diff --git a/build/darknet/x64/calc_anchors.cmd b/build/darknet/x64/calc_anchors.cmd index f8a77ad5..497b30b0 100644 --- a/build/darknet/x64/calc_anchors.cmd +++ b/build/darknet/x64/calc_anchors.cmd @@ -1,10 +1,10 @@ rem # How to calculate Yolo v2 anchors using K-means++ -darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13 +darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -width 416 -heigh 416 -rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13 -show +rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -width 416 -heigh 416 -show diff --git a/build/darknet/x64/calc_mAP.cmd b/build/darknet/x64/calc_mAP.cmd index 37cad1bb..614a422a 100644 --- a/build/darknet/x64/calc_mAP.cmd +++ b/build/darknet/x64/calc_mAP.cmd @@ -1,10 +1,10 @@ rem # How to calculate mAP (mean average precision) -rem darknet.exe detector map data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights +rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -darknet.exe detector map data/voc.data yolo-voc.cfg yolo-voc.weights +darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights diff --git a/build/darknet/x64/darknet_yolo_v3_video.cmd b/build/darknet/x64/darknet_yolo_v3_video.cmd new file mode 100644 index 00000000..729ec08f --- /dev/null +++ b/build/darknet/x64/darknet_yolo_v3_video.cmd @@ -0,0 +1,5 @@ + +darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 test.mp4 + + +pause \ No newline at end of file diff --git a/build/darknet/x64/data/voc.data b/build/darknet/x64/data/voc.data index 046019f2..d6775870 100644 --- a/build/darknet/x64/data/voc.data +++ b/build/darknet/x64/data/voc.data @@ -1,7 +1,7 @@ classes= 20 train = data/train_voc.txt -valid = data/voc/2007_test.txt -#difficult = data/voc/difficult_2007_test.txt +valid = data/2007_test.txt +#difficult = data/difficult_2007_test.txt names = data/voc.names backup = backup/ diff --git a/build/darknet/x64/yolov3.cfg b/build/darknet/x64/yolov3.cfg index 5f3ab621..33d94217 100644 --- a/build/darknet/x64/yolov3.cfg +++ b/build/darknet/x64/yolov3.cfg @@ -1,10 +1,10 @@ [net] # Testing -batch=1 -subdivisions=1 +#batch=1 +#subdivisions=1 # Training -# batch=64 -# subdivisions=16 +batch=64 +subdivisions=16 width=416 height=416 channels=3 diff --git a/src/data.c b/src/data.c index d9cedf5b..88d5720b 100644 --- a/src/data.c +++ b/src/data.c @@ -827,6 +827,9 @@ void *load_thread(void *ptr) } else if (a.type == IMAGE_DATA){ *(a.im) = load_image_color(a.path, 0, 0); *(a.resized) = resize_image(*(a.im), a.w, a.h); + }else if (a.type == LETTERBOX_DATA) { + *(a.im) = load_image_color(a.path, 0, 0); + *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } diff --git a/src/data.h b/src/data.h index e80beaee..e50ad006 100644 --- a/src/data.h +++ b/src/data.h @@ -32,7 +32,7 @@ typedef struct{ } data; typedef enum { - CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA + CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, LETTERBOX_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA } data_type; typedef struct load_args{ diff --git a/src/detector.c b/src/detector.c index 77175b4b..ac84c591 100644 --- a/src/detector.c +++ b/src/detector.c @@ -170,7 +170,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); - printf("%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), sec(clock()-time), i*imgs); #ifdef OPENCV if(!dont_show) @@ -204,301 +204,290 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i static int get_coco_image_id(char *filename) { - char *p = strrchr(filename, '_'); - return atoi(p+1); + 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, box *boxes, float **probs, int num_boxes, int classes, int w, int h) +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); - for(i = 0; i < num_boxes; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].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 (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]); - } - } + int i, j; + int image_id = get_coco_image_id(image_path); + 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]) fprintf(fp, "{\"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]); + } + } } -void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) +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 = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].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 (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], - xmin, ymin, xmax, ymax); - } - } + 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, box *boxes, float **probs, int total, int classes, int w, int h) +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 = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].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 class_id = j; - if (probs[i][class_id]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class_id], - xmin, ymin, xmax, ymax); - } - } + 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 class = j; + if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class], + xmin, ymin, xmax, ymax); + } + } } -void validate_detector(char *datacfg, char *cfgfile, char *weightfile) +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); - if(weightfile){ - load_weights(&net, weightfile); - } - 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)); - - char *base = "comp4_det_test_"; - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); + 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); - layer l = net.layers[net.n-1]; - int classes = l.classes; + network net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + 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)); - char buff[1024]; - char *type = option_find_str(options, "eval", "voc"); - FILE *fp = 0; - FILE **fps = 0; - int coco = 0; - int imagenet = 0; - if(0==strcmp(type, "coco")){ - snprintf(buff, 1024, "%s/coco_results.json", prefix); - fp = fopen(buff, "w"); - fprintf(fp, "[\n"); - coco = 1; - } else if(0==strcmp(type, "imagenet")){ - snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix); - fp = fopen(buff, "w"); - imagenet = 1; - classes = 200; - } else { - fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]); - fps[j] = fopen(buff, "w"); - } - } + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + layer l = net.layers[net.n - 1]; + int classes = l.classes; - 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] = calloc(classes, sizeof(float *)); + char buff[1024]; + char *type = option_find_str(options, "eval", "voc"); + FILE *fp = 0; + FILE **fps = 0; + int coco = 0; + int imagenet = 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, "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 = calloc(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 = .005; - float nms = .45; + int m = plist->size; + int i = 0; + int t; - int detection_count = 0; + float thresh = .005; + float nms = .45; - int nthreads = 4; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + int nthreads = 4; + image *val = calloc(nthreads, sizeof(image)); + image *val_resized = calloc(nthreads, sizeof(image)); + image *buf = calloc(nthreads, sizeof(image)); + image *buf_resized = calloc(nthreads, sizeof(image)); + pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; + load_args args = { 0 }; + args.w = net.w; + args.h = net.h; + args.type = IMAGE_DATA; + //args.type = LETTERBOX_DATA; - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - get_region_boxes(l, w, h, thresh, probs, boxes, 0, map); - if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms); - - int x, y; - for (x = 0; x < (l.w*l.h*l.n); ++x) { - for (y = 0; y < classes; ++y) - { - if (probs[x][y]) ++detection_count; - } + for (t = 0; t < nthreads; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + time_t start = time(0); + for (i = nthreads; i < m + nthreads; i += nthreads) { + fprintf(stderr, "%d\n", i); + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + pthread_join(thr[t], 0); + val[t] = buf[t]; + val_resized[t] = buf_resized[t]; + } + for (t = 0; t < nthreads && i + t < m; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + char *path = paths[i + t - nthreads]; + char *id = basecfg(path); + float *X = val_resized[t].data; + network_predict(net, X); + int w = val[t].w; + int h = val[t].h; + int nboxes = 0; + int letterbox = (args.type == LETTERBOX_DATA); + detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); + if (nms) do_nms_sort_v3(dets, nboxes, classes, nms); + if (coco) { + print_cocos(fp, path, dets, nboxes, classes, w, h); } - - if (coco){ - print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else if (imagenet){ - print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h); - } else { - print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h); - } - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - for(j = 0; j < classes; ++j){ - if(fps) fclose(fps[j]); - } - if(coco){ - fseek(fp, -2, SEEK_CUR); - fprintf(fp, "\n]\n"); - fclose(fp); - } - printf("\n detection_count = %d \n", detection_count); - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + else if (imagenet) { + print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h); + } + 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]); + } + } + for (j = 0; j < classes; ++j) { + if (fps) fclose(fps[j]); + } + if (coco) { + fseek(fp, -2, SEEK_CUR); + fprintf(fp, "\n]\n"); + fclose(fp); + } + fprintf(stderr, "Total Detection Time: %f Seconds\n", time(0) - start); } void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) { - network net = parse_network_cfg_custom(cfgfile, 1); - if(weightfile){ - load_weights(&net, weightfile); - } - 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)); + network net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + 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); + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n-1]; - int classes = l.classes; + layer l = net.layers[net.n - 1]; - int j, k; - 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] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001;// .001; // .2; - float iou_thresh = .5; - float nms = .4; - - int detection_count = 0, truth_count = 0; - - 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_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0); - if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 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, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - truth_count += num_labels; - for(k = 0; k < l.w*l.h*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } + 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_color(path, 0, 0); + 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_v3(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, ".JPEG", ".txt", 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 < l.w*l.h*l.n; ++k) { - float iou = box_iou(boxes[k], t); - if (probs[k][0] > thresh && iou > best_iou) { + 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; - } - } + avg_iou += best_iou; + if (best_iou > iou_thresh) { + ++correct; + } + } - 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); - } - printf("\n truth_count = %d \n", truth_count); + 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 { @@ -537,7 +526,6 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float load_weights(&net, weightfile); } 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)); list *plist = get_paths(valid_images); @@ -553,10 +541,6 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float layer l = net.layers[net.n - 1]; int classes = l.classes; - 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] = calloc(classes, sizeof(float *)); - int m = plist->size; int i = 0; int t; @@ -576,6 +560,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float args.w = net.w; args.h = net.h; args.type = IMAGE_DATA; + //args.type = LETTERBOX_DATA; //const float thresh_calc_avg_iou = 0.24; float avg_iou = 0; @@ -614,8 +599,12 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float char *id = basecfg(path); float *X = val_resized[t].data; network_predict(net, X); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); - if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms); + + 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); char labelpath[4096]; find_replace(path, "images", "labels", labelpath); @@ -646,15 +635,15 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float truth_dif = read_boxes(labelpath_dif, &num_labels_dif); } - for (i = 0; i < (l.w*l.h*l.n); ++i) { + for (i = 0; i < nboxes; ++i) { int class_id; for (class_id = 0; class_id < classes; ++class_id) { - float prob = probs[i][class_id]; + float prob = dets[i].prob[class_id]; if (prob > 0) { detections_count++; detections = realloc(detections, detections_count * sizeof(box_prob)); - detections[detections_count - 1].b = boxes[i]; + 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; @@ -667,8 +656,8 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float { 