nnet-copy.cc
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// nnetbin/nnet-copy.cc
// Copyright 2012-2015 Brno University of Technology (author: Karel Vesely)
// 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 "nnet/nnet-nnet.h"
#include "nnet/nnet-parallel-component.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet1;
typedef kaldi::int32 int32;
const char *usage =
"Copy Neural Network model (and possibly change binary/text format)\n"
"Usage: nnet-copy [options] <model-in> <model-out>\n"
"e.g.:\n"
" nnet-copy --binary=false nnet.mdl nnet_txt.mdl\n";
bool binary_write = true;
int32 remove_first_components = 0;
int32 remove_last_components = 0;
BaseFloat dropout_rate = -1.0;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("remove-first-layers", &remove_first_components,
"Deprecated, please use --remove-first-components");
po.Register("remove-last-layers", &remove_last_components,
"Deprecated, please use --remove-last-components");
po.Register("remove-first-components", &remove_first_components,
"Remove N first Components from the Nnet");
po.Register("remove-last-components", &remove_last_components,
"Remove N last layers Components from the Nnet");
po.Register("dropout-rate", &dropout_rate,
"Probability that neuron is dropped"
"(-1.0 keeps original value).");
std::string from_parallel_component;
po.Register("from-parallel-component", &from_parallel_component,
"Extract nested network from parallel component (two possibilities: "
"'3' = search for ParallelComponent and get its 3rd network; "
"'1:3' = get 3nd network from 1st component; ID = 1..N).");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string model_in_filename = po.GetArg(1),
model_out_filename = po.GetArg(2);
// load the network
Nnet nnet;
{
bool binary_read;
Input ki(model_in_filename, &binary_read);
nnet.Read(ki.Stream(), binary_read);
}
// eventually replace 'nnet' by nested network from <ParallelComponent>,
if (from_parallel_component != "") {
std::vector<int32> component_id_nested_id;
kaldi::SplitStringToIntegers(from_parallel_component, ":", false,
&component_id_nested_id);
// parse the argument,
int32 component_id = -1, nested_id = 0;
switch (component_id_nested_id.size()) {
case 1:
nested_id = component_id_nested_id[0];
break;
case 2:
component_id = component_id_nested_id[0];
nested_id = component_id_nested_id[1];
break;
default:
KALDI_ERR << "Check the csl '--from-parallel-component='"
<< from_parallel_component
<< " There must be 1 or 2 elements.";
}
// search for first <ParallelComponent> (we don't know component_id yet),
if (component_id == -1) {
for (int32 i = 0; i < nnet.NumComponents(); i++) {
if (nnet.GetComponent(i).GetType() == Component::kParallelComponent) {
component_id = i+1;
break;
}
}
}
// replace the nnet,
KALDI_ASSERT(nnet.GetComponent(component_id-1).GetType() ==
Component::kParallelComponent);
ParallelComponent& parallel_comp =
dynamic_cast<ParallelComponent&>(nnet.GetComponent(component_id-1));
nnet = parallel_comp.GetNestedNnet(nested_id-1); // replace!
}
// optionally remove N first components,
if (remove_first_components > 0) {
for (int32 i = 0; i < remove_first_components; i++) {
nnet.RemoveComponent(0);
}
}
// optionally remove N last components,
if (remove_last_components > 0) {
for (int32 i = 0; i < remove_last_components; i++) {
nnet.RemoveLastComponent();
}
}
// dropout,
if (dropout_rate != -1.0) {
nnet.SetDropoutRate(dropout_rate);
}
// store the network,
{
Output ko(model_out_filename, binary_write);
nnet.Write(ko.Stream(), binary_write);
}
KALDI_LOG << "Written 'nnet1' to " << model_out_filename;
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}