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from torch.utils.data.dataset import ConcatDataset as _ConcatDataset |
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from .builder import DATASETS |
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@DATASETS.register_module() |
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class ConcatDataset(_ConcatDataset): |
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"""A wrapper of concatenated dataset. |
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Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but |
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concat the group flag for image aspect ratio. |
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Args: |
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datasets (list[:obj:`Dataset`]): A list of datasets. |
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""" |
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def __init__(self, datasets): |
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super(ConcatDataset, self).__init__(datasets) |
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self.CLASSES = datasets[0].CLASSES |
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self.PALETTE = datasets[0].PALETTE |
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@DATASETS.register_module() |
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class RepeatDataset(object): |
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"""A wrapper of repeated dataset. |
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The length of repeated dataset will be `times` larger than the original |
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dataset. This is useful when the data loading time is long but the dataset |
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is small. Using RepeatDataset can reduce the data loading time between |
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epochs. |
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Args: |
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dataset (:obj:`Dataset`): The dataset to be repeated. |
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times (int): Repeat times. |
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""" |
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def __init__(self, dataset, times): |
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self.dataset = dataset |
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self.times = times |
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self.CLASSES = dataset.CLASSES |
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self.PALETTE = dataset.PALETTE |
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self._ori_len = len(self.dataset) |
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def __getitem__(self, idx): |
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"""Get item from original dataset.""" |
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return self.dataset[idx % self._ori_len] |
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def __len__(self): |
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"""The length is multiplied by ``times``""" |
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return self.times * self._ori_len |
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