text: #include <stdlib.h>
  #include "darknet.h"
  #include "network.h"
  #include "region_layer.h"
  #include "cost_layer.h"
  #include "utils.h"
  #include "parser.h"
  #include "box.h"
  #include "demo.h"
  #include "option_list.h"
  
  #ifndef __COMPAR_FN_T
  #define __COMPAR_FN_T
  typedef int (*__compar_fn_t)(const void*, const void*);
  #ifdef __USE_GNU
  typedef __compar_fn_t comparison_fn_t;
  #endif
  #endif
  
  #include "http_stream.h"
  
  int check_mistakes = 0;
  
  static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 };
  
  void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path)
  {
      list *options = read_data_cfg(datacfg);
      char *train_images = option_find_str(options, "train", "data/train.txt");
      char *valid_images = option_find_str(options, "valid", train_images);
      char *backup_directory = option_find_str(options, "backup", "/backup/");
  
      network net_map;
      if (calc_map) {
          FILE* valid_file = fopen(valid_images, "r");
          if (!valid_file) {
              printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
              getchar();
              exit(-1);
          }
          else fclose(valid_file);
  
          cuda_set_device(gpus[0]);
          printf(" Prepare additional network for mAP calculation...\n");
          net_map = parse_network_cfg_custom(cfgfile, 1, 1);
          net_map.benchmark_layers = benchmark_layers;
          const int net_classes = net_map.layers[net_map.n - 1].classes;
  
          int k;  // free memory unnecessary arrays
          for (k = 0; k < net_map.n - 1; ++k) free_layer_custom(net_map.layers[k], 1);
  
          char *name_list = option_find_str(options, "names", "data/names.list");
          int names_size = 0;
          char **names = get_labels_custom(name_list, &names_size);
          if (net_classes != names_size) {
              printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
                  name_list, names_size, net_classes, cfgfile);
              if (net_classes > names_size) getchar();
          }
          free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes);
      }
  
      srand(time(0));
      char *base = basecfg(cfgfile);
      printf("%s\n", base);
      float avg_loss = -1;
      network* nets = (network*)xcalloc(ngpus, sizeof(network));
  
      srand(time(0));
      int seed = rand();
      int k;
      for (k = 0; k < ngpus; ++k) {
          srand(seed);
  #ifdef GPU
          cuda_set_device(gpus[k]);
  #endif
          nets[k] = parse_network_cfg(cfgfile);
          nets[k].benchmark_layers = benchmark_layers;
          if (weightfile) {
              load_weights(&nets[k], weightfile);
          }
          if (clear) {
              *nets[k].seen = 0;
              *nets[k].cur_iteration = 0;
          }
          nets[k].learning_rate *= ngpus;
      }
      srand(time(0));
      network net = nets[0];
  
      const int actual_batch_size = net.batch * net.subdivisions;
      if (actual_batch_size == 1) {
          printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");
          getchar();
      }
      else if (actual_batch_size < 8) {
          printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);
      }
  
      int imgs = net.batch * net.subdivisions * ngpus;
      printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      data train, buffer;
  
      layer l = net.layers[net.n - 1];
  
      int classes = l.classes;
      float jitter = l.jitter;
  
      list *plist = get_paths(train_images);
      int train_images_num = plist->size;
      char **paths = (char **)list_to_array(plist);
  
      const int init_w = net.w;
      const int init_h = net.h;
      const int init_b = net.batch;
      int iter_save, iter_save_last, iter_map;
      iter_save = get_current_iteration(net);
      iter_save_last = get_current_iteration(net);
      iter_map = get_current_iteration(net);
      float mean_average_precision = -1;
      float best_map = mean_average_precision;
  
      load_args args = { 0 };
      args.w = net.w;
      args.h = net.h;
      args.c = net.c;
      args.paths = paths;
      args.n = imgs;
      args.m = plist->size;
      args.classes = classes;
      args.flip = net.flip;
      args.jitter = jitter;
      args.num_boxes = l.max_boxes;
      net.num_boxes = args.num_boxes;
      net.train_images_num = train_images_num;
      args.d = &buffer;
      args.type = DETECTION_DATA;
      args.threads = 64;    // 16 or 64
  
      args.angle = net.angle;
      args.gaussian_noise = net.gaussian_noise;
      args.blur = net.blur;
      args.mixup = net.mixup;
      args.exposure = net.exposure;
      args.saturation = net.saturation;
      args.hue = net.hue;
      args.letter_box = net.letter_box;
      if (dont_show && show_imgs) show_imgs = 2;
      args.show_imgs = show_imgs;
  
  #ifdef OPENCV
      args.threads = 6 * ngpus;   // 3 for - Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge
      //args.threads = 12 * ngpus;    // Ryzen 7 2700X (16 logical cores)
      mat_cv* img = NULL;
      float max_img_loss = 5;
      int number_of_lines = 100;
      int img_size = 1000;
      char windows_name[100];
      sprintf(windows_name, "chart_%s.png", base);
      img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
  #endif    //OPENCV
      if (net.track) {
          args.track = net.track;
          args.augment_speed = net.augment_speed;
          if (net.sequential_subdivisions) args.threads = net.sequential_subdivisions * ngpus;
          else args.threads = net.subdivisions * ngpus;
          args.mini_batch = net.batch / net.time_steps;
          printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch);
      }
      //printf(" imgs = %d \n", imgs);
  
      pthread_t load_thread = load_data(args);
  
      int count = 0;
      double time_remaining, avg_time = -1, alpha_time = 0.01;
  
      //while(i*imgs < N*120){
      while (get_current_iteration(net) < net.max_batches) {
          if (l.random && count++ % 10 == 0) {
              float rand_coef = 1.4;
              if (l.random != 1.0) rand_coef = l.random;
              printf("Resizing, random_coef = %.2f \n", rand_coef);
              float random_val = rand_scale(rand_coef);    // *x or /x
              int dim_w = roundl(random_val*init_w / net.resize_step + 1) * net.resize_step;
              int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step;
              if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h;
  
              int max_dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step;
              int max_dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step;
  
              // at the beginning (check if enough memory) and at the end (calc rolling mean/variance)
              if (avg_loss < 0 || get_current_iteration(net) > net.max_batches - 100) {
                  dim_w = max_dim_w;
                  dim_h = max_dim_h;
              }
  
              if (dim_w < net.resize_step) dim_w = net.resize_step;
              if (dim_h < net.resize_step) dim_h = net.resize_step;
              int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h);
              int new_dim_b = (int)(dim_b * 0.8);
              if (new_dim_b > init_b) dim_b = new_dim_b;
  
              args.w = dim_w;
              args.h = dim_h;
  
              int k;
              if (net.dynamic_minibatch) {
                  for (k = 0; k < ngpus; ++k) {
                      (*nets[k].seen) = init_b * net.subdivisions * get_current_iteration(net); // remove this line, when you will save to weights-file both: seen & cur_iteration
                      nets[k].batch = dim_b;
                      int j;
                      for (j = 0; j < nets[k].n; ++j)
                          nets[k].layers[j].batch = dim_b;
                  }
                  net.batch = dim_b;
                  imgs = net.batch * net.subdivisions * ngpus;
                  args.n = imgs;
                  printf("\n %d x %d  (batch = %d) \n", dim_w, dim_h, net.batch);
              }
              else
                  printf("\n %d x %d \n", dim_w, dim_h);
  
              pthread_join(load_thread, 0);
              train = buffer;
              free_data(train);
              load_thread = load_data(args);
  
              for (k = 0; k < ngpus; ++k) {
                  resize_network(nets + k, dim_w, dim_h);
              }
              net = nets[0];
          }
          double time = what_time_is_it_now();
          pthread_join(load_thread, 0);
          train = buffer;
          if (net.track) {
              net.sequential_subdivisions = get_current_seq_subdivisions(net);
              args.threads = net.sequential_subdivisions * ngpus;
              printf(" sequential_subdivisions = %d, sequence = %d \n", net.sequential_subdivisions, get_sequence_value(net));
          }
          load_thread = load_data(args);
  
