# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from auto_scan_test import OPConvertAutoScanTest from hypothesis import reproduce_failure from onnxbase import randtool import hypothesis.strategies as st import numpy as np import unittest min_opset_version_map = { "Add": 7, "Sub": 7, "Div": 7, "Mul": 7, "Pow": 7, "Max": 9, "Min": 9, } class TestElementwiseopsConvert(OPConvertAutoScanTest): """ ONNX op: elementwise ops OPset version: 7~15 """ def sample_convert_config(self, draw): input1_shape = draw( st.lists( st.integers( min_value=10, max_value=20), min_size=2, max_size=4)) if draw(st.booleans()): input2_shape = [input1_shape[-1]] else: input2_shape = input1_shape def generator_data(): input_data = randtool("float", -5.0, 5.0, input2_shape) input_data[abs(input_data) < 1.0] = 1.0 return input_data input_dtype = draw(st.sampled_from(["float32", "float64"])) config = { "op_names": ["Add", "Sub", "Div", "Mul", "Pow", "Max", "Min"], "test_data_shapes": [input1_shape, generator_data], "test_data_types": [[input_dtype], [input_dtype]], "inputs_shape": [], "min_opset_version": 7, "inputs_name": ["x", "y"], "outputs_name": ["z"], "delta": 1e-4, "rtol": 1e-4 } min_opset_versions = list() for op_name in config["op_names"]: min_opset_versions.append(min_opset_version_map[op_name]) config["min_opset_version"] = min_opset_versions attrs = {} return (config, attrs) def test(self): self.run_and_statis(max_examples=30) if __name__ == "__main__": unittest.main()