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)

Batcher

class torchdata.datapipes.iter.Batcher(datapipe: IterDataPipe, batch_size: int, drop_last: bool = False, wrapper_class: Type[DataChunk] = List)

Creates mini-batches of data (functional name: batch).

An outer dimension will be added as batch_size if drop_last is set to True, or length % batch_size for the last batch if drop_last is set to False.

Parameters:

• datapipe – Iterable DataPipe being batched

• batch_size – The size of each batch

• drop_last – Option to drop the last batch if it’s not full

• wrapper_class – wrapper to apply onto each batch (type List) before yielding, defaults to DataChunk

Example

>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp = IterableWrapper(range(10))
>>> dp = dp.batch(batch_size=3, drop_last=True)
>>> list(dp)
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]