snapshot...

modules/deeplearning/unet.py

@@ -286,7 +286,7 @@ class UNET:
     def get_in_mem_data_batch(self, idxs, is_training):
         if is_training:
             train_data = []
-            label_data = []
+            train_label = []
             for k in idxs:
                 f = self.train_data_files[k]
                 nda = np.load(f)
@@ -294,35 +294,35 @@ class UNET:
 
                 f = self.train_label_files[k]
                 nda = np.load(f)
-                label_data.append(nda)
+                train_label.append(nda)
 
             data = np.concatenate(train_data)
             data = np.expand_dims(data, axis=3)
-            label = np.concatenate(label_data)
+            label = np.concatenate(train_label)
             label = np.expand_dims(label, axis=3)
         else:
-            train_data = []
-            label_data = []
+            test_data = []
+            test_label = []
             for k in idxs:
                 f = self.test_data_files[k]
                 nda = np.load(f)
-                train_data.append(nda)
+                test_data.append(nda)
 
                 f = self.test_label_files[k]
                 nda = np.load(f)
-                label_data.append(nda)
+                test_label.append(nda)
 
-            data = np.concatenate(train_data)
+            data = np.concatenate(test_data)
             data = np.expand_dims(data, axis=3)
 
-            label = np.concatenate(label_data)
+            label = np.concatenate(test_label)
             label = np.expand_dims(label, axis=3)
 
         data = data.astype(np.float32)
         label = label.astype(np.float32)
 
-        normalize(data, 'M15', mean_std_dct)
-        normalize(label, 'M15', mean_std_dct)
+        data = normalize(data, 'M15', mean_std_dct)
+        label = normalize(label, 'M15', mean_std_dct)
 
         if is_training and DO_AUGMENT:
             data_ud = np.flip(data, axis=1)