forked from nudt_dsp/netrans
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
import math
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import torch
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from . import Sampler
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import torch.distributed as dist
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class DistributedSampler(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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shuffle (optional): If true (default), sampler will shuffle the indices
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.. warning::
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In distributed mode, calling the ``set_epoch`` method is needed to
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make shuffling work; each process will use the same random seed
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otherwise.
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Example::
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>>> sampler = DistributedSampler(dataset) if is_distributed else None
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>>> loader = DataLoader(dataset, shuffle=(sampler is None),
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... sampler=sampler)
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>>> for epoch in range(start_epoch, n_epochs):
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... if is_distributed:
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"""
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def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
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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) * 1.0 / self.num_replicas))
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self.total_size = self.num_samples * self.num_replicas
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self.shuffle = shuffle
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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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if self.shuffle:
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indices = torch.randperm(len(self.dataset), generator=g).tolist()
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else:
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indices = list(range(len(self.dataset)))
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# add extra samples to make it evenly divisible
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indices += indices[:(self.total_size - len(indices))]
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assert len(indices) == self.total_size
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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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