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/reference/ops/op_optional_has_element.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 OptionalHasElement(OpRun):
def _run(self, x=None):
return (np.array(x is not None),)
| 14 | 282 |
onnx | onnx/reference/ops/op_exp.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 Exp(OpRunUnaryNum):
def _run(self, x):
return (np.exp(x).astype(x.dtype),)
| 14 | 281 |
onnx | onnx/reference/ops/op_gru.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 CommonGRU(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
self.n_out... | 188 | 5,444 |
onnx | onnx/reference/ops/op_cos.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 Cos(OpRunUnaryNum):
def _run(self, x):
return (np.cos(x),)
| 14 | 265 |
onnx | onnx/reference/ops/op_lrn.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import math
import numpy as np
from onnx.reference.op_run import OpRun
class LRN(OpRun):
def _run(self, x, alpha=None, beta=None, bias=None, size=None):
if len(x.shape) != 4:
... | 29 | 880 |
onnx | onnx/reference/ops/op_qlinear_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.op_run import OpRun
class QLinearMatMul(OpRun):
def _run(
self, a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point
):
... | 30 | 894 |
onnx | onnx/reference/ops/op_cum_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.op_run import OpRun
class CumSum(OpRun):
def _run(self, x, axis, exclusive=None, reverse=None):
axis = np.asarray(axis)
if axis.ndim != 0:
... | 32 | 1,085 |
onnx | onnx/reference/ops/op_atan.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 Atan(OpRunUnaryNum):
def _run(self, x):
return (np.arctan(x),)
| 14 | 269 |
onnx | onnx/reference/ops/_op_common_window.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class _CommonWindow(OpRun):
@staticmethod
def _begin(size, periodic, output_datatype... | 27 | 750 |
onnx | onnx/reference/ops/op_lp_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_pool_common import CommonPool
class LpPool(CommonPool):
def _run(
self,
x,
auto_pad=None,
ceil_mode=None,
dila... | 44 | 1,252 |
onnx | onnx/reference/ops/op_topk.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 topk_sorted_implementation(X, k, axis, largest):
"""See function `_kneighbors_reduce_func
<https://github.com/scikit-learn/scikit... | 111 | 4,040 |
onnx | onnx/reference/ops/op_attribute_has_value.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 AttributeHasValue(OpRun):
def _run(
self,
value_float=None, # noqa: ARG002
value_floats=None, # noqa: ARG... | 34 | 1,084 |
onnx | onnx/reference/ops/op_tile.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 Tile(OpRun):
def _run(self, x, repeats):
return (np.tile(x, repeats),)
| 14 | 268 |
onnx | onnx/reference/ops/op_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
class Concat(OpRun):
def _preprocess(self, a: np.ndarray, axis: int) -> np.ndarray:
if len(a.shape) == 0:
raise Runti... | 23 | 682 |
onnx | onnx/reference/ops/op_eyelike.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.onnx_pb import TensorProto
from onnx.reference.op_run import OpRun
class EyeLike(OpRun):
def _run(self, data, *args, d... | 32 | 991 |
onnx | onnx/reference/ops/op_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 OpRunBinaryNumpy
class Min(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.minimum, o... | 26 | 729 |
onnx | onnx/reference/ops/op_mean.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 Mean(OpRun):
def _run(self, *args):
res = args[0].copy()
for m in args[1:]:
res += m
return ((res / len(args)).asty... | 15 | 340 |
onnx | onnx/reference/ops/op_average_pool.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.ops.op_pool_common import CommonPool
class AveragePool_1(CommonPool):
def _run(
self,
x,
auto_pad=None,
ceil_mode=None,
kernel_shape=None... | 108 | 2,349 |
onnx | onnx/reference/ops/op_gathernd.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_nd_impl(
data: np.ndarray, indices: np.ndarray, batch_dims: int
) -> tuple[np.ndarray]:
# Note the data rank - will be re... | 59 | 1,992 |
onnx | onnx/reference/ops/op_causal_conv_with_state.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 CausalConvWithState(OpRun):
def _run(
self,
input,
... | 72 | 2,163 |
onnx | onnx/reference/ops/op_linear_attention.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 _unpack_3d_to_4d(x: np.ndarray, num_heads: int) -> np.ndarray:
"""Reshape (B, T, H*D) -> (B, H, T, D)."""