          /*
          int k;
          for(k = 0; k < l.max_boxes; ++k){
          box b = float_to_box(train.y.vals[10] + 1 + k*5);
          if(!b.x) break;
          printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
          }
          image im = float_to_image(448, 448, 3, train.X.vals[10]);
          int k;
          for(k = 0; k < l.max_boxes; ++k){
          box b = float_to_box(train.y.vals[10] + 1 + k*5);
          printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
          draw_bbox(im, b, 8, 1,0,0);
          }
          save_image(im, "truth11");
          */
  
          const double load_time = (what_time_is_it_now() - time);
          printf("Loaded: %lf seconds", load_time);
          if (load_time > 0.1 && avg_loss > 0) printf(" - performance bottleneck on CPU or Disk HDD/SSD");
          printf("\n");
  
          time = what_time_is_it_now();
          float loss = 0;
  #ifdef GPU
          if (ngpus == 1) {
              int wait_key = (dont_show) ? 0 : 1;
              loss = train_network_waitkey(net, train, wait_key);
          }
          else {
              loss = train_networks(nets, ngpus, train, 4);
          }
  #else
          loss = train_network(net, train);
  #endif
          if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss;    // if(-inf or nan)
          avg_loss = avg_loss*.9 + loss*.1;
  
          const int iteration = get_current_iteration(net);
          //i = get_current_batch(net);
  
          int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochs
          calc_map_for_each = fmax(calc_map_for_each, 100);
          int next_map_calc = iter_map + calc_map_for_each;
          next_map_calc = fmax(next_map_calc, net.burn_in);
          //next_map_calc = fmax(next_map_calc, 400);
          if (calc_map) {
              printf("\n (next mAP calculation at %d iterations) ", next_map_calc);
              if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100);
          }
  
          if (net.cudnn_half) {
              if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
              else fprintf(stderr, "\n Tensor Cores are used.");
          }
          printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f hours left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time);
  
          int draw_precision = 0;
          if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) {
              if (l.random) {
                  printf("Resizing to initial size: %d x %d ", init_w, init_h);
                  args.w = init_w;
                  args.h = init_h;
                  int k;
                  if (net.dynamic_minibatch) {
                      for (k = 0; k < ngpus; ++k) {
                          for (k = 0; k < ngpus; ++k) {
                              nets[k].batch = init_b;
                              int j;
                              for (j = 0; j < nets[k].n; ++j)
                                  nets[k].layers[j].batch = init_b;
                          }
                      }
                      net.batch = init_b;
                      imgs = init_b * net.subdivisions * ngpus;
                      args.n = imgs;
                      printf("\n %d x %d  (batch = %d) \n", init_w, init_h, init_b);
                  }
                  pthread_join(load_thread, 0);
                  free_data(train);
                  train = buffer;
                  load_thread = load_data(args);
                  for (k = 0; k < ngpus; ++k) {
                      resize_network(nets + k, init_w, init_h);
                  }
                  net = nets[0];
              }
  
              copy_weights_net(net, &net_map);
  
              // combine Training and Validation networks
              //network net_combined = combine_train_valid_networks(net, net_map);
  
              iter_map = iteration;
              mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined);
              printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision);
              if (mean_average_precision > best_map) {
                  best_map = mean_average_precision;
                  printf("New best mAP!\n");
                  char buff[256];
                  sprintf(buff, "%s/%s_best.weights", backup_directory, base);
                  save_weights(net, buff);
              }
  
              draw_precision = 1;
          }
          time_remaining = (net.max_batches - iteration)*(what_time_is_it_now() - time + load_time) / 60 / 60;
          // set initial value, even if resume training from 10000 iteration
          if (avg_time < 0) avg_time = time_remaining;
          else avg_time = alpha_time * time_remaining + (1 -  alpha_time) * avg_time;
  #ifdef OPENCV
          draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_time);
  #endif    // OPENCV
  
          //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
          //if (i % 100 == 0) {
          if (iteration >= (iter_save + 1000) || iteration % 1000 == 0) {
              iter_save = iteration;
  #ifdef GPU
              if (ngpus != 1) sync_nets(nets, ngpus, 0);
  #endif
              char buff[256];
              sprintf(buff, "%s/%s_%d.weights", backup_directory, base, iteration);
              save_weights(net, buff);
          }
  
          if (iteration >= (iter_save_last + 100) || (iteration % 100 == 0 && iteration > 1)) {
              iter_save_last = iteration;
  #ifdef GPU
              if (ngpus != 1) sync_nets(nets, ngpus, 0);
  #endif
              char buff[256];
              sprintf(buff, "%s/%s_last.weights", backup_directory, base);
              save_weights(net, buff);
          }
          free_data(train);
      }
  #ifdef GPU
      if (ngpus != 1) sync_nets(nets, ngpus, 0);
  #endif
      char buff[256];
      sprintf(buff, "%s/%s_final.weights", backup_directory, base);
      save_weights(net, buff);
  
  #ifdef OPENCV
      release_mat(&img);
      destroy_all_windows_cv();
  #endif
  
      // free memory
      pthread_join(load_thread, 0);
      free_data(buffer);
  
      free_load_threads(&args);
  
      free(base);
      free(paths);
      free_list_contents(plist);
      free_list(plist);
  
      free_list_contents_kvp(options);
      free_list(options);
  
      for (k = 0; k < ngpus; ++k) free_network(nets[k]);
      free(nets);
      //free_network(net);
  
      if (calc_map) {
          net_map.n = 0;
          free_network(net_map);
      }
  }
  
  
  static int get_coco_image_id(char *filename)
  {
      char *p = strrchr(filename, '/');
      char *c = strrchr(filename, '_');
      if (c) p = c;
      return atoi(p + 1);
  }
  
  static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
  {
      int i, j;
      //int image_id = get_coco_image_id(image_path);
      char *p = basecfg(image_path);
      int image_id = atoi(p);
      for (i = 0; i < num_boxes; ++i) {
          float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
          float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
          float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
          float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
  
          if (xmin < 0) xmin = 0;
          if (ymin < 0) ymin = 0;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          float bx = xmin;
          float by = ymin;
          float bw = xmax - xmin;
          float bh = ymax - ymin;
  
          for (j = 0; j < classes; ++j) {
              if (dets[i].prob[j] > 0) {
                  char buff[1024];
                  sprintf(buff, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
                  fprintf(fp, buff);
                  //printf("%s", buff);
              }
          }
      }
  }
  
  void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
  {
      int i, j;
      for (i = 0; i < total; ++i) {
          float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
          float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
          float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
          float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1;
  
          if (xmin < 1) xmin = 1;
          if (ymin < 1) ymin = 1;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          for (j = 0; j < classes; ++j) {
              if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
                  xmin, ymin, xmax, ymax);
          }
      }
  }
  
