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F?0F?@F?PF?`F?pF?F?@S?PS?`S?pS?U?0o?po?p?r?@t?u?u? x?px?x?py?y?pz?z?@{?{? ?p?p??????А??0?? ?@?`?p??`?0?`??г? ?P???0????p????`@ @@ @`@(@0(@P(@p(@(@)@0)@P)@)@ *@*@@+@ ,@0,@,@,@,@,@-@-@:@;@?@@@P@@@@@@f@h@i@ i@0i@ j@k@l@ n@`q@r@pz@{@{@|@`}@}@`~@@@ @@`@ @е@@@P@@@@p@@@P@@@AAA A A0A AAA@"A0#A)A)A*A4A9A0?ApDA`IA0]AwA@xA0AA@A`AAA`A`A`A0ABp+B01B=BYB@}B BB0B@BBBPBBPBBpBCC0$C%Cp4C@P@PAPPBP@CP`GPGP@HPRPRPPTPTPWPXP[P[P[P[P ]P]P_P_P@`P hP0hP@hPPhP`hPphPjPpPrP0xPP|Pp}PPpPPPP0P@PPPP PpP P0P@PPP`P`PP P QFQFQGQ GQ@GQ`GQGQGQvQwQQQQQQQ@QRRR$R $Rp.R.R9R9R:R0IRNRQRURXRXR0\R_R_R_R `RiRiR0kR mRnRrRJUKUpKUKUKULULU MUNUOUOUPUQUPRU@SUSU0TUTUUUUUUU`VUVU@WUWU`XUpXUXUXUXUYUYUYU`ZUZU@[UP[U[U[U\UP\U\U\U]UP]U`]Up]U]U]U]U]U]U]U]U]U^U^U ^U0^Up^U^U^U0_Up_U_U_U0`Up`U`U`U0aUpaUaUaU0bUpbUbUbU0cUpcUcUcU0dUpdUdUdU0eUpeUeUeU0fUpfUfUfU0gUpgUgUgU0hUphUhUhU0iUpiUiU0jUjU@kUPkUkUkUlUlUlUmUpmUmU`nUnU`oUoUoUoU`pUpUpU`U]?QUnknown exception(]?Q]?Qbad array new lengthstring too long]P?]?`y@(cannot determine missing fields for lite message)D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too long]XcD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\stubs\common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s ]`X^``@c`p` b0P ]W%I64u00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899>?456789:;<=  !"#$%&'()*+,-./0123>456789:;<= ? !"#$%&'()*+,-./0123ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_falsetrue\x%02xty10invalid string position@D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google/protobuf/parse_context.hCan't happen]ЖЗК]PP``y@D:\a\ml-builds\ml-builds\onnxruntime\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_): ]]]Ц]@]@P]`@`y@]P]У``y@D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: INVALID_ARGUMENT:  @ V@VDVPV]@D0CD:\a\ml-builds\ml-builds\onnxruntime\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. ]  `...\\?\\/D:\a\ml-builds\ml-builds\onnxruntime\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_api-ms-win-core-synch-l1-2-0.dllkernel32.dllSleepConditionVariableCSWakeAllConditionVariable@]bad allocationiip33P0 DFCGEEEEEEGGG0GFF @@`0` 33333`3333 p  3`3 333`333о3`33333 @33333 PKAP @!#Л 3 :`=p3@3 F@03 3P3333 33p@` PE3P3`&Ap56 pC'(( )@*`+,105P5p6Ч9P9333B094`39 :"V/\ ]= was false. Stacktrace: ]`!=D:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()CPUExecutionProvider]ORT_LOAD_CONFIG_FROM_MODELInteger overflowSafeIntExceptionHandler::SafeIntOnOverflowD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/common/safeint.hTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\a\ml-builds\ml-builds\onnxruntime\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()OrtCreateValueImplSeqHelperD:\a\ml-builds\ml-builds\onnxruntime\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 rangeCUDA execution provider is not enabled.1.6.0p]#@"0T 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 > > >::checkD:\a\ml-builds\ml-builds\onnxruntime\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]p!p!p$p0 "0*p]!!p$0 "0*]!!$00"0* != onnxruntime::DataTypeImpl::GetType() == type_len >= 0 && static_cast(len) < std::numeric_limits::max()OrtCreateMapMLValuep] #@"`@*Encountered nullptr.not implementedexecution_mode is not validgraph_optimization_level is not valid]`JpL@]KȰ]?Qunknown]?Q is not implementedȲ]onnxruntime::OpKernel::ComputeAsyncResult buffer is not large enough]`00i<l<]' in custom op 'Unsupported version 'onnxruntime::CustomOpKernel::CustomOpKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\custom_ops.ccDescriptionAonnxruntime::CreateCustomRegistry]@"]#@"]0@"]  `0 "0*]`  "0*]P "0*No requested allocator availableEnv is nullOrtMemoryInfo is null]?Qbad castȺ]?Q, Attempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/common/logging/logging.h]`jPo@x0@Ћp]i]CpuH]`HHȻ]o`J5J5ОPQ5PQ5PQ5PQ50J50J50J5CUDAExecutionProviderDmlExecutionProvider has already been registered.Provider onnxruntime::ExecutionProviders::AddD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/execution_providers.hTrying to get a SparseTensor, but got: IsSparseTensor()onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/feeds_fetches_manager.h]oCompute_Create_State_Release_State_@]@o@0]p@0L]o xx]@p@MemcpyTransformer]n`J5@ J5 ОPQ5PQ5PQ5PQ50J50J50J5x]n]n`x}ORTMp]p] [json.exception.]pother_errorsession.use_env_allocatorssession.load_model_formatsession.save_model_formatsession.set_denormal_as_zero%Y-%m-%d_%H-%M-%S21.4.0The environment variable contained the value: are '0' and '1'. The only supported values for the environment variable Reading the provided model for the ORT configonnxruntime::FinalizeSessionOptionsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedonoffFlush-to-zero and denormal-as-zero are onnxruntime::InferenceSession::ConstructorCommon::::operator ()Could not finalize session options while constructing the inference session. Error Message: status.IsOK()onnxruntime::InferenceSession::ConstructorCommongraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is trueintra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing 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.session_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionCould not parse model successfully while constructing the inference sessionst.IsOK()resultError during EndProfiling(): onnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec providerExecution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.So disabling it for this session since it uses the DML 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 DML Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Parallel execution mode does not support the CUDA Execution Provider. onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryonnxruntime::InferenceSession::SaveToOrtFormatException during loading: This session already contains a loaded model.onnxruntime::InferenceSession::LoadUnknown exception in Load()Encountered unknown exception in Load()ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Failed to load model because protobuf parsing failed.model_loading_arrayModelProto 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_protoonnxruntime::InferenceSession::TransformGraphCould not find an implementation for the node ( (Node placements].All nodes have been placed on [: [ Provider: [onnxruntime::InferenceSession::PartitionOrtFormatModelonnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModelThis session has already been initialized.Not satisfied: fbs::VerifyInferenceSessionBuffer(verifier) InferenceSession is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_session Serialized version info is null. Invalid ORT format model.Satisfied, but should not be: fbs_ort_model_version == nullptr ] is not supported this build The ORT format model version [Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) Missing Model. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_model SessionState is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sess_state Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Initializing session.onnxruntime::InferenceSession::InitializeModel was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.