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import struct |
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import numpy as np |
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import datasets |
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from datasets.tasks import ImageClassification |
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_CITATION = """\ |
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@article{lecun2010mnist, |
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title={MNIST handwritten digit database}, |
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author={LeCun, Yann and Cortes, Corinna and Burges, CJ}, |
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journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist}, |
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volume={2}, |
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year={2010} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000 |
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images per class. There are 60,000 training images and 10,000 test images. |
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""" |
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_URL = "https://huggingface.co/datasets/AnaChikashua/handwriting/resolve/main/handwriting_dataset.zip" |
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_NAMES = ['ა', 'ბ', 'გ', 'დ', 'ე', 'ვ', 'ზ', 'თ', 'ი', 'კ', 'ლ', 'მ', 'ნ', 'ო', 'პ', 'ჟ', 'რ', 'ს', 'ტ', 'უ', 'ფ', 'ქ', 'ღ', 'ყ', 'შ', 'ჩ', 'ც', 'ძ', 'წ', 'ჭ', 'ხ', 'ჯ', 'ჰ'] |
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class MNIST(datasets.GeneratorBasedBuilder): |
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"""MNIST Data Set""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="data", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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) |
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] |
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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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"image": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=("image", "label"), |
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citation=_CITATION, |
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task_templates=[ |
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ImageClassification( |
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image_column="image", |
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label_column="label", |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.dowload(_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={"images": image_iters} |
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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 form.""" |
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for idx, filepath, image in enumerate(images): |
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text = [c for c in str(filepath) if not 0 <= ord(c) <= 127][0] |
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yield idx, { |
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"label": text, |
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"image": {"path": filepath, "bytes": image.read()} |
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} |
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