Function RunOnce

tensorflow/cc/saved_model/loader.cc:193–223

Like Session::Run(), but uses the Make/Run/ReleaseCallable() API to avoid leaving behind non-GC'ed state. Detailed motivation behind this approach, from ashankar@: Each call to Session::Run() that identifies a new subgraph (based on feeds and fetches) creates some datastructures that live as long as the session (the partitioned graph, associated executors etc.). A pathological case of this woul

//
// However, the resource manager state remains.
Status RunOnce(const RunOptions& run_options,
               const std::vector<std::pair<string, Tensor>>& inputs,
               const std::vector<string>& output_tensor_names,
               const std::vector<string>& target_node_names,
               std::vector<Tensor>* outputs, RunMetadata* run_metadata,
               Session* session) {
  CallableOptions callable_options;
  std::vector<Tensor> feed_tensors;
  *callable_options.mutable_run_options() = run_options;
  for (const auto& input : inputs) {
    const string& name = input.first;
    const Tensor& tensor = input.second;
    callable_options.add_feed(name);
    feed_tensors.push_back(tensor);
  }
  for (const string& output_tensor_name : output_tensor_names) {
    callable_options.add_fetch(output_tensor_name);
  }
  for (const string& target_node_name : target_node_names) {
    callable_options.add_target(target_node_name);
  }

  Session::CallableHandle callable_handle;
  TF_RETURN_IF_ERROR(session->MakeCallable(callable_options, &callable_handle));
  const Status run_status = session->RunCallable(callable_handle, feed_tensors,
                                                 outputs, run_metadata);
  // Be sure to call ReleaseCallable() regardless of the outcome of
  // RunCallable().
  session->ReleaseCallable(callable_handle).IgnoreError();
  return run_status;
}

// RunInitOp will return OK if the initialization op was run successfully.
// An empty init_op_name indicates that there are no init ops to run.