From 393dc8eb6f3a9dd92ec665200444186c1addc5d2 Mon Sep 17 00:00:00 2001 From: Joseph Redmon Date: Wed, 9 Sep 2015 12:48:40 -0700 Subject: [PATCH] stable --- Makefile | 2 +- cfg/darknet.cfg | 14 +- cfg/yolo.cfg | 7 +- src/darknet.c | 3 + src/detection_layer.c | 17 ++- src/network.c | 13 +- src/network.h | 6 +- src/parser.c | 37 ++++- src/yolo.c | 18 +-- src/yoloplus.c | 334 ++++++++++++++++++++++++++++++++++++++++++ 10 files changed, 415 insertions(+), 36 deletions(-) create mode 100644 src/yoloplus.c diff --git a/Makefile b/Makefile index 65264de2..581b6d77 100644 --- a/Makefile +++ b/Makefile @@ -34,7 +34,7 @@ CFLAGS+= -DGPU LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand endif -OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o +OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o yoloplus.o ifeq ($(GPU), 1) OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o endif diff --git a/cfg/darknet.cfg b/cfg/darknet.cfg index f52ff3f8..eb1310a7 100644 --- a/cfg/darknet.cfg +++ b/cfg/darknet.cfg @@ -27,7 +27,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -38,7 +38,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -49,7 +49,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -60,7 +60,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -71,7 +71,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -82,7 +82,7 @@ pad=1 activation=leaky [maxpool] -size=3 +size=2 stride=2 [convolutional] @@ -99,7 +99,7 @@ probability=.5 [connected] output=1000 -activation=linear +activation=leaky [softmax] diff --git a/cfg/yolo.cfg b/cfg/yolo.cfg index eef0b695..88176a65 100644 --- a/cfg/yolo.cfg +++ b/cfg/yolo.cfg @@ -4,10 +4,15 @@ subdivisions=64 height=448 width=448 channels=3 -learning_rate=0.01 +learning_rate=0.001 momentum=0.9 decay=0.0005 +policy=steps +steps=50, 5000 +scales=10, .1 +max_batches = 8000 + [crop] crop_width=448 crop_height=448 diff --git a/src/darknet.c b/src/darknet.c index 3709ed1e..833f89ec 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -13,6 +13,7 @@ extern void run_imagenet(int argc, char **argv); extern void run_yolo(int argc, char **argv); +extern void run_yoloplus(int argc, char **argv); extern void run_coco(int argc, char **argv); extern void run_writing(int argc, char **argv); extern void run_captcha(int argc, char **argv); @@ -178,6 +179,8 @@ int main(int argc, char **argv) average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ run_yolo(argc, argv); + } else if (0 == strcmp(argv[1], "yoloplus")){ + run_yoloplus(argc, argv); } else if (0 == strcmp(argv[1], "coco")){ run_coco(argc, argv); } else if (0 == strcmp(argv[1], "compare")){ diff --git a/src/detection_layer.c b/src/detection_layer.c index 80b606b5..daeee042 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -85,11 +85,12 @@ void forward_detection_layer(const detection_layer l, network_state state) int size = get_detection_layer_output_size(l) * l.batch; memset(l.delta, 0, size * sizeof(float)); for (i = 0; i < l.batch*locations; ++i) { - int classes = l.objectness+l.classes; + int classes = (l.objectness || l.background)+l.classes; int offset = i*(classes+l.coords); for (j = offset; j < offset+classes; ++j) { *(l.cost) += pow(state.truth[j] - l.output[j], 2); l.delta[j] = state.truth[j] - l.output[j]; + if(l.background && j == offset) l.delta[j] *= .1; } box truth; @@ -115,9 +116,15 @@ void forward_detection_layer(const detection_layer l, network_state state) l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]); l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]); if(l.rescore){ - for (j = offset; j < offset+classes; ++j) { - if(state.truth[j]) state.truth[j] = iou; - l.delta[j] = state.truth[j] - l.output[j]; + if(l.objectness){ + state.truth[offset] = iou; + l.delta[offset] = state.truth[offset] - l.output[offset]; + } + else{ + for (j = offset; j < offset+classes; ++j) { + if(state.truth[j]) state.truth[j] = iou; + l.delta[j] = state.truth[j] - l.output[j]; + } } } } @@ -145,7 +152,7 @@ void backward_detection_layer(const detection_layer l, network_state state) if (l.objectness) { }else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i); - for(j = 0; j < l.coords; ++j){ + for (j = 0; j < l.coords; ++j){ state.delta[in_i++] += l.delta[out_i++]; } if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] += latent_delta; diff --git a/src/network.c b/src/network.c index d823c157..af4861a3 