Hacked By AnonymousFox
| Current Path : C:/Windows/System32/ |
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| Current File : C:/Windows/System32/Windows.AI.MachineLearning.dll |
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W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < S t r i n g , I n t 6 4 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < I n t 6 4 , I n t 6 4 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < U I n t 1 6 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , D o u b l e > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < U I n t 8 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < I n t 6 4 , S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < U I n t 1 6 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < B o o l e a n > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , D o u b l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , I n t 6 4 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < D o u b l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < I n t 3 2 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < U I n t 3 2 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , I n t 6 4 > W W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , D o u b l e > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , I n t 6 4 > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < S t r i n g , S i n g l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < I n t 6 4 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , S i n g l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , S i n g l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < S i n g l e > FW[LB ("W4 W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < I n t 3 2 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , S t r i n g > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , S i n g l e > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < B o o l e a n > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , S i n g l e > > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < I n t 6 4 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < I n t 1 6 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < S t r i n g , S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , D o u b l e > @ W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < I n t 6 4 , S t r i n g > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < I n t 6 4 , S i n g l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < S i n g l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < S t r i n g , D o u b l e > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < U I n t 3 2 > W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < D o u b l e > Unknown exception bad allocation n t d l l . d l l k e r n e l b a s e . d l l Exception ReturnHr LogHr FailFast % h s ( % d ) \ % h s ! % p : % h s ! % p : ( c a l l e r : % p ) % h s ( % d ) t i d ( % x ) % 0 8 X % w s M s g : [ % w s ] C a l l C o n t e x t : [ % h s ] [ % h s ( % h s ) ]
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RaiseFailFastException RtlDllShutdownInProgress %ws wil s t d : : e x c e p t i o n : % h s internal\sdk\inc\wil\resource.h _ p 0 WilError_02 C++/WinRT version:1.0.180821.2 W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . P r o p e r t y S e t ai.onnx.ml CPUExecutionProvider W i n d o w s . S t o r a g e . S t r e a m s . B u f f e r W i n d o w s . G r a p h i c s . I m a g i n g . S o f t w a r e B i t m a p W i n d o w s . M e d i a . V i d e o F r a m e W i n d o w s . A I . M a c h i n e L e a r n i n g . I L e a r n i n g M o d e l F e a t u r e V a l u e W i n d o w s . A I . M a c h i n e L e a r n i n g . I m a g e F e a t u r e V a l u e W i n d o w s . A I . M a c h i n e L e a r n i n g . L e a r n i n g M o d e l W i n d o w s . A I . M a c h i n e L e a r n i n g . L e a r n i n g M o d e l B i n d i n g W i n d o w s . A I . M a c h i n e L e a r n i n g . L e a r n i n g M o d e l D e v i c e W i n d o w s . A I . M a c h i n e L e a r n i n g . L e a r n i n g M o d e l S e s s i o n W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r B o o l e a n W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r D o u b l e W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r F l o a t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r F l o a t 1 6 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r I n t 1 6 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r I n t 3 2 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r I n t 6 4 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r I n t 8 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r S t r i n g W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r U I n t 1 6 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r U I n t 3 2 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r U I n t 6 4 B i t W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r U I n t 8 B i t \ G P U E n g i n e ( p i d _ * _ * ) \ U t i l i z a t i o n P e r c e n t a g e \ G P U P r o c e s s M e m o r y ( p i d _ * _ * ) \ D e d i c a t e d U s a g e \ G P U P r o c e s s M e m o r y ( p i d _ * _ * ) \ S h a r e d U s a g e W i n d o w s . A p p l i c a t i o n M o d e l . C o r e . C o r e A p p l i c a t i o n onecoreuap\windows\windowsai\winml\dll\module.cpp L o c a l \ S M 0 : % d : % d : % h s invalid string position internal\sdk\inc\wil\wrl.h string too long bad function call Bdi.Y@y[\ 5 FK B;7_T$9N8d[a颂J2HS*V
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Stacktrace:
Cpu onecoreuap\windows\windowsai\winml\dll\tensorbase.h Windows::AI::MachineLearning::Tensor<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::copy_into_buffer Argument dataSize (%d) exceeds the tensor size (%d). Windows::AI::MachineLearning::TensorBase<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct winrt::hstring,struct winrt::Windows::AI::MachineLearning::implementation::TensorString,struct winrt::Windows::AI::MachineLearning::ITensorString,struct winrt::Windows::AI::MachineLearning::implementation::TensorString_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorString,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize onecoreuap\windows\windowsai\winml\dll\tensor.h Windows::AI::MachineLearning::TensorBase<union Lotus::MLFloat16,float,struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat16Bit,struct winrt::Windows::AI::MachineLearning::ITensorFloat16Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat16Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat16Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<union Lotus::MLFloat16>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<__int64,__int64,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt64Bit,struct winrt::Windows::AI::MachineLearning::ITensorInt64Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt64Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorInt64Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<__int64>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<unsigned __int64,unsigned __int64,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt64Bit,struct winrt::Windows::AI::MachineLearning::ITensorUInt64Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt64Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt64Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<unsigned __int64>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<int,int,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt32Bit,struct winrt::Windows::AI::MachineLearning::ITensorInt32Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt32Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorInt32Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<int>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<unsigned int,unsigned int,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt32Bit,struct winrt::Windows::AI::MachineLearning::ITensorUInt32Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt32Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt32Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<unsigned int>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<short,short,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt16Bit,struct winrt::Windows::AI::MachineLearning::ITensorInt16Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt16Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorInt16Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<short>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<unsigned short,unsigned short,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt16Bit,struct winrt::Windows::AI::MachineLearning::ITensorUInt16Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt16Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt16Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<unsigned short>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<unsigned char,unsigned char,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt8Bit,struct winrt::Windows::AI::MachineLearning::ITensorUInt8Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt8Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorUInt8Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<unsigned char>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<signed char,unsigned char,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt8Bit,struct winrt::Windows::AI::MachineLearning::ITensorInt8Bit,struct winrt::Windows::AI::MachineLearning::implementation::TensorInt8Bit_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorInt8Bit,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<signed char>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<double,double,struct winrt::Windows::AI::MachineLearning::implementation::TensorDouble,struct winrt::Windows::AI::MachineLearning::ITensorDouble,struct winrt::Windows::AI::MachineLearning::implementation::TensorDouble_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorDouble,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<double>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<float,float,struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat,struct winrt::Windows::AI::MachineLearning::ITensorFloat,struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorFloat,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<float>::copy_into_buffer Windows::AI::MachineLearning::TensorBase<bool,bool,struct winrt::Windows::AI::MachineLearning::implementation::TensorBoolean,struct winrt::Windows::AI::MachineLearning::ITensorBoolean,struct winrt::Windows::AI::MachineLearning::implementation::TensorBoolean_base<struct winrt::Windows::AI::MachineLearning::implementation::TensorBoolean,struct ITensorNative,struct Windows::AI::MachineLearning::ILotusValueProviderPrivate> >::CPUTensorize Windows::AI::MachineLearning::Tensor<bool>::copy_into_buffer %p Ld Lu ld lu eE vector<T> too long . pP Windows::AI::MachineLearning::Tensor<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::move_into_buffer Vector argument other has size (%zd) which is greater than tensor size(%d) vector<bool> too long Windows::AI::MachineLearning::Tensor<signed char>::move_into_buffer != a c t i v a t i b l e C l a s s I d Lotus::MLValue::Get DataTypeImpl::GetType<T>() == type_ Windows::AI::MachineLearning::Tensor<__int64>::move_into_buffer onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\ml_value.h Windows::AI::MachineLearning::Tensor<int>::move_into_buffer Windows::AI::MachineLearning::Tensor<unsigned __int64>::move_into_buffer Windows::AI::MachineLearning::Tensor<short>::move_into_buffer Windows::AI::MachineLearning::Tensor<unsigned int>::move_into_buffer Windows::AI::MachineLearning::Tensor<unsigned char>::move_into_buffer Windows::AI::MachineLearning::Tensor<unsigned short>::move_into_buffer Windows::AI::MachineLearning::Tensor<float>::move_into_buffer Windows::AI::MachineLearning::Tensor<double>::move_into_buffer Windows::AI::MachineLearning::Tensor<bool>::move_into_buffer map/set<T> too long p d h . d l l PdhAddCounterW PdhOpenQueryW PdhGetFormattedCounterArrayW PdhCollectQueryData PdhCloseQuery PdhGetFormattedCounterValue onecoreuap\windows\windowsai\winml\dll\mloperatorauthorimpl.h onecoreuap\windows\windowsai\winml\dll\mloperatorauthorimpl.cpp winrt::Windows::AI::MachineLearning::implementation::OpKernelContextWrapper::AllocateTemporaryData winrt::Windows::AI::MachineLearning::implementation::AbiCustomRegistry::RegisterOperatorSetSchema tensor(complext64) tensor(complext128) Reshape Tile MemcpyToHost MemcpyFromHost Identity winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<class Lotus::ProtoHelperNodeContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct Microsoft::WRL::ChainInterfaces<struct IMLOperatorKernelCreationContextPrivate,struct IMLOperatorKernelCreationContext,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil>,struct IMLOperatorTensorShapeDescription>,struct Lotus::null_type>::GetAttributeArrayHelper winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<struct onnx::InferenceContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct IMLOperatorShapeInferenceContext,struct IMLOperatorTypeInferenceContext,struct IMLOperatorAttributes>,struct Lotus::null_type>::GetAttributeHelper winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<class Lotus::ProtoHelperNodeContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct Microsoft::WRL::ChainInterfaces<struct IMLOperatorKernelCreationContextPrivate,struct IMLOperatorKernelCreationContext,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil,class Microsoft::WRL::Details::Nil>,struct IMLOperatorTensorShapeDescription>,struct Lotus::null_type>::GetAttributeHelper winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<class Lotus::ProtoHelperNodeContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct IMLOperatorShapeInferenceContext,struct IMLOperatorAttributes>,struct Lotus::null_type>::GetAttributeArrayHelper Lotus::ExecutionFrame::GetValue winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<struct onnx::InferenceContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct IMLOperatorShapeInferenceContext,struct IMLOperatorTypeInferenceContext,struct IMLOperatorAttributes>,struct Lotus::null_type>::GetAttributeArrayHelper winrt::Windows::AI::MachineLearning::implementation::OpNodeInfoWrapper<class Lotus::ProtoHelperNodeContext,class Microsoft::WRL::RuntimeClass<struct Microsoft::WRL::RuntimeClassFlags<2>,struct IMLOperatorShapeInferenceContext,struct IMLOperatorAttributes>,struct Lotus::null_type>::GetAttributeHelper list<T> too long u
yYEC)#3r;8{Bu8 F-*rFS x9I<)k[ThJY Z4A 0DTAmȹ=YTeFp 1YK@lF4L(jS"iG?'þ(;HBonFCtensor(uint64) tensor(int32) tensor(uint32) tensor(float) tensor(double) tensor(int64) tensor(float16) tensor(int8) tensor(int16) tensor(uint8) tensor(uint16) tensor(string) tensor(bool) is not implemented index >= 0 && static_cast<size_t>(index) < node_values_.size() onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\execution_frame.h Lotus::OpKernel::ComputeAsync Lotus::ExecutionFrame::GetType TempSpace allocator not found DmlExecutionProvider winrt::Windows::AI::MachineLearning::implementation::LearningModelDevice::RegisterKernels Failed to create operator registry. Failed to create LearningModelDevice. Ivalid argument device. winrt::Windows::AI::MachineLearning::factory_implementation::LearningModelDevice::CreateFromD3D12CommandQueue Failed to create LearningModelDevice. Ivalid argument queue. onecoreuap\windows\windowsai\winml\dll\learningmodeldevice.cpp Image.ColorSpaceGamma Image.BitmapPixelFormat | Image.NominalPixelRange Tɦ@Ҹ
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J\kcPZxr\)1Default onecoreuap\windows\windowsai\common\operatorauthorhelper\mloperatorauthorhelper.h onecoreuap\windows\windowsai\winml\dll\learningmodel.h group output_shape pads Model stream is invalid. ConvTranspose AveragePool MaxPool LpPool winrt::Windows::AI::MachineLearning::implementation::LearningModel::_ZeroCopyInputStreamWrapper::Next onecoreuap\windows\windowsai\winml\dll\learningmodel.cpp Failed to acquire buffer from model stream. Conv Crop Upsample Slice winrt::Windows::AI::MachineLearning::implementation::LearningModel::LoadModel onecoreuap\windows\windowsai\common\operatorauthorhelper\operatorhelper.h ib-GMOj݂KTu@eN~bU6*J_&QYӉZoU*a3K+ zOAD|07p5
w^,;9PjBad optional access B i t m a p P i x e l F o r m a t BitmapPixelFormat must be either Rgba8, Bgra8, or Gray8 onecoreuap\windows\windowsai\winml\dll\imagefeaturevalue.cpp winrt::Windows::AI::MachineLearning::implementation::GetBitmapPixelFormatFromMetadata Model image inputs must have tensor type of Float or Float16. winrt::Windows::AI::MachineLearning::implementation::ImageFeatureValue::CenterAndCropBounds winrt::Windows::AI::MachineLearning::implementation::GetSizeFromTensorDataType winrt::Windows::AI::MachineLearning::implementation::GetTensorDataTypeFromTensorKind BitmapBounds must reference a property value with type UInt32Array with 4 elements. B i t m a p B o u n d s Failed to center crop the provided input image. The calculated bounds exceed the dimensions of the image, or do not match the model inputs dimensions. winrt::Windows::AI::MachineLearning::implementation::GetBoundsFromMetadata %f L}! OAD|07@M \% h s UInt16 Int8 Int32 Int16 Undefined L e a r n i n g M o d e l B i n d i n g d e s t r u c t o r UInt8 Float UInt32 Double Complex64 UInt64 String Int64 Float16 Boolean onecoreuap\windows\windowsai\winml\dll\featurecompatibility.h Unexpected TensorKind Complex128. Unexpected TensorKind Undefined. Unexpected TensorKind Complex64. [ Complex128 ] , Unexpected descriptor LearningModelFeatureKind. Image[ Windows::AI::MachineLearning::compatibility_details::not_compatible_hr Model variable %s, expects %s, but binding was attempted with an incompatible type %s. Map< > x Sequence< The model variable %s is an input, but has no associated resources to bind. The model variable %s failed tensorization. The binding collection does not contain a variable with name %s. winrt::Windows::AI::MachineLearning::implementation::LearningModelBinding::Bind winrt::Windows::AI::MachineLearning::implementation::LearningModelBinding::CreateBinding The model has no variable with name %s. The model variable %s cannot be bound with the provided type. onecoreuap\windows\windowsai\winml\dll\learningmodelbinding.cpp The engine produced an unexpected evaluation output %s, that is not a model variable output. winrt::Windows::AI::MachineLearning::implementation::LearningModelBinding::CreateUnboundOutput Failed to return bound object for model variable output %s Failed to update bound object for model variable output %s The engine produced an unexpected evaluation output for unbound output variable %s. winrt::Windows::AI::MachineLearning::implementation::LearningModelBinding::Lookup The engine produced an unexpected evaluation output %s, that is not a model variable. winrt::Windows::AI::MachineLearning::implementation::CreateUnboundOuputFeatureValue winrt::Windows::AI::MachineLearning::implementation::LearningModelBinding::UpdateProviders Evaluation produced unexpected output variables. onecoreuap\windows\windowsai\winml\dll\mapbase.h onecoreuap\windows\windowsai\winml\dll\sequencebase.h W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I I t e r a t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I K e y V a l u e P a i r ` 2 < S t r i n g , O b j e c t > > y*ƸD[q /1O.zJW i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < I n t 6 4 , S i n g l e > > ݠDx[^?xX{UW i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I V e c t o r ` 1 < W i n d o w s . F o u n d a t i o n . C o l l e c t i o n s . I M a p ` 2 < S t r i n g , S i n g l e > > C[ۢ3,Y XFԊXXr<ZUtje#z^
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(|n@+O W i n d o w s . A I . M a c h i n e L e a r n i n g . T e n s o r F e a t u r e D e s c r i p t o r W i n d o w s . A I . M a c h i n e L e a r n i n g . M a p F e a t u r e D e s c r i p t o r $SWmC` ||n@+Ostring IMAGE float16 boolean uint32 double complex64 uint64 undefined complex128 float int8 uint8 int16 uint16 int64 int32 Unsupported Image.NominalRange: NominalRange_0_255 Unsupported image with found. Rgb8 Gray8 Unsupported Image.BitmapPixelFormat: Bgr8 Unsupported Image.ColorSpaceGamma: Linear W i n d o w s . A I . M a c h i n e L e a r n i n g . I m a g e F e a t u r e D e s c r i p t o r Normalized_0_1 SRGB NominalRange_16_235 Normalized_1_1 Bgra8 Rgba8 U6*J_&QYӉZ|n@+OpB[b/!4݂KTu@eN~b
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[MDM^ OMpB[b/!4Target tensor description must either be IMG_TENSOR_DATA_TYPE_FLOAT32, or IMG_TENSOR_DATA_TYPE_FLOAT16. %d was supplied. Target tensor description has batch size %d. Batch size must be 1. Windows::AI::MachineLearning::Internal::VideoFrameToTensorConverter::ConvertDX12TextureToGPUTensor Invalid input image height provided. Height is set to zero. Target tensor description expects IMG_TENSOR_CHANNEL_TYPE_GRAY_8, but has %d channels specified instead of 1. Target tensor height (%d) does not match input height (%d). Target tensor description expects IMG_TENSOR_CHANNEL_TYPE_RGB_8, but has %d channels specified instead of 3. Target tensor description expects IMG_TENSOR_CHANNEL_TYPE_BGR_8, but has %d channels specified instead of 3. CreateUAVDescription Format was input image %d. Input image format must DXGI_FORMAT_B8G8R8X8_UNORM. onecoreuap\windows\windowsai\winml\dll\surfacetensorconvertor.cpp Tensorization conversion is only supported to IMG_TENSOR_DATA_TYPE_FLOAT32, or IMG_TENSOR_DATA_TYPE_FLOAT16. Target tensor description expects IMG_TENSOR_CHANNEL_TYPE_GRAY_8, IMG_TENSOR_CHANNEL_TYPE_BGR_8, or IMG_TENSOR_CHANNEL_TYPE_RGB_8 but has %d was specified. ImageConversionHelpers::ConvertCPUTensorToVideoFrameWithSoftwareBitmap ImageConversionHelpers::ConvertVideoFrameWithSoftwareBitmapToCPUTensor Invalid input image width provided. Height is set to zero. Windows::AI::MachineLearning::Internal::VideoFrameToTensorConverter::VideoFrameToDX12Tensor Windows::AI::MachineLearning::Internal::TensorToVideoFrameConverter::DX12TensorToVideoFrame Output input image height provided. Height is set to zero. Output input image width provided. Height is set to zero. CreateSRVDescriptor Windows::AI::MachineLearning::Internal::TensorToVideoFrameConverter::ConvertGPUTensorToDX12Texture Target tensor width (%d) does not match input width (%d). T e n s o r i z e D e s c r i p t o r H e a p D e t e n s o r i z e D e s c r i p t o r S R V H e a p Format was input image %d. Input image format must Bgra8. Format was input image %d. output image format must DXGI_FORMAT_B8G8R8X8_UNORM. D e t e n s o r i z e D e s c r i p t o r R e n d e r T a r g e t H e a p GlaIO{cQ WKT!3
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W i n d o w s . F o u n d a t i o n . I R e f e r e n c e ` 1 < W i n d o w s . G r a p h i c s . I m a g i n g . B i t m a p B o u n d s > *[$h#& ISink must be provided. Lotus Attempt to use DefaultLogger but none has been registered. Default logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. default_logger_id must be provided if instance_type is InstanceType::Default [...truncated...]
