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舛HL$ 67HHK8Hu bEHH@fhEHL$ SHL$ 87HHK8HuaEHH@1hEHL$ HL$ :7HHK8HuaEHH@gEHL$ HL$ <7HHK8HumaEHH@gEHL$ 贚HL$ _7HHK8Hu8aEHH@gEHL$ HL$ a7HHK8HuaEHH@]gEHL$ JHL$ b7HHK8Hu`EHH@(gEHL$ HL$ d7HHK8Hu`EHH@fEHL$ HL$ &j7HHK8Hud`EHH@fEHL$ 諙HL$ !r7HHK8Hu/`EHH@fEHL$ vHL$ t7HHK8Hu_EHH@TfEHL$ AHL$ 7w7HHK8Hu_EHH@fEHL$ HL$ x7HHK8Hu_EHH@eEHL$ טHL$ mz7HHK8Hu[_EHH@eEHL$ 袘HL$ H7HHK8Hu&_EHH@eEHL$ mHL$ Ӑ7HHK8Hu^EHH@KeEHL$ 8HL$ N7HHK8Hu^EHH@eEHL$ HL$ ٶ7HHK8Hu^EHH@dEHL$ ΗHL$ Ĺ7HHK8HuR^EHH@dEHL$ 虗HL$ _Z8HHK8Hu^EHH@wdEHL$ dHL$ j6HHK8Hu]EHH@BdEHL$ /HL$ 6HHK8Hu]EHH@ dEHL$ HL$ 6HHK8Hu~]EHH@cEHL$ ŖHL$ 6HHK8HuI]EHH@cEHL$ 萖HL$ V6HHK8Hu]EHH@ncEHL$ [HL$ !X6HHK8Hu\EHH@9cEHL$ &HL$ \Y6HHK8Hu\EHH@cEHL$ HL$ Z6HHK8Huu\EHH@bEHL$ 輕HL$ [6HHK8Hu@\EHH@bEHL$ 臕HL$ ]6HHK8Hu \EHH@ebEHL$ RHL$ H^6HHK8Hu[EHH@0bEHL$ HL$ _6HHK8Hu[EHH@aEHL$ HL$ `6HHK8Hul[EHH@aEHL$ 賔HL$ a6HHK8Hu7[EHH@aEHL$ ~HL$ h6HHK8Hu[EHH@\aEHL$ IHL$ i6HHK8HuZEHH@'aEHL$ HL$ Zw5HHK8HuZEHH@`EHL$ ߓHz5HHK8HuaZEHH@`EH覓HL$ L5HHK8Hu*ZEHH@`EHL$ qH赈5HHK8HuYEHH@M`EH8HL$ ΋5HHK8HuYEHH@`EHL$ HW5HHK8HuYEHH@_EHʒHL$ 谛5HHK8HuNYEHH@_EHL$ 蕒H蹫5HHK8HuYEHH@q_EH\HL$ b5HHK8HuXEHH@:_EHL$ 'H 5HHK8HuXEHH@_EHHL$ D5HHK8HurXEHH@^EHL$ 蹑Hݽ5HHK8Hu;XEHH@^EH耑HL$ F5HHK8HuXEHH@^^EHL$ KH5HHK8HuWEHH@'^EHHL$ 5HHK8HuWEHH@]EHL$ ݐHQ5HHK8Hu_WEHH@]EH褐HL$ 5HHK8Hu(WEHH@]EHL$ oH5HHK8HuVEHH@K]EH6HL$ 5HHK8HuVEHH@]EHL$ Hu5HHK8HuVEHH@\EHȏHL$ n5HHK8HuLVEHH@\EHL$ 蓏H6HHK8HuVEHH@o\EHZHL$ P 6HHK8HuUEHH@8\EHL$ %H96HHK8HuUEHH@\EHHL$ !6HHK8HupUEHH@[EHL$ 跎Hk75HHK8Hu9UEHH@[EH~HL$ :5HHK8HuUEHH@\[EHL$ IH=5HHK8HuTEHH@%[EHHL$ 5HHK8HuTEHH@ZEHL$ ۍHK8HtHH;H@ ZEHC8H$H]H\$HL$UH$HHHL$ 評4HHK8HuTEHH@aZEHL$ NHL$ 蔗4HHK8HuSEHH@,ZEHL$ HL$ _4HHK8HuSEHH@YEHL$ HL$ *4HHK8HuhSEHH@YEHL$ 诌HL$ 4HHK8Hu3SEHH@YEHL$ zHL$ 蠣4HHK8HuREHH@XYEHL$ EHL$ k4HHK8HuREHH@#YEHL$ HL$ F4HHK8HuREHH@XEHL$ ۋHL$ 4HHK8Hu_REHH@XEHL$ 見HL$ ̸4HHK8Hu*REHH@XEHL$ qHL$ 74HHK8HuQEHH@OXEHL$ HD$0Dt$ LLD$0HH%H>HtHt2H?HH$H@A_A^_^]H\$ VWAVH0IHHT$ LD$ MHA(Lq ML;tSA{I9zu>AK9Ju5AK9Ju,HRMKL+DB D+uH…uEt IL;uI%I;FtL 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?`:?0?? :?0????0p?p?:`?0??0:?0???p?0??:?0??:?0????0??`:?0P?P?@?0?0??:?0??:?0??`:?0`?`?P?@?00?0?0: ?00?0?: ?0??:?0P?P?:@?0??0:?0??:?00?0?: ?0p?p?:`?00?0? ??0p?p?:`?0P?P?@?0?0????0??p?`?0??:?0 ? ???0P?P?:@?0??p:?0??:?0????0p?p?`:`?0??:?0????0??0:?0??:?0????0P@P@:@@00@0@p@`@0@@ @ @00@0@ @@0`@`@:P@0@@@p@0`@`@P@@@0@@@@0@@@@00@0@: @0@!@@!@ @p @0p!@p!@`!@P!@0`@`@P@@@0@@@@0@@Е@@0@@@Ж@0@@@p@0@@@@0@@@@00@0@ @@0@@)=@0@@)=г@0@@@@0@@@@0p@p@@@0@@@@00@0@0g5 @0@@Pf5@0`@`@P@@@0 @ @@@0P@P@@@0@0aAbA bAbAcAcApdAfA@Ay8BUBAA=B4NBKBлA>BEB B2BAA"B40.B*BлA#B'BAB`APA`B4 B BлA`B`B2B07BAмAp3B46B6BлA3B 5BBpBAнAnB4rB`qBлAnBppBBBApAB4B`BлABBUB]B AAWB4\B[BлA@XB`ZB`BpBAAB4B`BлABBAA AAA4 APAлAAAsBxBAA uB4wBwBлApuBvBBBAAB4BBлA@BBЎBB@A0AB4BpBлABBAA@A0AA4A0AлA@AAyB~BAAzB40~B}BлA {B|BgBmB`APAiB4PlBkBлAPiBjBAAAAA4A0AлAPA0AmBrBApAnB4rB`qBлAnBppBApAAAA4pA0AлA@AA^BgB@A0A`B4eB`dBлAPaBPcBPB B AA B4B BлAB`B@B0BPB@BB4B@BлABB`CC(C(CC*C(C(C(CPvD4C0ICPJCpHCICZCpWCWCWCPvD*CjCkC(CkCCCCC0CPCpCCEDODODFDODEDWDGDFDGDEDWDWDWDPvD`C`[D^D(CUnknown exceptionbad array new lengthstring too longC:\a\5\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\a\5\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 longC:\a\5\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.C:\a\5\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\a\5\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 %I64uMKMKMKMKC:\a\5\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xty1invalid string position...\\?\\/INVALID_ARGUMENT:C:\a\5\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t., C:\a\5\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: Cpuai.onnx.mlai.onnx.trainingai.onnx.preview.training!=C:\a\5\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetC:\a\5\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()VIWEFCPUExecutionProviderInteger overflowSafeIntExceptionHandler::SafeIntOnOverflowC:\a\5\s\onnxruntime\core/common/safeint.hORT_LOAD_CONFIG_FROM_MODELTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeC:\a\5\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Gettried creating tensor with negative value in shapesize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryonnxruntime_profile_input name cannot be emptyoutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.input array is too shortUnsupported 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()OrtCreateValueImplSeqHelperC:\a\5\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::GetOpaqueValuethis API does not support stringslocation dimensions do not match shape sizeinvalid location range1.5.1T Tensor type mismatch. utils::IsPrimitiveDataType(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::allocator > > >::checkC:\a\5\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscript != onnxruntime::DataTypeImpl::GetType() == type_len >= 0 && static_cast(len) < std::numeric_limits::max()OrtCreateMapMLValueEncountered nullptr.SKnot implementedgraph_optimization_level is not validexecution_mode is not validWARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads. unknown is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'C:\a\5\s\onnxruntime\core\session\custom_ops.cconnxruntime::CreateCustomRegistryANo requested allocator availableEnv is nullReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.OrtMemoryInfo is nullOnly CPU devices are supported for now.env_ptr == p_instance_OrtEnv::ReleaseC:\a\5\s\onnxruntime\core\session\ort_env.cconnxruntime::IOBinding::BindInputC:\a\5\s\onnxruntime\core\session\IOBinding.ccp_providerbad castonnxruntime::logging::LoggingManager::DefaultLoggerC:\a\5\s\include\onnxruntime\core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.session.load_model_formatsession.save_model_formatsession.use_env_allocators]ORTMCUDAExecutionProviderDmlExecutionProvideronnxruntime::ExecutionProviders::AddC:\a\5\s\onnxruntime\core/framework/execution_providers.h has already been registered.Provider onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\a\5\s\onnxruntime\core/framework/feeds_fetches_manager.hTrying to get a SparseTensor, but got: IsSparseTensor()Release_State_Compute_Create_State_MemcpyTransformer] [json.exception.other_error1.4.0The environment variable contained the value: %Y-%m-%d_%H-%M-%SReading the provided model for the ORT configonnxruntime::FinalizeSessionOptions are '0' and '1'. The only supported values for the environment variable is_model_proto_parsedCould not finalize session options while constructing the inference session. Error Message: C:\a\5\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itgraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truestatus.IsOK()onnxruntime::InferenceSession::ConstructorCommonUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.intra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionsession_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerresultError during EndProfiling(): Could not parse model successfully while constructing the inference sessionst.IsOK()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec provideronnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()Execution providers must be registered before the session is initialized.So disabling it for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderParallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Parallel execution mode does not support the CUDA Execution Provider. onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryException during loading: onnxruntime::InferenceSession::SaveToOrtFormatUnknown exception in Load()Encountered unknown exception in Load()This session already contains a loaded model.onnxruntime::InferenceSession::LoadFailed to load model because protobuf parsing failed.model_loading_arrayORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto 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.model_loading_from_saved_proto( (onnxruntime::InferenceSession::TransformGraphCould not find an implementation for the node All nodes have been placed on [: [Node placements].onnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModel Provider: [Satisfied, but should not be: nullptr == fbs_session Serialized version info is null. Invalid ORT format model.This session has already been initialized.InferenceSession is null. Invalid ORT format model.The ORT format model version [Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) Satisfied, but should not be: fbs_ort_model_version == nullptr ] is not supported this build SessionState is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sess_state Missing Model. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_model onnxruntime::InferenceSession::Initialize::::operator ()Initializing session.Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()Model was not loaded.Session has already been initialized.onnxruntime::InferenceSession::InitializeModel was not loadedSession successfully initialized.Encountered unknown exception in Initialize()Adding default CPU execution provider.Serializing optimized 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. Expected: Got: session_initialization Please fix either the inputs or the model.Got invalid dimensions for input: index: Invalid rank for input: for the following indices elements, but feeds has Size mismatch: feed_names has Unexpected input data type elements.Input with name: onnxruntime::InferenceSession::ValidateInputsInvalid Feed Input Name: is not expected to be of type tensor.Output vector pointer is NULLAt least one output should be requested. is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Invalid Output Name:Session was not initializedp_fetches->size(): Output vector incorrectly sized: output_names.size(): Running with tag: Encountered unknown exception in Run()onnxruntime::InferenceSession::RunSession not initialized.onnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersmodel_runonnxruntime::InferenceSession::GetModelMetadataProfiler is disabled.onnxruntime::InferenceSession::EndProfilingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingCould not write a profile because no model was loaded.onnxruntime::InferenceSession::CreateLoggerForRunInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggerInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::AddPredefinedTransformersExceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel/961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_Load model from onnxruntime::LoadOrtModelBytesmodel_loading_uri failed:list too long bytes were able to be read. failed. Only localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringcom.microsoft.mlfeaturizerscom.microsoft.nchwccom.microsoft[TypeInferenceError] Element type of input unknownInput expected to have tensor type expected to have type but instead is null expected to have sequence typeOutput tensor(int32)tensor(uint64)tensor(uint32)tensor(double)tensor(float)tensor(float16)tensor(int64)tensor(int8)tensor(uint16)tensor(uint8)tensor(bfloat16)tensor(int16)tensor(complex128)tensor(complex64)tensor(bool)tensor(string) version: ) from file , but it is already registered from file line , but it its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: , but it its version is not in the inclusive range [inter-opException caught: Allocator with this OrtMemoryInfo is already registered.inputTMemcpyFromHostC:\a\5\s\onnxruntime\core\session\environment.ccConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.YoutputXMemcpyToHost: value'parse_errorparse errorinvalid_iterator at line , column session_optionsort_configout_of_rangetype_errorSetting intra_op_num_threads to C:\a\5\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Unsupported execution_mode value in ORT config: Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting execution_mode to Parallel modeSequential modeonnxruntime::SetExecutionModeSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to The Model Proto hasn't been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelintra_op_num_threads option in the model file must be an integerintra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelexecution_mode option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsenable_profiling option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelkey '' not foundIgnoring unsupported session option in ORT config: cannot use at() with cannot use key() for non-object iteratorsnullarrayobjectinvalid map keynumberdiscardedbooleanstringcannot get valuecannot compare iterators of different containerssyntax error unexpected ; last read: '- while parsing invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if given; expected null literalfalse literaltrue literal'{''['number literalstring literal','':''}'']'unknown token'[', '{', or a literalend of inputinvalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid number; expected digit after '-'invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Bvector too longinvalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Fnumber overflow parsing 'object separatorobject keycannot use erase() with iterator out of rangeiterator does not fit current valuetype must be number, but is Max:C:\a\5\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got:onnxruntime::GraphTransformer::RecurseCastcast node to cast from float16 to float32 on cpuInserted_CastC:\a\5\s\include\onnxruntime\core/optimizer/graph_transformer.hC:\a\5\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::RemoveDuplicateCastTransformer::ApplyImplRemoveDuplicateCastTransformertodtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtypeonnxruntime::InsertCastTransformer::ApplyImplACLExecutionProviderAttentionFusionCommonSubexpressionEliminationCastEliminationBiasGeluFusionConvActivationFusionConvMulFusionConvBNFusionConvConvAddFusionEmbedLayerNormFusionDynamicQuantizeMatMulFusionDropoutEliminateDropoutGeluApproximationFastGeluFusionExpandExpandEliminationIdentityEliminateIdentityGemmActivationFusionGeluFusionNchwcTransformerMatMulScaleFusionMatMulAddFusionLayerNormFusionShapeToInitializerReshapeFusionReluFuseReluClipSliceEliminateSliceSkipLayerNormFusionShapeLevel_RuleBasedTransformerUnsqueezeUnsqueezeEliminationUnsupported level C:\a\5\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelonnxruntime::optimizer_utils::GenerateTransformersOpenVINOExecutionProviderNupharExecutionProviderVitisAIExecutionProviderTensorrtExecutionProviderDnnlExecutionProviderNGRAPHExecutionProvideronnxruntime::Node::ForEachWithIndexNnapiExecutionProviderArmNNExecutionProvideronnxruntime::MemcpyTransformer::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'Memcpyonnxruntime::GraphTransformer::ApplyC:\a\5\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersC:\a\5\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::Initializer::InitializerC:\a\5\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: Could not find OrtValue with name 'C:\a\5\s\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold Could not find a CPU kernel and hence ConstantFoldingonnxruntime::ConstantFolding::ApplyImplfetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of C:\a\5\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccDivdiv_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeConstant initializer NodeArg shape should not be null. NodeArg: shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializeroutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesMatMulTransposeMatMulMulmul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergeshas_value_C:\a\5\s\include\onnxruntime\core/common/optional.honnxruntime::optional::valuealphaFused MatMul and Scale_FusedMatMulAndScaleonnxruntime::MatMulScaleFusion::ApplyImplUnsupported data type: onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Getonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Slice ends is less than INT_MAXSlice does not have enough number of inputsSlice parameter is not expected. Input index:Expected value:input_indices.size() == expected_values.size() && input_indices.size() > 0C:\a\5\s\onnxruntime\core/optimizer/attention_fusion_helper.honnxruntime::AttentionFusionHelper::CheckSliceParametersSqueezeConcatInput of reshape_before_gemm is not the input of subgraphFaild to match gemm pathonnxruntime::AttentionFusionHelper::MatchGemmSubgraphStart MatchGemmSubgraphGemmReshapeconcat first input value is not -1CheckSliceParameters return falseconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsGemm does not have 3 inputsOutput edge count not expected for nodes in gemm pathGemm bias shape not expectedGemm bias is not constantunsqueeze_after_gather axes value not expectedaxesgather axis value not expectedaxisGatherconcat_after_gather input 2 does not have expected valueOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathGemm bias shape is not expectedGemm bias is not constant initializerGemm weight shape is not expectedGemm weight is not constant initializerPass MatchGemmSubgraphgather input 1 value