100644 --- a/src/network.c +++ b/src/network.c @@ -29,15 +29,26 @@ int get_current_batch(network net) float get_current_rate(network net) { int batch_num = get_current_batch(net); + int i; + float rate; switch (net.policy) { case CONSTANT: return net.learning_rate; case STEP: - return net.learning_rate * pow(net.gamma, batch_num/net.step); + return net.learning_rate * pow(net.scale, batch_num/net.step); + case STEPS: + rate = net.learning_rate; + for(i = 0; i < net.num_steps; ++i){ + if(net.steps[i] > batch_num) return rate; + rate *= net.scales[i]; + } + return rate; case EXP: return net.learning_rate * pow(net.gamma, batch_num); case POLY: return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); + case SIG: + return net.learning_rate * (1/(1+exp(net.gamma*(batch_num - net.step)))); default: fprintf(stderr, "Policy is weird!\n"); return net.learning_rate; diff --git a/src/network.h b/src/network.h index 85e5dbc9..5a39f08f 100644 --- a/src/network.h +++ b/src/network.h @@ -8,7 +8,7 @@ #include "data.h" typedef enum { - CONSTANT, STEP, EXP, POLY + CONSTANT, STEP, EXP, POLY, STEPS, SIG } learning_rate_policy; typedef struct { @@ -25,9 +25,13 @@ typedef struct { float learning_rate; float gamma; + float scale; float power; int step; int max_batches; + float *scales; + int *steps; + int num_steps; int inputs; int h, w, c; diff --git a/src/parser.c b/src/parser.c index b9f6cb63..94dc0fad 100644 --- a/src/parser.c +++ b/src/parser.c @@ -169,7 +169,7 @@ detection_layer parse_detection(list *options, size_params params) int rescore = option_find_int(options, "rescore", 0); int joint = option_find_int(options, "joint", 0); int objectness = option_find_int(options, "objectness", 0); - int background = 0; + int background = option_find_int(options, "background", 0); detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness); return layer; } @@ -312,6 +312,8 @@ learning_rate_policy get_policy(char *s) if (strcmp(s, "constant")==0) return CONSTANT; if (strcmp(s, "step")==0) return STEP; if (strcmp(s, "exp")==0) return EXP; + if (strcmp(s, "sigmoid")==0) return SIG; + if (strcmp(s, "steps")==0) return STEPS; fprintf(stderr, "Couldn't find policy %s, going with constant\n", s); return CONSTANT; } @@ -337,9 +339,36 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); - net->gamma = option_find_float(options, "gamma", 1); + net->scale = option_find_float(options, "scale", 1); + } else if (net->policy == STEPS){ + char *l = option_find(options, "steps"); + char *p = option_find(options, "scales"); + if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); + + int len = strlen(l); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (l[i] == ',') ++n; + } + int *steps = calloc(n, sizeof(int)); + float *scales = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + int step = atoi(l); + float scale = atof(p); + l = strchr(l, ',')+1; + p = strchr(p, ',')+1; + steps[i] = step; + scales[i] = scale; + } + net->scales = scales; + net->steps = steps; + net->num_steps = n; } else if (net->policy == EXP){ net->gamma = option_find_float(options, "gamma", 1); + } else if (net->policy == SIG){ + net->gamma = option_find_float(options, "gamma", 1); + net->step = option_find_int(options, "step", 1); } else if (net->policy == POLY){ net->power = option_find_float(options, "power", 1); } @@ -401,10 +430,10 @@ network parse_network_cfg(char *filename) l = parse_dropout(options, params); l.output = net.layers[count-1].output; l.delta = net.layers[count-1].delta; - #ifdef GPU +#ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; - #endif +#endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } diff --git a/src/yolo.c b/src/yolo.c index 61a5344e..9b229e27 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -66,7 +66,6 @@ void train_yolo(char *cfgfile, char *weightfile) load_weights(&net, weightfile); } detection_layer layer = get_network_detection_layer(net); - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int imgs = 128; int i = *net.seen/imgs; @@ -75,10 +74,6 @@ void train_yolo(char *cfgfile, char *weightfile) int N = plist->size; paths = (char **)list_to_array(plist); - if(i*imgs > N*80){ - net.layers[net.n-1].joint = 1; - net.layers[net.n-1].objectness = 0; - } if(i*imgs > N*120){ net.layers[net.n-1].rescore = 1; } @@ -102,7 +97,7 @@ void train_yolo(char *cfgfile, char *weightfile) pthread_t load_thread = load_data_in_thread(args); clock_t time; - while(i*imgs < N*130){ + while(get_current_batch(net) < net.max_batches){ i += 1; time=clock(); pthread_join(load_thread, 0); @@ -115,19 +110,10 @@ void train_yolo(char *cfgfile, char *weightfile) if (avg_loss < 0) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images, epoch: %f\n", i, loss, avg_loss, sec(clock()-time), i*imgs, ((float)i)*imgs/N); - - if((i-1)*imgs <= N && i*imgs > N){ - fprintf(stderr, "First stage done\n"); - net.learning_rate *= 10; - char buff[256]; - sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base); - save_weights(net, buff); - } + printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N); if((i-1)*imgs <= 80*N && i*imgs > N*80){ fprintf(stderr, "Second stage done.\n"); - net.learning_rate *= .1; char buff[256]; sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base); save_weights(net, buff); diff --git a/src/yoloplus.c b/src/yoloplus.c new file mode 100644 index 00000000..dcae7bce --- /dev/null +++ b/src/yoloplus.c @@ -0,0 +1,334 @@ +#include "network.h" +#include "detection_layer.h" +#include "cost_layer.h" +#include "utils.h" +#include "parser.h" +#include "box.h" + +#ifdef OPENCV +#include "opencv2/highgui/highgui_c.h" +#endif + +char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; + +void draw_yoloplus(image im, float *box, int side, int objectness, char *label, float thresh) +{ + int classes = 20; + int elems = 4+classes+objectness; + int j; + int r, c; + + for(r = 0; r < side; ++r){ + for(c = 0; c < side; ++c){ + j = (r*side + c) * elems; + float scale = 1; + if(objectness) scale = 1 - box[j++]; + int class = max_index(box+j, classes); + if(scale * box[j+class] > thresh){ + int width = sqrt(scale*box[j+class])*5 + 1; + printf("%f %s\n", scale * box[j+class], voc_names[class]); + float red = get_color(0,class,classes); + float green = get_color(1,class,classes); + float blue = get_color(2,class,classes); + + j += classes; + float x = box[j+0]; + float y = box[j+1]; + x = (x+c)/side; + y = (y+r)/side; + float w = box[j+2]; //*maxwidth; + float h = box[j+3]; //*maxheight; + h = h*h; + w = w*w; + + int left = (x-w/2)*im.w; + int right = (x+w/2)*im.w; + int top = (y-h/2)*im.h; + int bot = (y+h/2)*im.h; + draw_box_width(im, left, top, right, bot, width, red, green, blue); + } + } + } + show_image(im, label); +} + +void train_yoloplus(char *cfgfile, char *weightfile) +{ + char *train_images = "/home/pjreddie/data/voc/test/train.txt"; + char *backup_directory = "/home/pjreddie/backup/"; + srand(time(0)); + data_seed = time(0); + char *base = basecfg(cfgfile); + printf("%s\n", base); + float avg_loss = -1; + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + detection_layer layer = get_network_detection_layer(net); + int imgs = 128; + int i = *net.seen/imgs; + + char **paths; + list *plist = get_paths(train_images); + int N = plist->size; + paths = (char **)list_to_array(plist); + + if(i*imgs > N*120){ + net.layers[net.n-1].rescore = 1; + } + data train, buffer; + + int classes = layer.classes; + int background = layer.objectness; + int side = sqrt(get_detection_layer_locations(layer)); + + load_args args = {0}; + args.w = net.w; + args.h = net.h; + args.paths = paths; + args.n = imgs; + args.m = plist->size; + args.classes = classes; + args.num_boxes = side; + args.background = background; + args.d = &buffer; + args.type = DETECTION_DATA; + + pthread_t load_thread = load_data_in_thread(args); + clock_t time; + while(get_current_batch(net) < net.max_batches){ + i += 1; + time=clock(); + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data_in_thread(args); + + printf("Loaded: %lf seconds\n", sec(clock()-time)); + time=clock(); + float loss = train_network(net, train); + if (avg_loss < 0) avg_loss = loss; + avg_loss = avg_loss*.9 + loss*.1; + + printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N); + + if((i-1)*imgs <= 80*N && i*imgs > N*80){ + fprintf(stderr, "Second stage done.\n"); + char buff[256]; + sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base); + save_weights(net, buff); + net.layers[net.n-1].joint = 1; + net.layers[net.n-1].objectness = 0; + background = 0; + + pthread_join(load_thread, 0); + free_data(buffer); + args.background = background; + load_thread = load_data_in_thread(args); + } + + if((i-1)*imgs <= 120*N && i*imgs > N*120){ + fprintf(stderr, "Third stage done.