fix export onnx script bug

Classification/cnns/resnet_to_onnx.py

 """
 Copyright 2020 The OneFlow Authors. All rights reserved.
-
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
-
     http://www.apache.org/licenses/LICENSE-2.0
-
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -22,8 +19,6 @@ from typing import Callable, Text
 
 import numpy as np
 import oneflow as flow
-import oneflow_onnx
-from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
 import oneflow.typing as tp
 import onnx
 import onnxruntime as ort
@@ -31,6 +26,7 @@ import onnxruntime as ort
 from resnet_model import resnet50
 import config as configs
 from imagenet1000_clsidx_to_labels import clsidx_2_labels
+from oneflow_onnx.oneflow2onnx.util import export_onnx_model
 
 parser = configs.get_parser()
 args = parser.parse_args()
@@ -94,9 +90,15 @@ def oneflow_to_onnx(
     assert os.path.exists(flow_weights_path) and os.path.isdir(onnx_model_dir)
 
     onnx_model_path = os.path.join(
-        onnx_model_dir, os.path.basename(flow_weights_path) + ".onnx"
+        onnx_model_dir, "model.onnx"
+    )
+    export_onnx_model(
+        job_func,
+        flow_weight_dir=flow_weights_path,
+        onnx_model_path=onnx_model_dir,
+        opset=11,
+        external_data=external_data,
     )
-    convert_to_onnx_and_check(job_func, flow_weight_dir=flow_weights_path, onnx_model_path=onnx_model_path, opset=11, external_data=external_data)
     print("Convert to onnx success! >> ", onnx_model_path)
     return onnx.load_model(onnx_model_path)
 
@@ -116,8 +118,6 @@ if __name__ == "__main__":
     # set up your model path
     flow_weights_path = "resnet_v15_of_best_model_val_top1_77318"
     onnx_model_dir = "onnx/model"
-    
-    flow.train.CheckPoint().init()
 
     flow.load_variables(flow.checkpoint.get(flow_weights_path))
 
@@ -130,4 +130,4 @@ if __name__ == "__main__":
     are_equal, onnx_res = check_equality(InferenceNet, onnx_model, image_path)
     clsidx_onnx = onnx_res.argmax()
     print("Are the results equal? {}".format("Yes" if are_equal else "No"))
-    print("Class: {}; score: {}".format(clsidx_2_labels[clsidx_onnx], onnx_res.max()))
+    print("Class: {}; score: {}".format(clsidx_2_labels[clsidx_onnx], onnx_res.max()))
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