/*!
 * Copyright (c) 2015 by Contributors
 */
#include <iostream>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include "mxnet-cpp/MxNetCpp.h"
using namespace std;
using namespace mxnet::cpp;

/*
 * This example shows how to extract features with a pretrained model.
 * Get the model here:
 *   https://github.com/dmlc/mxnet-model-gallery
 * */

/*The global context, change them if necessary*/
Context global_ctx(kGPU, 0);
// Context global_ctx(kCPU,0);

class FeatureExtractor {
 private:
  /*the mean image, get from the pretrained model*/
  NDArray mean_img;
  /*the following two maps store all the paramters need by the model*/
  map<string, NDArray> args_map;
  map<string, NDArray> aux_map;
  Symbol net;
  Executor *executor;
  /*Get the feature layer we want to extract*/
  void GetFeatureSymbol() {
    /*
     * use the following to check all the layers' names:
     * */
    /*
    net=Symbol::Load("./model/Inception_BN-symbol.json").GetInternals();
    for(const auto & layer_name:net.ListOutputs()){
      LG<<layer_name;
    }
    */
    net = Symbol::Load("./model/Inception_BN-symbol.json")
              .GetInternals()["global_pool_output"];
  }
  /*Fill the trained paramters into the model, a.k.a. net, executor*/
  void LoadParameters() {
    map<string, NDArray> paramters;
    NDArray::Load("./model/Inception_BN-0039.params", 0, &paramters);
    for (const auto &k : paramters) {
      if (k.first.substr(0, 4) == "aux:") {
        auto name = k.first.substr(4, k.first.size() - 4);
        aux_map[name] = k.second.Copy(global_ctx);
      }
      if (k.first.substr(0, 4) == "arg:") {
        auto name = k.first.substr(4, k.first.size() - 4);
        args_map[name] = k.second.Copy(global_ctx);
      }
    }
    /*WaitAll is need when we copy data between GPU and the main memory*/
    NDArray::WaitAll();
  }
  void GetMeanImg() {
    mean_img = NDArray(Shape(1, 3, 224, 224), global_ctx, false);
    mean_img.SyncCopyFromCPU(
        NDArray::LoadToMap("./model/mean_224.nd")["mean_img"].GetData(),
        1 * 3 * 224 * 224);
    NDArray::WaitAll();
  }

 public:
  FeatureExtractor() {
    /*prepare the model, fill the pretrained parameters, get the mean image*/
    GetFeatureSymbol();
    LoadParameters();
    GetMeanImg();
  }

  void Extract(NDArray data) {
    /*Normalize the pictures*/
    data.Slice(0, 1) -= mean_img;
    data.Slice(1, 2) -= mean_img;
    args_map["data"] = data;
    /*bind the excutor*/
    executor = net.SimpleBind(global_ctx, args_map, map<string, NDArray>(),
                              map<string, OpReqType>(), aux_map);
    executor->Forward(false);
    /*print out the features*/
    auto array = executor->outputs[0].Copy(Context(kCPU, 0));
    NDArray::WaitAll();
    for (int i = 0; i < 1024; ++i) {
      cout << array.At(0, i) << ",";
    }
    cout << endl;
  }
};

NDArray Data2NDArray() {
  NDArray ret(Shape(2, 3, 224, 224), global_ctx, false);
  ifstream inf("./img.dat", ios::binary);
  vector<float> data(2 * 3 * 224 * 224);
  inf.read(reinterpret_cast<char *>data.data(), 2 * 3 * 224 * 224 * sizeof(float));
  inf.close();
  ret.SyncCopyFromCPU(data.data(), 2 * 3 * 224 * 224);
  NDArray::WaitAll();
  return ret;
}

int main() {
  /*
   * get the data from a binary file ./img.data
   * this file is generated by ./prepare_data_with_opencv
   * it stores 2 pictures in NDArray format
   *
   */
  auto data = Data2NDArray();
  FeatureExtractor fe;
  fe.Extract(data);
  return 0;
}