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b557dbc2d8
* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test Co-authored-by: Jason <jiangjiajun@baidu.com>
117 lines
3.9 KiB
C++
Executable File
117 lines
3.9 KiB
C++
Executable File
// 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.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace classification {
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YOLOv5Cls::YOLOv5Cls(const std::string& model_file,
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const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::OPENVINO, Backend::ORT};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool YOLOv5Cls::Initialize() {
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// preprocess parameters
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size = {224, 224};
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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bool YOLOv5Cls::Preprocess(Mat* mat, FDTensor* output,
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const std::vector<int>& size) {
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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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Normalize::Run(mat, mean, std, false);
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1);
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return true;
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}
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bool YOLOv5Cls::Postprocess(const FDTensor& infer_result,
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ClassifyResult* result, int topk) {
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// Softmax
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FDTensor infer_result_softmax;
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Softmax(infer_result, &infer_result_softmax, 1);
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int num_classes = infer_result_softmax.shape[1];
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const float* infer_result_buffer =
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reinterpret_cast<const float*>(infer_result_softmax.Data());
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topk = std::min(num_classes, topk);
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result->label_ids =
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utils::TopKIndices(infer_result_buffer, num_classes, topk);
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result->scores.resize(topk);
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for (int i = 0; i < topk; ++i) {
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result->scores[i] = *(infer_result_buffer + result->label_ids[i]);
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}
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return true;
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}
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bool YOLOv5Cls::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
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Mat mat(*im);
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std::vector<FDTensor> input_tensors(1);
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if (!Preprocess(&mat, &input_tensors[0], size)) {
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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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input_tensors[0].name = InputInfoOfRuntime(0).name;
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std::vector<FDTensor> output_tensors(1);
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if (!Infer(input_tensors, &output_tensors)) {
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FDERROR << "Failed to inference." << std::endl;
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return false;
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}
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if (!Postprocess(output_tensors[0], result, topk)) {
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FDERROR << "Failed to post process." << std::endl;
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return false;
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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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