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)

UnBatcher

class torchdata.datapipes.iter.UnBatcher(datapipe: IterDataPipe, unbatch_level: int = 1)

Undos batching of data (functional name: unbatch).

In other words, it flattens the data up to the specified level within a batched DataPipe.

Parameters:

• datapipe – Iterable DataPipe being un-batched

• unbatch_level – Defaults to 1 (only flattening the top level). If set to 2, it will flatten the top two levels, and -1 will flatten the entire DataPipe.

Example

>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper
>>> source_dp = IterableWrapper([[[0, 1], [2]], [[3, 4], [5]], [[6]]])
>>> dp1 = source_dp.unbatch()
>>> list(dp1)
[[0, 1], [2], [3, 4], [5], [6]]
>>> dp2 = source_dp.unbatch(unbatch_level=2)
>>> list(dp2)
[0, 1, 2, 3, 4, 5, 6]