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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, 11])
@pytest.mark.parametrize("sigma", [1.5, 5.0])
def test_get_gaussian_kernel(window_size, sigma):
kernel = kornia.filters.get_gaussian_kernel1d(window_size, sigma)
assert kernel.shape == (window_size,)
assert kernel.sum().item() == pytest.approx(1.0)
@pytest.mark.parametrize("window_size", [5, 11])
@pytest.mark.parametrize("sigma", [1.5, 5.0])
def test_get_gaussian_discrete_kernel(window_size, sigma):
kernel = kornia.filters.get_gaussian_discrete_kernel1d(window_size, sigma)
assert kernel.shape == (window_size,)
assert kernel.sum().item() == pytest.approx(1.0)
@pytest.mark.parametrize("window_size", [5, 11])
@pytest.mark.parametrize("sigma", [1.5, 5.0])
def test_get_gaussian_erf_kernel(window_size, sigma):
kernel = kornia.filters.get_gaussian_erf_kernel1d(window_size, sigma)
assert kernel.shape == (window_size,)
assert kernel.sum().item() == pytest.approx(1.0)
@pytest.mark.parametrize("ksize_x", [5, 11])
@pytest.mark.parametrize("ksize_y", [3, 7])
@pytest.mark.parametrize("sigma", [1.5, 2.1])
def test_get_gaussian_kernel2d(ksize_x, ksize_y, sigma):
kernel = kornia.filters.get_gaussian_kernel2d((ksize_x, ksize_y), (sigma, sigma))
assert kernel.shape == (ksize_x, ksize_y)
assert kernel.sum().item() == pytest.approx(1.0)
@pytest.mark.parametrize("ksize_x", [5, 11])
@pytest.mark.parametrize("ksize_y", [3, 7])
@pytest.mark.parametrize("sigma", [1.5, 2.1])
def test_separable(ksize_x, ksize_y, sigma, device, dtype):
input = torch.rand(2, 3, 16, 16, device=device, dtype=dtype)
out = kornia.filters.gaussian_blur2d(input, (ksize_x, ksize_y), (sigma, sigma), "replicate", separable=False)
out_sep = kornia.filters.gaussian_blur2d(input, (ksize_x, ksize_y), (sigma, sigma), "replicate", separable=True)
assert_close(out, out_sep)
class TestGaussianBlur2d:
@pytest.mark.parametrize("batch_shape", [(1, 4, 8, 15), (2, 3, 11, 7)])
def test_cardinality(self, batch_shape, device, dtype):
kernel_size = (5, 7)
sigma = (1.5, 2.1)
input = torch.rand(batch_shape, device=device, dtype=dtype)
actual = kornia.filters.gaussian_blur2d(input, kernel_size, sigma, "replicate")
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, 3)
sigma = (1.5, 2.1)
actual = kornia.filters.gaussian_blur2d(input, kernel_size, sigma, "replicate")
assert_close(actual, actual)
def test_gradcheck(self, device, dtype):
# test parameters
batch_shape = (1, 3, 5, 5)
kernel_size = (3, 3)
sigma = (1.5, 2.1)
# evaluate function gradient
input = torch.rand(batch_shape, device=device, dtype=dtype)
input = utils.tensor_to_gradcheck_var(input) # to var
assert gradcheck(kornia.filters.gaussian_blur2d, (input, kernel_size, sigma, "replicate"), raise_exception=True)
def test_jit(self, device, dtype):
op = kornia.filters.gaussian_blur2d
op_script = torch.jit.script(op)
params = [(3, 3), (1.5, 1.5)]
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, 3), (1.5, 1.5)]
op = kornia.filters.gaussian_blur2d
op_module = kornia.filters.GaussianBlur2d(*params)
img = torch.ones(1, 3, 5, 5, device=device, dtype=dtype)
assert_close(op(img, *params), op_module(img))
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