index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
58,922 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_scaler.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class Scaler(OpRunAiOnnxMl):
def _run(self, x, offset=None, scale=None): # type: ignore
dx = x - offset
return ((dx * scale).astype(x.dtype),)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,923 | onnx/onnx | refs/heads/main | /onnx/test/relu_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from onnx import defs, helper
class TestRelu(unittest.TestCase):
def test_relu(self) -> None:
self.assertTrue(defs.has("Relu"))
helper.make_node("Relu", ["X"], ["Y"])
if __name__ == "__main__":
unittest.main()
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58,924 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_ceil.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Ceil(OpRunUnaryNum):
def _run(self, x): # type: ignore
return (np.ceil(x),)
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58,925 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_argmin.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _argmin(data, axis=0, keepdims=True): # type: ignore
result = np.argmin(data, axis=axis)
if keepdims and len(result.shape) < len(data.shape):
result = np.expand_dims(result, axis)
return result.astype(np.int64)
def _argmin_use_numpy_select_last_index(data, axis=0, keepdims=True): # type: ignore
data = np.flip(data, axis)
result = np.argmin(data, axis=axis)
result = data.shape[axis] - result - 1
if keepdims:
result = np.expand_dims(result, axis)
return result.astype(np.int64)
class _ArgMin(OpRun):
def _run(self, data, axis=None, keepdims=None): # type: ignore
return (_argmin(data, axis=axis, keepdims=keepdims),)
class ArgMin_1(_ArgMin):
pass
class ArgMin_12(_ArgMin):
def _run(self, data, axis=None, keepdims=None, select_last_index=None): # type: ignore
if select_last_index == 0: # type: ignore
return _ArgMin._run(self, data, axis=axis, keepdims=keepdims)
return (
_argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims),
)
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58,926 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/compress.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Compress(Base):
@staticmethod
def export_compress_0() -> None:
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=0,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1, 1])
output = np.compress(condition, input, axis=0)
# print(output)
# [[ 3. 4.]
# [ 5. 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_0",
)
@staticmethod
def export_compress_1() -> None:
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=1,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1])
output = np.compress(condition, input, axis=1)
# print(output)
# [[ 2.]
# [ 4.]
# [ 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_1",
)
@staticmethod
def export_compress_default_axis() -> None:
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1, 0, 0, 1])
output = np.compress(condition, input)
# print(output)
# [ 2., 5.]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_default_axis",
)
@staticmethod
def export_compress_negative_axis() -> None:
node = onnx.helper.make_node(
"Compress",
inputs=["input", "condition"],
outputs=["output"],
axis=-1,
)
input = np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)
condition = np.array([0, 1])
output = np.compress(condition, input, axis=-1)
# print(output)
# [[ 2.]
# [ 4.]
# [ 6.]]
expect(
node,
inputs=[input, condition.astype(bool)],
outputs=[output],
name="test_compress_negative_axis",
)
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58,927 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_dynamic_quantize_linear.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class DynamicQuantizeLinear(OpRun):
def _run(self, x): # type: ignore
# args: x, y_scale, zero_point
dtype, qmin, qmax = np.uint8, 0, 255
maxx = np.float32(np.maximum(0, np.max(x)))
minx = np.float32(np.minimum(0, np.min(x)))
y_scale = np.float32(1.0 if maxx == minx else (maxx - minx)) / np.float32(
qmax - qmin
)
# scale = max == min ? 1.0f : (max - min) / float(qmax - qmin);
initial_zero_point = np.float32(qmin) - minx / y_scale
zp = max(qmin, min(qmax, initial_zero_point))
zpi = np.rint(zp)
y = np.clip(np.rint(x / y_scale) + zpi, qmin, qmax)
return (
y.astype(dtype),
y_scale.astype(x.dtype),
zpi.astype(dtype),
)
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,928 | onnx/onnx | refs/heads/main | /onnx/backend/test/report/__init__.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, Sequence
import _pytest
import pytest
from onnx.backend.test.report.coverage import Coverage
_coverage = Coverage()
_marks: Dict[str, Sequence[Any]] = {}
def _add_mark(mark: Any, bucket: str) -> None:
proto = mark.args[0]
if isinstance(proto, list):
assert len(proto) == 1
proto = proto[0]
if proto is not None:
_coverage.add_proto(proto, bucket, mark.args[1] == "RealModel")
def pytest_runtest_call(item: _pytest.nodes.Item) -> None:
mark = item.get_closest_marker("onnx_coverage")
if mark:
assert item.nodeid not in _marks
_marks[item.nodeid] = mark
def pytest_runtest_logreport(report: Any) -> None:
if report.when == "call" and report.outcome == "passed" and report.nodeid in _marks:
mark = _marks[report.nodeid]
_add_mark(mark, "passed")
@pytest.hookimpl(trylast=True) # type: ignore
def pytest_terminal_summary(
terminalreporter: _pytest.terminal.TerminalReporter, exitstatus: int
) -> None:
for mark in _marks.values():
_add_mark(mark, "loaded")
_coverage.report_text(terminalreporter)
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,929 | onnx/onnx | refs/heads/main | /onnx/backend/test/loader/__init__.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import json
import os
from typing import List, Optional
from onnx.backend.test.case.test_case import TestCase
DATA_DIR = os.path.join(
os.path.dirname(os.path.realpath(os.path.dirname(__file__))), "data"
)
def load_model_tests(
data_dir: str = DATA_DIR,
kind: Optional[str] = None,
) -> List[TestCase]:
"""Load model test cases from on-disk data files."""
supported_kinds = os.listdir(data_dir)
if kind not in supported_kinds:
raise ValueError(f"kind must be one of {supported_kinds}")
testcases = []
kind_dir = os.path.join(data_dir, kind)
for test_name in os.listdir(kind_dir):
case_dir = os.path.join(kind_dir, test_name)
# skip the non-dir files, such as generated __init__.py.
rtol = 1e-3
atol = 1e-7
if not os.path.isdir(case_dir):
continue
if os.path.exists(os.path.join(case_dir, "model.onnx")):
url = None
model_name = test_name[len("test_")]
model_dir: Optional[str] = case_dir
else:
with open(os.path.join(case_dir, "data.json")) as f:
data = json.load(f)
url = data["url"]
model_name = data["model_name"]
rtol = data.get("rtol", 1e-3)
atol = data.get("atol", 1e-7)
model_dir = None
testcases.append(
TestCase(
name=test_name,
url=url,
model_name=model_name,
model_dir=model_dir,
model=None,
data_sets=None,
kind=kind,
rtol=rtol,
atol=atol,
)
)
return testcases
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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58,930 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_concat.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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 RuntimeError(f"Concat: one input has an empty shape: {a!r}.")
if axis >= len(a.shape): # type: ignore
new_shape = a.shape + (1,) * (axis + 1 - len(a.shape)) # type: ignore
return a.reshape(new_shape)
return a
def _run(self, *args, axis=None): # type: ignore
targs = tuple(self._preprocess(a, axis) for a in args)
return (np.concatenate(targs, axis),) # type: ignore
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58,931 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/add.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Add(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add")
@staticmethod
def export_add_uint8() -> None:
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8")
@staticmethod
def export_add_broadcast() -> None:
node = onnx.helper.make_node(
"Add",
inputs=["x", "y"],
outputs=["sum"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast")
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": 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58,932 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict
import numpy as np
from onnx.onnx_pb import NodeProto
from onnx.reference.op_run import OpRun, RuntimeTypeError
class OpRunUnary(OpRun): # pylint: disable=W0223
"""
Ancestor to all unary operators in this subfolder.
Checks that input and output types are the same.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRun.__init__(self, onnx_node, run_params)
def run(self, x): # type: ignore # pylint: disable=W0221
"""
Calls method ``_run``, catches exceptions,
displays a longer error message.
Supports only unary operators.
"""
self._log("-- begin %s.run(1 input)", self.__class__.__name__)
try:
res = self._run(x)
except TypeError as e:
raise TypeError(
f"Issues with types {', '.join(str(type(_)) for _ in [x])} "
f"(unary operator {self.__class__.__name__!r})."
) from e
self._log("-- done %s.run -> %d outputs", self.__class__.__name__, len(res))
return res
class OpRunUnaryNum(OpRunUnary): # pylint: disable=W0223
"""
Ancestor to all unary and numerical operators
in this subfolder. Checks that input and output types
are the same.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRunUnary.__init__(self, onnx_node, run_params)
def run(self, x): # type: ignore # pylint: disable=W0221
"""
Calls method ``OpRunUnary.run``, catches exceptions,
displays a longer error message.
Checks that the result is not empty.
"""
res = OpRunUnary.run(self, x)
if len(res) == 0 or res[0] is None:
return res
if not isinstance(res[0], list) and res[0].dtype != x.dtype:
raise RuntimeTypeError(
f"Output type mismatch: input '{x.dtype}' != output '{res[0].dtype}' "
f"(operator {self.__class__.__name__!r})."
)
return res
class OpRunBinary(OpRun): # pylint: disable=W0223
"""
Ancestor to all binary operators in this subfolder.
Checks that input and output types are the same.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRun.__init__(self, onnx_node, run_params)
def run(self, x, y): # type: ignore # pylint: disable=W0221
"""
Calls method ``_run``, catches exceptions,
displays a longer error message.
Supports only binary operators.
"""
self._log("-- begin %s.run(2 inputs)", self.__class__.__name__)
if x is None or y is None:
raise RuntimeError(
f"x and y have different dtype: {type(x)} != {type(y)} ({type(self)})"
)
if x.dtype != y.dtype:
raise RuntimeTypeError(
f"Input type mismatch: {x.dtype} != {y.dtype} "
f"(operator '{self.__class__.__name__!r}', "
f"shapes {x.shape}, {y.shape})."
)
try:
res = self._run(x, y)
except (TypeError, ValueError) as e:
raise TypeError(
f"Issues with types {', '.join(str(type(_)) for _ in [x, y])} "
f"(binary operator {self.__class__.__name__!r})."
) from e
self._log("-- done %s.run -> %d outputs", self.__class__.__name__, len(res))
return res
class OpRunBinaryComparison(OpRunBinary): # pylint: disable=W0223
"""
Ancestor to all binary operators in this subfolder
comparing tensors.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRunBinary.__init__(self, onnx_node, run_params)
class OpRunBinaryNum(OpRunBinary): # pylint: disable=W0223
"""
Ancestor to all binary operators in this subfolder.
Checks that input oud output types are the same.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRunBinary.__init__(self, onnx_node, run_params)
def run(self, x, y): # type: ignore # pylint: disable=W0221
"""
Calls method ``OpRunBinary.run``, catches exceptions,
displays a longer error message.
"""
res = OpRunBinary.run(self, x, y)
if res[0].dtype != x.dtype:
raise RuntimeTypeError(
f"Output type mismatch: {x.dtype} != {res[0].dtype} or {y.dtype} "
f"(operator {self.__class__.__name__!r})"
f" type(x)={type(x)} type(y)={type(y)}"
)
return res
class OpRunBinaryNumpy(OpRunBinaryNum):
"""
*numpy_fct* is a binary numpy function which
takes two matrices.
"""
def __init__(
self, numpy_fct: Any, onnx_node: NodeProto, run_params: Dict[str, Any]
):
OpRunBinaryNum.__init__(self, onnx_node, run_params)
self.numpy_fct = numpy_fct
def _run(self, a, b): # type: ignore # pylint: disable=W0221
return (self.numpy_fct(a, b),)
class OpRunReduceNumpy(OpRun): # type: ignore
"""
Implements the reduce logic.
It must have a parameter *axes*.
"""
def __init__(self, onnx_node: NodeProto, run_params: Dict[str, Any]):
OpRun.__init__(self, onnx_node, run_params)
if hasattr(self, "axes"):
if isinstance(self.axes, np.ndarray): # type: ignore # pylint: disable=E0203
if len(self.axes.shape) == 0 or self.axes.shape[0] == 0: # type: ignore # pylint: disable=E0203
self.axes = None
else:
self.axes = tuple(self.axes)
elif self.axes in [[], ()]:
self.axes = None
elif isinstance(self.axes, list):
self.axes = tuple(self.axes)
def is_axes_empty(self, axes):
return axes is None
def handle_axes(self, axes):
if isinstance(axes, tuple):
if len(axes) == 0:
return None
return axes
if axes is None:
return None
if isinstance(axes, (int, tuple)):
return axes
if not isinstance(axes, np.ndarray):
raise TypeError(f"axes must be an array, not {type(axes)}.")
if len(axes.shape) == 0:
return int(axes)
if 0 in axes.shape:
return None
return tuple(axes.ravel().tolist())
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58,933 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_global_average_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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 axis:
y = np.expand_dims(y, -1)
return y # type: ignore
class GlobalAveragePool(OpRun):
def _run(self, x): # type: ignore
return (_global_average_pool(x).astype(x.dtype),)
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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,934 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import sys
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import onnx
from onnx.backend.test.case.test_case import TestCase
from onnx.backend.test.case.utils import import_recursive
from onnx.onnx_pb import (
AttributeProto,
FunctionProto,
GraphProto,
ModelProto,
NodeProto,
TensorProto,
TypeProto,
)
_NodeTestCases = []
_TargetOpType = None
def _rename_edges_helper(
internal_node: NodeProto,
rename_helper: Callable[[str], str],
attribute_map: Dict[str, AttributeProto],
prefix: str,
) -> NodeProto:
new_node = NodeProto()
new_node.CopyFrom(internal_node)
new_node.ClearField("input")
new_node.ClearField("output")
new_node.ClearField("attribute")
for internal_name in internal_node.input:
new_node.input.append(rename_helper(internal_name))
for internal_name in internal_node.output:
new_node.output.append(rename_helper(internal_name))
for attr in internal_node.attribute:
if attr.HasField("ref_attr_name"):
if attr.ref_attr_name in attribute_map:
new_attr = AttributeProto()
new_attr.CopyFrom(attribute_map[attr.ref_attr_name]) # type: ignore
new_attr.name = attr.name
new_node.attribute.extend([new_attr])
else:
new_attr = AttributeProto()
new_attr.CopyFrom(attr)
if attr.type == AttributeProto.GRAPH:
new_graph = new_attr.g
sg_rename = {}
for in_desc in new_graph.input:
sg_rename[in_desc.name] = in_desc.name = prefix + in_desc.name
for out_desc in new_graph.output:
sg_rename[out_desc.name] = out_desc.name = prefix + out_desc.name
for init_desc in new_graph.initializer:
sg_rename[init_desc.name] = init_desc.name = prefix + init_desc.name
for sparse_init_desc in new_graph.sparse_initializer:
sg_rename[
sparse_init_desc.values.name
] = sparse_init_desc.values.name = (
prefix + sparse_init_desc.values.name
)
for sparse_init_desc in new_graph.sparse_initializer:
sg_rename[
sparse_init_desc.indices.name
] = sparse_init_desc.indices.name = (
prefix + sparse_init_desc.indices.name
)
def subgraph_rename_helper(name: str) -> Any:
if name in sg_rename: # noqa: B023
return sg_rename[name] # noqa: B023
return rename_helper(name)
new_nodes = [
_rename_edges_helper(
node_desc, subgraph_rename_helper, attribute_map, prefix
)
for node_desc in new_graph.node
]
new_graph.ClearField("node")
new_graph.node.extend(new_nodes)
new_node.attribute.extend([new_attr])
return new_node
# FIXME(TMVector): Any reason we can't get rid of this and use the C++ helper directly?
def function_expand_helper(
node: NodeProto, function_proto: FunctionProto, op_prefix: str
) -> List[NodeProto]:
io_names_map = {}
attribute_map = {a.name: a for a in node.attribute}
for idx in range(len(function_proto.input)):
io_names_map[function_proto.input[idx]] = (
node.input[idx] if idx in range(len(node.input)) else ""
)
for idx in range(len(function_proto.output)):
# Even if the node has been created with optional outputs missing, we
# can't assume that the function body handles this correctly, such as in
# the case that output is also an intermediate value.
# So we only add a name mapping if the output is present. An internal
# name will be generated if the missing output is used, the same as any
# other internal tensor.
if idx in range(len(node.output)) and node.output[idx] != "":
io_names_map[function_proto.output[idx]] = node.output[idx]
def rename_helper(internal_name: str) -> Any:
if internal_name in io_names_map:
return io_names_map[internal_name]
elif internal_name == "":
return ""
return op_prefix + internal_name
new_node_list = [
_rename_edges_helper(internal_node, rename_helper, attribute_map, op_prefix)
for internal_node in function_proto.node
]
return new_node_list
def function_testcase_helper(
node: NodeProto, input_types: List[TypeProto], name: str
) -> Tuple[List[Tuple[List[NodeProto], Any]], int]:
test_op = node.op_type
op_prefix = test_op + "_" + name + "_expanded_function_"
schema = onnx.defs.get_schema(test_op, domain=node.domain)
# an op schema may have several functions, each for one opset version
# opset versions include the op's since_version and other opset versions
# if it is needed to define the op for a opset version other than the op's since_version.
function_protos = []
for opset_version in schema.function_opset_versions: # type: ignore
function_proto_str = schema.get_function_with_opset_version(opset_version) # type: ignore
function_proto = FunctionProto()
function_proto.ParseFromString(function_proto_str)
function_protos.append(function_proto)
for opset_version in schema.context_dependent_function_opset_versions: # type: ignore
function_proto_str = schema.get_context_dependent_function_with_opset_version( # type: ignore
opset_version,
node.SerializeToString(),
[t.SerializeToString() for t in input_types],
)
function_proto = FunctionProto()
function_proto.ParseFromString(function_proto_str)
function_protos.append(function_proto)
expanded_tests = []
for function_proto in function_protos:
for attr in schema.attributes:
if attr in [a.name for a in node.attribute]:
continue
if schema.attributes[attr].default_value:
node.attribute.extend([schema.attributes[attr].default_value])
# function_proto.attributes
node_list = function_expand_helper(node, function_proto, op_prefix)
expanded_tests.append((node_list, function_proto.opset_import))
return expanded_tests, schema.since_version
def _extract_value_info(
input: Union[List[Any], np.ndarray, None],
name: str,
type_proto: Optional[TypeProto] = None,
) -> onnx.ValueInfoProto:
if type_proto is None:
if input is None:
raise NotImplementedError(
"_extract_value_info: both input and type_proto arguments cannot be None."
)
elif isinstance(input, list):
elem_type = onnx.helper.np_dtype_to_tensor_dtype(input[0].dtype)
shape = None
tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
elif isinstance(input, TensorProto):
elem_type = input.data_type
shape = tuple(input.dims)
type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
else:
elem_type = onnx.helper.np_dtype_to_tensor_dtype(input.dtype)
shape = input.shape
type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
return onnx.helper.make_value_info(name, type_proto)
def _make_test_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto:
latest_onnx_version, latest_ml_version, latest_training_version = onnx.helper.VERSION_TABLE[-1][2:5] # type: ignore
if "opset_imports" in kwargs:
for opset in kwargs["opset_imports"]:
# If the test model uses an unreleased opset version (latest_version+1),
# directly use make_model to create a model with the latest ir version
if (
(
(opset.domain in {"", "ai.onnx"})
and opset.version == latest_onnx_version + 1
)
or (
opset.domain == "ai.onnx.ml"
and opset.version == latest_ml_version + 1
)
or (
(
opset.domain
in {"ai.onnx.training version", "ai.onnx.preview.training"}
)
and opset.version == latest_training_version + 1
)
):
return onnx.helper.make_model(graph, **kwargs)
# Otherwise, find and use the corresponding ir version according to given opset version
return onnx.helper.make_model_gen_version(graph, **kwargs)
# In the case of ops with optional inputs and outputs, node_op.input and node_op.output indicate
# which inputs/outputs are present and which are omitted. However, the parameter inputs
# and outputs of this function include values only for inputs/outputs that are present.
# E.g., for an op with 3 inputs, if the second parameter is optional and we wish to omit it,
# node_op.inputs would look like ["Param1", "", "Param3"], while inputs would look like
# [input-1-value, input-3-value]
# Instead of creating model with latest version, it now generates models for since_version by default.
# Thus it can make every model uses the same opset version after every opset change.
# Besides, user can specify "use_max_opset_version" to generate models for
# the latest opset vesion that supports before targeted opset version
def expect(
node_op: onnx.NodeProto,
inputs: Sequence[Union[np.ndarray, TensorProto]],
outputs: Sequence[Union[np.ndarray, TensorProto]],
name: str,
**kwargs: Any,
) -> None:
# skip if the node_op's op_type is not same as the given one
if _TargetOpType and node_op.op_type != _TargetOpType:
return
# in case node_op is modified
node = deepcopy(node_op)
present_inputs = [x for x in node.input if (x != "")]
present_outputs = [x for x in node.output if (x != "")]
input_type_protos = [None] * len(inputs)
if "input_type_protos" in kwargs:
input_type_protos = kwargs["input_type_protos"]
del kwargs["input_type_protos"]
output_type_protos = [None] * len(outputs)
if "output_type_protos" in kwargs:
output_type_protos = kwargs["output_type_protos"]
del kwargs["output_type_protos"]
inputs_vi = [
_extract_value_info(arr, arr_name, input_type)
for arr, arr_name, input_type in zip(inputs, present_inputs, input_type_protos)
]
outputs_vi = [
_extract_value_info(arr, arr_name, output_type)
for arr, arr_name, output_type in zip(
outputs, present_outputs, output_type_protos
)
]
graph = onnx.helper.make_graph(
nodes=[node], name=name, inputs=inputs_vi, outputs=outputs_vi
)
kwargs["producer_name"] = "backend-test"
if "opset_imports" not in kwargs:
# To make sure the model will be produced with the same opset_version after opset changes
# By default, it uses since_version as opset_version for produced models
produce_opset_version = onnx.defs.get_schema(
node.op_type, domain=node.domain
).since_version
kwargs["opset_imports"] = [
onnx.helper.make_operatorsetid(node.domain, produce_opset_version)
]
model = _make_test_model_gen_version(graph, **kwargs)
_NodeTestCases.append(
TestCase(
name=name,
model_name=name,
url=None,
model_dir=None,
model=model,
data_sets=[(inputs, outputs)],
kind="node",
rtol=1e-3,
atol=1e-7,
)
)
# Create list of types for node.input, filling a default TypeProto for missing inputs:
# E.g. merge(["x", "", "y"], [x-value-info, y-value-info]) will return [x-type, default-type, y-type]
def merge(
node_inputs: List[str], present_value_info: List[onnx.ValueInfoProto]
) -> List[TypeProto]:
if node_inputs:
if node_inputs[0] != "":
return [
present_value_info[0].type,
*merge(node_inputs[1:], present_value_info[1:]),
]
else:
return [TypeProto(), *merge(node_inputs[1:], present_value_info)]
return []
merged_types = merge(list(node.input), inputs_vi)
(
expanded_tests,
since_version,
) = function_testcase_helper(node, merged_types, name)
for expanded_function_nodes, func_opset_import in expanded_tests:
kwargs["producer_name"] = "backend-test"
# TODO: if kwargs["opset_imports"] already exists, only generate test case for the opset version.
# replace opset versions with what are specified in function proto
if "opset_imports" not in kwargs:
kwargs["opset_imports"] = func_opset_import
else:
for opset_import in func_opset_import:
matches = [
opset
for opset in kwargs["opset_imports"]
if opset.domain == opset_import.domain
]
if matches:
matches[0].version = opset_import.version
else:
kwargs["opset_imports"].append(opset_import)
onnx_ai_opset_version = ""
if "opset_imports" in kwargs:
onnx_ai_opset_imports = [
oi for oi in kwargs["opset_imports"] if oi.domain in ("", "ai.onnx")
]
if len(onnx_ai_opset_imports) == 1:
onnx_ai_opset_version = onnx_ai_opset_imports[0].version
function_test_name = name + "_expanded"
if onnx_ai_opset_version and onnx_ai_opset_version != since_version:
function_test_name += f"_ver{onnx_ai_opset_version}"
graph = onnx.helper.make_graph(
nodes=expanded_function_nodes,
name=function_test_name,
inputs=inputs_vi,
outputs=outputs_vi,
)
model = _make_test_model_gen_version(graph, **kwargs)
_NodeTestCases.append(
TestCase(
name=function_test_name,
model_name=function_test_name,
url=None,
model_dir=None,
model=model,
data_sets=[(inputs, outputs)],
kind="node",
rtol=1e-3,
atol=1e-7,
)
)
def collect_testcases(op_type: str) -> List[TestCase]:
"""Collect node test cases"""
# only keep those tests related to this operator
global _TargetOpType
_TargetOpType = op_type
import_recursive(sys.modules[__name__])
return _NodeTestCases
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": 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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], 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58,935 | onnx/onnx | refs/heads/main | /onnx/test/schema_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Sequence
import parameterized
import onnx
from onnx import defs
class TestSchema(unittest.TestCase):
def test_get_schema(self) -> None:
defs.get_schema("Relu")
def test_typecheck(self) -> None:
defs.get_schema("Conv")
def test_attr_default_value(self) -> None:
v = defs.get_schema("BatchNormalization").attributes["epsilon"].default_value
self.assertEqual(type(v), onnx.AttributeProto)
self.assertEqual(v.type, onnx.AttributeProto.FLOAT)
def test_function_body(self) -> None:
self.assertEqual(
type(defs.get_schema("Selu").function_body), onnx.FunctionProto
)
class TestOpSchema(unittest.TestCase):
def test_init(self):
# Test that the constructor creates an OpSchema object
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertIsInstance(schema, defs.OpSchema)
def test_init_with_inputs(self) -> None:
op_schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=[defs.OpSchema.FormalParameter("input1", "T")],
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(op_schema.name, "test_op")
self.assertEqual(op_schema.domain, "test_domain")
self.assertEqual(op_schema.since_version, 1)
self.assertEqual(len(op_schema.inputs), 1)
self.assertEqual(op_schema.inputs[0].name, "input1")
self.assertEqual(op_schema.inputs[0].type_str, "T")
self.assertEqual(len(op_schema.type_constraints), 1)
self.assertEqual(op_schema.type_constraints[0].type_param_str, "T")
self.assertEqual(
op_schema.type_constraints[0].allowed_type_strs, ["tensor(int64)"]
)
def test_init_creates_multi_input_output_schema(self) -> None:
op_schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=[
defs.OpSchema.FormalParameter("input1", "T"),
defs.OpSchema.FormalParameter("input2", "T"),
],
outputs=[
defs.OpSchema.FormalParameter("output1", "T"),
defs.OpSchema.FormalParameter("output2", "T"),
],
type_constraints=[("T", ["tensor(int64)"], "")],
attributes=[
defs.OpSchema.Attribute(
"attr1", defs.OpSchema.AttrType.INTS, "attr1 description"
)
],
)
self.assertEqual(len(op_schema.inputs), 2)
self.assertEqual(op_schema.inputs[0].name, "input1")
self.assertEqual(op_schema.inputs[0].type_str, "T")
self.assertEqual(op_schema.inputs[1].name, "input2")
self.assertEqual(op_schema.inputs[1].type_str, "T")
self.assertEqual(len(op_schema.outputs), 2)
self.assertEqual(op_schema.outputs[0].name, "output1")
self.assertEqual(op_schema.outputs[0].type_str, "T")
self.assertEqual(op_schema.outputs[1].name, "output2")
self.assertEqual(op_schema.outputs[1].type_str, "T")
self.assertEqual(len(op_schema.type_constraints), 1)
self.assertEqual(op_schema.type_constraints[0].type_param_str, "T")
self.assertEqual(
op_schema.type_constraints[0].allowed_type_strs, ["tensor(int64)"]
)
self.assertEqual(len(op_schema.attributes), 1)
self.assertEqual(op_schema.attributes["attr1"].name, "attr1")
self.assertEqual(
op_schema.attributes["attr1"].type, defs.OpSchema.AttrType.INTS
)
self.assertEqual(op_schema.attributes["attr1"].description, "attr1 description")
def test_init_without_optional_arguments(self) -> None:
op_schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(op_schema.name, "test_op")
self.assertEqual(op_schema.domain, "test_domain")
self.assertEqual(op_schema.since_version, 1)
self.assertEqual(len(op_schema.inputs), 0)
self.assertEqual(len(op_schema.outputs), 0)
self.assertEqual(len(op_schema.type_constraints), 0)
def test_name(self):
# Test that the name parameter is required and is a string
with self.assertRaises(TypeError):
defs.OpSchema(domain="test_domain", since_version=1) # type: ignore
with self.assertRaises(TypeError):
defs.OpSchema(123, "test_domain", 1) # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.name, "test_op")
def test_domain(self):
# Test that the domain parameter is required and is a string
with self.assertRaises(TypeError):
defs.OpSchema(name="test_op", since_version=1) # type: ignore
with self.assertRaises(TypeError):
defs.OpSchema("test_op", 123, 1) # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.domain, "test_domain")
def test_since_version(self):
# Test that the since_version parameter is required and is an integer
with self.assertRaises(TypeError):
defs.OpSchema("test_op", "test_domain") # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.since_version, 1)
def test_doc(self):
schema = defs.OpSchema("test_op", "test_domain", 1, doc="test_doc")
self.assertEqual(schema.doc, "test_doc")
def test_inputs(self):
# Test that the inputs parameter is optional and is a sequence of FormalParameter tuples
inputs = [
defs.OpSchema.FormalParameter(
name="input1", type_str="T", description="The first input."
)
]
schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=inputs,
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(len(schema.inputs), 1)
self.assertEqual(schema.inputs[0].name, "input1")
self.assertEqual(schema.inputs[0].type_str, "T")
self.assertEqual(schema.inputs[0].description, "The first input.")
def test_outputs(self):
# Test that the outputs parameter is optional and is a sequence of FormalParameter tuples
outputs = [
defs.OpSchema.FormalParameter(
name="output1", type_str="T", description="The first output."
)
]
schema = defs.OpSchema(
"test_op",
"test_domain",
1,
outputs=outputs,
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(len(schema.outputs), 1)
self.assertEqual(schema.outputs[0].name, "output1")
self.assertEqual(schema.outputs[0].type_str, "T")
self.assertEqual(schema.outputs[0].description, "The first output.")
class TestFormalParameter(unittest.TestCase):
def test_init(self):
name = "input1"
type_str = "tensor(float)"
description = "The first input."
param_option = defs.OpSchema.FormalParameterOption.Single
is_homogeneous = True
min_arity = 1
differentiation_category = defs.OpSchema.DifferentiationCategory.Unknown
formal_parameter = defs.OpSchema.FormalParameter(
name,
type_str,
description,
param_option=param_option,
is_homogeneous=is_homogeneous,
min_arity=min_arity,
differentiation_category=differentiation_category,
)
self.assertEqual(formal_parameter.name, name)
self.assertEqual(formal_parameter.type_str, type_str)
self.assertEqual(formal_parameter.description, description)
self.assertEqual(formal_parameter.option, param_option)
self.assertEqual(formal_parameter.is_homogeneous, is_homogeneous)
self.assertEqual(formal_parameter.min_arity, min_arity)
self.assertEqual(
formal_parameter.differentiation_category, differentiation_category
)
class TestTypeConstraintParam(unittest.TestCase):
@parameterized.parameterized.expand(
[
("single_type", "T", ["tensor(float)"], "Test description"),
(
"double_types",
"T",
["tensor(float)", "tensor(int64)"],
"Test description",
),
("tuple", "T", ("tensor(float)", "tensor(int64)"), "Test description"),
]
)
def test_init(
self,
_: str,
type_param_str: str,
allowed_types: Sequence[str],
description: str,
) -> None:
type_constraint = defs.OpSchema.TypeConstraintParam(
type_param_str, allowed_types, description
)
self.assertEqual(type_constraint.description, description)
self.assertEqual(type_constraint.allowed_type_strs, list(allowed_types))
self.assertEqual(type_constraint.type_param_str, type_param_str)
class TestAttribute(unittest.TestCase):
def test_init(self):
name = "test_attr"
type_ = defs.OpSchema.AttrType.STRINGS
description = "Test attribute"
attribute = defs.OpSchema.Attribute(name, type_, description)
self.assertEqual(attribute.name, name)
self.assertEqual(attribute.type, type_)
self.assertEqual(attribute.description, description)
def test_init_with_default_value(self):
default_value = (
defs.get_schema("BatchNormalization").attributes["epsilon"].default_value
)
self.assertIsInstance(default_value, onnx.AttributeProto)
attribute = defs.OpSchema.Attribute("attr1", default_value, "attr1 description")
self.assertEqual(default_value, attribute.default_value)
self.assertEqual("attr1", attribute.name)
self.assertEqual("attr1 description", attribute.description)
if __name__ == "__main__":
unittest.main()
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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58,936 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bitshift.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class BitShift(Base):
@staticmethod
def export_right_unit8() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint8)
y = np.array([1, 2, 3]).astype(np.uint8)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8")
@staticmethod
def export_right_unit16() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint16)
y = np.array([1, 2, 3]).astype(np.uint16)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16")
@staticmethod
def export_right_unit32() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint32)
y = np.array([1, 2, 3]).astype(np.uint32)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32")
@staticmethod
def export_right_unit64() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
)
x = np.array([16, 4, 1]).astype(np.uint64)
y = np.array([1, 2, 3]).astype(np.uint64)
z = x >> y # expected output [8, 1, 0]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64")
@staticmethod
def export_left_unit8() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint8)
y = np.array([1, 2, 3]).astype(np.uint8)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8")
@staticmethod
def export_left_unit16() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint16)
y = np.array([1, 2, 3]).astype(np.uint16)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16")
@staticmethod
def export_left_unit32() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint32)
y = np.array([1, 2, 3]).astype(np.uint32)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32")
@staticmethod
def export_left_unit64() -> None:
node = onnx.helper.make_node(
"BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
)
x = np.array([16, 4, 1]).astype(np.uint64)
y = np.array([1, 2, 3]).astype(np.uint64)
z = x << y # expected output [32, 16, 8]
expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64")
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,937 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_imputer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class Imputer(OpRunAiOnnxMl):
def _run( # type: ignore
self,
x,
imputed_value_floats=None,
imputed_value_int64s=None,
replaced_value_float=None,
replaced_value_int64=None,
):
if imputed_value_floats is not None and len(imputed_value_floats) > 0:
values = imputed_value_floats
replace = replaced_value_float
elif imputed_value_int64s is not None and len(imputed_value_int64s) > 0:
values = imputed_value_int64s
replace = replaced_value_int64
else:
raise ValueError("Missing are not defined.")
if isinstance(values, list):
values = np.array(values)
if len(x.shape) != 2:
raise TypeError(f"x must be a matrix but shape is {x.shape}")
if values.shape[0] not in (x.shape[1], 1):
raise TypeError( # pragma: no cover
f"Dimension mismatch {values.shape[0]} != {x.shape[1]}"
)
x = x.copy()
if np.isnan(replace):
for i in range(0, x.shape[1]):
val = values[min(i, values.shape[0] - 1)]
x[np.isnan(x[:, i]), i] = val
else:
for i in range(0, x.shape[1]):
val = values[min(i, values.shape[0] - 1)]
x[x[:, i] == replace, i] = val
return (x,)
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58,938 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_if.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
from onnx.reference.op_run import OpRun
class If(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
if "opsets" not in self.run_params:
raise KeyError("run_params must contains key 'opsets'.")
if "verbose" not in run_params:
raise KeyError("run_params must contains key 'verbose'.")
def need_context(self) -> bool:
"""
Tells the runtime if this node needs the context
(all the results produced so far) as it may silently access
one of them (operator Loop).
The default answer is `False`.
"""
return True
def _run(self, cond, context=None, else_branch=None, then_branch=None, attributes=None): # type: ignore
if len(cond.shape) > 0:
try:
evaluated_condition = all(cond)
except ValueError as e:
raise ValueError(
f"Unable to evaluate the condition with {type(cond)}, "
f"shape={cond.shape}, dtype={cond.dtype}."
) from e
if evaluated_condition:
self._log(" -- then> {%r}", context)
outputs = self._run_then_branch(context, attributes=attributes) # type: ignore
self._log(" -- then<")
final = tuple(outputs)
branch = "then"
else:
self._log(" -- else> {%r}", context)
outputs = self._run_else_branch(context, attributes=attributes) # type: ignore
self._log(" -- else<")
final = tuple(outputs)
branch = "else"
elif cond:
self._log(" -- then> {%r}", context)
outputs = self._run_then_branch(context, attributes=attributes) # type: ignore
self._log(" -- then<")
final = tuple(outputs)
branch = "then"
else:
self._log(" -- else> {%r}", context)
outputs = self._run_else_branch(context, attributes=attributes) # type: ignore
self._log(" -- else<")
final = tuple(outputs)
branch = "else"
if not final:
raise RuntimeError( # pragma: no cover
f"Operator If ({self.onnx_node.name!r}) does not have any output."
)
for i, f in enumerate(final):
if f is None:
br = self.then_branch if branch == "then" else self.else_branch # type: ignore
names = br.output_names
inits = [i.name for i in br.obj.graph.initializer]
raise RuntimeError( # pragma: no cover
f"Output {i!r} (branch={branch!r}, name={names[i]!r}) is None, "
f"available inputs={sorted(context)}, initializers={inits}."
)
return final
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,939 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/mean.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Mean(Base):
@staticmethod
def export() -> None:
data_0 = np.array([3, 0, 2]).astype(np.float32)
data_1 = np.array([1, 3, 4]).astype(np.float32)
data_2 = np.array([2, 6, 6]).astype(np.float32)
result = np.array([2, 3, 4]).astype(np.float32)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_mean_example",
)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input")
result = np.divide(np.add(data_0, data_1), 2.0)
node = onnx.helper.make_node(
"Mean",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs"
)
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58,940 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_attribute_has_value.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class AttributeHasValue(OpRun):
def _run( # type: ignore
self,
value_float=None,
value_floats=None,
value_graph=None,
value_graphs=None,
value_int=None,
value_ints=None,
value_sparse_tensor=None,
value_sparse_tensors=None,
value_string=None,
value_strings=None,
value_tensor=None,
value_tensors=None,
value_type_proto=None,
value_type_protos=None,
):
# TODO: support overridden attributes.
for att in self.onnx_node.attribute:
if att.name.startswith("value_"):
return (np.array([True]),)
return (np.array([False]),)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], 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58,941 | onnx/onnx | refs/heads/main | /onnx/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
__all__ = [
# Constants
"ONNX_ML",
"IR_VERSION",
"IR_VERSION_2017_10_10",
"IR_VERSION_2017_10_30",
"IR_VERSION_2017_11_3",
"IR_VERSION_2019_1_22",
"IR_VERSION_2019_3_18",
"IR_VERSION_2019_9_19",
"IR_VERSION_2020_5_8",
"IR_VERSION_2021_7_30",
"EXPERIMENTAL",
"STABLE",
# Modules
"checker",
"compose",
"defs",
"gen_proto",
"helper",
"hub",
"mapping",
"numpy_helper",
"parser",
"printer",
"shape_inference",
"utils",
"version_converter",
# Proto classes
"AttributeProto",
"FunctionProto",
"GraphProto",
"MapProto",
"ModelProto",
"NodeProto",
"OperatorProto",
"OperatorSetIdProto",
"OperatorSetProto",
"OperatorStatus",
"OptionalProto",
"SequenceProto",
"SparseTensorProto",
"StringStringEntryProto",
"TensorAnnotation",
"TensorProto",
"TensorShapeProto",
"TrainingInfoProto",
"TypeProto",
"ValueInfoProto",
"Version",
# Utility functions
"convert_model_to_external_data",
"load_external_data_for_model",
"load_model_from_string",
"load_model",
"load_tensor_from_string",
"load_tensor",
"save_model",
"save_tensor",
"write_external_data_tensors",
]
# isort:skip_file
import os
import typing
from typing import IO, Literal, Union
from onnx import serialization
from onnx.onnx_cpp2py_export import ONNX_ML
from onnx.external_data_helper import (
load_external_data_for_model,
write_external_data_tensors,
convert_model_to_external_data,
)
from onnx.onnx_pb import (
AttributeProto,
EXPERIMENTAL,
FunctionProto,
GraphProto,
IR_VERSION,
IR_VERSION_2017_10_10,
IR_VERSION_2017_10_30,
IR_VERSION_2017_11_3,
IR_VERSION_2019_1_22,
IR_VERSION_2019_3_18,
IR_VERSION_2019_9_19,
IR_VERSION_2020_5_8,
IR_VERSION_2021_7_30,
ModelProto,
NodeProto,
OperatorSetIdProto,
OperatorStatus,
STABLE,
SparseTensorProto,
StringStringEntryProto,
TensorAnnotation,
TensorProto,
TensorShapeProto,
TrainingInfoProto,
TypeProto,
ValueInfoProto,
Version,
)
from onnx.onnx_operators_pb import OperatorProto, OperatorSetProto
from onnx.onnx_data_pb import MapProto, OptionalProto, SequenceProto
from onnx.version import version as __version__
# Import common subpackages so they're available when you 'import onnx'
from onnx import (
checker,
compose,
defs,
gen_proto,
helper,
hub,
mapping,
numpy_helper,
parser,
printer,
shape_inference,
utils,
version_converter,
)
# Supported model formats that can be loaded from and saved to
# The literals are formats with built-in support. But we also allow users to
# register their own formats. So we allow str as well.
_SupportedFormat = Union[Literal["protobuf", "textproto"], str]
# Default serialization format
_DEFAULT_FORMAT = "protobuf"
def _load_bytes(f: IO[bytes] | str | os.PathLike) -> bytes:
if hasattr(f, "read") and callable(typing.cast(IO[bytes], f).read):
content = typing.cast(IO[bytes], f).read()
else:
f = typing.cast(Union[str, os.PathLike], f)
with open(f, "rb") as readable:
content = readable.read()
return content
def _save_bytes(content: bytes, f: IO[bytes] | str | os.PathLike) -> None:
if hasattr(f, "write") and callable(typing.cast(IO[bytes], f).write):
typing.cast(IO[bytes], f).write(content)
else:
f = typing.cast(Union[str, os.PathLike], f)
with open(f, "wb") as writable:
writable.write(content)
def _get_file_path(f: IO[bytes] | str | os.PathLike | None) -> str | None:
if isinstance(f, (str, os.PathLike)):
return os.path.abspath(f)
if hasattr(f, "name"):
assert f is not None
return os.path.abspath(f.name)
return None
def _get_serializer(
fmt: _SupportedFormat | None, f: str | os.PathLike | IO[bytes] | None = None
) -> serialization.ProtoSerializer:
"""Get the serializer for the given path and format from the serialization registry."""
# Use fmt if it is specified
if fmt is not None:
return serialization.registry.get(fmt)
if (file_path := _get_file_path(f)) is not None:
_, ext = os.path.splitext(file_path)
fmt = serialization.registry.get_format_from_file_extension(ext)
# Failed to resolve format if fmt is None. Use protobuf as default
fmt = fmt or _DEFAULT_FORMAT
assert fmt is not None
return serialization.registry.get(fmt)
def load_model(
f: IO[bytes] | str | os.PathLike,
format: _SupportedFormat | None = None, # pylint: disable=redefined-builtin
load_external_data: bool = True,
) -> ModelProto:
"""Loads a serialized ModelProto into memory.
Args:
f: can be a file-like object (has "read" function) or a string/PathLike containing a file name
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
load_external_data: Whether to load the external data.
Set to True if the data is under the same directory of the model.
If not, users need to call :func:`load_external_data_for_model`
with directory to load external data from.
Returns:
Loaded in-memory ModelProto.
"""
model = _get_serializer(format, f).deserialize_proto(_load_bytes(f), ModelProto())
if load_external_data:
model_filepath = _get_file_path(f)
if model_filepath:
base_dir = os.path.dirname(model_filepath)
load_external_data_for_model(model, base_dir)
return model
def load_tensor(
f: IO[bytes] | str | os.PathLike,
format: _SupportedFormat | None = None, # pylint: disable=redefined-builtin
) -> TensorProto:
"""Loads a serialized TensorProto into memory.
Args:
f: can be a file-like object (has "read" function) or a string/PathLike containing a file name
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
Returns:
Loaded in-memory TensorProto.
"""
return _get_serializer(format, f).deserialize_proto(_load_bytes(f), TensorProto())
def load_model_from_string(
s: bytes | str,
format: _SupportedFormat = _DEFAULT_FORMAT, # pylint: disable=redefined-builtin
) -> ModelProto:
"""Loads a binary string (bytes) that contains serialized ModelProto.
Args:
s: a string, which contains serialized ModelProto
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
Returns:
Loaded in-memory ModelProto.
"""
return _get_serializer(format).deserialize_proto(s, ModelProto())
def load_tensor_from_string(
s: bytes,
format: _SupportedFormat = _DEFAULT_FORMAT, # pylint: disable=redefined-builtin
) -> TensorProto:
"""Loads a binary string (bytes) that contains serialized TensorProto.
Args:
s: a string, which contains serialized TensorProto
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
Returns:
Loaded in-memory TensorProto.
"""
return _get_serializer(format).deserialize_proto(s, TensorProto())
def save_model(
proto: ModelProto | bytes,
f: IO[bytes] | str | os.PathLike,
format: _SupportedFormat | None = None, # pylint: disable=redefined-builtin
*,
save_as_external_data: bool = False,
all_tensors_to_one_file: bool = True,
location: str | None = None,
size_threshold: int = 1024,
convert_attribute: bool = False,
) -> None:
"""
Saves the ModelProto to the specified path and optionally, serialize tensors with raw data as external data before saving.
Args:
proto: should be a in-memory ModelProto
f: can be a file-like object (has "write" function) or a string containing
a file name or a pathlike object
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
save_as_external_data: If true, save tensors to external file(s).
all_tensors_to_one_file: Effective only if save_as_external_data is True.
If true, save all tensors to one external file specified by location.
If false, save each tensor to a file named with the tensor name.
location: Effective only if save_as_external_data is true.
Specify the external file that all tensors to save to.
If not specified, will use the model name.
size_threshold: Effective only if save_as_external_data is True.
Threshold for size of data. Only when tensor's data is >= the size_threshold it will be converted
to external data. To convert every tensor with raw data to external data set size_threshold=0.
convert_attribute: Effective only if save_as_external_data is True.
If true, convert all tensors to external data
If false, convert only non-attribute tensors to external data
"""
if isinstance(proto, bytes):
proto = _get_serializer(_DEFAULT_FORMAT).deserialize_proto(proto, ModelProto())
if save_as_external_data:
convert_model_to_external_data(
proto, all_tensors_to_one_file, location, size_threshold, convert_attribute
)
model_filepath = _get_file_path(f)
if model_filepath is not None:
basepath = os.path.dirname(model_filepath)
proto = write_external_data_tensors(proto, basepath)
serialized = _get_serializer(format, model_filepath).serialize_proto(proto)
_save_bytes(serialized, f)
def save_tensor(
proto: TensorProto,
f: IO[bytes] | str | os.PathLike,
format: _SupportedFormat | None = None, # pylint: disable=redefined-builtin
) -> None:
"""
Saves the TensorProto to the specified path.
Args:
proto: should be a in-memory TensorProto
f: can be a file-like object (has "write" function) or a string
containing a file name or a pathlike object.
format: The serialization format. When it is not specified, it is inferred
from the file extension when ``f`` is a path. If not specified _and_
``f`` is not a path, 'protobuf' is used. The encoding is assumed to
be "utf-8" when the format is a text format.
"""
serialized = _get_serializer(format, f).serialize_proto(proto)
_save_bytes(serialized, f)
# For backward compatibility
load = load_model
load_from_string = load_model_from_string
save = save_model
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58,942 | onnx/onnx | refs/heads/main | /onnx/reference/reference_evaluator.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C3001,C0415,R0902,R0912,R0913,R0914,R0915
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from onnx import load
from onnx.defs import onnx_opset_version
from onnx.onnx_pb import FunctionProto, GraphProto, ModelProto, NodeProto, TypeProto
from onnx.reference.op_run import (
OpFunctionContextDependant,
OpRun,
OpRunExpand,
RuntimeContextError,
to_array_extended,
)
from onnx.reference.ops_optimized import optimized_operators
class ReferenceEvaluator:
"""
Computes the outputs of an ONNX proto
(`ModelProto`, `FunctionProto`, `GraphProto`, `NodeProto`).
This is a pure python implementation of ONNX specifications.
Mismatches may remain between the official specifications and the implementation here.
In the case of such a mismatch, the official spec overrides this implementation.
:param proto: :class:`onnx.ModelProto`, :class:`onnx.GraphProto`,
:class:`onnx.FunctionProto`, :class:`onnx.NodeProto`,
filename or bytes
:param verbose: display intermediate results
on the standard output during the execution
:param opsets: if *proto* is an instance of *GraphProto*,
opsets must be defined by a dictionary of
:param functions: known onnx functions
:param new_ops: this runtime can be used to test the implementations
of new operators, *new_ops* is a list of classes
derived from :class:`OpRun <onnx.reference.op_run.OpRun>`,
every class must define the static attribute `domain`,
there may be multiple implementations for the same operator,
the first one in the list is used.
:param optimized: some operators have two implementations,
a naive one corresponding to definition of the mathematical
definition of the operator, another one more efficient.
This is the case for operator Conv. The naive version is ten times
slower than the optimized one using a decomposition
into *Conv = im2col + Gemm*. If True, all optimized
kernels are added in `new_ops` and are used instead of the
inner implementation if list *new_ops* does not already contain
one.
The class maps every node to its associated implementation.
When a subgraph of a function is met,
it uses this class to execute the subgraph or the function.
Next example shows how to run `ReferenceEvaluator` with an onnx model
stored in file `model.onnx`.
::
import numpy as np
from onnx.reference import ReferenceEvaluator
X = np.array(...)
sess = ReferenceEvaluator("model.onnx")
results = sess.run(None, {"X": X})
print(results[0]) # display the first result
Parameter *verbose* may be used to show intermediate results.
::
import numpy as np
from onnx.reference import ReferenceEvaluator
X = np.array(...)
sess = ReferenceEvaluator("model.onnx", verbose=1)
results = sess.run(None, {"X": X})
print(results[0]) # display the first result
The class can use any implementation available in folder
`ops <https://github.com/onnx/onnx/tree/main/onnx/reference/ops>`_.
Adding an implementation requires two changes. The first one is
the implementation itself. Any existing node can be used as a template.
The second is one line in file `_op_list.py
<https://github.com/onnx/onnx/tree/main/onnx/reference/ops/_op_list.py>`_
to import the file and let the reference evaluator know it exists.
This class can also be used to test an implementation of
a custom operator. Let's assume this new operator
is `InvAlpha` from domain `custom`. The implementation
must take place in a class inheriting from
:class:`OpRun <onnx.reference.op_run.OpRun>`.
It must also define attribute `op_domain`.
Here is an example which computes :math:`\\frac{1}{X + \\alpha}`.
.. exec_code::
from onnx.reference.op_run import OpRun
class InvAlpha(OpRun):
op_domain = "custom"
def _run(self, x, alpha=None): # type: ignore
# None must be the default value, it is automatically
# replaced by class OpRun with either the default value
# specified in the NodeProto or an attribute value defined
# in a `FunctionProto`.
return (1 / (x + alpha),)
`alpha` is an attribute. It can be defined by the onnx node or
be defined by the function using this node. It is safe to assume
that attributes are known at the same time as the input.
Class `ReferenceEvaluator` must know about this new implementation
and this can be done by specified argument *new_ops*.
::
sess = ReferenceEvaluator(onnx_model, new_ops=[InvAlpha])
got = sess.run(None, {"X": x})[0]
A specific node can be simply evaluated.
.. exec_code::
import numpy as np
from onnx.reference.ops._op_list import Celu
x = np.array([[0, 1], [-1, 2]], dtype=np.float32)
y = Celu.eval(x, alpha=0.5)
print(y)
This can also be expressed as:
.. exec_code::
import numpy as np
from onnx.reference.ops import load_op
Celu = load_op("", "Celu") # domain is ""
x = np.array([[0, 1], [-1, 2]], dtype=np.float32)
y = Celu.eval(x, alpha=0.5)
print(y)
It is possible to overwrite an existing operator.
The class name must be the same. The domain does not have
to be specified for the default domain. However, by default,
class `OpRun` will load the most recent for this operator.
It can be explicitly specified by adding static attribute
`op_schema` of type :class:`OpSchema
<onnx.onnx_cpp2py_export.defs.OpSchema>`.
::
from onnx.reference.op_run.op_conv import Conv as _Conv
class Conv(_Conv):
op_schema = instance_of_OpSchema()
def _run(self, ...):
...
An operator may be different in a later opset. In that case,
a new implementation needs to be registered. `Pad_11`, `Pad_18`.
`Pad_11` is the implementation chose for opset in [11, 17].
`Pad_18` is selected for any greater opset. Both classes must be
imported into file `_op_list.py` to register their existence to the
runtime.
An operator may have a reference implementation such as `CastLike`
and still be defined as a function. By default, the reference implementation
is used. This behaviour can be changed by adding a class to the list
of overwritten operators. It must inherit from :class:`OpRunExpand`.
::
from onnx.reference.op_run import OpRunExpand
class CastLike(OpRunExpand):
op_domain = ""
ref = ReferenceEvaluator(model, new_ops=[CastLike])
# ...
This mechanism is used in unit test to check the function
implementation a schema may define.
"""
def __init__( # type: ignore
self,
proto: Any,
opsets: Optional[Dict[str, int]] = None,
functions: Optional[List[Union["ReferenceEvaluator", FunctionProto]]] = None, # type: ignore
verbose: int = 0,
new_ops: Optional[List[OpRun]] = None,
optimized: bool = True,
):
if optimized:
if new_ops is None:
new_ops = optimized_operators.copy()
else:
set_new_ops = set(new_ops)
for op in optimized_operators:
if op not in set_new_ops:
new_ops.append(op)
self.output_types_ = None
self.input_types_ = None
if isinstance(proto, str):
with open(proto, "rb") as f:
proto = load(f)
elif isinstance(proto, bytes):
proto = load(BytesIO(proto))
self.proto_ = proto
self.functions_: Dict[Tuple[str, str], ReferenceEvaluator] = {}
self.attributes_: List[str] = []
if isinstance(proto, ModelProto):
self.onnx_graph_ = proto.graph
self.opsets_ = {d.domain: d.version for d in proto.opset_import}
if opsets is not None:
raise ValueError("opsets must be None if proto is ModelProto.")
if functions is not None:
raise ValueError("functions must be None if proto is ModelProto.")
functions = proto.functions # type: ignore[assignment]
elif isinstance(proto, GraphProto):
self.onnx_graph_ = proto
if not isinstance(opsets, dict):
raise ValueError("opsets must be a dictionary if proto is GraphProto.")
self.opsets_ = opsets
elif isinstance(proto, FunctionProto):
self.onnx_graph_ = None # type: ignore
self.opsets_ = {d.domain: d.version for d in proto.opset_import}
if opsets is not None:
raise ValueError("opsets must be None if proto is FunctionProto.")
self.attributes_ = list(proto.attribute)
elif isinstance(proto, NodeProto):
self.onnx_graph_ = None # type: ignore
self.opsets_ = {
proto.domain: 1 if proto.domain != "" else onnx_opset_version()
}
else:
raise TypeError(f"Unexpected type {type(proto)} for proto.")
if self.onnx_graph_:
self.input_names_ = [i.name for i in self.onnx_graph_.input]
self.input_types_ = [i.type for i in self.onnx_graph_.input]
self.output_names_ = [o.name for o in self.onnx_graph_.output]
self.output_types_ = [i.type for i in self.onnx_graph_.output]
self.inits_ = list(self.onnx_graph_.initializer) + list(
self.onnx_graph_.sparse_initializer # type: ignore
)
self.nodes_ = self.onnx_graph_.node
all_types = {i.name: i.type for i in self.onnx_graph_.input}
if hasattr(self.proto_, "value_info"):
for shape_type in self.proto_.value_info:
all_types[shape_type.name] = shape_type.type
self.all_types_ = all_types
else:
self.input_names_ = list(proto.input)
self.output_names_ = list(proto.output)
self.inits_ = []
if isinstance(proto, NodeProto):
self.nodes_ = [proto] # type: ignore[assignment]
else:
self.nodes_ = proto.node
if functions is not None:
for f in functions: # type: ignore
if isinstance(f, FunctionProto):
existing_functions = list(self.functions_.values())
self.functions_[f.domain, f.name] = ReferenceEvaluator(
f, verbose=verbose, functions=existing_functions
)
elif isinstance(f, ReferenceEvaluator):
onx = f.proto_ # type: ignore
self.functions_[onx.domain, onx.name] = f
else:
raise TypeError(f"Unexpected type {type(f)!r} for a function.")
self.verbose = verbose
self.new_ops_: Dict[Tuple[str, str], OpRun] = {}
if new_ops is not None:
for cl in new_ops:
if not hasattr(cl, "op_domain"):
raise AttributeError(
f"Class {cl} must define attribute 'op_domain'."
)
if not issubclass(cl, OpRun): # type: ignore
raise TypeError(f"Class {cl} must inherit from OpRun (in new_ops).")
key = cl.op_domain, cl.__name__ # type: ignore
if key in self.new_ops_:
# Already an implementation, the first one is used.
continue
self.new_ops_[key] = cl
self._init()
def _log_arg(self, a: Any) -> Any:
if isinstance(a, (str, int, float)):
return a
if isinstance(a, np.ndarray):
if self.verbose < 4:
return f"{a.dtype}:{a.shape} in [{a.min()}, {a.max()}]"
elements = a.ravel().tolist()
if len(elements) > 5:
elements = elements[:5]
return f"{a.dtype}:{a.shape}:{','.join(map(str, elements))}..."
return f"{a.dtype}:{a.shape}:{elements}"
if hasattr(a, "append"):
return ", ".join(map(self._log_arg, a))
return a
def _log(self, level: int, pattern: str, *args: List[Any]) -> None:
if level < self.verbose:
new_args = [self._log_arg(a) for a in args]
print(pattern % tuple(new_args))
@property
def input_names(self): # type: ignore
"Returns the input names."
return self.input_names_
@property
def input_types(self): # type: ignore
"Returns the input types if any specified."
return self.input_types_
@property
def output_names(self): # type: ignore
"Returns the output names."
return self.output_names_
@property
def output_types(self): # type: ignore
"Returns the output types."
return self.output_types_
@property
def opsets(self): # type: ignore
"Returns the opsets."
return self.opsets_
@property
def has_linked_attribute(self):
"""
Checks if the graph has a linked attribute (= an attribute whose value is defined
by a function attribute.
"""
return any(node.has_linked_attribute for node in self.rt_nodes_)
def __str__(self) -> str:
return f"{self.__class__.__name__}({', '.join(self.input_names)}) -> {', '.join(self.output_names)}"
def get_result_types(self, name: str, exc: bool = True) -> Any:
if self.all_types_ is None:
raise RuntimeError(
f"Unable to return type for name {name!r}. Run shape_inference first."
)
if name not in self.all_types_:
if exc:
raise RuntimeError(
f"Unable to return type for name {name!r}, it was not found in {sorted(self.all_types_)}."
)
return None
return self.all_types_[name]
def _init(self) -> None:
"""
Loads the implementation for every node in the graph.
"""
self.rt_inits_ = {}
self.rt_nodes_ = []
for init in self.inits_:
self.rt_inits_[init.name] = to_array_extended(init) # type: ignore[union-attr,arg-type]
run_params = {
"log": lambda pattern, *args: self._log(10, pattern, *args),
"opsets": self.opsets,
"verbose": self.verbose,
"new_ops": self.new_ops_,
}
if self.input_types_:
all_types = {i.name: i.type for i in self.onnx_graph_.input}
if hasattr(self.proto_, "value_info"):
for shape_type in self.proto_.value_info:
all_types[shape_type.name] = shape_type.type
self.all_types_ = all_types
else:
self.all_types_ = None # type: ignore
for node in self.nodes_:
try:
cl = self._load_impl(node)
except RuntimeContextError as e:
# A node has a context dependent implementation.
# Shape inference must be run to get the input types.
if self.all_types_:
it = [self.get_result_types(i, exc=False) for i in node.input]
if None in it:
# One input does not exist. It must be done while executing the graph.
cl = lambda *args, parent=self: OpFunctionContextDependant( # noqa: E731
*args, parent=parent
)
else:
cl = self._load_impl(node, it) # type: ignore
else:
raise RuntimeContextError(
f"No implementation was found for node type {node.op_type!r} from domain {node.domain!r}. "
f"If this node has a context dependent implementation, you should run function infer_shapes "
f"before calling ReferenceEvaluator."
) from e
try:
inst = cl(node, run_params)
except TypeError as e:
raise TypeError(
f"Unable to instantiate class {cl!r} with "
f"run_params={run_params} and node={node}."
) from e
self.rt_nodes_.append(inst)
def _load_impl(
self, node: NodeProto, input_types: Optional[TypeProto] = None
) -> Any:
"""
Loads the implementation for a specified runtime.
"""
if node.domain not in self.opsets:
raise RuntimeError(
f"Domain {node.domain!r} (node type: {node.op_type!r}) "
f"is not specified. Known opsets: {self.opsets!r}."
)
version = self.opsets[node.domain]
key = node.domain, node.op_type
expand = False
if key in self.new_ops_:
# This operator has a custom implementation.
# This mechanism can be used to implement a custom onnx node
# or to overwrite an existing one.
cl = self.new_ops_[key]
if not issubclass(cl, OpRunExpand):
return cl
# It must be replaced by its implementation defined in its schema.
expand = True
if node.domain == "":
from onnx.reference.ops import load_op
try:
return load_op(node.domain, node.op_type, version, expand=expand)
except RuntimeContextError:
if input_types is None:
raise
return load_op(
node.domain,
node.op_type,
version,
node=node,
input_types=input_types, # type: ignore[arg-type]
expand=expand,
)
if expand:
raise NotImplementedError(
f"Expanding an operator with its function definition "
f"is only implemented for the main opset. Remove operator "
f"{node.domain},{node.op_type} from the list of inlined operator."
)
if node.domain == "ai.onnx.preview.training":
from onnx.reference.ops.aionnx_preview_training import load_op as load_op_pt
return load_op_pt(node.domain, node.op_type, version)
if node.domain == "experimental":
from onnx.reference.ops.experimental import load_op as load_op_exp
return load_op_exp(node.domain, node.op_type, version)
if node.domain == "ai.onnx.ml":
from onnx.reference.ops.aionnxml import load_op as load_op_ml
return load_op_ml(node.domain, node.op_type, version)
# It has to be a function.
if key in self.functions_:
from onnx.reference.ops import load_op
impl = self.functions_[key]
return load_op(node.domain, node.op_type, version, custom=impl)
raise NotImplementedError(
f"Node type {node.op_type!r} from domain {node.domain!r} "
f"is unknown, known functions: {sorted(self.functions_)}."
)
def run(self, output_names, feed_inputs: Dict[str, Any], attributes: Optional[Dict[str, Any]] = None): # type: ignore
"""
Executes the onnx model.
:param output_names: requested outputs by names,
None for all
:param feed_inputs: dictionary `{ input name: input value }`
:param attributes: attributes value if the instance runs a FunctionProto
:return: list of requested outputs
"""
if output_names is None:
output_names = self.output_names
if isinstance(self.proto_, FunctionProto) and attributes is None:
raise TypeError()
# step 1: inputs and initializers
results = {"": None} # optional input
results.update(self.rt_inits_) # type: ignore[arg-type]
results.update(feed_inputs)
for k, v in self.rt_inits_.items():
self._log(2, " +C %s: %s", k, v) # type: ignore[arg-type]
for k, v in feed_inputs.items():
self._log(2, " +I %s: %s", k, v) # type: ignore[arg-type]
# step 2: execute nodes
for node in self.rt_nodes_:
self._log(1, "%s(%s) -> %s", node.op_type, node.input, node.output)
inputs = [results[i] for i in node.input]
linked_attributes = {}
if node.has_linked_attribute and attributes:
linked_attributes["linked_attributes"] = attributes
if node.need_context():
outputs = node.run(*inputs, context=results, **linked_attributes)
else:
outputs = node.run(*inputs, **linked_attributes)
for name, value in zip(node.output, outputs):
if isinstance(value, tuple):
raise TypeError(
f"Unexected type {type(value)} for output {name!r}."
)
self._log(2, " + %s: %s", name, value) # type: ignore[arg-type]
results[name] = value
# return the results
list_results: List[Any] = []
for name in output_names:
if name not in results:
raise RuntimeError(
f"Unable to find output name {name!r} in {sorted(results)}, proto is\n{self.proto_}"
)
list_results.append(results[name])
return list_results
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58,943 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/or.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Or(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Or",
inputs=["x", "y"],
outputs=["or"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or4d")
@staticmethod
def export_or_broadcast() -> None:
node = onnx.helper.make_node(
"Or",
inputs=["x", "y"],
outputs=["or"],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d")
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d")
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d")
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d")
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d")
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58,944 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_div.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Div(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunBinaryNumpy.__init__(self, np.divide, onnx_node, run_params)
def _run(self, a, b): # type: ignore
res = OpRunBinaryNumpy._run(self, a, b)
if res[0].dtype != a.dtype:
return (res[0].astype(a.dtype),)
return res
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,945 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/single-relu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
class SingleRelu(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node("Relu", ["x"], ["y"], name="test")
graph = onnx.helper.make_graph(
nodes=[node],
name="SingleRelu",
inputs=[
onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [1, 2])
],
outputs=[
onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [1, 2])
],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
x = np.random.randn(1, 2).astype(np.float32)
y = np.maximum(x, 0)
expect(model, inputs=[x], outputs=[y], name="test_single_relu_model")
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58,946 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_matmul_integer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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): # type: ignore
A32 = A.astype(np.int32)
if a_zero_point is not None:
A32 -= a_zero_point
B32 = B.astype(np.int32)
if b_zero_point is not None:
B32 -= b_zero_point
return (A32 @ B32,)
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58,947 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gemm.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def gemm_reference_implementation(
A: np.ndarray,
B: np.ndarray,
C: Optional[np.ndarray] = None,
alpha: float = 1.0,
beta: float = 1.0,
transA: int = 0,
transB: int = 0,
) -> np.ndarray:
A = A if transA == 0 else A.T
B = B if transB == 0 else B.T
C = C if C is not None else np.array(0)
Y = alpha * np.dot(A, B) + beta * C
return Y
class Gemm(Base):
@staticmethod
def export_default_zero_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias")
@staticmethod
def export_default_no_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"])
a = np.random.ranf([2, 10]).astype(np.float32)
b = np.random.ranf([10, 3]).astype(np.float32)
y = gemm_reference_implementation(a, b)
expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias")
@staticmethod
def export_default_scalar_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 3]).astype(np.float32)
b = np.random.ranf([3, 4]).astype(np.float32)
c = np.array(3.14).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias"
)
@staticmethod
def export_default_single_elem_vector_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 7]).astype(np.float32)
b = np.random.ranf([7, 3]).astype(np.float32)
c = np.random.ranf([1]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node,
inputs=[a, b, c],
outputs=[y],
name="test_gemm_default_single_elem_vector_bias",
)
@staticmethod
def export_default_vector_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias"
)
@staticmethod
def export_default_matrix_bias() -> None:
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.random.ranf([3, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias"
)
@staticmethod
def export_transposeA() -> None:
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1
)
a = np.random.ranf([6, 3]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transA=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA")
@staticmethod
def export_transposeB() -> None:
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1
)
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([4, 6]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transB=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB")
@staticmethod
def export_alpha() -> None:
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5
)
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, alpha=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha")
@staticmethod
def export_beta() -> None:
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5
)
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, beta=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta")
@staticmethod
def export_all_attributes() -> None:
node = onnx.helper.make_node(
"Gemm",
inputs=["a", "b", "c"],
outputs=["y"],
alpha=0.25,
beta=0.35,
transA=1,
transB=1,
)
a = np.random.ranf([4, 3]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.random.ranf([1, 5]).astype(np.float32)
y = gemm_reference_implementation(
a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35
)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes")
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58,948 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/isinf.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class IsInf(Base):
@staticmethod
def export_infinity() -> None:
node = onnx.helper.make_node(
"IsInf",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf], dtype=np.float32)
y = np.isinf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf")
@staticmethod
def export_positive_infinity_only() -> None:
node = onnx.helper.make_node(
"IsInf", inputs=["x"], outputs=["y"], detect_negative=0
)
x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf], dtype=np.float32)
y = np.isposinf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_positive")
@staticmethod
def export_negative_infinity_only() -> None:
node = onnx.helper.make_node(
"IsInf", inputs=["x"], outputs=["y"], detect_positive=0
)
x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf], dtype=np.float32)
y = np.isneginf(x)
expect(node, inputs=[x], outputs=[y], name="test_isinf_negative")
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58,949 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_isinf.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class IsInf(OpRun):
def _run(self, data, detect_negative=None, detect_positive=None): # type: ignore
if detect_negative:
if detect_positive:
return (np.isinf(data),)
return (np.isneginf(data),)
if detect_positive:
return (np.isposinf(data),)
res = np.full(data.shape, dtype=np.bool_, fill_value=False)
return (res,)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,950 | onnx/onnx | refs/heads/main | /onnx/test/symbolic_shape_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import List, Optional
import onnx.shape_inference
from onnx import ModelProto, TensorProto, TensorShapeProto, ValueInfoProto, helper
from onnx.helper import make_model, make_tensor_value_info
class TestSymbolicShape(unittest.TestCase):
def _assert_valueinfo_shape(
self, onnx_model: ModelProto, value_infos: List[ValueInfoProto]
) -> None:
"""
Assert onnx_model.value_info should be the same as expected value_infos
Instead of exact symbol, use -1 to represent symbolic shape in expected value_infos
"""
for expected_vi in value_infos:
shape = self._get_shape_from_name(onnx_model, expected_vi.name)
assert shape is not None, f"{onnx_model}"
if expected_vi.type.HasField("tensor_type"):
expected_shape = expected_vi.type.tensor_type.shape
elif expected_vi.type.HasField("sparse_tensor_type"):
expected_shape = expected_vi.type.sparse_tensor_type.shape
assert len(shape.dim) == len(expected_shape.dim), f"{onnx_model}"
for dim_i, dim in enumerate(shape.dim):
expected_dim = expected_shape.dim[dim_i]
# -1 means it's a symbolic shape
if expected_dim.dim_value == -1:
# symbolic dimension must exist
assert dim.dim_param, f"{onnx_model}"
else:
assert dim.dim_value == expected_dim.dim_value, f"{onnx_model}"
def _count_unique_dim_param_number(self, onnx_model: ModelProto) -> int:
"""
return the total number of unique symbolic shape
"""
symbol_shape_set = set()
inputs = list(onnx_model.graph.input)
outputs = list(onnx_model.graph.output)
valueinfos = list(onnx_model.graph.value_info)
for v in inputs + outputs + valueinfos:
for dim in v.type.tensor_type.shape.dim:
if dim.dim_param:
symbol_shape_set.add(dim.dim_param)
return len(symbol_shape_set)
def _get_shape_from_name(
self, onnx_model: ModelProto, name: str
) -> Optional[TensorShapeProto]:
"""
Get shape from tensor_type or sparse_tensor_type according to given name
"""
inputs = list(onnx_model.graph.input)
outputs = list(onnx_model.graph.output)
valueinfos = list(onnx_model.graph.value_info)
for v in inputs + outputs + valueinfos:
if v.name == name:
if v.type.HasField("tensor_type"):
return v.type.tensor_type.shape # type: ignore
if v.type.HasField("sparse_tensor_type"):
return v.type.sparse_tensor_type.shape # type: ignore
return None
def test_concat_enable_symbolic(self) -> None:
concat = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["C"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "A"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (2, -1))]
)
# the symbolic shape of C and output should be the same
assert self._get_shape_from_name(
inferred_model, "C"
) == self._get_shape_from_name(inferred_model, "output")
def test_two_symbolic_concat(self) -> None:
concat1 = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
concat2 = helper.make_node(
"Concat", inputs=["C", "D"], outputs=["E"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["E"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat1, concat2, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "A"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
helper.make_tensor_value_info("D", TensorProto.FLOAT, [2, "D"]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model,
[
make_tensor_value_info("C", TensorProto.FLOAT, (2, -1)),
make_tensor_value_info("E", TensorProto.FLOAT, (2, -1)),
],
)
# the symbolic shape of E and output should be the same
assert self._get_shape_from_name(
inferred_model, "E"
) == self._get_shape_from_name(inferred_model, "output")
def test_duplicate_symbolic_shape(self) -> None:
concat1 = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
concat2 = helper.make_node(
"Concat", inputs=["C", "D"], outputs=["E"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["E"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat1, concat2, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "unk__0"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
helper.make_tensor_value_info("D", TensorProto.FLOAT, [2, "unk__1"]),
],
outputs=[
helper.make_tensor_value_info(
"output", TensorProto.FLOAT, [2, "unk__0"]
)
],
)
onnx_model = make_model(graph_def)
original_count = self._count_unique_dim_param_number(onnx_model)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
inferred_count = self._count_unique_dim_param_number(inferred_model)
# to prevent duplicate so the inferred count will be count + 2
# new symbol 'unk__2' and 'unk__3' should be generated
# original: {'unk_0', 'unk__1'}
# inferred: {'unk_0', 'unk__1', 'unk__2', 'unk__3'}
assert inferred_count == original_count + 2, f"{inferred_model}{onnx_model}"
def test_unknown_shape(self) -> None:
concat = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["C"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat, cast],
inputs=[
helper.make_tensor_value_info(
"A", TensorProto.FLOAT, [3, None]
), # unknown shape
helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, None]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [3, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (3, -1))]
)
# the symbolic shape of C and output should be the same
# ('unk__0', 'unk__1')
assert self._get_shape_from_name(
inferred_model, "C"
) == self._get_shape_from_name(inferred_model, "output")
if __name__ == "__main__":
unittest.main()
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58,951 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/det.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Det(Base):
@staticmethod
def export_2d() -> None:
node = onnx.helper.make_node(
"Det",
inputs=["x"],
outputs=["y"],
)
x = np.arange(4).reshape(2, 2).astype(np.float32)
y = np.linalg.det(x) # expect -2
expect(node, inputs=[x], outputs=[y], name="test_det_2d")
@staticmethod
def export_nd() -> None:
node = onnx.helper.make_node(
"Det",
inputs=["x"],
outputs=["y"],
)
x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(
np.float32
)
y = np.linalg.det(x) # expect array([-2., -3., -8.])
expect(node, inputs=[x], outputs=[y], name="test_det_nd")
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58,952 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_grid_sample.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,R1716,W0221
import numbers
from typing import List
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/blob/v2.0.0/aten/src/ATen/native/GridSampler.h#L26
def _gs_denormalize(self, n, length: int, align_corners: bool): # type: ignore
# n is the normalized coordinate (float)
# x is the unormalized coordinate (float)
if align_corners:
# Align to corners
# x_min = 0
# x_max = d-1
# Linear mapping from [x_min, x_max] to [-1, 1]
# Solving linear equation n = ax + b
# a = 2/(d-1)
# b = -1
# n = 2/(d-1) x - 1
# n(d-1) = 2x - (d-1)
# x = (n+1)(d-1) / 2
x = (n + 1) / 2.0 * (length - 1)
else:
# Not align to corners
# x_min = -0.5
# x_max = d-0.5
# Linear mapping from [x_min, x_max] to [-1, 1]
# Solving linear equation n = ax + b
# a = 2/d
# b = 1/d - 1
# n = 2/d x + 1/d - 1
# nd = 2x + 1 - d
# x = (nd + d - 1) / 2
# x = ((n + 1) d - 1) / 2
x = ((n + 1) * length - 1) / 2.0
return x
def _gs_denormalize_coordinates(self, n, dims, align_corners: bool):
x = np.zeros(len(n), dtype=np.float32)
for i, (v, dim) in enumerate(zip(n, dims)):
x[i] = self._gs_denormalize(n=v, length=dim, align_corners=align_corners)
return x
def _gs_reflect(self, x, x_min, x_max): # type: ignore
"""
Reflect by the near border till within the borders
Use float for borders to avoid potential issues with integer T
"""
fx = x
rng = x_max - x_min
if fx < x_min:
dx = x_min - fx
n = int(dx / rng)
r = dx - n * rng
if n % 2 == 0:
fx = x_min + r
else:
fx = x_max - r
elif fx > x_max:
dx = fx - x_max
n = int(dx / rng)
r = dx - n * rng
if n % 2 == 0:
fx = x_max - r
else:
fx = x_min + r
return fx
def _gs_get_cubic_coeffs(self, x, coeffs): # type: ignore
"""
Calculate cubic convolution interpolation coefficients
ROBERT G. KEYS https://ieeexplore.ieee.org/document/1163711
Use float to avoid potential issues with integer.
