Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,687 Bytes
0324143 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import math
import torch
from torch.utils.data.sampler import Sampler
class EnlargedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
Modified from torch.utils.data.distributed.DistributedSampler
Support enlarging the dataset for iteration-based training, for saving
time when restart the dataloader after each epoch
Args:
dataset (torch.utils.data.Dataset): Dataset used for sampling.
num_replicas (int | None): Number of processes participating in
the training. It is usually the world_size.
rank (int | None): Rank of the current process within num_replicas.
ratio (int): Enlarging ratio. Default: 1.
"""
def __init__(self, dataset, num_replicas, rank, ratio=1):
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.num_samples = 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()
dataset_size = len(self.dataset)
indices = [v % dataset_size 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
|