b, t, hd = x.shape
... | 182 | 7,881 |
onnx | onnx/reference/ops/op_concat_from_sequence.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import Any
import numpy as np
from onnx.reference.op_run import OpRun
def _concat_from_sequence(seq: list[Any], axis: int, new_axis: int = 0) -> np.ndarray:
if new_axis == 1:
... | 32 | 887 |
onnx | onnx/reference/ops/op_lstm.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 CommonLSTM(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
self.n_ou... | 213 | 6,254 |
onnx | onnx/reference/ops/op_bitwise_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 BitwiseOr(OpRunBinary):
def _run(self, x, y):
return (np.bitwise_or(x, y),)
| 14 | 280 |
onnx | onnx/reference/ops/op_less_or_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 LessOrEqual(OpRunBinaryComparison):
def _run(self, a, b):
return (np.less_equal(a, b),)
| 14 | 302 |
onnx | onnx/reference/ops/op_random_uniform.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 RandomUniform(_CommonRandom):
def _run(self, dtype=None, high=None, low=None, seed=None, shape=None):
dtype = self._dtyp... | 17 | 501 |
onnx | onnx/reference/ops/op_hard_sigmoid.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 HardSigmoid(OpRunUnaryNum):
def _run(self, x, alpha=None, beta=None):
alpha = alpha or self.alpha
beta = b... | 17 | 432 |
onnx | onnx/reference/ops/_helpers.py | .py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import Any
from onnx.reference.op_run import OpRun
def _split_class_name(name):
if "_" in name:
prefix, vers = name.rsplit("_", maxsplit=1)
try:
v = int(ve... | 68 | 2,139 |
onnx | onnx/reference/ops/op_reduce_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 ReduceSum_1(OpRunReduceNumpy):
def _run(self, x, axes=None, keepdims=None):
axes = tuple(axes) if axes is not N... | 36 | 1,249 |
onnx | onnx/reference/ops/op_einsum.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 Einsum(OpRun):
def _run(self, *args, equation=None):
if not isinstance(equation, str):
raise TypeError(f"equati... | 22 | 630 |
onnx | onnx/reference/ops/op_one_hot.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 _one_hot(indices, depth, axis=-1, dtype=np.float32):
values = np.asarray(indices)
rank = len(values.shape)
depth_range = np.a... | 33 | 1,057 |
onnx | onnx/reference/ops/_op_common_pool.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import itertools
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops._op_common_indices import _get_index, _get_indices
def _get_pad_shape(
auto_pad: str,
input... | 302 | 10,283 |
onnx | onnx/reference/ops/op_tan.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 Tan(OpRunUnaryNum):
def _run(self, x):
return (np.tan(x),)
| 14 | 265 |
onnx | onnx/reference/ops/op_reduce_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 OpRunReduceNumpy
class ReduceMax_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is no... | 47 | 1,605 |
onnx | onnx/reference/ops/op_gemm.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 _gemm00(a, b, c, alpha, beta):
o = np.dot(a, b) * alpha
if c is not None and beta != 0:
o += c * beta
return o
def ... | 78 | 1,945 |
onnx | onnx/reference/ops/op_string_normalizer.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import locale as pylocale
import unicodedata
import warnings
import numpy as np
from onnx.reference.op_run import OpRun, RuntimeTypeError
class StringNormalizer(OpRun):
"""The operator is not rea... | 150 | 4,941 |
onnx | onnx/reference/ops/op_pow.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from warnings import catch_warnings, simplefilter
import numpy as np
from onnx.reference.op_run import OpRun
class Pow(OpRun):
def _run(self, a, b):
with catch_warnings():
sim... | 18 | 393 |
onnx | onnx/reference/ops/op_flatten.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 Flatten(OpRunUnary):
def _run(self, x, axis=None):
i = axis or self.axis
shape = x.shape
new_shape = ... | 17 | 419 |
onnx | onnx/reference/ops/op_lp_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.ops._op import OpRunUnaryNum
class LpNormalization(OpRunUnaryNum):
def _run(self, x, axis=None, p=None):
axis = axis or self.axis
p = p or se... | 20 | 582 |
onnx | onnx/reference/ops/op_bitwise_xor.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 BitwiseXor(OpRunBinary):
def _run(self, x, y):
return (np.bitwise_xor(x, y),)
| 14 | 282 |
onnx | onnx/reference/ops/op_greater_or_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 GreaterOrEqual(OpRunBinaryComparison):