is not expectedonnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerSubFaild to match the path (Div-->Where-->Add) for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskFaild to match path 1 for unidirectional maskStart MatchUnidirMaskSubgraphPass ValidateGemmInitializerWhereonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskCheckSliceParameters returns false for last_sliceDiv and Shape1 does not have edgeCheckSliceParameters returns false for slice1CheckSliceParameters returns false for mask_sliceStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskSoftmax attribute axis is expected to be 3Output edge count not expected for mask nodesmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Output edge count not expected for SoftmaxFailed to find Softmax nodeFailed to find path for maskAddFailed to find mask pathEqualFailed to find shape pathwhere const not matched.mask_mul const input not matchedmask_sub const input not matchedStart MatchInputMaskSubgraphDistilBertPass MatchInputMaskSubgraphStart MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertTransposeonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find reshape shape path 1equal const not matched.gather indices not matched.Failed to find reshape shape path 2past_k_transpose perm attribute not matchedpermpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedFailed to find path for present_kFailed to find path for past_kFailed to match v_concatFailed to find path for present_v and past_vPass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapepast_v_gather indices != 1present_v_unsqueeze axes value not expectedpast_v_gather and past_k_gather does not have same past inputpast_k_gather indices != 0v_reshape initializer value is not expectedhidden_size != num_heads * head_sizereshape initializer value is not expectedPass CheckNodesInPathVOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3Pass CheckNodesInPathQq_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQqk_div const not matched.q_reshape const not matchedCast mask from int64 to int32onnxruntime::AttentionFusionHelper::FuseGptAttentionStart FuseGptAttentionk_reshape const not matchedk_transpose perm attribute not matchedMask_Int32Pass CheckNodesInPathKMatchUnidirMaskSubgraph returns NULLMatchInputMaskSubgraph returns falseq and v are not from same Split nodeFailed to find path for qSplitFaild to find path to qkv_matmulCheckNodesInPathV return falseFaild to find path v to SplitFused Attention subgraphs MatchPastSubgraph returns falsenum_headsAttentionFailed to find path for kCheckNodesInPathQ returns falseCheckNodesInPathK returns falsek and v are not from same Split nodeonnxruntime::ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32C:\a\5\s\onnxruntime\core\optimizer\attention_fusion.ccFused an attention node for GPT.unidirectionalqkv_biasqkv_weightsq root should be layer normalizationonnxruntime::FuseSubGraphQKImplk root is not layer normq_matmul and q_add shape not matchedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplTotal fused Attention node count: shape of layer norm bias tensor not expectedFused an attention node.onnxruntime::FuseSubGraphQKFaild to find path vonnxruntime::FuseSubGraphQKDistilBertFailed to load Q, K and V weights, or data type is not float or float16.k_matmul and k_add shape not matchedFailed to convert mask to int32Failed to load Q, K and V bias tensors, or data type is not float or float16.Output edge count not expected for Add or MatMul in path vonnxruntime::AttentionFusion::FuseSubGraphFailed in match input mask subgraphFailed in match v_matmul and v_add input shapedata type is not supportedonnxruntime::ConvBNFusion::Applybn_scale_tensor_protobn_B_tensor_protoC:\a\5\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::Initializer::ToProtoepsilonConvBnFusion_BN_B_ConvBnFusion_W_BatchNormalizationbn_var_tensor_protobn_mean_tensor_protoconv_B_tensor_protoconv_W_tensor_protoonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of ClipC:\a\5\s\onnxruntime\core\optimizer\conv_activation_fusion.ccLeakyReluTanhfused Conv with activation maxminSigmoidonnxruntime::ConvActivationFusion::ApplyImplfused FusedConvactivation_paramsactivationUnsqueezeElimination_onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node C:\a\5\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccis not supported.data type FuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of C:\a\5\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyGeluBiasGeluC:\a\5\s\onnxruntime\core\optimizer\gelu_approximation.cconnxruntime::GeluApproximation::ApplyImplFastGeluGelu approximationTotal Gelu Approximation (FastGelu) node count: Fused reshape node: C:\a\5\s\onnxruntime\core\optimizer\reshape_fusion.ccCannot replace concat node with initializer:Total fused reshape node count: onnxruntime::ReshapeFusion::ApplyImplonnxruntime::ReshapeFusion::Fuse_Subgraphonnxruntime::CheckInputC:\a\5\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_CastInput shape is unknown or not 2D, or data type unknown_Int32Cast Input from int64 to int32Output 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 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Input data type is not int32 or int64Failed to find path 1 of position shape.Output edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherConstantOfShapeRangetwo paths share the same shapeNonZeroSecond 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.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Second input of Gather should be a constant with value 1. Failed to match Shape node. The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Word embedding shape not expected.onnxruntime::FuseSubGraphfused EmbedLayerNorm subgraphs EmbedLayerNormalizationposition_embeddingsmask_indexSegment id is not valid. Input_ids and segment id should have the same shape. Position embedding shape is not expected.Input id is not valid. Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape not matched.Position embedding data type shall be float or float16.onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplGamma should be of shape (hidden_size). Beta should be of shape (hidden_size). ̼+MatMulIntegerToFloatnullptr != fused_nodeDynamicQuantizeLinearDynamicQuantizeMatMulC:\a\5\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegeronnxruntime::DynamicQuantizeMatMulFusion::ApplyImplstartsendsConflicting free dimension overrides.which does not equal the specified override of onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerC:\a\5\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccFreeDimensionOverrideTransformerInvalid free dimension override.The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplwith a fixed dimension size ' C:\a\5\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add onnxruntime::MatMulAddFusion::ApplyImplSoftplusSoftsignHardSigmoidSeluEluFusedGemmactivation_C:\a\5\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused Gemm ThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplScaledTanhParametricSoftplusConvMulFusion_Mul_B_ConvMulFusion_W_mul_B_tensor_protoC:\a\5\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplySkipLayerNormalizationfused SkipLayerNorm subgraphs C:\a\5\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cconnxruntime::SkipLayerNormFusion::ApplyImpl̼+stridespadsReorderInputreordergroupVALIDSAME_LOWERSAME_UPPERNOTSETauto_padkernel_shapedilationschannels_lastchannelsReorderOutput_bn_nchwcspatial_nchwcscalesUpsamplefloornearest_modeasymmetriccoordinate_transformation_modenearestmodeC:\a\5\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImplGlobalAveragePoolGlobalMaxPoolResizeSumAveragePoolMaxPooldeque too long'7ReduceMeanC:\a\5\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplinvalid unordered_map keyfused LayerNorm subgraphs PowSqrtonnxruntime::CommonSubexpressionElimination::ApplyImplNot eliminating output of node [] because it's the graph's output.representative.output_index != kInvalidOutputIndexC:\a\5\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccC:\a\5\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::GeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs onnxruntime::BiasGeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccConvAddFusion_B_ConvAddFusion_Add_B_onnxruntime::ConvAddFusion::ApplyC:\a\5\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoonnxruntime::Initializer::ReadExternalRawDataC:\a\5\s\onnxruntime\core\optimizer\initializer.ccNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING model_path must not be empty. Ensure that a path is provided when the model is created or loaded.Satisfied, but should not be: model_path.IsEmpty() , external_data.length: Computed size: TensorProto external data size mismatch. External data type must not be UNDEFINED or STRING.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()C:\a\5\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=C:\a\5\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryC:\a\5\s\include\onnxruntime\core/framework/op_kernel.h is not in valid range [-axis >= -tensor_rank && axis <= tensor_rank - 1axis C:\a\5\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisx_zero_point must be null or 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimDequantizeLinearzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)T2T1QuantizeLinearx_zero_point must be null or a scalar or 1D tensor or size 1.onnxruntime::PrepareForQDQzero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size C:\a\5\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.cconnxruntime::OpKernelContext::InputMissing Input: helper.called_ < 2Check for duplicate types in MLTypeCallDispatcherhelper.called_ == 1onnxruntime::utils::MLTypeCallDispatcher::InvokeSigninvalid stol argumentstoi argument out of rangeinvalid stoul argumentstol argument out of rangeinvalid stoll argumentstoul argument out of rangeinvalid stoull argumentstoll argument out of rangeinvalid stoi argumentinvalid stof argumentstoull argument out of rangeinvalid stod argumentstof argument out of rangestod argument out of rangeonnxruntime::Cast::ComputeAttribute to is not set.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast::Castonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::InvokeUnsupported type in cast oponnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<__int64,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::InvokeNaNonnxruntime::`anonymous-namespace'::CastFromStringDataINF-INFonnxruntime::Tensor::MutableDataAsSpanonnxruntime::Tensor::DataAsSpanSizeinput count mismatchseed!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeX and mask should have the same shapeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)ratio input should have a single value.ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0f((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOPlease fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Output by onnxruntime::BroadcastIterator::InitC:\a\5\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestExpLogCeilReciprocalLessGreaterMaxMinCan broadcast 0 by 0 or 1. largest <= 1onnxruntime::BroadcastIterator::Append is invalid.NegFlooronnxruntime::Broadcaster::BroadcasterAbsC:\a\5\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeinput_count >= 1onnxruntime::Sum_6::ComputeinputCount >= 1onnxruntime::Min_6::Computedata_1.Shape() == shapedata_n.Shape() == shapeNotAndMeanBitShiftUnsupported X type: Must have 1 or more inputsOrXorAsinhAcoshSinhCoshfmodfmod must have value either 0 or 1AtanhPReluSinCosonnxruntime::Max_6::Computeonnxruntime::Mean_6::ComputeAcosAtanTanAsin) for tensor of length:Invalid start/ending offset [fmod_onnxruntime::Mod::Compute) are not at boundary of span with size:Broadcast Output range [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenModfailed to get first output!(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModX and Y input types do not match: fmod attribute must be true for float, float16 and double typesonnxruntime::mod_internal::BroadCastMFloat16FMod vs onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.hstart_offset % span_size == 0 && real_end % span_size == 0input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeLEFTRIGHTdirectiononnxruntime::BitShift::BitShiftonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BroadcastVariadic(output_size % span_size) == 0onnxruntime::BitShift::Compute::::operator ()Unsupported Y type: tensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8::Computeonnxruntime::BroadcastTwoShape must be 1 dimensional as it's tensor data is a shapecur1 == end1onnxruntime::BitShift::Compute::::operator ()'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftcur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::Expand_8::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8::Computeonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::TBroadcaster::AdvanceByoffset % span_size_ == 0onnxruntime::TBroadcaster::AdvanceByonnxruntime::BroadcastOneSpaninput0_size == output_sizeonnxruntime::TensorAllocator::TensorAllocatorinput1_size == output_sizeonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::TBroadcastOutput<__int64>::TBroadcastOutputonnxruntime::TBroadcastOutput::TBroadcastOutputTBroadcaster can only start at span boundary!onnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::TBroadcastOutput::TBroadcastOutputonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TBroadcaster::AdvanceByonnxruntime::TBroadcaster<__int64,__int64>::AdvanceByonnxruntime::TBroadcaster::AdvanceByonnxruntime::TBroadcaster::AdvanceByonnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TBroadcaster::AdvanceByonnxruntime::TBroadcaster::AdvanceByMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseC:\a\5\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hX != nullptr'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::UnsqueezeBase::PrepareComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccnullptr != p.output_tensoronnxruntime::Unsqueeze::Computeequation->C:\a\5\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsuminfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeonnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one inputEinsumEinsum op: An implementation for the input type is not supported yetC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum.ccC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computeinput_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchindices tensor data type not supportedlast dimension of indices must not be larger than rank of input tensorGatherNDTindbatch_dimsinvalid index found, index = indices tensor must has rank larger than 0ReduceL1ReduceMaxReduceLogSumExpReduceLogSumReduceL2C:\a\5\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<0>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()keepdimsReduceSumSquareReduceSumReduceProdReduceMinArgMinArgMaxInput to be reduced is nullC:\a\5\s\onnxruntime\core/providers/cpu/containers.honnxruntime::GetAllocatorThe input shape override's size does not match the input tensor's shape sizeC:\a\5\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::PrepareForReduceinput_tensor_ptr != nullptronnxruntime::optional<__int64>::valueonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseselect_last_indexnoop_with_empty_axesin_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:input_tensor_ptr->Shape().Size() == input_shape_override->Size()onnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringinfo.GetAttr("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()Pad should be smaller than kernel.kernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()storage_orderGlobalLpPoolC:\a\5\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.ponnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizeInput dimension cannot be less than 3.C:\a\5\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]input_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:Unsupported pooling size.Not satisfied: input_dims >= 3 C:\a\5\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeLpPoolINot satisfied: pooling_dims == pool_attrs_.kernel_shape.size() kernel_shape num_dims is not compatible with X num_dims.onnxruntime::Pool::Computeonnxruntime::MaxPoolV8::ComputeImplUnsupported pooling size : Not satisfied: x_shape.NumDimensions() >= 3 C:\a\5\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Basemin_ <= max_C:\a\5\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl::operator ()min->Shape().NumDimensions() == 0min should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().NumDimensions() == 0max should be a scalar.onnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()extrapolation_valueinfo.GetAttrs("scales", scales_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBaseexclude_outsidecubic_coeff_around_prefer_floorhalf_pixelinfo.GetAttr("mode", &mode).IsOK()cubiclinearcoordinate_transform_mode:[] is not