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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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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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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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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 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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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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... sampler.set_epoch(epoch)
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... train(loader)
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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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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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self.shuffle = shuffle
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self.seed = seed
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def __iter__(self):
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if self.shuffle:
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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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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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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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