mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
linyangshi
9164796645
* 添加paddleclas模型 * 更新README_CN * 更新README_CN * 更新README * update get_model.sh * update get_models.sh * update paddleseg models * update paddle_seg models * update paddle_seg models * modified test resources * update benchmark_gpu_trt.sh * add paddle detection * add paddledetection to benchmark * modified benchmark cmakelists * update benchmark scripts * modified benchmark function calling * modified paddledetection documents * add PaddleDetectonModel * reset examples/paddledetection * resolve conflict * update pybind * resolve conflict * fix bug * delete debug mode * update checkarch log * update trt inputs example * Update README.md --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
105 lines
3.2 KiB
C++
Executable File
105 lines
3.2 KiB
C++
Executable File
#include "fastdeploy/vision/detection/ppdet/base.h"
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#include "fastdeploy/utils/unique_ptr.h"
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#include "fastdeploy/vision/utils/utils.h"
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#include "yaml-cpp/yaml.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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PPDetBase::PPDetBase(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format)
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: preprocessor_(config_file), postprocessor_(preprocessor_.GetArch()) {
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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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}
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std::unique_ptr<PPDetBase> PPDetBase::Clone() const {
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std::unique_ptr<PPDetBase> clone_model =
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fastdeploy::utils::make_unique<PPDetBase>(PPDetBase(*this));
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clone_model->SetRuntime(clone_model->CloneRuntime());
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return clone_model;
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}
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bool PPDetBase::Initialize() {
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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 PPDetBase::Predict(cv::Mat* im, DetectionResult* result) {
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return Predict(*im, result);
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}
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bool PPDetBase::Predict(const cv::Mat& im, DetectionResult* result) {
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std::vector<DetectionResult> results;
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if (!BatchPredict({im}, &results)) {
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return false;
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}
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*result = std::move(results[0]);
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return true;
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}
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bool PPDetBase::BatchPredict(const std::vector<cv::Mat>& imgs,
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std::vector<DetectionResult>* results) {
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std::vector<FDMat> fd_images = WrapMat(imgs);
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
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FDERROR << "Failed to preprocess the input image." << std::endl;
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return false;
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}
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reused_input_tensors_[0].name = "image";
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reused_input_tensors_[1].name = "scale_factor";
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reused_input_tensors_[2].name = "im_shape";
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if (NumInputsOfRuntime() == 1) {
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auto scale_factor = static_cast<float*>(reused_input_tensors_[1].Data());
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postprocessor_.SetScaleFactor({scale_factor[0], scale_factor[1]});
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}
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// Some models don't need scale_factor and im_shape as input
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while (reused_input_tensors_.size() != NumInputsOfRuntime()) {
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reused_input_tensors_.pop_back();
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}
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, results)) {
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FDERROR << "Failed to postprocess the inference results by runtime."
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<< 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 PPDetBase::CheckArch(){
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std::vector<std::string> archs = {"SOLOv2","YOLO","SSD","RetinaNet","RCNN","Face","GFL","YOLOX","YOLOv5","YOLOv6","YOLOv7","RTMDet","FCOS","TTFNet","TOOD","DETR"};
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auto arch_ = preprocessor_.GetArch();
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for (auto item : archs) {
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if (arch_ == item) {
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return true;
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}
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}
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FDWARNING << "Please set model arch,"
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<< "support value : SOLOv2, YOLO, SSD, RetinaNet, RCNN, Face , GFL , RTMDet ,"\
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<<"FCOS , TTFNet , TOOD , DETR." << std::endl;
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return false;
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}
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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