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215 lines
8.3 KiB
215 lines
8.3 KiB
/**
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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 "common/cvop_common.h"
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#include "minddata/dataset/core/de_tensor.h"
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#include "minddata/dataset/include/execute.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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#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::CVOP::CVOpCommon {
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protected:
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MindDataTestExecute() : CVOpCommon() {}
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std::shared_ptr<Tensor> output_tensor_;
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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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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<DETensor>(de_tensor));
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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, 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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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 = 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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// Read images
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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
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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(new vision::Decode()); // auto will create raw pointer to Decode class
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auto resize(new vision::Resize({224, 224}));
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auto 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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auto hwc2chw(new vision::HWC2CHW());
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std::vector<TensorTransform *> op_list = {decode, resize, normalize, hwc2chw};
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mindspore::dataset::Execute Transform(op_list);
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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, 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 images
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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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auto decode(new vision::Decode()); // auto will create raw pointer to Decode class
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auto resize(new vision::Resize({224, 224}));
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auto 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<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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auto hwc2chw(new vision::HWC2CHW());
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TensorTransform *op_single = hwc2chw;
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mindspore::dataset::Execute Transform2(op_single);
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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 auto pointers
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// Read image
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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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auto decode(new vision::Decode());
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auto resize(new vision::Resize(resize_paras));
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auto centercrop(new vision::CenterCrop(crop_paras));
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auto hwc2chw(new vision::HWC2CHW());
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std::vector<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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