Change README.md, add config.yaml, refactoring

Signed-off-by: xavier <micaelaverucchi@gmail.com>

CMakeLists.txt

@@ -17,7 +17,7 @@ endif()
 find_package(CUDA 9.0 REQUIRED)
 SET(CUDA_SEPARABLE_COMPILATION ON)
 #set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch=sm_30 --compiler-options '-fPIC'")
-#set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
 
 find_package(CUDNN REQUIRED)
 
@@ -39,7 +39,7 @@ find_package(yaml-cpp REQUIRED)
 # Build Libraries
 #-------------------------------------------------------------------------------
 file(GLOB tkdnn_SRC "src/*.cpp")
-set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS})
+set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES} ${OpenCV_LIBS} yaml-cpp)
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -std=c++11")
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${OPENCV_INCLUDE_DIRS} ${NVINFER_INCLUDES})
@@ -120,7 +120,7 @@ add_executable(centernet_demo demo/demo/demo_centernet.cpp)
 target_link_libraries(centernet_demo tkDNN)
 
 add_executable(map_demo demo/demo/map.cpp)
-target_link_libraries(map_demo tkDNN yaml-cpp)
+target_link_libraries(map_demo tkDNN)
 
 
 #-------------------------------------------------------------------------------

README.md

@@ -7,6 +7,7 @@ this branch actually work on every NVIDIA GPU that support the dependencies:
 * CUDNN 7.603
 * TENSORRT 6.01
 * OPENCV 4.1
+* yaml-cpp 0.5.2 (sudo apt install libyaml-cpp-dev)
 
