Attention

June 2024 Status Update: Removing DataPipes and DataLoader V2

We are re-focusing the torchdata repo to be an iterative enhancement of torch.utils.data.DataLoader. We do not plan on continuing development or maintaining the [DataPipes] and [DataLoaderV2] solutions, and they will be removed from the torchdata repo. We’ll also be revisiting the DataPipes references in pytorch/pytorch. In release torchdata==0.8.0 (July 2024) they will be marked as deprecated, and in 0.9.0 (Oct 2024) they will be deleted. Existing users are advised to pin to torchdata==0.8.0 or an older version until they are able to migrate away. Subsequent releases will not include DataPipes or DataLoaderV2. Please reach out if you suggestions or comments (please use this issue for feedback)

Dropper

class torchdata.datapipes.iter.Dropper(datapipe: IterDataPipe, indices: Union[Hashable, List[Hashable]])

Drop columns/elements in input DataPipe via its indices (functional name: drop).

Parameters:

• datapipe – IterDataPipe with columns to be dropped

• indices –

  a single column index to be dropped or a list of indices

  • Integer(s) is/are used for list/tuple.

  • Key(s) is/are used for dict.

Example

>>> from torchdata.datapipes.iter import IterableWrapper, ZipperMapDataPipe
>>> dp1 = IterableWrapper(range(5))
>>> dp2 = IterableWrapper(range(10, 15))
>>> dp = dp1.zip(dp2)
>>> list(dp)
[(0, 10), (1, 11), (2, 12), (3, 13), (4, 14)]
>>> drop_dp = dp.drop(1)
>>> list(drop_dp)
[(0), (1), (2), (3), (4)]