code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from collections import OrderedDict
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.jit.annotations import Tuple, List, Dict
class FeaturePyramidNetwork(nn.Module):
"""
Module that adds a FPN from on top of a set of feature maps. This is based on
`"Feature Pyramid Ne... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/feature_pyramid_network.py | 0.943699 | 0.688796 | feature_pyramid_network.py | pypi |
import importlib
import math
import os
import warnings
from fractions import Fraction
from typing import List, Tuple
import numpy as np
import torch
_HAS_VIDEO_OPT = False
try:
lib_dir = os.path.join(os.path.dirname(__file__), "..")
loader_details = (
importlib.machinery.ExtensionFileLoader,
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/io/_video_opt.py | 0.769427 | 0.213685 | _video_opt.py | pypi |
import os
import tarfile
import collections
from .vision import VisionDataset
import xml.etree.ElementTree as ET
from PIL import Image
from .utils import download_url, check_integrity, verify_str_arg
DATASET_YEAR_DICT = {
'2012': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/voc.py | 0.63624 | 0.255544 | voc.py | pypi |
from collections import defaultdict
from PIL import Image
from html.parser import HTMLParser
import glob
import os
from .vision import VisionDataset
class Flickr8kParser(HTMLParser):
"""Parser for extracting captions from the Flickr8k dataset web page."""
def __init__(self, root):
super(Flickr8kPars... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/flickr.py | 0.872768 | 0.304436 | flickr.py | pypi |
import glob
import os
from .utils import list_dir
from .folder import make_dataset
from .video_utils import VideoClips
from .vision import VisionDataset
class HMDB51(VisionDataset):
"""
`HMDB51 <http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/>`_
dataset.
HMDB51 is an act... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/hmdb51.py | 0.870405 | 0.546073 | hmdb51.py | pypi |
from PIL import Image
from os.path import join
import os
from .vision import VisionDataset
from .utils import download_and_extract_archive, check_integrity, list_dir, list_files
class Omniglot(VisionDataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ Dataset.
Args:
root (string): Root d... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/omniglot.py | 0.840652 | 0.413832 | omniglot.py | pypi |
import warnings
from contextlib import contextmanager
import os
import shutil
import tempfile
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': ('ILS... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/imagenet.py | 0.661486 | 0.303409 | imagenet.py | pypi |
from functools import partial
import torch
import os
import PIL
from .vision import VisionDataset
from .utils import download_file_from_google_drive, check_integrity, verify_str_arg
class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.htm... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/celeba.py | 0.800536 | 0.47658 | celeba.py | pypi |
from PIL import Image
import os
import os.path
import numpy as np
from .vision import VisionDataset
from .utils import download_url, check_integrity
class SEMEION(VisionDataset):
"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.
Args:
root (string): Root directo... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/semeion.py | 0.813572 | 0.365825 | semeion.py | pypi |
import json
import os
from collections import namedtuple
import zipfile
from .utils import extract_archive, verify_str_arg, iterable_to_str
from .vision import VisionDataset
from PIL import Image
class Cityscapes(VisionDataset):
"""`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset.
Args:
ro... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/cityscapes.py | 0.770896 | 0.40486 | cityscapes.py | pypi |
from PIL import Image
import os
import os.path
from .vision import VisionDataset
from .utils import download_and_extract_archive, verify_str_arg
class Caltech101(VisionDataset):
"""`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset.
.. warning::
This class needs `scip... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/caltech.py | 0.817866 | 0.517998 | caltech.py | pypi |
import os
import os.path
import hashlib
import gzip
import errno
import tarfile
import zipfile
import torch
from torch.utils.model_zoo import tqdm
def gen_bar_updater():
pbar = tqdm(total=None)
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.tota... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/utils.py | 0.567577 | 0.18374 | utils.py | pypi |
from PIL import Image
import os
import os.path
import numpy as np
import pickle
from .vision import VisionDataset
from .utils import check_integrity, download_and_extract_archive
class CIFAR10(VisionDataset):
"""`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
Args:
root (string): R... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/cifar.py | 0.737158 | 0.336277 | cifar.py | pypi |
import os
import shutil
from .vision import VisionDataset
import numpy as np
from PIL import Image
from .utils import download_url, verify_str_arg
from .voc import download_extract
class SBDataset(VisionDataset):
"""`Semantic Boundaries Dataset <http://home.bharathh.info/pubs/codes/SBD/download.html>`_
The... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/sbd.py | 0.756358 | 0.490114 | sbd.py | pypi |
from PIL import Image
import os
import numpy as np
from .utils import download_url
from .vision import VisionDataset
class USPS(VisionDataset):
"""`USPS <https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps>`_ Dataset.
