Enhance rescale_array

monai/transforms/intensity/array.py

@@ -436,14 +436,14 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         if self.minv is not None and self.maxv is not None:
             if self.channel_wise:
                 out = [rescale_array(d, self.minv, self.maxv, dtype=self.dtype) for d in img]
-                return torch.stack(out) if isinstance(img, torch.Tensor) else np.stack(out)  # type: ignore
-            return rescale_array(img, self.minv, self.maxv, dtype=self.dtype)
-        if self.factor is not None:
-            ret = img * (1 + self.factor)
-            if self.dtype is not None:
-                ret, *_ = convert_data_type(ret, dtype=self.dtype or img.dtype)
-            return ret
-        raise ValueError("Incompatible values: minv=None or maxv=None and factor=None.")
+                ret = torch.stack(out) if isinstance(img, torch.Tensor) else np.stack(out)  # type: ignore
+            else:
+                ret = rescale_array(img, self.minv, self.maxv, dtype=self.dtype)
+        else:
+            ret = (img * (1 + self.factor)) if self.factor is not None else img
+
+        ret, *_ = convert_data_type(ret, dtype=self.dtype or img.dtype)
+        return ret
 
 
 class RandScaleIntensity(RandomizableTransform):

monai/transforms/utils.py

@@ -156,6 +156,13 @@ def rescale_array(
 ) -> NdarrayOrTensor:
     """
     Rescale the values of numpy array `arr` to be from `minv` to `maxv`.
+
+    Args:
+        arr: input array to rescale.
+        minv: minimum value of target rescaled array.
+        maxv: maxmum value of target rescaled array.
+        dtype: if not None, convert input array to dtype before computation.
+
     """
     if dtype is not None:
         arr, *_ = convert_data_type(arr, dtype=dtype)