index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
58,822 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_hamming_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HammingWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = \\alpha - \\beta \\cos \\left( \\frac{\\pi n}{N-1} \\right)`
where *N* is the window length.
See `hamming_window
<https://pytorch.org/docs/stable/generated/torch.hamming_window.html>`_.
`alpha=0.54, beta=0.46`
"""
def _run(self, size, output_datatype=None, periodic=None): # type: ignore
ni, N_1 = self._begin(size, periodic, output_datatype)
alpha = 25.0 / 46.0
beta = 1 - alpha
res = alpha - np.cos(ni * np.pi * 2 / N_1) * beta
return self._end(size, res, output_datatype)
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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"]} |
58,823 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_unique.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,W0622
import numpy as np
from onnx.reference.op_run import OpRun
def _specify_int64(indices, inverse_indices, counts): # type: ignore
return (
np.array(indices, dtype=np.int64),
np.array(inverse_indices, dtype=np.int64),
np.array(counts, dtype=np.int64),
)
class Unique(OpRun):
def _run(self, x, axis=None, sorted=None): # type: ignore
if axis is None or np.isnan(axis):
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
else:
y, indices, inverse_indices, counts = np.unique(
x, True, True, True, axis=axis
)
if len(self.onnx_node.output) == 1:
return (y,)
if not sorted:
argsorted_indices = np.argsort(indices)
inverse_indices_map = dict(
zip(argsorted_indices, np.arange(len(argsorted_indices)))
)
indices = indices[argsorted_indices]
y = np.take(x, indices, axis=0)
inverse_indices = np.asarray(
[inverse_indices_map[i] for i in inverse_indices], dtype=np.int64
)
counts = counts[argsorted_indices]
indices, inverse_indices, counts = _specify_int64(
indices, inverse_indices, counts
)
if len(self.onnx_node.output) == 2:
return (y, indices)
if len(self.onnx_node.output) == 3:
return (y, indices, inverse_indices)
return (y, indices, inverse_indices, counts)
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58,824 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sqrt.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.ops._op import OpRunUnaryNum
class Sqrt(OpRunUnaryNum):
def _run(self, x): # type: ignore
with catch_warnings():
simplefilter("ignore")
return (np.sqrt(x).astype(x.dtype),)
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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"]} |
58,825 | onnx/onnx | refs/heads/main | /onnx/test/serialization_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import os
import tempfile
import unittest
import onnx
_TEST_MODEL = """\
<
ir_version: 8,
opset_import: ["" : 17, "local" : 1]
>
agraph (float[N] X) => (float[N] Y) {
Y = local.foo (X)
}
<opset_import: ["" : 17, "local" : 1], domain: "local">
foo (x) => (y) {
temp = Add(x, x)
y = local.bar(temp)
}
<opset_import: ["" : 17], domain: "local">
bar (x) => (y) {
y = Mul (x, x)
}"""
class _OnnxTestTextualSerializer(onnx.serialization.ProtoSerializer):
"""Serialize and deserialize the ONNX textual representation."""
supported_format = "onnxtext"
file_extensions = frozenset({".onnxtext"})
def serialize_proto(self, proto) -> bytes:
text = onnx.printer.to_text(proto)
return text.encode("utf-8")
def deserialize_proto(self, serialized: bytes, proto):
text = serialized.decode("utf-8")
if isinstance(proto, onnx.ModelProto):
return onnx.parser.parse_model(text)
if isinstance(proto, onnx.GraphProto):
return onnx.parser.parse_graph(text)
if isinstance(proto, onnx.FunctionProto):
return onnx.parser.parse_function(text)
if isinstance(proto, onnx.NodeProto):
return onnx.parser.parse_node(text)
raise ValueError(f"Unsupported proto type: {type(proto)}")
class TestRegistry(unittest.TestCase):
def setUp(self) -> None:
self.serializer = _OnnxTestTextualSerializer()
onnx.serialization.registry.register(self.serializer)
def test_get_returns_the_registered_instance(self) -> None:
serializer = onnx.serialization.registry.get("onnxtext")
self.assertIs(serializer, self.serializer)
def test_get_raises_for_unsupported_format(self) -> None:
with self.assertRaises(ValueError):
onnx.serialization.registry.get("unsupported")
def test_onnx_save_load_model_uses_the_custom_serializer(self) -> None:
model = onnx.parser.parse_model(_TEST_MODEL)
with tempfile.TemporaryDirectory() as tmpdir:
model_path = os.path.join(tmpdir, "model.onnx")
onnx.save_model(model, model_path, format="onnxtext")
# Check the file content
with open(model_path, encoding="utf-8") as f:
content = f.read()
self.assertEqual(content, onnx.printer.to_text(model))
loaded_model = onnx.load_model(model_path, format="onnxtext")
self.assertEqual(
model.SerializeToString(deterministic=True),
loaded_model.SerializeToString(deterministic=True),
)
class TestCustomSerializer(unittest.TestCase):
def test_serialize_deserialize_model(self) -> None:
serializer = _OnnxTestTextualSerializer()
model = onnx.parser.parse_model(_TEST_MODEL)
serialized = serializer.serialize_proto(model)
deserialized = serializer.deserialize_proto(serialized, onnx.ModelProto())
self.assertEqual(
model.SerializeToString(deterministic=True),
deserialized.SerializeToString(deterministic=True),
)
| {"/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": 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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"]} |
58,826 | onnx/onnx | refs/heads/main | /onnx/defs/gen_shape_inference_information.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from onnx import defs
def main() -> None:
# domain -> support level -> name -> [schema]
with_inference = []
without_inference = []
for schema in defs.get_all_schemas():
domain, name, has_inference = (
schema.domain,
schema.name,
schema.has_type_and_shape_inference_function,
)
elem = (domain, name)
if has_inference:
with_inference.append(elem)
else:
without_inference.append(elem)
print(len(with_inference), "operators have a type/shape inference function.")
print(len(without_inference), "do not. These are:")
for domain, name in sorted(without_inference):
print(domain, name)
if __name__ == "__main__":
main()
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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"]} |
58,827 | onnx/onnx | refs/heads/main | /onnx/test/elu_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from onnx import checker, defs, helper
class TestRelu(unittest.TestCase):
def test_elu(self) -> None:
self.assertTrue(defs.has("Elu"))
node_def = helper.make_node("Elu", ["X"], ["Y"], alpha=1.0)
checker.check_node(node_def)
if __name__ == "__main__":
unittest.main()
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58,828 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bitwiseor.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 BitwiseOr(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d")
# 4d
x = create_random_int((3, 4, 5, 6), np.int8)
y = create_random_int((3, 4, 5, 6), np.int8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d")
@staticmethod
def export_bitwiseor_broadcast() -> None:
node = onnx.helper.make_node(
"BitwiseOr",
inputs=["x", "y"],
outputs=["bitwiseor"],
)
# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d")
# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d")
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58,829 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/conv.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 Conv(Base):
@staticmethod
def export() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with padding
node_with_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[1, 1, 1, 1],
)
y_with_padding = np.array(
[
[
[
[12.0, 21.0, 27.0, 33.0, 24.0], # (1, 1, 5, 5) output tensor
[33.0, 54.0, 63.0, 72.0, 51.0],
[63.0, 99.0, 108.0, 117.0, 81.0],
[93.0, 144.0, 153.0, 162.0, 111.0],
[72.0, 111.0, 117.0, 123.0, 84.0],
]
]
]
).astype(np.float32)
expect(
node_with_padding,
inputs=[x, W],
outputs=[y_with_padding],
name="test_basic_conv_with_padding",
)
# Convolution without padding
node_without_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[0, 0, 0, 0],
)
y_without_padding = np.array(
[
[
[
[54.0, 63.0, 72.0], # (1, 1, 3, 3) output tensor
[99.0, 108.0, 117.0],
[144.0, 153.0, 162.0],
]
]
]
).astype(np.float32)
expect(
node_without_padding,
inputs=[x, W],
outputs=[y_without_padding],
name="test_basic_conv_without_padding",
)
@staticmethod
def export_conv_with_strides() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 7, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with strides=2 and padding
node_with_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_padding = np.array(
[
[
[
[12.0, 27.0, 24.0], # (1, 1, 4, 3) output tensor
[63.0, 108.0, 81.0],
[123.0, 198.0, 141.0],
[112.0, 177.0, 124.0],
]
]
]
).astype(np.float32)
expect(
node_with_padding,
inputs=[x, W],
outputs=[y_with_padding],
name="test_conv_with_strides_padding",
)
# Convolution with strides=2 and no padding
node_without_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[0, 0, 0, 0],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_without_padding = np.array(
[
[
[
[54.0, 72.0], # (1, 1, 3, 2) output tensor
[144.0, 162.0],
[234.0, 252.0],
]
]
]
).astype(np.float32)
expect(
node_without_padding,
inputs=[x, W],
outputs=[y_without_padding],
name="test_conv_with_strides_no_padding",
)
# Convolution with strides=2 and padding only along one dimension (the H dimension in NxCxHxW tensor)
node_with_asymmetric_padding = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[1, 0, 1, 0],
strides=[
2,
2,
], # Default values for other attributes: dilations=[1, 1], groups=1
)
y_with_asymmetric_padding = np.array(
[
[
[
[21.0, 33.0], # (1, 1, 4, 2) output tensor
[99.0, 117.0],
[189.0, 207.0],
[171.0, 183.0],
]
]
]
).astype(np.float32)
expect(
node_with_asymmetric_padding,
inputs=[x, W],
outputs=[y_with_asymmetric_padding],
name="test_conv_with_strides_and_asymmetric_padding",
)
@staticmethod
def export_conv_with_autopad_same() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 5, 5) input tensor
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
W = np.array(
[
[
[
[1.0, 1.0, 1.0], # (1, 1, 3, 3) tensor for convolution weights
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
# Convolution with auto_pad='SAME_LOWER' and strides=2
node = onnx.helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
auto_pad="SAME_LOWER",
kernel_shape=[3, 3],
strides=[2, 2],
)
y = np.array(
[[[[12.0, 27.0, 24.0], [63.0, 108.0, 81.0], [72.0, 117.0, 84.0]]]]
).astype(np.float32)
expect(node, inputs=[x, W], outputs=[y], name="test_conv_with_autopad_same")
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58,830 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/pow.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 pow(x, y): # type: ignore
z = np.power(x, y).astype(x.dtype)
return z
class Pow(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")
x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow")
@staticmethod
def export_pow_broadcast() -> None:
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array(2).astype(np.float32)
z = pow(x, y) # expected output [1., 4., 9.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
y = np.array([1, 2, 3]).astype(np.float32)
# expected output [[1, 4, 27], [4, 25, 216]]
z = pow(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")
@staticmethod
def export_types() -> None:
node = onnx.helper.make_node(
"Pow",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")
x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")
x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.float32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint64)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.uint32)
z = pow(x, y) # expected output [1., 32., 729.]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")
x = np.array([1, 2, 3]).astype(np.int64)
y = np.array([4, 5, 6]).astype(np.int64)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")
x = np.array([1, 2, 3]).astype(np.int32)
y = np.array([4, 5, 6]).astype(np.int32)
z = pow(x, y) # expected output [1, 32, 729]
expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")
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58,831 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_scan.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0914,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class Scan(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
if not hasattr(self.body, "run"): # type: ignore
raise RuntimeError(
f"Parameter 'body' must have a method 'run', type {type(self.body)}." # type: ignore
)
self.input_directions_ = [
0
if self.scan_input_directions is None # type: ignore
or i >= len(self.scan_input_directions) # type: ignore
else self.scan_input_directions[i] # type: ignore
for i in range(self.num_scan_inputs) # type: ignore
]
max_dir_in = max(self.input_directions_)
if max_dir_in != 0:
raise RuntimeError(
"Scan is not implemented for other output input_direction than 0."
)
self.input_axes_ = [
0
if self.scan_input_axes is None or i >= len(self.scan_input_axes) # type: ignore
else self.scan_input_axes[i] # type: ignore
for i in range(self.num_scan_inputs) # type: ignore
]
max_axe_in = max(self.input_axes_)
if max_axe_in != 0:
raise RuntimeError("Scan is not implemented for other input axes than 0.")
self.input_names = self.body.input_names # type: ignore
self.output_names = self.body.output_names # type: ignore
def _common_run_shape(self, *args): # type: ignore
num_loop_state_vars = len(args) - self.num_scan_inputs # type: ignore
num_scan_outputs = len(args) - num_loop_state_vars
output_directions = [
0
if self.scan_output_directions is None # type: ignore
or i >= len(self.scan_output_directions) # type: ignore
else self.scan_output_directions[i] # type: ignore
for i in range(num_scan_outputs)
]
max_dir_out = max(output_directions)
if max_dir_out != 0:
raise RuntimeError(
"Scan is not implemented for other output output_direction than 0."
)
output_axes = [
0
if self.scan_output_axes is None or i >= len(self.scan_output_axes) # type: ignore
else self.scan_output_axes[i] # type: ignore
for i in range(num_scan_outputs)
]
max_axe_out = max(output_axes)
if max_axe_out != 0:
raise RuntimeError("Scan is not implemented for other output axes than 0.")
state_names_in = self.input_names[: self.num_scan_inputs] # type: ignore
state_names_out = self.output_names[: len(state_names_in)]
scan_names_in = self.input_names[num_loop_state_vars:]
scan_names_out = self.output_names[num_loop_state_vars:]
scan_values = args[num_loop_state_vars:]
states = args[:num_loop_state_vars]
return (
num_loop_state_vars,
num_scan_outputs,
output_directions,
max_dir_out,
output_axes,
max_axe_out,
state_names_in,
state_names_out,
scan_names_in,
scan_names_out,
scan_values,
states,
)
def _run( # type:ignore
self,
*args,
body=None,
num_scan_inputs=None,
scan_input_axes=None,
scan_input_directions=None,
scan_output_axes=None,
scan_output_directions=None,
attributes=None,
):
# TODO: support overridden attributes.
(
num_loop_state_vars,
num_scan_outputs, # pylint: disable=W0612
output_directions, # pylint: disable=W0612
max_dir_out, # pylint: disable=W0612
output_axes, # pylint: disable=W0612
max_axe_out, # pylint: disable=W0612
state_names_in,
state_names_out,
scan_names_in,
scan_names_out,
scan_values,
states,
) = self._common_run_shape(*args)
max_iter = args[num_loop_state_vars].shape[self.input_axes_[0]]
results = [[] for _ in scan_names_out] # type: ignore
for it in range(max_iter):
inputs = {}
for name, value in zip(state_names_in, states):
inputs[name] = value
for name, value in zip(scan_names_in, scan_values):
inputs[name] = value[it]
try:
outputs_list = self._run_body(inputs) # type: ignore
except TypeError as e:
raise TypeError(
f"Unable to call 'run' for type '{type(self.body)}'." # type: ignore
) from e
outputs = dict(zip(self.output_names, outputs_list))
states = [outputs[name] for name in state_names_out]
for i, name in enumerate(scan_names_out):
results[i].append(np.expand_dims(outputs[name], axis=0))
for res in results:
conc = np.vstack(res)
states.append(conc)
return tuple(states)
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58,832 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reduce_mean.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
class ReduceMean_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None): # type: ignore
axes = tuple(axes) if axes is not None else None
res = np.mean(data, axis=axes, keepdims=keepdims, dtype=data.dtype)
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,)
class ReduceMean_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
try:
res = np.mean(data, axis=axes, keepdims=keepdims, dtype=data.dtype) # type: ignore
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,) # type: ignore
except TypeError as e:
raise TypeError(
f"Unable to reduce shape {data.shape!r} with axes={axes!r} and keepdims={keepdims}."
) from e
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58,833 | onnx/onnx | refs/heads/main | /onnx/tools/net_drawer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# A library and utility for drawing ONNX nets. Most of this implementation has
# been borrowed from the caffe2 implementation
# https://github.com/pytorch/pytorch/blob/master/caffe2/python/net_drawer.py
#
# The script takes two required arguments:
# -input: a path to a serialized ModelProto .pb file.
# -output: a path to write a dot file representation of the graph
#
# Given this dot file representation, you can-for example-export this to svg
# with the graphviz `dot` utility, like so:
#
# $ dot -Tsvg my_output.dot -o my_output.svg
import argparse
import json
from collections import defaultdict
from typing import Any, Callable, Dict, Optional
import pydot
from onnx import GraphProto, ModelProto, NodeProto
OP_STYLE = {
"shape": "box",
"color": "#0F9D58",
"style": "filled",
"fontcolor": "#FFFFFF",
}
BLOB_STYLE = {"shape": "octagon"}
_NodeProducer = Callable[[NodeProto, int], pydot.Node]
def _escape_label(name: str) -> str:
# json.dumps is poor man's escaping
return json.dumps(name)
def _form_and_sanitize_docstring(s: str) -> str:
url = "javascript:alert("
url += _escape_label(s).replace('"', "'").replace("<", "").replace(">", "")
url += ")"
return url
def GetOpNodeProducer( # noqa: N802
embed_docstring: bool = False, **kwargs: Any
) -> _NodeProducer:
def really_get_op_node(op: NodeProto, op_id: int) -> pydot.Node:
if op.name:
node_name = f"{op.name}/{op.op_type} (op#{op_id})"
else:
node_name = f"{op.op_type} (op#{op_id})"
for i, input_ in enumerate(op.input):
node_name += "\n input" + str(i) + " " + input_
for i, output in enumerate(op.output):
node_name += "\n output" + str(i) + " " + output
node = pydot.Node(node_name, **kwargs)
if embed_docstring:
url = _form_and_sanitize_docstring(op.doc_string)
node.set_URL(url)
return node
return really_get_op_node
def GetPydotGraph( # noqa: N802
graph: GraphProto,
name: Optional[str] = None,
rankdir: str = "LR",
node_producer: Optional[_NodeProducer] = None,
embed_docstring: bool = False,
) -> pydot.Dot:
if node_producer is None:
node_producer = GetOpNodeProducer(embed_docstring=embed_docstring, **OP_STYLE)
pydot_graph = pydot.Dot(name, rankdir=rankdir)
pydot_nodes: Dict[str, pydot.Node] = {}
pydot_node_counts: Dict[str, int] = defaultdict(int)
for op_id, op in enumerate(graph.node):
op_node = node_producer(op, op_id)
pydot_graph.add_node(op_node)
for input_name in op.input:
if input_name not in pydot_nodes:
input_node = pydot.Node(
_escape_label(input_name + str(pydot_node_counts[input_name])),
label=_escape_label(input_name),
**BLOB_STYLE,
)
pydot_nodes[input_name] = input_node
else:
input_node = pydot_nodes[input_name]
pydot_graph.add_node(input_node)
pydot_graph.add_edge(pydot.Edge(input_node, op_node))
for output_name in op.output:
if output_name in pydot_nodes:
pydot_node_counts[output_name] += 1
output_node = pydot.Node(
_escape_label(output_name + str(pydot_node_counts[output_name])),
label=_escape_label(output_name),
**BLOB_STYLE,
)
pydot_nodes[output_name] = output_node
pydot_graph.add_node(output_node)
pydot_graph.add_edge(pydot.Edge(op_node, output_node))
return pydot_graph
def main() -> None:
parser = argparse.ArgumentParser(description="ONNX net drawer")
parser.add_argument(
"--input",
type=str,
required=True,
help="The input protobuf file.",
)
parser.add_argument(
"--output",
type=str,
required=True,
help="The output protobuf file.",
)
parser.add_argument(
"--rankdir",
type=str,
default="LR",
help="The rank direction of the pydot graph.",
)
parser.add_argument(
"--embed_docstring",
action="store_true",
help="Embed docstring as javascript alert. Useful for SVG format.",
)
args = parser.parse_args()
model = ModelProto()
with open(args.input, "rb") as fid:
content = fid.read()
model.ParseFromString(content)
pydot_graph = GetPydotGraph(
model.graph,
name=model.graph.name,
rankdir=args.rankdir,
node_producer=GetOpNodeProducer(
embed_docstring=args.embed_docstring, **OP_STYLE
),
)
pydot_graph.write_dot(args.output)
if __name__ == "__main__":
main()
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58,834 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/momentum.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.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
def apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Coefficient of gradient should be 1 at the first iteration.
beta_adjusted = beta if t > 0 else 1
# Update momentum.
v_new = alpha * v + beta_adjusted * g_regularized
# Apply SG with momentum update rule.
x_new = x - r * v_new
return x_new, v_new
def apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Coefficient of gradient should be 1 at the first iteration.
beta_adjusted = beta if t > 0 else 1
# Update momentum.
v_new = alpha * v + beta_adjusted * g_regularized
# Apply Nesterov with momentum update rule.
x_new = x - r * (g_regularized + alpha * v_new)
return x_new, v_new
class Momentum(Base):
@staticmethod
def export_momentum() -> None:
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.1
# Create operator.
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X", "G", "V"],
outputs=["X_new", "V_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="standard",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
# Compute expected outputs of Momentum.
x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x, g, v],
outputs=[x_new, v_new],
name="test_momentum",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
@staticmethod
def export_nesterov_momentum() -> None:
# Define operator attributes.
norm_coefficient = 0.01
alpha = 0.95
beta = 1.0
# Create operator.
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X", "G", "V"],
outputs=["X_new", "V_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="nesterov",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
# Compute expected outputs of Momentum.
x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x, g, v],
outputs=[x_new, v_new],
name="test_nesterov_momentum",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
@staticmethod
def export_momentum_multiple() -> None:
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.85
node = onnx.helper.make_node(
"Momentum",
inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
outputs=["X1_new", "X2_new", "V1_new", "V2_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode="standard",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
v1 = np.array([2.0], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
v2 = np.array([4.0, 1.0], dtype=np.float32)
# Compute expected outputs of Momentum.
x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta)
x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, v1, v2],
outputs=[x1_new, x2_new, v1_new, v2_new],
name="test_momentum_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
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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"]} |
58,835 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_shape.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.reference.op_run import OpRun
class Shape_1(OpRun):
def _run(self, data): # type: ignore
return (np.array(data.shape, dtype=np.int64),)
class Shape_15(Shape_1):
@staticmethod
def _interval(
n: int, start: Optional[int], end: Optional[int]
) -> Optional[Tuple[int, int]]:
if start == 0:
if end is None or np.isnan(end):
return None
if end < 0:
return (0, n + end)
return (0, end)
if end is None or np.isnan(end):
return (start, n) # type: ignore
if end < 0:
return (start, n + end) # type: ignore
return (start, end) # type: ignore
def _run(self, data, end=None, start=None): # type: ignore
ab = self._interval(len(data.shape), start=start, end=end)
if ab is None:
return (np.array(data.shape, dtype=np.int64),)
return (np.array(data.shape[ab[0] : ab[1]], dtype=np.int64),)
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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": ["/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"]} |
58,836 | onnx/onnx | refs/heads/main | /onnx/test/reference_evaluator_backend_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# type: ignore
# pylint: disable=C0415,R0912,R0913,R0914,R0915,W0613,W0640,W0703
"""
These test evaluates the python runtime (class ReferenceEvaluator) against
all the backend tests (in onnx/backend/test/case/node) and checks
the runtime produces the expected outputs.
You may run one specific test with following command line:
::
python onnx/test/reference_evaluator_backend_test.py TestOnnxBackEndWithReferenceEvaluator.test_group_normalization_example
You may bypass a test newly added by adding to the global variable `SKIP_TESTS`.
You may refine the absolute or relative tolerance for a test by
adding an item in method `setUpClass` and attributes
`atol` or `rtol`.
"""
import os
import pprint
import sys
import unittest
try:
from packaging.version import parse as version
except ImportError:
from distutils.version import ( # noqa: N813 # pylint: disable=deprecated-module
StrictVersion as version,
)
from os import getenv
import numpy as np
from numpy import __version__ as npver
from numpy import object_ as dtype_object
from numpy.testing import assert_allclose # type: ignore
from onnx import ONNX_ML, OptionalProto, SequenceProto, TensorProto, load
from onnx.backend.test import __file__ as backend_folder
from onnx.helper import __file__ as onnx_file
from onnx.numpy_helper import bfloat16_to_float32, to_list, to_optional
from onnx.reference import ReferenceEvaluator
from onnx.reference.op_run import to_array_extended
from onnx.reference.ops.op_cast import cast_to
# TODO (https://github.com/microsoft/onnxruntime/issues/14932): Get max supported version from onnxruntime directly
# For now, bump the version in CIs whenever there is a new onnxruntime release
ORT_MAX_IR_SUPPORTED_VERSION = int(getenv("ORT_MAX_IR_SUPPORTED_VERSION", "8"))
ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION = int(
getenv("ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION", "18")
)
# Number of tests expected to pass without raising an exception.
MIN_PASSING_TESTS = 1235
# Update this list if one new operator does not have any implementation.
SKIP_TESTS = {
# mismatches
# shapes (10, 9, 3), (10, 8, 3) shape mismatch unexpected as the operator is inlined
"test_center_crop_pad_crop_axes_hwc_expanded",
# deprecated
"test_scan_sum", # deprecated, opset 8 -> not implemented
"test_scatter_with_axis", # deprecated, scatter is removed
"test_scatter_without_axis", # deprecated, scatter is removed
# not implemented
"test__simple_gradient_of_add", # gradient not implemented
"test__simple_gradient_of_add_and_mul", # gradient not implemented
}
if version(npver) < version("1.21.5"):
SKIP_TESTS |= {
"test_cast_FLOAT_to_BFLOAT16",
"test_castlike_FLOAT_to_BFLOAT16",
"test_castlike_FLOAT_to_BFLOAT16_expanded",
}
if version(npver) < version("1.21.5"):
SKIP_TESTS |= {
"test_cast_FLOAT_to_BFLOAT16",
"test_castlike_FLOAT_to_BFLOAT16",
"test_castlike_FLOAT_to_BFLOAT16_expanded",
}
if sys.platform == "win32":
SKIP_TESTS |= {
"test_regex_full_match_basic",
"test_regex_full_match_email_domain",
"test_regex_full_match_empty",
}
def assert_allclose_string(expected, value):
"""
Compares two arrays knowing they contain strings.
Raises an exception if the test fails.
:param expected: expected array
:param value: value
"""
def is_float(x):
try:
float(x)
return True
except ValueError:
return False
if all(map(is_float, expected.ravel())):
expected_float = expected.astype(np.float32)
value_float = value.astype(np.float32)
assert_allclose(expected_float, value_float)
else:
if expected.tolist() != value.tolist():
raise AssertionError(f"Mismatches {expected} != {value}.")
class OnnxBackendTest:
"""
Definition of a backend test. It starts with a folder,
in this folder, one onnx file must be there, then a subfolder
for each test to run with this model.
:param folder: test folder
:param onnx_path: onnx file
:param onnx_model: loaded onnx file
:param tests: list of test
"""
@staticmethod
def _sort(filenames):
temp = []
for f in filenames:
name = os.path.splitext(f)[0]
i = name.split("_")[-1]
temp.append((int(i), f))
temp.sort()
return [_[1] for _ in temp]
@staticmethod
def _read_proto_from_file(full):
if not os.path.exists(full):
raise FileNotFoundError(f"File not found: {full!r}.")
with open(full, "rb") as f:
serialized = f.read()
return OnnxBackendTest._read_proto_from_serialized(serialized, full)
@staticmethod
def _read_proto_from_serialized(serialized, full):
if not os.path.exists(full):
raise FileNotFoundError(f"File not found: {full!r}.")
with open(full, "rb") as f:
serialized = f.read()
proto_types = [
(TensorProto, to_array_extended),
(SequenceProto, to_list),
(OptionalProto, to_optional),
]
exc = None
for pt, cvt in proto_types:
obj = pt()
try:
obj.ParseFromString(serialized)
try:
return cvt(obj)
except ValueError as e:
exc = e
continue
except Exception as e:
exc = e
raise RuntimeError(
f"Unable to read {full!r}, error is {exc}, "
f"content is {serialized[:100]!r}."
) from exc
@staticmethod
def _load(folder, names):
res = []
for name in names:
full = os.path.join(folder, name)
obj = OnnxBackendTest._read_proto_from_file(full)
res.append(obj)
return res
def __repr__(self):
"usual"
return f"{self.__class__.__name__}({self.folder!r})"
def __init__(self, folder):
if not os.path.exists(folder):
raise FileNotFoundError(f"Unable to find folder {folder!r}.")
content = os.listdir(folder)
onx = [c for c in content if os.path.splitext(c)[-1] in {".onnx"}]
if len(onx) != 1:
raise ValueError(
f"There is more than one onnx file in {folder!r} ({onx!r})."
)
self.folder = folder
self.onnx_path = os.path.join(folder, onx[0])
self.onnx_model = load(self.onnx_path)
self.tests = []
for sub in content:
full = os.path.join(folder, sub)
if os.path.isdir(full):
pb = [c for c in os.listdir(full) if os.path.splitext(c)[-1] in {".pb"}]
inputs = OnnxBackendTest._sort(c for c in pb if c.startswith("input_"))
outputs = OnnxBackendTest._sort(
c for c in pb if c.startswith("output_")
)
self.tests.append(
{
"inputs": OnnxBackendTest._load(full, inputs),
"outputs": OnnxBackendTest._load(full, outputs),
}
)
@property
def name(self):
"Returns the test name."
return os.path.split(self.folder)[-1]
@property
def fname(self):
folder = self.folder.replace("\\", "/").split("/")[-2]
if folder.endswith("node"):
fname = self.name
else:
fname = f"test__{folder.replace('-', '_')}_{self.name[5:]}"
if "/" in fname or fname == "test__test_AvgPool1d_AvgPool1d":
raise AssertionError(
f"name={self.name!r}, folder={folder!r}, self.folder={self.folder}."
)
return fname
def __len__(self):
"Returns the number of tests."
return len(self.tests)
def _compare_results(
self, index, i_output, desired, output, rtol=0, atol=0, comment="", inputs=None
):
"""
Compares the expected output and the output produced
by the runtime. Raises an exception if not equal.
:param index: test index
:param i_output: output index
:param desired: expected output
:param output: output
:param rtol: relative tolerance
:param atol: absolute tolerance
:param comment: addition text to give more insights to the user
:param inputs: inputs to the model
"""
if comment == "":
raise RuntimeError("Argument comment should be filled.")
if atol is None:
atol = 0
if rtol is None:
rtol = 0
if isinstance(desired, np.ndarray):
if isinstance(output, np.ndarray):
if rtol == 0:
if desired.dtype == np.float32:
rtl = 1e-5
elif desired.dtype == np.float64:
rtl = 1e-12
else:
rtl = rtol
else:
rtl = rtol
if desired.dtype == dtype_object:
try:
assert_allclose_string(desired, output)
except AssertionError as ex:
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} failed, comment={comment}."
) from ex
else:
equal_nan = desired.dtype in (np.float16, np.float32, np.float64)
if equal_nan:
try:
assert_allclose(
desired,
output,
atol=atol,
rtol=rtl,
equal_nan=equal_nan,
)
except AssertionError as ex:
try:
diff = output - desired
except ValueError:
diff = None
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} failed "
f"(rtol={rtl}, atol={atol}), comment={comment}\n---\n{desired}\n----"
f"\n{output}\n-----\n{diff}\n------INPUTS----\n{pprint.pformat(inputs)}."
) from ex
else:
# float 8 types
if desired.dtype != output.dtype:
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} "
f"has unexpected type {output.dtype} (expecting {desired.dtype}.)"
)
if desired.tolist() != output.tolist():
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} "
f"has unexpected values {output} (expecting {desired}.)"
)
if desired.shape != output.shape:
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} failed "
f"(expected shape={desired.shape} but shape={output.shape}), "
f"comment={comment}\n---\n{desired}\n----"
f"\n{output}\n------INPUTS----\n{pprint.pformat(inputs)}."
)
elif hasattr(output, "is_compatible"):
# A shape
if desired.dtype != output.dtype:
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} failed "
f"(desired.dtype={desired.dtype!r}, output={output!r}), comment={comment}."
)
if not output.is_compatible(desired.shape):
raise AssertionError(
f"Output {i_output} of test {index} in folder {self.folder!r} failed "
f"(desired.shape={desired.shape}, output={output!r}), comment={comment}."
)
elif isinstance(desired, list):
if not isinstance(output, list):
raise AssertionError(
f"Expected result is 'list' but output type is {type(output)} for output {i_output}"
f", comment={comment}\n--EXPECTED--\n{desired}\n--GOT--\n{output}."
)
if len(desired) != len(output):
raise AssertionError(
f"Expected has {len(desired)} but output has {len(output)} for output {i_output}"
f", comment={comment}\n--EXPECTED--\n{desired}\n--GOT--\n{output}."
)
for a, b in zip(desired, output):
self._compare_results(
index, i_output, a, b, rtol=rtol, atol=atol, comment=comment
)
else:
raise NotImplementedError(
f"Comparison not implemented for type {type(desired)} and output {i_output}, comment={comment}."
)
def is_random(self):
"Tells if a test is random or not."
if "bernoulli" in self.folder:
return True
return False
def run(
self,
load_fct,
run_fct,
index=None,
rtol=1e-07,
atol=0,
comment="",
print_io=False,
):
"""
Executes a tests or all tests if index is None.
The function crashes if the tests fails.
:param load_fct: loading function, takes a loaded onnx graph,
and returns an object
:param run_fct: running function, takes the result of previous
function, the inputs, and returns the outputs
:param index: index of the test to run or all.
:param rtol: relative tolerance
:param atol: absolute tolerance
:param comment: additional information for the user
:param print_io: prints out the input and output
"""
if index is None:
res = []
for i in range(len(self)):
res.append(
self.run(
load_fct,
run_fct,
index=i,
atol=atol,
rtol=rtol,
comment=comment,
print_io=print_io,
)
)
return res
if print_io:
print("------ INPUTS")
for k, v in enumerate(self.tests[index]["inputs"]):
print(f"input {k!r}, shape={v.shape}, dtype={v.dtype}")
print("------ EXPECTED OUTPUTS")
for k, v in enumerate(self.tests[index]["outputs"]):
print(f"output {k!r}, shape={v.shape}, dtype={v.dtype}")
obj = load_fct(self.onnx_model)
got = run_fct(obj, *self.tests[index]["inputs"])
expected = self.tests[index]["outputs"]
if len(got) != len(expected):
raise AssertionError(
f"Unexpected number of output (test {index}, folder {self.folder!r}), "
f"got {len(got)}, expected {len(expected)}."
)
res = {
"inputs": self.tests[index]["inputs"],
"expected": self.tests[index]["outputs"],
"results": got,
}
for i, (e, o) in enumerate(zip(expected, got)):
if self.is_random():
if e.dtype != o.dtype:
raise AssertionError(
f"Output {i} of test {index} in folder {self.folder!r} failed "
f"(type mismatch {e.dtype} != {o.dtype!r})."
)
if e.shape != o.shape:
raise AssertionError(
f"Output {i} of test {index} in folder {self.folder!r} failed "
f"(shape mismatch {e.shape} != {o.shape})."
)
else:
self._compare_results(
index,
i,
e,
o,
atol=atol,
rtol=rtol,
comment=comment + "\n" + str(self.onnx_model),
inputs=self.tests[index]["inputs"],
)
return res
def enumerate_onnx_tests(series, fct_filter=None):
"""
Collects test from a sub folder of `onnx/backend/test`.
Works as an enumerator to start processing them
without waiting or storing too much of them.
:param series: which subfolder to load, possible values:
(`'node'`, ...)
:param fct_filter: function `lambda testname: boolean`
to load or skip the test, None for all
:return: list of @see cl OnnxBackendTest
"""
root = os.path.dirname(backend_folder)
sub = os.path.join(root, "data", series)
if not os.path.exists(sub):
content = "\n".join(os.listdir(root))
raise FileNotFoundError(
f"Unable to find series of tests in {root!r}, subfolders:\n{content}"
)
tests = os.listdir(sub)
for t in tests:
if fct_filter is not None and not fct_filter(t):
continue
folder = os.path.join(sub, t)
if not ONNX_ML and "ai_onnx_ml" in folder:
continue
content = os.listdir(folder)
onx = [c for c in content if os.path.splitext(c)[-1] in {".onnx"}]
if len(onx) == 1:
yield OnnxBackendTest(folder)
class TestOnnxBackEndWithReferenceEvaluator(unittest.TestCase):
folder = os.path.join(
os.path.abspath(os.path.dirname(__file__)), "onnx_backend_test_code"
)
@classmethod
def add_test_methods(cls):
for folder in ["node", "pytorch-converted", "pytorch-operator", "simple"]:
for te in enumerate_onnx_tests(folder):
def _test_(
self, te=te, check_other_runtime=None, verbose=0, print_io=False
):
if te.fname in getattr(cls, "skip_test", set()):
cls.skipped.append((te, None))
return
rtol = getattr(cls, "rtol", {})
atol = getattr(cls, "atol", {})
if len(rtol) == 0 or len(atol) == 0:
raise AssertionError("rtol or atol is empty.")
self.common_test_onnx_test_run(
te,
getattr(cls, "successes", []),
getattr(cls, "missed", []),
getattr(cls, "skipped", []),
getattr(cls, "load_failed", []),
getattr(cls, "exec_failed", []),
getattr(cls, "mismatch", []),
verbose=verbose,
rtol=rtol,
atol=atol,
check_other_runtime=check_other_runtime,
print_io=print_io,
)
setattr(TestOnnxBackEndWithReferenceEvaluator, te.fname, _test_)
def test_onnx_backend_test_abs(self):
name = "test_abs"
code = []
for te in enumerate_onnx_tests("node", lambda folder: folder == name):
code.append(te)
self.assertEqual(len(code), 1)
def test_onnx_backend_test_expand_shape_model1(self):
name = "test_expand_shape_model1"
code = []
for te in enumerate_onnx_tests("simple", lambda folder: folder == name):
code.append(te)
self.assertEqual(len(code), 1)
@staticmethod
def load_fct(obj, verbose=0):
return ReferenceEvaluator(obj, verbose=verbose)
@staticmethod
def run_fct(obj, *inputs, verbose=0): # pylint: disable=W0613
if hasattr(obj, "input_names"):
input_names = obj.input_names
elif hasattr(obj, "get_inputs"):
input_names = [_.name for _ in obj.get_inputs()]
else:
raise AttributeError(
f"Unable to extract the number to guess the number of inputs for type {type(obj)}."
)
if len(input_names) < len(inputs):
raise AssertionError(
f"Got {len(inputs)} inputs but expecting {len(obj.input_names)}."
)
rewrite = False
for i in range(len(inputs)): # pylint: disable=C0200
if (
isinstance(inputs[i], np.ndarray)
and inputs[i].dtype == np.uint16
and obj.input_types[i].tensor_type.elem_type != TensorProto.UINT16
):
rewrite = True
if rewrite:
# bfloat16 does not exist for numpy.
inputs = list(inputs)
for i in range(len(inputs)): # pylint: disable=C0200
if (
isinstance(inputs[i], np.ndarray)
and inputs[i].dtype == np.uint16
and obj.input_types[i].tensor_type.elem_type != TensorProto.UINT16
):
xr = inputs[i].ravel()
xf = np.empty(xr.shape[0], dtype=np.float32)
for ie in range(xr.shape[0]):
el = bfloat16_to_float32(xr[ie])
xf[ie] = el
inputs[i] = cast_to(
xf.astype(np.float32).reshape(inputs[i].shape),
TensorProto.BFLOAT16,
True,
)
feeds = {input_names[i]: inputs[i] for i in range(len(inputs))}
got = obj.run(None, feeds)
return got
# def test_onnx_test_run_test_abs(self):
# done = 0
# for te in enumerate_onnx_tests("node", lambda folder: folder == "test_abs"):
# self.assertIn(te.name, repr(te))
# self.assertGreater(len(te), 0)
# te.run(
# TestOnnxBackEndWithReferenceEvaluator.load_fct,
# TestOnnxBackEndWithReferenceEvaluator.run_fct,
# comment="[runtime=ReferenceEvaluator]",
# )
# done += 1
# self.assertEqual(done, 1)
def common_test_onnx_test_run(
self,
te,
successes,
missed,
skipped,
load_failed,
exec_failed,
mismatch,
verbose=0,
rtol=None,
atol=None,
check_other_runtime=None,
print_io=False,
):
if verbose > 6:
print("TEST:", te.name)
if verbose > 7:
print(" check runtime")
self.assertIn(te.name, repr(te))
self.assertGreater(len(te), 0)
try:
if verbose > 7:
print(" run")
if verbose > 5:
te.run(
lambda *args, verbose=verbose: TestOnnxBackEndWithReferenceEvaluator.load_fct(
*args, verbose
),
TestOnnxBackEndWithReferenceEvaluator.run_fct,
atol=atol.get(te.name, None),
rtol=rtol.get(te.name, None),
comment=f"[runtime=ReferenceEvaluator, verbose={verbose}]",
print_io=print_io,
)
else:
te.run(
TestOnnxBackEndWithReferenceEvaluator.load_fct,
TestOnnxBackEndWithReferenceEvaluator.run_fct,
atol=atol.get(te.fname, atol.get(te.name, None)),
rtol=rtol.get(te.fname, rtol.get(te.name, None)),
comment="[runtime=ReferenceEvaluator]",
print_io=print_io,
)
if verbose > 7:
print(" end run")
if verbose > 8:
print(te.onnx_model)
except NotImplementedError as e:
if verbose > 7:
print(" ", e, type(e))
missed.append((te, e))
with open(f"missed_{te.name}.onnx", "wb") as f:
f.write(te.onnx_model.SerializeToString())
raise e
except (AssertionError, ValueError) as e:
if verbose > 7:
print(" ", e, type(e))
mismatch.append((te, e))
with open(f"mismatch_{te.name}.onnx", "wb") as f:
f.write(te.onnx_model.SerializeToString())
if check_other_runtime is None:
raise e
if "onnxruntime" in check_other_runtime:
print("CHECK RUNTIME onnxruntime")
from onnxruntime import InferenceSession
onnx_domain_opset = ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
for opset in te.onnx_model.opset_import:
if opset.domain in ("", "ai.onnx"):
onnx_domain_opset = opset.version
break
# The new IR or opset version is not supported by onnxruntime yet
if (
te.onnx_model.ir_version > ORT_MAX_IR_SUPPORTED_VERSION
or onnx_domain_opset > ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
):
print(
"Skip test because of IR or opset version is not supported by onnxruntime yet"
)
return
te.run(
lambda obj: InferenceSession(
obj.SerializeToString(), providers=["CPUExecutionProvider"]
),
lambda *a, **b: TestOnnxBackEndWithReferenceEvaluator.run_fct(
*a, verbose=1, **b
),
atol=1e-5,
rtol=1e-3,
comment="[runtime=onnxruntime]",
)
print("done")
raise e
except Exception as e:
if verbose > 7:
print(" ", e, type(e))
with open(f"issue_{te.name}.onnx", "wb") as f:
f.write(te.onnx_model.SerializeToString())
raise AssertionError(
f"Unable to run test {te.name!r} due to {e}\n{te.onnx_model}"
) from e
successes.append((te, atol.get(te.fname, None), rtol.get(te.fname, None)))
if verbose > 7:
print(" end example.")
@staticmethod
def _postprocess(
successes, missed, skipped, load_failed, exec_failed, mismatch, verbose
):
success = len(successes)
failed = [
len(missed),
len(skipped),
len(load_failed),
len(exec_failed),
len(mismatch),
]
coverage = success / (success + sum(failed))
if verbose:
path = os.path.dirname(onnx_file)
print("-----------")
print(
f"success={success}, skipped={len(skipped)}, missed={len(missed)}, load_failed={len(load_failed)}, "
f"exec_failed={len(exec_failed)}, mismatch={len(mismatch)}"
)
print(
f"coverage {coverage * 100:.1f}% out of {success + sum(failed)} tests"
)
if verbose > 3:
def _print(s, path):
return (
str(s)
.replace("\\\\", "\\")
.replace(path, "onnx")
.replace("\\", "/")
)
print("-----------")
for t in sorted(load_failed, key=lambda m: m[0].fname):
print("loading failed", t[0].fname, "---", _print(t[0], path))
for t in sorted(exec_failed, key=lambda m: m[0].fname):
print("execution failed", t[0].fname, "---", _print(t[0], path))
for t in sorted(mismatch, key=lambda m: m[0].fname):
print("mismatch", t[0].fname, "---", _print(t[0], path))
for t in sorted(missed, key=lambda m: m[0].fname):
print("missed ", t[0].fname, "---", _print(t[0], path))
for t in sorted(skipped, key=lambda m: m[0].fname):
print("skipped", t[0].fname, "---", _print(t[0], path))
if success > 30:
print("-----------")
print(
f"success={success}, skipped={len(skipped)}, missed={len(missed)}, load_failed={len(load_failed)}, "
f"exec_failed={len(exec_failed)}, mismatch={len(mismatch)}"
)
print(
f"coverage {coverage * 100:.1f}% out of {success + sum(failed)} tests"
)
print("-----------")
if len(mismatch) > 0:
te, e = mismatch[0]
raise AssertionError(
f"Mismatch in test {te.name!r}\n{te.onnx_model}."
