import os import datasets from datasets.tasks import ImageClassification from .classes_rod import ROD_CLASSES _CITATION = """\ @misc{lee2023hardwiring, title={Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing}, author={Ariel N. Lee and Sarah Adel Bargal and Janavi Kasera and Stan Sclaroff and Kate Saenko and Nataniel Ruiz}, year={2023}, eprint={2306.17848}, archivePrefix={arXiv}, primaryClass={cs.CV} } """ _HOMEPAGE = "https://arielnlee.github.io/PatchMixing/" _DESCRIPTION = """\ ROD is meant to serve as a metric for evaluating models' robustness to occlusion. It is the product of a meticulous object collection protocol aimed at collecting and capturing 40+ distinct, real-world objects from 16 classes. """ _DATA_URL = { "rod": [ f"https://huggingface.co/datasets/ariellee/Realistic-Occlusion-Dataset/resolve/main/rod_{i}.tar.gz" for i in range(2) ] } class ROD(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") DEFAULT_WRITER_BATCH_SIZE = 16 def _info(self): assert len(ROD_CLASSES) == 16 return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "image": datasets.Image(), "label": datasets.ClassLabel(names=list(ROD_CLASSES.values())), } ), homepage=_HOMEPAGE, citation=_CITATION, task_templates=[ImageClassification(image_column="image", label_column="label")], ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" archives = dl_manager.download(_DATA_URL) return [ datasets.SplitGenerator( name="ROD", gen_kwargs={ "archives": [dl_manager.iter_archive(archive) for archive in archives["rod"]], }, ), ] def _generate_examples(self, archives): """Yields examples.""" idx = 0 for archive in archives: for path, file in archive: if path.endswith(".jpg"): synset_id = os.path.basename(os.path.dirname(path)) ex = {"image": {"path": path, "bytes": file.read()}, "label": synset_id} yield idx, ex idx += 1