mindspore-ci-bot
4 years ago
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Gitee
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/**
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* Copyright 2021 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/cvop_common.h"
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#include "minddata/dataset/core/cv_tensor.h"
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#include "minddata/dataset/kernels/image/affine_op.h"
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#include "minddata/dataset/kernels/image/math_utils.h"
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#include <opencv2/opencv.hpp>
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#include <opencv2/imgproc/types_c.h>
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#include "lite_cv/lite_mat.h"
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#include "lite_cv/image_process.h"
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using namespace mindspore::dataset;
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using mindspore::dataset::InterpolationMode;
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class MindDataTestAffineOp : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestAffineOp() : CVOpCommon() {}
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};
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// Helper function, consider moving this to helper class for UT
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double Mse(cv::Mat img1, cv::Mat img2) {
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// clone to get around open cv optimization
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cv::Mat output1 = img1.clone();
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cv::Mat output2 = img2.clone();
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// input check
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if (output1.rows < 0 || output1.rows != output2.rows || output1.cols < 0 || output1.cols != output2.cols) {
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return 10000.0;
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}
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return cv::norm(output1, output2, cv::NORM_L1);
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}
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// helper function to generate corresponding affine matrix
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std::vector<double> GenerateMatrix(const std::shared_ptr<Tensor> &input, float_t degrees,
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const std::vector<float_t> &translation, float_t scale,
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const std::vector<float_t> &shear) {
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float_t translation_x = translation[0];
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float_t translation_y = translation[1];
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DegreesToRadians(degrees, °rees);
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float_t shear_x = shear[0];
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float_t shear_y = shear[1];
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DegreesToRadians(shear_x, &shear_x);
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DegreesToRadians(-1 * shear_y, &shear_y);
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float_t cx = ((input->shape()[1] - 1) / 2.0);
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float_t cy = ((input->shape()[0] - 1) / 2.0);
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// Calculate RSS
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std::vector<double> matrix{
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static_cast<double>(scale * cos(degrees + shear_y) / cos(shear_y)),
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static_cast<double>(scale * (-1 * cos(degrees + shear_y) * tan(shear_x) / cos(shear_y) - sin(degrees))),
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0,
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static_cast<double>(scale * sin(degrees + shear_y) / cos(shear_y)),
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static_cast<double>(scale * (-1 * sin(degrees + shear_y) * tan(shear_x) / cos(shear_y) + cos(degrees))),
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0};
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// Compute T * C * RSS * C^-1
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matrix[2] = (1 - matrix[0]) * cx - matrix[1] * cy + translation_x;
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matrix[5] = (1 - matrix[4]) * cy - matrix[3] * cx + translation_y;
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return matrix;
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}
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TEST_F(MindDataTestAffineOp, TestAffineLite) {
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MS_LOG(INFO) << "Doing MindDataTestAffine-TestAffineLite.";
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// create input tensor and
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float degree = 0.0;
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std::vector<float> translation = {0.0, 0.0};
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float scale = 0.0;
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std::vector<float> shear = {0.0, 0.0};
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// Create affine object with default values
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std::shared_ptr<AffineOp> op(new AffineOp(degree, translation, scale, shear, InterpolationMode::kLinear));
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// output tensor
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std::shared_ptr<Tensor> output_tensor;
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// output
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LiteMat dst;
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LiteMat lite_mat_rgb(input_tensor_->shape()[1], input_tensor_->shape()[0], input_tensor_->shape()[2],
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const_cast<void *>(reinterpret_cast<const void *>(input_tensor_->GetBuffer())),
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LDataType::UINT8);
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std::vector<double> matrix = GenerateMatrix(input_tensor_, degree, translation, scale, shear);
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int height = lite_mat_rgb.height_;
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int width = lite_mat_rgb.width_;
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std::vector<size_t> dsize;
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dsize.push_back(width);
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dsize.push_back(height);
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double M[6] = {};
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for (int i = 0; i < matrix.size(); i++) {
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M[i] = static_cast<double>(matrix[i]);
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}
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EXPECT_TRUE(Affine(lite_mat_rgb, dst, M, dsize, UINT8_C3(0, 0, 0)));
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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// output tensor is a cv tenosr, we can compare mat values
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cv::Mat lite_cv_out(dst.height_, dst.width_, CV_8UC3, dst.data_ptr_);
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double mse = Mse(lite_cv_out, CVTensor(output_tensor).mat());
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MS_LOG(INFO) << "mse: " << std::to_string(mse) << std::endl;
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EXPECT_LT(mse, 1); // predetermined magic number
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}
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@ -0,0 +1,97 @@
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/**
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* Copyright 2021 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 "minddata/dataset/include/datasets.h"
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#include "minddata/dataset/include/transforms.h"
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#include "minddata/dataset/include/vision.h"
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using namespace mindspore::dataset;
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using mindspore::dataset::InterpolationMode;
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using mindspore::dataset::Tensor;
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class MindDataTestPipeline : public UT::DatasetOpTesting {
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protected:
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};
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TEST_F(MindDataTestPipeline, TestAffineAPI) {
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MS_LOG(INFO) << "Doing MindDataTestAffine-TestAffineAPI.";
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// Create an ImageFolder Dataset
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std::string folder_path = datasets_root_path_ + "/testPK/data/";
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std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
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// Create a Repeat operation on ds
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int32_t repeat_num = 3;
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ds = ds->Repeat(repeat_num);
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// Create auto contrast object with default values
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std::shared_ptr<TensorTransform> crop(new vision::RandomCrop({256, 256}));
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std::shared_ptr<TensorTransform> affine(
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new vision::Affine(0.0, {0.0, 0.0}, 0.0, {0.0, 0.0}, InterpolationMode::kLinear));
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// Create a Map operation on ds
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ds = ds->Map({crop, affine});
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// Create an iterator over the result of the above dataset
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// This will trigger the creation of the Execution Tree and launch it.
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std::shared_ptr<Iterator> iter = ds->CreateIterator();
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// Iterate the dataset and get each row
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std::unordered_map<std::string, mindspore::MSTensor> row;
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iter->GetNextRow(&row);
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uint64_t i = 0;
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while (row.size() != 0) {
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i++;
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// auto image = row["image"];
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// MS_LOG(INFO) << "Tensor image shape: " << image->shape();
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iter->GetNextRow(&row);
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// EXPECT_EQ(row["image"].Shape()[0], 256);
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}
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EXPECT_EQ(i, 15);
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// Manually terminate the pipeline
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iter->Stop();
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}
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TEST_F(MindDataTestPipeline, TestAffineAPIFail) {
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MS_LOG(INFO) << "Doing MindDataTestAffine-TestAffineAPI.";
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// Create an ImageFolder Dataset
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std::string folder_path = datasets_root_path_ + "/testPK/data/";
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std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
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// Create a Repeat operation on ds
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int32_t repeat_num = 3;
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ds = ds->Repeat(repeat_num);
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// Create auto contrast object with default values
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std::shared_ptr<TensorTransform> crop(new vision::RandomCrop({256, 256}));
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std::shared_ptr<TensorTransform> affine(
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new vision::Affine(0.0, {2.0, -1.0}, 0.0, {0.0, 0.0}, InterpolationMode::kLinear));
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// Create a Map operation on ds
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ds = ds->Map({crop, affine});
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// Create an iterator over the result of the above dataset
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// This will trigger the creation of the Execution Tree and launch it.
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std::shared_ptr<Iterator> iter = ds->CreateIterator();
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EXPECT_EQ(iter, nullptr);
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}
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