| | |
| | | network net = parse_network_cfg("cfg/detnet.cfg"); |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = 1000/net.batch+1; |
| | | srand(time(0)); |
| | | //srand(time(0)); |
| | | srand(23410); |
| | | int i = 0; |
| | | list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt"); |
| | | char **paths = (char **)list_to_array(plist); |
| | |
| | | time=clock(); |
| | | data train = load_data_detection_random(imgs*net.batch, paths, plist->size, 256, 256, 8, 8, 256); |
| | | //translate_data_rows(train, -144); |
| | | /* |
| | | image im = float_to_image(256, 256, 3, train.X.vals[0]); |
| | | float *truth = train.y.vals[0]; |
| | | int j; |
| | | int r, c; |
| | | for(r = 0; r < 8; ++r){ |
| | | for(c = 0; c < 8; ++c){ |
| | | j = (r*8 + c) * 5; |
| | | if(truth[j]){ |
| | | int d = 256/8; |
| | | int y = r*d+truth[j+1]*d; |
| | | int x = c*d+truth[j+2]*d; |
| | | int h = truth[j+3]*256; |
| | | int w = truth[j+4]*256; |
| | | printf("%f %f %f %f\n", truth[j+1], truth[j+2], truth[j+3], truth[j+4]); |
| | | printf("%d %d %d %d\n", x, y, w, h); |
| | | printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2); |
| | | draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2); |
| | | } |
| | | } |
| | | } |
| | | show_image(im, "box"); |
| | | cvWaitKey(0); |
| | | */ |
| | | |
| | | normalize_data_rows(train); |
| | | printf("Loaded: %lf seconds\n", sec(clock()-time)); |
| | | time=clock(); |
| | |
| | | free_data(train); |
| | | if(i%10==0){ |
| | | char buff[256]; |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i); |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i); |
| | | save_network(net, buff); |
| | | } |
| | | } |
| | |
| | | { |
| | | float avg_loss = 1; |
| | | //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg"); |
| | | network net = parse_network_cfg("cfg/trained_alexnet.cfg"); |
| | | network net = parse_network_cfg("cfg/alexnet.part"); |
| | | printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
| | | int imgs = 1000/net.batch+1; |
| | | srand(time(0)); |
| | |
| | | free_data(train); |
| | | if(i%10==0){ |
| | | char buff[256]; |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i); |
| | | sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i); |
| | | save_network(net, buff); |
| | | } |
| | | } |
| | |
| | | char **part = paths+(i*m/splits); |
| | | int num = (i+1)*m/splits - i*m/splits; |
| | | data val = load_data(part, num, labels, 1000, 256, 256); |
| | | |
| | | normalize_data_rows(val); |
| | | printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time)); |
| | | time=clock(); |
| | |
| | | } |
| | | } |
| | | |
| | | void draw_detection(image im, float *box) |
| | | { |
| | | int j; |
| | | int r, c; |
| | | for(r = 0; r < 8; ++r){ |
| | | for(c = 0; c < 8; ++c){ |
| | | j = (r*8 + c) * 5; |
| | | printf("Prob: %f\n", box[j]); |
| | | if(box[j] > .999){ |
| | | int d = 256/8; |
| | | int y = r*d+box[j+1]*d; |
| | | int x = c*d+box[j+2]*d; |
| | | int h = box[j+3]*256; |
| | | int w = box[j+4]*256; |
| | | printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]); |
| | | printf("%d %d %d %d\n", x, y, w, h); |
| | | printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2); |
| | | draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2); |
