TEST( SLOW_testforward, perf_kgsgo_fc500 ) {
int batchSize = 128;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputSize(19).setNumFilters( 500 ).setFilterSize( 19 )
.setPadZeros( false ).setBiased( true );
testPerf( -1, 128, batchSize, dim );
}
TEST( SLOW_testforward, perf_mnist_finallayer ) {
int batchSize = 128;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputSize(28).setNumFilters( 10 ).setFilterSize( 28 )
.setPadZeros( false ).setBiased( true );
testPerf( -1, 128, batchSize, dim );
}
TEST( SLOW_testforward, perf_mnist_intlayers_1024ex ) {
int batchSize = 1024;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputSize(28).setNumFilters( 32 ).setFilterSize( 5 )
.setPadZeros( true ).setBiased( true );
testPerf( -1, 128, batchSize, dim );
}
TEST( SLOW_testpropagate, perf_mnist_finallayer ) {
int batchSize = 128;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputImageSize(28).setNumFilters( 10 ).setFilterSize( 28 )
.setPadZeros( false ).setBiased( true );
testPerf( -1, 128, batchSize, dim, new ReluActivation() );
}
TEST( SLOW_testpropagate, perf_kgsgo_fc500 ) {
int batchSize = 128;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputImageSize(19).setNumFilters( 500 ).setFilterSize( 19 )
.setPadZeros( false ).setBiased( true );
testPerf( -1, 128, batchSize, dim, new TanhActivation() );
}
TEST( SLOW_testpropagate, compare_args ) {
LayerDimensions dim;
int batchSize = 128;
// int imageSize = 19;
// int filterSize = 7;
// int inputPlanes = 64;
// int numFilters = 64;
int instance0 = 1;
int instance1 = 3;
int N = 128;
bool debug = false;
string activationName = "tanh";
dim.setInputPlanes( 64 ).setInputImageSize(19).setNumFilters( 64 )
.setFilterSize( 7 )
.setPadZeros( true ).setBiased( false );
TestArgsParser::arg( "n", &N );
DimFromArgs::arg( &dim );
TestArgsParser::arg( "instance0", &instance0 );
TestArgsParser::arg( "instance1", &instance1 );
TestArgsParser::arg( "debug", &debug );
TestArgsParser::arg( "batchsize", &batchSize );
TestArgsParser::arg( "activation", &activationName );
TestArgsParser::go();
dim.deriveOthers();
ActivationFunction *fn = ActivationFunction::fromName( activationName );
compareSpecific( debug, N, batchSize, dim, fn, instance0, instance1 );
}
TEST( testforward, compare_1_n_biased_pad ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
int maxWorkgroupSize = cl->getMaxWorkgroupSize();
delete cl;
LayerDimensions dim;
int batchSize = 4;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputSize(19).setNumFilters( 8 )
.setFilterSize( 5 )
.setPadZeros( true ).setBiased( true );
for( int instance = 2; instance <= 7; instance++ ) {
if( instance == 5 ) {
continue; // forwardfc, cant use for inputimagesize != filtersize
}
dim.setInputSize(19);
if(instance == 2 && maxWorkgroupSize < 19 * 19) {
dim.setInputSize(15);
}
if(instance == 3 && maxWorkgroupSize < 19 * 19) {
dim.setInputSize(15);
}
cout << "instance: " << instance << endl;
compareSpecific( false, N, batchSize, dim, 1, instance );
}
}
TEST( testforward, compare_1_4_fcscenario ) { // only need to do nopad, since fc wont work with pad
LayerDimensions dim;
int batchSize = 4;
int N = 4;
dim.setInputPlanes( 10 ).setInputSize(24).setNumFilters( 10 )
.setFilterSize( 24 )
.setPadZeros( false ).setBiased( true );