"""
cubic_alpha = -0.75
x = abs(x)
coeffs[0] = (
(cubic_alpha * (x + 1) - 5 * cubic_alpha) * (x + 1) + 8 * cubic_alpha
) * (x + 1) - 4 * cubic_alpha
coeffs[1] = ((cubic_alpha + 2) * x - (cubic_alpha + 3)) * x * x + 1
coeffs[2] = ((cubic_alpha + 2) * (1 - x) - (cubic_alpha + 3)) * (1 - x) * (
1 - x
) + 1
coeffs[3] = (
(cubic_alpha * (2 - x) - 5 * cubic_alpha) * (2 - x) + 8 * cubic_alpha
) * (2 - x) - 4 * cubic_alpha
def _gs_get_linear_coeffs(self, x, coeffs):
x = abs(x)
coeffs[0] = 1 - x
coeffs[1] = x
def _gs_bicubic_interpolate(self, p, x, y): # type: ignore
v = np.empty((4,), dtype=p.dtype)
coeffs = np.empty((4,), dtype=p.dtype)
self._gs_get_cubic_coeffs(x, coeffs)
for i in range(4):
v[i] = coeffs @ p[i, :]
self._gs_get_cubic_coeffs(y, coeffs)
return coeffs @ v
def _gs_cubic_interpolation_1d_with_x(self, data, x, border, padding_mode):
v = np.empty((4,), dtype=data.dtype)
coeffs = np.empty((4,), dtype=data.dtype)
x_0 = int(np.floor(x))
x_1 = x_0 + 1
x_2 = x_0 + 2
x_minus_1 = x_0 - 1
self._gs_get_cubic_coeffs(x - x_0, coeffs)
v[0] = self._pixel_at_array(
array=data, i=x_minus_1, border=border, padding_mode=padding_mode
)
v[1] = self._pixel_at_array(
array=data, i=x_0, border=border, padding_mode=padding_mode
)
v[2] = self._pixel_at_array(
array=data, i=x_1, border=border, padding_mode=padding_mode
)
v[3] = self._pixel_at_array(
array=data, i=x_2, border=border, padding_mode=padding_mode
)
return coeffs @ v
def _gs_linear_interpolation_1d_with_x(self, data, x, border, padding_mode):
v = np.empty((2,), dtype=data.dtype)
coeffs = np.empty((2,), dtype=data.dtype)
x_0 = int(np.floor(x))
x_1 = x_0 + 1
self._gs_get_linear_coeffs(x - x_0, coeffs)
v[0] = self._pixel_at_array(
array=data, i=x_0, border=border, padding_mode=padding_mode
)
v[1] = self._pixel_at_array(
array=data, i=x_1, border=border, padding_mode=padding_mode
)
return coeffs @ v
def _gs_linear_interpolation_nd_with_x(self, data, x, border, padding_mode):
num_dims = data.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return self._gs_linear_interpolation_1d_with_x(
data=data, x=x[0], border=border, padding_mode=padding_mode
)
res1d = []
for i in range(data.shape[0]):
r = self._gs_linear_interpolation_nd_with_x(
data=data[i],
x=x[1:],
border=list(border[1:num_dims])
+ list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
res1d.append(r)
res1d = np.array(res1d)
return self._gs_linear_interpolation_1d_with_x(
data=res1d,
x=x[0],
border=[border[0], border[num_dims]],
padding_mode=padding_mode,
)
def _gs_cubic_interpolation_nd_with_x(self, data, x, border, padding_mode):
num_dims = data.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return self._gs_cubic_interpolation_1d_with_x(
data=data, x=x[0], border=border, padding_mode=padding_mode
)
res1d = []
for i in range(data.shape[0]):
r = self._gs_cubic_interpolation_nd_with_x(
data=data[i],
x=x[1:],
border=list(border[1:num_dims])
+ list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
res1d.append(r)
res1d = np.array(res1d)
return self._gs_cubic_interpolation_1d_with_x(
data=res1d,
x=x[0],
border=[border[0], border[num_dims]],
padding_mode=padding_mode,
)
def _clamp(self, val, lo, hi): # type: ignore
if val < lo:
return lo
if val > hi:
return hi
return val
def _pixel_at_ndarray(self, ndarray, x: List, border, padding_mode): # type: ignore
# boarder: [x_1_min, x_2_min, ..., x_1_max, x_2_max, ...]
num_dims = ndarray.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return self._pixel_at_array(
array=ndarray, i=x[0], border=border, padding_mode=padding_mode
)
i = x[0]
d = ndarray.shape[0]
if padding_mode == "zeros":
if i >= 0 and i < d:
ndarray = ndarray[i]
else:
# Trick
i = 0
ndarray = np.zeros_like(ndarray[i])
elif padding_mode == "border":
i = self._clamp(i, 0, d - 1)
ndarray = ndarray[i]
else: # padding_mode == "reflection"
i = int(self._gs_reflect(i, border[0], border[num_dims]))
ndarray = ndarray[i]
return self._pixel_at_ndarray(
ndarray=ndarray,
x=x[1:],
border=list(border[1:num_dims]) + list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
def _pixel_at_array(self, array, i: int, border, padding_mode): # type: ignore
assert array.ndim == 1
d = array.shape[0]
if padding_mode == "zeros":
if i >= 0 and i < d:
pixel = array[i]
else:
pixel = 0
elif padding_mode == "border":
i = self._clamp(i, 0, d - 1)
pixel = array[i]
else: # padding_mode == "reflection"
i = int(self._gs_reflect(i, border[0], border[1]))
pixel = array[i]
return pixel
def _prepare_border(self, dims, align_corners: bool):
# boarder: [x_1_min, x_2_min, ..., x_1_max, x_2_max, ...]
num_dims = len(dims)
borders = np.zeros(num_dims * 2)
for i in range(num_dims):
# min
borders[i] = -0.5
# max
borders[i + num_dims] = dims[i] - 0.5
if align_corners:
# min
borders[i] = 0.0
# max
borders[i + num_dims] = dims[i] - 1.0
return borders
def _cpp_std_round(self, x):
# https://en.cppreference.com/w/cpp/numeric/math/round
def round_single_value(v):
if v >= 0.0:
return np.floor(v + 0.5)
else:
return np.ceil(v - 0.5)
if isinstance(x, numbers.Number):
return round_single_value(x)
else:
assert x.ndim == 1
x_rounded = np.zeros_like(x)
for i in range(x.shape[0]):
x_rounded[i] = round_single_value(x[i])
x_rounded = x_rounded.astype(np.int32)
return x_rounded
def _run(self, X, grid, mode=None, padding_mode=None, align_corners=None):
# This implementation supports GridSample arbitrary dimensions.
mode = mode or self.mode # type: ignore
padding_mode = padding_mode or self.padding_mode # type: ignore
align_corners = align_corners or self.align_corners # type: ignore
x_dims = X.shape
grid_dims = grid.shape
N = x_dims[0]
C = x_dims[1]
y_dims = (N, C, *grid_dims[1:-1])
if np.prod(y_dims) == 0:
return np.array([], dtype=X.dtype)
Y = np.empty(y_dims, dtype=X.dtype)
for n in range(N):
grid_data = grid[n]
for c in range(C):
# Because the indices in the grid_data are always in the "reverse" dimensional order.
# To interpolate for certain positions, we either have to transpose the X_data or
# reverse the indices.
# In this implementation, we took the latter approach.
X_data = X[n, c]
num_dims = len(x_dims[2:])
dims = x_dims[2:]
# Prepare borders.
border = self._prepare_border(dims, align_corners=align_corners)
for ox in _get_all_coords(Y[n, c]):
# normalized coordinates.
nx = grid_data[tuple(ox)]
nx = nx[::-1]
# denormalized coordinates.
x = self._gs_denormalize_coordinates(
n=nx, dims=dims, align_corners=align_corners
)
if mode == "nearest":
# PyTorch round the index to nearest even.
# https://github.com/pytorch/pytorch/pull/97000
x = np.rint(x)
# https://github.com/pytorch/pytorch/blob/v2.0.0/aten/src/ATen/native/GridSampler.h#L142
for i, v in enumerate(x):
x_min = border[i]
x_max = border[i + num_dims]
if v < x_min or v > x_max:
if padding_mode == "border":
x[i] = self._clamp(v, 0, dims[i] - 1)
elif padding_mode == "reflection":
x[i] = self._gs_reflect(v, x_min, x_max)
if mode == "nearest":
x = x.astype(np.int32)
Y[n][c][tuple(ox)] = self._pixel_at_ndarray(
ndarray=X_data,
x=x,
border=border,
padding_mode=padding_mode,
)
elif mode == "linear":
Y[n][c][tuple(ox)] = self._gs_linear_interpolation_nd_with_x(
data=X_data, x=x, border=border, padding_mode=padding_mode
)
elif mode == "cubic":
Y[n][c][tuple(ox)] = self._gs_cubic_interpolation_nd_with_x(
data=X_data, x=x, border=border, padding_mode=padding_mode
)
else:
raise RuntimeError(
"GridSample interpolation only supports nearest, linear, and cubic modes."
)
return (Y.astype(X.dtype),)
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58,953 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/sequenceinsert.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, List
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def sequence_insert_reference_implementation(
sequence: List[Any], tensor: np.ndarray, position: np.ndarray = None
) -> List[Any]:
# make a copy of input sequence
seq = list(sequence)
if position is not None:
# In these cases, insert_position will be between [-len(sequence), len(sequence)]
# The position argument will be in the format np.array([pos_index])
insert_position = position[0]
seq.insert(insert_position, tensor)
else:
# Default position of insertion is at the end of the sequence.
seq.append(tensor)
return seq
class SequenceInsert(Base):
@staticmethod
def export() -> None:
test_cases = {
"at_back": [np.array([10, 11, 12]).astype(np.int64)],
"at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)],
}
sequence = [
np.array([1, 2, 3, 4]).astype(np.int64),
np.array([5, 6, 7]).astype(np.int64),
np.array([8, 9]).astype(np.int64),
]
for test_name, test_inputs in test_cases.items():
tensor = test_inputs[0].astype(np.int64)
if len(test_inputs) > 1:
node = onnx.helper.make_node(
"SequenceInsert",
inputs=["sequence", "tensor", "position"],
outputs=["output_sequence"],
)
position = test_inputs[1]
inserted = sequence_insert_reference_implementation(
sequence, tensor, position
)
expect(
node,
inputs=[sequence, tensor, position],
outputs=[inserted],
name="test_sequence_insert_" + test_name,
)
else:
node = onnx.helper.make_node(
"SequenceInsert",
inputs=["sequence", "tensor"],
outputs=["output_sequence"],
)
inserted = sequence_insert_reference_implementation(sequence, tensor)
expect(
node,
inputs=[sequence, tensor],
outputs=[inserted],
name="test_sequence_insert_" + test_name,
)
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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,954 | onnx/onnx | refs/heads/main | /onnx/backend/test/report/coverage.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import csv
import datetime
import os
from collections import OrderedDict, defaultdict
from typing import IO, Any, Dict, List, Optional, Set
from tabulate import tabulate
import onnx
from onnx import GraphProto, defs, helper
_all_schemas = defs.get_all_schemas()
class AttrCoverage:
def __init__(self) -> None:
self.name: Optional[str] = None
self.values: Set[str] = set()
def add(self, attr: onnx.AttributeProto) -> None:
assert self.name in {None, attr.name}
self.name = attr.name
value = helper.get_attribute_value(attr)
# Turn list into tuple so we can put it into set
# As value can be string, don't blindly turn `collections.Iterable`
# into tuple.
if isinstance(value, list):
value = tuple(value)
self.values.add(str(value))
class NodeCoverage:
def __init__(self) -> None:
self.op_type: Optional[str] = None
self.attr_coverages: Dict[str, AttrCoverage] = defaultdict(AttrCoverage)
def add(self, node: onnx.NodeProto) -> None:
assert self.op_type in [None, node.op_type]
if self.op_type is None:
self.op_type = node.op_type
assert self.op_type is not None
self.schema = defs.get_schema(self.op_type, domain=node.domain)
for attr in node.attribute:
self.attr_coverages[attr.name].add(attr)
class ModelCoverage:
def __init__(self) -> None:
self.name: Optional[str] = None
self.graph: Optional[GraphProto] = None
self.node_coverages: Dict[str, NodeCoverage] = defaultdict(NodeCoverage)
def add(self, model: onnx.ModelProto) -> None:
assert self.name in [None, model.graph.name]
if self.name is None:
self.name = model.graph.name
assert self.name is not None
self.graph = model.graph
for node in model.graph.node:
self.node_coverages[node.op_type].add(node)
class Coverage:
def __init__(self) -> None:
self.buckets: Dict[str, Dict[str, NodeCoverage]] = {
"loaded": defaultdict(NodeCoverage),
"passed": defaultdict(NodeCoverage),
}
self.models: Dict[str, Dict[str, ModelCoverage]] = {
"loaded": defaultdict(ModelCoverage),
"passed": defaultdict(ModelCoverage),
}
def add_node(self, node: onnx.NodeProto, bucket: str) -> None:
self.buckets[bucket][node.op_type].add(node)
def add_graph(self, graph: onnx.GraphProto, bucket: str) -> None:
for node in graph.node:
self.add_node(node, bucket)
def add_model(self, model: onnx.ModelProto, bucket: str, is_model: bool) -> None:
self.add_graph(model.graph, bucket)
# Only add model if name does not start with test
if is_model:
self.models[bucket][model.graph.name].add(model)
def add_proto(self, proto: onnx.ModelProto, bucket: str, is_model: bool) -> None:
assert isinstance(proto, onnx.ModelProto)
self.add_model(proto, bucket, is_model)
def report_text(self, writer: IO[str]) -> None:
writer.write("---------- onnx coverage: ----------\n")
writer.write(
f"Operators (passed/loaded/total): {len(self.buckets['passed'])}/{len(self.buckets['loaded'])}/{len(_all_schemas)}\n"
)
writer.write("------------------------------------\n")
rows = []
passed = []
all_ops: List[str] = []
experimental: List[str] = []
for op_cov in self.buckets["passed"].values():
covered_attrs = [
f"{attr_cov.name}: {len(attr_cov.values)}"
for attr_cov in op_cov.attr_coverages.values()
]
uncovered_attrs = [
f"{attr}: 0"
for attr in op_cov.schema.attributes
if attr not in op_cov.attr_coverages
]
attrs = sorted(covered_attrs) + sorted(uncovered_attrs)
if attrs:
attrs_column = os.linesep.join(attrs)
else:
attrs_column = "No attributes"
rows.append([op_cov.op_type, attrs_column])
passed.append(op_cov.op_type)
writer.write(
tabulate(
rows,
headers=["Operator", "Attributes\n(name: #values)"],
tablefmt="plain",
)
)
writer.write("\n")
if os.environ.get("CSVDIR") is not None:
self.report_csv(all_ops, passed, experimental)
# This function writes the coverage report to a set of CSV files for
# the Backend Scoreboard (onnx.ai/backend-scoreboard). To enable this
# feature, set a CSVDIR environment variable locally with the directory
# where you would like the files to be written, relative to the
# directory from which you're running pytest. The format of the CSV
# files is a column naming each op or model and columns for each
# backend with indications of whether the tests passed or failed for
# each row.
def report_csv(
self, all_ops: List[str], passed: List[Optional[str]], experimental: List[str]
) -> None:
for schema in _all_schemas:
if schema.domain == "" or schema.domain == "ai.onnx":
all_ops.append(schema.name)
if schema.support_level == defs.OpSchema.SupportType.EXPERIMENTAL:
experimental.append(schema.name)
all_ops.sort()
nodes_path = os.path.join(
str(os.environ.get("CSVDIR")), "nodes.csv" # type: ignore
) # type: ignore
models_path = os.path.join(
str(os.environ.get("CSVDIR")), "models.csv" # type: ignore
) # type: ignore
existing_nodes: OrderedDict[str, Dict[str, str]] = OrderedDict()
existing_models: OrderedDict[str, Dict[str, str]] = OrderedDict()
frameworks: List[str] = []
if os.path.isfile(nodes_path):
with open(nodes_path) as nodes_file:
reader = csv.DictReader(nodes_file)
assert reader.fieldnames
frameworks = list(reader.fieldnames)
for row in reader:
op = row["Op"]
del row["Op"]
existing_nodes[str(op)] = row
if os.path.isfile(models_path):
with open(models_path) as models_file:
reader = csv.DictReader(models_file)
for row in reader:
model = row["Model"]
del row["Model"]
existing_models[str(model)] = row
backend = os.environ.get("BACKEND")
other_frameworks = frameworks[1:]
with open(nodes_path, "w") as nodes_file:
if "Op" not in frameworks:
frameworks.append("Op")
if backend not in frameworks:
frameworks.append(str(backend))
else:
other_frameworks.remove(str(backend))
node_writer = csv.DictWriter(nodes_file, fieldnames=frameworks)
node_writer.writeheader()
for node in all_ops:
node_name = node
if node in experimental:
node_name = node + " (Experimental)"
if node_name not in existing_nodes:
# Also add Skipped for other nodes
existing_nodes[node_name] = OrderedDict()
for other_framework in other_frameworks:
existing_nodes[node_name][other_framework] = "Skipped!"
if node in passed:
existing_nodes[node_name][str(backend)] = "Passed!"
else:
existing_nodes[node_name][str(backend)] = "Failed!"
summaries: Dict[Any, Any] = {}
if "Summary" in existing_nodes:
summaries = existing_nodes["Summary"]
del existing_nodes["Summary"]
summaries[str(backend)] = f"{len(passed)}/{len(all_ops)} node tests passed"
summaries["Op"] = "Summary"
for node in existing_nodes:
existing_nodes[node]["Op"] = str(node)
node_writer.writerow(existing_nodes[node])
node_writer.writerow(summaries)
with open(models_path, "w") as models_file:
frameworks[0] = "Model"
model_writer = csv.DictWriter(models_file, fieldnames=frameworks)
model_writer.writeheader()
# Consider both buckets
num_models = 0
for bucket in self.models:
for model in self.models[bucket]: # type: ignore
# Both analyze and run the model on the backend
num_covered = 0
for node in self.models[bucket][model].node_coverages:
if node in passed:
num_covered += 1
# TODO: Identify if there are models that are being
# skipped/not loaded, but that are in other frameworks
msg = "Passed!"
if bucket == "loaded":
if model in self.models["passed"]:
continue
msg = "Failed!"
num_models += 1
if model not in existing_models:
# Also add Skipped for other models
existing_models[model] = OrderedDict()
for other_framework in other_frameworks:
existing_models[model][other_framework] = "Skipped!"
existing_models[model][str(backend)] = str(
f"{num_covered}/{len(self.models[bucket][model].node_coverages)} nodes covered: {msg}"
)
summaries.clear()
if "Summary" in existing_models:
summaries = existing_models["Summary"]
del existing_models["Summary"]
if str(backend) in summaries:
del summaries[str(backend)]
summaries[
str(backend)
] = f"{len(self.models['passed'])}/{num_models} model tests passed"
summaries["Model"] = "Summary"
for model in existing_models: # type: ignore
existing_models[model]["Model"] = model
model_writer.writerow(existing_models[model])
model_writer.writerow(summaries)
with open(
os.path.join(str(os.environ.get("CSVDIR")), "metadata.csv"), # type: ignore
"w",
) as metadata_file: # type: ignore
metadata_writer = csv.writer(metadata_file)
metadata_writer.writerow(
["Latest Update", datetime.datetime.now().isoformat().replace("T", " ")]
)
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58,955 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_concat_from_sequence.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Any, List
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:
seq2 = [s[..., np.newaxis] for s in seq]
res = np.concatenate(seq2, axis=-1)
else:
res = np.concatenate(seq, axis=axis)
return res # type: ignore
class ConcatFromSequence(OpRun):
def _run(self, seq, axis=None, new_axis=None): # type: ignore
if seq is None:
raise RuntimeError("A sequence cannot be null.")
res = _concat_from_sequence(seq, axis, new_axis=new_axis)
return (res,)
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58,956 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_dropout.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Optional, Tuple
import numpy as np
from numpy.random import RandomState # type: ignore
from onnx.reference.op_run import OpRun
def _dropout(
X: np.ndarray,
drop_probability: float = 0.5,
seed: Optional[int] = None,
training_mode: bool = False,
return_mask: bool = False,
) -> Tuple[np.ndarray]:
if drop_probability == 0 or not training_mode:
if return_mask:
return X, np.ones(X.shape, dtype=bool) # type: ignore
return (X,)
rnd = RandomState(seed)
mask = rnd.uniform(0, 1.0, X.shape) >= drop_probability
scale = 1.0 / (1.0 - drop_probability)
return (mask * X * scale, mask.astype(bool)) if return_mask else (mask * X * scale,) # type: ignore
class DropoutBase(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_node.output)
def _private_run(
self,
X: np.ndarray,
seed: Optional[int] = None,
ratio: float = 0.5,
training_mode: bool = False,
) -> Tuple[np.ndarray]:
return _dropout(
X,
ratio,
seed=seed, # type: ignore
return_mask=self.n_outputs == 2,
training_mode=training_mode,
)
class Dropout_7(DropoutBase):
def _run(self, X, ratio=None): # type: ignore
return self._private_run(X, ratio)
class Dropout_12(DropoutBase):
def _run(self, *inputs, seed=None): # type: ignore
X = inputs[0]
ratio = 0.5 if len(inputs) <= 1 else inputs[1]
training_mode = False if len(inputs) <= 2 else inputs[2]
return self._private_run(
X, seed=seed, ratio=ratio, training_mode=training_mode # type: ignore
)
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,957 | onnx/onnx | refs/heads/main | /onnx/numpy_helper.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C3001,isinstance-second-argument-not-valid-type
import sys
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from onnx import MapProto, OptionalProto, SequenceProto, TensorProto, helper
from onnx.external_data_helper import load_external_data_for_tensor, uses_external_data
def combine_pairs_to_complex(fa: Sequence[int]) -> List[complex]:
return [complex(fa[i * 2], fa[i * 2 + 1]) for i in range(len(fa) // 2)]
def bfloat16_to_float32(
data: Union[np.int16, np.int32, np.ndarray],
dims: Optional[Union[int, Sequence[int]]] = None,
) -> np.ndarray:
"""Converts ndarray of bf16 (as uint32) to f32 (as uint32).
:param data: a numpy array, empty dimensions are allowed if dims is None
:param dims: if specified, the function reshapes the results
:return: a numpy array of float32 with the same dimension if dims is None,
or reshaped to dims if specified"""
shift = lambda x: x << 16 # noqa: E731
if dims is None:
if len(data.shape) == 0:
return shift(np.array([data]).astype(np.int32)).view(np.float32)[0] # type: ignore[no-any-return]
return shift(data.astype(np.int32)).view(np.float32) # type: ignore[no-any-return]
return shift(data.astype(np.int32)).reshape(dims).view(np.float32) # type: ignore[no-any-return]
def _float8e4m3_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
if not fn:
raise NotImplementedError("fn=False is not implemented.")
if ival < 0 or ival > 255:
raise ValueError(f"{ival} is not a float8.")
if uz:
exponent_bias = 8
if ival == 0x80:
return np.nan # type: ignore[return-value]
else:
exponent_bias = 7
if ival == 255:
return np.float32(-np.nan)
if ival == 127:
return np.float32(np.nan)
expo = (ival & 0x78) >> 3
mant = ival & 0x07
sign = ival & 0x80
res = sign << 24
if expo == 0:
if mant > 0:
expo = 0x7F - exponent_bias
if mant & 0x4 == 0:
mant &= 0x3
mant <<= 1
expo -= 1
if mant & 0x4 == 0:
mant &= 0x3
mant <<= 1
expo -= 1
res |= (mant & 0x3) << 21
res |= expo << 23
else:
res |= mant << 20
expo += 0x7F - exponent_bias
res |= expo << 23
f = np.uint32(res).view(np.float32) # pylint: disable=E1121
return f
_float8e4m3_to_float32 = np.vectorize(
_float8e4m3_to_float32_scalar, excluded=["fn", "uz"]
)
def float8e4m3_to_float32(
data: Union[np.int16, np.int32, np.ndarray],
dims: Optional[Union[int, Sequence[int]]] = None,
fn: bool = True,
uz: bool = False,
) -> np.ndarray:
"""Converts ndarray of float8, e4m3 (as uint32) to f32 (as uint32).
:param data: a numpy array, empty dimensions are allowed if dims is None
:param dims: if specified, the function reshapes the results
:param fn: no infinite values
:param uz: no negative zero
:return: a numpy array of float32 with the same dimension if dims is None,
or reshaped to dims if specified.
See :ref:`onnx-detail-float8` for technical details.
"""
if not fn:
raise NotImplementedError(
"float32_to_float8e4m3 not implemented with fn=False."
)
res = _float8e4m3_to_float32(data, fn=fn, uz=uz)
if dims is None:
return res # type: ignore[no-any-return]
return res.reshape(dims) # type: ignore[no-any-return]
def _float8e5m2_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
if fn and uz:
if ival == 0x80:
return np.float32(np.nan)
exponent_bias = 16
elif not fn and not uz:
if ival in {253, 254, 255}:
return np.float32(-np.nan)
if ival in {125, 126, 127}:
return np.float32(np.nan)
if ival == 252:
return np.float32(-np.inf)
if ival == 124:
return np.float32(np.inf)
exponent_bias = 15
else:
raise NotImplementedError("fn and uz must be both False or True.")
expo = (ival & 0x7C) >> 2
mant = ival & 0x03
sign = ival & 0x80
res = sign << 24
if expo == 0:
if mant > 0:
expo = 0x7F - exponent_bias
if mant & 0x2 == 0:
mant &= 0x1
mant <<= 1
expo -= 1
res |= (mant & 0x1) << 22
res |= expo << 23
else:
res |= mant << 21
expo += 0x7F - exponent_bias
res |= expo << 23
f = np.uint32(res).view(np.float32) # pylint: disable=E1121
return f
_float8e5m2_to_float32 = np.vectorize(
_float8e5m2_to_float32_scalar, excluded=["fn", "uz"]
)
def float8e5m2_to_float32(
data: Union[np.int16, np.int32, np.ndarray],
dims: Optional[Union[int, Sequence[int]]] = None,
fn: bool = False,
uz: bool = False,
) -> np.ndarray:
"""Converts ndarray of float8, e5m2 (as uint32) to f32 (as uint32).
:param data: a numpy array, empty dimensions are allowed if dims is None
:param dims: if specified, the function reshapes the results
:param fn: no infinite values
:param uz: no negative zero
:return: a numpy array of float32 with the same dimension if dims is None,
or reshaped to dims if specified"""
res = _float8e5m2_to_float32(data, fn=fn, uz=uz)
if dims is None:
return res # type: ignore[no-any-return]
return res.reshape(dims) # type: ignore[no-any-return]
def to_array( # pylint: disable=too-many-branches
tensor: TensorProto, base_dir: str = ""
) -> np.ndarray:
"""Converts a tensor def object to a numpy array.
Args:
tensor: a TensorProto object.
base_dir: if external tensor exists, base_dir can help to find the path to it
Returns:
arr: the converted array.
"""
if tensor.HasField("segment"):
raise ValueError("Currently not supporting loading segments.")
if tensor.data_type == TensorProto.UNDEFINED:
raise TypeError("The element type in the input tensor is not defined.")
tensor_dtype = tensor.data_type
np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype)
storage_np_dtype = helper.tensor_dtype_to_np_dtype(
helper.tensor_dtype_to_storage_tensor_dtype(tensor_dtype)
)
storage_field = helper.tensor_dtype_to_field(tensor_dtype)
dims = tensor.dims
if tensor.data_type == TensorProto.STRING:
utf8_strings = getattr(tensor, storage_field)
ss = [s.decode("utf-8") for s in utf8_strings]
return np.asarray(ss).astype(np_dtype).reshape(dims)
# Load raw data from external tensor if it exists
if uses_external_data(tensor):
load_external_data_for_tensor(tensor, base_dir)
if tensor.HasField("raw_data"):
# Raw_bytes support: using frombuffer.
if sys.byteorder == "big":
# Convert endian from little to big
convert_endian(tensor)
# manually convert bf16 since there's no numpy support
if tensor_dtype == TensorProto.BFLOAT16:
data = np.frombuffer(tensor.raw_data, dtype=np.int16)
return bfloat16_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FN:
data = np.frombuffer(tensor.raw_data, dtype=np.int8)
return float8e4m3_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FNUZ:
data = np.frombuffer(tensor.raw_data, dtype=np.int8)
return float8e4m3_to_float32(data, dims, uz=True)
if tensor_dtype == TensorProto.FLOAT8E5M2:
data = np.frombuffer(tensor.raw_data, dtype=np.int8)
return float8e5m2_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E5M2FNUZ:
data = np.frombuffer(tensor.raw_data, dtype=np.int8)
return float8e5m2_to_float32(data, dims, fn=True, uz=True)
return np.frombuffer(tensor.raw_data, dtype=np_dtype).reshape(dims) # type: ignore[no-any-return]
# float16 is stored as int32 (uint16 type); Need view to get the original value
if tensor_dtype == TensorProto.FLOAT16:
return (
np.asarray(tensor.int32_data, dtype=np.uint16)
.reshape(dims)
.view(np.float16)
)
# bfloat16 is stored as int32 (uint16 type); no numpy support for bf16
if tensor_dtype == TensorProto.BFLOAT16:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return bfloat16_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FN:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e4m3_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E4M3FNUZ:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e4m3_to_float32(data, dims, uz=True)
if tensor_dtype == TensorProto.FLOAT8E5M2:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e5m2_to_float32(data, dims)
if tensor_dtype == TensorProto.FLOAT8E5M2FNUZ:
data = np.asarray(tensor.int32_data, dtype=np.int32)
return float8e5m2_to_float32(data, dims, fn=True, uz=True)
data = getattr(tensor, storage_field)
if tensor_dtype in (TensorProto.COMPLEX64, TensorProto.COMPLEX128):
data = combine_pairs_to_complex(data) # type: ignore[assignment,arg-type]
return np.asarray(data, dtype=storage_np_dtype).astype(np_dtype).reshape(dims)
def from_array(arr: np.ndarray, name: Optional[str] = None) -> TensorProto:
"""Converts a numpy array to a tensor def.
Args:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
TensorProto: the converted tensor def.
"""
tensor = TensorProto()
tensor.dims.extend(arr.shape)
if name:
tensor.name = name
if arr.dtype == object:
# Special care for strings.
tensor.data_type = helper.np_dtype_to_tensor_dtype(arr.dtype)
# TODO: Introduce full string support.
# We flatten the array in case there are 2-D arrays are specified
# We throw the error below if we have a 3-D array or some kind of other
# object. If you want more complex shapes then follow the below instructions.
# Unlike other types where the shape is automatically inferred from
# nested arrays of values, the only reliable way now to feed strings
# is to put them into a flat array then specify type astype(object)
# (otherwise all strings may have different types depending on their length)
# and then specify shape .reshape([x, y, z])
flat_array = arr.flatten()
for e in flat_array:
if isinstance(e, str):
tensor.string_data.append(e.encode("utf-8"))
elif isinstance(e, np.ndarray):
for s in e:
if isinstance(s, str):
tensor.string_data.append(s.encode("utf-8"))
elif isinstance(s, bytes):
tensor.string_data.append(s)
elif isinstance(e, bytes):
tensor.string_data.append(e)
else:
raise NotImplementedError(
"Unrecognized object in the object array, expect a string, or array of bytes: ",
str(type(e)),
)
return tensor
# For numerical types, directly use numpy raw bytes.
try:
dtype = helper.np_dtype_to_tensor_dtype(arr.dtype)
except KeyError as e:
raise RuntimeError(f"Numpy data type not understood yet: {arr.dtype!r}") from e
tensor.data_type = dtype
tensor.raw_data = arr.tobytes() # note: tobytes() is only after 1.9.
if sys.byteorder == "big":
# Convert endian from big to little
convert_endian(tensor)
return tensor
def to_list(sequence: SequenceProto) -> List[Any]:
"""Converts a sequence def to a Python list.
Args:
sequence: a SequenceProto object.
Returns:
list: the converted list.
"""
elem_type = sequence.elem_type
if elem_type == SequenceProto.TENSOR:
return [to_array(v) for v in sequence.tensor_values] # type: ignore[arg-type]
if elem_type == SequenceProto.SPARSE_TENSOR:
return [to_array(v) for v in sequence.sparse_tensor_values] # type: ignore[arg-type]
if elem_type == SequenceProto.SEQUENCE:
return [to_list(v) for v in sequence.sequence_values]
if elem_type == SequenceProto.MAP:
return [to_dict(v) for v in sequence.map_values]
raise TypeError("The element type in the input sequence is not supported.")
def from_list( # pylint: disable=too-many-branches
lst: List[Any], name: Optional[str] = None, dtype: Optional[int] = None
) -> SequenceProto: # pylint: disable=too-many-branches
"""Converts a list into a sequence def.
Args:
lst: a Python list
name: (optional) the name of the sequence.
dtype: (optional) type of element in the input list, used for specifying
sequence values when converting an empty list.
Returns:
SequenceProto: the converted sequence def.
"""
sequence = SequenceProto()
if name:
sequence.name = name
if dtype:
elem_type = dtype
elif len(lst) > 0:
first_elem = lst[0]
if isinstance(first_elem, dict):
elem_type = SequenceProto.MAP
elif isinstance(first_elem, list):
elem_type = SequenceProto.SEQUENCE
else:
elem_type = SequenceProto.TENSOR
else:
# if empty input list and no dtype specified
# choose sequence of tensors on default
elem_type = SequenceProto.TENSOR
sequence.elem_type = elem_type
if (len(lst) > 0) and not all(isinstance(elem, type(lst[0])) for elem in lst):
raise TypeError(
"The element type in the input list is not the same "
"for all elements and therefore is not supported as a sequence."
)
if elem_type == SequenceProto.TENSOR:
for tensor in lst:
sequence.tensor_values.extend([from_array(tensor)])
elif elem_type == SequenceProto.SEQUENCE:
for seq in lst:
sequence.sequence_values.extend([from_list(seq)])
elif elem_type == SequenceProto.MAP:
for mapping in lst:
sequence.map_values.extend([from_dict(mapping)])
else:
raise TypeError(
"The element type in the input list is not a tensor, "
"sequence, or map and is not supported."
)
return sequence
def to_dict(map_proto: MapProto) -> Dict[Any, Any]:
"""Converts a map def to a Python dictionary.
Args:
map: a MapProto object.
Returns:
dict: the converted dictionary.
"""
key_list: List[Any] = []
if map_proto.key_type == TensorProto.STRING:
key_list = list(map_proto.string_keys)
else:
key_list = list(map_proto.keys)
value_list = to_list(map_proto.values)
if len(key_list) != len(value_list):
raise IndexError(
"Length of keys and values for MapProto (map name: ",
map_proto.name,
") are not the same.",
)
dictionary = dict(zip(key_list, value_list))
return dictionary
def from_dict(dict_: Dict[Any, Any], name: Optional[str] = None) -> MapProto:
"""Converts a Python dictionary into a map def.
Args:
dict: Python dictionary
name: (optional) the name of the map.
Returns:
MapProto: the converted map def.
"""
map_proto = MapProto()
if name:
map_proto.name = name
keys = list(dict_)
raw_key_type = np.array(keys[0]).dtype
key_type = helper.np_dtype_to_tensor_dtype(raw_key_type)
valid_key_int_types = [
TensorProto.INT8,
TensorProto.INT16,
TensorProto.INT32,
TensorProto.INT64,
TensorProto.UINT8,
TensorProto.UINT16,
TensorProto.UINT32,
TensorProto.UINT64,
]
if not all(
isinstance(
key,
raw_key_type, # type: ignore[arg-type]
)
for key in keys
):
raise TypeError(
"The key type in the input dictionary is not the same "
"for all keys and therefore is not valid as a map."
)
values = list(dict_.values())
raw_value_type = type(values[0])
if not all(isinstance(val, raw_value_type) for val in values):
raise TypeError(
"The value type in the input dictionary is not the same "
"for all values and therefore is not valid as a map."
)
value_seq = from_list(values)
map_proto.key_type = key_type
if key_type == TensorProto.STRING:
map_proto.string_keys.extend(keys)
elif key_type in valid_key_int_types:
map_proto.keys.extend(keys)
map_proto.values.CopyFrom(value_seq)
return map_proto
def to_optional(optional: OptionalProto) -> Optional[Any]:
"""Converts an optional def to a Python optional.
Args:
optional: an OptionalProto object.
Returns:
opt: the converted optional.
"""
elem_type = optional.elem_type
if elem_type == OptionalProto.UNDEFINED:
return None
if elem_type == OptionalProto.TENSOR:
return to_array(optional.tensor_value)
if elem_type == OptionalProto.SPARSE_TENSOR:
return to_array(optional.sparse_tensor_value) # type: ignore[arg-type]
if elem_type == OptionalProto.SEQUENCE:
return to_list(optional.sequence_value)
if elem_type == OptionalProto.MAP:
return to_dict(optional.map_value)
if elem_type == OptionalProto.OPTIONAL:
return to_optional(optional.optional_value)
raise TypeError("The element type in the input optional is not supported.")
def from_optional(
opt: Optional[Any], name: Optional[str] = None, dtype: Optional[int] = None
) -> OptionalProto:
"""Converts an optional value into a Optional def.
Args:
opt: a Python optional
name: (optional) the name of the optional.
dtype: (optional) type of element in the input, used for specifying
optional values when converting empty none. dtype must
be a valid OptionalProto.DataType value
Returns:
optional: the converted optional def.
"""
# TODO: create a map and replace conditional branches
optional = OptionalProto()
if name:
optional.name = name
if dtype:
# dtype must be a valid OptionalProto.DataType
valid_dtypes = list(OptionalProto.DataType.values())
if dtype not in valid_dtypes:
raise TypeError(f"{dtype} must be a valid OptionalProto.DataType.")
elem_type = dtype
elif isinstance(opt, dict):
elem_type = OptionalProto.MAP
elif isinstance(opt, list):
elem_type = OptionalProto.SEQUENCE
elif opt is None:
elem_type = OptionalProto.UNDEFINED
else:
elem_type = OptionalProto.TENSOR
optional.elem_type = elem_type
if opt is not None:
if elem_type == OptionalProto.TENSOR:
optional.tensor_value.CopyFrom(from_array(opt))
elif elem_type == OptionalProto.SEQUENCE:
optional.sequence_value.CopyFrom(from_list(opt))
elif elem_type == OptionalProto.MAP:
optional.map_value.CopyFrom(from_dict(opt))
else:
raise TypeError(
"The element type in the input is not a tensor, "
"sequence, or map and is not supported."
)
return optional
def convert_endian(tensor: TensorProto) -> None:
"""
Call to convert endianess of raw data in tensor.
Arguments:
tensor (TensorProto): TensorProto to be converted.
"""
tensor_dtype = tensor.data_type
np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype)
tensor.raw_data = (
np.frombuffer(tensor.raw_data, dtype=np_dtype).byteswap().tobytes()
)
def create_random_int(
input_shape: Tuple[int], dtype: np.dtype, seed: int = 1
) -> np.ndarray:
"""
Create random integer array for backend/test/case/node.
Args:
input_shape: specify the shape for the returned integer array.
dtype: specify the NumPy data type for the returned integer array.
seed: (optional) the seed for np.random.
Returns:
np.ndarray: the created random integer array.
"""
np.random.seed(seed)
if dtype in (
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.int8,
np.int16,
np.int32,
np.int64,
):
# the range of np.random.randint is int32; set a fixed boundary if overflow
end = min(np.iinfo(dtype).max, np.iinfo(np.int32).max)
start = max(np.iinfo(dtype).min, np.iinfo(np.int32).min)
return np.random.randint(start, end, size=input_shape).astype(dtype)
else:
raise TypeError(f"{dtype} is not supported by create_random_int.")
