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‎README.md‎

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## 简介
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X2Paddle是飞桨生态下的模型转换工具，致力于帮助其它深度学习框架用户快速迁移至飞桨框架。目前支持**推理模型的框架转换**与**PyTorch训练代码迁移**，我们还提供了详细的不同框架间API对比文档，降低开发者上手飞桨核心的学习成本。
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X2Paddle是飞桨生态下的模型转换工具，致力于帮助其它深度学习框架用户快速迁移至飞桨框架。目前支持**推理模型的框架转换**与**PyTorch训练代码迁移**，我们还提供了详细的不同框架间API对比文档，降低开发者将模型迁移到飞桨的时间成本。
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- **支持的模型丰富**
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- 在主流的CV和NLP模型上均支持转换，涵盖了19+个Caffe预测模型转换、27+个TensorFlow预测模型转换、32+个ONNX预测模型转换、27+个PyTorch预测模型转换、2+个PyTorch训练项目转换，详见 ***[支持列表](./docs/introduction/x2paddle_model_zoo.md)***
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- 在主流的CV和NLP模型上支持大部分模型转换，目前X2Paddle支持130+ PyTorch OP，90+ ONNX OP，90+ TensorFlow OP 以及 30+ Caffe OP，详见 ***[支持列表](./docs/inference_model_convertor/op_list.md)***
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- **简洁易用**
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‎docs/inference_model_convertor/op_list.md‎

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# X2Paddle支持OP列表
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> 目前X2Paddle支持90+的TensorFlow OP，30+的Caffe Layer，80+的ONNX OP，120+的PyTorch Aten，10+的PyTorch Prim覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。
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> 目前X2Paddle支持90+ TensorFlow OP，30+ Caffe OP，90+ ONNX OP，130+ PyTorch OP，覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。
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**注：** 目前，部分OP暂未支持，如您在转换过程中出现OP不支持的情况，可自行添加或反馈给我们。欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：）
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‎tests/onnx/test_auto_scan_conv2d.py‎

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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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import hypothesis.strategies as st
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import onnx
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from onnx import helper
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from onnx import TensorProto
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import numpy as np
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import unittest
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‎tests/onnx/test_auto_scan_hardsigmoid.py‎

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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import random
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class TestHardSigmoidCovert(OPConvertAutoScanTest):
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"""
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ONNX op: HardSigmoid
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OPset version: 7~15
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"""
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def sample_convert_config(self, draw):
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input_shape = draw(
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st.lists(
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st.integers(
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min_value=2, max_value=6), min_size=2, max_size=5))
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input_dtype = draw(st.sampled_from(["float32"]))
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alpha = random.random()
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beta = random.random()
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config = {
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"op_names": ["HardSigmoid"],
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"test_data_shapes": [input_shape],
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"test_data_types": [[input_dtype]],
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"inputs_shape": [input_shape],
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"min_opset_version": 7,
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"inputs_name": ["x"],
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"outputs_name": ["y"],
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"delta": 1e-4,
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"rtol": 1e-4
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}
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attrs = {
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"alpha": alpha,
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"beta": beta,
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}
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return (config, attrs)
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def test(self):
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self.run_and_statis(max_examples=30)
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if __name__ == "__main__":
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unittest.main()

‎tests/onnx/test_auto_scan_isinf.py‎

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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import random
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class TestIsInfConvert(OPConvertAutoScanTest):
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"""
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ONNX op: IsInf
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OPset version: 10~15
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"""
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def sample_convert_config(self, draw):
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input_shape = draw(
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st.lists(
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st.integers(
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min_value=20, max_value=30), min_size=3, max_size=5))
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input_dtype = draw(st.sampled_from(["float32"]))
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config = {
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"op_names": ["IsInf"],
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"test_data_shapes": [input_shape],
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"test_data_types": [input_dtype],
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"inputs_shape": [input_shape],
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"min_opset_version": 10,
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"max_opset_version": 15,
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"inputs_name": ["x"],
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"outputs_name": ["y"],
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"delta": 1e-4,
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"rtol": 1e-4,
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"run_dynamic": True,
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}
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attrs = {}
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return (config, attrs)
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def test(self):
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self.run_and_statis(max_examples=50)
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if __name__ == "__main__":
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unittest.main()