supportted!tf_crop_and_resizealign_corners] is not supported!ceilround_prefer_ceilonnxruntime::UpsampleBase::StringToCoordinateTransformationMode. It can only be (default) or or exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to tf_half_pixel_for_nnpytorch_half_pixelonnxruntime::UpsampleBase::StringToUpsampleModemode attribute is scales size should be greater than 0.scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'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 Upsample operatoronnxruntime::UpsampleBase::ParseScalesDatascales_size > 0scale >= 1Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModenearest_mode:[Resize operatorscale > 0Scale value should be greater than 0.onnxruntime::UpsampleBase::ScalesValidationbad allocationT3onnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\onehot.ccOneHotInvalid 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 elementsonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::ComputeDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeC:\a\5\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedkop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()TopKonnxruntime::TopkOpset11ConstructorCommonlargestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sorted] should not be greater than specified axis dim value [k argument [output count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of limit in Range operator should be scalar like tensor, yet got shape:delta 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:C:\a\5\s\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valuereflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseconstantUnexpected mode of Unsupported input data type of onnxruntime::PadValueFromFloatC:\a\5\s\onnxruntime\core\providers\cpu\tensor\pad.ccPadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:onnxruntime::SliceSkips::SliceSkipsC:\a\5\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::Initdims.size() == extents.size() && dims.size() >= steps.size()value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1pads_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 pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorPads tensor should be an INT64 tensorpads_tensor.IsDataType()onnxruntime::Pad::ComputePads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]data_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroInput tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesinfo.GetAttr("axis", &axis_).IsOK()onnxruntime::GatherElements::GatherElementsC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsMissing/Invalid 'axis' attribute valueGatherElements 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 output , 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: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'onnxruntime::parse_and_validate_indices_tensorC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cc]. Actual value is C:\a\5\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperinfo.GetAttr("alpha", &alpha_).IsOK()betainfo.GetAttr("beta", &beta_).IsOK()onnxruntime::Gemm::GemmC:\a\5\s\onnxruntime\core/providers/cpu/math/gemm.htransBinfo.GetAttr("transB", &temp).IsOK()transAinfo.GetAttr("transA", &temp).IsOK()GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0c_shape != nullptronnxruntime::Gemm::ComputeGemmc_shape is required if c_data is providedScatterNDC:\a\5\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterND::Computeindice_tensor != nullptronnxruntime::ScatterNDBase::PrepareForCompute, indices shape: update_tensor != nullptrinput_tensor != nullptrinvalid indice found, indice = updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. input tensor and indices tensor must has rank larger than 0. input shape: updates shape: , data shape: ) != new size (shape_.Size() == new_shape.Size()Tensor size (onnxruntime::Tensor::ReshapeY != nullptrSequenceLength: Got nullptr for output tensoronnxruntime::GetSeqIdxSequenceAtGot nullptr for sequence input.SequenceLengthC:\a\5\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceLength::ComputeSonnxruntime::SequenceInsert::ComputeS != nullptr), input tensor data type (Got nullptr for input tensor.Unsupported 'dtype' value: onnxruntime::SequenceEmpty::Computeonnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertSequenceAt: Got nullptr for output tensorInvalid sequence index (SequenceEmpty: Got nullptr for output sequenceSequenceEmptyGot nullptr input for index tensoronnxruntime::SequenceAt::Compute) specified for sequence of size (I != nullptronnxruntime::SplitToSequence::ComputeSplitToSequence operator does not support split_size_sum () != split_dim_size (Violation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequence yetSplitToSequenceSequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceConstruct::Computenum_inputs >= 1SequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceErase::ComputeSequenceErasesplit_scalar > 0Split should be > 0onnxruntime::GetScalarSplitInputInvalid data type for split tensor onnxruntime::GetSplitSizesInputInvalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeConcatFromSequenceC:\a\5\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::Computenew_axisC:\a\5\s\onnxruntime\core/providers/cpu//tensor/concat.honnxruntime::If::Ifinfo.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)then_branchC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\if.cc outputs.If'If' node has outputs which doesn't match the subgraph's VBonnxruntime::IfImpl::Executeonnxruntime::IfImpl::Initializesession_stateFailed to create output tensor for If output Failed to create output tensor for onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.CreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::If::SetupSubgraphExecutionInfo X: kernel_shape is not compatible with W shape. group: X num_dims does not match W num_dims. W: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: output_paddinggroup count is <= 0output_shapeInvalid input shape: onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0pad type not supportedC:\a\5\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeonnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0 filter_number: num_input_channels: Input channels is not divisible by group.filter number not equal to input channel number.ConvTransposeC:\a\5\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeonnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported. C: kernel channels: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.C:\a\5\s\onnxruntime\core/providers/cpu/nn/conv_attributes.hNot enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: onnxruntime::ConvAttributes::InferOutputShapeNot enough elements in pads. Expected: M: Input channels C is not equal to kernel channels * group.Not enough elements in strides. Expected: Output channels M is not divisible by group.d_iter[d_i] < d_maxC:\a\5\s\onnxruntime\core\providers\cpu\nn\conv.ccC:\a\5\s\onnxruntime\core/util/math.honnxruntime::math::Im2colNd::operator ()onnxruntime::Conv::Computepads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0strides_.size() == kernel_shape_.size()info.GetAttrs("kernel_shape", kernel_shape_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 C:\a\5\s\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satisfied: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.onnxruntime::MaxUnpool::ComputeMaxUnpoolNot satisfied: given_shape.Size() >= shape.Size() Not satisfied: tensor_shape->Shape().GetDims().size() == 1 Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:boxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeC:\a\5\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppression0 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBasecenter_point_boxcenter_point_box only support 0 or 1Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) onnxruntime::NonMaxSuppression::Computeiou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsThe most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[2] == 4 boxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[1] == scores_dims[2] boxes and scores should have same num_batches.Not satisfied: boxes_dims[0] == scores_dims[0] scores must be a 3D tensor.Not satisfied: scores_shape.NumDimensions() == 3 boxes must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 C:\a\5\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorNot satisfied: ret.IsOK() output != nullptrExpecting indices to be either int32_t or int64_tUnknown tensor type of onnxruntime::Scatter::Compute at pos=Indices dim=Indices must have the same rank as Input. Indices rank= is greater than input dim=Indices vs updates dimensions differs at position=. Input rank=data type is different from updates typeIndices and updates must have the same rankScatterScatterElementsonnxruntime::Scatter::ScatterC:\a\5\s\onnxruntime\core\providers\cpu\tensor\scatter.cc must be within the inclusive range [indices element out of data bounds, idx=DetMatrix dimensions are not equal. Square matrix is expectedC:\a\5\s\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2onnxruntime::Det::ComputeDimension of input input_shape[i] == 1. shape= must be 1 instead of onnxruntime::SqueezeBase::ComputeOutputShapeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SplitBase::SplitBaseC:\a\5\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; })split Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Axis=Input cannot be split evenly on selected axis. Input shape= NumOutputs=C:\a\5\s\onnxruntime\core\providers\cpu\tensor\split.ccSplit operator does not support onnxruntime::Split::Compute Input shape=Cannot split using values in 'split' attribute. Axis=onnxruntime::Split::ComputeImpl!has_axes || attr_axes_.size() == attr_starts_.size()C:\a\5\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::SliceBaseMissing or invalid starts and ends attributeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arraystart_tensor.Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInput'step' value cannot be 0Starts must be a 1-D array'axes' has an axis outside of the tensor dimension count'axes' has duplicatesUnexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepCannot slice scalarsMissing input tensor to be processedonnxruntime::SliceBase::Computenullptr == 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 nullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()Starts and steps shape mismatchstart_tensor.Shape() == ends_tensor.Shape()Starts and axes shape mismatchends_tensor.Shape().NumDimensions() == 1Starts and ends shape mismatchonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()Type for Tind not supported yet in Gather.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase and Not satisfied: data_n.DataType() == p.output_tensor->DataType() Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of inputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: inputs_0_rank > 0 Cannot concatenate scalarsNot satisfied: input_count >= 1 C:\a\5\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()CompressFlattenonnxruntime::Flatten::Computegsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisC:\a\5\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenC:\a\5\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.num_scan_inputsC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Scaninfo.GetAttr("body", &proto).IsOK()bodyonnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.onnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_input_axesscan_output_directionsscan_input_directionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsSubgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid scan input: Expected dimensions or more but input had shape of Scanonnxruntime::ScanImpl::TransposeOutputOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueOutput OrtValue has not been created for loop state variable output C:\a\5\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.blocksizeDCRCRDinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace::Computeinput.Shape().NumDimensions() == 4input_width % this->blocksize_ == 0input_height % this->blocksize_ == 0C:\a\5\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::Computeinput_shape.NumDimensions() == 4DepthToSpaceSpaceToDepthonnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:Loop was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Executeonnxruntime::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_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::LoopInconsistent shape in loop output for output. Expected: Got:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has Loop had zero iterations and the shape of subgraph output LogSoftmax, D=. N=Hardmax inputs N, D and N * D must be < Hardmaxx_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccRoundexclusiveC:\a\5\s\onnxruntime\core\providers\cpu\math\cumsum.ccreverseCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`CumSumonnxruntime::CumSum<__int64>::Computestarts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initbatch_axisReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccinfo.GetAttr("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpC:\a\5\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisavg specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase::RoiAlignBaseC:\a\5\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchsampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaleRoiAlignIsInfFailed to obtain detect_positivedetect_positiveC:\a\5\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfFailed to obtain detect_negativedetect_negativeonnxruntime::IsInf::ComputeData type X must be float or double, but instead got Tensor type mismatch.onnxruntime::Tensor::MutableDataRawtype == dtype_onnxruntime::Tensor::DataRawC:\a\5\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::IdentityOp<1>::Computeonnxruntime::math::Im2colNd::operator ()QLinearConvT4IsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::ComputeIsScalarOr1ElementVector(W_zero_point)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1ConvIntegerIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.C:\a\5\s\onnxruntime\core\providers\cpu\nn\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.onnxruntime::MatMulComputeHelper::ComputeNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 C:\a\5\s\onnxruntime\core/providers/cpu/math/matmul_helper.hNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] MatMul dimension mismatchNot satisfied: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim onnxruntime::MatMulInteger::ComputeMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)IsScalarOr1ElementVector(b_zero_point)Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: K_ == right_shape[0] Not satisfied: num_dims_with_pad - 2 == num_output_dims QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)IsScalarOr1ElementVector(y_scale)onnxruntime::QLinearMatMul::ComputeQLinearMatMulQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1C:\a\5\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)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)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelNo attribute with name:''is defined.onnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::Initunknown kernel typeonnxruntime::functors::ParametricSoftplus::InitC:\a\5\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::InitgammaC:\a\5\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::HardSigmoid::Init*NONEFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleConversion Error:Please, install necessary language-pack-XX and configure localesis_case_sensitiveattribute is_case_sensitive is not setC:\a\5\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USattribute case_change_action is not setLOWERonnxruntime::StringNormalizer::StringNormalizercase_change_actionlocalestopwordsUPPERattribute case_change_action has invalid valueStringNormalizerConversion Errorbad conversionDuplicate stopwords not allowedp.secondEmpty stopwords not allowed!sw.empty()Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorInput contains invalid utf8 chars at: onnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\math\matmul.cccondition, X, and Y inputs are required!condition && X && Yonnxruntime::Where::Computeonnxruntime::Where<__int64>::Computeonnxruntime::Where::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::TensorAllocator,class std::allocator > >::TensorAllocatoronnxruntime::Where::Computeonnxruntime::Where,class std::allocator > >::Computeonnxruntime::TensorAllocator::TensorAllocatorX input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeTfIdfVectorizerC:\a\5\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizer is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFmin_gram_length is requiredRequired min_gram_length must be positive: impl_->weighting_criteria_ != kNonemin_gram_lengthNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesweights must be of equal sizeNegative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings but ngram_indexes size: Got weights of size: pool_int64snon-empty pool_int64s is required if pool_strings not providedpool_strings must not be empty if specified!impl_->pool_strings_.empty() >= min_gram_length >= max_gram_length required: impl_->min_gram_length_ > 0max_gram_lengthmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countssize_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: <= min_gram_length must be inbounds of ngram_counts: id: Duplicate ngram detected, size: p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsstatus.IsOK() && !pool_int64s.empty()-gramsInput shape must have either [C] or [B,C] dimensions with B > 0.Number of items must compose whole (items % ngram_size == 0)biasC:\a\5\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkShrinkop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()Null input ptrIsNaNC:\a\5\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]MeanVarianceNormalizationC:\a\5\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceMust have a single dimensionUnsupported value attribute datatype: unpack_status.IsOK()Value attribute unpacking failed:t_proto.dims()[0] == 1Must have a single dimension of 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaset_proto.dims_size() == 1onnxruntime::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase::PrepareComputeinput_shape.NumDimensions() > 0Must have a valid input shape.C:\a\5\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.honnxruntime::ConstantOfShapeBase::SetValueUnsupported value attribute datatype with sizeof=: TensorProto::DataType_IsValid(t_proto.data_type())C:\a\5\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoutils::HasDataType(t_proto)onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was sequence_lens length of did not match batch size of position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueC:\a\5\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *C:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::ScanScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Computeonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsonnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was but has batch size of roi_input_idx_ > 0Invalid roi input index.