The data-format is : [label [index:value ]*256 \\n] * num_lines, w... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/usps.py | 0.820793 | 0.519095 | usps.py | pypi |
from PIL import Image
from .utils import download_url, check_integrity
import os
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicente/sbucaptions/>`_ Dataset.
Args:
root (string): Root directory of dataset where tarball
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/sbu.py | 0.836421 | 0.465448 | sbu.py | pypi |
import os
import numpy as np
from PIL import Image
import torch
from .vision import VisionDataset
from .utils import download_url
class PhotoTour(VisionDataset):
"""`Learning Local Image Descriptors Data <http://phototour.cs.washington.edu/patches/default.htm>`_ Dataset.
Args:
root (string): Root ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/phototour.py | 0.640973 | 0.3295 | phototour.py | pypi |
from PIL import Image
import os
import os.path
import numpy as np
from .vision import VisionDataset
from .utils import check_integrity, download_and_extract_archive, verify_str_arg
class STL10(VisionDataset):
"""`STL10 <https://cs.stanford.edu/~acoates/stl10/>`_ Dataset.
Args:
root (string): Root di... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/stl10.py | 0.719679 | 0.418756 | stl10.py | pypi |
from .utils import list_dir
from .folder import make_dataset
from .video_utils import VideoClips
from .vision import VisionDataset
class Kinetics400(VisionDataset):
"""
`Kinetics-400 <https://deepmind.com/research/open-source/open-source-datasets/kinetics/>`_
dataset.
Kinetics-400 is an action recogn... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/kinetics.py | 0.937247 | 0.564639 | kinetics.py | pypi |
from .vision import VisionDataset
from PIL import Image
import os
import os.path
class CocoCaptions(VisionDataset):
"""`MS Coco Captions <http://mscoco.org/dataset/#captions-challenge2015>`_ Dataset.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/coco.py | 0.918199 | 0.570989 | coco.py | pypi |
from .vision import VisionDataset
from PIL import Image
import os
import os.path
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/folder.py | 0.889493 | 0.386995 | folder.py | pypi |
import glob
import os
from .utils import list_dir
from .folder import make_dataset
from .video_utils import VideoClips
from .vision import VisionDataset
class UCF101(VisionDataset):
"""
`UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset.
UCF101 is an action recognition video dataset.
This ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/ucf101.py | 0.867373 | 0.603611 | ucf101.py | pypi |
import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
from torchvision.datasets.video_utils import VideoClips
class DistributedSampler(Sampler):
"""
Extension of DistributedSampler, as discussed in
https://github.com/pytorch/pytorch/issues/23430
Example:
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/datasets/samplers/clip_sampler.py | 0.887089 | 0.535706 | clip_sampler.py | pypi |
rpi\_ws281x
===========
Userspace Raspberry Pi library for controlling WS281X LEDs. This
includes WS2812 and SK6812RGB RGB LEDs Preliminary support is now
included for SK6812RGBW LEDs (yes, RGB + W) The LEDs can be controlled
by either the PWM (2 independent channels) or PCM controller (1 channel)
or the SPI interface... | /rpi_ws281x_3bp_spi1-0.0.1.tar.gz/rpi_ws281x_3bp_spi1-0.0.1/README.rst | 0.8474 | 0.67996 | README.rst | pypi |
import _rpi_ws281x as ws
import atexit
try:
xrange(0)
except NameError:
xrange = range
def Color(red, green, blue, white=0):
"""Convert the provided red, green, blue color to a 24-bit color value.
Each color component should be a value 0-255 where 0 is the lowest intensity
and 255 is the highest... | /rpi_ws281x_3bp_spi1-0.0.1.tar.gz/rpi_ws281x_3bp_spi1-0.0.1/rpi_ws281x/rpi_ws281x.py | 0.76769 | 0.491883 | rpi_ws281x.py | pypi |
import time
from abc import ABC, abstractmethod
from colour import Color as C
import random
from enum import Enum
import math
from operator import add
from pydantic import BaseModel
from easing_functions import *
from rpi_ws281x_hub.strip import ColorPixelStrip
RAINBOW = list(C('#FF0000').range_to(C('#00FFFE'), 128))... | /rpi_ws281x_hub-1.0.3-py3-none-any.whl/rpi_ws281x_hub/tasks.py | 0.763307 | 0.172398 | tasks.py | pypi |
import atexit
def Color(red, green, blue, white=0):
"""Convert the provided red, green, blue color to a 24-bit color value.
Each color component should be a value 0-255 where 0 is the lowest intensity
and 255 is the highest intensity.
