Upload path_vqa.py

path_vqa.py

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+"""PathVQA: 30000+ Questions for Medical Visual Question Answering"""
+
+import pandas
+import os
+
+import datasets
+
+_CITATION = """\
+    @article{he2020pathvqa,
+        title={PathVQA: 30000+ Questions for Medical Visual Question Answering},
+        author={He, Xuehai and Zhang, Yichen and Mou, Luntian and Xing, Eric and Xie, Pengtao},
+        journal={arXiv preprint arXiv:2003.10286},
+        year={2020}
+    }
+"""
+
+_DESCRIPTION = """\
+    PathVQA is a dataset of question-answer pairs on pathology images. The questions are similar to those in the
+    American Board of Pathology (ABP) test. The dataset includes both open-ended questions and binary "yes/no"
+    questions. The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and
+    "Basic Pathology", and a publicly-available digital library: "Pathology Education Informational Resource"
+    (PEIR). The copyrights of images and captions belong to the publishers and authors of these two books,
+    and the owners of the PEIR digital library.
+"""
+
+_HOMEPAGE = "https://github.com/UCSD-AI4H/PathVQA"
+
+_LICENSE = "MIT"
+
+_URLS = {
+    "image_train": "data/image/train_img.tar",
+    "image_val": "data/image/val_img.tar",
+    "image_test": "data/image/test_img.tar",
+    "text_train": "data/text/train_qa.jsonl",
+    "text_val": "data/text/val_qa.jsonl",
+    "text_test": "data/text/test_qa.jsonl",
+}
+
+class PathVQA(datasets.GeneratorBasedBuilder):
+    
+    """
+    PathVQA: 30000+ Questions for Medical Visual Question Answering.
+    
+    The data was obtained from the updated Google Drive link shared by the authors in their GitHub repository
+    on Feb 15, 2023, see https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab.
+    
+    This version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs. Of the
+    5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images are not used.
+    Furthermore, there are several duplicates, i.e. there are some image-question-answer triplets which occur
+    more than once in the same split (train, val, test). After dropping the duplicate image-question-answer
+    triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.
+    """
+
+    VERSION = datasets.Version("0.1.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="full", version=VERSION, description="Original dataset."),
+        datasets.BuilderConfig(name="de-duped", version=VERSION, description="De-duplicated dataset."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "de-duped"
+    
+    def _info(self):
+        
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "question": datasets.Value("string"),
+                "answer": datasets.Value("string")
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        # images
+        image_train_dir = dl_manager.download_and_extract(_URLS["image_train"])
+        image_val_dir = dl_manager.download_and_extract(_URLS["image_val"])
+        image_test_dir = dl_manager.download_and_extract(_URLS["image_test"])
+
+        # question-answer pairs
+        text_train_dir = dl_manager.download(_URLS["text_train"])
+        text_val_dir = dl_manager.download(_URLS["text_val"])
+        text_test_dir = dl_manager.download(_URLS["text_test"])
+
+        return [
+            
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_filepath": os.path.join(image_train_dir),
+                    "text_filepath": os.path.join(text_train_dir),
+                    "split": "train",
+                },
+            ),
+            
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "image_filepath": os.path.join(image_val_dir),
+                    "text_filepath": os.path.join(text_val_dir),
+                    "split": "val",
+                },
+            ),
+            
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "image_filepath": os.path.join(image_test_dir),
+                    "text_filepath": os.path.join(text_test_dir),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, image_filepath, text_filepath, split):
+        
+        df = pandas.read_json(text_filepath, orient='records', lines=True)
+        if self.config.name == "de-duped":
+            df = df.drop_duplicates(ignore_index=True)
+        
+        for key, row in df.iterrows():
+            yield key, {
+                "image": os.path.join(image_filepath, row['image']),
+                "question": row["question"],
+                "answer": row["answer"]
+            }
+