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| # 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 | |