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; } }
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 train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display) { #ifdef GPU //char *train_images = "/home/kunle12/data/coco/train1.txt"; //char *train_images = "/home/kunle12/data/coco/trainvalno5k.txt"; char *train_images = "/home/kunle12/data/imagenet/imagenet1k.train.list"; char *backup_directory = "/home/kunle12/backup/"; srand(time(0)); char *base = basecfg(cfg); char *abase = basecfg(acfg); printf("%s\n", base); network *net = load_network(cfg, weight, clear); network *anet = load_network(acfg, aweight, clear); int i, j, k; layer imlayer = {0}; for (i = 0; i < net->n; ++i) { if (net->layers[i].out_c == 3) { imlayer = net->layers[i]; break; } } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay); int imgs = net->batch*net->subdivisions; i = *net->seen/imgs; data train, buffer; list *plist = get_paths(train_images); //int N = plist->size; char **paths = (char **)list_to_array(plist); load_args args= get_base_args(net); args.paths = paths; args.n = imgs; args.m = plist->size; args.d = &buffer; args.type = CLASSIFICATION_DATA; args.classes = 1; char *ls[2] = {"imagenet"}; args.labels = ls; pthread_t load_thread = load_data_in_thread(args); clock_t time; int x_size = net->inputs*net->batch; //int y_size = x_size; net->delta = 0; net->train = 1; float *pixs = calloc(x_size, sizeof(float)); float *graypixs = calloc(x_size, sizeof(float)); //float *y = calloc(y_size, sizeof(float)); //int ay_size = anet->outputs*anet->batch; anet->delta = 0; anet->train = 1; float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch); float aloss_avg = -1; float gloss_avg = -1; //data generated = copy_data(train); 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)); data gray = copy_data(train); for(j = 0; j < imgs; ++j){ image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]); grayscale_image_3c(gim); train.y.vals[j][0] = .95; gray.y.vals[j][0] = .05; } time=clock(); float gloss = 0; for(j = 0; j < net->subdivisions; ++j){ get_next_batch(train, net->batch, j*net->batch, pixs, 0); get_next_batch(gray, net->batch, j*net->batch, graypixs, 0); cuda_push_array(net->input_gpu, graypixs, net->inputs*net->batch); cuda_push_array(net->truth_gpu, pixs, net->truths*net->batch); /* image origi = float_to_image(net->w, net->h, 3, pixs); image grayi = float_to_image(net->w, net->h, 3, graypixs); show_image(grayi, "gray"); show_image(origi, "orig"); cvWaitKey(0); */ *net->seen += net->batch; forward_network_gpu(net); fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1); copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1); fill_gpu(anet->inputs*anet->batch, .95, anet->truth_gpu, 1); anet->delta_gpu = imerror; forward_network_gpu(anet); backward_network_gpu(anet); scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net->layers[net->n-1].delta_gpu, 1); scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1); printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch)); printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs*imlayer.batch)); axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net->layers[net->n-1].delta_gpu, 1); backward_network_gpu(net); gloss += *net->cost /(net->subdivisions*net->batch); for(k = 0; k < net->batch; ++k){ int index = j*net->batch + k; copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1); } } harmless_update_network_gpu(anet); data merge = concat_data(train, gray); //randomize_data(merge); float aloss = train_network(anet, merge); update_network_gpu(net); #ifdef OPENCV if(display){ image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]); image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]); show_image(im, "gen", 1); show_image(im2, "train", 1); } #endif free_data(merge); free_data(train); free_data(gray); if (aloss_avg < 0) aloss_avg = aloss; aloss_avg = aloss_avg*.9 + aloss*.1; gloss_avg = gloss_avg*.9 + gloss*.1; printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs); if(i%1000==0){ char buff[256]; sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); save_weights(net, buff); sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); save_weights(anet, buff); } if(i%100==0){ char buff[256]; sprintf(buff, "%s/%s.backup", backup_directory, base); save_weights(net, buff); sprintf(buff, "%s/%s.backup", backup_directory, abase); save_weights(anet, buff); } } char buff[256]; sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); #endif }
