neodroidvision.utilities.torch_utilities.distributing.distributed.DistributedSampler

class neodroidvision.utilities.torch_utilities.distributing.distributed.DistributedSampler(dataset: Sized, num_replicas: Optional[int] = None, rank=None, shuffle: bool = True)[source]

Bases: 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. Arguments: 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.

__init__(dataset: Sized, num_replicas: Optional[int] = None, rank=None, shuffle: bool = True)[source]
Parameters

  • dataset –

  • num_replicas –

  • rank –

  • shuffle –

Methods

__init__(dataset[, num_replicas, rank, shuffle])

param dataset

set_epoch(epoch)

param epoch
set_epoch(epoch)[source]
Parameters

epoch –