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import os
from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Version, Features, Value, Sequence, Image, Split
_CITATION = """\
@inproceedings{ma2023crepe,
title={Crepe: Can vision-language foundation models reason compositionally?},
author={Ma, Zixian and Hong, Jerry and Gul, Mustafa Omer and Gandhi, Mona and Gao, Irena and Krishna, Ranjay},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10910--10921},
year={2023}
}
"""
_DESCRIPTION = """\
Code and datasets for "CREPE: Can Vision-Language Foundation Models Reason Compositionally?".
"""
_HOMEPAGE = "https://huggingface.co/datasets/Mayfull/crepe_vlms"
_LICENSE = "MIT License"
class CREPEVLMsDataset(GeneratorBasedBuilder):
VERSION = Version("1.0.0")
def _info(self):
return DatasetInfo(
description=_DESCRIPTION,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
features=Features(
{
"images": Sequence(Image()), # Sequence of images
"positive_caption": Sequence(Value("string")),
"negative_caption": Sequence(Value("string")),
"x": Value("float"),
"y": Value("float"),
"width": Value("float"),
"height": Value("float"),
"original_file_name": Value("string"),
}
),
)
def _split_generators(self, dl_manager):
# URLs for images.zip and examples.jsonl
urls_to_download = {
"images": "https://huggingface.co/datasets/Mayfull/crepe_vlms/resolve/main/images.zip",
"examples": "https://huggingface.co/datasets/Mayfull/crepe_vlms/resolve/main/examples.jsonl",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"examples_file": downloaded_files["examples"],
"images_dir": downloaded_files["images"],
},
),
]
def _generate_examples(self, examples_file, images_dir):
# Read the examples.jsonl file
with open(examples_file, "r", encoding="utf-8") as f:
for idx, line in enumerate(f):
data = eval(line.strip())
# Get image file path
image_file_name = data.get("image")
image_path = os.path.join(images_dir, image_file_name)
# Ensure the image file exists
images = [image_path] if os.path.exists(image_path) else []
# Convert bounding box values to float
x = float(data.get("x", 0.0))
y = float(data.get("y", 0.0))
width = float(data.get("width", 0.0))
height = float(data.get("height", 0.0))
# Prepare the example
yield idx, {
"images": images,
"positive_caption": data.get("positive_caption", []),
"negative_caption": data.get("negative_caption", []),
"x": x,
"y": y,
"width": width,
"height": height,
"original_file_name": data.get("original_file_name", ""),
}
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