"""
return (white << 24) | (red << 16) | (green << 8) | blue
... | /rpi_ws281x_mock-0.2.2.tar.gz/rpi_ws281x_mock-0.2.2/rpi_ws281x/rpi_ws281x_mock.py | 0.818664 | 0.606003 | rpi_ws281x_mock.py | pypi |
rpi\_ws281x
===========
Userspace Raspberry Pi library for controlling WS281X LEDs. This
includes WS2812 and SK6812RGB RGB LEDs Preliminary support is now
included for SK6812RGBW LEDs (yes, RGB + W) The LEDs can be controlled
by either the PWM (2 independent channels) or PCM controller (1 channel)
or the SPI interface... | /rpi_ws281x-5.0.0.tar.gz/rpi_ws281x-5.0.0/README.rst | 0.8474 | 0.67996 | README.rst | pypi |
import _rpi_ws281x as ws
import atexit
class RGBW(int):
def __new__(self, r, g=None, b=None, w=None):
if (g, b, w) == (None, None, None):
return int.__new__(self, r)
else:
if w is None:
w = 0
return int.__new__(self, (w << 24) | (r << 16) | (g <<... | /rpi_ws281x-5.0.0.tar.gz/rpi_ws281x-5.0.0/rpi_ws281x/rpi_ws281x.py | 0.820685 | 0.336576 | rpi_ws281x.py | pypi |
rpi2caster
==========
Raspberry Pi controls a Monotype composition caster.
----------------------------------------------------
Based on computer2caster by John Cornelisse Original idea described at
http://letterpress.ch
Typesetting and casting software for a Raspberry Pi-based computer
control attachment for Monoty... | /rpi2caster-2.5.0.tar.gz/rpi2caster-2.5.0/README.rst | 0.582966 | 0.666314 | README.rst | pypi |
rpi2casterd
===========
Hardware driver and web API for rpi2caster
------------------------------------------
This is a machine control daemon for the ``rpi2caster`` typesetting and casting software.
It is supposed to run on a Raspberry Pi (any model) with an output expander based on two
MCP23017 chips to provide 32 ... | /rpi2casterd-2.5.12.tar.gz/rpi2casterd-2.5.12/README.rst | 0.831622 | 0.865679 | README.rst | pypi |
```
%load_ext autoreload
autoreload 2
from rpi2mqtt.config import *
import yaml
from collections import deque
class HestiaPi:
def __init__(self, **kwargs):
# self._modes = HVAC.HEAT_PUMP_MODES
# super(HestiaPi, self).__init__(kwargs.get('name'), None, kwargs.get('topic'), 'climate', 'HestiaPi')... | /rpi2mqtt-0.5.29.tar.gz/rpi2mqtt-0.5.29/Untitled.ipynb | 0.521959 | 0.177668 | Untitled.ipynb | pypi |
import math
import torch
from functools import reduce
from sys import float_info
class __PrinterOptions(object):
precision = 4
threshold = 1000
edgeitems = 3
linewidth = 80
PRINT_OPTS = __PrinterOptions()
SCALE_FORMAT = '{:.5e} *\n'
# We could use **kwargs, but this will give better docs
def set_p... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/_tensor_str.py | 0.535827 | 0.297387 | _tensor_str.py | pypi |
import torch
import contextlib
import warnings
from torch._C import default_generator
def set_rng_state(new_state):
r"""Sets the random number generator state.
Args:
new_state (torch.ByteTensor): The desired state
"""
default_generator.set_state(new_state)
def get_rng_state():
r"""Retu... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/random.py | 0.895912 | 0.453625 | random.py | pypi |
import torch
from ._utils import _type, _cuda
class _StorageBase(object):
is_cuda = False
is_sparse = False
def __str__(self):
content = ' ' + '\n '.join(str(self[i]) for i in range(len(self)))
return content + '\n[{} of size {}]'.format(torch.typename(self), len(self))
def __repr__(... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/storage.py | 0.758332 | 0.236307 | storage.py | pypi |
import sys
import torch
import torch._C as _C
from collections import OrderedDict
import torch.utils.hooks as hooks
import warnings
import weakref
from torch._six import imap
from torch._C import _add_docstr
class Tensor(torch._C._TensorBase):
def __deepcopy__(self, memo):
if not self.is_leaf:
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/tensor.py | 0.77569 | 0.337204 | tensor.py | pypi |
import torch
import importlib
import warnings
from collections import defaultdict
def _type(self, dtype=None, non_blocking=False, **kwargs):
"""Returns the type if `dtype` is not provided, else casts this object to
the specified type.