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.Session successfully initialized.Encountered unknown exception in Initialize()session_initialization Please fix either the inputs or the model. Expected: Got: Invalid rank for input: for the following indices Got invalid dimensions for input: index: ) , expected: (Unexpected input data type. Actual: ( elements.elements, but feeds has Size mismatch: feed_names has Invalid Feed Input Name: is not expected to be of type tensor.Input with name: onnxruntime::InferenceSession::ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output vector pointer is NULLAt least one output should be requested.p_fetches->size(): Output vector incorrectly sized: output_names.size(): Invalid Output Name:Session was not initializedonnxruntime::InferenceSession::RunSession not initialized.Running with tag: Encountered unknown exception in Run()model_runonnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingProfiler is disabled.onnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggerExceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevelonnxruntime::InferenceSession::AddPredefinedTransformers/961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1]g@"j0]i.json_model_loading_uri failed:Load model from ]ionnxruntime::LoadOrtModelBytes bytes were able to be read. failed. Only list too long] g@"0j o]PfPfhPp "0*localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeString]ffhppg0*]ffpjp "0*]ffkp0"0*]`f`fh`pg0*]ffjp "0*??onnxruntime::IOBinding::BindInputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\IOBinding.ccp_providerh]0j@@]0j;@env_ptr == p_instance_OrtEnv::ReleaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\ort_env.cc oVsession_options: value']parse errorparse_error, column at line P]pinvalid_iterator]ptype_error`]pout_of_range8]_0jort_configUnsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeSequential modeParallel modeSetting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting enable_profiling to Json stored in the `ort_config` key cannot be parsed. Error message: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()The Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoORT config json from the model: The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: ' not foundkey 'cannot use at() with nullcannot use key() for non-object iteratorsinvalid map keyobjectarraystringbooleandiscardednumbercannot compare iterators of different containerscannot get valuesyntax error while parsing - ; last read: 'unexpected ; expected invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literaltrue literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 bytevector too longobject keyobject separatornumber overflow parsing 'iterator does not fit current valueiterator out of rangecannot use erase() with ] >@"type must be number, but is com.microsoftcom.microsoft.nchwccom.microsoft.mlfeaturizers]0,[TypeInferenceError] expected to have tensor typeInput unknownElement type of input Output expected to have sequence type expected to have type but instead is null@]0,tensor(uint32)tensor(uint64)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(bfloat16)tensor(uint8)tensor(uint16)tensor(int8)tensor(int16)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)8] line , but it is already registered from file ) from file version: (domain: Trying to register schema with name known by the checker., but it its domain is notin onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you in the inclusive range [, but it its version is not Schema error: Allocator with this OrtMemoryInfo is already registered.Only CPU devices are supported for now.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.Exception caught: inter-opD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\environment.ccMemcpyFromHostTinputXoutputYConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHosth]P "0*]00g0*h]  ` "0*]P "0*onnxruntime::GraphTransformer::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered D:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/graph/graph.h Max:Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplonnxruntime::GraphTransformer::RecurseD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/optimizer/graph_transformer.hInserted_Castcast node to cast from float16 to float32 on cpuCasttoh]@o@\RemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImpldtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()VitisAIExecutionProviderTensorrtExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::MemcpyTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'MemcpyCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializers8]0*]0"0*8]й0"0*ACLExecutionProviderArmNNExecutionProvider]0]@ ]@o@:AttentionFusion0]@o@ОBiasGeluFusion]@o@BiasSoftmaxFusion0]PCastElimination]@o@0CommonSubexpressionElimination0]@o@- ConvActivationFusion]I @@ ConvAddFusionConv0]h Y ConvBNFusion]0  ConvMulFusion0] EliminateDropoutDropout]@o@P DynamicQuantizeMatMulFusion0]@o@ EmbedLayerNormFusion]p" ExpandEliminationExpand0]@o@# FastGeluFusion]@o@Q GeluApproximation0]@o@[ GeluFusion]@o@Pk GemmActivationFusion0]0 EliminateIdentityIdentity]@o@ LayerNormFusion0]@o@ SimplifiedLayerNormFusion]@o@ MatMulAddFusion0]@o@ MatMulIntegerToFloatFusion]@ MatMulScaleFusion0]@o@. NchwcTransformer]@o@ NhwcTransformer0] p FuseReluClipRelu]@o@ ReshapeFusion0]  ShapeToInitializerShape]@o@ SkipLayerNormFusion0] EliminateSliceSlice]PP(  UnsqueezeEliminationUnsqueeze_RuleBasedTransformerLevelUnsupported levelonnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer_utils.ccUnsupported level onnxruntime::optimizer_utils::GenerateTransformersonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::Initializer::InitializerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsSlice ends is less than INT_MAXExpected value:Slice parameter is not expected. Input index:Start MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeGemmConcatSqueezeFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueGatherFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathaxesunsqueeze_after_gather axes value not expectedaxisgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerunidir mask is not constantonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphWhereDivFaild to match the path (Div-->Where-->Add) for unidirectional maskSubFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphSoftmaxFailed to find Softmax nodeOutput edge count not expected for SoftmaxAddMulFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertEqualFailed to find mask pathwhere const not matched.MatMulFailed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphTransposeFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpermpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32Cast mask from int64 to int32MaskCastStart FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulSplitFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs Attentionnum_headsunidirectionalFused an attention node for GPT.qkv_weightsqkv_biasMask shape is unknown or not 2D, or data type unknownonnxruntime::ConvertMaskToInt32D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplLayerNormalizationshape of layer norm bias tensor not expectedTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32onnxruntime::FuseSubGraphQKFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraph@  ai.onnxonnxruntime::BiasGeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\bias_gelu_fusion.ccGeluFastGeluBiasGelufused Add and Gelu and is not in valid range [-axis axis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxisD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/common.h into softmax(input + bias)fused BiasSoftmaxsoftmax_axisbroadcast_axisonnxruntime::BiasSoftmaxFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\bias_softmax_fusion.ccinvalid unordered_map keyonnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.[ of node Not eliminating output 0]0 @p Could not find OrtValue with name 'ConstantFoldingonnxruntime::ConstantFolding::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of Unexpected data type for Clip input of onnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_activation_fusion.ccClipminmaxonnxruntime::ConvActivationFusion::ApplyImplSigmoidTanhLeakyRelualphawith activation fused Conv FusedConvactivationactivation_params data type is not supportedonnxruntime::Initializer::ToProtoconv_W_tensor_protoonnxruntime::ConvAddFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_epsilonbn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_BatchNormalizationonnxruntime::ConvMulFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_ onnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeMatMul̼+_Int32Cast Input from int64 to int32_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second 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.two paths share the same shapeNonZeroConstantOfShapeRangeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.position_embeddingsmask_indexfused EmbedLayerNorm subgraphs EmbedLayerNormalizationWord embedding shape not expected.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplPowonnxruntime::FastGeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs GPT2Gelu'7=*BL?@@]0J @J FreeDimensionOverrideTransformerInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of with a fixed dimension size ' The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplonnxruntime::GeluApproximation::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: onnxruntime::GeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs ??EluHardSigmoidSeluSoftplusSoftsignScaledTanhParametricSoftplusThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused Gemm FusedGemmactivation_ '7onnxruntime::LayerNormFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\layer_norm_fusion.ccReduceMeanSqrtfused LayerNorm subgraphs onnxruntime::SimplifiedLayerNormFusion::ApplyImplSimplifiedLayerNormalizationonnxruntime::MatMulAddFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add gemmonnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\matmul_integer_to_float.ccMatMulIntegerConstant initializer NodeArg shape should not be null. NodeArg: shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\matmul_scale_fusion.ccdiv_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodemul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesFusedMatMulTransposeMatMulFused MatMul and Scale_FusedMatMulAndScaleonnxruntime::MatMulScaleFusion::ApplyImplonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::GetUnsupported data type: onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`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 ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()reorderReorderInputpadsstridesdilationskernel_shapeauto_padgroup_nchwcspatialbn_scalebn_B_bn_nchwcReorderOutputchannelschannels_lastmodenearestcoordinate_transformation_modeasymmetricnearest_modefloorUpsamplescalesMaxPoolAveragePoolSumResizeGlobalMaxPoolGlobalAveragePoolonnxruntime::NchwcTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\nchwc_transformer.ccdeque too long  DequantizeLinear_nhwcQLinearConvQLinearAddQLinearMulQLinearLeakyReluQLinearSigmoidQLinearGlobalAveragePoolonnxruntime::NhwcTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\nhwc_transformer.ccis not supported.data type Unexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantFuseReluClip_onnxruntime::ReshapeFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: startsendsCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph ̼+onnxruntime::SkipLayerNormFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs SkipLayerNormalizationUnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\unsqueeze_elimination.ccExternal data type must not be UNDEFINED or STRING.onnxruntime::Initializer::ReadExternalRawDataD:\a\ml-builds\ml-builds\onnxruntime\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. Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomial Vw+2'7]u 07P707`7]0u @7 Px pu onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\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=]@ @ ` P g0*]Ѓ   ]#@" Ѕ @   0 p     P 0 0 _`b`]`[v u w x p `      0 f d @j g  h 0i n Pk `l pm           0  У P   0 @    y   ` 0   Й `  0  p      z o | p*)n@0P0@v@0 0@@@@@Pб`pPИpp0DP-00>Ш0[0P GFKHIJPLMOpE`D 0 Pcp  P       3LKTn `0л pr  { p } P^ Т          ,+-/PP@  j p P!Q!S!p ^!]!b!a!v!@!P!p!+P!"""`" ""`""@ Б   p -#,#+#5#0 0PP`@X>#`@!0p@P/@ua#p#p# #p#P#I$#N$0h$ ~  `:$P$$klPPШвЭPT! U!0V! 0 0@ 0`p0@   ` @ PP`p@P`p$n%`;`:`?`<`=`>`C`@`A`B`9`8  %0%%%C P%P$P&'` 0 p R `!_!@e! d!p%P%%С%0   p P 0  P p ж    r q s t  ` ` ` `          p [ Z _ \ ] ^ c ` a b      P Т С P  Z 0 i$@O$ /JIН О П Р С $@<@@PPP@W!@X!@Y!P0bPPonnxruntime::RegisterOnnxOperatorKernelsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\cpu_execution_provider.ccЅ %%%%%%%`&@0& /&.&,&+&*&H&W&i&&&'5'4'3'6'k'j'0m' l'p''p''' ')p)+ n&v&Pp&k&t&y&r&onnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryЅ :+;+6+9+8+5+7+<+onnxruntime::contrib::RegisterNchwcKernelsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.ccЅ a+Д+ ++`+@+ + + + +++++ ,09,:,pL,!onnxruntime::contrib::RegisterQuantizationKernelsЅ Pg,pk, ,,,,@,-$`-$0----`90-Э-p----p..p.(.<.0?.L.p.. .p.S.R.U.T.p.p.p.C0onnxruntime::contrib::RegisterCpuContribKernelsD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/op_kernel.h]@  ]д  min_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/clip.hMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::utils::MLTypeCallDispatcher::Invokehelper.called_ == 1 ]  P@  "0*min should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.max->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()'is defined.No attribute with name:'Attribute name and type don't match for 'p]]   @ ]P   @ ] P P  ] p ] `   ] P ] p   ] @ ]   @ ]  ]  `  ]  ]  ]   @ ]  ]  ` @ ]  ]  ]p    ] p input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::functors::HardSigmoid::Initbetah]@  P  ]    unknown kernel typeonnxruntime::functors::ScaledTanh::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ParametricSoftplus::Inith]  `    0*]  `  @ 0*h]` ` `  @ 0*]    `  @ 0*h]  `  @ 0*]  `    0*h]` ` `  @ 0*]    `    0*h]  `    0*]  `    0*~3a*7.A\3Ӡ5JCy7֕89 ':;Y;Y;??@@@.@9@>@@@O@pAAA%O~3a*~3a*~3a*~3a*A\3A\3A\3A\3Ӡ5Ӡ5Ӡ5Ӡ5JCy7JCy7JCy7JCy7֕8֕8֕8֕8giP9giP9giP9giP99999 ': ': ': ':C:C:C:C:;;;;Y;Y;Y;Y;Y;Y;Y;Y;<<<<*=*=*=*=*>*>*>*>????1?1?1?1?????;?;?;?;?@@O@O@O@O@BBBBBBBB%%%%7777^^^^OOOO¥¥¥Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Output((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOh]]ph]]`h]]0h]` by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::BroadcastIterator::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid.Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!offset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:Invalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Broadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocator] P@ h]@]h]]@h^^0h^0^h^^@h^@^h^`^h^^`h^ @ ^ `Abs^h^ p8^ @0 ^^ P ^ ^X ^ ( ^` ^ ^   ^^H ^0 `^P ^^P 0^0^8^Ђ ^^^ p@^px^(^p Ѝ^^ 0@  ^ Neg^ p ^ ^ P ^P^P  ^0^0  ^P^І @  ^ Floor^ @  ^ Ceil^p  @  ^ Reciprocal^ p@  ^ ^ p ^ pT1^ иExp ^0 p^  ^p @@  ^ pLog !^0!^@ "^Max"^pMin #^0Less#^ $^$^0 %^0Greater%^ &^&^0 '^P'^P (^(^ )^P)^Mean *^*^д BitShift +^д  +^д  ,^NotAndOrXorUnsupported X type: Must have 1 or more inputsinput_count >= 1onnxruntime::Sum_6::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapedata_1.Shape() == shapedata_n.Shape() == shapeinputCount >= 1onnxruntime::Min_6::Computeonnxruntime::Max_6::Computeonnxruntime::Mean_6::Compute,^Sin -^-^ Cos .^Tan.^Asin /^PAcos/^0Atan 0^Sinh0^0Cosh 1^pAsinh1^Acosh 2^Atanh2^@PReluExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpand 3^`3^д fmodfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModModfmod attribute must be true for float, float16 and double typesfmod_onnxruntime::Mod::Computeonnxruntime::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 >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKerneldirectiononnxruntime::BitShift::BitShiftLEFTRIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()onnxruntime::utils::MLTypeCallDispatcher::InvokeBroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooper 4^``!0*4^`  @ 0* 5^```  p0*5^  `  @ 0* 6^` P!p0*6^` @!@ 0* 7^``` 0!@ 0*7^`  @ 0* 8^`  @ 0*8^  `  !0* 9^```  00*9^  `  0* :^` !p0*:^` !@ 0* ;^`  0*;^` @ P0* <^  `  0*<^`  0* =^``` p 0*=^``` 0 00* >^`  0*>^``` 0* ?^  ` ` p0*?^` P @ 0* @^  `!0"0*@^@@p!0"0* A^!0"0*-DT!<@C,FFt.t.t.t..4.4.4.4 s7 s7 s7 s7Ӆ<Ӆ<Ӆ<Ӆ<*=*=*=*=!=!=!=!=%=%=%=%=>>>>L>L>L>L>>>>>ʫy>ʫy>_6.>_6.>K͉ ?K͉ ?"?"?"?"?5?5?5?5?9d?9d?~, ?~, ?????t ?t ???@.?@.???+eG?+eG?@@ @ @"~+@"~+@BBBB,F,F,F,FFFFF@K@K@K@KpMpMpMpMnnnn뽸뽸뽸O]O]O]O]****P*P*P*P*ɿɿɿɿW6}+W6}+A^meaninfo.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/generator/random.hscaleinfo.GetAttr("scale", &scale_).IsOK()seedinfo.GetAttr("dtype", &dtype).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK() B^onnxruntime::RandomNormalLike::RandomNormalLikeB^0highinfo.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformlowinfo.GetAttr("low", &low_).IsOK() C^Чonnxruntime::RandomUniformLike::RandomUniformLikeC^Psample_sizeinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDT2input count mismatchD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of FLOAT16 is not supportedonnxruntime::RandomNormalComputeonnxruntime::RandomUniformComputeonnxruntime::MultinomialComputeJ@AAOleft.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0 D^ptransAinfo.GetAttr("transA", &temp).IsOK()onnxruntime::Gemm::GemmD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/gemm.htransBinfo.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\gemm.ccD^ "0* E^д 0onnxruntime::Hardmax::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\hardmax.cc, D=. N=Hardmax inputs N, D and N * D must be < HardmaxE^ P F^ LogSoftmaxonnxruntime::Softmax::ComputeImplOpset13D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13F^`8;gonnxruntime::MatMulComputeHelper::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/matmul_helper.hNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 MatMul dimension mismatchNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: K_ == right_shape[0] G^p?G^8 H^Aonnxruntime::MatMul::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::ComputeH^ kop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKD:\a\ml-builds\ml-builds\onnxruntime\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 processedpI^ onnxruntime::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 negativeonnxruntime::TopkOpset11ConstructorCommonlargestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()I^ pJ^ 0ITopK] should not be greater than specified axis dim value [k argument [output count mismatch, expected 2 outputs to be present for TopK operatorJ^0*pK^00*K^`0*pL^@0*L^@p0*pM^0*M^0*pN^  0*N^p00*pO^p0*O^PP0*pP^0*333333?Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: P^д pQ^д  onnxruntime::BatchNorm::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormNOTSETVALIDSAME_UPPERSAME_LOWERUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShape W: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. X: X num_dims does not match W num_dims. group: kernel channels: C: Input channels C is not equal to kernel channels * group. M: Output channels M is not divisible by group.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: onnxruntime::ConvAttributes::InferOutputShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/conv_attributes.hInvalid input shape: Q^ABonnxruntime::Conv::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv.ccoutput_paddingoutput_shapegroup count is <= 0 num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapepad type not supportedpR^ }`}ConvTransposeonnxruntime::ConvTranspose::DoConvTransposeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv_transpose.ccR^д 0info.GetAttr("axis", &axis_).IsOK()onnxruntime::Flatten::FlattenD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeFlattenInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: vs. Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count pS^д pInstanceNormalizationonnxruntime::InstanceNorm::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\instance_norm.ccop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/instance_norm.hS^  LpNormalizationop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/lp_norm.hpop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2pT^ X->Shape().NumDimensions() == 4onnxruntime::LRN::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\lrn.ccLRNsizeinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRND:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fbiasT^0*t@GlobalLpPoolNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolProcessContext::initD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/pool_base.hpU^@S_Input dimension cannot be less than 3.onnxruntime::PoolBase::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\pool.ccNot satisfied: input_dims >= 3 Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() pV^SSV^SSW^SSLpPoolonnxruntime::Pool::ComputeNot satisfied: x_shape.NumDimensions() >= 3 Unsupported pooling size : onnxruntime::MaxPoolV8::ComputeImplX^u|`0w0*X^Pv~p`w0*Y^v~pw0*Y^qx~0w0*Z^rP|~@w0*Z^pt|0Pw0*[^pw~0w0*[^0r@|~@w0*\^s| Pw0*\^q{~0w0*]^sp|@w0*]^pu|PPw0*^^pqy~0w0*^^s`|@w0*_^t|@Pw0*_^0`MaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0a^ReduceL1Hb^b^ReduceL2hc^c^@ReduceLogSumd^`e^ReduceLogSumExpe^ 8f^ReduceMaxf^Xg^`g^xh^i^ i^@(j^ ReduceMinj^ Hk^ k^ hl^ l^@ReduceProdm^` n^ n^ReduceSum8o^o^Xp^p^`ReduceSumSquarexq^r^@r^ ArgMax(s^0s^Ht^ArgMint^Input to be reduced is nullinput_tensor_ptr != nullptronnxruntime::SetupForReduceD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccThe input shape override's size does not match the input tensor's shape sizeinput_tensor_ptr->Shape().Size() == input_shape_override->Size()keepdimsinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/reduction/reduction_ops.hnoop_with_empty_axesselect_last_indexonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:keep_dimsonnxruntime::ReduceSum::Implonnxruntime::ReduceSum::Implonnxruntime::ReduceSum<__int64>::Implonnxruntime::ReduceSum::ImplAxes