def test_toy(self, device):
inp = torch.zeros(1, 1, 32, 32, device=device)
inp[:, :, :16, :] = 1
ori = OriNet(True).to(device=device, dtype=inp.dtype).eval()
ang = ori(inp)
expected = torch.tensor([70.58], device=device)
assert_allclose(kornia.rad2deg(ang), expected, atol=1e-2, rtol=1e-3)
def test_toy(self, device):
ori = PatchDominantGradientOrientation(19).to(device)
inp = torch.zeros(1, 1, 19, 19, device=device)
inp[:, :, :10, :] = 1
ang = ori(inp)
expected = torch.tensor([90.], device=device)
assert_allclose(kornia.rad2deg(ang), expected)
def test_deg2rad(batch_shape, device, dtype):
# generate input data
x_deg = 180. * torch.rand(batch_shape, device=device, dtype=dtype)
# convert radians/degrees
x_rad = kornia.deg2rad(x_deg)
x_rad_to_deg = kornia.rad2deg(x_rad)
assert_allclose(x_deg, x_rad_to_deg, atol=1e-4, rtol=1e-4)
assert gradcheck(kornia.deg2rad, (tensor_to_gradcheck_var(x_deg), ),
raise_exception=True)
def test_rad2deg(batch_shape, device, dtype):
# generate input data
x_rad = kornia.pi * torch.rand(batch_shape, device=device, dtype=dtype)
# convert radians/degrees
x_deg = kornia.rad2deg(x_rad)
x_deg_to_rad = kornia.deg2rad(x_deg)
# compute error
assert_allclose(x_rad, x_deg_to_rad)
# evaluate function gradient
assert gradcheck(kornia.rad2deg, (tensor_to_gradcheck_var(x_rad), ),
raise_exception=True)
def test_deg2rad(batch_shape, device_type):
# generate input data
x_deg = 180. * torch.rand(batch_shape)
x_deg = x_deg.to(torch.device(device_type))
# convert radians/degrees
x_rad = kornia.deg2rad(x_deg)
x_rad_to_deg = kornia.rad2deg(x_rad)
# compute error
error = utils.compute_mse(x_deg, x_rad_to_deg)
assert pytest.approx(error.item(), 0.0)
assert gradcheck(kornia.deg2rad, (utils.tensor_to_gradcheck_var(x_deg),),
raise_exception=True)
def test_rad2deg(batch_shape, device_type):
# generate input data
x_rad = kornia.pi * torch.rand(batch_shape)
x_rad = x_rad.to(torch.device(device_type))
# convert radians/degrees
x_deg = kornia.rad2deg(x_rad)
x_deg_to_rad = kornia.deg2rad(x_deg)
# compute error
error = utils.compute_mse(x_rad, x_deg_to_rad)
# evaluate function gradient
assert gradcheck(kornia.rad2deg, (utils.tensor_to_gradcheck_var(x_rad),),
raise_exception=True)
def get_laf_orientation(LAF: torch.Tensor) -> torch.Tensor:
"""Returns orientation of the LAFs, in degrees.
Args:
LAF: (torch.Tensor): tensor [BxNx2x3].
Returns:
torch.Tensor: tensor BxNx1 .
Shape:
- Input: :math: `(B, N, 2, 3)`
- Output: :math: `(B, N, 1)`
Example:
>>> input = torch.ones(1, 5, 2, 3) # BxNx2x3
>>> output = get_laf_orientation(input) # BxNx1
"""
raise_error_if_laf_is_not_valid(LAF)
angle_rad: torch.Tensor = torch.atan2(LAF[..., 0, 1], LAF[..., 0, 0])
return kornia.rad2deg(angle_rad).unsqueeze(-1)
