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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
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