// nnet2bin/nnet-am-limit-rank-final.cc

// Copyright 2012 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/nnet-limit-rank.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 =

"Copy a (cpu-based) neural net and its associated transition model,\n"

"but modify it to reduce the effective parameter count by limiting\n"

"the rank of the last affine component, which is replaced with two\n"

"affine components. You specify the number of singlular values to\n"

"retain as e.g. --dim=200\n"

"\n"

"Usage: nnet-am-limit-rank-final [options] <nnet-in> <nnet-out>\n"

"e.g.:\n"

" nnet-am-limit-rank-final --dim=200 1.mdl 1_limited.mdl\n";

bool binary_write = true;

int32 dim = 200;

ParseOptions po(usage);

po.Register("binary", &binary_write, "Write output in binary mode");

po.Register("dim", &dim, "Dimension to retain");

po.Read(argc, argv);

if (po.NumArgs() != 2) {

po.PrintUsage();

exit(1);

}

std::string nnet_rxfilename = po.GetArg(1),

nnet_wxfilename = po.GetArg(2);

TransitionModel trans_model;

AmNnet am_nnet;

{

bool binary;

Input ki(nnet_rxfilename, &binary);

trans_model.Read(ki.Stream(), binary);

am_nnet.Read(ki.Stream(), binary);

}

am_nnet.GetNnet().LimitRankOfLastLayer(dim);

{

Output ko(nnet_wxfilename, binary_write);

trans_model.Write(ko.Stream(), binary_write);

am_nnet.Write(ko.Stream(), binary_write);

}

KALDI_LOG << "Limited rank of neural net " << nnet_rxfilename

<< " and copied to " << nnet_wxfilename;

return 0;

} catch(const std::exception &e) {

std::cerr << e.what() << '\n';

return -1;

}

}