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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.geometry.epipolar as epi |
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from kornia.testing import assert_close |
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class TestSkewSymmetric: |
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def test_smoke(self, device, dtype): |
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vec = torch.rand(1, 3, device=device, dtype=dtype) |
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cross_product_matrix = epi.cross_product_matrix(vec) |
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assert cross_product_matrix.shape == (1, 3, 3) |
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@pytest.mark.parametrize("batch_size", [1, 2, 4, 7]) |
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def test_shape(self, batch_size, device, dtype): |
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B = batch_size |
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vec = torch.rand(B, 3, device=device, dtype=dtype) |
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cross_product_matrix = epi.cross_product_matrix(vec) |
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assert cross_product_matrix.shape == (B, 3, 3) |
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def test_mean_std(self, device, dtype): |
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vec = torch.tensor([[1.0, 2.0, 3.0]], device=device, dtype=dtype) |
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cross_product_matrix = epi.cross_product_matrix(vec) |
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assert_close(cross_product_matrix[..., 0, 1], -cross_product_matrix[..., 1, 0]) |
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assert_close(cross_product_matrix[..., 0, 2], -cross_product_matrix[..., 2, 0]) |
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assert_close(cross_product_matrix[..., 1, 2], -cross_product_matrix[..., 2, 1]) |
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def test_gradcheck(self, device): |
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vec = torch.ones(2, 3, device=device, requires_grad=True, dtype=torch.float64) |
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assert gradcheck(epi.cross_product_matrix, (vec,), raise_exception=True) |
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class TestEyeLike: |
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def test_smoke(self, device, dtype): |
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image = torch.rand(1, 3, 4, 4, device=device, dtype=dtype) |
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identity = kornia.eye_like(3, image) |
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assert identity.shape == (1, 3, 3) |
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assert identity.device == image.device |
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assert identity.dtype == image.dtype |
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@pytest.mark.parametrize("batch_size, eye_size", [(1, 2), (2, 3), (3, 3), (2, 4)]) |
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def test_shape(self, batch_size, eye_size, device, dtype): |
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B, N = batch_size, eye_size |
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image = torch.rand(B, 3, 4, 4, device=device, dtype=dtype) |
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identity = kornia.eye_like(N, image) |
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assert identity.shape == (B, N, N) |
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assert identity.device == image.device |
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assert identity.dtype == image.dtype |
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class TestVecLike: |
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def test_smoke(self, device, dtype): |
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image = torch.rand(1, 3, 4, 4, device=device, dtype=dtype) |
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vec = kornia.vec_like(3, image) |
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assert vec.shape == (1, 3, 1) |
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assert vec.device == image.device |
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assert vec.dtype == image.dtype |
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@pytest.mark.parametrize("batch_size, eye_size", [(1, 2), (2, 3), (3, 3), (2, 4)]) |
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def test_shape(self, batch_size, eye_size, device, dtype): |
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B, N = batch_size, eye_size |
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image = torch.rand(B, 3, 4, 4, device=device, dtype=dtype) |
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vec = kornia.vec_like(N, image) |
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assert vec.shape == (B, N, 1) |
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assert vec.device == image.device |
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assert vec.dtype == image.dtype |
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