‎tests/onnx/test_auto_scan_isnan.py‎

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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import random
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class TestIsNaNConcert(OPConvertAutoScanTest):
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"""
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ONNX op: IsNaN
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OPset version: 9~15
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"""
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def sample_convert_config(self, draw):
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input_shape = draw(
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st.lists(
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st.integers(
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min_value=20, max_value=30), min_size=3, max_size=5))
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input_dtype = draw(st.sampled_from(["float32"]))
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config = {
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"op_names": ["IsNaN", ],
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"test_data_shapes": [input_shape],
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"test_data_types": [input_dtype],
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"inputs_shape": [input_shape],
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"min_opset_version": 9,
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"inputs_name": ["x"],
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"outputs_name": ["y"],
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"delta": 1e-4,
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"rtol": 1e-4,
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"run_dynamic": True,
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}
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attrs = {}
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return (config, attrs)
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def test(self):
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self.run_and_statis(max_examples=50)
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if __name__ == "__main__":
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unittest.main()

‎tests/onnx/test_auto_scan_reduce_ops.py‎

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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import random
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class TestReduceOpsConvert(OPConvertAutoScanTest):
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"""
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ONNX op: Reduce Ops
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OPset version: 7~15
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"""
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def sample_convert_config(self, draw):
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input_shape = draw(
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st.lists(
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st.integers(
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min_value=20, max_value=30), min_size=3, max_size=5))
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input_dtype = draw(st.sampled_from(["float32", "int32", "int64"]))
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axes = draw(
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st.lists(
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st.integers(
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min_value=-len(input_shape), max_value=len(input_shape) -
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1),
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min_size=1,
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max_size=1))
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keep_dim = draw(st.integers(min_value=0, max_value=1))
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config = {
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"op_names": ["ReduceL1", "ReduceL2"],
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"test_data_shapes": [input_shape],
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"test_data_types": [input_dtype],
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"inputs_shape": [input_shape],
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"min_opset_version": 7,
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"max_opset_version": 15,
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"inputs_name": ["x"],
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"outputs_name": ["y"],
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"delta": 1e-4,
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"rtol": 1e-4,
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}
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attrs = {
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"axes": axes,
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"keepdims": keep_dim,
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}
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return (config, attrs)
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def test(self):
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self.run_and_statis(max_examples=50)
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if __name__ == "__main__":
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unittest.main()

‎tests/onnx/test_auto_scan_sum_7.py‎

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from auto_scan_test import OPConvertAutoScanTest
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from hypothesis import reproduce_failure
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from onnxbase import randtool
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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class TestSumConvert(OPConvertAutoScanTest):
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"""
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ONNX op: Sum
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OPset version: 7
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"""
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def sample_convert_config(self, draw):
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input1_shape = draw(
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st.lists(
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st.integers(
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min_value=10, max_value=20), min_size=2, max_size=4))
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input_dtype = draw(st.sampled_from(["float32"]))
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config = {
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"op_names": ["Sum"],
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"test_data_shapes": [input1_shape, input1_shape],
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"test_data_types": [[input_dtype], [input_dtype]],
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"inputs_shape": [],
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"min_opset_version": 7,
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"max_opset_version": 7,
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"inputs_name": ["x", "y"],
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"outputs_name": ["z"],
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"delta": 1e-4,
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"rtol": 1e-4
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}
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attrs = {}
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return (config, attrs)
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def test(self):
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self.run_and_statis(max_examples=30)
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if __name__ == "__main__":
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unittest.main()

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