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- # mypy: allow-untyped-defs
- import copy
- import warnings
- from torch.utils.data.datapipes.datapipe import MapDataPipe
- __all__ = ["SequenceWrapperMapDataPipe", ]
- class SequenceWrapperMapDataPipe(MapDataPipe):
- r"""
- Wraps a sequence object into a MapDataPipe.
- Args:
- sequence: Sequence object to be wrapped into an MapDataPipe
- deepcopy: Option to deepcopy input sequence object
- .. note::
- If ``deepcopy`` is set to False explicitly, users should ensure
- that data pipeline doesn't contain any in-place operations over
- the iterable instance, in order to prevent data inconsistency
- across iterations.
- Example:
- >>> # xdoctest: +SKIP
- >>> from torchdata.datapipes.map import SequenceWrapper
- >>> dp = SequenceWrapper(range(10))
- >>> list(dp)
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
- >>> dp = SequenceWrapper({'a': 100, 'b': 200, 'c': 300, 'd': 400})
- >>> dp['a']
- 100
- """
- def __init__(self, sequence, deepcopy=True):
- if deepcopy:
- try:
- self.sequence = copy.deepcopy(sequence)
- except TypeError:
- warnings.warn(
- "The input sequence can not be deepcopied, "
- "please be aware of in-place modification would affect source data"
- )
- self.sequence = sequence
- else:
- self.sequence = sequence
- def __getitem__(self, index):
- return self.sequence[index]
- def __len__(self):
- return len(self.sequence)
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