snapshot...

modules/deeplearning/unet.py

 import glob
 import tensorflow as tf
 from util.setup import logdir, modeldir, cachepath, now, ancillary_path, home_dir
-from util.util import EarlyStop, normalize, make_for_full_domain_predict
+from util.util import EarlyStop, normalize, make_for_full_domain_predict, scale
 
 import os, datetime
 import numpy as np
@@ -199,8 +199,8 @@ class UNET:
         data = data.astype(np.float32)
         label = label.astype(np.float32)
 
-        data = normalize(data, param, mean_std_dct)
-        label = normalize(label, param, mean_std_dct)
+        data = scale(data, param, None)
+        label = scale(label, param, None)
 
         if is_training and DO_AUGMENT:
             data_ud = np.flip(data, axis=1)