) from e
if sum(failed) > len(SKIP_TESTS):
raise AssertionError(
f"Unexpected failures. {sum(failed)}/{success + sum(failed)} tests have failed."
f"The coverage is {coverage * 100:.1f}%. "
f"New operators were added with no corresponding runtime."
)
@classmethod
def setUpClass(cls, all_tests=False):
# test not supported yet
# not supported yet
# see https://onnx.ai/backend-scoreboard/onnxruntime_details_stable.html
# to compare with onnxruntime
cls.rtol = {
"test_adam_multiple": 1e-2,
"test_blackmanwindow_expanded": 0,
"test_blackmanwindow_symmetric_expanded": 0,
"test_simple_rnn_batchwise": 0,
"test__pytorch_converted_Conv1d_pad1": 1e-4,
"test__pytorch_converted_Conv2d": 1e-5,
"test__pytorch_converted_Conv2d_no_bias": 1e-3,
"test__pytorch_converted_Conv2d_strided": 1e-4,
"test_layer_normalization_4d_axis1_expanded_ver18": 1e-4,
"test_layer_normalization_4d_axis_negative_1_expanded_ver18": 1e-4,
"test_layer_normalization_4d_axis_negative_3_expanded_ver18": 1e-4,
}
cls.atol = {
"test_blackmanwindow": 1e-7,
"test_blackmanwindow_expanded": 1e-4,
"test_blackmanwindow_symmetric": 1e-7,
"test_blackmanwindow_symmetric_expanded": 1e-4,
"test_Conv1d": 1e-6,
"test_Conv2d_depthwise_padded": 1e-7,
"test_Conv3d_dilated": 1e-6,
"test_gridsample_bicubic": 1e-4,
"test_gru_seq_length": 1e-7,
"test_hammingwindow_expanded": 1e-4,
"test_hammingwindow_symmetric_expanded": 1e-4,
"test_hannwindow_expanded": 1e-4,
"test_hannwindow_symmetric": 1e-7,
"test_hannwindow_symmetric_expanded": 1e-4,
"test_layer_normalization_4d_axis_negative_1_expanded": 1e-6,
"test_layer_normalization_4d_axis1_expanded": 1e-6,
"test_layer_normalization_4d_axis_negative_3_expanded": 1e-6,
"test_mish": 1e-6,
"test_mish_expanded": 1e-6,
"test_roialign_aligned_false": 1e-4,
"test_roialign_aligned_true": 1e-4,
# extended list
"test__pytorch_converted_ConvTranspose2d_no_bias": 1e-4,
"test__pytorch_converted_Linear_no_bias": 1e-5,
"test_Linear_no_bias": 1e-5,
"test__pytorch_converted_Conv1d_pad1": 1e-6,
"test__pytorch_converted_Conv2d": 1e-5,
"test__pytorch_converted_Conv2d_depthwise": 1e-4,
"test__pytorch_converted_Conv2d_depthwise_strided": 1e-4,
"test__pytorch_converted_Conv2d_depthwise_with_multiplier": 1e-4,
"test__pytorch_converted_Conv2d_depthwise_padded": 1e-4,
"test__pytorch_converted_Conv2d_groups": 1e-4,
"test__pytorch_converted_Conv2d_groups_thnn": 1e-4,
"test__pytorch_converted_Conv2d_no_bias": 1e-5,
"test__pytorch_converted_Conv2d_strided": 1e-4,
"test__pytorch_operator_operator_symbolic_override": 1e-5,
"test_operator_symbolic_override": 1e-4,
"test__pytorch_converted_Conv3d_dilated_strided": 1e-4,
"test__pytorch_converted_Conv3d_groups": 1e-4,
"test_affine_grid_2d": 1e-4,
"test_affine_grid_2d_expanded": 1e-4,
"test_affine_grid_2d_align_corners": 1e-4,
"test_affine_grid_2d_align_corners_expanded": 1e-4,
"test_affine_grid_3d": 1e-4,
"test_affine_grid_3d_expanded": 1e-4,
"test_affine_grid_3d_align_corners": 1e-4,
"test_affine_grid_3d_align_corners_expanded": 1e-4,
}
if version(npver) < version("1.21.5"):
cls.atol.update(
{
"test_dft": 1e-11,
"test_dft_axis": 1e-11,
"test_dft_inverse": 1e-11,
}
)
cls.skip_test = SKIP_TESTS
if all_tests:
cls.skip_test = set()
cls.successes = []
cls.missed = []
cls.skipped = []
cls.load_failed = []
cls.exec_failed = []
cls.mismatch = []
@classmethod
def tearDownClass(cls):
if len(cls.successes) == 0:
failed = cls.mismatch + cls.missed + cls.load_failed + cls.exec_failed
if len(failed) > 0:
raise RuntimeError(
f"No test was successful, {len(failed)} failed."
) from failed[0][1]
raise RuntimeError("No test was successful.")
cls._postprocess(
cls.successes,
cls.missed,
cls.skipped,
cls.load_failed,
cls.exec_failed,
cls.mismatch,
10,
)
TestOnnxBackEndWithReferenceEvaluator.add_test_methods()
if __name__ == "__main__":
unittest.main(verbosity=2)
| {"/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/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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58,837 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_string_normalizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
import locale as pylocale
import unicodedata
import warnings
import numpy as np
from onnx.reference.op_run import OpRun, RuntimeTypeError
class StringNormalizer(OpRun):
"""
The operator is not really threadsafe as python cannot
play with two locales at the same time. stop words
should not be implemented here as the tokenization
usually happens after this steps.
"""
def _run( # type: ignore
self,
x,
case_change_action=None,
is_case_sensitive=None,
locale=None,
stopwords=None,
):
slocale = locale
if stopwords is None:
raw_stops = set()
stops = set()
else:
raw_stops = set(stopwords)
if case_change_action == "LOWER":
stops = {w.lower() for w in stopwords}
elif case_change_action == "UPPER":
stops = {w.upper() for w in stopwords}
else:
stops = set(stopwords)
res = np.empty(x.shape, dtype=x.dtype)
if len(x.shape) == 2:
for i in range(0, x.shape[1]):
self._run_column(
x[:, i],
res[:, i],
slocale=slocale,
stops=stops,
raw_stops=raw_stops,
is_case_sensitive=is_case_sensitive,
case_change_action=case_change_action,
)
elif len(x.shape) == 1:
self._run_column(
x,
res,
slocale=slocale,
stops=stops,
raw_stops=raw_stops,
is_case_sensitive=is_case_sensitive,
case_change_action=case_change_action,
)
else:
raise RuntimeTypeError("x must be a matrix or a vector.")
if len(res.shape) == 2 and res.shape[0] == 1:
res = np.array([[w for w in res.tolist()[0] if len(w) > 0]])
if res.shape[1] == 0:
res = np.array([[""]])
elif len(res.shape) == 1:
res = np.array([w for w in res.tolist() if len(w) > 0])
if len(res) == 0:
res = np.array([""])
return (res,)
@staticmethod
def _run_column( # type: ignore
cin,
cout,
slocale=None,
stops=None,
raw_stops=None,
is_case_sensitive=None,
case_change_action=None,
):
if pylocale.getlocale() != slocale:
try:
pylocale.setlocale(pylocale.LC_ALL, slocale)
except pylocale.Error as e:
warnings.warn(
f"Unknown local setting {slocale!r} (current: {pylocale.getlocale()!r}) - {e!r}.",
stacklevel=1,
)
cout[:] = cin[:]
for i in range(0, cin.shape[0]):
if isinstance(cout[i], float):
# nan
cout[i] = ""
else:
cout[i] = StringNormalizer.strip_accents_unicode(cout[i])
if is_case_sensitive and len(stops) > 0:
for i in range(0, cin.shape[0]):
cout[i] = StringNormalizer._remove_stopwords(cout[i], raw_stops)
if case_change_action == "LOWER":
for i in range(0, cin.shape[0]):
cout[i] = cout[i].lower()
elif case_change_action == "UPPER":
for i in range(0, cin.shape[0]):
cout[i] = cout[i].upper()
elif case_change_action != "NONE":
raise RuntimeError(
f"Unknown option for case_change_action: {case_change_action!r}."
)
if not is_case_sensitive and len(stops) > 0:
for i in range(0, cin.shape[0]):
cout[i] = StringNormalizer._remove_stopwords(cout[i], stops)
return cout
@staticmethod
def _remove_stopwords(text, stops): # type: ignore
spl = text.split(" ")
return " ".join(filter(lambda s: s not in stops, spl))
@staticmethod
def strip_accents_unicode(s): # type: ignore
"""
Transforms accentuated unicode symbols into their simple counterpart.
Source: `sklearn/feature_extraction/text.py
<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/
feature_extraction/text.py#L115>`_.
:param s: string
The string to strip
:return: the cleaned string
"""
try:
# If `s` is ASCII-compatible, then it does not contain any accented
# characters and we can avoid an expensive list comprehension
s.encode("ASCII", errors="strict")
return s
except UnicodeEncodeError:
normalized = unicodedata.normalize("NFKD", s)
s = "".join([c for c in normalized if not unicodedata.combining(c)])
return s
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58,838 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/adam.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.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
def apply_adam(r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Update momentum.
v_new = alpha * v + (1 - alpha) * g_regularized
# Update second-order momentum.
h_new = beta * h + (1 - beta) * (g_regularized * g_regularized)
# Compute element-wise square root.
h_sqrt = np.sqrt(h_new) + epsilon
# Adjust learning rate.
r_adjusted = None
if t > 0:
# Consider bias correction on momentums.
r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t)
else:
# No bias correction on momentums.
r_adjusted = r
# Apply Adam update rule.
x_new = x - r_adjusted * (v_new / h_sqrt)
# It's possible to apply regularization in the end.
x_final = (1 - norm_coefficient_post) * x_new
return x_final, v_new, h_new
class Adam(Base):
@staticmethod
def export_adam() -> None:
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.1
epsilon = 1e-7
# Create operator.
node = onnx.helper.make_node(
"Adam",
inputs=["R", "T", "X", "G", "V", "H"],
outputs=["X_new", "V_new", "H_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
epsilon=epsilon,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.2, 2.8], dtype=np.float32)
g = np.array([-0.94, -2.5], dtype=np.float32)
v = np.array([1.7, 3.6], dtype=np.float32)
h = np.array([0.1, 0.1], dtype=np.float32)
# Compute expected outputs of Adam.
x_new, v_new, h_new = apply_adam(
r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon
)
# Check results.
expect(
node,
inputs=[r, t, x, g, v, h],
outputs=[x_new, v_new, h_new],
name="test_adam",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
@staticmethod
def export_adam_multiple() -> None:
# Define operator attributes.
norm_coefficient = 0.001
alpha = 0.95
beta = 0.85
epsilon = 1e-2
node = onnx.helper.make_node(
"Adam",
inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"],
outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
v1 = np.array([2.0], dtype=np.float32)
h1 = np.array([0.5], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
v2 = np.array([4.0, 1.0], dtype=np.float32)
h2 = np.array([1.0, 10.0], dtype=np.float32)
# Compute expected outputs of Adam.
x1_new, v1_new, h1_new = apply_adam(
r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon
)
x2_new, v2_new, h2_new = apply_adam(
r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon
)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
name="test_adam_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
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58,839 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/ceil.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 Ceil(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Ceil",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.5, 1.2]).astype(np.float32)
y = np.ceil(x) # expected output [-1., 2.]
expect(node, inputs=[x], outputs=[y], name="test_ceil_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.ceil(x)
expect(node, inputs=[x], outputs=[y], name="test_ceil")
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58,840 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bitwiseand.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 BitwiseAnd(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d")
# 3d
x = create_random_int((3, 4, 5), np.int16)
y = create_random_int((3, 4, 5), np.int16)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d")
@staticmethod
def export_bitwiseand_broadcast() -> None:
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_and(x, y)
expect(
node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d"
)
# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d")
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58,841 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/onehot.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 one_hot(indices, depth, axis=-1, dtype=np.float32): # type: ignore
"""Compute one hot from indices at a specific axis"""
values = np.asarray(indices)
rank = len(values.shape)
depth_range = np.arange(depth)
if axis < 0:
axis += rank + 1
ls = values.shape[0:axis]
rs = values.shape[axis:rank]
targets = np.reshape(
depth_range, (1,) * len(ls) + depth_range.shape + (1,) * len(rs)
)
values = np.reshape(np.mod(values, depth), (*ls, 1, *rs))
return np.asarray(targets == values, dtype=dtype)
class OneHot(Base):
@staticmethod
def export_without_axis() -> None:
on_value = 5
off_value = 2
output_type = np.int32
node = onnx.helper.make_node(
"OneHot", inputs=["indices", "depth", "values"], outputs=["y"]
)
indices = np.array([0, 7, 8], dtype=np.int64)
depth = np.float32(12)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_without_axis",
)
@staticmethod
def export_with_axis() -> None:
axisValue = 1
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_with_axis",
)
@staticmethod
def export_with_negative_indices() -> None:
axisValue = 1
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([0, -7, -8], dtype=np.int64)
# print(y)
# [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
# [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
# [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_negative_indices",
)
@staticmethod
def export_with_negative_axis() -> None:
axisValue = -2
on_value = 3
off_value = 1
output_type = np.float32
node = onnx.helper.make_node(
"OneHot",
inputs=["indices", "depth", "values"],
outputs=["y"],
axis=axisValue,
)
indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
depth = np.float32(10)
values = np.array([off_value, on_value], dtype=output_type)
y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
y = y * (on_value - off_value) + off_value
expect(
node,
inputs=[indices, depth, values],
outputs=[y],
name="test_onehot_with_negative_axis",
)
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58,842 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/cumsum.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 CumSum(Base):
@staticmethod
def export_cumsum_1d() -> None:
node = onnx.helper.make_node("CumSum", inputs=["x", "axis"], outputs=["y"])
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([1.0, 3.0, 6.0, 10.0, 15.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d")
@staticmethod
def export_cumsum_1d_exclusive() -> None:
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], exclusive=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([0.0, 1.0, 3.0, 6.0, 10.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_exclusive")
@staticmethod
def export_cumsum_1d_reverse() -> None:
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([15.0, 14.0, 12.0, 9.0, 5.0]).astype(np.float64)
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse")
@staticmethod
def export_cumsum_1d_reverse_exclusive() -> None:
node = onnx.helper.make_node(
"CumSum", inputs=["x", "axis"], outputs=["y"], reverse=1, exclusive=1
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype(np.float64)
axis = np.int32(0)
y = np.array([14.0, 12.0, 9.0, 5.0, 0.0]).astype(np.float64)
expect(
node, inputs=[x, axis], outputs=[y], name="test_cumsum_1d_reverse_exclusive"
)
@staticmethod
def export_cumsum_2d_axis_0() -> None:
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(0)
y = np.array([1.0, 2.0, 3.0, 5.0, 7.0, 9.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_0")
@staticmethod
def export_cumsum_2d_axis_1() -> None:
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(1)
y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_axis_1")
@staticmethod
def export_cumsum_2d_negative_axis() -> None:
node = onnx.helper.make_node(
"CumSum",
inputs=["x", "axis"],
outputs=["y"],
)
x = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float64).reshape((2, 3))
axis = np.int32(-1)
y = np.array([1.0, 3.0, 6.0, 4.0, 9.0, 15.0]).astype(np.float64).reshape((2, 3))
expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis")
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58,843 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reshape.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 reshape_reference_implementation(
data: np.ndarray, shape: np.ndarray, allowzero: int = 0
) -> np.ndarray:
# replace zeros with corresponding dim size
# we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set
new_shape = np.copy(shape)
if allowzero == 0:
zeros_index = np.where(shape == 0)
new_shape[zeros_index] = np.array(data.shape)[zeros_index]
reshaped = np.reshape(data, new_shape)
return reshaped
class CommonReshape(OpRun):
def _run(self, data, shape): # type: ignore
return (reshape_reference_implementation(data, shape, 0),)
class Reshape_5(CommonReshape):
pass
class Reshape_14(CommonReshape):
def _run(self, data, shape, allowzero=None): # type: ignore
if allowzero is None:
allowzero = getattr(self, "allowzero", 0) == 1
else:
allowzero = allowzero == 1
return (reshape_reference_implementation(data, shape, allowzero),)
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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"]} |
58,844 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_qlinear_conv.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_conv import _conv_implementation
class QLinearConv(OpRun):
def _run( # type: ignore
self,
x,
x_scale,
x_zero_point,
w,
w_scale,
w_zero_point,
y_scale,
y_zero_point,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
pads=None,
strides=None,
):
auto_pad = auto_pad or self.auto_pad # type: ignore
dilations = dilations or self.dilations # type: ignore
group = group or self.group # type: ignore
kernel_shape = kernel_shape or self.kernel_shape # type: ignore
pads = pads or self.pads # type: ignore
strides = strides or self.strides # type: ignore
X = x.astype(np.int32)
if x_zero_point is not None:
X -= x_zero_point
W = w.astype(np.int32)
if w_zero_point is not None:
if len(w_zero_point.shape) == 1 and w_zero_point.shape[0] == W.shape[0]:
missing = (w_zero_point.shape[0],) + (1,) * (len(W.shape) - 1)
W -= w_zero_point.reshape(missing)
else:
W -= w_zero_point
res = _conv_implementation(
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
).astype(np.int32)
R = res * (x_scale * w_scale / y_scale)
if y_zero_point is not None:
R += y_zero_point
if y_zero_point.dtype == np.int8:
R = np.clip(R, -128, 127)
else:
R = np.clip(R, 0, 255)
return (np.round(R).astype(y_zero_point.dtype),)
if x.dtype == np.int8:
R = np.clip(R, -128, 127)
else:
R = np.clip(R, 0, 255)
return (np.round(R).astype(x.dtype),)
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58,845 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gathernd.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 gather_nd_impl(
data: np.ndarray, indices: np.ndarray, batch_dims: int
) -> np.ndarray:
# Note the data rank - will be reused multiple times later
data_rank = len(data.shape)
# Check input tensors' shape/rank condition
assert indices.shape[-1] <= data_rank
# The list of data/indice shape of batch_dims
batch_dims_shape = []
# The number of elements in the batch_dims for data/indice array
batch_dims_size = 1
# Check the shape of indice and data are identicial for batch dims.
for i in range(batch_dims):
batch_dims_shape.append(indices.shape[i])
batch_dims_size *= indices.shape[i]
# Compute output of the op as below
# Compute shape of output array
output_shape = (
batch_dims_shape + list(indices.shape)[batch_dims:-1]
if (indices.shape[-1] == data_rank - batch_dims)
else batch_dims_shape
+ list(indices.shape)[batch_dims:-1]
+ list(data.shape)[batch_dims + indices.shape[-1] :]
)
# Placeholder for output data
output_data_buffer = []
# Flatten 'indices' to 2D array
reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1])
# Flatten 'data' to array of shape (batch_dim_size, data.shape[batch_dimes:])
reshaped_data = data.reshape((batch_dims_size,) + data.shape[batch_dims:])
# gather each scalar value from 'data'
for batch_dim in range(reshaped_indices.shape[0]):
for outer_dim in range(reshaped_indices.shape[1]):
gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
output_data_buffer.append(reshaped_data[(batch_dim, *gather_index)])
return np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape)
class GatherND(Base):
@staticmethod
def export_int32() -> None:
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
)
data = np.array([[0, 1], [2, 3]], dtype=np.int32)
indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
output = gather_nd_impl(data, indices, 0)
expected_output = np.array([0, 3], dtype=np.int32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_int32",
)
@staticmethod
def export_float32() -> None:
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
)
data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
output = gather_nd_impl(data, indices, 0)
expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_float32",
)
@staticmethod
def export_int32_batchdim_1() -> None:
node = onnx.helper.make_node(
"GatherND",
inputs=["data", "indices"],
outputs=["output"],
batch_dims=1,
)
data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
indices = np.array([[1], [0]], dtype=np.int64)
output = gather_nd_impl(data, indices, 1)
expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
assert np.array_equal(output, expected_output)
expect(
node,
inputs=[data, indices],
outputs=[output],
name="test_gathernd_example_int32_batch_dim1",
)
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58,846 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/negativeloglikelihoodloss.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 compute_negative_log_likelihood_loss(input, target, weight=None, reduction="mean", ignore_index=None): # type: ignore
input_shape = input.shape
if len(input_shape) == 1:
raise RuntimeError("Unsupported shape")
target_shape = target.shape
N = input_shape[0]
C = input_shape[1]
# initialize the positional weights when required
gather_weight = None
if weight is not None:
# setting mode='clip' to deal with ignore_index > C or < 0 cases.
# when the target value is > C or < 0, it doesn't matter which value we are
# taking in gather_weight, since it will be set to 0 in the following if-block
# use np.int32 to make it compatible with x86 machines
gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
# set `ignore_index`'s loss weight to 0.
# The loss tensor will be multiplied by this weight tensor,
# so `ingore_index`'s loss value will be eliminated.
if ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
dtype=np.float32
)
elif ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
# if input is 4-d and above, make it 3-d
if len(input_shape) != 3:
input = input.reshape((N, C, -1))
target = target.reshape((N, -1))
# Get a dimension from the reshaped input.
# If the original input shape is [N, C, H, W],
# the D here should be H * W because we reshape
# [N, C, H, W] to [N, C, H * W].
D = input.shape[2]
neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
for i in range(N):
for d in range(D):
if target[i][d] != ignore_index:
neg_gather_element_input[i][d] = -input[i][target[i][d]][d]
loss = neg_gather_element_input
# if the input was 4-d or above reshape to the right shape
if len(input_shape) != 3:
loss = loss.reshape(target_shape)
# apply the weights when required
if gather_weight is not None:
loss = gather_weight * loss
if reduction == "mean":
loss = loss.sum() / gather_weight.sum()
return loss
if reduction == "mean":
loss = np.mean(loss)
elif reduction == "sum":
loss = np.sum(loss)
return loss
class NegativeLogLikelihoodLoss(Base):
@staticmethod
def export_input_shape_is_NC() -> None:
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N,)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NC",
)
@staticmethod
def export_input_shape_is_NCd1d2() -> None:
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2",
)
@staticmethod
def export_input_shape_is_NCd1d2_reduction_mean() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_reduction_mean",
)
@staticmethod
def export_input_shape_is_NCd1d2_reduction_sum() -> None:
reduction = "sum"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_reduction_sum",
)
@staticmethod
def export_input_shape_is_NCd1d2_with_weight() -> None:
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight",
)
@staticmethod
def export_input_shape_is_NCd1d2_with_weight_reduction_mean() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_mean",
)
@staticmethod
def export_input_shape_is_NCd1d2_with_weight_reduction_sum() -> None:
reduction = "sum"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_sum",
)
@staticmethod
def export_input_shape_is_NCd1d2_with_weight_reduction_sum_ii() -> None:
reduction = "sum"
ignore_index = np.int64(0)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(0)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() -> None:
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(1)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii",
)
@staticmethod
def export_input_shape_is_NCd1() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1",
)
@staticmethod
def export_input_shape_is_NCd1_weight() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_weight",
)
@staticmethod
def export_input_shape_is_NCd1_ii() -> None:
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=None, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_ii",
)
@staticmethod
def export_input_shape_is_NCd1_weight_ii() -> None:
reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_weight_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3d4d5_mean_weight",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
reduction = "none"
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3d4d5_none_no_weight",
)
@staticmethod
def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
target[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1_mean_weight_negative_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
target[0][0][0][0] = -5
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"NegativeLogLikelihoodLoss",
inputs=["input", "target", "weight"],
outputs=["loss"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N)).astype(np.int64)
target[0] = 10
weight = np.random.rand(C).astype(np.float32)
negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[input, target, weight],
outputs=[negative_log_likelihood_loss],
name="test_nllloss_NCd1d2d3_sum_weight_high_ii",
)
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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"], 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58,847 | onnx/onnx | refs/heads/main | /onnx/test/function_test.py | # SPDX-License-Identifier: Apache-2.0
# Copyright (c) ONNX Project Contributors
import unittest
import onnx
from onnx import checker, utils
class TestFunction(unittest.TestCase):
def _verify_function_set(self, extracted_model, function_set, func_domain): # type: ignore
checker.check_model(extracted_model)
self.assertEqual(len(extracted_model.functions), len(function_set))
for function in function_set:
self.assertIsNotNone(
next(
(
f
for f in extracted_model.functions
if f.name == function and f.domain == func_domain
),
None,
)
)
def test_extract_model_with_local_function(self) -> None:
r"""
# 1. build a model with graph below. extract models with output combinations
# 2. validate extracted models' local functions
#
# model graph:
# i0 i1 i2
# | __________________|__________________/_________
# | | | | / |
# | | | | / |
# func_add func_identity add identity
# | ___\___________\____________________|_________ |
# | | \ \ | _______|___|
# | | \ \ | | | |
# add function_nested_identity_add add function_nested_identity_add
# | | | |
# | | | |
# o_func_add o_all_func0 o_no_func o_all_func1
#
# where function_nested_identity_add is a function that is defined with functions:
# a b
# | |
# func_identity func_identity
# \ /
# func_add
# |
# c
#
"""
# function common
func_domain = "local"
func_opset_imports = [onnx.helper.make_opsetid("", 14)]
func_nested_opset_imports = [
onnx.helper.make_opsetid("", 14),
onnx.helper.make_opsetid(func_domain, 1),
]
# add function
func_add_name = "func_add"
func_add_inputs = ["a", "b"]
func_add_outputs = ["c"]
func_add_nodes = [onnx.helper.make_node("Add", ["a", "b"], ["c"])]
func_add = onnx.helper.make_function(
func_domain,
func_add_name,
func_add_inputs,
func_add_outputs,
func_add_nodes,
func_opset_imports,
)
# identity function
func_identity_name = "func_identity"
func_identity_inputs = ["a"]
func_identity_outputs = ["b"]
func_identity_nodes = [onnx.helper.make_node("Identity", ["a"], ["b"])]
func_identity = onnx.helper.make_function(
func_domain,
func_identity_name,
func_identity_inputs,
func_identity_outputs,
func_identity_nodes,
func_opset_imports,
)
# nested identity/add function
func_nested_identity_add_name = "func_nested_identity_add"
func_nested_identity_add_inputs = ["a", "b"]
func_nested_identity_add_outputs = ["c"]
func_nested_identity_add_nodes = [
onnx.helper.make_node("func_identity", ["a"], ["a1"], domain=func_domain),
onnx.helper.make_node("func_identity", ["b"], ["b1"], domain=func_domain),
onnx.helper.make_node("func_add", ["a1", "b1"], ["c"], domain=func_domain),
]
func_nested_identity_add = onnx.helper.make_function(
func_domain,
func_nested_identity_add_name,
func_nested_identity_add_inputs,
func_nested_identity_add_outputs,
func_nested_identity_add_nodes,
func_nested_opset_imports,
)
# create graph nodes
node_func_add = onnx.helper.make_node(
func_add_name, ["i0", "i1"], ["t0"], domain=func_domain
)
node_add0 = onnx.helper.make_node("Add", ["i1", "i2"], ["t2"])
node_add1 = onnx.helper.make_node("Add", ["t0", "t2"], ["o_func_add"])
node_func_identity = onnx.helper.make_node(
func_identity_name, ["i1"], ["t1"], domain=func_domain
)
node_identity = onnx.helper.make_node("Identity", ["i1"], ["t3"])
node_add2 = onnx.helper.make_node("Add", ["t3", "t2"], ["o_no_func"])
node_func_nested0 = onnx.helper.make_node(
func_nested_identity_add_name,
["t0", "t1"],
["o_all_func0"],
domain=func_domain,
)
node_func_nested1 = onnx.helper.make_node(
func_nested_identity_add_name,
["t3", "t2"],
["o_all_func1"],
domain=func_domain,
)
graph_name = "graph_with_imbedded_functions"
ir_version = 8
opset_imports = [
onnx.helper.make_opsetid("", 14),
onnx.helper.make_opsetid("local", 1),
]
tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type=2, shape=[5])
graph = onnx.helper.make_graph(
[
node_func_add,
node_add0,
node_add1,
node_func_identity,
node_identity,
node_func_nested0,
node_func_nested1,
node_add2,
],
graph_name,
[
onnx.helper.make_value_info(name="i0", type_proto=tensor_type_proto),
onnx.helper.make_value_info(name="i1", type_proto=tensor_type_proto),
onnx.helper.make_value_info(name="i2", type_proto=tensor_type_proto),
],
[
onnx.helper.make_value_info(
name="o_no_func", type_proto=tensor_type_proto
),
onnx.helper.make_value_info(
name="o_func_add", type_proto=tensor_type_proto
),
onnx.helper.make_value_info(
name="o_all_func0", type_proto=tensor_type_proto
),
onnx.helper.make_value_info(
name="o_all_func1", type_proto=tensor_type_proto
),
],
)
meta = {
"ir_version": ir_version,
"opset_imports": opset_imports,
"producer_name": "test_extract_model_with_local_function",
"functions": [func_identity, func_add, func_nested_identity_add],
}
model = onnx.helper.make_model(graph, **meta)
checker.check_model(model)
extracted_with_no_funcion = utils.Extractor(model).extract_model(
["i0", "i1", "i2"], ["o_no_func"]
)
self._verify_function_set(extracted_with_no_funcion, {}, func_domain)
extracted_with_add_funcion = utils.Extractor(model).extract_model(
["i0", "i1", "i2"], ["o_func_add"]
)
self._verify_function_set(
extracted_with_add_funcion, {func_add_name}, func_domain
)
extracted_with_o_all_funcion0 = utils.Extractor(model).extract_model(
["i0", "i1", "i2"], ["o_all_func0"]
)
self._verify_function_set(
extracted_with_o_all_funcion0,
{func_add_name, func_identity_name, func_nested_identity_add_name},
func_domain,
)
extracted_with_o_all_funcion1 = utils.Extractor(model).extract_model(
["i0", "i1", "i2"], ["o_all_func1"]
)
self._verify_function_set(
extracted_with_o_all_funcion1,
{func_add_name, func_identity_name, func_nested_identity_add_name},
func_domain,
)
extracted_with_o_all_funcion2 = utils.Extractor(model).extract_model(
["i0", "i1", "i2"],
["o_no_func", "o_func_add", "o_all_func0", "o_all_func1"],
)
self._verify_function_set(
extracted_with_o_all_funcion2,
{func_add_name, func_identity_name, func_nested_identity_add_name},
func_domain,
)
if __name__ == "__main__":
unittest.main()
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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": 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58,848 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/clip.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 Clip(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min", "max"],
outputs=["y"],
)
x = np.array([-2, 0, 2]).astype(np.float32)
min_val = np.float32(-1)
max_val = np.float32(1)
y = np.clip(x, min_val, max_val) # expected output [-1., 0., 1.]
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example"
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, max_val)
expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip")
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min", "max"],
outputs=["y"],
)
min_val = np.float32(-5)
max_val = np.float32(5)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds"
)
x = np.array([-6, 0, 6]).astype(np.float32)
y = np.array([-5, 0, 5]).astype(np.float32)
expect(
node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds"
)
x = np.array([-1, 0, 6]).astype(np.float32)
y = np.array([-1, 0, 5]).astype(np.float32)
expect(
node,
inputs=[x, min_val, max_val],
outputs=[y],
name="test_clip_splitbounds",
)
@staticmethod
def export_clip_default() -> None:
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min"],
outputs=["y"],
)
min_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, min_val, np.inf)
expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min")
no_min = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, "max"],
outputs=["y"],
)
max_val = np.float32(0)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, -np.inf, max_val)
expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max")
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, no_max],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.array([-1, 0, 1]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds")
@staticmethod
def export_clip_default_int8() -> None:
node = onnx.helper.make_node(
"Clip",
inputs=["x", "min"],
outputs=["y"],
)
min_val = np.int8(0)
x = np.random.randn(3, 4, 5).astype(np.int8)
y = np.clip(x, min_val, np.iinfo(np.int8).max)
expect(
node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min"
)
no_min = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, "max"],
outputs=["y"],
)
max_val = np.int8(0)
x = np.random.randn(3, 4, 5).astype(np.int8)
y = np.clip(x, np.iinfo(np.int8).min, max_val)
expect(
node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max"
)
no_max = "" # optional input, not supplied
node = onnx.helper.make_node(
"Clip",
inputs=["x", no_min, no_max],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.int8)
y = np.array([-1, 0, 1]).astype(np.int8)
expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds")
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58,849 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reduce_l1.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
class ReduceL1_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=None): # type: ignore
axes = tuple(axes) if axes is not None else None
res = np.sum(np.abs(data), axis=axes, keepdims=keepdims).astype(
dtype=data.dtype
)
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,)
class ReduceL1_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
res = np.sum(np.abs(data), axis=axes, keepdims=keepdims).astype(
dtype=data.dtype
)
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,)
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58,850 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/mod.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 Mod(Base):
@staticmethod
def export_mod_mixed_sign_float64() -> None:
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
z = np.fmod(x, y) # expected output [-0.1, 0.4, 5. , 0.1, -0.4, 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64")
@staticmethod
def export_mod_mixed_sign_float32() -> None:
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
z = np.fmod(
x, y
) # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32")
@staticmethod
def export_mod_mixed_sign_float16() -> None:
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
z = np.fmod(
x, y
) # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 , 3.]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16")
@staticmethod
def export_mod_mixed_sign_int64() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64")
@staticmethod
def export_mod_mixed_sign_int32() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32")
@staticmethod
def export_mod_mixed_sign_int16() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16")
@staticmethod
def export_mod_mixed_sign_int8() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
z = np.mod(x, y) # expected output [ 0, -2, 5, 0, 2, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8")
@staticmethod
def export_mod_uint8() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint8)
y = np.array([2, 3, 8]).astype(np.uint8)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8")
@staticmethod
def export_mod_uint16() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint16)
y = np.array([2, 3, 8]).astype(np.uint16)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16")
@staticmethod
def export_mod_uint32() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint32)
y = np.array([2, 3, 8]).astype(np.uint32)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32")
@staticmethod
def export_mod_uint64() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([4, 7, 5]).astype(np.uint64)
y = np.array([2, 3, 8]).astype(np.uint64)
z = np.mod(x, y) # expected output [0, 1, 5]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64")
@staticmethod
def export_mod_int64_fmod() -> None:
node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
z = np.fmod(x, y) # expected output [ 0, 1, 5, 0, -1, 3]
expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod")
@staticmethod
def export_mod_broadcast() -> None:
node = onnx.helper.make_node(
"Mod",
inputs=["x", "y"],
outputs=["z"],
)
x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
y = np.array([7]).astype(np.int32)
z = np.mod(x, y)
# array([[[0, 1, 2, 3, 4],
# [5, 6, 0, 1, 2]],
# [[3, 4, 5, 6, 0],
# [1, 2, 3, 4, 5]],
# [[6, 0, 1, 2, 3],
# [4, 5, 6, 0, 1]]], dtype=int32)
expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast")
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"/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", 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"/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"], 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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": 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58,851 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/equal.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 Equal(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal")
@staticmethod
def export_equal_broadcast() -> None:
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
y = (np.random.randn(5) * 10).astype(np.int32)
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast")
@staticmethod
def export_equal_string() -> None:
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array(["string1", "string2"], dtype=np.dtype(object))
y = np.array(["string1", "string3"], dtype=np.dtype(object))
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal_string")
@staticmethod
def export_equal_string_broadcast() -> None:
node = onnx.helper.make_node(
"Equal",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array(["string1", "string2"], dtype=np.dtype(object))
y = np.array(["string1"], dtype=np.dtype(object))
z = np.equal(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_equal_string_broadcast")
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58,852 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_loop.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0914,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Loop(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'.")
self.output_index = {n: i for i, n in enumerate(self.body.output_names)} # type: ignore
self.N = len(self.body.input_names) - 2 # type: ignore
self.K = len(self.body.output_names) - self.N - 1 # type: ignore
def need_context(self) -> bool:
"""
The operator Loop needs to know all results produced
so far as the loop may silently access one of them.
Some information are not always referred in the list of inputs
(kind of static variables).
"""
return True
def _run(self, M, cond, *args, context=None, body=None, attributes=None): # type: ignore
if args:
v_initial = args[0]
args = args[1:]
else:
v_initial = None
if not hasattr(M, "dtype"):
raise TypeError(f"M must be an array or a numpy number not {type(M)}.")
body = self.body # type: ignore
loop_inputs = body.input_names
inputs = {name: None for name in loop_inputs}
if v_initial is not None:
inputs[loop_inputs[2]] = v_initial
cond_name = body.output_names[0]
if args:
begin = len(loop_inputs) - len(args)
all_inputs = loop_inputs[begin:]
for name, val in zip(all_inputs, args):
inputs[name] = val
if context is not None:
for a in context:
inputs[a] = context[a]
k_carried_away = [[] for i in range(self.K)] # type: ignore
it = 0
while cond and it < M:
self._log(" -- loop> {%r}", context)
if len(body.input_names) > 0 and body.input_names[0] is not None:
inputs[body.input_names[0]] = np.array(it, dtype=M.dtype) # type: ignore
if len(body.input_names) > 1 and body.input_names[1] is not None:
inputs[body.input_names[1]] = cond
outputs = self._run_body(inputs, attributes=attributes) # type: ignore
if self.K > 0:
for k in range(self.K):
k_carried_away[k].append(outputs[-self.K + k])
index_cond = self.output_index[cond_name]
cond = outputs[index_cond]
if cond is None:
raise RuntimeError(
f"Condition {cond_name!r} returned by the subgraph cannot be None."
)
for i, o in zip(body.input_names[2:], body.output_names[1:]):
inputs[i] = outputs[self.output_index[o]]
it += 1
self._log(" -- loop<")
if it == 0:
outputs = [inputs[i] for i in body.input_names[2:]]
else:
outputs = outputs[1 : 1 + self.N]
outputs.extend(k_carried_away)
while len(outputs) < len(self.onnx_node.output):
outputs.append(np.empty(shape=()))
res = tuple(outputs)
return res
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58,853 | onnx/onnx | refs/heads/main | /onnx/reference/ops/experimental/op_im2col.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from onnx.reference.ops.experimental._op_run_experimental import OpRunExperimental
from onnx.reference.ops_optimized.op_conv_optimized import im2col_fast
class Im2Col(OpRunExperimental):
def _run(self, img, kernel_shape, dilations=None, pads=None, strides=None): # type: ignore
if dilations is None:
dilations = [1 for s in img.shape[2:]]
if pads is None:
pads = [0 for s in img.shape[2:]] * 2
if strides is None:
strides = [1 for s in img.shape[2:]]
if min(dilations) == max(dilations) == 1:
return (im2col_fast(img, tuple(kernel_shape[2:]), pads, strides)[0],) # type: ignore
if dilations[0] != 1 or min(dilations) != max(dilations):
# Let's compute the dilated kernel.
nd = len(dilations)
new_kernel_shape = []
new_shape = list(kernel_shape)
for i, d in enumerate(dilations):
di = len(kernel_shape) - nd + i
new_shape.append(kernel_shape[di] + (kernel_shape[di] - 1) * (d - 1))
new_kernel_shape.append(
kernel_shape[i] + (kernel_shape[i] - 1) * (d - 1)
)
kernel_shape = new_kernel_shape
return (im2col_fast(img, tuple(kernel_shape[2:]), pads, strides),) # type: ignore
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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": 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58,854 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/stringnormalizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Sequence
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
class NormalizeStrings(Base):
@staticmethod
def export() -> None:
def make_graph(
node: onnx.helper.NodeProto,
input_shape: Sequence[int],
output_shape: Sequence[int],
) -> onnx.helper.GraphProto:
graph = onnx.helper.make_graph(
nodes=[node],
name="StringNormalizer",
inputs=[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.STRING, input_shape
)
],
outputs=[
onnx.helper.make_tensor_value_info(
"y", onnx.TensorProto.STRING, output_shape
)
],
)
return graph
# 1st model_monday_casesensintive_nochangecase
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
is_case_sensitive=1,
stopwords=stopwords,
)
x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
y = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
graph = make_graph(node, [4], [3])
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_monday_casesensintive_nochangecase",
)
# 2nd model_nostopwords_nochangecase
node = onnx.helper.make_node(
"StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1
)
x = np.array(["monday", "tuesday"]).astype(object)
y = x
graph = make_graph(node, [2], [2])
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_nostopwords_nochangecase",
)
# 3rd model_monday_casesensintive_lower
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="LOWER",
is_case_sensitive=1,
stopwords=stopwords,
)
x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
y = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
graph = make_graph(node, [4], [3])
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_monday_casesensintive_lower",
)
# 4 model_monday_casesensintive_upper
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=1,
stopwords=stopwords,
)
x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
y = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object)
graph = make_graph(node, [4], [3])
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_monday_casesensintive_upper",
)
# 5 monday_insensintive_upper_twodim
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
stopwords=stopwords,
)
input_shape = [1, 6]
output_shape = [1, 4]
x = (
np.array(
["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"]
)
.astype(object)
.reshape(input_shape)
)
y = (
np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"])
.astype(object)
.reshape(output_shape)
)
graph = make_graph(node, input_shape, output_shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_monday_insensintive_upper_twodim",
)
# 6 monday_empty_output
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=0,
stopwords=stopwords,
)
x = np.array(["monday", "monday"]).astype(object)
y = np.array([""]).astype(object)
graph = make_graph(node, [2], [1])
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
expect(
model,
inputs=[x],
outputs=[y],
name="test_strnorm_model_monday_empty_output",
)
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58,855 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_insert.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Any, List, Optional, Union
import numpy as np
from onnx.reference.op_run import OpRun
def sequence_insert_reference_implementation(
sequence: Union[List[Any], np.ndarray],
tensor: np.ndarray,
position: Optional[np.ndarray] = None,
) -> List[Any]:
# make a copy of input sequence
seq: List[Any] = []
if sequence is not None and (
not isinstance(sequence, np.ndarray) or len(sequence.shape) > 0
):
try:
seq.extend(sequence)
except TypeError as e:
raise TypeError(
f"Unable to iterate on type {type(sequence)}: {sequence}."
) from e
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] + len(seq)) % len(seq)
seq.insert(insert_position, tensor)
else:
# Default position of insertion is at the end of the sequence.
seq.append(tensor)
return seq
class SequenceInsert(OpRun):
def _run(self, S, T, ind=None): # type: ignore
if ind is None:
res = sequence_insert_reference_implementation(S, T)
elif isinstance(ind, int):
res = sequence_insert_reference_implementation(S, T, [ind]) # type: ignore[arg-type]
elif len(ind.shape) > 0:
res = sequence_insert_reference_implementation(S, T, ind)
elif len(ind.shape) == 0:
res = sequence_insert_reference_implementation(S, T, [int(ind)]) # type: ignore[arg-type]
else:
res = sequence_insert_reference_implementation(S, T)
return (res,)
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58,856 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/matmul.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 MatMul(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"MatMul",
inputs=["a", "b"],
outputs=["c"],
)
# 2d
a = np.random.randn(3, 4).astype(np.float32)
b = np.random.randn(4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d")
# 3d
a = np.random.randn(2, 3, 4).astype(np.float32)
b = np.random.randn(2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d")
# 4d
a = np.random.randn(1, 2, 3, 4).astype(np.float32)
b = np.random.randn(1, 2, 4, 3).astype(np.float32)
c = np.matmul(a, b)
expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d")
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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", "/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"], 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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", 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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"], 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58,857 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/quantizelinear.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx import TensorProto
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.helper import make_tensor
class QuantizeLinear(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = np.uint8(128)
y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear",
)
@staticmethod
def export_axis() -> None:
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array(
[
[
[[-162, 10], [-100, 232], [-20, -50]],
[[-76, 0], [0, 252], [32, -44]],
[[245, -485], [-960, -270], [-375, -470]],
],
],
dtype=np.float32,
)
y_scale = np.array([2, 4, 5], dtype=np.float32)
y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(
np.uint8
)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear_axis",
)
@staticmethod
def export_e4m3fn() -> None:
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = make_tensor("zero_point", TensorProto.FLOAT8E4M3FN, [1], [0])
y = make_tensor(
"zero_point", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96]
)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear_e4m3fn",
)
@staticmethod
def export_e5m2() -> None:
node = onnx.helper.make_node(
"QuantizeLinear",
inputs=["x", "y_scale", "y_zero_point"],
outputs=["y"],
)
x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = make_tensor("zero_point", TensorProto.FLOAT8E5M2, [1], [0.0])
y = make_tensor(
"zero_point", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96]
)
expect(
node,
inputs=[x, y_scale, y_zero_point],
outputs=[y],
name="test_quantizelinear_e5m2",
)
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58,858 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0911,R0913,R0914,W0221
import numpy as np
class TreeEnsembleAttributes:
def __init__(self):
self._names = []
def add(self, name, value):
if not name.endswith("_as_tensor"):
self._names.append(name)
if isinstance(value, list):
if name in {
"base_values",
"class_weights",
"nodes_values",
"nodes_hitrates",
}:
value = np.array(value, dtype=np.float32)
elif name.endswith("as_tensor"):
value = np.array(value)
setattr(self, name, value)
def __str__(self):
rows = ["Attributes"]
for name in self._names:
if name.endswith("_as_tensor"):
name = name.replace("_as_tensor", "")
rows.append(f" {name}={getattr(self, name)}")
return "\n".join(rows)
class TreeEnsemble:
def __init__(self, **kwargs):
self.atts = TreeEnsembleAttributes()
for name, value in kwargs.items():
self.atts.add(name, value)
self.tree_ids = sorted(set(self.atts.nodes_treeids)) # type: ignore
self.root_index = {
tid: len(self.atts.nodes_treeids) for tid in self.tree_ids # type: ignore
}
for index, tree_id in enumerate(self.atts.nodes_treeids): # type: ignore
self.root_index[tree_id] = min(self.root_index[tree_id], index)
self.node_index = {
(tid, nid): i
for i, (tid, nid) in enumerate(
zip(self.atts.nodes_treeids, self.atts.nodes_nodeids) # type: ignore
)
}
def __str__(self) -> str:
rows = ["TreeEnsemble", f"root_index={self.root_index}", str(self.atts)]
return "\n".join(rows)
def leaf_index_tree(self, X: np.ndarray, tree_id: int) -> int:
"""
Computes the leaf index for one tree.
