Upload openvivqa.py with huggingface_hub

openvivqa.py

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+# coding=utf-8
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+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 = "openvivqa"
+_DESCRIPTION = """\
+OpenViVQA (Open-domain Vietnamese Visual Question Answering) is a dataset for VQA (Visual Question Answering) with
+open-ended answers in Vietnamese. It consisted of 11199 images associated with 37914 question-answer pairs (QAs).
+Images in the OpenViVQA dataset are captured in Vietnam and question-answer pairs are created manually by Vietnamese
+crowd workers.
+"""
+_HOMEPAGE = "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset"
+_LANGUAGES = ["vie"]
+_LICENSE = Licenses.MIT.value
+_LOCAL = False
+_HF_URL = "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset"
+_URLS = {
+    "dataset": {
+        "train": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/raw/main/vlsp2023_train_data.json",
+        "test": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/raw/main/vlsp2023_test_data.json",
+        "dev": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/raw/main/vlsp2023_dev_data.json",
+    },
+    "images": {
+        "train": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/resolve/main/train-images.zip?download=true",
+        "test": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/resolve/main/test-images.zip?download=true",
+        "dev": "https://huggingface.co/datasets/uitnlp/OpenViVQA-dataset/resolve/main/dev-images.zip?download=true",
+    },
+}
+_SUPPORTED_TASKS = [Tasks.VISUAL_QUESTION_ANSWERING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class OpenViVQADataset(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_path": datasets.Value("string"),
+                                          "question": datasets.Value("string"),
+                                          "answer": datasets.Value("string"),
+                                          "id": datasets.Value("string")})
+        elif self.config.schema == "seacrowd_imqa":
+            features = schemas.imqa_features
+            # features["meta"] = {"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."""
+        data_dir = dl_manager.download_and_extract(_URLS["dataset"])
+        image_dir = dl_manager.download_and_extract(_URLS["images"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "imagepath": os.path.join(image_dir["train"], "training-images"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "imagepath": os.path.join(image_dir["test"], "test-images"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["dev"],
+                    "imagepath": os.path.join(image_dir["dev"], "dev-images"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, imagepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        raw_examples = json.load(open(filepath, "r"))
+        images = raw_examples["images"]
+        data_annotations = raw_examples["annotations"]
+        for sample_id, q_key in enumerate(list(data_annotations.keys())):
+            quest_id = q_key
+            sample = data_annotations[q_key]
+            sample_img_id = sample["image_id"]
+            sample_img_name = images[str(sample_img_id)]
+            sample_img_path = os.path.join(imagepath, sample_img_name)
+            sample_question = sample["question"]
+            sample_answer = sample["answer"]
+            if self.config.schema == "source":
+                example = {
+                    "img_path": sample_img_path,
+                    "question": sample_question,
+                    "answer": sample_answer,
+                    "id": quest_id,
+                }
+            elif self.config.schema == "seacrowd_imqa":
+                example = {
+                    "id": q_key,
+                    "question_id": q_key,
+                    "document_id": q_key,
+                    "questions": [sample_question],
+                    "type": None,
+                    "choices": None,
+                    "context": sample_img_id,
+                    "answer": [sample_answer],
+                    "image_paths": [sample_img_path],
+                    "meta": {},
+                }
+            yield sample_id, example