|
1
|
|
|
import warnings |
|
2
|
|
|
|
|
3
|
|
|
from torchio.transforms.intensity_transform import IntensityTransform |
|
4
|
|
|
from ..utils import TorchioTestCase |
|
5
|
|
|
|
|
6
|
|
|
|
|
7
|
|
|
class TestInvertibility(TorchioTestCase): |
|
8
|
|
|
|
|
9
|
|
|
def test_all_random_transforms(self): |
|
10
|
|
|
transform = self.get_large_composed_transform() |
|
11
|
|
|
# Remove RandomLabelsToImage as it will add a new image to the subject |
|
12
|
|
|
for t in transform.transforms: |
|
13
|
|
|
if t.name == 'RandomLabelsToImage': |
|
14
|
|
|
transform.transforms.remove(t) |
|
15
|
|
|
break |
|
16
|
|
|
# Ignore elastic deformation and gamma warnings during execution |
|
17
|
|
|
# Ignore some transforms not invertible |
|
18
|
|
|
with warnings.catch_warnings(): |
|
19
|
|
|
warnings.simplefilter('ignore', RuntimeWarning) |
|
20
|
|
|
transformed = transform(self.sample_subject) |
|
21
|
|
|
inverting_transform = transformed.get_inverse_transform() |
|
22
|
|
|
transformed_back = inverting_transform(transformed) |
|
23
|
|
|
self.assertEqual( |
|
24
|
|
|
transformed.t1.shape, |
|
25
|
|
|
transformed_back.t1.shape, |
|
26
|
|
|
) |
|
27
|
|
|
self.assertTensorEqual( |
|
28
|
|
|
transformed.label.affine, |
|
29
|
|
|
transformed_back.label.affine, |
|
30
|
|
|
) |
|
31
|
|
|
|
|
32
|
|
|
def test_ignore_intensity(self): |
|
33
|
|
|
composed = self.get_large_composed_transform() |
|
34
|
|
|
with warnings.catch_warnings(): |
|
35
|
|
|
warnings.simplefilter('ignore', RuntimeWarning) |
|
36
|
|
|
transformed = composed(self.sample_subject) |
|
37
|
|
|
inverse_transform = transformed.get_inverse_transform(warn=False) |
|
38
|
|
|
for transform in inverse_transform: |
|
39
|
|
|
assert not isinstance(transform, IntensityTransform) |
|
40
|
|
|
|
fepegar /
torchio
