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