Simplified code

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -45,7 +45,8 @@ def _const_weight_or_none(node, necessary=False):
 def _rename_or_remove_weight(weights,
                              origin_name,
                              target_name=None,
-                             is_remove=True):
+                             is_remove=True,
+                             rename_mapper=None):
     '''
     Rename parameters by Paddle's naming rule of parameters.
 
@@ -56,9 +57,13 @@ def _rename_or_remove_weight(weights,
             {target_name:weights[origin_name]} to weights, and target_name must follow paddle's
             naming rule of parameters. Default: None.
         is_remove: if is_remove is True, remove origin key-value pair. Default: True.
+        rename_mapper: Solved the same data is used for multiple OPs, key is old_name, value is new_name.
     Returns:
         None
     '''
+    if origin_name in rename_mapper:
+        origin_name = rename_mapper[origin_name]
+        is_remove = False
     if origin_name not in weights:
         raise KeyError('{} not a key in {}'.format(origin_name, weights.keys()))
     if is_remove:
@@ -69,6 +74,7 @@ def _rename_or_remove_weight(weights,
     if target_name is not None:
         # rename weight
         weights[target_name] = data
+        rename_mapper[origin_name] = target_name
 
 
 def _is_static_shape(shape):
@@ -1682,38 +1688,26 @@ class OpSet9():
         c = val_x.out_shapes[0][1]
 
         # solved the same data is used as an argument to multiple OPs.
-        if val_scale.name in self.rename_mapper:
-            new_name = self.rename_mapper[val_scale.name]
-            _rename_or_remove_weight(self.weights, new_name,
-                                     op_name + '.weight', False)
-        else:
-            _rename_or_remove_weight(self.weights, val_scale.name,
-                                     op_name + '.weight')
-            self.rename_mapper[val_scale.name] = op_name + '.weight'
-        if val_b.name in self.rename_mapper:
-            new_name = self.rename_mapper[val_b.name]
-            _rename_or_remove_weight(self.weights, new_name, op_name + '.bias',
-                                     False)
-        else:
-            _rename_or_remove_weight(self.weights, val_b.name,
-                                     op_name + '.bias')
-            self.rename_mapper[val_b.name] = op_name + '.bias'
-        if val_var.name in self.rename_mapper:
-            new_name = self.rename_mapper[val_var.name]
-            _rename_or_remove_weight(self.weights, new_name,
-                                     op_name + '._variance', False)
-        else:
-            _rename_or_remove_weight(self.weights, val_var.name,
-                                     op_name + '._variance')
-            self.rename_mapper[val_var.name] = op_name + '._variance'
-        if val_mean.name in self.rename_mapper:
-            new_name = self.rename_mapper[val_mean.name]
-            _rename_or_remove_weight(self.weights, new_name, op_name + '._mean',
-                                     False)
-        else:
-            _rename_or_remove_weight(self.weights, val_mean.name,
-                                     op_name + '._mean')
-            self.rename_mapper[val_mean.name] = op_name + '._mean'
+        _rename_or_remove_weight(
+            self.weights,
+            val_scale.name,
+            op_name + '.weight',
+            rename_mapper=self.rename_mapper)
+        _rename_or_remove_weight(
+            self.weights,
+            val_b.name,
+            op_name + '.bias',
+            rename_mapper=self.rename_mapper)
+        _rename_or_remove_weight(
+            self.weights,
+            val_var.name,
+            op_name + '._variance',
+            rename_mapper=self.rename_mapper)
+        _rename_or_remove_weight(
+            self.weights,
+            val_mean.name,
+            op_name + '._mean',
+            rename_mapper=self.rename_mapper)
 
         # Attribute: spatial is used in BatchNormalization-1,6,7
         spatial = bool(node.get_attr('spatial'))
@@ -2255,14 +2249,22 @@ class OpSet9():
         remove_weight = True if val_w.name in self.done_weight_list else False
         if remove_weight:
             self.done_weight_list.append(val_w.name)
-        _rename_or_remove_weight(self.weights, val_w.name, op_name + '.weight',
-                                 remove_weight)
+        _rename_or_remove_weight(
+            self.weights,
+            val_w.name,
+            op_name + '.weight',
+            remove_weight,
+            rename_mapper=self.rename_mapper)
         if has_bias:
             remove_bias = True if val_b.name in self.done_weight_list else False
             if remove_bias:
-                self.done_weight_list.append(val_b_name)
-            _rename_or_remove_weight(self.weights, val_b.name,
-                                     op_name + '.bias', remove_bias)
+                self.done_weight_list.append(val_b.name)
+            _rename_or_remove_weight(
+                self.weights,
+                val_b.name,
+                op_name + '.bias',
+                remove_bias,
+                rename_mapper=self.rename_mapper)
         else:
             layer_attrs["bias_attr"] = False
         if reduce(lambda x, y: x * y,
@@ -2382,10 +2384,14 @@ class OpSet9():
         _rename_or_remove_weight(
             self.weights,
             val_w.name,
-            op_name + '.weight', )
+            op_name + '.weight',
+            rename_mapper=self.rename_mapper)
         if val_b is not None:
-            _rename_or_remove_weight(self.weights, val_b.name,
-                                     op_name + '.bias')
+            _rename_or_remove_weight(
+                self.weights,
+                val_b.name,
+                op_name + '.bias',
+                rename_mapper=self.rename_mapper)
         else:
             layer_attrs["bias_attr"] = False
         self.paddle_graph.add_layer(