fedlab_core.utils.sampler

Module Contents

Classes

DistributedSampler	Sampler that restricts data loading to a subset of the dataset.
NonIIDDistributedSampler	This is a copy of torch.utils.data.distributed.DistributedSampler (28 March 2019)

class fedlab_core.utils.sampler.DistributedSampler(dataset, rank, num_replicas, shuffle=True)

Bases: torch.utils.data.distributed.Sampler

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.

Note

Dataset is assumed to be of constant size.

Parameters

• dataset – Dataset used for sampling.

• num_replicas (optional) – Number of processes participating in distributed training.

• rank (optional) – Rank of the current process within num_replicas.

• shuffle (optional) – If true (default), sampler will shuffle the indices

__iter__(self)

__len__(self)

set_epoch(self, epoch)

class fedlab_core.utils.sampler.NonIIDDistributedSampler(dataset, add_extra_samples=True)

Bases: torch.utils.data.distributed.Sampler

This is a copy of torch.utils.data.distributed.DistributedSampler (28 March 2019) with the option to turn off adding extra samples to divide the work evenly.

__iter__(self)

__len__(self)

set_epoch(self, epoch)