compareSpecific( false, N, batchSize, dim, 1, 4 );
}
TEST(SLOW_testbackward, perf_kgsgo_32c5) {
int batchSize = 128;
LayerDimensions dim;
dim.setInputPlanes(32).setInputSize(19).setNumFilters(32).setFilterSize(5)
.setPadZeros(true).setBiased(true);
cout << dim.buildOptionsString() << endl;
// ActivationFunction *fn = new ReluActivation();
measurePerf(2, batchSize, dim);
}
TEST( testforward, compare_1_5_biased_nopad ) { // only need to do nopad, since fc wont work with pad
LayerDimensions dim;
int batchSize = 4;
// int instance0 = 1;
// int instance1 = 1;
int N = 4;
dim.setInputPlanes( 8 ).setInputSize(19).setNumFilters( 8 )
.setFilterSize( 19 )
.setPadZeros( false ).setBiased( true );
compareSpecific( false, N, batchSize, dim, 1, 5 );
}
TEST(SLOW_testbackward, compare_kgsgo_32c5mini) {
int batchSize = 4;
LayerDimensions dim;
dim.setInputPlanes(2).setInputSize(3).setNumFilters(2).setFilterSize(3)
.setPadZeros(true).setBiased(true);
cout << dim.buildOptionsString() << endl;
// ActivationFunction *fn = new ReluActivation();
compareSpecific(1, 2, 1, batchSize, dim);
}
TEST( testpropagate, compare_1_4_fcscenario ) { // only need to do nopad, since fc wont work with pad
LayerDimensions dim;
int batchSize = 4;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 10 ).setInputImageSize(24).setNumFilters( 10 )
.setFilterSize( 24 )
.setPadZeros( false ).setBiased( true );
ActivationFunction *fn = ActivationFunction::fromName( activationName );
compareSpecific( false, N, batchSize, dim, fn, 1, 4 );
}
TEST( testforward, compare_0_1_biased_pad ) {
LayerDimensions dim;
int batchSize = 4;
// int instance0 = 1;
// int instance1 = 1;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputSize(19).setNumFilters( 8 )
.setFilterSize( 5 )
.setPadZeros( true ).setBiased( true );
compareSpecific( false, N, batchSize, dim, 0, 1 );
}
TEST( SLOW_testforward, soumith2 ) {
int batchSize = 128;
LayerDimensions dim;
int instance = 4;
bool biased = true;
TestArgsParser::arg( "instance", &instance );
TestArgsParser::arg( "biased", &biased );
TestArgsParser::go();
dim.setInputPlanes( 64 ).setInputSize( 64 ).setNumFilters( 128 ).setFilterSize( 9 )
.setPadZeros( false ).setBiased( biased );
testPerf( instance, 128, batchSize, dim );
}
TEST(testbackward, compare_1_n_kgsgo_32c5) {
int batchSize = 8;
LayerDimensions dim;
dim.setInputPlanes(32).setInputSize(19).setNumFilters(32).setFilterSize(5)
.setPadZeros(true).setBiased(true);
cout << dim.buildOptionsString() << endl;
// ActivationFunction *fn = new ReluActivation();
compareSpecific(0, 1, 1, batchSize, dim);
for(int i=2; i < Backward::getNumImplementations(); i++) {
compareSpecific(1, i, 1, batchSize, dim);
}
}
TEST( testpropagate, compare_0_1_biased_pad ) {
LayerDimensions dim;
int batchSize = 4;
// int instance0 = 1;
// int instance1 = 1;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputImageSize(19).setNumFilters( 8 )
.setFilterSize( 5 )
.setPadZeros( true ).setBiased( true );
ActivationFunction *fn = ActivationFunction::fromName( activationName );
compareSpecific( false, N, batchSize, dim, fn, 0, 1 );
}
TEST( testpropagate, compare_1_5_biased_nopad ) { // only need to do nopad, since fc wont work with pad
LayerDimensions dim;
int batchSize = 4;
// int instance0 = 1;