  void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
  {
      int i, j;
      for (i = 0; i < total; ++i) {
          float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
          float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
          float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
          float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
  
          if (xmin < 0) xmin = 0;
          if (ymin < 0) ymin = 0;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          for (j = 0; j < classes; ++j) {
              int myclass = j;
              if (dets[i].prob[myclass] > 0) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass],
                  xmin, ymin, xmax, ymax);
          }
      }
  }
  
  static void print_kitti_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h, char *outfile, char *prefix)
  {
      char *kitti_ids[] = { "car", "pedestrian", "cyclist" };
      FILE *fpd = 0;
      char buffd[1024];
      snprintf(buffd, 1024, "%s/%s/data/%s.txt", prefix, outfile, id);
  
      fpd = fopen(buffd, "w");
      int i, j;
      for (i = 0; i < total; ++i)
      {
          float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
          float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
          float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
          float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
  
          if (xmin < 0) xmin = 0;
          if (ymin < 0) ymin = 0;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          for (j = 0; j < classes; ++j)
          {
              //if (dets[i].prob[j]) fprintf(fpd, "%s 0 0 0 %f %f %f %f -1 -1 -1 -1 0 0 0 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
              if (dets[i].prob[j]) fprintf(fpd, "%s -1 -1 -10 %f %f %f %f -1 -1 -1 -1000 -1000 -1000 -10 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
          }
      }
      fclose(fpd);
  }
  
  static void eliminate_bdd(char *buf, char *a)
  {
      int n = 0;
      int i, k;
      for (i = 0; buf[i] != '\0'; i++)
      {
          if (buf[i] == a[n])
          {
              k = i;
              while (buf[i] == a[n])
              {
                  if (a[++n] == '\0')
                  {
                      for (k; buf[k + n] != '\0'; k++)
                      {
                          buf[k] = buf[k + n];
                      }
                      buf[k] = '\0';
                      break;
                  }
                  i++;
              }
              n = 0; i--;
          }
      }
  }
  
  static void get_bdd_image_id(char *filename)
  {
      char *p = strrchr(filename, '/');
      eliminate_bdd(p, ".jpg");
      eliminate_bdd(p, "/");
      strcpy(filename, p);
  }
  
  static void print_bdd_detections(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
  {
      char *bdd_ids[] = { "bike" , "bus" , "car" , "motor" ,"person", "rider", "traffic light", "traffic sign", "train", "truck" };
      get_bdd_image_id(image_path);
      int i, j;
  
      for (i = 0; i < num_boxes; ++i)
      {
          float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
          float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
          float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
          float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
  
          if (xmin < 0) xmin = 0;
          if (ymin < 0) ymin = 0;
          if (xmax > w) xmax = w;
          if (ymax > h) ymax = h;
  
          float bx1 = xmin;
          float by1 = ymin;
          float bx2 = xmax;
          float by2 = ymax;
  
          for (j = 0; j < classes; ++j)
          {
              if (dets[i].prob[j])
              {
                  fprintf(fp, "\t{\n\t\t\"name\":\"%s\",\n\t\t\"category\":\"%s\",\n\t\t\"bbox\":[%f, %f, %f, %f],\n\t\t\"score\":%f\n\t},\n", image_path, bdd_ids[j], bx1, by1, bx2, by2, dets[i].prob[j]);
              }
          }
      }
  }
  
  void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
  {
      int j;
      list *options = read_data_cfg(datacfg);
      char *valid_images = option_find_str(options, "valid", "data/train.list");
      char *name_list = option_find_str(options, "names", "data/names.list");
      char *prefix = option_find_str(options, "results", "results");
      char **names = get_labels(name_list);
      char *mapf = option_find_str(options, "map", 0);
      int *map = 0;
      if (mapf) map = read_map(mapf);
  
      network net = parse_network_cfg_custom(cfgfile, 1, 1);    // set batch=1
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      //set_batch_network(&net, 1);
      fuse_conv_batchnorm(net);
      calculate_binary_weights(net);
      fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
      srand(time(0));
  
      list *plist = get_paths(valid_images);
      char **paths = (char **)list_to_array(plist);
  
      layer l = net.layers[net.n - 1];
      int classes = l.classes;
  
      char buff[1024];
      char *type = option_find_str(options, "eval", "voc");
      FILE *fp = 0;
      FILE **fps = 0;
      int coco = 0;
      int imagenet = 0;
      int bdd = 0;
      int kitti = 0;
  
      if (0 == strcmp(type, "coco")) {
          if (!outfile) outfile = "coco_results";
          snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
          fp = fopen(buff, "w");
          fprintf(fp, "[\n");
          coco = 1;
      }
      else if (0 == strcmp(type, "bdd")) {
          if (!outfile) outfile = "bdd_results";
          snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
          fp = fopen(buff, "w");
          fprintf(fp, "[\n");
          bdd = 1;
      }
      else if (0 == strcmp(type, "kitti")) {
          char buff2[1024];
          if (!outfile) outfile = "kitti_results";
          printf("%s\n", outfile);
          snprintf(buff, 1024, "%s/%s", prefix, outfile);
          int mkd = make_directory(buff, 0777);
          snprintf(buff2, 1024, "%s/%s/data", prefix, outfile);
          int mkd2 = make_directory(buff2, 0777);
          kitti = 1;
      }
      else if (0 == strcmp(type, "imagenet")) {
          if (!outfile) outfile = "imagenet-detection";
          snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
          fp = fopen(buff, "w");
          imagenet = 1;
          classes = 200;
      }
      else {
          if (!outfile) outfile = "comp4_det_test_";
          fps = (FILE**) xcalloc(classes, sizeof(FILE *));
          for (j = 0; j < classes; ++j) {
              snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
              fps[j] = fopen(buff, "w");
          }
      }
  
  
      int m = plist->size;
      int i = 0;
      int t;
  
      float thresh = .001;
      float nms = .45;
  
      int nthreads = 4;
      if (m < 4) nthreads = m;
      image* val = (image*)xcalloc(nthreads, sizeof(image));
      image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
      image* buf = (image*)xcalloc(nthreads, sizeof(image));
      image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
      pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
  
      load_args args = { 0 };
      args.w = net.w;
      args.h = net.h;
      args.c = net.c;
      args.type = IMAGE_DATA;
      //args.type = LETTERBOX_DATA;
  
      for (t = 0; t < nthreads; ++t) {
          args.path = paths[i + t];
          args.im = &buf[t];
          args.resized = &buf_resized[t];
          thr[t] = load_data_in_thread(args);
      }
      time_t start = time(0);
      for (i = nthreads; i < m + nthreads; i += nthreads) {
          fprintf(stderr, "%d\n", i);
          for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
              pthread_join(thr[t], 0);
              val[t] = buf[t];
              val_resized[t] = buf_resized[t];
          }
          for (t = 0; t < nthreads && i + t < m; ++t) {
              args.path = paths[i + t];
              args.im = &buf[t];
              args.resized = &buf_resized[t];
              thr[t] = load_data_in_thread(args);
          }
          for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
              char *path = paths[i + t - nthreads];
              char *id = basecfg(path);
              float *X = val_resized[t].data;
              network_predict(net, X);
              int w = val[t].w;
              int h = val[t].h;
              int nboxes = 0;
              int letterbox = (args.type == LETTERBOX_DATA);
              detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
              if (nms) {
                  if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
                  else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
              }
  
              if (coco) {
                  print_cocos(fp, path, dets, nboxes, classes, w, h);
              }
              else if (imagenet) {
                  print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
              }
              else if (bdd) {
                  print_bdd_detections(fp, path, dets, nboxes, classes, w, h);
              }
              else if (kitti) {
                  print_kitti_detections(fps, id, dets, nboxes, classes, w, h, outfile, prefix);
              }
              else {
                  print_detector_detections(fps, id, dets, nboxes, classes, w, h);
              }
  
              free_detections(dets, nboxes);
              free(id);
              free_image(val[t]);
              free_image(val_resized[t]);
          }
      }
      if (fps) {
          for (j = 0; j < classes; ++j) {
              fclose(fps[j]);
          }
          free(fps);
      }
      if (coco) {
  #ifdef WIN32
          fseek(fp, -3, SEEK_CUR);
  #else
          fseek(fp, -2, SEEK_CUR);
  #endif
          fprintf(fp, "\n]\n");
      }
  
      if (bdd) {
  #ifdef WIN32
          fseek(fp, -3, SEEK_CUR);
  #else
          fseek(fp, -2, SEEK_CUR);
  #endif
          fprintf(fp, "\n]\n");
          fclose(fp);
      }
  
      if (fp) fclose(fp);
  
      if (val) free(val);
      if (val_resized) free(val_resized);
      if (thr) free(thr);
      if (buf) free(buf);
      if (buf_resized) free(buf_resized);
  
      fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start);
  }
  
  void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
  {
      network net = parse_network_cfg_custom(cfgfile, 1, 1);    // set batch=1
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      //set_batch_network(&net, 1);
      fuse_conv_batchnorm(net);
      srand(time(0));
  
      //list *plist = get_paths("data/coco_val_5k.list");
      list *options = read_data_cfg(datacfg);
      char *valid_images = option_find_str(options, "valid", "data/train.txt");
      list *plist = get_paths(valid_images);
      char **paths = (char **)list_to_array(plist);
  
      //layer l = net.layers[net.n - 1];
  
      int j, k;
  
      int m = plist->size;
      int i = 0;
  
      float thresh = .001;
      float iou_thresh = .5;
      float nms = .4;
  
      int total = 0;
      int correct = 0;
      int proposals = 0;
      float avg_iou = 0;
  
      for (i = 0; i < m; ++i) {
          char *path = paths[i];
          image orig = load_image(path, 0, 0, net.c);
          image sized = resize_image(orig, net.w, net.h);
          char *id = basecfg(path);
          network_predict(net, sized.data);
          int nboxes = 0;
          int letterbox = 0;
          detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
          if (nms) do_nms_obj(dets, nboxes, 1, nms);
  
          char labelpath[4096];
          replace_image_to_label(path, labelpath);
  
          int num_labels = 0;
          box_label *truth = read_boxes(labelpath, &num_labels);
          for (k = 0; k < nboxes; ++k) {
              if (dets[k].objectness > thresh) {
                  ++proposals;
              }
          }
          for (j = 0; j < num_labels; ++j) {
              ++total;
              box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
              float best_iou = 0;
              for (k = 0; k < nboxes; ++k) {
                  float iou = box_iou(dets[k].bbox, t);
                  if (dets[k].objectness > thresh && iou > best_iou) {
                      best_iou = iou;
                  }
              }
              avg_iou += best_iou;
              if (best_iou > iou_thresh) {
                  ++correct;
              }
          }
          //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
          fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total);
          free(id);
          free_image(orig);
          free_image(sized);
      }
  }
  
  typedef struct {
      box b;
      float p;
      int class_id;
      int image_index;
      int truth_flag;
      int unique_truth_index;
  } box_prob;
  
  int detections_comparator(const void *pa, const void *pb)
  {
      box_prob a = *(const box_prob *)pa;
      box_prob b = *(const box_prob *)pb;
      float diff = a.p - b.p;
      if (diff < 0) return 1;
      else if (diff > 0) return -1;
      return 0;
  }
  
  float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net)
  {
      int j;
      list *options = read_data_cfg(datacfg);
      char *valid_images = option_find_str(options, "valid", "data/train.txt");
      char *difficult_valid_images = option_find_str(options, "difficult", NULL);
      char *name_list = option_find_str(options, "names", "data/names.list");
      int names_size = 0;
      char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
      //char *mapf = option_find_str(options, "map", 0);
      //int *map = 0;
      //if (mapf) map = read_map(mapf);
      FILE* reinforcement_fd = NULL;
  
      network net;
      //int initial_batch;
      if (existing_net) {
          char *train_images = option_find_str(options, "train", "data/train.txt");
          valid_images = option_find_str(options, "valid", train_images);
          net = *existing_net;
          remember_network_recurrent_state(*existing_net);
          free_network_recurrent_state(*existing_net);
      }
      else {
          net = parse_network_cfg_custom(cfgfile, 1, 1);    // set batch=1
          if (weightfile) {
              load_weights(&net, weightfile);
          }
          //set_batch_network(&net, 1);
          fuse_conv_batchnorm(net);
          calculate_binary_weights(net);
      }
      if (net.layers[net.n - 1].classes != names_size) {
          printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
              name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
          getchar();
      }
      srand(time(0));
      printf("\n calculation mAP (mean average precision)...\n");
  
      list *plist = get_paths(valid_images);
      char **paths = (char **)list_to_array(plist);
  
      char **paths_dif = NULL;
      if (difficult_valid_images) {
          list *plist_dif = get_paths(difficult_valid_images);
          paths_dif = (char **)list_to_array(plist_dif);
      }
  
  
      layer l = net.layers[net.n - 1];
      int classes = l.classes;
  
      int m = plist->size;
      int i = 0;
      int t;
  
      const float thresh = .005;
      const float nms = .45;
      //const float iou_thresh = 0.5;
  
      int nthreads = 4;
      if (m < 4) nthreads = m;
      image* val = (image*)xcalloc(nthreads, sizeof(image));
      image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
      image* buf = (image*)xcalloc(nthreads, sizeof(image));
      image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
      pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
  
      load_args args = { 0 };
      args.w = net.w;
      args.h = net.h;
      args.c = net.c;
      if (letter_box) args.type = LETTERBOX_DATA;
      else args.type = IMAGE_DATA;
  
      //const float thresh_calc_avg_iou = 0.24;
      float avg_iou = 0;
      int tp_for_thresh = 0;
      int fp_for_thresh = 0;
  
      box_prob* detections = (box_prob*)xcalloc(1, sizeof(box_prob));
      int detections_count = 0;
      int unique_truth_count = 0;
  
      int* truth_classes_count = (int*)xcalloc(classes, sizeof(int));
  
