mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

it's raining really hard outside :-( :rain: :storm: ☁️

Browse Source
This commit is contained in:
Joseph Redmon
2017-10-17 11:41:34 -07:00
parent 532c6e1481
commit cd5d393b46
27 changed files with 1340 additions and 1669 deletions
Download Patch File Download Diff File Expand all files Collapse all files

96
examples/detector.c
View File

@ -12,7 +12,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network *nets = calloc(ngpus, sizeof(network));
network **nets = calloc(ngpus, sizeof(network));
srand(time(0));
int seed = rand();
@ -23,16 +23,16 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
cuda_set_device(gpus[i]);
#endif
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i].learning_rate *= ngpus;
nets[i]->learning_rate *= ngpus;
}
srand(time(0));
network net = nets[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 * ngpus;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
data train, buffer;
layer l = net.layers[net.n - 1];
layer l = net->layers[net->n - 1];
int classes = l.classes;
float jitter = l.jitter;
@ -58,11 +58,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
double time;
int count = 0;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
while(get_current_batch(net) < net->max_batches){
if(l.random && count++%10 == 0){
printf("Resizing\n");
int dim = (rand() % 10 + 10) * 32;
if (get_current_batch(net)+200 > net.max_batches) dim = 608;
if (get_current_batch(net)+200 > net->max_batches) dim = 608;
//int dim = (rand() % 4 + 16) * 32;
printf("%d\n", dim);
args.w = dim;
@ -74,7 +74,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
load_thread = load_data(args);
for(i = 0; i < ngpus; ++i){
resize_network(nets + i, dim, dim);
resize_network(nets[i], dim, dim);
}
net = nets[0];
}
@ -94,7 +94,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
/*
int zz;
for(zz = 0; zz < train.X.cols; ++zz){
image im = float_to_image(net.w, net.h, 3, train.X.vals[zz]);
image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]);
int k;
for(k = 0; k < l.max_boxes; ++k){
box b = float_to_box(train.y.vals[zz] + k*5, 1);
@ -239,18 +239,15 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
int *map = 0;
if (mapf) map = read_map(mapf);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 2);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 2);
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);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
layer l = net->layers[net->n-1];
int classes = l.classes;
char buff[1024];
@ -299,11 +296,11 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
image input = make_image(net.w, net.h, net.c*2);
image input = make_image(net->w, net->h, net->c*2);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
//args.type = IMAGE_DATA;
args.type = LETTERBOX_DATA;
@ -330,14 +327,14 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data, 1);
copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1);
flip_image(val_resized[t]);
copy_cpu(net.w*net.h*net.c, val_resized[t].data, 1, input.data + net.w*net.h*net.c, 1);
copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1);
network_predict(net, input.data);
int w = val[t].w;
int h = val[t].h;
get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
get_region_boxes(l, w, h, net->w, net->h, thresh, probs, boxes, 0, 0, map, .5, 0);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
if (coco){
print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
@ -375,18 +372,15 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
int *map = 0;
if (mapf) map = read_map(mapf);
network net = parse_network_cfg(cfgfile);
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);
network *net = load_network(cfgfile, weightfile, 0);
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);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
layer l = net->layers[net->n-1];
int classes = l.classes;
char buff[1024];
@ -436,8 +430,8 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
//args.type = IMAGE_DATA;
args.type = LETTERBOX_DATA;
@ -468,7 +462,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
get_region_boxes(l, w, h, net->w, net->h, thresh, probs, boxes, 0, 0, map, .5, 0);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
if (coco){
print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
@ -495,18 +489,15 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
void validate_detector_recall(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
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);
network *net = load_network(cfgfile, weightfile, 0);
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("data/coco_val_5k.list");
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n-1];
layer l = net->layers[net->n-1];
int classes = l.classes;
int j, k;
@ -529,10 +520,10 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
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);
image sized = resize_image(orig, net->w, net->h);
char *id = basecfg(path);
network_predict(net, sized.data);
get_region_boxes(l, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 0, 1, 0, .5, 1);
get_region_boxes(l, sized.w, sized.h, net->w, net->h, thresh, probs, boxes, 0, 1, 0, .5, 1);
if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
char labelpath[4096];
@ -578,11 +569,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
double time;
char buff[256];
@ -600,12 +588,12 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
image sized = letterbox_image(im, net.w, net.h);
//image sized = resize_image(im, net.w, net.h);
//image sized2 = resize_max(im, net.w);
//image sized = crop_image(sized2, -((net.w - sized2.w)/2), -((net.h - sized2.h)/2), net.w, net.h);
//resize_network(&net, sized.w, sized.h);
layer l = net.layers[net.n-1];
image sized = letterbox_image(im, net->w, net->h);
//image sized = resize_image(im, net->w, net->h);
//image sized2 = resize_max(im, net->w);
//image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
//resize_network(net, sized.w, sized.h);
layer l = net->layers[net->n-1];
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
@ -620,7 +608,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
time=what_time_is_it_now();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, masks, 0, 0, hier_thresh, 1);
get_region_boxes(l, im.w, im.h, net->w, net->h, thresh, probs, boxes, masks, 0, 0, hier_thresh, 1);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
//else 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, masks, names, alphabet, l.classes);