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Make normalization stable when target_dtype is float16 #2941

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Make normalization stable when target_dtype is float16 #2941

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11 changes: 7 additions & 4 deletions nnunetv2/preprocessing/normalization/default_normalization_schemes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray:
here seg is used to store the zero valued region. The value for that region in the segmentation is -1 by
default.
"""
eps = 1e-8 if not self.target_dtype == np.float16 else 1e-4
image = image.astype(self.target_dtype, copy=False)
if self.use_mask_for_norm is not None and self.use_mask_for_norm:
# negative values in the segmentation encode the 'outside' region (think zero values around the brain as
Expand All @@ -41,12 +42,12 @@ def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray:
mask = seg >= 0
mean = image[mask].mean()
std = image[mask].std()
image[mask] = (image[mask] - mean) / (max(std, 1e-8))
image[mask] = (image[mask] - mean) / (max(std, eps))
else:
mean = image.mean()
std = image.std()
image -= mean
image /= (max(std, 1e-8))
image /= (max(std, eps))
return image


Expand All @@ -55,6 +56,7 @@ class CTNormalization(ImageNormalization):

def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray:
assert self.intensityproperties is not None, "CTNormalization requires intensity properties"
eps = 1e-8 if not self.target_dtype == np.float16 else 1e-4
mean_intensity = self.intensityproperties['mean']
std_intensity = self.intensityproperties['std']
lower_bound = self.intensityproperties['percentile_00_5']
Expand All @@ -63,7 +65,7 @@ def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray:
image = image.astype(self.target_dtype, copy=False)
np.clip(image, lower_bound, upper_bound, out=image)
image -= mean_intensity
image /= max(std_intensity, 1e-8)
image /= max(std_intensity, eps)
return image


Expand All @@ -78,9 +80,10 @@ class RescaleTo01Normalization(ImageNormalization):
leaves_pixels_outside_mask_at_zero_if_use_mask_for_norm_is_true = False

def run(self, image: np.ndarray, seg: np.ndarray = None) -> np.ndarray:
eps = 1e-8 if not self.target_dtype == np.float16 else 1e-4
image = image.astype(self.target_dtype, copy=False)
image -= image.min()
image /= np.clip(image.max(), a_min=1e-8, a_max=None)
image /= np.clip(image.max(), a_min=eps, a_max=None)
return image


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