nnet3-copy.cc
4.34 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
// nnet3bin/nnet3-copy.cc
// Copyright 2012 Johns Hopkins University (author: Daniel Povey)
// 2015 Xingyu Na
// 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 "nnet3/am-nnet-simple.h"
#include "nnet3/nnet-utils.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet3;
typedef kaldi::int32 int32;
const char *usage =
"Copy 'raw' nnet3 neural network to standard output\n"
"Also supports setting all the learning rates to a value\n"
"(the --learning-rate option)\n"
"\n"
"Usage: nnet3-copy [options] <nnet-in> <nnet-out>\n"
"e.g.:\n"
" nnet3-copy --binary=false 0.raw text.raw\n";
bool binary_write = true;
BaseFloat learning_rate = -1;
std::string nnet_config, edits_config, edits_str;
BaseFloat scale = 1.0;
bool prepare_for_test = false;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("learning-rate", &learning_rate,
"If supplied, all the learning rates of updatable components"
"are set to this value.");
po.Register("nnet-config", &nnet_config,
"Name of nnet3 config file that can be used to add or replace "
"components or nodes of the neural network (the same as you "
"would give to nnet3-init).");
po.Register("edits-config", &edits_config,
"Name of edits-config file that can be used to modify the network "
"(applied after nnet-config). See comments for ReadEditConfig()"
"in nnet3/nnet-utils.h to see currently supported commands.");
po.Register("edits", &edits_str,
"Can be used as an inline alternative to edits-config; semicolons "
"will be converted to newlines before parsing. E.g. "
"'--edits=remove-orphans'.");
po.Register("scale", &scale, "The parameter matrices are scaled"
" by the specified value.");
po.Register("prepare-for-test", &prepare_for_test,
"If true, prepares the model for test time (may reduce model size "
"slightly. Involves setting test mode in dropout and batch-norm "
"components, and calling CollapseModel() which may remove some "
"components.");
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 (!nnet_config.empty()) {
Input ki(nnet_config);
nnet.ReadConfig(ki.Stream());
}
if (learning_rate >= 0)
SetLearningRate(learning_rate, &nnet);
if (scale != 1.0)
ScaleNnet(scale, &nnet);
if (!edits_config.empty()) {
Input ki(edits_config);
ReadEditConfig(ki.Stream(), &nnet);
}
if (!edits_str.empty()) {
for (size_t i = 0; i < edits_str.size(); i++)
if (edits_str[i] == ';')
edits_str[i] = '\n';
std::istringstream is(edits_str);
ReadEditConfig(is, &nnet);
}
if (prepare_for_test) {
SetBatchnormTestMode(true, &nnet);
SetDropoutTestMode(true, &nnet);
CollapseModel(CollapseModelConfig(), &nnet);
}
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;
}
}