Should I transpose a Tensor when feeding it into a CNN
I am using a custom dataset with images of different sizes in the Lab format (Lightness, a, b) which are feed into a CNN. The input layer has 3 in-channels and so my idea was to split all 3 channels (L, a, b) and feed those into the network. Next I was wondering if each tensor needs to be transposed? My doubt is that it would lose its dimensions which are variable from image to image and I would not be able to reconstruct the image in the end. Any thoughts or ideas how I should normalize the image?

You can normalise without the need of transposing the image or splitting it based on its channels 

torchvision.transforms.Normalize(mean=[l_channel_mean, a_channel_mean , b_channel_mean], std= [l_channel_mean, a_channel_mean , b_channel_mean])

The only required transform is the one that converts the images to tensors : 

torchvision.transforms.ToTensor()

