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darknet
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pull/5316/head
Joseph Redmon 9 years ago
parent 4b60afcc64
commit 9a01e6ccb7
4 changed files with 339 additions and 181 deletions
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  1. 79
      src/classifier.c
  2. 55
      src/darknet.c
  3. 129
      src/detector.c
  4. 67
      src/region_layer.c

79
src/classifier.c
Unescape View File

@ -25,9 +25,8 @@ float *get_regression_values(char **labels, int n)
return v;
}
void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
#ifdef GPU
int i;
float avg_loss = -1;
@ -40,7 +39,9 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
int seed = rand();
for(i = 0; i < ngpus; ++i){
srand(seed);
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&nets[i], weightfile);
@ -107,7 +108,16 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_networks(nets, ngpus, train, 4);
float loss = 0;
#ifdef GPU
if(ngpus == 1){
loss = train_network(net, train);
} else {
loss = train_networks(nets, ngpus, train, 4);
}
#else
loss = train_network(net, train);
#endif
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
@ -133,12 +143,12 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
#endif
}
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
/*
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
@ -206,44 +216,45 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
time=clock();
#ifdef OPENCV
if(0){
int u;
for(u = 0; u < imgs; ++u){
if(0){
int u;
for(u = 0; u < imgs; ++u){
image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
show_image(im, "loaded");
cvWaitKey(0);
}
}
}
}
#endif
float loss = train_network(net, train);
free_data(train);
float loss = train_network(net, train);
free_data(train);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
if(get_current_batch(net)%100 == 0){
}
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
}
*/
void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
@ -1108,6 +1119,7 @@ void run_classifier(int argc, char **argv)
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);
@ -1122,6 +1134,10 @@ void run_classifier(int argc, char **argv)
gpus[i] = atoi(gpu_list);
gpu_list = strchr(gpu_list, ',')+1;
}
} else {
gpu = gpu_index;
gpus = &gpu;
ngpus = 1;
}
int cam_index = find_int_arg(argc, argv, "-c", 0);
@ -1135,8 +1151,7 @@ void run_classifier(int argc, char **argv)
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
else if(0==strcmp(argv[2], "trainm")) train_classifier_multi(data, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);

55
src/darknet.c
Unescape View File

@ -30,20 +30,6 @@ extern void run_go(int argc, char **argv);
extern void run_art(int argc, char **argv);
extern void run_super(int argc, char **argv);
void change_rate(char *filename, float scale, float add)
{
// Ready for some weird shit??
FILE *fp = fopen(filename, "r+b");
if(!fp) file_error(filename);
float rate = 0;
fread(&rate, sizeof(float), 1, fp);
printf("Scaling learning rate from %f to %f\n", rate, rate*scale+add);
rate = rate*scale + add;
fseek(fp, 0, SEEK_SET);
fwrite(&rate, sizeof(float), 1, fp);
fclose(fp);
}
void average(int argc, char *argv[])
{
char *cfgfile = argv[2];
@ -67,6 +53,11 @@ void average(int argc, char *argv[])
int num = l.n*l.c*l.size*l.size;
axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
if(l.batch_normalize){
axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
}
}
if(l.type == CONNECTED){
axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
@ -81,6 +72,11 @@ void average(int argc, char *argv[])
int num = l.n*l.c*l.size*l.size;
scal_cpu(l.n, 1./n, l.biases, 1);
scal_cpu(num, 1./n, l.weights, 1);
if(l.batch_normalize){
scal_cpu(l.n, 1./n, l.scales, 1);
scal_cpu(l.n, 1./n, l.rolling_mean, 1);
scal_cpu(l.n, 1./n, l.rolling_variance, 1);
}
}
if(l.type == CONNECTED){
scal_cpu(l.outputs, 1./n, l.biases, 1);
@ -125,6 +121,31 @@ void operations(char *cfgfile)
printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
}
void oneoff(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
int oldn = net.layers[net.n - 2].n;
int c = net.layers[net.n - 2].c;
net.layers[net.n - 2].n = 7879;
net.layers[net.n - 2].biases += 5;
net.layers[net.n - 2].weights += 5*c;
if(weightfile){
load_weights(&net, weightfile);
}
net.layers[net.n - 2].biases -= 5;
net.layers[net.n - 2].weights -= 5*c;
net.layers[net.n - 2].n = oldn;
printf("%d\n", oldn);
layer l = net.layers[net.n - 2];
copy_cpu(l.n/3, l.biases, 1, l.biases + l.n/3, 1);
copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1);
copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
*net.seen = 0;
save_weights(net, outfile);
}
void partial(char *cfgfile, char *weightfile, char *outfile, int max)
{
gpu_index = -1;
@ -387,8 +408,6 @@ int main(int argc, char **argv)
run_captcha(argc, argv);
} else if (0 == strcmp(argv[1], "nightmare")){
run_nightmare(argc, argv);
} else if (0 == strcmp(argv[1], "change")){
change_rate(argv[2], atof(argv[3]), (argc > 4) ? atof(argv[4]) : 0);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "reset")){
@ -404,7 +423,9 @@ int main(int argc, char **argv)
} else if (0 == strcmp(argv[1], "ops")){
operations(argv[2]);
} else if (0 == strcmp(argv[1], "speed")){
speed(argv[2], (argc > 3) ? atoi(argv[3]) : 0);
speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
} else if (0 == strcmp(argv[1], "oneoff")){
oneoff(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "average")){

