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)