Machine learning framework for both deep learning and traditional algorithms

Complete log:

$ cmake -G Ninja ../NeoML -DCMAKE_BUILD_TYPE=Debug -DCMAKE_INSTALL_PREFIX=test
-- Found OpenMP: TRUE (found version "4.5")  
-- No CUDA support.
-- Found OpenMP: TRUE (found version "4.5")  
CMake Error at /usr/share/cmake/Modules/FindPackageHandleStandardArgs.cmake:230 (message):
  Could NOT find Protobuf (missing: Protobuf_LIBRARIES) (found version
  "3.19.4")
Call Stack (most recent call first):
  /usr/share/cmake/Modules/FindPackageHandleStandardArgs.cmake:594 (_FPHSA_FAILURE_MESSAGE)
  /usr/share/cmake/Modules/FindProtobuf.cmake:650 (FIND_PACKAGE_HANDLE_STANDARD_ARGS)
  /home/<redacted>/Desktop/neoml/NeoOnnx/src/CMakeLists.txt:166 (find_package)


-- Configuring incomplete, errors occurred!
See also "/home/<redacted>/Desktop/neoml/Build/CMakeFiles/CMakeOutput.log".
See also "/home/<redacted>/Desktop/neoml/Build/CMakeFiles/CMakeError.log".

What can be done to build this ML library with the newer version of Protobuf? It would be inconvenient for me to build older version of Protobuf.

NeoML showing linker error. In Qt it looks like this:

:-1: error: LNK1104: cannot open file 'NeoMathEngine.x64.Debug.lib'

And in built libraries there is nowhere to be found NeoMathEngine.x64.Debug.lib. When I'm trying with Visual studio, I'm getting following output:

Severity	Code	Description	Project	File	Line	Suppression State
Error	C3861	'NOT_FOUND': identifier not found	NeoMLTest2	K:\NeoML\build64\debug\include\NeoML\TraditionalML\LdGraph.h	438	

Code snippet where error is located seems to be some kind of assertion, but I can't understand what's wrong:

inline void CLdGraph<Arc>::DetachArc( Arc* arc )
{
	// Delete the arcs from the starting node
	CLdGraphVertex* initial = vertices[arc->InitialCoord() - begin];
	NeoPresume( initial != 0 );
	int i = initial->OutgoingArcs.Find(arc);
	NeoAssert( i != NOT_FOUND ); //     PROBLEM IS HERE
	initial->OutgoingArcs.DeleteAt(i);
	// Delete the hanging node
	if( initial->OutgoingArcs.Size() == 0
		&&  initial->IncomingArcs.Size() == 0 )
	{
		delete initial;
		vertices[arc->InitialCoord() - begin] = 0;
	}

I have 4 of those errors.

Why do you use both keywords 'virtual' and 'override' in methods definition? It's redundantly and hard to read, because semantics for this keywords are different.

Please implement calculation of positivesCorrect, positivesTotal, negativesCorrect, negativesTotal on device side.

https://github.com/neoml-lib/neoml/blob/7b7653645c777f70acb461202032e118c2f1b470/NeoML/src/Dnn/Layers/PrecisionRecallLayer.cpp#L65-L91

Currently DNN graph structure is inaccessible from NeoML/Python - you can get the list of layers, but you can not read how these layers are connected with each other. With input names getter it is possible to traverse the whole graph starting from sinks and retrieve it's structure.

It could be used in theory to plot the model, similar to torchviz or maybe even in a plugin for netron.

Also fixes incorrect assertTrue which are supposed to be assertEqual

Раз PR вы не принимаете, оформлю как issue. Сделайте в namespace NeoML себе что-то такое и перестаньте писать многоэтажные сравнения:

#if __cplusplus >= 201703L
using std::clamp;
#else
template<typename T, typename Compare = std::less<T>> constexpr const T& clamp(const T &v, const T &lo, const T &hi, Compare comp = Compare()) {
    return comp(v, lo) ? lo : comp(hi, v) ? hi : v;
}
#endif

Have a bunch mkl related linker errors when compiling NeoMathEngine. Could someone help me and tell what is this all about, since I can't look inside mkl dll? Neo ML was build with FineObjects option turned off (if it makes any difference).

Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx512_jit_destroy.obj) 1 Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MT_StaticRelease' doesn't match value 'MDd_DynamicDebug' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx512_jit_destroy.obj) 1 Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx2_jit_destroy.obj) 1 Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MT_StaticRelease' doesn't match value 'MDd_DynamicDebug' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx2_jit_destroy.obj) 1 Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx_jit_destroy.obj) 1 Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MT_StaticRelease' doesn't match value 'MDd_DynamicDebug' in common.obj NeoMathEngine K:\NeoML\build\NeoMathEngine\src\mkl_core.lib(_avx_jit_destroy.obj) 1

Hello! I'm trying to recreate simple net example, but I'm stuck here:

for( int epoch = 1; epoch < 15; ++epoch ) {
    float epochLoss = 0; // total loss for the epoch
    for( int iter = 0; iter < iterationPerEpoch; ++iter ) {
        // trainData methods are used to transmit the data into the blob
        trainData.GetSamples( iter * batchSize, dataBlob );
        trainData.GetLabels( iter * batchSize, labelBlob );

        net.RunAndLearnOnce(); // run the learning iteration
        epochLoss += loss->GetLastLoss(); // add the loss value on the last step
    }

    ::printf( "Epoch #%02d    avg loss: %f\n", epoch, epochLoss / iterationPerEpoch );
    trainData.ReShuffle( random ); // reshuffle the data
}

I don't understand what class is trainData (and testData) and can't find GetSamples and GetLabels functions on my own. Please help.

