mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-27 10:30:34 +08:00
9937b6c325
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST * Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg * Add multi thread demo * Add python model clone function * Add multi thread python && C++ example * Fix bug Co-authored-by: Jason <jiangjiajun@baidu.com>
85 lines
2.6 KiB
C++
Executable File
85 lines
2.6 KiB
C++
Executable File
#include "fastdeploy/vision/detection/ppdet/base.h"
|
|
#include "fastdeploy/vision/utils/utils.h"
|
|
#include "yaml-cpp/yaml.h"
|
|
#include "fastdeploy/utils/unique_ptr.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace detection {
|
|
|
|
PPDetBase::PPDetBase(const std::string& model_file,
|
|
const std::string& params_file,
|
|
const std::string& config_file,
|
|
const RuntimeOption& custom_option,
|
|
const ModelFormat& model_format)
|
|
: preprocessor_(config_file) {
|
|
runtime_option = custom_option;
|
|
runtime_option.model_format = model_format;
|
|
runtime_option.model_file = model_file;
|
|
runtime_option.params_file = params_file;
|
|
}
|
|
|
|
std::unique_ptr<PPDetBase> PPDetBase::Clone() const {
|
|
std::unique_ptr<PPDetBase> clone_model = fastdeploy::utils::make_unique<PPDetBase>(PPDetBase(*this));
|
|
clone_model->SetRuntime(clone_model->CloneRuntime());
|
|
return clone_model;
|
|
}
|
|
|
|
bool PPDetBase::Initialize() {
|
|
if (!InitRuntime()) {
|
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool PPDetBase::Predict(cv::Mat* im, DetectionResult* result) {
|
|
return Predict(*im, result);
|
|
}
|
|
|
|
bool PPDetBase::Predict(const cv::Mat& im, DetectionResult* result) {
|
|
std::vector<DetectionResult> results;
|
|
if (!BatchPredict({im}, &results)) {
|
|
return false;
|
|
}
|
|
*result = std::move(results[0]);
|
|
return true;
|
|
}
|
|
|
|
bool PPDetBase::BatchPredict(const std::vector<cv::Mat>& imgs,
|
|
std::vector<DetectionResult>* results) {
|
|
std::vector<FDMat> fd_images = WrapMat(imgs);
|
|
if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
|
|
FDERROR << "Failed to preprocess the input image." << std::endl;
|
|
return false;
|
|
}
|
|
reused_input_tensors_[0].name = "image";
|
|
reused_input_tensors_[1].name = "scale_factor";
|
|
reused_input_tensors_[2].name = "im_shape";
|
|
|
|
if(postprocessor_.DecodeAndNMSApplied()){
|
|
postprocessor_.SetScaleFactor(static_cast<float*>(reused_input_tensors_[1].Data()));
|
|
}
|
|
|
|
// Some models don't need scale_factor and im_shape as input
|
|
while (reused_input_tensors_.size() != NumInputsOfRuntime()) {
|
|
reused_input_tensors_.pop_back();
|
|
}
|
|
|
|
if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
|
|
FDERROR << "Failed to inference by runtime." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
if (!postprocessor_.Run(reused_output_tensors_, results)) {
|
|
FDERROR << "Failed to postprocess the inference results by runtime."
|
|
<< std::endl;
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
} // namespace detection
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|