vendor/onnxruntime.dll in onnxruntime-0.2.3 vs vendor/onnxruntime.dll in onnxruntime-0.3.0

- old
+ new

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444@=P=HLP4P4@404@=P=0L4444@=P=L@4@4@404@=P=L4444@=P=hL`4`4P4@4P=8L44p4`4@=P=`L4444@=P=L4444@=P=L`5`555@=P=XL5555@=P=LЌ5Ќ5 55@=P=p)L0505 55@=P=L@5@55Ѕ5@=P=p$L555p5@=P=(L5555@=P=)L`5`5P5@5@=P=XL5555@=P=$L5555@=P=!L5555@=P=L5555@=P=$L`5`5 55@=P='L5555@=P=p(L0505 55@=P='LЊ5Њ555@=P=XLp5p5`5P5@=P=(Lp5p5`5P5@=P=0(L`5`5P5@5@=P=#LP5P5@505@=P=&L555p5@=P=L555p5@=P=!LЋ5Ћ55p5@=P=L555p5@=P=0%L555Ў5@=P="L@5@505 5@=P=L55p5`5@=P=*L5555@=P=0L 5 555@=P=H$L`5`5P5@5@=P=*L`5`555@=P=@+L 5 555@=P=*LP5P5@505@=P=8,L0505 55@=P=x,L555p5@=P=P-L55p5`5@=P=+L`5`5P5@5@=P=+L555p5@=P=p:L{6{6 5{6@=P=1L{6{6 5{6@=P=0L|6|61|6@=P=8L66116@=P=6LP6P6@606@=P=/L66@606@=P=/L{6{6{6z6@=P=2L66p6`6@=P=07LP|6P|61@|6@=P=01Lz6z6z6z6@=P=.L~6~6p~6`~6@=P=4L6666 $P=09LP~6P~6@}60}6@=P= 4L6666@=P=`4Lz6z6pz6`z6@=P=X1L|6|6p|6`|6P=/L~6~611~6@=P=4L }6 }6}6}6@=P=5L|6|6|6|6@=P=6L{6{6 5{6@=P=`5L~6~6~6~6@=P=x3Lz6z6z6z6@=P=3L666~6@=P=p6L |6 |6|6|6P=p;L`6`6P6@6@=P=;LМ6М666@=P=;L6666@=P=p<L6666@=P=h=L0606 66@=P=@L`6`6P6@6@=P=?L6666@=P=<L6666@=P=?L6666@=P=@L6666@=P=>L666p6@=P=@L6666@=P==L6666@=P=AL !7 !701!7@=P=AL!7!7/1 7@=P=XBL37373737@=P=0BL@57@574747@=P=BL3737437@=P=CL87877777@=P=JL7777@=P=HL7757@=P=KLP7P7 5@7@=P=IL77`57@=P=FL777p7@=P=DLp7p7 5`7@=P=EL`7`7P7@7@=P=DL07075 7@=P=GL7757@=P=XKL77`5Й7@=P=KL@7@707 7@=P=JL777И7@=P=KLp7p7`7P7@=P=EL7777@=P= EL0707 77@=P=IL7777@=P=@OL7767@=P=RL7707 7@=P=QL77@707@=P=ML7777 $P=RL77 57@=P=(SL7777@=P=LL7777@=P=(RL7777@=P=0ML7777@=P=OLp7p7`7P7@=P=NL7777@=P=hOL7777@=P=NL@7@7}607@=P=ML77774P=PLp7p7 5`7@=P=VLpS8pS8@4`S8@=P=0WL0S80S8 S8S8@=P=PVLR8R84R8@=P=xVLT8T8T8S8@=P=WLS8S8S8S8@=P=ULPT8PT8@T80T8P=WLPS8PS8@4@S8@=P=TLS8S8S8S8@=P=ULS8S8R8R8@=P=XLpw8pw8@v80v8@=P=HXLx8x8w8w8@=P=YL`8`8P8@8@=P=ZL8888@=P=YL8888@=P=ZLО8О8478@=P=[LP8P8@808@=P=`[L88p8`8@=P=]L8888@=P=]L888p8@=P=\L 8 888@=P=]L`8`8P8@8@=P=X^L89p_LPB9C9 C9C9D9D9E9G9^Lo9`ZbL99 99p9> 5099099P9hLg:Pv:99i:> 5s:@q:09`j: n:8eLpU:P`:`9P9PX:> 5 ^:P]:09X:[:bL@2:::9p94:> 58:8:095:7:xaL :(:@909:> 5 #:@:09:@:hL9:999> 5:p909@99HdL: : 99:> 5 : +:09@: :aL9999 9> 59909 9p9piLS:T:@909p9> 59P90999cL):1:`9P9+:> 5`0:/:09,:0.:bL`R:S: 99A:> 50E:D:09B:C:fL9999p9> 59P90999fLF:K:9М9 H:> 5J:J:09pH:I:cLP9p99p9`9> 59909 9@9_L9`99Л99> 5990999cLa: f:9p9a:> 50e:d:09b:c:`hL@L:Q:99M:> 5P:`P:09M:pO:dL99`9P99> 590909P909gL@;:p@:99p<:> 5?:?:09<: >:aL A: F:99A:> 50E:D:09B:C:jL::: :O<XjL`::: ::`kL;;;;`;jL);%;%;%;O<kL:9;9; ::;0;P;p;;;е;;; lL`<(<(<<(<plL`<0< <<<lL`<0<0<0<O<mL`:4<@7< :Unknown exceptionbad array new lengthD:\4\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)D:\4\s\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longstring too longD:\4\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\4\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: D:\4\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: 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(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s +%I64uBBBBD:\4\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xty10invalid string position.OKINVALID_ARGUMENT:D:\4\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits<size_t>::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t., D:\4\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): map/set too longunordered_map/set too longinvalid hash bucket count/\ was false. Stacktrace: -CpuVIWEFai.onnx.ml!=D:\30\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetD:\30\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()ai.onnxCPUExecutionProviderDmlExecutionProviderTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\30\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Getenv_ptr == p_instance_OrtEnv::ReleaseD:\30\s\onnxruntime\core\session\onnxruntime_c_api.ccsize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.onnxruntime_profile_input name cannot be emptyoutput name cannot be emptyinput array is too shortshape is invalidspace is not enoughout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.Unsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperSequences must have tensors of the same data type. There was at least one tensor in the input that was different.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.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValue1.1.1vector<T> too longinvalid string positionT Tensor type mismatch. utils::IsPrimitiveDataType<T>(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,double,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,double> > > >::checkD:\30\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,__int64,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<struct OrtStatus *,struct c_api_internal::UnsupportedReturnFailStatus>::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > >,class std::allocator<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > >,class std::allocator<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > > > >::checkstring too longinvalid vector<T> subscript != onnxruntime::DataTypeImpl::GetType<T>() == type_map/set<T> too longEncountered nullptr.not implementedexecution_mode is not validgraph_optimization_level is not valid is not implementedunknownonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughDescriptionUnsupported version 'onnxruntime::CreateCustomRegistryAD:\30\s\onnxruntime\core\session\custom_ops.cclist<T> too longcom.microsoft.nchwccom.microsoftcom.microsoft.automl[TypeInferenceError] expected to have tensor type unknownInput Output Element type of input tensor(uint32)tensor(int32)tensor(uint64)tensor(float16)tensor(int64)tensor(double)tensor(float)tensor(uint16)tensor(uint8)tensor(int16)tensor(int8)tensor(string)tensor(complex64)tensor(bool)tensor(complex128) line ) from file , but it is already registered from file (domain: version: known by the checker.Trying to register schema with name in onnx/defs/schema.h)., but it its domain is notbumped the operator version but forgot to update the version range in DomainToVersionRange , ] (usually, this means you , but it its version is not in the inclusive range [Schema error: MemcpyFromHostD:\30\s\onnxruntime\core\session\environment.ccinputToutputXConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.YMemcpyToHostException caught: bad castAttempt to use DefaultLogger but none has been registered.]CUDAExecutionProviderProvider has already been registered.D:\30\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddTrying to get a SparseTensor, but got: allocator already registered.IsSparseTensor()onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\30\s\onnxruntime\core/framework/feeds_fetches_manager.hCreate_State_Compute_Release_State_onnxruntime::DummyArena::Maxonnxruntime::DummyArena::Used] MemcpyTransformer[json.exception..ort_configother_errorORT_LOAD_CONFIG_FROM_MODELsession_options%Y-%m-%d_%H-%M-%S are '0' and '1'. The environment variable contained the value: Reading the provided model for the ORT configThe only supported values for the environment variable D:\30\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsmodel_protoModel needs to be provided to check for ORT config within itEnvironment::IsInitialized()Environment must be initialized before creating an InferenceSession.Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommonintra_op_thread_poolstatus.IsOK()CastFloat16Transformerinter_op_thread_poolonnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::~InferenceSessionError during EndProfiling(): Received nullptr for exec providerUnknown error during EndProfiling()Sequential execution must be enabled before registering DMLExecutionProviderMemory pattern must be disabled before registering DMLExecutionProviderReceived nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::LoadThis session already contains a loaded model.Unknown exception in Load()Exception during loading: Encountered unknown exception in Load()model_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.Could not find an implementation for the node onnxruntime::InferenceSession::TransformGraph ((].Node placements: [All nodes have been placed on [Main Graph instance should have populated all subgraphs when being resolved. Provider: [onnxruntime::InferenceSession::CreateSubgraphSessionStatesubgraphsubgraph_session_stateCreateSubgraphSessionState should have created an entry earlier.p_op_kernelonnxruntime::InferenceSession::InitializeSubgraphSessionsonnxruntime::InferenceSession::InitializeInitializing session.Model was not loaded.Model was not loadedAdding default CPU execution provider.Session has already been initialized.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Parallel execution is currently not supported for the registered CUDA Execution Provider.Exception during initialization: Session successfully initialized.session_initializationEncountered unknown exception in Initialize() Expected: Please fix either the inputs or the model.Invalid rank for input: Got: Got invalid dimensions for input: for the following indices -) , expected: ( index: elements.Unexpected input data type. Actual: (Size mismatch: feed_names has elements, but feeds has is not expected to be of type tensor.Invalid Feed Input Name:onnxruntime::InferenceSession::ValidateInputsInput with name: is not expected to be of type tensor sequence. is not expected to be of type sparse tensor.At least one output should be requested.Output vector pointer is NULLOutput vector incorrectly sized: output_names.size(): p_fetches->size(): Session was not initializedInvalid Output Name:Session not initialized.onnxruntime::InferenceSession::RunEncountered unknown exception in Run()Running with tag: model_runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::EndProfilingProfiler is disabled.Could not write a profile because no model was loaded.run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to onnxruntime::InferenceSession::AddPredefinedTransformersinvalid hash bucket count961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeString0Y=p_providertruefalse: value', column parse errorparse_errortype_errorout_of_range at line invalid_iteratorD:\30\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsParallel modeSetting execution_mode to onnxruntime::SetExecutionModeSequential modeSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelUnsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::InferenceSessionUtils::ParseOrtConfigJsonInModelProtoSetting enable_profiling to The Model Proto has already been checked for the ORT config json.The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: intra_op_num_threads option in the model file must be an integerinter_op_num_threadsonnxruntime::InferenceSessionUtils::ParseSessionOptionsFromModelProtointra_op_num_threadsexecution_mode option in the model file must be an integergraph_optimization_levelinter_op_num_threads option in the model file must be an integerexecution_modeenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: graph_optimization_level option in the model file must be an integerenable_profilingkey 'cannot use at() with ' not foundinvalid map<K, T> keycannot use key() for non-object iterators0objectnullbooleandiscardedarraystringcannot compare iterators of different containersnumbercannot get value<U+%.4X>syntax error while parsing unexpected ; expected - ; last read: '<uninitialized>true literalinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literalstring literalnumber literalfalse literalnull literal']''}''[''{'<parse error>end of input':'','invalid number; expected digit after '-'invalid number; expected digit after '.''[', '{', or a literalunknown tokeninvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Evector<bool> too longnumber overflow parsing 'object keyobject separatorcannot use erase() with iterator does not fit current valueiterator out of rangetype must be number, but is D:\30\s\include\onnxruntime\core/graph/graph.h Max:onnxruntime::Graph::NodeAtIndexImplValidating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()D:\30\s\include\onnxruntime\core/optimizer/graph_transformer.hCastDef_%donnxruntime::GraphTransformer::RecursetoRemoveDuplicateCastTransformercast node to cast from float16 to float32 on cpuCastonnxruntime::InsertCastTransformer::ApplyImplonnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\30\s\onnxruntime\core\optimizer\insert_cast_transformer.ccEliminateIdentityIdentityConvMulFusionConvEliminateSliceSliceConvBNFusionConvAddFusionRandomNormalRandomUniformLikeConstantFoldingRandomUniformUnsqueezeEliminationUnsqueezeRandomNormalLikeMultinomialMatMulAddFusionEliminateDropoutConvActivationFusionGemmActivationFusionReluShapeToInitializerDropoutFuseReluClipBiasGeluGeluFusionShapeNchwcTransformerSkipLayerNormFusionEmbedLayerNormFusionGeluApproximationLayerNormFusionReshapeFusionAttentionFusionUnsupported levelonnxruntime::optimizer_utils::GenerateRewriteRules_RuleBasedTransformerLevelonnxruntime::optimizer_utils::GenerateTransformersD:\30\s\onnxruntime\core\optimizer\graph_transformer_utils.ccUnsupported level DnnlExecutionProviderNupharExecutionProviderNGRAPHExecutionProviderOpenVINOExecutionProviderACLExecutionProvideronnxruntime::Node::ForEachWithIndexTensorrtExecutionProvideronnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy onnxruntime::MemcpyTransformer::ApplyImplD:\30\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()Execution type 'Memcpyonnxruntime::TransformerMemcpyImpl::ProcessInitializers::<lambda_1e5d1c881204fc115ab0f0a9641af688>::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::GraphTransformer::ApplyD:\30\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\30\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered Requested size is too large to fit into size_t.GeluAddD:\30\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGelu::ApplyImplfused Add and GeluCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()D:\30\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplort_value.IsTensor()Unsupported output type of . Can't constant fold node 'size_ == sizesize is differentD:\30\s\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::Initializerqkv_biasqkv_weightsdata type is not supportedMaskIndex_Int32MaskCastCast mask from int64 to int32Mask_Int32axesMaskIndexReduceSumCount number of wordsD:\30\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ProcessMaskMask shape is unknown or not 2D, or data type unknownkeepdimsshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64ReshapeTotal fused Attention node count: Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.MatMulOutput edge count not expected for nodes in path vonnxruntime::AttentionFusion::FuseSubGraphFaild to find path vTransposev_reshape initializer value is not expectedFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3permMulSoftmaxFailed in match v_matmul and v_add input shapereshape initializer value is not expectedaxisOutput edge count not expected for mask nodesFailed to find path for maskSubmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3q root should be layer normalizationFailed to find path for qDivmask_mul const input not matchedq_matmul and q_add shape not matchedq_transpose perm attribute not matchedqk_div const not matched.q_reshape const not matchedk_matmul and k_add shape not matchedk_transpose perm attribute not matchedk root is not layer normFailed to find path for kFailed to create mask indexFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.k_reshape const not matchedFused an attention node.num_headsAttentionFused Attention subgraphs onnxruntime::ConvBNFusion::Applybn_scale_tensor_protoepsilononnxruntime::Initializer::ToProtobn_var_tensor_protobn_mean_tensor_protobn_B_tensor_protoD:\30\s\onnxruntime\core\optimizer\conv_bn_fusion.ccConvBnFusion_BN_B_ConvBnFusion_W_conv_B_tensor_protoconv_W_tensor_protoBatchNormalizationD:\30\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::GetClipConstantMinMax::<lambda_95ba7040e9169255988b71d576dbf5c2>::operator ()Unexpected data type for Clip input of onnxruntime::ConvActivationFusion::ApplyImplmaxminClipalphaLeakyReluTanhSigmoidfused FusedConvfused Conv with activation activation_paramsactivationUnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyD:\30\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccdata type Unexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyD:\30\s\onnxruntime\core\optimizer\relu_clip_fusion.ccis not supported._min_zero_constantFuseReluClip_onnxruntime::GeluApproximation::ApplyImplD:\30\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationFastGeluTotal Gelu Approximation (FastGelu) node count: ConcatGatherCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph1onnxruntime::ReshapeFusion::ApplyImplD:\30\s\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: _CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputD:\30\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_Int32Cast Input from int64 to int32Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Input data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchPositionSubgraphOutput edge count not expected for nodes in path 1 of position shape.NonZeroConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraph1Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.ExpandSqueezeRangeFailed to find path 1.onnxruntime::MatchPositionEmbeddingSubgraph2Output edge count not expected for nodes in path 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.position_embeddingsonnxruntime::EmbedLayerNormFusion::ApplyImplInput id is not valid. Segment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.EmbedLayerNormalizationInput_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs startsendsFreeDimensionOverrideTransformerThe model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplD:\30\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccInvalid free dimension override.does not equal the specified override ofbut the size of this dimension ' with a fixed dimension denotation 'gemmGemmonnxruntime::MatMulAddFusion::ApplyImplfused Matmul and Add D:\30\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::GemmActivationFusion::ApplyImplfused Gemm D:\30\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccleaky_relu_FusedGemmonnxruntime::ConvMulFusion::Applymul_B_tensor_protoD:\30\s\onnxruntime\core\optimizer\conv_mul_fusion.ccConvMulFusion_Mul_B_ConvMulFusion_W_onnxruntime::SkipLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs D:\30\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccSkipLayerNormalizationreorderpadsReorderInputSAME_UPPERNOTSETVALIDSAME_LOWERdilationsstridesauto_padkernel_shapeGlobalAveragePoolGlobalMaxPoolReorderOutputSum_nchwcgroupAveragePoolMaxPoolonnxruntime::NchwcTransformer::ApplyImplchannelsD:\30\s\onnxruntime\core\optimizer\nchwc_transformer.ccdeque<T> too longfused LayerNorm subgraphs PowD:\30\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplSqrtReduceMeanonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplD:\30\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::GeluFusion::ApplyImplD:\30\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs ConvAddFusion_B_ConvAddFusion_Add_B_D:\30\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoonnxruntime::ConvAddFusion::ApplyD:\30\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::{ctor}::<lambda_7ec6fb0dac869cff5994429850ef203c>::operator ()Tried to allocate without valid type information, ort_value index=invalid unordered_map<K, T> keyallocator_ptr_ != nullptronnxruntime::OptimizerExecutionFrame::Info::Infoonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryD:\30\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterOnnxOperatorKernelslinearcubicnearestextrapolation_valuehalf_pixelscalesinfo.GetAttrs<float>("scales", scales_).IsOK()onnxruntime::UpsampleBase::UpsampleBaseD:\30\s\onnxruntime\core/providers/cpu/tensor/upsample.hmodeinfo.GetAttr<std::string>("mode", &mode).IsOK()exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outsideround_prefer_floornearest_modecoordinate_transformation_modeasymmetricalign_cornerstf_crop_and_resizepytorch_half_pixeltf_half_pixel_for_nnmode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be nearst_mode:[onnxruntime::UpsampleBase::StringToNearstModefloorceilonnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceil] is not supportted!coordinate_transform_mode:[Upsample operator'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scale > 0Resize operatoronnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.Scale value should be greater than or equal to 1.scale >= 1Resizescales_size > 0onnxruntime::UpsampleBase::ParseScalesDatascales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)scales size should be greater than 0.bad allocationD:\30\s\include\onnxruntime\core/framework/op_kernel.hT3T2T1Invalid 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.OneHotonnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::OneHotOp<int,float,int>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<float,__int64,__int64>::Compute'axis' attribute must have a value in the range [Depth is negative.D:\30\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<float,float,float>::Computeonnxruntime::OneHotOp<int,float,float>::Computeonnxruntime::OpKernelContext::InputMissing Input: onnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-D:\30\s\onnxruntime\core/providers/common.hkoutput count mismatch, expected 2 outputs to be present for TopK operatork argument [] should not be greater than specified axis dim value [input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 1 input - the tensor to be processedop_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK()k_temp > 0D:\30\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK()Iop_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK()sortedop_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK()largestonnxruntime::TopK<11,float>::TopKvalue of k must not be negativek tensor should be a 1D tensor of size 1TopKUniqueUnsupported tensor type of onnxruntime::Tensor::MutableDataAsSpanonnxruntime::Tensor::DataAsSpaninput count mismatchonnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status>::operator ()Unsupported data type: onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<class onnxruntime::common::Status,struct onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status> >::Getdelta in Range operator can not be zero!delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:D:\30\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::ComputeGatherNDTindinvalid index found, index = last dimension of indices must not be larger than rank of input tensorindices tensor must has rank larger than 0onnxruntime::GatherNDBase::PrepareForComputeinput_tensor != nullptr && indices_tensor != nullptrGatherND op: Input count mismatchonnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueedgereflectconstantInvalid 'pads' attribute valueD:\30\s\onnxruntime\core/providers/cpu/tensor/pad.hdims.size() == extents.size() && dims.size() >= steps.size()Unexpected mode of Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:PadD:\30\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkipsPads tensor size should be equal to twice the input dimension count pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]D:\30\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::Pad<float>::Computepads_tensor.IsDataType<int64_t>()Pads tensor should be an INT64 tensoronnxruntime::Pad<__int64>::Computeonnxruntime::Pad<int>::Computeonnxruntime::Pad<double>::Computevalue_tensor->IsDataType<T>() && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()data_rank * 2 == pads.size()'pads' has wrong number of valuesonnxruntime::PadCpuImpldata_rank > 0Input tensor has no dimensionsonnxruntime::PadInputWithDimValueOfZeroonnxruntime::SliceIterator<float>::Initonnxruntime::SliceIterator<double>::Initonnxruntime::SliceIterator<int>::Initonnxruntime::SliceIterator<__int64>::InitPlease fetch output tensor with specified shape.onnxruntime::OpKernelContext::Outputp_ml_valueaxis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by Absonnxruntime::Broadcaster::Broadcasterlargest <= 1Can broadcast 0 by 0 or 1. is invalid.onnxruntime::BroadcastIterator::AppendD:\30\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::InitMaxMinLogExpReciprocalCeilFloorNegOrAndNotBitShiftMeanEqualGreaterLessdata_n.Shape() == shapedata_1.Shape() == shapeAll inputs must have the same shapeD:\30\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Sum_6<float>::Computeinput_count >= 1Must have 1 or more inputsXorAsinTanCosSinonnxruntime::Mean_6<float>::Computeonnxruntime::Max_6<float>::Computeonnxruntime::Min_6<float>::ComputeinputCount >= 1PReluAtanhAcoshAsinhCoshSinhAtanAcosfailed to get first output!Modonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1fmodonnxruntime::Erf<float>::ComputeX_ptr != nullptronnxruntime::Mod::Computefmod_fmod attribute must be true for float, float16 and double typesX and Y input types do not match: vs onnxruntime::mod_internal::BroadCastMFloat16FModInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.RIGHTLEFTonnxruntime::BitShift<unsigned char>::BitShiftdirectiononnxruntime::BroadcastVariadiconnxruntime::BitShift<unsigned __int64>::Compute::<lambda_6854f6970408c01d53b015feb9056541>::operator ()onnxruntime::BitShift<unsigned __int64>::BitShiftonnxruntime::BitShift<unsigned int>::Compute::<lambda_a52bdf88c33ca724246acf24e6ac3e6e>::operator ()onnxruntime::BitShift<unsigned int>::BitShiftcur_out == end_outonnxruntime::BitShift<unsigned char>::Compute::<lambda_5162488dba51b830a7f18d693c9d8bc3>::operator ()cur1 == end1onnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8<int>::Computeonnxruntime::Expand_8<short>::Computeonnxruntime::Expand_8<signed char>::Computeonnxruntime::Expand_8<double>::Computeonnxruntime::Expand_8<float>::Computetensor_shape.Shape().GetDims().size() == 1Shape must be 1 dimensional as it's tensor data is a shapeCheck for duplicate types in MLTypeCallDispatcheronnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8<union onnxruntime::MLFloat16>::Computeonnxruntime::Expand_8<bool>::Computeonnxruntime::Expand_8<unsigned __int64>::Computeonnxruntime::Expand_8<unsigned int>::Computeonnxruntime::Expand_8<unsigned short>::Computeonnxruntime::Expand_8<unsigned char>::Computeonnxruntime::TensorAllocator<float>::TensorAllocatorhelper.called_ == 1onnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::mod_internal::CallModImpl,unsigned char,signed char,unsigned short,short,unsigned int,int,unsigned __int64,__int64>::Invokehelper.called_ < 2onnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator<double>::TensorAllocator((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOMissing/Invalid 'axis' attribute valueinfo.GetAttr<int64_t>("axis", &axis_).IsOK()onnxruntime::GatherElements::GatherElementsd:\30\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'onnxruntime::parse_and_validate_indices_tensorD:\30\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cc]. Actual value is , GatherElements op: Value in indices must be within bounds [GatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputM_ >= 0 && K_ > 0 && N_ >= 0left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperd:\30\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcasttransAinfo.GetAttr<int64_t>("transA", &temp).IsOK()onnxruntime::Gemm<float>::GemmD:\30\s\onnxruntime\core/providers/cpu/math/gemm.htransBinfo.GetAttr<int64_t>("transB", &temp).IsOK()info.GetAttr<float>("alpha", &alpha_).IsOK()betaNot implemented fused activation: info.GetAttr<float>("beta", &beta_).IsOK()ScatterNDonnxruntime::ScatterND::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.ccinput_tensor != nullptronnxruntime::ScatterNDBase::PrepareForComputeindice_tensor != nullptrupdate_tensor != nullptr, indices shape: input shape: input tensor and indices tensor must has rank larger than 0. , data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = ) != new size (Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::ReshapeD:\30\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLength: Got nullptr for output tensorY != nullptrSequenceAtonnxruntime::GetSeqIdxonnxruntime::SequenceAt::ComputeGot nullptr input for index tensorI != nullptrSSequenceLengthGot nullptr for sequence input.X != nullptronnxruntime::SequenceLength::ComputeSequenceInsertonnxruntime::CreateCopyAndAppendCpuTensorS != nullptronnxruntime::SequenceInsert::ComputeGot nullptr for input tensor.), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequence) specified for sequence of size (Invalid sequence index (SequenceAt: Got nullptr for output tensorSequenceEmptydtypeSequenceEmpty: Got nullptr for output sequenceonnxruntime::SequenceEmpty::ComputeUnsupported 'dtype' value: SplitToSequence yetSplitToSequence operator does not support onnxruntime::SplitToSequence::Compute) != split_dim_size (split_size_sum (Invalid data type for split tensor onnxruntime::GetScalarSplitInputSequenceEraseonnxruntime::SequenceErase::ComputeSequenceErase: Got nullptr for output sequenceSequenceConstructnum_inputs >= 1onnxruntime::SequenceConstruct::ComputeSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.Split should be > 0split_scalar > 0onnxruntime::GetSplitSizesInputonnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })Must have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\30\s\onnxruntime\core/providers/cpu//tensor/concat.hnew_axisConcatFromSequenceonnxruntime::ConcatFromSequence::ComputeD:\30\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccnum_subgraph_outputs == static_cast<size_t>(num_outputs)'If' node has D:\30\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infoinfo.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK()then_branchelse_branchonnxruntime::If::IfBIfV outputs which doesn't match the subgraph's outputs.session_stateSubgraph SessionState was not found for ' did not.onnxruntime::IfImpl::InitializeFailed to create output tensor for Subgraph must have the shape set for all outputs but onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output SetupSubgraphExecutionInfo should only be called once for each subgraph.info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK()onnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.' attribute.onnxruntime::If::ComputeUnknown AutoPadType StringD:\30\s\onnxruntime\core/providers/cpu/nn/autopad_type.honnxruntime::StringToAutoPadType kernel_shape: W: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims.output_shapeoutput_paddingOnly 1D and 2D ConvTranspose is supported.group count is <= 0 filter_number: num_input_channels: Input channels is not divisible by group.filter number not equal to input channel number.X num_dims does not match W num_dims. X: group: D:\30\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputedim_size > 0Invalid input shape: *out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapepad type not supportedonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeConvTransposeD:\30\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose<float>::DoConvTranspose C: kernel channels: M: Input channels C is not equal to kernel channels * group.D:\30\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv<float>::Computeonnxruntime::math::Im2colNd<float,2>::operator ()d_iter[d_i] < d_maxOutput channels M is not divisible by group.Out of bound access to arrayD:\30\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::InferOutputShapeD:\30\s\onnxruntime\core/util/math.hdilation == 1Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.pad type not supported.onnxruntime::ComputePadAndOutputShapestorage_orderinfo.GetAttr<int64_t>("count_include_pad", &temp).IsOK()Pad should be smaller than kernel.kernel_shape[dim] > 0strides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapeNo kernel shape is set.GlobalLpPoolonnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK()info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK()D:\30\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hcount_include_padceil_modeonnxruntime::PoolProcessContext::initinfo.GetAttr<int64_t>("p", &p_).IsOK()Input dimension cannot be less than 3.D:\30\s\onnxruntime\core/providers/cpu/nn/pool_base.hD:\30\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeUnsupported pooling size.Not satsified: input_dims >= 3 -input_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:input_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizeponnxruntime::PoolAttributes::ComputeSizePadDilationsNot satsified: pooling_dims == pool_attrs_.kernel_shape.size() -kernel_shape num_dims is not compatible with X num_dims.Not satsified: x_shape.NumDimensions() >= 3 -onnxruntime::Pool<float,class onnxruntime::MaxPool<8> >::ComputeLpPoolUnsupported pooling size : onnxruntime::Pool<float,class onnxruntime::LpPool>::Computeonnxruntime::MaxUnpool::MaxUnpoolinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0D:\30\s\onnxruntime\core/providers/cpu/nn/unpool.hstrides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]MaxUnpoolD:\30\s\onnxruntime\core\providers\cpu\nn\Unpool.cconnxruntime::MaxUnpool::ComputeIndex tensor shape should be same as that of the input data tensor to unpool.Not satsified: X_shape.NumDimensions() >= 3 -Shape must be 1 dimensional as it's tensor data of a shapeNot satsified: I_shape == X_shape - inferred output shape:Not satsified: tensor_shape->Shape().GetDims().size() == 1 -Not satsified: given_shape.Size() >= shape.Size() -output_shape is smaller than minimum required. output_shape:0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1d:\30\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseboxes_tensorNonMaxSuppressionD:\30\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputecenter_point_boxThe most inner dimension in boxes must have 4 data.Not satsified: boxes_dims[1] == scores_dims[2] -iou_threshold must be in range [0, 1].Not satsified: boxes_dims[2] == 4 -Not satsified: (iou_threshold >= 0 && iou_threshold <= 1.f) -onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satsified: ret.IsOK() -onnxruntime::NonMaxSuppression::Computeboxes must be a 3D tensor.scores_tensorscores must be a 3D tensor.Not satsified: boxes_shape.NumDimensions() == 3 -boxes and scores should have same num_batches.Not satsified: scores_shape.NumDimensions() == 3 -boxes and scores should have same spatial_dimension.Not satsified: boxes_dims[0] == scores_dims[0] -output != nullptrdata type is different from updates typeScatterElementsIndices vs updates dimensions differs at position=Indices and updates must have the same rankIndices must have the same rank as Input. Indices rank=. Input rank= at pos= is greater than input dim=onnxruntime::Scatter::ScatterScatterD:\30\s\onnxruntime\core\providers\cpu\tensor\scatter.ccUnknown tensor type of Indices dim=Expecting indices to be either int32_t or int64_tonnxruntime::Scatter::Computeindices element out of data bounds, idx= must be within the inclusive range [DetD:\30\s\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2onnxruntime::Det<float>::ComputeMatrix dimensions are not equal. Square matrix is expectedD:\30\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseMissing/Invalid 'axes' attribute valueonnxruntime::Unsqueeze::Compute'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::UnsqueezeBase::PrepareComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cc. shape= must be 1 instead of onnxruntime::SqueezeBase::ComputeOutputShapeD:\30\s\onnxruntime\core/providers/cpu/tensor/squeeze.hDimension of input input_shape[i] == 1onnxruntime::SplitBase::SplitBaseD:\30\s\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })splitInput cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was NumOutputs= Axis=Cannot split using values in 'split' attribute. Axis=Split operator does not support Num entries in 'split' (must equal number of outputs) was Input shape=Splitonnxruntime::Split::ComputeImplonnxruntime::Split::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\split.ccMissing or invalid starts and ends attributeD:\30\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributeshas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBase'axes' has an axis outside of the tensor dimension countStarts must be a 1-D arraynullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1'axes' has duplicates'step' value cannot be 0!has_axes || attr_axes_.size() == attr_starts_.size()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and steps shape mismatchnullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape()Ends must be a 1-D arraynullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInputD:\30\s\onnxruntime\core\providers\cpu\tensor\slice.ccStarts and axes shape mismatchnullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()Starts and ends shape mismatchstart_tensor->Shape() == ends_tensor->Shape()Cannot slice scalarsonnxruntime::Slice<unsigned short,0>::Computeinput_tensor_ptr != nullptronnxruntime::Slice<unsigned char,0>::Computeonnxruntime::Slice<signed char,0>::Computeonnxruntime::Slice<short,0>::Computeonnxruntime::Slice<unsigned int,0>::Computeonnxruntime::Slice<unsigned __int64,0>::ComputeMissing input tensor to be processedonnxruntime::Slice<unsigned __int64,1>::Computeonnxruntime::Slice<signed char,1>::Computeonnxruntime::Slice<unsigned short,1>::Computeonnxruntime::Slice<unsigned int,1>::Computeonnxruntime::Slice<__int64,1>::Computeonnxruntime::Slice<float,1>::Computeonnxruntime::Slice<short,1>::Computeonnxruntime::Slice<int,1>::Computeonnxruntime::Slice<float,0>::Computeonnxruntime::Slice<double,0>::Computeonnxruntime::Slice<int,0>::Computeonnxruntime::Slice<__int64,0>::Computeonnxruntime::Slice<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,0>::Computeonnxruntime::Slice<unsigned char,1>::Computeonnxruntime::Slice<union onnxruntime::MLFloat16,0>::Computeonnxruntime::Slice<bool,0>::Computeonnxruntime::SliceImpl::<lambda_da95c811eeedce732ddb5e4757e3d89f>::operator ()onnxruntime::SliceImpl::<lambda_e275596fde644351b0f4c031ca0531ff>::operator ()output == output_endonnxruntime::SliceImpl::<lambda_a7b49f3e20e43e9fc0638a44b63e9b91>::operator ()onnxruntime::SliceImpl::<lambda_9a8afa08c06815da723eee9e2fb72c5e>::operator ()onnxruntime::SliceImpl::<lambda_f6caaedca46f94b8b8c3cb32c737131a>::operator ()onnxruntime::SliceImpl::<lambda_145f5cf68628c407aeabc61501ffa04d>::operator ()onnxruntime::SliceImpl::<lambda_ded130cf5245585a2793943e10dba2c5>::operator ()onnxruntime::Slice<bool,1>::Computeonnxruntime::Slice<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,1>::Computeonnxruntime::Slice<double,1>::Computeonnxruntime::Slice<union onnxruntime::MLFloat16,1>::Computeonnxruntime::SliceIterator<signed char>::Initonnxruntime::SliceIterator<unsigned __int64>::Initonnxruntime::SliceIterator<union onnxruntime::MLFloat16>::Initonnxruntime::SliceIterator<short>::Initonnxruntime::SliceIterator<unsigned char>::Initonnxruntime::SliceIterator<unsigned int>::Initonnxruntime::SliceIterator<unsigned short>::Initonnxruntime::SliceImpl::<lambda_9b0c3fd7e6a625ca609d219133431c26>::operator ()onnxruntime::SliceImpl::<lambda_2bbed03e472bcb7ef8230a400a2c7bd8>::operator ()onnxruntime::SliceImpl::<lambda_8b340f56ea70d730460eca84008e7b4b>::operator ()onnxruntime::SliceImpl::<lambda_92a57b0084db555419cc832e10e5ebdf>::operator ()onnxruntime::SliceIterator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Initonnxruntime::SliceIterator<bool>::Initonnxruntime::SliceImpl::<lambda_dd568ba036dfa4912f48c16edf51252a>::operator ()onnxruntime::SliceImpl::<lambda_2c8d78553f9b7144abe0af58c0fd8278>::operator ()onnxruntime::GatherBase::GatherBaseD:\30\s\onnxruntime\core/providers/cpu/tensor/gather.hType for Tind not supported yet in Gather.onnxruntime::Gather::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\gather.cc and D:\30\s\onnxruntime\core\providers\cpu\tensor\concat.ccNot satsified: input_count >= 1 -onnxruntime::ConcatBase::PrepareForCompute got: Ranks of input data are different, cannot concatenate them. expected rank: Cannot concatenate scalarsNot satsified: inputs_0_rank > 0 -Non concat axis dimensions must match: Axis Not satsified: dim_value == inputs_0_dims[axis_index] -inputs_n_rank == inputs_0_rank has mismatched dimensions of Not satsified: data_n.DataType() == p.output_tensor->DataType() -axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()CompressD:\30\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeonnxruntime::Flatten::Computegsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisD:\30\s\onnxruntime\core/providers/cpu/nn/flatten.hFlattenonnxruntime::Flatten::FlattenAttempt to retrieve final output before it was set.D:\30\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_ but expected scan_input_axesscan_output_directionsscan_input_directionsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK()bodyinfo.