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vivqa.py

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+# coding=utf-8
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{tran2021vivqa,
+  title={ViVQA: Vietnamese visual question answering},
+  author={Tran, Khanh Quoc and Nguyen, An Trong and Le, An Tran-Hoai and Van Nguyen, Kiet},
+  booktitle={Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation},
+  pages={683--691},
+  year={2021}
+}
+"""
+_DATASETNAME = "vivqa"
+_DESCRIPTION = """\
+Vietnamese Visual Question Answering (ViVQA) consist of 10328 images and 15000 question-answer
+pairs in Vietnamese for evaluating Vietnamese VQA models. This dataset is built based on 10328 randomly
+selected images from MS COCO dataset. The question-answer pairs were based on the COCO-QA dataset that
+was automatically translated from English to Vietnamese.
+"""
+_HOMEPAGE = "https://github.com/kh4nh12/ViVQA"
+_LANGUAGES = ["vie"]
+_LICENSE = Licenses.UNKNOWN.value
+_LOCAL = False
+_URLS = {
+    "viviq": {"train": "https://raw.githubusercontent.com/kh4nh12/ViVQA/main/train.csv",
+              "test": "https://raw.githubusercontent.com/kh4nh12/ViVQA/main/test.csv"},
+    "cocodata": {
+        "coco2014_train_val_annots": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip",
+        "coco2014_train_images": "http://images.cocodataset.org/zips/train2014.zip",
+        "coco2014_val_images": "http://images.cocodataset.org/zips/val2014.zip",
+    },
+}
+_SUPPORTED_TASKS = [Tasks.VISUAL_QUESTION_ANSWERING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VivQADataset(datasets.GeneratorBasedBuilder):
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_imqa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_imqa",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "img_id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    "coco_url": datasets.Value("string"),
+                    "flickr_url": datasets.Value("string"),
+                    "img_name": datasets.Value("string"),
+                    "coco_license": datasets.Value("int32"),
+                    "coco_width": datasets.Value("int32"),
+                    "coco_height": datasets.Value("int32"),
+                    "coco_date_captured": datasets.Value("string"),
+                    "image_path": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_imqa":
+            features = schemas.imqa_features
+            features["meta"] = {
+                "coco_img_id": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "flickr_url": datasets.Value("string"),
+                "coco_url": datasets.Value("string"),
+                "img_name": datasets.Value("string"),
+                "coco_license": datasets.Value("int32"),
+                "coco_width": datasets.Value("int32"),
+                "coco_height": datasets.Value("int32"),
+                "coco_date_captured": datasets.Value("string"),
+                "image_path": datasets.Value("string"),
+            }
+        else:
+            raise ValueError(f"No schema matched for {self.config.schema}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS["viviq"]
+        data_dir = dl_manager.download_and_extract(urls)
+        cocodata = dl_manager.download_and_extract(_URLS["cocodata"])
+        Coco_Dict = self._get_image_detail(cocodata)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                    "coco_dict": Coco_Dict,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                    "coco_dict": Coco_Dict,
+                },
+            ),
+        ]
+
+    def _get_image_detail(self, coco_dir) -> Dict:
+        coco2014_train_val_annots = os.path.join(coco_dir["coco2014_train_val_annots"], "annotations")
+        train_ann_2014_path = os.path.join(coco2014_train_val_annots, "captions_train2014.json")
+        val_ann_2014_path = os.path.join(coco2014_train_val_annots, "captions_val2014.json")
+        coco_dict_val = {itm["id"]: itm for itm in json.load(open(val_ann_2014_path, "r"))["images"]}
+        coco_dict_train = {itm["id"]: itm for itm in json.load(open(train_ann_2014_path, "r"))["images"]}
+        coco_train_path = os.path.join(coco_dir["coco2014_train_images"], "train2014")
+        coco_val_path = os.path.join(coco_dir["coco2014_val_images"], "val2014")
+        coco_dict = {"train": coco_dict_train, "val": coco_dict_val, "coco_train_path": coco_train_path, "coco_val_path": coco_val_path}
+
+        return coco_dict
+
+    def _generate_examples(self, filepath: Path, split: str, coco_dict: Dict = None) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        raw_examples = pd.read_csv(filepath)
+        coco_train_ref = coco_dict["train"]
+        coco_val_ref = coco_dict["val"]
+        coco_ref = {**coco_train_ref, **coco_val_ref}
+        coco_train_path = coco_dict["coco_train_path"]
+        coco_val_path = coco_dict["coco_val_path"]
+
+        for eid, exam in raw_examples.iterrows():
+            assert len(exam) == 5
+            exam_id, exam_quest, exam_answer, exam_img_id, exam_type = exam
+            coco_info = coco_ref[exam_img_id]
+            flickr_url = coco_info["flickr_url"]
+            img_name = coco_info["file_name"]
+            coco_url = coco_info["coco_url"]
+            coco_license = coco_info["license"]
+            coco_width = coco_info["width"]
+            coco_height = coco_info["height"]
+            coco_date_captured = coco_info["date_captured"]
+            coco_path = coco_train_path if exam_img_id in coco_train_ref else coco_val_path
+            image_path = os.path.join(coco_path, img_name)
+
+            if self.config.schema == "source":
+                yield eid, {
+                    "img_id": str(exam_img_id),
+                    "question": exam_quest,
+                    "answer": exam_answer,
+                    "type": exam_type,
+                    "coco_url": coco_url,
+                    "flickr_url": flickr_url,
+                    "img_name": img_name,
+                    "coco_license": coco_license,
+                    "coco_width": coco_width,
+                    "coco_height": coco_height,
+                    "coco_date_captured": coco_date_captured,
+                    "image_path": image_path,
+                }
+
+            elif self.config.schema == "seacrowd_imqa":
+                example = {
+                    "id": str(eid),
+                    "question_id": str(exam_id),
+                    "document_id": str(eid),
+                    "questions": [exam_quest],
+                    "type": None,
+                    "choices": None,
+                    "context": None,
+                    "answer": [exam_answer],
+                    "image_paths": [image_path],
+                    "meta": {
+                        "coco_img_id": str(exam_img_id),
+                        "type": exam_type,
+                        "flickr_url": flickr_url,
+                        "coco_url": coco_url,
+                        "img_name": img_name,
+                        "coco_license": coco_license,
+                        "coco_width": coco_width,
+                        "coco_height": coco_height,
+                        "coco_date_captured": coco_date_captured,
+                        "image_path": image_path,
+                    },
+                }
+
+                yield eid, example