How to simplify DataLoader for Autoencoder in Pytorch

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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.

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)

and train like this:

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)
autoencoder pytorch
2 Answers

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]

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)
user_35819030
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