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import tarfile |
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import numpy as np |
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import datasets |
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class STL10(datasets.GeneratorBasedBuilder): |
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"""STL-10 Dataset |
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The STL-10 dataset is a dataset for developing unsupervised feature learning, |
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deep learning, and self-taught learning algorithms. |
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""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="STL-10 dataset for unsupervised feature learning. " |
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"Includes labeled and unlabeled images.", |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.ClassLabel( |
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names=[ |
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"airplane", "bird", "car", "cat", "deer", |
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"dog", "horse", "monkey", "ship", "truck" |
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] |
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), |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage="https://cs.stanford.edu/~acoates/stl10/", |
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citation="""@article{coates2011analysis, |
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title={An analysis of single-layer networks in unsupervised feature learning}, |
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author={Coates, Adam and Ng, Andrew Y}, |
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journal={AISTATS}, |
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year={2011}}""", |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download( |
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"http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz" |
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) |
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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={"archive_path": archive_path, "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"archive_path": archive_path, "split": "test"}, |
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), |
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datasets.SplitGenerator( |
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name="unlabeled", |
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gen_kwargs={"archive_path": archive_path, "split": "unlabeled"}, |
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), |
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] |
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def _generate_examples(self, archive_path, split): |
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with tarfile.open(archive_path, "r:gz") as tar: |
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if split == "train": |
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images_file = "stl10_binary/train_X.bin" |
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labels_file = "stl10_binary/train_y.bin" |
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elif split == "test": |
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images_file = "stl10_binary/test_X.bin" |
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labels_file = "stl10_binary/test_y.bin" |
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else: |
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images_file = "stl10_binary/unlabeled_X.bin" |
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labels_file = None |
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images = tar.extractfile(images_file).read() |
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images = ( |
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np.frombuffer(images, dtype=np.uint8) |
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.reshape(-1, 3, 96, 96) |
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.transpose((0, 2, 3, 1)) |
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) |
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if labels_file: |
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labels = tar.extractfile(labels_file).read() |
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labels = np.frombuffer(labels, dtype=np.uint8) - 1 |
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for idx, (image, label) in enumerate(zip(images, labels)): |
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yield idx, {"image": image, "label": label} |
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else: |
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for idx, image in enumerate(images): |
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yield idx, {"image": image, "label": -1} |
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