diff --git a/mace/python/tools/converter_tool/transformer.py b/mace/python/tools/converter_tool/transformer.py
index 9a4985b779edc8e9d95624cad45d4b29927438a6..1f2e986d5c5b04a64bc9b5a5395716ccee5b2e28 100644
--- a/mace/python/tools/converter_tool/transformer.py
+++ b/mace/python/tools/converter_tool/transformer.py
@@ -137,7 +137,6 @@ class Transformer(base_converter.ConverterInterface):
                 changed = transformer()
                 if not changed:
                     break
-        self.delete_after_check_nodes()
         return self._model, self._quantize_activation_info
 
     def initialize_name_map(self):
@@ -1332,7 +1331,10 @@ class Transformer(base_converter.ConverterInterface):
         visited = set()
         sorted_nodes = []
 
-        for output_node in self._option.output_nodes:
+        output_nodes = self._option.check_nodes
+        if not self._quantize_activation_info:
+            output_nodes.update(self._option.output_nodes)
+        for output_node in output_nodes:
             mace_check(output_node in self._producer,
                        "output_tensor %s not existed in model" % output_node)
             self.sort_dfs(self._producer[output_node], visited, sorted_nodes)
@@ -2010,18 +2012,6 @@ class Transformer(base_converter.ConverterInterface):
         arg.i = mace_pb2.GPU_IMAGE if self._option.cl_mem_type == "image"\
             else mace_pb2.GPU_BUFFER
 
-    def delete_after_check_nodes(self):
-        if self._option.check_nodes != self._option.output_nodes:
-            mace_check(len(self._option.check_nodes) == 1,
-                       "Only support one check node now.")
-            check_node = None
-            for i in six.moves.range(len(self._model.op)):
-                if self._model.op[i].output[0] in self._option.check_nodes:
-                    check_node = self._model.op[i]
-                    del self._model.op[i+1:]
-                    break
-            mace_check(check_node is not None, "check node not found.")
-
     def transform_caffe_reshape_and_flatten(self):
         net = self._model
         for op in net.op: