def test_sequential(self, device):
f = nn.Sequential(
RandomDepthicalFlip3D(p=1.0, return_transform=True),
RandomDepthicalFlip3D(p=1.0, return_transform=True),
)
f1 = nn.Sequential(
RandomDepthicalFlip3D(p=1.0, return_transform=True),
RandomDepthicalFlip3D(p=1.0),
)
input = torch.tensor([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 1.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 2.]]]) # 2 x 3 x 4
input = input.to(device)
expected_transform = torch.tensor([[[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., -1., 1.],
[0., 0., 0., 1.]]]) # 1 x 4 x 4
expected_transform = expected_transform.to(device)
expected_transform_1 = expected_transform @ expected_transform
assert_allclose(f(input)[0], input.squeeze())
assert_allclose(f(input)[1], expected_transform_1)
assert_allclose(f1(input)[0], input.squeeze())
assert_allclose(f1(input)[1], expected_transform)
def test_random_dflip(self, device):
f = RandomDepthicalFlip3D(p=1.0, return_transform=True)
f1 = RandomDepthicalFlip3D(p=0., return_transform=True)
f2 = RandomDepthicalFlip3D(p=1.)
f3 = RandomDepthicalFlip3D(p=0.)
input = torch.tensor([[[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 1.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 2.]]]) # 2 x 3 x 4
input = input.to(device)
expected = torch.tensor([[[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 2.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.],
[0., 0., 0., 1.]]]) # 2 x 3 x 4
expected = expected.to(device)
expected_transform = torch.tensor([[1., 0., 0., 0.], [0., 1., 0., 0.],
[0., 0., -1., 1.], [0., 0., 0.,
1.]]) # 4 x 4
expected_transform = expected_transform.to(device)
identity = torch.tensor([[1., 0., 0., 0.], [0., 1., 0., 0.],
[0., 0., 1., 0.], [0., 0., 0., 1.]]) # 4 x 4
identity = identity.to(device)
assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_allclose(f2(input), expected)
assert_allclose(f3(input), input)
def test_random_dflip(self, device, dtype):
f = RandomDepthicalFlip3D(p=1.0, return_transform=True)
f1 = RandomDepthicalFlip3D(p=0.0, return_transform=True)
f2 = RandomDepthicalFlip3D(p=1.0)
f3 = RandomDepthicalFlip3D(p=0.0)
input = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 2.0]],
]
]
],
device=device,
dtype=dtype,
) # 2 x 3 x 4
expected = torch.tensor(
[
[
[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 2.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
]
]
],
device=device,
dtype=dtype,
) # 2 x 3 x 4
expected_transform = torch.tensor(
[[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, -1.0, 1.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
) # 4 x 4
identity = torch.tensor(
[[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
) # 4 x 4
assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
assert_allclose(f2(input), expected)
assert_allclose(f3(input), input)
def test_random_dflip(self, device, dtype):
f = RandomDepthicalFlip3D(p=1.0)
f1 = RandomDepthicalFlip3D(p=0.0)
input = torch.tensor(
[[[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 2.0]],
]]],
device=device,
dtype=dtype,
) # 2 x 3 x 4
expected = torch.tensor(
[[[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 2.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0]],
]]],
device=device,
dtype=dtype,
) # 2 x 3 x 4
expected_transform = torch.tensor(
[[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 1.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
) # 4 x 4
identity = torch.tensor(
[[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
) # 4 x 4
assert_close(f(input), expected)
assert_close(f.transform_matrix, expected_transform)
assert_close(f1(input), input)
assert_close(f1.transform_matrix, identity)
def test_batch_random_dflip(self, device):
f = RandomDepthicalFlip3D(p=1.0, return_transform=True)
f1 = RandomDepthicalFlip3D(p=0.0, return_transform=True)
input = torch.tensor([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 1.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 2.]]]) # 2 x 3 x 4
input = input.to(device)
expected = torch.tensor([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 2.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 1.]]]) # 2 x 3 x 4
expected = expected.to(device)
expected_transform = torch.tensor([[[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., -1., 1.],
[0., 0., 0., 1.]]]) # 1 x 4 x 4
expected_transform = expected_transform.to(device)
identity = torch.tensor([[[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]]]) # 1 x 4 x 4
identity = identity.to(device)
input = input.repeat(5, 3, 1, 1, 1) # 5 x 3 x 3 x 3 x 3
expected = expected.repeat(5, 3, 1, 1, 1) # 5 x 3 x 3 x 3 x 3
expected_transform = expected_transform.repeat(5, 1, 1) # 5 x 4 x 4
identity = identity.repeat(5, 1, 1) # 5 x 4 x 4
assert_allclose(f(input)[0], expected)
assert_allclose(f(input)[1], expected_transform)
assert_allclose(f1(input)[0], input)
assert_allclose(f1(input)[1], identity)
def test_sequential(self, device):
f = AugmentationSequential(RandomDepthicalFlip3D(p=1.0),
RandomDepthicalFlip3D(p=1.0))
input = torch.tensor([[[
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 2.0]],
]]]) # 2 x 3 x 4
input = input.to(device)
expected_transform = torch.tensor([[[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 1.0],
[0.0, 0.0, 0.0,
1.0]]]) # 1 x 4 x 4
expected_transform = expected_transform.to(device)
expected_transform_1 = expected_transform @ expected_transform
assert_close(f(input), input)
assert_close(f.transform_matrix, expected_transform_1)
def test_batch_random_dflip(self, device):
f = RandomDepthicalFlip3D(p=1.0)
f1 = RandomDepthicalFlip3D(p=0.0)
input = torch.tensor([
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 2.0]],
]) # 2 x 3 x 4
input = input.to(device)
expected = torch.tensor([
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 2.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
]) # 2 x 3 x 4
expected = expected.to(device)
expected_transform = torch.tensor([[[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 1.0],
[0.0, 0.0, 0.0,
1.0]]]) # 1 x 4 x 4
expected_transform = expected_transform.to(device)
identity = torch.tensor([[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0,
1.0]]]) # 1 x 4 x 4
identity = identity.to(device)
input = input.repeat(5, 3, 1, 1, 1) # 5 x 3 x 3 x 3 x 3
expected = expected.repeat(5, 3, 1, 1, 1) # 5 x 3 x 3 x 3 x 3
expected_transform = expected_transform.repeat(5, 1, 1) # 5 x 4 x 4
identity = identity.repeat(5, 1, 1) # 5 x 4 x 4
assert_close(f(input), expected)
assert_close(f.transform_matrix, expected_transform)
assert_close(f1(input), input)
assert_close(f1.transform_matrix, identity)
def test_gradcheck(self, device):
input = torch.rand((1, 3, 3)).to(device) # 4 x 4
input = utils.tensor_to_gradcheck_var(input) # to var
assert gradcheck(RandomDepthicalFlip3D(p=1.), (input, ), raise_exception=True)
def test_same_on_batch(self, device):
f = RandomDepthicalFlip3D(p=0.5, same_on_batch=True)
input = torch.eye(3).unsqueeze(dim=0).unsqueeze(dim=0).repeat(2, 1, 2, 1, 1)
res = f(input)
assert (res[0] == res[1]).all()
def test_smoke(self):
f = RandomDepthicalFlip3D(0.5)
repr = "RandomDepthicalFlip3D(p=0.5, p_batch=1.0, same_on_batch=False, return_transform=0.5)"
assert str(f) == repr
def smoke_test(self, device):
f = RandomDepthicalFlip3D(0.5)
repr = "RandomDepthicalFlip3D(p=0.5, return_transform=False)"
assert str(f) == repr
