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// nnet3/nnet-utils-test.cc
// Copyright 2015 Johns Hopkins University (author: Daniel Povey)
// 2016 Daniel Galvez
// 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/nnet-nnet.h"
#include "nnet3/nnet-simple-component.h"
#include "nnet3/nnet-test-utils.h"
namespace kaldi {
namespace nnet3 {
void UnitTestNnetContext() {
for (int32 n = 0; n < 20; n++) {
struct NnetGenerationOptions gen_config;
std::vector<std::string> configs;
GenerateConfigSequence(gen_config, &configs);
Nnet nnet;
std::istringstream is(configs[0]);
nnet.ReadConfig(is);
// this test doesn't really test anything except that it runs;
// we manually inspect the output.
int32 left_context, right_context;
ComputeSimpleNnetContext(nnet, &left_context, &right_context);
KALDI_LOG << "Left,right-context= " << left_context << ","
<< right_context << " for config: " << configs[0];
KALDI_LOG << "Info for nnet is: " << NnetInfo(nnet);
}
}
void UnitTestConvertRepeatedToBlockAffine() {
// a test without a composite component.
std::string config =
"component name=repeated-affine1 type=RepeatedAffineComponent "
"input-dim=100 output-dim=200 num-repeats=20
"
"component name=relu1 type=RectifiedLinearComponent dim=200
"
"component name=block-affine1 type=BlockAffineComponent "
"input-dim=200 output-dim=100 num-blocks=10
"
"component name=relu2 type=RectifiedLinearComponent dim=100
"
"component name=repeated-affine2 type=NaturalGradientRepeatedAffineComponent "
"input-dim=100 output-dim=200 num-repeats=10
"
"
"
"input-node name=input dim=100
"
"component-node name=repeated-affine1 component=repeated-affine1 input=input
"
"component-node name=relu1 component=relu1 input=repeated-affine1
"
"component-node name=block-affine1 component=block-affine1 input=relu1
"
"component-node name=relu2 component=relu2 component=relu2 input=block-affine1
"
"component-node name=repeated-affine2 component=repeated-affine2 input=relu2
"
"output-node name=output input=repeated-affine2
";
Nnet nnet;
std::istringstream is(config);
nnet.ReadConfig(is);
ConvertRepeatedToBlockAffine(&nnet);
for(int i = 0; i < nnet.NumComponents(); i++) {
Component *c = nnet.GetComponent(i);
KALDI_ASSERT(c->Type() != "RepeatedAffineComponent"
&& c->Type() != "NaturalGradientRepeatedAffineComponent");
}
}
void UnitTestConvertRepeatedToBlockAffineComposite() {
// test that repeated affine components nested within a CompositeComponent
// are converted.
struct NnetGenerationOptions gen_config;
gen_config.output_dim = 0;
std::vector<std::string> configs;
// this function generates a neural net with one component:
// a composite component.
GenerateConfigSequenceCompositeBlock(gen_config, &configs);
Nnet nnet;
std::istringstream is(configs[0]);
nnet.ReadConfig(is);
KALDI_ASSERT(nnet.NumComponents() == 1);
ConvertRepeatedToBlockAffine(&nnet);
CompositeComponent *cc = dynamic_cast<CompositeComponent*>(nnet.GetComponent(0));
for(int i = 0; i < cc->NumComponents(); i++) {
const Component *c = cc->GetComponent(i);
KALDI_ASSERT(c->Type() == "BlockAffineComponent");
}
}
} // namespace nnet3
} // namespace kaldi
int main() {
using namespace kaldi;
using namespace kaldi::nnet3;
SetVerboseLevel(2);
UnitTestNnetContext();
UnitTestConvertRepeatedToBlockAffine();
UnitTestConvertRepeatedToBlockAffineComposite();
KALDI_LOG << "Nnet tests succeeded.";
return 0;
}
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