      // For multi-class precision and recall computation
      float *avg_iou_per_class = (float*)xcalloc(classes, sizeof(float));
      int *tp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int));
      int *fp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int));
  
      for (t = 0; t < nthreads; ++t) {
          args.path = paths[i + t];
          args.im = &buf[t];
          args.resized = &buf_resized[t];
          thr[t] = load_data_in_thread(args);
      }
      time_t start = time(0);
      for (i = nthreads; i < m + nthreads; i += nthreads) {
          fprintf(stderr, "\r%d", i);
          for (t = 0; t < nthreads && (i + t - nthreads) < m; ++t) {
              pthread_join(thr[t], 0);
              val[t] = buf[t];
              val_resized[t] = buf_resized[t];
          }
          for (t = 0; t < nthreads && (i + t) < m; ++t) {
              args.path = paths[i + t];
              args.im = &buf[t];
              args.resized = &buf_resized[t];
              thr[t] = load_data_in_thread(args);
          }
          for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
              const int image_index = i + t - nthreads;
              char *path = paths[image_index];
              char *id = basecfg(path);
              float *X = val_resized[t].data;
              network_predict(net, X);
  
              int nboxes = 0;
              float hier_thresh = 0;
              detection *dets;
              if (args.type == LETTERBOX_DATA) {
                  dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
              }
              else {
                  dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letter_box);
              }
              //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1
              if (nms) {
                  if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
                  else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
              }
              //if (nms) do_nms_obj(dets, nboxes, l.classes, nms);
  
              char labelpath[4096];
              replace_image_to_label(path, labelpath);
              int num_labels = 0;
              box_label *truth = read_boxes(labelpath, &num_labels);
              int j;
              for (j = 0; j < num_labels; ++j) {
                  truth_classes_count[truth[j].id]++;
              }
  
              // difficult
              box_label *truth_dif = NULL;
              int num_labels_dif = 0;
              if (paths_dif)
              {
                  char *path_dif = paths_dif[image_index];
  
                  char labelpath_dif[4096];
                  replace_image_to_label(path_dif, labelpath_dif);
  
                  truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
              }
  
              const int checkpoint_detections_count = detections_count;
  
              int i;
              for (i = 0; i < nboxes; ++i) {
  
                  int class_id;
                  for (class_id = 0; class_id < classes; ++class_id) {
                      float prob = dets[i].prob[class_id];
                      if (prob > 0) {
                          detections_count++;
                          detections = (box_prob*)xrealloc(detections, detections_count * sizeof(box_prob));
                          detections[detections_count - 1].b = dets[i].bbox;
                          detections[detections_count - 1].p = prob;
                          detections[detections_count - 1].image_index = image_index;
                          detections[detections_count - 1].class_id = class_id;
                          detections[detections_count - 1].truth_flag = 0;
                          detections[detections_count - 1].unique_truth_index = -1;
  
                          int truth_index = -1;
                          float max_iou = 0;
                          for (j = 0; j < num_labels; ++j)
                          {
                              box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
                              //printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n",
                              //    box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
                              float current_iou = box_iou(dets[i].bbox, t);
                              if (current_iou > iou_thresh && class_id == truth[j].id) {
                                  if (current_iou > max_iou) {
                                      max_iou = current_iou;
                                      truth_index = unique_truth_count + j;
                                  }
                              }
                          }
  
                          // best IoU
                          if (truth_index > -1) {
                              detections[detections_count - 1].truth_flag = 1;
                              detections[detections_count - 1].unique_truth_index = truth_index;
                          }
                          else {
                              // if object is difficult then remove detection
                              for (j = 0; j < num_labels_dif; ++j) {
                                  box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
                                  float current_iou = box_iou(dets[i].bbox, t);
                                  if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
                                      --detections_count;
                                      break;
                                  }
                              }
                          }
  
                          // calc avg IoU, true-positives, false-positives for required Threshold
                          if (prob > thresh_calc_avg_iou) {
                              int z, found = 0;
                              for (z = checkpoint_detections_count; z < detections_count - 1; ++z) {
                                  if (detections[z].unique_truth_index == truth_index) {
                                      found = 1; break;
                                  }
                              }
  
                              if (truth_index > -1 && found == 0) {
                                  avg_iou += max_iou;
                                  ++tp_for_thresh;
                                  avg_iou_per_class[class_id] += max_iou;
                                  tp_for_thresh_per_class[class_id]++;
                              }
                              else{
                                  fp_for_thresh++;
                                  fp_for_thresh_per_class[class_id]++;
                              }
                          }
                      }
                  }
              }
  
              unique_truth_count += num_labels;
  
              //static int previous_errors = 0;
              //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
              //int errors_in_this_image = total_errors - previous_errors;
              //previous_errors = total_errors;
              //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
              //char buff[1000];
              //sprintf(buff, "%s\n", path);
              //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);
  
              free_detections(dets, nboxes);
              free(id);
              free_image(val[t]);
              free_image(val_resized[t]);
          }
      }
  
      //for (t = 0; t < nthreads; ++t) {
      //    pthread_join(thr[t], 0);
      //}
  
      if ((tp_for_thresh + fp_for_thresh) > 0)
          avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
  
      int class_id;
      for(class_id = 0; class_id < classes; class_id++){
          if ((tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]) > 0)
              avg_iou_per_class[class_id] = avg_iou_per_class[class_id] / (tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]);
      }
  
      // SORT(detections)
      qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
  
      typedef struct {
          double precision;
          double recall;
          int tp, fp, fn;
      } pr_t;
  
      // for PR-curve
      pr_t** pr = (pr_t**)xcalloc(classes, sizeof(pr_t*));
      for (i = 0; i < classes; ++i) {
          pr[i] = (pr_t*)xcalloc(detections_count, sizeof(pr_t));
      }
      printf("\n detections_count = %d, unique_truth_count = %d  \n", detections_count, unique_truth_count);
  
  
      int* detection_per_class_count = (int*)xcalloc(classes, sizeof(int));
      for (j = 0; j < detections_count; ++j) {
          detection_per_class_count[detections[j].class_id]++;
      }
  
      int* truth_flags = (int*)xcalloc(unique_truth_count, sizeof(int));
  
      int rank;
      for (rank = 0; rank < detections_count; ++rank) {
          if (rank % 100 == 0)
              printf(" rank = %d of ranks = %d \r", rank, detections_count);
  
          if (rank > 0) {
              int class_id;
              for (class_id = 0; class_id < classes; ++class_id) {
                  pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
                  pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
              }
          }
  
          box_prob d = detections[rank];
          // if (detected && isn't detected before)
          if (d.truth_flag == 1) {
              if (truth_flags[d.unique_truth_index] == 0)
              {
                  truth_flags[d.unique_truth_index] = 1;
                  pr[d.class_id][rank].tp++;    // true-positive
              } else
                  pr[d.class_id][rank].fp++;
          }
          else {
              pr[d.class_id][rank].fp++;    // false-positive
          }
  
          for (i = 0; i < classes; ++i)
          {
              const int tp = pr[i][rank].tp;
              const int fp = pr[i][rank].fp;
              const int fn = truth_classes_count[i] - tp;    // false-negative = objects - true-positive
              pr[i][rank].fn = fn;
  
              if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
              else pr[i][rank].precision = 0;
  
              if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
              else pr[i][rank].recall = 0;
  
              if (rank == (detections_count - 1) && detection_per_class_count[i] != (tp + fp)) {    // check for last rank
                      printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, detection_per_class_count[i], tp+fp, tp, fp);
              }
          }
      }
  
      free(truth_flags);
  
  
      double mean_average_precision = 0;
  
      for (i = 0; i < classes; ++i) {
          double avg_precision = 0;
  
          // MS COCO - uses 101-Recall-points on PR-chart.
          // PascalVOC2007 - uses 11-Recall-points on PR-chart.
          // PascalVOC2010-2012 - uses Area-Under-Curve on PR-chart.
          // ImageNet - uses Area-Under-Curve on PR-chart.
  
