repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
onnx | onnx/version.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
"""Backward-compatibility shim for onnx.version.
This module is deprecated. Use ``onnx.__version__`` instead.
"""
from __future__ import annotations
import warnings
from onnx import __version__ as version
warnings.warn(
"onnx.ve... | 25 | 473 |
onnx | onnx/gen_proto.py | .py | #!/usr/bin/env python
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import argparse
import glob
import os
import re
import subprocess
from textwrap import dedent
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import I... | 273 | 8,718 |
onnx | onnx/checker.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
"""Graph utilities for checking whether an ONNX proto message is legal."""
from __future__ import annotations
__all__ = [
"check_attribute",
"check_function",
"check_graph",
"check_model",
"check_node",
"check_sp... | 172 | 5,554 |
onnx | onnx/helper.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import collections.abc
import functools
import math
import numbers
import typing
from typing import TYPE_CHECKING, Any, TypeVar
import google.protobuf.message
import numpy as np
import typing_extension... | 1,382 | 46,471 |
onnx | onnx/serialization.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import warnings
__all__ = [
"registry",
]
import typing
from typing import Any, Protocol, TypeVar
import google.protobuf.json_format
import google.protobuf.message
import google.protobuf.text_fo... | 213 | 7,910 |
onnx | onnx/bin/checker.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import argparse
from onnx import NodeProto, checker, load
def check_model() -> None:
parser = argparse.ArgumentParser("check-model")
parser.add_argument("model_pb", type=argparse.FileType("rb... | 28 | 693 |
onnx | onnx/reference/reference_evaluator.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from io import BytesIO
from typing import Any
import numpy as np
import onnx
import onnx.model_container
from onnx.onnx_pb import (
FunctionProto,
GraphProto,
ModelProto,
NodeProto,
... | 618 | 24,519 |
onnx | onnx/reference/op_run.py | .py | # SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import abc
from typing import TYPE_CHECKING, Any
import numpy as np
import onnx
if TYPE_CHECKING:
from collections.abc import Sequence
class RuntimeTypeError(RuntimeError):
"""Raised when a type of a variable is unexpected."""
cl... | 683 | 25,409 |
onnx | onnx/reference/ops_optimized/op_conv_optimized.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _make_ind(dim, shape):
m = np.empty(shape, dtype=np.int64)
ind = [slice(0, shape[i]) for i in range(len(shape))]
new_shape = ... | 190 | 6,398 |
onnx | onnx/reference/ops/op_max.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Max(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.maximum, o... | 26 | 729 |
onnx | onnx/reference/ops/op_or.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinary
class Or(OpRunBinary):
def _run(self, x, y):
return (np.logical_or(x, y),)
| 14 | 273 |
onnx | onnx/reference/ops/op_matmul_integer.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class MatMulInteger(OpRun):
def _run(self, A, B, a_zero_point=None, b_zero_point=None):
A32 = A.astype(np.int32)
if a_zer... | 20 | 503 |
onnx | onnx/reference/ops/op_optional_get_element.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class OptionalGetElement(OpRun):
def _run(self, x):
if x is None:
raise ValueError("The requested optional input has no value.")
... | 14 | 332 |
onnx | onnx/reference/ops/op_and.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinary
class And(OpRunBinary):
def _run(self, x, y):
return (np.logical_and(x, y),)
| 14 | 275 |
onnx | onnx/reference/ops/op_expand.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def common_reference_implementation(data: np.ndarray, shape: np.ndarray) -> np.ndarray:
ones = np.ones(shape, dtype=data.dtype)
retur... | 19 | 453 |
onnx | onnx/reference/ops/op_reduce_min.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceMin_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is no... | 51 | 1,647 |
onnx | onnx/reference/ops/op_log.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Log(OpRunUnaryNum):
def _run(self, x):
return (np.log(x).astype(x.dtype),)
| 14 | 281 |
onnx | onnx/reference/ops/op_affine_grid.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def construct_original_grid(data_size, align_corners):
is_2d = len(data_size) == 2
size_zeros = np.zeros(data_size)
original_grid... | 91 | 3,490 |
onnx | onnx/reference/ops/op_sin.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Sin(OpRunUnaryNum):
def _run(self, x):
return (np.sin(x),)
| 14 | 265 |
onnx | onnx/reference/ops/op_matmul.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNum
def numpy_matmul(a, b):
"""Implements a matmul product. See :func:`np.matmul`.
