57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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import math
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import torch
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from torch.utils.data.sampler import Sampler
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class EnlargedSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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Modified from torch.utils.data.distributed.DistributedSampler
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Support enlarging the dataset for iteration-based training, for saving
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time when restart the dataloader after each epoch
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Args:
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dataset (torch.utils.data.Dataset): Dataset used for sampling.
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num_replicas (int | None): Number of processes participating in
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the training. It is usually the world_size.
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rank (int | None): Rank of the current process within num_replicas.
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ratio (int): Enlarging ratio. Default: 1.
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"""
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def __init__(self, dataset, num_replicas, rank, ratio=1):
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self.dataset = dataset
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self.num_replicas = num_replicas
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self.rank = rank
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self.epoch = 0
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self.num_samples = math.ceil(
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len(self.dataset) * ratio / self.num_replicas)
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self.total_size = self.num_samples * self.num_replicas
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def __iter__(self):
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices = torch.randperm(self.total_size, generator=g).tolist()
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dataset_size = len(self.dataset)
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indices = [v % dataset_size for v in indices]
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# subsample
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indices = indices[self.rank:self.total_size:self.num_replicas]
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assert len(indices) == self.num_samples
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return iter(indices)
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch):
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self.epoch = epoch
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