wehub-resource-sync
3a2c66702c
Tests on CPU (scheduled) / check-skip (push) Has been cancelled
Tests on CPU (scheduled) / pre-tests (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-ubuntu (float32) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-ubuntu (float64) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float64, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.11, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float64, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.12, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.13, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-windows (3.13, float64, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.11, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.11, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.12, float32, 2.5.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.12, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / tests-cpu-mac (3.13, float32, 2.9.1) (push) Has been cancelled
Tests on CPU (scheduled) / coverage (push) Has been cancelled
Tests on CPU (scheduled) / typing (push) Has been cancelled
Tests on CPU (scheduled) / tutorials (push) Has been cancelled
Tests on CPU (scheduled) / docs (push) Has been cancelled
Lint / TOML Format (push) Has been cancelled
350 lines
12 KiB
Python
350 lines
12 KiB
Python
# 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
|
|
from kornia.core._compat import torch_version
|
|
|
|
from testing.base import BaseTester
|
|
|
|
|
|
class TestCropAndResize3D(BaseTester):
|
|
def test_crop(self, device, dtype):
|
|
inp = torch.arange(0.0, 64.0, device=device, dtype=dtype).view(1, 1, 4, 4, 4)
|
|
|
|
depth, height, width = 2, 2, 2
|
|
expected = torch.tensor(
|
|
[[[[[25.1667, 27.1667], [30.5000, 32.5000]], [[46.5000, 48.5000], [51.8333, 53.8333]]]]],
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
|
|
boxes = torch.tensor(
|
|
[[[0, 0, 1], [3, 0, 1], [3, 2, 1], [0, 2, 1], [0, 0, 3], [3, 0, 3], [3, 2, 3], [0, 2, 3]]],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
patches = kornia.geometry.transform.crop_and_resize3d(inp, boxes, (depth, height, width))
|
|
self.assert_close(patches, expected)
|
|
|
|
def test_crop_batch(self, device, dtype):
|
|
inp = torch.cat(
|
|
[
|
|
torch.arange(0.0, 64.0, device=device, dtype=dtype).view(1, 1, 4, 4, 4),
|
|
torch.arange(0.0, 128.0, step=2, device=device, dtype=dtype).view(1, 1, 4, 4, 4),
|
|
],
|
|
dim=0,
|
|
)
|
|
|
|
depth, height, width = 2, 2, 2
|
|
expected = torch.tensor(
|
|
[
|
|
[[[[16.0000, 19.0000], [24.0000, 27.0000]], [[48.0000, 51.0000], [56.0000, 59.0000]]]],
|
|
[[[[0.0000, 6.0000], [16.0000, 22.0000]], [[64.0000, 70.0000], [80.0000, 86.0000]]]],
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
|
|
boxes = torch.tensor(
|
|
[
|
|
[[0, 0, 1], [3, 0, 1], [3, 2, 1], [0, 2, 1], [0, 0, 3], [3, 0, 3], [3, 2, 3], [0, 2, 3]],
|
|
[[0, 0, 0], [3, 0, 0], [3, 2, 0], [0, 2, 0], [0, 0, 2], [3, 0, 2], [3, 2, 2], [0, 2, 2]],
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 2x8x3
|
|
|
|
patches = kornia.geometry.transform.crop_and_resize3d(inp, boxes, (depth, height, width), align_corners=True)
|
|
self.assert_close(patches, expected)
|
|
|
|
def test_gradcheck(self, device):
|
|
img = torch.arange(0.0, 64.0, device=device, dtype=torch.float64).view(1, 1, 4, 4, 4)
|
|
|
|
boxes = torch.tensor(
|
|
[[[0, 0, 1], [3, 0, 1], [3, 2, 1], [0, 2, 1], [0, 0, 3], [3, 0, 3], [3, 2, 3], [0, 2, 3]]],
|
|
device=device,
|
|
dtype=torch.float64,
|
|
) # 1x8x3
|
|
|
|
self.gradcheck(kornia.geometry.transform.crop_and_resize3d, (img, boxes, (4, 3, 2)))
|
|
|
|
def test_dynamo(self, device, dtype, torch_optimizer):
|
|
# Define script
|
|
op = kornia.geometry.transform.crop_and_resize3d
|
|
op_script = torch_optimizer(op)
|
|
|
|
