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src/nnet3bin/nnet3-discriminative-shuffle-egs.cc
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// nnet3bin/nnet3-discriminative-shuffle-egs.cc
// Copyright 2012-2015 Johns Hopkins University (author: Daniel Povey)
// 2014-2015 Vimal Manohar
// 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 "hmm/transition-model.h"
#include "nnet3/nnet-discriminative-example.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet3;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Copy nnet3 discriminative training examples from the input to output,
"
"while randomly shuffling the order. This program will keep all of the examples
"
"in memory at once, unless you use the --buffer-size option
"
"
"
"Usage: nnet3-discriminative-shuffle-egs [options] <egs-rspecifier> <egs-wspecifier>
"
"
"
"nnet3-discriminative-shuffle-egs --srand=1 ark:train.egs ark:shuffled.egs
";
int32 srand_seed = 0;
int32 buffer_size = 0;
ParseOptions po(usage);
po.Register("srand", &srand_seed, "Seed for random number generator ");
po.Register("buffer-size", &buffer_size, "If >0, size of a buffer we use "
"to do limited-memory partial randomization. Otherwise, do "
"full randomization.");
po.Read(argc, argv);
srand(srand_seed);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string examples_rspecifier = po.GetArg(1),
examples_wspecifier = po.GetArg(2);
int64 num_done = 0;
std::vector<std::pair<std::string, NnetDiscriminativeExample*> > egs;
SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);
NnetDiscriminativeExampleWriter example_writer(examples_wspecifier);
if (buffer_size == 0) { // Do full randomization
// Putting in an extra level of indirection here to avoid excessive
// computation and memory demands when we have to resize the vector.
for (; !example_reader.Done(); example_reader.Next())
egs.push_back(std::pair<std::string, NnetDiscriminativeExample*>(
example_reader.Key(),
new NnetDiscriminativeExample(example_reader.Value())));
std::random_shuffle(egs.begin(), egs.end());
} else {
KALDI_ASSERT(buffer_size > 0);
egs.resize(buffer_size,
std::pair<std::string, NnetDiscriminativeExample*>("", NULL));
for (; !example_reader.Done(); example_reader.Next()) {
int32 index = RandInt(0, buffer_size - 1);
if (egs[index].second == NULL) {
egs[index] = std::pair<std::string, NnetDiscriminativeExample*>(
example_reader.Key(),
new NnetDiscriminativeExample(example_reader.Value()));
} else {
example_writer.Write(egs[index].first, *(egs[index].second));
egs[index].first = example_reader.Key();
*(egs[index].second) = example_reader.Value();
num_done++;
}
}
}
for (size_t i = 0; i < egs.size(); i++) {
if (egs[i].second != NULL) {
example_writer.Write(egs[i].first, *(egs[i].second));
delete egs[i].second;
num_done++;
}
}
KALDI_LOG << "Shuffled order of " << num_done
<< " neural-network training examples "
<< (buffer_size ? "using a buffer (partial randomization)" : "");
return (num_done == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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