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src/sgmm2/am-sgmm2-test.cc
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// sgmm2/am-sgmm2-test.cc
// Copyright 2012 Arnab Ghoshal
// 2009-2011 Saarland University
// 2012 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 "gmm/model-test-common.h"
#include "sgmm2/am-sgmm2.h"
#include "util/kaldi-io.h"
using kaldi::AmSgmm2;
using kaldi::int32;
using kaldi::BaseFloat;
namespace ut = kaldi::unittest;
// Tests the initialization routines: InitializeFromFullGmm(), CopyFromSgmm2()
// and CopyGlobalsInitVecs().
void TestSgmm2Init(const AmSgmm2 &sgmm) {
using namespace kaldi;
int32 dim = sgmm.FeatureDim();
kaldi::Sgmm2GselectConfig config;
config.full_gmm_nbest = std::min(config.full_gmm_nbest, sgmm.NumGauss());
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
kaldi::Sgmm2PerFrameDerivedVars frame_vars;
frame_vars.Resize(sgmm.NumGauss(), sgmm.FeatureDim(),
sgmm.PhoneSpaceDim());
std::vector<int32> gselect;
sgmm.GaussianSelection(config, feat, &gselect);
Sgmm2PerSpkDerivedVars empty;
Sgmm2PerFrameDerivedVars per_frame;
sgmm.ComputePerFrameVars(feat, gselect, empty, &per_frame);
Sgmm2LikelihoodCache sgmm_cache(sgmm.NumGroups(), sgmm.NumPdfs());
BaseFloat loglike = sgmm.LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
sgmm_cache.NextFrame();
// First, test the CopyFromSgmm2() method:
AmSgmm2 *sgmm1 = new AmSgmm2();
sgmm1->CopyFromSgmm2(sgmm, true, true);
sgmm1->GaussianSelection(config, feat, &gselect);
sgmm1->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike1 = sgmm1->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike1, 1e-4);
delete sgmm1;
AmSgmm2 *sgmm2 = new AmSgmm2();
sgmm2->CopyFromSgmm2(sgmm, false, false);
sgmm2->ComputeNormalizers();
sgmm2->ComputeWeights();
sgmm2->GaussianSelection(config, feat, &gselect);
sgmm2->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike2 = sgmm2->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike2, 1e-4);
delete sgmm2;
// Next, initialize using the UBM from the current model
AmSgmm2 *sgmm3 = new AmSgmm2();
{
std::vector<int32> pdf2group(sgmm.NumPdfs());
for (int32 i = 0; i < sgmm.NumPdfs(); i++) pdf2group[i] = sgmm.Pdf2Group(i);
sgmm3->InitializeFromFullGmm(sgmm.full_ubm(), pdf2group,
sgmm.PhoneSpaceDim(), sgmm.SpkSpaceDim(), true, 0.9);
}
sgmm3->ComputeNormalizers();
sgmm3->GaussianSelection(config, feat, &gselect);
sgmm3->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike3 = sgmm3->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike3, 1e-4);
delete sgmm3;
}
// Tests the Read() and Write() methods, in both binary and ASCII mode, as well
// as Check(), and methods in likelihood computations.
