御网杯 御网杯新手教程 包含说明和注释

# Copyright (c) OpenMMLab. All rights reserved. import itertools from typing import Iterator, List, Optional, Sized, Union import numpy as np import torch from mmengine.dataset import BaseDataset from mmengine.dist import get_dist_info, sync_random_seed from torch.utils.data import Sampler from mmdet.registry import DATA_SAMPLERS @DATA_SAMPLERS.register_module() class MultiSourceSampler(Sampler): r"""Multi-Source Infinite Sampler. According to the sampling ratio, sample data from different datasets to form batches. Args: dataset (Sized): The dataset. batch_size (int): Size of mini-batch. source_ratio (list[int | float]): The sampling ratio of different source datasets in a 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. Examples: >>> dataset_type = 'ConcatDataset' >>> sub_dataset_type = 'CocoDataset' >>> data_root = 'data/coco/' >>> sup_ann = '../coco_semi_annos/[email protected]' >>> unsup_ann = '../coco_semi_annos/' \ >>> '[email protected]' >>> dataset = dict(type=dataset_type, >>> datasets=[ >>> dict( >>> type=sub_dataset_type, >>> data_root=data_root, >>> ann_file=sup_ann, >>> data_prefix=dict(img='train2017/'), >>> filter_cfg=dict(filter_empty_gt=True, min_size=32), >>> pipeline=sup_pipeline), >>> dict( >>> type=sub_dataset_type, >>> data_root=data_root, >>> ann_file=unsup_ann, >>> data_prefix=dict(img='train2017/'), >>> filter_cfg=dict(filter_empty_gt=True, min_size=32), >>> pipeline=unsup_pipeline), >>> ]) >>> train_dataloader = dict( >>> batch_size=5, >>> num_workers=5, >>> persistent_workers=True, >>> sampler=dict(type='MultiSourceSampler', >>> batch_size=5, source_ratio=[1, 4]), >>> batch_sampler=None, >>> dataset=dataset) """ def __init__(self, dataset: Sized, batch_size: int, source_ratio: List[Union[int, float]], 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 isinstance(source_ratio, list), \ f'source_ratio must be a list, but got source_ratio={source_ratio}' assert len(source_ratio) == len(dataset.cumulative_sizes), \ 'The length of source_ratio must be equal to ' \ f'the number of datasets, but got source_ratio={source_ratio}' 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_size = batch_size self.source_ratio = source_ratio self.num_per_source = [ int(batch_size * sr / sum(source_ratio)) for sr in source_ratio ] self.num_per_source[0] = batch_size - sum(self.num_per_source[1:]) assert sum(self.num_per_source) == batch_size, \ 'The sum of num_per_source must be equal to ' \ f'batch_size, but get {self.num_per_source}' 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 __iter__(self) -> Iterator[int]: batch_buffer = [] while True: for source, num in enumerate(self.num_per_source): batch_buffer_per_source = [] for idx in self.source2inds[source]: idx += self.cumulative_sizes[source] batch_buffer_per_source.append(idx) if len(batch_buffer_per_source) == num: batch_buffer += batch_buffer_per_source break yield from batch_buffer batch_buffer = [] def __len__(self) -> int: return len(self.dataset) def set_epoch(self, epoch: int) -> None: """Not supported in `epoch-based runner.""" pass @DATA_SAMPLERS.register_module() class GroupMultiSourceSampler(MultiSourceSampler): r"""Group Multi-Source Infinite Sampler. According to the sampling ratio, sample data from different datasets but the same group to form batches. Args: dataset (Sized): The dataset. batch_size (int): Size of mini-batch. source_ratio (list[int | float]): The sampling ratio of different source datasets in a 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, dataset: BaseDataset, batch_size: int, source_ratio: List[Union[int, float]], shuffle: bool = True, seed: Optional[int] = None) -> None: super().__init__( dataset=dataset, batch_size=batch_size, source_ratio=source_ratio, shuffle=shuffle, seed=seed) self._get_source_group_info() self.group_source2inds = [{ source: self._indices_of_rank(self.group2size_per_source[source][group]) for source in range(len(dataset.datasets)) } for group in range(len(self.group_ratio))] def _get_source_group_info(self) -> None: self.group2size_per_source = [{0: 0, 1: 0}, {0: 0, 1: 0}] self.group2inds_per_source = [{0: [], 1: []}, {0: [], 1: []}] for source, dataset in enumerate(self.dataset.datasets): for idx in range(len(dataset)): data_info = dataset.get_data_info(idx) width, height = data_info['width'], data_info['height'] group = 0 if width < height else 1 self.group2size_per_source[source][group] += 1 self.group2inds_per_source[source][group].append(idx) self.group_sizes = np.zeros(2, dtype=np.int64) for group2size in self.group2size_per_source: for group, size in group2size.items(): self.group_sizes[group] += size self.group_ratio = self.group_sizes / sum(self.group_sizes) def __iter__(self) -> Iterator[int]: batch_buffer = [] while True: group = np.random.choice( list(range(len(self.group_ratio))), p=self.group_ratio)