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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("batch_size", [0, 1, 5])
@pytest.mark.parametrize("ksize", [3, 11])
@pytest.mark.parametrize("angle", [0.0, 360.0])
@pytest.mark.parametrize("direction", [-1.0, 1.0])
def test_get_motion_kernel2d(batch_size, ksize, angle, direction):
if batch_size != 0:
angle = torch.tensor([angle] * batch_size)
direction = torch.tensor([direction] * batch_size)
else:
batch_size = 1
kernel = kornia.filters.kernels_geometry.get_motion_kernel2d(ksize, angle, direction)
assert kernel.shape == (batch_size, ksize, ksize)
assert kernel.sum().item() == pytest.approx(batch_size)
class TestMotionBlur:
@pytest.mark.parametrize("batch_shape", [(1, 4, 8, 15), (2, 3, 11, 7)])
def test_motion_blur(self, batch_shape, device, dtype):
ksize = 5
angle = 200.0
direction = 0.3
input = torch.rand(batch_shape, device=device, dtype=dtype)
motion = kornia.filters.MotionBlur(ksize, angle, direction)
assert motion(input).shape == batch_shape
def test_noncontiguous(self, device, dtype):
batch_size = 3
inp = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(batch_size, -1, -1, -1)
kernel_size = 3
angle = 200.0
direction = 0.3
actual = kornia.filters.motion_blur(inp, kernel_size, angle, direction)
assert_close(actual, actual)
def test_gradcheck(self, device, dtype):
batch_shape = (1, 3, 4, 5)
ksize = 9
angle = 34.0
direction = -0.2
input = torch.rand(batch_shape, device=device, dtype=dtype)
input = utils.tensor_to_gradcheck_var(input)
assert gradcheck(
kornia.filters.motion_blur, (input, ksize, angle, direction, "replicate"), raise_exception=True
)
@pytest.mark.skip("angle can be Union")
def test_jit(self, device, dtype):
img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype)
ksize = 5
angle = 65.0
direction = 0.1
op = kornia.filters.motion_blur
op_script = torch.jit.script(op)
actual = op_script(img, ksize, angle, direction)
expected = op(img, ksize, angle, direction)
assert_close(actual, expected)
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