054095d922edda5134e520522bc82a1b95cc5bd4,onnx_tf/backend.py,TensorflowBackend,run_node,#Any#Any#Any#Any#Any#,167

Before Change

    
    super(TensorflowBackend, cls).run_node(node, inputs, device)
    node_graph = tf.Graph()
    with node_graph.as_default():
      node = OnnxNode(node)
      device_option = get_device_option(Device(device))
      input_tensors = []
      for i in inputs:
        input_tensors.append(tf.constant(i))

      if isinstance(inputs, dict):
        feed_dict_raw = inputs
      else:
        assert len(node.inputs) == len(inputs)
        feed_dict_raw = dict(zip(node.inputs, inputs))

      // TODO: is constant the best way for feeding inputs?
      input_dict = dict([
          (x[0], tf.constant(x[1])) for x in feed_dict_raw.items()
      ])
      ops = cls._onnx_node_to_tensorflow_op(node, input_dict)

      with tf.compat.v1.Session() as sess:
        with tf.device(device_option):
          sess.run(tf.compat.v1.global_variables_initializer())
          output_vals = sess.run(ops)

    return namedtupledict("Outputs", node.outputs)(*output_vals)

  @classmethod
  def _onnx_initializer_to_input_dict_items(cls, initializer):

After Change

    module = TFModule(node)

    output_vals = module(**input_dict)
    output_vals = [val.numpy() if isinstance(val, tf.Tensor) else val for val in output_vals]

    return namedtupledict("Outputs", node.outputs)(*output_vals)

  @classmethod

Instances