107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
import math
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import torch
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from mmengine.runner import get_dist_info
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from torch.utils.data import Sampler
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from mmcls.core.utils import sync_random_seed
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from mmcls.datasets import SAMPLERS
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@SAMPLERS.register_module()
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class RepeatAugSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset for
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distributed, with repeated augmentation. It ensures that different each
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augmented version of a sample will be visible to a different process (GPU).
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Heavily based on torch.utils.data.DistributedSampler.
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This sampler was taken from
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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__(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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seed=0):
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default_rank, default_world_size = get_dist_info()
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rank = default_rank if rank is None else rank
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num_replicas = (
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default_world_size if num_replicas is None else num_replicas)
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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(
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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
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# 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
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# selected samples by, num_replicas if 0
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if selected_round:
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self.num_selected_samples = int(
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math.floor(
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len(self.dataset) // selected_round * selected_round /
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selected_ratio))
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else:
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self.num_selected_samples = int(
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math.ceil(len(self.dataset) / selected_ratio))
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# In distributed sampling, different ranks should sample
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# non-overlapped data in the dataset. Therefore, this function
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# is used to make sure that each rank shuffles the data indices
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# in the same order based on the same seed. Then different ranks
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# could use different indices to select non-overlapped data from the
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# same data list.
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self.seed = sync_random_seed(seed)
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def __iter__(self):
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# deterministically shuffle based on epoch
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if self.shuffle:
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if self.num_replicas > 1: # In distributed environment
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# deterministically shuffle based on epoch
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g = torch.Generator()
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# When :attr:`shuffle=True`, this ensures all replicas
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# use a different random ordering for each epoch.
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# Otherwise, the next iteration of this sampler will
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# yield the same ordering.
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g.manual_seed(self.epoch + self.seed)
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indices = torch.randperm(
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len(self.dataset), generator=g).tolist()
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else:
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indices = torch.randperm(len(self.dataset)).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
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