ERROR LOG MSG NOTIFICATION: Failure to successfully parse the message onecoreuap\windows\windowsai\engine\lotus\lotus\core\common\status.cc Lotus::Common::Status::Status code != static_cast<int>(MLStatus::OK) other.buffer_deleter_ == nullptr onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\tensor.cc Lotus::Tensor::ShallowCopy Can't copy tensor with its owned buffer. Please transfer ownership by move. Lotus::TensorTypeBase::IsCompatible Lotus::TensorTypeBase::GetElementType Sequence type that has a map of value type other than float not supported for now. Map with value type: Map with key type: Nested map/sequence type is not supported Onnx type: is not supported. type is not supported Sequence with value type: tensor_type.has_elem_type() tensor type is not supported onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\data_types.cc Lotus::DataTypeImpl::TypeFromProto (null) Invalid tensor shape slice argument. Lotus::TensorShape::SizeFromDimension onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\tensor_shape.cc Lotus::TensorShape::SizeToDimension } { Lotus::TensorShape::Slice dimstart <= dimend && dimend <= size() dimension <= num_dims Invalid dimension of for SizeFromDimension. Tensor has dimensions. but the actually size is: , fall back to default allocation behavior Lotus::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper p_mlvalue->Fence() == nullptr failed: , block not found in target loation. fall back to default allocation behavior For mlvalue with index: , block in memory pattern size is: onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\execution_frame.cc Lotus::ExecutionFrame::ExecutionFrame buffers_.find(mem_patterns_->locations[i]) == buffers_.end() Tensor shape cannot contain any negative value Trying to allocate memory for unused optional inputs/outputs Lotus::ExecutionFrame::AllocateMLValueTensorSelfOwnBuffer mlvalue_index >= 0 && mlvalue_index < all_values_.size() Tried to allocate without valid type information, mlvalue index= Lotus::ExecutionFrame::AllocateAsPerAllocationPlan mlvalue_index >= 0 && mlvalue_index < alloc_plan.size() Tried to allocated with invalid mlvalue index: Lotus::ExecutionFrame::TraceAllocate TraceAllocation for mlvalue_idx= size= mlvalue_index_reuse >= 0 && mlvalue_index_reuse < all_values_.size() Lotus::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer mlvalue_index_to_allocate >= 0 && mlvalue_index_to_allocate < all_values_.size() Lotus::ExecutionFrame::TraceFree TraceFree for mlvalue_idx= Lotus::ExecutionFrame::SetupNodeArg arg invalid vector<T> subscript Lotus::ExecutionFrame::GetAllocationPlan mlvalue_idx >= 0 && mlvalue_idx < alloc_plan.size() Memory pattern planner is not enabled on this execution framework. status.IsOK() Lotus::ExecutionFrame::Init graph Invalid allocation kind: node.Index() < node_offsets_.size() output_names.size() == fetches.size() output_names vector size: does not match that of fetches vector: Lotus::MLValue::GetMutable No attribute with this name is defined. Attibute name and type don't match No attribute with name:' 'is defined. Lotus::OpNodeProtoHelper<struct onnx::InferenceContext>::GetAttrs onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\op_node_proto_helper.cc Lotus::OpNodeProtoHelper<class Lotus::ProtoHelperNodeContext>::GetAttrs values.size() == attr->floats_size() values.size() == attr->ints_size() _kFunctionOp Lotus::ExecutionFrame::GetFirstArgIndex index < node_offsets_.size() Lotus::OpKernelContext::Output Acutal: Domain mistmatch: Expected: Actual: node_version: kernel start version: kernel_end_version: Execution provider mismatch. len <= op_schema.inputs().size() Op: Incompatible types. onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\op_kernel.cc Lotus::FindTypeBinding KernelDef not found. Finding function kernel def for op: since we couldn't find kernel def. Failed to find kernel def for op: Execution frame was null kernel is not supported in Lotus::KernelRegistry::CanExecutionProviderCreateKernel Failed to create kernel for op: since it was illformed during registration Failed to add kernel for : Conflicting with a registered kernel with op versions. Version mismatch. on CPU execution provider Encountered following errors: Lotus::KernelRegistry::SearchKernelRegistry Failed to search kernel def for op: Try to access with invalid node value index: kernel != nullptr OpKernel was null Lotus::OpKernelContext::OpKernelContext frame != nullptr Lotus::VerifyShape MLValue shape verification failed. com.microsoft [TypeInferenceError] Input expected to have tensor type ) from file , but it is already registered from file line unknown Output Element type of input unknown , but it its version is not in the inclusive range [ , ] (usually, this means you Schema error: known by the checker. Trying to register schema with name (domain: version: bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h). , but it its domain is not Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. Y output X Exception caught:
Internal copy node
input T onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\environment.cc X h Lotus::AllocateHelper p_provider p_input_provider buffer allocator != nullptr onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\iobinding.cc node_exec_provider node_provider Lotus::IOBinding::CopyOneInputAcrossDevices %Y-%m-%d_%H-%M-%S type: ) AllocatorInfo: name: id: mem_type: Received nullptr for exec provider CastFloat16Transformer Split the graph according to the kernel registrations. Transformer to insert cast node that cast float16 to float for cpu nodes Exception during loading: This session already contains a loaded model. Loading model using model_proto Encountered unknown exception in Load() Unknown exception in Load() onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\inference_session.cc Lotus::InferenceSession::Impl::Load Received nullptr for custom registry Session has already been initialized. Model was not loaded. Model was not loaded Lotus::InferenceSession::Impl::Initialize Exception during initialization: Session successfully initialized. non sequential execution is not implemented Adding default CPU execution provider. model_loading_proto Model successfully loaded. Initializing session. p_fetches->size(): At least one output should be requested. Output vector pointer is NULL Invalid Feed Input Names: p_graph Invalid Output Names: Valid output names are: Output vector incorrectly sized: output_names.size(): ) , expected: ( session_initialization Encountered unknown exception in Initialize() Unknown exception in Initialize() Valid input names are: The number of feeds is not same as the number of the model input, expect got Unexpected input data type. Actual: ( Lotus::InferenceSession::Impl::Run Session was not initialized p_output_provider Lotus::InferenceSession::Impl::CopyOutputsAcrossDevices model_run Encountered unknown exception in Run() Running with tag: Session not initialized. output size mismatch, expected Output with name is not a weight. Lotus::InferenceSession::Impl::MatchOutputsWithProviders p_fetched_provider invalid buffer invalid allocator invalid provider_type Got null output nodearg ptr Got null input nodearg ptr Lotus::InferenceSession::Impl::NewIOBinding _ .json p_execution_plan session_state_.GetNumMLValues() > 0 Lotus::InferenceSession::Impl::SaveInitializedTensorsWithSeperateBuffer Done saving initialized tensors The buffer planner is not consistent with tensor buffer size Failed to get allocator for alloc_info: GraphPlacementPlanner InferenceSession Saving initialized tensors. Failed to copy tensor to execution provider: Lotus::InferenceSession::Impl::DeserializeTensorProto provider != nullptr Saving kernels. Done saving MLValue mappings. Could not create kernel for node: as there's no execution provider allocated. Done saving kernels. Lotus::InferenceSession::Impl::SaveKernels Failed to get allocator for location: weights_buffers_.find(location) == weights_buffers_.end() execution_plan Lotus::InferenceSession::Impl::SaveInitializedTensorsWithMemPattern Lotus::InferenceSession::Impl::SaveMLValueNameIndexMapping Saving MLValue mappings. Weight buffer not found Kernel creation failed for node: with error: Lotus::InferenceSession::Impl::CreateOpKernel invalid unordered_map<K, T> key Lotus::BFCArena::RegionManager::RegionFor Creating bin of max chunk size Lotus::BFCArena::BFCArena onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\bfc_arena.cc p_int >= base_int Lotus::BFCArena::AllocationRegion::IndexFor p_int < base_int + memory_size_ Could not find Region for 0 == memory_size % kMinAllocationSize Lotus::BFCArena::AllocationRegion::AllocationRegion onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\bfc_arena.h reserved_chunks_.find(ptr) == reserved_chunks_.end() Lotus::BFCArena::Reserve Lotus::BFCArena::Extend Total allocated bytes: to Allocated memory at h < chunks_.size() Lotus::BFCArena::ChunkFromHandle bytes. Extending allocation by BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) Lotus::BFCArena::DeallocateRawInternal !c1->in_use() && !c2->in_use() Lotus::BFCArena::Merge c2->prev == h1 !chunk->in_use() Lotus::BFCArena::FindChunkPtr !c->in_use() && (c->bin_num == kInvalidBinNum) Lotus::BFCArena::SplitChunk Lotus::BFCArena::AllocateRawInternal h != kInvalidChunkHandle tried to allocate 0 bytes Could not find chunk in bin BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) Lotus::BFCArena::FreeAndMaybeCoalesce Lotus::BFCArena::InsertFreeChunkIntoBin !c->in_use() && (c->bin_num != kInvalidBinNum) Lotus::BFCArena::RemoveFreeChunkIterFromBin Lotus::BFCArena::RemoveFreeChunkFromBin Validating no unexpected access using an invalid node_index. node_index < nodes_.size() LotusIR::GraphBase::NodeAtIndexImpl onecoreuap\windows\windowsai\engine\lotus\lotus\core\graph\graph.h exec_provider_set_.provider_idx_map.end() != iter nullptr != allocator already register in session state , type: nullptr != node Lotus::SessionState::GetKernelDef onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\session_state.cc Lotus::SessionState::GetAllocatorInfo Failed to find input name in the mapping Lotus::SessionState::GetExecutionProvider Could not find MLValue with name: mlvalue_index >= 0 && mlvalue_index <= GetMaxMLValueIdx() Lotus::SessionState::AddInitializedTensor , id: Execution Provider's allocator with info: Lotus::SessionState::AddExecutionProvider it->second < exec_provider_set_.exec_providers.size() Initialized tensor with unexpected type: UnpackTensor: the pre-allocated size does not match the raw data size UnpackTensor: the pre-allocated size does not match the size in proto , got The buffer planner is not consistent with tensor buffer size, expected Can't plan on string tensors. Lotus::Utils::TraceTensorAllocFromTensorProto onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\tensorprotoutils.cc cast node to cast from float16 to float32 on cpu CastDef_%d Kernel not found. Kernel def not found. Begin execution invalid index mlvalue_index >= 0 && static_cast<size_t>(mlvalue_index) < all_values_.size() Lotus::ExecutionFrame::GetMLValue Lotus::ExecutionFrame::GetFence p_fetches Lotus::SequentialExecutor::FetchOutput output_names.size() == p_fetches->size() _fence_after Excutor::Execute op_name _fence_before _kernel_time Lotus::SequentialExecutor::Execute onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\executor.cc Got nullptr from GetKernel for node: No provider preference found, at least CPU provider should be chosed. No kernel registry found. Lotus::PlannerImpl::Reuse nullptr != p_opkernelDef Lotus::PlannerImpl::FindReusableInput nullptr != tensor_type_base Lotus::PlannerImpl::Index onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\allocation_planner.cc reused != reused_for ] ( Lotus::PlannerImpl::CreatePlan Lotus::PlannerImpl::GetElementSize No suitable kernel definition found for op (node Lotus::PlannerImpl::GeneratePlanForWeights::<lambda_f26670cd82cd2c0dc215d9c18141b8fe>::operator () GetOpKernelRegistry().SearchKernelRegistry(node, &kci).IsOK() Lotus::TransformerMemcpyImpl::ProcessDefs onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\transformer_memcpy.cc LotusIR::Node::ForEachWithIndex( node.InputDefs(), [this, &input_mem_types](const LotusIR::NodeArg& arg, size_t index) { if (input_mem_types && MemTypeOnCpuExplicitly(*input_mem_types, index)) non_provider_input_defs_.insert(&arg); else provider_input_defs_.insert(&arg); return Status::OK(); }) .IsOK() LotusIR::Node::ForEachWithIndex( node.OutputDefs(), [this, &output_mem_types](const LotusIR::NodeArg& arg, size_t index) { if (output_mem_types && MemTypeOnCpuExplicitly(*output_mem_types, index)) non_provider_output_defs_.insert(&arg); else provider_output_defs_.insert(&arg); return Status::OK(); }) .IsOK() node.GetExecutionProviderType() == LotusIR::kCpuExecutionProvider || node.GetExecutionProviderType().empty() Memcpy Copy from/to host memory Domain already set in registry than the operator set version , but it its version is higher deque<T> too long NoOp Constant ai.onnx Error: two nodes with same node name ( Error: Duplicate definition of name ( ) does not exist in the graph. ) of Node ( graph_proto != nullptr LotusIR::Graph::Graph onecoreuap\windows\windowsai\engine\lotus\lotus\core\graph\graph.cc ). The sum of input arg count is not equal to size of input defs in node ( Expected either name or graph_proto. ' of input parameter ( Type Error: Type ' in node ( and Node ( ) is invalid. ) in node ( ) of operator ( LotusIR::GraphBase::BuildConnections Error: the graph is not acyclic. ) does not have type information set by parent node. ) input arg ( The control input ( src= ControlInputs should not have invalid nodes. dst= dst && src Type Error: Shape of initializer ) is required but not specified. ) attribute ( Error: Duplicate definition-site for ( ) does not have type information. Model input ( does not match its type. Type Error: Value of initializer ) does not match expected type ( ) of node ( ) of output arg ( Type Error: Type ( ) bound to different types ( Type Error: Type parameter ( ) type inference failed ) output arg ( %s exists in this graph's initializers but it is not used by any node Some graph outputs do not exist in the graph. ( ) should be a graph input or initializer. Node input ( LotusIR::AddNodeArgs Cannot find NodArg for LotusIR::GraphBase::FindNodeArg LotusIR::Graph::CleanUnusedInitializers _Graph_Sink != input definition. Existing entry in NodeArg map for node_arg == input_arg Resolve() must be called before using the graph as modifications have been made to it. Source node internally in a graph. _Graph_Source Sink node internally in a graph. Requested size is too large to fit into size_t. index < data_.size() ConstPointerContainer<class std::vector<class LotusIR::NodeArg *,class std::allocator<class LotusIR::NodeArg *> > >::at onecoreuap\windows\windowsai\engine\lotus\lotus\core\common\const_pointer_container.h invalid hash bucket count Protobuf parsing failed. Failed to load model with error: <p_fd> less than 0. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. No graph was found in the protobuf. system error number Load model failed. File doesn't exist UnpackTensor: the pre-allocate size does not match the raw data size UnpackTensor: the pre-allocate size does not match the size in proto A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\coded_stream.cc CHECK failed: (buffer_size) >= (0): CHECK failed: !is_closed_: close() failed: onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\zero_copy_stream_impl.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\zero_copy_stream.cc CHECK failed: (count) <= (last_returned_size_): BackUp() can only be called after a successful Next(). CHECK failed: target_ != NULL: CHECK failed: (count) >= (0): CHECK failed: (last_returned_size_) > (0): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\zero_copy_stream_impl_lite.cc CHECK failed: (backup_bytes_) == (0): CHECK failed: (count) <= (buffer_used_): BackUp() can only be called after Next(). Parameter to BackUp() can't be negative. Can't back up over more bytes than were returned by the last call to Next(). StringOutputStream. Cannot allocate buffer larger than kint32max for CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: CHECK failed: (count) <= (target_->size()): [libprotobuf %s %s:%d] %s
FATAL %d INFO ERROR WARNING %u CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kHeaderSize): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\arena.cc CHECK failed: (new_size) <= ((std::numeric_limits<size_t>::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\repeated_field.cc This shouldn't be called if all the sizes are equal. Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of message of type " Can't CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\message_lite.cc CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): was modified concurrently during serialization. (cannot determine missing fields for lite message) Exceeded maximum protobuf size of 2GB: CHECK failed: !coded_out.HadError(): parse " because it is missing required fields: key onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\reflection_internal.h value Unimplemented Map Reflection API. File is already registered: : Tried to copy from a message with a different type. to: CHECK failed: (from.GetDescriptor()) == (descriptor): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\message.cc , from: : Tried to merge from a message with a different type. to: CHECK failed: this == other_mutator: Not implemented. registered: Should not reach here. CHECK failed: field->is_repeated(): Type is already registered: Type appears to be in generated pool but wasn't File appears to be in generated pool but wasn't registered: with type Not implemented field number onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\generated_message_util.cc onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\map_field.h Unsupported Unsupported: type mismatch Can't get here. MapKey::GetUInt32Value MapKey::GetInt32Value MapKey::GetStringValue MapKey::GetBoolValue Expected : type does not match
MapKey::GetUInt64Value Actual : MapKey::type MapKey is not initialized. Protocol Buffer map usage error:
MapKey::GetInt64Value Call set methods to initialize MapKey. MapValueRef::GetEnumValue MapValueRef::GetBoolValue MapValueRef::GetFloatValue MapValueRef::GetStringValue MapValueRef::GetUInt64Value MapValueRef::GetInt64Value MapValueRef::GetUInt32Value MapValueRef::GetInt32Value onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\map_field.cc MapValueRef::GetMessageValue MapValueRef::GetDoubleValue MapValueRef::type MapValueRef is not initialized. Invalid key for map field. onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\dynamic_message.h Extensions of MessageSets must be optional messages. onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\wire_format.cc Cannot get here : Protocol message serialized to a size different from what was originally expected. Perhaps it was modified by another thread during serialization? CHECK failed: (output->ByteCount()) == (expected_endpoint): Tried to merge messages of different types CHECK failed: (to->GetDescriptor()) == (descriptor): (merge CHECK failed: (&from) != (to): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\reflection_ops.cc
message bool enum bytes fixed32 fixed64 MessageOptions FileOptions EnumOptions FieldOptions onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\descriptor.cc proto3 google.protobuf.Empty can't reach here. required optional proto2 repeated sfixed64 sfixed32 sint64 sint32 " No default value Can't get here: failed to get default value as string Messages can't have default values! UNKNOWN_ENUM_VALUE_%s_%d CHECK failed: has_default_value(): CHECK failed: generated_database_->Add(encoded_file_descriptor, size): 2. google.protobuf. proto ServiceOptions EnumValueOptions OneofOptions MethodOptions extend .$0 {
{
$0message $1 $0option $1;
Found invalid proto option data for: $0// $1
{
= (. json_name = " [default = $0 { ... };
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$0$1$2 $3 = $4 map<$0, $1> $0 to $1, $0, "$0", ;
$0 }
$0 extensions $1 to $2;
$0 reserved $0 extend .$1 {
Invalid proto descriptor for file " ": 'out_location' must not be NULL [$0] ... }
$0oneof $1 { $0$1 = $2 $0enum $1 {
" is already defined in " ". " is already defined (as something other than a package) in file " " is already defined in file " .PLACEHOLDER_VALUE PLACEHOLDER_VALUE " is already defined. " not previously defined in symbols_by_name_, but was defined in symbols_by_parent_; this shouldn't be possible. ", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., ". ") to start from the outermost scope. .placeholder.proto " is resolved to " ". To use it here, please add the necessary import. " is not defined. " seems to be defined in " ", which is not imported by " Invalid weak dependency index. Invalid public dependency index. Field name "$0" is reserved multiple times. Reserved range $0 to $1 overlaps with already-defined range $2 to $3. Missing field: FileDescriptorProto.name. " was not found or had errors. A file with this name is already in the pool. Unrecognized syntax: " was listed twice. -> " has not been loaded. Import " " is not a valid identifier. Missing name. File recursively imports itself: .dummy Field numbers must be positive integers. Couldn't parse default value " Field numbers $0 through $1 are reserved for the protocol buffer library implementation. Field numbers cannot be greater than $0. nan -inf Messages can't have default values. Boolean default must be true or false. Message extensions cannot have required fields. Extension range $0 to $1 overlaps with already-defined range $2 to $3. inf Repeated fields can't have default values. Field "$0" uses reserved number $1. Extension range $0 to $1 includes field "$2" ($3). Extension range $0 to $1 overlaps with reserved range $2 to $3. Field name "$0" is reserved. Enum value "$0" uses reserved number $1. Enum value "$0" is reserved multiple times. the global scope Enum value "$0" is reserved. ), this value label conflicts with . This will make the proto fail to compile for some languages, such as C#. Enums must contain at least one value. When enum name is stripped and label is PascalCased ( Extension range end number must be greater than start number. Extension numbers must be positive integers. Reserved range end number must be greater than start number. Reserved numbers must be positive integers. FieldDescriptorProto.oneof_index should not be set for extensions. FieldDescriptorProto.extendee not set for extension field. FieldDescriptorProto.oneof_index $0 is out of range for type "$1". FieldDescriptorProto.extendee set for non-extension field. Field with primitive type has type_name. Enum type " Extension number $0 has already been used in "$1" by extension "$2". Field with message or enum type missing type_name. " is not an enum type. " is not a type. " has no value named " Default value for an enum field must be an identifier. " is not a message type. Oneof must have at least one field. Fields of oneofs must themselves have label LABEL_OPTIONAL. "$0" does not declare $1 as an extension number. " must be unique within , not just within " Fields in the same oneof must be defined consecutively. "$0" cannot be defined before the completion of the "$1" oneof definition. Note that enum values use C++ scoping rules, meaning that enum values are siblings of their type, not children of it. Therefore, " " which is a proto3 message type. Explicit default values are not allowed in proto3. Groups are not supported in proto3 syntax. " is not a proto3 enum, but is used in " The JSON camel-case name of field " " conflicts with field " Required fields are not allowed in proto3. Extensions in proto3 are only allowed for defining options. MessageSet is not supported in proto3. Extension ranges are not allowed in proto3. ". This is not allowed in proto3. Extension number $0 has already been used in "$1" by extension "$2" defined in $3. Field number $0 has already been used in "$1" by field "$2". Files that do not use optimize_for = LITE_RUNTIME cannot import files which do use this option. This file is not lite, but it imports " " which is. Enum value in map must define 0 as the first value. Key in map fields cannot be float/double, bytes or message types. Expanded map entry type conflicts with an existing nested message type. Files with optimize_for = LITE_RUNTIME cannot define services unless you set both options cc_generic_services and java_generic_sevices to false. " uses the same enum value as " Key in map fields cannot be enum types. Entry Extensions to non-lite types can only be declared in non-lite files. Note that you cannot extend a non-lite type to contain a lite type, but the reverse is allowed. MessageSets cannot have fields, only extensions. ". If this is intended, set 'option allow_alias = true;' to the enum definition. map_entry should not be set explicitly. Use map<KeyType, ValueType> instead. Extension numbers cannot be greater than $0. The first enum value must be zero in proto3. [packed = true] can only be specified for repeated primitive fields. [lazy = true] can only be specified for submessage fields. Protocol message serialized itself in invalid fashion. CHECK failed: options->ParsePartialFromString(buf): Option must have a name. Options could not be fully parsed using the proto descriptors compiled into this binary. Missing required fields: CHECK failed: original_uninterpreted_options_field != NULL: No field named "uninterpreted_option" in the Options proto. Protocol message could not be serialized. CHECK failed: options->AppendPartialToString(&buf): CHECK failed: builder_: jstype is only allowed on int64, uint64, sint64, fixed64 or sfixed64 fields. CHECK failed: uninterpreted_options_field != NULL: uninterpreted_option conflicts with an existing enum type. conflicts with an existing field. Illegal jstype for int64, uint64, sint64, fixed64 or sfixed64 field: conflicts with an existing oneof type. CHECK failed: field != NULL: Invalid wire type for CPPTYPE_MESSAGE: Value out of range for int32 option " " was already set. " is a repeated message. Repeated message options must be initialized using an aggregate value. " is an atomic type, not a message. Unexpected failure while serializing option submessage CHECK failed: !out.HadError(): " unknown. Option " Option field " " is not a field or extension of message " CHECK failed: options_descriptor: Option must not use reserved name "uninterpreted_option". " is resolved to "( )", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., "(. Value must be quoted string for string option " " for option " .foo = value". ; Value must be "true" or "false" for boolean option " Value must be identifier for boolean option " ". This appears to be a value from a sibling type. 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com.google.protobufBDescriptorProtosHZ>github.com/golang/protobuf/protoc-gen-go/descriptor;descriptorGPBGoogle.Protobuf.Reflection CHECK failed: (temp[size-1]) == ('5'): CHECK failed: (temp[0]) == ('1'): CHECK failed: (size) <= (6): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\strtod.cc %.1f Unexpected end of string. Comment started here. End-of-file inside block comment. Invalid control characters encountered in text. / Already saw decimal point or exponent; can't have another one. Need space between number and identifier. "/*" inside block comment. Block comments cannot be nested. Hex and octal numbers must be integers. "0x" must be followed by hex digits. Invalid escape sequence in string literal. "e" must be followed by exponent. Numbers starting with leading zero must be in octal. Expected hex digits for escape sequence. String literals cannot cross line boundaries. Expected eight hex digits up to 10ffff for \U escape sequence Expected four hex digits for \u escape sequence. Tokenizer::ParseStringAppend() passed text that could not have been tokenized as a string: \U%08x Tokenizer::ParseFloat() passed text that could not have been tokenized as a float: Interpreting non ascii codepoint %d. Need space between identifier and decimal point. onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\io\tokenizer.cc CHECK failed: is_prototype(): onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\dynamic_message.cc args were given. Full format string was: " ", but only Invalid strings::Substitute() format string: " strings::Substitute format string invalid: asked for "$ onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\stubs\substitute.cc Warning parsing text-format Error parsing text-format Non-repeated Any specified multiple times. < CHECK failed: trees: onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\text_format.cc False True Invalid value for boolean field " ". Value: " Field " " is specified along with field " text format contains deprecated field " ; " is specified multiple times. CHECK failed: allow_unknown_field_ || reserved_field: ", another member of oneof " Non-repeated field " Extension " " is not defined or is not an extension of " Message type " " has no field named " TextFormat::Parser for Any supports only type.googleapis.com and type.googleprod.com, but found " Expected double, got: Could not find type " " stored in google.protobuf.Any. Expected integer, got: Expected string, got: Expect a decimal number, got: Integer out of range ( infinity Reached an unintended state: CPPTYPE_MESSAGE Expected identifier, got: Invalid float number: Expected integer or identifier, got: - Unknown enumeration value of " " for field " : [ : failed to parse contents ]
