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"""PMC-OA Dataset"""
import os
import jsonlines
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{lin2023pmc,
title={PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents},
author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2303.07240},
year={2023}
}
"""
_DESCRIPTION = """\
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity.
To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before.
PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption.
While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP achieves state-of-the-art results on various downstream tasks,
including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, i.e. +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.
"""
_HOMEPAGE = "https://weixionglin.github.io/PMC-CLIP/"
_URLs = {
"images": "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/images.zip",
# "pmc_oa_beta": "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/test.jsonl",
# "pmc_oa": "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/valid.jsonl",
"demo_images": "https://huggingface.co/datasets/axiong/test/resolve/main/images.zip",
"demo_captions": "https://huggingface.co/datasets/axiong/test/resolve/main/mini_dataset.jsonl",
"pmc_oa_beta": "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/pmc_oa_beta.jsonl",
"pmc_oa": "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/pmc_oa.jsonl",
}
class PMC_OA_Config(datasets.BuilderConfig):
"""BuilderConfig for PMC_OA"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PMC_OA_Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class PMC_OA(datasets.GeneratorBasedBuilder):
"""PMC_OA Dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
PMC_OA_Config(
name="demo",
description="Demo subset to show dataset samples.",
),
PMC_OA_Config(
name="pmc_oa_beta",
description="<subfigure, caption> pairs. Subfigures detected by a DETR model.",
),
PMC_OA_Config(
name="pmc_oa",
description="<subfigure, subcaption> pairs. Subfigures detected by a DETR model. Subcaptions detected by ChatGPT and aligned with subfigures.",
),
]
def _info(self):
if self.config.name == "demo":
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Value("string"),
"caption": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
homepage=_HOMEPAGE,
)
elif self.config.name == "pmc_oa_beta":
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Value("string"),
"caption": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
homepage=_HOMEPAGE,
)
elif self.config.name == "pmc_oa":
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Value("string"),
"caption": datasets.Value("string"),
# "alignment_type": datasets.Value("string"),
# "alignment_score": datasets.Value("float"),
}
),
supervised_keys=None,
citation=_CITATION,
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_files = dl_manager.download_and_extract(_URLs)
if self.config.name == "demo":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["demo_captions"], "image_dir": downloaded_files['demo_images']}
)
]
elif self.config.name == "pmc_oa_beta":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["pmc_oa_beta"], "image_dir": downloaded_files['images']}
)
]
elif self.config.name == "pmc_oa":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["pmc_oa"], "image_dir": downloaded_files['images']}
)
]
def _generate_examples(self, filepath, image_dir):
"""Yields examples."""
logger.info("generating examples from = %s", filepath)
with jsonlines.open(filepath) as reader:
for _id, obj in enumerate(reader):
if self.config.name == "demo":
relative_image_path = obj['image']
image_path = os.path.join(image_dir, "images", relative_image_path)
caption = obj['caption']
yield _id, {
"image": {
"path": image_path,
"bytes": open(image_path, "rb").read(),
},
"caption": caption,
}
elif self.config.name == "pmc_oa_beta":
relative_image_path = obj['image']
image_path = os.path.join(image_dir, "caption_T060_filtered_top4_sep_v0_subfigures", relative_image_path)
caption = obj['caption']
yield _id, {
"image": {
"path": image_path,
"bytes": open(image_path, "rb").read(),
},
"caption": caption,
}
elif self.config.name == "pmc_oa":
relative_image_path = obj['image']
image_path = os.path.join(image_dir, "caption_T060_filtered_top4_sep_v0_subfigures", relative_image_path)
caption = obj['caption']
# alignment_type = obj['alignment_type']
# alignment_score = obj['alignment_score']
yield _id, {
"image": {
"path": image_path,
"bytes": open(image_path, "rb").read(),
},
"caption": caption,
# "alignment_type": alignment_type,
# "alignment_score": alignment_score,
} |