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import os |
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import datasets |
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import joblib |
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from pathlib import Path |
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from tqdm import tqdm |
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_BASE_HF_URL = Path("./data") |
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_CITATION = "" |
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_HOMEPAGE = "" |
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_DESCRIPTION = "" |
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_DATA_URL = { |
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"train": [_BASE_HF_URL/"images.tar.gz"] |
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} |
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class AVA(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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DEFAULT_WRITER_BATCH_SIZE = 1000 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"filename": datasets.Value("string"), |
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"rating_counts": datasets.features.Sequence(datasets.Value("int32")), |
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"text_tag_0": datasets.Value("string"), |
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"text_tag_1": datasets.Value("string") |
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} |
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), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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archives = dl_manager.download(_DATA_URL) |
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self.dict_metadata = joblib.load(Path(dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"archives": [dl_manager.iter_archive(archive) for archive in archives["train"]], |
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"split": "train", |
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}, |
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) |
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] |
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def _generate_examples(self, archives, split): |
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"""Yields examples.""" |
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idx = 0 |
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for archive in archives: |
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for path, file in tqdm(archive): |
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if path.endswith(".jpg"): |
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_id = int(os.path.splitext(path)[0].split('/')[-1]) |
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_metadata = self.dict_metadata[_id] |
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ex = {"image": {"path": path, "bytes": file.read()}, |
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"filename": str(path).split('/')[-1], |
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"rating_counts": _metadata[0], |
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"text_tag_0":_metadata[1], |
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"text_tag_1": _metadata[2]} |
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yield idx, ex |
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idx += 1 |
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