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chore: import upstream snapshot with attribution
2026-07-13 12:49:27 +08:00

376 行
16 KiB
Python
原始文件 永久链接 Blame 文件历史

# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import pytest
import torch
import kornia
import kornia.geometry.transform as proj
from kornia.core.utils import _torch_inverse_cast
from testing.base import BaseTester
class TestWarpAffine3d(BaseTester):
def test_smoke(self, device, dtype):
sample = torch.rand(1, 3, 3, 4, 5, device=device, dtype=dtype)
P = torch.rand(1, 3, 4, device=device, dtype=dtype)
output = proj.warp_affine3d(sample, P, (3, 4, 5))
assert output.shape == (1, 3, 3, 4, 5)
@pytest.mark.parametrize("batch_size", [1, 3])
@pytest.mark.parametrize("num_channels", [1, 3, 5])
@pytest.mark.parametrize("out_shape", [(3, 3, 3), (4, 5, 6)])
def test_batch(self, batch_size, num_channels, out_shape, device, dtype):
B, C = batch_size, num_channels
sample = torch.rand(B, C, 3, 4, 5, device=device, dtype=dtype)
P = torch.rand(B, 3, 4, device=device, dtype=dtype)
output = proj.warp_affine3d(sample, P, out_shape)
assert list(output.shape) == [B, C, *list(out_shape)]
def test_gradcheck(self, device):
# generate input data
sample = torch.rand(1, 3, 3, 4, 5, device=device, dtype=torch.float64, requires_grad=True)
P = torch.rand(1, 3, 4, device=device, dtype=torch.float64)
self.gradcheck(proj.warp_affine3d, (sample, P, (3, 3, 3)))
def test_forth_back(self, device, dtype):
out_shape = (3, 4, 5)
sample = torch.rand(2, 5, 3, 4, 5, device=device, dtype=dtype)
P = torch.rand(2, 3, 4, device=device, dtype=dtype)
P = kornia.geometry.convert_affinematrix_to_homography3d(P)
P_hat = (_torch_inverse_cast(P) @ P)[:, :3]
output = proj.warp_affine3d(sample, P_hat, out_shape, flags="nearest")
self.assert_close(output, sample, rtol=1e-4, atol=1e-4)
def test_rotate_x(self, device, dtype):
sample = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
expected = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 2.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
_, _, D, H, W = sample.shape
center = torch.tensor([[(W - 1) / 2, (H - 1) / 2, (D - 1) / 2]], device=device, dtype=dtype)
angles = torch.tensor([[90.0, 0.0, 0.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angles, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angles, scales)
output = proj.warp_affine3d(sample, P, (3, 3, 3))
self.assert_close(output, expected, rtol=1e-4, atol=1e-4)
def test_rotate_y(self, device, dtype):
sample = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
expected = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [2.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
_, _, D, H, W = sample.shape
center = torch.tensor([[(W - 1) / 2, (H - 1) / 2, (D - 1) / 2]], device=device, dtype=dtype)
angles = torch.tensor([[0.0, 90.0, 0.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angles, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angles, scales)
output = proj.warp_affine3d(sample, P, (3, 3, 3))
self.assert_close(output, expected, rtol=1e-4, atol=1e-4)
def test_rotate_z(self, device, dtype):
sample = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 2.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
expected = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 1.0, 2.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
]
],
device=device,
dtype=dtype,
)
_, _, D, H, W = sample.shape
center = torch.tensor([[(W - 1) / 2, (H - 1) / 2, (D - 1) / 2]], device=device, dtype=dtype)
angles = torch.tensor([[0.0, 0.0, 90.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angles, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angles, scales)
output = proj.warp_affine3d(sample, P, (3, 3, 3))
self.assert_close(output, expected, rtol=1e-4, atol=1e-4)
def test_rotate_y_large(self, device, dtype):
"""Rotates 90deg anti-clockwise."""
