| |
| 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 |
|
|
