| | |
| | | data train, buffer; |
| | | int im_dim = 512; |
| | | int jitter = 64; |
| | | int classes = 21; |
| | | pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer); |
| | | int classes = 20; |
| | | int background = 1; |
| | | pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer); |
| | | clock_t time; |
| | | while(1){ |
| | | i += 1; |
| | | time=clock(); |
| | | pthread_join(load_thread, 0); |
| | | train = buffer; |
| | | load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer); |
| | | load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer); |
| | | |
| | | /* |
| | | image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]); |
| | |
| | | srand(time(0)); |
| | | |
| | | list *plist = get_paths("/home/pjreddie/data/voc/val.txt"); |
| | | //list *plist = get_paths("/home/pjreddie/data/voc/train.txt"); |
| | | char **paths = (char **)list_to_array(plist); |
| | | int num_output = 1225; |
| | | int im_size = 448; |
| | | int classes = 21; |
| | | int classes = 20; |
| | | int background = 0; |
| | | int num_output = 7*7*(4+classes+background); |
| | | |
| | | int m = plist->size; |
| | | int i = 0; |
| | |
| | | matrix pred = network_predict_data(net, val); |
| | | int j, k, class; |
| | | for(j = 0; j < pred.rows; ++j){ |
| | | for(k = 0; k < pred.cols; k += classes+4){ |
| | | |
| | | /* |
| | | int z; |
| | | for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]); |
| | | printf("\n"); |
| | | */ |
| | | |
| | | //if (pred.vals[j][k] > .001){ |
| | | for(class = 0; class < classes-1; ++class){ |
| | | int index = (k)/(classes+4); |
| | | for(k = 0; k < pred.cols; k += classes+4+background){ |
| | | for(class = 0; class < classes; ++class){ |
| | | int index = (k)/(classes+4+background); |
| | | int r = index/7; |
| | | int c = index%7; |
| | | float y = (r + pred.vals[j][k+0+classes])/7.; |
| | | float x = (c + pred.vals[j][k+1+classes])/7.; |
| | | float h = pred.vals[j][k+2+classes]; |
| | | float w = pred.vals[j][k+3+classes]; |
| | | int ci = k+classes+background; |
| | | float y = (r + pred.vals[j][ci + 0])/7.; |
| | | float x = (c + pred.vals[j][ci + 1])/7.; |
| | | float h = pred.vals[j][ci + 2]; |
| | | float w = pred.vals[j][ci + 3]; |
| | | printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class], y, x, h, w); |
| | | } |
| | | //} |
| | | } |
| | | } |
| | | |