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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
def identity_matrix(batch_size, device, dtype):
r"""Create a batched homogeneous identity matrix"""
return torch.eye(4, device=device, dtype=dtype).repeat(batch_size, 1, 1) # Nx4x4
def euler_angles_to_rotation_matrix(x, y, z):
r"""Create a rotation matrix from x, y, z angles"""
assert x.dim() == 1, x.shape
assert x.shape == y.shape == z.shape
ones, zeros = torch.ones_like(x), torch.zeros_like(x)
# the rotation matrix for the x-axis
rx_tmp = [
ones,
zeros,
zeros,
zeros,
zeros,
torch.cos(x),
-torch.sin(x),
zeros,
zeros,
torch.sin(x),
torch.cos(x),
zeros,
zeros,
zeros,
zeros,
ones,
]
rx = torch.stack(rx_tmp, dim=-1).view(-1, 4, 4)
# the rotation matrix for the y-axis
ry_tmp = [
torch.cos(y),
zeros,
torch.sin(y),
zeros,
zeros,
ones,
zeros,
zeros,
-torch.sin(y),
zeros,
torch.cos(y),
zeros,
zeros,
zeros,
zeros,
ones,
]
ry = torch.stack(ry_tmp, dim=-1).view(-1, 4, 4)
# the rotation matrix for the z-axis
rz_tmp = [
torch.cos(z),
-torch.sin(z),
zeros,
zeros,
torch.sin(z),
torch.cos(z),
zeros,
zeros,
zeros,
zeros,
ones,
zeros,
zeros,
zeros,
zeros,
ones,
]
rz = torch.stack(rz_tmp, dim=-1).view(-1, 4, 4)
return torch.matmul(rz, torch.matmul(ry, rx)) # Bx4x4
class TestTransformPoints:
@pytest.mark.parametrize("batch_size", [1, 2, 5])
@pytest.mark.parametrize("num_points", [2, 3, 5])
@pytest.mark.parametrize("num_dims", [2, 3])
def test_transform_points(self, batch_size, num_points, num_dims, device, dtype):
# generate input data
eye_size = num_dims + 1
points_src = torch.rand(batch_size, num_points, num_dims, device=device, dtype=dtype)
dst_homo_src = utils.create_random_homography(batch_size, eye_size).to(device=device, dtype=dtype)
dst_homo_src = dst_homo_src.to(device)
# transform the points from dst to ref
points_dst = kornia.geometry.linalg.transform_points(dst_homo_src, points_src)
# transform the points from ref to dst
src_homo_dst = torch.inverse(dst_homo_src)
points_dst_to_src = kornia.geometry.linalg.transform_points(src_homo_dst, points_dst)
# projected should be equal as initial
assert_close(points_src, points_dst_to_src, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device, dtype):
# generate input data
batch_size, num_points, num_dims = 2, 3, 2
eye_size = num_dims + 1
points_src = torch.rand(batch_size, num_points, num_dims, device=device, dtype=dtype)
dst_homo_src = utils.create_random_homography(batch_size, eye_size).to(device=device, dtype=dtype)
# evaluate function gradient
points_src = utils.tensor_to_gradcheck_var(points_src) # to var
dst_homo_src = utils.tensor_to_gradcheck_var(dst_homo_src) # to var
assert gradcheck(kornia.geometry.transform_points, (dst_homo_src, points_src), raise_exception=True)
def test_jit(self, device, dtype):
points = torch.ones(1, 2, 2, device=device, dtype=dtype)
transform = kornia.eye_like(3, points)
op = kornia.geometry.transform_points
op_script = torch.jit.script(op)
actual = op_script(transform, points)
expected = op(transform, points)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
class TestComposeTransforms:
def test_smoke(self, device, dtype):
batch_size = 2
trans_01 = identity_matrix(batch_size=batch_size, device=device, dtype=dtype)
trans_12 = identity_matrix(batch_size=batch_size, device=device, dtype=dtype)
to_check_1 = kornia.geometry.compose_transformations(trans_01, trans_12)
to_check_2 = kornia.geometry.compose_transformations(trans_01[0], trans_12[0])
assert to_check_1.shape == (batch_size, 4, 4)
assert to_check_2.shape == (4, 4)
def test_exception(self, device, dtype):
to_check_1 = torch.rand((7, 4, 4, 3), device=device, dtype=dtype)
to_check_2 = torch.rand((5, 10, 10), device=device, dtype=dtype)
to_check_3 = torch.rand((6, 4, 4), device=device, dtype=dtype)
to_check_4 = torch.rand((4, 4), device=device, dtype=dtype)
to_check_5 = torch.rand((3, 3), device=device, dtype=dtype)
# Testing if exception is thrown when both inputs have shape (3, 3)
with pytest.raises(ValueError):
