nnet-am-reinitialize.cc
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// nnet2bin/nnet-am-reinitialize.cc
// Copyright 2014 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "nnet2/am-nnet.h"
#include "hmm/transition-model.h"
#include "tree/context-dep.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
const char *usage =
"This program can used when transferring a neural net from one language\n"
"to another (or one tree to another). It takes a neural net and a\n"
"transition model from a different neural net, resizes the last layer\n"
"to match the new transition model, zeroes it, and writes out the new,\n"
"resized .mdl file. If the original model had been 'mixed-up', the associated\n"
"SumGroupComponent will be removed.\n"
"\n"
"Usage: nnet-am-reinitialize [options] <nnet-in> <new-transition-model> <nnet-out>\n"
"e.g.:\n"
" nnet-am-reinitialize 1.mdl exp/tri6/final.mdl 2.mdl\n";
bool binary_write = true;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string nnet_rxfilename = po.GetArg(1),
transition_model_rxfilename = po.GetArg(2),
nnet_wxfilename = po.GetArg(3);
TransitionModel orig_trans_model;
AmNnet am_nnet;
{
bool binary;
Input ki(nnet_rxfilename, &binary);
orig_trans_model.Read(ki.Stream(), binary);
am_nnet.Read(ki.Stream(), binary);
}
TransitionModel new_trans_model;
ReadKaldiObject(transition_model_rxfilename, &new_trans_model);
am_nnet.ResizeOutputLayer(new_trans_model.NumPdfs());
{
Output ko(nnet_wxfilename, binary_write);
new_trans_model.Write(ko.Stream(), binary_write);
am_nnet.Write(ko.Stream(), binary_write);
}
KALDI_LOG << "Resized neural net from " << nnet_rxfilename
<< " to " << am_nnet.NumPdfs()
<< " pdfs, and wrote to " << nnet_wxfilename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}