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[Benchmark]Compare diff for OCR (#1415)
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug
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@@ -20,18 +20,19 @@ namespace vision {
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namespace classification {
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YOLOv5ClsPreprocessor::YOLOv5ClsPreprocessor() {
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size_ = {224, 224}; //{h,w}
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size_ = {224, 224}; //{h,w}
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}
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bool YOLOv5ClsPreprocessor::Preprocess(FDMat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info) {
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bool YOLOv5ClsPreprocessor::Preprocess(
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FDMat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info) {
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// Record the shape of image and the shape of preprocessed image
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(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width())};
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// process after image load
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double ratio = (size_[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width()));
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static_cast<float>(mat->Width()));
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// yolov5cls's preprocess steps
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// 1. CenterCrop
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@@ -54,20 +55,22 @@ bool YOLOv5ClsPreprocessor::Preprocess(FDMat* mat, FDTensor* output,
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static_cast<float>(mat->Width())};
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mat->ShareWithTensor(output);
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output->ExpandDim(0); // reshape to n, h, w, c
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output->ExpandDim(0); // reshape to n, c, h, w
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return true;
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}
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bool YOLOv5ClsPreprocessor::Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
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std::vector<std::map<std::string, std::array<float, 2>>>* ims_info) {
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bool YOLOv5ClsPreprocessor::Run(
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std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
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std::vector<std::map<std::string, std::array<float, 2>>>* ims_info) {
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if (images->size() == 0) {
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FDERROR << "The size of input images should be greater than 0." << std::endl;
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FDERROR << "The size of input images should be greater than 0."
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<< std::endl;
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return false;
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}
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ims_info->resize(images->size());
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outputs->resize(1);
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// Concat all the preprocessed data to a batch tensor
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std::vector<FDTensor> tensors(images->size());
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std::vector<FDTensor> tensors(images->size());
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for (size_t i = 0; i < images->size(); ++i) {
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if (!Preprocess(&(*images)[i], &tensors[i], &(*ims_info)[i])) {
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FDERROR << "Failed to preprocess input image." << std::endl;
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