torch.utils.data.DataLoader()详解【Pytorch入门手册】

函数原型

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None, *, prefetch_factor=2,
           persistent_workers=False)

功能

根据自定义的格式将数据封装成Tensor。

参数说明

• 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）

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