/usr/bin/ld: /usr/lib/x86_64-linux-gnu/libprotobuf.a(arena.o): relocation R_X86_64_TPOFF32 against symbol `_ZN6google8protobuf5Arena13thread_cache_E' can not be used when making a shared object; перекомпилируйте с параметром -fPIC /usr/bin/ld: final link failed: Раздел, непредставимый для вывода collect2: error: ld returned 1 exit status NeoOnnx/src/CMakeFiles/NeoOnnx.dir/build.make:581: ошибка выполнения рецепта для цели «NeoOnnx/src/libNeoOnnx.so» make[2]: *** [NeoOnnx/src/libNeoOnnx.so] Ошибка 1 CMakeFiles/Makefile2:266: ошибка выполнения рецепта для цели «NeoOnnx/src/CMakeFiles/NeoOnnx.dir/all» make[1]: *** [NeoOnnx/src/CMakeFiles/NeoOnnx.dir/all] Ошибка 2 Makefile:160: ошибка выполнения рецепта для цели «all» make: *** [all] Ошибка 2

There are many ML problems where TP/FP/FN/TN counters make sense, however they differ in terms of how these counters should be calculated. It would be nice to re-use PrecisionRecallLayer through inheritance for these ML problems.

Currently PrecisionRecallLayer counters are private, so they are inaccessible in the inherited classes, and one can not change the way these counters are calculated.

PR adds virtual reference-getters and uses them instead of direct changes of member variables. Now inherited classes have an access to the counters, and if this access is not sufficient somehow, it is possible to override the getters to use some other member variables.

Hello. I am building a library on windows 10, MSVC 2019 x64, protobuf 3.18.1 . I get two errors:

onnx.pb.obj : error LNK2019: ссылка на неразрешенный внешний символ "class google::protobuf::internal::ExplicitlyConstr ucted<class std::basic_string<char,struct std::char_traits,class std::allocator > > google::protobuf::inter nal::fixed_address_empty_string" (?fixed_address_empty_string@internal@protobuf@google@@3V?$ExplicitlyConstructed@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@123@A) в функции "public: virtual class onnx::ValueInfoProto* __cdecl onnx::ValueInfoProto::New(void)const " (?New@ValueInfoProto@onnx@@UEBAPEAV12@XZ). [H:\libs\neoml2.0.22.0\Build\NeoOnnx\src\NeoOnnx.vcxproj]

onnx.pb.obj : error LNK2019: ссылка на неразрешенный внешний символ "struct std::atomic google::protobuf::interna l::init_protobuf_defaults_state" (?init_protobuf_defaults_state@internal@protobuf@google@@3U?$atomic@_N@std@@A) в функции "public: virtual unsigned char * __cdecl onnx::TensorShapeProto_Dimension::_InternalSerialize(unsigned char *,class google::protobuf::io::EpsCopyOutputStream *)const " (?_InternalSerialize@TensorShapeProto_Dimension@onnx@@UEBAPEAEPEAEPEAVEpsCopyOutputStream@io@protobuf@google@@@Z). [H:\libs\neoml2.0.22.0\Build\NeoOnnx\src\NeoOnnx.vcxproj]

H:\libs\neoml2.0.22.0\Build\NeoOnnx\src\Release\NeoOnnx.dll : fatal error LNK1120: неразрешенных внешних элементов: 2 [ H:\libs\neoml2.0.22.0\Build\NeoOnnx\src\NeoOnnx.vcxproj]

How fix it? Thank you!

Comparison by relation < is undefined for boolean data type: https://github.com/neoml-lib/neoml/blob/5f0e8928c0150723a6504c6d05cc48643ed23ecd/NeoML/src/TraditionalML/GradientBoostQSEnsemble.cpp#L662-L665

In most cases .onnx models are produced from Python environment - e.g. from tensorflow + tf2onnx or torch + torch.onnx.export). Currently to convert such exported onnx models into NeoML you can use either executable app (NeoOnnx/Onnx2NeoML) or C++ API (NeoOnnx::LoadFromOnnx).

It would be nice to wrap NeoOnnx API into python bindings. With such Python API it would be possible to run the whole conversion pipeline (third-party-framework export, neoml import, models comparison and checks) in one environment.

Whenever I try to build NeoML/Python with python setup.py install, I get the following error:

...
-- Configuring done
CMake Error in CMakeLists.txt:
  CUDA_ARCHITECTURES is empty for target "PythonWrapper".
...
CMake Generate step failed.  Build files cannot be regenerated correctly.

As far as I understand, this is because I do not specify CMAKE_CUDA_ARCHITECTURES variable. This PR sets it to native when variable is empty. Somehow works for my CMake 3.18.