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