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import json |
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import datasets |
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from datasets.tasks import QuestionAnsweringExtractive |
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import re |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{tobedetermined, |
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author = {Zihao}, |
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title = "{Food Images}", |
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journal = {Nah}, |
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year = 2022, |
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eid = {arXiv:Nah}, |
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pages = {arXiv:Nah}, |
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archivePrefix = {arXiv}, |
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eprint = {Nah}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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For finetunning stable diffuser with chinese food images |
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""" |
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_URL = "https://huggingface.co/datasets/zmao/chinese_food_caption/resolve/main/chinese_food_caption.tar.gz" |
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class FoodCaption(datasets.GeneratorBasedBuilder): |
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"""Chinese_food image and captions Dataset. Version 1.1.""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"image": datasets.Image(), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://huggingface.co/datasets/zmao/food_img_caption", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.download(_URL) |
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image_iters = dl_manager.iter_archive(path) |
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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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"images": image_iters |
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} |
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), |
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] |
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def _generate_examples(self, images): |
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"""This function returns the examples in the raw (text) form.""" |
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idx = 0 |
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for filepath, image in images: |
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text = filepath[14:-4] |
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text = text.replace('-',' ') |
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text = re.sub("^\d+\s|\s\d+\s|\s\d+$", " ", text) |
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text = text.strip() |
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yield idx, { |
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"image" : {"path": filepath, "bytes":image.read()}, |
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"text": text |
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} |
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idx += 1 |
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