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK()num_scan_inputsD:\30\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Scan' dimension has length of Invalid scan input: Expected of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was but input 'feeds_fetches_manager_ && info_onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrgsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputs dimensions or more but input had shape of onnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.onnxruntime::Scan<9>::ComputeInvalid value in scan_output_axes for output . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueScanonnxruntime::ScanImpl::TransposeOutputOutputs from Scan are not optional and should never be null. outputs but Scan expects onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input Output OrtValue has not been created for loop state variable output onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsSubgraph in 'body' produces Attribute blocksize is not set.blocksizeD:\30\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()input_width % this->blocksize_ == 0input_height % this->blocksize_ == 0D:\30\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth<float>::ComputeDCRCRDinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace<float>::Computeinput.Shape().NumDimensions() == 4DepthToSpaceSpaceToDepthonnxruntime::DepthToSpace<float>::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supported Expected: Got:num_subgraph_outputs - 1 == num_outputs'Loop' node has onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::LoopInconsistent shape in loop output for output. static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:Loop outputs so the subgraph requires but has D:\30\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infoonnxruntime::LoopImpl::ConcatenateLoopOutput'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::ExecuteLogSoftmax, D=. N=Hardmax inputs N, D and N * D must be < HardmaxReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceL1info.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseD:\30\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseArgMinReduceMinReduceProdReduceSumSquareArgMaxin_dim != 0onnxruntime::PrepareForReduceD:\30\s\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:DynamicQuantizeLinearD:\30\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptronnxruntime::DynamicQuantizeLinear<unsigned char>::ComputeRoundCumSumAxis tensor should be 0D or 1D) but got: Axis should be in the range [reverseexclusivedims.size() == extents_.size()steps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator<int>::Initdims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=starts.size()=onnxruntime::WritableSliceIterator<double>::Initonnxruntime::WritableSliceIterator<float>::Initinfo.GetAttr<T>("max", &max_).IsOK()onnxruntime::Clip_6<float>::Clip_6info.GetAttr<T>("min", &min_).IsOK()onnxruntime::Clip<float>::ComputeD:\30\s\onnxruntime\core/providers/cpu/math/clip.hmax should be a scalar.max->Shape().NumDimensions() == 0min should be a scalar.min->Shape().NumDimensions() == 0batch_axisinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOptime_axis and batch_axis must have different values but both are batch_axis != time_axisInvalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2d:\30\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr<int64_t>("time_axis", &time_axis).IsOK(). Must be 0 or 1ReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccNull input X ptrRoiAlignInvalid mode of value specified. It should be either avg or maxd:\30\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase<double>::RoiAlignBaseavgFirst dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly Null batch_indices_ptrNull rois_ptrNumber of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1spatial_scalesampling_ratio_ >= 0onnxruntime::RoiAlignBase<float>::RoiAlignBaseoutput_widthoutput_heightSampling ratio should be >=0, but it was sampling_ratioFailed to obtain detect_negativedetect_negativeonnxruntime::IsInf::ComputeData type X must be float or double, but instead got Failed to obtain detect_positivedetect_positiveD:\30\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfIsInfTensor type mismatch.onnxruntime::IdentityOp<1>::ComputeD:\30\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::Tensor::MutableDataRawtype == dtype_onnxruntime::Tensor::DataRawQLinearConvT4IsScalarOr1ElementVector(result_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1onnxruntime::math::Im2colNd<unsigned char,2>::operator ()IsScalarOr1ElementVector(input_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(filter_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(filter_offset)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_offset)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(input_offset)QLinearConv : input zero point must be a scalar or 1D tensor of size 1D:\30\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::ComputeD:\30\s\onnxruntime\core\providers\cpu\nn\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.ConvIntegerIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.Not satsified: K_ == right_shape[right_num_dims - 2] -MatMul dimension mismatch, Left:Not satsified: left_padded_dims_[idx_dim] == 1 -left operand cannot broadcast on dim D:\30\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::Compute Right:Not satsified: left_num_dims >= 1 && right_num_dims >= 1 -IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1a != nullptr && b != nullptrMatMulIntegerD:\30\s\onnxruntime\core\providers\cpu\math\matmul_integer.cconnxruntime::MatMulInteger<unsigned char,unsigned char>::ComputeNot satsified: num_dims_with_pad - 1 == num_output_dims -Not satsified: M_ == 1 && N_ == 1 -Not satsified: K_ == right_shape[0] -Not satsified: num_dims_with_pad - 2 == num_output_dims -Not satsified: right_padded_dims_[idx_dim] == 1 -right operand cannot broadcast on dim Not satsified: num_dims_with_pad == num_output_dims -MatMul dimension mismatchMatMulInteger: Unsupported input types with zero pointonnxruntime::MatMulInteger<unsigned char,signed char>::Computet->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1Currently only scalar zero_point is supported. TODO: add per channel zero point support.t->IsDataType<int8_t>() || t->IsDataType<uint8_t>()onnxruntime::MatMulInteger<unsigned char,signed char>::Compute::<lambda_bb5968a6c8261e871ab5b3bffee781e1>::operator ()QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\30\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)onnxruntime::QLinearMatMul<unsigned char,unsigned char,unsigned char>::ComputeQLinearMatMulIsScalarOr1ElementVector(y_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)xQuantizeLineary_zero_pointx_zero_pointx_scaleDequantizeLinearyIsScalarOr1ElementVector(&y_scale)onnxruntime::DequantizeLinear<signed char>::ComputeIsScalarOr1ElementVector(&y_zero_point)onnxruntime::QuantizeLinear<unsigned char>::ComputeIsScalarOr1ElementVector(&x_scale)x_scale must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&x_zero_point)x_zero_point must be a scalar or 1D tensor or size 1.D:\30\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.cconnxruntime::DequantizeLinear<unsigned char>::Computex_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimx_zero_point must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_scale must be 1D tensor with size y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimy_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimonnxruntime::QuantizeLinear<signed char>::ComputegammaSoftsignThresholdedReluSeluSoftplusEluHardSigmoid*Conversion ErrorConversion ErrorStringNormalizerlocalestopwordsNONEattribute case_change_action has invalid valueLOWERUPPERcase_change_actionattribute case_change_action is not setattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizeren-USis_case_sensitiveonnxruntime::string_normalizer::Locale::LocaleD:\30\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cc:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:Input contains invalid utf8 chars at: Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorDuplicate stopwords not allowedp.secondEmpty stopwords not allowed!sw.empty()bad conversiononnxruntime::MatMul<int>::Computeonnxruntime::MatMul<unsigned int>::ComputeD:\30\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul<double>::Computeonnxruntime::MatMul<float>::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul<unsigned __int64>::ComputeInvalid input B: NumDimensions() != Invalid input B: 0th dimension != dimension != Invalid input scale: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input X: The rank of input X must be atleast 2. Got rank: D:\30\s\onnxruntime\core/providers/cpu/nn/batch_norm.hspatialst.IsOK()onnxruntime::BatchNorm<float>::BatchNormonnxruntime::BatchNorm<float>::ComputeD:\30\s\onnxruntime\core\providers\cpu\nn\batch_norm.ccmeanvarInvalid input var: scaleInvalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input mean: Invalid input B: Invalid input mean: NumDimensions() != Whereonnxruntime::TensorAllocator<int>::TensorAllocatoronnxruntime::TensorAllocator<unsigned char>::TensorAllocatoronnxruntime::TensorAllocator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::Where<float>::Computeonnxruntime::Where<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Computeonnxruntime::Where<int>::Computeonnxruntime::Where<__int64>::Computeonnxruntime::Where<unsigned char>::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\where_op.cccondition, X, and Y inputs are required!condition && X && Yonnxruntime::NonZero<float>::Computeonnxruntime::NonZero<int>::Computeonnxruntime::NonZero<__int64>::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero<unsigned char>::ComputeX input is required!onnxruntime::NonZero<bool>::Computeonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\30\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTfIdfVectorizermode is requiredmax_skip_countmax_skip_count is requiredmin_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_gram_length >= Required min_gram_length must be positive: impl_->min_gram_length_ > 0min_gram_lengthmin_gram_length is requiredmode: impl_->weighting_criteria_ != kNoneTFIDF is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedngram_indexesNon-empty ngram_indexes is requiredmax_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()status.IsOK() && !impl_->ngram_counts_.empty() <= ngram_countsNon-empty ngram_counts is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0(items % ngram_size == 0)-grams detectedend_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole -gramsn-gram counts out of bounds for non-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()!impl_->pool_strings_.empty()pool_int64spool_stringspool_strings must not be empty if specifiedGot weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size() must be of equal size but ngram_indexes size: Invalid type of the input argumentpoll_strings duplicate (before_insert + ngrams) == impl_->str_set_.size()pool_int64s duplicate (before_insert + ngrams) == impl_->int64_set_.size()Input shape must have either [C] or [B,C] dimensions with B > 0.op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK()lambdD:\30\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK()biasShrinkSignonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::sign_internal::CallSignImpl,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64>::InvokeNull input ptrIsNaNstoll argument out of rangeinvalid stoll argumentstoul argument out of rangeinvalid stoul argumentstol argument out of rangeinvalid stol argumentstoi argument out of rangeinvalid stoi argumentstod argument out of rangeinvalid stod argumentstof argument out of rangeinvalid stof argumentstoull argument out of rangeinvalid stoull argumentonnxruntime::Cast<bool>::Computeonnxruntime::Cast<__int64>::Computeonnxruntime::Cast<int>::Computeonnxruntime::Cast<short>::Computeonnxruntime::Cast<signed char>::Computeonnxruntime::Cast<unsigned __int64>::Computeonnxruntime::Cast<unsigned int>::Computeonnxruntime::Cast<unsigned short>::ComputeUnexpected 'to' argument value: D:\30\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast<unsigned char>::ComputeCast op must have 'to' argument of type DataTypeonnxruntime::Cast<signed char>::Castonnxruntime::Cast<short>::Castonnxruntime::Cast<int>::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast<bool>::Castonnxruntime::Cast<float>::Castonnxruntime::Cast<double>::Castonnxruntime::Cast<union onnxruntime::MLFloat16>::Castonnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::CastAttribute to is not set.onnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::ComputeInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast<union onnxruntime::MLFloat16>::ComputeCasting from 'float16' to 'string' is not supported yet.onnxruntime::Cast<double>::Computeonnxruntime::Cast<float>::Computeonnxruntime::Cast<unsigned char>::Castonnxruntime::Cast<unsigned short>::Castonnxruntime::Cast<unsigned int>::Castonnxruntime::Cast<unsigned __int64>::Castonnxruntime::Cast<int>::CastFloat16Dataonnxruntime::Cast<short>::CastFloat16Dataonnxruntime::Cast<signed char>::CastFloat16Dataonnxruntime::Cast<unsigned __int64>::CastFloat16Dataonnxruntime::Cast<unsigned int>::CastFloat16Dataonnxruntime::Cast<unsigned short>::CastFloat16Dataonnxruntime::Cast<unsigned char>::CastFloat16Datalen > 0onnxruntime::CastFloat16Dataallocator != nullptronnxruntime::Cast<union onnxruntime::MLFloat16>::CastFloat16Dataonnxruntime::Cast<double>::CastFloat16Dataonnxruntime::Cast<bool>::CastFloat16Dataonnxruntime::Cast<__int64>::CastFloat16Dataonnxruntime::CastFromStringDataINF-INFNaNonnxruntime::CastToStringDatabufferD:\30\s\onnxruntime\core\providers\cpu\tensor\eye_like.cconnxruntime::EyeLike::ComputeT1 != nullptrEyeLikeEyeLike : Input tensor dimension is not 2Input is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationinfo.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK()normalize_varianceD:\30\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0<float>::MeanVarianceNormalization_0info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK()across_channelsUnsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShapeBase::SetValueD:\30\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.hutils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoD:\30\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:unpack_status.IsOK()Unsupported value attribute datatype: Must have a single dimensiont_proto.dims_size() == 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseMust have a single dimension of 1t_proto.dims()[0] == 1Must have a valid input shape.input_shape.NumDimensions() > 0onnxruntime::ConstantOfShapeBase::PrepareComputeonnxruntime::ConstantOfShape::ComputeD:\30\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initialize has batch size of but Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::ValidateInput did not match batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::ExecuteScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Scanposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Iterator::operator *D:\30\s\onnxruntime\core/framework/ort_value_tensor_slicer.hUpsampleonnxruntime::Upsample<float>::BaseComputeRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample<int>::BaseComputeonnxruntime::Upsample<unsigned char>::BaseComputeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchonnxruntime::Upsample<float>::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.roi_input_idx_ > 0scales != nullptrOnly one of scales or sizes must be provided as input.sizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample<int>::Computeonnxruntime::Upsample<unsigned char>::ComputeResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.perm: v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max()onnxruntime::TransposeBase::TransposeBaseD:\30\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: Mismatched data types between input and output Tensors. onnxruntime::Transpose::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\transpose.ccTileInput 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::Tile::ComputeD:\30\s\onnxruntime\core\providers\cpu\tensor\tile.ccSizeA dimension cannot be less than -1.onnxruntime::ReshapeHelper::ReshapeHelperrequested_shape[i] >= -1At most one dimension can be -1.d:\30\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hThe dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1, requested shape:i < input_shape.NumDimensions()(input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:A shape tensor must be a vector tensor.onnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1shapeD:\30\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1Attribute shape is not set.Reshape_1gsl::narrow_cast<int64_t>(input_shape.Size()) == sizeforwardbidirectionalRNND:\30\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN<float>::ComputeScaledTanhAffineinfo.GetAttr("direction", &direction_).IsOK()clipD:\30\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN<float>::RNNactivation_alphaallowed_directions.find(direction_) != allowed_directions.end()activationsactivation_betahidden_sizeinfo.GetAttrs("activations", activations_).IsOK()activations_.size() == static_cast<size_t>(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()allowed_activations.find(activations_[direction]) != allowed_activations.end()input_forgetclip_ > 0.ftanhsigmoidLSTMactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3Invalid data type for LSTM operator of LSTM operator does not support double yetD:\30\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::Compute}. Actual:onnxruntime::DeepCpuLstmOp::ValidateInputsInput P must have shape {Input initial_c must have shape {future already retrievedbroken promiseno statepromise already satisfied'. Must be one of 'forward', 'reverse', or 'bidirectional'.futureonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of 'DEEPCPU_LSTMD:\30\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOpinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0D:\30\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.h rows[..onnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= NB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endcur + size <= endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointeronnxruntime::rnn::detail::SafeRawConstPointeronnxruntime::DeepCpuLstmOp::ComputeImplProcessing batchonnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of D:\30\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::DeepCpuGruOp::DeepCpuGruOplinear_before_resetD:\30\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()GRU operator does not support double yetGRUR->Shape()[1] == 5MaxRoiPoolD:\30\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool<float>::Computeroi_batch_id < batch_sizeroi_batch_id >= 0info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK()pooled_shapeD:\30\s\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool<float>::RoiPoolpooled_height_ > 0pooled_shape.size() == 2info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK()pooled_width_ > 0spatial_scale_ > 0onnxruntime::LRN<float>::ComputeX->Shape().NumDimensions() == 4LRND:\30\s\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr<int64_t>("size", &size).IsOK()sizeD:\30\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN<float>::LRNsize_ % 2 == 1size_ > 0beta_ > 0.0falpha_ > 0.0fop_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK()LpNormalizationD:\30\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm<float>::LpNormp_ == 1 || p_ == 2op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK()onnxruntime::InstanceNorm<float>::ComputeD:\30\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInstanceNormalizationD:\30\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm<float>::InstanceNorm vs. Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count info.GetAttr<float>("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalonnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDseedinfo.GetAttr<int64_t>("dtype", &dtype).IsOK()D:\30\s\onnxruntime\core/providers/cpu/generator/random.hinfo.GetAttr<float>("scale", &scale_).IsOK()info.GetAttrs<int64_t>("shape", shape).IsOK()onnxruntime::RandomNormalLike::RandomNormalLikeInvalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomUniform::RandomUniformlowhighinfo.GetAttr<float>("high", &high_).IsOK()sample_sizeinfo.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()info.GetAttr<float>("low", &low_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikeonnxruntime::MultinomialComputeCould not infer data type from input tensor with data type onnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::ComputeD:\30\s\onnxruntime\core\providers\cpu\generator\random.ccnum_classes is < 1num_samples is < 1Empty dimensions for input tensorbatch_size is < 1onnxruntime::RandomNormalComputeonnxruntime::RandomUniformComputeInvalid data type of FLOAT16 is not supportedonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsD:\30\s\onnxruntime\contrib_ops\cpu_contrib_kernels.ccclasses_stringsinfo.GetAttrs<std::string>("classes_strings", string_classes).IsOK()default_stringinfo.GetAttr<std::string>("default_string", &default_string_).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderD:\30\s\onnxruntime\core/providers/cpu/ml/label_encoder.hHowever, the number of key is ) must have the same length. values is and the number of The num_keys == num_values(name: attribtues in LabelEncoder onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2LabelEncoderInput of tensor(string) must have output of tensor(int64)default_int64info.GetAttr<int64_t>("default_int64", &default_int_).IsOK()values_strings_UnusedInput of tensor(int64) must have output of tensor(string)keys_floatsdefault_floatkeys_int64skeys_stringsvalues_floatsonnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::LabelEncoder_2info.GetAttrs<TValue>(_value_field_name, values).IsOK()values_int64sinfo.GetAttrs<TKey>(_key_field_name, keys).IsOK()classlabels_int64sclasslabels_stringsZipMaponnxruntime::ml::ZipMapOp::ZipMapOpD:\30\s\onnxruntime\core\providers\cpu\ml\zipmap.ccMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()] != number of classlabels[Input features_per_batch[Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsLEAFBRANCH_LTBRANCH_LEQBRANCH_EQLOGISTICBRANCH_GTEBRANCH_GTTreeEnsembleRegressorAVERAGESUMSOFTMAXSOFTMAX_ZEROMINd:\30\s\onnxruntime\core\providers\cpu\ml\ml_common.hbase_valuespost_transformtarget_idstarget_weightsinfo.GetAttr<int64_t>("n_targets", &n_targets_).IsOK()onnxruntime::ml::TreeEnsembleRegressor<float>::TreeEnsembleRegressoraggregate_functionn_targetsnodes_modestarget_nodeids_.size() == target_ids_.size()D:\30\s\onnxruntime\core\providers\cpu\ml\treeregressor.cc!nodes_treeids_.empty()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_treeidsnodes_nodeidsnodes_hitratesnodes_truenodeidsnodes_featureidsnodes_valuestarget_nodeidstarget_treeidsnodes_falsenodeidsnodes_missing_value_tracks_trueonnxruntime::ml::write_scores(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())it != parents.end()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()Input shape needs to be at least a single dimension.onnxruntime::ml::TreeEnsembleRegressor<float>::Computeit != indices.end()base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_)onnxruntime::ml::TreeEnsembleRegressor<double>::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor<double>::Computelength overflowtreeindex evaluated to a negative value, which should not happen.TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<float>::Compute1std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })X dims is empty.onnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::Computeonnxruntime::ml::TreeEnsembleClassifier<double>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<double>::Computeonnxruntime::ml::TreeEnsembleClassifier<int>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<int>::Computebase_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size()Invalid mode of value: onnxruntime::ml::TreeEnsembleClassifier<float>::Initializeclass_nodeidsclass_treeidsonnxruntime::ml::TreeEnsembleClassifier<float>::TreeEnsembleClassifierD:\30\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.ccclass_idsclass_weightsnodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()class_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()onnxruntime::ml::TreeEnsembleClassifier<double>::Initializeonnxruntime::ml::TreeEnsembleClassifier<double>::ProcessTreeNodetreeindex >= 0onnxruntime::ml::TreeEnsembleClassifier<float>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<int>::Initializeonnxruntime::ml::TreeEnsembleClassifier<int>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Initializeonnxruntime::ml::TreeEnsembleClassifier<__int64>::ProcessTreeNodePOLYLINEARRBFSVMRegressorinfo.GetAttrs<float>("kernel_params", kernel_params).IsOK()kernel_paramskernel_typeone_classd:\30\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon<float>::SVMCommonn_supportsonnxruntime::ml::SVMRegressor<float>::SVMRegressorinfo.GetAttrs<float>("rho", rho_).IsOK()rhosupport_vectors!coefficients_.empty()info.GetAttrs<float>("coefficients", coefficients_).IsOK()coefficientsD:\30\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccSVMClassifieronnxruntime::ml::SVMCommon<double>::SVMCommononnxruntime::ml::SVMCommon<__int64>::SVMCommonD:\30\s\onnxruntime\core/providers/cpu/ml/svmclassifier.hprob_bprob_avectors_per_classclasslabels_intsproba_.size() == probb_.size()D:\30\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier<float>::SVMClassifierNo support vectors.coefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK()onnxruntime::ml::SVMCommon<int>::SVMCommononnxruntime::ml::SVMClassifier<int>::SVMClassifieronnxruntime::ml::SVMClassifier<__int64>::SVMClassifieronnxruntime::ml::SVMClassifier<double>::SVMClassifierScaleronnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp<double>::ScalerOpEither both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp<int>::ScalerOpoffsetD:\30\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp<float>::ScalerOp!scale_.empty()Empty scale in attributesInvalid argument: input has empty dimensions.scale_.size() == offset_.size()Scale size: () != (One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64szerosUnknown Category and zeros = 0.OneHotEncodernum_categories_ > 0D:\30\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<double>::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<float>::OneHotEncoderOpD:\30\s\onnxruntime\core/providers/cpu/ml/ml_common.hL1MAXnormonnxruntime::ml::MakeNormalizeInvalid normalize value of L2NormalizerD:\30\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr<std::string>("norm", &norm).IsOK()Unexpected NORMALIZE value of onnxruntime::ml::Normalizer::NormalizeD:\30\s\onnxruntime\core\providers\cpu\ml\normalizer.cconnxruntime::ml::Normalizer::Computeonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::ml::Normalizer::CallNormalizerImpl,float,double,__int64,int>::InvokeLinearRegressortargetsinterceptsD:\30\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor<float>::LinearRegressorinfo.GetAttr<int64_t>("targets", &targets_).IsOK()LinearClassifieronnxruntime::ml::LinearClassifier<float>::LinearClassifiermulti_classonnxruntime::ml::LinearClassifier<int>::LinearClassifieronnxruntime::ml::LinearClassifier<__int64>::LinearClassifieronnxruntime::ml::LinearClassifier<double>::LinearClassifierD:\30\s\onnxruntime\core\providers\cpu\ml\linearclassifier.ccD:\30\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64Invalid typeonnxruntime::ml::ImputerOp::Computeimputed_value_int64simputed_value_floatsImputerEmpty input dimensions.Empty value of imputed values.inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::Computeonnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\30\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer).D:\30\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:DictVectorizerstring_vocabularyonnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::DictVectorizerOpint64_vocabularyinfo.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\30\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::DictVectorizerOpnum_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string info.GetAttrs<std::string>("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperD:\30\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK()%f Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSETO_FLOATTO_STRINGTO_INT64cast_toinfo.GetAttr<std::string>("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\30\s\onnxruntime\core/providers/cpu/ml/cast_map.hSPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\30\s\onnxruntime\core\providers\cpu\ml\cast_map.ccmax_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map<int64_t, float> or std::map<int64_t, std::string>map_forminfo.GetAttr<std::string>("map_form", &attr).IsOK()max_mapinfo.GetAttr<int64_t>("max_map", &max_map_).IsOK()Invalid input type of value: 0.fUnexpected CAST_TO value of Binarizerthreshold is NaNInput data with index: ArrayFeatureExtractor) >=] (Invalid Y argument: index is out of range: Y[Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0onnxruntime::scan::detail::OutputIterator::InitializeFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocatoriteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *D:\30\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'num_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entries inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::Infoonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::<lambda_df18081ccc34b5e376ea2f099a6c86d1>::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'validonnxruntime::OrtValueTensorSlicer<struct OrtValue>::Iterator::operator *Input X must have 3 dimensions only. Actual:Input R must have shape {Input W must have shape {Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid activation function of Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNamereluaffinethresholdedreluleakyreluInput sequence_lens must have shape {Input B must have shape {Input initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusD:\30\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCounthardsigmoidscaledtanhsoftsigneluinput is expected to have 3 dimensions, got bias is expected to have 1 dimension, got Last dimension of beta and input does not matchLast dimension of bias and input does not matchgamma is expected to have 1 dimension, got skip is expected to have same shape as inputbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchop_kernel_info.GetAttr("axis", &axis_).IsOK()D:\30\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm<float>::LayerNormonnxruntime::contrib::LayerNorm<double>::LayerNormonnxruntime::contrib::LayerNorm<float>::Computeonnxruntime::contrib::LayerNorm<double>::Computeonnxruntime::contrib::Affine<float>::Affineinfo.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()info.GetAttr("scale", &scale_).IsOK()Scaled:\30\s\onnxruntime\contrib_ops\cpu\element_wise_ops.honnxruntime::contrib::Scale<float>::ScaleParametricSoftplusInput is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerBias size () does not match the number of channels (d:\30\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler<float>::ImageScalerinfo.GetAttrs<float>("bias", bias_).IsOK()DynamicSliceAttribute border needs to be specified with four border elements, got input dimensions insteadborder) + scale_[0] () + scale_[1] () needs to be greater than or equal to the leftBorder (Input's width (Input's height () + rightBorder () + bottomBorder () needs to be greater than or equal to the topBorder (d:\30\s\onnxruntime\contrib_ops\cpu\crop.hCroponnxruntime::contrib::Crop<float>::ComputeD:\30\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu_fusion.ccdimension 0 of Input 1 should have same length as the last dimension of input 0onnxruntime::contrib::BiasGelu<float>::ComputeShape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got euclideanonnxruntime::contrib::CDist<float>::CDistCDistd:\30\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaninfo.GetAttr<std::string>("metric", &metric).IsOK()onnxruntime::contrib::CDist<double>::CDistInput shape dimensions mismatch:metricThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: y_scaleonnxruntime::contrib::CropAndResize<float>::CropAndResizeD:\30\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hbilinear specified. It should be either bilinear or nearestCropAndResizeNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskd:\30\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeD:\30\s\onnxruntime\contrib_ops\cpu\matmul_integer16.cconnxruntime::contrib::MatMulInteger16<short,short,int>::ComputeA != nullptr && B != nullptrMatMulInteger16keysMurmurHash3positivesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeD:\30\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computechar_embedding_sizeconv_window_sizeembedding_size Char embedding size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Conv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: WordConvEmbeddingD:\30\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2. Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is settokenexpseparatorsmincharnum_ > 0attribute mincharnum must have a positive valueattribute mincharnum is not setmincharnumInput string contains invalid utf8 chars: Can not digest tokenexp: Can not digest separators: !char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezation!separators.empty()separators must not be empty!tokenexp.empty()attribute pad_value is not setpad_valueD:\30\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute mark is not setmarkTokenizerInput dimensions are either [C] or [N][C] allowedtensor(string) expected as inputMatch contains invalid utf8 chars: DEEPCPU_ATTN_LSTMstatic_cast<int>(activation_func_names.size()) == num_directions_ * 3d:\30\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp}. Got: Attention query layer weight shape error! Expected:{}, Got: Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, ], while found!onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention v weight shape error! Expected:{Attention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {}, actural: D:\30\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplleaky_relu_alphaGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\30\s\onnxruntime\contrib_ops\cpu\fused_conv.ccAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::Computed:\30\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::EmbedLayerNorm<float>::ComputeD:\30\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccinput index out of rangeinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseD:\30\s\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 1 is expected to have 2 dimensions, got onnxruntime::contrib::Attention<float>::ComputeInput 1 dimension 0 should have same length as dimension 2 of input 0Input 1 dimension 1 should be 3 times of dimension 0Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0SampleOpinfo.GetAttr<int64_t>("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput<float>::ReorderOutputinvalid channel countchannels_ > 0D:\30\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccX_shape.NumDimensions() == 4(static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchNot satsified: Y->Shape() == sum_shape -onnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::contrib::NchwcConv::NchwcConvd:\30\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseonnxruntime::contrib::NchwcConv::Computeonnxruntime::contrib::ReorderInput<float>::Computeonnxruntime::contrib::ReorderOutput<float>::Computechannels_ <= Y_shape[1] [seqno= row[not support normalize yet.onnxruntime::contrib::BahdanauAttention<float>::BahdanauAttention!normalize_ is not in (0, D:\30\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps onnxruntime::contrib::BahdanauAttention<float>::PrepareMemoryunimplemented activation: activation_params count mismatchsegment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1beta is expected to have size of beta is expected to have 1 dimensions, got gamma is expected to have size of gamma is expected to have 1 dimensions, got Input 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapeword_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got D:\30\s\onnxruntime\core\util\protobuf_parsing_utils.ccCHECK failed: !is_closed_: close() failed: gemmlowp error: %s -WidthMajorallocation failurereference(Lhs: %d cells %dx%d %s, Rhs: %d cells %dx%d %s) is not supportedTensor types should have been handled already type: id: name: mem_type:CudaPinnedOrtMemoryInfo: [CudaSpecified device is not supported.Not implementedOrtValue is not a TensorD:\30\s\onnxruntime\core\framework\tensor_type_and_shape.ccArgument is not a tensortype != nullptrOrtApis::GetTensorTypeAndShapeTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrD:\30\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0p_type != nullptronnxruntime::Tensor::Tensoronnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Inittensor failed memory size calculationtensor size overflowonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatible is not currently registered or supportedMLDataType for: tensor type Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatible(null)tensor(bfloat16)onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())proto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeD:\30\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())We do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::data_types_internal::SetSequenceType<bool>::Setonnxruntime::data_types_internal::SetSequenceType<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned __int64>::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<union onnxruntime::MLFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<struct onnxruntime::BFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<signed char>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned char>::Setonnxruntime::data_types_internal::SetSequenceType<float>::Setonnxruntime::data_types_internal::SetSequenceType<double>::Setonnxruntime::data_types_internal::SetSequenceType<int>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned int>::Setonnxruntime::data_types_internal::SetSequenceType<short>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned short>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::SetD:\30\s\include\onnxruntime\core/framework/data_types.h expected to be a registered ONNX typevalue_proto != nullptr{}dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::SliceInvalid dimension of dimension <= num_dims dimensions. for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.onnxruntime::TensorShape::SizeToDimensionD:\30\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::utils::CopyMLValueD:\30\s\onnxruntime\core\framework\utils.ccinvalid allocatorNnapiExecutionProvideronnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()'is defined.No attribute with name:'Attibute name and type don't matchNo attribute with this name is defined.D:\30\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocatoronnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_D:\30\s\onnxruntime\core/framework/node_index_info.honnxruntime::OpKernelContext::GetOrCreateOutputMLValue. Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Invalid arg_num of D:\30\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was nullDevice: [ memory_type: device_id:Kernel creation failed for node: with error: Could not create kernel for node: as there's no execution provider allocated. is used by node ) and node duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?D:\30\s\onnxruntime\core\framework\session_state.ccDone saving OrtValue mappings.onnxruntime::SessionState::SetGraphSaveMLValueNameIndexMappingnode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::GetNodeIndexInfooutput_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input:Entry exists in node for attribute onnxruntime::SessionState::AddOutputNameToNodeInfoMappingGraphSubgraph input with name Failed to copy tensor to execution provider: Internal error. The preallocated buffer is too small. Requires , Got onnxruntime::DeserializeTensorProto is not used by any node.onnxruntime::SaveInputOutputNamesToNodeMappingonnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_8f8c0c3c57010134a4c11b3447bd920e>::operator ()onnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_fa9620d02174af4aeb88191a68ed0828>::operator ()onnxruntime::SessionStateInitializer::CreatePlanstring tensor is not supported for copying between allocatorsInvalid allocation info. Provider name = Execution plan was not found in SessionState. CreatePlan must be called first.D:\30\s\onnxruntime\core\framework\session_state_initializer.ccDone saving initialized tensorsort_value_name_idx_map.MaxIdx() > 0OrtValue indexes should have been populated.onnxruntime::SaveInitializedTensorsSaving initialized tensors.Deserialize tensor failed.Failed to find kernel for The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for (node The node is not placed on any Execution Providerfound duplicated provider in KernelRegistryManagerThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsonnxruntime::utils::TensorProtoToMLValuestring tensor can not have raw dataInitialized tensor with unexpected type: initialize preallocated buffer failedcorrupted protobuf data: tensor shape size() does not match the data size() in protoD:\30\s\onnxruntime\core\framework\tensorprotoutils.cconnxruntime::utils::GetFileContentdata overflowUnpackTensor: the pre-allocate size does not match the size in protoNot satsified: source_bytes.size_bytes() == destination.size_bytes() -D:\30\s\onnxruntime\core/framework/endian_utils.honnxruntime::utils::ReadLittleEndiansource and destination buffer size mismatchInvalid TensorProto`anonymous-namespace'::UnpackTensorWithRawDataUnpackTensor: the pre-allocated size does not match the raw data size, expected nullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\30\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::IExecutionProvider::InsertAllocatorD:\30\s\onnxruntime\core\framework\execution_provider.ccduplicated allocatorError mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\30\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: onnxruntime::GraphPartitioner::PartitionProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()onnxruntime::PlaceNodeD:\30\s\onnxruntime\core\framework\graph_partitioner.ccNo provider specified.onnxruntime::FunctionKernel::FunctionKernelD:\30\s\onnxruntime\core/framework/func_kernel.hcreate_func(&context, &func_state_) == 01 == capability->nodes.size()Tensor size mismatch to There's no data transfer registered for copying tensors from data_transfer registered is nullptr.Caught exception while destructing CustomOpsLoader with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderD:\30\s\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: Caught exception while loading custom ops with message: ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueoffset >= 0 && static_cast<size_t>(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndex 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()onnxruntime::ExecutionFrame::ExecutionFrameOrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index 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() Requested shape:feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::IExecutionFrameD:\30\s\onnxruntime\core\framework\execution_frame.ccD:\30\s\onnxruntime\core/framework/execution_frame.hMemory pattern planner is not enabled on this execution framework.TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplmlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan, block not found in target location. fall back to default allocation behavior. 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.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer, fall back to default allocation behavior but the actually size is: , block in memory pattern size is: For ort_value with index: Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper kernel is not supported in Failed to create kernel for op: onnxruntime::KernelRegistry::TryFindKernelD:\30\s\onnxruntime\core\framework\kernel_registry.cc Encountered following errors: (kernel def can't be NULL: Conflicting with a registered kernel with op versions.Failed to add kernel for since it was ill-formed during registration kernel_end_version: kernel start version: node_version: Version mismatch. Incompatible types.Op: len <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoD:\30\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensorsrc.SizeInBytes() == dst.SizeInBytes()D:\30\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::<lambda_04eedca63eb721c5b4620f0e1a122648>::operator ()onnxruntime::NodeIndexInfo::Init::<lambda_458270087b990ddc192eb1d0283590c4>::operator ()onnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Createdim0_offset < dim0_sizegsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on Invalid dim0_offset of . Dimension 0 is OrtValue has not been allocated so can't be sliced.D:\30\s\onnxruntime\core\framework\ort_value_tensor_slicer.cc. Shape:ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer<struct OrtValue>::CreateCan't slice a non-tensor OrtValue. Type was onnxruntime::ParallelExecutor::RunNodeAsyncBegin executionGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.D:\30\s\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::ExecuteD:\30\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalonnxruntime::ParallelExecutor::ExecuteMultiple errors were found. does not.entry != nullptrAll implicit inputs should have OrtValue instances by now. Unknown exception was caught by catch-all handler._fence_afterException running nodes starting at '. Non-zero status code returned while running node. Name:'_kernel_timeprovider_fence_beforeop_name' Status Message: onnxruntime::ReleaseNodeMLValuesSequentialExecutor::ExecuteD:\30\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::ExecuteD:\30\s\onnxruntime\core\framework\fuse_nodes_funcs.ccCan't use func with null ptrfunc info for node: onnxruntime::FuncManager::GetFuncs not found. already exist.' failedWeight buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer ' is not foundInternal error.' is not foundMem pattern for initializer duplicated locationFailed to get allocator for location: d:\30\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlan[D:\30\s\onnxruntime\core\framework\allocation_planner.cc : ) onnxruntime::PlannerImpl::CreatePlankernel_create_info != nullptr && kernel_create_info->kernel_def != nullptronnxruntime::PlannerImpl::GetLocationForNodeInputonnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeUseCountsCan not find the node Can not find the execution provider No suitable kernel definition found for op onnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseonnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast<size_t>(id) < ort_value_info_.size()n >= 0 && static_cast<size_t>(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Indexonnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountillegal input path:.. unexpected failuremodel format error!model format error! Missing 'location'lengthchecksum failedparsing model format error! Need a value for the external data infolocationmodel format error! Need a key for the external data infoCreating bin of max chunk size onnxruntime::BFCArena::BFCArenaCould not find Region for onnxruntime::BFCArena::RegionManager::RegionForonnxruntime::BFCArena::AllocationRegion::IndexForp_int < base_int + memory_size_D:\30\s\onnxruntime\core/framework/bfc_arena.hp_int >= base_int0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::AllocationRegiononnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunk!chunk->in_use()onnxruntime::BFCArena::FindChunkPtronnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytesh != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::ReserveTotal allocated bytes: Allocated memory at Extended allocation by onnxruntime::BFCArena::Extendonnxruntime::BFCArena::ChunkFromHandle bytes.BinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)D:\30\s\onnxruntime\core\framework\bfc_arena.ccBinForSize(bin_size) == BinFromIndex(b) was c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesceCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)Failed to get allocator for initializer 'D:\30\s\onnxruntime\core\framework\simple_tensor_allocator.cc', location: invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferCan't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source= Dimension= Target=[ShapeInferenceError] NoOpConstantD:\30\s\onnxruntime\core\graph\graph.ccOutput:Error merging shape info for output. 'onnxruntime::MergeShapeInfo target:' source:. Falling back to lenient merge. in node No Graph instance was found for attribute This is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Invalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeThis is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsconstant_attribute.has_t()This is an invalid model. Error: two nodes with same node name (onnxruntime::Graph::GraphOnly 'value' attribute is supported within a 'Constant' node in ORTgraph_proto cannot be nullgraph_proto != nullptrSparseTensor element type mismatch. This is an invalid model. The sum of input arg count is not equal to size of input defs in node (Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShape Input=Type mismatch. Current= does not match. Type Error: Shape of initializer does not match its type.Type Error: Value of initializer ) does not have type information.This is an invalid model. Model input () of output arg (Type Error: Type () does not match expected type () of node () type inference failed) output arg () Op (Node () bound to different types (Type Error: Type parameter (This is an invalid model. Type Error: Type ' in node () of operator (' of input parameter () is invalid.) in node () input arg (This is an invalid model. Node (onnxruntime::Graph::InferAndVerifySubgraphTypes) does not have type information set by parent node.Node:Subgraph input missing type. inputs but subgraph has Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires Can't remove node node->GetOutputEdgesCount() == 0 as it still has output edges.!foundonnxruntime::Graph::RemoveInitializedTensoronnxruntime::Graph::AddInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.AddInitializedTensor already has tensor with name existing->second == &tensorouter_scope_node_args_consumed.empty() but different TensorProto.onnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsError: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputs) is required but not specified.) attribute (Fatal error: onnxruntime::Graph::VerifyNodeAndOpMatchThis is an invalid model. Error in Node: is not a registered function/oponnxruntime::ConstPointerContainer<class std::vector<class onnxruntime::NodeArg *,class std::allocator<class onnxruntime::NodeArg *> > >::atD:\30\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::Graph::SetOutputsindex < data_.size()This API is not supported when model is loaded from proto file right now.onnxruntime::Graph::SetInputsonnxruntime::Graph::InlineFunctionnullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraphnodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max())onnxruntime::Graph::AllocateNodeThis is an invalid model. Graph output ( must be either specified in graph inputs or graph initializers.onnxruntime::Graph::SetGraphInputsOutputs) does not exist in the graph.'. It is no longer used by any node.Graph ctor should have created NodeArg for initializer.Removing initializer 'onnxruntime::Graph::CleanUnusedInitializersonnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model.Outer scope node arg name 'node_argonnxruntime::Graph::RemoveNode'was added but does not exist. , but it its version is higherthan the operator set version known by the checker.D:\30\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryCAttribute Negative values are not allowed in a shape specification should specify a shapeConstrain input and output types to float tensors.3D output tensor with shape (batch_size, sequence_length, hidden_size)mask_indexAttention mask index with shape (batch_size)M1D input tensor with shape (3 * hidden_size)weight2D input tensor with shape (hidden_size, 3 * hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeNumber of attention headsword_embedding should have 2 dimensions and dimension size is known.Inputs 0 shall be 2 dimensionsWConstrain input and output types to float/quantized tensorsD:\30\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cctensor rank too smallConstrain input and output types to float tensorsFor internal use.Crop and image to the specified spatial dimensions. If scale is given, -then optionally start the crop offset by the left/top border amounts. -If scale is not provided, crop the borders as provided.Scale and bias the input image. Bias values are stored in -the same ordering as the image pixel format. -ParametricSoftplus takes one input data (Tensor<T>) and produces one output data -(Tensor<T>) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to -the tensor elementwise. - -Affine takes one input data (Tensor<T>) and produces one output data -(Tensor<T>) where the affine function, y = alpha * x + beta, -is applied to the tensor elementwise. -1D bias tensor with shape (hidden_size1D skip tensor with shape (hidden_size1D input tensor with shape (hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to float or half tensors.output tensorbias tensorinput tensorConstrain input and output float tensors types.Constrain input and output integer tensors types1D mask_index tensor with shape (batch_size)mask2D attention mask with shape (batch_size, sequence_length)1D beta tensor for layer normalization with shape (hidden_size)1D gamma tensor for layer normalization with shape (hidden_size)segment_embeddingposition_embedding2D with shape (, hidden_size)word_embedding2D with shape (,hidden_size)segment_ids2D segment IDs with shape (batch_size, sequence_length)input_ids2D words IDs with shape (batch_size, sequence_length)Constrain mask index to integer typesValue of alphaBias Gelu. -It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Gaussian Error Linear Unit. -A high-performing neural network activation function.The GELU nonlinearity is -the expected transformation of a stochastic regularizer which randomly applies -the identity or zero map to a neuron's input. The GELU nonlinearity weights -inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionfirst input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.inputs are expected to have tensor type and output type should not be null. - Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. - If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. - Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. - The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, - the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. - "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. - Let's assume "separators" is [" "] and consider an example. - If input is - ["Hello World", "I love computer science !"] whose shape is [2], - then the output would be - [["Hello", "World", padvalue, padvalue, padvalue], - ["I", "love", "computer", "science", "!"]] - whose shape is [2, 5] because you can find at most 5 tokens per input string. - Note that the input at most can have two axes, so 3-D and higher dimension are not supported. - If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings - will be broken part into individual characters. - For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. - The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). - This operator searches for the first match starting from the beginning of the considered string, - and then launches another search starting from the first remained character after the first matched token. - If no match found, this operator will remove the first character from the remained string and do another search. - This procedure will be repeated until reaching the end of the considered string. - Let's consider another example to illustrate the effect of setting "mark" to true. - If input is ["Hello", "World"], - then the corresponding output would be [0x02, "Hello", "World", 0x03]. - This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. -If tokenizer removes the entire content of [C]-input, it will produce [[]]. -I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. -If the tokenizer receives empty input of [0] then the output is [0] if empty input -of [N, 0] then [N, 0]. - -The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. -The dequantization formula is y = (x - x_zero_point) * x_scale. -Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). -The linear quantization operator. It consumes a full precision data, a scale, a zero point and computes the quantized data. -The quantization formula is y = (x / y_scale) + y_zero_point. For (x / y_scale), it computes the nearest integer value to arg (in floating-point format), - rounding halfway cases away from zero. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').Input axis is invalid: Second input does not have rank 2First input does not have rank 2) + scale[1] () + scale[0] () needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D -GRUUnit computes the activations of a standard GRU, -in a sequence-length aware fashion. -Concretely, given the (fused) inputs X (TxNxD), the previous hidden -state (NxD), and the sequence lengths (N), computes the GRU -activations, avoiding computation if the input is invalid (as in, the -value at X[t][n] >= seqLengths[n]. - -Scale takes one input data (Tensor<float>) and produces one output data -(Tensor<float>) whose value is the input data tensor scaled element-wise. -extra_shapeinput_as_shape -Produces a slice of the input tensor along multiple axes. Similar to numpy: -https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html -Slices uses `axes`, `starts` and `ends` inputs to specify the start and end -dimension for each axis in the list of axes, it uses this information to -slice the input `data` tensor. If a negative value is passed for any of the -start or end indices, it represent number of elements before the end of that -dimension. If the value passed to start or end is larger than the `n` (the -number of elements in this dimension), it represents `n`. For slicing to the -end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. -If `axes` are omitted, they are set to `[0, ..., ndim-1]`. -Example 1: - data = [ - [1, 2, 3, 4], - [5, 6, 7, 8], - ] - axes = [0, 1] - starts = [1, 0] - ends = [2, 3] - result = [ - [5, 6, 7], - ] -Example 2: - data = [ - [1, 2, 3, 4], - [5, 6, 7, 8], - ] - starts = [0, 1] - ends = [-1, 1000] - result = [ - [2, 3, 4], - ] -hidden_prevThe previous GRU hidden state.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitOutput data after scalingInput data to be scaledvaluesThe filled tensorThe shape of filled tensorGivenTensorFillConstrain indices to integer typesConstrain input and output types to all tensor types.Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`dataTensor of data to extract slices from.Output tensorInput tensorThreshold valueResult, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Result, has same shape and type as inputInput tensor of shape [N,C,H,W]The scale to apply.Bias applied to each channel, same size as C.1D output tensor1D input tensorValue of betaScale 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'.N-D full precision Input tensor to be quantized.The 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.ExpandDims echo operator.Specified axis to insert a dimensionScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).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 A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. -The FusedGemm operator schema is the same as Gemm besides it includes attributes -activation and leaky_relu_alpha. -The fused convolution operator schema is the same as Conv besides it includes an attribute -activation.PadsConstrain input0 and output types to float tensors -Sample echo operator.The scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.hiddenThe new GRU hidden state calculated by this op.tThe timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.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).Constrain input type to 8-bit integer tensor.reducedReduced output tensor.An input tensor. -Computes the sum of the low-precision input tensor's element along the provided axes. -The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, -then the resulting 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.ReduceSumIntegerConstrain 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 B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BN-dimensional matrix BN-dimensional matrix A -Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. - The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan 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.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.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.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorTokenized stringsStrings to tokenizeConstrain 'x_zero_point' and 'x' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.N-D full precision output tensor. It has same shape as 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'.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'.N-D quantized Input tensor to be de-quantized.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.N-D quantized output tensor. It has same shape as 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'.Tensor after padding.(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).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] 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`.Input tensor.Three 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 arrayThe WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Constrain to tensor(int32).Specify embedding vector of charSpecify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This 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 word.If not provide, use the fileter size of conv weight -Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather -slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. -Example 1: - data = [[0,1],[2,3]] - indices = [[0,0],[1,1]] - output = [0,3] -Example 2: - data = [[0,1],[2,3]] - indices = [[1],[0]] - output = [[2,3],[0,1]] -Example 3: - data = [[[0,1],[2,3]],[[4,5],[6,7]]] - indices = [[0,1],[1,0]] - output = [[2,3],[4,5]] -Example 4: - data = [[[0,1],[2,3]],[[4,5],[6,7]]] - indices = [[[0,1]],[[1,0]]] - output = [[[2,3]],[[4,5]]] -Constrain indice type to int32 or int64Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].indicesTensor of rank q >= 1.Tensor of rank r >= 1.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.Keep the reduced dimension or not, default 1 mean keep reduced dimension.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Bias tensor.Scale tensor.Input data tensor from the previous layer.The epsilon value to use to avoid division by zero.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). - Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling - (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. - Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. - The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to - a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. - The resizing is corner aligned.Constrain types to int tensors.Constrain types to float tensors.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].crop_size1-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.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisRoIs (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.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.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)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". - Finds all the unique values (deduped list) present in the given input tensor. - This operator returns 3 outputs. - The first output tensor 'uniques' contains all of the unique elements of the input, - sorted in the same order that they occur in the input. - The second output tensor 'idx' is the same size as the input and it contains the index - of each value of the input in 'uniques'. - The third output tensor 'counts' contains the count of each element of 'uniques' in the input. - Example: - input_x = [2, 1, 1, 3, 4, 3] - output_uniques = [2, 1, 3, 4] - output_idx = [0, 1, 1, 2, 3, 2] - output_counts = [1, 2, 2, 1] - Input can be of any tensor type.countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 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 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 input tensor that is to be processed. - Given `data` tensor, pads, mode, and value. - Example: - Insert 0 pads to the beginning of the second dimension. - data = [ - [1.0, 1.2], - [2.3, 3.4], - [4.5, 5.7], - ] - pads = [0, 2, 0, 0] - output = [ - [ - [0.0, 0.0, 1.0, 1.2], - [0.0, 0.0, 2.3, 3.4], - [0.0, 0.0, 4.5, 5.7], - ], - ] - The bias input data that is a 1D tensor.The normal input data.The output.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.inv_std_varSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationUOutput data tensor.onnxruntime::Model::LoadFailed to load model with error: No graph was found in the protobuf.D:\30\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelONNX 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.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.ModelProto was null.system error number Load model failed. File doesn't existonnxruntime::SaveModelProtobuf serialization failed.onnxruntime::Model::Save<p_fd> is less than 0.<p_fd> less than 0.Protobuf parsing failed.Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.D:\30\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast<size_t>(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index: ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast<size_t>(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast<size_t>(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Resolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplinput_arg->Type() != nullptrfused_function_subgraphD:\30\s\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyA node with a function body within a subgraph within another function body is currently not supported in ORToutinY=1-D Tensor of the range.deltaTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.limitTensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.startTensor(scalar, or dims=[1]). First entry in the range.Constrain input and output types.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!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.RThe 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 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 input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Constrain seq_lens to integral tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.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.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.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.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`AWThe 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.` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` QWThe 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]` PThe 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.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.sequence_lensOptional 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]` [ is not a valid year is not a valid day is not a valid dateDefault logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::DefaultISink must be provided.onnxruntime - error code: Failed to close file handle onnxruntime::`anonymous-namespace'::FileHandleTraits::CleanUpD:\30\s\onnxruntime\core\platform\windows\env.ccFatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation fail, errcode = GetFileSizeEx onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satsified: file_path -Not satsified: offset >= 0 -Not satsified: length <= buffer.size() -open file SetFilePointerEx ReadFile fail: unexpected endMapFileIntoMemory is not implemented on Windows.Failed to unload libraryFailed to find symbol in libraryonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNamekernel32.dllGetSystemTimePreciseAsFileTimeSUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORcode != static_cast<int>(common::OK)onnxruntime::common::Status::StatusD:\30\s\onnxruntime\core\common\status.ccOKSystemError[ONNXRuntimeError]"pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} -}, +CpuVIWEFai.onnx.ml!=D:\4\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetD:\4\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()ai.onnxCPUExecutionProviderDmlExecutionProviderTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\4\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Getsize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.onnxruntime_profile_input name cannot be emptyoutput name cannot be emptyinput array is too shortshape is invalidspace is not enoughout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.Unsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperD:\4\s\onnxruntime\core\session\onnxruntime_c_api.ccSequences must have tensors of the same data type. There was at least one tensor in the input that was different.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.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValue1.2.0T Tensor type mismatch. utils::IsPrimitiveDataType<T>(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,double,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,double> > > >::checkD:\4\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,__int64,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<struct OrtStatus *,struct c_api_internal::UnsupportedReturnFailStatus>::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > >,class std::allocator<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > >,class std::allocator<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > > > >::checkinvalid vector subscript != onnxruntime::DataTypeImpl::GetType<T>() == type_len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max()OrtCreateMapMLValueEncountered nullptr.not implementedgraph_optimization_level is not validexecution_mode is not validunknown is not implementedonnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughUnsupported version '' in custom op 'ADescriptionD:\4\s\onnxruntime\core\session\custom_ops.cconnxruntime::CreateCustomRegistryenv_ptr == p_instance_D:\4\s\onnxruntime\core\session\ort_env.ccOrtEnv::Releasebad castAttempt to use DefaultLogger but none has been registered.]CUDAExecutionProviderProvider has already been registered.D:\4\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddTrying to get a SparseTensor, but got: allocator already registered.IsSparseTensor()onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\4\s\onnxruntime\core/framework/feeds_fetches_manager.hCreate_State_Compute_Release_State_MemcpyTransformer[json.exception.] other_errorORT_LOAD_CONFIG_FROM_MODEL%Y-%m-%d_%H-%M-%S are '0' and '1'. The environment variable contained the value: Reading the provided model for the ORT configThe only supported values for the environment variable D:\4\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsmodel_protoModel needs to be provided to check for ORT config within itstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: intra_op_thread_poolonnxruntime::InferenceSession::ConstructorCommonCastFloat16Transformerinter_op_thread_poolonnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::~InferenceSessionError during EndProfiling(): Received nullptr for exec providerUnknown error during EndProfiling()Sequential execution must be enabled before registering DMLExecutionProviderMemory pattern must be disabled before registering DMLExecutionProviderReceived nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::LoadThis session already contains a loaded model.model_proto_ == nullptrFailed to clear up model_proto_ in Inference SessionUnknown exception in Load()Exception during loading: ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Encountered unknown exception in Load()model_loading_protomodel_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.Could not find an implementation for the node onnxruntime::InferenceSession::TransformGraph ((].Node placements: [All nodes have been placed on [Main Graph instance should have populated all subgraphs when being resolved. Provider: [onnxruntime::InferenceSession::CreateSubgraphSessionStatesubgraphsubgraph_session_stateCreateSubgraphSessionState should have created an entry earlier.p_op_kernelonnxruntime::InferenceSession::InitializeSubgraphSessionsonnxruntime::InferenceSession::InitializeInitializing session.Model was not loaded.Model was not loadedAdding default CPU execution provider.Session has already been initialized.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Parallel execution is currently not supported for the registered CUDA Execution Provider.Exception during initialization: Session successfully initialized.session_initializationEncountered unknown exception in Initialize() Expected: Please fix either the inputs or the model.Invalid rank for input: Got: Got invalid dimensions for input: for the following indices +) , expected: ( index: elements.Unexpected input data type. Actual: (Size mismatch: feed_names has elements, but feeds has is not expected to be of type tensor.Invalid Feed Input Name:onnxruntime::InferenceSession::ValidateInputsInput with name: is not expected to be of type tensor sequence. is not expected to be of type sparse tensor.At least one output should be requested.Output vector pointer is NULLOutput vector incorrectly sized: output_names.size(): p_fetches->size(): Session was not initializedInvalid Output Name:Session not initialized.onnxruntime::InferenceSession::RunEncountered unknown exception in Run()Running with tag: onnxruntime::InferenceSession::GetModelMetadatamodel_runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::EndProfilingProfiler is disabled.Could not write a profile because no model was loaded.run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to onnxruntime::InferenceSession::AddPredefinedTransformers961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_list too longlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringBcom.microsoft.nchwccom.microsoft.mlfeaturizerscom.microsoft[TypeInferenceError] unknownElement type of input expected to have tensor typeInput Output tensor(int32)tensor(int64)tensor(uint32)tensor(uint64)tensor(double)tensor(uint8)tensor(float16)tensor(float)tensor(int16)tensor(uint16)tensor(int8)tensor(string)tensor(bool)tensor(complex64)tensor(complex128) line , but it is already registered from file (domain: Trying to register schema with name ) from file version: in onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange known by the checker., but it its domain is notin the inclusive range [, but it its version is not bumped the operator version but ] (usually, this means you Schema error: TinputD:\4\s\onnxruntime\core\session\environment.ccMemcpyFromHostYConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.XoutputException caught: MemcpyToHostp_provider: value'parse errorparse_error at line invalid_iterator, column ort_configsession_optionstype_errorout_of_rangeD:\4\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsParallel modeSetting execution_mode to onnxruntime::SetExecutionModeSequential modeSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelUnsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::InferenceSessionUtils::ParseOrtConfigJsonInModelProtoSetting enable_profiling to The Model Proto has already been checked for the ORT config json.The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: intra_op_num_threads option in the model file must be an integerinter_op_num_threadsonnxruntime::InferenceSessionUtils::ParseSessionOptionsFromModelProtointra_op_num_threadsexecution_mode option in the model file must be an integergraph_optimization_levelinter_op_num_threads option in the model file must be an integerexecution_modeenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: graph_optimization_level option in the model file must be an integerenable_profilingkey 'cannot use at() with ' not foundnullcannot use key() for non-object iteratorsarraystringinvalid map<K, T> keyobjectnumberbooleandiscardedcannot get valuecannot compare iterators of different containerssyntax error ; last read: 'unexpected while parsing - invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literal; expected <U+%.4X>false literalnull literal<uninitialized>true literal'[''{'string literalnumber literal':'','']''}''[', '{', or a literalunknown token<parse error>end of inputinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cvector<bool> too longinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 bytenumber overflow parsing 'object keyobject separatoriterator out of rangecannot use erase() with iterator does not fit current valuetype must be number, but is ExceptionFailFast%hs(%u)\%hs!%p: ReturnHrLogHr%hs(%d) tid(%x) %08X %ws %hs!%p: (caller: %p) [%hs(%hs)] +[%hs] +Msg:[%ws] CallContext:[%hs] RaiseFailFastException +kernelbase.dll%wswil_p0hD:\4\s\cmake\external\wil\include\wil\resource.hWilError_03Local\SM0:%d:%d:%hsproviderop_name +C +CSessionNodeModel file not found!Failed to parse model file!), but the current device does not support 16-bit float.The model contains a 16-bit input (Failed to parse model stream!Engine failed to create a model!CasttoOut of memoryWindows::AI::MachineLearning::Adapter::SessionRegisterCustomRegistryD:\4\s\winml\adapter\winml_adapter_session.cppD:\4\s\include\onnxruntime\core/graph/graph.hnode_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImpl Max:Validating no unexpected access using an invalid node_index. Got:onnxruntime::GraphTransformer::Recursecast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformerD:\4\s\include\onnxruntime\core/optimizer/graph_transformer.hCastDef_%donnxruntime::InsertCastTransformer::ApplyImplonnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\4\s\onnxruntime\core\optimizer\insert_cast_transformer.ccEliminateSliceIdentityEliminateIdentityConvBNFusionConvConvMulFusionSliceConvAddFusionRandomUniformLikeRandomNormalRandomUniformConstantFoldingUnsqueezeUnsqueezeEliminationMultinomialRandomNormalLikeEliminateDropoutMatMulAddFusionGemmActivationFusionConvActivationFusionShapeToInitializerReluFuseReluClipDropoutGeluFusionBiasGeluNchwcTransformerShapeSkipLayerNormFusionLayerNormFusionFastGeluFusionGeluApproximation_RuleBasedTransformerAttentionFusionReshapeFusionEmbedLayerNormFusionD:\4\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelLevelonnxruntime::optimizer_utils::GenerateTransformersUnsupported level OpenVINOExecutionProviderNGRAPHExecutionProviderDnnlExecutionProviderTensorrtExecutionProviderNupharExecutionProvideronnxruntime::Node::ForEachWithIndexACLExecutionProviderD:\4\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImplMemcpyExecution type '' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessDefsonnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::<lambda_9836964a0f220270e640a83a0a4b8b24>::operator ()dup_replacements.find(&arg) == dup_replacements.end()Copy from/to host memoryD:\4\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::ApplyThis transformer is already registered D:\4\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersAddD:\4\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGelu::ApplyImplfused Add and GeluFastGeluGeluCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()D:\4\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplort_value.IsTensor()Unsupported output type of . Can't constant fold node 'size_ == sizesize is differentD:\4\s\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::Initializerqkv_biasqkv_weightsdata type is not supportedMaskIndex_Int32MaskCastCast mask from int64 to int32Mask_Int32axesMaskIndexReduceSumCount number of wordsD:\4\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ProcessMaskMask shape is unknown or not 2D, or data type unknownkeepdimsshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64ReshapeTotal fused Attention node count: Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.MatMulOutput edge count not expected for nodes in path vonnxruntime::AttentionFusion::FuseSubGraphFaild to find path vTransposev_reshape initializer value is not expectedFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3permMulSoftmaxFailed in match v_matmul and v_add input shapereshape initializer value is not expectedaxisOutput edge count not expected for mask nodesFailed to find path for maskSubmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3q root should be layer normalizationFailed to find path for qDivmask_mul const input not matchedq_matmul and q_add shape not matchedq_transpose perm attribute not matchedqk_div const not matched.q_reshape const not matchedk_matmul and k_add shape not matchedk_transpose perm attribute not matchedk root is not layer normFailed to find path for kFailed to create mask indexFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.k_reshape const not matchedFused an attention node.num_headsAttentionFused Attention subgraphs onnxruntime::ConvBNFusion::Applybn_scale_tensor_protoepsilononnxruntime::Initializer::ToProtobn_var_tensor_protobn_mean_tensor_protobn_B_tensor_protoD:\4\s\onnxruntime\core\optimizer\conv_bn_fusion.ccConvBnFusion_BN_B_ConvBnFusion_W_conv_B_tensor_protoconv_W_tensor_protoBatchNormalizationonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::<lambda_19e34746ceb372ac4d53d2f562025063>::operator ()Unexpected data type for Clip input of maxminClipD:\4\s\onnxruntime\core\optimizer\conv_activation_fusion.ccLeakyReluTanhSigmoidonnxruntime::ConvActivationFusion::ApplyImplFusedConvfused Conv with activation alphaactivation_paramsactivationfused UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyD:\4\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccUnsqueezeElimination_onnxruntime::FuseReluClip::ApplyD:\4\s\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantFuseReluClip_is not supported.data type Unexpected data type for Clip 'min' input of onnxruntime::GeluApproximation::ApplyImplD:\4\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: D:\4\s\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: Concatonnxruntime::ReshapeFusion::ApplyImplGatherCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph1_Int32Cast Input from int64 to int32_CastFailed to find path 1 of position shape.onnxruntime::MatchPositionSubgraphOutput edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Input shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputD:\4\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64The parent of two shape nodes are expected to be input_ids.ExpandSqueezeNonZeroFailed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.RangeConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraph1Second input of Gather should be a constant with value 1. The third input of Range should be a constant with value 1.position_embeddingsonnxruntime::EmbedLayerNormFusion::ApplyImplWord embedding shape not expected.Failed to find path 1.onnxruntime::MatchPositionEmbeddingSubgraph2Output edge count not expected for nodes in path 1.The first input of Range should be a constant with value 0.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs EmbedLayerNormalizationSegment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. startsendswith a fixed dimension denotation 'The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplD:\4\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccFreeDimensionOverrideTransformerdoes not equal the specified override ofbut the size of this dimension ' Invalid free dimension override.onnxruntime::MatMulAddFusion::ApplyImplD:\4\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add Gemmgemmonnxruntime::GemmActivationFusion::ApplyImplD:\4\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused Gemm FusedGemmleaky_relu_onnxruntime::ConvMulFusion::ApplyD:\4\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_D:\4\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs SkipLayerNormalizationonnxruntime::SkipLayerNormFusion::ApplyImplreorderauto_padkernel_shapeSAME_UPPERNOTSETpadsReorderInputdilationsstridesbn_scalespatial_bn_nchwcbn_BVALIDSAME_LOWER_nchwcgroupGlobalMaxPoolAveragePoolSumGlobalAveragePoolchannelsReorderOutputMaxPoolchannels_lastD:\4\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImpldeque<T> too longfused LayerNorm subgraphs Powsaved_inv_std_varsaved_meanD:\4\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplSqrtReduceMeanonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplD:\4\s\onnxruntime\core\optimizer\rule_based_graph_transformer.ccD:\4\s\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::GeluFusion::ApplyImplfused Gelu subgraphs ErfGPT2Gelufused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplfast_gelu_outputD:\4\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccD:\4\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_B_add_B_tensor_protoConvAddFusion_Add_B_onnxruntime::OptimizerExecutionFrame::Info::{ctor}::<lambda_85bf080c1fc848656699606d1eef7a11>::operator ()Failed to get allocator for optimizerD:\4\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Infoallocator_ptr_ != nullptrinvalid unordered_map<K, T> keyTried to allocate without valid type information, ort_value index=onnxruntime::RegisterOnnxOperatorKernelsD:\4\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::UpsampleBase::UpsampleBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/upsample.hmodeinfo.GetAttr<std::string>("mode", &mode).IsOK()linearcubicnearestround_prefer_floornearest_modecoordinate_transformation_modeasymmetricextrapolation_valuehalf_pixelscalesinfo.GetAttrs<float>("scales", scales_).IsOK()mode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outsideonnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceil] is not supportted!coordinate_transform_mode:[align_cornerstf_crop_and_resizepytorch_half_pixeltf_half_pixel_for_nnonnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.Scale value should be greater than or equal to 1.scale >= 1nearst_mode:[onnxruntime::UpsampleBase::StringToNearstModefloorceilscales_size > 0onnxruntime::UpsampleBase::ParseScalesDatascales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)scales size should be greater than 0.Upsample operator'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scale > 0Resize operatorResizeT1bad allocationD:\4\s\include\onnxruntime\core/framework/op_kernel.hInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsT3OneHotT2'axis' attribute must have a value in the range [onnxruntime::OneHotOp<float,__int64,__int64>::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<int,float,float>::Computeonnxruntime::OneHotOp<float,float,float>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<int,float,int>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OpKernelContext::Inputonnxruntime::OneHotOp<__int64,float,int>::ComputeMissing Input: is not in valid range [-onnxruntime::HandleNegativeAxisD:\4\s\onnxruntime\core/providers/common.haxis axis >= -tensor_rank && axis <= tensor_rank - 1konnxruntime::TopK<10,float>::TopKinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueop_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedD:\4\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK()Iop_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK()sortedonnxruntime::TopkOpset11ConstructorCommonlargestk tensor should be a 1D tensor of size 1value of k must not be negativeTopKoutput count mismatch, expected 2 outputs to be present for TopK operator] should not be greater than specified axis dim value [k argument [UniqueUnsupported tensor type of onnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataAsSpaninput count mismatchonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<class onnxruntime::common::Status,struct onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status> >::Getdelta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:Unsupported data type: onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status>::operator ()GatherNDonnxruntime::GatherND::ComputeTindD:\4\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherNDBase::PrepareForComputeindices tensor must has rank larger than 0GatherND op: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorinvalid index found, index = constantreflectInvalid 'pads' attribute valueonnxruntime::PadBase::PadBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/pad.hedgeInvalid 'mode' attribute valueCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of D:\4\s\onnxruntime\core/providers/cpu/tensor/utils.hPaddims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\4\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_tensor.IsDataType<int64_t>()onnxruntime::Pad<float>::ComputePads tensor should be an INT64 tensoronnxruntime::Pad<int>::Computeonnxruntime::Pad<__int64>::Computevalue_tensor->IsDataType<T>() && value_tensor->Shape().Size() == 1onnxruntime::Pad<double>::Computepads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_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 onnxruntime::SliceIterator<__int64>::Initonnxruntime::SliceIterator<int>::Initdata_rank * 2 == pads.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::PadCpuImpl'pads' has wrong number of valuesInput tensor has no dimensionsdata_rank > 0onnxruntime::PadInputWithDimValueOfZeroonnxruntime::SliceIterator<double>::Initonnxruntime::SliceIterator<float>::Initp_ml_valueonnxruntime::OpKernelContext::OutputPlease fetch output tensor with specified shape.onnxruntime::BroadcastIterator::InitD:\4\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largest by NegFlooronnxruntime::Broadcaster::BroadcasterAbsCan broadcast 0 by 0 or 1. largest <= 1onnxruntime::BroadcastIterator::Append is invalid.LessGreaterMinMaxExpLogCeilReciprocalXorMust have 1 or more inputsAndOrBitShiftNotEqualMeaninputCount >= 1onnxruntime::Min_6<float>::Computedata_1.Shape() == shapedata_n.Shape() == shapeD:\4\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeinput_count >= 1onnxruntime::Sum_6<float>::ComputeAcosAtanTanAsinSinCosonnxruntime::Max_6<float>::Computeonnxruntime::Mean_6<float>::ComputeX_ptr != nullptronnxruntime::Erf<float>::ComputeAtanhPReluAsinhAcoshSinhCoshonnxruntime::mod_internal::BroadCastMFloat16FMod vs Modfailed to get first output!(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfmodfmod must have value either 0 or 1fmod_onnxruntime::Mod::ComputeX and Y input types do not match: fmod attribute must be true for float, float16 and double typesdirectiononnxruntime::BitShift<unsigned char>::Compute::<lambda_4715894455eb36139678760f63bfaebb>::operator ()cur_out == end_outInvalid direction value of 'cur1 == end1RIGHT'. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift<unsigned char>::BitShiftLEFTtensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8<float>::Computeonnxruntime::BroadcastVariadicShape must be 1 dimensional as it's tensor data is a shapeonnxruntime::BitShift<unsigned __int64>::BitShiftonnxruntime::BitShift<unsigned __int64>::Compute::<lambda_d24bfb18330c4a1cb23d8e1537b1d77e>::operator ()onnxruntime::BitShift<unsigned int>::BitShiftonnxruntime::BitShift<unsigned int>::Compute::<lambda_1b188dbdddbf1b4ca214438118d6d1b6>::operator ()onnxruntime::Expand_8<unsigned short>::Computeonnxruntime::Expand_8<unsigned int>::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8<unsigned char>::Computeonnxruntime::Expand_8<short>::Computeonnxruntime::Expand_8<int>::Computeonnxruntime::Expand_8<double>::Computeonnxruntime::Expand_8<signed char>::Computeonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::mod_internal::CallModImpl,unsigned char,signed char,unsigned short,short,unsigned int,int,unsigned __int64,__int64>::Invokehelper.called_ == 1Check for duplicate types in MLTypeCallDispatcherhelper.called_ < 2onnxruntime::Expand_8<union onnxruntime::MLFloat16>::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8<unsigned __int64>::Computeonnxruntime::Expand_8<bool>::Computeonnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator<float>::TensorAllocatoronnxruntime::TensorAllocator<double>::TensorAllocator((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOonnxruntime::GatherElements::GatherElementsinfo.GetAttr<int64_t>("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueGatherElements op: Value in indices must be within bounds [ , ]. Actual value is D:\4\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::parse_and_validate_indices_tensorGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElementsD:\4\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGEMM: Dimension mismatch, W: K: N:right.NumDimensions() == 2D:\4\s\onnxruntime\core\providers\cpu\math\gemm_helper.honnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1info.GetAttr<int64_t>("transB", &temp).IsOK()transBD:\4\s\onnxruntime\core/providers/cpu/math/gemm.honnxruntime::Gemm<float>::Gemminfo.GetAttr<int64_t>("transA", &temp).IsOK()transAM_ >= 0 && K_ > 0 && N_ >= 0Gemm: Invalid bias shape for broadcastinfo.GetAttr<float>("beta", &beta_).IsOK()betainfo.GetAttr<float>("alpha", &alpha_).IsOK()Not implemented fused activation: D:\4\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterND::ComputeScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: update_tensor != nullptrindice_tensor != nullptronnxruntime::ScatterNDBase::PrepareForComputeinput_tensor != nullptrinvalid indice found, indice = updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: onnxruntime::Tensor::Reshapeshape_.Size() == new_shape.Size()Tensor size () != new size (onnxruntime::SequenceLength::ComputeX != nullptrGot nullptr for sequence input.SequenceLengthSI != nullptrGot nullptr input for index tensoronnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxSequenceAtY != nullptrSequenceLength: Got nullptr for output tensorD:\4\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSequenceEmpty: Got nullptr for output sequencedtypeSequenceEmptySequenceAt: Got nullptr for output tensorInvalid sequence index () specified for sequence of size (SequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (Got nullptr for input tensor.onnxruntime::SequenceInsert::ComputeS != nullptronnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertViolation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequenceonnxruntime::SequenceConstruct::Computenum_inputs >= 1SequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceErase::ComputeSequenceEraseonnxruntime::GetScalarSplitInputInvalid data type for split tensor split_size_sum () != split_dim_size (onnxruntime::SplitToSequence::ComputeSplitToSequence operator does not support yetSplitToSequenceonnxruntime::GetSplitSizesInputonnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0Split should be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })Invalid value in 'split' input. All values must be > 0D:\4\s\onnxruntime\core/providers/cpu//tensor/concat.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributenew_axisD:\4\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeConcatFromSequenceBD:\4\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infonum_subgraph_outputs == static_cast<size_t>(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.IfVonnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK()else_branchonnxruntime::If::Ifinfo.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK()then_branch did not.onnxruntime::IfImpl::Initializesession_stateSubgraph SessionState was not found for '' attribute.onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output Failed to create output tensor for Subgraph must have the shape set for all outputs but Unknown AutoPadType StringX num_dims does not match W num_dims. X: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims. kernel_shape: W: D:\4\s\onnxruntime\core/providers/cpu/nn/autopad_type.honnxruntime::StringToAutoPadTypeoutput_shapeoutput_padding group: D:\4\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.filter number not equal to input channel number. filter_number: num_input_channels: Only 1D and 2D ConvTranspose is supported.group count is <= 0SafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnOverflowInteger overflowpad type not supportedonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0Invalid input shape: D:\4\s\onnxruntime\core/common/safeint.hConvTransposeD:\4\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose<float>::DoConvTransposeInput channels C is not equal to kernel channels * group. C: kernel channels: Output channels M is not divisible by group. M: onnxruntime::Conv<float>::ComputeD:\4\s\onnxruntime\core/util/math.honnxruntime::math::Im2colNd<float,2>::operator ()d_iter[d_i] < d_maxD:\4\s\onnxruntime\core\providers\cpu\nn\conv.ccOut of bound access to arraypad type not supported.onnxruntime::ComputePadAndOutputShapedilation == 1Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.D:\4\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::InferOutputShapeGlobalLpPoolNo kernel shape is set.info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesD:\4\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr<std::string>("auto_pad", &auto_padding).IsOK()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] == 0onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.ceil_modecount_include_padinfo.GetAttr<int64_t>("count_include_pad", &temp).IsOK()storage_orderkernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Not satisfied: input_dims >= 3 +Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() +onnxruntime::Pool<float,class onnxruntime::MaxPool<8> >::ComputeNot satisfied: x_shape.NumDimensions() >= 3 +Unsupported pooling size : LpPoolonnxruntime::PoolAttributes::ComputeSizePadDilationspinfo.GetAttr<int64_t>("p", &p_).IsOK()onnxruntime::PoolProcessContext::initD:\4\s\onnxruntime\core/providers/cpu/nn/pool_base.hInput dimension cannot be less than 3.onnxruntime::PoolBase::ComputeD:\4\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::Pool<float,class onnxruntime::LpPool>::Computestrides_.size() == kernel_shape_.size()info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolD:\4\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]MaxUnpoolonnxruntime::MaxUnpool::ComputeNot satisfied: given_shape.Size() >= shape.Size() +D:\4\s\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satisfied: X_shape.NumDimensions() >= 3 +Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: I_shape == X_shape +Shape must be 1 dimensional as it's tensor data of a shapeNot satisfied: tensor_shape->Shape().GetDims().size() == 1 + inferred output shape:output_shape is smaller than minimum required. output_shape:onnxruntime::NonMaxSuppressionBase::PrepareComputeD:\4\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 +scores must be a 3D tensor.Not satisfied: scores_shape.NumDimensions() == 3 +boxes and scores should have same num_batches.center_point_boxcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\4\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppression::ComputeNot satisfied: ret.IsOK() +output != nullptrNot satisfied: boxes_dims[0] == scores_dims[0] +boxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[1] == scores_dims[2] +The most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[2] == 4 +iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) +ScatterScatterElementsdata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=. Input rank=Indices must have the same rank as Input. Indices rank= is greater than input dim=onnxruntime::Scatter::ScatterD:\4\s\onnxruntime\core\providers\cpu\tensor\scatter.cc at pos=Indices dim=Unknown tensor type of onnxruntime::Scatter::ComputeExpecting indices to be either int32_t or int64_t must be within the inclusive range [indices element out of data bounds, idx=Detonnxruntime::Det<float>::ComputeD:\4\s\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::PrepareComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cc'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::Unsqueeze::Compute. shape= must be 1 instead of Dimension of input input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\4\s\onnxruntime\core/providers/cpu/tensor/squeeze.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })onnxruntime::SplitBase::SplitBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/split.hsplitInvalid value in 'split' attribute. All values must be > 0 Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=Cannot split using values in 'split' attribute. Axis=Split operator does not support onnxruntime::Split::ComputeSplit NumOutputs=onnxruntime::Split::ComputeImplD:\4\s\onnxruntime\core\providers\cpu\tensor\split.cchas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attribute'axes' has duplicates'step' value cannot be 0Starts must be a 1-D arraynullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInputD:\4\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arraynullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1'axes' has an axis outside of the tensor dimension countStarts and ends shape mismatchstart_tensor->Shape() == ends_tensor->Shape()Starts and axes shape mismatchnullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()Starts and steps shape mismatchnullptr == 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 onnxruntime::Slice<unsigned __int64,0>::Computeonnxruntime::Slice<signed char,0>::Computeonnxruntime::Slice<short,0>::Computeonnxruntime::Slice<int,0>::Computeonnxruntime::Slice<__int64,0>::Computeonnxruntime::Slice<float,0>::Computeonnxruntime::Slice<double,0>::Computeonnxruntime::Slice<union onnxruntime::MLFloat16,0>::ComputeMissing input tensor to be processedinput_tensor_ptr != nullptronnxruntime::Slice<unsigned char,0>::ComputeCannot slice scalarsonnxruntime::Slice<unsigned short,0>::Computeonnxruntime::Slice<unsigned int,0>::Computeonnxruntime::Slice<int,1>::Computeonnxruntime::Slice<__int64,1>::Computeonnxruntime::Slice<float,1>::Computeonnxruntime::Slice<double,1>::Computeonnxruntime::Slice<union onnxruntime::MLFloat16,1>::Computeonnxruntime::Slice<bool,1>::Computeonnxruntime::Slice<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,1>::Computeoutput == output_endonnxruntime::Slice<bool,0>::Computeonnxruntime::Slice<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,0>::Computeonnxruntime::Slice<unsigned char,1>::Computeonnxruntime::Slice<unsigned short,1>::Computeonnxruntime::Slice<unsigned int,1>::Computeonnxruntime::Slice<unsigned __int64,1>::Computeonnxruntime::Slice<signed char,1>::Computeonnxruntime::Slice<short,1>::Computeonnxruntime::SliceImpl::<lambda_0ebc6ffbead786f49394762740e9be28>::operator ()onnxruntime::SliceImpl::<lambda_b19d6003f38ca8b3120d36ffb43df279>::operator ()onnxruntime::SliceImpl::<lambda_92ec66c7223cf11a6620040f06f359c0>::operator ()onnxruntime::SliceImpl::<lambda_486887758c2ee082cbe76f20a7d3a5b2>::operator ()onnxruntime::SliceImpl::<lambda_8cab13ee559d7cd92597f7d95713eca2>::operator ()onnxruntime::SliceIterator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Initonnxruntime::SliceIterator<bool>::Initonnxruntime::SliceIterator<union onnxruntime::MLFloat16>::Initonnxruntime::SliceImpl::<lambda_250d89763c102833a65d93a626e2814f>::operator ()onnxruntime::SliceImpl::<lambda_4c6420586cc61ac5aee9e5f6b1fe5fc6>::operator ()onnxruntime::SliceImpl::<lambda_a9b6a496f4f9006eeea1da83409b4f01>::operator ()onnxruntime::SliceImpl::<lambda_5cf79f49f1f540ef1770047ce884b25b>::operator ()onnxruntime::SliceImpl::<lambda_5a90419231598d1467999b4ab0e4d66b>::operator ()onnxruntime::SliceImpl::<lambda_9a0fd5fa330f7eb76e441bed23d8d211>::operator ()onnxruntime::SliceImpl::<lambda_22ba7718989a469c7a34dd56799375ce>::operator ()onnxruntime::SliceImpl::<lambda_d733b1184a068aeae21fb343a0433a43>::operator ()onnxruntime::SliceIterator<short>::Initonnxruntime::SliceIterator<signed char>::Initonnxruntime::SliceIterator<unsigned __int64>::Initonnxruntime::SliceIterator<unsigned int>::Initonnxruntime::SliceIterator<unsigned short>::Initonnxruntime::SliceIterator<unsigned char>::Initonnxruntime::GatherBase::GatherBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::Gather::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\gather.ccType for Tind not supported yet in Gather. and onnxruntime::ConcatBase::PrepareForComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\concat.ccNot satisfied: input_count >= 1 +Cannot concatenate scalarsNot satisfied: inputs_0_rank > 0 + got: Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Not satisfied: dim_value == inputs_0_dims[axis_index] +Not satisfied: data_n.DataType() == p.output_tensor->DataType() +onnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()D:\4\s\onnxruntime\core\providers\cpu\tensor\compress.ccCompressD:\4\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flattengsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisFlattenonnxruntime::Flatten::Computefinal_output_mlvalue_Attempt to retrieve final output before it was set.D:\4\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputscan_input_directionsinfo.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_axesscan_output_directionsNumber of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_bodyonnxruntime::Scan<9>::Scaninfo.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK()num_scan_inputsD:\4\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cc Expected dimensions or more but input had shape of has length of Invalid scan input: but input '' dimension . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was gsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptronnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.output_mlvalueOutput OrtValue has not been created for loop state variable output . Output tensor rank was onnxruntime::ScanImpl::ExecuteOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output Scanonnxruntime::ScanImpl::TransposeOutputInvalid value in scan_input_axes for input of onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputSubgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsAttribute blocksize is not set.blocksizeonnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()D:\4\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceToDepthinput_shape.NumDimensions() == 4DepthToSpaceD:\4\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth<float>::Computeonnxruntime::DepthToSpace<float>::DepthToSpaceinput_width % this->blocksize_ == 0input_height % this->blocksize_ == 0onnxruntime::DepthToSpace<float>::Computeinput.Shape().NumDimensions() == 4CRDinput_depth % (blocksize_ * blocksize_) == 0DepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCR inputs. Found:Loopstatic_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have D:\4\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infoonnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfo'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::LoopImpl::ConcatenateLoopOutput'Loop' input 'cond' should be a scalar tensor. Got shape of was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Execute outputs so the subgraph requires but has num_subgraph_outputs - 1 == num_outputs'Loop' node has Expected: Got:onnxruntime::Loop::LoopInconsistent shape in loop output for output. Loop had zero iterations and the shape of subgraph output LogSoftmax. N=, D=HardmaxHardmax inputs N, D and N * D must be < ReduceL2ReduceL1ReduceLogSumExpReduceLogSumReduceMinReduceMaxReduceSumSquareReduceProdonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\4\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::optional<__int64>::valuehas_value_D:\4\s\include\onnxruntime\core/common/optional.hArgMinArgMaxin_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:D:\4\s\onnxruntime\core/providers/cpu/containers.honnxruntime::GetAllocatorD:\4\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::PrepareForReduceDynamicQuantizeLinearx_ptr != nullptrD:\4\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear<unsigned char>::ComputeRoundAxis tensor must be provided to the CumSum opCumSumAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`exclusiveD:\4\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum<__int64>::Computestarts.size()=onnxruntime::CumSum<int>::Computeonnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size()=dims.size() == starts.size()Cannot apply CumSum operator on a scalarreverseonnxruntime::CumSum<double>::Computeonnxruntime::CumSum<float>::Computedims.size() == steps.size()onnxruntime::WritableSliceIterator<int>::Initdims.size() == extents_.size()steps.size()=onnxruntime::WritableSliceIterator<double>::Initonnxruntime::WritableSliceIterator<float>::Initonnxruntime::Clip_6<float>::Clip_6info.GetAttr<T>("min", &min_).IsOK()max->Shape().NumDimensions() == 0info.GetAttr<T>("max", &max_).IsOK()min should be a scalar.D:\4\s\onnxruntime\core/providers/cpu/math/clip.hmax should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip<float>::ComputeInvalid batch_axis of batch_axis < 2info.GetAttr<int64_t>("time_axis", &time_axis).IsOK(). Must be 0 or 1time_axis and batch_axis must have different values but both are batch_axis != time_axisInvalid time_axis of time_axis < 2batch_axisD:\4\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOponnxruntime::ReverseSequenceOp::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cc. batch_size=sequence_lens shape must be {batch_size}. Got:ReverseSequenceSecond dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Null rois_ptrNull batch_indices_ptrRoiAlignNull input X ptrspatial_scaleonnxruntime::RoiAlignBase<float>::RoiAlignBaseSampling ratio should be >=0, but it was sampling_ratio_ >= 0Invalid mode of value onnxruntime::RoiAlignBase<double>::RoiAlignBaseavg specified. It should be either avg or maxoutput_widthsampling_ratioD:\4\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightdetect_positiveFailed to obtain detect_positiveIsInfdetect_negativeFailed to obtain detect_negativeonnxruntime::IsInf::IsInfD:\4\s\onnxruntime\core\providers\cpu\tensor\isinf.ccData type X must be float or double, but instead got onnxruntime::IsInf::ComputeTensor type mismatch.type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawD:\4\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<1>::Computeonnxruntime::IdentityOp<0>::ComputeIsScalarOr1ElementVector(filter_offset)QLinearConv : result zero point must be a scalar or 1D tensor of size 1D:\4\s\onnxruntime\core\providers\cpu\nn\qlinearconv.ccQLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(input_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_offset)QLinearConv : input scale must be a scalar or 1D tensor of size 1T4IsScalarOr1ElementVector(input_offset)onnxruntime::QLinearConv::ComputeQLinearConvQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_scale)onnxruntime::math::Im2colNd<unsigned char,2>::operator ()IsScalarOr1ElementVector(filter_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(W_Zero_Point)D:\4\s\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.onnxruntime::MatMulComputeHelper::ComputeMatMul dimension mismatchNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] +D:\4\s\onnxruntime\core/providers/cpu/math/matmul_helper.hNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 +MatMulIntegera != nullptr && b != nullptrNot satisfied: num_dims_with_pad - 2 == num_output_dims +Not satisfied: K_ == right_shape[0] +MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)onnxruntime::MatMulInteger<unsigned char,unsigned char>::ComputeD:\4\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccright operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 +left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1 +Not satisfied: M_ == 1 && N_ == 1 +Not satisfied: num_dims_with_pad - 1 == num_output_dims +Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] +Not satisfied: num_dims_with_pad == num_output_dims +Currently only scalar zero_point is supported. TODO: add per channel zero point support.t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)onnxruntime::MatMulInteger<unsigned char,signed char>::ComputeMatMulInteger: Unsupported input types with zero pointonnxruntime::MatMulInteger<unsigned char,signed char>::Compute::<lambda_df3f3659732d59f1707eb0c75336cd47>::operator ()t->IsDataType<int8_t>() || t->IsDataType<uint8_t>()QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\4\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.cconnxruntime::QLinearMatMul<unsigned char,unsigned char,unsigned char>::ComputeQLinearMatMulQLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)x_scalexx_scale must be 1D tensor with size QuantizeLineary_zero_pointDequantizeLinearx_zero_pointonnxruntime::QuantizeLinear<unsigned char>::ComputeIsScalarOr1ElementVector(&y_scale)onnxruntime::DequantizeLinear<signed char>::ComputeIsScalarOr1ElementVector(&x_zero_point)onnxruntime::QuantizeLinear<signed char>::Computey_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimy_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimIsScalarOr1ElementVector(&y_zero_point)x_zero_point must be 1D tensor with size D:\4\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.cconnxruntime::DequantizeLinear<unsigned char>::Computex_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_zero_point must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&x_scale)x_scale must be a scalar or 1D tensor or size 1.x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimSoftplusSeluHardSigmoidElugammaThresholdedReluSoftsign*D:\4\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cconnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesonnxruntime::StringNormalizer::StringNormalizerattribute is_case_sensitive is not setis_case_sensitiveen-USConversion ErrorConversion ErrorStringNormalizerp.secondDuplicate stopwords not allowed!sw.empty()Empty stopwords not allowedInput dimensions are either[C > 0] or [1][C > 0] allowedSingle dimension value must be greater than 0wstr != wconv_errorStopword contains invalid utf8 charsUPPERLOWERattribute case_change_action is not setcase_change_actionstopwordslocaleattribute case_change_action has invalid valueNONEbad conversionInput contains invalid utf8 chars at: onnxruntime::MatMul<unsigned __int64>::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul<float>::Computeonnxruntime::MatMul<unsigned int>::Computeonnxruntime::MatMul<int>::Computeonnxruntime::MatMul<double>::ComputeD:\4\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::Where<unsigned char>::Computecondition && X && Ycondition, X, and Y inputs are required!Whereonnxruntime::TensorAllocator<unsigned char>::TensorAllocatoronnxruntime::Where<float>::Computeonnxruntime::Where<__int64>::Computeonnxruntime::Where<int>::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::TensorAllocator<int>::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::TensorAllocatoronnxruntime::Where<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero<bool>::ComputeX input is required!onnxruntime::NonZero<float>::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero<int>::Computeonnxruntime::NonZero<unsigned char>::Computemin_gram_lengthimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmax_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: min_gram_length is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizermode is requiredTfIdfVectorizerTFIDFIDFTFD:\4\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccmin_gram_length must be inbounds of ngram_counts: <= status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= ngram_countsimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: max_skip_count is required!impl_->pool_strings_.empty()pool_strings must not be empty if specifiedpool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()-gramsstatus.