not found Proto type Outdent() without matching Indent(). } Message missing required fields: Value of type " " stored in google.protobuf.Any has missing required fields Expected " ", found " " : " "
...<truncated>... : 0x CPPTYPE_BOOL CPPTYPE_FLOAT CPPTYPE_STRING CPPTYPE_ENUM CPPTYPE_UINT32 CPPTYPE_INT64 CPPTYPE_DOUBLE CPPTYPE_UINT64
Problem :
Field : CPPTYPE_INT32 INVALID_CPPTYPE onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\generated_message_reflection.cc
Message type: Protocol Buffer reflection usage error:
Method : google::protobuf::Reflection:: Second argument to SwapFields() (of type " First argument to SwapFields() (of type " HasField Field does not match message type. "). Note that the exact same class is required; not just the same descriptor. ") is not compatible with this reflection object (which is for type " Second argument to Swap() (of type " CHECK failed: (message2->GetReflection()) == (this): Unimplemented type:
Actual : First argument to Swap() (of type " CHECK failed: (message1->GetReflection()) == (this):
Problem : Field is not the right type for this message:
Expected : CPPTYPE_MESSAGE
Problem : Enum value did not match field type:
Expected :
Field type: GetInt64 AddInt32 GetRepeatedInt64 SetInt64 SetInt32 GetInt32 SetRepeatedInt32 GetRepeatedInt32 ReleaseLast RemoveLast Swap FieldSize Field is repeated; the method requires a singular field. ClearField Field is singular; the method requires a repeated field. SetFloat GetFloat SetRepeatedFloat GetRepeatedFloat GetRepeatedUInt64 SetUInt64 AddUInt64 SetRepeatedUInt64 SetRepeatedUInt32 GetRepeatedUInt32 GetUInt64 AddUInt32 AddInt64 SetRepeatedInt64 SetUInt32 GetUInt32 SetString GetStringReference GetRepeatedStringReference GetRepeatedString SetRepeatedBool GetRepeatedBool GetString AddBool AddDouble SetRepeatedDouble SetBool GetBool GetDouble AddFloat GetRepeatedDouble SetDouble GetMessage AddEnumValue accepts only valid integer values: value SetAllocatedMessage MutableMessage SetRepeatedEnumValue accepts only valid integer values: SetRepeatedEnum AddEnum value SetEnumValue accepts only valid integer values: value SetEnumValue GetRepeatedEnumValue unexpected for field AddString SetRepeatedString SetEnum GetEnumValue Field is not a map field. GetRawRepeatedField "InsertOrLookupMapValue" "LookupMapValue" CHECK failed: (field->message_type()) == (desc): subtype mismatch "GetRawRepeatedField" wrong submessage type "MutableRawRepeatedField" AddAllocatedMessage CHECK failed: (field->options().ctype()) == (ctype): MutableRawRepeatedField GetRepeatedMessage ReleaseMessage AddMessage MutableRepeatedMessage CHECK failed: file != NULL: "GetMapData" the actual field type (for enums T should be the generated enum The type parameter T in RepeatedFieldRef<T> API doesn't match CHECK failed: (message_type) == (field->message_type()): type or int32). CHECK failed: field->cpp_type() == cpp_type || (field->cpp_type() == FieldDescriptor::CPPTYPE_ENUM && cpp_type == FieldDescriptor::CPPTYPE_INT32): Reached impossible case in HasBit(). "MapBegin" "DeleteMapValue" "MapSize" "MapEnd" CHECK failed: prototype != NULL: Extension factory's GetPrototype() returned NULL for extension: CHECK failed: output->message_prototype != NULL: onecoreuap\windows\windowsai\opensource\protobuf\google\protobuf\extension_set_heavy.cc google.protobuf.Any type.googleprod.com/ type.googleapis.com/ seq map( tensor map seq( tensor( complext128 complext64 not in range [min= , max= not in allowed input sizes. Input size ]. ' is expected to have field 't' ' is expected to have field 's' ' is expected to have field 'floats' ' is expected to have field 'g' Unrecognized attribute: Attribute ' ' is expected to have field 'i' ' is expected to have field 'f' is marked single but has an empty string in the graph ) has more inputs ( ' appeared multiple times. ) has more outputs ( not in allowed output sizes. Output size ) than declared ( ) in op definition. !(it.GetName().empty()) (outputs_.size() - 1) == i (inputs_.size() - 1) == i Duplicate type constraint name ONNX Schema : failed validating the check: ' is missing. has unknown expected type Attribute specification type mismatch. Required attribute ' ' is expected to have field 'strings' ' is expected to have field 'ints' ' is expected to have field 'graphs' ' is expected to have field 'tensors' B Condition for the if If conditional A maximum trip-count for the loop specified at runtime. Optional. pass empty string to skip. M
Generic Looping construct. This loop has multiple termination conditions:
1) Trip count. Iteration count specified at runtime. Set by
specifying the input M. Optional. Set to empty string to omit.
Note that a static trip count (specified at graph construction time) can be
specified by passing in a constant node for input M.
2) Loop termination condition. This is an input to the op that determines
whether to run the first iteration and also a loop-carried dependency for
the body graph. The body graph must yield a value for the condition variable,
whether this input is provided or not.
This table summarizes the operating modes of this operator with equivalent
C-style code:
Operator inputs defined as (max_trip_count, condition_var).
input ("", ""):
for (int i=0; ; ++i) {
cond = ... // Note this value is ignored, but is required in the body
}
input ("", cond) // Note this is analogous to a while loop
bool cond = ...;
for (int i=0; cond; ++i) {
cond = ...;
}
input ("", 1) // Note this is analogous to a do-while loop
bool cond = true
for (int i=0; cond; ++i) {
cond = ...;
}
input (trip_count, "") // Note this is analogous to a for loop
int trip_count = ...
for (int i=0; i < trip_count; ++i) {
cond = ...; // ignored
}
input (trip_count, cond)
int trip_count = ...;
bool cond = ...;
for (int i=0; i < trip_count && cond; ++i) {
cond = ...;
}
*Sample usage - cond as well as trip count*
graph predict-net {
%a = Constant[value = <Scalar Tensor [3]>]()
%b = Constant[value = <Scalar Tensor [6]>]()
%keepgoing = Constant[value = <Scalar Tensor [1]>]()
%max_trip_count = Constant[value = <Scalar Tensor [10]>]()
%keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
return
}
graph body-net (
%i[INT32, scalar]
%keepgoing[BOOL, scalar]
%b[INT32, scalar]
) {
%my_local = Add(%a, %b)
%b_out = Sub(%a, %b)
%keepgoing_out = Greater(%my_local, %b_out)
%user_defined_vals = Add(%b, %b)
return %keepgoing_out, %b_out, %user_defined_vals
}
*Sample equivalent C code*
{
/* User-defined code (enclosing scope) */
int a = 3, b = 6;
bool keepgoing = true; // Analogous to input cond
/* End user-defined code */
/* Implicitly-defined code */
const int max_trip_count = 10; // Analogous to input M
int user_defined_vals[]; // Imagine this is resizable
/* End implicitly-defined code */
for (int i=0; i < max_trip_count && keepgoing; ++i) {
/* User-defined code (loop body) */
int my_local = a + b; // Reading values in the enclosing scope is fine
b = a - b; // writes fine if we specify b as a loop-carried dependency
keepgoing = my_local > b; // keepgoing is a loop-carried dependency
user_defined_vals[i] = b + b;
/* End user-defined code */
}
// my_local = 123; // Can't do this. my_local was defined in the the body
// These below values are live-out from the loop and therefore accessible
b_out; user_defined_vals; keepgoing_out;
}
There are several things of note in this code snippet:
1) Values from the enclosing scope (i.e. variable a here) are in scope and can
be referenced in the inputs of the loop.
2) Any variables which you wish to make available in the enclosing scope (i.e.
the variables b and keepgoing) must be declared as either loop-carried
dependencies (both at the op inputs and output and at the body net input and
output) or scan_outputs.
3) Values created in the body cannot be accessed in the enclosing scope.
Note that the semantics of this op support "diagonal" or "wavefront" execution.
(See Step 3 here for an example:
https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
Frontends should emit multi-layer RNNs as a series of While operators (with
time being the inner looping dimension), with each successive layer consuming
the scan_outputs from the previous layer, possibly going through several
point-wise operators (e.g. dropout, residual connections, linear layer).
Concretely, the (possibly transformed) scan_outputs are referenced by the
subsequent layer as a LoopIndexTensor operating on a value in scope, not
necessarily a loop-carried dependency. Backends can recognize this pattern and
are permitted to schedule the execution of the multi-layer network in a
pipelined/"wavefront" fashion.
I If onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\controlflow\defs.cc All Tensor types Only bool Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. else_branch Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. then_branch Values that are live-out to the enclosing scope. outputs cond V axis Tensor of N - 1 dims that is a sub tensor of T loop_idx Axis on which to index Tensor to be indexed (has N dimensions) Loop index provided as input to the body graph Loop This is a special operator only valid inside the loop that supports the common case behavior of accessing the correct element of the input sequence in an RNN. This operator MUST be directly given the passed-in iteration number to the body of a Loop graph. This signals to back-ends that this is a direct indexing operation, with no transforms applied to the index. body Only int64 v_final_and_scan_outputs The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations. v_initial Final N loop carried dependency values then K scan_outputs A boolean termination condition. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) LoopIndexTensor O Indices [ShapeInferenceError] Negative values are not allowed in a shape specification does not specify a valid type. should specify a shape should be of integer type and specify a type. Attribute not specified Value of attribute
ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
is applied to the tensor elementwise.
Threshold value Affine onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\experiments\defs.cc 1D output tensor Constrain input and output types to float tensors. beta 1D input tensor alpha Value of beta
Affine takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the affine function, y = alpha * x + beta,
is applied to the tensor elementwise.
Value of alpha The value for the elements of the output tensor. Default is 0. The data type for the elements of the output tensor.Strictly must be one of the types from DataType enum in TensorProto. input_as_shape extra_shape dtype shape ParametricSoftplus
The operator fills the elements of the output tensor with a constant value
specified by the 'value' attribute.
The data type is specified by the 'dtype' attribute. The 'dtype' attribute must
be one of the data types specified in the 'DataType' enum field in the
TensorProto message. If the 'dtype' attribute is not provided, the data type of
'value' is used.
The output tensor shape is specified by the 'shape' attribute. If the number of
input is 1, the shape will be identical to that of the input at run time with
optional additional dimensions appended at the end as specified by 'extra_shape'
attribute. In that case the 'shape' attribute should not be set.
If input_as_shape is set to true, then the input should be a 1D tensor
containing the desired output shape (the dimensions specified in extra_shape
will also be appended)
NOTE: Currently, it supports data type of float, int32, int64, and bool.
ScaledTanh
ParametricSoftplus takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to
the tensor elementwise.
Scaling value The scaled hyperbolic tangent values of the input tensor computed element-wise ThresholdedRelu
Calculates the scaled hyperbolic tangent of the given input tensor element-wise,
alpha * tanh(beta * x). This operation can be done in an in-place fashion too,
by providing the same input and output blobs.
Input tensor Output tensor
Scale takes one input data (Tensor<float>) and produces one output data
(Tensor<float>) whose value is the input data tensor scaled element-wise.
Input data to be scaled values GivenTensorFill The shape of filled tensor The filled tensor Constrain output types to float, int32, int64, bool tensors. ConstantFill Output tensor of constant values specified by 'value'argument and its type is specified by the 'dtype' argument Constrain input types to float, int32, int64, bool tensors. Input tensor (optional) to provide shape information. T2 1D tensor containing the desired output shape. First input must be in CPU context. T1 The shape of the output tensor. Cannot set the shape argument and pass in an input at the same time. The additional dimensions appended at the end of the shape indicatedby the input blob.Cannot set the extra_shape argument when there is no input blob. The new GRU hidden state calculated by this op. hidden seq_lengths The timestep for this operation. gates Array of sequence lengths. len(seq_lengths) should equal batch size N. hidden_prev Unactivated gate outputs from forget, update, and output gates, pre-activation. drop_states The previous GRU hidden state.
GRUUnit computes the activations of a standard GRU,
in a sequence-length aware fashion.
Concretely, given the (fused) inputs X (TxNxD), the previous hidden
state (NxD), and the sequence lengths (N), computes the GRU
activations, avoiding computation if the input is invalid (as in, the
value at X[t][n] >= seqLengths[n].
Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. scale Scale Output data after scaling (float, default 1.0) the scale to apply. across_channels If 0, normalize the mean only. Default is 1. Perform mean variance normalization. If 1, mean and variance are computed across channels. Default is 0. Result, has same shape and type as input ImageScaler bias Input tensor of shape [N,C,H,W] Scale and bias the input image. Bias values are stored in
the same ordering as the image pixel format. Bias applied to each channel, same size as C. Constrain output types to bool, int32, int64, float16, float, double tensors. ATen Arbitrary input Arbitrary output GRUUnit
Experimental allowing ATen operations to be accessed directly from Caffe2
to allow for quick prototyping when ONNX is missing standard versions of
and op Result, has same type as input, with H and W dimensions reduced. border A 1-D values of (height, width). Crop and image to the specified spatial dimensions. If scale is given,
then optionally start the crop offset by the left/top border amounts.
If scale is not provided, crop the borders as provided. A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder). normalize_variance MeanVarianceNormalization A constant tensor. RandomUniform
Generate a tensor with random values drawn from a normal distribution. The shape
of the tensor is specified by the `shape` argument and the parameter of the normal distribution
specified by `mean` and `scale`.
The data type is specified by the 'dtype' argument. The 'dtype' argument must
be one of the data types specified in the 'DataType' enum field in the
TensorProto message.
Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. (Optional) Seed to the random generator, if not specified we will auto generate one. seed Upper boundary of the output values. If not specified, default is 1. high Lower boundary of the output values. If not specified, default is 0. low onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\generator\defs.cc
Generate a tensor with random values drawn from a uniform distribution. The shape
of the tensor is specified by the `shape` argument and the range by `low` and `high`.
The data type is specified by the 'dtype' argument. The 'dtype' argument must
be one of the data types specified in the 'DataType' enum field in the
TensorProto message.
The value for the elements of the output tensor. Output tensor containing the same value of the provided tensor. Input tensor must have rank 2 sample_size
Generate a tensor of samples from a multinomial distribution according to the probabilities
of each of the possible outcomes.
Output type must be int32 or int64
Generate a tensor with random values drawn from a normal distribution.
The shape of the output tensor is copied from the shape of the input tensor,
and the parameters of the normal distribution are specified by `mean` and `scale`.
The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
TensorProto message, and be valid as an output type.
RandomNormalLike Input tensor to copy shape and optionally type information from. RandomUniformLike
Generate a tensor with random values drawn from a uniform distribution.
The shape of the output tensor is copied from the shape of the input tensor,
and the parameters of the uniform distribution are specified by `low` and `high`.
The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided.
The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the
TensorProto message and be valid as an output type.
(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor. Output tensor of random values drawn from normal distribution RandomNormal The standard deviation of the normal distribution. If not specified, default is 1. The data type for the elements of the output tensor. Default is TensorProto::FLOAT. The mean of the normal distribution. If not specified, default is 0. mean Multinomial Constrain input types to float tensors. Constrain output types to integral tensors. Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch. Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch. Number of times to sample. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32. C This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Incompatible dimensions Or xor onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\logical\defs.cc or Constrains output to boolean tensor. And and Constrains input to boolean tensor. Second input operand for the logical operator. Result tensor. First input operand for the logical operator. A {name} {broadcast_doc}
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
{broadcast_doc}
Constrains input/output to boolean tensors. Not Equal
Returns the negation of the input tensor element-wise.
equal Constrains input to integral tensors. less Less Constrains input to float tensors. Greater Xor greater Enable broadcasting broadcast
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B`.
If broadcasting is enabled, the right-hand-side argument will be broadcasted
to match the shape of left-hand-side argument. See the doc of `Add` for a
detailed description of the broadcasting rules.
Right input tensor for the logical operator. onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\logical\old.cc If set, defines the broadcast dimensions. Left input tensor for the logical operator. First operand. Second operand. should be unidirectional broadcastable to
Performs element-wise binary {name} (with Numpy-style broadcasting support).
{broadcast_doc}
This operator supports **unidirectional broadcasting** ( ); for more details please check [the doc](Broadcasting.md). division Div multiplication Mul subtraction Sub Add onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\math\defs.cc The output values with the same shape as input tensor. addition (int) default to 1; describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size The input tensor that's coerced into a 2D matrix of size (NxD) as described above.
The operator computes the {name} ({description}) values for each layer in the batch
of the given input. The input is a 2-D tensor (Tensor<float>) of size
(batch_size x input_feature_dimensions). The output tensor has the same shape
and contains the {name} values of the corresponding input.
X does not need to explicitly be a 2D vector; rather, it will be
coerced into one. For an arbitrary n-dimensional tensor
X \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
the axis provided, then X will be coerced into a 2-dimensional tensor with
dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
case where axis=1, this means the X tensor will be coerced into a 2D tensor
of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
Each of these dimensions must be matched correctly, or else the operator
will throw errors.
{description} Result, has same element type as two inputs Constrain input and output types to high-precision numeric tensors.
Relu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the rectified linear function, y = max(0, x), is applied to
the tensor elementwise.
Relu
Square root takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the square root is, y = x^0.5, is applied to
the tensor elementwise. If x is negative, then it will return NaN.
Sqrt
Ceil takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the ceil is, y = ceil(x), is applied to
the tensor elementwise.
Ceil
Floor takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the floor is, y = floor(x), is applied to
the tensor elementwise.
Floor
Reciprocal takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the reciprocal is, y = 1/x, is applied to
the tensor elementwise.
Reciprocal Constrain input and output types to all numeric tensors. Abs Neg
Absolute takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the absolute is, y = abs(x), is applied to
the tensor elementwise.
Neg takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where each element flipped sign, y = -x, is applied to
the tensor elementwise.
Constrain input and output types to signed numeric tensors.
Calculates the natural log of the given input tensor, element-wise.
The natural log of the input tensor computed element-wise The exponential of the input tensor computed element-wise Exp Elu
Calculates the exponential of the given input tensor, element-wise.
Elu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.
Coefficient of ELU default to 1.0. gamma Selu Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). LeakyRelu
Selu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the scaled exponential linear unit function,
`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
is applied to the tensor elementwise.
LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
output data (Tensor<T>) where the function `f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`, is applied to the data tensor elementwise.
Coefficient of leakage default to 0.01. slope PRelu tensor slope Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X
PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
input tensor X Z Pow Second operand, power of the exponent. Output tensor (same size as X)
Pow takes input data (Tensor<T>) and exponent Tensor, and
produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
is applied to the data tensor elementwise.
First operand, base of the exponent. The hyperbolic tangent values of the input tensor computed element-wise Tanh Log
Calculates the hyperbolic tangent of the given input tensor element-wise.
min Min
Element-wise min of each of the input tensors. All inputs and outputs must
have the same shape and data type.
List of tensors for Min max Max data_0 Output tensor. Same dimension as inputs.
Element-wise max of each of the input tensors. All inputs and outputs must
have the same shape and data type.
List of tensors for Max. Value of beta default to 0.5 HardSigmoid
HardSigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
is applied to the tensor elementwise.
Value of alpha default to 0.2
Sigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the
tensor elementwise.
