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.Hflip(), (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.Hflip(), (input, ), raise_exception=True)
def test_hflip(self):
f = kornia.Hflip()
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1.,
1.]]) # 3 x 3
expected = torch.tensor([[0., 0., 0.], [0., 0., 0.], [1., 1.,
0.]]) # 3 x 3
assert (f(input) == expected).all()
def test_hflip(self, device, dtype):
f = kornia.Hflip()
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1., 1.]],
device=device,
dtype=dtype) # 3 x 3
expected = torch.tensor([[0., 0., 0.], [0., 0., 0.], [1., 1., 0.]],
device=device,
dtype=dtype) # 3 x 3
assert (f(input) == expected).all()
def test_batch_hflip(self):
input = torch.tensor([[0., 0., 0.], [0., 0., 0.], [0., 1.,
1.]]) # 1 x 3 x 3
input = input.repeat(2, 1, 1) # 2 x 3 x 3
f = kornia.Hflip()
expected = torch.tensor([[[0., 0., 0.], [0., 0., 0.], [1., 1.,
0.]]]) # 3 x 3
expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
assert (f(input) == expected).all()
def test_batch_hflip(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) # 1 x 3 x 3
input = input.repeat(2, 1, 1) # 2 x 3 x 3
f = kornia.Hflip()
expected = torch.tensor(
[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]]],
device=device,
dtype=dtype) # 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.Hflip()
repr = "Hflip()"
assert str(f) == repr
def smoke_test(self):
f = kornia.Hflip()
repr = "Hflip()"
assert str(f) == repr
def op_script(data: torch.Tensor) -> torch.Tensor:
return kornia.Hflip(data)
