|
|
|
@ -137,14 +137,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i |
|
|
|
|
|
|
|
|
|
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); |
|
|
|
|
if(i%1000==0 || (i < 1000 && i%100 == 0)){ |
|
|
|
|
if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { |
|
|
|
|
#ifdef GPU |
|
|
|
|
if(ngpus != 1) sync_nets(nets, ngpus, 0); |
|
|
|
|
if (ngpus != 1) sync_nets(nets, ngpus, 0); |
|
|
|
|
#endif |
|
|
|
|
char buff[256]; |
|
|
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); |
|
|
|
|
save_weights(net, buff); |
|
|
|
|
} |
|
|
|
|
char buff[256]; |
|
|
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); |
|
|
|
|
save_weights(net, buff); |
|
|
|
|
} |
|
|
|
|
free_data(train); |
|
|
|
|
} |
|
|
|
|
#ifdef GPU |
|
|
|
@ -358,7 +358,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile) |
|
|
|
|
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); |
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
void validate_detector_recall(char *cfgfile, char *weightfile) |
|
|
|
|
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) |
|
|
|
|
{ |
|
|
|
|
network net = parse_network_cfg(cfgfile); |
|
|
|
|
if(weightfile){ |
|
|
|
@ -368,7 +368,9 @@ void validate_detector_recall(char *cfgfile, char *weightfile) |
|
|
|
|
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
|
|
|
|
srand(time(0)); |
|
|
|
|
|
|
|
|
|
list *plist = get_paths("data/voc.2007.test"); |
|
|
|
|
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]; |
|
|
|
@ -382,7 +384,7 @@ void validate_detector_recall(char *cfgfile, char *weightfile) |
|
|
|
|
int m = plist->size; |
|
|
|
|
int i=0; |
|
|
|
|
|
|
|
|
|
float thresh = .001; |
|
|
|
|
float thresh = .2;// .001;
|
|
|
|
|
float iou_thresh = .5; |
|
|
|
|
float nms = .4; |
|
|
|
|
|
|
|
|
@ -413,16 +415,16 @@ void validate_detector_recall(char *cfgfile, char *weightfile) |
|
|
|
|
++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){ |
|
|
|
|
best_iou = iou; |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|
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) { |
|
|
|
|
best_iou = iou; |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|
avg_iou += best_iou; |
|
|
|
|
if(best_iou > iou_thresh){ |
|
|
|
|
++correct; |
|
|
|
@ -536,7 +538,7 @@ void run_detector(int argc, char **argv) |
|
|
|
|
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh); |
|
|
|
|
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear); |
|
|
|
|
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights); |
|
|
|
|
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights); |
|
|
|
|
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); |
|
|
|
|
else if(0==strcmp(argv[2], "demo")) { |
|
|
|
|
list *options = read_data_cfg(datacfg); |
|
|
|
|
int classes = option_find_int(options, "classes", 20); |
|
|
|
|