text: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you 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.  See the License for the
   * specific language governing permissions and limitations
   * under the License.
   */
  
  /*
   * Copyright (c) 2021, OPEN AI LAB
   * Author:   bzhang@openailab.com
   */
  
  #ifndef __CAFFE2TENGINE_HPP__
  #define __CAFFE2TENGINE_HPP__
  
  #include <string>
  #include <iostream>
  #include <fstream>
  #include "te_caffe.pb.h"
  #include <vector>
  
  #include <google/protobuf/io/coded_stream.h>
  #include <google/protobuf/io/zero_copy_stream_impl.h>
  #include <google/protobuf/text_format.h>
  #include <google/protobuf/message.h>
  
  extern "C" {
  #include "api/c_api.h"
  #include "graph/graph.h"
  #include "graph/subgraph.h"
  #include "graph/node.h"
  #include "graph/tensor.h"
  #include "executer/executer.h"
  #include "module/module.h"
  #include "utility/log.h"
  #include "utility/sys_port.h"
  #include "utility/vector.h"
  #include "save_graph/op_include.h"
  }
  #define LRN_ACROSS_CHANNELS 0
  #define LRN_WITHIN_CHANNEL  1
  enum PoolArg
  {
      kPoolMax,
      kPoolAvg,
      kPoolRand
  };
  
  class caffe_serializer
  {
  public:
      graph_t caffe2tengine(std::string model_file, std::string proto_file);
      typedef int (*op_load_t)(ir_graph_t* graph, ir_node_t* node, const te_caffe::LayerParameter& layer_param);
      typedef int (*blob_load_t)(ir_graph_t* graph, ir_node_t* node, const te_caffe::LayerParameter& layer_param);
      using name_map_t = std::unordered_map<std::string, std::string>;
  
  private:
      std::unordered_map<std::string, std::pair<int, op_load_t> > op_load_map;
      std::unordered_map<std::string, blob_load_t> blob_load_map;
      int load_model(ir_graph_t* graph, std::string model_file, std::string proto_file);
      int load_graph_node(ir_graph_t* graph, const te_caffe::NetParameter test_net, const te_caffe::NetParameter train_net);
      int load_tensor_data(ir_graph_t* graph, const te_caffe::NetParameter test_net, const te_caffe::NetParameter train_net);
      int load_text_file(std::string model_file, te_caffe::NetParameter& caffe_net);
      int load_binary_file(std::string model_file, te_caffe::NetParameter& caffe_net);
      int set_graph_input(ir_graph_t* graph, const te_caffe::NetParameter test_net, const te_caffe::NetParameter train_net);
      int set_graph_output(ir_graph_t* graph, const te_caffe::NetParameter test_net, const te_caffe::NetParameter train_net);
      bool find_op_load_method(const std::string& op_name);
      void register_op_load();
  
      std::unordered_map<std::string, const te_caffe::LayerParameter*> train_name_map;
      std::vector<std::string> output_tensors;
      std::vector<std::string> input_tensors;
  };
  
  #endif