          // correct mAP calculation: ImageNet, PascalVOC 2010-2012
          if (map_points == 0)
          {
              double last_recall = pr[i][detections_count - 1].recall;
              double last_precision = pr[i][detections_count - 1].precision;
              for (rank = detections_count - 2; rank >= 0; --rank)
              {
                  double delta_recall = last_recall - pr[i][rank].recall;
                  last_recall = pr[i][rank].recall;
  
                  if (pr[i][rank].precision > last_precision) {
                      last_precision = pr[i][rank].precision;
                  }
  
                  avg_precision += delta_recall * last_precision;
              }
          }
          // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points
          else
          {
              int point;
              for (point = 0; point < map_points; ++point) {
                  double cur_recall = point * 1.0 / (map_points-1);
                  double cur_precision = 0;
                  for (rank = 0; rank < detections_count; ++rank)
                  {
                      if (pr[i][rank].recall >= cur_recall) {    // > or >=
                          if (pr[i][rank].precision > cur_precision) {
                              cur_precision = pr[i][rank].precision;
                          }
                      }
                  }
                  //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision);
  
                  avg_precision += cur_precision;
              }
              avg_precision = avg_precision / map_points;
          }
  
          printf("class_id = %d, name = %s, ap = %2.2f%%   \t (TP = %d, FP = %d) \n",
              i, names[i], avg_precision * 100, tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]);
  
          float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);
          float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));
          //printf("Precision = %1.2f, Recall = %1.2f, avg IOU = %2.2f%% \n\n", class_precision, class_recall, avg_iou_per_class[i]);
  
          mean_average_precision += avg_precision;
      }
  
      const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
      const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
      const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
      printf("\n for conf_thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n",
          thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);
  
      printf(" for conf_thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n",
          thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);
  
      mean_average_precision = mean_average_precision / classes;
      printf("\n IoU threshold = %2.0f %%, ", iou_thresh * 100);
      if (map_points) printf("used %d Recall-points \n", map_points);
      else printf("used Area-Under-Curve for each unique Recall \n");
  
      printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100);
  
      for (i = 0; i < classes; ++i) {
          free(pr[i]);
      }
      free(pr);
      free(detections);
      free(truth_classes_count);
      free(detection_per_class_count);
  
      free(avg_iou_per_class);
      free(tp_for_thresh_per_class);
      free(fp_for_thresh_per_class);
  
      fprintf(stderr, "Total Detection Time: %d Seconds\n", (int)(time(0) - start));
      printf("\nSet -points flag:\n");
      printf(" `-points 101` for MS COCO \n");
      printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n");
      printf(" `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n");
      if (reinforcement_fd != NULL) fclose(reinforcement_fd);
  
      // free memory
      free_ptrs((void**)names, net.layers[net.n - 1].classes);
      free_list_contents_kvp(options);
      free_list(options);
  
      if (existing_net) {
          //set_batch_network(&net, initial_batch);
          //free_network_recurrent_state(*existing_net);
          restore_network_recurrent_state(*existing_net);
          //randomize_network_recurrent_state(*existing_net);
      }
      else {
          free_network(net);
      }
      if (val) free(val);
      if (val_resized) free(val_resized);
      if (thr) free(thr);
      if (buf) free(buf);
      if (buf_resized) free(buf_resized);
  
      return mean_average_precision;
  }
  
  typedef struct {
      float w, h;
  } anchors_t;
  
  int anchors_comparator(const void *pa, const void *pb)
  {
      anchors_t a = *(const anchors_t *)pa;
      anchors_t b = *(const anchors_t *)pb;
      float diff = b.w*b.h - a.w*a.h;
      if (diff < 0) return 1;
      else if (diff > 0) return -1;
      return 0;
  }
  
  int anchors_data_comparator(const float **pa, const float **pb)
  {
      float *a = (float *)*pa;
      float *b = (float *)*pb;
      float diff = b[0] * b[1] - a[0] * a[1];
      if (diff < 0) return 1;
      else if (diff > 0) return -1;
      return 0;
  }
  
  
  void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
  {
      printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height);
      if (width < 0 || height < 0) {
          printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 \n");
          printf("Error: set width and height \n");
          return;
      }
  
      //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
      float* rel_width_height_array = (float*)xcalloc(1000, sizeof(float));
  
  
      list *options = read_data_cfg(datacfg);
      char *train_images = option_find_str(options, "train", "data/train.list");
      list *plist = get_paths(train_images);
      int number_of_images = plist->size;
      char **paths = (char **)list_to_array(plist);
  
      int classes = option_find_int(options, "classes", 1);
      int* counter_per_class = (int*)xcalloc(classes, sizeof(int));
  
      srand(time(0));
      int number_of_boxes = 0;
      printf(" read labels from %d images \n", number_of_images);
  
      int i, j;
      for (i = 0; i < number_of_images; ++i) {
          char *path = paths[i];
          char labelpath[4096];
          replace_image_to_label(path, labelpath);
  
          int num_labels = 0;
          box_label *truth = read_boxes(labelpath, &num_labels);
          //printf(" new path: %s \n", labelpath);
          char *buff = (char*)xcalloc(6144, sizeof(char));
          for (j = 0; j < num_labels; ++j)
          {
              if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
                  truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
              {
                  printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n",
                      labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                  sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list",
                      labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
                  system(buff);
                  if (check_mistakes) getchar();
              }
              if (truth[j].id >= classes) {
                  classes = truth[j].id + 1;
                  counter_per_class = (int*)xrealloc(counter_per_class, classes * sizeof(int));
              }
              counter_per_class[truth[j].id]++;
  
              number_of_boxes++;
              rel_width_height_array = (float*)xrealloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
  
              rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
              rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
              printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes);
          }
          free(buff);
      }
      printf("\n all loaded. \n");
      printf("\n calculating k-means++ ...");
  
      matrix boxes_data;
      model anchors_data;
      boxes_data = make_matrix(number_of_boxes, 2);
  
      printf("\n");
      for (i = 0; i < number_of_boxes; ++i) {
          boxes_data.vals[i][0] = rel_width_height_array[i * 2];
          boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1];
          //if (w > 410 || h > 410) printf("i:%d,  w = %f, h = %f \n", i, w, h);
      }
  
      // Is used: distance(box, centroid) = 1 - IoU(box, centroid)
  
      // K-means
      anchors_data = do_kmeans(boxes_data, num_of_clusters);
  
      qsort((void*)anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), (__compar_fn_t)anchors_data_comparator);
  
