BMM_attention_model.py 文件源码

def crosschannelnormalization(alpha = 1e-4, k=2, beta=0.75, n=5,**kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """
    def f(X):
        b, ch, r, c = X.shape
        half = n // 2
        square = K.square(X)
        extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0,2,3,1))
                                              , (0,half))
        extra_channels = K.permute_dimensions(extra_channels, (0,3,1,2))
        scale = k
        for i in range(n):
            scale += alpha * extra_channels[:,i:i+ch,:,:]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape:input_shape,**kwargs)