input is nullaxes_tensor != nullptronnxruntime::CommonReduceAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1keepdims_Reduction on all axes, output size should be 1.count == 1onnxruntime::NoTransposeReducelast_results.last_loop_red_size > 0last_results.last_loop_size > 0last_results.projected_index.size() > 0hu^0[0[b0*u^\\Ppp0*hv^^^pp0*v^]]|0*hw^P^P^@0*w^\\j 0*hx^0^0^`~00*x^p^p^ЀP0*hy^]]y0*y^p]p]wЏ0*hz^[[pb0*z^[[j0*h{^__00*{^\\o`0*h|^ZZ0a0*|^[[h0*h}^]]s0*}^p_p_0А0*h~^^^0`0*~^p\p\mP0*h^^^ } 0*^P]P]Pv0*h^__`0*^0\0\Pk00*h^[[g0*^__P0*h^]]{0*^]]x0*h^0]0]u0*^\\Pq0*h^^^Ѕ0*^p[p[eЎ0*h^P\P\pl@0*^P[P[c0*h^0_0_P0*^[[pf0*h^^^p0*^ZZ_0*h^\\pr0*^P_P_0*forwardreversebidirectional'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hh^`info.GetAttr("direction", &direction).IsOK()onnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hlinear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclipclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2GRUGRU operator does not support double yetInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataAsSpanlda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointer^`p0*^p0*onnxruntime::LSTMBase::LSTMBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\lstm_base.hinput_forgetactivation_func_names.size() == static_cast(num_directions_) * 3^`PLSTMLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cc^,,RNNonnxruntime::RNN::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\rnn.ccAffineinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNND:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()X^??@ @ "0*invalid stoi argumentstoi argument out of rangeinvalid stol argumentstol argument out of rangeinvalid stoul argumentstoul argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stof argumentstof argument out of rangeinvalid stod argumentstod argument out of range؍^д Attribute to is not set.onnxruntime::Cast::CastD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast::ComputeCheck for duplicate types in MLTypeCallDispatcherhelper.called_ < 2onnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::utils::MLTypeCallDispatcher::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>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::InvokeUnsupported type in cast op: from String to onnxruntime::`anonymous-namespace'::CastFromStringDataNaN-INFINFMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/concat.hnew_axisX^ onnxruntime::ConcatBase::PrepareForComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\concat.ccNot satisfied: input_count >= 1 Cannot concatenate scalarsNot satisfied: inputs_0_rank > 0 got: Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Not satisfied: dim_value == inputs_0_dims[axis_index] Not satisfied: data_n.DataType() == p.output_tensor->DataType() Missing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/gather.hX^д "Tindonnxruntime::Gather::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather.ccType for Tind not supported yet in Gather. must be within the inclusive range [indices element out of data bounds, idx=X^((*P+ "0*ؐ^(((@+ "0*Tensor type mismatch.type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawX^@4ؑ^5X != nullptronnxruntime::IdentityOp<0>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<1>::ComputeconstantreflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueX^pdims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::InitPadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Unsupported input data type of onnxruntime::PadValueFromFloatD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\pad.ccPads 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]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Input tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesdata_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroA dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\ml-builds\ml-builds\onnxruntime\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.i < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeX^@A shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/reshape.hؓ^Attribute shape is not set.onnxruntime::Reshape_1::Reshape_1X^ؔ^SizeMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()X^X^Unexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0Starts must be a 1-D arraystart_tensor.Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arrayends_tensor.Shape().NumDimensions() == 1Starts and ends shape mismatchstart_tensor.Shape() == ends_tensor.Shape()Starts and axes shape mismatchnullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()Starts and steps shape mismatchnullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Missing input tensor to be processedonnxruntime::SliceBase::ComputeCannot slice scalarsoutput == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()^д blocksizeAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/space_depth_ops.h`^д @1SpaceToDepth^ .DepthToSpaceinput_shape.NumDimensions() == 4onnxruntime::SpaceToDepth::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccinput_height % this->blocksize_ == 0input_width % this->blocksize_ == 0input.Shape().NumDimensions() == 4onnxruntime::DepthToSpace::Computeinput_depth % (blocksize_ * blocksize_) == 0DepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpace*>e@splitInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/split.hp^ЀP NumOutputs= Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=Cannot split using values in 'split' attribute. Axis= yetSplit operator does not support onnxruntime::Split::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\split.ccAn split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1onnxruntime::Split::ComputeImpl. shape= must be 1 instead of Dimension of input input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/squeeze.hp^0onnxruntime::Squeeze::Computep^pTileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\tile.ccv >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: ^Method IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 0onnxruntime::IncrementIndexAndComputeOffsetSetupD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\transpose.cc(local_source >= source) && (local_source < source + num_blocks * blocksize)onnxruntime::DoTransposeImplTranspose not implemented for empty tensors.num_axes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)onnxruntime::DoTransposeEltWise(local_source >= source) && (local_source < source + num_blocks)Mismatched data types between input and output Tensors. onnxruntime::Transpose::Compute(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::TypedDoTransposeEltWiseMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/unsqueeze.h^onnxruntime::UnsqueezeBase::PrepareComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::Computelinearcubicinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/upsample.hinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuehalf_pixel model uses the deprecated attributeyet this opset `tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or (default) or . It can only be mode attribute is onnxruntime::UpsampleBase::StringToUpsampleModepytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resize] is