一、DataLoader创建过程中

在训练过程train_detector函数中调用build_dataloader函数

train_detector(model, datasets, cfg, distributed=False, validate=True)
#train_detector函数中
data_loaders = [build_dataloader(ds,cfg.data.samples_per_gpu,cfg.data.workers_per_gpu,# `num_gpus` will be ignored if distributednum_gpus=len(cfg.gpu_ids),dist=distributed,seed=cfg.seed,runner_type=runner_type,persistent_workers=cfg.data.get('persistent_workers', False))for ds in dataset]

build_dataloader函数中最关键的步骤就是DataLoader类的实例化
DataLoader：包括 dataset and sampler, an iterable

def build_dataloader(dataset,samples_per_gpu,workers_per_gpu,num_gpus=1,dist=True,shuffle=True,seed=None,runner_type='EpochBasedRunner',persistent_workers=False,**kwargs):data_loader = DataLoader(dataset,batch_size=batch_size,sampler=sampler,num_workers=num_workers,batch_sampler=batch_sampler,collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),pin_memory=False,worker_init_fn=init_fn,**kwargs)return data_loader

可以看到初始化参数里有两种sampler：sampler和batch_sampler，都默认为None。前者的作用是生成一系列的index，而batch_sampler则是将sampler生成的indices打包分组，得到一个又一个batch的index。

class DataLoader(Generic[T_co]):def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1,shuffle: bool = False, sampler: Optional[Sampler[int]] = None,batch_sampler: Optional[Sampler[Sequence[int]]] = None,num_workers: int = 0, collate_fn: _collate_fn_t = None,pin_memory: bool = False, drop_last: bool = False,timeout: float = 0, worker_init_fn: _worker_init_fn_t = None,multiprocessing_context=None, generator=None,*, prefetch_factor: int = 2,persistent_workers: bool = False):torch._C._log_api_usage_once("python.data_loader")  # type: ignore

二、利用实例化data_loaders进行训练

runner.run(data_loaders, cfg.workflow)