| import copy |
| import warnings |
| from torch.utils.data.datapipes.datapipe import IterDataPipe |
|
|
| __all__ = ["IterableWrapperIterDataPipe", ] |
|
|
|
|
| class IterableWrapperIterDataPipe(IterDataPipe): |
| r""" |
| Wraps an iterable object to create an IterDataPipe. |
| |
| Args: |
| iterable: Iterable object to be wrapped into an IterDataPipe |
| deepcopy: Option to deepcopy input iterable object for each |
| iterator. The copy is made when the first element is read in ``iter()``. |
| |
| .. note:: |
| If ``deepcopy`` is explicitly set to ``False``, users should ensure |
| that the data pipeline doesn't contain any in-place operations over |
| the iterable instance to prevent data inconsistency across iterations. |
| |
| Example: |
| >>> # xdoctest: +SKIP |
| >>> from torchdata.datapipes.iter import IterableWrapper |
| >>> dp = IterableWrapper(range(10)) |
| >>> list(dp) |
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] |
| """ |
| def __init__(self, iterable, deepcopy=True): |
| self.iterable = iterable |
| self.deepcopy = deepcopy |
|
|
| def __iter__(self): |
| source_data = self.iterable |
| if self.deepcopy: |
| try: |
| source_data = copy.deepcopy(self.iterable) |
| |
| |
| |
| |
| except TypeError: |
| warnings.warn( |
| "The input iterable can not be deepcopied, " |
| "please be aware of in-place modification would affect source data." |
| ) |
| for data in source_data: |
| yield data |
|
|
| def __len__(self): |
| return len(self.iterable) |
|
|
