resnext101_32x8d( + *, + weights: Optional[Union[ResNeXt101_32X8D_QuantizedWeights, ResNeXt101_32X8D_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableResNet: + """ResNeXt-101 32x8d model from + `Aggregated Residual Transformation for Deep Neural Networks `_ + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ResNet101_32X8D_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool, optional): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights + :members: + :noindex: + """ + weights = (ResNeXt101_32X8D_QuantizedWeights if quantize else ResNeXt101_32X8D_Weights).verify(weights) + + _ovewrite_named_param(kwargs, "groups", 32) + _ovewrite_named_param(kwargs, "width_per_group", 8) + return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs) + + +@register_model(name="quantized_resnext101_64x4d") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ResNeXt101_64X4D_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ResNeXt101_64X4D_Weights.IMAGENET1K_V1, + ) +) +def resnext101_64x4d( + *, + weights: Optional[Union[ResNeXt101_64X4D_QuantizedWeights, ResNeXt101_64X4D_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableResNet: + """ResNeXt-101 64x4d model from + `Aggregated Residual Transformation for Deep Neural Networks `_ + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + quantize (bool, optional): If True, return a quantized version of the model. Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights + :members: + :noindex: + """ + weights = (ResNeXt101_64X4D_QuantizedWeights if quantize else ResNeXt101_64X4D_Weights).verify(weights) + + _ovewrite_named_param(kwargs, "groups", 64) + _ovewrite_named_param(kwargs, "width_per_group", 4) + return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/shufflenetv2.py b/lib/python3.11/site-packages/torchvision/models/quantization/shufflenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..3e1b01356a74b8e4f16d66811060be698cfed199 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/shufflenetv2.py @@ -0,0 +1,427 @@ +from functools import partial +from typing import Any, List, Optional, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torchvision.models import shufflenetv2 + +from ...transforms._presets import ImageClassification +from .._api import register_model, Weights, WeightsEnum +from .._meta import _IMAGENET_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from ..shufflenetv2 import ( + ShuffleNet_V2_X0_5_Weights, + ShuffleNet_V2_X1_0_Weights, + ShuffleNet_V2_X1_5_Weights, + ShuffleNet_V2_X2_0_Weights, +) +from .utils import _fuse_modules, _replace_relu, quantize_model + + +__all__ = [ + "QuantizableShuffleNetV2", + "ShuffleNet_V2_X0_5_QuantizedWeights", + "ShuffleNet_V2_X1_0_QuantizedWeights", + "ShuffleNet_V2_X1_5_QuantizedWeights", + "ShuffleNet_V2_X2_0_QuantizedWeights", + "shufflenet_v2_x0_5", + "shufflenet_v2_x1_0", + "shufflenet_v2_x1_5", + "shufflenet_v2_x2_0", +] + + +class QuantizableInvertedResidual(shufflenetv2.InvertedResidual): + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.cat = nn.quantized.FloatFunctional() + + def forward(self, x: Tensor) -> Tensor: + if self.stride == 1: + x1, x2 = x.chunk(2, dim=1) + out = self.cat.cat([x1, self.branch2(x2)], dim=1) + else: + out = self.cat.cat([self.branch1(x), self.branch2(x)], dim=1) + + out = shufflenetv2.channel_shuffle(out, 2) + + return out + + +class QuantizableShuffleNetV2(shufflenetv2.ShuffleNetV2): + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, inverted_residual=QuantizableInvertedResidual, **kwargs) # type: ignore[misc] + self.quant = torch.ao.quantization.QuantStub() + self.dequant = torch.ao.quantization.DeQuantStub() + + def forward(self, x: Tensor) -> Tensor: + x = self.quant(x) + x = self._forward_impl(x) + x = self.dequant(x) + return x + + def fuse_model(self, is_qat: Optional[bool] = None) -> None: + r"""Fuse conv/bn/relu modules in shufflenetv2 model + + Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization. + Model is modified in place. + + .. note:: + Note that this operation does not change numerics + and the model after modification is in floating point + """ + for name, m in self._modules.items(): + if name in ["conv1", "conv5"] and m is not None: + _fuse_modules(m, [["0", "1", "2"]], is_qat, inplace=True) + for m in self.modules(): + if type(m) is QuantizableInvertedResidual: + if len(m.branch1._modules.items()) > 0: + _fuse_modules(m.branch1, [["0", "1"], ["2", "3", "4"]], is_qat, inplace=True) + _fuse_modules( + m.branch2, + [["0", "1", "2"], ["3", "4"], ["5", "6", "7"]], + is_qat, + inplace=True, + ) + + +def _shufflenetv2( + stages_repeats: List[int], + stages_out_channels: List[int], + *, + weights: Optional[WeightsEnum], + progress: bool, + quantize: bool, + **kwargs: Any, +) -> QuantizableShuffleNetV2: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + if "backend" in weights.meta: + _ovewrite_named_param(kwargs, "backend", weights.meta["backend"]) + backend = kwargs.pop("backend", "fbgemm") + + model = QuantizableShuffleNetV2(stages_repeats, stages_out_channels, **kwargs) + _replace_relu(model) + if quantize: + quantize_model(model, backend) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "backend": "fbgemm", + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models", + "_docs": """ + These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized + weights listed below. + """, +} + + +class ShuffleNet_V2_X0_5_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 1366792, + "unquantized": ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 57.972, + "acc@5": 79.780, + } + }, + "_ops": 0.04, + "_file_size": 1.501, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-1e62bb32.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 2278604, + "unquantized": ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 68.360, + "acc@5": 87.582, + } + }, + "_ops": 0.145, + "_file_size": 2.334, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +class ShuffleNet_V2_X1_5_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/5906", + "num_params": 3503624, + "unquantized": ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.052, + "acc@5": 90.700, + } + }, + "_ops": 0.296, + "_file_size": 3.672, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +class ShuffleNet_V2_X2_0_QuantizedWeights(WeightsEnum): + IMAGENET1K_FBGEMM_V1 = Weights( + url="https://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/5906", + "num_params": 7393996, + "unquantized": ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1, + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.354, + "acc@5": 92.488, + } + }, + "_ops": 0.583, + "_file_size": 7.467, + }, + ) + DEFAULT = IMAGENET1K_FBGEMM_V1 + + +@register_model(name="quantized_shufflenet_v2_x0_5") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ShuffleNet_V2_X0_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1, + ) +) +def shufflenet_v2_x0_5( + *, + weights: Optional[Union[ShuffleNet_V2_X0_5_QuantizedWeights, ShuffleNet_V2_X0_5_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableShuffleNetV2: + """ + Constructs a ShuffleNetV2 with 0.5x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. + Default is True. + quantize (bool, optional): If True, return a quantized version of the model. + Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights + :members: + :noindex: + """ + weights = (ShuffleNet_V2_X0_5_QuantizedWeights if quantize else ShuffleNet_V2_X0_5_Weights).verify(weights) + return _shufflenetv2( + [4, 8, 4], [24, 48, 96, 192, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs + ) + + +@register_model(name="quantized_shufflenet_v2_x1_0") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1, + ) +) +def shufflenet_v2_x1_0( + *, + weights: Optional[Union[ShuffleNet_V2_X1_0_QuantizedWeights, ShuffleNet_V2_X1_0_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableShuffleNetV2: + """ + Constructs a ShuffleNetV2 with 1.0x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. + Default is True. + quantize (bool, optional): If True, return a quantized version of the model. + Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights + :members: + :noindex: + """ + weights = (ShuffleNet_V2_X1_0_QuantizedWeights if quantize else ShuffleNet_V2_X1_0_Weights).verify(weights) + return _shufflenetv2( + [4, 8, 4], [24, 116, 232, 464, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs + ) + + +@register_model(name="quantized_shufflenet_v2_x1_5") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ShuffleNet_V2_X1_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1, + ) +) +def shufflenet_v2_x1_5( + *, + weights: Optional[Union[ShuffleNet_V2_X1_5_QuantizedWeights, ShuffleNet_V2_X1_5_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableShuffleNetV2: + """ + Constructs a ShuffleNetV2 with 1.5x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. + Default is True. + quantize (bool, optional): If True, return a quantized version of the model. + Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights + :members: + :noindex: + """ + weights = (ShuffleNet_V2_X1_5_QuantizedWeights if quantize else ShuffleNet_V2_X1_5_Weights).verify(weights) + return _shufflenetv2( + [4, 8, 4], [24, 176, 352, 704, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs + ) + + +@register_model(name="quantized_shufflenet_v2_x2_0") +@handle_legacy_interface( + weights=( + "pretrained", + lambda kwargs: ShuffleNet_V2_X2_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1 + if kwargs.get("quantize", False) + else ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1, + ) +) +def shufflenet_v2_x2_0( + *, + weights: Optional[Union[ShuffleNet_V2_X2_0_QuantizedWeights, ShuffleNet_V2_X2_0_Weights]] = None, + progress: bool = True, + quantize: bool = False, + **kwargs: Any, +) -> QuantizableShuffleNetV2: + """ + Constructs a ShuffleNetV2 with 2.0x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + .. note:: + Note that ``quantize = True`` returns a quantized model with 8 bit + weights. Quantized models only support inference and run on CPUs. + GPU inference is not yet supported. + + Args: + weights (:class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The + pretrained weights for the model. See + :class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. + Default is True. + quantize (bool, optional): If True, return a quantized version of the model. + Default is False. + **kwargs: parameters passed to the ``torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights + :members: + + .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights + :members: + :noindex: + """ + weights = (ShuffleNet_V2_X2_0_QuantizedWeights if quantize else ShuffleNet_V2_X2_0_Weights).verify(weights) + return _shufflenetv2( + [4, 8, 4], [24, 244, 488, 976, 2048], weights=weights, progress=progress, quantize=quantize, **kwargs + ) diff --git a/lib/python3.11/site-packages/torchvision/models/quantization/utils.py b/lib/python3.11/site-packages/torchvision/models/quantization/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a21e2af8e016568e79b25e35ec774f39f0595c3a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/quantization/utils.py @@ -0,0 +1,51 @@ +from typing import Any, List, Optional, Union + +import torch +from torch import nn + + +def _replace_relu(module: nn.Module) -> None: + reassign = {} + for name, mod in module.named_children(): + _replace_relu(mod) + # Checking for explicit type instead of instance + # as we only want to replace modules of the exact type + # not inherited classes + if type(mod) is nn.ReLU or type(mod) is nn.ReLU6: + reassign[name] = nn.ReLU(inplace=False) + + for key, value in reassign.items(): + module._modules[key] = value + + +def quantize_model(model: nn.Module, backend: str) -> None: + _dummy_input_data = torch.rand(1, 3, 299, 299) + if backend not in torch.backends.quantized.supported_engines: + raise RuntimeError("Quantized backend not supported ") + torch.backends.quantized.engine = backend + model.eval() + # Make sure that weight qconfig matches that of the serialized models + if backend == "fbgemm": + model.qconfig = torch.ao.quantization.QConfig( # type: ignore[assignment] + activation=torch.ao.quantization.default_observer, + weight=torch.ao.quantization.default_per_channel_weight_observer, + ) + elif backend == "qnnpack": + model.qconfig = torch.ao.quantization.QConfig( # type: ignore[assignment] + activation=torch.ao.quantization.default_observer, weight=torch.ao.quantization.default_weight_observer + ) + + # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659 + model.fuse_model() # type: ignore[operator] + torch.ao.quantization.prepare(model, inplace=True) + model(_dummy_input_data) + torch.ao.quantization.convert(model, inplace=True) + + +def _fuse_modules( + model: nn.Module, modules_to_fuse: Union[List[str], List[List[str]]], is_qat: Optional[bool], **kwargs: Any +): + if is_qat is None: + is_qat = model.training + method = torch.ao.quantization.fuse_modules_qat if is_qat else torch.ao.quantization.fuse_modules + return method(model, modules_to_fuse, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/regnet.py b/lib/python3.11/site-packages/torchvision/models/regnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f37b2994e48fd202cccd4db6dc63b97f7c0c1ae7 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/regnet.py @@ -0,0 +1,1571 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Tuple + +import torch +from torch import nn, Tensor + +from ..ops.misc import Conv2dNormActivation, SqueezeExcitation +from ..transforms._presets import ImageClassification, InterpolationMode +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "RegNet", + "RegNet_Y_400MF_Weights", + "RegNet_Y_800MF_Weights", + "RegNet_Y_1_6GF_Weights", + "RegNet_Y_3_2GF_Weights", + "RegNet_Y_8GF_Weights", + "RegNet_Y_16GF_Weights", + "RegNet_Y_32GF_Weights", + "RegNet_Y_128GF_Weights", + "RegNet_X_400MF_Weights", + "RegNet_X_800MF_Weights", + "RegNet_X_1_6GF_Weights", + "RegNet_X_3_2GF_Weights", + "RegNet_X_8GF_Weights", + "RegNet_X_16GF_Weights", + "RegNet_X_32GF_Weights", + "regnet_y_400mf", + "regnet_y_800mf", + "regnet_y_1_6gf", + "regnet_y_3_2gf", + "regnet_y_8gf", + "regnet_y_16gf", + "regnet_y_32gf", + "regnet_y_128gf", + "regnet_x_400mf", + "regnet_x_800mf", + "regnet_x_1_6gf", + "regnet_x_3_2gf", + "regnet_x_8gf", + "regnet_x_16gf", + "regnet_x_32gf", +] + + +class SimpleStemIN(Conv2dNormActivation): + """Simple stem for ImageNet: 3x3, BN, ReLU.""" + + def __init__( + self, + width_in: int, + width_out: int, + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + ) -> None: + super().__init__( + width_in, width_out, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=activation_layer + ) + + +class BottleneckTransform(nn.Sequential): + """Bottleneck transformation: 1x1, 3x3 [+SE], 1x1.""" + + def __init__( + self, + width_in: int, + width_out: int, + stride: int, + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + group_width: int, + bottleneck_multiplier: float, + se_ratio: Optional[float], + ) -> None: + layers: OrderedDict[str, nn.Module] = OrderedDict() + w_b = int(round(width_out * bottleneck_multiplier)) + g = w_b // group_width + + layers["a"] = Conv2dNormActivation( + width_in, w_b, kernel_size=1, stride=1, norm_layer=norm_layer, activation_layer=activation_layer + ) + layers["b"] = Conv2dNormActivation( + w_b, w_b, kernel_size=3, stride=stride, groups=g, norm_layer=norm_layer, activation_layer=activation_layer + ) + + if se_ratio: + # The SE reduction ratio is defined with respect to the + # beginning of the block + width_se_out = int(round(se_ratio * width_in)) + layers["se"] = SqueezeExcitation( + input_channels=w_b, + squeeze_channels=width_se_out, + activation=activation_layer, + ) + + layers["c"] = Conv2dNormActivation( + w_b, width_out, kernel_size=1, stride=1, norm_layer=norm_layer, activation_layer=None + ) + super().__init__(layers) + + +class ResBottleneckBlock(nn.Module): + """Residual bottleneck block: x + F(x), F = bottleneck transform.""" + + def __init__( + self, + width_in: int, + width_out: int, + stride: int, + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + group_width: int = 1, + bottleneck_multiplier: float = 1.0, + se_ratio: Optional[float] = None, + ) -> None: + super().__init__() + + # Use skip connection with projection if shape changes + self.proj = None + should_proj = (width_in != width_out) or (stride != 1) + if should_proj: + self.proj = Conv2dNormActivation( + width_in, width_out, kernel_size=1, stride=stride, norm_layer=norm_layer, activation_layer=None + ) + self.f = BottleneckTransform( + width_in, + width_out, + stride, + norm_layer, + activation_layer, + group_width, + bottleneck_multiplier, + se_ratio, + ) + self.activation = activation_layer(inplace=True) + + def forward(self, x: Tensor) -> Tensor: + if self.proj is not None: + x = self.proj(x) + self.f(x) + else: + x = x + self.f(x) + return self.activation(x) + + +class AnyStage(nn.Sequential): + """AnyNet stage (sequence of blocks w/ the same output shape).""" + + def __init__( + self, + width_in: int, + width_out: int, + stride: int, + depth: int, + block_constructor: Callable[..., nn.Module], + norm_layer: Callable[..., nn.Module], + activation_layer: Callable[..., nn.Module], + group_width: int, + bottleneck_multiplier: float, + se_ratio: Optional[float] = None, + stage_index: int = 0, + ) -> None: + super().__init__() + + for i in range(depth): + block = block_constructor( + width_in if i == 0 else width_out, + width_out, + stride if i == 0 else 1, + norm_layer, + activation_layer, + group_width, + bottleneck_multiplier, + se_ratio, + ) + + self.add_module(f"block{stage_index}-{i}", block) + + +class BlockParams: + def __init__( + self, + depths: List[int], + widths: List[int], + group_widths: List[int], + bottleneck_multipliers: List[float], + strides: List[int], + se_ratio: Optional[float] = None, + ) -> None: + self.depths = depths + self.widths = widths + self.group_widths = group_widths + self.bottleneck_multipliers = bottleneck_multipliers + self.strides = strides + self.se_ratio = se_ratio + + @classmethod + def from_init_params( + cls, + depth: int, + w_0: int, + w_a: float, + w_m: float, + group_width: int, + bottleneck_multiplier: float = 1.0, + se_ratio: Optional[float] = None, + **kwargs: Any, + ) -> "BlockParams": + """ + Programmatically compute all the per-block settings, + given the RegNet parameters. + + The first step is to compute the quantized linear block parameters, + in log space. Key parameters are: + - `w_a` is the width progression slope + - `w_0` is the initial width + - `w_m` is the width stepping in the log space + + In other terms + `log(block_width) = log(w_0) + w_m * block_capacity`, + with `bock_capacity` ramping up following the w_0 and w_a params. + This block width is finally quantized to multiples of 8. + + The second step is to compute the parameters per stage, + taking into account the skip connection and the final 1x1 convolutions. + We use the fact that the output width is constant within a stage. + """ + + QUANT = 8 + STRIDE = 2 + + if w_a < 0 or w_0 <= 0 or w_m <= 1 or w_0 % 8 != 0: + raise ValueError("Invalid RegNet settings") + # Compute the block widths. Each stage has one unique block width + widths_cont = torch.arange(depth) * w_a + w_0 + block_capacity = torch.round(torch.log(widths_cont / w_0) / math.log(w_m)) + block_widths = (torch.round(torch.divide(w_0 * torch.pow(w_m, block_capacity), QUANT)) * QUANT).int().tolist() + num_stages = len(set(block_widths)) + + # Convert to per stage parameters + split_helper = zip( + block_widths + [0], + [0] + block_widths, + block_widths + [0], + [0] + block_widths, + ) + splits = [w != wp or r != rp for w, wp, r, rp in split_helper] + + stage_widths = [w for w, t in zip(block_widths, splits[:-1]) if t] + stage_depths = torch.diff(torch.tensor([d for d, t in enumerate(splits) if t])).int().tolist() + + strides = [STRIDE] * num_stages + bottleneck_multipliers = [bottleneck_multiplier] * num_stages + group_widths = [group_width] * num_stages + + # Adjust the compatibility of stage widths and group widths + stage_widths, group_widths = cls._adjust_widths_groups_compatibilty( + stage_widths, bottleneck_multipliers, group_widths + ) + + return cls( + depths=stage_depths, + widths=stage_widths, + group_widths=group_widths, + bottleneck_multipliers=bottleneck_multipliers, + strides=strides, + se_ratio=se_ratio, + ) + + def _get_expanded_params(self): + return zip(self.widths, self.strides, self.depths, self.group_widths, self.bottleneck_multipliers) + + @staticmethod + def _adjust_widths_groups_compatibilty( + stage_widths: List[int], bottleneck_ratios: List[float], group_widths: List[int] + ) -> Tuple[List[int], List[int]]: + """ + Adjusts the compatibility of widths and groups, + depending on the bottleneck ratio. + """ + # Compute all widths for the current settings + widths = [int(w * b) for w, b in zip(stage_widths, bottleneck_ratios)] + group_widths_min = [min(g, w_bot) for g, w_bot in zip(group_widths, widths)] + + # Compute the adjusted widths so that stage and group widths fit + ws_bot = [_make_divisible(w_bot, g) for w_bot, g in zip(widths, group_widths_min)] + stage_widths = [int(w_bot / b) for w_bot, b in zip(ws_bot, bottleneck_ratios)] + return stage_widths, group_widths_min + + +class RegNet(nn.Module): + def __init__( + self, + block_params: BlockParams, + num_classes: int = 1000, + stem_width: int = 32, + stem_type: Optional[Callable[..., nn.Module]] = None, + block_type: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + activation: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if stem_type is None: + stem_type = SimpleStemIN + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if block_type is None: + block_type = ResBottleneckBlock + if activation is None: + activation = nn.ReLU + + # Ad hoc stem + self.stem = stem_type( + 3, # width_in + stem_width, + norm_layer, + activation, + ) + + current_width = stem_width + + blocks = [] + for i, ( + width_out, + stride, + depth, + group_width, + bottleneck_multiplier, + ) in enumerate(block_params._get_expanded_params()): + blocks.append( + ( + f"block{i+1}", + AnyStage( + current_width, + width_out, + stride, + depth, + block_type, + norm_layer, + activation, + group_width, + bottleneck_multiplier, + block_params.se_ratio, + stage_index=i + 1, + ), + ) + ) + + current_width = width_out + + self.trunk_output = nn.Sequential(OrderedDict(blocks)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(in_features=current_width, out_features=num_classes) + + # Performs ResNet-style weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # Note that there is no bias due to BN + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + nn.init.normal_(m.weight, mean=0.0, std=math.sqrt(2.0 / fan_out)) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, mean=0.0, std=0.01) + nn.init.zeros_(m.bias) + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + x = self.trunk_output(x) + + x = self.avgpool(x) + x = x.flatten(start_dim=1) + x = self.fc(x) + + return x + + +def _regnet( + block_params: BlockParams, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> RegNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + norm_layer = kwargs.pop("norm_layer", partial(nn.BatchNorm2d, eps=1e-05, momentum=0.1)) + model = RegNet(block_params, norm_layer=norm_layer, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META: Dict[str, Any] = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, +} + +_COMMON_SWAG_META = { + **_COMMON_META, + "recipe": "https://github.com/facebookresearch/SWAG", + "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE", +} + + +class RegNet_Y_400MF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_400mf-c65dace8.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 4344144, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.046, + "acc@5": 91.716, + } + }, + "_ops": 0.402, + "_file_size": 16.806, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 4344144, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.804, + "acc@5": 92.742, + } + }, + "_ops": 0.402, + "_file_size": 16.806, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_800MF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_800mf-1b27b58c.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 6432512, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.420, + "acc@5": 93.136, + } + }, + "_ops": 0.834, + "_file_size": 24.774, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 6432512, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.828, + "acc@5": 94.502, + } + }, + "_ops": 0.834, + "_file_size": 24.774, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_1_6GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_1_6gf-b11a554e.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 11202430, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.950, + "acc@5": 93.966, + } + }, + "_ops": 1.612, + "_file_size": 43.152, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 11202430, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.876, + "acc@5": 95.444, + } + }, + "_ops": 1.612, + "_file_size": 43.152, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_3_2GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_3_2gf-b5a9779c.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 19436338, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#medium-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.948, + "acc@5": 94.576, + } + }, + "_ops": 3.176, + "_file_size": 74.567, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 19436338, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.982, + "acc@5": 95.972, + } + }, + "_ops": 3.176, + "_file_size": 74.567, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_8GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_8gf-d0d0e4a8.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 39381472, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#medium-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.032, + "acc@5": 95.048, + } + }, + "_ops": 8.473, + "_file_size": 150.701, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 39381472, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.828, + "acc@5": 96.330, + } + }, + "_ops": 8.473, + "_file_size": 150.701, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_16GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_16gf-9e6ed7dd.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 83590140, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#large-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.424, + "acc@5": 95.240, + } + }, + "_ops": 15.912, + "_file_size": 319.49, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 83590140, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.886, + "acc@5": 96.328, + } + }, + "_ops": 15.912, + "_file_size": 319.49, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_16gf_swag-43afe44d.pth", + transforms=partial( + ImageClassification, crop_size=384, resize_size=384, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 83590140, + "_metrics": { + "ImageNet-1K": { + "acc@1": 86.012, + "acc@5": 98.054, + } + }, + "_ops": 46.735, + "_file_size": 319.49, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_16gf_lc_swag-f3ec0043.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=224, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 83590140, + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.976, + "acc@5": 97.244, + } + }, + "_ops": 15.912, + "_file_size": 319.49, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_32GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_32gf-4dee3f7a.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 145046770, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#large-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.878, + "acc@5": 95.340, + } + }, + "_ops": 32.28, + "_file_size": 554.076, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 145046770, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.368, + "acc@5": 96.498, + } + }, + "_ops": 32.28, + "_file_size": 554.076, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_32gf_swag-04fdfa75.pth", + transforms=partial( + ImageClassification, crop_size=384, resize_size=384, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 145046770, + "_metrics": { + "ImageNet-1K": { + "acc@1": 86.838, + "acc@5": 98.362, + } + }, + "_ops": 94.826, + "_file_size": 554.076, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_32gf_lc_swag-e1583746.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=224, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 145046770, + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.622, + "acc@5": 97.480, + } + }, + "_ops": 32.28, + "_file_size": 554.076, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_Y_128GF_Weights(WeightsEnum): + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_128gf_swag-c8ce3e52.pth", + transforms=partial( + ImageClassification, crop_size=384, resize_size=384, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 644812894, + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.228, + "acc@5": 98.682, + } + }, + "_ops": 374.57, + "_file_size": 2461.564, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/regnet_y_128gf_lc_swag-cbe8ce12.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=224, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 644812894, + "_metrics": { + "ImageNet-1K": { + "acc@1": 86.068, + "acc@5": 97.844, + } + }, + "_ops": 127.518, + "_file_size": 2461.564, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_SWAG_E2E_V1 + + +class RegNet_X_400MF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_400mf-adf1edd5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 5495976, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.834, + "acc@5": 90.950, + } + }, + "_ops": 0.414, + "_file_size": 21.258, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 5495976, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.864, + "acc@5": 92.322, + } + }, + "_ops": 0.414, + "_file_size": 21.257, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_800MF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_800mf-ad17e45c.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 7259656, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.212, + "acc@5": 92.348, + } + }, + "_ops": 0.8, + "_file_size": 27.945, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 7259656, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.522, + "acc@5": 93.826, + } + }, + "_ops": 0.8, + "_file_size": 27.945, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_1_6GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_1_6gf-e3633e7f.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 9190136, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#small-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.040, + "acc@5": 93.440, + } + }, + "_ops": 1.603, + "_file_size": 35.339, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 9190136, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.668, + "acc@5": 94.922, + } + }, + "_ops": 1.603, + "_file_size": 35.339, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_3_2GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_3_2gf-f342aeae.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 15296552, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#medium-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.364, + "acc@5": 93.992, + } + }, + "_ops": 3.177, + "_file_size": 58.756, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 15296552, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.196, + "acc@5": 95.430, + } + }, + "_ops": 3.177, + "_file_size": 58.756, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_8GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_8gf-03ceed89.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 39572648, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#medium-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.344, + "acc@5": 94.686, + } + }, + "_ops": 7.995, + "_file_size": 151.456, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 39572648, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.682, + "acc@5": 95.678, + } + }, + "_ops": 7.995, + "_file_size": 151.456, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_16GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_16gf-2007eb11.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 54278536, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#medium-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.058, + "acc@5": 94.944, + } + }, + "_ops": 15.941, + "_file_size": 207.627, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 54278536, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.716, + "acc@5": 96.196, + } + }, + "_ops": 15.941, + "_file_size": 207.627, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class RegNet_X_32GF_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/regnet_x_32gf-9d47f8d0.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 107811560, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#large-models", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.622, + "acc@5": 95.248, + } + }, + "_ops": 31.736, + "_file_size": 412.039, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 107811560, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.014, + "acc@5": 96.288, + } + }, + "_ops": 31.736, + "_file_size": 412.039, + "_docs": """ + These weights improve upon the results of the original paper by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_400MF_Weights.IMAGENET1K_V1)) +def regnet_y_400mf(*, weights: Optional[RegNet_Y_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_400MF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_400MF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_400MF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_400MF_Weights + :members: + """ + weights = RegNet_Y_400MF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=16, w_0=48, w_a=27.89, w_m=2.09, group_width=8, se_ratio=0.25, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_800MF_Weights.IMAGENET1K_V1)) +def regnet_y_800mf(*, weights: Optional[RegNet_Y_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_800MF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_800MF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_800MF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_800MF_Weights + :members: + """ + weights = RegNet_Y_800MF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=14, w_0=56, w_a=38.84, w_m=2.4, group_width=16, se_ratio=0.25, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_1_6GF_Weights.IMAGENET1K_V1)) +def regnet_y_1_6gf(*, weights: Optional[RegNet_Y_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_1.6GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_1_6GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_1_6GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_1_6GF_Weights + :members: + """ + weights = RegNet_Y_1_6GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=27, w_0=48, w_a=20.71, w_m=2.65, group_width=24, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_3_2GF_Weights.IMAGENET1K_V1)) +def regnet_y_3_2gf(*, weights: Optional[RegNet_Y_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_3.2GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_3_2GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_3_2GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_3_2GF_Weights + :members: + """ + weights = RegNet_Y_3_2GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=21, w_0=80, w_a=42.63, w_m=2.66, group_width=24, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_8GF_Weights.IMAGENET1K_V1)) +def regnet_y_8gf(*, weights: Optional[RegNet_Y_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_8GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_8GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_8GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_8GF_Weights + :members: + """ + weights = RegNet_Y_8GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=17, w_0=192, w_a=76.82, w_m=2.19, group_width=56, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_16GF_Weights.IMAGENET1K_V1)) +def regnet_y_16gf(*, weights: Optional[RegNet_Y_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_16GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_16GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_16GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_16GF_Weights + :members: + """ + weights = RegNet_Y_16GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=18, w_0=200, w_a=106.23, w_m=2.48, group_width=112, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_Y_32GF_Weights.IMAGENET1K_V1)) +def regnet_y_32gf(*, weights: Optional[RegNet_Y_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_32GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_32GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_32GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_32GF_Weights + :members: + """ + weights = RegNet_Y_32GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=20, w_0=232, w_a=115.89, w_m=2.53, group_width=232, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", None)) +def regnet_y_128gf(*, weights: Optional[RegNet_Y_128GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetY_128GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_Y_128GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_Y_128GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_Y_128GF_Weights + :members: + """ + weights = RegNet_Y_128GF_Weights.verify(weights) + + params = BlockParams.from_init_params( + depth=27, w_0=456, w_a=160.83, w_m=2.52, group_width=264, se_ratio=0.25, **kwargs + ) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_400MF_Weights.IMAGENET1K_V1)) +def regnet_x_400mf(*, weights: Optional[RegNet_X_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_400MF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_400MF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_400MF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_400MF_Weights + :members: + """ + weights = RegNet_X_400MF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=22, w_0=24, w_a=24.48, w_m=2.54, group_width=16, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_800MF_Weights.IMAGENET1K_V1)) +def regnet_x_800mf(*, weights: Optional[RegNet_X_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_800MF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_800MF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_800MF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_800MF_Weights + :members: + """ + weights = RegNet_X_800MF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=16, w_0=56, w_a=35.73, w_m=2.28, group_width=16, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_1_6GF_Weights.IMAGENET1K_V1)) +def regnet_x_1_6gf(*, weights: Optional[RegNet_X_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_1.6GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_1_6GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_1_6GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_1_6GF_Weights + :members: + """ + weights = RegNet_X_1_6GF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=18, w_0=80, w_a=34.01, w_m=2.25, group_width=24, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_3_2GF_Weights.IMAGENET1K_V1)) +def regnet_x_3_2gf(*, weights: Optional[RegNet_X_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_3.2GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_3_2GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_3_2GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_3_2GF_Weights + :members: + """ + weights = RegNet_X_3_2GF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=25, w_0=88, w_a=26.31, w_m=2.25, group_width=48, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_8GF_Weights.IMAGENET1K_V1)) +def regnet_x_8gf(*, weights: Optional[RegNet_X_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_8GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_8GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_8GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_8GF_Weights + :members: + """ + weights = RegNet_X_8GF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=23, w_0=80, w_a=49.56, w_m=2.88, group_width=120, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_16GF_Weights.IMAGENET1K_V1)) +def regnet_x_16gf(*, weights: Optional[RegNet_X_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_16GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_16GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_16GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_16GF_Weights + :members: + """ + weights = RegNet_X_16GF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=22, w_0=216, w_a=55.59, w_m=2.1, group_width=128, **kwargs) + return _regnet(params, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", RegNet_X_32GF_Weights.IMAGENET1K_V1)) +def regnet_x_32gf(*, weights: Optional[RegNet_X_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet: + """ + Constructs a RegNetX_32GF architecture from + `Designing Network Design Spaces `_. + + Args: + weights (:class:`~torchvision.models.RegNet_X_32GF_Weights`, optional): The pretrained weights to use. + See :class:`~torchvision.models.RegNet_X_32GF_Weights` below for more details and possible values. + By default, no pretrained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to either ``torchvision.models.regnet.RegNet`` or + ``torchvision.models.regnet.BlockParams`` class. Please refer to the `source code + `_ + for more detail about the classes. + + .. autoclass:: torchvision.models.RegNet_X_32GF_Weights + :members: + """ + weights = RegNet_X_32GF_Weights.verify(weights) + + params = BlockParams.from_init_params(depth=23, w_0=320, w_a=69.86, w_m=2.0, group_width=168, **kwargs) + return _regnet(params, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/resnet.py b/lib/python3.11/site-packages/torchvision/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..83c0340cef74d9cb4c1dc92c38f4b3024be1f731 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/resnet.py @@ -0,0 +1,985 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Type, Union + +import torch +import torch.nn as nn +from torch import Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "ResNet", + "ResNet18_Weights", + "ResNet34_Weights", + "ResNet50_Weights", + "ResNet101_Weights", + "ResNet152_Weights", + "ResNeXt50_32X4D_Weights", + "ResNeXt101_32X8D_Weights", + "ResNeXt101_64X4D_Weights", + "Wide_ResNet50_2_Weights", + "Wide_ResNet101_2_Weights", + "resnet18", + "resnet34", + "resnet50", + "resnet101", + "resnet152", + "resnext50_32x4d", + "resnext101_32x8d", + "resnext101_64x4d", + "wide_resnet50_2", + "wide_resnet101_2", +] + + +def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion: int = 1 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition" https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion: int = 4 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + num_classes: int = 1000, + zero_init_residual: bool = False, + groups: int = 1, + width_per_group: int = 64, + replace_stride_with_dilation: Optional[List[bool]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + f"or a 3-element tuple, got {replace_stride_with_dilation}" + ) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck) and m.bn3.weight is not None: + nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] + elif isinstance(m, BasicBlock) and m.bn2.weight is not None: + nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] + + def _make_layer( + self, + block: Type[Union[BasicBlock, Bottleneck]], + planes: int, + blocks: int, + stride: int = 1, + dilate: bool = False, + ) -> nn.Sequential: + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _resnet( + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> ResNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ResNet(block, layers, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, +} + + +class ResNet18_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet18-f37072fd.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 11689512, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.758, + "acc@5": 89.078, + } + }, + "_ops": 1.814, + "_file_size": 44.661, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ResNet34_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet34-b627a593.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 21797672, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.314, + "acc@5": 91.420, + } + }, + "_ops": 3.664, + "_file_size": 83.275, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ResNet50_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet50-0676ba61.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 25557032, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.130, + "acc@5": 92.862, + } + }, + "_ops": 4.089, + "_file_size": 97.781, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 25557032, + "recipe": "https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621", + "_metrics": { + "ImageNet-1K": { + "acc@1": 80.858, + "acc@5": 95.434, + } + }, + "_ops": 4.089, + "_file_size": 97.79, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNet101_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet101-63fe2227.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 44549160, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.374, + "acc@5": 93.546, + } + }, + "_ops": 7.801, + "_file_size": 170.511, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet101-cd907fc2.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 44549160, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 95.780, + } + }, + "_ops": 7.801, + "_file_size": 170.53, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNet152_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnet152-394f9c45.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 60192808, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.312, + "acc@5": 94.046, + } + }, + "_ops": 11.514, + "_file_size": 230.434, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnet152-f82ba261.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 60192808, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.284, + "acc@5": 96.002, + } + }, + "_ops": 11.514, + "_file_size": 230.474, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt50_32X4D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 25028904, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", + "_metrics": { + "ImageNet-1K": { + "acc@1": 77.618, + "acc@5": 93.698, + } + }, + "_ops": 4.23, + "_file_size": 95.789, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 25028904, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.198, + "acc@5": 95.340, + } + }, + "_ops": 4.23, + "_file_size": 95.833, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt101_32X8D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88791336, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.312, + "acc@5": 94.526, + } + }, + "_ops": 16.414, + "_file_size": 339.586, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 88791336, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.834, + "acc@5": 96.228, + } + }, + "_ops": 16.414, + "_file_size": 339.673, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class ResNeXt101_64X4D_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 83455272, + "recipe": "https://github.com/pytorch/vision/pull/5935", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.246, + "acc@5": 96.454, + } + }, + "_ops": 15.46, + "_file_size": 319.318, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Wide_ResNet50_2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 68883240, + "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.468, + "acc@5": 94.086, + } + }, + "_ops": 11.398, + "_file_size": 131.82, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 68883240, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.602, + "acc@5": 95.758, + } + }, + "_ops": 11.398, + "_file_size": 263.124, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +class Wide_ResNet101_2_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 126886696, + "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", + "_metrics": { + "ImageNet-1K": { + "acc@1": 78.848, + "acc@5": 94.284, + } + }, + "_ops": 22.753, + "_file_size": 242.896, + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", + }, + ) + IMAGENET1K_V2 = Weights( + url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "num_params": 126886696, + "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.510, + "acc@5": 96.020, + } + }, + "_ops": 22.753, + "_file_size": 484.747, + "_docs": """ + These weights improve upon the results of the original paper by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V2 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNet18_Weights.IMAGENET1K_V1)) +def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-18 from `Deep Residual Learning for Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet18_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet18_Weights + :members: + """ + weights = ResNet18_Weights.verify(weights) + + return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNet34_Weights.IMAGENET1K_V1)) +def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-34 from `Deep Residual Learning for Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.ResNet34_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet34_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet34_Weights + :members: + """ + weights = ResNet34_Weights.verify(weights) + + return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNet50_Weights.IMAGENET1K_V1)) +def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-50 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet50_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet50_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet50_Weights + :members: + """ + weights = ResNet50_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNet101_Weights.IMAGENET1K_V1)) +def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-101 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet101_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet101_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet101_Weights + :members: + """ + weights = ResNet101_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNet152_Weights.IMAGENET1K_V1)) +def resnet152(*, weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: + """ResNet-152 from `Deep Residual Learning for Image Recognition `__. + + .. note:: + The bottleneck of TorchVision places the stride for downsampling to the second 3x3 + convolution while the original paper places it to the first 1x1 convolution. + This variant improves the accuracy and is known as `ResNet V1.5 + `_. + + Args: + weights (:class:`~torchvision.models.ResNet152_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNet152_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ResNet152_Weights + :members: + """ + weights = ResNet152_Weights.verify(weights) + + return _resnet(Bottleneck, [3, 8, 36, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNeXt50_32X4D_Weights.IMAGENET1K_V1)) +def resnext50_32x4d( + *, weights: Optional[ResNeXt50_32X4D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-50 32x4d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt50_32X4D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNext50_32X4D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt50_32X4D_Weights + :members: + """ + weights = ResNeXt50_32X4D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 32) + _ovewrite_named_param(kwargs, "width_per_group", 4) + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.IMAGENET1K_V1)) +def resnext101_32x8d( + *, weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-101 32x8d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNeXt101_32X8D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights + :members: + """ + weights = ResNeXt101_32X8D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 32) + _ovewrite_named_param(kwargs, "width_per_group", 8) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ResNeXt101_64X4D_Weights.IMAGENET1K_V1)) +def resnext101_64x4d( + *, weights: Optional[ResNeXt101_64X4D_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """ResNeXt-101 64x4d model from + `Aggregated Residual Transformation for Deep Neural Networks `_. + + Args: + weights (:class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ResNeXt101_64X4D_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights + :members: + """ + weights = ResNeXt101_64X4D_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "groups", 64) + _ovewrite_named_param(kwargs, "width_per_group", 4) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Wide_ResNet50_2_Weights.IMAGENET1K_V1)) +def wide_resnet50_2( + *, weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """Wide ResNet-50-2 model from + `Wide Residual Networks `_. + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + weights (:class:`~torchvision.models.Wide_ResNet50_2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Wide_ResNet50_2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.Wide_ResNet50_2_Weights + :members: + """ + weights = Wide_ResNet50_2_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) + return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Wide_ResNet101_2_Weights.IMAGENET1K_V1)) +def wide_resnet101_2( + *, weights: Optional[Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any +) -> ResNet: + """Wide ResNet-101-2 model from + `Wide Residual Networks `_. + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-101 has 2048-512-2048 + channels, and in Wide ResNet-101-2 has 2048-1024-2048. + + Args: + weights (:class:`~torchvision.models.Wide_ResNet101_2_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Wide_ResNet101_2_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + .. autoclass:: torchvision.models.Wide_ResNet101_2_Weights + :members: + """ + weights = Wide_ResNet101_2_Weights.verify(weights) + + _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) + return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/segmentation/__init__.py b/lib/python3.11/site-packages/torchvision/models/segmentation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d6f37f958a131b76ce80306718b77d78bc3f045 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/segmentation/__init__.py @@ -0,0 +1,3 @@ +from .deeplabv3 import * +from .fcn import * +from .lraspp import * diff --git a/lib/python3.11/site-packages/torchvision/models/segmentation/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/models/segmentation/__pycache__/__init__.cpython-311.pyc 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self.aux_classifier = aux_classifier + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + input_shape = x.shape[-2:] + # contract: features is a dict of tensors + features = self.backbone(x) + + result = OrderedDict() + x = features["out"] + x = self.classifier(x) + x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False) + result["out"] = x + + if self.aux_classifier is not None: + x = features["aux"] + x = self.aux_classifier(x) + x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False) + result["aux"] = x + + return result diff --git a/lib/python3.11/site-packages/torchvision/models/segmentation/deeplabv3.py b/lib/python3.11/site-packages/torchvision/models/segmentation/deeplabv3.py new file mode 100644 index 0000000000000000000000000000000000000000..a92ddfe3b7af41a9ffd371d34cd459ba57965c53 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/segmentation/deeplabv3.py @@ -0,0 +1,390 @@ +from functools import partial +from typing import Any, Optional, Sequence + +import torch +from torch import nn +from torch.nn import functional as F + +from ...transforms._presets import SemanticSegmentation +from .._api import register_model, Weights, WeightsEnum +from .._meta import _VOC_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter +from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights, MobileNetV3 +from ..resnet import ResNet, resnet101, ResNet101_Weights, resnet50, ResNet50_Weights +from ._utils import _SimpleSegmentationModel +from .fcn import FCNHead + + +__all__ = [ + "DeepLabV3", + "DeepLabV3_ResNet50_Weights", + "DeepLabV3_ResNet101_Weights", + "DeepLabV3_MobileNet_V3_Large_Weights", + "deeplabv3_mobilenet_v3_large", + "deeplabv3_resnet50", + "deeplabv3_resnet101", +] + + +class DeepLabV3(_SimpleSegmentationModel): + """ + Implements DeepLabV3 model from + `"Rethinking Atrous Convolution for Semantic Image Segmentation" + `_. + + Args: + backbone (nn.Module): the network used to compute the features for the model. + The backbone should return an OrderedDict[Tensor], with the key being + "out" for the last feature map used, and "aux" if an auxiliary classifier + is used. + classifier (nn.Module): module that takes the "out" element returned from + the backbone and returns a dense prediction. + aux_classifier (nn.Module, optional): auxiliary classifier used during training + """ + + pass + + +class DeepLabHead(nn.Sequential): + def __init__(self, in_channels: int, num_classes: int, atrous_rates: Sequence[int] = (12, 24, 36)) -> None: + super().__init__( + ASPP(in_channels, atrous_rates), + nn.Conv2d(256, 256, 3, padding=1, bias=False), + nn.BatchNorm2d(256), + nn.ReLU(), + nn.Conv2d(256, num_classes, 1), + ) + + +class ASPPConv(nn.Sequential): + def __init__(self, in_channels: int, out_channels: int, dilation: int) -> None: + modules = [ + nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + ] + super().__init__(*modules) + + +class ASPPPooling(nn.Sequential): + def __init__(self, in_channels: int, out_channels: int) -> None: + super().__init__( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + size = x.shape[-2:] + for mod in self: + x = mod(x) + return F.interpolate(x, size=size, mode="bilinear", align_corners=False) + + +class ASPP(nn.Module): + def __init__(self, in_channels: int, atrous_rates: Sequence[int], out_channels: int = 256) -> None: + super().__init__() + modules = [] + modules.append( + nn.Sequential(nn.Conv2d(in_channels, out_channels, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU()) + ) + + rates = tuple(atrous_rates) + for rate in rates: + modules.append(ASPPConv(in_channels, out_channels, rate)) + + modules.append(ASPPPooling(in_channels, out_channels)) + + self.convs = nn.ModuleList(modules) + + self.project = nn.Sequential( + nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.Dropout(0.5), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + _res = [] + for conv in self.convs: + _res.append(conv(x)) + res = torch.cat(_res, dim=1) + return self.project(res) + + +def _deeplabv3_resnet( + backbone: ResNet, + num_classes: int, + aux: Optional[bool], +) -> DeepLabV3: + return_layers = {"layer4": "out"} + if aux: + return_layers["layer3"] = "aux" + backbone = IntermediateLayerGetter(backbone, return_layers=return_layers) + + aux_classifier = FCNHead(1024, num_classes) if aux else None + classifier = DeepLabHead(2048, num_classes) + return DeepLabV3(backbone, classifier, aux_classifier) + + +_COMMON_META = { + "categories": _VOC_CATEGORIES, + "min_size": (1, 1), + "_docs": """ + These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC + dataset. + """, +} + + +class DeepLabV3_ResNet50_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + **_COMMON_META, + "num_params": 42004074, + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 66.4, + "pixel_acc": 92.4, + } + }, + "_ops": 178.722, + "_file_size": 160.515, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +class DeepLabV3_ResNet101_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + **_COMMON_META, + "num_params": 60996202, + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 67.4, + "pixel_acc": 92.4, + } + }, + "_ops": 258.743, + "_file_size": 233.217, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +class DeepLabV3_MobileNet_V3_Large_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + **_COMMON_META, + "num_params": 11029328, + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_large", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 60.3, + "pixel_acc": 91.2, + } + }, + "_ops": 10.452, + "_file_size": 42.301, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +def _deeplabv3_mobilenetv3( + backbone: MobileNetV3, + num_classes: int, + aux: Optional[bool], +) -> DeepLabV3: + backbone = backbone.features + # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks. + # The first and last blocks are always included because they are the C0 (conv1) and Cn. + stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1] + out_pos = stage_indices[-1] # use C5 which has output_stride = 16 + out_inplanes = backbone[out_pos].out_channels + aux_pos = stage_indices[-4] # use C2 here which has output_stride = 8 + aux_inplanes = backbone[aux_pos].out_channels + return_layers = {str(out_pos): "out"} + if aux: + return_layers[str(aux_pos)] = "aux" + backbone = IntermediateLayerGetter(backbone, return_layers=return_layers) + + aux_classifier = FCNHead(aux_inplanes, num_classes) if aux else None + classifier = DeepLabHead(out_inplanes, num_classes) + return DeepLabV3(backbone, classifier, aux_classifier) + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def deeplabv3_resnet50( + *, + weights: Optional[DeepLabV3_ResNet50_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + aux_loss: Optional[bool] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> DeepLabV3: + """Constructs a DeepLabV3 model with a ResNet-50 backbone. + + .. betastatus:: segmentation module + + Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. + + Args: + weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet50_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.DeepLabV3_ResNet50_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + aux_loss (bool, optional): If True, it uses an auxiliary loss + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for the + backbone + **kwargs: unused + + .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet50_Weights + :members: + """ + weights = DeepLabV3_ResNet50_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True) + elif num_classes is None: + num_classes = 21 + + backbone = resnet50(weights=weights_backbone, replace_stride_with_dilation=[False, True, True]) + model = _deeplabv3_resnet(backbone, num_classes, aux_loss) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", ResNet101_Weights.IMAGENET1K_V1), +) +def deeplabv3_resnet101( + *, + weights: Optional[DeepLabV3_ResNet101_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + aux_loss: Optional[bool] = None, + weights_backbone: Optional[ResNet101_Weights] = ResNet101_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> DeepLabV3: + """Constructs a DeepLabV3 model with a ResNet-101 backbone. + + .. betastatus:: segmentation module + + Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. + + Args: + weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + aux_loss (bool, optional): If True, it uses an auxiliary loss + weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights for the + backbone + **kwargs: unused + + .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet101_Weights + :members: + """ + weights = DeepLabV3_ResNet101_Weights.verify(weights) + weights_backbone = ResNet101_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True) + elif num_classes is None: + num_classes = 21 + + backbone = resnet101(weights=weights_backbone, replace_stride_with_dilation=[False, True, True]) + model = _deeplabv3_resnet(backbone, num_classes, aux_loss) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", DeepLabV3_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1), +) +def deeplabv3_mobilenet_v3_large( + *, + weights: Optional[DeepLabV3_MobileNet_V3_Large_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + aux_loss: Optional[bool] = None, + weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> DeepLabV3: + """Constructs a DeepLabV3 model with a MobileNetV3-Large backbone. + + Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. + + Args: + weights (:class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background) + aux_loss (bool, optional): If True, it uses an auxiliary loss + weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights + for the backbone + **kwargs: unused + + .. autoclass:: torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights + :members: + """ + weights = DeepLabV3_MobileNet_V3_Large_Weights.verify(weights) + weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True) + elif num_classes is None: + num_classes = 21 + + backbone = mobilenet_v3_large(weights=weights_backbone, dilated=True) + model = _deeplabv3_mobilenetv3(backbone, num_classes, aux_loss) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/segmentation/fcn.py b/lib/python3.11/site-packages/torchvision/models/segmentation/fcn.py new file mode 100644 index 0000000000000000000000000000000000000000..fb2e242adac0e7430bab6155ae0347770e29fee9 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/segmentation/fcn.py @@ -0,0 +1,232 @@ +from functools import partial +from typing import Any, Optional + +from torch import nn + +from ...transforms._presets import SemanticSegmentation +from .._api import register_model, Weights, WeightsEnum +from .._meta import _VOC_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter +from ..resnet import ResNet, resnet101, ResNet101_Weights, resnet50, ResNet50_Weights +from ._utils import _SimpleSegmentationModel + + +__all__ = ["FCN", "FCN_ResNet50_Weights", "FCN_ResNet101_Weights", "fcn_resnet50", "fcn_resnet101"] + + +class FCN(_SimpleSegmentationModel): + """ + Implements FCN model from + `"Fully Convolutional Networks for Semantic Segmentation" + `_. + + Args: + backbone (nn.Module): the network used to compute the features for the model. + The backbone should return an OrderedDict[Tensor], with the key being + "out" for the last feature map used, and "aux" if an auxiliary classifier + is used. + classifier (nn.Module): module that takes the "out" element returned from + the backbone and returns a dense prediction. + aux_classifier (nn.Module, optional): auxiliary classifier used during training + """ + + pass + + +class FCNHead(nn.Sequential): + def __init__(self, in_channels: int, channels: int) -> None: + inter_channels = in_channels // 4 + layers = [ + nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), + nn.BatchNorm2d(inter_channels), + nn.ReLU(), + nn.Dropout(0.1), + nn.Conv2d(inter_channels, channels, 1), + ] + + super().__init__(*layers) + + +_COMMON_META = { + "categories": _VOC_CATEGORIES, + "min_size": (1, 1), + "_docs": """ + These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC + dataset. + """, +} + + +class FCN_ResNet50_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + **_COMMON_META, + "num_params": 35322218, + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet50", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 60.5, + "pixel_acc": 91.4, + } + }, + "_ops": 152.717, + "_file_size": 135.009, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +class FCN_ResNet101_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + **_COMMON_META, + "num_params": 54314346, + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet101", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 63.7, + "pixel_acc": 91.9, + } + }, + "_ops": 232.738, + "_file_size": 207.711, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +def _fcn_resnet( + backbone: ResNet, + num_classes: int, + aux: Optional[bool], +) -> FCN: + return_layers = {"layer4": "out"} + if aux: + return_layers["layer3"] = "aux" + backbone = IntermediateLayerGetter(backbone, return_layers=return_layers) + + aux_classifier = FCNHead(1024, num_classes) if aux else None + classifier = FCNHead(2048, num_classes) + return FCN(backbone, classifier, aux_classifier) + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FCN_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1), +) +def fcn_resnet50( + *, + weights: Optional[FCN_ResNet50_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + aux_loss: Optional[bool] = None, + weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> FCN: + """Fully-Convolutional Network model with a ResNet-50 backbone from the `Fully Convolutional + Networks for Semantic Segmentation `_ paper. + + .. betastatus:: segmentation module + + Args: + weights (:class:`~torchvision.models.segmentation.FCN_ResNet50_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.FCN_ResNet50_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background). + aux_loss (bool, optional): If True, it uses an auxiliary loss. + weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained + weights for the backbone. + **kwargs: parameters passed to the ``torchvision.models.segmentation.fcn.FCN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.segmentation.FCN_ResNet50_Weights + :members: + """ + + weights = FCN_ResNet50_Weights.verify(weights) + weights_backbone = ResNet50_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True) + elif num_classes is None: + num_classes = 21 + + backbone = resnet50(weights=weights_backbone, replace_stride_with_dilation=[False, True, True]) + model = _fcn_resnet(backbone, num_classes, aux_loss) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", FCN_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", ResNet101_Weights.IMAGENET1K_V1), +) +def fcn_resnet101( + *, + weights: Optional[FCN_ResNet101_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + aux_loss: Optional[bool] = None, + weights_backbone: Optional[ResNet101_Weights] = ResNet101_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> FCN: + """Fully-Convolutional Network model with a ResNet-101 backbone from the `Fully Convolutional + Networks for Semantic Segmentation `_ paper. + + .. betastatus:: segmentation module + + Args: + weights (:class:`~torchvision.models.segmentation.FCN_ResNet101_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.FCN_ResNet101_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background). + aux_loss (bool, optional): If True, it uses an auxiliary loss. + weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained + weights for the backbone. + **kwargs: parameters passed to the ``torchvision.models.segmentation.fcn.FCN`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.segmentation.FCN_ResNet101_Weights + :members: + """ + + weights = FCN_ResNet101_Weights.verify(weights) + weights_backbone = ResNet101_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True) + elif num_classes is None: + num_classes = 21 + + backbone = resnet101(weights=weights_backbone, replace_stride_with_dilation=[False, True, True]) + model = _fcn_resnet(backbone, num_classes, aux_loss) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/segmentation/lraspp.py b/lib/python3.11/site-packages/torchvision/models/segmentation/lraspp.py new file mode 100644 index 0000000000000000000000000000000000000000..70bced70fd37c3c681915492cea0c68c87cf0a7e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/segmentation/lraspp.py @@ -0,0 +1,178 @@ +from collections import OrderedDict +from functools import partial +from typing import Any, Dict, Optional + +from torch import nn, Tensor +from torch.nn import functional as F + +from ...transforms._presets import SemanticSegmentation +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _VOC_CATEGORIES +from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter +from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights, MobileNetV3 + + +__all__ = ["LRASPP", "LRASPP_MobileNet_V3_Large_Weights", "lraspp_mobilenet_v3_large"] + + +class LRASPP(nn.Module): + """ + Implements a Lite R-ASPP Network for semantic segmentation from + `"Searching for MobileNetV3" + `_. + + Args: + backbone (nn.Module): the network used to compute the features for the model. + The backbone should return an OrderedDict[Tensor], with the key being + "high" for the high level feature map and "low" for the low level feature map. + low_channels (int): the number of channels of the low level features. + high_channels (int): the number of channels of the high level features. + num_classes (int, optional): number of output classes of the model (including the background). + inter_channels (int, optional): the number of channels for intermediate computations. + """ + + def __init__( + self, backbone: nn.Module, low_channels: int, high_channels: int, num_classes: int, inter_channels: int = 128 + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.backbone = backbone + self.classifier = LRASPPHead(low_channels, high_channels, num_classes, inter_channels) + + def forward(self, input: Tensor) -> Dict[str, Tensor]: + features = self.backbone(input) + out = self.classifier(features) + out = F.interpolate(out, size=input.shape[-2:], mode="bilinear", align_corners=False) + + result = OrderedDict() + result["out"] = out + + return result + + +class LRASPPHead(nn.Module): + def __init__(self, low_channels: int, high_channels: int, num_classes: int, inter_channels: int) -> None: + super().__init__() + self.cbr = nn.Sequential( + nn.Conv2d(high_channels, inter_channels, 1, bias=False), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + ) + self.scale = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(high_channels, inter_channels, 1, bias=False), + nn.Sigmoid(), + ) + self.low_classifier = nn.Conv2d(low_channels, num_classes, 1) + self.high_classifier = nn.Conv2d(inter_channels, num_classes, 1) + + def forward(self, input: Dict[str, Tensor]) -> Tensor: + low = input["low"] + high = input["high"] + + x = self.cbr(high) + s = self.scale(high) + x = x * s + x = F.interpolate(x, size=low.shape[-2:], mode="bilinear", align_corners=False) + + return self.low_classifier(low) + self.high_classifier(x) + + +def _lraspp_mobilenetv3(backbone: MobileNetV3, num_classes: int) -> LRASPP: + backbone = backbone.features + # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks. + # The first and last blocks are always included because they are the C0 (conv1) and Cn. + stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1] + low_pos = stage_indices[-4] # use C2 here which has output_stride = 8 + high_pos = stage_indices[-1] # use C5 which has output_stride = 16 + low_channels = backbone[low_pos].out_channels + high_channels = backbone[high_pos].out_channels + backbone = IntermediateLayerGetter(backbone, return_layers={str(low_pos): "low", str(high_pos): "high"}) + + return LRASPP(backbone, low_channels, high_channels, num_classes) + + +class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum): + COCO_WITH_VOC_LABELS_V1 = Weights( + url="https://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pth", + transforms=partial(SemanticSegmentation, resize_size=520), + meta={ + "num_params": 3221538, + "categories": _VOC_CATEGORIES, + "min_size": (1, 1), + "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_large", + "_metrics": { + "COCO-val2017-VOC-labels": { + "miou": 57.9, + "pixel_acc": 91.2, + } + }, + "_ops": 2.086, + "_file_size": 12.49, + "_docs": """ + These weights were trained on a subset of COCO, using only the 20 categories that are present in the + Pascal VOC dataset. + """, + }, + ) + DEFAULT = COCO_WITH_VOC_LABELS_V1 + + +@register_model() +@handle_legacy_interface( + weights=("pretrained", LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1), + weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1), +) +def lraspp_mobilenet_v3_large( + *, + weights: Optional[LRASPP_MobileNet_V3_Large_Weights] = None, + progress: bool = True, + num_classes: Optional[int] = None, + weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1, + **kwargs: Any, +) -> LRASPP: + """Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from + `Searching for MobileNetV3 `_ paper. + + .. betastatus:: segmentation module + + Args: + weights (:class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + num_classes (int, optional): number of output classes of the model (including the background). + aux_loss (bool, optional): If True, it uses an auxiliary loss. + weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained + weights for the backbone. + **kwargs: parameters passed to the ``torchvision.models.segmentation.LRASPP`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights + :members: + """ + if kwargs.pop("aux_loss", False): + raise NotImplementedError("This model does not use auxiliary loss") + + weights = LRASPP_MobileNet_V3_Large_Weights.verify(weights) + weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone) + + if weights is not None: + weights_backbone = None + num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"])) + elif num_classes is None: + num_classes = 21 + + backbone = mobilenet_v3_large(weights=weights_backbone, dilated=True) + model = _lraspp_mobilenetv3(backbone, num_classes) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/shufflenetv2.py b/lib/python3.11/site-packages/torchvision/models/shufflenetv2.py new file mode 100644 index 0000000000000000000000000000000000000000..3f3322b7a88f183c15838308f39c38d36dea13c0 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/shufflenetv2.py @@ -0,0 +1,408 @@ +from functools import partial +from typing import Any, Callable, List, Optional + +import torch +import torch.nn as nn +from torch import Tensor + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "ShuffleNetV2", + "ShuffleNet_V2_X0_5_Weights", + "ShuffleNet_V2_X1_0_Weights", + "ShuffleNet_V2_X1_5_Weights", + "ShuffleNet_V2_X2_0_Weights", + "shufflenet_v2_x0_5", + "shufflenet_v2_x1_0", + "shufflenet_v2_x1_5", + "shufflenet_v2_x2_0", +] + + +def channel_shuffle(x: Tensor, groups: int) -> Tensor: + batchsize, num_channels, height, width = x.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, channels_per_group, height, width) + + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, num_channels, height, width) + + return x + + +class InvertedResidual(nn.Module): + def __init__(self, inp: int, oup: int, stride: int) -> None: + super().__init__() + + if not (1 <= stride <= 3): + raise ValueError("illegal stride value") + self.stride = stride + + branch_features = oup // 2 + if (self.stride == 1) and (inp != branch_features << 1): + raise ValueError( + f"Invalid combination of stride {stride}, inp {inp} and oup {oup} values. If stride == 1 then inp should be equal to oup // 2 << 1." + ) + + if self.stride > 1: + self.branch1 = nn.Sequential( + self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), + nn.BatchNorm2d(inp), + nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(branch_features), + nn.ReLU(inplace=True), + ) + else: + self.branch1 = nn.Sequential() + + self.branch2 = nn.Sequential( + nn.Conv2d( + inp if (self.stride > 1) else branch_features, + branch_features, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ), + nn.BatchNorm2d(branch_features), + nn.ReLU(inplace=True), + self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), + nn.BatchNorm2d(branch_features), + nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(branch_features), + nn.ReLU(inplace=True), + ) + + @staticmethod + def depthwise_conv( + i: int, o: int, kernel_size: int, stride: int = 1, padding: int = 0, bias: bool = False + ) -> nn.Conv2d: + return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) + + def forward(self, x: Tensor) -> Tensor: + if self.stride == 1: + x1, x2 = x.chunk(2, dim=1) + out = torch.cat((x1, self.branch2(x2)), dim=1) + else: + out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) + + out = channel_shuffle(out, 2) + + return out + + +class ShuffleNetV2(nn.Module): + def __init__( + self, + stages_repeats: List[int], + stages_out_channels: List[int], + num_classes: int = 1000, + inverted_residual: Callable[..., nn.Module] = InvertedResidual, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if len(stages_repeats) != 3: + raise ValueError("expected stages_repeats as list of 3 positive ints") + if len(stages_out_channels) != 5: + raise ValueError("expected stages_out_channels as list of 5 positive ints") + self._stage_out_channels = stages_out_channels + + input_channels = 3 + output_channels = self._stage_out_channels[0] + self.conv1 = nn.Sequential( + nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False), + nn.BatchNorm2d(output_channels), + nn.ReLU(inplace=True), + ) + input_channels = output_channels + + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + # Static annotations for mypy + self.stage2: nn.Sequential + self.stage3: nn.Sequential + self.stage4: nn.Sequential + stage_names = [f"stage{i}" for i in [2, 3, 4]] + for name, repeats, output_channels in zip(stage_names, stages_repeats, self._stage_out_channels[1:]): + seq = [inverted_residual(input_channels, output_channels, 2)] + for i in range(repeats - 1): + seq.append(inverted_residual(output_channels, output_channels, 1)) + setattr(self, name, nn.Sequential(*seq)) + input_channels = output_channels + + output_channels = self._stage_out_channels[-1] + self.conv5 = nn.Sequential( + nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False), + nn.BatchNorm2d(output_channels), + nn.ReLU(inplace=True), + ) + + self.fc = nn.Linear(output_channels, num_classes) + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + x = self.conv1(x) + x = self.maxpool(x) + x = self.stage2(x) + x = self.stage3(x) + x = self.stage4(x) + x = self.conv5(x) + x = x.mean([2, 3]) # globalpool + x = self.fc(x) + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _shufflenetv2( + weights: Optional[WeightsEnum], + progress: bool, + *args: Any, + **kwargs: Any, +) -> ShuffleNetV2: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = ShuffleNetV2(*args, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/ericsun99/Shufflenet-v2-Pytorch", +} + + +class ShuffleNet_V2_X0_5_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorch + url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 1366792, + "_metrics": { + "ImageNet-1K": { + "acc@1": 60.552, + "acc@5": 81.746, + } + }, + "_ops": 0.04, + "_file_size": 5.282, + "_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ShuffleNet_V2_X1_0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + # Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorch + url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 2278604, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.362, + "acc@5": 88.316, + } + }, + "_ops": 0.145, + "_file_size": 8.791, + "_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ShuffleNet_V2_X1_5_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/5906", + "num_params": 3503624, + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.996, + "acc@5": 91.086, + } + }, + "_ops": 0.296, + "_file_size": 13.557, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ShuffleNet_V2_X2_0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=232), + meta={ + **_COMMON_META, + "recipe": "https://github.com/pytorch/vision/pull/5906", + "num_params": 7393996, + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.230, + "acc@5": 93.006, + } + }, + "_ops": 0.583, + "_file_size": 28.433, + "_docs": """ + These weights were trained from scratch by using TorchVision's `new training recipe + `_. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1)) +def shufflenet_v2_x0_5( + *, weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any +) -> ShuffleNetV2: + """ + Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + Args: + weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights + :members: + """ + weights = ShuffleNet_V2_X0_5_Weights.verify(weights) + + return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1)) +def shufflenet_v2_x1_0( + *, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any +) -> ShuffleNetV2: + """ + Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + Args: + weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights + :members: + """ + weights = ShuffleNet_V2_X1_0_Weights.verify(weights) + + return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1)) +def shufflenet_v2_x1_5( + *, weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any +) -> ShuffleNetV2: + """ + Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + Args: + weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights + :members: + """ + weights = ShuffleNet_V2_X1_5_Weights.verify(weights) + + return _shufflenetv2(weights, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1)) +def shufflenet_v2_x2_0( + *, weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any +) -> ShuffleNetV2: + """ + Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in + `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design + `__. + + Args: + weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights + :members: + """ + weights = ShuffleNet_V2_X2_0_Weights.verify(weights) + + return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/squeezenet.py b/lib/python3.11/site-packages/torchvision/models/squeezenet.py new file mode 100644 index 0000000000000000000000000000000000000000..982b32107b09c280b4c7caa61e6b80be0cbf041e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/squeezenet.py @@ -0,0 +1,223 @@ +from functools import partial +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.init as init + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = ["SqueezeNet", "SqueezeNet1_0_Weights", "SqueezeNet1_1_Weights", "squeezenet1_0", "squeezenet1_1"] + + +class Fire(nn.Module): + def __init__(self, inplanes: int, squeeze_planes: int, expand1x1_planes: int, expand3x3_planes: int) -> None: + super().__init__() + self.inplanes = inplanes + self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) + self.squeeze_activation = nn.ReLU(inplace=True) + self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1) + self.expand1x1_activation = nn.ReLU(inplace=True) + self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1) + self.expand3x3_activation = nn.ReLU(inplace=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.squeeze_activation(self.squeeze(x)) + return torch.cat( + [self.expand1x1_activation(self.expand1x1(x)), self.expand3x3_activation(self.expand3x3(x))], 1 + ) + + +class SqueezeNet(nn.Module): + def __init__(self, version: str = "1_0", num_classes: int = 1000, dropout: float = 0.5) -> None: + super().__init__() + _log_api_usage_once(self) + self.num_classes = num_classes + if version == "1_0": + self.features = nn.Sequential( + nn.Conv2d(3, 96, kernel_size=7, stride=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(96, 16, 64, 64), + Fire(128, 16, 64, 64), + Fire(128, 32, 128, 128), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(256, 32, 128, 128), + Fire(256, 48, 192, 192), + Fire(384, 48, 192, 192), + Fire(384, 64, 256, 256), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(512, 64, 256, 256), + ) + elif version == "1_1": + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, stride=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(64, 16, 64, 64), + Fire(128, 16, 64, 64), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(128, 32, 128, 128), + Fire(256, 32, 128, 128), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(256, 48, 192, 192), + Fire(384, 48, 192, 192), + Fire(384, 64, 256, 256), + Fire(512, 64, 256, 256), + ) + else: + # FIXME: Is this needed? SqueezeNet should only be called from the + # FIXME: squeezenet1_x() functions + # FIXME: This checking is not done for the other models + raise ValueError(f"Unsupported SqueezeNet version {version}: 1_0 or 1_1 expected") + + # Final convolution is initialized differently from the rest + final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout), final_conv, nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d((1, 1)) + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + if m is final_conv: + init.normal_(m.weight, mean=0.0, std=0.01) + else: + init.kaiming_uniform_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.classifier(x) + return torch.flatten(x, 1) + + +def _squeezenet( + version: str, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> SqueezeNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = SqueezeNet(version, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717", + "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", +} + + +class SqueezeNet1_0_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "min_size": (21, 21), + "num_params": 1248424, + "_metrics": { + "ImageNet-1K": { + "acc@1": 58.092, + "acc@5": 80.420, + } + }, + "_ops": 0.819, + "_file_size": 4.778, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class SqueezeNet1_1_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "min_size": (17, 17), + "num_params": 1235496, + "_metrics": { + "ImageNet-1K": { + "acc@1": 58.178, + "acc@5": 80.624, + } + }, + "_ops": 0.349, + "_file_size": 4.729, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", SqueezeNet1_0_Weights.IMAGENET1K_V1)) +def squeezenet1_0( + *, weights: Optional[SqueezeNet1_0_Weights] = None, progress: bool = True, **kwargs: Any +) -> SqueezeNet: + """SqueezeNet model architecture from the `SqueezeNet: AlexNet-level + accuracy with 50x fewer parameters and <0.5MB model size + `_ paper. + + Args: + weights (:class:`~torchvision.models.SqueezeNet1_0_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.SqueezeNet1_0_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.SqueezeNet1_0_Weights + :members: + """ + weights = SqueezeNet1_0_Weights.verify(weights) + return _squeezenet("1_0", weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", SqueezeNet1_1_Weights.IMAGENET1K_V1)) +def squeezenet1_1( + *, weights: Optional[SqueezeNet1_1_Weights] = None, progress: bool = True, **kwargs: Any +) -> SqueezeNet: + """SqueezeNet 1.1 model from the `official SqueezeNet repo + `_. + + SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters + than SqueezeNet 1.0, without sacrificing accuracy. + + Args: + weights (:class:`~torchvision.models.SqueezeNet1_1_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.SqueezeNet1_1_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.SqueezeNet1_1_Weights + :members: + """ + weights = SqueezeNet1_1_Weights.verify(weights) + return _squeezenet("1_1", weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/swin_transformer.py b/lib/python3.11/site-packages/torchvision/models/swin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..2035f659bfc7f4e4f98d36cabff9be3802d49e59 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/swin_transformer.py @@ -0,0 +1,1033 @@ +import math +from functools import partial +from typing import Any, Callable, List, Optional + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ..ops.misc import MLP, Permute +from ..ops.stochastic_depth import StochasticDepth +from ..transforms._presets import ImageClassification, InterpolationMode +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "SwinTransformer", + "Swin_T_Weights", + "Swin_S_Weights", + "Swin_B_Weights", + "Swin_V2_T_Weights", + "Swin_V2_S_Weights", + "Swin_V2_B_Weights", + "swin_t", + "swin_s", + "swin_b", + "swin_v2_t", + "swin_v2_s", + "swin_v2_b", +] + + +def _patch_merging_pad(x: torch.Tensor) -> torch.Tensor: + H, W, _ = x.shape[-3:] + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + x0 = x[..., 0::2, 0::2, :] # ... H/2 W/2 C + x1 = x[..., 1::2, 0::2, :] # ... H/2 W/2 C + x2 = x[..., 0::2, 1::2, :] # ... H/2 W/2 C + x3 = x[..., 1::2, 1::2, :] # ... H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # ... H/2 W/2 4*C + return x + + +torch.fx.wrap("_patch_merging_pad") + + +def _get_relative_position_bias( + relative_position_bias_table: torch.Tensor, relative_position_index: torch.Tensor, window_size: List[int] +) -> torch.Tensor: + N = window_size[0] * window_size[1] + relative_position_bias = relative_position_bias_table[relative_position_index] # type: ignore[index] + relative_position_bias = relative_position_bias.view(N, N, -1) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0) + return relative_position_bias + + +torch.fx.wrap("_get_relative_position_bias") + + +class PatchMerging(nn.Module): + """Patch Merging Layer. + Args: + dim (int): Number of input channels. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + """ + + def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm): + super().__init__() + _log_api_usage_once(self) + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x: Tensor): + """ + Args: + x (Tensor): input tensor with expected layout of [..., H, W, C] + Returns: + Tensor with layout of [..., H/2, W/2, 2*C] + """ + x = _patch_merging_pad(x) + x = self.norm(x) + x = self.reduction(x) # ... H/2 W/2 2*C + return x + + +class PatchMergingV2(nn.Module): + """Patch Merging Layer for Swin Transformer V2. + Args: + dim (int): Number of input channels. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + """ + + def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm): + super().__init__() + _log_api_usage_once(self) + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) # difference + + def forward(self, x: Tensor): + """ + Args: + x (Tensor): input tensor with expected layout of [..., H, W, C] + Returns: + Tensor with layout of [..., H/2, W/2, 2*C] + """ + x = _patch_merging_pad(x) + x = self.reduction(x) # ... H/2 W/2 2*C + x = self.norm(x) + return x + + +def shifted_window_attention( + input: Tensor, + qkv_weight: Tensor, + proj_weight: Tensor, + relative_position_bias: Tensor, + window_size: List[int], + num_heads: int, + shift_size: List[int], + attention_dropout: float = 0.0, + dropout: float = 0.0, + qkv_bias: Optional[Tensor] = None, + proj_bias: Optional[Tensor] = None, + logit_scale: Optional[torch.Tensor] = None, + training: bool = True, +) -> Tensor: + """ + Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + input (Tensor[N, H, W, C]): The input tensor or 4-dimensions. + qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value. + proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection. + relative_position_bias (Tensor): The learned relative position bias added to attention. + window_size (List[int]): Window size. + num_heads (int): Number of attention heads. + shift_size (List[int]): Shift size for shifted window attention. + attention_dropout (float): Dropout ratio of attention weight. Default: 0.0. + dropout (float): Dropout ratio of output. Default: 0.0. + qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None. + proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None. + logit_scale (Tensor[out_dim], optional): Logit scale of cosine attention for Swin Transformer V2. Default: None. + training (bool, optional): Training flag used by the dropout parameters. Default: True. + Returns: + Tensor[N, H, W, C]: The output tensor after shifted window attention. + """ + B, H, W, C = input.shape + # pad feature maps to multiples of window size + pad_r = (window_size[1] - W % window_size[1]) % window_size[1] + pad_b = (window_size[0] - H % window_size[0]) % window_size[0] + x = F.pad(input, (0, 0, 0, pad_r, 0, pad_b)) + _, pad_H, pad_W, _ = x.shape + + shift_size = shift_size.copy() + # If window size is larger than feature size, there is no need to shift window + if window_size[0] >= pad_H: + shift_size[0] = 0 + if window_size[1] >= pad_W: + shift_size[1] = 0 + + # cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2)) + + # partition windows + num_windows = (pad_H // window_size[0]) * (pad_W // window_size[1]) + x = x.view(B, pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1], C) + x = x.permute(0, 1, 3, 2, 4, 5).reshape(B * num_windows, window_size[0] * window_size[1], C) # B*nW, Ws*Ws, C + + # multi-head attention + if logit_scale is not None and qkv_bias is not None: + qkv_bias = qkv_bias.clone() + length = qkv_bias.numel() // 3 + qkv_bias[length : 2 * length].zero_() + qkv = F.linear(x, qkv_weight, qkv_bias) + qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, C // num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + if logit_scale is not None: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp(logit_scale, max=math.log(100.0)).exp() + attn = attn * logit_scale + else: + q = q * (C // num_heads) ** -0.5 + attn = q.matmul(k.transpose(-2, -1)) + # add relative position bias + attn = attn + relative_position_bias + + if sum(shift_size) > 0: + # generate attention mask + attn_mask = x.new_zeros((pad_H, pad_W)) + h_slices = ((0, -window_size[0]), (-window_size[0], -shift_size[0]), (-shift_size[0], None)) + w_slices = ((0, -window_size[1]), (-window_size[1], -shift_size[1]), (-shift_size[1], None)) + count = 0 + for h in h_slices: + for w in w_slices: + attn_mask[h[0] : h[1], w[0] : w[1]] = count + count += 1 + attn_mask = attn_mask.view(pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1]) + attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(num_windows, window_size[0] * window_size[1]) + attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1)) + attn = attn + attn_mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, num_heads, x.size(1), x.size(1)) + + attn = F.softmax(attn, dim=-1) + attn = F.dropout(attn, p=attention_dropout, training=training) + + x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), C) + x = F.linear(x, proj_weight, proj_bias) + x = F.dropout(x, p=dropout, training=training) + + # reverse windows + x = x.view(B, pad_H // window_size[0], pad_W // window_size[1], window_size[0], window_size[1], C) + x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, pad_H, pad_W, C) + + # reverse cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2)) + + # unpad features + x = x[:, :H, :W, :].contiguous() + return x + + +torch.fx.wrap("shifted_window_attention") + + +class ShiftedWindowAttention(nn.Module): + """ + See :func:`shifted_window_attention`. + """ + + def __init__( + self, + dim: int, + window_size: List[int], + shift_size: List[int], + num_heads: int, + qkv_bias: bool = True, + proj_bias: bool = True, + attention_dropout: float = 0.0, + dropout: float = 0.0, + ): + super().__init__() + if len(window_size) != 2 or len(shift_size) != 2: + raise ValueError("window_size and shift_size must be of length 2") + self.window_size = window_size + self.shift_size = shift_size + self.num_heads = num_heads + self.attention_dropout = attention_dropout + self.dropout = dropout + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + + self.define_relative_position_bias_table() + self.define_relative_position_index() + + def define_relative_position_bias_table(self): + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), self.num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02) + + def define_relative_position_index(self): + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij")) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1).flatten() # Wh*Ww*Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + def get_relative_position_bias(self) -> torch.Tensor: + return _get_relative_position_bias( + self.relative_position_bias_table, self.relative_position_index, self.window_size # type: ignore[arg-type] + ) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x (Tensor): Tensor with layout of [B, H, W, C] + Returns: + Tensor with same layout as input, i.e. [B, H, W, C] + """ + relative_position_bias = self.get_relative_position_bias() + return shifted_window_attention( + x, + self.qkv.weight, + self.proj.weight, + relative_position_bias, + self.window_size, + self.num_heads, + shift_size=self.shift_size, + attention_dropout=self.attention_dropout, + dropout=self.dropout, + qkv_bias=self.qkv.bias, + proj_bias=self.proj.bias, + training=self.training, + ) + + +class ShiftedWindowAttentionV2(ShiftedWindowAttention): + """ + See :func:`shifted_window_attention_v2`. + """ + + def __init__( + self, + dim: int, + window_size: List[int], + shift_size: List[int], + num_heads: int, + qkv_bias: bool = True, + proj_bias: bool = True, + attention_dropout: float = 0.0, + dropout: float = 0.0, + ): + super().__init__( + dim, + window_size, + shift_size, + num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attention_dropout=attention_dropout, + dropout=dropout, + ) + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False) + ) + if qkv_bias: + length = self.qkv.bias.numel() // 3 + self.qkv.bias[length : 2 * length].data.zero_() + + def define_relative_position_bias_table(self): + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) + relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w], indexing="ij")) + relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / 3.0 + ) + self.register_buffer("relative_coords_table", relative_coords_table) + + def get_relative_position_bias(self) -> torch.Tensor: + relative_position_bias = _get_relative_position_bias( + self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads), + self.relative_position_index, # type: ignore[arg-type] + self.window_size, + ) + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + return relative_position_bias + + def forward(self, x: Tensor): + """ + Args: + x (Tensor): Tensor with layout of [B, H, W, C] + Returns: + Tensor with same layout as input, i.e. [B, H, W, C] + """ + relative_position_bias = self.get_relative_position_bias() + return shifted_window_attention( + x, + self.qkv.weight, + self.proj.weight, + relative_position_bias, + self.window_size, + self.num_heads, + shift_size=self.shift_size, + attention_dropout=self.attention_dropout, + dropout=self.dropout, + qkv_bias=self.qkv.bias, + proj_bias=self.proj.bias, + logit_scale=self.logit_scale, + training=self.training, + ) + + +class SwinTransformerBlock(nn.Module): + """ + Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (List[int]): Window size. + shift_size (List[int]): Shift size for shifted window attention. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttention + """ + + def __init__( + self, + dim: int, + num_heads: int, + window_size: List[int], + shift_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_layer: Callable[..., nn.Module] = ShiftedWindowAttention, + ): + super().__init__() + _log_api_usage_once(self) + + self.norm1 = norm_layer(dim) + self.attn = attn_layer( + dim, + window_size, + shift_size, + num_heads, + attention_dropout=attention_dropout, + dropout=dropout, + ) + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + self.norm2 = norm_layer(dim) + self.mlp = MLP(dim, [int(dim * mlp_ratio), dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.mlp.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def forward(self, x: Tensor): + x = x + self.stochastic_depth(self.attn(self.norm1(x))) + x = x + self.stochastic_depth(self.mlp(self.norm2(x))) + return x + + +class SwinTransformerBlockV2(SwinTransformerBlock): + """ + Swin Transformer V2 Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (List[int]): Window size. + shift_size (List[int]): Shift size for shifted window attention. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttentionV2. + """ + + def __init__( + self, + dim: int, + num_heads: int, + window_size: List[int], + shift_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_layer: Callable[..., nn.Module] = ShiftedWindowAttentionV2, + ): + super().