add _rename_or_remove_weight

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -42,14 +42,17 @@ def _const_weight_or_none(node, necessary=False):
     return None
 
 
-def _rename_or_remove_weight(weights, origin_name, target_name=None):
+def _rename_or_remove_weight(weights, origin_name, target_name=None, is_remove=True):
     if origin_name not in weights:
         raise KeyError('{} not a key in {}'.format(origin_name, weights))
-    if target_name is None:
+    if is_remove:
         # remove weight
-        weights.pop(origin_name)
+        data = weights.pop(origin_name)
+    else:
+        data = weights[origin_name]
+    if target_name is not None:
         # rename weight
-    weights[target_name] = weights.pop(origin_name)
+        weights[target_name] = data
 
 def _is_static_shape(shape):
     negtive_dims = 0
@@ -1700,19 +1703,17 @@ class OpSet9():
             "dilation": dilations,
             "groups": num_groups,
         }
-        val_w_name = val_w.name
-        while val_w_name in self.done_weight_list:
-            val_w_name += "__repeat"
-        self.done_weight_list.append(val_w_name)
+        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)
         #self.weights[op_name + '.weight'] = self.weights[val_w.name]
-        _rename_or_remove_weight(self.weights, val_w.name, op_name+'.weight')
+        _rename_or_remove_weight(self.weights, val_w.name, op_name+'.weight', remove_weight)
         if has_bias:
-            val_b_name = val_b.name
-            while val_b_name in self.done_weight_list:
-                val_b_name += "__repeat"
+            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)
             #self.weights[op_name + '.bias'] = self.weights[val_b.name]
-            _rename_or_remove_weight(self.weights, val_b.name, op_name+'.bias')
+            _rename_or_remove_weight(self.weights, val_b.name, op_name+'.bias', remove_bias)
         else:
             layer_attrs["bias_attr"] = False
         input_shape = val_x.out_shapes[0]