Set transform kwargs in every instantiation of another transform with…

…in the original transform

src/torchio/transforms/augmentation/composition.py

@@ -67,7 +67,7 @@ def inverse(self, warn: bool = True) -> Compose:
                 message = f'Skipping {transform.name} as it is not invertible'
                 warnings.warn(message, RuntimeWarning, stacklevel=2)
         transforms.reverse()
-        result = Compose(transforms)
+        result = Compose(transforms, **self.get_init_args())
         if not transforms and warn:
             warnings.warn(
                 'No invertible transforms found',

src/torchio/transforms/augmentation/intensity/random_bias_field.py

@@ -60,7 +60,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             coefficients = self.get_params(self.order, self.coefficients_range)
             arguments['coefficients'][image_name] = coefficients
             arguments['order'][image_name] = self.order
-        transform = BiasField(**self.add_include_exclude(arguments))
+        transform = BiasField(**arguments, **self.get_init_args())
         transformed = transform(subject)
         return transformed
 

src/torchio/transforms/augmentation/intensity/random_blur.py

@@ -47,7 +47,7 @@ def apply_transform(self, subject: Subject) -> Subject:
         for name in images_dict:
             std = self.get_params(self.std_ranges)  # type: ignore[arg-type]
             arguments['std'][name] = std
-        transform = Blur(**self.add_include_exclude(arguments))
+        transform = Blur(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_gamma.py

@@ -77,7 +77,7 @@ def apply_transform(self, subject: Subject) -> Subject:
         for name, image in images_dict.items():
             gammas = [self.get_params(self.log_gamma_range) for _ in image.data]
             arguments['gamma'][name] = gammas
-        transform = Gamma(**self.add_include_exclude(arguments))
+        transform = Gamma(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_ghosting.py

@@ -106,7 +106,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             arguments['axis'][name] = axis_param
             arguments['intensity'][name] = intensity_param
             arguments['restore'][name] = self.restore
-        transform = Ghosting(**self.add_include_exclude(arguments))
+        transform = Ghosting(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_labels_to_image.py

@@ -268,7 +268,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             means.append(mean)
             stds.append(std)
 
-        transform = LabelsToImage(**self.add_include_exclude(arguments))
+        transform = LabelsToImage(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_motion.py

@@ -90,7 +90,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             arguments['degrees'][name] = degrees_params
             arguments['translation'][name] = translation_params
             arguments['image_interpolation'][name] = self.image_interpolation
-        transform = Motion(**self.add_include_exclude(arguments))
+        transform = Motion(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_noise.py

@@ -54,7 +54,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             arguments['mean'][image_name] = mean
             arguments['std'][image_name] = std
             arguments['seed'][image_name] = seed
-        transform = Noise(**self.add_include_exclude(arguments))
+        transform = Noise(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_spike.py

@@ -73,7 +73,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             )
             arguments['spikes_positions'][image_name] = spikes_positions_param
             arguments['intensity'][image_name] = intensity_param
-        transform = Spike(**self.add_include_exclude(arguments))
+        transform = Spike(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/intensity/random_swap.py

@@ -102,7 +102,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             )
             arguments['locations'][name] = locations
             arguments['patch_size'][name] = self.patch_size
-        transform = Swap(**self.add_include_exclude(arguments))
+        transform = Swap(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/random_transform.py

@@ -21,11 +21,6 @@ class RandomTransform(Transform):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
-    def add_include_exclude(self, kwargs):
-        kwargs['include'] = self.include
-        kwargs['exclude'] = self.exclude
-        return kwargs
-
     def parse_degrees(
         self,
         degrees: TypeRangeFloat,

src/torchio/transforms/augmentation/spatial/random_affine.py

@@ -184,7 +184,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             'label_interpolation': self.label_interpolation,
             'check_shape': self.check_shape,
         }
-        transform = Affine(**self.add_include_exclude(arguments))
+        transform = Affine(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/spatial/random_anisotropy.py