Sigmoid softmax Softmax Clip normalized exponential Input tensor whose elements to be clipped Output tensor with clipped input elements Minimum value, under which element is replaced by min Maximum value, above which element is replaced by max Mean
Clip operator limits the given input within an interval. The interval is
specified with arguments 'min' and 'max'. They default to
numeric_limits::lowest() and numeric_limits::max() respectively.
Element-wise mean of each of the input tensors. All inputs and outputs must
have the same shape and data type.
List of tensors for Mean. sum Sum
Element-wise sum of each of the input tensors. All inputs and outputs must
have the same shape and data type.
List of tensors for Sum. First input does not have rank 2 Second input does not have rank 2 transA transB Softplus General Matrix multiplication:
https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A' = transpose(A) if transA else A
B' = transpose(B) if transB else B
Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
and output tensor Y has shape (M, N). A will be transposed before doing the
computation if attribute transA is non-zero, same for B and transB.
Softsign
Softplus takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
the tensor elementwise.
Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.
The softsign (x/(1+|x|)) values of the input tensor computed element-wise hardmax Hardmax LogSoftmax 1 for the first maximum value, and 0 for all others log of softmax logsoftmax N-dimensional matrix A N-dimensional matrix B Input tensors of wrong rank (0). Incompatible dimensions for matrix multiplication Gemm
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
Scalar multiplier for the product of input tensors A * B Scalar multiplier for input tensor C Whether A should be transposed Whether B should be transposed Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Output tensor of shape (M, N). Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero. tensor A * B tensor C
Calculates the sine of the given input tensor, element-wise.
The sine of the input tensor computed element-wise Dimension on which to do the sort. Default -1, which indicates the last axis TopK Constrain index tensor to int64 Number of top elements to retrieve Values Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values. Tensor of shape [a_1, a_2, ..., a_n, r] Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor k Invalid value for attribute k
Retrieve the top-K elements along a specified axis. Given an input tensor of
shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
-Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
which contains the values of the top k elements along the specified axis
-Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
contains the indices of the top k elements (original indices from the input
tensor).
Given two equivalent values, this operator uses the indices along the axis as
a tiebreaker. That is, the element with the lower index will appear first.
Invalid value for attribute axis Matrix multiply results from A * B MatMul The arctangent of the input tensor computed element-wise Atan Acos
Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
The arccosine of the input tensor computed element-wise The arcsine of the input tensor computed element-wise Asin Tan
Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
Calculates the tangent of the given input tensor, element-wise.
The tangent of the input tensor computed element-wise The cosine of the input tensor computed element-wise Cos Sin
Calculates the cosine of the given input tensor, element-wise.
If necessary the right-hand-side argument will be broadcasted to match the
shape of left-hand-side argument. When broadcasting is specified, the second
tensor can either be of element size 1 (including a scalar tensor and any
tensor with rank equal to or smaller than the first tensor), or having its
shape as a contiguous subset of the first tensor's shape. The starting of the
mutually equal shape is specified by the argument "axis", and if it is not set,
suffix matching is assumed. 1-dim expansion doesn't work yet.
For example, the following tensor shapes are supported (with broadcast=1):
shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
Attribute `broadcast=1` needs to be passed to enable broadcasting.
Performs element-wise binary {name} (with limited broadcast support).
{broadcast_doc} Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels General Matrix multiplication:
https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
Compute Y = alpha * A * B + beta * C, where input tensor A has
dimension (M X K), input tensor B has dimension (K X N), input tensor C and
output tensor Y have dimension (M X N).
If attribute broadcast is non-zero, input tensor C will be broadcasted to match
the dimension requirement. A will be transposed before doing the computation
if attribute transA is non-zero, same for B and transB.
1-D input tensor
PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`,
`f(x) = x for x >= 0`., is applied to the data tensor elementwise.
Coefficient of SELU default to 1.6732. Coefficient of SELU default to 1.0507. Input tensor of any shape, base of the exponent. Input tensor of any shape broadcastable to X shape, the exponent component. Result, has same dimensions and type as A onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\math\old.cc First operand, should share the type with the second operand. Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. consumed_inputs If set, defines the broadcast dimensions. See doc for details. Pass 1 to enable broadcasting legacy optimization attribute. Whether C should be broadcasted Input tensor C Input tensor C, can be inplace. Output tensor. Input tensor A Input tensor B dilations Attribute dilations has incorrect size auto_pad Input tensor must have atleast 2 dimensions Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each axis. auto_pad must be either SAME_UPPER, SAME_LOWER or VALID. Where SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used The output of each pooling window is divided by the number of elements exclude pad. NOTSET Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. The size of the kernel along each axis. Stride along each axis. If not present, the stride defaults to 1 along each axis. {opName} {additionalDescription} Second input tensor has wrong dimension
{name} consumes an input tensor X and applies {opName} pooling across the
the tensor according to kernel sizes, stride sizes, and pad lengths.
{opName} pooling consisting of computing the {opName} on all values of a
subset of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. The output spatial shape will be following:
```
output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
* pad_shape[i] is sum of pads along axis i
```
`auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
```
VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
```
And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
```
pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
```
{additionalDescription}
Attribute kernel_shape has incorrect size Attribute kernel_shape must be specified Attribute strides has incorrect size kernel_shape Attribute pads has incorrect size strides pooled_shape Attribute pooled_shape has incorrect length Input tensor must have at least 2 dimensions RoIs tensor must have 2 dimensions Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. p value of the Lp norm used to pool over the input data, default is 2. p
{name} consumes an input tensor X and applies Lp pooling across the
the tensor according to kernel sizes, stride sizes, and pad lengths.
Lp pooling consisting of computing the Lp norm on all values of a subset
of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. Stride along each axis. If not present, the stride defaults to 0 along each axis. count_include_pad The output of each pooling window is maximum number of elements exclude pad. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Whether include pad pixels when calculating values for the edges. average onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\nn\defs.cc W Optional 1D bias to be added to the convolution, has size of M. Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. The weight tensor that will be used in the convolutions; has size (M x C x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_IN_CHANNEL, FILTER_OUT_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...].
The convolution operator consumes an input tensor and {filter_desc}, and
computes the output. {filter_desc} RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). MaxRoiPool RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. rois spatial_scale Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. ROI pool output shape (height, width). Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling, default is 1.0f. Attribute pooled_shape must be specified
ROI {name} pool consumes an input tensor X and region of interests (RoIs) to
apply {name} pooling across each RoI, to produce output 4-D tensor of shape
(num_rois, channels, pooled_shape[0], pooled_shape[1]). {op} Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1
Global{op_type} consumes an input tensor X and applies {op} pooling across the
the values in the same channel. This is equivalent to {op_type} with kernel size
equal to the spatial dimension of input tensor. {op_type} The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks. The weight tensor that will be used in the convolutions; has size (C x M x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel Optional 1D bias to be added to the convolution, has size of C.
The convolution transpose operator consumes an input tensor and {filter_desc},
and computes the output.
If the pads parameter is provided the shape of the output is calculated via the following equation:
output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + kernel_shape[i] - pads[start_i] - pads[end_i]
output_shape can also be explicitly specified in which case pads values are auto generated using these equations:
total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + kernel_shape[i] - output_shape[i]
If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).
Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image.Otherwise the size is (N x D1 x D2 ... x Dn) a filter output_padding dilation value along each axis of the filter. If not present, the dilation defaults to 1 along each axis. number of groups input channels and output channels are divided into, default is 1. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. The shape of the convolution kernel. If not present, should be inferred from input W. The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C. var The bias as a 1-dimensional tensor of size C to be applied to the output. The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C. momentum The scale as a 1-dimensional tensor of size C to be applied to the output. epsilon Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f. spatial The epsilon value to use to avoid division by zero, default is 1e-5f.
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:
Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)
If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1. lp pool GlobalLpPool GlobalAveragePool GlobalMaxPool Input matrix Matrix after normalization InstanceNormalization
Given a matrix, apply Lp-normalization along the provided axis.
The input 1-dimensional bias tensor of size C. The output tensor of the same shape as input.
Carries out instance normalization as described in the paper
https://arxiv.org/abs/1607.08022.
y = scale * (x - mean) / sqrt(variance + epsilon) + B,
where mean and variance are computed per instance per channel.
The input 1-dimensional scale tensor of size C. saved_var BatchNormalization saved_mean Saved variance used during training to speed up gradient computation. The running variance after the BatchNormalization operator. Saved mean used during training to speed up gradient computation. The output tensor of the same shape as X. The running mean after the BatchNormalization operator. Invalid value( A tensor of rank >= axis.
Flattens the input tensor into a 2D matrix. If input tensor has shape
(d_0, d_1, ... d_n) then the output will have shape
(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
) for attribute 'axis' mask Dropout The output. The output mask. The input data as Tensor. data (float, default 0.5) the ratio of random dropout ratio LpNormalization
Dropout takes one input data (Tensor<float>) and produces two Tensor outputs,
output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in
test mode or not, the output Y will either be a random dropout, or a simple
copy of the input. Note that our implementation of Dropout does scaling in
the training phase, so during testing nothing needs to be done.
(int64, default -1) the axis on which to apply normalization, -1 mean last axis. (int64, default 2) the order of the normalization, only 1 or 2 are supported. Constrain input and output types to float tensors. LRN Default to 1.0f Output tensor, which has the shape and type as input tensor Scaling parameter, default is 1e-4f. The exponent, default is 0.75f The number of channels to sum over size Flatten
Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
It normalizes over local input regions.
The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor
of shape (N x C x D1 x D2, ..., Dk), its region is
{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2),
where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output. (Default to 1) Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing. The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing. The input 4-dimensional tensor of shape NCHW. The output 4-dimensional tensor of the same shape as X. If set to nonzero, run spatial batch normalization in test mode, default is 0. is_test onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\nn\old.cc
GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
the values in the same channel. This is equivalent to LpPool with kernel size
equal to the spatial dimension of input tensor. p value of the Lp norm used to pool over the input data, default is 2.0. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.
LpPool consumes an input tensor X and applies Lp pooling across the
the tensor according to kernel sizes, stride sizes, and pad lengths.
Lp pooling consisting of computing the Lp norm on all values of a subset
of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. Stride along each axis. Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. The output mask. If is_test is nonzero, this output is not filled.
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:
Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)
The output 4-dimensional tensor of the same shape as input. (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. Saved mean used during training to speed up gradient computation. Should not be used for testing. Saved variance used during training to speed up gradient computation. Should not be used for testing. ReduceMin ReduceSum ReduceMax onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\reduction\defs.cc Reduced output tensor. reduced Keep the reduced dimension or not, default 1 mean keep reduced dimension. An input tensor.
Computes the {name} of the input tensor's element along the provided axes. The resulted
tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then
the resulted tensor have the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy default keepdims to
False instead of True. A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. keepdims axes The axis in which to compute the arg indices. Default is 0. Reduced output tensor with integer data type. ReduceL2
Computes the indices of the {name} elements of the input tensor's element along the
provided axis. The resulted tensor has the same rank as the input if keepdims equal 1.
If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
The type of the output tensor is integer. ReduceL1 L2 norm ReduceLogSumExp L1 norm ReduceLogSum log sum exponent ReduceProd log sum ReduceMean product sum square ReduceSumSquare ArgMax ArgMin Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. activation_beta Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01. activation_alpha Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Number of neurons in the hidden layer hidden_size First input tensor must have rank 3 reverse bidirectional forward direction The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`. One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. activations Constrain seq_lens to integer tensor.
Computes an one-layer simple RNN. This operator is usually supported
via some custom implementation such as CuDNN.
Notations:
`X` - input tensor
`i` - input gate
`t` - time step (t-1 means previous time step)
`Wi` - W parameter weight matrix for input gate
`Ri` - R recurrence weight matrix for input gate
`Wbi` - W parameter bias vector for input gate
`Rbi` - R parameter bias vector for input gate
`WBi` - W parameter weight matrix for backward input gate
`RBi` - R recurrence weight matrix for backward input gate
`WBbi` - WR bias vectors for backward input gate
`RBbi` - RR bias vectors for backward input gate
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Tanh):
- Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. Y_h initial_h A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. sequence_lens Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`. Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified. clip Couple the input and forget gates if 1, default 0. input_forget
Computes an one-layer LSTM. This operator is usually supported via some
custom implementation such as CuDNN.
Notations:
`X` - input tensor
`i` - input gate
`o` - output gate
`f` - forget gate
`c` - cell gate
`t` - time step (t-1 means previous time step)
`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
`P[iof]` - P peephole weight vector for input, output, and forget gates
`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
`PB[iof]` - P peephole weight vector for backward input, output, and forget gates
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
- it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
- ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
- ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
- Ct = ft (.) Ct-1 + it (.) ct
- ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
- Ht = ot (.) h(Ct)
A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 GRU The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. linear_before_reset
Computes an one-layer GRU. This operator is usually supported via some custom
implementation such as CuDNN.
Notations:
`X` - input tensor
`z` - update gate
`r` - reset gate
`h` - hidden gate
`t` - time step (t-1 means previous time step)
`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh):
- zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
- rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
- ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
- ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh) + Wbh) # when linear_before_reset != 0
- Ht = (1 - zt) (.) ht + zt (.) Ht-1
A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. RNN onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\rnn\defs.cc R The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. Y_c P LSTM initial_c The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0. The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
Computes an one-layer LSTM. This operator is usually supported via some
custom implementation such as CuDNN.
Notations:
`X` - input tensor
`i` - input gate
`o` - output gate
`f` - forget gate
`c` - cell gate
`t` - time step (t-1 means previous time step)
`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
`P[iof]` - P peephole weight vector for input, output, and forget gates
`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
`PB[iof]` - P peephole weight vector for backward input, output, and forget gates
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
- it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi)
- ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf)
- ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc)
- Ct = ft (.) Ct-1 + it (.) ct
- ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo)
- Ht = ot (.) h(Ct)
onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\rnn\old.cc
Computes an one-layer simple RNN. This operator is usually supported
via some custom implementation such as CuDNN.
Notations:
`X` - input tensor
`i` - input gate
`t` - time step (t-1 means previous time step)
`Wi` - W parameter weight matrix for input gate
`Ri` - R recurrence weight matrix for input gate
`Wbi` - W parameter bias vector for input gate
`Rbi` - R parameter bias vector for input gate
`WBi` - W parameter weight matrix for backward input gate
`RBi` - R recurrence weight matrix for backward input gate
`WBbi` - WR bias vectors for backward input gate
`RBbi` - RR bias vectors for backward input gate
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Tanh):
- Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi)
A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Computes an one-layer GRU. This operator is usually supported via some custom
implementation such as CuDNN.
Notations:
`X` - input tensor
`z` - update gate
`r` - reset gate
`h` - hidden gate
`t` - time step (t-1 means previous time step)
`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
`H` - Hidden state
`num_directions` - 2 if direction == bidirectional else 1
Activation functions:
Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha*Tanh(beta*x)
HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
Elu(x) - x if x >= 0 else alpha*(e^x - 1)
Softsign(x) - x/(1 + |x|)
Softplus(x) - log(1 + e^x)
Equations (Default: f=Sigmoid, g=Tanh):
- zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)
- rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)
- ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0
- ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0
- Ht = (1 - zt) (.) ht + zt (.) Ht-1
The sequence output for the hidden is optional if 0. Default 0. output_sequence foward Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.
The operator casts the elements of a given input tensor to a data type
specified by the 'to' argument and returns an output tensor of the same size in
the converted type. The 'to' argument must be one of the data types specified
in the 'DataType' enum field in the TensorProto message.
NOTE: Casting to and from strings is not supported yet.
Shape
Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
Input tensor can be of arbitrary type. Constrains output to int64 tensor.
Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
Shape of the input tensor Reshaped data. reshaped
Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). Specified shape for output. Constrain output types. Casting to strings and complex are not supported. onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\tensor\defs.cc Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from strings and complex are not supported. The data type to which the elements of the input tensor are cast.Strictly must be one of the types from DataType enum in TensorProto Input tensor to be cast. Split a tensor into a list of tensors, along the specified
'axis'. Lengths of the parts can be specified using argument 'split'.
Otherwise, the tensor is split to equal sized parts.
split Constrain output types to any tensor type. Concat Concatenated tensor concat_result List of tensors for concatenation inputs Which axis to concat on Concatenate a list of tensors into a single tensor All inputs to Concat must have same rank Dimension mismatch Size Required attribute axis is missing Total number of elements of the input tensor Constrains output to int64 tensor, which should be a scalar though. Ending indices (exclusive) of corresponding axis in axes` Sliced data tensor. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Attribute axes has incorrect length Tensor of data to extract slices from. ends Incorrect or missing attribute value for starts and ends
Produces a slice of the input tensor along multiple axes. Similar to numpy:
https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Slices uses `axes`, `starts` and `ends` attributes to specify the start and end
dimension for each axis in the list of axes, it uses this information to
slice the input `data` tensor. If a negative value is passed for any of the
start or end indices, it represent number of elements before the end of that
dimension. If the value passed to start or end is larger than the `n` (the
number of elements in this dimension), it represents `n`. For slicing to the
end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
Example 1:
data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
result = [
[5, 6, 7],
]
Example 2:
data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
starts = [0, 1]
ends = [-1, 1000]
result = [
[2, 3, 4],
]
starts length of each output Split One or more outputs forming list of tensors after splitting Which axis to split on (defaults to 0) Invalid value of attribute 'axis' The tensor to split Tensor of rank q + (r - 1). Constrain input and output types to any tensor type. Tensor of int32/int64 indices, of any rank q. indices Tensor of rank r >= 1. Tind
Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
them in an output tensor of rank q + (r - 1).
Example 1:
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
indices = [
[0, 1],
[1, 2],
]
output = [
[
[1.0, 1.2],
[2.3, 3.4],
],
[
[2.3, 3.4],
[4.5, 5.7],
],
]
Example 2:
data = [
[1.0, 1.2, 1.9],
[2.3, 3.4, 3.9],
[4.5, 5.7, 5.9],
]
indices = [
[0, 2],
]
axis = 1,
output = [
[
[1.0, 1.9],
[2.3, 3.9],
[4.5, 5.9],
],
]
Which axis to gather on, defaults to 0. Negative value means counting dimensions from the back. Accepted range in [-r, r-1] transposed Transpose A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Transposed output. Invalid attribute perm { }, input shape = {
Transpose the input tensor similar to numpy.transpose. For example, when
perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape
will be (2, 1, 3).
perm Attribute value for pads is required Attribute pads has incorrect length Unsqueeze
Given `data` tensor, pads, mode, and value.
Example:
Insert 0 pads to the beginning of the second dimension.
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
pads = [0, 2, 0, 0]
output = [
[
[0.0, 0.0, 1.0, 1.2],
[0.0, 0.0, 2.3, 3.4],
[0.0, 0.0, 4.5, 5.7],
],
]
Original tensor expanded
Insert single-dimensional entries to the shape of a tensor.
Takes one required argument `axes`, a list of dimensions that will be inserted.
Dimension indices in `axes` are as seen in the output tensor. For example:
Given a tensor such that tensor with shape [3, 4, 5], then
Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
List of positive integers, indicate the dimensions to be inserted squeezed Squeeze Tensors with at least max(dims) dimensions. Reshaped tensor with same data as input.
Remove single-dimensional entries from the shape of a tensor.
Takes a parameter `axes` with a list of axes to squeeze.
If an axis is selected with shape entry not equal to one, an error is raised.
List of positive integers, indicate the dimensions to squeeze. Constrain indices to integer types Gather Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize]. SpaceToDepth Blocks of [blocksize, blocksize] are moved. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width. Blocksize must be positive Input tensor must be 4-dimensional SpaceToDepth rearranges blocks of spatial data into depth. More specifically,
this op outputs a copy of the input tensor where values from the height and width dimensions
are moved to the depth dimension.
blocksize Tensor after padding. Pad One float, indicates the value to be filled, default is 0 Input tensor. Three modes: constant(default), reflect, edge mode List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. constant Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) N-D tensor scales nearest
Upsample the input tensor.
Each dimension value of the output tensor is:
output_dimension = floor(input_dimension * scale).
The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'. Constrain input and output's types to float tensors. Constrain repeat's type to int64 tensors. repeats Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i] Input tensor of any shape. 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions. DepthToSpace Constructs a tensor by tiling a given tensor.
This is the same as function `tile` in Numpy, but no broadcast.
For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
the input tensor where values from the depth dimension are moved in spatial blocks to the height
and width dimensions.
Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. Constrain input and output types to all tensor types. Identity operator Tensor to copy input into. N-D tensor after resizing Constrain input/output types to all tensor types.
Upsample the input tensor.
The width and height of the output tensor are:
output_width = floor(input_width * width_scale),
output_height = floor(input_height * height_scale).
Example:
Given `data` tensor, width_scale, height_scale, mode,
Upsample the input 4-D tensor in nearest mode:
data = [[[
[1, 2],
[3, 4]
]]]
width_scale = 2
height_scale = 2
mode = "nearest"
output = [[[
[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]
]]]
Repeat the elements of a tensor along an axis.
Reshape the input tensor similar to numpy.reshape.
It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). New shape List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. paddings Which axis to split on
Given `data` tensor, paddings, mode, and value.
Example:
Insert 0 paddings to the beginning of the second dimension.
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
paddings = [0, 0, 2, 0]
output = [
[
[0.0, 0.0, 1.0, 1.2],
[0.0, 0.0, 2.3, 3.4],
[0.0, 0.0, 4.5, 5.7],
],
]
Optional list of output lengths (see also arg 'split') outputs... Which axis to concat on. Default value is 1. Split a tensor into a list of tensors, along the specified
'axis'. The lengths of the split can be specified using argument 'axis' or
optional second input blob to the operator. Otherwise, the tensor is split
to equal sized parts.
onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\tensor\old.cc 4-D tensor, [N,C,H,W] 4-D tensor after resizing, [N,C,H,W] height_scale Two interpolation modes: nearest(default), bilinear width_scale The scale along height dimension. It takes value greater than or equal to 1. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. Axis along which to repeat. Output tensor of same shape and type as input. Number of repeated copies to make of the input tensor. tiles Data to be binarized Binarized output data onecoreuap\windows\windowsai\opensource\onnx\onnx\defs\traditionalml\defs.cc
Makes values 1 or 0 based on a single threshold.
allowed types. ArrayFeatureExtractor The index values to select as a int64 tensor Selected output data as an array
Select a subset of the data X based on the indices provided Y.
