def test_structural_element(self, device, dtype):
tensor = torch.tensor(
[[0.5, 1.0, 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]],
device=device,
dtype=dtype)[None, None, :, :]
structural_element = torch.tensor(
[[-1.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, -1.0]],
device=device,
dtype=dtype)
expected = torch.tensor(
[[1.0, 1.0, 1.0], [0.7, 1.0, 0.8], [0.9, 0.9, 0.9]],
device=device,
dtype=dtype)[None, None, :, :]
assert_close(
dilation(tensor,
torch.ones_like(structural_element),
structuring_element=structural_element,
engine='unfold'),
expected,
atol=1e-3,
rtol=1e-3,
)
assert_close(
dilation(tensor,
torch.ones_like(structural_element),
structuring_element=structural_element,
engine='convolution'),
expected,
atol=1e-3,
rtol=1e-3,
)
def test_kernel(self, device, dtype):
tensor = torch.tensor([[0.5, 1.0, 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]], device=device, dtype=dtype)[
None, None, :, :
]
kernel = torch.tensor([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]], device=device, dtype=dtype)
expected = torch.tensor([[1.0, 1.0, 1.0], [0.7, 1.0, 0.8], [0.9, 0.9, 0.9]], device=device, dtype=dtype)[
None, None, :, :
]
assert_close(dilation(tensor, kernel, engine='unfold'), expected, atol=1e-4, rtol=1e-4)
assert_close(dilation(tensor, kernel, engine='convolution'), expected, atol=1e-3, rtol=1e-3)
def test_exception(self, device, dtype):
tensor = torch.ones(1, 1, 3, 4, device=device, dtype=dtype)
kernel = torch.ones(3, 3, device=device, dtype=dtype)
with pytest.raises(TypeError):
assert dilation([0.0], kernel)
with pytest.raises(TypeError):
assert dilation(tensor, [0.0])
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert dilation(test, kernel)
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert dilation(tensor, test)
def test_flip(self, device, dtype):
tensor = torch.tensor([[0.5, 1.0, 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]], device=device, dtype=dtype)[
None, None, :, :
]
kernel = torch.tensor([[0.0, 1.0, 1.0], [0.0, 1.0, 1.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype)
expected = torch.tensor([[0.7, 1.0, 1.0], [0.7, 1.0, 1.0], [0.7, 0.9, 0.9]], device=device, dtype=dtype)[
None, None, :, :
]
assert_close(dilation(tensor, kernel), expected, atol=1e-3, rtol=1e-3)
def test_structural_element(self, device, dtype):
tensor = torch.tensor([[0.5, 1.0, 0.3], [0.7, 0.3, 0.8], [0.4, 0.9, 0.2]], device=device, dtype=dtype)[
None, None, :, :
]
structural_element = torch.tensor(
[[-1.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, -1.0]], device=device, dtype=dtype
)
expected = torch.tensor([[1.0, 1.0, 1.0], [0.7, 1.0, 0.8], [0.9, 0.9, 0.9]], device=device, dtype=dtype)[
None, None, :, :
]
assert_allclose(
dilation(tensor, torch.ones_like(structural_element), structuring_element=structural_element), expected
)
def test_cardinality(self, device, dtype, shape, kernel):
img = torch.ones(shape, device=device, dtype=dtype)
krnl = torch.ones(kernel, device=device, dtype=dtype)
assert dilation(img, krnl).shape == shape