Handles sparse matrices.
"""
try:
... | 26 | 644 |
onnx | onnx/reference/ops/op_negative_log_likelihood_loss.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _compute_negative_log_likelihood_loss(
x, target, weight=None, reduction="mean", ignore_index=None
):
input_shape = x.shape
i... | 85 | 2,902 |
onnx | onnx/reference/ops/op_hardmax.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Hardmax(OpRunUnaryNum):
def _run(self, x, axis=None):
axis = axis or self.axis
if x.size == 0:
... | 25 | 564 |
onnx | onnx/reference/ops/op_conv_transpose.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_col2im import col2im_naive_implementation
class ConvTranspose(OpRun):
def _run(
self,
X,
... | 140 | 5,166 |
onnx | onnx/reference/ops/op_sub.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Sub(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.subtract, ... | 14 | 343 |
onnx | onnx/reference/ops/op_leaky_relu.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import TYPE_CHECKING
from onnx.reference.ops._op import OpRunUnaryNum
if TYPE_CHECKING:
import numpy as np
def _leaky_relu(x: np.ndarray, alpha: float) -> np.ndarray:
sign = (x > ... | 24 | 569 |
onnx | onnx/reference/ops/op_cosh.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Cosh(OpRunUnaryNum):
def _run(self, x):
return (np.cosh(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_atanh.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Atanh(OpRunUnaryNum):
def _run(self, x):
return (np.arctanh(x),)
| 14 | 271 |
onnx | onnx/reference/ops/op_tanh.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Tanh(OpRunUnaryNum):
def _run(self, x):
return (np.tanh(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_sequence_erase.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class SequenceErase(OpRun):
def _run(self, S, ind=None):
if ind is None:
ind = -1
else:
ind = int(ind)
S2 = S... | 18 | 369 |
onnx | onnx/reference/ops/op_softsign.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Softsign(OpRunUnaryNum):
def _run(self, X):
tmp = np.abs(X)
tmp += 1
np.divide(X, tmp, out=tmp)
... | 17 | 340 |
onnx | onnx/reference/ops/op_random_uniform_like.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomUniformLike(_CommonRandom):
def _run(self, x, dtype=None, high=None, low=... | 20 | 630 |
onnx | onnx/reference/ops/op_rotary_embedding.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def rotary_embedding(
input: np.ndarray,
cos_cache: np.ndarray,
sin_cache: np.ndarray,
position_ids: np.ndarray | None = Non... | 120 | 4,316 |
onnx | onnx/reference/ops/_quant_utils.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
def reshape_input(
value: np.ndarray,
shape: tuple[int, ...],
axis: int,
block_size: int | None = None,
) -> np.ndarray:
"""Reshape/Replicate scale/zero-point to ... | 57 | 1,907 |
onnx | onnx/reference/ops/op_not.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnary
class Not(OpRunUnary):
def _run(self, x):
return (np.logical_not(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_softplus.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Softplus(OpRunUnaryNum):
def _run(self, X):
tmp = np.asarray(np.exp(X), dtype=X.dtype)
tmp += 1
np... | 17 | 361 |
onnx | onnx/reference/ops/op_elu.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Elu(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = alpha or self.alpha
return (np.where(x > 0, x, ... | 15 | 363 |
onnx | onnx/reference/ops/op_round.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Round(OpRunUnaryNum):
def _run(self, x):
return (np.round(x).astype(x.dtype),)
| 14 | 285 |
onnx | onnx/reference/ops/op_reshape.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def reshape_reference_implementation(
data: np.ndarray, shape: np.ndarray, allowzero: int = 0
) -> np.ndarray:
# replace zeros with c... | 39 | 1,118 |
onnx | onnx/reference/ops/op_swiglu.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class SwiGLU(OpRun):
def _run(self, a, b, alpha=None):
# SwiGLU requires identical shapes and dtypes for A and B: broadcasting is... | 33 | 1,352 |
onnx | onnx/reference/ops/op_pool_common.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import itertools
import math
from typing import TYPE_CHECKING
import numpy as np
from onnx.reference.op_run import OpRun
if TYPE_CHECKING:
from collections.abc import Sequence
def get_pad_shape... | 346 | 12,618 |