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not providedpool_int64sstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowedstatus.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredGot weights of size: but ngram_indexes size: must be of equal sizeweightsInvalid type of the input argument(before_insert + ngrams) == impl_->str_set_.size()(items % ngram_size == 0)Number of items must compose whole end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for pool_strings duplicate (before_insert + ngrams) == impl_->int64_set_.size()pool_int64s duplicate -grams detectedInput shape must have either [C] or [B,C] dimensions with B > 0.biaslambdD:\4\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK()op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK()ShrinkSignonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::sign_internal::CallSignImpl,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64>::InvokeNull input ptrIsNaNinvalid stoi argumentstoi argument out of rangeinvalid stol argumentstof argument out of rangeinvalid stod argumentstod argument out of rangestoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stof argumentstol argument out of rangeinvalid stoul argumentstoul argument out of rangeinvalid stoll argumentD:\4\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccUnexpected 'to' argument value: onnxruntime::Cast<unsigned short>::Computeonnxruntime::Cast<unsigned int>::ComputeCast op must have 'to' argument of type DataTypeonnxruntime::Cast<unsigned char>::ComputeAttribute to is not set.onnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Castonnxruntime::Cast<union onnxruntime::MLFloat16>::Castonnxruntime::Cast<double>::CastCasting from 'float16' to 'string' is not supported yet.onnxruntime::Cast<union onnxruntime::MLFloat16>::ComputeInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Computeonnxruntime::Cast<__int64>::Computeonnxruntime::Cast<bool>::Computeonnxruntime::Cast<float>::Computeonnxruntime::Cast<double>::Computeonnxruntime::Cast<unsigned __int64>::Computeonnxruntime::Cast<signed char>::Computeonnxruntime::Cast<short>::Computeonnxruntime::Cast<int>::Computeonnxruntime::Cast<unsigned short>::Castonnxruntime::Cast<unsigned char>::Castonnxruntime::Cast<short>::Castonnxruntime::Cast<signed char>::Castonnxruntime::Cast<unsigned __int64>::Castonnxruntime::Cast<unsigned int>::Castonnxruntime::Cast<float>::Castonnxruntime::Cast<bool>::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast<int>::Castonnxruntime::Cast<unsigned char>::CastFloat16Dataonnxruntime::Cast<unsigned short>::CastFloat16Dataallocator != nullptronnxruntime::CastFloat16Dataonnxruntime::Cast<double>::CastFloat16Dataonnxruntime::Cast<union onnxruntime::MLFloat16>::CastFloat16Dataonnxruntime::Cast<int>::CastFloat16Dataonnxruntime::Cast<__int64>::CastFloat16Dataonnxruntime::Cast<bool>::CastFloat16Dataonnxruntime::Cast<unsigned int>::CastFloat16Dataonnxruntime::Cast<unsigned __int64>::CastFloat16Dataonnxruntime::Cast<signed char>::CastFloat16Dataonnxruntime::Cast<short>::CastFloat16Data-INFINFonnxruntime::CastFromStringDatalen > 0bufferonnxruntime::CastToStringDataNaNEyeLikeT1 != nullptronnxruntime::EyeLike::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\eye_like.ccEyeLike : Input tensor dimension is not 2MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]normalize_varianceinfo.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK()across_channelsinfo.GetAttr<int64_t>("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0<float>::MeanVarianceNormalization_0D:\4\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseMust have a single dimension of 1t_proto.dims()[0] == 1Must have a valid input shape.unpack_status.IsOK()Unsupported value attribute datatype: Must have a single dimensiont_proto.dims_size() == 1onnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoD:\4\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:Unsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShapeBase::SetValueD:\4\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.hutils::HasDataType(t_proto)input_shape.NumDimensions() > 0onnxruntime::ConstantOfShapeBase::PrepareComputeonnxruntime::ConstantOfShape::Computeonnxruntime::Scan8Impl::Initialize has batch size of but Scan inputs have inconsistent batch size. Previous value was D:\4\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::ComputeScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Scanposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Iterator::operator *D:\4\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::ValidateInput did not match batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was Upsamplesizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.Invalid roi input index.roi_input_idx_ > 0scales != nullptrOnly one of scales or sizes must be provided as input.onnxruntime::Upsample<float>::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample<int>::BaseComputeonnxruntime::Upsample<unsigned char>::BaseComputeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize: unexpected modeUpsample: unexpected modeRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.X->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample<int>::Computeonnxruntime::Upsample<unsigned char>::Computeonnxruntime::Upsample<float>::BaseComputeResize: 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.perm: Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max()onnxruntime::TransposeBase::TransposeBaseD:\4\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.D:\4\s\onnxruntime\core\providers\cpu\tensor\transpose.ccMismatched data types between input and output Tensors. onnxruntime::Transpose::Computeonnxruntime::Tile::ComputeD:\4\s\onnxruntime\core\providers\cpu\tensor\tile.ccthe 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: TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptySizeD:\4\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelperunknown_dim == -1At most one dimension can be -1.requested_shape[i] >= -1A dimension cannot be less than -1.gsl::narrow_cast<int64_t>(input_shape.Size()) == size(input_shape.Size() % size) == 0i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:shapeTensor->Shape().NumDimensions() == 1A shape tensor must be a vector tensor.D:\4\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape::ComputeAttribute shape is not set.shapeReshape_1onnxruntime::Reshape_1::Reshape_1clipScaledTanhonnxruntime::RNN<float>::RNNinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN<float>::ComputeRNNAffineD:\4\s\onnxruntime\core\providers\cpu\rnn\rnn.ccbidirectionalforwardallowed_activations.find(activations_[direction]) != allowed_activations.end()activations_.size() == static_cast<size_t>(num_directions)info.GetAttrs("activations", activations_).IsOK()activationsinfo.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizeallowed_directions.find(direction_) != allowed_directions.end()D:\4\s\onnxruntime\core/providers/cpu/rnn/rnn.hactivation_betaactivation_alphafuture already retrievedbroken promiseno statepromise already satisfiedtanhsigmoidLSTMactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0D:\4\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hinput_forgetclip_ > 0.fDEEPCPU_LSTMD:\4\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOpinfo.GetAttr("direction", &direction).IsOK()'. Must be one of 'forward', 'reverse', or 'bidirectional'.futureonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of '}. Actual:onnxruntime::DeepCpuLstmOp::ValidateInputsInput P must have shape {Input initial_c must have shape {Invalid data type for LSTM operator of LSTM operator does not support double yetD:\4\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endonnxruntime::rnn::detail::SafeRawPointercur + size <= endlda >= K && ldb >= K && ldc >= N rows[A + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmProcessing batch..onnxruntime::DeepCpuLstmOp::ComputeImplonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())D:\4\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeGRUactivation_func_names.size() == static_cast<size_t>(num_directions_) * 2Invalid data type for GRU operator of GRU operator does not support double yetD:\4\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::RoiPool<float>::RoiPoolinfo.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2D:\4\s\onnxruntime\core/providers/cpu/nn/roi_pool.hroi_batch_id >= 0D:\4\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccpooled_shaperoi_batch_id < batch_sizeMaxRoiPoolonnxruntime::RoiPool<float>::ComputeR->Shape()[1] == 5pooled_width_ > 0pooled_height_ > 0spatial_scale_ > 0info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK()size_ % 2 == 1size_ > 0beta_ > 0.0falpha_ > 0.0finfo.GetAttr<int64_t>("size", &size).IsOK()sizeD:\4\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN<float>::LRNonnxruntime::LRN<float>::ComputeX->Shape().NumDimensions() == 4LRND:\4\s\onnxruntime\core\providers\cpu\nn\lrn.ccp_ == 1 || p_ == 2op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK()op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK()LpNormalizationD:\4\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm<float>::LpNormMismatch between input data and B: size of B != input channel count vs. Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: D:\4\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm<float>::InstanceNormonnxruntime::InstanceNorm<float>::ComputeInstanceNormalizationop_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK()D:\4\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInvalid input X: The rank of input X must be atleast 2. Got rank: D:\4\s\onnxruntime\core/providers/cpu/nn/batch_norm.hst.IsOK()Invalid input var: onnxruntime::BatchNorm<double>::ComputeInvalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input mean: Invalid input B: Invalid input mean: NumDimensions() != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != dimension != Invalid input scale: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != onnxruntime::BatchNorm<float>::BatchNormonnxruntime::BatchNorm<double>::BatchNormonnxruntime::BatchNorm<float>::ComputeD:\4\s\onnxruntime\core/providers/cpu/generator/random.hscaleinfo.GetAttr<float>("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalmeaninfo.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::Multinomialonnxruntime::RandomUniformLike::RandomUniformLikesample_sizelowinfo.GetAttr<float>("low", &low_).IsOK()info.GetAttr<float>("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformonnxruntime::RandomNormalLike::RandomNormalLikehighONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs<int64_t>("shape", shape).IsOK()info.GetAttr<int64_t>("dtype", &dtype).IsOK()Invalid dtype of info.GetAttr<float>("scale", &scale_).IsOK()seedONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomUniformComputeFLOAT16 is not supportedonnxruntime::RandomNormalComputenum_samples is < 1Invalid data type of batch_size is < 1num_classes is < 1onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorD:\4\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::Computeonnxruntime::MultinomialComputeD:\4\s\onnxruntime\contrib_ops\cpu_contrib_kernels.cconnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelskeys_stringsvalues_floatsvalues_strings_UnusedInput of tensor(int64) must have output of tensor(string)keys_floatsLabelEncoderInput of tensor(string) must have output of tensor(int64)default_int64info.GetAttr<int64_t>("default_int64", &default_int_).IsOK()default_stringinfo.GetAttr<std::string>("default_string", &default_string_).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderD:\4\s\onnxruntime\core/providers/cpu/ml/label_encoder.hclasses_stringsinfo.GetAttrs<std::string>("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2The num_keys == num_values(name: attribtues in LabelEncoder However, the number of key is ) must have the same length. values is and the number of onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs<TValue>(_value_field_name, values).IsOK()values_int64sinfo.GetAttrs<TKey>(_key_field_name, keys).IsOK()default_floatkeys_int64sonnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2Input features_per_batch[Zipmap only supports 1D or 2D input tensors] != number of classlabels[D:\4\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_stringsMust provide classlabels_strings or classlabels_int64s but not both.ZipMapclasslabels_int64sBRANCH_LTBRANCH_GTEBRANCH_LEQLEAFD:\4\s\onnxruntime\core\providers\cpu\ml\ml_common.hSUMMINSOFTMAX_ZEROAVERAGELOGISTICSOFTMAXBRANCH_GTBRANCH_EQTreeEnsembleRegressornodes_falsenodeidsnodes_missing_value_tracks_truenodes_hitratesnodes_truenodeidsnodes_featureidsnodes_valuesnodes_treeidsnodes_nodeidstarget_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_modestarget_nodeids_.size() == target_ids_.size()D:\4\s\onnxruntime\core\providers\cpu\ml\treeregressor.cc!nodes_treeids_.empty()info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK()onnxruntime::ml::TreeEnsembleRegressor<float>::TreeEnsembleRegressoraggregate_functionn_targetsbase_valuespost_transformtarget_idstarget_weightstarget_nodeidstarget_treeidsonnxruntime::ml::write_scorestreeindex evaluated to a negative value, which should not happen.length overflowonnxruntime::ml::TreeEnsembleRegressor<double>::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor<double>::ComputeInput shape needs to be at least a single dimension.onnxruntime::ml::TreeEnsembleRegressor<float>::Computeit != indices.end()base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_)(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())it != parents.end()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()class_idsclass_weightsclass_nodeidsclass_treeidsTreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<double>::Computeonnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<float>::Computeonnxruntime::ml::TreeEnsembleClassifier<double>::TreeEnsembleClassifierstd::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })X dims is empty.nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()class_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()onnxruntime::ml::TreeEnsembleClassifier<float>::TreeEnsembleClassifierD:\4\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier<int>::Initializeonnxruntime::ml::TreeEnsembleClassifier<int>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Initializeonnxruntime::ml::TreeEnsembleClassifier<__int64>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<double>::Initializeonnxruntime::ml::TreeEnsembleClassifier<double>::ProcessTreeNodetreeindex >= 0onnxruntime::ml::TreeEnsembleClassifier<float>::ProcessTreeNodebase_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size()Invalid mode of value: onnxruntime::ml::TreeEnsembleClassifier<int>::Computeonnxruntime::ml::TreeEnsembleClassifier<float>::Initializeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Computeonnxruntime::ml::TreeEnsembleClassifier<int>::TreeEnsembleClassifierSVMRegressorRBFLINEARPOLYonnxruntime::ml::SVMRegressor<float>::SVMRegressorD:\4\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccrhoinfo.GetAttrs<float>("rho", rho_).IsOK()n_supportssupport_vectorsD:\4\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hinfo.GetAttrs<float>("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon<float>::SVMCommonkernel_typekernel_params!coefficients_.empty()one_classcoefficientsinfo.GetAttrs<float>("coefficients", coefficients_).IsOK()SVMClassifierclasslabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0classlabels_intsinfo.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK()D:\4\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()prob_bonnxruntime::ml::SVMClassifier<float>::SVMClassifiervectors_per_classprob_aonnxruntime::ml::SVMCommon<__int64>::SVMCommononnxruntime::ml::SVMCommon<double>::SVMCommononnxruntime::ml::SVMCommon<int>::SVMCommonD:\4\s\onnxruntime\core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMClassifier<__int64>::SVMClassifieronnxruntime::ml::SVMClassifier<int>::SVMClassifierNo support vectors.onnxruntime::ml::SVMClassifier<double>::SVMClassifierScalerEither both scale and offset can be of feature size (onnxruntime::ml::ScalerOp<double>::ScalerOpInvalid argument: input has empty dimensions.) or 1Scale size: (scale_.size() == offset_.size()D:\4\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (!scale_.empty()onnxruntime::ml::ScalerOp<float>::ScalerOpoffsetEmpty scale in attributesonnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp<int>::ScalerOpUnknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<float>::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<double>::OneHotEncoderOpD:\4\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0tmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedzeroscats_int64sMAXD:\4\s\onnxruntime\core/providers/cpu/ml/ml_common.hD:\4\s\onnxruntime\core\providers\cpu\ml\normalizer.cconnxruntime::ml::Normalizer::ComputeNormalizerD:\4\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr<std::string>("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1onnxruntime::ml::Normalizer::NormalizeUnexpected NORMALIZE value of onnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::ml::Normalizer::CallNormalizerImpl,float,double,__int64,int>::InvokeLinearRegressorD:\4\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor<float>::LinearRegressorinfo.GetAttr<int64_t>("targets", &targets_).IsOK()targetsinterceptsLinearClassifieronnxruntime::ml::LinearClassifier<int>::LinearClassifieronnxruntime::ml::LinearClassifier<__int64>::LinearClassifieronnxruntime::ml::LinearClassifier<double>::LinearClassifierD:\4\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier<float>::LinearClassifiermulti_classMust provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64D:\4\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerEmpty input dimensions.Empty value of imputed values.Invalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()).FeatureVectorizerD:\4\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsInvalid input type:D:\4\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (int64_vocabularystring_vocabularyDictVectorizeronnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpD:\4\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()Input of int64 must have output of string Input of string must have output of int64CategoryMappernum_entries == int_categories.size()info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK()D:\4\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs<std::string>("cats_strings", string_categories).IsOK()%fInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64TO_STRINGTO_FLOATmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSEinfo.GetAttr<int64_t>("max_map", &max_map_).IsOK()max_mapinfo.GetAttr<std::string>("map_form", &attr).IsOK()map_formD:\4\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapinfo.GetAttr<std::string>("cast_to", &attr).IsOK()cast_toonnxruntime::ml::MakePackInvalid PACK_MAP value of Expected DENSE or SPARSESPARSEDENSEonnxruntime::ml::MakeCastUnexpected CAST_TO value of 0.fInvalid input type of value: Expected std::map<int64_t, float> or std::map<int64_t, std::string>CastMapD:\4\s\onnxruntime\core\providers\cpu\ml\cast_map.cconnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of BinarizerInput data with index: is NaNthresholdArrayFeatureExtractorInvalid Y argument: index is out of range: Y[] () >=Invalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions. inputs but Scan was only given iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::<lambda_8456f2a97a0c16e7b73f03be87ea8102>::operator ()onnxruntime::scan::detail::CreateFeedsFetchesManagervalidInvalid values in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsnum_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entriesNumber of entries in '' was D:\4\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::Infonum_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires onnxruntime::OrtValueTensorSlicer<struct OrtValue>::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first output is not compatible with onnxruntime::scan::detail::LoopStateVariable::NextExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\4\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInput X must have 3 dimensions only. Actual:Input W must have shape {Input R must have shape {Input B must have shape {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 {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchinput is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchonnxruntime::contrib::LayerNorm<float>::Computeonnxruntime::contrib::LayerNorm<double>::LayerNormonnxruntime::contrib::LayerNorm<double>::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm<float>::LayerNormD:\4\s\onnxruntime\contrib_ops\cpu\layer_norm.ccScaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale<float>::ScaleD:\4\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine<float>::Affineinfo.GetAttr("beta", &beta_).IsOK()ParametricSoftplusImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler<float>::ImageScalerD:\4\s\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs<float>("bias", bias_).IsOK()DynamicSlice) + scale_[0] () + scale_[1] (Croponnxruntime::contrib::Crop<float>::ComputeD:\4\s\onnxruntime\contrib_ops\cpu\crop.hborderAttribute border needs to be specified with four border elements, got input dimensions instead) + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder () needs to be greater than or equal to the leftBorder (Input's width (Shape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0onnxruntime::contrib::BiasGelu<float>::ComputeD:\4\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu_fusion.ccCDistThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:metricinfo.GetAttr<std::string>("metric", &metric).IsOK()onnxruntime::contrib::CDist<double>::CDistD:\4\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist<float>::CDisty_scalebilinearCropAndResizeonnxruntime::contrib::CropAndResize<float>::CropAndResize specified. It should be either bilinear or nearestD:\4\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskD:\4\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMatMulInteger16onnxruntime::contrib::MatMulInteger16<short,short,int>::ComputeA != nullptr && B != nullptrD:\4\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMurmurHash3positiveonnxruntime::contrib::MurmurHash3::ComputekeysInvalid assumption of output element sizeD:\4\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccsizeof(uint32_t) == output_element_bytesconv_window_sizeembedding_sizechar_embedding_sizeWordConvEmbedding conv filter size: Conv filter size does not match embedding_size attribute. embedding_size attribute: conv_window_size attribute: conv kernal size 1: Char embedding size: 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. Conv kernal size 2 : D:\4\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::Computeattribute mark is not setmarkD:\4\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute pad_value is not setpad_valueattribute mincharnum is not setmincharnummincharnum_ > 0attribute mincharnum must have a positive valuetokenexpseparatorsExpecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setseparators must not be empty!tokenexp.empty()Tokenizermincharnum is too big for char level tokenezation!separators.empty()Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 chars: Can not digest tokenexp: tensor(string) expected as inputMatch contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedD:\4\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpstatic_cast<int>(activation_func_names.size()) == num_directions_ * 3DEEPCPU_ATTN_LSTM, aw_attn_size}. Got:Attention v weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTM}, actural: D:\4\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.ccAttention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {], while found!, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, }, Got: Attention memory layer weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{leaky_relu_alphaGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\4\s\onnxruntime\contrib_ops\cpu\fused_conv.ccAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar() Axis is Axis must be within range [onnxruntime::contrib::ExpandDims::ComputeD:\4\s\onnxruntime\contrib_ops\cpu\expand_dims.haxis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::EmbedLayerNorm<float>::ComputeD:\4\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccinput index out of rangeonnxruntime::contrib::Attention<float>::Computeinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.onnxruntime::contrib::AttentionBase::AttentionBaseD:\4\s\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 1 dimension 1 should be 3 times of dimension 0Input 2 is expected to have 1 dimension, got Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Inputs 3 and 0 shall have same length at dimension 0Input 2 dimension 0 should have same length as dimension 1 of input 1Input 3 is expected to have 1 dimension, got SampleOpinfo.GetAttr<int64_t>("channels", &channels_).IsOK()invalid channel countchannels_ > 0onnxruntime::contrib::ReorderOutput::ReorderOutputD:\4\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttr<int64_t>("channels_last", &channels_last_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvD:\4\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.NumDimensions() == 4onnxruntime::contrib::ReorderInput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_rank == 4onnxruntime::contrib::ReorderOutput::Computeoutput and sum shape must matchNot satisfied: Y->Shape() == sum_shape +(static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPool [seqno= row[not support normalize yet.!normalize_ is not in (0, Real memory steps onnxruntime::contrib::BahdanauAttention<float>::BahdanauAttentionD:\4\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention<float>::PrepareMemoryactivation_params count mismatchunimplemented activation: input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapeword_embedding and position_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got gamma is expected to have 1 dimensions, got beta is expected to have size of beta is expected to have 1 dimensions, got word_embedding and segment_embedding shall have same dimension 1gamma is expected to have size of allocation failureWidthMajorgemmlowp error: %s +reference(Lhs: %d cells %dx%d %s, Rhs: %d cells %dx%d %s)Tensor types should have been handled already is not supported mem_type: alloc_type:CudaOrtMemoryInfo: [ name: id:CudaPinnedSpecified device is not supported.D:\4\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignmentOrtApis::GetTensorTypeAndShapetype != nullptrOrtValue is not a TensorNot implementedArgument is not a tensorD:\4\s\onnxruntime\core\framework\tensor_type_and_shape.cctype_proto is not of type sequence!type_proto is not of type map!dtype_ != nullptrp_type != nullptrtensor failed memory size calculationshape.Size() must >=0D:\4\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::TensorTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContainerOnly ONNX MLDataType can be registeredD:\4\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeproto != nullptronnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::SetD:\4\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetSequenceType<signed char>::Setonnxruntime::data_types_internal::SetSequenceType<double>::Setonnxruntime::data_types_internal::SetSequenceType<float>::Setelem_proto != nullptronnxruntime::data_types_internal::SetSequenceType<int>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned short>::Setonnxruntime::data_types_internal::SetSequenceType<short>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned char>::Settensor type tensor(bfloat16)(null)onnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitionMLDataType for: is not currently registered or supportedonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setvalue_proto != nullptr expected to be a registered ONNX typeonnxruntime::data_types_internal::SetSequenceType<bool>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned __int64>::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned int>::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<struct onnxruntime::BFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<union onnxruntime::MLFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::Setdimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionD:\4\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimension dimensions. for SizeFromDimension. Tensor has Invalid dimension of }Invalid tensor shape slice argument.dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Slice{invalid allocatoronnxruntime::utils::CopyMLValueD:\4\s\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueNnapiExecutionProviderfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphcopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevices'is defined.No attribute with name:'Attibute name and type don't matchNo attribute with this name is defined.onnxruntime::OpKernelInfo::GetMemoryInfoD:\4\s\onnxruntime\core\framework\op_kernel_info.cccannot find allocatornode_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetD:\4\s\onnxruntime\core/framework/node_index_info.hExecution frame was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is Invalid arg_num of frame != nullptronnxruntime::OpKernelContext::OpKernelContextD:\4\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullarg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValue memory_type: type:Device: [ device_id:SaveMLValueNameIndexMappingonnxruntime::SessionState::SetGraphD:\4\s\onnxruntime\core\framework\session_state.ccCould not create kernel for node: with error: Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function?Done saving OrtValue mappings. as there's no execution provider allocated.Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMappingduplicated ort_value index:) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::GetNodeIndexInfo for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::DeserializeTensorProto, Got Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: D:\4\s\onnxruntime\core\framework\session_state_initializer.ccExecution plan was not found in SessionState. CreatePlan must be called first.Invalid allocation info. Provider name = string tensor is not supported for copying between allocators input with name SubgraphGraphonnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_3fada3e6e238c777f0cc2fe21f5c767c>::operator ()onnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_f94dd7e431e532fd5d301c7120845926>::operator ()onnxruntime::SaveInputOutputNamesToNodeMapping is not used by any node.onnxruntime::SessionStateInitializer::CreatePlanDeserialize tensor Done saving initialized tensorsonnxruntime::SaveInitializedTensorsOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1 failed.Saving initialized tensors.Failed to find kernel for in KernelRegistryManagerfound duplicated provider The node is not placed on any Execution Provider (node The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for data overflowonnxruntime::utils::GetFileContentD:\4\s\onnxruntime\core\framework\tensorprotoutils.ccstring tensor can not have raw data) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in proto`anonymous-namespace'::UnpackTensorWithRawDataInvalid TensorProtosource and destination buffer size mismatchonnxruntime::utils::ReadLittleEndianUnpackTensor: the pre-allocated size does not match the raw data size, expected D:\4\s\onnxruntime\core/framework/endian_utils.hNot satisfied: source_bytes.size_bytes() == destination.size_bytes() +Initialized tensor with unexpected type: onnxruntime::utils::TensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedD:\4\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypenullptr != type_protoonnxruntime::IExecutionProvider::InsertAllocatorduplicated allocatorD:\4\s\onnxruntime\core\framework\execution_provider.ccD:\4\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping output names: Error mapping feeds: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FunctionKernel::FunctionKernelcreate_func(&context, &func_state_) == 0D:\4\s\onnxruntime\core/framework/func_kernel.honnxruntime::GraphPartitioner::PartitionNo provider specified.node_compute_funcs.size() == nodes_need_compile.size()Provider doesn't return correct number of compiled functionsonnxruntime::PlaceNode1 == capability->nodes.size()D:\4\s\onnxruntime\core\framework\graph_partitioner.ccThere's no data transfer registered for copying tensors from data_transfer registered is nullptr. to Tensor size mismatchUnloading DSO D:\4\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::~ExLibLoaderCaught exception while loading custom ops with message: onnxruntime::ExLibLoader::LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Failed to unload DSO: A dso with name has already been loaded.ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexD:\4\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueoffset >= 0 && static_cast<size_t>(offset) < node_values_size_onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shape entries which doesn't match the number of fetches the frame was initialized with of invalid index node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedOrtValue shape verification failed. Current shape: Requested shape:Tensor shape cannot contain any negative valueonnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperort_value.Fence() == nullptrbuffers_.find(mem_patterns_->locations[i]) == buffers_.end()Fetches vector passed to GetOutputs contains Trying to allocate memory for unused optional inputs/outputsonnxruntime::ExecutionFrame::ExecutionFrameonnxruntime::IExecutionFrame::IExecutionFramefeeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()D:\4\s\onnxruntime\core\framework\execution_frame.cconnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx=TraceFree for ort_value_idx=onnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::GetAllocationPlanort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() size= failed: Memory pattern planner is not enabled on this execution framework.. 