129
src/detector.c
Unescape View File

@ -10,8 +10,9 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
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 clear)
void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
list *options = read_data_cfg(datacfg);
char *train_images = option_find_str(options, "train", "data/train.list");
@ -21,14 +22,28 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int clear)
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
network *nets = calloc(ngpus, sizeof(network));
srand(time(0));
int seed = rand();
int i;
for(i = 0; i < ngpus; ++i){
srand(seed);
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
load_weights(&nets[i], weightfile);
}
if(clear) *nets[i].seen = 0;
nets[i].learning_rate *= ngpus;
}
if(clear) *net.seen = 0;
srand(time(0));
network net = nets[0];
int imgs = net.batch * net.subdivisions * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
data train, buffer;
layer l = net.layers[net.n - 1];
@ -62,13 +77,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int clear)
clock_t time;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
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);
@ -83,16 +97,26 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int clear)
draw_bbox(im, b, 8, 1,0,0);
}
save_image(im, "truth11");
*/
*/
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
float loss = 0;
#ifdef GPU
if(ngpus == 1){
loss = train_network(net, train);
} else {
loss = train_networks(nets, ngpus, train, 4);
}
#else
loss = train_network(net, train);
#endif
if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
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)){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
@ -105,6 +129,39 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int clear)
save_weights(net, buff);
}
static int get_coco_image_id(char *filename)
{
char *p = strrchr(filename, '_');
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)
{
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]);
}
}
}
void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
int i, j;
@ -131,8 +188,19 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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 buff[1024];
int coco = option_find_int_quiet(options, "coco", 0);
FILE *coco_fp = 0;
if(coco){
snprintf(buff, 1024, "%s/coco_results.json", prefix);
coco_fp = fopen(buff, "w");
fprintf(coco_fp, "[\n");
}
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
@ -141,7 +209,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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_";
char *base = "comp4_det_test_";
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
@ -151,8 +219,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
int j;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, names[j]);
snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
fps[j] = fopen(buff, "w");
}
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
@ -207,7 +274,11 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
int h = val[t].h;
get_region_boxes(l, w, h, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
if(coco_fp){
print_cocos(coco_fp, path, 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]);
@ -216,6 +287,11 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
for(j = 0; j < classes; ++j){
fclose(fps[j]);
}
if(coco_fp){
fseek(coco_fp, -2, SEEK_CUR);
fprintf(coco_fp, "\n]\n");
fclose(coco_fp);
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
@ -361,6 +437,29 @@ void run_detector(int argc, char **argv)
fprintf(stderr, "usage: %s %s [train/test/valid] [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 = strlen(gpu_list);
ngpus = 1;
int i;
for(i = 0; i < len; ++i){
if (gpu_list[i] == ',') ++ngpus;
}
gpus = calloc(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];
@ -368,7 +467,7 @@ void run_detector(int argc, char **argv)
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
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, clear);
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], "demo")) {

