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import os |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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TODO |
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""" |
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_HOMEPAGE = "" |
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_DESCRIPTION = """\ |
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Text To Image Evaluation (TeTIm-Eval) |
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""" |
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_URLS = { |
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"mini": "https://huggingface.co/datasets/galatolo/TeTIm-Eval/resolve/main/data/TeTIm-Eval-Mini.zip" |
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} |
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_CATEGORIES = [ |
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"digital_art", |
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"sketch_art", |
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"traditional_art", |
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"baroque_painting", |
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"high_renaissance_painting", |
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"neoclassical_painting", |
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"animal_photo", |
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"food_photo", |
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"landscape_photo", |
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"person_photo" |
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] |
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_FOLDERS = { |
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"mini": { |
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_CATEGORIES[0]: "TeTIm-Eval-Mini/sampled_art_digital", |
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_CATEGORIES[1]: "TeTIm-Eval-Mini/sampled_art_sketch", |
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_CATEGORIES[2]: "TeTIm-Eval-Mini/sampled_art_traditional", |
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_CATEGORIES[3]: "TeTIm-Eval-Mini/sampled_painting_baroque", |
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_CATEGORIES[4]: "TeTIm-Eval-Mini/sampled_painting_high-renaissance", |
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_CATEGORIES[5]: "TeTIm-Eval-Mini/sampled_painting_neoclassicism", |
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_CATEGORIES[6]: "TeTIm-Eval-Mini/sampled_photo_animal", |
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_CATEGORIES[7]: "TeTIm-Eval-Mini/sampled_photo_food", |
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_CATEGORIES[8]: "TeTIm-Eval-Mini/sampled_photo_landscape", |
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_CATEGORIES[9]: "TeTIm-Eval-Mini/sampled_photo_person", |
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} |
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} |
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class TeTImConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(TeTImConfig, self).__init__(**kwargs) |
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class TeTIm(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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TeTImConfig( |
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name="mini", |
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version=datasets.Version("1.0.0", ""), |
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description="A random sampling of 300 images (30 for category) from the TeTIm dataset, manually annotated by the same person", |
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), |
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] |
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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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{ |
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"id": datasets.Value("int32"), |
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"image": datasets.Image(), |
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"caption": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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} |
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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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target = os.environ.get(f"TETIMEVAL_{self.config.name}", _URLS[self.config.name]) |
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downloaded_files = dl_manager.download_and_extract(target) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"path": downloaded_files}), |
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] |
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def _generate_examples(self, path): |
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id = 0 |
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for category, folder in _FOLDERS[self.config.name].items(): |
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images_folder = os.path.join(path, folder, "images") |
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annotations_folder = os.path.join(path, folder, "annotations") |
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for image in os.listdir(images_folder): |
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image_id = int(image.split(".")[0]) |
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annotation_file = os.path.join(annotations_folder, f"{image_id}.json") |
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with open(annotation_file) as f: |
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annotation = json.load(f) |
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yield id, { |
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"id": id, |
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"image": os.path.join(images_folder, image), |
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"caption": annotation["caption"], |
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"category": category |
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} |
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id += 1 |
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if __name__ == "__main__": |
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from datasets import load_dataset |
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dataset_config = { |
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"LOADING_SCRIPT_FILES": os.path.join(os.getcwd(), "TeTIm-Eval.py"), |
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"CONFIG_NAME": "mini", |
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} |
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ds = load_dataset( |
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dataset_config["LOADING_SCRIPT_FILES"], |
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dataset_config["CONFIG_NAME"], |
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) |
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print(ds) |
