import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
[文档]class DistributedSampler(_DistributedSampler):
"""DistributedSampler inheriting from
`torch.utils.data.DistributedSampler`.
In pytorch of lower versions, there is no `shuffle` argument. This child
class will port one to DistributedSampler.
"""
def __init__(self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
seed=0):
super().__init__(
dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
# for the compatibility from PyTorch 1.3+
self.seed = seed if seed is not None else 0
def __iter__(self):
"""Deterministically shuffle based on epoch."""
if self.shuffle:
g = torch.Generator()
g.manual_seed(self.epoch + self.seed)
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = torch.arange(len(self.dataset)).tolist()
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)