From 9a01e6ccb7a74ff77e99060cf18acd6cfdb74b8e Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 11 Nov 2016 16:48:40 +0000
Subject: [PATCH] :fire: crush. crush. admit. :fire:
---
src/detector.c | 165 ++++++++++++++---
src/classifier.c | 203 ++++++++++++----------
src/region_layer.c | 75 +++++--
src/darknet.c | 55 ++++-
4 files changed, 328 insertions(+), 170 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 2ce6207..586530a 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -25,9 +25,8 @@
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 @@
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 @@
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);
@@ -133,117 +143,118 @@
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
-#endif
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
-{
- srand(time(0));
- float avg_loss = -1;
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- if(clear) *net.seen = 0;
+/*
+ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+ {
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ if(clear) *net.seen = 0;
- int imgs = net.batch * net.subdivisions;
+ int imgs = net.batch * net.subdivisions;
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- list *options = read_data_cfg(datacfg);
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ list *options = read_data_cfg(datacfg);
- char *backup_directory = option_find_str(options, "backup", "/backup/");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
- char *train_list = option_find_str(options, "train", "data/train.list");
- int classes = option_find_int(options, "classes", 2);
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *train_list = option_find_str(options, "train", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
- char **labels = get_labels(label_list);
- list *plist = get_paths(train_list);
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
- int N = plist->size;
- clock_t time;
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(train_list);
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ int N = plist->size;
+ clock_t time;
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.threads = 8;
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.threads = 8;
- args.min = net.min_crop;
- args.max = net.max_crop;
- args.angle = net.angle;
- args.aspect = net.aspect;
- args.exposure = net.exposure;
- args.saturation = net.saturation;
- args.hue = net.hue;
- args.size = net.w;
- args.hierarchy = net.hierarchy;
+ args.min = net.min_crop;
+ args.max = net.max_crop;
+ args.angle = net.angle;
+ args.aspect = net.aspect;
+ args.exposure = net.exposure;
+ args.saturation = net.saturation;
+ args.hue = net.hue;
+ args.size = net.w;
+ args.hierarchy = net.hierarchy;
- args.paths = paths;
- args.classes = classes;
- args.n = imgs;
- args.m = N;
- args.labels = labels;
- args.type = CLASSIFICATION_DATA;
+ args.paths = paths;
+ args.classes = classes;
+ args.n = imgs;
+ args.m = N;
+ args.labels = labels;
+ args.type = CLASSIFICATION_DATA;
- data train;
- data buffer;
- pthread_t load_thread;
- args.d = &buffer;
- load_thread = load_data(args);
+ data train;
+ data buffer;
+ pthread_t load_thread;
+ args.d = &buffer;
+ load_thread = load_data(args);
- int epoch = (*net.seen)/N;
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- time=clock();
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
- printf("Loaded: %lf seconds\n", sec(clock()-time));
- time=clock();
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
#ifdef OPENCV
- 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);
- }
- }
+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){
- 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){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
- }
+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.weights", backup_directory, base);
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free_ptrs((void**)paths, plist->size);
- free_list(plist);
- free(base);
}
+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);
+}
+*/
void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
@@ -1108,6 +1119,7 @@
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 @@
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 @@
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);
diff --git a/src/darknet.c b/src/darknet.c
index 3bc0c6a..989bf6f 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -30,20 +30,6 @@
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 @@
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 @@
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 @@
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 @@
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 @@
} 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")){
diff --git a/src/detector.c b/src/detector.c
index e020be5..f18ae51 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -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 @@
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
+ 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(&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];
@@ -62,37 +77,46 @@
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);
- if(!b.x) break;
- printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
- }
- image im = float_to_image(448, 448, 3, train.X.vals[10]);
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
- draw_bbox(im, b, 8, 1,0,0);
- }
- save_image(im, "truth11");
-*/
+ /*
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ if(!b.x) break;
+ printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
+ }
+ image im = float_to_image(448, 448, 3, train.X.vals[10]);
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
+ 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 @@
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 @@
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 @@
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 @@
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 @@
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);
- print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
+ 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 @@
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));
}
@@ -300,8 +376,8 @@
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh)
{
list *options = read_data_cfg(datacfg);
- char *name_list = option_find_str(options, "names", "data/names.list");
- char **names = get_labels(name_list);
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ char **names = get_labels(name_list);
image **alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
@@ -361,6 +437,29 @@
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 @@
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")) {
diff --git a/src/region_layer.c b/src/region_layer.c
index 269be1f..ac30e88 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -48,19 +48,18 @@
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.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 @@
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]);
-
- 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]));
+ if(DOABS){
+ tw = log(truth.w*w / biases[2*n]);
+ th = log(truth.h*h / biases[2*n + 1]);
}
+
+ 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 @@
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 @@
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 @@
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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