You would have to transform the label tensor to a FloatTensor via labels.float().
Also, you should get a warning about a shape mismatch in the criterion between the model output and target, so unsqueeze the feature dimension in the labels via:
loss = criterion(outputs, labels.float().unsqueeze(1))