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(boxes[i], t), prob, class_id, truth[j].id); - float current_iou = box_iou(boxes[i], t); + // 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; @@ -686,7 +675,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float // 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(boxes[i], t); + float current_iou = box_iou(dets[i].bbox, t); if (current_iou > iou_thresh && class_id == truth_dif[j].id) { --detections_count; break; @@ -709,6 +698,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float unique_truth_count += num_labels; + free_detections(dets, nboxes); free(id); free_image(val[t]); free_image(val_resized[t]); @@ -830,9 +820,9 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float } #ifdef OPENCV -void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) +void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { - printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height); + printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height); //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; float *rel_width_height_array = calloc(1000, sizeof(float)); @@ -862,8 +852,8 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final { number_of_boxes++; rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); - rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * final_width; - rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_height; + 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); } } @@ -967,15 +957,15 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final for (j = 0; j < num_of_clusters; ++j) { CvPoint pt1, pt2; pt1.x = pt1.y = 0; - pt2.x = centers->data.fl[j * 2] * img_size / final_width; - pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_height; + pt2.x = centers->data.fl[j * 2] * img_size / width; + pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); } for (i = 0; i < number_of_boxes; ++i) { CvPoint pt; - pt.x = points->data.fl[i * 2] * img_size / final_width; - pt.y = points->data.fl[i * 2 + 1] * img_size / final_height; + pt.x = points->data.fl[i * 2] * img_size / width; + pt.y = points->data.fl[i * 2 + 1] * img_size / height; int cluster_idx = labels->data.i[i]; int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; @@ -995,7 +985,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final cvReleaseMat(&labels); } #else -void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) { +void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); } #endif // OPENCV @@ -1030,9 +1020,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam strtok(input, "\n"); } image im = load_image_color(input,0,0); - int letter = 0; + int letterbox = 0; image sized = resize_image(im, net.w, net.h); - //image sized = letterbox_image(im, net.w, net.h); letter = 1; + //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; layer l = net.layers[net.n-1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); @@ -1047,7 +1037,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam // if (nms) do_nms_sort(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, letter); + 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); draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes); free_detections(dets, nboxes); @@ -1076,14 +1066,15 @@ void run_detector(int argc, char **argv) int show = find_arg(argc, argv, "-show"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); 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 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 final_width = find_int_arg(argc, argv, "-final_width", 13); - int final_heigh = find_int_arg(argc, argv, "-final_heigh", 13); + int width = find_int_arg(argc, argv, "-width", 13); + int heigh = find_int_arg(argc, argv, "-heigh", 13); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -1121,10 +1112,10 @@ void run_detector(int argc, char **argv) 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); + 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, final_width, final_heigh, show); + else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, heigh, show); else if(0==strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); int classes = option_find_int(options, "classes", 20); diff --git a/src/network.c b/src/network.c index 8619158a..51d290c9 100644 --- a/src/network.c +++ b/src/network.c @@ -374,10 +374,14 @@ int resize_network(network *net, int w, int h) resize_maxpool_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); + }else if (l.type == YOLO) { + resize_yolo_layer(&l, w, h); }else if(l.type == ROUTE){ resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { resize_shortcut_layer(&l, w, h); + }else if (l.type == UPSAMPLE) { + resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ resize_reorg_layer(&l, w, h); }else if(l.type == AVGPOOL){ @@ -539,12 +543,14 @@ void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, f float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); int i, j; for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); + get_region_boxes(l, w, h, thresh, probs, boxes, 0, map); for (j = 0; j < l.w*l.h*l.n; ++j) { dets[j].classes = l.classes; dets[j].bbox = boxes[j]; dets[j].objectness = 1; - for (i = 0; i < l.classes; ++i) dets[j].prob[i] = probs[j][i]; + for (i = 0; i < l.classes; ++i) { + dets[j].prob[i] = probs[j][i]; + } } free(boxes); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index c8e2ff59..2925b263 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -378,9 +378,26 @@ void forward_yolo_layer_gpu(const layer l, network_state state) return; } - cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs); - forward_yolo_layer(l, state); + //cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs); + float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); + cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); + float *truth_cpu = 0; + if (state.truth) { + int num_truth = l.batch*l.truths; + truth_cpu = calloc(num_truth, sizeof(float)); + cuda_pull_array(state.truth, truth_cpu, num_truth); + } + network_state cpu_state = state; + cpu_state.net = state.net; + cpu_state.index = state.index; + cpu_state.train = state.train; + cpu_state.truth = truth_cpu; + cpu_state.input = in_cpu; + forward_yolo_layer(l, cpu_state); + //forward_yolo_layer(l, state); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); + free(in_cpu); + if (cpu_state.truth) free(cpu_state.truth); } void backward_yolo_layer_gpu(const layer l, network_state state)