fix RandomCropDecodeResize test
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9be1a01db6
commit
d37b8c4f2d
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@ -32,7 +32,7 @@ SET(DE_UT_SRCS
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project_op_test.cc
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queue_test.cc
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random_crop_op_test.cc
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random_crop_decode_resizeOp_test.cc
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random_crop_decode_resize_op_test.cc
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random_crop_and_resize_op_test.cc
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random_color_adjust_op_test.cc
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random_horizontal_flip_op_test.cc
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@ -20,35 +20,17 @@
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#include "utils/log_adapter.h"
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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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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::MsLogLevel::INFO;
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class MindDataTestRandomCropAndResizeOp : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestRandomCropAndResizeOp() : CVOpCommon() {}
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};
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TEST_F(MindDataTestRandomCropAndResizeOp, TestOpDefault) {
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MS_LOG(INFO) << "Doing testRandomCropAndResize.";
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TensorShape s_in = input_tensor_->shape();
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std::shared_ptr<Tensor> output_tensor;
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int h_out = 512;
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int w_out = 512;
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TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]});
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std::unique_ptr<RandomCropAndResizeOp> op(new RandomCropAndResizeOp(h_out, w_out));
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Status s;
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for (auto i = 0; i < 100; i++) {
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s = op->Compute(input_tensor_, &output_tensor);
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}
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EXPECT_TRUE(s.IsOk());
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MS_LOG(INFO) << "testRandomCropAndResize end.";
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}
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TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) {
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MS_LOG(INFO) << "Doing testRandomCropAndResize.";
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TEST_F(MindDataTestRandomCropAndResizeOp, TestOpSimpleTest) {
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MS_LOG(INFO) << " starting RandomCropAndResizeOp simple test";
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TensorShape s_in = input_tensor_->shape();
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std::shared_ptr<Tensor> output_tensor;
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int h_out = 1024;
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@ -58,14 +40,14 @@ TEST_F(MindDataTestRandomCropAndResizeOp, TestOpExtended) {
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float scale_lb = 0.0001;
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float scale_ub = 1.0;
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TensorShape s_out({(uint32_t) h_out, (uint32_t) w_out, (uint32_t) s_in[2]});
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TensorShape s_out({h_out, w_out, s_in[2]});
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std::unique_ptr<RandomCropAndResizeOp> op(
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new RandomCropAndResizeOp(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub));
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auto op = std::make_unique<RandomCropAndResizeOp>(h_out, w_out, scale_lb, scale_ub, aspect_lb, aspect_ub);
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Status s;
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for (auto i = 0; i < 100; i++) {
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s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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}
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EXPECT_TRUE(s.IsOk());
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MS_LOG(INFO) << "testRandomCropAndResize end.";
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MS_LOG(INFO) << "RandomCropAndResizeOp simple test finished";
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}
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@ -23,9 +23,10 @@
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#include "utils/log_adapter.h"
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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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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::MsLogLevel::INFO;
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constexpr double kMseThreshold = 2.0;
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class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon {
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public:
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@ -33,39 +34,38 @@ class MindDataTestRandomCropDecodeResizeOp : public UT::CVOP::CVOpCommon {
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};
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TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
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MS_LOG(INFO) << "Doing testRandomCropDecodeResizeOp Test";
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MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 1";
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std::shared_ptr<Tensor> output_tensor1;
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std::shared_ptr<Tensor> output_tensor2;
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std::shared_ptr<Tensor> decode_and_crop_output;
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std::shared_ptr<Tensor> crop_and_decode_output;
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int target_height = 884;
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int target_width = 718;
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float scale_lb = 0.08;
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float scale_ub = 1.0;
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float aspect_lb = 0.75;
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float aspect_ub = 1.333333;
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InterpolationMode interpolation = InterpolationMode::kLinear;
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uint32_t max_iter = 10;
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std::unique_ptr<RandomCropAndResizeOp> op1(new RandomCropAndResizeOp(
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target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter));
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EXPECT_TRUE(op1->OneToOne());
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std::unique_ptr<RandomCropDecodeResizeOp> op2(new RandomCropDecodeResizeOp(
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target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation, max_iter));
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EXPECT_TRUE(op2->OneToOne());
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Status s1, s2;
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constexpr int target_height = 884;
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constexpr int target_width = 718;
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constexpr float scale_lb = 0.08;
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constexpr float scale_ub = 1.0;
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constexpr float aspect_lb = 0.75;
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constexpr float aspect_ub = 1.333333;
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const InterpolationMode interpolation = InterpolationMode::kLinear;
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constexpr uint32_t max_iter = 10;
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auto crop_and_decode = RandomCropDecodeResizeOp(target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub,
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interpolation, max_iter);
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auto crop_and_decode_copy = crop_and_decode;
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auto decode_and_crop = static_cast<RandomCropAndResizeOp>(crop_and_decode_copy);
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EXPECT_TRUE(crop_and_decode.OneToOne());
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GlobalContext::config_manager()->set_seed(42);
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for (int i = 0; i < 100; i++) {
