Merge 6827698 into ba5be71

docs/source/transforms/transforms.rst

@@ -125,10 +125,10 @@ or `aleatoric uncertainty estimation <https://www.sciencedirect.com/science/arti
     >>> segmentations = []
     >>> num_segmentations = 10
     >>> for _ in range(num_segmentations):
-    ...     transform = tio.RandomAffine()
+    ...     transform = tio.RandomAffine(image_interpolation='bspline')
     ...     transformed = transform(subject)
     ...     segmentation = model(transformed)
-    ...     transformed_native_space = segmentation.apply_inverse_transform()
+    ...     transformed_native_space = segmentation.apply_inverse_transform(image_interpolation='linear')
     ...     segmentations.append(transformed_native_space)
     ...
 

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]

torchio/data/subject.py

@@ -120,6 +120,7 @@ def history(self):
     def get_applied_transforms(
             self,
             ignore_intensity: bool = False,
+            image_interpolation: Optional[str] = None,
             ) -> List['Transform']:
         from ..transforms.transform import Transform
         from ..transforms.intensity_transform import IntensityTransform
@@ -132,22 +133,30 @@ def get_applied_transforms(
             transform = name_to_transform[transform_name](**arguments)
             if ignore_intensity and isinstance(transform, IntensityTransform):
                 continue
+            resamples = hasattr(transform, 'image_interpolation')
+            if resamples and image_interpolation is not None:
+                parsed = transform.parse_interpolation(image_interpolation)
+                transform.image_interpolation = parsed
             transforms_list.append(transform)
         return transforms_list
 
     def get_composed_history(
             self,
             ignore_intensity: bool = False,
+            image_interpolation: Optional[str] = None,
             ) -> 'Compose':
         from ..transforms.augmentation.composition import Compose
         transforms = self.get_applied_transforms(
-            ignore_intensity=ignore_intensity)
+            ignore_intensity=ignore_intensity,
+            image_interpolation=image_interpolation,
+        )
         return Compose(transforms)
 
     def get_inverse_transform(
             self,
             warn: bool = True,
             ignore_intensity: bool = True,
+            image_interpolation: Optional[str] = None,
             ) ->  'Compose':
         """Get a reversed list of the inverses of the applied transforms.
 
@@ -156,9 +165,13 @@ def get_inverse_transform(
             ignore_intensity: If ``True``, all instances of
                 :class:`~torchio.transforms.intensity_transform.IntensityTransform`
                 will be ignored.
+            image_interpolation: Modify interpolation for scalar images inside
+                transforms that perform resampling.
         """
         history_transform = self.get_composed_history(
-            ignore_intensity=ignore_intensity)
+            ignore_intensity=ignore_intensity,
+            image_interpolation=image_interpolation,
+        )
         inverse_transform = history_transform.inverse(warn=warn)
         return inverse_transform