raw-nnet-copy.cc
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// nnet2bin/raw-nnet-copy.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 <typeinfo>
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "nnet2/am-nnet.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 raw neural net (this version works on raw nnet2 neural nets,\n"
"without the transition model. Supports the 'truncate' option.\n"
"\n"
"Usage: raw-nnet-copy [options] <raw-nnet-in> <raw-nnet-out>\n"
"e.g.:\n"
" raw-nnet-copy --binary=false 1.mdl text.mdl\n"
"See also: nnet-to-raw-nnet, nnet-am-copy\n";
int32 truncate = -1;
bool binary_write = true;
std::string learning_rate_scales_str = " ";
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("truncate", &truncate, "If set, will truncate the neural net "
"to this many components by removing the last components.");
po.Register("learning-rate-scales", &learning_rate_scales_str,
"Colon-separated list of scaling factors for learning rates, "
"applied after the --learning-rate and --learning-rates options."
"Used to scale learning rates for particular layer types. E.g."
"--learning-rate-scales=AffineComponent=0.5");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string raw_nnet_rxfilename = po.GetArg(1),
raw_nnet_wxfilename = po.GetArg(2);
Nnet nnet;
ReadKaldiObject(raw_nnet_rxfilename, &nnet);
if (truncate >= 0)
nnet.Resize(truncate);
if (learning_rate_scales_str != " ") {
// parse the learning_rate_scales provided as an option
std::map<std::string, BaseFloat> learning_rate_scales;
std::vector<std::string> learning_rate_scale_vec;
SplitStringToVector(learning_rate_scales_str, ":", true,
&learning_rate_scale_vec);
for (int32 index = 0; index < learning_rate_scale_vec.size();
index++) {
std::vector<std::string> parts;
BaseFloat scale_factor;
SplitStringToVector(learning_rate_scale_vec[index],
"=", false, &parts);
if (!ConvertStringToReal(parts[1], &scale_factor)) {
KALDI_ERR << "Unknown format for --learning-rate-scales option. "
<< "Expected format is "
<< "--learning-rate-scales=AffineComponent=0.1:AffineComponentPreconditioned=0.5 "
<< "instead got "
<< learning_rate_scales_str;
}
learning_rate_scales.insert(std::pair<std::string, BaseFloat>(
parts[0], scale_factor));
}
// use the learning_rate_scales to scale the component learning rates
nnet.ScaleLearningRates(learning_rate_scales);
}
WriteKaldiObject(nnet, raw_nnet_wxfilename, binary_write);
KALDI_LOG << "Copied raw neural net from " << raw_nnet_rxfilename
<< " to " << raw_nnet_wxfilename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}