import pytest import torch from torch.autograd import gradcheck from kornia.geometry.line import ParametrizedLine, fit_line from kornia.testing import BaseTester class TestParametrizedLine(BaseTester): def test_smoke(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) d0 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) l0 = ParametrizedLine(p0, d0) self.assert_close(l0.origin, p0) self.assert_close(l0.direction, d0) assert l0.dim() == 2 def test_through(self, device, dtype): p0 = torch.tensor([-1.0, -1.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 1.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) direction_expected = torch.tensor([0.7071, 0.7071], device=device, dtype=dtype) self.assert_close(l1.origin, p0) self.assert_close(l1.direction, direction_expected) def test_point_at(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) self.assert_close(l1.point_at(0.0), torch.tensor([0.0, 0.0], device=device, dtype=dtype)) self.assert_close(l1.point_at(0.5), torch.tensor([0.5, 0.0], device=device, dtype=dtype)) self.assert_close(l1.point_at(1.0), torch.tensor([1.0, 0.0], device=device, dtype=dtype)) def test_projection1(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) p2 = torch.tensor([0.5, 0.5], device=device, dtype=dtype) p3_expected = torch.tensor([0.5, 0.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) p3 = l1.projection(p2) self.assert_close(p3, p3_expected) def test_projection2(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([0.0, 1.0], device=device, dtype=dtype) p2 = torch.tensor([0.5, 0.5], device=device, dtype=dtype) p3_expected = torch.tensor([0.0, 0.5], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) p3 = l1.projection(p2) self.assert_close(p3, p3_expected) def test_projection(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) point = torch.tensor([1.0, 2.0], device=device, dtype=dtype) point_projection = torch.tensor([1.0, 0.0], device=device, dtype=dtype) self.assert_close(l1.projection(point), point_projection) def test_distance(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) point = torch.tensor([1.0, 4.0], device=device, dtype=dtype) distance_expected = torch.tensor(4.0, device=device, dtype=dtype) self.assert_close(l1.distance(point), distance_expected) def test_squared_distance(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 0.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) point = torch.tensor([1.0, 4.0], device=device, dtype=dtype) distance_expected = torch.tensor(16.0, device=device, dtype=dtype) self.assert_close(l1.squared_distance(point), distance_expected) @pytest.mark.skip(reason="not implemented yet") def test_cardinality(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_jit(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_exception(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_module(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_gradcheck(self, device): pass class TestFitLine(BaseTester): @pytest.mark.parametrize("B", (1, 2)) @pytest.mark.parametrize("D", (2, 3, 4)) def test_smoke(self, device, dtype, B, D): N: int = 10 # num points points = torch.ones(B, N, D, device=device, dtype=dtype) line = fit_line(points) assert isinstance(line, ParametrizedLine) assert line.origin.shape == (B, D) assert line.direction.shape == (B, D) assert (line.origin == line[0]).all() assert (line.direction == line[1]).all() origin, direction = fit_line(points) assert (line.origin == origin).all() assert (line.direction == direction).all() def test_fit_line2(self, device, dtype): p0 = torch.tensor([0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 1.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) num_points: int = 10 pts = [] for t in torch.linspace(-10, 10, num_points): p2 = l1.point_at(t) pts.append(p2) pts = torch.stack(pts) line_est = fit_line(pts[None]) dir_exp = torch.tensor([0.7071, 0.7071], device=device, dtype=dtype) # NOTE: for some reason the result in c[u/cuda differs angle_est = torch.nn.functional.cosine_similarity(line_est.direction, dir_exp, -1) angle_exp = torch.tensor([1.0], device=device, dtype=dtype) self.assert_close(angle_est.abs(), angle_exp) def test_fit_line3(self, device, dtype): p0 = torch.tensor([0.0, 0.0, 0.0], device=device, dtype=dtype) p1 = torch.tensor([1.0, 1.0, 1.0], device=device, dtype=dtype) l1 = ParametrizedLine.through(p0, p1) num_points: int = 10 pts = [] for t in torch.linspace(-10, 10, num_points): p2 = l1.point_at(t) pts.append(p2) pts = torch.stack(pts) line_est = fit_line(pts[None]) dir_exp = torch.tensor([0.7071, 0.7071, 0.7071], device=device, dtype=dtype) # NOTE: result differs with the sign between cpu/cuda angle_est = torch.nn.functional.cosine_similarity(line_est.direction, dir_exp, -1) angle_exp = torch.tensor([1.0], device=device, dtype=dtype) self.assert_close(angle_est.abs(), angle_exp) @pytest.mark.skip(reason="numerical do not match with analytical") def test_gradcheck(self, device): def proxy_func(pts, weights): line = fit_line(pts, weights) return line.projection(pts[:, 0].T) pts = torch.rand(1, 3, 2, device=device, dtype=torch.float64, requires_grad=True) weights = torch.rand(1, 3, device=device, dtype=torch.float64, requires_grad=False) assert gradcheck(proxy_func, (pts, weights), raise_exception=True) @pytest.mark.skip(reason="not implemented yet") def test_cardinality(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_jit(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_exception(self, device, dtype): pass @pytest.mark.skip(reason="not implemented yet") def test_module(self, device, dtype): pass