fix mypy

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

monai/transforms/intensity/array.py

@@ -666,7 +666,7 @@ def randomize(self, data: Any | None = None) -> None:
         if not self._do_transform:
             return None
         if self.channel_wise:
-            self.factor = [self.R.uniform(low=self.factors[0], high=self.factors[1]) for _ in range(data.shape[0])]
+            self.factor = [self.R.uniform(low=self.factors[0], high=self.factors[1]) for _ in range(data.shape[0])]  # type: ignore
         else:
             self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])
 
@@ -681,12 +681,13 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
         if not self._do_transform:
             return convert_data_type(img, dtype=self.dtype)[0]
 
+        ret: NdarrayOrTensor
         if self.channel_wise:
             out = []
             for i, d in enumerate(img):
-                out_channel = ScaleIntensity(minv=None, maxv=None, factor=self.factor[i], dtype=self.dtype)(d)
+                out_channel = ScaleIntensity(minv=None, maxv=None, factor=self.factor[i], dtype=self.dtype)(d)  # type: ignore
                 out.append(out_channel)
-            ret = torch.stack(out)
+            ret = torch.stack(out)  # type: ignore
         else:
             ret = ScaleIntensity(minv=None, maxv=None, factor=self.factor, dtype=self.dtype)(img)
         return ret

monai/transforms/intensity/dictionary.py

@@ -369,7 +369,6 @@ def __init__(
         keys: KeysCollection,
         offsets: tuple[float, float] | float,
         safe: bool = False,
-        channel_wise: bool = False,
         factor_key: str | None = None,
         meta_keys: KeysCollection | None = None,
         meta_key_postfix: str = DEFAULT_POST_FIX,
@@ -384,8 +383,6 @@ def __init__(
                 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.
             factor_key: if not None, use it as the key to extract a value from the corresponding
                 metadata dictionary of `key` at runtime, and multiply the random `offset` to shift intensity.
                 Usually, `IntensityStatsd` transform can pre-compute statistics of intensity values
@@ -412,7 +409,7 @@ def __init__(
         if len(self.keys) != len(self.meta_keys):
             raise ValueError("meta_keys should have the same length as keys.")
         self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))
-        self.shifter = RandShiftIntensity(offsets=offsets, safe=safe, channel_wise=channel_wise, prob=1.0)
+        self.shifter = RandShiftIntensity(offsets=offsets, safe=safe, prob=1.0)
 
     def set_random_state(
         self, seed: int | None = None, state: np.random.RandomState | None = None