import pytest import torch from torch.autograd import gradcheck import kornia import kornia.testing as utils # test utils from kornia.testing import assert_close class TestSpatialGradient: def test_shape(self, device, dtype): inp = torch.zeros(1, 3, 4, 4, device=device, dtype=dtype) sobel = kornia.filters.SpatialGradient() assert sobel(inp).shape == (1, 3, 2, 4, 4) def test_shape_batch(self, device, dtype): inp = torch.zeros(2, 6, 4, 4, device=device, dtype=dtype) sobel = kornia.filters.SpatialGradient() assert sobel(inp).shape == (2, 6, 2, 4, 4) def test_edges(self, device, dtype): inp = torch.tensor( [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ] ] ], device=device, dtype=dtype, ) expected = torch.tensor( [ [ [ [ [0.0, 1.0, 0.0, -1.0, 0.0], [1.0, 3.0, 0.0, -3.0, -1.0], [2.0, 4.0, 0.0, -4.0, -2.0], [1.0, 3.0, 0.0, -3.0, -1.0], [0.0, 1.0, 0.0, -1.0, 0.0], ], [ [0.0, 1.0, 2.0, 1.0, 0.0], [1.0, 3.0, 4.0, 3.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0], [-1.0, -3.0, -4.0, -3.0, -1], [0.0, -1.0, -2.0, -1.0, 0.0], ], ] ] ], device=device, dtype=dtype, ) edges = kornia.filters.spatial_gradient(inp, normalized=False) assert_close(edges, expected) def test_edges_norm(self, device, dtype): inp = torch.tensor( [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ] ] ], device=device, dtype=dtype, ) expected = ( torch.tensor( [ [ [ [ [0.0, 1.0, 0.0, -1.0, 0.0], [1.0, 3.0, 0.0, -3.0, -1.0], [2.0, 4.0, 0.0, -4.0, -2.0], [1.0, 3.0, 0.0, -3.0, -1.0], [0.0, 1.0, 0.0, -1.0, 0.0], ], [ [0.0, 1.0, 2.0, 1.0, 0.0], [1.0, 3.0, 4.0, 3.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0], [-1.0, -3.0, -4.0, -3.0, -1], [0.0, -1.0, -2.0, -1.0, 0.0], ], ] ] ], device=device, dtype=dtype, ) / 8.0 ) edges = kornia.filters.spatial_gradient(inp, normalized=True) assert_close(edges, expected) def test_edges_sep(self, device, dtype): inp = torch.tensor( [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ] ] ], device=device, dtype=dtype, ) expected = torch.tensor( [ [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, -1.0, 0.0], [1.0, 1.0, 0.0, -1.0, -1.0], [0.0, 1.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, -1.0, -1.0, 0.0], [0.0, 0.0, -1.0, 0.0, 0.0], ], ] ] ], device=device, dtype=dtype, ) edges = kornia.filters.spatial_gradient(inp, 'diff', normalized=False) assert_close(edges, expected) def test_edges_sep_norm(self, device, dtype): inp = torch.tensor( [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ] ] ], device=device, dtype=dtype, ) expected = ( torch.tensor( [ [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, -1.0, 0.0], [1.0, 1.0, 0.0, -1.0, -1.0], [0.0, 1.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, -1.0, -1.0, 0.0], [0.0, 0.0, -1.0, 0.0, 0.0], ], ] ] ], device=device, dtype=dtype, ) / 2.0 ) edges = kornia.filters.spatial_gradient(inp, 'diff', normalized=True) assert_close(edges, expected) def test_noncontiguous(self, device, dtype): batch_size = 3 inp = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(batch_size, -1, -1, -1) actual = kornia.filters.spatial_gradient(inp) assert inp.is_contiguous() is False assert actual.is_contiguous() assert actual.shape == (3, 3, 2, 5, 5) def test_gradcheck(self, device, dtype): batch_size, channels, height, width = 1, 1, 3, 4 img = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype) img = utils.tensor_to_gradcheck_var(img) # to var assert gradcheck(kornia.filters.spatial_gradient, (img,), raise_exception=True) def test_jit(self, device, dtype): img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) op = kornia.filters.spatial_gradient op_script = torch.jit.script(op) actual = op_script(img) expected = op(img) assert_close(actual, expected) def test_module(self, device, dtype): img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) op = kornia.filters.spatial_gradient op_module = kornia.filters.SpatialGradient() expected = op(img) actual = op_module(img) assert_close(actual, expected) class TestSpatialGradient3d: def test_shape(self, device, dtype): inp = torch.zeros(1, 2, 4, 5, 6, device=device, dtype=dtype) sobel = kornia.filters.SpatialGradient3d() assert sobel(inp).shape == (1, 2, 3, 4, 5, 6) def test_shape_batch(self, device, dtype): inp = torch.zeros(7, 2, 4, 5, 6, device=device, dtype=dtype) sobel = kornia.filters.SpatialGradient3d() assert sobel(inp).shape == (7, 2, 3, 4, 5, 6) @pytest.mark.skip("fix due to bug in kernel_flip") def test_edges(self, device, dtype): inp = torch.tensor( [ [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ], [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ], ] ] ], device=device, dtype=dtype, ) expected = torch.tensor( [ [ [ [ [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.5000, 0.0000, -0.5000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.5000, 0.0000, -0.5000, 0.0000], [0.5000, 0.5000, 0.0000, -0.5000, -0.5000], [0.0000, 0.5000, 0.0000, -0.5000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.5000, 0.0000, -0.5000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], ], [ [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, -0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.5000, 0.0000, 0.0000], [0.0000, 0.5000, 0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, -0.5000, -0.5000, -0.5000, 0.0000], [0.0000, 0.0000, -0.5000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, -0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], ], [ [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.5000, 0.0000, 0.0000], [0.0000, 0.5000, 0.0000, 0.5000, 0.0000], [0.0000, 0.0000, 0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.0000, -0.5000, 0.0000, 0.0000], [0.0000, -0.5000, 0.0000, -0.5000, 0.0000], [0.0000, 0.0000, -0.5000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000], ], ], ] ] ], device=device, dtype=dtype, ) edges = kornia.filters.spatial_gradient3d(inp) assert_close(edges, expected) def test_gradcheck(self, device, dtype): img = torch.rand(1, 1, 1, 3, 4, device=device, dtype=dtype) img = utils.tensor_to_gradcheck_var(img) # to var assert gradcheck(kornia.filters.spatial_gradient3d, (img,), raise_exception=True) @pytest.mark.skip("issue with device in kernel generation") def test_jit(self, device, dtype): img = torch.rand(2, 3, 1, 4, 5, device=device, dtype=dtype) op = kornia.filters.spatial_gradient3d op_script = torch.jit.script(op) expected = op(img) actual = op_script(img) assert_close(actual, expected) def test_module(self, device, dtype): img = torch.rand(2, 3, 1, 4, 5, device=device, dtype=dtype) op = kornia.filters.spatial_gradient3d op_module = kornia.filters.SpatialGradient3d() expected = op(img) actual = op_module(img) assert_close(actual, expected) class TestSobel: def test_shape(self, device, dtype): inp = torch.zeros(1, 3, 4, 4, device=device, dtype=dtype) sobel = kornia.filters.Sobel() assert sobel(inp).shape == (1, 3, 4, 4) def test_shape_batch(self, device, dtype): inp = torch.zeros(3, 2, 4, 4, device=device, dtype=dtype) sobel = kornia.filters.Sobel() assert sobel(inp).shape == (3, 2, 4, 4) def test_magnitude(self, device, dtype): inp = torch.tensor( [ [ [ [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], ] ] ], device=device, dtype=dtype, ) expected = torch.tensor( [ [ [ [0.0, 1.4142, 2.0, 1.4142, 0.0], [1.4142, 4.2426, 4.00, 4.2426, 1.4142], [2.0, 4.0000, 0.00, 4.0000, 2.0], [1.4142, 4.2426, 4.00, 4.2426, 1.4142], [0.0, 1.4142, 2.0, 1.4142, 0.0], ] ] ], device=device, dtype=dtype, ) edges = kornia.filters.sobel(inp, normalized=False, eps=0.0) assert_close(edges, expected) def test_noncontiguous(self, device, dtype): batch_size = 3 inp = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(batch_size, -1, -1, -1) sobel = kornia.filters.Sobel() actual = sobel(inp) assert inp.is_contiguous() is False assert actual.is_contiguous() assert actual.shape == (3, 3, 5, 5) def test_gradcheck_unnorm(self, device, dtype): if "cuda" in str(device): pytest.skip("RuntimeError: Backward is not reentrant, i.e., running backward,") batch_size, channels, height, width = 1, 1, 3, 4 img = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype) img = utils.tensor_to_gradcheck_var(img) # to var assert gradcheck(kornia.filters.sobel, (img, False), raise_exception=True) def test_gradcheck(self, device, dtype): if "cuda" in str(device): pytest.skip("RuntimeError: Backward is not reentrant, i.e., running backward,") batch_size, channels, height, width = 1, 1, 3, 4 img = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype) img = utils.tensor_to_gradcheck_var(img) # to var assert gradcheck(kornia.filters.sobel, (img, True), raise_exception=True) def test_jit(self, device, dtype): img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) op = kornia.filters.sobel op_script = torch.jit.script(op) expected = op(img) actual = op_script(img) assert_close(actual, expected) def test_module(self, device, dtype): img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) op = kornia.filters.sobel op_module = kornia.filters.Sobel() expected = op(img) actual = op_module(img) assert_close(actual, expected)