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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
from maskrcnn_benchmark.utils.comm import shared_random_seed
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`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.
"""
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, use_random=False):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.use_random = use_random
def __iter__(self):
if self.shuffle:
# deterministically shuffle based on epoch
_seed = self.epoch
if self.use_random:
_seed = int(shared_random_seed())
g = torch.Generator()
g.manual_seed(_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
offset = self.num_samples * self.rank
indices = indices[offset : offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
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