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::ComputeCannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeUpsample: unexpected modeResize: unexpected modeUpsample: input/output value's dimension mismatchResize: input/output value's dimension mismatchUpsample: input/output value is nullptrResize: input/output value is nullptrUpsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensionEither scales or sizes MUST be provided as input.sizes == nullptrOnly one of scales or sizes must be provided as input.scales != nullptronnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.sizes != nullptr && sizes->Shape().Size() != 0output_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::Compute'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Upsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.onnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max() is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.C:\a\5\s\onnxruntime\core/providers/cpu/tensor/transpose.hperm: does not align with rank of input data: [ C:\a\5\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::Transpose::ComputeMismatched data types between input and output Tensors. '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::ComputeTileInput 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 dimensionalC:\a\5\s\onnxruntime\core\providers\cpu\tensor\tile.ccgsl::narrow_cast(input_shape.Size()) == sizeshapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.A shape tensor must be a vector tensor.A dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperi < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0C:\a\5\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.onnxruntime::Reshape_1::Reshape_1Reshape_1clipinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNNC:\a\5\s\onnxruntime\core/providers/cpu/rnn/rnn.hRNNonnxruntime::RNN::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\rnn\rnn.ccAffineinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betaactivationsforwardbidirectionalRNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()C:\a\5\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ValidateInputs}. Actual:Input initial_c must have shape {LSTMLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeInput P must have shape {Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionC:\a\5\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()'. Must be one of 'forward', 'reverse', or 'bidirectional'.input_forgetsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 3onnxruntime::DeepCpuLstmOp::DeepCpuLstmOpC:\a\5\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.foffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointeronnxruntime::DeepCpuLstmOp::ComputeImpl..B + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawPointer rows[lda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endonnxruntime::DeepCpuGruOp::DeepCpuGruOpC:\a\5\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hlinear_before_resetonnxruntime::DeepCpuGruOp::ComputeImplInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccinfo.GetAttr("linear_before_reset", &int64_value).IsOK()activation_func_names.size() == static_cast(num_directions_) * 2GRUGRU operator does not support double yetonnxruntime::RoiPool::RoiPoolC:\a\5\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0roi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0MaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccalpha_ > 0.0fsize_ % 2 == 1beta_ > 0.0fonnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()size_ > 0C:\a\5\s\onnxruntime\core/providers/cpu/nn/lrn.hC:\a\5\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputesizeLRNX->Shape().NumDimensions() == 4op_kernel_info.GetAttr("p", &p_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/lp_norm.hp_ == 1 || p_ == 2LpNormalizationonnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()InstanceNormalizationC:\a\5\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::Computeonnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/instance_norm.hInvalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: Mismatch between input data and scale: size of scale != input channel count vs. Mismatch between input data and B: size of B != input channel count Invalid input B: number of dimensions is not 1: Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Invalid input scale: dimension != Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input mean: Invalid input mean: 0th dimension != Invalid input var: 0th dimension != Invalid input var: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: NumDimensions() != Invalid input mean: NumDimensions() != Invalid input B: onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::ComputeInvalid input var: onnxruntime::BatchNorm::BatchNormC:\a\5\s\onnxruntime\core/providers/cpu/nn/batch_norm.hinfo.GetAttr("mean", &mean_).IsOK()meanInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDC:\a\5\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("scale", &scale_).IsOK()scaleCould not infer data type from input tensor with data type C:\a\5\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::Computeinfo.GetAttr("low", &low_).IsOK()lowsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikehighonnxruntime::RandomNormalLike::RandomNormalLikeonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()onnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::MultinomialComputeonnxruntime::RandomNormalComputeFLOAT16 is not supportedonnxruntime::RandomUniformComputenum_classes is < 1batch_size is < 1Invalid data type of num_samples is < 1C:\a\5\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsinfo.GetAttr("default_string", &default_string_).IsOK()default_stringinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsC:\a\5\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder_Unusedvalues_stringsvalues_floatskeys_stringsInput of tensor(string) must have output of tensor(int64)LabelEncoderkeys_floatsInput of tensor(int64) must have output of tensor(string)onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2 and the number of values is keys_int64sdefault_floatinfo.GetAttrs(_key_field_name, keys).IsOK()values_int64snum_keys == num_valuesThe onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2) must have the same length. However, the number of key is attribtues in LabelEncoder (name: Input features_per_batch[ZipMapclasslabels_int64sZipmap only supports 1D or 2D input tensors] != number of classlabels[C:\a\5\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.BRANCH_LEQLEAFTreeEnsembleRegressorSOFTMAX_ZEROAVERAGELOGISTICSOFTMAXBRANCH_GTBRANCH_EQBRANCH_LTBRANCH_GTESUMMINnodes_featureidsnodes_falsenodeidsnodes_missing_value_tracks_truenodes_hitratesnodes_nodeidsnodes_modesnodes_truenodeidsnodes_treeidsC:\a\5\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_falsenodeids.size() == nodes_featureids.size()n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonaggregate_functionInput shape needs to be at least a single dimension.n_targetsbase_valuespost_transformnodes_valuestarget_nodeidstarget_idstarget_weightstarget_treeidsC:\a\5\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMin::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::MergePredictionit->i < (int64_t)predictions.size()this->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScorestarget_class_ids.size() == target_class_treeids.size() is already there.nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size() (falsenode). (weights).Unable to find node One falsenode is pointing either to itself, either to another tree. (truenode).- in tree Node onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonpredictions.size() == 2X dims is empty.onnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictionTreeEnsembleClassifierclass_nodeidsclass_idsclass_weightsclass_treeidsonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::SVMRegressor::SVMRegressorC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccrhoinfo.GetAttrs("rho", rho_).IsOK()n_supportssupport_vectorsPOLYRBFLINEARSVMRegressoronnxruntime::ml::SVMCommon::SVMCommonC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hkernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_typeUnexpected mode:num_features == feature_count_onnxruntime::ml::SVMRegressor::Compute!coefficients_.empty()one_classcoefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()C:\a\5\s\onnxruntime\core\providers\cpu\ml\ml_common.hprob_aprob_bSVMClassifiervectors_per_classUnexpected value for 'add_second_class' of scores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplacecoefficients_.size() > 0onnxruntime::ml::SVMClassifier::Computeinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0proba_.size() == probb_.size()classlabels_intsonnxruntime::ml::SVMClassifier::SVMClassifierC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccUnsupported data type of C:\a\5\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (!scale_.empty()onnxruntime::ml::ScalerOp::ScalerOpoffsetEmpty scale in attributesonnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpEither both scale and offset can be of feature size (onnxruntime::ml::ScalerOp::ScalerOpInvalid argument: input has empty dimensions.) or 1Scale size: (scale_.size() == offset_.size()ScalerUnknown Category and zeros = 0.OneHotEncodertmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedzeroscats_int64sonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpC:\a\5\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0C:\a\5\s\onnxruntime\core/providers/cpu/ml/ml_common.hUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got onnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1MAXNormalizerC:\a\5\s\onnxruntime\core/providers/cpu/ml/normalizer.hC:\a\5\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()targetsinterceptsLinearRegressorInput shape had more than 2 dimension. Dims=scores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:C:\a\5\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifiermulti_classLinearClassifierUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplonnxruntime::ml::CastInputToFloatshape_size == out.length()Empty input dimensions.Empty value of imputed values.Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_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_int64C:\a\5\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpInvalid input type:C:\a\5\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values ().FeatureVectorizerC:\a\5\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsonnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\a\5\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularystring_vocabularyDictVectorizerInput of int64 must have output of string Input of string must have output of int64CategoryMappernum_entries == int_categories.size()info.GetAttrs("cats_int64s", int_categories).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs("cats_strings", string_categories).IsOK()%fTO_FLOATUnexpected CAST_TO value of max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapInvalid input type of value: 0.fInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\a\5\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()TO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSENegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\a\5\s\onnxruntime\core\providers\cpu\ml\cast_map.ccthreshold is NaNInput data with index: BinarizerArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputRight shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not align Left shape override: Left shape: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Number of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionAnother operand has a dim value of onnxruntime::EinsumComputePreprocessor::RunC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccRank of the input must match number of subscript labels corresponding to the inputdim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Output subscript contains repeated lettersOutput subscript contains letters not seen in the inputs. The shape is: is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]input_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Data types of the inputs must match for MatMulonnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()Einsum op: Exception during MatMul operation: onnxruntime::scan::detail::CreateFeedsFetchesManagerSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::NextNumber of entries in '' was onnxruntime::scan::detail::ReadDirectionsnum_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesInvalid values in ''. 0 == forward. 