47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import numpy as np
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import torch
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import torch.distributed as dist
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from mmcv.runner import get_dist_info
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def check_dist_init():
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return dist.is_available() and dist.is_initialized()
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def sync_random_seed(seed=None, device='cuda'):
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"""Make sure different ranks share the same seed. All workers must call
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this function, otherwise it will deadlock. This method is generally used in
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`DistributedSampler`, because the seed should be identical across all
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processes in the distributed group.
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In distributed sampling, different ranks should sample non-overlapped
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data in the dataset. Therefore, this function is used to make sure that
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each rank shuffles the data indices in the same order based
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on the same seed. Then different ranks could use different indices
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to select non-overlapped data from the same data list.
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Args:
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seed (int, Optional): The seed. Default to None.
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device (str): The device where the seed will be put on.
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Default to 'cuda'.
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Returns:
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int: Seed to be used.
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"""
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if seed is None:
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seed = np.random.randint(2**31)
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assert isinstance(seed, int)
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rank, world_size = get_dist_info()
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if world_size == 1:
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return seed
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if rank == 0:
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random_num = torch.tensor(seed, dtype=torch.int32, device=device)
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else:
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random_num = torch.tensor(0, dtype=torch.int32, device=device)
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dist.broadcast(random_num, src=0)
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return random_num.item()
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