easy_rec/python/tools/split_model_pai.py [211:250]:
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        graph.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, variable)
      saver = tf_saver.Saver()
      saver.restore(sess, get_variables_path(model_dir))

      builder = tf.saved_model.builder.SavedModelBuilder(part_dir)
      signature_inputs = {}
      for input_name in input_tensor_names:
        try:
          tensor_info = tf.saved_model.utils.build_tensor_info(
              graph.get_tensor_by_name(input_tensor_names[input_name]))
          signature_inputs[input_name] = tensor_info
        except Exception:
          print('ignore input: %s' % input_name)

      signature_outputs = {}
      for output_name in output_tensor_names:
        tensor_info = tf.saved_model.utils.build_tensor_info(
            graph.get_tensor_by_name(output_tensor_names[output_name]))
        signature_outputs[output_name] = tensor_info

      prediction_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs=signature_inputs,
              outputs=signature_outputs,
              method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
          ))

      builder.add_meta_graph_and_variables(
          sess, [tf.saved_model.tag_constants.SERVING],
          signature_def_map={
              signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  prediction_signature,
          })
      builder.save()
  config_path = os.path.join(model_dir, 'assets/pipeline.config')
  assert tf.gfile.Exists(config_path)
  dst_path = os.path.join(part_dir, 'assets')
  dst_config_path = os.path.join(dst_path, 'pipeline.config')
  tf.gfile.MkDir(dst_path)
  tf.gfile.Copy(config_path, dst_config_path)
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easy_rec/python/tools/split_pdn_model_pai.py [203:242]:
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        graph.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, variable)
      saver = tf_saver.Saver()
      saver.restore(sess, get_variables_path(model_dir))

      builder = tf.saved_model.builder.SavedModelBuilder(part_dir)
      signature_inputs = {}
      for input_name in input_tensor_names:
        try:
          tensor_info = tf.saved_model.utils.build_tensor_info(
              graph.get_tensor_by_name(input_tensor_names[input_name]))
          signature_inputs[input_name] = tensor_info
        except Exception:
          print('ignore input: %s' % input_name)

      signature_outputs = {}
      for output_name in output_tensor_names:
        tensor_info = tf.saved_model.utils.build_tensor_info(
            graph.get_tensor_by_name(output_tensor_names[output_name]))
        signature_outputs[output_name] = tensor_info

      prediction_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs=signature_inputs,
              outputs=signature_outputs,
              method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
          ))

      builder.add_meta_graph_and_variables(
          sess, [tf.saved_model.tag_constants.SERVING],
          signature_def_map={
              signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  prediction_signature,
          })
      builder.save()
  config_path = os.path.join(model_dir, 'assets/pipeline.config')
  assert tf.gfile.Exists(config_path)
  dst_path = os.path.join(part_dir, 'assets')
  dst_config_path = os.path.join(dst_path, 'pipeline.config')
  tf.gfile.MkDir(dst_path)
  tf.gfile.Copy(config_path, dst_config_path)
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