Shuffler

class torchdata.datapipes.iter.Shuffler(datapipe: IterDataPipe[T_co], *, buffer_size: int = 10000, unbatch_level: int = 0)

Shuffle the input DataPipe with a buffer (functional name: shuffle).

The buffer with buffer_size is filled with elements from the datapipe first. Then, each item will be yielded from the buffer by reservoir sampling via iterator.

buffer_size is required to be larger than 0. For buffer_size == 1, the datapipe is not shuffled. In order to fully shuffle all elements from datapipe, buffer_size is required to be greater than or equal to the size of datapipe.

When it is used with torch.utils.data.DataLoader, the methods to set up random seed are different based on num_workers.

For single-process mode (num_workers == 0), the random seed is set before the DataLoader in the main process. For multi-process mode (num_worker > 0), worker_init_fn is used to set up a random seed for each worker process.

Parameters:

• datapipe – The IterDataPipe being shuffled

• buffer_size – The buffer size for shuffling (default to 10000)

• unbatch_level – Specifies if it is necessary to unbatch source data before applying the shuffle

Example

>>> # xdoctest: +SKIP
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp = IterableWrapper(range(10))
>>> shuffle_dp = dp.shuffle()
>>> list(shuffle_dp)
[0, 4, 1, 6, 3, 2, 9, 5, 7, 8]