array transforms inherit Transform

monai/apps/deepgrow/transforms.py

@@ -61,7 +61,7 @@ def __call__(self, data):
         return d
 
 
-class AddInitialSeedPointd(Randomizable):
+class AddInitialSeedPointd(Randomizable, Transform):
     """
     Add random guidance as initial seed point for a given label.
 
@@ -283,7 +283,7 @@ def __call__(self, data):
         return d
 
 
-class AddRandomGuidanced(Randomizable):
+class AddRandomGuidanced(Randomizable, Transform):
     """
     Add random guidance based on discrepancies that were found between label and prediction.
 

monai/transforms/croppad/array.py

@@ -314,7 +314,7 @@ def __call__(self, img: np.ndarray):
         return super().__call__(img=img)
 
 
-class RandSpatialCrop(Randomizable):
+class RandSpatialCrop(Randomizable, Transform):
     """
     Crop image with random size or specific size ROI. It can crop at a random position as center
     or at the image center. And allows to set the minimum and maximum size to limit the randomly generated ROI.
@@ -416,7 +416,7 @@ def __call__(self, img: np.ndarray):
         return super().__call__(img=img)
 
 
-class RandSpatialCropSamples(Randomizable):
+class RandSpatialCropSamples(Randomizable, Transform):
     """
     Crop image with random size or specific size ROI to generate a list of N samples.
     It can crop at a random position as center or at the image center. And allows to set
@@ -575,7 +575,7 @@ def __call__(self, img: np.ndarray):
         return cropped
 
 
-class RandWeightedCrop(Randomizable):
+class RandWeightedCrop(Randomizable, Transform):
     """
     Samples a list of `num_samples` image patches according to the provided `weight_map`.
 
@@ -627,7 +627,7 @@ def __call__(self, img: np.ndarray, weight_map: Optional[np.ndarray] = None) ->
         return results
 
 
-class RandCropByPosNegLabel(Randomizable):
+class RandCropByPosNegLabel(Randomizable, Transform):
     """
     Crop random fixed sized regions with the center being a foreground or background voxel
     based on the Pos Neg Ratio.

monai/transforms/spatial/array.py

@@ -1009,7 +1009,7 @@ def __call__(
         return grid if self.as_tensor_output else np.asarray(grid.cpu().numpy()), affine
 
 
-class RandAffineGrid(Randomizable):
+class RandAffineGrid(Randomizable, Transform):
     """
     Generate randomised affine grid.
     """
@@ -1106,7 +1106,7 @@ def get_transformation_matrix(self) -> Optional[Union[np.ndarray, torch.Tensor]]
         return self.affine
 
 
-class RandDeformGrid(Randomizable):
+class RandDeformGrid(Randomizable, Transform):
     """
     Generate random deformation grid.
     """

monai/transforms/utility/array.py

@@ -686,7 +686,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         return np.stack(result, axis=0)
 
 
-class AddExtremePointsChannel(Randomizable):
+class AddExtremePointsChannel(Randomizable, Transform):
     """
     Add extreme points of label to the image as a new channel. This transform generates extreme
     point from label and applies a gaussian filter. The pixel values in points image are rescaled