| import json | |
| import os | |
| from pathlib import Path | |
| import datasets | |
| from datasets import DownloadManager | |
| from PIL import Image | |
| _CITATION = "" | |
| _HOMEPAGE = "" | |
| _DESCRIPTION = "" | |
| _LICENSE = "" | |
| _DATA_URL = {"train" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/train.zip", | |
| "test" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip", | |
| "val" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/val.zip" } | |
| _ANNOTATION_URL = "https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip" | |
| _FEATURES = datasets.Features( | |
| { | |
| "id" : datasets.Value("int32"), | |
| "image": datasets.Image(), | |
| "filename": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answers": datasets.Sequence(datasets.Value("string")), | |
| "answers_original": [ | |
| { | |
| "answer": datasets.Value("string"), | |
| "answer_confidence": datasets.Value("string"), | |
| } | |
| ], | |
| "answer_type": datasets.Value("string"), | |
| "answerable": datasets.Value("int32") | |
| } | |
| ) | |
| class VizWiz(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description = _DESCRIPTION, | |
| features = _FEATURES, | |
| homepage = _HOMEPAGE, | |
| license = _LICENSE, | |
| citation = _CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| ann_file_train = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "train.json") | |
| ann_file_val = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "val.json") | |
| ann_file_test = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "test.json") | |
| image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_DATA_URL).items()} | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "annotation_file": ann_file_train, | |
| "image_folders": image_folders, | |
| "split_key": 'train' | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "annotation_file": ann_file_val, | |
| "image_folders": image_folders, | |
| "split_key": "val" | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "annotation_file": ann_file_test, | |
| "image_folders": image_folders, | |
| "split_key": "test" | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, annotation_file,image_folders,split_key): | |
| counter = 0 | |
| info = {} | |
| annotations = json.load(open(annotation_file)) | |
| for ann in annotations: | |
| if split_key in ['train','val']: | |
| answers = [answer["answer"] for answer in ann["answers"]] | |
| answers_original = ann['answers'] | |
| answer_type = ann["answer_type"] | |
| answerable = ann["answerable"] | |
| else: | |
| answers = None | |
| answers_original = None | |
| answer_type = None | |
| answerable = None | |
| yield counter, { | |
| "id" : counter, | |
| "image": str(image_folders[split_key]/split_key/ann['image']), | |
| "filename" : ann['image'], | |
| "question" : ann["question"], | |
| "answers" : answers, | |
| "answers_original" : answers_original, | |
| "answer_type" : answer_type, | |
| "answerable" : answerable | |
| } | |
| counter += 1 | |