forked from huawei/mindspore2022
298 lines
11 KiB
C++
298 lines
11 KiB
C++
/**
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* Copyright 2020-2021 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 "include/api/types.h"
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#include "minddata/dataset/core/de_tensor.h"
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#include "minddata/dataset/include/dataset/execute.h"
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#include "minddata/dataset/include/dataset/transforms.h"
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#include "minddata/dataset/include/dataset/vision.h"
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#include "minddata/dataset/include/dataset/text.h"
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#include "utils/log_adapter.h"
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using namespace mindspore::dataset;
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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 MindDataTestExecute : public UT::DatasetOpTesting {
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protected:
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};
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TEST_F(MindDataTestExecute, TestComposeTransforms) {
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MS_LOG(INFO) << "Doing TestComposeTransforms.";
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// Read images
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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// Transform params
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std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
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std::shared_ptr<TensorTransform> center_crop(new vision::CenterCrop({30}));
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std::shared_ptr<TensorTransform> rescale = std::make_shared<vision::Rescale>(1. / 3, 0.5);
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auto transform = Execute({decode, center_crop, rescale});
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Status rc = transform(image, &image);
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EXPECT_EQ(rc, Status::OK());
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EXPECT_EQ(30, image.Shape()[0]);
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EXPECT_EQ(30, image.Shape()[1]);
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}
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TEST_F(MindDataTestExecute, TestCrop) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestCrop.";
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// Read images
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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// Transform params
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auto decode = vision::Decode();
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auto crop = vision::Crop({10, 30}, {10, 15});
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auto transform = Execute({decode, crop});
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Status rc = transform(image, &image);
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EXPECT_EQ(rc, Status::OK());
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EXPECT_EQ(image.Shape()[0], 10);
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EXPECT_EQ(image.Shape()[1], 15);
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}
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TEST_F(MindDataTestExecute, TestTransformInput1) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput1.";
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// Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
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// instantiated via mix of make_shared and new
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// Read images
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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// Define transform operations
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std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
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std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
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std::shared_ptr<TensorTransform> normalize(
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new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
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std::shared_ptr<TensorTransform> hwc2chw = std::make_shared<vision::HWC2CHW>();
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mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
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// Apply transform on image
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Status rc = Transform(image, &image);
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// Check image info
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ASSERT_TRUE(rc.IsOk());
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ASSERT_EQ(image.Shape().size(), 3);
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ASSERT_EQ(image.Shape()[0], 3);
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ASSERT_EQ(image.Shape()[1], 224);
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ASSERT_EQ(image.Shape()[2], 224);
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}
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TEST_F(MindDataTestExecute, TestTransformInput2) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput2.";
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// Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
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// instantiated via new
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// With this way of creating TensorTransforms, we don't need to explicitly delete the object created with the
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// "new" keyword. When the shared pointer goes out of scope the object destructor will be called.
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// Read image, construct MSTensor from dataset tensor
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std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
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mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
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auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define transform operations
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std::shared_ptr<TensorTransform> decode(new vision::Decode());
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std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
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std::shared_ptr<TensorTransform> normalize(
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new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
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std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
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mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
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// Apply transform on image
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Status rc = Transform(image, &image);
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// Check image info
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ASSERT_TRUE(rc.IsOk());
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ASSERT_EQ(image.Shape().size(), 3);
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ASSERT_EQ(image.Shape()[0], 3);
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ASSERT_EQ(image.Shape()[1], 224);
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ASSERT_EQ(image.Shape()[2], 224);
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}
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TEST_F(MindDataTestExecute, TestTransformInput3) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput3.";
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// Test Execute with transform op input using API constructors, with auto pointers
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// Read image, construct MSTensor from dataset tensor
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std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
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mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
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auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define transform operations
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auto decode = vision::Decode();
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mindspore::dataset::Execute Transform1(decode);
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auto resize = vision::Resize({224, 224});
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mindspore::dataset::Execute Transform2(resize);
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// Apply transform on image
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Status rc;
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rc = Transform1(image, &image);
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ASSERT_TRUE(rc.IsOk());
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rc = Transform2(image, &image);
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ASSERT_TRUE(rc.IsOk());
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// Check image info
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ASSERT_EQ(image.Shape().size(), 3);
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ASSERT_EQ(image.Shape()[0], 224);
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ASSERT_EQ(image.Shape()[1], 224);
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ASSERT_EQ(image.Shape()[2], 3);
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}
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TEST_F(MindDataTestExecute, TestTransformInputSequential) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInputSequential.";