Data to be selected what type of tensor to cast the input to, enum 'TO_FLOAT','TO_STRING','TO_INT64', default is 'TO_FLOAT' DENSE map(int64, float) allowed output types. allowed input types. map(int64, string) Data to be encoded encoded output data TO_INT64 TO_STRING cast_to TO_FLOAT Binarizer
Converts a map to a tensor. Map key must be int64 and the values will be ordered
in ascending order based on this key. Supports dense packing or sparse packing.
If using sparse packing, the key cannot exceed the max_map-1 value.
Values greater than this are set to 1, else set to 0 threshold string value to use if the int is not in the map default_string cats_int64s _Unused cats_strings ints part of the input map, must be same size and the strings allowed types. strings part of the input map, must be same size and the ints Input data Output data, if strings are input, then output is int64s, and vice versa. CastMap
Convert strings to int64s and vice versa.
Takes in a map to use for the conversion.
The index position in the strings and ints repeated inputs
is used to do the mapping.
Each instantiated operator converts either ints to strings or strings to ints.
This behavior is triggered based on which default value is set.
If the string default value is set, it will convert ints to strings.
If the int default value is set, it will convert strings to ints.
if map_form packing is SPARSE, what is the total length of each output in N (max index value) max_map whether to only output as many values as are in the input, or position the input based on using the key of the map as the index of the output (sparse), enum 'DENSE', 'SPARSE', default is 'DENSE' map_form
Replace imputs that equal replaceValue/s with imputeValue/s.
All other inputs are copied to the output unchanged.
This op is used to replace missing values where we know what a missing value looks like.
Only one of imputed_value_floats or imputed_value_int64s should be used.
The size can be 1 element, which will be reused, or the size of the feature set F in input N,F
Data to be imputed int64_vocabulary DictVectorizer The vocabulary vector string_vocabulary map(string, float) map(string, double) map(string, int64) map(int64, double) The input dictionary The tensor CategoryMapper
Uses an index mapping to convert a dictionary to an array.
The output array will be equal in length to the index mapping vector parameter.
All keys in the input dictionary must be present in the index mapping vector.
For each item in the input dictionary, insert its value in the output array.
The position of the insertion is determined by the position of the item's key
in the index mapping. Any keys not present in the input dictionary, will be
zero in the output array. Use either string_vocabulary or int64_vocabulary, not both.
For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``,
then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``.
int value to use if the string is not in the map default_int64 LabelEncoder
Linear classifier prediction (choose class)
Default value if not in class list as int64 Default value if not in class list as string List of class label strings to be encoded as int64s classes_strings
Convert class label to their integral type and vice versa.
In both cases the operator is instantiated with the list of class strings.
The integral value of the string is the index position in the list.
Encoded output data replaced_value_int64 Imputer replaced_value_float imputed_value_int64s imputed_value_floats value that needs replacing Imputed output data value to change to enum NONE, SOFTMAX, LOGISTIC, SOFTMAX_ZERO, PROBIT post_transform classlabels_ints NONE class labels if using string labels class labels if using int labels whether to do OvR or multinomial (0=OvR and is default) multi_class weights of the intercepts (if used) intercepts weights of the model(s) coefficients Classification outputs (one class per example Classification scores (N,E - one score for each class, for each example classlabels_strings Data to be classified list of categories, strings if true and category is not present, will return all zeros, if false and missing category, operator will return false. Default is true (1).
Replace the inputs with an array of ones and zeros, where the only
one is the zero-based category that was passed in. The total category count
will determine the length of the vector. For example if we pass a
tensor with a single value of 4, and a category count of 8, the
output will be a tensor with 0,0,0,0,1,0,0,0 .
This operator assumes every input in X is of the same category set
(meaning there is only one category count).
If the input is a tensor of float, int32, or double, the data will be cast
to int64s and the cats_int64s category list will be used for the lookups.
list of categories, ints norm Normalizer MAX enum 'MAX', 'L1', 'L2' LinearRegressor
Normalize the input. There are three normalization modes,
which have the corresponding formulas:
Max .. math:: max(x_i)
L1 .. math:: z = ||x||_1 = \sum_{i=1}^{n} |x_i|
L2 .. math:: z = ||x||_2 = \sqrt{\sum_{i=1}^{n} x_i^2}
total number of regression targets (default is 1) targets Data to be regressed Regression outputs (one per target, per example LinearClassifier
Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence
and M results will be output for each input n in N.
Coefficients are of the same length as an n, and coefficients for each target are contiguous.
Intercepts are optional but if provided must match the number of targets.
kernel_type Tensor of 3 elements containing gamma, coef0, degree in that order. Zero if unused for the kernel. LINEAR enum LINEAR, POLY, RBF, SIGMOID, defaults to linear Classification outputs (one class per example) Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class otherwise they are raw scores. Scaler
SVM classifier prediction
first, offset by this, must be same length as scale offset Scaled output data second, multiply by this, can be length of features or length 1
Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.
Data to be scaled zeros OneHotEncoder one_class support vector coefficients chosen support vectors bool whether the regression is a one class svm or not, defaults to false
SVM regression prediction and one-class svm anomaly detection
Regression outputs (one score per target per example) post eval transform for score, enum NONE, SOFTMAX, LOGISTIC, SOFTMAX_ZERO, PROBIT SVMClassifier prob_b rho prob_a Second set of probability coefficients, must be same size as prob_a, if these are provided then output Z are probability estimates. support_vectors First set of probability coefficients kernel_params vectors_per_class nodes_values nodes_hitrates nodes_featureids thresholds to do the splitting on for this node. nodes_nodeids feature id for this node nodes_treeids node id for this node, node ids may restart at zero for each tree (but not required). N,E the class score for each class, for each point tree id for this node Input N,F N, Top class for each point SVMRegressor
Tree Ensemble classifier. Returns the top class for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and
it is assumed they are the same length, and an index i will decode the
tuple across these inputs. Each node id can appear only once
for each tree id.
All fields prefixed with class_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by
the associated class_weights index.
It is expected that either classlabels_strings or classlabels_int64s
will be passed and the class_ids are an index into this list.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
number of support vectors n_supports the weight for the class in class_id class_weights index of the class list that this weight is for class_ids node id that this weight is for class_nodeids tree that this node is in class_treeids for each node, decide if the value is missing (nan) then use true branch, this field can be left unset and will assume false for all nodes nodes_missing_value_tracks_true child node if expression is false nodes_falsenodeids child node if expression is true nodes_truenodeids enum of behavior for this node 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' nodes_modes total number of targets n_targets the weight for the class in target_id target_weights target_nodeids target_ids enum of behavior for this node as enum of BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF target_treeids node id for this node, node ids must restart at zero for each tree and increase sequentially. popularity of the node, used for performance and may be omitted
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and
it is assumed they are the same length, and an index i will decode the
tuple across these inputs. Each node id can appear only once
for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by
the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
N classes base_values TreeEnsembleClassifier classlabels_int64s base values for classification, added to final class score, size must be the same as classes or can be left unassigned (assumed 0) ordered input tensors Output array, in same order as Input, as floats ZipMap
Concatenates input features into one continuous output of floats.
inputdimensions is the size of each input feature.
Inputs will be written to the output in the order of the input arguments.
If an input tensor is shorter than its matching input dimension the output will be padded with zeros.
If an input tensor is longer than its matching input dimension, the additional input will be ignored.
Input tensors must all be of the same type. Use Cast as needed.
Input tensors must all be of the same batch size.
keys if using string keys keys if using int keys seq(map(string, float)) seq(map(int64, float)) The input values The output map TreeEnsembleRegressor
Makes a map from the input and the attributes.
Assumes input 0 are the values, and the keys are specified by the attributes.
Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
Input 0 may have a batch size larger than 1,
but each input in the batch must be the size of the keys specified by the attributes.
The order of the input and attributes determines the key-value mapping.
aggregate_function base values for regression, added to final score, size must be the same as n_outputs or can be left unassigned (assumed 0) SUM enum, how to aggregate leaf values within a target, AVERAGE,SUM,MIN,MAX inputdimensions FeatureVectorizer Allowed input types the size of each input in the input list
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is not output of any previous nodes. ' of node:
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nљ<OA,f{Nonecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\executionprovider.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\gpuevent.h DML CPU onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\bucketizedbufferallocator.cpp DML allocator onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\pooleduploadheap.cpp fused_activation_domain fused_activation_since_version fused_activation fused op ( onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\graphtransformer.cpp ' ) + ( Transformer for DML execution provider onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\dmlcommon.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\readbackheap.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\dmlcommandrecorder.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\d3d12commandrecorder.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\commandqueue.cpp fused_gamma fused_alpha fused_beta FusedMatMul onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\operatorregistration.cpp FusedMeanVarianceNormalization FusedGemm FusedInstanceNormalization FusedBatchNormalization FusedConv FusedConvTranspose ~ ~ o
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Fused fused_ onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperator.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorgather.cpp gNayonecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorgemm.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorelementwise.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorconvolution.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorreduce.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorpooling.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatoractivation.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorbatchnormalization.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorsplit.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatormemcpy.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorroipooling.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorrecurrentneuralnetwork.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorflatten.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorlpnormalization.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorrandomnormal.cpp NN BILINEAR onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorupsample2d.cpp bilinear nn NEAREST onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorinstancenormalization.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatormeanvariancenormalization.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatormatmul.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorslice.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatoraffine.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorreshape.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatordepthtospace.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorneg.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorrandomuniform.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorunsqueeze.cpp edge reflect onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorpadding.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorcast.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatortopk.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatoridentity.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorspacetodepth.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorcrop.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatortile.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorlocalresponsenormalization.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatormultinomial.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorconcat.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorsqueeze.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatortranspose.cpp onecoreuap\windows\windowsai\engine\dmlexecutionprovider\src\operators\dmloperatorvaluescale2d.cpp onecoreuap\windows\windowsai\common\operatorauthorhelper\operatorhelper.cpp kernels SAME_UPPER VALID system unknown error * address family not supported address in use address not available already connected argument list too long argument out of domain function not supported host unreachable identifier removed illegal byte sequence inappropriate io control operation interrupted invalid argument invalid seek io error is a directory message size network down network reset network unreachable no buffer space no child process bad address bad file descriptor bad message broken pipe connection aborted connection already in progress connection refused connection reset cross device link destination address required device or resource busy directory not empty executable format error file exists file too large filename too long not supported operation canceled operation in progress operation not permitted operation not supported operation would block owner dead permission denied protocol error protocol not supported read only file system resource deadlock would occur resource unavailable try again result out of range state not recoverable stream timeout no link no lock available no message available no message no protocol option no space on device no stream resources no such device or address no such device no such file or directory no such process not a directory not a socket not a stream not connected not enough memory text file busy timed out too many files open in system too many files open too many links too many symbolic link levels value too large wrong protocol type
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@' d A' e ?' f 5' g ' E' j M' k F' l 7' m ' Q' n 4' p ' &' H' q (' 8' s O' & B' t D' u C' v G' w :' { I' ~ 6' =' ;' 9' L' 3' $ bad exception session_logger != nullptr onecoreuap\windows\windowsai\engine\lotus\lotus\core\common\profiler.cc Lotus::Profiling::Profiler::StartProfiling Lotus::Profiling::Profiler::EndTimeAndRecordEvent Maximum number of events reached, could not record profile event. %s:%d GetFileSizeEx %ls fail, errcode = %d %s:%d open file %ls fail, errcode = %d %s:%d ReadFileEx %ls fail, errcode = %d %s:%d READ file %ls fail, file size too long GetSystemTimePreciseAsFileTime k e r n e l 3 2 . d l l unknown error inside FileExists is not a regular file Lotus::`anonymous-namespace'::WindowsEnv::GetNumCpuCores Fatal error: 0 count processors from GetSystemInfo Fatal error: 0 count processors from GetLogicalProcessorInformation onecoreuap\windows\windowsai\engine\lotus\lotus\core\platform\windows\env.cc input path too long file name is nullptr 'out' cannot be NULL MultiByteToWideChar error onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\tensor.h != DataTypeImpl::GetType<T>() == dtype_ Tensor type mismatch. Lotus::Tensor::MutableData Lotus::Tensor::Data Empty dimensions for input tensor Could not infer data type from input tensor with data type Lotus::RandomNormal::RandomNormal info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\generator\random.h Invalid dtype of info.GetAttr<int64_t>("dtype", &dtype).IsOK() info.GetAttrs<int64_t>("shape", shape).IsOK() onnx::TensorProto::DataType_IsValid(dtype_) && dtype_ != onnx::TensorProto::UNDEFINED info.GetAttr<float>("high", &high_).IsOK() Lotus::RandomNormalLike::RandomNormalLike info.GetAttr<float>("low", &low_).IsOK() Lotus::RandomUniform::RandomUniform info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() Lotus::RandomUniformLike::RandomUniformLike onnx::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != onnx::TensorProto::UNDEFINED Lotus::Multinomial::Multinomial num_classes is < 1 batch_size is < 1 Invalid data type of num_samples is < 1 Lotus::RandomNormalCompute FLOAT16 is not supported Lotus::RandomUniformCompute onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\generator\random.cc ) != new size ( shape_.Size() == new_shape.Size() Tensor size ( Lotus::Tensor::Reshape Lotus::TopK<float>::TopK op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() k_temp > 0 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\top_k.h op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() k <= dims.size() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\top_k.cc Lotus::SizeToDim k argment [ ] should not be greater than last dim [ axis is not in valid range [- Lotus::HandleNegativeAxis axis >= -tensor_rank && axis <= tensor_rank - 1 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\common.h Not satsified: M_ == 1 && N_ == 1
