Datasets:
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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
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