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,958 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/maxpool.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_pool_common import (
get_output_shape_auto_pad,
get_output_shape_explicit_padding,
get_pad_shape,
pool,
)
class MaxPool(Base):
@staticmethod
def export_maxpool_2d_uint8() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.uint8)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.uint8)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8")
@staticmethod
def export_maxpool_2d_precomputed_pads() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads")
@staticmethod
def export_maxpool_with_argmax_2d_precomputed_pads() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
z = np.array(
[
[
[
[12, 13, 14, 14, 14],
[17, 18, 19, 19, 19],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
]
]
]
).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_pads",
)
@staticmethod
def export_maxpool_2d_precomputed_strides() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2]
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides"
)
@staticmethod
def export_maxpool_with_argmax_2d_precomputed_strides() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
kernel_shape=[2, 2],
strides=[2, 2],
storage_order=1,
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_strides",
)
@staticmethod
def export_maxpool_2d_precomputed_same_upper() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper"
)
@staticmethod
def export_maxpool_1d_default() -> None:
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2],
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = [2]
strides = [1]
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default")
@staticmethod
def export_maxpool_2d_default() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = (2, 2)
strides = (1, 1)
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default")
@staticmethod
def export_maxpool_3d_default() -> None:
"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default")
@staticmethod
def export_maxpool_2d_same_upper() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_UPPER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper")
@staticmethod
def export_maxpool_2d_same_lower() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_LOWER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower")
@staticmethod
def export_maxpool_2d_pads() -> None:
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads")
@staticmethod
def export_maxpool_2d_strides() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
"MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3]
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = (5, 5)
strides = (3, 3)
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides")
@staticmethod
def export_maxpool_2d_ceil() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil")
@staticmethod
def export_maxpool_2d_dilations() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations")
@staticmethod
def export_maxpool_3d_dilations() -> None:
"""
input_shape: [1, 1, 4, 4, 4]
output_shape: [1, 1, 2, 2, 2]
"""
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
strides=[1, 1, 1],
dilations=[2, 2, 2],
)
x = np.array(
[
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
]
]
]
).astype(np.float32)
y = np.array([[[[[11, 12], [15, 16]], [[11, 12], [15, 16]]]]]).astype(
np.float32
)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations")
@staticmethod
def export_maxpool_3d_dilations_use_ref_impl() -> None:
"""
input_shape: [1, 1, 4, 4, 4]
output_shape: [1, 1, 2, 2, 2]
"""
dilations = [2, 2, 2]
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
ceil_mode = False
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
strides=[1, 1, 1],
dilations=dilations,
)
x = np.array(
[
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
]
]
]
).astype(np.float32)
x_shape = x.shape[2:]
out_shape, pads = get_output_shape_explicit_padding(
None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode
)
padded = x
y = pool(
padded,
(1, 1, *x_shape),
kernel_shape,
strides,
out_shape,
"MAX",
pads,
dilations=dilations,
)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl"
)
@staticmethod
def export_maxpool_3d_dilations_use_ref_impl_large() -> None:
x_shape = (32, 32, 32)
dilations = (2, 2, 2)
kernel_shape = (5, 5, 5)
strides = (3, 3, 3)
ceil_mode = True
node = onnx.helper.make_node(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
ceil_mode=ceil_mode,
)
x = np.random.randn(1, 1, *x_shape).astype(np.float32)
out_shape, pads = get_output_shape_explicit_padding(
None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode
)
padded = np.pad(
x,
(
(0, 0),
(0, 0),
(pads[0], pads[3]),
(pads[1], pads[4]),
(pads[2], pads[5]),
),
mode="constant",
constant_values=0,
)
y = pool(
padded,
(1, 1, *x_shape),
kernel_shape,
strides,
out_shape,
"MAX",
pads,
dilations=dilations,
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_maxpool_3d_dilations_use_ref_impl_large",
)
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58,959 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnx_preview_training/op_adagrad.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
import numpy as np
from onnx.reference.ops.aionnx_preview_training._op_run_training import OpRunTraining
def _apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor): # type: ignore
# Compute adjusted learning-rate.
r_ = r / (1 + t * decay_factor)
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Update squared accumulated gradient.
h_new = h + g_regularized * g_regularized
# Compute ADAGRAD's gradient scaling factors
h_sqrt = np.sqrt(h_new) + epsilon
# Apply ADAGRAD update rule.
x_new = x - r_ * g_regularized / h_sqrt
return (x_new, h_new)
class Adagrad(OpRunTraining):
def _run(self, *data, decay_factor=None, epsilon=None, norm_coefficient=None): # type: ignore
if len(data) == 5:
return self._run1( # type: ignore
*data,
decay_factor=decay_factor,
epsilon=epsilon,
norm_coefficient=norm_coefficient,
)
n = (len(data) - 2) // 3
xs = []
hs = []
for i in range(0, n):
a, b = self._run1( # type: ignore
*data[:2],
data[2 + i],
data[2 + n + i],
data[2 + n * 2 + i],
decay_factor=decay_factor,
epsilon=epsilon,
norm_coefficient=norm_coefficient,
)
xs.append(a)
hs.append(b)
return tuple(xs + hs)
def _run1(self, r, t, x, g, h, decay_factor=None, epsilon=None, norm_coefficient=None): # type: ignore
x_new, h_new = _apply_adagrad(
r, t, x, g, h, norm_coefficient, epsilon, decay_factor # type: ignore
)
return x_new, h_new
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58,960 | onnx/onnx | refs/heads/main | /onnx/test/printer_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
import onnx
from onnx import parser, printer
class TestBasicFunctions(unittest.TestCase):
def check_graph(self, graph: onnx.GraphProto) -> None:
self.assertEqual(len(graph.node), 3)
self.assertEqual(graph.node[0].op_type, "MatMul")
self.assertEqual(graph.node[1].op_type, "Add")
self.assertEqual(graph.node[2].op_type, "Softmax")
def test_parse_graph(self) -> None:
text0 = """
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
graph1 = parser.parse_graph(text0)
text1 = printer.to_text(graph1)
graph2 = parser.parse_graph(text1)
text2 = printer.to_text(graph2)
# Note that text0 and text1 should be semantically-equivalent, but may differ
# in white-space and other syntactic sugar. However, we expect text1 and text2
# to be identical.
self.assertEqual(text1, text2)
self.check_graph(graph2)
if __name__ == "__main__":
unittest.main()
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58,961 | onnx/onnx | refs/heads/main | /onnx/reference/ops_optimized/__init__.py | # Copyright (c) ONNX Project Contributors
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from onnx.reference.ops_optimized.op_conv_optimized import Conv
optimized_operators = [Conv]
__all__ = ["Conv", "optimized_operators"]
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], 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58,962 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reduceprod.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceProd(Base):
@staticmethod
def export_do_not_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[3., 8.]
# [35., 48.]
# [99., 120.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_do_not_keepdims_random",
)
@staticmethod
def export_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3., 8.]]
# [[35., 48.]]
# [[99., 120.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_keepdims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = None
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd", inputs=["data"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
# print(reduced)
# [[[4.790016e+08]]]
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_prod_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_prod_default_axes_keepdims_random",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceProd",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3., 8.]]
# [[35., 48.]]
# [[99., 120.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.prod(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_prod_negative_axes_keepdims_random",
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,963 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_pow.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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): # type: ignore
with catch_warnings():
simplefilter("ignore")
return (np.power(a, b).astype(a.dtype),)
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58,964 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/sequence.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import typing
import numpy as np
import onnx
from onnx import TensorProto
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
def SequenceEmptyImpl() -> list[np.ndarray | None]:
return []
def SequenceConstructImpl(*tensors: np.ndarray) -> list[np.ndarray]:
return list(tensors)
def SequenceInsertImpl(
sequence: list[np.ndarray], tensor: np.ndarray, position: int | None = None
) -> list[np.ndarray]:
if position is None:
position = len(sequence)
sequence.insert(position, tensor)
return sequence
def SequenceAtImpl(sequence: list[np.ndarray], position: int) -> np.ndarray:
return sequence[position]
def SequenceEraseImpl(
sequence: list[np.ndarray], position: int | None = None
) -> list[np.ndarray | None]:
if position is None:
position = -1
del sequence[position]
return sequence
def SequenceLengthImpl(sequence: list[np.ndarray]) -> np.int64:
return np.int64(len(sequence))
def SplitToSequenceImpl(
tensor: np.ndarray,
split: int | list[int] | None = None,
axis: int = 0,
keepdims: int = 1,
) -> list[np.ndarray]:
dim_size = tensor.shape[axis]
if split is None:
split = 1
split_indices = [
i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size
]
if not keepdims:
results = np.array_split(tensor, split_indices, axis)
return [np.squeeze(res, axis) for res in results]
if np.isscalar(split):
split_indices = [i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size] # type: ignore
else:
split_indices = np.cumsum(split) + 1
return np.array_split(tensor, split_indices, axis) # type: ignore
def ConcatFromSequenceImpl(
sequence: list[np.ndarray], axis: int, new_axis: int | None = 0
) -> np.ndarray:
if not new_axis:
return np.concatenate(sequence, axis)
return np.stack(sequence, axis)
class Sequence(Base):
@staticmethod
def export() -> None:
def make_graph(
nodes: list[onnx.helper.NodeProto],
input_shapes: list[typing.Sequence[str | int] | None],
output_shapes: list[typing.Sequence[str | int] | None],
input_names: list[str],
output_names: list[str],
input_types: list[TensorProto.DataType],
output_types: list[TensorProto.DataType],
initializers: list[TensorProto] | None = None,
) -> onnx.helper.GraphProto:
graph = onnx.helper.make_graph(
nodes=nodes,
name="Sequence",
inputs=[
onnx.helper.make_tensor_value_info(name, input_type, input_shape)
for name, input_type, input_shape in zip(
input_names, input_types, input_shapes
)
],
outputs=[
onnx.helper.make_tensor_value_info(name, output_type, output_shape)
for name, output_type, output_shape in zip(
output_names, output_types, output_shapes
)
],
initializer=initializers,
)
return graph
# 1st testcase - insert and at.
# 1. SequenceEmpty: -> []
# 2. SequenceInsert(x): -> [x]
# 3. SequenceInsert(y): -> [x, y]
# 4. SequenceInsert(z, 1): -> [x, z, y]
# 5. SequenceAt(2): -> y
seq_empty_node = onnx.helper.make_node("SequenceEmpty", [], ["Seq_empty"])
seq_insert_node = onnx.helper.make_node(
"SequenceInsert", ["Seq_empty", "X"], ["Seq_1"]
)
seq_insert_node2 = onnx.helper.make_node(
"SequenceInsert", ["Seq_1", "Y"], ["Seq_2"]
)
seq_insert_node3 = onnx.helper.make_node(
"SequenceInsert", ["Seq_2", "Z", "pos"], ["Seq_3"]
)
seq_at_node = onnx.helper.make_node("SequenceAt", ["Seq_3", "pos_at"], ["out"])
x_shape = [2, 3, 4]
y_shape = [1, 3, 4]
z_shape = [3, 3, 4]
out_shape = [None, 3, 4]
x = np.ones(x_shape, dtype=np.float32)
y = np.zeros(y_shape, dtype=np.float32)
z = np.ones(z_shape, dtype=np.float32) * 2
pos_val = 1
pos_at_val = 2
out = SequenceEmptyImpl()
out = SequenceInsertImpl(out, x)
out = SequenceInsertImpl(out, y)
out = SequenceInsertImpl(out, z, pos_val)
out = SequenceAtImpl(out, pos_at_val)
assert np.array_equal(out, y)
pos = onnx.helper.make_tensor("pos", TensorProto.INT64, (), (pos_val,))
pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
graph = make_graph(
[
seq_empty_node,
seq_insert_node,
seq_insert_node2,
seq_insert_node3,
seq_at_node,
],
[x_shape, y_shape, z_shape, [], []], # type: ignore
[out_shape], # type: ignore
["X", "Y", "Z", "pos", "pos_at"],
["out"],
[onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2, # type: ignore
[onnx.TensorProto.FLOAT],
[pos, pos_at],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model1")
# 2nd testcase - erase and at.
# 1. SequenceConstruct(x, y, z): -> [x, y, z]
# 2. SequenceErase(1): -> [x, z]
# 3. SequenceAt(1): -> z
seq_construct_node = onnx.helper.make_node(
"SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
)
seq_erase_node = onnx.helper.make_node(
"SequenceErase", ["seq_1", "pos_erase"], ["seq_2"]
)
seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_2", "pos_at"], ["out"])
tensor_shape = [2, 3, 4]
x = np.ones(tensor_shape, dtype=np.float32)
y = np.zeros(tensor_shape, dtype=np.float32)
z = np.ones(tensor_shape, dtype=np.float32) * 2
pos_erase_val = 1
pos_at_val = 1
out = SequenceConstructImpl(x, y, z)
out = SequenceEraseImpl(out, pos_erase_val)
out = SequenceAtImpl(out, pos_at_val)
assert np.array_equal(out, z)
pos_erase = onnx.helper.make_tensor(
"pos_erase", TensorProto.INT64, (), (pos_erase_val,)
)
pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
graph = make_graph(
[seq_construct_node, seq_erase_node, seq_at_node],
[tensor_shape, tensor_shape, tensor_shape, [], []], # type: ignore
[tensor_shape], # type: ignore
["X", "Y", "Z", "pos_erase", "pos_at"],
["out"],
[onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2, # type: ignore
[onnx.TensorProto.FLOAT],
[pos_erase, pos_at],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model2")
# 3rd testcase - erase, insert and at, with negative index value.
# 1. SequenceConstruct(x, y, z): -> [x, y, z]
# 2. SequenceErase(-3): -> [y, z]
# 3. SequenceInsert(x, -1): -> [y, x, z]
# 4. SequenceAt(-1): -> z
seq_construct_node = onnx.helper.make_node(
"SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
)
seq_erase_node = onnx.helper.make_node(
"SequenceErase", ["seq_1", "pos_erase"], ["seq_2"]
)
seq_insert_node = onnx.helper.make_node(
"SequenceInsert", ["seq_2", "X", "pos_insert"], ["seq_3"]
)
seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_3", "pos_at"], ["out"])
tensor_shape = [2, 3, 4]
x = np.ones(tensor_shape, dtype=np.float32)
y = np.zeros(tensor_shape, dtype=np.float32)
z = np.ones(tensor_shape, dtype=np.float32) * 2
pos_erase_val = -3
pos_insert_val = -1
pos_at_val = -1
out = SequenceConstructImpl(x, y, z)
out = SequenceEraseImpl(out, pos_erase_val)
out = SequenceInsertImpl(out, x, pos_insert_val)
out = SequenceAtImpl(out, pos_at_val)
assert np.array_equal(out, z)
pos_erase = onnx.helper.make_tensor(
"pos_erase", TensorProto.INT64, (), (pos_erase_val,)
)
pos_insert = onnx.helper.make_tensor(
"pos_insert", TensorProto.INT64, (), (pos_insert_val,)
)
pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
graph = make_graph(
[seq_construct_node, seq_erase_node, seq_insert_node, seq_at_node],
[tensor_shape, tensor_shape, tensor_shape, [], [], []], # type: ignore
[tensor_shape], # type: ignore
["X", "Y", "Z", "pos_erase", "pos_insert", "pos_at"],
["out"],
[onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 3, # type: ignore
[onnx.TensorProto.FLOAT],
[pos_erase, pos_insert, pos_at],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model3")
# 4th testcase - concat
seq_construct_node = onnx.helper.make_node(
"SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
)
seq_concat_node = onnx.helper.make_node(
"ConcatFromSequence", ["seq_1"], ["out"], axis=1
)
tensor_shape = [2, 3, 4]
concat_out_shape = [2, None, 4]
x = np.ones(tensor_shape, dtype=np.float32)
y = np.zeros(tensor_shape, dtype=np.float32)
z = np.ones(tensor_shape, dtype=np.float32) * 2
out = SequenceConstructImpl(x, y, z)
concat_out = ConcatFromSequenceImpl(out, 1)
graph = make_graph(
[seq_construct_node, seq_concat_node],
[tensor_shape] * 3, # type: ignore
[concat_out_shape], # type: ignore
["X", "Y", "Z"],
["out"],
[onnx.TensorProto.FLOAT] * 3, # type: ignore
[onnx.TensorProto.FLOAT],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(
model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model4"
)
# 5th testcase - concat with new_axis = 1
seq_construct_node = onnx.helper.make_node(
"SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
)
seq_concat_node = onnx.helper.make_node(
"ConcatFromSequence", ["seq_1"], ["out"], axis=-1, new_axis=1
)
tensor_shape = [2, 3, 4]
concat_out_shape = [2, 3, 4, 3]
x = np.ones(tensor_shape, dtype=np.float32)
y = np.zeros(tensor_shape, dtype=np.float32)
z = np.ones(tensor_shape, dtype=np.float32) * 2
out = SequenceConstructImpl(x, y, z)
concat_out = ConcatFromSequenceImpl(out, -1, 1)
graph = make_graph(
[seq_construct_node, seq_concat_node],
[tensor_shape] * 3, # type: ignore
[concat_out_shape], # type: ignore
["X", "Y", "Z"],
["out"],
[onnx.TensorProto.FLOAT] * 3, # type: ignore
[onnx.TensorProto.FLOAT],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(
model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model5"
)
# 6th testcase - split and len
seq_split_node = onnx.helper.make_node(
"SplitToSequence", ["X"], ["seq_1"], axis=-1
)
seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"])
tensor_shape = [2, 3, 4]
len_shape = [] # type: ignore
x = np.ones(tensor_shape, dtype=np.float32)
out = SplitToSequenceImpl(x, axis=-1)
out = SequenceLengthImpl(out)
assert np.array_equal(out, np.int64(4))
graph = onnx.helper.make_graph(
nodes=[seq_split_node, seq_len_node],
name="Sequence",
inputs=[
onnx.helper.make_tensor_value_info(
"X", onnx.TensorProto.FLOAT, tensor_shape
)
],
outputs=[
onnx.helper.make_tensor_value_info(
"len", onnx.TensorProto.INT64, len_shape
)
],
) # type: ignore
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(model, inputs=[x], outputs=[out], name="test_sequence_model6")
# 7th testcase - split with keepdims=0, and SequenceAt
seq_split_node = onnx.helper.make_node(
"SplitToSequence", ["X"], ["seq_1"], axis=0, keepdims=0
)
seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_1", "pos_at"], ["out"])
tensor_shape = [2, 3, 4]
out_shape = [3, 4]
x = np.random.rand(*tensor_shape)
pos_at_val = 1
out = SplitToSequenceImpl(x, axis=0, keepdims=0)
out = SequenceAtImpl(out, pos_at_val)
assert np.array_equal(out, x[pos_at_val])
pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
graph = make_graph(
[seq_split_node, seq_at_node],
[tensor_shape, []], # type: ignore
[out_shape], # type: ignore
["X", "pos_at"],
["out"],
[onnx.TensorProto.DOUBLE, onnx.TensorProto.INT64],
[onnx.TensorProto.DOUBLE],
[pos_at],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(model, inputs=[x], outputs=[out], name="test_sequence_model7")
# 8th testcase - split zero length
seq_split_node = onnx.helper.make_node(
"SplitToSequence", ["X", "Splits"], ["seq_1"]
)
seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"])
tensor_shape = ["n"] # type: ignore
splits_shape = [3] # type: ignore
x = np.array([]).astype(np.float32)
splits = np.array([0, 0, 0]).astype(np.int64)
out_len = np.int64(3)
graph = onnx.helper.make_graph(
nodes=[seq_split_node, seq_len_node],
name="Sequence",
inputs=[
onnx.helper.make_tensor_value_info(
"X", onnx.TensorProto.FLOAT, tensor_shape
), # type: ignore
onnx.helper.make_tensor_value_info(
"Splits", onnx.TensorProto.INT64, splits_shape
),
], # type: ignore
outputs=[
onnx.helper.make_tensor_value_info(
"len", onnx.TensorProto.INT64, len_shape
)
],
) # type: ignore
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 12)],
)
expect(
model, inputs=[x, splits], outputs=[out_len], name="test_sequence_model8"
)
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58,965 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_global_max_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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_shape + 2)))
for _ in range(spatial_shape):
y = np.expand_dims(y, -1)
return y # type: ignore
class GlobalMaxPool(OpRun):
def _run(self, x): # type: ignore
res = _global_max_pool(x)
return (res,)
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58,966 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class Sum(OpRun):
def _run(self, *args): # type: ignore
return (sum(args).astype(args[0].dtype),)
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58,967 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_clip.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0622,W0622,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Clip_6(OpRun):
def _run(self, data, min=None, max=None): # type: ignore
amin = min
amax = max
if amin is amax is None:
return (data,)
res = np.clip(data, amin, amax) # type: ignore
return (res,) if res.dtype == data.dtype else (res.astype(data.dtype),)
class Clip_11(OpRun):
def _run(self, data, *minmax): # type: ignore
le = len(minmax)
amin = minmax[0] if le > 0 else None
amax = minmax[1] if le > 1 else None
if amin is amax is None:
return (data,)
res = np.clip(data, amin, amax)
return (res,) if res.dtype == data.dtype else (res.astype(data.dtype),)
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58,968 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/convinteger.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ConvInteger(Base):
@staticmethod
def export_without_padding() -> None:
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
# ConvInteger without padding
convinteger_node = onnx.helper.make_node(
"ConvInteger", inputs=["x", "w", "x_zero_point"], outputs=["y"]
)
expect(
convinteger_node,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_without_padding",
)
@staticmethod
def export_with_padding() -> None:
x = (
np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
.astype(np.uint8)
.reshape((1, 1, 3, 3))
)
x_zero_point = np.uint8(1)
w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
y = (
np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9])
.astype(np.int32)
.reshape((1, 1, 4, 4))
)
# ConvInteger with padding
convinteger_node_with_padding = onnx.helper.make_node(
"ConvInteger",
inputs=["x", "w", "x_zero_point"],
outputs=["y"],
pads=[1, 1, 1, 1],
)
expect(
convinteger_node_with_padding,
inputs=[x, w, x_zero_point],
outputs=[y],
name="test_convinteger_with_padding",
)
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58,969 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/dynamicquantizelinear.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class DynamicQuantizeLinear(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"DynamicQuantizeLinear",
inputs=["x"],
outputs=["y", "y_scale", "y_zero_point"],
)
# expected scale 0.0196078438 and zero point 153
X = np.array([0, 2, -3, -2.5, 1.34, 0.5]).astype(np.float32)
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear",
)
# expected scale 0.0156862754 and zero point 255
X = np.array([-1.0, -2.1, -1.3, -2.5, -3.34, -4.0]).astype(np.float32)
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear_max_adjusted",
)
X = (
np.array([1, 2.1, 1.3, 2.5, 3.34, 4.0, 1.5, 2.6, 3.9, 4.0, 3.0, 2.345])
.astype(np.float32)
.reshape((3, 4))
)
# expected scale 0.0156862754 and zero point 0
x_min = np.minimum(0, np.min(X))
x_max = np.maximum(0, np.max(X))
Y_Scale = np.float32((x_max - x_min) / (255 - 0)) # uint8 -> [0, 255]
Y_ZeroPoint = np.clip(round((0 - x_min) / Y_Scale), 0, 255).astype(np.uint8)
Y = np.clip(np.round(X / Y_Scale) + Y_ZeroPoint, 0, 255).astype(np.uint8)
expect(
node,
inputs=[X],
outputs=[Y, Y_Scale, Y_ZeroPoint],
name="test_dynamicquantizelinear_min_adjusted",
)
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["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,970 | onnx/onnx | refs/heads/main | /onnx/checker.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_sparse_tensor",
"check_tensor",
"check_value_info",
"DEFAULT_CONTEXT",
"ValidationError",
"C",
"MAXIMUM_PROTOBUF",
]
import os
import sys
from typing import Any, Callable, TypeVar
from google.protobuf.message import Message
import onnx.defs
import onnx.onnx_cpp2py_export.checker as C # noqa: N812
import onnx.shape_inference
from onnx import (
IR_VERSION,
AttributeProto,
FunctionProto,
GraphProto,
ModelProto,
NodeProto,
SparseTensorProto,
TensorProto,
ValueInfoProto,
helper,
)
# Limitation of single protobuf file is 2GB
MAXIMUM_PROTOBUF = 2000000000
# TODO: This thing where we reserialize the protobuf back into the
# string, only to deserialize it at the call site, is really goofy.
# Stop doing that.
# NB: Please don't edit this context!
DEFAULT_CONTEXT = C.CheckerContext()
DEFAULT_CONTEXT.ir_version = IR_VERSION
# TODO: Maybe ONNX-ML should also be defaulted?
DEFAULT_CONTEXT.opset_imports = {"": onnx.defs.onnx_opset_version()}
FuncType = TypeVar("FuncType", bound=Callable[..., Any])
def _ensure_proto_type(proto: Message, proto_type: type[Message]) -> None:
if not isinstance(proto, proto_type):
raise TypeError(
f"The proto message needs to be of type '{proto_type.__name__}'"
)
def check_value_info(
value_info: ValueInfoProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
) -> None:
_ensure_proto_type(value_info, ValueInfoProto)
return C.check_value_info(value_info.SerializeToString(), ctx)
def check_tensor(tensor: TensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
_ensure_proto_type(tensor, TensorProto)
return C.check_tensor(tensor.SerializeToString(), ctx)
def check_attribute(
attr: AttributeProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
) -> None:
_ensure_proto_type(attr, AttributeProto)
return C.check_attribute(attr.SerializeToString(), ctx)
def check_node(node: NodeProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
_ensure_proto_type(node, NodeProto)
return C.check_node(node.SerializeToString(), ctx)
def check_function(
function: FunctionProto, ctx: C.CheckerContext | None = None
) -> None:
_ensure_proto_type(function, FunctionProto)
if ctx is None:
ctx = C.CheckerContext()
ctx.ir_version = helper.find_min_ir_version_for(
list(function.opset_import), True
)
function_opset_dic = {}
for domain_version in function.opset_import:
function_opset_dic[domain_version.domain] = domain_version.version
ctx.opset_imports = function_opset_dic
C.check_function(function.SerializeToString(), ctx)
def check_graph(graph: GraphProto, ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
_ensure_proto_type(graph, GraphProto)
return C.check_graph(graph.SerializeToString(), ctx)
def check_sparse_tensor(
sparse: SparseTensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
) -> None:
_ensure_proto_type(sparse, SparseTensorProto)
C.check_sparse_tensor(sparse.SerializeToString(), ctx)
def check_model(
model: ModelProto | str | bytes | os.PathLike,
full_check: bool = False,
skip_opset_compatibility_check: bool = False,
) -> None:
"""Check the consistency of a model. An exception is raised if the test fails.
Args:
model: Model to check.
full_check: If True, the function also checks for shapes that can be inferred.
skip_opset_compatibility_check: If True, the function skips the check for
opset compatibility.
"""
# If model is a path instead of ModelProto
if isinstance(model, (str, os.PathLike)):
C.check_model_path(os.fspath(model), full_check, skip_opset_compatibility_check)
else:
protobuf_string = (
model if isinstance(model, bytes) else model.SerializeToString()
)
# If the protobuf is larger than 2GB,
# remind users should use the model path to check
if sys.getsizeof(protobuf_string) > MAXIMUM_PROTOBUF:
raise ValueError(
"This protobuf of onnx model is too large (>2GB). Call check_model with model path instead."
)
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
ValidationError = C.ValidationError
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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58,971 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/hardmax.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def hardmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_argmax = np.argmax(x, axis=axis)
y = np.zeros_like(x)
np.put_along_axis(y, np.expand_dims(x_argmax, axis=axis), 1, axis=axis)
return y
class Hardmax(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype(
np.float32
)
# expect result:
# [[1. 0. 0. 0.]
# [0. 1. 0. 0.]
# [0. 0. 1. 0.]
# [0. 0. 0. 1.]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_example")
# For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
x = np.array([[3, 3, 3, 1]]).astype(np.float32)
# expect result:
# [[1, 0, 0, 0]]
y = hardmax(x)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot")
@staticmethod
def export_hardmax_axis() -> None:
x = np.random.randn(3, 4, 5).astype(np.float32)
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = hardmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = hardmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = hardmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2")
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = hardmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"Hardmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis")
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"/onnx/reference/ops/op_random_normal.py", "/onnx/reference/ops/op_random_normal_like.py", "/onnx/reference/ops/op_random_uniform.py", "/onnx/reference/ops/op_reduce_l1.py", "/onnx/reference/ops/op_reduce_log_sum.py", "/onnx/reference/ops/op_reduce_log_sum_exp.py", "/onnx/reference/ops/op_reduce_mean.py", "/onnx/reference/ops/op_reduce_sum.py", "/onnx/reference/ops/op_regex_full_match.py", "/onnx/reference/ops/op_reshape.py", "/onnx/reference/ops/op_resize.py", "/onnx/reference/ops/op_reverse_sequence.py", "/onnx/reference/ops/op_rnn.py", "/onnx/reference/ops/op_roi_align.py", "/onnx/reference/ops/op_scan.py", "/onnx/reference/ops/op_scatter_elements.py", "/onnx/reference/ops/op_scatternd.py", "/onnx/reference/ops/op_selu.py", "/onnx/reference/ops/op_sequence_construct.py", "/onnx/reference/ops/op_sequence_empty.py", "/onnx/reference/ops/op_sequence_erase.py", "/onnx/reference/ops/op_sequence_insert.py", "/onnx/reference/ops/op_sequence_length.py", "/onnx/reference/ops/op_sequence_map.py", "/onnx/reference/ops/op_shape.py", "/onnx/reference/ops/op_shrink.py", "/onnx/reference/ops/op_sigmoid.py", "/onnx/reference/ops/op_slice.py", "/onnx/reference/ops/op_softmax.py", "/onnx/reference/ops/op_softmax_cross_entropy_loss.py", "/onnx/reference/ops/op_softplus.py", "/onnx/reference/ops/op_space_to_depth.py", "/onnx/reference/ops/op_split.py", "/onnx/reference/ops/op_split_to_sequence.py", "/onnx/reference/ops/op_sqrt.py", "/onnx/reference/ops/op_squeeze.py", "/onnx/reference/ops/op_stft.py", "/onnx/reference/ops/op_string_concat.py", "/onnx/reference/ops/op_string_normalizer.py", "/onnx/reference/ops/op_string_split.py", "/onnx/reference/ops/op_sub.py", "/onnx/reference/ops/op_sum.py", "/onnx/reference/ops/op_tfidf_vectorizer.py", "/onnx/reference/ops/op_thresholded_relu.py", "/onnx/reference/ops/op_tile.py", "/onnx/reference/ops/op_topk.py", "/onnx/reference/ops/op_transpose.py", "/onnx/reference/ops/op_trilu.py", "/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,972 | onnx/onnx | refs/heads/main | /onnx/test/test_backend_reference.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import os
import platform
import sys
import unittest
from typing import Any
import numpy
import onnx.backend.base
import onnx.backend.test
import onnx.shape_inference
import onnx.version_converter
from onnx import ModelProto
from onnx.backend.base import Device, DeviceType
from onnx.reference import ReferenceEvaluator
# The following just executes a backend based on ReferenceEvaluator through the backend test
class ReferenceEvaluatorBackendRep(onnx.backend.base.BackendRep):
def __init__(self, session):
self._session = session
def run(self, inputs, **kwargs):
if isinstance(inputs, numpy.ndarray):
inputs = [inputs]
if isinstance(inputs, list):
if len(inputs) == len(self._session.input_names):
feeds = dict(zip(self._session.input_names, inputs))
else:
feeds = {}
pos_inputs = 0
for inp, tshape in zip(
self._session.input_names, self._session.input_types
):
shape = tuple(d.dim_value for d in tshape.tensor_type.shape.dim)
if shape == inputs[pos_inputs].shape:
feeds[inp] = inputs[pos_inputs]
pos_inputs += 1
if pos_inputs >= len(inputs):
break
elif isinstance(inputs, dict):
feeds = inputs
else:
raise TypeError(f"Unexpected input type {type(inputs)!r}.")
outs = self._session.run(None, feeds)
return outs
class ReferenceEvaluatorBackend(onnx.backend.base.Backend):
@classmethod
def is_opset_supported(cls, model): # pylint: disable=unused-argument
return True, ""
@classmethod
def supports_device(cls, device: str) -> bool:
d = Device(device)
return d.type == DeviceType.CPU # type: ignore[no-any-return]
@classmethod
def create_inference_session(cls, model):
return ReferenceEvaluator(model)
@classmethod
def prepare(
cls, model: Any, device: str = "CPU", **kwargs: Any
) -> ReferenceEvaluatorBackendRep:
# if isinstance(model, ReferenceEvaluatorBackendRep):
# return model
if isinstance(model, ReferenceEvaluator):
return ReferenceEvaluatorBackendRep(model)
if isinstance(model, (str, bytes, ModelProto)):
inf = cls.create_inference_session(model)
return cls.prepare(inf, device, **kwargs)
raise TypeError(f"Unexpected type {type(model)} for model.")
@classmethod
def run_model(cls, model, inputs, device=None, **kwargs):
rep = cls.prepare(model, device, **kwargs)
return rep.run(inputs, **kwargs)
@classmethod
def run_node(cls, node, inputs, device=None, outputs_info=None, **kwargs):
raise NotImplementedError("Unable to run the model node by node.")
backend_test = onnx.backend.test.BackendTest(ReferenceEvaluatorBackend, __name__)
if os.getenv("APPVEYOR"):
backend_test.exclude("(test_vgg19|test_zfnet)")
if platform.architecture()[0] == "32bit":
backend_test.exclude("(test_vgg19|test_zfnet|test_bvlc_alexnet)")
if platform.system() == "Windows":
backend_test.exclude("test_sequence_model")
# The following tests are not supported.
backend_test.exclude(
"(test_gradient"
"|test_if_opt"
"|test_loop16_seq_none"
"|test_range_float_type_positive_delta_expanded"
"|test_range_int32_type_negative_delta_expanded"
"|test_scan_sum)"
)
# The following tests are about deprecated operators.
backend_test.exclude("(test_scatter_with_axis|test_scatter_without)")
# The following tests are using types not supported by numpy.
# They could be if method to_array is extended to support custom
# types the same as the reference implementation does
# (see onnx.reference.op_run.to_array_extended).
backend_test.exclude(
"(test_cast_FLOAT_to_FLOAT8"
"|test_cast_FLOAT16_to_FLOAT8"
"|test_castlike_FLOAT_to_FLOAT8"
"|test_castlike_FLOAT16_to_FLOAT8"
"|test_cast_no_saturate_FLOAT_to_FLOAT8"
"|test_cast_no_saturate_FLOAT16_to_FLOAT8"
"|test_cast_BFLOAT16_to_FLOAT"
"|test_castlike_BFLOAT16_to_FLOAT"
"|test_quantizelinear_e4m3"
"|test_quantizelinear_e5m2"
")"
)
# The following tests are using types not supported by NumPy.
# They could be if method to_array is extended to support custom
# types the same as the reference implementation does
# (see onnx.reference.op_run.to_array_extended).
backend_test.exclude(
"(test_cast_FLOAT_to_BFLOAT16"
"|test_castlike_FLOAT_to_BFLOAT16"
"|test_castlike_FLOAT_to_BFLOAT16_expanded"
")"
)
# The following tests are too slow with the reference implementation (Conv).
backend_test.exclude(
"(test_bvlc_alexnet"
"|test_densenet121"
"|test_inception_v1"
"|test_inception_v2"
"|test_resnet50"
"|test_shufflenet"
"|test_squeezenet"
"|test_vgg19"
"|test_zfnet512)"
)
# The following tests cannot pass because they consists in generating random number.
backend_test.exclude("(test_bernoulli)")
# The following tests fail due to a bug in the backend test comparison.
backend_test.exclude(
"(test_cast_FLOAT_to_STRING|test_castlike_FLOAT_to_STRING|test_strnorm)"
)
# The following tests fail due to a shape mismatch.
backend_test.exclude(
"(test_center_crop_pad_crop_axes_hwc_expanded"
"|test_lppool_2d_dilations"
"|test_averagepool_2d_dilations)"
)
# The following tests fail due to a type mismatch.
backend_test.exclude("(test_eyelike_without_dtype)")
# The following tests fail due to discrepancies (small but still higher than 1e-7).
backend_test.exclude("test_adam_multiple") # 1e-2
# Currently google-re2 is not supported on Win32 and is required for the reference implementation of RegexFullMatch.
if sys.platform == "win32":
backend_test.exclude("test_regex_full_match_basic_cpu")
backend_test.exclude("test_regex_full_match_email_domain_cpu")
backend_test.exclude("test_regex_full_match_empty_cpu")
# import all test cases at global scope to make them visible to python.unittest
globals().update(backend_test.test_cases)
if __name__ == "__main__":
res = unittest.main(verbosity=2, exit=False)
tests_run = res.result.testsRun
errors = len(res.result.errors)
skipped = len(res.result.skipped)
unexpected_successes = len(res.result.unexpectedSuccesses)
expected_failures = len(res.result.expectedFailures)
print("---------------------------------")
print(
f"tests_run={tests_run} errors={errors} skipped={skipped} "
f"unexpected_successes={unexpected_successes} "
f"expected_failures={expected_failures}"
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_non_zero.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/prelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_conv.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lppool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/hardswish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/thresholdedrelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_leaky_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_pool_common.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_backend_test.py": ["/onnx/backend/base.py", "/onnx/backend/test/__init__.py", "/onnx/shape_inference.py", "/onnx/version_converter.py", "/onnx/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/backend/test/case/__init__.py": ["/onnx/backend/test/case/base.py", "/onnx/backend/test/case/utils.py"], "/onnx/reference/ops/op_instance_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/sequence_map.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_hamming_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_unique.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sqrt.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/serialization_test.py": ["/onnx/__init__.py"], "/onnx/defs/gen_shape_inference_information.py": ["/onnx/__init__.py"], "/onnx/test/elu_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/bitwiseor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/conv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/pow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_scan.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_reduce_mean.py": ["/onnx/reference/ops/_op.py"], "/onnx/tools/net_drawer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/momentum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_shape.py": 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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/softmaxcrossentropy.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/hub.py": ["/onnx/__init__.py"], "/onnx/defs/gen_doc.py": ["/onnx/__init__.py", "/onnx/backend/sample/ops/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/defs/__init__.py"], "/onnx/backend/test/case/model/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/reference/ops/op_eyelike.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lstm.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lrn.py": ["/onnx/reference/op_run.py"], "/onnx/version_converter.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/neg.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/softmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_compress.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_hard_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/gru.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/string_concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/tools_test.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/tools/replace_constants.py"], "/onnx/reference/ops/op_hardmax.py": ["/onnx/reference/ops/_op.py"], 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58,973 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_identity.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.ops._op import OpRunUnaryNum
class Identity(OpRunUnaryNum):
def _run(self, a): # type: ignore
if a is None:
return (None,)
return (a.copy(),)
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58,974 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op_common_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0613,W0221
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): # type: ignore
dtype = tensor_dtype_to_np_dtype(output_datatype)
if periodic == 1:
N_1 = size
else:
N_1 = size - 1
ni = np.arange(size, dtype=dtype)
return ni, N_1
@staticmethod
def _end(size, res, output_datatype): # type: ignore
dtype = tensor_dtype_to_np_dtype(output_datatype)
return (res.astype(dtype),)
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58,975 | onnx/onnx | refs/heads/main | /onnx/backend/sample/ops/abs.py | # SPDX-License-Identifier: Apache-2.0
import numpy as np
def abs(input: np.ndarray) -> np.ndarray:
return np.abs(input) # type: ignore[no-any-return]
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58,976 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/where.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Where(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Where",
inputs=["condition", "x", "y"],
outputs=["z"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
x = np.array([[1, 2], [3, 4]], dtype=np.float32)
y = np.array([[9, 8], [7, 6]], dtype=np.float32)
z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example")
@staticmethod
def export_long() -> None:
node = onnx.helper.make_node(
"Where",
inputs=["condition", "x", "y"],
outputs=["z"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
x = np.array([[1, 2], [3, 4]], dtype=np.int64)
y = np.array([[9, 8], [7, 6]], dtype=np.int64)
z = np.where(condition, x, y) # expected output [[1, 8], [3, 4]]
expect(
node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example"
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,977 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_space_to_depth.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class SpaceToDepth(OpRun):
def _run(self, data, blocksize=None): # type: ignore
if len(data.shape) != 4:
raise RuntimeError(f"Unexpected shape {data.shape!r}.")
b, C, H, W = data.shape
tmpshape = (
b,
C,
H // blocksize,
blocksize,
W // blocksize,
blocksize,
)
reshaped = np.reshape(data, tmpshape)
transposed = np.transpose(reshaped, [0, 3, 5, 1, 2, 4])
finalshape = (
b,
C * blocksize * blocksize,
H // blocksize,
W // blocksize,
)
y = np.reshape(transposed, finalshape).astype(data.dtype)
return (y,)
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58,978 | onnx/onnx | refs/heads/main | /onnx/tools/update_model_dims.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, List, Set
import onnx.checker
from onnx import ModelProto, ValueInfoProto
def update_inputs_outputs_dims(
model: ModelProto,
input_dims: Dict[str, List[Any]],
output_dims: Dict[str, List[Any]],
) -> ModelProto:
"""
This function updates the dimension sizes of the model's inputs and outputs to the values
provided in input_dims and output_dims. if the dim value provided is negative, a unique dim_param
will be set for that dimension.
