QBatchNormalization quantizers

[AdrianAlan]

Hey, I have a question regarding the QBatchNorm implementation. I think I must be overlooking something, but are these used anywhere?

Perhaps the QBN parameters could be directly quantized here before scale and bias calculation?

class BatchNormalization(Layer):
    _expected_attributes = [
        Attribute('n_in'),
        Attribute('n_filt', default=0),

        WeightAttribute('scale'),
        WeightAttribute('bias'),

        TypeAttribute('scale'),
        TypeAttribute('bias'),
    ]

    def initialize(self):
        inp = self.get_input_variable()
        shape = inp.shape
        dims = inp.dim_names
        self.add_output_variable(shape, dims)

        gamma = self.model.get_weights_data(self.name, 'gamma')
        beta = self.model.get_weights_data(self.name, 'beta')
        mean = self.model.get_weights_data(self.name, 'moving_mean')
        var = self.model.get_weights_data(self.name, 'moving_variance')

        if self.get_attr('gamma_quantizer'):
            gamma = self.get_attr('gamma_quantizer')(gamma)
            beta = self.get_attr('beta_quantizer')(beta)
            mean = self.get_attr('mean_quantizer')(mean)
            var = self.get_attr('variance_quantizer')(var)

        scale = gamma / np.sqrt(var + self.get_attr('epsilon'))
        bias = beta - mean * scale

        self.add_weights_variable(name='scale', var_name='s{index}', data=scale)
        self.add_weights_variable(name='bias', var_name='b{index}', data=bias)