| | |
| | | args.n = imgs; |
| | | args.m = plist->size; |
| | | args.classes = classes; |
| | | args.flip = net.flip; |
| | | args.jitter = jitter; |
| | | args.num_boxes = l.max_boxes; |
| | | args.small_object = l.small_object; |
| | | args.small_object = net.small_object; |
| | | args.d = &buffer; |
| | | args.type = DETECTION_DATA; |
| | | args.threads = 64; // 8 |
| | | args.threads = 16; // 64 |
| | | |
| | | args.angle = net.angle; |
| | | args.exposure = net.exposure; |
| | |
| | | args.hue = net.hue; |
| | | |
| | | #ifdef OPENCV |
| | | args.threads = 3; |
| | | IplImage* img = NULL; |
| | | float max_img_loss = 5; |
| | | int number_of_lines = 100; |
| | |
| | | #endif //OPENCV |
| | | |
| | | pthread_t load_thread = load_data(args); |
| | | clock_t time; |
| | | double time; |
| | | int count = 0; |
| | | //while(i*imgs < N*120){ |
| | | while(get_current_batch(net) < net.max_batches){ |
| | |
| | | } |
| | | net = nets[0]; |
| | | } |
| | | time=clock(); |
| | | time=what_time_is_it_now(); |
| | | pthread_join(load_thread, 0); |
| | | train = buffer; |
| | | load_thread = load_data(args); |
| | |
| | | save_image(im, "truth11"); |
| | | */ |
| | | |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | printf("Loaded: %lf seconds\n", (what_time_is_it_now()-time)); |
| | | |
| | | time=clock(); |
| | | time=what_time_is_it_now(); |
| | | float loss = 0; |
| | | #ifdef GPU |
| | | if(ngpus == 1){ |
| | |
| | | #else |
| | | loss = train_network(net, train); |
| | | #endif |
| | | if (avg_loss < 0) avg_loss = loss; |
| | | if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan) |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | |
| | | i = get_current_batch(net); |
| | | printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); |
| | | printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); |
| | | |
| | | #ifdef OPENCV |
| | | if(!dont_show) |
| | |
| | | int *map = 0; |
| | | if (mapf) map = read_map(mapf); |
| | | |
| | | network net = parse_network_cfg_custom(cfgfile, 1); |
| | | network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 |
| | | if (weightfile) { |
| | | load_weights(&net, weightfile); |
| | | } |
| | | set_batch_network(&net, 1); |
| | | //set_batch_network(&net, 1); |
| | | fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | srand(time(0)); |
| | | |
| | |
| | | int nboxes = 0; |
| | | int letterbox = (args.type == LETTERBOX_DATA); |
| | | detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); |
| | | if (nms) do_nms_sort_v3(dets, nboxes, classes, nms); |
| | | if (nms) do_nms_sort(dets, nboxes, classes, nms); |
| | | if (coco) { |
| | | print_cocos(fp, path, dets, nboxes, classes, w, h); |
| | | } |
| | |
| | | |
| | | void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) |
| | | { |
| | | network net = parse_network_cfg_custom(cfgfile, 1); |
| | | network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 |
| | | if (weightfile) { |
| | | load_weights(&net, weightfile); |
| | | } |
| | | set_batch_network(&net, 1); |
| | | //set_batch_network(&net, 1); |
| | | fuse_conv_batchnorm(net); |
| | | srand(time(0)); |
| | | |
| | | //list *plist = get_paths("data/coco_val_5k.list"); |
| | |
| | | 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_v3(dets, nboxes, 1, nms); |
| | | if (nms) do_nms_obj(dets, nboxes, 1, nms); |
| | | |
| | | char labelpath[4096]; |
| | | find_replace(path, "images", "labels", labelpath); |
| | | find_replace(labelpath, "JPEGImages", "labels", labelpath); |
| | | find_replace(labelpath, ".jpg", ".txt", labelpath); |
| | | find_replace(labelpath, ".png", ".txt", labelpath); |
| | | find_replace(labelpath, ".bmp", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPG", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPEG", ".txt", labelpath); |
| | | |
| | | int num_labels = 0; |
| | |
| | | ++total; |
| | | box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h }; |
| | | float best_iou = 0; |
| | | for (k = 0; k < l.w*l.h*l.n; ++k) { |
| | | 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; |
| | |
| | | ++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); |
| | |
| | | int *map = 0; |
| | | if (mapf) map = read_map(mapf); |
| | | |
| | | network net = parse_network_cfg_custom(cfgfile, 1); |
| | | network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 |
| | | if (weightfile) { |
| | | load_weights(&net, weightfile); |
| | | } |
| | | set_batch_network(&net, 1); |
| | | //set_batch_network(&net, 1); |
| | | fuse_conv_batchnorm(net); |
| | | srand(time(0)); |
| | | |
| | | list *plist = get_paths(valid_images); |
| | |
| | | int nboxes = 0; |
| | | int letterbox = (args.type == LETTERBOX_DATA); |
| | | float hier_thresh = 0; |
| | | detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 1, &nboxes, letterbox); |
| | | if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms); |
| | | detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); |
| | | //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1 |
| | | if (nms) do_nms_sort(dets, nboxes, l.classes, nms); |
| | | |
| | | char labelpath[4096]; |
| | | find_replace(path, "images", "labels", labelpath); |
| | | find_replace(labelpath, "JPEGImages", "labels", labelpath); |
| | | find_replace(labelpath, ".jpg", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPEG", ".txt", labelpath); |
| | | find_replace(labelpath, ".png", ".txt", labelpath); |
