派生自 Algorithm/baseDetector
lib/detecter_tools/darknet/detector.c
@@ -1,1970 +1,2042 @@ #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; 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 = l.jitter; args.resize = l.resize; 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 //int num_threads = get_num_threads(); //if(num_threads > 2) args.threads = get_num_threads() - 2; 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.\n", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used.\n"); fflush(stderr); } 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); fflush(stdout); 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) / ngpus)*(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; const int letter_box = net.letter_box; if (letter_box) 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; detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &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); } 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 dontdraw_bbox = find_arg(argc, argv, "-dontdraw_bbox"); 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, dontdraw_bbox, 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); } #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; float avg_contrastive_acc = 0; 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]; for (k = 0; k < net.n; ++k) { layer lk = net.layers[k]; if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { l = lk; printf(" Detection layer: %d - type = %d \n", k, l.type); } } int classes = l.classes; 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 = l.jitter; args.resize = l.resize; args.num_boxes = l.max_boxes; args.truth_size = l.truth_size; 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; args.mosaic_bound = net.mosaic_bound; args.contrastive = net.contrastive; args.contrastive_jit_flip = net.contrastive_jit_flip; args.contrastive_color = net.contrastive_color; if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; #ifdef OPENCV //int num_threads = get_num_threads(); //if(num_threads > 2) args.threads = get_num_threads() - 2; 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 = net.max_chart_loss; 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.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2; 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); //wait_key_cv(500); /* 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.\n", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used.\n"); fflush(stderr); } 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); fflush(stdout); 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) / ngpus)*(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 if (net.contrastive) { float cur_con_acc = -1; for (k = 0; k < net.n; ++k) if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss; if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01; printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc); } draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", avg_contrastive_acc / 100, 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 + 10000) || iteration % 10000 == 0) || (iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000) { 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); if (net.ema_alpha && is_ema_initialized(net)) { sprintf(buff, "%s/%s_ema.weights", backup_directory, base); save_weights_upto(net, buff, net.n, 1); printf(" EMA weights are saved to the file: %s \n", 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); printf("If you want to train from the beginning, then use flag in the end of training command: -clear \n"); #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 k; for (k = 0; k < net.n; ++k) { layer lk = net.layers[k]; if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { l = lk; printf(" Detection layer: %d - type = %d \n", k, l.type); } } 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 = .6; 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; const int letter_box = net.letter_box; if (letter_box) 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; detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &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); } 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 k; for (k = 0; k < net.n; ++k) { layer lk = net.layers[k]; if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { l = lk; printf(" Detection layer: %d - type = %d \n", k, l.type); } } 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; letter_box = net.letter_box; 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 (l.embedding_size) set_track_id(dets, nboxes, thresh, l.sim_thresh, l.track_ciou_norm, l.track_history_size, l.dets_for_track, l.dets_for_show); 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; } //add remaining area of PR curve when recall isn't 0 at rank-1 double delta_recall = last_recall - 0; 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); } if (net.letter_box) letter_box = 1; 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]; int k; for (k = 0; k < net.n; ++k) { layer lk = net.layers[k]; if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { l = lk; printf(" Detection layer: %d - type = %d \n", k, l.type); } } //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]; int k; for (k = 0; k < net.n; ++k) { layer lk = net.layers[k]; if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { l = lk; printf(" Detection layer: %d - type = %d \n", k, l.type); } } 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%", 0, 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 avgframes = find_int_arg(argc, argv, "-avgframes", 3); int dontdraw_bbox = find_arg(argc, argv, "-dontdraw_bbox"); 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, avgframes, frame_skip, prefix, out_filename, mjpeg_port, dontdraw_bbox, 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); }
