forked from mindspore-Ecosystem/mindspore
96 lines
3.6 KiB
C++
96 lines
3.6 KiB
C++
/**
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* Copyright 2019-2022 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/data/data_utils.h"
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#include "minddata/dataset/kernels/image/normalize_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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class MindDataTestNormalizeOP : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestNormalizeOP() : CVOpCommon() {}
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};
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/// Feature: Normalize
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/// Description: Normalize the image and save
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/// Expectation: normalized image saves successfully
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TEST_F(MindDataTestNormalizeOP, TestOp) {
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MS_LOG(INFO) << "Doing TestNormalizeOp::TestOp2.";
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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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std::vector<float> mean = {121.0, 115.0, 100.0};
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std::vector<float> std = {70.0, 68.0, 71.0};
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// Normalize Op
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std::unique_ptr<NormalizeOp> op = std::make_unique<NormalizeOp>(mean, std, true);
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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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std::string output_filename = GetFilename();
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output_filename.replace(output_filename.end() - 8, output_filename.end(), "imagefolder/normalizeOpOut.yml");
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std::shared_ptr<CVTensor> p = CVTensor::AsCVTensor(output_tensor);
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cv::Mat cv_output_image;
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cv_output_image = p->mat();
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MS_LOG(DEBUG) << "Storing output file to : " << output_filename << std::endl;
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cv::FileStorage file(output_filename, cv::FileStorage::WRITE);
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file << "imageData" << cv_output_image;
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}
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/// Feature: Normalize
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/// Description: Test Normalize with 4 dimension tensor
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/// Expectation: The result is as expected
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TEST_F(MindDataTestNormalizeOP, TestOp4Dim) {
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MS_LOG(INFO) << "Doing TestNormalizeOp-TestOp4Dim.";
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std::shared_ptr<Tensor> output_tensor;
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// construct a fake 4 dimension data
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std::shared_ptr<Tensor> input_tensor_cp;
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ASSERT_OK(Tensor::CreateFromTensor(input_tensor_, &input_tensor_cp));
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std::vector<std::shared_ptr<Tensor>> tensor_list;
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tensor_list.push_back(input_tensor_cp);
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tensor_list.push_back(input_tensor_cp);
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TensorShape shape = input_tensor_cp->shape();
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std::shared_ptr<Tensor> input_4d;
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ASSERT_OK(TensorVectorToBatchTensor(tensor_list, &input_4d));
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std::vector<float> mean = {121.0, 115.0, 100.0};
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std::vector<float> std = {70.0, 68.0, 71.0};
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// Normalize Op
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std::unique_ptr<NormalizeOp> op = std::make_unique<NormalizeOp>(mean, std, true);
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_4d, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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std::string output_filename = GetFilename();
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output_filename.replace(output_filename.end() - 8, output_filename.end(), "imagefolder/normalizeOpVideoOut.yml");
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std::shared_ptr<CVTensor> p = CVTensor::AsCVTensor(output_tensor);
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cv::Mat cv_output_video;
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cv_output_video = p->mat();
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MS_LOG(DEBUG) << "Storing output file to : " << output_filename << std::endl;
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cv::FileStorage file(output_filename, cv::FileStorage::WRITE);
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file << "videoData" << cv_output_video;
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}
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