It looks like I was mishandling the transformations, as outputting the normalised test data doesn’t seem to have scaled the values:
data = np.random.uniform(0,10,[64,64])
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0],[1])])
d = transform(data.reshape(*data.shape, 1)).unsqueeze(0).float().cuda()
output = torch.nn.functional.softmax(tester(d),dim=1).cpu().detach().numpy()
plt.figure()
plt.imshow(d[0,0,:,:],cmap='jet')
plt.colorbar()


Upon doing more research, I’ve figured out that this is because the scaling does the operation: (x-0)/1 which of course makes no change to the data