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128 lines
4.9 KiB
128 lines
4.9 KiB
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 OrderedDistributedSampler(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):
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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.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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def __iter__(self):
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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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class RepeatAugSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset for distributed,
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with repeated augmentation.
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It ensures that different each augmented version of a sample will be visible to a
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different process (GPU). Heavily based on torch.utils.data.DistributedSampler
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This sampler was taken from https://github.com/facebookresearch/deit/blob/0c4b8f60/samplers.py
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Used in
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Copyright (c) 2015-present, Facebook, Inc.
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"""
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def __init__(
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self,
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dataset,
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num_replicas=None,
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rank=None,
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shuffle=True,
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num_repeats=3,
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selected_round=256,
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selected_ratio=0,
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):
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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.shuffle = shuffle
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self.num_repeats = num_repeats
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self.epoch = 0
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self.num_samples = int(math.ceil(len(self.dataset) * num_repeats / self.num_replicas))
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self.total_size = self.num_samples * self.num_replicas
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# Determine the number of samples to select per epoch for each rank.
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# num_selected logic defaults to be the same as original RASampler impl, but this one can be tweaked
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# via selected_ratio and selected_round args.
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selected_ratio = selected_ratio or num_replicas # ratio to reduce selected samples by, num_replicas if 0
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if selected_round:
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self.num_selected_samples = int(math.floor(
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len(self.dataset) // selected_round * selected_round / selected_ratio))
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else:
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self.num_selected_samples = int(math.ceil(len(self.dataset) / selected_ratio))
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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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# produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....]
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indices = [x for x in indices for _ in range(self.num_repeats)]
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# add extra samples to make it evenly divisible
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padding_size = self.total_size - len(indices)
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indices += indices[:padding_size]
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assert len(indices) == self.total_size
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# subsample per rank
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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 up to num selected samples
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return iter(indices[:self.num_selected_samples])
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def __len__(self):
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return self.num_selected_samples
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def set_epoch(self, epoch):
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self.epoch = epoch |