1 == reverse. did not.valid inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires C:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputSoftmaxCPU inputs N, D and N * D must be < Input X must have 3 dimensions only. Actual:Invalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameC:\a\5\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountInvalid activation function of Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNamethresholdedreluleakyreluhardsigmoidscaledtanhsoftsigneluExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusInput R must have shape {Input W must have shape {Input 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=reluaffineonnxruntime::contrib::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()TriluC:\a\5\s\onnxruntime\contrib_ops\cpu\trilu.hIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.C:\a\5\s\onnxruntime\contrib_ops\cpu\trilu.cconnxruntime::contrib::Trilu::ComputeupperInverseonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::contrib::SkipLayerNorm::SkipLayerNormLast dimension of bias and input does not matchinput is expected to have 3 dimensions, got epsilon_ >= 0gamma 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 matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchC:\a\5\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::LayerNorm::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()C:\a\5\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::contrib::Scale::ScaleC:\a\5\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernel) does not match the number of channels (Bias size (ImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs("bias", bias_).IsOK()onnxruntime::contrib::ImageScaler::ImageScalerC:\a\5\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceonnxruntime::contrib::Crop::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\crop.h) + scale_[1] (Crop) needs to be greater than or equal to the topBorder (Input's height ( input dimensions instead) + bottomBorder (Input's width () + scale_[0] () + rightBorder () needs to be greater than or equal to the leftBorder (borderAttribute border needs to be specified with four border elements, got onnxruntime::contrib::BiasGelu::Computeuse_approximationC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::ComputeeuclideansqeuclideanC:\a\5\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDistonnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()metricCDistInput shape dimensions mismatch:The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: C:\a\5\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestbilinearCropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskC:\a\5\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::Computeonnxruntime::contrib::TransposeMatMul::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\transpose_scale_matmul.cconnxruntime::contrib::TransposeMatMul::TransposeMatMulMurmurHash3positiveInvalid assumption of output element sizeC:\a\5\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computekeysinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytes Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Conv filter size does not match embedding_size attribute. conv kernal size 1: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute . conv filter size: embedding_size attribute: embedding_sizeconv_window_sizechar_embedding_sizeChar embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbeddingattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerC:\a\5\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueTokenizermarkInput string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Expecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valueC:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {C:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::Computeonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplAttention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{}. Got: found!], while Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:onnxruntime::contrib::FusedGemm::FusedGemmC:\a\5\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatC:\a\5\s\onnxruntime\contrib_ops\cpu\fused_conv.ccaxis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::ExpandDims::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [An axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::EmbedLayerNorm::EmbedLayerNormC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 'input' is expected to have 3 dimensions, got C:\a\5\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionCPUBase::ApplyAttentiononnxruntime::contrib::Attention::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionBase::GetPresentInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeExpect to have present state output when past state input is givenInput 'mask_index' is expected to have 1 or 2 dimensions, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 2 shall have length of Inputs 'past' dimension 2 shall have length of num_headsInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'bias' is expected to have 1 dimension, got Input 'past' is expected to have 5 dimension, got Input 'past' is only allowed for unidirectionalInput 'weights' is expected to have 2 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' dimension 1 should be 3 times of dimension 0Input 1 dimension 0 should have same length as dimension 2 of input 0SampleOponnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonMatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.C:\a\5\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsScalarOr1ElementVector(b_scale_tensor)input zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) weight zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) input scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccweight must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(input_scale_tensor) onnxruntime::contrib::QAttention::ComputeQAttentionNot satisfied: IsScalarOr1ElementVector(w_zp_tensor) QLinearAddtensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenonnxruntime::TBroadcaster::AdvanceBytensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.cconnxruntime::contrib::QLinearBroadcastTwoQLinearMulQLinearLeakyRelutensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQLinearSigmoidonnxruntime::contrib::MatMulInteger16::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptr(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_rank == 4onnxruntime::contrib::ReorderInput::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccscales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1X_shape.NumDimensions() == 4onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2channels_ > 0info.GetAttr("channels_last", &channels_last_).IsOK()C:\a\5\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinvalid channel countinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputonnxruntime::contrib::NchwcUpsample::ComputeNot satisfied: Y->Shape() == sum_shape onnxruntime::contrib::NchwcPoolBase::NchwcPoolUnsupported convolution size.output and sum shape must matchonnxruntime::contrib::NchwcConv::Compute(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got row[ [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemory is not in (0, Real memory steps onnxruntime::contrib::BahdanauAttention::BahdanauAttentionC:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccnot support normalize yet.!normalize_activation_params count mismatchunimplemented activation: Input 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 1 shall have same shapegamma is expected to have size of beta is expected to have size of gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got Received invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptions is not supportedTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType.CudaPinnedCudaOrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:Device:[DeviceType: MemoryType: DeviceId:Specified device is not supported.C:\a\5\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Argument is not a tensorC:\a\5\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShapetype != nullptrOrtValue is not a TensorNot implementedtype_proto is not of type sequence!type_proto is not of type map!C:\a\5\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::Tensorp_type != nullptrdtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowtensor failed memory size calculationshape.Size() must >=0(unknown type)(null)onnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeproto != nullptrOnly ONNX MLDataType can be registeredC:\a\5\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setelem_proto != nullptronnxruntime::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,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::SetC:\a\5\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::Setvalue_proto != nullptrexpected a registered ONNX typeonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitionMLDataType for: is not currently registered or supportedtensor type onnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType::Set dimensions. for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionC:\a\5\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Slice{}Config key is empty or longer than maximum length 128Config value is longer than maximum length 1024]. It will be overwritten] already exists with value [Session Config with key [onnxruntime::SessionOptions::AddConfigEntryC:\a\5\s\onnxruntime\core\framework\session_options.ccinvalid allocatorFailed to find allocator for device allocator != nullptronnxruntime::utils::BatchOrCopyMLValueC:\a\5\s\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphRknpuExecutionProviderAttibute name and type don't matchNo attribute with this name is defined.cannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoC:\a\5\s\onnxruntime\core\framework\op_kernel_info.ccnode_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetC:\a\5\s\onnxruntime\core/framework/node_index_info.hExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextC:\a\5\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is Invalid arg_num of arg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueReceived invalid value of arena_extend_strategy C:\a\5\s\onnxruntime\core\framework\allocatormgr.cconnxruntime::CreateAllocatorsession.disable_prepackingCould not find OrtValue with idx 'node_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::SaveToOrtFormatonnxruntime::GetSubGraphSessionStatesOrtFormatsubgraphMain Graph instance should have populated all subgraphs when being resolved.Kernel create info is null. Invalid ORT format model.onnxruntime::SessionState::CreateSubgraphSessionStateSatisfied, but should not be: nullptr == fbs_kcis onnxruntime::SessionState::LoadFromOrtFormatSatisfied, but should not be: nullptr == node_indices Kernel create info node indices are null. Invalid ORT format model.Satisfied, but should not be: nullptr == kernel_def_hashes Kernel create info hashes are null. Invalid ORT format model.Not satisfied: node_indices->size() == kernel_def_hashes->size() Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Can't find node with index . Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Satisfied, but should not be: node == nullptr is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states Satisfied, but should not be: nullptr == fbs_sub_graph_ss Subgraph SessionState entry for Subgraph SessionState for is null. Invalid ORT format model.onnxruntime::SessionState::FinalizeSessionStateSatisfied, but should not be: nullptr == fbs_sub_session_state Attribute:onnxruntime::SessionState::FinalizeSessionStateImpl. Ignoring allocator from onnxruntime::SessionState::SetupAllocatorsAllocator already registered for SaveMLValueNameIndexMappingC:\a\5\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::CreateGraphInfoDone saving OrtValue mappings.entry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfo. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::PrepackInitializedConstantTensorsduplicated ort_value index: is used by node ) and node Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input:output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for onnxruntime::SessionState::AddOutputNameToNodeInfoMappingEntry exists in node for attribute onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()' OpType: Index:entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'p_op_kernelfound duplicated provider in KernelRegistryManager (node ). Failed to find kernel for The node is not placed on any Execution Provider. ) in protocorrupted protobuf data: tensor shape size() does not match the data size(data overflowUnpackTensor: the pre-allocate size does not match the size in protoC:\a\5\s\onnxruntime\core\framework\tensorprotoutils.cconnxruntime::utils::GetFileContentonnxruntime::utils::TensorProtoToMLValuestring tensor can not have raw dataThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsInitialized tensor with unexpected type: initialize preallocated buffer failed in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoonnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported attribute value type of Unsupported type: Unsupported sparse tensor data type of UnpackTensor: the pre-allocated size does not match the raw data size, expected Invalid TensorProto`anonymous-namespace'::UnpackTensorWithRawDataonnxruntime::utils::CopySparseDatarank == dims.size() && rank > 0Invalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend()onnxruntime::utils::ReadLittleEndiansource and destination buffer size mismatchNot satisfied: source_bytes.size_bytes() == destination.size_bytes() C:\a\5\s\onnxruntime\core/framework/endian_utils.hnullptr != type_protoC:\a\5\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypeonnxruntime::IExecutionProvider::InsertAllocatorduplicated allocatorC:\a\5\s\onnxruntime\core\framework\execution_provider.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping feeds: C:\a\5\s\onnxruntime\core\framework\feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FunctionKernel::FunctionKernelcreate_func(&context, &func_state_) == 0C:\a\5\s\onnxruntime\core/framework/func_kernel.honnxruntime::PlaceNode1 == capability->nodes.size()C:\a\5\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::GraphPartitioner::PartitionNo provider specified.node_compute_funcs.size() == nodes_need_compile.size()Provider doesn't return correct number of compiled functionsdata_transfer registered is nullptr.Tensor size mismatchonnxruntime::DataTransferManager::CopyTensorsC:\a\5\s\onnxruntime\core\framework\data_transfer_manager.cc to There's no data transfer registered for copying tensors from onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: C:\a\5\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO A dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while loading custom ops with message: has already been loaded.Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()offset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\a\5\s\onnxruntime\core/framework/execution_frame.hnode_index_info and ort_value_idx_map are out of sync and cannot be usedmlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlanInvalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocate failed: size=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=C:\a\5\s\onnxruntime\core\framework\execution_frame.cc Requested shape:node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapefetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::InitAllocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()Fetches vector passed to GetOutputs contains failed. Error: should only appear once.Memory type buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrameTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valuestatic_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() returned nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behaviorort_value.Fence() == nullptrFor ort_value with index: . 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. but the actually size is: , block in memory pattern size is: model format error!model format error! Missing 'location'lengthchecksummodel format error! Need a key for the external data info failedparsing model format error! Need a value for the external data infolocation kernel start version: node_version: kernel_end_version: Op with name ( This op has been implemented only for the following types ( Version mismatch. and type ( in the supported version rangeFound kernel for Op with name ( However the types are incompatible. (node_version: onnxruntime::KernelRegistry::TryCreateKernelC:\a\5\s\onnxruntime\core\framework\kernel_registry.cc), but the node in the model has the following type ( Encountered following errors: ( kernel is not supported in No matching hash for kernel not found in : Conflicting with a registered kernel with op versions.Failed to add kernel for Kernel not foundkernel def can't be NULLlen <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoonnxruntime::IDataTransfer::CopyTensorsonnxruntime::CPUDataTransfer::CopyTensorC:\a\5\s\onnxruntime\core\framework\data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()C:\a\5\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer::CreateC:\a\5\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccgsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is . Shape:Insufficient dimensions to slice on onnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::CreateInvalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratorentry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternal does not.All implicit inputs should have OrtValue instances by now. onnxruntime::ParallelExecutor::ExecuteC:\a\5\s\onnxruntime\core\framework\parallel_executor.ccC:\a\5\s\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.ParallelExecutor::ExecuteBegin executionGot nullptr from GetKernel for node: op_nameonnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.' Status Message: node. Name:'_fence_before_kernel_time_fence_afterNon-zero status code returned while running providerException running nodes starting at Unknown exception was caught by catch-all handler.'