|
|
import pytest |
|
|
import torch |
|
|
from torch.autograd import gradcheck |
|
|
|
|
|
import kornia |
|
|
import kornia.testing as utils |
|
|
from kornia.testing import assert_close |
|
|
|
|
|
|
|
|
class TestRandomPerspective: |
|
|
|
|
|
torch.manual_seed(0) |
|
|
|
|
|
def test_smoke_no_transform(self, device): |
|
|
x_data = torch.rand(1, 2, 8, 9).to(device) |
|
|
|
|
|
aug = kornia.augmentation.RandomPerspective(0.5, p=0.5, return_transform=False) |
|
|
|
|
|
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): |
|
|
x_data = torch.rand(2, 2, 8, 9).to(device) |
|
|
|
|
|
aug = kornia.augmentation.RandomPerspective(0.5, p=0.5, return_transform=False) |
|
|
|
|
|
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): |
|
|
x_data = torch.rand(1, 2, 4, 5).to(device) |
|
|
|
|
|
aug = kornia.augmentation.RandomPerspective(0.5, p=0.5, return_transform=True) |
|
|
|
|
|
out_perspective = aug(x_data) |
|
|
|
|
|
assert isinstance(out_perspective, tuple) |
|
|
assert len(out_perspective) == 2 |
|
|
assert out_perspective[0].shape == x_data.shape |
|
|
assert out_perspective[1].shape == (1, 3, 3) |
|
|
assert aug.inverse(out_perspective).shape == x_data.shape |
|
|
|
|
|
def test_no_transform_module(self, device): |
|
|
x_data = torch.rand(1, 2, 8, 9).to(device) |
|
|
aug = kornia.augmentation.RandomPerspective() |
|
|
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): |
|
|
torch.manual_seed(0) |
|
|
x_data = torch.rand(1, 2, 4, 5).to(device) |
|
|
|
|
|
aug = kornia.augmentation.RandomPerspective(p=0.0, return_transform=True) |
|
|
|
|
|
out_perspective = aug(x_data) |
|
|
assert isinstance(out_perspective, tuple) |
|
|
assert len(out_perspective) == 2 |
|
|
assert out_perspective[0].shape == x_data.shape |
|
|
assert out_perspective[1].shape == (1, 3, 3) |
|
|
assert_close(out_perspective[0], x_data) |
|
|
assert_close(out_perspective[1], torch.eye(3, device=device)[None]) |
|
|
assert aug.inverse(out_perspective).shape == x_data.shape |
|
|
|
|
|
def test_transform_module_should_return_expected_transform(self, device): |
|
|
torch.manual_seed(0) |
|
|
x_data = torch.rand(1, 2, 4, 5).to(device) |
|
|
|
|
|
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(p=0.99999999, return_transform=True) |
|
|
|
|
|
out_perspective = aug(x_data) |
|
|
|
|
|
assert isinstance(out_perspective, tuple) |
|
|
assert len(out_perspective) == 2 |
|
|
assert out_perspective[0].shape == x_data.shape |
|
|
assert out_perspective[1].shape == (1, 3, 3) |
|
|
assert_close(out_perspective[0], expected_output, atol=1e-4, rtol=1e-4) |
|
|
assert_close(out_perspective[1], expected_transform, atol=1e-4, rtol=1e-4) |
|
|
assert aug.inverse(out_perspective).shape == x_data.shape |
|
|
|
|
|
def test_gradcheck(self, device): |
|
|
input = torch.rand(1, 2, 5, 7).to(device) |
|
|
input = utils.tensor_to_gradcheck_var(input) |
|
|
|
|
|
assert gradcheck(kornia.augmentation.RandomPerspective(p=0.0), (input,), raise_exception=True) |
|
|
|
|
|
|
|
|
class TestRandomAffine: |
|
|
|
|
|
torch.manual_seed(0) |
|
|
|
|
|
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 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, return_transform=True) |
|
|
out = aug(x_data) |
|
|
|
|
|
assert isinstance(out, tuple) |
|
|
assert len(out) == 2 |
|
|
assert out[0].shape == x_data.shape |
|
|
assert out[1].shape == (1, 3, 3) |
|
|
assert aug.inverse(out).shape == x_data.shape |
|
|
|
|
|
def test_gradcheck(self, device): |
|
|
input = torch.rand(1, 2, 5, 7).to(device) |
|
|
input = utils.tensor_to_gradcheck_var(input) |
|
|
|
|
|
assert gradcheck(kornia.augmentation.RandomAffine(10, p=0.0), (input,), raise_exception=True) |
|
|
|
