nnet-pdf-prior.cc
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// nnet/nnet-pdf-prior.cc
// Copyright 2013 Brno University of Technology (Author: Karel Vesely);
// Arnab Ghoshal
// 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 "nnet/nnet-pdf-prior.h"
namespace kaldi {
namespace nnet1 {
PdfPrior::PdfPrior(const PdfPriorOptions &opts)
: prior_scale_(opts.prior_scale) {
if (opts.class_frame_counts == "") {
// class_frame_counts is empty, the PdfPrior is deactivated...
// (for example when 'nnet-forward' generates bottleneck features)
return;
}
KALDI_LOG << "Computing pdf-priors from : " << opts.class_frame_counts;
Vector<double> frame_counts, rel_freq, log_priors;
{
Input in;
in.OpenTextMode(opts.class_frame_counts);
frame_counts.Read(in.Stream(), false);
in.Close();
}
// get relative frequencies,
rel_freq = frame_counts;
rel_freq.Scale(1.0/frame_counts.Sum());
// get the log-prior,
log_priors = rel_freq;
log_priors.Add(1e-20);
log_priors.ApplyLog();
// Make the priors for classes with low counts +inf (i.e. -log(0))
// such that the classes have 0 likelihood (i.e. -inf log-likelihood).
// We use sqrt(FLT_MAX) instead of -kLogZeroFloat to prevent NANs
// from appearing in computation.
int32 num_floored = 0;
for (int32 i = 0; i < log_priors.Dim(); i++) {
if (rel_freq(i) < opts.prior_floor) {
log_priors(i) = sqrt(FLT_MAX);
num_floored++;
}
}
KALDI_LOG << "Floored " << num_floored << " pdf-priors "
<< "(hard-set to " << sqrt(FLT_MAX)
<< ", which disables DNN output when decoding)";
// sanity check,
KALDI_ASSERT(KALDI_ISFINITE(log_priors.Sum()));
// push to GPU,
log_priors_ = Vector<BaseFloat>(log_priors);
}
void PdfPrior::SubtractOnLogpost(CuMatrixBase<BaseFloat> *llk) {
if (log_priors_.Dim() == 0) {
KALDI_ERR << "--class-frame-counts is empty: Cannot initialize priors "
<< "without the counts.";
}
if (log_priors_.Dim() != llk->NumCols()) {
KALDI_ERR << "Dimensionality mismatch,"
<< " class_frame_counts " << log_priors_.Dim()
<< " pdf_output_llk " << llk->NumCols();
}
llk->AddVecToRows(-prior_scale_, log_priors_);
}
} // namespace nnet1
} // namespace kaldi