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becf13a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
from typing import Iterator, Optional, Sized
import torch
from mmengine.dist import get_dist_info, sync_random_seed
from torch.utils.data import Sampler
class FixedBatchMultiSourceSampler(Sampler):
r"""Multi-Source Infinite Sampler.
According to the sampling ratio, sample data from different
datasets to form batches.
Args:
repeat (tuple): repeat factor
dataset (Sized): The dataset.
batch_size (int): Size of mini-batch.
shuffle (bool): Whether shuffle the dataset or not. Defaults to True.
seed (int, optional): Random seed. If None, set a random seed.
Defaults to None.
"""
def __init__(self,
repeat,
dataset: Sized,
batch_size: int,
shuffle: bool = True,
seed: Optional[int] = None) -> None:
assert hasattr(dataset, 'cumulative_sizes'),\
f'The dataset must be ConcatDataset, but get {dataset}'
assert isinstance(batch_size, int) and batch_size > 0, \
'batch_size must be a positive integer value, ' \
f'but got batch_size={batch_size}'
assert len(repeat) == len(dataset.cumulative_sizes), \
'The length of repeat must be equal to ' \
f'the number of datasets, but got repeat={repeat}'
rank, world_size = get_dist_info()
self.rank = rank
self.world_size = world_size
self.dataset = dataset
self.repeat = repeat
self.cumulative_sizes = [0] + dataset.cumulative_sizes
self.batch_size = batch_size
self.seed = sync_random_seed() if seed is None else seed
self.shuffle = shuffle
self.source2inds = {
source: self._indices_of_rank(len(ds))
for source, ds in enumerate(dataset.datasets)
}
def _infinite_indices(self, sample_size: int) -> Iterator[int]:
"""Infinitely yield a sequence of indices."""
g = torch.Generator()
g.manual_seed(self.seed)
while True:
if self.shuffle:
yield from torch.randperm(sample_size, generator=g).tolist()
else:
yield from torch.arange(sample_size).tolist()
def _indices_of_rank(self, sample_size: int) -> Iterator[int]:
"""Slice the infinite indices by rank."""
yield from itertools.islice(
self._infinite_indices(sample_size), self.rank, None,
self.world_size)
def __len__(self) -> int:
return len(self.dataset)
def set_epoch(self, epoch: int) -> None:
"""Not supported in `epoch-based runner."""
pass
def __iter__(self) -> Iterator[int]:
while True:
for source, repeat in enumerate(self.repeat):
for _ in range(repeat):
batch_buffer_per_source = []
while len(batch_buffer_per_source) < self.batch_size:
idx = next(self.source2inds[source])
idx += self.cumulative_sizes[source]
batch_buffer_per_source.append(idx)
yield from batch_buffer_per_source
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