def test_real_sift_preextract(self, device, dtype, data):
torch.random.manual_seed(0)
# This is not unit test, but that is quite good integration test
feat = SIFTFeature(2000)
matcher = LocalFeatureMatcher(feat, DescriptorMatcher('snn',
0.8)).to(device)
ransac = RANSAC('homography', 1.0, 2048, 10).to(device, dtype)
data_dev = utils.dict_to(data, device, dtype)
pts_src = data_dev['pts0']
pts_dst = data_dev['pts1']
lafs, _, descs = feat(data_dev["image0"])
data_dev["lafs0"] = lafs
data_dev["descriptors0"] = descs
lafs2, _, descs2 = feat(data_dev["image1"])
data_dev["lafs1"] = lafs2
data_dev["descriptors1"] = descs2
with torch.no_grad():
out = matcher(data_dev)
homography, inliers = ransac(out['keypoints0'], out['keypoints1'])
assert inliers.sum().item() > 50 # we have enough inliers
# Reprojection error of 5px is OK
assert_close(transform_points(homography[None], pts_src[None]),
pts_dst[None],
rtol=5e-2,
atol=5)
def test_gradcheck(self, device):
B, C, H, W = 1, 1, 32, 32
img = torch.rand(B, C, H, W, device=device)
local_feature = SIFTFeature(2, True).to(device).to(device)
img = utils.tensor_to_gradcheck_var(img) # to var
assert gradcheck(local_feature,
img,
eps=1e-4,
atol=1e-4,
raise_exception=True)
def test_gradcheck(self, device):
matcher = LocalFeatureMatcher(SIFTFeature(5),
DescriptorMatcher('nn', 1.0)).to(device)
patches = torch.rand(1, 1, 32, 32, device=device)
patches05 = resize(patches, (48, 48))
patches = utils.tensor_to_gradcheck_var(patches) # to var
patches05 = utils.tensor_to_gradcheck_var(patches05) # to var
def proxy_forward(x, y):
return matcher({"image0": x, "image1": y})["keypoints0"]
assert gradcheck(proxy_forward, (patches, patches05),
eps=1e-4,
atol=1e-4,
raise_exception=True)
def test_smoke(self, device):
matcher = LocalFeatureMatcher(SIFTFeature(5),
DescriptorMatcher('snn', 0.8)).to(device)
assert matcher is not None
def test_smoke(self, device, dtype):
sift = SIFTFeature()
assert sift is not None