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193 lines
7.6 KiB
193 lines
7.6 KiB
/**
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* Copyright 2020 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 <string>
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#include <vector>
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#include "common/common_test.h"
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#include "include/api/types.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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#ifdef ENABLE_ACL
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#include "minddata/dataset/include/vision_ascend.h"
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#endif
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#include "minddata/dataset/kernels/tensor_op.h"
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#include "include/api/model.h"
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#include "include/api/serialization.h"
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#include "include/api/context.h"
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using namespace mindspore;
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using namespace mindspore::dataset;
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using namespace mindspore::dataset::vision;
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class TestDE : public ST::Common {
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public:
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TestDE() {}
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};
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TEST_F(TestDE, TestResNetPreprocess) {
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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 images
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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(TestDE, TestDvpp) {
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#ifdef ENABLE_ACL
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// Read images from target directory
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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 = MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define dvpp transform
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std::vector<uint32_t> crop_paras = {224, 224};
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std::vector<uint32_t> resize_paras = {256, 256};
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std::shared_ptr<TensorTransform> decode_resize_crop(new vision::DvppDecodeResizeCropJpeg(crop_paras, resize_paras));
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mindspore::dataset::Execute Transform(decode_resize_crop, MapTargetDevice::kAscend310);
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// Apply transform on images
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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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int32_t real_h = 0;
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int32_t real_w = 0;
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int32_t remainder = crop_paras[crop_paras.size() - 1] % 16;
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if (crop_paras.size() == 1) {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[0] : crop_paras[0] + 16 - remainder;
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} else {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
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}
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/* TODO Use in the future after compute college finish their job
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ASSERT_EQ(image.Shape()[0], real_h); // For image in YUV format, each pixel takes 1.5 byte
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ASSERT_EQ(image.Shape()[1], real_w);
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ASSERT_EQ(image.DataSize(), real_h * real_w * 1.5);
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*/
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ASSERT_EQ(image.Shape()[0], 1.5 * real_h * real_w); // For image in YUV format, each pixel takes 1.5 byte
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ASSERT_EQ(image.Shape()[1], 1);
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ASSERT_EQ(image.Shape()[2], 1);
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ASSERT_EQ(image.DataSize(), real_h * real_w * 1.5);
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#endif
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}
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TEST_F(TestDE, TestDvppSinkMode) {
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#ifdef ENABLE_ACL
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// Read images from target directory
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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 = MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define dvpp transform
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std::vector<int32_t> crop_paras = {224, 224};
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std::vector<int32_t> resize_paras = {256};
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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::vector<std::shared_ptr<TensorTransform>> trans_list = {decode, resize, centercrop};
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mindspore::dataset::Execute Transform(trans_list, MapTargetDevice::kAscend310);
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// Apply transform on images
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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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int32_t real_h = 0;
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int32_t real_w = 0;
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int32_t remainder = crop_paras[crop_paras.size() - 1] % 16;
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if (crop_paras.size() == 1) {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[0] : crop_paras[0] + 16 - remainder;
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} else {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
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}
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ASSERT_EQ(image.Shape()[0], 1.5 * real_h * real_w); // For image in YUV format, each pixel takes 1.5 byte
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ASSERT_EQ(image.Shape()[1], 1);
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ASSERT_EQ(image.Shape()[2], 1);
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ASSERT_EQ(image.DataSize(), real_h * real_w * 1.5);
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Transform.DeviceMemoryRelease();
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#endif
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}
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TEST_F(TestDE, TestDvppDecodeResizeCropNormalize) {
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#ifdef ENABLE_ACL
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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 = MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
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// Define dvpp transform
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std::vector<int32_t> crop_paras = {416};
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std::vector<int32_t> resize_paras = {512};
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std::vector<float> mean = {0.485 * 255, 0.456 * 255, 0.406 * 255};
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std::vector<float> std = {0.229 * 255, 0.224 * 255, 0.225 * 255};
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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> normalize(new vision::Normalize(mean, std));
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std::vector<std::shared_ptr<TensorTransform>> trans_list = {decode, resize, centercrop, normalize};
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mindspore::dataset::Execute Transform(trans_list, MapTargetDevice::kAscend310);
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std::string aipp_cfg = Transform.AippCfgGenerator();
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ASSERT_EQ(aipp_cfg, "./aipp.cfg");
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// Apply transform on images
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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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int32_t real_h = 0;
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int32_t real_w = 0;
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int32_t remainder = crop_paras[crop_paras.size() - 1] % 16;
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if (crop_paras.size() == 1) {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[0] : crop_paras[0] + 16 - remainder;
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} else {
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real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
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real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
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}
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ASSERT_EQ(image.Shape()[0], 1.5 * real_h * real_w); // For image in YUV format, each pixel takes 1.5 byte
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ASSERT_EQ(image.Shape()[1], 1);
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ASSERT_EQ(image.Shape()[2], 1);
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ASSERT_EQ(image.DataSize(), real_h * real_w * 1.5);
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Transform.DeviceMemoryRelease();
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#endif
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}
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