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., block not found in target location. fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferShape mismatch attempting to re-use buffer. but the actually size is: , fall back to default allocation behaviorFor ort_value with index: , block in memory pattern size is: Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::ReleaseMLValueImplInvalid allocation kind: onnxruntime::AllocateSparseTensormlvalue.Fence() == nullptronnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() Version mismatch. node_version: : Conflicting with a registered kernel with op versions.kernel def can't be NULLOp: Incompatible types. kernel start version: kernel_end_version: onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size() Encountered following errors: (D:\4\s\onnxruntime\core\framework\kernel_registry.cc kernel is not supported in since it was ill-formed during registrationFailed to add kernel for onnxruntime::KernelRegistry::TryFindKernelFailed to create kernel for op: onnxruntime::IDataTransfer::CopyTensorsnullptr != src && nullptr != dstsrc.SizeInBytes() == dst.SizeInBytes()D:\4\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensoronnxruntime::NodeIndexInfo::Init::<lambda_fbf1a8eac9a1b2a8b5a9e6050d980b68>::operator ()D:\4\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::<lambda_8f4c2b119e33b42171a0a55fa88769bc>::operator ()ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.Insufficient dimensions to slice on . Shape:Can't slice a non-tensor OrtValue. Type was D:\4\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer<struct OrtValue>::Createonnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Create. Dimension 0 is gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimensiondim0_offset < dim0_sizeInvalid dim0_offset of Multiple errors were found.onnxruntime::ParallelExecutor::Executeonnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\4\s\onnxruntime\core/framework/op_kernel_context_internal.hAll implicit inputs should have OrtValue instances by now. entry != nullptr does not.Exiting due to terminate flag being set to true.Got nullptr from GetKernel for node: Begin executiononnxruntime::ParallelExecutor::RunNodeAsyncParallelExecutor::ExecuteD:\4\s\onnxruntime\core\framework\parallel_executor.cc_fence_afterUnknown exception was caught by catch-all handler.Non-zero status code returned while running _kernel_time' Status Message: node. Name:'_fence_before'. Exception running nodes starting at SequentialExecutor::ExecuteD:\4\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::Executeonnxruntime::ReleaseNodeMLValuesD:\4\s\onnxruntime\core\framework\fuse_nodes_funcs.cc not found.onnxruntime::FuncManager::GetFuncsfunc info for node: Can't use func with null ptr already exist.Mem pattern for initializer ' is not foundInternal error. is not foundonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanD:\4\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hFailed to get allocator for location: duplicated locationGet preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::TraceWeight buffer for initializer '' failedid >= 0 && static_cast<size_t>(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefn >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::Indexn >= 0 && static_cast<size_t>(n) < ort_value_info_.size()No suitable kernel definition found for op Can not find the execution provider Can not find the node onnxruntime::PlannerImpl::ComputeUseCountsnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizereused != reused_foronnxruntime::PlannerImpl::Reuse) : D:\4\s\onnxruntime\core\framework\allocation_planner.cc[onnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::GetLocationForNodeInputkernel_create_info != nullptr && kernel_create_info->kernel_def != nullptrunexpected failure. illegal input path:model format error! Missing 'location'checksummodel format error!parsing lengthlocation failedmodel format error! Need a key for the external data infomodel format error! Need a value for the external data infoSafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnDivZeroDivide by zero0 == memory_size % kMinAllocationSizeExtended allocation by Total allocated bytes: onnxruntime::BFCArena::Extend bytes.cudaMallocFailed to allocate memory for requested buffer of size h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleonnxruntime::BFCArena::Reserveh != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()Allocated memory at onnxruntime::BFCArena::RegionManager::RegionForCreating BFCArena for p_int < base_int + memory_size_Could not find Region for p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\4\s\onnxruntime\core/framework/bfc_arena.hBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) bins of max chunk size Creating onnxruntime::BFCArena::BFCArenaD:\4\s\onnxruntime\core\framework\bfc_arena.ccc->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesceCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)Extending BFCArena for rounded_bytes:. bin_num:tried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternalonnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunk!chunk->in_use()onnxruntime::BFCArena::FindChunkPtrD:\4\s\onnxruntime\core\framework\simple_tensor_allocator.cc', location: invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferFailed to get allocator for initializer ' Dimension= Target=[ShapeInferenceError] Can't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source=NoOpConstant target:' source:. Falling back to lenient merge.Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShape Input=Type mismatch. Current=D:\4\s\onnxruntime\core\graph\graph.ccOutput:Error merging shape info for output. 'onnxruntime::MergeShapeInfoThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge. inputs but subgraph has Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires in node No Graph instance was found for attribute This is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: constant_attribute.has_t()This is an invalid model. Error: two nodes with same node name (onnxruntime::Graph::GraphOnly 'value' attribute is supported within a 'Constant' node in ORTgraph_proto cannot be nullgraph_proto != nullptrSparseTensor element type mismatch. This is an invalid model. The sum of input arg count is not equal to size of input defs in node (Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Invalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeThis is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgs) does not have type information.This is an invalid model. Model input () of output arg (Type Error: Type () does not match expected type () of node () type inference failed) output arg ( is not a registered function/opFatal error: Type Error: Shape of initializer This is an invalid model. Error in Node:Type Error: Data in initializer ' does not match. but usage of initializer in graph expects ' has element type ) is invalid.) in node () input arg (This is an invalid model. Node (onnxruntime::Graph::InferAndVerifySubgraphTypes) does not have type information set by parent node.Node:Subgraph input missing type.) Op (Node () bound to different types (Type Error: Type parameter (This is an invalid model. Type Error: Type ' in node () of operator (' of input parameter (onnxruntime::Graph::RemoveInitializedTensor'was added but does not exist. Outer scope node arg name 'node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode as it still has output edges.Can't remove node onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::Resolveonnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencing) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::VerifyNodeAndOpMatch) is required but not specified.graph_proto_ is not in sync with name_to_initial_tensor_.!foundexisting->second == &tensoronnxruntime::Graph::AddInitializedTensor but different TensorProto.AddInitializedTensor already has tensor with name Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()D:\4\s\include\onnxruntime\core/common/const_pointer_container.hindex < data_.size()onnxruntime::ConstPointerContainer<class std::vector<class onnxruntime::NodeArg *,class std::allocator<class onnxruntime::NodeArg *> > >::atonnxruntime::Graph::SetInputsonnxruntime::Graph::SetOutputsonnxruntime::Graph::InlineFunctionThis API is not supported when model is loaded from proto file right now.Graph ctor should have created NodeArg for initializer.onnxruntime::Graph::SetGraphInputsOutputsonnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node.'. It is not used by any node and should be removed from the model.Removing initializer 'node_argonnxruntime::Graph::ToGraphProtoInternalonnxruntime::Graph::FuseSubGraphonnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef() must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max())) does not exist in the graph.This is an invalid model. Graph output (D:\4\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registry, but it its version is higherthan the operator set version known by the checker.Unknown model file format version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.ModelProto was null.Failed to load model with error: No graph was found in the protobuf.D:\4\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelONNX 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.system error number Load model failed. File doesn't existonnxruntime::SaveModelProtobuf serialization failed.onnxruntime::Model::Save<p_fd> is less than 0.<p_fd> less than 0.Protobuf parsing failed.onnxruntime::Model::LoadCIncompatible dimensionsNegative values are not allowed in a shape specification should specify a shapeAttribute Pads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64{name} +Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). + +{additionalDocumentation} +inputs are expected to have tensor type.Scale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is Input data type does not match the expected data typeD:\4\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccword_embedding should have 2 dimensions and dimension size is known.Inputs 0 shall be 2 dimensionsauto_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.Constrain input and output types to 8 bit signed and unsigned tensors.Attention mask index with shape (batch_size)M1D input tensor with shape (3 * hidden_size)weight2D input tensor with shape (hidden_size, 3 * hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeNumber of attention headsInput B's scale. It's a scalar, which means a per-tensor/layer quantization.Second operand.A_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.A_scaleInput A's scale. It's a scalar, which means a per-tensor/layer quantization.First operand.{additionalDocumentation}Result, has same element type as two inputsC_zero_pointOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_scaleOutput scale. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_scaleConstrain input and output types to float or half tensors.output tensorbias tensorinput tensorConstrain input and output float tensors types.Constrain input and output integer tensors types1D mask_index tensor with shape (batch_size)maskU1D bias tensor with shape (hidden_size1D skip tensor with shape (hidden_size1D input tensor with shape (hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size)segment_ids2D segment IDs with shape (batch_size, sequence_length)input_ids2D words IDs with shape (batch_size, sequence_length)Constrain mask index to integer typesConstrain input and output types to float tensors.3D output tensor with shape (batch_size, sequence_length, hidden_size)mask_index2D attention mask with shape (batch_size, sequence_length)1D beta tensor for layer normalization with shape (hidden_size)1D gamma tensor for layer normalization with shape (hidden_size)segment_embeddingposition_embedding2D with shape (, hidden_size)word_embedding2D with shape (,hidden_size)First input does not have rank 2) + scale[1] () + scale[0] () needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)both data and indices tensor need to have rank larger than zero.axis must be in [-rank, rank-1]. input rank was inputs are expected to have tensor type and output type should not be null.Input axis is invalid: Second input does not have rank 2Constrain mean and inv_std_var to float tensors.inv_std_varSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computation'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-Dextra_shapeinput_as_shapedataTensor of data to extract slices from.Output tensorInput tensorThreshold valueResult, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).The shape of filled tensorGivenTensorFillConstrain indices to integer typesConstrain input and output types to all tensor types.Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionfirst input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64last dimension of indices must not be larger and rank of data tensorResult, has same shape and type as inputInput tensor of shape [N,C,H,W]The scale to apply.Bias applied to each channel, same size as C.1D output tensor1D input tensorValue of betaValue of alphaConstrain input0 and output types to float tensorsThe scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).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 A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Constrain input and output types to float tensorsPadsWThe previous GRU hidden state.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitOutput data after scalingInput data to be scaledvaluesThe filled tensorhiddenThe new GRU hidden state calculated by this op.The timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevInput/Output is a string tensorTokenized stringsStrings to tokenizeConstrain 'x_zero_point' and 'x' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.N-D full precision output tensor. It has same shape as 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'.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'.N-dimensional matrix BN-dimensional matrix AMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan 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.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.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.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Specified axis to insert a dimensionScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.Output tensor of shape (M, N).N-D quantized Input tensor to be de-quantized.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.N-D quantized output tensor. It has same shape as 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'.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'.N-D full precision Input tensor to be quantized.The 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.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_scaleInput scale. It's a scalar, which means a per-tensor/layer quantization.QLinearReduceMeanmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointQLinearMulConstrain output types to 32 bit tensors.Constrain output to 32 bit tensorMulIntegerConstrain input types to 8 bit signed and unsigned tensors.reduced_zero_pointreduced_scaledata_zero_pointReduced output tensor.An input tensor.ReduceSumIntegerConstrain 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 B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BadditionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointQLinearAddKeep the reduced dimension or not, default 1 mean keep reduced dimension.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input 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).Constrain input type to 8-bit integer tensor.reducedInteger representing the embedding vector size for each word.If not provide, use the fileter size of conv weightConstrain indice type to int32 or int64Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].indicesTensor of rank q >= 1.Tensor of rank r >= 1.Constrain to tensor(int32).Specify embedding vector of charSpecify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This 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.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 usedInput 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 ...].Whether to use ceil or floor (default) to compute the output shape.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.QLinearAveragePoolIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.Constrain input and output types to 8 bit tensors.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.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)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".Constrain types to float tensors.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].crop_size1-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.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisRoIs (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.Tensor after padding.(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).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] 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`.Input tensor.Three 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 arrayConstrain to tensor(float).Input can be of any tensor type.countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 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 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 input tensor that is to be processed.Output data tensor.Bias tensor.Scale tensor.Input data tensor from the previous layer.The epsilon value to use to avoid division by zero.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Constrain types to int tensors.The bias input data that is a 1D tensor.The normal input data.The output.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.D:\4\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Attempting to get an input that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast<size_t>(target_input_idx)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast<size_t>(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast<size_t>(index) < inputs.size()Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index: ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)fused_function_subgraphD:\4\s\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyA node with a function body within a subgraph within another function body is currently not supported in ORToutinResolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplinput_arg->Type() != nullptr1-D Tensor of the range.deltaTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.limitTensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.startTensor(scalar, or dims=[1]). First entry in the range.Constrain input and output types.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!D:\4\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cctensor rank too smallCell 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.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.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.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` QWThe 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]` PY_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`AWThe 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.` memory_seq_lensRThe 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 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 input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Constrain seq_lens to integral tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.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.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.sequence_lensOptional 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]` 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.GetFileSizeEx fail, errcode = Fatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfoD:\4\s\onnxruntime\core\platform\windows\env.cconnxruntime::`anonymous-namespace'::FileHandleTraits::CleanUp fail: unexpected endReadFile SetFilePointerEx open file Not satisfied: length <= buffer.size() +Not satisfied: offset >= 0 +Not satisfied: file_path +onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferFailed to close file handle - error code: GetSystemTimePreciseAsFileTimekernel32.dllonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameFailed to find symbol in libraryFailed to unload libraryMapFileIntoMemory is not implemented on Windows.pmam is not a valid date is not a valid year is not a valid day [Default logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::DefaultISink must be provided.onnxruntimeENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORSUCCESSFAILNO_SUCHFILENO_MODELcode != static_cast<int>(common::OK)onnxruntime::common::Status::StatusD:\4\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]enabled_onnxruntime::profiling::Profiler::StartTimeD:\4\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::Initializecustom_logger != nullptronnxruntime::profiling::Profiler::StartProfilingMaximum number of events reached, could not record profile event.}, ] -SessionNodeenabled_onnxruntime::profiling::Profiler::StartTimeD:\30\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ -",{"cat" : "X@`@1.033{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;   - Operator '' has been deprecated since version not in range [min=, max=(outputs_.size() - 1) == iONNX Schema !(it.GetName().empty()) not in allowed input sizes.) has input size not in allowed output sizes.) has output size ) in op definition.has output size ) has more inputs () than declared ()'s input is marked single but has an empty string in the graph)'s output ) has more outputs (Attribute '' appeared multiple times.Unrecognized attribute: for operator ' is expected to have field 't'Mismatched attribute type in '' is expected to have field 'g'' is expected to have field 'sparse_tensor'' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'tensors'' is expected to have field 'strings' has unknown expected type' is expected to have field 'graphs'Required attribute '' is missing.Duplicate type constraint name: failed validating the check: (inputs_.size() - 1) == iA 1-D tensor holding values from the input dictionary.A dictionary.map(string, int64)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.map(string, float)map(int64, double)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.map(string, double)An integer vocabulary array.<br>One and only one of the vocabularies must be defined.A string vocabulary array.<br>One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The input type must be a tensor of a numeric type.The output array, elements ordered as the inputs.The size of each input in the input listImputed output dataData to be processed.Value(s) to change toThe 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.Value(s) to change to.A value that needs replacing.Label encoder has only one input.Only one of keys_*'s can be set in label encoder.Label encoder has only one output.Input type is not int64 tensor but keys_int64s is setInput type is not string tensor but key_strings is setOnly one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setOutput data.Input data. It can be either tensor or scalar.Data to be selectedSelected output data as an arrayThe indices, based on 0 as the first index of any dimension.The input must be a tensor of a numeric type or string. The output will be of the same tensor type.ZD:\30\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccBinarized output dataData to be binarizedValues greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type. The output will be of the same tensor type.The input map that is to be cast to a tensorThe input must be an integer map to either string or float.A tensor representing the same data as the input map, ordered by their keysmap(int64, float)map(int64, string)A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.The output is a 1-D tensor of string, float, or integer.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.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).<br>One of 'DENSE', 'SPARSE'.Input dataThe input must be a tensor of strings or integers, either [N,C] or [C].Output data. If strings are input, the output values are integers, and vice versa.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe output is a tensor of strings or integers. Its shape will be the same as the input shape.A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.One of 'MAX,' 'L1,' 'L2'Encoded output dataData to be encoded.List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.Encoded output data, having one more dimension than X.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.Data to be scaled.First, offset by this.<br>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.Scaled output data.Second, multiply by this.<br>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.<br>Must be same length as 'offset'The input must be a tensor of a numeric type, either [C] or [N,C].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 kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.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.First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The number of support vectors.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.A list of ints.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.A list of floats.An integer.A string.A float.Data to be classified.Input's shape should be 1D or 2DClassification scores ([N,E] - one score for each class and exampleClassification outputs (one class per example).The output will be a tensor of strings or integers.The 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]A collection of intercepts.A collection of weights of the model(s).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates whether to do OvR or multinomial (0=OvR is the default).Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Data to be regressed.The input must be a tensor of a numeric type.Regression outputs (one per target, per example).Weights of the model(s).Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.Data to be encoded, a tensor of shape [N,C] or [C]The input valuesThe output will be a sequence of string or integer maps to float.The output mapseq(map(int64, float))seq(map(string, float))The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.Input of shape [N,F]The class score for each class, for each point, a tensor of shape [N,E].N, Top class for each pointTree id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Child node if expression is true.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'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.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.Child node if expression is false.node id that this weight is for.The id of the tree that this node is in.The weight for the class in class_id.The index of the class list that each weight is for.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)Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'N classesChild node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.For 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.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.Child node if expression is falseThe node id of each weightThe id of the tree that each node is in.The weight for each targetThe index of the target that each weight is forDefines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The input type must be a tensor of integers or strings, of any shape.Input data.A list of labels.The output type will be a tensor of strings or integers, and will have the same shape as the input.D:\30\s\cmake\external\onnx\onnx\defs\traditionalml\old.cc expected to have rank but has rank expected to have rank >Dimension mismatch in unification between default 1; Pooled output Y's height.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. 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.default 1; Pooled output Y's width.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.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.D:\30\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccRoI 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].An 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]boxesInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.scoresFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.max_output_boxes_per_classFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.iou_thresholdselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].score_thresholdInteger 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.selected_indicesInput type was nullElement type of input was unknownInput was expected to have tensor type. Got Input element type of does not match existing output type of Output was expected to have tensor type. Got then_branch and else_branch produce different number of outputs. Scan 'body' subgraph outputs should all be tensors but output If node has but subgraphs produce then= else=Mismatched tensor element type for output Mismatched type for output Loop 'body' subgraph outputs should all be tensors but output condCondition for the ifoutputsValues 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 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.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.Only boolAll Tensor typesD:\30\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccA boolean termination condition. Optional. Pass empty string to skip.A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputstensor of int64, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of bool, which should be a scalar.initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputs is invalid for a tensor of rank ) is not equal to number of scan inputs ( axis value ) is not equal to number of scan outputs (Number of scan input axes specified ( was not a tensor.Number of scan output axes specified ( outputs. Expected Scan input was notGraph attribute inferencing returned type information for final_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsAn attribute specifying the number of scan_inputs M. The 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 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 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. 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].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).Int64 tensorIncompatible dimensionsadditionThe output values with the same shape as input tensor (the original size without coercion).subtractionD:\30\s\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 treatmentDivisor tensorDividend tensormultiplicationRemainder tensordivisionConstrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.Coefficient of leakage.Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of ELU.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. -); for more details please check [the doc](Broadcasting.md).This operator supports **unidirectional broadcasting** ( +"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} +onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ +",{"cat" : ""pid" :"tid" :1.033{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;   +  is marked single but has an empty string in the graph)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute ' for operator Unrecognized attribute: ) has input size not in allowed input sizes.) has output size not in allowed output sizes.has output size ) in op definition.) than declared () has more inputs (' has been deprecated since version Operator ', max= not in range [min=ONNX Schema (outputs_.size() - 1) == i!(it.GetName().empty())' is expected to have field 'graphs' has unknown expected type' is missing.Required attribute 'Duplicate type constraint name(inputs_.size() - 1) == i: failed validating the check: 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'The input must be a tensor of a numeric type or string. The output will be of the same tensor type.D:\4\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccData to be binarizedBinarized output dataThe 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.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayZmap(string, float)map(string, double)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.<br>One and only one of the vocabularies must be defined.An integer vocabulary array.<br>One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.A string to use when an input integer value is not found in the map.<br>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.<br>One and only one of the 'default_*' attributes must be defined.A dictionary.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.map(string, int64)map(int64, double)If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput 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.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.map(int64, string)map(int64, 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).<br>One of 'DENSE', 'SPARSE'.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Input's shape should be 1D or 2DData to be classified.Classification outputs (one class per example).Only one of values_*'s can be set in label encoder.Input data. It can be either tensor or scalar.Output data.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 ints.A list of floats.Value(s) to change to.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 setThe input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe 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.Value(s) to change toA value that needs replacing.List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.List of categories, strings.<br>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.Data to be scaled.Scaled output data.First, offset by this.<br>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.<br>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.<br>Must be same length as 'offset'The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.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.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.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).A collection of intercepts.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.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.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.<br>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.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.<br>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].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.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.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.seq(map(int64, float))The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.The weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapThe output will be a sequence of string or integer maps to float.seq(map(string, float))N classesNode 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.<br>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 node id of each weightThe index of the target that each weight is forFor 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.<br>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. <br> 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)Input data.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.A list of labels.D:\4\s\cmake\external\onnx\onnx\defs\traditionalml\old.cc expected to have rank > but has rank expected to have rank Dimension mismatch in unification between 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.iou_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.score_thresholdselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].selected_indicesInteger 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.D:\4\s\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.boxesAn input tensor with shape [num_batches, num_classes, spatial_dimension]scoresInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.max_output_boxes_per_classMultiplicative 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]. does not match existing output type of Input element type of Output was expected to have tensor type. Got Input type was nullInput was expected to have tensor type. Got Element type of input was unknown outputs. Expected Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. but subgraphs produce If node has else= is invalid for a tensor of rank axis value ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified ( was not a tensor.Scan input tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsinitial_state_and_scan_inputsFinal values of the loop's N state variables followed by K scan_outputsfinal_state_and_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. A 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)v_initialFinal N loop carried dependency values then K scan_outputsv_final_and_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.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.outputsGraph 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.All Tensor typesOnly boolD:\4\s\cmake\external\onnx\onnx\defs\controlflow\defs.cc then=Mismatched type for output Mismatched tensor element type for output was Loop 'body' subgraph outputs should all be tensors but output Condition for the ifcondAn 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].Int64 tensorThe input tensor that's coerced into a 2D matrix of size (NxD) as described above.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).D:\4\s\cmake\external\onnx\onnx\defs\math\defs.ccThe output values with the same shape as input tensor (the original size without coercion).subtractionDividend tensorWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} - should be unidirectional broadcastable to {broadcast_doc}{name}Second operand.First operand.Constrain input and output types to high-precision numeric tensors.Result, has same element type as two inputs'axis' must be in []. Its actual value is: {description} + should be unidirectional broadcastable to Constrain input and output types to high-precision numeric tensors.{broadcast_doc}'axis' must be in []. Its actual value is: {description} 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 @@ -5565,34 +6191,35 @@ 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. -The input tensor that's coerced into a 2D matrix of size (NxD) as described above.