estimate-am-sgmm2-test.cc
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// sgmm2/estimate-am-sgmm2-test.cc
// Copyright 2009-2011 Saarland University (author: Arnab Ghoshal)
// 2012-2013 Johns Hopkins University (author: Daniel Povey)
// 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 "base/kaldi-math.h"
#include "gmm/model-test-common.h"
#include "sgmm2/am-sgmm2.h"
#include "sgmm2/estimate-am-sgmm2.h"
#include "util/kaldi-io.h"
using kaldi::AmSgmm2;
using kaldi::MleAmSgmm2Accs;
using kaldi::int32;
using kaldi::BaseFloat;
using kaldi::Exp;
namespace ut = kaldi::unittest;
// Tests the Read() and Write() methods for the accumulators, in both binary
// and ASCII mode, as well as Check().
void TestSgmm2AccsIO(const AmSgmm2 &sgmm,
const kaldi::Matrix<BaseFloat> &feats) {
using namespace kaldi;
kaldi::SgmmUpdateFlagsType flags = kaldi::kSgmmAll & ~kSgmmSpeakerWeightProjections;
kaldi::Sgmm2PerFrameDerivedVars frame_vars;
kaldi::Sgmm2PerSpkDerivedVars empty;
frame_vars.Resize(sgmm.NumGauss(), sgmm.FeatureDim(),
sgmm.PhoneSpaceDim());
kaldi::Sgmm2GselectConfig sgmm_config;
sgmm_config.full_gmm_nbest = std::min(sgmm_config.full_gmm_nbest,
sgmm.NumGauss());
MleAmSgmm2Accs accs(sgmm, flags, true);
BaseFloat loglike = 0.0;
for (int32 i = 0; i < feats.NumRows(); i++) {
std::vector<int32> gselect;
sgmm.GaussianSelection(sgmm_config, feats.Row(i), &gselect);
sgmm.ComputePerFrameVars(feats.Row(i), gselect, empty, &frame_vars);
loglike += accs.Accumulate(sgmm, frame_vars, 0, 1.0, &empty);
}
accs.CommitStatsForSpk(sgmm, empty);
kaldi::MleAmSgmm2Options update_opts;
AmSgmm2 *sgmm1 = new AmSgmm2();
sgmm1->CopyFromSgmm2(sgmm, false, false);
kaldi::MleAmSgmm2Updater updater(update_opts);
updater.Update(accs, sgmm1, flags);
sgmm1->ComputeDerivedVars();
std::vector<int32> gselect;
Sgmm2LikelihoodCache like_cache(sgmm.NumGroups(), sgmm.NumPdfs());
sgmm1->GaussianSelection(sgmm_config, feats.Row(0), &gselect);
sgmm1->ComputePerFrameVars(feats.Row(0), gselect, empty, &frame_vars);
BaseFloat loglike1 = sgmm1->LogLikelihood(frame_vars, 0, &like_cache, &empty);
delete sgmm1;
// First, non-binary write
accs.Write(kaldi::Output("tmpf", false).Stream(), false);
bool binary_in;
MleAmSgmm2Accs *accs1 = new MleAmSgmm2Accs();
// Non-binary read
kaldi::Input ki1("tmpf", &binary_in);
accs1->Read(ki1.Stream(), binary_in, false);
accs1->Check(sgmm, true);
AmSgmm2 *sgmm2 = new AmSgmm2();
sgmm2->CopyFromSgmm2(sgmm, false, false);
updater.Update(*accs1, sgmm2, flags);
sgmm2->ComputeDerivedVars();
sgmm2->GaussianSelection(sgmm_config, feats.Row(0), &gselect);
sgmm2->ComputePerFrameVars(feats.Row(0), gselect, empty, &frame_vars);
Sgmm2LikelihoodCache like_cache2(sgmm2->NumGroups(), sgmm2->NumPdfs());
BaseFloat loglike2 = sgmm2->LogLikelihood(frame_vars, 0, &like_cache2, &empty);
kaldi::AssertEqual(loglike1, loglike2, 1e-4);
delete accs1;
// Next, binary write
accs.Write(kaldi::Output("tmpfb", true).Stream(), true);
MleAmSgmm2Accs *accs2 = new MleAmSgmm2Accs();
// Binary read
kaldi::Input ki2("tmpfb", &binary_in);
accs2->Read(ki2.Stream(), binary_in, false);
accs2->Check(sgmm, true);
AmSgmm2 *sgmm3 = new AmSgmm2();
sgmm3->CopyFromSgmm2(sgmm, false, false);
updater.Update(*accs2, sgmm3, flags);
sgmm3->ComputeDerivedVars();
sgmm3->GaussianSelection(sgmm_config, feats.Row(0), &gselect);
sgmm3->ComputePerFrameVars(feats.Row(0), gselect, empty, &frame_vars);
Sgmm2LikelihoodCache like_cache3(sgmm3->NumGroups(), sgmm3->NumPdfs());
BaseFloat loglike3 = sgmm3->LogLikelihood(frame_vars, 0, &like_cache3, &empty);
kaldi::AssertEqual(loglike1, loglike3, 1e-6);
// Testing the MAP update of M
update_opts.tau_map_M = 10;
update_opts.full_col_cov = (RandUniform() > 0.5)? true : false;
update_opts.full_row_cov = (RandUniform() > 0.5)? true : false;
kaldi::MleAmSgmm2Updater updater_map(update_opts);
sgmm3->CopyFromSgmm2(sgmm, false, false);
updater_map.Update(*accs2, sgmm3, flags);
delete accs2;
delete sgmm2;
delete sgmm3;
unlink("tmpf");
unlink("tmpfb");
}
void UnitTestEstimateSgmm2() {
int32 dim = 1 + kaldi::RandInt(0, 9); // random dimension of the gmm
int32 num_comp = 2 + kaldi::RandInt(0, 9); // random mixture size
kaldi::FullGmm full_gmm;
ut::InitRandFullGmm(dim, num_comp, &full_gmm);
AmSgmm2 sgmm;
kaldi::Sgmm2GselectConfig config;
std::vector<int32> pdf2group;
pdf2group.push_back(0);
sgmm.InitializeFromFullGmm(full_gmm, pdf2group, dim+1, dim, false, 0.9); // TODO-- make this true!
sgmm.ComputeNormalizers();
kaldi::Matrix<BaseFloat> feats;
{ // First, generate random means and variances
int32 num_feat_comp = num_comp + kaldi::RandInt(-num_comp/2, num_comp/2);
kaldi::Matrix<BaseFloat> means(num_feat_comp, dim),
vars(num_feat_comp, dim);
for (int32 m = 0; m < num_feat_comp; m++) {
for (int32 d= 0; d < dim; d++) {
means(m, d) = kaldi::RandGauss();
vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
}
}
// Now generate random features with those means and variances.
feats.Resize(num_feat_comp * 200, dim);
for (int32 m = 0; m < num_feat_comp; m++) {
kaldi::SubMatrix<BaseFloat> tmp(feats, m*200, 200, 0, dim);
ut::RandDiagGaussFeatures(200, means.Row(m), vars.Row(m), &tmp);
}
}
sgmm.ComputeDerivedVars();
TestSgmm2AccsIO(sgmm, feats);
}
int main() {
for (int i = 0; i < 10; i++)
UnitTestEstimateSgmm2();
std::cout << "Test OK.\n";
return 0;
}