// int instance1 = 1;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputImageSize(19).setNumFilters( 8 )
.setFilterSize( 19 )
.setPadZeros( false ).setBiased( true );
ActivationFunction *fn = ActivationFunction::fromName( activationName );
compareSpecific( false, N, batchSize, dim, fn, 1, 5 );
}
TEST( SLOW_testforward, perf_kgsgo_64c7_args ) {
int instance = 3;
int batchSize = 128;
int N = 1000;
LayerDimensions dim;
dim.setInputPlanes( 64 ).setInputSize(19).setNumFilters( 64 ).setFilterSize( 7 )
.setPadZeros( true ).setBiased( true );
DimFromArgs::arg( &dim );
TestArgsParser::arg( "instance", &instance );
TestArgsParser::arg( "n", &N );
TestArgsParser::arg( "batchsize", &batchSize );
TestArgsParser::go();
testPerf( instance, N, batchSize, dim );
}
TEST( testforward, crash_from_jm ) {
int instance = 1;
int batchSize = 64;
int N = 64;
LayerDimensions dim;
dim.setInputPlanes( 32 ).setInputSize(28).setNumFilters( 20 ).setFilterSize( 28 )
.setPadZeros( false ).setBiased( false );
DimFromArgs::arg( &dim );
TestArgsParser::arg( "instance", &instance );
TestArgsParser::arg( "n", &N );
TestArgsParser::arg( "batchsize", &batchSize );
TestArgsParser::go();
testPerf( instance, N, batchSize, dim );
}
TEST( testforward, compare_1_n_biased_nopad ) {
LayerDimensions dim;
int batchSize = 4;
// int instance0 = 1;
// int instance1 = 1;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputSize(19).setNumFilters( 8 )
.setFilterSize( 5 )
.setPadZeros( false ).setBiased( true );
for( int instance = 2; instance <= 7; instance++ ) {
if( instance == 5 ) {
continue; // forwardfc, cant use for inputimagesize != filtersize
}
cout << "instance: " << instance << endl;
compareSpecific( false, N, batchSize, dim, 1, instance );
}
}
TEST( testforward, comparespecific_break2 ) { // this breaks on v5.7.0 for example
LayerDimensions dim;
int batchSize = 4;
int instance0 = 1;
int instance1 = 5;
int N = 4;
bool debug = false;
dim.setInputPlanes( 64 ).setInputSize(19).setNumFilters( 64 )
.setFilterSize( 19 )
.setPadZeros( false ).setBiased( false );
TestArgsParser::arg( "n", &N );
DimFromArgs::arg( &dim );
TestArgsParser::arg( "instance0", &instance0 );
TestArgsParser::arg( "instance1", &instance1 );
TestArgsParser::arg( "debug", &debug );
TestArgsParser::arg( "batchsize", &batchSize );
TestArgsParser::go();
dim.deriveOthers();
compareSpecific( debug, N, batchSize, dim, instance0, instance1 );
}
TEST( SLOW_testforward, compare_args ) {
LayerDimensions dim;
int batchSize = 128;
int instance0 = 1;
int instance1 = 3;
int N = 128;
bool debug = false;
dim.setInputPlanes( 64 ).setInputSize(19).setNumFilters( 64 )
.setFilterSize( 7 )
.setPadZeros( true ).setBiased( false );
TestArgsParser::arg( "n", &N );
DimFromArgs::arg( &dim );
TestArgsParser::arg( "instance0", &instance0 );
TestArgsParser::arg( "instance1", &instance1 );
TestArgsParser::arg( "debug", &debug );
TestArgsParser::arg( "batchsize", &batchSize );
TestArgsParser::go();
dim.deriveOthers();
compareSpecific( debug, N, batchSize, dim, instance0, instance1 );
}
TEST( testforward, compare_break1_0_4 ) {
LayerDimensions dim;
dim.setInputPlanes( 1 ).setInputSize( 33 ).setNumFilters( 1 ).setFilterSize( 1 )
.setPadZeros( false ).setBiased( false );
compareSpecific( false, 1, 1, dim, 0, 4 );
}