Example. if we have the following shape for inputs and outputs:
* shape(input_1) = ('b', 3, 'w', 'h')
* shape(input_2) = ('b', 4)
* shape(output) = ('b', 'd', 5)
The parameters can be provided as:
::
input_dims = {
"input_1": ['b', 3, 'w', 'h'],
"input_2": ['b', 4],
}
output_dims = {
"output": ['b', -1, 5]
}
Putting it together:
::
model = onnx.load('model.onnx')
updated_model = update_inputs_outputs_dims(model, input_dims, output_dims)
onnx.save(updated_model, 'model.onnx')
"""
dim_param_set: Set[str] = set()
def init_dim_param_set(
dim_param_set: Set[str], value_infos: List[ValueInfoProto]
) -> None:
for info in value_infos:
shape = info.type.tensor_type.shape
for dim in shape.dim:
if dim.HasField("dim_param"):
dim_param_set.add(dim.dim_param) # type: ignore
init_dim_param_set(dim_param_set, model.graph.input) # type: ignore
init_dim_param_set(dim_param_set, model.graph.output) # type: ignore
init_dim_param_set(dim_param_set, model.graph.value_info) # type: ignore
def update_dim(tensor: ValueInfoProto, dim: Any, j: int, name: str) -> None:
dim_proto = tensor.type.tensor_type.shape.dim[j]
if isinstance(dim, int):
if dim >= 0:
if dim_proto.HasField("dim_value") and dim_proto.dim_value != dim:
raise ValueError(
f"Unable to set dimension value to {dim} for axis {j} of {name}. Contradicts existing dimension value {dim_proto.dim_value}."
)
dim_proto.dim_value = dim
else:
generated_dim_param = name + "_" + str(j)
if generated_dim_param in dim_param_set:
raise ValueError(
f"Unable to generate unique dim_param for axis {j} of {name}. Please manually provide a dim_param value."
)
dim_proto.dim_param = generated_dim_param
elif isinstance(dim, str):
dim_proto.dim_param = dim
else:
raise ValueError(
f"Only int or str is accepted as dimension value, incorrect type: {type(dim)}"
)
for input_ in model.graph.input:
input_name = input_.name
input_dim_arr = input_dims[input_name]
for j, dim in enumerate(input_dim_arr):
update_dim(input_, dim, j, input_name)
for output in model.graph.output:
output_name = output.name
output_dim_arr = output_dims[output_name]
for j, dim in enumerate(output_dim_arr):
update_dim(output, dim, j, output_name)
onnx.checker.check_model(model)
return model
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58,979 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_trilu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Trilu(OpRun):
def _run(self, x, k=None, upper=None): # type: ignore
k = 0 if k is None else int(k)
if upper: # type: ignore
return (np.triu(x, k),)
return (np.tril(x, k).astype(x.dtype),)
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["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,980 | onnx/onnx | refs/heads/main | /onnx/utils.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import os
import onnx.checker
import onnx.helper
import onnx.shape_inference
from onnx import FunctionProto, ModelProto, NodeProto, TensorProto, ValueInfoProto
class Extractor:
def __init__(self, model: ModelProto) -> None:
self.model = onnx.shape_inference.infer_shapes(model)
self.graph = self.model.graph
self.wmap = self._build_name2obj_dict(self.graph.initializer)
self.vimap = self._build_name2obj_dict(self.graph.value_info)
@staticmethod
def _build_name2obj_dict(objs): # type: ignore
return {obj.name: obj for obj in objs}
def _collect_new_io_core(self, original_io, io_names_to_extract): # type: ignore
original_io_map = self._build_name2obj_dict(original_io)
original_io_names = set(original_io_map)
s_io_names_to_extract = set(io_names_to_extract)
io_names_to_keep = s_io_names_to_extract & original_io_names
new_io_names_to_add = s_io_names_to_extract - original_io_names
new_io_tensors = []
for name in io_names_to_keep:
new_io_tensors.append(original_io_map[name])
for name in new_io_names_to_add:
# activation become input or output
new_io_tensors.append(self.vimap[name])
# adjust sequence
new_io_tensors_map = self._build_name2obj_dict(new_io_tensors)
return [new_io_tensors_map[name] for name in io_names_to_extract]
def _collect_new_inputs(self, names: list[str]) -> list[ValueInfoProto]:
return self._collect_new_io_core(self.graph.input, names) # type: ignore
def _collect_new_outputs(self, names: list[str]) -> list[ValueInfoProto]:
return self._collect_new_io_core(self.graph.output, names) # type: ignore
def _dfs_search_reachable_nodes(
self,
node_output_name: str,
graph_input_names: list[str],
reachable_nodes: list[NodeProto],
) -> None:
if node_output_name in graph_input_names:
return
for node in self.graph.node:
# check output_name first to reduce run time
if node_output_name not in node.output:
continue
if node in reachable_nodes:
continue
reachable_nodes.append(node)
for name in node.input:
self._dfs_search_reachable_nodes(
name, graph_input_names, reachable_nodes
)
def _collect_reachable_nodes(
self,
input_names: list[str],
output_names: list[str],
) -> list[NodeProto]:
reachable_nodes = [] # type: ignore[var-annotated]
for name in output_names:
self._dfs_search_reachable_nodes(name, input_names, reachable_nodes)
# needs to be topology sorted.
nodes = [n for n in self.graph.node if n in reachable_nodes]
return nodes
def _collect_referred_local_functions(
self,
nodes, # type: list[NodeProto]
): # type: (...) -> list[FunctionProto]
# a node in a model graph may refer a function.
# a function contains nodes, some of which may in turn refer a function.
# we need to find functions referred by graph nodes and
# by nodes used to define functions.
def find_referred_funcs(nodes, referred_local_functions): # type: ignore
new_nodes = [] # type: list[NodeProto]
for node in nodes:
# check if the node is a function op
match_function = next(
(
f
for f in self.model.functions
if f.name == node.op_type and f.domain == node.domain
),
None,
)
if match_function and match_function not in referred_local_functions:
referred_local_functions.append(match_function)
new_nodes.extend(match_function.node)
return new_nodes
referred_local_functions = [] # type: list[FunctionProto]
new_nodes = find_referred_funcs(nodes, referred_local_functions)
while new_nodes:
new_nodes = find_referred_funcs(new_nodes, referred_local_functions)
return referred_local_functions
def _collect_reachable_tensors(
self,
nodes: list[NodeProto],
) -> tuple[list[TensorProto], list[ValueInfoProto]]:
all_tensors_names: set[str] = set()
for node in nodes:
all_tensors_names.update(node.input)
all_tensors_names.update(node.output)
initializer = [self.wmap[t] for t in self.wmap if t in all_tensors_names]
value_info = [self.vimap[t] for t in self.vimap if t in all_tensors_names]
len_sparse_initializer = len(self.graph.sparse_initializer)
if len_sparse_initializer != 0:
raise ValueError(
f"len_sparse_initializer is {len_sparse_initializer}, it must be 0."
)
len_quantization_annotation = len(self.graph.quantization_annotation)
if len_quantization_annotation != 0:
raise ValueError(
f"len_quantization_annotation is {len_quantization_annotation}, it must be 0."
)
return initializer, value_info
def _make_model(
self,
nodes: list[NodeProto],
inputs: list[ValueInfoProto],
outputs: list[ValueInfoProto],
initializer: list[TensorProto],
value_info: list[ValueInfoProto],
local_functions: list[FunctionProto],
) -> ModelProto:
name = "Extracted from {" + self.graph.name + "}"
graph = onnx.helper.make_graph(
nodes, name, inputs, outputs, initializer=initializer, value_info=value_info
)
meta = {
"ir_version": self.model.ir_version,
"opset_imports": self.model.opset_import,
"producer_name": "onnx.utils.extract_model",
"functions": local_functions,
}
return onnx.helper.make_model(graph, **meta)
def extract_model(
self,
input_names: list[str],
output_names: list[str],
) -> ModelProto:
inputs = self._collect_new_inputs(input_names)
outputs = self._collect_new_outputs(output_names)
nodes = self._collect_reachable_nodes(input_names, output_names)
initializer, value_info = self._collect_reachable_tensors(nodes)
local_functions = self._collect_referred_local_functions(nodes)
model = self._make_model(
nodes, inputs, outputs, initializer, value_info, local_functions
)
return model
def extract_model(
input_path: str | os.PathLike,
output_path: str | os.PathLike,
input_names: list[str],
output_names: list[str],
check_model: bool = True,
) -> None:
"""Extracts sub-model from an ONNX model.
The sub-model is defined by the names of the input and output tensors *exactly*.
Note: For control-flow operators, e.g. If and Loop, the _boundary of sub-model_,
which is defined by the input and output tensors, should not _cut through_ the
subgraph that is connected to the _main graph_ as attributes of these operators.
Arguments:
input_path (str | os.PathLike): The path to original ONNX model.
output_path (str | os.PathLike): The path to save the extracted ONNX model.
input_names (list of string): The names of the input tensors that to be extracted.
output_names (list of string): The names of the output tensors that to be extracted.
check_model (bool): Whether to run model checker on the extracted model.
"""
if not os.path.exists(input_path):
raise ValueError(f"Invalid input model path: {input_path}")
if not output_path:
raise ValueError("Output model path shall not be empty!")
if not output_names:
raise ValueError("Output tensor names shall not be empty!")
onnx.checker.check_model(input_path)
model = onnx.load(input_path)
e = Extractor(model)
extracted = e.extract_model(input_names, output_names)
onnx.save(extracted, output_path)
if check_model:
onnx.checker.check_model(output_path)
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["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,981 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_resize.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import Any, Callable
import numpy as np
from onnx.reference.op_run import OpRun
def _cartesian(arrays: list[np.ndarray], out: np.ndarray | None = None) -> np.ndarray:
"""
From https://stackoverflow.com/a/1235363
Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray
Array to place the cartesian product in.
Returns
-------
out : ndarray
2-D array of shape (M, len(arrays)) containing cartesian products
formed of input arrays.
Examples
--------
>>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
"""
arrays = [np.asarray(x) for x in arrays]
dtype = arrays[0].dtype
n = np.prod([x.size for x in arrays])
if out is None:
out = np.zeros([n, len(arrays)], dtype=dtype)
m = n // arrays[0].size
out[:, 0] = np.repeat(arrays[0], m)
if arrays[1:]:
_cartesian(arrays[1:], out=out[0:m, 1:])
for j in range(1, arrays[0].size):
out[j * m : (j + 1) * m, 1:] = out[0:m, 1:]
return out
def _nearest_coeffs(
ratio: float | int | np.ndarray, mode: str = "round_prefer_floor"
) -> np.ndarray:
if isinstance(ratio, int) or ratio.is_integer():
return np.array([0, 1])
if mode == "round_prefer_floor":
return np.array([ratio <= 0.5, ratio > 0.5])
if mode == "round_prefer_ceil":
return np.array([ratio < 0.5, ratio >= 0.5])
if mode == "floor":
return np.array([1, 0])
if mode == "ceil":
return np.array([0, 1])
raise ValueError(f"Unexpected value {mode!r}.")
def _cubic_coeffs(
ratio: float, scale: float | None = None, A: float = -0.75
) -> np.ndarray:
del scale # Unused
coeffs = [
((A * (ratio + 1) - 5 * A) * (ratio + 1) + 8 * A) * (ratio + 1) - 4 * A,
((A + 2) * ratio - (A + 3)) * ratio * ratio + 1,
((A + 2) * (1 - ratio) - (A + 3)) * (1 - ratio) * (1 - ratio) + 1,
((A * ((1 - ratio) + 1) - 5 * A) * ((1 - ratio) + 1) + 8 * A)
* ((1 - ratio) + 1)
- 4 * A,
]
return np.array(coeffs)
def _cubic_coeffs_antialias(ratio: float, scale: float, A: float = -0.75) -> np.ndarray:
# Antialias is applied when downsampling
scale = min(scale, 1.0)
def compute_coeff(x: float) -> float:
x = abs(x)
x_2 = x * x
x_3 = x * x_2
if x <= 1:
return (A + 2) * x_3 - (A + 3) * x_2 + 1
if x < 2:
return A * x_3 - 5 * A * x_2 + 8 * A * x - 4 * A
return 0.0
i_start = int(np.floor(-2 / scale) + 1)
i_end = 2 - i_start
args = [scale * (i - ratio) for i in range(i_start, i_end)]
coeffs = [compute_coeff(x) for x in args]
return np.array(coeffs) / sum(coeffs)
def _linear_coeffs(ratio: float, scale: float | None = None) -> np.ndarray:
del scale # unused
return np.array([1 - ratio, ratio])
def _linear_coeffs_antialias(ratio: float, scale: float) -> np.ndarray:
# Antialias is applied when downsampling
scale = min(scale, 1.0)
start = int(np.floor(-1 / scale) + 1)
footprint = 2 - 2 * start
args = (np.arange(start, start + footprint) - ratio) * scale
coeffs = np.clip(1 - np.abs(args), 0, 1)
return np.array(coeffs) / sum(coeffs) # type: ignore[no-any-return]
def _get_neighbor_idxes(x: float, n: int, limit: int) -> np.ndarray:
"""
Return the n nearest indexes to x among `[0, limit)`,
prefer the indexes smaller than x.
As a result, the ratio must be in `(0, 1]`.
Examples::
get_neighbor_idxes(4, 2, 10) == [3, 4]
get_neighbor_idxes(4, 3, 10) == [3, 4, 5]
get_neighbor_idxes(4.4, 3, 10) == [3, 4, 5]
get_neighbor_idxes(4.5, 3, 10) == [3, 4, 5]
get_neighbor_idxes(4.6, 3, 10) == [4, 5, 6]
get_neighbor_idxes(4.4, 1, 10) == [4]
get_neighbor_idxes(4.6, 1, 10) == [5]
:param x:
:param n: the number of the wanted indexes
:param limit: the maximum value of index
:return: An np.array containing n nearest indexes in ascending order
"""
idxes = sorted(range(limit), key=lambda idx: (abs(x - idx), idx))[:n]
idxes = sorted(idxes)
return np.array(idxes)
def _get_neighbor(x: float, n: int, data: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
"""
Pad `data` in 'edge' mode, and get n nearest elements in the padded array
and their indexes in the original array.
:param x: center index (in the unpadded coordinate system) of the found nearest elements.
:param n: the number of neighbors.
:param data: the array
:return: A tuple containing the indexes of neighbor elements
(the index can be smaller than 0 or higher than len(data))
and the value of these elements
"""
pad_width = np.ceil(n / 2).astype(int)
padded = np.pad(data, pad_width, mode="edge")
x += pad_width
idxes = _get_neighbor_idxes(x, n, len(padded))
ret = padded[idxes]
return idxes - pad_width, ret
def _interpolate_1d_with_x( # pylint: disable=too-many-branches
data: np.ndarray,
scale_factor: float,
output_width_int: int,
x: float,
get_coeffs: Callable[[float, float], np.ndarray],
roi: np.ndarray | None = None,
extrapolation_value: float = 0.0,
coordinate_transformation_mode: str = "half_pixel",
exclude_outside: bool = False,
) -> np.ndarray:
input_width = len(data)
output_width = scale_factor * input_width
if coordinate_transformation_mode == "align_corners":
if output_width == 1:
x_ori = 0.0
else:
x_ori = x * (input_width - 1) / (output_width - 1)
elif coordinate_transformation_mode == "asymmetric":
x_ori = x / scale_factor
elif coordinate_transformation_mode == "tf_crop_and_resize":
if roi is None:
raise ValueError("roi cannot be None.")
if output_width == 1:
x_ori = (roi[1] - roi[0]) * (input_width - 1) / 2
else:
x_ori = x * (roi[1] - roi[0]) * (input_width - 1) / (output_width - 1)
x_ori += roi[0] * (input_width - 1)
# Return extrapolation_value directly as what TF CropAndResize does
if x_ori < 0 or x_ori > input_width - 1:
return np.array(extrapolation_value)
elif coordinate_transformation_mode == "pytorch_half_pixel":
if output_width == 1:
x_ori = -0.5
else:
x_ori = (x + 0.5) / scale_factor - 0.5
elif coordinate_transformation_mode == "half_pixel":
x_ori = (x + 0.5) / scale_factor - 0.5
elif coordinate_transformation_mode == "half_pixel_symmetric":
# Maps the center of the implicit ROI to the center of the output canvas.
# The difference with `half_pixel` will be only relevant
# when output_width_int != output_width
adjustment = output_width_int / output_width
center = input_width / 2
offset = center * (1 - adjustment)
x_ori = offset + (x + 0.5) / scale_factor - 0.5
else:
raise ValueError(
f"Invalid coordinate_transformation_mode: {coordinate_transformation_mode!r}."
)
x_ori_int = np.floor(x_ori).astype(int).item()
# ratio must be in (0, 1] since we prefer the pixel on the left of `x_ori`
if x_ori.is_integer():
ratio = 1
else:
ratio = x_ori - x_ori_int
coeffs = get_coeffs(ratio, scale_factor)
n = len(coeffs)
idxes, points = _get_neighbor(x_ori, n, data)
if exclude_outside:
for i, idx in enumerate(idxes):
if idx < 0 or idx >= input_width:
coeffs[i] = 0
coeffs /= sum(coeffs)
return np.dot(coeffs, points).item() # type: ignore[no-any-return]
def _interpolate_nd_with_x(
data: np.ndarray,
n: int,
scale_factors: list[float],
output_size: list[int],
x: list[float],
get_coeffs: Callable[[float, float], np.ndarray],
roi: np.ndarray | None = None,
exclude_outside: bool = False,
**kwargs: Any,
) -> np.ndarray:
if n == 1:
return _interpolate_1d_with_x(
data,
scale_factors[0],
output_size[0],
x[0],
get_coeffs,
roi=roi,
exclude_outside=exclude_outside,
**kwargs,
)
res1d = []
for i in range(data.shape[0]):
r = _interpolate_nd_with_x(
data[i],
n - 1,
scale_factors[1:],
output_size[1:],
x[1:],
get_coeffs,
roi=None if roi is None else np.concatenate([roi[1:n], roi[n + 1 :]]),
exclude_outside=exclude_outside,
**kwargs,
)
res1d.append(r)
return _interpolate_1d_with_x(
res1d, # type: ignore[arg-type] # FIXME
scale_factors[0],
output_size[0],
x[0],
get_coeffs,
roi=None if roi is None else [roi[0], roi[n]], # type: ignore[arg-type] # FIXME
exclude_outside=exclude_outside,
**kwargs,
)
def _get_all_coords(data: np.ndarray) -> np.ndarray:
# FIXME: Fix input type
return _cartesian(
[list(range(data.shape[i])) for i in range(len(data.shape))] # type: ignore[arg-type,misc]
)
def _interpolate_nd( # pylint: disable=too-many-branches
data: np.ndarray,
get_coeffs: Callable[[float, float], np.ndarray],
output_size: list[int] | None = None,
scale_factors: list[float] | None = None,
axes: list[int] | None = None,
roi: np.ndarray | None = None,
keep_aspect_ratio_policy: str | None = "stretch",
exclude_outside: bool = False,
**kwargs: Any,
) -> np.ndarray:
if output_size is None and scale_factors is None:
raise ValueError("output_size is None and scale_factors is None.")
r = len(data.shape)
if axes is not None:
if scale_factors is not None:
new_scale_factors = [1.0] * r
for i, d in enumerate(axes):
new_scale_factors[d] = scale_factors[i]
scale_factors = new_scale_factors
if output_size is not None:
new_output_size = [data.shape[i] for i in range(r)]
for i, d in enumerate(axes):
new_output_size[d] = output_size[i]
output_size = new_output_size
if roi is not None:
new_roi = ([0.0] * r) + ([1.0] * r)
naxes = len(axes)
for i, d in enumerate(axes):
new_roi[d] = roi[i]
new_roi[r + d] = roi[naxes + i]
roi = new_roi # type: ignore[assignment] # FIXME
else:
axes = list(range(r))
if output_size is not None:
scale_factors = [output_size[i] / data.shape[i] for i in range(r)]
if keep_aspect_ratio_policy != "stretch":
if keep_aspect_ratio_policy == "not_larger":
scale = np.array(scale_factors)[axes].min()
elif keep_aspect_ratio_policy == "not_smaller":
scale = np.array(scale_factors)[axes].max()
else:
raise ValueError(
f"Invalid keep_aspect_ratio_policy={keep_aspect_ratio_policy!r}"
)
scale_factors = [scale if i in axes else 1.0 for i in range(r)]
def round_half_up(x: float) -> int:
return int(x + 0.5)
output_size = [
round_half_up(scale * data.shape[i]) if i in axes else data.shape[i]
for i in range(r)
]
else:
output_size = (scale_factors * np.array(data.shape)).astype(int) # type: ignore[union-attr]
if scale_factors is None:
raise ValueError("scale_factors is None.")
if output_size is None:
raise ValueError("output_size is None.")
ret = np.zeros(output_size)
for x in _get_all_coords(ret):
ret[tuple(x)] = _interpolate_nd_with_x(
data,
len(data.shape),
scale_factors,
output_size,
x,
get_coeffs,
roi=roi,
exclude_outside=exclude_outside,
**kwargs,
)
return ret
class Resize(OpRun):
def _run( # type: ignore # pylint: disable=arguments-differ
self,
X,
roi,
scales=None,
sizes=None,
antialias=None,
axes=None,
coordinate_transformation_mode=None,
cubic_coeff_a=None,
exclude_outside=None,
extrapolation_value=None,
keep_aspect_ratio_policy=None,
mode: str | None = None,
nearest_mode=None,
):
if mode == "nearest":
if antialias:
raise RuntimeError(
f"antilias={antialias!r} is not supported for mode={mode!r}."
)
if nearest_mode is not None:
def fct(x, scale_factor):
del scale_factor # unused
return _nearest_coeffs(x, mode=nearest_mode)
else:
fct = _nearest_coeffs
elif mode == "cubic":
fct_ = _cubic_coeffs_antialias if antialias else _cubic_coeffs
def fct(x, scale):
return fct_(x, scale, A=cubic_coeff_a)
elif mode == "linear":
fct = _linear_coeffs_antialias if antialias else _linear_coeffs
else:
raise ValueError(f"Unexpected value {mode!r} for mode.")
if axes is None:
output = _interpolate_nd(
X,
fct,
scale_factors=scales,
output_size=sizes,
roi=roi,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
exclude_outside=exclude_outside,
coordinate_transformation_mode=coordinate_transformation_mode, # type: ignore
extrapolation_value=extrapolation_value, # type: ignore
).astype(X.dtype)
return (output,)
# axes is not None
not_axes = [a for a in range(len(X.shape)) if a not in axes]
perm = tuple(not_axes + axes)
permuted = np.transpose(X, perm)
new_shape = (-1, *tuple(X.shape[a] for a in axes))
reshaped = permuted.reshape(new_shape)
res = None
for i in range(reshaped.shape[0]):
output = _interpolate_nd(
reshaped[i],
fct,
scale_factors=scales,
output_size=sizes,
roi=roi,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
exclude_outside=exclude_outside,
coordinate_transformation_mode=coordinate_transformation_mode, # type: ignore
extrapolation_value=extrapolation_value, # type: ignore
).astype(X.dtype)
if res is None:
res = np.empty((reshaped.shape[0], *output.shape), dtype=output.dtype)
res[i] = output
res_reshaped = res.reshape(tuple(X.shape[a] for a in not_axes) + res[0].shape) # type: ignore
new_perm = list(perm)
for i, a in enumerate(perm):
new_perm[a] = i
final = np.transpose(res_reshaped, tuple(new_perm))
return (final,)
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58,982 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/hardsigmoid.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class HardSigmoid(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1) # expected output [0.1, 0.6, 1.]
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")
@staticmethod
def export_hardsigmoid_default() -> None:
default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
"HardSigmoid",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,983 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_max.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Max(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunBinaryNumpy.__init__(self, np.maximum, onnx_node, run_params)
def run(self, *data): # type: ignore
if len(data) == 2:
return OpRunBinaryNumpy.run(self, *data)
if len(data) == 1:
return (data[0].copy(),)
if len(data) > 2:
a = data[0]
for i in range(1, len(data)):
a = np.maximum(a, data[i])
return (a,)
raise RuntimeError("Unexpected turn of events.")
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,984 | onnx/onnx | refs/heads/main | /tools/gen_coverage_report.py | #!/usr/bin/env python
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import subprocess
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(os.path.basename(__file__))
parser.add_argument(
"-r",
"--root",
default=os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
help="onnx root directory (default: %(default)s)",
)
parser.add_argument("-o", "--out", required=True, help="output directory")
return parser.parse_args()
def gen_trace_file(root_dir: str, out_path: str) -> None:
subprocess.check_output(
[
"lcov",
"-c",
"-d",
root_dir,
"--no-external",
"--path",
root_dir,
"-o",
out_path,
]
)
subprocess.check_output(
[
"lcov",
"-r",
out_path,
os.path.join(root_dir, "third_party", "*"),
"-o",
out_path,
]
)
subprocess.check_output(
[
"lcov",
"-r",
out_path,
os.path.join(root_dir, ".setuptools-cmake-build", "*"),
"-o",
out_path,
]
)
def gen_html_files(root_dir: str, trace_path: str, out_dir: str) -> None:
subprocess.check_output(
[
"genhtml",
trace_path,
"-p",
root_dir,
"-o",
out_dir,
]
)
def main() -> None:
args = parse_args()
root = os.path.abspath(args.root)
out = os.path.abspath(args.out)
if not os.path.exists(out):
os.makedirs(out)
trace_path = os.path.join(out, "onnx-coverage.info")
gen_trace_file(root, trace_path)
html_dir = os.path.join(out, "html")
gen_html_files(root, trace_path, html_dir)
print(f"Static HTML files have been generated at:\n\t{html_dir}")
if __name__ == "__main__":
main()
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["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_non_zero.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/prelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_conv.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lppool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/hardswish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/thresholdedrelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_leaky_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_pool_common.py": 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58,985 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_array_feature_extractor.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
def _array_feature_extrator(data, indices): # type: ignore
"""
Implementation of operator *ArrayFeatureExtractor*.
"""
if len(indices.shape) == 2 and indices.shape[0] == 1:
index = indices.ravel().tolist()
add = len(index)
elif len(indices.shape) == 1:
index = indices.tolist()
add = len(index)
else:
add = 1
for s in indices.shape:
add *= s
index = indices.ravel().tolist()
if len(data.shape) == 1:
new_shape = (1, add)
else:
new_shape = [*data.shape[:-1], add]
try:
tem = data[..., index]
except IndexError as e:
raise RuntimeError(f"data.shape={data.shape}, indices={indices}") from e
res = tem.reshape(new_shape)
return res
class ArrayFeatureExtractor(OpRunAiOnnxMl):
def _run(self, data, indices): # type: ignore
"""
Runtime for operator *ArrayFeatureExtractor*.
.. warning::
ONNX specifications may be imprecise in some cases.
When the input data is a vector (one dimension),
the output has still two like a matrix with one row.
The implementation follows what onnxruntime does in
`array_feature_extractor.cc
<https://github.com/microsoft/onnxruntime/blob/main/
onnxruntime/core/providers/cpu/ml/array_feature_extractor.cc#L84>`_.
"""
res = _array_feature_extrator(data, indices)
return (res,)
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58,986 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/ai_onnx_ml/array_feature_extractor.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ArrayFeatureExtractor(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"ArrayFeatureExtractor",
inputs=["x", "y"],
outputs=["z"],
domain="ai.onnx.ml",
)
x = np.arange(12).reshape((3, 4)).astype(np.float32)
y = np.array([0, 1], dtype=np.int64)
z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T
expect(
node,
inputs=[x, y],
outputs=[z],
name="test_ai_onnx_ml_array_feature_extractor",
)
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58,987 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/regex_full_match.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class RegexFullMatch(Base):
@staticmethod
def export_basic() -> None:
node = onnx.helper.make_node(
"RegexFullMatch",
inputs=["X"],
outputs=["Y"],
pattern=r"www\.[\w.-]+\.\bcom\b",
)
x = np.array(["www.google.com", "www.facebook.com", "www.bbc.co.uk"]).astype(
object
)
result = np.array([True, True, False])
expect(node, inputs=[x], outputs=[result], name="test_regex_full_match_basic")
@staticmethod
def export_match_email_domain() -> None:
node = onnx.helper.make_node(
"RegexFullMatch",
inputs=["X"],
outputs=["Y"],
pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
)
x = np.array(
[
["account@gmail.com", "account@hotmail.com"],
["not email", "account2@yahoo.com"],
]
).astype(object)
result = np.array([[True, False], [False, True]])
expect(
node,
inputs=[x],
outputs=[result],
name="test_regex_full_match_email_domain",
)
@staticmethod
def export_match_empty() -> None:
node = onnx.helper.make_node(
"RegexFullMatch",
inputs=["X"],
outputs=["Y"],
pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
)
x = np.array([[], []]).astype(object)
result = np.array([[], []]).astype(bool)
expect(
node,
inputs=[x],
outputs=[result],
name="test_regex_full_match_empty",
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,988 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_erase.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class SequenceErase(OpRun):
def _run(self, S, ind=None): # type: ignore
if ind is None:
ind = -1
else:
ind = int(ind)
S2 = S.copy()
del S2[ind]
return (S2,)
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58,989 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_elu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Elu(OpRunUnaryNum):
def _run(self, x, alpha=None): # type: ignore
alpha = alpha or self.alpha # type: ignore
return (np.where(x > 0, x, alpha * (np.exp(x) - 1)).astype(x.dtype),)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,990 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op_common_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913,R0914
import itertools
from typing import Optional, Tuple
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_spatial_shape: Tuple[int],
kernel_spatial_shape: Tuple[int],
strides_spatial: Tuple[int],
output_spatial_shape: Tuple[int],
) -> Tuple[int]:
pad_shape = [0] * len(input_spatial_shape)
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
for i in range(len(input_spatial_shape)): # pylint: disable=C0200
pad_shape[i] = (
(output_spatial_shape[i] - 1) * strides_spatial[i]
+ kernel_spatial_shape[i]
- input_spatial_shape[i]
)
elif auto_pad == "VALID":
pass
if len(pad_shape) == 0:
raise RuntimeError(
f"Unable to compute pad shape, auto_pad={auto_pad!r}, "
f"input_spatial_shape={input_spatial_shape!r}, "
f"kernel_spatial_shape={kernel_spatial_shape!r}, "
f"strides_spatial={strides_spatial!r}."
)
return tuple(pad_shape) # type: ignore
def _get_output_shape_no_ceil(
auto_pad: str,
input_spatial_shape: Tuple[int],
kernel_spatial_shape: Tuple[int],
strides_spatial: Tuple[int],
) -> Tuple[int]:
out_shape = [0] * len(input_spatial_shape)
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
for i in range(len(input_spatial_shape)): # pylint: disable=C0200
out_shape[i] = int(
np.ceil(float(input_spatial_shape[i]) / float(strides_spatial[i]))
)
elif auto_pad == "VALID":
for i in range(len(input_spatial_shape)): # pylint: disable=C0200
out_shape[i] = int(
np.ceil(
float(input_spatial_shape[i] - (kernel_spatial_shape[i] - 1))
/ float(strides_spatial[i])
)
)
return tuple(out_shape) # type: ignore
def _get_output_shape(
auto_pad: str,
input_spatial_shape: Tuple[int],
kernel_spatial_shape: Tuple[int],
strides_spatial: Tuple[int],
pad_shape: Optional[Tuple[int]] = None,
ceil_mode: Optional[int] = 0,
) -> Tuple[int]:
if not ceil_mode:
out_shape = _get_output_shape_no_ceil(
auto_pad, input_spatial_shape, kernel_spatial_shape, strides_spatial
)
else:
round_fct = np.ceil if ceil_mode else np.floor
out_shape = [0] * len(input_spatial_shape) # type: ignore
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
for i in range(len(input_spatial_shape)): # pylint: disable=C0200
out_shape[i] = int( # type: ignore
round_fct(float(input_spatial_shape[i]) / float(strides_spatial[i])) # type: ignore
)
elif auto_pad == "VALID":
if pad_shape is None:
raise ValueError( # pragma: no cogitver
"pad_shape cannot be None if auto_pad is "
"'VALID' and ceil_mode is 1."
)
for i in range(len(input_spatial_shape)): # pylint: disable=C0200
out_shape[i] = int( # type: ignore
round_fct( # type: ignore
float(
input_spatial_shape[i]
+ pad_shape[i]
- kernel_spatial_shape[i]
)
/ float(strides_spatial[i])
+ 1
)
)
if len(out_shape) == 0:
raise RuntimeError(
f"Unable to compute output shape, auto_pad={auto_pad!r}, "
f"input_spatial_shape={input_spatial_shape!r}, "
f"kernel_spatial_shape={kernel_spatial_shape!r}, "
f"strides_spatial={strides_spatial!r}, ceil_mode={ceil_mode!r}."
)
if min(out_shape) <= 0:
raise RuntimeError(
f"output shape cannot be null or negative, out_shape={out_shape!r}, "
f"auto_pad={auto_pad!r}, input_spatial_shape={input_spatial_shape!r}, "
f"kernel_spatial_shape={kernel_spatial_shape!r}, "
f"strides_spatial={strides_spatial!r}, ceil_mode={ceil_mode!r}."
)
return tuple(out_shape) # type: ignore
def _pool(
padded: np.ndarray,
x_shape: Tuple[int],
kernel_shape: Tuple[int],
strides_shape: Tuple[int],
out_shape: Tuple[int],
pad_shape: Tuple[int],
pooling_type: str,
count_include_pad: Optional[int] = 0,
ceil_mode: Optional[int] = 0,
indices: bool = False,
pads: Optional[np.ndarray] = None,
) -> np.ndarray:
if pooling_type == "AVG":
fpool = np.average
elif pooling_type == "MAX":
fpool = np.max
else:
raise NotImplementedError(
f"Pooling type {pooling_type!r} does not support. Should be AVG, MAX."
