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
228 lines
8.8 KiB
Python
228 lines
8.8 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 testing.base import BaseTester
|
|
|
|
|
|
class TestRandomPerspective(BaseTester):
|
|
torch.manual_seed(0) # for random reproductibility
|
|
|
|
def test_smoke_no_transform_float(self, device):
|
|
x_data = torch.rand(1, 2, 8, 9).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(0.5, p=0.5)
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_smoke_no_transform(self, device, dtype):
|
|
x_data = torch.rand(1, 2, 8, 9, dtype=dtype).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype), p=0.5)
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_smoke_no_transform_batch(self, device, dtype):
|
|
x_data = torch.rand(2, 2, 8, 9, dtype=dtype).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype), p=0.5)
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_smoke_transform(self, device, dtype):
|
|
x_data = torch.rand(1, 2, 4, 5, dtype=dtype).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype), p=0.5)
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.transform_matrix.shape == torch.Size([1, 3, 3])
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_smoke_transform_sampling_method(self, device, dtype):
|
|
x_data = torch.rand(1, 2, 4, 5, dtype=dtype).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(
|
|
torch.tensor(0.5, device=device, dtype=dtype), p=0.5, sampling_method="area_preserving"
|
|
)
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.transform_matrix.shape == torch.Size([1, 3, 3])
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_no_transform_module(self, device, dtype):
|
|
x_data = torch.rand(1, 2, 8, 9, dtype=dtype).to(device)
|
|
aug = kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype))
|
|
out_perspective = aug(x_data)
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_transform_module_should_return_identity(self, device, dtype):
|
|
torch.manual_seed(0)
|
|
x_data = torch.rand(1, 2, 4, 5, dtype=dtype).to(device)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype), p=0.0)
|
|
|
|
out_perspective = aug(x_data)
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.transform_matrix.shape == (1, 3, 3)
|
|
self.assert_close(out_perspective, x_data)
|
|
self.assert_close(aug.transform_matrix, torch.eye(3, device=device, dtype=dtype)[None])
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_transform_module_should_return_expected_transform(self, device, dtype):
|
|
torch.manual_seed(0)
|
|
x_data = torch.rand(1, 2, 4, 5).to(device).type(dtype)
|
|
|
|
expected_output = torch.tensor(
|
|
[
|
|
[
|
|
[
|
|
[0.0000, 0.0000, 0.0000, 0.0197, 0.0429],
|
|
[0.0000, 0.5632, 0.5322, 0.3677, 0.1430],
|
|
[0.0000, 0.3083, 0.4032, 0.1761, 0.0000],
|
|
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
|
|
],
|
|
[
|
|
[0.0000, 0.0000, 0.0000, 0.1189, 0.0586],
|
|
[0.0000, 0.7087, 0.5420, 0.3995, 0.0863],
|
|
[0.0000, 0.2695, 0.5981, 0.5888, 0.0000],
|
|
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
|
|
],
|
|
]
|
|
],
|
|
device=device,
|
|
dtype=x_data.dtype,
|
|
)
|
|
|
|
expected_transform = torch.tensor(
|
|
[[[1.0523, 0.3493, 0.3046], [-0.1066, 1.0426, 0.5846], [0.0351, 0.1213, 1.0000]]],
|
|
device=device,
|
|
dtype=x_data.dtype,
|
|
)
|
|
|
|
aug = kornia.augmentation.RandomPerspective(
|
|
torch.tensor(0.5, device=device, dtype=dtype), p=0.99999999
|
|
) # step one the random state
|
|
|
|
out_perspective = aug(x_data)
|
|
|
|