 ## Workflow
 The recommended workflow follow these step:
@@ -112,4 +113,23 @@ this will genereate resnet101_cnet.rt and dla34_cnet.rt file that can be used fo
 ./centernet_demo                               # launch detection on a demo video
 ./centernet_demo resnet101_cnet.rt /dev/video0 # launch detection on device 0
 ./centernet_demo dla34_cnet.rt /dev/video0     # launch detection on device 0
+```
+
+## mAP demo
+To compute mAP, precision, recall and f1score, run the map_demo.
+
+The following parameters are needed:
+```
+./map_demo <network rt> <network type [y|c]> <labels file path> <config file path>
+```
+where 
+* <network rt>: rt file of a choosen network on wich compute the mAP.
+* <network type [y|c]>: type of network. Right now only y(yolo) and c(centernet) are allowed
+* <labels file path>: path to a text file containing all the paths of the groundtruth labels. It is important that all the labels of the groundtruth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the groundtruth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg. 
+* <config file path>: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml
+
+Example:
+
+```
+./map_demo dla34_cnet.rt c path/to/labelsCOCO_val2017.txt ../demo/config.yaml
 ```

demo/config.yaml

@@ -0,0 +1,7 @@
+classes     : 80    #number of classes
+map_points  : 101   #number of recall points (0 for all, 101 for COCO, 11 PascalVOC)
+map_levels  : 10    #number of IoU step for the AP
+map_step    : 0.05  #step of IoU
+IoU_thresh  : 0.5   #starting IoU threshold
+conf_thresh : 0.3   #threshold on the condifence of the bbox
+verbose     : false #print on screen information

demo/demo/map.cpp

@@ -17,7 +17,7 @@
 #include "evaluation.h"
 
 #include <map>
-#include <yaml-cpp/yaml.h>
+
 
 
 void convertFilename(std::string &filename,const std::string l_folder, const std::string i_folder, const std::string l_ext,const std::string i_ext)
@@ -26,49 +26,24 @@ void convertFilename(std::string &filename,const std::string l_folder, const std
     filename.replace(filename.find(l_ext),l_ext.length(),i_ext);
 }
 
-void readParams(char* config_filename, std::string& net, char &ntype, std::string& labels_path, 
-                bool &show, bool& write_dets, int& classes, int& n_images, 
-                int& map_points, int& map_levels, float& map_step, 
-                float& IoU_thresh, float& conf_thresh, bool& verbose)
-{
-    YAML::Node config   = YAML::LoadFile(config_filename);
-    net         = config["net"].as<std::string>();
-    ntype       = config["ntype"].as<char>();
-    labels_path = config["labels_path"].as<std::string>();
-    show        = config["show"].as<bool>();
-    write_dets  = config["write_dets"].as<bool>();
-    classes     = config["classes"].as<int>();
-    n_images    = config["n_images"].as<int>();
-    map_points  = config["map_points"].as<int>();
-    map_levels  = config["map_levels"].as<int>();
-    map_step    = config["map_step"].as<float>();
-    IoU_thresh  = config["IoU_thresh"].as<float>();
-    conf_thresh = config["conf_thresh"].as<float>();
-    verbose     = config["verbose"].as<bool>();
-
-}
-
 int main(int argc, char *argv[]) 
 {
-
+    char ntype = 'y';
-    char *config_filename = "config.yaml";
+    char *config_filename = "../demo/config.yaml";
+    char * net = "yolo3.rt";
+    char * labels_path = "/media/887E650E7E64F67A/val2017/all_labels2017.txt";
+    bool show = false;
+    bool write_dets = false;
+    int n_images = 1000;
+    
     if(argc > 1)
-        config_filename = argv[1]; 
+        net = argv[1]; 
-
-    char ntype;
-    std::string net, labels_path;
-    bool show, write_dets, verbose;
-    int classes, map_points, map_levels, n_images;
-    float map_step, IoU_thresh, conf_thresh;
-
-    readParams( config_filename, net, ntype, labels_path, show, write_dets, 
-                classes, n_images, map_points, map_levels, map_step, 
-                IoU_thresh, conf_thresh, verbose);
-
     if(argc > 2)
-        net = argv[2]; 
+        ntype = argv[2][0];    
     if(argc > 3)
-        ntype = argv[3][0];    
+        labels_path = argv[3]; 
+    if(argc > 3)
+        config_filename = argv[4]; 
 
     tk::dnn::Yolo3Detection yolo;
     tk::dnn::CenternetDetection cnet;
@@ -183,6 +158,14 @@ int main(int argc, char *argv[])
     }
 
     std::cout<<"Done."<<std::endl;
+
+    bool verbose;
+    int classes, map_points, map_levels;
+    float map_step, IoU_thresh, conf_thresh;
+
+    //read mAP parameters
+    readParams( config_filename, classes,  map_points, map_levels, map_step, 
+                IoU_thresh, conf_thresh, verbose);
     
     //compute mAP
     double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose);

include/evaluation.h

@@ -5,6 +5,8 @@
 #include <vector>
 #include <algorithm>
 
+#include <yaml-cpp/yaml.h>
+
 #include "tkdnn.h"
 
 
@@ -45,6 +47,10 @@ float boxIntersection(const BoundingBox &a, const BoundingBox &b);
 float boxUnion(const BoundingBox &a, const BoundingBox &b);
 float boxIoU(const BoundingBox &a, const BoundingBox &b);
 
+void readParams(char* config_filename, int& classes, int& map_points, 
+                int& map_levels, float& map_step, float& IoU_thresh, 
+                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);
 

src/evaluation.cpp

@@ -76,6 +76,21 @@ float boxIoU(const BoundingBox &a, const BoundingBox &b)
     return I / U;
 }
 
+void readParams(char* config_filename, int& classes, int& map_points, 
+                int& map_levels, float& map_step, float& IoU_thresh, 
+                float& conf_thresh, bool& verbose)
+{
+    YAML::Node config   = YAML::LoadFile(config_filename);
+    classes     = config["classes"].as<int>();
+    map_points  = config["map_points"].as<int>();
+    map_levels  = config["map_levels"].as<int>();
+    map_step    = config["map_step"].as<float>();
+    IoU_thresh  = config["IoU_thresh"].as<float>();
+    conf_thresh = config["conf_thresh"].as<float>();
+    verbose     = config["verbose"].as<bool>();
+
+}
+
 /* Credits to https://github.com/AlexeyAB/darknet/blob/master/src/detector.c*/
 double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, const int map_points, const bool verbose)
 {