Fix error with invertible custom transforms, also keep include/exclude lists for inverse transforms.

torchio/data/subject.py

@@ -6,6 +6,7 @@
 
 from ..constants import TYPE, INTENSITY
 from .image import Image
+from ..utils import get_subclasses
 
 
 class Subject(dict):
@@ -110,10 +111,12 @@ def spacing(self):
 
  @property
  def history(self):
- from .. import transforms
+ from ..transforms.transform import Transform
+ transform_classes = {cls.__name__: cls for cls in get_subclasses(Transform)}
+
  transforms_list = []
  for transform_name, arguments in self.applied_transforms:
- transform = getattr(transforms, transform_name)(**arguments)
+ transform = transform_classes[transform_name](**arguments)
  transforms_list.append(transform)
  return transforms_list
 

torchio/transforms/transform.py

@@ -311,7 +311,9 @@ def _get_reproducing_arguments(self):
  Return a dictionary with the arguments that would be necessary to
  reproduce the transform exactly.
  """
- return {name: getattr(self, name) for name in self.args_names}
+ reproducing_arguments = dict(include=self.include, exclude=self.exclude, copy=self.copy)
+ reproducing_arguments.update({name: getattr(self, name) for name in self.args_names})
+ return reproducing_arguments
 
  def is_invertible(self):
  return hasattr(self, 'invert_transform')

torchio/utils.py

@@ -209,3 +209,9 @@ def history_collate(batch: Sequence, collate_transforms=True):
  if hasattr(first_element, attr):
  dictionary.update({attr: [getattr(d, attr) for d in batch]})
  return dictionary
+
+
+def get_subclasses(target_class: type) -> List[type]:
+ subclasses = target_class.__subclasses__()
+ subclasses += sum([get_subclasses(cls) for cls in subclasses], [])
+ return subclasses