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""" | |
Modified from torch.utils.data.distributed.DistributedSampler | |
Support enlarging the dataset for *iteration-oriented* training, | |
for saving time when restart the dataloader after each epoch | |
""" | |
import math | |
import torch | |
import torch.distributed as dist | |
from torch.utils.data.sampler import Sampler | |
class DistIterSampler(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, ratio=100): | |
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) * ratio / self.num_replicas)) | |
self.total_size = self.num_samples * self.num_replicas | |
def __iter__(self): | |
# deterministically shuffle based on epoch | |
g = torch.Generator() | |
g.manual_seed(self.epoch) | |
indices = torch.randperm(self.total_size, generator=g).tolist() | |
dsize = len(self.dataset) | |
indices = [v % dsize for v in indices] | |
# subsample | |
indices = indices[self.rank : self.total_size : self.num_replicas] | |
assert len(indices) == self.num_samples | |
return iter(indices) | |
def __len__(self): | |
return self.num_samples | |
def set_epoch(self, epoch): | |
self.epoch = epoch | |