diff --git a/tools/check_code_style.sh b/tools/check_code_style.sh
index 235c291e6800fe2c668786c8d422a791aa08c0b2..fa5126cd537ae6c299dfa73cdff9743586c0d738 100644
--- a/tools/check_code_style.sh
+++ b/tools/check_code_style.sh
@@ -7,6 +7,7 @@ function abort(){
 
 trap 'abort' 0
 set -e
+TRAVIS_BUILD_DIR=${PWD}
 cd $TRAVIS_BUILD_DIR
 export PATH=/usr/bin:$PATH
 pre-commit install
diff --git a/x2paddle/op_mapper/onnx_op_mapper.py b/x2paddle/op_mapper/onnx_op_mapper.py
index 161c44492b1cf92d3fb2702f2604ee24ca63e08b..48245cb8e04e4d4bd066450db87a7fb1bd2f1fcc 100644
--- a/x2paddle/op_mapper/onnx_op_mapper.py
+++ b/x2paddle/op_mapper/onnx_op_mapper.py
@@ -51,14 +51,15 @@ def get_same_padding(in_size, kernel_size, stride):
     return [pad0, pad1]
 
 
-class ONNXOpMapper(OpMapper):        
+class ONNXOpMapper(OpMapper):
     elementwise_ops = {
         'Add': 'elementwise_add',
         'Div': 'elementwise_div',
         'Sub': 'elementwise_sub',
         'Mul': 'elementwise_mul',
-        'Pow': 'elementwise_pow',}
-    
+        'Pow': 'elementwise_pow',
+    }
+
     def __init__(self, decoder, save_dir):
         super(ONNXOpMapper, self).__init__()
         self.decoder = decoder
@@ -70,10 +71,10 @@ class ONNXOpMapper(OpMapper):
         self.is_inference = False
         self.tmp_data_dir = os.path.join(save_dir, 'tmp_data')
         self.get_output_shapes()
-        
+
         if not self.op_checker():
             raise Exception("Model are not supported yet.")
-            
+
         #mapping op
         print("Total nodes: {}".format(
             sum([
@@ -160,8 +161,8 @@ class ONNXOpMapper(OpMapper):
             for opt in layer.output:
                 if opt in value_infos:
                     value_info = value_infos[opt]
-                    if len(value_info['shape']
-                           ) == 0 or value_info['dtype'] is None or 0 in value_info['shape']:
+                    if len(value_info['shape']) == 0 or value_info[
+                            'dtype'] is None or 0 in value_info['shape']:
                         if self.is_inference == False:
                             self.get_results_of_inference(
                                 onnx_model, value_infos,
@@ -258,25 +259,25 @@ class ONNXOpMapper(OpMapper):
                 if child_func_code is not None:
                     self.used_custom_layers[op +
                                             '_child_func'] = child_func_code
+
     def elementwise_map(self, node):
         assert node.layer_type in self.elementwise_ops
         op_type = self.elementwise_ops[node.layer_type]
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_y = self.graph.get_input_node(node, idx=1, copy=True)
-        
-        if len(val_x.out_shapes[0])<len(val_y.out_shapes[0]):
+
+        if len(val_x.out_shapes[0]) < len(val_y.out_shapes[0]):
             val_x, val_y = val_y, val_x
-        
+
         val_y_shape = val_y.out_shapes[0]
         val_x_shape = val_x.out_shapes[0]
-        
+
         slice_idx = 0
         for dim in val_y_shape:
             if dim == 1:
                 slice_idx += 1
             else:
                 break
-                
         attr = {"name": string(node.layer_name)}
         if slice_idx < len(val_y_shape) and slice_idx > 0:
             val_y_reshaped = val_y_shape[slice_idx:]
@@ -351,7 +352,7 @@ class ONNXOpMapper(OpMapper):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_scales = self.graph.get_input_node(node, idx=1, copy=True)
         val_y = self.graph.get_node(node.layer.output[0], copy=True)
-        
+
         out_shape_ = val_y.out_shapes[0]
         if out_shape_ is not None:
             assert len(out_shape_) == 4, 'only 4-D Tensor as X and Y supported'
@@ -375,17 +376,19 @@ class ONNXOpMapper(OpMapper):
                 assert len(
                     in_shape) == 4, 'only 4-D Tensor as X and Y supported'
                 out_shape_ = [in_shape[2] * scale, in_shape[3] * scale]
-        
+
         mode = node.get_attr('mode', 'nearest')
-        
+
         fluid_op = 'resize_{}'.format(mode)
         if 'linear' in mode:
-            print('Warnning: paddle not support resize wiht mode: linear, we use bilinear replace linear')
+            print(
+                'Warnning: paddle not support resize wiht mode: linear, we use bilinear replace linear'
+            )
             fluid_op = 'resize_bilinear'
-        
+
         if isinstance(val_scales, ONNXGraphNode):
             scale, _, _ = self.get_dynamic_shape(val_scales.layer_name)
-            
+
         attr = {
             'scale': scale,
             'out_shape': out_shape,
@@ -446,12 +449,12 @@ class ONNXOpMapper(OpMapper):
     def Unsqueeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
-        
-        if len(val_x.out_shapes[0])==0:
+
+        if len(val_x.out_shapes[0]) == 0:
             node.fluid_code.add_layer('assign',
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=None)
+                                      inputs=val_x,
+                                      output=node,
+                                      param_attr=None)
         else:
             attr = {'axes': axes, 'name': string(node.layer_name)}
             node.fluid_code.add_layer('unsqueeze',
@@ -459,9 +462,6 @@ class ONNXOpMapper(OpMapper):
                                       output=node,
                                       param_attr=attr)
 
-
-            
-
     def Shrink(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         bias = node.get_attr('bias')
@@ -845,7 +845,6 @@ class ONNXOpMapper(OpMapper):
                                           output=node,
                                           param_attr=attr)
 
-
     def Sum(self, node):
         val_inps = node.layer.input
         inputs = {