def test_shift(self, device, dtype):
pts1 = torch.zeros(3, 2, device=device, dtype=dtype)[None]
pts2 = torch.tensor([[1.0, 0.0], [2.0, 0.0], [2.0, 2.0]],
device=device,
dtype=dtype)[None]
H = torch.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
dtype=dtype,
device=device)[None]
expected = torch.tensor([0.0, 1.0, 5.0], device=device,
dtype=dtype)[None]
assert_close(oneway_transfer_error(pts1, pts2, H),
expected,
atol=1e-4,
rtol=1e-4)
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 oneway_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 oneway_transfer_error(pts1, pts2, H).shape == (1, 6)