"""
index = self.root_index[tree_id]
while self.atts.nodes_modes[index] != "LEAF": # type: ignore
x = X[self.atts.nodes_featureids[index]] # type: ignore
if np.isnan(x):
r = self.atts.nodes_missing_value_tracks_true[index] >= 1 # type: ignore
else:
rule = self.atts.nodes_modes[index] # type: ignore
th = self.atts.nodes_values[index] # type: ignore
if rule == "BRANCH_LEQ":
r = x <= th
elif rule == "BRANCH_LT":
r = x < th
elif rule == "BRANCH_GTE":
r = x >= th
elif rule == "BRANCH_GT":
r = x > th
elif rule == "BRANCH_EQ":
r = x == th
elif rule == "BRANCH_NEQ":
r = x != th
else:
raise ValueError(
f"Unexpected rule {rule!r} for node index {index}."
)
nid = (
self.atts.nodes_truenodeids[index] # type: ignore
if r
else self.atts.nodes_falsenodeids[index] # type: ignore
)
index = self.node_index[tree_id, nid]
return index
def leave_index_tree(self, X: np.ndarray) -> np.ndarray:
"""
Computes the leave index for all trees.
"""
if len(X.shape) == 1:
X = X.reshape((1, -1))
outputs = []
for row in X:
outs = []
for tree_id in self.tree_ids:
outs.append(self.leaf_index_tree(row, tree_id))
outputs.append(outs)
return np.array(outputs)
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"/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"]} |
58,859 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/optionalhaselement.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 optional_has_element_reference_implementation(
optional: Optional[np.ndarray],
) -> np.ndarray:
if optional is None:
return np.array(False)
else:
return np.array(True)
class OptionalHasElement(Base):
@staticmethod
def export() -> None:
optional = np.array([1, 2, 3, 4]).astype(np.float32)
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.FLOAT,
shape=[
4,
],
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
# OptionalHasElement takes a tensor or optional as input
for input_type_protos in [tensor_type_proto, optional_type_proto]:
node = onnx.helper.make_node(
"OptionalHasElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_has_element_reference_implementation(optional)
test_name = "test_optional_has_element_" + (
"optional_input"
if input_type_protos == optional_type_proto
else "tensor_input"
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name=test_name,
)
@staticmethod
def export_empty() -> None:
optional = None
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.INT32, shape=[]
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
# OptionalHasElement takes a tensor or optional as input
for input_type_proto in [tensor_type_proto, optional_type_proto]:
input_name_options = {
"empty": "optional_input",
"empty_no_input_name": "",
"empty_no_input": None,
}
for test_name_surfix, input_name in input_name_options.items():
if input_type_proto == tensor_type_proto and input_name:
# the input tensor cannot be empty if input name is provided.
continue
node = onnx.helper.make_node(
"OptionalHasElement",
inputs=[] if input_name is None else [input_name],
outputs=["output"],
)
output = optional_has_element_reference_implementation(optional)
test_name = (
"test_optional_has_element_"
+ test_name_surfix
+ (
"_optional_input"
if input_type_proto == optional_type_proto
else "_tensor_input"
)
)
expect(
node,
inputs=[optional] if input_name else [],
outputs=[output],
input_type_protos=[input_type_proto] if input_name else [],
name=test_name,
)
| {"/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": 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58,860 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/melweightmatrix.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 MelWeightMatrix(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"MelWeightMatrix",
inputs=[
"num_mel_bins",
"dft_length",
"sample_rate",
"lower_edge_hertz",
"upper_edge_hertz",
],
outputs=["output"],
)
num_mel_bins = np.int32(8)
dft_length = np.int32(16)
sample_rate = np.int32(8192)
lower_edge_hertz = np.float32(0)
upper_edge_hertz = np.float32(8192 / 2)
num_spectrogram_bins = dft_length // 2 + 1
frequency_bins = np.arange(0, num_mel_bins + 2)
low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0]
frequency_bins = frequency_bins * mel_step + low_frequency_mel
frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate
frequency_bins = frequency_bins.astype(int)
output = np.zeros((num_spectrogram_bins, num_mel_bins))
output.flags.writeable = True
for i in range(num_mel_bins):
lower_frequency_value = frequency_bins[i] # left
center_frequency_point = frequency_bins[i + 1] # center
higher_frequency_point = frequency_bins[i + 2] # right
low_to_center = center_frequency_point - lower_frequency_value
if low_to_center == 0:
output[center_frequency_point, i] = 1
else:
for j in range(lower_frequency_value, center_frequency_point + 1):
output[j, i] = float(j - lower_frequency_value) / float(
low_to_center
)
center_to_high = higher_frequency_point - center_frequency_point
if center_to_high > 0:
for j in range(center_frequency_point, higher_frequency_point):
output[j, i] = float(higher_frequency_point - j) / float(
center_to_high
)
# Expected output
# 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
# 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
output = output.astype(np.float32)
expect(
node,
inputs=[
num_mel_bins,
dft_length,
sample_rate,
lower_edge_hertz,
upper_edge_hertz,
],
outputs=[output],
name="test_melweightmatrix",
)
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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"]} |
58,861 | onnx/onnx | refs/heads/main | /onnx/mapping.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import warnings
from typing import Any, Dict, NamedTuple, Union, cast
import numpy as np
from onnx import OptionalProto, SequenceProto, TensorProto
class TensorDtypeMap(NamedTuple):
np_dtype: np.dtype
storage_dtype: int
name: str
# tensor_dtype: (numpy type, storage type, string name)
TENSOR_TYPE_MAP = {
int(TensorProto.FLOAT): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.FLOAT), "TensorProto.FLOAT"
),
int(TensorProto.UINT8): TensorDtypeMap(
np.dtype("uint8"), int(TensorProto.INT32), "TensorProto.UINT8"
),
int(TensorProto.INT8): TensorDtypeMap(
np.dtype("int8"), int(TensorProto.INT32), "TensorProto.INT8"
),
int(TensorProto.UINT16): TensorDtypeMap(
np.dtype("uint16"), int(TensorProto.INT32), "TensorProto.UINT16"
),
int(TensorProto.INT16): TensorDtypeMap(
np.dtype("int16"), int(TensorProto.INT32), "TensorProto.INT16"
),
int(TensorProto.INT32): TensorDtypeMap(
np.dtype("int32"), int(TensorProto.INT32), "TensorProto.INT32"
),
int(TensorProto.INT64): TensorDtypeMap(
np.dtype("int64"), int(TensorProto.INT64), "TensorProto.INT64"
),
int(TensorProto.BOOL): TensorDtypeMap(
np.dtype("bool"), int(TensorProto.INT32), "TensorProto.BOOL"
),
int(TensorProto.FLOAT16): TensorDtypeMap(
np.dtype("float16"), int(TensorProto.UINT16), "TensorProto.FLOAT16"
),
# Native numpy does not support bfloat16 so now use float32.
int(TensorProto.BFLOAT16): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.UINT16), "TensorProto.BFLOAT16"
),
int(TensorProto.DOUBLE): TensorDtypeMap(
np.dtype("float64"), int(TensorProto.DOUBLE), "TensorProto.DOUBLE"
),
int(TensorProto.COMPLEX64): TensorDtypeMap(
np.dtype("complex64"), int(TensorProto.FLOAT), "TensorProto.COMPLEX64"
),
int(TensorProto.COMPLEX128): TensorDtypeMap(
np.dtype("complex128"), int(TensorProto.DOUBLE), "TensorProto.COMPLEX128"
),
int(TensorProto.UINT32): TensorDtypeMap(
np.dtype("uint32"), int(TensorProto.UINT32), "TensorProto.UINT32"
),
int(TensorProto.UINT64): TensorDtypeMap(
np.dtype("uint64"), int(TensorProto.UINT64), "TensorProto.UINT64"
),
int(TensorProto.STRING): TensorDtypeMap(
np.dtype("object"), int(TensorProto.STRING), "TensorProto.STRING"
),
# Native numpy does not support float8 types, so now use float32 for these types.
int(TensorProto.FLOAT8E4M3FN): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.UINT8), "TensorProto.FLOAT8E4M3FN"
),
int(TensorProto.FLOAT8E4M3FNUZ): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.UINT8), "TensorProto.FLOAT8E4M3FNUZ"
),
int(TensorProto.FLOAT8E5M2): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.UINT8), "TensorProto.FLOAT8E5M2"
),
int(TensorProto.FLOAT8E5M2FNUZ): TensorDtypeMap(
np.dtype("float32"), int(TensorProto.UINT8), "TensorProto.FLOAT8E5M2FNUZ"
),
}
class DeprecatedWarningDict(dict): # type: ignore
def __init__(
self,
dictionary: Dict[int, Union[int, str, np.dtype]],
original_function: str,
future_function: str = "",
) -> None:
super().__init__(dictionary)
self._origin_function = original_function
self._future_function = future_function
def __eq__(self, other: object) -> bool:
if not isinstance(other, DeprecatedWarningDict):
return False
return (
self._origin_function == other._origin_function
and self._future_function == other._future_function
)
def __getitem__(self, key: Union[int, str, np.dtype]) -> Any:
if not self._future_function:
warnings.warn(
str(
f"`mapping.{self._origin_function}` is now deprecated and will be removed in a future release."
"To silence this warning, please simply use if-else statement to get the corresponding value."
),
DeprecationWarning,
stacklevel=2,
)
else:
warnings.warn(
str(
f"`mapping.{self._origin_function}` is now deprecated and will be removed in a future release."
f"To silence this warning, please use `helper.{self._future_function}` instead."
),
DeprecationWarning,
stacklevel=2,
)
return super().__getitem__(key)
# This map is used for converting TensorProto values into numpy arrays
TENSOR_TYPE_TO_NP_TYPE = DeprecatedWarningDict(
{tensor_dtype: value.np_dtype for tensor_dtype, value in TENSOR_TYPE_MAP.items()},
"TENSOR_TYPE_TO_NP_TYPE",
"tensor_dtype_to_np_dtype",
)
# This is only used to get keys into STORAGE_TENSOR_TYPE_TO_FIELD.
# TODO(https://github.com/onnx/onnx/issues/4554): Move these variables into _mapping.py
TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE = DeprecatedWarningDict(
{
tensor_dtype: value.storage_dtype
for tensor_dtype, value in TENSOR_TYPE_MAP.items()
},
"TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE",
"tensor_dtype_to_storage_tensor_dtype",
)
# NP_TYPE_TO_TENSOR_TYPE will be eventually removed in the future
# and _NP_TYPE_TO_TENSOR_TYPE will only be used internally
_NP_TYPE_TO_TENSOR_TYPE = {
v: k
for k, v in TENSOR_TYPE_TO_NP_TYPE.items()
if k
not in (
TensorProto.BFLOAT16,
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
)
}
# Currently native numpy does not support bfloat16 so TensorProto.BFLOAT16 is ignored for now
# Numpy float32 array is only reversed to TensorProto.FLOAT
NP_TYPE_TO_TENSOR_TYPE = DeprecatedWarningDict(
cast(Dict[int, Union[int, str, Any]], _NP_TYPE_TO_TENSOR_TYPE),
"NP_TYPE_TO_TENSOR_TYPE",
"np_dtype_to_tensor_dtype",
)
# STORAGE_TENSOR_TYPE_TO_FIELD will be eventually removed in the future
# and _STORAGE_TENSOR_TYPE_TO_FIELD will only be used internally
_STORAGE_TENSOR_TYPE_TO_FIELD = {
int(TensorProto.FLOAT): "float_data",
int(TensorProto.INT32): "int32_data",
int(TensorProto.INT64): "int64_data",
int(TensorProto.UINT8): "int32_data",
int(TensorProto.UINT16): "int32_data",
int(TensorProto.DOUBLE): "double_data",
int(TensorProto.COMPLEX64): "float_data",
int(TensorProto.COMPLEX128): "double_data",
int(TensorProto.UINT32): "uint64_data",
int(TensorProto.UINT64): "uint64_data",
int(TensorProto.STRING): "string_data",
int(TensorProto.BOOL): "int32_data",
}
STORAGE_TENSOR_TYPE_TO_FIELD = DeprecatedWarningDict(
cast(Dict[int, Union[int, str, Any]], _STORAGE_TENSOR_TYPE_TO_FIELD),
"STORAGE_TENSOR_TYPE_TO_FIELD",
)
# This map will be removed and there is no replacement for it
STORAGE_ELEMENT_TYPE_TO_FIELD = DeprecatedWarningDict(
{
int(SequenceProto.TENSOR): "tensor_values",
int(SequenceProto.SPARSE_TENSOR): "sparse_tensor_values",
int(SequenceProto.SEQUENCE): "sequence_values",
int(SequenceProto.MAP): "map_values",
int(OptionalProto.OPTIONAL): "optional_value",
},
"STORAGE_ELEMENT_TYPE_TO_FIELD",
)
# This map will be removed and there is no replacement for it
OPTIONAL_ELEMENT_TYPE_TO_FIELD = DeprecatedWarningDict(
{
int(OptionalProto.TENSOR): "tensor_value",
int(OptionalProto.SPARSE_TENSOR): "sparse_tensor_value",
int(OptionalProto.SEQUENCE): "sequence_value",
int(OptionalProto.MAP): "map_value",
int(OptionalProto.OPTIONAL): "optional_value",
},
"OPTIONAL_ELEMENT_TYPE_TO_FIELD",
)
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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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"/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"]} |
58,862 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_length.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 SequenceLength(OpRun):
def _run(self, input_sequence): # type: ignore
if not isinstance(input_sequence, list):
raise TypeError(
f"input_sequence must be a list not {type(input_sequence)}."
)
return (np.array(len(input_sequence), dtype=np.int64),)
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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"]} |
58,863 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_mod.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 Mod(OpRun):
def _run(self, a, b, fmod=None): # type: ignore
fmod = fmod or self.fmod # type: ignore
if fmod == 1: # type: ignore
return (np.fmod(a, b),)
if a.dtype in (np.float16, np.float32, np.float64):
return (np.nan_to_num(np.fmod(a, b)),)
return (np.nan_to_num(np.mod(a, b)),)
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58,864 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_optional_get_element.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class OptionalGetElement(OpRun):
def _run(self, x): # type: ignore
if x is None:
raise ValueError("The requested optional input has no value.")
return (x,)
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58,865 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_one_hot.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 _one_hot(indices, depth, axis=-1, dtype=np.float32): # type: ignore
values = np.asarray(indices)
rank = len(values.shape)
depth_range = np.arange(depth)
if axis < 0:
axis += rank + 1
ls = values.shape[0:axis]
rs = values.shape[axis:rank]
new_shape = (1,) * len(ls) + depth_range.shape + (1,) * len(rs)
targets = np.reshape(depth_range, new_shape)
values = np.reshape(np.mod(values, depth), (*ls, 1, *rs))
return np.asarray(targets == values, dtype=dtype)
class OneHot(OpRun):
def _run(self, indices, depth, values, axis=None): # type: ignore
off_value, on_value = values
y = _one_hot(indices, depth, axis=axis, dtype=values.dtype) # type: ignore
y = y * (on_value - off_value) + off_value
return (y,)
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58,866 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/affinegrid.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_affine_grid import (
apply_affine_transform,
construct_original_grid,
)
def create_affine_matrix_3d(
angle1,
angle2,
offset_x,
offset_y,
offset_z,
shear_x,
shear_y,
shear_z,
scale_x,
scale_y,
scale_z,
):
rot_x = np.stack(
[
np.ones_like(angle1),
np.zeros_like(angle1),
np.zeros_like(angle1),
np.zeros_like(angle1),
np.cos(angle1),
-np.sin(angle1),
np.zeros_like(angle1),
np.sin(angle1),
np.cos(angle1),
],
axis=-1,
).reshape(-1, 3, 3)
rot_y = np.stack(
[
np.cos(angle2),
np.zeros_like(angle2),
np.sin(angle2),
np.zeros_like(angle2),
np.ones_like(angle2),
np.zeros_like(angle2),
-np.sin(angle2),
np.zeros_like(angle2),
np.cos(angle2),
],
axis=-1,
).reshape(-1, 3, 3)
shear = np.stack(
[
np.ones_like(shear_x),
shear_x,
shear_y,
shear_z,
np.ones_like(shear_x),
shear_x,
shear_y,
shear_x,
np.ones_like(shear_x),
],
axis=-1,
).reshape(-1, 3, 3)
scale = np.stack(
[
scale_x,
np.zeros_like(scale_x),
np.zeros_like(scale_x),
np.zeros_like(scale_x),
scale_y,
np.zeros_like(scale_x),
np.zeros_like(scale_x),
np.zeros_like(scale_x),
scale_z,
],
axis=-1,
).reshape(-1, 3, 3)
translation = np.transpose(np.array([offset_x, offset_y, offset_z])).reshape(
-1, 1, 3
)
rotation_matrix = rot_y @ rot_x @ shear @ scale # (N, 3, 3)
rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1))
affine_matrix = np.hstack((rotation_matrix, translation))
affine_matrix = np.transpose(affine_matrix, (0, 2, 1))
return affine_matrix
def create_affine_matrix_2d(
angle1, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y
):
rot = np.stack(
[np.cos(angle1), -np.sin(angle1), np.sin(angle1), np.cos(angle1)], axis=-1
).reshape(-1, 2, 2)
shear = np.stack(
[np.ones_like(shear_x), shear_x, shear_y, np.ones_like(shear_x)], axis=-1
).reshape(-1, 2, 2)
scale = np.stack(
[scale_x, np.zeros_like(scale_x), np.zeros_like(scale_x), scale_y], axis=-1
).reshape(-1, 2, 2)
translation = np.transpose(np.array([offset_x, offset_y])).reshape(-1, 1, 2)
rotation_matrix = rot @ shear @ scale # (N, 3, 3)
rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1))
affine_matrix = np.hstack((rotation_matrix, translation))
affine_matrix = np.transpose(affine_matrix, (0, 2, 1))
return affine_matrix
def create_theta_2d():
angle = np.array([np.pi / 4, np.pi / 3])
offset_x = np.array([5.0, 2.5])
offset_y = np.array([-3.3, 1.1])
shear_x = np.array([-0.5, 0.5])
shear_y = np.array([0.3, -0.3])
scale_x = np.array([2.2, 1.1])
scale_y = np.array([3.1, 0.9])
theta_2d = create_affine_matrix_2d(
angle, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y
)
return theta_2d
def create_theta_3d():
angle1 = np.array([np.pi / 4, np.pi / 3])
angle2 = np.array([np.pi / 6, np.pi / 2])
offset_x = np.array([5.0, 2.5])
offset_y = np.array([-3.3, 1.1])
offset_z = np.array([-1.1, 2.2])
shear_x = np.array([-0.5, 0.5])
shear_y = np.array([0.3, -0.3])
shear_z = np.array([0.7, -0.2])
scale_x = np.array([2.2, 1.1])
scale_y = np.array([3.1, 0.9])
scale_z = np.array([0.5, 1.5])
theta_3d = create_affine_matrix_3d(
angle1,
angle2,
offset_x,
offset_y,
offset_z,
shear_x,
shear_y,
shear_z,
scale_x,
scale_y,
scale_z,
)
return theta_3d
class AffineGrid(Base):
@staticmethod
def export_2d_no_reference_evaluator() -> None:
theta_2d = create_theta_2d()
N, C, W, H = len(theta_2d), 3, 5, 6
data_size = (W, H)
for align_corners in (0, 1):
node = onnx.helper.make_node(
"AffineGrid",
inputs=["theta", "size"],
outputs=["grid"],
align_corners=align_corners,
)
original_grid = construct_original_grid(data_size, align_corners)
grid = apply_affine_transform(theta_2d, original_grid)
test_name = "test_affine_grid_2d"
if align_corners == 1:
test_name += "_align_corners"
expect(
node,
inputs=[theta_2d, np.array([N, C, W, H], dtype=np.int64)],
outputs=[grid],
name=test_name,
)
@staticmethod
def export_3d_no_reference_evaluator() -> None:
theta_3d = create_theta_3d()
N, C, D, W, H = len(theta_3d), 3, 4, 5, 6
data_size = (D, W, H)
for align_corners in (0, 1):
node = onnx.helper.make_node(
"AffineGrid",
inputs=["theta", "size"],
outputs=["grid"],
align_corners=align_corners,
)
original_grid = construct_original_grid(data_size, align_corners)
grid = apply_affine_transform(theta_3d, original_grid)
test_name = "test_affine_grid_3d"
if align_corners == 1:
test_name += "_align_corners"
expect(
node,
inputs=[theta_3d, np.array([N, C, D, W, H], dtype=np.int64)],
outputs=[grid],
name=test_name,
)
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58,867 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/batchnorm.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 _batchnorm_test_mode(x, s, bias, mean, var, epsilon=1e-5): # type: ignore
dims_x = len(x.shape)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
mean = mean.reshape(-1, *dim_ones)
var = var.reshape(-1, *dim_ones)
return s * (x - mean) / np.sqrt(var + epsilon) + bias
def _batchnorm_training_mode(x, s, bias, mean, var, momentum=0.9, epsilon=1e-5): # type: ignore
axis = tuple(np.delete(np.arange(len(x.shape)), 1))
saved_mean = x.mean(axis=axis)
saved_var = x.var(axis=axis)
output_mean = mean * momentum + saved_mean * (1 - momentum)
output_var = var * momentum + saved_var * (1 - momentum)
y = _batchnorm_test_mode(x, s, bias, saved_mean, saved_var, epsilon=epsilon)
return y.astype(np.float32), output_mean, output_var
class BatchNormalization(Base):
@staticmethod
def export() -> None:
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"],
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y],
name="test_batchnorm_example",
)
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
epsilon = 1e-2
y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"],
epsilon=epsilon,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y],
name="test_batchnorm_epsilon",
)
@staticmethod
def export_train() -> None:
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
# using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
# working around by using np.byte(1).astype(bool)
training_mode = 1
y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y", "output_mean", "output_var"],
training_mode=training_mode,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y, output_mean, output_var],
name="test_batchnorm_example_training_mode",
)
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
mean = np.random.randn(3).astype(np.float32)
var = np.random.rand(3).astype(np.float32)
training_mode = 1
momentum = 0.9
epsilon = 1e-2
y, output_mean, output_var = _batchnorm_training_mode(
x, s, bias, mean, var, momentum, epsilon
)
node = onnx.helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y", "output_mean", "output_var"],
epsilon=epsilon,
training_mode=training_mode,
)
# output size: (2, 3, 4, 5)
expect(
node,
inputs=[x, s, bias, mean, var],
outputs=[y, output_mean, output_var],
name="test_batchnorm_epsilon_training_mode",
)
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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": 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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"]} |
58,868 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/maxunpool.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 MaxUnpool(Base):
@staticmethod
def export_without_output_shape() -> None:
node = onnx.helper.make_node(
"MaxUnpool",
inputs=["xT", "xI"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
y = np.array(
[[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]],
dtype=np.float32,
)
expect(
node,
inputs=[xT, xI],
outputs=[y],
name="test_maxunpool_export_without_output_shape",
)
@staticmethod
def export_with_output_shape() -> None:
node = onnx.helper.make_node(
"MaxUnpool",
inputs=["xT", "xI", "output_shape"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32)
xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
y = np.array(
[
[
[
[0, 0, 0, 0, 0],
[0, 5, 0, 6, 0],
[0, 0, 0, 0, 0],
[0, 7, 0, 8, 0],
[0, 0, 0, 0, 0],
]
]
],
dtype=np.float32,
)
expect(
node,
inputs=[xT, xI, output_shape],
outputs=[y],
name="test_maxunpool_export_with_output_shape",
)
| {"/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/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"]} |
58,869 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/softmaxcrossentropy.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 softmaxcrossentropy(x, target, weight=None, reduction="mean", ignore_index=None, get_log_prob=None): # type: ignore
input_shape = x.shape
if len(input_shape) == 1:
raise RuntimeError("Unsupported shape")
target_shape = target.shape
N = input_shape[0]
C = input_shape[1]
# compute log_softmax
max_x = np.max(x, axis=1, keepdims=True)
exp_x = np.exp(x - max_x)
p = exp_x / np.sum(exp_x, axis=1, keepdims=True)
inp = np.log(p)
log_prob = None
if get_log_prob is True:
log_prob = np.copy(inp)
# initialize the positional weights when required
gather_weight = None
if weight is not None:
# setting mode='clip' to deal with ignore_index > C or < 0 cases.
# when the target value is > C or < 0, it doesn't matter which value we are
# taking in gather_weight, since it will be set to 0 in the following if-block
# use np.int32 to make it compatible with x86 machines
gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
# set `ignore_index`'s loss weight to 0.
# The loss tensor will be multiplied by this weight tensor,
# so `ingore_index`'s loss value will be eliminated.
if ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
dtype=np.float32
)
elif ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
# if input is 4-d and above, make it 3-d
if len(input_shape) != 3:
inp = inp.reshape((N, C, -1))
target = target.reshape((N, -1))
# Get a dimension from the reshaped input.
# If the original input shape is [N, C, H, W],
# the D here should be H * W because we reshape
# [N, C, H, W] to [N, C, H * W].
D = inp.shape[2]
neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
for i in range(N):
for d in range(D):
if target[i][d] != ignore_index:
neg_gather_element_input[i][d] = -inp[i][target[i][d]][d]
loss = neg_gather_element_input
# if the input was 4-d or above reshape to the right shape
if len(input_shape) != 3:
loss = loss.reshape(target_shape)
# apply the weights when required
if gather_weight is not None:
loss = gather_weight * loss
if reduction == "mean":
loss = loss.sum() / gather_weight.sum()
if get_log_prob is True:
return loss, log_prob
else:
return loss
if reduction == "mean":
loss = np.mean(loss)
elif reduction == "sum":
loss = np.sum(loss)
if get_log_prob:
return loss, log_prob
return loss
class SoftmaxCrossEntropyLoss(Base):
@staticmethod
def export_softmaxcrossentropy_none() -> None:
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, reduction="none")
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none")
@staticmethod
def export_softmaxcrossentropy_none_log_prob() -> None:
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction="none", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_none_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_none_weights() -> None:
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none")
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_none_weights",
)
@staticmethod
def export_softmaxcrossentropy_none_weights_log_prob() -> None:
# Define operator attributes.
reduction = "none"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, reduction="none", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_none_weights_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_sum() -> None:
# Define operator attributes.
reduction = "sum"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, reduction="sum")
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum")
@staticmethod
def export_softmaxcrossentropy_sum_log_prob() -> None:
# Define operator attributes.
reduction = "sum"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction="sum", get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_sum_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels)
# Check results
expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean")
@staticmethod
def export_softmaxcrossentropy_mean_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_3d() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, y)
# Check results
expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d")
@staticmethod
def export_softmaxcrossentropy_mean_3d_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
# Check results
expect(
node,
inputs=[x, y],
outputs=[loss, log_prob],
name="test_sce_mean_3d_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(0)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(0)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(0)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(0)
weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
# Check results
expect(
node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii"
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
labels[0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii_3d() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(1)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(1)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii_3d",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii_3d_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(1)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(1)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_3d_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii_3d() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_mean_no_weight_ii_3d",
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii_3d_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
labels[0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_3d_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii_4d() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(
x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[sce],
name="test_sce_mean_weight_ii_4d",
)
@staticmethod
def export_softmaxcrossentropy_mean_weights_ii_4d_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x,
labels,
reduction=reduction,
weight=weights,
ignore_index=ignore_index,
get_log_prob=True,
)
# Check results
expect(
node,
inputs=[x, labels, weights],
outputs=[loss, log_prob],
name="test_sce_mean_weight_ii_4d_log_prob",
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii_4d() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
sce = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_mean_no_weight_ii_4d",
)
@staticmethod
def export_softmaxcrossentropy_mean_no_weights_ii_4d_log_prob() -> None:
# Define operator attributes.
reduction = "mean"
ignore_index = np.int64(2)
# Create operator.
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
# Define operator inputs.
np.random.seed(0)
x = np.random.rand(3, 5, 2, 7).astype(np.float32)
labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
labels[0][0][0] = np.int64(2)
# Compute SoftmaxCrossEntropyLoss
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
)
# Check results
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_mean_no_weight_ii_4d_log_prob",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1d2d3d4d5_mean_weight",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() -> None:
reduction = "mean"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, get_log_prob=True
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
reduction = "none"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
sce = softmaxcrossentropy(x, labels, reduction=reduction)
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_NCd1d2d3d4d5_none_no_weight",
)
@staticmethod
def export_input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob() -> None:
reduction = "none"
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
)
N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
labels = np.random.randint(
0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, get_log_prob=True
)
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob",
)
@staticmethod
def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
x = np.random.rand(N, C, dim1).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
labels[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1_mean_weight_negative_ii",
)
@staticmethod
def export_input_shape_is_NCd1_mean_weight_negative_ii_log_prob() -> None:
reduction = "mean"
ignore_index = np.int64(-1)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1 = 3, 5, 6
np.random.seed(0)
x = np.random.rand(N, C, dim1).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
labels[0][0] = -1
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x,
labels,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
get_log_prob=True,
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1_mean_weight_negative_ii_log_prob",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
labels[0][0][0][0] = -5
sce = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels],
outputs=[sce],
name="test_sce_NCd1d2d3_none_no_weight_negative_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob() -> None:
reduction = "none"
ignore_index = np.int64(-5)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
np.int64
)
labels[0][0][0][0] = -5
loss, log_prob = softmaxcrossentropy(
x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
)
expect(
node,
inputs=[x, labels],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
x = np.random.rand(N, C).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
labels[0] = 10
weight = np.random.rand(C).astype(np.float32)
sce = softmaxcrossentropy(
x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
)
expect(
node,
inputs=[x, labels, weight],
outputs=[sce],
name="test_sce_NCd1d2d3_sum_weight_high_ii",
)
@staticmethod
def export_input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob() -> None:
reduction = "sum"
ignore_index = np.int64(10)
node = onnx.helper.make_node(
"SoftmaxCrossEntropyLoss",
inputs=["x", "y", "w"],
outputs=["z", "log_prob"],
reduction=reduction,
ignore_index=ignore_index,
)
N, C = 3, 5
np.random.seed(0)
x = np.random.rand(N, C).astype(np.float32)
labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
labels[0] = 10
weight = np.random.rand(C).astype(np.float32)
loss, log_prob = softmaxcrossentropy(
x,
labels,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
get_log_prob=True,
)
expect(
node,
inputs=[x, labels, weight],
outputs=[loss, log_prob],
name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob",
)
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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": ["/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": 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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"]} |
58,870 | onnx/onnx | refs/heads/main | /onnx/hub.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
"""ONNX Model Hub
This implements the python client for the ONNX model hub.
"""
import hashlib
import json
import os
import sys
import tarfile
from io import BytesIO
from os.path import join
from typing import IO, Any, Dict, List, Optional, Set, Tuple, cast
from urllib.error import HTTPError
from urllib.request import urlopen
import onnx
if "ONNX_HOME" in os.environ:
_ONNX_HUB_DIR = join(os.environ["ONNX_HOME"], "hub")
elif "XDG_CACHE_HOME" in os.environ:
_ONNX_HUB_DIR = join(os.environ["XDG_CACHE_HOME"], "onnx", "hub")
else:
_ONNX_HUB_DIR = join(os.path.expanduser("~"), ".cache", "onnx", "hub")
class ModelInfo:
"""
A class to represent a model's property and metadata in the ONNX Hub.
It extracts model name, path, sha, tags, etc. from the passed in raw_model_info dict.
Attributes:
model: The name of the model.
model_path: The path to the model, relative to the model zoo (https://github.com/onnx/models/) repo root.
metadata: Additional metadata of the model, such as the size of the model, IO ports, etc.
model_sha: The SHA256 digest of the model file.
tags: A set of tags associated with the model.
opset: The opset version of the model.
"""
def __init__(self, raw_model_info: Dict[str, Any]) -> None:
"""
Parameters:
raw_model_info: A JSON dict containing the model info.
"""
self.model = cast(str, raw_model_info["model"])
self.model_path = cast(str, raw_model_info["model_path"])
self.metadata: Dict[str, Any] = cast(Dict[str, Any], raw_model_info["metadata"])
self.model_sha: Optional[str] = None
if "model_sha" in self.metadata:
self.model_sha = cast(str, self.metadata["model_sha"])
self.tags: Set[str] = set()
if "tags" in self.metadata:
self.tags = set(cast(List[str], self.metadata["tags"]))
self.opset = cast(int, raw_model_info["opset_version"])
self.raw_model_info: Dict[str, Any] = raw_model_info
def __str__(self) -> str:
return f"ModelInfo(model={self.model}, opset={self.opset}, path={self.model_path}, metadata={self.metadata})"
def __repr__(self) -> str:
return self.__str__()
def set_dir(new_dir: str) -> None:
"""
Sets the current ONNX hub cache location
:param new_dir: location of new model hub cache
"""
global _ONNX_HUB_DIR # pylint: disable=global-statement
_ONNX_HUB_DIR = new_dir
def get_dir() -> str:
"""
Gets the current ONNX hub cache location
:return: The location of the ONNX hub model cache
"""
return _ONNX_HUB_DIR
def _parse_repo_info(repo: str) -> Tuple[str, str, str]:
"""
Gets the repo owner, name and ref from a repo specification string.
"""
repo_owner = repo.split(":")[0].split("/")[0]
repo_name = repo.split(":")[0].split("/")[1]
if ":" in repo:
repo_ref = repo.split(":")[1]
else:
repo_ref = "main"
return repo_owner, repo_name, repo_ref
def _verify_repo_ref(repo: str) -> bool:
"""
Verifies whether the given model repo can be trusted.
A model repo can be trusted if it matches onnx/models:main.
"""
repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
return (repo_owner == "onnx") and (repo_name == "models") and (repo_ref == "main")
def _get_base_url(repo: str, lfs: bool = False) -> str:
"""
Gets the base github url from a repo specification string
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param lfs: whether the url is for downloading lfs models
:return: the base github url for downloading
"""
repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
if lfs:
return f"https://media.githubusercontent.com/media/{repo_owner}/{repo_name}/{repo_ref}/"
return f"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{repo_ref}/"
def _download_file(url: str, file_name: str) -> None:
"""
Downloads the file with specifed file_name from the url
:param url: a url of download link
:param file_name: a specified file name for the downloaded file
"""
chunk_size = 16384 # 1024 * 16
with urlopen(url) as response, open(file_name, "wb") as f:
# Loads processively with chuck_size for huge models
while True:
chunk = response.read(chunk_size)
if not chunk:
break
f.write(chunk)
def list_models(
repo: str = "onnx/models:main",
model: Optional[str] = None,
tags: Optional[List[str]] = None,
) -> List[ModelInfo]:
"""
Gets the list of model info consistent with a given name and tags
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param model: The name of the model to search for. If `None`, will return all models with matching tags.
:param tags: A list of tags to filter models by. If `None`, will return all models with matching name.
:return: list of ModelInfo
"""
base_url = _get_base_url(repo)
manifest_url = base_url + "ONNX_HUB_MANIFEST.json"
try:
with urlopen(manifest_url) as response:
manifest: List[ModelInfo] = [
ModelInfo(info) for info in json.load(cast(IO[str], response))
]
except HTTPError as e:
raise AssertionError(f"Could not find manifest at {manifest_url}") from e
# Filter by model name first.
matching_models = (
manifest
if model is None
else [m for m in manifest if m.model.lower() == model.lower()]
)
# Filter by tags
if tags is None:
return matching_models
canonical_tags = {t.lower() for t in tags}
matching_info_list: List[ModelInfo] = []
for m in matching_models:
model_tags = {t.lower() for t in m.tags}
if len(canonical_tags.intersection(model_tags)) > 0:
matching_info_list.append(m)
return matching_info_list
def get_model_info(
model: str, repo: str = "onnx/models:main", opset: Optional[int] = None
) -> ModelInfo:
"""
Gets the model info matching the given name and opset.
:param model: The name of the onnx model in the manifest. This field is case-sensitive
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param opset: The opset of the model to get. The default of `None` will return the model with largest opset.
:return: ModelInfo
"""
matching_models = list_models(repo, model)
if not matching_models:
raise AssertionError(f"No models found with name {model}")
if opset is None:
selected_models = sorted(matching_models, key=lambda m: -m.opset)
else:
selected_models = [m for m in matching_models if m.opset == opset]
if not selected_models:
valid_opsets = [m.opset for m in matching_models]
raise AssertionError(
f"{model} has no version with opset {opset}. Valid opsets: {valid_opsets}"
)
return selected_models[0]
def load(
model: str,
repo: str = "onnx/models:main",
opset: Optional[int] = None,
force_reload: bool = False,
silent: bool = False,
) -> Optional[onnx.ModelProto]:
"""
Downloads a model by name from the onnx model hub
:param model: The name of the onnx model in the manifest. This field is case-sensitive
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param opset: The opset of the model to download. The default of `None` automatically chooses the largest opset
:param force_reload: Whether to force the model to re-download even if its already found in the cache
:param silent: Whether to suppress the warning message if the repo is not trusted.
:return: ModelProto or None
"""
selected_model = get_model_info(model, repo, opset)
local_model_path_arr = selected_model.model_path.split("/")
if selected_model.model_sha is not None:
local_model_path_arr[
-1
] = f"{selected_model.model_sha}_{local_model_path_arr[-1]}"
local_model_path = join(_ONNX_HUB_DIR, os.sep.join(local_model_path_arr))
if force_reload or not os.path.exists(local_model_path):
if not _verify_repo_ref(repo) and not silent:
msg = f"The model repo specification {repo} is not trusted and may contain security vulnerabilities. Only continue if you trust this repo."
print(msg, file=sys.stderr)
print("Continue?[y/n]")
if input().lower() != "y":
return None
os.makedirs(os.path.dirname(local_model_path), exist_ok=True)
lfs_url = _get_base_url(repo, True)
print(f"Downloading {model} to local path {local_model_path}")
_download_file(lfs_url + selected_model.model_path, local_model_path)
else:
print(f"Using cached {model} model from {local_model_path}")
with open(local_model_path, "rb") as f:
model_bytes = f.read()
if selected_model.model_sha is not None:
downloaded_sha = hashlib.sha256(model_bytes).hexdigest()
if not downloaded_sha == selected_model.model_sha:
raise AssertionError(
f"The cached model {selected_model.model} has SHA256 {downloaded_sha} "
f"while checksum should be {selected_model.model_sha}. "
"The model in the hub may have been updated. Use force_reload to "
"download the model from the model hub."
)
return onnx.load(cast(IO[bytes], BytesIO(model_bytes)))
def download_model_with_test_data(
model: str,
repo: str = "onnx/models:main",
opset: Optional[int] = None,
force_reload: bool = False,
silent: bool = False,
) -> Optional[str]:
"""
Downloads a model along with test data by name from the onnx model hub and returns the directory to which the files have been extracted.
:param model: The name of the onnx model in the manifest. This field is case-sensitive
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param opset: The opset of the model to download. The default of `None` automatically chooses the largest opset
:param force_reload: Whether to force the model to re-download even if its already found in the cache
:param silent: Whether to suppress the warning message if the repo is not trusted.
:return: str or None
"""
selected_model = get_model_info(model, repo, opset)
local_model_with_data_path_arr = selected_model.metadata[
"model_with_data_path"
].split("/")
model_with_data_sha = selected_model.metadata["model_with_data_sha"]
if model_with_data_sha is not None:
local_model_with_data_path_arr[
-1
] = f"{model_with_data_sha}_{local_model_with_data_path_arr[-1]}"
local_model_with_data_path = join(
_ONNX_HUB_DIR, os.sep.join(local_model_with_data_path_arr)
)
if force_reload or not os.path.exists(local_model_with_data_path):
if not _verify_repo_ref(repo) and not silent:
msg = f"The model repo specification {repo} is not trusted and may contain security vulnerabilities. Only continue if you trust this repo."
print(msg, file=sys.stderr)
print("Continue?[y/n]")
if input().lower() != "y":
return None
os.makedirs(os.path.dirname(local_model_with_data_path), exist_ok=True)
lfs_url = _get_base_url(repo, True)
print(f"Downloading {model} to local path {local_model_with_data_path}")
_download_file(
lfs_url + selected_model.metadata["model_with_data_path"],
local_model_with_data_path,
)
else:
print(f"Using cached {model} model from {local_model_with_data_path}")
with open(local_model_with_data_path, "rb") as f:
model_with_data_bytes = f.read()
if model_with_data_sha is not None:
downloaded_sha = hashlib.sha256(model_with_data_bytes).hexdigest()
if not downloaded_sha == model_with_data_sha:
raise AssertionError(
f"The cached model {selected_model.model} has SHA256 {downloaded_sha} "
f"while checksum should be {model_with_data_sha}. "
"The model in the hub may have been updated. Use force_reload to "
"download the model from the model hub."
)
with tarfile.open(local_model_with_data_path) as model_with_data_zipped:
# FIXME: Avoid index manipulation with magic numbers
local_model_with_data_dir_path = local_model_with_data_path[
0 : len(local_model_with_data_path) - 7
]
model_with_data_zipped.extractall(local_model_with_data_dir_path)
model_with_data_path = (
local_model_with_data_dir_path
+ "/"
+ os.listdir(local_model_with_data_dir_path)[0]
)
return model_with_data_path
def load_composite_model(
network_model: str,
preprocessing_model: str,
network_repo: str = "onnx/models:main",
preprocessing_repo: str = "onnx/models:main",
opset: Optional[int] = None,
force_reload: bool = False,
silent: bool = False,
) -> Optional[onnx.ModelProto]:
"""
Builds a composite model including data preprocessing by downloading a network and a preprocessing model
and combine it into a single model
:param model: The name of the onnx model in the manifest. This field is case-sensitive
:param repo: The location of the model repo in format "user/repo[:branch]".
If no branch is found will default to "main"
:param opset: The opset of the model to download. The default of `None` automatically chooses the largest opset
:param force_reload: Whether to force the model to re-download even if its already found in the cache
:param silent: Whether to suppress the warning message if the repo is not trusted.
