torch.utils.data.DataLoader()的使用

数据加载器，结合了数据集和取样器，并且可以提供多个线程处理数据集。在训练模型时使用到此函数，用来把训练数据分成多个小组，此函数每次抛出一组数据。直至把所有的数据都抛出。就是做一个数据的初始化。

官网上对于torch.utils.data.DataLoader的讲解：

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): 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``).  每个 epoch 重新随机数据
        sampler (Sampler, optional): defines the strategy to draw samples from
            the dataset. If specified, ``shuffle`` must be False.  定义抽样方法
        batch_sampler (Sampler, optional): like sampler, but returns a batch of
            indices at a time. Mutually exclusive with :attr:`batch_size`,
            :attr:`shuffle`, :attr:`sampler`, and :attr:`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``)  多少个线程 用于 加载数据
        collate_fn (callable, optional): merges a list of samples to form a mini-batch. 把 list sample 合并成 mini-batch
        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
            see the example below.
        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``)  当 batch 很大是，最后一轮可能样本数量偏少，影响模型训练
        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 (an int in ``[0, num_workers - 1]``) as
            input, after seeding and before data loading. (default: ``None``)
    """

下面看一个简单的使用实例：

"""
    批训练，把数据变成一小批一小批数据进行训练。
    DataLoader就是用来包装所使用的数据，每次抛出一批数据
"""
import torch
import torch.utils.data as Data

BATCH_SIZE = 5

x = torch.linspace(1, 10, 10) # linspace: 返回一个1维张量,包含在区间start和end上均匀间隔的step个点
y = torch.linspace(10, 1, 10)
# 把数据放在数据集中
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
    # 从数据集中每次抽出batch size个样本
    dataset=torch_dataset,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2,
)

def show_batch():
    for epoch in range(3): # epoch: 迭代次数
        print('Epoch:', epoch)
        for batch_id, (batch_x, batch_y) in enumerate(loader):
            print("  batch_id:{}, batch_x:{}, batch_y:{}".format(batch_id, batch_x, batch_y))
            # print(f'  batch_id:{batch_id}, batch_x:{batch_x}, batch_y:{batch_y}')

if __name__ == '__main__':
    show_batch()

输出结果：

Epoch: 0
  batch_id:0, batch_x:tensor([ 7.,  4.,  3.,  9., 10.]), batch_y:tensor([4., 7., 8., 2., 1.])
  batch_id:1, batch_x:tensor([6., 2., 1., 5., 8.]), batch_y:tensor([ 5.,  9., 10.,  6.,  3.])
Epoch: 1
  batch_id:0, batch_x:tensor([ 2.,  7., 10.,  8.,  3.]), batch_y:tensor([9., 4., 1., 3., 8.])
  batch_id:1, batch_x:tensor([6., 9., 1., 4., 5.]), batch_y:tensor([ 5.,  2., 10.,  7.,  6.])
Epoch: 2
  batch_id:0, batch_x:tensor([10.,  3.,  9.,  6.,  8.]), batch_y:tensor([1., 8., 2., 5., 3.])
  batch_id:1, batch_x:tensor([1., 4., 2., 7., 5.]), batch_y:tensor([10.,  7.,  9.,  4.,  6.])