If this is already of the correct type, no copy is performed and the
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/_utils.py | 0.896206 | 0.358493 | _utils.py | pypi |
import warnings
import math
from operator import mul
from functools import reduce
import torch
from torch._C import _infer_size, _add_docstr
from . import _functions
from .modules import utils
from ._functions.padding import ConstantPadNd
from ._functions import vision
from ._functions.thnn.fold import Col2Im, Im2Col... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/functional.py | 0.9255 | 0.606906 | functional.py | pypi |
import torch
from .modules.utils import _single, _pair, _triple
def _grad_input_padding(grad_output, input_size, stride, padding, kernel_size):
input_size = list(input_size)
k = grad_output.dim() - 2
if len(input_size) == k + 2:
input_size = input_size[-k:]
if len(input_size) != k:
r... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/grad.py | 0.911648 | 0.583975 | grad.py | pypi |
import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .module import Module
from .utils import _single, _pair, _triple
class _ConvNd(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_p... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/conv.py | 0.951363 | 0.417568 | conv.py | pypi |
from .module import Module
from .. import functional as F
class Fold(Module):
"""
De-interleaves vectors of length :math:`\prod(kernel_size)` from the "channel"
dimension of the input tensor to generate blocks of size :math:`kernel_size`
of the output. These blocks populate the "spatial" dimensions [... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/fold.py | 0.952541 | 0.841565 | fold.py | pypi |
from .module import Module
from .utils import _pair, _quadruple, _ntuple
from .. import functional as F
# TODO: grad_output size asserts in THNN
class _ConstantPadNd(Module):
def __init__(self, value):
super(_ConstantPadNd, self).__init__()
self.value = value
def forward(self, input):
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/padding.py | 0.784319 | 0.585131 | padding.py | pypi |
import warnings
import torch
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
class Threshold(Module):
r"""Thresholds each element of the input Tensor
Threshold is defined as:
.. math::
y =
\begin{cases}
x, &\text{ if } x > \text... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/activation.py | 0.921759 | 0.674225 | activation.py | pypi |
from .module import Module
from .. import functional as F
class _DropoutNd(Module):
def __init__(self, p=0.5, inplace=False):
super(_DropoutNd, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/dropout.py | 0.940817 | 0.775732 | dropout.py | pypi |
import torch
import numbers
from torch.nn.parameter import Parameter
from .module import Module
from .batchnorm import _BatchNorm
from .. import functional as F
class LocalResponseNorm(Module):
r"""Applies local response normalization over an input signal composed
of several input planes, where channels occup... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/normalization.py | 0.963386 | 0.680112 | normalization.py | pypi |
import torch
from .module import Module
from .. import functional as F
class PairwiseDistance(Module):
r"""
Computes the batchwise pairwise distance between vectors :math:`v_1`,:math:`v_2` using the p-norm:
.. math ::
\Vert x \Vert _p := \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/distance.py | 0.958109 | 0.761538 | distance.py | pypi |
from numbers import Integral
import warnings
from .module import Module
from .. import functional as F
class Upsample(Module):
r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
The input data is assumed to be of the form
`minibatch x channels x [optional depth] x [... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/upsampling.py | 0.899348 | 0.887253 | upsampling.py | pypi |
import torch
from .module import Module
from .utils import _single, _pair, _triple
from .. import functional as F
class _MaxPoolNd(Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False):
super(_MaxPoolNd, self).__init__()
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/pooling.py | 0.953416 | 0.54692 | pooling.py | pypi |
import torch
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
class Embedding(Module):
r"""A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them using indices.
Th... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/sparse.py | 0.964187 | 0.76819 | sparse.py | pypi |
import warnings
from collections import OrderedDict, Iterable
from itertools import islice
import operator
import torch
from .module import Module
class Container(Module):
def __init__(self, **kwargs):
super(Container, self).__init__()
# DeprecationWarning is ignored by default <sigh>
wa... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/container.py | 0.85449 | 0.269656 | container.py | pypi |
import warnings
import torch
from .module import Module
from .container import Sequential
from .activation import LogSoftmax
from .. import functional as F
def _assert_no_grad(tensor):
assert not tensor.requires_grad, \
"nn criterions don't compute the gradient w.r.t. targets - please " \
"mark t... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/loss.py | 0.942049 | 0.665431 | loss.py | pypi |
from collections import OrderedDict
import functools
import itertools
import torch
from ..backends.thnn import backend as thnn_backend
from ..parameter import Parameter
import torch.utils.hooks as hooks
def _addindent(s_, numSpaces):
s = s_.split('\n')
# don't do anything for single-line stuff
if len(s) ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/module.py | 0.898522 | 0.305477 | module.py | pypi |