:return: ModelProto or None
"""
preprocessing = load(
preprocessing_model, preprocessing_repo, opset, force_reload, silent
)
if preprocessing is None:
raise RuntimeError(
f"Could not load the preprocessing model: {preprocessing_model}"
)
network = load(network_model, network_repo, opset, force_reload, silent)
if network is None:
raise RuntimeError(f"Could not load the network model: {network_model}")
all_domains: Set[str] = set()
domains_to_version_network: Dict[str, int] = {}
domains_to_version_preprocessing: Dict[str, int] = {}
for opset_import_entry in network.opset_import:
domain = (
"ai.onnx" if opset_import_entry.domain == "" else opset_import_entry.domain
)
all_domains.add(domain)
domains_to_version_network[domain] = opset_import_entry.version
for opset_import_entry in preprocessing.opset_import:
domain = (
"ai.onnx" if opset_import_entry.domain == "" else opset_import_entry.domain
)
all_domains.add(domain)
domains_to_version_preprocessing[domain] = opset_import_entry.version
preprocessing_opset_version = -1
network_opset_version = -1
for domain in all_domains:
if domain == "ai.onnx":
preprocessing_opset_version = domains_to_version_preprocessing[domain]
network_opset_version = domains_to_version_network[domain]
elif (
domain in domains_to_version_preprocessing
and domain in domains_to_version_network
and domains_to_version_preprocessing[domain]
!= domains_to_version_preprocessing[domain]
):
raise ValueError(
f"Can not merge {preprocessing_model} and {network_model} because they contain "
f"different opset versions for domain {domain} ({domains_to_version_preprocessing[domain]}) "
f"and {domains_to_version_network[domain]}). Only the default domain can be "
"automatically converted to the highest version of the two."
)
if preprocessing_opset_version > network_opset_version:
network = onnx.version_converter.convert_version(
network, preprocessing_opset_version
)
network.ir_version = preprocessing.ir_version
onnx.checker.check_model(network)
elif network_opset_version > preprocessing_opset_version:
preprocessing = onnx.version_converter.convert_version(
preprocessing, network_opset_version
)
preprocessing.ir_version = network.ir_version
onnx.checker.check_model(preprocessing)
io_map = [
(out_entry.name, in_entry.name)
for out_entry, in_entry in zip(preprocessing.graph.output, network.graph.input)
]
model_with_preprocessing = onnx.compose.merge_models(
preprocessing, network, io_map=io_map
)
return model_with_preprocessing
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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"]} |
58,871 | onnx/onnx | refs/heads/main | /onnx/defs/gen_doc.py | #!/usr/bin/env python
# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import os
from collections import defaultdict
from typing import Any, Dict, List, NamedTuple, Sequence, Set, Tuple
import numpy as np
from onnx import defs, helper
from onnx.backend.sample.ops import collect_sample_implementations
from onnx.backend.test.case import collect_snippets
from onnx.defs import ONNX_ML_DOMAIN, OpSchema
SNIPPETS = collect_snippets()
SAMPLE_IMPLEMENTATIONS = collect_sample_implementations()
ONNX_ML = not bool(os.getenv("ONNX_ML") == "0")
def display_number(v: int) -> str:
if defs.OpSchema.is_infinite(v):
return "∞"
return str(v)
def should_render_domain(domain: str, output: str) -> bool:
is_ml = "-ml" in output
if domain == ONNX_ML_DOMAIN:
return is_ml
else:
return not is_ml
def format_name_with_domain(domain: str, schema_name: str) -> str:
if domain:
return f"{domain}.{schema_name}"
return schema_name
def format_function_versions(function_versions: Sequence[int]) -> str:
return f"{', '.join([str(v) for v in function_versions])}"
def format_versions(versions: Sequence[OpSchema], changelog: str) -> str:
return f"{', '.join(display_version_link(format_name_with_domain(v.domain, v.name), v.since_version, changelog) for v in versions[::-1])}"
def display_attr_type(v: OpSchema.AttrType) -> str:
assert isinstance(v, OpSchema.AttrType)
s = str(v)
s = s[s.rfind(".") + 1 :].lower()
if s[-1] == "s":
s = "list of " + s
return s
def display_domain(domain: str) -> str:
if domain:
return f"the '{domain}' operator set"
return "the default ONNX operator set"
def display_domain_short(domain: str) -> str:
if domain:
return domain
return "ai.onnx (default)"
def display_version_link(name: str, version: int, changelog: str) -> str:
name_with_ver = f"{name}-{version}"
return f'<a href="{changelog}#{name_with_ver}">{version}</a>'
def generate_formal_parameter_tags(formal_parameter: OpSchema.FormalParameter) -> str:
tags: List[str] = []
if OpSchema.FormalParameterOption.Optional == formal_parameter.option:
tags = ["optional"]
elif OpSchema.FormalParameterOption.Variadic == formal_parameter.option:
if formal_parameter.is_homogeneous:
tags = ["variadic"]
else:
tags = ["variadic", "heterogeneous"]
differentiable: OpSchema.DifferentiationCategory = (
OpSchema.DifferentiationCategory.Differentiable
)
non_differentiable: OpSchema.DifferentiationCategory = (
OpSchema.DifferentiationCategory.NonDifferentiable
)
if differentiable == formal_parameter.differentiation_category:
tags.append("differentiable")
elif non_differentiable == formal_parameter.differentiation_category:
tags.append("non-differentiable")
return "" if len(tags) == 0 else " (" + ", ".join(tags) + ")"
def display_schema( # pylint: disable=too-many-branches,too-many-statements
schema: OpSchema, versions: Sequence[OpSchema], changelog: str
) -> str:
s = ""
# doc
if schema.doc:
s += "\n"
s += "\n".join(
(" " + line).rstrip() for line in schema.doc.lstrip().splitlines()
)
s += "\n"
# since version
s += "\n#### Version\n"
if schema.support_level == OpSchema.SupportType.EXPERIMENTAL:
s += "\nNo versioning maintained for experimental ops."
else:
s += (
"\nThis version of the operator has been "
+ ("deprecated" if schema.deprecated else "available")
+ f" since version {schema.since_version}"
)
s += f" of {display_domain(schema.domain)}.\n"
if len(versions) > 1:
# TODO: link to the Changelog.md
s += "\nOther versions of this operator: {}\n".format( # pylint: disable=consider-using-f-string
", ".join(
display_version_link(
format_name_with_domain(v.domain, v.name),
v.since_version,
changelog,
)
for v in versions[:-1]
)
)
# If this schema is deprecated, don't display any of the following sections
if schema.deprecated:
return s
# attributes
if schema.attributes:
s += "\n#### Attributes\n\n"
s += "<dl>\n"
for _, attr in sorted(schema.attributes.items()):
# option holds either required or default value
opt = ""
if attr.required:
opt = "required"
elif attr.default_value.name:
default_value = helper.get_attribute_value(attr.default_value)
def format_value(value: Any) -> str:
if isinstance(value, float):
formatted = str(np.round(value, 5))
# use default formatting, unless too long.
if len(formatted) > 10:
formatted = str(f"({value:e})")
return formatted
if isinstance(value, (bytes, bytearray)):
return str(value.decode("utf-8"))
return str(value)
if isinstance(default_value, list):
default_value = [format_value(val) for val in default_value]
else:
default_value = format_value(default_value)
opt = f"default is {default_value}"
s += f"<dt><tt>{attr.name}</tt> : {display_attr_type(attr.type)}{f' ({opt})' if opt else ''}</dt>\n"
s += f"<dd>{attr.description}</dd>\n"
s += "</dl>\n"
# inputs
s += "\n#### Inputs"
if schema.min_input != schema.max_input:
s += f" ({display_number(schema.min_input)} - {display_number(schema.max_input)})"
s += "\n\n"
if schema.inputs:
s += "<dl>\n"
for input_ in schema.inputs:
option_str = generate_formal_parameter_tags(input_)
s += f"<dt><tt>{input_.name}</tt>{option_str} : {input_.type_str}</dt>\n"
s += f"<dd>{input_.description}</dd>\n"
s += "</dl>\n"
# outputs
s += "\n#### Outputs"
if schema.min_output != schema.max_output:
s += f" ({display_number(schema.min_output)} - {display_number(schema.max_output)})"
s += "\n\n"
if schema.outputs:
s += "<dl>\n"
for output in schema.outputs:
option_str = generate_formal_parameter_tags(output)
s += f"<dt><tt>{output.name}</tt>{option_str} : {output.type_str}</dt>\n"
s += f"<dd>{output.description}</dd>\n"
s += "</dl>\n"
# type constraints
s += "\n#### Type Constraints"
s += "\n\n"
if schema.type_constraints:
s += "<dl>\n"
for type_constraint in schema.type_constraints:
allowedTypes = type_constraint.allowed_type_strs
if len(allowedTypes) > 0:
allowedTypeStr = allowedTypes[0]
for allowedType in allowedTypes[1:]:
allowedTypeStr += ", " + allowedType
s += f"<dt><tt>{type_constraint.type_param_str}</tt> : {allowedTypeStr}</dt>\n"
s += f"<dd>{type_constraint.description}</dd>\n"
s += "</dl>\n"
# Function Body
# TODO: this should be refactored to show the function body graph's picture (DAG).
# if schema.has_function or schema.has_context_dependent_function: # type: ignore
# s += '\n#### Function\n'
# s += '\nThe Function can be represented as a function.\n'
return s
def support_level_str(level: OpSchema.SupportType) -> str:
return (
"<sub>experimental</sub> " if level == OpSchema.SupportType.EXPERIMENTAL else ""
)
class Args(NamedTuple):
output: str
changelog: str
def main(args: Args) -> None: # pylint: disable=too-many-branches,too-many-statements
base_dir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
)
docs_dir = os.path.join(base_dir, "docs")
with open(
os.path.join(docs_dir, args.changelog), "w", newline="", encoding="utf-8"
) as fout:
fout.write("<!--- SPDX-License-Identifier: Apache-2.0 -->\n")
fout.write("## Operator Changelog\n")
fout.write(
"*This file is automatically generated from the\n"
" [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).\n"
" Do not modify directly and instead edit operator definitions.*\n"
"\n"
"For an operator input/output's differentiability, it can be differentiable,\n"
" non-differentiable, or undefined. If a variable's differentiability\n"
" is not specified, that variable has undefined differentiability.\n"
)
# domain -> version -> [schema]
dv_index: Dict[str, Dict[int, List[OpSchema]]] = defaultdict(
lambda: defaultdict(list)
)
for schema in defs.get_all_schemas_with_history():
dv_index[schema.domain][schema.since_version].append(schema)
fout.write("\n")
for domain, versionmap in sorted(dv_index.items()):
if not should_render_domain(domain, args.output):
continue
s = f"# {display_domain_short(domain)}\n"
for version, unsorted_schemas in sorted(versionmap.items()):
s += f"## Version {version} of {display_domain(domain)}\n"
for schema in sorted(unsorted_schemas, key=lambda s: s.name):
name_with_ver = f"{format_name_with_domain(domain, schema.name)}-{schema.since_version}"
s += (
'### <a name="{}"></a>**{}**'
+ (" (deprecated)" if schema.deprecated else "")
+ "</a>\n"
).format(name_with_ver, name_with_ver)
s += display_schema(schema, [schema], args.changelog)
s += "\n"
fout.write(s)
with open(
os.path.join(docs_dir, args.output), "w", newline="", encoding="utf-8"
) as fout:
fout.write("<!--- SPDX-License-Identifier: Apache-2.0 -->\n")
fout.write("## Operator Schemas\n")
fout.write(
"*This file is automatically generated from the\n"
" [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).\n"
" Do not modify directly and instead edit operator definitions.*\n"
"\n"
"For an operator input/output's differentiability, it can be differentiable,\n"
" non-differentiable, or undefined. If a variable's differentiability\n"
" is not specified, that variable has undefined differentiability.\n"
)
# domain -> support level -> name -> [schema]
index: Dict[str, Dict[int, Dict[str, List[OpSchema]]]] = defaultdict(
lambda: defaultdict(lambda: defaultdict(list))
)
for schema in defs.get_all_schemas_with_history():
index[schema.domain][int(schema.support_level)][schema.name].append(schema)
fout.write("\n")
# Preprocess the Operator Schemas
# [(domain, [(support_level, [(schema name, current schema, all versions schemas)])])]
operator_schemas: List[
Tuple[str, List[Tuple[int, List[Tuple[str, OpSchema, List[OpSchema]]]]]]
] = []
existing_ops: Set[str] = set()
for domain, _supportmap in sorted(index.items()):
if not should_render_domain(domain, args.output):
continue
processed_supportmap = []
for _support, _namemap in sorted(_supportmap.items()):
processed_namemap = []
for n, unsorted_versions in sorted(_namemap.items()):
versions = sorted(unsorted_versions, key=lambda s: s.since_version)
schema = versions[-1]
if schema.name in existing_ops:
continue
existing_ops.add(schema.name)
processed_namemap.append((n, schema, versions))
processed_supportmap.append((_support, processed_namemap))
operator_schemas.append((domain, processed_supportmap))
# Table of contents
for domain, supportmap in operator_schemas:
s = f"### {display_domain_short(domain)}\n"
fout.write(s)
fout.write("|**Operator**|**Since version**||\n")
fout.write("|-|-|-|\n")
function_ops = []
for _, namemap in supportmap:
for n, schema, versions in namemap:
if schema.has_function or schema.has_context_dependent_function: # type: ignore
function_versions = schema.all_function_opset_versions # type: ignore
function_ops.append((n, schema, versions, function_versions))
continue
s = '|{}<a href="#{}">{}</a>{}|{}|\n'.format( # pylint: disable=consider-using-f-string
support_level_str(schema.support_level),
format_name_with_domain(domain, n),
format_name_with_domain(domain, n),
" (deprecated)" if schema.deprecated else "",
format_versions(versions, args.changelog),
)
fout.write(s)
if function_ops:
fout.write("|**Function**|**Since version**|**Function version**|\n")
for n, schema, versions, function_versions in function_ops:
s = '|{}<a href="#{}">{}</a>|{}|{}|\n'.format( # pylint: disable=consider-using-f-string
support_level_str(schema.support_level),
format_name_with_domain(domain, n),
format_name_with_domain(domain, n),
format_versions(versions, args.changelog),
format_function_versions(function_versions),
)
fout.write(s)
fout.write("\n")
fout.write("\n")
for domain, supportmap in operator_schemas:
s = f"## {display_domain_short(domain)}\n"
fout.write(s)
for _, namemap in supportmap:
for op_type, schema, versions in namemap:
# op_type
s = (
'### {}<a name="{}"></a><a name="{}">**{}**'
+ (" (deprecated)" if schema.deprecated else "")
+ "</a>\n"
).format(
support_level_str(schema.support_level),
format_name_with_domain(domain, op_type),
format_name_with_domain(domain, op_type.lower()),
format_name_with_domain(domain, op_type),
)
s += display_schema(schema, versions, args.changelog)
s += "\n\n"
if op_type in SNIPPETS:
s += "#### Examples\n\n"
for summary, code in sorted(SNIPPETS[op_type]):
s += "<details>\n"
s += f"<summary>{summary}</summary>\n\n"
s += f"```python\n{code}\n```\n\n"
s += "</details>\n"
s += "\n\n"
if op_type.lower() in SAMPLE_IMPLEMENTATIONS:
s += "#### Sample Implementation\n\n"
s += "<details>\n"
s += f"<summary>{op_type}</summary>\n\n"
s += f"```python\n{SAMPLE_IMPLEMENTATIONS[op_type.lower()]}\n```\n\n"
s += "</details>\n"
s += "\n\n"
fout.write(s)
if __name__ == "__main__":
if ONNX_ML:
main(
Args(
"Operators-ml.md",
"Changelog-ml.md",
)
)
main(
Args(
"Operators.md",
"Changelog.md",
)
)
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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"]} |
58,872 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/shrink.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 ShrinkTest(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Shrink",
["x"],
["y"],
lambd=1.5,
bias=1.5,
)
graph = onnx.helper.make_graph(
nodes=[node],
name="Shrink",
inputs=[
onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [5])
],
outputs=[
onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [5])
],
)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 10)],
)
x = np.array([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=np.float32)
y = np.array([-0.5, 0.0, 0.0, 0.0, 0.5], dtype=np.float32)
expect(model, inputs=[x], outputs=[y], name="test_shrink")
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58,873 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_eyelike.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.onnx_pb import TensorProto
from onnx.reference.op_run import OpRun
class EyeLike(OpRun):
def _run(self, data, *args, dtype=None, k=None):
if dtype is None:
if data is None:
_dtype = np.float32
else:
_dtype = data.dtype
elif dtype == TensorProto.STRING:
_dtype = np.str_ # type: ignore[assignment]
else:
_dtype = tensor_dtype_to_np_dtype(dtype) # type: ignore[assignment]
shape = data.shape
if len(shape) == 1:
sh = (shape[0], shape[0])
elif len(shape) == 2:
sh = shape
else:
raise RuntimeError(f"EyeLike only accept 1D or 2D tensors not {shape!r}.")
return (np.eye(*sh, k=k, dtype=_dtype),) # type: ignore
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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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58,874 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/lstm.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Tuple
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class LSTMHelper:
def __init__(self, **params: Any) -> None:
# LSTM Input Names
X = "X"
W = "W"
R = "R"
B = "B"
H_0 = "initial_h"
C_0 = "initial_c"
P = "P"
LAYOUT = "layout"
number_of_gates = 4
number_of_peepholes = 3
required_inputs = [X, W, R]
for i in required_inputs:
assert i in params, f"Missing Required Input: {i}"
self.num_directions = params[W].shape[0]
if self.num_directions == 1:
for k in params:
if k != X:
params[k] = np.squeeze(params[k], axis=0)
hidden_size = params[R].shape[-1]
batch_size = params[X].shape[1]
layout = params[LAYOUT] if LAYOUT in params else 0
x = params[X]
x = x if layout == 0 else np.swapaxes(x, 0, 1)
b = (
params[B]
if B in params
else np.zeros(2 * number_of_gates * hidden_size, dtype=np.float32)
)
p = (
params[P]
if P in params
else np.zeros(number_of_peepholes * hidden_size, dtype=np.float32)
)
h_0 = (
params[H_0]
if H_0 in params
else np.zeros((batch_size, hidden_size), dtype=np.float32)
)
c_0 = (
params[C_0]
if C_0 in params
else np.zeros((batch_size, hidden_size), dtype=np.float32)
)
self.X = x
self.W = params[W]
self.R = params[R]
self.B = b
self.P = p
self.H_0 = h_0
self.C_0 = c_0
self.LAYOUT = layout
else:
raise NotImplementedError()
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) -> Tuple[np.ndarray, np.ndarray]:
seq_length = self.X.shape[0]
hidden_size = self.H_0.shape[-1]
batch_size = self.X.shape[1]
Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
h_list = []
[p_i, p_o, p_f] = np.split(self.P, 3)
H_t = self.H_0
C_t = self.C_0
for x in np.split(self.X, self.X.shape[0], axis=0):
gates = (
np.dot(x, np.transpose(self.W))
+ np.dot(H_t, np.transpose(self.R))
+ np.add(*np.split(self.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 self.num_directions == 1:
Y[:, 0, :, :] = concatenated
if self.LAYOUT == 0:
Y_h = Y[-1]
else:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
return Y, Y_h
class LSTM(Base):
@staticmethod
def export_defaults() -> None:
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 3
weight_scale = 0.1
number_of_gates = 4
node = onnx.helper.make_node(
"LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
lstm = LSTMHelper(X=input, W=W, R=R)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_defaults",
)
@staticmethod
def export_initial_bias() -> None:
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 4
weight_scale = 0.1
custom_bias = 0.1
number_of_gates = 4
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
np.float32
)
R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), 1)
lstm = LSTMHelper(X=input, W=W, R=R, B=B)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_with_initial_bias",
)
@staticmethod
def export_peepholes() -> None:
input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype(
np.float32
)
input_size = 4
hidden_size = 3
weight_scale = 0.1
number_of_gates = 4
number_of_peepholes = 3
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
# Initializing Inputs
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(
np.float32
)
lstm = LSTMHelper(
X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h
)
_, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R, B, seq_lens, init_h, init_c, P],
outputs=[Y_h.astype(np.float32)],
name="test_lstm_with_peepholes",
)
@staticmethod
def export_batchwise() -> None:
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 7
weight_scale = 0.3
number_of_gates = 4
layout = 1
node = onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
lstm = LSTMHelper(X=input, W=W, R=R, layout=layout)
Y, Y_h = lstm.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_lstm_batchwise",
)
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58,875 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_lrn.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
import math
import numpy as np
from onnx.reference.op_run import OpRun
class LRN(OpRun):
def _run(self, x, alpha=None, beta=None, bias=None, size=None): # type: ignore
if len(x.shape) != 4:
raise RuntimeError(
f"LRN only applies on 4D tensors but shape is {x.shape!r}."
)
square_sum = np.zeros(x.shape).astype(x.dtype)
minc = x.shape[1]
c1 = int(math.floor((size - 1) / 2))
c2 = int(math.ceil((size - 1) / 2)) + 1
for c in range(x.shape[0]):
begin = max(0, c - c1)
end = min(minc, c + c2)
square_sum[:, c, :, :] = np.sum(x[:, begin:end, :, :] ** 2, axis=1)
y = x / ((bias + (alpha / size) * square_sum) ** beta)
return (y.astype(x.dtype),)
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58,876 | onnx/onnx | refs/heads/main | /onnx/version_converter.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
"""onnx version converter
This enables users to convert their models between different opsets within the
default domain ("" or "ai.onnx").
"""
import onnx
import onnx.onnx_cpp2py_export.version_converter as C # noqa: N812
from onnx import ModelProto
def convert_version(model: ModelProto, target_version: int) -> ModelProto:
"""Apply the version conversion on the serialized ModelProto.
Arguments:
input (ModelProto): model
target_version (int): target opset version
Returns:
return (ModelProto) converted model
Raises Exceptions:
RuntimeError when some necessary conversion is not supported
Supported adapters:
- Add from Opset 7 to Opset 6
- Add from Opset 6 to Opset 5
- Add from Opset 6 to Opset 7
- Add from Opset 5 to Opset 6
- Mul from Opset 6 to Opset 7
- Mul from Opset 7 to Opset 6
- Mul from Opset 6 to Opset 5
- Mul from Opset 5 to Opset 6
- Gemm from Opset 7 to Opset 6
- Gemm from Opset 6 to Opset 5
- Gemm from Opset 6 to Opset 7
- Gemm from Opset 5 to Opset 6
- Relu from Opset 6 to Opset 5
- Relu from Opset 5 to Opset 6
- BatchNorm from Opset 7 to Opset 6
- BatchNorm from Opset 6 to Opset 7
- BatchNorm from Opset 6 to Opset 5
- BatchNorm from Opset 5 to Opset 6
- Concat from Opset 4 to Opset 3
- Concat from Opset 3 to Opset 4
- Reshape from Opset 5 to Opset 4
- Reshape from Opset 4 to Opset 5
- Sum from Opset 7 to Opset 8
- Sum from Opset 8 to Opset 7
- Sum from Opset 6 to Opset 5
- Sum from Opset 5 to Opset 6
- MaxPool from Opset 8 to Opset 7
- MaxPool from Opset 7 to Opset 8
- AveragePool from Opset 7 to Opset 6
- AveragePool from Opset 6 to Opset 7
- Dropout from Opset 7 to Opset 6
- Dropout from Opset 6 to Opset 5
- Dropout from Opset 6 to Opset 7
- Dropout from Opset 5 to Opset 6
- RNN from Opset 13 to Opset 14
- RNN from Opset 14 to Opset 13
- GRU from Opset 13 to Opset 14
- GRU from Opset 14 to Opset 13
- LSTM from Opset 13 to Opset 14
- LSTM from Opset 14 to Opset 13
Unsupported adapters:
- Min from Opset 8 to Opset 7
- Min from Opset 7 to Opset 8
- Min from Opset 6 to Opset 5
- Min from Opset 5 to Opset 6
- Mean from Opset 8 to Opset 7
- Mean from Opset 7 to Opset 8
- Mean from Opset 6 to Opset 5
- Mean from Opset 5 to Opset 6
- Max from Opset 8 to Opset 7
- Max from Opset 7 to Opset 8
- Max from Opset 6 to Opset 5
- Max from Opset 5 to Opset 6
- Xor from Opset 6 to Opset 7
- Xor from Opset 7 to Opset 6
- Upsample from Opset 6 to Opset 7
- Upsample from Opset 7 to Opset 6
- Sub from Opset 6 to Opset 7
- Sub from Opset 7 to Opset 6
- Sub from Opset 6 to Opset 5
- Sub from Opset 5 to Opset 6
- RNN from Opset 6 to Opset 7
- RNN from Opset 7 to Opset 6
- Pow from Opset 6 to Opset 7
- Pow from Opset 7 to Opset 6
- PRelu from Opset 6 to Opset 7
- PRelu from Opset 7 to Opset 6
- PRelu from Opset 6 to Opset 5
- PRelu from Opset 5 to Opset 6
- Or from Opset 6 to Opset 7
- Or from Opset 7 to Opset 6
- Less from Opset 6 to Opset 7
- Less from Opset 7 to Opset 6
- LSTM from Opset 6 to Opset 7
- LSTM from Opset 7 to Opset 6
- Greater from Opset 6 to Opset 7
- Greater from Opset 7 to Opset 6
- GRU from Opset 6 to Opset 7
- GRU from Opset 7 to Opset 6
- GRU from Opset 3 to Opset 2
- GRU from Opset 2 to Opset 3
- Equal from Opset 6 to Opset 7
- Equal from Opset 7 to Opset 6
- Div from Opset 6 to Opset 7
- Div from Opset 7 to Opset 6
- Div from Opset 6 to Opset 5
- Div from Opset 5 to Opset 6
- And from Opset 6 to Opset 7
- And from Opset 7 to Opset 6
- And from Opset 6 to Opset 5
- And from Opset 5 to Opset 6
- Tile from Opset 6 to Opset 5
- Tile from Opset 5 to Opset 6
- Sqrt from Opset 6 to Opset 5
- Sqrt from Opset 5 to Opset 6
- Sigmoid from opset 6 to opset 5
- Sigmoid from opset 5 to opset 6
- Selu from opset 6 to opset 5
- Selu from opset 5 to opset 6
- Reciprocal from opset 6 to opset 5
- Reciprocal from opset 5 to opset 6
- Neg from opset 6 to opset 5
- Neg from opset 5 to opset 6
- Log from opset 6 to opset 5
- Log from opset 5 to opset 6
- LeakyRelu from opset 6 to opset 5
- LeakyRelu from opset 5 to opset 6
- InstanceNormalization from opset 6 to opset 5
- InstanceNormalization from opset 5 to opset 6
- HardSigmoid from opset 6 to opset 5
- HardSigmoid from opset 5 to opset 6
- Floor from opset 6 to opset 5
- Floor from opset 5 to opset 6
- Exp from opset 6 to opset 5
- Exp from opset 5 to opset 6
- Elu from opset 6 to opset 5
- Elu from opset 5 to opset 6
- Clip from opset 6 to opset 5
- Clip from opset 5 to opset 6
- Ceil from opset 6 to opset 5
- Ceil from opset 5 to opset 6
- Cast from opset 6 to opset 5
- Cast from opset 5 to opset 6
- Abs from opset 6 to opset 5
- Abs from opset 5 to opset 6
- Split from opset 2 to opset 1
- Split from opset 1 to opset 2
- Pad from opset 2 to opset 1
- Pad from opset 1 to opset 2
- LpPool from opset 2 to opset 1
- LpPool from opset 1 to opset 2
- GlobalLpPool from opset 2 to opset 1
- GlobalLpPool from opset 1 to opset 2
"""
if not isinstance(model, ModelProto):
raise ValueError(
f"VersionConverter only accepts ModelProto as model, incorrect type: {type(model)}"
)
if not isinstance(target_version, int):
raise ValueError(
f"VersionConverter only accepts int as target_version, incorrect type: {type(target_version)}"
)
model_str = model.SerializeToString()
converted_model_str = C.convert_version(model_str, target_version)
return onnx.load_from_string(converted_model_str)
ConvertError = C.ConvertError
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58,877 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/pad.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 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 != raw_pads.size:
raise Exception("The number of elements in raw_pads should be 2 * num_axes")
pad_width = []
for _ in range(input_rank):
pad_width += [[0, 0]] # init to zero
# re-order to np.pad accepted order ((x1_begin, x1_end), (x2_begin, x2_end), ...)
for i in range(num_axes):
axis = axes[i]
if axis < 0:
axis = input_rank + axis
pad_width[axis] = [raw_pads[i], raw_pads[i + num_axes]]
if mode == "constant":
y = np.pad(
data,
pad_width=pad_width,
mode=mode,
constant_values=constant_values,
)
return y
y = np.pad(
data,
pad_width=pad_width,
mode=mode,
)
return y
class Pad(Base):
@staticmethod
def export_constant_pad() -> None:
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant"
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
y = pad_impl(x, pads, "constant", 1.2)
expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad")
@staticmethod
def export_reflection_edge_and_wrap_pad() -> None:
for mode in ("edge", "reflect", "wrap"):
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads"], outputs=["y"], mode=mode
)
x = np.random.randn(1, 3, 4, 5).astype(np.int32)
pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
y = pad_impl(x, pads, mode)
expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad")
@staticmethod
def export_constant_pad_axes() -> None:
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant"
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 3, 0, 4]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
axes = np.array([1, 3], dtype=np.int64)
y = pad_impl(
x,
pads,
"constant",
1.2,
[1, 3],
)
expect(
node,
inputs=[x, pads, value, axes],
outputs=[y],
name="test_constant_pad_axes",
)
@staticmethod
def export_constant_pad_negative_axes() -> None:
node = onnx.helper.make_node(
"Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant"
)
x = np.random.randn(1, 3, 4, 5).astype(np.float32)
pads = np.array([0, 3, 0, 4]).astype(
np.int64
) # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
value = np.float32(1.2)
axes = np.array([-3, -1], dtype=np.int64)
y = pad_impl(
x,
pads,
"constant",
1.2,
[-3, -1],
)
expect(
node,
inputs=[x, pads, value, axes],
outputs=[y],
name="test_constant_pad_negative_axes",
)
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58,878 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/neg.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 Neg(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Neg",
inputs=["x"],
outputs=["y"],
)
x = np.array([-4, 2]).astype(np.float32)
y = np.negative(x) # expected output [4., -2.],
expect(node, inputs=[x], outputs=[y], name="test_neg_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.negative(x)
expect(node, inputs=[x], outputs=[y], name="test_neg")
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58,879 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/softmax.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 softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
s = np.sum(tmp, axis=axis, keepdims=True)
return tmp / s
class Softmax(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[-1, 0, 1]]).astype(np.float32)
# expected output [[0.09003058, 0.24472848, 0.66524094]]
y = softmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_example")
@staticmethod
def export_softmax_axis() -> None:
x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
# expected output
# [[0.032058604 0.08714432 0.23688284 0.6439143 ]
# [0.032058604 0.08714432 0.23688284 0.6439143 ]]
y = softmax(x)
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number")
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = softmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = softmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = softmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2")
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = softmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"Softmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis")
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58,880 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_compress.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 Compress(OpRun):
def _run(self, x, condition, axis=None): # type: ignore
return (np.compress(condition, x, axis=axis),) # type: ignore
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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"]} |
58,881 | onnx/onnx | refs/heads/main | /workflow_scripts/config.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
SKIP_VERSION_CONVERTER_MODELS = {
"vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-6.onnx", # the converted opset 7 model cannot pass shape inference:
# [ShapeInferenceError] (op_type:Mul, node name: ): [ShapeInferenceError] Inferred shape and existing shape differ in dimension 0: (64) vs (1)
"vision/classification/resnet/preproc/resnet-preproc-v1-18.onnx", # preprocessing model contains unknown domain "local"
"vision/classification/vgg/model/vgg16-bn-7.onnx", # version_converter/adapters/transformers.h:30: operator(): Assertion `node->i(attr) == value` failed: Attribute spatial must have value 1
"vision/classification/vgg/model/vgg19-bn-7.onnx", # version_converter/adapters/transformers.h:30: operator(): Assertion `node->i(attr) == value` failed: Attribute spatial must have value 1
"vision/object_detection_segmentation/mask-rcnn/model/MaskRCNN-12-int8.onnx", # unordered_map::at: key not found
}
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58,882 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_hard_sigmoid.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 HardSigmoid(OpRunUnaryNum):
def _run(self, x, alpha=None, beta=None): # type: ignore
alpha = alpha or self.alpha # type: ignore
beta = beta or self.beta # type: ignore
y = np.maximum(0, np.minimum(1, x * alpha + beta))
return (y,)
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"/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"], 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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": 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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"]} |
58,883 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gru.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Tuple
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class GRUHelper:
def __init__(self, **params: Any) -> None:
# GRU Input Names
X = "X"
W = "W"
R = "R"
B = "B"
H_0 = "initial_h"
LBR = "linear_before_reset"
LAYOUT = "layout"
number_of_gates = 3
required_inputs = [X, W, R]
for i in required_inputs:
assert i in params, f"Missing Required Input: {i}"
self.num_directions = params[W].shape[0]
if self.num_directions == 1:
for k in params:
if k != X:
params[k] = np.squeeze(params[k], axis=0)
hidden_size = params[R].shape[-1]
batch_size = params[X].shape[1]
layout = params[LAYOUT] if LAYOUT in params else 0
x = params[X]
x = x if layout == 0 else np.swapaxes(x, 0, 1)
b = (
params[B]
if B in params
else np.zeros(2 * number_of_gates * hidden_size)
)
h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size))
lbr = params[LBR] if LBR in params else 0
self.X = x
self.W = params[W]
self.R = params[R]
self.B = b
self.H_0 = h_0
self.LBR = lbr
self.LAYOUT = layout
else:
raise NotImplementedError()
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 step(self) -> Tuple[np.ndarray, np.ndarray]:
seq_length = self.X.shape[0]
hidden_size = self.H_0.shape[-1]
batch_size = self.X.shape[1]
Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
h_list = []
[w_z, w_r, w_h] = np.split(self.W, 3)
[r_z, r_r, r_h] = np.split(self.R, 3)
[w_bz, w_br, w_bh, r_bz, r_br, r_bh] = np.split(self.B, 6)
gates_w = np.transpose(np.concatenate((w_z, w_r)))
gates_r = np.transpose(np.concatenate((r_z, r_r)))
gates_b = np.add(np.concatenate((w_bz, w_br)), np.concatenate((r_bz, r_br)))
H_t = self.H_0
for x in np.split(self.X, self.X.shape[0], axis=0):
gates = np.dot(x, gates_w) + np.dot(H_t, gates_r) + gates_b
z, r = np.split(gates, 2, -1)
z = self.f(z)
r = self.f(r)
h_default = self.g(
np.dot(x, np.transpose(w_h))
+ np.dot(r * H_t, np.transpose(r_h))
+ w_bh
+ r_bh
)
h_linear = self.g(
np.dot(x, np.transpose(w_h))
+ r * (np.dot(H_t, np.transpose(r_h)) + r_bh)
+ w_bh
)
h = h_linear if self.LBR else h_default
H = (1 - z) * h + z * H_t
h_list.append(H)
H_t = H
concatenated = np.concatenate(h_list)
if self.num_directions == 1:
Y[:, 0, :, :] = concatenated
if self.LAYOUT == 0:
Y_h = Y[-1]
else:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
return Y, Y_h
class GRU(Base):
@staticmethod
def export_defaults() -> None:
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 5
weight_scale = 0.1
number_of_gates = 3
node = onnx.helper.make_node(
"GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
gru = GRUHelper(X=input, W=W, R=R)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_gru_defaults",
)
@staticmethod
def export_initial_bias() -> None:
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 3
weight_scale = 0.1
custom_bias = 0.1
number_of_gates = 3
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
np.float32
)
R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
gru = GRUHelper(X=input, W=W, R=R, B=B)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_gru_with_initial_bias",
)
@staticmethod
def export_seq_length() -> None:
input = np.array(
[
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
[[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
]
).astype(np.float32)
input_size = 3
hidden_size = 5
number_of_gates = 3
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(
np.float32
)
R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(
np.float32
)
# Adding custom bias
W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
gru = GRUHelper(X=input, W=W, R=R, B=B)
_, Y_h = gru.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_gru_seq_length",
)
@staticmethod
def export_batchwise() -> None:
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 6
number_of_gates = 3
weight_scale = 0.2
layout = 1
node = onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones(
(1, number_of_gates * hidden_size, input_size)
).astype(np.float32)
R = weight_scale * np.ones(
(1, number_of_gates * hidden_size, hidden_size)
).astype(np.float32)
gru = GRUHelper(X=input, W=W, R=R, layout=layout)
Y, Y_h = gru.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_gru_batchwise",
)
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58,884 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/string_concat.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 StringConcat(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"StringConcat",
inputs=["x", "y"],
outputs=["result"],
)
x = np.array(["abc", "def"]).astype("object")
y = np.array([".com", ".net"]).astype("object")
result = np.array(["abc.com", "def.net"]).astype("object")
expect(node, inputs=[x, y], outputs=[result], name="test_string_concat")
x = np.array(["cat", "dog", "snake"]).astype("object")
y = np.array(["s"]).astype("object")
result = np.array(["cats", "dogs", "snakes"]).astype("object")
expect(
node,
inputs=[x, y],
outputs=[result],
name="test_string_concat_broadcasting",
)
x = np.array("cat").astype("object")
y = np.array("s").astype("object")
result = np.array("cats").astype("object")
expect(
node,
inputs=[x, y],
outputs=[result],
name="test_string_concat_zero_dimensional",
)
x = np.array(["abc", ""]).astype("object")
y = np.array(["", "abc"]).astype("object")
result = np.array(["abc", "abc"]).astype("object")
expect(
node,
inputs=[x, y],
outputs=[result],
name="test_string_concat_empty_string",
)
x = np.array(["的", "中"]).astype("object")
y = np.array(["的", "中"]).astype("object")
result = np.array(["的的", "中中"]).astype("object")
expect(
node,
inputs=[x, y],
outputs=[result],
name="test_string_concat_utf8",
)
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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"]} |
58,885 | onnx/onnx | refs/heads/main | /onnx/test/tools_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
import numpy as np
from numpy.testing import assert_allclose
import onnx
from onnx import TensorProto, helper, numpy_helper
from onnx.defs import onnx_opset_version
from onnx.reference import ReferenceEvaluator
from onnx.tools import update_model_dims
from onnx.tools.replace_constants import replace_initializer_by_constant_of_shape
class TestToolsFunctions(unittest.TestCase):
def test_update_inputs_outputs_dim(self) -> None:
node_def = helper.make_node(
"Conv",
inputs=["x", "W"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
)
graph_def = helper.make_graph(
[node_def],
"test",
[
helper.make_tensor_value_info("x", TensorProto.FLOAT, [1, 1, 5, 5]),
helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 1, 3, 3]),
],
[helper.make_tensor_value_info("y", TensorProto.FLOAT, [1, 1, 2, 2])],
)
model_def = helper.make_model(graph_def, producer_name="test")
updated_def = update_model_dims.update_inputs_outputs_dims(
model_def,
{
"x": [1, 1, "x1", -1],
"W": [1, 1, 3, 3],
},
{
"y": [1, 1, -1, -1],
},
)
onnx.checker.check_model(updated_def)
self.assertEqual(
updated_def.graph.input[0].type.tensor_type.shape.dim[2].dim_param, "x1"
)
self.assertEqual(
updated_def.graph.input[0].type.tensor_type.shape.dim[3].dim_param, "x_3"
)
self.assertEqual(
updated_def.graph.output[0].type.tensor_type.shape.dim[2].dim_param, "y_2"
)
self.assertEqual(
updated_def.graph.output[0].type.tensor_type.shape.dim[3].dim_param, "y_3"
)
def test_replace_initializer(self):
dtype = np.float32
value = np.random.randn(2, 100).astype(dtype)
A = numpy_helper.from_array(value, name="A")
value = np.array([1], dtype=dtype)
C = numpy_helper.from_array(value, name="C")
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = helper.make_node("MatMul", ["X", "A"], ["AX"])
node2 = helper.make_node("Sub", ["AX", "C"], ["Y"])
graph = helper.make_graph([node1, node2], "lr", [X], [Y], [A, C])
model_def = helper.make_model(graph)
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
oinf1 = ReferenceEvaluator(model_def)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(model_def)
node_types = {n.op_type for n in repl.graph.node}
self.assertIn("ConstantOfShape", node_types)
oinf2 = ReferenceEvaluator(repl)
y1[:, :] = 3.5
y1[0, :] = 0.5
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1, y2)
def test_replace_constant(self):
dtype = np.float32
value = np.random.randn(2, 100).astype(dtype)
A = numpy_helper.from_array(value, name="A")
value = np.array([1], dtype=dtype)
C = numpy_helper.from_array(value, name="C")
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node0 = helper.make_node("Constant", [], ["A"], value=A)
node1 = helper.make_node("MatMul", ["X", "A"], ["AX"])
node2 = helper.make_node("Sub", ["AX", "C"], ["Y"])
graph = helper.make_graph([node0, node1, node2], "lr", [X], [Y], [C])
model_def = helper.make_model(graph)
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
oinf1 = ReferenceEvaluator(model_def)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(model_def)
node_types = {n.op_type for n in repl.graph.node}
self.assertIn("ConstantOfShape", node_types)
oinf2 = ReferenceEvaluator(repl)
y1[:, :] = 3.5
y1[0, :] = 0.5
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1, y2)
def test_replace_range(self):
dtype = np.float32
value = np.random.randn(2, 100).astype(dtype)
A = numpy_helper.from_array(value, name="A")
value = np.array([1], dtype=dtype)
C = numpy_helper.from_array(value, name="C")
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node0 = helper.make_node("Constant", [], ["A"], value=A)
node1 = helper.make_node("MatMul", ["X", "A"], ["AX"])
node2 = helper.make_node("Sub", ["AX", "C"], ["Y"])
graph = helper.make_graph([node0, node1, node2], "lr", [X], [Y], [C])
model_def = helper.make_model(graph)
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
oinf1 = ReferenceEvaluator(model_def)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(model_def, use_range=True)
node_types = {n.op_type for n in repl.graph.node}
self.assertIn("Range", node_types)
self.assertNotIn("ConstantOfShape", node_types)
oinf2 = ReferenceEvaluator(repl)
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1.shape, y2.shape)
def test_replace_constant_function(self):
dtype = np.float32
value = np.random.randn(2, 100).astype(dtype)
A = numpy_helper.from_array(value, name="A")
value = np.array([1], dtype=dtype)
C = numpy_helper.from_array(value, name="C")
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None])
nodeC = helper.make_node("Constant", [], ["C"], value=C)
node0 = helper.make_node("Constant", [], ["A"], value=A)
node1 = helper.make_node("MatMul", ["X", "A"], ["AX"])
node2 = helper.make_node("Sub", ["AX", "C"], ["Y"])
opset_imports = [
helper.make_opsetid("", onnx_opset_version()),
helper.make_opsetid("custom", 1),
]
fct = helper.make_function(
"custom",
"unittest",
["X"],
["Y"],
[nodeC, node0, node1, node2],
opset_imports,
)
node = helper.make_node("unittest", ["X"], ["Y"], domain="custom")
graph = helper.make_graph([node], "lr", [X], [Y], [C])
model_def = helper.make_model(
graph, functions=[fct], opset_imports=opset_imports
)
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
oinf1 = ReferenceEvaluator(model_def)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(model_def)
node_types = {n.op_type for n in repl.functions[0].node}
self.assertIn("ConstantOfShape", node_types)
oinf2 = ReferenceEvaluator(repl)
y1[:, :] = 3.5
y1[0, :] = 0.5
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1, y2)
def test_replace_range_function(self):
dtype = np.float32
value = np.random.randn(2, 100).astype(dtype)
A = numpy_helper.from_array(value, name="A")
value = np.array([1], dtype=dtype)
C = numpy_helper.from_array(value, name="C")
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None])
nodeC = helper.make_node("Constant", [], ["C"], value=C)
node0 = helper.make_node("Constant", [], ["A"], value=A)
node1 = helper.make_node("MatMul", ["X", "A"], ["AX"])
node2 = helper.make_node("Sub", ["AX", "C"], ["Y"])
opset_imports = [
helper.make_opsetid("", onnx_opset_version()),
helper.make_opsetid("custom", 1),
]
fct = helper.make_function(
"custom",
"unittest",
["X"],
["Y"],
[nodeC, node0, node1, node2],
opset_imports,
)
node = helper.make_node("unittest", ["X"], ["Y"], domain="custom")
graph = helper.make_graph([node], "lr", [X], [Y], [C])
model_def = helper.make_model(
graph, functions=[fct], opset_imports=opset_imports
)
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
oinf1 = ReferenceEvaluator(model_def)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(model_def, use_range=True)
node_types = {n.op_type for n in repl.functions[0].node}
self.assertIn("Range", node_types)
self.assertNotIn("ConstantOfShape", node_types)
oinf2 = ReferenceEvaluator(repl)
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1.shape, y2.shape)
def test_replace_constant_graph(self):
value = np.array([0], dtype=np.float32)
zero = numpy_helper.from_array(value, name="zero")
X = helper.make_tensor_value_info("X", onnx.TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", onnx.TensorProto.FLOAT, [None])
rsum = helper.make_node("ReduceSum", ["X"], ["rsum"])
cond = helper.make_node("Greater", ["rsum", "zero"], ["cond"])
then_out = helper.make_tensor_value_info(
"then_out", onnx.TensorProto.FLOAT, None
)
then_cst = numpy_helper.from_array(np.array([1] * 129).astype(np.float32))
then_const_node = helper.make_node(
"Constant", inputs=[], outputs=["then_out"], value=then_cst, name="cst1"
)
then_body = helper.make_graph([then_const_node], "then_body", [], [then_out])
else_out = helper.make_tensor_value_info(
"else_out", onnx.TensorProto.FLOAT, None
)
else_cst = numpy_helper.from_array(np.array([-1] * 129).astype(np.float32))
else_const_node = helper.make_node(
"Constant", inputs=[], outputs=["else_out"], value=else_cst, name="cst2"
)
else_body = helper.make_graph([else_const_node], "else_body", [], [else_out])
if_node = onnx.helper.make_node(
"If", ["cond"], ["Y"], then_branch=then_body, else_branch=else_body
)
graph = helper.make_graph([rsum, cond, if_node], "if", [X], [Y], [zero])
onnx_model = helper.make_model(
graph, opset_imports=[helper.make_opsetid("", onnx_opset_version())]
)
self.assertNotIn("ConstantOfShape", str(onnx_model))
x = np.ones((3, 2), dtype=np.float32)
oinf1 = ReferenceEvaluator(onnx_model)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(onnx_model)
self.assertIn("ConstantOfShape", str(repl))
oinf2 = ReferenceEvaluator(repl)
y2 = oinf2.run(None, {"X": x})[0]
y1 = y1.copy()
y1[:] = 0.5
assert_allclose(y1, y2)
def test_replace_range_graph(self):
value = np.array([0], dtype=np.float32)
zero = numpy_helper.from_array(value, name="zero")
X = helper.make_tensor_value_info("X", onnx.TensorProto.FLOAT, [None, None])
Y = helper.make_tensor_value_info("Y", onnx.TensorProto.FLOAT, [None])
rsum = helper.make_node("ReduceSum", ["X"], ["rsum"])
cond = helper.make_node("Greater", ["rsum", "zero"], ["cond"])
then_out = helper.make_tensor_value_info(
"then_out", onnx.TensorProto.FLOAT, None
)
then_cst = numpy_helper.from_array(np.array([1] * 129).astype(np.float32))
then_const_node = helper.make_node(
"Constant", inputs=[], outputs=["then_out"], value=then_cst, name="cst1"
)
then_body = helper.make_graph([then_const_node], "then_body", [], [then_out])
else_out = helper.make_tensor_value_info(
"else_out", onnx.TensorProto.FLOAT, None
)
else_cst = numpy_helper.from_array(np.array([-1] * 129).astype(np.float32))
else_const_node = helper.make_node(
"Constant", inputs=[], outputs=["else_out"], value=else_cst, name="cst2"
)
else_body = helper.make_graph([else_const_node], "else_body", [], [else_out])
if_node = onnx.helper.make_node(
"If", ["cond"], ["Y"], then_branch=then_body, else_branch=else_body
)
graph = helper.make_graph([rsum, cond, if_node], "if", [X], [Y], [zero])
onnx_model = helper.make_model(
graph, opset_imports=[helper.make_opsetid("", onnx_opset_version())]
)
self.assertNotIn("ConstantOfShape", str(onnx_model))
x = np.ones((3, 2), dtype=np.float32)
oinf1 = ReferenceEvaluator(onnx_model)
y1 = oinf1.run(None, {"X": x})[0]
repl = replace_initializer_by_constant_of_shape(onnx_model, use_range=True)
self.assertNotIn("ConstantOfShape", str(repl))
self.assertIn("Range", str(repl))
oinf2 = ReferenceEvaluator(repl)
y2 = oinf2.run(None, {"X": x})[0]
assert_allclose(y1.shape, y2.shape)
if __name__ == "__main__":
unittest.main(verbosity=2)
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58,886 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_hardmax.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 Hardmax(OpRunUnaryNum):
def _run(self, x, axis=None): # type: ignore
axis = axis or self.axis # type: ignore
x_argmax = np.argmax(x, axis=axis) # type: ignore
y = np.zeros_like(x)
np.put_along_axis(
y, np.expand_dims(x_argmax, axis=axis), 1, axis=axis # type: ignore
)
return (y,)
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"/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": 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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"]} |
58,887 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/if.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 compute_if_outputs(x, cond): # type: ignore
if cond:
return []
else:
return x
class If(Base):
@staticmethod
def export_if() -> None:
# Given a bool scalar input cond.
# return constant tensor x if cond is True, otherwise return constant tensor y.
then_out = onnx.helper.make_tensor_value_info(
"then_out", onnx.TensorProto.FLOAT, [5]
)
else_out = onnx.helper.make_tensor_value_info(
"else_out", onnx.TensorProto.FLOAT, [5]
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
then_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["then_out"],
value=onnx.numpy_helper.from_array(x),
)
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["else_out"],
value=onnx.numpy_helper.from_array(y),
)
then_body = onnx.helper.make_graph(
[then_const_node], "then_body", [], [then_out]
)
else_body = onnx.helper.make_graph(
[else_const_node], "else_body", [], [else_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["res"],
then_branch=then_body,
else_branch=else_body,
)
cond = np.array(1).astype(bool)
res = x if cond else y
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if",
opset_imports=[onnx.helper.make_opsetid("", 11)],
)
@staticmethod
def export_if_seq() -> None:
# Given a bool scalar input cond.
# return constant sequence x if cond is True, otherwise return constant sequence y.
then_out = onnx.helper.make_tensor_sequence_value_info(
"then_out", onnx.TensorProto.FLOAT, shape=[5]
)
else_out = onnx.helper.make_tensor_sequence_value_info(
"else_out", onnx.TensorProto.FLOAT, shape=[5]
)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
then_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.numpy_helper.from_array(x[0]),
)
then_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["x"], outputs=["then_out"]
)
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["y"],
value=onnx.numpy_helper.from_array(y[0]),
)
else_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["y"], outputs=["else_out"]
)
then_body = onnx.helper.make_graph(
[then_const_node, then_seq_node], "then_body", [], [then_out]
)
else_body = onnx.helper.make_graph(
[else_const_node, else_seq_node], "else_body", [], [else_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["res"],
then_branch=then_body,
else_branch=else_body,
)
cond = np.array(1).astype(bool)
res = x if cond else y
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if_seq",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
@staticmethod
def export_if_optional() -> None:
# Given a bool scalar input cond, return an empty optional sequence of
# tensor if True, return an optional sequence with value x
# (the input optional sequence) otherwise.
ten_in_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
then_out_tensor_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp)
else_out_tensor_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
cond = np.array(0).astype(bool)
res = compute_if_outputs(x, cond)
opt_empty_in = onnx.helper.make_node(
"Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp
)
then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out])
else_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.numpy_helper.from_array(x[0]),
)
else_seq_node = onnx.helper.make_node(
"SequenceConstruct", inputs=["x"], outputs=["else_seq"]
)
else_optional_seq_node = onnx.helper.make_node(
"Optional", inputs=["else_seq"], outputs=["else_opt"]
)
else_body = onnx.helper.make_graph(
[else_const_node, else_seq_node, else_optional_seq_node],
"else_body",
[],
[else_out],
)
if_node = onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["sequence"],
then_branch=then_body,
else_branch=else_body,
)
expect(
if_node,
inputs=[cond],
outputs=[res],
name="test_if_opt",
output_type_protos=[else_out_opt_tp],
opset_imports=[onnx.helper.make_opsetid("", 16)],
)
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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": 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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"]} |
58,888 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reducesum.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 ReduceSum(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(
"ReduceSum", 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.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[4., 6.]
# [12., 14.]
# [20., 22.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_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(
"ReduceSum", 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.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[[4., 6.]]
# [[12., 14.]]
# [[20., 22.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_keepdims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum", 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.sum(data, axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[78.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_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(
"ReduceSum", 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.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
# print(reduced)
# [[[4., 6.]]
# [[12., 14.]]
# [[20., 22.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(data, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_negative_axes_keepdims_random",
)
@staticmethod
def export_empty_axes_input_noop() -> None:
shape = [3, 2, 2]
keepdims = 1
node = onnx.helper.make_node(
"ReduceSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
noop_with_empty_axes=True,
)
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], dtype=np.float32
)
axes = np.array([], dtype=np.int64)
reduced = np.array(data)
# print(reduced)
# [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_empty_axes_input_noop_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.array(data)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_sum_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/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"]} |
58,889 | onnx/onnx | refs/heads/main | /onnx/test/basic_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import io
import os
import pathlib
import tempfile
import unittest
import google.protobuf.message
import google.protobuf.text_format
import parameterized
import onnx
from onnx import serialization
def _simple_model() -> onnx.ModelProto:
model = onnx.ModelProto()
model.ir_version = onnx.IR_VERSION
return model
def _simple_tensor() -> onnx.TensorProto:
tensor = onnx.helper.make_tensor(
name="test-tensor",
data_type=onnx.TensorProto.FLOAT,
dims=(2, 3, 4),
vals=[x + 0.5 for x in range(24)],
)
return tensor
@parameterized.parameterized_class(
[
{"format": "protobuf"},
{"format": "textproto"},
]
)
class TestIO(unittest.TestCase):
format: str
def test_load_model_when_input_is_bytes(self) -> None:
proto = _simple_model()
proto_string = serialization.registry.get(self.format).serialize_proto(proto)
loaded_proto = onnx.load_model_from_string(proto_string, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_has_read_function(self) -> None:
proto = _simple_model()
# When the proto is a bytes representation provided to `save_model`,
# it should always be a serialized binary protobuf representation. Aka. format="protobuf"
# The saved file format is specified by the `format` argument.
proto_string = serialization.registry.get("protobuf").serialize_proto(proto)
f = io.BytesIO()
onnx.save_model(proto_string, f, format=self.format)
loaded_proto = onnx.load_model(io.BytesIO(f.getvalue()), format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_is_file_name(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.onnx")
onnx.save_model(proto, model_path, format=self.format)
loaded_proto = onnx.load_model(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_is_pathlike(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = pathlib.Path(temp_dir, "model.onnx")
onnx.save_model(proto, model_path, format=self.format)
loaded_proto = onnx.load_model(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_load_tensor_when_input_is_bytes(self) -> None:
proto = _simple_tensor()
proto_string = serialization.registry.get(self.format).serialize_proto(proto)
loaded_proto = onnx.load_tensor_from_string(proto_string, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_has_read_function(self) -> None:
# Test if input has a read function
proto = _simple_tensor()
f = io.BytesIO()
onnx.save_tensor(proto, f, format=self.format)
loaded_proto = onnx.load_tensor(io.BytesIO(f.getvalue()), format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_is_file_name(self) -> None:
# Test if input is a file name
proto = _simple_tensor()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.onnx")
onnx.save_tensor(proto, model_path, format=self.format)
loaded_proto = onnx.load_tensor(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_is_pathlike(self) -> None:
# Test if input is a file name
proto = _simple_tensor()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = pathlib.Path(temp_dir, "model.onnx")
onnx.save_tensor(proto, model_path, format=self.format)
loaded_proto = onnx.load_tensor(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
class TestSaveAndLoadFileExtensions(unittest.TestCase):
def test_save_model_picks_correct_format_from_extension(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
# No format is specified, so the extension should be used to determine the format
onnx.save_model(proto, model_path)
loaded_proto = onnx.load_model(model_path, format="textproto")
self.assertEqual(proto, loaded_proto)
def test_load_model_picks_correct_format_from_extension(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
onnx.save_model(proto, model_path, format="textproto")
# No format is specified, so the extension should be used to determine the format
loaded_proto = onnx.load_model(model_path)
self.assertEqual(proto, loaded_proto)
def test_save_model_uses_format_when_it_is_specified(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
# `format` is specified. It should take precedence over the extension
onnx.save_model(proto, model_path, format="protobuf")
loaded_proto = onnx.load_model(model_path, format="protobuf")
self.assertEqual(proto, loaded_proto)
with self.assertRaises(google.protobuf.text_format.ParseError):
# Loading it as textproto (by file extension) should fail
onnx.load_model(model_path)
def test_load_model_uses_format_when_it_is_specified(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.protobuf")
onnx.save_model(proto, model_path)
with self.assertRaises(google.protobuf.text_format.ParseError):
# `format` is specified. It should take precedence over the extension
# Loading it as textproto should fail
onnx.load_model(model_path, format="textproto")
loaded_proto = onnx.load_model(model_path, format="protobuf")
self.assertEqual(proto, loaded_proto)
def test_load_and_save_model_to_path_without_specifying_extension_succeeds(
self,
) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
# No extension is specified
model_path = os.path.join(temp_dir, "model")
onnx.save_model(proto, model_path, format="textproto")
with self.assertRaises(google.protobuf.message.DecodeError):
# `format` is not specified. load_model should assume protobuf
# and fail to load it
onnx.load_model(model_path)
loaded_proto = onnx.load_model(model_path, format="textproto")
self.assertEqual(proto, loaded_proto)
def test_load_and_save_model_without_specifying_extension_or_format_defaults_to_protobuf(
self,
) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
# No extension is specified
model_path = os.path.join(temp_dir, "model")
onnx.save_model(proto, model_path)
with self.assertRaises(google.protobuf.text_format.ParseError):
# The model is saved as protobuf, so loading it as textproto should fail
onnx.load_model(model_path, format="textproto")
loaded_proto = onnx.load_model(model_path)
self.assertEqual(proto, loaded_proto)
loaded_proto_as_explicitly_protobuf = onnx.load_model(
model_path, format="protobuf"
)
self.assertEqual(proto, loaded_proto_as_explicitly_protobuf)
class TestBasicFunctions(unittest.TestCase):
def test_protos_exist(self) -> None:
# The proto classes should exist
_ = onnx.AttributeProto
_ = onnx.NodeProto
_ = onnx.GraphProto
_ = onnx.ModelProto
def test_version_exists(self) -> None:
model = onnx.ModelProto()
# When we create it, graph should not have a version string.
self.assertFalse(model.HasField("ir_version"))
# We should touch the version so it is annotated with the current
# ir version of the running ONNX
model.ir_version = onnx.IR_VERSION
model_string = model.SerializeToString()
model.ParseFromString(model_string)
self.assertTrue(model.HasField("ir_version"))
# Check if the version is correct.
self.assertEqual(model.ir_version, onnx.IR_VERSION)
if __name__ == "__main__":
unittest.main()
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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"]} |
58,890 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/transpose.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import itertools
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Transpose(Base):
@staticmethod
def export_default() -> None:
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
node = onnx.helper.make_node(
"Transpose", inputs=["data"], outputs=["transposed"]
)
transposed = np.transpose(data)
expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default")
@staticmethod
def export_all_permutations() -> None:
shape = (2, 3, 4)
data = np.random.random_sample(shape).astype(np.float32)
permutations = list(itertools.permutations(np.arange(len(shape))))
for i, permutation in enumerate(permutations):
node = onnx.helper.make_node(
"Transpose",
inputs=["data"],
outputs=["transposed"],
perm=permutation,
)
transposed = np.transpose(data, permutation)
expect(
node,
inputs=[data],
outputs=[transposed],
name=f"test_transpose_all_permutations_{i}",
)
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58,891 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_conv_transpose.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
from onnx.reference.ops.op_col2im import col2im_naive_implementation
class ConvTranspose(OpRun):
def _run( # type: ignore
self,
X,
W,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
output_padding=None,
output_shape=None,
pads=None,
strides=None,
):
if group != 1:
raise RuntimeError(f"group={group} != 1 is not implemented yet.")
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if output_padding is None:
output_padding = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
if pads is None and auto_pad not in {"SAME_UPPER", "SAME_LOWER"}:
pads = [0 for i in range(2 * len(strides))]
if pads is None:
if output_shape is None:
output_shape = [
X.shape[i + 2] * strides[i] for i in range(len(strides))
]
total_padding = [
strides[i] * (X.shape[i + 2] - 1)
+ output_padding[i]
+ ((kernel_shape[i] - 1) * dilations[i] + 1)
- output_shape[i]
for i in range(len(output_shape))
]
pads_1 = []
pads_2 = []
for i in range(len(output_shape)):
if auto_pad == "SAME_UPPER":
pads_1.append(total_padding[i] // 2)
pads_2.append(total_padding[i] - (total_padding[i] // 2))
else:
pads_1.append(total_padding[i] - (total_padding[i] // 2))
pads_2.append(total_padding[i] // 2)
pads = pads_1 + pads_2
n_dims = len(pads) // 2
else:
n_dims = len(X.shape) - 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
if output_shape is None:
output_shape = [
strides[i] * (X.shape[i + 2] - 1)
+ output_padding[i]
+ ((kernel_shape[i] - 1) * dilations[i] + 1)
- new_pads[i, :].sum()
for i in range(n_dims)
]
kernel_shape = W.shape[2:]
kernel_size = np.prod(kernel_shape)
num_output_channels = W.shape[1] * group
kernel_dim = num_output_channels // group * kernel_size
C = X.shape[1] # num_inputs_channels
m = kernel_dim # kernel_dim
n = np.prod(X.shape[2:]) # input_image_size
k = C // group
w_reshaped = W.reshape((group, k, m))
final = None
# N x C x H x W = X.shape
# C x M/group x k1 x k2 = W.shape
if group == 1:
for image_id in range(X.shape[0]):
w_t = w_reshaped[0].T
gemm = np.matmul(w_t, X[image_id].reshape((k, n)))
gemmc = gemm.reshape((num_output_channels, -1, gemm.shape[-1]))
for c in range(num_output_channels):
res = col2im_naive_implementation(
gemmc[c], output_shape, kernel_shape, dilations, pads, strides
)
if final is None:
final = np.empty(
X.shape[:1] + (num_output_channels,) + res.shape,
dtype=X.dtype,
)
if B is not None:
res += B[c]
final[image_id, c, ...] = res[...]
else:
raise NotImplementedError(
f"Implementation for group={group} > 1 is not available yet."
)
return (final.astype(X.dtype),) # type: ignore[union-attr]
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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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58,892 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/string_split.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 StringSplit(Base):
@staticmethod
def export_basic() -> None:
node = onnx.helper.make_node(
"StringSplit",
inputs=["x"],
outputs=["substrings", "length"],
delimiter=".",
maxsplit=None,
)
x = np.array(["abc.com", "def.net"]).astype(object)
substrings = np.array([["abc", "com"], ["def", "net"]]).astype(object)
length = np.array([2, 2], dtype=np.int64)
expect(
node,
inputs=[x],
outputs=[substrings, length],
name="test_string_split_basic",
)
@staticmethod
def export_maxsplit() -> None:
node = onnx.helper.make_node(
"StringSplit",
inputs=["x"],
outputs=["substrings", "length"],
maxsplit=2,
)
x = np.array(
[["hello world", "def.net"], ["o n n x", "the quick brown fox"]]
).astype(object)
substrings = np.array(
[
[["hello", "world", ""], ["def.net", "", ""]],
[["o", "n", "n x"], ["the", "quick", "brown fox"]],
]
).astype(object)
length = np.array([[2, 1], [3, 3]], np.int64)
expect(
node,
inputs=[x],
outputs=[substrings, length],
name="test_string_split_maxsplit",
)
@staticmethod
def export_consecutive_delimiters() -> None:
node = onnx.helper.make_node(
"StringSplit",
inputs=["x"],
outputs=["substrings", "length"],
delimiter="-",
maxsplit=None,
)
x = np.array(["o-n-n--x-", "o-n----nx"]).astype(object)
substrings = np.array(
[["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]]
).astype(object)
length = np.array([6, 6], dtype=np.int64)
expect(
node,
inputs=[x],
outputs=[substrings, length],
name="test_string_split_consecutive_delimiters",
)
@staticmethod
def export_empty_string_delimiter() -> None:
for delimiter, test_name in (
("", "test_string_split_empty_string_delimiter"),
(None, "test_string_split_no_delimiter"),
):
node = onnx.helper.make_node(
"StringSplit",
inputs=["x"],
outputs=["substrings", "length"],
delimiter=delimiter,
maxsplit=None,
)
x = np.array(
["hello world !", " hello world !", " hello world ! "]
).astype(object)
substrings = np.array(
[
["hello", "world", "!"],
["hello", "world", "!"],
["hello", "world", "!"],
]
).astype(object)
length = np.array([3, 3, 3], dtype=np.int64)
expect(
node,
inputs=[x],
outputs=[substrings, length],
name=test_name,
)
@staticmethod
def export_empty_string_split() -> None:
node = onnx.helper.make_node(
"StringSplit",
inputs=["x"],
outputs=["substrings", "length"],
delimiter=None,
maxsplit=None,
)
x = np.array([]).astype(object)
substrings = np.array([]).astype(object).reshape(0, 0)
length = np.array([], dtype=np.int64)
expect(
node,
inputs=[x],
outputs=[substrings, length],
name="test_string_split_empty_tensor",
output_type_protos=[
onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, (0, None)),
None,
],
)
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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"]} |
58,893 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_qlinear_matmul.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
class QLinearMatMul(OpRun):
def _run( # type: ignore
self, a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point
):
A = a.astype(np.int32)
if a_zero_point is not None:
A -= a_zero_point.astype(np.int32)
B = b.astype(np.int32)
if b_zero_point is not None:
B -= b_zero_point.astype(np.int32)
C = np.matmul(A, B)
D = C * (a_scale * b_scale / y_scale)
if y_zero_point is not None:
D += y_zero_point
return (np.round(D).astype(y_zero_point.dtype),)
return (np.round(D).astype(a.dtype),)
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"/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"]} |
58,894 | onnx/onnx | refs/heads/main | /onnx/reference/ops/experimental/_op_list.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0415,R0912,W0611,W0603
import textwrap
from typing import Any, Dict
from typing import Optional as TOptional
from typing import Union
from onnx.reference.op_run import OpFunction
from onnx.reference.ops._helpers import build_registered_operators_any_domain
from onnx.reference.ops.experimental._op_run_experimental import OpRunExperimental
from onnx.reference.ops.experimental.op_im2col import Im2Col # noqa: F401
def _build_registered_operators() -> (
Dict[str, Dict[Union[int, None], OpRunExperimental]]
):
return build_registered_operators_any_domain(globals().copy()) # type: ignore[return-value]
def load_op(
domain: str, op_type: str, version: Union[None, int], custom: Any = None
) -> Any:
"""
Loads the implemented for a specified operator.
:param domain: domain
:param op_type: oprator type
:param version: requested version
:param custom: custom implementation (like a function)
:return: class
"""
global _registered_operators
if _registered_operators is None:
_registered_operators = _build_registered_operators() # type: ignore[assignment]
if custom is not None:
return lambda *args: OpFunction(*args, impl=custom) # type: ignore
if domain != "experimental":
raise ValueError(f"Domain must be '' not {domain!r}.")
if op_type not in _registered_operators: # type: ignore
available = "\n".join(textwrap.wrap(", ".join(sorted(_registered_operators)))) # type: ignore
raise NotImplementedError(
f"No registered implementation for operator {op_type!r} "
f"and domain {domain!r} in\n{available}"
)
impl = _registered_operators[op_type] # type: ignore
if None not in impl:
raise RuntimeError(
f"No default implementation for operator {op_type!r} "
f"and domain {domain!r}, found "
f"{', '.join(map(str, impl))}."
)
if version is None or len(impl) == 1:
cl = impl[None]
else:
best = -1
for v in impl:
if v is None:
continue
if best < v <= version:
best = v
if best == -1:
raise RuntimeError(
f"No implementation for operator {op_type!r} "
f"domain {domain!r} and version {version!r}, found "
f"{', '.join(map(str, impl))}."
)
cl = impl[best]
if cl is None:
available = "\n".join(textwrap.wrap(", ".join(sorted(_registered_operators)))) # type: ignore
raise ValueError(
f"Not registered implementation for operator {op_type!r}, "
f"domain {domain!r}, and {version!r} in\n{available}"
)
return cl
_registered_operators: TOptional[
Dict[str, Dict[Union[int, None], OpRunExperimental]]
] = None
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58,895 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reverse_sequence.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0123,R0912,R0913,R0914,W0221
from onnx.reference.op_run import OpRun
class ReverseSequence(OpRun):
def _run(self, data, sequence_lens, batch_axis=None, time_axis=None): # type: ignore
index = [slice(0, s) for s in data.shape]
index_data = [slice(0, s) for s in data.shape]
result = data.copy()
for i, sl in enumerate(sequence_lens):
index[batch_axis] = i # type: ignore
index[time_axis] = slice(0, sl)
index_data[batch_axis] = i # type: ignore
index_data[time_axis] = slice(sl - 1, None, -1) # type: ignore
result[tuple(index)] = data[tuple(index_data)]
return (result,)
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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"]} |
58,896 | onnx/onnx | refs/heads/main | /onnx/test/version_converter_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import struct
import unittest
import numpy as np
import onnx.version_converter
from onnx import (
GraphProto,
ModelProto,
OperatorSetIdProto,
TensorProto,
checker,
helper,
)
class TestVersionConverter(unittest.TestCase):
def _converted(
self,
graph: GraphProto,
initial_version: OperatorSetIdProto,
target_version: int,
) -> ModelProto:
orig_model = helper.make_model(
graph, producer_name="onnx-test", opset_imports=[initial_version]
)
# print(type(orig_model))
converted_model = onnx.version_converter.convert_version(
orig_model, target_version
)
checker.check_model(converted_model)
return converted_model
# Test 1: Backwards Incompatible Conversion: Reshape: 8 -> 2
def test_backwards_incompatible(self) -> None:
def test() -> None:
nodes = [
helper.make_node("Add", ["W", "Z"], ["shape"]),
helper.make_node("Reshape", ["X", "shape"], ["A"]),
helper.make_node("Add", ["A", "W"], ["Y"]),
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
self._converted(graph, helper.make_operatorsetid("", 8), 2)
self.assertRaises(RuntimeError, test)
# Test 2: Backwards Compatible Conversion (No Adaptations): Add: 3 -> 2
def test_backwards_compatible(self) -> None:
nodes = [helper.make_node("Add", ["X1", "X2"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (5,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 3), 2)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Add"
assert converted_model.opset_import[0].version == 2
# Test 3: Non-Existent Op Conversion: Cos: 8 -> 6
def test_non_existent_op(self) -> None:
def test() -> None:
nodes = [helper.make_node("Cos", ["X"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
self._converted(graph, helper.make_operatorsetid("", 8), 6)
self.assertRaises(RuntimeError, test)
# Test Add Adapter: 8 -> 5
def test_add_8_5(self) -> None:
nodes = [helper.make_node("Add", ["X1", "X2"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Add"
assert converted_model.opset_import[0].version == 5
# Test Add Adapter: 5 -> 8
def test_add_5_8(self) -> None:
nodes = [helper.make_node("Add", ["X1", "X2"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Add"
assert converted_model.opset_import[0].version == 8
# Test Add Adapter: 5 -> 8, requiring insertion of an Unsqueeze node
def test_add_5_8_with_unsqueeze(self) -> None:
nodes = [helper.make_node("Add", ["X1", "X2"], ["Y"], axis=0, broadcast=1)]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5, 2)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (5,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Unsqueeze"
assert converted_model.graph.node[1].op_type == "Add"
assert converted_model.opset_import[0].version == 8
# Test Mul Adapter: 8 -> 5
def test_mul_8_5(self) -> None:
nodes = [helper.make_node("Mul", ["X1", "X2"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Mul"
assert converted_model.opset_import[0].version == 5
# Test Mul Adapter: 5 -> 8
def test_mul_5_8(self) -> None:
nodes = [helper.make_node("Mul", ["X1", "X2"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Mul"
assert converted_model.opset_import[0].version == 8
# Test Gemm Adapter: 1 -> 8
def test_gemm_up(self) -> None:
nodes = [helper.make_node("Gemm", ["A", "B", "C"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info(
"A",
TensorProto.FLOAT,
(
5,
5,
),
),
helper.make_tensor_value_info(
"B",
TensorProto.FLOAT,
(
5,
5,
),
),
helper.make_tensor_value_info(
"C",
TensorProto.FLOAT,
(
5,
5,
),
),
],
[
helper.make_tensor_value_info(
"Y",
TensorProto.FLOAT,
(
5,
5,
),
)
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 1), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Gemm"
assert converted_model.opset_import[0].version == 8
# Test Gemm Adapter: 8 -> 1
def test_gemm_down(self) -> None:
nodes = [helper.make_node("Gemm", ["A", "B", "C"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info(
"A",
TensorProto.FLOAT,
(
5,
5,
),
),
helper.make_tensor_value_info(
"B",
TensorProto.FLOAT,
(
5,
5,
),
),
helper.make_tensor_value_info(
"C",
TensorProto.FLOAT,
(
5,
5,
),
),
],
[
helper.make_tensor_value_info(
"Y",
TensorProto.FLOAT,
(
5,
5,
),
)
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Gemm"
assert converted_model.opset_import[0].version == 1
# Test Relu Adapter: 5 -> 7
def test_relu_5_7(self) -> None:
nodes = [helper.make_node("Relu", ["X"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 7)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Relu"
assert converted_model.opset_import[0].version == 7
# Test Relu Adapter: 7 -> 5
def test_relu_7_5(self) -> None:
nodes = [helper.make_node("Relu", ["X"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 7), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Relu"
assert converted_model.opset_import[0].version == 5
# Test BatchNormalization Adapter: 8 -> 5
def test_batch_normalization_8_5(self) -> None:
nodes = [
helper.make_node(
"BatchNormalization", ["X", "scale", "B", "mean", "var"], ["Y"]
)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("scale", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("B", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("mean", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("var", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == 5
# Test BatchNormalization Adapter: 5 -> 8
def test_batch_normalization_5_8(self) -> None:
nodes = [
helper.make_node(
"BatchNormalization", ["X", "scale", "B", "mean", "var"], ["Y"]
)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("scale", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("B", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("mean", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("var", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == 8
# Test Concat Adapter: 3 -> 5
def test_concat_3_5(self) -> None:
nodes = [helper.make_node("Concat", ["X1", "X2", "X3", "X4", "X5"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X3", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X4", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X5", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 3), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Concat"
assert converted_model.opset_import[0].version == 5
# Test Concat Adapter: 5 -> 3
def test_concat_5_3(self) -> None:
nodes = [
helper.make_node("Concat", ["X1", "X2", "X3", "X4", "X5"], ["Y"], axis=0)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X3", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X4", TensorProto.FLOAT, (1,)),
helper.make_tensor_value_info("X5", TensorProto.FLOAT, (1,)),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 3)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Concat"
assert converted_model.opset_import[0].version == 3
# Test Reshape Adapter: 6 -> 4
def test_reshape_6_4(self) -> None:
nodes = [
helper.make_node(
"Constant",
[],
["shape"],
value=helper.make_tensor("", TensorProto.INT64, [1], [5]),
),
helper.make_node("Reshape", ["X", "shape"], ["Y"]),
]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 6), 4)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Reshape"
assert converted_model.opset_import[0].version == 4
# Test Reshape Adapter: 4 -> 6
def test_reshape_4_6(self) -> None:
nodes = [helper.make_node("Reshape", ["X"], ["Y"], shape=[5])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 4), 6)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.node[1].op_type == "Reshape"
assert converted_model.opset_import[0].version == 6
# Test Sum Adapter: 7 -> 8
def test_sum_7_8(self) -> None:
nodes = [
helper.make_node(
"Sum", ["data_0", "data_1", "data_2", "data_3", "data_4"], ["sum"]
)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,)),
],
[helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 7), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Sum"
assert converted_model.opset_import[0].version == 8
# Test Sum Adapter: 5 -> 8
def test_sum_5_8(self) -> None:
nodes = [
helper.make_node(
"Sum", ["data_0", "data_1", "data_2", "data_3", "data_4"], ["sum"]
)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,)),
],
[helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 5), 7)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Sum"
assert converted_model.opset_import[0].version == 7
# Test Sum Adapter: 8 -> 5
def test_sum_8_5(self) -> None:
nodes = [
helper.make_node(
"Sum", ["data_0", "data_1", "data_2", "data_3", "data_4"], ["sum"]
)
]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info("data_0", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_1", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_2", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_3", TensorProto.FLOAT, (5,)),
helper.make_tensor_value_info("data_4", TensorProto.FLOAT, (5,)),
],
[helper.make_tensor_value_info("sum", TensorProto.FLOAT, (5,))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 5)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Sum"
assert converted_model.opset_import[0].version == 5
# Test AveragePool Adapter: 1 -> 8
def test_averagepool_up(self) -> None:
nodes = [helper.make_node("AveragePool", ["X"], ["Y"], kernel_shape=[1, 1])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 1), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "AveragePool"
assert converted_model.opset_import[0].version == 8
# Test AveragePool Adapter: 8 -> 1
def test_averagepool_down(self) -> None:
nodes = [helper.make_node("AveragePool", ["X"], ["Y"], kernel_shape=[1, 1])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "AveragePool"
assert converted_model.opset_import[0].version == 1
# Test Dropout Adapter: 1 -> 8
def test_dropout_up(self) -> None:
nodes = [helper.make_node("Dropout", ["data"], ["output"], is_test=1)]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info(
"data",
TensorProto.FLOAT,
(
5,
5,
),
)
],
[
helper.make_tensor_value_info(
"output",
TensorProto.FLOAT,
(
5,
5,
),
)
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 1), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Dropout"
assert converted_model.opset_import[0].version == 8
# Test Dropout Adapter: 8 -> 1
def test_dropout_down(self) -> None:
nodes = [helper.make_node("Dropout", ["data"], ["output"])]
graph = helper.make_graph(
nodes,
"test",
[
helper.make_tensor_value_info(
"data",
TensorProto.FLOAT,
(
5,
5,
),
)
],
[
helper.make_tensor_value_info(
"output",
TensorProto.FLOAT,
(
5,
5,
),
)
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Dropout"
assert converted_model.opset_import[0].version == 1
# Test Max Adapter: 7 -> 8
def test_max_7_8(self) -> None:
from_opset = 7
to_opset = 8
data_type = TensorProto.FLOAT
data_shape = (2, 3, 4)
nodes = [onnx.helper.make_node("Max", inputs=["X"], outputs=["Y"])]
graph = helper.make_graph(
nodes,
"test_max",
[onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
[onnx.helper.make_tensor_value_info("Y", data_type, data_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Max"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Min Adapter: 7 -> 8
def test_min_7_8(self) -> None:
from_opset = 7
to_opset = 8
data_type = TensorProto.FLOAT
data_shape = (2, 3, 4)
nodes = [onnx.helper.make_node("Min", inputs=["X"], outputs=["Y"])]
graph = helper.make_graph(
nodes,
"test_min",
[onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
[onnx.helper.make_tensor_value_info("Y", data_type, data_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Min"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Mean Adapter: 7 -> 8
def test_mean_7_8(self) -> None:
from_opset = 7
to_opset = 8
data_type = TensorProto.FLOAT
data_shape = (3,)
nodes = [onnx.helper.make_node("Mean", inputs=["X"], outputs=["Y"])]
graph = helper.make_graph(
nodes,
"test_mean",
[onnx.helper.make_tensor_value_info("X", data_type, data_shape)],
[onnx.helper.make_tensor_value_info("Y", data_type, data_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Mean"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test MaxPool Adapter: 1 -> 8
def test_maxpool_up(self) -> None:
nodes = [helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[1, 1])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 1), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "MaxPool"
assert converted_model.opset_import[0].version == 8
# Test Upsample Adapter: 6 -> 7
def test_upsample_6_7(self) -> None:
from_opset = 6
to_opset = 7
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
"Upsample",
inputs=["X"],
outputs=["Y"],
mode="nearest",
width_scale=3.0,
height_scale=2.0,
)
]
graph = helper.make_graph(
nodes,
"test_upsample_6_7",
[onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
[onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert len(converted_model.graph.node) == 1
assert converted_model.graph.node[0].op_type == "Upsample"
attribute_names = [
attr.name for attr in converted_model.graph.node[0].attribute
]
assert "scales" in attribute_names
assert "width_scale" not in attribute_names
assert "height_scale" not in attribute_names
assert converted_model.opset_import[0].version == to_opset
# Test MaxPool Adapter: 8 -> 1
def test_maxpool_down(self) -> None:
nodes = [helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[1, 1])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5, 5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "MaxPool"
assert converted_model.opset_import[0].version == 1
# Test BatchNormalization Adapter: 8 -> 9
def test_batch_normalization_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [
helper.make_node(
"BatchNormalization",
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"],
)
]
input_shape = (1, 2, 1, 3)
x = helper.make_tensor_value_info("x", data_type, input_shape)
scale = helper.make_tensor_value_info("s", data_type, [input_shape[1]])
B = helper.make_tensor_value_info("bias", data_type, [input_shape[1]])
mean = helper.make_tensor_value_info("mean", data_type, [input_shape[1]])
var = helper.make_tensor_value_info("var", data_type, [input_shape[1]])
y = helper.make_tensor_value_info("y", data_type, input_shape)
graph = helper.make_graph(
nodes, "test_batchnormalization_8_9", [x, scale, B, mean, var], [y]
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == to_opset
# Test BatchNormalization Adapter: 9 -> 8
def test_batchnormalization_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
"BatchNormalization",
inputs=["X", "scale", "B", "mean", "var"],
outputs=["Y"],
)
]
input_shape = (2, 3, 4, 5)
x = onnx.helper.make_tensor_value_info("X", data_type, input_shape)
scale = onnx.helper.make_tensor_value_info("scale", data_type, [input_shape[1]])
B = onnx.helper.make_tensor_value_info("B", data_type, [input_shape[1]])
mean = onnx.helper.make_tensor_value_info("mean", data_type, [input_shape[1]])
var = onnx.helper.make_tensor_value_info("var", data_type, [input_shape[1]])
y = onnx.helper.make_tensor_value_info("Y", data_type, input_shape)
graph = onnx.helper.make_graph(
nodes, "test_batchnormalization", [x, scale, B, mean, var], [y]
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == to_opset
# Test Constant Adapter: 8 -> 9
def test_constant_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
output_shape = [2, 3, 4]
output_value = np.arange(24)
nodes = [
helper.make_node(
"Constant",
inputs=[],
outputs=["Y"],
value=helper.make_tensor("", data_type, output_shape, output_value),
)
]
graph = helper.make_graph(
nodes,
"test_constant",
[],
[onnx.helper.make_tensor_value_info("Y", data_type, output_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Constant Adapter: 9 -> 8
def test_constant_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
output_shape = [2, 3, 4]
output_value = np.arange(24)
nodes = [
helper.make_node(
"Constant",
inputs=[],
outputs=["Y"],
value=helper.make_tensor("", data_type, output_shape, output_value),
)
]
graph = helper.make_graph(
nodes,
"test_constant",
[],
[onnx.helper.make_tensor_value_info("Y", data_type, output_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Flatten Adapter: 8 -> 9
def test_flatten_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [onnx.helper.make_node("Flatten", inputs=["X"], outputs=["Y"], axis=1)]
graph = helper.make_graph(
nodes,
"test_flatten",
[onnx.helper.make_tensor_value_info("X", data_type, [2, 3, 4])],
[onnx.helper.make_tensor_value_info("Y", data_type, [2, 12])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Flatten"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Flatten Adapter: 9 -> 8
def test_flatten_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [onnx.helper.make_node("Flatten", inputs=["X"], outputs=["Y"], axis=1)]
graph = helper.make_graph(
nodes,
"test_flatten",
[onnx.helper.make_tensor_value_info("X", data_type, [2, 3, 4])],
[onnx.helper.make_tensor_value_info("Y", data_type, [2, 12])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[1].op_type == "Flatten"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test PRelu Adapter: 8 -> 9
def test_prelu_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [onnx.helper.make_node("PRelu", inputs=["X", "Slope"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_prelu",
[
onnx.helper.make_tensor_value_info("X", data_type, input_shape),
onnx.helper.make_tensor_value_info("Slope", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", data_type, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "PRelu"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test PRelu Adapter: 9 -> 8
def test_prelu_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [onnx.helper.make_node("PRelu", inputs=["X", "Slope"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_prelu",
[
onnx.helper.make_tensor_value_info("X", data_type, input_shape),
onnx.helper.make_tensor_value_info("Slope", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", data_type, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[2].op_type == "PRelu"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Greater Adapter: 8 -> 9
def test_greater_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [onnx.helper.make_node("Greater", inputs=["X1", "X2"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_greater",
[
onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
onnx.helper.make_tensor_value_info("X2", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Greater"
assert (
converted_model.graph.output[0].type.tensor_type.elem_type
== TensorProto.BOOL
)
assert converted_model.opset_import[0].version == to_opset
# Test Greater Adapter: 9 -> 8
def test_greater_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [onnx.helper.make_node("Greater", inputs=["X1", "X2"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_greater",
[
onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
onnx.helper.make_tensor_value_info("X2", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[2].op_type == "Greater"
assert (
converted_model.graph.output[0].type.tensor_type.elem_type
== TensorProto.BOOL
)
assert converted_model.opset_import[0].version == to_opset
# Test Less Adapter: 8 -> 9
def test_less_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [onnx.helper.make_node("Less", inputs=["X1", "X2"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_less",
[
onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
onnx.helper.make_tensor_value_info("X2", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Less"
assert (
converted_model.graph.output[0].type.tensor_type.elem_type
== TensorProto.BOOL
)
assert converted_model.opset_import[0].version == to_opset
# Test Less Adapter: 9 -> 8
def test_less_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [onnx.helper.make_node("Less", inputs=["X1", "X2"], outputs=["Y"])]
input_shape = [2, 3, 4]
graph = helper.make_graph(
nodes,
"test_less",
[
onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
onnx.helper.make_tensor_value_info("X2", data_type, input_shape),
],
[onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL, input_shape)],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[2].op_type == "Less"
assert (
converted_model.graph.output[0].type.tensor_type.elem_type
== TensorProto.BOOL
)
assert converted_model.opset_import[0].version == to_opset
# Test MatMul Adapter: 8 -> 9
def test_matmul_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [onnx.helper.make_node("MatMul", inputs=["X1", "X2"], outputs=["Y"])]
graph = helper.make_graph(
nodes,
"test_matmul",
[
onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
],
[onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "MatMul"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test MatMul Adapter: 9 -> 8
def test_matmul_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [onnx.helper.make_node("MatMul", inputs=["X1", "X2"], outputs=["Y"])]
graph = helper.make_graph(
nodes,
"test_matmul",
[
onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
],
[onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[2].op_type == "MatMul"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Gemm Adapter: 8 -> 9
def test_gemm_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node("Gemm", inputs=["X1", "X2", "X3"], outputs=["Y"])
]
graph = helper.make_graph(
nodes,
"test_gemm",
[
onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
onnx.helper.make_tensor_value_info("X3", data_type, [3, 3]),
],
[onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Gemm"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Gemm Adapter: 9 -> 8
def test_gemm_9_8(self) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [
onnx.helper.make_node("Gemm", inputs=["X1", "X2", "X3"], outputs=["Y"])
]
graph = helper.make_graph(
nodes,
"test_gemm",
[
onnx.helper.make_tensor_value_info("X1", data_type, [3, 4]),
onnx.helper.make_tensor_value_info("X2", data_type, [4, 3]),
onnx.helper.make_tensor_value_info("X3", data_type, [3, 3]),
],
[onnx.helper.make_tensor_value_info("Y", data_type, [3, 3])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[3].op_type == "Gemm"
assert converted_model.graph.output[0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
# Test Upsample Adapter: 8 -> 9
def test_upsample_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
"Upsample",
inputs=["X"],
outputs=["Y"],
mode="nearest",
scales=[1.0, 1.0, 2.0, 3.0],
)
]
graph = helper.make_graph(
nodes,
"test_upsample_8_9",
[onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
[onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert len(converted_model.graph.node) == 2
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.node[1].op_type == "Upsample"
assert len(converted_model.graph.node[1].attribute) == 1
assert converted_model.graph.node[1].attribute[0].name == "mode"
assert converted_model.opset_import[0].version == to_opset
# Test Helper for Upsample Adapter: 9 -> 8
def helper_upsample_with_initializer(self, raw_scale: bool = False) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
"Upsample", inputs=["X", "Scales"], outputs=["Y"], mode="nearest"
)
]
scale_value = [1.0, 1.0, 2.0, 3.0]
scale_tensor = onnx.helper.make_tensor(
"Scales",
onnx.TensorProto.FLOAT,
[4],
bytes(struct.pack("4f", *scale_value)) if raw_scale else scale_value,
raw_scale,
)
graph = helper.make_graph(
nodes,
"test_upsample",
[
onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2]),
onnx.helper.make_tensor_value_info("Scales", data_type, [4]),
],
[onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
[scale_tensor],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Upsample"
assert len(converted_model.graph.initializer) == 0
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "scales"
assert converted_model.opset_import[0].version == to_opset
# Test Helper for Upsample Adapter: 9 -> 8
def helper_upsample_with_constant(self, raw_scale: bool = False) -> None:
from_opset = 9
to_opset = 8
data_type = TensorProto.FLOAT
scale_value = [1.0, 1.0, 2.0, 3.0]
scale_tensor = onnx.helper.make_tensor(
"const_value",
onnx.TensorProto.FLOAT,
[4],
bytes(struct.pack("4f", *scale_value)) if raw_scale else scale_value,
raw_scale,
)
nodes = [
onnx.helper.make_node(
"Constant", inputs=[], outputs=["Constant_Output"], value=scale_tensor
),
onnx.helper.make_node(
"Upsample",
inputs=["X", "Constant_Output"],
outputs=["Y"],
mode="nearest",
),
]
graph = helper.make_graph(
nodes,
"test_upsample",
[onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
[onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
value_info=[
onnx.helper.make_tensor_value_info("Constant_Output", data_type, [4])
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert len(converted_model.graph.node) == 1
assert converted_model.graph.node[0].op_type == "Upsample"
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "scales"
assert converted_model.opset_import[0].version == to_opset
# Test Upsample Adapter: 9 -> 8
def test_upsample_with_constant_node_9_8(self) -> None:
self.helper_upsample_with_constant(raw_scale=False)
# Test Upsample Adapter: 9 -> 8
def test_upsample_with_initializer_9_8(self) -> None:
self.helper_upsample_with_initializer(raw_scale=False)
# Test Upsample Adapter: 9 -> 8
def test_upsample_with_raw_initializer_9_8(self) -> None:
self.helper_upsample_with_constant(raw_scale=True)
# Test Upsample Adapter: 9 -> 8
def test_upsample_with_raw_constant_node_9_8(self) -> None:
self.helper_upsample_with_constant(raw_scale=True)
# Test Scan Adapter: 8 -> 9
def test_scan_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
node1 = onnx.helper.make_node(
"Add",
inputs=["sum_in", "next"],
outputs=["sum_out"],
)
node2 = onnx.helper.make_node(
"Identity",
inputs=["sum_out"],
outputs=["scan_out"],
)
g = onnx.helper.make_graph(
[node1, node2],
"scan_body",
[
onnx.helper.make_tensor_value_info("sum_in", data_type, [2]),
onnx.helper.make_tensor_value_info("next", data_type, [2]),
],
[
onnx.helper.make_tensor_value_info("sum_out", data_type, [2]),
onnx.helper.make_tensor_value_info("scan_out", data_type, [2]),
],
)
no_sequence_lens = "" # optional input, not supplied
nodes = [
onnx.helper.make_node(
"Scan",
inputs=[no_sequence_lens, "initial", "x"],
outputs=["y", "z"],
body=g,
num_scan_inputs=1,
)
]
initial = onnx.helper.make_tensor_value_info("initial", data_type, [1, 2])
x = onnx.helper.make_tensor_value_info("x", data_type, [1, 3, 2])
y = onnx.helper.make_tensor_value_info("y", data_type, [1, 2])
z = onnx.helper.make_tensor_value_info("z", data_type, [1, 3, 2])
graph = onnx.helper.make_graph(nodes, "test_scan_8_9", [initial, x], [y, z])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Scan"
assert converted_model.opset_import[0].version == to_opset
# Test Cast Adapter: 8 -> 9
def test_cast_8_9(self) -> None:
from_opset = 8
to_opset = 9
data_type_from = TensorProto.FLOAT
data_type_to = TensorProto.UINT32
nodes = [
onnx.helper.make_node(
"Cast", inputs=["X"], outputs=["Y"], to=TensorProto.UINT32
)
]
graph = helper.make_graph(
nodes,
"test_cast",
[onnx.helper.make_tensor_value_info("X", data_type_from, [2, 3])],
[onnx.helper.make_tensor_value_info("Y", data_type_to, [2, 3])],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "Cast"
assert (
converted_model.graph.output[0].type.tensor_type.elem_type == data_type_to
)
assert converted_model.opset_import[0].version == to_opset
# Test Split Adapter: 13 -> 12
def test_split_13_12(self) -> None:
nodes = [
helper.make_node(
"Constant",
[],
["split"],
value=helper.make_tensor("", TensorProto.INT64, [2], [2, 3]),
),
helper.make_node("Split", ["X", "split"], ["Y1", "Y2"]),
]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[
helper.make_tensor_value_info("Y1", TensorProto.FLOAT, (2,)),
helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (3,)),
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 13), 12)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Split"
assert converted_model.opset_import[0].version == 12
# Test Split Adapter: 12 -> 13
def test_split_12_13(self) -> None:
nodes = [helper.make_node("Split", ["X"], ["Y1", "Y2"], split=[2, 3])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
[
helper.make_tensor_value_info("Y1", TensorProto.FLOAT, (2,)),
helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (3,)),
],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 12), 13)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.node[1].op_type == "Split"
assert converted_model.opset_import[0].version == 13
# Test AxesInputToAttribute Adapter: 13 -> 12
def test_axes_input_to_attr_13_12(self) -> None:
nodes = [
helper.make_node(
"Constant",
[],
["axes"],
value=helper.make_tensor("", TensorProto.INT64, [1], [0]),
),
helper.make_node("ReduceSum", ["X", "axes"], ["Y"]),
]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 13), 12)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "ReduceSum"
assert converted_model.opset_import[0].version == 12
# Test AxesAttributeToInput Adapter: 12 -> 13
def test_axes_attr_to_input_12_13(self) -> None:
nodes = [helper.make_node("ReduceSum", ["X"], ["Y"], axes=[0])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 12), 13)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.opset_import[0].version == 13
# Test Slice Adapter: 9 -> 10
def test_slice_9_10(self) -> None:
nodes = [
helper.make_node(
"Slice", ["X"], ["Y"], axes=[0, 1], starts=[0, 0], ends=[3, 10]
)
]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (20, 10, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 10, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 9), 10)
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.node[1].op_type == "Constant"
assert converted_model.graph.node[2].op_type == "Constant"
assert converted_model.graph.node[3].op_type == "Slice"
assert converted_model.opset_import[0].version == 10
assert len(converted_model.graph.node[3].input) == 4
assert len(converted_model.graph.node[3].attribute) == 0
# Test RNN Adapter: 13 -> 14
def test_rnn_13_14(self) -> None:
from_opset = 13
to_opset = 14
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
]
graph = helper.make_graph(
nodes,
"test_rnn",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 2 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "RNN"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "layout"
# Test GRU Adapter: 13 -> 14
def test_gru_13_14(self) -> None:
from_opset = 13
to_opset = 14
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
]
graph = helper.make_graph(
nodes,
"test_gru",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, 3 * hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, 3 * hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 6 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "GRU"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "layout"
# Test LSTM Adapter: 13 -> 14
def test_lstm_13_14(self) -> None:
from_opset = 13
to_opset = 14
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
]
graph = helper.make_graph(
nodes,
"test_lstm",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, 4 * hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, 4 * hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 8 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "LSTM"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "layout"
# Test RNN Adapter: 14 -> 13
def test_rnn_14_13(self) -> None:
from_opset = 14
to_opset = 13
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
layout=0,
)
]
graph = helper.make_graph(
nodes,
"test_rnn",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 2 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "RNN"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 1
# Test GRU Adapter: 14 -> 13
def test_gru_14_13(self) -> None:
from_opset = 14
to_opset = 13
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"GRU",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
layout=0,
)
]
graph = helper.make_graph(
nodes,
"test_gru",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, 3 * hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, 3 * hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 6 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "GRU"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 1
# Test LSTM Adapter: 14 -> 13
def test_lstm_14_13(self) -> None:
from_opset = 14
to_opset = 13
data_type = TensorProto.FLOAT
seq_length = 1
batch_size = 2
input_size = 3
num_directions = 1
hidden_size = 5
nodes = [
onnx.helper.make_node(
"LSTM",
inputs=["X", "W", "R"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
layout=0,
)
]
graph = helper.make_graph(
nodes,
"test_lstm",
[
onnx.helper.make_tensor_value_info(
"X", data_type, [seq_length, batch_size, input_size]
),
onnx.helper.make_tensor_value_info(
"W", data_type, [num_directions, 4 * hidden_size, input_size]
),
onnx.helper.make_tensor_value_info(
"R", data_type, [num_directions, 4 * hidden_size, hidden_size]
),
onnx.helper.make_tensor_value_info(
"B", data_type, [num_directions, 8 * hidden_size]
),
],
[
onnx.helper.make_tensor_value_info(
"Y_h", data_type, [num_directions, batch_size, hidden_size]
)
],
)
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted_model.graph.node[0].op_type == "LSTM"
assert converted_model.opset_import[0].version == to_opset
assert len(converted_model.graph.node[0].attribute) == 1
# Test Pad Adapter: 10 -> 11
def test_pad_10_11(self) -> None:
pads = (0, 1, 2, 0, 2, 1)
nodes = [helper.make_node("Pad", ["X"], ["Y"], pads=pads)]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2, 2))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 10), 11)
# Assert equality of graph and converted_model
assert converted_model.graph.node[1].op_type == "Pad"
assert converted_model.opset_import[0].version == 11
def test_pad_with_value_10_11(self) -> None:
pads = (0, 1, 2, 0, 2, 1)
nodes = [helper.make_node("Pad", ["X"], ["Y"], pads=pads, value=1.0)]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2, 2))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 5, 5))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 10), 11)
# Assert equality of graph and converted_model
assert converted_model.graph.node[1].op_type == "Pad"
assert converted_model.opset_import[0].version == 11
# Test that subgraphs are converted
def test_if_subgraph_10_11(self) -> None:
from_opset = 10
to_opset = 11
data_type = TensorProto.FLOAT
data_shape = [2]
subg1_node = [
onnx.helper.make_node(
"Clip", inputs=["sub_in"], outputs=["sub_out"], min=2.0, max=3.0
)
]
subg1_input = [
onnx.helper.make_tensor_value_info("sub_in", data_type, data_shape)
]
subg1_output = [
onnx.helper.make_tensor_value_info("sub_out", data_type, data_shape)
]
subg1 = helper.make_graph(subg1_node, "then_g", subg1_input, subg1_output)
subg2_node = [
onnx.helper.make_node(
"Clip", inputs=["sub_in"], outputs=["sub_out"], min=2.0, max=3.0
)
]
subg2_input = [
onnx.helper.make_tensor_value_info("sub_in", data_type, data_shape)
]
subg2_output = [
onnx.helper.make_tensor_value_info("sub_out", data_type, data_shape)
]
subg2 = helper.make_graph(subg2_node, "then_g", subg2_input, subg2_output)
node = [
onnx.helper.make_node(
"If",
inputs=["cond"],
outputs=["out"],
then_branch=subg1,
else_branch=subg2,
)
]
input = [onnx.helper.make_tensor_value_info("cond", TensorProto.BOOL, [])]
output = [onnx.helper.make_tensor_value_info("out", data_type, data_shape)]
init = [helper.make_tensor("sub_in", data_type, data_shape, [4.0, 5.0])]
graph = helper.make_graph(node, "test_subgraphs", input, output, init)
converted = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset
)
assert converted.graph.node[0].op_type == "If"
assert converted.opset_import[0].version == to_opset
assert converted.graph.node[0].attribute[0].g.node[2].op_type == "Clip"
assert len(converted.graph.node[0].attribute[0].g.node[2].attribute) == 0
assert converted.graph.node[0].attribute[1].g.node[2].op_type == "Clip"
assert len(converted.graph.node[0].attribute[1].g.node[2].attribute) == 0
# Use initializer as node inputs (instead of graph input)
# to test whether IR (version_converter) can handle it
def test_initializer_not_in_input_above_ir4(self): # type: () -> None
nodes = [
helper.make_node(
"BatchNormalization", ["X", "scale", "B", "mean", "var"], ["Y"]
)
]
scale_value = [0.55, 0.72]
scale_tensor = onnx.helper.make_tensor(
"scale", onnx.TensorProto.FLOAT, [2], scale_value
)
b_value = [0.60, 0.54]
b_tensor = onnx.helper.make_tensor("B", onnx.TensorProto.FLOAT, [2], b_value)
mean_value = [0.42, 0.65]
mean_tensor = onnx.helper.make_tensor(
"mean", onnx.TensorProto.FLOAT, [2], mean_value
)
var_value = [0.44, 0.89]
var_tensor = onnx.helper.make_tensor(
"var", onnx.TensorProto.FLOAT, [2], var_value
)
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2, 2, 3))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2, 2, 3))],
[scale_tensor, b_tensor, mean_tensor, var_tensor],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 11), 12)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == 12
def test_softmax_12_13(self) -> None:
axis = 0
nodes = [helper.make_node("Softmax", ["X"], ["Y"], axis=axis)]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2, 3))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2, 3))],
)
converted_model = self._converted(graph, helper.make_operatorsetid("", 11), 13)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Shape"
assert converted_model.graph.node[1].op_type == "Flatten"
assert converted_model.graph.node[1].attribute[0].name == "axis"
assert converted_model.graph.node[1].attribute[0].i == axis
assert converted_model.graph.node[2].op_type == "Softmax"
assert converted_model.graph.node[2].attribute[0].name == "axis"
assert converted_model.graph.node[2].attribute[0].i == -1
assert converted_model.graph.node[3].op_type == "Reshape"
assert converted_model.opset_import[0].version == 13
if __name__ == "__main__":
unittest.main()
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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"]} |
58,897 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_selu.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 Selu(OpRun):
def _run(self, x, alpha=None, gamma=None): # type: ignore
return (
(np.where(x > 0, x, np.exp(x) * alpha - alpha) * gamma).astype(x.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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58,898 | onnx/onnx | refs/heads/main | /onnx/shape_inference.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
"""onnx shape inference. Shape inference is not guaranteed to be
complete.
"""
from __future__ import annotations
import os
from typing import Sequence
import onnx
import onnx.onnx_cpp2py_export.shape_inference as C # noqa: N812
from onnx import AttributeProto, FunctionProto, ModelProto, TypeProto
def infer_shapes(
model: ModelProto | bytes,
check_type: bool = False,
strict_mode: bool = False,
data_prop: bool = False,
) -> ModelProto:
"""Apply shape inference to the provided ModelProto.
Inferred shapes are added to the value_info field of the graph.
If the inferred values conflict with values already provided in the
graph, that means that the provided values are invalid (or there is a
bug in shape inference), and the result is unspecified.
Arguments:
model (Union[ModelProto, bytes], bool, bool, bool) -> ModelProto
check_type (bool): Checks the type-equality for input and output
strict_mode (bool): Stricter shape inference, it will throw errors if any;
Otherwise, simply stop if any error
data_prop (bool): Enables data propagation for limited operators to perform shape computation
Returns:
(ModelProto) model with inferred shape information
"""
if isinstance(model, (ModelProto, bytes)):
model_str = model if isinstance(model, bytes) else model.SerializeToString()
inferred_model_str = C.infer_shapes(
model_str, check_type, strict_mode, data_prop
)
return onnx.load_from_string(inferred_model_str)
if isinstance(model, str):
raise TypeError(
"infer_shapes only accepts ModelProto or bytes,"
"you can use infer_shapes_path for the model path (String)."
)
raise TypeError(
f"infer_shapes only accepts ModelProto or bytes, incorrect type: {type(model)}"
)
def infer_shapes_path(
model_path: str | os.PathLike,
output_path: str | os.PathLike = "",
check_type: bool = False,
strict_mode: bool = False,
data_prop: bool = False,
) -> None:
"""
Take model path for shape_inference same as infer_shape; it support >2GB models
Directly output the inferred model to the output_path; Default is the original model path
"""
if isinstance(model_path, ModelProto):
raise TypeError(
"infer_shapes_path only accepts model Path (String),"
"you can use infer_shapes for the ModelProto."
)
try:
model_path = os.fspath(model_path)
except TypeError as exp:
raise TypeError(
"infer_shapes_path only accepts model path as a string or PathLike, "
f"incorrect model path type: {type(model_path)}"
) from exp
try:
output_path = os.fspath(output_path)
except TypeError as exp:
raise TypeError(
"infer_shapes_path only accepts output path as a string or PathLike, "
f"incorrect output path type: {type(output_path)}"
) from exp
if output_path == "":
output_path = model_path
C.infer_shapes_path(model_path, output_path, check_type, strict_mode, data_prop)
def infer_node_outputs(
schema: onnx.defs.OpSchema,
node: onnx.NodeProto,
input_types: dict[str, onnx.TypeProto],
input_data: dict[str, onnx.TensorProto] | None = None,
input_sparse_data: dict[str, onnx.SparseTensorProto] | None = None,
opset_imports: list[onnx.OperatorSetIdProto] | None = None,
ir_version: int = onnx.IR_VERSION,
) -> dict[str, onnx.TypeProto]:
if not schema.has_type_and_shape_inference_function: # type: ignore
return {}
if input_data is None:
input_data = {}
if input_sparse_data is None:
input_sparse_data = {}
if opset_imports is None:
passed_opset_imports = {}
else:
passed_opset_imports = {opset.domain: opset.version for opset in opset_imports}
# catch KeyError if node's input does not exist in input_types
passed_input_types = {
key: input_types[key].SerializeToString() for key in node.input
}
# input_types will also be used as outer_scope_value_types so do not filter by node's input here
for key in input_types:
if key not in passed_input_types:
passed_input_types[key] = input_types[key].SerializeToString()
passed_input_data = {
key: input_data[key].SerializeToString()
for key in node.input
if key in input_data
}
passed_sparse_input_data = {
key: input_sparse_data[key].SerializeToString()
for key in node.input
if key in input_sparse_data
}
outputs = schema._infer_node_outputs( # pylint: disable=protected-access
node.SerializeToString(),
passed_input_types,
passed_input_data,
passed_sparse_input_data,
passed_opset_imports,
ir_version,
) # type: ignore[call-arg]
return {key: onnx.TypeProto.FromString(out) for key, out in outputs.items()}
def infer_function_output_types(
function: FunctionProto,
input_types: Sequence[TypeProto],
attributes: Sequence[AttributeProto],
) -> list[TypeProto]:
"""
Apply type-and-shape-inference to given function body, with given input types
and given input attribute values.
"""
result = C.infer_function_output_types(
function.SerializeToString(),
[x.SerializeToString() for x in input_types],
[x.SerializeToString() for x in attributes],
)
def to_type_proto(x) -> TypeProto:
type_proto = onnx.TypeProto()
type_proto.ParseFromString(x)
return type_proto
return [to_type_proto(x) for x in result]
InferenceError = C.InferenceError
| {"/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/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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58,899 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/base.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import inspect
from collections import defaultdict
from textwrap import dedent
from typing import Any, ClassVar, Dict, List, Tuple, Type
import numpy as np
def process_snippet(op_name: str, name: str, export: Any) -> Tuple[str, str]:
snippet_name = name[len("export_") :] or op_name.lower()
source_code = dedent(inspect.getsource(export))
# remove the function signature line
lines = source_code.splitlines()
assert lines[0] == "@staticmethod"
assert lines[1].startswith("def export")
return snippet_name, dedent("\n".join(lines[2:]))
Snippets: Dict[str, List[Tuple[str, str]]] = defaultdict(list)
class _Exporter(type):
exports: ClassVar[Dict[str, List[Tuple[str, str]]]] = defaultdict(list)
def __init__(
cls, name: str, bases: Tuple[Type[Any], ...], dct: Dict[str, Any]
) -> None:
for k, v in dct.items():
if k.startswith("export"):
if not isinstance(v, staticmethod):
raise ValueError("Only staticmethods could be named as export.*")
export = getattr(cls, k)
Snippets[name].append(process_snippet(name, k, export))
# export functions should call expect and so populate
# TestCases
np.random.seed(seed=0)
export()
super().__init__(name, bases, dct)
class Base(metaclass=_Exporter):
pass
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58,900 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/instancenorm.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 InstanceNormalization(Base):
@staticmethod
def export() -> None:
def _instancenorm_test_mode(x, s, bias, epsilon=1e-5): # type: ignore
dims_x = len(x.shape)
axis = tuple(range(2, dims_x))
mean = np.mean(x, axis=axis, keepdims=True)
var = np.var(x, axis=axis, keepdims=True)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
return s * (x - mean) / np.sqrt(var + epsilon) + bias
# input size: (1, 2, 1, 3)
x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
s = np.array([1.0, 1.5]).astype(np.float32)
bias = np.array([0, 1]).astype(np.float32)
y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
node = onnx.helper.make_node(
"InstanceNormalization",
inputs=["x", "s", "bias"],
outputs=["y"],
)
# output size: (1, 2, 1, 3)
expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example")
# input size: (2, 3, 4, 5)
x = np.random.randn(2, 3, 4, 5).astype(np.float32)
s = np.random.randn(3).astype(np.float32)
bias = np.random.randn(3).astype(np.float32)
epsilon = 1e-2
y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"InstanceNormalization",
inputs=["x", "s", "bias"],
outputs=["y"],
epsilon=epsilon,
)
# output size: (2, 3, 4, 5)
expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon")
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58,901 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_tree_ensemble_classifier.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._common_classifier import (
logistic,
probit,
softmax,
softmax_zero,
)
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
from onnx.reference.ops.aionnxml.op_tree_ensemble_helper import TreeEnsemble
class TreeEnsembleClassifier(OpRunAiOnnxMl):
def _run( # type: ignore
self,
X,
base_values=None,
base_values_as_tensor=None,
class_ids=None,
class_nodeids=None,
class_treeids=None,
class_weights=None,
class_weights_as_tensor=None,
classlabels_int64s=None,
classlabels_strings=None,
nodes_falsenodeids=None,
nodes_featureids=None,
nodes_hitrates=None,
nodes_hitrates_as_tensor=None,
nodes_missing_value_tracks_true=None,
nodes_modes=None,
nodes_nodeids=None,
nodes_treeids=None,
nodes_truenodeids=None,
nodes_values=None,
nodes_values_as_tensor=None,
post_transform=None,
):
nmv = nodes_missing_value_tracks_true
tr = TreeEnsemble(
base_values=base_values,
base_values_as_tensor=base_values_as_tensor,
nodes_falsenodeids=nodes_falsenodeids,
nodes_featureids=nodes_featureids,
nodes_hitrates=nodes_hitrates,
nodes_hitrates_as_tensor=nodes_hitrates_as_tensor,
nodes_missing_value_tracks_true=nmv,
nodes_modes=nodes_modes,
nodes_nodeids=nodes_nodeids,
nodes_treeids=nodes_treeids,
nodes_truenodeids=nodes_truenodeids,
nodes_values=nodes_values,
nodes_values_as_tensor=nodes_values_as_tensor,
class_weights=class_weights,
class_weights_as_tensor=class_weights_as_tensor,
)
# unused unless for debugging purposes
self._tree = tr # pylint: disable=W0201
if X.dtype not in (np.float32, np.float64):
X = X.astype(np.float32)
leaves_index = tr.leave_index_tree(X)
n_classes = max(len(classlabels_int64s or []), len(classlabels_strings or []))
res = np.empty((leaves_index.shape[0], n_classes), dtype=np.float32)
if tr.atts.base_values is None: # type: ignore
res[:, :] = 0
else:
res[:, :] = np.array(tr.atts.base_values).reshape((1, -1)) # type: ignore
class_index = {} # type: ignore
for i, (tid, nid) in enumerate(zip(class_treeids, class_nodeids)):
if (tid, nid) not in class_index:
class_index[tid, nid] = []
class_index[tid, nid].append(i)
for i in range(res.shape[0]):
indices = leaves_index[i]
t_index = [class_index[nodes_treeids[i], nodes_nodeids[i]] for i in indices]
for its in t_index:
for it in its:
res[i, class_ids[it]] += tr.atts.class_weights[it] # type: ignore
# post_transform
binary = len(set(class_ids)) == 1
classes = classlabels_int64s or classlabels_strings
post_function = {
None: lambda x: x,
"NONE": lambda x: x,
"LOGISTIC": logistic,
"SOFTMAX": softmax,
"SOFTMAX_ZERO": softmax_zero,
"PROBIT": probit,
}
if binary:
if res.shape[1] == len(classes) == 1:
new_res = np.zeros((res.shape[0], 2), res.dtype)
new_res[:, 1] = res[:, 0]
res = new_res
else:
res[:, 1] = res[:, 0]
if post_transform in (None, "NONE", "PROBIT"):
res[:, 0] = 1 - res[:, 1]
else:
res[:, 0] = -res[:, 1]
new_scores = post_function[post_transform](res) # type: ignore
labels = np.argmax(new_scores, axis=1)
# labels
if classlabels_int64s is not None:
if len(classlabels_int64s) == 1:
if classlabels_int64s[0] == 1:
d = {1: 1}
labels = np.array([d.get(i, 0) for i in labels], dtype=np.int64)
else:
raise NotImplementedError(
f"classlabels_int64s={classlabels_int64s}, not supported."
)
else:
labels = np.array(
[classlabels_int64s[i] for i in labels], dtype=np.int64
)
elif classlabels_strings is not None:
if len(classlabels_strings) == 1:
raise NotImplementedError(
f"classlabels_strings={classlabels_strings}, not supported."
)
labels = np.array([classlabels_strings[i] for i in labels])
return labels, new_scores
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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"]} |
58,902 | onnx/onnx | refs/heads/main | /setup.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import glob
import multiprocessing
import os
import pathlib
import platform
import shlex
import subprocess
import sys
from collections import namedtuple
from contextlib import contextmanager
from datetime import date
from distutils import log, sysconfig
from distutils.spawn import find_executable
from textwrap import dedent
from typing import ClassVar, List
import setuptools
import setuptools.command.build_ext
import setuptools.command.build_py
import setuptools.command.develop
TOP_DIR = os.path.realpath(os.path.dirname(__file__))
SRC_DIR = os.path.join(TOP_DIR, "onnx")
TP_DIR = os.path.join(TOP_DIR, "third_party")
CMAKE_BUILD_DIR = os.path.join(TOP_DIR, ".setuptools-cmake-build")
PACKAGE_NAME = "onnx"
WINDOWS = os.name == "nt"
CMAKE = find_executable("cmake3") or find_executable("cmake")
MAKE = find_executable("make")
install_requires = []
setup_requires = []
tests_require = []
extras_require = {}
################################################################################
# Global variables for controlling the build variant
################################################################################
# Default value is set to TRUE\1 to keep the settings same as the current ones.
# However going forward the recommended way to is to set this to False\0
ONNX_ML = not bool(os.getenv("ONNX_ML") == "0")
ONNX_VERIFY_PROTO3 = bool(os.getenv("ONNX_VERIFY_PROTO3") == "1")
ONNX_NAMESPACE = os.getenv("ONNX_NAMESPACE", "onnx")
ONNX_BUILD_TESTS = bool(os.getenv("ONNX_BUILD_TESTS") == "1")
ONNX_DISABLE_EXCEPTIONS = bool(os.getenv("ONNX_DISABLE_EXCEPTIONS") == "1")
ONNX_DISABLE_STATIC_REGISTRATION = bool(
os.getenv("ONNX_DISABLE_STATIC_REGISTRATION") == "1"
)
USE_MSVC_STATIC_RUNTIME = bool(os.getenv("USE_MSVC_STATIC_RUNTIME", "0") == "1")
DEBUG = bool(os.getenv("DEBUG", "0") == "1")
COVERAGE = bool(os.getenv("COVERAGE", "0") == "1")
################################################################################
# Version
################################################################################
try:
git_version = (
subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=TOP_DIR)
.decode("ascii")
.strip()
)
except (OSError, subprocess.CalledProcessError):
git_version = None
with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
VERSION_NUMBER = version_file.read().strip()
if "--weekly_build" in sys.argv:
today_number = date.today().strftime("%Y%m%d")
VERSION_NUMBER += ".dev" + today_number
PACKAGE_NAME = "onnx-weekly"
sys.argv.remove("--weekly_build")
VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
version=VERSION_NUMBER, git_version=git_version
)
################################################################################
# Pre Check
################################################################################
assert CMAKE, "Could not find cmake executable!"
################################################################################
# Utilities
################################################################################
@contextmanager
def cd(path):
if not os.path.isabs(path):
raise RuntimeError(f"Can only cd to absolute path, got: {path}")
orig_path = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(orig_path)
################################################################################
# Customized commands
################################################################################
class ONNXCommand(setuptools.Command):
user_options: ClassVar[list] = []
def initialize_options(self):
pass
def finalize_options(self):
pass
class CreateVersion(ONNXCommand):
def run(self):
with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
f.write(
dedent(
"""\
# This file is generated by setup.py. DO NOT EDIT!
version = "{version}"
git_version = "{git_version}"
""".format(
**dict(VersionInfo._asdict())
)
)
)
class CmakeBuild(setuptools.Command):
"""
Compiles everything when `python setup.py build` is run using cmake.
Custom args can be passed to cmake by specifying the `CMAKE_ARGS`
environment variable.
The number of CPUs used by `make` can be specified by passing `-j<ncpus>`
to `setup.py build`. By default all CPUs are used.
"""
user_options: ClassVar[list] = [
("jobs=", "j", "Specifies the number of jobs to use with make")
]
built = False
def initialize_options(self):
self.jobs = None
def finalize_options(self):
self.set_undefined_options("build", ("parallel", "jobs"))
if self.jobs is None and os.getenv("MAX_JOBS") is not None:
self.jobs = os.getenv("MAX_JOBS")
self.jobs = multiprocessing.cpu_count() if self.jobs is None else int(self.jobs)
def run(self):
if CmakeBuild.built:
return
CmakeBuild.built = True
if not os.path.exists(CMAKE_BUILD_DIR):
os.makedirs(CMAKE_BUILD_DIR)
with cd(CMAKE_BUILD_DIR):
build_type = "Release"
# configure
cmake_args = [
CMAKE,
f"-DPYTHON_INCLUDE_DIR={sysconfig.get_python_inc()}",
f"-DPYTHON_EXECUTABLE={sys.executable}",
"-DBUILD_ONNX_PYTHON=ON",
"-DCMAKE_EXPORT_COMPILE_COMMANDS=ON",
f"-DONNX_NAMESPACE={ONNX_NAMESPACE}",
f"-DPY_EXT_SUFFIX={sysconfig.get_config_var('EXT_SUFFIX') or ''}",
]
if COVERAGE:
cmake_args.append("-DONNX_COVERAGE=ON")
if COVERAGE or DEBUG:
# in order to get accurate coverage information, the
# build needs to turn off optimizations
build_type = "Debug"
cmake_args.append(f"-DCMAKE_BUILD_TYPE={build_type}")
if WINDOWS:
cmake_args.extend(
[
# we need to link with libpython on windows, so
# passing python version to window in order to
# find python in cmake
f"-DPY_VERSION={'{}.{}'.format(*sys.version_info[:2])}",
]
)
if USE_MSVC_STATIC_RUNTIME:
cmake_args.append("-DONNX_USE_MSVC_STATIC_RUNTIME=ON")
if platform.architecture()[0] == "64bit":
if "arm" in platform.machine().lower():
cmake_args.extend(["-A", "ARM64"])
else:
cmake_args.extend(["-A", "x64", "-T", "host=x64"])
else:
if "arm" in platform.machine().lower():
cmake_args.extend(["-A", "ARM"])
else:
cmake_args.extend(["-A", "Win32", "-T", "host=x86"])
if ONNX_ML:
cmake_args.append("-DONNX_ML=1")
if ONNX_VERIFY_PROTO3:
cmake_args.append("-DONNX_VERIFY_PROTO3=1")
if ONNX_BUILD_TESTS:
cmake_args.append("-DONNX_BUILD_TESTS=ON")
if ONNX_DISABLE_EXCEPTIONS:
cmake_args.append("-DONNX_DISABLE_EXCEPTIONS=ON")
if ONNX_DISABLE_STATIC_REGISTRATION:
cmake_args.append("-DONNX_DISABLE_STATIC_REGISTRATION=ON")
if "CMAKE_ARGS" in os.environ:
extra_cmake_args = shlex.split(os.environ["CMAKE_ARGS"])
# prevent crossfire with downstream scripts
del os.environ["CMAKE_ARGS"]
log.info(f"Extra cmake args: {extra_cmake_args}")
cmake_args.extend(extra_cmake_args)
cmake_args.append(TOP_DIR)
log.info(f"Using cmake args: {cmake_args}")
if "-DONNX_DISABLE_EXCEPTIONS=ON" in cmake_args:
raise RuntimeError(
"-DONNX_DISABLE_EXCEPTIONS=ON option is only available for c++ builds. Python binding require exceptions to be enabled."
)
if (
"PYTHONPATH" in os.environ
and "pip-build-env" in os.environ["PYTHONPATH"]
):
# When the users use `pip install -e .` to install onnx and
# the cmake executable is a python entry script, there will be
# `Fix ModuleNotFoundError: No module named 'cmake'` from the cmake script.
# This is caused by the additional PYTHONPATH environment variable added by pip,
# which makes cmake python entry script not able to find correct python cmake packages.
# Actually, sys.path is well enough for `pip install -e .`.
# Therefore, we delete the PYTHONPATH variable.
del os.environ["PYTHONPATH"]
subprocess.check_call(cmake_args)
build_args = [CMAKE, "--build", os.curdir]
if WINDOWS:
build_args.extend(["--config", build_type])
build_args.extend(["--", f"/maxcpucount:{self.jobs}"])
else:
build_args.extend(["--", "-j", str(self.jobs)])
subprocess.check_call(build_args)
class BuildPy(setuptools.command.build_py.build_py):
def run(self):
self.run_command("create_version")
self.run_command("cmake_build")
generated_python_files = glob.glob(
os.path.join(CMAKE_BUILD_DIR, "onnx", "*.py")
) + glob.glob(os.path.join(CMAKE_BUILD_DIR, "onnx", "*.pyi"))
for src in generated_python_files:
dst = os.path.join(TOP_DIR, os.path.relpath(src, CMAKE_BUILD_DIR))
self.copy_file(src, dst)
# TODO (https://github.com/pypa/setuptools/issues/3606)
# Review the command customisations to enable editable_mode
self.editable_mode = False
return setuptools.command.build_py.build_py.run(self)
class Develop(setuptools.command.develop.develop):
def run(self):
self.run_command("build_py")
setuptools.command.develop.develop.run(self)
class BuildExt(setuptools.command.build_ext.build_ext):
def run(self):
self.run_command("cmake_build")
setuptools.command.build_ext.build_ext.run(self)
def build_extensions(self):
for ext in self.extensions:
fullname = self.get_ext_fullname(ext.name)
filename = os.path.basename(self.get_ext_filename(fullname))
lib_path = CMAKE_BUILD_DIR
if os.name == "nt":
debug_lib_dir = os.path.join(lib_path, "Debug")
release_lib_dir = os.path.join(lib_path, "Release")
if os.path.exists(debug_lib_dir):
lib_path = debug_lib_dir
elif os.path.exists(release_lib_dir):
lib_path = release_lib_dir
src = os.path.join(lib_path, filename)
dst = os.path.join(os.path.realpath(self.build_lib), "onnx", filename)
self.copy_file(src, dst)
CMDCLASS = {
"create_version": CreateVersion,
"cmake_build": CmakeBuild,
"build_py": BuildPy,
"develop": Develop,
"build_ext": BuildExt,
}
################################################################################
# Extensions
################################################################################
ext_modules = [setuptools.Extension(name="onnx.onnx_cpp2py_export", sources=[])]
################################################################################
# Packages
################################################################################
# Add package directories here if you want to package them with the source
# TODO try to remove unnecessary .cpp files
include_dirs = [
"onnx.backend.test.data.*",
"onnx.common",
"onnx.defs.*",
"onnx.examples*",
"onnx.shape_inference",
"onnx.test.cpp",
"onnx.version_converter*",
]
packages = setuptools.find_packages() + setuptools.find_namespace_packages(
include=include_dirs
)
def load_packages_from_requirements(requirements_file: str) -> List[str]:
"""Load required packages from requirements-*.txt.
Arguments:
requirements_file {str} -- requirements file name (e.g. requirements.txt)
Returns:
List[str] -- list of required packages
"""
requirements_path = os.path.join(os.getcwd(), requirements_file)
if not os.path.exists(requirements_path):
this = os.path.dirname(__file__)
requirements_path = os.path.join(this, requirements_file)
if not os.path.exists(requirements_path):
raise FileNotFoundError("Unable to find " + requirements_file)
requires_list = []
with open(requirements_path) as f:
requires_list = f.read().splitlines()
return requires_list
install_requires = load_packages_from_requirements("requirements.txt")
################################################################################
# Test
################################################################################
setup_requires.append("pytest-runner")
tests_require.append("pytest")
tests_require.append("nbval")
tests_require.append("tabulate")
extras_require["lint"] = [
"lintrunner>=0.10.0",
"lintrunner-adapters>=0.3",
]
extras_require["reference"] = load_packages_from_requirements(
"requirements-reference.txt"
)
################################################################################
# Final
################################################################################
setuptools.setup(
name=PACKAGE_NAME,
version=VersionInfo.version,
description="Open Neural Network Exchange",
long_description=pathlib.Path("README.md").read_text(),
long_description_content_type="text/markdown",
ext_modules=ext_modules,
cmdclass=CMDCLASS,
packages=packages,
license="Apache License v2.0",
include_package_data=True,
package_data={"onnx": ["py.typed", "*.pyi"]},
install_requires=install_requires,
setup_requires=setup_requires,
tests_require=tests_require,
extras_require=extras_require,
author="ONNX",
author_email="onnx-technical-discuss@lists.lfaidata.foundation",
url="https://github.com/onnx/onnx",
entry_points={
"console_scripts": [
"check-model = onnx.bin.checker:check_model",
"check-node = onnx.bin.checker:check_node",
"backend-test-tools = onnx.backend.test.cmd_tools: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": ["/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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58,903 | onnx/onnx | refs/heads/main | /onnx/external_data_helper.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import os
import re
import sys
import uuid
from itertools import chain
from typing import Callable, Iterable, Optional
from onnx.onnx_pb import AttributeProto, GraphProto, ModelProto, TensorProto
class ExternalDataInfo:
def __init__(self, tensor: TensorProto) -> None:
self.location = ""
self.offset = None
self.length = None
self.checksum = None
self.basepath = ""
for entry in tensor.external_data:
setattr(self, entry.key, entry.value)
if self.offset:
self.offset = int(self.offset)
if self.length:
self.length = int(self.length)
def load_external_data_for_tensor(tensor: TensorProto, base_dir: str) -> None:
"""
Loads data from an external file for tensor.
Ideally TensorProto should not hold any raw data but if it does it will be ignored.
Arguments:
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
info = ExternalDataInfo(tensor)
file_location = _sanitize_path(info.location)
external_data_file_path = os.path.join(base_dir, file_location)
with open(external_data_file_path, "rb") as data_file:
if info.offset:
data_file.seek(info.offset)
if info.length:
tensor.raw_data = data_file.read(info.length)
else:
tensor.raw_data = data_file.read()
def load_external_data_for_model(model: ModelProto, base_dir: str) -> None:
"""
Loads external tensors into model
Arguments:
model: ModelProto to load external data to
base_dir: directory that contains external data
"""
for tensor in _get_all_tensors(model):
if uses_external_data(tensor):
load_external_data_for_tensor(tensor, base_dir)
# After loading raw_data from external_data, change the state of tensors
tensor.data_location = TensorProto.DEFAULT
# and remove external data
del tensor.external_data[:]
def set_external_data(
tensor: TensorProto,
location: str,
offset: Optional[int] = None,
length: Optional[int] = None,
checksum: Optional[str] = None,
basepath: Optional[str] = None,
) -> None:
if not tensor.HasField("raw_data"):
raise ValueError(
"Tensor "
+ tensor.name
+ "does not have raw_data field. Cannot set external data for this tensor."
)
del tensor.external_data[:]
tensor.data_location = TensorProto.EXTERNAL
for k, v in {
"location": location,
"offset": int(offset) if offset is not None else None,
"length": int(length) if length is not None else None,
"checksum": checksum,
"basepath": basepath,
}.items():
if v is not None:
entry = tensor.external_data.add()
entry.key = k
entry.value = str(v)
def convert_model_to_external_data(
model: ModelProto,
all_tensors_to_one_file: bool = True,
location: Optional[str] = None,
size_threshold: int = 1024,
convert_attribute: bool = False,
) -> None:
"""
Call to set all tensors with raw data as external data. This call should preceed 'save_model'.
'save_model' saves all the tensors data as external data after calling this function.
Arguments:
model (ModelProto): Model to be converted.
all_tensors_to_one_file (bool): 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: specify the external file that all tensors to save to.
If not specified, will use the model name.
size_threshold: 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 (bool): If true, convert all tensors to external data
If false, convert only non-attribute tensors to external data
"""
tensors = _get_initializer_tensors(model)
if convert_attribute:
tensors = _get_all_tensors(model)
if all_tensors_to_one_file:
file_name = str(uuid.uuid1())
if location:
file_name = location
for tensor in tensors:
if (
tensor.HasField("raw_data")
and sys.getsizeof(tensor.raw_data) >= size_threshold
):
set_external_data(tensor, file_name)
else:
for tensor in tensors:
if (
tensor.HasField("raw_data")
and sys.getsizeof(tensor.raw_data) >= size_threshold
):
tensor_location = tensor.name
if not _is_valid_filename(tensor_location):
tensor_location = str(uuid.uuid1())
set_external_data(tensor, tensor_location)
def convert_model_from_external_data(model: ModelProto) -> None:
"""
Call to set all tensors which use external data as embedded data.
save_model saves all the tensors data as embedded data after
calling this function.
Arguments:
model (ModelProto): Model to be converted.
"""
for tensor in _get_all_tensors(model):
if uses_external_data(tensor):
if not tensor.HasField("raw_data"):
raise ValueError("raw_data field doesn't exist.")
del tensor.external_data[:]
tensor.data_location = TensorProto.DEFAULT
def save_external_data(tensor: TensorProto, base_path: str) -> None:
"""
Writes tensor data to an external file according to information in the `external_data` field.
Arguments:
tensor (TensorProto): Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
"""
info = ExternalDataInfo(tensor)
external_data_file_path = os.path.join(base_path, info.location)
# Retrieve the tensor's data from raw_data or load external file
if not tensor.HasField("raw_data"):
raise ValueError("raw_data field doesn't exist.")
# Create file if it doesn't exist
if not os.path.isfile(external_data_file_path):
with open(external_data_file_path, "ab"):
pass
# Open file for reading and writing at random locations ('r+b')
with open(external_data_file_path, "r+b") as data_file:
data_file.seek(0, 2)
if info.offset is not None:
# Pad file to required offset if needed
file_size = data_file.tell()
if info.offset > file_size:
data_file.write(b"\0" * (info.offset - file_size))
data_file.seek(info.offset)
offset = data_file.tell()
data_file.write(tensor.raw_data)
set_external_data(tensor, info.location, offset, data_file.tell() - offset)
def _get_all_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
"""Scan an ONNX model for all tensors and return as an iterator."""
return chain(
_get_initializer_tensors(onnx_model_proto),
_get_attribute_tensors(onnx_model_proto),
)
def _recursive_attribute_processor(
attribute: AttributeProto, func: Callable[[GraphProto], Iterable[TensorProto]]
) -> Iterable[TensorProto]:
"""Create an iterator through processing ONNX model attributes with functor."""
if attribute.type == AttributeProto.GRAPH:
yield from func(attribute.g)
if attribute.type == AttributeProto.GRAPHS:
for graph in attribute.graphs:
yield from func(graph)
def _get_initializer_tensors_from_graph(
onnx_model_proto_graph: GraphProto,
) -> Iterable[TensorProto]:
"""Create an iterator of initializer tensors from ONNX model graph."""
yield from onnx_model_proto_graph.initializer
for node in onnx_model_proto_graph.node:
for attribute in node.attribute:
yield from _recursive_attribute_processor(
attribute, _get_initializer_tensors_from_graph
)
def _get_initializer_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
"""Create an iterator of initializer tensors from ONNX model."""
yield from _get_initializer_tensors_from_graph(onnx_model_proto.graph)
def _get_attribute_tensors_from_graph(
onnx_model_proto_graph: GraphProto,
) -> Iterable[TensorProto]:
"""Create an iterator of tensors from node attributes of an ONNX model graph."""
for node in onnx_model_proto_graph.node:
for attribute in node.attribute:
if attribute.HasField("t"):
yield attribute.t
yield from attribute.tensors
yield from _recursive_attribute_processor(
attribute, _get_attribute_tensors_from_graph
)
def _get_attribute_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
"""Create an iterator of tensors from node attributes of an ONNX model."""
yield from _get_attribute_tensors_from_graph(onnx_model_proto.graph)
def _sanitize_path(path: str) -> str:
"""Remove path components which would allow traversing up a directory tree from a base path.
