void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 0);
int i;
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {
denormalize_convolutional_layer(l);
net->layers[i].batch_normalize=0;
}
if (l.type == CONNECTED && l.batch_normalize) {
denormalize_connected_layer(l);
net->layers[i].batch_normalize=0;
}
if (l.type == GRU && l.batch_normalize) {
denormalize_connected_layer(*l.input_z_layer);
denormalize_connected_layer(*l.input_r_layer);
denormalize_connected_layer(*l.input_h_layer);
denormalize_connected_layer(*l.state_z_layer);
denormalize_connected_layer(*l.state_r_layer);
denormalize_connected_layer(*l.state_h_layer);
l.input_z_layer->batch_normalize = 0;
l.input_r_layer->batch_normalize = 0;
l.input_h_layer->batch_normalize = 0;
l.state_z_layer->batch_normalize = 0;
l.state_r_layer->batch_normalize = 0;
l.state_h_layer->batch_normalize = 0;
net->layers[i].batch_normalize=0;
}
}
save_weights(net, outfile);
free_network(net);
}
void normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 0);
int i;
for(i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL && !l.batch_normalize){
net->layers[i] = normalize_layer(l, l.n);
}
if (l.type == CONNECTED && !l.batch_normalize) {
net->layers[i] = normalize_layer(l, l.outputs);
}
if (l.type == GRU && l.batch_normalize) {
*l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
*l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
*l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
*l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
*l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
*l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
net->layers[i].batch_normalize=1;
}
}
save_weights(net, outfile);
free_network(net);
}
void statistics_net(char *cfgfile, char *weightfile)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 0);
int i;
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if (l.type == CONNECTED && l.batch_normalize) {
printf("Connected Layer %d\n", i);
statistics_connected_layer(l);
}
if (l.type == GRU && l.batch_normalize) {
printf("GRU Layer %d\n", i);
printf("Input Z\n");
statistics_connected_layer(*l.input_z_layer);
printf("Input R\n");
statistics_connected_layer(*l.input_r_layer);
printf("Input H\n");
statistics_connected_layer(*l.input_h_layer);
printf("State Z\n");
statistics_connected_layer(*l.state_z_layer);
printf("State R\n");
statistics_connected_layer(*l.state_r_layer);
printf("State H\n");
statistics_connected_layer(*l.state_h_layer);
}
printf("\n");
}
free_network(net);
}
void label_classifier(char *datacfg, char *filename, char *weightfile)
{
int i;
network *net = load_network(filename, weightfile, 0);
set_batch_network(net, 1);
srand(time(0));
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "names", "data/labels.list");
char *test_list = option_find_str(options, "test", "data/train.list");
int classes = option_find_int(options, "classes", 2);
char **labels = get_labels(label_list);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
for(i = 0; i < m; ++i){
image im = load_image_color(paths[i], 0, 0);
image resized = resize_min(im, net->w);
image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
float *pred = network_predict(net, crop.data);
if(resized.data != im.data) free_image(resized);
free_image(im);
free_image(crop);
int ind = max_index(pred, classes);
printf("%s\n", labels[ind]);
}
}
void partial(char *cfgfile, char *weightfile, char *outfile, int max)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 1);
save_weights_upto(net, outfile, max);
free_network(net);
}
void predict_regressor(char *cfgfile, char *weightfile, char *filename)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image sized = letterbox_image(im, net->w, net->h);
float *X = sized.data;
time=clock();
float *predictions = network_predict(net, X);
printf("Predicted: %f\n", predictions[0]);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
free_image(im);
free_image(sized);
if (filename) break;
}
free_network(net);
}
void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
{
network *net = load_network(cfgfile, weightfile, 0);
image *ims = get_weights(net->layers[0]);
int n = net->layers[0].n;
int z;
for(z = 0; z < num; ++z){
image im = make_image(h, w, 3);
fill_image(im, .5);
int i;
for(i = 0; i < 100; ++i){
image r = copy_image(ims[rand()%n]);
rotate_image_cw(r, rand()%4);
random_distort_image(r, 1, 1.5, 1.5);
int dx = rand()%(w-r.w);
int dy = rand()%(h-r.h);
ghost_image(r, im, dx, dy);
free_image(r);
}
char buff[256];
sprintf(buff, "%s/gen_%d", prefix, z);
save_image(im, buff);
free_image(im);
}
free_network(net);
}
void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
{
int i, j;
network *net = load_network(filename, weightfile, 0);
set_batch_network(net, 1);
srand(time(0));
list *options = read_data_cfg(datacfg);
char *label_list = option_find_str(options, "labels", "data/labels.list");