void TestSgmm2IO(const AmSgmm2 &sgmm) {
using namespace kaldi;
int32 dim = sgmm.FeatureDim();
kaldi::Sgmm2GselectConfig config;
config.full_gmm_nbest = std::min(config.full_gmm_nbest, sgmm.NumGauss());
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
kaldi::Sgmm2PerFrameDerivedVars frame_vars;
frame_vars.Resize(sgmm.NumGauss(), sgmm.FeatureDim(),
sgmm.PhoneSpaceDim());
std::vector<int32> gselect;
sgmm.GaussianSelection(config, feat, &gselect);
Sgmm2PerSpkDerivedVars empty;
Sgmm2PerFrameDerivedVars per_frame;
sgmm.ComputePerFrameVars(feat, gselect, empty, &per_frame);
Sgmm2LikelihoodCache sgmm_cache(sgmm.NumGroups(), sgmm.NumPdfs());
BaseFloat loglike = sgmm.LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
// First, non-binary write
sgmm.Write(kaldi::Output("tmpf", false).Stream(), false,
kaldi::kSgmmWriteAll);
bool binary_in;
AmSgmm2 *sgmm1 = new AmSgmm2();
// Non-binary read
kaldi::Input ki1("tmpf", &binary_in);
sgmm1->Read(ki1.Stream(), binary_in);
sgmm1->Check(true);
sgmm1->GaussianSelection(config, feat, &gselect);
sgmm1->ComputePerFrameVars(feat, gselect, empty, &per_frame);
BaseFloat loglike1 = sgmm1->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike1, 1e-4);
// Next, binary write
sgmm1->Write(kaldi::Output("tmpfb", true).Stream(), true,
kaldi::kSgmmWriteAll);
delete sgmm1;
AmSgmm2 *sgmm2 = new AmSgmm2();
// Binary read
kaldi::Input ki2("tmpfb", &binary_in);
sgmm2->Read(ki2.Stream(), binary_in);
sgmm2->Check(true);
sgmm2->GaussianSelection(config, feat, &gselect);
sgmm2->ComputePerFrameVars(feat, gselect, empty, &per_frame);
BaseFloat loglike2 = sgmm2->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike2, 1e-4);
delete sgmm2;
unlink("tmpf");
unlink("tmpfb");
}
void TestSgmm2Substates(const AmSgmm2 &sgmm) {
using namespace kaldi;
int32 target_substates = 2 * sgmm.NumPdfs();
kaldi::Vector<BaseFloat> occs(sgmm.NumPdfs());
for (int32 i = 0; i < occs.Dim(); i++)
occs(i) = std::fabs(kaldi::RandGauss()) * (kaldi::RandUniform()+1);
AmSgmm2 *sgmm1 = new AmSgmm2();
sgmm1->CopyFromSgmm2(sgmm, false, false);
Sgmm2SplitSubstatesConfig cfg;
cfg.split_substates = target_substates;
sgmm1->SplitSubstates(occs, cfg);
sgmm1->ComputeNormalizers();
sgmm1->ComputeWeights();
sgmm1->Check(true);
int32 dim = sgmm.FeatureDim();
kaldi::Sgmm2GselectConfig config;
config.full_gmm_nbest = std::min(config.full_gmm_nbest, sgmm.NumGauss());
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
std::vector<int32> gselect;
sgmm.GaussianSelection(config, feat, &gselect);
Sgmm2PerSpkDerivedVars empty;
Sgmm2PerFrameDerivedVars per_frame;
sgmm.ComputePerFrameVars(feat, gselect, empty, &per_frame);
Sgmm2LikelihoodCache sgmm_cache(sgmm.NumGroups(), sgmm.NumPdfs());
BaseFloat loglike = sgmm.LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
sgmm1->GaussianSelection(config, feat, &gselect);
sgmm1->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike1 = sgmm1->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike1, 1e-2);
delete sgmm1;
}
void TestSgmm2IncreaseDim(const AmSgmm2 &sgmm) {
using namespace kaldi;
