layers.py 文件源码

def __init__(self, values, ref_img, kern_std, norm_type="sym",
                 name=None, trainable_kernels=False, _bilateral=False):

        assert(norm_type in ["sym", "pre", "post", None])
        super(GaussianFilterLayer, self).__init__(incomings=[values, ref_img],
                                                  name=name)

        self.val_dim = ll.get_output_shape(values)[1]
        self.ref_dim = ll.get_output_shape(ref_img)[1]

        if None in (self.val_dim, self.ref_dim):
            raise ValueError("Gaussian filtering requires known channel \
dimensions for all inputs.")

        self.norm_type = norm_type

        if _bilateral:
            self.ref_dim += 2

        if len(kern_std) != self.ref_dim:
            raise ValueError("Number of kernel weights must match reference \
dimensionality. Got %d weights for %d reference dims." % (len(kern_std),
                                                          self.ref_dim))

        self.kern_std = self.add_param(kern_std, (self.ref_dim,),
                                       name="kern_std",
                                       trainable=trainable_kernels,
                                       regularizable=False)