Not satsified: num_dims_with_pad == num_output_dims
Not satsified: num_dims_with_pad - 2 == num_output_dims
Not satsified: num_dims_with_pad - 1 == num_output_dims
Not satsified: K_ == right_shape[0]
Lotus::MatMulComputeHelper::Compute Not satsified: left_num_dims >= 1 && right_num_dims >= 1
onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\matmul_helper.h Not satsified: K_ == right_shape[right_num_dims - 2]
MatMul dimension mismatch Not satsified: left_padded_dims_[idx_dim] == 1
left operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1
right operand cannot broadcast on dim . N= , D= Invalid axis provided. Hardmax inputs N, D and N * D must be < Lotus::Hardmax<float>::Hardmax axis_ >= 0 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\hardmax.h left.NumDimensions() == 2 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\gemm_helper.h Lotus::GemmHelper::GemmHelper N: right.NumDimensions() == 2 GEMM: Dimension mismatch, W: K: M_ > 0 && N_ > 0 && K_ > 0 Gemm: Invalid bias shape for broadcast Lotus::Gemm<float,float,float,float>::Gemm info.GetAttr<int64_t>("transA", &temp).IsOK() info.GetAttr<int64_t>("transB", &temp).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\gemm.h info.GetAttr<float>("beta", &beta_).IsOK() info.GetAttr<float>("alpha", &alpha_).IsOK() Attempting to broadcast an axis by a dimension other than 1. by Lotus::BroadcastIterator::Init axis == 1 || axis == largest Lotus::BroadcastIterator::Append onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\element_wise_ops.cc input_count >= 1 Must have 1 or more inputs All inputs must have the same shape Lotus::Sum<float>::Compute data_n.Shape() == shape data_1.Shape() == shape Lotus::Min<float>::Compute inputCount >= 1 Lotus::Mean<float>::Compute Lotus::Max<float>::Compute Lotus::Affine<float>::Affine info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\element_wise_ops.h Lotus::Clip<float>::Clip info.GetAttr<T>("max", &max_).IsOK() info.GetAttr<T>("min", &min_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\math\clip.h SoftmaxCPU inputs N, D and N * D must be < onecoreuap\windows\windowsai\engine\lotus\lotus\core\framework\op_kernel.h classlabels_strings_.empty() ^ classlabels_int64s_.empty() Must provide classlabels_strings or classlabels_int64s but not both. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\zipmap.cc Lotus::ML::ZipMapOp::ZipMapOp Zipmap only supports 1D or 2D input tensors Zipmap does not support empty dim count Input features_per_batch[ ] != number of classlabels[ execution_frame_->GetOrCreateMLValue<T>(output_arg_index, parameters, ret).IsOK() LEAF BRANCH_LEQ BRANCH_GTE BRANCH_LT BRANCH_EQ BRANCH_GT SOFTMAX LOGISTIC AVERAGE SOFTMAX_ZERO MIN target_nodeids_.size() == target_weights_.size() target_nodeids_.size() == target_ids_.size() nodes_id_size == nodes_values_.size() nodes_id_size == nodes_featureids_.size() nodes_id_size == nodes_truenodeids_.size() nodes_id_size == nodes_modes_.size() (nodes_id_size == nodes_hitrates_.size()) || (0 == nodes_hitrates_.size()) nodes_id_size == nodes_falsenodeids_.size() base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_) it != parents.end() treeindex evaluated to a negative value, which should not happen. Input shape needs to be at least a single dimension. Lotus::ML::TreeEnsembleRegressor<float>::TreeEnsembleRegressor info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK() !nodes_treeids_.empty() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\treeregressor.cc Lotus::ML::TreeEnsembleClassifier<float>::TreeEnsembleClassifier class_nodeids_.size() == class_ids_.size() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\tree_ensemble_classifier.cc Lotus::ML::TreeEnsembleClassifier<double>::Initialize Lotus::ML::TreeEnsembleClassifier<float>::ProcessTreeNode Lotus::ML::TreeEnsembleClassifier<__int64>::Initialize Lotus::ML::TreeEnsembleClassifier<double>::ProcessTreeNode Lotus::ML::TreeEnsembleClassifier<int>::Initialize Lotus::ML::TreeEnsembleClassifier<__int64>::ProcessTreeNode Lotus::ML::TreeEnsembleClassifier<int>::ProcessTreeNode nodes_nodeids_.size() == nodes_featureids_.size() class_nodeids_.size() == class_weights_.size() nodes_nodeids_.size() == nodes_values_.size() nodes_nodeids_.size() == nodes_modes_names_.size() nodes_nodeids_.size() == nodes_falsenodeids_.size() nodes_nodeids_.size() == nodes_truenodeids_.size() std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; }) (nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) Lotus::ML::TreeEnsembleClassifier<double>::TreeEnsembleClassifier X dims is empty. Lotus::ML::TreeEnsembleClassifier<int>::TreeEnsembleClassifier Lotus::ML::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifier base_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size() Lotus::ML::TreeEnsembleClassifier<float>::Initialize treeindex >= 0 Invalid mode of value: onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\svmclassifier.h RBF POLY info.GetAttrs<float>("rho", rho_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\svmregressor.cc Lotus::ML::SVMRegressor<float>::SVMRegressor coefficients_.size() > 0 info.GetAttrs<float>("coefficients", coefficients_).IsOK() Lotus::ML::SVMCommon<float>::SVMCommon info.GetAttrs<float>("kernel_params", kernel_params).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() Lotus::ML::SVMClassifier<__int64>::SVMClassifier Lotus::ML::SVMClassifier<double>::SVMClassifier Lotus::ML::SVMCommon<int>::SVMCommon Lotus::ML::SVMClassifier<int>::SVMClassifier Lotus::ML::SVMCommon<double>::SVMCommon Lotus::ML::SVMCommon<__int64>::SVMCommon Lotus::ML::SVMClassifier<float>::SVMClassifier proba_.size() == probb_.size() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\svmclassifier.cc !scale_.empty() Empty scale in attributes onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\scaler.cc Lotus::ML::ScalerOp<float>::ScalerOp Scale size: ( ) != ( Invalid argument: input has empty dimensions. scale_.size() == offset_.size() Either both scale and offset can be of feature size ( ) or 1 Lotus::ML::ScalerOp<__int64>::ScalerOp Lotus::ML::ScalerOp<double>::ScalerOp Lotus::ML::ScalerOp<int>::ScalerOp Lotus::ML::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::OneHotEncoderOp Lotus::ML::OneHotEncoderOp<double>::Compute Lotus::ML::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Compute input_shape.NumDimensions() <= 2 Unknown Category and zeros = 0. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() num_categories_ > 0 Lotus::ML::OneHotEncoderOp<__int64>::OneHotEncoderOp Lotus::ML::OneHotEncoderOp<float>::OneHotEncoderOp Lotus::ML::OneHotEncoderOp<__int64>::Compute Lotus::ML::OneHotEncoderOp<double>::OneHotEncoderOp Lotus::ML::OneHotEncoderOp<float>::Compute Invalid normalize value of Lotus::ML::MakeNormalize L1 L2 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\normalizer.h Invalid input type of info.GetAttr<std::string>("norm", &norm).IsOK() Lotus::ML::Normalizer::Normalizer Lotus::ML::Normalizer::Compute onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\normalizer.cc onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\ml_common.h Lotus::ML::Normalizer::Normalize Unexpected NORMALIZE value of Lotus::ML::LinearRegressor<float>::LinearRegressor onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\linearregressor.cc info.GetAttr<int64_t>("targets", &targets_).IsOK() !coefficients_.empty() Lotus::ML::LinearClassifier<double>::LinearClassifier Lotus::ML::LinearClassifier<__int64>::LinearClassifier Lotus::ML::LinearClassifier<float>::LinearClassifier onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\linearclassifier.cc Lotus::ML::LinearClassifier<int>::LinearClassifier Lotus::ML::LabelEncoder::LabelEncoder onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\label_encoder.h info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified Lotus::ML::ImputerOp::ImputerOp Invalid type Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. Empty input dimensions. arg_num < arg_counts.size() Lotus::OpKernelContext::NumInputs . Num args is Invalid arg_num of status.IsOK() && !input_dimensions_.empty() Lotus::ML::FeatureVectorizer::FeatureVectorizer inputdimensions attribute must be provided Number of inputs ( input_count == input_dimensions_.size() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\feature_vectorizer.h ) does not match number of inputdimensions values ( Invalid input type: Lotus::ML::FeatureVectorizer::Compute onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\feature_vectorizer.cc Lotus::ML::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::DictVectorizerOp Lotus::ML::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::DictVectorizerOp Lotus::ML::DictVectorizerOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::DictVectorizerOp Lotus::ML::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::DictVectorizerOp onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\dictvectorizer.h Lotus::ML::DictVectorizerOp<__int64,float>::DictVectorizerOp info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() Lotus::ML::DictVectorizerOp<__int64,double>::DictVectorizerOp info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() num_entries == int_categories.size() Lotus::ML::CategoryMapper::CategoryMapper onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\category_mapper.h Input of string must have output of int64 Input of int64 must have output of string invalid stoll argument stoll argument out of range invalid stof argument stof argument out of range %f 0.f Unexpected CAST_TO value of cur_input == end_input || cur_input->first >= 0 Lotus::ML::CastMap::ComputeImpl Negative index values are not permitted. First entry in map has index value of onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\cast_map.cc Lotus::ML::MakeCast SPARSE Expected TO_FLOAT, TO_STRING or TO_INT64 Invalid CAST_TO value of Lotus::ML::MakePack info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected DENSE or SPARSE Invalid PACK_MAP value of info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() Lotus::ML::CastMap::CastMap onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\ml\cast_map.h Expected std::map<int64_t, float> or std::map<int64_t, std::string> Invalid input type of value: max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 Input data with index: is NaN Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices - 1 ( Invalid Y argument: num_indices = 0 ) >= stride ( Lotus::RoiPool<float>::Compute onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\roi_pool.cc R->Shape()[1] == 5 info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() Lotus::RoiPool<float>::RoiPool roi_batch_id >= 0 roi_batch_id < batch_size pooled_height_ > 0 pooled_width_ > 0 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\roi_pool.h pooled_shape.size() == 2 info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() spatial_scale_ > 0 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\autopad_type.h No kernel shape is set. Unknown AutoPadType String Lotus::StringToAutoPadType onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\pool_base.h info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK() info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() Lotus::PoolBase::PoolBase Pad should be smaller than kernel. pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] info.GetAttr<int64_t>("count_include_pad", &temp).IsOK() kernel_shape_[dim] > 0 Lotus::PoolBase::SetOutputSize Unsupported AutoPad Type. strides_.size() == kernel_shape_.size() input_shape.Size() > 0 SAME_LOWER Lotus::PoolProcessContext::init onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\pool.h Lotus::PoolBase::ComputeSizeAndPad info.GetAttr<int64_t>("p", &p_).IsOK() Input dimension cannot be less than 3. Unsupported pooling size : Lotus::LRN<float>::Compute onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\lrn.cc X->Shape().NumDimensions() == 4 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\lrn.h size_ > 0 info.GetAttr<int64_t>("size", &size).IsOK() Lotus::LRN<float>::LRN beta_ > 0.0f size_ % 2 == 1 alpha_ > 0.0f onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\lp_norm.h op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() Lotus::LpNorm<float>::LpNorm p_ == 1 || p_ == 2 Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. Mismatch between input data and scale: size of scale != input channel count onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() Lotus::InstanceNorm<float>::InstanceNorm The rank of input tensor must be >= axis onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis_ Lotus::Flatten<float>::Compute Lotus::Flatten<float>::Flatten group count is <= 0 X: pad type not supported group: X num_dims does not match W num_dims. num_input_channels: Input X must be 4-dimensional. W: Input channels is not divisible by group. filter_height: filter_number: filter number not equal to input channel number. filter_width: kernel_width: kernel_height: kernel height does not match filter height. Lotus::`anonymous-namespace'::ComputeTransposePadAndOutputShape onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\conv_transpose.h *out_size >= 0 output_height >= H Lotus::ConvTranspose<float>::ComputePadsAndOutputShape kernel width does not match filter width. Output height cannot be smaller than input height. Output width cannot be smaller than input width. output_width >= W Lotus::`anonymous-namespace'::ComputePadAndOutputShape onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\nn\conv_base.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. dilation == 1 Output channels M is not divisible by group. kernel_shape: Input channels C is not equal to kernel channels * group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. Invalid input shape: pad type not supported. Out of bound access to array kernel channels: C: Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input X: Empty dimensions Invalid input scale: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input var: 0th dimension != Lotus::PrepareForReduce onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\reduction\reduction_ops.cc Axis attribute out of range axe >= 0 && axe < (int64_t)ndim onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\rnn.h allowed_directions.find(direction_) != allowed_directions.end() info.GetAttr("direction", &direction_).IsOK() Lotus::RNN<float>::RNN activations_.size() == num_directions allowed_activations.find(activations_[direction]) != allowed_activations.end() info.GetAttrs("activations", activations_).IsOK() info.GetAttr("hidden_size", &hidden_size_).IsOK() '. Must be one of 'forward', 'reverse', or 'bidirectional'. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\rnn_helpers.h info.GetAttr("direction", &direction).IsOK() Invalid 'direction' argument of ' Lotus::Rnn::detail::MakeDirection info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 clip_ > 0.f Lotus::DeepCpuLstmOp::DeepCpuLstmOp onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\deep_cpu_lstm.h activation_func_names.size() == num_directions_ * 3 LSTM operator does not support double yet sigmoid tanh promise already satisfied no state broken promise future already retrieved Lotus::TaskThreadPool::~TaskThreadPool onecoreuap\windows\windowsai\engine\lotus\lotus\core\lib\task_thread_pool.h future Exception joining threads in TaskThreadPool: .. rows[ Calculating Xt*(W[ifoc]^T) + Ht-1*R[ifoc]) row[ A + (M * lda - (lda - K)) <= A_end B + (K * ldb - (ldb - N)) <= B_end lda >= K && ldb >= N && ldc >= N Lotus::Rnn::detail::ComputeGemm Lotus::Rnn::detail::ExecuteLambdaInParallel C + (M * ldc - (ldc - N)) <= C_end - exception running tasks: cur + size <= end Lotus::Rnn::detail::SafeRawPointer onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\deep_cpu_lstm.cc }. Actual: Invalid data type for LSTM operator of Lotus::DeepCpuLstmOp::Compute Input initial_c must have shape { Input P must have shape { Lotus::Tensor::MutableDataAsSpan ,seqno= Lotus::Tensor::DataAsSpan Processing batch [seqno= [row= Applying weights to inputs Loading weights - exception running tasks: Lotus::detail::UniDirectionalLstm<float>::LoadWeightsWithTranspose offset + size <= size_t(span.size()) Lotus::Rnn::detail::SafeRawConstPointer linear_before_reset_ == 0 linear_before_reset is not currently supported. info.GetAttr("linear_before_reset", &int64_value).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\deep_cpu_gru.h Lotus::DeepCpuGruOp::Compute Invalid data type for GRU operator of GRU operator does not support double yet activation_func_names.size() == num_directions_ * 2 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\deep_cpu_gru.cc input_num_threads_ >= 1 Lotus::detail::UniDirectionalGru<float>::SetNumThreads Calculating Xt*(Wh^T) + rt (.) Ht-1 * Rh Calculating Ht-1*R[zh] + Xt*(W[zh]^T) Lotus::DeepCpuGruOp::DeepCpuGruOp hidden_num_threads_ >= 1 Input initial_h must have shape { Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input sequence_lens must have shape { Input B must have shape { thresholdedrelu leakyrelu relu affine softsign elu hardsigmoid scaledtanh onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\rnn\rnn_helpers.cc Lotus::Rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplus Input R must have shape { Input W must have shape { Input X must have 3 dimensions only. Actual: Invalid LSTM merge activation function of Lotus::Rnn::detail::deepcpu::ActivationFuncByName Invalid activation function of Invalid GRU hidden gate activation function: Lotus::Rnn::detail::deepcpu::GruResetGateFuncByName Invalid GRU reset gate activation function: Lotus::Rnn::detail::deepcpu::LstmMergeGatesFuncByName Lotus::Rnn::detail::deepcpu::GruOutputGateFuncByName info.GetAttr<std::string>("mode", &mode).IsOK() linear Scale value should be greater than or equal to 1. info.GetAttrs<float>("scales", scales_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\upsample.h Lotus::Upsample<int>::Upsample . It can only be (default) or Lotus::Upsample<float>::Upsample scale >= 1 Lotus::Upsample<float>::StringToUpsampleMode Lotus::Upsample<int>::StringToUpsampleMode mode attribute is Upsample: liner mode upsample only support 4-D tensor with NCHW layout Upsample: input tensor's dimension does not match the scales. Upsample: input/output value's dimension mismatch Upsample: input/output value is nullptr Upsample: unexpected mode info.GetAttrs("axes", axes_).IsOK() Missing/Invalid 'axes' attribute value Lotus::Unsqueeze<float>::Unsqueeze onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\unsqueeze.h Lotus::Unsqueeze<int>::Unsqueeze 'axes' has a duplicate axis 'axes' has an out of range axis is repeated. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\transpose.h Lotus::TransposeBase::TransposeBase Attribute perm of Transpose has an invalid value. Value is outside range. 'repeat' input tensor must be 1 dimensional 'repeat' input tensor must have the same length as the 'input' tensor Lotus::Squeeze<float>::Squeeze onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\squeeze.h Lotus::Squeeze<int>::Squeeze Attribute axes is not set. Lotus::Squeeze<int>::ComputeOutputShape input_shape[i] == 1 Dimension of input must be 1 instead of Lotus::Squeeze<float>::ComputeOutputShape std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; }) Invalid value in 'split' attribute. All values must be > 0 Lotus::Split::Compute Invalid data type for Split operator of Split operator does not support double yet onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\split.h Lotus::Split::Split Missing 'axis' attribute value onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\split.cc Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape= Axis= NumOutputs= Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was input.Shape().NumDimensions() == 4 input_width % this->blocksize_ == 0 input_height % this->blocksize_ == 0 onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\space_depth_ops.cc Lotus::SpaceToDepth<float>::Compute Lotus::SpaceDepthBase::SpaceDepthBase info.GetAttr("blocksize", &blocksize_).IsOK() Attribute blocksize is not set. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\space_depth_ops.h input_depth % (blocksize_ * blocksize_) == 0 Lotus::DepthToSpace<float>::Compute Lotus::SliceSkips::SliceSkips static_cast<ptrdiff_t>(dims.size()) == extents.size() 'axes' has an axis outside of the tensor dimension count 'axes' has more entries than the 'ends' attribute holds 'axes' has more entries than the 'starts' attribute holds onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\utils.h Invalid 'starts' attribute value Lotus::SliceIterator<float>::SliceIterator static_cast<ptrdiff_t>(dims.size()) == starts.size() && static_cast<ptrdiff_t>(dims.size()) == extents.size() 'starts' and 'ends' values resulted in a negative dimension Invalid 'ends' attribute value onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\slice.h Lotus::Slice<float>::Slice Attribute shape is not set. Reshape_double Reshape_float Reshape_MLFloat16 shapeTensor->Shape().NumDimensions() == 1 A shape tensor must be a vector tensor. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\reshape.h Lotus::Reshape_1<double>::Reshape_1 Lotus::Reshape_1<union Lotus::MLFloat16>::Reshape_1 Lotus::Reshape<float>::Compute Lotus::Reshape_1<float>::Reshape_1 Lotus::Reshape<double>::Compute Lotus::Reshape<union Lotus::MLFloat16>::Compute Lotus::ReshapeHelper::ReshapeHelper requested_shape[i] >= -1 A dimension cannot be less than -1. The dimension with value zero exceeds the dimension size of the input tensor. unknown_dim == -1 At most one dimension can be -1. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\reshape_helper.h gsl::narrow_cast<int64_t>(input_shape.Size()) == size (input_shape.Size() % size) == 0 The input tensor cannot be reshaped to the requested shape. Input shape: i < input_shape.NumDimensions() Lotus::PadBase::PadBase Invalid 'mode' attribute value dimension_count * 2 == pads_.size() 'pads' attribute has wrong number of values Invalid 'pads' attribute value onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\pad.h onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\pad.cc Lotus::Pad<float>::Compute info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\mean_variance_normalization.h Lotus::MeanVarianceNormalization<float>::MeanVarianceNormalization info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() Input is expected to have four dimensions corresponding to [N,C,H,W] ) does not match the number of channels ( Input is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs<float>("bias", bias_).IsOK() onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\image_scaler.h Lotus::ImageScaler<float>::ImageScaler Bias size ( Lotus::Tensor::MutableDataRaw type == dtype_ Tensor type mismatch. Lotus::Tensor::DataRaw indices element out of data bounds, idx= Type for Tind not supported yet in Gather. onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\gather.h Lotus::Gather::Gather data_dim= ) needs to be greater than the topBorder ( ) + bottomBorder ( Attribute border needs to be specified with four border elements, got Input's width ( ) needs to be greater than the leftBorder ( ) + rightBorder ( Input's height ( ) + scale_[1] ( ) + scale_[0] ( Lotus::Concat<int>::Concat Must have valid 'axis' attribute Lotus::Concat<float>::Concat onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\concat.h has mismatched dimensions of onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\concat.cc Lotus::Concat<float>::Compute Lotus::Concat<int>::Compute data_n.Shape()[axis_index] == inputs_0.Shape()[axis_index] Non concat axis dimensions must match: Axis Casting to and from strings is not supported yet. Unexpected 'to' argument value: Cast op must have 'to' argument of type DataType onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\cast_op.cc Lotus::Cast<unsigned char>::Compute Lotus::Cast<short>::Cast Lotus::Cast<int>::Cast Lotus::Cast<__int64>::Cast Lotus::Cast<bool>::Cast Lotus::Cast<unsigned short>::Cast Lotus::Cast<unsigned int>::Cast Lotus::Cast<unsigned __int64>::Cast Lotus::Cast<signed char>::Cast Lotus::Cast<unsigned char>::Cast Lotus::CastFloat16Data Lotus::Cast<signed char>::Compute Lotus::Cast<unsigned __int64>::Compute Lotus::Cast<unsigned int>::Compute Lotus::Cast<unsigned short>::Compute Lotus::Cast<bool>::Compute Lotus::Cast<__int64>::Compute Lotus::Cast<int>::Compute Lotus::Cast<short>::Compute Attribute to is not set. Lotus::Cast<union Lotus::MLFloat16>::Compute Lotus::Cast<double>::Compute Lotus::Cast<float>::Compute Lotus::Cast<float>::Cast Lotus::Cast<double>::Cast onecoreuap\windows\windowsai\engine\lotus\lotus\core\providers\cpu\tensor\cast_op.h Lotus::Cast<union Lotus::MLFloat16>::Cast onecoreuap\windows\windowsai\engine\lotus\lotus\core\util\math_cpu.cc Lotus::Math::Im2colNd d_iter[d_i] < d_max w+b a+b r w r+ w+ a+ r+b a rb wb ab
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schemaVersion modelname isCpu
adapterLuidLowPart adapterLuidHighPart runtimeSessionId @ ModelCreation UTCReplace_AppSessionGuid
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author name domain description version usefp16
bitmappixelformat colorspacegamma nominalpixelrange metaDataCount
metaDataKeys metaDataValues runtimeSessionId @ s RegisterOperatorKernel UTCReplace_AppSessionGuid
schemaVersion name domain executionType runtimeSessionId @ 9 ProcessInfo UTCReplace_AppSessionGuid
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schemaVersion name inputCount outputCount typeConstraintCount attributeCount defaultAttributeCount m_runtimeSessionId @ z RuntimeError UTCReplace_AppSessionGuid
schemaVersion hResult errormessage file function line runtimeSessionId WinMLLogSink message.Category() (UINT32)message.Severity() message.Message().c_str() message.Location().ToString(Lotus::CodeLocation::kFilenameAndPath).c_str() WinMLLogSink message.Category() (UINT32)message.Severity() message.Message().c_str() message.Location().ToString(Lotus::CodeLocation::kFilenameAndPath).c_str() WinMLLogSink message.Category() (UINT32)message.Severity() message.Message().c_str() message.Location().ToString(Lotus::CodeLocation::kFilenameAndPath).c_str() @ WinMLLogSink message.Category() (UINT32)message.Severity() message.Message().c_str() message.Location().ToString(Lotus::CodeLocation::kFilenameAndPath).c_str() @ WinMLLogSink message.Category() (UINT32)message.Severity() message.Message().c_str() message.Location().ToString(Lotus::CodeLocation::kFilenameAndPath).c_str() % WinMLInputValidation msg.c_str() # DX12TextureToGPUTensor HRESULT n InputVideoFrame Convert
HRESULT iWidth oWidth iHeight oHeight iLuid oLuid iFormat rX rY rW rH / DX12TextureToGPUTensor Type Height Width # GPUTensorToDX12Texture HRESULT G ConvertVideoFrameWithSoftwareBitmapToCPUTensor Type Height Width / GPUTensorToDX12Texture Type Height Width G ConvertCPUTensorToVideoFrameWithSoftwareBitmap Type Height Width ; ConvertCPUTensorToVideoFrameWithSoftwareBitmap HRESULT ; ConvertVideoFrameWithSoftwareBitmapToCPUTensor HRESULT jfOLCF=: Microsoft.Windows.AI.MachineLearning sPOωGvjfOLCF=: Microsoft.Windows.AI.MachineLearning sPOωGv RSDSCHAK
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/M/M.M.萷 h ,'蠷 u $ 3ɉM89 d9E u,Mu,/ܟM M}txEĉMEEEEĉEEEt}Eu
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؈AudMPE_^[] fUQSVWٍO A+Pj ϋ% _s^[]fUQSVWٍO A+Pj ϋ'& _s^[]fU`M VWj yM V Rj 1։D$L$HNT$|$$L$XL$$T$T$ T$8L$<L$(xL$DT$$T$L$|$ T$(D$,u
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ك;LǍL$ +ƙ3VD$; YL$D$<D$T;|эL$ W; Y.MPE_^] h$ -' u `3ɉM9 x;|5 EЉE܍EEEEEEh
p Px}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E: YM EE EX ;|ߋэMW: xY;| 4 <9x
;| EЉE܍EEEEEEh0t Px}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E9 YM EE EX ;|ߋэMW9 xY;| ;| EЉE܍EEEEE荅PE썍EPx}MХu}h}M}č}؉xEEEE+Ʌy<3VM5 Y! EX ;|uMx5 xY;|蒑MP EP h$ -'p u 3ɉM9 x;|> EЉE܍EEEEEEhjp E썅Px}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉E|7 YM EfnEfp Ef ;|эMWH7 xY;|9 <9x ;| EЉE܍EEEEEEh0Et E썅Px}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉E6 YM EfnEfp Ef ;|эMWM6 xY;| ;| EЉE܍EEEEE荅PE썍EPx}MХu}h}M}č}؉xEEEE
+Ʌy<3VM5 Y" Ef ;|uM^5 xY;|MPuE觛 h$ -' u 3ɉM9 x;|5 EЉE܍EEEEEEh躱p PMx}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉EO4 YM EM E\ ;|ߋэMW 4 xY;| 4 <9x
;| EЉE܍EEEEEEh0虰t PWx}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E[3 YM EE E\ ;|ߋэMW)3 xY;| ;| EЉE܍EEEEE荅P:E썍EPSx}MХu}h}M}č}؉xEEEE:
+Ʌy<3VMs2 Y! E\ ;|uM>2 xY;|BMPUE h$ -' u 5 3ɉM9 x;|> EЉE܍EEEEEEhp E썅Px}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉ER1 YM EfnEfp Ef ;|эMW 1 xY;|9 <9x ;| EЉE܍EEEEEEh0t E썅Px}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉ES0 YM EfnEfp Ef ;|эMW 0 xY;| ;| EЉE܍EEEEE荅PE썍EPx}MХu}h}M}č}؉xEEEE+Ʌy<3VMf/ Y" Ef ;|uM0/ xY;|虆MP%EW h$ -'p u 3ɉM9 x;|5 EЉE܍EEEEEEhjp Px}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E}* YM EE EY ;|ߋэMWK* xY;| 4 <9x
;| EЉE܍EEEEEEh0It Px}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E( YM EE EY ;|ߋэMW( xY;| ;| EЉE܍EEEEE荅PE썍EPx}MХu}h}M}č}؉xEEEE+Ʌy<3VM+ Y! EY ;|uM+ xY;|MP~E谐 h$ -'А u 3ɉM9. x;| EЉE܍EEEEEEhʦp E썅P\x}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉E* YM6EfnEfp fp˱Efp±ffوfp;|ƋэMW* xY;|g <9x( ;|G EЉE܍EEEEEEh0莥t E썅PKx}MЋ𥥥hM}ĉ}؍xEEEEEEu
ك;L+Ƌy3VM܉E) YM6EfnEfp fp± Efp˱ffوfp;|ƋэMWn) xY;|+ ;| EЉE܍EEEEE荅PE썍EP-x}MХu}h}M}č}؉xEEEE+Ʌy<3VM( Y9fp± Efp˱ffوfp;|uMi( xY;|MPE h$ -' u 03ɉM9 x;|5 EЉE܍EEEEEEhڢp Pmx}EMХhM}ĉ}؍xEEEEu
ك;L+Ƌy3VM܉E`' YM EM E^ ;|ߋэMW.' xY;| 4 <9x
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cE? d S t r i n g F i l e I n f o @ 0 4 0 9 0 4 B 0 L C o m p a n y N a m e M i c r o s o f t C o r p o r a t i o n j ! F i l e D e s c r i p t i o n W i n d o w s M a c h i n e L e a r n i n g R u n t i m e n ' F i l e V e r s i o n 1 0 . 0 . 1 7 7 6 3 . 1 6 9 7 ( W i n B u i l d . 1 6 0 1 0 1 . 0 8 0 0 ) f # I n t e r n a l N a m e W i n d o w s . A I . M a c h i n e L e a r n i n g . R u n t i m e . L e g a l C o p y r i g h t M i c r o s o f t C o r p o r a t i o n . 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???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 0 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? @ 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? P 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? ` 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? p 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9`9d9h9l9p9t9x9|999999999999999999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::::::::::::::: ;;; ;;;; ; ;$;(;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<0<4<8<<<@<D<H<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x=|================================= >>> >>>> > >$>(>,>0>4>8><>@>D>H>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|????????????????????????????????? $ 000 0000 0 0$0(0,0004080<0@0D0H0L0P0T0X0\0`0d0h0l0p0t0x0|000000000000000000000000000000000 111 1111 1 1$1(1,1014181<1@1D1H1L1P1T1X1\1`1d1h1l1p1t1x1|111111111111111111111111111111111 222 2222 2 2$2(2,2024282<2@2D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222222222222222222222 333 3333 3 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|333333333333333333333333333333333 444 4444 4 4$4(4,4044484<4@4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|444444444444444444444444444444444 555 5555 5 5$5(5,5054585<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x5|555555555555555555555555555555555 666 6666 6 6$6(6,6064686<6@6D6H6L6P6T6X6\6`6d6h6l6p6t6x6|666666666666666666666666666666666 777 7777 7 7$7(7,7074787<7@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|777777777777777777777777777777777 888 8888 8 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8d8h8l8p8t8x8|888888888888888888888888888888888 999 9999 9 9$9(9,9094989<9@9D9H9L9P9T9X9\9d9l9t9|99999999999999999: :: :$:,:4:<:D:L:T:\:d:l:t:|:::::::::::::::::; ;; ;$;,;4;<;D;L;T;\;d;l;t;|;;;;;;;;;;;;;;;;;;;;;;; <<< <<<< < <d<h<p<x<<<<<<<<= ==,=<=@=P=T=X=\=d=|============ >> > >$>,>@>D>T>X>p>t>>>>>>>>>>>>?? 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D 0 00(0,0D0T0d0h0x00000000000000 11 1 14181H1L1P1T1\1t1x1|11111111111111111111 222 2222 2 242D2H2`2d2h2l2p2t2x2|22222222222222233$343D3H3`3d3|33333333333333 44 4 444D4H4X4\4p4t4444444444444444444 55 5555 5 5$5(5,5@5D5\5l5p555555555555 6666(6,6D6H6`6d6|666666666677 707@7D7H7L7P7T7X7\7`7d7h7l7p7t7x7|7777777777 88 8 888<8T8X8p8888888888889 9999 989<9@9D9H9\9`9x9|9999999999 :: :4:8:P:`:p::::::::::::::; ;;; ; ;$;(;,;0;4;8;<;@;H;L;P;T;\;t;x;;;;;;;;;;<$<(<8<H<L<d<h<<<<<<<<<<<<<<<== =0=@=D=H=`=d=|=================== >>(>,>D>T>X>p>t>>>>>>>>>>>>>>>? ?$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?d?h?|????????? 00 0 0(0@0D0\0`0h0l0p0t0x00000000000110141L1P1h1l11111111111111111 2 2 2$2<2L2P2T2\2`2d2h2l2p2t2x2|22222222222222222222222222333,3034383<3@3H3`3d3h3l3t3x3|333333333333333333334$4(4,404D4H4L4d4h444444444455 5555 54585<5@5D5X5h5x555555555555566 646D6H6`6p6t6666666666667 77$7(7,7074787<7P7T7X7\7`7d7h7l7p7t7x7|77777777777 888,808H8X8h8l8888888888888888888889 9999 9 9$9(9,9094989<9@9D9H9P9T9X9`9h9999999999 ::::0:@:D:\:l:|:::::::::::::::::;; ;$;<;L;P;h;x;|;;;;;;;;;;;;;;;;;;;; <<< <<<< <0<@<P<T<X<p<<<<<<<<<<<== =$=<=L=\=l=p=t=x=|=============== >>> > >4>D>H>L>P>T>X>l>p>>>>>>>>>>>>>> ?? ?,?0?4?L?P?h?x?|????????????????????????? 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X 00 0$0(0,00040H0L0d0t0x000000000011 1,101H1L1P1T1X1\1d1|1111111111111111 22 20242D2T2X2p2222222222222233 3 3$3,30343@3D3H3L3P3T3X3\3`3d3h3l3p3t3x3|3333333333333 4 4 484H4L4d4t444444444444444 55 5 5$5(5,545L5\5`5x5|55555555555555555 66660646L6\6`6d6h6l6t6666666666677 7777 7 7$7,70747H7L7\7`7d7|77777777778 88(8,8D8H8L8P8X8\8`8d8x8|8888888888888888 999 9 9$9(9@9P9T9l9|99999999999999 ::(:,:0:4:8:<:@:D:H:L:P:T:X:`:x::::::::::;; ;4;8;P;T;l;p;t;x;|;;;;;;;;;;;;;; <<(<,<D<H<`<d<|<<<<<<<<<<<<<<<== =4=D=T=X=l=p=x=|========================== >>>>(>,>D>H>`>p>>>>>>>>>>>>>>???0?4?L?\?l?p?t????????????????? 000 00040L0P0h0l0000000000000 111 1,1014181<1P1T1l1|11111111111111111222 2 2$2(202D2H2\2`2d2h2l2p2t222222222222222 333 333 3034383P3T3X3\3d3|333333333334 44(484<4T4d4h444444444444444445 5$5(5@5D5H5L5P5T5\5p5t5x5|55555555555555555555555666 6$6(6,6064686<6D6\6l6|6666666666666667707@7D7T7X7p7t777777777777 88 8$84888@8X8\8t8x888888888888 999 99(9,9D9H9L9T9l9p9999999999999 :::,:<:@:H:L:P:T:X:\:`:d:h:l:p:t:x:|::::::::::::::::::::; ;;;; ;$;(;,;0;8;P;T;h;l;|;;;;;;;;;;;;; <<(<8<<<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<<<<<<<<<<<<<< == =4=D=T=X=p====================> >>>> >8><>T>X>\>`>d>l>>>>>>>>>>>>>>>>>>> ??? ???? ? ?$?(?,?4?8?<?@?H?`?d?l??????????????? 0 0$0(0,0004080L0\0`0x0000000000000000 111 11,1<1@1D1\1`1h1l1p1t1x1|1111111111111111111111222 2 2(2,2024282<2@2D2L2T2X2\2`2d2h2l2p2t2x2|2222222222222222 3333(383H3L3P3T3X3\3p3t3x3|3333333333333333 444$4(484<4@4X4\4p4444444444445 5,505H5L5T5X5\5p5t5x5|5555555555555556$64686P6`6d6l6666666666666667707@7P7T7X7\7`7d7h7l7p7x7|77777777777 88 8,8<8@8P8`8d8h8l8t8x8|888888888888888899,9<9@9D9H9L9P9T9X9\9`9d9x9|99999999999:$:(:@:D:H:L:P:T:X:`:x:|:::::::::::::::::: ;; ;;;;,;0;H;L;P;T;X;`;h;;;;;;;;;;;<<$<(<@<D<\<`<d<h<p<<<<<<<<<<<<<<< == = =$=(=<=@=D=H=L=T=X=\=`=d=h=l=p=t=x=|=================== >>>>(>,>D>H>L>P>T>\>t>x>>>>>>>>>>>>?? ?$?<?@?X?\?t?x???????????????? 00 0$04080<0@0D0H0L0P0T0X0\0d0h0l0t0000000000000011 111 1 1$1(1,1014181<1@1D1X1\1`1d1l11111111111111111112$2(2@2D2\2`2x2222222222 33 3 383<3@3D3X3h3x3|3333333333333333333334 44(4,4D4H4L4P4T4X4\4d4h4l4p4444444444444444455 5$5(5,5054585<5D5\5l5|555555555555566 666 6 64686P6`6d6|6666666666666666666666 7777(7,7@7D7H7`7d7|777777777788 8888,8<8L8P8h8l8888888888888888888 99 9 9$9(9,90989<9@9D9H9L9P9T9\9`9d9x999999999999::: : :4:8:<:@:D:H:L:P:T:X:\:`:d:h:p:x::::::::::: ;;; ; ;$;(;,;0;4;H;L;`;d;h;;;;;;;;;;;;;; <<< <<$<4<D<T<X<\<p<t<x<<<<<<<<<<<<<== =$=<=@=X=\=t=x=|================== >>> >>>> >$>(>0>H>X>\>t>x>>>>>>>>>>>>> ?? 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? ?8?<?T?X?`?x???????????????????? 000(080<0@0D0H0L0P0T0X0\0`0d0h0|00000000000000001 1$1(1,101D1H1`1p1t1x1|11111111111111 22 20242L2P2h2l222222222222223 3$3(3,3034383<3@3D3H3L3P3T3X3\3`3d3h3p3t3x3|333333333333333344 4$4<4L4P4h4l44444444444444444455,5<5@5D5\5l5p5555555555 6 6 686<6T6X6\6`6d6l666666666667 7777 7 7(7,7074787<7@7D7H7L7P7T7X7`7x7|7777777777 88 8 84888H8L8d8h88888888888888 9999 9 9$9(9,90949H9L9P9h9l999999999999:::(:,:0:H:L:T:l:|::::::::::::;;0;4;L;P;h;l;;;;;;;;;;;; <<< <$<<<L<P<T<l<|<<<<<<<<<<<<<< === === =4=8=<=@=D=H=L=P=T=X=\=`=d=h=l=p=t=x============= >>> >>>> > >$>(>,>@>D>T>X>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>>>>>?? ???? ? ?$?(?,?0?4?8?<?@?D?L?d?h?p??????????????????? 000 0000 0 04080H0L0\0`0p00000000000 1 1 181<1@1D1X1h1l111111111111111111111111222 24282@2D2H2L2P2T2X2\2`2h2222222222222 333 3333 3 3$3(3,3034383<3@3T3d3h3p3t3x3|33333333333333333 44(4,4D4H4L4P4T4X4\4`4d4h4l4p4t4x4|4444444444444444 55 5$5(5,5054585<5@5T5X5\5`5d5h5l5p5t5x5|55555555555555555 666 66,6064686<6@6D6H6L6P6T6X6\6d6|666666666666666666 77(7,7D7T7d7h7x7|77777777777777777777777 888 84888<8@8D8H8P8h8l8p8x8|88888888888888888899094989@9D9H9P9h9x9|99999999999999999999 ::: :::: : :$:(:,:0:4:8:<:@:D:H:L:P:d:t:::::::::::::::: ;;; ;;;; ; ;$;(;,;4;8;<;@;D;H;L;P;T;\;t;;;;;;;;;;;;;;;; <<<,<0<4<<<T<X<p<<<<<<<<<<== =$=4=8=P=`=d=h=l=p=t============ >> >$>4>D>H>L>T>l>|>>>>>>>>>>>? 