import torch
from .module import Module
from torch.nn.parameter import Parameter
from .. import functional as F
# TODO: check contiguous in THNN
# TODO: use separate backend functions?
class _BatchNorm(Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_s... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/batchnorm.py | 0.761361 | 0.51379 | batchnorm.py | pypi |
import math
import torch
import warnings
import itertools
import numbers
from .module import Module
from ..parameter import Parameter
from ..utils.rnn import PackedSequence
class RNNBase(Module):
def __init__(self, mode, input_size, hidden_size,
num_layers=1, bias=True, batch_first=False,
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/rnn.py | 0.785966 | 0.34834 | rnn.py | pypi |
from .module import Module
from .linear import Linear, Bilinear
from .conv import Conv1d, Conv2d, Conv3d, \
ConvTranspose1d, ConvTranspose2d, ConvTranspose3d
from .activation import Threshold, ReLU, Hardtanh, ReLU6, Sigmoid, Tanh, \
Softmax, Softmax2d, LogSoftmax, ELU, SELU, Hardshrink, LeakyReLU, LogSigmoid, \... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/__init__.py | 0.819135 | 0.434641 | __init__.py | pypi |
from .batchnorm import _BatchNorm
from .. import functional as F
class _InstanceNorm(_BatchNorm):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(_InstanceNorm, self).__init__(
num_features, eps, momentum, affine, track_... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/instancenorm.py | 0.846483 | 0.503601 | instancenorm.py | pypi |
import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .module import Module
class Linear(Module):
r"""Applies a linear transformation to the incoming data: :math:`y = Ax + b`
Args:
in_features: size of each input sample
out_features: size of ea... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/modules/linear.py | 0.911024 | 0.799638 | linear.py | pypi |
import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch._thnn import type2backend
from .thnn.auto import function_by_name
import torch.backends.cudnn as cudnn
MODE_ZEROS = 0
MODE_BORDER = 1
def grid_sampler(input, grid, padding_mode):
if cudnn.is_accept... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/vision.py | 0.679604 | 0.553324 | vision.py | pypi |
from torch.autograd import Function, Variable
from torch.autograd._functions.utils import prepare_onnx_paddings
class ConstantPadNd(Function):
@staticmethod
def symbolic(g, input, pad, value=0):
paddings = prepare_onnx_paddings(len(input.type().sizes()), pad)
return g.op("Pad", input, pads_i=... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/padding.py | 0.78838 | 0.5425 | padding.py | pypi |
import torch
from torch.autograd.function import InplaceFunction
from itertools import repeat
class Dropout(InplaceFunction):
@staticmethod
def _make_noise(input):
return input.new().resize_as_(input)
@staticmethod
def symbolic(g, input, p=0.5, train=False, inplace=False):
# See Note... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/dropout.py | 0.801897 | 0.387545 | dropout.py | pypi |
import warnings
from torch.autograd import NestedIOFunction
import torch.backends.cudnn as cudnn
from .. import functional as F
from .thnn import rnnFusedPointwise as fusedBackend
import itertools
from functools import partial
try:
import torch.backends.cudnn.rnn
except ImportError:
pass
def RNNReLUCell(inpu... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/rnn.py | 0.710126 | 0.335991 | rnn.py | pypi |
from torch.autograd.function import Function, once_differentiable
from torch._thnn import type2backend
from . import _all_functions
class Col2Im(Function):
@staticmethod
def forward(ctx, input, output_size, kernel_size, dilation, padding, stride):
ctx.output_size = output_size
ctx.kernel_si... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/thnn/fold.py | 0.92944 | 0.448607 | fold.py | pypi |
import torch
from torch.autograd.function import Function, InplaceFunction, once_differentiable
from torch._thnn import type2backend
class GRUFused(Function):
@staticmethod
def forward(ctx, input_gate, hidden_gate, hx, ibias=None, hbias=None):
ctx.backend = type2backend[input_gate.type()]
hy ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/thnn/rnnFusedPointwise.py | 0.873228 | 0.394726 | rnnFusedPointwise.py | pypi |
import torch
from torch.autograd.function import Function
from torch._thnn import type2backend
from . import _all_functions
class CrossMapLRN2d(Function):
def __init__(self, size, alpha=1e-4, beta=0.75, k=1):
super(CrossMapLRN2d, self).__init__()
self.size = size
self.alpha = alpha
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/thnn/normalization.py | 0.811303 | 0.325253 | normalization.py | pypi |
import torch
from torch.autograd.function import Function
from torch._thnn import type2backend
from torch.autograd.function import once_differentiable
from . import _all_functions
MODE_SUM = 0
MODE_MEAN = 1
class EmbeddingBag(Function):
@staticmethod
def _renorm(ctx, indices, weight, max_norm, norm_type):... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/thnn/sparse.py | 0.87456 | 0.433682 | sparse.py | pypi |
import torch
def elu_double_backwards(ctx, ggI):
t = ctx.saved_tensors
input, grad_output = t[0], t[1]
alpha = ctx.additional_args[0]
negative_mask = (input < 0).type_as(ggI)
exp_alpha = input.exp() * alpha * negative_mask
gI = ggI * grad_output * exp_alpha
non_negative_mask = (input >= ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/_functions/thnn/auto_double_backwards.py | 0.658088 | 0.33876 | auto_double_backwards.py | pypi |
import operator
import torch
import warnings
from ..modules import Module
from .scatter_gather import scatter_kwargs, gather
from .replicate import replicate
from .parallel_apply import parallel_apply
def _check_balance(device_ids):
imbalance_warn = """
There is an imbalance between your GPUs. You may want to... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/parallel/data_parallel.py | 0.81283 | 0.425068 | data_parallel.py | pypi |
import torch
from ._functions import Scatter, Gather
def scatter(inputs, target_gpus, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are not tensors. Does not
support Tensors.