img = torch.arange(0.0, 64.0, device=device, dtype=dtype).view(1, 1, 4, 4, 4)
|
|
|
|
boxes = torch.tensor(
|
|
[[[0, 0, 1], [3, 0, 1], [3, 2, 1], [0, 2, 1], [0, 0, 3], [3, 0, 3], [3, 2, 3], [0, 2, 3]]],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
actual = op_script(img, boxes, (4, 3, 2))
|
|
expected = op(img, boxes, (4, 3, 2))
|
|
self.assert_close(actual, expected)
|
|
|
|
|
|
class TestCenterCrop3D(BaseTester):
|
|
@pytest.mark.parametrize("crop_size", [(3, 5, 7), (5, 3, 7), (7, 3, 5)])
|
|
def test_center_crop_357(self, crop_size, device, dtype):
|
|
inp = torch.arange(0.0, 343.0, device=device, dtype=dtype).view(1, 1, 7, 7, 7)
|
|
expected = inp[
|
|
:,
|
|
:,
|
|
(inp.size(2) // 2 - crop_size[0] // 2) : (inp.size(2) // 2 + crop_size[0] // 2 + 1),
|
|
(inp.size(3) // 2 - crop_size[1] // 2) : (inp.size(3) // 2 + crop_size[1] // 2 + 1),
|
|
(inp.size(4) // 2 - crop_size[2] // 2) : (inp.size(4) // 2 + crop_size[2] // 2 + 1),
|
|
]
|
|
out_crop = kornia.geometry.transform.center_crop3d(inp, crop_size, align_corners=True)
|
|
self.assert_close(out_crop, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
@pytest.mark.parametrize("crop_size", [(3, 5, 7), (5, 3, 7), (7, 3, 5)])
|
|
def test_center_crop_357_batch(self, crop_size, device, dtype):
|
|
inp = torch.cat(
|
|
[
|
|
torch.arange(0.0, 343.0, device=device, dtype=dtype).view(1, 1, 7, 7, 7),
|
|
torch.arange(343.0, 686.0, device=device, dtype=dtype).view(1, 1, 7, 7, 7),
|
|
]
|
|
)
|
|
expected = inp[
|
|
:,
|
|
:,
|
|
(inp.size(2) // 2 - crop_size[0] // 2) : (inp.size(2) // 2 + crop_size[0] // 2 + 1),
|
|
(inp.size(3) // 2 - crop_size[1] // 2) : (inp.size(3) // 2 + crop_size[1] // 2 + 1),
|
|
(inp.size(4) // 2 - crop_size[2] // 2) : (inp.size(4) // 2 + crop_size[2] // 2 + 1),
|
|
]
|
|
out_crop = kornia.geometry.transform.center_crop3d(inp, crop_size, align_corners=True)
|
|
self.assert_close(out_crop, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
def test_gradcheck(self, device):
|
|
img = torch.arange(0.0, 343.0, device=device, dtype=torch.float64).view(1, 1, 7, 7, 7)
|
|
|
|
self.gradcheck(kornia.geometry.transform.center_crop3d, (img, (3, 5, 7)))
|
|
|
|
@pytest.mark.skipif(
|
|
torch_version() == "2.1.0",
|
|
reason=(
|
|
"https://github.com/pytorch/pytorch/issues/110680"
|
|
" - unsupported operand type(s) for @: 'FakeTensor' and 'FakeTensor' on `normalize_homography3d`"
|
|
),
|
|
)
|
|
def test_dynamo(self, device, dtype, torch_optimizer):
|
|
# Define script
|
|
op = kornia.geometry.transform.center_crop3d
|
|
op_script = torch_optimizer(op)
|
|
img = torch.ones(4, 3, 5, 6, 7, device=device, dtype=dtype)
|
|
|
|
actual = op_script(img, (4, 3, 2))
|
|
expected = kornia.geometry.transform.center_crop3d(img, (4, 3, 2))
|
|
self.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
|
|
class TestCropByBoxes3D(BaseTester):
|
|
def test_crop_by_boxes_no_resizing(self, device, dtype):
|
|
inp = torch.arange(0.0, 343.0, device=device, dtype=dtype).view(1, 1, 7, 7, 7)
|
|
src_box = torch.tensor(
|
|
[
|
|
[
|
|
[1.0, 1.0, 1.0],
|
|
[3.0, 1.0, 1.0],
|
|
[3.0, 3.0, 1.0],
|
|
[1.0, 3.0, 1.0],
|
|
[1.0, 1.0, 2.0],
|
|
[3.0, 1.0, 2.0],
|
|
[3.0, 3.0, 2.0],
|
|
[1.0, 3.0, 2.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
dst_box = torch.tensor(
|
|
[
|
|
[
|
|
[0.0, 0.0, 0.0],
|
|
[2.0, 0.0, 0.0],
|
|
[2.0, 2.0, 0.0],
|
|
[0.0, 2.0, 0.0],
|
|
[0.0, 0.0, 1.0],
|
|
[2.0, 0.0, 1.0],
|
|
[2.0, 2.0, 1.0],
|
|
[0.0, 2.0, 1.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
expected = inp[:, :, 1:3, 1:4, 1:4]
|
|
|
|
patches = kornia.geometry.transform.crop_by_boxes3d(inp, src_box, dst_box, align_corners=True)
|
|
self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
def test_crop_by_boxes_resizing(self, device, dtype):
|
|