      //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
      //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };
  
      printf("\n");
      float avg_iou = 0;
      for (i = 0; i < number_of_boxes; ++i) {
          float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2];
          float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1];
                                                           //int cluster_idx = labels->data.i[i];
          int cluster_idx = 0;
          float min_dist = FLT_MAX;
          float best_iou = 0;
          for (j = 0; j < num_of_clusters; ++j) {
              float anchor_w = anchors_data.centers.vals[j][0];   // centers->data.fl[j * 2];
              float anchor_h = anchors_data.centers.vals[j][1];   // centers->data.fl[j * 2 + 1];
              float min_w = (box_w < anchor_w) ? box_w : anchor_w;
              float min_h = (box_h < anchor_h) ? box_h : anchor_h;
              float box_intersect = min_w*min_h;
              float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect;
              float iou = box_intersect / box_union;
              float distance = 1 - iou;
              if (distance < min_dist) {
                min_dist = distance;
                cluster_idx = j;
                best_iou = iou;
              }
          }
  
          float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2];
          float anchor_h = anchors_data.centers.vals[cluster_idx][1]; //centers->data.fl[cluster_idx * 2 + 1];
          if (best_iou > 1 || best_iou < 0) { // || box_w > width || box_h > height) {
              printf(" Wrong label: i = %d, box_w = %f, box_h = %f, anchor_w = %f, anchor_h = %f, iou = %f \n",
                  i, box_w, box_h, anchor_w, anchor_h, best_iou);
          }
          else avg_iou += best_iou;
      }
  
      char buff[1024];
      FILE* fwc = fopen("counters_per_class.txt", "wb");
      if (fwc) {
          sprintf(buff, "counters_per_class = ");
          printf("\n%s", buff);
          fwrite(buff, sizeof(char), strlen(buff), fwc);
          for (i = 0; i < classes; ++i) {
              sprintf(buff, "%d", counter_per_class[i]);
              printf("%s", buff);
              fwrite(buff, sizeof(char), strlen(buff), fwc);
              if (i < classes - 1) {
                  fwrite(", ", sizeof(char), 2, fwc);
                  printf(", ");
              }
          }
          printf("\n");
          fclose(fwc);
      }
      else {
          printf(" Error: file counters_per_class.txt can't be open \n");
      }
  
      avg_iou = 100 * avg_iou / number_of_boxes;
      printf("\n avg IoU = %2.2f %% \n", avg_iou);
  
  
      FILE* fw = fopen("anchors.txt", "wb");
      if (fw) {
          printf("\nSaving anchors to the file: anchors.txt \n");
          printf("anchors = ");
          for (i = 0; i < num_of_clusters; ++i) {
              float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2];
              float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1];
              if (width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h);
              else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h);
              printf("%s", buff);
              fwrite(buff, sizeof(char), strlen(buff), fw);
              if (i + 1 < num_of_clusters) {
                  fwrite(", ", sizeof(char), 2, fw);
                  printf(", ");
              }
          }
          printf("\n");
          fclose(fw);
      }
      else {
          printf(" Error: file anchors.txt can't be open \n");
      }
  
      if (show) {
  #ifdef OPENCV
          show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height);
  #endif // OPENCV
      }
      free(rel_width_height_array);
      free(counter_per_class);
  
      getchar();
  }
  
  
  void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
      float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers)
  {
      list *options = read_data_cfg(datacfg);
      char *name_list = option_find_str(options, "names", "data/names.list");
      int names_size = 0;
      char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
  
      image **alphabet = load_alphabet();
      network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      net.benchmark_layers = benchmark_layers;
      fuse_conv_batchnorm(net);
      calculate_binary_weights(net);
      if (net.layers[net.n - 1].classes != names_size) {
          printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
              name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
          if (net.layers[net.n - 1].classes > names_size) getchar();
      }
      srand(2222222);
      char buff[256];
      char *input = buff;
      char *json_buf = NULL;
      int json_image_id = 0;
      FILE* json_file = NULL;
      if (outfile) {
          json_file = fopen(outfile, "wb");
          if(!json_file) {
            error("fopen failed");
          }
          char *tmp = "[\n";
          fwrite(tmp, sizeof(char), strlen(tmp), json_file);
      }
      int j;
      float nms = .45;    // 0.4F
      while (1) {
          if (filename) {
              strncpy(input, filename, 256);
              if (strlen(input) > 0)
                  if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
          }
          else {
              printf("Enter Image Path: ");
              fflush(stdout);
              input = fgets(input, 256, stdin);
              if (!input) break;
              strtok(input, "\n");
          }
          //image im;
          //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
          image im = load_image(input, 0, 0, net.c);
          image sized;
          if(letter_box) sized = letterbox_image(im, net.w, net.h);
          else sized = resize_image(im, net.w, net.h);
          layer l = net.layers[net.n - 1];
  
          //box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
          //float **probs = calloc(l.w*l.h*l.n, sizeof(float*));
          //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float));
  
          float *X = sized.data;
  
          //time= what_time_is_it_now();
          double time = get_time_point();
          network_predict(net, X);
          //network_predict_image(&net, im); letterbox = 1;
          printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
          //printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
  
          int nboxes = 0;
          detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
          if (nms) {
              if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
              else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
          }
          draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
          save_image(im, "predictions");
          if (!dont_show) {
              show_image(im, "predictions");
          }
  
          if (json_file) {
              if (json_buf) {
                  char *tmp = ", \n";
                  fwrite(tmp, sizeof(char), strlen(tmp), json_file);
              }
              ++json_image_id;
              json_buf = detection_to_json(dets, nboxes, l.classes, names, json_image_id, input);
  
              fwrite(json_buf, sizeof(char), strlen(json_buf), json_file);
              free(json_buf);
          }
  
          // pseudo labeling concept - fast.ai
          if (save_labels)
          {
              char labelpath[4096];
              replace_image_to_label(input, labelpath);
  
              FILE* fw = fopen(labelpath, "wb");
              int i;
              for (i = 0; i < nboxes; ++i) {
                  char buff[1024];
                  int class_id = -1;
                  float prob = 0;
                  for (j = 0; j < l.classes; ++j) {
                      if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
                          prob = dets[i].prob[j];
                          class_id = j;
                      }
                  }
                  if (class_id >= 0) {
                      sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
                      fwrite(buff, sizeof(char), strlen(buff), fw);
                  }
              }
              fclose(fw);
          }
  
          free_detections(dets, nboxes);
          free_image(im);
          free_image(sized);
  
          if (!dont_show) {
              wait_until_press_key_cv();
              destroy_all_windows_cv();
          }
  
          if (filename) break;
      }
  
      if (json_file) {
          char *tmp = "\n]";
          fwrite(tmp, sizeof(char), strlen(tmp), json_file);
          fclose(json_file);
      }
  
      // free memory
      free_ptrs((void**)names, net.layers[net.n - 1].classes);
      free_list_contents_kvp(options);
      free_list(options);
  
      int i;
      const int nsize = 8;
      for (j = 0; j < nsize; ++j) {
          for (i = 32; i < 127; ++i) {
              free_image(alphabet[j][i]);
          }
          free(alphabet[j]);
      }
      free(alphabet);
  
      free_network(net);
  }
  
  #if defined(OPENCV) && defined(GPU)
  
