Add test for different interpolation when inverting

tests/transforms/test_invertibility.py

@@ -1,5 +1,8 @@
+import copy
 import warnings
 
+import torch
+import torchio as tio
 from torchio.transforms.intensity_transform import IntensityTransform
 from ..utils import TorchioTestCase
 
@@ -37,3 +40,41 @@ def test_ignore_intensity(self):
         inverse_transform = transformed.get_inverse_transform(warn=False)
         for transform in inverse_transform:
             assert not isinstance(transform, IntensityTransform)
+
+    def test_different_interpolation(self):
+        def model_probs(subject):
+            subject = copy.deepcopy(subject)
+            subject.im.set_data(torch.rand_like(subject.im.data))
+            return subject
+
+        def model_label(subject):
+            subject = model_probs(subject)
+            subject.im.set_data(torch.bernoulli(subject.im.data))
+            return subject
+
+        transform = tio.RandomAffine(image_interpolation='bspline')
+        subject = copy.deepcopy(self.sample_subject)
+        tensor = (torch.rand(1, 20, 20, 20) > 0.5).float()  # 0s and 1s
+        subject = tio.Subject(im=tio.ScalarImage(tensor=tensor))
+        transformed = transform(subject)
+        assert transformed.im.data.min() < 0
+        assert transformed.im.data.max() > 1
+
+        subject_probs = model_probs(transformed)
+        transformed_back = subject_probs.apply_inverse_transform()
+        assert transformed_back.im.data.min() < 0
+        assert transformed_back.im.data.max() > 1
+        transformed_back_linear = subject_probs.apply_inverse_transform(
+            image_interpolation='linear',
+        )
+        assert transformed_back_linear.im.data.min() >= 0
+        assert transformed_back_linear.im.data.max() <= 1
+
+        subject_label = model_label(transformed)
+        transformed_back = subject_label.apply_inverse_transform()
+        assert transformed_back.im.data.min() < 0
+        assert transformed_back.im.data.max() > 1
+        transformed_back_linear = subject_label.apply_inverse_transform(
+            image_interpolation='nearest',
+        )
+        assert transformed_back_linear.im.data.unique().tolist() == [0, 1]