not supportted!coordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceilceil] is not supported!nearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.scale >= 1onnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.scale > 0Resize operatorUpsample operator'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)scales size should be greater than 0.scales_size > 0onnxruntime::UpsampleBase::ParseScalesData Output dim value: Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. output_dims[i] == 0onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize^@ ^@`^@onnxruntime::Upsample::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.roi_input_idx_ > 0Only one of scales or sizes must be provided as input.sizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Resize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseCompute^++/2 "0* ^,,/@3 "0*^@*@*.2 "0* ^0+0+@/3 "0*^P*P*0.2 "0* ^++.2 "0*^`*`*.2 "0* ^ + + /3 "0*^--/p3 "0* ^**.2 "0*^0*0*-2 "0*Resize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension. ^-/`3Pw0*^@+/ 3-0* ^,/P3Pw0*^)-p2-0* ^p*.2Pw0*^+/03-0*A@ ^0^ ^`^z ^^ ^m^ ^^ ^`^Pinvalid expand shape ^ 0*^0* ^`0*^  0* ^Pp 0*^@ 0* ^0P 0*^pp0* ^ 0*^ 0* ^P0*^ 0* ^Pp0*^  @0* ^00*^@ 0* ^0*^PP 0* ^P0 0*^  0* ^ 0*^  @` 0* ^0 0*^P0*0^0,p3JScan<8> spec does not support transpose of output. This should never be called.bodyinfo.GetAttr("body", &proto).IsOK()onnxruntime::Scan<8>::ScanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_8.ccnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()directionsSetupSubgraphExecutionInfo should only be called once for each subgraph.info_ == nullptronnxruntime::Scan<8>::SetupSubgraphExecutionInfoCreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_onnxruntime::Scan<8>::ComputeSubgraph SessionState was not found for 'body' attribute.session_stateonnxruntime::Scan8Impl::Initialize dimensions or more but input had shape of Expected Invalid scan input: has batch size of but Scan inputs have inconsistent batch size. Previous value was has length of Scan inputs have inconsistent sequence lengths. Previous value was onnxruntime::Scan8Impl::ValidateInput did not match batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was outputs but Scan expects Subgraph in 'body' produces onnxruntime::Scan8Impl::AllocateOutputTensors^p,8Output OrtValue has not been created for loop state variable output p_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::ExecuteVScanposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/ort_value_tensor_slicer.h^`Z`Z[p^ "0*8^ZZ[^ "0*^ZZ\^ "0*8^ZZp\^ "0*^ypBIf outputs. outputs which doesn't match the subgraph's 'If' node has num_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\if.ccthen_branchinfo.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::Ifelse_branchinfo.GetAttr("else_branch", &proto).IsOK()info == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::Compute' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::InitializeFailed to create output tensor for Only tensors or sequence of tensors are suppportedFailed to create output tensor for If output onnxruntime::IfImpl::Execute^`g0*^P`Loop inputs. Found:Graph in 'body' attribute of Loop should have static_cast(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\loop.cc but has outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputs Got: Expected:Inconsistent shape in loop output for output. onnxruntime::Loop::Looponnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of All scan outputs MUST be tensorslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::LoopImpl::ConcatenateLoopOutputUnable to get an allocatoronnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::Execute was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output ^   "0*h^ Compressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()onnxruntime::Compress::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\compress.ccUnsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShapeBase::SetValueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/generator/constant_of_shape.h^ utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:unpack_status.IsOK()Unsupported value attribute datatype: Must have a single dimensiont_proto.dims_size() == 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseMust have a single dimension of 1t_proto.dims()[0] == 1Must have a valid input shape.input_shape.NumDimensions() > 0onnxruntime::ConstantOfShapeBase::PrepareComputeonnxruntime::ConstantOfShape::Compute^ $^ $MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]across_channelsinfo.GetAttr("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hnormalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()^0FEyeLikeT1 != nullptronnxruntime::EyeLike::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\eye_like.ccUnsupported 'dtype' value: EyeLike : Input tensor dimension is not 2p^ OIsNaN^PNull input ptrp^iSignonnxruntime::utils::MLTypeCallDispatcher::Invoke^д pop_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()Shrinkp^д 0T3OneHot^д `p^д  ^д p^д p^д 0p^д ^д p^д ^д p^д Invalid 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,__int64,__int64>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Compute> @^ yPzinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()MaxUnpoolonnxruntime::MaxUnpool::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satisfied: X_shape.NumDimensions() >= 3 Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: I_shape == X_shape Shape must be 1 dimensional as it's tensor data of a shapeNot satisfied: tensor_shape->Shape().GetDims().size() == 1 inferred output shape:output_shape is smaller than minimum required. output_shape:Not satisfied: given_shape.Size() >= shape.Size() Attempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/controlflow/scan_utils.hp^onnxruntime::Scan<9>::ScanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_9.ccscan_input_directionsscan_output_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsonnxruntime::Scan<9>::SetupSubgraphExecutionInfoonnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initialize' dimension but input '. Input tensor rank was of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariablesoutput_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutput^ "0*x^p\ "0*^д N onnxruntime::Scatter::ScatterD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\scatter.ccScatterScatterElementsdata type is different from updates typeIndices and updates must have the same rank vs Indices vs updates dimensions differs at position=. Input rank=Indices must have the same rank as Input. Indices rank= is greater than input dim= at pos=Indices dim=Unknown tensor type of onnxruntime::Scatter::ComputeExpecting indices to be either int32_t or int64_tx^  TfIdfVectorizermode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDF is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNonemin_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0max_gram_length >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0ngram_countsNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weights must be of equal size but ngram_indexes size: Got weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specified!