def test_2d(self):
sample = self.make_2d(self.sample)
transform = RandomFlip(axes=(1, 2), flip_probability=1)
transformed = transform(sample)
assert_array_equal(
sample.t1.data.numpy()[:, :, ::-1, ::-1],
transformed.t1.data.numpy())
def test_apply_transform_to_file(self):
transform = RandomFlip()
apply_transform_to_file(
self.get_image_path('input'),
transform,
self.get_image_path('output'),
verbose=True,
)
def test_2d(self):
sample = self.make_2d(self.sample)
transform = RandomFlip(axes=(1, 2), flip_probability=1)
transformed = transform(sample)
self.assertTensorEqual(
sample.t1.data.numpy()[..., ::-1, ::-1],
transformed.t1.data.numpy(),
)
def test_anatomical_axis(self):
transform = RandomFlip(axes=['i'], flip_probability=1)
tensor = torch.rand(1, 2, 3, 4)
transformed = transform(tensor)
self.assertTensorEqual(
tensor.numpy()[..., ::-1],
transformed.numpy(),
)
def test_tensor_flip(self):
sample_input = torch.ones((4, 30, 30, 30))
RandomFlip()(sample_input)
def test_history(self):
transformed = RandomFlip()(self.sample)
self.assertIs(len(transformed.history), 1)
def test_no_sample(self):
with tempfile.NamedTemporaryFile() as f:
input_dict = {'image': ScalarImage(f.name)}
subject = Subject(input_dict)
with self.assertRaises(RuntimeError):
RandomFlip()(subject)
def test_wrong_flip_probability_type(self):
with self.assertRaises(ValueError):
RandomFlip(flip_probability='wrong')
def test_wrong_axes_type(self):
with self.assertRaises(ValueError):
RandomFlip(axes=None)
def test_out_of_range_axis_in_tuple(self):
with self.assertRaises(ValueError):
RandomFlip(axes=(0, -1, 2))
def test_out_of_range_axis(self):
with self.assertRaises(ValueError):
RandomFlip(axes=3)