diff --git a/x2paddle/convert.py b/x2paddle/convert.py
index d5c78e761..86f62104e 100644
--- a/x2paddle/convert.py
+++ b/x2paddle/convert.py
@@ -190,6 +190,7 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     mapper = ONNXOpMapper(model)
     print("Model optimizing ...")
     optimizer = ONNXOptimizer(mapper)
+    optimizer.delete_redundance_code()
     print("Model optimized.")
 
     print("Paddle model and code generating ...")
diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
index 38087558f..ead2370ef 100644
--- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
+++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
@@ -550,8 +550,6 @@ def InstanceNormalization(self, node):
     def Expand(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_shape = self.graph.get_input_node(node, idx=1, copy=True)
-        if len(val_shape.outputs) == 1:
-            self.omit_nodes.append(val_shape.layer_name)
         val_x_dtype = val_x.dtype
         name_ones = node.layer_name + '_ones'
         attr_ones = {
@@ -1256,10 +1254,19 @@ def PRelu(self, node):
 
         mode = 'channel'
         shape_slope = val_slope.out_shapes[0]
-        if len(shape_slope) == 1:
+
+        if shape_slope == [1]:
             mode = 'all'
         elif len(shape_slope) > 2:
             mode = 'element'
+
+        if mode == 'channel' and len(shape_slope) == 1:
+            # paddle params shape need be [1, channel]
+            slope_data = _const_weight_or_none(val_slope)
+            slope_data = np.reshape(slope_data, [1] + shape_slope)
+            self.weights[val_slope.layer_name] = slope_data
+
+        self.omit_nodes.append(val_slope.layer_name)
         attr = {
             "param_attr": string(val_slope.layer_name),
             'mode': string(mode)