_ = kornia.geometry.compose_transformations(to_check_5, to_check_5)
# Testing if exception is thrown when both inputs have shape (5, 10, 10)
with pytest.raises(ValueError):
_ = kornia.geometry.compose_transformations(to_check_2, to_check_2)
# Testing if exception is thrown when one input has shape (6, 4, 4)
# whereas the other input has shape (4, 4)
with pytest.raises(ValueError):
_ = kornia.geometry.compose_transformations(to_check_3, to_check_4)
# Testing if exception is thrown when one input has shape (7, 4, 4, 3)
# whereas the other input has shape (4, 4)
with pytest.raises(ValueError):
_ = kornia.geometry.compose_transformations(to_check_1, to_check_4)
def test_translation_4x4(self, device, dtype):
offset = 10
trans_01 = identity_matrix(batch_size=1, device=device, dtype=dtype)[0]
trans_12 = identity_matrix(batch_size=1, device=device, dtype=dtype)[0]
trans_12[..., :3, -1] += offset # add offset to translation vector
trans_02 = kornia.geometry.linalg.compose_transformations(trans_01, trans_12)
assert_close(trans_02, trans_12, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_translation_Bx4x4(self, batch_size, device, dtype):
offset = 10
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_12 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_12[..., :3, -1] += offset # add offset to translation vector
trans_02 = kornia.geometry.linalg.compose_transformations(trans_01, trans_12)
assert_close(trans_02, trans_12, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_gradcheck(self, batch_size, device, dtype):
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_12 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_01 = utils.tensor_to_gradcheck_var(trans_01) # to var
trans_12 = utils.tensor_to_gradcheck_var(trans_12) # to var
assert gradcheck(kornia.geometry.linalg.compose_transformations, (trans_01, trans_12), raise_exception=True)
class TestInverseTransformation:
def test_smoke(self, device, dtype):
batch_size = 2
trans_01 = identity_matrix(batch_size=batch_size, device=device, dtype=dtype)
to_check_1 = kornia.geometry.inverse_transformation(trans_01)
to_check_2 = kornia.geometry.inverse_transformation(trans_01[0])
assert to_check_1.shape == (batch_size, 4, 4)
assert to_check_2.shape == (4, 4)
def test_exception(self, device, dtype):
to_check_1 = torch.rand((7, 4, 4, 3), device=device, dtype=dtype)
to_check_2 = torch.rand((5, 10, 10), device=device, dtype=dtype)
to_check_3 = torch.rand((3, 3), device=device, dtype=dtype)
# Testing if exception is thrown when the input has shape (7, 4, 4, 3)
with pytest.raises(ValueError):
_ = kornia.geometry.inverse_transformation(to_check_1)
# Testing if exception is thrown when the input has shape (5, 10, 10)
with pytest.raises(ValueError):
_ = kornia.geometry.inverse_transformation(to_check_2)
# Testing if exception is thrown when the input has shape (3, 3)
with pytest.raises(ValueError):
_ = kornia.geometry.inverse_transformation(to_check_3)
def test_translation_4x4(self, device, dtype):
offset = 10
trans_01 = identity_matrix(batch_size=1, device=device, dtype=dtype)[0]
trans_01[..., :3, -1] += offset # add offset to translation vector
trans_10 = kornia.geometry.linalg.inverse_transformation(trans_01)
trans_01_hat = kornia.geometry.linalg.inverse_transformation(trans_10)
assert_close(trans_01, trans_01_hat, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_translation_Bx4x4(self, batch_size, device, dtype):
offset = 10
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_01[..., :3, -1] += offset # add offset to translation vector
trans_10 = kornia.geometry.linalg.inverse_transformation(trans_01)
trans_01_hat = kornia.geometry.linalg.inverse_transformation(trans_10)
assert_close(trans_01, trans_01_hat, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_rotation_translation_Bx4x4(self, batch_size, device, dtype):
offset = 10
x, y, z = 0, 0, kornia.pi
ones = torch.ones(batch_size, device=device, dtype=dtype)
rmat_01 = euler_angles_to_rotation_matrix(x * ones, y * ones, z * ones)
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_01[..., :3, -1] += offset # add offset to translation vector
trans_01[..., :3, :3] = rmat_01[..., :3, :3]
trans_10 = kornia.geometry.linalg.inverse_transformation(trans_01)
trans_01_hat = kornia.geometry.linalg.inverse_transformation(trans_10)
assert_close(trans_01, trans_01_hat, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_gradcheck(self, batch_size, device, dtype):
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_01 = utils.tensor_to_gradcheck_var(trans_01) # to var
assert gradcheck(kornia.geometry.linalg.inverse_transformation, (trans_01,), raise_exception=True)
class TestRelativeTransformation:
def test_smoke(self, device, dtype):
batch_size = 2
trans_01 = identity_matrix(batch_size=batch_size, device=device, dtype=dtype)
trans_02 = identity_matrix(batch_size=batch_size, device=device, dtype=dtype)
to_check_1 = kornia.geometry.relative_transformation(trans_01, trans_02)
to_check_2 = kornia.geometry.relative_transformation(trans_01[0], trans_02[0])
assert to_check_1.shape == (batch_size, 4, 4)
assert to_check_2.shape == (4, 4)
def test_exception(self, device, dtype):
to_check_1 = torch.rand((7, 4, 4, 3), device=device, dtype=dtype)
to_check_2 = torch.rand((5, 10, 10), device=device, dtype=dtype)
to_check_3 = torch.rand((6, 4, 4), device=device, dtype=dtype)
to_check_4 = torch.rand((4, 4), device=device, dtype=dtype)
to_check_5 = torch.rand((3, 3), device=device, dtype=dtype)
# Testing if exception is thrown when both inputs have shape (3, 3)
with pytest.raises(ValueError):
_ = kornia.geometry.relative_transformation(to_check_5, to_check_5)
# Testing if exception is thrown when both inputs have shape (5, 10, 10)
with pytest.raises(ValueError):
_ = kornia.geometry.relative_transformation(to_check_2, to_check_2)
# Testing if exception is thrown when one input has shape (6, 4, 4)
# whereas the other input has shape (4, 4)
with pytest.raises(ValueError):
_ = kornia.geometry.relative_transformation(to_check_3, to_check_4)
# Testing if exception is thrown when one input has shape (7, 4, 4, 3)
# whereas the other input has shape (4, 4)
with pytest.raises(ValueError):
_ = kornia.geometry.relative_transformation(to_check_1, to_check_4)
def test_translation_4x4(self, device, dtype):
offset = 10.0
trans_01 = identity_matrix(batch_size=1, device=device, dtype=dtype)[0]
trans_02 = identity_matrix(batch_size=1, device=device, dtype=dtype)[0]
trans_02[..., :3, -1] += offset # add offset to translation vector
trans_12 = kornia.geometry.linalg.relative_transformation(trans_01, trans_02)
trans_02_hat = kornia.geometry.linalg.compose_transformations(trans_01, trans_12)
assert_close(trans_02_hat, trans_02, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_rotation_translation_Bx4x4(self, batch_size, device, dtype):
offset = 10.0
x, y, z = 0.0, 0.0, kornia.pi
ones = torch.ones(batch_size, device=device, dtype=dtype)
rmat_02 = euler_angles_to_rotation_matrix(x * ones, y * ones, z * ones)
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_02 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_02[..., :3, -1] += offset # add offset to translation vector
trans_02[..., :3, :3] = rmat_02[..., :3, :3]
trans_12 = kornia.geometry.linalg.relative_transformation(trans_01, trans_02)
trans_02_hat = kornia.geometry.linalg.compose_transformations(trans_01, trans_12)
assert_close(trans_02_hat, trans_02, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_gradcheck(self, batch_size, device, dtype):
trans_01 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_02 = identity_matrix(batch_size, device=device, dtype=dtype)
trans_01 = utils.tensor_to_gradcheck_var(trans_01) # to var
trans_02 = utils.tensor_to_gradcheck_var(trans_02) # to var
assert gradcheck(kornia.geometry.linalg.relative_transformation, (trans_01, trans_02), raise_exception=True)
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