symbolize-model.C

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#include <string>
#include <cstdlib>
#include <memory>
#include <vector>

#include "../proto/dataio.h"
#include "model-proto-writer.H"
#include "model-reader.H"
#include "symbol-table.H"

#define PROG_NAME "symbolize-model"

// We use two levels of macros to get the string version of an int constant.
#define XSTR(arg) STR(arg)
#define STR(arg) #arg

using namespace std;
using namespace reranker;
using confusion_learning::SymbolMessage;

const char *usage_msg[] = {
  "Usage:\n",
  PROG_NAME " <model file> <symbol file> <output model file>\n",
};

void usage() {
  int usage_msg_len = sizeof(usage_msg)/sizeof(const char *);
  for (int i = 0; i < usage_msg_len; ++i) {
    cout << usage_msg[i];
  }
  cout.flush();
}

int
main(int argc, char **argv) {
  // Required parameters.
  string model_file;
  string symbol_file;
  string model_output_file;

  if (argc != 4) {
    usage();
    return -1;
  }

  model_file = argv[1];
  symbol_file = argv[2];
  model_output_file = argv[3];

  bool compressed = true;
  bool use_base64 = true;

  // Now, we finally get to the meat of the code for this executable.
  shared_ptr<Symbols> symbols(new LocalSymbolTable());
  if (symbol_file != "") {
    ConfusionProtoIO proto_reader(symbol_file,
                                  ConfusionProtoIO::READ,
                                  compressed, use_base64);
    SymbolMessage symbol_message;
    while (proto_reader.Read(&symbol_message)) {
      symbols->SetIndex(symbol_message.symbol(), symbol_message.index());
    }
    proto_reader.Close();
  }

  ModelReader model_reader(1);
  shared_ptr<Model> model =
      model_reader.Read(model_file, compressed, use_base64);
  model->set_symbols(symbols.get());
  
  // Serialize model.
  Factory<ModelProtoWriter> proto_writer_factory;
  shared_ptr<ModelProtoWriter> model_writer =
      proto_writer_factory.CreateOrDie(model->proto_writer_spec(),
                                       "model proto writer");
  if (model_writer.get() == NULL) {
    return -1;
  }

  cerr << "Writing out model to file \"" << model_output_file << "\"...";
  cerr.flush();
  confusion_learning::ModelMessage model_message;
  model_writer->Write(model.get(), &model_message, false);

  ConfusionProtoIO *proto_writer;
  proto_writer = new ConfusionProtoIO(model_output_file,
                                      ConfusionProtoIO::WRITE,
                                      compressed, use_base64);
  proto_writer->Write(model_message);
  // Write out features.
  bool output_best_epoch = true;
  bool output_key = false;
  model_writer->WriteFeatures(model.get(), 
                              *(proto_writer->outputstream()),
                              output_best_epoch,
                              model->num_training_errors(),
                              output_key);
  delete proto_writer;
  cerr << "done." << endl;  

  TearDown();
  google::protobuf::ShutdownProtobufLibrary();
}