void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear) { #ifdef GPU //char *train_images = "/home/pjreddie/data/coco/train1.txt"; //char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt"; char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list"; char *backup_directory = "/home/pjreddie/backup/"; srand(time(0)); char *base = basecfg(cfg); char *abase = basecfg(acfg); printf("%s\n", base); network net = load_network(cfg, weight, clear); network anet = load_network(acfg, aweight, clear); int i, j, k; layer imlayer = {}; for (i = 0; i < net.n; ++i) { if (net.layers[i].out_c == 3) { imlayer = net.layers[i]; break; } } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int imgs = net.batch*net.subdivisions; i = *net.seen/imgs; data train, buffer; list *plist = get_paths(train_images); //int N = plist->size; char **paths = (char **)list_to_array(plist); load_args args = {}; args.w = net.w; args.h = net.h; args.paths = paths; args.n = imgs; args.m = plist->size; args.d = &buffer; 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.type = CLASSIFICATION_DATA; args.classes = 1; char *ls[1] = {"imagenet"}; args.labels = ls; pthread_t load_thread = load_data_in_thread(args); clock_t time; network_state gstate = {}; gstate.index = 0; gstate.net = net; int x_size = get_network_input_size(net)*net.batch; int y_size = x_size; gstate.input = cuda_make_array(0, x_size); gstate.truth = cuda_make_array(0, y_size); gstate.delta = 0; gstate.train = 1; float *pixs = (float*)calloc(x_size, sizeof(float)); float *graypixs = (float*)calloc(x_size, sizeof(float)); float *y = (float*)calloc(y_size, sizeof(float)); network_state astate = {}; astate.index = 0; astate.net = anet; int ay_size = get_network_output_size(anet)*anet.batch; astate.input = 0; astate.truth = 0; astate.delta = 0; astate.train = 1; float *imerror = cuda_make_array(0, imlayer.outputs); float *ones_gpu = cuda_make_array(0, ay_size); fill_ongpu(ay_size, .99, ones_gpu, 1); float aloss_avg = -1; float gloss_avg = -1; //data generated = copy_data(train); 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)); data gray = copy_data(train); for(j = 0; j < imgs; ++j){ image gim = float_to_image(net.w, net.h, net.c, gray.X.vals[j]); grayscale_image_3c(gim); train.y.vals[j][0] = .99; image yim = float_to_image(net.w, net.h, net.c, train.X.vals[j]); //rgb_to_yuv(yim); } time=clock(); float gloss = 0; for(j = 0; j < net.subdivisions; ++j){ get_next_batch(train, net.batch, j*net.batch, pixs, y); get_next_batch(gray, net.batch, j*net.batch, graypixs, y); cuda_push_array(gstate.input, graypixs, x_size); cuda_push_array(gstate.truth, pixs, x_size); /* image origi = float_to_image(net.w, net.h, 3, pixs); image grayi = float_to_image(net.w, net.h, 3, graypixs); show_image(grayi, "gray"); show_image(origi, "orig"); cvWaitKey(0); */ *net.seen += net.batch; forward_network_gpu(net, gstate); fill_ongpu(imlayer.outputs, 0, imerror, 1); astate.input = imlayer.output_gpu; astate.delta = imerror; astate.truth = ones_gpu; forward_network_gpu(anet, astate); backward_network_gpu(anet, astate); scal_ongpu(imlayer.outputs, .1, net.layers[net.n-1].delta_gpu, 1); backward_network_gpu(net, gstate); scal_ongpu(imlayer.outputs, 100, imerror, 1); printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs)); printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs)); axpy_ongpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1); gloss += get_network_cost(net) /(net.subdivisions*net.batch); cuda_pull_array(imlayer.output_gpu, imlayer.output, x_size); for(k = 0; k < net.batch; ++k){ int index = j*net.batch + k; copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1); gray.y.vals[index][0] = .01; } } harmless_update_network_gpu(anet); data merge = concat_data(train, gray); randomize_data(merge); float aloss = train_network(anet, merge); update_network_gpu(net); update_network_gpu(anet); free_data(merge); free_data(train); free_data(gray); if (aloss_avg < 0) aloss_avg = aloss; aloss_avg = aloss_avg*.9 + aloss*.1; gloss_avg = gloss_avg*.9 + gloss*.1; printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs); if(i%1000==0){ char buff[256]; sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); save_weights(net, buff); sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i); save_weights(anet, buff); } if(i%100==0){ char buff[256]; sprintf(buff, "%s/%s.backup", backup_directory, base); save_weights(net, buff); sprintf(buff, "%s/%s.backup", backup_directory, abase); save_weights(anet, buff); } } char buff[256]; sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); #endif }