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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{tbd, |
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title={tbd}, |
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author={tbd}, |
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year={2024}, |
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url={https://huggingface.co/datasets/faridlab/deepaction_v1} |
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} |
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""" |
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_DESCRIPTION = """\ |
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TBD |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/faridlab/deepaction_v1" |
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_LICENSE = "TBD" |
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SUPPORTED = ["VideoPoet", "BDAnimateDiffLightning", "CogVideoX5B", "Pexels", "RunwayML", "StableDiffusion"] |
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class DeepActionV1(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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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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"video": datasets.Video(), |
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"label": datasets.ClassLabel(names=SUPPORTED), |
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}), |
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supervised_keys=("video", "label"), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = [] |
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base_url = "https://huggingface.co/datasets/faridlab/deepaction_v1/resolve/main/" |
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for engine in SUPPORTED: |
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for i in list(range(95)) + list(range(99, 104)): |
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if engine in ["Pexels", "Veo"]: |
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urls.append(os.path.join(base_url, engine, "{}".format(i), "a.mp4")) |
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else: |
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for vid_file in ["a.mp4", "b.mp4", "c.mp4", "d.mp4", "e.mp4"]: |
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urls.append(os.path.join(base_url, engine, "{}".format(i), vid_file)) |
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data_dir = dl_manager.download_and_extract(urls) |
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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={"data_dir": data_dir}, |
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), |
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] |
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def _generate_examples(self, data_dir): |
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for label in os.listdir(data_dir): |
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label_path = os.path.join(data_dir, label) |
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if os.path.isdir(label_path): |
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for subfolder in os.listdir(label_path): |
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subfolder_path = os.path.join(label_path, subfolder) |
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if os.path.isdir(subfolder_path): |
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for video_file in os.listdir(subfolder_path): |
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if video_file.endswith(".mp4"): |
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video_path = os.path.join(subfolder_path, video_file) |
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yield video_path, { |
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"video": video_path, |
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"label": label, |
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} |