1349 Align output data type with input in NormalizeIntensity

monai/transforms/intensity/array.py

@@ -200,6 +200,7 @@ class NormalizeIntensity(Transform):
         nonzero: whether only normalize non-zero values.
         channel_wise: if using calculated mean and std, calculate on each channel separately
             or calculate on the entire image directly.
+        dtype: output data type, defaut to float32.
     """
 
     def __init__(
@@ -208,11 +209,13 @@ def __init__(
         divisor: Optional[Sequence] = None,
         nonzero: bool = False,
         channel_wise: bool = False,
+        dtype: np.dtype = np.float32,
     ) -> None:
         self.subtrahend = subtrahend
         self.divisor = divisor
         self.nonzero = nonzero
         self.channel_wise = channel_wise
+        self.dtype = dtype
 
     def _normalize(self, img: np.ndarray, sub=None, div=None) -> np.ndarray:
         slices = (img != 0) if self.nonzero else np.ones(img.shape, dtype=np.bool_)
@@ -252,7 +255,7 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         else:
             img = self._normalize(img, self.subtrahend, self.divisor)
 
-        return img
+        return img.astype(self.dtype)
 
 
 class ThresholdIntensity(Transform):

monai/transforms/intensity/dictionary.py

@@ -227,6 +227,7 @@ class NormalizeIntensityd(MapTransform):
         nonzero: whether only normalize non-zero values.
         channel_wise: if using calculated mean and std, calculate on each channel separately
             or calculate on the entire image directly.
+        dtype: output data type, defaut to float32.
     """
 
     def __init__(
@@ -236,9 +237,10 @@ def __init__(
         divisor: Optional[np.ndarray] = None,
         nonzero: bool = False,
         channel_wise: bool = False,
+        dtype: np.dtype = np.float32,
     ) -> None:
         super().__init__(keys)
-        self.normalizer = NormalizeIntensity(subtrahend, divisor, nonzero, channel_wise)
+        self.normalizer = NormalizeIntensity(subtrahend, divisor, nonzero, channel_wise, dtype)
 
     def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
         d = dict(data)

tests/test_normalize_intensity.py

@@ -59,6 +59,7 @@ class TestNormalizeIntensity(NumpyImageTestCase2D):
     def test_default(self):
         normalizer = NormalizeIntensity()
         normalized = normalizer(self.imt)
+        self.assertTrue(normalized.dtype == np.float32)
         expected = (self.imt - np.mean(self.imt)) / np.std(self.imt)
         np.testing.assert_allclose(normalized, expected, rtol=1e-6)