"""
def scatter_map(obj... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/parallel/scatter_gather.py | 0.733929 | 0.54353 | scatter_gather.py | pypi |
import torch
import torch.cuda.comm as comm
from torch.autograd import Function
class Broadcast(Function):
@staticmethod
def forward(ctx, target_gpus, *inputs):
if not all(input.is_cuda for input in inputs):
raise TypeError('Broadcast function not implemented for CPU tensors')
ctx... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/parallel/_functions.py | 0.853898 | 0.395105 | _functions.py | pypi |
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
import torch.distributed as dist
from torch.nn.modules import Module
from collections import defaultdict
from torch.autograd import Variable
class DistributedDataParallelCPU(Module):
r"""Implements distributed data parallelism ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/parallel/distributed_cpu.py | 0.934761 | 0.577019 | distributed_cpu.py | pypi |
import threading
import torch
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):
for result in map(get_a_var, obj):
if isinstance(result, torch.Tensor):
return result
if isinstance(obj, dict):
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/parallel/parallel_apply.py | 0.425605 | 0.335773 | parallel_apply.py | pypi |
r"""
Weight Normalization from https://arxiv.org/abs/1602.07868
"""
from torch.nn.parameter import Parameter
def _norm(p, dim):
"""Computes the norm over all dimensions except dim"""
if dim is None:
return p.norm()
elif dim == 0:
output_size = (p.size(0),) + (1,) * (p.dim() - 1)
re... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/utils/weight_norm.py | 0.953848 | 0.733667 | weight_norm.py | pypi |
import torch
from torch.nn.functional import normalize
from torch.nn.parameter import Parameter
class SpectralNorm(object):
def __init__(self, name='weight', n_power_iterations=1, eps=1e-12):
self.name = name
self.n_power_iterations = n_power_iterations
self.eps = eps
def compute_wei... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/utils/spectral_norm.py | 0.935065 | 0.596844 | spectral_norm.py | pypi |
import warnings
def clip_grad_norm_(parameters, max_norm, norm_type=2):
r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Arguments:
parameters (Iterable[... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/utils/clip_grad.py | 0.907476 | 0.654522 | clip_grad.py | pypi |
import torch
def parameters_to_vector(parameters):
r"""Convert parameters to one vector
Arguments:
parameters (Iterable[Tensor]): an iterator of Tensors that are the
parameters of a model.
Returns:
The parameters represented by a single vector
"""
# Flag for the devic... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/nn/utils/convert_parameters.py | 0.900606 | 0.890008 | convert_parameters.py | pypi |
import ctypes
import torch
from . import cudart, check_error, cudaStatus
class Stream(torch._C._CudaStreamBase):
"""Wrapper around a CUDA stream.
A CUDA stream is a linear sequence of execution that belongs to a specific
device, independent from other streams. See :ref:`cuda-semantics` for
details.
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/cuda/streams.py | 0.885841 | 0.339691 | streams.py | pypi |
from torch import _C
from . import _lazy_init, _lazy_call, device_count, device as device_ctx_manager
def get_rng_state(device=-1):
r"""Returns the random number generator state of the current
GPU as a ByteTensor.
Args:
device (int, optional): The device to return the RNG state of.
De... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/cuda/random.py | 0.921362 | 0.456289 | random.py | pypi |
import torch
from . import nccl
from torch._utils import _accumulate, _take_tensors, _flatten_dense_tensors, \
_flatten_sparse_tensors, _unflatten_dense_tensors, \
_unflatten_sparse_tensors, _reorder_tensors_as
def broadcast(tensor, devices):
"""Broadcasts a tensor to a number of GPUs.
Arguments:
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/cuda/comm.py | 0.852076 | 0.731634 | comm.py | pypi |
import functools
import types
import torch._C as _C
TensorProtoDataType = _C._onnx.TensorProtoDataType
ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"
class ExportTypes:
PROTOBUF_FILE = 1
ZIP_ARCHIVE = 2
COMPRESSED_ZIP_ARCHIVE = 3
DIRECTORY = 4
def _export(*args, **kwargs):
from torch.onnx im... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/onnx/__init__.py | 0.831314 | 0.492676 | __init__.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class SpatialFullConvolution(Module):
def __init__(self, nInputPlane, nOutputPlane, kW, kH, dW=1, dH=1, padW=0, padH=None, adjW=0, adjH=0):
super(SpatialFullConvolution, self).__init__()
self.nInputPlane = nInputPlane
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialFullConvolution.py | 0.839767 | 0.345851 | SpatialFullConvolution.py | pypi |
import torch
from .Module import Module
from .utils import clear, addSingletondimension
class Max(Module):
def __init__(self, dimension=0):
super(Max, self).__init__()
self.dimension = dimension
self._output = None
self._indices = None
def _getPositiveDimension(self, input):
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Max.py | 0.826327 | 0.248854 | Max.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class SpatialConvolutionLocal(Module):
def __init__(self, nInputPlane, nOutputPlane, iW, iH, kW, kH, dW=1, dH=1, padW=0, padH=None):
super(SpatialConvolutionLocal, self).__init__()
self.nInputPlane = nInputPlane
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialConvolutionLocal.py | 0.755276 | 0.355076 | SpatialConvolutionLocal.py | pypi |
import torch
from .Module import Module
class FlattenTable(Module):
def __init__(self):
super(FlattenTable, self).__init__()
self.output = []
self.input_map = []
self.gradInput = []
def _flatten(self, output, input):
if isinstance(input, list):
input_map ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/FlattenTable.py | 0.7478 | 0.357483 | FlattenTable.py | pypi |
import torch
from .Module import Module
from .utils import clear
class BatchNormalization(Module):
# expected dimension of input
nDim = 2
def __init__(self, nOutput, eps=1e-5, momentum=0.1, affine=True):
super(BatchNormalization, self).__init__()
assert nOutput != 0
self.affine =... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/BatchNormalization.py | 0.857574 | 0.396185 | BatchNormalization.py | pypi |
import torch
from .Module import Module
from .utils import clear, recursiveResizeAs
class MixtureTable(Module):
def __init__(self, dim=1):
super(MixtureTable, self).__init__()
self.dim = dim
self.size = torch.Size()
self.size2 = torch.Size()
self.batchSize = 0
self... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/MixtureTable.py | 0.752286 | 0.192691 | MixtureTable.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class Euclidean(Module):
def __init__(self, inputSize, outputSize):
super(Euclidean, self).__init__()
self.weight = torch.Tensor(inputSize, outputSize)
self.gradWeight = torch.Tensor(inputSize, outputSize)
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Euclidean.py | 0.696887 | 0.277234 | Euclidean.py | pypi |
import torch
from .Module import Module
from .Identity import Identity
from .LookupTable import LookupTable
from .Sequential import Sequential
from .ParallelTable import ParallelTable
from .MM import MM
class PartialLinear(Module):
"""
PartialLinear is a Linear layer that allows the user to a set a collection... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/PartialLinear.py | 0.897753 | 0.548492 | PartialLinear.py | pypi |
import torch
from .Module import Module
class MM(Module):
def __init__(self, transA=False, transB=False):
super(MM, self).__init__()
self.transA = transA
self.transB = transB
self.gradInput = [torch.Tensor(), torch.Tensor()]
def updateOutput(self, input):
assert len(i... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/MM.py | 0.701815 | 0.659433 | MM.py | pypi |
import math
import torch
from .Module import Module
class SpatialFractionalMaxPooling(Module):
# Usage:
# nn.SpatialFractionalMaxPooling(poolSizeW, poolSizeH, outW, outH)
# the output should be the exact size (outH x outW)
# nn.SpatialFractionalMaxPooling(poolSizeW, poolSizeH, ratioW, ratioH)
# ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialFractionalMaxPooling.py | 0.862656 | 0.427397 | SpatialFractionalMaxPooling.py | pypi |
import torch
from .Criterion import Criterion
# TODO: use THNN
class BCECriterion(Criterion):
eps = 1e-12
def __init__(self, weights=None, sizeAverage=True):
if weights is not None and weights.dim() != 1:
raise ValueError("weights input should be 1D Tensor")
super(BCECriterion, ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/BCECriterion.py | 0.432782 | 0.331823 | BCECriterion.py | pypi |
import torch
from .Module import Module
from .utils import clear
class SpatialCrossMapLRN(Module):
def __init__(self, size, alpha=1e-4, beta=0.75, k=1):
super(SpatialCrossMapLRN, self).__init__()
self.size = size
self.alpha = alpha
self.beta = beta
self.k = k
self... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialCrossMapLRN.py | 0.870941 | 0.379608 | SpatialCrossMapLRN.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class Cosine(Module):
def __init__(self, inputSize, outputSize):
super(Cosine, self).__init__()
self.weight = torch.Tensor(outputSize, inputSize)
self.gradWeight = torch.Tensor(outputSize, inputSize)
self... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/Cosine.py | 0.715225 | 0.377139 | Cosine.py | pypi |
import torch
from .Module import Module
class MV(Module):
"""Module to perform matrix vector multiplication on two minibatch inputs,
producing a minibatch.
"""
def __init__(self, trans=False):
super(MV, self).__init__()
self.trans = trans
self.gradInput = [torch.Tensor(), ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/MV.py | 0.825027 | 0.733786 | MV.py | pypi |
import math
import torch
from .Module import Module
from .Sequential import Sequential
from .SpatialZeroPadding import SpatialZeroPadding
from .SpatialConvolution import SpatialConvolution
from .SpatialConvolutionMap import SpatialConvolutionMap
from .Replicate import Replicate
from .CSubTable import CSubTable
from .CD... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialSubtractiveNormalization.py | 0.675978 | 0.453927 | SpatialSubtractiveNormalization.py | pypi |
import random
import math
import torch
from .Module import Module
# TODO fix THNN...
class SpatialConvolutionMap(Module):
class maps(object):
@staticmethod
def full(nin, nout):
ft = torch.Tensor(nin * nout, 2)
p = 0
for j in range(nout):
for i... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialConvolutionMap.py | 0.407216 | 0.312331 | SpatialConvolutionMap.py | pypi |
import math
import torch
from .Concat import Concat
class DepthConcat(Concat):
def windowNarrow(self, output, currentOutput, offset):
outputWindow = output.narrow(self.dimension, offset, currentOutput.size(self.dimension))
for dim in range(len(self.outputSize)):
currentSize = current... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/DepthConcat.py | 0.54577 | 0.296457 | DepthConcat.py | pypi |
import torch
from .Module import Module
class SpatialZeroPadding(Module):
def __init__(self, pad_l, pad_r=None, pad_t=None, pad_b=None):
super(SpatialZeroPadding, self).__init__()
self.pad_l = pad_l
self.pad_r = pad_r if pad_r is not None else pad_l
self.pad_t = pad_t if pad_t is ... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialZeroPadding.py | 0.681197 | 0.419945 | SpatialZeroPadding.py | pypi |
import math
import torch
from .Module import Module
from .utils import clear
class SpatialConvolution(Module):
def __init__(self, nInputPlane, nOutputPlane, kW, kH, dW=1, dH=1, padW=0, padH=None):
super(SpatialConvolution, self).__init__()
self.nInputPlane = nInputPlane
self.nOutputPlane... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialConvolution.py | 0.786254 | 0.23206 | SpatialConvolution.py | pypi |
import torch
from .Module import Module
from .utils import clear
class PairwiseDistance(Module):
def __init__(self, p):
super(PairwiseDistance, self).__init__()
assert p % 1 == 0
self.gradInput = []
self.diff = torch.Tensor()
self.norm = p
self.outExpand = None
... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/PairwiseDistance.py | 0.756717 | 0.542439 | PairwiseDistance.py | pypi |
import random
import math
import torch
from .Module import Module
class SpatialFullConvolutionMap(Module):
def __init__(self, conMatrix, kW, kH, dW=1, dH=1):
super(SpatialFullConvolutionMap, self).__init__()
self.kW = kW
self.kH = kH
self.dW = dW
self.dH = dH
self... | /rpi3.torch-0.1.0-cp35-cp35m-linux_armv7l.whl/torch/legacy/nn/SpatialFullConvolutionMap.py | 0.557364 | 0.290226 | SpatialFullConvolutionMap.py | pypi |
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