Create pmc_oa.py

pmc_oa.py

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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/"
+
+_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_name": datasets.Value("string"),
+                    "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": os.path.join(data_dir, "train.jsonl")}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "valid.jsonl")}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """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("images", relative_image_path)
+                caption = obj['caption']
+                yield _id, {
+                    "image": {
+                        "path": image_path,
+                        "bytes": open(image_path, "rb").read(),
+                    },
+                    "caption": caption,
+                }
+                _id += 1
+