@@ -106,9 +106,7 @@ def apply_transform(self, subject: Subject) -> Subject:
         }
 
         sx, sy, sz = target_spacing  # for mypy
-        downsample = Resample(
-            target=(sx, sy, sz), **self.add_include_exclude(arguments)
-        )
+        downsample = Resample(target=(sx, sy, sz), **arguments, **self.get_init_args())
         downsampled = downsample(subject)
         image = subject.get_first_image()
         target = image.spatial_shape, image.affine

src/torchio/transforms/augmentation/spatial/random_elastic_deformation.py

@@ -190,7 +190,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             'label_interpolation': self.label_interpolation,
         }
 
-        transform = ElasticDeformation(**self.add_include_exclude(arguments))
+        transform = ElasticDeformation(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/augmentation/spatial/random_flip.py

@@ -60,7 +60,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             return subject
 
         arguments = {'axes': axes}
-        transform = Flip(**self.add_include_exclude(arguments))
+        transform = Flip(**arguments, **self.get_init_args())
         transformed = transform(subject)
         assert isinstance(transformed, Subject)
         return transformed

src/torchio/transforms/preprocessing/label/sequential_labels.py

@@ -52,10 +52,13 @@ def apply_transform(self, subject):
             remapping = {
                 unique_labels[i].item(): i for i in range(0, len(unique_labels))
             }
+            init_kwargs = self.get_init_args()
+            init_kwargs['include'] = [name]
+
             transform = RemapLabels(
                 remapping=remapping,
                 masking_method=self.masking_method,
-                include=[name],
+                **init_kwargs,
             )
             subject = transform(subject)
         return subject

src/torchio/transforms/preprocessing/spatial/crop_or_pad.py

@@ -279,9 +279,9 @@ def apply_transform(self, subject: Subject) -> Subject:
         padding_params, cropping_params = self.compute_crop_or_pad(subject)
         padding_kwargs = {'padding_mode': self.padding_mode}
         if padding_params is not None:
-            pad = Pad(padding_params, **padding_kwargs)
+            pad = Pad(padding_params, **self.get_init_args(), **padding_kwargs)
             subject = pad(subject)  # type: ignore[assignment]
         if cropping_params is not None:
-            crop = Crop(cropping_params)
+            crop = Crop(cropping_params, **self.get_init_args())
             subject = crop(subject)  # type: ignore[assignment]
         return subject

src/torchio/transforms/preprocessing/spatial/ensure_shape_multiple.py

@@ -133,6 +133,6 @@ def apply_transform(self, subject: Subject) -> Subject:
         integer_ratio = function(source_shape / self.target_multiple)
         target_shape = integer_ratio * self.target_multiple
         target_shape = np.maximum(target_shape, 1)
-        transform = CropOrPad(target_shape.astype(int))
+        transform = CropOrPad(target_shape.astype(int), **self.get_init_args())
         subject = transform(subject)  # type: ignore[assignment]
         return subject

src/torchio/transforms/preprocessing/spatial/resize.py

@@ -61,6 +61,7 @@ def apply_transform(self, subject: Subject) -> Subject:
             spacing_out,
             image_interpolation=self.image_interpolation,
             label_interpolation=self.label_interpolation,
+            **self.get_init_args(),
         )
         resampled = resample(subject)
         assert isinstance(resampled, Subject)
@@ -72,7 +73,7 @@ def apply_transform(self, subject: Subject) -> Subject:
                 f' != target shape {tuple(shape_out)}. Fixing with CropOrPad'
             )
             warnings.warn(message, RuntimeWarning, stacklevel=2)
-            crop_pad = CropOrPad(shape_out)  # type: ignore[arg-type]
+            crop_pad = CropOrPad(shape_out, **self.get_init_args())  # type: ignore[arg-type]
             resampled = crop_pad(resampled)
         assert isinstance(resampled, Subject)
         return resampled

src/torchio/transforms/transform.py

@@ -189,6 +189,17 @@ def __repr__(self):
         else:
             return super().__repr__()
 
+    def get_init_args(self) -> dict:
+        return {
+            'p': self.probability,
+            'copy': self.copy,
+            'include': self.include,
+            'exclude': self.exclude,
+            'keep': self.keep,
+            'parse_input': self.parse_input,
+            'label_keys': self.label_keys,
+        }
+
     @property
     def name(self):
         return self.__class__.__name__