char *leaf_list = option_find_str(options, "leaves", 0);
if(leaf_list) change_leaves(net->hierarchy, leaf_list);
char *valid_list = option_find_str(options, "valid", "data/train.list");
int classes = option_find_int(options, "classes", 2);
int topk = option_find_int(options, "top", 1);
char **labels = get_labels(label_list);
list *plist = get_paths(valid_list);
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
float avg_acc = 0;
float avg_topk = 0;
int *indexes = calloc(topk, sizeof(int));
for(i = 0; i < m; ++i){
int class = -1;
char *path = paths[i];
for(j = 0; j < classes; ++j){
if(strstr(path, labels[j])){
class = j;
break;
}
}
image im = load_image_color(paths[i], 0, 0);
image resized = resize_min(im, net->w);
image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
//show_image(im, "orig");
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
if(resized.data != im.data) free_image(resized);
free_image(im);
free_image(crop);
top_k(pred, classes, topk, indexes);
if(indexes[0] == class) avg_acc += 1;
for(j = 0; j < topk; ++j){
if(indexes[j] == class) avg_topk += 1;
}
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
}
}
void inter_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
image start = random_unit_vector_image(net->w, net->h, net->c);
image end = random_unit_vector_image(net->w, net->h, net->c);
image im = make_image(net->w, net->h, net->c);
image orig = copy_image(start);
int c = 0;
int count = 0;
int max_count = 15;
while(1){
++c;
if(count == max_count){
count = 0;
free_image(start);
start = end;
end = random_unit_vector_image(net->w, net->h, net->c);
if(c > 300){
end = orig;
}
if(c>300 + max_count) return;
}
++count;
slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
normalize_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
//char buff[256];
sprintf(buff, "out%05d", c);
save_image(out, "out");
save_image(out, buff);
show_image(out, "out", 0);
}
}
void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed,
real_t 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 = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int i, j;
for (i = 0; i < net->n; ++i)
net->layers[i].temperature = temp;
int c = 0;
int len = strlen(seed);
real_t *input = calloc(inputs, sizeof(real_t));
/*
fill_cpu(inputs, 0, input, 1);
for(i = 0; i < 10; ++i){
network_predict(net, input);
}
fill_cpu(inputs, 0, input, 1);
*/
for (i = 0; i < len - 1; ++i) {
c = seed[i];
input[c] = 1;
network_predict(net, input);
input[c] = 0;
print_symbol(c, tokens);
}
if (len)
c = seed[len - 1];
print_symbol(c, tokens);
for (i = 0; i < num; ++i) {
input[c] = 1;
real_t *out = network_predict(net, input);
input[c] = 0;
for (j = 32; j < 127; ++j) {
//printf("%d %c %f\n",j, j, out[j]);
}
for (j = 0; j < inputs; ++j) {
if (out[j] < .0001)
out[j] = 0;
}
c = sample_array(out, inputs);
print_symbol(c, tokens);
}
printf("\n");
}
void test_lsd(char *cfg, char *weights, char *filename, int gray)
{
network *net = load_network(cfg, weights, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image resized = resize_min(im, net->w);
image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
if(gray) grayscale_image_3c(crop);
float *X = crop.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
constrain_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
show_image(out, "out");
show_image(crop, "crop");
save_image(out, "out");
#ifdef OPENCV
cvWaitKey(0);
#endif
free_image(im);
free_image(resized);
free_image(crop);
if (filename) break;
}
}
int main ( int argc , char** argv ) {
double start, end;
// get cmdline args
std::string input_file = argv[1];
std::string output_file = argv[2];
int num_steps = atoi( argv[3] );
// check for valid input file
if ( !utils::fexists( input_file ) ) {
printf( "Error: Input file does not exist.\n" );
return 1;
}
// initialize adjacency list vector hash
printf( "Loading network edges from %s\n", input_file.c_str() );
start = omp_get_wtime();
load_network( input_file );
end = omp_get_wtime();
printf( "Time to read input file = %lf seconds\n", end - start );
// compute the normalized credit after numSteps
printf("\nComputing the Credit Values for %d Rounds:\n", num_steps);
CreditVec C( nodevec.size(), 1 ); // initialize credit at t=0 to 1 for each node
CreditVec C_( nodevec.size(), 0 );
std::vector<CreditVec> updates( num_steps );
for (int i=0; i<num_steps; ++i) {
printf("round %d = ", i+1);
start = omp_get_wtime();
credit_update(C, C_);
end = omp_get_wtime();
printf( "%f seconds\n", end - start );
// store credit update before overwriting timestep t
updates[i] = C_;
C = C_; // C(t+1) becomes C(t) for next iteration
}
// output credit value results after the final step
printf( "\nOutputting Network and Random Walk Data to %s\n", output_file.c_str() );
write_output( output_file, updates );
// free heap memory
