From my dataset I have three numpy variables/channels which can be characterized as RGB respectively. Furthermore I converted them into tensors using torch.from_numpy(u) and concatenated them into an image tensor as:
HR_data = torch.cat((u_tensor,v_tensor,w_tensor), dim=1)
Then I normalized like this: HR_data_norm = (HR_data - HR_data.mean()) / HR_data.std()
and used TensorDataset to put it into a dataloader.
My question is, should I normalize the numpy arrays before I convert them into tensors or is my method ok?