__init__( + dim, + num_heads, + window_size, + shift_size, + mlp_ratio=mlp_ratio, + dropout=dropout, + attention_dropout=attention_dropout, + stochastic_depth_prob=stochastic_depth_prob, + norm_layer=norm_layer, + attn_layer=attn_layer, + ) + + def forward(self, x: Tensor): + # Here is the difference, we apply norm after the attention in V2. + # In V1 we applied norm before the attention. + x = x + self.stochastic_depth(self.norm1(self.attn(x))) + x = x + self.stochastic_depth(self.norm2(self.mlp(x))) + return x + + +class SwinTransformer(nn.Module): + """ + Implements Swin Transformer from the `"Swin Transformer: Hierarchical Vision Transformer using + Shifted Windows" `_ paper. + Args: + patch_size (List[int]): Patch size. + embed_dim (int): Patch embedding dimension. + depths (List(int)): Depth of each Swin Transformer layer. + num_heads (List(int)): Number of attention heads in different layers. + window_size (List[int]): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob (float): Stochastic depth rate. Default: 0.1. + num_classes (int): Number of classes for classification head. Default: 1000. + block (nn.Module, optional): SwinTransformer Block. Default: None. + norm_layer (nn.Module, optional): Normalization layer. Default: None. + downsample_layer (nn.Module): Downsample layer (patch merging). Default: PatchMerging. + """ + + def __init__( + self, + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.1, + num_classes: int = 1000, + norm_layer: Optional[Callable[..., nn.Module]] = None, + block: Optional[Callable[..., nn.Module]] = None, + downsample_layer: Callable[..., nn.Module] = PatchMerging, + ): + super().__init__() + _log_api_usage_once(self) + self.num_classes = num_classes + + if block is None: + block = SwinTransformerBlock + if norm_layer is None: + norm_layer = partial(nn.LayerNorm, eps=1e-5) + + layers: List[nn.Module] = [] + # split image into non-overlapping patches + layers.append( + nn.Sequential( + nn.Conv2d( + 3, embed_dim, kernel_size=(patch_size[0], patch_size[1]), stride=(patch_size[0], patch_size[1]) + ), + Permute([0, 2, 3, 1]), + norm_layer(embed_dim), + ) + ) + + total_stage_blocks = sum(depths) + stage_block_id = 0 + # build SwinTransformer blocks + for i_stage in range(len(depths)): + stage: List[nn.Module] = [] + dim = embed_dim * 2**i_stage + for i_layer in range(depths[i_stage]): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1) + stage.append( + block( + dim, + num_heads[i_stage], + window_size=window_size, + shift_size=[0 if i_layer % 2 == 0 else w // 2 for w in window_size], + mlp_ratio=mlp_ratio, + dropout=dropout, + attention_dropout=attention_dropout, + stochastic_depth_prob=sd_prob, + norm_layer=norm_layer, + ) + ) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + # add patch merging layer + if i_stage < (len(depths) - 1): + layers.append(downsample_layer(dim, norm_layer)) + self.features = nn.Sequential(*layers) + + num_features = embed_dim * 2 ** (len(depths) - 1) + self.norm = norm_layer(num_features) + self.permute = Permute([0, 3, 1, 2]) # B H W C -> B C H W + self.avgpool = nn.AdaptiveAvgPool2d(1) + self.flatten = nn.Flatten(1) + self.head = nn.Linear(num_features, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x): + x = self.features(x) + x = self.norm(x) + x = self.permute(x) + x = self.avgpool(x) + x = self.flatten(x) + x = self.head(x) + return x + + +def _swin_transformer( + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> SwinTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = SwinTransformer( + patch_size=patch_size, + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=window_size, + stochastic_depth_prob=stochastic_depth_prob, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "categories": _IMAGENET_CATEGORIES, +} + + +class Swin_T_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_t-704ceda3.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=232, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 28288354, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.474, + "acc@5": 95.776, + } + }, + "_ops": 4.491, + "_file_size": 108.19, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_S_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_s-5e29d889.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=246, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 49606258, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.196, + "acc@5": 96.360, + } + }, + "_ops": 8.741, + "_file_size": 189.786, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_B_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_b-68c6b09e.pth", + transforms=partial( + ImageClassification, crop_size=224, resize_size=238, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 87768224, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.582, + "acc@5": 96.640, + } + }, + "_ops": 15.431, + "_file_size": 335.364, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_V2_T_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_v2_t-b137f0e2.pth", + transforms=partial( + ImageClassification, crop_size=256, resize_size=260, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 28351570, + "min_size": (256, 256), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2", + "_metrics": { + "ImageNet-1K": { + "acc@1": 82.072, + "acc@5": 96.132, + } + }, + "_ops": 5.94, + "_file_size": 108.626, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_V2_S_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_v2_s-637d8ceb.pth", + transforms=partial( + ImageClassification, crop_size=256, resize_size=260, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 49737442, + "min_size": (256, 256), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2", + "_metrics": { + "ImageNet-1K": { + "acc@1": 83.712, + "acc@5": 96.816, + } + }, + "_ops": 11.546, + "_file_size": 190.675, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class Swin_V2_B_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/swin_v2_b-781e5279.pth", + transforms=partial( + ImageClassification, crop_size=256, resize_size=272, interpolation=InterpolationMode.BICUBIC + ), + meta={ + **_COMMON_META, + "num_params": 87930848, + "min_size": (256, 256), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2", + "_metrics": { + "ImageNet-1K": { + "acc@1": 84.112, + "acc@5": 96.864, + } + }, + "_ops": 20.325, + "_file_size": 336.372, + "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""", + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_T_Weights.IMAGENET1K_V1)) +def swin_t(*, weights: Optional[Swin_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_tiny architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_T_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_T_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_T_Weights + :members: + """ + weights = Swin_T_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=[7, 7], + stochastic_depth_prob=0.2, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_S_Weights.IMAGENET1K_V1)) +def swin_s(*, weights: Optional[Swin_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_small architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_S_Weights + :members: + """ + weights = Swin_S_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 18, 2], + num_heads=[3, 6, 12, 24], + window_size=[7, 7], + stochastic_depth_prob=0.3, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_B_Weights.IMAGENET1K_V1)) +def swin_b(*, weights: Optional[Swin_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_base architecture from + `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows `_. + + Args: + weights (:class:`~torchvision.models.Swin_B_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_B_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_B_Weights + :members: + """ + weights = Swin_B_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=[7, 7], + stochastic_depth_prob=0.5, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_V2_T_Weights.IMAGENET1K_V1)) +def swin_v2_t(*, weights: Optional[Swin_V2_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_v2_tiny architecture from + `Swin Transformer V2: Scaling Up Capacity and Resolution `_. + + Args: + weights (:class:`~torchvision.models.Swin_V2_T_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_V2_T_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_V2_T_Weights + :members: + """ + weights = Swin_V2_T_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=[8, 8], + stochastic_depth_prob=0.2, + weights=weights, + progress=progress, + block=SwinTransformerBlockV2, + downsample_layer=PatchMergingV2, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_V2_S_Weights.IMAGENET1K_V1)) +def swin_v2_s(*, weights: Optional[Swin_V2_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_v2_small architecture from + `Swin Transformer V2: Scaling Up Capacity and Resolution `_. + + Args: + weights (:class:`~torchvision.models.Swin_V2_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_V2_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_V2_S_Weights + :members: + """ + weights = Swin_V2_S_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=96, + depths=[2, 2, 18, 2], + num_heads=[3, 6, 12, 24], + window_size=[8, 8], + stochastic_depth_prob=0.3, + weights=weights, + progress=progress, + block=SwinTransformerBlockV2, + downsample_layer=PatchMergingV2, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin_V2_B_Weights.IMAGENET1K_V1)) +def swin_v2_b(*, weights: Optional[Swin_V2_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer: + """ + Constructs a swin_v2_base architecture from + `Swin Transformer V2: Scaling Up Capacity and Resolution `_. + + Args: + weights (:class:`~torchvision.models.Swin_V2_B_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.Swin_V2_B_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.Swin_V2_B_Weights + :members: + """ + weights = Swin_V2_B_Weights.verify(weights) + + return _swin_transformer( + patch_size=[4, 4], + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=[8, 8], + stochastic_depth_prob=0.5, + weights=weights, + progress=progress, + block=SwinTransformerBlockV2, + downsample_layer=PatchMergingV2, + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/vgg.py b/lib/python3.11/site-packages/torchvision/models/vgg.py new file mode 100644 index 0000000000000000000000000000000000000000..0a548570dd41fa87309e8aa4bdb373517f265060 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/vgg.py @@ -0,0 +1,511 @@ +from functools import partial +from typing import Any, cast, Dict, List, Optional, Union + +import torch +import torch.nn as nn + +from ..transforms._presets import ImageClassification +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "VGG", + "VGG11_Weights", + "VGG11_BN_Weights", + "VGG13_Weights", + "VGG13_BN_Weights", + "VGG16_Weights", + "VGG16_BN_Weights", + "VGG19_Weights", + "VGG19_BN_Weights", + "vgg11", + "vgg11_bn", + "vgg13", + "vgg13_bn", + "vgg16", + "vgg16_bn", + "vgg19", + "vgg19_bn", +] + + +class VGG(nn.Module): + def __init__( + self, features: nn.Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5 + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(p=dropout), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(p=dropout), + nn.Linear(4096, num_classes), + ) + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + +def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential: + layers: List[nn.Module] = [] + in_channels = 3 + for v in cfg: + if v == "M": + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + v = cast(int, v) + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs: Dict[str, List[Union[str, int]]] = { + "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"], + "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"], +} + + +def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> VGG: + if weights is not None: + kwargs["init_weights"] = False + if weights.meta["categories"] is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs) + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + return model + + +_COMMON_META = { + "min_size": (32, 32), + "categories": _IMAGENET_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg", + "_docs": """These weights were trained from scratch by using a simplified training recipe.""", +} + + +class VGG11_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg11-8a719046.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 132863336, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.020, + "acc@5": 88.628, + } + }, + "_ops": 7.609, + "_file_size": 506.84, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG11_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg11_bn-6002323d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 132868840, + "_metrics": { + "ImageNet-1K": { + "acc@1": 70.370, + "acc@5": 89.810, + } + }, + "_ops": 7.609, + "_file_size": 506.881, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG13_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg13-19584684.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 133047848, + "_metrics": { + "ImageNet-1K": { + "acc@1": 69.928, + "acc@5": 89.246, + } + }, + "_ops": 11.308, + "_file_size": 507.545, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG13_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg13_bn-abd245e5.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 133053736, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.586, + "acc@5": 90.374, + } + }, + "_ops": 11.308, + "_file_size": 507.59, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg16-397923af.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 138357544, + "_metrics": { + "ImageNet-1K": { + "acc@1": 71.592, + "acc@5": 90.382, + } + }, + "_ops": 15.47, + "_file_size": 527.796, + }, + ) + IMAGENET1K_FEATURES = Weights( + # Weights ported from https://github.com/amdegroot/ssd.pytorch/ + url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + mean=(0.48235, 0.45882, 0.40784), + std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0), + ), + meta={ + **_COMMON_META, + "num_params": 138357544, + "categories": None, + "recipe": "https://github.com/amdegroot/ssd.pytorch#training-ssd", + "_metrics": { + "ImageNet-1K": { + "acc@1": float("nan"), + "acc@5": float("nan"), + } + }, + "_ops": 15.47, + "_file_size": 527.802, + "_docs": """ + These weights can't be used for classification because they are missing values in the `classifier` + module. Only the `features` module has valid values and can be used for feature extraction. The weights + were trained using the original input standardization method as described in the paper. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG16_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg16_bn-6c64b313.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 138365992, + "_metrics": { + "ImageNet-1K": { + "acc@1": 73.360, + "acc@5": 91.516, + } + }, + "_ops": 15.47, + "_file_size": 527.866, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG19_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg19-dcbb9e9d.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 143667240, + "_metrics": { + "ImageNet-1K": { + "acc@1": 72.376, + "acc@5": 90.876, + } + }, + "_ops": 19.632, + "_file_size": 548.051, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class VGG19_BN_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vgg19_bn-c79401a0.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 143678248, + "_metrics": { + "ImageNet-1K": { + "acc@1": 74.218, + "acc@5": 91.842, + } + }, + "_ops": 19.632, + "_file_size": 548.143, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG11_Weights.IMAGENET1K_V1)) +def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-11 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG11_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG11_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG11_Weights + :members: + """ + weights = VGG11_Weights.verify(weights) + + return _vgg("A", False, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1)) +def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-11-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG11_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG11_BN_Weights + :members: + """ + weights = VGG11_BN_Weights.verify(weights) + + return _vgg("A", True, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1)) +def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG13_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG13_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG13_Weights + :members: + """ + weights = VGG13_Weights.verify(weights) + + return _vgg("B", False, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1)) +def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-13-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG13_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG13_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG13_BN_Weights + :members: + """ + weights = VGG13_BN_Weights.verify(weights) + + return _vgg("B", True, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1)) +def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-16 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG16_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG16_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG16_Weights + :members: + """ + weights = VGG16_Weights.verify(weights) + + return _vgg("D", False, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.IMAGENET1K_V1)) +def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-16-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG16_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG16_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG16_BN_Weights + :members: + """ + weights = VGG16_BN_Weights.verify(weights) + + return _vgg("D", True, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1)) +def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-19 from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG19_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG19_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG19_Weights + :members: + """ + weights = VGG19_Weights.verify(weights) + + return _vgg("E", False, weights, progress, **kwargs) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.IMAGENET1K_V1)) +def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: + """VGG-19_BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition `__. + + Args: + weights (:class:`~torchvision.models.VGG19_BN_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.VGG19_BN_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vgg.VGG`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.VGG19_BN_Weights + :members: + """ + weights = VGG19_BN_Weights.verify(weights) + + return _vgg("E", True, weights, progress, **kwargs) diff --git a/lib/python3.11/site-packages/torchvision/models/video/__init__.py b/lib/python3.11/site-packages/torchvision/models/video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f1eedd3116001af22ec202d2ccec6eefad8090ae --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/video/__init__.py @@ -0,0 +1,4 @@ +from .mvit import * +from .resnet import * +from .s3d import * +from .swin_transformer import * diff --git a/lib/python3.11/site-packages/torchvision/models/video/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/models/video/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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a/lib/python3.11/site-packages/torchvision/models/video/mvit.py b/lib/python3.11/site-packages/torchvision/models/video/mvit.py new file mode 100644 index 0000000000000000000000000000000000000000..159c12a4f3eac579f4e122741f57cc60f5cd0a23 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/video/mvit.py @@ -0,0 +1,897 @@ +import math +from dataclasses import dataclass +from functools import partial +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple + +import torch +import torch.fx +import torch.nn as nn + +from ...ops import MLP, StochasticDepth +from ...transforms._presets import VideoClassification +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _KINETICS400_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "MViT", + "MViT_V1_B_Weights", + "mvit_v1_b", + "MViT_V2_S_Weights", + "mvit_v2_s", +] + + +@dataclass +class MSBlockConfig: + num_heads: int + input_channels: int + output_channels: int + kernel_q: List[int] + kernel_kv: List[int] + stride_q: List[int] + stride_kv: List[int] + + +def _prod(s: Sequence[int]) -> int: + product = 1 + for v in s: + product *= v + return product + + +def _unsqueeze(x: torch.Tensor, target_dim: int, expand_dim: int) -> Tuple[torch.Tensor, int]: + tensor_dim = x.dim() + if tensor_dim == target_dim - 1: + x = x.unsqueeze(expand_dim) + elif tensor_dim != target_dim: + raise ValueError(f"Unsupported input dimension {x.shape}") + return x, tensor_dim + + +def _squeeze(x: torch.Tensor, target_dim: int, expand_dim: int, tensor_dim: int) -> torch.Tensor: + if tensor_dim == target_dim - 1: + x = x.squeeze(expand_dim) + return x + + +torch.fx.wrap("_unsqueeze") +torch.fx.wrap("_squeeze") + + +class Pool(nn.Module): + def __init__( + self, + pool: nn.Module, + norm: Optional[nn.Module], + activation: Optional[nn.Module] = None, + norm_before_pool: bool = False, + ) -> None: + super().__init__() + self.pool = pool + layers = [] + if norm is not None: + layers.append(norm) + if activation is not None: + layers.append(activation) + self.norm_act = nn.Sequential(*layers) if layers else None + self.norm_before_pool = norm_before_pool + + def forward(self, x: torch.Tensor, thw: Tuple[int, int, int]) -> Tuple[torch.Tensor, Tuple[int, int, int]]: + x, tensor_dim = _unsqueeze(x, 4, 1) + + # Separate the class token and reshape the input + class_token, x = torch.tensor_split(x, indices=(1,), dim=2) + x = x.transpose(2, 3) + B, N, C = x.shape[:3] + x = x.reshape((B * N, C) + thw).contiguous() + + # normalizing prior pooling is useful when we use BN which can be absorbed to speed up inference + if self.norm_before_pool and self.norm_act is not None: + x = self.norm_act(x) + + # apply the pool on the input and add back the token + x = self.pool(x) + T, H, W = x.shape[2:] + x = x.reshape(B, N, C, -1).transpose(2, 3) + x = torch.cat((class_token, x), dim=2) + + if not self.norm_before_pool and self.norm_act is not None: + x = self.norm_act(x) + + x = _squeeze(x, 4, 1, tensor_dim) + return x, (T, H, W) + + +def _interpolate(embedding: torch.Tensor, d: int) -> torch.Tensor: + if embedding.shape[0] == d: + return embedding + + return ( + nn.functional.interpolate( + embedding.permute(1, 0).unsqueeze(0), + size=d, + mode="linear", + ) + .squeeze(0) + .permute(1, 0) + ) + + +def _add_rel_pos( + attn: torch.Tensor, + q: torch.Tensor, + q_thw: Tuple[int, int, int], + k_thw: Tuple[int, int, int], + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + rel_pos_t: torch.Tensor, +) -> torch.Tensor: + # Modified code from: https://github.com/facebookresearch/SlowFast/commit/1aebd71a2efad823d52b827a3deaf15a56cf4932 + q_t, q_h, q_w = q_thw + k_t, k_h, k_w = k_thw + dh = int(2 * max(q_h, k_h) - 1) + dw = int(2 * max(q_w, k_w) - 1) + dt = int(2 * max(q_t, k_t) - 1) + + # Scale up rel pos if shapes for q and k are different. + q_h_ratio = max(k_h / q_h, 1.0) + k_h_ratio = max(q_h / k_h, 1.0) + dist_h = torch.arange(q_h)[:, None] * q_h_ratio - (torch.arange(k_h)[None, :] + (1.0 - k_h)) * k_h_ratio + q_w_ratio = max(k_w / q_w, 1.0) + k_w_ratio = max(q_w / k_w, 1.0) + dist_w = torch.arange(q_w)[:, None] * q_w_ratio - (torch.arange(k_w)[None, :] + (1.0 - k_w)) * k_w_ratio + q_t_ratio = max(k_t / q_t, 1.0) + k_t_ratio = max(q_t / k_t, 1.0) + dist_t = torch.arange(q_t)[:, None] * q_t_ratio - (torch.arange(k_t)[None, :] + (1.0 - k_t)) * k_t_ratio + + # Interpolate rel pos if needed. + rel_pos_h = _interpolate(rel_pos_h, dh) + rel_pos_w = _interpolate(rel_pos_w, dw) + rel_pos_t = _interpolate(rel_pos_t, dt) + Rh = rel_pos_h[dist_h.long()] + Rw = rel_pos_w[dist_w.long()] + Rt = rel_pos_t[dist_t.long()] + + B, n_head, _, dim = q.shape + + r_q = q[:, :, 1:].reshape(B, n_head, q_t, q_h, q_w, dim) + rel_h_q = torch.einsum("bythwc,hkc->bythwk", r_q, Rh) # [B, H, q_t, qh, qw, k_h] + rel_w_q = torch.einsum("bythwc,wkc->bythwk", r_q, Rw) # [B, H, q_t, qh, qw, k_w] + # [B, H, q_t, q_h, q_w, dim] -> [q_t, B, H, q_h, q_w, dim] -> [q_t, B*H*q_h*q_w, dim] + r_q = r_q.permute(2, 0, 1, 3, 4, 5).reshape(q_t, B * n_head * q_h * q_w, dim) + # [q_t, B*H*q_h*q_w, dim] * [q_t, dim, k_t] = [q_t, B*H*q_h*q_w, k_t] -> [B*H*q_h*q_w, q_t, k_t] + rel_q_t = torch.matmul(r_q, Rt.transpose(1, 2)).transpose(0, 1) + # [B*H*q_h*q_w, q_t, k_t] -> [B, H, q_t, q_h, q_w, k_t] + rel_q_t = rel_q_t.view(B, n_head, q_h, q_w, q_t, k_t).permute(0, 1, 4, 2, 3, 5) + + # Combine rel pos. + rel_pos = ( + rel_h_q[:, :, :, :, :, None, :, None] + + rel_w_q[:, :, :, :, :, None, None, :] + + rel_q_t[:, :, :, :, :, :, None, None] + ).reshape(B, n_head, q_t * q_h * q_w, k_t * k_h * k_w) + + # Add it to attention + attn[:, :, 1:, 1:] += rel_pos + + return attn + + +def _add_shortcut(x: torch.Tensor, shortcut: torch.Tensor, residual_with_cls_embed: bool): + if residual_with_cls_embed: + x.add_(shortcut) + else: + x[:, :, 1:, :] += shortcut[:, :, 1:, :] + return x + + +torch.fx.wrap("_add_rel_pos") +torch.fx.wrap("_add_shortcut") + + +class MultiscaleAttention(nn.Module): + def __init__( + self, + input_size: List[int], + embed_dim: int, + output_dim: int, + num_heads: int, + kernel_q: List[int], + kernel_kv: List[int], + stride_q: List[int], + stride_kv: List[int], + residual_pool: bool, + residual_with_cls_embed: bool, + rel_pos_embed: bool, + dropout: float = 0.0, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + ) -> None: + super().__init__() + self.embed_dim = embed_dim + self.output_dim = output_dim + self.num_heads = num_heads + self.head_dim = output_dim // num_heads + self.scaler = 1.0 / math.sqrt(self.head_dim) + self.residual_pool = residual_pool + self.residual_with_cls_embed = residual_with_cls_embed + + self.qkv = nn.Linear(embed_dim, 3 * output_dim) + layers: List[nn.Module] = [nn.Linear(output_dim, output_dim)] + if dropout > 0.0: + layers.append(nn.Dropout(dropout, inplace=True)) + self.project = nn.Sequential(*layers) + + self.pool_q: Optional[nn.Module] = None + if _prod(kernel_q) > 1 or _prod(stride_q) > 1: + padding_q = [int(q // 2) for q in kernel_q] + self.pool_q = Pool( + nn.Conv3d( + self.head_dim, + self.head_dim, + kernel_q, # type: ignore[arg-type] + stride=stride_q, # type: ignore[arg-type] + padding=padding_q, # type: ignore[arg-type] + groups=self.head_dim, + bias=False, + ), + norm_layer(self.head_dim), + ) + + self.pool_k: Optional[nn.Module] = None + self.pool_v: Optional[nn.Module] = None + if _prod(kernel_kv) > 1 or _prod(stride_kv) > 1: + padding_kv = [int(kv // 2) for kv in kernel_kv] + self.pool_k = Pool( + nn.Conv3d( + self.head_dim, + self.head_dim, + kernel_kv, # type: ignore[arg-type] + stride=stride_kv, # type: ignore[arg-type] + padding=padding_kv, # type: ignore[arg-type] + groups=self.head_dim, + bias=False, + ), + norm_layer(self.head_dim), + ) + self.pool_v = Pool( + nn.Conv3d( + self.head_dim, + self.head_dim, + kernel_kv, # type: ignore[arg-type] + stride=stride_kv, # type: ignore[arg-type] + padding=padding_kv, # type: ignore[arg-type] + groups=self.head_dim, + bias=False, + ), + norm_layer(self.head_dim), + ) + + self.rel_pos_h: Optional[nn.Parameter] = None + self.rel_pos_w: Optional[nn.Parameter] = None + self.rel_pos_t: Optional[nn.Parameter] = None + if rel_pos_embed: + size = max(input_size[1:]) + q_size = size // stride_q[1] if len(stride_q) > 0 else size + kv_size = size // stride_kv[1] if len(stride_kv) > 0 else size + spatial_dim = 2 * max(q_size, kv_size) - 1 + temporal_dim = 2 * input_size[0] - 1 + self.rel_pos_h = nn.Parameter(torch.zeros(spatial_dim, self.head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(spatial_dim, self.head_dim)) + self.rel_pos_t = nn.Parameter(torch.zeros(temporal_dim, self.head_dim)) + nn.init.trunc_normal_(self.rel_pos_h, std=0.02) + nn.init.trunc_normal_(self.rel_pos_w, std=0.02) + nn.init.trunc_normal_(self.rel_pos_t, std=0.02) + + def forward(self, x: torch.Tensor, thw: Tuple[int, int, int]) -> Tuple[torch.Tensor, Tuple[int, int, int]]: + B, N, C = x.shape + q, k, v = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(dim=2) + + if self.pool_k is not None: + k, k_thw = self.pool_k(k, thw) + else: + k_thw = thw + if self.pool_v is not None: + v = self.pool_v(v, thw)[0] + if self.pool_q is not None: + q, thw = self.pool_q(q, thw) + + attn = torch.matmul(self.scaler * q, k.transpose(2, 3)) + if self.rel_pos_h is not None and self.rel_pos_w is not None and self.rel_pos_t is not None: + attn = _add_rel_pos( + attn, + q, + thw, + k_thw, + self.rel_pos_h, + self.rel_pos_w, + self.rel_pos_t, + ) + attn = attn.softmax(dim=-1) + + x = torch.matmul(attn, v) + if self.residual_pool: + _add_shortcut(x, q, self.residual_with_cls_embed) + x = x.transpose(1, 2).reshape(B, -1, self.output_dim) + x = self.project(x) + + return x, thw + + +class MultiscaleBlock(nn.Module): + def __init__( + self, + input_size: List[int], + cnf: MSBlockConfig, + residual_pool: bool, + residual_with_cls_embed: bool, + rel_pos_embed: bool, + proj_after_attn: bool, + dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + ) -> None: + super().__init__() + self.proj_after_attn = proj_after_attn + + self.pool_skip: Optional[nn.Module] = None + if _prod(cnf.stride_q) > 1: + kernel_skip = [s + 1 if s > 1 else s for s in cnf.stride_q] + padding_skip = [int(k // 2) for k in kernel_skip] + self.pool_skip = Pool( + nn.MaxPool3d(kernel_skip, stride=cnf.stride_q, padding=padding_skip), None # type: ignore[arg-type] + ) + + attn_dim = cnf.output_channels if proj_after_attn else cnf.input_channels + + self.norm1 = norm_layer(cnf.input_channels) + self.norm2 = norm_layer(attn_dim) + self.needs_transposal = isinstance(self.norm1, nn.BatchNorm1d) + + self.attn = MultiscaleAttention( + input_size, + cnf.input_channels, + attn_dim, + cnf.num_heads, + kernel_q=cnf.kernel_q, + kernel_kv=cnf.kernel_kv, + stride_q=cnf.stride_q, + stride_kv=cnf.stride_kv, + rel_pos_embed=rel_pos_embed, + residual_pool=residual_pool, + residual_with_cls_embed=residual_with_cls_embed, + dropout=dropout, + norm_layer=norm_layer, + ) + self.mlp = MLP( + attn_dim, + [4 * attn_dim, cnf.output_channels], + activation_layer=nn.GELU, + dropout=dropout, + inplace=None, + ) + + self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row") + + self.project: Optional[nn.Module] = None + if cnf.input_channels != cnf.output_channels: + self.project = nn.Linear(cnf.input_channels, cnf.output_channels) + + def forward(self, x: torch.Tensor, thw: Tuple[int, int, int]) -> Tuple[torch.Tensor, Tuple[int, int, int]]: + x_norm1 = self.norm1(x.transpose(1, 2)).transpose(1, 2) if self.needs_transposal else self.norm1(x) + x_attn, thw_new = self.attn(x_norm1, thw) + x = x if self.project is None or not self.proj_after_attn else self.project(x_norm1) + x_skip = x if self.pool_skip is None else self.pool_skip(x, thw)[0] + x = x_skip + self.stochastic_depth(x_attn) + + x_norm2 = self.norm2(x.transpose(1, 2)).transpose(1, 2) if self.needs_transposal else self.norm2(x) + x_proj = x if self.project is None or self.proj_after_attn else self.project(x_norm2) + + return x_proj + self.stochastic_depth(self.mlp(x_norm2)), thw_new + + +class PositionalEncoding(nn.Module): + def __init__(self, embed_size: int, spatial_size: Tuple[int, int], temporal_size: int, rel_pos_embed: bool) -> None: + super().__init__() + self.spatial_size = spatial_size + self.temporal_size = temporal_size + + self.class_token = nn.Parameter(torch.zeros(embed_size)) + self.spatial_pos: Optional[nn.Parameter] = None + self.temporal_pos: Optional[nn.Parameter] = None + self.class_pos: Optional[nn.Parameter] = None + if not rel_pos_embed: + self.spatial_pos = nn.Parameter(torch.zeros(self.spatial_size[0] * self.spatial_size[1], embed_size)) + self.temporal_pos = nn.Parameter(torch.zeros(self.temporal_size, embed_size)) + self.class_pos = nn.Parameter(torch.zeros(embed_size)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + class_token = self.class_token.expand(x.size(0), -1).unsqueeze(1) + x = torch.cat((class_token, x), dim=1) + + if self.spatial_pos is not None and self.temporal_pos is not None and self.class_pos is not None: + hw_size, embed_size = self.spatial_pos.shape + pos_embedding = torch.repeat_interleave(self.temporal_pos, hw_size, dim=0) + pos_embedding.add_(self.spatial_pos.unsqueeze(0).expand(self.temporal_size, -1, -1).reshape(-1, embed_size)) + pos_embedding = torch.cat((self.class_pos.unsqueeze(0), pos_embedding), dim=0).unsqueeze(0) + x.add_(pos_embedding) + + return x + + +class MViT(nn.Module): + def __init__( + self, + spatial_size: Tuple[int, int], + temporal_size: int, + block_setting: Sequence[MSBlockConfig], + residual_pool: bool, + residual_with_cls_embed: bool, + rel_pos_embed: bool, + proj_after_attn: bool, + dropout: float = 0.5, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.0, + num_classes: int = 400, + block: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + patch_embed_kernel: Tuple[int, int, int] = (3, 7, 7), + patch_embed_stride: Tuple[int, int, int] = (2, 4, 4), + patch_embed_padding: Tuple[int, int, int] = (1, 3, 3), + ) -> None: + """ + MViT main class. + + Args: + spatial_size (tuple of ints): The spacial size of the input as ``(H, W)``. + temporal_size (int): The temporal size ``T`` of the input. + block_setting (sequence of MSBlockConfig): The Network structure. + residual_pool (bool): If True, use MViTv2 pooling residual connection. + residual_with_cls_embed (bool): If True, the addition on the residual connection will include + the class embedding. + rel_pos_embed (bool): If True, use MViTv2's relative positional embeddings. + proj_after_attn (bool): If True, apply the projection after the attention. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0. + num_classes (int): The number of classes. + block (callable, optional): Module specifying the layer which consists of the attention and mlp. + norm_layer (callable, optional): Module specifying the normalization layer to use. + patch_embed_kernel (tuple of ints): The kernel of the convolution that patchifies the input. + patch_embed_stride (tuple of ints): The stride of the convolution that patchifies the input. + patch_embed_padding (tuple of ints): The padding of the convolution that patchifies the input. + """ + super().__init__() + # This implementation employs a different parameterization scheme than the one used at PyTorch Video: + # https://github.com/facebookresearch/pytorchvideo/blob/718d0a4/pytorchvideo/models/vision_transformers.py + # We remove any experimental configuration that didn't make it to the final variants of the models. To represent + # the configuration of the architecture we use the simplified form suggested at Table 1 of the paper. + _log_api_usage_once(self) + total_stage_blocks = len(block_setting) + if total_stage_blocks == 0: + raise ValueError("The configuration parameter can't be empty.") + + if block is None: + block = MultiscaleBlock + + if norm_layer is None: + norm_layer = partial(nn.LayerNorm, eps=1e-6) + + # Patch Embedding module + self.conv_proj = nn.Conv3d( + in_channels=3, + out_channels=block_setting[0].input_channels, + kernel_size=patch_embed_kernel, + stride=patch_embed_stride, + padding=patch_embed_padding, + ) + + input_size = [size // stride for size, stride in zip((temporal_size,) + spatial_size, self.conv_proj.stride)] + + # Spatio-Temporal Class Positional Encoding + self.pos_encoding = PositionalEncoding( + embed_size=block_setting[0].input_channels, + spatial_size=(input_size[1], input_size[2]), + temporal_size=input_size[0], + rel_pos_embed=rel_pos_embed, + ) + + # Encoder module + self.blocks = nn.ModuleList() + for stage_block_id, cnf in enumerate(block_setting): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0) + + self.blocks.append( + block( + input_size=input_size, + cnf=cnf, + residual_pool=residual_pool, + residual_with_cls_embed=residual_with_cls_embed, + rel_pos_embed=rel_pos_embed, + proj_after_attn=proj_after_attn, + dropout=attention_dropout, + stochastic_depth_prob=sd_prob, + norm_layer=norm_layer, + ) + ) + + if len(cnf.stride_q) > 0: + input_size = [size // stride for size, stride in zip(input_size, cnf.stride_q)] + self.norm = norm_layer(block_setting[-1].output_channels) + + # Classifier module + self.head = nn.Sequential( + nn.Dropout(dropout, inplace=True), + nn.Linear(block_setting[-1].output_channels, num_classes), + ) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0.0) + elif isinstance(m, nn.LayerNorm): + if m.weight is not None: + nn.init.constant_(m.weight, 1.0) + if m.bias is not None: + nn.init.constant_(m.bias, 0.0) + elif isinstance(m, PositionalEncoding): + for weights in m.parameters(): + nn.init.trunc_normal_(weights, std=0.02) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # Convert if necessary (B, C, H, W) -> (B, C, 1, H, W) + x = _unsqueeze(x, 5, 2)[0] + # patchify and reshape: (B, C, T, H, W) -> (B, embed_channels[0], T', H', W') -> (B, THW', embed_channels[0]) + x = self.conv_proj(x) + x = x.flatten(2).transpose(1, 2) + + # add positional encoding + x = self.pos_encoding(x) + + # pass patches through the encoder + thw = (self.pos_encoding.temporal_size,) + self.pos_encoding.spatial_size + for block in self.blocks: + x, thw = block(x, thw) + x = self.norm(x) + + # classifier "token" as used by standard language architectures + x = x[:, 0] + x = self.head(x) + + return x + + +def _mvit( + block_setting: List[MSBlockConfig], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> MViT: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "spatial_size", weights.meta["min_size"]) + _ovewrite_named_param(kwargs, "temporal_size", weights.meta["min_temporal_size"]) + spatial_size = kwargs.pop("spatial_size", (224, 224)) + temporal_size = kwargs.pop("temporal_size", 16) + + model = MViT( + spatial_size=spatial_size, + temporal_size=temporal_size, + block_setting=block_setting, + residual_pool=kwargs.pop("residual_pool", False), + residual_with_cls_embed=kwargs.pop("residual_with_cls_embed", True), + rel_pos_embed=kwargs.pop("rel_pos_embed", False), + proj_after_attn=kwargs.pop("proj_after_attn", False), + stochastic_depth_prob=stochastic_depth_prob, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +class MViT_V1_B_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/mvit_v1_b-dbeb1030.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.45, 0.45, 0.45), + std=(0.225, 0.225, 0.225), + ), + meta={ + "min_size": (224, 224), + "min_temporal_size": 16, + "categories": _KINETICS400_CATEGORIES, + "recipe": "https://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.md", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=7.5`, `clips_per_video=5`, and `clip_len=16`" + ), + "num_params": 36610672, + "_metrics": { + "Kinetics-400": { + "acc@1": 78.477, + "acc@5": 93.582, + } + }, + "_ops": 70.599, + "_file_size": 139.764, + }, + ) + DEFAULT = KINETICS400_V1 + + +class MViT_V2_S_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/mvit_v2_s-ae3be167.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.45, 0.45, 0.45), + std=(0.225, 0.225, 0.225), + ), + meta={ + "min_size": (224, 224), + "min_temporal_size": 16, + "categories": _KINETICS400_CATEGORIES, + "recipe": "https://github.com/facebookresearch/SlowFast/blob/main/MODEL_ZOO.md", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=7.5`, `clips_per_video=5`, and `clip_len=16`" + ), + "num_params": 34537744, + "_metrics": { + "Kinetics-400": { + "acc@1": 80.757, + "acc@5": 94.665, + } + }, + "_ops": 64.224, + "_file_size": 131.884, + }, + ) + DEFAULT = KINETICS400_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MViT_V1_B_Weights.KINETICS400_V1)) +def mvit_v1_b(*, weights: Optional[MViT_V1_B_Weights] = None, progress: bool = True, **kwargs: Any) -> MViT: + """ + Constructs a base MViTV1 architecture from + `Multiscale Vision Transformers `__. + + .. betastatus:: video module + + Args: + weights (:class:`~torchvision.models.video.MViT_V1_B_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.MViT_V1_B_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.MViT`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.MViT_V1_B_Weights + :members: + """ + weights = MViT_V1_B_Weights.verify(weights) + + config: Dict[str, List] = { + "num_heads": [1, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8], + "input_channels": [96, 192, 192, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 768, 768], + "output_channels": [192, 192, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 768, 768, 768], + "kernel_q": [[], [3, 3, 3], [], [3, 3, 3], [], [], [], [], [], [], [], [], [], [], [3, 3, 3], []], + "kernel_kv": [ + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + ], + "stride_q": [[], [1, 2, 2], [], [1, 2, 2], [], [], [], [], [], [], [], [], [], [], [1, 2, 2], []], + "stride_kv": [ + [1, 8, 8], + [1, 4, 4], + [1, 4, 4], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 1, 1], + [1, 1, 1], + ], + } + + block_setting = [] + for i in range(len(config["num_heads"])): + block_setting.append( + MSBlockConfig( + num_heads=config["num_heads"][i], + input_channels=config["input_channels"][i], + output_channels=config["output_channels"][i], + kernel_q=config["kernel_q"][i], + kernel_kv=config["kernel_kv"][i], + stride_q=config["stride_q"][i], + stride_kv=config["stride_kv"][i], + ) + ) + + return _mvit( + spatial_size=(224, 224), + temporal_size=16, + block_setting=block_setting, + residual_pool=False, + residual_with_cls_embed=False, + stochastic_depth_prob=kwargs.pop("stochastic_depth_prob", 0.2), + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MViT_V2_S_Weights.KINETICS400_V1)) +def mvit_v2_s(*, weights: Optional[MViT_V2_S_Weights] = None, progress: bool = True, **kwargs: Any) -> MViT: + """Constructs a small MViTV2 architecture from + `Multiscale Vision Transformers `__ and + `MViTv2: Improved Multiscale Vision Transformers for Classification + and Detection `__. + + .. betastatus:: video module + + Args: + weights (:class:`~torchvision.models.video.MViT_V2_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.MViT_V2_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.MViT`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.MViT_V2_S_Weights + :members: + """ + weights = MViT_V2_S_Weights.verify(weights) + + config: Dict[str, List] = { + "num_heads": [1, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8], + "input_channels": [96, 96, 192, 192, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 768], + "output_channels": [96, 192, 192, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 384, 768, 768], + "kernel_q": [ + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + ], + "kernel_kv": [ + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + [3, 3, 3], + ], + "stride_q": [ + [1, 1, 1], + [1, 2, 2], + [1, 1, 1], + [1, 2, 2], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 1, 1], + [1, 2, 2], + [1, 1, 1], + ], + "stride_kv": [ + [1, 8, 8], + [1, 4, 4], + [1, 4, 4], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 2, 2], + [1, 1, 1], + [1, 1, 1], + ], + } + + block_setting = [] + for i in range(len(config["num_heads"])): + block_setting.append( + MSBlockConfig( + num_heads=config["num_heads"][i], + input_channels=config["input_channels"][i], + output_channels=config["output_channels"][i], + kernel_q=config["kernel_q"][i], + kernel_kv=config["kernel_kv"][i], + stride_q=config["stride_q"][i], + stride_kv=config["stride_kv"][i], + ) + ) + + return _mvit( + spatial_size=(224, 224), + temporal_size=16, + block_setting=block_setting, + residual_pool=True, + residual_with_cls_embed=False, + rel_pos_embed=True, + proj_after_attn=True, + stochastic_depth_prob=kwargs.pop("stochastic_depth_prob", 0.2), + weights=weights, + progress=progress, + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/video/resnet.py b/lib/python3.11/site-packages/torchvision/models/video/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..a1cb2884013c053118555344617e4b1efb8ddaab --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/video/resnet.py @@ -0,0 +1,503 @@ +from functools import partial +from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union + +import torch.nn as nn +from torch import Tensor + +from ...transforms._presets import VideoClassification +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _KINETICS400_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "VideoResNet", + "R3D_18_Weights", + "MC3_18_Weights", + "R2Plus1D_18_Weights", + "r3d_18", + "mc3_18", + "r2plus1d_18", +] + + +class Conv3DSimple(nn.Conv3d): + def __init__( + self, in_planes: int, out_planes: int, midplanes: Optional[int] = None, stride: int = 1, padding: int = 1 + ) -> None: + + super().__init__( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=(3, 3, 3), + stride=stride, + padding=padding, + bias=False, + ) + + @staticmethod + def get_downsample_stride(stride: int) -> Tuple[int, int, int]: + return stride, stride, stride + + +class Conv2Plus1D(nn.Sequential): + def __init__(self, in_planes: int, out_planes: int, midplanes: int, stride: int = 1, padding: int = 1) -> None: + super().__init__( + nn.Conv3d( + in_planes, + midplanes, + kernel_size=(1, 3, 3), + stride=(1, stride, stride), + padding=(0, padding, padding), + bias=False, + ), + nn.BatchNorm3d(midplanes), + nn.ReLU(inplace=True), + nn.Conv3d( + midplanes, out_planes, kernel_size=(3, 1, 1), stride=(stride, 1, 1), padding=(padding, 0, 0), bias=False + ), + ) + + @staticmethod + def get_downsample_stride(stride: int) -> Tuple[int, int, int]: + return stride, stride, stride + + +class Conv3DNoTemporal(nn.Conv3d): + def __init__( + self, in_planes: int, out_planes: int, midplanes: Optional[int] = None, stride: int = 1, padding: int = 1 + ) -> None: + + super().__init__( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=(1, 3, 3), + stride=(1, stride, stride), + padding=(0, padding, padding), + bias=False, + ) + + @staticmethod + def get_downsample_stride(stride: int) -> Tuple[int, int, int]: + return 1, stride, stride + + +class BasicBlock(nn.Module): + + expansion = 1 + + def __init__( + self, + inplanes: int, + planes: int, + conv_builder: Callable[..., nn.Module], + stride: int = 1, + downsample: Optional[nn.Module] = None, + ) -> None: + midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes) + + super().__init__() + self.conv1 = nn.Sequential( + conv_builder(inplanes, planes, midplanes, stride), nn.BatchNorm3d(planes), nn.ReLU(inplace=True) + ) + self.conv2 = nn.Sequential(conv_builder(planes, planes, midplanes), nn.BatchNorm3d(planes)) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + residual = x + + out = self.conv1(x) + out = self.conv2(out) + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__( + self, + inplanes: int, + planes: int, + conv_builder: Callable[..., nn.Module], + stride: int = 1, + downsample: Optional[nn.Module] = None, + ) -> None: + + super().__init__() + midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes) + + # 1x1x1 + self.conv1 = nn.Sequential( + nn.Conv3d(inplanes, planes, kernel_size=1, bias=False), nn.BatchNorm3d(planes), nn.ReLU(inplace=True) + ) + # Second kernel + self.conv2 = nn.Sequential( + conv_builder(planes, planes, midplanes, stride), nn.BatchNorm3d(planes), nn.ReLU(inplace=True) + ) + + # 1x1x1 + self.conv3 = nn.Sequential( + nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False), + nn.BatchNorm3d(planes * self.expansion), + ) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + residual = x + + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class BasicStem(nn.Sequential): + """The default conv-batchnorm-relu stem""" + + def __init__(self) -> None: + super().__init__( + nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False), + nn.BatchNorm3d(64), + nn.ReLU(inplace=True), + ) + + +class R2Plus1dStem(nn.Sequential): + """R(2+1)D stem is different than the default one as it uses separated 3D convolution""" + + def __init__(self) -> None: + super().__init__( + nn.Conv3d(3, 45, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False), + nn.BatchNorm3d(45), + nn.ReLU(inplace=True), + nn.Conv3d(45, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False), + nn.BatchNorm3d(64), + nn.ReLU(inplace=True), + ) + + +class VideoResNet(nn.Module): + def __init__( + self, + block: Type[Union[BasicBlock, Bottleneck]], + conv_makers: Sequence[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]], + layers: List[int], + stem: Callable[..., nn.Module], + num_classes: int = 400, + zero_init_residual: bool = False, + ) -> None: + """Generic resnet video generator. + + Args: + block (Type[Union[BasicBlock, Bottleneck]]): resnet building block + conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator + function for each layer + layers (List[int]): number of blocks per layer + stem (Callable[..., nn.Module]): module specifying the ResNet stem. + num_classes (int, optional): Dimension of the final FC layer. Defaults to 400. + zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False. + """ + super().__init__() + _log_api_usage_once(self) + self.inplanes = 64 + + self.stem = stem() + + self.layer1 = self._make_layer(block, conv_makers[0], 64, layers[0], stride=1) + self.layer2 = self._make_layer(block, conv_makers[1], 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=2) + + self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + # init weights + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) # type: ignore[union-attr, arg-type] + + def forward(self, x: Tensor) -> Tensor: + x = self.stem(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + # Flatten the layer to fc + x = x.flatten(1) + x = self.fc(x) + + return x + + def _make_layer( + self, + block: Type[Union[BasicBlock, Bottleneck]], + conv_builder: Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]], + planes: int, + blocks: int, + stride: int = 1, + ) -> nn.Sequential: + downsample = None + + if stride != 1 or self.inplanes != planes * block.expansion: + ds_stride = conv_builder.get_downsample_stride(stride) + downsample = nn.Sequential( + nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=1, stride=ds_stride, bias=False), + nn.BatchNorm3d(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, conv_builder, stride, downsample)) + + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, conv_builder)) + + return nn.Sequential(*layers) + + +def _video_resnet( + block: Type[Union[BasicBlock, Bottleneck]], + conv_makers: Sequence[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]], + layers: List[int], + stem: Callable[..., nn.Module], + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> VideoResNet: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = VideoResNet(block, conv_makers, layers, stem, **kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "min_size": (1, 1), + "categories": _KINETICS400_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification", + "_docs": ( + "The weights reproduce closely the accuracy of the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=15`, `clips_per_video=5`, and `clip_len=16`." + ), +} + + +class R3D_18_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/r3d_18-b3b3357e.pth", + transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)), + meta={ + **_COMMON_META, + "num_params": 33371472, + "_metrics": { + "Kinetics-400": { + "acc@1": 63.200, + "acc@5": 83.479, + } + }, + "_ops": 40.697, + "_file_size": 127.359, + }, + ) + DEFAULT = KINETICS400_V1 + + +class MC3_18_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/mc3_18-a90a0ba3.pth", + transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)), + meta={ + **_COMMON_META, + "num_params": 11695440, + "_metrics": { + "Kinetics-400": { + "acc@1": 63.960, + "acc@5": 84.130, + } + }, + "_ops": 43.343, + "_file_size": 44.672, + }, + ) + DEFAULT = KINETICS400_V1 + + +class R2Plus1D_18_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth", + transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)), + meta={ + **_COMMON_META, + "num_params": 31505325, + "_metrics": { + "Kinetics-400": { + "acc@1": 67.463, + "acc@5": 86.175, + } + }, + "_ops": 40.519, + "_file_size": 120.318, + }, + ) + DEFAULT = KINETICS400_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", R3D_18_Weights.KINETICS400_V1)) +def r3d_18(*, weights: Optional[R3D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet: + """Construct 18 layer Resnet3D model. + + .. betastatus:: video module + + Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition `__. + + Args: + weights (:class:`~torchvision.models.video.R3D_18_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.R3D_18_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class. + Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.R3D_18_Weights + :members: + """ + weights = R3D_18_Weights.verify(weights) + + return _video_resnet( + BasicBlock, + [Conv3DSimple] * 4, + [2, 2, 2, 2], + BasicStem, + weights, + progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", MC3_18_Weights.KINETICS400_V1)) +def mc3_18(*, weights: Optional[MC3_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet: + """Construct 18 layer Mixed Convolution network as in + + .. betastatus:: video module + + Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition `__. + + Args: + weights (:class:`~torchvision.models.video.MC3_18_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.MC3_18_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class. + Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.MC3_18_Weights + :members: + """ + weights = MC3_18_Weights.verify(weights) + + return _video_resnet( + BasicBlock, + [Conv3DSimple] + [Conv3DNoTemporal] * 3, # type: ignore[list-item] + [2, 2, 2, 2], + BasicStem, + weights, + progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", R2Plus1D_18_Weights.KINETICS400_V1)) +def r2plus1d_18(*, weights: Optional[R2Plus1D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet: + """Construct 18 layer deep R(2+1)D network as in + + .. betastatus:: video module + + Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition `__. + + Args: + weights (:class:`~torchvision.models.video.R2Plus1D_18_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.R2Plus1D_18_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class. + Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.R2Plus1D_18_Weights + :members: + """ + weights = R2Plus1D_18_Weights.verify(weights) + + return _video_resnet( + BasicBlock, + [Conv2Plus1D] * 4, + [2, 2, 2, 2], + R2Plus1dStem, + weights, + progress, + **kwargs, + ) + + +# The dictionary below is internal implementation detail and will be removed in v0.15 +from .._utils import _ModelURLs + + +model_urls = _ModelURLs( + { + "r3d_18": R3D_18_Weights.KINETICS400_V1.url, + "mc3_18": MC3_18_Weights.KINETICS400_V1.url, + "r2plus1d_18": R2Plus1D_18_Weights.KINETICS400_V1.url, + } +) diff --git a/lib/python3.11/site-packages/torchvision/models/video/s3d.py b/lib/python3.11/site-packages/torchvision/models/video/s3d.py new file mode 100644 index 0000000000000000000000000000000000000000..4b202829b24fb1dc314452d38a521dfe6c8e446f --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/video/s3d.py @@ -0,0 +1,219 @@ +from functools import partial +from typing import Any, Callable, Optional + +import torch +from torch import nn +from torchvision.ops.misc import Conv3dNormActivation + +from ...transforms._presets import VideoClassification +from ...utils import _log_api_usage_once +from .._api import register_model, Weights, WeightsEnum +from .._meta import _KINETICS400_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "S3D", + "S3D_Weights", + "s3d", +] + + +class TemporalSeparableConv(nn.Sequential): + def __init__( + self, + in_planes: int, + out_planes: int, + kernel_size: int, + stride: int, + padding: int, + norm_layer: Callable[..., nn.Module], + ): + super().__init__( + Conv3dNormActivation( + in_planes, + out_planes, + kernel_size=(1, kernel_size, kernel_size), + stride=(1, stride, stride), + padding=(0, padding, padding), + bias=False, + norm_layer=norm_layer, + ), + Conv3dNormActivation( + out_planes, + out_planes, + kernel_size=(kernel_size, 1, 1), + stride=(stride, 1, 1), + padding=(padding, 0, 0), + bias=False, + norm_layer=norm_layer, + ), + ) + + +class SepInceptionBlock3D(nn.Module): + def __init__( + self, + in_planes: int, + b0_out: int, + b1_mid: int, + b1_out: int, + b2_mid: int, + b2_out: int, + b3_out: int, + norm_layer: Callable[..., nn.Module], + ): + super().__init__() + + self.branch0 = Conv3dNormActivation(in_planes, b0_out, kernel_size=1, stride=1, norm_layer=norm_layer) + self.branch1 = nn.Sequential( + Conv3dNormActivation(in_planes, b1_mid, kernel_size=1, stride=1, norm_layer=norm_layer), + TemporalSeparableConv(b1_mid, b1_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer), + ) + self.branch2 = nn.Sequential( + Conv3dNormActivation(in_planes, b2_mid, kernel_size=1, stride=1, norm_layer=norm_layer), + TemporalSeparableConv(b2_mid, b2_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer), + ) + self.branch3 = nn.Sequential( + nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1), + Conv3dNormActivation(in_planes, b3_out, kernel_size=1, stride=1, norm_layer=norm_layer), + ) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + x2 = self.branch2(x) + x3 = self.branch3(x) + out = torch.cat((x0, x1, x2, x3), 1) + + return out + + +class S3D(nn.Module): + """S3D main class. + + Args: + num_class (int): number of classes for the classification task. + dropout (float): dropout probability. + norm_layer (Optional[Callable]): Module specifying the normalization layer to use. + + Inputs: + x (Tensor): batch of videos with dimensions (batch, channel, time, height, width) + """ + + def __init__( + self, + num_classes: int = 400, + dropout: float = 0.2, + norm_layer: Optional[Callable[..., torch.nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + + if norm_layer is None: + norm_layer = partial(nn.BatchNorm3d, eps=0.001, momentum=0.001) + + self.features = nn.Sequential( + TemporalSeparableConv(3, 64, 7, 2, 3, norm_layer), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), + Conv3dNormActivation( + 64, + 64, + kernel_size=1, + stride=1, + norm_layer=norm_layer, + ), + TemporalSeparableConv(64, 192, 3, 1, 1, norm_layer), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), + SepInceptionBlock3D(192, 64, 96, 128, 16, 32, 32, norm_layer), + SepInceptionBlock3D(256, 128, 128, 192, 32, 96, 64, norm_layer), + nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)), + SepInceptionBlock3D(480, 192, 96, 208, 16, 48, 64, norm_layer), + SepInceptionBlock3D(512, 160, 112, 224, 24, 64, 64, norm_layer), + SepInceptionBlock3D(512, 128, 128, 256, 24, 64, 64, norm_layer), + SepInceptionBlock3D(512, 112, 144, 288, 32, 64, 64, norm_layer), + SepInceptionBlock3D(528, 256, 160, 320, 32, 128, 128, norm_layer), + nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0)), + SepInceptionBlock3D(832, 256, 160, 320, 32, 128, 128, norm_layer), + SepInceptionBlock3D(832, 384, 192, 384, 48, 128, 128, norm_layer), + ) + self.avgpool = nn.AvgPool3d(kernel_size=(2, 7, 7), stride=1) + self.classifier = nn.Sequential( + nn.Dropout(p=dropout), + nn.Conv3d(1024, num_classes, kernel_size=1, stride=1, bias=True), + ) + + def forward(self, x): + x = self.features(x) + x = self.avgpool(x) + x = self.classifier(x) + x = torch.mean(x, dim=(2, 3, 4)) + return x + + +class S3D_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/s3d-d76dad2f.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256, 256), + ), + meta={ + "min_size": (224, 224), + "min_temporal_size": 14, + "categories": _KINETICS400_CATEGORIES, + "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification#s3d", + "_docs": ( + "The weights aim to approximate the accuracy of the paper. The accuracies are estimated on clip-level " + "with parameters `frame_rate=15`, `clips_per_video=1`, and `clip_len=128`." + ), + "num_params": 8320048, + "_metrics": { + "Kinetics-400": { + "acc@1": 68.368, + "acc@5": 88.050, + } + }, + "_ops": 17.979, + "_file_size": 31.972, + }, + ) + DEFAULT = KINETICS400_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", S3D_Weights.KINETICS400_V1)) +def s3d(*, weights: Optional[S3D_Weights] = None, progress: bool = True, **kwargs: Any) -> S3D: + """Construct Separable 3D CNN model. + + Reference: `Rethinking Spatiotemporal Feature Learning `__. + + .. betastatus:: video module + + Args: + weights (:class:`~torchvision.models.video.S3D_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.S3D_Weights` + below for more details, and possible values. By default, no + pre-trained weights are used. + progress (bool): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.S3D`` base class. + Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.S3D_Weights + :members: + """ + weights = S3D_Weights.verify(weights) + + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = S3D(**kwargs) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model diff --git a/lib/python3.11/site-packages/torchvision/models/video/swin_transformer.py b/lib/python3.11/site-packages/torchvision/models/video/swin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..a8d87ffbe5af6caa1de0a3760fa5c506fdf8e231 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/video/swin_transformer.py @@ -0,0 +1,743 @@ +# Modified from 2d Swin Transformers in torchvision: +# https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py + +from functools import partial +from typing import Any, Callable, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ...transforms._presets import VideoClassification + +from ...utils import _log_api_usage_once + +from .._api import register_model, Weights, WeightsEnum + +from .._meta import _KINETICS400_CATEGORIES +from .._utils import _ovewrite_named_param, handle_legacy_interface +from ..swin_transformer import PatchMerging, SwinTransformerBlock + +__all__ = [ + "SwinTransformer3d", + "Swin3D_T_Weights", + "Swin3D_S_Weights", + "Swin3D_B_Weights", + "swin3d_t", + "swin3d_s", + "swin3d_b", +] + + +def _get_window_and_shift_size( + shift_size: List[int], size_dhw: List[int], window_size: List[int] +) -> Tuple[List[int], List[int]]: + for i in range(3): + if size_dhw[i] <= window_size[i]: + # In this case, window_size will adapt to the input size, and no need to shift + window_size[i] = size_dhw[i] + shift_size[i] = 0 + + return window_size, shift_size + + +torch.fx.wrap("_get_window_and_shift_size") + + +def _get_relative_position_bias( + relative_position_bias_table: torch.Tensor, relative_position_index: torch.Tensor, window_size: List[int] +) -> Tensor: + window_vol = window_size[0] * window_size[1] * window_size[2] + # In 3d case we flatten the relative_position_bias + relative_position_bias = relative_position_bias_table[ + relative_position_index[:window_vol, :window_vol].flatten() # type: ignore[index] + ] + relative_position_bias = relative_position_bias.view(window_vol, window_vol, -1) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0) + return relative_position_bias + + +torch.fx.wrap("_get_relative_position_bias") + + +def _compute_pad_size_3d(size_dhw: Tuple[int, int, int], patch_size: Tuple[int, int, int]) -> Tuple[int, int, int]: + pad_size = [(patch_size[i] - size_dhw[i] % patch_size[i]) % patch_size[i] for i in range(3)] + return pad_size[0], pad_size[1], pad_size[2] + + +torch.fx.wrap("_compute_pad_size_3d") + + +def _compute_attention_mask_3d( + x: Tensor, + size_dhw: Tuple[int, int, int], + window_size: Tuple[int, int, int], + shift_size: Tuple[int, int, int], +) -> Tensor: + # generate attention mask + attn_mask = x.new_zeros(*size_dhw) + num_windows = (size_dhw[0] // window_size[0]) * (size_dhw[1] // window_size[1]) * (size_dhw[2] // window_size[2]) + slices = [ + ( + (0, -window_size[i]), + (-window_size[i], -shift_size[i]), + (-shift_size[i], None), + ) + for i in range(3) + ] + count = 0 + for d in slices[0]: + for h in slices[1]: + for w in slices[2]: + attn_mask[d[0] : d[1], h[0] : h[1], w[0] : w[1]] = count + count += 1 + + # Partition window on attn_mask + attn_mask = attn_mask.view( + size_dhw[0] // window_size[0], + window_size[0], + size_dhw[1] // window_size[1], + window_size[1], + size_dhw[2] // window_size[2], + window_size[2], + ) + attn_mask = attn_mask.permute(0, 2, 4, 1, 3, 5).reshape( + num_windows, window_size[0] * window_size[1] * window_size[2] + ) + attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + return attn_mask + + +torch.fx.wrap("_compute_attention_mask_3d") + + +def shifted_window_attention_3d( + input: Tensor, + qkv_weight: Tensor, + proj_weight: Tensor, + relative_position_bias: Tensor, + window_size: List[int], + num_heads: int, + shift_size: List[int], + attention_dropout: float = 0.0, + dropout: float = 0.0, + qkv_bias: Optional[Tensor] = None, + proj_bias: Optional[Tensor] = None, + training: bool = True, +) -> Tensor: + """ + Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + input (Tensor[B, T, H, W, C]): The input tensor, 5-dimensions. + qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value. + proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection. + relative_position_bias (Tensor): The learned relative position bias added to attention. + window_size (List[int]): 3-dimensions window size, T, H, W . + num_heads (int): Number of attention heads. + shift_size (List[int]): Shift size for shifted window attention (T, H, W). + attention_dropout (float): Dropout ratio of attention weight. Default: 0.0. + dropout (float): Dropout ratio of output. Default: 0.0. + qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None. + proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None. + training (bool, optional): Training flag used by the dropout parameters. Default: True. + Returns: + Tensor[B, T, H, W, C]: The output tensor after shifted window attention. + """ + b, t, h, w, c = input.shape + # pad feature maps to multiples of window size + pad_size = _compute_pad_size_3d((t, h, w), (window_size[0], window_size[1], window_size[2])) + x = F.pad(input, (0, 0, 0, pad_size[2], 0, pad_size[1], 0, pad_size[0])) + _, tp, hp, wp, _ = x.shape + padded_size = (tp, hp, wp) + + # cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3)) + + # partition windows + num_windows = ( + (padded_size[0] // window_size[0]) * (padded_size[1] // window_size[1]) * (padded_size[2] // window_size[2]) + ) + x = x.view( + b, + padded_size[0] // window_size[0], + window_size[0], + padded_size[1] // window_size[1], + window_size[1], + padded_size[2] // window_size[2], + window_size[2], + c, + ) + x = x.permute(0, 1, 3, 5, 2, 4, 6, 7).reshape( + b * num_windows, window_size[0] * window_size[1] * window_size[2], c + ) # B*nW, Wd*Wh*Ww, C + + # multi-head attention + qkv = F.linear(x, qkv_weight, qkv_bias) + qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, c // num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + q = q * (c // num_heads) ** -0.5 + attn = q.matmul(k.transpose(-2, -1)) + # add relative position bias + attn = attn + relative_position_bias + + if sum(shift_size) > 0: + # generate attention mask to handle shifted windows with varying size + attn_mask = _compute_attention_mask_3d( + x, + (padded_size[0], padded_size[1], padded_size[2]), + (window_size[0], window_size[1], window_size[2]), + (shift_size[0], shift_size[1], shift_size[2]), + ) + attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1)) + attn = attn + attn_mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, num_heads, x.size(1), x.size(1)) + + attn = F.softmax(attn, dim=-1) + attn = F.dropout(attn, p=attention_dropout, training=training) + + x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), c) + x = F.linear(x, proj_weight, proj_bias) + x = F.dropout(x, p=dropout, training=training) + + # reverse windows + x = x.view( + b, + padded_size[0] // window_size[0], + padded_size[1] // window_size[1], + padded_size[2] // window_size[2], + window_size[0], + window_size[1], + window_size[2], + c, + ) + x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).reshape(b, tp, hp, wp, c) + + # reverse cyclic shift + if sum(shift_size) > 0: + x = torch.roll(x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3)) + + # unpad features + x = x[:, :t, :h, :w, :].contiguous() + return x + + +torch.fx.wrap("shifted_window_attention_3d") + + +class ShiftedWindowAttention3d(nn.Module): + """ + See :func:`shifted_window_attention_3d`. + """ + + def __init__( + self, + dim: int, + window_size: List[int], + shift_size: List[int], + num_heads: int, + qkv_bias: bool = True, + proj_bias: bool = True, + attention_dropout: float = 0.0, + dropout: float = 0.0, + ) -> None: + super().__init__() + if len(window_size) != 3 or len(shift_size) != 3: + raise ValueError("window_size and shift_size must be of length 2") + + self.window_size = window_size # Wd, Wh, Ww + self.shift_size = shift_size + self.num_heads = num_heads + self.attention_dropout = attention_dropout + self.dropout = dropout + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim, bias=proj_bias) + + self.define_relative_position_bias_table() + self.define_relative_position_index() + + def define_relative_position_bias_table(self) -> None: + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros( + (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1), + self.num_heads, + ) + ) # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH + nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02) + + def define_relative_position_index(self) -> None: + # get pair-wise relative position index for each token inside the window + coords_dhw = [torch.arange(self.window_size[i]) for i in range(3)] + coords = torch.stack( + torch.meshgrid(coords_dhw[0], coords_dhw[1], coords_dhw[2], indexing="ij") + ) # 3, Wd, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 3, Wd*Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 3, Wd*Wh*Ww, Wd*Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wd*Wh*Ww, Wd*Wh*Ww, 3 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 2] += self.window_size[2] - 1 + + relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1) + relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1 + # We don't flatten the relative_position_index here in 3d case. + relative_position_index = relative_coords.sum(-1) # Wd*Wh*Ww, Wd*Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + def get_relative_position_bias(self, window_size: List[int]) -> torch.Tensor: + return _get_relative_position_bias(self.relative_position_bias_table, self.relative_position_index, window_size) # type: ignore + + def forward(self, x: Tensor) -> Tensor: + _, t, h, w, _ = x.shape + size_dhw = [t, h, w] + window_size, shift_size = self.window_size.copy(), self.shift_size.copy() + # Handle case where window_size is larger than the input tensor + window_size, shift_size = _get_window_and_shift_size(shift_size, size_dhw, window_size) + + relative_position_bias = self.get_relative_position_bias(window_size) + + return shifted_window_attention_3d( + x, + self.qkv.weight, + self.proj.weight, + relative_position_bias, + window_size, + self.num_heads, + shift_size=shift_size, + attention_dropout=self.attention_dropout, + dropout=self.dropout, + qkv_bias=self.qkv.bias, + proj_bias=self.proj.bias, + training=self.training, + ) + + +# Modified from: +# https://github.com/SwinTransformer/Video-Swin-Transformer/blob/master/mmaction/models/backbones/swin_transformer.py +class PatchEmbed3d(nn.Module): + """Video to Patch Embedding. + + Args: + patch_size (List[int]): Patch token size. + in_channels (int): Number of input channels. Default: 3 + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, + patch_size: List[int], + in_channels: int = 3, + embed_dim: int = 96, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.tuple_patch_size = (patch_size[0], patch_size[1], patch_size[2]) + + self.proj = nn.Conv3d( + in_channels, + embed_dim, + kernel_size=self.tuple_patch_size, + stride=self.tuple_patch_size, + ) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + """Forward function.""" + # padding + _, _, t, h, w = x.size() + pad_size = _compute_pad_size_3d((t, h, w), self.tuple_patch_size) + x = F.pad(x, (0, pad_size[2], 0, pad_size[1], 0, pad_size[0])) + x = self.proj(x) # B C T Wh Ww + x = x.permute(0, 2, 3, 4, 1) # B T Wh Ww C + if self.norm is not None: + x = self.norm(x) + return x + + +class SwinTransformer3d(nn.Module): + """ + Implements 3D Swin Transformer from the `"Video Swin Transformer" `_ paper. + Args: + patch_size (List[int]): Patch size. + embed_dim (int): Patch embedding dimension. + depths (List(int)): Depth of each Swin Transformer layer. + num_heads (List(int)): Number of attention heads in different layers. + window_size (List[int]): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + dropout (float): Dropout rate. Default: 0.0. + attention_dropout (float): Attention dropout rate. Default: 0.0. + stochastic_depth_prob (float): Stochastic depth rate. Default: 0.1. + num_classes (int): Number of classes for classification head. Default: 400. + norm_layer (nn.Module, optional): Normalization layer. Default: None. + block (nn.Module, optional): SwinTransformer Block. Default: None. + downsample_layer (nn.Module): Downsample layer (patch merging). Default: PatchMerging. + patch_embed (nn.Module, optional): Patch Embedding layer. Default: None. + """ + + def __init__( + self, + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + mlp_ratio: float = 4.0, + dropout: float = 0.0, + attention_dropout: float = 0.0, + stochastic_depth_prob: float = 0.1, + num_classes: int = 400, + norm_layer: Optional[Callable[..., nn.Module]] = None, + block: Optional[Callable[..., nn.Module]] = None, + downsample_layer: Callable[..., nn.Module] = PatchMerging, + patch_embed: Optional[Callable[..., nn.Module]] = None, + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.num_classes = num_classes + + if block is None: + block = partial(SwinTransformerBlock, attn_layer=ShiftedWindowAttention3d) + + if norm_layer is None: + norm_layer = partial(nn.LayerNorm, eps=1e-5) + + if patch_embed is None: + patch_embed = PatchEmbed3d + + # split image into non-overlapping patches + self.patch_embed = patch_embed(patch_size=patch_size, embed_dim=embed_dim, norm_layer=norm_layer) + self.pos_drop = nn.Dropout(p=dropout) + + layers: List[nn.Module] = [] + total_stage_blocks = sum(depths) + stage_block_id = 0 + # build SwinTransformer blocks + for i_stage in range(len(depths)): + stage: List[nn.Module] = [] + dim = embed_dim * 2**i_stage + for i_layer in range(depths[i_stage]): + # adjust stochastic depth probability based on the depth of the stage block + sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1) + stage.append( + block( + dim, + num_heads[i_stage], + window_size=window_size, + shift_size=[0 if i_layer % 2 == 0 else w // 2 for w in window_size], + mlp_ratio=mlp_ratio, + dropout=dropout, + attention_dropout=attention_dropout, + stochastic_depth_prob=sd_prob, + norm_layer=norm_layer, + attn_layer=ShiftedWindowAttention3d, + ) + ) + stage_block_id += 1 + layers.append(nn.Sequential(*stage)) + # add patch merging layer + if i_stage < (len(depths) - 1): + layers.append(downsample_layer(dim, norm_layer)) + self.features = nn.Sequential(*layers) + + self.num_features = embed_dim * 2 ** (len(depths) - 1) + self.norm = norm_layer(self.num_features) + self.avgpool = nn.AdaptiveAvgPool3d(1) + self.head = nn.Linear(self.num_features, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x: Tensor) -> Tensor: + # x: B C T H W + x = self.patch_embed(x) # B _T _H _W C + x = self.pos_drop(x) + x = self.features(x) # B _T _H _W C + x = self.norm(x) + x = x.permute(0, 4, 1, 2, 3) # B, C, _T, _H, _W + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.head(x) + return x + + +def _swin_transformer3d( + patch_size: List[int], + embed_dim: int, + depths: List[int], + num_heads: List[int], + window_size: List[int], + stochastic_depth_prob: float, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> SwinTransformer3d: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + + model = SwinTransformer3d( + patch_size=patch_size, + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=window_size, + stochastic_depth_prob=stochastic_depth_prob, + **kwargs, + ) + + if weights is not None: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META = { + "categories": _KINETICS400_CATEGORIES, + "min_size": (1, 1), + "min_temporal_size": 1, +} + + +class Swin3D_T_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/swin3d_t-7615ae03.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.4850, 0.4560, 0.4060), + std=(0.2290, 0.2240, 0.2250), + ), + meta={ + **_COMMON_META, + "recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`" + ), + "num_params": 28158070, + "_metrics": { + "Kinetics-400": { + "acc@1": 77.715, + "acc@5": 93.519, + } + }, + "_ops": 43.882, + "_file_size": 121.543, + }, + ) + DEFAULT = KINETICS400_V1 + + +class Swin3D_S_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/swin3d_s-da41c237.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.4850, 0.4560, 0.4060), + std=(0.2290, 0.2240, 0.2250), + ), + meta={ + **_COMMON_META, + "recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`" + ), + "num_params": 49816678, + "_metrics": { + "Kinetics-400": { + "acc@1": 79.521, + "acc@5": 94.158, + } + }, + "_ops": 82.841, + "_file_size": 218.288, + }, + ) + DEFAULT = KINETICS400_V1 + + +class Swin3D_B_Weights(WeightsEnum): + KINETICS400_V1 = Weights( + url="https://download.pytorch.org/models/swin3d_b_1k-24f7c7c6.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.4850, 0.4560, 0.4060), + std=(0.2290, 0.2240, 0.2250), + ), + meta={ + **_COMMON_META, + "recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`" + ), + "num_params": 88048984, + "_metrics": { + "Kinetics-400": { + "acc@1": 79.427, + "acc@5": 94.386, + } + }, + "_ops": 140.667, + "_file_size": 364.134, + }, + ) + KINETICS400_IMAGENET22K_V1 = Weights( + url="https://download.pytorch.org/models/swin3d_b_22k-7c6ae6fa.pth", + transforms=partial( + VideoClassification, + crop_size=(224, 224), + resize_size=(256,), + mean=(0.4850, 0.4560, 0.4060), + std=(0.2290, 0.2240, 0.2250), + ), + meta={ + **_COMMON_META, + "recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400", + "_docs": ( + "The weights were ported from the paper. The accuracies are estimated on video-level " + "with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`" + ), + "num_params": 88048984, + "_metrics": { + "Kinetics-400": { + "acc@1": 81.643, + "acc@5": 95.574, + } + }, + "_ops": 140.667, + "_file_size": 364.134, + }, + ) + DEFAULT = KINETICS400_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin3D_T_Weights.KINETICS400_V1)) +def swin3d_t(*, weights: Optional[Swin3D_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d: + """ + Constructs a swin_tiny architecture from + `Video Swin Transformer `_. + + Args: + weights (:class:`~torchvision.models.video.Swin3D_T_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.Swin3D_T_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.Swin3D_T_Weights + :members: + """ + weights = Swin3D_T_Weights.verify(weights) + + return _swin_transformer3d( + patch_size=[2, 4, 4], + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=[8, 7, 7], + stochastic_depth_prob=0.1, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin3D_S_Weights.KINETICS400_V1)) +def swin3d_s(*, weights: Optional[Swin3D_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d: + """ + Constructs a swin_small architecture from + `Video Swin Transformer `_. + + Args: + weights (:class:`~torchvision.models.video.Swin3D_S_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.Swin3D_S_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.Swin3D_S_Weights + :members: + """ + weights = Swin3D_S_Weights.verify(weights) + + return _swin_transformer3d( + patch_size=[2, 4, 4], + embed_dim=96, + depths=[2, 2, 18, 2], + num_heads=[3, 6, 12, 24], + window_size=[8, 7, 7], + stochastic_depth_prob=0.1, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", Swin3D_B_Weights.KINETICS400_V1)) +def swin3d_b(*, weights: Optional[Swin3D_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d: + """ + Constructs a swin_base architecture from + `Video Swin Transformer `_. + + Args: + weights (:class:`~torchvision.models.video.Swin3D_B_Weights`, optional): The + pretrained weights to use. See + :class:`~torchvision.models.video.Swin3D_B_Weights` below for + more details, and possible values. By default, no pre-trained + weights are used. + progress (bool, optional): If True, displays a progress bar of the + download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.video.Swin3D_B_Weights + :members: + """ + weights = Swin3D_B_Weights.verify(weights) + + return _swin_transformer3d( + patch_size=[2, 4, 4], + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=[8, 7, 7], + stochastic_depth_prob=0.1, + weights=weights, + progress=progress, + **kwargs, + ) diff --git a/lib/python3.11/site-packages/torchvision/models/vision_transformer.py b/lib/python3.11/site-packages/torchvision/models/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f2983ef9db0a3adf232dd6fe1b90ce9417ce0853 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/models/vision_transformer.py @@ -0,0 +1,864 @@ +import math +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, Dict, List, NamedTuple, Optional + +import torch +import torch.nn as nn + +from ..ops.misc import Conv2dNormActivation, MLP +from ..transforms._presets import ImageClassification, InterpolationMode +from ..utils import _log_api_usage_once +from ._api import register_model, Weights, WeightsEnum +from ._meta import _IMAGENET_CATEGORIES +from ._utils import _ovewrite_named_param, handle_legacy_interface + + +__all__ = [ + "VisionTransformer", + "ViT_B_16_Weights", + "ViT_B_32_Weights", + "ViT_L_16_Weights", + "ViT_L_32_Weights", + "ViT_H_14_Weights", + "vit_b_16", + "vit_b_32", + "vit_l_16", + "vit_l_32", + "vit_h_14", +] + + +class ConvStemConfig(NamedTuple): + out_channels: int + kernel_size: int + stride: int + norm_layer: Callable[..., nn.Module] = nn.BatchNorm2d + activation_layer: Callable[..., nn.Module] = nn.ReLU + + +class MLPBlock(MLP): + """Transformer MLP block.""" + + _version = 2 + + def __init__(self, in_dim: int, mlp_dim: int, dropout: float): + super().__init__(in_dim, [mlp_dim, in_dim], activation_layer=nn.GELU, inplace=None, dropout=dropout) + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.normal_(m.bias, std=1e-6) + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # Replacing legacy MLPBlock with MLP. See https://github.com/pytorch/vision/pull/6053 + for i in range(2): + for type in ["weight", "bias"]: + old_key = f"{prefix}linear_{i+1}.{type}" + new_key = f"{prefix}{3*i}.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + +class EncoderBlock(nn.Module): + """Transformer encoder block.""" + + def __init__( + self, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6), + ): + super().__init__() + self.num_heads = num_heads + + # Attention block + self.ln_1 = norm_layer(hidden_dim) + self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True) + self.dropout = nn.Dropout(dropout) + + # MLP block + self.ln_2 = norm_layer(hidden_dim) + self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + x = self.ln_1(input) + x, _ = self.self_attention(x, x, x, need_weights=False) + x = self.dropout(x) + x = x + input + + y = self.ln_2(x) + y = self.mlp(y) + return x + y + + +class Encoder(nn.Module): + """Transformer Model Encoder for sequence to sequence translation.""" + + def __init__( + self, + seq_length: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float, + attention_dropout: float, + norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6), + ): + super().__init__() + # Note that batch_size is on the first dim because + # we have batch_first=True in nn.MultiAttention() by default + self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT + self.dropout = nn.Dropout(dropout) + layers: OrderedDict[str, nn.Module] = OrderedDict() + for i in range(num_layers): + layers[f"encoder_layer_{i}"] = EncoderBlock( + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.layers = nn.Sequential(layers) + self.ln = norm_layer(hidden_dim) + + def forward(self, input: torch.Tensor): + torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}") + input = input + self.pos_embedding + return self.ln(self.layers(self.dropout(input))) + + +class VisionTransformer(nn.Module): + """Vision Transformer as per https://arxiv.org/abs/2010.11929.""" + + def __init__( + self, + image_size: int, + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + dropout: float = 0.0, + attention_dropout: float = 0.0, + num_classes: int = 1000, + representation_size: Optional[int] = None, + norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6), + conv_stem_configs: Optional[List[ConvStemConfig]] = None, + ): + super().__init__() + _log_api_usage_once(self) + torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!") + self.image_size = image_size + self.patch_size = patch_size + self.hidden_dim = hidden_dim + self.mlp_dim = mlp_dim + self.attention_dropout = attention_dropout + self.dropout = dropout + self.num_classes = num_classes + self.representation_size = representation_size + self.norm_layer = norm_layer + + if conv_stem_configs is not None: + # As per https://arxiv.org/abs/2106.14881 + seq_proj = nn.Sequential() + prev_channels = 3 + for i, conv_stem_layer_config in enumerate(conv_stem_configs): + seq_proj.add_module( + f"conv_bn_relu_{i}", + Conv2dNormActivation( + in_channels=prev_channels, + out_channels=conv_stem_layer_config.out_channels, + kernel_size=conv_stem_layer_config.kernel_size, + stride=conv_stem_layer_config.stride, + norm_layer=conv_stem_layer_config.norm_layer, + activation_layer=conv_stem_layer_config.activation_layer, + ), + ) + prev_channels = conv_stem_layer_config.out_channels + seq_proj.add_module( + "conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1) + ) + self.conv_proj: nn.Module = seq_proj + else: + self.conv_proj = nn.Conv2d( + in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size + ) + + seq_length = (image_size // patch_size) ** 2 + + # Add a class token + self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim)) + seq_length += 1 + + self.encoder = Encoder( + seq_length, + num_layers, + num_heads, + hidden_dim, + mlp_dim, + dropout, + attention_dropout, + norm_layer, + ) + self.seq_length = seq_length + + heads_layers: OrderedDict[str, nn.Module] = OrderedDict() + if representation_size is None: + heads_layers["head"] = nn.Linear(hidden_dim, num_classes) + else: + heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size) + heads_layers["act"] = nn.Tanh() + heads_layers["head"] = nn.Linear(representation_size, num_classes) + + self.heads = nn.Sequential(heads_layers) + + if isinstance(self.conv_proj, nn.Conv2d): + # Init the patchify stem + fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1] + nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in)) + if self.conv_proj.bias is not None: + nn.init.zeros_(self.conv_proj.bias) + elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d): + # Init the last 1x1 conv of the conv stem + nn.init.normal_( + self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels) + ) + if self.conv_proj.conv_last.bias is not None: + nn.init.zeros_(self.conv_proj.conv_last.bias) + + if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear): + fan_in = self.heads.pre_logits.in_features + nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in)) + nn.init.zeros_(self.heads.pre_logits.bias) + + if isinstance(self.heads.head, nn.Linear): + nn.init.zeros_(self.heads.head.weight) + nn.init.zeros_(self.heads.head.bias) + + def _process_input(self, x: torch.Tensor) -> torch.Tensor: + n, c, h, w = x.shape + p = self.patch_size + torch._assert(h == self.image_size, f"Wrong image height! Expected {self.image_size} but got {h}!") + torch._assert(w == self.image_size, f"Wrong image width! Expected {self.image_size} but got {w}!") + n_h = h // p + n_w = w // p + + # (n, c, h, w) -> (n, hidden_dim, n_h, n_w) + x = self.conv_proj(x) + # (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w)) + x = x.reshape(n, self.hidden_dim, n_h * n_w) + + # (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim) + # The self attention layer expects inputs in the format (N, S, E) + # where S is the source sequence length, N is the batch size, E is the + # embedding dimension + x = x.permute(0, 2, 1) + + return x + + def forward(self, x: torch.Tensor): + # Reshape and permute the input tensor + x = self._process_input(x) + n = x.shape[0] + + # Expand the class token to the full batch + batch_class_token = self.class_token.expand(n, -1, -1) + x = torch.cat([batch_class_token, x], dim=1) + + x = self.encoder(x) + + # Classifier "token" as used by standard language architectures + x = x[:, 0] + + x = self.heads(x) + + return x + + +def _vision_transformer( + patch_size: int, + num_layers: int, + num_heads: int, + hidden_dim: int, + mlp_dim: int, + weights: Optional[WeightsEnum], + progress: bool, + **kwargs: Any, +) -> VisionTransformer: + if weights is not None: + _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) + assert weights.meta["min_size"][0] == weights.meta["min_size"][1] + _ovewrite_named_param(kwargs, "image_size", weights.meta["min_size"][0]) + image_size = kwargs.pop("image_size", 224) + + model = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + num_layers=num_layers, + num_heads=num_heads, + hidden_dim=hidden_dim, + mlp_dim=mlp_dim, + **kwargs, + ) + + if weights: + model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) + + return model + + +_COMMON_META: Dict[str, Any] = { + "categories": _IMAGENET_CATEGORIES, +} + +_COMMON_SWAG_META = { + **_COMMON_META, + "recipe": "https://github.com/facebookresearch/SWAG", + "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE", +} + + +class ViT_B_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16-c867db91.