Dataset是一个包裹训练数据的对象，它的主要功能是组织数据、标签，可能还有它们的扩充。
在Dataset之上，通常使用DataLoader：该对象从底层Dataset收集训练样本，并将它们放入表示用于训练的小批量的Tensor中。
您的程式码应该看起来像这样：

# one-time setup of training data

train_dataset = ...  # your code that construct the Dataset
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)

# training loop

for e in range(num_epochs):
  # each epoch represents one pass over all the training samples.
  for (x, y) in train_loader:  # iterate over the data one _batch_ at a time
    # move training tensors, extracted from the Dataset, to GPU
    x = x.to(device)  
    y = y.to(device)
    # rest of your training code here:
    pred = model(x) 
    # ...