def test_inverse(self):
x = torch.tensor([0.1])
y = torch.tensor([0.2, 2.0])
z = torch.tensor([0.3, 3.0])
t = Scale(x, y, z)
im = t.inverse()._matrix
im_2 = t._matrix.inverse()
im_comp = t.get_matrix().inverse()
self.assertTrue(torch.allclose(im, im_comp))
self.assertTrue(torch.allclose(im, im_2))
def test_stack(self):
rotations = random_rotations(3)
transform3 = Transform3d().rotate(rotations).translate(
torch.full((3, 3), 0.3))
transform1 = Scale(37)
transform4 = transform1.stack(transform3)
self.assertEqual(len(transform1), 1)
self.assertEqual(len(transform3), 3)
self.assertEqual(len(transform4), 4)
self.assertClose(
transform4.get_matrix(),
torch.cat([transform1.get_matrix(),
transform3.get_matrix()]),
)
points = torch.rand(4, 5, 3)
new_points_expect = torch.cat([
transform1.transform_points(points[:1]),
transform3.transform_points(points[1:]),
])
new_points = transform4.transform_points(points)
self.assertClose(new_points, new_points_expect)
