def test_gradcheck(self, device, dtype):
# generate input data
batch_size, num_points = 2, 3
eye_size = 3
points_src = torch.rand(batch_size, num_points, 2, 3, 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.feature.perspective_transform_lafs, (dst_homo_src, points_src), raise_exception=True)
def test_gradcheck(self):
# 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)
dst_homo_src = utils.create_random_homography(batch_size, eye_size)
# 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.transform_points, (dst_homo_src, points_src,),
raise_exception=True)
def test_transform_points(self, batch_size, num_points, device, dtype):
# generate input data
eye_size = 3
lafs_src = torch.rand(batch_size, num_points, 2, 3, device=device, dtype=dtype)
dst_homo_src = utils.create_random_homography(batch_size, eye_size).to(device=device, dtype=dtype)
# transform the points from dst to ref
lafs_dst = kornia.feature.perspective_transform_lafs(dst_homo_src, lafs_src)
# transform the points from ref to dst
src_homo_dst = torch.inverse(dst_homo_src)
lafs_dst_to_src = kornia.feature.perspective_transform_lafs(src_homo_dst, lafs_dst)
# projected should be equal as initial
assert_close(lafs_src, lafs_dst_to_src)
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):
# 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)
H = utils.create_random_homography(batch_size,
3).type_as(points1).to(device)
assert gradcheck(symmetric_transfer_error, (points1, points2, H),
raise_exception=True)
def test_transform_points(self, batch_size, num_points, num_dims,
device_type):
# generate input data
eye_size = num_dims + 1
points_src = torch.rand(batch_size, num_points, num_dims)
points_src = points_src.to(torch.device(device_type))
dst_homo_src = utils.create_random_homography(batch_size, eye_size)
dst_homo_src = dst_homo_src.to(torch.device(device_type))
# transform the points from dst to ref
points_dst = kornia.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.transform_points(src_homo_dst, points_dst)
# projected should be equal as initial
assert_allclose(points_src, points_dst_to_src)
def test_batch(self, device, dtype):
batch_size = 5
pts1 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
pts2 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert symmetric_transfer_error(pts1, pts2, H).shape == (batch_size, 3)
def test_smoke(self, device, dtype):
pts1 = torch.rand(1, 6, 2, device=device, dtype=dtype)
pts2 = torch.rand(1, 6, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert symmetric_transfer_error(pts1, pts2, H).shape == (1, 6)