. C:\a\5\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::Executeparameter_sizeactivation_sizeexec_plan_indexgraph_indexSequentialExecutor::Executeoutput_size[Memory] ExecutionFrame dynamically allocates [Memory] ExecutionFrame statically allocates bytes for onnxruntime::ReleaseNodeMLValuesCan't use func with null ptrfunc info for node: already exist.C:\a\5\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncs not found.BinForSize(bin_size) == BinFromIndex(b)Creating bins of max chunk size C:\a\5\s\onnxruntime\core\framework\bfc_arena.cch < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.onnxruntime::BFCArena::ChunkFromHandle bytes.Failed to allocate memory for requested buffer of size Available memory of is smaller than requested bytes of Allocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendExtended allocation by onnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()tried to allocate 0 bytesh != kInvalidChunkHandleExtending BFCArena for . bin_num: rounded_bytes:onnxruntime::BFCArena::AllocateRawInternalonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizep_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_intC:\a\5\s\onnxruntime\core/framework/bfc_arena.h memory limit: arena_extend_strategy onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: !chunk->in_use()Failed to find a free memory block despite calling Extend. rounded_bytes=onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtronnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0. string tensor is not supported for copying between allocatorsInternal error. The preallocated buffer is too small. Requires , Got C:\a\5\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::DeserializeTensorProtoOrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.Failed to copy tensor to Deserialize tensor failed.[Memory] SessionStateInitializer statically allocates ort_value_name_idx_map.MaxIdx() > -1onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()Done saving initialized tensors input with name is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()GraphSubgraphduplicated locationFailed to get allocator for location: is not foundInternal error.C:\a\5\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlan' failedWeight buffer for initializer '' is not foundMem pattern for initializer onnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer ') : C:\a\5\s\onnxruntime\core\framework\allocation_planner.ccentry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Indexonnxruntime::GetKernelCreateInfoid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Buffernullptr != tensor_type_baseonnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::ProcessDefp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the node onnxruntime::PlannerImpl::GetElementSizeonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider onnxruntime::PlannerImpl::ComputeUseCountsonnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::ComputeReusePlanSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zero', location: Failed to get allocator for initializer 'invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferC:\a\5\s\onnxruntime\core\framework\simple_tensor_allocator.cc[ShapeInferenceError] Dimension= Target=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=NoOpNode [Serialization of fused function body is not currently supported, does not have the graph for key onnxruntime::Node::SaveToOrtFormatTensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. ] op_type [node_arg_name cannot be nullSatisfied, but should not be: nullptr == node_arg_name ], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [Not satisfied: it != attr_to_subgraph_map_.cend() fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_node_arg_names Serialization error. Graph attribute was serialized without Graph instanceNot satisfied: subgraph Node::LoadFromOrtFormat, input_arg_counts is missingSatisfied, but should not be: nullptr == fbs_input_arg_counts Satisfied, but should not be: nullptr == node_arg onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSatisfied, but should not be: nullptr == fbs_attr input index: onnxruntime::Node::LoadEdgesFromOrtFormatSatisfied, but should not be: fbs_node_edges.node_index() != index_ input edgesNode::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_edge is not the same as this node's index:Constant are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetC:\a\5\s\onnxruntime\core/graph/model_load_utils.hALLOW_RELEASED_ONNX_OPSET_ONLYOutput:onnxruntime::MergeShapeInfoC:\a\5\s\onnxruntime\core\graph\graph.cc Input=Type mismatch. Current=. Falling back to lenient merge. target:' source:Error merging shape info for output. 'This is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: in node No Graph instance was found for attribute onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.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=Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got ) does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.node.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) Op (Node ( in node () bound to different types (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) in node () of operator (' of input parameter (This is an invalid model. Type Error: Type 'onnxruntime::Graph::GraphThis is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter output edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptrGraph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgenode_arg) does not exist in the graph.This is an invalid model. Graph output (This is an invalid model. Error: two nodes with same node name (onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.!foundonnxruntime::Graph::RemoveInitializedTensor_token_onnxruntime::Graph::SaveToOrtFormat'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model. as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodenodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraphRemoving initializer 'onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.) of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input () type inference failed) output arg () does not match expected type () of node (Type Error: Shape of initializer This is an invalid model. Error in Node:onnxruntime::Graph::VerifyNodeAndOpMatch is not a registered function/op but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.onnxruntime::Graph::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::SetInputs's number of inputs is different from function body graph's number of input.'s number of outputs is different from function body graph's number of outputs.node.MutableOutputDefs().size() == subgraph.GetOutputs().size()onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_value_info Node is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node NodeEdge is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_arg_name Initializer tensor is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensor NodeArg is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_edge index < data_.size()onnxruntime::ConstPointerContainer > >::atC:\a\5\s\include\onnxruntime\core/common/const_pointer_container.h8NDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetC:\a\5\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker.than the operator set version , but it its version is higher is till opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::Model::ModelC:\a\5\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unknown model file format version.onnxruntime::model_load_utils::ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph.Failed to load model with error: No graph was found in the protobuf.onnxruntime::Model::Load model may run depending upon legacy support of some older opset version operators.ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset onnxruntime::Model::SaveProtobuf serialization failed.onnxruntime::Model::SaveToOrtFormatModel must have opset imports. Invalid ORT format model.Protobuf parsing failed. less than 0. is less than 0.Graph is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_graph onnxruntime::Model::LoadFromOrtFormatSatisfied, but should not be: nullptr == fbs_op_set_ids opset import domain is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_domain Load model system error number onnxruntime::SaveModel failed. File doesn't existC should specify a shapeNegative values are not allowed in a shape specificationIncompatible dimensionsAttribute Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput data type does not match the expected data typeFirst operand.A_scaleInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.inputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointOutput scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleA_zero_pointSecond operand.B_scalePadding 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.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Inputs 0 shall be 3 dimensionsOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.segment_idsword_embeddingConstrain input and output types to int8 tensors.The epsilon value to use to avoid division by zero.input_idsmaskposition_embeddingsegment_embeddingoutput tensorConstrain input and output types to float or half tensors.Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorUSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.skipC:\a\5\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.Inputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.MpastweightConstrain mask index to integer typesinput_scalepresentConstrain input and output types to float tensors.input_zero_pointweight_zero_pointweight_scalerois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionGaussian 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.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimension Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal. Value of alphaValue of beta1D input tensorBias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Input rank must be >= 2. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]data1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format.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. 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], ] inv_std_varConstrain mean and inv_std_var to float tensors. Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) + bottom_border (input_as_shapeextra_shape Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. 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[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: ) needs to be greater than or equal to the top_border () + right_border () needs to be greater than or equal to the left_border () + scale[0] ( Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationaxis must be in [-rank, rank-1]. input rank was The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). 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'). 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]. inputs are expected to have tensor type and output type should not be null.input_1ComplexMulConjWPadsonesidedIrfftComplexMulinput_0Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Constrain input and output types to float tensors The fused convolution operator schema is the same as Conv besides it includes an attribute activation. The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.activation_gammaSpecified axis to insert a dimensionExpandDims echo operator.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.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.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'.y_zero_pointN-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.N-D full precision Input tensor to be quantized.xScale 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'.y_scaleThe shape of filled tensorThe filled tensorvaluesInput data to be scaledSliced data tensor.Constrain input and output types to all tensor types.Constrain indices to integer typesGivenTensorFillThe previous GRU hidden state.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesOutput data after scalingGRUUnitBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_stateshiddenPerform mean variance normalization.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Array of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsThe timestep for this operation.The new GRU hidden state calculated by this op.Constrain input0 and output types to float tensorsRfftsignal_ndimnormalizedScaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wise Sample echo operator.For internal use.Constrain input a_scale, b_scale and output Y data type as float tensor.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationa_scaleZero 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-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_zero_pointConstrain input A data type to 8-bit integer tensor.Reduced output tensor.reducedConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).Matrix multiply resultsReduceSumInteger 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.An input tensor.C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationQLinearReduceMean Computes the mean 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. Input and Output scales and zero points are used to requantize the output in a new range. This helps to improve accuracy as after ReduceMean operation the range of the output is expected to decrease. ``` "Output = Dequantize(Input) -> ReduceMean on fp32 data -> Quantize(output)", ``` A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionreduced_scalereduced_zero_pointConstrain input types to 8 bit signed and unsigned tensors.MulIntegerInput scale. It's a scalar, which means a per-tensor/layer quantization.data_scaleInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointZero 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'.x_zero_pointN-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.x_scaleBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Constrain 'y', 'x_scale' to float tensors.Strings to tokenizeTokenized stringsInput/Output is a string tensorMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored 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.N-dimensional matrix AN-dimensional matrix B1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Scale of quantized input 'A'. 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 'A'.Scale of quantized input 'B'. 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'.b_scaleZero point 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'.b_zero_point 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]]] Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.indicesTensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Specify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Specify batchs of sequence words to embeddingSequenceSpecify weights of convSpecify bias of convTensor after padding. 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], ], ] A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'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 arrayInput tensor.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`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Input can be of any tensor type. 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] 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".2D matrix with shape (M,N)A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'idxA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'countsQLinearAveragePoolWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.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.Performs element-wise binary quantized multiplication (with Numpy-style broadcasting support). "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**" The output of this op is the int32 accumulated result of the mul operation ``` C (int32) = (A - A_zero_point) * (B - B_zero_point) ``` Constrain output to 32 bit tensorConstrain output types to 32 bit tensors. QLinearAveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Input and output scales and zero points are used to convert the output to a new quantization range. Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output) QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, `f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. Coefficient of leakage.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleWhether to use ceil or floor (default) to compute the output shape.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_zero_point QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` 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.Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_scaleSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.Tensor of rank q >= 1.An input tensor to hash.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input and output types to all numeric tensors and bool tensors.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.rois2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors. 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.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.batch_indices1-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.crop_sizeOutput data tensor.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Input data tensor from the previous layer.Scale tensor.Bias tensor.Output tensor of the same type and shape as the input tensor.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.