def _run(self, a, b):
return (np.greater_equal(a, b),)
| 14 | 308 |
onnx | onnx/reference/ops/op_swish.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 Swish(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = self.alpha if alpha is None else alpha
return... | 15 | 375 |
onnx | onnx/reference/ops/op_split_to_sequence.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.op_run import OpRun
if TYPE_CHECKING:
import numpy as np
class SplitToSequence(OpRun):
def common_run(
self, mat: np.ndarray, spli... | 55 | 1,528 |
onnx | onnx/reference/ops/op_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 Relu(OpRunUnaryNum):
def _run(self, x):
return (np.maximum(x, 0).astype(x.dtype),)
| 14 | 289 |
onnx | onnx/reference/ops/op_split.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 CommonSplit(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_no... | 52 | 1,535 |
onnx | onnx/reference/ops/op_reduce_l2.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 ReduceL2_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is not... | 35 | 1,164 |
onnx | onnx/reference/ops/op_layer_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
def _layer_normalization(
X: np.ndarray,
W: np.ndarray,
B: np.ndarray,
axis: int = -1,
epsilon: float = 1e-5,
) -> tuple[... | 74 | 2,595 |
onnx | onnx/reference/ops/op_log_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_softmax import Softmax
class LogSoftmax(Softmax):
def _run(self, X):
Y = Softmax._run(self, X)[0]
np.log(Y, out=Y)
return (Y,)... | 16 | 321 |
onnx | onnx/reference/ops/op_sequence_construct.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 SequenceConstruct(OpRun):
def _run(self, *data):
return (list(data),)
| 12 | 247 |
onnx | onnx/reference/ops/op_attention.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.reference.op_run import OpRun
def _softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
# A fully-masked r... | 337 | 14,743 |
onnx | onnx/reference/ops/op_sum.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 Sum(OpRun):
def _run(self, *args):
return (sum(args).astype(args[0].dtype),)
| 12 | 254 |
onnx | onnx/reference/ops/op_add.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 Add(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.add, onnx_... | 14 | 338 |
onnx | onnx/reference/ops/op_roi_align.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 PreCalc:
def __init__(self, pos1=0, pos2=0, pos3=0, pos4=0, w1=0, w2=0, w3=0, w4=0):
self.pos1 = pos1
self.pos2 = p... | 297 | 10,843 |
onnx | onnx/reference/ops/op_dequantize_linear.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx import TensorProto
from onnx.helper import np_dtype_to_tensor_dtype, tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
from onnx.reference.ops._quant_utils im... | 92 | 3,182 |
onnx | onnx/reference/ops/op_floor.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 Floor(OpRunUnaryNum):
def _run(self, x):
return (np.floor(x),)
| 14 | 269 |
onnx | onnx/reference/ops/op_bitwise_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 BitwiseNot(OpRunUnary):
def _run(self, X):
return (np.bitwise_not(X),)
| 14 | 274 |
onnx | onnx/reference/ops/op_sequence_length.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 SequenceLength(OpRun):
def _run(self, input_sequence):
if not isinstance(input_sequence, list):
raise TypeError... | 18 | 477 |
onnx | onnx/reference/ops/op_sign.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 Sign(OpRunUnaryNum):
def _run(self, x):
return (np.sign(x),)
| 14 | 267 |
onnx | onnx/reference/ops/op_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
class Transpose(OpRun):
def _run(self, data, perm=None):
perm_ = None if (perm is None or len(perm) == 0) else perm
if pe... | 21 | 592 |
onnx | onnx/reference/ops/op_col2im.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_common_indices import _get_indices, _is_out
def _col2im_shape_check_2d(X, output_shape, kernel_shape, dilations, ... | 214 | 7,976 |
onnx | onnx/reference/ops/op_xor.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 Xor(OpRunBinary):
def _run(self, x, y):
return (np.logical_xor(x, y),)
| 14 | 275 |
onnx | onnx/reference/ops/op_asinh.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 Asinh(OpRunUnaryNum):
def _run(self, x):
return (np.arcsinh(x),)
| 14 | 271 |
onnx | onnx/reference/ops/op_reverse_sequence.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 ReverseSequence(OpRun):