| | | } |
| | | } |
| | | } |
| | | show_image(im, "box"); |
| | | cvWaitKey(0); |
| | | } |
| | | |
| | | void test_detection() |
| | | { |
| | | network net = parse_network_cfg("cfg/detnet_test.cfg"); |
| | | //imgs=1; |
| | | srand(2222222); |
| | | int i = 0; |
| | | clock_t time; |
| | | char filename[256]; |
| | | int indexes[10]; |
| | | while(1){ |
| | | fgets(filename, 256, stdin); |
| | | strtok(filename, "\n"); |
| | | image im = load_image_color(filename, 256, 256); |
| | | z_normalize_image(im); |
| | | printf("%d %d %d\n", im.h, im.w, im.c); |
| | | float *X = im.data; |
| | | time=clock(); |
| | | float *predictions = network_predict(net, X); |
| | | top_predictions(net, 10, indexes); |
| | | printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time)); |
| | | draw_detection(im, predictions); |
| | | free_image(im); |
| | | } |
| | | } |
| | |
| | | int indexes[10]; |
| | | while(1){ |
| | | fgets(filename, 256, stdin); |
| | | strtok(filename, "\n"); |
| | | image im = load_image_color(filename, 256, 256); |
| | | z_normalize_image(im); |
| | | printf("%d %d %d\n", im.h, im.w, im.c); |
| | |
| | | #endif |
| | | } |
| | | |
| | | void test_server() |
| | | { |
| | | server_update(); |
| | | } |
| | | void test_client() |
| | | { |
| | | client_update(); |
| | | } |
| | | |
| | | int main(int argc, char *argv[]) |
| | | { |
| | |
| | | else if(0==strcmp(argv[1], "nist")) train_nist(); |
| | | else if(0==strcmp(argv[1], "test_correct")) test_gpu_net(); |
| | | else if(0==strcmp(argv[1], "test")) test_imagenet(); |
| | | else if(0==strcmp(argv[1], "server")) test_server(); |
| | | else if(0==strcmp(argv[1], "client")) test_client(); |
| | | else if(0==strcmp(argv[1], "detect")) test_detection(); |
| | | else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]); |
| | | else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]); |
| | | #ifdef GPU |
| | |
| | | } |
| | | |
| | | /* |
| | | void visualize_imagenet_topk(char *filename) |
| | | { |
| | | int i,j,k,l; |
| | | int topk = 10; |
| | | network net = parse_network_cfg("cfg/voc_imagenet.cfg"); |
| | | list *plist = get_paths(filename); |
| | | node *n = plist->front; |
| | | int h = voc_size(1), w = voc_size(1); |
| | | int num = get_network_image(net).c; |
| | | image **vizs = calloc(num, sizeof(image*)); |
| | | float **score = calloc(num, sizeof(float *)); |
| | | for(i = 0; i < num; ++i){ |
| | | vizs[i] = calloc(topk, sizeof(image)); |
| | | for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3); |
| | | score[i] = calloc(topk, sizeof(float)); |
| | | } |
| | | void visualize_imagenet_topk(char *filename) |
| | | { |
| | | int i,j,k,l; |
| | | int topk = 10; |
| | | network net = parse_network_cfg("cfg/voc_imagenet.cfg"); |
| | | list *plist = get_paths(filename); |
| | | node *n = plist->front; |
| | | int h = voc_size(1), w = voc_size(1); |
| | | int num = get_network_image(net).c; |
| | | image **vizs = calloc(num, sizeof(image*)); |
| | | float **score = calloc(num, sizeof(float *)); |
| | | for(i = 0; i < num; ++i){ |
| | | vizs[i] = calloc(topk, sizeof(image)); |
| | | for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3); |
| | | score[i] = calloc(topk, sizeof(float)); |
| | | } |
| | | |
