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#6\Ll$0H|$ LD$0LvHD$0HxH;=6H|$ H 6HWH賬HD$ D$@HxH;=6Hѩ6LxLhIEH@HpH91ɺL耧H5] L1HXH{LlLH{H5= H$vL $Ll$@VH5 1LWH{LLM9MM)HT$1L菫IHL@uOAEAH; è6Ll$0H|$ LD$0LLLLH $逸H $IYLLLH $ѩH $III>It$XH|$ 蜥WMMH=g6RLl$0HD$ LHx\H=@6+HHD$@Ht$0Hx5H荩AL9BD9HD$0Ht$HxAL9BD9I|$H菪H7I|$HIGH $L|$HHLHHa6I|$`HH$HH=Ө6HWt#XHt$0\IPJHHL H袨H=LƥH=v6HWt#8Ht$0)PJHH8H=06HWtbHt$0蹠HD$ Ht$Hx豞PH=6HWt+_Ht$uPPJH똋PJHfDAWAVAUATUSHH<H HHcHA~(DHĸD[]A\A]A^A_AH}H5ؙfDE1HH5<諙CtLH{ AtH}H5肙Hs H}EH}H5r`KLd$@D$@I|$`$H6Ƅ$1HDŽ$xƄ$HDŽ$HDŽ$HHDŽ$HDŽ$H$H6LpIFLt$HI|H6LxL?WL-P6I|$HHD$XHD$`HD$hHD$pHD$xIEHDŽ$HD$HIE@H$H6HHD$PHHA6It$I|$`DŽ$HHD$PHܣ6HH$諡I|$H535I|$H5BI|$iH5HI|$H5柧|$@IEHD$HIE@H$H6HHD$PH$HxH;=#6(H6I|$HHHD$PsIFLt$HI|$`L|HH٢6HH$xoI|$H5-:H6HHD$ HD$xHHt$hHT$$H9H|$pH蘥Lt$0H|$ HD$0LѣHD$0HxH;=Q6H|$ Hg6HWHHD$ D$@HxH;=6usH06LpLxH|$pLt$0H|$ HD$0LR|It$XH|$ HL}HYI|$H蜤HDH=$6HWtGpHt$0譛aHD$0Ht$Hx襙HD$ LHx蔙OPJHI|$H襪IFLt$HL|HH6I|$`HH$誖H袢HE1%H=u6HWtqHt$0HD$ Ht$Hx]4Lt$0=H="6HWt+:Ht$諚+PJH뉋PJHfff.AWAVAUIATUSHHHH\$@I}H\$@D$HD$LHHD$X,>ImLE8LMHHu0@L9IMcL$Mt$AAm}AT$HLLD$@|$@AD$A~AD$ID$fD~H咧AVID$McL$AL$ffIFD9|JI}HD$HL$LHD$XHD$@/=ImHu0LE8LMHL9IMaIMcL$Mt$A@AVAL$fmAT$MD$LLfA~I|$HD$赛ImD$H}0LE8L9HwHu0LMHHL)HLH+}(HHHHHU H+UHHL9HHJHHczB fImH}0LE8L9D$"HUHD$HJHMHHRHHU8HHU0HM@ImLE8Hu0?H\$@D$@H{`蝒L56H_61Ƅ$HDŽ$xƄ$HDŽ$MnHM~H$HDŽ$HDŽ$HDŽ$IELl$HH|L?ךH-Н6H{HHD$XHD$`HD$hHD$pHD$xHEHDŽ$HD$HHE@H$H6HHD$PɟH6L%{6HsH{`DŽ$HHD$PID$H$-H{H54踘H{H5ƺ裘H{蕝H5H聘H{H5 l|$@HEHD$HHE@H$H6HHD$PH$HxL9H6H{HHHD$PIELl$HH{`L|HHe6HH$Hĸ[]A\A]A^A_fDAEH1IT$E1IT$IFB IIHE19H{H5DMl$LHD$xLl$ HoHT$hL|$pH9L9KM]L)1IHT$L/IHL@AAL9Ll$0H|$ LD$0L跛HD$0HxL9H|$ HQ6HWHHD$ D$@HxL9MnM~L9ML)HT$1HI}IHL@uLAAL98Ll$0H|$ LD$0L MLLLHL$ᚧHL$I LLLHL$HL$IMMHsXH|$ 茖HH=W'BLLl$0HxLN fH=)'HD$ Ll$0H=f6HWt" Ht$0풧ċPJHHHtH6H{HzH"H{H桨IELl$HL|HHQ6H{`HH$썧H䙧HHD$ Ht$HxlH=6HWtSHt$.H=y6HWt2Ht$0PJH멋PJHHD$0Ht$HxُHD$ uAL)BD)AL)BD)fAUIATIUSH8HI6H\$HHH%+H-n6LHLd$(D$$HEHD$|$ tH5G L2HK6HHHD$Ll$MtBI}Ht1IEHI](HhH9vfDH;HH9wI}摧LޑH8L[]A\A]HI<$Ht$H諎HIHEHLHD$f.1f.ATH6UHSHHLcMtMI<$Ht.ID$HI\$(HhH9vH;H4H9wI<$&[]LA\f.[]A\f.AUIATUSHH6HHpMeMtAI<$Ht0ID$HI\$(HhH9vH;H謐H9wI<$螐L薐HL[]A\A]鄐@U1H& .SHHxGGGGGG G G HfG Ht$0H|$HH?HHH)HT$闧Ht|T$8D$DVUUUL$f.sH,HEH[]\H,HEHH1EF>DSHHH H;WHHT$l$Ht$Hl$ Ht$re \$D$X% =Ht$f.sjH,HHH9HC H H¸[D\$D$襏 u=f.swH,HCfDH 11[fD\HH,H1&=Ht$Hl$Ht$=!tf.\H,HCHH1Cxf.@HHɉ6H!H6SHH H[@H6HHMf.SHH[Έf.@H6HHHG؏SHH[鎈f.@H6SHH8HHGH{HtHHC[f.fSHH[.f.@=6tH6H=6H舎tKH=u6Hj6Hv6H=g6HP6HL5H5=6H=~)H*6Hf.H8HwHPH|$ HQHD$ HxH;=6uH8H=6HWt# ʅHt$HD$HD$ʋPJH@USHHH6HH~Nt)udH5pcHH[]fH5?HH[]DuH56HH[]H5b HHH[]fH5߇HH[]H;Ht$HH1H艌fHHfHI6HHHG耧H6HHÐH6HHÐHH6HH魌f.SHH[鞅HH1.0.0out of indexinternal errorindex out of range/\basic_string::substrRegisterCustomOps was false.Stacktrace: opaque(ml_type != nullptrnon_tensor_base != nullptronnxruntime_profile_input name cannot be emptyCPUExecutionProvideroutput name cannot be emptynot enough space: expected , got !=Tensor type mismatch. input array is too shortspace is not enoughshape is invalidvector::reservevector::_M_default_appendvector::_M_range_checkDmlExecutionProviderEncountered nullptr.Unsupported data type: Unsupported input typeRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in librarySpecified domain and type names combination does not refer to a registered opaque type/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccOpaque type is not a non_tensor type!!!/onnxruntime_src/include/onnxruntime/core/framework/ml_value.honnxruntime::DataTypeImpl::GetType() == type_ is not supported in this function/onnxruntime_src/include/onnxruntime/core/framework/tensor.hDataTypeImpl::GetType() == dtype_Input is not of one of the supported sequence types.Input is not of type sequence or map.Invalid index requested for map type.Invalid tensor element type in the input.Mem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Input is not of one of the supported map types.Number of values should be at least 1.For map type num_values MUST be 2Key and value tensors have unequal number of elements.Value type is not supported yet.Key type is not supported yet.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Either the key tensor or the value tensor has NumDimensions > 1` @` @``x76666667747L7d7|777746OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = std::map]const T& OrtValue::Get() const [with T = std::map]const T& OrtValue::Get() const [with T = std::map]const T& OrtValue::Get() const [with T = std::map >]const T& OrtValue::Get() const [with T = std::map, double>]const T& OrtValue::Get() const [with T = std::map, float>]const T& OrtValue::Get() const [with T = std::map, long int>]const T& OrtValue::Get() const [with T = std::map, std::basic_string >]const T& OrtValue::Get() const [with T = std::vector >]const T& OrtValue::Get() const [with T = std::vector, float> >]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEPFvPvESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEN11onnxruntime10IAllocatorEN11onnxruntime16AllocatorWrapperEN11onnxruntime7logging5ISinkEN11onnxruntime7logging8CLogSinkE8OrtValue14LoggingWrapperSt19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughbasic_string::_S_construct null not valid/onnxruntime_src/onnxruntime/core/session/custom_ops.cc%uUnsupported version '' in custom op ' is not implementedDescriptionCreateCustomRegistryComputeAsync*ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_N4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime8OpKernelEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_ESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE12OrtAllocatorN3Ort9ExceptionE16OrtAllocatorImpl19OrtDefaultAllocatortensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128) unknownElement type of input [TypeInferenceError] expected to have tensor typeInput Output com.microsoftcom.microsoft.nchwccom.microsoft.automl (domain: ) from file line , but it its domain is not known by the checker., but it its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Schema error: MemcpyFromHostinputMemcpyToHostException caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type./onnxruntime_src/onnxruntime/core/session/environment.ccFvON4onnx8OpSchemaEEFvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEEPFvON4onnx8OpSchemaEECpu) , expected: (Model was not loadedModel was not loaded.Profiler is disabled.Session was not initializedSession not initialized.subgraph_session_statep_op_kernel%Y-%m-%d_%H-%M-%S.jsonCompute_Create_State_Release_State_Error during EndProfiling(): intra_op_thread_poolinter_op_thread_poolCastFloat16TransformerEnvironment::IsInitialized()Invalid rank for input: Expected: for the following indices Output vector pointer is NULLp_fetches->size(): Invalid Output Name:Provider has already been registered.elements, but feeds has elements.Invalid Feed Input Name:Input with name: Running with tag: model_runException during loading: Unknown exception in Load()model_loading_uriLoad model from model_loading_protomodel_loading_istreammodel_loading_arrayNode placements]. Provider: [MemcpyTransformerInitializing session.CUDAExecutionProvidersession_initializationUnexpected input data type. Actual: (Attempt to use DefaultLogger but none has been registered.Received nullptr for graph transformer/onnxruntime_src/onnxruntime/core/session/inference_session.ccCould not write a profile because no model was loaded.Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)CreateSubgraphSessionState should have created an entry earlier./onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.hAllowed values are 1 and 2. Current level is set to graph_optimization_level < TransformerLevel::MaxTransformerLeveltimeout_in_ms >0 is not supportedMain Graph instance should have populated all subgraphs when being resolved.Unknown error during EndProfiling()Environment must be initialized before creating an InferenceSession.Received nullptr for custom registry Please fix either the inputs or the model.Got invalid dimensions for input: At least one output should be requested.Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered.Received nullptr for exec providerMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderSize mismatch: feed_names has is not expected to be of type tensor.Encountered unknown exception in Run()This session already contains a loaded model.Encountered unknown exception in Load()Could not find an implementation for the node All nodes have been placed on [Session has already been initialized.Adding default CPU execution provider.Parallel execution is currently not supported for the registered CUDA Execution Provider.Serializing Optimized ONNX model with Graph Optimization level greater than 2 is not supported.Session successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()Failed to load model because protobuf parsing failed.void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector >&)void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr&)EndProfilingGetModelOutputsGetOverridableInitializersGetModelInputsonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector >&, const std::vector >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoRunValidateInputsInitializeInitializeSubgraphSessionsonnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&)CreateSubgraphSessionStateonnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&)TransformGraphLoadAddCustomOpDomainsAdd~InferenceSessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*)MaxUsedSt14default_deleteIN11onnxruntime10DummyArenaEE*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_N11onnxruntime16IDeviceAllocatorEN6google8protobuf2io19ZeroCopyInputStreamEN5Eigen19ThreadPoolInterfaceEN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN11onnxruntime15IArenaAllocatorEN11onnxruntime10DummyArenaEN11onnxruntime11controlflow18IControlFlowKernelEZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE__C/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccThis transformer is already registered ApplyTransformersLevel_RuleBasedTransformerNchwcTransformerEliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutFuseReluClipShapeToInitializerConvAddFusionConvMulFusionConvBNFusionUnsupported levelfalseConstantFoldingRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialMatMulAddFusionGemmActivationFusionConvActivationFusionGeluFusionUnsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span, const std::vector >&)std::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector >&)N11onnxruntime11RewriteRuleEN11onnxruntime17EliminateIdentityECast Max:node_index < nodes_.size()CastDef_%dValidating no unexpected access using an invalid node_index. Got:/onnxruntime_src/include/onnxruntime/core/graph/graph.h/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cccast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformerApplyImplRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerEai.onnxAddfused Matmul and Add gemm/onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl N11onnxruntime15MatMulAddFusionEpadsstridesdilationskernel_shapeauto_padSAME_UPPERSAME_LOWERNOTSETVALIDReorderOutputsize is differentsize_ == sizedata type is not supportedreorderReorderInputactivation_nchwcFusedConvGlobalMaxPoolGlobalAveragePoolConcat/onnxruntime_src/onnxruntime/core/optimizer/initializer.h/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc|{||||{{||{{11111ĆD11·=Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplInitializeronnxruntime::Initializer::Initializer(const onnx::TensorProto&)    N11onnxruntime16NchwcTransformerEdata type is not supported.minFuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.ccHHHHHXHH Q  h   X   onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constToProtoonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector&)virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const N11onnxruntime12FuseReluClipE/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplApplyRulesOnNodeN11onnxruntime25RuleBasedGraphTransformerEN11onnxruntime18ShapeToInitializerEstartsends=SDSDSDSD==SDSD==/>CCCC">>CC>=}>@@@@k>Y>@@G>5>>BBBB>>BB>>AAAAAAAAAAA+E8E8E8E8EEE8E8EED  N11onnxruntime14EliminateSliceETensorrtExecutionProviderNGRAPHExecutionProvider' doesn't support memcpy Execution type 'MemcpyCopy from/to host memoryMKLDNNExecutionProviderNupharExecutionProviderOpenVINOExecutionProvider/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)ForEachWithIndexvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)RecurseApplyImpl*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_N11onnxruntime17MemcpyTransformerEUnsqueezeUnsqueezeElimination_UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccApplyN11onnxruntime20UnsqueezeEliminationEUnsupported output type of . Can't constant fold node 'ort_value.IsTensor()/onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()const T& OrtValue::Get() const [with T = onnxruntime::Tensor]Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int) constApplyImplN11onnxruntime15OpKernelContextEN11onnxruntime15ConstantFoldingELeakyRelufused Conv with activation fused activation_params/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccUnexpected data type for Clip input of zm]  ?2??????onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15RecurseApplyImpl  N11onnxruntime20ConvActivationFusionEconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_ x        X 0 xxxxxH xx8888888x? 3*!2 xof{ of]/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.ccxXToProtoonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const N11onnxruntime13ConvAddFusionEBatchNormalizationepsilonbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_+,++++X,,++,+-0----}0q0--h0_0*&0****00**0/ ./ . . . .// . .//,,0,,,, 00,, 00:,/0:,:,:,:,#00:,:,001 21111h221131 4 7 4 4 4 466 4 46606000066006|6D4C6D4D4D4D476+6D4D4"66262222662266J26J2J2J2J266J2J2669z999999999>:;9:IA::::=A1A::(AA3=@3=3=3=3=@@3=3=@@;@;;;;w@k@;;b@Y@(> @(>(>(>(>@@(>(>??9@9999@@99@@/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.ccNMNNNN_MMNNLLUcTUUUUNTBTUUZTSLUTLULULULUTTLULUTS UT U U U UTT U UTTTTTTTT%TTTTTSTbTTTTTTTTTTLonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const  N11onnxruntime12ConvBNFusionEmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc`o3n`o`o`o`om{m`o`omlsrssssrrssqqpsMrssss7rrss'rqs0rssss0qqssAqo^s.s^s^s^s^sMsr&, const InitializedTensorSet&)*ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_N11onnxruntime23OptimizerExecutionFrameE/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constGetKernelRegistryRegisterCPUKernelsRegisterOnnxMLOperatorKernelsRegisterOnnxOperatorKernelsN11onnxruntime20CPUExecutionProviderEN11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEutils::HasDataType(t_proto)unpack_status.IsOK()T1T2ConstantOfShapeMissing Input: vector::_M_range_insertMust have a single dimensiont_proto.dims_size() == 1t_proto.dims()[0] == 1Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.hMust have a valid input shape.input_shape.NumDimensions() > 0Must have a single dimension of 1=E\EEDD("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorT* onnxruntime::Tensor::MutableData() [with T = long int]MultinomialComputeT* onnxruntime::Tensor::MutableData() [with T = int]MultinomialComputeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialE0AC?"~+@"~+@W6}+W6}++eG?+eG?????@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?@@delta in Range operator can not be zero!Range op: Unsupported tensor data type:start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5RangeEmin should be a scalar.max should be a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hinfo.GetAttr("max", &max_).IsOK()info.GetAttr("min", &min_).IsOK()min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Clip::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Clip_6::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime6Clip_6IfEEN11onnxruntime4ClipIfEEexclusivereverseCumSumstarts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()) but got: Axis should be in the range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()dims.size() == extents_.size()Axis tensor should be 0D or 1Dvoid onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long int]onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span, gsl::span)T* onnxruntime::Tensor::MutableData() [with T = long int]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEEDDD4$TDTdTdtTdDDtTdtdTdttDetX != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime3DetIfEEj@fmod(fmod == 0) || (fmod == 1)SubAbsNegFloorCeilReciprocalSqrtPowLogLessGreaterEqualBitShiftNotAndOrXorSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandModaxis == 1 || axis == largestdirectionLEFTRIGHTInvalid direction value of 'X_ptr != nullptrvector::_M_fill_insertCan broadcast 0 by 0 or 1. largest <= 1failed to get first output! vs fmod_Unsupported data typeMust have 1 or more inputsinputCount >= 1data_n.Shape() == shapeinput_count >= 1data_1.Shape() == shapep_ml_valuecur1 == end1cur_out == end_outfmod must have value either 0 or 1/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccAttempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h'. Valid values are 'LEFT' or 'RIGHT'.Shape must be 1 dimensional as it's tensor data is a shapetensor_shape.Shape().GetDims().size() == 1X and Y input types do not match: fmod attribute must be true for float, float16 and double typesAll inputs must have the same shapePlease fetch output tensor with specified shape.onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102]onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float]virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*)onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)onnxruntime::common::Status onnxruntime::Erf::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123]onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99]onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)onnxruntime::Broadcaster::Broadcaster(const std::vector&, const std::vector&)onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96]onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_N11onnxruntime3NotEN11onnxruntime3AndEN11onnxruntime2OrEN11onnxruntime3XorEN11onnxruntime3AddIfEEN11onnxruntime3AddIdEEN11onnxruntime3AddIiEEN11onnxruntime3AddIlEEN11onnxruntime3SubIfEEN11onnxruntime3SubIdEEN11onnxruntime3SubIiEEN11onnxruntime3SubIlEEN11onnxruntime3MulIfEEN11onnxruntime3MulIdEEN11onnxruntime3MulIiEEN11onnxruntime3MulIlEEN11onnxruntime3DivIfEEN11onnxruntime3DivIdEEN11onnxruntime3DivIiEEN11onnxruntime3DivIlEEN11onnxruntime3AbsIfEEN11onnxruntime3AbsIdEEN11onnxruntime3AbsIaEEN11onnxruntime3AbsIsEEN11onnxruntime3AbsIiEEN11onnxruntime3AbsIlEEN11onnxruntime3AbsIhEEN11onnxruntime3AbsItEEN11onnxruntime3AbsIjEEN11onnxruntime3AbsImEEN11onnxruntime3NegIfEEN11onnxruntime3NegIdEEN11onnxruntime3NegIaEEN11onnxruntime3NegIiEEN11onnxruntime5FloorIfEEN11onnxruntime4CeilIfEEN11onnxruntime10ReciprocalIfEEN11onnxruntime4SqrtIfEEN11onnxruntime4SqrtIdEEN11onnxruntime3PowIfEEN11onnxruntime3PowIdEEN11onnxruntime3ExpIfEEN11onnxruntime3ExpIdEEN11onnxruntime3LogIfEEN11onnxruntime5Sum_6IfEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Min_6IfEEN11onnxruntime5Min_8IfEEN11onnxruntime5Max_6IfEEN11onnxruntime5Max_8IfEEN11onnxruntime5Max_8IdEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8IfEEN11onnxruntime8Expand_8IdEEN11onnxruntime8Expand_8IaEEN11onnxruntime8Expand_8IsEEN11onnxruntime8Expand_8IiEEN11onnxruntime8Expand_8IlEEN11onnxruntime8Expand_8IhEEN11onnxruntime8Expand_8ItEEN11onnxruntime8Expand_8IjEEN11onnxruntime8Expand_8ImEEN11onnxruntime8Expand_8IbEEN11onnxruntime8Expand_8INS_9MLFloat16EEEN11onnxruntime3ModE,FF@@@????BBBB????5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^1?1?1?1?BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>BBBB????"?"?"?"?@K@K@K@Kɿɿɿɿ.4.4.4.4t.t.t.t.,F,F,F,F s7 s7 s7 s7nnnn*=*=*=*=pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<****FFFF-DT!<@@right.NumDimensions() == 2%ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0transAtransB/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hleft.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcast/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.hinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()Not implemented fused activation: T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime4GemmIfEEA%~3a*7.A\3JCy7 ':Y;Ӡ5֕8;Y;AAAA%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [-axis . N=, D=/onnxruntime_src/onnxruntime/core/providers/common.haxis >= -tensor_rank && axis <= tensor_rank - 1Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7HardmaxIfEE Right:MatMul dimension mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satsified: left_num_dims >= 1 && right_num_dims >= 1 MatMul dimension mismatch, Left:Not satsified: K_ == right_shape[right_num_dims - 2] left operand cannot broadcast on dim Not satsified: left_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1 Not satsified: num_dims_with_pad == num_output_dims Not satsified: M_ == 1 && N_ == 1 Not satsified: num_dims_with_pad - 1 == num_output_dims Not satsified: num_dims_with_pad - 2 == num_output_dims Not satsified: K_ == right_shape[0] /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]Computeconst T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]Computeconst T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeconst T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]Computeconst T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]Computeconst T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]ComputeComputeN11onnxruntime6MatMulIfEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIjEEN11onnxruntime6MatMulIlEEN11onnxruntime6MatMulImEET3MatMulIntegera != nullptr && b != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cct->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1t->DataType() == DataTypeImpl::GetType() || t->DataType() == DataTypeImpl::GetType()Currently only scalar zero_point is supported. TODO: add per channel zero point support.IsScalarOr1ElementVector(a_zero_point)IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1MatMulInteger: Unsupported input types with zero pointconst T* onnxruntime::Tensor::Data() const [with T = signed char]onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9Computeonnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char]N11onnxruntime13MatMulIntegerIhhEEN11onnxruntime13MatMulIntegerIhaEEQLinearMatMul/onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccIsScalarOr1ElementVector(a_offset)IsScalarOr1ElementVector(b_offset)IsScalarOr1ElementVector(y_offset)IsScalarOr1ElementVector(a_scale)IsScalarOr1ElementVector(b_scale)IsScalarOr1ElementVector(y_scale)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1QLinearMatmul : input scale must be a scalar or 1D tensor of size 1QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1QLinearMatmul : result scale must be a scalar or 1D tensor of size 1const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]Computeonnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char]N11onnxruntime13QLinearMatMulIhhhEEORoundT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundIdEESignUnsupported input datatypeT* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]N11onnxruntime4SignELogSoftmaxint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)N11onnxruntime7SoftmaxIfLb0EEEN11onnxruntime7SoftmaxIfLb1EEEk_temp > 0sortedITopKk argument [/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()op_kernel_info.GetAttr("axis", &axis_temp).IsOK()op_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuevalue of k must not be negativek tensor should be a 1D tensor of size 1onnxruntime::TopK::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 11; T = float]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::TopK::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::TopK::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float]N11onnxruntime4TopKILi9EfEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi11EfEEArrayFeatureExtractorai.onnx.ml) >=Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]N11onnxruntime2ml23ArrayFeatureExtractorOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpISsEEBinarizer%luInput data with index: is NaNT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime2ml11BinarizerOpIfEEcast_toTO_FLOATTO_STRINGTO_INT64Invalid CAST_TO value of map_formDENSE Expected DENSE or SPARSEInvalid PACK_MAP value of max_mapCastMapstofstoll%fInvalid input type of value: Unexpected CAST_TO value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hinfo.GetAttr("map_form", &attr).IsOK()info.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cccur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of Expected std::map or std::maponnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = std::basic_string]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]const T& OrtValue::Get() const [with T = std::map >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&)onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&)onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml7CastMapECategoryMappercats_stringscats_int64sdefault_stringdefault_int64Input of string must have output of int64Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_strings", string_categories).IsOK()info.GetAttrs("cats_int64s", int_categories).IsOK()info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml14CategoryMapperEstring_vocabularyint64_vocabularyDictVectorizer/onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.hinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()T* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = std::map, long int>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, long int>]const T& OrtValue::Get() const [with T = std::map, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, float>]const T& OrtValue::Get() const [with T = std::map, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, double>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T& OrtValue::Get() const [with T = std::map >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]T* onnxruntime::Tensor::MutableData() [with T = double]const T& OrtValue::Get() const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = long int]N11onnxruntime2ml16DictVectorizerOpISslEEN11onnxruntime2ml16DictVectorizerOpISsfEEN11onnxruntime2ml16DictVectorizerOpISsdEEN11onnxruntime2ml16DictVectorizerOpIlSsEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIldEEinputdimensionsFeatureVectorizerinput_tensor_ptr != nullptrInvalid input type:Number of inputs (/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be provided/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()) does not match number of inputdimensions values (const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = float]virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml17FeatureVectorizerEimputed_value_floatsimputed_value_int64sreplaced_value_floatreplaced_value_int64ImputerEmpty input dimensions.Invalid typeExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specified/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()Empty value of imputed values.T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml9ImputerOpEkeys_floatsvalues_strings_Unusedkeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sLabelEncoder attribtues in LabelEncoder and The values is and the number of ) must have the same length. num_keys == num_valuesclasses_strings/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.hinfo.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()However, the number of key is Input of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)info.GetAttrs("classes_strings", string_classes).IsOK()gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml14LabelEncoder_2IfSsEEN11onnxruntime2ml14LabelEncoder_2ISsfEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2IlSsEEN11onnxruntime2ml14LabelEncoder_2ISslEEmulti_classNONEpost_transformLOGISTICSOFTMAXSOFTMAX_ZEROinterceptsclasslabels_stringsclasslabels_intscoefficients!coefficients_.empty()LinearClassifierlength overflow/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.ccInput shape needs to be at least a single dimension.const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml16LinearClassifierIfEEN11onnxruntime2ml16LinearClassifierIdEEN11onnxruntime2ml16LinearClassifierIlEEN11onnxruntime2ml16LinearClassifierIiEE3ֳ@@LinearRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.ccinfo.GetAttr("targets", &targets_).IsOK()info.GetAttrs("coefficients", coefficients_).IsOK()T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml15LinearRegressorIfEEInvalid normalize value of Invalid input type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.ccconst T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = long int]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = double]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&)onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml10NormalizerEOneHotEncoderzerosnum_categories_ > 0Unknown Category and zeros = 0./onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cctmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedconst T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]N11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpISsEE/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccscale_.size() == offset_.size()Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size (offsetEmpty scale in attributes!scale_.empty()Scale size: () != () or 1const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIiEESVMClassifierLINEARkernel_typePOLYRBFkernel_paramsvectors_per_classprob_aprob_bsupport_vectorsrhocoefficients_.size() > 0No support vectors./onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.hinfo.GetAttrs("kernel_params", kernel_params).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.ccinfo.GetAttrs("rho", rho_).IsOK()proba_.size() == probb_.size()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml9SVMCommonIfEEN11onnxruntime2ml9SVMCommonIdEEN11onnxruntime2ml9SVMCommonIlEEN11onnxruntime2ml9SVMCommonIiEEN11onnxruntime2ml13SVMClassifierIfEEN11onnxruntime2ml13SVMClassifierIdEEN11onnxruntime2ml13SVMClassifierIlEEN11onnxruntime2ml13SVMClassifierIiEE ף;?n_supportsone_classSVMRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.ccT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml12SVMRegressorIfEEBRANCH_EQTreeEnsembleClassifierBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTit != parents.end()nodes_treeidsnodes_nodeidsnodes_featureidsnodes_valuesnodes_hitratesnodes_modesnodes_truenodeidsnodes_falsenodeidsclass_nodeidsclass_treeidsclass_idsclass_weightsbase_valuesclasslabels_int64s!nodes_treeids_.empty()Invalid mode of value: treeindex >= 0X dims is empty./onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.ccbase_values_.empty() || base_values_.size() == static_cast(class_count_) || base_values_.size() == weights_classes_.size()nodes_missing_value_tracks_trueclass_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())Must provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })׼׼׼ּּ׼Pݼ8ݼ ݼ`ܼ8ܼhݼ@((Ponnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = double]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computevoid onnxruntime::ml::TreeEnsembleClassifier::Initialize() [with T = int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [with T = long int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [with T = double]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [with T = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml22TreeEnsembleClassifierIfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml22TreeEnsembleClassifierIiEETreeEnsembleRegressortarget_nodeidstarget_treeidstarget_idstarget_weightsSUMaggregate_functionAVERAGEMINn_targetsit != indices.end()/onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.ccinfo.GetAttr("n_targets", &n_targets_).IsOK()target_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())base_values_.empty() || base_values_.size() == static_cast(n_targets_)treeindex evaluated to a negative value, which should not happen.const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml21TreeEnsembleRegressorIfEEZipMapInput features_per_batch[] != number of classlabels[/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector >]T* OrtValue::GetMutable() [with T = std::vector, float> >]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml8ZipMapOpEMaxUnpoolUnsupported pooling size. inferred output shape:No kernel shape is set.kernel_shape_[dim] > 0Input dimension cannot be less than 3./onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satsified: X_shape.NumDimensions() >= 3 Not satsified: I_shape == X_shape Shape must be 1 dimensional as it's tensor data of a shapeNot satsified: tensor_shape->Shape().GetDims().size() == 1 output_shape is smaller than minimum required. output_shape:Not satsified: given_shape.Size() >= shape.Size() Index tensor shape should be same as that of the input data tensor to unpool./onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.hinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Pad should be smaller than kernel.T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)N11onnxruntime9MaxUnpoolEst.IsOK()spatialInvalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.hInvalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.ccT* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime9BatchNormIfEEkernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape.X num_dims does not match W num_dims.Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group./onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.hDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc/onnxruntime_src/onnxruntime/core/util/math.h kernel_shape: X: C: kernel channels: group: M: Unknown AutoPadType Stringdilation == 1pad type not supported.Invalid input shape: Out of bound access to arrayd_iter[d_i] < d_maxonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)void onnxruntime::math::Im2colNd::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, Provider*, bool, T) [with T = float; Provider = onnxruntime::CPUMathUtil; int64_t = long int]onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int]InferOutputShapeComputeN11onnxruntime4ConvIfEE/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.ccIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)Must be a scalar or 1D tensor or size 1.Non per-tensor quantization is not supported now.ConvIntegervoid onnxruntime::math::Im2colNd::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, Provider*, bool, T) [with T = unsigned char; Provider = onnxruntime::CPUMathUtil; int64_t = long int]Computevirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime11ConvIntegerEpad type not supportedoutput_height >= Houtput_width >= W*out_size >= 0ConvTransposeoutput_paddingoutput_shapegroup count is <= 0 filter_number: num_input_channels: Output height cannot be smaller than input height./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hOutput width cannot be smaller than input width.Input X must be 4-dimensional.filter number not equal to input channel number.Input channels is not divisible by group./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) constvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector*, std::vector*) constconst T* onnxruntime::Tensor::Data() const [with T = long int]PrepareForComputeDoConvTransposeN11onnxruntime13ConvTransposeIfEEFlatten/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hinfo.GetAttr("axis", &axis_).IsOK()The rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7FlattenEInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalizationconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime12InstanceNormIfEEp_ == 1 || p_ == 2LpNormalization/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime6LpNormIfEEsize_ > 0size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fbiasLRN/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr("size", &size).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4onnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo&) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime3LRNIfEEGlobalLpPoolceil_modecount_include_padstorage_orderkernel_shape[dim] > 0input_dims.size() >= 2Unsupported AutoPad Type.Unsupported pooling size : /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.ccNot satsified: input_dims >= 3 kernel_shape num_dims is not compatible with X num_dims.Not satsified: pooling_dims == pool_attrs_.kernel_shape.size() Not satsified: x_shape.NumDimensions() >= 3 Computevoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const string&, int)T* onnxruntime::Tensor::MutableData() [with T = long int]Computevoid onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constvoid onnxruntime::PoolAttributes::InferOutputSize(const std::vector&, std::vector*, std::vector*) conststd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector*) constComputeN11onnxruntime8PoolBaseEN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime4PoolIfNS_7MaxPoolILi8EEEEEN11onnxruntime4PoolIfNS_6LpPoolEEE/onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.ccIsScalarOr1ElementVector(input_offset)IsScalarOr1ElementVector(filter_offset)IsScalarOr1ElementVector(result_offset)IsScalarOr1ElementVector(input_scale)IsScalarOr1ElementVector(filter_scale)IsScalarOr1ElementVector(result_scale)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : filter zero point must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : filter scale must be a scalar or 1D tensor of size 1QLinearConv : result scale must be a scalar or 1D tensor of size 1T4QLinearConvconst T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::math::Im2colNd::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, Provider*, bool, T) [with T = unsigned char; Provider = onnxruntime::CPUMathUtil; int64_t = long int]Computevirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime11QLinearConvEpooled_shapepooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scalespatial_scale_ > 0MaxRoiPoolR->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_size/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.hinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()info.GetAttr("spatial_scale", &spatial_scale_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.ccT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7RoiPoolIfEEInput types for the Shrink operator are constrained to all numeric types only. Got bool type here.Input types for the Shrink operator are constrained to all numeric types only. Got std::string type here./onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.cc/onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()ShrinkUnknown tensor type of lambdT* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Shrink::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&)N11onnxruntime6ShrinkEWCHAR_TStringNormalizeris_case_sensitivecase_change_actionlocalestopwordsEmpty stopwords not allowed!sw.empty()p.secondwstr != wconv_errorConversion Erroren_US.UTF-8:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:/onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.ccattribute is_case_sensitive is not setattribute case_change_action is not setattribute case_change_action has invalid valueDuplicate stopwords not allowedStopword contains invalid utf8 charsInput contains invalid utf8 chars at: Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedConversion Errorconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::string_normalizer::Locale::Locale(const string&)onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)N11onnxruntime16StringNormalizerETfIdfVectorizermode is requiredTFTFIDFmode: min_gram_lengthmin_gram_length is requiredimpl_->min_gram_length_ > 0max_gram_length >= max_skip_countmax_skip_count is requiredimpl_->max_skip_count_ >= 0ngram_counts <= ngram_indexesGot weights of size: but ngram_indexes size: must be of equal sizepool_strings!impl_->pool_strings_.empty()pool_int64s-grams(items % ngram_size == 0)-grams detectedpool_int64s duplicate poll_strings duplicate Input shape must have either [C] or [B,C] dimensions with B > 0.Invalid type of the input argument/onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()status.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings must not be empty if specifiedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (before_insert + ngrams) == impl_->int64_set_.size()(before_insert + ngrams) == impl_->str_set_.size()Non-empty ngram_counts is requiredNon-empty ngram_indexes is requirednon-empty pool_int64s is required if pool_strings not providedconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&)N11onnxruntime15TfIdfVectorizerEGGGGcenter_point_boxNonMaxSuppressionboxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.Not satsified: ret.IsOK() output != nullptriou_threshold must be in range [0, 1]./onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccNot satsified: (iou_threshold >= 0 && iou_threshold <= 1.f) center_point_box only support 0 or 1/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h0 == center_point_box_ || 1 == center_point_box_Not satsified: boxes_shape.NumDimensions() == 3 Not satsified: scores_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satsified: boxes_dims[0] == scores_dims[0] boxes and scores should have same spatial_dimension.Not satsified: boxes_dims[1] == scores_dims[2] The most inner dimension in boxes must have 4 data.Not satsified: boxes_dims[2] == 4 virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constComputeconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]PrepareComputestatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)GetThresholdsFromInputsonnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)N11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionENull input X ptrNull rois_ptrNull batch_indices_ptrRoiAlignavgoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Invalid mode of value Number of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was specified. It should be either avg or maxconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float]*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_N11onnxruntime12RoiAlignBaseIfEEN11onnxruntime12RoiAlignBaseIdEEN11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEEReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMinkeepdimsin_dim != 0/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector&, bool, bool) [with T = long int; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector&, bool, bool) [with T = double; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector&, bool, bool) [with T = float; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector&, bool, bool) [with T = int; int64_t = long int]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true]N11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime12ReduceKernelILb1EEEN11onnxruntime12ReduceKernelILb0EEEN11onnxruntime8ReduceL1IfEEN11onnxruntime8ReduceL1IiEEN11onnxruntime8ReduceL2IfEEN11onnxruntime8ReduceL2IiEEN11onnxruntime12ReduceLogSumIfEEN11onnxruntime12ReduceLogSumIiEEN11onnxruntime15ReduceLogSumExpIfEEN11onnxruntime15ReduceLogSumExpIiEEN11onnxruntime9ReduceMaxIfEEN11onnxruntime9ReduceMaxIiEEN11onnxruntime9ReduceMaxIlEEN11onnxruntime10ReduceMeanIfEEN11onnxruntime10ReduceMeanIiEEN11onnxruntime9ReduceMinIfEEN11onnxruntime9ReduceMinIiEEN11onnxruntime9ReduceMinIlEEN11onnxruntime10ReduceProdIfEEN11onnxruntime10ReduceProdIiEEN11onnxruntime9ReduceSumIfEEN11onnxruntime9ReduceSumIiEEN11onnxruntime9ReduceSumIlEEN11onnxruntime9ReduceSumIdEEN11onnxruntime15ReduceSumSquareIfEEN11onnxruntime15ReduceSumSquareIiEEN11onnxruntime15ReduceSumSquareIdEEN11onnxruntime6ArgMaxIfEEN11onnxruntime6ArgMaxIiEEN11onnxruntime6ArgMinIfEEN11onnxruntime6ArgMinIiEEGRUhidden_sizeactivationsactivation_alphaactivation_betaclipclip_ > 0.fforwardbidirectionallinear_before_resetcur + size <= end/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'./onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast(num_directions_) * 2lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccGRU operator does not support double yetInvalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = float*; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [with T = float; typename gsl::span::const_iterator = const float*; size_t = long unsigned int]T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)N3gsl15narrowing_errorEN11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_}. Actual:Input P must have shape {std::future_errorDEEPCPU_LSTMinput_forget rows[Processing batchInput initial_c must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.hactivation_func_names.size() == static_cast(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of ValidateInputsconst T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = const float; size_t = long unsigned int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&)NSt13__future_base7_ResultIvEEN11onnxruntime13DeepCpuLstmOpENSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEENSt13__future_base11_State_base7_SetterIvvEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EERNNAffineThresholdedReluScaledTanhHardSigmoidEluSoftsign/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo&) [with T = float]FffffEN11onnxruntime3RNNIfEEPFffffEDump matrix: affineleakyreluthresholdedreluscaledtanhhardsigmoidsoftsignsoftplusInput B must have shape {*Input W must have shape {Input R must have shape {Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccInvalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual:Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector::const_iterator&, const const_iterator&, std::vector::const_iterator&, const const_iterator&, float&, float&)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]7.7.7.7.A Anew_axisConcatFromSequenceMust have valid 'axis' attribute/onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.hGot nullptr for sequence input./onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccconst T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceETensor size () != new size (SequenceEmptySequenceLengthSequenceConstructSequenceInsertSequenceEraseSequenceAtSplitToSequenceY != nullptrUnsupported 'dtype' value: I != nullptrInvalid sequence index (split_size_sum () != split_dim_size (S != nullptrGot nullptr for input tensor.), input tensor data type (num_inputs >= 1Split should be > 0split_scalar > 0 yetshape_.Size() == new_shape.Size()SequenceEmpty: Got nullptr for output sequence/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccSequenceLength: Got nullptr for output tensorGot nullptr input for index tensor) specified for sequence of size (SequenceAt: Got nullptr for output tensorInvalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceSequenceErase: Got nullptr for output sequenceSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.Invalid value in 'split' input. All values must be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })SplitToSequence operator does not support L| ,<LLLint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string]T* onnxruntime::Tensor::MutableData() [with T = int]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double]void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector&)int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float]virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) constCreateCopyAndAppendCpuTensorT* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) constconst T* onnxruntime::Tensor::Data() const [with T = long int]int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&)virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEAttribute to is not set.Tensor type mismatch.type == dtype_allocator != nullptrlen > 0bufferstoistolstoul-INFstoullstod/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccCasting from 'float16' to 'string' is not supported yet.Cast op must have 'to' argument of type DataTypeUnexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input,dlT!, 4l#d l\ ]^aTXY<[\Dd\d\gtgVW Txdytz{|Ԅ}|}uv<<\,\ܓ| l,$ĩ4L d|Į<$T4,DTT>>>> ?AAA\@@L<void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int]onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float]onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = long int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = short int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char]N11onnxruntime4CastIhEEN11onnxruntime4CastItEEN11onnxruntime4CastIjEEN11onnxruntime4CastImEEN11onnxruntime4CastIaEEN11onnxruntime4CastIsEEN11onnxruntime4CastIiEEN11onnxruntime4CastIlEEN11onnxruntime4CastIbEEN11onnxruntime4CastIfEEN11onnxruntime4CastIdEEN11onnxruntime4CastINS_9MLFloat16EEEN11onnxruntime4CastISsEE8OOOOAAAACompressaxis greater than input data dimension!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxis < static_cast(rank)axes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()const T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime8CompressECannot concatenate scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.ccNot satsified: input_count >= 1 Not satsified: inputs_0_rank > 0 Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rankNon concat axis dimensions must match: Axis has mismatched dimensions of Not satsified: dim_value == inputs_0_dims[axis_index] Not satsified: data_n.DataType() == p.output_tensor->DataType() const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector&, onnxruntime::Prepare&) constPrepareForComputeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime6ConcatEDynamicQuantizeLinearx_ptr != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccT* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]N11onnxruntime21DynamicQuantizeLinearIhEECEyeLikeT1 != nullptrEyeLike : Input tensor dimension is not 2/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.ccԓT* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7EyeLikeETindGatherMissing/Invalid 'axis' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.hindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccType for Tind not supported yet in Gather.const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)N11onnxruntime10GatherBaseEN11onnxruntime6GatherEGatherElements]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElements op: Value in indices must be within bounds [GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Data type of input 'data' should match the data type of the outputconst T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]std::vector onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)N11onnxruntime14GatherElementsEGatherNDinvalid index found, index = GatherND op: Input count mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrindices tensor must has rank larger than 0last dimension of indices must not be larger than rank of input tensorconst T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEdetect_positivedetect_negativeIsInfFailed to obtain detect_positive/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeData type X must be float or double, but instead got const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) constonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)N11onnxruntime5IsInfEIsNaNNull input ptrconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNINS_9MLFloat16EEE||||MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime27MeanVarianceNormalization_0IfEEN11onnxruntime27MeanVarianceNormalization_1IfEENonZeroX input is required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccconst T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEOneHotDepth is negative.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc'axis' attribute must have a value in the range [ComputeComputeComputeComputeComputeComputeComputeComputeComputeComputeN11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEE>Unexpected mode of Padreflectedgedata_rank > 0data_rank * 2 == pads.size()pads_size == 2 * data_rankCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Invalid 'mode' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueInput tensor has no dimensions/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc'pads' has wrong number of valuesPads tensor should be an INT64 tensorpads_tensor.DataType() == DataTypeImpl::GetType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count value_tensor->DataType() == DataTypeImpl::GetType() && value_tensor->Shape().Size() == 1Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvoid onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = float]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = float]onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = double]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = double]onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const [with T = double]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = int]onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const [with T = int]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = long int]onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)N11onnxruntime7PadBaseEN11onnxruntime3PadIfEEN11onnxruntime3PadIdEEN11onnxruntime3PadIiEEN11onnxruntime3PadIlEEy_zero_pointx_scalex_zero_pointDequantizeLinear/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccIsScalarOr1ElementVector(&y_scale)IsScalarOr1ElementVector(&y_zero_point)y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimx_zero_point must be 1D tensor with size y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimx_scale must be a scalar or 1D tensor or size 1.x_zero_point must be a scalar or 1D tensor or size 1.x_scale must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimIsScalarOr1ElementVector(&x_scale)IsScalarOr1ElementVector(&x_zero_point)onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]N11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearIaEEBAttribute shape is not set.Reshape_1Reshaperequested_shape[i] >= -1unknown_dim == -1, requested shape:/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hA dimension cannot be less than -1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hAt most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:(input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7ReshapeEN11onnxruntime9Reshape_1EScale value should be greater than or equal to 1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.hScale value should be greater than 0.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)info.GetAttr("mode", &mode).IsOK()info.GetAttrs("scales", scales_).IsOK()coordinate_transformation_modeexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0.Resize operatorUpsample operatorscale >= 1scale > 0nearestcubicmode attribute is . It can only be (default) or scalesextrapolation_valueasymmetricpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeround_prefer_floornearest_moderound_prefer_ceilcubic_coeff_aexclude_outsidescales_size > 0const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector&) constvoid onnxruntime::UpsampleBase::ScalesValidation(const std::vector&, onnxruntime::UpsampleMode) constonnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)N11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE0_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE1_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE2_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE3_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE4_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE0_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE1_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE2_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE3_@?batch_axisInvalid batch_axis of . Must be 0 or 1batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axisReverseSequence. batch_size=/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()info.GetAttr("time_axis", &time_axis).IsOK()time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccsequence_lens shape must be {batch_size}. Got:gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)N11onnxruntime17ReverseSequenceOpE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccdata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=Expecting indices to be either int32_t or int64_tScatterElementsScatter. Input rank=Indices dim= at pos= is greater than input dim=int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) constonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&)N11onnxruntime7ScatterEScatterNDinput_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptr, indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinput tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int]ComputeN11onnxruntime13ScatterNDBaseEN11onnxruntime9ScatterNDET* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5ShapeESizeT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime4SizeEStarts must be a 1-D arrayEnds must be a 1-D array'axes' has duplicates'step' value cannot be 0Cannot slice scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attributeInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccnullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1start_tensor->Shape() == ends_tensor->Shape()nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatch'axes' has an axis outside of the tensor dimension countMissing input tensor to be processedComputeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string; bool dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = bool]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true]void onnxruntime::SliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]Computeonnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string; bool dynamic = true]void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector&, std::vector&, std::vector&, std::vector&) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime9SliceBaseEN11onnxruntime5SliceIhLb0EEEN11onnxruntime5SliceItLb0EEEN11onnxruntime5SliceIjLb0EEEN11onnxruntime5SliceImLb0EEEN11onnxruntime5SliceIaLb0EEEN11onnxruntime5SliceIsLb0EEEN11onnxruntime5SliceIiLb0EEEN11onnxruntime5SliceIlLb0EEEN11onnxruntime5SliceIfLb0EEEN11onnxruntime5SliceIdLb0EEEN11onnxruntime5SliceINS_9MLFloat16ELb0EEEN11onnxruntime5SliceIbLb0EEEN11onnxruntime5SliceISsLb0EEEN11onnxruntime5SliceIhLb1EEEN11onnxruntime5SliceItLb1EEEN11onnxruntime5SliceIjLb1EEEN11onnxruntime5SliceImLb1EEEN11onnxruntime5SliceIaLb1EEEN11onnxruntime5SliceIsLb1EEEN11onnxruntime5SliceIiLb1EEEN11onnxruntime5SliceIlLb1EEEN11onnxruntime5SliceIfLb1EEEN11onnxruntime5SliceIdLb1EEEN11onnxruntime5SliceINS_9MLFloat16ELb1EEEN11onnxruntime5SliceIbLb1EEEN11onnxruntime5SliceISsLb1EEEblocksizeCRDDCRSpaceToDepthDepthToSpaceAttribute blocksize is not set./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()DepthToSpace op: only 'DCR' and 'CRD' modes are supported/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccinput.Shape().NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0input_depth % (blocksize_ * blocksize_) == 0onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthIfEEN11onnxruntime12DepthToSpaceIfEE*> Axis= NumOutputs= Input shape=SplitInvalid value in 'split' attribute. All values must be > 0/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccSplit operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]ComputeImplconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&)N11onnxruntime9SplitBaseEN11onnxruntime5SplitESqueeze. shape= must be 1 instead of Dimension of input input_shape[i] == 1/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)static std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeETileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.ccconst T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime4TileEperm is outside range. is repeated.[ perm: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = std::basic_string]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string]N11onnxruntime13TransposeBaseEN11onnxruntime9TransposeEUniquevector::_M_emplace_back_auxUnsupported tensor type of gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]N11onnxruntime6UniqueE'axes' has a duplicate axisMissing/Invalid 'axes' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc'axes' has an out of range axisComputeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) constonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)N11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEUpsampleResize: unexpected modeUpsample: unexpected modescales != nullptrsizes == nullptrInvalid roi input index.roi_input_idx_ > 0Resize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccResize: size of roi array should be 2 * N where N is the rank of input tensor X.sizes != nullptr && sizes->Shape().Size() != 0Either scales input or sizes input must be provided and the number of elements of scales or sizes should be the same as the rank of the input.BaseComputeonnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&) const [with T = float]onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = float]BaseComputeonnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&) const [with T = int]onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = int]BaseComputeonnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector&) const@A@Wherecondition && X && Ycondition, X, and Y inputs are required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.ccT* onnxruntime::Tensor::MutableData() [with T = int]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int]onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = long int]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int]onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext*) const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string]onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string]N11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereISsEE/onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.ccRegisterCpuContribKernelsRegisterNchwcKernels/onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.ccRegisterCpuAutoMLKernelsgammaSeluconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7SigmoidIfEEN11onnxruntime4TanhIfEEN11onnxruntime3EluIfEEN11onnxruntime11HardSigmoidIfEEN11onnxruntime9LeakyReluIfEEN11onnxruntime4ReluIfEEN11onnxruntime4SeluIfEEN11onnxruntime18ParametricSoftplusIfEEN11onnxruntime8SoftsignIfEEN11onnxruntime15ThresholdedReluIfEE #}-?_}?VIf'If' node has outputs.then_branchinfo == nullptr did not.else_branch' attribute.Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.ccnum_subgraph_outputs == static_cast(num_outputs)SetupSubgraphExecutionInfo should only be called once for each subgraph.Subgraph must have the shape set for all outputs but Failed to create output tensor for then_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for 'CreateFeedsFetchesManager must be called prior to execution of graph.info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()ExecuteInitializeconst T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computevirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constonnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)onnxruntime::If::If(const onnxruntime::OpKernelInfo&)*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_N11onnxruntime2IfELoop inputs. Found:'Loop' node has but has info_ == nullptr Expected:bodyfeeds_fetches_manager_Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccstatic_cast(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Inconsistent shape in loop output for output Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Subgraph SessionState was not found for 'body' attribute.info.GetAttr("body", &proto).IsOK()ExecuteT* onnxruntime::Tensor::MutableData() [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = long int]Initializeconst T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]Computevirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) constonnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)onnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo&)N11onnxruntime4LoopEScanInvalid scan input: has batch size of has length of sequence_lens length of did not match batch size of p_mlvaluenum_scan_inputsSubgraph in 'body' produces outputs but Scan expects /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cc dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid entries in sequence_lens. Max sequence length was Output OrtValue has not been created for loop state variable output /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()feeds_fetches_manager_ && info_T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]Executetypename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = const OrtValue&]CreateLoopStateVariablesonnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)AllocateOutputTensorsgsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]ValidateInputInitializeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]onnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string]onnxruntime::Scan::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]N11onnxruntime4ScanILi8EEEfinal_output_mlvalue_. Output tensor rank was but input '' dimension . Input tensor rank was output_mlvaluescan_input_directionsscan_output_directionsscan_input_axes but expected scan_output_axes/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.ccAttempt to retrieve final output before it was set./onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.Invalid value in scan_input_axes for input Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsconst OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() constTransposeOutputonnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()Executeonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&)CreateLoopStateVariablesAllocateOutputTensorsT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]SetupInputsValidateInputInitializeComputeonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]onnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string]onnxruntime::Scan::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]N11onnxruntime4ScanILi9EEENumber of entries in '' was Invalid values in ' is not compatible with !is_concrete_shape_Misuse of LoopStateVariable. Attempt to move beyond end of sequence/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cciteration_num_ < sequence_len_Expected AllocateFinalOutput to have been called to before we increment the iteratornum_entries < 0 || gsl::narrow_cast(directions.size()) == num_entries'. 