You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
41 lines
1.4 KiB
41 lines
1.4 KiB
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import torch
|
|
from torch.utils.data import DistributedSampler as _DistributedSampler
|
|
|
|
|
|
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)
|
|
|