\n"); + char buff[256]; + sprintf(buff, "%s/%s_final.weights", backup_directory, base); + net.layers[net.n-1].rescore = 1; + save_weights(net, buff); + } + + if(i%1000==0){ + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + } + free_data(train); + } + char buff[256]; + sprintf(buff, "%s/%s_rescore.weights", backup_directory, base); + save_weights(net, buff); +} + +void convert_yoloplus_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes) +{ + int i,j; + int per_box = 4+classes+(background || objectness); + for (i = 0; i < num_boxes*num_boxes; ++i){ + float scale = 1; + if(objectness) scale = 1-predictions[i*per_box]; + int offset = i*per_box+(background||objectness); + for(j = 0; j < classes; ++j){ + float prob = scale*predictions[offset+j]; + probs[i][j] = (prob > thresh) ? prob : 0; + } + int row = i / num_boxes; + int col = i % num_boxes; + offset += classes; + boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w; + boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h; + boxes[i].w = pow(predictions[offset + 2], 2) * w; + boxes[i].h = pow(predictions[offset + 3], 2) * h; + } +} + +void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h) +{ + int i, j; + for(i = 0; i < num_boxes*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; + + 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); + } + } +} + +void validate_yoloplus(char *cfgfile, char *weightfile) +{ + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + detection_layer layer = get_network_detection_layer(net); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + srand(time(0)); + + char *base = "results/comp4_det_test_"; + list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); + char **paths = (char **)list_to_array(plist); + + int classes = layer.classes; + int objectness = layer.objectness; + int background = layer.background; + int num_boxes = sqrt(get_detection_layer_locations(layer)); + + int j; + FILE **fps = calloc(classes, sizeof(FILE *)); + for(j = 0; j < classes; ++j){ + char buff[1024]; + snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); + fps[j] = fopen(buff, "w"); + } + box *boxes = calloc(num_boxes*num_boxes, sizeof(box)); + float **probs = calloc(num_boxes*num_boxes, sizeof(float *)); + for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *)); + + int m = plist->size; + int i=0; + int t; + + float thresh = .001; + int nms = 1; + float iou_thresh = .5; + + int nthreads = 8; + 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; + + 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; + float *predictions = network_predict(net, X); + int w = val[t].w; + int h = val[t].h; + convert_yoloplus_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes); + if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh); + print_yoloplus_detections(fps, id, boxes, probs, num_boxes, classes, w, h); + free(id); + free_image(val[t]); + free_image(val_resized[t]); + } + } + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); +} + +void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh) +{ + + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + detection_layer layer = get_network_detection_layer(net); + set_batch_network(&net, 1); + srand(2222222); + clock_t time; + char input[256]; + while(1){ + if(filename){ + strncpy(input, filename, 256); + } else { + printf("Enter Image Path: "); + fflush(stdout); + fgets(input, 256, stdin); + strtok(input, "\n"); + } + image im = load_image_color(input,0,0); + image sized = resize_image(im, net.w, net.h); + float *X = sized.data; + time=clock(); + float *predictions = network_predict(net, X); + printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + draw_yoloplus(im, predictions, 7, layer.objectness, "predictions", thresh); + free_image(im); + free_image(sized); +#ifdef OPENCV + cvWaitKey(0); + cvDestroyAllWindows(); +#endif + if (filename) break; + } +} + +void run_yoloplus(int argc, char **argv) +{ + float thresh = find_float_arg(argc, argv, "-thresh", .2); + if(argc < 4){ + fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + return; + } + + char *cfg = argv[3]; + char *weights = (argc > 4) ? argv[4] : 0; + char *filename = (argc > 5) ? argv[5]: 0; + if(0==strcmp(argv[2], "test")) test_yoloplus(cfg, weights, filename, thresh); + else if(0==strcmp(argv[2], "train")) train_yoloplus(cfg, weights); + else if(0==strcmp(argv[2], "valid")) validate_yoloplus(cfg, weights); +}