diff --git a/lanenet_model/lanenet.py b/lanenet_model/lanenet.py
index 9822a0ee..17c4d506 100644
--- a/lanenet_model/lanenet.py
+++ b/lanenet_model/lanenet.py
@@ -97,42 +97,3 @@ def compute_loss(self, input_tensor, binary_label, instance_label, name):
                 self._reuse = True
 
         return calculated_losses
-
-
-if __name__ == '__main__':
-    """
-    test code
-    """
-    tusimple_dataset = lanenet_data_feed_pipline.LaneNetDataFeeder(
-        dataset_dir='/IMAGE_SCENE_SEGMENTATION/TUSIMPLE_DATASET/train_set/training',
-        flags='train'
-    )
-
-    test_in_tensor, test_binary_tensor, test_instance_tensor = tusimple_dataset.inputs(4, 1)
-
-    train_model = LaneNet(phase='train', net_flag='vgg')
-    val_model = LaneNet(phase='val', net_flag='vgg', reuse=True)
-
-    train_inference = train_model.inference(input_tensor=test_in_tensor, name='lanenet')
-    train_loss = train_model.compute_loss(
-        input_tensor=test_in_tensor,
-        binary_label=test_binary_tensor,
-        instance_label=test_instance_tensor,
-        name='lanenet'
-    )
-
-    val_inference = val_model.inference(input_tensor=test_in_tensor, name='lanenet')
-    val_loss = val_model.compute_loss(
-        input_tensor=test_in_tensor,
-        binary_label=test_binary_tensor,
-        instance_label=test_instance_tensor,
-        name='lanenet'
-    )
-
-    for vv in tf.global_variables():
-        print(vv.name)
-
-    print(train_inference)
-
-    for layer_name, layer_info in train_loss.items():
-        print('layer name: {:s} shape: {}'.format(layer_name, layer_info.get_shape().as_list()))