B, C, preprocess, random_transform=False) train_generator = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=min(num_gpu * 8, 16)) val_generator = data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=min(num_gpu * 8, 16)) # This is just to check that labels are correctly encoded. for pos in range(20): img1, target1 = train_dataset.__getitem__(pos) gt_color = (0, 0, 255) #red # Get numpy image in opencv format np_img = preprocess.post_process_image(img1) # get bounding boxes: xyxy + class + confidence img_h, img_w, _ = np_img.shape target_bboxes = preprocess.decode_label(target1, img_h, img_w) draw_all_bboxes(np_img, target_bboxes, preprocess, gt_color, "images_transformed/gt_{}.jpg".format(pos)) import os os._exit(1) # Enable anomaly detection for debugging purpose