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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
class TestRandomPerspective:
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.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) # step one the random state
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) # to var
# TODO: turned off with p=0
assert gradcheck(kornia.augmentation.RandomPerspective(p=0.0), (input,), raise_exception=True)
class TestRandomAffine:
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 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) # to var
# TODO: turned off with p=0
assert gradcheck(kornia.augmentation.RandomAffine(10, p=0.0), (input,), raise_exception=True)
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