I do not understand why you need torch.mean to normalize all feature maps to a range of [0, 1].
If you want standardized normalization：
X = torch.randn(1,20,64,64)
X -= X.mean((2,3),keepdim=True)
X /= X.std((2,3),keepdim=True)

If you want them in [0,1]:
X = torch.randn(1,20,64,64)
X -= X.min()
X /= X.max()

Further, if you want to do this for each feature map independently:
X = torch.randn(1,20,64,64)
min_val = X.min(-1)[0].min(-1)[0]
max_val = X.max(-1)[0].max(-1)[0]
X = (X-min_val[:,:,None,None])/(max_val[:,:,None,None]-min_val[:,:,None,None])