for ( auto& node: nodevec ) {
delete node;
}
return 0 ;
}
void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) {
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network *net = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int count = 0;
int words = 1;
int c;
int len = strlen(seed);
real_t *input = calloc(inputs, sizeof(real_t));
int i;
for (i = 0; i < len; ++i) {
c = seed[i];
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
}
real_t sum = 0;
c = getc(stdin);
real_t log2 = log(2);
int in = 0;
while (c != EOF) {
int next = getc(stdin);
if (next == EOF)
break;
if (next < 0 || next >= 255)
error("Out of range character");
input[c] = 1;
real_t *out = network_predict(net, input);
input[c] = 0;
if (c == '.' && next == '\n')
in = 0;
if (!in) {
if (c == '>' && next == '>') {
in = 1;
++words;
}
c = next;
continue;
}
++count;
sum += log(out[next]) / log2;
c = next;
printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count,
words, pow(2, -sum / count), pow(2, -sum / words));
}
}
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", 0);
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
if(top == 0) top = option_find_int(options, "top", 1);
int i = 0;
char **names = get_labels(name_list);
clock_t time;
int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
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;
time=clock();
float *predictions = network_predict(net, X);
if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
top_k(predictions, net->outputs, top, indexes);
fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
for(i = 0; i < top; ++i){
int index = indexes[i];
//if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root");
//else printf("%s: %f\n",names[index], predictions[index]);
printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
}
if(r.data != im.data) free_image(r);
free_image(im);
if (filename) break;
}
}
void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 0);
int i;
for(i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
rgbgr_weights(l);
break;
}
}
save_weights(net, outfile);
free_network(net);
}
void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
printf("Regressor Demo\n");
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 1);
char *name_list = option_find_str(options, "names", 0);
char **names = get_labels(name_list);
void * cap = open_video_stream(filename, cam_index, 0,0,0);
if(!cap) error("Couldn't connect to webcam.\n");
float fps = 0;
while(1){
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
image in = get_image_from_stream(cap);
image crop = center_crop_image(in, net->w, net->h);
grayscale_image_3c(crop);
float *predictions = network_predict(net, crop.data);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
int i;
for(i = 0; i < classes; ++i){
printf("%s: %f\n", names[i], predictions[i]);
}
show_image(crop, "Regressor", 10);
free_image(in);
free_image(crop);
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;
}
free_network(net);
#endif
}
void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num,
real_t 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 = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int i, j;
for (i = 0; i < net->n; ++i)
net->layers[i].temperature = temp;
int c = 0;
real_t *input = calloc(inputs, sizeof(real_t));
real_t *out = 0;
while (1) {
reset_network_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_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
{
image **alphabet = load_alphabet();
network *net = load_network(cfgfile, weightfile, 0);
layer l = net->layers[net->n-1];
set_batch_network(net, 1);
srand(2222222);
float nms = .4;
clock_t time;
char buff[256];
char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
} else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
image sized = resize_image(im, net->w, net->h);
float *X = sized.data;
time=clock();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
int nboxes = 0;
detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
save_image(im, "prediction");
show_image(im, "predictions");
free_detections(dets, nboxes);
free_image(im);
free_image(sized);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
void demo_art(char *cfgfile, char *weightfile, int cam_index)
{
#ifdef OPENCV
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
void * cap = open_video_stream(0, cam_index, 0,0,0);
char *window = "ArtJudgementBot9000!!!";
if(!cap) error("Couldn't connect to webcam.\n");
int i;
int idx[] = {37, 401, 434};
int n = sizeof(idx)/sizeof(idx[0]);
while(1){
image in = get_image_from_stream(cap);
image in_s = resize_image(in, net->w, net->h);
float *p = network_predict(net, in_s.data);
printf("\033[2J");
printf("\033[1;1H");
float score = 0;
for(i = 0; i < n; ++i){
float s = p[idx[i]];
if (s > score) score = s;
}
score = score;
printf("I APPRECIATE THIS ARTWORK: %10.7f%%\n", score*100);