0 == forward. 1 == reverse.cur_iteration_ < num_iterations_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.Mismatch between expected shape and shape from first outputThe subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsFailed to create output tensor for output #If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = OrtValue&]OrtValue& onnxruntime::scan::detail::OutputIterator::operator*()AllocateFinalOutputonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)AllocateFinalBufferconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Initializevoid onnxruntime::scan::detail::LoopStateVariable::Next()const T& OrtValue::Get() const [with T = onnxruntime::Tensor]operator()typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = const OrtValue&]IterateSequenceCreateFeedsFetchesManagervoid onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector&, int64_t)onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_ParametricSoftplusT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7contrib10ScaledTanhIfEEN11onnxruntime7contrib4GeluIfEE5?AttnLSTMDEEPCPU_ATTN_LSTM}, actural: ], while found!, am_attn_size}, Got:}, Got: }. Got: , aw_attn_size}. Got:/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{Attention query layer weight shape error! Expected:{Attention v weight shape error! Expected:{Attention layer weight shape error! Expected: {/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccValidateInputsComputeImplvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib17DeepCpuAttnLstmOpEN11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno=sqeuclideanCDist/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:The first input of CDist kernel has wrong shape: T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib5CDistIfEEN11onnxruntime7contrib5CDistIdEEConvTransposeWithDynamicPadsN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEborder input dimensions insteadInput's height () + bottomBorder (Input's width () + rightBorder () + scale_[0] () + scale_[1] (CropAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEEbilinearCropAndResizeNull crop_size_ptr/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearestNumber of dimensions for crop size should be exactly 1const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_N11onnxruntime7contrib13CropAndResizeIfEEDynamicSliceScale/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo&) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEExpandDimsAxis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.haxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAn axis tensor must be a scalar tensor.const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = int]virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib10ExpandDimsE/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib14FusedConvFloatEleaky_relu_alphaonnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib9FusedGemmIfEEImageScalerBias size (/onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()) does not match the number of channels (T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib11ImageScalerIfEE/onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.ccop_kernel_info.GetAttr("axis", &axis_).IsOK()const T* onnxruntime::Tensor::Data() const [with T = float]Computeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T* onnxruntime::Tensor::Data() const [with T = double]Computeonnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib9LayerNormIfEEN11onnxruntime7contrib9LayerNormIdEEMatMulInteger16A != nullptr && B != nullptr/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.ccconst T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]N11onnxruntime7contrib15MatMulInteger16IssiEEMaxpoolWithMask/onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constvoid onnxruntime::PoolAttributes::InferOutputSize(const std::vector&, std::vector*, std::vector*) conststd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector*) constconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib15MaxpoolWithMaskEacross_channelsnormalize_variance/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.hinfo.GetAttr("across_channels", &across_channels_).IsOK()info.GetAttr("normalize_variance", &normalize_variance_).IsOK()onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float]MurmurHash3keysType not supported./onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib11MurmurHash3Einvalid channel countchannels_ > 0X_shape.NumDimensions() == 4channels_ <= Y_shape[1]Unsupported convolution size./onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()pool_attrs_.kernel_shape.size() == 2/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Not satsified: Y->Shape() == sum_shape output and sum shape must matchonnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constvoid onnxruntime::PoolAttributes::InferOutputSize(const std::vector&, std::vector*, std::vector*) conststd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector*) constonnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) constComputeonnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib13NchwcPoolBaseEN11onnxruntime7contrib9NchwcConvEN11onnxruntime7contrib12NchwcMaxPoolEN11onnxruntime7contrib16NchwcAveragePoolEN11onnxruntime7contrib12ReorderInputIfEEN11onnxruntime7contrib13ReorderOutputIfEEy_scaleSampleOpT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7contrib8SampleOpIfEETokenizermarkattribute mark is not setpad_valuemincharnummincharnum_ > 0separatorstokenexp!tokenexp.empty()separators must not be empty!separators.empty()Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute pad_value is not setattribute mincharnum is not setattribute mincharnum must have a positive valueEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib9TokenizerEInput tensor to Unique op should be 1DT* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7contrib6UniqueIfEEconv_window_sizechar_embedding_size embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbeddingConv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute .Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2./onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib17WordConvEmbeddingEhӥhӥhӥhDateTimeTransformerconst T* onnxruntime::Tensor::Data() const [with T = long int]T* OrtValue::GetMutable() [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime6automl19DateTimeTransformerEReal memory steps is not in (0, not support normalize yet.!normalize_/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]onnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]N11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEEunimplemented activation: activation_params count mismatchExpecting an instance of ExternalTimePoint/onnxruntime_src/onnxruntime/automl_ops/automl_types.ccsizeof(DateTimeFeaturizerTimePointData) == data_sizeconst T& OrtValue::Get() const [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint]static void onnxruntime::NonTensorTypeConverter::ToContainer(const OrtValue&, size_t, void*)static void onnxruntime::NonTensorTypeConverter::FromContainer(onnxruntime::MLDataType, const void*, size_t, OrtValue&)N11onnxruntime13NonTensorTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEEEN11onnxruntime10OpaqueTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEXadL_ZNS_15kMsAutoMLDomainEEEXadL_ZNS_14kTimepointNameEEEEEDateTimeFeaturizer_TimePointcom.microsoft.automlSSE, 12x4, depth 2allocation failuregemmlowp error: %s N8gemmlowp10KernelBaseEN8gemmlowp24SSE4_64_Kernel12x4Depth2EN8gemmlowp17DefaultKernelImplILb0ELb0EEEN8gemmlowp4TaskEN8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE@@/onnxruntime_src/onnxruntime/core/util/protobuf_parsing_utils.ccCan't BackUp() after failed Next().CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEOrtMemoryInfo: [ id: mem_type:CudaCudaPinnedSpecified device is not supported.N11onnxruntime12CPUAllocatorESt14default_deleteIN11onnxruntime16IDeviceAllocatorEESt14default_deleteIN11onnxruntime8BFCArenaEESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE/onnxruntime_src/onnxruntime/core/framework/bfc_arena.h/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc!c->in_use() && (c->bin_num != kInvalidBinNum)!c->in_use() && (c->bin_num == kInvalidBinNum)p_int < base_int + memory_size_!c1->in_use() && !c2->in_use()bin->free_chunks.count(h) == 1Creating bin of max chunk size BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)0 == memory_size % kMinAllocationSizeBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use client-requested in use in bin. Summary of in-use Chunks by size: BFC Arena ran out of memory trying . Current allocation summary follows.reserved_chunks_.find(ptr) == reserved_chunks_.end()Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_intc2->prev == h1h != kInvalidChunkHandlec->bin_num == bin_num!chunk->in_use()Extended allocation by bytes.Total allocated bytes: Allocated memory at Could not find chunk in binChunkFree Bin (): Total Chunks: , Chunks in use: allocated for chunks. in use in bin. Bin for , Chunk State: , prev: , next: Chunks of size totalling Sum Total of in-use chunks: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Stats: tried to allocate 0 bytesto allocate DumpMemoryLogvoid onnxruntime::BFCArena::DumpMemoryLog(size_t)std::array onnxruntime::BFCArena::get_bin_debug_info()void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&)void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::DeallocateRawInternal(void*)void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t)void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)AllocateRawInternalvirtual void* onnxruntime::BFCArena::Reserve(size_t)int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) constRegionForonnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t)Extendonnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t)BFCArenaN11onnxruntime8BFCArenaEfff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) constN11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEDevice: [ memory_type: device_id:Tensor size mismatchThere's no data transfer registered for copying tensors from data_transfer registered is nullptr.(null)Not implementedtensor(MLFloat16)tensor(bfloat16)sparse tensor type value_proto != nullptrelem_proto != nullptrMap with key type: value type: is not registered/onnxruntime_src/onnxruntime/core/framework/data_types.ccthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kOpaqueTypeOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: expected to be a registered ONNX type/onnxruntime_src/include/onnxruntime/core/framework/data_types.hutils::HasElemType(tensor_type)utils::HasElemType(sparse_tensor_type) is not currently registered or supportedl$L h (  X98 99,9<9L9\9l999999998(PhOxOOOOOO(POOOPP(P(PPOstatic const onnxruntime::DataTypeImpl* onnxruntime::DataTypeImpl::TypeFromProto(const onnx::TypeProto&)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::map]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::map, float>]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = long unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = long int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = short int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = short unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = bool]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = float]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = onnxruntime::Tensor]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = double]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = long int]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = double]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = long int]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = std::basic_string]virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) constbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)GetElementTypeGetElementTypeN11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime13NonTensorTypeINS_9TensorSeqEEEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime12SequenceTypeINS_9TensorSeqEEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime11NonOnnxTypeIiEEN11onnxruntime11NonOnnxTypeIfEEN11onnxruntime11NonOnnxTypeIbEEN11onnxruntime11NonOnnxTypeISsEEN11onnxruntime11NonOnnxTypeIaEEN11onnxruntime11NonOnnxTypeIhEEN11onnxruntime11NonOnnxTypeItEEN11onnxruntime11NonOnnxTypeIsEEN11onnxruntime11NonOnnxTypeIlEEN11onnxruntime11NonOnnxTypeIdEEN11onnxruntime11NonOnnxTypeIjEEN11onnxruntime11NonOnnxTypeImEEN11onnxruntime11NonOnnxTypeINS_9MLFloat16EEEN11onnxruntime11NonOnnxTypeINS_8BFloat16EEEZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_SsN11onnxruntime6TensorEN11onnxruntime9MLFloat16EN11onnxruntime8BFloat16ESt3mapISsfSt4lessISsESaISt4pairIKSsfEEESt3mapIlfSt4lessIlESaISt4pairIKlfEEECaught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLib~ExLibLoaderN11onnxruntime11ExLibLoaderEMemory pattern planner is not enabled on this execution framework./onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast(offset) < node_values_size_/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocation for ort_value_idx=Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative value, block in memory pattern size is: , fall back to default allocation behavior, block not found in target location. fall back to default allocation behaviorfeeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains buffers_.find(mem_patterns_->locations[i]) == buffers_.end()Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shape size=TraceFree for ort_value_idx=ort_value.Fence() == nullptrFor ort_value with index: but the actually size is: invalid index Invalid allocation kind: mlvalue.Fence() == nullptr Requested shape:TraceFreevoid onnxruntime::ExecutionFrame::TraceFree(int)TraceAllocateconst onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int)ReleaseMLValueImplonnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&)AllocateAsPerAllocationPlanonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]AllocateMLValueTensorPreAllocateBufferAllocateMLValueTensorSelfOwnBufferHelperonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool)onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::unordered_map >&, const onnxruntime::SessionState&)int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constconst OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) constconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t)onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector&, const std::vector&, const std::unordered_map&, const std::vector&, const std::vector&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&)N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameEduplicated allocator/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)N11onnxruntime18IExecutionProviderEError mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownvoid onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector >&, const std::vector >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoMapNamesToMLValueIdxsNo provider specified.1 == capability->nodes.size()/onnxruntime_src/onnxruntime/core/framework/func_kernel.hcreate_func(&context, &func_state_) == 0/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr, const onnxruntime::KernelRegistryManager&, const string&, int&)onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) constPartitionN11onnxruntime14FunctionKernelEOp: Incompatible types.kernel def can't be NULLFailed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions./onnxruntime_src/onnxruntime/core/framework/kernel_registry.cclen <= op_schema.inputs().size()Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: (TryFindKernelvoid onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10]void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8]The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node found duplicated provider in KernelRegistryManagernullptr != type_proto/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_:tensor type Tensor types should have been handled already\\\,\const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]TempSpace allocator not foundExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptr. Num args is Invalid arg_num of arg_num < arg_counts.size()/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccnode_offsets_index < node_offsets_size_OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constOrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constonnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)cannot find allocator/onnxruntime_src/onnxruntime/core/framework/op_kernel_info.