| import torch |
| from torch.autograd import gradcheck |
|
|
| import kornia |
| import kornia.testing as utils |
| from kornia.feature.scale_space_detector import ScaleSpaceDetector |
| from kornia.testing import assert_close |
|
|
|
|
| class TestScaleSpaceDetector: |
| def test_shape(self, device, dtype): |
| inp = torch.rand(1, 1, 32, 32, device=device, dtype=dtype) |
| n_feats = 10 |
| det = ScaleSpaceDetector(n_feats).to(device, dtype) |
| lafs, resps = det(inp) |
| assert lafs.shape == torch.Size([1, n_feats, 2, 3]) |
| assert resps.shape == torch.Size([1, n_feats]) |
|
|
| def test_shape_batch(self, device, dtype): |
| inp = torch.rand(7, 1, 32, 32, device=device, dtype=dtype) |
| n_feats = 10 |
| det = ScaleSpaceDetector(n_feats).to(device, dtype) |
| lafs, resps = det(inp) |
| assert lafs.shape == torch.Size([7, n_feats, 2, 3]) |
| assert resps.shape == torch.Size([7, n_feats]) |
|
|
| def test_toy(self, device, dtype): |
| inp = torch.zeros(1, 1, 33, 33, device=device, dtype=dtype) |
| inp[:, :, 13:-13, 13:-13] = 1.0 |
| n_feats = 1 |
| det = ScaleSpaceDetector(n_feats, resp_module=kornia.feature.BlobHessian(), mr_size=3.0).to(device, dtype) |
| lafs, resps = det(inp) |
| expected_laf = torch.tensor([[[[9.5823, 0.0000, 16.0], [0.0, 9.5823, 16.0]]]], device=device, dtype=dtype) |
| expected_resp = torch.tensor([[0.0857]], device=device, dtype=dtype) |
| assert_close(lafs, expected_laf, rtol=0.001, atol=1e-03) |
| assert_close(resps, expected_resp, rtol=0.001, atol=1e-03) |
|
|
| def test_toy_mask(self, device, dtype): |
| inp = torch.zeros(1, 1, 33, 33, device=device, dtype=dtype) |
| inp[:, :, 13:-13, 13:-13] = 1.0 |
|
|
| mask = torch.zeros(1, 1, 33, 33, device=device, dtype=dtype) |
| mask[:, :, 1:-1, 3:-3] = 1.0 |
|
|
| n_feats = 1 |
| det = ScaleSpaceDetector(n_feats, resp_module=kornia.feature.BlobHessian(), mr_size=3.0).to(device, dtype) |
| lafs, resps = det(inp, mask) |
| expected_laf = torch.tensor([[[[9.5823, 0.0000, 16.0], [0.0, 9.5823, 16.0]]]], device=device, dtype=dtype) |
| expected_resp = torch.tensor([[0.0857]], device=device, dtype=dtype) |
| assert_close(lafs, expected_laf, rtol=0.001, atol=1e-03) |
| assert_close(resps, expected_resp, rtol=0.001, atol=1e-03) |
|
|
| def test_gradcheck(self, device): |
| batch_size, channels, height, width = 1, 1, 7, 7 |
| patches = torch.rand(batch_size, channels, height, width, device=device) |
| patches = utils.tensor_to_gradcheck_var(patches) |
| assert gradcheck(ScaleSpaceDetector(2).to(device), patches, raise_exception=True, nondet_tol=1e-4) |
|
|