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 softsign (x/(1+|x|)) values of the input tensor computed element-wisetensor A * BGeneral Matrix multiplication: +This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. +); for more details please check [the doc](Broadcasting.md).This operator supports **unidirectional broadcasting** (The hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent. +Pow takes input data (Tensor<T>) and exponent Tensor, and +produces one output data (Tensor<T>) where the function `f(x) = x^exponent`, +is applied to the data tensor elementwise. +Output tensor (same size as X)Second operand, power of the exponent.input tensor X +PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one +output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`, +`f(x) = x for x >= 0`., is applied to the data tensor elementwise. +Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of ELU.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseCoefficient of leakage.Remainder tensorDivisor tensordivisionConstrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.The softsign (x/(1+|x|)) values of the input tensor computed element-wisetensor A * BGeneral 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).tensor CIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).K input must be a one-dimensional tensor of size 1.Invalid value for attribute axisAxis has less than the requested k elements.K input must be of type int64.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_n, r]Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorKTensor 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.ValuesConstrain input and output types to numeric tensors.IndicesDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain index tensor to int64Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.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 hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent. -Pow takes input data (Tensor<T>) and exponent Tensor, and -produces one output data (Tensor<T>) where the function `f(x) = x^exponent`, -is applied to the data tensor elementwise. -Output tensor (same size as X)Second operand, power of the exponent.input tensor X -PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one -output data (Tensor<T>) where the function `f(x) = slope * x for x < 0`, -`f(x) = x for x >= 0`., is applied to the data tensor elementwise. -Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to Xtensor slopeslopeValue of alpha. +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).tensor CIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).normalized exponentialOutput tensor with clipped input elementslog of softmaxsoftmax1 for the first maximum value, and 0 for all otherslogsoftmaxhardmaxdata_0List of tensors for sumOutput tensor.Input tensor whose elements to be clippedMaximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to Xtensor slopeslopeValue of alpha. 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} -Value of beta.data_0List of tensors for sumOutput tensor.Input tensor whose elements to be clippedMaximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).normalized exponentialOutput tensor with clipped input elementslog of softmaxsoftmax1 for the first maximum value, and 0 for all otherslogsoftmaxhardmaxa_zero_pointzero point of quantized input abN-dimensional quantized matrix bb_scalescale of quantized input bb_zero_pointzero point of quantized input bzero point of quantized output yscale of quantized output yConstrain input a and its zero point data type to 8-bit integer tensor.Quantized matrix multiply results from a * bConstrain output y 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.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'.Constrain input A data type to 8-bit integer tensor.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 output Y data type as 32-bit integer tensor.Constrain input B data type to 8-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.An input tensor that is to be processed.If set to 1 will perform the sums in reverse direction.Output tensor of the same type as 'x' with cumulative sums of the x's elements(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.axis tensor can be int32 or int64 onlyInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:The arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wise'shape' input must be 1D tensor of type INT64Constrain 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.inputs are expected to have tensor type.input and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.aN-dimensional quantized matrix aa_scalescale of quantized input aConstrain input and output types to floating-point tensors.Value of attribute not specified does not specify a valid type. should be of integer type and specify a type.The 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' argumentInput tensor to be cast.Constrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.length of each outputWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsIncorrect or missing input value for starts and endsSlice op must have either three, four or five inputs.Input steps has incorrect lengthInput axes has incorrect length'step' cannot be 0Input axes has invalid data1-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 ending indices (exclusive) of corresponding axis in `axes`steps1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Invalid attribute perm {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.}, input shape = {transposedTransposed output.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 rank r >=1 (same rank and shape as indices)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 as input).updatesInput and output types can be of any tensor type.Tensor of rank q + r - indices_shape[-1] - 1.data tensor must have rank >= 1D:\30\s\cmake\external\onnx\onnx\defs\tensor\defs.ccInvalid position of 0Target shape may not have multiple -1 dimensionsInvalid Target shape product of 0Invalid dimension value: Specified shape for output.Dimension could not be inferred: incompatible shapesreshapedReshaped data.Shape of the input tensorConstrain output to int64 tensor.Input tensor can be of arbitrary type.Total number of elements of the input tensorRequired attribute axis is missingConstrain output to int64 tensor, which should be a scalar though.All inputs to Concat must have same rankaxis must be in [-rank, rank-1].Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..inputsList of tensors for concatenationconcat_resultConcatenated tensorConstrain output types to any tensor type.Invalid value of attribute 'axis'. Rank= Value=One or more outputs forming list of tensors after splittingThe tensor to splitOutput tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]repeatsConstrain repeat's type to int64 tensors.N-D tensorTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensor after resizingThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Constrain input 'X' and output 'Y' to all tensor types.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). +Value of beta.The arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wise'shape' input must be 1D tensor of type INT64Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorKTensor 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.ValuesConstrain input and output types to numeric tensors.IndicesDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain index tensor to int64K input must be a one-dimensional tensor of size 1.Invalid value for attribute axisAxis has less than the requested k elements.K input must be of type int64.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_n, r]N-dimensional quantized matrix ba_zero_pointscale of quantized input bbzero point of quantized input bb_scalescale of quantized output yb_zero_pointinput and zero_point pair is expected to have be same type.N-dimensional quantized matrix ainput and zero_point pair is expected to have same type.scale of quantized input aazero point of quantized input aa_scaleThe 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.Constrain 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-wiseaxis tensor can be int32 or int64 onlyOutput tensor of the same type as 'x' with cumulative sums of the x's elements != mat_h:Input rank must be >= 2.Constrain input and output types to floating-point tensors.The inner-most 2 dimensions must have the same size (mat_w:Constrain input B data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If set to 1 will perform the sums in reverse direction.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.(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.An input tensor that is to be processed.Quantized matrix multiply results from a * bzero point of quantized output yConstrain input b and its zero point data type to 8-bit integer tensor.Constrain input a 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.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'.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'.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoValue of attribute not specified does not specify a valid type. should be of integer type and specify a type.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..inputsList of tensors for concatenationconcat_resultConcatenated tensorConstrain output types to any tensor type.Constrain output to int64 tensor.Input tensor can be of arbitrary type.Total number of elements of the input tensorRequired attribute axis is missingConstrain output to int64 tensor, which should be a scalar though.All inputs to Concat must have same rankaxis must be in [-rank, rank-1].Invalid Target shape product of 0Invalid dimension value: Specified shape for output.Dimension could not be inferred: incompatible shapesreshapedReshaped data.Shape of the input tensorOutput tensor with the same shape as input with type specified by the 'to' argumentInput tensor to be cast.Constrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.D:\4\s\cmake\external\onnx\onnx\defs\tensor\defs.ccInvalid position of 0Target shape may not have multiple -1 dimensionsWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of rank r >=1 (same rank and shape as indices)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 as input).updatesInput and output types can be of any tensor type.steps1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Invalid attribute perm {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.}, input shape = {transposedTransposed output.Incorrect or missing input value for starts and endsSlice op must have either three, four or five inputs.Input steps has incorrect lengthInput axes has incorrect length'step' cannot be 0Input axes has invalid data1-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 ending indices (exclusive) of corresponding axis in `axes`Invalid value of attribute 'axis'. Rank= Value=One or more outputs forming list of tensors after splittingThe tensor to splitlength of each outputWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsBlocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalOutput tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].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.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].'axes' attribute must not contain any duplicatesList 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).values in 'axes' are beyond the bounds of the computed output shapeexpandedOriginal tensorBlocksize must be positiveTensor 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.Tensors with at least max(dims) dimensions.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).squeezedReshaped tensor with same data as input.Tensor of rank q + r - indices_shape[-1] - 1.data tensor must have rank >= 1Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]Tensor of rank q + (r - 1).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.Constrain roi type to float or double.sizesTensor to copy input into.Indices tensor must have rank >= 1(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).'axis' must be in [-rank(indices), rank(indices)-1]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.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".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.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".roi1-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 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.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.N-D tensorTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensor after resizingThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Constrain input 'X' and output 'Y' to all tensor types.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). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/> 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, <br/> @@ -5610,11 +6237,11 @@ if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/> x_original = (x_resized + 0.5) / scale, <br/> if coordinate_transformation_mode is "tf_crop_and_resize", <br/> -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).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.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".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.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".roi1-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 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.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.Constrain roi type to float or double.sizesTensor to copy input into.Indices tensor must have rank >= 1(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).'axis' must be in [-rank(indices), rank(indices)-1]conditionRank 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.Constrains to boolean tensors.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]Tensor of rank q + (r - 1).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.Tensors with at least max(dims) dimensions.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).squeezedReshaped tensor with same data as input.'axes' attribute must not contain any duplicatesList 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).values in 'axes' are beyond the bounds of the computed output shapeexpandedOriginal tensorBlocksize must be positiveBlocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalOutput tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].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.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].'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int641D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Input tensor of any shape.Tensor of rank r >= 2.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor with same shape of input.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).A N-D input tensor that is to be processed.(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 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 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'inverse_indicesA 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'Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.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.Pads has incorrect number of values'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Tensor 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`.Supported modes: `constant`(default), `reflect`, `edge`constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrains input and output to only numeric types.OneHot node must have three inputs.Input 'depth' must have exactly one element.Input 'depth' must be a scalar or rank 1 tensor.Input 'values' must have exactly two elements.Input 'values' must be rank 1 tensor.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.(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).depthScalar 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.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.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. Constrain to any tensor type.Constrain output types to boolean tensors.Constrain input types to float 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.values selected at indices where condition is TrueWhen True (nonzero), yield X, otherwise yield YTensor of shape equal to the broadcasted shape of condition, X, and Y.values selected at indices where condition is FalseConstrain to boolean tensors.Constrain to all tensor types.output (always 2D tensor)'input' must have rank >= 2(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'sequence_lens' must have rank of 1seq(tensor(uint16))seq(tensor(uint8))seq(tensor(uint64))seq(tensor(uint32))seq(tensor(int16))seq(tensor(int8))Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Length of input sequence. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for split sum of split values=Input 'split' can not be empty.Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain input types to all tensor types.One or more outputs forming a sequence of tensors after splittingConstrain output types to all tensor types.Constrain split size to integral tensor.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Sequence of tensors for concatenationseq(tensor(int64))seq(tensor(int32))seq(tensor(float))seq(tensor(float16))seq(tensor(string))seq(tensor(double))seq(tensor(complex64))seq(tensor(bool))seq(tensor(complex128))(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.D:\30\s\cmake\external\onnx\onnx\defs\sequence\defs.ccEmpty sequence.SequenceConstruct is expected to have at least 1 input.Tensors.Element type of inputs are expected to be the same.output_sequenceSequence enclosing the input tensors.Constrain input types to any tensor type.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=input_sequenceInput sequence.tensorInput tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.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.Attribute dilations has incorrect sizeAttribute strides has incorrect sizeauto_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.Input tensor must have atleast 2 dimensionsAttribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specified{additionalDescription}((kernel_spatial_shape[i] - 1) * dilations[i] + 1) +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).'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int641D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Input tensor of any shape.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]repeatsConstrain repeat's type to int64 tensors.values selected at indices where condition is TrueWhen True (nonzero), yield X, otherwise yield YTensor of shape equal to the broadcasted shape of condition, X, and Y.values selected at indices where condition is FalseConstrain to boolean tensors.Constrain to all tensor types.output (always 2D tensor)Constrain to any tensor type.Constrain output types to boolean tensors.Constrain input types to float 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.Input 'values' must have exactly two elements.Input 'values' must be rank 1 tensor.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.(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).depthScalar 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.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.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. conditionRank 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.Constrains to boolean tensors.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.OneHot node must have three inputs.Input 'depth' must have exactly one element.Input 'depth' must be a scalar or rank 1 tensor.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).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`.Constrains input and output to only numeric types.constant_valueA 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.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.Supported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).A N-D input tensor that is to be processed.(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 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 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'inverse_indicesA 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'. 'input' must have rank >= 2(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'sequence_lens' must have rank of 1Tensor of rank r >= 2.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor with same shape of input.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Attribute dtype should be of integer type and specify a type.Empty sequence.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.D:\4\s\cmake\external\onnx\onnx\defs\sequence\defs.ccElement type of inputs are expected to be the same.SequenceConstruct is expected to have at least 1 input.seq(tensor(float16))seq(tensor(int64))seq(tensor(double))seq(tensor(float))seq(tensor(bool))seq(tensor(string))seq(tensor(complex128))seq(tensor(complex64))seq(tensor(uint8))seq(tensor(uint32))seq(tensor(uint16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(int32))seq(tensor(int16))Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitSum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=One or more outputs forming a sequence of tensors after splittingLength of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. Constrain split size to integral tensor.Constrain input types to all tensor types.Output tensor at the specified position in the input sequence.Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Input tensor to be inserted into the input sequence.input_sequencePosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).tensorOutput sequence that contains the inserted tensor at given position.positionConstrain position to integral tensor. It must be a scalar(tensor of empty shape).Sequence enclosing the input tensors.Tensors.Constrain input types to any tensor type.output_sequence Tensor=Input sequence.Input Sequence and Tensor are expected to have the same elem type. Sequence=Sequence of tensors for concatenationInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output types to all tensor types.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.], Value=new_axis must be either 0 or 1Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Invalid value of attribute 'axis'. Accepted range=[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).Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Input tensor X must have atleast 2 dimensions.MaxUnpool op must have either two or three inputs.Attribute strides has incorrect size.Attribute pads has incorrect size.Attribute kernel_shape must be specified.Attribute kernel_shape has incorrect size.{kernelSpatialShape}kernel_spatial_shape[i]The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).Wether to use ceil or floor (default) to compute the output shape.D:\4\s\cmake\external\onnx\onnx\defs\nn\defs.ccaverageThe storage order of the tensor. 0 is row major, and 1 is column major.The output of each pooling window is maximum number of elements exclude pad.Attribute kernel_shape must be specifiedAttribute kernel_shape has incorrect sizeSecond input tensor has wrong dimensionAttribute pads has incorrect size{opName} {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: @@ -5639,27 +6266,21 @@ 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} - {opName}Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsMultiplicative 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. + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1){additionalDescription}Input tensor must have atleast 2 dimensionsAttribute strides has incorrect sizeAttribute dilations has incorrect sizeOptional 1D bias to be added to the convolution, has size of M.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. The shape of the convolution kernel. If not present, should be inferred from input W.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.a filternumber of groups input channels and output channels are divided into.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).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], ...].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. +The convolution operator consumes an input tensor and {filter_desc}, and +computes the output.RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).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 ...].{filter_desc}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.p value of the Lp norm used to pool over the input data.Input tensor must have at least 2 dimensionsOutput data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Attribute pooled_shape has incorrect lengthRoIs tensor must have 2 dimensions 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). -The convolution operator consumes an input tensor and {filter_desc}, and -computes the output.{filter_desc}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]).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.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. 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 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.weight and zero_point pair is expected to have same type.Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.a filterScale 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).w_scaleZero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.wConstrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.w_zero_pointScale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.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.kernel_spatial_shape[i]{kernelSpatialShape}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 usedThe output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).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 ...].D:\30\s\cmake\external\onnx\onnx\defs\nn\defs.ccThe output of each pooling window is maximum number of elements exclude pad.averageWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.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).MaxUnpool op must have either two or three inputs.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.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.'output_shape' must have same number of elements as the shape of input tensor X.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.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.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).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. + (num_rois, channels, pooled_shape[0], pooled_shape[1]).Attribute pooled_shape must be specified'output_shape' must have same number of elements as the shape of input tensor X.'output_shape' must be rank 1 tensor.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 ...].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.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). {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.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.The output tensor of the same shape as XThe running mean after the BatchNormalization operator.saved_varsaved_meanSaved variance used during training to speed up gradient computation.The output tensor of the same shape as input.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.Input matrixMatrix after normalizationratioThe output mask. -Dropout takes one input floating tensor and produces two tensor outputs, -output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is -in test mode or not, the output Y will either be a random dropout, or a simple -copy of the input. Note that our implementation of Dropout does scaling in -the training phase, so during testing nothing needs to be done. -The ratio of random dropoutThe lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain output mask types to boolean tensors.Invalid value(A tensor of rank >= axis.) for attribute 'axis'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). 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.Constrain input and output to all tensor types.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.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.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.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)number of groups input channels and output channels are divided into. default is 1.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.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.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)Stride along each spatial axis. If not present, the stride defaults to 1 along each axis. + data into the output tensor Y for further processing.Output data tensor that contains the result of the unpooling.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 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: @@ -5669,39 +6290,45 @@ 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). - Additional 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. + 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 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)Additional 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.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads{op_type} 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 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 padsOutput 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.lp pool{op_type}{op}momentumInput 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 1 + equal to the spatial dimension of input tensor.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.Constrain input x and its zero point data type to 8-bit integer tensor.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 output y data type to 32-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain filter type to 8-bit integer tensor.Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain bias type to 32-bit integer tensor.Constrain output type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input 'w'.number of groups input channels and output channels are divided into. default is 1.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.weight and zero_point pair is expected to have same type.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Scale 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).ww_zero_pointw_scaleThe axis on which to apply normalization, -1 mean last axis.Matrix after normalization +Dropout takes one input floating tensor and produces two tensor outputs, +output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is +in test mode or not, the output Y will either be a random dropout, or a simple +copy of the input. Note that our implementation of Dropout does scaling in +the training phase, so during testing nothing needs to be done. +The order of the normalization, only 1 or 2 are supported.ratioThe ratio of random dropoutConstrain output mask types to boolean tensors.The output mask.saved_varSaved variance used during training to speed up gradient computation.The input 1-dimensional scale tensor of size C.The output tensor of the same shape as input.The input 1-dimensional bias tensor of size C.Input matrixrunning (training) or estimated (testing) mean tensor of shape (C).Bias tensor of shape (C).varrunning (training) or estimated (testing) variance tensor of shape (C).The running mean after the BatchNormalization operator.The output tensor of the same shape as XSaved mean used during training to speed up gradient computation.The running variance after the BatchNormalization operator.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.{op} 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).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).Scale tensor of shape (C).Bias tensor of shape (C).EX_squaredX_squaredEpsilonX_RMSTDX_varianceE_XsquaredVarianceProcessed_STDThe exponent.Output tensor, which has the shape and type as input tensorThe number of channels to sum overScaling parameter.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Constrain input and output types to float tensors.Input is ether string UTF-8 or int32/int641-D tensor of floatsInput for n-gram extractionNgram resultsMaximum 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.Maximum 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.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.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.Input shape must have either [C] or [1,C] dimensions where C > 0UTF-8 strings to normalizeThe 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 falseList of stop words. If not set, no word would be removed from X.UTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"A 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.ExponentEnvironment 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.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).D:\30\s\cmake\external\onnx\onnx\defs\reduction\defs.ccaxis must be in [-rank, rank-1]. input rank was +lp poolmomentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Scale tensor of shape (C).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 1Maximum 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=1Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.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.List 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 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.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.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)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.ngram_indexes must be non-empty with no negative valuesInput for n-gram extractionInput tensor must have rank 1 or 2Input is ether string UTF-8 or int32/int64Ngram resultsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.1-D tensor of floatsIndicate 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). Constrain input and output to all tensor types.The number of channels to sum overThe exponent.Scaling parameter.Constrain input and output types to float tensors.Output tensor, which has the shape and type as input tensorThe lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Invalid value() for attribute 'axis'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.A tensor of rank >= axis.STDVarianceProcessed_STDX_varianceA 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.EpsilonExponentEX_squaredX_RME_XsquaredX_squaredInput shape must have either [C] or [1,C] dimensions where C > 0UTF-8 Normalized stringsUTF-8 strings to normalizeBoolean. 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"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.List of stop words. If not set, no word would be removed from X.D:\4\s\cmake\external\onnx\onnx\defs\reduction\defs.ccA 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).productsum squarelog sum exponentlog sumL2 normL1 norm 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.log sumlog sum exponentsum squareproduct +False instead of True.The axis in which to compute the arg indices. 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).L1 normL2 normReduced output tensor with integer data type.Output tensor containing the same value of the provided tensor.D:\30\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.Invalid shape value: (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 float32Shape input must be a one-dimensional tensor.Constrain input types.Constrain output types to be numerics.1D 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.(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'.Input tensor must be 2-dimensionalConstrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Upper boundary of the output values.(Optional) Seed to the random generator, if not specified we will auto generate one.Lower boundary of the output values.Output tensor of random values drawn from uniform distributionConstrain output types to float tensors.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The mean of the normal distribution.Attribute expected to have tensor typeAttribute expected to have a one-dim tensorOnly 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.sparse_valueConstrain input types to common numeric type tensors.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)(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.Output tensor of random values drawn from normal distributionOutput type must be int32 or int64Input tensor must have rank 2Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.loop_body_attributeiConstrain output types to integral tensors.currentrangeprevcond_outdelta_casteddiv_resultsub_resultsub_result_castedceil_result_relu_intceil_result_relu_boolceil_resultceil_result_reluScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.variadic_outputAll inputs to 'Range' op must be of the same typeConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Scale 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.N-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.D:\30\s\cmake\external\onnx\onnx\defs\quantization\defs.ccQuantized output tensorZero 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.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned 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.X_MinX_Min_AdjustedQ_MinQ_MaxX_RangeMin_ScaledX_MaxX_Max_AdjustedRounded_ZeroPoint_FPZeropointInitial_ZeroPoint_FPClipped_ZeroPoint_FP +The type of the output tensor is integer.Reduced output tensor with integer data type.Attribute expected to have tensor typeAttribute expected to have a one-dim tensorOnly one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.sparse_valueThe data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.(Optional) Seed to the random generator, if not specified we will auto generate one.Output tensor of random values drawn from uniform distributionThe shape of the output tensor.Constrain output types to float tensors.The standard deviation of the normal distribution.The mean of the normal distribution.2D input tensor to copy shape, and optionally, type information from.(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'.Constrain input types. Strings and complex are not supported.Output tensor, same shape as input tensor T1.Constrain output types. Strings and complex are not supported.Upper boundary of the output values.Lower boundary of the output values.1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.(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 float32Constrain input types.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.Input tensor must be 2-dimensionalThe value for the elements of the output tensor.