为更清楚这些参数的含义，可以参考一下DataLoader类的定义。
class DataLoader(object): r""" Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. Arguments: dataset(Dataset): 传入的数据集 batch_size(int, optional): 每个batch有多少个样本 shuffle(bool, optional): 在每个epoch开始的时候，对数据进行重新排序 sampler(Sampler, optional): 自定义从数据集中取样本的策略，如果指定这个参数，那么shuffle必须为False batch_sampler(Sampler, optional): 与sampler类似，但是一次只返回一个batch的indices（索引），需要注意的是，一旦指定了这个参数，那么batch_size,shuffle,sampler,drop_last就不能再制定了（互斥——Mutually exclusive） num_workers (int, optional): 这个参数决定了有几个进程来处理data loading。0意味着所有的数据都会被load进主进程。（默认为0） collate_fn (callable, optional): 将一个list的sample组成一个mini-batch的函数 pin_memory (bool, optional)： 如果设置为True，那么data loader将会在返回它们之前，将tensors拷贝到CUDA中的固定内存（CUDA pinned memory）中. drop_last (bool, optional): 如果设置为True：这个是对最后的未完成的batch来说的，比如你的batch_size设置为64，而一个epoch只有100个样本，那么训练的时候后面的36个就被扔掉了… 如果为False（默认），那么会继续正常执行，只是最后的batch_size会小一点。 timeout(numeric, optional): 如果是正数，表明等待从worker进程中收集一个batch等待的时间，若超出设定的时间还没有收集到，那就不收集这个内容了。这个numeric应总是大于等于0。默认为0 worker_init_fn (callable, optional): 每个worker初始化函数 If not None, this will be called on each worker subprocess with the worker id (an int in [0, num_workers - 1]) as input, after seeding and before data loading. (default: None) .. note:: By default, each worker will have its PyTorch seed set to ``base_seed + worker_id``, where ``base_seed`` is a long generated by main process using its RNG. However, seeds for other libraies may be duplicated upon initializing workers (w.g., NumPy), causing each worker to return identical random numbers. (See :ref:`dataloader-workers-random-seed` section in FAQ.) You may use ``torch.initial_seed()`` to access the PyTorch seed for each worker in :attr:`worker_init_fn`, and use it to set other seeds before data loading. .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an unpicklable object, e.g., a lambda function. """ __initialized = False def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None): self.dataset = dataset self.batch_size = batch_size self.num_workers = num_workers self.collate_fn = collate_fn self.pin_memory = pin_memory self.drop_last = drop_last self.timeout = timeout self.worker_init_fn = worker_init_fn if timeout < 0: raise ValueError('timeout option should be non-negative') if batch_sampler is not None: if batch_size > 1 or shuffle or sampler is not None or drop_last: raise ValueError('batch_sampler option is mutually exclusive ' 'with batch_size, shuffle, sampler, and ' 'drop_last') self.batch_size = None self.drop_last = None if sampler is not None and shuffle: raise ValueError('sampler option is mutually exclusive with ' 'shuffle') if self.num_workers < 0: raise ValueError('num_workers option cannot be negative; ' 'use num_workers=0 to disable multiprocessing.') if batch_sampler is None: if sampler is None: if shuffle: sampler = RandomSampler(dataset) //将list打乱 else: sampler = SequentialSampler(dataset) batch_sampler = BatchSampler(sampler, batch_size, drop_last) self.sampler = sampler self.batch_sampler = batch_sampler self.__initialized = True def __setattr__(self, attr, val): if self.__initialized and attr in ('batch_size', 'sampler', 'drop_last'): raise ValueError('{} attribute should not be set after {} is ' 'initialized'.format(attr, self.__class__.__name__)) super(DataLoader, self).__setattr__(attr, val) def __iter__(self): return _DataLoaderIter(self) def __len__(self): return len(self.batch_sampler) pspnet.py