revised Normalize function according to reviewer comments.

pytorch3dunet/augment/transforms.py

@@ -549,22 +549,27 @@ class Normalize:
     Apply simple min-max scaling to a given input tensor, i.e. shrinks the range of the data in a fixed range of [-1, 1].
     """
 
-    def __init__(self, min_value=None, max_value=None, norm01=False, **kwargs):
+    def __init__(self, min_value=None, max_value=None, norm01=False, eps=1e-10, **kwargs):
         if min_value is not None and max_value is not None:
           assert max_value > min_value
         self.min_value = min_value
         self.max_value = max_value
         self.norm01 = norm01
+        self.eps = eps
 
     def __call__(self, m):
         if self.min_value is None:
-          self.min_value = np.min(m)
+          min_value = np.min(m)
+        else:
+          min_value = self.min_value
+
         if self.max_value is None:
-          self.max_value = np.max(m)
-        self.value_range = self.max_value - self.min_value
-        if self.value_range == 0:
-          self.value_range += 10**-100
-        norm_0_1 = (m - self.min_value) / self.value_range
+          max_value = np.max(m)
+        else:
+          max_value = self.max_value
+
+        norm_0_1 = (m - min_value) / (max_value - min_value + self.eps)
+
         if self.norm01 is True:
           return np.clip(norm_0_1, 0, 1)
         else: