Refine ce support for object detection (#1165)

* update ce

fluid/object_detection/_ce.py

@@ -8,7 +8,7 @@ from kpi import CostKpi, DurationKpi, AccKpi
 #### NOTE kpi.py should shared in models in some way!!!!
 
 train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True)
-test_acc_kpi = AccKpi('test_acc', 0.01, 0, actived=True)
+test_acc_kpi = AccKpi('test_acc', 0.01, 0, actived=False)
 train_speed_kpi = AccKpi('train_speed', 0.2, 0, actived=False)
 train_cost_card4_kpi = CostKpi('train_cost_card4', 0.02, 0, actived=True)
 test_acc_card4_kpi = AccKpi('test_acc_card4', 0.01, 0, actived=True)

fluid/object_detection/train.py

@@ -65,7 +65,6 @@ def train(args,
         name='gt_label', shape=[1], dtype='int32', lod_level=1)
     difficult = fluid.layers.data(
         name='gt_difficult', shape=[1], dtype='int32', lod_level=1)
-
     locs, confs, box, box_var = mobile_net(num_classes, image, image_shape)
     nmsed_out = fluid.layers.detection_output(
         locs, confs, box, box_var, nms_threshold=args.nms_threshold)
@@ -126,6 +125,9 @@ def train(args,
         train_reader = paddle.batch(
             reader.train(data_args, train_file_list), batch_size=batch_size)
     else:
+        import random
+        random.seed(0)
+        np.random.seed(0)
         train_reader = paddle.batch(
             reader.train(data_args, train_file_list, False), batch_size=batch_size)
     test_reader = paddle.batch(
@@ -166,8 +168,6 @@ def train(args,
         start_time = time.time()
         prev_start_time = start_time
         every_pass_loss = []
-        iter = 0
-        pass_duration = 0.0
         for batch_id, data in enumerate(train_reader()):
             prev_start_time = start_time
             start_time = time.time()