def _run(self, data, sequence_lens, batch_axis=None, time_axis=None):
index = [slice(0, s) for s in data.shape]
... | 21 | 705 |
onnx | onnx/reference/ops/op_acosh.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 Acosh(OpRunUnaryNum):
def _run(self, x):
return (np.arccosh(x),)
| 14 | 271 |
onnx | onnx/reference/ops/op_max_unpool.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 MaxUnpool(OpRun):
def _run(
self, X, indices, output_shape=None, kernel_shape=None, pads=None, strides=None
):
... | 59 | 1,794 |
onnx | onnx/reference/ops/op_quantize_linear.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx import TensorProto
from onnx.helper import (
np_dtype_to_tensor_dtype,
tensor_dtype_to_np_dtype,
)
from onnx.reference.op_run import OpRun
from onnx.re... | 191 | 5,708 |
onnx | onnx/reference/ops/op_scan.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 Scan(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init__(self, onnx_node, run_params)
if not hasattr(... | 182 | 6,573 |
onnx | onnx/reference/ops/_op_common_random.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class _CommonRandom(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__i... | 56 | 1,771 |
onnx | onnx/reference/ops/op_prelu.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 PRelu(OpRun):
def _run(self, x, slope):
try:
return (np.where(x > 0, x, x * slope).astype(x.dtype),)
ex... | 35 | 1,170 |
onnx | onnx/reference/ops/op_conv_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
from onnx.reference.ops.op_conv import _conv_implementation
class ConvInteger(OpRun):
def _run(
self,
X,
W,
... | 53 | 1,436 |
onnx | onnx/reference/ops/op_grid_sample.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numbers
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_resize import _get_all_coords
class GridSample(OpRun):
# https://github.com/pytorch/pytorch/bl... | 366 | 13,169 |
onnx | onnx/reference/ops/op_sqrt.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from warnings import catch_warnings, simplefilter
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Sqrt(OpRunUnaryNum):
def _run(self, x):
with catch_warnings():
... | 18 | 404 |
onnx | onnx/reference/ops/op_selu.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 Selu(OpRun):
def _run(self, x, alpha=None, gamma=None):
return (
(np.where(x > 0, x, np.exp(x) * alpha - alpha)... | 16 | 357 |
onnx | onnx/reference/ops/op_upsample.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 Upsample(OpRun):
def _run(self, x, scale, mode=None):
if mode == "nearest" and scale.astype(np.int64).tolist() == scale.tol... | 21 | 608 |
onnx | onnx/reference/ops/op_sequence_insert.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import Any
import numpy as np
from onnx.reference.op_run import OpRun
def sequence_insert_reference_implementation(
sequence: list[Any] | np.ndarray,
tensor: np.ndarray,
posit... | 53 | 1,796 |
onnx | onnx/reference/ops/op_constant.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, RefAttrName
def _check_dtype(val):
dtype = val.dtype
if not isinstance(dtype, np.dtype):
raise TypeError(
f"Type... | 120 | 4,332 |
onnx | onnx/reference/ops/op_mel_weight_matrix.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class MelWeightMatrix(OpRun):
def _run(
self,
num_mel_bins,
dft_... | 62 | 2,353 |
onnx | onnx/reference/ops/op_cast.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.reference.op_run import OpRun
def cast_to(
x: np.ndarray, to: onnx.TensorProto.DataType, saturate: bool, round_mode: str = "up"
):
if to == onnx.Tensor... | 55 | 1,417 |
onnx | onnx/reference/ops/op_image_decoder.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import io
import numpy as np
from onnx.reference.op_run import OpRun
class ImageDecoder(OpRun):
def _run(self, encoded: np.ndarray, pixel_format="RGB") -> tuple[np.ndarray]:
try:
... | 34 | 1,077 |
onnx | onnx/reference/ops/op_if.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.op_run import OpRun
if TYPE_CHECKING:
import numpy as np
class If(OpRun):
def __init__(self, onnx_node, run_params):
OpRun.__init... | 73 | 2,578 |
onnx | onnx/reference/ops/op_reduce_mean.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 ReduceMean_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None):
axes = tuple(axes) if axes is n... | 36 | 1,283 |
onnx | onnx/reference/ops/op_blackman_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 BlackmanWindow(_CommonWindow):
r"""Blankman windowing function.
