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

GUYS I KNOW HOW TO MULTITHREAD :SNAKE:

Browse Source
This commit is contained in:
Joseph Redmon
2017-07-11 16:44:09 -07:00
parent 59ed1719d4
commit 616e6305e2
18 changed files with 319 additions and 151 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
examples/classifier.c
View File

@ -58,7 +58,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.threads = 32;
args.threads = 64;
args.hierarchy = net.hierarchy;
args.min = net.min_crop;
@ -670,7 +670,6 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
int size = net.w;
while(1){
if(filename){
strncpy(input, filename, 256);
@ -682,8 +681,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image r = resize_min(im, size);
resize_network(&net, r.w, r.h);
image r = letterbox_image(im, net.w, net.h);
//resize_network(&net, r.w, r.h);
//printf("%d %d\n", r.w, r.h);
float *X = r.data;

2
examples/coco.c
View File

@ -342,7 +342,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, 0, coco_classes, alphabet, 80);
save_image(im, "prediction");
show_image(im, "predictions");
free_image(im);

38
examples/detector.c
View File

@ -41,9 +41,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
load_args args = get_base_args(net);
args.coords = l.coords;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
@ -52,13 +51,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
args.num_boxes = l.max_boxes;
args.d = &buffer;
args.type = DETECTION_DATA;
//args.type = INSTANCE_DATA;
args.threads = 8;
args.angle = net.angle;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
pthread_t load_thread = load_data(args);
clock_t time;
int count = 0;
@ -102,7 +97,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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);
box b = float_to_box(train.y.vals[zz] + k*5, 1);
printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
draw_bbox(im, b, 1, 1,0,0);
}
@ -130,7 +125,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
i = get_current_batch(net);
printf("%ld: %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){
if(i%100==0){
#ifdef GPU
if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
@ -342,7 +337,7 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
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, 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);
@ -473,7 +468,7 @@ 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, 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);
@ -537,7 +532,7 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
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, 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];
@ -589,11 +584,11 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
}
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
double time;
char buff[256];
char *input = buff;
int j;
float nms=.4;
float nms=.3;
while(1){
if(filename){
strncpy(input, filename, 256);
@ -615,15 +610,20 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
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(l.classes + 1, sizeof(float *));
float **masks = 0;
if (l.coords > 4){
masks = calloc(l.w*l.h*l.n, sizeof(float*));
for(j = 0; j < l.w*l.h*l.n; ++j) masks[j] = calloc(l.coords-4, sizeof(float *));
}
float *X = sized.data;
time=clock();
time=what_time_is_it_now();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, 0, 0, hier_thresh, 1);
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);
if (nms) do_nms_obj(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, names, alphabet, l.classes);
draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, masks, names, alphabet, l.classes);
if(outfile){
save_image(im, outfile);
}

48
examples/rnn.c
View File

@ -279,6 +279,54 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
printf("\n");
}
void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
{
char **tokens = 0;
if(token_file){
size_t n;
tokens = read_tokens(token_file, &n);
}
srand(rseed);
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
int inputs = net.inputs;
int i, j;
for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
int c = 0;
float *input = calloc(inputs, sizeof(float));
float *out = 0;
while(1){
reset_rnn_state(net, 0);
while((c = getc(stdin)) != EOF && c != 0){
input[c] = 1;
out = network_predict(net, input);
input[c] = 0;
}
for(i = 0; i < num; ++i){
for(j = 0; j < inputs; ++j){
if (out[j] < .0001) out[j] = 0;
}
int next = sample_array(out, inputs);
if(c == '.' && next == '\n') break;
c = next;
print_symbol(c, tokens);
input[c] = 1;
out = network_predict(net, input);
input[c] = 0;
}
printf("\n");
}
}
void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
{
char **tokens = 0;

3
examples/yolo.c
View File

@ -308,8 +308,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
//draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, 0, voc_names, alphabet, 20);
save_image(im, "predictions");
show_image(im, "predictions");