diff --git a/tools/program.py b/tools/program.py
index b63d416c709279a8b18fe322d90aded83e821b16..34541043623ea9c4f78387488203f57e7fa8a0c7 100644
--- a/tools/program.py
+++ b/tools/program.py
@@ -49,7 +49,7 @@ def create_dataloader():
         dataloader(fluid dataloader):
     """
     trainer_num = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
-    capacity = 64 if trainer_num <= 1 else 8
+    capacity = 64 if trainer_num == 1 else 8
     dataloader = fluid.io.DataLoader.from_generator(
         capacity=capacity, use_double_buffer=True, iterable=True)
 
@@ -163,15 +163,7 @@ def create_metric(out,
     return fetchs
 
 
-def create_fetchs(feeds,
-                  out,
-                  config,
-                  architecture,
-                  topk=5,
-                  classes_num=1000,
-                  epsilon=None,
-                  use_mix=False,
-                  use_distillation=False):
+def create_fetchs(feeds, net, config, mode="train"):
     """
     Create fetchs as model outputs(included loss and measures),
     will call create_loss and create_metric(if use_mix).
@@ -190,6 +182,15 @@ def create_fetchs(feeds,
     Returns:
         fetchs(dict): dict of model outputs(included loss and measures)
     """
+    architecture = config.ARCHITECTURE
+    topk = config.topk
+    classes_num = config.classes_num
+    epsilon = config.get('ls_epsilon')
+    use_mix = config.get('use_mix') and mode == 'train'
+    use_distillation = config.get('use_distillation')
+
+    out = net(feeds["image"])
+
     fetchs = OrderedDict()
     fetchs['loss'] = create_loss(feeds, out, architecture, classes_num,
                                  epsilon, use_mix, use_distillation)
@@ -276,40 +277,6 @@ def mixed_precision_optimizer(config, optimizer):
     return optimizer
 
 
-def compute(feeds, net, config, mode='train'):
-    """
-    Build a program using a model and an optimizer
-        1. create feeds
-        2. create a dataloader
-        3. create a model
-        4. create fetchs
-        5. create an optimizer
-
-    Args:
-        config(dict): config
-        main_prog(): main program
-        startup_prog(): startup program
-        is_train(bool): train or valid
-
-    Returns:
-        dataloader(): a bridge between the model and the data
-        fetchs(dict): dict of model outputs(included loss and measures)
-    """
-    out = net(feeds["image"])
-    fetchs = create_fetchs(
-        feeds,
-        out,
-        config,
-        config.ARCHITECTURE,
-        config.topk,
-        config.classes_num,
-        epsilon=config.get('ls_epsilon'),
-        use_mix=config.get('use_mix') and mode == 'train',
-        use_distillation=config.get('use_distillation'))
-
-    return fetchs
-
-
 def create_feeds(batch, use_mix):
     image = to_variable(batch[0].numpy().astype("float32"))
     if use_mix:
@@ -360,7 +327,7 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
     for idx, batch in enumerate(dataloader()):
         batch_size = len(batch[0])
         feeds = create_feeds(batch, use_mix)
-        fetchs = compute(feeds, net, config, mode)
+        fetchs = create_fetchs(feeds, net, config, mode)
         if mode == 'train':
             avg_loss = net.scale_loss(fetchs['loss'])
             avg_loss.backward()