dmlc--dgl
Hongzhi (Steve), Chen
47 行
1.1 KiB
Python
47 行
1.1 KiB
Python
"""Mini-batch transformer"""
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from torch.utils.data import functional_datapipe
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from torch.utils.data.datapipes.iter import Mapper
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from .minibatch import MiniBatch
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__all__ = [
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"MiniBatchTransformer",
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]
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@functional_datapipe("transform")
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class MiniBatchTransformer(Mapper):
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"""A mini-batch transformer used to manipulate mini-batch.
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Functional name: :obj:`transform`.
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Parameters
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----------
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datapipe : DataPipe
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The datapipe.
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transformer:
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The function applied to each minibatch which is responsible for
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transforming the minibatch.
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"""
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def __init__(
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self,
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datapipe,
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transformer=None,
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):
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super().__init__(datapipe, self._transformer)
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self.transformer = transformer or self._identity
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def _transformer(self, minibatch):
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minibatch = self.transformer(minibatch)
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assert isinstance(
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minibatch, (MiniBatch,)
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), "The transformer output should be an instance of MiniBatch"
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return minibatch
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@staticmethod
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def _identity(minibatch):
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return minibatch
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