From 73f7aacf35ec9b1d0f9de9ddf38af0889f213e99 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 20 Sep 2016 18:34:49 +0000
Subject: [PATCH] better multigpu
---
src/classifier.c | 204 ++++++++++++++++++++++++++++++++-------------------
1 files changed, 128 insertions(+), 76 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 3424216..b42d010 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -55,10 +55,8 @@
void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
#ifdef GPU
- int nthreads = 8;
int i;
- data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
@@ -68,17 +66,20 @@
for(i = 0; i < ngpus; ++i){
cuda_set_device(gpus[i]);
nets[i] = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&(nets[i]), weightfile);
- }
if(clear) *nets[i].seen = 0;
+ if(weightfile){
+ load_weights(&nets[i], weightfile);
+ }
}
network net = nets[0];
+ for(i = 0; i < ngpus; ++i){
+ *nets[i].seen = *net.seen;
+ nets[i].learning_rate *= ngpus;
+ }
+
+ 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*ngpus/nthreads;
- assert(net.batch*ngpus % nthreads == 0);
-
list *options = read_data_cfg(datacfg);
char *backup_directory = option_find_str(options, "backup", "/backup/");
@@ -93,13 +94,10 @@
int N = plist->size;
clock_t time;
- pthread_t *load_threads = calloc(nthreads, sizeof(pthread_t));
- data *trains = calloc(nthreads, sizeof(data));
- data *buffers = calloc(nthreads, sizeof(data));
-
load_args args = {0};
args.w = net.w;
args.h = net.h;
+ args.threads = 16;
args.min = net.min_crop;
args.max = net.max_crop;
@@ -117,36 +115,28 @@
args.labels = labels;
args.type = CLASSIFICATION_DATA;
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(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();
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- trains[i] = buffers[i];
- }
- data train = concat_datas(trains, nthreads);
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(args);
- }
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- float loss = train_networks(nets, ngpus, train);
+ float loss = train_networks(nets, ngpus, train, 4);
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);
free_data(train);
- for(i = 0; i < nthreads; ++i){
- free_data(trains[i]);
- }
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
@@ -163,14 +153,6 @@
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- free_data(buffers[i]);
- }
- free(buffers);
- free(trains);
- free(load_threads);
-
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -182,10 +164,6 @@
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
- int nthreads = 8;
- int i;
-
- data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
@@ -195,10 +173,10 @@
load_weights(&net, weightfile);
}
if(clear) *net.seen = 0;
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = net.batch*net.subdivisions/nthreads;
- assert(net.batch*net.subdivisions % nthreads == 0);
+ 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);
char *backup_directory = option_find_str(options, "backup", "/backup/");
@@ -213,13 +191,10 @@
int N = plist->size;
clock_t time;
- pthread_t *load_threads = calloc(nthreads, sizeof(pthread_t));
- data *trains = calloc(nthreads, sizeof(data));
- data *buffers = calloc(nthreads, sizeof(data));
-
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;
@@ -237,24 +212,19 @@
args.labels = labels;
args.type = CLASSIFICATION_DATA;
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(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();
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- trains[i] = buffers[i];
- }
- data train = concat_datas(trains, nthreads);
- for(i = 0; i < nthreads; ++i){
- args.d = buffers + i;
- load_threads[i] = load_data_in_thread(args);
- }
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -271,13 +241,11 @@
#endif
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);
- free_data(train);
- for(i = 0; i < nthreads; ++i){
- free_data(trains[i]);
- }
if(*net.seen/N > epoch){
epoch = *net.seen/N;
char buff[256];
@@ -294,14 +262,6 @@
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
- for(i = 0; i < nthreads; ++i){
- pthread_join(load_threads[i], 0);
- free_data(buffers[i]);
- }
- free(buffers);
- free(trains);
- free(load_threads);
-
free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
@@ -934,7 +894,19 @@
int w = x2 - x1 - 2*border;
float *predictions = network_predict(net, in_s.data);
- float curr_threat = predictions[0] * 0 + predictions[1] * .6 + predictions[2];
+ float curr_threat = 0;
+ if(1){
+ curr_threat = predictions[0] * 0 +
+ predictions[1] * .6 +
+ predictions[2];
+ } else {
+ curr_threat = predictions[218] +
+ predictions[539] +
+ predictions[540] +
+ predictions[368] +
+ predictions[369] +
+ predictions[370];
+ }
threat = roll * curr_threat + (1-roll) * threat;
draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
@@ -970,7 +942,7 @@
top_predictions(net, top, indexes);
char buff[256];
sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count);
- save_image(out, buff);
+ //save_image(out, buff);
printf("\033[2J");
printf("\033[1;1H");
@@ -981,7 +953,7 @@
printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
}
- if(0){
+ if(1){
show_image(out, "Threat");
cvWaitKey(10);
}
@@ -997,6 +969,85 @@
}
+void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+ int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
+
+ printf("Classifier Demo\n");
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ list *options = read_data_cfg(datacfg);
+
+ srand(2222222);
+ CvCapture * cap;
+
+ if(filename){
+ cap = cvCaptureFromFile(filename);
+ }else{
+ cap = cvCaptureFromCAM(cam_index);
+ }
+
+ int top = option_find_int(options, "top", 1);
+
+ char *name_list = option_find_str(options, "names", 0);
+ char **names = get_labels(name_list);
+
+ int *indexes = calloc(top, sizeof(int));
+
+ if(!cap) error("Couldn't connect to webcam.\n");
+ cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
+ cvResizeWindow("Threat Detection", 512, 512);
+ float fps = 0;
+ int i;
+
+ while(1){
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+
+ image in = get_image_from_stream(cap);
+ image in_s = resize_image(in, net.w, net.h);
+ show_image(in, "Threat Detection");
+
+ float *predictions = network_predict(net, in_s.data);
+ top_predictions(net, top, indexes);
+
+ printf("\033[2J");
+ printf("\033[1;1H");
+
+ int threat = 0;
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("Threat Detected!\n");
+ threat = 1;
+ break;
+ }
+ }
+ if(!threat) printf("Scanning...\n");
+ for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
+ int index = bad_cats[i];
+ if(predictions[index] > .01){
+ printf("%s\n", names[index]);
+ }
+ }
+
+ free_image(in_s);
+ free_image(in);
+
+ cvWaitKey(10);
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
+ }
+#endif
+}
+
void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
@@ -1102,6 +1153,7 @@
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], "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);
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
--
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