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9cd00ad4c5
* Refactoring code of YOLOv5Cls with new model type * fix reviewed problem * Normalize&HWC2CHW -> NormalizeAndPermute * remove cast()
89 lines
3.2 KiB
C++
89 lines
3.2 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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#include "fastdeploy/vision/classification/contrib/yolov5cls/preprocessor.h"
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#include "fastdeploy/function/concat.h"
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namespace fastdeploy {
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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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}
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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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// 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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// yolov5cls's preprocess steps
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// 1. CenterCrop
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// 2. Normalize
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// CenterCrop
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int crop_size = std::min(mat->Height(), mat->Width());
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CenterCrop::Run(mat, crop_size, crop_size);
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Resize::Run(mat, size_[0], size_[1], -1, -1, cv::INTER_LINEAR);
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// Normalize
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BGR2RGB::Run(mat);
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std::vector<float> alpha = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
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std::vector<float> beta = {0.0f, 0.0f, 0.0f};
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Convert::Run(mat, alpha, beta);
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std::vector<float> mean = {0.485f, 0.456f, 0.406f};
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std::vector<float> std = {0.229f, 0.224f, 0.225f};
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NormalizeAndPermute::Run(mat, mean, std, false);
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// Record output shape of preprocessed image
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(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
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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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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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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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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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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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return false;
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}
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}
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if (tensors.size() == 1) {
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(*outputs)[0] = std::move(tensors[0]);
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} else {
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function::Concat(tensors, &((*outputs)[0]), 0);
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}
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return true;
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}
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} // namespace classification
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} // namespace vision
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} // namespace fastdeploy
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