IterableWrapper

class torchdata.datapipes.iter.IterableWrapper(iterable, deepcopy=True)

Wraps an iterable object to create an IterDataPipe.

Parameters:

• 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]