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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]) |
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def test_get_laplacian_kernel(window_size): |
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kernel = kornia.filters.get_laplacian_kernel1d(window_size) |
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assert kernel.shape == (window_size,) |
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assert kernel.sum().item() == pytest.approx(0.0) |
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@pytest.mark.parametrize("window_size", [7]) |
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def test_get_laplacian_kernel2d(window_size): |
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kernel = kornia.filters.get_laplacian_kernel2d(window_size) |
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assert kernel.shape == (window_size, window_size) |
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assert kernel.sum().item() == pytest.approx(0.0) |
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expected = torch.tensor( |
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[ |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, -48.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], |
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] |
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) |
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assert_close(expected, kernel) |
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class TestLaplacian: |
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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 |
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input = torch.rand(batch_shape, device=device, dtype=dtype) |
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actual = kornia.filters.laplacian(input, kernel_size) |
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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 |
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actual = kornia.filters.laplacian(input, kernel_size) |
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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, 2, 5, 7) |
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kernel_size = 3 |
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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.laplacian, (input, kernel_size), raise_exception=True) |
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def test_jit(self, device, dtype): |
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op = kornia.filters.laplacian |
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op_script = torch.jit.script(op) |
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params = [3] |
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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] |
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op = kornia.filters.laplacian |
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op_module = kornia.filters.Laplacian(*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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