def test_multiple_broadcast_compose(self):
t1 = Scale(0.1, 0.1, 0.1)
t2 = Scale(0.2, 0.2, 0.2)
N = 10
scale_n = torch.tensor([0.3] * N)
tN = Scale(scale_n)
t1N2 = t1.compose(tN.compose(t2))
composed_mat = t1N2.get_matrix()
self.assertTrue(composed_mat.shape == (N, 4, 4))
expected_mat = torch.eye(3, dtype=torch.float32) * 0.3 * 0.2 * 0.1
self.assertTrue(torch.allclose(composed_mat[0, :3, :3], expected_mat))
def test_broadcast_compose(self):
t1 = Scale(0.1, 0.1, 0.1)
N = 10
scale_n = torch.tensor([0.3] * N)
tN = Scale(scale_n)
t1N = t1.compose(tN)
self.assertTrue(t1._matrix.shape == (1, 4, 4))
self.assertTrue(tN._matrix.shape == (N, 4, 4))
self.assertTrue(t1N.get_matrix().shape == (N, 4, 4))
t11 = t1.compose(t1)
self.assertTrue(t11.get_matrix().shape == (1, 4, 4))
def test_broadcast_compose_fail(self):
# Cannot compose two transforms which have batch dimensions N and M
# other than the case where either N or M is 1
N = 10
M = 20
scale_n = torch.tensor([0.3] * N)
tN = Scale(scale_n)
x = torch.tensor([0.2] * M)
y = torch.tensor([0.3] * M)
z = torch.tensor([0.4] * M)
tM = Translate(x, y, z)
with self.assertRaises(ValueError):
t = tN.compose(tM)
t.get_matrix()
def test_compose_fail(self):
# Only composing Transform3d objects is possible
t1 = Scale(0.1, 0.1, 0.1)
with self.assertRaises(ValueError):
t1.compose(torch.randn(100))
