| import os |
| import argparse |
| from torchvision import transforms, datasets |
| from torch.utils.data import DataLoader |
| from util.crop import center_crop_arr |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--data_path', type=str, required=True, |
| help='Path to ImageNet root directory') |
| parser.add_argument('--output_path', type=str, default='imagenet-train-256', |
| help='Folder where transformed images will be saved') |
| parser.add_argument('--img_size', type=int, default=256, |
| help='Resolution to center-crop and resize') |
| args = parser.parse_args() |
|
|
| transform_train = transforms.Compose([ |
| transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.img_size)), |
| transforms.CenterCrop(args.img_size), |
| transforms.ToTensor(), |
| ]) |
|
|
| dataset_train = datasets.ImageFolder( |
| os.path.join(args.data_path, 'train'), |
| transform=transform_train |
| ) |
|
|
| data_loader = DataLoader( |
| dataset_train, |
| batch_size=256, |
| num_workers=32, |
| shuffle=False, |
| pin_memory=False |
| ) |
|
|
| os.makedirs(args.output_path, exist_ok=True) |
|
|
| to_pil = transforms.ToPILImage() |
| global_idx = 0 |
|
|
| from tqdm import tqdm |
| for batch_images, batch_labels in tqdm(data_loader): |
| for i in range(batch_images.size(0)): |
| img_tensor = batch_images[i] |
|
|
| pil_img = to_pil(img_tensor) |
| out_path = os.path.join( |
| args.output_path, |
| f"transformed_{global_idx:08d}.png" |
| ) |
| pil_img.save(out_path, format='PNG', compress_level=0) |
| global_idx += 1 |
|
|
| print(f"Saved batch up to index={global_idx} ...") |
|
|
| print("Finished saving all images.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
