images2D_carrier.py 文件源码

def _HamiltonianShootingCarrying(self, q, p, i0) :
        """
        Given initial control points/momentums q0 and p0 given as n-by-d matrices,
        and a "template" image i0, outputs the trajectories q_t, p_t, I_t = I0 \circ phi_{t->0}.
        """
        # Here, we use the "scan" theano routine, which  can be understood as a "for" loop
        identity = T.as_tensor_variable(0. * self.dense_grid())  # We encode the identity as a null displacement field.

        # Here, we use the "scan" theano routine, which  can be understood as a "for" loop
        result, updates = theano.scan(fn            = lambda x,y,z : self._hamiltonian_step_carrying2(x,y,z),
                                      outputs_info  = [q,p, identity],
                                      n_steps       = int(np.round(1/self.dt) ))

        phi_inv_1 = result[2][-1]  # We do not store the intermediate results
        I1 = self._image_circ_diffeo(i0, self.dense_grid() + phi_inv_1)  # instead of interpolating the images It at all timesteps, we only do it in the end.
        return [result[0][-1], result[1][-1], I1]                       # and only return the final state + momentum + image