| """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/" | |
| _URL = "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/" | |
| _URLS = { | |
| "images": _URL + "images.zip", | |
| "train": _URL + "train.jsonl", | |
| "valid": _URL + "valid.jsonl", | |
| "test": _URL + "test.jsonl" | |
| } | |
| class PMC_OA(datasets.GeneratorBasedBuilder): | |
| """PMC_OA Dataset.""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "caption": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_URLS) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir['train'], "image_dir": data_dir['images']}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir['valid'], "image_dir": data_dir['images']}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir['test'], "image_dir": data_dir['images']}), | |
| ] | |
| def _generate_examples(self, filepath, image_dir): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| with jsonlines.open(filepath) as reader: | |
| _id = 0 | |
| for obj in reader: | |
| 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, | |
| } | |
| _id += 1 | |