def test_gradcheck(self):
input = torch.tensor([[0., 0., 0.], [0., 0., 0.],
[0., 1., 1.]]).double() # 3 x 3
input = utils.tensor_to_gradcheck_var(input) # to var
assert gradcheck(kornia.Rot180(), (input, ), raise_exception=True)
def test_gradcheck(self, device, dtype):
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1., 1.]],
device=device,
dtype=dtype) # 3 x 3
input = utils.tensor_to_gradcheck_var(input) # to var
assert gradcheck(kornia.Rot180(), (input, ), raise_exception=True)
def test_rot180(self):
f = kornia.Rot180()
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1.,
1.]]) # 3 x 3
expected = torch.tensor([[1., 1., 0.], [0., 0., 0.], [0., 0.,
0.]]) # 3 x 3
assert (f(input) == expected).all()
def test_rot180(self, device, dtype):
f = kornia.Rot180()
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1., 1.]],
device=device,
dtype=dtype) # 3 x 3
expected = torch.tensor([[1., 1., 0.], [0., 0., 0.], [0., 0., 0.]],
device=device,
dtype=dtype) # 3 x 3
assert (f(input) == expected).all()
def test_batch_rot180(self):
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1.,
1.]]) # 3 x 3
input = input.repeat(2, 1, 1) # 2 x 3 x 3
f = kornia.Rot180()
expected = torch.tensor([[1., 1., 0.], [0., 0., 0.],
[0., 0., 0.]]) # 1 x 3 x 3
expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
assert (f(input) == expected).all()
def test_batch_rot180(self, device, dtype):
input = torch.tensor(
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]],
device=device,
dtype=dtype) # 3 x 3
input = input.repeat(2, 1, 1) # 2 x 3 x 3
f = kornia.Rot180()
expected = torch.tensor(
[[1.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
device=device,
dtype=dtype) # 1 x 3 x 3
expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
assert (f(input) == expected).all()
def smoke_test(self, device, dtype):
f = kornia.Rot180()
repr = "Rot180()"
assert str(f) == repr
def op_script(data: torch.Tensor) -> torch.Tensor:
return kornia.Rot180(data)
def smoke_test(self):
f = kornia.Rot180()
repr = "Rot180()"
assert str(f) == repr
