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Code Issues Actions 2 Packages Projects Releases Wiki Activity

[Model] Add Solov2 For PaddleDetection (#1435)

Browse Source 
* update solov2

* Repair note

* update solov2 postprocess

* update

* update solov2

* update solov2

* fixed bug

* fixed bug

* update solov2

* update solov2

* fix build android bug

* update docs

* update docs

* update docs

* update

* update

* update arch and docs

* update

* update

* update solov2 python

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
Zheng-Bicheng
2023-03-08 10:01:32 +08:00
committed by GitHub
parent 96a3698271
commit 0687d3b0ad
21 changed files with 840 additions and 474 deletions
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136
fastdeploy/vision/tracking/pptracking/model.cc Executable file → Normal file
View File

@@ -13,6 +13,7 @@
// limitations under the License.
#include "fastdeploy/vision/tracking/pptracking/model.h"
#include "fastdeploy/vision/tracking/pptracking/letter_box_resize.h"
#include "yaml-cpp/yaml.h"
@@ -24,8 +25,8 @@ PPTracking::PPTracking(const std::string& model_file,
const std::string& params_file,
const std::string& config_file,
const RuntimeOption& custom_option,
const ModelFormat& model_format){
config_file_=config_file;
const ModelFormat& model_format) {
config_file_ = config_file;
valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
@@ -37,30 +38,29 @@ PPTracking::PPTracking(const std::string& model_file,
initialized = Initialize();
}
bool PPTracking::BuildPreprocessPipelineFromConfig(){
bool PPTracking::BuildPreprocessPipelineFromConfig() {
processors_.clear();
YAML::Node cfg;
try {
cfg = YAML::LoadFile(config_file_);
cfg = YAML::LoadFile(config_file_);
} catch (YAML::BadFile& e) {
FDERROR << "Failed to load yaml file " << config_file_
<< ", maybe you should check this file." << std::endl;
return false;
FDERROR << "Failed to load yaml file " << config_file_
<< ", maybe you should check this file." << std::endl;
return false;
}
// Get draw_threshold for visualization
if (cfg["draw_threshold"].IsDefined()) {
draw_threshold_ = cfg["draw_threshold"].as<float>();
draw_threshold_ = cfg["draw_threshold"].as<float>();
} else {
FDERROR << "Please set draw_threshold." << std::endl;
return false;
FDERROR << "Please set draw_threshold." << std::endl;
return false;
}
// Get config for tracker
if (cfg["tracker"].IsDefined()) {
if (cfg["tracker"]["conf_thres"].IsDefined()) {
conf_thresh_ = cfg["tracker"]["conf_thres"].as<float>();
}
else {
} else {
std::cerr << "Please set conf_thres in tracker." << std::endl;
return false;
}
@@ -86,48 +86,47 @@ bool PPTracking::BuildPreprocessPipelineFromConfig(){
int width = target_size[1];
int height = target_size[0];
processors_.push_back(
std::make_shared<Resize>(width, height, -1.0, -1.0, interp, false));
std::make_shared<Resize>(width, height, -1.0, -1.0, interp, false));
} else {
int min_target_size = std::min(target_size[0], target_size[1]);
int max_target_size = std::max(target_size[0], target_size[1]);
std::vector<int> max_size;
if (max_target_size > 0) {
max_size.push_back(max_target_size);
max_size.push_back(max_target_size);
max_size.push_back(max_target_size);
max_size.push_back(max_target_size);
}
processors_.push_back(std::make_shared<ResizeByShort>(
min_target_size, interp, true, max_size));
min_target_size, interp, true, max_size));
}
}
else if(op_name == "LetterBoxResize"){
