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src/nnet/nnet-component-test.cc
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// nnet/nnet-component-test.cc
// Copyright 2014-2015 Brno University of Technology (author: Karel Vesely),
// The Johns Hopkins University (author: Sri Harish Mallidi)
// 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 <sstream>
#include <fstream>
#include <algorithm>
#include "nnet/nnet-component.h"
#include "nnet/nnet-nnet.h"
#include "nnet/nnet-convolutional-component.h"
#include "nnet/nnet-max-pooling-component.h"
#include "util/common-utils.h"
namespace kaldi {
namespace nnet1 {
/*
* Helper functions
*/
template<typename Real>
void ReadCuMatrixFromString(const std::string& s, CuMatrix<Real>* m) {
std::istringstream is(s + "
");
m->Read(is, false); // false for ascii
}
Component* ReadComponentFromString(const std::string& s) {
std::istringstream is(s + "
");
return Component::Read(is, false); // false for ascii
}
/*
* Unit tests,
*/
void UnitTestLengthNorm() {
// make L2-length normalization component,
Component* c = ReadComponentFromString("<LengthNormComponent> 5 5");
// prepare input,
CuMatrix<BaseFloat> mat_in;
ReadCuMatrixFromString("[ 1 2 3 4 5
2 3 5 6 8 ] ", &mat_in);
// propagate,
CuMatrix<BaseFloat> mat_out;
c->Propagate(mat_in, &mat_out);
// check the length,
mat_out.MulElements(mat_out); // ^2,
CuVector<BaseFloat> check_length_is_one(2);
check_length_is_one.AddColSumMat(1.0, mat_out, 0.0); // sum_of_cols(x^2),
check_length_is_one.ApplyPow(0.5); // L2norm = sqrt(sum_of_cols(x^2)),
CuVector<BaseFloat> ones(2);
ones.Set(1.0);
AssertEqual(check_length_is_one, ones);
}
void UnitTestSimpleSentenceAveragingComponent() {
// make SimpleSentenceAveraging component,
Component* c = ReadComponentFromString(
"<SimpleSentenceAveragingComponent> 2 2 <GradientBoost> 10.0"
);
// prepare input,
CuMatrix<BaseFloat> mat_in;
ReadCuMatrixFromString("[ 0 0.5
1 1
2 1.5 ] ", &mat_in);
// propagate,
CuMatrix<BaseFloat> mat_out;
c->Propagate(mat_in, &mat_out);
// check the output,
CuVector<BaseFloat> ones(2);
ones.Set(1.0);
for (int32 i = 0; i < mat_out.NumRows(); i++) {
AssertEqual(mat_out.Row(i), ones);
}
// backpropagate,
CuMatrix<BaseFloat> dummy1(3, 2), dummy2(3, 2), diff_out(mat_in), diff_in;
// the average 1.0 in 'diff_in' will be boosted by 10.0,
c->Backpropagate(dummy1, dummy2, diff_out, &diff_in);
// check the output,
CuVector<BaseFloat> tens(2); tens.Set(10);
for (int32 i = 0; i < diff_in.NumRows(); i++) {
AssertEqual(diff_in.Row(i), tens);
}
}
void UnitTestConvolutionalComponentUnity() {
// make 'identity' convolutional component,
Component* c = ReadComponentFromString("<ConvolutionalComponent> 5 5 \
<PatchDim> 1 <PatchStep> 1 <PatchStride> 5 \
<LearnRateCoef> 1.0 <BiasLearnRateCoef> 1.0 \
<MaxNorm> 0 \
<Filters> [ 1 \
] <Bias> [ 0 ]"
);
// prepare input,
CuMatrix<BaseFloat> mat_in;
ReadCuMatrixFromString("[ 1 2 3 4 5 ] ", &mat_in);
// propagate,
CuMatrix<BaseFloat> mat_out;
c->Propagate(mat_in, &mat_out);
KALDI_LOG << "mat_in" << mat_in << "mat_out" << mat_out;
AssertEqual(mat_in, mat_out);
// backpropagate,
CuMatrix<BaseFloat> mat_out_diff(mat_in), mat_in_diff;
c->Backpropagate(mat_in, mat_out, mat_out_diff, &mat_in_diff);
KALDI_LOG << "mat_out_diff " << mat_out_diff
<< " mat_in_diff " << mat_in_diff;
AssertEqual(mat_out_diff, mat_in_diff);
// clean,
delete c;
}
void UnitTestConvolutionalComponent3x3() {
// make 3x3 convolutional component,
// design such weights and input so output is zero,
Component* c = ReadComponentFromString("<ConvolutionalComponent> 9 15 \
<PatchDim> 3 <PatchStep> 1 <PatchStride> 5 \
<LearnRateCoef> 1.0 <BiasLearnRateCoef> 1.0 \
<MaxNorm> 0 \
<Filters> [ -1 -2 -7 0 0 0 1 2 7 ; \
