mirror of https://github.com/YifanXu74/MQ-Det.git
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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# Code is copy-pasted exactly as in torch.utils.data.distributed.
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# FIXME remove this once c10d fixes the bug it has
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import math
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import torch
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import torch.distributed as dist
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from torch.utils.data.sampler import Sampler
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from maskrcnn_benchmark.utils.comm import shared_random_seed
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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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"""
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def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, use_random=False):
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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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self.use_random = use_random
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def __iter__(self):
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if self.shuffle:
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# deterministically shuffle based on epoch
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_seed = self.epoch
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if self.use_random:
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_seed = int(shared_random_seed())
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g = torch.Generator()
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g.manual_seed(_seed)
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indices = torch.randperm(len(self.dataset), generator=g).tolist()
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else:
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indices = torch.arange(len(self.dataset)).tolist()
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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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offset = self.num_samples * self.rank
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indices = indices[offset : offset + self.num_samples]
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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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