forked from mrq/DL-Art-School
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
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"""
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Modified from torch.utils.data.distributed.DistributedSampler
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Support enlarging the dataset for *iteration-oriented* training, for saving time when restart the
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dataloader after each epoch
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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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import torch.distributed as dist
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class DistIterSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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It is especially useful in conjunction with
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:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
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process can pass a DistributedSampler instance as a DataLoader sampler,
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and load a subset of the original dataset that is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size.
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Arguments:
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dataset: Dataset used for sampling.
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num_replicas (optional): Number of processes participating in
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distributed training.
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rank (optional): Rank of the current process within num_replicas.
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"""
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def __init__(self, dataset, num_replicas=None, rank=None, ratio=100):
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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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 = int(math.ceil(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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dsize = len(self.dataset)
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indices = [v % dsize 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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