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 86567656, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.072, + "acc@5": 95.318, + } + }, + "_ops": 17.564, + "_file_size": 330.285, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pth", + transforms=partial( + ImageClassification, + crop_size=384, + resize_size=384, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 86859496, + "min_size": (384, 384), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.304, + "acc@5": 97.650, + } + }, + "_ops": 55.484, + "_file_size": 331.398, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 86567656, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 81.886, + "acc@5": 96.180, + } + }, + "_ops": 17.564, + "_file_size": 330.285, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_B_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 88224232, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 75.912, + "acc@5": 92.466, + } + }, + "_ops": 4.409, + "_file_size": 336.604, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_16_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth", + transforms=partial(ImageClassification, crop_size=224, resize_size=242), + meta={ + **_COMMON_META, + "num_params": 304326632, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16", + "_metrics": { + "ImageNet-1K": { + "acc@1": 79.662, + "acc@5": 94.638, + } + }, + "_ops": 61.555, + "_file_size": 1161.023, + "_docs": """ + These weights were trained from scratch by using a modified version of TorchVision's + `new training recipe + `_. + """, + }, + ) + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pth", + transforms=partial( + ImageClassification, + crop_size=512, + resize_size=512, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 305174504, + "min_size": (512, 512), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.064, + "acc@5": 98.512, + } + }, + "_ops": 361.986, + "_file_size": 1164.258, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 304326632, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.146, + "acc@5": 97.422, + } + }, + "_ops": 61.555, + "_file_size": 1161.023, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_L_32_Weights(WeightsEnum): + IMAGENET1K_V1 = Weights( + url="https://download.pytorch.org/models/vit_l_32-c7638314.pth", + transforms=partial(ImageClassification, crop_size=224), + meta={ + **_COMMON_META, + "num_params": 306535400, + "min_size": (224, 224), + "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32", + "_metrics": { + "ImageNet-1K": { + "acc@1": 76.972, + "acc@5": 93.07, + } + }, + "_ops": 15.378, + "_file_size": 1169.449, + "_docs": """ + These weights were trained from scratch by using a modified version of `DeIT + `_'s training recipe. + """, + }, + ) + DEFAULT = IMAGENET1K_V1 + + +class ViT_H_14_Weights(WeightsEnum): + IMAGENET1K_SWAG_E2E_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_swag-80465313.pth", + transforms=partial( + ImageClassification, + crop_size=518, + resize_size=518, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "num_params": 633470440, + "min_size": (518, 518), + "_metrics": { + "ImageNet-1K": { + "acc@1": 88.552, + "acc@5": 98.694, + } + }, + "_ops": 1016.717, + "_file_size": 2416.643, + "_docs": """ + These weights are learnt via transfer learning by end-to-end fine-tuning the original + `SWAG `_ weights on ImageNet-1K data. + """, + }, + ) + IMAGENET1K_SWAG_LINEAR_V1 = Weights( + url="https://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pth", + transforms=partial( + ImageClassification, + crop_size=224, + resize_size=224, + interpolation=InterpolationMode.BICUBIC, + ), + meta={ + **_COMMON_SWAG_META, + "recipe": "https://github.com/pytorch/vision/pull/5793", + "num_params": 632045800, + "min_size": (224, 224), + "_metrics": { + "ImageNet-1K": { + "acc@1": 85.708, + "acc@5": 97.730, + } + }, + "_ops": 167.295, + "_file_size": 2411.209, + "_docs": """ + These weights are composed of the original frozen `SWAG `_ trunk + weights and a linear classifier learnt on top of them trained on ImageNet-1K data. + """, + }, + ) + DEFAULT = IMAGENET1K_SWAG_E2E_V1 + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1)) +def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_16_Weights + :members: + """ + weights = ViT_B_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.IMAGENET1K_V1)) +def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_b_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_B_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_B_32_Weights + :members: + """ + weights = ViT_B_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=12, + num_heads=12, + hidden_dim=768, + mlp_dim=3072, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.IMAGENET1K_V1)) +def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_16 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_16_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_16_Weights + :members: + """ + weights = ViT_L_16_Weights.verify(weights) + + return _vision_transformer( + patch_size=16, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.IMAGENET1K_V1)) +def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_l_32 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_L_32_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_L_32_Weights + :members: + """ + weights = ViT_L_32_Weights.verify(weights) + + return _vision_transformer( + patch_size=32, + num_layers=24, + num_heads=16, + hidden_dim=1024, + mlp_dim=4096, + weights=weights, + progress=progress, + **kwargs, + ) + + +@register_model() +@handle_legacy_interface(weights=("pretrained", None)) +def vit_h_14(*, weights: Optional[ViT_H_14_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer: + """ + Constructs a vit_h_14 architecture from + `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. + + Args: + weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained + weights to use. See :class:`~torchvision.models.ViT_H_14_Weights` + below for more details and possible values. By default, no pre-trained weights are used. + progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. + **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` + base class. Please refer to the `source code + `_ + for more details about this class. + + .. autoclass:: torchvision.models.ViT_H_14_Weights + :members: + """ + weights = ViT_H_14_Weights.verify(weights) + + return _vision_transformer( + patch_size=14, + num_layers=32, + num_heads=16, + hidden_dim=1280, + mlp_dim=5120, + weights=weights, + progress=progress, + **kwargs, + ) + + +def interpolate_embeddings( + image_size: int, + patch_size: int, + model_state: "OrderedDict[str, torch.Tensor]", + interpolation_mode: str = "bicubic", + reset_heads: bool = False, +) -> "OrderedDict[str, torch.Tensor]": + """This function helps interpolate positional embeddings during checkpoint loading, + especially when you want to apply a pre-trained model on images with different resolution. + + Args: + image_size (int): Image size of the new model. + patch_size (int): Patch size of the new model. + model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. + interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. + reset_heads (bool): If true, not copying the state of heads. Default: False. + + Returns: + OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. + """ + # Shape of pos_embedding is (1, seq_length, hidden_dim) + pos_embedding = model_state["encoder.pos_embedding"] + n, seq_length, hidden_dim = pos_embedding.shape + if n != 1: + raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}") + + new_seq_length = (image_size // patch_size) ** 2 + 1 + + # Need to interpolate the weights for the position embedding. + # We do this by reshaping the positions embeddings to a 2d grid, performing + # an interpolation in the (h, w) space and then reshaping back to a 1d grid. + if new_seq_length != seq_length: + # The class token embedding shouldn't be interpolated, so we split it up. + seq_length -= 1 + new_seq_length -= 1 + pos_embedding_token = pos_embedding[:, :1, :] + pos_embedding_img = pos_embedding[:, 1:, :] + + # (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length) + pos_embedding_img = pos_embedding_img.permute(0, 2, 1) + seq_length_1d = int(math.sqrt(seq_length)) + if seq_length_1d * seq_length_1d != seq_length: + raise ValueError( + f"seq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = {seq_length_1d * seq_length_1d } and seq_length = {seq_length}" + ) + + # (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d) + pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d) + new_seq_length_1d = image_size // patch_size + + # Perform interpolation. + # (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) + new_pos_embedding_img = nn.functional.interpolate( + pos_embedding_img, + size=new_seq_length_1d, + mode=interpolation_mode, + align_corners=True, + ) + + # (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length) + new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length) + + # (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim) + new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1) + new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1) + + model_state["encoder.pos_embedding"] = new_pos_embedding + + if reset_heads: + model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict() + for k, v in model_state.items(): + if not k.startswith("heads"): + model_state_copy[k] = v + model_state = model_state_copy + + return model_state diff --git a/lib/python3.11/site-packages/torchvision/ops/__init__.py b/lib/python3.11/site-packages/torchvision/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..827505b842d4f1ad0e16dfe54ef28658364cc9ac --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/__init__.py @@ -0,0 +1,73 @@ +from ._register_onnx_ops import _register_custom_op +from .boxes import ( + batched_nms, + box_area, + box_convert, + box_iou, + clip_boxes_to_image, + complete_box_iou, + distance_box_iou, + generalized_box_iou, + masks_to_boxes, + nms, + remove_small_boxes, +) +from .ciou_loss import complete_box_iou_loss +from .deform_conv import deform_conv2d, DeformConv2d +from .diou_loss import distance_box_iou_loss +from .drop_block import drop_block2d, drop_block3d, DropBlock2d, DropBlock3d +from .feature_pyramid_network import FeaturePyramidNetwork +from .focal_loss import sigmoid_focal_loss +from .giou_loss import generalized_box_iou_loss +from .misc import Conv2dNormActivation, Conv3dNormActivation, FrozenBatchNorm2d, MLP, Permute, SqueezeExcitation +from .poolers import MultiScaleRoIAlign +from .ps_roi_align import ps_roi_align, PSRoIAlign +from .ps_roi_pool import ps_roi_pool, PSRoIPool +from .roi_align import roi_align, RoIAlign +from .roi_pool import roi_pool, RoIPool +from .stochastic_depth import stochastic_depth, StochasticDepth + +_register_custom_op() + + +__all__ = [ + "masks_to_boxes", + "deform_conv2d", + "DeformConv2d", + "nms", + "batched_nms", + "remove_small_boxes", + "clip_boxes_to_image", + "box_convert", + "box_area", + "box_iou", + "generalized_box_iou", + "distance_box_iou", + "complete_box_iou", + "roi_align", + "RoIAlign", + "roi_pool", + "RoIPool", + "ps_roi_align", + "PSRoIAlign", + "ps_roi_pool", + "PSRoIPool", + "MultiScaleRoIAlign", + "FeaturePyramidNetwork", + "sigmoid_focal_loss", + "stochastic_depth", + "StochasticDepth", + "FrozenBatchNorm2d", + "Conv2dNormActivation", + "Conv3dNormActivation", + "SqueezeExcitation", + "MLP", + "Permute", + "generalized_box_iou_loss", + "distance_box_iou_loss", + "complete_box_iou_loss", + "drop_block2d", + "DropBlock2d", + "drop_block3d", + "DropBlock3d", +] diff --git a/lib/python3.11/site-packages/torchvision/ops/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/ops/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6d041fd7428c418743c8306aebeb2d1b3521d756 Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/ops/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/ops/__pycache__/_box_convert.cpython-311.pyc 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torch import Tensor + + +def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor: + """ + Converts bounding boxes from (cx, cy, w, h) format to (x1, y1, x2, y2) format. + (cx, cy) refers to center of bounding box + (w, h) are width and height of bounding box + Args: + boxes (Tensor[N, 4]): boxes in (cx, cy, w, h) format which will be converted. + + Returns: + boxes (Tensor(N, 4)): boxes in (x1, y1, x2, y2) format. + """ + # We need to change all 4 of them so some temporary variable is needed. + cx, cy, w, h = boxes.unbind(-1) + x1 = cx - 0.5 * w + y1 = cy - 0.5 * h + x2 = cx + 0.5 * w + y2 = cy + 0.5 * h + + boxes = torch.stack((x1, y1, x2, y2), dim=-1) + + return boxes + + +def _box_xyxy_to_cxcywh(boxes: Tensor) -> Tensor: + """ + Converts bounding boxes from (x1, y1, x2, y2) format to (cx, cy, w, h) format. + (x1, y1) refer to top left of bounding box + (x2, y2) refer to bottom right of bounding box + Args: + boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format which will be converted. + + Returns: + boxes (Tensor(N, 4)): boxes in (cx, cy, w, h) format. + """ + x1, y1, x2, y2 = boxes.unbind(-1) + cx = (x1 + x2) / 2 + cy = (y1 + y2) / 2 + w = x2 - x1 + h = y2 - y1 + + boxes = torch.stack((cx, cy, w, h), dim=-1) + + return boxes + + +def _box_xywh_to_xyxy(boxes: Tensor) -> Tensor: + """ + Converts bounding boxes from (x, y, w, h) format to (x1, y1, x2, y2) format. + (x, y) refers to top left of bounding box. + (w, h) refers to width and height of box. + Args: + boxes (Tensor[N, 4]): boxes in (x, y, w, h) which will be converted. + + Returns: + boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format. + """ + x, y, w, h = boxes.unbind(-1) + boxes = torch.stack([x, y, x + w, y + h], dim=-1) + return boxes + + +def _box_xyxy_to_xywh(boxes: Tensor) -> Tensor: + """ + Converts bounding boxes from (x1, y1, x2, y2) format to (x, y, w, h) format. + (x1, y1) refer to top left of bounding box + (x2, y2) refer to bottom right of bounding box + Args: + boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) which will be converted. + + Returns: + boxes (Tensor[N, 4]): boxes in (x, y, w, h) format. + """ + x1, y1, x2, y2 = boxes.unbind(-1) + w = x2 - x1 # x2 - x1 + h = y2 - y1 # y2 - y1 + boxes = torch.stack((x1, y1, w, h), dim=-1) + return boxes diff --git a/lib/python3.11/site-packages/torchvision/ops/_register_onnx_ops.py b/lib/python3.11/site-packages/torchvision/ops/_register_onnx_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..5dd263a5d8ef497becc4aa39252a93c913b84880 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/_register_onnx_ops.py @@ -0,0 +1,107 @@ +import sys +import warnings + +import torch +from torch.onnx import symbolic_opset11 as opset11 +from torch.onnx.symbolic_helper import parse_args + +_ONNX_OPSET_VERSION_11 = 11 +_ONNX_OPSET_VERSION_16 = 16 +BASE_ONNX_OPSET_VERSION = _ONNX_OPSET_VERSION_11 + + +@parse_args("v", "v", "f") +def symbolic_multi_label_nms(g, boxes, scores, iou_threshold): + boxes = opset11.unsqueeze(g, boxes, 0) + scores = opset11.unsqueeze(g, opset11.unsqueeze(g, scores, 0), 0) + max_output_per_class = g.op("Constant", value_t=torch.tensor([sys.maxsize], dtype=torch.long)) + iou_threshold = g.op("Constant", value_t=torch.tensor([iou_threshold], dtype=torch.float)) + + # Cast boxes and scores to float32 in case they are float64 inputs + nms_out = g.op( + "NonMaxSuppression", + g.op("Cast", boxes, to_i=torch.onnx.TensorProtoDataType.FLOAT), + g.op("Cast", scores, to_i=torch.onnx.TensorProtoDataType.FLOAT), + max_output_per_class, + iou_threshold, + ) + return opset11.squeeze( + g, opset11.select(g, nms_out, 1, g.op("Constant", value_t=torch.tensor([2], dtype=torch.long))), 1 + ) + + +def _process_batch_indices_for_roi_align(g, rois): + indices = opset11.squeeze( + g, opset11.select(g, rois, 1, g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))), 1 + ) + return g.op("Cast", indices, to_i=torch.onnx.TensorProtoDataType.INT64) + + +def _process_rois_for_roi_align(g, rois): + return opset11.select(g, rois, 1, g.op("Constant", value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long))) + + +def _process_sampling_ratio_for_roi_align(g, sampling_ratio: int): + if sampling_ratio < 0: + warnings.warn( + "ONNX export for RoIAlign with a non-zero sampling_ratio is not supported. " + "The model will be exported with a sampling_ratio of 0." + ) + sampling_ratio = 0 + return sampling_ratio + + +@parse_args("v", "v", "f", "i", "i", "i", "i") +def roi_align_opset11(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned): + batch_indices = _process_batch_indices_for_roi_align(g, rois) + rois = _process_rois_for_roi_align(g, rois) + if aligned: + warnings.warn( + "ROIAlign with aligned=True is only supported in opset >= 16. " + "Please export with opset 16 or higher, or use aligned=False." + ) + sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio) + return g.op( + "RoiAlign", + input, + rois, + batch_indices, + spatial_scale_f=spatial_scale, + output_height_i=pooled_height, + output_width_i=pooled_width, + sampling_ratio_i=sampling_ratio, + ) + + +@parse_args("v", "v", "f", "i", "i", "i", "i") +def roi_align_opset16(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned): + batch_indices = _process_batch_indices_for_roi_align(g, rois) + rois = _process_rois_for_roi_align(g, rois) + coordinate_transformation_mode = "half_pixel" if aligned else "output_half_pixel" + sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio) + return g.op( + "RoiAlign", + input, + rois, + batch_indices, + coordinate_transformation_mode_s=coordinate_transformation_mode, + spatial_scale_f=spatial_scale, + output_height_i=pooled_height, + output_width_i=pooled_width, + sampling_ratio_i=sampling_ratio, + ) + + +@parse_args("v", "v", "f", "i", "i") +def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width): + roi_pool = g.op( + "MaxRoiPool", input, rois, pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale + ) + return roi_pool, None + + +def _register_custom_op(): + torch.onnx.register_custom_op_symbolic("torchvision::nms", symbolic_multi_label_nms, _ONNX_OPSET_VERSION_11) + torch.onnx.register_custom_op_symbolic("torchvision::roi_align", roi_align_opset11, _ONNX_OPSET_VERSION_11) + torch.onnx.register_custom_op_symbolic("torchvision::roi_align", roi_align_opset16, _ONNX_OPSET_VERSION_16) + torch.onnx.register_custom_op_symbolic("torchvision::roi_pool", roi_pool, _ONNX_OPSET_VERSION_11) diff --git a/lib/python3.11/site-packages/torchvision/ops/_utils.py b/lib/python3.11/site-packages/torchvision/ops/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a6ca557a98b899b7c2a11ba0dca3d64730af4268 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/_utils.py @@ -0,0 +1,106 @@ +from typing import List, Optional, Tuple, Union + +import torch +from torch import nn, Tensor + + +def _cat(tensors: List[Tensor], dim: int = 0) -> Tensor: + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + # TODO add back the assert + # assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +def convert_boxes_to_roi_format(boxes: List[Tensor]) -> Tensor: + concat_boxes = _cat([b for b in boxes], dim=0) + temp = [] + for i, b in enumerate(boxes): + temp.append(torch.full_like(b[:, :1], i)) + ids = _cat(temp, dim=0) + rois = torch.cat([ids, concat_boxes], dim=1) + return rois + + +def check_roi_boxes_shape(boxes: Union[Tensor, List[Tensor]]): + if isinstance(boxes, (list, tuple)): + for _tensor in boxes: + torch._assert( + _tensor.size(1) == 4, "The shape of the tensor in the boxes list is not correct as List[Tensor[L, 4]]" + ) + elif isinstance(boxes, torch.Tensor): + torch._assert(boxes.size(1) == 5, "The boxes tensor shape is not correct as Tensor[K, 5]") + else: + torch._assert(False, "boxes is expected to be a Tensor[L, 5] or a List[Tensor[K, 4]]") + return + + +def split_normalization_params( + model: nn.Module, norm_classes: Optional[List[type]] = None +) -> Tuple[List[Tensor], List[Tensor]]: + # Adapted from https://github.com/facebookresearch/ClassyVision/blob/659d7f78/classy_vision/generic/util.py#L501 + if not norm_classes: + norm_classes = [ + nn.modules.batchnorm._BatchNorm, + nn.LayerNorm, + nn.GroupNorm, + nn.modules.instancenorm._InstanceNorm, + nn.LocalResponseNorm, + ] + + for t in norm_classes: + if not issubclass(t, nn.Module): + raise ValueError(f"Class {t} is not a subclass of nn.Module.") + + classes = tuple(norm_classes) + + norm_params = [] + other_params = [] + for module in model.modules(): + if next(module.children(), None): + other_params.extend(p for p in module.parameters(recurse=False) if p.requires_grad) + elif isinstance(module, classes): + norm_params.extend(p for p in module.parameters() if p.requires_grad) + else: + other_params.extend(p for p in module.parameters() if p.requires_grad) + return norm_params, other_params + + +def _upcast(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +def _upcast_non_float(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.dtype not in (torch.float32, torch.float64): + return t.float() + return t + + +def _loss_inter_union( + boxes1: torch.Tensor, + boxes2: torch.Tensor, +) -> Tuple[torch.Tensor, torch.Tensor]: + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsctk = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk + + return intsctk, unionk diff --git a/lib/python3.11/site-packages/torchvision/ops/boxes.py b/lib/python3.11/site-packages/torchvision/ops/boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..96631278d48ea0d956d5d98a3e3fe1cbc4e51c19 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/boxes.py @@ -0,0 +1,439 @@ +from typing import Tuple + +import torch +import torchvision +from torch import Tensor +from torchvision.extension import _assert_has_ops + +from ..utils import _log_api_usage_once +from ._box_convert import _box_cxcywh_to_xyxy, _box_xywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xyxy_to_xywh +from ._utils import _upcast + + +def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor: + """ + Performs non-maximum suppression (NMS) on the boxes according + to their intersection-over-union (IoU). + + NMS iteratively removes lower scoring boxes which have an + IoU greater than ``iou_threshold`` with another (higher scoring) + box. + + If multiple boxes have the exact same score and satisfy the IoU + criterion with respect to a reference box, the selected box is + not guaranteed to be the same between CPU and GPU. This is similar + to the behavior of argsort in PyTorch when repeated values are present. + + Args: + boxes (Tensor[N, 4])): boxes to perform NMS on. They + are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and + ``0 <= y1 < y2``. + scores (Tensor[N]): scores for each one of the boxes + iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold + + Returns: + Tensor: int64 tensor with the indices of the elements that have been kept + by NMS, sorted in decreasing order of scores + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(nms) + _assert_has_ops() + return torch.ops.torchvision.nms(boxes, scores, iou_threshold) + + +def batched_nms( + boxes: Tensor, + scores: Tensor, + idxs: Tensor, + iou_threshold: float, +) -> Tensor: + """ + Performs non-maximum suppression in a batched fashion. + + Each index value correspond to a category, and NMS + will not be applied between elements of different categories. + + Args: + boxes (Tensor[N, 4]): boxes where NMS will be performed. They + are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and + ``0 <= y1 < y2``. + scores (Tensor[N]): scores for each one of the boxes + idxs (Tensor[N]): indices of the categories for each one of the boxes. + iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold + + Returns: + Tensor: int64 tensor with the indices of the elements that have been kept by NMS, sorted + in decreasing order of scores + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(batched_nms) + # Benchmarks that drove the following thresholds are at + # https://github.com/pytorch/vision/issues/1311#issuecomment-781329339 + if boxes.numel() > (4000 if boxes.device.type == "cpu" else 20000) and not torchvision._is_tracing(): + return _batched_nms_vanilla(boxes, scores, idxs, iou_threshold) + else: + return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold) + + +@torch.jit._script_if_tracing +def _batched_nms_coordinate_trick( + boxes: Tensor, + scores: Tensor, + idxs: Tensor, + iou_threshold: float, +) -> Tensor: + # strategy: in order to perform NMS independently per class, + # we add an offset to all the boxes. The offset is dependent + # only on the class idx, and is large enough so that boxes + # from different classes do not overlap + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.int64, device=boxes.device) + max_coordinate = boxes.max() + offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes)) + boxes_for_nms = boxes + offsets[:, None] + keep = nms(boxes_for_nms, scores, iou_threshold) + return keep + + +@torch.jit._script_if_tracing +def _batched_nms_vanilla( + boxes: Tensor, + scores: Tensor, + idxs: Tensor, + iou_threshold: float, +) -> Tensor: + # Based on Detectron2 implementation, just manually call nms() on each class independently + keep_mask = torch.zeros_like(scores, dtype=torch.bool) + for class_id in torch.unique(idxs): + curr_indices = torch.where(idxs == class_id)[0] + curr_keep_indices = nms(boxes[curr_indices], scores[curr_indices], iou_threshold) + keep_mask[curr_indices[curr_keep_indices]] = True + keep_indices = torch.where(keep_mask)[0] + return keep_indices[scores[keep_indices].sort(descending=True)[1]] + + +def remove_small_boxes(boxes: Tensor, min_size: float) -> Tensor: + """ + Remove every box from ``boxes`` which contains at least one side length + that is smaller than ``min_size``. + + .. note:: + For sanitizing a :class:`~torchvision.tv_tensors.BoundingBoxes` object, consider using + the transform :func:`~torchvision.transforms.v2.SanitizeBoundingBoxes` instead. + + Args: + boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format + with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + min_size (float): minimum size + + Returns: + Tensor[K]: indices of the boxes that have both sides + larger than ``min_size`` + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(remove_small_boxes) + ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1] + keep = (ws >= min_size) & (hs >= min_size) + keep = torch.where(keep)[0] + return keep + + +def clip_boxes_to_image(boxes: Tensor, size: Tuple[int, int]) -> Tensor: + """ + Clip boxes so that they lie inside an image of size ``size``. + + .. note:: + For clipping a :class:`~torchvision.tv_tensors.BoundingBoxes` object, consider using + the transform :func:`~torchvision.transforms.v2.ClampBoundingBoxes` instead. + + Args: + boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format + with ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + size (Tuple[height, width]): size of the image + + Returns: + Tensor[N, 4]: clipped boxes + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(clip_boxes_to_image) + dim = boxes.dim() + boxes_x = boxes[..., 0::2] + boxes_y = boxes[..., 1::2] + height, width = size + + if torchvision._is_tracing(): + boxes_x = torch.max(boxes_x, torch.tensor(0, dtype=boxes.dtype, device=boxes.device)) + boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device)) + boxes_y = torch.max(boxes_y, torch.tensor(0, dtype=boxes.dtype, device=boxes.device)) + boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device)) + else: + boxes_x = boxes_x.clamp(min=0, max=width) + boxes_y = boxes_y.clamp(min=0, max=height) + + clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim) + return clipped_boxes.reshape(boxes.shape) + + +def box_convert(boxes: Tensor, in_fmt: str, out_fmt: str) -> Tensor: + """ + Converts :class:`torch.Tensor` boxes from a given ``in_fmt`` to ``out_fmt``. + + .. note:: + For converting a :class:`torch.Tensor` or a :class:`~torchvision.tv_tensors.BoundingBoxes` object + between different formats, + consider using :func:`~torchvision.transforms.v2.functional.convert_bounding_box_format` instead. + Or see the corresponding transform :func:`~torchvision.transforms.v2.ConvertBoundingBoxFormat`. + + Supported ``in_fmt`` and ``out_fmt`` strings are: + + ``'xyxy'``: boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right. + This is the format that torchvision utilities expect. + + ``'xywh'``: boxes are represented via corner, width and height, x1, y2 being top left, w, h being width and height. + + ``'cxcywh'``: boxes are represented via centre, width and height, cx, cy being center of box, w, h + being width and height. + + Args: + boxes (Tensor[N, 4]): boxes which will be converted. + in_fmt (str): Input format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh']. + out_fmt (str): Output format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh'] + + Returns: + Tensor[N, 4]: Boxes into converted format. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(box_convert) + allowed_fmts = ("xyxy", "xywh", "cxcywh") + if in_fmt not in allowed_fmts or out_fmt not in allowed_fmts: + raise ValueError("Unsupported Bounding Box Conversions for given in_fmt and out_fmt") + + if in_fmt == out_fmt: + return boxes.clone() + + if in_fmt != "xyxy" and out_fmt != "xyxy": + # convert to xyxy and change in_fmt xyxy + if in_fmt == "xywh": + boxes = _box_xywh_to_xyxy(boxes) + elif in_fmt == "cxcywh": + boxes = _box_cxcywh_to_xyxy(boxes) + in_fmt = "xyxy" + + if in_fmt == "xyxy": + if out_fmt == "xywh": + boxes = _box_xyxy_to_xywh(boxes) + elif out_fmt == "cxcywh": + boxes = _box_xyxy_to_cxcywh(boxes) + elif out_fmt == "xyxy": + if in_fmt == "xywh": + boxes = _box_xywh_to_xyxy(boxes) + elif in_fmt == "cxcywh": + boxes = _box_cxcywh_to_xyxy(boxes) + return boxes + + +def box_area(boxes: Tensor) -> Tensor: + """ + Computes the area of a set of bounding boxes, which are specified by their + (x1, y1, x2, y2) coordinates. + + Args: + boxes (Tensor[N, 4]): boxes for which the area will be computed. They + are expected to be in (x1, y1, x2, y2) format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Returns: + Tensor[N]: the area for each box + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(box_area) + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> Tuple[Tensor, Tensor]: + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = _upcast(rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + return inter, union + + +def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor: + """ + Return intersection-over-union (Jaccard index) between two sets of boxes. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(box_iou) + inter, union = _box_inter_union(boxes1, boxes2) + iou = inter / union + return iou + + +# Implementation adapted from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py +def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor: + """ + Return generalized intersection-over-union (Jaccard index) between two sets of boxes. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values + for every element in boxes1 and boxes2 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(generalized_box_iou) + + inter, union = _box_inter_union(boxes1, boxes2) + iou = inter / union + + lti = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rbi = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + whi = _upcast(rbi - lti).clamp(min=0) # [N,M,2] + areai = whi[:, :, 0] * whi[:, :, 1] + + return iou - (areai - union) / areai + + +def complete_box_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tensor: + """ + Return complete intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values + for every element in boxes1 and boxes2 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(complete_box_iou) + + boxes1 = _upcast(boxes1) + boxes2 = _upcast(boxes2) + + diou, iou = _box_diou_iou(boxes1, boxes2, eps) + + w_pred = boxes1[:, None, 2] - boxes1[:, None, 0] + h_pred = boxes1[:, None, 3] - boxes1[:, None, 1] + + w_gt = boxes2[:, 2] - boxes2[:, 0] + h_gt = boxes2[:, 3] - boxes2[:, 1] + + v = (4 / (torch.pi**2)) * torch.pow(torch.atan(w_pred / h_pred) - torch.atan(w_gt / h_gt), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + return diou - alpha * v + + +def distance_box_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tensor: + """ + Return distance intersection-over-union (Jaccard index) between two sets of boxes. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values + for every element in boxes1 and boxes2 + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(distance_box_iou) + + boxes1 = _upcast(boxes1) + boxes2 = _upcast(boxes2) + diou, _ = _box_diou_iou(boxes1, boxes2, eps=eps) + return diou + + +def _box_diou_iou(boxes1: Tensor, boxes2: Tensor, eps: float = 1e-7) -> Tuple[Tensor, Tensor]: + + iou = box_iou(boxes1, boxes2) + lti = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rbi = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + whi = _upcast(rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + # centers of boxes + x_p = (boxes1[:, 0] + boxes1[:, 2]) / 2 + y_p = (boxes1[:, 1] + boxes1[:, 3]) / 2 + x_g = (boxes2[:, 0] + boxes2[:, 2]) / 2 + y_g = (boxes2[:, 1] + boxes2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (_upcast((x_p[:, None] - x_g[None, :])) ** 2) + ( + _upcast((y_p[:, None] - y_g[None, :])) ** 2 + ) + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + return iou - (centers_distance_squared / diagonal_distance_squared), iou + + +def masks_to_boxes(masks: torch.Tensor) -> torch.Tensor: + """ + Compute the bounding boxes around the provided masks. + + Returns a [N, 4] tensor containing bounding boxes. The boxes are in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 <= x2`` and ``0 <= y1 <= y2``. + + .. warning:: + + In most cases the output will guarantee ``x1 < x2`` and ``y1 < y2``. But + if the input is degenerate, e.g. if a mask is a single row or a single + column, then the output may have x1 = x2 or y1 = y2. + + Args: + masks (Tensor[N, H, W]): masks to transform where N is the number of masks + and (H, W) are the spatial dimensions. + + Returns: + Tensor[N, 4]: bounding boxes + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(masks_to_boxes) + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device, dtype=torch.float) + + n = masks.shape[0] + + bounding_boxes = torch.zeros((n, 4), device=masks.device, dtype=torch.float) + + for index, mask in enumerate(masks): + y, x = torch.where(mask != 0) + + bounding_boxes[index, 0] = torch.min(x) + bounding_boxes[index, 1] = torch.min(y) + bounding_boxes[index, 2] = torch.max(x) + bounding_boxes[index, 3] = torch.max(y) + + return bounding_boxes diff --git a/lib/python3.11/site-packages/torchvision/ops/ciou_loss.py b/lib/python3.11/site-packages/torchvision/ops/ciou_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..75a1c4cb1f39f7b8ff33bdac3641ca71b6413d20 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/ciou_loss.py @@ -0,0 +1,78 @@ +import torch + +from ..utils import _log_api_usage_once +from ._utils import _upcast_non_float +from .diou_loss import _diou_iou_loss + + +def complete_box_iou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + + """ + Gradient-friendly IoU loss with an additional penalty that is non-zero when the + boxes do not overlap. This loss function considers important geometrical + factors such as overlap area, normalized central point distance and aspect ratio. + This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the + same dimensions. + + Args: + boxes1 : (Tensor[N, 4] or Tensor[4]) first set of boxes + boxes2 : (Tensor[N, 4] or Tensor[4]) second set of boxes + reduction : (string, optional) Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be + applied to the output. ``'mean'``: The output will be averaged. + ``'sum'``: The output will be summed. Default: ``'none'`` + eps : (float): small number to prevent division by zero. Default: 1e-7 + + Returns: + Tensor: Loss tensor with the reduction option applied. + + Reference: + Zhaohui Zheng et al.: Complete Intersection over Union Loss: + https://arxiv.org/abs/1911.08287 + + """ + + # Original Implementation from https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/losses.py + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(complete_box_iou_loss) + + boxes1 = _upcast_non_float(boxes1) + boxes2 = _upcast_non_float(boxes2) + + diou_loss, iou = _diou_iou_loss(boxes1, boxes2) + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # width and height of boxes + w_pred = x2 - x1 + h_pred = y2 - y1 + w_gt = x2g - x1g + h_gt = y2g - y1g + v = (4 / (torch.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + + loss = diou_loss + alpha * v + + # Check reduction option and return loss accordingly + if reduction == "none": + pass + elif reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + else: + raise ValueError( + f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'" + ) + return loss diff --git a/lib/python3.11/site-packages/torchvision/ops/deform_conv.py b/lib/python3.11/site-packages/torchvision/ops/deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..b3cc83332a0a780710cdd61f0930f81ca9ab18b9 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/deform_conv.py @@ -0,0 +1,195 @@ +import math +from typing import Optional, Tuple + +import torch +from torch import nn, Tensor +from torch.nn import init +from torch.nn.modules.utils import _pair +from torch.nn.parameter import Parameter +from torchvision.extension import _assert_has_ops + +from ..utils import _log_api_usage_once + + +def deform_conv2d( + input: Tensor, + offset: Tensor, + weight: Tensor, + bias: Optional[Tensor] = None, + stride: Tuple[int, int] = (1, 1), + padding: Tuple[int, int] = (0, 0), + dilation: Tuple[int, int] = (1, 1), + mask: Optional[Tensor] = None, +) -> Tensor: + r""" + Performs Deformable Convolution v2, described in + `Deformable ConvNets v2: More Deformable, Better Results + `__ if :attr:`mask` is not ``None`` and + Performs Deformable Convolution, described in + `Deformable Convolutional Networks + `__ if :attr:`mask` is ``None``. + + Args: + input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor + offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]): + offsets to be applied for each position in the convolution kernel. + weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]): convolution weights, + split into groups of size (in_channels // groups) + bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None + stride (int or Tuple[int, int]): distance between convolution centers. Default: 1 + padding (int or Tuple[int, int]): height/width of padding of zeroes around + each image. Default: 0 + dilation (int or Tuple[int, int]): the spacing between kernel elements. Default: 1 + mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): + masks to be applied for each position in the convolution kernel. Default: None + + Returns: + Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution + + Examples:: + >>> input = torch.rand(4, 3, 10, 10) + >>> kh, kw = 3, 3 + >>> weight = torch.rand(5, 3, kh, kw) + >>> # offset and mask should have the same spatial size as the output + >>> # of the convolution. In this case, for an input of 10, stride of 1 + >>> # and kernel size of 3, without padding, the output size is 8 + >>> offset = torch.rand(4, 2 * kh * kw, 8, 8) + >>> mask = torch.rand(4, kh * kw, 8, 8) + >>> out = deform_conv2d(input, offset, weight, mask=mask) + >>> print(out.shape) + >>> # returns + >>> torch.Size([4, 5, 8, 8]) + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(deform_conv2d) + _assert_has_ops() + out_channels = weight.shape[0] + + use_mask = mask is not None + + if mask is None: + mask = torch.zeros((input.shape[0], 1), device=input.device, dtype=input.dtype) + + if bias is None: + bias = torch.zeros(out_channels, device=input.device, dtype=input.dtype) + + stride_h, stride_w = _pair(stride) + pad_h, pad_w = _pair(padding) + dil_h, dil_w = _pair(dilation) + weights_h, weights_w = weight.shape[-2:] + _, n_in_channels, _, _ = input.shape + + n_offset_grps = offset.shape[1] // (2 * weights_h * weights_w) + n_weight_grps = n_in_channels // weight.shape[1] + + if n_offset_grps == 0: + raise RuntimeError( + "the shape of the offset tensor at dimension 1 is not valid. It should " + "be a multiple of 2 * weight.size[2] * weight.size[3].\n" + f"Got offset.shape[1]={offset.shape[1]}, while 2 * weight.size[2] * weight.size[3]={2 * weights_h * weights_w}" + ) + + return torch.ops.torchvision.deform_conv2d( + input, + weight, + offset, + mask, + bias, + stride_h, + stride_w, + pad_h, + pad_w, + dil_h, + dil_w, + n_weight_grps, + n_offset_grps, + use_mask, + ) + + +class DeformConv2d(nn.Module): + """ + See :func:`deform_conv2d`. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + ): + super().__init__() + _log_api_usage_once(self) + + if in_channels % groups != 0: + raise ValueError("in_channels must be divisible by groups") + if out_channels % groups != 0: + raise ValueError("out_channels must be divisible by groups") + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + + self.weight = Parameter( + torch.empty(out_channels, in_channels // groups, self.kernel_size[0], self.kernel_size[1]) + ) + + if bias: + self.bias = Parameter(torch.empty(out_channels)) + else: + self.register_parameter("bias", None) + + self.reset_parameters() + + def reset_parameters(self) -> None: + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + if self.bias is not None: + fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) + bound = 1 / math.sqrt(fan_in) + init.uniform_(self.bias, -bound, bound) + + def forward(self, input: Tensor, offset: Tensor, mask: Optional[Tensor] = None) -> Tensor: + """ + Args: + input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor + offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]): + offsets to be applied for each position in the convolution kernel. + mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): + masks to be applied for each position in the convolution kernel. + """ + return deform_conv2d( + input, + offset, + self.weight, + self.bias, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + mask=mask, + ) + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"{self.in_channels}" + f", {self.out_channels}" + f", kernel_size={self.kernel_size}" + f", stride={self.stride}" + ) + s += f", padding={self.padding}" if self.padding != (0, 0) else "" + s += f", dilation={self.dilation}" if self.dilation != (1, 1) else "" + s += f", groups={self.groups}" if self.groups != 1 else "" + s += ", bias=False" if self.bias is None else "" + s += ")" + + return s diff --git a/lib/python3.11/site-packages/torchvision/ops/diou_loss.py b/lib/python3.11/site-packages/torchvision/ops/diou_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..c64c6673a882f439f8ba9b0e25d1387d8d2f4284 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/diou_loss.py @@ -0,0 +1,94 @@ +from typing import Tuple + +import torch + +from ..utils import _log_api_usage_once +from ._utils import _loss_inter_union, _upcast_non_float + + +def distance_box_iou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + + """ + Gradient-friendly IoU loss with an additional penalty that is non-zero when the + distance between boxes' centers isn't zero. Indeed, for two exactly overlapping + boxes, the distance IoU is the same as the IoU loss. + This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the + same dimensions. + + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[N, 4]): second set of boxes + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be + applied to the output. ``'mean'``: The output will be averaged. + ``'sum'``: The output will be summed. Default: ``'none'`` + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + + Returns: + Tensor: Loss tensor with the reduction option applied. + + Reference: + Zhaohui Zheng et al.: Distance Intersection over Union Loss: + https://arxiv.org/abs/1911.08287 + """ + + # Original Implementation from https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/losses.py + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(distance_box_iou_loss) + + boxes1 = _upcast_non_float(boxes1) + boxes2 = _upcast_non_float(boxes2) + + loss, _ = _diou_iou_loss(boxes1, boxes2, eps) + + # Check reduction option and return loss accordingly + if reduction == "none": + pass + elif reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + else: + raise ValueError( + f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'" + ) + return loss + + +def _diou_iou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + eps: float = 1e-7, +) -> Tuple[torch.Tensor, torch.Tensor]: + + intsct, union = _loss_inter_union(boxes1, boxes2) + iou = intsct / (union + eps) + # smallest enclosing box + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + # The diagonal distance of the smallest enclosing box squared + diagonal_distance_squared = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps + # centers of boxes + x_p = (x2 + x1) / 2 + y_p = (y2 + y1) / 2 + x_g = (x1g + x2g) / 2 + y_g = (y1g + y2g) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + loss = 1 - iou + (centers_distance_squared / diagonal_distance_squared) + return loss, iou diff --git a/lib/python3.11/site-packages/torchvision/ops/drop_block.py b/lib/python3.11/site-packages/torchvision/ops/drop_block.py new file mode 100644 index 0000000000000000000000000000000000000000..e65496ea29ad1ee6e04bc19280a00a397a4f1040 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/drop_block.py @@ -0,0 +1,155 @@ +import torch +import torch.fx +import torch.nn.functional as F +from torch import nn, Tensor + +from ..utils import _log_api_usage_once + + +def drop_block2d( + input: Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True +) -> Tensor: + """ + Implements DropBlock2d from `"DropBlock: A regularization method for convolutional networks" + `. + + Args: + input (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one + being its batch i.e. a batch with ``N`` rows. + p (float): Probability of an element to be dropped. + block_size (int): Size of the block to drop. + inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. + eps (float): A value added to the denominator for numerical stability. Default: 1e-6. + training (bool): apply dropblock if is ``True``. Default: ``True``. + + Returns: + Tensor[N, C, H, W]: The randomly zeroed tensor after dropblock. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(drop_block2d) + if p < 0.0 or p > 1.0: + raise ValueError(f"drop probability has to be between 0 and 1, but got {p}.") + if input.ndim != 4: + raise ValueError(f"input should be 4 dimensional. Got {input.ndim} dimensions.") + if not training or p == 0.0: + return input + + N, C, H, W = input.size() + block_size = min(block_size, W, H) + # compute the gamma of Bernoulli distribution + gamma = (p * H * W) / ((block_size**2) * ((H - block_size + 1) * (W - block_size + 1))) + noise = torch.empty((N, C, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device) + noise.bernoulli_(gamma) + + noise = F.pad(noise, [block_size // 2] * 4, value=0) + noise = F.max_pool2d(noise, stride=(1, 1), kernel_size=(block_size, block_size), padding=block_size // 2) + noise = 1 - noise + normalize_scale = noise.numel() / (eps + noise.sum()) + if inplace: + input.mul_(noise).mul_(normalize_scale) + else: + input = input * noise * normalize_scale + return input + + +def drop_block3d( + input: Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True +) -> Tensor: + """ + Implements DropBlock3d from `"DropBlock: A regularization method for convolutional networks" + `. + + Args: + input (Tensor[N, C, D, H, W]): The input tensor or 5-dimensions with the first one + being its batch i.e. a batch with ``N`` rows. + p (float): Probability of an element to be dropped. + block_size (int): Size of the block to drop. + inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. + eps (float): A value added to the denominator for numerical stability. Default: 1e-6. + training (bool): apply dropblock if is ``True``. Default: ``True``. + + Returns: + Tensor[N, C, D, H, W]: The randomly zeroed tensor after dropblock. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(drop_block3d) + if p < 0.0 or p > 1.0: + raise ValueError(f"drop probability has to be between 0 and 1, but got {p}.") + if input.ndim != 5: + raise ValueError(f"input should be 5 dimensional. Got {input.ndim} dimensions.") + if not training or p == 0.0: + return input + + N, C, D, H, W = input.size() + block_size = min(block_size, D, H, W) + # compute the gamma of Bernoulli distribution + gamma = (p * D * H * W) / ((block_size**3) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1))) + noise = torch.empty( + (N, C, D - block_size + 1, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device + ) + noise.bernoulli_(gamma) + + noise = F.pad(noise, [block_size // 2] * 6, value=0) + noise = F.max_pool3d( + noise, stride=(1, 1, 1), kernel_size=(block_size, block_size, block_size), padding=block_size // 2 + ) + noise = 1 - noise + normalize_scale = noise.numel() / (eps + noise.sum()) + if inplace: + input.mul_(noise).mul_(normalize_scale) + else: + input = input * noise * normalize_scale + return input + + +torch.fx.wrap("drop_block2d") + + +class DropBlock2d(nn.Module): + """ + See :func:`drop_block2d`. + """ + + def __init__(self, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06) -> None: + super().__init__() + + self.p = p + self.block_size = block_size + self.inplace = inplace + self.eps = eps + + def forward(self, input: Tensor) -> Tensor: + """ + Args: + input (Tensor): Input feature map on which some areas will be randomly + dropped. + Returns: + Tensor: The tensor after DropBlock layer. + """ + return drop_block2d(input, self.p, self.block_size, self.inplace, self.eps, self.training) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(p={self.p}, block_size={self.block_size}, inplace={self.inplace})" + return s + + +torch.fx.wrap("drop_block3d") + + +class DropBlock3d(DropBlock2d): + """ + See :func:`drop_block3d`. + """ + + def __init__(self, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06) -> None: + super().__init__(p, block_size, inplace, eps) + + def forward(self, input: Tensor) -> Tensor: + """ + Args: + input (Tensor): Input feature map on which some areas will be randomly + dropped. + Returns: + Tensor: The tensor after DropBlock layer. + """ + return drop_block3d(input, self.p, self.block_size, self.inplace, self.eps, self.training) diff --git a/lib/python3.11/site-packages/torchvision/ops/feature_pyramid_network.py b/lib/python3.11/site-packages/torchvision/ops/feature_pyramid_network.py new file mode 100644 index 0000000000000000000000000000000000000000..2e7aef0e2fad773ac884d576f781a305e5e880d0 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/feature_pyramid_network.py @@ -0,0 +1,250 @@ +from collections import OrderedDict +from typing import Callable, Dict, List, Optional, Tuple + +import torch.nn.functional as F +from torch import nn, Tensor + +from ..ops.misc import Conv2dNormActivation +from ..utils import _log_api_usage_once + + +class ExtraFPNBlock(nn.Module): + """ + Base class for the extra block in the FPN. + + Args: + results (List[Tensor]): the result of the FPN + x (List[Tensor]): the original feature maps + names (List[str]): the names for each one of the + original feature maps + + Returns: + results (List[Tensor]): the extended set of results + of the FPN + names (List[str]): the extended set of names for the results + """ + + def forward( + self, + results: List[Tensor], + x: List[Tensor], + names: List[str], + ) -> Tuple[List[Tensor], List[str]]: + pass + + +class FeaturePyramidNetwork(nn.Module): + """ + Module that adds a FPN from on top of a set of feature maps. This is based on + `"Feature Pyramid Network for Object Detection" `_. + + The feature maps are currently supposed to be in increasing depth + order. + + The input to the model is expected to be an OrderedDict[Tensor], containing + the feature maps on top of which the FPN will be added. + + Args: + in_channels_list (list[int]): number of channels for each feature map that + is passed to the module + out_channels (int): number of channels of the FPN representation + extra_blocks (ExtraFPNBlock or None): if provided, extra operations will + be performed. It is expected to take the fpn features, the original + features and the names of the original features as input, and returns + a new list of feature maps and their corresponding names + norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None + + Examples:: + + >>> m = torchvision.ops.FeaturePyramidNetwork([10, 20, 30], 5) + >>> # get some dummy data + >>> x = OrderedDict() + >>> x['feat0'] = torch.rand(1, 10, 64, 64) + >>> x['feat2'] = torch.rand(1, 20, 16, 16) + >>> x['feat3'] = torch.rand(1, 30, 8, 8) + >>> # compute the FPN on top of x + >>> output = m(x) + >>> print([(k, v.shape) for k, v in output.items()]) + >>> # returns + >>> [('feat0', torch.Size([1, 5, 64, 64])), + >>> ('feat2', torch.Size([1, 5, 16, 16])), + >>> ('feat3', torch.Size([1, 5, 8, 8]))] + + """ + + _version = 2 + + def __init__( + self, + in_channels_list: List[int], + out_channels: int, + extra_blocks: Optional[ExtraFPNBlock] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + ): + super().__init__() + _log_api_usage_once(self) + self.inner_blocks = nn.ModuleList() + self.layer_blocks = nn.ModuleList() + for in_channels in in_channels_list: + if in_channels == 0: + raise ValueError("in_channels=0 is currently not supported") + inner_block_module = Conv2dNormActivation( + in_channels, out_channels, kernel_size=1, padding=0, norm_layer=norm_layer, activation_layer=None + ) + layer_block_module = Conv2dNormActivation( + out_channels, out_channels, kernel_size=3, norm_layer=norm_layer, activation_layer=None + ) + self.inner_blocks.append(inner_block_module) + self.layer_blocks.append(layer_block_module) + + # initialize parameters now to avoid modifying the initialization of top_blocks + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_uniform_(m.weight, a=1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + if extra_blocks is not None: + if not isinstance(extra_blocks, ExtraFPNBlock): + raise TypeError(f"extra_blocks should be of type ExtraFPNBlock not {type(extra_blocks)}") + self.extra_blocks = extra_blocks + + def _load_from_state_dict( + self, + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + num_blocks = len(self.inner_blocks) + for block in ["inner_blocks", "layer_blocks"]: + for i in range(num_blocks): + for type in ["weight", "bias"]: + old_key = f"{prefix}{block}.{i}.{type}" + new_key = f"{prefix}{block}.{i}.0.{type}" + if old_key in state_dict: + state_dict[new_key] = state_dict.pop(old_key) + + super()._load_from_state_dict( + state_dict, + prefix, + local_metadata, + strict, + missing_keys, + unexpected_keys, + error_msgs, + ) + + def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor: + """ + This is equivalent to self.inner_blocks[idx](x), + but torchscript doesn't support this yet + """ + num_blocks = len(self.inner_blocks) + if idx < 0: + idx += num_blocks + out = x + for i, module in enumerate(self.inner_blocks): + if i == idx: + out = module(x) + return out + + def get_result_from_layer_blocks(self, x: Tensor, idx: int) -> Tensor: + """ + This is equivalent to self.layer_blocks[idx](x), + but torchscript doesn't support this yet + """ + num_blocks = len(self.layer_blocks) + if idx < 0: + idx += num_blocks + out = x + for i, module in enumerate(self.layer_blocks): + if i == idx: + out = module(x) + return out + + def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]: + """ + Computes the FPN for a set of feature maps. + + Args: + x (OrderedDict[Tensor]): feature maps for each feature level. + + Returns: + results (OrderedDict[Tensor]): feature maps after FPN layers. + They are ordered from the highest resolution first. + """ + # unpack OrderedDict into two lists for easier handling + names = list(x.keys()) + x = list(x.values()) + + last_inner = self.get_result_from_inner_blocks(x[-1], -1) + results = [] + results.append(self.get_result_from_layer_blocks(last_inner, -1)) + + for idx in range(len(x) - 2, -1, -1): + inner_lateral = self.get_result_from_inner_blocks(x[idx], idx) + feat_shape = inner_lateral.shape[-2:] + inner_top_down = F.interpolate(last_inner, size=feat_shape, mode="nearest") + last_inner = inner_lateral + inner_top_down + results.insert(0, self.get_result_from_layer_blocks(last_inner, idx)) + + if self.extra_blocks is not None: + results, names = self.extra_blocks(results, x, names) + + # make it back an OrderedDict + out = OrderedDict([(k, v) for k, v in zip(names, results)]) + + return out + + +class LastLevelMaxPool(ExtraFPNBlock): + """ + Applies a max_pool2d (not actual max_pool2d, we just subsample) on top of the last feature map + """ + + def forward( + self, + x: List[Tensor], + y: List[Tensor], + names: List[str], + ) -> Tuple[List[Tensor], List[str]]: + names.append("pool") + # Use max pooling to simulate stride 2 subsampling + x.append(F.max_pool2d(x[-1], kernel_size=1, stride=2, padding=0)) + return x, names + + +class LastLevelP6P7(ExtraFPNBlock): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7. + """ + + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + nn.init.kaiming_uniform_(module.weight, a=1) + nn.init.constant_(module.bias, 0) + self.use_P5 = in_channels == out_channels + + def forward( + self, + p: List[Tensor], + c: List[Tensor], + names: List[str], + ) -> Tuple[List[Tensor], List[str]]: + p5, c5 = p[-1], c[-1] + x = p5 if self.use_P5 else c5 + p6 = self.p6(x) + p7 = self.p7(F.relu(p6)) + p.extend([p6, p7]) + names.extend(["p6", "p7"]) + return p, names diff --git a/lib/python3.11/site-packages/torchvision/ops/focal_loss.py b/lib/python3.11/site-packages/torchvision/ops/focal_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..08c282555fca01cbee78d79c2a0fa7bb8a9df570 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/focal_loss.py @@ -0,0 +1,58 @@ +import torch +import torch.nn.functional as F + +from ..utils import _log_api_usage_once + + +def sigmoid_focal_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + alpha: float = 0.25, + gamma: float = 2, + reduction: str = "none", +) -> torch.Tensor: + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + + Args: + inputs (Tensor): A float tensor of arbitrary shape. + The predictions for each example. + targets (Tensor): A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha (float): Weighting factor in range (0,1) to balance + positive vs negative examples or -1 for ignore. Default: ``0.25``. + gamma (float): Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. Default: ``2``. + reduction (string): ``'none'`` | ``'mean'`` | ``'sum'`` + ``'none'``: No reduction will be applied to the output. + ``'mean'``: The output will be averaged. + ``'sum'``: The output will be summed. Default: ``'none'``. + Returns: + Loss tensor with the reduction option applied. + """ + # Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(sigmoid_focal_loss) + p = torch.sigmoid(inputs) + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = p * targets + (1 - p) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + # Check reduction option and return loss accordingly + if reduction == "none": + pass + elif reduction == "mean": + loss = loss.mean() + elif reduction == "sum": + loss = loss.sum() + else: + raise ValueError( + f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'" + ) + return loss diff --git a/lib/python3.11/site-packages/torchvision/ops/giou_loss.py b/lib/python3.11/site-packages/torchvision/ops/giou_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..ec8bc8852fe71258cb5a46fdf5428581a23b4c3e --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/giou_loss.py @@ -0,0 +1,76 @@ +import torch + +from ..utils import _log_api_usage_once +from ._utils import _loss_inter_union, _upcast_non_float + + +def generalized_box_iou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + + """ + Gradient-friendly IoU loss with an additional penalty that is non-zero when the + boxes do not overlap and scales with the size of their smallest enclosing box. + This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the + same dimensions. + + Args: + boxes1 (Tensor[N, 4] or Tensor[4]): first set of boxes + boxes2 (Tensor[N, 4] or Tensor[4]): second set of boxes + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be + applied to the output. ``'mean'``: The output will be averaged. + ``'sum'``: The output will be summed. Default: ``'none'`` + eps (float): small number to prevent division by zero. Default: 1e-7 + + Returns: + Tensor: Loss tensor with the reduction option applied. + + Reference: + Hamid Rezatofighi et al.: Generalized Intersection over Union: + A Metric and A Loss for Bounding Box Regression: + https://arxiv.org/abs/1902.09630 + """ + + # Original implementation from https://github.com/facebookresearch/fvcore/blob/bfff2ef/fvcore/nn/giou_loss.py + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(generalized_box_iou_loss) + + boxes1 = _upcast_non_float(boxes1) + boxes2 = _upcast_non_float(boxes2) + intsctk, unionk = _loss_inter_union(boxes1, boxes2) + iouk = intsctk / (unionk + eps) + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + + area_c = (xc2 - xc1) * (yc2 - yc1) + miouk = iouk - ((area_c - unionk) / (area_c + eps)) + + loss = 1 - miouk + + # Check reduction option and return loss accordingly + if reduction == "none": + pass + elif reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + else: + raise ValueError( + f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'" + ) + return loss diff --git a/lib/python3.11/site-packages/torchvision/ops/misc.py b/lib/python3.11/site-packages/torchvision/ops/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..0bbea6bce43425dec79a655857a92b1f36883098 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/misc.py @@ -0,0 +1,320 @@ +import warnings +from typing import Callable, List, Optional, Sequence, Tuple, Union + +import torch +from torch import Tensor + +from ..utils import _log_api_usage_once, _make_ntuple + + +interpolate = torch.nn.functional.interpolate + + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed + + Args: + num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)`` + eps (float): a value added to the denominator for numerical stability. Default: 1e-5 + """ + + def __init__( + self, + num_features: int, + eps: float = 1e-5, + ): + super().__init__() + _log_api_usage_once(self) + self.eps = eps + self.register_buffer("weight", torch.ones(num_features)) + self.register_buffer("bias", torch.zeros(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features)) + + def _load_from_state_dict( + self, + state_dict: dict, + prefix: str, + local_metadata: dict, + strict: bool, + missing_keys: List[str], + unexpected_keys: List[str], + error_msgs: List[str], + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x: Tensor) -> Tensor: + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + scale = w * (rv + self.eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps})" + + +class ConvNormActivation(torch.nn.Sequential): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, ...]] = 3, + stride: Union[int, Tuple[int, ...]] = 1, + padding: Optional[Union[int, Tuple[int, ...], str]] = None, + groups: int = 1, + norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d, + activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU, + dilation: Union[int, Tuple[int, ...]] = 1, + inplace: Optional[bool] = True, + bias: Optional[bool] = None, + conv_layer: Callable[..., torch.nn.Module] = torch.nn.Conv2d, + ) -> None: + + if padding is None: + if isinstance(kernel_size, int) and isinstance(dilation, int): + padding = (kernel_size - 1) // 2 * dilation + else: + _conv_dim = len(kernel_size) if isinstance(kernel_size, Sequence) else len(dilation) + kernel_size = _make_ntuple(kernel_size, _conv_dim) + dilation = _make_ntuple(dilation, _conv_dim) + padding = tuple((kernel_size[i] - 1) // 2 * dilation[i] for i in range(_conv_dim)) + if bias is None: + bias = norm_layer is None + + layers = [ + conv_layer( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=dilation, + groups=groups, + bias=bias, + ) + ] + + if norm_layer is not None: + layers.append(norm_layer(out_channels)) + + if activation_layer is not None: + params = {} if inplace is None else {"inplace": inplace} + layers.append(activation_layer(**params)) + super().__init__(*layers) + _log_api_usage_once(self) + self.out_channels = out_channels + + if self.__class__ == ConvNormActivation: + warnings.warn( + "Don't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead." + ) + + +class Conv2dNormActivation(ConvNormActivation): + """ + Configurable block used for Convolution2d-Normalization-Activation blocks. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block + kernel_size: (int, optional): Size of the convolving kernel. Default: 3 + stride (int, optional): Stride of the convolution. Default: 1 + padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation`` + groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 + norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm2d`` + activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` + dilation (int): Spacing between kernel elements. Default: 1 + inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True`` + bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``. + + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]] = 3, + stride: Union[int, Tuple[int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int], str]] = None, + groups: int = 1, + norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d, + activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU, + dilation: Union[int, Tuple[int, int]] = 1, + inplace: Optional[bool] = True, + bias: Optional[bool] = None, + ) -> None: + + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups, + norm_layer, + activation_layer, + dilation, + inplace, + bias, + torch.nn.Conv2d, + ) + + +class Conv3dNormActivation(ConvNormActivation): + """ + Configurable block used for Convolution3d-Normalization-Activation blocks. + + Args: + in_channels (int): Number of channels in the input video. + out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block + kernel_size: (int, optional): Size of the convolving kernel. Default: 3 + stride (int, optional): Stride of the convolution. Default: 1 + padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation`` + groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 + norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm3d`` + activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` + dilation (int): Spacing between kernel elements. Default: 1 + inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True`` + bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int, int]] = 3, + stride: Union[int, Tuple[int, int, int]] = 1, + padding: Optional[Union[int, Tuple[int, int, int], str]] = None, + groups: int = 1, + norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm3d, + activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU, + dilation: Union[int, Tuple[int, int, int]] = 1, + inplace: Optional[bool] = True, + bias: Optional[bool] = None, + ) -> None: + + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups, + norm_layer, + activation_layer, + dilation, + inplace, + bias, + torch.nn.Conv3d, + ) + + +class SqueezeExcitation(torch.nn.Module): + """ + This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1). + Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3. + + Args: + input_channels (int): Number of channels in the input image + squeeze_channels (int): Number of squeeze channels + activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU`` + scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid`` + """ + + def __init__( + self, + input_channels: int, + squeeze_channels: int, + activation: Callable[..., torch.nn.Module] = torch.nn.ReLU, + scale_activation: Callable[..., torch.nn.Module] = torch.nn.Sigmoid, + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.avgpool = torch.nn.AdaptiveAvgPool2d(1) + self.fc1 = torch.nn.Conv2d(input_channels, squeeze_channels, 1) + self.fc2 = torch.nn.Conv2d(squeeze_channels, input_channels, 1) + self.activation = activation() + self.scale_activation = scale_activation() + + def _scale(self, input: Tensor) -> Tensor: + scale = self.avgpool(input) + scale = self.fc1(scale) + scale = self.activation(scale) + scale = self.fc2(scale) + return self.scale_activation(scale) + + def forward(self, input: Tensor) -> Tensor: + scale = self._scale(input) + return scale * input + + +class MLP(torch.nn.Sequential): + """This block implements the multi-layer perceptron (MLP) module. + + Args: + in_channels (int): Number of channels of the input + hidden_channels (List[int]): List of the hidden channel dimensions + norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the linear layer. If ``None`` this layer won't be used. Default: ``None`` + activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the linear layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU`` + inplace (bool, optional): Parameter for the activation layer, which can optionally do the operation in-place. + Default is ``None``, which uses the respective default values of the ``activation_layer`` and Dropout layer. + bias (bool): Whether to use bias in the linear layer. Default ``True`` + dropout (float): The probability for the dropout layer. Default: 0.0 + """ + + def __init__( + self, + in_channels: int, + hidden_channels: List[int], + norm_layer: Optional[Callable[..., torch.nn.Module]] = None, + activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU, + inplace: Optional[bool] = None, + bias: bool = True, + dropout: float = 0.0, + ): + # The addition of `norm_layer` is inspired from the implementation of TorchMultimodal: + # https://github.com/facebookresearch/multimodal/blob/5dec8a/torchmultimodal/modules/layers/mlp.py + params = {} if inplace is None else {"inplace": inplace} + + layers = [] + in_dim = in_channels + for hidden_dim in hidden_channels[:-1]: + layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias)) + if norm_layer is not None: + layers.append(norm_layer(hidden_dim)) + layers.append(activation_layer(**params)) + layers.append(torch.nn.Dropout(dropout, **params)) + in_dim = hidden_dim + + layers.append(torch.nn.Linear(in_dim, hidden_channels[-1], bias=bias)) + layers.append(torch.nn.Dropout(dropout, **params)) + + super().__init__(*layers) + _log_api_usage_once(self) + + +class Permute(torch.nn.Module): + """This module returns a view of the tensor input with its dimensions permuted. + + Args: + dims (List[int]): The desired ordering of dimensions + """ + + def __init__(self, dims: List[int]): + super().__init__() + self.dims = dims + + def forward(self, x: Tensor) -> Tensor: + return torch.permute(x, self.dims) diff --git a/lib/python3.11/site-packages/torchvision/ops/poolers.py b/lib/python3.11/site-packages/torchvision/ops/poolers.py new file mode 100644 index 0000000000000000000000000000000000000000..9cdd83a598bea0a044179269f8369b58401801d1 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/poolers.py @@ -0,0 +1,327 @@ +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.fx +import torchvision +from torch import nn, Tensor +from torchvision.ops.boxes import box_area + +from ..utils import _log_api_usage_once +from .roi_align import roi_align + + +# copying result_idx_in_level to a specific index in result[] +# is not supported by ONNX tracing yet. +# _onnx_merge_levels() is an implementation supported by ONNX +# that merges the levels to the right indices +@torch.jit.unused +def _onnx_merge_levels(levels: Tensor, unmerged_results: List[Tensor]) -> Tensor: + first_result = unmerged_results[0] + dtype, device = first_result.dtype, first_result.device + res = torch.zeros( + (levels.size(0), first_result.size(1), first_result.size(2), first_result.size(3)), dtype=dtype, device=device + ) + for level in range(len(unmerged_results)): + index = torch.where(levels == level)[0].view(-1, 1, 1, 1) + index = index.expand( + index.size(0), + unmerged_results[level].size(1), + unmerged_results[level].size(2), + unmerged_results[level].size(3), + ) + res = res.scatter(0, index, unmerged_results[level]) + return res + + +# TODO: (eellison) T54974082 https://github.com/pytorch/pytorch/issues/26744/pytorch/issues/26744 +def initLevelMapper( + k_min: int, + k_max: int, + canonical_scale: int = 224, + canonical_level: int = 4, + eps: float = 1e-6, +): + return LevelMapper(k_min, k_max, canonical_scale, canonical_level, eps) + + +class LevelMapper: + """Determine which FPN level each RoI in a set of RoIs should map to based + on the heuristic in the FPN paper. + + Args: + k_min (int) + k_max (int) + canonical_scale (int) + canonical_level (int) + eps (float) + """ + + def __init__( + self, + k_min: int, + k_max: int, + canonical_scale: int = 224, + canonical_level: int = 4, + eps: float = 1e-6, + ): + self.k_min = k_min + self.k_max = k_max + self.s0 = canonical_scale + self.lvl0 = canonical_level + self.eps = eps + + def __call__(self, boxlists: List[Tensor]) -> Tensor: + """ + Args: + boxlists (list[BoxList]) + """ + # Compute level ids + s = torch.sqrt(torch.cat([box_area(boxlist) for boxlist in boxlists])) + + # Eqn.(1) in FPN paper + target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0) + torch.tensor(self.eps, dtype=s.dtype)) + target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max) + return (target_lvls.to(torch.int64) - self.k_min).to(torch.int64) + + +def _convert_to_roi_format(boxes: List[Tensor]) -> Tensor: + concat_boxes = torch.cat(boxes, dim=0) + device, dtype = concat_boxes.device, concat_boxes.dtype + ids = torch.cat( + [torch.full_like(b[:, :1], i, dtype=dtype, layout=torch.strided, device=device) for i, b in enumerate(boxes)], + dim=0, + ) + rois = torch.cat([ids, concat_boxes], dim=1) + return rois + + +def _infer_scale(feature: Tensor, original_size: List[int]) -> float: + # assumption: the scale is of the form 2 ** (-k), with k integer + size = feature.shape[-2:] + possible_scales: List[float] = [] + for s1, s2 in zip(size, original_size): + approx_scale = float(s1) / float(s2) + scale = 2 ** float(torch.tensor(approx_scale).log2().round()) + possible_scales.append(scale) + return possible_scales[0] + + +@torch.fx.wrap +def _setup_scales( + features: List[Tensor], image_shapes: List[Tuple[int, int]], canonical_scale: int, canonical_level: int +) -> Tuple[List[float], LevelMapper]: + if not image_shapes: + raise ValueError("images list should not be empty") + max_x = 0 + max_y = 0 + for shape in image_shapes: + max_x = max(shape[0], max_x) + max_y = max(shape[1], max_y) + original_input_shape = (max_x, max_y) + + scales = [_infer_scale(feat, original_input_shape) for feat in features] + # get the levels in the feature map by leveraging the fact that the network always + # downsamples by a factor of 2 at each level. + lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item() + lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item() + + map_levels = initLevelMapper( + int(lvl_min), + int(lvl_max), + canonical_scale=canonical_scale, + canonical_level=canonical_level, + ) + return scales, map_levels + + +@torch.fx.wrap +def _filter_input(x: Dict[str, Tensor], featmap_names: List[str]) -> List[Tensor]: + x_filtered = [] + for k, v in x.items(): + if k in featmap_names: + x_filtered.append(v) + return x_filtered + + +@torch.fx.wrap +def _multiscale_roi_align( + x_filtered: List[Tensor], + boxes: List[Tensor], + output_size: List[int], + sampling_ratio: int, + scales: Optional[List[float]], + mapper: Optional[LevelMapper], +) -> Tensor: + """ + Args: + x_filtered (List[Tensor]): List of input tensors. + boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in + (x1, y1, x2, y2) format and in the image reference size, not the feature map + reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + output_size (Union[List[Tuple[int, int]], List[int]]): size of the output + sampling_ratio (int): sampling ratio for ROIAlign + scales (Optional[List[float]]): If None, scales will be automatically inferred. Default value is None. + mapper (Optional[LevelMapper]): If none, mapper will be automatically inferred. Default value is None. + Returns: + result (Tensor) + """ + if scales is None or mapper is None: + raise ValueError("scales and mapper should not be None") + + num_levels = len(x_filtered) + rois = _convert_to_roi_format(boxes) + + if num_levels == 1: + return roi_align( + x_filtered[0], + rois, + output_size=output_size, + spatial_scale=scales[0], + sampling_ratio=sampling_ratio, + ) + + levels = mapper(boxes) + + num_rois = len(rois) + num_channels = x_filtered[0].shape[1] + + dtype, device = x_filtered[0].dtype, x_filtered[0].device + result = torch.zeros( + ( + num_rois, + num_channels, + ) + + output_size, + dtype=dtype, + device=device, + ) + + tracing_results = [] + for level, (per_level_feature, scale) in enumerate(zip(x_filtered, scales)): + idx_in_level = torch.where(levels == level)[0] + rois_per_level = rois[idx_in_level] + + result_idx_in_level = roi_align( + per_level_feature, + rois_per_level, + output_size=output_size, + spatial_scale=scale, + sampling_ratio=sampling_ratio, + ) + + if torchvision._is_tracing(): + tracing_results.append(result_idx_in_level.to(dtype)) + else: + # result and result_idx_in_level's dtypes are based on dtypes of different + # elements in x_filtered. x_filtered contains tensors output by different + # layers. When autocast is active, it may choose different dtypes for + # different layers' outputs. Therefore, we defensively match result's dtype + # before copying elements from result_idx_in_level in the following op. + # We need to cast manually (can't rely on autocast to cast for us) because + # the op acts on result in-place, and autocast only affects out-of-place ops. + result[idx_in_level] = result_idx_in_level.to(result.dtype) + + if torchvision._is_tracing(): + result = _onnx_merge_levels(levels, tracing_results) + + return result + + +class MultiScaleRoIAlign(nn.Module): + """ + Multi-scale RoIAlign pooling, which is useful for detection with or without FPN. + + It infers the scale of the pooling via the heuristics specified in eq. 1 + of the `Feature Pyramid Network paper `_. + They keyword-only parameters ``canonical_scale`` and ``canonical_level`` + correspond respectively to ``224`` and ``k0=4`` in eq. 1, and + have the following meaning: ``canonical_level`` is the target level of the pyramid from + which to pool a region of interest with ``w x h = canonical_scale x canonical_scale``. + + Args: + featmap_names (List[str]): the names of the feature maps that will be used + for the pooling. + output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region + sampling_ratio (int): sampling ratio for ROIAlign + canonical_scale (int, optional): canonical_scale for LevelMapper + canonical_level (int, optional): canonical_level for LevelMapper + + Examples:: + + >>> m = torchvision.ops.MultiScaleRoIAlign(['feat1', 'feat3'], 3, 2) + >>> i = OrderedDict() + >>> i['feat1'] = torch.rand(1, 5, 64, 64) + >>> i['feat2'] = torch.rand(1, 5, 32, 32) # this feature won't be used in the pooling + >>> i['feat3'] = torch.rand(1, 5, 16, 16) + >>> # create some random bounding boxes + >>> boxes = torch.rand(6, 4) * 256; boxes[:, 2:] += boxes[:, :2] + >>> # original image size, before computing the feature maps + >>> image_sizes = [(512, 512)] + >>> output = m(i, [boxes], image_sizes) + >>> print(output.shape) + >>> torch.Size([6, 5, 3, 3]) + + """ + + __annotations__ = {"scales": Optional[List[float]], "map_levels": Optional[LevelMapper]} + + def __init__( + self, + featmap_names: List[str], + output_size: Union[int, Tuple[int], List[int]], + sampling_ratio: int, + *, + canonical_scale: int = 224, + canonical_level: int = 4, + ): + super().__init__() + _log_api_usage_once(self) + if isinstance(output_size, int): + output_size = (output_size, output_size) + self.featmap_names = featmap_names + self.sampling_ratio = sampling_ratio + self.output_size = tuple(output_size) + self.scales = None + self.map_levels = None + self.canonical_scale = canonical_scale + self.canonical_level = canonical_level + + def forward( + self, + x: Dict[str, Tensor], + boxes: List[Tensor], + image_shapes: List[Tuple[int, int]], + ) -> Tensor: + """ + Args: + x (OrderedDict[Tensor]): feature maps for each level. They are assumed to have + all the same number of channels, but they can have different sizes. + boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in + (x1, y1, x2, y2) format and in the image reference size, not the feature map + reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + image_shapes (List[Tuple[height, width]]): the sizes of each image before they + have been fed to a CNN to obtain feature maps. This allows us to infer the + scale factor for each one of the levels to be pooled. + Returns: + result (Tensor) + """ + x_filtered = _filter_input(x, self.featmap_names) + if self.scales is None or self.map_levels is None: + self.scales, self.map_levels = _setup_scales( + x_filtered, image_shapes, self.canonical_scale, self.canonical_level + ) + + return _multiscale_roi_align( + x_filtered, + boxes, + self.output_size, + self.sampling_ratio, + self.scales, + self.map_levels, + ) + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(featmap_names={self.featmap_names}, " + f"output_size={self.output_size}, sampling_ratio={self.sampling_ratio})" + ) diff --git a/lib/python3.11/site-packages/torchvision/ops/ps_roi_align.py b/lib/python3.11/site-packages/torchvision/ops/ps_roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..82809b8f8885667b28eccd22aca60d1dca02f3bf --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/ps_roi_align.py @@ -0,0 +1,90 @@ +import torch +import torch.fx +from torch import nn, Tensor +from torch.nn.modules.utils import _pair +from torchvision.extension import _assert_has_ops + +from ..utils import _log_api_usage_once +from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format + + +@torch.fx.wrap +def ps_roi_align( + input: Tensor, + boxes: Tensor, + output_size: int, + spatial_scale: float = 1.0, + sampling_ratio: int = -1, +) -> Tensor: + """ + Performs Position-Sensitive Region of Interest (RoI) Align operator + mentioned in Light-Head R-CNN. + + Args: + input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element + contains ``C`` feature maps of dimensions ``H x W``. + boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) + format where the regions will be taken from. + The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + If a single Tensor is passed, then the first column should + contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. + If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i + in the batch. + output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling + is performed, as (height, width). + spatial_scale (float): a scaling factor that maps the box coordinates to + the input coordinates. For example, if your boxes are defined on the scale + of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of + the original image), you'll want to set this to 0.5. Default: 1.0 + sampling_ratio (int): number of sampling points in the interpolation grid + used to compute the output value of each pooled output bin. If > 0, + then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If + <= 0, then an adaptive number of grid points are used (computed as + ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 + + Returns: + Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(ps_roi_align) + _assert_has_ops() + check_roi_boxes_shape(boxes) + rois = boxes + output_size = _pair(output_size) + if not isinstance(rois, torch.Tensor): + rois = convert_boxes_to_roi_format(rois) + output, _ = torch.ops.torchvision.ps_roi_align( + input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio + ) + return output + + +class PSRoIAlign(nn.Module): + """ + See :func:`ps_roi_align`. + """ + + def __init__( + self, + output_size: int, + spatial_scale: float, + sampling_ratio: int, + ): + super().__init__() + _log_api_usage_once(self) + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input: Tensor, rois: Tensor) -> Tensor: + return ps_roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio) + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"output_size={self.output_size}" + f", spatial_scale={self.spatial_scale}" + f", sampling_ratio={self.sampling_ratio}" + f")" + ) + return s diff --git a/lib/python3.11/site-packages/torchvision/ops/ps_roi_pool.py b/lib/python3.11/site-packages/torchvision/ops/ps_roi_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..15292dcad97490aaa740cdec2d0aedb31e5662eb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/ps_roi_pool.py @@ -0,0 +1,70 @@ +import torch +import torch.fx +from torch import nn, Tensor +from torch.nn.modules.utils import _pair +from torchvision.extension import _assert_has_ops + +from ..utils import _log_api_usage_once +from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format + + +@torch.fx.wrap +def ps_roi_pool( + input: Tensor, + boxes: Tensor, + output_size: int, + spatial_scale: float = 1.0, +) -> Tensor: + """ + Performs Position-Sensitive Region of Interest (RoI) Pool operator + described in R-FCN + + Args: + input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element + contains ``C`` feature maps of dimensions ``H x W``. + boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) + format where the regions will be taken from. + The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + If a single Tensor is passed, then the first column should + contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. + If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i + in the batch. + output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling + is performed, as (height, width). + spatial_scale (float): a scaling factor that maps the box coordinates to + the input coordinates. For example, if your boxes are defined on the scale + of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of + the original image), you'll want to set this to 0.5. Default: 1.0 + + Returns: + Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(ps_roi_pool) + _assert_has_ops() + check_roi_boxes_shape(boxes) + rois = boxes + output_size = _pair(output_size) + if not isinstance(rois, torch.Tensor): + rois = convert_boxes_to_roi_format(rois) + output, _ = torch.ops.torchvision.ps_roi_pool(input, rois, spatial_scale, output_size[0], output_size[1]) + return output + + +class PSRoIPool(nn.Module): + """ + See :func:`ps_roi_pool`. + """ + + def __init__(self, output_size: int, spatial_scale: float): + super().__init__() + _log_api_usage_once(self) + self.output_size = output_size + self.spatial_scale = spatial_scale + + def forward(self, input: Tensor, rois: Tensor) -> Tensor: + return ps_roi_pool(input, rois, self.output_size, self.spatial_scale) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(output_size={self.output_size}, spatial_scale={self.spatial_scale})" + return s diff --git a/lib/python3.11/site-packages/torchvision/ops/roi_align.py b/lib/python3.11/site-packages/torchvision/ops/roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..8b616ca9161c64e3a1fe1004776e466c5118d17d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/roi_align.py @@ -0,0 +1,293 @@ +import functools +from typing import List, Union + +import torch +import torch.fx +from torch import nn, Tensor +from torch._dynamo.utils import is_compile_supported +from torch.jit.annotations import BroadcastingList2 +from torch.nn.modules.utils import _pair +from torchvision.extension import _assert_has_ops, _has_ops + +from ..utils import _log_api_usage_once +from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format + + +def lazy_compile(**compile_kwargs): + """Lazily wrap a function with torch.compile on the first call + + This avoids eagerly importing dynamo. + """ + + def decorate_fn(fn): + @functools.wraps(fn) + def compile_hook(*args, **kwargs): + compiled_fn = torch.compile(fn, **compile_kwargs) + globals()[fn.__name__] = functools.wraps(fn)(compiled_fn) + return compiled_fn(*args, **kwargs) + + return compile_hook + + return decorate_fn + + +# NB: all inputs are tensors +def _bilinear_interpolate( + input, # [N, C, H, W] + roi_batch_ind, # [K] + y, # [K, PH, IY] + x, # [K, PW, IX] + ymask, # [K, IY] + xmask, # [K, IX] +): + _, channels, height, width = input.size() + + # deal with inverse element out of feature map boundary + y = y.clamp(min=0) + x = x.clamp(min=0) + y_low = y.int() + x_low = x.int() + y_high = torch.where(y_low >= height - 1, height - 1, y_low + 1) + y_low = torch.where(y_low >= height - 1, height - 1, y_low) + y = torch.where(y_low >= height - 1, y.to(input.dtype), y) + + x_high = torch.where(x_low >= width - 1, width - 1, x_low + 1) + x_low = torch.where(x_low >= width - 1, width - 1, x_low) + x = torch.where(x_low >= width - 1, x.to(input.dtype), x) + + ly = y - y_low + lx = x - x_low + hy = 1.0 - ly + hx = 1.0 - lx + + # do bilinear interpolation, but respect the masking! + # TODO: It's possible the masking here is unnecessary if y and + # x were clamped appropriately; hard to tell + def masked_index( + y, # [K, PH, IY] + x, # [K, PW, IX] + ): + if ymask is not None: + assert xmask is not None + y = torch.where(ymask[:, None, :], y, 0) + x = torch.where(xmask[:, None, :], x, 0) + return input[ + roi_batch_ind[:, None, None, None, None, None], + torch.arange(channels, device=input.device)[None, :, None, None, None, None], + y[:, None, :, None, :, None], # prev [K, PH, IY] + x[:, None, None, :, None, :], # prev [K, PW, IX] + ] # [K, C, PH, PW, IY, IX] + + v1 = masked_index(y_low, x_low) + v2 = masked_index(y_low, x_high) + v3 = masked_index(y_high, x_low) + v4 = masked_index(y_high, x_high) + + # all ws preemptively [K, C, PH, PW, IY, IX] + def outer_prod(y, x): + return y[:, None, :, None, :, None] * x[:, None, None, :, None, :] + + w1 = outer_prod(hy, hx) + w2 = outer_prod(hy, lx) + w3 = outer_prod(ly, hx) + w4 = outer_prod(ly, lx) + + val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4 + return val + + +# TODO: this doesn't actually cache +# TODO: main library should make this easier to do +def maybe_cast(tensor): + if torch.is_autocast_enabled() and tensor.is_cuda and tensor.dtype != torch.double: + return tensor.float() + else: + return tensor + + +# This is a pure Python and differentiable implementation of roi_align. When +# run in eager mode, it uses a lot of memory, but when compiled it has +# acceptable memory usage. The main point of this implementation is that +# its backwards is deterministic. +# It is transcribed directly off of the roi_align CUDA kernel, see +# https://dev-discuss.pytorch.org/t/a-pure-python-implementation-of-roi-align-that-looks-just-like-its-cuda-kernel/1266 +@lazy_compile(dynamic=True) +def _roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned): + orig_dtype = input.dtype + + input = maybe_cast(input) + rois = maybe_cast(rois) + + _, _, height, width = input.size() + + ph = torch.arange(pooled_height, device=input.device) # [PH] + pw = torch.arange(pooled_width, device=input.device) # [PW] + + # input: [N, C, H, W] + # rois: [K, 5] + + roi_batch_ind = rois[:, 0].int() # [K] + offset = 0.5 if aligned else 0.0 + roi_start_w = rois[:, 1] * spatial_scale - offset # [K] + roi_start_h = rois[:, 2] * spatial_scale - offset # [K] + roi_end_w = rois[:, 3] * spatial_scale - offset # [K] + roi_end_h = rois[:, 4] * spatial_scale - offset # [K] + + roi_width = roi_end_w - roi_start_w # [K] + roi_height = roi_end_h - roi_start_h # [K] + if not aligned: + roi_width = torch.clamp(roi_width, min=1.0) # [K] + roi_height = torch.clamp(roi_height, min=1.0) # [K] + + bin_size_h = roi_height / pooled_height # [K] + bin_size_w = roi_width / pooled_width # [K] + + exact_sampling = sampling_ratio > 0 + + roi_bin_grid_h = sampling_ratio if exact_sampling else torch.ceil(roi_height / pooled_height) # scalar or [K] + roi_bin_grid_w = sampling_ratio if exact_sampling else torch.ceil(roi_width / pooled_width) # scalar or [K] + + """ + iy, ix = dims(2) + """ + + if exact_sampling: + count = max(roi_bin_grid_h * roi_bin_grid_w, 1) # scalar + iy = torch.arange(roi_bin_grid_h, device=input.device) # [IY] + ix = torch.arange(roi_bin_grid_w, device=input.device) # [IX] + ymask = None + xmask = None + else: + count = torch.clamp(roi_bin_grid_h * roi_bin_grid_w, min=1) # [K] + # When doing adaptive sampling, the number of samples we need to do + # is data-dependent based on how big the ROIs are. This is a bit + # awkward because first-class dims can't actually handle this. + # So instead, we inefficiently suppose that we needed to sample ALL + # the points and mask out things that turned out to be unnecessary + iy = torch.arange(height, device=input.device) # [IY] + ix = torch.arange(width, device=input.device) # [IX] + ymask = iy[None, :] < roi_bin_grid_h[:, None] # [K, IY] + xmask = ix[None, :] < roi_bin_grid_w[:, None] # [K, IX] + + def from_K(t): + return t[:, None, None] + + y = ( + from_K(roi_start_h) + + ph[None, :, None] * from_K(bin_size_h) + + (iy[None, None, :] + 0.5).to(input.dtype) * from_K(bin_size_h / roi_bin_grid_h) + ) # [K, PH, IY] + x = ( + from_K(roi_start_w) + + pw[None, :, None] * from_K(bin_size_w) + + (ix[None, None, :] + 0.5).to(input.dtype) * from_K(bin_size_w / roi_bin_grid_w) + ) # [K, PW, IX] + val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask) # [K, C, PH, PW, IY, IX] + + # Mask out samples that weren't actually adaptively needed + if not exact_sampling: + val = torch.where(ymask[:, None, None, None, :, None], val, 0) + val = torch.where(xmask[:, None, None, None, None, :], val, 0) + + output = val.sum((-1, -2)) # remove IY, IX ~> [K, C, PH, PW] + if isinstance(count, torch.Tensor): + output /= count[:, None, None, None] + else: + output /= count + + output = output.to(orig_dtype) + + return output + + +@torch.fx.wrap +def roi_align( + input: Tensor, + boxes: Union[Tensor, List[Tensor]], + output_size: BroadcastingList2[int], + spatial_scale: float = 1.0, + sampling_ratio: int = -1, + aligned: bool = False, +) -> Tensor: + """ + Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN. + + Args: + input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element + contains ``C`` feature maps of dimensions ``H x W``. + If the tensor is quantized, we expect a batch size of ``N == 1``. + boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) + format where the regions will be taken from. + The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + If a single Tensor is passed, then the first column should + contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. + If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i + in the batch. + output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling + is performed, as (height, width). + spatial_scale (float): a scaling factor that maps the box coordinates to + the input coordinates. For example, if your boxes are defined on the scale + of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of + the original image), you'll want to set this to 0.5. Default: 1.0 + sampling_ratio (int): number of sampling points in the interpolation grid + used to compute the output value of each pooled output bin. If > 0, + then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If + <= 0, then an adaptive number of grid points are used (computed as + ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 + aligned (bool): If False, use the legacy implementation. + If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two + neighboring pixel indices. This version is used in Detectron2 + + Returns: + Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(roi_align) + check_roi_boxes_shape(boxes) + rois = boxes + output_size = _pair(output_size) + if not isinstance(rois, torch.Tensor): + rois = convert_boxes_to_roi_format(rois) + if not torch.jit.is_scripting(): + if ( + not _has_ops() or (torch.are_deterministic_algorithms_enabled() and (input.is_cuda or input.is_mps)) + ) and is_compile_supported(input.device.type): + return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned) + _assert_has_ops() + return torch.ops.torchvision.roi_align( + input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned + ) + + +class RoIAlign(nn.Module): + """ + See :func:`roi_align`. + """ + + def __init__( + self, + output_size: BroadcastingList2[int], + spatial_scale: float, + sampling_ratio: int, + aligned: bool = False, + ): + super().__init__() + _log_api_usage_once(self) + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + self.aligned = aligned + + def forward(self, input: Tensor, rois: Union[Tensor, List[Tensor]]) -> Tensor: + return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned) + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"output_size={self.output_size}" + f", spatial_scale={self.spatial_scale}" + f", sampling_ratio={self.sampling_ratio}" + f", aligned={self.aligned}" + f")" + ) + return s diff --git a/lib/python3.11/site-packages/torchvision/ops/roi_pool.py b/lib/python3.11/site-packages/torchvision/ops/roi_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..96282418f0769bc91018a872dbd0a106742bf884 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/roi_pool.py @@ -0,0 +1,72 @@ +from typing import List, Union + +import torch +import torch.fx +from torch import nn, Tensor +from torch.jit.annotations import BroadcastingList2 +from torch.nn.modules.utils import _pair +from torchvision.extension import _assert_has_ops + +from ..utils import _log_api_usage_once +from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format + + +@torch.fx.wrap +def roi_pool( + input: Tensor, + boxes: Union[Tensor, List[Tensor]], + output_size: BroadcastingList2[int], + spatial_scale: float = 1.0, +) -> Tensor: + """ + Performs Region of Interest (RoI) Pool operator described in Fast R-CNN + + Args: + input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element + contains ``C`` feature maps of dimensions ``H x W``. + boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) + format where the regions will be taken from. + The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + If a single Tensor is passed, then the first column should + contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. + If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i + in the batch. + output_size (int or Tuple[int, int]): the size of the output after the cropping + is performed, as (height, width) + spatial_scale (float): a scaling factor that maps the box coordinates to + the input coordinates. For example, if your boxes are defined on the scale + of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of + the original image), you'll want to set this to 0.5. Default: 1.0 + + Returns: + Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(roi_pool) + _assert_has_ops() + check_roi_boxes_shape(boxes) + rois = boxes + output_size = _pair(output_size) + if not isinstance(rois, torch.Tensor): + rois = convert_boxes_to_roi_format(rois) + output, _ = torch.ops.torchvision.roi_pool(input, rois, spatial_scale, output_size[0], output_size[1]) + return output + + +class RoIPool(nn.Module): + """ + See :func:`roi_pool`. + """ + + def __init__(self, output_size: BroadcastingList2[int], spatial_scale: float): + super().__init__() + _log_api_usage_once(self) + self.output_size = output_size + self.spatial_scale = spatial_scale + + def forward(self, input: Tensor, rois: Union[Tensor, List[Tensor]]) -> Tensor: + return roi_pool(input, rois, self.output_size, self.spatial_scale) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(output_size={self.output_size}, spatial_scale={self.spatial_scale})" + return s diff --git a/lib/python3.11/site-packages/torchvision/ops/stochastic_depth.py b/lib/python3.11/site-packages/torchvision/ops/stochastic_depth.py new file mode 100644 index 0000000000000000000000000000000000000000..ff8167b2315e941f7e31a0626eeec270d350a710 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/ops/stochastic_depth.py @@ -0,0 +1,66 @@ +import torch +import torch.fx +from torch import nn, Tensor + +from ..utils import _log_api_usage_once + + +def stochastic_depth(input: Tensor, p: float, mode: str, training: bool = True) -> Tensor: + """ + Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth" + `_ used for randomly dropping residual + branches of residual architectures. + + Args: + input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one + being its batch i.e. a batch with ``N`` rows. + p (float): probability of the input to be zeroed. + mode (str): ``"batch"`` or ``"row"``. + ``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes + randomly selected rows from the batch. + training: apply stochastic depth if is ``True``. Default: ``True`` + + Returns: + Tensor[N, ...]: The randomly zeroed tensor. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(stochastic_depth) + if p < 0.0 or p > 1.0: + raise ValueError(f"drop probability has to be between 0 and 1, but got {p}") + if mode not in ["batch", "row"]: + raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}") + if not training or p == 0.0: + return input + + survival_rate = 1.0 - p + if mode == "row": + size = [input.shape[0]] + [1] * (input.ndim - 1) + else: + size = [1] * input.ndim + noise = torch.empty(size, dtype=input.dtype, device=input.device) + noise = noise.bernoulli_(survival_rate) + if survival_rate > 0.0: + noise.div_(survival_rate) + return input * noise + + +torch.fx.wrap("stochastic_depth") + + +class StochasticDepth(nn.Module): + """ + See :func:`stochastic_depth`. + """ + + def __init__(self, p: float, mode: str) -> None: + super().__init__() + _log_api_usage_once(self) + self.p = p + self.mode = mode + + def forward(self, input: Tensor) -> Tensor: + return stochastic_depth(input, self.p, self.mode, self.training) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})" + return s diff --git a/lib/python3.11/site-packages/torchvision/transforms/__init__.py b/lib/python3.11/site-packages/torchvision/transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..77680a14f0d0599f4004a2ce5c299c0f5e13a0d5 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/__init__.py @@ -0,0 +1,2 @@ +from .transforms import * +from .autoaugment import * diff --git a/lib/python3.11/site-packages/torchvision/transforms/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/transforms/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28fddb7ee860fdac06a637cc96cd4e36674a01f7 Binary files /dev/null and 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index 0000000000000000000000000000000000000000..e5a0ec4a1961e95463040d48bb277edf38ab9d6b Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/transforms/__pycache__/functional.