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H 0000(0,0004080<0P0T0d0h0l000000000000011 1 181H1X1h1l1111111111111112$2(2,2024282L2\2`2d2h2p222222222222222 333 3333 303@3D3H3L3T3X3\3`3h333333333333333344(4,404D4H4`4p4444444444 55 5555 5 5(5@5D5H5L5P5T5X5\5p555555555555555 66 6 6$686<6@6X6h6l6p6t6|66666666666 77 74787<7@7T7d7h7l7p7t7x7|7777777777 888 8888 8 8$8(8,80888P8T8l8|88888888888888888 999 99,9<9@9X9h9l99999999999999 :::(:8:<:@:T:d:t:x:::::::::::::; ;;;; ;$;(;,;0;8;P;T;l;|;;;;;;;;;;;;;;;;;<< <$<<<L<P<T<X<\<`<d<h<l<p<t<x<|<<<<<<<<<< == = =$=(=0=H=L=d=h==============>>,><>@>X>\>t>>>>>>>>>>>>>>>> ?? ?$?<?@?T?X?h?x????????????? 8 000 0 0$080<0L0\0`0x0|0000000000000001 1$14181P1T1X1\1`1h11111111111111111122 22,2<2L2P2T2X2`2d2h2l2p2t2222222222 333 3 3$3(3034383@3X3h3l3p3x3333333333333 44(4,44484<4@4D4H4L4P4T4X4\4`4d4x4444444444444444445550545H5X5\5`5x5|55555555555555555 666,606H6X6\6`6d6x6|66666666677(7,7D7T7X7p77777777777 888 8 8$8(8,8@8D8\8`8x8|88888888889 9$9(9@9D9\9`9x9|999999999999999999: :$:(:@:D:X:\:`:x:|:::::::::::: ;;;,;0;4;8;@;D;H;P;T;X;\;p;;;;;;;;;;;;;;;;;;;;; <<(<8<<<@<X<\<p<t<<<<<<<<<< === === =4=8=P=T=l=p========== >>(>,>4>8><>P>`>d>|>>>>>>>>>>??? ? ?$?(?,?0?4?H?L?P?h?x?|???????????????? 0000040L0P0h0x0|00000000000000 11 1 181<1T1d1h1l1t11111111111111111 22 2,2<2L2P2T2h2x2|22222222222222222233 3034383<3@3D3L3P3T3X3\3`3d3h3l3p3t3x3|333333333344 444 4 4$4(4,40484P4T4l4p4x44444444444444 555 555,5054585<5@5D5H5L5P5T5X5\5d5|55555555555555 66 6$6(6,60646H6L6P6T6X6\6d6h6l6p6t66666666666666677 7 7$7(7,7074787<7@7D7H7\7l7|77777777777 888 88$8(8@8D8H8L8T8X8\8`8t8x8|888888888888888 999 999 949D9T9X9\9t999999999999999::,:0:4:8:<:D:\:`:x:|:::::::::::::::::::: ;; ; ;$;(;,;0;4;<;@;D;H;\;`;x;;;;;;;;;; <<<0<4<L<\<l<p<<<<<<<<<<<<<<<<<< === = =$=<=L=P=`=d=t======================= >>> >>> > >4>8><>@>D>H>P>T>X>\>`>h>l>p>x>|>>>>>>>>>>>>>>>>>>?? ? ?$?(?0?H?X?\?`?d?h?l?p?????????? ` 000(0,040L0\0`0x00000000000000 11 1 1$1(1,141H1L1P1T1X1\1`1d1h1p1t1x1|11111111111111111111111111112 2$2(2@2P2`2p2t2222222222 333,3<3@3P3T3X3\3`3h3|333333333344 4$4<4@4X4\4p4t4x4|44444444444444444444 55 54585<5@5D5H5L5P5T5X5\5`5d5x5|55555555555 66 64686<6@6D6L6d6h6l6p6t6666666666666666677 7777 7 7$7(7,7074787L7\7l7p77777777777 88 8$8<8@8X8\8p8t888888888888889 909@9D9\9`9d9h9l9p9t9|9999999999 ::(:,:0:4:8:<:@:D:X:\:`:d:h:l:p:t:::::::::: ;;; ;;;; ; ;(;@;D;\;`;x;|;;;;;;;;;;<<< <4<8<P<T<X<\<d<h<l<p<t<x<|<<<<<<<<<<<< = =,=0=@=D=T=X=\=`=d=h=l=p=t=|============== >>> >>>> >0>@>P>T>d>h>l>>>>>>>>>>??(?,?0?4?8?<?@?H?`?d?|????????? 0000(080H0X0\0l0p00000000001 111 1$1(1<1L1P1T1X1`1x1|11111111111111 22 2,2<2@2P2`2p22222222222222222330343L3\3`3x3|3333333333333 44 4 484<4T4X4\4`4h444444444445 555(585H5L5\5l5p5t5x5|5555555555555 666 6,606H6X6\6l6p66666666666677 787<7T7X7p777777777778 8$8(8,8@8P8T8d8t8x888888888888888888899$9(9,9D9T9X9p9999999999999 ::: ::$:(:,:@:D:H:L:T:l:p::::::::::::::::::::::: ;; ;;;; ; ;$;(;0;H;L;P;T;\;t;;;;;;;;;;;< <$<(<@<D<H<L<P<T<X<\<`<h<<<<<<<<<<<== = =$=(=,=0=4=8=<=@=D=H=L=P=d=t=x=|========= >> >$>(>@>P>T>l>|>>>>>>>>>>>>>>> ?? ?$?4?8?H?L?d?h????????????? 00 0$0(0,00040H0X0\0t0x00000000000000000000 11$1(1@1D1\1`1x1111111111 22 22,2<2L2\2`2d2l2p2t2x2|22222222222222222222 33(3,3034383<3@3D3H3L3P3T3X3\3`3h3l3p3t3x33333333333334 44$44484<4@4T4X4`4d4h4l4p4x4|444444444445 5$54585L5\5`5d5h5l5p5t5x5|5555555555555 6666,6064686<6@6D6H6L6P6T6X6\6p6t66666666666666666 77 7777 7 7$7,7D7H7P7h7x77777777777778 8$8(808H8L8d8t8x88888888888888888899 9$9<9L9\9`9p9t99999999999999999999999: :,:<:L:P:h:l:::::::::::;; ;;;; ; ;$;(;,;0;4;<;T;X;p;t;;;;;;;;;;; <<< <$<8<<<@<D<H<P<T<X<\<`<h<l<p<t<x<<<<<<<<<<== =$=<=L=P=h=x=|===============> >$>(>,>@>P>T>d>h>l>>>>>>>>>>>>>>>>>? ?$?(?,?0?4?8?<?@?D?H?P?h?x?|?????????????????? ` 0 0000 0 0$0(0,0004080<0@0T0X0\0`0d0l0p0t0|00000000011 1$14181P1`1p1t1111111111111111111112 2,202@2P2T2l2p2x2222222222 333,303H3L3T3l3|333333333333 44 4 484<4T4d4h444444444444 55(585<5T5d5h5l5555555555555 66 6$6(6,646L6P6h6x6|66666666666666677 7074787P7`7d7h7l7p7t7x7777777777 8888 8$8<8@8X8\8`8d8h8l8p8t8x8|8888888888888888888888 999 9999 9 9(9,909D9T9X9\9`9d9h9l9p9t9999999999999 ::: : :$:(:0:H:L:d:t:x:|::::::::::::::::; ;$;(;@;D;\;`;x;;;;;;;;;;;;;;;;;; <<< <<<(<,<0<4<H<L<P<T<X<\<d<h<l<<<<<<<<<<<< == =0=4=D=H=L=d=h=l=p==============>>> >4>8>P>T>X>\>d>|>>>>>>>>>>? ??? ? ?$?(?,?0?8?P?`?d?|????????????????? 0000 080<0T0X0\0`0h00000000000 111 111 1 1$1,1D1T1X1\1`1d1h1p1111111111111111111 22 2 2$2(2,24282<2@2D2H2\2l2p22222222222222222222 33 3$3(3,3@3D3H3L3P3d3h333333333333334 4$4(4@4D4H4L4P4T4X4\4`4d4h4l4p4x4|4444444444 555,5<5L5P5h5x5|555555555555555555556$6(6,60686<6P6T6d6h66666666666666777 7 7$7(7,7074787L7P7`7d7|777777777 888 888 8$8<8@8X8\8`8h8l8p8t8x8|888888888888888888 99 9$9(9,9094989<9@9H9L9P9T9X9`9x9|99999999999999999:: : :$:(:,:0:4:8:@:X:h:l::::::::::::::::::: ;; ;$;,;D;H;`;d;x;|;;;;;;;;;;;;;;; << <4<8<P<T<l<|<<<<<<<<<< ==== =$=(=,=0=4=8=@=X=\=t=x=|============ >>>>(>8>H>L>d>h>>>>>>>>>>>>>>>>?$?4?D?H?`?p?t????????????????? < 000 040D0T0X0h0l0p00000000000000001 10141D1T1X1\1`1d1h1|11111111111122 20242L2\2`2d2h2l2p2t2x2|222222222222222233,303H3X3\3`3x3|333333333334404@4D4H4P4T4X4`4d4h4l4t4|44444444444 55 5$5(5,50585<5@5D5X5\5l5|55555555555 66 60646L6\6`6p6t6x666666666677 777,70747L7P7h7l7t77777777777778 8888 8$8(8,8084888<8@8H8P8h8l8p8t8888888888888 9999 9$9(9,949H9L9P9h9l9999999999:: : :0:@:D:H:L:P:T:X:\:d:|::::::::::::: ;;; ;,;0;4;8;<;@;D;H;L;P;X;p;;;;;;;;;;<<<0<@<D<H<L<P<X<p<t<<<<<<<<<<<<<< == =,=0=@=D=\=`=x=|====================> > >8>H>L>d>t>x>|>>>>>>>>>>>>>>>? ?$?<?@?X?\?t?x?|????????????????????? 0 H 0$04080P0T0X0\0p0t0x0|00000000000000 11 1 1$1(1,10141H1L1d1t1x1|111111111111111 22 2$2<2@2D2H2L2P2X2p2t22222222222222223330343L3\3`3x33333333333333 4444 4 444D4T4X4l4p4t4x4|4444444444444 55 54585P5T5X5\5d5|555555555555566 6 6$6(606H6X6\6t6666666666666 77 7 7$7(7,7074787<7@7H7L7P7T7X7\7`7h7l7p77777777777777 888 88 84888P8T8h8x8|888888888889 9$94989<9@9D9H9L9P9T9\9t9x9|9999999999999 ::: ::: :4:8:<:@:H:P:T:X:\:p:t:|:::::::::::::: ;;; ; ;0;4;8;<;@;D;H;\;`;d;x;|;;;;;;;;;;<<0<4<8<<<@<D<L<d<h<<<<<<<<<<<<< ==(=8=<=L=\=l=|============>>> > >4>8><>T>X>p>>>>>>>>>>>>>>>>? 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P 000 04080P0`0d0t0x0|000000000000 11 1 181H1X1\1`1x1|11111111111112220242L2P2T2h2l2p222222222233 333(3,3<3@3P3T3d3h3l3p333333333333333333333333 444 44$4(4,4D4T4X4p44444444444 55 5054585<5D5H5L5P5T5h5l5p5t5|555555555555555556 6,60646L6\6`6p6t6x6666666666 7777 7$7<7L7\7`7x77777777777788 8$8(8<8@8P8T8X8l8p8888888888889 9 9 9$9,909D9T9X9\9t999999999999 :::::0:4:L:P:h:x::::::::::::;; ;4;D;H;X;\;`;d;l;t;x;;;;;;;;;;;;;;;<<<<(<8<<<L<P<T<l<|<<<<<<<<<<=== =4=8=<=D=\=l=p=t============ >> >0>4>8>P>`>d>h>l>>>>>>>>>>>>>??? ? ?(?@?P?`?d?|?????????????? ` 00 0$0,00040<0T0d0t0x0|000000000000000 11 1 1$1(101H1L1d1t1111111111111112 2$24282H2L2\2`2d2|222222222222222333 3 343D3H3`3p3t3333333333333333 44 4$4(4,40484P4T4l4|4444444444444455 55505@5D5\5`5d5h5p5555555555555 66(6,6D6T6X6h6x6|6666666666666 77 777 7 7$7,747L7\7`7x7|77777777777777 88$8(8@8D8\8l8p88888888888888889$9(9,94989@9D9X9\9l9p9t999999999999:$:4:8:P:`:d:h::::::::::::;; ; ;$;,;@;D;\;`;x;|;;;;;;;;;;;; < < <0<@<D<H<L<`<d<h<<<<<<<<<<<<<<< ==(=,=D=T=X=p=t=x=|===========> >>>(>,>0>H>L>P>T>\>t>x>|>>>>>>>>>>>> ???,?0?H?X?h?l?p???????????? p 00 000(0,0<0@0D0H0L0T0X0\0d0|0000000000000011(1,1014181L1P1T1X1\1d1l1111111111111 222$2(2,20242<2@2H2`2p2t2x2|222222222222 333 3 383H3X3\3l3p33333333333333444 4044484<4@4H4L4P4X4\4`4t4x4|444444444444 55 5$5(5@5D5H5\5`5d5|5555555555555566$6(6@6D6\6`6t6666666666666 7777074787@7X7h7l7p77777777777777 888 8 80848L8\8`8p8t888888888888 9999,9<9L9P9T9l9p9999999999999: ::$:4:D:H:L:P:d:t:x:|:::::::::::;;; ;0;4;8;P;`;d;h;p;;;;;;;;;;;; << < <$<8<H<L<\<l<|<<<<<<<<<<<<< ===,=0=4=<=@=T=d=t=x=|============ >>>> >8><>T>X>\>p>t>x>>>>>>>>>>>>> ??? ?4?D?H?`?d?|??????????? 00 00$0(0,0D0H0`0p0t0x0|000000000000 111,1<1@1P1T1d1h1111111111111122 2 2$2,202D2H2`2d2h2|22222222222223 333 34383<3P3T3X3p3t333333333333333 44(484H4L4d4t44444444444445 55(5,50585<5P5T5l5|5555555555555 666 6 64686P6`6d6t6x6|666666666666 77 7 747D7H7L7d7h7l777777777777888,808H8X8\8t888888888888888 999 9 9094989P9T9X9l9|999999999999:: :0:4:8:L:\:`:d:h:p:::::::::::::: ;;;(;,;<;@;P;T;X;p;;;;;;;;;;;;;<< <0<4<D<H<`<p<t<<<<<<<<<<<<=== =$=,=0=8=P=`=p=t============= >>> >>> >8><>@>D>L>P>T>\>`>d>l>p>t>|>>>>>>>>>>>?$?(?@?D?H?L?`?d?t?x???????????????? 0000 0 0$080<0@0D0X0h0l0p0t0x00000000000 11 1$1(1@1P1T1l1p11111111111112 2 2 282<2@2D2X2\2l2p2t2x2222222222222 333 3333 3 343D3H3X3\3l3p3t3|33333333333 444 4444 4$4(4,4044484<4@4D4X4\4l4p44444444444444444444444 55(5,50585<5@5D5H5L5P5T5X5\5`5d5h5l555555555556 6$6(6,60646<6T6X6p666666666666666 77 7 7(7@7D7\7l7p7t7x7|77777777777777777777 88 848D8H8X8\8t888888888888 9999094989L9P9T9\9t9999999999999 :: :$:(:,:4:L:P:X:p::::::::::::::; ;$;4;8;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;<< <<<< < <$<8<<<T<X<\<`<d<x<<<<<<<<<<<<<<<<<<==0=4=L=P=T=X=\=`=d=h=l=p=t=x=|============>>0>@>D>T>X>\>t>x>|>>>>>>>>>>>>?? ? ?$?(?<?@?D?L?P?T?X?\?`?d?h?l?p???????????????????? 00(0,0<0@0H0`0d0|000000000000111(1,1<1@1D1\1l1|111111111111111111111111111 222 222024282<2@2D2H2L2P2T2X2\2`2d2h2|222222222233 3333 3 3$3(3,3034383<3P3`3d3t3x33333333333333344,404H4X4h4l4p444444444444555 5 5$585<5L5P5T5X5l5p555555555555666 6 686<6T6X6p6t6x6|666666666666666666677 77$7(7,7D7T7d7h7l7p7t7|777777777777778 8 888H8L8\8`8d8h8l8p8t8x8|88888888888888888888888888 999 9999 9 9$9(9,9@9D9\9l9p9t9x9999999999999999999999 ::(:8:<:L:P:T:l:|:::::::::::;; ;,;<;@;X;h;l;p;;;;;;;;;;;;;;;;;;;;;<< <$<(<@<P<T<X<p<<<<<<<<<<<<<<<<<<<<<<<< === ==== = =$=,=D=H=`=p=t=x=|==================> > > >0>4>D>H>`>d>h>l>>>>>>>>>>>>>>> ??$?(?,?0?4?8?<?@?D?H?L?P?T?X?\?`?t???????????????? 000 0000 0 0$0(000H0X0\0t000000000000000 11(181H1L1P1T1X1`1d1h1l1p1t1x1|11111111111111111111111111 222 2222 2 2$2(202H2L2P2T2X2\2`2d2h2l2p2t2x2|22222222222223303@3D3T3X3h3l3|333333333333 44(484H4L4\4`4x4444444444 55 5 585H5L5P5T5X5`5d5h5|555555555555555555555566 6,6<6@6D6\6`6d6h6l6p6t6x6|66666666666666666666 777 7777 7 7(7,70787<7@7H7L7P7T7X7\7`7d7h7l7p7t7x7|7777777777788$8(888<8L8\8`8d8|88888888888899 909@9D9\9`9x9|99999999999999999999 :::$:4:8:<:T:X:p:t:::::::::::::::::::::::::;; ;$;(;@;D;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;<$<4<8<H<X<\<`<d<h<p<t<x<|<<<<<<<<<< === ==== = =$=(=,=0=4=8=<=P=T=X=p=t=x=|===========>>> > >$>(>,>4>8><>@>H>`>d>|>>>>>>>>>>>> ??? ???? ? ?$?(?,?0?4?8?<?D?H?L?P?T?X?\?`?d?h?l?p?t?x?|??????????????????????? 00 0$0<0L0P0`0p0t0000000000000000 111 1111 1 1$1(101H1L1d1t1x1|1111111111222 242D2H2L2P2T2X2\2`2d2h2l2p2t2x2|222222222222222 333(383<3T3d3t3x33333333333333333333 44 4$44484<4@4D4H4L4P4T4X4\4`4d4h4l4p4x44444444444 55 5$5(5@5P5T5X5\5`5d5x55555555555 6 6 6$6(6,6@6D6T6X6\6t6666666666666666666666666666 777 777 7 7$7(7,7074787<7@7D7H7L7P7X7\7`7d7x7|77777777777777 888 8888 8 8$8(8,8084888L8P8T8X8\8`8d8h8l8p8t8x8|8888888888888 999$9(9,90949H9L9P9T9X9\9`9d9h9l9p9t9x9|99999999999 :: :$:(:0:4:8:<:P:T:l:p::::::::::: ; ;,;0;@;D;H;L;P;T;h;l;p;t;x;|;;;;;;;;;;;;;;;;;;;;;;;;;;;; <<< <<$<(<,<D<H<`<d<|<<<<<<<<<<<< ===$=(=@=D=\=`=x============== >>> >>>> > >$>(>,>0>4>8>@>X>\>t>>>>>>>>>>>>>> ??(?,?<?@?X?h?l?p???????????????????? 00 0 0$0(0,0004080<0@0D0H0L0P0X0p0t0x0|0000000000111 14181<1@1H1`1d1h1l1111111111111111111111 222 2 282H2L2P2T2X2\2`2d2h2l2p2t2x2|22222222222222 333 3 30343D3H3L3P3T3X3\3`3d3h3l3p3t3x3|3333333333333333333333 444 4 4$4<4L4P4`4p4t4444444444444 55(5,50545H5L5\5`5p5t5555555555555555555555 66 6,6064686<6@6D6H6L6P6T6X6\6`6d6h6|66666666666 77 7$7(7@7P7`7d7t7x777777777777777 888 8888 8084888P8T8l8p8t8x88888888888 9999,909@9P9T9d9h9l9999999999999999999999 :::: : :$:(:,:0:4:8:<:@:D:X:\:l:p:t:x::::::::::::;$;(;8;H;L;P;T;X;\;`;d;h;l;p;t;x;|;;;;;;;;;;;;;<<< < <8<<<T<X<\<`<t<x<|<<<<<<<<<<<<<<<<<<<<<<<<<< === === = =$=(=,=0=4=8=<=@=D=H=\=`=p=========== >>(>8><>L>P>T>X>\>`>d>h>l>p>t>x>|>>>>>>>>>>>>>>>? ??(?,?D?H?`?d?h?l?p?t?x?|??????????????? 0 0$04080<0@0D0H0L0P0T0X0\0`0d0h0|00000000000011 1$181<1@1D1L1P1T1X1\1`1d1h1l1p1t1x1|1111111111122(282<2@2X2h2l2|22222222222222222222 333 3034383P3`3d3h3l3t3333333333 444,404H4L4P4T4X4l4p4444444444444444444444 5 5 5$5<5@5D5H5L5P5T5X5\5`5d5h5l5p5t5x555555555556 6$6(6@6P6T6d6t66666666666666666666 777 777 7 7$787<7L7P7`7p7t7777777777777 88 8$8(8,8084888<8@8D8H8L8P8T8X8\8`8h8888888888888889 9$9<9@9X9\9d9|999999999999999999999 :: :$:4:D:H:L:d:t:::::::::::::::::::: ;; ;$;(;@;P;T;X;p;t;x;;;;;;;;;;;;;;;; <<< <<<< < <$<(<,<4<8<<<P<T<d<h<l<<<<<<<<<<<== =$=<=@=D=H=L=`=d=t=x========== >>>> > >$>(>,>0>4>8><>@>D>H>\>`>x>|>>>>>>>>>>? ??? ?4?8?<?@?D?H?L?P?T?X?\?`?d?h?l?t???????????????????? 00 040D0H0`0p0t0x0000000000000011,1<1L1\1`1d1h1l1p1t1x1|111111111111111111 222 2222 2 2$282<2@2X2\2`2t2x2222222222 333 3 383H3L3P3h3x3|333333333333333333334 4$444D4H4`4p4t4x4|44444444444444444444 55 5 5$5(5,5054585<5@5D5H5L5`5p5t5555555555555 6666 6 6$6(6,6064686<6@6D6L6P6T6\6d6|666666666667$7(787<7@7X7\7t77777777777777777777 8888,80848L8\8`8d8|88888888889 9$949D9H9L9d9h9l9p9t9999999999999999 :: :$:<:L:P:h:x:|::::::::::::::::::::::::::::::: ;;(;,;<;@;D;\;`;d;h;p;;;;;;;;;;; <<<< < <$<(<,<0<4<8<<<@<D<L<d<t<x<|<<<<<<<<<<<<<<<<<<<== =$=4=8=P=T=l=p=t=x=========== >> >4>D>H>X>\>l>|>>>>>>>>>>>>>>>>>>>> ??? ??,?<?@?D?L?d?h????????????????????? t 00(080<0@0X0\0`0t0x000000000000000000 111 1,10141L1\1`1d1h1p1111111111222 24282P2T2X2\2`2d2h2l2p2t2x2|22222222222222222222222 3 3 383<3T3d3h3x3|3333333333334 4 4,404H4L4d4t4x4|44444444444444444444 555,5<5@5P5T5X5\5`5d5h5l5p5t5x5|555555555555 6666(6,60686P6`6d6t6x6|6666666666666666666 777 7777 7 7$7(7,70787P7`7d7t7x7|77777777777888 84888P8`8p8t8x88888888888889$9(989<9T9X9p9t9999999999999999999 ::: ::,:<:@:X:h:l:p::::::::::::::::::::;; ;,;0;4;8;<;@;D;H;L;P;T;X;\;`;d;l;;;;;;;;;;;; << <$<4<8<<<@<T<X<p<<<<<<<<<<<<<<< = = =$=,=D=H=`=p=t=x============= >>>(>8><>L>P>h>l>t>>>>>>>>>>>> ???,?<?L?P?h?x?|??????????????? 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P) 0$00080l0|000000 11141@1d1l1t1|11111111111112 22 2$2,242<2D2P2p2x222222233T3d3p3x3333344 4@4L4l4x4444444 5555 505T5\5d5l5t5|555555555555555556 6686@6H6d6666666666667 77 7$7,747<7D7T7\7d7l7t7|777777777778 88 8$8,848<8D8L8T8\8d8l8t8|88888888888888889 99 9$9,949<9D9L9T9\9d9l9|999999999: :: :$:,:4:<:H:l:t:|:::::::::::: ;$;,;4;<;D;L;T;\;d;l;x;;;;;;;;;;< << <$<,<4<<<H<P<t<|<<<<<<<<<<<<<<<<== =$=,=4=<=L=X=x=============(>0>L>l>>>>>>>> ?? ?$?,?4?<?D?L?T?\?d?l?t?|????????????? `) ( 0 00 0$0,080\0d0l0t0|0000000001 11 1$1,141<1D1L1T1\1d1l1t1|1111111112 2(2H2P2X2`2h2x2222222222 3343<3D3L3T3\3d3l3t3|33333333334(444T4\4h444444445 5(5H5T5t5555555$646@6H6|6666666667707<7\7d7p7777777777777778 88 8$8,848<8D8P8p8x8888888888889 9$9,949L9X9x9999999999 ::: :<:D:L:T:\:d:p:x:::::::: ;,;8;@;;;;;;;;< << <$<,<4<<<D<L<X<x<<<<<<<<<< = =,=4=@=`=h=p=x==========>H>X>d>l>>>>>??$?,?p???????? p) @0P0\0d00000 11 1$1h1x1111111(282D2L222222222222 343D3P3X3333333 44<4L4X4`44444445 5585T5t5|55555555555 66 6$6,686X6`6h6p6x6666666666 7777$7D7L7T7`77777777777778 88 8$8,888@888888889 9,9L9T9`9999999999999: :: :$:,:T:\:d:l:t:::::::::::::0;d;;;;;;;;;;<(<0<8<@<L<l<t<|<<<<<<<=0=8=@=H=T=t=|==========>>>L>\>h>p>>>>>?,?4?<?D?L?T?\?d?l?t?|????????????? ) 0 00 0(0H0P0\0|000000000 141D1P1X111111111112 22 2$2,242<2D2L2T2\2d2l2t2|222222222222222223 33 3$3,343<3D3L3T3\3d3l3t3|333333333333333334 44 4$4,444<4D4L4T4\4d4l4t4|444444444444444445 55 5$5,545<5D5L5T5\5d5l5t5|555555555555555556 66 6$6,646<6D6L6T6\6d6l6x666666666666667 77 7$7,747<7D7L7T7\7d7l7t7|77777777777777778$8D8L8T8\8h88888888889 99 9$9,989X9d9999999999:(:4:T:`::::::::; ;$;,;4;<;D;L;X;|;;;;;;;;;;;;< <(<H<T<t<<<<<<<<<<= == =$=,=4=\=d=p===========> >> >$>,>4><>D>P>p>x>>>>>>>>>>? ?? ?$?,?4?<?D?L?T?\?d?l?t?|??????????????? ) 0 00 0$0,040<0D0L0T0\0d0p000000000001141<1D1L1d1l1t1|11111111112 22 2$2,242@2d2l2t2|22222222222 33 383X3`3l3333333334\4|44444444444445 55 5$5,545<5D5L5T5\5d5p55555555 6 6,686X6`6l66666666 7 7(747<7p7777777778 8@8P8\8d88888 99 9$9X9h9t9|999999 :::::: ;4;D;P;X;;;;;<< <(<H<d<t<<<<<<< = =,=8=@=======>>T>d>p>x>>>>>>>?$?0?T?\?d?l?t?|???????????? ) 00 0$0,080@0X0`0p000000000000 111 141<1D1L1d1x11111111 22 2T2d2p222222 3 343@3`3l3333333334(444T4`4444444444 5(5d5555556 66 6@6H6P6\6|6666666667$7D7`7777777777 888,8L8X8x888888889 9@9H9P9\9|9999999999:(:4:T:`:::::;$;D;P;;;;;<< <`<p<|<<<<<<0=@=L=T====>4?<?D?P?p?x????????? ) 0 0@0L0l0t0|000000000 1(1<1D1\11111111102@2L2l2222222 333<3D3L3T3\3d3l3t3|333333333 4,4L4T4\4d4l4t444444455 5<5D5L5X5x55555556606<6\6h666666666 7 7,747<7H7h7p7|77777777 8888D8l8x8888888 9999(9L9T9\9d9l9t9|9999999999999: :: :(:H:T:|::::::::::;;@;L;l;x;;;;;;;;<<<$<D<P<p<|<<<<<<<<< =$=0=X=h=x===========>>> >,>L>T>`>>>>>>>>>>?$?,?4?<?H?h?x????????????? ) 0 0(000<0\0d0l0t0|0000000001P1p1|111111111 2$2,242<2D2L2T2\2d2l2t2|2222222333$3D3L3T3\3d3l3x333333333333334(40484@4H4P4\4|4444444 55 555555555 66<6666777778,8D8`88888888888889 99 9$9,949<9D9L9T9\9d99: :4:<:T:p::::::::::P;;;;;;<4<<<D<L<T<\<d<l<t<|<<<<<<<<<<<<<<<== =(=8====$>,>4><>D>L>T>`>>>>>> ?(?\?d?l?t?|????????? ) ( 0 00,040<0D0L0T0`00000000000001,1D1L1T1\111111111111111112 22 2$2,242<2D2L2T2`22222222223,343<3D3L3T3\3d3l3t3|333333333333333334 44 4$4,444<4D4L4T4\4d4l4x444444444444445 5$505T5\5d5l5t5|5555555555555 6 6,686X6`6h6p6x6666666666 7 7,747<7H7h7p7x7777777777 8$8,888X8d888888888990989@9L9T99999 :: :$:h:x:::::::8;H;T;\;;;;;<<$<,<`<p<|<<<<<< =0=<=\=d=l=x=========>> >,>4>h>x>>>>>>>8?H?T?\????? 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P* 0 00,080X0`0l0000000001(101<1\1h111111111 2 2,282X2d2222222233$3D3P3X3p33333334,444<4D4L4T4\4d4l4t4|44444444444445$5@5P5\5|5555555555 66 6$6,646<6D6L6X6x6666666667 7@7L7l7x7777777778 8<8D8P8X8888888 9 9(90989@9L9l9t9|999999 :::: :(:8:\:d:l:t:|::::::::::; ;;8;@;L;l;t;|;;;;;;;;;;< <<8<@<H<P<X<d<<<<<<<<<<< == =(=H=P=X=d==========> >>8>@>H>P>X>d>>>>>>>>>? ?,?L?T?\?d?p??????? `* 0 0@0H0X0|0000000000001 1@1H1T1t1|11111112242<2D2P2p2|22222222223$3@3P3\3d3333333444 4,4L4T4\4d4l4t4444444 5 5,545<5D5P5t5|55555555555 66 6@6\6l6x6666666 7 7,787@7`7|77777777 8 8<8L8X8`88888888 99 9@9\9l9x9999999 : :,:8:@:`:|:::::::: ; ;<;L;X;`;;;;;;;; << <@<\<l<x<<<<<<< = =,=8=@=`=|======== > ><>L>X>x>>>>>>>?(?4?T?`??????? p* 0000<0\0h0000000 1181D1d1p11111112 2@2L2l2t2|22222222223 3383@3H3P3\3|33333333334 44 4(4H4P4X4`4l444444444445 5$5,585X5`5h5p5|55555555556$6,646<6H6h6p6x6666666666 77747<7D7L7X7x7777777777 8888$8D8L8T8\8h8888888888 99 9$9D9`9p9|9999999::0:8:@:H:T:t:|:::::::::::::; ;; ;@;\;l;x;;;;;;; < <,<8<@<`<|<<<<<<<< = =<=L=X=`======== >> >@>\>l>x>>>>>>> ? 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Hacked By AnonymousFox1.0, Coded By AnonymousFox