am-nnet-simple.cc
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// nnet3/am-nnet-simple.cc
// Copyright 2012-2015 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 "nnet3/am-nnet-simple.h"
#include "nnet3/nnet-utils.h"
namespace kaldi {
namespace nnet3 {
int32 AmNnetSimple::NumPdfs() const {
int32 ans = nnet_.OutputDim("output");
KALDI_ASSERT(ans > 0);
return ans;
}
void AmNnetSimple::Write(std::ostream &os, bool binary) const {
// We don't write any header or footer like <AmNnetSimple> and </AmNnetSimple> -- we just
// write the neural net and then the priors. Who knows, there might be some
// situation where we want to just read the neural net.
nnet_.Write(os, binary);
WriteToken(os, binary, "<LeftContext>");
WriteBasicType(os, binary, left_context_);
WriteToken(os, binary, "<RightContext>");
WriteBasicType(os, binary, right_context_);
WriteToken(os, binary, "<Priors>");
priors_.Write(os, binary);
}
void AmNnetSimple::Read(std::istream &is, bool binary) {
nnet_.Read(is, binary);
ExpectToken(is, binary, "<LeftContext>");
ReadBasicType(is, binary, &left_context_);
ExpectToken(is, binary, "<RightContext>");
ReadBasicType(is, binary, &right_context_);
SetContext(); // temporarily, I'm not trusting the written ones (there was
// briefly a bug)
ExpectToken(is, binary, "<Priors>");
priors_.Read(is, binary);
}
void AmNnetSimple::SetNnet(const Nnet &nnet) {
nnet_ = nnet;
SetContext();
if (priors_.Dim() != 0 && priors_.Dim() != nnet_.OutputDim("output")) {
KALDI_WARN << "Removing priors since there is a dimension mismatch after "
<< "changing the nnet: " << priors_.Dim() << " vs. "
<< nnet_.OutputDim("output");
priors_.Resize(0);
}
}
void AmNnetSimple::SetPriors(const VectorBase<BaseFloat> &priors) {
priors_ = priors;
if (priors_.Dim() != nnet_.OutputDim("output") &&
priors_.Dim() != 0) {
KALDI_ERR << "Dimension mismatch when setting priors: priors have dim "
<< priors.Dim() << ", model expects "
<< nnet_.OutputDim("output");
}
}
std::string AmNnetSimple::Info() const {
std::ostringstream ostr;
ostr << "input-dim: " << nnet_.InputDim("input") << "\n";
ostr << "ivector-dim: " << nnet_.InputDim("ivector") << "\n";
ostr << "num-pdfs: " << nnet_.OutputDim("output") << "\n";
ostr << "prior-dimension: " << priors_.Dim() << "\n";
if (priors_.Dim() != 0) {
ostr << "prior-sum: " << priors_.Sum() << "\n";
ostr << "prior-min: " << priors_.Min() << "\n";
ostr << "prior-max: " << priors_.Max() << "\n";
}
ostr << "# Nnet info follows.\n";
return ostr.str() + nnet_.Info();
}
void AmNnetSimple::SetContext() {
if (!IsSimpleNnet(nnet_)) {
KALDI_ERR << "Class AmNnetSimple is only intended for a restricted type of "
<< "nnet, and this one does not meet the conditions.";
}
ComputeSimpleNnetContext(nnet_,
&left_context_,
&right_context_);
}
} // namespace nnet3
} // namespace kaldi