def test_batch(self, device, dtype):
batch_size = 5
pts1 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype)
pts2 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype)
Fm = utils.create_random_fundamental_matrix(1).type_as(pts1)
assert epi.symmetrical_epipolar_distance(pts1, pts2,
Fm).shape == (5, 4)
def test_gradcheck(self, device):
# generate input data
batch_size, num_points, num_dims = 2, 3, 2
points1 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64, requires_grad=True)
points2 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64)
Fm = utils.create_random_fundamental_matrix(batch_size).type_as(points2)
assert gradcheck(epi.sampson_epipolar_distance, (points1, points2, Fm),
raise_exception=True)
def test_smoke(self, device, dtype):
pts1 = torch.rand(1, 4, 3, device=device, dtype=dtype)
pts2 = torch.rand(1, 4, 3, device=device, dtype=dtype)
Fm = utils.create_random_fundamental_matrix(1).type_as(pts1)
assert epi.sampson_epipolar_distance(pts1, pts2, Fm).shape == (1, 4)