printf("[");
int upper = 30;
for(i = 0; i < upper; ++i){
printf("%c", ((i+.5) < score*upper) ? 219 : ' ');
}
printf("]\n");
show_image(in, window, 1);
free_image(in_s);
free_image(in);
}
#endif
}
void print_weights(char *cfgfile, char *weightfile, int n)
{
gpu_index = -1;
network *net = load_network(cfgfile, weightfile, 1);
layer l = net->layers[n];
int i, j;
//printf("[");
for(i = 0; i < l.n; ++i){
//printf("[");
for(j = 0; j < l.size*l.size*l.c; ++j){
//if(j > 0) printf(",");
printf("%g ", l.weights[i*l.size*l.size*l.c + j]);
}
printf("\n");
//printf("]%s\n", (i == l.n-1)?"":",");
}
//printf("]");
}
void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) {
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
network *net = load_network(cfgfile, weightfile, 0);
int inputs = net->inputs;
int c;
int seed_len = strlen(seed);
real_t *input = calloc(inputs, sizeof(real_t));
int i;
char *line;
while ((line = fgetl(stdin)) != 0) {
reset_network_state(net, 0);
for (i = 0; i < seed_len; ++i) {
c = seed[i];
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
}
strip(line);
int str_len = strlen(line);
for (i = 0; i < str_len; ++i) {
c = line[i];
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
}
c = ' ';
input[(int) c] = 1;
network_predict(net, input);
input[(int) c] = 0;
layer l = net->layers[0];
#ifdef GPU
cuda_pull_array(l.output_gpu, l.output, l.outputs);
#endif
printf("%s", line);
for (i = 0; i < l.outputs; ++i) {
printf(",%g", l.output[i]);
}
printf("\n");
}
}
void test_tag(char *cfgfile, char *weightfile, char *filename) {
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
int i = 0;
char **names = get_labels("data/tags.txt");
clock_t time;
int indexes[10];
char buff[256];
char *input = buff;
int size = net->w;
while (1) {
if (filename) {
strncpy(input, filename, 256);
} else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if (!input)
return;
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image r = resize_min(im, size);
resize_network(net, r.w, r.h);
printf("%d %d\n", r.w, r.h);
real_t *X = r.data;
time = clock();
real_t *predictions = network_predict(net, X);
top_predictions(net, 10, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock() - time));
for (i = 0; i < 10; ++i) {
int index = indexes[i];
printf("%.1f%%: %s\n", predictions[index] * 100, names[index]);
}
if (r.data != im.data)
free_image(r);
free_image(im);
if (filename)
break;
}
}
void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
{
#ifdef OPENCV
printf("Classifier Demo\n");
network *net = load_network(cfg, weights, 0);
set_batch_network(net, 1);
srand(2222222);
void * cap = open_video_stream(filename, cam_index, 0,0,0);
if(!cap) error("Couldn't connect to webcam.\n");
float fps = 0;
while(1){
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
image in = get_image_from_stream(cap);
image in_s = letterbox_image(in, net->w, net->h);
network_predict(net, in_s.data);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
image pred = get_network_image(net);
image prmask = mask_to_rgb(pred);
show_image(prmask, "Segmenter", 10);
free_image(in_s);
free_image(in);
free_image(prmask);
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;
}
free_network(net);
#endif
}
void test_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
while(1){
image im = make_image(net->w, net->h, net->c);
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
im.data[i] = rand_normal();
}
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
normalize_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
show_image(out, "out");
save_image(out, "out");
#ifdef OPENCV
cvWaitKey(0);
#endif
free_image(im);
}
}
void test_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int imlayer = 0;
imlayer = net->n-1;
while(1){
image im = make_image(net->w, net->h, net->c);
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
im.data[i] = rand_normal();
}
//float mag = mag_array(im.data, im.w*im.h*im.c);
//scale_array(im.data, im.w*im.h*im.c, 1./mag);
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
normalize_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
save_image(out, "out");
show_image(out, "out", 0);
free_image(im);
}
free_network(net);
}
void validate_yolo(char *cfg, char *weights)
{
network *net = load_network(cfg, weights, 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));
char *base = "results/comp4_det_test_";
//list *plist = get_paths("data/voc.2007.test");
list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt");
//list *plist = get_paths("data/voc.2012.test");
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int classes = l.classes;
int j;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
int m = plist->size;
int i=0;
int t;
float thresh = .001;
int nms = 1;
float iou_thresh = .5;
int nthreads = 8;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
load_args args = {0};