int32 target_phn_dim = static_cast<int32>(1.5 * sgmm.PhoneSpaceDim());
int32 target_spk_dim = sgmm.PhoneSpaceDim() - 1;
int32 dim = sgmm.FeatureDim();
kaldi::Sgmm2GselectConfig config;
config.full_gmm_nbest = std::min(config.full_gmm_nbest, sgmm.NumGauss());
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
kaldi::Sgmm2PerFrameDerivedVars frame_vars;
std::vector<int32> gselect;
sgmm.GaussianSelection(config, feat, &gselect);
Sgmm2PerSpkDerivedVars empty;
Sgmm2PerFrameDerivedVars per_frame;
sgmm.ComputePerFrameVars(feat, gselect, empty, &per_frame);
Sgmm2LikelihoodCache sgmm_cache(sgmm.NumGroups(), sgmm.NumPdfs());
BaseFloat loglike = sgmm.LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::Matrix<BaseFloat> norm_xform;
kaldi::ComputeFeatureNormalizingTransform(sgmm.full_ubm(), &norm_xform);
AmSgmm2 *sgmm1 = new AmSgmm2();
sgmm1->CopyFromSgmm2(sgmm, false, false);
sgmm1->Check(true);
sgmm1->IncreasePhoneSpaceDim(target_phn_dim, norm_xform);
sgmm1->ComputeNormalizers();
sgmm1->Check(true);
sgmm1->GaussianSelection(config, feat, &gselect);
sgmm1->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike1 = sgmm1->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike1, 1e-4);
sgmm1->IncreaseSpkSpaceDim(target_spk_dim, norm_xform, true);
sgmm1->Check(true);
sgmm1->GaussianSelection(config, feat, &gselect);
sgmm1->ComputePerFrameVars(feat, gselect, empty, &per_frame);
sgmm_cache.NextFrame();
BaseFloat loglike2 = sgmm1->LogLikelihood(per_frame, 0, &sgmm_cache, &empty);
kaldi::AssertEqual(loglike, loglike2, 1e-4);
delete sgmm1;
}
void TestSgmm2PreXform(const AmSgmm2 &sgmm) {
kaldi::Matrix<BaseFloat> xform, inv_xform;
kaldi::Vector<BaseFloat> diag_scatter;
kaldi::Vector<BaseFloat> occs(sgmm.NumPdfs());
occs.Set(100);
sgmm.ComputeFmllrPreXform(occs, &xform, &inv_xform, &diag_scatter);
int32 dim = xform.NumRows();
kaldi::SubMatrix<BaseFloat> a_pre(xform, 0, dim, 0, dim),
a_inv(inv_xform, 0, dim, 0, dim);
kaldi::Vector<BaseFloat> b_pre(dim), b_inv(dim);
b_pre.CopyColFromMat(xform, dim);
b_inv.CopyColFromMat(inv_xform, dim);
kaldi::Matrix<BaseFloat> res_mat(dim, dim, kaldi::kSetZero);
res_mat.AddMatMat(1.0, a_pre, kaldi::kNoTrans, a_inv, kaldi::kNoTrans, 0.0);
KALDI_ASSERT(res_mat.IsUnit(1.0e-5));
kaldi::Vector<BaseFloat> res_vec(dim, kaldi::kSetZero);
res_vec.AddMatVec(1.0, a_inv, kaldi::kNoTrans, b_pre, 0.0);
res_vec.AddVec(1.0, b_inv);
KALDI_ASSERT(res_vec.IsZero(1.0e-5));
}
void UnitTestSgmm2() {
size_t dim = 1 + kaldi::RandInt(0, 9); // random dimension of the gmm
size_t num_comp = 1 + kaldi::RandInt(0, 9); // random number of mixtures
kaldi::FullGmm full_gmm;
ut::InitRandFullGmm(dim, num_comp, &full_gmm);
std::vector<int32> pdf2group;
pdf2group.push_back(0);
AmSgmm2 sgmm;
kaldi::Sgmm2GselectConfig config;
sgmm.InitializeFromFullGmm(full_gmm, pdf2group, dim+1, 0, true, 0.9);
sgmm.ComputeNormalizers();
TestSgmm2Init(sgmm);
TestSgmm2IO(sgmm);
TestSgmm2Substates(sgmm);
TestSgmm2IncreaseDim(sgmm);
TestSgmm2PreXform(sgmm);
}
int main() {
for (int i = 0; i < 10; i++)
UnitTestSgmm2();
std::cout << "Test OK.
";
return 0;
}
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