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/transforms/_functional_pil.py b/lib/python3.11/site-packages/torchvision/transforms/_functional_pil.py new file mode 100644 index 0000000000000000000000000000000000000000..bd943b6ee1016b45bf2e17ec169fc45b1cc311f4 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/_functional_pil.py @@ -0,0 +1,393 @@ +import numbers +from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union + +import numpy as np +import torch +from PIL import Image, ImageEnhance, ImageOps + +try: + import accimage +except ImportError: + accimage = None + + +@torch.jit.unused +def _is_pil_image(img: Any) -> bool: + if accimage is not None: + return isinstance(img, (Image.Image, accimage.Image)) + else: + return isinstance(img, Image.Image) + + +@torch.jit.unused +def get_dimensions(img: Any) -> List[int]: + if _is_pil_image(img): + if hasattr(img, "getbands"): + channels = len(img.getbands()) + else: + channels = img.channels + width, height = img.size + return [channels, height, width] + raise TypeError(f"Unexpected type {type(img)}") + + +@torch.jit.unused +def get_image_size(img: Any) -> List[int]: + if _is_pil_image(img): + return list(img.size) + raise TypeError(f"Unexpected type {type(img)}") + + +@torch.jit.unused +def get_image_num_channels(img: Any) -> int: + if _is_pil_image(img): + if hasattr(img, "getbands"): + return len(img.getbands()) + else: + return img.channels + raise TypeError(f"Unexpected type {type(img)}") + + +@torch.jit.unused +def hflip(img: Image.Image) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + return img.transpose(Image.FLIP_LEFT_RIGHT) + + +@torch.jit.unused +def vflip(img: Image.Image) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + return img.transpose(Image.FLIP_TOP_BOTTOM) + + +@torch.jit.unused +def adjust_brightness(img: Image.Image, brightness_factor: float) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + enhancer = ImageEnhance.Brightness(img) + img = enhancer.enhance(brightness_factor) + return img + + +@torch.jit.unused +def adjust_contrast(img: Image.Image, contrast_factor: float) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + enhancer = ImageEnhance.Contrast(img) + img = enhancer.enhance(contrast_factor) + return img + + +@torch.jit.unused +def adjust_saturation(img: Image.Image, saturation_factor: float) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + enhancer = ImageEnhance.Color(img) + img = enhancer.enhance(saturation_factor) + return img + + +@torch.jit.unused +def adjust_hue(img: Image.Image, hue_factor: float) -> Image.Image: + if not (-0.5 <= hue_factor <= 0.5): + raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].") + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + input_mode = img.mode + if input_mode in {"L", "1", "I", "F"}: + return img + + h, s, v = img.convert("HSV").split() + + np_h = np.array(h, dtype=np.uint8) + # This will over/underflow, as desired + np_h += np.int32(hue_factor * 255).astype(np.uint8) + + h = Image.fromarray(np_h, "L") + + img = Image.merge("HSV", (h, s, v)).convert(input_mode) + return img + + +@torch.jit.unused +def adjust_gamma( + img: Image.Image, + gamma: float, + gain: float = 1.0, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + if gamma < 0: + raise ValueError("Gamma should be a non-negative real number") + + input_mode = img.mode + img = img.convert("RGB") + gamma_map = [int((255 + 1 - 1e-3) * gain * pow(ele / 255.0, gamma)) for ele in range(256)] * 3 + img = img.point(gamma_map) # use PIL's point-function to accelerate this part + + img = img.convert(input_mode) + return img + + +@torch.jit.unused +def pad( + img: Image.Image, + padding: Union[int, List[int], Tuple[int, ...]], + fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0, + padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant", +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + if not isinstance(padding, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate padding arg") + if fill is not None and not isinstance(fill, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate fill arg") + if not isinstance(padding_mode, str): + raise TypeError("Got inappropriate padding_mode arg") + + if isinstance(padding, list): + padding = tuple(padding) + + if isinstance(padding, tuple) and len(padding) not in [1, 2, 4]: + raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple") + + if isinstance(padding, tuple) and len(padding) == 1: + # Compatibility with `functional_tensor.pad` + padding = padding[0] + + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + if padding_mode == "constant": + opts = _parse_fill(fill, img, name="fill") + if img.mode == "P": + palette = img.getpalette() + image = ImageOps.expand(img, border=padding, **opts) + image.putpalette(palette) + return image + + return ImageOps.expand(img, border=padding, **opts) + else: + if isinstance(padding, int): + pad_left = pad_right = pad_top = pad_bottom = padding + if isinstance(padding, tuple) and len(padding) == 2: + pad_left = pad_right = padding[0] + pad_top = pad_bottom = padding[1] + if isinstance(padding, tuple) and len(padding) == 4: + pad_left = padding[0] + pad_top = padding[1] + pad_right = padding[2] + pad_bottom = padding[3] + + p = [pad_left, pad_top, pad_right, pad_bottom] + cropping = -np.minimum(p, 0) + + if cropping.any(): + crop_left, crop_top, crop_right, crop_bottom = cropping + img = img.crop((crop_left, crop_top, img.width - crop_right, img.height - crop_bottom)) + + pad_left, pad_top, pad_right, pad_bottom = np.maximum(p, 0) + + if img.mode == "P": + palette = img.getpalette() + img = np.asarray(img) + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), mode=padding_mode) + img = Image.fromarray(img) + img.putpalette(palette) + return img + + img = np.asarray(img) + # RGB image + if len(img.shape) == 3: + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), padding_mode) + # Grayscale image + if len(img.shape) == 2: + img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode) + + return Image.fromarray(img) + + +@torch.jit.unused +def crop( + img: Image.Image, + top: int, + left: int, + height: int, + width: int, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + return img.crop((left, top, left + width, top + height)) + + +@torch.jit.unused +def resize( + img: Image.Image, + size: Union[List[int], int], + interpolation: int = Image.BILINEAR, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + if not (isinstance(size, list) and len(size) == 2): + raise TypeError(f"Got inappropriate size arg: {size}") + + return img.resize(tuple(size[::-1]), interpolation) + + +@torch.jit.unused +def _parse_fill( + fill: Optional[Union[float, List[float], Tuple[float, ...]]], + img: Image.Image, + name: str = "fillcolor", +) -> Dict[str, Optional[Union[float, List[float], Tuple[float, ...]]]]: + + # Process fill color for affine transforms + num_channels = get_image_num_channels(img) + if fill is None: + fill = 0 + if isinstance(fill, (int, float)) and num_channels > 1: + fill = tuple([fill] * num_channels) + if isinstance(fill, (list, tuple)): + if len(fill) == 1: + fill = fill * num_channels + elif len(fill) != num_channels: + msg = "The number of elements in 'fill' does not match the number of channels of the image ({} != {})" + raise ValueError(msg.format(len(fill), num_channels)) + + fill = tuple(fill) # type: ignore[arg-type] + + if img.mode != "F": + if isinstance(fill, (list, tuple)): + fill = tuple(int(x) for x in fill) + else: + fill = int(fill) + + return {name: fill} + + +@torch.jit.unused +def affine( + img: Image.Image, + matrix: List[float], + interpolation: int = Image.NEAREST, + fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + output_size = img.size + opts = _parse_fill(fill, img) + return img.transform(output_size, Image.AFFINE, matrix, interpolation, **opts) + + +@torch.jit.unused +def rotate( + img: Image.Image, + angle: float, + interpolation: int = Image.NEAREST, + expand: bool = False, + center: Optional[Tuple[int, int]] = None, + fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + opts = _parse_fill(fill, img) + return img.rotate(angle, interpolation, expand, center, **opts) + + +@torch.jit.unused +def perspective( + img: Image.Image, + perspective_coeffs: List[float], + interpolation: int = Image.BICUBIC, + fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None, +) -> Image.Image: + + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + opts = _parse_fill(fill, img) + + return img.transform(img.size, Image.PERSPECTIVE, perspective_coeffs, interpolation, **opts) + + +@torch.jit.unused +def to_grayscale(img: Image.Image, num_output_channels: int) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + if num_output_channels == 1: + img = img.convert("L") + elif num_output_channels == 3: + img = img.convert("L") + np_img = np.array(img, dtype=np.uint8) + np_img = np.dstack([np_img, np_img, np_img]) + img = Image.fromarray(np_img, "RGB") + else: + raise ValueError("num_output_channels should be either 1 or 3") + + return img + + +@torch.jit.unused +def invert(img: Image.Image) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + return ImageOps.invert(img) + + +@torch.jit.unused +def posterize(img: Image.Image, bits: int) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + return ImageOps.posterize(img, bits) + + +@torch.jit.unused +def solarize(img: Image.Image, threshold: int) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + return ImageOps.solarize(img, threshold) + + +@torch.jit.unused +def adjust_sharpness(img: Image.Image, sharpness_factor: float) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + + enhancer = ImageEnhance.Sharpness(img) + img = enhancer.enhance(sharpness_factor) + return img + + +@torch.jit.unused +def autocontrast(img: Image.Image) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + return ImageOps.autocontrast(img) + + +@torch.jit.unused +def equalize(img: Image.Image) -> Image.Image: + if not _is_pil_image(img): + raise TypeError(f"img should be PIL Image. Got {type(img)}") + return ImageOps.equalize(img) diff --git a/lib/python3.11/site-packages/torchvision/transforms/_functional_tensor.py b/lib/python3.11/site-packages/torchvision/transforms/_functional_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..618bbfbab7c8f40b34216c211a4a39f2a87ff72d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/_functional_tensor.py @@ -0,0 +1,962 @@ +import warnings +from typing import List, Optional, Tuple, Union + +import torch +from torch import Tensor +from torch.nn.functional import conv2d, grid_sample, interpolate, pad as torch_pad + + +def _is_tensor_a_torch_image(x: Tensor) -> bool: + return x.ndim >= 2 + + +def _assert_image_tensor(img: Tensor) -> None: + if not _is_tensor_a_torch_image(img): + raise TypeError("Tensor is not a torch image.") + + +def get_dimensions(img: Tensor) -> List[int]: + _assert_image_tensor(img) + channels = 1 if img.ndim == 2 else img.shape[-3] + height, width = img.shape[-2:] + return [channels, height, width] + + +def get_image_size(img: Tensor) -> List[int]: + # Returns (w, h) of tensor image + _assert_image_tensor(img) + return [img.shape[-1], img.shape[-2]] + + +def get_image_num_channels(img: Tensor) -> int: + _assert_image_tensor(img) + if img.ndim == 2: + return 1 + elif img.ndim > 2: + return img.shape[-3] + + raise TypeError(f"Input ndim should be 2 or more. Got {img.ndim}") + + +def _max_value(dtype: torch.dtype) -> int: + if dtype == torch.uint8: + return 255 + elif dtype == torch.int8: + return 127 + elif dtype == torch.int16: + return 32767 + elif dtype == torch.uint16: + return 65535 + elif dtype == torch.int32: + return 2147483647 + elif dtype == torch.int64: + return 9223372036854775807 + else: + # This is only here for completeness. This value is implicitly assumed in a lot of places so changing it is not + # easy. + return 1 + + +def _assert_channels(img: Tensor, permitted: List[int]) -> None: + c = get_dimensions(img)[0] + if c not in permitted: + raise TypeError(f"Input image tensor permitted channel values are {permitted}, but found {c}") + + +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor: + if image.dtype == dtype: + return image + + if image.is_floating_point(): + + # TODO: replace with dtype.is_floating_point when torchscript supports it + if torch.tensor(0, dtype=dtype).is_floating_point(): + return image.to(dtype) + + # float to int + if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or ( + image.dtype == torch.float64 and dtype == torch.int64 + ): + msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely." + raise RuntimeError(msg) + + # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321 + # For data in the range 0-1, (float * 255).to(uint) is only 255 + # when float is exactly 1.0. + # `max + 1 - epsilon` provides more evenly distributed mapping of + # ranges of floats to ints. + eps = 1e-3 + max_val = float(_max_value(dtype)) + result = image.mul(max_val + 1.0 - eps) + return result.to(dtype) + else: + input_max = float(_max_value(image.dtype)) + + # int to float + # TODO: replace with dtype.is_floating_point when torchscript supports it + if torch.tensor(0, dtype=dtype).is_floating_point(): + image = image.to(dtype) + return image / input_max + + output_max = float(_max_value(dtype)) + + # int to int + if input_max > output_max: + # factor should be forced to int for torch jit script + # otherwise factor is a float and image // factor can produce different results + factor = int((input_max + 1) // (output_max + 1)) + image = torch.div(image, factor, rounding_mode="floor") + return image.to(dtype) + else: + # factor should be forced to int for torch jit script + # otherwise factor is a float and image * factor can produce different results + factor = int((output_max + 1) // (input_max + 1)) + image = image.to(dtype) + return image * factor + + +def vflip(img: Tensor) -> Tensor: + _assert_image_tensor(img) + + return img.flip(-2) + + +def hflip(img: Tensor) -> Tensor: + _assert_image_tensor(img) + + return img.flip(-1) + + +def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor: + _assert_image_tensor(img) + + _, h, w = get_dimensions(img) + right = left + width + bottom = top + height + + if left < 0 or top < 0 or right > w or bottom > h: + padding_ltrb = [ + max(-left + min(0, right), 0), + max(-top + min(0, bottom), 0), + max(right - max(w, left), 0), + max(bottom - max(h, top), 0), + ] + return pad(img[..., max(top, 0) : bottom, max(left, 0) : right], padding_ltrb, fill=0) + return img[..., top:bottom, left:right] + + +def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor: + if img.ndim < 3: + raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}") + _assert_channels(img, [1, 3]) + + if num_output_channels not in (1, 3): + raise ValueError("num_output_channels should be either 1 or 3") + + if img.shape[-3] == 3: + r, g, b = img.unbind(dim=-3) + # This implementation closely follows the TF one: + # https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/image_ops_impl.py#L2105-L2138 + l_img = (0.2989 * r + 0.587 * g + 0.114 * b).to(img.dtype) + l_img = l_img.unsqueeze(dim=-3) + else: + l_img = img.clone() + + if num_output_channels == 3: + return l_img.expand(img.shape) + + return l_img + + +def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor: + if brightness_factor < 0: + raise ValueError(f"brightness_factor ({brightness_factor}) is not non-negative.") + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + + return _blend(img, torch.zeros_like(img), brightness_factor) + + +def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor: + if contrast_factor < 0: + raise ValueError(f"contrast_factor ({contrast_factor}) is not non-negative.") + + _assert_image_tensor(img) + + _assert_channels(img, [3, 1]) + c = get_dimensions(img)[0] + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + if c == 3: + mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True) + else: + mean = torch.mean(img.to(dtype), dim=(-3, -2, -1), keepdim=True) + + return _blend(img, mean, contrast_factor) + + +def adjust_hue(img: Tensor, hue_factor: float) -> Tensor: + if not (-0.5 <= hue_factor <= 0.5): + raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].") + + if not (isinstance(img, torch.Tensor)): + raise TypeError("Input img should be Tensor image") + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + if get_dimensions(img)[0] == 1: # Match PIL behaviour + return img + + orig_dtype = img.dtype + img = convert_image_dtype(img, torch.float32) + + img = _rgb2hsv(img) + h, s, v = img.unbind(dim=-3) + h = (h + hue_factor) % 1.0 + img = torch.stack((h, s, v), dim=-3) + img_hue_adj = _hsv2rgb(img) + + return convert_image_dtype(img_hue_adj, orig_dtype) + + +def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor: + if saturation_factor < 0: + raise ValueError(f"saturation_factor ({saturation_factor}) is not non-negative.") + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + + if get_dimensions(img)[0] == 1: # Match PIL behaviour + return img + + return _blend(img, rgb_to_grayscale(img), saturation_factor) + + +def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: + if not isinstance(img, torch.Tensor): + raise TypeError("Input img should be a Tensor.") + + _assert_channels(img, [1, 3]) + + if gamma < 0: + raise ValueError("Gamma should be a non-negative real number") + + result = img + dtype = img.dtype + if not torch.is_floating_point(img): + result = convert_image_dtype(result, torch.float32) + + result = (gain * result**gamma).clamp(0, 1) + + result = convert_image_dtype(result, dtype) + return result + + +def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor: + ratio = float(ratio) + bound = _max_value(img1.dtype) + return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype) + + +def _rgb2hsv(img: Tensor) -> Tensor: + r, g, b = img.unbind(dim=-3) + + # Implementation is based on https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/ + # src/libImaging/Convert.c#L330 + maxc = torch.max(img, dim=-3).values + minc = torch.min(img, dim=-3).values + + # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN + # from happening in the results, because + # + S channel has division by `maxc`, which is zero only if `maxc = minc` + # + H channel has division by `(maxc - minc)`. + # + # Instead of overwriting NaN afterwards, we just prevent it from occurring, so + # we don't need to deal with it in case we save the NaN in a buffer in + # backprop, if it is ever supported, but it doesn't hurt to do so. + eqc = maxc == minc + + cr = maxc - minc + # Since `eqc => cr = 0`, replacing denominator with 1 when `eqc` is fine. + ones = torch.ones_like(maxc) + s = cr / torch.where(eqc, ones, maxc) + # Note that `eqc => maxc = minc = r = g = b`. So the following calculation + # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it + # would not matter what values `rc`, `gc`, and `bc` have here, and thus + # replacing denominator with 1 when `eqc` is fine. + cr_divisor = torch.where(eqc, ones, cr) + rc = (maxc - r) / cr_divisor + gc = (maxc - g) / cr_divisor + bc = (maxc - b) / cr_divisor + + hr = (maxc == r) * (bc - gc) + hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc) + hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc) + h = hr + hg + hb + h = torch.fmod((h / 6.0 + 1.0), 1.0) + return torch.stack((h, s, maxc), dim=-3) + + +def _hsv2rgb(img: Tensor) -> Tensor: + h, s, v = img.unbind(dim=-3) + i = torch.floor(h * 6.0) + f = (h * 6.0) - i + i = i.to(dtype=torch.int32) + + p = torch.clamp((v * (1.0 - s)), 0.0, 1.0) + q = torch.clamp((v * (1.0 - s * f)), 0.0, 1.0) + t = torch.clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0) + i = i % 6 + + mask = i.unsqueeze(dim=-3) == torch.arange(6, device=i.device).view(-1, 1, 1) + + a1 = torch.stack((v, q, p, p, t, v), dim=-3) + a2 = torch.stack((t, v, v, q, p, p), dim=-3) + a3 = torch.stack((p, p, t, v, v, q), dim=-3) + a4 = torch.stack((a1, a2, a3), dim=-4) + + return torch.einsum("...ijk, ...xijk -> ...xjk", mask.to(dtype=img.dtype), a4) + + +def _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor: + # padding is left, right, top, bottom + + # crop if needed + if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0: + neg_min_padding = [-min(x, 0) for x in padding] + crop_left, crop_right, crop_top, crop_bottom = neg_min_padding + img = img[..., crop_top : img.shape[-2] - crop_bottom, crop_left : img.shape[-1] - crop_right] + padding = [max(x, 0) for x in padding] + + in_sizes = img.size() + + _x_indices = [i for i in range(in_sizes[-1])] # [0, 1, 2, 3, ...] + left_indices = [i for i in range(padding[0] - 1, -1, -1)] # e.g. [3, 2, 1, 0] + right_indices = [-(i + 1) for i in range(padding[1])] # e.g. [-1, -2, -3] + x_indices = torch.tensor(left_indices + _x_indices + right_indices, device=img.device) + + _y_indices = [i for i in range(in_sizes[-2])] + top_indices = [i for i in range(padding[2] - 1, -1, -1)] + bottom_indices = [-(i + 1) for i in range(padding[3])] + y_indices = torch.tensor(top_indices + _y_indices + bottom_indices, device=img.device) + + ndim = img.ndim + if ndim == 3: + return img[:, y_indices[:, None], x_indices[None, :]] + elif ndim == 4: + return img[:, :, y_indices[:, None], x_indices[None, :]] + else: + raise RuntimeError("Symmetric padding of N-D tensors are not supported yet") + + +def _parse_pad_padding(padding: Union[int, List[int]]) -> List[int]: + if isinstance(padding, int): + if torch.jit.is_scripting(): + # This maybe unreachable + raise ValueError("padding can't be an int while torchscripting, set it as a list [value, ]") + pad_left = pad_right = pad_top = pad_bottom = padding + elif len(padding) == 1: + pad_left = pad_right = pad_top = pad_bottom = padding[0] + elif len(padding) == 2: + pad_left = pad_right = padding[0] + pad_top = pad_bottom = padding[1] + else: + pad_left = padding[0] + pad_top = padding[1] + pad_right = padding[2] + pad_bottom = padding[3] + + return [pad_left, pad_right, pad_top, pad_bottom] + + +def pad( + img: Tensor, padding: Union[int, List[int]], fill: Optional[Union[int, float]] = 0, padding_mode: str = "constant" +) -> Tensor: + _assert_image_tensor(img) + + if fill is None: + fill = 0 + + if not isinstance(padding, (int, tuple, list)): + raise TypeError("Got inappropriate padding arg") + if not isinstance(fill, (int, float)): + raise TypeError("Got inappropriate fill arg") + if not isinstance(padding_mode, str): + raise TypeError("Got inappropriate padding_mode arg") + + if isinstance(padding, tuple): + padding = list(padding) + + if isinstance(padding, list): + # TODO: Jit is failing on loading this op when scripted and saved + # https://github.com/pytorch/pytorch/issues/81100 + if len(padding) not in [1, 2, 4]: + raise ValueError( + f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple" + ) + + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + p = _parse_pad_padding(padding) + + if padding_mode == "edge": + # remap padding_mode str + padding_mode = "replicate" + elif padding_mode == "symmetric": + # route to another implementation + return _pad_symmetric(img, p) + + need_squeeze = False + if img.ndim < 4: + img = img.unsqueeze(dim=0) + need_squeeze = True + + out_dtype = img.dtype + need_cast = False + if (padding_mode != "constant") and img.dtype not in (torch.float32, torch.float64): + # Here we temporarily cast input tensor to float + # until pytorch issue is resolved : + # https://github.com/pytorch/pytorch/issues/40763 + need_cast = True + img = img.to(torch.float32) + + if padding_mode in ("reflect", "replicate"): + img = torch_pad(img, p, mode=padding_mode) + else: + img = torch_pad(img, p, mode=padding_mode, value=float(fill)) + + if need_squeeze: + img = img.squeeze(dim=0) + + if need_cast: + img = img.to(out_dtype) + + return img + + +def resize( + img: Tensor, + size: List[int], + interpolation: str = "bilinear", + antialias: Optional[bool] = True, +) -> Tensor: + _assert_image_tensor(img) + + if isinstance(size, tuple): + size = list(size) + + if antialias is None: + antialias = False + + if antialias and interpolation not in ["bilinear", "bicubic"]: + # We manually set it to False to avoid an error downstream in interpolate() + # This behaviour is documented: the parameter is irrelevant for modes + # that are not bilinear or bicubic. We used to raise an error here, but + # now we don't as True is the default. + antialias = False + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64]) + + # Define align_corners to avoid warnings + align_corners = False if interpolation in ["bilinear", "bicubic"] else None + + img = interpolate(img, size=size, mode=interpolation, align_corners=align_corners, antialias=antialias) + + if interpolation == "bicubic" and out_dtype == torch.uint8: + img = img.clamp(min=0, max=255) + + img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype) + + return img + + +def _assert_grid_transform_inputs( + img: Tensor, + matrix: Optional[List[float]], + interpolation: str, + fill: Optional[Union[int, float, List[float]]], + supported_interpolation_modes: List[str], + coeffs: Optional[List[float]] = None, +) -> None: + + if not (isinstance(img, torch.Tensor)): + raise TypeError("Input img should be Tensor") + + _assert_image_tensor(img) + + if matrix is not None and not isinstance(matrix, list): + raise TypeError("Argument matrix should be a list") + + if matrix is not None and len(matrix) != 6: + raise ValueError("Argument matrix should have 6 float values") + + if coeffs is not None and len(coeffs) != 8: + raise ValueError("Argument coeffs should have 8 float values") + + if fill is not None and not isinstance(fill, (int, float, tuple, list)): + warnings.warn("Argument fill should be either int, float, tuple or list") + + # Check fill + num_channels = get_dimensions(img)[0] + if fill is not None and isinstance(fill, (tuple, list)) and len(fill) > 1 and len(fill) != num_channels: + msg = ( + "The number of elements in 'fill' cannot broadcast to match the number of " + "channels of the image ({} != {})" + ) + raise ValueError(msg.format(len(fill), num_channels)) + + if interpolation not in supported_interpolation_modes: + raise ValueError(f"Interpolation mode '{interpolation}' is unsupported with Tensor input") + + +def _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]: + need_squeeze = False + # make image NCHW + if img.ndim < 4: + img = img.unsqueeze(dim=0) + need_squeeze = True + + out_dtype = img.dtype + need_cast = False + if out_dtype not in req_dtypes: + need_cast = True + req_dtype = req_dtypes[0] + img = img.to(req_dtype) + return img, need_cast, need_squeeze, out_dtype + + +def _cast_squeeze_out(img: Tensor, need_cast: bool, need_squeeze: bool, out_dtype: torch.dtype) -> Tensor: + if need_squeeze: + img = img.squeeze(dim=0) + + if need_cast: + if out_dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): + # it is better to round before cast + img = torch.round(img) + img = img.to(out_dtype) + + return img + + +def _apply_grid_transform( + img: Tensor, grid: Tensor, mode: str, fill: Optional[Union[int, float, List[float]]] +) -> Tensor: + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [grid.dtype]) + + if img.shape[0] > 1: + # Apply same grid to a batch of images + grid = grid.expand(img.shape[0], grid.shape[1], grid.shape[2], grid.shape[3]) + + # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice + if fill is not None: + mask = torch.ones((img.shape[0], 1, img.shape[2], img.shape[3]), dtype=img.dtype, device=img.device) + img = torch.cat((img, mask), dim=1) + + img = grid_sample(img, grid, mode=mode, padding_mode="zeros", align_corners=False) + + # Fill with required color + if fill is not None: + mask = img[:, -1:, :, :] # N * 1 * H * W + img = img[:, :-1, :, :] # N * C * H * W + mask = mask.expand_as(img) + fill_list, len_fill = (fill, len(fill)) if isinstance(fill, (tuple, list)) else ([float(fill)], 1) + fill_img = torch.tensor(fill_list, dtype=img.dtype, device=img.device).view(1, len_fill, 1, 1).expand_as(img) + if mode == "nearest": + mask = mask < 0.5 + img[mask] = fill_img[mask] + else: # 'bilinear' + img = img * mask + (1.0 - mask) * fill_img + + img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype) + return img + + +def _gen_affine_grid( + theta: Tensor, + w: int, + h: int, + ow: int, + oh: int, +) -> Tensor: + # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/ + # AffineGridGenerator.cpp#L18 + # Difference with AffineGridGenerator is that: + # 1) we normalize grid values after applying theta + # 2) we can normalize by other image size, such that it covers "extend" option like in PIL.Image.rotate + + d = 0.5 + base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device) + x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device) + output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta) + return output_grid.view(1, oh, ow, 2) + + +def affine( + img: Tensor, + matrix: List[float], + interpolation: str = "nearest", + fill: Optional[Union[int, float, List[float]]] = None, +) -> Tensor: + _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"]) + + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3) + shape = img.shape + # grid will be generated on the same device as theta and img + grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]) + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]: + + # Inspired of PIL implementation: + # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054 + + # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points. + # Points are shifted due to affine matrix torch convention about + # the center point. Center is (0, 0) for image center pivot point (w * 0.5, h * 0.5) + pts = torch.tensor( + [ + [-0.5 * w, -0.5 * h, 1.0], + [-0.5 * w, 0.5 * h, 1.0], + [0.5 * w, 0.5 * h, 1.0], + [0.5 * w, -0.5 * h, 1.0], + ] + ) + theta = torch.tensor(matrix, dtype=torch.float).view(2, 3) + new_pts = torch.matmul(pts, theta.T) + min_vals, _ = new_pts.min(dim=0) + max_vals, _ = new_pts.max(dim=0) + + # shift points to [0, w] and [0, h] interval to match PIL results + min_vals += torch.tensor((w * 0.5, h * 0.5)) + max_vals += torch.tensor((w * 0.5, h * 0.5)) + + # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0 + tol = 1e-4 + cmax = torch.ceil((max_vals / tol).trunc_() * tol) + cmin = torch.floor((min_vals / tol).trunc_() * tol) + size = cmax - cmin + return int(size[0]), int(size[1]) # w, h + + +def rotate( + img: Tensor, + matrix: List[float], + interpolation: str = "nearest", + expand: bool = False, + fill: Optional[Union[int, float, List[float]]] = None, +) -> Tensor: + _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"]) + w, h = img.shape[-1], img.shape[-2] + ow, oh = _compute_affine_output_size(matrix, w, h) if expand else (w, h) + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3) + # grid will be generated on the same device as theta and img + grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh) + + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device) -> Tensor: + # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/ + # src/libImaging/Geometry.c#L394 + + # + # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1) + # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1) + # + theta1 = torch.tensor( + [[[coeffs[0], coeffs[1], coeffs[2]], [coeffs[3], coeffs[4], coeffs[5]]]], dtype=dtype, device=device + ) + theta2 = torch.tensor([[[coeffs[6], coeffs[7], 1.0], [coeffs[6], coeffs[7], 1.0]]], dtype=dtype, device=device) + + d = 0.5 + base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device) + x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device) + output_grid1 = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta1) + output_grid2 = base_grid.view(1, oh * ow, 3).bmm(theta2.transpose(1, 2)) + + output_grid = output_grid1 / output_grid2 - 1.0 + return output_grid.view(1, oh, ow, 2) + + +def perspective( + img: Tensor, + perspective_coeffs: List[float], + interpolation: str = "bilinear", + fill: Optional[Union[int, float, List[float]]] = None, +) -> Tensor: + if not (isinstance(img, torch.Tensor)): + raise TypeError("Input img should be Tensor.") + + _assert_image_tensor(img) + + _assert_grid_transform_inputs( + img, + matrix=None, + interpolation=interpolation, + fill=fill, + supported_interpolation_modes=["nearest", "bilinear"], + coeffs=perspective_coeffs, + ) + + ow, oh = img.shape[-1], img.shape[-2] + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device) + return _apply_grid_transform(img, grid, interpolation, fill=fill) + + +def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> Tensor: + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, dtype=dtype, device=device) + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + kernel1d = pdf / pdf.sum() + + return kernel1d + + +def _get_gaussian_kernel2d( + kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device +) -> Tensor: + kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device) + kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device) + kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :]) + return kernel2d + + +def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor: + if not (isinstance(img, torch.Tensor)): + raise TypeError(f"img should be Tensor. Got {type(img)}") + + _assert_image_tensor(img) + + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device) + kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype]) + + # padding = (left, right, top, bottom) + padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] + img = torch_pad(img, padding, mode="reflect") + img = conv2d(img, kernel, groups=img.shape[-3]) + + img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype) + return img + + +def invert(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}") + + _assert_channels(img, [1, 3]) + + return _max_value(img.dtype) - img + + +def posterize(img: Tensor, bits: int) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}") + if img.dtype != torch.uint8: + raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}") + + _assert_channels(img, [1, 3]) + mask = -int(2 ** (8 - bits)) # JIT-friendly for: ~(2 ** (8 - bits) - 1) + return img & mask + + +def solarize(img: Tensor, threshold: float) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}") + + _assert_channels(img, [1, 3]) + + if threshold > _max_value(img.dtype): + raise TypeError("Threshold should be less than bound of img.") + + inverted_img = invert(img) + return torch.where(img >= threshold, inverted_img, img) + + +def _blurred_degenerate_image(img: Tensor) -> Tensor: + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + + kernel = torch.ones((3, 3), dtype=dtype, device=img.device) + kernel[1, 1] = 5.0 + kernel /= kernel.sum() + kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype]) + result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3]) + result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype) + + result = img.clone() + result[..., 1:-1, 1:-1] = result_tmp + + return result + + +def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor: + if sharpness_factor < 0: + raise ValueError(f"sharpness_factor ({sharpness_factor}) is not non-negative.") + + _assert_image_tensor(img) + + _assert_channels(img, [1, 3]) + + if img.size(-1) <= 2 or img.size(-2) <= 2: + return img + + return _blend(img, _blurred_degenerate_image(img), sharpness_factor) + + +def autocontrast(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if img.ndim < 3: + raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}") + + _assert_channels(img, [1, 3]) + + bound = _max_value(img.dtype) + dtype = img.dtype if torch.is_floating_point(img) else torch.float32 + + minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype) + maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype) + scale = bound / (maximum - minimum) + eq_idxs = torch.isfinite(scale).logical_not() + minimum[eq_idxs] = 0 + scale[eq_idxs] = 1 + + return ((img - minimum) * scale).clamp(0, bound).to(img.dtype) + + +def _scale_channel(img_chan: Tensor) -> Tensor: + # TODO: we should expect bincount to always be faster than histc, but this + # isn't always the case. Once + # https://github.com/pytorch/pytorch/issues/53194 is fixed, remove the if + # block and only use bincount. + if img_chan.is_cuda: + hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255) + else: + hist = torch.bincount(img_chan.reshape(-1), minlength=256) + + nonzero_hist = hist[hist != 0] + step = torch.div(nonzero_hist[:-1].sum(), 255, rounding_mode="floor") + if step == 0: + return img_chan + + lut = torch.div(torch.cumsum(hist, 0) + torch.div(step, 2, rounding_mode="floor"), step, rounding_mode="floor") + lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255) + + return lut[img_chan.to(torch.int64)].to(torch.uint8) + + +def _equalize_single_image(img: Tensor) -> Tensor: + return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))]) + + +def equalize(img: Tensor) -> Tensor: + + _assert_image_tensor(img) + + if not (3 <= img.ndim <= 4): + raise TypeError(f"Input image tensor should have 3 or 4 dimensions, but found {img.ndim}") + if img.dtype != torch.uint8: + raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}") + + _assert_channels(img, [1, 3]) + + if img.ndim == 3: + return _equalize_single_image(img) + + return torch.stack([_equalize_single_image(x) for x in img]) + + +def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor: + _assert_image_tensor(tensor) + + if not tensor.is_floating_point(): + raise TypeError(f"Input tensor should be a float tensor. Got {tensor.dtype}.") + + if tensor.ndim < 3: + raise ValueError( + f"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = {tensor.size()}" + ) + + if not inplace: + tensor = tensor.clone() + + dtype = tensor.dtype + mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device) + std = torch.as_tensor(std, dtype=dtype, device=tensor.device) + if (std == 0).any(): + raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.") + if mean.ndim == 1: + mean = mean.view(-1, 1, 1) + if std.ndim == 1: + std = std.view(-1, 1, 1) + return tensor.sub_(mean).div_(std) + + +def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor: + _assert_image_tensor(img) + + if not inplace: + img = img.clone() + + img[..., i : i + h, j : j + w] = v + return img + + +def _create_identity_grid(size: List[int]) -> Tensor: + hw_space = [torch.linspace((-s + 1) / s, (s - 1) / s, s) for s in size] + grid_y, grid_x = torch.meshgrid(hw_space, indexing="ij") + return torch.stack([grid_x, grid_y], -1).unsqueeze(0) # 1 x H x W x 2 + + +def elastic_transform( + img: Tensor, + displacement: Tensor, + interpolation: str = "bilinear", + fill: Optional[Union[int, float, List[float]]] = None, +) -> Tensor: + + if not (isinstance(img, torch.Tensor)): + raise TypeError(f"img should be Tensor. Got {type(img)}") + + size = list(img.shape[-2:]) + displacement = displacement.to(img.device) + + identity_grid = _create_identity_grid(size) + grid = identity_grid.to(img.device) + displacement + return _apply_grid_transform(img, grid, interpolation, fill) diff --git a/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py b/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py new file mode 100644 index 0000000000000000000000000000000000000000..91df7d42cd71fc554aba51fcf5e90db30e3c3851 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py @@ -0,0 +1,114 @@ +import warnings + +import torch + + +warnings.warn( + "The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. " + "Please use the 'torchvision.transforms.functional' module instead." +) + + +def _is_tensor_video_clip(clip): + if not torch.is_tensor(clip): + raise TypeError("clip should be Tensor. Got %s" % type(clip)) + + if not clip.ndimension() == 4: + raise ValueError("clip should be 4D. Got %dD" % clip.dim()) + + return True + + +def crop(clip, i, j, h, w): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) + """ + if len(clip.size()) != 4: + raise ValueError("clip should be a 4D tensor") + return clip[..., i : i + h, j : j + w] + + +def resize(clip, target_size, interpolation_mode): + if len(target_size) != 2: + raise ValueError(f"target size should be tuple (height, width), instead got {target_size}") + return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False) + + +def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"): + """ + Do spatial cropping and resizing to the video clip + Args: + clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) + i (int): i in (i,j) i.e coordinates of the upper left corner. + j (int): j in (i,j) i.e coordinates of the upper left corner. + h (int): Height of the cropped region. + w (int): Width of the cropped region. + size (tuple(int, int)): height and width of resized clip + Returns: + clip (torch.tensor): Resized and cropped clip. Size is (C, T, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + clip = crop(clip, i, j, h, w) + clip = resize(clip, size, interpolation_mode) + return clip + + +def center_crop(clip, crop_size): + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + h, w = clip.size(-2), clip.size(-1) + th, tw = crop_size + if h < th or w < tw: + raise ValueError("height and width must be no smaller than crop_size") + + i = int(round((h - th) / 2.0)) + j = int(round((w - tw) / 2.0)) + return crop(clip, i, j, th, tw) + + +def to_tensor(clip): + """ + Convert tensor data type from uint8 to float, divide value by 255.0 and + permute the dimensions of clip tensor + Args: + clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C) + Return: + clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W) + """ + _is_tensor_video_clip(clip) + if not clip.dtype == torch.uint8: + raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype)) + return clip.float().permute(3, 0, 1, 2) / 255.0 + + +def normalize(clip, mean, std, inplace=False): + """ + Args: + clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W) + mean (tuple): pixel RGB mean. Size is (3) + std (tuple): pixel standard deviation. Size is (3) + Returns: + normalized clip (torch.tensor): Size is (C, T, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + if not inplace: + clip = clip.clone() + mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device) + std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device) + clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None]) + return clip + + +def hflip(clip): + """ + Args: + clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W) + Returns: + flipped clip (torch.tensor): Size is (C, T, H, W) + """ + if not _is_tensor_video_clip(clip): + raise ValueError("clip should be a 4D torch.tensor") + return clip.flip(-1) diff --git a/lib/python3.11/site-packages/torchvision/transforms/_presets.py b/lib/python3.11/site-packages/torchvision/transforms/_presets.py new file mode 100644 index 0000000000000000000000000000000000000000..fb6f4ad5ca543ddaf41dbdaa4161cd89193f5506 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/_presets.py @@ -0,0 +1,216 @@ +""" +This file is part of the private API. Please do not use directly these classes as they will be modified on +future versions without warning. The classes should be accessed only via the transforms argument of Weights. +""" +from typing import Optional, Tuple, Union + +import torch +from torch import nn, Tensor + +from . import functional as F, InterpolationMode + + +__all__ = [ + "ObjectDetection", + "ImageClassification", + "VideoClassification", + "SemanticSegmentation", + "OpticalFlow", +] + + +class ObjectDetection(nn.Module): + def forward(self, img: Tensor) -> Tensor: + if not isinstance(img, Tensor): + img = F.pil_to_tensor(img) + return F.convert_image_dtype(img, torch.float) + + def __repr__(self) -> str: + return self.__class__.__name__ + "()" + + def describe(self) -> str: + return ( + "Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. " + "The images are rescaled to ``[0.0, 1.0]``." + ) + + +class ImageClassification(nn.Module): + def __init__( + self, + *, + crop_size: int, + resize_size: int = 256, + mean: Tuple[float, ...] = (0.485, 0.456, 0.406), + std: Tuple[float, ...] = (0.229, 0.224, 0.225), + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ) -> None: + super().__init__() + self.crop_size = [crop_size] + self.resize_size = [resize_size] + self.mean = list(mean) + self.std = list(std) + self.interpolation = interpolation + self.antialias = antialias + + def forward(self, img: Tensor) -> Tensor: + img = F.resize(img, self.resize_size, interpolation=self.interpolation, antialias=self.antialias) + img = F.center_crop(img, self.crop_size) + if not isinstance(img, Tensor): + img = F.pil_to_tensor(img) + img = F.convert_image_dtype(img, torch.float) + img = F.normalize(img, mean=self.mean, std=self.std) + return img + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + format_string += f"\n crop_size={self.crop_size}" + format_string += f"\n resize_size={self.resize_size}" + format_string += f"\n mean={self.mean}" + format_string += f"\n std={self.std}" + format_string += f"\n interpolation={self.interpolation}" + format_string += "\n)" + return format_string + + def describe(self) -> str: + return ( + "Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. " + f"The images are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``, " + f"followed by a central crop of ``crop_size={self.crop_size}``. Finally the values are first rescaled to " + f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``." + ) + + +class VideoClassification(nn.Module): + def __init__( + self, + *, + crop_size: Tuple[int, int], + resize_size: Union[Tuple[int], Tuple[int, int]], + mean: Tuple[float, ...] = (0.43216, 0.394666, 0.37645), + std: Tuple[float, ...] = (0.22803, 0.22145, 0.216989), + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + ) -> None: + super().__init__() + self.crop_size = list(crop_size) + self.resize_size = list(resize_size) + self.mean = list(mean) + self.std = list(std) + self.interpolation = interpolation + + def forward(self, vid: Tensor) -> Tensor: + need_squeeze = False + if vid.ndim < 5: + vid = vid.unsqueeze(dim=0) + need_squeeze = True + + N, T, C, H, W = vid.shape + vid = vid.view(-1, C, H, W) + # We hard-code antialias=False to preserve results after we changed + # its default from None to True (see + # https://github.com/pytorch/vision/pull/7160) + # TODO: we could re-train the video models with antialias=True? + vid = F.resize(vid, self.resize_size, interpolation=self.interpolation, antialias=False) + vid = F.center_crop(vid, self.crop_size) + vid = F.convert_image_dtype(vid, torch.float) + vid = F.normalize(vid, mean=self.mean, std=self.std) + H, W = self.crop_size + vid = vid.view(N, T, C, H, W) + vid = vid.permute(0, 2, 1, 3, 4) # (N, T, C, H, W) => (N, C, T, H, W) + + if need_squeeze: + vid = vid.squeeze(dim=0) + return vid + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + format_string += f"\n crop_size={self.crop_size}" + format_string += f"\n resize_size={self.resize_size}" + format_string += f"\n mean={self.mean}" + format_string += f"\n std={self.std}" + format_string += f"\n interpolation={self.interpolation}" + format_string += "\n)" + return format_string + + def describe(self) -> str: + return ( + "Accepts batched ``(B, T, C, H, W)`` and single ``(T, C, H, W)`` video frame ``torch.Tensor`` objects. " + f"The frames are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``, " + f"followed by a central crop of ``crop_size={self.crop_size}``. Finally the values are first rescaled to " + f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``. Finally the output " + "dimensions are permuted to ``(..., C, T, H, W)`` tensors." + ) + + +class SemanticSegmentation(nn.Module): + def __init__( + self, + *, + resize_size: Optional[int], + mean: Tuple[float, ...] = (0.485, 0.456, 0.406), + std: Tuple[float, ...] = (0.229, 0.224, 0.225), + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ) -> None: + super().__init__() + self.resize_size = [resize_size] if resize_size is not None else None + self.mean = list(mean) + self.std = list(std) + self.interpolation = interpolation + self.antialias = antialias + + def forward(self, img: Tensor) -> Tensor: + if isinstance(self.resize_size, list): + img = F.resize(img, self.resize_size, interpolation=self.interpolation, antialias=self.antialias) + if not isinstance(img, Tensor): + img = F.pil_to_tensor(img) + img = F.convert_image_dtype(img, torch.float) + img = F.normalize(img, mean=self.mean, std=self.std) + return img + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + format_string += f"\n resize_size={self.resize_size}" + format_string += f"\n mean={self.mean}" + format_string += f"\n std={self.std}" + format_string += f"\n interpolation={self.interpolation}" + format_string += "\n)" + return format_string + + def describe(self) -> str: + return ( + "Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. " + f"The images are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``. " + f"Finally the values are first rescaled to ``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and " + f"``std={self.std}``." + ) + + +class OpticalFlow(nn.Module): + def forward(self, img1: Tensor, img2: Tensor) -> Tuple[Tensor, Tensor]: + if not isinstance(img1, Tensor): + img1 = F.pil_to_tensor(img1) + if not isinstance(img2, Tensor): + img2 = F.pil_to_tensor(img2) + + img1 = F.convert_image_dtype(img1, torch.float) + img2 = F.convert_image_dtype(img2, torch.float) + + # map [0, 1] into [-1, 1] + img1 = F.normalize(img1, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + img2 = F.normalize(img2, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + + img1 = img1.contiguous() + img2 = img2.contiguous() + + return img1, img2 + + def __repr__(self) -> str: + return self.__class__.__name__ + "()" + + def describe(self) -> str: + return ( + "Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. " + "The images are rescaled to ``[-1.0, 1.0]``." + ) diff --git a/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py b/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py new file mode 100644 index 0000000000000000000000000000000000000000..a04da4f74849805641e4c470f6b6b8d5f7000e3a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 + +import numbers +import random +import warnings + +from torchvision.transforms import RandomCrop, RandomResizedCrop + +from . import _functional_video as F + + +__all__ = [ + "RandomCropVideo", + "RandomResizedCropVideo", + "CenterCropVideo", + "NormalizeVideo", + "ToTensorVideo", + "RandomHorizontalFlipVideo", +] + + +warnings.warn( + "The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. " + "Please use the 'torchvision.transforms' module instead." +) + + +class RandomCropVideo(RandomCrop): + def __init__(self, size): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) + Returns: + torch.tensor: randomly cropped/resized video clip. + size is (C, T, OH, OW) + """ + i, j, h, w = self.get_params(clip, self.size) + return F.crop(clip, i, j, h, w) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +class RandomResizedCropVideo(RandomResizedCrop): + def __init__( + self, + size, + scale=(0.08, 1.0), + ratio=(3.0 / 4.0, 4.0 / 3.0), + interpolation_mode="bilinear", + ): + if isinstance(size, tuple): + if len(size) != 2: + raise ValueError(f"size should be tuple (height, width), instead got {size}") + self.size = size + else: + self.size = (size, size) + + self.interpolation_mode = interpolation_mode + self.scale = scale + self.ratio = ratio + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) + Returns: + torch.tensor: randomly cropped/resized video clip. + size is (C, T, H, W) + """ + i, j, h, w = self.get_params(clip, self.scale, self.ratio) + return F.resized_crop(clip, i, j, h, w, self.size, self.interpolation_mode) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}, scale={self.scale}, ratio={self.ratio})" + + +class CenterCropVideo: + def __init__(self, crop_size): + if isinstance(crop_size, numbers.Number): + self.crop_size = (int(crop_size), int(crop_size)) + else: + self.crop_size = crop_size + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) + Returns: + torch.tensor: central cropping of video clip. Size is + (C, T, crop_size, crop_size) + """ + return F.center_crop(clip, self.crop_size) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(crop_size={self.crop_size})" + + +class NormalizeVideo: + """ + Normalize the video clip by mean subtraction and division by standard deviation + Args: + mean (3-tuple): pixel RGB mean + std (3-tuple): pixel RGB standard deviation + inplace (boolean): whether do in-place normalization + """ + + def __init__(self, mean, std, inplace=False): + self.mean = mean + self.std = std + self.inplace = inplace + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W) + """ + return F.normalize(clip, self.mean, self.std, self.inplace) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})" + + +class ToTensorVideo: + """ + Convert tensor data type from uint8 to float, divide value by 255.0 and + permute the dimensions of clip tensor + """ + + def __init__(self): + pass + + def __call__(self, clip): + """ + Args: + clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C) + Return: + clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W) + """ + return F.to_tensor(clip) + + def __repr__(self) -> str: + return self.__class__.__name__ + + +class RandomHorizontalFlipVideo: + """ + Flip the video clip along the horizontal direction with a given probability + Args: + p (float): probability of the clip being flipped. Default value is 0.5 + """ + + def __init__(self, p=0.5): + self.p = p + + def __call__(self, clip): + """ + Args: + clip (torch.tensor): Size is (C, T, H, W) + Return: + clip (torch.tensor): Size is (C, T, H, W) + """ + if random.random() < self.p: + clip = F.hflip(clip) + return clip + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" diff --git a/lib/python3.11/site-packages/torchvision/transforms/autoaugment.py b/lib/python3.11/site-packages/torchvision/transforms/autoaugment.py new file mode 100644 index 0000000000000000000000000000000000000000..9dbbe91e741093b01ff8491ba8b39d9b6f578103 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/autoaugment.py @@ -0,0 +1,615 @@ +import math +from enum import Enum +from typing import Dict, List, Optional, Tuple + +import torch +from torch import Tensor + +from . import functional as F, InterpolationMode + +__all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment", "TrivialAugmentWide", "AugMix"] + + +def _apply_op( + img: Tensor, op_name: str, magnitude: float, interpolation: InterpolationMode, fill: Optional[List[float]] +): + if op_name == "ShearX": + # magnitude should be arctan(magnitude) + # official autoaug: (1, level, 0, 0, 1, 0) + # https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290 + # compared to + # torchvision: (1, tan(level), 0, 0, 1, 0) + # https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976 + img = F.affine( + img, + angle=0.0, + translate=[0, 0], + scale=1.0, + shear=[math.degrees(math.atan(magnitude)), 0.0], + interpolation=interpolation, + fill=fill, + center=[0, 0], + ) + elif op_name == "ShearY": + # magnitude should be arctan(magnitude) + # See above + img = F.affine( + img, + angle=0.0, + translate=[0, 0], + scale=1.0, + shear=[0.0, math.degrees(math.atan(magnitude))], + interpolation=interpolation, + fill=fill, + center=[0, 0], + ) + elif op_name == "TranslateX": + img = F.affine( + img, + angle=0.0, + translate=[int(magnitude), 0], + scale=1.0, + interpolation=interpolation, + shear=[0.0, 0.0], + fill=fill, + ) + elif op_name == "TranslateY": + img = F.affine( + img, + angle=0.0, + translate=[0, int(magnitude)], + scale=1.0, + interpolation=interpolation, + shear=[0.0, 0.0], + fill=fill, + ) + elif op_name == "Rotate": + img = F.rotate(img, magnitude, interpolation=interpolation, fill=fill) + elif op_name == "Brightness": + img = F.adjust_brightness(img, 1.0 + magnitude) + elif op_name == "Color": + img = F.adjust_saturation(img, 1.0 + magnitude) + elif op_name == "Contrast": + img = F.adjust_contrast(img, 1.0 + magnitude) + elif op_name == "Sharpness": + img = F.adjust_sharpness(img, 1.0 + magnitude) + elif op_name == "Posterize": + img = F.posterize(img, int(magnitude)) + elif op_name == "Solarize": + img = F.solarize(img, magnitude) + elif op_name == "AutoContrast": + img = F.autocontrast(img) + elif op_name == "Equalize": + img = F.equalize(img) + elif op_name == "Invert": + img = F.invert(img) + elif op_name == "Identity": + pass + else: + raise ValueError(f"The provided operator {op_name} is not recognized.") + return img + + +class AutoAugmentPolicy(Enum): + """AutoAugment policies learned on different datasets. + Available policies are IMAGENET, CIFAR10 and SVHN. + """ + + IMAGENET = "imagenet" + CIFAR10 = "cifar10" + SVHN = "svhn" + + +# FIXME: Eliminate copy-pasted code for fill standardization and _augmentation_space() by moving stuff on a base class +class AutoAugment(torch.nn.Module): + r"""AutoAugment data augmentation method based on + `"AutoAugment: Learning Augmentation Strategies from Data" `_. + If the image is torch Tensor, it should be of type torch.uint8, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + policy (AutoAugmentPolicy): Desired policy enum defined by + :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + def __init__( + self, + policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, + interpolation: InterpolationMode = InterpolationMode.NEAREST, + fill: Optional[List[float]] = None, + ) -> None: + super().__init__() + self.policy = policy + self.interpolation = interpolation + self.fill = fill + self.policies = self._get_policies(policy) + + def _get_policies( + self, policy: AutoAugmentPolicy + ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]: + if policy == AutoAugmentPolicy.IMAGENET: + return [ + (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)), + (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), + (("Equalize", 0.8, None), ("Equalize", 0.6, None)), + (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)), + (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), + (("Equalize", 0.4, None), ("Rotate", 0.8, 8)), + (("Solarize", 0.6, 3), ("Equalize", 0.6, None)), + (("Posterize", 0.8, 5), ("Equalize", 1.0, None)), + (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)), + (("Equalize", 0.6, None), ("Posterize", 0.4, 6)), + (("Rotate", 0.8, 8), ("Color", 0.4, 0)), + (("Rotate", 0.4, 9), ("Equalize", 0.6, None)), + (("Equalize", 0.0, None), ("Equalize", 0.8, None)), + (("Invert", 0.6, None), ("Equalize", 1.0, None)), + (("Color", 0.6, 4), ("Contrast", 1.0, 8)), + (("Rotate", 0.8, 8), ("Color", 1.0, 2)), + (("Color", 0.8, 8), ("Solarize", 0.8, 7)), + (("Sharpness", 0.4, 7), ("Invert", 0.6, None)), + (("ShearX", 0.6, 5), ("Equalize", 1.0, None)), + (("Color", 0.4, 0), ("Equalize", 0.6, None)), + (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), + (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), + (("Invert", 0.6, None), ("Equalize", 1.0, None)), + (("Color", 0.6, 4), ("Contrast", 1.0, 8)), + (("Equalize", 0.8, None), ("Equalize", 0.6, None)), + ] + elif policy == AutoAugmentPolicy.CIFAR10: + return [ + (("Invert", 0.1, None), ("Contrast", 0.2, 6)), + (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)), + (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)), + (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)), + (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)), + (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)), + (("Color", 0.4, 3), ("Brightness", 0.6, 7)), + (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)), + (("Equalize", 0.6, None), ("Equalize", 0.5, None)), + (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)), + (("Color", 0.7, 7), ("TranslateX", 0.5, 8)), + (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)), + (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)), + (("Brightness", 0.9, 6), ("Color", 0.2, 8)), + (("Solarize", 0.5, 2), ("Invert", 0.0, None)), + (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)), + (("Equalize", 0.2, None), ("Equalize", 0.6, None)), + (("Color", 0.9, 9), ("Equalize", 0.6, None)), + (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)), + (("Brightness", 0.1, 3), ("Color", 0.7, 0)), + (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)), + (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)), + (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)), + (("Equalize", 0.8, None), ("Invert", 0.1, None)), + (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)), + ] + elif policy == AutoAugmentPolicy.SVHN: + return [ + (("ShearX", 0.9, 4), ("Invert", 0.2, None)), + (("ShearY", 0.9, 8), ("Invert", 0.7, None)), + (("Equalize", 0.6, None), ("Solarize", 0.6, 6)), + (("Invert", 0.9, None), ("Equalize", 0.6, None)), + (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), + (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)), + (("ShearY", 0.9, 8), ("Invert", 0.4, None)), + (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)), + (("Invert", 0.9, None), ("AutoContrast", 0.8, None)), + (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), + (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)), + (("ShearY", 0.8, 8), ("Invert", 0.7, None)), + (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)), + (("Invert", 0.9, None), ("Equalize", 0.6, None)), + (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)), + (("Invert", 0.8, None), ("TranslateY", 0.0, 2)), + (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)), + (("Invert", 0.6, None), ("Rotate", 0.8, 4)), + (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)), + (("ShearX", 0.1, 6), ("Invert", 0.6, None)), + (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)), + (("ShearY", 0.8, 4), ("Invert", 0.8, None)), + (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)), + (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)), + (("ShearX", 0.7, 2), ("Invert", 0.1, None)), + ] + else: + raise ValueError(f"The provided policy {policy} is not recognized.") + + def _augmentation_space(self, num_bins: int, image_size: Tuple[int, int]) -> Dict[str, Tuple[Tensor, bool]]: + return { + # op_name: (magnitudes, signed) + "ShearX": (torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True), + "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True), + "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), + "Brightness": (torch.linspace(0.0, 0.9, num_bins), True), + "Color": (torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True), + "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False), + "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), + "AutoContrast": (torch.tensor(0.0), False), + "Equalize": (torch.tensor(0.0), False), + "Invert": (torch.tensor(0.0), False), + } + + @staticmethod + def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]: + """Get parameters for autoaugment transformation + + Returns: + params required by the autoaugment transformation + """ + policy_id = int(torch.randint(transform_num, (1,)).item()) + probs = torch.rand((2,)) + signs = torch.randint(2, (2,)) + + return policy_id, probs, signs + + def forward(self, img: Tensor) -> Tensor: + """ + img (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: AutoAugmented image. + """ + fill = self.fill + channels, height, width = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + elif fill is not None: + fill = [float(f) for f in fill] + + transform_id, probs, signs = self.get_params(len(self.policies)) + + op_meta = self._augmentation_space(10, (height, width)) + for i, (op_name, p, magnitude_id) in enumerate(self.policies[transform_id]): + if probs[i] <= p: + magnitudes, signed = op_meta[op_name] + magnitude = float(magnitudes[magnitude_id].item()) if magnitude_id is not None else 0.0 + if signed and signs[i] == 0: + magnitude *= -1.0 + img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) + + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(policy={self.policy}, fill={self.fill})" + + +class RandAugment(torch.nn.Module): + r"""RandAugment data augmentation method based on + `"RandAugment: Practical automated data augmentation with a reduced search space" + `_. + If the image is torch Tensor, it should be of type torch.uint8, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + num_ops (int): Number of augmentation transformations to apply sequentially. + magnitude (int): Magnitude for all the transformations. + num_magnitude_bins (int): The number of different magnitude values. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + def __init__( + self, + num_ops: int = 2, + magnitude: int = 9, + num_magnitude_bins: int = 31, + interpolation: InterpolationMode = InterpolationMode.NEAREST, + fill: Optional[List[float]] = None, + ) -> None: + super().__init__() + self.num_ops = num_ops + self.magnitude = magnitude + self.num_magnitude_bins = num_magnitude_bins + self.interpolation = interpolation + self.fill = fill + + def _augmentation_space(self, num_bins: int, image_size: Tuple[int, int]) -> Dict[str, Tuple[Tensor, bool]]: + return { + # op_name: (magnitudes, signed) + "Identity": (torch.tensor(0.0), False), + "ShearX": (torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True), + "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True), + "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), + "Brightness": (torch.linspace(0.0, 0.9, num_bins), True), + "Color": (torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True), + "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False), + "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), + "AutoContrast": (torch.tensor(0.0), False), + "Equalize": (torch.tensor(0.0), False), + } + + def forward(self, img: Tensor) -> Tensor: + """ + img (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: Transformed image. + """ + fill = self.fill + channels, height, width = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + elif fill is not None: + fill = [float(f) for f in fill] + + op_meta = self._augmentation_space(self.num_magnitude_bins, (height, width)) + for _ in range(self.num_ops): + op_index = int(torch.randint(len(op_meta), (1,)).item()) + op_name = list(op_meta.keys())[op_index] + magnitudes, signed = op_meta[op_name] + magnitude = float(magnitudes[self.magnitude].item()) if magnitudes.ndim > 0 else 0.0 + if signed and torch.randint(2, (1,)): + magnitude *= -1.0 + img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) + + return img + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"num_ops={self.num_ops}" + f", magnitude={self.magnitude}" + f", num_magnitude_bins={self.num_magnitude_bins}" + f", interpolation={self.interpolation}" + f", fill={self.fill}" + f")" + ) + return s + + +class TrivialAugmentWide(torch.nn.Module): + r"""Dataset-independent data-augmentation with TrivialAugment Wide, as described in + `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" `_. + If the image is torch Tensor, it should be of type torch.uint8, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + num_magnitude_bins (int): The number of different magnitude values. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + def __init__( + self, + num_magnitude_bins: int = 31, + interpolation: InterpolationMode = InterpolationMode.NEAREST, + fill: Optional[List[float]] = None, + ) -> None: + super().__init__() + self.num_magnitude_bins = num_magnitude_bins + self.interpolation = interpolation + self.fill = fill + + def _augmentation_space(self, num_bins: int) -> Dict[str, Tuple[Tensor, bool]]: + return { + # op_name: (magnitudes, signed) + "Identity": (torch.tensor(0.0), False), + "ShearX": (torch.linspace(0.0, 0.99, num_bins), True), + "ShearY": (torch.linspace(0.0, 0.99, num_bins), True), + "TranslateX": (torch.linspace(0.0, 32.0, num_bins), True), + "TranslateY": (torch.linspace(0.0, 32.0, num_bins), True), + "Rotate": (torch.linspace(0.0, 135.0, num_bins), True), + "Brightness": (torch.linspace(0.0, 0.99, num_bins), True), + "Color": (torch.linspace(0.0, 0.99, num_bins), True), + "Contrast": (torch.linspace(0.0, 0.99, num_bins), True), + "Sharpness": (torch.linspace(0.0, 0.99, num_bins), True), + "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6)).round().int(), False), + "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), + "AutoContrast": (torch.tensor(0.0), False), + "Equalize": (torch.tensor(0.0), False), + } + + def forward(self, img: Tensor) -> Tensor: + """ + img (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: Transformed image. + """ + fill = self.fill + channels, height, width = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + elif fill is not None: + fill = [float(f) for f in fill] + + op_meta = self._augmentation_space(self.num_magnitude_bins) + op_index = int(torch.randint(len(op_meta), (1,)).item()) + op_name = list(op_meta.keys())[op_index] + magnitudes, signed = op_meta[op_name] + magnitude = ( + float(magnitudes[torch.randint(len(magnitudes), (1,), dtype=torch.long)].item()) + if magnitudes.ndim > 0 + else 0.0 + ) + if signed and torch.randint(2, (1,)): + magnitude *= -1.0 + + return _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill) + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"num_magnitude_bins={self.num_magnitude_bins}" + f", interpolation={self.interpolation}" + f", fill={self.fill}" + f")" + ) + return s + + +class AugMix(torch.nn.Module): + r"""AugMix data augmentation method based on + `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" `_. + If the image is torch Tensor, it should be of type torch.uint8, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + severity (int): The severity of base augmentation operators. Default is ``3``. + mixture_width (int): The number of augmentation chains. Default is ``3``. + chain_depth (int): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3]. + Default is ``-1``. + alpha (float): The hyperparameter for the probability distributions. Default is ``1.0``. + all_ops (bool): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + def __init__( + self, + severity: int = 3, + mixture_width: int = 3, + chain_depth: int = -1, + alpha: float = 1.0, + all_ops: bool = True, + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + fill: Optional[List[float]] = None, + ) -> None: + super().__init__() + self._PARAMETER_MAX = 10 + if not (1 <= severity <= self._PARAMETER_MAX): + raise ValueError(f"The severity must be between [1, {self._PARAMETER_MAX}]. Got {severity} instead.") + self.severity = severity + self.mixture_width = mixture_width + self.chain_depth = chain_depth + self.alpha = alpha + self.all_ops = all_ops + self.interpolation = interpolation + self.fill = fill + + def _augmentation_space(self, num_bins: int, image_size: Tuple[int, int]) -> Dict[str, Tuple[Tensor, bool]]: + s = { + # op_name: (magnitudes, signed) + "ShearX": (torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": (torch.linspace(0.0, image_size[1] / 3.0, num_bins), True), + "TranslateY": (torch.linspace(0.0, image_size[0] / 3.0, num_bins), True), + "Rotate": (torch.linspace(0.0, 30.0, num_bins), True), + "Posterize": (4 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False), + "Solarize": (torch.linspace(255.0, 0.0, num_bins), False), + "AutoContrast": (torch.tensor(0.0), False), + "Equalize": (torch.tensor(0.0), False), + } + if self.all_ops: + s.update( + { + "Brightness": (torch.linspace(0.0, 0.9, num_bins), True), + "Color": (torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True), + } + ) + return s + + @torch.jit.unused + def _pil_to_tensor(self, img) -> Tensor: + return F.pil_to_tensor(img) + + @torch.jit.unused + def _tensor_to_pil(self, img: Tensor): + return F.to_pil_image(img) + + def _sample_dirichlet(self, params: Tensor) -> Tensor: + # Must be on a separate method so that we can overwrite it in tests. + return torch._sample_dirichlet(params) + + def forward(self, orig_img: Tensor) -> Tensor: + """ + img (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: Transformed image. + """ + fill = self.fill + channels, height, width = F.get_dimensions(orig_img) + if isinstance(orig_img, Tensor): + img = orig_img + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + elif fill is not None: + fill = [float(f) for f in fill] + else: + img = self._pil_to_tensor(orig_img) + + op_meta = self._augmentation_space(self._PARAMETER_MAX, (height, width)) + + orig_dims = list(img.shape) + batch = img.view([1] * max(4 - img.ndim, 0) + orig_dims) + batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1) + + # Sample the beta weights for combining the original and augmented image. To get Beta, we use a Dirichlet + # with 2 parameters. The 1st column stores the weights of the original and the 2nd the ones of augmented image. + m = self._sample_dirichlet( + torch.tensor([self.alpha, self.alpha], device=batch.device).expand(batch_dims[0], -1) + ) + + # Sample the mixing weights and combine them with the ones sampled from Beta for the augmented images. + combined_weights = self._sample_dirichlet( + torch.tensor([self.alpha] * self.mixture_width, device=batch.device).expand(batch_dims[0], -1) + ) * m[:, 1].view([batch_dims[0], -1]) + + mix = m[:, 0].view(batch_dims) * batch + for i in range(self.mixture_width): + aug = batch + depth = self.chain_depth if self.chain_depth > 0 else int(torch.randint(low=1, high=4, size=(1,)).item()) + for _ in range(depth): + op_index = int(torch.randint(len(op_meta), (1,)).item()) + op_name = list(op_meta.keys())[op_index] + magnitudes, signed = op_meta[op_name] + magnitude = ( + float(magnitudes[torch.randint(self.severity, (1,), dtype=torch.long)].item()) + if magnitudes.ndim > 0 + else 0.0 + ) + if signed and torch.randint(2, (1,)): + magnitude *= -1.0 + aug = _apply_op(aug, op_name, magnitude, interpolation=self.interpolation, fill=fill) + mix.add_(combined_weights[:, i].view(batch_dims) * aug) + mix = mix.view(orig_dims).to(dtype=img.dtype) + + if not isinstance(orig_img, Tensor): + return self._tensor_to_pil(mix) + return mix + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"severity={self.severity}" + f", mixture_width={self.mixture_width}" + f", chain_depth={self.chain_depth}" + f", alpha={self.alpha}" + f", all_ops={self.all_ops}" + f", interpolation={self.interpolation}" + f", fill={self.fill}" + f")" + ) + return s diff --git a/lib/python3.11/site-packages/torchvision/transforms/functional.py b/lib/python3.11/site-packages/torchvision/transforms/functional.py new file mode 100644 index 0000000000000000000000000000000000000000..8efe2a8878a06b37d7ede9496b213076c1f59c01 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/functional.py @@ -0,0 +1,1586 @@ +import math +import numbers +import sys +import warnings +from enum import Enum +from typing import Any, List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL import Image +from PIL.Image import Image as PILImage +from torch import Tensor + +try: + import accimage +except ImportError: + accimage = None + +from ..utils import _log_api_usage_once +from . import _functional_pil as F_pil, _functional_tensor as F_t + + +class InterpolationMode(Enum): + """Interpolation modes + Available interpolation methods are ``nearest``, ``nearest-exact``, ``bilinear``, ``bicubic``, ``box``, ``hamming``, + and ``lanczos``. + """ + + NEAREST = "nearest" + NEAREST_EXACT = "nearest-exact" + BILINEAR = "bilinear" + BICUBIC = "bicubic" + # For PIL compatibility + BOX = "box" + HAMMING = "hamming" + LANCZOS = "lanczos" + + +# TODO: Once torchscript supports Enums with staticmethod +# this can be put into InterpolationMode as staticmethod +def _interpolation_modes_from_int(i: int) -> InterpolationMode: + inverse_modes_mapping = { + 0: InterpolationMode.NEAREST, + 2: InterpolationMode.BILINEAR, + 3: InterpolationMode.BICUBIC, + 4: InterpolationMode.BOX, + 5: InterpolationMode.HAMMING, + 1: InterpolationMode.LANCZOS, + } + return inverse_modes_mapping[i] + + +pil_modes_mapping = { + InterpolationMode.NEAREST: 0, + InterpolationMode.BILINEAR: 2, + InterpolationMode.BICUBIC: 3, + InterpolationMode.NEAREST_EXACT: 0, + InterpolationMode.BOX: 4, + InterpolationMode.HAMMING: 5, + InterpolationMode.LANCZOS: 1, +} + +_is_pil_image = F_pil._is_pil_image + + +def get_dimensions(img: Tensor) -> List[int]: + """Returns the dimensions of an image as [channels, height, width]. + + Args: + img (PIL Image or Tensor): The image to be checked. + + Returns: + List[int]: The image dimensions. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(get_dimensions) + if isinstance(img, torch.Tensor): + return F_t.get_dimensions(img) + + return F_pil.get_dimensions(img) + + +def get_image_size(img: Tensor) -> List[int]: + """Returns the size of an image as [width, height]. + + Args: + img (PIL Image or Tensor): The image to be checked. + + Returns: + List[int]: The image size. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(get_image_size) + if isinstance(img, torch.Tensor): + return F_t.get_image_size(img) + + return F_pil.get_image_size(img) + + +def get_image_num_channels(img: Tensor) -> int: + """Returns the number of channels of an image. + + Args: + img (PIL Image or Tensor): The image to be checked. + + Returns: + int: The number of channels. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(get_image_num_channels) + if isinstance(img, torch.Tensor): + return F_t.get_image_num_channels(img) + + return F_pil.get_image_num_channels(img) + + +@torch.jit.unused +def _is_numpy(img: Any) -> bool: + return isinstance(img, np.ndarray) + + +@torch.jit.unused +def _is_numpy_image(img: Any) -> bool: + return img.ndim in {2, 3} + + +def to_tensor(pic: Union[PILImage, np.ndarray]) -> Tensor: + """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor. + This function does not support torchscript. + + See :class:`~torchvision.transforms.ToTensor` for more details. + + Args: + pic (PIL Image or numpy.ndarray): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(to_tensor) + if not (F_pil._is_pil_image(pic) or _is_numpy(pic)): + raise TypeError(f"pic should be PIL Image or ndarray. Got {type(pic)}") + + if _is_numpy(pic) and not _is_numpy_image(pic): + raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.") + + default_float_dtype = torch.get_default_dtype() + + if isinstance(pic, np.ndarray): + # handle numpy array + if pic.ndim == 2: + pic = pic[:, :, None] + + img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous() + # backward compatibility + if isinstance(img, torch.ByteTensor): + return img.to(dtype=default_float_dtype).div(255) + else: + return img + + if accimage is not None and isinstance(pic, accimage.Image): + nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32) + pic.copyto(nppic) + return torch.from_numpy(nppic).to(dtype=default_float_dtype) + + # handle PIL Image + mode_to_nptype = {"I": np.int32, "I;16" if sys.byteorder == "little" else "I;16B": np.int16, "F": np.float32} + img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)) + + if pic.mode == "1": + img = 255 * img + img = img.view(pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic)) + # put it from HWC to CHW format + img = img.permute((2, 0, 1)).contiguous() + if isinstance(img, torch.ByteTensor): + return img.to(dtype=default_float_dtype).div(255) + else: + return img + + +def pil_to_tensor(pic: Any) -> Tensor: + """Convert a ``PIL Image`` to a tensor of the same type. + This function does not support torchscript. + + See :class:`~torchvision.transforms.PILToTensor` for more details. + + .. note:: + + A deep copy of the underlying array is performed. + + Args: + pic (PIL Image): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(pil_to_tensor) + if not F_pil._is_pil_image(pic): + raise TypeError(f"pic should be PIL Image. Got {type(pic)}") + + if accimage is not None and isinstance(pic, accimage.Image): + # accimage format is always uint8 internally, so always return uint8 here + nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.uint8) + pic.copyto(nppic) + return torch.as_tensor(nppic) + + # handle PIL Image + img = torch.as_tensor(np.array(pic, copy=True)) + img = img.view(pic.size[1], pic.size[0], F_pil.get_image_num_channels(pic)) + # put it from HWC to CHW format + img = img.permute((2, 0, 1)) + return img + + +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor: + """Convert a tensor image to the given ``dtype`` and scale the values accordingly + This function does not support PIL Image. + + Args: + image (torch.Tensor): Image to be converted + dtype (torch.dtype): Desired data type of the output + + Returns: + Tensor: Converted image + + .. note:: + + When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. + If converted back and forth, this mismatch has no effect. + + Raises: + RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as + well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to + overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range + of the integer ``dtype``. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(convert_image_dtype) + if not isinstance(image, torch.Tensor): + raise TypeError("Input img should be Tensor Image") + + return F_t.convert_image_dtype(image, dtype) + + +def to_pil_image(pic, mode=None): + """Convert a tensor or an ndarray to PIL Image. This function does not support torchscript. + + See :class:`~torchvision.transforms.ToPILImage` for more details. + + Args: + pic (Tensor or numpy.ndarray): Image to be converted to PIL Image. + mode (`PIL.Image mode`_): color space and pixel depth of input data (optional). + + .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes + + Returns: + PIL Image: Image converted to PIL Image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(to_pil_image) + + if isinstance(pic, torch.Tensor): + if pic.ndim == 3: + pic = pic.permute((1, 2, 0)) + pic = pic.numpy(force=True) + elif not isinstance(pic, np.ndarray): + raise TypeError(f"pic should be Tensor or ndarray. Got {type(pic)}.") + + if pic.ndim == 2: + # if 2D image, add channel dimension (HWC) + pic = np.expand_dims(pic, 2) + if pic.ndim != 3: + raise ValueError(f"pic should be 2/3 dimensional. Got {pic.ndim} dimensions.") + + if pic.shape[-1] > 4: + raise ValueError(f"pic should not have > 4 channels. Got {pic.shape[-1]} channels.") + + npimg = pic + + if np.issubdtype(npimg.dtype, np.floating) and mode != "F": + npimg = (npimg * 255).astype(np.uint8) + + if npimg.shape[2] == 1: + expected_mode = None + npimg = npimg[:, :, 0] + if npimg.dtype == np.uint8: + expected_mode = "L" + elif npimg.dtype == np.int16: + expected_mode = "I;16" if sys.byteorder == "little" else "I;16B" + elif npimg.dtype == np.int32: + expected_mode = "I" + elif npimg.dtype == np.float32: + expected_mode = "F" + if mode is not None and mode != expected_mode: + raise ValueError(f"Incorrect mode ({mode}) supplied for input type {np.dtype}. Should be {expected_mode}") + mode = expected_mode + + elif npimg.shape[2] == 2: + permitted_2_channel_modes = ["LA"] + if mode is not None and mode not in permitted_2_channel_modes: + raise ValueError(f"Only modes {permitted_2_channel_modes} are supported for 2D inputs") + + if mode is None and npimg.dtype == np.uint8: + mode = "LA" + + elif npimg.shape[2] == 4: + permitted_4_channel_modes = ["RGBA", "CMYK", "RGBX"] + if mode is not None and mode not in permitted_4_channel_modes: + raise ValueError(f"Only modes {permitted_4_channel_modes} are supported for 4D inputs") + + if mode is None and npimg.dtype == np.uint8: + mode = "RGBA" + else: + permitted_3_channel_modes = ["RGB", "YCbCr", "HSV"] + if mode is not None and mode not in permitted_3_channel_modes: + raise ValueError(f"Only modes {permitted_3_channel_modes} are supported for 3D inputs") + if mode is None and npimg.dtype == np.uint8: + mode = "RGB" + + if mode is None: + raise TypeError(f"Input type {npimg.dtype} is not supported") + + return Image.fromarray(npimg, mode=mode) + + +def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor: + """Normalize a float tensor image with mean and standard deviation. + This transform does not support PIL Image. + + .. note:: + This transform acts out of place by default, i.e., it does not mutates the input tensor. + + See :class:`~torchvision.transforms.Normalize` for more details. + + Args: + tensor (Tensor): Float tensor image of size (C, H, W) or (B, C, H, W) to be normalized. + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation inplace. + + Returns: + Tensor: Normalized Tensor image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(normalize) + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"img should be Tensor Image. Got {type(tensor)}") + + return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace) + + +def _compute_resized_output_size( + image_size: Tuple[int, int], + size: Optional[List[int]], + max_size: Optional[int] = None, + allow_size_none: bool = False, # only True in v2 +) -> List[int]: + h, w = image_size + short, long = (w, h) if w <= h else (h, w) + if size is None: + if not allow_size_none: + raise ValueError("This should never happen!!") + if not isinstance(max_size, int): + raise ValueError(f"max_size must be an integer when size is None, but got {max_size} instead.") + new_short, new_long = int(max_size * short / long), max_size + new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short) + elif len(size) == 1: # specified size only for the smallest edge + requested_new_short = size if isinstance(size, int) else size[0] + new_short, new_long = requested_new_short, int(requested_new_short * long / short) + + if max_size is not None: + if max_size <= requested_new_short: + raise ValueError( + f"max_size = {max_size} must be strictly greater than the requested " + f"size for the smaller edge size = {size}" + ) + if new_long > max_size: + new_short, new_long = int(max_size * new_short / new_long), max_size + + new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short) + else: # specified both h and w + new_w, new_h = size[1], size[0] + return [new_h, new_w] + + +def resize( + img: Tensor, + size: List[int], + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, +) -> Tensor: + r"""Resize the input image to the given size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + img (PIL Image or Tensor): Image to be resized. + size (sequence or int): Desired output size. If size is a sequence like + (h, w), the output size will be matched to this. If size is an int, + the smaller edge of the image will be matched to this number maintaining + the aspect ratio. i.e, if height > width, then image will be rescaled to + :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`. + + .. note:: + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. + Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, + ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are + supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + max_size (int, optional): The maximum allowed for the longer edge of + the resized image. If the longer edge of the image is greater + than ``max_size`` after being resized according to ``size``, + ``size`` will be overruled so that the longer edge is equal to + ``max_size``. + As a result, the smaller edge may be shorter than ``size``. This + is only supported if ``size`` is an int (or a sequence of length + 1 in torchscript mode). + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + + Returns: + PIL Image or Tensor: Resized image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(resize) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + elif not isinstance(interpolation, InterpolationMode): + raise TypeError( + "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" + ) + + if isinstance(size, (list, tuple)): + if len(size) not in [1, 2]: + raise ValueError( + f"Size must be an int or a 1 or 2 element tuple/list, not a {len(size)} element tuple/list" + ) + if max_size is not None and len(size) != 1: + raise ValueError( + "max_size should only be passed if size specifies the length of the smaller edge, " + "i.e. size should be an int or a sequence of length 1 in torchscript mode." + ) + + _, image_height, image_width = get_dimensions(img) + if isinstance(size, int): + size = [size] + output_size = _compute_resized_output_size((image_height, image_width), size, max_size) + + if [image_height, image_width] == output_size: + return img + + if not isinstance(img, torch.Tensor): + if antialias is False: + warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.") + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.resize(img, size=output_size, interpolation=pil_interpolation) + + return F_t.resize(img, size=output_size, interpolation=interpolation.value, antialias=antialias) + + +def pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = "constant") -> Tensor: + r"""Pad the given image on all sides with the given "pad" value. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric, + at most 3 leading dimensions for mode edge, + and an arbitrary number of leading dimensions for mode constant + + Args: + img (PIL Image or Tensor): Image to be padded. + padding (int or sequence): Padding on each border. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + + .. note:: + In torchscript mode padding as single int is not supported, use a sequence of + length 1: ``[padding, ]``. + fill (number or tuple): Pixel fill value for constant fill. Default is 0. + If a tuple of length 3, it is used to fill R, G, B channels respectively. + This value is only used when the padding_mode is constant. + Only number is supported for torch Tensor. + Only int or tuple value is supported for PIL Image. + padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. + Default is constant. + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value at the edge of the image. + If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 + + - reflect: pads with reflection of image without repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + + Returns: + PIL Image or Tensor: Padded image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(pad) + if not isinstance(img, torch.Tensor): + return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) + + return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode) + + +def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor: + """Crop the given image at specified location and output size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then cropped. + + Args: + img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. + top (int): Vertical component of the top left corner of the crop box. + left (int): Horizontal component of the top left corner of the crop box. + height (int): Height of the crop box. + width (int): Width of the crop box. + + Returns: + PIL Image or Tensor: Cropped image. + """ + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(crop) + if not isinstance(img, torch.Tensor): + return F_pil.crop(img, top, left, height, width) + + return F_t.crop(img, top, left, height, width) + + +def center_crop(img: Tensor, output_size: List[int]) -> Tensor: + """Crops the given image at the center. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + img (PIL Image or Tensor): Image to be cropped. + output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, + it is used for both directions. + + Returns: + PIL Image or Tensor: Cropped image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(center_crop) + if isinstance(output_size, numbers.Number): + output_size = (int(output_size), int(output_size)) + elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: + output_size = (output_size[0], output_size[0]) + + _, image_height, image_width = get_dimensions(img) + crop_height, crop_width = output_size + + if crop_width > image_width or crop_height > image_height: + padding_ltrb = [ + (crop_width - image_width) // 2 if crop_width > image_width else 0, + (crop_height - image_height) // 2 if crop_height > image_height else 0, + (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, + (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, + ] + img = pad(img, padding_ltrb, fill=0) # PIL uses fill value 0 + _, image_height, image_width = get_dimensions(img) + if crop_width == image_width and crop_height == image_height: + return img + + crop_top = int(round((image_height - crop_height) / 2.0)) + crop_left = int(round((image_width - crop_width) / 2.0)) + return crop(img, crop_top, crop_left, crop_height, crop_width) + + +def resized_crop( + img: Tensor, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, +) -> Tensor: + """Crop the given image and resize it to desired size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + Notably used in :class:`~torchvision.transforms.RandomResizedCrop`. + + Args: + img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. + top (int): Vertical component of the top left corner of the crop box. + left (int): Horizontal component of the top left corner of the crop box. + height (int): Height of the crop box. + width (int): Width of the crop box. + size (sequence or int): Desired output size. Same semantics as ``resize``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. + Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``, + ``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are + supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + Returns: + PIL Image or Tensor: Cropped image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(resized_crop) + img = crop(img, top, left, height, width) + img = resize(img, size, interpolation, antialias=antialias) + return img + + +def hflip(img: Tensor) -> Tensor: + """Horizontally flip the given image. + + Args: + img (PIL Image or Tensor): Image to be flipped. If img + is a Tensor, it is expected to be in [..., H, W] format, + where ... means it can have an arbitrary number of leading + dimensions. + + Returns: + PIL Image or Tensor: Horizontally flipped image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(hflip) + if not isinstance(img, torch.Tensor): + return F_pil.hflip(img) + + return F_t.hflip(img) + + +def _get_perspective_coeffs(startpoints: List[List[int]], endpoints: List[List[int]]) -> List[float]: + """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms. + + In Perspective Transform each pixel (x, y) in the original image gets transformed as, + (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) ) + + Args: + startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. + endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. + + Returns: + octuple (a, b, c, d, e, f, g, h) for transforming each pixel. + """ + if len(startpoints) != 4 or len(endpoints) != 4: + raise ValueError( + f"Please provide exactly four corners, got {len(startpoints)} startpoints and {len(endpoints)} endpoints." + ) + a_matrix = torch.zeros(2 * len(startpoints), 8, dtype=torch.float64) + + for i, (p1, p2) in enumerate(zip(endpoints, startpoints)): + a_matrix[2 * i, :] = torch.tensor([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]]) + a_matrix[2 * i + 1, :] = torch.tensor([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]]) + + b_matrix = torch.tensor(startpoints, dtype=torch.float64).view(8) + # do least squares in double precision to prevent numerical issues + res = torch.linalg.lstsq(a_matrix, b_matrix, driver="gels").solution.to(torch.float32) + + output: List[float] = res.tolist() + return output + + +def perspective( + img: Tensor, + startpoints: List[List[int]], + endpoints: List[List[int]], + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + fill: Optional[List[float]] = None, +) -> Tensor: + """Perform perspective transform of the given image. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): Image to be transformed. + startpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the original image. + endpoints (list of list of ints): List containing four lists of two integers corresponding to four corners + ``[top-left, top-right, bottom-right, bottom-left]`` of the transformed image. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + + .. note:: + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + + Returns: + PIL Image or Tensor: transformed Image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(perspective) + + coeffs = _get_perspective_coeffs(startpoints, endpoints) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + elif not isinstance(interpolation, InterpolationMode): + raise TypeError( + "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" + ) + + if not isinstance(img, torch.Tensor): + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.perspective(img, coeffs, interpolation=pil_interpolation, fill=fill) + + return F_t.perspective(img, coeffs, interpolation=interpolation.value, fill=fill) + + +def vflip(img: Tensor) -> Tensor: + """Vertically flip the given image. + + Args: + img (PIL Image or Tensor): Image to be flipped. If img + is a Tensor, it is expected to be in [..., H, W] format, + where ... means it can have an arbitrary number of leading + dimensions. + + Returns: + PIL Image or Tensor: Vertically flipped image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(vflip) + if not isinstance(img, torch.Tensor): + return F_pil.vflip(img) + + return F_t.vflip(img) + + +def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + """Crop the given image into four corners and the central crop. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + .. Note:: + This transform returns a tuple of images and there may be a + mismatch in the number of inputs and targets your ``Dataset`` returns. + + Args: + img (PIL Image or Tensor): Image to be cropped. + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + Returns: + tuple: tuple (tl, tr, bl, br, center) + Corresponding top left, top right, bottom left, bottom right and center crop. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(five_crop) + if isinstance(size, numbers.Number): + size = (int(size), int(size)) + elif isinstance(size, (tuple, list)) and len(size) == 1: + size = (size[0], size[0]) + + if len(size) != 2: + raise ValueError("Please provide only two dimensions (h, w) for size.") + + _, image_height, image_width = get_dimensions(img) + crop_height, crop_width = size + if crop_width > image_width or crop_height > image_height: + msg = "Requested crop size {} is bigger than input size {}" + raise ValueError(msg.format(size, (image_height, image_width))) + + tl = crop(img, 0, 0, crop_height, crop_width) + tr = crop(img, 0, image_width - crop_width, crop_height, crop_width) + bl = crop(img, image_height - crop_height, 0, crop_height, crop_width) + br = crop(img, image_height - crop_height, image_width - crop_width, crop_height, crop_width) + + center = center_crop(img, [crop_height, crop_width]) + + return tl, tr, bl, br, center + + +def ten_crop( + img: Tensor, size: List[int], vertical_flip: bool = False +) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: + """Generate ten cropped images from the given image. + Crop the given image into four corners and the central crop plus the + flipped version of these (horizontal flipping is used by default). + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + .. Note:: + This transform returns a tuple of images and there may be a + mismatch in the number of inputs and targets your ``Dataset`` returns. + + Args: + img (PIL Image or Tensor): Image to be cropped. + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + vertical_flip (bool): Use vertical flipping instead of horizontal + + Returns: + tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip) + Corresponding top left, top right, bottom left, bottom right and + center crop and same for the flipped image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(ten_crop) + if isinstance(size, numbers.Number): + size = (int(size), int(size)) + elif isinstance(size, (tuple, list)) and len(size) == 1: + size = (size[0], size[0]) + + if len(size) != 2: + raise ValueError("Please provide only two dimensions (h, w) for size.") + + first_five = five_crop(img, size) + + if vertical_flip: + img = vflip(img) + else: + img = hflip(img) + + second_five = five_crop(img, size) + return first_five + second_five + + +def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor: + """Adjust brightness of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + brightness_factor (float): How much to adjust the brightness. Can be + any non-negative number. 0 gives a black image, 1 gives the + original image while 2 increases the brightness by a factor of 2. + + Returns: + PIL Image or Tensor: Brightness adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_brightness) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_brightness(img, brightness_factor) + + return F_t.adjust_brightness(img, brightness_factor) + + +def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor: + """Adjust contrast of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + contrast_factor (float): How much to adjust the contrast. Can be any + non-negative number. 0 gives a solid gray image, 1 gives the + original image while 2 increases the contrast by a factor of 2. + + Returns: + PIL Image or Tensor: Contrast adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_contrast) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_contrast(img, contrast_factor) + + return F_t.adjust_contrast(img, contrast_factor) + + +def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor: + """Adjust color saturation of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + saturation_factor (float): How much to adjust the saturation. 0 will + give a black and white image, 1 will give the original image while + 2 will enhance the saturation by a factor of 2. + + Returns: + PIL Image or Tensor: Saturation adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_saturation) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_saturation(img, saturation_factor) + + return F_t.adjust_saturation(img, saturation_factor) + + +def adjust_hue(img: Tensor, hue_factor: float) -> Tensor: + """Adjust hue of an image. + + The image hue is adjusted by converting the image to HSV and + cyclically shifting the intensities in the hue channel (H). + The image is then converted back to original image mode. + + `hue_factor` is the amount of shift in H channel and must be in the + interval `[-0.5, 0.5]`. + + See `Hue`_ for more details. + + .. _Hue: https://en.wikipedia.org/wiki/Hue + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image mode "1", "I", "F" and modes with transparency (alpha channel) are not supported. + Note: the pixel values of the input image has to be non-negative for conversion to HSV space; + thus it does not work if you normalize your image to an interval with negative values, + or use an interpolation that generates negative values before using this function. + hue_factor (float): How much to shift the hue channel. Should be in + [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in + HSV space in positive and negative direction respectively. + 0 means no shift. Therefore, both -0.5 and 0.5 will give an image + with complementary colors while 0 gives the original image. + + Returns: + PIL Image or Tensor: Hue adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_hue) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_hue(img, hue_factor) + + return F_t.adjust_hue(img, hue_factor) + + +def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: + r"""Perform gamma correction on an image. + + Also known as Power Law Transform. Intensities in RGB mode are adjusted + based on the following equation: + + .. math:: + I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma} + + See `Gamma Correction`_ for more details. + + .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction + + Args: + img (PIL Image or Tensor): PIL Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, modes with transparency (alpha channel) are not supported. + gamma (float): Non negative real number, same as :math:`\gamma` in the equation. + gamma larger than 1 make the shadows darker, + while gamma smaller than 1 make dark regions lighter. + gain (float): The constant multiplier. + Returns: + PIL Image or Tensor: Gamma correction adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_gamma) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_gamma(img, gamma, gain) + + return F_t.adjust_gamma(img, gamma, gain) + + +def _get_inverse_affine_matrix( + center: List[float], angle: float, translate: List[float], scale: float, shear: List[float], inverted: bool = True +) -> List[float]: + # Helper method to compute inverse matrix for affine transformation + + # Pillow requires inverse affine transformation matrix: + # Affine matrix is : M = T * C * RotateScaleShear * C^-1 + # + # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1] + # C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1] + # RotateScaleShear is rotation with scale and shear matrix + # + # RotateScaleShear(a, s, (sx, sy)) = + # = R(a) * S(s) * SHy(sy) * SHx(sx) + # = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(sx)/cos(sy) - sin(a)), 0 ] + # [ s*sin(a - sy)/cos(sy), s*(-sin(a - sy)*tan(sx)/cos(sy) + cos(a)), 0 ] + # [ 0 , 0 , 1 ] + # where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears: + # SHx(s) = [1, -tan(s)] and SHy(s) = [1 , 0] + # [0, 1 ] [-tan(s), 1] + # + # Thus, the inverse is M^-1 = C * RotateScaleShear^-1 * C^-1 * T^-1 + + rot = math.radians(angle) + sx = math.radians(shear[0]) + sy = math.radians(shear[1]) + + cx, cy = center + tx, ty = translate + + # RSS without scaling + a = math.cos(rot - sy) / math.cos(sy) + b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot) + c = math.sin(rot - sy) / math.cos(sy) + d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot) + + if inverted: + # Inverted rotation matrix with scale and shear + # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1 + matrix = [d, -b, 0.0, -c, a, 0.0] + matrix = [x / scale for x in matrix] + # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1 + matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty) + matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty) + # Apply center translation: C * RSS^-1 * C^-1 * T^-1 + matrix[2] += cx + matrix[5] += cy + else: + matrix = [a, b, 0.0, c, d, 0.0] + matrix = [x * scale for x in matrix] + # Apply inverse of center translation: RSS * C^-1 + matrix[2] += matrix[0] * (-cx) + matrix[1] * (-cy) + matrix[5] += matrix[3] * (-cx) + matrix[4] * (-cy) + # Apply translation and center : T * C * RSS * C^-1 + matrix[2] += cx + tx + matrix[5] += cy + ty + + return matrix + + +def rotate( + img: Tensor, + angle: float, + interpolation: InterpolationMode = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[int]] = None, + fill: Optional[List[float]] = None, +) -> Tensor: + """Rotate the image by angle. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): image to be rotated. + angle (number): rotation angle value in degrees, counter-clockwise. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + expand (bool, optional): Optional expansion flag. + If true, expands the output image to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the input image. + Note that the expand flag assumes rotation around the center and no translation. + center (sequence, optional): Optional center of rotation. Origin is the upper left corner. + Default is the center of the image. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + + .. note:: + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + Returns: + PIL Image or Tensor: Rotated image. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(rotate) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + elif not isinstance(interpolation, InterpolationMode): + raise TypeError( + "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" + ) + + if not isinstance(angle, (int, float)): + raise TypeError("Argument angle should be int or float") + + if center is not None and not isinstance(center, (list, tuple)): + raise TypeError("Argument center should be a sequence") + + if not isinstance(img, torch.Tensor): + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.rotate(img, angle=angle, interpolation=pil_interpolation, expand=expand, center=center, fill=fill) + + center_f = [0.0, 0.0] + if center is not None: + _, height, width = get_dimensions(img) + # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. + center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, [width, height])] + + # due to current incoherence of rotation angle direction between affine and rotate implementations + # we need to set -angle. + matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0]) + return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill) + + +def affine( + img: Tensor, + angle: float, + translate: List[int], + scale: float, + shear: List[float], + interpolation: InterpolationMode = InterpolationMode.NEAREST, + fill: Optional[List[float]] = None, + center: Optional[List[int]] = None, +) -> Tensor: + """Apply affine transformation on the image keeping image center invariant. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + img (PIL Image or Tensor): image to transform. + angle (number): rotation angle in degrees between -180 and 180, clockwise direction. + translate (sequence of integers): horizontal and vertical translations (post-rotation translation) + scale (float): overall scale + shear (float or sequence): shear angle value in degrees between -180 to 180, clockwise direction. + If a sequence is specified, the first value corresponds to a shear parallel to the x-axis, while + the second value corresponds to a shear parallel to the y-axis. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + + .. note:: + In torchscript mode single int/float value is not supported, please use a sequence + of length 1: ``[value, ]``. + center (sequence, optional): Optional center of rotation. Origin is the upper left corner. + Default is the center of the image. + + Returns: + PIL Image or Tensor: Transformed image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(affine) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + elif not isinstance(interpolation, InterpolationMode): + raise TypeError( + "Argument interpolation should be a InterpolationMode or a corresponding Pillow integer constant" + ) + + if not isinstance(angle, (int, float)): + raise TypeError("Argument angle should be int or float") + + if not isinstance(translate, (list, tuple)): + raise TypeError("Argument translate should be a sequence") + + if len(translate) != 2: + raise ValueError("Argument translate should be a sequence of length 2") + + if scale <= 0.0: + raise ValueError("Argument scale should be positive") + + if not isinstance(shear, (numbers.Number, (list, tuple))): + raise TypeError("Shear should be either a single value or a sequence of two values") + + if isinstance(angle, int): + angle = float(angle) + + if isinstance(translate, tuple): + translate = list(translate) + + if isinstance(shear, numbers.Number): + shear = [shear, 0.0] + + if isinstance(shear, tuple): + shear = list(shear) + + if len(shear) == 1: + shear = [shear[0], shear[0]] + + if len(shear) != 2: + raise ValueError(f"Shear should be a sequence containing two values. Got {shear}") + + if center is not None and not isinstance(center, (list, tuple)): + raise TypeError("Argument center should be a sequence") + + _, height, width = get_dimensions(img) + if not isinstance(img, torch.Tensor): + # center = (width * 0.5 + 0.5, height * 0.5 + 0.5) + # it is visually better to estimate the center without 0.5 offset + # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine + if center is None: + center = [width * 0.5, height * 0.5] + matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear) + pil_interpolation = pil_modes_mapping[interpolation] + return F_pil.affine(img, matrix=matrix, interpolation=pil_interpolation, fill=fill) + + center_f = [0.0, 0.0] + if center is not None: + _, height, width = get_dimensions(img) + # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. + center_f = [1.0 * (c - s * 0.5) for c, s in zip(center, [width, height])] + + translate_f = [1.0 * t for t in translate] + matrix = _get_inverse_affine_matrix(center_f, angle, translate_f, scale, shear) + return F_t.affine(img, matrix=matrix, interpolation=interpolation.value, fill=fill) + + +# Looks like to_grayscale() is a stand-alone functional that is never called +# from the transform classes. Perhaps it's still here for BC? I can't be +# bothered to dig. +@torch.jit.unused +def to_grayscale(img, num_output_channels=1): + """Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image. + This transform does not support torch Tensor. + + Args: + img (PIL Image): PIL Image to be converted to grayscale. + num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1. + + Returns: + PIL Image: Grayscale version of the image. + + - if num_output_channels = 1 : returned image is single channel + - if num_output_channels = 3 : returned image is 3 channel with r = g = b + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(to_grayscale) + if isinstance(img, Image.Image): + return F_pil.to_grayscale(img, num_output_channels) + + raise TypeError("Input should be PIL Image") + + +def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor: + """Convert RGB image to grayscale version of image. + If the image is torch Tensor, it is expected + to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions + + Note: + Please, note that this method supports only RGB images as input. For inputs in other color spaces, + please, consider using :meth:`~torchvision.transforms.functional.to_grayscale` with PIL Image. + + Args: + img (PIL Image or Tensor): RGB Image to be converted to grayscale. + num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default, 1. + + Returns: + PIL Image or Tensor: Grayscale version of the image. + + - if num_output_channels = 1 : returned image is single channel + - if num_output_channels = 3 : returned image is 3 channel with r = g = b + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(rgb_to_grayscale) + if not isinstance(img, torch.Tensor): + return F_pil.to_grayscale(img, num_output_channels) + + return F_t.rgb_to_grayscale(img, num_output_channels) + + +def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor: + """Erase the input Tensor Image with given value. + This transform does not support PIL Image. + + Args: + img (Tensor Image): Tensor image of size (C, H, W) to be erased + i (int): i in (i,j) i.e coordinates of the upper left corner. + j (int): j in (i,j) i.e coordinates of the upper left corner. + h (int): Height of the erased region. + w (int): Width of the erased region. + v: Erasing value. + inplace(bool, optional): For in-place operations. By default, is set False. + + Returns: + Tensor Image: Erased image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(erase) + if not isinstance(img, torch.Tensor): + raise TypeError(f"img should be Tensor Image. Got {type(img)}") + + return F_t.erase(img, i, j, h, w, v, inplace=inplace) + + +def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor: + """Performs Gaussian blurring on the image by given kernel + + The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means at most one leading dimension. + + Args: + img (PIL Image or Tensor): Image to be blurred + kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers + like ``(kx, ky)`` or a single integer for square kernels. + + .. note:: + In torchscript mode kernel_size as single int is not supported, use a sequence of + length 1: ``[ksize, ]``. + sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a + sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the + same sigma in both X/Y directions. If None, then it is computed using + ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``. + Default, None. + + .. note:: + In torchscript mode sigma as single float is + not supported, use a sequence of length 1: ``[sigma, ]``. + + Returns: + PIL Image or Tensor: Gaussian Blurred version of the image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(gaussian_blur) + if not isinstance(kernel_size, (int, list, tuple)): + raise TypeError(f"kernel_size should be int or a sequence of integers. Got {type(kernel_size)}") + if isinstance(kernel_size, int): + kernel_size = [kernel_size, kernel_size] + if len(kernel_size) != 2: + raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}") + for ksize in kernel_size: + if ksize % 2 == 0 or ksize < 0: + raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}") + + if sigma is None: + sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] + + if sigma is not None and not isinstance(sigma, (int, float, list, tuple)): + raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}") + if isinstance(sigma, (int, float)): + sigma = [float(sigma), float(sigma)] + if isinstance(sigma, (list, tuple)) and len(sigma) == 1: + sigma = [sigma[0], sigma[0]] + if len(sigma) != 2: + raise ValueError(f"If sigma is a sequence, its length should be 2. Got {len(sigma)}") + for s in sigma: + if s <= 0.0: + raise ValueError(f"sigma should have positive values. Got {sigma}") + + t_img = img + if not isinstance(img, torch.Tensor): + if not F_pil._is_pil_image(img): + raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}") + + t_img = pil_to_tensor(img) + + output = F_t.gaussian_blur(t_img, kernel_size, sigma) + + if not isinstance(img, torch.Tensor): + output = to_pil_image(output, mode=img.mode) + return output + + +def invert(img: Tensor) -> Tensor: + """Invert the colors of an RGB/grayscale image. + + Args: + img (PIL Image or Tensor): Image to have its colors inverted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Returns: + PIL Image or Tensor: Color inverted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(invert) + if not isinstance(img, torch.Tensor): + return F_pil.invert(img) + + return F_t.invert(img) + + +def posterize(img: Tensor, bits: int) -> Tensor: + """Posterize an image by reducing the number of bits for each color channel. + + Args: + img (PIL Image or Tensor): Image to have its colors posterized. + If img is torch Tensor, it should be of type torch.uint8, and + it is expected to be in [..., 1 or 3, H, W] format, where ... means + it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + bits (int): The number of bits to keep for each channel (0-8). + Returns: + PIL Image or Tensor: Posterized image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(posterize) + if not (0 <= bits <= 8): + raise ValueError(f"The number if bits should be between 0 and 8. Got {bits}") + + if not isinstance(img, torch.Tensor): + return F_pil.posterize(img, bits) + + return F_t.posterize(img, bits) + + +def solarize(img: Tensor, threshold: float) -> Tensor: + """Solarize an RGB/grayscale image by inverting all pixel values above a threshold. + + Args: + img (PIL Image or Tensor): Image to have its colors inverted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + threshold (float): All pixels equal or above this value are inverted. + Returns: + PIL Image or Tensor: Solarized image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(solarize) + if not isinstance(img, torch.Tensor): + return F_pil.solarize(img, threshold) + + return F_t.solarize(img, threshold) + + +def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor: + """Adjust the sharpness of an image. + + Args: + img (PIL Image or Tensor): Image to be adjusted. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + sharpness_factor (float): How much to adjust the sharpness. Can be + any non-negative number. 0 gives a blurred image, 1 gives the + original image while 2 increases the sharpness by a factor of 2. + + Returns: + PIL Image or Tensor: Sharpness adjusted image. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(adjust_sharpness) + if not isinstance(img, torch.Tensor): + return F_pil.adjust_sharpness(img, sharpness_factor) + + return F_t.adjust_sharpness(img, sharpness_factor) + + +def autocontrast(img: Tensor) -> Tensor: + """Maximize contrast of an image by remapping its + pixels per channel so that the lowest becomes black and the lightest + becomes white. + + Args: + img (PIL Image or Tensor): Image on which autocontrast is applied. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Returns: + PIL Image or Tensor: An image that was autocontrasted. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(autocontrast) + if not isinstance(img, torch.Tensor): + return F_pil.autocontrast(img) + + return F_t.autocontrast(img) + + +def equalize(img: Tensor) -> Tensor: + """Equalize the histogram of an image by applying + a non-linear mapping to the input in order to create a uniform + distribution of grayscale values in the output. + + Args: + img (PIL Image or Tensor): Image on which equalize is applied. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + The tensor dtype must be ``torch.uint8`` and values are expected to be in ``[0, 255]``. + If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". + + Returns: + PIL Image or Tensor: An image that was equalized. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(equalize) + if not isinstance(img, torch.Tensor): + return F_pil.equalize(img) + + return F_t.equalize(img) + + +def elastic_transform( + img: Tensor, + displacement: Tensor, + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + fill: Optional[List[float]] = None, +) -> Tensor: + """Transform a tensor image with elastic transformations. + Given alpha and sigma, it will generate displacement + vectors for all pixels based on random offsets. Alpha controls the strength + and sigma controls the smoothness of the displacements. + The displacements are added to an identity grid and the resulting grid is + used to grid_sample from the image. + + Applications: + Randomly transforms the morphology of objects in images and produces a + see-through-water-like effect. + + Args: + img (PIL Image or Tensor): Image on which elastic_transform is applied. + If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". + displacement (Tensor): The displacement field. Expected shape is [1, H, W, 2]. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. + Default is ``InterpolationMode.BILINEAR``. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. + If a tuple of length 3, it is used to fill R, G, B channels respectively. + This value is only used when the padding_mode is constant. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(elastic_transform) + # Backward compatibility with integer value + if isinstance(interpolation, int): + warnings.warn( + "Argument interpolation should be of type InterpolationMode instead of int. " + "Please, use InterpolationMode enum." + ) + interpolation = _interpolation_modes_from_int(interpolation) + + if not isinstance(displacement, torch.Tensor): + raise TypeError("Argument displacement should be a Tensor") + + t_img = img + if not isinstance(img, torch.Tensor): + if not F_pil._is_pil_image(img): + raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}") + t_img = pil_to_tensor(img) + + shape = t_img.shape + shape = (1,) + shape[-2:] + (2,) + if shape != displacement.shape: + raise ValueError(f"Argument displacement shape should be {shape}, but given {displacement.shape}") + + # TODO: if image shape is [N1, N2, ..., C, H, W] and + # displacement is [1, H, W, 2] we need to reshape input image + # such grid_sampler takes internal code for 4D input + + output = F_t.elastic_transform( + t_img, + displacement, + interpolation=interpolation.value, + fill=fill, + ) + + if not isinstance(img, torch.Tensor): + output = to_pil_image(output, mode=img.mode) + return output diff --git a/lib/python3.11/site-packages/torchvision/transforms/transforms.py b/lib/python3.11/site-packages/torchvision/transforms/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..07932390efeceb8e0ae1d1aada6faddd56b17461 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/transforms.py @@ -0,0 +1,2153 @@ +import math +import numbers +import random +import warnings +from collections.abc import Sequence +from typing import List, Optional, Tuple, Union + +import torch +from torch import Tensor + +try: + import accimage +except ImportError: + accimage = None + +from ..utils import _log_api_usage_once +from . import functional as F +from .functional import _interpolation_modes_from_int, InterpolationMode + +__all__ = [ + "Compose", + "ToTensor", + "PILToTensor", + "ConvertImageDtype", + "ToPILImage", + "Normalize", + "Resize", + "CenterCrop", + "Pad", + "Lambda", + "RandomApply", + "RandomChoice", + "RandomOrder", + "RandomCrop", + "RandomHorizontalFlip", + "RandomVerticalFlip", + "RandomResizedCrop", + "FiveCrop", + "TenCrop", + "LinearTransformation", + "ColorJitter", + "RandomRotation", + "RandomAffine", + "Grayscale", + "RandomGrayscale", + "RandomPerspective", + "RandomErasing", + "GaussianBlur", + "InterpolationMode", + "RandomInvert", + "RandomPosterize", + "RandomSolarize", + "RandomAdjustSharpness", + "RandomAutocontrast", + "RandomEqualize", + "ElasticTransform", +] + + +class Compose: + """Composes several transforms together. This transform does not support torchscript. + Please, see the note below. + + Args: + transforms (list of ``Transform`` objects): list of transforms to compose. + + Example: + >>> transforms.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.PILToTensor(), + >>> transforms.ConvertImageDtype(torch.float), + >>> ]) + + .. note:: + In order to script the transformations, please use ``torch.nn.Sequential`` as below. + + >>> transforms = torch.nn.Sequential( + >>> transforms.CenterCrop(10), + >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + >>> ) + >>> scripted_transforms = torch.jit.script(transforms) + + Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require + `lambda` functions or ``PIL.Image``. + + """ + + def __init__(self, transforms): + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(self) + self.transforms = transforms + + def __call__(self, img): + for t in self.transforms: + img = t(img) + return img + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += f" {t}" + format_string += "\n)" + return format_string + + +class ToTensor: + """Convert a PIL Image or ndarray to tensor and scale the values accordingly. + + This transform does not support torchscript. + + Converts a PIL Image or numpy.ndarray (H x W x C) in the range + [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] + if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) + or if the numpy.ndarray has dtype = np.uint8 + + In the other cases, tensors are returned without scaling. + + .. note:: + Because the input image is scaled to [0.0, 1.0], this transformation should not be used when + transforming target image masks. See the `references`_ for implementing the transforms for image masks. + + .. _references: https://github.com/pytorch/vision/tree/main/references/segmentation + """ + + def __init__(self) -> None: + _log_api_usage_once(self) + + def __call__(self, pic): + """ + Args: + pic (PIL Image or numpy.ndarray): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + return F.to_tensor(pic) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}()" + + +class PILToTensor: + """Convert a PIL Image to a tensor of the same type - this does not scale values. + + This transform does not support torchscript. + + Converts a PIL Image (H x W x C) to a Tensor of shape (C x H x W). + """ + + def __init__(self) -> None: + _log_api_usage_once(self) + + def __call__(self, pic): + """ + .. note:: + + A deep copy of the underlying array is performed. + + Args: + pic (PIL Image): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + return F.pil_to_tensor(pic) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}()" + + +class ConvertImageDtype(torch.nn.Module): + """Convert a tensor image to the given ``dtype`` and scale the values accordingly. + + This function does not support PIL Image. + + Args: + dtype (torch.dtype): Desired data type of the output + + .. note:: + + When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. + If converted back and forth, this mismatch has no effect. + + Raises: + RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as + well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to + overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range + of the integer ``dtype``. + """ + + def __init__(self, dtype: torch.dtype) -> None: + super().__init__() + _log_api_usage_once(self) + self.dtype = dtype + + def forward(self, image): + return F.convert_image_dtype(image, self.dtype) + + +class ToPILImage: + """Convert a tensor or an ndarray to PIL Image + + This transform does not support torchscript. + + Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape + H x W x C to a PIL Image while adjusting the value range depending on the ``mode``. + + Args: + mode (`PIL.Image mode`_): color space and pixel depth of input data (optional). + If ``mode`` is ``None`` (default) there are some assumptions made about the input data: + + - If the input has 4 channels, the ``mode`` is assumed to be ``RGBA``. + - If the input has 3 channels, the ``mode`` is assumed to be ``RGB``. + - If the input has 2 channels, the ``mode`` is assumed to be ``LA``. + - If the input has 1 channel, the ``mode`` is determined by the data type (i.e ``int``, ``float``, ``short``). + + .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes + """ + + def __init__(self, mode=None): + _log_api_usage_once(self) + self.mode = mode + + def __call__(self, pic): + """ + Args: + pic (Tensor or numpy.ndarray): Image to be converted to PIL Image. + + Returns: + PIL Image: Image converted to PIL Image. + + """ + return F.to_pil_image(pic, self.mode) + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + if self.mode is not None: + format_string += f"mode={self.mode}" + format_string += ")" + return format_string + + +class Normalize(torch.nn.Module): + """Normalize a tensor image with mean and standard deviation. + This transform does not support PIL Image. + Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n`` + channels, this transform will normalize each channel of the input + ``torch.*Tensor`` i.e., + ``output[channel] = (input[channel] - mean[channel]) / std[channel]`` + + .. note:: + This transform acts out of place, i.e., it does not mutate the input tensor. + + Args: + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation in-place. + + """ + + def __init__(self, mean, std, inplace=False): + super().__init__() + _log_api_usage_once(self) + self.mean = mean + self.std = std + self.inplace = inplace + + def forward(self, tensor: Tensor) -> Tensor: + """ + Args: + tensor (Tensor): Tensor image to be normalized. + + Returns: + Tensor: Normalized Tensor image. + """ + return F.normalize(tensor, self.mean, self.std, self.inplace) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})" + + +class Resize(torch.nn.Module): + """Resize the input image to the given size. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means a maximum of two leading dimensions + + Args: + size (sequence or int): Desired output size. If size is a sequence like + (h, w), output size will be matched to this. If size is an int, + smaller edge of the image will be matched to this number. + i.e, if height > width, then image will be rescaled to + (size * height / width, size). + + .. note:: + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + max_size (int, optional): The maximum allowed for the longer edge of + the resized image. If the longer edge of the image is greater + than ``max_size`` after being resized according to ``size``, + ``size`` will be overruled so that the longer edge is equal to + ``max_size``. + As a result, the smaller edge may be shorter than ``size``. This + is only supported if ``size`` is an int (or a sequence of length + 1 in torchscript mode). + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + def __init__(self, size, interpolation=InterpolationMode.BILINEAR, max_size=None, antialias=True): + super().__init__() + _log_api_usage_once(self) + if not isinstance(size, (int, Sequence)): + raise TypeError(f"Size should be int or sequence. Got {type(size)}") + if isinstance(size, Sequence) and len(size) not in (1, 2): + raise ValueError("If size is a sequence, it should have 1 or 2 values") + self.size = size + self.max_size = max_size + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + + self.interpolation = interpolation + self.antialias = antialias + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be scaled. + + Returns: + PIL Image or Tensor: Rescaled image. + """ + return F.resize(img, self.size, self.interpolation, self.max_size, self.antialias) + + def __repr__(self) -> str: + detail = f"(size={self.size}, interpolation={self.interpolation.value}, max_size={self.max_size}, antialias={self.antialias})" + return f"{self.__class__.__name__}{detail}" + + +class CenterCrop(torch.nn.Module): + """Crops the given image at the center. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + """ + + def __init__(self, size): + super().__init__() + _log_api_usage_once(self) + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + return F.center_crop(img, self.size) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +class Pad(torch.nn.Module): + """Pad the given image on all sides with the given "pad" value. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric, + at most 3 leading dimensions for mode edge, + and an arbitrary number of leading dimensions for mode constant + + Args: + padding (int or sequence): Padding on each border. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + + .. note:: + In torchscript mode padding as single int is not supported, use a sequence of + length 1: ``[padding, ]``. + fill (number or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of + length 3, it is used to fill R, G, B channels respectively. + This value is only used when the padding_mode is constant. + Only number is supported for torch Tensor. + Only int or tuple value is supported for PIL Image. + padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. + Default is constant. + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value at the edge of the image. + If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 + + - reflect: pads with reflection of image without repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + """ + + def __init__(self, padding, fill=0, padding_mode="constant"): + super().__init__() + _log_api_usage_once(self) + if not isinstance(padding, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate padding arg") + + if not isinstance(fill, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate fill arg") + + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]: + raise ValueError( + f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple" + ) + + self.padding = padding + self.fill = fill + self.padding_mode = padding_mode + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be padded. + + Returns: + PIL Image or Tensor: Padded image. + """ + return F.pad(img, self.padding, self.fill, self.padding_mode) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(padding={self.padding}, fill={self.fill}, padding_mode={self.padding_mode})" + + +class Lambda: + """Apply a user-defined lambda as a transform. This transform does not support torchscript. + + Args: + lambd (function): Lambda/function to be used for transform. + """ + + def __init__(self, lambd): + _log_api_usage_once(self) + if not callable(lambd): + raise TypeError(f"Argument lambd should be callable, got {repr(type(lambd).__name__)}") + self.lambd = lambd + + def __call__(self, img): + return self.lambd(img) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}()" + + +class RandomTransforms: + """Base class for a list of transformations with randomness + + Args: + transforms (sequence): list of transformations + """ + + def __init__(self, transforms): + _log_api_usage_once(self) + if not isinstance(transforms, Sequence): + raise TypeError("Argument transforms should be a sequence") + self.transforms = transforms + + def __call__(self, *args, **kwargs): + raise NotImplementedError() + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += f" {t}" + format_string += "\n)" + return format_string + + +class RandomApply(torch.nn.Module): + """Apply randomly a list of transformations with a given probability. + + .. note:: + In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of + transforms as shown below: + + >>> transforms = transforms.RandomApply(torch.nn.ModuleList([ + >>> transforms.ColorJitter(), + >>> ]), p=0.3) + >>> scripted_transforms = torch.jit.script(transforms) + + Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require + `lambda` functions or ``PIL.Image``. + + Args: + transforms (sequence or torch.nn.Module): list of transformations + p (float): probability + """ + + def __init__(self, transforms, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.transforms = transforms + self.p = p + + def forward(self, img): + if self.p < torch.rand(1): + return img + for t in self.transforms: + img = t(img) + return img + + def __repr__(self) -> str: + format_string = self.__class__.__name__ + "(" + format_string += f"\n p={self.p}" + for t in self.transforms: + format_string += "\n" + format_string += f" {t}" + format_string += "\n)" + return format_string + + +class RandomOrder(RandomTransforms): + """Apply a list of transformations in a random order. This transform does not support torchscript.""" + + def __call__(self, img): + order = list(range(len(self.transforms))) + random.shuffle(order) + for i in order: + img = self.transforms[i](img) + return img + + +class RandomChoice(RandomTransforms): + """Apply single transformation randomly picked from a list. This transform does not support torchscript.""" + + def __init__(self, transforms, p=None): + super().__init__(transforms) + if p is not None and not isinstance(p, Sequence): + raise TypeError("Argument p should be a sequence") + self.p = p + + def __call__(self, *args): + t = random.choices(self.transforms, weights=self.p)[0] + return t(*args) + + def __repr__(self) -> str: + return f"{super().__repr__()}(p={self.p})" + + +class RandomCrop(torch.nn.Module): + """Crop the given image at a random location. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions, + but if non-constant padding is used, the input is expected to have at most 2 leading dimensions + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + padding (int or sequence, optional): Optional padding on each border + of the image. Default is None. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + + .. note:: + In torchscript mode padding as single int is not supported, use a sequence of + length 1: ``[padding, ]``. + pad_if_needed (boolean): It will pad the image if smaller than the + desired size to avoid raising an exception. Since cropping is done + after padding, the padding seems to be done at a random offset. + fill (number or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of + length 3, it is used to fill R, G, B channels respectively. + This value is only used when the padding_mode is constant. + Only number is supported for torch Tensor. + Only int or tuple value is supported for PIL Image. + padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. + Default is constant. + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value at the edge of the image. + If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 + + - reflect: pads with reflection of image without repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + """ + + @staticmethod + def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int, int]: + """Get parameters for ``crop`` for a random crop. + + Args: + img (PIL Image or Tensor): Image to be cropped. + output_size (tuple): Expected output size of the crop. + + Returns: + tuple: params (i, j, h, w) to be passed to ``crop`` for random crop. + """ + _, h, w = F.get_dimensions(img) + th, tw = output_size + + if h < th or w < tw: + raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}") + + if w == tw and h == th: + return 0, 0, h, w + + i = torch.randint(0, h - th + 1, size=(1,)).item() + j = torch.randint(0, w - tw + 1, size=(1,)).item() + return i, j, th, tw + + def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"): + super().__init__() + _log_api_usage_once(self) + + self.size = tuple(_setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")) + + self.padding = padding + self.pad_if_needed = pad_if_needed + self.fill = fill + self.padding_mode = padding_mode + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + if self.padding is not None: + img = F.pad(img, self.padding, self.fill, self.padding_mode) + + _, height, width = F.get_dimensions(img) + # pad the width if needed + if self.pad_if_needed and width < self.size[1]: + padding = [self.size[1] - width, 0] + img = F.pad(img, padding, self.fill, self.padding_mode) + # pad the height if needed + if self.pad_if_needed and height < self.size[0]: + padding = [0, self.size[0] - height] + img = F.pad(img, padding, self.fill, self.padding_mode) + + i, j, h, w = self.get_params(img, self.size) + + return F.crop(img, i, j, h, w) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, padding={self.padding})" + + +class RandomHorizontalFlip(torch.nn.Module): + """Horizontally flip the given image randomly with a given probability. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading + dimensions + + Args: + p (float): probability of the image being flipped. Default value is 0.5 + """ + + def __init__(self, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be flipped. + + Returns: + PIL Image or Tensor: Randomly flipped image. + """ + if torch.rand(1) < self.p: + return F.hflip(img) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomVerticalFlip(torch.nn.Module): + """Vertically flip the given image randomly with a given probability. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading + dimensions + + Args: + p (float): probability of the image being flipped. Default value is 0.5 + """ + + def __init__(self, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be flipped. + + Returns: + PIL Image or Tensor: Randomly flipped image. + """ + if torch.rand(1) < self.p: + return F.vflip(img) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomPerspective(torch.nn.Module): + """Performs a random perspective transformation of the given image with a given probability. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1. + Default is 0.5. + p (float): probability of the image being transformed. Default is 0.5. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (sequence or number): Pixel fill value for the area outside the transformed + image. Default is ``0``. If given a number, the value is used for all bands respectively. + """ + + def __init__(self, distortion_scale=0.5, p=0.5, interpolation=InterpolationMode.BILINEAR, fill=0): + super().__init__() + _log_api_usage_once(self) + self.p = p + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + + self.interpolation = interpolation + self.distortion_scale = distortion_scale + + if fill is None: + fill = 0 + elif not isinstance(fill, (Sequence, numbers.Number)): + raise TypeError("Fill should be either a sequence or a number.") + + self.fill = fill + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be Perspectively transformed. + + Returns: + PIL Image or Tensor: Randomly transformed image. + """ + + fill = self.fill + channels, height, width = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + else: + fill = [float(f) for f in fill] + + if torch.rand(1) < self.p: + startpoints, endpoints = self.get_params(width, height, self.distortion_scale) + return F.perspective(img, startpoints, endpoints, self.interpolation, fill) + return img + + @staticmethod + def get_params(width: int, height: int, distortion_scale: float) -> Tuple[List[List[int]], List[List[int]]]: + """Get parameters for ``perspective`` for a random perspective transform. + + Args: + width (int): width of the image. + height (int): height of the image. + distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1. + + Returns: + List containing [top-left, top-right, bottom-right, bottom-left] of the original image, + List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image. + """ + half_height = height // 2 + half_width = width // 2 + topleft = [ + int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1,)).item()), + int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1,)).item()), + ] + topright = [ + int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1,)).item()), + int(torch.randint(0, int(distortion_scale * half_height) + 1, size=(1,)).item()), + ] + botright = [ + int(torch.randint(width - int(distortion_scale * half_width) - 1, width, size=(1,)).item()), + int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1,)).item()), + ] + botleft = [ + int(torch.randint(0, int(distortion_scale * half_width) + 1, size=(1,)).item()), + int(torch.randint(height - int(distortion_scale * half_height) - 1, height, size=(1,)).item()), + ] + startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]] + endpoints = [topleft, topright, botright, botleft] + return startpoints, endpoints + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomResizedCrop(torch.nn.Module): + """Crop a random portion of image and resize it to a given size. + + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions + + A crop of the original image is made: the crop has a random area (H * W) + and a random aspect ratio. This crop is finally resized to the given + size. This is popularly used to train the Inception networks. + + Args: + size (int or sequence): expected output size of the crop, for each edge. If size is an + int instead of sequence like (h, w), a square output size ``(size, size)`` is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + .. note:: + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + scale (tuple of float): Specifies the lower and upper bounds for the random area of the crop, + before resizing. The scale is defined with respect to the area of the original image. + ratio (tuple of float): lower and upper bounds for the random aspect ratio of the crop, before + resizing. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + def __init__( + self, + size, + scale=(0.08, 1.0), + ratio=(3.0 / 4.0, 4.0 / 3.0), + interpolation=InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ): + super().__init__() + _log_api_usage_once(self) + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + if not isinstance(scale, Sequence): + raise TypeError("Scale should be a sequence") + if not isinstance(ratio, Sequence): + raise TypeError("Ratio should be a sequence") + if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): + warnings.warn("Scale and ratio should be of kind (min, max)") + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + + self.interpolation = interpolation + self.antialias = antialias + self.scale = scale + self.ratio = ratio + + @staticmethod + def get_params(img: Tensor, scale: List[float], ratio: List[float]) -> Tuple[int, int, int, int]: + """Get parameters for ``crop`` for a random sized crop. + + Args: + img (PIL Image or Tensor): Input image. + scale (list): range of scale of the origin size cropped + ratio (list): range of aspect ratio of the origin aspect ratio cropped + + Returns: + tuple: params (i, j, h, w) to be passed to ``crop`` for a random + sized crop. + """ + _, height, width = F.get_dimensions(img) + area = height * width + + log_ratio = torch.log(torch.tensor(ratio)) + for _ in range(10): + target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item() + aspect_ratio = torch.exp(torch.empty(1).uniform_(log_ratio[0], log_ratio[1])).item() + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if 0 < w <= width and 0 < h <= height: + i = torch.randint(0, height - h + 1, size=(1,)).item() + j = torch.randint(0, width - w + 1, size=(1,)).item() + return i, j, h, w + + # Fallback to central crop + in_ratio = float(width) / float(height) + if in_ratio < min(ratio): + w = width + h = int(round(w / min(ratio))) + elif in_ratio > max(ratio): + h = height + w = int(round(h * max(ratio))) + else: # whole image + w = width + h = height + i = (height - h) // 2 + j = (width - w) // 2 + return i, j, h, w + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped and resized. + + Returns: + PIL Image or Tensor: Randomly cropped and resized image. + """ + i, j, h, w = self.get_params(img, self.scale, self.ratio) + return F.resized_crop(img, i, j, h, w, self.size, self.interpolation, antialias=self.antialias) + + def __repr__(self) -> str: + interpolate_str = self.interpolation.value + format_string = self.__class__.__name__ + f"(size={self.size}" + format_string += f", scale={tuple(round(s, 4) for s in self.scale)}" + format_string += f", ratio={tuple(round(r, 4) for r in self.ratio)}" + format_string += f", interpolation={interpolate_str}" + format_string += f", antialias={self.antialias})" + return format_string + + +class FiveCrop(torch.nn.Module): + """Crop the given image into four corners and the central crop. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading + dimensions + + .. Note:: + This transform returns a tuple of images and there may be a mismatch in the number of + inputs and targets your Dataset returns. See below for an example of how to deal with + this. + + Args: + size (sequence or int): Desired output size of the crop. If size is an ``int`` + instead of sequence like (h, w), a square crop of size (size, size) is made. + If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + Example: + >>> transform = Compose([ + >>> FiveCrop(size), # this is a list of PIL Images + >>> Lambda(lambda crops: torch.stack([PILToTensor()(crop) for crop in crops])) # returns a 4D tensor + >>> ]) + >>> #In your test loop you can do the following: + >>> input, target = batch # input is a 5d tensor, target is 2d + >>> bs, ncrops, c, h, w = input.size() + >>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops + >>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops + """ + + def __init__(self, size): + super().__init__() + _log_api_usage_once(self) + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + tuple of 5 images. Image can be PIL Image or Tensor + """ + return F.five_crop(img, self.size) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +class TenCrop(torch.nn.Module): + """Crop the given image into four corners and the central crop plus the flipped version of + these (horizontal flipping is used by default). + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading + dimensions + + .. Note:: + This transform returns a tuple of images and there may be a mismatch in the number of + inputs and targets your Dataset returns. See below for an example of how to deal with + this. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + vertical_flip (bool): Use vertical flipping instead of horizontal + + Example: + >>> transform = Compose([ + >>> TenCrop(size), # this is a tuple of PIL Images + >>> Lambda(lambda crops: torch.stack([PILToTensor()(crop) for crop in crops])) # returns a 4D tensor + >>> ]) + >>> #In your test loop you can do the following: + >>> input, target = batch # input is a 5d tensor, target is 2d + >>> bs, ncrops, c, h, w = input.size() + >>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops + >>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops + """ + + def __init__(self, size, vertical_flip=False): + super().__init__() + _log_api_usage_once(self) + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + self.vertical_flip = vertical_flip + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + tuple of 10 images. Image can be PIL Image or Tensor + """ + return F.ten_crop(img, self.size, self.vertical_flip) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size}, vertical_flip={self.vertical_flip})" + + +class LinearTransformation(torch.nn.Module): + """Transform a tensor image with a square transformation matrix and a mean_vector computed + offline. + This transform does not support PIL Image. + Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and + subtract mean_vector from it which is then followed by computing the dot + product with the transformation matrix and then reshaping the tensor to its + original shape. + + Applications: + whitening transformation: Suppose X is a column vector zero-centered data. + Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X), + perform SVD on this matrix and pass it as transformation_matrix. + + Args: + transformation_matrix (Tensor): tensor [D x D], D = C x H x W + mean_vector (Tensor): tensor [D], D = C x H x W + """ + + def __init__(self, transformation_matrix, mean_vector): + super().__init__() + _log_api_usage_once(self) + if transformation_matrix.size(0) != transformation_matrix.size(1): + raise ValueError( + "transformation_matrix should be square. Got " + f"{tuple(transformation_matrix.size())} rectangular matrix." + ) + + if mean_vector.size(0) != transformation_matrix.size(0): + raise ValueError( + f"mean_vector should have the same length {mean_vector.size(0)}" + f" as any one of the dimensions of the transformation_matrix [{tuple(transformation_matrix.size())}]" + ) + + if transformation_matrix.device != mean_vector.device: + raise ValueError( + f"Input tensors should be on the same device. Got {transformation_matrix.device} and {mean_vector.device}" + ) + + if transformation_matrix.dtype != mean_vector.dtype: + raise ValueError( + f"Input tensors should have the same dtype. Got {transformation_matrix.dtype} and {mean_vector.dtype}" + ) + + self.transformation_matrix = transformation_matrix + self.mean_vector = mean_vector + + def forward(self, tensor: Tensor) -> Tensor: + """ + Args: + tensor (Tensor): Tensor image to be whitened. + + Returns: + Tensor: Transformed image. + """ + shape = tensor.shape + n = shape[-3] * shape[-2] * shape[-1] + if n != self.transformation_matrix.shape[0]: + raise ValueError( + "Input tensor and transformation matrix have incompatible shape." + + f"[{shape[-3]} x {shape[-2]} x {shape[-1]}] != " + + f"{self.transformation_matrix.shape[0]}" + ) + + if tensor.device.type != self.mean_vector.device.type: + raise ValueError( + "Input tensor should be on the same device as transformation matrix and mean vector. " + f"Got {tensor.device} vs {self.mean_vector.device}" + ) + + flat_tensor = tensor.view(-1, n) - self.mean_vector + transformation_matrix = self.transformation_matrix.to(flat_tensor.dtype) + transformed_tensor = torch.mm(flat_tensor, transformation_matrix) + tensor = transformed_tensor.view(shape) + return tensor + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(transformation_matrix=" + f"{self.transformation_matrix.tolist()}" + f", mean_vector={self.mean_vector.tolist()})" + ) + return s + + +class ColorJitter(torch.nn.Module): + """Randomly change the brightness, contrast, saturation and hue of an image. + If the image is torch Tensor, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, mode "1", "I", "F" and modes with transparency (alpha channel) are not supported. + + Args: + brightness (float or tuple of float (min, max)): How much to jitter brightness. + brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness] + or the given [min, max]. Should be non negative numbers. + contrast (float or tuple of float (min, max)): How much to jitter contrast. + contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast] + or the given [min, max]. Should be non-negative numbers. + saturation (float or tuple of float (min, max)): How much to jitter saturation. + saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation] + or the given [min, max]. Should be non negative numbers. + hue (float or tuple of float (min, max)): How much to jitter hue. + hue_factor is chosen uniformly from [-hue, hue] or the given [min, max]. + Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5. + To jitter hue, the pixel values of the input image has to be non-negative for conversion to HSV space; + thus it does not work if you normalize your image to an interval with negative values, + or use an interpolation that generates negative values before using this function. + """ + + def __init__( + self, + brightness: Union[float, Tuple[float, float]] = 0, + contrast: Union[float, Tuple[float, float]] = 0, + saturation: Union[float, Tuple[float, float]] = 0, + hue: Union[float, Tuple[float, float]] = 0, + ) -> None: + super().__init__() + _log_api_usage_once(self) + self.brightness = self._check_input(brightness, "brightness") + self.contrast = self._check_input(contrast, "contrast") + self.saturation = self._check_input(saturation, "saturation") + self.hue = self._check_input(hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False) + + @torch.jit.unused + def _check_input(self, value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True): + if isinstance(value, numbers.Number): + if value < 0: + raise ValueError(f"If {name} is a single number, it must be non negative.") + value = [center - float(value), center + float(value)] + if clip_first_on_zero: + value[0] = max(value[0], 0.0) + elif isinstance(value, (tuple, list)) and len(value) == 2: + value = [float(value[0]), float(value[1])] + else: + raise TypeError(f"{name} should be a single number or a list/tuple with length 2.") + + if not bound[0] <= value[0] <= value[1] <= bound[1]: + raise ValueError(f"{name} values should be between {bound}, but got {value}.") + + # if value is 0 or (1., 1.) for brightness/contrast/saturation + # or (0., 0.) for hue, do nothing + if value[0] == value[1] == center: + return None + else: + return tuple(value) + + @staticmethod + def get_params( + brightness: Optional[List[float]], + contrast: Optional[List[float]], + saturation: Optional[List[float]], + hue: Optional[List[float]], + ) -> Tuple[Tensor, Optional[float], Optional[float], Optional[float], Optional[float]]: + """Get the parameters for the randomized transform to be applied on image. + + Args: + brightness (tuple of float (min, max), optional): The range from which the brightness_factor is chosen + uniformly. Pass None to turn off the transformation. + contrast (tuple of float (min, max), optional): The range from which the contrast_factor is chosen + uniformly. Pass None to turn off the transformation. + saturation (tuple of float (min, max), optional): The range from which the saturation_factor is chosen + uniformly. Pass None to turn off the transformation. + hue (tuple of float (min, max), optional): The range from which the hue_factor is chosen uniformly. + Pass None to turn off the transformation. + + Returns: + tuple: The parameters used to apply the randomized transform + along with their random order. + """ + fn_idx = torch.randperm(4) + + b = None if brightness is None else float(torch.empty(1).uniform_(brightness[0], brightness[1])) + c = None if contrast is None else float(torch.empty(1).uniform_(contrast[0], contrast[1])) + s = None if saturation is None else float(torch.empty(1).uniform_(saturation[0], saturation[1])) + h = None if hue is None else float(torch.empty(1).uniform_(hue[0], hue[1])) + + return fn_idx, b, c, s, h + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Input image. + + Returns: + PIL Image or Tensor: Color jittered image. + """ + fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = self.get_params( + self.brightness, self.contrast, self.saturation, self.hue + ) + + for fn_id in fn_idx: + if fn_id == 0 and brightness_factor is not None: + img = F.adjust_brightness(img, brightness_factor) + elif fn_id == 1 and contrast_factor is not None: + img = F.adjust_contrast(img, contrast_factor) + elif fn_id == 2 and saturation_factor is not None: + img = F.adjust_saturation(img, saturation_factor) + elif fn_id == 3 and hue_factor is not None: + img = F.adjust_hue(img, hue_factor) + + return img + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}(" + f"brightness={self.brightness}" + f", contrast={self.contrast}" + f", saturation={self.saturation}" + f", hue={self.hue})" + ) + return s + + +class RandomRotation(torch.nn.Module): + """Rotate the image by angle. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + degrees (sequence or number): Range of degrees to select from. + If degrees is a number instead of sequence like (min, max), the range of degrees + will be (-degrees, +degrees). + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + expand (bool, optional): Optional expansion flag. + If true, expands the output to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the input image. + Note that the expand flag assumes rotation around the center and no translation. + center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner. + Default is the center of the image. + fill (sequence or number): Pixel fill value for the area outside the rotated + image. Default is ``0``. If given a number, the value is used for all bands respectively. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + + def __init__(self, degrees, interpolation=InterpolationMode.NEAREST, expand=False, center=None, fill=0): + super().__init__() + _log_api_usage_once(self) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + + self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,)) + + if center is not None: + _check_sequence_input(center, "center", req_sizes=(2,)) + + self.center = center + + self.interpolation = interpolation + self.expand = expand + + if fill is None: + fill = 0 + elif not isinstance(fill, (Sequence, numbers.Number)): + raise TypeError("Fill should be either a sequence or a number.") + + self.fill = fill + + @staticmethod + def get_params(degrees: List[float]) -> float: + """Get parameters for ``rotate`` for a random rotation. + + Returns: + float: angle parameter to be passed to ``rotate`` for random rotation. + """ + angle = float(torch.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item()) + return angle + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be rotated. + + Returns: + PIL Image or Tensor: Rotated image. + """ + fill = self.fill + channels, _, _ = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + else: + fill = [float(f) for f in fill] + angle = self.get_params(self.degrees) + + return F.rotate(img, angle, self.interpolation, self.expand, self.center, fill) + + def __repr__(self) -> str: + interpolate_str = self.interpolation.value + format_string = self.__class__.__name__ + f"(degrees={self.degrees}" + format_string += f", interpolation={interpolate_str}" + format_string += f", expand={self.expand}" + if self.center is not None: + format_string += f", center={self.center}" + if self.fill is not None: + format_string += f", fill={self.fill}" + format_string += ")" + return format_string + + +class RandomAffine(torch.nn.Module): + """Random affine transformation of the image keeping center invariant. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + degrees (sequence or number): Range of degrees to select from. + If degrees is a number instead of sequence like (min, max), the range of degrees + will be (-degrees, +degrees). Set to 0 to deactivate rotations. + translate (tuple, optional): tuple of maximum absolute fraction for horizontal + and vertical translations. For example translate=(a, b), then horizontal shift + is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is + randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default. + scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is + randomly sampled from the range a <= scale <= b. Will keep original scale by default. + shear (sequence or number, optional): Range of degrees to select from. + If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear) + will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the + range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values, + an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied. + Will not apply shear by default. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (sequence or number): Pixel fill value for the area outside the transformed + image. Default is ``0``. If given a number, the value is used for all bands respectively. + center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner. + Default is the center of the image. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + + def __init__( + self, + degrees, + translate=None, + scale=None, + shear=None, + interpolation=InterpolationMode.NEAREST, + fill=0, + center=None, + ): + super().__init__() + _log_api_usage_once(self) + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + + self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,)) + + if translate is not None: + _check_sequence_input(translate, "translate", req_sizes=(2,)) + for t in translate: + if not (0.0 <= t <= 1.0): + raise ValueError("translation values should be between 0 and 1") + self.translate = translate + + if scale is not None: + _check_sequence_input(scale, "scale", req_sizes=(2,)) + for s in scale: + if s <= 0: + raise ValueError("scale values should be positive") + self.scale = scale + + if shear is not None: + self.shear = _setup_angle(shear, name="shear", req_sizes=(2, 4)) + else: + self.shear = shear + + self.interpolation = interpolation + + if fill is None: + fill = 0 + elif not isinstance(fill, (Sequence, numbers.Number)): + raise TypeError("Fill should be either a sequence or a number.") + + self.fill = fill + + if center is not None: + _check_sequence_input(center, "center", req_sizes=(2,)) + + self.center = center + + @staticmethod + def get_params( + degrees: List[float], + translate: Optional[List[float]], + scale_ranges: Optional[List[float]], + shears: Optional[List[float]], + img_size: List[int], + ) -> Tuple[float, Tuple[int, int], float, Tuple[float, float]]: + """Get parameters for affine transformation + + Returns: + params to be passed to the affine transformation + """ + angle = float(torch.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item()) + if translate is not None: + max_dx = float(translate[0] * img_size[0]) + max_dy = float(translate[1] * img_size[1]) + tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item())) + ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item())) + translations = (tx, ty) + else: + translations = (0, 0) + + if scale_ranges is not None: + scale = float(torch.empty(1).uniform_(scale_ranges[0], scale_ranges[1]).item()) + else: + scale = 1.0 + + shear_x = shear_y = 0.0 + if shears is not None: + shear_x = float(torch.empty(1).uniform_(shears[0], shears[1]).item()) + if len(shears) == 4: + shear_y = float(torch.empty(1).uniform_(shears[2], shears[3]).item()) + + shear = (shear_x, shear_y) + + return angle, translations, scale, shear + + def forward(self, img): + """ + img (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: Affine transformed image. + """ + fill = self.fill + channels, height, width = F.get_dimensions(img) + if isinstance(img, Tensor): + if isinstance(fill, (int, float)): + fill = [float(fill)] * channels + else: + fill = [float(f) for f in fill] + + img_size = [width, height] # flip for keeping BC on get_params call + + ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size) + + return F.affine(img, *ret, interpolation=self.interpolation, fill=fill, center=self.center) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(degrees={self.degrees}" + s += f", translate={self.translate}" if self.translate is not None else "" + s += f", scale={self.scale}" if self.scale is not None else "" + s += f", shear={self.shear}" if self.shear is not None else "" + s += f", interpolation={self.interpolation.value}" if self.interpolation != InterpolationMode.NEAREST else "" + s += f", fill={self.fill}" if self.fill != 0 else "" + s += f", center={self.center}" if self.center is not None else "" + s += ")" + + return s + + +class Grayscale(torch.nn.Module): + """Convert image to grayscale. + If the image is torch Tensor, it is expected + to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + num_output_channels (int): (1 or 3) number of channels desired for output image + + Returns: + PIL Image: Grayscale version of the input. + + - If ``num_output_channels == 1`` : returned image is single channel + - If ``num_output_channels == 3`` : returned image is 3 channel with r == g == b + + """ + + def __init__(self, num_output_channels=1): + super().__init__() + _log_api_usage_once(self) + self.num_output_channels = num_output_channels + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be converted to grayscale. + + Returns: + PIL Image or Tensor: Grayscaled image. + """ + return F.rgb_to_grayscale(img, num_output_channels=self.num_output_channels) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(num_output_channels={self.num_output_channels})" + + +class RandomGrayscale(torch.nn.Module): + """Randomly convert image to grayscale with a probability of p (default 0.1). + If the image is torch Tensor, it is expected + to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + p (float): probability that image should be converted to grayscale. + + Returns: + PIL Image or Tensor: Grayscale version of the input image with probability p and unchanged + with probability (1-p). + - If input image is 1 channel: grayscale version is 1 channel + - If input image is 3 channel: grayscale version is 3 channel with r == g == b + + """ + + def __init__(self, p=0.1): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be converted to grayscale. + + Returns: + PIL Image or Tensor: Randomly grayscaled image. + """ + num_output_channels, _, _ = F.get_dimensions(img) + if torch.rand(1) < self.p: + return F.rgb_to_grayscale(img, num_output_channels=num_output_channels) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomErasing(torch.nn.Module): + """Randomly selects a rectangle region in a torch.Tensor image and erases its pixels. + This transform does not support PIL Image. + 'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896 + + Args: + p: probability that the random erasing operation will be performed. + scale: range of proportion of erased area against input image. + ratio: range of aspect ratio of erased area. + value: erasing value. Default is 0. If a single int, it is used to + erase all pixels. If a tuple of length 3, it is used to erase + R, G, B channels respectively. + If a str of 'random', erasing each pixel with random values. + inplace: boolean to make this transform inplace. Default set to False. + + Returns: + Erased Image. + + Example: + >>> transform = transforms.Compose([ + >>> transforms.RandomHorizontalFlip(), + >>> transforms.PILToTensor(), + >>> transforms.ConvertImageDtype(torch.float), + >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + >>> transforms.RandomErasing(), + >>> ]) + """ + + def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False): + super().__init__() + _log_api_usage_once(self) + if not isinstance(value, (numbers.Number, str, tuple, list)): + raise TypeError("Argument value should be either a number or str or a sequence") + if isinstance(value, str) and value != "random": + raise ValueError("If value is str, it should be 'random'") + if not isinstance(scale, Sequence): + raise TypeError("Scale should be a sequence") + if not isinstance(ratio, Sequence): + raise TypeError("Ratio should be a sequence") + if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): + warnings.warn("Scale and ratio should be of kind (min, max)") + if scale[0] < 0 or scale[1] > 1: + raise ValueError("Scale should be between 0 and 1") + if p < 0 or p > 1: + raise ValueError("Random erasing probability should be between 0 and 1") + + self.p = p + self.scale = scale + self.ratio = ratio + self.value = value + self.inplace = inplace + + @staticmethod + def get_params( + img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None + ) -> Tuple[int, int, int, int, Tensor]: + """Get parameters for ``erase`` for a random erasing. + + Args: + img (Tensor): Tensor image to be erased. + scale (sequence): range of proportion of erased area against input image. + ratio (sequence): range of aspect ratio of erased area. + value (list, optional): erasing value. If None, it is interpreted as "random" + (erasing each pixel with random values). If ``len(value)`` is 1, it is interpreted as a number, + i.e. ``value[0]``. + + Returns: + tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing. + """ + img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1] + area = img_h * img_w + + log_ratio = torch.log(torch.tensor(ratio)) + for _ in range(10): + erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item() + aspect_ratio = torch.exp(torch.empty(1).uniform_(log_ratio[0], log_ratio[1])).item() + + h = int(round(math.sqrt(erase_area * aspect_ratio))) + w = int(round(math.sqrt(erase_area / aspect_ratio))) + if not (h < img_h and w < img_w): + continue + + if value is None: + v = torch.empty([img_c, h, w], dtype=torch.float32).normal_() + else: + v = torch.tensor(value)[:, None, None] + + i = torch.randint(0, img_h - h + 1, size=(1,)).item() + j = torch.randint(0, img_w - w + 1, size=(1,)).item() + return i, j, h, w, v + + # Return original image + return 0, 0, img_h, img_w, img + + def forward(self, img): + """ + Args: + img (Tensor): Tensor image to be erased. + + Returns: + img (Tensor): Erased Tensor image. + """ + if torch.rand(1) < self.p: + + # cast self.value to script acceptable type + if isinstance(self.value, (int, float)): + value = [float(self.value)] + elif isinstance(self.value, str): + value = None + elif isinstance(self.value, (list, tuple)): + value = [float(v) for v in self.value] + else: + value = self.value + + if value is not None and not (len(value) in (1, img.shape[-3])): + raise ValueError( + "If value is a sequence, it should have either a single value or " + f"{img.shape[-3]} (number of input channels)" + ) + + x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value) + return F.erase(img, x, y, h, w, v, self.inplace) + return img + + def __repr__(self) -> str: + s = ( + f"{self.__class__.__name__}" + f"(p={self.p}, " + f"scale={self.scale}, " + f"ratio={self.ratio}, " + f"value={self.value}, " + f"inplace={self.inplace})" + ) + return s + + +class GaussianBlur(torch.nn.Module): + """Blurs image with randomly chosen Gaussian blur. + If the image is torch Tensor, it is expected + to have [..., C, H, W] shape, where ... means at most one leading dimension. + + Args: + kernel_size (int or sequence): Size of the Gaussian kernel. + sigma (float or tuple of float (min, max)): Standard deviation to be used for + creating kernel to perform blurring. If float, sigma is fixed. If it is tuple + of float (min, max), sigma is chosen uniformly at random to lie in the + given range. + + Returns: + PIL Image or Tensor: Gaussian blurred version of the input image. + + """ + + def __init__(self, kernel_size, sigma=(0.1, 2.0)): + super().__init__() + _log_api_usage_once(self) + self.kernel_size = _setup_size(kernel_size, "Kernel size should be a tuple/list of two integers") + for ks in self.kernel_size: + if ks <= 0 or ks % 2 == 0: + raise ValueError("Kernel size value should be an odd and positive number.") + + if isinstance(sigma, numbers.Number): + if sigma <= 0: + raise ValueError("If sigma is a single number, it must be positive.") + sigma = (sigma, sigma) + elif isinstance(sigma, Sequence) and len(sigma) == 2: + if not 0.0 < sigma[0] <= sigma[1]: + raise ValueError("sigma values should be positive and of the form (min, max).") + else: + raise ValueError("sigma should be a single number or a list/tuple with length 2.") + + self.sigma = sigma + + @staticmethod + def get_params(sigma_min: float, sigma_max: float) -> float: + """Choose sigma for random gaussian blurring. + + Args: + sigma_min (float): Minimum standard deviation that can be chosen for blurring kernel. + sigma_max (float): Maximum standard deviation that can be chosen for blurring kernel. + + Returns: + float: Standard deviation to be passed to calculate kernel for gaussian blurring. + """ + return torch.empty(1).uniform_(sigma_min, sigma_max).item() + + def forward(self, img: Tensor) -> Tensor: + """ + Args: + img (PIL Image or Tensor): image to be blurred. + + Returns: + PIL Image or Tensor: Gaussian blurred image + """ + sigma = self.get_params(self.sigma[0], self.sigma[1]) + return F.gaussian_blur(img, self.kernel_size, [sigma, sigma]) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(kernel_size={self.kernel_size}, sigma={self.sigma})" + return s + + +def _setup_size(size, error_msg): + if isinstance(size, numbers.Number): + return int(size), int(size) + + if isinstance(size, Sequence) and len(size) == 1: + return size[0], size[0] + + if len(size) != 2: + raise ValueError(error_msg) + + return size + + +def _check_sequence_input(x, name, req_sizes): + msg = req_sizes[0] if len(req_sizes) < 2 else " or ".join([str(s) for s in req_sizes]) + if not isinstance(x, Sequence): + raise TypeError(f"{name} should be a sequence of length {msg}.") + if len(x) not in req_sizes: + raise ValueError(f"{name} should be a sequence of length {msg}.") + + +def _setup_angle(x, name, req_sizes=(2,)): + if isinstance(x, numbers.Number): + if x < 0: + raise ValueError(f"If {name} is a single number, it must be positive.") + x = [-x, x] + else: + _check_sequence_input(x, name, req_sizes) + + return [float(d) for d in x] + + +class RandomInvert(torch.nn.Module): + """Inverts the colors of the given image randomly with a given probability. + If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + p (float): probability of the image being color inverted. Default value is 0.5 + """ + + def __init__(self, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be inverted. + + Returns: + PIL Image or Tensor: Randomly color inverted image. + """ + if torch.rand(1).item() < self.p: + return F.invert(img) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomPosterize(torch.nn.Module): + """Posterize the image randomly with a given probability by reducing the + number of bits for each color channel. If the image is torch Tensor, it should be of type torch.uint8, + and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + bits (int): number of bits to keep for each channel (0-8) + p (float): probability of the image being posterized. Default value is 0.5 + """ + + def __init__(self, bits, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.bits = bits + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be posterized. + + Returns: + PIL Image or Tensor: Randomly posterized image. + """ + if torch.rand(1).item() < self.p: + return F.posterize(img, self.bits) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(bits={self.bits},p={self.p})" + + +class RandomSolarize(torch.nn.Module): + """Solarize the image randomly with a given probability by inverting all pixel + values above a threshold. If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + threshold (float): all pixels equal or above this value are inverted. + p (float): probability of the image being solarized. Default value is 0.5 + """ + + def __init__(self, threshold, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.threshold = threshold + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be solarized. + + Returns: + PIL Image or Tensor: Randomly solarized image. + """ + if torch.rand(1).item() < self.p: + return F.solarize(img, self.threshold) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(threshold={self.threshold},p={self.p})" + + +class RandomAdjustSharpness(torch.nn.Module): + """Adjust the sharpness of the image randomly with a given probability. If the image is torch Tensor, + it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + sharpness_factor (float): How much to adjust the sharpness. Can be + any non-negative number. 0 gives a blurred image, 1 gives the + original image while 2 increases the sharpness by a factor of 2. + p (float): probability of the image being sharpened. Default value is 0.5 + """ + + def __init__(self, sharpness_factor, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.sharpness_factor = sharpness_factor + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be sharpened. + + Returns: + PIL Image or Tensor: Randomly sharpened image. + """ + if torch.rand(1).item() < self.p: + return F.adjust_sharpness(img, self.sharpness_factor) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(sharpness_factor={self.sharpness_factor},p={self.p})" + + +class RandomAutocontrast(torch.nn.Module): + """Autocontrast the pixels of the given image randomly with a given probability. + If the image is torch Tensor, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + p (float): probability of the image being autocontrasted. Default value is 0.5 + """ + + def __init__(self, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be autocontrasted. + + Returns: + PIL Image or Tensor: Randomly autocontrasted image. + """ + if torch.rand(1).item() < self.p: + return F.autocontrast(img) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class RandomEqualize(torch.nn.Module): + """Equalize the histogram of the given image randomly with a given probability. + If the image is torch Tensor, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". + + Args: + p (float): probability of the image being equalized. Default value is 0.5 + """ + + def __init__(self, p=0.5): + super().__init__() + _log_api_usage_once(self) + self.p = p + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be equalized. + + Returns: + PIL Image or Tensor: Randomly equalized image. + """ + if torch.rand(1).item() < self.p: + return F.equalize(img) + return img + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(p={self.p})" + + +class ElasticTransform(torch.nn.Module): + """Transform a tensor image with elastic transformations. + Given alpha and sigma, it will generate displacement + vectors for all pixels based on random offsets. Alpha controls the strength + and sigma controls the smoothness of the displacements. + The displacements are added to an identity grid and the resulting grid is + used to grid_sample from the image. + + Applications: + Randomly transforms the morphology of objects in images and produces a + see-through-water-like effect. + + Args: + alpha (float or sequence of floats): Magnitude of displacements. Default is 50.0. + sigma (float or sequence of floats): Smoothness of displacements. Default is 5.0. + interpolation (InterpolationMode): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (sequence or number): Pixel fill value for the area outside the transformed + image. Default is ``0``. If given a number, the value is used for all bands respectively. + + """ + + def __init__(self, alpha=50.0, sigma=5.0, interpolation=InterpolationMode.BILINEAR, fill=0): + super().__init__() + _log_api_usage_once(self) + if not isinstance(alpha, (float, Sequence)): + raise TypeError(f"alpha should be float or a sequence of floats. Got {type(alpha)}") + if isinstance(alpha, Sequence) and len(alpha) != 2: + raise ValueError(f"If alpha is a sequence its length should be 2. Got {len(alpha)}") + if isinstance(alpha, Sequence): + for element in alpha: + if not isinstance(element, float): + raise TypeError(f"alpha should be a sequence of floats. Got {type(element)}") + + if isinstance(alpha, float): + alpha = [float(alpha), float(alpha)] + if isinstance(alpha, (list, tuple)) and len(alpha) == 1: + alpha = [alpha[0], alpha[0]] + + self.alpha = alpha + + if not isinstance(sigma, (float, Sequence)): + raise TypeError(f"sigma should be float or a sequence of floats. Got {type(sigma)}") + if isinstance(sigma, Sequence) and len(sigma) != 2: + raise ValueError(f"If sigma is a sequence its length should be 2. Got {len(sigma)}") + if isinstance(sigma, Sequence): + for element in sigma: + if not isinstance(element, float): + raise TypeError(f"sigma should be a sequence of floats. Got {type(element)}") + + if isinstance(sigma, float): + sigma = [float(sigma), float(sigma)] + if isinstance(sigma, (list, tuple)) and len(sigma) == 1: + sigma = [sigma[0], sigma[0]] + + self.sigma = sigma + + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + self.interpolation = interpolation + + if isinstance(fill, (int, float)): + fill = [float(fill)] + elif isinstance(fill, (list, tuple)): + fill = [float(f) for f in fill] + else: + raise TypeError(f"fill should be int or float or a list or tuple of them. Got {type(fill)}") + self.fill = fill + + @staticmethod + def get_params(alpha: List[float], sigma: List[float], size: List[int]) -> Tensor: + dx = torch.rand([1, 1] + size) * 2 - 1 + if sigma[0] > 0.0: + kx = int(8 * sigma[0] + 1) + # if kernel size is even we have to make it odd + if kx % 2 == 0: + kx += 1 + dx = F.gaussian_blur(dx, [kx, kx], sigma) + dx = dx * alpha[0] / size[0] + + dy = torch.rand([1, 1] + size) * 2 - 1 + if sigma[1] > 0.0: + ky = int(8 * sigma[1] + 1) + # if kernel size is even we have to make it odd + if ky % 2 == 0: + ky += 1 + dy = F.gaussian_blur(dy, [ky, ky], sigma) + dy = dy * alpha[1] / size[1] + return torch.concat([dx, dy], 1).permute([0, 2, 3, 1]) # 1 x H x W x 2 + + def forward(self, tensor: Tensor) -> Tensor: + """ + Args: + tensor (PIL Image or Tensor): Image to be transformed. + + Returns: + PIL Image or Tensor: Transformed image. + """ + _, height, width = F.get_dimensions(tensor) + displacement = self.get_params(self.alpha, self.sigma, [height, width]) + return F.elastic_transform(tensor, displacement, self.interpolation, self.fill) + + def __repr__(self): + format_string = self.__class__.__name__ + format_string += f"(alpha={self.alpha}" + format_string += f", sigma={self.sigma}" + format_string += f", interpolation={self.interpolation}" + format_string += f", fill={self.fill})" + return format_string diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/__init__.py b/lib/python3.11/site-packages/torchvision/transforms/v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2d66917b6eaceb6281b7d809cbb22462a511a91d --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/__init__.py @@ -0,0 +1,60 @@ +from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip + +from . import functional # usort: skip + +from ._transform import Transform # usort: skip + +from ._augment import CutMix, JPEG, MixUp, RandomErasing +from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide +from ._color import ( + ColorJitter, + Grayscale, + RandomAdjustSharpness, + RandomAutocontrast, + RandomChannelPermutation, + RandomEqualize, + RandomGrayscale, + RandomInvert, + RandomPhotometricDistort, + RandomPosterize, + RandomSolarize, + RGB, +) +from ._container import Compose, RandomApply, RandomChoice, RandomOrder +from ._geometry import ( + CenterCrop, + ElasticTransform, + FiveCrop, + Pad, + RandomAffine, + RandomCrop, + RandomHorizontalFlip, + RandomIoUCrop, + RandomPerspective, + RandomResize, + RandomResizedCrop, + RandomRotation, + RandomShortestSize, + RandomVerticalFlip, + RandomZoomOut, + Resize, + ScaleJitter, + TenCrop, +) +from ._meta import ClampBoundingBoxes, ConvertBoundingBoxFormat +from ._misc import ( + ConvertImageDtype, + GaussianBlur, + GaussianNoise, + Identity, + Lambda, + LinearTransformation, + Normalize, + SanitizeBoundingBoxes, + ToDtype, +) +from ._temporal import UniformTemporalSubsample +from 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--git a/lib/python3.11/site-packages/torchvision/transforms/v2/_augment.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_augment.py new file mode 100644 index 0000000000000000000000000000000000000000..93d4ba45d6593c02bc010f4a57980ece15476183 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_augment.py @@ -0,0 +1,369 @@ +import math +import numbers +import warnings +from typing import Any, Callable, Dict, List, Optional, Sequence, Union + +import PIL.Image +import torch +from torch.nn.functional import one_hot +from torch.utils._pytree import tree_flatten, tree_unflatten +from torchvision import transforms as _transforms, tv_tensors +from torchvision.transforms.v2 import functional as F + +from ._transform import _RandomApplyTransform, Transform +from ._utils import _check_sequence_input, _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size + + +class RandomErasing(_RandomApplyTransform): + """Randomly select a rectangle region in the input image or video and erase its pixels. + + This transform does not support PIL Image. + 'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896 + + Args: + p (float, optional): probability that the random erasing operation will be performed. + scale (tuple of float, optional): range of proportion of erased area against input image. + ratio (tuple of float, optional): range of aspect ratio of erased area. + value (number or tuple of numbers): erasing value. Default is 0. If a single int, it is used to + erase all pixels. If a tuple of length 3, it is used to erase + R, G, B channels respectively. + If a str of 'random', erasing each pixel with random values. + inplace (bool, optional): boolean to make this transform inplace. Default set to False. + + Returns: + Erased input. + + Example: + >>> from torchvision.transforms import v2 as transforms + >>> + >>> transform = transforms.Compose([ + >>> transforms.RandomHorizontalFlip(), + >>> transforms.PILToTensor(), + >>> transforms.ConvertImageDtype(torch.float), + >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + >>> transforms.RandomErasing(), + >>> ]) + """ + + _v1_transform_cls = _transforms.RandomErasing + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + return dict( + super()._extract_params_for_v1_transform(), + value="random" if self.value is None else self.value, + ) + + def __init__( + self, + p: float = 0.5, + scale: Sequence[float] = (0.02, 0.33), + ratio: Sequence[float] = (0.3, 3.3), + value: float = 0.0, + inplace: bool = False, + ): + super().__init__(p=p) + if not isinstance(value, (numbers.Number, str, tuple, list)): + raise TypeError("Argument value should be either a number or str or a sequence") + if isinstance(value, str) and value != "random": + raise ValueError("If value is str, it should be 'random'") + if not isinstance(scale, Sequence): + raise TypeError("Scale should be a sequence") + if not isinstance(ratio, Sequence): + raise TypeError("Ratio should be a sequence") + if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): + warnings.warn("Scale and ratio should be of kind (min, max)") + if scale[0] < 0 or scale[1] > 1: + raise ValueError("Scale should be between 0 and 1") + self.scale = scale + self.ratio = ratio + if isinstance(value, (int, float)): + self.value = [float(value)] + elif isinstance(value, str): + self.value = None + elif isinstance(value, (list, tuple)): + self.value = [float(v) for v in value] + else: + self.value = value + self.inplace = inplace + + self._log_ratio = torch.log(torch.tensor(self.ratio)) + + def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any: + if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)): + warnings.warn( + f"{type(self).__name__}() is currently passing through inputs of type " + f"tv_tensors.{type(inpt).__name__}. This will likely change in the future." + ) + return super()._call_kernel(functional, inpt, *args, **kwargs) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + img_c, img_h, img_w = query_chw(flat_inputs) + + if self.value is not None and not (len(self.value) in (1, img_c)): + raise ValueError( + f"If value is a sequence, it should have either a single value or {img_c} (number of inpt channels)" + ) + + area = img_h * img_w + + log_ratio = self._log_ratio + for _ in range(10): + erase_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item() + aspect_ratio = torch.exp( + torch.empty(1).uniform_( + log_ratio[0], # type: ignore[arg-type] + log_ratio[1], # type: ignore[arg-type] + ) + ).item() + + h = int(round(math.sqrt(erase_area * aspect_ratio))) + w = int(round(math.sqrt(erase_area / aspect_ratio))) + if not (h < img_h and w < img_w): + continue + + if self.value is None: + v = torch.empty([img_c, h, w], dtype=torch.float32).normal_() + else: + v = torch.tensor(self.value)[:, None, None] + + i = torch.randint(0, img_h - h + 1, size=(1,)).item() + j = torch.randint(0, img_w - w + 1, size=(1,)).item() + break + else: + i, j, h, w, v = 0, 0, img_h, img_w, None + + return dict(i=i, j=j, h=h, w=w, v=v) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if params["v"] is not None: + inpt = self._call_kernel(F.erase, inpt, **params, inplace=self.inplace) + + return inpt + + +class _BaseMixUpCutMix(Transform): + def __init__(self, *, alpha: float = 1.0, num_classes: Optional[int] = None, labels_getter="default") -> None: + super().__init__() + self.alpha = float(alpha) + self._dist = torch.distributions.Beta(torch.tensor([alpha]), torch.tensor([alpha])) + + self.num_classes = num_classes + + self._labels_getter = _parse_labels_getter(labels_getter) + + def forward(self, *inputs): + inputs = inputs if len(inputs) > 1 else inputs[0] + flat_inputs, spec = tree_flatten(inputs) + needs_transform_list = self._needs_transform_list(flat_inputs) + + if has_any(flat_inputs, PIL.Image.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask): + raise ValueError(f"{type(self).__name__}() does not support PIL images, bounding boxes and masks.") + + labels = self._labels_getter(inputs) + if not isinstance(labels, torch.Tensor): + raise ValueError(f"The labels must be a tensor, but got {type(labels)} instead.") + if labels.ndim not in (1, 2): + raise ValueError( + f"labels should be index based with shape (batch_size,) " + f"or probability based with shape (batch_size, num_classes), " + f"but got a tensor of shape {labels.shape} instead." + ) + if labels.ndim == 2 and self.num_classes is not None and labels.shape[-1] != self.num_classes: + raise ValueError( + f"When passing 2D labels, " + f"the number of elements in last dimension must match num_classes: " + f"{labels.shape[-1]} != {self.num_classes}. " + f"You can Leave num_classes to None." + ) + if labels.ndim == 1 and self.num_classes is None: + raise ValueError("num_classes must be passed if the labels are index-based (1D)") + + params = { + "labels": labels, + "batch_size": labels.shape[0], + **self.make_params( + [inpt for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) if needs_transform] + ), + } + + # By default, the labels will be False inside needs_transform_list, since they are a torch.Tensor coming + # after an image or video. However, we need to handle them in _transform, so we make sure to set them to True + needs_transform_list[next(idx for idx, inpt in enumerate(flat_inputs) if inpt is labels)] = True + flat_outputs = [ + self.transform(inpt, params) if needs_transform else inpt + for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) + ] + + return tree_unflatten(flat_outputs, spec) + + def _check_image_or_video(self, inpt: torch.Tensor, *, batch_size: int): + expected_num_dims = 5 if isinstance(inpt, tv_tensors.Video) else 4 + if inpt.ndim != expected_num_dims: + raise ValueError( + f"Expected a batched input with {expected_num_dims} dims, but got {inpt.ndim} dimensions instead." + ) + if inpt.shape[0] != batch_size: + raise ValueError( + f"The batch size of the image or video does not match the batch size of the labels: " + f"{inpt.shape[0]} != {batch_size}." + ) + + def _mixup_label(self, label: torch.Tensor, *, lam: float) -> torch.Tensor: + if label.ndim == 1: + label = one_hot(label, num_classes=self.num_classes) # type: ignore[arg-type] + if not label.dtype.is_floating_point: + label = label.float() + return label.roll(1, 0).mul_(1.0 - lam).add_(label.mul(lam)) + + +class MixUp(_BaseMixUpCutMix): + """Apply MixUp to the provided batch of images and labels. + + Paper: `mixup: Beyond Empirical Risk Minimization `_. + + .. note:: + This transform is meant to be used on **batches** of samples, not + individual images. See + :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage + examples. + The sample pairing is deterministic and done by matching consecutive + samples in the batch, so the batch needs to be shuffled (this is an + implementation detail, not a guaranteed convention.) + + In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed + into a tensor of shape ``(batch_size, num_classes)``. + + Args: + alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1. + num_classes (int, optional): number of classes in the batch. Used for one-hot-encoding. + Can be None only if the labels are already one-hot-encoded. + labels_getter (callable or "default", optional): indicates how to identify the labels in the input. + By default, this will pick the second parameter as the labels if it's a tensor. This covers the most + common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``. + It can also be a callable that takes the same input as the transform, and returns the labels. + """ + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + return dict(lam=float(self._dist.sample(()))) # type: ignore[arg-type] + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + lam = params["lam"] + + if inpt is params["labels"]: + return self._mixup_label(inpt, lam=lam) + elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt): + self._check_image_or_video(inpt, batch_size=params["batch_size"]) + + output = inpt.roll(1, 0).mul_(1.0 - lam).add_(inpt.mul(lam)) + + if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)): + output = tv_tensors.wrap(output, like=inpt) + + return output + else: + return inpt + + +class CutMix(_BaseMixUpCutMix): + """Apply CutMix to the provided batch of images and labels. + + Paper: `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features + `_. + + .. note:: + This transform is meant to be used on **batches** of samples, not + individual images. See + :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage + examples. + The sample pairing is deterministic and done by matching consecutive + samples in the batch, so the batch needs to be shuffled (this is an + implementation detail, not a guaranteed convention.) + + In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed + into a tensor of shape ``(batch_size, num_classes)``. + + Args: + alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1. + num_classes (int, optional): number of classes in the batch. Used for one-hot-encoding. + Can be None only if the labels are already one-hot-encoded. + labels_getter (callable or "default", optional): indicates how to identify the labels in the input. + By default, this will pick the second parameter as the labels if it's a tensor. This covers the most + common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``. + It can also be a callable that takes the same input as the transform, and returns the labels. + """ + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + lam = float(self._dist.sample(())) # type: ignore[arg-type] + + H, W = query_size(flat_inputs) + + r_x = torch.randint(W, size=(1,)) + r_y = torch.randint(H, size=(1,)) + + r = 0.5 * math.sqrt(1.0 - lam) + r_w_half = int(r * W) + r_h_half = int(r * H) + + x1 = int(torch.clamp(r_x - r_w_half, min=0)) + y1 = int(torch.clamp(r_y - r_h_half, min=0)) + x2 = int(torch.clamp(r_x + r_w_half, max=W)) + y2 = int(torch.clamp(r_y + r_h_half, max=H)) + box = (x1, y1, x2, y2) + + lam_adjusted = float(1.0 - (x2 - x1) * (y2 - y1) / (W * H)) + + return dict(box=box, lam_adjusted=lam_adjusted) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if inpt is params["labels"]: + return self._mixup_label(inpt, lam=params["lam_adjusted"]) + elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt): + self._check_image_or_video(inpt, batch_size=params["batch_size"]) + + x1, y1, x2, y2 = params["box"] + rolled = inpt.roll(1, 0) + output = inpt.clone() + output[..., y1:y2, x1:x2] = rolled[..., y1:y2, x1:x2] + + if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)): + output = tv_tensors.wrap(output, like=inpt) + + return output + else: + return inpt + + +class JPEG(Transform): + """Apply JPEG compression and decompression to the given images. + + If the input is a :class:`torch.Tensor`, it is expected + to be of dtype uint8, on CPU, and have [..., 3 or 1, H, W] shape, + where ... means an arbitrary number of leading dimensions. + + Args: + quality (sequence or number): JPEG quality, from 1 to 100. Lower means more compression. + If quality is a sequence like (min, max), it specifies the range of JPEG quality to + randomly select from (inclusive of both ends). + + Returns: + image with JPEG compression. + """ + + def __init__(self, quality: Union[int, Sequence[int]]): + super().__init__() + if isinstance(quality, int): + quality = [quality, quality] + else: + _check_sequence_input(quality, "quality", req_sizes=(2,)) + + if not (1 <= quality[0] <= quality[1] <= 100 and isinstance(quality[0], int) and isinstance(quality[1], int)): + raise ValueError(f"quality must be an integer from 1 to 100, got {quality =}") + + self.quality = quality + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + quality = torch.randint(self.quality[0], self.quality[1] + 1, ()).item() + return dict(quality=quality) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.jpeg, inpt, quality=params["quality"]) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_auto_augment.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_auto_augment.py new file mode 100644 index 0000000000000000000000000000000000000000..4dd7ba343aa360c38192d2c8ac88481093ed8c93 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_auto_augment.py @@ -0,0 +1,627 @@ +import math +from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Type, Union + +import PIL.Image +import torch + +from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec +from torchvision import transforms as _transforms, tv_tensors +from torchvision.transforms import _functional_tensor as _FT +from torchvision.transforms.v2 import AutoAugmentPolicy, functional as F, InterpolationMode, Transform +from torchvision.transforms.v2.functional._geometry import _check_interpolation +from torchvision.transforms.v2.functional._meta import get_size +from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT + +from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor + + +ImageOrVideo = Union[torch.Tensor, PIL.Image.Image, tv_tensors.Image, tv_tensors.Video] + + +class _AutoAugmentBase(Transform): + def __init__( + self, + *, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None, + ) -> None: + super().__init__() + self.interpolation = _check_interpolation(interpolation) + self.fill = fill + self._fill = _setup_fill_arg(fill) + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + params = super()._extract_params_for_v1_transform() + + if isinstance(params["fill"], dict): + raise ValueError(f"{type(self).__name__}() can not be scripted for when `fill` is a dictionary.") + + return params + + def _get_random_item(self, dct: Dict[str, Tuple[Callable, bool]]) -> Tuple[str, Tuple[Callable, bool]]: + keys = tuple(dct.keys()) + key = keys[int(torch.randint(len(keys), ()))] + return key, dct[key] + + def _flatten_and_extract_image_or_video( + self, + inputs: Any, + unsupported_types: Tuple[Type, ...] = (tv_tensors.BoundingBoxes, tv_tensors.Mask), + ) -> Tuple[Tuple[List[Any], TreeSpec, int], ImageOrVideo]: + flat_inputs, spec = tree_flatten(inputs if len(inputs) > 1 else inputs[0]) + needs_transform_list = self._needs_transform_list(flat_inputs) + + image_or_videos = [] + for idx, (inpt, needs_transform) in enumerate(zip(flat_inputs, needs_transform_list)): + if needs_transform and check_type( + inpt, + ( + tv_tensors.Image, + PIL.Image.Image, + is_pure_tensor, + tv_tensors.Video, + ), + ): + image_or_videos.append((idx, inpt)) + elif isinstance(inpt, unsupported_types): + raise TypeError(f"Inputs of type {type(inpt).__name__} are not supported by {type(self).__name__}()") + + if not image_or_videos: + raise TypeError("Found no image in the sample.") + if len(image_or_videos) > 1: + raise TypeError( + f"Auto augment transformations are only properly defined for a single image or video, " + f"but found {len(image_or_videos)}." + ) + + idx, image_or_video = image_or_videos[0] + return (flat_inputs, spec, idx), image_or_video + + def _unflatten_and_insert_image_or_video( + self, + flat_inputs_with_spec: Tuple[List[Any], TreeSpec, int], + image_or_video: ImageOrVideo, + ) -> Any: + flat_inputs, spec, idx = flat_inputs_with_spec + flat_inputs[idx] = image_or_video + return tree_unflatten(flat_inputs, spec) + + def _apply_image_or_video_transform( + self, + image: ImageOrVideo, + transform_id: str, + magnitude: float, + interpolation: Union[InterpolationMode, int], + fill: Dict[Union[Type, str], _FillTypeJIT], + ) -> ImageOrVideo: + # Note: this cast is wrong and is only here to make mypy happy (it disagrees with torchscript) + image = cast(torch.Tensor, image) + fill_ = _get_fill(fill, type(image)) + + if transform_id == "Identity": + return image + elif transform_id == "ShearX": + # magnitude should be arctan(magnitude) + # official autoaug: (1, level, 0, 0, 1, 0) + # https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290 + # compared to + # torchvision: (1, tan(level), 0, 0, 1, 0) + # https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976 + return F.affine( + image, + angle=0.0, + translate=[0, 0], + scale=1.0, + shear=[math.degrees(math.atan(magnitude)), 0.0], + interpolation=interpolation, + fill=fill_, + center=[0, 0], + ) + elif transform_id == "ShearY": + # magnitude should be arctan(magnitude) + # See above + return F.affine( + image, + angle=0.0, + translate=[0, 0], + scale=1.0, + shear=[0.0, math.degrees(math.atan(magnitude))], + interpolation=interpolation, + fill=fill_, + center=[0, 0], + ) + elif transform_id == "TranslateX": + return F.affine( + image, + angle=0.0, + translate=[int(magnitude), 0], + scale=1.0, + interpolation=interpolation, + shear=[0.0, 0.0], + fill=fill_, + ) + elif transform_id == "TranslateY": + return F.affine( + image, + angle=0.0, + translate=[0, int(magnitude)], + scale=1.0, + interpolation=interpolation, + shear=[0.0, 0.0], + fill=fill_, + ) + elif transform_id == "Rotate": + return F.rotate(image, angle=magnitude, interpolation=interpolation, fill=fill_) + elif transform_id == "Brightness": + return F.adjust_brightness(image, brightness_factor=1.0 + magnitude) + elif transform_id == "Color": + return F.adjust_saturation(image, saturation_factor=1.0 + magnitude) + elif transform_id == "Contrast": + return F.adjust_contrast(image, contrast_factor=1.0 + magnitude) + elif transform_id == "Sharpness": + return F.adjust_sharpness(image, sharpness_factor=1.0 + magnitude) + elif transform_id == "Posterize": + return F.posterize(image, bits=int(magnitude)) + elif transform_id == "Solarize": + bound = _FT._max_value(image.dtype) if isinstance(image, torch.Tensor) else 255.0 + return F.solarize(image, threshold=bound * magnitude) + elif transform_id == "AutoContrast": + return F.autocontrast(image) + elif transform_id == "Equalize": + return F.equalize(image) + elif transform_id == "Invert": + return F.invert(image) + else: + raise ValueError(f"No transform available for {transform_id}") + + +class AutoAugment(_AutoAugmentBase): + r"""AutoAugment data augmentation method based on + `"AutoAugment: Learning Augmentation Strategies from Data" `_. + + This transformation works on images and videos only. + + If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + policy (AutoAugmentPolicy, optional): Desired policy enum defined by + :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + _v1_transform_cls = _transforms.AutoAugment + + _AUGMENTATION_SPACE = { + "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": ( + lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins), + True, + ), + "TranslateY": ( + lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins), + True, + ), + "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True), + "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Posterize": ( + lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(), + False, + ), + "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False), + "AutoContrast": (lambda num_bins, height, width: None, False), + "Equalize": (lambda num_bins, height, width: None, False), + "Invert": (lambda num_bins, height, width: None, False), + } + + def __init__( + self, + policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None, + ) -> None: + super().__init__(interpolation=interpolation, fill=fill) + self.policy = policy + self._policies = self._get_policies(policy) + + def _get_policies( + self, policy: AutoAugmentPolicy + ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]: + if policy == AutoAugmentPolicy.IMAGENET: + return [ + (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)), + (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), + (("Equalize", 0.8, None), ("Equalize", 0.6, None)), + (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)), + (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), + (("Equalize", 0.4, None), ("Rotate", 0.8, 8)), + (("Solarize", 0.6, 3), ("Equalize", 0.6, None)), + (("Posterize", 0.8, 5), ("Equalize", 1.0, None)), + (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)), + (("Equalize", 0.6, None), ("Posterize", 0.4, 6)), + (("Rotate", 0.8, 8), ("Color", 0.4, 0)), + (("Rotate", 0.4, 9), ("Equalize", 0.6, None)), + (("Equalize", 0.0, None), ("Equalize", 0.8, None)), + (("Invert", 0.6, None), ("Equalize", 1.0, None)), + (("Color", 0.6, 4), ("Contrast", 1.0, 8)), + (("Rotate", 0.8, 8), ("Color", 1.0, 2)), + (("Color", 0.8, 8), ("Solarize", 0.8, 7)), + (("Sharpness", 0.4, 7), ("Invert", 0.6, None)), + (("ShearX", 0.6, 5), ("Equalize", 1.0, None)), + (("Color", 0.4, 0), ("Equalize", 0.6, None)), + (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), + (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), + (("Invert", 0.6, None), ("Equalize", 1.0, None)), + (("Color", 0.6, 4), ("Contrast", 1.0, 8)), + (("Equalize", 0.8, None), ("Equalize", 0.6, None)), + ] + elif policy == AutoAugmentPolicy.CIFAR10: + return [ + (("Invert", 0.1, None), ("Contrast", 0.2, 6)), + (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)), + (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)), + (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)), + (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)), + (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)), + (("Color", 0.4, 3), ("Brightness", 0.6, 7)), + (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)), + (("Equalize", 0.6, None), ("Equalize", 0.5, None)), + (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)), + (("Color", 0.7, 7), ("TranslateX", 0.5, 8)), + (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)), + (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)), + (("Brightness", 0.9, 6), ("Color", 0.2, 8)), + (("Solarize", 0.5, 2), ("Invert", 0.0, None)), + (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)), + (("Equalize", 0.2, None), ("Equalize", 0.6, None)), + (("Color", 0.9, 9), ("Equalize", 0.6, None)), + (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)), + (("Brightness", 0.1, 3), ("Color", 0.7, 0)), + (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)), + (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)), + (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)), + (("Equalize", 0.8, None), ("Invert", 0.1, None)), + (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)), + ] + elif policy == AutoAugmentPolicy.SVHN: + return [ + (("ShearX", 0.9, 4), ("Invert", 0.2, None)), + (("ShearY", 0.9, 8), ("Invert", 0.7, None)), + (("Equalize", 0.6, None), ("Solarize", 0.6, 6)), + (("Invert", 0.9, None), ("Equalize", 0.6, None)), + (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), + (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)), + (("ShearY", 0.9, 8), ("Invert", 0.4, None)), + (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)), + (("Invert", 0.9, None), ("AutoContrast", 0.8, None)), + (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), + (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)), + (("ShearY", 0.8, 8), ("Invert", 0.7, None)), + (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)), + (("Invert", 0.9, None), ("Equalize", 0.6, None)), + (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)), + (("Invert", 0.8, None), ("TranslateY", 0.0, 2)), + (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)), + (("Invert", 0.6, None), ("Rotate", 0.8, 4)), + (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)), + (("ShearX", 0.1, 6), ("Invert", 0.6, None)), + (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)), + (("ShearY", 0.8, 4), ("Invert", 0.8, None)), + (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)), + (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)), + (("ShearX", 0.7, 2), ("Invert", 0.1, None)), + ] + else: + raise ValueError(f"The provided policy {policy} is not recognized.") + + def forward(self, *inputs: Any) -> Any: + flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs) + height, width = get_size(image_or_video) # type: ignore[arg-type] + + policy = self._policies[int(torch.randint(len(self._policies), ()))] + + for transform_id, probability, magnitude_idx in policy: + if not torch.rand(()) <= probability: + continue + + magnitudes_fn, signed = self._AUGMENTATION_SPACE[transform_id] + + magnitudes = magnitudes_fn(10, height, width) + if magnitudes is not None: + magnitude = float(magnitudes[magnitude_idx]) + if signed and torch.rand(()) <= 0.5: + magnitude *= -1 + else: + magnitude = 0.0 + + image_or_video = self._apply_image_or_video_transform( + image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill + ) + + return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video) + + +class RandAugment(_AutoAugmentBase): + r"""RandAugment data augmentation method based on + `"RandAugment: Practical automated data augmentation with a reduced search space" + `_. + + This transformation works on images and videos only. + + If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + num_ops (int, optional): Number of augmentation transformations to apply sequentially. + magnitude (int, optional): Magnitude for all the transformations. + num_magnitude_bins (int, optional): The number of different magnitude values. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + _v1_transform_cls = _transforms.RandAugment + _AUGMENTATION_SPACE = { + "Identity": (lambda num_bins, height, width: None, False), + "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": ( + lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins), + True, + ), + "TranslateY": ( + lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins), + True, + ), + "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True), + "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Posterize": ( + lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(), + False, + ), + "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False), + "AutoContrast": (lambda num_bins, height, width: None, False), + "Equalize": (lambda num_bins, height, width: None, False), + } + + def __init__( + self, + num_ops: int = 2, + magnitude: int = 9, + num_magnitude_bins: int = 31, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None, + ) -> None: + super().__init__(interpolation=interpolation, fill=fill) + self.num_ops = num_ops + self.magnitude = magnitude + self.num_magnitude_bins = num_magnitude_bins + + def forward(self, *inputs: Any) -> Any: + flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs) + height, width = get_size(image_or_video) # type: ignore[arg-type] + + for _ in range(self.num_ops): + transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE) + magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width) + if magnitudes is not None: + magnitude = float(magnitudes[self.magnitude]) + if signed and torch.rand(()) <= 0.5: + magnitude *= -1 + else: + magnitude = 0.0 + image_or_video = self._apply_image_or_video_transform( + image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill + ) + + return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video) + + +class TrivialAugmentWide(_AutoAugmentBase): + r"""Dataset-independent data-augmentation with TrivialAugment Wide, as described in + `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" `_. + + This transformation works on images and videos only. + + If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + num_magnitude_bins (int, optional): The number of different magnitude values. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + _v1_transform_cls = _transforms.TrivialAugmentWide + _AUGMENTATION_SPACE = { + "Identity": (lambda num_bins, height, width: None, False), + "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True), + "TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True), + "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 135.0, num_bins), True), + "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True), + "Posterize": ( + lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6))).round().int(), + False, + ), + "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False), + "AutoContrast": (lambda num_bins, height, width: None, False), + "Equalize": (lambda num_bins, height, width: None, False), + } + + def __init__( + self, + num_magnitude_bins: int = 31, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None, + ): + super().__init__(interpolation=interpolation, fill=fill) + self.num_magnitude_bins = num_magnitude_bins + + def forward(self, *inputs: Any) -> Any: + flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs) + height, width = get_size(image_or_video) # type: ignore[arg-type] + + transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE) + + magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width) + if magnitudes is not None: + magnitude = float(magnitudes[int(torch.randint(self.num_magnitude_bins, ()))]) + if signed and torch.rand(()) <= 0.5: + magnitude *= -1 + else: + magnitude = 0.0 + + image_or_video = self._apply_image_or_video_transform( + image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill + ) + return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video) + + +class AugMix(_AutoAugmentBase): + r"""AugMix data augmentation method based on + `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" `_. + + This transformation works on images and videos only. + + If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + severity (int, optional): The severity of base augmentation operators. Default is ``3``. + mixture_width (int, optional): The number of augmentation chains. Default is ``3``. + chain_depth (int, optional): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3]. + Default is ``-1``. + alpha (float, optional): The hyperparameter for the probability distributions. Default is ``1.0``. + all_ops (bool, optional): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + fill (sequence or number, optional): Pixel fill value for the area outside the transformed + image. If given a number, the value is used for all bands respectively. + """ + + _v1_transform_cls = _transforms.AugMix + + _PARTIAL_AUGMENTATION_SPACE = { + "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True), + "TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, width / 3.0, num_bins), True), + "TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, height / 3.0, num_bins), True), + "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True), + "Posterize": ( + lambda num_bins, height, width: (4 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(), + False, + ), + "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False), + "AutoContrast": (lambda num_bins, height, width: None, False), + "Equalize": (lambda num_bins, height, width: None, False), + } + _AUGMENTATION_SPACE: Dict[str, Tuple[Callable[[int, int, int], Optional[torch.Tensor]], bool]] = { + **_PARTIAL_AUGMENTATION_SPACE, + "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True), + } + + def __init__( + self, + severity: int = 3, + mixture_width: int = 3, + chain_depth: int = -1, + alpha: float = 1.0, + all_ops: bool = True, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None, + ) -> None: + super().__init__(interpolation=interpolation, fill=fill) + self._PARAMETER_MAX = 10 + if not (1 <= severity <= self._PARAMETER_MAX): + raise ValueError(f"The severity must be between [1, {self._PARAMETER_MAX}]. Got {severity} instead.") + self.severity = severity + self.mixture_width = mixture_width + self.chain_depth = chain_depth + self.alpha = alpha + self.all_ops = all_ops + + def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor: + # Must be on a separate method so that we can overwrite it in tests. + return torch._sample_dirichlet(params) + + def forward(self, *inputs: Any) -> Any: + flat_inputs_with_spec, orig_image_or_video = self._flatten_and_extract_image_or_video(inputs) + height, width = get_size(orig_image_or_video) # type: ignore[arg-type] + + if isinstance(orig_image_or_video, torch.Tensor): + image_or_video = orig_image_or_video + else: # isinstance(inpt, PIL.Image.Image): + image_or_video = F.pil_to_tensor(orig_image_or_video) + + augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE + + orig_dims = list(image_or_video.shape) + expected_ndim = 5 if isinstance(orig_image_or_video, tv_tensors.Video) else 4 + batch = image_or_video.reshape([1] * max(expected_ndim - image_or_video.ndim, 0) + orig_dims) + batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1) + + # Sample the beta weights for combining the original and augmented image or video. To get Beta, we use a + # Dirichlet with 2 parameters. The 1st column stores the weights of the original and the 2nd the ones of + # augmented image or video. + m = self._sample_dirichlet( + torch.tensor([self.alpha, self.alpha], device=batch.device).expand(batch_dims[0], -1) + ) + + # Sample the mixing weights and combine them with the ones sampled from Beta for the augmented images or videos. + combined_weights = self._sample_dirichlet( + torch.tensor([self.alpha] * self.mixture_width, device=batch.device).expand(batch_dims[0], -1) + ) * m[:, 1].reshape([batch_dims[0], -1]) + + mix = m[:, 0].reshape(batch_dims) * batch + for i in range(self.mixture_width): + aug = batch + depth = self.chain_depth if self.chain_depth > 0 else int(torch.randint(low=1, high=4, size=(1,)).item()) + for _ in range(depth): + transform_id, (magnitudes_fn, signed) = self._get_random_item(augmentation_space) + + magnitudes = magnitudes_fn(self._PARAMETER_MAX, height, width) + if magnitudes is not None: + magnitude = float(magnitudes[int(torch.randint(self.severity, ()))]) + if signed and torch.rand(()) <= 0.5: + magnitude *= -1 + else: + magnitude = 0.0 + + aug = self._apply_image_or_video_transform(aug, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill) # type: ignore[assignment] + mix.add_(combined_weights[:, i].reshape(batch_dims) * aug) + mix = mix.reshape(orig_dims).to(dtype=image_or_video.dtype) + + if isinstance(orig_image_or_video, (tv_tensors.Image, tv_tensors.Video)): + mix = tv_tensors.wrap(mix, like=orig_image_or_video) + elif isinstance(orig_image_or_video, PIL.Image.Image): + mix = F.to_pil_image(mix) + + return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, mix) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_color.