| 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) |
|
|
