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""" |
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Generate resized ImageNet-100 dataset. |
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""" |
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from argparse import ArgumentParser |
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from functools import partial |
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from pathlib import Path |
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from datasets import load_dataset |
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from torchvision.transforms import InterpolationMode |
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from torchvision.transforms.functional import resize |
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SCRIPT = str(Path(__file__).parent / "imagenet-100.py") |
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def transforms(examples, size: int = 160): |
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examples["image"] = [ |
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resize(image, size, interpolation=InterpolationMode.BICUBIC) |
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for image in examples["image"] |
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] |
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return examples |
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if __name__ == "__main__": |
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parser = ArgumentParser() |
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parser.add_argument("--outdir", "-o", type=str, default="cache") |
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parser.add_argument("--size", "-s", type=int, default=160) |
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parser.add_argument("--num-proc", "-n", type=int, default=8) |
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args = parser.parse_args() |
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dataset = load_dataset(SCRIPT) |
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dataset = dataset.map( |
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partial(transforms, size=args.size), |
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batched=True, |
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batch_size=256, |
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num_proc=args.num_proc, |
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) |
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print(dataset) |
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print(dataset["validation"][0]) |
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outdir = Path(args.outdir) / f"imagenet-100_{args.size}" |
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dataset.save_to_disk(outdir, num_proc=args.num_proc) |
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