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import pytest
import torch
from torch.autograd import gradcheck
from kornia.morphology import top_hat
from kornia.testing import assert_close
class TestTopHat:
def test_smoke(self, device, dtype):
kernel = torch.rand(3, 3, device=device, dtype=dtype)
assert kernel is not None
@pytest.mark.parametrize("shape", [(1, 3, 4, 4), (2, 3, 2, 4), (3, 3, 4, 1), (3, 2, 5, 5)])
@pytest.mark.parametrize("kernel", [(3, 3), (5, 5)])
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 top_hat(img, krnl).shape == shape
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([[0.0, 0.5, 0.0], [0.2, 0.0, 0.5], [0.0, 0.5, 0.0]], device=device, dtype=dtype)[
None, None, :, :
]
assert_close(top_hat(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([[0.0, 0.5, 0.0], [0.2, 0.0, 0.5], [0.0, 0.5, 0.0]], device=device, dtype=dtype)[
None, None, :, :
]
assert_close(
top_hat(tensor, torch.ones_like(structural_element), structuring_element=structural_element),
expected,
atol=1e-3,
rtol=1e-3,
)
def test_exception(self, device, dtype):
input = torch.ones(1, 1, 3, 4, device=device, dtype=dtype)
kernel = torch.ones(3, 3, device=device, dtype=dtype)
with pytest.raises(TypeError):
assert top_hat([0.0], kernel)
with pytest.raises(TypeError):
assert top_hat(input, [0.0])
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert top_hat(test, kernel)
with pytest.raises(ValueError):
test = torch.ones(2, 3, 4, device=device, dtype=dtype)
assert top_hat(input, test)
@pytest.mark.grad
def test_gradcheck(self, device, dtype):
input = torch.rand(2, 3, 4, 4, requires_grad=True, device=device, dtype=torch.float64)
kernel = torch.rand(3, 3, requires_grad=True, device=device, dtype=torch.float64)
assert gradcheck(top_hat, (input, kernel), raise_exception=True)
@pytest.mark.jit
def test_jit(self, device, dtype):
op = top_hat
op_script = torch.jit.script(op)
input = torch.rand(1, 2, 7, 7, device=device, dtype=dtype)
kernel = torch.ones(3, 3, device=device, dtype=dtype)
actual = op_script(input, kernel)
expected = op(input, kernel)
assert_close(actual, expected)
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