| |
| import itertools |
| from typing import Iterator, List, Optional, Sized, Union |
| 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 |
|
|
|
|
| class MultiSourceSampler(Sampler): |
| def __init__(self, |
| repeats, |
| dataset: Sized, |
| batch_sizes: list[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_sizes, list), \ |
| f'source_ratio must be a list, but got batch_sizes={batch_sizes}' |
| assert len(batch_sizes) == len(dataset.cumulative_sizes), \ |
| 'The length of batch_sizes must be equal to ' \ |
| f'the number of datasets, but got batch_sizes={batch_sizes}' |
|
|
| rank, world_size = get_dist_info() |
| self.rank = rank |
| self.world_size = world_size |
|
|
| self.dataset = dataset |
| self.cumulative_sizes = [0] + dataset.cumulative_sizes |
| self.batch_sizes = batch_sizes |
|
|
|
|
| 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) |
| } |
|
|
| self.repeats = repeats |
| assert len(self.repeats) == len(self.batch_sizes) |
|
|
| 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, (batch_size, repeat) in enumerate(zip(self.batch_sizes, self.repeats)): |
| for _ in range(repeat): |
| batch_buffer_per_source = [] |
| while len(batch_buffer_per_source) < batch_size: |
| idx = next(self.source2inds[source]) |
| idx += self.cumulative_sizes[source] |
| batch_buffer_per_source.append(idx) |
|
|
| yield from batch_buffer_per_source |
|
|
| @property |
| def batch_size(self): |
| batch_size_sum = sum([batch_size * repeat for batch_size, repeat in zip(self.batch_sizes, self.repeats)]) |
| batch_size_ave = batch_size_sum // sum(self.repeats) |
|
|
| return batch_size_ave |
|
|
|
|
| class MultiSourceBatchSampler(Sampler[list[int]]): |
| def __init__( |
| self, |
| sampler: Union[FixedBatchMultiSourceSampler, MultiSourceSampler], |
| batch_sizes: list[int], |
| repeats: list[int], |
| **kwargs |
| ) -> None: |
| self.sampler = sampler |
| self.batch_sizes = batch_sizes |
| self.repeats = repeats |
|
|
| def __iter__(self) -> Iterator[list[int]]: |
| |
| sampler_iter = iter(self.sampler) |
|
|
| while True: |
| for source, (batch_size, repeat) in enumerate(zip(self.batch_sizes, self.repeats)): |
| for _ in range(repeat): |
| batch = [*itertools.islice(sampler_iter, batch_size)] |
| yield batch |
|
|
| @property |
| def batch_size(self): |
| batch_size_sum = sum([batch_size * repeat for batch_size, repeat in zip(self.batch_sizes, self.repeats)]) |
| batch_size_ave = batch_size_sum // sum(self.repeats) |
|
|
| return batch_size_ave |
|
|
| def __len__(self) -> int: |
| return len(self.sampler) // self.batch_size |
|
|
