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