| | | int count = 0; |
| | | while(n){ |
| | | ++count; |
| | | char *image_path = (char *)n->val; |
| | | image im = load_image(image_path, 0, 0); |
| | | n = n->next; |
| | | if(im.h < 200 || im.w < 200) continue; |
| | | printf("Processing %dx%d image\n", im.h, im.w); |
| | | resize_network(net, im.h, im.w, im.c); |
| | | //scale_image(im, 1./255); |
| | | translate_image(im, -144); |
| | | forward_network(net, im.data, 0, 0); |
| | | image out = get_network_image(net); |
| | | int count = 0; |
| | | while(n){ |
| | | ++count; |
| | | char *image_path = (char *)n->val; |
| | | image im = load_image(image_path, 0, 0); |
| | | n = n->next; |
| | | if(im.h < 200 || im.w < 200) continue; |
| | | printf("Processing %dx%d image\n", im.h, im.w); |
| | | resize_network(net, im.h, im.w, im.c); |
| | | //scale_image(im, 1./255); |
| | | translate_image(im, -144); |
| | | forward_network(net, im.data, 0, 0); |
| | | image out = get_network_image(net); |
| | | |
| | | int dh = (im.h - h)/(out.h-1); |
| | | int dw = (im.w - w)/(out.w-1); |
| | | //printf("%d %d\n", dh, dw); |
| | | for(k = 0; k < out.c; ++k){ |
| | | float topv = 0; |
| | | int topi = -1; |
| | | int topj = -1; |
| | | for(i = 0; i < out.h; ++i){ |
| | | for(j = 0; j < out.w; ++j){ |
| | | float val = get_pixel(out, i, j, k); |
| | | if(val > topv){ |
| | | topv = val; |
| | | topi = i; |
| | | topj = j; |
| | | } |
| | | } |
| | | } |
| | | if(topv){ |
| | | image sub = get_sub_image(im, dh*topi, dw*topj, h, w); |
| | | for(l = 0; l < topk; ++l){ |
| | | if(topv > score[k][l]){ |
| | | float swap = score[k][l]; |
| | | score[k][l] = topv; |
| | | topv = swap; |
| | | int dh = (im.h - h)/(out.h-1); |
| | | int dw = (im.w - w)/(out.w-1); |
| | | //printf("%d %d\n", dh, dw); |
| | | for(k = 0; k < out.c; ++k){ |
| | | float topv = 0; |
| | | int topi = -1; |
| | | int topj = -1; |
| | | for(i = 0; i < out.h; ++i){ |
| | | for(j = 0; j < out.w; ++j){ |
| | | float val = get_pixel(out, i, j, k); |
| | | if(val > topv){ |
| | | topv = val; |
| | | topi = i; |
| | | topj = j; |
| | | } |
| | | } |
| | | } |
| | | if(topv){ |
| | | image sub = get_sub_image(im, dh*topi, dw*topj, h, w); |
| | | for(l = 0; l < topk; ++l){ |
| | | if(topv > score[k][l]){ |
| | | float swap = score[k][l]; |
| | | score[k][l] = topv; |
| | | topv = swap; |
| | | |
| | | image swapi = vizs[k][l]; |
| | | vizs[k][l] = sub; |
| | | sub = swapi; |
| | | } |
| | | } |
| | | free_image(sub); |
| | | } |
| | | } |
| | | free_image(im); |
| | | if(count%50 == 0){ |
| | | image grid = grid_images(vizs, num, topk); |
| | | //show_image(grid, "IMAGENET Visualization"); |
| | | save_image(grid, "IMAGENET Grid Single Nonorm"); |
| | | free_image(grid); |
| | | } |
| | | } |
| | | //cvWaitKey(0); |
| | | image swapi = vizs[k][l]; |
| | | vizs[k][l] = sub; |
| | | sub = swapi; |
| | | } |
| | | } |
| | | free_image(sub); |
| | | } |
| | | } |
| | | free_image(im); |
| | | if(count%50 == 0){ |
| | | image grid = grid_images(vizs, num, topk); |
| | | //show_image(grid, "IMAGENET Visualization"); |
| | | save_image(grid, "IMAGENET Grid Single Nonorm"); |
| | | free_image(grid); |
| | | } |
| | | } |
| | | //cvWaitKey(0); |
| | | } |
| | | |
| | | void visualize_imagenet_features(char *filename) |