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import datasets |
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import json |
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_CITATION = "" |
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_DESCRIPTION = "Dataset for training agents with Maze-v0 environment." |
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_HOMEPAGE = "https://huggingface.co/datasets/NathanGavenski/imagetest" |
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_LICENSE = "" |
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_REPO = "https://huggingface.co/datasets/NathanGavenski/imagetest" |
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class ImageSet(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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"obs": datasets.Image(), |
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"actions": datasets.Value("int32"), |
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"rewards": datasets.Value("float32"), |
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"episode_starts": datasets.Value("bool"), |
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"maze": datasets.Value("string"), |
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"image": datasets.Image() |
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}), |
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supervised_keys=None, |
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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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image_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/images.tar.gz") |
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info_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/dataset.tar.gz") |
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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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"images": f"{image_path}/images", |
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"infos": info_path |
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} |
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), |
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] |
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def _generate_examples(self, images, infos): |
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print(images) |
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with open(f"{infos}/dataset.jsonl", encoding="utf-8") as data: |
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for idx, line in enumerate(data): |
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record = json.loads(line) |
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image = record["obs"].split(".")[0] |
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yield idx, { |
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"obs": |
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} |
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