| import math
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| import torch
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| from torch.utils.data import Sampler
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| import torch.distributed as dist
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|
|
|
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| class DistributedEvalSampler(Sampler):
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| r"""
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| DistributedEvalSampler is different from DistributedSampler.
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| It does NOT add extra samples to make it evenly divisible.
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| DistributedEvalSampler should NOT be used for training. The distributed processes could hang forever.
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| See this issue for details: https://github.com/pytorch/pytorch/issues/22584
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| shuffle is disabled by default
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|
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| DistributedEvalSampler is for evaluation purpose where synchronization does not happen every epoch.
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| Synchronization should be done outside the dataloader loop.
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|
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| Sampler that restricts data loading to a subset of the dataset.
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|
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| It is especially useful in conjunction with
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| :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
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| process can pass a :class`~torch.utils.data.DistributedSampler` instance as a
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| :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
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| 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 (int, optional): Number of processes participating in
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| distributed training. By default, :attr:`rank` is retrieved from the
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| current distributed group.
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| rank (int, optional): Rank of the current process within :attr:`num_replicas`.
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| By default, :attr:`rank` is retrieved from the current distributed
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| group.
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| shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
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| indices.
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| seed (int, optional): random seed used to shuffle the sampler if
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| :attr:`shuffle=True`. This number should be identical across all
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| processes in the distributed group. Default: ``0``.
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|
|
| .. warning::
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| In distributed mode, calling the :meth`set_epoch(epoch) <set_epoch>` method at
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| the beginning of each epoch **before** creating the :class:`DataLoader` iterator
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| is necessary to make shuffling work properly across multiple epochs. Otherwise,
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| the same ordering will be always used.
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|
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| Example::
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|
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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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| ... sampler.set_epoch(epoch)
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| ... train(loader)
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| """
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|
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| def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False, seed=0):
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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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|
|
|
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| self.total_size = len(self.dataset)
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| indices = list(range(self.total_size))
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| indices = indices[self.rank:self.total_size:self.num_replicas]
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| self.num_samples = len(indices)
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|
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| self.shuffle = shuffle
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| self.seed = seed
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|
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| def __iter__(self):
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| if self.shuffle:
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|
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| g = torch.Generator()
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| g.manual_seed(self.seed + self.epoch)
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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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|
|
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|
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|
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|
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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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|
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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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| r"""
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| Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas
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| use a different random ordering for each epoch. Otherwise, the next iteration of this
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| sampler will yield the same ordering.
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|
|
| Arguments:
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| epoch (int): _epoch number.
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| """
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| self.epoch = epoch
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|
|
