Normalize channels separately?

[vzinche]

• inferno version: n/a
• Python version: n/a
• Operating System: n/a

Description

inferno/io/trasnform/generic.py
class Normalize(Transform)

def tensor_function(self, tensor):
    mean = np.asarray(tensor.mean()) if self.mean is None else self.mean
    std = np.asarray(tensor.std()) if self.std is None else self.std
    # Figure out how to reshape mean and std
    reshape_as = [-1] + [1] * (tensor.ndim - 1)
    # Normalize
    tensor = (tensor - mean.reshape(*reshape_as))/(std.reshape(*reshape_as) + self.eps)
    return tensor

Issue

I am not sure I'm getting the intentions here, but I guess this reshaping the mean and std part is meant to apply separate means and stds for channels, right?
In this case it looks like it wouldn't work if the mean and std were not supplied as arguments (tensor.mean() would return the mean of a flattened array by default?)
Was it meant like this?