Skip to content

Commit

Permalink
better multigpu
Browse files Browse the repository at this point in the history
  • Loading branch information
@pjreddie
pjreddie committed Sep 20, 2016
1 parent 5c067dc commit 73f7aac
Show file tree
Hide file tree
Showing 26 changed files with 401 additions and 197 deletions.
2 changes: 1 addition & 1 deletion src/blas_kernels.cu
View file
Original file line number Diff line number Diff line change
Expand Up @@ -365,7 +365,7 @@ __global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < N) X[i*INCX] = min(ALPHA, max(-ALPHA, X[i*INCX]));
if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX]));
}

__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX)
Expand Down
1 change: 0 additions & 1 deletion src/captcha.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,6 @@ void fix_data_captcha(data d, int mask)

void train_captcha(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
Expand Down
2 changes: 0 additions & 2 deletions src/cifar.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@

void train_cifar(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
Expand Down Expand Up @@ -59,7 +58,6 @@ void train_cifar(char *cfgfile, char *weightfile)

void train_cifar_distill(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
Expand Down
202 changes: 127 additions & 75 deletions src/classifier.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -55,10 +55,8 @@ float *get_regression_values(char **labels, int n)
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);
Expand All @@ -68,17 +66,20 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
for(i = 0; i < ngpus; ++i){
cuda_set_device(gpus[i]);
nets[i] = parse_network_cfg(cfgfile);
if(clear) *nets[i].seen = 0;
if(weightfile){
load_weights(&(nets[i]), weightfile);
load_weights(&nets[i], weightfile);
}
if(clear) *nets[i].seen = 0;
}
network net = nets[0];
for(i = 0; i < ngpus; ++i){
*nets[i].seen = *net.seen;
nets[i].learning_rate *= 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);
int imgs = net.batch * net.subdivisions * ngpus;

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/");
Expand All @@ -93,13 +94,10 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
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;
Expand All @@ -117,36 +115,28 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
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];
Expand All @@ -163,14 +153,6 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
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);
Expand All @@ -182,10 +164,6 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int

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);
Expand All @@ -195,10 +173,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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/");
Expand All @@ -213,13 +191,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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;
Expand All @@ -237,24 +212,19 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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();
Expand All @@ -271,13 +241,11 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
#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];
Expand All @@ -294,14 +262,6 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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);
Expand Down Expand Up @@ -934,7 +894,19 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
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);
Expand Down Expand Up @@ -970,7 +942,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
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");
Expand All @@ -981,7 +953,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
}

if(0){
if(1){
show_image(out, "Threat");
cvWaitKey(10);
}
Expand All @@ -997,6 +969,85 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
}


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
Expand Down Expand Up @@ -1102,6 +1153,7 @@ void run_classifier(int argc, char **argv)
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);
Expand Down
1 change: 0 additions & 1 deletion src/coco.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,6 @@ void train_coco(char *cfgfile, char *weightfile)
//char *train_images = "data/bags.train.list";
char *backup_directory = "/home/pjreddie/backup/";
srand(time(0));
data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
Expand Down
1 change: 0 additions & 1 deletion src/compare.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@

void train_compare(char *cfgfile, char *weightfile)
{
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
Expand Down
Loading

0 comments on commit 73f7aac

Please sign in to comment.