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61 lines
2.1 KiB
61 lines
2.1 KiB
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "common/common.h"
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#include "common/cvop_common.h"
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#include "minddata/dataset/kernels/image/normalize_pad_op.h"
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#include "minddata/dataset/core/cv_tensor.h"
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#include "utils/log_adapter.h"
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#include <opencv2/opencv.hpp>
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using namespace mindspore::dataset;
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using mindspore::MsLogLevel::INFO;
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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::LogStream;
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class MindDataTestNormalizePadOP : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestNormalizePadOP() : CVOpCommon() {}
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};
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TEST_F(MindDataTestNormalizePadOP, TestFloat32) {
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MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat32.";
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std::shared_ptr<Tensor> output_tensor;
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// Numbers are from the resnet50 model implementation
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float mean[3] = {121.0, 115.0, 100.0};
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float std[3] = {70.0, 68.0, 71.0};
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// NormalizePad Op
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std::unique_ptr<NormalizePadOp> op(new NormalizePadOp(mean[0], mean[1], mean[2], std[0], std[1], std[2], "float32"));
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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}
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TEST_F(MindDataTestNormalizePadOP, TestFloat16) {
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MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat16.";
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std::shared_ptr<Tensor> output_tensor;
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// Numbers are from the resnet50 model implementation
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float mean[3] = {121.0, 115.0, 100.0};
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float std[3] = {70.0, 68.0, 71.0};
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// NormalizePad Op
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std::unique_ptr<NormalizePadOp> op(new NormalizePadOp(mean[0], mean[1], mean[2], std[0], std[1], std[2], "float16"));
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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} |