| |
| 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 |
| |
| 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 |
|
|
