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"""Augmented MNIST Data Set""" |
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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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_DESCRIPTION = """\ |
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The dataset is built on top of MNIST. |
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It consists from 130K of images in 10 classes - 120K training and 10K test samples. |
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The training set was augmented with additional 60K images. |
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""" |
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_URLS = { |
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"train_images": "data/train-images-idx3-ubyte.gz", |
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"train_labels": "data/train-labels-idx1-ubyte.gz", |
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"test_images": "data/t10k-images-idx3-ubyte.gz", |
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"test_labels": "data/t10k-labels-idx1-ubyte.gz", |
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} |
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class AMNIST(datasets.GeneratorBasedBuilder): |
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"""A-MNIST Data Set""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="amnist", |
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version=datasets.Version("1.1.0"), |
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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=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]), |
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} |
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), |
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supervised_keys=("image", "label"), |
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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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urls_to_download = _URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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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": [downloaded_files["train_images"], |
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downloaded_files["train_labels"]], |
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"split": "train", |
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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": [downloaded_files["test_images"], |
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downloaded_files["test_labels"]], |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""This function returns the examples in the raw form.""" |
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with open(filepath[0], "rb") as f: |
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_ = f.read(4) |
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size = struct.unpack(">I", f.read(4))[0] |
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_ = f.read(8) |
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images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28) |
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with open(filepath[1], "rb") as f: |
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_ = f.read(8) |
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labels = np.frombuffer(f.read(), dtype=np.uint8) |
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for idx in range(size): |
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yield idx, {"image": images[idx], "label": str(labels[idx])} |
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