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Running
on
Zero
| 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 | |