revert RandShiftIntensity

Signed-off-by: KumoLiu <[email protected]>

monai/transforms/intensity/array.py

@@ -255,17 +255,13 @@ class RandShiftIntensity(RandomizableTransform):
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
-    def __init__(
-        self, offsets: tuple[float, float] | float, safe: bool = False, channel_wise: bool = False, prob: float = 0.1
-    ) -> None:
+    def __init__(self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1) -> None:
         """
         Args:
             offsets: offset range to randomly shift.
                 if single number, offset value is picked from (-offsets, offsets).
             safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
                 E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
-            channel_wise: if True, calculate on each channel separately. Please ensure
-                that the first dimension represents the channel of the image if True.
             prob: probability of shift.
         """
         RandomizableTransform.__init__(self, prob)
@@ -276,17 +272,13 @@ def __init__(
         else:
             self.offsets = (min(offsets), max(offsets))
         self._offset = self.offsets[0]
-        self.channel_wise = channel_wise
         self._shifter = ShiftIntensity(self._offset, safe)
 
     def randomize(self, data: Any | None = None) -> None:
         super().randomize(None)
         if not self._do_transform:
             return None
-        if self.channel_wise:
-            self._offset = [self.R.uniform(low=self.offsets[0], high=self.offsets[1]) for _ in range(data.shape[0])]
-        else:
-            self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
+        self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])
 
     def __call__(self, img: NdarrayOrTensor, factor: float | None = None, randomize: bool = True) -> NdarrayOrTensor:
         """
@@ -300,17 +292,12 @@ def __call__(self, img: NdarrayOrTensor, factor: float | None = None, randomize:
         """
         img = convert_to_tensor(img, track_meta=get_track_meta())
         if randomize:
-            self.randomize(img)
+            self.randomize()
 
         if not self._do_transform:
             return img
 
-        if self.channel_wise:
-            for i, d in enumerate(img):
-                img[i] = self._shifter(d, self._offset[i] if factor is None else self._offset * factor)
-        else:
-            img = self._shifter(img, self._offset if factor is None else self._offset * factor)
-        return img
+        return self._shifter(img, self._offset if factor is None else self._offset * factor)
 
 
 class StdShiftIntensity(Transform):