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const'is defined.No attribute with name:'attr != nullptrNo attribute with this name is defined./onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.ccvalues.size() == static_cast(attr->floats_size())values.size() == static_cast(attr->ints_size())values.size() == static_cast(attr->strings_size())Attibute name and type don't match/onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.hvalues.size() == static_cast(attr->tensors_size())values.size() == static_cast(attr->graphs_size())ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeCan't slice a non-tensor OrtValue. Type was /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.ccInsufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionOrtValue has not been allocated so can't be sliced.static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]node_index_info_ with error: duplicated ort_value index:SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo./onnxruntime_src/onnxruntime/core/framework/session_state.ccFailed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated.Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function?Done saving OrtValue mappings.Using an input in multiple nodes on different devices is not supported currently. Input:Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()existing_entries.find(attribute_name) == existing_entries.cend()const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constvoid onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr)void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)SetGraph/onnxruntime_src/onnxruntime/core/framework/session_state_initializer.ccOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > 0Invalid allocation info. Provider name = string tensor is not supported for copying between allocatorsInternal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensorsExecution plan was not found in SessionState. CreatePlan must be called first.Saving initialized tensors., Got Deserialize tensor SubgraphGraphexec_plan_ptr input with name is not used by any node.ForEachWithIndexoperator()operator()SaveInputOutputNamesToNodeMappingDeserializeTensorProtoonnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]SaveInitializedTensorsonnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer >*, ExecutionMode)CreatePlan*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextEtensor size overflowshape.Size() must >=0p_type != nullptr/onnxruntime_src/onnxruntime/core/framework/tensor.ccvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, int64_t)size_t onnxruntime::Tensor::SizeInBytes() constonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr, int64_t)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_t)Internal error.Mem pattern for initializer ' is not found' failedduplicated location/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hWeight buffer for initializer 'Get preallocated buffer for initializer 'Failed to get allocator for location: TraceFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEInvalid dimension of dimension <= num_dims/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccdimstart <= dimend && dimend <= size()Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constOrtValue is not a TensorArgument is not a tensor/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)UnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensor: the pre-allocate size does not match the size in proto) does not match the data size(corrupted protobuf data: tensor shape size(string tensor can not have raw data/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cctensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedInitialized tensor with unexpected type: data overflow) in protoInvalid TensorProto&, std::vector&, const std::vector&, const onnxruntime::DataTransferManager&)ExecuteGraphImplExecuteGraphT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]CopyMLValueconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, const std::vector&, std::vector&)bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector&, std::vector&)CalculateStaticCopyInfoForFeedCalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfoconst OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&)FindMemoryInfoForValueconst OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&)N11onnxruntime9IExecutorE/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccid >= 0 && static_cast(id) < ort_value_info_.size()n >= 0 && static_cast(n) < ort_value_info_.size()n >= 0 && static_cast(n) < plan_.allocation_plan.size()kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr(ort_value_idx) output_name : No suitable kernel definition found for op Can not find the execution provider AllocateAllocateStaticallyPreExistingReuseAllocateOutputSharenullptr != tensor_type_basereused != reused_forCan not find the node p_providerAllocation Plan: Execution Plan: ) , use fence when asyncIndex out-of-range!nullptr != nodeFree ml-values: INVALID INDEXvector::_M_fill_insertonnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&)OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&)GeneratePlanForWeightsComputeReusePlanonnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&)ForEachWithIndexComputeUseCountsCreatePlanN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanEZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_func info for node: already exist.Can't use func with null ptr/onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_'. Begin execution does not.entry != nullptrop_name_fence_before node. Name:'' Status Message: _kernel_time_fence_afterMultiple errors were found.ParallelExecutor::ExecuteUnknown exception was caught by catch-all handler.Exception running nodes starting at Exiting due to terminate flag being set to true./onnxruntime_src/onnxruntime/core/framework/parallel_executor.ccGot nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hNon-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&)onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&)RunNodeAsyncconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]Execute*ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_N11onnxruntime16ParallelExecutorEN11onnxruntime23OpKernelContextInternalESequentialExecutor::Execute/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]ReleaseNodeMLValuesonnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&)ExecuteN11onnxruntime18SequentialExecutorEinvalid ort_value_index:', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.ccFailed to get allocator for initializer 'GetPreallocatedBufferN11onnxruntime21SimpleTensorAllocatorEmodel format error! Need a key for the external data infomodel format error! Need a value for the external data infomodel format error! Missing 'location'parsing checksummodel format error!/onnxruntime_src/onnxruntime/core/graph/automl_ops/automl_defs.ccThe input represents a number of seconds passed since the epoch, suitable to properly constructan instance of std::chrono::system_clock::time_pointThe output which is a Microsoft::DateTimeFeaturizer::TimePoint structureConstrain input type to int64 scalar tensor.Constrain output type to an AutoML specific Microsoft::Featurizers::TimePoint typecurrently not part of ONNX standard. When it becomes a part of the standard we will adjust thiskernel definition and move it to ONNX repoopaque(com.microsoft.automl,DateTimeFeaturizer_TimePoint)[ShapeInferenceError] Input's shape must be 4-D) + bottom_border () + right_border () + scale[0] () + scale[1] (tensor rank too smallAttentionNumber of attention headsnum_headsmask_indexEmbedLayerNormalizationinput_idssegment_idsmask2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embeddingSkipLayerNormalizationskipValue of alphaValue of beta1D input tensor1D output tensorThe scale to apply.Threshold valueInput tensorOutput tensorSliced data tensor.GivenTensorFillThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingGRUUnitdrop_stateshidden_prevgatesseq_lengthshiddenScaling valueStrings to tokenizeTokenized stringsN-dimensional matrix AN-dimensional matrix BReduceSumIntegerAn input tensor.Reduced output tensor.reducedAn input tensor to hash.32-bit hash value.Tensor of rank r >= 1.Tensor of rank q >= 1.Specify weights of convSpecify bias of convConstrain to tensor(int32).Constrain to tensor(float).Input tensor.Tensor after padding.idx2D matrix with shape (M,N)2D matrix with shape (K,N)roisbatch_indicescrop_sizeScale tensor.Bias tensor.Output data tensor.inv_std_varThe input data as Tensor.The output.gamma should have 1 dimensionbeta should have 1 dimensionAttribute should specify a shapeInput axis is invalid: Constrain input and output types to float tensorsfirst input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimension'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () needs to be greater than or equal to the left_border (First input does not have rank 2Second input does not have rank 2inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64/onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input and output types to float/quantized tensors3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)Attention mask index with shape (batch_size)3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to float tensors.Constrain mask index to integer types2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)2D attention mask with shape (batch_size, sequence_length)1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_sizeConstrain input and output types to float or half tensors.Bias applied to each channel, same size as C.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Constrain input and output types to all tensor types.Tensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Constrain indices to integer typesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.The previous GRU hidden state.Unactivated gate outputs from forget, update, and output gates, pre-activation.Array of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.The new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.The scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain input0 and output types to float tensorsInput 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.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Specified axis to insert a dimensionThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'y', 'x_scale' to float tensors.Constrain 'x_zero_point' and 'x' to 8-bit integer tensors.Input/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embeddingSpecify embedding vector of charThree modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayTensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is 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`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Input can be of any tensor type.A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, 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.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).The epsilon value to use to avoid division by zero.Input data tensor from the previous layer.Saved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.Inputs 0, 1 and 2 shall be 2 dimensionsInputs 0, 1 and 2 shall have same value in dimension 1position_embedding should have 2 dimensionsThe last dimension of word_embedding and position_embedding does not match.segment_embedding should have 2 dimensionsThe last dimension of word_embedding and segment_embedding does not match.The last dimension of word_embedding and gamma does not match.The last dimension of word_embedding and beta does not match.Inputs 0, 1 and 2 shall have value in dimension 1word_embedding should have 2 dimensions and dimension size is known.Negative values are not allowed in a shape specificationFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_PFvRN4onnx8OpSchemaEEstartlimitdelta1-D Tensor of the range.Unsupported type:Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.Can not get shape initializer data!Unsupported non-raw-data data type!*ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_/onnxruntime_src/onnxruntime/core/graph/graph.ccThis API is not supported when model is loaded from proto file right now./onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hGraph attribute inferencing failed: Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when adding edge.This is an invalid model. The sum of input arg count is not equal to size of input defs in node (Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.Invalid node indexes specified when removing edge.No Graph instance was found for attribute Error: Duplicate definition-site for ('. It is not used by any node and should be removed from the model.'. It is no longer used by any node.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: the graph is not acyclic.Graph ctor should have created NodeArg for initializer.This is an invalid model. Graph output () does not exist in the graph. must be either specified in graph inputs or graph initializers. as it still has output edges.node->GetOutputEdgesCount() == 0This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=nodes_.size() < static_cast(std::numeric_limits::max())Mismatch between number of source and target dimensions. Source=Can't merge shape info. Both source and target dimension have values but they differ. Source=Error merging shape info for output. '. Falling back to lenient merge.Tensor element type mismatch. SparseTensor element type mismatch. Only 'value' attribute is supported within a 'Constant' node in ORTnullptr != sub_graph && nullptr != sub_graph->GetMetaDef()This is an invalid model. Node () does not have type information set by parent node.This is an invalid model. Type Error: Type ') does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Value of initializer Type Error: Shape of initializer is not a registered function/op) is required but not specified.This is an invalid model. Error in Node:Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs.Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()'was added but does not exist. 8hddddindex < data_.size()Removing initializer 'Constantnode_argCan't remove node Target= Dimension=' source: target:Output:Type mismatch. Current= Input=graph_proto cannot be nullgraph_proto != nullptrconstant_attribute.has_t()NoOp) input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () bound to different types (Node () output arg () type inference failedType Error: Type () of output arg () of node (Node: does not match its type. does not match. Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type.Outer scope node arg name 'void onnxruntime::Graph::SetOutputs(const std::vector&)void onnxruntime::Graph::SetInputs(const std::vector&)InlineFunctiononnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr, const string&)gsl::not_null onnxruntime::Graph::AllocateNode()onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs()CleanUnusedInitializersvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)onnxruntime::common::Status onnxruntime::Graph::Resolve(bool)ResolveForThisAndAllSubgraphsPerformTypeAndShapeInferencingInitInputsInitializersOutputsVerifyNodeAndOpMatchInferAndVerifySubgraphTypesconst T* onnxruntime::ConstPointerContainer::at(size_t) const [with Container = std::vector; onnxruntime::ConstPointerContainer::T = onnxruntime::NodeArg; size_t = long unsigned int]onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constBuildConnectionsvoid onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)SetOuterScopeNodeArgsGraphonnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const std::unordered_map, const onnx::FunctionProto*>&)MergeShapeInfoUpdateTypeAndShapeFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_N4onnx15GraphInferencerEN4onnx16InferenceContextEN11onnxruntime5GraphEN11onnxruntime19GraphInferencerImplEN11onnxruntime20InferenceContextImplEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.index >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Node must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing) ExplicitInputs:. Index:Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs.void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&)bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constCanUpdateImplicitInputNameInSubgraphsonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const*ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_N11onnxruntime11NodeCompareEModelProto was null.Protobuf parsing failed. is less than 0.Invalid istream object.Null model_proto ptr. less than 0.Load model failed. File doesn't existsystem error number ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators./onnxruntime_src/onnxruntime/core/graph/model.ccNo graph was found in the protobuf.Failed to load model with error: Protobuf serialization failed.SaveLoadSaveModelLoadModelSt15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEDomain already set in registry, but it its version is higherthan the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.ccRegisterOpSetN11onnxruntime27OnnxRuntimeOpSchemaRegistryEN11onnxruntime21SchemaRegistryManagerEsequence_lensinitial_hinitial_cQWMWmemory_seq_lensAWY_hY_cA 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.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.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.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.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_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]`.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 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]` Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.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 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 weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.fused_function_subgraphinput_arg->Type() != nullptrResolve subgraph failed:A node with a function body within a subgraph within another function body is currently not supported in ORT/onnxruntime_src/onnxruntime/core/graph/function.ccvoid onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map, int>&, const std::unordered_map, int>&)onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr)*ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoEEUlRNS6_16InferenceContextEE_N11onnxruntime8FunctionEN11onnxruntime12FunctionImplEDefault logger already set. ISink must be provided.Systemonnxruntimedefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.N@VIWEF is not a valid year is not a valid day is not a valid dateN11onnxruntime7logging11OStreamSinkEWriting profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.ccMaximum number of events reached, could not record profile event.[ ] {"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {} }, session_logger != nullptrcustom_logger != nullptrSessionNodeEndProfilingEndTimeAndRecordEventvoid onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)SUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROROKSystemError[ONNXRuntimeError]9qcUG/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&)*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_NSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEEZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EEFailed to get symbol Got null library handlelib.so file "" failed: closeopenFailed to load library munmap failed. error code:fstat' fail, error code = read file 'Failed to unload library with error: /onnxruntime_src/onnxruntime/core/platform/posix/env.ccReadFileAsString: 'fname' cannot be NULLReadFileAsString: offset must be non-negativeReadFileAsString: input is not a regular fileUnmapFile*N11onnxruntime12_GLOBAL__N_18PosixEnvEN11onnxruntime3EnvEmutex lock failedOrtCondVar wait failed: mutex not lockedOrtCondVar wait failed, error code=OrtCondVar timed_wait failed, error code=N11onnxruntime9TelemetryE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeEN11onnxruntime7EnvTimeE0A>    ?@@@@@@@@pB`A@>??AQ5@.N3;A8X} <2?~>++{16?:>=4?~??AQ5@.N3;A8X} <2?~>++{16?:>=4?~?AAAAQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} 2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KUB5?l?33{@7f9~;;?@Kr1mL:e9 <*=*>>A ;E<# =p>u>33{@33{@33{@33{@A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>l?l?l?l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>r1r1r1r1mL:mL:mL:mL:e9 *>*>*>>>>>*Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUliE_ is required to be non-empty.value_info' of Field ' is required but missing.elem_typekey_typevalue_typedata_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_dataTensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Sparse tensor indices () has values, but NNZ is ] not in sorted order.) index value at position [Sparse tensor (] out of range [0, ] out of range.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.op_typeNodeProto (name: , type: ATenConstantFillNo opset import for domain 'No Op registered for with domain_version of Warning: was a removed as possible.' of node: Bad node spec: ==> Context: attrAttribute (name: since_versionfunction (Unable to open model file:\/Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field.Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location., but it doesn't exist or is not accessible.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: ) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.) dimensions are not positive.) has zero input and zero output. is deprecated in domain_version of experimental ops. In the future, we may directly reject this operator. Please update your model as soon ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph inputNodes in a graph must be topologically sorted, however input ' is not output of any previous nodes.' has been used as output names multiple times.) should not contain more than one value field.type field and data field mismatch in attribute ) should refer to attribute in parent node.The model does not have an ir_version set properly.Your model ir_version is higher than the checker's.Your model has duplicate keys in metadata_props.model with IR version >= 3 must specify opset_import for ONNXmodel with IR version < 3 cannot have opset_import specified' has been used multiple times.) should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however '. Please check if it is a valid file.. Please check if it is a valid protobuf file of model.Unable to parse model from file:\ed\e\eddc\eccPt0ssssssrrssqpp0sqsN4onnx7checker15ValidationErrorEInput type was nullInput element type of axis value All Tensor typesCondition for the ifOnly boolv_initialv_final_and_scan_outputsinitial_state_and_scan_inputsfinal_state_and_scan_outputsInt64 tensorScan input was not a tensor. outputs. Expected was notIf node has but subgraphs produce Mismatched type for output then= else=Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.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.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./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.ccA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputsThe 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 or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors)./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.ccAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.bfloat16doubleuint8uint16uint32uint64complex64complex128seq(map(sparse_tensor(seqopaquesparse_tensor\sparse_value not specifiedValue of attribute Invalid shape value: Constrain input types.Number of times to sample.Input tensor must have rank 2loop_body_attributecond_outcurrentsub_resultsub_result_casteddelta_casteddiv_resultceil_resultceil_result_reluceil_result_relu_intceil_result_relu_boolvariadic_outputScalar. Value to step by.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute expected to have tensor typeAttribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type.Input tensor must be 2-dimensionalInput to 'Range' op should be scalars (Tensor with only one element and shape empty)All inputs to 'Range' op must be of the same typeShape input must be a one-dimensional tensor.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.Output tensor containing the same value of the provided tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.(Optional) Seed to the random generator, if not specified we will auto generate one.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionConstrain output types to float tensors.The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor.Input tensor to copy shape and optionally type information from.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.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.Constrain input types to float tensors.Constrain output types to integral tensors.Output type must be int32 or int64Scalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Attribute 'value' of Constant node must exist with 'Tensor' data./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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} This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).First input operand for the logical operator.Second input operand for the logical operator.Constrains input to boolean tensor.Constrains output to boolean tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input types to all numeric tensors. Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrains input/output to boolean tensors.{name}{broadcast_doc}Result tensor.equalZlessxorgreaterIncompatible dimensions*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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. If set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Constrains input to float tensors./onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to integral tensors.Enable broadcastingbroadcast*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input 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 input 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. The output tensor has the same shape and contains the {name} values of the corresponding input. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion). Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. {broadcast_doc} This operator supports **unidirectional broadcasting** ( should be unidirectional broadcastable to ); for more details please check [the doc](Broadcasting.md). Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} Result, has same element type as two inputsConstrain input and output types to high-precision numeric tensors./onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.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).The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wise Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. First operand, base of the exponent.Second operand, power of the exponent.Output tensor (same size as X) PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XInput tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elements1 for the first maximum value, and 0 for all othersThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseThis 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. 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. Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Constrain input and output types to numeric tensors.Tensor of shape [a_1, a_2, ..., a_n, r]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorTensor 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.Constrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseConstrain input and output types to all tensors.A 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.N-dimensional quantized matrix azero point of quantized input aN-dimensional quantized matrix bzero point of quantized input bzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Scale tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.The inner-most 2 dimensions must have the same size (mat_w:Invalid value for attribute axisK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.'shape' input must be 1D tensor of type INT64Incompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.{description}List of tensors for data_0Output tensor.'axis' must be in []. Its actual value is: First operand.Second operand.additionsubtractionDividend tensorDivisor tensorRemainder tensormultiplicationdivisionCoefficient of leakage.Coefficient of ELU.input tensor Xtensor slopeValue of alpha.Value of beta.normalized exponentiallog of softmaxlogsoftmaxhardmaxtensor A * BValuesIndicesscale of quantized input aa_scalea_zero_pointscale of quantized input bb_scaleb_zero_pointscale of quantized output yInput rank must be >= 2. != mat_h:*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input 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 input 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. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc}legacy optimization attribute.If set, defines the broadcast dimensions. See doc for details.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.Result, has same dimensions and type as A/onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.ccInput tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Slope tensor. If `Slope` is of size 1, the value is sharedacross different channelsOutput tensor. Same dimension as inputs.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Number of top elements to retrieveDimension on which to do the sort. 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. Pass 1 to enable broadcastingconsumed_inputs1-D input tensorValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Input tensor AInput tensor BInput tensor CInvalid value for attribute k*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_((kernel_spatial_shape[i] - 1) * dilations[i] + 1) {name} consumes an input tensor X and applies {opName} pooling across 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] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * 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] - {kernelSpatialShape} + 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] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Wether to use ceil or floor (default) to compute the output shape.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 ...].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 {name} consumes an input tensor X and applies Lp pooling across 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.p value of the Lp norm used to pool over the input data.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. The convolution operator consumes an input tensor and {filter_desc}, and computes the output.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/group 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/group 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_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.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.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into. 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] - 1) * dilations[i] + 1) - 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] - 1) * dilations[i] + 1) - 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 C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group 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/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)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 padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis. Global{op_type} consumes an input tensor X and applies {op} pooling across the values in the same channel. This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1. 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]).ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.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.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], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).