args.w = net->w;
args.h = net->h;
args.type = IMAGE_DATA;
for(t = 0; t < nthreads; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
time_t start = time(0);
for(i = nthreads; i < m+nthreads; i += nthreads){
fprintf(stderr, "%d\n", i);
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
pthread_join(thr[t], 0);
val[t] = buf[t];
val_resized[t] = buf_resized[t];
}
for(t = 0; t < nthreads && i+t < m; ++t){
args.path = paths[i+t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
int nboxes = 0;
detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes);
if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh);
print_yolo_detections(fps, id, l.side*l.side*l.n, classes, w, h, dets);
free_detections(dets, nboxes);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
free_network( net );
}
void train_yolo(char *cfgfile, char *weightfile)
{
char *train_images = "/data/voc/train.txt";
char *backup_directory = "/home/kunle12/backup/";
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network * net = load_network(cfgfile, weightfile, 0);
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];
int side = l.side;
int classes = l.classes;
float jitter = l.jitter;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net->w;
args.h = net->h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.classes = classes;
args.jitter = jitter;
args.num_boxes = side;
args.d = &buffer;
args.type = REGION_DATA;
args.angle = net->angle;
args.exposure = net->exposure;
args.saturation = net->saturation;
args.hue = net->hue;
pthread_t load_thread = load_data_in_thread(args);
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_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
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);
if(i%1000==0 || (i < 1000 && i%100 == 0)){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(net, buff);
free_network( net );
}
void validate_yolo_recall(char *cfg, char *weights)
{
network *net = load_network(cfg, weights, 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));
char *base = "results/comp4_det_test_";
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);
layer l = net->layers[net->n-1];
int classes = l.classes;
int side = l.side;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
for(j = 0; j < classes; ++j){
char buff[1024];
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
int m = plist->size;
int i=0;
float thresh = .001;
float iou_thresh = .5;
float nms = 0;
int total = 0;
int correct = 0;
int proposals = 0;
float avg_iou = 0;
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);
char *id = basecfg(path);
network_predict(net, sized.data);
int nboxes = 0;
detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
for(k = 0; k < side*side*l.n; ++k){
if(dets[k].objectness > thresh){
++proposals;
}
}
for (j = 0; j < num_labels; ++j) {
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
for(k = 0; k < side*side*l.n; ++k){
float iou = box_iou(dets[k].bbox, t);
if(dets[k].objectness > thresh && iou > best_iou){
best_iou = iou;
}
}
avg_iou += best_iou;
if(best_iou > iou_thresh){
++correct;
}
}
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
free_detections(dets, nboxes);
free(id);
free_image(orig);
free_image(sized);
}
free_network( net );
}
network *load_network_p(char *cfg, char *weights, int clear)
{
network *net = calloc(1, sizeof(network));
*net = load_network(cfg, weights, clear);
return net;
}
void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
int i;
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
printf("%d\n", ngpus);
network **nets = calloc(ngpus, sizeof(network*));
srand(time(0));
int seed = rand();
for(i = 0; i < ngpus; ++i){
srand(seed);
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i]->learning_rate *= ngpus;
}
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);
list *options = read_data_cfg(datacfg);
char *backup_directory = option_find_str(options, "backup", "/backup/");
char *train_list = option_find_str(options, "train", "data/train.list");
int classes = option_find_int(options, "classes", 1);
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 = 32;
args.classes = classes;
args.min = net->min_ratio*net->w;
args.max = net->max_ratio*net->w;
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.paths = paths;
args.n = imgs;
args.m = N;
args.type = REGRESSION_DATA;
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();
pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
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("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld 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);
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);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);
for(i = 0; i < ngpus; ++i){
free_network(nets[i]);
}
free(nets);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
}