assert out_perspective.shape == x_data.shape
|
|
assert aug.transform_matrix.shape == (1, 3, 3)
|
|
self.assert_close(out_perspective, expected_output, atol=1e-4, rtol=1e-4)
|
|
self.assert_close(aug.transform_matrix, expected_transform, atol=1e-4, rtol=1e-4)
|
|
assert aug.inverse(out_perspective).shape == x_data.shape
|
|
|
|
def test_gradcheck(self, device, dtype):
|
|
input = torch.rand(1, 2, 5, 7, dtype=torch.float64, device=device)
|
|
# TODO: turned off with p=0
|
|
self.gradcheck(
|
|
kornia.augmentation.RandomPerspective(torch.tensor(0.5, device=device, dtype=dtype), p=0.0),
|
|
(input,),
|
|
)
|
|
|
|
|
|
class TestRandomAffine(BaseTester):
|
|
torch.manual_seed(0) # for random reproductibility
|
|
|
|
def test_smoke_no_transform(self, device):
|
|
x_data = torch.rand(1, 2, 8, 9).to(device)
|
|
aug = kornia.augmentation.RandomAffine(0.0)
|
|
out = aug(x_data)
|
|
assert out.shape == x_data.shape
|
|
assert aug.inverse(out).shape == x_data.shape
|
|
assert aug.inverse(out, aug._params).shape == x_data.shape
|
|
|
|
def test_smoke_no_transform_batch(self, device):
|
|
x_data = torch.rand(2, 2, 8, 9).to(device)
|
|
aug = kornia.augmentation.RandomAffine(0.0)
|
|
out = aug(x_data)
|
|
assert out.shape == x_data.shape
|
|
# assert False, (aug.transform_matrix.shape, out.shape, aug._params)
|
|
assert aug.inverse(out).shape == x_data.shape
|
|
assert aug.inverse(out, aug._params).shape == x_data.shape
|
|
|
|
@pytest.mark.parametrize("degrees", [45.0, (-45.0, 45.0), torch.tensor([45.0, 45.0])])
|
|
@pytest.mark.parametrize("translate", [(0.1, 0.1), torch.tensor([0.1, 0.1])])
|
|
@pytest.mark.parametrize(
|
|
"scale", [(0.8, 1.2), (0.8, 1.2, 0.9, 1.1), torch.tensor([0.8, 1.2]), torch.tensor([0.8, 1.2, 0.7, 1.3])]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"shear",
|
|
[
|
|
5.0,
|
|
(-5.0, 5.0),
|
|
(-5.0, 5.0, -3.0, 3.0),
|
|
torch.tensor(5.0),
|
|
torch.tensor([-5.0, 5.0]),
|
|
torch.tensor([-5.0, 5.0, -3.0, 3.0]),
|
|
],
|
|
)
|
|
def test_batch_multi_params(self, degrees, translate, scale, shear, device, dtype):
|
|
x_data = torch.rand(2, 2, 8, 9).to(device)
|
|
aug = kornia.augmentation.RandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)
|
|
out = aug(x_data)
|
|
assert out.shape == x_data.shape
|
|
assert aug.inverse(out).shape == x_data.shape
|
|
|
|
def test_smoke_transform(self, device):
|
|
x_data = torch.rand(1, 2, 4, 5).to(device)
|
|
aug = kornia.augmentation.RandomAffine(0.0)
|
|
out = aug(x_data)
|
|
|
|
assert out.shape == x_data.shape
|
|
assert aug.transform_matrix.shape == torch.Size([1, 3, 3])
|
|
assert aug.inverse(out).shape == x_data.shape
|
|
|
|
def test_gradcheck(self, device):
|
|
input = torch.rand(1, 2, 5, 7, device=device, dtype=torch.float64)
|
|
# TODO: turned off with p=0
|
|
self.gradcheck(kornia.augmentation.RandomAffine(10, p=0.0), (input,))
|
|
|
|
|
|
class TestRandomShear(BaseTester):
|
|
torch.manual_seed(0) # for random reproductibility
|
|
|
|
def test_smoke_no_transform(self, device):
|
|
x_data = torch.rand(1, 2, 8, 9).to(device)
|
|
aug = kornia.augmentation.RandomShear((10.0, 10.0))
|
|
out = aug(x_data)
|
|
assert out.shape == x_data.shape
|
|
assert aug.inverse(out).shape == x_data.shape
|
|
assert aug.inverse(out, aug._params).shape == x_data.shape
|
|
|
|
def test_gradcheck(self, device):
|
|
input = torch.rand(1, 2, 5, 7, device=device, dtype=torch.float64)
|
|
# TODO: turned off with p=0
|
|
self.gradcheck(kornia.augmentation.RandomShear((10.0, 10.0), p=1.0), (input,))
|