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. Default is 0, doesn't count include pad./onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.ccThe output of each pooling window is maximum number of elements exclude pad.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.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 ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).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.Output data tensor that contains the result of the unpooling.Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial 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 ofpixels 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 defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Scale tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis. 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) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported. Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). 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. Constrain output mask types to boolean tensors.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain input and output to all tensor types.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.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 [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. 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 number of channels to sum overOutput tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Input is ether string UTF-8 or int32/int64Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Boolean. Whether the identification of stop words in X is case-sensitive. Default is falsestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"List of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionweight and zero_point pair is expected to have same type.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input shape must have either [C] or [1,C] dimensions where C > 0A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial 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.Padding for the beginning and ending along each spatial 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 spatial axis.kernel_spatial_shape[i]{opName}{additionalDescription}{kernelSpatialShape}{filter_desc}{op_type}{op}averagea filterw_scalew_zero_pointlp poolmomentumScale tensor of shape (C).Bias tensor of shape (C).saved_meansaved_varInput matrixMatrix after normalizationThe ratio of random dropoutThe output mask.A tensor of rank >= axis.Scaling parameter.The exponent.Input for n-gram extractionNgram results1-D tensor of floatsUTF-8 strings to normalizeUTF-8 Normalized stringsInvalid value() for attribute 'axis'ExponentEpsilonX_RMEX_squaredE_XsquaredVarianceX_varianceProcessed_STD*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ {name} consumes an input tensor X and applies {opName} pooling across 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} Stride along each spatial axis.dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.The output of each pooling window is divided by the number of elements exclude pad./onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.ccDilation value along each spatial axis of filter.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.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.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.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.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.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.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled. Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). 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. The output tensor of the same shape as X.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). 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 per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. 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.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.Stride along each axis.is_test*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.ccAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.Dimension mismatch in unification between boxesscoresmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indices expected to have rank but has rank expected to have rank >*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_/onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_N-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.ccScale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Output scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinQ_MaxX_MinX_Min_AdjustedX_MaxX_Max_AdjustedX_RangeMin_ScaledInitial_ZeroPoint_FPClipped_ZeroPoint_FPRounded_ZeroPoint_FPZeropointQuantized output tensor*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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. Accepted range is [-r, r-1] where r = rank(data). 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.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Reduced output tensor with integer data type.'axis' must be in [-rank(indices), rank(indices)-1]axis must be in [-rank, rank-1]. input rank was /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.ccproductL2 normsum squarelog sumL1 normlog sum exponent*ZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ 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.The axis in which to compute the arg indices./onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc*ZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_Number of neurons in the hidden layerThe 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]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.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. 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 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]`.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./onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc 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) Couple the input and forget gates if 1. 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.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.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]`.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 0First input tensor must have rank 3*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_fowardoutput_sequenceOptional 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 sequence output for the hidden is optional if 0. Default 0.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./onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_Attributes:(no type)(no doc)(unnamed)Outputs:Defined at (no documentation yet)Operator ') has input size not in range [min=, max= not in allowed input sizes.) has output size not in allowed output sizes.Attribute ' has unknown expected type' is missing.Required attribute ') than declared () has more inputs () in op definition.)'s input ) has more outputs ()'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()) (no explicit description available)' has been deprecated since version Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' is marked single but has an empty string in the graph: failed validating the check: Duplicate type constraint nameAttribute specification type mismatch.?p7p7p7X7p>== 2Invalid attribute perm {}, input shape = {Invalid position of 0Invalid dimension value: Input tensor to be cast.Specified shape for output.Reshaped data.reshapedShape of the input tensorlength of each outputstepsTransposed output.transposedupdatesTensor of rank q + (r - 1).squeezedOriginal tensorexpandedInput tensor of any shape.repeatsN-D tensorN-D tensor after resizingroisizesTensor to copy input into.conditiondepthConstrain to boolean tensors.output (always 2D tensor)Tensor of rank r >= 2.inverse_indicesconstant_valueInput axes has invalid data'step' cannot be 0Blocksize must be positive'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.axis must be in [-rank, rank-1].All inputs to Concat must have same rankdata tensor must have rank >= 1Indices tensor must have rank >= 1OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of values'sequence_lens' must have rank of 1Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorTarget shape may not have multiple -1 dimensionsInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesvalues in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatesOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.ccInput tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Default to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Input and output types can be of any tensor type.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Tensor of rank q + r - indices_shape[-1] - 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.Reshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).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.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Constrain input 'X' and output 'Y' to all tensor types.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)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'.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain roi type to float or double.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Constrains input and output to only numeric types.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Slice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput tensor must be 4-dimensional*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Ranks inferred (Which axis to concat onoutputs...Which axis to split onpaddingsNew shapetilesAxis along which to repeat.width_scaleheight_scale4-D tensor, [N,C,H,W]Which axis to split on. 'axis' must be in [-rank(indices)-1, rank(indices)]rank must be greater than axis) is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Attribute value for pads is requiredAttribute pads has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthInvalid value of attribute 'axis'Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.ccWhich axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')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`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0Number of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.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`Ending indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.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`.One float, indicates the value to be filled.*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_) is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Dimension value inferred (Data to be selectedData to be binarizedBinarized output dataInput dataA dictionary.Data to be processed.Imputed output dataValue(s) to change toA value that needs replacing.Value(s) to change to.Output data.A list of ints.A list of floats.A string.An integer.A float.Data to be classified.A collection of intercepts.Data to be regressed.Weights of the model(s).Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Data to be scaled.Scaled output data.Chosen support vectorsSupport vector coefficients.Input of shape [N,F]N, Top class for each pointTree id for each node.Feature id for each node.N classesThe node id of each weightThe weight for each targetThe total number of targets.The input valuesThe output mapseq(map(string, float))seq(map(int64, float))map(string, int64)map(int64, string)map(int64, float)map(int64, double)map(string, float)map(string, double)Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.The indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.ccThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Output data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Input data. It can be either tensor or scalar.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'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.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The class score for each class, for each point, a tensor of shape [N,E].The input type must be a tensor of a numeric type.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The size of each input in the input listInput's shape should be 1D or 2D*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.A list of labels.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ is out of bounds) vs ( inferred=type case mismatch. existing=type mismatch. existing= does not contain a graph.no name(op_type:, name: inputs but Graph has were providedGraph input #Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension type case unsupported. existing=GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplEFunc_Input for function node Output for function node onnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.TensorShapeProto.Dimension/build/Release/onnx/onnx-ml.pb.ccEEEEEFEEEOOOOOOOOOl (px|T\|d|||||| ld DT|l$L,T4|8P888888h8888888888888888888888888888888888888888888888888888888888888888888888888888888888888(<EhhEhEhhiEhPhh   d     N4onnx14AttributeProtoEN4onnx14ValueInfoProtoEN4onnx9NodeProtoEN4onnx10ModelProtoEN4onnx22StringStringEntryProtoEN4onnx16TensorAnnotationEN4onnx10GraphProtoEN4onnx19TensorProto_SegmentEN4onnx11TensorProtoEN4onnx17SparseTensorProtoEN4onnx26TensorShapeProto_DimensionEN4onnx16TensorShapeProtoEN4onnx16TypeProto_TensorEN4onnx18TypeProto_SequenceEN4onnx13TypeProto_MapEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_OpaqueEN4onnx9TypeProtoEN4onnx18OperatorSetIdProtoEN4onnx13FunctionProtoEN6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type ZjOMz`]j'ޱMOX~7 VbҪH$(Xʣ4zBx(HJƖz ϔU֓XnБRِ*ܼ D$̾= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next().CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative.CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream.N6google8protobuf2io16ArrayInputStreamEN6google8protobuf2io17ArrayOutputStreamEN6google8protobuf2io18StringOutputStreamEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io26CopyingOutputStreamAdaptorE(cannot determine missing fields for lite message)/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.ccCHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Exceeded maximum protobuf size of 2GB: Can't parse message of type "N6google8protobuf11MessageLiteE[libprotobuf %s %s:%d] %s %d.%d.%d%c%g%p%lld%llu".)INFOWARNINGFATAL/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "N6google8protobuf7ClosureEN6google8protobuf8internal16FunctionClosure0EN6google8protobuf14FatalExceptionE, lo=/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=CANCELLEDUNKNOWNDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSSHIHIIJ J@J`JJJJJK@II`IJ!KJMKcKyKKKKKKKL)LJ7K K OHOhONNORR`SQpQS  /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw parsingserializing '%s'String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperEN6google8protobuf8internal29CodedOutputStreamFieldSkipperE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.ccNon-primitive types can't be packed.Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Multiple extension registrations for type "CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here.Can't get here.Extension not found.", field number 2r@r@r@r@r@r@r@r@rrrt wv|zzzxLwwwydyyy %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] -> %dcapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail%d. %s %d+ %s [%02x-%02x] -> %d /onnxruntime_src/cmake/external/re2/re2/prog.ccUnexpected opcode in IsMatch: SSTHThTTSTxxwwwxwxyxwe{{|||{|{Malformed repeat Simplify case not handled: Simplify failed on /onnxruntime_src/cmake/external/re2/re2/simplify.ccCoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledCase not handled in ComputeSimple: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is țț` hȧ((n(hh((((((h PP8N3re26Regexp6WalkerIPS0_EEN3re214CoalesceWalkerEN3re214SimplifyWalkerE[]^-\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(?:kRegexpCapture cap() == 0?P< [truncated]/onnxruntime_src/cmake/external/re2/re2/tostring.ccL$.TT,|dD$ldd<N3re214ToStringWalkerEAZ  .@26@9G@Jv@xxy}@@O@@@@@@  ~"2@::+*;;==]>>(*AACC=DDEEEGFN@EEtpr@vvt&%@?  @~P/ `@@@.@1V0```É@@@A@(/8?HMYY[[]]__ho&!&!*!*!A+!+!2!2!`!o!!!$$,.,0`,`,b,b, c,c,d,d,g,k,@m,m,n,n,o,o,p,p,r,r,u,u,~,,,,@,,@,,@l@@".@2n@y{@}}u~@Z@@ZZZZZZZZ@p0h!: '((  @ @n_n !"]AZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,.,00,^,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~ZZZZZZZZZSS`p0h!: AZ'((O(  @   @n_n `nn!""C      68<>>@ACDFO 5 9 ? 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