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import random
import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils
from kornia.geometry.homography import (
find_homography_dlt,
find_homography_dlt_iterated,
oneway_transfer_error,
symmetric_transfer_error,
)
from kornia.testing import assert_close
class TestOneWayError:
def test_smoke(self, device, dtype):
pts1 = torch.rand(1, 6, 2, device=device, dtype=dtype)
pts2 = torch.rand(1, 6, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert oneway_transfer_error(pts1, pts2, H).shape == (1, 6)
def test_batch(self, device, dtype):
batch_size = 5
pts1 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
pts2 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert oneway_transfer_error(pts1, pts2, H).shape == (batch_size, 3)
def test_gradcheck(self, device):
# generate input data
batch_size, num_points, num_dims = 2, 3, 2
points1 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64, requires_grad=True)
points2 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64)
H = utils.create_random_homography(batch_size, 3).type_as(points1).to(device)
assert gradcheck(oneway_transfer_error, (points1, points2, H), raise_exception=True)
def test_shift(self, device, dtype):
pts1 = torch.zeros(3, 2, device=device, dtype=dtype)[None]
pts2 = torch.tensor([[1.0, 0.0], [2.0, 0.0], [2.0, 2.0]], device=device, dtype=dtype)[None]
H = torch.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], dtype=dtype, device=device)[None]
expected = torch.tensor([0.0, 1.0, 5.0], device=device, dtype=dtype)[None]
assert_close(oneway_transfer_error(pts1, pts2, H), expected, atol=1e-4, rtol=1e-4)
class TestSymmetricTransferError:
def test_smoke(self, device, dtype):
pts1 = torch.rand(1, 6, 2, device=device, dtype=dtype)
pts2 = torch.rand(1, 6, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert symmetric_transfer_error(pts1, pts2, H).shape == (1, 6)
def test_batch(self, device, dtype):
batch_size = 5
pts1 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
pts2 = torch.rand(batch_size, 3, 2, device=device, dtype=dtype)
H = utils.create_random_homography(1, 3).type_as(pts1).to(device)
assert symmetric_transfer_error(pts1, pts2, H).shape == (batch_size, 3)
def test_gradcheck(self, device):
# generate input data
batch_size, num_points, num_dims = 2, 3, 2
points1 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64, requires_grad=True)
points2 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64)
H = utils.create_random_homography(batch_size, 3).type_as(points1).to(device)
assert gradcheck(symmetric_transfer_error, (points1, points2, H), raise_exception=True)
def test_shift(self, device, dtype):
pts1 = torch.zeros(3, 2, device=device, dtype=dtype)[None]
pts2 = torch.tensor([[1.0, 0.0], [2.0, 0.0], [2.0, 2.0]], device=device, dtype=dtype)[None]
H = torch.tensor([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], dtype=dtype, device=device)[None]
expected = torch.tensor([0.0, 2.0, 10.0], device=device, dtype=dtype)[None]
assert_close(symmetric_transfer_error(pts1, pts2, H), expected, atol=1e-4, rtol=1e-4)
class TestFindHomographyDLT:
def test_smoke(self, device, dtype):
points1 = torch.rand(1, 4, 2, device=device, dtype=dtype)
points2 = torch.rand(1, 4, 2, device=device, dtype=dtype)
weights = torch.ones(1, 4, device=device, dtype=dtype)
H = find_homography_dlt(points1, points2, weights)
assert H.shape == (1, 3, 3)
@pytest.mark.parametrize("batch_size, num_points", [(1, 4), (2, 5), (3, 6)])
def test_shape(self, batch_size, num_points, device, dtype):
B, N = batch_size, num_points
points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
weights = torch.ones(B, N, device=device, dtype=dtype)
H = find_homography_dlt(points1, points2, weights)
assert H.shape == (B, 3, 3)
@pytest.mark.parametrize("batch_size, num_points", [(1, 4), (2, 5), (3, 6)])
def test_shape_noweights(self, batch_size, num_points, device, dtype):
B, N = batch_size, num_points
points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
H = find_homography_dlt(points1, points2, None)
assert H.shape == (B, 3, 3)
@pytest.mark.parametrize("batch_size, num_points", [(1, 4), (2, 5), (3, 6)])
def test_points_noweights(self, batch_size, num_points, device, dtype):
B, N = batch_size, num_points
points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
weights = torch.ones(B, N, device=device, dtype=dtype)
H_noweights = find_homography_dlt(points1, points2, None)
H_withweights = find_homography_dlt(points1, points2, weights)
assert H_noweights.shape == (B, 3, 3) and H_withweights.shape == (B, 3, 3)
assert_close(H_noweights, H_withweights, rtol=1e-3, atol=1e-4)
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_clean_points(self, batch_size, device, dtype):
# generate input data
points_src = torch.rand(batch_size, 10, 2, device=device, dtype=dtype)