onnx | onnx/reference/ops/op_pad.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _pad_impl(data, raw_pads, mode, constant_values=0.0, axes=None):
input_rank = data.ndim
if axes is None:
axes = list(rang... | 66 | 2,004 |
onnx | onnx/reference/ops/op_neg.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Neg(OpRunUnaryNum):
def _run(self, x):
return (np.negative(x),)
| 14 | 270 |
onnx | onnx/reference/ops/op_ceil.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Ceil(OpRunUnaryNum):
def _run(self, x):
return (np.ceil(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_hann_window.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HannWindow(_CommonWindow):
r"""Returns :math:`\\omega_n = \\sin^2\\left( \\frac{\\pi n}{N-1} \\right)` where... | 21 | 670 |
onnx | onnx/reference/ops/op_tfidf_vectorizer.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import collections
from enum import IntEnum
import numpy as np
from onnx.reference.op_run import OpRun
class IntMap(collections.UserDict):
def __init__(self):
super().__init__()
s... | 381 | 12,527 |
onnx | onnx/reference/ops/op_global_max_pool.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _global_max_pool(x: np.ndarray) -> np.ndarray:
spatial_shape = np.ndim(x) - 2
y = x.max(axis=tuple(range(spatial_shape, spatial_s... | 23 | 523 |
onnx | onnx/reference/ops/op_tensor_scatter.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class TensorScatter(OpRun):
def _run(self, past_cache, update, write_indices=None, mode="linear", axis=-2):
if mode not in {"line... | 51 | 1,910 |
onnx | onnx/reference/ops/op_cast_like.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_cast import cast_to
def _cast_like(x, y, saturate: bool):
return (cast_to(x, np_d... | 28 | 679 |
onnx | onnx/reference/ops/op_softmax.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Softmax(OpRunUnaryNum):
def _run(self, X, axis=None):
if X.size == 0:
return (X,)
axis = axis ... | 20 | 482 |
onnx | onnx/reference/ops/op_celu.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _vcelu1(x: np.ndarray, alpha: float = 1.0) -> np.ndarray:
positive_input = np.maximum(0, x)
negative_input = np.minimum(0, alpha ... | 20 | 498 |
onnx | onnx/reference/ops/op_optional.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class Optional(OpRun):
def _run(self, x=None, type=None): # type: ignore[override] # noqa: A002
if... | 19 | 601 |
onnx | onnx/reference/ops/op_isnan.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnary
class IsNaN(OpRunUnary):
def _run(self, data):
return (np.isnan(data),)
| 14 | 269 |
onnx | onnx/reference/ops/op_unique.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _specify_int64(indices, inverse_indices, counts):
return (
np.array(indices, dtype=np.int64),
np.array(inverse_indice... | 52 | 1,773 |
onnx | onnx/reference/ops/op_bernoulli.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class Bernoulli(_CommonRandom):
def _run(self, x, dtype=None, seed=None):
if ... | 18 | 576 |
onnx | onnx/reference/ops/op_dft.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _fft(x: np.ndarray, fft_length: int, axis: int) -> np.ndarray:
"""Compute the FFT return the real representation of the complex resul... | 147 | 4,821 |
onnx | onnx/reference/ops/op_equal.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryComparison
class Equal(OpRunBinaryComparison):
def _run(self, a, b):
return (np.equal(a, b),)
| 14 | 291 |
onnx | onnx/reference/ops/op_center_crop_pad.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class CenterCropPad(OpRun):
def _run(self, input_data, shape, axes=None):
axes = axes or self.axes
input_rank = len(input... | 51 | 1,626 |
onnx | onnx/reference/ops/op_sinh.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Sinh(OpRunUnaryNum):
def _run(self, x):
return (np.sinh(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_asin.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Asin(OpRunUnaryNum):
def _run(self, x):
return (np.arcsin(x),)
| 14 | 269 |
onnx | onnx/reference/ops/op_isinf.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class IsInf(OpRun):
def _run(self, data, detect_negative=None, detect_positive=None):
if detect_negative:
if detect_p... | 21 | 568 |
onnx | onnx/reference/ops/op_string_concat.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
_acceptable_str_dtypes = ("U", "O")
class StringConcat(OpRun):
def _run(self, x, y):
