参考：
/docs/stable/?highlight=torch%20utils%20data%20dataloader#
/u014380165/article/details/79058479
官方函数定义：
class torch.utils.data.DataLoader(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):
作用：
概括：根据自定义的格式封装成Tensor。
PyTorch中数据读取的一个重要接口是，该接口定义在脚本中，只要是用PyTorch来训练模型基本都会用到该接口，该接口主要用来将自定义的数据读取接口的输出或者PyTorch已有的数据读取接口的输入按照batch size封装成Tensor，后续只需要再包装成Variable即可作为模型的输入，因此该接口有点承上启下的作用。
函数说明：Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset.
The DataLoader supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching (collation) and memory pinning.
Data loader。组合数据集和采样器，并提供给定数据集的可迭代对象。
DataLoader支持映射样式和可迭代样式的数据集，支持单进程或多进程加载、自定义加载顺序、可选的自动批处理(排序)和内存固定。
Parameters 参数：
dataset (Dataset) – dataset from which to load the data.
要从中加载数据的数据集。
batch_size (int, optional) – how many samples per batch to load (default: 1).
每批次要装载多少样品
shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).
设置为True以使数据在每个时期都重新洗牌
sampler (Sampler or Iterable, optional) – defines the strategy to draw samples from the dataset. Can be any Iterable with __len__ implemented. If specified, shuffle must not be specified.
定义从数据集中抽取样本的策略
batch_sampler (Sampler or Iterable, optional) – like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last.
类似于采样器，但一次返回一批索引。 与batch_size，shuffle，sampler和drop_last互斥。
num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0)
多少个子流程用于数据加载。 0表示将在主进程中加载数据。 （默认值：0）
collate_fn (callable, optional) – merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset.
合并样本列表以形成张量的小批量。
pin_memory (bool, optional) – If True, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your collate_fn returns a batch that is a custom type.
如果为True，则数据加载器在将张量返回之前将其复制到CUDA固定的内存中。 如果您的数据元素是自定义类型，或者您的collate_fn返回的是一个自定义类型的批处理
drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)
如果数据集大小不能被批量大小整除，则设置为True以删除最后一个不完整的批量。 如果为False并且数据集的大小不能被批次大小整除，则最后一批将较小。
timeout (numeric, optional) – if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0)
如果为正，则为从工作人员收集批次的超时值。 应始终为非负数。 （默认值：0）
worker_init_fn (callable, optional) – 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)
prefetch_factor (int, optional, keyword-only arg) – Number of samples loaded in advance by each worker. 2 means there will be a total of 2 * num_workers samples prefetched across all workers. (default: 2)
每个子流程预先加载的样本数。 2表示将在所有子流程中预取总共2 * num_workers个样本。 （默认值：2）
persistent_workers (bool, optional) – If True, the data loader will not shutdown the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. (default: False)
如果为True，则一次使用数据集后，数据加载器将不会关闭工作进程。 这样可以使Worker Dataset实例保持活动状态。 （默认值：False）
使用示例：
# 加载数据集，batch_size设为1，每轮epoch不打乱，只在主进程加载，不预先复制到CUDA固定的内存中
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=0,
pin_memory=False
)
源码：
class DataLoader(object):
"""
Data loader. Combines a dataset and a sampler, and provides
single- or multi-process iterators over the dataset.
Arguments:
dataset (Dataset): dataset from which to load the data.
batch_size (int, optional): how many samples per batch to load
(default: 1).
shuffle (bool, optional): set to ``True`` to have the data reshuffled
at every epoch (default: False).
sampler (Sampler, optional): defines the strategy to draw samples from
the dataset. If specified, ``shuffle`` must be False.
batch_sampler (Sampler, optional): like sampler, but returns a batch of
indices at a time. Mutually exclusive with batch_size, shuffle,
sampler, and drop_last.
num_workers (int, optional): how many subprocesses to use for data
loading. 0 means that the data will be loaded in the main process.
(default: 0)
collate_fn (callable, optional): merges a list of samples to form a mini-batch.
pin_memory (bool, optional): If ``True``, the data loader will copy tensors
into CUDA pinned memory before returning them.
drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If ``False`` and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: False)
timeout (numeric, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative. (default: 0)
worker_init_fn (callable, optional): If not None, this will be called on each
worker subprocess with the worker id as input, after seeding and before data
loading. (default: None)
"""
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 is mutually exclusive with '
'batch_size, shuffle, sampler, and drop_last')
if sampler is not None and shuffle:
raise ValueError('sampler is mutually exclusive with shuffle')
if self.num_workers < 0:
raise ValueError('num_workers cannot be negative; '
'use num_workers=0 to disable multiprocessing.')
if batch_sampler is None:
if sampler is None:
if shuffle:
sampler = RandomSampler(dataset)
else:
sampler = SequentialSampler(dataset)
batch_sampler = BatchSampler(sampler, batch_size, drop_last)
self.sampler = sampler
self.batch_sampler = batch_sampler
def __iter__(self):
return DataLoaderIter(self)
def __len__(self):
return len(self.batch_sampler)