| | | find_replace(labelpath, ".bmp", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPG", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPEG", ".txt", labelpath); |
| | | int num_labels = 0; |
| | | box_label *truth = read_boxes(labelpath, &num_labels); |
| | | int i, j; |
| | |
| | | } |
| | | |
| | | #ifdef OPENCV |
| | | typedef struct { |
| | | float w, h; |
| | | } anchors_t; |
| | | |
| | | int anchors_comparator(const void *pa, const void *pb) |
| | | { |
| | | anchors_t a = *(anchors_t *)pa; |
| | | anchors_t b = *(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; |
| | | } |
| | | |
| | | 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 = calloc(1000, sizeof(float)); |
| | |
| | | find_replace(path, "images", "labels", labelpath); |
| | | find_replace(labelpath, "JPEGImages", "labels", labelpath); |
| | | find_replace(labelpath, ".jpg", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPEG", ".txt", labelpath); |
| | | find_replace(labelpath, ".png", ".txt", labelpath); |
| | | find_replace(labelpath, ".bmp", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPG", ".txt", labelpath); |
| | | find_replace(labelpath, ".JPEG", ".txt", labelpath); |
| | | int num_labels = 0; |
| | | box_label *truth = read_boxes(labelpath, &num_labels); |
| | | //printf(" new path: %s \n", labelpath); |
| | |
| | | cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps, |
| | | 0, KMEANS_PP_CENTERS, |
| | | centers, &compactness); |
| | | |
| | | |
| | | // sort anchors |
| | | qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator); |
| | | |
| | | //orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 |
| | | //float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 }; |
| | | // worse than ours (even for 19x19 final size - for input size 608x608) |
| | |
| | | printf("anchors = "); |
| | | for (i = 0; i < num_of_clusters; ++i) { |
| | | sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); |
| | | printf("%s, ", buff); |
| | | printf("%s", buff); |
| | | fwrite(buff, sizeof(char), strlen(buff), fw); |
| | | if (i + 1 < num_of_clusters) fwrite(", ", sizeof(char), 2, fw);; |
| | | if (i + 1 < num_of_clusters) { |
| | | fwrite(", ", sizeof(char), 2, fw); |
| | | printf(", "); |
| | | } |
| | | } |
| | | printf("\n"); |
| | | fclose(fw); |
| | |
| | | char **names = get_labels(name_list); |
| | | |
| | | image **alphabet = load_alphabet(); |
| | | network net = parse_network_cfg_custom(cfgfile, 1); |
| | | network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 |
| | | if(weightfile){ |
| | | load_weights(&net, weightfile); |
| | | } |
| | | set_batch_network(&net, 1); |
| | | //set_batch_network(&net, 1); |
| | | fuse_conv_batchnorm(net); |
| | | srand(2222222); |
| | | clock_t time; |
| | | double time; |
| | | char buff[256]; |
| | | char *input = buff; |
| | | int j; |
| | |
| | | while(1){ |
| | | if(filename){ |
| | | strncpy(input, filename, 256); |
| | | if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; |
| | | if(strlen(input) > 0) |
| | | if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; |
| | | } else { |
| | | printf("Enter Image Path: "); |
| | | fflush(stdout); |
| | |
| | | //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); |
| | | |
| | | float *X = sized.data; |
| | | time=clock(); |
| | | network_predict(net, X); |
| | | printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); |
| | | time= what_time_is_it_now(); |
| | | //network_predict(net, X); |
| | | network_predict_image(&net, im); letterbox = 1; |
| | | printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time)); |
| | | //get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0); |
| | | // if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms); |
| | | // if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms); |
| | | //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); |
| | | int nboxes = 0; |
| | | detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); |
| | | if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms); |
| | | if (nms) do_nms_sort(dets, nboxes, l.classes, nms); |
| | | draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes); |
| | | free_detections(dets, nboxes); |
| | | save_image(im, "predictions"); |
| | |
| | | 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", 13); |
| | | int heigh = find_int_arg(argc, argv, "-heigh", 13); |
| | | int width = find_int_arg(argc, argv, "-width", -1); |
| | | int height = find_int_arg(argc, argv, "-height", -1); |
| | | if(argc < 4){ |
| | | fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); |
| | | return; |
| | |
| | | char *cfg = argv[4]; |
| | | char *weights = (argc > 5) ? argv[5] : 0; |
| | | if(weights) |
| | | if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; |
| | | 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); |
| | | else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show); |
| | | 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); |
| | | else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, heigh, show); |
| | | else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); |
| | | 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 (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; |
| | | 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, |
| | | http_stream_port, dont_show); |
| | | } |
| | | else printf(" There isn't such command: %s", argv[2]); |
| | | } |