（九）mmdetection源码解读：训练过程中训练数据的调用DataLoader

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 distributed
            num_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)