inp = torch.arange(0.0, 343.0, device=device, dtype=dtype).view(1, 1, 7, 7, 7)
|
|
src_box = torch.tensor(
|
|
[
|
|
[
|
|
[1.0, 1.0, 1.0],
|
|
[3.0, 1.0, 1.0],
|
|
[3.0, 3.0, 1.0],
|
|
[1.0, 3.0, 1.0],
|
|
[1.0, 1.0, 2.0],
|
|
[3.0, 1.0, 2.0],
|
|
[3.0, 3.0, 2.0],
|
|
[1.0, 3.0, 2.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
dst_box = torch.tensor(
|
|
[
|
|
[
|
|
[0.0, 0.0, 0.0],
|
|
[1.0, 0.0, 0.0],
|
|
[1.0, 1.0, 0.0],
|
|
[0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 1.0],
|
|
[1.0, 0.0, 1.0],
|
|
[1.0, 1.0, 1.0],
|
|
[0.0, 1.0, 1.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
expected = torch.tensor(
|
|
[[[[[57.0000, 59.0000], [71.0000, 73.0000]], [[106.0000, 108.0000], [120.0000, 122.0000]]]]],
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
|
|
patches = kornia.geometry.transform.crop_by_boxes3d(inp, src_box, dst_box, align_corners=True)
|
|
self.assert_close(patches, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
def test_dynamo(self, device, dtype, torch_optimizer):
|
|
# Define script
|
|
op = kornia.geometry.transform.crop_by_boxes3d
|
|
op_script = torch_optimizer(op)
|
|
# Define input
|
|
inp = torch.randn((1, 1, 7, 7, 7), device=device, dtype=dtype)
|
|
src_box = torch.tensor(
|
|
[
|
|
[
|
|
[1.0, 1.0, 1.0],
|
|
[3.0, 1.0, 1.0],
|
|
[3.0, 3.0, 1.0],
|
|
[1.0, 3.0, 1.0],
|
|
[1.0, 1.0, 2.0],
|
|
[3.0, 1.0, 2.0],
|
|
[3.0, 3.0, 2.0],
|
|
[1.0, 3.0, 2.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
dst_box = torch.tensor(
|
|
[
|
|
[
|
|
[0.0, 0.0, 0.0],
|
|
[1.0, 0.0, 0.0],
|
|
[1.0, 1.0, 0.0],
|
|
[0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 1.0],
|
|
[1.0, 0.0, 1.0],
|
|
[1.0, 1.0, 1.0],
|
|
[0.0, 1.0, 1.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
actual = op_script(inp, src_box, dst_box, align_corners=True)
|
|
expected = op(inp, src_box, dst_box, align_corners=True)
|
|
self.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
|
|
|
|
def test_gradcheck(self, device):
|
|
dtype = torch.float64
|
|
inp = torch.randn((1, 1, 7, 7, 7), device=device, dtype=dtype)
|
|
src_box = torch.tensor(
|
|
[
|
|
[
|
|
[1.0, 1.0, 1.0],
|
|
[3.0, 1.0, 1.0],
|
|
[3.0, 3.0, 1.0],
|
|
[1.0, 3.0, 1.0],
|
|
[1.0, 1.0, 2.0],
|
|
[3.0, 1.0, 2.0],
|
|
[3.0, 3.0, 2.0],
|
|
[1.0, 3.0, 2.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
dst_box = torch.tensor(
|
|
[
|
|
[
|
|
[0.0, 0.0, 0.0],
|
|
[1.0, 0.0, 0.0],
|
|
[1.0, 1.0, 0.0],
|
|
[0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 1.0],
|
|
[1.0, 0.0, 1.0],
|
|
[1.0, 1.0, 1.0],
|
|
[0.0, 1.0, 1.0],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=dtype,
|
|
) # 1x8x3
|
|
|
|
self.gradcheck(
|
|
kornia.geometry.transform.crop_by_boxes3d, (inp, src_box, dst_box), requires_grad=(True, False, False)
|
|
)
|
|
|
|
|
|
class TestCrop3DSizeValidation:
|
|
"""Tests that 3D crop functions properly reject invalid size arguments."""
|
|
|
|
def test_crop_and_resize3d_rejects_wrong_length(self, device, dtype):
|
|
inp = torch.rand(1, 1, 4, 4, 4, device=device, dtype=dtype)
|
|
boxes = torch.rand(1, 8, 3, device=device, dtype=dtype)
|
|
with pytest.raises(ValueError, match="tuple/list of length 3"):
|
|
kornia.geometry.transform.crop_and_resize3d(inp, boxes, (2, 2))
|
|
|
|
def test_crop_and_resize3d_rejects_non_tuple(self, device, dtype):
|
|
inp = torch.rand(1, 1, 4, 4, 4, device=device, dtype=dtype)
|
|
boxes = torch.rand(1, 8, 3, device=device, dtype=dtype)
|
|
with pytest.raises((ValueError, TypeError)):
|
|
kornia.geometry.transform.crop_and_resize3d(inp, boxes, 2)
|
|
|
|
def test_center_crop3d_rejects_wrong_length(self, device, dtype):
|
|
inp = torch.rand(1, 1, 4, 4, 4, device=device, dtype=dtype)
|
|
with pytest.raises(ValueError, match="tuple/list of length 3"):
|
|
kornia.geometry.transform.center_crop3d(inp, (2, 2))
|