Source code for mmdet.datasets.samplers.distributed_sampler

import math

import torch
from import DistributedSampler as _DistributedSampler

[docs]class DistributedSampler(_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 # in case that indices is shorter than half of total_size indices = (indices * math.ceil(self.total_size / len(indices)))[:self.total_size] 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)