)
spatial_size = len(x_shape) - 2
y = np.zeros([x_shape[0], x_shape[1], *list(out_shape)]) # type: ignore
if indices:
z = np.full(y.shape, fill_value=-1, dtype=np.int64)
round_fct = np.ceil if ceil_mode else np.floor
def loop_range(): # type: ignore
return [
range(
int(
round_fct( # type: ignore
float(x_shape[i + 2] + pad_shape[i] - kernel_shape[i])
/ float(strides_shape[i])
+ 1
)
)
)
for i in range(spatial_size)
]
for shape in itertools.product(range(x_shape[0]), range(x_shape[1]), *loop_range()): # type: ignore
window = padded[shape[0], shape[1]]
listi = [
range(
strides_shape[i] * shape[i + 2],
strides_shape[i] * shape[i + 2] + kernel_shape[i],
)
for i in range(spatial_size)
]
listi2 = list(itertools.product(*listi))
values = []
for i in listi2:
try:
values.append(window[i])
except IndexError:
continue
window_vals = np.array(values)
if count_include_pad == 1 and pooling_type == "AVG":
y[shape] = fpool(window_vals)
else:
no_nan = window_vals[np.where(~np.isnan(window_vals))]
y[shape] = fpool(no_nan)
if indices:
try:
window_vals_min = np.nan_to_num(window_vals, nan=no_nan.min())
except TypeError:
# argument nan was introduced in numpy 1.17
window_vals_min = window_vals.copy()
window_vals_min[np.isnan(window_vals_min)] = no_nan.min()
arg = np.argmax(window_vals_min)
coordinates = _get_indices(arg, out_shape)
delta = shape[2:] - pads[:, 0] # type: ignore
coordinates += delta
new_arg = _get_index(coordinates, x_shape[2:])
z[shape] = new_arg
if indices:
return y.astype(padded.dtype), z # type: ignore
return y.astype(padded.dtype) # type: ignore
class CommonPool(OpRun):
def _run( # type: ignore
self,
pooling_type,
count_include_pad,
x,
auto_pad=None,
ceil_mode=None,
dilations=None,
kernel_shape=None,
pads=None,
storage_order=None, # pylint: disable=W0613
strides=None,
):
if pooling_type == "MAX" and dilations is None:
dilations = [1 for s in kernel_shape]
if pads is None:
pads = [0 for s in kernel_shape] * 2
if strides is None or len(strides) == 0:
strides = [1] * (len(x.shape) - 2)
kernel_shape = list(kernel_shape)
auto_pad = "VALID" if auto_pad == "NOTSET" else auto_pad
if pads is None or len(pads) == 0:
pad_shape = [0] * (len(x.shape) - 2)
x_shape = x.shape[2:]
padded = x
elif len(pads) == 4:
pad_top, pad_bottom, pad_left, pad_right = pads
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
x_shape = np.array(x.shape[2:]) + np.array(pad_shape)
const = np.nan if count_include_pad == 0 else 0
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=const,
)
else:
pad_shape = pads
x_shape = x.shape[2:]
padded = x
if auto_pad in ("SAME_LOWER", "SAME_UPPER"):
const = np.nan if count_include_pad == 0 else 0
out_shape = _get_output_shape(
auto_pad, x_shape, kernel_shape, strides, pad_shape, ceil_mode # type: ignore
)
pad_shape = _get_pad_shape( # type: ignore
auto_pad, x_shape, kernel_shape, strides, out_shape
)
if auto_pad == "SAME_LOWER":
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
else:
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
padded,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=const,
)
else:
out_shape = _get_output_shape(
auto_pad, x_shape, kernel_shape, strides, pad_shape, ceil_mode # type: ignore
)
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
res = _pool(
padded,
x.shape,
kernel_shape,
strides,
out_shape,
pad_shape, # type: ignore
pooling_type,
count_include_pad=count_include_pad,
ceil_mode=ceil_mode,
indices=len(self.output) > 1, # type: ignore
pads=new_pads,
)
if isinstance(res, tuple):
return res
return (res,)
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58,991 | onnx/onnx | refs/heads/main | /onnx/backend/test/report/base.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
class ReporterBase:
pass
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58,992 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reduce_log_sum_exp.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
def compute_log_sum_exp(data, axes, keepdims):
data_max = data.copy()
ind = np.isinf(data_max)
data_max[ind] = -np.inf
mx = data_max.max(axis=axes, keepdims=True)
sub = np.subtract(data, mx)
exp = np.exp(sub, out=sub)
mxs = np.sum(exp, axis=axes, keepdims=True, dtype=data.dtype)
res = np.log(mxs) + mx
if not keepdims: # type: ignore
res = np.squeeze(res, axis=axes)
return (res,)
class ReduceLogSumExp_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None): # type: ignore
tax = tuple(axes) if axes is not None else None
return compute_log_sum_exp(data, tax, keepdims)
class ReduceLogSumExp_18(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0): # type: ignore
if self.is_axes_empty(axes) and noop_with_empty_axes: # type: ignore
return (data,)
axes = self.handle_axes(axes)
keepdims = keepdims != 0 # type: ignore
return compute_log_sum_exp(data, axes, keepdims)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,993 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/squeeze.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Squeeze(Base):
@staticmethod
def export_squeeze() -> None:
node = onnx.helper.make_node(
"Squeeze",
inputs=["x", "axes"],
outputs=["y"],
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
axes = np.array([0], dtype=np.int64)
y = np.squeeze(x, axis=0)
expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze")
@staticmethod
def export_squeeze_negative_axes() -> None:
node = onnx.helper.make_node(
"Squeeze",
inputs=["x", "axes"],
outputs=["y"],
)
x = np.random.randn(1, 3, 1, 5).astype(np.float32)
axes = np.array([-2], dtype=np.int64)
y = np.squeeze(x, axis=-2)
expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes")
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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,994 | onnx/onnx | refs/heads/main | /onnx/reference/ops/experimental/_op_run_experimental.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from onnx.reference.op_run import OpRun
class OpRunExperimental(OpRun):
op_domain = "experimental"
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,995 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/castlike.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import sys
import numpy as np
import onnx
from onnx import TensorProto, helper
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.helper import float32_to_float8e4m3, float32_to_float8e5m2, make_tensor
from onnx.numpy_helper import float8e4m3_to_float32, float8e5m2_to_float32
class CastLike(Base):
@staticmethod
def export() -> None:
shape = (3, 4)
test_cases = [
("FLOAT", "FLOAT16"),
("FLOAT", "DOUBLE"),
("FLOAT16", "FLOAT"),
("FLOAT16", "DOUBLE"),
("DOUBLE", "FLOAT"),
("DOUBLE", "FLOAT16"),
("FLOAT", "STRING"),
("STRING", "FLOAT"),
("FLOAT", "BFLOAT16"),
("BFLOAT16", "FLOAT"),
("FLOAT", "FLOAT8E4M3FN"),
("FLOAT", "FLOAT8E4M3FNUZ"),
("FLOAT8E4M3FN", "FLOAT"),
("FLOAT8E4M3FNUZ", "FLOAT"),
("FLOAT", "FLOAT8E5M2"),
("FLOAT", "FLOAT8E5M2FNUZ"),
("FLOAT8E5M2", "FLOAT"),
("FLOAT8E5M2FNUZ", "FLOAT"),
]
vect_float32_to_float8e4m3 = np.vectorize(float32_to_float8e4m3)
vect_float32_to_float8e5m2 = np.vectorize(float32_to_float8e5m2)
for from_type, to_type in test_cases:
input_type_proto = None
output_type_proto = None
if from_type == "BFLOAT16" or to_type == "BFLOAT16":
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
little_endisan = sys.byteorder == "little"
np_uint16_view = np_fp32.view(dtype=np.uint16)
np_bfp16 = (
np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
)
if to_type == "BFLOAT16":
assert from_type == "FLOAT"
input = np_fp32.reshape([3, 4])
output = np_bfp16.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), output.shape
)
else:
assert to_type == "FLOAT"
input = np_bfp16.reshape([3, 4])
# convert bfloat to FLOAT
np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
if little_endisan:
np_fp32_zeros[1::2] = np_bfp16
else:
np_fp32_zeros[0::2] = np_bfp16
np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
output = np_fp32_from_bfloat.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), output.shape
)
like = output.flatten()[0:1]
elif from_type in (
"FLOAT8E4M3FN",
"FLOAT8E4M3FNUZ",
"FLOAT8E5M2",
"FLOAT8E5M2FNUZ",
) or to_type in (
"FLOAT8E4M3FN",
"FLOAT8E4M3FNUZ",
"FLOAT8E5M2",
"FLOAT8E5M2FNUZ",
):
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
if to_type == "FLOAT8E4M3FN":
expected = float8e4m3_to_float32(
vect_float32_to_float8e4m3(np_fp32)
)
expected_tensor = make_tensor(
"x", TensorProto.FLOAT8E4M3FN, [3, 4], expected.tolist()
)
like_tensor = make_tensor(
"x", TensorProto.FLOAT8E4M3FN, [1], expected[:1]
)
elif to_type == "FLOAT8E4M3FNUZ":
expected = float8e4m3_to_float32(
vect_float32_to_float8e4m3(np_fp32, uz=True), uz=True
)
expected_tensor = make_tensor(
"x", TensorProto.FLOAT8E4M3FNUZ, [3, 4], expected.tolist()
)
like_tensor = make_tensor(
"x", TensorProto.FLOAT8E4M3FNUZ, [1], expected[:1]
)
elif to_type == "FLOAT8E5M2":
expected = float8e5m2_to_float32(
vect_float32_to_float8e5m2(np_fp32)
)
expected_tensor = make_tensor(
"x", TensorProto.FLOAT8E5M2, [3, 4], expected.tolist()
)
like_tensor = make_tensor(
"x", TensorProto.FLOAT8E5M2, [1], expected[:1]
)
elif to_type == "FLOAT8E5M2FNUZ":
expected = float8e5m2_to_float32(
vect_float32_to_float8e5m2(np_fp32, fn=True, uz=True),
fn=True,
uz=True,
)
expected_tensor = make_tensor(
"x", TensorProto.FLOAT8E5M2FNUZ, [3, 4], expected.tolist()
)
like_tensor = make_tensor(
"x", TensorProto.FLOAT8E5M2FNUZ, [1], expected[:1]
)
if from_type == "FLOAT":
input = np_fp32.reshape((3, 4))
output = expected_tensor
like = like_tensor
else:
assert to_type == "FLOAT"
input = expected_tensor
output = expected.reshape((3, 4))
like = output.flatten()[:1]
elif from_type != "STRING":
input = np.random.random_sample(shape).astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, from_type))
)
if to_type == "STRING":
# Converting input to str, then give it object dtype for generating script
ss = []
for i in input.flatten():
s = str(i).encode("utf-8")
su = s.decode("utf-8")
ss.append(su)
output = np.array(ss).astype(object).reshape([3, 4])
else:
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
like = output.flatten()[0:1]
else:
input = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.dtype(object),
).reshape([3, 4])
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
like = output.flatten()[0:1]
node = onnx.helper.make_node(
"CastLike",
inputs=["input", "like"],
outputs=["output"],
)
if input_type_proto and output_type_proto:
like_type_proto = onnx.helper.make_tensor_type_proto(
output_type_proto.tensor_type.elem_type, like.shape
)
expect(
node,
inputs=[input, like],
outputs=[output],
name="test_castlike_" + from_type + "_to_" + to_type,
input_type_protos=[input_type_proto, like_type_proto],
output_type_protos=[output_type_proto],
)
else:
expect(
node,
inputs=[input, like],
outputs=[output],
name="test_castlike_" + from_type + "_to_" + to_type,
)
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58,996 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/einsum.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Tuple
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def einsum_reference_implementation(
Eqn: str, Operands: Tuple[np.ndarray, ...]
) -> np.ndarray:
Z = np.einsum(Eqn, *Operands)
return Z
class Einsum(Base):
@staticmethod
def export_einsum_transpose() -> None:
Eqn = "ij->ji"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Y = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Y], name="test_einsum_transpose")
@staticmethod
def export_einsum_sum() -> None:
Eqn = "ij->i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 4)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_sum")
@staticmethod
def export_einsum_batch_diagonal() -> None:
Eqn = "...ii ->...i"
node = onnx.helper.make_node(
"Einsum", inputs=["x"], outputs=["y"], equation=Eqn
)
X = np.random.randn(3, 5, 5)
Z = einsum_reference_implementation(Eqn, (X,))
expect(node, inputs=[X], outputs=[Z], name="test_einsum_batch_diagonal")
@staticmethod
def export_einsum_inner_prod() -> None:
Eqn = "i,i"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5)
Y = np.random.randn(5)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_inner_prod")
@staticmethod
def export_einsum_batch_matmul() -> None:
Eqn = "bij, bjk -> bik"
node = onnx.helper.make_node(
"Einsum", inputs=["x", "y"], outputs=["z"], equation=Eqn
)
X = np.random.randn(5, 2, 3)
Y = np.random.randn(5, 3, 4)
Z = einsum_reference_implementation(Eqn, (X, Y))
expect(node, inputs=[X, Y], outputs=[Z], name="test_einsum_batch_matmul")
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58,997 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_col2im.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
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, pads, strides): # type: ignore
output_height, output_width = output_shape
kernel_height, kernel_width = kernel_shape
dilation_height, dilation_width = dilations
stride_height, stride_width = strides
ndim = len(X.shape)
if not (
(ndim == 2 and X.shape[0] != 0 and X.shape[1] != 0)
or (ndim == 3 and X.shape[1] != 0 and X.shape[2] != 0)
):
raise ValueError(
"Expected 2D or 3D (batch mode) tensor for input with possibly 0 batch size and non-zero dimensions for input."
)
batch_dim = 0 if len(X.shape) == 3 else -1
n_input_plane = X.shape[batch_dim + 1]
if n_input_plane % (kernel_width * kernel_height) != 0:
raise ValueError(
f"Expected size of input's dimension 1 to be divisible by the "
f"product of kernel_size, but got input.size(1)={n_input_plane} "
f"and kernel_size={kernel_shape}."
)
input_length = X.shape[batch_dim + 2]
n_blocks_height = (
output_height + pads[0, :].sum() - dilation_height * (kernel_height - 1) - 1
) // stride_height + 1
n_blocks_width = (
output_width + pads[1, :].sum() - dilation_width * (kernel_width - 1) - 1
) // stride_width + 1
if input_length != (n_blocks_height * n_blocks_width):
raise ValueError(
f"Given batch_dim={batch_dim}, n_input_plane={n_input_plane}, X.shape={X.shape}, "
f"output_shape={output_shape}, kernel_shape={kernel_shape}, "
f"dilations={dilations}, pads={pads}, strides={strides}, "
f"expected size of input's dimension 2 to match the calculated number of ",
f"sliding blocks {n_blocks_height} * {n_blocks_width} = {n_blocks_height * n_blocks_width}, "
f"but got input.size(2)={input_length}.",
)
if not (n_blocks_height >= 1 and n_blocks_width >= 1):
raise ValueError(
f"Given batch_dim={batch_dim}, n_input_plane={n_input_plane}, X.shape={X.shape}, "
f"output_shape={output_shape}, kernel_shape={kernel_shape}, "
f"dilations={dilations}, pads={pads}, strides={strides}, "
f"calculated shape of the array of sliding blocks as ({n_blocks_height}, {n_blocks_width}), "
f"which is too small (non-positive)."
)
def _col2im_naive_implementation_2d(res, image_shape, kernel_shape, dilations, pads, strides): # type: ignore
# source: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/im2col.h
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
_col2im_shape_check_2d(res, image_shape, kernel_shape, dilations, new_pads, strides)
data_col = res.ravel()
data_im = np.zeros(image_shape, dtype=res.dtype).flatten()
kernel_h, kernel_w = kernel_shape
channels_col = kernel_h * kernel_w
stride_h, stride_w = strides
dilation_h, dilation_w = dilations
pad_h, pad_w = new_pads[:, 0]
height, width = image_shape
output_height, output_width = image_shape
height_col = (
output_height + new_pads[0, :].sum() - (dilation_h * (kernel_h - 1) + 1)
) // stride_h + 1
width_col = (
output_width + new_pads[1, :].sum() - (dilation_w * (kernel_w - 1) + 1)
) // stride_w + 1
for c_col in range(channels_col):
w_offset = c_col % kernel_w
h_offset = (c_col // kernel_w) % kernel_h
c_im = c_col // (kernel_h * kernel_w)
for h_col in range(height_col):
h_im = h_col * stride_h - pad_h + h_offset * dilation_h
for w_col in range(width_col):
w_im = w_col * stride_w - pad_w + w_offset * dilation_w
if 0 <= h_im < height and 0 <= w_im < width:
i_im = (c_im * height + h_im) * width + w_im
i_col = (c_col * height_col + h_col) * width_col + w_col
if 0 <= i_col < data_col.shape[0]:
data_im[i_im] += data_col[i_col]
return data_im.reshape(image_shape)
def _col2im_shape_check(X, output_shape, kernel_shape, dilations, pads, strides): # type: ignore
n_input_plane = X.shape[0]
kernel_size = np.prod(kernel_shape)
if n_input_plane % kernel_size != 0:
raise ValueError(
f"Expected size of input's dimension 1 to be divisible by the "
f"product of kernel_size={kernel_size}, "
f"but got input.size(1)={n_input_plane} "
f"and kernel_shape={kernel_shape}, X.shape={X.shape}, output_shape={output_shape}."
)
input_length = X.shape[1]
n_dims = len(output_shape)
n_blocks = []
for i in range(n_dims):
n_block = (
output_shape[i]
+ pads[i, :].sum()
- dilations[i] * (kernel_shape[i] - 1)
- 1
) // strides[i] + 1
n_blocks.append(n_block)
block_size = np.prod(n_blocks)
if input_length != block_size:
raise ValueError(
f"Given n_input_plane={n_input_plane}, X.shape={X.shape}, "
f"output_shape={output_shape}, kernel_shape={kernel_shape}, "
f"dilations={dilations}, pads={pads}, strides={strides}, "
f"expected size of input's dimension 2 to match the calculated number of "
f"sliding blocks {n_blocks} = {block_size}, "
f"but got input.size(2)={input_length}.",
)
def col2im_naive_implementation(data, image_shape, kernel_shape, dilations, pads, strides): # type: ignore
"""
Naive implementation for `col2im`.
"""
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
_col2im_shape_check(data, image_shape, kernel_shape, dilations, new_pads, strides)
data_col = data
data_im = np.zeros(image_shape, dtype=data.dtype)
dim_col = []
for i in range(n_dims):
col = (
image_shape[i]
+ new_pads[i, :].sum()
- (dilations[i] * (kernel_shape[i] - 1) + 1)
) // strides[i] + 1
dim_col.append(col)
kernel_size = np.prod(kernel_shape)
col_size = np.prod(dim_col)
for c_col in range(kernel_size):
offset = _get_indices(c_col, kernel_shape)
for col in range(col_size):
ind_col = _get_indices(col, dim_col)
ind_im = []
for i in range(n_dims):
ind = (
ind_col[i] * strides[i] - new_pads[i, 0] + offset[i] * dilations[i]
)
ind_im.append(ind)
if not _is_out(ind_im, data_im.shape):
data_im[tuple(ind_im)] += data_col[c_col, col]
return data_im
class Col2Im(OpRun):
def _run(self, data, image_shape, block_shape, dilations=None, pads=None, strides=None): # type: ignore
if dilations is None:
dilations = [1 for s in image_shape]
if pads is None:
pads = [0 for s in image_shape] * 2
if strides is None:
strides = [1 for s in image_shape]
bl = np.prod(block_shape)
C = data.shape[1] // bl
data = data.reshape(data.shape[:1] + (C,) + (bl,) + data.shape[2:])
ks = tuple(block_shape)
res = None
for n in range(data.shape[0]):
for c in range(data.shape[1]):
out = col2im_naive_implementation(
data[n, c, ...], image_shape, ks, dilations, pads, strides
)
if res is None:
new_shape = data.shape[:2] + out.shape
res = np.empty(new_shape, dtype=data.dtype)
res[n, c, ...] = out
return (res,) # type: ignore
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"/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,998 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_max_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0200,R0912,R0913,R0914,R0915,R0916,R1702,W0221
import numpy as np
from onnx.reference.ops._op_common_pool import CommonPool
class MaxPool(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
dilations=None,
kernel_shape=None,
pads=None,
storage_order=None,
strides=None,
):
if (
dilations is not None
and (min(dilations) != max(dilations) or min(dilations) != 1)
) or (
strides is not None and (min(strides) != max(strides) or min(strides) != 1)
):
return self._max_pool(
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=dilations,
kernel_shape=kernel_shape,
pads=pads,
storage_order=storage_order,
strides=strides,
)
return CommonPool._run(
self,
"MAX",
0,
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=dilations,
kernel_shape=kernel_shape,
pads=pads,
storage_order=storage_order,
strides=strides,
)
def _max_pool( # type: ignore
self,
x,
auto_pad,
ceil_mode,
dilations,
kernel_shape,
pads,
storage_order,
strides,
):
if pads is None:
pads = [0 for i in range(len(kernel_shape) * 2)]
if strides is None:
strides = [1 for i in range(len(kernel_shape))]
if dilations is None:
dilations = [1 for i in range(len(kernel_shape))]
n_dims = len(kernel_shape)
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
input_spatial_shape = x.shape[2:]
output_spatial_shape = [0 for s in input_spatial_shape]
if ceil_mode:
for i in range(len(input_spatial_shape)):
output_spatial_shape[i] = int(
np.ceil(
(
input_spatial_shape[i]
+ new_pads[i].sum()
- ((kernel_shape[i] - 1) * dilations[i] + 1)
)
/ strides[i]
+ 1
)
)
else:
for i in range(len(input_spatial_shape)):
output_spatial_shape[i] = int(
np.floor(
(
input_spatial_shape[i]
+ new_pads[i].sum()
- ((kernel_shape[i] - 1) * dilations[i] + 1)
)
/ strides[i]
+ 1
)
)
if auto_pad and auto_pad != "NOTSET":
# Deprecated attribute
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
for i in range(len(input_spatial_shape)):
if auto_pad == "SAME_UPPER":
output_spatial_shape[i] = int(
np.ceil(input_spatial_shape[i] / strides[i])
)
else:
output_spatial_shape[i] = int(
np.floor(input_spatial_shape[i] / strides[i])
)
pad_i = (
(output_spatial_shape[i] - 1) * strides[i]
+ ((kernel_shape[i] - 1) * dilations[i] + 1)
- input_spatial_shape[i]
)
new_pads[i, 0] = pad_i // 2
new_pads[i, 1] = pad_i - new_pads[i, 0]
else:
for i in range(len(input_spatial_shape)):
output_spatial_shape[i] = int(
np.ceil(
(
input_spatial_shape[i]
- ((kernel_shape[i] - 1) * dilations[i] + 1)
+ 1
)
/ strides[i]
)
)
if len(input_spatial_shape) == 1:
return self._max_pool_1d(
x,
auto_pad,
ceil_mode,
dilations,
kernel_shape,
new_pads,
storage_order,
strides,
output_spatial_shape,
)
if len(input_spatial_shape) == 2:
return self._max_pool_2d(
x,
auto_pad,
ceil_mode,
dilations,
kernel_shape,
new_pads,
storage_order,
strides,
output_spatial_shape,
)
if len(input_spatial_shape) == 3:
return self._max_pool_3d(
x,
auto_pad,
ceil_mode,
dilations,
kernel_shape,
new_pads,
storage_order,
strides,
output_spatial_shape,
)
raise RuntimeError(f"Not implemented yet for shape {x.shape}.")
def _max_pool_1d( # type: ignore
self,
x,
auto_pad, # pylint: disable=W0613
ceil_mode, # pylint: disable=W0613
dilations,
kernel_shape,
new_pads,
storage_order, # pylint: disable=W0613
strides,
output_spatial_shape,
):
global_pooling = False
y_dims = x.shape[:2] + tuple(output_spatial_shape)
y = np.zeros(y_dims, dtype=x.dtype)
indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
x_dims = x.shape
channels = x_dims[1]
height = x_dims[2]
pooled_height = y_dims[2]
total_channels = x_dims[0] * channels
stride_h = 1 if global_pooling else strides[0]
x_step = height
y_step = pooled_height
dilation_h = dilations[0]
X_data = x.ravel()
Y_data = y.ravel()
I_data = indices.ravel()
def iteration(c):
x_d = c * x_step
y_d = c * y_step
i_d = c * y_step
for ph in range(pooled_height):
hstart = ph * stride_h - new_pads[0, 0]
hend = hstart + kernel_shape[0] * dilation_h
Yh = None
h_index = -1
for h in range(hstart, hend, dilation_h):
if h < 0 or h >= height:
continue
if Yh is None or X_data[x_d + h] > Yh:
Yh = X_data[x_d + h]
h_index = h
Y_data[y_d + ph] = Yh
I_data[i_d + ph] = c * x_step + h_index
for c in range(total_channels):
iteration(c)
if len(self.output) == 1: # type: ignore
return (Y_data.reshape(y_dims),)
return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
def _max_pool_2d( # type: ignore
self,
x,
auto_pad, # pylint: disable=W0613
ceil_mode, # pylint: disable=W0613
dilations,
kernel_shape,
new_pads,
storage_order,
strides,
output_spatial_shape,
):
global_pooling = False
y_dims = x.shape[:2] + tuple(output_spatial_shape)
y = np.zeros(y_dims, dtype=x.dtype)
indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
x_dims = x.shape
channels = x_dims[1]
height = x_dims[2]
width = x_dims[3] if len(kernel_shape) > 1 else 1
pooled_height = y_dims[2]
pooled_width = y_dims[3] if len(kernel_shape) > 1 else 1
total_channels = x_dims[0] * channels
stride_h = 1 if global_pooling else strides[0]
stride_w = 1 if global_pooling else strides[1]
x_step = height * width
y_step = pooled_height * pooled_width
dilation_h = dilations[0]
dilation_w = dilations[1]
X_data = x.ravel()
Y_data = y.ravel()
I_data = indices.ravel()
def iteration(c): # type: ignore
x_d = c * x_step # X_data
y_d = c * y_step # Y_data
for ph in range(pooled_height):
hstart = ph * stride_h - new_pads[0, 0]
hend = hstart + kernel_shape[0] * dilation_h
for pw in range(pooled_width):
wstart = pw * stride_w - new_pads[1, 0]
wend = wstart + kernel_shape[1] * dilation_w
pool_index = ph * pooled_width + pw
Yh = None
h_index = -1
w_index = -1
for h in range(hstart, hend, dilation_h):
if h < 0 or h >= height:
continue
for w in range(wstart, wend, dilation_w):
if w < 0 or w >= width:
continue
input_index = h * width + w
if input_index < 0 or input_index > X_data.shape[0]:
continue
if Yh is None or X_data[x_d + input_index] > Yh:
Yh = X_data[x_d + input_index]
h_index = h
w_index = w
if Yh is None:
continue
Y_data[y_d + pool_index] = Yh
I_data[y_d + pool_index] = (
c * x_step + h_index * width + w_index
if storage_order == 0
else c * x_step + h_index + w_index * height
)
for c in range(total_channels):
iteration(c)
if len(self.output) == 1: # type: ignore
return (Y_data.reshape(y_dims),)
return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
def _max_pool_3d( # type: ignore
self,
x,
auto_pad, # pylint: disable=W0613
ceil_mode, # pylint: disable=W0613
dilations,
kernel_shape,
new_pads,
storage_order,
strides,
output_spatial_shape,
):
global_pooling = False
y_dims = x.shape[:2] + tuple(output_spatial_shape)
y = np.zeros(y_dims, dtype=x.dtype)
indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
x_dims = x.shape
channels = x_dims[1]
height = x_dims[2]
width = x_dims[3] if len(kernel_shape) > 1 else 1
depth = x_dims[4] if len(kernel_shape) > 2 else 1
pooled_height = y_dims[2]
pooled_width = y_dims[3] if len(kernel_shape) > 1 else 1
pooled_depth = y_dims[4] if len(kernel_shape) > 2 else 1
total_channels = x_dims[0] * channels
stride_h = 1 if global_pooling else strides[0]
stride_w = 1 if global_pooling else strides[1]
stride_d = 1 if global_pooling else strides[2]
x_step = height * width * depth
y_step = pooled_height * pooled_width * pooled_depth
dilation_h = dilations[0]
dilation_w = dilations[1]
dilation_d = dilations[2]
X_data = x.ravel()
Y_data = y.ravel()
I_data = indices.ravel()
def iteration(c):
x_d = c * x_step
y_d = c * y_step
i_d = c * y_step
for ph in range(pooled_height):
hstart = ph * stride_h - new_pads[0, 0]
hend = hstart + kernel_shape[0] * dilation_h
for pw in range(pooled_width):
wstart = pw * stride_w - new_pads[1, 0]
wend = wstart + kernel_shape[1] * dilation_w
for pd in range(pooled_depth):
dstart = pd * stride_d - new_pads[2, 0]
dend = dstart + kernel_shape[2] * dilation_d
pool_index = (
ph * pooled_width * pooled_depth + pw * pooled_depth + pd
)
Yh = None
h_index = -1
w_index = -1
d_index = -1
for h in range(hstart, hend, dilation_h):
if h < 0 or h >= height:
continue
for w in range(wstart, wend, dilation_w):
if w < 0 or w >= width:
continue
for d in range(dstart, dend, dilation_d):
if d < 0 or d >= depth:
continue
input_index = h * width * depth + w * depth + d
if Yh is None or X_data[x_d + input_index] > Yh:
Yh = X_data[x_d + input_index]
h_index = h
w_index = w
d_index = d
Y_data[y_d + pool_index] = Yh
I_data[i_d + pool_index] = (
(
c * x_step
+ h_index * width * depth
+ w_index * depth
+ d_index
)
if storage_order == 0
else (
c * x_step
+ h_index
+ w_index * height
+ d_index * height * width
)
)
for c in range(total_channels):
iteration(c)
if len(self.output) == 1: # type: ignore
return (Y_data.reshape(y_dims),)
return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
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"/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,999 | onnx/onnx | refs/heads/main | /onnx/test/automatic_upgrade_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import string
import unittest
from typing import Any, Dict, List, Optional, Sequence, Union, cast
import numpy as np
import onnx
from onnx import TensorProto, ValueInfoProto, helper, shape_inference, version_converter
#####################################################################################
# Every test creates a model containing a single operator from the lowest possible
# opset version, upgrades it to the most recent opset version and then runs checker +
# shape inference on the upgraded model.
####################################################################################
LATEST_OPSET = onnx.defs.onnx_opset_version()
tested_ops = []
class TestAutomaticUpgrade(unittest.TestCase):
def _test_op_upgrade(
self,
op: str,
from_opset: int,
input_shapes: Sequence[Union[Sequence[Optional[int]], str]] = ((3, 4, 5),),
output_shapes: Sequence[Sequence[Optional[int]]] = ((3, 4, 5),),
input_types: Optional[Sequence[Any]] = None,
output_types: Optional[Sequence[Any]] = None,
initializer: Sequence[Any] = (),
attrs: Optional[Dict[str, Any]] = None,
seq_inputs: Sequence[int] = (),
seq_outputs: Sequence[int] = (),
optional_inputs: Sequence[int] = (),
optional_outputs: Sequence[int] = (),
) -> None:
if attrs is None:
attrs = {}
tested_ops.append(op)
n_inputs = len(input_shapes)
letters = list(string.ascii_lowercase)[:n_inputs]
input_names = [
letter if shape != "" else ""
for (letter, shape) in zip(letters, input_shapes)
]
if input_types is None:
input_types = [TensorProto.FLOAT] * n_inputs
is_sequence = [0 if id not in seq_inputs else 1 for id in range(n_inputs)]
is_optional = [0 if id not in optional_inputs else 1 for id in range(n_inputs)]
# turn empty strings into [0] to ease type analysis, even though those entries
# will be ignored
input_shapes_cast = cast(
List[List[int]],
[[0] if isinstance(shape, str) else shape for shape in input_shapes],
)
inputs: List[ValueInfoProto] = []
for name, ttype, shape, is_seq, is_opt in zip(
input_names, input_types, input_shapes_cast, is_sequence, is_optional
):
if name != "":
if is_seq:
inputs += [
helper.make_tensor_sequence_value_info(name, ttype, shape)
]
elif is_opt:
type_proto = helper.make_tensor_type_proto(ttype, shape)
optional_type_proto = helper.make_optional_type_proto(type_proto)
inputs += [helper.make_value_info(name, optional_type_proto)]
else:
inputs += [helper.make_tensor_value_info(name, ttype, shape)]
n_outputs = len(output_shapes)
output_names = list(string.ascii_lowercase)[n_inputs : n_inputs + n_outputs]
if output_types is None:
output_types = [TensorProto.FLOAT] * n_outputs
is_sequence = [0 if id not in seq_outputs else 1 for id in range(n_outputs)]
is_optional = [
0 if id not in optional_outputs else 1 for id in range(n_outputs)
]
output_shapes_cast = cast(
List[List[int]],
[[0] if isinstance(shape, str) else shape for shape in output_shapes],
)
outputs: List[ValueInfoProto] = []
for name, ttype, shape, is_seq, is_opt in zip(
output_names, output_types, output_shapes_cast, is_sequence, is_optional
):
if is_seq:
outputs += [helper.make_tensor_sequence_value_info(name, ttype, shape)]
elif is_opt:
type_proto = helper.make_tensor_type_proto(ttype, shape)
optional_type_proto = helper.make_optional_type_proto(type_proto)
outputs += [helper.make_value_info(name, optional_type_proto)]
else:
outputs += [helper.make_tensor_value_info(name, ttype, shape)]
node = helper.make_node(op, input_names, output_names, **attrs)
graph = helper.make_graph([node], op, inputs, outputs, initializer)
original = helper.make_model(
graph,
producer_name="test",
opset_imports=[helper.make_opsetid("", from_opset)],
)
onnx.checker.check_model(original)
shape_inference.infer_shapes(original, strict_mode=True)
converted = version_converter.convert_version(original, LATEST_OPSET)
onnx.checker.check_model(converted)
shape_inference.infer_shapes(converted, strict_mode=True)
def test_Abs(self) -> None:
self._test_op_upgrade("Abs", 1, attrs={"consumed_inputs": [0]})
def test_Acosh(self) -> None:
self._test_op_upgrade("Acosh", 9)
def test_Acos(self) -> None:
self._test_op_upgrade("Acos", 7)
def test_And(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"And",
7,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.BOOL, TensorProto.BOOL],
[TensorProto.BOOL],
)
def test_Asinh(self) -> None:
self._test_op_upgrade("Asinh", 9)
def test_Atanh(self) -> None:
self._test_op_upgrade("Atanh", 9)
def test_Add_1(self) -> None:
self._test_op_upgrade(
"Add", 1, [[3, 4, 5], [3, 4, 5]], attrs={"consumed_inputs": [0]}
)
def test_Add_2(self) -> None:
self._test_op_upgrade(
"Add", 1, [[3, 4, 5], [5]], attrs={"consumed_inputs": [0], "broadcast": 1}
)
def test_Add_3(self) -> None:
self._test_op_upgrade(
"Add",
1,
[[3, 4, 5], [3]],
attrs={"consumed_inputs": [0], "broadcast": 1, "axis": 0},
)
def test_AffineGrid_2D(self) -> None:
N, _, H, W = 2, 3, 5, 6
self._test_op_upgrade("AffineGrid", 20, [[N, 2, 3], [4]], [[N, H, W, 2]])
def test_AffineGrid_3D(self) -> None:
N, _, D, H, W = 2, 3, 4, 5, 6
self._test_op_upgrade("AffineGrid", 20, [[N, 3, 4], [5]], [[N, D, H, W, 3]])
def test_ArgMax_1(self) -> None:
self._test_op_upgrade(
"ArgMax", 7, [[2, 3, 4]], [[1, 3, 4]], output_types=[TensorProto.INT64]
)
def test_ArgMax_2(self) -> None:
self._test_op_upgrade(
"ArgMax",
7,
[[2, 3, 4]],
[[2, 1, 4]],
output_types=[TensorProto.INT64],
attrs={"axis": 1},
)
def test_ArgMin_1(self) -> None:
self._test_op_upgrade(
"ArgMin", 7, [[2, 3, 4]], [[1, 3, 4]], output_types=[TensorProto.INT64]
)
def test_ArgMin_2(self) -> None:
self._test_op_upgrade(
"ArgMin",
7,
[[2, 3, 4]],
[[2, 1, 4]],
output_types=[TensorProto.INT64],
attrs={"axis": 1},
)
def test_Asin(self) -> None:
self._test_op_upgrade("Asin", 7)
def test_Atan(self) -> None:
self._test_op_upgrade("Atan", 7)
def test_AveragePool(self) -> None:
self._test_op_upgrade(
"AveragePool",
1,
[[1, 1, 5, 5]],
[[1, 1, 4, 4]],
attrs={"kernel_shape": [2, 2]},
)
def test_Bernoulli(self) -> None:
self._test_op_upgrade("Bernoulli", 15)
def test_BitShift(self) -> None:
self._test_op_upgrade(
"BitShift",
11,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.UINT8, TensorProto.UINT8],
[TensorProto.UINT8],
attrs={"direction": "RIGHT"},
)
def test_BatchNormalization_1(self) -> None:
self._test_op_upgrade(
"BatchNormalization",
1,
[[1, 3], [3], [3], [3], [3]],
[[1, 3]],
attrs={"consumed_inputs": [1, 1], "is_test": 1, "spatial": 1},
)
def test_BatchNormalization_2(self) -> None:
self._test_op_upgrade(
"BatchNormalization",
14,
[[1, 3], [3], [3], [3], [3]],
[[1, 3], [3], [3]],
attrs={"training_mode": 1},
)
def test_Cast(self) -> None:
# 5->6 adapter is missing
self._test_op_upgrade(
"Cast", 6, [[2, 3]], [[2, 3]], [TensorProto.INT64], attrs={"to": 1}
)
def test_Ceil(self) -> None:
self._test_op_upgrade("Ceil", 1, attrs={"consumed_inputs": [0]})
def test_Celu(self) -> None:
self._test_op_upgrade("Celu", 12)
def test_Clip_1(self) -> None:
self._test_op_upgrade("Clip", 1, attrs={"consumed_inputs": [0]})
def test_Clip_2(self) -> None:
self._test_op_upgrade("Clip", 1, attrs={"consumed_inputs": [0], "min": -1.4})
def test_Clip_3(self) -> None:
self._test_op_upgrade("Clip", 1, attrs={"consumed_inputs": [0], "max": 2.6})
def test_Clip_4(self) -> None:
self._test_op_upgrade(
