2231 enhances a few transform backends

monai/transforms/intensity/array.py

@@ -1076,6 +1076,8 @@ class RandGaussianSmooth(RandomizableTransform):
 
     """
 
+    backend = GaussianSmooth.backend
+
     def __init__(
         self,
         sigma_x: Tuple[float, float] = (0.25, 1.5),

monai/transforms/post/array.py

@@ -25,7 +25,7 @@
 from monai.networks.layers import GaussianFilter
 from monai.transforms.transform import Transform
 from monai.transforms.utils import fill_holes, get_largest_connected_component_mask
-from monai.utils import deprecated_arg, ensure_tuple, look_up_option
+from monai.utils import TransformBackends, deprecated_arg, ensure_tuple, look_up_option
 
 __all__ = [
     "Activations",
@@ -57,6 +57,8 @@ class Activations(Transform):
 
     """
 
+    backend = [TransformBackends.TORCH]
+
     def __init__(self, sigmoid: bool = False, softmax: bool = False, other: Optional[Callable] = None) -> None:
         self.sigmoid = sigmoid
         self.softmax = softmax
@@ -134,6 +136,8 @@ class AsDiscrete(Transform):
 
     """
 
+    backend = [TransformBackends.TORCH]
+
     @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
@@ -655,9 +659,8 @@ def __call__(
                 prob_map = torch.as_tensor(prob_map, dtype=torch.float)
             self.filter.to(prob_map)
             prob_map = self.filter(prob_map)
-        else:
-            if not isinstance(prob_map, torch.Tensor):
-                prob_map = prob_map.copy()
+        elif not isinstance(prob_map, torch.Tensor):
+            prob_map = prob_map.copy()
 
         if isinstance(prob_map, torch.Tensor):
             prob_map = prob_map.detach().cpu().numpy()

monai/transforms/post/dictionary.py

@@ -86,6 +86,8 @@ class Activationsd(MapTransform):
     Add activation layers to the input data specified by `keys`.
     """
 
+    backend = Activations.backend
+
     def __init__(
         self,
         keys: KeysCollection,
@@ -126,6 +128,8 @@ class AsDiscreted(MapTransform):
     Dictionary-based wrapper of :py:class:`monai.transforms.AsDiscrete`.
     """
 
+    backend = AsDiscrete.backend
+
     @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,

monai/transforms/utility/array.py

@@ -887,9 +887,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         if img.ndim == 4 and img.shape[0] == 1:
             img = np.squeeze(img, axis=0)
 
-        result = []
-        # merge labels 1 (tumor non-enh) and 4 (tumor enh) to TC
-        result.append(np.logical_or(img == 1, img == 4))
+        result = [np.logical_or(img == 1, img == 4)]
         # merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
         result.append(np.logical_or(np.logical_or(img == 1, img == 4), img == 2))
         # label 4 is ET
@@ -1125,6 +1123,8 @@ class ToDevice(Transform):
 
     """
 
+    backend = [TransformBackends.TORCH]
+
     def __init__(self, device: Union[torch.device, str], **kwargs) -> None:
         """
         Args:

monai/transforms/utility/dictionary.py

@@ -701,8 +701,7 @@ class SelectItemsd(MapTransform):
     """
 
     def __call__(self, data):
-        result = {key: data[key] for key in self.key_iterator(data)}
-        return result
+        return {key: data[key] for key in self.key_iterator(data)}
 
 
 class SqueezeDimd(MapTransform):
@@ -1465,6 +1464,8 @@ class ToDeviced(MapTransform):
     Dictionary-based wrapper of :py:class:`monai.transforms.ToDevice`.
     """
 
+    backend = [TransformBackends.TORCH]
+
     def __init__(
         self,
         keys: KeysCollection,

monai/transforms/utils.py

@@ -1370,20 +1370,29 @@ def get_transform_backends():
             continue
         unique_transforms.append(obj)
 
-        if isclass(obj) and issubclass(obj, Transform):
-            if n in [
-                "Transform",
+        if (
+            isclass(obj)
+            and issubclass(obj, Transform)
+            and n
+            not in [
+                "BatchInverseTransform",
+                "Compose",
+                "Decollated",
+                "InvertD",
                 "InvertibleTransform",
                 "Lambda",
                 "LambdaD",
-                "Compose",
-                "RandomizableTransform",
+                "MapTransform",
                 "OneOf",
-                "BatchInverseTransform",
-                "InverteD",
-            ]:
-                continue
-
+                "PadListDataCollate",
+                "RandLambda",
+                "RandLambdaD",
+                "RandTorchVisionD",
+                "RandomizableTransform",
+                "TorchVisionD",
+                "Transform",
+            ]
+        ):
             backends[n] = [
                 TransformBackends.TORCH in obj.backend,
                 TransformBackends.NUMPY in obj.backend,