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_color.py new file mode 100644 index 0000000000000000000000000000000000000000..7a471e7c1f632b8dd68ca6ad02b92f947b8d5354 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_color.py @@ -0,0 +1,376 @@ +import collections.abc +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +import torch +from torchvision import transforms as _transforms +from torchvision.transforms.v2 import functional as F, Transform + +from ._transform import _RandomApplyTransform +from ._utils import query_chw + + +class Grayscale(Transform): + """Convert images or videos to grayscale. + + If the input is a :class:`torch.Tensor`, it is expected + to have [..., 3 or 1, H, W] shape, where ... means an arbitrary number of leading dimensions + + Args: + num_output_channels (int): (1 or 3) number of channels desired for output image + """ + + _v1_transform_cls = _transforms.Grayscale + + def __init__(self, num_output_channels: int = 1): + super().__init__() + self.num_output_channels = num_output_channels + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.rgb_to_grayscale, inpt, num_output_channels=self.num_output_channels) + + +class RandomGrayscale(_RandomApplyTransform): + """Randomly convert image or videos to grayscale with a probability of p (default 0.1). + + If the input is a :class:`torch.Tensor`, it is expected to have [..., 3 or 1, H, W] shape, + where ... means an arbitrary number of leading dimensions + + The output has the same number of channels as the input. + + Args: + p (float): probability that image should be converted to grayscale. + """ + + _v1_transform_cls = _transforms.RandomGrayscale + + def __init__(self, p: float = 0.1) -> None: + super().__init__(p=p) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + num_input_channels, *_ = query_chw(flat_inputs) + return dict(num_input_channels=num_input_channels) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.rgb_to_grayscale, inpt, num_output_channels=params["num_input_channels"]) + + +class RGB(Transform): + """Convert images or videos to RGB (if they are already not RGB). + + If the input is a :class:`torch.Tensor`, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions + """ + + def __init__(self): + super().__init__() + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.grayscale_to_rgb, inpt) + + +class ColorJitter(Transform): + """Randomly change the brightness, contrast, saturation and hue of an image or video. + + If the input is a :class:`torch.Tensor`, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, mode "1", "I", "F" and modes with transparency (alpha channel) are not supported. + + Args: + brightness (float or tuple of float (min, max)): How much to jitter brightness. + brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness] + or the given [min, max]. Should be non negative numbers. + contrast (float or tuple of float (min, max)): How much to jitter contrast. + contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast] + or the given [min, max]. Should be non-negative numbers. + saturation (float or tuple of float (min, max)): How much to jitter saturation. + saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation] + or the given [min, max]. Should be non negative numbers. + hue (float or tuple of float (min, max)): How much to jitter hue. + hue_factor is chosen uniformly from [-hue, hue] or the given [min, max]. + Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5. + To jitter hue, the pixel values of the input image has to be non-negative for conversion to HSV space; + thus it does not work if you normalize your image to an interval with negative values, + or use an interpolation that generates negative values before using this function. + """ + + _v1_transform_cls = _transforms.ColorJitter + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + return {attr: value or 0 for attr, value in super()._extract_params_for_v1_transform().items()} + + def __init__( + self, + brightness: Optional[Union[float, Sequence[float]]] = None, + contrast: Optional[Union[float, Sequence[float]]] = None, + saturation: Optional[Union[float, Sequence[float]]] = None, + hue: Optional[Union[float, Sequence[float]]] = None, + ) -> None: + super().__init__() + self.brightness = self._check_input(brightness, "brightness") + self.contrast = self._check_input(contrast, "contrast") + self.saturation = self._check_input(saturation, "saturation") + self.hue = self._check_input(hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False) + + def _check_input( + self, + value: Optional[Union[float, Sequence[float]]], + name: str, + center: float = 1.0, + bound: Tuple[float, float] = (0, float("inf")), + clip_first_on_zero: bool = True, + ) -> Optional[Tuple[float, float]]: + if value is None: + return None + + if isinstance(value, (int, float)): + if value < 0: + raise ValueError(f"If {name} is a single number, it must be non negative.") + value = [center - value, center + value] + if clip_first_on_zero: + value[0] = max(value[0], 0.0) + elif isinstance(value, collections.abc.Sequence) and len(value) == 2: + value = [float(v) for v in value] + else: + raise TypeError(f"{name}={value} should be a single number or a sequence with length 2.") + + if not bound[0] <= value[0] <= value[1] <= bound[1]: + raise ValueError(f"{name} values should be between {bound}, but got {value}.") + + return None if value[0] == value[1] == center else (float(value[0]), float(value[1])) + + @staticmethod + def _generate_value(left: float, right: float) -> float: + return torch.empty(1).uniform_(left, right).item() + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + fn_idx = torch.randperm(4) + + b = None if self.brightness is None else self._generate_value(self.brightness[0], self.brightness[1]) + c = None if self.contrast is None else self._generate_value(self.contrast[0], self.contrast[1]) + s = None if self.saturation is None else self._generate_value(self.saturation[0], self.saturation[1]) + h = None if self.hue is None else self._generate_value(self.hue[0], self.hue[1]) + + return dict(fn_idx=fn_idx, brightness_factor=b, contrast_factor=c, saturation_factor=s, hue_factor=h) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + output = inpt + brightness_factor = params["brightness_factor"] + contrast_factor = params["contrast_factor"] + saturation_factor = params["saturation_factor"] + hue_factor = params["hue_factor"] + for fn_id in params["fn_idx"]: + if fn_id == 0 and brightness_factor is not None: + output = self._call_kernel(F.adjust_brightness, output, brightness_factor=brightness_factor) + elif fn_id == 1 and contrast_factor is not None: + output = self._call_kernel(F.adjust_contrast, output, contrast_factor=contrast_factor) + elif fn_id == 2 and saturation_factor is not None: + output = self._call_kernel(F.adjust_saturation, output, saturation_factor=saturation_factor) + elif fn_id == 3 and hue_factor is not None: + output = self._call_kernel(F.adjust_hue, output, hue_factor=hue_factor) + return output + + +class RandomChannelPermutation(Transform): + """Randomly permute the channels of an image or video""" + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + num_channels, *_ = query_chw(flat_inputs) + return dict(permutation=torch.randperm(num_channels)) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.permute_channels, inpt, params["permutation"]) + + +class RandomPhotometricDistort(Transform): + """Randomly distorts the image or video as used in `SSD: Single Shot + MultiBox Detector `_. + + This transform relies on :class:`~torchvision.transforms.v2.ColorJitter` + under the hood to adjust the contrast, saturation, hue, brightness, and also + randomly permutes channels. + + Args: + brightness (tuple of float (min, max), optional): How much to jitter brightness. + brightness_factor is chosen uniformly from [min, max]. Should be non negative numbers. + contrast (tuple of float (min, max), optional): How much to jitter contrast. + contrast_factor is chosen uniformly from [min, max]. Should be non-negative numbers. + saturation (tuple of float (min, max), optional): How much to jitter saturation. + saturation_factor is chosen uniformly from [min, max]. Should be non negative numbers. + hue (tuple of float (min, max), optional): How much to jitter hue. + hue_factor is chosen uniformly from [min, max]. Should have -0.5 <= min <= max <= 0.5. + To jitter hue, the pixel values of the input image has to be non-negative for conversion to HSV space; + thus it does not work if you normalize your image to an interval with negative values, + or use an interpolation that generates negative values before using this function. + p (float, optional) probability each distortion operation (contrast, saturation, ...) to be applied. + Default is 0.5. + """ + + def __init__( + self, + brightness: Tuple[float, float] = (0.875, 1.125), + contrast: Tuple[float, float] = (0.5, 1.5), + saturation: Tuple[float, float] = (0.5, 1.5), + hue: Tuple[float, float] = (-0.05, 0.05), + p: float = 0.5, + ): + super().__init__() + self.brightness = brightness + self.contrast = contrast + self.hue = hue + self.saturation = saturation + self.p = p + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + num_channels, *_ = query_chw(flat_inputs) + params: Dict[str, Any] = { + key: ColorJitter._generate_value(range[0], range[1]) if torch.rand(1) < self.p else None + for key, range in [ + ("brightness_factor", self.brightness), + ("contrast_factor", self.contrast), + ("saturation_factor", self.saturation), + ("hue_factor", self.hue), + ] + } + params["contrast_before"] = bool(torch.rand(()) < 0.5) + params["channel_permutation"] = torch.randperm(num_channels) if torch.rand(1) < self.p else None + return params + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if params["brightness_factor"] is not None: + inpt = self._call_kernel(F.adjust_brightness, inpt, brightness_factor=params["brightness_factor"]) + if params["contrast_factor"] is not None and params["contrast_before"]: + inpt = self._call_kernel(F.adjust_contrast, inpt, contrast_factor=params["contrast_factor"]) + if params["saturation_factor"] is not None: + inpt = self._call_kernel(F.adjust_saturation, inpt, saturation_factor=params["saturation_factor"]) + if params["hue_factor"] is not None: + inpt = self._call_kernel(F.adjust_hue, inpt, hue_factor=params["hue_factor"]) + if params["contrast_factor"] is not None and not params["contrast_before"]: + inpt = self._call_kernel(F.adjust_contrast, inpt, contrast_factor=params["contrast_factor"]) + if params["channel_permutation"] is not None: + inpt = self._call_kernel(F.permute_channels, inpt, permutation=params["channel_permutation"]) + return inpt + + +class RandomEqualize(_RandomApplyTransform): + """Equalize the histogram of the given image or video with a given probability. + + If the input is a :class:`torch.Tensor`, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "P", "L" or "RGB". + + Args: + p (float): probability of the image being equalized. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomEqualize + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.equalize, inpt) + + +class RandomInvert(_RandomApplyTransform): + """Inverts the colors of the given image or video with a given probability. + + If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + p (float): probability of the image being color inverted. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomInvert + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.invert, inpt) + + +class RandomPosterize(_RandomApplyTransform): + """Posterize the image or video with a given probability by reducing the + number of bits for each color channel. + + If the input is a :class:`torch.Tensor`, it should be of type torch.uint8, + and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + bits (int): number of bits to keep for each channel (0-8) + p (float): probability of the image being posterized. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomPosterize + + def __init__(self, bits: int, p: float = 0.5) -> None: + super().__init__(p=p) + self.bits = bits + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.posterize, inpt, bits=self.bits) + + +class RandomSolarize(_RandomApplyTransform): + """Solarize the image or video with a given probability by inverting all pixel + values above a threshold. + + If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + threshold (float): all pixels equal or above this value are inverted. + p (float): probability of the image being solarized. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomSolarize + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + params = super()._extract_params_for_v1_transform() + params["threshold"] = float(params["threshold"]) + return params + + def __init__(self, threshold: float, p: float = 0.5) -> None: + super().__init__(p=p) + self.threshold = threshold + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.solarize, inpt, threshold=self.threshold) + + +class RandomAutocontrast(_RandomApplyTransform): + """Autocontrast the pixels of the given image or video with a given probability. + + If the input is a :class:`torch.Tensor`, it is expected + to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + If img is PIL Image, it is expected to be in mode "L" or "RGB". + + Args: + p (float): probability of the image being autocontrasted. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomAutocontrast + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.autocontrast, inpt) + + +class RandomAdjustSharpness(_RandomApplyTransform): + """Adjust the sharpness of the image or video with a given probability. + + If the input is a :class:`torch.Tensor`, + it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + sharpness_factor (float): How much to adjust the sharpness. Can be + any non-negative number. 0 gives a blurred image, 1 gives the + original image while 2 increases the sharpness by a factor of 2. + p (float): probability of the image being sharpened. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomAdjustSharpness + + def __init__(self, sharpness_factor: float, p: float = 0.5) -> None: + super().__init__(p=p) + self.sharpness_factor = sharpness_factor + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=self.sharpness_factor) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_container.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_container.py new file mode 100644 index 0000000000000000000000000000000000000000..54de601c6967001f4f7b5951d84b36b7353dbf23 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_container.py @@ -0,0 +1,174 @@ +from typing import Any, Callable, Dict, List, Optional, Sequence, Union + +import torch + +from torch import nn +from torchvision import transforms as _transforms +from torchvision.transforms.v2 import Transform + + +class Compose(Transform): + """Composes several transforms together. + + This transform does not support torchscript. + Please, see the note below. + + Args: + transforms (list of ``Transform`` objects): list of transforms to compose. + + Example: + >>> transforms.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.PILToTensor(), + >>> transforms.ConvertImageDtype(torch.float), + >>> ]) + + .. note:: + In order to script the transformations, please use ``torch.nn.Sequential`` as below. + + >>> transforms = torch.nn.Sequential( + >>> transforms.CenterCrop(10), + >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + >>> ) + >>> scripted_transforms = torch.jit.script(transforms) + + Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require + `lambda` functions or ``PIL.Image``. + + """ + + def __init__(self, transforms: Sequence[Callable]) -> None: + super().__init__() + if not isinstance(transforms, Sequence): + raise TypeError("Argument transforms should be a sequence of callables") + elif not transforms: + raise ValueError("Pass at least one transform") + self.transforms = transforms + + def forward(self, *inputs: Any) -> Any: + needs_unpacking = len(inputs) > 1 + for transform in self.transforms: + outputs = transform(*inputs) + inputs = outputs if needs_unpacking else (outputs,) + return outputs + + def extra_repr(self) -> str: + format_string = [] + for t in self.transforms: + format_string.append(f" {t}") + return "\n".join(format_string) + + +class RandomApply(Transform): + """Apply randomly a list of transformations with a given probability. + + .. note:: + In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of + transforms as shown below: + + >>> transforms = transforms.RandomApply(torch.nn.ModuleList([ + >>> transforms.ColorJitter(), + >>> ]), p=0.3) + >>> scripted_transforms = torch.jit.script(transforms) + + Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require + `lambda` functions or ``PIL.Image``. + + Args: + transforms (sequence or torch.nn.Module): list of transformations + p (float): probability of applying the list of transforms + """ + + _v1_transform_cls = _transforms.RandomApply + + def __init__(self, transforms: Union[Sequence[Callable], nn.ModuleList], p: float = 0.5) -> None: + super().__init__() + + if not isinstance(transforms, (Sequence, nn.ModuleList)): + raise TypeError("Argument transforms should be a sequence of callables or a `nn.ModuleList`") + self.transforms = transforms + + if not (0.0 <= p <= 1.0): + raise ValueError("`p` should be a floating point value in the interval [0.0, 1.0].") + self.p = p + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + return {"transforms": self.transforms, "p": self.p} + + def forward(self, *inputs: Any) -> Any: + needs_unpacking = len(inputs) > 1 + + if torch.rand(1) >= self.p: + return inputs if needs_unpacking else inputs[0] + + for transform in self.transforms: + outputs = transform(*inputs) + inputs = outputs if needs_unpacking else (outputs,) + return outputs + + def extra_repr(self) -> str: + format_string = [] + for t in self.transforms: + format_string.append(f" {t}") + return "\n".join(format_string) + + +class RandomChoice(Transform): + """Apply single transformation randomly picked from a list. + + This transform does not support torchscript. + + Args: + transforms (sequence or torch.nn.Module): list of transformations + p (list of floats or None, optional): probability of each transform being picked. + If ``p`` doesn't sum to 1, it is automatically normalized. If ``None`` + (default), all transforms have the same probability. + """ + + def __init__( + self, + transforms: Sequence[Callable], + p: Optional[List[float]] = None, + ) -> None: + if not isinstance(transforms, Sequence): + raise TypeError("Argument transforms should be a sequence of callables") + + if p is None: + p = [1] * len(transforms) + elif len(p) != len(transforms): + raise ValueError(f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}") + + super().__init__() + + self.transforms = transforms + total = sum(p) + self.p = [prob / total for prob in p] + + def forward(self, *inputs: Any) -> Any: + idx = int(torch.multinomial(torch.tensor(self.p), 1)) + transform = self.transforms[idx] + return transform(*inputs) + + +class RandomOrder(Transform): + """Apply a list of transformations in a random order. + + This transform does not support torchscript. + + Args: + transforms (sequence or torch.nn.Module): list of transformations + """ + + def __init__(self, transforms: Sequence[Callable]) -> None: + if not isinstance(transforms, Sequence): + raise TypeError("Argument transforms should be a sequence of callables") + super().__init__() + self.transforms = transforms + + def forward(self, *inputs: Any) -> Any: + needs_unpacking = len(inputs) > 1 + for idx in torch.randperm(len(self.transforms)): + transform = self.transforms[idx] + outputs = transform(*inputs) + inputs = outputs if needs_unpacking else (outputs,) + return outputs diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..51a4f076e49d16b8ccde6e9ed04f610ed9c22bff --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py @@ -0,0 +1,50 @@ +import warnings +from typing import Any, Dict, Union + +import numpy as np +import PIL.Image +import torch +from torchvision.transforms import functional as _F + +from torchvision.transforms.v2 import Transform + + +class ToTensor(Transform): + """[DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`` instead. + + Convert a PIL Image or ndarray to tensor and scale the values accordingly. + + .. warning:: + :class:`v2.ToTensor` is deprecated and will be removed in a future release. + Please use instead ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])``. + Output is equivalent up to float precision. + + This transform does not support torchscript. + + + Converts a PIL Image or numpy.ndarray (H x W x C) in the range + [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] + if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) + or if the numpy.ndarray has dtype = np.uint8 + + In the other cases, tensors are returned without scaling. + + .. note:: + Because the input image is scaled to [0.0, 1.0], this transformation should not be used when + transforming target image masks. See the `references`_ for implementing the transforms for image masks. + + .. _references: https://github.com/pytorch/vision/tree/main/references/segmentation + """ + + _transformed_types = (PIL.Image.Image, np.ndarray) + + def __init__(self) -> None: + warnings.warn( + "The transform `ToTensor()` is deprecated and will be removed in a future release. " + "Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`." + "Output is equivalent up to float precision." + ) + super().__init__() + + def transform(self, inpt: Union[PIL.Image.Image, np.ndarray], params: Dict[str, Any]) -> torch.Tensor: + return _F.to_tensor(inpt) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_geometry.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..c2461418a422861159e8b27cb4e8a3f7dfa1dcf8 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_geometry.py @@ -0,0 +1,1416 @@ +import math +import numbers +import warnings +from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union + +import PIL.Image +import torch + +from torchvision import transforms as _transforms, tv_tensors +from torchvision.ops.boxes import box_iou +from torchvision.transforms.functional import _get_perspective_coeffs +from torchvision.transforms.v2 import functional as F, InterpolationMode, Transform +from torchvision.transforms.v2.functional._utils import _FillType + +from ._transform import _RandomApplyTransform +from ._utils import ( + _check_padding_arg, + _check_padding_mode_arg, + _check_sequence_input, + _get_fill, + _setup_angle, + _setup_fill_arg, + _setup_number_or_seq, + _setup_size, + get_bounding_boxes, + has_all, + has_any, + is_pure_tensor, + query_size, +) + + +class RandomHorizontalFlip(_RandomApplyTransform): + """Horizontally flip the input with a given probability. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + p (float, optional): probability of the input being flipped. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomHorizontalFlip + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.horizontal_flip, inpt) + + +class RandomVerticalFlip(_RandomApplyTransform): + """Vertically flip the input with a given probability. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + p (float, optional): probability of the input being flipped. Default value is 0.5 + """ + + _v1_transform_cls = _transforms.RandomVerticalFlip + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.vertical_flip, inpt) + + +class Resize(Transform): + """Resize the input to the given size. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + size (sequence, int, or None): Desired + output size. + + - If size is a sequence like (h, w), output size will be matched to this. + - If size is an int, smaller edge of the image will be matched to this + number. i.e, if height > width, then image will be rescaled to + (size * height / width, size). + - If size is None, the output shape is determined by the ``max_size`` + parameter. + + .. note:: + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + max_size (int, optional): The maximum allowed for the longer edge of + the resized image. + + - If ``size`` is an int: if the longer edge of the image is greater + than ``max_size`` after being resized according to ``size``, + ``size`` will be overruled so that the longer edge is equal to + ``max_size``. As a result, the smaller edge may be shorter than + ``size``. This is only supported if ``size`` is an int (or a + sequence of length 1 in torchscript mode). + - If ``size`` is None: the longer edge of the image will be matched + to max_size. i.e, if height > width, then image will be rescaled + to (max_size, max_size * width / height). + + This should be left to ``None`` (default) when ``size`` is a + sequence. + + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + _v1_transform_cls = _transforms.Resize + + def __init__( + self, + size: Union[int, Sequence[int], None], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, + ) -> None: + super().__init__() + + if isinstance(size, int): + size = [size] + elif isinstance(size, Sequence) and len(size) in {1, 2}: + size = list(size) + elif size is None: + if not isinstance(max_size, int): + raise ValueError(f"max_size must be an integer when size is None, but got {max_size} instead.") + else: + raise ValueError( + f"size can be an integer, a sequence of one or two integers, or None, but got {size} instead." + ) + self.size = size + + self.interpolation = interpolation + self.max_size = max_size + self.antialias = antialias + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel( + F.resize, + inpt, + self.size, + interpolation=self.interpolation, + max_size=self.max_size, + antialias=self.antialias, + ) + + +class CenterCrop(Transform): + """Crop the input at the center. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + """ + + _v1_transform_cls = _transforms.CenterCrop + + def __init__(self, size: Union[int, Sequence[int]]): + super().__init__() + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.center_crop, inpt, output_size=self.size) + + +class RandomResizedCrop(Transform): + """Crop a random portion of the input and resize it to a given size. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + A crop of the original input is made: the crop has a random area (H * W) + and a random aspect ratio. This crop is finally resized to the given + size. This is popularly used to train the Inception networks. + + Args: + size (int or sequence): expected output size of the crop, for each edge. If size is an + int instead of sequence like (h, w), a square output size ``(size, size)`` is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + .. note:: + In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``. + scale (tuple of float, optional): Specifies the lower and upper bounds for the random area of the crop, + before resizing. The scale is defined with respect to the area of the original image. + ratio (tuple of float, optional): lower and upper bounds for the random aspect ratio of the crop, before + resizing. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + _v1_transform_cls = _transforms.RandomResizedCrop + + def __init__( + self, + size: Union[int, Sequence[int]], + scale: Tuple[float, float] = (0.08, 1.0), + ratio: Tuple[float, float] = (3.0 / 4.0, 4.0 / 3.0), + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ) -> None: + super().__init__() + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + if not isinstance(scale, Sequence): + raise TypeError("Scale should be a sequence") + if not isinstance(ratio, Sequence): + raise TypeError("Ratio should be a sequence") + if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): + warnings.warn("Scale and ratio should be of kind (min, max)") + + self.scale = scale + self.ratio = ratio + self.interpolation = interpolation + self.antialias = antialias + + self._log_ratio = torch.log(torch.tensor(self.ratio)) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + height, width = query_size(flat_inputs) + area = height * width + + log_ratio = self._log_ratio + for _ in range(10): + target_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item() + aspect_ratio = torch.exp( + torch.empty(1).uniform_( + log_ratio[0], # type: ignore[arg-type] + log_ratio[1], # type: ignore[arg-type] + ) + ).item() + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if 0 < w <= width and 0 < h <= height: + i = torch.randint(0, height - h + 1, size=(1,)).item() + j = torch.randint(0, width - w + 1, size=(1,)).item() + break + else: + # Fallback to central crop + in_ratio = float(width) / float(height) + if in_ratio < min(self.ratio): + w = width + h = int(round(w / min(self.ratio))) + elif in_ratio > max(self.ratio): + h = height + w = int(round(h * max(self.ratio))) + else: # whole image + w = width + h = height + i = (height - h) // 2 + j = (width - w) // 2 + + return dict(top=i, left=j, height=h, width=w) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel( + F.resized_crop, inpt, **params, size=self.size, interpolation=self.interpolation, antialias=self.antialias + ) + + +class FiveCrop(Transform): + """Crop the image or video into four corners and the central crop. + + If the input is a :class:`torch.Tensor` or a :class:`~torchvision.tv_tensors.Image` or a + :class:`~torchvision.tv_tensors.Video` it can have arbitrary number of leading batch dimensions. + For example, the image can have ``[..., C, H, W]`` shape. + + .. Note:: + This transform returns a tuple of images and there may be a mismatch in the number of + inputs and targets your Dataset returns. See below for an example of how to deal with + this. + + Args: + size (sequence or int): Desired output size of the crop. If size is an ``int`` + instead of sequence like (h, w), a square crop of size (size, size) is made. + If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + + Example: + >>> class BatchMultiCrop(transforms.Transform): + ... def forward(self, sample: Tuple[Tuple[Union[tv_tensors.Image, tv_tensors.Video], ...], int]): + ... images_or_videos, labels = sample + ... batch_size = len(images_or_videos) + ... image_or_video = images_or_videos[0] + ... images_or_videos = tv_tensors.wrap(torch.stack(images_or_videos), like=image_or_video) + ... labels = torch.full((batch_size,), label, device=images_or_videos.device) + ... return images_or_videos, labels + ... + >>> image = tv_tensors.Image(torch.rand(3, 256, 256)) + >>> label = 3 + >>> transform = transforms.Compose([transforms.FiveCrop(224), BatchMultiCrop()]) + >>> images, labels = transform(image, label) + >>> images.shape + torch.Size([5, 3, 224, 224]) + >>> labels + tensor([3, 3, 3, 3, 3]) + """ + + _v1_transform_cls = _transforms.FiveCrop + + def __init__(self, size: Union[int, Sequence[int]]) -> None: + super().__init__() + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any: + if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)): + warnings.warn( + f"{type(self).__name__}() is currently passing through inputs of type " + f"tv_tensors.{type(inpt).__name__}. This will likely change in the future." + ) + return super()._call_kernel(functional, inpt, *args, **kwargs) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.five_crop, inpt, self.size) + + def check_inputs(self, flat_inputs: List[Any]) -> None: + if has_any(flat_inputs, tv_tensors.BoundingBoxes, tv_tensors.Mask): + raise TypeError(f"BoundingBoxes'es and Mask's are not supported by {type(self).__name__}()") + + +class TenCrop(Transform): + """Crop the image or video into four corners and the central crop plus the flipped version of + these (horizontal flipping is used by default). + + If the input is a :class:`torch.Tensor` or a :class:`~torchvision.tv_tensors.Image` or a + :class:`~torchvision.tv_tensors.Video` it can have arbitrary number of leading batch dimensions. + For example, the image can have ``[..., C, H, W]`` shape. + + See :class:`~torchvision.transforms.v2.FiveCrop` for an example. + + .. Note:: + This transform returns a tuple of images and there may be a mismatch in the number of + inputs and targets your Dataset returns. See below for an example of how to deal with + this. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + vertical_flip (bool, optional): Use vertical flipping instead of horizontal + """ + + _v1_transform_cls = _transforms.TenCrop + + def __init__(self, size: Union[int, Sequence[int]], vertical_flip: bool = False) -> None: + super().__init__() + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + self.vertical_flip = vertical_flip + + def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any: + if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)): + warnings.warn( + f"{type(self).__name__}() is currently passing through inputs of type " + f"tv_tensors.{type(inpt).__name__}. This will likely change in the future." + ) + return super()._call_kernel(functional, inpt, *args, **kwargs) + + def check_inputs(self, flat_inputs: List[Any]) -> None: + if has_any(flat_inputs, tv_tensors.BoundingBoxes, tv_tensors.Mask): + raise TypeError(f"BoundingBoxes'es and Mask's are not supported by {type(self).__name__}()") + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.ten_crop, inpt, self.size, vertical_flip=self.vertical_flip) + + +class Pad(Transform): + """Pad the input on all sides with the given "pad" value. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + padding (int or sequence): Padding on each border. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + + .. note:: + In torchscript mode padding as single int is not supported, use a sequence of + length 1: ``[padding, ]``. + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric. + Default is "constant". + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value at the edge of the image. + + - reflect: pads with reflection of image without repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + """ + + _v1_transform_cls = _transforms.Pad + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + params = super()._extract_params_for_v1_transform() + + if not (params["fill"] is None or isinstance(params["fill"], (int, float))): + raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.") + + return params + + def __init__( + self, + padding: Union[int, Sequence[int]], + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant", + ) -> None: + super().__init__() + + _check_padding_arg(padding) + _check_padding_mode_arg(padding_mode) + + # This cast does Sequence[int] -> List[int] and is required to make mypy happy + if not isinstance(padding, int): + padding = list(padding) + self.padding = padding + self.fill = fill + self._fill = _setup_fill_arg(fill) + self.padding_mode = padding_mode + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel(F.pad, inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type] + + +class RandomZoomOut(_RandomApplyTransform): + """ "Zoom out" transformation from + `"SSD: Single Shot MultiBox Detector" `_. + + This transformation randomly pads images, videos, bounding boxes and masks creating a zoom out effect. + Output spatial size is randomly sampled from original size up to a maximum size configured + with ``side_range`` parameter: + + .. code-block:: python + + r = uniform_sample(side_range[0], side_range[1]) + output_width = input_width * r + output_height = input_height * r + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + side_range (sequence of floats, optional): tuple of two floats defines minimum and maximum factors to + scale the input size. + p (float, optional): probability that the zoom operation will be performed. + """ + + def __init__( + self, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + side_range: Sequence[float] = (1.0, 4.0), + p: float = 0.5, + ) -> None: + super().__init__(p=p) + + self.fill = fill + self._fill = _setup_fill_arg(fill) + + _check_sequence_input(side_range, "side_range", req_sizes=(2,)) + + self.side_range = side_range + if side_range[0] < 1.0 or side_range[0] > side_range[1]: + raise ValueError(f"Invalid side range provided {side_range}.") + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + orig_h, orig_w = query_size(flat_inputs) + + r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0]) + canvas_width = int(orig_w * r) + canvas_height = int(orig_h * r) + + r = torch.rand(2) + left = int((canvas_width - orig_w) * r[0]) + top = int((canvas_height - orig_h) * r[1]) + right = canvas_width - (left + orig_w) + bottom = canvas_height - (top + orig_h) + padding = [left, top, right, bottom] + + return dict(padding=padding) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel(F.pad, inpt, **params, fill=fill) + + +class RandomRotation(Transform): + """Rotate the input by angle. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + degrees (sequence or number): Range of degrees to select from. + If degrees is a number instead of sequence like (min, max), the range of degrees + will be (-degrees, +degrees). + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + expand (bool, optional): Optional expansion flag. + If true, expands the output to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the input image. + Note that the expand flag assumes rotation around the center (see note below) and no translation. + center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner. + Default is the center of the image. + + .. note:: + + In theory, setting ``center`` has no effect if ``expand=True``, since the image center will become the + center of rotation. In practice however, due to numerical precision, this can lead to off-by-one + differences of the resulting image size compared to using the image center in the first place. Thus, when + setting ``expand=True``, it's best to leave ``center=None`` (default). + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + + _v1_transform_cls = _transforms.RandomRotation + + def __init__( + self, + degrees: Union[numbers.Number, Sequence], + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[float]] = None, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + ) -> None: + super().__init__() + self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,)) + self.interpolation = interpolation + self.expand = expand + + self.fill = fill + self._fill = _setup_fill_arg(fill) + + if center is not None: + _check_sequence_input(center, "center", req_sizes=(2,)) + + self.center = center + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item() + return dict(angle=angle) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel( + F.rotate, + inpt, + **params, + interpolation=self.interpolation, + expand=self.expand, + center=self.center, + fill=fill, + ) + + +class RandomAffine(Transform): + """Random affine transformation the input keeping center invariant. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + degrees (sequence or number): Range of degrees to select from. + If degrees is a number instead of sequence like (min, max), the range of degrees + will be (-degrees, +degrees). Set to 0 to deactivate rotations. + translate (tuple, optional): tuple of maximum absolute fraction for horizontal + and vertical translations. For example translate=(a, b), then horizontal shift + is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is + randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default. + scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is + randomly sampled from the range a <= scale <= b. Will keep original scale by default. + shear (sequence or number, optional): Range of degrees to select from. + If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear) + will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the + range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values, + an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied. + Will not apply shear by default. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner. + Default is the center of the image. + + .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters + + """ + + _v1_transform_cls = _transforms.RandomAffine + + def __init__( + self, + degrees: Union[numbers.Number, Sequence], + translate: Optional[Sequence[float]] = None, + scale: Optional[Sequence[float]] = None, + shear: Optional[Union[int, float, Sequence[float]]] = None, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + center: Optional[List[float]] = None, + ) -> None: + super().__init__() + self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,)) + if translate is not None: + _check_sequence_input(translate, "translate", req_sizes=(2,)) + for t in translate: + if not (0.0 <= t <= 1.0): + raise ValueError("translation values should be between 0 and 1") + self.translate = translate + if scale is not None: + _check_sequence_input(scale, "scale", req_sizes=(2,)) + for s in scale: + if s <= 0: + raise ValueError("scale values should be positive") + self.scale = scale + + if shear is not None: + self.shear = _setup_angle(shear, name="shear", req_sizes=(2, 4)) + else: + self.shear = shear + + self.interpolation = interpolation + self.fill = fill + self._fill = _setup_fill_arg(fill) + + if center is not None: + _check_sequence_input(center, "center", req_sizes=(2,)) + + self.center = center + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + height, width = query_size(flat_inputs) + + angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item() + if self.translate is not None: + max_dx = float(self.translate[0] * width) + max_dy = float(self.translate[1] * height) + tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item())) + ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item())) + translate = (tx, ty) + else: + translate = (0, 0) + + if self.scale is not None: + scale = torch.empty(1).uniform_(self.scale[0], self.scale[1]).item() + else: + scale = 1.0 + + shear_x = shear_y = 0.0 + if self.shear is not None: + shear_x = torch.empty(1).uniform_(self.shear[0], self.shear[1]).item() + if len(self.shear) == 4: + shear_y = torch.empty(1).uniform_(self.shear[2], self.shear[3]).item() + + shear = (shear_x, shear_y) + return dict(angle=angle, translate=translate, scale=scale, shear=shear) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel( + F.affine, + inpt, + **params, + interpolation=self.interpolation, + fill=fill, + center=self.center, + ) + + +class RandomCrop(Transform): + """Crop the input at a random location. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + padding (int or sequence, optional): Optional padding on each border + of the image. Default is None. If a single int is provided this + is used to pad all borders. If sequence of length 2 is provided this is the padding + on left/right and top/bottom respectively. If a sequence of length 4 is provided + this is the padding for the left, top, right and bottom borders respectively. + + .. note:: + In torchscript mode padding as single int is not supported, use a sequence of + length 1: ``[padding, ]``. + pad_if_needed (boolean, optional): It will pad the image if smaller than the + desired size to avoid raising an exception. Since cropping is done + after padding, the padding seems to be done at a random offset. + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric. + Default is constant. + + - constant: pads with a constant value, this value is specified with fill + + - edge: pads with the last value at the edge of the image. + + - reflect: pads with reflection of image without repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode + will result in [3, 2, 1, 2, 3, 4, 3, 2] + + - symmetric: pads with reflection of image repeating the last value on the edge. + For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode + will result in [2, 1, 1, 2, 3, 4, 4, 3] + """ + + _v1_transform_cls = _transforms.RandomCrop + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + params = super()._extract_params_for_v1_transform() + + if not (params["fill"] is None or isinstance(params["fill"], (int, float))): + raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.") + + padding = self.padding + if padding is not None: + pad_left, pad_right, pad_top, pad_bottom = padding + padding = [pad_left, pad_top, pad_right, pad_bottom] + params["padding"] = padding + + return params + + def __init__( + self, + size: Union[int, Sequence[int]], + padding: Optional[Union[int, Sequence[int]]] = None, + pad_if_needed: bool = False, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant", + ) -> None: + super().__init__() + + self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.") + + if pad_if_needed or padding is not None: + if padding is not None: + _check_padding_arg(padding) + _check_padding_mode_arg(padding_mode) + + self.padding = F._geometry._parse_pad_padding(padding) if padding else None # type: ignore[arg-type] + self.pad_if_needed = pad_if_needed + self.fill = fill + self._fill = _setup_fill_arg(fill) + self.padding_mode = padding_mode + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + padded_height, padded_width = query_size(flat_inputs) + + if self.padding is not None: + pad_left, pad_right, pad_top, pad_bottom = self.padding + padded_height += pad_top + pad_bottom + padded_width += pad_left + pad_right + else: + pad_left = pad_right = pad_top = pad_bottom = 0 + + cropped_height, cropped_width = self.size + + if self.pad_if_needed: + if padded_height < cropped_height: + diff = cropped_height - padded_height + + pad_top += diff + pad_bottom += diff + padded_height += 2 * diff + + if padded_width < cropped_width: + diff = cropped_width - padded_width + + pad_left += diff + pad_right += diff + padded_width += 2 * diff + + if padded_height < cropped_height or padded_width < cropped_width: + raise ValueError( + f"Required crop size {(cropped_height, cropped_width)} is larger than " + f"{'padded ' if self.padding is not None else ''}input image size {(padded_height, padded_width)}." + ) + + # We need a different order here than we have in self.padding since this padding will be parsed again in `F.pad` + padding = [pad_left, pad_top, pad_right, pad_bottom] + needs_pad = any(padding) + + needs_vert_crop, top = ( + (True, int(torch.randint(0, padded_height - cropped_height + 1, size=()))) + if padded_height > cropped_height + else (False, 0) + ) + needs_horz_crop, left = ( + (True, int(torch.randint(0, padded_width - cropped_width + 1, size=()))) + if padded_width > cropped_width + else (False, 0) + ) + + return dict( + needs_crop=needs_vert_crop or needs_horz_crop, + top=top, + left=left, + height=cropped_height, + width=cropped_width, + needs_pad=needs_pad, + padding=padding, + ) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if params["needs_pad"]: + fill = _get_fill(self._fill, type(inpt)) + inpt = self._call_kernel(F.pad, inpt, padding=params["padding"], fill=fill, padding_mode=self.padding_mode) + + if params["needs_crop"]: + inpt = self._call_kernel( + F.crop, inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"] + ) + + return inpt + + +class RandomPerspective(_RandomApplyTransform): + """Perform a random perspective transformation of the input with a given probability. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + distortion_scale (float, optional): argument to control the degree of distortion and ranges from 0 to 1. + Default is 0.5. + p (float, optional): probability of the input being transformed. Default is 0.5. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + """ + + _v1_transform_cls = _transforms.RandomPerspective + + def __init__( + self, + distortion_scale: float = 0.5, + p: float = 0.5, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + ) -> None: + super().__init__(p=p) + + if not (0 <= distortion_scale <= 1): + raise ValueError("Argument distortion_scale value should be between 0 and 1") + + self.distortion_scale = distortion_scale + self.interpolation = interpolation + self.fill = fill + self._fill = _setup_fill_arg(fill) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + height, width = query_size(flat_inputs) + + distortion_scale = self.distortion_scale + + half_height = height // 2 + half_width = width // 2 + bound_height = int(distortion_scale * half_height) + 1 + bound_width = int(distortion_scale * half_width) + 1 + topleft = [ + int(torch.randint(0, bound_width, size=(1,))), + int(torch.randint(0, bound_height, size=(1,))), + ] + topright = [ + int(torch.randint(width - bound_width, width, size=(1,))), + int(torch.randint(0, bound_height, size=(1,))), + ] + botright = [ + int(torch.randint(width - bound_width, width, size=(1,))), + int(torch.randint(height - bound_height, height, size=(1,))), + ] + botleft = [ + int(torch.randint(0, bound_width, size=(1,))), + int(torch.randint(height - bound_height, height, size=(1,))), + ] + startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]] + endpoints = [topleft, topright, botright, botleft] + perspective_coeffs = _get_perspective_coeffs(startpoints, endpoints) + return dict(coefficients=perspective_coeffs) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel( + F.perspective, + inpt, + startpoints=None, + endpoints=None, + fill=fill, + interpolation=self.interpolation, + **params, + ) + + +class ElasticTransform(Transform): + """Transform the input with elastic transformations. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Given alpha and sigma, it will generate displacement + vectors for all pixels based on random offsets. Alpha controls the strength + and sigma controls the smoothness of the displacements. + The displacements are added to an identity grid and the resulting grid is + used to transform the input. + + .. note:: + Implementation to transform bounding boxes is approximative (not exact). + We construct an approximation of the inverse grid as ``inverse_grid = identity - displacement``. + This is not an exact inverse of the grid used to transform images, i.e. ``grid = identity + displacement``. + Our assumption is that ``displacement * displacement`` is small and can be ignored. + Large displacements would lead to large errors in the approximation. + + Applications: + Randomly transforms the morphology of objects in images and produces a + see-through-water-like effect. + + Args: + alpha (float or sequence of floats, optional): Magnitude of displacements. Default is 50.0. + sigma (float or sequence of floats, optional): Smoothness of displacements. Default is 5.0. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant. + Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. + Fill value can be also a dictionary mapping data type to the fill value, e.g. + ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and + ``Mask`` will be filled with 0. + """ + + _v1_transform_cls = _transforms.ElasticTransform + + def __init__( + self, + alpha: Union[float, Sequence[float]] = 50.0, + sigma: Union[float, Sequence[float]] = 5.0, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0, + ) -> None: + super().__init__() + self.alpha = _setup_number_or_seq(alpha, "alpha") + self.sigma = _setup_number_or_seq(sigma, "sigma") + + self.interpolation = interpolation + self.fill = fill + self._fill = _setup_fill_arg(fill) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + size = list(query_size(flat_inputs)) + + dx = torch.rand([1, 1] + size) * 2 - 1 + if self.sigma[0] > 0.0: + kx = int(8 * self.sigma[0] + 1) + # if kernel size is even we have to make it odd + if kx % 2 == 0: + kx += 1 + dx = self._call_kernel(F.gaussian_blur, dx, [kx, kx], list(self.sigma)) + dx = dx * self.alpha[0] / size[0] + + dy = torch.rand([1, 1] + size) * 2 - 1 + if self.sigma[1] > 0.0: + ky = int(8 * self.sigma[1] + 1) + # if kernel size is even we have to make it odd + if ky % 2 == 0: + ky += 1 + dy = self._call_kernel(F.gaussian_blur, dy, [ky, ky], list(self.sigma)) + dy = dy * self.alpha[1] / size[1] + displacement = torch.concat([dx, dy], 1).permute([0, 2, 3, 1]) # 1 x H x W x 2 + return dict(displacement=displacement) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + fill = _get_fill(self._fill, type(inpt)) + return self._call_kernel( + F.elastic, + inpt, + **params, + fill=fill, + interpolation=self.interpolation, + ) + + +class RandomIoUCrop(Transform): + """Random IoU crop transformation from + `"SSD: Single Shot MultiBox Detector" `_. + + This transformation requires an image or video data and ``tv_tensors.BoundingBoxes`` in the input. + + .. warning:: + In order to properly remove the bounding boxes below the IoU threshold, `RandomIoUCrop` + must be followed by :class:`~torchvision.transforms.v2.SanitizeBoundingBoxes`, either immediately + after or later in the transforms pipeline. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + min_scale (float, optional): Minimum factors to scale the input size. + max_scale (float, optional): Maximum factors to scale the input size. + min_aspect_ratio (float, optional): Minimum aspect ratio for the cropped image or video. + max_aspect_ratio (float, optional): Maximum aspect ratio for the cropped image or video. + sampler_options (list of float, optional): List of minimal IoU (Jaccard) overlap between all the boxes and + a cropped image or video. Default, ``None`` which corresponds to ``[0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]`` + trials (int, optional): Number of trials to find a crop for a given value of minimal IoU (Jaccard) overlap. + Default, 40. + """ + + def __init__( + self, + min_scale: float = 0.3, + max_scale: float = 1.0, + min_aspect_ratio: float = 0.5, + max_aspect_ratio: float = 2.0, + sampler_options: Optional[List[float]] = None, + trials: int = 40, + ): + super().__init__() + # Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174 + self.min_scale = min_scale + self.max_scale = max_scale + self.min_aspect_ratio = min_aspect_ratio + self.max_aspect_ratio = max_aspect_ratio + if sampler_options is None: + sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0] + self.options = sampler_options + self.trials = trials + + def check_inputs(self, flat_inputs: List[Any]) -> None: + if not ( + has_all(flat_inputs, tv_tensors.BoundingBoxes) + and has_any(flat_inputs, PIL.Image.Image, tv_tensors.Image, is_pure_tensor) + ): + raise TypeError( + f"{type(self).__name__}() requires input sample to contain tensor or PIL images " + "and bounding boxes. Sample can also contain masks." + ) + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + orig_h, orig_w = query_size(flat_inputs) + bboxes = get_bounding_boxes(flat_inputs) + + while True: + # sample an option + idx = int(torch.randint(low=0, high=len(self.options), size=(1,))) + min_jaccard_overlap = self.options[idx] + if min_jaccard_overlap >= 1.0: # a value larger than 1 encodes the leave as-is option + return dict() + + for _ in range(self.trials): + # check the aspect ratio limitations + r = self.min_scale + (self.max_scale - self.min_scale) * torch.rand(2) + new_w = int(orig_w * r[0]) + new_h = int(orig_h * r[1]) + aspect_ratio = new_w / new_h + if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio): + continue + + # check for 0 area crops + r = torch.rand(2) + left = int((orig_w - new_w) * r[0]) + top = int((orig_h - new_h) * r[1]) + right = left + new_w + bottom = top + new_h + if left == right or top == bottom: + continue + + # check for any valid boxes with centers within the crop area + xyxy_bboxes = F.convert_bounding_box_format( + bboxes.as_subclass(torch.Tensor), + bboxes.format, + tv_tensors.BoundingBoxFormat.XYXY, + ) + cx = 0.5 * (xyxy_bboxes[..., 0] + xyxy_bboxes[..., 2]) + cy = 0.5 * (xyxy_bboxes[..., 1] + xyxy_bboxes[..., 3]) + is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom) + if not is_within_crop_area.any(): + continue + + # check at least 1 box with jaccard limitations + xyxy_bboxes = xyxy_bboxes[is_within_crop_area] + ious = box_iou( + xyxy_bboxes, + torch.tensor([[left, top, right, bottom]], dtype=xyxy_bboxes.dtype, device=xyxy_bboxes.device), + ) + if ious.max() < min_jaccard_overlap: + continue + + return dict(top=top, left=left, height=new_h, width=new_w, is_within_crop_area=is_within_crop_area) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + + if len(params) < 1: + return inpt + + output = self._call_kernel( + F.crop, inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"] + ) + + if isinstance(output, tv_tensors.BoundingBoxes): + # We "mark" the invalid boxes as degenreate, and they can be + # removed by a later call to SanitizeBoundingBoxes() + output[~params["is_within_crop_area"]] = 0 + + return output + + +class ScaleJitter(Transform): + """Perform Large Scale Jitter on the input according to + `"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" `_. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + target_size (tuple of int): Target size. This parameter defines base scale for jittering, + e.g. ``min(target_size[0] / width, target_size[1] / height)``. + scale_range (tuple of float, optional): Minimum and maximum of the scale range. Default, ``(0.1, 2.0)``. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + def __init__( + self, + target_size: Tuple[int, int], + scale_range: Tuple[float, float] = (0.1, 2.0), + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ): + super().__init__() + self.target_size = target_size + self.scale_range = scale_range + self.interpolation = interpolation + self.antialias = antialias + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + orig_height, orig_width = query_size(flat_inputs) + + scale = self.scale_range[0] + torch.rand(1) * (self.scale_range[1] - self.scale_range[0]) + r = min(self.target_size[1] / orig_height, self.target_size[0] / orig_width) * scale + new_width = int(orig_width * r) + new_height = int(orig_height * r) + + return dict(size=(new_height, new_width)) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel( + F.resize, inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias + ) + + +class RandomShortestSize(Transform): + """Randomly resize the input. + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + min_size (int or sequence of int): Minimum spatial size. Single integer value or a sequence of integer values. + max_size (int, optional): Maximum spatial size. Default, None. + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + def __init__( + self, + min_size: Union[List[int], Tuple[int], int], + max_size: Optional[int] = None, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ): + super().__init__() + self.min_size = [min_size] if isinstance(min_size, int) else list(min_size) + self.max_size = max_size + self.interpolation = interpolation + self.antialias = antialias + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + orig_height, orig_width = query_size(flat_inputs) + + min_size = self.min_size[int(torch.randint(len(self.min_size), ()))] + r = min_size / min(orig_height, orig_width) + if self.max_size is not None: + r = min(r, self.max_size / max(orig_height, orig_width)) + + new_width = int(orig_width * r) + new_height = int(orig_height * r) + + return dict(size=(new_height, new_width)) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel( + F.resize, inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias + ) + + +class RandomResize(Transform): + """Randomly resize the input. + + This transformation can be used together with ``RandomCrop`` as data augmentations to train + models on image segmentation task. + + Output spatial size is randomly sampled from the interval ``[min_size, max_size]``: + + .. code-block:: python + + size = uniform_sample(min_size, max_size) + output_width = size + output_height = size + + If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`, + :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.) + it can have arbitrary number of leading batch dimensions. For example, + the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape. + + Args: + min_size (int): Minimum output size for random sampling + max_size (int): Maximum output size for random sampling + interpolation (InterpolationMode, optional): Desired interpolation enum defined by + :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``. + If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``, + ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported. + The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well. + antialias (bool, optional): Whether to apply antialiasing. + It only affects **tensors** with bilinear or bicubic modes and it is + ignored otherwise: on PIL images, antialiasing is always applied on + bilinear or bicubic modes; on other modes (for PIL images and + tensors), antialiasing makes no sense and this parameter is ignored. + Possible values are: + + - ``True`` (default): will apply antialiasing for bilinear or bicubic modes. + Other mode aren't affected. This is probably what you want to use. + - ``False``: will not apply antialiasing for tensors on any mode. PIL + images are still antialiased on bilinear or bicubic modes, because + PIL doesn't support no antialias. + - ``None``: equivalent to ``False`` for tensors and ``True`` for + PIL images. This value exists for legacy reasons and you probably + don't want to use it unless you really know what you are doing. + + The default value changed from ``None`` to ``True`` in + v0.17, for the PIL and Tensor backends to be consistent. + """ + + def __init__( + self, + min_size: int, + max_size: int, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, + ) -> None: + super().__init__() + self.min_size = min_size + self.max_size = max_size + self.interpolation = interpolation + self.antialias = antialias + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + size = int(torch.randint(self.min_size, self.max_size, ())) + return dict(size=[size]) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel( + F.resize, inpt, params["size"], interpolation=self.interpolation, antialias=self.antialias + ) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_meta.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..1890b43115afb7eda76e13688daee5121029dc62 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_meta.py @@ -0,0 +1,36 @@ +from typing import Any, Dict, Union + +from torchvision import tv_tensors +from torchvision.transforms.v2 import functional as F, Transform + + +class ConvertBoundingBoxFormat(Transform): + """Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY". + + Args: + format (str or tv_tensors.BoundingBoxFormat): output bounding box format. + Possible values are defined by :class:`~torchvision.tv_tensors.BoundingBoxFormat` and + string values match the enums, e.g. "XYXY" or "XYWH" etc. + """ + + _transformed_types = (tv_tensors.BoundingBoxes,) + + def __init__(self, format: Union[str, tv_tensors.BoundingBoxFormat]) -> None: + super().__init__() + self.format = format + + def transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes: + return F.convert_bounding_box_format(inpt, new_format=self.format) # type: ignore[return-value, arg-type] + + +class ClampBoundingBoxes(Transform): + """Clamp bounding boxes to their corresponding image dimensions. + + The clamping is done according to the bounding boxes' ``canvas_size`` meta-data. + + """ + + _transformed_types = (tv_tensors.BoundingBoxes,) + + def transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes: + return F.clamp_bounding_boxes(inpt) # type: ignore[return-value] diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_misc.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_misc.py new file mode 100644 index 0000000000000000000000000000000000000000..d38a6ad8767b7e366564bff4a28c0838abd8379b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_misc.py @@ -0,0 +1,451 @@ +import warnings +from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union + +import PIL.Image + +import torch +from torch.utils._pytree import tree_flatten, tree_unflatten + +from torchvision import transforms as _transforms, tv_tensors +from torchvision.transforms.v2 import functional as F, Transform + +from ._utils import _parse_labels_getter, _setup_number_or_seq, _setup_size, get_bounding_boxes, has_any, is_pure_tensor + + +# TODO: do we want/need to expose this? +class Identity(Transform): + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return inpt + + +class Lambda(Transform): + """Apply a user-defined function as a transform. + + This transform does not support torchscript. + + Args: + lambd (function): Lambda/function to be used for transform. + """ + + _transformed_types = (object,) + + def __init__(self, lambd: Callable[[Any], Any], *types: Type): + super().__init__() + self.lambd = lambd + self.types = types or self._transformed_types + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if isinstance(inpt, self.types): + return self.lambd(inpt) + else: + return inpt + + def extra_repr(self) -> str: + extras = [] + name = getattr(self.lambd, "__name__", None) + if name: + extras.append(name) + extras.append(f"types={[type.__name__ for type in self.types]}") + return ", ".join(extras) + + +class LinearTransformation(Transform): + """Transform a tensor image or video with a square transformation matrix and a mean_vector computed offline. + + This transform does not support PIL Image. + Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and + subtract mean_vector from it which is then followed by computing the dot + product with the transformation matrix and then reshaping the tensor to its + original shape. + + Applications: + whitening transformation: Suppose X is a column vector zero-centered data. + Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X), + perform SVD on this matrix and pass it as transformation_matrix. + + Args: + transformation_matrix (Tensor): tensor [D x D], D = C x H x W + mean_vector (Tensor): tensor [D], D = C x H x W + """ + + _v1_transform_cls = _transforms.LinearTransformation + + _transformed_types = (is_pure_tensor, tv_tensors.Image, tv_tensors.Video) + + def __init__(self, transformation_matrix: torch.Tensor, mean_vector: torch.Tensor): + super().__init__() + if transformation_matrix.size(0) != transformation_matrix.size(1): + raise ValueError( + "transformation_matrix should be square. Got " + f"{tuple(transformation_matrix.size())} rectangular matrix." + ) + + if mean_vector.size(0) != transformation_matrix.size(0): + raise ValueError( + f"mean_vector should have the same length {mean_vector.size(0)}" + f" as any one of the dimensions of the transformation_matrix [{tuple(transformation_matrix.size())}]" + ) + + if transformation_matrix.device != mean_vector.device: + raise ValueError( + f"Input tensors should be on the same device. Got {transformation_matrix.device} and {mean_vector.device}" + ) + + if transformation_matrix.dtype != mean_vector.dtype: + raise ValueError( + f"Input tensors should have the same dtype. Got {transformation_matrix.dtype} and {mean_vector.dtype}" + ) + + self.transformation_matrix = transformation_matrix + self.mean_vector = mean_vector + + def check_inputs(self, sample: Any) -> Any: + if has_any(sample, PIL.Image.Image): + raise TypeError(f"{type(self).__name__}() does not support PIL images.") + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + shape = inpt.shape + n = shape[-3] * shape[-2] * shape[-1] + if n != self.transformation_matrix.shape[0]: + raise ValueError( + "Input tensor and transformation matrix have incompatible shape." + + f"[{shape[-3]} x {shape[-2]} x {shape[-1]}] != " + + f"{self.transformation_matrix.shape[0]}" + ) + + if inpt.device.type != self.mean_vector.device.type: + raise ValueError( + "Input tensor should be on the same device as transformation matrix and mean vector. " + f"Got {inpt.device} vs {self.mean_vector.device}" + ) + + flat_inpt = inpt.reshape(-1, n) - self.mean_vector + + transformation_matrix = self.transformation_matrix.to(flat_inpt.dtype) + output = torch.mm(flat_inpt, transformation_matrix) + output = output.reshape(shape) + + if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)): + output = tv_tensors.wrap(output, like=inpt) + return output + + +class Normalize(Transform): + """Normalize a tensor image or video with mean and standard deviation. + + This transform does not support PIL Image. + Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n`` + channels, this transform will normalize each channel of the input + ``torch.*Tensor`` i.e., + ``output[channel] = (input[channel] - mean[channel]) / std[channel]`` + + .. note:: + This transform acts out of place, i.e., it does not mutate the input tensor. + + Args: + mean (sequence): Sequence of means for each channel. + std (sequence): Sequence of standard deviations for each channel. + inplace(bool,optional): Bool to make this operation in-place. + + """ + + _v1_transform_cls = _transforms.Normalize + + def __init__(self, mean: Sequence[float], std: Sequence[float], inplace: bool = False): + super().__init__() + self.mean = list(mean) + self.std = list(std) + self.inplace = inplace + + def check_inputs(self, sample: Any) -> Any: + if has_any(sample, PIL.Image.Image): + raise TypeError(f"{type(self).__name__}() does not support PIL images.") + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.normalize, inpt, mean=self.mean, std=self.std, inplace=self.inplace) + + +class GaussianBlur(Transform): + """Blurs image with randomly chosen Gaussian blur kernel. + + The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape. + + If the input is a Tensor, it is expected + to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions. + + Args: + kernel_size (int or sequence): Size of the Gaussian kernel. + sigma (float or tuple of float (min, max)): Standard deviation to be used for + creating kernel to perform blurring. If float, sigma is fixed. If it is tuple + of float (min, max), sigma is chosen uniformly at random to lie in the + given range. + """ + + _v1_transform_cls = _transforms.GaussianBlur + + def __init__( + self, kernel_size: Union[int, Sequence[int]], sigma: Union[int, float, Sequence[float]] = (0.1, 2.0) + ) -> None: + super().__init__() + self.kernel_size = _setup_size(kernel_size, "Kernel size should be a tuple/list of two integers") + for ks in self.kernel_size: + if ks <= 0 or ks % 2 == 0: + raise ValueError("Kernel size value should be an odd and positive number.") + + self.sigma = _setup_number_or_seq(sigma, "sigma") + + if not 0.0 < self.sigma[0] <= self.sigma[1]: + raise ValueError(f"sigma values should be positive and of the form (min, max). Got {self.sigma}") + + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + sigma = torch.empty(1).uniform_(self.sigma[0], self.sigma[1]).item() + return dict(sigma=[sigma, sigma]) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.gaussian_blur, inpt, self.kernel_size, **params) + + +class GaussianNoise(Transform): + """Add gaussian noise to images or videos. + + The input tensor is expected to be in [..., 1 or 3, H, W] format, + where ... means it can have an arbitrary number of leading dimensions. + Each image or frame in a batch will be transformed independently i.e. the + noise added to each image will be different. + + The input tensor is also expected to be of float dtype in ``[0, 1]``. + This transform does not support PIL images. + + Args: + mean (float): Mean of the sampled normal distribution. Default is 0. + sigma (float): Standard deviation of the sampled normal distribution. Default is 0.1. + clip (bool, optional): Whether to clip the values in ``[0, 1]`` after adding noise. Default is True. + """ + + def __init__(self, mean: float = 0.0, sigma: float = 0.1, clip=True) -> None: + super().__init__() + self.mean = mean + self.sigma = sigma + self.clip = clip + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.gaussian_noise, inpt, mean=self.mean, sigma=self.sigma, clip=self.clip) + + +class ToDtype(Transform): + """Converts the input to a specific dtype, optionally scaling the values for images or videos. + + .. note:: + ``ToDtype(dtype, scale=True)`` is the recommended replacement for ``ConvertImageDtype(dtype)``. + + Args: + dtype (``torch.dtype`` or dict of ``TVTensor`` -> ``torch.dtype``): The dtype to convert to. + If a ``torch.dtype`` is passed, e.g. ``torch.float32``, only images and videos will be converted + to that dtype: this is for compatibility with :class:`~torchvision.transforms.v2.ConvertImageDtype`. + A dict can be passed to specify per-tv_tensor conversions, e.g. + ``dtype={tv_tensors.Image: torch.float32, tv_tensors.Mask: torch.int64, "others":None}``. The "others" + key can be used as a catch-all for any other tv_tensor type, and ``None`` means no conversion. + scale (bool, optional): Whether to scale the values for images or videos. See :ref:`range_and_dtype`. + Default: ``False``. + """ + + _transformed_types = (torch.Tensor,) + + def __init__( + self, dtype: Union[torch.dtype, Dict[Union[Type, str], Optional[torch.dtype]]], scale: bool = False + ) -> None: + super().__init__() + + if not isinstance(dtype, (dict, torch.dtype)): + raise ValueError(f"dtype must be a dict or a torch.dtype, got {type(dtype)} instead") + + if ( + isinstance(dtype, dict) + and torch.Tensor in dtype + and any(cls in dtype for cls in [tv_tensors.Image, tv_tensors.Video]) + ): + warnings.warn( + "Got `dtype` values for `torch.Tensor` and either `tv_tensors.Image` or `tv_tensors.Video`. " + "Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) " + "in case a `tv_tensors.Image` or `tv_tensors.Video` is present in the input." + ) + self.dtype = dtype + self.scale = scale + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + if isinstance(self.dtype, torch.dtype): + # For consistency / BC with ConvertImageDtype, we only care about images or videos when dtype + # is a simple torch.dtype + if not is_pure_tensor(inpt) and not isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)): + return inpt + + dtype: Optional[torch.dtype] = self.dtype + elif type(inpt) in self.dtype: + dtype = self.dtype[type(inpt)] + elif "others" in self.dtype: + dtype = self.dtype["others"] + else: + raise ValueError( + f"No dtype was specified for type {type(inpt)}. " + "If you only need to convert the dtype of images or videos, you can just pass e.g. dtype=torch.float32. " + "If you're passing a dict as dtype, " + 'you can use "others" as a catch-all key ' + 'e.g. dtype={tv_tensors.Mask: torch.int64, "others": None} to pass-through the rest of the inputs.' + ) + + supports_scaling = is_pure_tensor(inpt) or isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) + if dtype is None: + if self.scale and supports_scaling: + warnings.warn( + "scale was set to True but no dtype was specified for images or videos: no scaling will be done." + ) + return inpt + + return self._call_kernel(F.to_dtype, inpt, dtype=dtype, scale=self.scale) + + +class ConvertImageDtype(Transform): + """[DEPRECATED] Use ``v2.ToDtype(dtype, scale=True)`` instead. + + Convert input image to the given ``dtype`` and scale the values accordingly. + + .. warning:: + Consider using ``ToDtype(dtype, scale=True)`` instead. See :class:`~torchvision.transforms.v2.ToDtype`. + + This function does not support PIL Image. + + Args: + dtype (torch.dtype): Desired data type of the output + + .. note:: + + When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. + If converted back and forth, this mismatch has no effect. + + Raises: + RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as + well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to + overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range + of the integer ``dtype``. + """ + + _v1_transform_cls = _transforms.ConvertImageDtype + + def __init__(self, dtype: torch.dtype = torch.float32) -> None: + super().__init__() + self.dtype = dtype + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.to_dtype, inpt, dtype=self.dtype, scale=True) + + +class SanitizeBoundingBoxes(Transform): + """Remove degenerate/invalid bounding boxes and their corresponding labels and masks. + + This transform removes bounding boxes and their associated labels/masks that: + + - are below a given ``min_size`` or ``min_area``: by default this also removes degenerate boxes that have e.g. X2 <= X1. + - have any coordinate outside of their corresponding image. You may want to + call :class:`~torchvision.transforms.v2.ClampBoundingBoxes` first to avoid undesired removals. + + It can also sanitize other tensors like the "iscrowd" or "area" properties from COCO + (see ``labels_getter`` parameter). + + It is recommended to call it at the end of a pipeline, before passing the + input to the models. It is critical to call this transform if + :class:`~torchvision.transforms.v2.RandomIoUCrop` was called. + If you want to be extra careful, you may call it after all transforms that + may modify bounding boxes but once at the end should be enough in most + cases. + + Args: + min_size (float, optional): The size below which bounding boxes are removed. Default is 1. + min_area (float, optional): The area below which bounding boxes are removed. Default is 1. + labels_getter (callable or str or None, optional): indicates how to identify the labels in the input + (or anything else that needs to be sanitized along with the bounding boxes). + By default, this will try to find a "labels" key in the input (case-insensitive), if + the input is a dict or it is a tuple whose second element is a dict. + This heuristic should work well with a lot of datasets, including the built-in torchvision datasets. + + It can also be a callable that takes the same input as the transform, and returns either: + + - A single tensor (the labels) + - A tuple/list of tensors, each of which will be subject to the same sanitization as the bounding boxes. + This is useful to sanitize multiple tensors like the labels, and the "iscrowd" or "area" properties + from COCO. + + If ``labels_getter`` is None then only bounding boxes are sanitized. + """ + + def __init__( + self, + min_size: float = 1.0, + min_area: float = 1.0, + labels_getter: Union[Callable[[Any], Any], str, None] = "default", + ) -> None: + super().__init__() + + if min_size < 1: + raise ValueError(f"min_size must be >= 1, got {min_size}.") + self.min_size = min_size + + if min_area < 1: + raise ValueError(f"min_area must be >= 1, got {min_area}.") + self.min_area = min_area + + self.labels_getter = labels_getter + self._labels_getter = _parse_labels_getter(labels_getter) + + def forward(self, *inputs: Any) -> Any: + inputs = inputs if len(inputs) > 1 else inputs[0] + + labels = self._labels_getter(inputs) + if labels is not None: + msg = "The labels in the input to forward() must be a tensor or None, got {type} instead." + if isinstance(labels, torch.Tensor): + labels = (labels,) + elif isinstance(labels, (tuple, list)): + for entry in labels: + if not isinstance(entry, torch.Tensor): + # TODO: we don't need to enforce tensors, just that entries are indexable as t[bool_mask] + raise ValueError(msg.format(type=type(entry))) + else: + raise ValueError(msg.format(type=type(labels))) + + flat_inputs, spec = tree_flatten(inputs) + boxes = get_bounding_boxes(flat_inputs) + + if labels is not None: + for label in labels: + if boxes.shape[0] != label.shape[0]: + raise ValueError( + f"Number of boxes (shape={boxes.shape}) and must match the number of labels." + f"Found labels with shape={label.shape})." + ) + + valid = F._misc._get_sanitize_bounding_boxes_mask( + boxes, + format=boxes.format, + canvas_size=boxes.canvas_size, + min_size=self.min_size, + min_area=self.min_area, + ) + + params = dict(valid=valid, labels=labels) + flat_outputs = [self.transform(inpt, params) for inpt in flat_inputs] + + return tree_unflatten(flat_outputs, spec) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + is_label = params["labels"] is not None and any(inpt is label for label in params["labels"]) + is_bounding_boxes_or_mask = isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)) + + if not (is_label or is_bounding_boxes_or_mask): + return inpt + + output = inpt[params["valid"]] + + if is_label: + return output + else: + return tv_tensors.wrap(output, like=inpt) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_temporal.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_temporal.py new file mode 100644 index 0000000000000000000000000000000000000000..687b50188a8d2eb7b03c8f04863652a1998974f3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_temporal.py @@ -0,0 +1,26 @@ +from typing import Any, Dict + +import torch +from torchvision.transforms.v2 import functional as F, Transform + + +class UniformTemporalSubsample(Transform): + """Uniformly subsample ``num_samples`` indices from the temporal dimension of the video. + + Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` denotes the temporal dimension. + + When ``num_samples`` is larger than the size of temporal dimension of the video, it + will sample frames based on nearest neighbor interpolation. + + Args: + num_samples (int): The number of equispaced samples to be selected + """ + + _transformed_types = (torch.Tensor,) + + def __init__(self, num_samples: int): + super().__init__() + self.num_samples = num_samples + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + return self._call_kernel(F.uniform_temporal_subsample, inpt, self.num_samples) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_transform.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..5f27458970990c86a8ee422de8e194675d7c4eb7 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_transform.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +import enum +from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union + +import PIL.Image +import torch +from torch import nn +from torch.utils._pytree import tree_flatten, tree_unflatten +from torchvision import tv_tensors +from torchvision.transforms.v2._utils import check_type, has_any, is_pure_tensor +from torchvision.utils import _log_api_usage_once + +from .functional._utils import _get_kernel + + +class Transform(nn.Module): + """Base class to implement your own v2 transforms. + + See :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py` for + more details. + """ + + # Class attribute defining transformed types. Other types are passed-through without any transformation + # We support both Types and callables that are able to do further checks on the type of the input. + _transformed_types: Tuple[Union[Type, Callable[[Any], bool]], ...] = (torch.Tensor, PIL.Image.Image) + + def __init__(self) -> None: + super().__init__() + _log_api_usage_once(self) + + def check_inputs(self, flat_inputs: List[Any]) -> None: + pass + + # When v2 was introduced, this method was private and called + # `_get_params()`. Now it's publicly exposed as `make_params()`. It cannot + # be exposed as `get_params()` because there is already a `get_params()` + # methods for v2 transforms: it's the v1's `get_params()` that we have to + # keep in order to guarantee 100% BC with v1. (It's defined in + # __init_subclass__ below). + def make_params(self, flat_inputs: List[Any]) -> Dict[str, Any]: + """Method to override for custom transforms. + + See :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`""" + return dict() + + def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any: + kernel = _get_kernel(functional, type(inpt), allow_passthrough=True) + return kernel(inpt, *args, **kwargs) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> Any: + """Method to override for custom transforms. + + See :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`""" + raise NotImplementedError + + def forward(self, *inputs: Any) -> Any: + """Do not override this! Use ``transform()`` instead.""" + flat_inputs, spec = tree_flatten(inputs if len(inputs) > 1 else inputs[0]) + + self.check_inputs(flat_inputs) + + needs_transform_list = self._needs_transform_list(flat_inputs) + params = self.make_params( + [inpt for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) if needs_transform] + ) + + flat_outputs = [ + self.transform(inpt, params) if needs_transform else inpt + for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) + ] + + return tree_unflatten(flat_outputs, spec) + + def _needs_transform_list(self, flat_inputs: List[Any]) -> List[bool]: + # Below is a heuristic on how to deal with pure tensor inputs: + # 1. Pure tensors, i.e. tensors that are not a tv_tensor, are passed through if there is an explicit image + # (`tv_tensors.Image` or `PIL.Image.Image`) or video (`tv_tensors.Video`) in the sample. + # 2. If there is no explicit image or video in the sample, only the first encountered pure tensor is + # transformed as image, while the rest is passed through. The order is defined by the returned `flat_inputs` + # of `tree_flatten`, which recurses depth-first through the input. + # + # This heuristic stems from two requirements: + # 1. We need to keep BC for single input pure tensors and treat them as images. + # 2. We don't want to treat all pure tensors as images, because some datasets like `CelebA` or `Widerface` + # return supplemental numerical data as tensors that cannot be transformed as images. + # + # The heuristic should work well for most people in practice. The only case where it doesn't is if someone + # tries to transform multiple pure tensors at the same time, expecting them all to be treated as images. + # However, this case wasn't supported by transforms v1 either, so there is no BC concern. + + needs_transform_list = [] + transform_pure_tensor = not has_any(flat_inputs, tv_tensors.Image, tv_tensors.Video, PIL.Image.Image) + for inpt in flat_inputs: + needs_transform = True + + if not check_type(inpt, self._transformed_types): + needs_transform = False + elif is_pure_tensor(inpt): + if transform_pure_tensor: + transform_pure_tensor = False + else: + needs_transform = False + needs_transform_list.append(needs_transform) + return needs_transform_list + + def extra_repr(self) -> str: + extra = [] + for name, value in self.__dict__.items(): + if name.startswith("_") or name == "training": + continue + + if not isinstance(value, (bool, int, float, str, tuple, list, enum.Enum)): + continue + + extra.append(f"{name}={value}") + + return ", ".join(extra) + + # This attribute should be set on all transforms that have a v1 equivalent. Doing so enables two things: + # 1. In case the v1 transform has a static `get_params` method, it will also be available under the same name on + # the v2 transform. See `__init_subclass__` for details. + # 2. The v2 transform will be JIT scriptable. See `_extract_params_for_v1_transform` and `__prepare_scriptable__` + # for details. + _v1_transform_cls: Optional[Type[nn.Module]] = None + + def __init_subclass__(cls) -> None: + # Since `get_params` is a `@staticmethod`, we have to bind it to the class itself rather than to an instance. + # This method is called after subclassing has happened, i.e. `cls` is the subclass, e.g. `Resize`. + if cls._v1_transform_cls is not None and hasattr(cls._v1_transform_cls, "get_params"): + cls.get_params = staticmethod(cls._v1_transform_cls.get_params) # type: ignore[attr-defined] + + def _extract_params_for_v1_transform(self) -> Dict[str, Any]: + # This method is called by `__prepare_scriptable__` to instantiate the equivalent v1 transform from the current + # v2 transform instance. It extracts all available public attributes that are specific to that transform and + # not `nn.Module` in general. + # Overwrite this method on the v2 transform class if the above is not sufficient. For example, this might happen + # if the v2 transform introduced new parameters that are not support by the v1 transform. + common_attrs = nn.Module().__dict__.keys() + return { + attr: value + for attr, value in self.__dict__.items() + if not attr.startswith("_") and attr not in common_attrs + } + + def __prepare_scriptable__(self) -> nn.Module: + # This method is called early on when `torch.jit.script`'ing an `nn.Module` instance. If it succeeds, the return + # value is used for scripting over the original object that should have been scripted. Since the v1 transforms + # are JIT scriptable, and we made sure that for single image inputs v1 and v2 are equivalent, we just return the + # equivalent v1 transform here. This of course only makes transforms v2 JIT scriptable as long as transforms v1 + # is around. + if self._v1_transform_cls is None: + raise RuntimeError( + f"Transform {type(self).__name__} cannot be JIT scripted. " + "torchscript is only supported for backward compatibility with transforms " + "which are already in torchvision.transforms. " + "For torchscript support (on tensors only), you can use the functional API instead." + ) + + return self._v1_transform_cls(**self._extract_params_for_v1_transform()) + + +class _RandomApplyTransform(Transform): + def __init__(self, p: float = 0.5) -> None: + if not (0.0 <= p <= 1.0): + raise ValueError("`p` should be a floating point value in the interval [0.0, 1.0].") + + super().__init__() + self.p = p + + def forward(self, *inputs: Any) -> Any: + # We need to almost duplicate `Transform.forward()` here since we always want to check the inputs, but return + # early afterwards in case the random check triggers. The same result could be achieved by calling + # `super().forward()` after the random check, but that would call `self.check_inputs` twice. + + inputs = inputs if len(inputs) > 1 else inputs[0] + flat_inputs, spec = tree_flatten(inputs) + + self.check_inputs(flat_inputs) + + if torch.rand(1) >= self.p: + return inputs + + needs_transform_list = self._needs_transform_list(flat_inputs) + params = self.make_params( + [inpt for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) if needs_transform] + ) + + flat_outputs = [ + self.transform(inpt, params) if needs_transform else inpt + for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) + ] + + return tree_unflatten(flat_outputs, spec) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_type_conversion.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_type_conversion.py new file mode 100644 index 0000000000000000000000000000000000000000..bf9f71852393efe9c83f3b012a54f22568e341c1 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_type_conversion.py @@ -0,0 +1,84 @@ +from typing import Any, Dict, Optional, Union + +import numpy as np +import PIL.Image +import torch + +from torchvision import tv_tensors +from torchvision.transforms.v2 import functional as F, Transform + +from torchvision.transforms.v2._utils import is_pure_tensor + + +class PILToTensor(Transform): + """Convert a PIL Image to a tensor of the same type - this does not scale values. + + This transform does not support torchscript. + + Converts a PIL Image (H x W x C) to a Tensor of shape (C x H x W). + """ + + _transformed_types = (PIL.Image.Image,) + + def transform(self, inpt: PIL.Image.Image, params: Dict[str, Any]) -> torch.Tensor: + return F.pil_to_tensor(inpt) + + +class ToImage(Transform): + """Convert a tensor, ndarray, or PIL Image to :class:`~torchvision.tv_tensors.Image` + ; this does not scale values. + + This transform does not support torchscript. + """ + + _transformed_types = (is_pure_tensor, PIL.Image.Image, np.ndarray) + + def transform( + self, inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray], params: Dict[str, Any] + ) -> tv_tensors.Image: + return F.to_image(inpt) + + +class ToPILImage(Transform): + """Convert a tensor or an ndarray to PIL Image + + This transform does not support torchscript. + + Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape + H x W x C to a PIL Image while adjusting the value range depending on the ``mode``. + + Args: + mode (`PIL.Image mode`_): color space and pixel depth of input data (optional). + If ``mode`` is ``None`` (default) there are some assumptions made about the input data: + + - If the input has 4 channels, the ``mode`` is assumed to be ``RGBA``. + - If the input has 3 channels, the ``mode`` is assumed to be ``RGB``. + - If the input has 2 channels, the ``mode`` is assumed to be ``LA``. + - If the input has 1 channel, the ``mode`` is determined by the data type (i.e ``int``, ``float``, + ``short``). + + .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes + """ + + _transformed_types = (is_pure_tensor, tv_tensors.Image, np.ndarray) + + def __init__(self, mode: Optional[str] = None) -> None: + super().__init__() + self.mode = mode + + def transform( + self, inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray], params: Dict[str, Any] + ) -> PIL.Image.Image: + return F.to_pil_image(inpt, mode=self.mode) + + +class ToPureTensor(Transform): + """Convert all TVTensors to pure tensors, removing associated metadata (if any). + + This doesn't scale or change the values, only the type. + """ + + _transformed_types = (tv_tensors.TVTensor,) + + def transform(self, inpt: Any, params: Dict[str, Any]) -> torch.Tensor: + return inpt.as_subclass(torch.Tensor) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/_utils.py b/lib/python3.11/site-packages/torchvision/transforms/v2/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dd65ca4d9c98b73b8e17eae972e352df08bacbdb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/_utils.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +import collections.abc +import numbers +from contextlib import suppress + +from typing import Any, Callable, Dict, List, Literal, Sequence, Tuple, Type, Union + +import PIL.Image +import torch + +from torchvision import tv_tensors + +from torchvision._utils import sequence_to_str + +from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size # noqa: F401 +from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor +from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT + + +def _setup_number_or_seq(arg: Union[int, float, Sequence[Union[int, float]]], name: str) -> Sequence[float]: + if not isinstance(arg, (int, float, Sequence)): + raise TypeError(f"{name} should be a number or a sequence of numbers. Got {type(arg)}") + if isinstance(arg, Sequence) and len(arg) not in (1, 2): + raise ValueError(f"If {name} is a sequence its length should be 1 or 2. Got {len(arg)}") + if isinstance(arg, Sequence): + for element in arg: + if not isinstance(element, (int, float)): + raise ValueError(f"{name} should be a sequence of numbers. Got {type(element)}") + + if isinstance(arg, (int, float)): + arg = [float(arg), float(arg)] + elif isinstance(arg, Sequence): + if len(arg) == 1: + arg = [float(arg[0]), float(arg[0])] + else: + arg = [float(arg[0]), float(arg[1])] + return arg + + +def _check_fill_arg(fill: Union[_FillType, Dict[Union[Type, str], _FillType]]) -> None: + if isinstance(fill, dict): + for value in fill.values(): + _check_fill_arg(value) + else: + if fill is not None and not isinstance(fill, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate fill arg, only Numbers, tuples, lists and dicts are allowed.") + + +def _convert_fill_arg(fill: _FillType) -> _FillTypeJIT: + # Fill = 0 is not equivalent to None, https://github.com/pytorch/vision/issues/6517 + # So, we can't reassign fill to 0 + # if fill is None: + # fill = 0 + if fill is None: + return fill + + if not isinstance(fill, (int, float)): + fill = [float(v) for v in list(fill)] + return fill # type: ignore[return-value] + + +def _setup_fill_arg(fill: Union[_FillType, Dict[Union[Type, str], _FillType]]) -> Dict[Union[Type, str], _FillTypeJIT]: + _check_fill_arg(fill) + + if isinstance(fill, dict): + for k, v in fill.items(): + fill[k] = _convert_fill_arg(v) + return fill # type: ignore[return-value] + else: + return {"others": _convert_fill_arg(fill)} + + +def _get_fill(fill_dict, inpt_type): + if inpt_type in fill_dict: + return fill_dict[inpt_type] + elif "others" in fill_dict: + return fill_dict["others"] + else: + RuntimeError("This should never happen, please open an issue on the torchvision repo if you hit this.") + + +def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None: + if not isinstance(padding, (numbers.Number, tuple, list)): + raise TypeError("Got inappropriate padding arg") + + if isinstance(padding, (tuple, list)) and len(padding) not in [1, 2, 4]: + raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple") + + +# TODO: let's use torchvision._utils.StrEnum to have the best of both worlds (strings and enums) +# https://github.com/pytorch/vision/issues/6250 +def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect", "symmetric"]) -> None: + if padding_mode not in ["constant", "edge", "reflect", "symmetric"]: + raise ValueError("Padding mode should be either constant, edge, reflect or symmetric") + + +def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor: + """ + This heuristic covers three cases: + + 1. The input is tuple or list whose second item is a labels tensor. This happens for already batched + classification inputs for MixUp and CutMix (typically after the Dataloder). + 2. The input is a tuple or list whose second item is a dictionary that contains the labels tensor + under a label-like (see below) key. This happens for the inputs of detection models. + 3. The input is a dictionary that is structured as the one from 2. + + What is "label-like" key? We first search for an case-insensitive match of 'labels' inside the keys of the + dictionary. This is the name our detection models expect. If we can't find that, we look for a case-insensitive + match of the term 'label' anywhere inside the key, i.e. 'FooLaBeLBar'. If we can't find that either, the dictionary + contains no "label-like" key. + """ + + if isinstance(inputs, (tuple, list)): + inputs = inputs[1] + + # MixUp, CutMix + if is_pure_tensor(inputs): + return inputs + + if not isinstance(inputs, collections.abc.Mapping): + raise ValueError( + f"When using the default labels_getter, the input passed to forward must be a dictionary or a two-tuple " + f"whose second item is a dictionary or a tensor, but got {inputs} instead." + ) + + candidate_key = None + with suppress(StopIteration): + candidate_key = next(key for key in inputs.keys() if key.lower() == "labels") + if candidate_key is None: + with suppress(StopIteration): + candidate_key = next(key for key in inputs.keys() if "label" in key.lower()) + if candidate_key is None: + raise ValueError( + "Could not infer where the labels are in the sample. Try passing a callable as the labels_getter parameter?" + "If there are no labels in the sample by design, pass labels_getter=None." + ) + + return inputs[candidate_key] + + +def _parse_labels_getter(labels_getter: Union[str, Callable[[Any], Any], None]) -> Callable[[Any], Any]: + if labels_getter == "default": + return _find_labels_default_heuristic + elif callable(labels_getter): + return labels_getter + elif labels_getter is None: + return lambda _: None + else: + raise ValueError(f"labels_getter should either be 'default', a callable, or None, but got {labels_getter}.") + + +def get_bounding_boxes(flat_inputs: List[Any]) -> tv_tensors.BoundingBoxes: + """Return the Bounding Boxes in the input. + + Assumes only one ``BoundingBoxes`` object is present. + """ + # This assumes there is only one bbox per sample as per the general convention + try: + return next(inpt for inpt in flat_inputs if isinstance(inpt, tv_tensors.BoundingBoxes)) + except StopIteration: + raise ValueError("No bounding boxes were found in the sample") + + +def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]: + """Return Channel, Height, and Width.""" + chws = { + tuple(get_dimensions(inpt)) + for inpt in flat_inputs + if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video)) + } + if not chws: + raise TypeError("No image or video was found in the sample") + elif len(chws) > 1: + raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}") + c, h, w = chws.pop() + return c, h, w + + +def query_size(flat_inputs: List[Any]) -> Tuple[int, int]: + """Return Height and Width.""" + sizes = { + tuple(get_size(inpt)) + for inpt in flat_inputs + if check_type( + inpt, + ( + is_pure_tensor, + tv_tensors.Image, + PIL.Image.Image, + tv_tensors.Video, + tv_tensors.Mask, + tv_tensors.BoundingBoxes, + ), + ) + } + if not sizes: + raise TypeError("No image, video, mask or bounding box was found in the sample") + elif len(sizes) > 1: + raise ValueError(f"Found multiple HxW dimensions in the sample: {sequence_to_str(sorted(sizes))}") + h, w = sizes.pop() + return h, w + + +def check_type(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool: + for type_or_check in types_or_checks: + if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj): + return True + return False + + +def has_any(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool: + for inpt in flat_inputs: + if check_type(inpt, types_or_checks): + return True + return False + + +def has_all(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool: + for type_or_check in types_or_checks: + for inpt in flat_inputs: + if isinstance(inpt, type_or_check) if isinstance(type_or_check, type) else type_or_check(inpt): + break + else: + return False + return True diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/__init__.