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import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from .sampler import SAMPLER
@SAMPLER.register_module()
class DistributedSampler(_DistributedSampler):
def __init__(
self, dataset=None, 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:
assert False
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
per_replicas = self.total_size // self.num_replicas
# indices = indices[self.rank:self.total_size:self.num_replicas]
indices = indices[self.rank * per_replicas : (self.rank + 1) * per_replicas]
assert len(indices) == self.num_samples
# print('\nDistributedSampler')
# print(indices)
# print(len(indices))
# print(self.epoch)
return iter(indices)
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