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92 lines
3.2 KiB
92 lines
3.2 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/minddata_eager.h"
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#include "minddata/dataset/include/vision.h"
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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::api;
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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 from target directory
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std::vector<std::shared_ptr<Tensor>> images;
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MindDataEager::LoadImageFromDir("/home/workspace/mindspore_dataset/imagenet/imagenet_original/val/n01440764",
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&images);
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// Define transform operations
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MindDataEager Transform({Decode(), Resize({224, 224}),
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Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}),
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HWC2CHW()});
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// Apply transform on images
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for (auto &img : images) {
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img = Transform(img);
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}
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// Check shape of result
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ASSERT_NE(images.size(), 0);
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ASSERT_EQ(images[0]->Shape().size(), 3);
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ASSERT_EQ(images[0]->Shape()[0], 3);
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ASSERT_EQ(images[0]->Shape()[1], 224);
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ASSERT_EQ(images[0]->Shape()[2], 224);
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}
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TEST_F(TestDE, TestDvpp) {
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ContextAutoSet();
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// Read images from target directory
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std::vector<std::shared_ptr<Tensor>> images;
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MindDataEager::LoadImageFromDir("/home/workspace/mindspore_dataset/imagenet/imagenet_original/val/n01440764",
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&images);
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// Define dvpp transform
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std::vector<uint32_t> crop_size = {224, 224};
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std::vector<uint32_t> resize_size = {256, 256};
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MindDataEager Transform({DvppDecodeResizeCropJpeg(crop_size, resize_size)});
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// Apply transform on images
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for (auto &img : images) {
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img = Transform(img);
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ASSERT_NE(img, nullptr);
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ASSERT_EQ(img->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_size[crop_size.size() - 1] % 16;
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if (crop_size.size() == 1) {
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real_h = (crop_size[0] % 2 == 0) ? crop_size[0] : crop_size[0] + 1;
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real_w = (remainder == 0) ? crop_size[0] : crop_size[0] + 16 - remainder;
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} else {
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real_h = (crop_size[0] % 2 == 0) ? crop_size[0] : crop_size[0] + 1;
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real_w = (remainder == 0) ? crop_size[1] : crop_size[1] + 16 - remainder;
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}
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ASSERT_EQ(img->Shape()[0], real_h * real_w * 1.5); // For image in YUV format, each pixel takes 1.5 byte
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ASSERT_EQ(img->Shape()[1], 1);
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ASSERT_EQ(img->Shape()[2], 1);
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}
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}
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