| import pytest |
| import torch |
| from torch.autograd import gradcheck |
|
|
| import kornia.testing as utils |
| from kornia.feature.siftdesc import get_sift_bin_ksize_stride_pad, get_sift_pooling_kernel, SIFTDescriptor |
| from kornia.testing import assert_close |
|
|
|
|
| @pytest.mark.parametrize("ksize", [5, 13, 25]) |
| def test_get_sift_pooling_kernel(ksize): |
| kernel = get_sift_pooling_kernel(ksize) |
| assert kernel.shape == (ksize, ksize) |
|
|
|
|
| @pytest.mark.parametrize("ps,n_bins,ksize,stride,pad", [(41, 3, 20, 13, 5), (32, 4, 12, 8, 3)]) |
| def test_get_sift_bin_ksize_stride_pad(ps, n_bins, ksize, stride, pad): |
| out = get_sift_bin_ksize_stride_pad(ps, n_bins) |
| assert out == (ksize, stride, pad) |
|
|
|
|
| class TestSIFTDescriptor: |
| def test_shape(self, device, dtype): |
| inp = torch.ones(1, 1, 32, 32, device=device, dtype=dtype) |
| sift = SIFTDescriptor(32).to(device, dtype) |
| out = sift(inp) |
| assert out.shape == (1, 128) |
|
|
| def test_batch_shape(self, device, dtype): |
| inp = torch.ones(2, 1, 15, 15, device=device, dtype=dtype) |
| sift = SIFTDescriptor(15).to(device, dtype) |
| out = sift(inp) |
| assert out.shape == (2, 128) |
|
|
| def test_batch_shape_non_std(self, device, dtype): |
| inp = torch.ones(3, 1, 19, 19, device=device, dtype=dtype) |
| sift = SIFTDescriptor(19, 5, 3).to(device, dtype) |
| out = sift(inp) |
| assert out.shape == (3, (3 ** 2) * 5) |
|
|
| def test_toy(self, device, dtype): |
| patch = torch.ones(1, 1, 6, 6, device=device, dtype=dtype) |
| patch[0, 0, :, 3:] = 0 |
| sift = SIFTDescriptor(6, num_ang_bins=4, num_spatial_bins=1, clipval=0.2, rootsift=False).to(device, dtype) |
| out = sift(patch) |
| expected = torch.tensor([[0, 0, 1.0, 0]], device=device, dtype=dtype) |
| assert_close(out, expected, atol=1e-3, rtol=1e-3) |
|
|
| def test_gradcheck(self, device): |
| dtype = torch.float64 |
| batch_size, channels, height, width = 1, 1, 15, 15 |
| patches = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype) |
| patches = utils.tensor_to_gradcheck_var(patches) |
| sift = SIFTDescriptor(15).to(device, dtype) |
| assert gradcheck(sift, (patches,), raise_exception=True, nondet_tol=1e-4) |
|
|
| @pytest.mark.skip("Compiled functions can't take variable number") |
| def test_jit(self, device, dtype): |
| B, C, H, W = 1, 1, 32, 32 |
| patches = torch.ones(B, C, H, W, device=device, dtype=dtype) |
| model = SIFTDescriptor(32).to(patches.device, patches.dtype).eval() |
| model_jit = torch.jit.script(model) |
| assert_close(model(patches), model_jit(patches)) |
|
|