"Clip", 1, attrs={"consumed_inputs": [0], "min": -1.4, "max": 2.6}
)
def test_Col2Im_4D(self) -> None:
self._test_op_upgrade("Col2Im", 18, [[1, 5, 5], [2], [2]], [[1, 1, 5, 5]])
def test_Col2Im_5D(self) -> None:
self._test_op_upgrade("Col2Im", 18, [[1, 10, 12], [3], [3]], [[1, 2, 3, 4, 5]])
def test_Compress(self) -> None:
self._test_op_upgrade(
"Compress",
9,
[[6, 7], [3]],
[[3]],
[TensorProto.FLOAT, TensorProto.BOOL],
[TensorProto.FLOAT],
)
def test_Concat(self) -> None:
self._test_op_upgrade("Concat", 1, [[2, 3], [2, 4]], [[2, 7]])
def test_constant(self) -> None:
value = helper.make_tensor(
"Value",
TensorProto.FLOAT,
dims=[3, 4, 5],
vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
raw=True,
)
self._test_op_upgrade("Constant", 1, [], attrs={"value": value})
def test_ConstantOfShape(self) -> None:
self._test_op_upgrade("ConstantOfShape", 9, [[3]])
def test_Conv_1(self) -> None:
self._test_op_upgrade(
"Conv", 1, [[1, 3, 5, 5], [4, 3, 2, 2], [4]], [[1, 4, 4, 4]]
)
def test_Conv_2(self) -> None:
self._test_op_upgrade(
"Conv", 1, [[1, 3, 5, 5], [4, 3, 2, 2], [4]], [[1, 4, 4, 4]]
)
def test_Conv_3(self) -> None:
self._test_op_upgrade(
"Conv",
1,
[[1, 3, 5, 5], [4, 1, 2, 2], [4]],
[[1, 4, 3, 7]],
attrs={
"dilations": [1, 2],
"group": 3,
"pads": [0, 1, 2, 3],
"strides": [2, 1],
},
)
def test_Convinteger(self) -> None:
self._test_op_upgrade(
"ConvInteger",
10,
[[1, 3, 5, 5], [4, 3, 2, 2], [4]],
[[1, 4, 4, 4]],
[TensorProto.UINT8, TensorProto.UINT8, TensorProto.UINT8],
[TensorProto.INT32],
)
def test_ConvTranspose(self) -> None:
self._test_op_upgrade(
"ConvTranspose", 1, [[1, 1, 5, 5], [1, 1, 3, 3]], [[1, 1, 7, 7]]
)
def test_DeformConv(self) -> None:
self._test_op_upgrade(
"DeformConv",
19,
[[1, 1, 3, 3], [1, 1, 2, 2], [1, 8, 2, 2]],
[[1, 1, 2, 2]],
)
def test_Cosh(self) -> None:
self._test_op_upgrade("Cosh", 9)
def test_Cos(self) -> None:
self._test_op_upgrade("Cos", 7)
def test_Cumsum(self) -> None:
self._test_op_upgrade(
"CumSum",
11,
[[3, 4, 5], []],
[[3, 4, 5]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_DepthToSpace(self) -> None:
self._test_op_upgrade(
"DepthToSpace", 1, [[1, 8, 3, 3]], [[1, 2, 6, 6]], attrs={"blocksize": 2}
)
def test_DequantizeLinear(self) -> None:
self._test_op_upgrade(
"DequantizeLinear",
10,
[[2, 3], [], []],
[[2, 3]],
[TensorProto.INT8, TensorProto.FLOAT, TensorProto.INT8],
)
def test_Det_1(self) -> None:
self._test_op_upgrade("Det", 11, [[3, 5, 5]], [[3]])
def test_Det_2(self) -> None:
self._test_op_upgrade("Det", 11, [[5, 5]], [[]])
def test_DynamicQuantizeLinear(self) -> None:
self._test_op_upgrade(
"DynamicQuantizeLinear",
11,
[[3, 4, 5]],
[[3, 4, 5], [], []],
output_types=[TensorProto.UINT8, TensorProto.FLOAT, TensorProto.UINT8],
)
def test_Div(self) -> None:
self._test_op_upgrade(
"Div", 1, [[3, 4, 5], [3, 1, 5]], attrs={"consumed_inputs": [0]}
)
def test_Dropout(self) -> None:
self._test_op_upgrade(
"Dropout", 1, attrs={"consumed_inputs": [0], "is_test": 1}
)
def test_Einsum_1(self) -> None:
self._test_op_upgrade(
"Einsum",
12,
[[3, 4, 5], [3, 5, 6]],
[[3, 4, 6]],
attrs={"equation": "bij, bjk -> bik"},
)
def test_Einsum_2(self) -> None:
self._test_op_upgrade(
"Einsum", 12, [[4, 5]], [[5, 4]], attrs={"equation": "ij->ji"}
)
def test_Elu(self) -> None:
self._test_op_upgrade("Elu", 1, attrs={"consumed_inputs": [0]})
def test_Equal(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"Equal", 7, [[2, 3], [2, 3]], [[2, 3]], output_types=[TensorProto.BOOL]
)
def test_Erf(self) -> None:
self._test_op_upgrade("Erf", 9)
def test_Exp(self) -> None:
self._test_op_upgrade("Exp", 1, attrs={"consumed_inputs": [0]})
def test_Expand(self) -> None:
shape = helper.make_tensor(
"b", TensorProto.INT64, dims=[4], vals=np.array([5, 2, 6, 4])
)
self._test_op_upgrade(
"Expand",
8,
[[2, 1, 4], [4]],
[[5, 2, 6, 4]],
[TensorProto.FLOAT, TensorProto.INT64],
initializer=[shape],
)
def test_EyeLike(self) -> None:
self._test_op_upgrade("EyeLike", 9, [[4, 5]], [[4, 5]])
def test_Flatten(self) -> None:
self._test_op_upgrade("Flatten", 1, [[3, 4, 5]], [[3, 20]], attrs={"axis": 1})
def test_Floor(self) -> None:
self._test_op_upgrade("Floor", 1, attrs={"consumed_inputs": [0]})
def test_Gather(self) -> None:
self._test_op_upgrade(
"Gather",
1,
[[3, 4, 5], [6, 7]],
[[6, 7, 4, 5]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_GatherElements(self) -> None:
self._test_op_upgrade(
"GatherElements",
11,
[[3, 4, 5], [6, 7]],
[[6, 7]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_GatherND(self) -> None:
self._test_op_upgrade("GatherND", 11, [[1, 2, 3], [1, 2, 3]], [[1, 2]])
def test_Gelu_approximate_tanh(self) -> None:
self._test_op_upgrade("Gelu", 20, attrs={"approximate": "tanh"})
def test_Gelu(self) -> None:
self._test_op_upgrade("Gelu", 20)
def test_Gemm(self) -> None:
self._test_op_upgrade("Gemm", 1, [[5, 4], [4, 3], [3]], [[5, 3]])
def test_GlobalAveragePool(self) -> None:
self._test_op_upgrade("GlobalAveragePool", 1, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
def test_GlobalMaxPool(self) -> None:
self._test_op_upgrade("GlobalMaxPool", 1, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
def test_GlobalLpPool(self) -> None:
# 1->2 adapter is missing
self._test_op_upgrade("GlobalLpPool", 2, [[1, 3, 10, 10]], [[1, 3, 1, 1]])
def test_Greater(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"Greater", 7, [[2, 3], [2, 3]], [[2, 3]], output_types=[TensorProto.BOOL]
)
def test_GreaterOrEqual(self) -> None:
self._test_op_upgrade(
"GreaterOrEqual",
12,
[[2, 3], [2, 3]],
[[2, 3]],
output_types=[TensorProto.BOOL],
)
def test_GridSample(self) -> None:
self._test_op_upgrade(
"GridSample",
16,
[[1, 1, 3, 3], [1, 3, 3, 2]],
[[1, 1, 3, 3]],
input_types=[TensorProto.FLOAT, TensorProto.FLOAT],
output_types=[TensorProto.FLOAT],
attrs={"mode": "nearest", "padding_mode": "border", "align_corners": 1},
)
def test_GRU_1(self) -> None:
# 2->3, 6->7 adapters are missing
self._test_op_upgrade(
"GRU",
7,
[[5, 3, 4], [1, 18, 4], [1, 18, 4]],
[[5, 1, 3, 6], [1, 3, 6]],
attrs={"hidden_size": 6},
)
def test_GRU_2(self) -> None:
# 2->3, 6->7 adapters are missing
self._test_op_upgrade(
"GRU",
7,
[[5, 3, 4], [2, 18, 4], [2, 18, 4]],
[[5, 2, 3, 6], [2, 3, 6]],
attrs={"hidden_size": 6, "direction": "bidirectional"},
)
def test_GRU_3(self) -> None:
# 2->3, 6->7 adapters are missing
self._test_op_upgrade(
"GRU",
7,
[[5, 3, 4], [1, 18, 4], [1, 18, 4], [1, 24], [5], [1, 5, 6]],
[[5, 1, 3, 6], [1, 3, 6]],
[
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
],
attrs={"hidden_size": 6},
)
def test_HardSigmoid(self) -> None:
self._test_op_upgrade("HardSigmoid", 1, attrs={"consumed_inputs": [0]})
def test_HardSwish(self) -> None:
self._test_op_upgrade("HardSwish", 14)
def test_Hardmax(self) -> None:
self._test_op_upgrade("Hardmax", 1)
def test_Identity(self) -> None:
self._test_op_upgrade("Identity", 1)
def test_If(self) -> None:
sub_output = [
helper.make_tensor_value_info("out", TensorProto.FLOAT, [3, 4, 5])
]
then_tensor = helper.make_tensor(
"Value",
TensorProto.FLOAT,
dims=[3, 4, 5],
vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
raw=True,
)
then_node = helper.make_node("Constant", [], ["out"], value=then_tensor)
then_graph = helper.make_graph([then_node], "then_graph", [], sub_output, [])
else_tensor = helper.make_tensor(
"Value",
TensorProto.FLOAT,
dims=[3, 4, 5],
vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
raw=True,
)
else_node = helper.make_node("Constant", [], ["out"], value=else_tensor)
else_graph = helper.make_graph([else_node], "else_graph", [], sub_output, [])
self._test_op_upgrade(
"If",
1,
[[0]],
[[3, 4, 5]],
[TensorProto.BOOL],
attrs={"then_branch": then_graph, "else_branch": else_graph},
)
def test_ImageDecoder(self) -> None:
self._test_op_upgrade(
"ImageDecoder",
20,
[[None]],
[[None, None, 3]],
input_types=[TensorProto.UINT8],
output_types=[TensorProto.UINT8],
)
def test_InstanceNormalization(self) -> None:
self._test_op_upgrade(
"InstanceNormalization",
1,
[[1, 3], [3], [3]],
[[1, 3]],
attrs={"consumed_inputs": [0]},
)
def test_IsInf(self) -> None:
self._test_op_upgrade(
"IsInf", 10, [[2, 3]], [[2, 3]], output_types=[TensorProto.BOOL]
)
def test_IsNaN(self) -> None:
self._test_op_upgrade(
"IsNaN", 9, [[2, 3]], [[2, 3]], output_types=[TensorProto.BOOL]
)
def test_LeakyRelu(self) -> None:
self._test_op_upgrade("LeakyRelu", 1, attrs={"consumed_inputs": [0]})
def test_Less(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"Less", 7, [[2, 3], [2, 3]], [[2, 3]], output_types=[TensorProto.BOOL]
)
def test_LessOrEqual(self) -> None:
self._test_op_upgrade(
"LessOrEqual",
12,
[[2, 3], [2, 3]],
[[2, 3]],
output_types=[TensorProto.BOOL],
)
def test_Log(self) -> None:
self._test_op_upgrade("Log", 1, attrs={"consumed_inputs": [0]})
def test_LogSoftmax(self) -> None:
self._test_op_upgrade("LogSoftmax", 1)
def test_Loop_1(self) -> None:
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
x_in = onnx.helper.make_tensor_value_info("x_in", onnx.TensorProto.FLOAT, [1])
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
x_out = onnx.helper.make_tensor_value_info("x_out", onnx.TensorProto.FLOAT, [1])
x_scan = onnx.helper.make_tensor_value_info(
"x_scan", onnx.TensorProto.FLOAT, [1]
)
const = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="value",
data_type=onnx.TensorProto.FLOAT,
dims=[1],
vals=np.array([1]).astype(np.float32).astype(float),
),
)
add = onnx.helper.make_node("Add", inputs=["x_in", "one"], outputs=["x_out"])
id_1 = onnx.helper.make_node("Identity", inputs=["x_out"], outputs=["x_scan"])
id_2 = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[const, add, id_1, id_2],
"loop_body",
[iter_count, cond_in, x_in],
[cond_out, x_out, x_scan],
)
self._test_op_upgrade(
"Loop",
1,
[[], "", [1]],
[[1], [5, 1]],
[TensorProto.INT64, TensorProto.BOOL, TensorProto.FLOAT],
attrs={"body": loop_body},
)
def test_Loop_2(self) -> None:
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
x_in = onnx.helper.make_tensor_value_info(
"x_in", onnx.TensorProto.FLOAT, [2, 1]
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
x_out = onnx.helper.make_tensor_value_info(
"x_out", onnx.TensorProto.FLOAT, [2, 1]
)
squeeze = onnx.helper.make_node(
"Squeeze", inputs=["x_in"], outputs=["squeeze_out"], axes=[1]
)
unsqueeze = onnx.helper.make_node(
"Unsqueeze", inputs=["squeeze_out"], outputs=["x_out"], axes=[1]
)
identity = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[squeeze, unsqueeze, identity],
"loop_body",
[iter_count, cond_in, x_in],
[cond_out, x_out],
)
self._test_op_upgrade(
"Loop",
12,
[[], "", [2, 1]],
[[2, 1]],
[TensorProto.INT64, TensorProto.BOOL, TensorProto.FLOAT],
attrs={"body": loop_body},
)
def test_LpNormalization(self) -> None:
self._test_op_upgrade("LpNormalization", 1)
def test_LpPool(self) -> None:
# 1->2 adapter is missing
self._test_op_upgrade(
"LpPool", 2, [[1, 1, 5, 5]], [[1, 1, 4, 4]], attrs={"kernel_shape": [2, 2]}
)
def test_LRN_1(self) -> None:
self._test_op_upgrade("LRN", 1, attrs={"size": 3})
def test_LRN_2(self) -> None:
self._test_op_upgrade(
"LRN", 1, [[2, 3, 4, 5]], [[2, 3, 4, 5]], attrs={"size": 3}
)
def test_LSTM_1(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"LSTM",
7,
[[5, 3, 4], [1, 24, 4], [1, 24, 4]],
[[5, 1, 3, 6], [1, 3, 6], [1, 3, 6]],
attrs={"hidden_size": 6},
)
def test_LSTM_2(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"LSTM",
7,
[[5, 3, 4], [2, 24, 4], [2, 24, 4]],
[[5, 2, 3, 6], [2, 3, 6], [2, 3, 6]],
attrs={"hidden_size": 6, "direction": "bidirectional"},
)
def test_LSTM_3(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"LSTM",
7,
[
[5, 3, 4],
[1, 24, 4],
[1, 24, 4],
[1, 48],
[5],
[1, 5, 6],
[1, 5, 6],
[1, 18],
],
[[5, 1, 3, 6], [1, 3, 6], [1, 3, 6]],
[
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
],
attrs={"hidden_size": 6},
)
def test_MatMul_1(self) -> None:
self._test_op_upgrade("MatMul", 1, [[2, 3], [3, 4]], [[2, 4]])
def test_MatMul_2(self) -> None:
self._test_op_upgrade("MatMul", 1, [[5, 2, 3], [5, 3, 4]], [[5, 2, 4]])
def test_MatMulInteger_1(self) -> None:
self._test_op_upgrade(
"MatMulInteger",
10,
[[2, 3], [3, 4]],
[[2, 4]],
[TensorProto.INT8, TensorProto.INT8],
[TensorProto.INT32],
)
def test_MatMulInteger_2(self) -> None:
self._test_op_upgrade(
"MatMulInteger",
10,
[[2, 3], [3, 4], [], []],
[[2, 4]],
[TensorProto.INT8, TensorProto.INT8, TensorProto.INT8, TensorProto.INT8],
[TensorProto.INT32],
)
def test_MatMulInteger_3(self) -> None:
self._test_op_upgrade(
"MatMulInteger",
10,
[[2, 3], [3, 4], [2], [4]],
[[2, 4]],
[TensorProto.INT8, TensorProto.INT8, TensorProto.INT8, TensorProto.INT8],
[TensorProto.INT32],
)
def test_Max(self) -> None:
self._test_op_upgrade(
"Max",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_MaxPool_1(self) -> None:
self._test_op_upgrade(
"MaxPool", 1, [[1, 1, 5, 5]], [[1, 1, 4, 4]], attrs={"kernel_shape": [2, 2]}
)
def test_MaxPool_2(self) -> None:
self._test_op_upgrade(
"MaxPool",
8,
[[1, 1, 5, 5]],
[[1, 1, 4, 4], [1, 1, 4, 4]],
output_types=[TensorProto.FLOAT, TensorProto.INT64],
attrs={"kernel_shape": [2, 2]},
)
def test_MaxRoiPool(self) -> None:
self._test_op_upgrade(
"MaxRoiPool",
1,
[[2, 3, 20, 20], [4, 5]],
[[4, 3, 3, 3]],
attrs={"pooled_shape": [3, 3]},
)
def test_MaxUnpool(self) -> None:
self._test_op_upgrade(
"MaxUnpool",
9,
[[1, 1, 5, 5], [1, 1, 5, 5]],
[[1, 1, 6, 6]],
[TensorProto.FLOAT, TensorProto.INT64],
attrs={"kernel_shape": [2, 2]},
)
def test_Mean(self) -> None:
self._test_op_upgrade(
"Mean",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_MeanVarianceNormalization(self) -> None:
self._test_op_upgrade("MeanVarianceNormalization", 9, attrs={"axes": [1, 2]})
def test_Min(self) -> None:
self._test_op_upgrade(
"Min",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_Mish(self) -> None:
self._test_op_upgrade("Mish", 18)
def test_Mod_1(self) -> None:
self._test_op_upgrade("Mod", 10, [[2, 3], [2, 3]], [[2, 3]])
def test_Mod_2(self) -> None:
self._test_op_upgrade("Mod", 10, [[2, 3], [2, 3]], [[2, 3]], attrs={"fmod": 1})
def test_Mul(self) -> None:
self._test_op_upgrade(
"Mul",
1,
[[2, 3, 4], [2, 1, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_Multinomial(self) -> None:
self._test_op_upgrade(
"Multinomial",
7,
[[3, 5]],
[[3, 7]],
output_types=[TensorProto.INT32],
attrs={"sample_size": 7},
)
def test_Neg(self) -> None:
self._test_op_upgrade("Neg", 1, attrs={"consumed_inputs": [0]})
def test_NegativeLogLikelihoodLoss_1(self) -> None:
self._test_op_upgrade(
"NegativeLogLikelihoodLoss",
12,
[[3, 4, 5], [3, 5]],
[[]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_NegativeLogLikelihoodLoss_2(self) -> None:
self._test_op_upgrade(
"NegativeLogLikelihoodLoss",
12,
[[3, 4, 5], [3, 5], [4]],
[[]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
)
def test_NonMaxSuppression(self) -> None:
self._test_op_upgrade(
"NonMaxSuppression",
10,
[[2, 3, 4], [3, 5, 6]],
[[2, 3]],
output_types=[TensorProto.INT64],
)
def test_NonZero(self) -> None:
self._test_op_upgrade(
"NonZero", 9, [[3, 3]], [[2, 4]], output_types=[TensorProto.INT64]
)
def test_Not(self) -> None:
self._test_op_upgrade(
"Not", 1, [[2, 3]], [[2, 3]], [TensorProto.BOOL], [TensorProto.BOOL]
)
def test_OneHot(self) -> None:
self._test_op_upgrade("OneHot", 9, [[3, 4, 5], [], [2]], [[3, 4, 5, 6]])
def test_Or(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"Or",
7,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.BOOL, TensorProto.BOOL],
[TensorProto.BOOL],
)
def test_Pad(self) -> None:
# 1->2 adapter is missing
self._test_op_upgrade(
"Pad", 2, [[3, 4]], [[5, 8]], attrs={"pads": [1, 2, 1, 2], "value": 1.5}
)
def test_Pow(self) -> None:
self._test_op_upgrade("Pow", 1, [[2, 3, 4], [2, 3, 4]], [[2, 3, 4]])
def test_PRelu(self) -> None:
self._test_op_upgrade(
"PRelu",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_QLinearConv(self) -> None:
self._test_op_upgrade(
"QLinearConv",
10,
[[1, 3, 5, 5], [], [], [4, 3, 2, 2], [], [], [], []],
[[1, 4, 4, 4]],
)
def test_QLinearMatMul(self) -> None:
self._test_op_upgrade(
"QLinearMatMul", 10, [[2, 3], [], [], [3, 4], [], [], [], []], [[2, 4]]
)
def test_QuantizeLinear(self) -> None:
self._test_op_upgrade(
"QuantizeLinear",
10,
[[3, 4, 5], [], []],
[[3, 4, 5]],
[TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.UINT8],
[TensorProto.UINT8],
)
def test_RandomNormal(self) -> None:
self._test_op_upgrade(
"RandomNormal", 1, [], [[3, 4, 5]], attrs={"shape": [3, 4, 5]}
)
def test_RandomNormalLike(self) -> None:
like = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[3, 4, 5],
vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
raw=True,
)
self._test_op_upgrade(
"RandomNormalLike", 1, [[3, 4, 5]], [[3, 4, 5]], initializer=[like]
)
def test_RandomUniform(self) -> None:
self._test_op_upgrade(
"RandomUniform", 1, [], [[3, 4, 5]], attrs={"shape": [3, 4, 5]}
)
def test_RandomUniformLike(self) -> None:
like = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[3, 4, 5],
vals=np.random.rand(3, 4, 5).astype(np.float32).tobytes(),
raw=True,
)
self._test_op_upgrade(
"RandomUniformLike", 1, [[3, 4, 5]], [[3, 4, 5]], initializer=[like]
)
def test_Range(self) -> None:
start = helper.make_tensor("a", TensorProto.FLOAT, dims=[], vals=np.array([0]))
end = helper.make_tensor("b", TensorProto.FLOAT, dims=[], vals=np.array([12]))
step = helper.make_tensor("c", TensorProto.FLOAT, dims=[], vals=np.array([2]))
self._test_op_upgrade(
"Range", 11, [[], [], []], [[6]], initializer=[start, end, step]
)
def test_Reciprocal(self) -> None:
self._test_op_upgrade("Reciprocal", 1, attrs={"consumed_inputs": [0]})
def test_ReduceL1(self) -> None:
self._test_op_upgrade("ReduceL1", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceL2(self) -> None:
self._test_op_upgrade("ReduceL2", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceLogSum(self) -> None:
self._test_op_upgrade("ReduceLogSum", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceLogSumExp(self) -> None:
self._test_op_upgrade("ReduceLogSumExp", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceMean(self) -> None:
self._test_op_upgrade("ReduceMean", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceMax(self) -> None:
self._test_op_upgrade("ReduceMax", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceMin(self) -> None:
self._test_op_upgrade("ReduceMin", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceProd(self) -> None:
self._test_op_upgrade("ReduceProd", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceSum(self) -> None:
self._test_op_upgrade("ReduceSum", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_ReduceSumSquare(self) -> None:
self._test_op_upgrade("ReduceSumSquare", 1, [[3, 4, 5]], [[1, 1, 1]])
def test_Relu(self) -> None:
self._test_op_upgrade("Relu", 1, attrs={"consumed_inputs": [0]})
def test_Reshape(self) -> None:
self._test_op_upgrade(
"Reshape",
1,
[[3, 4, 5]],
[[3, 10, 2]],
attrs={"consumed_inputs": [0], "shape": [3, 10, 2]},
)
def test_Resize(self) -> None:
self._test_op_upgrade("Resize", 10, [[3, 4, 5], [3]], [[3, 8, 15]])
def test_ReverseSequence(self) -> None:
self._test_op_upgrade(
"ReverseSequence",
10,
[[3, 4, 5], [4]],
[[3, 4, 5]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_RNN_1(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"RNN",
7,
[[5, 3, 4], [1, 6, 4], [1, 6, 4]],
[[5, 1, 3, 6], [1, 3, 6]],
attrs={"hidden_size": 6},
)
def test_RNN_2(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"RNN",
7,
[[5, 3, 4], [2, 6, 4], [2, 6, 4]],
[[5, 2, 3, 6], [2, 3, 6]],
attrs={"hidden_size": 6, "direction": "bidirectional"},
)
def test_RNN_3(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"RNN",
7,
[[5, 3, 4], [1, 6, 4], [1, 6, 4], [1, 12], [5], [1, 5, 6]],
[[5, 1, 3, 6], [1, 3, 6]],
[
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
],
attrs={"hidden_size": 6},
)
def test_RoiAlign_1(self) -> None:
self._test_op_upgrade(
"RoiAlign",
10,
[[2, 3, 20, 20], [10, 4], [10]],
[[10, 3, 1, 1]],
[TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64],
)
def test_RoiAlign_2(self) -> None:
self._test_op_upgrade(
"RoiAlign",
16,
[[2, 3, 20, 20], [10, 4], [10]],
[[10, 3, 1, 1]],
[TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.INT64],
attrs={"coordinate_transformation_mode": "half_pixel"},
)
def test_Round(self) -> None:
self._test_op_upgrade("Round", 11)
def test_Scatter(self) -> None:
self._test_op_upgrade(
"Scatter",
9,
[[2, 3], [1, 2], [1, 2]],
[[2, 3]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
[TensorProto.FLOAT],
)
def test_ScatterElements_1(self) -> None:
self._test_op_upgrade(
"ScatterElements",
11,
[[2, 3], [1, 2], [1, 2]],
[[2, 3]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
[TensorProto.FLOAT],
)
def test_ScatterElements_2(self) -> None:
self._test_op_upgrade(
"ScatterElements",
16,
[[2, 3], [1, 2], [1, 2]],
[[2, 3]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
[TensorProto.FLOAT],
attrs={"reduction": "add"},
)
def test_ScatterND_1(self) -> None:
self._test_op_upgrade(
"ScatterND",
11,
[[2, 3], [1, 2], [1, 2]],
[[2, 3]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
[TensorProto.FLOAT],
)
def test_ScatterND_2(self) -> None:
self._test_op_upgrade(
"ScatterND",
16,
[[2, 3], [1, 2], [1, 2]],
[[2, 3]],
[TensorProto.FLOAT, TensorProto.INT64, TensorProto.FLOAT],
[TensorProto.FLOAT],
attrs={"reduction": "mul"},
)
def test_Scan(self) -> None:
sum_in = onnx.helper.make_tensor_value_info(
"sum_in", onnx.TensorProto.FLOAT, [2]
)
next_in = onnx.helper.make_tensor_value_info(
"next_in", onnx.TensorProto.FLOAT, [2]
)
sum_out = onnx.helper.make_tensor_value_info(
"sum_out", onnx.TensorProto.FLOAT, [2]
)
scan_out = onnx.helper.make_tensor_value_info(
"scan_out", onnx.TensorProto.FLOAT, [2]
)
add_node = onnx.helper.make_node(
"Add", inputs=["sum_in", "next_in"], outputs=["sum_out"]
)
id_node = onnx.helper.make_node(
"Identity", inputs=["sum_out"], outputs=["scan_out"]
)
body = onnx.helper.make_graph(
[add_node, id_node], "scan_body", [sum_in, next_in], [sum_out, scan_out]
)
self._test_op_upgrade(
"Scan",
8,
["", [1, 2], [1, 3, 2]],
[[1, 2], [1, 3, 2]],
attrs={"body": body, "num_scan_inputs": 1},
)
def test_Selu(self) -> None:
self._test_op_upgrade("Selu", 1, attrs={"consumed_inputs": [0]})
def test_Shape(self) -> None:
self._test_op_upgrade(
"Shape", 1, [[3, 4, 5]], [[3]], output_types=[TensorProto.INT64]
)
def test_Shrink(self) -> None:
self._test_op_upgrade("Shrink", 9)
def test_Sigmoid(self) -> None:
self._test_op_upgrade("Sigmoid", 1, attrs={"consumed_inputs": [0]})
def test_Sign(self) -> None:
self._test_op_upgrade("Sign", 9)
def test_Sinh(self) -> None:
self._test_op_upgrade("Sinh", 9)
def test_Sin(self) -> None:
self._test_op_upgrade("Sin", 7)
def test_Size(self) -> None:
self._test_op_upgrade(
"Size", 1, [[3, 4, 5]], [[]], output_types=[TensorProto.INT64]
)
def test_Slice(self) -> None:
self._test_op_upgrade(
"Slice",
1,
[[3, 4, 5]],
[[3, 2, 2]],
attrs={"axes": [1, 2], "starts": [0, 1], "ends": [2, 3]},
)
def test_Softmax_0(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": 0})
def test_Softmax_1(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": 1})
def test_Softmax_2(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": 2})
def test_Softmax_3(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": -1})
def test_Softmax_4(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": -2})
def test_Softmax_5(self) -> None:
self._test_op_upgrade("Softmax", 1, attrs={"axis": -3})
def test_Softplus(self) -> None:
self._test_op_upgrade("Softplus", 1)
def test_Softsign(self) -> None:
self._test_op_upgrade("Softsign", 1)
def test_SoftmaxCrossEntropyLoss(self) -> None:
self._test_op_upgrade(
"SoftmaxCrossEntropyLoss",
12,
[[3, 4, 5, 6], [3, 6]],
[[]],
[TensorProto.FLOAT, TensorProto.INT64],
)
def test_SpaceToDepth(self) -> None:
self._test_op_upgrade(
"SpaceToDepth", 1, [[1, 3, 8, 8]], [[1, 12, 4, 4]], attrs={"blocksize": 2}
)
def test_Split(self) -> None:
# 1->2 adapter is missing
self._test_op_upgrade(
"Split",
2,
[[3, 4, 7]],
[[3, 4, 2], [3, 4, 1], [3, 4, 4]],
attrs={"axis": 2, "split": [2, 1, 4]},
)
def test_Sqrt(self) -> None:
self._test_op_upgrade("Sqrt", 1, attrs={"consumed_inputs": [0]})
def test_Squeeze(self) -> None:
self._test_op_upgrade("Squeeze", 1, [[2, 1, 3, 4, 1]], [[2, 3, 4]])
def test_StringNormalizer(self) -> None:
self._test_op_upgrade(
"StringNormalizer",
10,
[[1, 3]],
[[1, 3]],
[TensorProto.STRING],
[TensorProto.STRING],
attrs={"case_change_action": "LOWER"},
)
def test_Sub(self) -> None:
self._test_op_upgrade(
"Sub",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_Sum(self) -> None:
self._test_op_upgrade(
"Sum",
1,
[[2, 3, 4], [2, 3, 4]],
[[2, 3, 4]],
attrs={"consumed_inputs": [0]},
)
def test_Tanh(self) -> None:
self._test_op_upgrade("Tanh", 1, attrs={"consumed_inputs": [0]})
def test_Tan(self) -> None:
self._test_op_upgrade("Tan", 7)
def test_TfIdfVectorizer(self) -> None:
self._test_op_upgrade(
"TfIdfVectorizer",
9,
[[3]],
[[5]],
attrs={
"max_gram_length": 3,
"max_skip_count": 1,
"min_gram_length": 2,
"mode": "TFIDF",
"ngram_counts": [0, 20],
"ngram_indexes": [3, 4],
},
)
def test_ThresholdedRelu(self) -> None:
self._test_op_upgrade("ThresholdedRelu", 10)
def test_Tile(self) -> None:
# 5->6 adapter is missing
repeats = helper.make_tensor(
"b", TensorProto.INT64, dims=[3], vals=np.array([1, 2, 3])
)
self._test_op_upgrade(
"Tile",
6,
[[3, 4, 5], [3]],
[[3, 8, 15]],
[TensorProto.FLOAT, TensorProto.INT64],
initializer=[repeats],
)
def test_TopK(self) -> None:
self._test_op_upgrade(
"TopK",
1,
[[3, 4, 5]],
[[3, 4, 2], [3, 4, 2]],
output_types=[TensorProto.FLOAT, TensorProto.INT64],
attrs={"k": 2},
)
def test_Transpose(self) -> None:
self._test_op_upgrade(
"Transpose",
1,
[[1, 2, 5, 3, 7]],
[[1, 7, 5, 2, 3]],
attrs={"perm": [0, 4, 2, 1, 3]},
)
def test_Trilu(self) -> None:
self._test_op_upgrade("Trilu", 14)
def test_Unique_1(self) -> None:
self._test_op_upgrade("Unique", 11, [[3, 4, 5]], [[None]])
def test_Unique_2(self) -> None:
self._test_op_upgrade(
"Unique", 11, [[3, 4, 5]], [[3, None, 5]], attrs={"axis": 1}
)
def test_Unsqueeze(self) -> None:
self._test_op_upgrade(
"Unsqueeze", 1, [[3, 4, 5]], [[3, 4, 1, 5]], attrs={"axes": [2]}
)
def test_Upsample(self) -> None:
self._test_op_upgrade(
"Upsample",
1,
[[1, 3, 4, 5]],
[[1, 3, 6, 10]],
attrs={"width_scale": 2.0, "height_scale": 1.5},
)
def test_Where(self) -> None:
self._test_op_upgrade(
"Where",
9,
[[2, 3], [2, 3], [2, 3]],
[[2, 3]],
[TensorProto.BOOL, TensorProto.FLOAT, TensorProto.FLOAT],
)
def test_Xor(self) -> None:
# 6->7 adapter is missing
self._test_op_upgrade(
"Xor",
7,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.BOOL, TensorProto.BOOL],
[TensorProto.BOOL],
)
def test_CastLike(self) -> None:
self._test_op_upgrade(
"CastLike",
15,
[[2, 3, 4], [2, 1, 4]],
[[2, 3, 4]],
input_types=[TensorProto.FLOAT, TensorProto.FLOAT16],
output_types=[TensorProto.FLOAT16],
)
def test_LayerNormalization(self) -> None:
self._test_op_upgrade(
"LayerNormalization",
17,
[[2, 3, 4, 5], [4, 5], [4, 5]],
[[2, 3, 4, 5]],
input_types=[TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT],
output_types=[TensorProto.FLOAT],
attrs={"axis": 2},
)
def _test_window_function(self, window_function_name: str) -> None:
size = helper.make_tensor("a", TensorProto.INT64, dims=[], vals=np.array([10]))
self._test_op_upgrade(
window_function_name,
17,
[[]],
[[10]],
[TensorProto.INT64],
initializer=[size],
)
def test_BlackmanWindow(self) -> None:
self._test_window_function("BlackmanWindow")
def test_HannWindow(self) -> None:
self._test_window_function("HannWindow")
def test_HammingWindow(self) -> None:
self._test_window_function("HammingWindow")
def test_DFT(self) -> None:
self._test_op_upgrade("DFT", 17, [[2, 16, 1], []], [[2, 16, 2]])
self._test_op_upgrade("DFT", 17, [[2, 16, 2], []], [[2, 16, 2]])
self._test_op_upgrade(
"DFT", 17, [[2, 16, 1], []], [[2, 9, 2]], attrs={"onesided": 1}
)
self._test_op_upgrade(
"DFT", 17, [[2, 16, 2], []], [[2, 9, 2]], attrs={"onesided": 1}
)
self._test_op_upgrade(
"DFT", 17, [[2, 16, 1], []], [[2, 16, 2]], attrs={"inverse": 1}
)
self._test_op_upgrade(
"DFT", 17, [[2, 16, 2], []], [[2, 16, 2]], attrs={"inverse": 1}
)
def _test_short_time_fourier_transform(self, operator_name: str) -> None:
# Real
signal = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[2, 64],
vals=np.random.rand(2, 64).astype(np.float32),
)
frame_step = helper.make_tensor(
"b", TensorProto.INT64, dims=[1], vals=np.array([8])
)
window = helper.make_tensor(
"c", TensorProto.FLOAT, dims=[16], vals=np.ones(16).astype(np.float32)
)
self._test_op_upgrade(
operator_name,
17,
[[2, 64], [1], [16]],
[[2, 7, 16, 2]],
[
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.INT64,
],
initializer=[signal, frame_step, window],
)
# Real Onesided
signal = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[2, 64],
vals=np.random.rand(2, 64).astype(np.float32),
)
frame_step = helper.make_tensor(
"b", TensorProto.INT64, dims=[1], vals=np.array([8])
)
window = helper.make_tensor(
"c", TensorProto.FLOAT, dims=[16], vals=np.ones(16).astype(np.float32)
)
self._test_op_upgrade(
operator_name,
17,
[[2, 64], [1], [16]],
[[2, 7, 9, 2]],
[
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.INT64,
],
attrs={"onesided": 1},
initializer=[signal, frame_step, window],
)
# Complex
signal = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[2, 64, 2],
vals=np.random.rand(2, 64, 2).astype(np.float32),
)
frame_step = helper.make_tensor(
"b", TensorProto.INT64, dims=[1], vals=np.array([8])
)
window = helper.make_tensor(
"c", TensorProto.FLOAT, dims=[16], vals=np.ones(16).astype(np.float32)
)
self._test_op_upgrade(
operator_name,
17,
[[2, 64, 2], [1], [16]],
[[2, 7, 16, 2]],
[
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.INT64,
],
initializer=[signal, frame_step, window],
)
# Complex Onesided
signal = helper.make_tensor(
"a",
TensorProto.FLOAT,
dims=[2, 64, 2],
vals=np.random.rand(2, 64, 2).astype(np.float32),
)
frame_step = helper.make_tensor(
"b", TensorProto.INT64, dims=[1], vals=np.array([8])
)
window = helper.make_tensor(
"c", TensorProto.FLOAT, dims=[16], vals=np.ones(16).astype(np.float32)
)
frame_length = helper.make_tensor(
"e", TensorProto.INT64, dims=[1], vals=np.array([16])
)
self._test_op_upgrade(
operator_name,
17,
[[2, 64, 2], [1], [16]],
[[2, 7, 9, 2]],
[
TensorProto.FLOAT,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.INT64,
],
attrs={"onesided": 1},
initializer=[signal, frame_step, window, frame_length],
)
def test_STFT(self) -> None:
self._test_short_time_fourier_transform("STFT")
def test_MelWeightMatrix(self) -> None:
num_mel_bins = helper.make_tensor(
"a", TensorProto.INT64, dims=[], vals=np.array([10])
)
dft_length = helper.make_tensor(
"b", TensorProto.INT64, dims=[], vals=np.array([64])
)
sample_rate = helper.make_tensor(
"c", TensorProto.INT64, dims=[], vals=np.array([0])
)
lower_edge_hertz = helper.make_tensor(
"d", TensorProto.FLOAT, dims=[], vals=np.array([0])
)
upper_edge_hertz = helper.make_tensor(
"e", TensorProto.FLOAT, dims=[], vals=np.array([1])
)
self._test_op_upgrade(
"MelWeightMatrix",
17,
[[], [], [], [], []],
[[33, 10]],
[
TensorProto.INT64,
TensorProto.INT64,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.FLOAT,
],
initializer=[
num_mel_bins,
dft_length,
sample_rate,
lower_edge_hertz,
upper_edge_hertz,
],
)
num_mel_bins = helper.make_tensor(
"a", TensorProto.INT64, dims=[], vals=np.array([20])
)
dft_length = helper.make_tensor(
"b", TensorProto.INT64, dims=[], vals=np.array([31])
)
sample_rate = helper.make_tensor(
"c", TensorProto.INT64, dims=[], vals=np.array([0])
)
lower_edge_hertz = helper.make_tensor(
"d", TensorProto.FLOAT, dims=[], vals=np.array([0])
)
upper_edge_hertz = helper.make_tensor(
"e", TensorProto.FLOAT, dims=[], vals=np.array([1])
)
self._test_op_upgrade(
"MelWeightMatrix",
17,
[[], [], [], [], []],
[[16, 20]],
[
TensorProto.INT64,
TensorProto.INT64,
TensorProto.INT64,
TensorProto.FLOAT,
TensorProto.FLOAT,
],
initializer=[
num_mel_bins,
dft_length,
sample_rate,
lower_edge_hertz,
upper_edge_hertz,
],
)
def test_CenterCropPad(self) -> None:
input_ = helper.make_tensor(
"input",
TensorProto.FLOAT,
dims=[2, 4],
vals=np.array([1, 2, 3, 4, 5, 6, 7, 8]),
)
shape = helper.make_tensor(
"shape", TensorProto.INT64, dims=[2], vals=np.array([3, 3])
)
self._test_op_upgrade(
"CenterCropPad",
18,
[[], []],
[[3, 3]],
[TensorProto.FLOAT, TensorProto.INT64],
initializer=[input_, shape],
)
def test_BitwiseNot(self) -> None:
self._test_op_upgrade(
"BitwiseNot",
18,
[[2, 3]],
[[2, 3]],
[TensorProto.INT32],
[TensorProto.INT32],
)
def test_BitwiseAnd(self) -> None:
self._test_op_upgrade(
"BitwiseAnd",
18,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.INT16, TensorProto.INT16],
[TensorProto.INT16],
)
def test_BitwiseOr(self) -> None:
self._test_op_upgrade(
"BitwiseOr",
18,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.INT16, TensorProto.INT16],
[TensorProto.INT16],
)
def test_BitwiseXor(self) -> None:
self._test_op_upgrade(
"BitwiseXor",
18,
[[2, 3], [2, 3]],
[[2, 3]],
[TensorProto.INT16, TensorProto.INT16],
[TensorProto.INT16],
)
def test_GroupNormalization(self) -> None:
self._test_op_upgrade(
"GroupNormalization",
18,
[[3, 4, 2, 2], [1], [1]],
[[3, 4, 2, 2]],
attrs={"epsilon": 1e-5, "num_groups": 2},
)
def test_StringConcat(self) -> None:
self._test_op_upgrade(
"StringConcat",
20,
[[2, 3], [2, 3]],
[[2, 3]],
)
def test_RegexFullMatch(self) -> None:
self._test_op_upgrade(
"RegexFullMatch",
20,
[[2, 3]],
[[2, 3]],
[TensorProto.STRING],
[TensorProto.BOOL],
)
def test_ops_tested(self) -> None:
all_schemas = onnx.defs.get_all_schemas()
all_op_names = [schema.name for schema in all_schemas if schema.domain == ""]
excluded_ops = [
# Sequence-based and Optional-based ops disabled because
# the version converter doesn't play nicely with sequences
"ConcatFromSequence",
"SequenceAt",
"SequenceConstruct",
"SequenceEmpty",
"SequenceErase",
"SequenceInsert",
"SequenceLength",
"SequenceMap",
"SplitToSequence",
"Optional",
"OptionalGetElement",
"OptionalHasElement",
"StringSplit",
]
all_op_names = [op for op in all_op_names if op not in excluded_ops]
untested_ops = set(all_op_names) - set(tested_ops)
print(untested_ops)
assert len(untested_ops) == 0
if __name__ == "__main__":
unittest.main()
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59,000 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/expand.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Expand(Base):
@staticmethod
def export_dim_changed() -> None:
node = onnx.helper.make_node(
"Expand",
inputs=["data", "new_shape"],
outputs=["expanded"],
)
shape = [3, 1]
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[1.], [2.], [3.]]
new_shape = [2, 1, 6]
expanded = data * np.ones(new_shape, dtype=np.float32)
# print(expanded)
# [[[1., 1., 1., 1., 1., 1.],
# [2., 2., 2., 2., 2., 2.],
# [3., 3., 3., 3., 3., 3.]],
#
# [[1., 1., 1., 1., 1., 1.],
# [2., 2., 2., 2., 2., 2.],
# [3., 3., 3., 3., 3., 3.]]]
new_shape = np.array(new_shape, dtype=np.int64)
expect(
node,
inputs=[data, new_shape],
outputs=[expanded],
name="test_expand_dim_changed",
)
@staticmethod
def export_dim_unchanged() -> None:
node = onnx.helper.make_node(
"Expand",
inputs=["data", "new_shape"],
outputs=["expanded"],
)
shape = [3, 1]
new_shape = [3, 4]
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[1.], [2.], [3.]]
