def test_affine_rotate_translate(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")
batch_size = 2
input = torch.tensor(
[[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]],
device=device,
dtype=dtype,
).repeat(batch_size, 1, 1, 1)
angle = torch.tensor(180.0, device=device, dtype=dtype).repeat(batch_size)
translation = torch.tensor([1.0, 0.0], device=device, dtype=dtype).repeat(batch_size, 1)
expected = torch.tensor(
[[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0]]],
device=device,
dtype=dtype,
).repeat(batch_size, 1, 1, 1)
transform = kornia.Affine(angle=angle, translation=translation, align_corners=True).to(
device=device, dtype=dtype
)
actual = transform(input)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
def test_affine_shear(self, device):
torch.manual_seed(0)
shear = torch.rand(1, 2, device=device)
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(shear=shear).to(device)
actual = transform(input)
expected = kornia.shear(input, shear)
assert_allclose(actual, expected)
def test_affine_shear(self, device, dtype):
torch.manual_seed(0)
shear = torch.rand(1, 2, device=device, dtype=dtype)
input = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
transform = kornia.Affine(shear=shear, device=device, dtype=dtype)
actual = transform(input)
expected = kornia.shear(input, shear)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
def test_affine_translate(self, device):
torch.manual_seed(0)
translation = torch.rand(1, 2, device=device) * 2.0
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(translation=translation).to(device)
actual = transform(input)
expected = kornia.translate(input, translation)
assert_allclose(actual, expected)
def test_affine_rotate(self, device):
torch.manual_seed(0)
angle = torch.rand(1, device=device) * 90.0
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(angle=angle).to(device)
actual = transform(input)
expected = kornia.rotate(input, angle)
assert_allclose(actual, expected)
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_translate(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)
translation = torch.rand(1, 2, device=device, dtype=dtype) * 2.0
input = torch.rand(1, 2, 3, 4, device=device, dtype=dtype)
transform = kornia.Affine(translation=translation).to(device=device, dtype=dtype)
actual = transform(input)
expected = kornia.translate(input, translation)
assert_close(actual, expected, atol=1e-4, rtol=1e-4)
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 test_affine_rotate(self, device):
# 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)
angle = torch.rand(1, device=device) * 90.0
input = torch.rand(1, 2, 3, 4, device=device)
transform = kornia.Affine(angle=angle).to(device)
actual = transform(input)
expected = kornia.rotate(input, angle)
assert_allclose(actual, expected)
def test_affine_rotate_translate(self, device):
batch_size = 2
input = torch.tensor(
[[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]], device=device
).repeat(batch_size, 1, 1, 1)
angle = torch.tensor(180.0, device=device).repeat(batch_size)
translation = torch.tensor([1.0, 0.0]).repeat(batch_size, 1)
expected = torch.tensor(
[[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0]]], device=device,
).repeat(batch_size, 1, 1, 1)
transform = kornia.Affine(angle=angle, translation=translation, align_corners=True).to(device)
actual = transform(input)
assert_allclose(actual, expected)
def test_affine_batch_size_mismatch(self, device, dtype):
with pytest.raises(RuntimeError):
angle = torch.rand(1, device=device, dtype=dtype)
translation = torch.rand(2, 2, device=device, dtype=dtype)
kornia.Affine(angle, translation)
def test_affine_no_args(self):
with pytest.raises(RuntimeError):
kornia.Affine()
