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