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d5705d55c4b2d429e450dc0033c99f7fe16a8ddb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/__init__.py @@ -0,0 +1,153 @@ +from torchvision.transforms import InterpolationMode # usort: skip + +from ._utils import is_pure_tensor, register_kernel # usort: skip + +from ._meta import ( + clamp_bounding_boxes, + convert_bounding_box_format, + get_dimensions_image, + get_dimensions_video, + get_dimensions, + get_num_frames_video, + get_num_frames, + get_image_num_channels, + get_num_channels_image, + get_num_channels_video, + get_num_channels, + get_size_bounding_boxes, + get_size_image, + get_size_mask, + get_size_video, + get_size, +) # usort: skip + +from ._augment import erase, erase_image, erase_video, jpeg, jpeg_image, jpeg_video +from ._color import ( + adjust_brightness, + adjust_brightness_image, + adjust_brightness_video, + adjust_contrast, + adjust_contrast_image, + adjust_contrast_video, + adjust_gamma, + adjust_gamma_image, + adjust_gamma_video, + adjust_hue, + adjust_hue_image, + adjust_hue_video, + adjust_saturation, + adjust_saturation_image, + adjust_saturation_video, + adjust_sharpness, + adjust_sharpness_image, + adjust_sharpness_video, + autocontrast, + autocontrast_image, + autocontrast_video, + equalize, + equalize_image, + equalize_video, + grayscale_to_rgb, + grayscale_to_rgb_image, + invert, + invert_image, + invert_video, + permute_channels, + permute_channels_image, + permute_channels_video, + posterize, + posterize_image, + posterize_video, + rgb_to_grayscale, + rgb_to_grayscale_image, + solarize, + solarize_image, + solarize_video, + to_grayscale, +) +from ._geometry import ( + affine, + affine_bounding_boxes, + affine_image, + affine_mask, + affine_video, + center_crop, + center_crop_bounding_boxes, + center_crop_image, + center_crop_mask, + center_crop_video, + crop, + crop_bounding_boxes, + crop_image, + crop_mask, + crop_video, + elastic, + elastic_bounding_boxes, + elastic_image, + elastic_mask, + elastic_transform, + elastic_video, + five_crop, + five_crop_image, + five_crop_video, + hflip, # TODO: Consider moving all pure alias definitions at the bottom of the file + horizontal_flip, + horizontal_flip_bounding_boxes, + horizontal_flip_image, + horizontal_flip_mask, + horizontal_flip_video, + pad, + pad_bounding_boxes, + pad_image, + pad_mask, + pad_video, + perspective, + perspective_bounding_boxes, + perspective_image, + perspective_mask, + perspective_video, + resize, + resize_bounding_boxes, + resize_image, + resize_mask, + resize_video, + resized_crop, + resized_crop_bounding_boxes, + resized_crop_image, + resized_crop_mask, + resized_crop_video, + rotate, + rotate_bounding_boxes, + rotate_image, + rotate_mask, + rotate_video, + ten_crop, + ten_crop_image, + ten_crop_video, + vertical_flip, + vertical_flip_bounding_boxes, + vertical_flip_image, + vertical_flip_mask, + vertical_flip_video, + vflip, +) +from ._misc import ( + convert_image_dtype, + gaussian_blur, + gaussian_blur_image, + gaussian_blur_video, + gaussian_noise, + gaussian_noise_image, + gaussian_noise_video, + normalize, + normalize_image, + normalize_video, + sanitize_bounding_boxes, + to_dtype, + to_dtype_image, + to_dtype_video, +) +from ._temporal import uniform_temporal_subsample, uniform_temporal_subsample_video +from ._type_conversion import pil_to_tensor, to_image, to_pil_image + +from ._deprecated import get_image_size, to_tensor # usort: skip diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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h: int, + w: int, + v: torch.Tensor, + inplace: bool = False, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomErase` for details.""" + if torch.jit.is_scripting(): + return erase_image(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace) + + _log_api_usage_once(erase) + + kernel = _get_kernel(erase, type(inpt)) + return kernel(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace) + + +@_register_kernel_internal(erase, torch.Tensor) +@_register_kernel_internal(erase, tv_tensors.Image) +def erase_image( + image: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False +) -> torch.Tensor: + if not inplace: + image = image.clone() + + image[..., i : i + h, j : j + w] = v + return image + + +@_register_kernel_internal(erase, PIL.Image.Image) +def _erase_image_pil( + image: PIL.Image.Image, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False +) -> PIL.Image.Image: + t_img = pil_to_tensor(image) + output = erase_image(t_img, i=i, j=j, h=h, w=w, v=v, inplace=inplace) + return to_pil_image(output, mode=image.mode) + + +@_register_kernel_internal(erase, tv_tensors.Video) +def erase_video( + video: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False +) -> torch.Tensor: + return erase_image(video, i=i, j=j, h=h, w=w, v=v, inplace=inplace) + + +def jpeg(image: torch.Tensor, quality: int) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.JPEG` for details.""" + if torch.jit.is_scripting(): + return jpeg_image(image, quality=quality) + + _log_api_usage_once(jpeg) + + kernel = _get_kernel(jpeg, type(image)) + return kernel(image, quality=quality) + + +@_register_kernel_internal(jpeg, torch.Tensor) +@_register_kernel_internal(jpeg, tv_tensors.Image) +def jpeg_image(image: torch.Tensor, quality: int) -> torch.Tensor: + original_shape = image.shape + image = image.view((-1,) + image.shape[-3:]) + + if image.shape[0] == 0: # degenerate + return image.reshape(original_shape).clone() + + images = [] + for i in range(image.shape[0]): + # isinstance checks are needed for torchscript. + encoded_image = encode_jpeg(image[i], quality=quality) + assert isinstance(encoded_image, torch.Tensor) + decoded_image = decode_jpeg(encoded_image) + assert isinstance(decoded_image, torch.Tensor) + images.append(decoded_image) + + images = torch.stack(images, dim=0).view(original_shape) + return images + + +@_register_kernel_internal(jpeg, tv_tensors.Video) +def jpeg_video(video: torch.Tensor, quality: int) -> torch.Tensor: + return jpeg_image(video, quality=quality) + + +@_register_kernel_internal(jpeg, PIL.Image.Image) +def _jpeg_image_pil(image: PIL.Image.Image, quality: int) -> PIL.Image.Image: + raw_jpeg = io.BytesIO() + image.save(raw_jpeg, format="JPEG", quality=quality) + + # we need to copy since PIL.Image.open() will return PIL.JpegImagePlugin.JpegImageFile + # which is a sub-class of PIL.Image.Image. this will fail check_transform() test. + return PIL.Image.open(raw_jpeg).copy() diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_color.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_color.py new file mode 100644 index 0000000000000000000000000000000000000000..eb75f58cb7ac85a9478c26c4e86d9ab73b93d331 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_color.py @@ -0,0 +1,739 @@ +from typing import List + +import PIL.Image +import torch +from torch.nn.functional import conv2d +from torchvision import tv_tensors +from torchvision.transforms import _functional_pil as _FP +from torchvision.transforms._functional_tensor import _max_value + +from torchvision.utils import _log_api_usage_once + +from ._misc import _num_value_bits, to_dtype_image +from ._type_conversion import pil_to_tensor, to_pil_image +from ._utils import _get_kernel, _register_kernel_internal + + +def rgb_to_grayscale(inpt: torch.Tensor, num_output_channels: int = 1) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.Grayscale` for details.""" + if torch.jit.is_scripting(): + return rgb_to_grayscale_image(inpt, num_output_channels=num_output_channels) + + _log_api_usage_once(rgb_to_grayscale) + + kernel = _get_kernel(rgb_to_grayscale, type(inpt)) + return kernel(inpt, num_output_channels=num_output_channels) + + +# `to_grayscale` actually predates `rgb_to_grayscale` in v1, but only handles PIL images. Since `rgb_to_grayscale` is a +# superset in terms of functionality and has the same signature, we alias here to avoid disruption. +to_grayscale = rgb_to_grayscale + + +def _rgb_to_grayscale_image( + image: torch.Tensor, num_output_channels: int = 1, preserve_dtype: bool = True +) -> torch.Tensor: + # TODO: Maybe move the validation that num_output_channels is 1 or 3 to this function instead of callers. + if image.shape[-3] == 1 and num_output_channels == 1: + return image.clone() + if image.shape[-3] == 1 and num_output_channels == 3: + s = [1] * len(image.shape) + s[-3] = 3 + return image.repeat(s) + r, g, b = image.unbind(dim=-3) + l_img = r.mul(0.2989).add_(g, alpha=0.587).add_(b, alpha=0.114) + l_img = l_img.unsqueeze(dim=-3) + if preserve_dtype: + l_img = l_img.to(image.dtype) + if num_output_channels == 3: + l_img = l_img.expand(image.shape) + return l_img + + +@_register_kernel_internal(rgb_to_grayscale, torch.Tensor) +@_register_kernel_internal(rgb_to_grayscale, tv_tensors.Image) +def rgb_to_grayscale_image(image: torch.Tensor, num_output_channels: int = 1) -> torch.Tensor: + if num_output_channels not in (1, 3): + raise ValueError(f"num_output_channels must be 1 or 3, got {num_output_channels}.") + return _rgb_to_grayscale_image(image, num_output_channels=num_output_channels, preserve_dtype=True) + + +@_register_kernel_internal(rgb_to_grayscale, PIL.Image.Image) +def _rgb_to_grayscale_image_pil(image: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Image.Image: + if num_output_channels not in (1, 3): + raise ValueError(f"num_output_channels must be 1 or 3, got {num_output_channels}.") + return _FP.to_grayscale(image, num_output_channels=num_output_channels) + + +def grayscale_to_rgb(inpt: torch.Tensor) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RGB` for details.""" + if torch.jit.is_scripting(): + return grayscale_to_rgb_image(inpt) + + _log_api_usage_once(grayscale_to_rgb) + + kernel = _get_kernel(grayscale_to_rgb, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(grayscale_to_rgb, torch.Tensor) +@_register_kernel_internal(grayscale_to_rgb, tv_tensors.Image) +def grayscale_to_rgb_image(image: torch.Tensor) -> torch.Tensor: + if image.shape[-3] >= 3: + # Image already has RGB channels. We don't need to do anything. + return image + # rgb_to_grayscale can be used to add channels so we reuse that function. + return _rgb_to_grayscale_image(image, num_output_channels=3, preserve_dtype=True) + + +@_register_kernel_internal(grayscale_to_rgb, PIL.Image.Image) +def grayscale_to_rgb_image_pil(image: PIL.Image.Image) -> PIL.Image.Image: + return image.convert(mode="RGB") + + +def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor: + ratio = float(ratio) + fp = image1.is_floating_point() + bound = _max_value(image1.dtype) + output = image1.mul(ratio).add_(image2, alpha=(1.0 - ratio)).clamp_(0, bound) + return output if fp else output.to(image1.dtype) + + +def adjust_brightness(inpt: torch.Tensor, brightness_factor: float) -> torch.Tensor: + """Adjust brightness.""" + + if torch.jit.is_scripting(): + return adjust_brightness_image(inpt, brightness_factor=brightness_factor) + + _log_api_usage_once(adjust_brightness) + + kernel = _get_kernel(adjust_brightness, type(inpt)) + return kernel(inpt, brightness_factor=brightness_factor) + + +@_register_kernel_internal(adjust_brightness, torch.Tensor) +@_register_kernel_internal(adjust_brightness, tv_tensors.Image) +def adjust_brightness_image(image: torch.Tensor, brightness_factor: float) -> torch.Tensor: + if brightness_factor < 0: + raise ValueError(f"brightness_factor ({brightness_factor}) is not non-negative.") + + c = image.shape[-3] + if c not in [1, 3]: + raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}") + + fp = image.is_floating_point() + bound = _max_value(image.dtype) + output = image.mul(brightness_factor).clamp_(0, bound) + return output if fp else output.to(image.dtype) + + +@_register_kernel_internal(adjust_brightness, PIL.Image.Image) +def _adjust_brightness_image_pil(image: PIL.Image.Image, brightness_factor: float) -> PIL.Image.Image: + return _FP.adjust_brightness(image, brightness_factor=brightness_factor) + + +@_register_kernel_internal(adjust_brightness, tv_tensors.Video) +def adjust_brightness_video(video: torch.Tensor, brightness_factor: float) -> torch.Tensor: + return adjust_brightness_image(video, brightness_factor=brightness_factor) + + +def adjust_saturation(inpt: torch.Tensor, saturation_factor: float) -> torch.Tensor: + """Adjust saturation.""" + if torch.jit.is_scripting(): + return adjust_saturation_image(inpt, saturation_factor=saturation_factor) + + _log_api_usage_once(adjust_saturation) + + kernel = _get_kernel(adjust_saturation, type(inpt)) + return kernel(inpt, saturation_factor=saturation_factor) + + +@_register_kernel_internal(adjust_saturation, torch.Tensor) +@_register_kernel_internal(adjust_saturation, tv_tensors.Image) +def adjust_saturation_image(image: torch.Tensor, saturation_factor: float) -> torch.Tensor: + if saturation_factor < 0: + raise ValueError(f"saturation_factor ({saturation_factor}) is not non-negative.") + + c = image.shape[-3] + if c not in [1, 3]: + raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}") + + if c == 1: # Match PIL behaviour + return image + + grayscale_image = _rgb_to_grayscale_image(image, num_output_channels=1, preserve_dtype=False) + if not image.is_floating_point(): + grayscale_image = grayscale_image.floor_() + + return _blend(image, grayscale_image, saturation_factor) + + +_adjust_saturation_image_pil = _register_kernel_internal(adjust_saturation, PIL.Image.Image)(_FP.adjust_saturation) + + +@_register_kernel_internal(adjust_saturation, tv_tensors.Video) +def adjust_saturation_video(video: torch.Tensor, saturation_factor: float) -> torch.Tensor: + return adjust_saturation_image(video, saturation_factor=saturation_factor) + + +def adjust_contrast(inpt: torch.Tensor, contrast_factor: float) -> torch.Tensor: + """See :class:`~torchvision.transforms.RandomAutocontrast`""" + if torch.jit.is_scripting(): + return adjust_contrast_image(inpt, contrast_factor=contrast_factor) + + _log_api_usage_once(adjust_contrast) + + kernel = _get_kernel(adjust_contrast, type(inpt)) + return kernel(inpt, contrast_factor=contrast_factor) + + +@_register_kernel_internal(adjust_contrast, torch.Tensor) +@_register_kernel_internal(adjust_contrast, tv_tensors.Image) +def adjust_contrast_image(image: torch.Tensor, contrast_factor: float) -> torch.Tensor: + if contrast_factor < 0: + raise ValueError(f"contrast_factor ({contrast_factor}) is not non-negative.") + + c = image.shape[-3] + if c not in [1, 3]: + raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}") + fp = image.is_floating_point() + if c == 3: + grayscale_image = _rgb_to_grayscale_image(image, num_output_channels=1, preserve_dtype=False) + if not fp: + grayscale_image = grayscale_image.floor_() + else: + grayscale_image = image if fp else image.to(torch.float32) + mean = torch.mean(grayscale_image, dim=(-3, -2, -1), keepdim=True) + return _blend(image, mean, contrast_factor) + + +_adjust_contrast_image_pil = _register_kernel_internal(adjust_contrast, PIL.Image.Image)(_FP.adjust_contrast) + + +@_register_kernel_internal(adjust_contrast, tv_tensors.Video) +def adjust_contrast_video(video: torch.Tensor, contrast_factor: float) -> torch.Tensor: + return adjust_contrast_image(video, contrast_factor=contrast_factor) + + +def adjust_sharpness(inpt: torch.Tensor, sharpness_factor: float) -> torch.Tensor: + """See :class:`~torchvision.transforms.RandomAdjustSharpness`""" + if torch.jit.is_scripting(): + return adjust_sharpness_image(inpt, sharpness_factor=sharpness_factor) + + _log_api_usage_once(adjust_sharpness) + + kernel = _get_kernel(adjust_sharpness, type(inpt)) + return kernel(inpt, sharpness_factor=sharpness_factor) + + +@_register_kernel_internal(adjust_sharpness, torch.Tensor) +@_register_kernel_internal(adjust_sharpness, tv_tensors.Image) +def adjust_sharpness_image(image: torch.Tensor, sharpness_factor: float) -> torch.Tensor: + num_channels, height, width = image.shape[-3:] + if num_channels not in (1, 3): + raise TypeError(f"Input image tensor can have 1 or 3 channels, but found {num_channels}") + + if sharpness_factor < 0: + raise ValueError(f"sharpness_factor ({sharpness_factor}) is not non-negative.") + + if image.numel() == 0 or height <= 2 or width <= 2: + return image + + bound = _max_value(image.dtype) + fp = image.is_floating_point() + shape = image.shape + + if image.ndim > 4: + image = image.reshape(-1, num_channels, height, width) + needs_unsquash = True + else: + needs_unsquash = False + + # The following is a normalized 3x3 kernel with 1s in the edges and a 5 in the middle. + kernel_dtype = image.dtype if fp else torch.float32 + a, b = 1.0 / 13.0, 5.0 / 13.0 + kernel = torch.tensor([[a, a, a], [a, b, a], [a, a, a]], dtype=kernel_dtype, device=image.device) + kernel = kernel.expand(num_channels, 1, 3, 3) + + # We copy and cast at the same time to avoid modifications on the original data + output = image.to(dtype=kernel_dtype, copy=True) + blurred_degenerate = conv2d(output, kernel, groups=num_channels) + if not fp: + # it is better to round before cast + blurred_degenerate = blurred_degenerate.round_() + + # Create a view on the underlying output while pointing at the same data. We do this to avoid indexing twice. + view = output[..., 1:-1, 1:-1] + + # We speed up blending by minimizing flops and doing in-place. The 2 blend options are mathematically equivalent: + # x+(1-r)*(y-x) = x + (1-r)*y - (1-r)*x = x*r + y*(1-r) + view.add_(blurred_degenerate.sub_(view), alpha=(1.0 - sharpness_factor)) + + # The actual data of output have been modified by the above. We only need to clamp and cast now. + output = output.clamp_(0, bound) + if not fp: + output = output.to(image.dtype) + + if needs_unsquash: + output = output.reshape(shape) + + return output + + +_adjust_sharpness_image_pil = _register_kernel_internal(adjust_sharpness, PIL.Image.Image)(_FP.adjust_sharpness) + + +@_register_kernel_internal(adjust_sharpness, tv_tensors.Video) +def adjust_sharpness_video(video: torch.Tensor, sharpness_factor: float) -> torch.Tensor: + return adjust_sharpness_image(video, sharpness_factor=sharpness_factor) + + +def adjust_hue(inpt: torch.Tensor, hue_factor: float) -> torch.Tensor: + """Adjust hue""" + if torch.jit.is_scripting(): + return adjust_hue_image(inpt, hue_factor=hue_factor) + + _log_api_usage_once(adjust_hue) + + kernel = _get_kernel(adjust_hue, type(inpt)) + return kernel(inpt, hue_factor=hue_factor) + + +def _rgb_to_hsv(image: torch.Tensor) -> torch.Tensor: + r, g, _ = image.unbind(dim=-3) + + # Implementation is based on + # https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/src/libImaging/Convert.c#L330 + minc, maxc = torch.aminmax(image, dim=-3) + + # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN + # from happening in the results, because + # + S channel has division by `maxc`, which is zero only if `maxc = minc` + # + H channel has division by `(maxc - minc)`. + # + # Instead of overwriting NaN afterwards, we just prevent it from occurring so + # we don't need to deal with it in case we save the NaN in a buffer in + # backprop, if it is ever supported, but it doesn't hurt to do so. + eqc = maxc == minc + + channels_range = maxc - minc + # Since `eqc => channels_range = 0`, replacing denominator with 1 when `eqc` is fine. + ones = torch.ones_like(maxc) + s = channels_range / torch.where(eqc, ones, maxc) + # Note that `eqc => maxc = minc = r = g = b`. So the following calculation + # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it + # would not matter what values `rc`, `gc`, and `bc` have here, and thus + # replacing denominator with 1 when `eqc` is fine. + channels_range_divisor = torch.where(eqc, ones, channels_range).unsqueeze_(dim=-3) + rc, gc, bc = ((maxc.unsqueeze(dim=-3) - image) / channels_range_divisor).unbind(dim=-3) + + mask_maxc_neq_r = maxc != r + mask_maxc_eq_g = maxc == g + + hg = rc.add(2.0).sub_(bc).mul_(mask_maxc_eq_g & mask_maxc_neq_r) + hr = bc.sub_(gc).mul_(~mask_maxc_neq_r) + hb = gc.add_(4.0).sub_(rc).mul_(mask_maxc_neq_r.logical_and_(mask_maxc_eq_g.logical_not_())) + + h = hr.add_(hg).add_(hb) + h = h.mul_(1.0 / 6.0).add_(1.0).fmod_(1.0) + return torch.stack((h, s, maxc), dim=-3) + + +def _hsv_to_rgb(img: torch.Tensor) -> torch.Tensor: + h, s, v = img.unbind(dim=-3) + h6 = h.mul(6) + i = torch.floor(h6) + f = h6.sub_(i) + i = i.to(dtype=torch.int32) + + sxf = s * f + one_minus_s = 1.0 - s + q = (1.0 - sxf).mul_(v).clamp_(0.0, 1.0) + t = sxf.add_(one_minus_s).mul_(v).clamp_(0.0, 1.0) + p = one_minus_s.mul_(v).clamp_(0.0, 1.0) + i.remainder_(6) + + vpqt = torch.stack((v, p, q, t), dim=-3) + + # vpqt -> rgb mapping based on i + select = torch.tensor([[0, 2, 1, 1, 3, 0], [3, 0, 0, 2, 1, 1], [1, 1, 3, 0, 0, 2]], dtype=torch.long) + select = select.to(device=img.device, non_blocking=True) + + select = select[:, i] + if select.ndim > 3: + # if input.shape is (B, ..., C, H, W) then + # select.shape is (C, B, ..., H, W) + # thus we move C axis to get (B, ..., C, H, W) + select = select.moveaxis(0, -3) + + return vpqt.gather(-3, select) + + +@_register_kernel_internal(adjust_hue, torch.Tensor) +@_register_kernel_internal(adjust_hue, tv_tensors.Image) +def adjust_hue_image(image: torch.Tensor, hue_factor: float) -> torch.Tensor: + if not (-0.5 <= hue_factor <= 0.5): + raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].") + + c = image.shape[-3] + if c not in [1, 3]: + raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}") + + if c == 1: # Match PIL behaviour + return image + + if image.numel() == 0: + # exit earlier on empty images + return image + + orig_dtype = image.dtype + image = to_dtype_image(image, torch.float32, scale=True) + + image = _rgb_to_hsv(image) + h, s, v = image.unbind(dim=-3) + h.add_(hue_factor).remainder_(1.0) + image = torch.stack((h, s, v), dim=-3) + image_hue_adj = _hsv_to_rgb(image) + + return to_dtype_image(image_hue_adj, orig_dtype, scale=True) + + +_adjust_hue_image_pil = _register_kernel_internal(adjust_hue, PIL.Image.Image)(_FP.adjust_hue) + + +@_register_kernel_internal(adjust_hue, tv_tensors.Video) +def adjust_hue_video(video: torch.Tensor, hue_factor: float) -> torch.Tensor: + return adjust_hue_image(video, hue_factor=hue_factor) + + +def adjust_gamma(inpt: torch.Tensor, gamma: float, gain: float = 1) -> torch.Tensor: + """Adjust gamma.""" + if torch.jit.is_scripting(): + return adjust_gamma_image(inpt, gamma=gamma, gain=gain) + + _log_api_usage_once(adjust_gamma) + + kernel = _get_kernel(adjust_gamma, type(inpt)) + return kernel(inpt, gamma=gamma, gain=gain) + + +@_register_kernel_internal(adjust_gamma, torch.Tensor) +@_register_kernel_internal(adjust_gamma, tv_tensors.Image) +def adjust_gamma_image(image: torch.Tensor, gamma: float, gain: float = 1.0) -> torch.Tensor: + if gamma < 0: + raise ValueError("Gamma should be a non-negative real number") + + # The input image is either assumed to be at [0, 1] scale (if float) or is converted to that scale (if integer). + # Since the gamma is non-negative, the output remains at [0, 1] scale. + if not torch.is_floating_point(image): + output = to_dtype_image(image, torch.float32, scale=True).pow_(gamma) + else: + output = image.pow(gamma) + + if gain != 1.0: + # The clamp operation is needed only if multiplication is performed. It's only when gain != 1, that the scale + # of the output can go beyond [0, 1]. + output = output.mul_(gain).clamp_(0.0, 1.0) + + return to_dtype_image(output, image.dtype, scale=True) + + +_adjust_gamma_image_pil = _register_kernel_internal(adjust_gamma, PIL.Image.Image)(_FP.adjust_gamma) + + +@_register_kernel_internal(adjust_gamma, tv_tensors.Video) +def adjust_gamma_video(video: torch.Tensor, gamma: float, gain: float = 1) -> torch.Tensor: + return adjust_gamma_image(video, gamma=gamma, gain=gain) + + +def posterize(inpt: torch.Tensor, bits: int) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomPosterize` for details.""" + if torch.jit.is_scripting(): + return posterize_image(inpt, bits=bits) + + _log_api_usage_once(posterize) + + kernel = _get_kernel(posterize, type(inpt)) + return kernel(inpt, bits=bits) + + +@_register_kernel_internal(posterize, torch.Tensor) +@_register_kernel_internal(posterize, tv_tensors.Image) +def posterize_image(image: torch.Tensor, bits: int) -> torch.Tensor: + if image.is_floating_point(): + levels = 1 << bits + return image.mul(levels).floor_().clamp_(0, levels - 1).mul_(1.0 / levels) + else: + num_value_bits = _num_value_bits(image.dtype) + if bits >= num_value_bits: + return image + + mask = ((1 << bits) - 1) << (num_value_bits - bits) + return image & mask + + +_posterize_image_pil = _register_kernel_internal(posterize, PIL.Image.Image)(_FP.posterize) + + +@_register_kernel_internal(posterize, tv_tensors.Video) +def posterize_video(video: torch.Tensor, bits: int) -> torch.Tensor: + return posterize_image(video, bits=bits) + + +def solarize(inpt: torch.Tensor, threshold: float) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomSolarize` for details.""" + if torch.jit.is_scripting(): + return solarize_image(inpt, threshold=threshold) + + _log_api_usage_once(solarize) + + kernel = _get_kernel(solarize, type(inpt)) + return kernel(inpt, threshold=threshold) + + +@_register_kernel_internal(solarize, torch.Tensor) +@_register_kernel_internal(solarize, tv_tensors.Image) +def solarize_image(image: torch.Tensor, threshold: float) -> torch.Tensor: + if threshold > _max_value(image.dtype): + raise TypeError(f"Threshold should be less or equal the maximum value of the dtype, but got {threshold}") + + return torch.where(image >= threshold, invert_image(image), image) + + +_solarize_image_pil = _register_kernel_internal(solarize, PIL.Image.Image)(_FP.solarize) + + +@_register_kernel_internal(solarize, tv_tensors.Video) +def solarize_video(video: torch.Tensor, threshold: float) -> torch.Tensor: + return solarize_image(video, threshold=threshold) + + +def autocontrast(inpt: torch.Tensor) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomAutocontrast` for details.""" + if torch.jit.is_scripting(): + return autocontrast_image(inpt) + + _log_api_usage_once(autocontrast) + + kernel = _get_kernel(autocontrast, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(autocontrast, torch.Tensor) +@_register_kernel_internal(autocontrast, tv_tensors.Image) +def autocontrast_image(image: torch.Tensor) -> torch.Tensor: + c = image.shape[-3] + if c not in [1, 3]: + raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}") + + if image.numel() == 0: + # exit earlier on empty images + return image + + bound = _max_value(image.dtype) + fp = image.is_floating_point() + float_image = image if fp else image.to(torch.float32) + + minimum = float_image.amin(dim=(-2, -1), keepdim=True) + maximum = float_image.amax(dim=(-2, -1), keepdim=True) + + eq_idxs = maximum == minimum + inv_scale = maximum.sub_(minimum).mul_(1.0 / bound) + minimum[eq_idxs] = 0.0 + inv_scale[eq_idxs] = 1.0 + + if fp: + diff = float_image.sub(minimum) + else: + diff = float_image.sub_(minimum) + + return diff.div_(inv_scale).clamp_(0, bound).to(image.dtype) + + +_autocontrast_image_pil = _register_kernel_internal(autocontrast, PIL.Image.Image)(_FP.autocontrast) + + +@_register_kernel_internal(autocontrast, tv_tensors.Video) +def autocontrast_video(video: torch.Tensor) -> torch.Tensor: + return autocontrast_image(video) + + +def equalize(inpt: torch.Tensor) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomEqualize` for details.""" + if torch.jit.is_scripting(): + return equalize_image(inpt) + + _log_api_usage_once(equalize) + + kernel = _get_kernel(equalize, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(equalize, torch.Tensor) +@_register_kernel_internal(equalize, tv_tensors.Image) +def equalize_image(image: torch.Tensor) -> torch.Tensor: + if image.numel() == 0: + return image + + # 1. The algorithm below can easily be extended to support arbitrary integer dtypes. However, the histogram that + # would be needed to computed will have at least `torch.iinfo(dtype).max + 1` values. That is perfectly fine for + # `torch.int8`, `torch.uint8`, and `torch.int16`, at least questionable for `torch.int32` and completely + # unfeasible for `torch.int64`. + # 2. Floating point inputs need to be binned for this algorithm. Apart from converting them to an integer dtype, we + # could also use PyTorch's builtin histogram functionality. However, that has its own set of issues: in addition + # to being slow in general, PyTorch's implementation also doesn't support batches. In total, that makes it slower + # and more complicated to implement than a simple conversion and a fast histogram implementation for integers. + # Since we need to convert in most cases anyway and out of the acceptable dtypes mentioned in 1. `torch.uint8` is + # by far the most common, we choose it as base. + output_dtype = image.dtype + image = to_dtype_image(image, torch.uint8, scale=True) + + # The histogram is computed by using the flattened image as index. For example, a pixel value of 127 in the image + # corresponds to adding 1 to index 127 in the histogram. + batch_shape = image.shape[:-2] + flat_image = image.flatten(start_dim=-2).to(torch.long) + hist = flat_image.new_zeros(batch_shape + (256,), dtype=torch.int32) + hist.scatter_add_(dim=-1, index=flat_image, src=hist.new_ones(1).expand_as(flat_image)) + cum_hist = hist.cumsum(dim=-1) + + # The simplest form of lookup-table (LUT) that also achieves histogram equalization is + # `lut = cum_hist / flat_image.shape[-1] * 255` + # However, PIL uses a more elaborate scheme: + # https://github.com/python-pillow/Pillow/blob/eb59cb61d5239ee69cbbf12709a0c6fd7314e6d7/src/PIL/ImageOps.py#L368-L385 + # `lut = ((cum_hist + num_non_max_pixels // (2 * 255)) // num_non_max_pixels) * 255` + + # The last non-zero element in the histogram is the first element in the cumulative histogram with the maximum + # value. Thus, the "max" in `num_non_max_pixels` does not refer to 255 as the maximum value of uint8 images, but + # rather the maximum value in the image, which might be or not be 255. + index = cum_hist.argmax(dim=-1) + num_non_max_pixels = flat_image.shape[-1] - hist.gather(dim=-1, index=index.unsqueeze_(-1)) + + # This is performance optimization that saves us one multiplication later. With this, the LUT computation simplifies + # to `lut = (cum_hist + step // 2) // step` and thus saving the final multiplication by 255 while keeping the + # division count the same. PIL uses the variable name `step` for this, so we keep that for easier comparison. + step = num_non_max_pixels.div_(255, rounding_mode="floor") + + # Although it looks like we could return early if we find `step == 0` like PIL does, that is unfortunately not as + # easy due to our support for batched images. We can only return early if `(step == 0).all()` holds. If it doesn't, + # we have to go through the computation below anyway. Since `step == 0` is an edge case anyway, it makes no sense to + # pay the runtime cost for checking it every time. + valid_equalization = step.ne(0).unsqueeze_(-1) + + # `lut[k]` is computed with `cum_hist[k-1]` with `lut[0] == (step // 2) // step == 0`. Thus, we perform the + # computation only for `lut[1:]` with `cum_hist[:-1]` and add `lut[0] == 0` afterwards. + cum_hist = cum_hist[..., :-1] + ( + cum_hist.add_(step // 2) + # We need the `clamp_`(min=1) call here to avoid zero division since they fail for integer dtypes. This has no + # effect on the returned result of this kernel since images inside the batch with `step == 0` are returned as is + # instead of equalized version. + .div_(step.clamp_(min=1), rounding_mode="floor") + # We need the `clamp_` call here since PILs LUT computation scheme can produce values outside the valid value + # range of uint8 images + .clamp_(0, 255) + ) + lut = cum_hist.to(torch.uint8) + lut = torch.cat([lut.new_zeros(1).expand(batch_shape + (1,)), lut], dim=-1) + equalized_image = lut.gather(dim=-1, index=flat_image).view_as(image) + + output = torch.where(valid_equalization, equalized_image, image) + return to_dtype_image(output, output_dtype, scale=True) + + +_equalize_image_pil = _register_kernel_internal(equalize, PIL.Image.Image)(_FP.equalize) + + +@_register_kernel_internal(equalize, tv_tensors.Video) +def equalize_video(video: torch.Tensor) -> torch.Tensor: + return equalize_image(video) + + +def invert(inpt: torch.Tensor) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.RandomInvert`.""" + if torch.jit.is_scripting(): + return invert_image(inpt) + + _log_api_usage_once(invert) + + kernel = _get_kernel(invert, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(invert, torch.Tensor) +@_register_kernel_internal(invert, tv_tensors.Image) +def invert_image(image: torch.Tensor) -> torch.Tensor: + if image.is_floating_point(): + return 1.0 - image + elif image.dtype == torch.uint8: + return image.bitwise_not() + else: # signed integer dtypes + # We can't use `Tensor.bitwise_not` here, since we want to retain the leading zero bit that encodes the sign + return image.bitwise_xor((1 << _num_value_bits(image.dtype)) - 1) + + +_invert_image_pil = _register_kernel_internal(invert, PIL.Image.Image)(_FP.invert) + + +@_register_kernel_internal(invert, tv_tensors.Video) +def invert_video(video: torch.Tensor) -> torch.Tensor: + return invert_image(video) + + +def permute_channels(inpt: torch.Tensor, permutation: List[int]) -> torch.Tensor: + """Permute the channels of the input according to the given permutation. + + This function supports plain :class:`~torch.Tensor`'s, :class:`PIL.Image.Image`'s, and + :class:`torchvision.tv_tensors.Image` and :class:`torchvision.tv_tensors.Video`. + + Example: + >>> rgb_image = torch.rand(3, 256, 256) + >>> bgr_image = F.permute_channels(rgb_image, permutation=[2, 1, 0]) + + Args: + permutation (List[int]): Valid permutation of the input channel indices. The index of the element determines the + channel index in the input and the value determines the channel index in the output. For example, + ``permutation=[2, 0 , 1]`` + + - takes ``ìnpt[..., 0, :, :]`` and puts it at ``output[..., 2, :, :]``, + - takes ``ìnpt[..., 1, :, :]`` and puts it at ``output[..., 0, :, :]``, and + - takes ``ìnpt[..., 2, :, :]`` and puts it at ``output[..., 1, :, :]``. + + Raises: + ValueError: If ``len(permutation)`` doesn't match the number of channels in the input. + """ + if torch.jit.is_scripting(): + return permute_channels_image(inpt, permutation=permutation) + + _log_api_usage_once(permute_channels) + + kernel = _get_kernel(permute_channels, type(inpt)) + return kernel(inpt, permutation=permutation) + + +@_register_kernel_internal(permute_channels, torch.Tensor) +@_register_kernel_internal(permute_channels, tv_tensors.Image) +def permute_channels_image(image: torch.Tensor, permutation: List[int]) -> torch.Tensor: + shape = image.shape + num_channels, height, width = shape[-3:] + + if len(permutation) != num_channels: + raise ValueError( + f"Length of permutation does not match number of channels: " f"{len(permutation)} != {num_channels}" + ) + + if image.numel() == 0: + return image + + image = image.reshape(-1, num_channels, height, width) + image = image[:, permutation, :, :] + return image.reshape(shape) + + +@_register_kernel_internal(permute_channels, PIL.Image.Image) +def _permute_channels_image_pil(image: PIL.Image.Image, permutation: List[int]) -> PIL.Image.Image: + return to_pil_image(permute_channels_image(pil_to_tensor(image), permutation=permutation)) + + +@_register_kernel_internal(permute_channels, tv_tensors.Video) +def permute_channels_video(video: torch.Tensor, permutation: List[int]) -> torch.Tensor: + return permute_channels_image(video, permutation=permutation) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_deprecated.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..116ea31587a1d7f7172267898152e0167531f303 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_deprecated.py @@ -0,0 +1,24 @@ +import warnings +from typing import Any, List + +import torch + +from torchvision.transforms import functional as _F + + +@torch.jit.unused +def to_tensor(inpt: Any) -> torch.Tensor: + """[DEPREACTED] Use to_image() and to_dtype() instead.""" + warnings.warn( + "The function `to_tensor(...)` is deprecated and will be removed in a future release. " + "Instead, please use `to_image(...)` followed by `to_dtype(..., dtype=torch.float32, scale=True)`." + ) + return _F.to_tensor(inpt) + + +def get_image_size(inpt: torch.Tensor) -> List[int]: + warnings.warn( + "The function `get_image_size(...)` is deprecated and will be removed in a future release. " + "Instead, please use `get_size(...)` which returns `[h, w]` instead of `[w, h]`." + ) + return _F.get_image_size(inpt) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..da080e437c99e0b173c496a9097bbae6ae020d7c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py @@ -0,0 +1,2377 @@ +import math +import numbers +import warnings +from typing import Any, List, Optional, Sequence, Tuple, Union + +import PIL.Image +import torch +from torch.nn.functional import grid_sample, interpolate, pad as torch_pad + +from torchvision import tv_tensors +from torchvision.transforms import _functional_pil as _FP +from torchvision.transforms._functional_tensor import _pad_symmetric +from torchvision.transforms.functional import ( + _compute_resized_output_size as __compute_resized_output_size, + _get_perspective_coeffs, + _interpolation_modes_from_int, + InterpolationMode, + pil_modes_mapping, + pil_to_tensor, + to_pil_image, +) + +from torchvision.utils import _log_api_usage_once + +from ._meta import _get_size_image_pil, clamp_bounding_boxes, convert_bounding_box_format + +from ._utils import _FillTypeJIT, _get_kernel, _register_five_ten_crop_kernel_internal, _register_kernel_internal + + +def _check_interpolation(interpolation: Union[InterpolationMode, int]) -> InterpolationMode: + if isinstance(interpolation, int): + interpolation = _interpolation_modes_from_int(interpolation) + elif not isinstance(interpolation, InterpolationMode): + raise ValueError( + f"Argument interpolation should be an `InterpolationMode` or a corresponding Pillow integer constant, " + f"but got {interpolation}." + ) + return interpolation + + +def horizontal_flip(inpt: torch.Tensor) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomHorizontalFlip` for details.""" + if torch.jit.is_scripting(): + return horizontal_flip_image(inpt) + + _log_api_usage_once(horizontal_flip) + + kernel = _get_kernel(horizontal_flip, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(horizontal_flip, torch.Tensor) +@_register_kernel_internal(horizontal_flip, tv_tensors.Image) +def horizontal_flip_image(image: torch.Tensor) -> torch.Tensor: + return image.flip(-1) + + +@_register_kernel_internal(horizontal_flip, PIL.Image.Image) +def _horizontal_flip_image_pil(image: PIL.Image.Image) -> PIL.Image.Image: + return _FP.hflip(image) + + +@_register_kernel_internal(horizontal_flip, tv_tensors.Mask) +def horizontal_flip_mask(mask: torch.Tensor) -> torch.Tensor: + return horizontal_flip_image(mask) + + +def horizontal_flip_bounding_boxes( + bounding_boxes: torch.Tensor, format: tv_tensors.BoundingBoxFormat, canvas_size: Tuple[int, int] +) -> torch.Tensor: + shape = bounding_boxes.shape + + bounding_boxes = bounding_boxes.clone().reshape(-1, 4) + + if format == tv_tensors.BoundingBoxFormat.XYXY: + bounding_boxes[:, [2, 0]] = bounding_boxes[:, [0, 2]].sub_(canvas_size[1]).neg_() + elif format == tv_tensors.BoundingBoxFormat.XYWH: + bounding_boxes[:, 0].add_(bounding_boxes[:, 2]).sub_(canvas_size[1]).neg_() + else: # format == tv_tensors.BoundingBoxFormat.CXCYWH: + bounding_boxes[:, 0].sub_(canvas_size[1]).neg_() + + return bounding_boxes.reshape(shape) + + +@_register_kernel_internal(horizontal_flip, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _horizontal_flip_bounding_boxes_dispatch(inpt: tv_tensors.BoundingBoxes) -> tv_tensors.BoundingBoxes: + output = horizontal_flip_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size + ) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(horizontal_flip, tv_tensors.Video) +def horizontal_flip_video(video: torch.Tensor) -> torch.Tensor: + return horizontal_flip_image(video) + + +def vertical_flip(inpt: torch.Tensor) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomVerticalFlip` for details.""" + if torch.jit.is_scripting(): + return vertical_flip_image(inpt) + + _log_api_usage_once(vertical_flip) + + kernel = _get_kernel(vertical_flip, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(vertical_flip, torch.Tensor) +@_register_kernel_internal(vertical_flip, tv_tensors.Image) +def vertical_flip_image(image: torch.Tensor) -> torch.Tensor: + return image.flip(-2) + + +@_register_kernel_internal(vertical_flip, PIL.Image.Image) +def _vertical_flip_image_pil(image: PIL.Image.Image) -> PIL.Image.Image: + return _FP.vflip(image) + + +@_register_kernel_internal(vertical_flip, tv_tensors.Mask) +def vertical_flip_mask(mask: torch.Tensor) -> torch.Tensor: + return vertical_flip_image(mask) + + +def vertical_flip_bounding_boxes( + bounding_boxes: torch.Tensor, format: tv_tensors.BoundingBoxFormat, canvas_size: Tuple[int, int] +) -> torch.Tensor: + shape = bounding_boxes.shape + + bounding_boxes = bounding_boxes.clone().reshape(-1, 4) + + if format == tv_tensors.BoundingBoxFormat.XYXY: + bounding_boxes[:, [1, 3]] = bounding_boxes[:, [3, 1]].sub_(canvas_size[0]).neg_() + elif format == tv_tensors.BoundingBoxFormat.XYWH: + bounding_boxes[:, 1].add_(bounding_boxes[:, 3]).sub_(canvas_size[0]).neg_() + else: # format == tv_tensors.BoundingBoxFormat.CXCYWH: + bounding_boxes[:, 1].sub_(canvas_size[0]).neg_() + + return bounding_boxes.reshape(shape) + + +@_register_kernel_internal(vertical_flip, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _vertical_flip_bounding_boxes_dispatch(inpt: tv_tensors.BoundingBoxes) -> tv_tensors.BoundingBoxes: + output = vertical_flip_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size + ) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(vertical_flip, tv_tensors.Video) +def vertical_flip_video(video: torch.Tensor) -> torch.Tensor: + return vertical_flip_image(video) + + +# We changed the names to align them with the transforms, i.e. `RandomHorizontalFlip`. Still, `hflip` and `vflip` are +# prevalent and well understood. Thus, we just alias them without deprecating the old names. +hflip = horizontal_flip +vflip = vertical_flip + + +def _compute_resized_output_size( + canvas_size: Tuple[int, int], size: Optional[List[int]], max_size: Optional[int] = None +) -> List[int]: + if isinstance(size, int): + size = [size] + elif max_size is not None and size is not None and len(size) != 1: + raise ValueError( + "max_size should only be passed if size is None or specifies the length of the smaller edge, " + "i.e. size should be an int or a sequence of length 1 in torchscript mode." + ) + return __compute_resized_output_size(canvas_size, size=size, max_size=max_size, allow_size_none=True) + + +def resize( + inpt: torch.Tensor, + size: Optional[List[int]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.Resize` for details.""" + if torch.jit.is_scripting(): + return resize_image(inpt, size=size, interpolation=interpolation, max_size=max_size, antialias=antialias) + + _log_api_usage_once(resize) + + kernel = _get_kernel(resize, type(inpt)) + return kernel(inpt, size=size, interpolation=interpolation, max_size=max_size, antialias=antialias) + + +# This is an internal helper method for resize_image. We should put it here instead of keeping it +# inside resize_image due to torchscript. +# uint8 dtype support for bilinear and bicubic is limited to cpu and +# according to our benchmarks on eager, non-AVX CPUs should still prefer u8->f32->interpolate->u8 path for bilinear +def _do_native_uint8_resize_on_cpu(interpolation: InterpolationMode) -> bool: + if interpolation == InterpolationMode.BILINEAR: + if torch.compiler.is_compiling(): + return True + else: + return "AVX2" in torch.backends.cpu.get_cpu_capability() + + return interpolation == InterpolationMode.BICUBIC + + +@_register_kernel_internal(resize, torch.Tensor) +@_register_kernel_internal(resize, tv_tensors.Image) +def resize_image( + image: torch.Tensor, + size: Optional[List[int]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, +) -> torch.Tensor: + interpolation = _check_interpolation(interpolation) + antialias = False if antialias is None else antialias + align_corners: Optional[bool] = None + if interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC: + align_corners = False + else: + # The default of antialias is True from 0.17, so we don't warn or + # error if other interpolation modes are used. This is documented. + antialias = False + + shape = image.shape + numel = image.numel() + num_channels, old_height, old_width = shape[-3:] + new_height, new_width = _compute_resized_output_size((old_height, old_width), size=size, max_size=max_size) + + if (new_height, new_width) == (old_height, old_width): + return image + elif numel > 0: + dtype = image.dtype + acceptable_dtypes = [torch.float32, torch.float64] + if interpolation == InterpolationMode.NEAREST or interpolation == InterpolationMode.NEAREST_EXACT: + # uint8 dtype can be included for cpu and cuda input if nearest mode + acceptable_dtypes.append(torch.uint8) + elif image.device.type == "cpu": + if _do_native_uint8_resize_on_cpu(interpolation): + acceptable_dtypes.append(torch.uint8) + + image = image.reshape(-1, num_channels, old_height, old_width) + strides = image.stride() + if image.is_contiguous(memory_format=torch.channels_last) and image.shape[0] == 1 and numel != strides[0]: + # There is a weird behaviour in torch core where the output tensor of `interpolate()` can be allocated as + # contiguous even though the input is un-ambiguously channels_last (https://github.com/pytorch/pytorch/issues/68430). + # In particular this happens for the typical torchvision use-case of single CHW images where we fake the batch dim + # to become 1CHW. Below, we restride those tensors to trick torch core into properly allocating the output as + # channels_last, thus preserving the memory format of the input. This is not just for format consistency: + # for uint8 bilinear images, this also avoids an extra copy (re-packing) of the output and saves time. + # TODO: when https://github.com/pytorch/pytorch/issues/68430 is fixed (possibly by https://github.com/pytorch/pytorch/pull/100373), + # we should be able to remove this hack. + new_strides = list(strides) + new_strides[0] = numel + image = image.as_strided((1, num_channels, old_height, old_width), new_strides) + + need_cast = dtype not in acceptable_dtypes + if need_cast: + image = image.to(dtype=torch.float32) + + image = interpolate( + image, + size=[new_height, new_width], + mode=interpolation.value, + align_corners=align_corners, + antialias=antialias, + ) + + if need_cast: + if interpolation == InterpolationMode.BICUBIC and dtype == torch.uint8: + # This path is hit on non-AVX archs, or on GPU. + image = image.clamp_(min=0, max=255) + if dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): + image = image.round_() + image = image.to(dtype=dtype) + + return image.reshape(shape[:-3] + (num_channels, new_height, new_width)) + + +def _resize_image_pil( + image: PIL.Image.Image, + size: Union[Sequence[int], int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, +) -> PIL.Image.Image: + old_height, old_width = image.height, image.width + new_height, new_width = _compute_resized_output_size( + (old_height, old_width), + size=size, # type: ignore[arg-type] + max_size=max_size, + ) + + interpolation = _check_interpolation(interpolation) + + if (new_height, new_width) == (old_height, old_width): + return image + + return image.resize((new_width, new_height), resample=pil_modes_mapping[interpolation]) + + +@_register_kernel_internal(resize, PIL.Image.Image) +def __resize_image_pil_dispatch( + image: PIL.Image.Image, + size: Union[Sequence[int], int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, +) -> PIL.Image.Image: + if antialias is False: + warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.") + return _resize_image_pil(image, size=size, interpolation=interpolation, max_size=max_size) + + +def resize_mask(mask: torch.Tensor, size: Optional[List[int]], max_size: Optional[int] = None) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = resize_image(mask, size=size, interpolation=InterpolationMode.NEAREST, max_size=max_size) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(resize, tv_tensors.Mask, tv_tensor_wrapper=False) +def _resize_mask_dispatch( + inpt: tv_tensors.Mask, size: List[int], max_size: Optional[int] = None, **kwargs: Any +) -> tv_tensors.Mask: + output = resize_mask(inpt.as_subclass(torch.Tensor), size, max_size=max_size) + return tv_tensors.wrap(output, like=inpt) + + +def resize_bounding_boxes( + bounding_boxes: torch.Tensor, + canvas_size: Tuple[int, int], + size: Optional[List[int]], + max_size: Optional[int] = None, +) -> Tuple[torch.Tensor, Tuple[int, int]]: + old_height, old_width = canvas_size + new_height, new_width = _compute_resized_output_size(canvas_size, size=size, max_size=max_size) + + if (new_height, new_width) == (old_height, old_width): + return bounding_boxes, canvas_size + + w_ratio = new_width / old_width + h_ratio = new_height / old_height + ratios = torch.tensor([w_ratio, h_ratio, w_ratio, h_ratio], device=bounding_boxes.device) + return ( + bounding_boxes.mul(ratios).to(bounding_boxes.dtype), + (new_height, new_width), + ) + + +@_register_kernel_internal(resize, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _resize_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, size: Optional[List[int]], max_size: Optional[int] = None, **kwargs: Any +) -> tv_tensors.BoundingBoxes: + output, canvas_size = resize_bounding_boxes( + inpt.as_subclass(torch.Tensor), inpt.canvas_size, size, max_size=max_size + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +@_register_kernel_internal(resize, tv_tensors.Video) +def resize_video( + video: torch.Tensor, + size: Optional[List[int]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + max_size: Optional[int] = None, + antialias: Optional[bool] = True, +) -> torch.Tensor: + return resize_image(video, size=size, interpolation=interpolation, max_size=max_size, antialias=antialias) + + +def affine( + inpt: torch.Tensor, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomAffine` for details.""" + if torch.jit.is_scripting(): + return affine_image( + inpt, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + interpolation=interpolation, + fill=fill, + center=center, + ) + + _log_api_usage_once(affine) + + kernel = _get_kernel(affine, type(inpt)) + return kernel( + inpt, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + interpolation=interpolation, + fill=fill, + center=center, + ) + + +def _affine_parse_args( + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + interpolation: InterpolationMode = InterpolationMode.NEAREST, + center: Optional[List[float]] = None, +) -> Tuple[float, List[float], List[float], Optional[List[float]]]: + if not isinstance(angle, (int, float)): + raise TypeError("Argument angle should be int or float") + + if not isinstance(translate, (list, tuple)): + raise TypeError("Argument translate should be a sequence") + + if len(translate) != 2: + raise ValueError("Argument translate should be a sequence of length 2") + + if scale <= 0.0: + raise ValueError("Argument scale should be positive") + + if not isinstance(shear, (numbers.Number, (list, tuple))): + raise TypeError("Shear should be either a single value or a sequence of two values") + + if not isinstance(interpolation, InterpolationMode): + raise TypeError("Argument interpolation should be a InterpolationMode") + + if isinstance(angle, int): + angle = float(angle) + + if isinstance(translate, tuple): + translate = list(translate) + + if isinstance(shear, numbers.Number): + shear = [shear, 0.0] + + if isinstance(shear, tuple): + shear = list(shear) + + if len(shear) == 1: + shear = [shear[0], shear[0]] + + if len(shear) != 2: + raise ValueError(f"Shear should be a sequence containing two values. Got {shear}") + + if center is not None: + if not isinstance(center, (list, tuple)): + raise TypeError("Argument center should be a sequence") + else: + center = [float(c) for c in center] + + return angle, translate, shear, center + + +def _get_inverse_affine_matrix( + center: List[float], angle: float, translate: List[float], scale: float, shear: List[float], inverted: bool = True +) -> List[float]: + # Helper method to compute inverse matrix for affine transformation + + # Pillow requires inverse affine transformation matrix: + # Affine matrix is : M = T * C * RotateScaleShear * C^-1 + # + # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1] + # C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1] + # RotateScaleShear is rotation with scale and shear matrix + # + # RotateScaleShear(a, s, (sx, sy)) = + # = R(a) * S(s) * SHy(sy) * SHx(sx) + # = [ s*cos(a - sy)/cos(sy), s*(-cos(a - sy)*tan(sx)/cos(sy) - sin(a)), 0 ] + # [ s*sin(a - sy)/cos(sy), s*(-sin(a - sy)*tan(sx)/cos(sy) + cos(a)), 0 ] + # [ 0 , 0 , 1 ] + # where R is a rotation matrix, S is a scaling matrix, and SHx and SHy are the shears: + # SHx(s) = [1, -tan(s)] and SHy(s) = [1 , 0] + # [0, 1 ] [-tan(s), 1] + # + # Thus, the inverse is M^-1 = C * RotateScaleShear^-1 * C^-1 * T^-1 + + rot = math.radians(angle) + sx = math.radians(shear[0]) + sy = math.radians(shear[1]) + + cx, cy = center + tx, ty = translate + + # Cached results + cos_sy = math.cos(sy) + tan_sx = math.tan(sx) + rot_minus_sy = rot - sy + cx_plus_tx = cx + tx + cy_plus_ty = cy + ty + + # Rotate Scale Shear (RSS) without scaling + a = math.cos(rot_minus_sy) / cos_sy + b = -(a * tan_sx + math.sin(rot)) + c = math.sin(rot_minus_sy) / cos_sy + d = math.cos(rot) - c * tan_sx + + if inverted: + # Inverted rotation matrix with scale and shear + # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1 + matrix = [d / scale, -b / scale, 0.0, -c / scale, a / scale, 0.0] + # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1 + # and then apply center translation: C * RSS^-1 * C^-1 * T^-1 + matrix[2] += cx - matrix[0] * cx_plus_tx - matrix[1] * cy_plus_ty + matrix[5] += cy - matrix[3] * cx_plus_tx - matrix[4] * cy_plus_ty + else: + matrix = [a * scale, b * scale, 0.0, c * scale, d * scale, 0.0] + # Apply inverse of center translation: RSS * C^-1 + # and then apply translation and center : T * C * RSS * C^-1 + matrix[2] += cx_plus_tx - matrix[0] * cx - matrix[1] * cy + matrix[5] += cy_plus_ty - matrix[3] * cx - matrix[4] * cy + + return matrix + + +def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]: + if torch.compiler.is_compiling() and not torch.jit.is_scripting(): + return _compute_affine_output_size_python(matrix, w, h) + else: + return _compute_affine_output_size_tensor(matrix, w, h) + + +def _compute_affine_output_size_tensor(matrix: List[float], w: int, h: int) -> Tuple[int, int]: + # Inspired of PIL implementation: + # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054 + + # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points. + # Points are shifted due to affine matrix torch convention about + # the center point. Center is (0, 0) for image center pivot point (w * 0.5, h * 0.5) + half_w = 0.5 * w + half_h = 0.5 * h + pts = torch.tensor( + [ + [-half_w, -half_h, 1.0], + [-half_w, half_h, 1.0], + [half_w, half_h, 1.0], + [half_w, -half_h, 1.0], + ] + ) + theta = torch.tensor(matrix, dtype=torch.float).view(2, 3) + new_pts = torch.matmul(pts, theta.T) + min_vals, max_vals = new_pts.aminmax(dim=0) + + # shift points to [0, w] and [0, h] interval to match PIL results + halfs = torch.tensor((half_w, half_h)) + min_vals.add_(halfs) + max_vals.add_(halfs) + + # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0 + tol = 1e-4 + inv_tol = 1.0 / tol + cmax = max_vals.mul_(inv_tol).trunc_().mul_(tol).ceil_() + cmin = min_vals.mul_(inv_tol).trunc_().mul_(tol).floor_() + size = cmax.sub_(cmin) + return int(size[0]), int(size[1]) # w, h + + +def _compute_affine_output_size_python(matrix: List[float], w: int, h: int) -> Tuple[int, int]: + # Mostly copied from PIL implementation: + # The only difference is with transformed points as input matrix has zero translation part here and + # PIL has a centered translation part. + # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054 + + a, b, c, d, e, f = matrix + xx = [] + yy = [] + + half_w = 0.5 * w + half_h = 0.5 * h + for x, y in ((-half_w, -half_h), (half_w, -half_h), (half_w, half_h), (-half_w, half_h)): + nx = a * x + b * y + c + ny = d * x + e * y + f + xx.append(nx + half_w) + yy.append(ny + half_h) + + nw = math.ceil(max(xx)) - math.floor(min(xx)) + nh = math.ceil(max(yy)) - math.floor(min(yy)) + return int(nw), int(nh) # w, h + + +def _apply_grid_transform(img: torch.Tensor, grid: torch.Tensor, mode: str, fill: _FillTypeJIT) -> torch.Tensor: + input_shape = img.shape + output_height, output_width = grid.shape[1], grid.shape[2] + num_channels, input_height, input_width = input_shape[-3:] + output_shape = input_shape[:-3] + (num_channels, output_height, output_width) + + if img.numel() == 0: + return img.reshape(output_shape) + + img = img.reshape(-1, num_channels, input_height, input_width) + squashed_batch_size = img.shape[0] + + # We are using context knowledge that grid should have float dtype + fp = img.dtype == grid.dtype + float_img = img if fp else img.to(grid.dtype) + + if squashed_batch_size > 1: + # Apply same grid to a batch of images + grid = grid.expand(squashed_batch_size, -1, -1, -1) + + # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice + if fill is not None: + mask = torch.ones( + (squashed_batch_size, 1, input_height, input_width), dtype=float_img.dtype, device=float_img.device + ) + float_img = torch.cat((float_img, mask), dim=1) + + float_img = grid_sample(float_img, grid, mode=mode, padding_mode="zeros", align_corners=False) + + # Fill with required color + if fill is not None: + float_img, mask = torch.tensor_split(float_img, indices=(-1,), dim=-3) + mask = mask.expand_as(float_img) + fill_list = fill if isinstance(fill, (tuple, list)) else [float(fill)] # type: ignore[arg-type] + fill_img = torch.tensor(fill_list, dtype=float_img.dtype, device=float_img.device).view(1, -1, 1, 1) + if mode == "nearest": + float_img = torch.where(mask < 0.5, fill_img.expand_as(float_img), float_img) + else: # 'bilinear' + # The following is mathematically equivalent to: + # img * mask + (1.0 - mask) * fill = img * mask - fill * mask + fill = mask * (img - fill) + fill + float_img = float_img.sub_(fill_img).mul_(mask).add_(fill_img) + + img = float_img.round_().to(img.dtype) if not fp else float_img + + return img.reshape(output_shape) + + +def _assert_grid_transform_inputs( + image: torch.Tensor, + matrix: Optional[List[float]], + interpolation: str, + fill: _FillTypeJIT, + supported_interpolation_modes: List[str], + coeffs: Optional[List[float]] = None, +) -> None: + if matrix is not None: + if not isinstance(matrix, list): + raise TypeError("Argument matrix should be a list") + elif len(matrix) != 6: + raise ValueError("Argument matrix should have 6 float values") + + if coeffs is not None and len(coeffs) != 8: + raise ValueError("Argument coeffs should have 8 float values") + + if fill is not None: + if isinstance(fill, (tuple, list)): + length = len(fill) + num_channels = image.shape[-3] + if length > 1 and length != num_channels: + raise ValueError( + "The number of elements in 'fill' cannot broadcast to match the number of " + f"channels of the image ({length} != {num_channels})" + ) + elif not isinstance(fill, (int, float)): + raise ValueError("Argument fill should be either int, float, tuple or list") + + if interpolation not in supported_interpolation_modes: + raise ValueError(f"Interpolation mode '{interpolation}' is unsupported with Tensor input") + + +def _affine_grid( + theta: torch.Tensor, + w: int, + h: int, + ow: int, + oh: int, +) -> torch.Tensor: + # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/ + # AffineGridGenerator.cpp#L18 + # Difference with AffineGridGenerator is that: + # 1) we normalize grid values after applying theta + # 2) we can normalize by other image size, such that it covers "extend" option like in PIL.Image.rotate + dtype = theta.dtype + device = theta.device + + base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device) + x_grid = torch.linspace((1.0 - ow) * 0.5, (ow - 1.0) * 0.5, steps=ow, device=device) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace((1.0 - oh) * 0.5, (oh - 1.0) * 0.5, steps=oh, device=device).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta = theta.transpose(1, 2).div_(torch.tensor([0.5 * w, 0.5 * h], dtype=dtype, device=device)) + output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta) + return output_grid.view(1, oh, ow, 2) + + +@_register_kernel_internal(affine, torch.Tensor) +@_register_kernel_internal(affine, tv_tensors.Image) +def affine_image( + image: torch.Tensor, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, +) -> torch.Tensor: + interpolation = _check_interpolation(interpolation) + + angle, translate, shear, center = _affine_parse_args(angle, translate, scale, shear, interpolation, center) + + height, width = image.shape[-2:] + + center_f = [0.0, 0.0] + if center is not None: + # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. + center_f = [(c - s * 0.5) for c, s in zip(center, [width, height])] + + translate_f = [float(t) for t in translate] + matrix = _get_inverse_affine_matrix(center_f, angle, translate_f, scale, shear) + + _assert_grid_transform_inputs(image, matrix, interpolation.value, fill, ["nearest", "bilinear"]) + + dtype = image.dtype if torch.is_floating_point(image) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=image.device).reshape(1, 2, 3) + grid = _affine_grid(theta, w=width, h=height, ow=width, oh=height) + return _apply_grid_transform(image, grid, interpolation.value, fill=fill) + + +@_register_kernel_internal(affine, PIL.Image.Image) +def _affine_image_pil( + image: PIL.Image.Image, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, +) -> PIL.Image.Image: + interpolation = _check_interpolation(interpolation) + angle, translate, shear, center = _affine_parse_args(angle, translate, scale, shear, interpolation, center) + + # center = (img_size[0] * 0.5 + 0.5, img_size[1] * 0.5 + 0.5) + # it is visually better to estimate the center without 0.5 offset + # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine + if center is None: + height, width = _get_size_image_pil(image) + center = [width * 0.5, height * 0.5] + matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear) + + return _FP.affine(image, matrix, interpolation=pil_modes_mapping[interpolation], fill=fill) + + +def _affine_bounding_boxes_with_expand( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + center: Optional[List[float]] = None, + expand: bool = False, +) -> Tuple[torch.Tensor, Tuple[int, int]]: + if bounding_boxes.numel() == 0: + return bounding_boxes, canvas_size + + original_shape = bounding_boxes.shape + original_dtype = bounding_boxes.dtype + bounding_boxes = bounding_boxes.clone() if bounding_boxes.is_floating_point() else bounding_boxes.float() + dtype = bounding_boxes.dtype + device = bounding_boxes.device + bounding_boxes = ( + convert_bounding_box_format( + bounding_boxes, old_format=format, new_format=tv_tensors.BoundingBoxFormat.XYXY, inplace=True + ) + ).reshape(-1, 4) + + angle, translate, shear, center = _affine_parse_args( + angle, translate, scale, shear, InterpolationMode.NEAREST, center + ) + + if center is None: + height, width = canvas_size + center = [width * 0.5, height * 0.5] + + affine_vector = _get_inverse_affine_matrix(center, angle, translate, scale, shear, inverted=False) + transposed_affine_matrix = ( + torch.tensor( + affine_vector, + dtype=dtype, + device=device, + ) + .reshape(2, 3) + .T + ) + # 1) Let's transform bboxes into a tensor of 4 points (top-left, top-right, bottom-left, bottom-right corners). + # Tensor of points has shape (N * 4, 3), where N is the number of bboxes + # Single point structure is similar to + # [(xmin, ymin, 1), (xmax, ymin, 1), (xmax, ymax, 1), (xmin, ymax, 1)] + points = bounding_boxes[:, [[0, 1], [2, 1], [2, 3], [0, 3]]].reshape(-1, 2) + points = torch.cat([points, torch.ones(points.shape[0], 1, device=device, dtype=dtype)], dim=-1) + # 2) Now let's transform the points using affine matrix + transformed_points = torch.matmul(points, transposed_affine_matrix) + # 3) Reshape transformed points to [N boxes, 4 points, x/y coords] + # and compute bounding box from 4 transformed points: + transformed_points = transformed_points.reshape(-1, 4, 2) + out_bbox_mins, out_bbox_maxs = torch.aminmax(transformed_points, dim=1) + out_bboxes = torch.cat([out_bbox_mins, out_bbox_maxs], dim=1) + + if expand: + # Compute minimum point for transformed image frame: + # Points are Top-Left, Top-Right, Bottom-Left, Bottom-Right points. + height, width = canvas_size + points = torch.tensor( + [ + [0.0, 0.0, 1.0], + [0.0, float(height), 1.0], + [float(width), float(height), 1.0], + [float(width), 0.0, 1.0], + ], + dtype=dtype, + device=device, + ) + new_points = torch.matmul(points, transposed_affine_matrix) + tr = torch.amin(new_points, dim=0, keepdim=True) + # Translate bounding boxes + out_bboxes.sub_(tr.repeat((1, 2))) + # Estimate meta-data for image with inverted=True + affine_vector = _get_inverse_affine_matrix(center, angle, translate, scale, shear) + new_width, new_height = _compute_affine_output_size(affine_vector, width, height) + canvas_size = (new_height, new_width) + + out_bboxes = clamp_bounding_boxes(out_bboxes, format=tv_tensors.BoundingBoxFormat.XYXY, canvas_size=canvas_size) + out_bboxes = convert_bounding_box_format( + out_bboxes, old_format=tv_tensors.BoundingBoxFormat.XYXY, new_format=format, inplace=True + ).reshape(original_shape) + + out_bboxes = out_bboxes.to(original_dtype) + return out_bboxes, canvas_size + + +def affine_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + center: Optional[List[float]] = None, +) -> torch.Tensor: + out_box, _ = _affine_bounding_boxes_with_expand( + bounding_boxes, + format=format, + canvas_size=canvas_size, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + center=center, + expand=False, + ) + return out_box + + +@_register_kernel_internal(affine, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _affine_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + center: Optional[List[float]] = None, + **kwargs, +) -> tv_tensors.BoundingBoxes: + output = affine_bounding_boxes( + inpt.as_subclass(torch.Tensor), + format=inpt.format, + canvas_size=inpt.canvas_size, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + center=center, + ) + return tv_tensors.wrap(output, like=inpt) + + +def affine_mask( + mask: torch.Tensor, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, +) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = affine_image( + mask, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + interpolation=InterpolationMode.NEAREST, + fill=fill, + center=center, + ) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(affine, tv_tensors.Mask, tv_tensor_wrapper=False) +def _affine_mask_dispatch( + inpt: tv_tensors.Mask, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, + **kwargs, +) -> tv_tensors.Mask: + output = affine_mask( + inpt.as_subclass(torch.Tensor), + angle=angle, + translate=translate, + scale=scale, + shear=shear, + fill=fill, + center=center, + ) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(affine, tv_tensors.Video) +def affine_video( + video: torch.Tensor, + angle: Union[int, float], + translate: List[float], + scale: float, + shear: List[float], + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + fill: _FillTypeJIT = None, + center: Optional[List[float]] = None, +) -> torch.Tensor: + return affine_image( + video, + angle=angle, + translate=translate, + scale=scale, + shear=shear, + interpolation=interpolation, + fill=fill, + center=center, + ) + + +def rotate( + inpt: torch.Tensor, + angle: float, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomRotation` for details.""" + if torch.jit.is_scripting(): + return rotate_image(inpt, angle=angle, interpolation=interpolation, expand=expand, fill=fill, center=center) + + _log_api_usage_once(rotate) + + kernel = _get_kernel(rotate, type(inpt)) + return kernel(inpt, angle=angle, interpolation=interpolation, expand=expand, fill=fill, center=center) + + +@_register_kernel_internal(rotate, torch.Tensor) +@_register_kernel_internal(rotate, tv_tensors.Image) +def rotate_image( + image: torch.Tensor, + angle: float, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + angle = angle % 360 # shift angle to [0, 360) range + + # fast path: transpose without affine transform + if center is None: + if angle == 0: + return image.clone() + if angle == 180: + return torch.rot90(image, k=2, dims=(-2, -1)) + + if expand or image.shape[-1] == image.shape[-2]: + if angle == 90: + return torch.rot90(image, k=1, dims=(-2, -1)) + if angle == 270: + return torch.rot90(image, k=3, dims=(-2, -1)) + + interpolation = _check_interpolation(interpolation) + + input_height, input_width = image.shape[-2:] + + center_f = [0.0, 0.0] + if center is not None: + # Center values should be in pixel coordinates but translated such that (0, 0) corresponds to image center. + center_f = [(c - s * 0.5) for c, s in zip(center, [input_width, input_height])] + + # due to current incoherence of rotation angle direction between affine and rotate implementations + # we need to set -angle. + matrix = _get_inverse_affine_matrix(center_f, -angle, [0.0, 0.0], 1.0, [0.0, 0.0]) + + _assert_grid_transform_inputs(image, matrix, interpolation.value, fill, ["nearest", "bilinear"]) + + output_width, output_height = ( + _compute_affine_output_size(matrix, input_width, input_height) if expand else (input_width, input_height) + ) + dtype = image.dtype if torch.is_floating_point(image) else torch.float32 + theta = torch.tensor(matrix, dtype=dtype, device=image.device).reshape(1, 2, 3) + grid = _affine_grid(theta, w=input_width, h=input_height, ow=output_width, oh=output_height) + return _apply_grid_transform(image, grid, interpolation.value, fill=fill) + + +@_register_kernel_internal(rotate, PIL.Image.Image) +def _rotate_image_pil( + image: PIL.Image.Image, + angle: float, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, +) -> PIL.Image.Image: + interpolation = _check_interpolation(interpolation) + + return _FP.rotate( + image, angle, interpolation=pil_modes_mapping[interpolation], expand=expand, fill=fill, center=center + ) + + +def rotate_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + angle: float, + expand: bool = False, + center: Optional[List[float]] = None, +) -> Tuple[torch.Tensor, Tuple[int, int]]: + return _affine_bounding_boxes_with_expand( + bounding_boxes, + format=format, + canvas_size=canvas_size, + angle=-angle, + translate=[0.0, 0.0], + scale=1.0, + shear=[0.0, 0.0], + center=center, + expand=expand, + ) + + +@_register_kernel_internal(rotate, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _rotate_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, angle: float, expand: bool = False, center: Optional[List[float]] = None, **kwargs +) -> tv_tensors.BoundingBoxes: + output, canvas_size = rotate_bounding_boxes( + inpt.as_subclass(torch.Tensor), + format=inpt.format, + canvas_size=inpt.canvas_size, + angle=angle, + expand=expand, + center=center, + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +def rotate_mask( + mask: torch.Tensor, + angle: float, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = rotate_image( + mask, + angle=angle, + expand=expand, + interpolation=InterpolationMode.NEAREST, + fill=fill, + center=center, + ) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(rotate, tv_tensors.Mask, tv_tensor_wrapper=False) +def _rotate_mask_dispatch( + inpt: tv_tensors.Mask, + angle: float, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, + **kwargs, +) -> tv_tensors.Mask: + output = rotate_mask(inpt.as_subclass(torch.Tensor), angle=angle, expand=expand, fill=fill, center=center) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(rotate, tv_tensors.Video) +def rotate_video( + video: torch.Tensor, + angle: float, + interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, + expand: bool = False, + center: Optional[List[float]] = None, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + return rotate_image(video, angle, interpolation=interpolation, expand=expand, fill=fill, center=center) + + +def pad( + inpt: torch.Tensor, + padding: List[int], + fill: Optional[Union[int, float, List[float]]] = None, + padding_mode: str = "constant", +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.Pad` for details.""" + if torch.jit.is_scripting(): + return pad_image(inpt, padding=padding, fill=fill, padding_mode=padding_mode) + + _log_api_usage_once(pad) + + kernel = _get_kernel(pad, type(inpt)) + return kernel(inpt, padding=padding, fill=fill, padding_mode=padding_mode) + + +def _parse_pad_padding(padding: Union[int, List[int]]) -> List[int]: + if isinstance(padding, int): + pad_left = pad_right = pad_top = pad_bottom = padding + elif isinstance(padding, (tuple, list)): + if len(padding) == 1: + pad_left = pad_right = pad_top = pad_bottom = padding[0] + elif len(padding) == 2: + pad_left = pad_right = padding[0] + pad_top = pad_bottom = padding[1] + elif len(padding) == 4: + pad_left = padding[0] + pad_top = padding[1] + pad_right = padding[2] + pad_bottom = padding[3] + else: + raise ValueError( + f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple" + ) + else: + raise TypeError(f"`padding` should be an integer or tuple or list of integers, but got {padding}") + + return [pad_left, pad_right, pad_top, pad_bottom] + + +@_register_kernel_internal(pad, torch.Tensor) +@_register_kernel_internal(pad, tv_tensors.Image) +def pad_image( + image: torch.Tensor, + padding: List[int], + fill: Optional[Union[int, float, List[float]]] = None, + padding_mode: str = "constant", +) -> torch.Tensor: + # Be aware that while `padding` has order `[left, top, right, bottom]`, `torch_padding` uses + # `[left, right, top, bottom]`. This stems from the fact that we align our API with PIL, but need to use `torch_pad` + # internally. + torch_padding = _parse_pad_padding(padding) + + if padding_mode not in ("constant", "edge", "reflect", "symmetric"): + raise ValueError( + f"`padding_mode` should be either `'constant'`, `'edge'`, `'reflect'` or `'symmetric'`, " + f"but got `'{padding_mode}'`." + ) + + if fill is None: + fill = 0 + + if isinstance(fill, (int, float)): + return _pad_with_scalar_fill(image, torch_padding, fill=fill, padding_mode=padding_mode) + elif len(fill) == 1: + return _pad_with_scalar_fill(image, torch_padding, fill=fill[0], padding_mode=padding_mode) + else: + return _pad_with_vector_fill(image, torch_padding, fill=fill, padding_mode=padding_mode) + + +def _pad_with_scalar_fill( + image: torch.Tensor, + torch_padding: List[int], + fill: Union[int, float], + padding_mode: str, +) -> torch.Tensor: + shape = image.shape + num_channels, height, width = shape[-3:] + + batch_size = 1 + for s in shape[:-3]: + batch_size *= s + + image = image.reshape(batch_size, num_channels, height, width) + + if padding_mode == "edge": + # Similar to the padding order, `torch_pad`'s PIL's padding modes don't have the same names. Thus, we map + # the PIL name for the padding mode, which we are also using for our API, to the corresponding `torch_pad` + # name. + padding_mode = "replicate" + + if padding_mode == "constant": + image = torch_pad(image, torch_padding, mode=padding_mode, value=float(fill)) + elif padding_mode in ("reflect", "replicate"): + # `torch_pad` only supports `"reflect"` or `"replicate"` padding for floating point inputs. + # TODO: See https://github.com/pytorch/pytorch/issues/40763 + dtype = image.dtype + if not image.is_floating_point(): + needs_cast = True + image = image.to(torch.float32) + else: + needs_cast = False + + image = torch_pad(image, torch_padding, mode=padding_mode) + + if needs_cast: + image = image.to(dtype) + else: # padding_mode == "symmetric" + image = _pad_symmetric(image, torch_padding) + + new_height, new_width = image.shape[-2:] + + return image.reshape(shape[:-3] + (num_channels, new_height, new_width)) + + +# TODO: This should be removed once torch_pad supports non-scalar padding values +def _pad_with_vector_fill( + image: torch.Tensor, + torch_padding: List[int], + fill: List[float], + padding_mode: str, +) -> torch.Tensor: + if padding_mode != "constant": + raise ValueError(f"Padding mode '{padding_mode}' is not supported if fill is not scalar") + + output = _pad_with_scalar_fill(image, torch_padding, fill=0, padding_mode="constant") + left, right, top, bottom = torch_padding + + # We are creating the tensor in the autodetected dtype first and convert to the right one after to avoid an implicit + # float -> int conversion. That happens for example for the valid input of a uint8 image with floating point fill + # value. + fill = torch.tensor(fill, device=image.device).to(dtype=image.dtype).reshape(-1, 1, 1) + + if top > 0: + output[..., :top, :] = fill + if left > 0: + output[..., :, :left] = fill + if bottom > 0: + output[..., -bottom:, :] = fill + if right > 0: + output[..., :, -right:] = fill + return output + + +_pad_image_pil = _register_kernel_internal(pad, PIL.Image.Image)(_FP.pad) + + +@_register_kernel_internal(pad, tv_tensors.Mask) +def pad_mask( + mask: torch.Tensor, + padding: List[int], + fill: Optional[Union[int, float, List[float]]] = None, + padding_mode: str = "constant", +) -> torch.Tensor: + if fill is None: + fill = 0 + + if isinstance(fill, (tuple, list)): + raise ValueError("Non-scalar fill value is not supported") + + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = pad_image(mask, padding=padding, fill=fill, padding_mode=padding_mode) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +def pad_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + padding: List[int], + padding_mode: str = "constant", +) -> Tuple[torch.Tensor, Tuple[int, int]]: + if padding_mode not in ["constant"]: + # TODO: add support of other padding modes + raise ValueError(f"Padding mode '{padding_mode}' is not supported with bounding boxes") + + left, right, top, bottom = _parse_pad_padding(padding) + + if format == tv_tensors.BoundingBoxFormat.XYXY: + pad = [left, top, left, top] + else: + pad = [left, top, 0, 0] + bounding_boxes = bounding_boxes + torch.tensor(pad, dtype=bounding_boxes.dtype, device=bounding_boxes.device) + + height, width = canvas_size + height += top + bottom + width += left + right + canvas_size = (height, width) + + return clamp_bounding_boxes(bounding_boxes, format=format, canvas_size=canvas_size), canvas_size + + +@_register_kernel_internal(pad, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _pad_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, padding: List[int], padding_mode: str = "constant", **kwargs +) -> tv_tensors.BoundingBoxes: + output, canvas_size = pad_bounding_boxes( + inpt.as_subclass(torch.Tensor), + format=inpt.format, + canvas_size=inpt.canvas_size, + padding=padding, + padding_mode=padding_mode, + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +@_register_kernel_internal(pad, tv_tensors.Video) +def pad_video( + video: torch.Tensor, + padding: List[int], + fill: Optional[Union[int, float, List[float]]] = None, + padding_mode: str = "constant", +) -> torch.Tensor: + return pad_image(video, padding, fill=fill, padding_mode=padding_mode) + + +def crop(inpt: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomCrop` for details.""" + if torch.jit.is_scripting(): + return crop_image(inpt, top=top, left=left, height=height, width=width) + + _log_api_usage_once(crop) + + kernel = _get_kernel(crop, type(inpt)) + return kernel(inpt, top=top, left=left, height=height, width=width) + + +@_register_kernel_internal(crop, torch.Tensor) +@_register_kernel_internal(crop, tv_tensors.Image) +def crop_image(image: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: + h, w = image.shape[-2:] + + right = left + width + bottom = top + height + + if left < 0 or top < 0 or right > w or bottom > h: + image = image[..., max(top, 0) : bottom, max(left, 0) : right] + torch_padding = [ + max(min(right, 0) - left, 0), + max(right - max(w, left), 0), + max(min(bottom, 0) - top, 0), + max(bottom - max(h, top), 0), + ] + return _pad_with_scalar_fill(image, torch_padding, fill=0, padding_mode="constant") + return image[..., top:bottom, left:right] + + +_crop_image_pil = _FP.crop +_register_kernel_internal(crop, PIL.Image.Image)(_crop_image_pil) + + +def crop_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + top: int, + left: int, + height: int, + width: int, +) -> Tuple[torch.Tensor, Tuple[int, int]]: + + # Crop or implicit pad if left and/or top have negative values: + if format == tv_tensors.BoundingBoxFormat.XYXY: + sub = [left, top, left, top] + else: + sub = [left, top, 0, 0] + + bounding_boxes = bounding_boxes - torch.tensor(sub, dtype=bounding_boxes.dtype, device=bounding_boxes.device) + canvas_size = (height, width) + + return clamp_bounding_boxes(bounding_boxes, format=format, canvas_size=canvas_size), canvas_size + + +@_register_kernel_internal(crop, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _crop_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, top: int, left: int, height: int, width: int +) -> tv_tensors.BoundingBoxes: + output, canvas_size = crop_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, top=top, left=left, height=height, width=width + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +@_register_kernel_internal(crop, tv_tensors.Mask) +def crop_mask(mask: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = crop_image(mask, top, left, height, width) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(crop, tv_tensors.Video) +def crop_video(video: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: + return crop_image(video, top, left, height, width) + + +def perspective( + inpt: torch.Tensor, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomPerspective` for details.""" + if torch.jit.is_scripting(): + return perspective_image( + inpt, + startpoints=startpoints, + endpoints=endpoints, + interpolation=interpolation, + fill=fill, + coefficients=coefficients, + ) + + _log_api_usage_once(perspective) + + kernel = _get_kernel(perspective, type(inpt)) + return kernel( + inpt, + startpoints=startpoints, + endpoints=endpoints, + interpolation=interpolation, + fill=fill, + coefficients=coefficients, + ) + + +def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/ + # src/libImaging/Geometry.c#L394 + + # + # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1) + # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1) + # + theta1 = torch.tensor( + [[[coeffs[0], coeffs[1], coeffs[2]], [coeffs[3], coeffs[4], coeffs[5]]]], dtype=dtype, device=device + ) + theta2 = torch.tensor([[[coeffs[6], coeffs[7], 1.0], [coeffs[6], coeffs[7], 1.0]]], dtype=dtype, device=device) + + d = 0.5 + base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device) + x_grid = torch.linspace(d, ow + d - 1.0, steps=ow, device=device, dtype=dtype) + base_grid[..., 0].copy_(x_grid) + y_grid = torch.linspace(d, oh + d - 1.0, steps=oh, device=device, dtype=dtype).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + base_grid[..., 2].fill_(1) + + rescaled_theta1 = theta1.transpose(1, 2).div_(torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device)) + shape = (1, oh * ow, 3) + output_grid1 = base_grid.view(shape).bmm(rescaled_theta1) + output_grid2 = base_grid.view(shape).bmm(theta2.transpose(1, 2)) + + output_grid = output_grid1.div_(output_grid2).sub_(1.0) + return output_grid.view(1, oh, ow, 2) + + +def _perspective_coefficients( + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + coefficients: Optional[List[float]], +) -> List[float]: + if coefficients is not None: + if startpoints is not None and endpoints is not None: + raise ValueError("The startpoints/endpoints and the coefficients shouldn't be defined concurrently.") + elif len(coefficients) != 8: + raise ValueError("Argument coefficients should have 8 float values") + return coefficients + elif startpoints is not None and endpoints is not None: + return _get_perspective_coeffs(startpoints, endpoints) + else: + raise ValueError("Either the startpoints/endpoints or the coefficients must have non `None` values.") + + +@_register_kernel_internal(perspective, torch.Tensor) +@_register_kernel_internal(perspective, tv_tensors.Image) +def perspective_image( + image: torch.Tensor, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, +) -> torch.Tensor: + perspective_coeffs = _perspective_coefficients(startpoints, endpoints, coefficients) + interpolation = _check_interpolation(interpolation) + + _assert_grid_transform_inputs( + image, + matrix=None, + interpolation=interpolation.value, + fill=fill, + supported_interpolation_modes=["nearest", "bilinear"], + coeffs=perspective_coeffs, + ) + + oh, ow = image.shape[-2:] + dtype = image.dtype if torch.is_floating_point(image) else torch.float32 + grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=image.device) + return _apply_grid_transform(image, grid, interpolation.value, fill=fill) + + +@_register_kernel_internal(perspective, PIL.Image.Image) +def _perspective_image_pil( + image: PIL.Image.Image, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, +) -> PIL.Image.Image: + perspective_coeffs = _perspective_coefficients(startpoints, endpoints, coefficients) + interpolation = _check_interpolation(interpolation) + return _FP.perspective(image, perspective_coeffs, interpolation=pil_modes_mapping[interpolation], fill=fill) + + +def perspective_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + coefficients: Optional[List[float]] = None, +) -> torch.Tensor: + if bounding_boxes.numel() == 0: + return bounding_boxes + + perspective_coeffs = _perspective_coefficients(startpoints, endpoints, coefficients) + + original_shape = bounding_boxes.shape + # TODO: first cast to float if bbox is int64 before convert_bounding_box_format + bounding_boxes = ( + convert_bounding_box_format(bounding_boxes, old_format=format, new_format=tv_tensors.BoundingBoxFormat.XYXY) + ).reshape(-1, 4) + + dtype = bounding_boxes.dtype if torch.is_floating_point(bounding_boxes) else torch.float32 + device = bounding_boxes.device + + # perspective_coeffs are computed as endpoint -> start point + # We have to invert perspective_coeffs for bboxes: + # (x, y) - end point and (x_out, y_out) - start point + # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1) + # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1) + # and we would like to get: + # x = (inv_coeffs[0] * x_out + inv_coeffs[1] * y_out + inv_coeffs[2]) + # / (inv_coeffs[6] * x_out + inv_coeffs[7] * y_out + 1) + # y = (inv_coeffs[3] * x_out + inv_coeffs[4] * y_out + inv_coeffs[5]) + # / (inv_coeffs[6] * x_out + inv_coeffs[7] * y_out + 1) + # and compute inv_coeffs in terms of coeffs + + denom = perspective_coeffs[0] * perspective_coeffs[4] - perspective_coeffs[1] * perspective_coeffs[3] + if denom == 0: + raise RuntimeError( + f"Provided perspective_coeffs {perspective_coeffs} can not be inverted to transform bounding boxes. " + f"Denominator is zero, denom={denom}" + ) + + inv_coeffs = [ + (perspective_coeffs[4] - perspective_coeffs[5] * perspective_coeffs[7]) / denom, + (-perspective_coeffs[1] + perspective_coeffs[2] * perspective_coeffs[7]) / denom, + (perspective_coeffs[1] * perspective_coeffs[5] - perspective_coeffs[2] * perspective_coeffs[4]) / denom, + (-perspective_coeffs[3] + perspective_coeffs[5] * perspective_coeffs[6]) / denom, + (perspective_coeffs[0] - perspective_coeffs[2] * perspective_coeffs[6]) / denom, + (-perspective_coeffs[0] * perspective_coeffs[5] + perspective_coeffs[2] * perspective_coeffs[3]) / denom, + (-perspective_coeffs[4] * perspective_coeffs[6] + perspective_coeffs[3] * perspective_coeffs[7]) / denom, + (-perspective_coeffs[0] * perspective_coeffs[7] + perspective_coeffs[1] * perspective_coeffs[6]) / denom, + ] + + theta1 = torch.tensor( + [[inv_coeffs[0], inv_coeffs[1], inv_coeffs[2]], [inv_coeffs[3], inv_coeffs[4], inv_coeffs[5]]], + dtype=dtype, + device=device, + ) + + theta2 = torch.tensor( + [[inv_coeffs[6], inv_coeffs[7], 1.0], [inv_coeffs[6], inv_coeffs[7], 1.0]], dtype=dtype, device=device + ) + + # 1) Let's transform bboxes into a tensor of 4 points (top-left, top-right, bottom-left, bottom-right corners). + # Tensor of points has shape (N * 4, 3), where N is the number of bboxes + # Single point structure is similar to + # [(xmin, ymin, 1), (xmax, ymin, 1), (xmax, ymax, 1), (xmin, ymax, 1)] + points = bounding_boxes[:, [[0, 1], [2, 1], [2, 3], [0, 3]]].reshape(-1, 2) + points = torch.cat([points, torch.ones(points.shape[0], 1, device=points.device)], dim=-1) + # 2) Now let's transform the points using perspective matrices + # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1) + # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1) + + numer_points = torch.matmul(points, theta1.T) + denom_points = torch.matmul(points, theta2.T) + transformed_points = numer_points.div_(denom_points) + + # 3) Reshape transformed points to [N boxes, 4 points, x/y coords] + # and compute bounding box from 4 transformed points: + transformed_points = transformed_points.reshape(-1, 4, 2) + out_bbox_mins, out_bbox_maxs = torch.aminmax(transformed_points, dim=1) + + out_bboxes = clamp_bounding_boxes( + torch.cat([out_bbox_mins, out_bbox_maxs], dim=1).to(bounding_boxes.dtype), + format=tv_tensors.BoundingBoxFormat.XYXY, + canvas_size=canvas_size, + ) + + # out_bboxes should be of shape [N boxes, 4] + + return convert_bounding_box_format( + out_bboxes, old_format=tv_tensors.BoundingBoxFormat.XYXY, new_format=format, inplace=True + ).reshape(original_shape) + + +@_register_kernel_internal(perspective, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _perspective_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + coefficients: Optional[List[float]] = None, + **kwargs, +) -> tv_tensors.BoundingBoxes: + output = perspective_bounding_boxes( + inpt.as_subclass(torch.Tensor), + format=inpt.format, + canvas_size=inpt.canvas_size, + startpoints=startpoints, + endpoints=endpoints, + coefficients=coefficients, + ) + return tv_tensors.wrap(output, like=inpt) + + +def perspective_mask( + mask: torch.Tensor, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, +) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = perspective_image( + mask, startpoints, endpoints, interpolation=InterpolationMode.NEAREST, fill=fill, coefficients=coefficients + ) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(perspective, tv_tensors.Mask, tv_tensor_wrapper=False) +def _perspective_mask_dispatch( + inpt: tv_tensors.Mask, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, + **kwargs, +) -> tv_tensors.Mask: + output = perspective_mask( + inpt.as_subclass(torch.Tensor), + startpoints=startpoints, + endpoints=endpoints, + fill=fill, + coefficients=coefficients, + ) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(perspective, tv_tensors.Video) +def perspective_video( + video: torch.Tensor, + startpoints: Optional[List[List[int]]], + endpoints: Optional[List[List[int]]], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, + coefficients: Optional[List[float]] = None, +) -> torch.Tensor: + return perspective_image( + video, startpoints, endpoints, interpolation=interpolation, fill=fill, coefficients=coefficients + ) + + +def elastic( + inpt: torch.Tensor, + displacement: torch.Tensor, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.ElasticTransform` for details.""" + if torch.jit.is_scripting(): + return elastic_image(inpt, displacement=displacement, interpolation=interpolation, fill=fill) + + _log_api_usage_once(elastic) + + kernel = _get_kernel(elastic, type(inpt)) + return kernel(inpt, displacement=displacement, interpolation=interpolation, fill=fill) + + +elastic_transform = elastic + + +@_register_kernel_internal(elastic, torch.Tensor) +@_register_kernel_internal(elastic, tv_tensors.Image) +def elastic_image( + image: torch.Tensor, + displacement: torch.Tensor, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + if not isinstance(displacement, torch.Tensor): + raise TypeError("Argument displacement should be a Tensor") + + interpolation = _check_interpolation(interpolation) + + height, width = image.shape[-2:] + device = image.device + dtype = image.dtype if torch.is_floating_point(image) else torch.float32 + + # Patch: elastic transform should support (cpu,f16) input + is_cpu_half = device.type == "cpu" and dtype == torch.float16 + if is_cpu_half: + image = image.to(torch.float32) + dtype = torch.float32 + + # We are aware that if input image dtype is uint8 and displacement is float64 then + # displacement will be cast to float32 and all computations will be done with float32 + # We can fix this later if needed + + expected_shape = (1, height, width, 2) + if expected_shape != displacement.shape: + raise ValueError(f"Argument displacement shape should be {expected_shape}, but given {displacement.shape}") + + grid = _create_identity_grid((height, width), device=device, dtype=dtype).add_( + displacement.to(dtype=dtype, device=device) + ) + output = _apply_grid_transform(image, grid, interpolation.value, fill=fill) + + if is_cpu_half: + output = output.to(torch.float16) + + return output + + +@_register_kernel_internal(elastic, PIL.Image.Image) +def _elastic_image_pil( + image: PIL.Image.Image, + displacement: torch.Tensor, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, +) -> PIL.Image.Image: + t_img = pil_to_tensor(image) + output = elastic_image(t_img, displacement, interpolation=interpolation, fill=fill) + return to_pil_image(output, mode=image.mode) + + +def _create_identity_grid(size: Tuple[int, int], device: torch.device, dtype: torch.dtype) -> torch.Tensor: + sy, sx = size + base_grid = torch.empty(1, sy, sx, 2, device=device, dtype=dtype) + x_grid = torch.linspace((-sx + 1) / sx, (sx - 1) / sx, sx, device=device, dtype=dtype) + base_grid[..., 0].copy_(x_grid) + + y_grid = torch.linspace((-sy + 1) / sy, (sy - 1) / sy, sy, device=device, dtype=dtype).unsqueeze_(-1) + base_grid[..., 1].copy_(y_grid) + + return base_grid + + +def elastic_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + displacement: torch.Tensor, +) -> torch.Tensor: + expected_shape = (1, canvas_size[0], canvas_size[1], 2) + if not isinstance(displacement, torch.Tensor): + raise TypeError("Argument displacement should be a Tensor") + elif displacement.shape != expected_shape: + raise ValueError(f"Argument displacement shape should be {expected_shape}, but given {displacement.shape}") + + if bounding_boxes.numel() == 0: + return bounding_boxes + + # TODO: add in docstring about approximation we are doing for grid inversion + device = bounding_boxes.device + dtype = bounding_boxes.dtype if torch.is_floating_point(bounding_boxes) else torch.float32 + + if displacement.dtype != dtype or displacement.device != device: + displacement = displacement.to(dtype=dtype, device=device) + + original_shape = bounding_boxes.shape + # TODO: first cast to float if bbox is int64 before convert_bounding_box_format + bounding_boxes = ( + convert_bounding_box_format(bounding_boxes, old_format=format, new_format=tv_tensors.BoundingBoxFormat.XYXY) + ).reshape(-1, 4) + + id_grid = _create_identity_grid(canvas_size, device=device, dtype=dtype) + # We construct an approximation of inverse grid as inv_grid = id_grid - displacement + # This is not an exact inverse of the grid + inv_grid = id_grid.sub_(displacement) + + # Get points from bboxes + points = bounding_boxes[:, [[0, 1], [2, 1], [2, 3], [0, 3]]].reshape(-1, 2) + if points.is_floating_point(): + points = points.ceil_() + index_xy = points.to(dtype=torch.long) + index_x, index_y = index_xy[:, 0], index_xy[:, 1] + + # Transform points: + t_size = torch.tensor(canvas_size[::-1], device=displacement.device, dtype=displacement.dtype) + transformed_points = inv_grid[0, index_y, index_x, :].add_(1).mul_(0.5 * t_size).sub_(0.5) + + transformed_points = transformed_points.reshape(-1, 4, 2) + out_bbox_mins, out_bbox_maxs = torch.aminmax(transformed_points, dim=1) + out_bboxes = clamp_bounding_boxes( + torch.cat([out_bbox_mins, out_bbox_maxs], dim=1).to(bounding_boxes.dtype), + format=tv_tensors.BoundingBoxFormat.XYXY, + canvas_size=canvas_size, + ) + + return convert_bounding_box_format( + out_bboxes, old_format=tv_tensors.BoundingBoxFormat.XYXY, new_format=format, inplace=True + ).reshape(original_shape) + + +@_register_kernel_internal(elastic, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _elastic_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, displacement: torch.Tensor, **kwargs +) -> tv_tensors.BoundingBoxes: + output = elastic_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size, displacement=displacement + ) + return tv_tensors.wrap(output, like=inpt) + + +def elastic_mask( + mask: torch.Tensor, + displacement: torch.Tensor, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = elastic_image(mask, displacement=displacement, interpolation=InterpolationMode.NEAREST, fill=fill) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(elastic, tv_tensors.Mask, tv_tensor_wrapper=False) +def _elastic_mask_dispatch( + inpt: tv_tensors.Mask, displacement: torch.Tensor, fill: _FillTypeJIT = None, **kwargs +) -> tv_tensors.Mask: + output = elastic_mask(inpt.as_subclass(torch.Tensor), displacement=displacement, fill=fill) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(elastic, tv_tensors.Video) +def elastic_video( + video: torch.Tensor, + displacement: torch.Tensor, + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + fill: _FillTypeJIT = None, +) -> torch.Tensor: + return elastic_image(video, displacement, interpolation=interpolation, fill=fill) + + +def center_crop(inpt: torch.Tensor, output_size: List[int]) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomCrop` for details.""" + if torch.jit.is_scripting(): + return center_crop_image(inpt, output_size=output_size) + + _log_api_usage_once(center_crop) + + kernel = _get_kernel(center_crop, type(inpt)) + return kernel(inpt, output_size=output_size) + + +def _center_crop_parse_output_size(output_size: List[int]) -> List[int]: + if isinstance(output_size, numbers.Number): + s = int(output_size) + return [s, s] + elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: + return [output_size[0], output_size[0]] + else: + return list(output_size) + + +def _center_crop_compute_padding(crop_height: int, crop_width: int, image_height: int, image_width: int) -> List[int]: + return [ + (crop_width - image_width) // 2 if crop_width > image_width else 0, + (crop_height - image_height) // 2 if crop_height > image_height else 0, + (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, + (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, + ] + + +def _center_crop_compute_crop_anchor( + crop_height: int, crop_width: int, image_height: int, image_width: int +) -> Tuple[int, int]: + crop_top = int(round((image_height - crop_height) / 2.0)) + crop_left = int(round((image_width - crop_width) / 2.0)) + return crop_top, crop_left + + +@_register_kernel_internal(center_crop, torch.Tensor) +@_register_kernel_internal(center_crop, tv_tensors.Image) +def center_crop_image(image: torch.Tensor, output_size: List[int]) -> torch.Tensor: + crop_height, crop_width = _center_crop_parse_output_size(output_size) + shape = image.shape + if image.numel() == 0: + return image.reshape(shape[:-2] + (crop_height, crop_width)) + image_height, image_width = shape[-2:] + + if crop_height > image_height or crop_width > image_width: + padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width) + image = torch_pad(image, _parse_pad_padding(padding_ltrb), value=0.0) + + image_height, image_width = image.shape[-2:] + if crop_width == image_width and crop_height == image_height: + return image + + crop_top, crop_left = _center_crop_compute_crop_anchor(crop_height, crop_width, image_height, image_width) + return image[..., crop_top : (crop_top + crop_height), crop_left : (crop_left + crop_width)] + + +@_register_kernel_internal(center_crop, PIL.Image.Image) +def _center_crop_image_pil(image: PIL.Image.Image, output_size: List[int]) -> PIL.Image.Image: + crop_height, crop_width = _center_crop_parse_output_size(output_size) + image_height, image_width = _get_size_image_pil(image) + + if crop_height > image_height or crop_width > image_width: + padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width) + image = _pad_image_pil(image, padding_ltrb, fill=0) + + image_height, image_width = _get_size_image_pil(image) + if crop_width == image_width and crop_height == image_height: + return image + + crop_top, crop_left = _center_crop_compute_crop_anchor(crop_height, crop_width, image_height, image_width) + return _crop_image_pil(image, crop_top, crop_left, crop_height, crop_width) + + +def center_crop_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + output_size: List[int], +) -> Tuple[torch.Tensor, Tuple[int, int]]: + crop_height, crop_width = _center_crop_parse_output_size(output_size) + crop_top, crop_left = _center_crop_compute_crop_anchor(crop_height, crop_width, *canvas_size) + return crop_bounding_boxes( + bounding_boxes, format, top=crop_top, left=crop_left, height=crop_height, width=crop_width + ) + + +@_register_kernel_internal(center_crop, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _center_crop_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, output_size: List[int] +) -> tv_tensors.BoundingBoxes: + output, canvas_size = center_crop_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size, output_size=output_size + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +@_register_kernel_internal(center_crop, tv_tensors.Mask) +def center_crop_mask(mask: torch.Tensor, output_size: List[int]) -> torch.Tensor: + if mask.ndim < 3: + mask = mask.unsqueeze(0) + needs_squeeze = True + else: + needs_squeeze = False + + output = center_crop_image(image=mask, output_size=output_size) + + if needs_squeeze: + output = output.squeeze(0) + + return output + + +@_register_kernel_internal(center_crop, tv_tensors.Video) +def center_crop_video(video: torch.Tensor, output_size: List[int]) -> torch.Tensor: + return center_crop_image(video, output_size) + + +def resized_crop( + inpt: torch.Tensor, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.RandomResizedCrop` for details.""" + if torch.jit.is_scripting(): + return resized_crop_image( + inpt, + top=top, + left=left, + height=height, + width=width, + size=size, + interpolation=interpolation, + antialias=antialias, + ) + + _log_api_usage_once(resized_crop) + + kernel = _get_kernel(resized_crop, type(inpt)) + return kernel( + inpt, + top=top, + left=left, + height=height, + width=width, + size=size, + interpolation=interpolation, + antialias=antialias, + ) + + +@_register_kernel_internal(resized_crop, torch.Tensor) +@_register_kernel_internal(resized_crop, tv_tensors.Image) +def resized_crop_image( + image: torch.Tensor, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, +) -> torch.Tensor: + image = crop_image(image, top, left, height, width) + return resize_image(image, size, interpolation=interpolation, antialias=antialias) + + +def _resized_crop_image_pil( + image: PIL.Image.Image, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, +) -> PIL.Image.Image: + image = _crop_image_pil(image, top, left, height, width) + return _resize_image_pil(image, size, interpolation=interpolation) + + +@_register_kernel_internal(resized_crop, PIL.Image.Image) +def _resized_crop_image_pil_dispatch( + image: PIL.Image.Image, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, +) -> PIL.Image.Image: + if antialias is False: + warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.") + return _resized_crop_image_pil( + image, + top=top, + left=left, + height=height, + width=width, + size=size, + interpolation=interpolation, + ) + + +def resized_crop_bounding_boxes( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + top: int, + left: int, + height: int, + width: int, + size: List[int], +) -> Tuple[torch.Tensor, Tuple[int, int]]: + bounding_boxes, canvas_size = crop_bounding_boxes(bounding_boxes, format, top, left, height, width) + return resize_bounding_boxes(bounding_boxes, canvas_size=canvas_size, size=size) + + +@_register_kernel_internal(resized_crop, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def _resized_crop_bounding_boxes_dispatch( + inpt: tv_tensors.BoundingBoxes, top: int, left: int, height: int, width: int, size: List[int], **kwargs +) -> tv_tensors.BoundingBoxes: + output, canvas_size = resized_crop_bounding_boxes( + inpt.as_subclass(torch.Tensor), format=inpt.format, top=top, left=left, height=height, width=width, size=size + ) + return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size) + + +def resized_crop_mask( + mask: torch.Tensor, + top: int, + left: int, + height: int, + width: int, + size: List[int], +) -> torch.Tensor: + mask = crop_mask(mask, top, left, height, width) + return resize_mask(mask, size) + + +@_register_kernel_internal(resized_crop, tv_tensors.Mask, tv_tensor_wrapper=False) +def _resized_crop_mask_dispatch( + inpt: tv_tensors.Mask, top: int, left: int, height: int, width: int, size: List[int], **kwargs +) -> tv_tensors.Mask: + output = resized_crop_mask( + inpt.as_subclass(torch.Tensor), top=top, left=left, height=height, width=width, size=size + ) + return tv_tensors.wrap(output, like=inpt) + + +@_register_kernel_internal(resized_crop, tv_tensors.Video) +def resized_crop_video( + video: torch.Tensor, + top: int, + left: int, + height: int, + width: int, + size: List[int], + interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, + antialias: Optional[bool] = True, +) -> torch.Tensor: + return resized_crop_image( + video, top, left, height, width, antialias=antialias, size=size, interpolation=interpolation + ) + + +def five_crop( + inpt: torch.Tensor, size: List[int] +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """See :class:`~torchvision.transforms.v2.FiveCrop` for details.""" + if torch.jit.is_scripting(): + return five_crop_image(inpt, size=size) + + _log_api_usage_once(five_crop) + + kernel = _get_kernel(five_crop, type(inpt)) + return kernel(inpt, size=size) + + +def _parse_five_crop_size(size: List[int]) -> List[int]: + if isinstance(size, numbers.Number): + s = int(size) + size = [s, s] + elif isinstance(size, (tuple, list)) and len(size) == 1: + s = size[0] + size = [s, s] + + if len(size) != 2: + raise ValueError("Please provide only two dimensions (h, w) for size.") + + return size + + +@_register_five_ten_crop_kernel_internal(five_crop, torch.Tensor) +@_register_five_ten_crop_kernel_internal(five_crop, tv_tensors.Image) +def five_crop_image( + image: torch.Tensor, size: List[int] +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + crop_height, crop_width = _parse_five_crop_size(size) + image_height, image_width = image.shape[-2:] + + if crop_width > image_width or crop_height > image_height: + raise ValueError(f"Requested crop size {size} is bigger than input size {(image_height, image_width)}") + + tl = crop_image(image, 0, 0, crop_height, crop_width) + tr = crop_image(image, 0, image_width - crop_width, crop_height, crop_width) + bl = crop_image(image, image_height - crop_height, 0, crop_height, crop_width) + br = crop_image(image, image_height - crop_height, image_width - crop_width, crop_height, crop_width) + center = center_crop_image(image, [crop_height, crop_width]) + + return tl, tr, bl, br, center + + +@_register_five_ten_crop_kernel_internal(five_crop, PIL.Image.Image) +def _five_crop_image_pil( + image: PIL.Image.Image, size: List[int] +) -> Tuple[PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image]: + crop_height, crop_width = _parse_five_crop_size(size) + image_height, image_width = _get_size_image_pil(image) + + if crop_width > image_width or crop_height > image_height: + raise ValueError(f"Requested crop size {size} is bigger than input size {(image_height, image_width)}") + + tl = _crop_image_pil(image, 0, 0, crop_height, crop_width) + tr = _crop_image_pil(image, 0, image_width - crop_width, crop_height, crop_width) + bl = _crop_image_pil(image, image_height - crop_height, 0, crop_height, crop_width) + br = _crop_image_pil(image, image_height - crop_height, image_width - crop_width, crop_height, crop_width) + center = _center_crop_image_pil(image, [crop_height, crop_width]) + + return tl, tr, bl, br, center + + +@_register_five_ten_crop_kernel_internal(five_crop, tv_tensors.Video) +def five_crop_video( + video: torch.Tensor, size: List[int] +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + return five_crop_image(video, size) + + +def ten_crop( + inpt: torch.Tensor, size: List[int], vertical_flip: bool = False +) -> Tuple[ + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, +]: + """See :class:`~torchvision.transforms.v2.TenCrop` for details.""" + if torch.jit.is_scripting(): + return ten_crop_image(inpt, size=size, vertical_flip=vertical_flip) + + _log_api_usage_once(ten_crop) + + kernel = _get_kernel(ten_crop, type(inpt)) + return kernel(inpt, size=size, vertical_flip=vertical_flip) + + +@_register_five_ten_crop_kernel_internal(ten_crop, torch.Tensor) +@_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Image) +def ten_crop_image( + image: torch.Tensor, size: List[int], vertical_flip: bool = False +) -> Tuple[ + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, +]: + non_flipped = five_crop_image(image, size) + + if vertical_flip: + image = vertical_flip_image(image) + else: + image = horizontal_flip_image(image) + + flipped = five_crop_image(image, size) + + return non_flipped + flipped + + +@_register_five_ten_crop_kernel_internal(ten_crop, PIL.Image.Image) +def _ten_crop_image_pil( + image: PIL.Image.Image, size: List[int], vertical_flip: bool = False +) -> Tuple[ + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, + PIL.Image.Image, +]: + non_flipped = _five_crop_image_pil(image, size) + + if vertical_flip: + image = _vertical_flip_image_pil(image) + else: + image = _horizontal_flip_image_pil(image) + + flipped = _five_crop_image_pil(image, size) + + return non_flipped + flipped + + +@_register_five_ten_crop_kernel_internal(ten_crop, tv_tensors.Video) +def ten_crop_video( + video: torch.Tensor, size: List[int], vertical_flip: bool = False +) -> Tuple[ + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, +]: + return ten_crop_image(video, size, vertical_flip=vertical_flip) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_meta.