Although the proper way is to find the mean and variance for your whole training set and use that to normalise your images (scikit-learn has some classes for this) there is a quicker way to validate if normalisation helps.
Just add a batch normalisation (torch.nn.BatchNorm2d) as the very first layer of your network and it will normalise on a per batch basis (64 images in your case). Not perfect but should give enough intuition if this will solve your problem.
And when indeed that helps you can use normalise over your whole training set.