def test_same(self, device, dtype):
B, C, H, W = 1, 3, 64, 64
PS = 16
img = torch.rand(B, C, H, W, device=device, dtype=dtype)
img_gray = kornia.color.rgb_to_grayscale(img)
centers = torch.tensor([[H / 3.0, W / 3.0], [2.0 * H / 3.0, W / 2.0]],
device=device,
dtype=dtype).view(1, 2, 2)
scales = torch.tensor([(H + W) / 4.0, (H + W) / 8.0],
device=device,
dtype=dtype).view(1, 2, 1, 1)
ori = torch.tensor([0.0, 30.0], device=device,
dtype=dtype).view(1, 2, 1)
lafs = kornia.feature.laf_from_center_scale_ori(centers, scales, ori)
sift = SIFTDescriptor(PS).to(device, dtype)
descs_test_from_rgb = get_laf_descriptors(img, lafs, sift, PS, True)
descs_test_from_gray = get_laf_descriptors(img_gray, lafs, sift, PS,
True)
patches = extract_patches_from_pyramid(img_gray, lafs, PS)
B1, N1, CH1, H1, W1 = patches.size()
# Descriptor accepts standard tensor [B, CH, H, W], while patches are [B, N, CH, H, W] shape
# So we need to reshape a bit :)
descs_reference = sift(patches.view(B1 * N1, CH1, H1,
W1)).view(B1, N1, -1)
assert_close(descs_test_from_rgb, descs_reference)
assert_close(descs_test_from_gray, descs_reference)
def test_gradcheck(self, device):
B, C, H, W = 1, 1, 32, 32
PS = 16
img = torch.rand(B, C, H, W, device=device)
img = utils.tensor_to_gradcheck_var(img) # to var
local_feature = LocalFeature(ScaleSpaceDetector(2),
LAFDescriptor(SIFTDescriptor(PS),
PS)).to(device, img.dtype)
assert gradcheck(local_feature,
img,
eps=1e-4,
atol=1e-4,
raise_exception=True)
def test_jit(self, device, dtype):
B, C, H, W = 1, 1, 32, 32
patches = torch.rand(B, C, H, W, device=device, dtype=dtype)
patches2x = resize(patches, (48, 48))
inputs = {"image0": patches, "image1": patches2x}
model = LocalFeatureMatcher(SIFTDescriptor(32),
DescriptorMatcher('snn',
0.8)).to(device).eval()
model_jit = torch.jit.script(model)
out = model(inputs)
out_jit = model_jit(inputs)
for k, v in out.items():
assert_close(v, out_jit[k])
def test_same(self, device, dtype):
B, C, H, W = 1, 1, 64, 64
PS = 16
img = torch.rand(B, C, H, W, device=device, dtype=dtype)
det = ScaleSpaceDetector(10)
desc = SIFTDescriptor(PS)
local_feature = LocalFeature(det, LAFDescriptor(desc,
PS)).to(device, dtype)
lafs, responses, descs = local_feature(img)
lafs1, responses1 = det(img)
assert_close(lafs, lafs1)
assert_close(responses, responses1)
patches = extract_patches_from_pyramid(img, lafs1, PS)
B1, N1, CH1, H1, W1 = patches.size()
# Descriptor accepts standard tensor [B, CH, H, W], while patches are [B, N, CH, H, W] shape
# So we need to reshape a bit :)
descs1 = desc(patches.view(B1 * N1, CH1, H1, W1)).view(B1, N1, -1)
assert_close(descs, descs1)
def test_smoke(self, device, dtype):
det = ScaleSpaceDetector(10)
desc = SIFTDescriptor(32)
local_feature = LocalFeature(det, desc).to(device, dtype)
assert local_feature is not None