̼+̼+Attempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputAttempting to get index for an input which does not exist.itr != node.InputDefs().end()onnxruntime::graph_utils::GetNodeInputIndexFromInputNameInitializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializer ImplicitInputs:index >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::RemoveNodeCan only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)onnxruntime::graph_utils::AddNodeInput ExplicitInputs:. Index:Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputFailed since multiple edges matched:onnxruntime::graph_utils::FindPathstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphC:\a\5\s\onnxruntime\core\graph\graph_utils.cc in one of the subgraphs. cannot be safely updated to GRAPHSSPARSE_TENSORSPARSE_TENSORStensor_typesequence_typemap_typeUNDEFINEDFLOATFLOATSINTSSTRINGSTENSORSINTSTRINGTENSORGRAPHGraph attribute value was null. Invalid ORT format model.Satisfied, but should not be: nullptr == graph SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model. is missing type info.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::experimental::utils::SaveInitializerOrtFormatonnxruntime::experimental::utils::SaveAttributeOrtFormatMissing raw data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_raw_data dim_param value with no name. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatonnxruntime::experimental::utils::LoadInitializerOrtFormatSatisfied, but should not be: nullptr == fbs_dims Missing string data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_str_data Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatSatisfied, but should not be: nullptr == fbs_type_info Null value type info in fbs::MapType. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_dim_param Null entry in dimensions. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim Null sequence type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sequence_type Null map type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_map_type onnxruntime::experimental::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadTypeInfoOrtFormatSatisfied, but should not be: nullptr == fbs_tensor_type onnxruntime::experimental::utils::SaveTypeInfoOrtFormat] for nowWe do not support type [onnxruntime::experimental::utils::SaveValueInfoOrtFormatonnxruntime::experimental::utils::SaveSequenceTypeOrtFormatC:\a\5\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveMapTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatSatisfied, but should not be: !value_info_proto.name().empty() Null string attribute. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatSatisfied, but should not be: nullptr == fbs_str is not supported currentlyType:Null type info for onnxruntime::experimental::utils::LoadValueInfoOrtFormatSatisfied, but should not be: nullptr == fbs_floats Null ints attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_ints Null strings attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_strings Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensors NNNNNNNNNNXN`NpNLNNNfused_function_subgraphinput_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImplResolve subgraph failed:outA node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::update_subgraphs_within_function_bodyC:\a\5\s\onnxruntime\core\graph\function.ccinstartlimitCan not get shape initializer data!Constrain input and output types.Unsupported type:deltaUnsupported non-raw-data data type!invalid scales dimensiontensor rank too smallC:\a\5\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueA list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.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.Constrain seq_lens to integral tensors.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.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.initial_hinitial_cRsequence_lensMWPQWY_hmemory_seq_lensAWY_cExceptionLogHrReturnHr %hs(%d) tid(%x) %08X %wsCallContext:[%hs] Msg:[%ws] %hs(%u)\%hs!%p: FailFast(caller: %p) %hs!%p: RaiseFailFastException%ws[%hs] [%hs(%hs)] kernelbase.dll SetThreadDescriptionC:\a\5\s\onnxruntime\core\platform\windows\env.cconnxruntimekernel32.dll\*C:\a\5\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDirGetFileSizeEx Received negative size from stat callInvalid fd was supplied: onnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfo fail, errcode = Fatal error: 0 count processors from GetLogicalProcessorInformationopen file Not satisfied: length <= buffer.size() ReadFile SetFilePointerEx onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satisfied: offset >= 0 Not satisfied: file_path onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: GetFinalPathNameByHandle() failed: RemoveDirectory() failed - path: MapFileIntoMemory is not implemented on Windows. fail: unexpected end, error code: \/Failed to find symbol in library, error code: Failed to unload library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameNot satisfied: result_length > 0 onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathFailed to load library, error code: UNC\[ONNXRuntimeError]SystemErrorcode != static_cast(common::OK)C:\a\5\s\onnxruntime\core\common\status.cconnxruntime::common::Status::StatusNO_SUCHFILEFAILENGINE_ERRORNO_MODELSUCCESSEP_FAILINVALID_GRAPHGENERAL ERRORINVALID_PROTOBUFRUNTIME_EXCEPTIONNOT_IMPLEMENTEDMODEL_LOADED is not a valid year is not a valid day is not a valid datepmam [C:\a\5\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerOnly 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::DefaultonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. onnxruntime::concurrency::ThreadPool::ThreadPooldegree_of_parallelism >= 1fn != nullptrC:\a\5\s\onnxruntime\core\common\threadpool.cconnxruntime::concurrency::ThreadPool::TryParallelFortotal >= 0onnxruntime::concurrency::ThreadPool::Scheduleonnxruntime::ToMBStringlength overflowonnxruntime::ToWideStringC:\a\5\s\onnxruntime\core\common\str_helper.cc"pid" :{"cat" : ""dur" :"tid" :onnxruntime::profiling::Profiler::EndProfilingWriting profiler data to file ",[ " : ""}, } "ph" : "X","ts" :"args" : {"name" :"] enabled_SessionNodeonnxruntime::profiling::Profiler::EndTimeAndRecordEventMaximum number of events reached, could not record profile event.C:\a\5\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::StartTimeonnxruntime::profiling::Profiler::Initializesession_logger != nullptr`NhNC:\a\5\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRootonnxruntime::Path::ParseNot satisfied: has_trailing_separator Failed to parse path root: /\unexpected failureillegal input path:EventSetInformationadvapi32api-ms-win-eventing-provider-l1-1-0GetSystemTimePreciseAsFileTimekernel32.dllrB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@Kbad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;packing unavailable    ??Operator ', max=' has been deprecated since version (outputs_.size() - 1) == i!(it.GetName().empty()): failed validating the check: ONNX Schema ) in op definition.) than declared ( not in allowed output sizes.has output size not in allowed input sizes.) has output size not in range [min=) has input size Attribute ' for operator )'s output ' appeared multiple times.)'s input ) has more outputs () has more inputs ( is marked single but has an empty string in the graph' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 't'' is expected to have field 'sparse_tensor'Unrecognized attribute: Mismatched attribute type in 'Duplicate type constraint name(inputs_.size() - 1) == iRequired attribute ' has unknown expected type' is missing.' is expected to have field 'tensors'' is expected to have field 'graphs'C:\a\5\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccZThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.map(int64, string)map(int64, float)The input must be an integer map to either string or float.The 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.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Label encoder has only one input.A value that needs replacing.Value(s) to change to.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toThe input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Input type is not float tensor but keys_floats is setOnly one of values_*'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 setA float.A string.An integer.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A collection of weights of the model(s).A collection of intercepts.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]The output will be a tensor of strings or integers.Input's shape should be 1D or 2DThe input must be a tensor of strings or integers, either [N,C] or [C].Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.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'.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.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' sequenceA string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.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.map(int64, double)map(string, float)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)The size of each input in the input listThe input type must be a tensor of a numeric type.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Flag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsThresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature 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.Tree id for each node.The index of the class list that each weight is for.The weight for the class in class_id.The id of the tree that this node is in.node id that this weight is for.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.The input must be a tensor of a numeric type.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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.One of 'MAX,' 'L1,' 'L2'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.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'.The input must be a tensor of a numeric type, either [C] or [N,C].Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Child node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueIndicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)The total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The index of the target that each weight is forThe weight for each targetThe id of the tree that each node is in.The node id of each weightThe keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.The input type must be a tensor of integers or strings, of any shape.C:\a\5\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccInput element type of Output was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input type was nullInput was expected to have tensor type. Got Input was expected to have either tensor or sequence type. Got Mismatched type:Mismatched tensor element type: target= source=seq(tensor(uint8))seq(tensor(uint16))source sequence type missing element type.target sequence type missing element type.condLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output If node has was Only boolC:\a\5\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph 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 and Sequence typesoutputsGraph 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.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_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.v_initialAn attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.final_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.initial_state_and_scan_inputsseq(tensor(float16))seq(tensor(float))seq(tensor(int32))seq(tensor(int64))seq(tensor(int8))seq(tensor(int16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(complex128))seq(tensor(bool))seq(tensor(complex64))seq(tensor(double))seq(tensor(string))) is not equal to number of scan outputs (Number of scan output axes specified () is not equal to number of scan inputs (Number of scan input axes specified ( is invalid for a tensor of rank axis value then_branch and else_branch produce different number of outputs. but subgraphs produce was notScan 'body' subgraph outputs should all be tensors but output outputs. Expected Graph attribute inferencing returned type information for was not a tensor.Scan input All Tensor typesAn 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 tensorAn 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).Constrain 'x' to float tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.X_Max_AdjustedX_RangeX_Min_AdjustedX_MaxQ_MaxX_MinConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinZeropointClipped_ZeroPoint_FPRounded_ZeroPoint_FPMin_ScaledInitial_ZeroPoint_FPConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\a\5\s\cmake\external\onnx\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float or int32 tensor.Constrain input types. Casting from complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesInvalid position of 0Invalid dimension value: Target shape may not have multiple -1 dimensionsConstrain output types. Casting to complex is not supported.C:\a\5\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output to int64 tensor.Input tensor can be of arbitrary type.reshapedAll inputs to Concat must have same rankWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Required attribute axis is missingaxis must be in [-rank, rank-1].Constrain output to int64 tensor, which should be a scalar though. should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedaxis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).data tensor must have rank >= 1Input and output types can be of any tensor type.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).expandedvalues in 'axes' are beyond the bounds of the computed output shapeList 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).'axes' attribute must not contain any duplicatessqueezedInvalid value of attribute 'axis'. Rank=) and outputs ( Value=concat_resultConstrain output types to any tensor type.inputsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.Mismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (Input axes has invalid data'step' cannot be 0Input axes has incorrect lengthInput steps has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {stepsIndices tensor must have rank >= 1sizesConstrain roi type to float or double.OneHot node must have three inputs.Constrains to boolean tensors.condition'axis' must be in [-rank(indices), rank(indices)-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).depth(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.(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.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrain to any tensor type.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Blocksize must be positiverepeats'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Constrain input 'X' and output 'Y' to all tensor types.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.roiFour modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).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).Constrains input and output to only numeric types.constant_valueSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Constrain to boolean tensors.(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.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.(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).Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorinverse_indicesConstrain input and output types to all numeric tensors.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).C:\a\5\s\cmake\external\onnx\onnx\defs\reduction\defs.ccsumConstrain input and output types to high-precision and 8 bit numeric tensors.Constrain input and output types to high-precision numeric tensors.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).Attribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeInput tensor must have atleast 2 dimensionsDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.C:\a\5\s\cmake\external\onnx\onnx\defs\nn\defs.ccConstrain input and output types to float and 8 bit tensors.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.IndicesConstrain index tensor to int64p value of the Lp norm used to pool over the input data.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.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. Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.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 to 1 along each spatial axis.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter 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 axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor. 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. 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 padsvarFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).momentumROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsThe shape of the convolution kernel. If not present, should be inferred from input W.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.number of groups input channels and output channels are divided into.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.w_zero_pointw_scalewThe number of channels to sum overScaling parameter.