def test_affine_scale(self, device):
torch.manual_seed(0)
scale_factor = torch.rand(1, device=device) * 2.0
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(scale_factor=scale_factor).to(device)
actual = transform(input)
expected = kornia.scale(input, scale_factor)
assert_allclose(actual, expected)
def test_affine_scale(self, device, dtype):
# TODO: Remove when #666 is implemented
if device.type == 'cuda':
pytest.skip("Currently breaks in CUDA." "See https://github.com/kornia/kornia/issues/666")
torch.manual_seed(0)
_scale_factor = torch.rand(1, device=device, dtype=dtype) * 2.0
scale_factor = torch.stack([_scale_factor, _scale_factor], dim=1)
input = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
transform = kornia.Affine(scale_factor=scale_factor).to(device=device, dtype=dtype)
actual = transform(input)
expected = kornia.scale(input, scale_factor)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
def ZoomY(x, v):
batch_size = v.size(0)
zoom = torch.ones((batch_size, 2), device=x.device)
zoom[:, 1] = v
return kornia.scale(x, zoom)