update ce kpi

fluid/object_detection/train.py

@@ -149,7 +149,7 @@ def train(args,
             save_model('best_model')
         print("Pass {0}, test map {1}".format(pass_id, test_map))
         return best_map
-    '''
+
     def ce_map(pass_id, best_map):
         _, accum_map = map_eval.get_map_var()
         map_eval.reset(exe)
@@ -161,7 +161,7 @@ def train(args,
             if batch_id % 20 == 0:
                 every_train_map.append(out)
         train_map = np.mean(every_train_map)
-        
+
         _, accum_map = map_eval.get_map_var()
         map_eval.reset(exe)
         every_test_map = []
@@ -173,7 +173,7 @@ def train(args,
                 every_test_map.append(out)
         test_map = np.mean(every_test_map)
         return (train_map, test_map)
-    '''
+
     train_num = 0
     total_train_time = 0.0
     for pass_id in range(num_passes):
@@ -207,10 +207,10 @@ def train(args,
 
         best_map = test(pass_id, best_map)
         if args.for_model_ce:
-            #map_kpi = ce_map(pass_id, best_map)
-            #print ("kpis    train_acc         %f" % train_avg_acc)
+            train_avg_acc, test_avg_acc = ce_map(pass_id, best_map)
+            print ("kpis    train_acc         %f" % train_avg_acc)
             print ("kpis    train_cost        %f" % train_avg_loss)
-            #print ("kpis    test_acc          %f" % test_avg_acc)
+            print ("kpis    test_acc          %f" % test_avg_acc)
             print ("kpis    train_duration    %f" % (end_time - start_time))
 
         if pass_id % 10 == 0 or pass_id == num_passes - 1: