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Add writing results on file for map demo

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-03-10 14:35:25 +01:00
parent 0773f216b3
commit 75d5e20843
5 changed files with 71 additions and 13 deletions
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CMakeLists.txt
+3
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@@ -115,6 +115,9 @@ target_link_libraries(test_dla34_cnet tkDNN)
add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
target_link_libraries(test_rtinference tkDNN)
add_executable(map_demo demo/demo/map.cpp)
target_link_libraries(map_demo tkDNN)
add_executable(demo demo/demo/demo.cpp)
target_link_libraries(demo tkDNN)
demo/demo/map.cpp
+20 -2
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@@ -34,7 +34,15 @@ int main(int argc, char *argv[])
char * labels_path = "../demo/COCO_val2017/all_labels.txt";
bool show = false;
bool write_dets = false;
bool write_res_on_file = true;
int n_images = 5000;
std::ofstream times;
if(write_res_on_file)
{
times.open ("times.csv", std::ios_base::app);
times<<net<<";";
}
if(argc > 1)
net = argv[1];
@@ -91,6 +99,7 @@ int main(int argc, char *argv[])
//inference
detected_bbox.clear();
TIMER_START
switch(ntype)
{
case 'y':
@@ -104,6 +113,9 @@ int main(int argc, char *argv[])
default:
FatalError("Network type not allowed!\n");
}
TIMER_STOP
if(write_res_on_file)
times<<t_ns<<";";
std::ofstream myfile;
if(write_dets)
@@ -168,11 +180,17 @@ int main(int argc, char *argv[])
IoU_thresh, conf_thresh, verbose);
//compute mAP
double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose);
double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net);
std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
//compute average precision, recall and f1score
computeTPFPFN(images,classes,IoU_thresh,conf_thresh);
computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net);
if(write_res_on_file)
{
times<<"\n";
times.close();
}
return 0;
include/evaluation.h
+3 -3
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@@ -52,8 +52,8 @@ void readParams(char* config_filename, int& classes, int& map_points,
float& conf_thresh, bool& verbose);
double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh=0.3, const int map_points=101, const bool verbose=false);
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false);
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false, const bool write_on_file = false, std::string net = "");
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false);
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false, const bool write_on_file=false, std::string net="");
#endif /*EVALUATION_H*/
#endif /*EVALUATION_H*/
include/tkDNN/Yolo3Detection.h
+2 -2
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@@ -37,7 +37,7 @@ class Yolo3Detection {
int classes = 0;
int num = 0;
int n_masks = 0;
float thresh = 0.3;
float thresh = 0.05;
cv::Scalar colors[256];
// this is filled with results
@@ -70,4 +70,4 @@ class Yolo3Detection {
}}
#endif /* YOLODETECTION_H*/
#endif /* YOLODETECTION_H*/
src/evaluation.cpp
+43 -6
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@@ -1,4 +1,5 @@
#include "evaluation.h"
#include <fstream>
void BoundingBox::clear()
@@ -283,27 +284,51 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
return mean_average_precision;
}
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose)
double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose, const bool write_on_file, std::string net)
{
double AP = 0;
std::ofstream out_file;
if(write_on_file)
{
out_file.open("map.csv", std::ios_base::app);
out_file<<net<<";";
}
double AP = 0, cur_AP = 0;
float IoU_thresh = i_IoU_thresh;
for(int i=0; i<map_levels; ++i)
{
for(auto& img:images)
for(auto & d:img.det)
d.clear();
AP += computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
cur_AP = computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
if(write_on_file)
out_file<<cur_AP<<";";
AP += cur_AP;
IoU_thresh +=map_step;
}
AP/=map_levels;
if(write_on_file)
{
out_file<<AP<<"\n";
out_file.close();
}
return AP;
}
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose)
void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose, const bool write_on_file, std::string net)
{
std::ofstream out_file;
if(write_on_file)
{
out_file.open("pr.csv", std::ios_base::app);
out_file<<net<<";";
}
std::vector<int> truth_classes_count(classes,0);
std::vector<int> dets_classes_count(classes,0);
std::vector<PR> pr( classes);
std::vector<PR> pr(classes);
for(auto &img:images)
{
@@ -355,6 +380,8 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
double avg_precision = 0, avg_recall = 0, f1_score = 0;
int TP = 0, FP = 0, FN = 0;
for(size_t i=0; i<classes; i++)
{
pr[i].precision = (pr[i].tp + pr[i].fp) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fp) : 0;
@@ -364,13 +391,23 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
// std::cout<<i<<"\t"<<pr[i].tp<<"\t"<<pr[i].fp<<"\t"<<pr[i].fn<<"\t"<<pr[i].precision<<"\t"<<pr[i].recall<<std::endl;
avg_precision += pr[i].precision;
avg_recall += pr[i].recall;
TP += pr[i].tp;
FP += pr[i].fp;
FN += pr[i].fn;
}
avg_precision /= classes;
avg_recall /= classes;
f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
if(write_on_file)
{
out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n";
out_file.close();
}
std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
}
}