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). Constrain input and output to all tensor types.Invalid value(ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Constrain input and output types to float tensors.The exponent.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.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.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.Input is ether string UTF-8 or int32/int641-D tensor of floatsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)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.saved_varsaved_meanThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.training_moderatiotraining_mode of Dropout must be a scalar.(Optional) Seed to the random generator, if not specified we will auto generate one.Ratio of Dropout must be a scalar.) for attribute 'axis'The 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.Constrain input 'ratio' types to float tensors.EpsilonX_RMA 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.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.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.Processed_STDSTDX_varianceE_XsquaredVarianceEX_squaredX_squaredConstrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Elu_ResultX_alphaCoefficient of ELU.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).CeluConstrain input and output types to float32 tensors.C:\a\5\s\cmake\external\onnx\onnx\defs\math\defs.ccDescribes 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).'axis' must be in []. Its actual value is: KAxis has less than the requested k elements.K input must be of type int64.K input must be a one-dimensional tensor of size 1.Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).ValuesConstrain input and output types to all tensors.'shape' input must be 1D tensor of type INT64Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. data_0List of tensors for Value of beta.Value of alpha.slopeConstrain input and output types to numeric tensors.axis tensor can be int32 or int64 onlyIf 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.input_gather_elementignore_indexexpanded_targettargetconst_oneconst_zeroConstrain input and output types to floating-point tensors.loss_unweightedweight_gatherloss_Nddlossreductionnoneloss_N1ddloss_NCddinput_gather_element_transformconst_zero_floattransform_targetsexpanded_target_int64const_zero_target_typedconst_ignore_indexweight_gather_sumloss_sumainput and zero_point pair is expected to have same type.bConstrain output Y data type as 32-bit integer tensor.Constrain 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.Constrain input a and its zero point data type to 8-bit integer tensor.labelslog_probX_LogX_DivSoftmaxCrossEntropyLossWeight rank must be 1.Input and target dimension value mismatch.Target rank must be 1 less than the input rank.weight_gather_temp_1weight_gather_tempconst_one_floatsqueeze_maskNegativeLogLikelihoodLossConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.InputsEinsum expression string.Rank of input does not match the equation indices.Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.X_RSX_ExpX_SubscoresType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.OutputGreaterOrEqualC:\a\5\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains output to boolean tensor.Constrains input to boolean tensor.Constrains input/output to boolean tensors.Constrains input types to all numeric tensors.LessOrEqualDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.Attribute expected to have a one-dim tensorAttribute expected to have tensor typevalue_intsparse_valueConstrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.(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'.(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 mean of the normal distribution.Constrain output types to float tensors.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Upper boundary of the output values.Lower boundary of the output values.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Constrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64Attribute 'value_ints' expect a list of integers.Attribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_stringsvalue_stringvalue_floatsvalue_floatvalue_intsThe value for the sole element for the scalar, int64, output tensor.The value for the elements of the output tensor in sparse format.The value for the elements of the output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.Attribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.Attribute 'value_float' expect a float.C:\a\5\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, int64, output tensor.Constrain output types to be numerics.Constrain input types.(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 float32Invalid shape value: Shape input must be a one-dimensional tensor.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)sub_result_castedsub_resultrangecurrentcond_outpreviloop_body_attributeAll inputs to 'Range' op must be of the same typevariadic_outputceil_result_relu_boolceil_result_relu_intceil_result_reluceil_resultdiv_resultdelta_castedConstrain input types to common numeric type tensors.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).If set, defines the broadcast dimensions. See doc for details.consumed_inputslegacy optimization attribute.broadcastPass 1 to enable broadcasting If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeC:\a\5\s\cmake\external\onnx\onnx\defs\math\old.ccCoefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01.Value of beta default to 0.5Value of alpha default to 0.2Coefficient of ELU default to 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedMaximum value, above which element is replaced by maxMinimum value, under which element is replaced by minDimension on which to do the sort.Number of top elements to retrieveInvalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data.C:\a\5\s\cmake\external\onnx\onnx\defs\generator\old.ccThe epsilon value to use to avoid division by zero, default is 1e-5f.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.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 zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.(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 dropoutIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.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). Constrain output mask types to boolean tensors.The ratio of random dropoutStride along each spatial axis.C:\a\5\s\cmake\external\onnx\onnx\defs\nn\old.ccp value of the Lp norm used to pool over the input data, default is 2.0.Stride along each 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.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.Dilation value along each spatial axis of filter.Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.C:\a\5\s\cmake\external\onnx\onnx\defs\tensor\old.ccWhich axis to split onoutputs...Which axis to concat onrank must be greater than axisWhich axis to concat on. Default value is 1.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Ending indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Axes 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 lengthList of non-negative integers, indicate the dimensions to be insertedList 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]Invalid value of attribute 'axis'(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.'axis' must be in [-rank(indices)-1, rank(indices)]One 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`.Attribute pads has incorrect lengthAttribute value for pads is requiredWhich axis to split on. New shapeOne float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgepaddingsList 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`.length of each outputThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.tiles) is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.width_scaleIncorrect or missing attribute value for starts and endsAttribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'Ranks inferred ( Computes the indices of the {name} elements of the input tensor's element along the provided axis. 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 type of the output tensor is integer.The axis in which to compute the arg indices.C:\a\5\s\cmake\external\onnx\onnx\defs\reduction\old.ccInteger 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_indicesscore_thresholdiou_thresholdDimension mismatch in unification between expected to have rank but has rank expected to have rank >The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.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.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. max_output_boxes_per_classboxesC:\a\5\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccLoop 'body' subgraph outputs should all be tensors but output C:\a\5\s\cmake\external\onnx\onnx\defs\controlflow\old.ccMismatched type for output then= else=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.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.Mismatched tensor element type for output SequenceConstruct is expected to have at least 1 input.C:\a\5\s\cmake\external\onnx\onnx\defs\sequence\defs.cc(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.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].Constrain output types to all tensor types.Constrain split size to integral tensor.Constrain input types to all tensor types.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=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]`. Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1Constrain input types to any tensor type.output_sequenceElement type of inputs are expected to be the same.Input type for input at index is null. Type info is expected.tensorinput_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.Input type for input at index 0 is null. Type info is expected.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).position sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrains input to integral tensors.Constrains input to float tensors.If set, defines the broadcast dimensions.Enable broadcastingC:\a\5\s\cmake\external\onnx\onnx\defs\logical\old.ccC:\a\5\s\cmake\external\onnx\onnx\defs\object_detection\old.ccC:\a\5\s\cmake\external\onnx\onnx\defs\quantization\old.ccConstrain seq_lens to integer tensor.Number of neurons in the hidden layerFirst input tensor must have rank 3A 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.C:\a\5\s\cmake\external\onnx\onnx\defs\rnn\defs.ccOne (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.Couple the input and forget gates if 1.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.C:\a\5\s\cmake\external\onnx\onnx\defs\rnn\old.ccThe 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.fowardint64uint8uint16uint32uint64complex64complex128sparse_tensorfloatfloat16doubleint16int32tensor(seq(map(sparse_tensor(seqmapopaque is required but missing.typeelem_typekey_typevalue_type): Unrecognized type value case (value_info name: data_type is required to be non-empty.value_info' of nameField 'uint64_data) is stored externally and should not have data field.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.) should be stored in ) is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_dataraw_datadouble_data ==> Context: Attribute (name: ) should refer to attribute in parent node.nodeop_type) has zero input and zero output., type: NodeProto (name: Warning: Checker does not support models with experimental ops: ) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field. in initializer but not in graph input is not output of any previous nodes.' of node: Nodes in a graph must be topologically sorted, however input 'Bad node spec: ' has been used as output names multiple times.No opset import for domain ' with domain_version of No Op registered for is deprecated in domain_version of Op registered for graph' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'TensorProto (tensor name: ) should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values ( values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (ConstantFillopaque_typesparse_tensor_typeNOT_SET) vs (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: ( is out of boundsinput output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.Warning: Unsupported operator Shape inference error(s): were provided inputs but Graph has , node name: (op_type:: (Inferred shape and existing shape differ in dimension inferred=type case mismatch. existing=type case unsupported. existing= models with experimental operators: Warning: Shape inference does not support. No schema registered for this operator.) is not equal to the existing dim value (Dimension value inferred (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.onnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.FunctionProtolist count bytemap range DFA out of memory: size SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError reverse compiling 'pattern too large - reverse compile failedInvalid RE2: text size: endpos: startpos: RE2: invalid startpos, endpos pair. [Unknown encoding C:\a\5\s\cmake\external\re2\re2\re2.cc': Error parsing 'Error compiling 'pattern too large - compile failedUnexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )Regexp not destroyed.C:\a\5\s\cmake\external\re2\re2\regexp.ccBad reference count trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledStack not empty.C:\a\5\s\cmake\external\re2\re2/walker-inl.hWalk NULLOOOOOOOOOOOOOOshould never happenC:\a\5\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classMissing case in Compiler: AddFoldedRange recurses too much.C:\a\5\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad hex digit AnyBad call to ParseState::ParsePerlFlagsunhandled C:\a\5\s\cmake\external\re2\re2\nfa.cc in stepUnhandled in AddToThreadq in ComputeFirstBytecontext does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: unhandled opcode: C:\a\5\s\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByte|RunStateOnByteUnlocked failed after ResetFailed to analyze start state.Unexpected special state in RunStateOnByteStateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCachejob_.size() = njob_ = GrowStack() failed: C:\a\5\s\cmake\external\re2\re2\bitstate.cc arg Unexpected opcode: Bad arg in kInstAltMatch: Bad arg in kInstCapture: Cannot use SearchOnePass for unanchored matches.C:\a\5\s\cmake\external\re2\re2\onepass.ccUnexpected opcode in IsMatch: C:\a\5\s\cmake\external\re2\re2\prog.ccCase not handled in ComputeSimple: C:\a\5\s\cmake\external\re2\re2\simplify.ccDoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is Malformed repeat Simplify case not handled: OOxOOO OO OOTOOTOO(OO(O OOOO@OPO0OPO`OOPOOHO\O8O\OhOOXOOOOxOOOOOOOOOOO@OO@O(O0OO0OOXOOXOOOOOpOOlOO[:^blank:][:blank:][:^cntrl:][:cntrl:][:^digit:][:digit:][:^graph:][:graph:]\W\w[:^alnum:][:alnum:][:^alpha:][:alpha:][:^ascii:][:ascii:][:^upper:][:upper:][:^word:][:word:][:^xdigit:][:xdigit:][:^lower:][:lower:][:^print:][:print:][:^punct:][:punct:][:^space:][:space:]\D\d\S\s09AZ__az ~ !~!/:@[`{~ 09AZazAZAZaz 09AFaf09AZ__az0909azPIPXP0IP@PHPlP3P@AP#`PPP@xPxP(PPP-PP -PP~PPH~PPhPP0aP P8vPP$PLP$PDPP$PhPP@$PP`$P`PX$P%P$PHPp$PP$P8JP$PP@%Px'P4%PDPT%PEPL%PFPd%PIP \%Ph-Px%PXBPp%PP$PxP$PFP%PHP$PHP%PP %PVP@N[P %PP%PLP<$PP8$PpPAdlamBengaliBatakBopomofoBhaiksukiBrailleBrahmiBuhidBugineseAnatolian_HieroglyphsAhomArmenianArabicBalineseAvestanBassa_VahBamumJavaneseInscriptional_ParthianKannadaKaithiKayah_LiKatakanaKhmerKharoshthiHanunooHanifi_RohingyaHebrewHatranImperial_AramaicHiraganaInscriptional_PahlaviInheritedLisuLinear_BLmLlLtLoLycianLuKhudawadiKhojkiLaoLLepchaLatinLinear_ALimbuCommonCoCsCopticCypriotCuneiformDeseretCyrillicCarianCanadian_AboriginalCcCaucasian_AlbanianChakmaCfCherokeeChamGranthaGothicGujaratiGreekGurmukhiGunjala_GondiHangulHanDograDevanagariEgyptian_HieroglyphsDuployanEthiopicElbasanGlagoliticGeorgianOriyaOld_TurkicOsmanyaOsagePalmyrenePahawh_HmongPcPau_Cin_HauOld_HungarianOl_ChikiOld_North_ArabianOld_ItalicOld_PersianOld_PermicOld_South_ArabianOld_SogdianRejangPsalter_PahlaviSamaritanRunicScSaurashtraShavianSharadaPePdPhags_PaPfPiPhoenicianPsPoMeMcMeetei_MayekMedefaidrinMeroitic_CursiveMende_KikakuiMiaoMeroitic_HieroglyphsMahajaniLydianMalayalamMakasarManichaeanMandaicMasaram_GondiMarchenNew_Tai_LueNdNkoNewaNoNlOghamNushuModiMnMroMongolianMyanmarMultaniNabataeanNZsZpSundaneseSoyomboSyriacSyloti_NagriTagbanwaTagalogTai_ThamTai_LeSignWritingSiddhamSkSinhalaSoSmSora_SompengSogdianTirhutaTifinaghVaiUgariticYiWarang_CitiZlZanabazar_SquareTakriTai_VietTangutTamilThaanaTeluguTibetanThai JMO`j0J'  @n_n4Mh  89;>@DFFJPl!<Up4Vn!`l,.,0,^,$$   ?? 88iiª۪ߪ3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq @[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++++.N.//00000 00070<0?00000001111 2_222X33MM!09..ϩϩ[[>?0RTfhk ;@[eppooWW  ? ? 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