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// Test Execute with transform op input using API constructors, with auto pointers;
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// Apply 2 transformations sequentially, including single non-vector Transform op input
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// Read image, construct MSTensor from dataset tensor
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std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
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mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
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auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define transform#1 operations
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std::shared_ptr<TensorTransform> decode(new vision::Decode());
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std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
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std::shared_ptr<TensorTransform> normalize(
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new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
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std::vector<std::shared_ptr<TensorTransform>> op_list = {decode, resize, normalize};
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mindspore::dataset::Execute Transform(op_list);
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// Apply transform#1 on image
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Status rc = Transform(image, &image);
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// Define transform#2 operations
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std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
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mindspore::dataset::Execute Transform2(hwc2chw);
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// Apply transform#2 on image
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rc = Transform2(image, &image);
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// Check image info
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ASSERT_TRUE(rc.IsOk());
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ASSERT_EQ(image.Shape().size(), 3);
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ASSERT_EQ(image.Shape()[0], 3);
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ASSERT_EQ(image.Shape()[1], 224);
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ASSERT_EQ(image.Shape()[2], 224);
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}
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TEST_F(MindDataTestExecute, TestTransformDecodeResizeCenterCrop1) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformDecodeResizeCenterCrop1.";
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// Test Execute with Decode, Resize and CenterCrop transform ops input using API constructors, with shared pointers
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// Read image, construct MSTensor from dataset tensor
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std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
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mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
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auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define transform operations
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std::vector<int32_t> resize_paras = {256, 256};
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std::vector<int32_t> crop_paras = {224, 224};
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std::shared_ptr<TensorTransform> decode(new vision::Decode());
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std::shared_ptr<TensorTransform> resize(new vision::Resize(resize_paras));
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std::shared_ptr<TensorTransform> centercrop(new vision::CenterCrop(crop_paras));
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std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
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std::vector<std::shared_ptr<TensorTransform>> op_list = {decode, resize, centercrop, hwc2chw};
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mindspore::dataset::Execute Transform(op_list, MapTargetDevice::kCpu);
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// Apply transform on image
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Status rc = Transform(image, &image);
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// Check image info
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ASSERT_TRUE(rc.IsOk());
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ASSERT_EQ(image.Shape().size(), 3);
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ASSERT_EQ(image.Shape()[0], 3);
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ASSERT_EQ(image.Shape()[1], 224);
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ASSERT_EQ(image.Shape()[2], 224);
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}
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TEST_F(MindDataTestExecute, TestUniformAugment) {
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// Read images
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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std::vector<mindspore::MSTensor> image2;
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// Transform params
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std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
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std::shared_ptr<TensorTransform> resize_op(new vision::Resize({16, 16}));
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std::shared_ptr<TensorTransform> vertical = std::make_shared<vision::RandomVerticalFlip>();
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std::shared_ptr<TensorTransform> horizontal = std::make_shared<vision::RandomHorizontalFlip>();
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std::shared_ptr<TensorTransform> uniform_op(new vision::UniformAugment({resize_op, vertical, horizontal}, 3));
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auto transform1 = Execute({decode});
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Status rc = transform1(image, &image);
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ASSERT_TRUE(rc.IsOk());
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auto transform2 = Execute({uniform_op});
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rc = transform2({image}, &image2);
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ASSERT_TRUE(rc.IsOk());
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}
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TEST_F(MindDataTestExecute, TestBasicTokenizer) {
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std::shared_ptr<Tensor> de_tensor;
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Tensor::CreateScalar<std::string>("Welcome to China.", &de_tensor);
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auto txt = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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std::vector<mindspore::MSTensor> txt_result;
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// Transform params
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std::shared_ptr<TensorTransform> tokenizer =
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std::make_shared<text::BasicTokenizer>(false, false, NormalizeForm::kNone, false, true);
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// BasicTokenizer has 3 outputs so we need a vector to receive its result
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auto transform1 = Execute({tokenizer});
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Status rc = transform1({txt}, &txt_result);
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ASSERT_EQ(txt_result.size(), 3);
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ASSERT_TRUE(rc.IsOk());
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}
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TEST_F(MindDataTestExecute, TestRotate) {
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MS_LOG(INFO) << "Doing MindDataTestExecute-TestRotate.";
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// Read images
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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// Transform params
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auto decode = vision::Decode();
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auto rotate = vision::Rotate(10.5);
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auto transform = Execute({decode, rotate});
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Status rc = transform(image, &image);
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EXPECT_EQ(rc, Status::OK());
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}
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TEST_F(MindDataTestExecute, TestResizeWithBBox) {
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auto image = ReadFileToTensor("data/dataset/apple.jpg");
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std::shared_ptr<TensorTransform> decode_op = std::make_shared<vision::Decode>();
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std::shared_ptr<TensorTransform> resizewithbbox_op =
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std::make_shared<vision::ResizeWithBBox>(std::vector<int32_t>{250, 500});
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// Test Compute(Tensor, Tensor) method of ResizeWithBBox
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auto transform = Execute({decode_op, resizewithbbox_op});
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// Expect fail since Compute(Tensor, Tensor) is not a valid behaviour for this Op,
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// while Compute(TensorRow, TensorRow) is the correct one.
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Status rc = transform(image, &image);
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EXPECT_FALSE(rc.IsOk());
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}
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