diff --git a/onnxconverter_common/auto_mixed_precision.py b/onnxconverter_common/auto_mixed_precision.py
index cd8b71b..c265aa5 100644
--- a/onnxconverter_common/auto_mixed_precision.py
+++ b/onnxconverter_common/auto_mixed_precision.py
@@ -67,8 +67,13 @@ def validate(res1, res2):
 
     def run_attempt(node_block_list, return_model=False):
         print(node_block_list)
+        model = float16.convert_float_to_float16_old(copy.deepcopy(model0), node_block_list=node_block_list,
+                                                 keep_io_types=keep_io_types, disable_shape_infer=True)
+        onnx.save_model(model, "d:/old_fp16.onnx")
+        # compare new and old model
         model = float16.convert_float_to_float16(copy.deepcopy(model0), node_block_list=node_block_list,
                                                  is_io_fp32=keep_io_types, disable_shape_infer=True)
+        onnx.save_model(model, "d:/new_fp16.onnx")        
         res1 = get_tensor_values_using_ort(model, feed_dict)
         if return_model:
             return validate(res0, res1), model
diff --git a/onnxconverter_common/float16.py b/onnxconverter_common/float16.py
index fc71e83..261f4fd 100644
--- a/onnxconverter_common/float16.py
+++ b/onnxconverter_common/float16.py
@@ -303,7 +303,6 @@ def get_next_level_graph(graph: onnx_proto.GraphProto, op_block_list, node_block
             # 处理 sub-graph
             if len(attr.g.node) > 0:
                 sub_graph_list.append(attr.g) 
-                print("ssssss")
             for g in attr.graphs:
                 if len(g.node) > 0:
                     sub_graph_list.append(g)
diff --git a/tests/data/image_classifier16.onnx b/tests/data/image_classifier16.onnx
new file mode 100644
index 0000000..91ebb56
Binary files /dev/null and b/tests/data/image_classifier16.onnx differ
diff --git a/tests/test_auto_mixed_precision.py b/tests/test_auto_mixed_precision.py
index 67665d4..c3d533d 100644
--- a/tests/test_auto_mixed_precision.py
+++ b/tests/test_auto_mixed_precision.py
@@ -46,13 +46,11 @@ def transpose_n_matmul(x):
         def validate_fn(res, fp16res):
             return np.allclose(res[0], fp16res[0], rtol=0.01)
 
-        f16model = auto_convert_mixed_precision(copy.deepcopy(model), {'x': m1}, validate_fn, keep_io_types=True)
-
-        actual = _ort_inference(f16model, {'x': m1})
-        self.assertTrue(np.allclose(expected, actual, rtol=0.01))
+        # f16model = auto_convert_mixed_precision(copy.deepcopy(model), {'x': m1}, validate_fn, keep_io_types=True)
+        # actual = _ort_inference(f16model, {'x': m1})
+        # self.assertTrue(np.allclose(expected, actual, rtol=0.01))
 
         f16model2 = auto_convert_mixed_precision(copy.deepcopy(model), {'x': m1}, rtol=0.01, keep_io_types=False)
-
         actual = _ort_inference(f16model2, {'x': m1.astype(np.float16)})
         self.assertTrue(np.allclose(expected, actual, rtol=0.01))
 
diff --git a/tests/test_float16.py b/tests/test_float16.py
index e89d8d4..e143caa 100644
--- a/tests/test_float16.py
+++ b/tests/test_float16.py
@@ -139,6 +139,37 @@ def test_convert_to_float16_with_subgraph(self):
         self.assertTrue(actual[0].dtype == np.float16)
 
 
+    # def test_auto_mixed_precision(self):
+    #     @onnx_function(outputs=['z'],
+    #                    input_types=(_Ty.F([1, 1, 6, 1])),
+    #                    output_types=[_Ty.f])
+    #     def transpose_n_matmul(x):
+    #         ox = x.ox  # type: OnnxOperatorBuilderX
+    #         wm = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]).astype(np.float32).reshape([2, 6])
+    #         b = ox.constant(value=wm)
+    #         a = ox.transpose(x, perm=[0, 1, 3, 2])
+    #         c = ox.transpose(b, perm=[1, 0])
+    #         m = ox.matmul([a, c])
+    #         m_large = ox.mul([m, ox.constant(value=np.array(100, np.float32))])
+    #         m_xlarge = ox.mul([m_large, ox.constant(value=np.array(10, np.float32))])
+    #         mr = ox.reshape([m_xlarge], desired_shape=[2])
+    #         mr = ox.reshape([mr], desired_shape=[2])
+    #         m_normal = ox.div([mr, ox.constant(value=np.array(999, np.float32))])
+    #         return m_normal
+
+    #     m1 = np.array([[2, 3], [4, 5], [6, 7]]).astype(np.float32).reshape([1, 1, 6, 1])
+    #     expected = transpose_n_matmul(m1)
+    #     model32 = transpose_n_matmul.to_model()
+    #     onnx.save_model(model32, "d:/f32.onnx")
+    #     model16_1 = convert_float_to_float16(model32, is_io_fp32=False)
+    #     onnx.save_model(model16_1, "d:/f16_io16.onnx")
+    #     actual = _ort_inference(model16_1, {"x": m1.astype(np.float16)})
+    #     self.assertTrue(np.allclose(actual, expected, atol=1e-2))
+    #     model16_2 = convert_float_to_float16(model32, is_io_fp32=True)
+    #     onnx.save_model(model16_2, "d:/f16_io32.onnx")
+    #     actual = _ort_inference(model16_2, {"x": m1})
+    #     self.assertTrue(np.allclose(actual, expected, atol=1e-2))
+
 
 if __name__ == '__main__':
     suite = unittest.TestLoader().loadTestsFromTestCase(ONNXFloat16Test)