如何在 Pytorch 中简化 Autoencoder 的 DataLoader [英] How to simplify DataLoader for Autoencoder in Pytorch

问题描述

有没有更简单的方法来设置数据加载器，因为输入和目标数据在自动编码器的情况下是相同的，并且在训练期间加载数据?DataLoader 总是需要两个输入.

Is there any easier way to set up the dataloader, because input and target data is the same in case of an autoencoder and to load the data during training? The DataLoader always requires two inputs.

目前我这样定义我的数据加载器:

Currently I define my dataloader like this:

X_train     = rnd.random((300,100))
X_val       = rnd.random((75,100))
train       = data_utils.TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(X_train).float())
val         = data_utils.TensorDataset(torch.from_numpy(X_val).float(), torch.from_numpy(X_val).float())
train_loader= data_utils.DataLoader(train, batch_size=1)
val_loader  = data_utils.DataLoader(val, batch_size=1)

然后像这样训练:

for epoch in range(50):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target).detach()
        optimizer.zero_grad()
        output = model(data, x)
        loss = criterion(output, target)

推荐答案

为什么不继承 TensorDataset 使其与未标记数据兼容?

Why not subclassing TensorDataset to make it compatible with unlabeled data ?

class UnlabeledTensorDataset(TensorDataset):
    """Dataset wrapping unlabeled data tensors.

    Each sample will be retrieved by indexing tensors along the first
    dimension.

    Arguments:
        data_tensor (Tensor): contains sample data.
    """
    def __init__(self, data_tensor):
        self.data_tensor = data_tensor

    def __getitem__(self, index):
        return self.data_tensor[index]

还有一些类似的东西来训练你的自动编码器

And something along these lines for training your autoencoder

X_train     = rnd.random((300,100))
train       = UnlabeledTensorDataset(torch.from_numpy(X_train).float())
train_loader= data_utils.DataLoader(train, batch_size=1)

for epoch in range(50):
    for batch in train_loader:
        data = Variable(batch)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)

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