layers.py 文件源码

python
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项目:crfrnn_layer 作者: HapeMask 项目源码 文件源码
def get_output_for(self, inputs, **kwargs):
        unary, ref = inputs

        N, _, H, W = ref.shape
        yx = tt.cast(tt.stack(tt.mgrid[0:H, 0:W]), "float32")
        grid = tt.alloc(yx[np.newaxis, :, :, :], N, 2, H, W)
        stacked = tt.concatenate([grid, ref], axis=1)

        def _bilateral(V, R):
            o = tt.ones((1, V.shape[1], V.shape[2]), "float32")
            norm = tt.sqrt(gaussian_filter(R, o, self.kstd_bf,
                                           self.ref_dim)) + 1e-8
            return gaussian_filter(R, V/norm, self.kstd_bf, self.ref_dim,
                                   self.val_dim) / norm

        def _step(prev_q, U, ref, normalize=True):
            qbf = _bilateral(prev_q, ref,)
            qsf = tt.nnet.conv2d(prev_q[np.newaxis, :, :, :],
                                 self.W_spatial, border_mode="half")[0]

            q_hat = -self.compat_bf * qbf + -self.compat_spatial * qsf
            q_hat = U - q_hat

            return softmax(q_hat, axis=0) if normalize else q_hat

        def _inference(unary_i, ref_i):
            U = tt.log(tt.clip(unary_i, 1e-5, 1))
            prev_q = softmax(U, axis=0)

            # This is faster than using scan.
            for i in range(self.num_iter):
                normalize = self.normalize_final_iter or i < self.num_iter-1
                prev_q = _step(prev_q, U, ref_i, normalize)
            return prev_q

        return theano.scan(fn=_inference, sequences=[unary, stacked],
                           outputs_info=None)[0]
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