} else if (op_name == "LetterBoxResize") {
auto target_size = op["target_size"].as<std::vector<int>>();
FDASSERT(target_size.size() == 2,"Require size of target_size be 2, but now it's %lu.",
FDASSERT(target_size.size() == 2,
"Require size of target_size be 2, but now it's %lu.",
target_size.size());
std::vector<float> color{127.0f,127.0f,127.0f};
if (op["fill_value"].IsDefined()){
color =op["fill_value"].as<std::vector<float>>();
std::vector<float> color{127.0f, 127.0f, 127.0f};
if (op["fill_value"].IsDefined()) {
color = op["fill_value"].as<std::vector<float>>();
}
processors_.push_back(std::make_shared<LetterBoxResize>(target_size, color));
}
else if (op_name == "NormalizeImage") {
processors_.push_back(
std::make_shared<LetterBoxResize>(target_size, color));
} else if (op_name == "NormalizeImage") {
auto mean = op["mean"].as<std::vector<float>>();
auto std = op["std"].as<std::vector<float>>();
bool is_scale = true;
if (op["is_scale"]) {
is_scale = op["is_scale"].as<bool>();
is_scale = op["is_scale"].as<bool>();
}
std::string norm_type = "mean_std";
if (op["norm_type"]) {
norm_type = op["norm_type"].as<std::string>();
norm_type = op["norm_type"].as<std::string>();
}
if (norm_type != "mean_std") {
std::fill(mean.begin(), mean.end(), 0.0);
std::fill(std.begin(), std.end(), 1.0);
std::fill(mean.begin(), mean.end(), 0.0);
std::fill(std.begin(), std.end(), 1.0);
}
processors_.push_back(std::make_shared<Normalize>(mean, std, is_scale));
}
else if (op_name == "Permute") {
} else if (op_name == "Permute") {
// Do nothing, do permute as the last operation
continue;
// processors_.push_back(std::make_shared<HWC2CHW>());
@@ -136,11 +135,11 @@ bool PPTracking::BuildPreprocessPipelineFromConfig(){
auto value = op["fill_value"].as<std::vector<float>>();
processors_.push_back(std::make_shared<Cast>("float"));
processors_.push_back(
std::make_shared<PadToSize>(size[1], size[0], value));
std::make_shared<PadToSize>(size[1], size[0], value));
} else if (op_name == "PadStride") {
auto stride = op["stride"].as<int>();
processors_.push_back(
std::make_shared<StridePad>(stride, std::vector<float>(3, 0)));
std::make_shared<StridePad>(stride, std::vector<float>(3, 0)));
} else {
FDERROR << "Unexcepted preprocess operator: " << op_name << "."
<< std::endl;
@@ -168,7 +167,7 @@ bool PPTracking::Initialize() {
return true;
}
bool PPTracking::Predict(cv::Mat *img, MOTResult *result) {
bool PPTracking::Predict(cv::Mat* img, MOTResult* result) {
Mat mat(*img);
std::vector<FDTensor> input_tensors;
@@ -189,9 +188,7 @@ bool PPTracking::Predict(cv::Mat *img, MOTResult *result) {
return true;
}
bool PPTracking::Preprocess(Mat* mat, std::vector<FDTensor>* outputs) {
int origin_w = mat->Width();
int origin_h = mat->Height();
@@ -203,9 +200,9 @@ bool PPTracking::Preprocess(Mat* mat, std::vector<FDTensor>* outputs) {
}
}
// LetterBoxResize(mat);
// Normalize::Run(mat,mean_,scale_,is_scale_);
// HWC2CHW::Run(mat);
// LetterBoxResize(mat);
// Normalize::Run(mat,mean_,scale_,is_scale_);
// HWC2CHW::Run(mat);
Cast::Run(mat, "float");
outputs->resize(3);
@@ -226,8 +223,8 @@ bool PPTracking::Preprocess(Mat* mat, std::vector<FDTensor>* outputs) {
return true;
}
void FilterDets(const float conf_thresh,const cv::Mat& dets,std::vector<int>* index) {
void FilterDets(const float conf_thresh, const cv::Mat& dets,
std::vector<int>* index) {
for (int i = 0; i < dets.rows; ++i) {
float score = *dets.ptr<float>(i, 4);
if (score > conf_thresh) {
@@ -236,7 +233,8 @@ void FilterDets(const float conf_thresh,const cv::Mat& dets,std::vector<int>* in
}
}
bool PPTracking::Postprocess(std::vector<FDTensor>& infer_result, MOTResult *result){
bool PPTracking::Postprocess(std::vector<FDTensor>& infer_result,
MOTResult* result) {
auto bbox_shape = infer_result[0].shape;
auto bbox_data = static_cast<float*>(infer_result[0].Data());
@@ -252,15 +250,14 @@ bool PPTracking::Postprocess(std::vector<FDTensor>& infer_result, MOTResult *res
FilterDets(conf_thresh_, dets, &valid);
cv::Mat new_dets, new_emb;
for (int i = 0; i < valid.size(); ++i) {
new_dets.push_back(dets.row(valid[i]));
new_emb.push_back(emb.row(valid[i]));
new_dets.push_back(dets.row(valid[i]));
new_emb.push_back(emb.row(valid[i]));
}
jdeTracker_->update(new_dets, new_emb, &tracks);
if (tracks.size() == 0) {
std::array<int ,4> box={int(*dets.ptr<float>(0, 0)),
int(*dets.ptr<float>(0, 1)),
int(*dets.ptr<float>(0, 2)),
int(*dets.ptr<float>(0, 3))};
std::array<int, 4> box = {
int(*dets.ptr<float>(0, 0)), int(*dets.ptr<float>(0, 1)),
int(*dets.ptr<float>(0, 2)), int(*dets.ptr<float>(0, 3))};
result->boxes.push_back(box);
result->ids.push_back(1);
result->scores.push_back(*dets.ptr<float>(0, 4));
@@ -275,8 +272,8 @@ bool PPTracking::Postprocess(std::vector<FDTensor>& infer_result, MOTResult *res
bool vertical = w / h > 1.6;
float area = w * h;
if (area > min_box_area_ && !vertical) {
std::array<int ,4> box = {
int(titer->ltrb[0]), int(titer->ltrb[1]), int(titer->ltrb[2]), int(titer->ltrb[3])};
std::array<int, 4> box = {int(titer->ltrb[0]), int(titer->ltrb[1]),
int(titer->ltrb[2]), int(titer->ltrb[3])};
result->boxes.push_back(box);
result->ids.push_back(titer->id);
result->scores.push_back(titer->score);
@@ -286,34 +283,33 @@ bool PPTracking::Postprocess(std::vector<FDTensor>& infer_result, MOTResult *res
}
if (!is_record_trail_) return true;
int nums = result->boxes.size();
for (int i=0; i<nums; i++) {
for (int i = 0; i < nums; i++) {
float center_x = (result->boxes[i][0] + result->boxes[i][2]) / 2;
float center_y = (result->boxes[i][1] + result->boxes[i][3]) / 2;
int id = result->ids[i];
recorder_->Add(id,{int(center_x), int(center_y)});
recorder_->Add(id, {int(center_x), int(center_y)});
}
return true;
}
void PPTracking::BindRecorder(TrailRecorder* recorder){
recorder_ = recorder;
is_record_trail_ = true;
void PPTracking::BindRecorder(TrailRecorder* recorder) {
recorder_ = recorder;
is_record_trail_ = true;
}
void PPTracking::UnbindRecorder(){
is_record_trail_ = false;
std::map<int, std::vector<std::array<int, 2>>>::iterator iter;
for(iter = recorder_->records.begin(); iter != recorder_->records.end(); iter++){
iter->second.clear();
iter->second.shrink_to_fit();
}
recorder_->records.clear();
std::map<int, std::vector<std::array<int, 2>>>().swap(recorder_->records);
recorder_ = nullptr;
void PPTracking::UnbindRecorder() {
is_record_trail_ = false;
std::map<int, std::vector<std::array<int, 2>>>::iterator iter;
for (iter = recorder_->records.begin(); iter != recorder_->records.end();
iter++) {
iter->second.clear();
iter->second.shrink_to_fit();
}
recorder_->records.clear();
std::map<int, std::vector<std::array<int, 2>>>().swap(recorder_->records);
recorder_ = nullptr;
}
} // namespace tracking
} // namespace vision
} // namespace fastdeploy
} // namespace tracking
} // namespace vision
} // namespace fastdeploy