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import pandas as pd |
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from huggingface_hub import hf_hub_url |
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import datasets |
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import os |
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_VERSION = datasets.Version("0.0.2") |
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_DESCRIPTION = "TODO" |
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_HOMEPAGE = "TODO" |
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_LICENSE = "TODO" |
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_CITATION = "TODO" |
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_FEATURES = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"conditioning_image": datasets.Image(), |
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"text": datasets.Value("string"), |
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}, |
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) |
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METADATA_URL = hf_hub_url( |
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"Saail/CVUSA_subset", |
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filename="random.jsonl", |
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repo_type="dataset", |
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) |
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IMAGES_URL = hf_hub_url( |
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"Saail/CVUSA_subset", |
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filename="street_view.zip", |
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repo_type="dataset", |
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) |
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CONDITIONING_IMAGES_URL = hf_hub_url( |
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"Saail/CVUSA_subset", |
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filename="sat.zip", |
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repo_type="dataset", |
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) |
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_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION) |
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class Fill50k(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [_DEFAULT_CONFIG] |
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DEFAULT_CONFIG_NAME = "default" |
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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=_FEATURES, |
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supervised_keys=None, |
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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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metadata_path = dl_manager.download(METADATA_URL) |
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images_dir = dl_manager.download_and_extract(IMAGES_URL) |
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conditioning_images_dir = dl_manager.download_and_extract( |
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CONDITIONING_IMAGES_URL |
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) |
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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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"metadata_path": metadata_path, |
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"images_dir": images_dir, |
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"conditioning_images_dir": conditioning_images_dir, |
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}, |
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), |
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] |
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def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir): |
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metadata = pd.read_json(metadata_path, lines=True) |
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for _, row in metadata.iterrows(): |
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text = row["text"] |
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image_path = row["image"] |
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image_path = os.path.join(images_dir, image_path) |
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image = open(image_path, "rb").read() |
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conditioning_image_path = row["conditioning_image"] |
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conditioning_image_path = os.path.join( |
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conditioning_images_dir, row["conditioning_image"] |
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) |
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conditioning_image = open(conditioning_image_path, "rb").read() |
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yield row["image"], { |
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"text": text, |
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"image": { |
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"path": image_path, |
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"bytes": image, |
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}, |
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"conditioning_image": { |
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"path": conditioning_image_path, |
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"bytes": conditioning_image, |
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}, |
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} |
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