| | 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): |
| | |
| | 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] |
| |
|
| | |
| | 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 |
| |
|
