File size: 1,845 Bytes
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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() |