Note: This method is currently very basic and should be expanded.
"""
return path.lstrip("/.")
def _is_valid_filename(filename: str) -> bool:
"""Utility to check whether the provided filename is valid."""
exp = re.compile('^[^<>:;,?"*|/]+$')
match = exp.match(filename)
return bool(match)
def uses_external_data(tensor: TensorProto) -> bool:
"""Returns true if the tensor stores data in an external location."""
return (
tensor.HasField("data_location")
and tensor.data_location == TensorProto.EXTERNAL
)
def remove_external_data_field(tensor: TensorProto, field_key: str) -> None:
"""
Removes a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
Arguments:
tensor (TensorProto): Tensor object from which value will be removed
field_key (string): The key of the field to be removed
"""
for i, field in enumerate(tensor.external_data):
if field.key == field_key:
del tensor.external_data[i]
def write_external_data_tensors(model: ModelProto, filepath: str) -> ModelProto:
"""
Serializes data for all the tensors which have data location set to TensorProto.External.
Note: This function also strips basepath information from all tensors' external_data fields.
Arguments:
model (ModelProto): Model object which is the source of tensors to serialize.
filepath: System path to the directory which should be treated as base path for external data.
Returns:
ModelProto: The modified model object.
"""
for tensor in _get_all_tensors(model):
# Writing to external data happens in 2 passes:
# 1. Tensors with raw data which pass the necessary conditions (size threshold etc) are marked for serialization
# 2. The raw data in these tensors is serialized to a file
# Thus serialize only if tensor has raw data and it was marked for serialization
if uses_external_data(tensor) and tensor.HasField("raw_data"):
save_external_data(tensor, filepath)
tensor.ClearField("raw_data")
return model
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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"]} |
58,904 | onnx/onnx | refs/heads/main | /onnx/test/test_backend_onnxruntime.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import os
import platform
import sys
import unittest
from typing import Any, ClassVar
import numpy
from packaging.version import Version
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
try:
from onnxruntime import InferenceSession
from onnxruntime import __version__ as ort_version
from onnxruntime import get_available_providers
from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
except ImportError:
# onnxruntime is not installed, all tests are skipped.
InferenceSession = None
ort_version = None
def get_available_providers():
return []
# The following just executes a backend based on InferenceSession through the backend test
class InferenceSessionBackendRep(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):
input_names = [i.name for i in self._session.get_inputs()]
input_shapes = [i.shape for i in self._session.get_inputs()]
if len(inputs) == len(input_names):
feeds = dict(zip(input_names, inputs))
else:
feeds = {}
pos_inputs = 0
for inp, shape in zip(input_names, input_shapes):
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 InferenceSessionBackend(onnx.backend.base.Backend):
providers: ClassVar[set[str]] = set(get_available_providers())
@classmethod
def is_opset_supported(cls, model): # pylint: disable=unused-argument
return True, ""
@classmethod
def supports_device(cls, device: str) -> bool:
d = Device(device)
if d.type == DeviceType.CPU and "CPUExecutionProvider" in cls.providers:
return True
if d.type == DeviceType.CUDA and "CUDAExecutionProvider" in cls.providers:
return True
return False
@classmethod
def convert_version_opset_before(cls, model):
opsets = {d.domain: d.version for d in model.opset_import}
if "" not in opsets:
return None
try:
return onnx.version_converter.convert_version(model, opsets[""] - 1)
except RuntimeError:
# Let's try without any change.
del model.opset_import[:]
for k, v in opsets.items():
d = model.opset_import.add()
d.domain = k
d.version = v if k != "" else v - 1
return model
@classmethod
def create_inference_session(cls, model, device):
if device == "CPU":
providers = ["CPUExecutionProvider"]
elif device == "CUDA":
providers = ["CUDAExecutionProvider"]
else:
raise ValueError(f"Unexepcted device {device!r}.")
try:
return InferenceSession(model.SerializeToString(), providers=providers)
except InvalidArgument as e:
if "Unsupported model IR version" in str(e):
model.ir_version -= 1
return cls.create_inference_session(model, device)
if "Current official support for domain ai.onnx is till opset" in str(e):
new_model = cls.convert_version_opset_before(model)
if new_model is not None:
return cls.create_inference_session(new_model, device)
raise e
@classmethod
def prepare(
cls, model: Any, device: str = "CPU", **kwargs: Any
) -> InferenceSessionBackendRep:
# if isinstance(model, InferenceSessionBackendRep):
# return model
if isinstance(model, InferenceSession):
return InferenceSessionBackendRep(model)
if isinstance(model, (str, bytes, ModelProto)):
inf = cls.create_inference_session(model, device)
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(InferenceSessionBackend, __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 cannot pass because they consists in generating random number.
backend_test.exclude("(test_bernoulli)")
# The following tests are not supported by onnxruntime.
backend_test.exclude(
"("
"test_adagrad"
"|test_adam"
"|test_add_uint8"
"|bitshift_left_uint16"
"|bitshift_right_uint16"
"|cast_BFLOAT16_to_FLOAT"
"|cast_FLOAT_to_BFLOAT16"
"|castlike_BFLOAT16_to_FLOAT"
"|castlike_FLOAT_to_BFLOAT16"
"|clip_default_int8_min_expanded"
"|clip_default_int8_max_expanded"
"|div_uint8"
"|gru_batchwise" # Batchwise recurrent operations (layout == 1) are not supported.
"|loop16_seq_none" # The graph is missing type information needed to construct the ORT tensor.
"|lstm_batchwise" # Batchwise recurrent operations (layout == 1) are not supported.
"|m(in|ax)_u?int(16|8)"
"|momentum"
"|mul_uint8"
"|pow_types_float32_uint32"
"|pow_types_float32_uint64"
"|simple_rnn_batchwise" # Batchwise recurrent operations (layout == 1) are not supported.
"|sub_uint8"
"|gradient_of_add"
"|test_batchnorm_epsilon_training_mode" # Training mode does not support BN opset 14 (or higher) yet.
"|test_batchnorm_example_training_mode" # Training mode does not support BN opset 14 (or higher) yet.
"|_to_FLOAT8E4M3FN" # No corresponding Numpy type for Tensor Type.
"|_to_FLOAT8E5M2" # No corresponding Numpy type for Tensor Type.
"|cast_FLOAT8E" # No corresponding Numpy type for Tensor Type.
"|castlike_FLOAT8E" # No corresponding Numpy type for Tensor Type.
"|test_dequantizelinear_axis" # y_scale must be a scalar or 1D tensor of size 1.
"|test_dequantizelinear" # No corresponding Numpy type for Tensor Type.
"|test_quantizelinear_axis" # y_scale must be a scalar or 1D tensor of size 1.
"|test_quantizelinear" # No corresponding Numpy type for Tensor Type.
"|test_affine_grid_" # new IR version 9 and opset version 20 not supported yet.
")"
)
# The following tests fail due to small discrepancies.
backend_test.exclude("(cast_FLOAT_to_STRING|castlike_FLOAT_to_STRING|dft|stft)")
# The following tests fail due to huge discrepancies.
backend_test.exclude(
"("
"resize_downsample_scales_cubic_align_corners"
"|resize_downsample_scales_linear_align_corners"
"|training_dropout"
")"
)
# The following tests fail for no obvious reason.
backend_test.exclude(
"("
"maxunpool_export_with_output_shape" # not the same expected output
"|softplus_example_expanded" # Could not find an implementation for Exp(1) node with name ''
"|softplus_expanded" # Could not find an implementation for Exp(1) node with name ''
"|AvgPool[1-3]d" # Could not find an implementation for AveragePool(1) node with name ''
"|BatchNorm1d_3d_input_eval" # Could not find an implementation for BatchNormalization(6) node with name ''
"|BatchNorm[2-3]d_eval" # Could not find an implementation for BatchNormalization(6) node with name ''
"|GLU" # Could not find an implementation for Mul(6) node with name ''
"|Linear" # Could not find an implementation for Gemm(6) node with name ''
"|PReLU" # Could not find an implementation for PRelu(6) node with name ''
"|PoissonNLL" # Could not find an implementation for Mul(6) node with name ''
"|Softsign" # Could not find an implementation for Gemm(6) node with name ''
"|operator_add_broadcast" # Could not find an implementation for Gemm(6) node with name ''
"|operator_add_size1" # Could not find an implementation for Gemm(6) node with name ''
"|operator_addconstant" # Could not find an implementation for Gemm(6) node with name ''
"|operator_addmm" # Could not find an implementation for Gemm(6) node with name ''
"|operator_basic" # Could not find an implementation for Add(6) node with name ''
"|operator_mm" # Could not find an implementation for Gemm(6) node with name ''
"|operator_non_float_params" # Could not find an implementation for Add(6) node with name ''
"|operator_params" # Could not find an implementation for Add(6) node with name ''
"|operator_pow" # Could not find an implementation for Pow(1) node with name ''
")"
)
# The following tests are new with opset 19 and 20.
if ort_version is not None and Version(ort_version) < Version("1.16"):
# version should be 1.15 but there is no development version number.
backend_test.exclude(
"("
"averagepool"
"|deform_conv"
"|optional_get_element_optional_sequence"
"|identity_opt"
"|half_pixel_symmetric"
"|_pad_"
"|_resize_"
"|_size_"
"|equal_string"
"|equal_string_broadcast"
"|gridsample"
"|cast"
"|castlike"
"|equal"
"|identity"
"|reshape"
"|regex_full_match"
"|string_split"
"|string_concat"
"|gelu"
"|image_decoder"
")"
)
if sys.version_info[:2] < (3, 8) or Version(numpy.__version__) < Version("1.23.5"):
# Version 1.21.5 causes segmentation faults.
# onnxruntime should be tested with the same numpy API
# onnxruntime was compiled with.
backend_test.exclude("")
# import all test cases at global scope to make them visible to python.unittest
if InferenceSession is not None:
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}"
)
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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"]} |
58,905 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/flatten.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 Flatten(Base):
@staticmethod
def export() -> None:
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(len(shape)):
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"],
axis=i,
)
new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i))
@staticmethod
def export_flatten_with_default_axis() -> None:
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"], # Default value for axis: axis=1
)
shape = (5, 4, 3, 2)
a = np.random.random_sample(shape).astype(np.float32)
new_shape = (5, 24)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis")
@staticmethod
def export_flatten_negative_axis() -> None:
shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)
for i in range(-len(shape), 0):
node = onnx.helper.make_node(
"Flatten",
inputs=["a"],
outputs=["b"],
axis=i,
)
new_shape = (np.prod(shape[0:i]).astype(int), -1)
b = np.reshape(a, new_shape)
expect(
node,
inputs=[a],
outputs=[b],
name="test_flatten_negative_axis" + str(abs(i)),
)
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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": 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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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58,906 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reducemean.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 ReduceMean(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(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[12.5, 1.5]
# [35., 1.5]
# [57.5, 1.5]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_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(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[12.5, 1.5]]
# [[35., 1.5]]
# [[57.5, 1.5]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_keepdims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[18.25]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_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(
"ReduceMean",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[12.5, 1.5]]
# [[35., 1.5]]
# [[57.5, 1.5]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.mean(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_mean_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/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"]} |
58,907 | onnx/onnx | refs/heads/main | /onnx/test/test_external_data.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import os
import pathlib
import tempfile
import unittest
import uuid
from typing import Any
import numpy as np
import parameterized
import onnx
from onnx import ModelProto, TensorProto, checker, helper, shape_inference
from onnx.external_data_helper import (
convert_model_from_external_data,
convert_model_to_external_data,
load_external_data_for_model,
load_external_data_for_tensor,
set_external_data,
)
from onnx.numpy_helper import from_array, to_array
class TestLoadExternalDataBase(unittest.TestCase):
"""Base class for testing external data related behaviors.
Subclasses should be parameterized with a serialization format.
"""
serialization_format: str = "protobuf"
def setUp(self) -> None:
self._temp_dir_obj = (
tempfile.TemporaryDirectory() # pylint: disable=consider-using-with
)
self.temp_dir: str = self._temp_dir_obj.name
self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
self.model_filename = self.create_test_model()
def tearDown(self) -> None:
self._temp_dir_obj.cleanup()
def get_temp_model_filename(self) -> str:
return os.path.join(self.temp_dir, str(uuid.uuid4()) + ".onnx")
def create_external_data_tensor(
self, value: list[Any], tensor_name: str, location: str = ""
) -> TensorProto:
tensor = from_array(np.array(value))
tensor.name = tensor_name
tensor_filename = location or f"{tensor_name}.bin"
set_external_data(tensor, location=tensor_filename)
with open(os.path.join(self.temp_dir, tensor_filename), "wb") as data_file:
data_file.write(tensor.raw_data)
tensor.ClearField("raw_data")
tensor.data_location = onnx.TensorProto.EXTERNAL
return tensor
def create_test_model(self, location: str = "") -> str:
constant_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["values"],
value=self.create_external_data_tensor(
self.attribute_value, "attribute_value" # type: ignore[arg-type]
),
)
initializers = [
self.create_external_data_tensor(
self.initializer_value, "input_value", location # type: ignore[arg-type]
)
]
inputs = [
helper.make_tensor_value_info(
"input_value", onnx.TensorProto.FLOAT, self.initializer_value.shape
)
]
graph = helper.make_graph(
[constant_node],
"test_graph",
inputs=inputs,
outputs=[],
initializer=initializers,
)
model = helper.make_model(graph)
model_filename = os.path.join(self.temp_dir, "model.onnx")
onnx.save_model(model, model_filename, self.serialization_format)
return model_filename
def test_check_model(self) -> None:
if self.serialization_format != "protobuf":
self.skipTest(
"check_model supports protobuf only as binary when provided as a path"
)
checker.check_model(self.model_filename)
@parameterized.parameterized_class(
[
{"serialization_format": "protobuf"},
{"serialization_format": "textproto"},
]
)
class TestLoadExternalData(TestLoadExternalDataBase):
def test_load_external_data(self) -> None:
model = onnx.load_model(self.model_filename, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_load_external_data_for_model(self) -> None:
model = onnx.load_model(
self.model_filename, self.serialization_format, load_external_data=False
)
load_external_data_for_model(model, self.temp_dir)
initializer_tensor = model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_save_external_data(self) -> None:
model = onnx.load_model(self.model_filename, self.serialization_format)
temp_dir = os.path.join(self.temp_dir, "save_copy")
os.mkdir(temp_dir)
new_model_filename = os.path.join(temp_dir, "model.onnx")
onnx.save_model(model, new_model_filename, self.serialization_format)
new_model = onnx.load_model(new_model_filename, self.serialization_format)
initializer_tensor = new_model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = new_model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
@parameterized.parameterized_class(
[
{"serialization_format": "protobuf"},
{"serialization_format": "textproto"},
]
)
class TestLoadExternalDataSingleFile(TestLoadExternalDataBase):
def create_external_data_tensors(
self, tensors_data: list[tuple[list[Any], Any]]
) -> list[TensorProto]:
tensor_filename = "tensors.bin"
tensors = []
with open(os.path.join(self.temp_dir, tensor_filename), "ab") as data_file:
for value, tensor_name in tensors_data:
tensor = from_array(np.array(value))
offset = data_file.tell()
if offset % 4096 != 0:
data_file.write(b"\0" * (4096 - offset % 4096))
offset = offset + 4096 - offset % 4096
data_file.write(tensor.raw_data)
set_external_data(
tensor,
location=tensor_filename,
offset=offset,
length=data_file.tell() - offset,
)
tensor.name = tensor_name
tensor.ClearField("raw_data")
tensor.data_location = onnx.TensorProto.EXTERNAL
tensors.append(tensor)
return tensors
def test_load_external_single_file_data(self) -> None:
model = onnx.load_model(self.model_filename, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_save_external_single_file_data(self) -> None:
model = onnx.load_model(self.model_filename, self.serialization_format)
temp_dir = os.path.join(self.temp_dir, "save_copy")
os.mkdir(temp_dir)
new_model_filename = os.path.join(temp_dir, "model.onnx")
onnx.save_model(model, new_model_filename, self.serialization_format)
new_model = onnx.load_model(new_model_filename, self.serialization_format)
initializer_tensor = new_model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = new_model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
@parameterized.parameterized_class(
[
{"serialization_format": "protobuf"},
{"serialization_format": "textproto"},
]
)
class TestSaveAllTensorsAsExternalData(unittest.TestCase):
serialization_format: str = "protobuf"
def setUp(self) -> None:
self._temp_dir_obj = (
tempfile.TemporaryDirectory() # pylint: disable=consider-using-with
)
self.temp_dir: str = self._temp_dir_obj.name
self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
self.model = self.create_test_model_proto()
def get_temp_model_filename(self):
return os.path.join(self.temp_dir, str(uuid.uuid4()) + ".onnx")
def create_data_tensors(
self, tensors_data: list[tuple[list[Any], Any]]
) -> list[TensorProto]:
tensors = []
for value, tensor_name in tensors_data:
tensor = from_array(np.array(value))
tensor.name = tensor_name
tensors.append(tensor)
return tensors
def create_test_model_proto(self) -> ModelProto:
tensors = self.create_data_tensors(
[
(self.attribute_value, "attribute_value"), # type: ignore[list-item]
(self.initializer_value, "input_value"), # type: ignore[list-item]
]
)
constant_node = onnx.helper.make_node(
"Constant", inputs=[], outputs=["values"], value=tensors[0]
)
inputs = [
helper.make_tensor_value_info(
"input_value", onnx.TensorProto.FLOAT, self.initializer_value.shape
)
]
graph = helper.make_graph(
[constant_node],
"test_graph",
inputs=inputs,
outputs=[],
initializer=[tensors[1]],
)
return helper.make_model(graph)
@unittest.skipIf(
serialization_format != "protobuf",
"check_model supports protobuf only when provided as a path",
)
def test_check_model(self) -> None:
checker.check_model(self.model)
def test_convert_model_to_external_data_with_size_threshold(self) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(self.model, size_threshold=1024)
onnx.save_model(self.model, model_file_path, self.serialization_format)
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertFalse(initializer_tensor.HasField("data_location"))
def test_convert_model_to_external_data_without_size_threshold(self) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(self.model, size_threshold=0)
onnx.save_model(self.model, model_file_path, self.serialization_format)
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertTrue(initializer_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
def test_convert_model_to_external_data_from_one_file_with_location(self) -> None:
model_file_path = self.get_temp_model_filename()
external_data_file = str(uuid.uuid4())
convert_model_to_external_data(
self.model,
size_threshold=0,
all_tensors_to_one_file=True,
location=external_data_file,
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, external_data_file)))
model = onnx.load_model(model_file_path, self.serialization_format)
# test convert model from external data
convert_model_from_external_data(model)
model_file_path = self.get_temp_model_filename()
onnx.save_model(model, model_file_path, self.serialization_format)
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertFalse(len(initializer_tensor.external_data))
self.assertEqual(initializer_tensor.data_location, TensorProto.DEFAULT)
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
self.assertFalse(len(attribute_tensor.external_data))
self.assertEqual(attribute_tensor.data_location, TensorProto.DEFAULT)
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_convert_model_to_external_data_from_one_file_without_location_uses_model_name(
self,
) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(
self.model, size_threshold=0, all_tensors_to_one_file=True
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
self.assertTrue(os.path.isfile(model_file_path))
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, model_file_path)))
def test_convert_model_to_external_data_one_file_per_tensor_without_attribute(
self,
) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(
self.model,
size_threshold=0,
all_tensors_to_one_file=False,
convert_attribute=False,
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
self.assertTrue(os.path.isfile(model_file_path))
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
def test_convert_model_to_external_data_one_file_per_tensor_with_attribute(
self,
) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(
self.model,
size_threshold=0,
all_tensors_to_one_file=False,
convert_attribute=True,
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
self.assertTrue(os.path.isfile(model_file_path))
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
def test_convert_model_to_external_data_does_not_convert_attribute_values(
self,
) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(
self.model,
size_threshold=0,
convert_attribute=False,
all_tensors_to_one_file=False,
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertTrue(initializer_tensor.HasField("data_location"))
attribute_tensor = model.graph.node[0].attribute[0].t
self.assertFalse(attribute_tensor.HasField("data_location"))
def test_convert_model_to_external_data_converts_attribute_values(self) -> None:
model_file_path = self.get_temp_model_filename()
convert_model_to_external_data(
self.model, size_threshold=0, convert_attribute=True
)
onnx.save_model(self.model, model_file_path, self.serialization_format)
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
self.assertTrue(initializer_tensor.HasField("data_location"))
attribute_tensor = model.graph.node[0].attribute[0].t
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
self.assertTrue(attribute_tensor.HasField("data_location"))
def test_save_model_does_not_convert_to_external_data_and_saves_the_model(
self,
) -> None:
model_file_path = self.get_temp_model_filename()
onnx.save_model(
self.model,
model_file_path,
self.serialization_format,
save_as_external_data=False,
)
self.assertTrue(os.path.isfile(model_file_path))
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertFalse(initializer_tensor.HasField("data_location"))
attribute_tensor = model.graph.node[0].attribute[0].t
self.assertFalse(attribute_tensor.HasField("data_location"))
def test_save_model_does_convert_and_saves_the_model(self) -> None:
model_file_path = self.get_temp_model_filename()
onnx.save_model(
self.model,
model_file_path,
self.serialization_format,
save_as_external_data=True,
all_tensors_to_one_file=True,
location=None,
size_threshold=0,
convert_attribute=False,
)
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertTrue(initializer_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
self.assertFalse(attribute_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_save_model_without_loading_external_data(self) -> None:
model_file_path = self.get_temp_model_filename()
onnx.save_model(
self.model,
model_file_path,
self.serialization_format,
save_as_external_data=True,
location=None,
size_threshold=0,
convert_attribute=False,
)
# Save without load_external_data
model = onnx.load_model(
model_file_path, self.serialization_format, load_external_data=False
)
onnx.save_model(
model,
model_file_path,
self.serialization_format,
save_as_external_data=True,
location=None,
size_threshold=0,
convert_attribute=False,
)
# Load the saved model again; Only works if the saved path is under the same directory
model = onnx.load_model(model_file_path, self.serialization_format)
initializer_tensor = model.graph.initializer[0]
self.assertTrue(initializer_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
attribute_tensor = model.graph.node[0].attribute[0].t
self.assertFalse(attribute_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
def test_save_model_with_existing_raw_data_should_override(self) -> None:
model_file_path = self.get_temp_model_filename()
original_raw_data = self.model.graph.initializer[0].raw_data
onnx.save_model(
self.model,
model_file_path,
self.serialization_format,
save_as_external_data=True,
size_threshold=0,
)
self.assertTrue(os.path.isfile(model_file_path))
model = onnx.load_model(
model_file_path, self.serialization_format, load_external_data=False
)
initializer_tensor = model.graph.initializer[0]
initializer_tensor.raw_data = b"dummpy_raw_data"
# If raw_data and external tensor exist at the same time, override existing raw_data
load_external_data_for_tensor(initializer_tensor, self.temp_dir)
self.assertEqual(initializer_tensor.raw_data, original_raw_data)
@parameterized.parameterized_class(
[
{"serialization_format": "protobuf"},
{"serialization_format": "textproto"},
]
)
class TestExternalDataToArray(unittest.TestCase):
serialization_format: str = "protobuf"
def setUp(self) -> None:
self._temp_dir_obj = (
tempfile.TemporaryDirectory() # pylint: disable=consider-using-with
)
self.temp_dir: str = self._temp_dir_obj.name
self._model_file_path: str = os.path.join(self.temp_dir, "model.onnx")
self.large_data = np.random.rand(10, 60, 100).astype(np.float32)
self.small_data = (200, 300)
self.model = self.create_test_model()
@property
def model_file_path(self):
return self._model_file_path
def tearDown(self) -> None:
self._temp_dir_obj.cleanup()
def create_test_model(self) -> ModelProto:
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, self.large_data.shape)
input_init = helper.make_tensor(
name="X",
data_type=TensorProto.FLOAT,
dims=self.large_data.shape,
vals=self.large_data.tobytes(),
raw=True,
)
shape_data = np.array(self.small_data, np.int64)
shape_init = helper.make_tensor(
name="Shape",
data_type=TensorProto.INT64,
dims=shape_data.shape,
vals=shape_data.tobytes(),
raw=True,
)
C = helper.make_tensor_value_info("C", TensorProto.INT64, self.small_data)
reshape = onnx.helper.make_node(
"Reshape",
inputs=["X", "Shape"],
outputs=["Y"],
)
cast = onnx.helper.make_node(
"Cast", inputs=["Y"], outputs=["C"], to=TensorProto.INT64
)
graph_def = helper.make_graph(
[reshape, cast],
"test-model",
[X],
[C],
initializer=[input_init, shape_init],
)
model = helper.make_model(graph_def, producer_name="onnx-example")
return model
@unittest.skipIf(
serialization_format != "protobuf",
"check_model supports protobuf only when provided as a path",
)
def test_check_model(self) -> None:
checker.check_model(self.model)
def test_reshape_inference_with_external_data_fail(self) -> None:
onnx.save_model(
self.model,
self.model_file_path,
self.serialization_format,
save_as_external_data=True,
all_tensors_to_one_file=False,
size_threshold=0,
)
model_without_external_data = onnx.load(
self.model_file_path, self.serialization_format, load_external_data=False
)
# Shape inference of Reshape uses ParseData
# ParseData cannot handle external data and should throw the error as follows:
# Cannot parse data from external tensors. Please load external data into raw data for tensor: Shape
self.assertRaises(
shape_inference.InferenceError,
shape_inference.infer_shapes,
model_without_external_data,
strict_mode=True,
)
def test_to_array_with_external_data(self) -> None:
onnx.save_model(
self.model,
self.model_file_path,
self.serialization_format,
save_as_external_data=True,
all_tensors_to_one_file=False,
size_threshold=0,
)
# raw_data of external tensor is not loaded
model = onnx.load(
self.model_file_path, self.serialization_format, load_external_data=False
)
# Specify self.temp_dir to load external tensor
loaded_large_data = to_array(model.graph.initializer[0], self.temp_dir)
np.testing.assert_allclose(loaded_large_data, self.large_data)
def test_save_model_with_external_data_multiple_times(self) -> None:
# Test onnx.save should respectively handle typical tensor and external tensor properly
# 1st save: save two tensors which have raw_data
# Only w_large will be stored as external tensors since it's larger than 1024
onnx.save_model(
self.model,
self.model_file_path,
self.serialization_format,
save_as_external_data=True,
all_tensors_to_one_file=False,
location=None,
size_threshold=1024,
convert_attribute=True,
)
model_without_loading_external = onnx.load(
self.model_file_path, self.serialization_format, load_external_data=False
)
large_input_tensor = model_without_loading_external.graph.initializer[0]
self.assertTrue(large_input_tensor.HasField("data_location"))
np.testing.assert_allclose(
to_array(large_input_tensor, self.temp_dir), self.large_data
)
small_shape_tensor = model_without_loading_external.graph.initializer[1]
self.assertTrue(not small_shape_tensor.HasField("data_location"))
np.testing.assert_allclose(to_array(small_shape_tensor), self.small_data)
# 2nd save: one tensor has raw_data (small); one external tensor (large)
# Save them both as external tensors this time
onnx.save_model(
model_without_loading_external,
self.model_file_path,
self.serialization_format,
save_as_external_data=True,
all_tensors_to_one_file=False,
location=None,
size_threshold=0,
convert_attribute=True,
)
model_without_loading_external = onnx.load(
self.model_file_path, self.serialization_format, load_external_data=False
)
large_input_tensor = model_without_loading_external.graph.initializer[0]
self.assertTrue(large_input_tensor.HasField("data_location"))
np.testing.assert_allclose(
to_array(large_input_tensor, self.temp_dir), self.large_data
)
small_shape_tensor = model_without_loading_external.graph.initializer[1]
self.assertTrue(small_shape_tensor.HasField("data_location"))
np.testing.assert_allclose(
to_array(small_shape_tensor, self.temp_dir), self.small_data
)
class TestNotAllowToLoadExternalDataOutsideModelDirectory(TestLoadExternalDataBase):
"""Essential test to check that onnx (validate) C++ code will not allow to load external_data outside the model
directory."""
def create_external_data_tensor(
self, value: list[Any], tensor_name: str, location: str = ""
) -> TensorProto:
tensor = from_array(np.array(value))
tensor.name = tensor_name
tensor_filename = location or f"{tensor_name}.bin"
set_external_data(tensor, location=tensor_filename)
tensor.ClearField("raw_data")
tensor.data_location = onnx.TensorProto.EXTERNAL
return tensor
def test_check_model(self) -> None:
"""We only test the model validation as onnxruntime uses this to load the model."""
self.model_filename = self.create_test_model("../../file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
def test_check_model_relative(self) -> None:
"""More relative path test."""
self.model_filename = self.create_test_model("../test/../file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
def test_check_model_absolute(self) -> None:
"""ONNX checker disallows using absolute path as location in external tensor."""
self.model_filename = self.create_test_model("//file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
@unittest.skipIf(os.name != "nt", reason="Skip Windows test")
class TestNotAllowToLoadExternalDataOutsideModelDirectoryOnWindows(
TestNotAllowToLoadExternalDataOutsideModelDirectory
):
"""Essential test to check that onnx (validate) C++ code will not allow to load external_data outside the model
directory."""
def test_check_model(self) -> None:
"""We only test the model validation as onnxruntime uses this to load the model."""
self.model_filename = self.create_test_model("..\\..\\file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
def test_check_model_relative(self) -> None:
"""More relative path test."""
self.model_filename = self.create_test_model("..\\test\\..\\file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
def test_check_model_absolute(self) -> None:
"""ONNX checker disallows using absolute path as location in external tensor."""
self.model_filename = self.create_test_model("C:/file.bin")
with self.assertRaises(onnx.checker.ValidationError):
checker.check_model(self.model_filename)
class TestSaveAllTensorsAsExternalDataWithPath(TestSaveAllTensorsAsExternalData):
def get_temp_model_filename(self) -> pathlib.Path:
return pathlib.Path(super().get_temp_model_filename())
class TestExternalDataToArrayWithPath(TestExternalDataToArray):
@property
def model_file_path(self) -> pathlib.Path:
return pathlib.Path(self._model_file_path)
if __name__ == "__main__":
unittest.main()
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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"]} |
58,908 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/__init__.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import sys
from typing import List, Optional, Sequence
import numpy as np
from onnx import ModelProto
from onnx.backend.test.case.test_case import TestCase
from onnx.backend.test.case.utils import import_recursive
_SimpleModelTestCases = []
def expect(
model: ModelProto,
inputs: Sequence[np.ndarray],
outputs: Sequence[np.ndarray],
name: Optional[str] = None,
) -> None:
name = name or model.graph.name
_SimpleModelTestCases.append(
TestCase(
name=name,
model_name=model.graph.name,
url=None,
model_dir=None,
model=model,
data_sets=[(inputs, outputs)],
kind="simple",
rtol=1e-3,
atol=1e-7,
)
)
# BASE_URL = "https://download.onnxruntime.ai/onnx/models"
BASE_URL = "onnx/backend/test/data/light/light_%s.onnx"
def collect_testcases() -> List[TestCase]:
"""Collect model test cases defined in python/numpy code."""
real_model_testcases = []
model_tests = [
("test_bvlc_alexnet", "bvlc_alexnet", 1e-3, 1e-7),
("test_densenet121", "densenet121", 2e-3, 1e-7),
("test_inception_v1", "inception_v1", 1e-3, 1e-7),
("test_inception_v2", "inception_v2", 1e-3, 1e-7),
("test_resnet50", "resnet50", 1e-3, 1e-7),
("test_shufflenet", "shufflenet", 1e-3, 1e-7),
("test_squeezenet", "squeezenet", 1e-3, 1e-7),
("test_vgg19", "vgg19", 1e-3, 1e-7),
("test_zfnet512", "zfnet512", 1e-3, 1e-7),
]
for test_name, model_name, rtol, atol in model_tests:
url = BASE_URL % model_name
real_model_testcases.append(
TestCase(
name=test_name,
model_name=model_name,
url=url,
model_dir=None,
model=None,
data_sets=None,
kind="real",
rtol=rtol,
atol=atol,
)
)
import_recursive(sys.modules[__name__])
return real_model_testcases + _SimpleModelTestCases
| {"/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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58,909 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/eyelike.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 EyeLike(Base):
@staticmethod
def export_without_dtype() -> None:
shape = (4, 4)
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.int32)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype")
@staticmethod
def export_with_dtype() -> None:
shape = (3, 4)
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
dtype=onnx.TensorProto.DOUBLE,
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], dtype=np.float64)
expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype")
@staticmethod
def export_populate_off_main_diagonal() -> None:
shape = (4, 5)
off_diagonal_offset = 1
node = onnx.helper.make_node(
"EyeLike",
inputs=["x"],
outputs=["y"],
k=off_diagonal_offset,
dtype=onnx.TensorProto.FLOAT,
)
x = np.random.randint(0, 100, size=shape, dtype=np.int32)
y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_eyelike_populate_off_main_diagonal",
)
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58,910 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_tile.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 Tile(OpRun):
def _run(self, x, repeats): # type: ignore
return (np.tile(x, repeats),)
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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"]} |
58,911 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_transpose.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 Transpose(OpRun):
def _run(self, data, perm=None): # type: ignore
perm_ = None if (perm is None or len(perm) == 0) else perm
if perm_ is None:
return (np.transpose(data),)
if len(perm_) != len(data.shape):
raise RuntimeError(
f"Inconsistent permutation {perm_!r} with shape {data.shape!r}."
)
return (np.transpose(data, axes=perm_),)
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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"]} |
58,912 | onnx/onnx | refs/heads/main | /onnx/test/compose_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Callable, List, Optional, Sequence, Tuple
import numpy as np
from onnx import (
FunctionProto,
GraphProto,
ModelProto,
NodeProto,
SparseTensorProto,
TensorProto,
ValueInfoProto,
checker,
compose,
helper,
parser,
version_converter,
)
def _load_model(m_def: str) -> ModelProto:
"""
Parses a model from a string representation, including checking the model for correctness
"""
m = parser.parse_model(m_def)
checker.check_model(m)
return m
def _prefixed(prefix: str, s: str) -> str:
"""
Prefixes a string (if not empty)
"""
return prefix + s if len(s) > 0 else s
def _get_shape(value_info: ValueInfoProto) -> List[int]:
"""
Returns a list of integers representing the shape of the provided ValueInfoProto
"""
return [
value_info.type.tensor_type.shape.dim[d].dim_value
for d in range(len(value_info.type.tensor_type.shape.dim))
]
def _make_sparse_tensor(name: str) -> SparseTensorProto:
dense_shape = [3, 3]
linear_indices = [2, 3, 5]
sparse_values = [1.7, 0.4, 0.9]
values_tensor = helper.make_tensor(
name=name + "_values",
data_type=TensorProto.FLOAT,
dims=[len(sparse_values)],
vals=np.array(sparse_values).astype(np.float32),
raw=False,
)
indices_tensor = helper.make_tensor(
name=name + "_idx",
data_type=TensorProto.INT64,
dims=[len(linear_indices)],
vals=np.array(linear_indices).astype(np.int64),
raw=False,
)
return helper.make_sparse_tensor(values_tensor, indices_tensor, dense_shape)
M1_DEF = """
<
ir_version: 7,
opset_import: [ "": 10, "com.microsoft": 1]
>
agraph (float[N, M] A0, float[N, M] A1, float[N, M] _A) => (float[N, M] B00, float[N, M] B10, float[N, M] B20)
{
B00 = Add(A0, A1)
B10 = Sub(A0, A1)
B20 = Mul(A0, A1)
}
"""
M2_DEF = """
<
ir_version: 7,
opset_import: [ "": 10, "com.microsoft": 1]
>
agraph (float[N, M] B01, float[N, M] B11, float[N, M] B21) => (float[N, M] D0)
{
C0 = Add(B01, B11)
C1 = Sub(B11, B21)
M1 = Mul(C0, C1)
}
"""
class TestComposeFunctions(unittest.TestCase):
def _test_merge_models(
self,
m1def: str,
m2def: str,
io_map: List[Tuple[str, str]],
check_expectations: Callable[[GraphProto, GraphProto, GraphProto], None],
inputs: Optional[List[str]] = None,
outputs: Optional[List[str]] = None,
prefix1: Optional[str] = None,
prefix2: Optional[str] = None,
) -> None:
m1, m2 = _load_model(m1def), _load_model(m2def)
g3 = compose.merge_graphs(
m1.graph,
m2.graph,
io_map=io_map,
inputs=inputs,
outputs=outputs,
prefix1=prefix1,
prefix2=prefix2,
)
checker.check_graph(g3)
check_expectations(m1.graph, m2.graph, g3)
m3 = compose.merge_models(
m1,
m2,
io_map=io_map,
inputs=inputs,
outputs=outputs,
prefix1=prefix1,
prefix2=prefix2,
)
checker.check_model(m3)
check_expectations(m1.graph, m2.graph, m3.graph)
def test_case_connect_all_no_name_collision(self) -> None:
"""
Tests a simple scenario where two models without overlapping names are merged by
connecting all the outputs in the first models to all the inputs in the second model
"""
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
self.assertEqual(g3.input, g1.input)
self.assertEqual(g3.output, g2.output)
self.assertEqual(
["Add", "Sub", "Mul", "Add", "Sub", "Mul"],
[item.op_type for item in g3.node],
)
io_map = [("B00", "B01"), ("B10", "B11"), ("B20", "B21")]
self._test_merge_models(M1_DEF, M2_DEF, io_map, check_expectations)
def test_case_connect_same_output_twice(self) -> None:
"""
Tests a scenario where we merge two models by connecting a single output in the first model
to all the inputs in the second
"""
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
del g2 # Unused
self.assertEqual(g3.input, g1.input)
self.assertEqual(["B10", "B20", "D0"], [elem.name for elem in g3.output])
self.assertEqual(
["Add", "Sub", "Mul", "Add", "Sub", "Mul"],
[item.op_type for item in g3.node],
)
io_map = [("B00", "B01"), ("B00", "B11"), ("B00", "B21")]
self._test_merge_models(M1_DEF, M2_DEF, io_map, check_expectations)
def test_case_connect_same_output_drop_outputs(self) -> None:
"""
Tests a scenario where we merge two models by connecting a single output in the first model
to all the inputs in the second, while dropping the rest of the outputs in the first model
"""
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
del g2 # Unused
self.assertEqual(g3.input, g1.input)
self.assertEqual(["D0"], [elem.name for elem in g3.output])
self.assertEqual(
["Add", "Add", "Sub", "Mul"], [item.op_type for item in g3.node]
)
io_map = [("B00", "B01"), ("B00", "B11"), ("B00", "B21")]
outputs = ["D0"]
self._test_merge_models(
M1_DEF, M2_DEF, io_map, check_expectations, outputs=outputs
)
def test_case_connect_same_input_output_name(self) -> None:
"""
Tests a scenario where we merge two models, where the inputs/outputs connected
are named exactly the same
"""
m1_def = """
<
ir_version: 7,
opset_import: [ "": 10]
>
agraph (float[N, M] A) => (float[N, M] B)
{
B = Add(A, A)
}
"""
m2_def = """
<
ir_version: 7,
opset_import: [ "": 10]
>
agraph (float[N, M] B) => (float[N, M] C)
{
C = Add(B, B)
}
"""
io_map = [("B", "B")]
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
del g1, g2 # Unused
self.assertEqual(["A"], [elem.name for elem in g3.input])
self.assertEqual(["C"], [elem.name for elem in g3.output])
self._test_merge_models(m1_def, m2_def, io_map, check_expectations)
def test_case_drop_inputs_outputs(self) -> None:
"""
Tests a scenario where we merge two models, not including some of the inputs/outputs
"""
m1_def = """
<
ir_version: 7,
opset_import: [ "": 10]
>
agraph (float[N] A0, float[N] B0) => (float[N] A1, float[N] B1)
{
A1 = Add(A0, A0)
B1 = Sub(B0, B0)
}
"""
m2_def = """
<
ir_version: 7,
opset_import: [ "": 10]
>
agraph (float[N] A2, float[N] B2) => (float[N] A3, float[N] B3)
{
A3 = Add(A2, A2)
B3 = Sub(B2, B2)
}
"""
io_map = [("A1", "B2")]
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
del g1, g2 # Unused
self.assertEqual(["A0"], [elem.name for elem in g3.input])
self.assertEqual(["B3"], [elem.name for elem in g3.output])
self.assertEqual(["Add", "Sub"], [elem.op_type for elem in g3.node])
inputs = ["A0"]
outputs = ["B3"]
self._test_merge_models(
m1_def, m2_def, io_map, check_expectations, inputs=inputs, outputs=outputs
)
def test_case_name_collision_prefix(self) -> None:
"""
Tests a scenario where we merge two models that have name collisions, but they
are avoided by prefixing the models model.
"""
m1_def = """
<
ir_version: 7,
opset_import: [ "": 10]
>
agraph (float[N] A, float[N] B) => (float[N] C)
{
C = Add(A, B)
}
"""
io_map = [("C", "A")]
def check_expectations(g1: GraphProto, g2: GraphProto, g3: GraphProto) -> None:
del g1, g2 # Unused
self.assertEqual(["m1/A", "m1/B", "m2/B"], [elem.name for elem in g3.input])
self.assertEqual(["m2/C"], [elem.name for elem in g3.output])
self.assertEqual(["Add", "Add"], [elem.op_type for elem in g3.node])
self._test_merge_models(
m1_def, m1_def, io_map, check_expectations, prefix1="m1/", prefix2="m2/"
)
def test_case_connect_partially_no_name_collision(self) -> None:
"""
Tests a scenario where two models without overlapping names are merged by
connecting some outputs from the first model to some inputs in the second.
The remaining inputs/outputs should be present in the combined model
"""
def check_expectations(g1: GraphProto, g2: GraphProto, g4: GraphProto) -> None:
del g1, g2 # Unused
# B20 <-> B21 not connected. They should still be present
# in the inputs and outputs of the combined graph
self.assertEqual(
["A0", "A1", "_A", "B21"], [elem.name for elem in g4.input]
)
self.assertEqual(["B20", "D0"], [elem.name for elem in g4.output])
io_map = [("B00", "B01"), ("B10", "B11")]
self._test_merge_models(M1_DEF, M2_DEF, io_map, check_expectations)
def test_merge_models_with_metadata_props(self) -> None:
m1 = _load_model(M1_DEF)
helper.set_model_props(m1, {"p1": "v1", "p2": "v2"})
m2 = _load_model(M2_DEF)
helper.set_model_props(m2, {"p3": "v3", "p4": "v4"})
io_map = [("B00", "B01")]
m3 = compose.merge_models(m1, m2, io_map=io_map)
assert len(m3.metadata_props) == 4
# Overlap, but same value
helper.set_model_props(m2, {"p1": "v1", "p4": "v4"})
m3 = compose.merge_models(m1, m2, io_map=io_map)
assert len(m3.metadata_props) == 3
# Same keys but not same value. Error
helper.set_model_props(m2, {"p1": "v5", "p4": "v4"})
self.assertRaises(ValueError, compose.merge_models, m1, m2, io_map=io_map)
def test_error_wrong_input_output_name(self) -> None:
"""
Tests that providing a non existing output/input name in the io_map argument produces an error.
