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import datasets |
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import json |
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import numpy |
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import tarfile |
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import io |
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_FEATURES = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"prompt": datasets.Array3D(shape=(1, 77, 768), dtype="float32"), |
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"video": datasets.Sequence(feature=datasets.Array3D(shape=(4, 64, 64), dtype="float32")), |
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"description": datasets.Value("string"), |
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"videourl": datasets.Value("string"), |
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"categories": datasets.Value("string"), |
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"duration": datasets.Value("float"), |
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"full_metadata": datasets.Value("string"), |
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} |
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) |
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class FunkLoaderStream(datasets.GeneratorBasedBuilder): |
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"""TempoFunk Dataset""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="TempoFunk Dataset", |
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features=_FEATURES, |
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homepage="tempofunk.github.io", |
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citation=""" |
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@misc{TempoFunk2023, |
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author = {Lopho, Carlos Chavez}, |
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title = {TempoFunk: Extending latent diffusion image models to Video}, |
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url = {tempofunk.github.io}, |
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month = {5}, |
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year = {2023} |
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} |
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""", |
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license="AGPL v3" |
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) |
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def _split_generators(self, dl_manager): |
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print("PATH:", dl_manager.download("lists/chunk_list.json")) |
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thing = json.load(open(dl_manager.download("lists/chunk_list.json"), 'rb')) |
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_CHUNK_LIST = thing |
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_list = [] |
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for chunk in _CHUNK_LIST: |
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_list.append(dl_manager.download(f"data/{chunk}.tar")) |
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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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"chunks": _list, |
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}, |
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), |
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] |
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def _generate_examples(self, chunks): |
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"""Generate images and labels for splits.""" |
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for chunk in chunks: |
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tar_data = open(chunk, 'rb') |
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tar_bytes = tar_data.read() |
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tar_bytes_io = io.BytesIO(tar_bytes) |
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response_dict = {} |
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with tarfile.open(fileobj=tar_bytes_io, mode='r') as tar: |
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for file_info in tar: |
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if file_info.isfile(): |
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file_name = file_info.name |
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file_type = file_name.split('_')[0] |
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file_id = file_name.split('_')[1].split('.')[0] |
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file_ext = file_name.split('_')[1].split('.')[1] |
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file_contents = tar.extractfile(file_info).read() |
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if file_id not in response_dict: |
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response_dict[file_id] = {} |
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if file_type == 'txt' or file_type == 'vid': |
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response_dict[file_id][file_type] = numpy.load(io.BytesIO(file_contents)) |
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elif file_type == 'jso': |
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response_dict[file_id][file_type] = json.loads(file_contents) |
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for key, value in response_dict.items(): |
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yield key, { |
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"id": key, |
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"description": value['jso']['description'], |
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"prompt": value['txt'], |
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"video": value['vid'], |
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"videourl": value['jso']['videourl'], |
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"categories": value['jso']['categories'], |
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"duration": value['jso']['duration'], |
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"full_metadata": value['jso'] |
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} |