  // adversarial attack dnn
  void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
      int letter_box, int benchmark_layers)
  {
      list *options = read_data_cfg(datacfg);
      char *name_list = option_find_str(options, "names", "data/names.list");
      int names_size = 0;
      char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
  
      image **alphabet = load_alphabet();
      network net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
      net.adversarial = 1;
      set_batch_network(&net, 1);
      if (weightfile) {
          load_weights(&net, weightfile);
      }
      net.benchmark_layers = benchmark_layers;
      //fuse_conv_batchnorm(net);
      //calculate_binary_weights(net);
      if (net.layers[net.n - 1].classes != names_size) {
          printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
              name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
          if (net.layers[net.n - 1].classes > names_size) getchar();
      }
  
      srand(2222222);
      char buff[256];
      char *input = buff;
  
      int j;
      float nms = .45;    // 0.4F
      while (1) {
          if (filename) {
              strncpy(input, filename, 256);
              if (strlen(input) > 0)
                  if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
          }
          else {
              printf("Enter Image Path: ");
              fflush(stdout);
              input = fgets(input, 256, stdin);
              if (!input) break;
              strtok(input, "\n");
          }
          //image im;
          //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
          image im = load_image(input, 0, 0, net.c);
          image sized;
          if (letter_box) sized = letterbox_image(im, net.w, net.h);
          else sized = resize_image(im, net.w, net.h);
  
          image src_sized = copy_image(sized);
  
          layer l = net.layers[net.n - 1];
          net.num_boxes = l.max_boxes;
          int num_truth = l.truths;
          float *truth_cpu = (float *)xcalloc(num_truth, sizeof(float));
  
          int *it_num_set = (int *)xcalloc(1, sizeof(int));
          float *lr_set = (float *)xcalloc(1, sizeof(float));
          int *boxonly = (int *)xcalloc(1, sizeof(int));
  
          cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, boxonly, l.classes, names);
  
          net.learning_rate = *lr_set;
          it_num = *it_num_set;
  
          float *X = sized.data;
  
          mat_cv* img = NULL;
          float max_img_loss = 5;
          int number_of_lines = 100;
          int img_size = 1000;
          char windows_name[100];
          char *base = basecfg(cfgfile);
          sprintf(windows_name, "chart_%s.png", base);
          img = draw_train_chart(windows_name, max_img_loss, it_num, number_of_lines, img_size, dont_show, NULL);
  
          int iteration;
          for (iteration = 0; iteration < it_num; ++iteration)
          {
              forward_backward_network_gpu(net, X, truth_cpu);
  
              float avg_loss = get_network_cost(net);
              draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0);
  
              float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss);
              //net.learning_rate = *lr_set * inv_loss;
  
              if (*boxonly) {
                  int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h;
                  int dx = truth_cpu[0] * sized.w - dw / 2, dy = truth_cpu[1] * sized.h - dh / 2;
                  image crop = crop_image(sized, dx, dy, dw, dh);
                  copy_image_inplace(src_sized, sized);
                  embed_image(crop, sized, dx, dy);
              }
  
              show_image_cv(sized, "image_optimization");
              wait_key_cv(20);
          }
  
          net.train = 0;
          quantize_image(sized);
          network_predict(net, X);
  
          save_image_png(sized, "drawn");
          //sized = load_image("drawn.png", 0, 0, net.c);
  
          int nboxes = 0;
          detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, 0, 0, 1, &nboxes, letter_box);
          if (nms) {
              if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
              else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
          }
          draw_detections_v3(sized, dets, nboxes, thresh, names, alphabet, l.classes, 1);
          save_image(sized, "pre_predictions");
          if (!dont_show) {
              show_image(sized, "pre_predictions");
          }
  
          free_detections(dets, nboxes);
          free_image(im);
          free_image(sized);
          free_image(src_sized);
  
          if (!dont_show) {
              wait_until_press_key_cv();
              destroy_all_windows_cv();
          }
  
          free(lr_set);
          free(it_num_set);
  
          if (filename) break;
      }
  
      // free memory
      free_ptrs((void**)names, net.layers[net.n - 1].classes);
      free_list_contents_kvp(options);
      free_list(options);
  
      int i;
      const int nsize = 8;
      for (j = 0; j < nsize; ++j) {
          for (i = 32; i < 127; ++i) {
              free_image(alphabet[j][i]);
          }
          free(alphabet[j]);
      }
      free(alphabet);
  
      free_network(net);
  }
  #else // defined(OPENCV) && defined(GPU)
  void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
      int letter_box, int benchmark_layers)
  {
      printf(" ./darknet detector draw ... can't be used without OpenCV and CUDA! \n");
      getchar();
  }
  #endif // defined(OPENCV) && defined(GPU)
  
  void run_detector(int argc, char **argv)
  {
      int dont_show = find_arg(argc, argv, "-dont_show");
      int benchmark = find_arg(argc, argv, "-benchmark");
      int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
      //if (benchmark_layers) benchmark = 1;
      if (benchmark) dont_show = 1;
      int show = find_arg(argc, argv, "-show");
      int letter_box = find_arg(argc, argv, "-letter_box");
      int calc_map = find_arg(argc, argv, "-map");
      int map_points = find_int_arg(argc, argv, "-points", 0);
      check_mistakes = find_arg(argc, argv, "-check_mistakes");
      int show_imgs = find_arg(argc, argv, "-show_imgs");
      int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
      int json_port = find_int_arg(argc, argv, "-json_port", -1);
      char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0);
      int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0);
      char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
      char *outfile = find_char_arg(argc, argv, "-out", 0);
      char *prefix = find_char_arg(argc, argv, "-prefix", 0);
      float thresh = find_float_arg(argc, argv, "-thresh", .25);    // 0.24
      float iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5);    // 0.5 for mAP
      float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
      int cam_index = find_int_arg(argc, argv, "-c", 0);
      int frame_skip = find_int_arg(argc, argv, "-s", 0);
      int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
      int width = find_int_arg(argc, argv, "-width", -1);
      int height = find_int_arg(argc, argv, "-height", -1);
      // extended output in test mode (output of rect bound coords)
      // and for recall mode (extended output table-like format with results for best_class fit)
      int ext_output = find_arg(argc, argv, "-ext_output");
      int save_labels = find_arg(argc, argv, "-save_labels");
      char* chart_path = find_char_arg(argc, argv, "-chart", 0);
      if (argc < 4) {
          fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]);
          return;
      }
      char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
      int *gpus = 0;
      int gpu = 0;
      int ngpus = 0;
      if (gpu_list) {
          printf("%s\n", gpu_list);
          int len = (int)strlen(gpu_list);
          ngpus = 1;
          int i;
          for (i = 0; i < len; ++i) {
              if (gpu_list[i] == ',') ++ngpus;
          }
          gpus = (int*)xcalloc(ngpus, sizeof(int));
          for (i = 0; i < ngpus; ++i) {
              gpus[i] = atoi(gpu_list);
              gpu_list = strchr(gpu_list, ',') + 1;
          }
      }
      else {
          gpu = gpu_index;
          gpus = &gpu;
          ngpus = 1;
      }
  
      int clear = find_arg(argc, argv, "-clear");
  
      char *datacfg = argv[3];
      char *cfg = argv[4];
      char *weights = (argc > 5) ? argv[5] : 0;
      if (weights)
          if (strlen(weights) > 0)
              if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
      char *filename = (argc > 6) ? argv[6] : 0;
      if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers);
      else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers, chart_path);
      else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
      else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
      else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL);
      else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
      else if (0 == strcmp(argv[2], "draw")) {
          int it_num = 100;
          draw_object(datacfg, cfg, weights, filename, thresh, dont_show, it_num, letter_box, benchmark_layers);
      }
      else if (0 == strcmp(argv[2], "demo")) {
          list *options = read_data_cfg(datacfg);
          int classes = option_find_int(options, "classes", 20);
          char *name_list = option_find_str(options, "names", "data/names.list");
          char **names = get_labels(name_list);
          if (filename)
              if (strlen(filename) > 0)
                  if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
          demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
              mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers);
  
          free_list_contents_kvp(options);
          free_list(options);
      }
      else printf(" There isn't such command: %s", argv[2]);
  
      if (gpus && gpu_list && ngpus > 1) free(gpus);
  }
  