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Output tensor containing the same value of the provided tensor.The value for the elements of the output tensor in sparse format.D:\4\s\cmake\external\onnx\onnx\defs\generator\defs.ccInvalid shape value: Shape input must be a one-dimensional tensor.ceil_resultdiv_resultceil_result_relu_intceil_result_reluvariadic_outputceil_result_relu_boolScalar. First entry for the range of output values.All inputs to 'Range' op must be of the same typeprevicurrentcond_outsub_resultrangedelta_castedsub_result_castedNumber of times to sample.Input tensor must have rank 2Input 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.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Constrain output types to integral tensors.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.loop_body_attributeOutput tensor of random values drawn from normal distributionThe data type for the elements of the output tensor. Default is TensorProto::FLOAT.(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.Output type must be int32 or int64Input to 'Range' op should be scalars (Tensor with only one element and shape empty)Scalar. Value to step by.Scalar. Exclusive upper limit for the range of output values.Constrain input types to common numeric type tensors.A 1-D tensor with same type as the inputs containing generated range of values.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.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.Quantized output tensorConstrain 'x' to float tensor.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Q_MinConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.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.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x' to float or int32 tensor.D:\4\s\cmake\external\onnx\onnx\defs\quantization\defs.ccScale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.N-D quantized input tensor to be de-quantized.Rounded_ZeroPoint_FPClipped_ZeroPoint_FPZeropointX_MinQ_MaxX_MaxX_Min_AdjustedX_RangeX_Max_AdjustedInitial_ZeroPoint_FPMin_ScaledResult tensor.Second input operand for the logical operator.Constrains input to boolean tensor.andD:\4\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains output to boolean tensor.xororFirst input operand for the logical operator. 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} -First input operand for the logical operator.andConstrains input to boolean tensor.Second input operand for the logical operator.Result tensor.orxorConstrains output to boolean tensor.D:\30\s\cmake\external\onnx\onnx\defs\logical\defs.cclessequalgreaterConstrains input types to all numeric tensors. +Constrain input and output types to integer tensors.Second operand, amounts of shift.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrains input types to all numeric tensors.greaterequallessConstrains input/output to boolean tensors.First operand, input to be shifted. 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 @@ -5709,11 +6336,11 @@ 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.Constrains input/output to boolean tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Second operand, amounts of shift.Constrain input and output types to integer tensors.D:\30\s\cmake\external\onnx\onnx\defs\object_detection\old.ccConstrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.Axis along which to repeat.Output tensor of same shape and type as input.height_scaleTwo interpolation modes: nearest(default), bilinearwidth_scaleThe scale along height dimension. It takes value greater than or equal to 1.Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Attribute 'scales' must have floats type.Attribute 'scales' is required.Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Incorrect or missing attribute value for starts and endsThe 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'.Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Attribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Tensor of int32/int64 indices, of r >= 1 (same rank as input).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 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.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]D:\30\s\cmake\external\onnx\onnx\defs\tensor\old.ccConstrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat onWhich axis to concat on. Default value is 1.rank must be greater than axisWhich axis to split onOptional list of output lengths (see also arg 'split')outputs...Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0List 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`.paddingslegacy optimization attribute.consumed_inputsNew shapeNumber of repeated copies to make of the input tensor.tiles'axis' must be in [-rank(indices)-1, rank(indices)]List of non-negative integers, indicate the dimensions to be insertedScalar 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.(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.Which axis to split on. 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.Invalid value of attribute 'axis'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.Attribute value for pads is requiredAttribute pads has incorrect lengthThe output of each pooling window is divided by the number of elements exclude pad.D:\30\s\cmake\external\onnx\onnx\defs\nn\old.cc +D:\4\s\cmake\external\onnx\onnx\defs\object_detection\old.ccconsumed_inputslegacy optimization attribute.tilesNumber of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Axis along which to repeat.Which axis to split onpaddingsList 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`.One float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgeNew shapeD:\4\s\cmake\external\onnx\onnx\defs\tensor\old.ccrank must be greater than axisWhich axis to concat on. Default value is 1.Which axis to concat onoutputs...Optional list of output lengths (see also arg 'split')Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.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]List of non-negative integers, indicate the dimensions to squeeze.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 be insertedIncorrect or missing attribute value for starts and endsAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Attribute axes has incorrect lengthEnding indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]) is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.Number of elements of attribute 'scales' must be same as rank of input 'X'Ranks inferred (Attribute 'scales' is required.Attribute 'scales' must have floats type.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'.The scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.The scale along height dimension. It takes value greater than or equal to 1.width_scaleTwo interpolation modes: nearest(default), bilinearheight_scale4-D tensor after resizing, [N,C,H,W]4-D tensor, [N,C,H,W]Which axis to split on. Attribute pads has incorrect lengthAttribute value for pads is requiredOne float, indicates the value to be filled.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`.'axis' must be in [-rank(indices)-1, rank(indices)]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.(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.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.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.Invalid value of attribute 'axis'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.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The epsilon value to use to avoid division by zero, default is 1e-5f.The scale as a 1-dimensional tensor of size C to be applied to the output.The input 4-dimensional tensor of shape NCHW.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The bias as a 1-dimensional tensor of size C to be applied to the output.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.p value of the Lp norm used to pool over the input data, default is 2.0.dilation value along each spatial axis of the filter.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The output of each pooling window is divided by the number of elements exclude pad.Stride along each spatial axis.Dilation value along each spatial axis of filter.D:\4\s\cmake\external\onnx\onnx\defs\nn\old.ccauto_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. {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: @@ -5731,24 +6358,24 @@ 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.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.Dilation value along each spatial axis of filter.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.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.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1dilation 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.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.If 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 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.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 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 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.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.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.(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<float>) and produces two Tensor outputs, -output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in -test mode or not, the output Y will either be a random dropout, or a simple -copy of the input. Note that our implementation of Dropout does scaling in -the training phase, so during testing nothing needs to be done. -(float, default 0.5) the ratio of random dropoutIndicate 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). + 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 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 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).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).(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropoutThe output mask. If is_test is nonzero, this output is not filled.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The output tensor of the same shape as X.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) - The output tensor of the same shape as X.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 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 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).Slope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of alpha default to 0.21-D input tensorList of tensors for Max.Output tensor. Same dimension as inputs.Value of beta default to 0.5List of tensors for Sum.List of tensors for MinMinimum value, under which element is replaced by minList of tensors for Mean.Input tensor AInput tensor BMaximum value, above which element is replaced by maxScalar 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.Input tensor C, can be inplace.Whether C should be broadcastedInvalid value for attribute kNumber of top elements to retrieveInput tensor CDimension on which to do the sort. + The output 4-dimensional tensor of the same shape as X.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing. +Dropout takes one input data (Tensor<float>) and produces two Tensor outputs, +output (Tensor<float>) and mask (Tensor<bool>). Depending on whether it is in +test mode or not, the output Y will either be a random dropout, or a simple +copy of the input. Note that our implementation of Dropout does scaling in +the training phase, so during testing nothing needs to be done. +The output 4-dimensional tensor of the same shape as input.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor<float>) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. @@ -5779,39 +6406,43 @@ 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. - +D:\4\s\cmake\external\onnx\onnx\defs\math\old.ccPass 1 to enable broadcasting Performs element-wise binary {name} (with limited broadcast support). -{broadcast_doc}Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeD:\30\s\cmake\external\onnx\onnx\defs\math\old.ccIf set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Pass 1 to enable broadcastingbroadcastInput tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.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 ACoefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.D:\30\s\cmake\external\onnx\onnx\defs\controlflow\old.ccOptional 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).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.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.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.Attribute 'value' of Constant node must exist with 'Tensor' data.D:\30\s\cmake\external\onnx\onnx\defs\generator\old.ccThe input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Number of neurons in the hidden layerFirst input tensor must have rank 3Constrain seq_lens to integer tensor.The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_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]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. -Computes an one-layer simple RNN. This operator is usually supported -via some custom implementation such as CuDNN. +{broadcast_doc}If set, defines the broadcast dimensions. See doc for details.broadcastMaximum value, above which element is replaced by maxMinimum value, under which element is replaced by minInput tensor AInput tensor C, can be inplace.Input tensor BScalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedOutput tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.1-D input tensorCoefficient of ELU default to 1.0.Slope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of beta default to 0.5Value of alpha default to 0.2List of tensors for Max.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.First operand, should share the type with the second operand.Input tensor of any shape, base of the exponent.Result, has same dimensions and type as ACoefficient of leakage default to 0.01.Input tensor of any shape broadcastable to X shape, the exponent component.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Input tensor CScalar multiplier for input tensor C, the default value is 1.0.Invalid value for attribute kDimension on which to do the sort.Number of top elements to retrieveOptional 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).D:\4\s\cmake\external\onnx\onnx\defs\controlflow\old.ccAn 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.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.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.Attribute 'value' of Constant node must exist with 'Tensor' data.D:\4\s\cmake\external\onnx\onnx\defs\generator\old.ccThe recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.D:\4\s\cmake\external\onnx\onnx\defs\rnn\defs.ccThe 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.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. +Computes an one-layer GRU. This operator is usually supported via some custom +implementation such as CuDNN. Notations: `X` - input tensor -`i` - input gate +`z` - update gate +`r` - reset gate + +`h` - hidden gate + `t` - time step (t-1 means previous time step) -`Wi` - W parameter weight matrix for input gate +`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates -`Ri` - R recurrence weight matrix for input gate +`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates -`Wbi` - W parameter bias vector for input gate +`Wb[zrh]` - W bias vectors for update, reset, and hidden gates -`Rbi` - R parameter bias vector for input gate +`Rb[zrh]` - R bias vectors for update, reset, and hidden gates -`WBi` - W parameter weight matrix for backward input gate +`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates -`RBi` - R recurrence weight matrix for backward input gate +`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates -`WBbi` - WR bias vectors for backward input gate +`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates -`RBbi` - RR bias vectors for backward input gate +`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 @@ -5839,44 +6470,48 @@ Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) -Equations (Default: f=Tanh): +Equations (Default: f=Sigmoid, g=Tanh): - - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) -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. -Computes an one-layer GRU. This operator is usually supported via some custom -implementation such as CuDNN. + - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) -Notations: + - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) -`X` - input tensor + - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 -`z` - update gate + - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 -`r` - reset gate + - Ht = (1 - zt) (.) ht + zt (.) Ht-1 +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]`.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Number of neurons in the hidden layerA tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_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]`.Constrain seq_lens to integer tensor.The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. +Computes an one-layer simple RNN. This operator is usually supported +via some custom implementation such as CuDNN. -`h` - hidden gate +Notations: +`X` - input tensor + +`i` - input gate + `t` - time step (t-1 means previous time step) -`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates +`Wi` - W parameter weight matrix for input gate -`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates +`Ri` - R recurrence weight matrix for input gate -`Wb[zrh]` - W bias vectors for update, reset, and hidden gates +`Wbi` - W parameter bias vector for input gate -`Rb[zrh]` - R bias vectors for update, reset, and hidden gates +`Rbi` - R parameter bias vector for input gate -`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates +`WBi` - W parameter weight matrix for backward input gate -`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates +`RBi` - R recurrence weight matrix for backward input gate -`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates +`WBbi` - WR bias vectors for backward input gate -`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates +`RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 @@ -5904,22 +6539,14 @@ Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) -Equations (Default: f=Sigmoid, g=Tanh): +Equations (Default: f=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 -D:\30\s\cmake\external\onnx\onnx\defs\rnn\defs.ccThe bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.The 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 0The 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 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]`.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.Couple the input and forget gates if 1. + - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) +First input tensor must have rank 3The 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 0The 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]`.Couple the input and forget gates if 1. Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: @@ -5996,52 +6623,104 @@ - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) - +Enable broadcasting Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. -Right input tensor for the logical operator.Left input tensor for the logical operator.If set, defines the broadcast dimensions.Enable broadcastingConstrains input to integral tensors.Constrains input to float tensors.D:\30\s\cmake\external\onnx\onnx\defs\logical\old.ccfowardA 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.The sequence output for the hidden is optional if 0. Default 0.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.D:\30\s\cmake\external\onnx\onnx\defs\rnn\old.ccD:\30\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices. +Left input tensor for the logical operator.If set, defines the broadcast dimensions.D:\4\s\cmake\external\onnx\onnx\defs\logical\old.ccRight input tensor for the logical operator.Constrains input to integral tensors.Constrains input to float tensors.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.D:\4\s\cmake\external\onnx\onnx\defs\rnn\old.ccfowardThe sequence output for the hidden is optional if 0. Default 0.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. 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.sparse_tensor(map(seq(tensor(sparse_tensoropaquemapseqdoublebfloat16float16floatint64int32int16int8uint64uint32uint16uint8boolcomplex128complex64STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field.TensorProto (tensor name: Unrecognized data_type (tensor name: values of data_type '' should be stored in field '' instead of '] out of range [0, Sparse tensor indices () has values, but NNZ is ) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () index value at position [sparse_tensor_proto] not in lexicographic sorted order.] out of range.) second dimension size does not match rank of tensor.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values () must have rank 1.attr) has no index values.) must have rank 1 or 2.) must have INT64 type.) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.type field and data field mismatch in attribute is required to be non-empty. +The type of the output tensor is integer.D:\4\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices.int16int32doubleint8uint16uint32int64uint8mapopaquesparse_tensor(seqfloat16bfloat16sparse_tensorfloatseq(map(tensor(complex128booluint64complex64) should be stored in ) is stored externally but doesn't have a location.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.TensorProto (tensor name: ) should contain one and only one value field.TensorProto ( tensor name: ) is 0-element but contains data!string_dataint64_datafloat_dataint32_datauint64_data) is stored externally and should not have data field.raw_datadouble_datavalue_type): elem_typekey_type) to UNDEFINED is not allowedsetting data_type field (tensor name: Unrecognized type value case (value_info name: data_typevalue_info' of -==> Context: Field 'name' of value_infokey_typeelem_typetype is required but missing.data_typeUnrecognized type value case (value_info name: ): value_typeint32_datafloat_datasetting data_type field (tensor name: ) to UNDEFINED is not alloweddouble_dataraw_dataint64_datastring_data, but it doesn't exist or is not accessible.Data of TensorProto ( tensor name: ) is stored externally and should not have data field.uint64_data) is 0-element but contains data!TensorProto ( tensor name: ) is stored externally but doesn't have a location.) should be stored in , type: ) has zero input and zero output.op_typenodeas possible.ConstantFillATenNodeProto (name: Warning: was a removed experimental ops. In the future, we may directly reject this operator. Please update your model as soon is deprecated in domain_version of No Op registered for with domain_version of No opset import for domain 'Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.graphOp registered for Nodes in a graph must be topologically sorted, however input '' of node: - - is not output of any previous nodes. in initializer but not in graph input' has been used as output names multiple times.Bad node spec: Inferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (type mismatch. existing=type case unsupported. existing=type case mismatch. existing= inferred= were provided(op_type:, name:no nameGraph input # is tensor type, but provided type is Graph has inputs but input is out of bounds) vs ( does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.output Dimension value inferred () is not equal to the existing dim value (Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'onnx.AttributeProtoonnx.ModelProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.TensorProtoonnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Maponnx.FunctionProtoonnx.OperatorSetIdProtoINFO%d%uWARNINGERRORFATAL[libprotobuf %s %s:%d] %s -%I64uAAAACHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.D:\30\s\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_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): CHECK failed: (count) <= (target_->size()): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): (cannot determine missing fields for lite message)D:\30\s\cmake\external\protobuf\src\google\protobuf\message_lite.ccCHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): Can't message of type "" because it is missing required fields: parse 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.CHECK failed: !coded_out.HadError(): Exceeded maximum protobuf size of 2GB: CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): D:\30\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccNot implemented field number with type D:\30\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): D:\30\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits<size_t>::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): D:\30\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\30\s\cmake\external\protobuf\src\google\protobuf\io\coded_stream.ccA protocol message was rejected because it was too big (more than CHECK failed: (buffer_size) >= (0): bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.D:\30\s\cmake\external\protobuf\src\google\protobuf\wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): SearchBitState inconsistencySearchNFA inconsistencybytemap range DFA out of memory: size SearchDFA inconsistencySearchOnePass inconsistencyError parsing 'Error compiling 'pattern too large - compile failedError reverse compiling '...Unknown encoding D:\30\s\cmake\external\re2\re2\re2.cc': endpos: startpos: RE2: invalid startpos, endpos pair. [list count pattern too large - reverse compile failedInvalid RE2: text size: invalid character class rangemissing ]missing )trailing \no errorunexpected errorinvalid escape sequenceinvalid character classinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledno argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorStack not empty.D:\30\s\cmake\external\re2\re2/walker-inl.hWalk NULLRegexp not destroyed.D:\30\s\cmake\external\re2\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: AAAAhAAAAhAAAAA ANo ranges in char classMissing case in Compiler: should never happenD:\30\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!Concat of unknown round: RE2: unexpected op: Bad hex digit AddFoldedRange recurses too much.D:\30\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledAnyBad call to ParseState::ParsePerlFlagsUnhandled in stepunhandled in AddToThreadqD:\30\s\cmake\external\re2\re2\nfa.ccUnexpected opcode in short circuit: context does not contain textBad args: nsubmatch= in ComputeFirstByte|D:\30\s\cmake\external\re2\re2\dfa.ccNULL state in RunStateOnByteDeadState in RunStateOnByteunhandled opcode: RunStateOnByteUnlocked failed after ResetCacheStateSaver failed to restore state.Unexpected special state in RunStateOnByteRunStateOnByteUnlocked failed after ResetFailed to analyze start state.GrowStack() failed: njob_ = arg D:\30\s\cmake\external\re2\re2\bitstate.ccjob_.size() = Bad arg in kInstAltMatch: Unexpected opcode: Bad arg in kInstCapture: D:\30\s\cmake\external\re2\re2\onepass.ccCannot use SearchOnePass for unanchored matches.D:\30\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: D:\30\s\cmake\external\re2\re2\simplify.ccCoalesceWalker::ShortVisit calledCase not handled in ComputeSimple: Malformed repeat DoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is Simplify case not handled: SimplifyWalker::ShortVisit calledPA4ALA4AXAATAAAAAAA8AA8ApAA`AAAXAAXAA AA A8AA(AAA0AA0AAAAAA,AA,AA(AA(AXAHAHAHAxA`AhA`A AAAA@AA0AAAAAA[:^xdigit:][:xdigit:][:^upper:][:upper:][:^word:][:word:]\D\d\S\s[:^alpha:][:alpha:][:^ascii:][:ascii:]\W\w[:^alnum:][:alnum:][:^digit:][:digit:][:^graph:][:graph:][:^blank:][:blank:][:^cntrl:][:cntrl:][:^punct:][:punct:][:^space:][:space:][:^lower:][:lower:][:^print:][:print:]AZaz09AFafAZ!~09AZ__az - 09AZ__az ~az090909AZaz!/:@[`{~ B(3BpBCBXBCBB@,B:B#xBB0BYB BlBDBkB,B8B-BBGBBIBBbBB8vBBWBBWBBPuBBLuB ?UB%BXB<BLByBpB:BlBWBBLB YBB(BBBBWBB`uBBB?BZGBLBDBB@uB0ByB(BHCBHBVB@B9BB(vBBXuBBmBB#B BWBBxB 8B`yB -(B<BPBBhABBlBBB!BBBB,BpB0MBBpxB xIBBBPB8VBHBDuBhBh3B`B@3B B`ABP<BBVBBprBNBBNBBPABBMBBBBPB XB`VB BHBkBpBBdBVB4BCB(BB@B0&B`[B<B09BB -BBkBB0CBByBB[BBpIBB@bB\$B"BkB6:BBmB'-BBpAB - BBRAB%B8|BBCBX?0QBJBXB@<BB$BBPB`BWBPBBxBQBhBXmB(B0BnWB8$BB@B$B0BxPBBkBB,BB@|BBXxBBP|B3BiB0,BBlBHABB9BB9BBMBBYBCB:B8yBBuB%WBBJBB:BBABHB <ByBDB@B>B&TBCBLBBBBHuBBxCB8BhPB B9BB-BB3BBVBBmBhBDB\BBB8ABxBIB@PvB TB0BCBBBBH<BBh:BB=BB OBHB:B -Bx9BBxBB9B BrB`<B0BpMBKBHVB|BCBB`PBh ->6ByBIBBBBBP>BXyBB,BBBBHyBB(>BBNB P[BBtB0yBLB0FB5:B HB#Bp@DB>`B,BPBUB(B6BB8uB8BB0B@>BBCBBmBB9BB8[BpB>BhBUBBVB|BVBLBBB DB,B`B<BXBhuB(BB B(VB8BCB4BCBxBIBpBPB(@@PBB0uBB,9BlBxBhBDBBengaliBatakBopomofoBhaiksukiBalineseAvestanBassa_VahBamumCarianCanadian_AboriginalCcCaucasian_AlbanianBrailleBrahmiBuhidBugineseCommonCoCsCopticChakmaCfCherokeeChamDograDevanagariEgyptian_HieroglyphsDuployanCypriotCuneiformDeseretCyrillicAnatolian_HieroglyphsAhomArmenianArabicAdlamLisuLinear_BLmLlLepchaLatinLinear_ALimbuMahajaniLydianMalayalamMakasarLtLoLycianLuMeMcMeetei_MayekMedefaidrinManichaeanMandaicMasaram_GondiMarchenModiMnMroMongolianMeroitic_CursiveMende_KikakuiMiaoMeroitic_HieroglyphsGranthaGothicGujaratiGreekEthiopicElbasanGlagoliticGeorgianHanunooHanifi_RohingyaHebrewHatranGurmukhiGunjala_GondiHangulHanJavaneseInscriptional_ParthianKannadaKaithiImperial_AramaicHiraganaInscriptional_PahlaviInheritedKhudawadiKhojkiLaoLKayah_LiKatakanaKhmerKharoshthiRejangPsalter_PahlaviSamaritanRunicPiPhoenicianPsPoSignWritingSiddhamSkSinhalaScSaurashtraShavianSharadaSundaneseSoyomboSyriacSyloti_NagriSoSmSora_SompengSogdianTakriTai_VietTangutTamilTagbanwaTagalogTai_ThamTai_LeNew_Tai_LueNdNkoNewaMyanmarMultaniNabataeanNOld_HungarianOl_ChikiOld_North_ArabianOld_ItalicNoNlOghamNushuOriyaOld_TurkicOsmanyaOsageOld_PersianOld_PermicOld_South_ArabianOld_SogdianPePdPhags_PaPfPalmyrenePahawh_HmongPcPau_Cin_HauTirhutaTifinaghVaiUgariticThaanaTeluguTibetanThaiZsZpYiWarang_CitiZlZanabazar_Square.0/011122`2~2`|װנ +==> Context: is required to be non-empty. is required but missing.typenameField 'type field and data field mismatch in attribute ) should not contain more than one value field.) has no index values.attrnodeop_typeAttribute (name: ) should refer to attribute in parent node.) must have rank 1.Sparse tensor values (] not in lexicographic sorted order.sparse_tensor_proto) must have INT64 type.) must have rank 1 or 2.) must have a dense-rank > 0) dimensions are not positive.) index value at position [Sparse tensor (Sparse tensor indices (] out of range [0, ) second dimension size does not match rank of tensor.] out of range.] not in sorted order.) first dimension size does not equal NNZ.' instead of '' should be stored in field ') should not be stored in raw_data fieldSTRING data (tensor name: values, but NNZ is ) has values of data_type 'Unrecognized data_type (tensor name: in initializer but not in graph input + is not output of any previous nodes.' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'Bad node spec: ' has been used as output names multiple times.' of node: +Nodes in a graph must be topologically sorted, however input 'No opset import for domain ' with domain_version of was a removed Warning: Op registered for graphNo Op registered for is deprecated in domain_version of NodeProto (name: ATen) has zero input and zero output., type: reject this operator. Please update your model as soon experimental ops. In the future, we may directly ConstantFillas possible.Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: ( does not contain a graph.) vs ( inferred=type case mismatch. existing=: (Inferred shape and existing shape differ in dimension output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of boundsinput is tensor type, but provided type is Graph input #no name, name:type case unsupported. existing=type mismatch. existing= inputs but Graph has (op_type: were providedNumber of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.) is not equal to the existing dim value (Dimension value inferred (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.onnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.ModelProtoonnx.StringStringEntryProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.TensorAnnotationonnx.GraphProtoonnx.AttributeProtoonnx.FunctionProtoonnx.TypeProtoonnx.OperatorSetIdProtoDFA out of memory: size SearchDFA inconsistencylist count bytemap range SearchNFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencyInvalid RE2: Error reverse compiling 'pattern too large - reverse compile failedstartpos: RE2: invalid startpos, endpos pair. [text size: endpos: Unknown encoding D:\4\s\cmake\external\re2\re2\re2.cc...Error compiling 'pattern too large - compile failed': Error parsing 'invalid repetition sizebad repetition operatortrailing \no argument for repetition operatorinvalid named capture groupNumCapturesWalker::ShortVisit calledinvalid perl operatorinvalid UTF-8unexpected errorinvalid escape sequenceUnexpected op in Regexp::Equal: no errormissing ]missing )invalid character classinvalid character class rangeD:\4\s\cmake\external\re2\re2\regexp.ccBad reference count Regexp not destroyed.Walk NULLStack not empty.D:\4\s\cmake\external\re2\re2/walker-inl.hG@GXGGGGGGGhGGG0GGshould never happenD:\4\s\cmake\external\re2\re2\compile.ccMissing case in Compiler: Compiler::Copy called!No ranges in char classRE2: unexpected op: Bad hex digit Concat of unknown round: Bad call to ParseState::ParsePerlFlagsAnyD:\4\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledAddFoldedRange recurses too much. in ComputeFirstBytecontext does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: in stepunhandled D:\4\s\cmake\external\re2\re2\nfa.ccUnhandled in AddToThreadqD:\4\s\cmake\external\re2\re2\dfa.ccunhandled opcode: |RunStateOnByteUnlocked failed after ResetFailed to analyze start state.Unexpected special state in RunStateOnByteStateSaver failed to restore state.DeadState in RunStateOnByteNULL state in RunStateOnByteRunStateOnByteUnlocked failed after ResetCachejob_.size() = D:\4\s\cmake\external\re2\re2\bitstate.cc arg njob_ = GrowStack() failed: Bad arg in kInstCapture: Unexpected opcode: Bad arg in kInstAltMatch: Cannot use SearchOnePass for unanchored matches.D:\4\s\cmake\external\re2\re2\onepass.ccD:\4\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is Case not handled in ComputeSimple: CoalesceWalker::ShortVisit calledMalformed repeat SimplifyWalker::ShortVisit calledSimplify case not handled: D:\4\s\cmake\external\re2\re2\simplify.cc'G@)G'G@)G'G(G'G(G'G(G|'G(G'G()G`'G()Gp'G)G'G)G'G$)G'G$)G'G(G'G(G'G(G&G(G&GD)G@'GD)GP'G(G 'G(G0'GH)GP(GH)G`(G(G0(G(G@(G(G(G(G(G8)Gp(G8)G(G )G(G )G(G(G'G(G'G)G (G)G[:^cntrl:][:digit:][:^blank:][:cntrl:][:^graph:][:lower:][:^digit:][:graph:][:^alnum:][:alpha:]\W[:alnum:][:^ascii:][:blank:][:^alpha:][:ascii:]\d\S\w\D\s[:^word:][:xdigit:][:^upper:][:word:][:^xdigit:][:^print:][:punct:][:^lower:][:print:][:^space:][:upper:][:^punct:][:space:] ~09AZ__az 09AZ__az!/:@[`{~!~ + 09AFafAZazAZ09AZaz 0909azKGoGKGPGKGhG LG@vGfG#LGPGKG`GLGxGPMGGuGXMGuG@MGpGHMGGxMG GMGGdMG`GpMGxGMGGMGGtEG|GLGfGLGXG(MG@hG<MGpGMGpG pG MG8GLGxGLGGLGG_GLGaGZGLLGxGLGGLG`GLGG@LGЀGPLG iG(LGG4LGGpLGGLG`SGXLGG`LG G NGG +NGfGGNGHaGNGG OG_G!^GOGGNGvGOGGxNGG ؍G|NG_G`NG0GpNGGNGXoGNGpoG NGPaGpfGNGhGNGG`GNGGMGXaGMGPGLNGGXNGG 0NGGHG@NGGMGPGMGGMGGMG^GMGvG@GMG`iGMG@uGMGG<PG8GHPGG(PGG4PGG\PGG\|G"`PGG6hGTPGG'oGXPG aG 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<.?AV<lambda_ea73e250586924fb71aef6e9068c74b3>@@ <.?AV<lambda_8ea5687524a42143533b684f5c9ce181>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_ea73e250586924fb71aef6e9068c74b3>@@XAEAUInferenceContext@onnx@@@std@@ <.?AV?$_Func_impl_no_alloc@V<lambda_8ea5687524a42143533b684f5c9ce181>@@XAEAUInferenceContext@onnx@@@std@@ <.?AV?$_Func_impl_no_alloc@V<lambda_f985edc73297b31100389b5211e3935e>@@XAEAVOpSchema@onnx@@@std@@ <.?AV<lambda_f985edc73297b31100389b5211e3935e>@@ <.?AV<lambda_a6f452e793f181bfdb21f8008d20a820>@@ <.?AV<lambda_71346983b6599c47894abb35bff4747e>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_a6f452e793f181bfdb21f8008d20a820>@@XAEAVOpSchema@onnx@@@std@@ <.?AV?$_Func_impl_no_alloc@V<lambda_9dc19add650df7a8dd3a2c037757be68>@@XAEAUInferenceContext@onnx@@@std@@ <.?AV?$_Func_impl_no_alloc@V<lambda_71346983b6599c47894abb35bff4747e>@@XAEAVOpSchema@onnx@@@std@@ <.?AV<lambda_9dc19add650df7a8dd3a2c037757be68>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_c41fc543d857db8b3ca85cea5cadd247>@@XAEAVOpSchema@onnx@@@std@@ <.?AV<lambda_c41fc543d857db8b3ca85cea5cadd247>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_87f27c7b79f82015223cdf844a99cbbd>@@XAEAVOpSchema@onnx@@@std@@ <.?AV<lambda_87f27c7b79f82015223cdf844a99cbbd>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_79584b85d4c850093e6a9c5e19df1d8c>@@XAEAVOpSchema@onnx@@@std@@ <.?AV?$_Func_impl_no_alloc@V<lambda_161d552c3d13498fbdff138ac6194313>@@XAEAUInferenceContext@onnx@@@std@@ <.?AV<lambda_d0004e4a62297b0a0f1590f82488548b>@@ <.?AV<lambda_161d552c3d13498fbdff138ac6194313>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_d0004e4a62297b0a0f1590f82488548b>@@XAEAUInferenceContext@onnx@@@std@@ <.?AV<lambda_3828882ef4b24f198f58c51e8a0e3d81>@@ <.?AV<lambda_79584b85d4c850093e6a9c5e19df1d8c>@@ <.?AV?$_Func_impl_no_alloc@V<lambda_3828882ef4b24f198f58c51e8a0e3d81>@@XAEAVOpSchema@onnx@@@std@@ <.?AV?$basic_ifstream@DU?$char_traits@D@std@@@std@@ <.?AUInferenceContextImpl@shape_inference@onnx@@ <.?AVGraphInferencerImpl@shape_inference@onnx@@ <.?AVTensorProto@onnx@@ <.?AVTensorShapeProto_Dimension@onnx@@ <.?AVModelProto@onnx@@ <.?AVTensorAnnotation@onnx@@ <.?AVTensorProto_Segment@onnx@@ <.?AVTypeProto_Map@onnx@@ <.?AVOperatorSetIdProto@onnx@@ <.?AVValueInfoProto@onnx@@ <.?AVTypeProto_Tensor@onnx@@ <.?AVStringStringEntryProto@onnx@@ <.?AVGraphProto@onnx@@ <.?AVTypeProto_Opaque@onnx@@ <.?AVTypeProto_Sequence@onnx@@ <.?AVSparseTensorProto@onnx@@ <.?AVTypeProto_SparseTensor@onnx@@ <.?AUContainer@?$InternalMetadataWithArenaBase@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@VInternalMetadataWithArenaLite@internal@protobuf@google@@@internal@protobuf@google@@ <.?AVNodeProto@onnx@@ <.?AVFunctionProto@onnx@@ <.?AVAttributeProto@onnx@@ <.?AVMessageLite@protobuf@google@@ <.?AVTensorShapeProto@onnx@@ <.?AVTypeProto@onnx@@ <.?AVFatalException@protobuf@google@@ <.?AVStringOutputStream@io@protobuf@google@@ <.?AVCopyingOutputStreamAdaptor@io@protobuf@google@@ 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