H = kornia.eye_like(3, points_src)
H = H * 0.3 * torch.rand_like(H)
H = H / H[:, 2:3, 2:3]
points_dst = kornia.geometry.transform_points(H, points_src)
weights = torch.ones(batch_size, 10, device=device, dtype=dtype)
# compute transform from source to target
dst_homo_src = find_homography_dlt(points_src, points_dst, weights)
assert_close(kornia.geometry.transform_points(dst_homo_src, points_src), points_dst, rtol=1e-3, atol=1e-4)
@pytest.mark.grad
@pytest.mark.skipif(torch.__version__ < '1.7', reason="pytorch bug of incopatible types: #33546 fixed in v1.7")
def test_gradcheck(self, device):
# Save initial seed
initial_seed = torch.random.initial_seed()
max_number_of_checks = 10
# Test gradients for a max_number_of_checks times
current_seed = initial_seed
for i in range(max_number_of_checks):
torch.manual_seed(current_seed)
points_src = torch.rand(1, 10, 2, device=device, dtype=torch.float64, requires_grad=True)
points_dst = torch.rand_like(points_src)
weights = torch.ones_like(points_src)[..., 0]
try:
gradcheck(
find_homography_dlt, (points_src, points_dst, weights), rtol=1e-6, atol=1e-6, raise_exception=True
)
# Gradcheck failed
except RuntimeError:
# All iterations failed
if i == max_number_of_checks - 1:
assert gradcheck(
find_homography_dlt,
(points_src, points_dst, weights),
rtol=1e-6,
atol=1e-6,
raise_exception=True,
)
# Next iteration
else:
current_seed = random.randrange(0xFFFFFFFFFFFFFFFF)
continue
# Gradcheck succeed
torch.manual_seed(initial_seed)
return
class TestFindHomographyDLTIter:
def test_smoke(self, device, dtype):
points1 = torch.rand(1, 4, 2, device=device, dtype=dtype)
points2 = torch.rand(1, 4, 2, device=device, dtype=dtype)
weights = torch.ones(1, 4, device=device, dtype=dtype)
H = find_homography_dlt_iterated(points1, points2, weights, 5)
assert H.shape == (1, 3, 3)
@pytest.mark.parametrize("batch_size, num_points", [(1, 4), (2, 5), (3, 6)])
def test_shape(self, batch_size, num_points, device, dtype):
B, N = batch_size, num_points
points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
weights = torch.ones(B, N, device=device, dtype=dtype)
H = find_homography_dlt_iterated(points1, points2, weights, 5)
assert H.shape == (B, 3, 3)
@pytest.mark.parametrize("batch_size", [1, 2])
def test_clean_points(self, batch_size, device, dtype):
# generate input data
points_src = torch.rand(batch_size, 10, 2, device=device, dtype=dtype)
H = kornia.eye_like(3, points_src)
H = H * 0.3 * torch.rand_like(H)
H = H / H[:, 2:3, 2:3]
points_dst = kornia.geometry.transform_points(H, points_src)
weights = torch.ones(batch_size, 10, device=device, dtype=dtype)
# compute transform from source to target
dst_homo_src = find_homography_dlt_iterated(points_src, points_dst, weights, 10)
assert_close(kornia.geometry.transform_points(dst_homo_src, points_src), points_dst, rtol=1e-3, atol=1e-4)
@pytest.mark.grad
@pytest.mark.skipif(torch.__version__ < '1.7', reason="pytorch bug of incopatible types: #33546 fixed in v1.7")
def test_gradcheck(self, device):
# Save initial seed
initial_seed = torch.random.initial_seed()
max_number_of_checks = 10
# Test gradients for a max_number_of_checks times
current_seed = initial_seed
for i in range(max_number_of_checks):
torch.manual_seed(current_seed)
points_src = torch.rand(1, 10, 2, device=device, dtype=torch.float64, requires_grad=True)
points_dst = torch.rand_like(points_src)
weights = torch.ones_like(points_src)[..., 0]
try:
gradcheck(
find_homography_dlt_iterated,
(points_src, points_dst, weights),
rtol=1e-6,
atol=1e-6,
raise_exception=True,
)
# Gradcheck failed
except RuntimeError:
# All iterations failed
if i == max_number_of_checks - 1:
assert gradcheck(
find_homography_dlt_iterated,
(points_src, points_dst, weights),
rtol=1e-6,
atol=1e-6,
raise_exception=True,
)
# Next iteration
else:
current_seed = random.randrange(0xFFFFFFFFFFFFFFFF)
continue
# Gradcheck succeed
torch.manual_seed(initial_seed)
return
@pytest.mark.grad
@pytest.mark.parametrize("batch_size", [1, 2])
def test_dirty_points_and_gradcheck(self, batch_size, device, dtype):
# generate input data
points_src = torch.rand(batch_size, 10, 2, device=device, dtype=dtype)
H = kornia.eye_like(3, points_src)
H = H * (1 + torch.rand_like(H))
H = H / H[:, 2:3, 2:3]
points_src = 100.0 * torch.rand(batch_size, 20, 2, device=device, dtype=dtype)
points_dst = kornia.geometry.transform_points(H, points_src)
# making last point an outlier
points_dst[:, -1, :] += 20
weights = torch.ones(batch_size, 20, device=device, dtype=dtype)
# compute transform from source to target
dst_homo_src = find_homography_dlt_iterated(points_src, points_dst, weights, 0.5, 10)
assert_close(
kornia.geometry.transform_points(dst_homo_src, points_src[:, :-1]), points_dst[:, :-1], rtol=1e-3, atol=1e-3
)
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