67
src/region_layer.c
Unescape View File

@ -48,19 +48,18 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int
return l;
}
#define LOG 1
#define DOABS 1
box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
{
box b;
b.x = (i + .5)/w + x[index + 0] * biases[2*n];
b.y = (j + .5)/h + x[index + 1] * biases[2*n + 1];
if(LOG){
b.x = (i + logistic_activate(x[index + 0])) / w;
b.y = (j + logistic_activate(x[index + 1])) / h;
}
b.w = exp(x[index + 2]) * biases[2*n];
b.h = exp(x[index + 3]) * biases[2*n+1];
if(DOABS){
b.w = exp(x[index + 2]) * biases[2*n] / w;
b.h = exp(x[index + 3]) * biases[2*n+1] / h;
}
return b;
}
@ -69,21 +68,17 @@ float delta_region_box(box truth, float *x, float *biases, int n, int index, int
box pred = get_region_box(x, biases, n, index, i, j, w, h);
float iou = box_iou(pred, truth);
float tx = (truth.x - (i + .5)/w) / biases[2*n];
float ty = (truth.y - (j + .5)/h) / biases[2*n + 1];
if(LOG){
tx = (truth.x*w - i);
ty = (truth.y*h - j);
}
float tx = (truth.x*w - i);
float ty = (truth.y*h - j);
float tw = log(truth.w / biases[2*n]);
float th = log(truth.h / biases[2*n + 1]);
if(DOABS){
tw = log(truth.w*w / biases[2*n]);
th = log(truth.h*h / biases[2*n + 1]);
}
delta[index + 0] = scale * (tx - x[index + 0]);
delta[index + 1] = scale * (ty - x[index + 1]);
if(LOG){
delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
}
delta[index + 2] = scale * (tw - x[index + 2]);
delta[index + 3] = scale * (th - x[index + 3]);
return iou;
@ -135,9 +130,33 @@ void forward_region_layer(const region_layer l, network_state state)
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
l.output[index + 4] = logistic_activate(l.output[index + 4]);
if(l.softmax_tree){
}
}
if (l.softmax_tree){
#ifdef GPU
cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
int i;
int count = 5;
for (i = 0; i < l.softmax_tree->groups; ++i) {
int group_size = l.softmax_tree->group_size[i];
softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
count += group_size;
}
cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
#else
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
} else if(l.softmax){
}
}
#endif
} else if (l.softmax){
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
}
}
@ -188,11 +207,11 @@ void forward_region_layer(const region_layer l, network_state state)
truth.y = (j + .5)/l.h;
truth.w = l.biases[2*n];
truth.h = l.biases[2*n+1];
if(DOABS){
truth.w = l.biases[2*n]/l.w;
truth.h = l.biases[2*n+1]/l.h;
}
delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
//l.delta[index + 0] = .1 * (0 - l.output[index + 0]);
//l.delta[index + 1] = .1 * (0 - l.output[index + 1]);
//l.delta[index + 2] = .1 * (0 - l.output[index + 2]);
//l.delta[index + 3] = .1 * (0 - l.output[index + 3]);
}
}
}
@ -217,6 +236,10 @@ void forward_region_layer(const region_layer l, network_state state)
if(l.bias_match){
pred.w = l.biases[2*n];
pred.h = l.biases[2*n+1];
if(DOABS){
pred.w = l.biases[2*n]/l.w;
pred.h = l.biases[2*n+1]/l.h;
}
}
//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
pred.x = 0;

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