expanded = np.tile(data, 4)
# print(expanded)
# [[1., 1., 1., 1.],
# [2., 2., 2., 2.],
# [3., 3., 3., 3.]]
new_shape = np.array(new_shape, dtype=np.int64)
expect(
node,
inputs=[data, new_shape],
outputs=[expanded],
name="test_expand_dim_unchanged",
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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59,001 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_pad.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _pad_impl(data, raw_pads, mode, constant_values=0.0, axes=None): # type: ignore
input_rank = data.ndim
if axes is None:
axes = list(range(input_rank))
else:
axes = [axis if axis >= 0 else axis + input_rank for axis in axes]
num_axes = len(axes)
if num_axes * 2 != len(raw_pads):
raise RuntimeError(
"The number of elements in raw_pads should be 2 times the number of axes"
)
pad_width = [(0, 0)] * input_rank
for i, axis in enumerate(axes):
pad_begin = raw_pads[i]
pad_end = raw_pads[num_axes + i]
pad_width[axis] = (pad_begin, pad_end)
if mode == "constant":
return np.pad(
data, pad_width=pad_width, mode=mode, constant_values=constant_values
).astype(data.dtype)
return np.pad(data, pad_width=pad_width, mode=mode).astype(data.dtype)
class Pad_1(OpRun):
def _run(self, data, paddings=None, mode=None, value=None): # type: ignore
if value is None:
value = 0
return (_pad_impl(data, paddings, mode=mode, constant_values=value),)
class Pad_2(OpRun):
def _run(self, data, pads=None, mode=None, value=None): # type: ignore
if value is None:
value = 0
return (_pad_impl(data, pads, mode=mode, constant_values=value),)
class Pad_11(OpRun):
def _run(self, data, pads, constant_value=None, mode=None): # type: ignore
if constant_value is None:
constant_value = 0
return (
_pad_impl(data, pads, mode=mode, constant_values=constant_value, axes=None),
)
class Pad_18(OpRun):
def _run(self, data, pads, constant_value=None, axes=None, mode=None): # type: ignore
if constant_value is None:
constant_value = 0
return (
_pad_impl(data, pads, mode=mode, constant_values=constant_value, axes=axes),
)
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59,002 | onnx/onnx | refs/heads/main | /onnx/test/training_tool_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
import numpy as np
import onnx
from onnx import TensorProto, helper, numpy_helper, shape_inference
class TestTrainingTool(unittest.TestCase):
def test_training_info_proto(self) -> None:
# Inference graph.
A_shape = [2, 2]
A_name = "A"
A = np.random.rand(*A_shape).astype(np.float32)
A_initializer = numpy_helper.from_array(A, name=A_name)
A_value_info = helper.make_tensor_value_info(A_name, TensorProto.FLOAT, A_shape)
B_shape = [2, 2]
B_name = "B"
B = np.random.rand(*B_shape).astype(np.float32)
B_initializer = numpy_helper.from_array(B, name=B_name)
B_value_info = helper.make_tensor_value_info(B_name, TensorProto.FLOAT, B_shape)
C_shape = [2, 2]
C_name = "C"
C_value_info = helper.make_tensor_value_info(C_name, TensorProto.FLOAT, C_shape)
inference_node = helper.make_node(
"MatMul", inputs=[A_name, B_name], outputs=[C_name]
)
inference_graph = helper.make_graph(
[inference_node],
"simple_inference",
[A_value_info, B_value_info],
[C_value_info],
[A_initializer, B_initializer],
)
# Training graph
X_shape = [2, 2]
X_name = "X"
X = np.random.rand(*X_shape).astype(np.float32)
X_initializer = numpy_helper.from_array(X, name=X_name)
X_value_info = helper.make_tensor_value_info(X_name, TensorProto.FLOAT, X_shape)
Y_shape = [2, 2]
Y_name = "Y"
Y_value_info = helper.make_tensor_value_info(Y_name, TensorProto.FLOAT, Y_shape)
node = helper.make_node(
"MatMul",
inputs=[X_name, C_name], # tensor "C" is from inference graph.
outputs=[Y_name],
)
training_graph = helper.make_graph(
[node], "simple_training", [X_value_info], [Y_value_info], [X_initializer]
)
# Capture assignment of B <--- Y.
training_info = helper.make_training_info(
training_graph, [(B_name, Y_name)], None, None
)
# Create a model with both inference and training information.
model = helper.make_model(inference_graph)
# Check if the inference-only part is correct.
onnx.checker.check_model(model)
# Insert training information.
new_training_info = model.training_info.add()
new_training_info.CopyFrom(training_info)
# Generate the actual training graph from training information so that
# we can run onnx checker to check if the full training graph is a valid
# graph. As defined in spec, full training graph forms by concatenating
# corresponding fields.
full_training_graph = helper.make_graph(
list(model.graph.node) + list(model.training_info[0].algorithm.node),
"full_training_graph",
list(model.graph.input) + list(model.training_info[0].algorithm.input),
list(model.graph.output) + list(model.training_info[0].algorithm.output),
list(model.graph.initializer)
+ list(model.training_info[0].algorithm.initializer),
)
# Wrap full training graph as a ModelProto so that we can run checker.
full_training_model = helper.make_model(full_training_graph)
full_training_model_with_shapes = shape_inference.infer_shapes(
full_training_model
)
onnx.checker.check_model(full_training_model_with_shapes)
if __name__ == "__main__":
unittest.main()
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59,003 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_celu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
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 * (np.exp(x / alpha) - 1))
return positive_input + negative_input # type: ignore
class Celu(OpRun):
def _run(self, x, alpha=None): # type: ignore
return (_vcelu1(x, alpha).astype(x.dtype),)
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", 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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,004 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_shrink.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=E1130,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Shrink(OpRun):
def _run(self, x, bias=None, lambd=None): # type: ignore
return (
np.where(
x < -lambd,
x + bias,
np.where(x > lambd, x - bias, 0),
).astype(x.dtype),
)
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59,005 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bitwisenot.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np # type: ignore
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.numpy_helper import create_random_int
class BitwiseNot(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"BitwiseNot",
inputs=["x"],
outputs=["bitwise_not"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d")
# 3d
x = create_random_int((3, 4, 5), np.uint16)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d")
# 4d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = np.bitwise_not(x)
expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d")
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59,006 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gather.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Gather(Base):
@staticmethod
def export_gather_0() -> None:
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=0)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_0",
)
@staticmethod
def export_gather_1() -> None:
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_1",
)
@staticmethod
def export_gather_2d_indices() -> None:
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(3, 3).astype(np.float32)
indices = np.array([[0, 2]])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_2d_indices",
)
@staticmethod
def export_gather_negative_indices() -> None:
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.arange(10).astype(np.float32)
indices = np.array([0, -9, -10])
y = np.take(data, indices, axis=0)
# print(y)
# [0. 1. 0.]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_negative_indices",
)
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59,007 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_constant_of_shape.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class ConstantOfShape(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.cst = (
self.value[0] if isinstance(self.value, np.ndarray) else self.value # type: ignore
)
if isinstance(self.cst, int):
self.cst = np.int64(self.cst)
elif isinstance(self.cst, float):
self.cst = np.float64(self.cst)
elif self.cst is None:
self.cst = np.float32(0)
def _run(self, data, value=None): # type: ignore
try:
res = np.full(tuple(data), self.cst) # type: ignore
except TypeError as e:
raise RuntimeError(
f"Unable to create a constant of shape {data!r} with value {self.cst!r} " # type: ignore
f"(raw value={value!r})." # type: ignore
) from e
return (res,)
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59,008 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_feature_vectorizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class FeatureVectorizer(OpRunAiOnnxMl):
def _preprocess(self, a, cut): # type: ignore
if len(a.shape) == 1:
a = a.reshape((-1, 1))
if len(a.shape) != 2:
raise ValueError(f"Every input must have 1 or 2 dimensions not {a.shape}.")
if cut < a.shape[1]:
return a[:, :cut]
if cut > a.shape[1]:
b = np.zeros((a.shape[0], cut), dtype=a.dtype)
b[:, : a.shape[1]] = a
return b
return a
def _run(self, *args, inputdimensions=None): # type: ignore
args = [ # type: ignore
self._preprocess(a, axis) for a, axis in zip(args, inputdimensions)
]
res = np.concatenate(args, axis=1)
return (res,)
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59,009 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_lstm.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
from typing import Tuple
import numpy as np
from onnx.reference.op_run import OpRun
class CommonLSTM(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_node.output)
self.n_gates = 3
def f(self, x: np.ndarray) -> np.ndarray:
return 1 / (1 + np.exp(-x))
def g(self, x: np.ndarray) -> np.ndarray:
return np.tanh(x)
def h(self, x: np.ndarray) -> np.ndarray:
return np.tanh(x)
def _step(
self,
X: np.ndarray,
R: np.ndarray,
B: np.ndarray,
W: np.ndarray,
H_0: np.ndarray,
C_0: np.ndarray,
P: np.ndarray,
num_directions: int,
) -> Tuple[np.ndarray, np.ndarray]:
seq_length = X.shape[0]
hidden_size = H_0.shape[-1]
batch_size = X.shape[1]
Y = np.empty([seq_length, num_directions, batch_size, hidden_size])
h_list = []
[p_i, p_o, p_f] = np.split(P, 3)
H_t = H_0
C_t = C_0
for x in np.split(X, X.shape[0], axis=0):
gates = (
np.dot(x, np.transpose(W))
+ np.dot(H_t, np.transpose(R))
+ np.add(*np.split(B, 2))
)
i, o, f, c = np.split(gates, 4, -1)
i = self.f(i + p_i * C_t)
f = self.f(f + p_f * C_t)
c = self.g(c)
C = f * C_t + i * c
o = self.f(o + p_o * C)
H = o * self.h(C)
h_list.append(H)
H_t = H
C_t = C
concatenated = np.concatenate(h_list)
if num_directions == 1:
Y[:, 0, :, :] = concatenated
if self.layout == 0: # type: ignore
Y_h = Y[-1]
else:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
return Y, Y_h # type: ignore
def _run( # type: ignore
self,
X,
W,
R,
B=None,
sequence_lens=None,
initial_h=None,
initial_c=None,
P=None,
activation_alpha=None,
activation_beta=None,
activations=None,
clip=None,
direction=None,
hidden_size=None,
input_forget=None,
layout=None,
):
# TODO: support overridden attributes.
n_gates = 4
number_of_peepholes = 3
num_directions = W.shape[0]
if num_directions == 1:
R = np.squeeze(R, axis=0)
W = np.squeeze(W, axis=0)
if B is not None and len(B.shape) > 0 and B.shape[0] == 1:
B = np.squeeze(B, axis=0)
if (
sequence_lens is not None
and len(sequence_lens.shape) > 0
and sequence_lens.shape[0] == 1
):
sequence_lens = np.squeeze(sequence_lens, axis=0)
if (
initial_h is not None
and len(initial_h.shape) > 0
and initial_h.shape[0] == 1
):
initial_h = np.squeeze(initial_h, axis=0)
if (
initial_c is not None
and len(initial_c.shape) > 0
and initial_c.shape[0] == 1
):
initial_c = np.squeeze(initial_c, axis=0)
if P is not None and len(P.shape) > 0 and P.shape[0] == 1:
P = np.squeeze(P, axis=0)
hidden_size = R.shape[-1]
batch_size = X.shape[1]
if self.layout != 0: # type: ignore
X = np.swapaxes(X, 0, 1)
if B is None:
B = np.zeros(2 * n_gates * hidden_size, dtype=np.float32)
if P is None:
P = np.zeros(number_of_peepholes * hidden_size, dtype=np.float32)
if initial_h is None:
initial_h = np.zeros((batch_size, hidden_size), dtype=np.float32)
if initial_c is None:
initial_c = np.zeros((batch_size, hidden_size), dtype=np.float32)
else:
raise NotImplementedError( # pragma: no cover
f"Unsupported value {num_directions!r} for num_directions "
f"and operator {self.__class__.__name__!r}."
)
Y, Y_h = self._step(
X, R, B, W, initial_h, initial_c, P, num_directions=num_directions
)
Y = Y.astype(X.dtype)
return (Y,) if self.n_outputs == 1 else (Y, Y_h.astype(X.dtype)) # type: ignore
class LSTM(CommonLSTM):
def __init__(self, onnx_node, run_params): # type: ignore
CommonLSTM.__init__(self, onnx_node, run_params)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,010 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/lrn.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import math
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class LRN(Base):
@staticmethod
def export() -> None:
alpha = 0.0002
beta = 0.5
bias = 2.0
nsize = 3
node = onnx.helper.make_node(
"LRN",
inputs=["x"],
outputs=["y"],
alpha=alpha,
beta=beta,
bias=bias,
size=nsize,
)
x = np.random.randn(5, 5, 5, 5).astype(np.float32)
square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
for n, c, h, w in np.ndindex(x.shape):
square_sum[n, c, h, w] = sum(
x[
n,
max(0, c - int(math.floor((nsize - 1) / 2))) : min(
5, c + int(math.ceil((nsize - 1) / 2)) + 1
),
h,
w,
]
** 2
)
y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
expect(node, inputs=[x], outputs=[y], name="test_lrn")
@staticmethod
def export_default() -> None:
alpha = 0.0001
beta = 0.75
bias = 1.0
nsize = 3
node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3)
x = np.random.randn(5, 5, 5, 5).astype(np.float32)
square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
for n, c, h, w in np.ndindex(x.shape):
square_sum[n, c, h, w] = sum(
x[
n,
max(0, c - int(math.floor((nsize - 1) / 2))) : min(
5, c + int(math.ceil((nsize - 1) / 2)) + 1
),
h,
w,
]
** 2
)
y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
expect(node, inputs=[x], outputs=[y], name="test_lrn_default")
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], 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59,011 | onnx/onnx | refs/heads/main | /onnx/test/inference_function_test.py | # SPDX-License-Identifier: Apache-2.0
# Copyright (c) ONNX Project Contributors
import unittest
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from onnx import TensorProto, TypeProto
from onnx.checker import ValidationError
from onnx.defs import OpSchema, get_all_schemas_with_history, get_schema
from onnx.helper import (
make_graph,
make_node,
make_opsetid,
make_tensor_type_proto,
make_tensor_value_info,
)
from onnx.numpy_helper import from_array
from onnx.shape_inference import InferenceError, infer_node_outputs
ADD_SCHEMA = max(
(s for s in get_all_schemas_with_history() if s.name == "Add" and s.domain == ""),
key=lambda s: s.since_version,
)
RESHAPE_SCHEMA = max(
(
s
for s in get_all_schemas_with_history()
if s.name == "Reshape" and s.domain == ""
),
key=lambda s: s.since_version,
)
def _to_tensor_types(
tensor_types: Dict[str, Tuple[int, Tuple[Union[int, str, None], ...]]]
) -> Dict[str, TypeProto]:
return {key: make_tensor_type_proto(*value) for key, value in tensor_types.items()}
def _run_case(
schema: OpSchema,
input_names: List[str],
output_names: List[str],
input_types: Dict[str, TypeProto],
input_data: Optional[Dict[str, np.ndarray]] = None,
) -> Dict[str, TypeProto]:
if input_data is None:
input_data = {}
return infer_node_outputs(
schema,
make_node(schema.name, input_names, output_names, domain=schema.domain),
input_types,
{key: from_array(arr) for key, arr in input_data.items()},
)
class TestInferenceFunctionCall(unittest.TestCase):
def test_add_inference(self) -> None:
cases = [
(
{"A": (TensorProto.FLOAT, ()), "B": (TensorProto.FLOAT, ())},
{"C": (TensorProto.FLOAT, ())},
),
(
{
"A": (TensorProto.FLOAT, (None, 2)),
"B": (TensorProto.FLOAT, (2,)),
},
{"C": (TensorProto.FLOAT, (None, 2))},
),
(
{
"A": (TensorProto.FLOAT, (None, 2)),
"B": (TensorProto.FLOAT, (1, 2)),
},
{"C": (TensorProto.FLOAT, (None, 2))},
),
(
{
"A": (TensorProto.DOUBLE, ("n", "m")),
"B": (TensorProto.DOUBLE, (1, "n", "m")),
},
{"C": (TensorProto.DOUBLE, (1, "n", "m"))},
),
(
{
"A": (TensorProto.FLOAT, ("x", 2)),
"B": (TensorProto.FLOAT, ("y", 2)),
},
{"C": (TensorProto.FLOAT, (None, 2))},
),
]
for ins, outs in cases:
assert _run_case(ADD_SCHEMA, ["A", "B"], ["C"], _to_tensor_types(ins)) == _to_tensor_types(outs) # type: ignore
def test_add_inference_raises_errors(self) -> None:
with self.assertRaises(ValidationError):
_run_case(
ADD_SCHEMA,
["A"],
["C"],
_to_tensor_types({"A": (TensorProto.FLOAT, (3, 4))}),
)
with self.assertRaises(ValidationError):
_run_case(
ADD_SCHEMA,
["A", "B"],
["C"],
_to_tensor_types({"A": (TensorProto.FLOAT, (3, 4)), "B": (2, (3, 4))}),
)
with self.assertRaises(InferenceError):
_run_case(
ADD_SCHEMA,
["A", "B"],
["C"],
_to_tensor_types(
{
"A": (TensorProto.FLOAT, (2, 4)),
"B": (TensorProto.FLOAT, (3, 4)),
}
),
)
with self.assertRaises(KeyError):
_run_case(
ADD_SCHEMA,
["A", "B"],
["C"],
_to_tensor_types({"A": (TensorProto.FLOAT, (3, 4))}),
)
def test_reshape_inference(self) -> None:
assert _run_case(
RESHAPE_SCHEMA,
["x", "t"],
["y"],
_to_tensor_types(
{
"x": (TensorProto.FLOAT, (5, 4)),
"t": (TensorProto.INT64, (3,)),
}
),
{"t": np.array([2, 2, 5], dtype=np.int64)},
) == _to_tensor_types({"y": (TensorProto.FLOAT, (2, 2, 5))})
def test_scan_inference_with_subgraph(self) -> None:
seq_len = "sequence"
input_size = 2
loop_state_size = 3
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
make_tensor_value_info("outer", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.FLOAT, (seq_len, input_size)),
]
subgraph = make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Add", ["input", "outer"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
assert infer_node_outputs(
get_schema("Scan", 9),
make_node(
"Scan",
["loop_state_orig", "scan_input", "scan_outer"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
),
_to_tensor_types(
{
"loop_state_orig": (TensorProto.FLOAT, (loop_state_size,)),
"scan_input": (TensorProto.FLOAT, (seq_len, input_size)),
"scan_outer": (TensorProto.FLOAT, (input_size,)),
}
),
# Same as default value in Scan-9
opset_imports=[make_opsetid("", 9)],
ir_version=4,
) == _to_tensor_types(
{
"loop_state_final": (TensorProto.FLOAT, (loop_state_size,)),
"scan_output": (TensorProto.FLOAT, (seq_len, input_size)),
}
)
if __name__ == "__main__":
unittest.main()
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["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,012 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/qlinearconv.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class QLinearConv(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"QLinearConv",
inputs=[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
outputs=["y"],
)
x = np.array(
[
[255, 174, 162, 25, 203, 168, 58],
[15, 59, 237, 95, 129, 0, 64],
[56, 242, 153, 221, 168, 12, 166],
[232, 178, 186, 195, 237, 162, 237],
[188, 39, 124, 77, 80, 102, 43],
[127, 230, 21, 83, 41, 40, 134],
[255, 154, 92, 141, 42, 148, 247],
],
dtype=np.uint8,
).reshape((1, 1, 7, 7))
x_scale = np.float32(0.00369204697)
x_zero_point = np.uint8(132)
w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
w_scale = np.array([0.00172794575], dtype=np.float32)
w_zero_point = np.array([255], dtype=np.uint8)
y_scale = np.float32(0.00162681262)
y_zero_point = np.uint8(123)
output = np.array(
[
[0, 81, 93, 230, 52, 87, 197],
[240, 196, 18, 160, 126, 255, 191],
[199, 13, 102, 34, 87, 243, 89],
[23, 77, 69, 60, 18, 93, 18],
[67, 216, 131, 178, 175, 153, 212],
[128, 25, 234, 172, 214, 215, 121],
[0, 101, 163, 114, 213, 107, 8],
],
dtype=np.uint8,
).reshape((1, 1, 7, 7))
expect(
node,
inputs=[
x,
x_scale,
x_zero_point,
w,
w_scale,
w_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearconv",
)
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59,013 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_scatter_elements.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C3001,R0912,R0913,R0914,R0915,W0108,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def scatter_elements(data, indices, updates, axis=0, reduction=None): # type: ignore
"""
::
// for 3-dim and axis=0
// output[indices[i][j][k]][j][k] = updates[i][j][k]
// for axis 1
// output[i][indices[i][j][k]][k] = updates[i][j][k]
// and so on
"""
if reduction == "add":
def f(x, y):
return x + y
elif reduction == "min":
def f(x, y):
return min(x, y)
elif reduction == "max":
def f(x, y):
return max(x, y)
else:
def f(x, y): # pylint: disable=unused-argument
return y
if axis < 0:
axis = data.ndim + axis
if len(data.shape) == 1 and axis == 0:
scattered = np.copy(data)
for pos, up in zip(indices, updates):
scattered[pos] = f(scattered[pos], up)
return scattered
if len(indices.shape) == 2:
scattered = np.copy(data)
if axis == 0:
for i in range(indices.shape[0]):
for j in range(indices.shape[1]):
scattered[indices[i, j], j] = f(
scattered[indices[i, j], j], updates[i, j]
)
else:
for i in range(indices.shape[0]):
for j in range(indices.shape[1]):
scattered[i, indices[i, j]] = f(
scattered[i, indices[i, j]], updates[i, j]
)
return scattered
if len(indices.shape) == 3:
scattered = np.copy(data)
if axis == 0:
for i in range(indices.shape[0]):
for j in range(indices.shape[1]):
for k in range(indices.shape[2]):
scattered[indices[i, j, k], j, k] = f(
scattered[indices[i, j, k], j, k], updates[i, j, k]
)
elif axis == 1:
for i in range(indices.shape[0]):
for j in range(indices.shape[1]):
for k in range(indices.shape[2]):
scattered[i, indices[i, j, k], k] = f(
scattered[i, indices[i, j, k], k], updates[i, j, k]
)
elif axis == 2:
for i in range(indices.shape[0]):
for j in range(indices.shape[1]):
for k in range(indices.shape[2]):
scattered[i, j, indices[i, j, k]] = f(
scattered[i, j, indices[i, j, k]], updates[i, j, k]
)
return scattered
idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
def make_slice(arr, axis, i): # type: ignore
slc = [slice(None)] * arr.ndim
slc[axis] = i
return slc
def unpack(packed): # type: ignore
unpacked = packed[0]
for i in range(1, len(packed)):
unpacked = unpacked, packed[i]
return unpacked
# We use indices and axis parameters to create idx
# idx is in a form that can be used as a NumPy advanced
# indices for scattering of updates param. in data
idx = [
[
unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0],
]
for i in range(indices.shape[axis])
]
idx = list(np.concatenate(idx, axis=1))
idx.insert(axis, idx.pop())
# updates_idx is a NumPy advanced indices for indexing
# of elements in the updates
updates_idx = list(idx)
updates_idx.pop(axis)
updates_idx.insert( # type: ignore
axis,
np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape)), # type: ignore
)
scattered = np.copy(data)
if reduction == "min":
scattered[tuple(idx)] = np.minimum(
scattered[tuple(idx)], updates[tuple(updates_idx)]
)
elif reduction == "max":
scattered[tuple(idx)] = np.maximum(
scattered[tuple(idx)], updates[tuple(updates_idx)]
)
elif reduction == "add":
scattered[tuple(idx)] += updates[tuple(updates_idx)]
else:
scattered[tuple(idx)] = updates[tuple(updates_idx)]
return scattered
class ScatterElements(OpRun):
def _run(self, data, indices, updates, axis=None, reduction=None): # type: ignore
res = scatter_elements(data, indices, updates, axis=axis, reduction=reduction)
return (res,)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,014 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op_common_indices.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
def _get_indices(i, shape): # type: ignore
res = np.empty((len(shape),), dtype=np.int64)
k = len(shape) - 1
while k > 0:
m = i % shape[k]
res[k] = m
i -= m
i /= shape[k]
k -= 1
res[0] = i
return res
def _is_out(ind, shape): # type: ignore
for i, s in zip(ind, shape):
if i < 0:
return True
if i >= s:
return True
return False
def _get_index(indices, shape): # type: ignore
ind = 0
mul = 1
for pos, sh in zip(reversed(indices), reversed(shape)):
ind += pos * mul
mul *= sh
return ind
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59,015 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_bitshift.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class BitShift(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunBinaryNumpy.__init__(self, np.right_shift, onnx_node, run_params)
if self.direction not in ("LEFT", "RIGHT"): # type: ignore
raise ValueError(f"Unexpected value for direction ({self.direction!r}).") # type: ignore
if self.direction == "LEFT": # type: ignore
self.numpy_fct = np.left_shift
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59,016 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_log_softmax.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops.op_softmax import Softmax
class LogSoftmax(Softmax):
def _run(self, X): # type: ignore
Y = Softmax._run(self, X)[0]
np.log(Y, out=Y)
return (Y,)
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59,017 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_stft.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,R0915,W0613,W0221
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.reference.ops.op_slice import _slice
def _concat(*args, axis=0): # type: ignore
return np.concatenate(args, axis=axis)
def _unsqueeze(a, axis): # type: ignore
try:
return np.expand_dims(a, axis=axis)
except TypeError:
# numpy 1.18 supports axes as a tuple
if len(axis) == 1:
return np.expand_dims(a, axis=tuple(axis)[0])
for x in reversed(axis):
a = np.expand_dims(a, axis=x)
return a
def _stft(x, fft_length, hop_length, n_frames, window, onesided=False): # type: ignore
"""
Applies one dimensional FFT with window weights.
torch defines the number of frames as:
`n_frames = 1 + (len - n_fft) // hop_length`.
"""
last_axis = len(x.shape) - 1 # op.Sub(op.Shape(op.Shape(x)), one)
axis = [-2]
axis2 = [-3]
window_size = window.shape[0]
# building frames
seq = []
for fs in range(n_frames):
begin = fs * hop_length
end = begin + window_size
sliced_x = _slice(x, np.array([begin]), np.array([end]), axis) # type: ignore
# sliced_x may be smaller
new_dim = sliced_x.shape[-2:-1]
missing = (window_size - new_dim[0],)
new_shape = sliced_x.shape[:-2] + missing + sliced_x.shape[-1:]
cst = np.zeros(new_shape, dtype=x.dtype)
pad_sliced_x = _concat(sliced_x, cst, axis=-2)
# same size
un_sliced_x = _unsqueeze(pad_sliced_x, axis2)
seq.append(un_sliced_x)
# concatenation
new_x = _concat_from_sequence(seq, axis=-3, new_axis=0)
# calling weighted dft with weights=window
shape_x = new_x.shape
shape_x_short = shape_x[:-2]
shape_x_short_one = tuple(1 for _ in shape_x_short)
window_shape = (*shape_x_short_one, window_size, 1)
weights = np.reshape(window, window_shape)
weighted_new_x = new_x * weights
result = _dft(
weighted_new_x, fft_length, last_axis, onesided=onesided
) # normalize=False
return result
def _istft(x, fft_length, hop_length, window, onesided=False): # type: ignore
"""
Reverses of `stft`.
"""
zero = [0]
one = [1]
two = [2]
axisf = [-2]
n_frames = x.shape[-2]
expected_signal_len = fft_length[0] + hop_length * (n_frames - 1)
# building frames
seqr = []
seqi = []
seqc = []
for fs in range(n_frames):
begin = fs
end = fs + 1
frame_x = np.squeeze(
_slice(x, np.array([begin]), np.array([end]), axisf), axis=axisf[0] # type: ignore
)
# ifft
ift = _dft(frame_x, fft_length, axis=-1, onesided=onesided, normalize=True)
n_dims = len(ift.shape)
# real part
n_dims_1 = n_dims - 1
sliced = _slice(ift, np.array(zero), np.array(one), [n_dims_1]) # type: ignore
ytmp = np.squeeze(sliced, axis=n_dims_1)
ctmp = np.full(ytmp.shape, fill_value=1, dtype=x.dtype) * window
shape_begin = ytmp.shape[:-1]
n_left = fs * hop_length
size = ytmp.shape[-1]
n_right = expected_signal_len - (n_left + size)
left_shape = (*shape_begin, n_left)
right_shape = (*shape_begin, n_right)
right = np.zeros(right_shape, dtype=x.dtype)
left = np.zeros(left_shape, dtype=x.dtype)
y = _concat(left, ytmp, right, axis=-1)
yc = _concat(left, ctmp, right, axis=-1)
# imaginary part
sliced = _slice(ift, np.array(one), np.array(two), [n_dims_1]) # type: ignore
itmp = np.squeeze(sliced, axis=n_dims_1)
yi = _concat(left, itmp, right, axis=-1)
# append
seqr.append(_unsqueeze(y, axis=-1))
seqi.append(_unsqueeze(yi, axis=-1))
seqc.append(_unsqueeze(yc, axis=-1))
# concatenation
redr = _concat_from_sequence(seqr, axis=-1, new_axis=0)
redi = _concat_from_sequence(seqi, axis=-1, new_axis=0)
redc = _concat_from_sequence(seqc, axis=-1, new_axis=0)
# unweight
resr = redr.sum(axis=-1, keepdims=0) # type: ignore
resi = redi.sum(axis=-1, keepdims=0) # type: ignore
resc = redc.sum(axis=-1, keepdims=0) # type: ignore
rr = resr / resc
ri = resi / resc
# Make complex
rr0 = np.expand_dims(rr, axis=0)
ri0 = np.expand_dims(ri, axis=0)
conc = _concat(rr0, ri0, axis=0)
# rotation, bring first dimension to the last position
result_shape = conc.shape
reshaped_result = conc.reshape((2, -1))
transposed = np.transpose(reshaped_result, (1, 0))
other_dimensions = result_shape[1:]
final_shape = _concat(other_dimensions, two, axis=0)
final = transposed.reshape(final_shape)
return final
class STFT(OpRun):
def _run(self, x, frame_step, window=None, frame_length=None, onesided=None): # type: ignore
if frame_length is None:
if window is None:
frame_length = x.shape[-2]
else:
frame_length = window.shape[0]
hop_length = frame_step
if window is None:
window = np.ones((frame_length,), dtype=x.dtype)
n_frames = 1 + (x.shape[-2] - frame_length) // frame_step
res = _stft(x, [frame_length], hop_length, n_frames, window, onesided=onesided)
return (res.astype(x.dtype),)
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59,018 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_dequantize_linear.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Optional, Tuple
import numpy as np
from onnx import TensorProto
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.numpy_helper import float8e4m3_to_float32, float8e5m2_to_float32
from onnx.reference.custom_element_types import (
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
from onnx.reference.op_run import OpRun
class DequantizeLinear(OpRun):
def get_x_type(self, x: np.ndarray) -> int:
if x.dtype == float8e4m3fn and x.dtype.descr[0][0] == "e4m3fn":
return TensorProto.FLOAT8E4M3FN
if x.dtype == float8e4m3fnuz and x.dtype.descr[0][0] == "e4m3fnuz":
return TensorProto.FLOAT8E4M3FNUZ
if x.dtype == float8e5m2 and x.dtype.descr[0][0] == "e5m2":
return TensorProto.FLOAT8E5M2
if x.dtype == float8e5m2fnuz and x.dtype.descr[0][0] == "e5m2fnuz":
return TensorProto.FLOAT8E5M2FNUZ
return np_dtype_to_tensor_dtype(x.dtype)
@staticmethod
def reshape_input(
value: np.ndarray, shape: Tuple[int, ...], axis: Optional[int]
) -> np.ndarray:
if axis is None:
raise ValueError("axis cannot be None.")
if len(value.shape) == 0:
return value
dims = [1] * len(shape)
try:
dims[axis] = value.size
except IndexError as e:
raise IndexError(
f"axis is out of boundary, axis={axis}, "
f"value.shape={value.shape}, shape={shape}."
) from e
return value.reshape(tuple(dims))
def _run(
self,
x: np.ndarray,
x_scale: np.ndarray,
x_zero_point: Optional[np.ndarray] = None,
axis: Optional[int] = None,
): # type: ignore
if len(x_scale.shape) > 1:
raise RuntimeError("Input 2 must be a vector or a number.")
x_type = self.get_x_type(x)
f8_type = x_type in {
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
}
if x_zero_point is not None and not f8_type:
zero_type = self.get_x_type(x_zero_point)
if x_type != zero_type:
raise RuntimeError(
f"Type mismatch {x_type} != {zero_type} in DequantizeLinear."
)
dx = x.astype(np.float32) - DequantizeLinear.reshape_input(
x_zero_point, x.shape, axis
)
else:
if f8_type and x_zero_point is not None:
u_x_zero_point = x_zero_point.astype(np.uint8)
umi = u_x_zero_point.min()
uma = u_x_zero_point.max()
if umi != uma or umi != np.uint8(0):
raise RuntimeError(
"x_zero_point is not null but should be zero for float 8 types."
)
if x_type == TensorProto.FLOAT8E4M3FN:
dx = float8e4m3_to_float32(x)
elif x_type == TensorProto.FLOAT8E4M3FNUZ:
dx = float8e4m3_to_float32(x, uz=True)
elif x_type == TensorProto.FLOAT8E5M2:
dx = float8e5m2_to_float32(x)
elif x_type == TensorProto.FLOAT8E5M2FNUZ:
dx = float8e5m2_to_float32(x, fn=True, uz=True)
else:
dx = x.astype(np.float32)
y = dx * DequantizeLinear.reshape_input(x_scale, x.shape, axis)
return (y.astype(x_scale.dtype),)
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59,019 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_deform_conv.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _deform_conv_implementation( # type: ignore
X, W, offset, B, mask, dilations, group, kernel_shape, offset_group, pads, strides
):
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if pads is None:
pads = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
if group is None:
group = 1
if offset_group is None:
offset_group = 1
n, ic = X.shape[:2]
oc = W.shape[0]
output_shape = offset.shape[2:]
if ic != W.shape[1] * group or oc % group != 0:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={group}."
)
ics_per_group, ocs_per_group = W.shape[1], oc // group
if ic % offset_group != 0:
raise ValueError("Number of input channels must be divisible by offset_group.")
ics_per_offset_group = ic // offset_group
if offset_group * np.prod(kernel_shape) * len(kernel_shape) != offset.shape[1]:
raise ValueError(
f"Offset shape {offset.shape} is inconsistent with offset_group {offset_group} "
f"and kernel shape {kernel_shape}."
)
offset = offset.reshape(
(n, offset_group, *kernel_shape, len(kernel_shape), *output_shape)
)
if mask is None:
mask = np.ones((n, offset_group * np.prod(kernel_shape), *output_shape))
mask = mask.reshape((n, offset_group, *kernel_shape, *output_shape))
# pylint: disable=import-outside-toplevel
from onnx.reference.ops._op_list import GridSample
if len(X.shape) == 4:
ih, iw = X.shape[2:]
oh, ow = offset.shape[-2:]
kh, kw = kernel_shape
sth, stw = strides
dh, dw = dilations
kh_new, kw_new = (kh - 1) * dh + 1, (kw - 1) * dw + 1
if oh != int(((ih - kh_new + pads[0] + pads[2]) / sth) + 1) or ow != int(
((iw - kw_new + pads[1] + pads[3]) / stw) + 1
):
raise RuntimeError(
"Padding, dilation, stride, and kernel shape incompatible with output shape."
)
bh, bw = -pads[0], -pads[1]
res = np.zeros((n, oc, oh, ow), dtype=X.dtype)
if B is not None:
res[:, :, :, :] = B.reshape((1, -1, 1, 1))
# Calculate coordinates of sampling points within kernel
kernel_pos_w, kernel_pos_h = np.meshgrid(
np.arange(0, kw_new, dw), np.arange(0, kh_new, dh)
)
kernel_pos_wrt_first_elem = np.stack(
(kernel_pos_h, kernel_pos_w), axis=2
) # shape (kH, kW, 2)
for batch_idx in range(n):
for oc_idx in range(oc):
for ic_idx in range(ic):
# Group convolution logic
if ic_idx // ics_per_group != oc_idx // ocs_per_group:
# Input channel and output channel don't belong to same group
continue
# Offset group logic
offset_group_idx = ic_idx // ics_per_offset_group
for i in range(oh):
h_coord = bh + sth * i
for j in range(ow):
w_coord = bw + stw * j
# (h_coord, w_coord) is coord of top left elem of kernel
kernel = np.copy(kernel_pos_wrt_first_elem).astype(float)
kernel[:, :, 0] += (
h_coord
+ offset[batch_idx, offset_group_idx, :, :, 0, i, j]
)
kernel[:, :, 1] += (
w_coord
+ offset[batch_idx, offset_group_idx, :, :, 1, i, j]
)
# GridSample expects normalized grid coordinates
kernel[:, :, 0] = kernel[:, :, 0] / (ih - 1) * 2 - 1
kernel[:, :, 1] = kernel[:, :, 1] / (iw - 1) * 2 - 1
kernel = np.expand_dims(kernel, 0) # add batch dimension
kernel = np.flip(
kernel, 3
) # spatial GridSample expects (x, y) input
grid_sample_output = GridSample.eval(
X[batch_idx : batch_idx + 1, ic_idx : ic_idx + 1],
kernel,
align_corners=1,
)
conv_value = np.multiply(
grid_sample_output,
W[oc_idx, ic_idx % ics_per_group, :, :],
)
conv_value = np.multiply(
conv_value,
mask[batch_idx, offset_group_idx, :, :, i, j],
)
res[batch_idx, oc_idx, i, j] += np.sum(conv_value)
return res
raise RuntimeError(
f"The convolution for X.shape={X.shape}, W.shape={W.shape}, "
f"kernel_shape={kernel_shape} is not implemented yet."
)
class DeformConv(OpRun):
def _run( # type: ignore
self,
X,
W,
offset,
B=None,
mask=None,
dilations=None,
group=None,
kernel_shape=None,
offset_group=None,
pads=None,
strides=None,
):
if len(X.shape) < 3:
raise ValueError(
f"X must have at least 3 dimensions but its shape is {X.shape}."
)
return (
_deform_conv_implementation(
X,
W,
offset,
B,
mask,
dilations,
group,
kernel_shape,
offset_group,
pads,
strides,
),
)
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59,020 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/spacetodepth.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class SpaceToDepth(Base):
@staticmethod
def export() -> None:
b, c, h, w = shape = (2, 2, 6, 6)
blocksize = 2
node = onnx.helper.make_node(
"SpaceToDepth",
inputs=["x"],
outputs=["y"],
blocksize=blocksize,
)
x = np.random.random_sample(shape).astype(np.float32)
tmp = np.reshape(
x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]
)
tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth")
@staticmethod
def export_example() -> None:
node = onnx.helper.make_node(
"SpaceToDepth",
inputs=["x"],
outputs=["y"],
blocksize=2,
)
# (1, 1, 4, 6) input tensor
x = np.array(
[
[
[
[0, 6, 1, 7, 2, 8],
[12, 18, 13, 19, 14, 20],
[3, 9, 4, 10, 5, 11],
[15, 21, 16, 22, 17, 23],
]
]
]
).astype(np.float32)
# (1, 4, 2, 3) output tensor
y = np.array(
[
[
[[0, 1, 2], [3, 4, 5]],
[[6, 7, 8], [9, 10, 11]],
[[12, 13, 14], [15, 16, 17]],
[[18, 19, 20], [21, 22, 23]],
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example")
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], 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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,021 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_split.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class CommonSplit(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_node.output)
def common_run(self, mat, split, axis, num_outputs): # type: ignore
n_outputs = num_outputs or self.n_outputs
if split is None:
if mat.shape[axis] % n_outputs == 0:
div = mat.shape[axis] // n_outputs
split = [div] * n_outputs
else:
div = mat.shape[axis] // n_outputs + 1
split = [div] * n_outputs
split[-1] += mat.shape[axis] - sum(split) # type: ignore
sli = [slice(0, s) for s in mat.shape]
res = []
pos = 0
for spl in split:
sli[axis] = slice(pos, pos + spl) # type: ignore
pos += spl
res.append(mat[tuple(sli)])
return tuple(res)
class Split_2(CommonSplit):
def _run(self, mat, axis=None, split=None): # type: ignore
return self.common_run(mat, split, axis=axis, num_outputs=None) # type: ignore
class Split_11(Split_2):
pass
class Split_13(CommonSplit):
def _run(self, mat, split=None, axis=None): # type: ignore
return self.common_run(mat, split, axis=axis, num_outputs=None)
class Split_18(CommonSplit):
def _run(self, mat, split=None, axis=None, num_outputs=None): # type: ignore
return self.common_run(mat, split, axis=axis, num_outputs=num_outputs)
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