Computation seems to be worng with BatchNorm

[charmway]

In Group Normalization, it says that the Batchnorm is computed along the channel dimension. However in this implementation, the forward of the BN is just computed regardless of the channel dimension:

self.x = inpt.copy()
self.mean = self.x.mean(axis=0)                             # shape = (w, h, c)
self.var = 1. / np.sqrt((self.x.var(axis=0)) + self.epsil)  # shape = (w, h, c)

I think it should be like this:

mean = np.mean(X, axis=(0, 2, 3), keepdims=True)
variance = np.var(X, axis=(0, 2, 3), keepdims=True)