Best Python code snippet using lisa_python
quat_affine_test.py
Source: quat_affine_test.py
...23r2t = quat_affine.rot_list_to_tensor24v2t = quat_affine.vec_list_to_tensor25q2r = lambda q: r2t(quat_affine.quat_to_rot(q))26class QuatAffineTest(absltest.TestCase):27 def _assert_check(self, to_check, tol=1e-5):28 for k, (correct, generated) in to_check.items():29 if VERBOSE:30 logging.info(k)31 logging.info('Correct %s', correct)32 logging.info('Predicted %s', generated)33 self.assertLess(np.max(np.abs(correct - generated)), tol)34 def test_conversion(self):35 quat = jnp.array([-2., 5., -1., 4.])36 rotation = jnp.array([37 [0.26087, 0.130435, 0.956522],38 [-0.565217, -0.782609, 0.26087],39 [0.782609, -0.608696, -0.130435]])40 translation = jnp.array([1., -3., 4.])41 point = jnp.array([0.7, 3.2, -2.9])42 a = quat_affine.QuatAffine(quat, translation, unstack_inputs=True)43 true_new_point = jnp.matmul(rotation, point[:, None])[:, 0] + translation44 self._assert_check({45 'rot': (rotation, r2t(a.rotation)),46 'trans': (translation, v2t(a.translation)),47 'point': (true_new_point,48 v2t(a.apply_to_point(jnp.moveaxis(point, -1, 0)))),49 # Because of the double cover, we must be careful and compare rotations50 'quat': (q2r(a.quaternion),51 q2r(quat_affine.rot_to_quat(a.rotation))),52 })53 def test_double_cover(self):54 """Test that -q is the same rotation as q."""55 rng = jax.random.PRNGKey(42)56 keys = jax.random.split(rng)57 q = jax.random.normal(keys[0], (2, 4))58 trans = jax.random.normal(keys[1], (2, 3))59 a1 = quat_affine.QuatAffine(q, trans, unstack_inputs=True)60 a2 = quat_affine.QuatAffine(-q, trans, unstack_inputs=True)61 self._assert_check({62 'rot': (r2t(a1.rotation),63 r2t(a2.rotation)),64 'trans': (v2t(a1.translation),65 v2t(a2.translation)),66 })67 def test_homomorphism(self):68 rng = jax.random.PRNGKey(42)69 keys = jax.random.split(rng, 4)70 vec_q1 = jax.random.normal(keys[0], (2, 3))71 q1 = jnp.concatenate([72 jnp.ones_like(vec_q1)[:, :1],73 vec_q1], axis=-1)74 q2 = jax.random.normal(keys[1], (2, 4))75 t1 = jax.random.normal(keys[2], (2, 3))76 t2 = jax.random.normal(keys[3], (2, 3))77 a1 = quat_affine.QuatAffine(q1, t1, unstack_inputs=True)78 a2 = quat_affine.QuatAffine(q2, t2, unstack_inputs=True)79 a21 = a2.pre_compose(jnp.concatenate([vec_q1, t1], axis=-1))80 rng, key = jax.random.split(rng)81 x = jax.random.normal(key, (2, 3))82 new_x = a21.apply_to_point(jnp.moveaxis(x, -1, 0))83 new_x_apply2 = a2.apply_to_point(a1.apply_to_point(jnp.moveaxis(x, -1, 0)))84 self._assert_check({85 'quat': (q2r(quat_affine.quat_multiply(a2.quaternion, a1.quaternion)),86 q2r(a21.quaternion)),87 'rot': (jnp.matmul(r2t(a2.rotation), r2t(a1.rotation)),88 r2t(a21.rotation)),89 'point': (v2t(new_x_apply2),90 v2t(new_x)),91 'inverse': (x, v2t(a21.invert_point(new_x))),92 })93 def test_batching(self):94 """Test that affine applies batchwise."""95 rng = jax.random.PRNGKey(42)96 keys = jax.random.split(rng, 3)97 q = jax.random.uniform(keys[0], (5, 2, 4))98 t = jax.random.uniform(keys[1], (2, 3))99 x = jax.random.uniform(keys[2], (5, 1, 3))100 a = quat_affine.QuatAffine(q, t, unstack_inputs=True)101 y = v2t(a.apply_to_point(jnp.moveaxis(x, -1, 0)))102 y_list = []103 for i in range(5):104 for j in range(2):105 a_local = quat_affine.QuatAffine(q[i, j], t[j],106 unstack_inputs=True)107 y_local = v2t(a_local.apply_to_point(jnp.moveaxis(x[i, 0], -1, 0)))108 y_list.append(y_local)109 y_combine = jnp.reshape(jnp.stack(y_list, axis=0), (5, 2, 3))110 self._assert_check({111 'batch': (y_combine, y),112 'quat': (q2r(a.quaternion),113 q2r(quat_affine.rot_to_quat(a.rotation))),114 })115 def assertAllClose(self, a, b, rtol=1e-06, atol=1e-06):116 self.assertTrue(np.allclose(a, b, rtol=rtol, atol=atol))117 def assertAllEqual(self, a, b):118 self.assertTrue(np.all(np.array(a) == np.array(b)))119if __name__ == '__main__':...
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