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s1 = op1->Compute(input_tensor_, &output_tensor1);
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s2 = op2->Compute(raw_input_tensor_, &output_tensor2);
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cv::Mat output1(target_height, target_width, CV_8UC3, output_tensor1->StartAddr());
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cv::Mat output2(target_height, target_width, CV_8UC3, output_tensor2->StartAddr());
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(void)crop_and_decode.Compute(raw_input_tensor_, &crop_and_decode_output);
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(void)decode_and_crop.Compute(input_tensor_, &decode_and_crop_output);
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cv::Mat output1(target_height, target_width, CV_8UC3, crop_and_decode_output->StartAddr());
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cv::Mat output2(target_height, target_width, CV_8UC3, decode_and_crop_output->StartAddr());
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long int mse_sum = 0;
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long int count = 0;
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int a, b;
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for (int i = 0; i < target_height; i++) {
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for (int j = 0; j < target_width; j++) {
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a = (int)output1.at<cv::Vec3b>(i, j)[1];
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b = (int)output2.at<cv::Vec3b>(i, j)[1];
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for (int j = 0; j < target_height; j++) {
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for (int k = 0; k < target_width; k++) {
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a = static_cast<int>(output1.at<cv::Vec3b>(i, j)[1]);
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b = static_cast<int>(output2.at<cv::Vec3b>(i, j)[1]);
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mse_sum += sqrt((a - b) * (a - b));
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if (a != b) {
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count++;
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@ -73,24 +73,22 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
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}
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}
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double mse;
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if (count > 0) {
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mse = (double) mse_sum / count;
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} else {
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mse = mse_sum;
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}
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MS_LOG(DEBUG) << "mse: " << mse << std::endl;
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mse = count > 0 ? static_cast<double>(mse_sum) / count : mse_sum;
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MS_LOG(INFO) << "mse: " << mse << std::endl;
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EXPECT_LT(mse, kMseThreshold);
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}
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MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
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MS_LOG(INFO) << "RandomCropDecodeResizeOp test 1 finished";
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}
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TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
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MS_LOG(INFO) << "Doing MindDataTestRandomCropDecodeResizeOp";
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const unsigned int h = 884;
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const unsigned int w = 718;
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const float scale_lb = 0.1;
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const float scale_ub = 1;
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const float aspect_lb = 0.1;
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const float aspect_ub = 10;
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MS_LOG(INFO) << "starting RandomCropDecodeResizeOp test 2";
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constexpr int h = 884;
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constexpr int w = 718;
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constexpr float scale_lb = 0.1;
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constexpr float scale_ub = 1;
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constexpr float aspect_lb = 0.1;
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constexpr float aspect_ub = 10;
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std::shared_ptr<Tensor> decoded, decoded_and_cropped, cropped_and_decoded;
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std::mt19937 rd;
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@ -98,14 +96,14 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
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std::uniform_real_distribution<float> rd_aspect(aspect_lb, aspect_ub);
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DecodeOp op(true);
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op.Compute(raw_input_tensor_, &decoded);
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Status s1, s2;
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Status crop_and_decode_status, decode_and_crop_status;
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float scale, aspect;
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int crop_width, crop_height;
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bool crop_success = false;
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unsigned int mse_sum, m1, m2, count;
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float mse;
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int mse_sum, m1, m2, count;
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double mse;
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for (unsigned int k = 0; k < 100; ++k) {
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for (int k = 0; k < 100; ++k) {
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mse_sum = 0;
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count = 0;
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for (auto i = 0; i < 100; i++) {
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@ -132,13 +130,13 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
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int y = rd_y(rd);
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op.Compute(raw_input_tensor_, &decoded);
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s1 = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height);
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s2 = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height);
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crop_and_decode_status = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height);
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decode_and_crop_status = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height);
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{
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cv::Mat M1(crop_height, crop_width, CV_8UC3, decoded_and_cropped->StartAddr());
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cv::Mat M2(crop_height, crop_width, CV_8UC3, cropped_and_decoded->StartAddr());
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for (unsigned int i = 0; i < crop_height; ++i) {
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for (unsigned int j = 0; j < crop_width; ++j) {
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for (int i = 0; i < crop_height; ++i) {
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for (int j = 0; j < crop_width; ++j) {
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m1 = M1.at<cv::Vec3b>(i, j)[1];
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m2 = M2.at<cv::Vec3b>(i, j)[1];
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mse_sum += sqrt((m1 - m2) * (m1 - m2));
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@ -149,8 +147,9 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
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}
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}
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mse = (count == 0) ? mse_sum : static_cast<float>(mse_sum) / count;
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MS_LOG(DEBUG) << "mse: " << mse << std::endl;
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mse = count > 0 ? static_cast<double>(mse_sum) / count : mse_sum;
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MS_LOG(INFO) << "mse: " << mse << std::endl;
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EXPECT_LT(mse, kMseThreshold);
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}
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MS_LOG(INFO) << "MindDataTestRandomCropDecodeResizeOp end!";
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MS_LOG(INFO) << "RandomCropDecodeResizeOp test 2 finished";
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}
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