sample = torch.tensor(
[
[
[
[[0.0, 4.0, 0.0], [0.0, 3.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 2.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 9.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 6.0, 7.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 8.0, 0.0], [0.0, 0.0, 0.0]],
],
]
],
device=device,
dtype=dtype,
)
expected = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[4.0, 2.0, 0.0], [3.0, 1.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
[
[[0.0, 0.0, 0.0], [0.0, 7.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 6.0, 8.0], [9.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
],
]
],
device=device,
dtype=dtype,
)
_, _, D, H, W = sample.shape
center = torch.tensor([[(W - 1) / 2, (H - 1) / 2, (D - 1) / 2]], device=device, dtype=dtype)
angles = torch.tensor([[0.0, 90.0, 0.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angles, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angles, scales)
output = proj.warp_affine3d(sample, P, (3, 3, 3))
self.assert_close(output, expected, rtol=1e-4, atol=1e-4)
class TestGetRotationMatrix3d(BaseTester):
def test_smoke(self, device, dtype):
center = torch.rand(1, 3, device=device, dtype=dtype)
angle = torch.rand(1, 3, device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
assert P.shape == (1, 3, 4)
@pytest.mark.parametrize("batch_size", [1, 3, 6])
def test_batch(self, batch_size, device, dtype):
B: int = batch_size
center = torch.rand(B, 3, device=device, dtype=dtype)
angle = torch.rand(B, 3, device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
assert P.shape == (B, 3, 4)
def test_identity(self, device, dtype):
center = torch.zeros(1, 3, device=device, dtype=dtype)
angle = torch.zeros(1, 3, device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
P_expected = torch.tensor(
[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]], device=device, dtype=dtype
).unsqueeze(0)
self.assert_close(P, P_expected, atol=1e-4, rtol=1e-4)
def test_rot90x(self, device, dtype):
center = torch.zeros(1, 3, device=device, dtype=dtype)
angle = torch.tensor([[90.0, 0.0, 0.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
P_expected = torch.tensor(
[[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, -1.0, 0.0], [0.0, 1.0, 0.0, 0.0]], device=device, dtype=dtype
).unsqueeze(0)
self.assert_close(P, P_expected, atol=1e-4, rtol=1e-4)
def test_rot90y(self, device, dtype):
center = torch.zeros(1, 3, device=device, dtype=dtype)
angle = torch.tensor([[0.0, 90.0, 0.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
P_expected = torch.tensor(
[[0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0], [-1.0, 0.0, 0.0, 0.0]], device=device, dtype=dtype
).unsqueeze(0)
self.assert_close(P, P_expected, atol=1e-4, rtol=1e-4)
def test_rot90z(self, device, dtype):
center = torch.zeros(1, 3, device=device, dtype=dtype)
angle = torch.tensor([[0.0, 0.0, 90.0]], device=device, dtype=dtype)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=dtype)
P = proj.get_projective_transform(center, angle, scales)
P_expected = torch.tensor(
[[0.0, -1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]], device=device, dtype=dtype
).unsqueeze(0)
self.assert_close(P, P_expected, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device):
# generate input data
center = torch.rand(1, 3, device=device, dtype=torch.float64, requires_grad=True)
angle = torch.rand(1, 3, device=device, dtype=torch.float64)
scales: torch.Tensor = torch.ones_like(angle, device=device, dtype=torch.float64)
self.gradcheck(proj.get_projective_transform, (center, angle, scales))
class TestPerspectiveTransform3D(BaseTester):
@pytest.mark.skip("Not working")
@pytest.mark.parametrize("batch_size", [1, 2, 5])
def test_get_perspective_transform3d(self, batch_size, device, dtype):
# generate input data
# d_max, h_max, w_max = 16, 64, 32 # height, width
# d = torch.ceil(d_max * torch.rand(batch_size, device=device, dtype=dtype))
# h = torch.ceil(h_max * torch.rand(batch_size, device=device, dtype=dtype))
# w = torch.ceil(w_max * torch.rand(batch_size, device=device, dtype=dtype))
norm = torch.rand(batch_size, 8, 3, device=device, dtype=dtype)
points_src = torch.rand_like(norm, device=device, dtype=dtype)
points_dst = points_src + norm
# compute transform from source to target
dst_homo_src = kornia.geometry.transform.get_perspective_transform3d(points_src, points_dst)
# TODO: get_perspective_transform3d seems to be correct since it would result in the
# expected output for cropping volumes. Not sure what is going on here.
self.assert_close(
kornia.geometry.linalg.transform_points(dst_homo_src, points_src), points_dst, rtol=1e-4, atol=1e-4
)
# compute gradient check
self.gradcheck(kornia.geometry.transform.get_perspective_transform3d, (points_src, points_dst), fast_mode=False)
@pytest.mark.parametrize("batch_size", [1, 2])
def test_get_perspective_transform3d_2(self, batch_size, device, dtype):
torch.manual_seed(0)
src = kornia.geometry.bbox.bbox_generator3d(
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
).to(device=device, dtype=dtype)
dst = kornia.geometry.bbox.bbox_generator3d(
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1,), dtype=dtype).repeat(batch_size),
).to(device=device, dtype=dtype)
out = kornia.geometry.transform.get_perspective_transform3d(src, dst)
if batch_size == 1:
expected = torch.tensor(
[
[
[3.3000, 0.0000, 0.0000, -118.2000],
[0.0000, 0.0769, 0.0000, 0.0000],
[0.0000, 0.0000, 0.5517, 28.7930],
[0.0000, 0.0000, 0.0000, 1.0000],
]
],
device=device,
dtype=dtype,
)
if batch_size == 2:
expected = torch.tensor(
[
[
[0.9630, 0.0000, 0.0000, -9.3702],
[0.0000, 2.0000, 0.0000, -49.9999],
[0.0000, 0.0000, 0.3830, 44.0213],
[0.0000, 0.0000, 0.0000, 1.0000],
],
[
[0.9630, 0.0000, 0.0000, -36.5555],
[0.0000, 2.0000, 0.0000, -14.0000],
[0.0000, 0.0000, 0.3830, 16.8940],
[0.0000, 0.0000, 0.0000, 1.0000],
],
],
device=device,
dtype=dtype,
)
self.assert_close(out, expected, rtol=1e-4, atol=1e-4)
# compute gradient check
self.gradcheck(kornia.geometry.transform.get_perspective_transform3d, (src, dst))
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