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// nnet2bin/nnet-compute.cc
// Copyright 2012-2013 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 "hmm/transition-model.h"
#include "nnet2/train-nnet.h"
#include "nnet2/am-nnet.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Does the neural net computation for each file of input features, and
"
"outputs as a matrix the result. Used mostly for debugging.
"
"Note: if you want it to apply a log (e.g. for log-likelihoods), use
"
"--apply-log=true. Unlike nnet-am-compute, this version reads a 'raw'
"
"neural net
"
"
"
"Usage: nnet-compute [options] <raw-nnet-in> <feature-rspecifier> "
"<feature-or-loglikes-wspecifier>
";
bool apply_log = false;
bool pad_input = true;
ParseOptions po(usage);
po.Register("apply-log", &apply_log, "Apply a log to the result of the computation "
"before outputting.");
po.Register("pad-input", &pad_input, "If true, duplicate the first and last frames "
"of input features as required for temporal context, to prevent #frames "
"of output being less than those of input.");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string raw_nnet_rxfilename = po.GetArg(1),
features_rspecifier = po.GetArg(2),
features_or_loglikes_wspecifier = po.GetArg(3);
Nnet nnet;
ReadKaldiObject(raw_nnet_rxfilename, &nnet);
int64 num_done = 0, num_frames = 0;
SequentialBaseFloatCuMatrixReader feature_reader(features_rspecifier);
BaseFloatCuMatrixWriter writer(features_or_loglikes_wspecifier);
for (; !feature_reader.Done(); feature_reader.Next()) {
std::string utt = feature_reader.Key();
const CuMatrix<BaseFloat> &feats = feature_reader.Value();
int32 output_frames = feats.NumRows(), output_dim = nnet.OutputDim();
if (!pad_input)
output_frames -= nnet.LeftContext() + nnet.RightContext();
if (output_frames <= 0) {
KALDI_WARN << "Skipping utterance " << utt << " because output "
<< "would be empty.";
continue;
}
CuMatrix<BaseFloat> output(output_frames, output_dim);
NnetComputation(nnet, feats, pad_input, &output);
if (apply_log) {
output.ApplyFloor(1.0e-20);
output.ApplyLog();
}
writer.Write(utt, output);
num_frames += feats.NumRows();
num_done++;
}
KALDI_LOG << "Processed " << num_done << " feature files, "
<< num_frames << " frames of input were processed.";
return (num_done == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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