pytorch 的dataloader使用一个epoch后需要重新“装载”么？

Question

pytorch 的dataloader使用一个epoch后需要重新“装载”么？
我这样的代码有问题么

    data_train = Weibo_Dataset("./data/train/", train=True)
    train_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)
    for epoch in range(epochs):
        train_loop(train_loader, model, loss_fn, optimizer)
    print("Done!")

需要像下面这样么？

    data_train = Weibo_Dataset("./data/train/", train=True)
    for epoch in range(epochs):
        train_loader = DataLoader(data_train, batch_size=batch_size, shuffle=True)
        train_loop(train_loader, model, loss_fn, optimizer)
    print("Done!")

下面是train_loop函数的定义

    def train_loop(dataloader, model, loss_fn, optimizer):
        model.train()
        size = len(dataloader.dataset)
        for batch, (X, y) in enumerate(dataloader):
            X = get_feature_vector(X, time_step)
            X = X.to(device)
            y = y.to(device)
            # Compute prediction and loss
            pred = model(X)
            loss = loss_fn(pred, y)
            # Backpropagation
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if batch % 1 == 0:
                loss, current = loss.item(), batch * len(X)
                print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
            pass

Answer 1

你的数据很多吗？ 一般情况下是不需要的，DataLoader本身是个可迭代的东西，如果设计得当的话大部分情况是不需要多次加载的。