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https://github.com/PaddlePaddle/FastDeploy.git
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77cb9db6da
* [cmake] add faiss.cmake -> pp-shituv2 * [PP-ShiTuV2] Support PP-ShituV2-Det model * [PP-ShiTuV2] Support PP-ShiTuV2-Det model * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [Bug Fix] fix ppshitu_pybind error * [benchmark] Add ppshituv2-det c++ benchmark * [examples] Add PP-ShiTuV2 det & rec examples * [vision] Update vision classification result * [Bug Fix] fix trt shapes setting errors
109 lines
3.4 KiB
C++
109 lines
3.4 KiB
C++
#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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// Add "PicoDet" arch for backward compability with the
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// old ppdet model, such as picodet from PaddleClas
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// PP-ShiTuV2 pipeline.
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std::vector<std::string> archs = {
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"SOLOv2", "YOLO", "SSD", "RetinaNet", "RCNN", "Face",
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"GFL", "YOLOX", "YOLOv5", "YOLOv6", "YOLOv7", "RTMDet",
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"FCOS", "TTFNet", "TOOD", "DETR", "PicoDet"};
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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, "
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<< "RCNN, Face , GFL , RTMDet ,"
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<< "FCOS , TTFNet , TOOD , DETR, PicoDet" << 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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