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