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..b90e5fb7b5be887d24bb42725d59bb056ac126c2 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_meta.py @@ -0,0 +1,279 @@ +from typing import List, Optional, Tuple + +import PIL.Image +import torch +from torchvision import tv_tensors +from torchvision.transforms import _functional_pil as _FP +from torchvision.tv_tensors import BoundingBoxFormat + +from torchvision.utils import _log_api_usage_once + +from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor + + +def get_dimensions(inpt: torch.Tensor) -> List[int]: + if torch.jit.is_scripting(): + return get_dimensions_image(inpt) + + _log_api_usage_once(get_dimensions) + + kernel = _get_kernel(get_dimensions, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_dimensions, torch.Tensor) +@_register_kernel_internal(get_dimensions, tv_tensors.Image, tv_tensor_wrapper=False) +def get_dimensions_image(image: torch.Tensor) -> List[int]: + chw = list(image.shape[-3:]) + ndims = len(chw) + if ndims == 3: + return chw + elif ndims == 2: + chw.insert(0, 1) + return chw + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +_get_dimensions_image_pil = _register_kernel_internal(get_dimensions, PIL.Image.Image)(_FP.get_dimensions) + + +@_register_kernel_internal(get_dimensions, tv_tensors.Video, tv_tensor_wrapper=False) +def get_dimensions_video(video: torch.Tensor) -> List[int]: + return get_dimensions_image(video) + + +def get_num_channels(inpt: torch.Tensor) -> int: + if torch.jit.is_scripting(): + return get_num_channels_image(inpt) + + _log_api_usage_once(get_num_channels) + + kernel = _get_kernel(get_num_channels, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_num_channels, torch.Tensor) +@_register_kernel_internal(get_num_channels, tv_tensors.Image, tv_tensor_wrapper=False) +def get_num_channels_image(image: torch.Tensor) -> int: + chw = image.shape[-3:] + ndims = len(chw) + if ndims == 3: + return chw[0] + elif ndims == 2: + return 1 + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +_get_num_channels_image_pil = _register_kernel_internal(get_num_channels, PIL.Image.Image)(_FP.get_image_num_channels) + + +@_register_kernel_internal(get_num_channels, tv_tensors.Video, tv_tensor_wrapper=False) +def get_num_channels_video(video: torch.Tensor) -> int: + return get_num_channels_image(video) + + +# We changed the names to ensure it can be used not only for images but also videos. Thus, we just alias it without +# deprecating the old names. +get_image_num_channels = get_num_channels + + +def get_size(inpt: torch.Tensor) -> List[int]: + if torch.jit.is_scripting(): + return get_size_image(inpt) + + _log_api_usage_once(get_size) + + kernel = _get_kernel(get_size, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_size, torch.Tensor) +@_register_kernel_internal(get_size, tv_tensors.Image, tv_tensor_wrapper=False) +def get_size_image(image: torch.Tensor) -> List[int]: + hw = list(image.shape[-2:]) + ndims = len(hw) + if ndims == 2: + return hw + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +@_register_kernel_internal(get_size, PIL.Image.Image) +def _get_size_image_pil(image: PIL.Image.Image) -> List[int]: + width, height = _FP.get_image_size(image) + return [height, width] + + +@_register_kernel_internal(get_size, tv_tensors.Video, tv_tensor_wrapper=False) +def get_size_video(video: torch.Tensor) -> List[int]: + return get_size_image(video) + + +@_register_kernel_internal(get_size, tv_tensors.Mask, tv_tensor_wrapper=False) +def get_size_mask(mask: torch.Tensor) -> List[int]: + return get_size_image(mask) + + +@_register_kernel_internal(get_size, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def get_size_bounding_boxes(bounding_box: tv_tensors.BoundingBoxes) -> List[int]: + return list(bounding_box.canvas_size) + + +def get_num_frames(inpt: torch.Tensor) -> int: + if torch.jit.is_scripting(): + return get_num_frames_video(inpt) + + _log_api_usage_once(get_num_frames) + + kernel = _get_kernel(get_num_frames, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_num_frames, torch.Tensor) +@_register_kernel_internal(get_num_frames, tv_tensors.Video, tv_tensor_wrapper=False) +def get_num_frames_video(video: torch.Tensor) -> int: + return video.shape[-4] + + +def _xywh_to_xyxy(xywh: torch.Tensor, inplace: bool) -> torch.Tensor: + xyxy = xywh if inplace else xywh.clone() + xyxy[..., 2:] += xyxy[..., :2] + return xyxy + + +def _xyxy_to_xywh(xyxy: torch.Tensor, inplace: bool) -> torch.Tensor: + xywh = xyxy if inplace else xyxy.clone() + xywh[..., 2:] -= xywh[..., :2] + return xywh + + +def _cxcywh_to_xyxy(cxcywh: torch.Tensor, inplace: bool) -> torch.Tensor: + if not inplace: + cxcywh = cxcywh.clone() + + # Trick to do fast division by 2 and ceil, without casting. It produces the same result as + # `torchvision.ops._box_convert._box_cxcywh_to_xyxy`. + half_wh = cxcywh[..., 2:].div(-2, rounding_mode=None if cxcywh.is_floating_point() else "floor").abs_() + # (cx - width / 2) = x1, same for y1 + cxcywh[..., :2].sub_(half_wh) + # (x1 + width) = x2, same for y2 + cxcywh[..., 2:].add_(cxcywh[..., :2]) + + return cxcywh + + +def _xyxy_to_cxcywh(xyxy: torch.Tensor, inplace: bool) -> torch.Tensor: + if not inplace: + xyxy = xyxy.clone() + + # (x2 - x1) = width, same for height + xyxy[..., 2:].sub_(xyxy[..., :2]) + # (x1 * 2 + width) / 2 = x1 + width / 2 = x1 + (x2-x1)/2 = (x1 + x2)/2 = cx, same for cy + xyxy[..., :2].mul_(2).add_(xyxy[..., 2:]).div_(2, rounding_mode=None if xyxy.is_floating_point() else "floor") + + return xyxy + + +def _convert_bounding_box_format( + bounding_boxes: torch.Tensor, old_format: BoundingBoxFormat, new_format: BoundingBoxFormat, inplace: bool = False +) -> torch.Tensor: + + if new_format == old_format: + return bounding_boxes + + # TODO: Add _xywh_to_cxcywh and _cxcywh_to_xywh to improve performance + if old_format == BoundingBoxFormat.XYWH: + bounding_boxes = _xywh_to_xyxy(bounding_boxes, inplace) + elif old_format == BoundingBoxFormat.CXCYWH: + bounding_boxes = _cxcywh_to_xyxy(bounding_boxes, inplace) + + if new_format == BoundingBoxFormat.XYWH: + bounding_boxes = _xyxy_to_xywh(bounding_boxes, inplace) + elif new_format == BoundingBoxFormat.CXCYWH: + bounding_boxes = _xyxy_to_cxcywh(bounding_boxes, inplace) + + return bounding_boxes + + +def convert_bounding_box_format( + inpt: torch.Tensor, + old_format: Optional[BoundingBoxFormat] = None, + new_format: Optional[BoundingBoxFormat] = None, + inplace: bool = False, +) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ConvertBoundingBoxFormat` for details.""" + # This being a kernel / functional hybrid, we need an option to pass `old_format` explicitly for pure tensor + # inputs as well as extract it from `tv_tensors.BoundingBoxes` inputs. However, putting a default value on + # `old_format` means we also need to put one on `new_format` to have syntactically correct Python. Here we mimic the + # default error that would be thrown if `new_format` had no default value. + if new_format is None: + raise TypeError("convert_bounding_box_format() missing 1 required argument: 'new_format'") + + if not torch.jit.is_scripting(): + _log_api_usage_once(convert_bounding_box_format) + + if isinstance(old_format, str): + old_format = BoundingBoxFormat[old_format.upper()] + if isinstance(new_format, str): + new_format = BoundingBoxFormat[new_format.upper()] + + if torch.jit.is_scripting() or is_pure_tensor(inpt): + if old_format is None: + raise ValueError("For pure tensor inputs, `old_format` has to be passed.") + return _convert_bounding_box_format(inpt, old_format=old_format, new_format=new_format, inplace=inplace) + elif isinstance(inpt, tv_tensors.BoundingBoxes): + if old_format is not None: + raise ValueError("For bounding box tv_tensor inputs, `old_format` must not be passed.") + output = _convert_bounding_box_format( + inpt.as_subclass(torch.Tensor), old_format=inpt.format, new_format=new_format, inplace=inplace + ) + return tv_tensors.wrap(output, like=inpt, format=new_format) + else: + raise TypeError( + f"Input can either be a plain tensor or a bounding box tv_tensor, but got {type(inpt)} instead." + ) + + +def _clamp_bounding_boxes( + bounding_boxes: torch.Tensor, format: BoundingBoxFormat, canvas_size: Tuple[int, int] +) -> torch.Tensor: + # TODO: Investigate if it makes sense from a performance perspective to have an implementation for every + # BoundingBoxFormat instead of converting back and forth + in_dtype = bounding_boxes.dtype + bounding_boxes = bounding_boxes.clone() if bounding_boxes.is_floating_point() else bounding_boxes.float() + xyxy_boxes = convert_bounding_box_format( + bounding_boxes, old_format=format, new_format=tv_tensors.BoundingBoxFormat.XYXY, inplace=True + ) + xyxy_boxes[..., 0::2].clamp_(min=0, max=canvas_size[1]) + xyxy_boxes[..., 1::2].clamp_(min=0, max=canvas_size[0]) + out_boxes = convert_bounding_box_format( + xyxy_boxes, old_format=BoundingBoxFormat.XYXY, new_format=format, inplace=True + ) + return out_boxes.to(in_dtype) + + +def clamp_bounding_boxes( + inpt: torch.Tensor, + format: Optional[BoundingBoxFormat] = None, + canvas_size: Optional[Tuple[int, int]] = None, +) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ClampBoundingBoxes` for details.""" + if not torch.jit.is_scripting(): + _log_api_usage_once(clamp_bounding_boxes) + + if torch.jit.is_scripting() or is_pure_tensor(inpt): + + if format is None or canvas_size is None: + raise ValueError("For pure tensor inputs, `format` and `canvas_size` have to be passed.") + return _clamp_bounding_boxes(inpt, format=format, canvas_size=canvas_size) + elif isinstance(inpt, tv_tensors.BoundingBoxes): + if format is not None or canvas_size is not None: + raise ValueError("For bounding box tv_tensor inputs, `format` and `canvas_size` must not be passed.") + output = _clamp_bounding_boxes(inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size) + return tv_tensors.wrap(output, like=inpt) + else: + raise TypeError( + f"Input can either be a plain tensor or a bounding box tv_tensor, but got {type(inpt)} instead." + ) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_misc.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_misc.py new file mode 100644 index 0000000000000000000000000000000000000000..f40bf117753ef2af57d3e1f0f74df2274a8a05cd --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_misc.py @@ -0,0 +1,420 @@ +import math +from typing import List, Optional, Tuple + +import PIL.Image +import torch +from torch.nn.functional import conv2d, pad as torch_pad + +from torchvision import tv_tensors +from torchvision.transforms._functional_tensor import _max_value +from torchvision.transforms.functional import pil_to_tensor, to_pil_image + +from torchvision.utils import _log_api_usage_once + +from ._meta import _convert_bounding_box_format + +from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor + + +def normalize( + inpt: torch.Tensor, + mean: List[float], + std: List[float], + inplace: bool = False, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.Normalize` for details.""" + if torch.jit.is_scripting(): + return normalize_image(inpt, mean=mean, std=std, inplace=inplace) + + _log_api_usage_once(normalize) + + kernel = _get_kernel(normalize, type(inpt)) + return kernel(inpt, mean=mean, std=std, inplace=inplace) + + +@_register_kernel_internal(normalize, torch.Tensor) +@_register_kernel_internal(normalize, tv_tensors.Image) +def normalize_image(image: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: + if not image.is_floating_point(): + raise TypeError(f"Input tensor should be a float tensor. Got {image.dtype}.") + + if image.ndim < 3: + raise ValueError(f"Expected tensor to be a tensor image of size (..., C, H, W). Got {image.shape}.") + + if isinstance(std, (tuple, list)): + divzero = not all(std) + elif isinstance(std, (int, float)): + divzero = std == 0 + else: + divzero = False + if divzero: + raise ValueError("std evaluated to zero, leading to division by zero.") + + dtype = image.dtype + device = image.device + mean = torch.as_tensor(mean, dtype=dtype, device=device) + std = torch.as_tensor(std, dtype=dtype, device=device) + if mean.ndim == 1: + mean = mean.view(-1, 1, 1) + if std.ndim == 1: + std = std.view(-1, 1, 1) + + if inplace: + image = image.sub_(mean) + else: + image = image.sub(mean) + + return image.div_(std) + + +@_register_kernel_internal(normalize, tv_tensors.Video) +def normalize_video(video: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: + return normalize_image(video, mean, std, inplace=inplace) + + +def gaussian_blur(inpt: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.GaussianBlur` for details.""" + if torch.jit.is_scripting(): + return gaussian_blur_image(inpt, kernel_size=kernel_size, sigma=sigma) + + _log_api_usage_once(gaussian_blur) + + kernel = _get_kernel(gaussian_blur, type(inpt)) + return kernel(inpt, kernel_size=kernel_size, sigma=sigma) + + +def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + lim = (kernel_size - 1) / (2.0 * math.sqrt(2.0)) + x = torch.linspace(-lim, lim, steps=kernel_size, dtype=dtype, device=device) + kernel1d = torch.softmax(x.div(sigma).pow(2).neg(), dim=0) + return kernel1d + + +def _get_gaussian_kernel2d( + kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device +) -> torch.Tensor: + kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device) + kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device) + kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x + return kernel2d + + +@_register_kernel_internal(gaussian_blur, torch.Tensor) +@_register_kernel_internal(gaussian_blur, tv_tensors.Image) +def gaussian_blur_image( + image: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> torch.Tensor: + # TODO: consider deprecating integers from sigma on the future + if isinstance(kernel_size, int): + kernel_size = [kernel_size, kernel_size] + elif len(kernel_size) != 2: + raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}") + for ksize in kernel_size: + if ksize % 2 == 0 or ksize < 0: + raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}") + + if sigma is None: + sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] + else: + if isinstance(sigma, (list, tuple)): + length = len(sigma) + if length == 1: + s = sigma[0] + sigma = [s, s] + elif length != 2: + raise ValueError(f"If sigma is a sequence, its length should be 2. Got {length}") + elif isinstance(sigma, (int, float)): + s = float(sigma) + sigma = [s, s] + else: + raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}") + for s in sigma: + if s <= 0.0: + raise ValueError(f"sigma should have positive values. Got {sigma}") + + if image.numel() == 0: + return image + + dtype = image.dtype + shape = image.shape + ndim = image.ndim + if ndim == 3: + image = image.unsqueeze(dim=0) + elif ndim > 4: + image = image.reshape((-1,) + shape[-3:]) + + fp = torch.is_floating_point(image) + kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype if fp else torch.float32, device=image.device) + kernel = kernel.expand(shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + output = image if fp else image.to(dtype=torch.float32) + + # padding = (left, right, top, bottom) + padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] + output = torch_pad(output, padding, mode="reflect") + output = conv2d(output, kernel, groups=shape[-3]) + + if ndim == 3: + output = output.squeeze(dim=0) + elif ndim > 4: + output = output.reshape(shape) + + if not fp: + output = output.round_().to(dtype=dtype) + + return output + + +@_register_kernel_internal(gaussian_blur, PIL.Image.Image) +def _gaussian_blur_image_pil( + image: PIL.Image.Image, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> PIL.Image.Image: + t_img = pil_to_tensor(image) + output = gaussian_blur_image(t_img, kernel_size=kernel_size, sigma=sigma) + return to_pil_image(output, mode=image.mode) + + +@_register_kernel_internal(gaussian_blur, tv_tensors.Video) +def gaussian_blur_video( + video: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> torch.Tensor: + return gaussian_blur_image(video, kernel_size, sigma) + + +def gaussian_noise(inpt: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.GaussianNoise`""" + if torch.jit.is_scripting(): + return gaussian_noise_image(inpt, mean=mean, sigma=sigma) + + _log_api_usage_once(gaussian_noise) + + kernel = _get_kernel(gaussian_noise, type(inpt)) + return kernel(inpt, mean=mean, sigma=sigma, clip=clip) + + +@_register_kernel_internal(gaussian_noise, torch.Tensor) +@_register_kernel_internal(gaussian_noise, tv_tensors.Image) +def gaussian_noise_image(image: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor: + if not image.is_floating_point(): + raise ValueError(f"Input tensor is expected to be in float dtype, got dtype={image.dtype}") + if sigma < 0: + raise ValueError(f"sigma shouldn't be negative. Got {sigma}") + + noise = mean + torch.randn_like(image) * sigma + out = image + noise + if clip: + out = torch.clamp(out, 0, 1) + return out + + +@_register_kernel_internal(gaussian_noise, tv_tensors.Video) +def gaussian_noise_video(video: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor: + return gaussian_noise_image(video, mean=mean, sigma=sigma, clip=clip) + + +@_register_kernel_internal(gaussian_noise, PIL.Image.Image) +def _gaussian_noise_pil( + video: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True +) -> PIL.Image.Image: + raise ValueError("Gaussian Noise is not implemented for PIL images.") + + +def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ToDtype` for details.""" + if torch.jit.is_scripting(): + return to_dtype_image(inpt, dtype=dtype, scale=scale) + + _log_api_usage_once(to_dtype) + + kernel = _get_kernel(to_dtype, type(inpt)) + return kernel(inpt, dtype=dtype, scale=scale) + + +def _num_value_bits(dtype: torch.dtype) -> int: + if dtype == torch.uint8: + return 8 + elif dtype == torch.int8: + return 7 + elif dtype == torch.int16: + return 15 + elif dtype == torch.uint16: + return 16 + elif dtype == torch.int32: + return 31 + elif dtype == torch.int64: + return 63 + else: + raise TypeError(f"Number of value bits is only defined for integer dtypes, but got {dtype}.") + + +@_register_kernel_internal(to_dtype, torch.Tensor) +@_register_kernel_internal(to_dtype, tv_tensors.Image) +def to_dtype_image(image: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + + if image.dtype == dtype: + return image + elif not scale: + return image.to(dtype) + + float_input = image.is_floating_point() + if torch.jit.is_scripting(): + # TODO: remove this branch as soon as `dtype.is_floating_point` is supported by JIT + float_output = torch.tensor(0, dtype=dtype).is_floating_point() + else: + float_output = dtype.is_floating_point + + if float_input: + # float to float + if float_output: + return image.to(dtype) + + # float to int + if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or ( + image.dtype == torch.float64 and dtype == torch.int64 + ): + raise RuntimeError(f"The conversion from {image.dtype} to {dtype} cannot be performed safely.") + + # For data in the range `[0.0, 1.0]`, just multiplying by the maximum value of the integer range and converting + # to the integer dtype is not sufficient. For example, `torch.rand(...).mul(255).to(torch.uint8)` will only + # be `255` if the input is exactly `1.0`. See https://github.com/pytorch/vision/pull/2078#issuecomment-612045321 + # for a detailed analysis. + # To mitigate this, we could round before we convert to the integer dtype, but this is an extra operation. + # Instead, we can also multiply by the maximum value plus something close to `1`. See + # https://github.com/pytorch/vision/pull/2078#issuecomment-613524965 for details. + eps = 1e-3 + max_value = float(_max_value(dtype)) + # We need to scale first since the conversion would otherwise turn the input range `[0.0, 1.0]` into the + # discrete set `{0, 1}`. + return image.mul(max_value + 1.0 - eps).to(dtype) + else: + # int to float + if float_output: + return image.to(dtype).mul_(1.0 / _max_value(image.dtype)) + + # int to int + num_value_bits_input = _num_value_bits(image.dtype) + num_value_bits_output = _num_value_bits(dtype) + + # TODO: Remove if/else inner blocks once uint16 dtype supports bitwise shift operations. + shift_by = abs(num_value_bits_input - num_value_bits_output) + if num_value_bits_input > num_value_bits_output: + if image.dtype == torch.uint16: + return (image / 2 ** (shift_by)).to(dtype) + else: + return image.bitwise_right_shift(shift_by).to(dtype) + else: + if dtype == torch.uint16: + return image.to(dtype) * 2 ** (shift_by) + else: + return image.to(dtype).bitwise_left_shift_(shift_by) + + +# We encourage users to use to_dtype() instead but we keep this for BC +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float32) -> torch.Tensor: + """[DEPRECATED] Use to_dtype() instead.""" + return to_dtype_image(image, dtype=dtype, scale=True) + + +@_register_kernel_internal(to_dtype, tv_tensors.Video) +def to_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + return to_dtype_image(video, dtype, scale=scale) + + +@_register_kernel_internal(to_dtype, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +@_register_kernel_internal(to_dtype, tv_tensors.Mask, tv_tensor_wrapper=False) +def _to_dtype_tensor_dispatch(inpt: torch.Tensor, dtype: torch.dtype, scale: bool = False) -> torch.Tensor: + # We don't need to unwrap and rewrap here, since TVTensor.to() preserves the type + return inpt.to(dtype) + + +def sanitize_bounding_boxes( + bounding_boxes: torch.Tensor, + format: Optional[tv_tensors.BoundingBoxFormat] = None, + canvas_size: Optional[Tuple[int, int]] = None, + min_size: float = 1.0, + min_area: float = 1.0, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Remove degenerate/invalid bounding boxes and return the corresponding indexing mask. + + This removes bounding boxes that: + + - are below a given ``min_size`` or ``min_area``: by default this also removes degenerate boxes that have e.g. X2 <= X1. + - have any coordinate outside of their corresponding image. You may want to + call :func:`~torchvision.transforms.v2.functional.clamp_bounding_boxes` first to avoid undesired removals. + + It is recommended to call it at the end of a pipeline, before passing the + input to the models. It is critical to call this transform if + :class:`~torchvision.transforms.v2.RandomIoUCrop` was called. + If you want to be extra careful, you may call it after all transforms that + may modify bounding boxes but once at the end should be enough in most + cases. + + Args: + bounding_boxes (Tensor or :class:`~torchvision.tv_tensors.BoundingBoxes`): The bounding boxes to be sanitized. + format (str or :class:`~torchvision.tv_tensors.BoundingBoxFormat`, optional): The format of the bounding boxes. + Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object. + canvas_size (tuple of int, optional): The canvas_size of the bounding boxes + (size of the corresponding image/video). + Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object. + min_size (float, optional) The size below which bounding boxes are removed. Default is 1. + min_area (float, optional) The area below which bounding boxes are removed. Default is 1. + + Returns: + out (tuple of Tensors): The subset of valid bounding boxes, and the corresponding indexing mask. + The mask can then be used to subset other tensors (e.g. labels) that are associated with the bounding boxes. + """ + if torch.jit.is_scripting() or is_pure_tensor(bounding_boxes): + if format is None or canvas_size is None: + raise ValueError( + "format and canvas_size cannot be None if bounding_boxes is a pure tensor. " + f"Got format={format} and canvas_size={canvas_size}." + "Set those to appropriate values or pass bounding_boxes as a tv_tensors.BoundingBoxes object." + ) + if isinstance(format, str): + format = tv_tensors.BoundingBoxFormat[format.upper()] + valid = _get_sanitize_bounding_boxes_mask( + bounding_boxes, format=format, canvas_size=canvas_size, min_size=min_size, min_area=min_area + ) + bounding_boxes = bounding_boxes[valid] + else: + if not isinstance(bounding_boxes, tv_tensors.BoundingBoxes): + raise ValueError("bounding_boxes must be a tv_tensors.BoundingBoxes instance or a pure tensor.") + if format is not None or canvas_size is not None: + raise ValueError( + "format and canvas_size must be None when bounding_boxes is a tv_tensors.BoundingBoxes instance. " + f"Got format={format} and canvas_size={canvas_size}. " + "Leave those to None or pass bounding_boxes as a pure tensor." + ) + valid = _get_sanitize_bounding_boxes_mask( + bounding_boxes, + format=bounding_boxes.format, + canvas_size=bounding_boxes.canvas_size, + min_size=min_size, + min_area=min_area, + ) + bounding_boxes = tv_tensors.wrap(bounding_boxes[valid], like=bounding_boxes) + + return bounding_boxes, valid + + +def _get_sanitize_bounding_boxes_mask( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + min_size: float = 1.0, + min_area: float = 1.0, +) -> torch.Tensor: + + bounding_boxes = _convert_bounding_box_format( + bounding_boxes, new_format=tv_tensors.BoundingBoxFormat.XYXY, old_format=format + ) + + image_h, image_w = canvas_size + ws, hs = bounding_boxes[:, 2] - bounding_boxes[:, 0], bounding_boxes[:, 3] - bounding_boxes[:, 1] + valid = (ws >= min_size) & (hs >= min_size) & (bounding_boxes >= 0).all(dim=-1) & (ws * hs >= min_area) + # TODO: Do we really need to check for out of bounds here? All + # transforms should be clamping anyway, so this should never happen? + image_h, image_w = canvas_size + valid &= (bounding_boxes[:, 0] <= image_w) & (bounding_boxes[:, 2] <= image_w) + valid &= (bounding_boxes[:, 1] <= image_h) & (bounding_boxes[:, 3] <= image_h) + return valid diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_temporal.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_temporal.py new file mode 100644 index 0000000000000000000000000000000000000000..f932b06a295fd10316fba3e796ec4649053e92db --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_temporal.py @@ -0,0 +1,27 @@ +import torch + +from torchvision import tv_tensors + +from torchvision.utils import _log_api_usage_once + +from ._utils import _get_kernel, _register_kernel_internal + + +def uniform_temporal_subsample(inpt: torch.Tensor, num_samples: int) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.UniformTemporalSubsample` for details.""" + if torch.jit.is_scripting(): + return uniform_temporal_subsample_video(inpt, num_samples=num_samples) + + _log_api_usage_once(uniform_temporal_subsample) + + kernel = _get_kernel(uniform_temporal_subsample, type(inpt)) + return kernel(inpt, num_samples=num_samples) + + +@_register_kernel_internal(uniform_temporal_subsample, torch.Tensor) +@_register_kernel_internal(uniform_temporal_subsample, tv_tensors.Video) +def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int) -> torch.Tensor: + # Reference: https://github.com/facebookresearch/pytorchvideo/blob/a0a131e/pytorchvideo/transforms/functional.py#L19 + t_max = video.shape[-4] - 1 + indices = torch.linspace(0, t_max, num_samples, device=video.device).long() + return torch.index_select(video, -4, indices) diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_type_conversion.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_type_conversion.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a731fe143c365400d5905db8370c538097583a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_type_conversion.py @@ -0,0 +1,27 @@ +from typing import Union + +import numpy as np +import PIL.Image +import torch +from torchvision import tv_tensors +from torchvision.transforms import functional as _F + + +@torch.jit.unused +def to_image(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> tv_tensors.Image: + """See :class:`~torchvision.transforms.v2.ToImage` for details.""" + if isinstance(inpt, np.ndarray): + output = torch.from_numpy(np.atleast_3d(inpt)).permute((2, 0, 1)).contiguous() + elif isinstance(inpt, PIL.Image.Image): + output = pil_to_tensor(inpt) + elif isinstance(inpt, torch.Tensor): + output = inpt + else: + raise TypeError( + f"Input can either be a pure Tensor, a numpy array, or a PIL image, but got {type(inpt)} instead." + ) + return tv_tensors.Image(output) + + +to_pil_image = _F.to_pil_image +pil_to_tensor = _F.pil_to_tensor diff --git a/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_utils.py b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fe0faeddc1b9b56596783e4e4872b604edcd1555 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_utils.py @@ -0,0 +1,141 @@ +import functools +from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union + +import torch +from torchvision import tv_tensors + +_FillType = Union[int, float, Sequence[int], Sequence[float], None] +_FillTypeJIT = Optional[List[float]] + + +def is_pure_tensor(inpt: Any) -> bool: + return isinstance(inpt, torch.Tensor) and not isinstance(inpt, tv_tensors.TVTensor) + + +# {functional: {input_type: type_specific_kernel}} +_KERNEL_REGISTRY: Dict[Callable, Dict[Type, Callable]] = {} + + +def _kernel_tv_tensor_wrapper(kernel): + @functools.wraps(kernel) + def wrapper(inpt, *args, **kwargs): + # If you're wondering whether we could / should get rid of this wrapper, + # the answer is no: we want to pass pure Tensors to avoid the overhead + # of the __torch_function__ machinery. Note that this is always valid, + # regardless of whether we override __torch_function__ in our base class + # or not. + # Also, even if we didn't call `as_subclass` here, we would still need + # this wrapper to call wrap(), because the TVTensor type would be + # lost after the first operation due to our own __torch_function__ + # logic. + output = kernel(inpt.as_subclass(torch.Tensor), *args, **kwargs) + return tv_tensors.wrap(output, like=inpt) + + return wrapper + + +def _register_kernel_internal(functional, input_type, *, tv_tensor_wrapper=True): + registry = _KERNEL_REGISTRY.setdefault(functional, {}) + if input_type in registry: + raise ValueError(f"Functional {functional} already has a kernel registered for type {input_type}.") + + def decorator(kernel): + registry[input_type] = ( + _kernel_tv_tensor_wrapper(kernel) + if issubclass(input_type, tv_tensors.TVTensor) and tv_tensor_wrapper + else kernel + ) + return kernel + + return decorator + + +def _name_to_functional(name): + import torchvision.transforms.v2.functional # noqa + + try: + return getattr(torchvision.transforms.v2.functional, name) + except AttributeError: + raise ValueError( + f"Could not find functional with name '{name}' in torchvision.transforms.v2.functional." + ) from None + + +_BUILTIN_DATAPOINT_TYPES = { + obj for obj in tv_tensors.__dict__.values() if isinstance(obj, type) and issubclass(obj, tv_tensors.TVTensor) +} + + +def register_kernel(functional, tv_tensor_cls): + """Decorate a kernel to register it for a functional and a (custom) tv_tensor type. + + See :ref:`sphx_glr_auto_examples_transforms_plot_custom_tv_tensors.py` for usage + details. + """ + if isinstance(functional, str): + functional = _name_to_functional(name=functional) + elif not ( + callable(functional) + and getattr(functional, "__module__", "").startswith("torchvision.transforms.v2.functional") + ): + raise ValueError( + f"Kernels can only be registered on functionals from the torchvision.transforms.v2.functional namespace, " + f"but got {functional}." + ) + + if not (isinstance(tv_tensor_cls, type) and issubclass(tv_tensor_cls, tv_tensors.TVTensor)): + raise ValueError( + f"Kernels can only be registered for subclasses of torchvision.tv_tensors.TVTensor, " + f"but got {tv_tensor_cls}." + ) + + if tv_tensor_cls in _BUILTIN_DATAPOINT_TYPES: + raise ValueError(f"Kernels cannot be registered for the builtin tv_tensor classes, but got {tv_tensor_cls}") + + return _register_kernel_internal(functional, tv_tensor_cls, tv_tensor_wrapper=False) + + +def _get_kernel(functional, input_type, *, allow_passthrough=False): + registry = _KERNEL_REGISTRY.get(functional) + if not registry: + raise ValueError(f"No kernel registered for functional {functional.__name__}.") + + for cls in input_type.__mro__: + if cls in registry: + return registry[cls] + elif cls is tv_tensors.TVTensor: + # We don't want user-defined tv_tensors to dispatch to the pure Tensor kernels, so we explicit stop the + # MRO traversal before hitting torch.Tensor. We can even stop at tv_tensors.TVTensor, since we don't + # allow kernels to be registered for tv_tensors.TVTensor anyway. + break + + if allow_passthrough: + return lambda inpt, *args, **kwargs: inpt + + raise TypeError( + f"Functional F.{functional.__name__} supports inputs of type {registry.keys()}, " + f"but got {input_type} instead." + ) + + +# This basically replicates _register_kernel_internal, but with a specialized wrapper for five_crop / ten_crop +# We could get rid of this by letting _register_kernel_internal take arbitrary functionals rather than wrap_kernel: bool +def _register_five_ten_crop_kernel_internal(functional, input_type): + registry = _KERNEL_REGISTRY.setdefault(functional, {}) + if input_type in registry: + raise TypeError(f"Functional '{functional}' already has a kernel registered for type '{input_type}'.") + + def wrap(kernel): + @functools.wraps(kernel) + def wrapper(inpt, *args, **kwargs): + output = kernel(inpt, *args, **kwargs) + container_type = type(output) + return container_type(tv_tensors.wrap(o, like=inpt) for o in output) + + return wrapper + + def decorator(kernel): + registry[input_type] = wrap(kernel) if issubclass(input_type, tv_tensors.TVTensor) else kernel + return kernel + + return decorator diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/__init__.py b/lib/python3.11/site-packages/torchvision/tv_tensors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ba47f60a36ad3be8cb2f557adb57f1d2f1ba470 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/__init__.py @@ -0,0 +1,35 @@ +import torch + +from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat +from ._image import Image +from ._mask import Mask +from ._torch_function_helpers import set_return_type +from ._tv_tensor import TVTensor +from ._video import Video + + +# TODO: Fix this. We skip this method as it leads to +# RecursionError: maximum recursion depth exceeded while calling a Python object +# Until `disable` is removed, there will be graph breaks after all calls to functional transforms +@torch.compiler.disable +def wrap(wrappee, *, like, **kwargs): + """Convert a :class:`torch.Tensor` (``wrappee``) into the same :class:`~torchvision.tv_tensors.TVTensor` subclass as ``like``. + + If ``like`` is a :class:`~torchvision.tv_tensors.BoundingBoxes`, the ``format`` and ``canvas_size`` of + ``like`` are assigned to ``wrappee``, unless they are passed as ``kwargs``. + + Args: + wrappee (Tensor): The tensor to convert. + like (:class:`~torchvision.tv_tensors.TVTensor`): The reference. + ``wrappee`` will be converted into the same subclass as ``like``. + kwargs: Can contain "format" and "canvas_size" if ``like`` is a :class:`~torchvision.tv_tensor.BoundingBoxes`. + Ignored otherwise. + """ + if isinstance(like, BoundingBoxes): + return BoundingBoxes._wrap( + wrappee, + format=kwargs.get("format", like.format), + canvas_size=kwargs.get("canvas_size", like.canvas_size), + ) + else: + return wrappee.as_subclass(type(like)) diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/tv_tensors/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a927a8ca3b225f5ce9d609ce0bc563a261a0898a Binary files /dev/null and b/lib/python3.11/site-packages/torchvision/tv_tensors/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/__pycache__/_bounding_boxes.cpython-311.pyc b/lib/python3.11/site-packages/torchvision/tv_tensors/__pycache__/_bounding_boxes.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b642826de2e57f429b5606d11ea840486bf7d25a Binary files /dev/null and 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0000000000000000000000000000000000000000..ea02fa3dc7b3e25ba7f545ca64288fc241d47f2a --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_bounding_boxes.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +from enum import Enum +from typing import Any, Mapping, Optional, Sequence, Tuple, Union + +import torch +from torch.utils._pytree import tree_flatten + +from ._tv_tensor import TVTensor + + +class BoundingBoxFormat(Enum): + """Coordinate format of a bounding box. + + Available formats are + + * ``XYXY`` + * ``XYWH`` + * ``CXCYWH`` + """ + + XYXY = "XYXY" + XYWH = "XYWH" + CXCYWH = "CXCYWH" + + +class BoundingBoxes(TVTensor): + """:class:`torch.Tensor` subclass for bounding boxes with shape ``[N, 4]``. + + .. note:: + There should be only one :class:`~torchvision.tv_tensors.BoundingBoxes` + instance per sample e.g. ``{"img": img, "bbox": BoundingBoxes(...)}``, + although one :class:`~torchvision.tv_tensors.BoundingBoxes` object can + contain multiple bounding boxes. + + Args: + data: Any data that can be turned into a tensor with :func:`torch.as_tensor`. + format (BoundingBoxFormat, str): Format of the bounding box. + canvas_size (two-tuple of ints): Height and width of the corresponding image or video. + dtype (torch.dtype, optional): Desired data type of the bounding box. If omitted, will be inferred from + ``data``. + device (torch.device, optional): Desired device of the bounding box. If omitted and ``data`` is a + :class:`torch.Tensor`, the device is taken from it. Otherwise, the bounding box is constructed on the CPU. + requires_grad (bool, optional): Whether autograd should record operations on the bounding box. If omitted and + ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. + """ + + format: BoundingBoxFormat + canvas_size: Tuple[int, int] + + @classmethod + def _wrap(cls, tensor: torch.Tensor, *, format: Union[BoundingBoxFormat, str], canvas_size: Tuple[int, int], check_dims: bool = True) -> BoundingBoxes: # type: ignore[override] + if check_dims: + if tensor.ndim == 1: + tensor = tensor.unsqueeze(0) + elif tensor.ndim != 2: + raise ValueError(f"Expected a 1D or 2D tensor, got {tensor.ndim}D") + if isinstance(format, str): + format = BoundingBoxFormat[format.upper()] + bounding_boxes = tensor.as_subclass(cls) + bounding_boxes.format = format + bounding_boxes.canvas_size = canvas_size + return bounding_boxes + + def __new__( + cls, + data: Any, + *, + format: Union[BoundingBoxFormat, str], + canvas_size: Tuple[int, int], + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str, int]] = None, + requires_grad: Optional[bool] = None, + ) -> BoundingBoxes: + tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad) + return cls._wrap(tensor, format=format, canvas_size=canvas_size) + + @classmethod + def _wrap_output( + cls, + output: torch.Tensor, + args: Sequence[Any] = (), + kwargs: Optional[Mapping[str, Any]] = None, + ) -> BoundingBoxes: + # If there are BoundingBoxes instances in the output, their metadata got lost when we called + # super().__torch_function__. We need to restore the metadata somehow, so we choose to take + # the metadata from the first bbox in the parameters. + # This should be what we want in most cases. When it's not, it's probably a mis-use anyway, e.g. + # something like some_xyxy_bbox + some_xywh_bbox; we don't guard against those cases. + flat_params, _ = tree_flatten(args + (tuple(kwargs.values()) if kwargs else ())) # type: ignore[operator] + first_bbox_from_args = next(x for x in flat_params if isinstance(x, BoundingBoxes)) + format, canvas_size = first_bbox_from_args.format, first_bbox_from_args.canvas_size + + if isinstance(output, torch.Tensor) and not isinstance(output, BoundingBoxes): + output = BoundingBoxes._wrap(output, format=format, canvas_size=canvas_size, check_dims=False) + elif isinstance(output, (tuple, list)): + output = type(output)( + BoundingBoxes._wrap(part, format=format, canvas_size=canvas_size, check_dims=False) for part in output + ) + return output + + def __repr__(self, *, tensor_contents: Any = None) -> str: # type: ignore[override] + return self._make_repr(format=self.format, canvas_size=self.canvas_size) diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_dataset_wrapper.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_dataset_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..23683221f6005a9ce6a55e785e59409a649d7928 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_dataset_wrapper.py @@ -0,0 +1,666 @@ +# type: ignore + +from __future__ import annotations + +import collections.abc + +import contextlib +from collections import defaultdict +from copy import copy + +import torch + +from torchvision import datasets, tv_tensors +from torchvision.transforms.v2 import functional as F + +__all__ = ["wrap_dataset_for_transforms_v2"] + + +def wrap_dataset_for_transforms_v2(dataset, target_keys=None): + """Wrap a ``torchvision.dataset`` for usage with :mod:`torchvision.transforms.v2`. + + Example: + >>> dataset = torchvision.datasets.CocoDetection(...) + >>> dataset = wrap_dataset_for_transforms_v2(dataset) + + .. note:: + + For now, only the most popular datasets are supported. Furthermore, the wrapper only supports dataset + configurations that are fully supported by ``torchvision.transforms.v2``. If you encounter an error prompting you + to raise an issue to ``torchvision`` for a dataset or configuration that you need, please do so. + + The dataset samples are wrapped according to the description below. + + Special cases: + + * :class:`~torchvision.datasets.CocoDetection`: Instead of returning the target as list of dicts, the wrapper + returns a dict of lists. In addition, the key-value-pairs ``"boxes"`` (in ``XYXY`` coordinate format), + ``"masks"`` and ``"labels"`` are added and wrap the data in the corresponding ``torchvision.tv_tensors``. + The original keys are preserved. If ``target_keys`` is omitted, returns only the values for the + ``"image_id"``, ``"boxes"``, and ``"labels"``. + * :class:`~torchvision.datasets.VOCDetection`: The key-value-pairs ``"boxes"`` and ``"labels"`` are added to + the target and wrap the data in the corresponding ``torchvision.tv_tensors``. The original keys are + preserved. If ``target_keys`` is omitted, returns only the values for the ``"boxes"`` and ``"labels"``. + * :class:`~torchvision.datasets.CelebA`: The target for ``target_type="bbox"`` is converted to the ``XYXY`` + coordinate format and wrapped into a :class:`~torchvision.tv_tensors.BoundingBoxes` tv_tensor. + * :class:`~torchvision.datasets.Kitti`: Instead returning the target as list of dicts, the wrapper returns a + dict of lists. In addition, the key-value-pairs ``"boxes"`` and ``"labels"`` are added and wrap the data + in the corresponding ``torchvision.tv_tensors``. The original keys are preserved. If ``target_keys`` is + omitted, returns only the values for the ``"boxes"`` and ``"labels"``. + * :class:`~torchvision.datasets.OxfordIIITPet`: The target for ``target_type="segmentation"`` is wrapped into a + :class:`~torchvision.tv_tensors.Mask` tv_tensor. + * :class:`~torchvision.datasets.Cityscapes`: The target for ``target_type="semantic"`` is wrapped into a + :class:`~torchvision.tv_tensors.Mask` tv_tensor. The target for ``target_type="instance"`` is *replaced* by + a dictionary with the key-value-pairs ``"masks"`` (as :class:`~torchvision.tv_tensors.Mask` tv_tensor) and + ``"labels"``. + * :class:`~torchvision.datasets.WIDERFace`: The value for key ``"bbox"`` in the target is converted to ``XYXY`` + coordinate format and wrapped into a :class:`~torchvision.tv_tensors.BoundingBoxes` tv_tensor. + + Image classification datasets + + This wrapper is a no-op for image classification datasets, since they were already fully supported by + :mod:`torchvision.transforms` and thus no change is needed for :mod:`torchvision.transforms.v2`. + + Segmentation datasets + + Segmentation datasets, e.g. :class:`~torchvision.datasets.VOCSegmentation`, return a two-tuple of + :class:`PIL.Image.Image`'s. This wrapper leaves the image as is (first item), while wrapping the + segmentation mask into a :class:`~torchvision.tv_tensors.Mask` (second item). + + Video classification datasets + + Video classification datasets, e.g. :class:`~torchvision.datasets.Kinetics`, return a three-tuple containing a + :class:`torch.Tensor` for the video and audio and a :class:`int` as label. This wrapper wraps the video into a + :class:`~torchvision.tv_tensors.Video` while leaving the other items as is. + + .. note:: + + Only datasets constructed with ``output_format="TCHW"`` are supported, since the alternative + ``output_format="THWC"`` is not supported by :mod:`torchvision.transforms.v2`. + + Args: + dataset: the dataset instance to wrap for compatibility with transforms v2. + target_keys: Target keys to return in case the target is a dictionary. If ``None`` (default), selected keys are + specific to the dataset. If ``"all"``, returns the full target. Can also be a collection of strings for + fine grained access. Currently only supported for :class:`~torchvision.datasets.CocoDetection`, + :class:`~torchvision.datasets.VOCDetection`, :class:`~torchvision.datasets.Kitti`, and + :class:`~torchvision.datasets.WIDERFace`. See above for details. + """ + if not ( + target_keys is None + or target_keys == "all" + or (isinstance(target_keys, collections.abc.Collection) and all(isinstance(key, str) for key in target_keys)) + ): + raise ValueError( + f"`target_keys` can be None, 'all', or a collection of strings denoting the keys to be returned, " + f"but got {target_keys}" + ) + + # Imagine we have isinstance(dataset, datasets.ImageNet). This will create a new class with the name + # "WrappedImageNet" at runtime that doubly inherits from VisionDatasetTVTensorWrapper (see below) as well as the + # original ImageNet class. This allows the user to do regular isinstance(wrapped_dataset, datasets.ImageNet) checks, + # while we can still inject everything that we need. + wrapped_dataset_cls = type(f"Wrapped{type(dataset).__name__}", (VisionDatasetTVTensorWrapper, type(dataset)), {}) + # Since VisionDatasetTVTensorWrapper comes before ImageNet in the MRO, calling the class hits + # VisionDatasetTVTensorWrapper.__init__ first. Since we are never doing super().__init__(...), the constructor of + # ImageNet is never hit. That is by design, since we don't want to create the dataset instance again, but rather + # have the existing instance as attribute on the new object. + return wrapped_dataset_cls(dataset, target_keys) + + +class WrapperFactories(dict): + def register(self, dataset_cls): + def decorator(wrapper_factory): + self[dataset_cls] = wrapper_factory + return wrapper_factory + + return decorator + + +# We need this two-stage design, i.e. a wrapper factory producing the actual wrapper, since some wrappers depend on the +# dataset instance rather than just the class, since they require the user defined instance attributes. Thus, we can +# provide a wrapping from the dataset class to the factory here, but can only instantiate the wrapper at runtime when +# we have access to the dataset instance. +WRAPPER_FACTORIES = WrapperFactories() + + +class VisionDatasetTVTensorWrapper: + def __init__(self, dataset, target_keys): + dataset_cls = type(dataset) + + if not isinstance(dataset, datasets.VisionDataset): + raise TypeError( + f"This wrapper is meant for subclasses of `torchvision.datasets.VisionDataset`, " + f"but got a '{dataset_cls.__name__}' instead.\n" + f"For an example of how to perform the wrapping for custom datasets, see\n\n" + "https://pytorch.org/vision/main/auto_examples/plot_tv_tensors.html#do-i-have-to-wrap-the-output-of-the-datasets-myself" + ) + + for cls in dataset_cls.mro(): + if cls in WRAPPER_FACTORIES: + wrapper_factory = WRAPPER_FACTORIES[cls] + if target_keys is not None and cls not in { + datasets.CocoDetection, + datasets.VOCDetection, + datasets.Kitti, + datasets.WIDERFace, + }: + raise ValueError( + f"`target_keys` is currently only supported for `CocoDetection`, `VOCDetection`, `Kitti`, " + f"and `WIDERFace`, but got {cls.__name__}." + ) + break + elif cls is datasets.VisionDataset: + # TODO: If we have documentation on how to do that, put a link in the error message. + msg = f"No wrapper exists for dataset class {dataset_cls.__name__}. Please wrap the output yourself." + if dataset_cls in datasets.__dict__.values(): + msg = ( + f"{msg} If an automated wrapper for this dataset would be useful for you, " + f"please open an issue at https://github.com/pytorch/vision/issues." + ) + raise TypeError(msg) + + self._dataset = dataset + self._target_keys = target_keys + self._wrapper = wrapper_factory(dataset, target_keys) + + # We need to disable the transforms on the dataset here to be able to inject the wrapping before we apply them. + # Although internally, `datasets.VisionDataset` merges `transform` and `target_transform` into the joint + # `transforms` + # https://github.com/pytorch/vision/blob/135a0f9ea9841b6324b4fe8974e2543cbb95709a/torchvision/datasets/vision.py#L52-L54 + # some (if not most) datasets still use `transform` and `target_transform` individually. Thus, we need to + # disable all three here to be able to extract the untransformed sample to wrap. + self.transform, dataset.transform = dataset.transform, None + self.target_transform, dataset.target_transform = dataset.target_transform, None + self.transforms, dataset.transforms = dataset.transforms, None + + def __getattr__(self, item): + with contextlib.suppress(AttributeError): + return object.__getattribute__(self, item) + + return getattr(self._dataset, item) + + def __getitem__(self, idx): + # This gets us the raw sample since we disabled the transforms for the underlying dataset in the constructor + # of this class + sample = self._dataset[idx] + + sample = self._wrapper(idx, sample) + + # Regardless of whether the user has supplied the transforms individually (`transform` and `target_transform`) + # or joint (`transforms`), we can access the full functionality through `transforms` + if self.transforms is not None: + sample = self.transforms(*sample) + + return sample + + def __len__(self): + return len(self._dataset) + + # TODO: maybe we should use __getstate__ and __setstate__ instead of __reduce__, as recommended in the docs. + def __reduce__(self): + # __reduce__ gets called when we try to pickle the dataset. + # In a DataLoader with spawn context, this gets called `num_workers` times from the main process. + + # We have to reset the [target_]transform[s] attributes of the dataset + # to their original values, because we previously set them to None in __init__(). + dataset = copy(self._dataset) + dataset.transform = self.transform + dataset.transforms = self.transforms + dataset.target_transform = self.target_transform + + return wrap_dataset_for_transforms_v2, (dataset, self._target_keys) + + +def raise_not_supported(description): + raise RuntimeError( + f"{description} is currently not supported by this wrapper. " + f"If this would be helpful for you, please open an issue at https://github.com/pytorch/vision/issues." + ) + + +def identity(item): + return item + + +def identity_wrapper_factory(dataset, target_keys): + def wrapper(idx, sample): + return sample + + return wrapper + + +def pil_image_to_mask(pil_image): + return tv_tensors.Mask(pil_image) + + +def parse_target_keys(target_keys, *, available, default): + if target_keys is None: + target_keys = default + if target_keys == "all": + target_keys = available + else: + target_keys = set(target_keys) + extra = target_keys - available + if extra: + raise ValueError(f"Target keys {sorted(extra)} are not available") + + return target_keys + + +def list_of_dicts_to_dict_of_lists(list_of_dicts): + dict_of_lists = defaultdict(list) + for dct in list_of_dicts: + for key, value in dct.items(): + dict_of_lists[key].append(value) + return dict(dict_of_lists) + + +def wrap_target_by_type(target, *, target_types, type_wrappers): + if not isinstance(target, (tuple, list)): + target = [target] + + wrapped_target = tuple( + type_wrappers.get(target_type, identity)(item) for target_type, item in zip(target_types, target) + ) + + if len(wrapped_target) == 1: + wrapped_target = wrapped_target[0] + + return wrapped_target + + +def classification_wrapper_factory(dataset, target_keys): + return identity_wrapper_factory(dataset, target_keys) + + +for dataset_cls in [ + datasets.Caltech256, + datasets.CIFAR10, + datasets.CIFAR100, + datasets.ImageNet, + datasets.MNIST, + datasets.FashionMNIST, + datasets.GTSRB, + datasets.DatasetFolder, + datasets.ImageFolder, + datasets.Imagenette, +]: + WRAPPER_FACTORIES.register(dataset_cls)(classification_wrapper_factory) + + +def segmentation_wrapper_factory(dataset, target_keys): + def wrapper(idx, sample): + image, mask = sample + return image, pil_image_to_mask(mask) + + return wrapper + + +for dataset_cls in [ + datasets.VOCSegmentation, +]: + WRAPPER_FACTORIES.register(dataset_cls)(segmentation_wrapper_factory) + + +def video_classification_wrapper_factory(dataset, target_keys): + if dataset.video_clips.output_format == "THWC": + raise RuntimeError( + f"{type(dataset).__name__} with `output_format='THWC'` is not supported by this wrapper, " + f"since it is not compatible with the transformations. Please use `output_format='TCHW'` instead." + ) + + def wrapper(idx, sample): + video, audio, label = sample + + video = tv_tensors.Video(video) + + return video, audio, label + + return wrapper + + +for dataset_cls in [ + datasets.HMDB51, + datasets.Kinetics, + datasets.UCF101, +]: + WRAPPER_FACTORIES.register(dataset_cls)(video_classification_wrapper_factory) + + +@WRAPPER_FACTORIES.register(datasets.Caltech101) +def caltech101_wrapper_factory(dataset, target_keys): + if "annotation" in dataset.target_type: + raise_not_supported("Caltech101 dataset with `target_type=['annotation', ...]`") + + return classification_wrapper_factory(dataset, target_keys) + + +@WRAPPER_FACTORIES.register(datasets.CocoDetection) +def coco_dectection_wrapper_factory(dataset, target_keys): + target_keys = parse_target_keys( + target_keys, + available={ + # native + "segmentation", + "area", + "iscrowd", + "image_id", + "bbox", + "category_id", + # added by the wrapper + "boxes", + "masks", + "labels", + }, + default={"image_id", "boxes", "labels"}, + ) + + def segmentation_to_mask(segmentation, *, canvas_size): + from pycocotools import mask + + if isinstance(segmentation, dict): + # if counts is a string, it is already an encoded RLE mask + if not isinstance(segmentation["counts"], str): + segmentation = mask.frPyObjects(segmentation, *canvas_size) + elif isinstance(segmentation, list): + segmentation = mask.merge(mask.frPyObjects(segmentation, *canvas_size)) + else: + raise ValueError(f"COCO segmentation expected to be a dict or a list, got {type(segmentation)}") + return torch.from_numpy(mask.decode(segmentation)) + + def wrapper(idx, sample): + image_id = dataset.ids[idx] + + image, target = sample + + if not target: + return image, dict(image_id=image_id) + + canvas_size = tuple(F.get_size(image)) + + batched_target = list_of_dicts_to_dict_of_lists(target) + target = {} + + if "image_id" in target_keys: + target["image_id"] = image_id + + if "boxes" in target_keys: + target["boxes"] = F.convert_bounding_box_format( + tv_tensors.BoundingBoxes( + batched_target["bbox"], + format=tv_tensors.BoundingBoxFormat.XYWH, + canvas_size=canvas_size, + ), + new_format=tv_tensors.BoundingBoxFormat.XYXY, + ) + + if "masks" in target_keys: + target["masks"] = tv_tensors.Mask( + torch.stack( + [ + segmentation_to_mask(segmentation, canvas_size=canvas_size) + for segmentation in batched_target["segmentation"] + ] + ), + ) + + if "labels" in target_keys: + target["labels"] = torch.tensor(batched_target["category_id"]) + + for target_key in target_keys - {"image_id", "boxes", "masks", "labels"}: + target[target_key] = batched_target[target_key] + + return image, target + + return wrapper + + +WRAPPER_FACTORIES.register(datasets.CocoCaptions)(identity_wrapper_factory) + + +VOC_DETECTION_CATEGORIES = [ + "__background__", + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "diningtable", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", +] +VOC_DETECTION_CATEGORY_TO_IDX = dict(zip(VOC_DETECTION_CATEGORIES, range(len(VOC_DETECTION_CATEGORIES)))) + + +@WRAPPER_FACTORIES.register(datasets.VOCDetection) +def voc_detection_wrapper_factory(dataset, target_keys): + target_keys = parse_target_keys( + target_keys, + available={ + # native + "annotation", + # added by the wrapper + "boxes", + "labels", + }, + default={"boxes", "labels"}, + ) + + def wrapper(idx, sample): + image, target = sample + + batched_instances = list_of_dicts_to_dict_of_lists(target["annotation"]["object"]) + + if "annotation" not in target_keys: + target = {} + + if "boxes" in target_keys: + target["boxes"] = tv_tensors.BoundingBoxes( + [ + [int(bndbox[part]) for part in ("xmin", "ymin", "xmax", "ymax")] + for bndbox in batched_instances["bndbox"] + ], + format=tv_tensors.BoundingBoxFormat.XYXY, + canvas_size=(image.height, image.width), + ) + + if "labels" in target_keys: + target["labels"] = torch.tensor( + [VOC_DETECTION_CATEGORY_TO_IDX[category] for category in batched_instances["name"]] + ) + + return image, target + + return wrapper + + +@WRAPPER_FACTORIES.register(datasets.SBDataset) +def sbd_wrapper(dataset, target_keys): + if dataset.mode == "boundaries": + raise_not_supported("SBDataset with mode='boundaries'") + + return segmentation_wrapper_factory(dataset, target_keys) + + +@WRAPPER_FACTORIES.register(datasets.CelebA) +def celeba_wrapper_factory(dataset, target_keys): + if any(target_type in dataset.target_type for target_type in ["attr", "landmarks"]): + raise_not_supported("`CelebA` dataset with `target_type=['attr', 'landmarks', ...]`") + + def wrapper(idx, sample): + image, target = sample + + target = wrap_target_by_type( + target, + target_types=dataset.target_type, + type_wrappers={ + "bbox": lambda item: F.convert_bounding_box_format( + tv_tensors.BoundingBoxes( + item, + format=tv_tensors.BoundingBoxFormat.XYWH, + canvas_size=(image.height, image.width), + ), + new_format=tv_tensors.BoundingBoxFormat.XYXY, + ), + }, + ) + + return image, target + + return wrapper + + +KITTI_CATEGORIES = ["Car", "Van", "Truck", "Pedestrian", "Person_sitting", "Cyclist", "Tram", "Misc", "DontCare"] +KITTI_CATEGORY_TO_IDX = dict(zip(KITTI_CATEGORIES, range(len(KITTI_CATEGORIES)))) + + +@WRAPPER_FACTORIES.register(datasets.Kitti) +def kitti_wrapper_factory(dataset, target_keys): + target_keys = parse_target_keys( + target_keys, + available={ + # native + "type", + "truncated", + "occluded", + "alpha", + "bbox", + "dimensions", + "location", + "rotation_y", + # added by the wrapper + "boxes", + "labels", + }, + default={"boxes", "labels"}, + ) + + def wrapper(idx, sample): + image, target = sample + + if target is None: + return image, target + + batched_target = list_of_dicts_to_dict_of_lists(target) + target = {} + + if "boxes" in target_keys: + target["boxes"] = tv_tensors.BoundingBoxes( + batched_target["bbox"], + format=tv_tensors.BoundingBoxFormat.XYXY, + canvas_size=(image.height, image.width), + ) + + if "labels" in target_keys: + target["labels"] = torch.tensor([KITTI_CATEGORY_TO_IDX[category] for category in batched_target["type"]]) + + for target_key in target_keys - {"boxes", "labels"}: + target[target_key] = batched_target[target_key] + + return image, target + + return wrapper + + +@WRAPPER_FACTORIES.register(datasets.OxfordIIITPet) +def oxford_iiit_pet_wrapper_factor(dataset, target_keys): + def wrapper(idx, sample): + image, target = sample + + if target is not None: + target = wrap_target_by_type( + target, + target_types=dataset._target_types, + type_wrappers={ + "segmentation": pil_image_to_mask, + }, + ) + + return image, target + + return wrapper + + +@WRAPPER_FACTORIES.register(datasets.Cityscapes) +def cityscapes_wrapper_factory(dataset, target_keys): + if any(target_type in dataset.target_type for target_type in ["polygon", "color"]): + raise_not_supported("`Cityscapes` dataset with `target_type=['polygon', 'color', ...]`") + + def instance_segmentation_wrapper(mask): + # See https://github.com/mcordts/cityscapesScripts/blob/8da5dd00c9069058ccc134654116aac52d4f6fa2/cityscapesscripts/preparation/json2instanceImg.py#L7-L21 + data = pil_image_to_mask(mask) + masks = [] + labels = [] + for id in data.unique(): + masks.append(data == id) + label = id + if label >= 1_000: + label //= 1_000 + labels.append(label) + return dict(masks=tv_tensors.Mask(torch.stack(masks)), labels=torch.stack(labels)) + + def wrapper(idx, sample): + image, target = sample + + target = wrap_target_by_type( + target, + target_types=dataset.target_type, + type_wrappers={ + "instance": instance_segmentation_wrapper, + "semantic": pil_image_to_mask, + }, + ) + + return image, target + + return wrapper + + +@WRAPPER_FACTORIES.register(datasets.WIDERFace) +def widerface_wrapper(dataset, target_keys): + target_keys = parse_target_keys( + target_keys, + available={ + "bbox", + "blur", + "expression", + "illumination", + "occlusion", + "pose", + "invalid", + }, + default="all", + ) + + def wrapper(idx, sample): + image, target = sample + + if target is None: + return image, target + + target = {key: target[key] for key in target_keys} + + if "bbox" in target_keys: + target["bbox"] = F.convert_bounding_box_format( + tv_tensors.BoundingBoxes( + target["bbox"], format=tv_tensors.BoundingBoxFormat.XYWH, canvas_size=(image.height, image.width) + ), + new_format=tv_tensors.BoundingBoxFormat.XYXY, + ) + + return image, target + + return wrapper diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_image.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_image.py new file mode 100644 index 0000000000000000000000000000000000000000..2a0a2ec720966f849f8d832a1b9f2e640ba7dc2c --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_image.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from typing import Any, Optional, Union + +import PIL.Image +import torch + +from ._tv_tensor import TVTensor + + +class Image(TVTensor): + """:class:`torch.Tensor` subclass for images with shape ``[..., C, H, W]``. + + .. note:: + + In the :ref:`transforms `, ``Image`` instances are largely + interchangeable with pure :class:`torch.Tensor`. See + :ref:`this note ` for more details. + + Args: + data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as + well as PIL images. + dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from + ``data``. + device (torch.device, optional): Desired device. If omitted and ``data`` is a + :class:`torch.Tensor`, the device is taken from it. Otherwise, the image is constructed on the CPU. + requires_grad (bool, optional): Whether autograd should record operations. If omitted and + ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. + """ + + def __new__( + cls, + data: Any, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str, int]] = None, + requires_grad: Optional[bool] = None, + ) -> Image: + if isinstance(data, PIL.Image.Image): + from torchvision.transforms.v2 import functional as F + + data = F.pil_to_tensor(data) + + tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad) + if tensor.ndim < 2: + raise ValueError + elif tensor.ndim == 2: + tensor = tensor.unsqueeze(0) + + return tensor.as_subclass(cls) + + def __repr__(self, *, tensor_contents: Any = None) -> str: # type: ignore[override] + return self._make_repr() diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_mask.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..ef9d96159fb5e0ac64bb99774ae65be9e11325ae --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_mask.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +from typing import Any, Optional, Union + +import PIL.Image +import torch + +from ._tv_tensor import TVTensor + + +class Mask(TVTensor): + """:class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``. + + Args: + data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as + well as PIL images. + dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from + ``data``. + device (torch.device, optional): Desired device. If omitted and ``data`` is a + :class:`torch.Tensor`, the device is taken from it. Otherwise, the mask is constructed on the CPU. + requires_grad (bool, optional): Whether autograd should record operations. If omitted and + ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. + """ + + def __new__( + cls, + data: Any, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str, int]] = None, + requires_grad: Optional[bool] = None, + ) -> Mask: + if isinstance(data, PIL.Image.Image): + from torchvision.transforms.v2 import functional as F + + data = F.pil_to_tensor(data) + + tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad) + return tensor.as_subclass(cls) diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_torch_function_helpers.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_torch_function_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..e6ea5fddf35b4b585cb2527863fc2db1ba6c049b --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_torch_function_helpers.py @@ -0,0 +1,72 @@ +import torch + +_TORCHFUNCTION_SUBCLASS = False + + +class _ReturnTypeCM: + def __init__(self, to_restore): + self.to_restore = to_restore + + def __enter__(self): + return self + + def __exit__(self, *args): + global _TORCHFUNCTION_SUBCLASS + _TORCHFUNCTION_SUBCLASS = self.to_restore + + +def set_return_type(return_type: str): + """Set the return type of torch operations on :class:`~torchvision.tv_tensors.TVTensor`. + + This only affects the behaviour of torch operations. It has no effect on + ``torchvision`` transforms or functionals, which will always return as + output the same type that was passed as input. + + .. warning:: + + We recommend using :class:`~torchvision.transforms.v2.ToPureTensor` at + the end of your transform pipelines if you use + ``set_return_type("TVTensor")``. This will avoid the + ``__torch_function__`` overhead in the models ``forward()``. + + Can be used as a global flag for the entire program: + + .. code:: python + + img = tv_tensors.Image(torch.rand(3, 5, 5)) + img + 2 # This is a pure Tensor (default behaviour) + + set_return_type("TVTensor") + img + 2 # This is an Image + + or as a context manager to restrict the scope: + + .. code:: python + + img = tv_tensors.Image(torch.rand(3, 5, 5)) + img + 2 # This is a pure Tensor + with set_return_type("TVTensor"): + img + 2 # This is an Image + img + 2 # This is a pure Tensor + + Args: + return_type (str): Can be "TVTensor" or "Tensor" (case-insensitive). + Default is "Tensor" (i.e. pure :class:`torch.Tensor`). + """ + global _TORCHFUNCTION_SUBCLASS + to_restore = _TORCHFUNCTION_SUBCLASS + + try: + _TORCHFUNCTION_SUBCLASS = {"tensor": False, "tvtensor": True}[return_type.lower()] + except KeyError: + raise ValueError(f"return_type must be 'TVTensor' or 'Tensor', got {return_type}") from None + + return _ReturnTypeCM(to_restore) + + +def _must_return_subclass(): + return _TORCHFUNCTION_SUBCLASS + + +# For those ops we always want to preserve the original subclass instead of returning a pure Tensor +_FORCE_TORCHFUNCTION_SUBCLASS = {torch.Tensor.clone, torch.Tensor.to, torch.Tensor.detach, torch.Tensor.requires_grad_} diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_tv_tensor.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_tv_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..508e73724beb8e5ce1202bc35e079b5542149feb --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_tv_tensor.py @@ -0,0 +1,132 @@ +from __future__ import annotations + +from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, Type, TypeVar, Union + +import torch +from torch._C import DisableTorchFunctionSubclass +from torch.types import _device, _dtype, _size + +from torchvision.tv_tensors._torch_function_helpers import _FORCE_TORCHFUNCTION_SUBCLASS, _must_return_subclass + + +D = TypeVar("D", bound="TVTensor") + + +class TVTensor(torch.Tensor): + """Base class for all TVTensors. + + You probably don't want to use this class unless you're defining your own + custom TVTensors. See + :ref:`sphx_glr_auto_examples_transforms_plot_custom_tv_tensors.py` for details. + """ + + @staticmethod + def _to_tensor( + data: Any, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str, int]] = None, + requires_grad: Optional[bool] = None, + ) -> torch.Tensor: + if requires_grad is None: + requires_grad = data.requires_grad if isinstance(data, torch.Tensor) else False + return torch.as_tensor(data, dtype=dtype, device=device).requires_grad_(requires_grad) + + @classmethod + def _wrap_output( + cls, + output: torch.Tensor, + args: Sequence[Any] = (), + kwargs: Optional[Mapping[str, Any]] = None, + ) -> torch.Tensor: + # Same as torch._tensor._convert + if isinstance(output, torch.Tensor) and not isinstance(output, cls): + output = output.as_subclass(cls) + + if isinstance(output, (tuple, list)): + # Also handles things like namedtuples + output = type(output)(cls._wrap_output(part, args, kwargs) for part in output) + return output + + @classmethod + def __torch_function__( + cls, + func: Callable[..., torch.Tensor], + types: Tuple[Type[torch.Tensor], ...], + args: Sequence[Any] = (), + kwargs: Optional[Mapping[str, Any]] = None, + ) -> torch.Tensor: + """For general information about how the __torch_function__ protocol works, + see https://pytorch.org/docs/stable/notes/extending.html#extending-torch + + TL;DR: Every time a PyTorch operator is called, it goes through the inputs and looks for the + ``__torch_function__`` method. If one is found, it is invoked with the operator as ``func`` as well as the + ``args`` and ``kwargs`` of the original call. + + Why do we override this? Because the base implementation in torch.Tensor would preserve the TVTensor type + of the output. In our case, we want to return pure tensors instead (with a few exceptions). Refer to the + "TVTensors FAQ" gallery example for a rationale of this behaviour (TL;DR: perf + no silver bullet). + + Our implementation below is very similar to the base implementation in ``torch.Tensor`` - go check it out. + """ + if not all(issubclass(cls, t) for t in types): + return NotImplemented + + # Like in the base Tensor.__torch_function__ implementation, it's easier to always use + # DisableTorchFunctionSubclass and then manually re-wrap the output if necessary + with DisableTorchFunctionSubclass(): + output = func(*args, **kwargs or dict()) + + must_return_subclass = _must_return_subclass() + if must_return_subclass or (func in _FORCE_TORCHFUNCTION_SUBCLASS and isinstance(args[0], cls)): + # If you're wondering why we need the `isinstance(args[0], cls)` check, remove it and see what fails + # in test_to_tv_tensor_reference(). + # The __torch_function__ protocol will invoke the __torch_function__ method on *all* types involved in + # the computation by walking the MRO upwards. For example, + # `out = a_pure_tensor.to(an_image)` will invoke `Image.__torch_function__` with + # `args = (a_pure_tensor, an_image)` first. Without this guard, `out` would + # be wrapped into an `Image`. + return cls._wrap_output(output, args, kwargs) + + if not must_return_subclass and isinstance(output, cls): + # DisableTorchFunctionSubclass is ignored by inplace ops like `.add_(...)`, + # so for those, the output is still a TVTensor. Thus, we need to manually unwrap. + return output.as_subclass(torch.Tensor) + + return output + + def _make_repr(self, **kwargs: Any) -> str: + # This is a poor man's implementation of the proposal in https://github.com/pytorch/pytorch/issues/76532. + # If that ever gets implemented, remove this in favor of the solution on the `torch.Tensor` class. + extra_repr = ", ".join(f"{key}={value}" for key, value in kwargs.items()) + return f"{super().__repr__()[:-1]}, {extra_repr})" + + # Add properties for common attributes like shape, dtype, device, ndim etc + # this way we return the result without passing into __torch_function__ + @property + def shape(self) -> _size: # type: ignore[override] + with DisableTorchFunctionSubclass(): + return super().shape + + @property + def ndim(self) -> int: # type: ignore[override] + with DisableTorchFunctionSubclass(): + return super().ndim + + @property + def device(self, *args: Any, **kwargs: Any) -> _device: # type: ignore[override] + with DisableTorchFunctionSubclass(): + return super().device + + @property + def dtype(self) -> _dtype: # type: ignore[override] + with DisableTorchFunctionSubclass(): + return super().dtype + + def __deepcopy__(self: D, memo: Dict[int, Any]) -> D: + # We need to detach first, since a plain `Tensor.clone` will be part of the computation graph, which does + # *not* happen for `deepcopy(Tensor)`. A side-effect from detaching is that the `Tensor.requires_grad` + # attribute is cleared, so we need to refill it before we return. + # Note: We don't explicitly handle deep-copying of the metadata here. The only metadata we currently have is + # `BoundingBoxes.format` and `BoundingBoxes.canvas_size`, which are immutable and thus implicitly deep-copied by + # `BoundingBoxes.clone()`. + return self.detach().clone().requires_grad_(self.requires_grad) # type: ignore[return-value] diff --git a/lib/python3.11/site-packages/torchvision/tv_tensors/_video.py b/lib/python3.11/site-packages/torchvision/tv_tensors/_video.py new file mode 100644 index 0000000000000000000000000000000000000000..aa923e781ef0438e88c672f4f46d884689e206af --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/tv_tensors/_video.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from typing import Any, Optional, Union + +import torch + +from ._tv_tensor import TVTensor + + +class Video(TVTensor): + """:class:`torch.Tensor` subclass for videos with shape ``[..., T, C, H, W]``. + + Args: + data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`. + dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from + ``data``. + device (torch.device, optional): Desired device. If omitted and ``data`` is a + :class:`torch.Tensor`, the device is taken from it. Otherwise, the video is constructed on the CPU. + requires_grad (bool, optional): Whether autograd should record operations. If omitted and + ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``. + """ + + def __new__( + cls, + data: Any, + *, + dtype: Optional[torch.dtype] = None, + device: Optional[Union[torch.device, str, int]] = None, + requires_grad: Optional[bool] = None, + ) -> Video: + tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad) + if data.ndim < 4: + raise ValueError + return tensor.as_subclass(cls) + + def __repr__(self, *, tensor_contents: Any = None) -> str: # type: ignore[override] + return self._make_repr() diff --git a/lib/python3.11/site-packages/torchvision/utils.py b/lib/python3.11/site-packages/torchvision/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..eb1ec2ccf65bf18dacec7c828e527fb760205786 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/utils.py @@ -0,0 +1,666 @@ +import collections +import math +import pathlib +import warnings +from itertools import repeat +from types import FunctionType +from typing import Any, BinaryIO, List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL import Image, ImageColor, ImageDraw, ImageFont + + +__all__ = [ + "make_grid", + "save_image", + "draw_bounding_boxes", + "draw_segmentation_masks", + "draw_keypoints", + "flow_to_image", +] + + +@torch.no_grad() +def make_grid( + tensor: Union[torch.Tensor, List[torch.Tensor]], + nrow: int = 8, + padding: int = 2, + normalize: bool = False, + value_range: Optional[Tuple[int, int]] = None, + scale_each: bool = False, + pad_value: float = 0.0, +) -> torch.Tensor: + """ + Make a grid of images. + + Args: + tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W) + or a list of images all of the same size. + nrow (int, optional): Number of images displayed in each row of the grid. + The final grid size is ``(B / nrow, nrow)``. Default: ``8``. + padding (int, optional): amount of padding. Default: ``2``. + normalize (bool, optional): If True, shift the image to the range (0, 1), + by the min and max values specified by ``value_range``. Default: ``False``. + value_range (tuple, optional): tuple (min, max) where min and max are numbers, + then these numbers are used to normalize the image. By default, min and max + are computed from the tensor. + scale_each (bool, optional): If ``True``, scale each image in the batch of + images separately rather than the (min, max) over all images. Default: ``False``. + pad_value (float, optional): Value for the padded pixels. Default: ``0``. + + Returns: + grid (Tensor): the tensor containing grid of images. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(make_grid) + if not torch.is_tensor(tensor): + if isinstance(tensor, list): + for t in tensor: + if not torch.is_tensor(t): + raise TypeError(f"tensor or list of tensors expected, got a list containing {type(t)}") + else: + raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}") + + # if list of tensors, convert to a 4D mini-batch Tensor + if isinstance(tensor, list): + tensor = torch.stack(tensor, dim=0) + + if tensor.dim() == 2: # single image H x W + tensor = tensor.unsqueeze(0) + if tensor.dim() == 3: # single image + if tensor.size(0) == 1: # if single-channel, convert to 3-channel + tensor = torch.cat((tensor, tensor, tensor), 0) + tensor = tensor.unsqueeze(0) + + if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images + tensor = torch.cat((tensor, tensor, tensor), 1) + + if normalize is True: + tensor = tensor.clone() # avoid modifying tensor in-place + if value_range is not None and not isinstance(value_range, tuple): + raise TypeError("value_range has to be a tuple (min, max) if specified. min and max are numbers") + + def norm_ip(img, low, high): + img.clamp_(min=low, max=high) + img.sub_(low).div_(max(high - low, 1e-5)) + + def norm_range(t, value_range): + if value_range is not None: + norm_ip(t, value_range[0], value_range[1]) + else: + norm_ip(t, float(t.min()), float(t.max())) + + if scale_each is True: + for t in tensor: # loop over mini-batch dimension + norm_range(t, value_range) + else: + norm_range(tensor, value_range) + + if not isinstance(tensor, torch.Tensor): + raise TypeError("tensor should be of type torch.Tensor") + if tensor.size(0) == 1: + return tensor.squeeze(0) + + # make the mini-batch of images into a grid + nmaps = tensor.size(0) + xmaps = min(nrow, nmaps) + ymaps = int(math.ceil(float(nmaps) / xmaps)) + height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding) + num_channels = tensor.size(1) + grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value) + k = 0 + for y in range(ymaps): + for x in range(xmaps): + if k >= nmaps: + break + # Tensor.copy_() is a valid method but seems to be missing from the stubs + # https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_ + grid.narrow(1, y * height + padding, height - padding).narrow( # type: ignore[attr-defined] + 2, x * width + padding, width - padding + ).copy_(tensor[k]) + k = k + 1 + return grid + + +@torch.no_grad() +def save_image( + tensor: Union[torch.Tensor, List[torch.Tensor]], + fp: Union[str, pathlib.Path, BinaryIO], + format: Optional[str] = None, + **kwargs, +) -> None: + """ + Save a given Tensor into an image file. + + Args: + tensor (Tensor or list): Image to be saved. If given a mini-batch tensor, + saves the tensor as a grid of images by calling ``make_grid``. + fp (string or file object): A filename or a file object + format(Optional): If omitted, the format to use is determined from the filename extension. + If a file object was used instead of a filename, this parameter should always be used. + **kwargs: Other arguments are documented in ``make_grid``. + """ + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(save_image) + grid = make_grid(tensor, **kwargs) + # Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer + ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy() + im = Image.fromarray(ndarr) + im.save(fp, format=format) + + +@torch.no_grad() +def draw_bounding_boxes( + image: torch.Tensor, + boxes: torch.Tensor, + labels: Optional[List[str]] = None, + colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None, + fill: Optional[bool] = False, + width: int = 1, + font: Optional[str] = None, + font_size: Optional[int] = None, + label_colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None, +) -> torch.Tensor: + + """ + Draws bounding boxes on given RGB image. + The image values should be uint8 in [0, 255] or float in [0, 1]. + If fill is True, Resulting Tensor should be saved as PNG image. + + Args: + image (Tensor): Tensor of shape (C, H, W) and dtype uint8 or float. + boxes (Tensor): Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note that + the boxes are absolute coordinates with respect to the image. In other words: `0 <= xmin < xmax < W` and + `0 <= ymin < ymax < H`. + labels (List[str]): List containing the labels of bounding boxes. + colors (color or list of colors, optional): List containing the colors + of the boxes or single color for all boxes. The color can be represented as + PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``. + By default, random colors are generated for boxes. + fill (bool): If `True` fills the bounding box with specified color. + width (int): Width of bounding box. + font (str): A filename containing a TrueType font. If the file is not found in this filename, the loader may + also search in other directories, such as the `fonts/` directory on Windows or `/Library/Fonts/`, + `/System/Library/Fonts/` and `~/Library/Fonts/` on macOS. + font_size (int): The requested font size in points. + label_colors (color or list of colors, optional): Colors for the label text. See the description of the + `colors` argument for details. Defaults to the same colors used for the boxes. + + Returns: + img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted. + + """ + import torchvision.transforms.v2.functional as F # noqa + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(draw_bounding_boxes) + if not isinstance(image, torch.Tensor): + raise TypeError(f"Tensor expected, got {type(image)}") + elif not (image.dtype == torch.uint8 or image.is_floating_point()): + raise ValueError(f"The image dtype must be uint8 or float, got {image.dtype}") + elif image.dim() != 3: + raise ValueError("Pass individual images, not batches") + elif image.size(0) not in {1, 3}: + raise ValueError("Only grayscale and RGB images are supported") + elif (boxes[:, 0] > boxes[:, 2]).any() or (boxes[:, 1] > boxes[:, 3]).any(): + raise ValueError( + "Boxes need to be in (xmin, ymin, xmax, ymax) format. Use torchvision.ops.box_convert to convert them" + ) + + num_boxes = boxes.shape[0] + + if num_boxes == 0: + warnings.warn("boxes doesn't contain any box. No box was drawn") + return image + + if labels is None: + labels: Union[List[str], List[None]] = [None] * num_boxes # type: ignore[no-redef] + elif len(labels) != num_boxes: + raise ValueError( + f"Number of boxes ({num_boxes}) and labels ({len(labels)}) mismatch. Please specify labels for each box." + ) + + colors = _parse_colors(colors, num_objects=num_boxes) + if label_colors: + label_colors = _parse_colors(label_colors, num_objects=num_boxes) # type: ignore[assignment] + else: + label_colors = colors.copy() # type: ignore[assignment] + + if font is None: + if font_size is not None: + warnings.warn("Argument 'font_size' will be ignored since 'font' is not set.") + txt_font = ImageFont.load_default() + else: + txt_font = ImageFont.truetype(font=font, size=font_size or 10) + + # Handle Grayscale images + if image.size(0) == 1: + image = torch.tile(image, (3, 1, 1)) + + original_dtype = image.dtype + if original_dtype.is_floating_point: + image = F.to_dtype(image, dtype=torch.uint8, scale=True) + + img_to_draw = F.to_pil_image(image) + img_boxes = boxes.to(torch.int64).tolist() + + if fill: + draw = ImageDraw.Draw(img_to_draw, "RGBA") + else: + draw = ImageDraw.Draw(img_to_draw) + + for bbox, color, label, label_color in zip(img_boxes, colors, labels, label_colors): # type: ignore[arg-type] + if fill: + fill_color = color + (100,) + draw.rectangle(bbox, width=width, outline=color, fill=fill_color) + else: + draw.rectangle(bbox, width=width, outline=color) + + if label is not None: + margin = width + 1 + draw.text((bbox[0] + margin, bbox[1] + margin), label, fill=label_color, font=txt_font) # type: ignore[arg-type] + + out = F.pil_to_tensor(img_to_draw) + if original_dtype.is_floating_point: + out = F.to_dtype(out, dtype=original_dtype, scale=True) + return out + + +@torch.no_grad() +def draw_segmentation_masks( + image: torch.Tensor, + masks: torch.Tensor, + alpha: float = 0.8, + colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None, +) -> torch.Tensor: + + """ + Draws segmentation masks on given RGB image. + The image values should be uint8 in [0, 255] or float in [0, 1]. + + Args: + image (Tensor): Tensor of shape (3, H, W) and dtype uint8 or float. + masks (Tensor): Tensor of shape (num_masks, H, W) or (H, W) and dtype bool. + alpha (float): Float number between 0 and 1 denoting the transparency of the masks. + 0 means full transparency, 1 means no transparency. + colors (color or list of colors, optional): List containing the colors + of the masks or single color for all masks. The color can be represented as + PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``. + By default, random colors are generated for each mask. + + Returns: + img (Tensor[C, H, W]): Image Tensor, with segmentation masks drawn on top. + """ + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(draw_segmentation_masks) + if not isinstance(image, torch.Tensor): + raise TypeError(f"The image must be a tensor, got {type(image)}") + elif not (image.dtype == torch.uint8 or image.is_floating_point()): + raise ValueError(f"The image dtype must be uint8 or float, got {image.dtype}") + elif image.dim() != 3: + raise ValueError("Pass individual images, not batches") + elif image.size()[0] != 3: + raise ValueError("Pass an RGB image. Other Image formats are not supported") + if masks.ndim == 2: + masks = masks[None, :, :] + if masks.ndim != 3: + raise ValueError("masks must be of shape (H, W) or (batch_size, H, W)") + if masks.dtype != torch.bool: + raise ValueError(f"The masks must be of dtype bool. Got {masks.dtype}") + if masks.shape[-2:] != image.shape[-2:]: + raise ValueError("The image and the masks must have the same height and width") + + num_masks = masks.size()[0] + overlapping_masks = masks.sum(dim=0) > 1 + + if num_masks == 0: + warnings.warn("masks doesn't contain any mask. No mask was drawn") + return image + + original_dtype = image.dtype + colors = [ + torch.tensor(color, dtype=original_dtype, device=image.device) + for color in _parse_colors(colors, num_objects=num_masks, dtype=original_dtype) + ] + + img_to_draw = image.detach().clone() + # TODO: There might be a way to vectorize this + for mask, color in zip(masks, colors): + img_to_draw[:, mask] = color[:, None] + + img_to_draw[:, overlapping_masks] = 0 + + out = image * (1 - alpha) + img_to_draw * alpha + # Note: at this point, out is a float tensor in [0, 1] or [0, 255] depending on original_dtype + return out.to(original_dtype) + + +@torch.no_grad() +def draw_keypoints( + image: torch.Tensor, + keypoints: torch.Tensor, + connectivity: Optional[List[Tuple[int, int]]] = None, + colors: Optional[Union[str, Tuple[int, int, int]]] = None, + radius: int = 2, + width: int = 3, + visibility: Optional[torch.Tensor] = None, +) -> torch.Tensor: + + """ + Draws Keypoints on given RGB image. + The image values should be uint8 in [0, 255] or float in [0, 1]. + Keypoints can be drawn for multiple instances at a time. + + This method allows that keypoints and their connectivity are drawn based on the visibility of this keypoint. + + Args: + image (Tensor): Tensor of shape (3, H, W) and dtype uint8 or float. + keypoints (Tensor): Tensor of shape (num_instances, K, 2) the K keypoint locations for each of the N instances, + in the format [x, y]. + connectivity (List[Tuple[int, int]]]): A List of tuple where each tuple contains a pair of keypoints + to be connected. + If at least one of the two connected keypoints has a ``visibility`` of False, + this specific connection is not drawn. + Exclusions due to invisibility are computed per-instance. + colors (str, Tuple): The color can be represented as + PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``. + radius (int): Integer denoting radius of keypoint. + width (int): Integer denoting width of line connecting keypoints. + visibility (Tensor): Tensor of shape (num_instances, K) specifying the visibility of the K + keypoints for each of the N instances. + True means that the respective keypoint is visible and should be drawn. + False means invisible, so neither the point nor possible connections containing it are drawn. + The input tensor will be cast to bool. + Default ``None`` means that all the keypoints are visible. + For more details, see :ref:`draw_keypoints_with_visibility`. + + Returns: + img (Tensor[C, H, W]): Image Tensor with keypoints drawn. + """ + + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(draw_keypoints) + # validate image + if not isinstance(image, torch.Tensor): + raise TypeError(f"The image must be a tensor, got {type(image)}") + elif not (image.dtype == torch.uint8 or image.is_floating_point()): + raise ValueError(f"The image dtype must be uint8 or float, got {image.dtype}") + elif image.dim() != 3: + raise ValueError("Pass individual images, not batches") + elif image.size()[0] != 3: + raise ValueError("Pass an RGB image. Other Image formats are not supported") + + # validate keypoints + if keypoints.ndim != 3: + raise ValueError("keypoints must be of shape (num_instances, K, 2)") + + # validate visibility + if visibility is None: # set default + visibility = torch.ones(keypoints.shape[:-1], dtype=torch.bool) + if visibility.ndim == 3: + # If visibility was passed as pred.split([2, 1], dim=-1), it will be of shape (num_instances, K, 1). + # We make sure it is of shape (num_instances, K). This isn't documented, we're just being nice. + visibility = visibility.squeeze(-1) + if visibility.ndim != 2: + raise ValueError(f"visibility must be of shape (num_instances, K). Got ndim={visibility.ndim}") + if visibility.shape != keypoints.shape[:-1]: + raise ValueError( + "keypoints and visibility must have the same dimensionality for num_instances and K. " + f"Got {visibility.shape = } and {keypoints.shape = }" + ) + + original_dtype = image.dtype + if original_dtype.is_floating_point: + from torchvision.transforms.v2.functional import to_dtype # noqa + + image = to_dtype(image, dtype=torch.uint8, scale=True) + + ndarr = image.permute(1, 2, 0).cpu().numpy() + img_to_draw = Image.fromarray(ndarr) + draw = ImageDraw.Draw(img_to_draw) + img_kpts = keypoints.to(torch.int64).tolist() + img_vis = visibility.cpu().bool().tolist() + + for kpt_inst, vis_inst in zip(img_kpts, img_vis): + for kpt_coord, kp_vis in zip(kpt_inst, vis_inst): + if not kp_vis: + continue + x1 = kpt_coord[0] - radius + x2 = kpt_coord[0] + radius + y1 = kpt_coord[1] - radius + y2 = kpt_coord[1] + radius + draw.ellipse([x1, y1, x2, y2], fill=colors, outline=None, width=0) + + if connectivity: + for connection in connectivity: + if (not vis_inst[connection[0]]) or (not vis_inst[connection[1]]): + continue + start_pt_x = kpt_inst[connection[0]][0] + start_pt_y = kpt_inst[connection[0]][1] + + end_pt_x = kpt_inst[connection[1]][0] + end_pt_y = kpt_inst[connection[1]][1] + + draw.line( + ((start_pt_x, start_pt_y), (end_pt_x, end_pt_y)), + width=width, + ) + + out = torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1) + if original_dtype.is_floating_point: + out = to_dtype(out, dtype=original_dtype, scale=True) + return out + + +# Flow visualization code adapted from https://github.com/tomrunia/OpticalFlow_Visualization +@torch.no_grad() +def flow_to_image(flow: torch.Tensor) -> torch.Tensor: + + """ + Converts a flow to an RGB image. + + Args: + flow (Tensor): Flow of shape (N, 2, H, W) or (2, H, W) and dtype torch.float. + + Returns: + img (Tensor): Image Tensor of dtype uint8 where each color corresponds + to a given flow direction. Shape is (N, 3, H, W) or (3, H, W) depending on the input. + """ + + if flow.dtype != torch.float: + raise ValueError(f"Flow should be of dtype torch.float, got {flow.dtype}.") + + orig_shape = flow.shape + if flow.ndim == 3: + flow = flow[None] # Add batch dim + + if flow.ndim != 4 or flow.shape[1] != 2: + raise ValueError(f"Input flow should have shape (2, H, W) or (N, 2, H, W), got {orig_shape}.") + + max_norm = torch.sum(flow**2, dim=1).sqrt().max() + epsilon = torch.finfo((flow).dtype).eps + normalized_flow = flow / (max_norm + epsilon) + img = _normalized_flow_to_image(normalized_flow) + + if len(orig_shape) == 3: + img = img[0] # Remove batch dim + return img + + +@torch.no_grad() +def _normalized_flow_to_image(normalized_flow: torch.Tensor) -> torch.Tensor: + + """ + Converts a batch of normalized flow to an RGB image. + + Args: + normalized_flow (torch.Tensor): Normalized flow tensor of shape (N, 2, H, W) + Returns: + img (Tensor(N, 3, H, W)): Flow visualization image of dtype uint8. + """ + + N, _, H, W = normalized_flow.shape + device = normalized_flow.device + flow_image = torch.zeros((N, 3, H, W), dtype=torch.uint8, device=device) + colorwheel = _make_colorwheel().to(device) # shape [55x3] + num_cols = colorwheel.shape[0] + norm = torch.sum(normalized_flow**2, dim=1).sqrt() + a = torch.atan2(-normalized_flow[:, 1, :, :], -normalized_flow[:, 0, :, :]) / torch.pi + fk = (a + 1) / 2 * (num_cols - 1) + k0 = torch.floor(fk).to(torch.long) + k1 = k0 + 1 + k1[k1 == num_cols] = 0 + f = fk - k0 + + for c in range(colorwheel.shape[1]): + tmp = colorwheel[:, c] + col0 = tmp[k0] / 255.0 + col1 = tmp[k1] / 255.0 + col = (1 - f) * col0 + f * col1 + col = 1 - norm * (1 - col) + flow_image[:, c, :, :] = torch.floor(255 * col) + return flow_image + + +def _make_colorwheel() -> torch.Tensor: + """ + Generates a color wheel for optical flow visualization as presented in: + Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) + URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf. + + Returns: + colorwheel (Tensor[55, 3]): Colorwheel Tensor. + """ + + RY = 15 + YG = 6 + GC = 4 + CB = 11 + BM = 13 + MR = 6 + + ncols = RY + YG + GC + CB + BM + MR + colorwheel = torch.zeros((ncols, 3)) + col = 0 + + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = torch.floor(255 * torch.arange(0, RY) / RY) + col = col + RY + # YG + colorwheel[col : col + YG, 0] = 255 - torch.floor(255 * torch.arange(0, YG) / YG) + colorwheel[col : col + YG, 1] = 255 + col = col + YG + # GC + colorwheel[col : col + GC, 1] = 255 + colorwheel[col : col + GC, 2] = torch.floor(255 * torch.arange(0, GC) / GC) + col = col + GC + # CB + colorwheel[col : col + CB, 1] = 255 - torch.floor(255 * torch.arange(CB) / CB) + colorwheel[col : col + CB, 2] = 255 + col = col + CB + # BM + colorwheel[col : col + BM, 2] = 255 + colorwheel[col : col + BM, 0] = torch.floor(255 * torch.arange(0, BM) / BM) + col = col + BM + # MR + colorwheel[col : col + MR, 2] = 255 - torch.floor(255 * torch.arange(MR) / MR) + colorwheel[col : col + MR, 0] = 255 + return colorwheel + + +def _generate_color_palette(num_objects: int): + palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1]) + return [tuple((i * palette) % 255) for i in range(num_objects)] + + +def _parse_colors( + colors: Union[None, str, Tuple[int, int, int], List[Union[str, Tuple[int, int, int]]]], + *, + num_objects: int, + dtype: torch.dtype = torch.uint8, +) -> List[Tuple[int, int, int]]: + """ + Parses a specification of colors for a set of objects. + + Args: + colors: A specification of colors for the objects. This can be one of the following: + - None: to generate a color palette automatically. + - A list of colors: where each color is either a string (specifying a named color) or an RGB tuple. + - A string or an RGB tuple: to use the same color for all objects. + + If `colors` is a tuple, it should be a 3-tuple specifying the RGB values of the color. + If `colors` is a list, it should have at least as many elements as the number of objects to color. + + num_objects (int): The number of objects to color. + + Returns: + A list of 3-tuples, specifying the RGB values of the colors. + + Raises: + ValueError: If the number of colors in the list is less than the number of objects to color. + If `colors` is not a list, tuple, string or None. + """ + if colors is None: + colors = _generate_color_palette(num_objects) + elif isinstance(colors, list): + if len(colors) < num_objects: + raise ValueError( + f"Number of colors must be equal or larger than the number of objects, but got {len(colors)} < {num_objects}." + ) + elif not isinstance(colors, (tuple, str)): + raise ValueError(f"`colors` must be a tuple or a string, or a list thereof, but got {colors}.") + elif isinstance(colors, tuple) and len(colors) != 3: + raise ValueError(f"If passed as tuple, colors should be an RGB triplet, but got {colors}.") + else: # colors specifies a single color for all objects + colors = [colors] * num_objects + + colors = [ImageColor.getrgb(color) if isinstance(color, str) else color for color in colors] + if dtype.is_floating_point: # [0, 255] -> [0, 1] + colors = [tuple(v / 255 for v in color) for color in colors] # type: ignore[union-attr] + return colors # type: ignore[return-value] + + +def _log_api_usage_once(obj: Any) -> None: + + """ + Logs API usage(module and name) within an organization. + In a large ecosystem, it's often useful to track the PyTorch and + TorchVision APIs usage. This API provides the similar functionality to the + logging module in the Python stdlib. It can be used for debugging purpose + to log which methods are used and by default it is inactive, unless the user + manually subscribes a logger via the `SetAPIUsageLogger method `_. + Please note it is triggered only once for the same API call within a process. + It does not collect any data from open-source users since it is no-op by default. + For more information, please refer to + * PyTorch note: https://pytorch.org/docs/stable/notes/large_scale_deployments.html#api-usage-logging; + * Logging policy: https://github.com/pytorch/vision/issues/5052; + + Args: + obj (class instance or method): an object to extract info from. + """ + module = obj.__module__ + if not module.startswith("torchvision"): + module = f"torchvision.internal.{module}" + name = obj.__class__.__name__ + if isinstance(obj, FunctionType): + name = obj.__name__ + torch._C._log_api_usage_once(f"{module}.{name}") + + +def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]: + """ + Make n-tuple from input x. If x is an iterable, then we just convert it to tuple. + Otherwise, we will make a tuple of length n, all with value of x. + reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8 + + Args: + x (Any): input value + n (int): length of the resulting tuple + """ + if isinstance(x, collections.abc.Iterable): + return tuple(x) + return tuple(repeat(x, n)) diff --git a/lib/python3.11/site-packages/torchvision/version.py b/lib/python3.11/site-packages/torchvision/version.py new file mode 100644 index 0000000000000000000000000000000000000000..3e8c33bd46e080cbacfe14e88d1d44c0b49395d3 --- /dev/null +++ b/lib/python3.11/site-packages/torchvision/version.py @@ -0,0 +1,5 @@ +__version__ = '0.21.0+cu124' +git_version = '7af698794eded568735f9519593603c1ec889eba' +from torchvision.extension import _check_cuda_version +if _check_cuda_version() > 0: + cuda = _check_cuda_version()