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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
@pytest.mark.parametrize("window_size", [5])
def test_get_laplacian_kernel(window_size):
kernel = kornia.filters.get_laplacian_kernel1d(window_size)
assert kernel.shape == (window_size,)
assert kernel.sum().item() == pytest.approx(0.0)
@pytest.mark.parametrize("window_size", [7])
def test_get_laplacian_kernel2d(window_size):
kernel = kornia.filters.get_laplacian_kernel2d(window_size)
assert kernel.shape == (window_size, window_size)
assert kernel.sum().item() == pytest.approx(0.0)
expected = torch.tensor(
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, -48.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
]
)
assert_close(expected, kernel)
class TestLaplacian:
@pytest.mark.parametrize("batch_shape", [(1, 4, 8, 15), (2, 3, 11, 7)])
def test_cardinality(self, batch_shape, device, dtype):
kernel_size = 5
input = torch.rand(batch_shape, device=device, dtype=dtype)
actual = kornia.filters.laplacian(input, kernel_size)
assert actual.shape == batch_shape
def test_noncontiguous(self, device, dtype):
batch_size = 3
input = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(batch_size, -1, -1, -1)
kernel_size = 3
actual = kornia.filters.laplacian(input, kernel_size)
assert_close(actual, actual)
def test_gradcheck(self, device, dtype):
# test parameters
batch_shape = (1, 2, 5, 7)
kernel_size = 3
# evaluate function gradient
input = torch.rand(batch_shape, device=device, dtype=dtype)
input = utils.tensor_to_gradcheck_var(input)
assert gradcheck(kornia.filters.laplacian, (input, kernel_size), raise_exception=True)
def test_jit(self, device, dtype):
op = kornia.filters.laplacian
op_script = torch.jit.script(op)
params = [3]
img = torch.ones(1, 3, 5, 5, device=device, dtype=dtype)
assert_close(op(img, *params), op_script(img, *params))
def test_module(self, device, dtype):
params = [3]
op = kornia.filters.laplacian
op_module = kornia.filters.Laplacian(*params)
img = torch.ones(1, 3, 5, 5, device=device, dtype=dtype)
assert_close(op(img, *params), op_module(img))
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