def eval_vec(input):
return epi.decompose_essential_matrix(input)[2]
def test_shape(self, batch_shape, device, dtype):
E_mat = torch.rand(batch_shape, device=device, dtype=dtype)
R1, R2, t = epi.decompose_essential_matrix(E_mat)
assert R1.shape == batch_shape
assert R2.shape == batch_shape
assert t.shape == batch_shape[:-1] + (1, )
def eval_rot2(input):
return epi.decompose_essential_matrix(input)[1]
def test_smoke(self, device, dtype):
E_mat = torch.rand(1, 3, 3, device=device, dtype=dtype)
R1, R2, t = epi.decompose_essential_matrix(E_mat)
assert R1.shape == (1, 3, 3)
assert R2.shape == (1, 3, 3)
assert t.shape == (1, 3, 1)