-1 0 1 -3 0 3 -2 2 0 ; \
-4 0 0 -3 0 3 4 0 0 ] \
<Bias> [ -20 -20 -20 ]"
);
// prepare input, reference output,
CuMatrix<BaseFloat> mat_in;
ReadCuMatrixFromString("[ 1 3 5 7 9 2 4 6 8 10 3 5 7 9 11 ]", &mat_in);
CuMatrix<BaseFloat> mat_out_ref;
ReadCuMatrixFromString("[ 0 0 0 0 0 0 0 0 0 ]", &mat_out_ref);
// propagate,
CuMatrix<BaseFloat> mat_out;
c->Propagate(mat_in, &mat_out);
KALDI_LOG << "mat_in" << mat_in << "mat_out" << mat_out;
AssertEqual(mat_out, mat_out_ref);
// prepare mat_out_diff, mat_in_diff_ref,
CuMatrix<BaseFloat> mat_out_diff;
ReadCuMatrixFromString("[ 1 0 0 1 1 0 1 1 1 ]", &mat_out_diff);
// hand-computed back-propagated values,
CuMatrix<BaseFloat> mat_in_diff_ref;
ReadCuMatrixFromString("[ -1 -4 -15 -8 -6 0 -3 -6 3 6 1 1 14 11 7 ]",
&mat_in_diff_ref);
// backpropagate,
CuMatrix<BaseFloat> mat_in_diff;
c->Backpropagate(mat_in, mat_out, mat_out_diff, &mat_in_diff);
KALDI_LOG << "mat_in_diff " << mat_in_diff
<< " mat_in_diff_ref " << mat_in_diff_ref;
AssertEqual(mat_in_diff, mat_in_diff_ref);
// clean,
delete c;
}
void UnitTestMaxPoolingComponent() {
// make max-pooling component, assuming 4 conv. neurons,
// non-overlapping pool of size 3,
Component* c = Component::Init(
"<MaxPoolingComponent> <InputDim> 24 <OutputDim> 8 \
<PoolSize> 3 <PoolStep> 3 <PoolStride> 4"
);
// input matrix,
CuMatrix<BaseFloat> mat_in;
ReadCuMatrixFromString("[ 3 8 2 9 \
8 3 9 3 \
2 4 9 6 \
\
2 4 2 0 \
6 4 9 4 \
7 3 0 3;\
\
5 4 7 8 \
3 9 5 6 \
3 4 8 9 \
\
5 4 5 6 \
3 1 4 5 \
8 2 1 7 ]", &mat_in);
// expected output (max values in columns),
CuMatrix<BaseFloat> mat_out_ref;
ReadCuMatrixFromString("[ 8 8 9 9 \
7 4 9 4;\
5 9 8 9 \
8 4 5 7 ]", &mat_out_ref);
// propagate,
CuMatrix<BaseFloat> mat_out;
c->Propagate(mat_in, &mat_out);
KALDI_LOG << "mat_out" << mat_out << "mat_out_ref" << mat_out_ref;
AssertEqual(mat_out, mat_out_ref);
// locations of max values will be shown,
CuMatrix<BaseFloat> mat_out_diff(mat_out);
mat_out_diff.Set(1);
// expected backpropagated values (hand-computed),
CuMatrix<BaseFloat> mat_in_diff_ref;
ReadCuMatrixFromString("[ 0 1 0 1 \
1 0 1 0 \
0 0 1 0 \
\
0 1 0 0 \
0 1 1 1 \
1 0 0 0;\
\
1 0 0 0 \
0 1 0 0 \
0 0 1 1 \
\
0 1 1 0 \
0 0 0 0 \
1 0 0 1 ]", &mat_in_diff_ref);
// backpropagate,
CuMatrix<BaseFloat> mat_in_diff;
c->Backpropagate(mat_in, mat_out, mat_out_diff, &mat_in_diff);
KALDI_LOG << "mat_in_diff " << mat_in_diff
<< " mat_in_diff_ref " << mat_in_diff_ref;
AssertEqual(mat_in_diff, mat_in_diff_ref);
delete c;
}
void UnitTestDropoutComponent() {
Component* c = ReadComponentFromString("<Dropout> 100 100 <DropoutRetention> 0.7");
// buffers,
CuMatrix<BaseFloat> in(777, 100),
out,
out_diff,
in_diff;
// init,
in.Set(2.0);
// propagate,
c->Propagate(in, &out);
AssertEqual(in.Sum(), out.Sum(), 0.01);
// backprop,
out_diff = in;
c->Backpropagate(in, out, out_diff, &in_diff);
AssertEqual(in_diff, out);
delete c;
}
} // namespace nnet1
} // namespace kaldi
int main() {
using namespace kaldi;
using namespace kaldi::nnet1;
for (kaldi::int32 loop = 0; loop < 2; loop++) {
#if HAVE_CUDA == 1
if (loop == 0)
// use no GPU,
CuDevice::Instantiate().SelectGpuId("no");
else
// use GPU when available,
CuDevice::Instantiate().SelectGpuId("optional");
#endif
// unit-tests :
UnitTestLengthNorm();
UnitTestSimpleSentenceAveragingComponent();
UnitTestConvolutionalComponentUnity();
UnitTestConvolutionalComponent3x3();
UnitTestMaxPoolingComponent();
UnitTestDropoutComponent();
// end of unit-tests,
if (loop == 0)
KALDI_LOG << "Tests without GPU use succeeded.";
else
KALDI_LOG << "Tests with GPU use (if available) succeeded.";
}
#if HAVE_CUDA == 1
CuDevice::Instantiate().PrintProfile();
#endif
return 0;
}
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