links.py 文件源码

def __call__(self, x):
        xp = chainer.cuda.get_array_module(x.data)
        batchsize = x.shape[0]
        if self.train_weights == False and self.initial_T is not None:
            self.T.W.data = self.initial_T

        M = F.reshape(self.T(x), (-1, self.num_kernels, self.ndim_kernel))
        M = F.expand_dims(M, 3)
        M_T = F.transpose(M, (3, 1, 2, 0))
        M, M_T = F.broadcast(M, M_T)

        norm = F.sum(abs(M - M_T), axis=2)
        eraser = F.broadcast_to(xp.eye(batchsize, dtype=x.dtype).reshape((batchsize, 1, batchsize)), norm.shape)
        c_b = F.exp(-(norm + 1e6 * eraser))
        o_b = F.sum(c_b, axis=2)

        if self.train_weights == False:
            self.initial_T = self.T.W.data

        return F.concat((x, o_b), axis=1)