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import sys
import pytest
import torch
import torch.nn as nn
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.feature import (
DescriptorMatcher,
extract_patches_from_pyramid,
get_laf_descriptors,
GFTTAffNetHardNet,
LAFDescriptor,
LocalFeature,
ScaleSpaceDetector,
SIFTDescriptor,
SIFTFeature,
)
from kornia.feature.integrated import LocalFeatureMatcher
from kornia.geometry import RANSAC, resize, transform_points
from kornia.testing import assert_close
class TestGetLAFDescriptors:
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, dtype=torch.float64):
B, C, H, W = 1, 1, 32, 32
PS = 16
img = torch.rand(B, C, H, W, device=device)
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)
img = utils.tensor_to_gradcheck_var(img) # to var
lafs = utils.tensor_to_gradcheck_var(lafs) # to var
class _MeanPatch(nn.Module):
def forward(self, inputs):
return inputs.mean(dim=(2, 3))
desc = _MeanPatch()
img = utils.tensor_to_gradcheck_var(img) # to var
assert gradcheck(get_laf_descriptors, (img, lafs, desc, PS, True),
eps=1e-3, atol=1e-3, raise_exception=True, nondet_tol=1e-3)
class TestLAFDescriptor:
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)
lafsift = LAFDescriptor(sift, PS)
descs_test = lafsift(img, lafs)
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, descs_reference)
def test_gradcheck(self, device, dtype=torch.float64):
B, C, H, W = 1, 1, 32, 32
PS = 16
img = torch.rand(B, C, H, W, device=device)
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)
img = utils.tensor_to_gradcheck_var(img) # to var
lafs = utils.tensor_to_gradcheck_var(lafs) # to var
class _MeanPatch(nn.Module):
def forward(self, inputs):
return inputs.mean(dim=(2, 3))
lafdesc = LAFDescriptor(_MeanPatch(), PS)
img = utils.tensor_to_gradcheck_var(img) # to var
assert gradcheck(lafdesc, (img, lafs), eps=1e-3, atol=1e-3, raise_exception=True, nondet_tol=1e-3)
class TestLocalFeature:
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
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)
@pytest.mark.skip("Takes too long time (but works)")
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)
class TestSIFTFeature:
# The real test is in TestLocalFeatureMatcher
def test_smoke(self, device, dtype):
sift = SIFTFeature()
assert sift is not None
@pytest.mark.skip("jacobian not well computed")
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)
class TestGFTTAffNetHardNet:
# The real test is in TestLocalFeatureMatcher
def test_smoke(self, device, dtype):
feat = GFTTAffNetHardNet().to(device, dtype)
assert feat is not None
@pytest.mark.skip("jacobian not well computed")
def test_gradcheck(self, device):
B, C, H, W = 1, 1, 32, 32
img = torch.rand(B, C, H, W, device=device)
img = utils.tensor_to_gradcheck_var(img) # to var
local_feature = GFTTAffNetHardNet(2, True).to(device, img.dtype)
assert gradcheck(local_feature, img, eps=1e-4, atol=1e-4, raise_exception=True)
class TestLocalFeatureMatcher:
def test_smoke(self, device):
matcher = LocalFeatureMatcher(SIFTFeature(5), DescriptorMatcher('snn', 0.8)).to(device)
assert matcher is not None
@pytest.mark.parametrize("data", ["loftr_homo"], indirect=True)
def test_nomatch(self, device, dtype, data):
matcher = LocalFeatureMatcher(GFTTAffNetHardNet(100), DescriptorMatcher('snn', 0.8)).to(device, dtype)
data_dev = utils.dict_to(data, device, dtype)
with torch.no_grad():
out = matcher({"image0": data_dev["image0"], "image1": 0 * data_dev["image0"]})
assert len(out['keypoints0']) == 0
@pytest.mark.skip("Takes too long time (but works)")
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)
@pytest.mark.parametrize("data", ["loftr_homo"], indirect=True)
def test_real_sift(self, device, dtype, data):
torch.random.manual_seed(0)
# This is not unit test, but that is quite good integration test
matcher = LocalFeatureMatcher(SIFTFeature(2000), DescriptorMatcher('snn', 0.8)).to(device, dtype)
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']
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)
@pytest.mark.parametrize("data", ["loftr_homo"], indirect=True)
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)
@pytest.mark.skipif(sys.platform == "win32",
reason="this test takes so much memory in the CI with Windows")
@pytest.mark.parametrize("data", ["loftr_homo"], indirect=True)
def test_real_gftt(self, device, dtype, data):
torch.random.manual_seed(0)
# This is not unit test, but that is quite good integration test
matcher = LocalFeatureMatcher(GFTTAffNetHardNet(2000), DescriptorMatcher('snn', 0.8)).to(device, dtype)
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']
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)
@pytest.mark.skip("ScaleSpaceDetector now is not jittable")
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])
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