def test_param(self, angle, direction, border_type, resample,
return_transform, same_on_batch, device, dtype):
_angle = (angle if isinstance(angle, (float, int, list, tuple)) else
nn.Parameter(angle.clone().to(device=device, dtype=dtype)))
_direction = (direction if isinstance(direction, (list, tuple)) else
nn.Parameter(direction.clone().to(device=device,
dtype=dtype)))
torch.manual_seed(0)
input = torch.randint(255, (2, 3, 10, 10), device=device,
dtype=dtype) / 255.0
aug = RandomMotionBlur((3, 3),
_angle,
_direction,
border_type,
resample,
return_transform,
same_on_batch,
p=1.0)
if return_transform:
output, _ = aug(input)
else:
output = aug(input)
if len(list(aug.parameters())) != 0:
mse = nn.MSELoss()
opt = torch.optim.SGD(aug.parameters(), lr=0.1)
loss = mse(output, torch.ones_like(output) * 2)
loss.backward()
opt.step()
if not isinstance(angle, (float, int, list, tuple)):
assert isinstance(aug._param_generator.angle, torch.Tensor)
if resample == 'nearest' and aug._param_generator.angle.is_cuda:
# grid_sample in nearest mode and cuda device returns nan than 0
pass
elif resample == 'nearest' or torch.all(
aug._param_generator.angle._grad == 0.0):
# grid_sample will return grad = 0 for resample nearest
# https://discuss.pytorch.org/t/autograd-issue-with-f-grid-sample/76894
assert (angle.to(device=device, dtype=dtype) -
aug._param_generator.angle.data).sum() == 0
else:
# Assert if param not updated
assert (angle.to(device=device, dtype=dtype) -
aug._param_generator.angle.data).sum() != 0
if not isinstance(direction, (list, tuple)):
assert isinstance(aug._param_generator.direction, torch.Tensor)
if torch.all(aug._param_generator.direction._grad == 0.0):
# grid_sample will return grad = 0 for resample nearest
# https://discuss.pytorch.org/t/autograd-issue-with-f-grid-sample/76894
assert (direction.to(device=device, dtype=dtype) -
aug._param_generator.direction.data).sum() == 0
else:
# Assert if param not updated
assert (direction.to(device=device, dtype=dtype) -
aug._param_generator.direction.data).sum() != 0
def test_smoke(self, device):
f = RandomMotionBlur(kernel_size=(3, 5), angle=(10, 30), direction=0.5)
repr = (
"RandomMotionBlur(kernel_size=(3, 5), angle=tensor([10., 30.]), direction=tensor([-0.5000, 0.5000]), "
"border_type='constant', p=0.5, p_batch=1.0, same_on_batch=False, return_transform=False)"
)
assert str(f) == repr
def test_random_motion_blur(self, same_on_batch, return_transform, p,
device, dtype):
f = RandomMotionBlur(kernel_size=(3, 5),
angle=(10, 30),
direction=0.5,
same_on_batch=same_on_batch,
return_transform=return_transform,
p=p)
torch.manual_seed(0)
batch_size = 2
input = torch.randn(1, 3, 5, 6).repeat(batch_size, 1, 1, 1)
output = f(input)
if return_transform:
assert len(
output
) == 2, f"must return a length 2 tuple if return_transform is True. Got {len(output)}."
identity = kornia.eye_like(3, input)
output, mat = output
assert_allclose(mat, identity, rtol=1e-4, atol=1e-4)
if same_on_batch:
assert_allclose(output[0], output[1], rtol=1e-4, atol=1e-4)
elif p == 0:
assert_allclose(output, input, rtol=1e-4, atol=1e-4)
else:
assert not torch.allclose(
output[0], output[1], rtol=1e-4, atol=1e-4)
assert output.shape == torch.Size([batch_size, 3, 5, 6])
def test_gradcheck(self, device):
torch.manual_seed(0) # for random reproductibility
inp = torch.rand((1, 3, 11, 7)).to(device)
inp = utils.tensor_to_gradcheck_var(inp) # to var
# TODO: Gradcheck for param random gen failed. Suspect get_motion_kernel2d issue.
params = {
'batch_prob': torch.tensor([True]),
'ksize_factor': torch.tensor([31]),
'angle_factor': torch.tensor([30.]),
'direction_factor': torch.tensor([-0.5]),
'border_type': torch.tensor([0]),
}
assert gradcheck(RandomMotionBlur(
kernel_size=3, angle=(10, 30), direction=(-0.5, 0.5), p=1.0), (inp, params), raise_exception=True)
def test_against_functional(self, input_shape):
input = torch.randn(*input_shape)
f = RandomMotionBlur(kernel_size=(3, 5), angle=(10, 30), direction=0.5, p=1.0)
output = f(input)
expected = motion_blur(
input,
f._params['ksize_factor'].unique().item(),
f._params['angle_factor'],
f._params['direction_factor'],
f.border_type.name.lower(),
)
assert_allclose(output, expected, rtol=1e-4, atol=1e-4)
