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import datasets |
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import glob |
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import os |
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import numpy as np |
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SHARD_SIZE = 2500 |
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NUM_SHARDS = 40 |
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_URLS = { |
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'train': [ |
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f'https://huggingface.co/datasets/commaai/commavq/resolve/main/data_{i*SHARD_SIZE}_to_{(i+1)*SHARD_SIZE}.zip' for i in range(NUM_SHARDS) |
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], |
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'val': f'https://huggingface.co/datasets/commaai/commavq/resolve/main/val.zip' |
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} |
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_DESCRIPTION = """\ |
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TODO |
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""" |
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class CommaVQ(datasets.GeneratorBasedBuilder): |
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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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{"path": datasets.Value("string")} |
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) |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_manager.download_config.ignore_url_params = True |
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downloaded_files = dl_manager.download(_URLS) |
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local_extracted_archive = dl_manager.extract(downloaded_files) if not dl_manager.is_streaming else None |
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return [ |
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datasets.SplitGenerator( |
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name=f'train_{i}', |
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gen_kwargs={"local_extracted_archive":local_extracted_archive['train'][i], "files": dl_manager.iter_archive(downloaded_files['train'][i])} |
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) for i in range(len(downloaded_files['train']))] + [ |
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datasets.SplitGenerator( |
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name='val', |
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gen_kwargs={"local_extracted_archive":local_extracted_archive['val'], "files": dl_manager.iter_archive(downloaded_files['val'])} |
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) |
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] |
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def _generate_examples(self, local_extracted_archive, files): |
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files = glob.glob(os.path.join(local_extracted_archive, '*.npy')) |
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for path in files: |
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file_name = os.path.basename(path) |
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yield file_name, {'path': path} |
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def _get_examples_iterable_for_split(self, split_generator): |
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for path in split_generator.gen_kwargs['files']: |
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yield path[0], {'path': path[0]} |
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