if (
x.dtype.kind not in _accep... | 24 | 708 |
onnx | onnx/reference/ops/op_argmax.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _argmax(data, axis=0, keepdims=True):
result = np.argmax(data, axis=axis)
if keepdims and len(result.shape) < len(data.shape):
... | 43 | 1,182 |
onnx | onnx/reference/ops/op_non_max_suppression.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import dataclasses
import numpy as np
from onnx.reference.op_run import OpRun
@dataclasses.dataclass
class PrepareContext:
boxes_data_: np.ndarray | None = None
boxes_size_: int = 0
score... | 272 | 9,282 |
onnx | onnx/reference/ops/op_thresholded_relu.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class ThresholdedRelu(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = alpha or self.alpha
return (np.wher... | 15 | 357 |
onnx | onnx/reference/ops/op_gather.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Gather(OpRun):
def _run(self, x, indices, axis=None):
if not x.flags["C_CONTIGUOUS"]:
x = np.ascontiguousarray(... | 22 | 625 |
onnx | onnx/reference/ops/op_max_pool.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op_common_pool import CommonPool
class MaxPool(CommonPool):
def _run(
self,
x,
auto_pad=None,
ceil_mode=None,
di... | 414 | 14,553 |
onnx | onnx/reference/ops/op_reduce_l1.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceL1_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is not... | 35 | 1,140 |
onnx | onnx/reference/ops/op_reduce_log_sum.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceLogSum_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=True):
tax = tuple(axes) if axes is ... | 35 | 1,093 |
onnx | onnx/reference/ops/op_scatter_elements.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def scatter_elements(data, indices, updates, axis=0, reduction=None):
"""Scatter elements.
::
for 3-dim and axis=0
... | 120 | 3,806 |
onnx | onnx/reference/ops/op_dropout.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from numpy.random import RandomState
from onnx.reference.op_run import OpRun
def _dropout(
X: np.ndarray,
drop_probability: float = 0.5,
seed: int | None = None,
tra... | 67 | 1,779 |
onnx | onnx/reference/ops/op_size.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Size(OpRun):
def _run(self, data):
return (np.array(data.size, dtype=np.int64),)
| 14 | 278 |
onnx | onnx/reference/ops/_op_common_indices.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
def _get_indices(i, shape):
res = np.empty((len(shape),), dtype=np.int64)
k = len(shape) - 1
while k > 0:
m = i % shape[k]
res[k] = m
i -= m
... | 38 | 746 |
onnx | onnx/reference/ops/op_compress.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Compress(OpRun):
def _run(self, x, condition, axis=None):
return (np.compress(condition, x, axis=axis),)
| 14 | 302 |
onnx | onnx/reference/ops/op_loop.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Loop(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
if "opsets" not... | 87 | 3,454 |
onnx | onnx/reference/ops/op_reduce_prod.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceProd_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is n... | 31 | 1,071 |
onnx | onnx/reference/ops/op_qlinear_conv.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_conv import _conv_implementation
class QLinearConv(OpRun):
def _run(
self,
x,
x_scale,... | 79 | 2,516 |
onnx | onnx/reference/ops/op_gather_elements.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def gather_numpy_2(self: np.ndarray, index: np.ndarray) -> np.ndarray:
res = []
for a, b in zip(self, index, strict=True):
re... | 48 | 1,598 |
onnx | onnx/reference/ops/op_bitwise_and.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinary
class BitwiseAnd(OpRunBinary):
def _run(self, x, y):
return (np.bitwise_and(x, y),)
| 14 | 282 |
onnx | onnx/reference/ops/op_reduce_sum_square.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceSumSquare_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes... | 31 | 1,066 |
onnx | onnx/reference/ops/op_rnn.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class CommonRNN(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
if self.d... | 203 | 6,149 |
onnx | onnx/reference/ops/op_cum_prod.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class CumProd(OpRun):
def _run(self, x, axis, exclusive=None, reverse=None):
axis = np.asarray(axis)
if axis.ndim != 0:
... | 32 | 1,087 |
onnx | onnx/reference/ops/op_shape.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Shape_1(OpRun):
def _run(self, data):
return (np.array(data.shape, dtype=np.int64),)
class Shape_15(Shape_1):
@static... | 36 | 1,023 |
onnx | onnx/reference/ops/op_hamming_window.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HammingWindow(_CommonWindow):
r"""Returns :math:`\\omega_n = \\alpha - \\beta \\cos \\left( \\frac{\\pi n}{N... | 24 | 791 |
onnx | onnx/reference/ops/op_random_normal.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomNormal(_CommonRandom):
def _run(self, dtype=None, mean=None, scale=None, seed=None, shape=None):
state = self._get... | 17 | 512 |
onnx | onnx/reference/ops/op_where.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Where(OpRun):
def _run(self, condition, x, y):
if (
x.dtype not in (y.dtype, object)
and x.dtype.ty... | 22 | 577 |
onnx | onnx/reference/ops/op_identity.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class Identity(OpRun):
def _run(self, a):
if a is None:
return (None,)
return (a.copy(),)
| 14 | 281 |
onnx | onnx/reference/ops/op_greater.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryComparison
class Greater(OpRunBinaryComparison):
def _run(self, a, b):
return (np.greater(a, b),)
| 14 | 295 |
onnx | onnx/reference/ops/_op_list.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
"""Every class imported in this module defines an implementation of
an operator of the main domain. Any class name uses `_` to specify a
version defined in a specific opset. The class name without `_`
defines the current implementation. If... | 630 | 21,806 |
onnx | onnx/reference/ops/op_unsqueeze.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Unsqueeze_1(OpRun):
def _run(self, data, axes=None):
if isinstance(axes, np.ndarray):
axes = tuple(axes)
... | 53 | 1,592 |
onnx | onnx/reference/ops/op_instance_normalization.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class InstanceNormalization(OpRun):
def _run(self, x, s, bias, epsilon=None):
dims_x = len(x.shape)
axis = tuple(range(2,... | 22 | 646 |
onnx | onnx/reference/ops/op_mul.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Mul(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.multiply, ... | 14 | 343 |
onnx | onnx/reference/ops/op_erf.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from math import erf
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Erf(OpRunUnaryNum):
def __init__(self, onnx_node, run_params):
OpRunUnaryNum.__init__(self, ... | 20 | 466 |
onnx | onnx/reference/ops/op_argmin.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _argmin(data, axis=0, keepdims=True):
result = np.argmin(data, axis=axis)
if keepdims and len(result.shape) < len(data.shape):
... | 43 | 1,182 |
onnx | onnx/reference/ops/op_sequence_empty.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class SequenceEmpty(OpRun):
def _run(self, dtype=None): # type: ignore[override] # noqa: ARG002
return ([],)
| 12 | 282 |
onnx | onnx/reference/ops/op_string_split.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
_acceptable_str_dtypes = ("U", "O")
def pad_empty_string(
split_lists: list | np.ndarray, padding_requirement: list | int
) -> list:
... | 47 | 1,695 |
onnx | onnx/reference/ops/op_softmax_cross_entropy_loss.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def softmaxcrossentropy(
x, target, weight=None, reduction="mean", ignore_index=None, get_log_prob=None
):
input_shape = x.shape
... | 95 | 2,950 |
onnx | onnx/reference/ops/op_less.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryComparison
class Less(OpRunBinaryComparison):
def _run(self, a, b):
return (np.less(a, b),)
| 14 | 289 |
onnx | onnx/reference/ops/op_scatternd.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _scatter_nd_impl(data, indices, updates, reduction=None):
output = np.copy(data)
for i in np.ndindex(indices.shape[:-1]):
... | 35 | 1,050 |
onnx | onnx/reference/ops/op_space_to_depth.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class SpaceToDepth(OpRun):
def _run(self, data, blocksize=None):
if len(data.shape) != 4:
raise RuntimeError(f"Unexpe... | 34 | 883 |
onnx | onnx/reference/ops/op_acos.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Acos(OpRunUnaryNum):
def _run(self, x):
return (np.arccos(x),)
| 14 | 269 |
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