Returns :math:`\\omega_n = 0.42 - 0.5 \... | 31 | 945 |
onnx | onnx/reference/ops/op_div.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 Div(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
def func(x, y):
if issubclass(x.d... | 31 | 1,034 |
onnx | onnx/reference/ops/op_trilu.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 Trilu(OpRun):
def _run(self, x, k=None, upper=None):
k = 0 if k is None else k.item()
if upper:
return ... | 17 | 385 |
onnx | onnx/reference/ops/op_bitshift.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 BitShift(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.right... | 18 | 569 |
onnx | onnx/reference/ops/op_depth_to_space.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 DepthToSpace(OpRun):
def _run(self, data, blocksize=None, mode=None):
if len(data.shape) != 4:
raise RuntimeErr... | 47 | 1,298 |
onnx | onnx/reference/ops/op_shrink.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 Shrink(OpRun):
def _run(self, x, bias=None, lambd=None):
return (
np.where(
x < -lambd,
... | 20 | 430 |
onnx | onnx/reference/ops/op_sigmoid.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
def sigmoid(x: np.ndarray) -> np.ndarray:
"""Numerically stable sigmoid implementation that supports scalars and nd-arrays."""
... | 28 | 680 |
onnx | onnx/reference/ops/op_constant_of_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 ConstantOfShape(OpRun):
def _run(self, data, value: np.array | None = None):
if self.value is None:
value = np.... | 30 | 951 |
onnx | onnx/reference/ops/op_reciprocal.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 Reciprocal(OpRunUnaryNum):
def _run(self, x):
with np.errstate(divide="ignore"):
return (np.reciprocal... | 15 | 342 |
onnx | onnx/reference/ops/op_mod.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 Mod(OpRun):
def _run(self, a, b, fmod=None):
fmod = fmod or self.fmod
if fmod == 1:
return (np.fmod(a, ... | 19 | 482 |
onnx | onnx/reference/ops/op_det.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 Det(OpRun):
def _run(self, x):
return (np.array(np.linalg.det(x)),)
| 14 | 265 |
onnx | onnx/reference/ops/op_random_normal_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 RandomNormalLike(_CommonRandom):
def _run(self, x, dtype=None, mean=None, scale... | 20 | 628 |
onnx | onnx/reference/ops/_op.py | .py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import numpy as np
from onnx.reference.op_run import OpRun, RuntimeTypeError
if TYPE_CHECKING:
from onnx.onnx_pb import NodeProto
class OpRunUnary(OpRun):
... | 183 | 6,219 |
onnx | onnx/reference/ops/op_squeeze.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 Squeeze_1(OpRun):
def _run(self, data, axes=None):
if isinstance(axes, np.ndarray):
axes = tuple(axes)
... | 46 | 1,154 |
onnx | onnx/reference/ops/op_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
def _conv_implementation(
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
):
if dilations is None:
dilations... | 317 | 13,111 |
onnx | onnx/reference/ops/op_batch_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
def _batchnorm_test_mode(
x: np.ndarray,
s: np.ndarray,
bias: np.ndarray,
mean: np.ndarray,
var: np.ndarray,
epsilon:... | 102 | 3,018 |
onnx | onnx/reference/ops/op_global_average_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_average_pool(x: np.ndarray) -> np.ndarray:
axis = tuple(range(2, np.ndim(x)))
y = np.average(x, axis=axis)
for _ in a... | 22 | 487 |
onnx | onnx/reference/ops/op_sequence_map.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 SequenceMap(OpRun):
def _run(self, input_sequence, *additional_inputs, body=None, attributes=None):
if len(additional_inputs) == 1 and isinstan... | 34 | 1,245 |
onnx | onnx/reference/ops/op_stft.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_concat_from_sequence import _concat_from_sequence
from onnx.reference.ops.op_dft import _cfft as _dft
from onnx.refe... | 168 | 5,349 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.