"""
m1, m2 = _load_model(M1_DEF), _load_model(M2_DEF)
self.assertRaises(
ValueError,
compose.merge_models,
m1,
m2,
io_map=[("wrong_outname", "B01"), ("B10", "B11"), ("B20", "B21")],
)
# Wrong output name
self.assertRaises(
ValueError,
compose.merge_models,
m1,
m2,
io_map=[("B00", "wrong_input"), ("B10", "B11"), ("B20", "B21")],
)
def test_error_ir_version_mismatch(self) -> None:
m1 = _load_model(
"""
<
ir_version: 7,
opset_import: [ "": 13]
>
agraph (float[N, M] X0) => (float[N, M] Y0)
{
Y0 = Add(X0, X0)
}
"""
)
m2 = _load_model(
"""
<
ir_version: 6,
opset_import: [ "": 13]
>
agraph (float[N, M] X1) => (float[N, M] Y1)
{
Y1 = Add(X1, X1)
}
"""
)
# Wrong IR version name
self.assertRaises(
ValueError, compose.merge_models, m1, m2, io_map=[("Y0", "X1")]
)
def test_error_opset_import_mismatch(self) -> None:
"""Tests that providing models with different operator set imported produces an error."""
m1, m2 = _load_model(M1_DEF), _load_model(M2_DEF)
m1 = helper.make_model(
m1.graph, producer_name="test", opset_imports=[helper.make_opsetid("", 10)]
)
m2 = helper.make_model(
m2.graph, producer_name="test", opset_imports=[helper.make_opsetid("", 15)]
)
io_map = [("B00", "B01"), ("B10", "B11"), ("B20", "B21")]
self.assertRaises(ValueError, compose.merge_models, m1, m2, io_map)
# Converting to the same Operator set version, should work
m1 = version_converter.convert_version(m1, 15)
m3 = compose.merge_models(m1, m2, io_map=io_map)
checker.check_model(m3)
# FIXME: This function should be removed, as tests should not contain a copy of the tested logic.
def _test_add_prefix( # pylint: disable=too-many-branches
self,
rename_nodes: bool = False,
rename_edges: bool = False,
rename_inputs: bool = False,
rename_outputs: bool = False,
rename_initializers: bool = False,
rename_value_infos: bool = False,
inplace: bool = False,
) -> None:
m1 = _load_model(M1_DEF)
prefix = "pre/"
if inplace:
m2 = ModelProto()
m2.CopyFrom(m1)
compose.add_prefix(
m2,
prefix,
rename_nodes=rename_nodes,
rename_edges=rename_edges,
rename_inputs=rename_inputs,
rename_outputs=rename_outputs,
rename_initializers=rename_initializers,
rename_value_infos=rename_value_infos,
inplace=True,
)
else:
m2 = compose.add_prefix(
m1,
prefix,
rename_nodes=rename_nodes,
rename_edges=rename_edges,
rename_inputs=rename_inputs,
rename_outputs=rename_outputs,
rename_initializers=rename_initializers,
rename_value_infos=rename_value_infos,
)
g_in = m1.graph
g_out = m2.graph
if (
rename_edges
or rename_inputs
or rename_outputs
or rename_initializers
or rename_value_infos
):
name_mapping = {}
# Rename inputs/outputs/edges. Propagate name changes from and to edges
if rename_edges:
for n in g_in.node:
for e in n.input:
name_mapping[e] = _prefixed(prefix, e)
for e in n.output:
name_mapping[e] = _prefixed(prefix, e)
if rename_inputs:
for elem in g_in.input:
name_mapping[elem.name] = _prefixed(prefix, elem.name)
if rename_outputs:
for elem in g_in.output:
name_mapping[elem.name] = _prefixed(prefix, elem.name)
if rename_initializers:
for init in g_in.initializer:
name_mapping[init.name] = _prefixed(prefix, init.name)
for sparse_init in g_in.sparse_initializer:
name_mapping[sparse_init.values.name] = _prefixed(
prefix, sparse_init.values.name
)
name_mapping[sparse_init.indices.name] = _prefixed(
prefix, sparse_init.indices.name
)
if rename_value_infos:
for value_info in g_in.output:
name_mapping[value_info.name] = _prefixed(prefix, value_info.name)
for n1, n0 in zip(g_out.node, g_in.node):
for e1, e0 in zip(n1.input, n0.input):
self.assertEqual(name_mapping.get(e0, e0), e1)
for e1, e0 in zip(n1.output, n0.output):
self.assertEqual(name_mapping.get(e0, e0), e1)
for i1, i0 in zip(g_out.input, g_in.input):
self.assertEqual(name_mapping.get(i0.name, i0.name), i1.name)
for o1, o0 in zip(g_out.output, g_in.output):
self.assertEqual(name_mapping.get(o0.name, o0.name), o1.name)
for init1, init0 in zip(g_out.initializer, g_in.initializer):
self.assertEqual(name_mapping.get(init0.name, init0.name), init1.name)
for sparse_init1, sparse_init0 in zip(
g_out.sparse_initializer, g_in.sparse_initializer
):
self.assertEqual(
name_mapping.get(
sparse_init0.values.name, sparse_init0.values.name
),
sparse_init1.values.name,
)
self.assertEqual(
name_mapping.get(
sparse_init0.indices.name, sparse_init0.indices.name
),
sparse_init1.indices.name,
)
for vi1, vi0 in zip(g_out.value_info, g_in.value_info):
self.assertEqual(name_mapping.get(vi0.name, vi0.name), vi1.name)
if rename_nodes:
for n1, n0 in zip(g_out.node, g_in.node):
self.assertEqual(_prefixed(prefix, n0.name), n1.name)
def test_add_prefix_nodes(self) -> None:
"""
Tests renaming nodes only
"""
self._test_add_prefix(rename_nodes=True)
def test_add_prefix_edges(self) -> None:
"""
Tests prefixing nodes edges. This will also rename inputs/outputs, since the names are shared
"""
self._test_add_prefix(rename_edges=True)
def test_add_prefix_inputs(self) -> None:
"""
Tests prefixing graph inputs only. Relevant node edges should be renamed as well
"""
self._test_add_prefix(rename_inputs=True)
def test_add_prefix_outputs(self) -> None:
"""
Tests prefixing graph outputs only. Relevant node edges should be renamed as well
"""
self._test_add_prefix(rename_outputs=True)
def test_add_prefix_attribute_subgraph(self) -> None:
"""
Tests prefixing attribute's subgraph. Relevant subgraph should be renamed as well
"""
C = helper.make_tensor_value_info("C", TensorProto.BOOL, [1])
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, 1])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, 1])
Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [None, 1])
Out = helper.make_tensor_value_info("Out", TensorProto.FLOAT, [None, 1])
XY = helper.make_node("Mul", inputs=["X", "Y"], outputs=["XY"])
add = helper.make_node("Add", inputs=["XY", "Z"], outputs=["Out"])
sub = helper.make_node("Sub", inputs=["XY", "Z"], outputs=["Out"])
cond = helper.make_node(
"If",
inputs=["C"],
outputs=["Out"],
then_branch=helper.make_graph(
nodes=[add], name="then", inputs=[], outputs=[Out]
),
else_branch=helper.make_graph(
nodes=[sub], name="else", inputs=[], outputs=[Out]
),
)
graph = helper.make_graph(
nodes=[XY, cond], name="graph", inputs=[C, X, Y, Z], outputs=[Out]
)
prefix = "prefix."
prefixed_graph = compose.add_prefix_graph(graph, prefix)
checker.check_graph(prefixed_graph)
for n1, n0 in zip(prefixed_graph.node, graph.node):
self.assertEqual(_prefixed(prefix, n0.name), n1.name)
for attribute1, attribute0 in zip(n1.attribute, n0.attribute):
if attribute1.g:
for subgraph_n1, subgraph_n0 in zip(
attribute1.g.node, attribute0.g.node
):
for input_n1, input_n0 in zip(
subgraph_n1.input, subgraph_n0.input
):
self.assertEqual(_prefixed(prefix, input_n0), input_n1)
for output_n1, output_n0 in zip(
subgraph_n1.output, subgraph_n0.output
):
self.assertEqual(_prefixed(prefix, output_n0), output_n1)
def test_add_prefix_all(self) -> None:
"""
Tests prefixing all names in the graph
"""
self._test_add_prefix(True, True, True, True, True, True)
def test_add_prefix_inplace(self) -> None:
"""
Tests prefixing inplace
"""
self._test_add_prefix(inplace=True)
def test_expand_out_dim(self) -> None:
"""
Tests expanding output dimensions. The resulting graph should have the same output names,
but with one more dimension at the specified index.
"""
m1 = _load_model(M1_DEF)
def _check_model(m1: ModelProto, m2: ModelProto, dim_idx: int) -> None:
for out_g2, out_g1 in zip(m2.graph.output, m1.graph.output):
self.assertEqual(out_g2.name, out_g1.name)
self.assertEqual(
out_g2.type.tensor_type.elem_type, out_g1.type.tensor_type.elem_type
)
expected_out_shape = _get_shape(out_g1)
expected_out_shape.insert(dim_idx, 1)
self.assertEqual(_get_shape(out_g2), expected_out_shape)
for dim_idx in [0, 2, -1, -3]:
m2 = compose.expand_out_dim(m1, dim_idx)
_check_model(m1, m2, dim_idx)
# Test inplace
m2 = ModelProto()
m2.CopyFrom(m1)
dim_idx = 0
compose.expand_out_dim(m2, dim_idx, inplace=True)
_check_model(m1, m2, dim_idx)
def _test_overlapping_names(
self,
inputs0: Sequence[str] = ("i0", "i1"),
inputs1: Sequence[str] = ("i2", "i3"),
outputs0: Sequence[str] = ("o0", "o1"),
outputs1: Sequence[str] = ("o2", "o3"),
value_info0: Sequence[str] = ("v0", "v1"),
value_info1: Sequence[str] = ("v2", "v3"),
initializer0: Sequence[str] = ("init0", "init1"),
initializer1: Sequence[str] = ("init2", "init3"),
sparse_initializer0: Sequence[str] = ("sparse_init0", "sparse_init1"),
sparse_initializer1: Sequence[str] = ("sparse_init2", "sparse_init3"),
) -> None:
n0 = [
helper.make_node("Identity", inputs=[inputs0[i]], outputs=[outputs0[i]])
for i in range(len(inputs0))
]
i0 = [
helper.make_tensor_value_info(inputs0[i], TensorProto.FLOAT, [])
for i in range(len(inputs0))
]
o0 = [
helper.make_tensor_value_info(outputs0[i], TensorProto.FLOAT, [])
for i in range(len(outputs0))
]
vi0 = [
helper.make_tensor_value_info(value_info0[i], TensorProto.FLOAT, [])
for i in range(len(value_info0))
]
init0 = [
helper.make_tensor(
name=initializer0[i], data_type=TensorProto.INT64, dims=(), vals=[1]
)
for i in range(len(initializer0))
]
sparse_init0 = [
_make_sparse_tensor(sparse_initializer0[i])
for i in range(len(sparse_initializer0))
]
n1 = [
helper.make_node("Identity", inputs=[inputs1[i]], outputs=[outputs1[i]])
for i in range(len(inputs1))
]
i1 = [
helper.make_tensor_value_info(inputs1[i], TensorProto.FLOAT, [])
for i in range(len(inputs1))
]
o1 = [
helper.make_tensor_value_info(outputs1[i], TensorProto.FLOAT, [])
for i in range(len(outputs1))
]
vi1 = [
helper.make_tensor_value_info(value_info1[i], TensorProto.FLOAT, [])
for i in range(len(value_info1))
]
init1 = [
helper.make_tensor(
name=initializer1[i], data_type=TensorProto.INT64, dims=(), vals=[1]
)
for i in range(len(initializer1))
]
sparse_init1 = [
_make_sparse_tensor(sparse_initializer1[i])
for i in range(len(sparse_initializer1))
]
ops = [helper.make_opsetid("", 10)]
m0 = helper.make_model(
helper.make_graph(
nodes=n0,
name="g0",
inputs=i0,
outputs=o0,
value_info=vi0,
initializer=init0,
sparse_initializer=sparse_init0,
),
producer_name="test",
opset_imports=ops,
)
m1 = helper.make_model(
helper.make_graph(
nodes=n1,
name="g1",
inputs=i1,
outputs=o1,
value_info=vi1,
initializer=init1,
sparse_initializer=sparse_init1,
),
producer_name="test",
opset_imports=ops,
)
overlap = compose.check_overlapping_names(m0.graph, m1.graph)
i = 0
overlapping_inputs = list(set(inputs0) & set(inputs1))
overlapping_outputs = list(set(outputs0) & set(outputs1))
overlapping_edges = list(set(overlapping_inputs + overlapping_outputs))
if overlapping_edges:
self.assertEqual(overlap[i], ("edge", overlapping_edges))
i += 1
overlapping_vis = list(set(value_info0) & set(value_info1))
if overlapping_vis:
self.assertEqual(overlap[i], ("value_info", overlapping_vis))
i += 1
overlapping_init = list(set(initializer0) & set(initializer1))
if overlapping_init:
self.assertEqual(overlap[i], ("initializer", overlapping_init))
i += 1
overlapping_sparse_init = list(
set(sparse_initializer0) & set(sparse_initializer1)
)
if overlapping_sparse_init:
expected_overlap = []
for overlapping_name in overlapping_sparse_init:
expected_overlap.append(overlapping_name + "_values")
expected_overlap.append(overlapping_name + "_idx")
self.assertEqual(overlap[i], ("sparse_initializer", expected_overlap))
i += 1
m0_new = compose.add_prefix(m0, prefix="g0/")
overlap = compose.check_overlapping_names(m0_new.graph, m1.graph)
self.assertEqual(0, len(overlap))
def test_overlapping_input_names(self) -> None:
"""
Tests error checking when the name of the inputs overlaps
"""
self._test_overlapping_names(inputs0=["i0", "i1"], inputs1=["i1", "i2"])
def test_overlapping_output_names(self) -> None:
"""
Tests error checking when the name of the output overlaps
"""
self._test_overlapping_names(outputs0=["o0", "o1"], outputs1=["o1", "o2"])
def test_overlapping_value_info_names(self) -> None:
"""
Tests error checking when the name of value_info entries overlaps
"""
self._test_overlapping_names(
value_info0=["vi0", "vi1"], value_info1=["vi1", "vi2"]
)
def test_overlapping_initializer_names(self) -> None:
"""
Tests error checking when the name of initializer entries overlaps
"""
self._test_overlapping_names(
initializer0=["init0", "init1"], initializer1=["init1", "init2"]
)
def test_overlapping_sparse_initializer_names(self) -> None:
"""
Tests error checking when the name of sparse_initializer entries overlaps
"""
self._test_overlapping_names(
sparse_initializer0=["sparse_init0", "sparse_init1"],
sparse_initializer1=["sparse_init1", "sparse_init2"],
)
def test_overlapping_function_names(self) -> None:
"""
Tests error checking when the name of local function entries overlaps
"""
ops = [helper.make_opsetid("", 10), helper.make_opsetid("local", 10)]
def _make_function(
domain: str,
fname: str,
inputs: List[str],
outputs: List[str],
nodes: List[NodeProto],
) -> FunctionProto:
f = FunctionProto()
f.domain = domain
f.name = fname
f.input.extend(inputs)
f.output.extend(outputs)
f.node.extend(nodes)
f.opset_import.extend(ops)
return f
ops = [helper.make_opsetid("", 10), helper.make_opsetid("local", 10)]
g = GraphProto()
g.input.extend(
[
helper.make_tensor_value_info("x0", TensorProto.FLOAT, []),
helper.make_tensor_value_info("x1", TensorProto.FLOAT, []),
]
)
g.output.extend(
[
helper.make_tensor_value_info("y", TensorProto.FLOAT, []),
]
)
g.node.extend(
[helper.make_node("f1", domain="local", inputs=["x0", "x1"], outputs=["y"])]
)
g1 = GraphProto()
g1.CopyFrom(g)
g1.name = "g1"
m1 = helper.make_model(g1, producer_name="test", opset_imports=ops)
m1.functions.extend(
[
_make_function(
"local",
"f1",
["x0", "x1"],
["y"],
[helper.make_node("Add", inputs=["x0", "x1"], outputs=["y"])],
)
]
)
checker.check_model(m1)
g2 = GraphProto()
g2.CopyFrom(g)
g2.name = "g2"
m2 = helper.make_model(g2, producer_name="test", opset_imports=ops)
m2.functions.extend(
[
_make_function(
"local",
"f1",
["x0", "x1"],
["y"],
[helper.make_node("Mul", inputs=["x0", "x1"], outputs=["y"])],
)
]
)
checker.check_model(m2)
m = compose.merge_models(
m1, m2, io_map=[("y", "x0"), ("y", "x1")], prefix1="m1/", prefix2="m2/"
)
checker.check_model(m)
nodes = [n.op_type for n in m.graph.node]
self.assertEqual(["m1/f1", "m2/f1"], nodes)
functions = [f.name for f in m.functions]
self.assertEqual(["m1/f1", "m2/f1"], functions)
g3 = GraphProto()
g3.CopyFrom(g)
g3.name = "g3"
g3.node[0].op_type = "f2"
m3 = helper.make_model(g3, producer_name="test", opset_imports=ops)
m3.functions.extend(
[
_make_function(
"local",
"f1",
["x0", "x1"],
["y"],
[
helper.make_node("Add", inputs=["x0", "x1"], outputs=["y0"]),
helper.make_node("Mul", inputs=["x0", "x1"], outputs=["y1"]),
helper.make_node("Add", inputs=["y0", "y1"], outputs=["y"]),
],
),
_make_function(
"local",
"f2",
["x0", "x1"],
["y"],
[
helper.make_node(
"f1", domain="local", inputs=["x0", "x1"], outputs=["y0"]
),
helper.make_node("Mul", inputs=["x0", "x1"], outputs=["y1"]),
helper.make_node("Add", inputs=["y0", "y1"], outputs=["y"]),
],
),
]
)
checker.check_model(m3)
m = compose.merge_models(
m1, m3, io_map=[("y", "x0"), ("y", "x1")], prefix1="m1/", prefix2="m3/"
)
checker.check_model(m)
nodes = [n.op_type for n in m.graph.node]
self.assertEqual(["m1/f1", "m3/f2"], nodes)
functions = [f.name for f in m.functions]
self.assertEqual(["m1/f1", "m3/f1", "m3/f2"], functions)
self.assertEqual(["Add"], [n.op_type for n in m.functions[0].node])
self.assertEqual(
["Add", "Mul", "Add"], [n.op_type for n in m.functions[1].node]
)
self.assertEqual(
["m3/f1", "Mul", "Add"], [n.op_type for n in m.functions[2].node]
)
def test_merge_drop_unnecessary_initializers_and_value_info(self) -> None:
"""
Tests automatic removal of initializers when merging graphs
"""
ops = [helper.make_opsetid("", 10)]
g = GraphProto()
g.input.extend([helper.make_tensor_value_info("x", TensorProto.FLOAT, [])])
g.output.extend([helper.make_tensor_value_info("y", TensorProto.FLOAT, [])])
g.node.extend([helper.make_node("Identity", inputs=["x"], outputs=["y"])])
g1 = GraphProto()
g1.CopyFrom(g)
g1.name = "g1"
m1 = helper.make_model(g1, producer_name="test", opset_imports=ops)
checker.check_model(m1)
g2 = GraphProto()
g2.CopyFrom(g)
g2.name = "g2"
g2.initializer.extend(
[
helper.make_tensor(
name="x", data_type=TensorProto.FLOAT, dims=(), vals=[0]
)
]
)
m2 = helper.make_model(g2, producer_name="test", opset_imports=ops)
checker.check_model(m2)
g3 = GraphProto()
g3.CopyFrom(g)
g3.name = "g3"
g3.sparse_initializer.extend([_make_sparse_tensor("x")])
m3 = helper.make_model(g3, producer_name="test", opset_imports=ops)
checker.check_model(m3)
g4 = GraphProto()
g4.CopyFrom(g)
g4.name = "g3"
g4.value_info.extend(
[helper.make_tensor_value_info("x", TensorProto.FLOAT, [])]
)
m4 = helper.make_model(g4, producer_name="test", opset_imports=ops)
checker.check_model(m4)
# Initializer 'x' from m1 is removed, because there is no longer an input with that name
out_m1 = compose.merge_models(m1, m2, prefix1="m1/", io_map=[("y", "x")])
self.assertEqual(0, len(out_m1.graph.initializer))
# Sparse initializer 'x' from m1 is removed, because there is no longer an input with that name
out_m2 = compose.merge_models(m1, m3, prefix1="m1/", io_map=[("y", "x")])
self.assertEqual(0, len(out_m2.graph.initializer))
# Value info 'x' from m1 is removed, because there is no longer an input with that name
out_m3 = compose.merge_models(m1, m4, prefix1="m1/", io_map=[("y", "x")])
self.assertEqual(0, len(out_m3.graph.value_info))
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": ["/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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"/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"]} |
58,913 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/rangeop.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 Range(Base):
@staticmethod
def export_range_float_type_positive_delta() -> None:
node = onnx.helper.make_node(
"Range",
inputs=["start", "limit", "delta"],
outputs=["output"],
)
start = np.float32(1)
limit = np.float32(5)
delta = np.float32(2)
output = np.arange(
start, limit, delta, dtype=np.float32
) # expected output [1.0, 3.0]
expect(
node,
inputs=[start, limit, delta],
outputs=[output],
name="test_range_float_type_positive_delta",
)
@staticmethod
def export_range_int32_type_negative_delta() -> None:
node = onnx.helper.make_node(
"Range",
inputs=["start", "limit", "delta"],
outputs=["output"],
)
start = np.int32(10)
limit = np.int32(6)
delta = np.int32(-3)
output = np.arange(
start, limit, delta, dtype=np.int32
) # expected output [10, 7]
expect(
node,
inputs=[start, limit, delta],
outputs=[output],
name="test_range_int32_type_negative_delta",
)
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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"]} |
58,914 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/trilu.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 triu_reference_implementation(x, k=0): # type: ignore
return np.triu(x, k)
def tril_reference_implementation(x, k=0): # type: ignore
return np.tril(x, k)
class Trilu(Base):
@staticmethod
def export_triu() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [0, 2, 8, 6, 9],
# [0, 0, 0, 8, 7],
# [0, 0, 0, 2, 4]]
y = triu_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_triu")
@staticmethod
def export_triu_neg() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [0, 4, 0, 8, 7],
# [0, 0, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg")
@staticmethod
def export_triu_out_neg_out() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out")
@staticmethod
def export_triu_pos() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 3, 7, 9],
# [0, 0, 0, 6, 9],
# [0, 0, 0, 0, 7],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos")
@staticmethod
def export_triu_out_pos() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 0, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos")
@staticmethod
def export_triu_square() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
y = triu_reference_implementation(x)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [0, 5, 4],
# [0, 0, 2]],
#
# [[1, 4, 9],
# [0, 6, 3],
# [0, 0, 8]]]
expect(node, inputs=[x], outputs=[y], name="test_triu_square")
@staticmethod
def export_triu_square_neg() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[4, 6, 9],
# [7, 5, 4],
# [8, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [8, 9, 8]]]
# expect result:
# [[[4, 6, 9],
# [7, 5, 4],
# [0, 1, 2]],
#
# [[1, 4, 9],
# [9, 6, 3],
# [0, 9, 8]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg")
@staticmethod
def export_triu_one_row() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
k = np.array(1).astype(np.int64)
# X:
# [[[1, 4, 9, 7, 1]],
#
# [[9, 2, 8, 8, 4]],
#
# [[3, 9, 7, 4, 2]]]
# expect result:
# [[[0, 4, 9, 7, 1]],
#
# [[0, 2, 8, 8, 4]],
#
# [[0, 9, 7, 4, 2]]]
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row")
@staticmethod
def export_triu_zero() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
)
x = np.random.randint(10, size=(0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = triu_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero")
@staticmethod
def export_tril() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 0, 0, 0, 0],
# [1, 2, 0, 0, 0],
# [9, 4, 1, 0, 0],
# [4, 3, 4, 2, 0]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril")
@staticmethod
def export_tril_neg() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [1, 0, 0, 0, 0],
# [9, 4, 0, 0, 0],
# [4, 3, 4, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg")
@staticmethod
def export_tril_out_neg() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(-7).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg")
@staticmethod
def export_tril_pos() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(2).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 0, 0],
# [1, 2, 8, 6, 0],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos")
@staticmethod
def export_tril_out_pos() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(4, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
# expect result:
# [[4, 7, 3, 7, 9],
# [1, 2, 8, 6, 9],
# [9, 4, 1, 8, 7],
# [4, 3, 4, 2, 4]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos")
@staticmethod
def export_tril_square() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 5]],
#
# [[2, 0, 0],
# [2, 6, 0],
# [2, 6, 5]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril_square")
@staticmethod
def export_tril_square_neg() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
k = np.array(-1).astype(np.int64)
# X:
# [[[0, 4, 3],
# [2, 0, 9],
# [8, 2, 5]],
#
# [[2, 7, 2],
# [2, 6, 0],
# [2, 6, 5]]]
# expect result:
# [[[0, 0, 0],
# [2, 0, 0],
# [8, 2, 0]],
#
# [[0, 0, 0],
# [2, 0, 0],
# [2, 6, 0]]]
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg")
@staticmethod
def export_tril_one_row() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
# X:
# [[[6, 2, 4, 1, 6]],
#
# [[8, 3, 8, 7, 0]],
#
# [[2, 2, 9, 5, 9]]]
# expect result:
# [[[6, 0, 0, 0, 0]],
#
# [[8, 0, 0, 0, 0]],
#
# [[2, 0, 0, 0, 0]]]
y = tril_reference_implementation(x)
expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg")
@staticmethod
def export_tril_zero() -> None:
node = onnx.helper.make_node(
"Trilu",
inputs=["x", "k"],
outputs=["y"],
upper=0,
)
x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
k = np.array(6).astype(np.int64)
# X:
# []
# expect result:
# []
y = tril_reference_implementation(x, int(k))
expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero")
| {"/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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58,915 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/scan.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 Scan(Base):
@staticmethod
def export_scan_8() -> None:
# Given an input sequence [x1, ..., xN], sum up its elements using a scan
# returning the final state (x1+x2+...+xN) as well the scan_output
# [x1, x1+x2, ..., x1+x2+...+xN]
#
# create graph to represent scan body
sum_in = onnx.helper.make_tensor_value_info(
"sum_in", onnx.TensorProto.FLOAT, [2]
)
next = onnx.helper.make_tensor_value_info("next", 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"], outputs=["sum_out"]
)
id_node = onnx.helper.make_node(
"Identity", inputs=["sum_out"], outputs=["scan_out"]
)
scan_body = onnx.helper.make_graph(
[add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
)
# create scan op node
no_sequence_lens = "" # optional input, not supplied
node = onnx.helper.make_node(
"Scan",
inputs=[no_sequence_lens, "initial", "x"],
outputs=["y", "z"],
num_scan_inputs=1,
body=scan_body,
)
# create inputs for batch-size 1, sequence-length 3, inner dimension 2
initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
# final state computed = [1 + 3 + 5, 2 + 4 + 6]
y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
# scan-output computed
z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
expect(
node,
inputs=[initial, x],
outputs=[y, z],
name="test_scan_sum",
opset_imports=[onnx.helper.make_opsetid("", 8)],
)
@staticmethod
def export_scan_9() -> None:
# Given an input sequence [x1, ..., xN], sum up its elements using a scan
# returning the final state (x1+x2+...+xN) as well the scan_output
# [x1, x1+x2, ..., x1+x2+...+xN]
#
# create graph to represent scan body
sum_in = onnx.helper.make_tensor_value_info(
"sum_in", onnx.TensorProto.FLOAT, [2]
)
next = onnx.helper.make_tensor_value_info("next", 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"], outputs=["sum_out"]
)
id_node = onnx.helper.make_node(
"Identity", inputs=["sum_out"], outputs=["scan_out"]
)
scan_body = onnx.helper.make_graph(
[add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
)
# create scan op node
node = onnx.helper.make_node(
"Scan",
inputs=["initial", "x"],
outputs=["y", "z"],
num_scan_inputs=1,
body=scan_body,
)
# create inputs for sequence-length 3, inner dimension 2
initial = np.array([0, 0]).astype(np.float32).reshape((2,))
x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
# final state computed = [1 + 3 + 5, 2 + 4 + 6]
y = np.array([9, 12]).astype(np.float32).reshape((2,))
# scan-output computed
z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
expect(
node,
inputs=[initial, x],
outputs=[y, z],
name="test_scan9_sum",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
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58,916 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_negative_log_likelihood_loss.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _compute_negative_log_likelihood_loss(x, target, weight=None, reduction="mean", ignore_index=None): # type: ignore
input_shape = x.shape
if len(input_shape) == 1:
raise RuntimeError(f"Unsupported shape {input_shape!r}.")
target_shape = target.shape
N = input_shape[0]
C = input_shape[1]
# initialize the positional weights when required
gather_weight = None
if weight is not None:
# setting mode='clip' to deal with ignore_index > C or < 0 cases.
# when the target value is > C or < 0, it doesn't matter which value we are
# taking in gather_weight, since it will be set to 0 in the following if-block
# use np.int32 to make it compatible with x86 machines
gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
# set `ignore_index`'s loss weight to 0.
# The loss tensor will be multiplied by this weight tensor,
# so `ingore_index`'s loss value will be eliminated.
if ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
dtype=x.dtype
)
elif ignore_index != -1:
gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=x.dtype)
# if input is 4-d and above, make it 3-d
if len(input_shape) != 3:
x = x.reshape((N, C, -1))
target = target.reshape((N, -1))
# Get a dimension from the reshaped input.
# If the original input shape is [N, C, H, W],
# the D here should be H * W because we reshape
# [N, C, H, W] to [N, C, H * W].
D = x.shape[2]
neg_gather_element_input = np.zeros((N, D), dtype=x.dtype)
for i in range(N):
for d in range(D):
if target[i][d] != ignore_index:
neg_gather_element_input[i][d] = -x[i][target[i][d]][d]
loss = neg_gather_element_input
# if the input was 4-d or above reshape to the right shape
if len(input_shape) != 3:
loss = loss.reshape(target_shape)
# apply the weights when required
if gather_weight is not None:
loss = gather_weight * loss
if reduction == "mean":
loss = loss.sum() / gather_weight.sum()
return (loss,)
if reduction == "mean":
loss = np.mean(loss)
elif reduction == "sum":
loss = np.sum(loss)
return (loss.astype(x.dtype),)
class NegativeLogLikelihoodLoss(OpRun):
def _run(self, x, target, weight=None, ignore_index=None, reduction=None): # type: ignore
return _compute_negative_log_likelihood_loss(
x,
target,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
)
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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"]} |
58,917 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_depth_to_space.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 DepthToSpace(OpRun):
def _run(self, data, blocksize=None, mode=None): # type: ignore
if len(data.shape) != 4:
raise RuntimeError(f"Unexpected shape {data.shape!r}.")
b, c, h, w = data.shape
if mode == "DCR":
tmpshape = (
b,
blocksize,
blocksize,
c // (blocksize * blocksize),
h,
w,
)
reshaped = data.reshape(tmpshape)
transposed = np.transpose(reshaped, [0, 3, 4, 1, 5, 2])
else:
# assert mode == "CRD"
tmpshape = (
b,
c // (blocksize * blocksize),
blocksize,
blocksize,
h,
w,
)
reshaped = data.reshape(tmpshape)
transposed = np.transpose(reshaped, [0, 1, 4, 2, 5, 3])
finalshape = (
b,
c // (blocksize * blocksize),
h * blocksize,
w * blocksize,
)
y = np.reshape(transposed, finalshape)
return (y,)
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58,918 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_split_to_sequence.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0200,W0221
from typing import List, Optional, Tuple
import numpy as np
from onnx.reference.op_run import OpRun
class SplitToSequence(OpRun):
def common_run(
self, mat: np.ndarray, split: Optional[np.ndarray], axis: int
) -> List[np.ndarray]:
if split is None:
split_length = [1 for _ in range(mat.shape[axis])]
elif len(split.shape) == 0:
# A scalar
dim = mat.shape[axis]
length = int(split)
n = dim // int(length)
split_length = [length] * n
left = dim - length * n
if left > 0:
split_length.append(left)
else:
split_length = list(split)
sli = [slice(0, s) for s in mat.shape]
res = []
pos = 0
for spl in split_length:
sli[axis] = slice(pos, pos + spl) # type: ignore
pos += spl
res.append(mat[tuple(sli)])
return res
def _run(
self,
mat: np.ndarray,
split: Optional[np.ndarray] = None,
axis: int = 0,
keepdims: int = 1,
) -> Tuple[np.ndarray]:
res = self.common_run(mat, split, axis=axis)
if split is None and not keepdims:
for i in range(len(res)):
shape = list(res[i].shape)
del shape[axis]
res[i] = res[i].reshape(tuple(shape))
return (res,)
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58,919 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/adagrad.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.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
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(Base):
@staticmethod
def export_adagrad() -> None:
# Define operator attributes.
norm_coefficient = 0.001
epsilon = 1e-5
decay_factor = 0.1
# Create operator.
node = onnx.helper.make_node(
"Adagrad",
inputs=["R", "T", "X", "G", "H"],
outputs=["X_new", "H_new"],
norm_coefficient=norm_coefficient,
epsilon=epsilon,
decay_factor=decay_factor,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x = np.array([1.0], dtype=np.float32)
g = np.array([-1.0], dtype=np.float32)
h = np.array([2.0], dtype=np.float32)
# Compute expected outputs of Adagrad.
x_new, h_new = apply_adagrad(
r, t, x, g, h, norm_coefficient, epsilon, decay_factor
)
# Check results.
expect(
node,
inputs=[r, t, x, g, h],
outputs=[x_new, h_new],
name="test_adagrad",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
@staticmethod
def export_adagrad_multiple() -> None:
# Define operator attributes.
norm_coefficient = 0.001
epsilon = 1e-5
decay_factor = 0.1
node = onnx.helper.make_node(
"Adagrad",
inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
outputs=["X1_new", "X2_new", "H1_new", "H2_new"],
norm_coefficient=norm_coefficient,
epsilon=epsilon,
decay_factor=decay_factor,
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
# Define operator inputs.
r = np.array(0.1, dtype=np.float32) # scalar
t = np.array(0, dtype=np.int64) # scalar
x1 = np.array([1.0], dtype=np.float32)
g1 = np.array([-1.0], dtype=np.float32)
h1 = np.array([2.0], dtype=np.float32)
x2 = np.array([1.0, 2.0], dtype=np.float32)
g2 = np.array([-1.0, -3.0], dtype=np.float32)
h2 = np.array([4.0, 1.0], dtype=np.float32)
# Compute expected outputs of Adagrad.
x1_new, h1_new = apply_adagrad(
r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor
)
x2_new, h2_new = apply_adagrad(
r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor
)
# Check results.
expect(
node,
inputs=[r, t, x1, x2, g1, g2, h1, h2],
outputs=[x1_new, x2_new, h1_new, h2_new],
name="test_adagrad_multiple",
opset_imports=[
onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
],
)
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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"]} |
58,920 | onnx/onnx | refs/heads/main | /onnx/gen_proto.py | #!/usr/bin/env python
# SPDX-License-Identifier: Apache-2.0
import argparse
import glob
import os
import re
import subprocess
from textwrap import dedent
from typing import Iterable, Optional
autogen_header = """\
//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
"""
LITE_OPTION = """
// For using protobuf-lite
option optimize_for = LITE_RUNTIME;
"""
DEFAULT_PACKAGE_NAME = "onnx"
IF_ONNX_ML_REGEX = re.compile(r"\s*//\s*#if\s+ONNX-ML\s*$")
ENDIF_ONNX_ML_REGEX = re.compile(r"\s*//\s*#endif\s*$")
ELSE_ONNX_ML_REGEX = re.compile(r"\s*//\s*#else\s*$")
def process_ifs(lines: Iterable[str], onnx_ml: bool) -> Iterable[str]:
in_if = 0
for line in lines:
if IF_ONNX_ML_REGEX.match(line):
assert in_if == 0
in_if = 1
elif ELSE_ONNX_ML_REGEX.match(line):
assert in_if == 1
in_if = 2
elif ENDIF_ONNX_ML_REGEX.match(line):
assert in_if == 1 or in_if == 2 # pylint: disable=consider-using-in
in_if = 0
else:
if in_if == 0:
yield line
elif in_if == 1 and onnx_ml:
yield line
elif in_if == 2 and not onnx_ml:
yield line
IMPORT_REGEX = re.compile(r'(\s*)import\s*"([^"]*)\.proto";\s*$')
PACKAGE_NAME_REGEX = re.compile(r"\{PACKAGE_NAME\}")
ML_REGEX = re.compile(r"(.*)\-ml")
def process_package_name(lines: Iterable[str], package_name: str) -> Iterable[str]:
need_rename = package_name != DEFAULT_PACKAGE_NAME
for line in lines:
m = IMPORT_REGEX.match(line) if need_rename else None
if m:
include_name = m.group(2)
ml = ML_REGEX.match(include_name)
if ml:
include_name = f"{ml.group(1)}_{package_name}-ml"
else:
include_name = f"{include_name}_{package_name}"
yield m.group(1) + f'import "{include_name}.proto";'
else:
yield PACKAGE_NAME_REGEX.sub(package_name, line)
PROTO_SYNTAX_REGEX = re.compile(r'(\s*)syntax\s*=\s*"proto2"\s*;\s*$')
OPTIONAL_REGEX = re.compile(r"(\s*)optional\s(.*)$")
def convert_to_proto3(lines: Iterable[str]) -> Iterable[str]:
for line in lines:
# Set the syntax specifier
m = PROTO_SYNTAX_REGEX.match(line)
if m:
yield m.group(1) + 'syntax = "proto3";'
continue
# Remove optional keywords
m = OPTIONAL_REGEX.match(line)
if m:
yield m.group(1) + m.group(2)
continue
# Rewrite import
m = IMPORT_REGEX.match(line)
if m:
yield m.group(1) + f'import "{m.group(2)}.proto3";'
continue
yield line
def gen_proto3_code(
protoc_path: str, proto3_path: str, include_path: str, cpp_out: str, python_out: str
) -> None:
print(f"Generate pb3 code using {protoc_path}")
build_args = [protoc_path, proto3_path, "-I", include_path]
build_args.extend(["--cpp_out", cpp_out, "--python_out", python_out])
subprocess.check_call(build_args)
def translate(source: str, proto: int, onnx_ml: bool, package_name: str) -> str:
lines: Iterable[str] = source.splitlines()
lines = process_ifs(lines, onnx_ml=onnx_ml)
lines = process_package_name(lines, package_name=package_name)
if proto == 3:
lines = convert_to_proto3(lines)
else:
assert proto == 2
return "\n".join(lines) # TODO: not Windows friendly
def qualify(f: str, pardir: Optional[str] = None) -> str:
if pardir is None:
pardir = os.path.realpath(os.path.dirname(__file__))
return os.path.join(pardir, f)
def convert( # pylint: disable=too-many-branches,too-many-statements
stem: str,
package_name: str,
output: str,
do_onnx_ml: bool = False,
lite: bool = False,
protoc_path: str = "",
) -> None:
proto_in = qualify(f"{stem}.in.proto")
need_rename = package_name != DEFAULT_PACKAGE_NAME
# Having a separate variable for import_ml ensures that the import statements for the generated
# proto files can be set separately from the ONNX_ML environment variable setting.
import_ml = do_onnx_ml
# We do not want to generate the onnx-data-ml.proto files for onnx-data.in.proto,
# as there is no change between onnx-data.proto and the ML version.
if "onnx-data" in proto_in:
do_onnx_ml = False
if do_onnx_ml:
proto_base = f"{stem}_{package_name}-ml" if need_rename else f"{stem}-ml"
else:
proto_base = f"{stem}_{package_name}" if need_rename else f"{stem}"
proto = qualify(f"{proto_base}.proto", pardir=output)
proto3 = qualify(f"{proto_base}.proto3", pardir=output)
print(f"Processing {proto_in}")
with open(proto_in, encoding="utf-8") as fin:
source = fin.read()
print(f"Writing {proto}")
with open(proto, "w", newline="", encoding="utf-8") as fout:
fout.write(autogen_header)
fout.write(
translate(source, proto=2, onnx_ml=import_ml, package_name=package_name)
)
if lite:
fout.write(LITE_OPTION)
print(f"Writing {proto3}")
with open(proto3, "w", newline="", encoding="utf-8") as fout:
fout.write(autogen_header)
fout.write(
translate(source, proto=3, onnx_ml=import_ml, package_name=package_name)
)
if lite:
fout.write(LITE_OPTION)
if protoc_path:
porto3_dir = os.path.dirname(proto3)
base_dir = os.path.dirname(porto3_dir)
gen_proto3_code(protoc_path, proto3, base_dir, base_dir, base_dir)
pb3_files = glob.glob(os.path.join(porto3_dir, f"{proto_base}.proto3.*"))
for pb3_file in pb3_files:
print(f"Removing {pb3_file}")
os.remove(pb3_file)
if need_rename:
if do_onnx_ml:
proto_header = qualify(f"{stem}-ml.pb.h", pardir=output)
else:
proto_header = qualify(f"{stem}.pb.h", pardir=output)
print(f"Writing {proto_header}")
with open(proto_header, "w", newline="", encoding="utf-8") as fout:
fout.write("#pragma once\n")
fout.write(f'#include "{proto_base}.pb.h"\n')
# Generate py mapping
# "-" is invalid in python module name, replaces '-' with '_'
pb_py = qualify(f"{stem.replace('-', '_')}_pb.py", pardir=output)
if need_rename:
pb2_py = qualify(f"{proto_base.replace('-', '_')}_pb2.py", pardir=output)
else:
if do_onnx_ml:
pb2_py = qualify(f"{stem.replace('-', '_')}_ml_pb2.py", pardir=output)
else:
pb2_py = qualify(f"{stem.replace('-', '_')}_pb2.py", pardir=output)
print(f"generating {pb_py}")
with open(pb_py, "w", encoding="utf-8") as f:
f.write(
dedent(
f"""\
# This file is generated by setup.py. DO NOT EDIT!
from .{os.path.splitext(os.path.basename(pb2_py))[0]} import * # noqa
"""
)
)
def main() -> None:
parser = argparse.ArgumentParser(
description="Generates .proto file variations from .in.proto"
)
parser.add_argument(
"-p",
"--package",
default="onnx",
help="package name in the generated proto files (default: %(default)s)",
)
parser.add_argument("-m", "--ml", action="store_true", help="ML mode")
parser.add_argument(
"-l",
"--lite",
action="store_true",
help="generate lite proto to use with protobuf-lite",
)
parser.add_argument(
"-o",
"--output",
default=os.path.realpath(os.path.dirname(__file__)),
help="output directory (default: %(default)s)",
)
parser.add_argument(
"--protoc_path", default="", help="path to protoc for proto3 file validation"
)
parser.add_argument(
"stems",
nargs="*",
default=["onnx", "onnx-operators", "onnx-data"],
help="list of .in.proto file stems (default: %(default)s)",
)
args = parser.parse_args()
if not os.path.exists(args.output):
os.makedirs(args.output)
for stem in args.stems:
convert(
stem,
package_name=args.package,
output=args.output,
do_onnx_ml=args.ml,
lite=args.lite,
protoc_path=args.protoc_path,
)
if __name__ == "__main__":
main()
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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"]} |
58,921 | onnx/onnx | refs/heads/main | /onnx/test/utils_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import os
import shutil
import tempfile
import unittest
import onnx
from onnx import TensorProto, helper
class TestUtilityFunctions(unittest.TestCase):
def test_extract_model(self) -> None:
def create_tensor(name): # type: ignore
return helper.make_tensor_value_info(name, TensorProto.FLOAT, [1, 2])
A0 = create_tensor("A0")
A1 = create_tensor("A1")
B0 = create_tensor("B0")
B1 = create_tensor("B1")
B2 = create_tensor("B2")
C0 = create_tensor("C0")
C1 = create_tensor("C1")
D0 = create_tensor("D0")
L0_0 = helper.make_node("Add", ["A0", "A1"], ["B0"])
L0_1 = helper.make_node("Sub", ["A0", "A1"], ["B1"])
L0_2 = helper.make_node("Mul", ["A0", "A1"], ["B2"])
L1_0 = helper.make_node("Add", ["B0", "B1"], ["C0"])
L1_1 = helper.make_node("Sub", ["B1", "B2"], ["C1"])
L2_0 = helper.make_node("Mul", ["C0", "C1"], ["D0"])
g0 = helper.make_graph(
[L0_0, L0_1, L0_2, L1_0, L1_1, L2_0], "test", [A0, A1], [D0]
)
m0 = helper.make_model(g0, producer_name="test")
tdir = tempfile.mkdtemp()
p0 = os.path.join(tdir, "original.onnx")
onnx.save(m0, p0)
p1 = os.path.join(tdir, "extracted.onnx")
input_names = ["B0", "B1", "B2"]
output_names = ["C0", "C1"]
onnx.utils.extract_model(p0, p1, input_names, output_names)
m1 = onnx.load(p1)
self.assertEqual(m1.producer_name, "onnx.utils.extract_model")
self.assertEqual(m1.ir_version, m0.ir_version)
self.assertEqual(m1.opset_import, m0.opset_import)
self.assertEqual(len(m1.graph.node), 2)
self.assertEqual(len(m1.graph.input), 3)
self.assertEqual(len(m1.graph.output), 2)
self.assertEqual(m1.graph.input[0], B0)
self.assertEqual(m1.graph.input[1], B1)
self.assertEqual(m1.graph.input[2], B2)
self.assertEqual(m1.graph.output[0], C0)
self.assertEqual(m1.graph.output[1], C1)
shutil.rmtree(tdir, ignore_errors=True)
if __name__ == "__main__":
unittest.main()
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