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()-gramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0. id: Duplicate ngram detected, size: p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGrams^  P  0*x^0 0   g0*OOOO^ x^ ^ x^ ^0 X input is required!onnxruntime::NonZero::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computex^ ^ x^ ^ x^ Conversion ErrorConversion Erroren-US^?Q*^P/!/!StringNormalizer:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\string_normalizer.cc^.!iRiR@D!A!D!E!C!^/!is_case_sensitiveattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizercase_change_actionattribute case_change_action is not setLOWERUPPERNONEattribute case_change_action has invalid valuelocalestopwordsEmpty stopwords not allowed!sw.empty()Duplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_errorSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: bad conversion^@\!^@[!8^@[!x_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccscale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)^д  j!H^д l!^д n!QuantizeLinearH^д p!^@y!QLinearMatMulonnxruntime::QLinearMatMul::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)^!!P!onnxruntime::MatMulInteger::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)(^!!ConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)^!!!T4QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\qlinearconv.ccQLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point must be constantQLinearConv : filter zero point shape invalidQLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalid(^!!!!0*0Acenter_point_boxcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.h^д  !NonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 scores must be a 3D tensor.Not satisfied: scores_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satisfied: boxes_dims[0] == scores_dims[0] boxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[1] == scores_dims[2] The most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[2] == 4 iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) onnxruntime::NonMaxSuppression::ComputeNot satisfied: ret.IsOK() output != nullptr^ `"IsInfdetect_positiveFailed to obtain detect_positiveonnxruntime::IsInf::IsInfD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativeFailed to obtain detect_negativeData type X must be float or double, but instead got onnxruntime::IsInf::Compute specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase::RoiAlignBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthsampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0(^pS" W"RoiAlign(^pS" ["Null 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 match^_"`" `"_"0*0^ _"_"`"_"0*^д "batch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpD:\a\ml-builds\ml-builds\onnxruntime\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_axisReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`0^ 0"CumSum^ "0^ "^  #exclusiveD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\cumsum.ccCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::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::Init0^3#Round^@0#0^2#^7#DynamicQuantizeLinearx_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccC0^#Detonnxruntime::Det::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expected) != new size (Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape^06$0^ !$^д 0)$0^.$^ +$0^&$^07$SSequenceLengthGot nullptr for sequence input.onnxruntime::SequenceLength::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLength: Got nullptr for output tensorY != nullptrSequenceAtonnxruntime::GetSeqIdxonnxruntime::SequenceAt::ComputeGot nullptr input for index tensorI != nullptr) specified for sequence of size (Invalid sequence index (SequenceAt: Got nullptr for output tensorSequenceEmptySequenceEmpty: Got nullptr for output sequenceonnxruntime::SequenceEmpty::ComputeSequenceInsertonnxruntime::CreateCopyAndAppendCpuTensorS != nullptronnxruntime::SequenceInsert::ComputeGot nullptr for input tensor.), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceSequenceEraseonnxruntime::SequenceErase::ComputeSequenceErase: Got nullptr for output sequenceSequenceConstructnum_inputs >= 1onnxruntime::SequenceConstruct::ComputeSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.SplitToSequenceSplitToSequence operator does not support onnxruntime::SplitToSequence::Compute) != split_dim_size (split_size_sum (Invalid data type for split tensor onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputSplit should be > 0split_scalar > 0onnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })0^ K$ConcatFromSequenceonnxruntime::ConcatFromSequence::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cc^b$ScatterNDonnxruntime::ScatterND::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\scatter_nd.ccinput_tensor != nullptronnxruntime::ScatterNDBase::PrepareForComputeindice_tensor != nullptrupdate_tensor != nullptr, indices shape: input shape: input tensor and indices tensor must has rank larger than 0. last dimension of indices must not be larger than rank of input tensor, data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = ^e$e$e$Pf$ "0*8^d$d$ e$@f$ "0*^д $onnxruntime::GatherElements::GatherElementsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output]. Actual value is , GatherElements op: Value in indices must be within bounds [onnxruntime::core_impl::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()8^p$$$0$0*^@$0$$0$0*8^$$0$0$0*^$$p$0$0*8^$$$p$0*^`$$@$$0*8^$0$ $p$0*^$p$P$$0*8^$$ `$g0*^д $batch_dimsGatherNDGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor data type not supportedindices tensor must has rank larger than 0invalid index found, index = ^$$$`$ "0*8^$$$p$ "0*^P$P$$@$ "0*8^p$p$$P$ "0*^$start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!8^ g%UniqueUnsupported tensor type of ^%%P%equationMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()onnxruntime::Einsum::EinsumD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum.hEinsumEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetEinsum op: An implementation for the input type ^@%@%@%% "0*8^`%`%%% "0*^%%%К% "0*8^ % %Й%% "0*^%%p%% "0*8^%%%% "0*^%%%% "0*8^%%%% "0*^%P%8^%%^%%8_%%X and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeratio input should have a single value.ratio_tensor->Shape().Size() == 1onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fai.onnx.ml_p%ArrayFeatureExtractor8_@%_%8_%_ %Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[8_д %Binarizerthreshold is NaNInput data with index: %fTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/ml_common.hDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePack_ p &cast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()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.fUnexpected CAST_TO value of Negative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\cast_map.cc8_%&%&cats_stringsinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/category_mapper.hcats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string _>&A&DictVectorizer8_>&?&_>&p@&8_F&_C&8_D&string_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOp_Q&inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer).) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:8_d&pe&Imputerimputed_value_floatsimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeEmpty value of imputed values.Empty input dimensions._%&0&classes_stringsinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/label_encoder.hLabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)8 _&`&keys_floatsvalues_strings_Unused _`&@&keys_stringsvalues_floatsdefault_float8 _`&&keys_int64s _`&&values_int64s8 _&`& _`&&8 _`&P&info.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_value_field_name, values).IsOK()values is and the number of However, the number of key is ) must have the same length. (name: attribtues in LabelEncoder The num_keys == num_valuesonnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2LOGISTICSOFTMAXSOFTMAX_ZEROD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\ml_common.h _&@&LinearClassifiermulti_classpost_transforminterceptsclasslabels_stringsclasslabels_intscoefficients!coefficients_.empty()onnxruntime::ml::LinearClassifier::LinearClassifierD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplInput shape needs to be at least a single dimension.Unsupported input element type of scores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of shape_size == out.length()onnxruntime::ml::CastInputToFloat _&&& & "0*_&&&0& "0*_&&&@& "0*3@@ֳ_@' 'LinearRegressortargetsinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\linearregressor.ccinfo.GetAttrs("coefficients", coefficients_).IsOK()Input shape had more than 2 dimension. Dims=Unsupported data type of Invalid normalize value of onnxruntime::ml::MakeNormalize_д 1'norminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of _U'c'OneHotEncoder_U'V'_U'Z'_U'_'Unknown Category and zeros = 0.zerosOne and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOp_}''Scaler_}''_}''_}'~'offsetEmpty scale in attributes!scale_.empty()onnxruntime::ml::ScalerOp::ScalerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\scaler.cc) != (Scale size: (scale_.size() == offset_.size()Invalid argument: input has empty dimensions.) or 1Either both scale and offset can be of feature size (onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOp_''p''0*_0'0''Б'g0*_''@''0*_''''g0*_Ў'Ў'''0*_p'p'''g0*_''0''0*_P'P'''g0*LINEARPOLYkernel_typekernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/svmclassifier.h_''SVMClassifiervectors_per_classprob_aprob_bsupport_vectorsrhoinfo.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMClassifier::SVMClassifierD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0onnxruntime::ml::SVMClassifier::Compute_''' 'g0* ף;?_@''SVMRegressorn_supportsonnxruntime::ml::SVMRegressor::SVMRegressorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\svmregressor.ccone_classnum_features == feature_count_onnxruntime::ml::SVMRegressor::ComputeUnexpected mode:SUM_P) R)TreeEnsembleClassifier_P)@S)_P)`T)_P)Q)classlabels_int64sclass_weightsclass_treeidsclass_nodeidsclass_idsnodes_valuesnodes_truenodeidsnodes_treeidsnodes_nodeidsnodes_modesnodes_missing_value_tracks_truenodes_hitratesnodes_featureidsnodes_falsenodeidsbase_valuesaggregate_functionX dims is empty.onnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computen_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonnodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() is already there. in tree Node (truenode).-Unable to find node One falsenode is pointing either to itself, either to another tree. (falsenode). (weights).onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary_)p)))0*_)))p))0*_0)0)0)К)0*_))))0*_ ) )))0*_p)0)Й)0*_))))0*_P)P))P))0* _))))0* _P))))0*!_)@)0))0*!_@) ))0*"_@)Б)))0*"_)))))0*#_ ) )))0*#_))))0*$_)Ќ)`))0*$_ )))0*%_)0) ))0*%_)))))0*&_))))0*&_Њ))))0*'_Є))p))0*'_0)P)@)0*(_))p)0)g0*(_`)`)@))g0*)_p)p)) )g0*)_))`)P)g0**_0)0))@)g0**_))))g0*+_))@))g0*+_)))`)g0*,_0*p*TreeEnsembleRegressor,_0*`*target_weightstarget_treeidstarget_nodeidstarget_idsn_targetsUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregator::FinalizeScores-_*p)@+)0*-_**0*+)0*._**0)P+0*._*)+)0*/_@*@++)0*/_*P*+0*0_`*p) +)0*0_**P++)0*1_**0)@+0*1_*) +)0*2_ *p ++)0*2_P*P+ +0*3_**+)0*3_@*@*++)0*4_p*p*p*p+0*4_*P*@+)0*5_p*+p+)0*5_*`*P+0*6_**+)0*6_P*P*`*+)0*7_0*0*@* +0*7_*P +p+)0*8_@* + +)0*8_*0++0*9_P*)+)0*9_**0*P+)0*:_0*0*)+0*:_`*)0+)0*;_*p ++)0*;_* ++0*<_*)+)0*<_**P++)0*=_**)+0*=_0*)`+)0*>_** +)0*>_@**+0*?_@**0+)0*?_p*p*++)0*@_@*@*p+`+0*@_* *+)0*A_*` ++)0*A_* ++0*B_*0+@+)0*B_**`*p+)0*C_** ++0*C_* ++)0*D_**+)0*D_*++0*E_**p)+g0*E_0*0* ++g0*F_P*P*p)`+g0*F_**0++g0*G_p*p*++g0*G_`*`*++g0*H_**0+P+g0*H_***+g0*I_**)`+g0*I_**`*+g0*J_0*0*)+g0*J_***+g0*K_**P++g0*K_** ++g0*L_**`+0+g0*L_** +0+g0*M_#+@$+ZipMapMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensors] != number of classlabels[Input features_per_batch[M_Y+N_ 0[+info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\nchwc_ops.hinvalid channel countchannels_ > 0info.GetAttr("channels_last", &channels_last_).IsOK()N_pF+F+GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseO_@SV+ P_@SF+P_W+info.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1X_shape.NumDimensions() == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_rank == 4onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::Compute(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchNot satisfied: Y->Shape() == sum_shape onnxruntime::contrib::NchwcPoolBase::NchwcPoolonnxruntime::contrib::NchwcUpsample::Compute0Q_0+MatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\matmul_integer16.ccQ_д +Input x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\qlinear_global_average_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)Not satisfied: x_shape.size() >= 3 0R_++ ++0*R_+++0*0S_`+`+++)0*P@S_++T_++pU_++U_++QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QlinearBuildLookupTableD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.ccQLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point) W_p+p+++ "0*W_++++ "0* X_++P++0*X_ + ++`+ "0* Y_++++ "0*Y_++P+P+0* Z_++0++ "0*Z_@+@+0+p+ "0* [_++P+0+0*[_++0+ + "0* \_++0++ "0*\_++P+@+0* ]_P+P+++ "0*]_++++ "0* #< ^_+^_@+ __+__@+MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)onnxruntime::contrib::`anonymous-namespace'::QLinearImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\qlinear_binary_op.ccMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point) `_++`+,0"0*`_++,,0"0* a_++ +,0"0*a_0Apb_,P,2,QAttentiononnxruntime::contrib::QAttention::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(input_scale_tensor) weight must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) input zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) weight zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) onnxruntime::contrib::AttentionCPUBase::ApplyAttentionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/bert/attention_cpu_base.hc_@8,8,9,8,0*xd_7,8,8,`w0*d_7,8,8,`w0*@F@@@@onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cce_! >,P!hf_! D,P!DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_scale_tensor)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeDynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_scale_tensor)onnxruntime::contrib::MatMulIntegerToFloat::ComputeMatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.f_Q,R,a,} for per-channel quantization. Actual:, 4*} for per-tensor/layer quantization or shape { must have shape {W_zero_pointR_zero_pointW_scaleR_scaleWeight zero point must be zeroInputDynamicQuantizeLSTM : Weight point must be constantRecurrentDynamicQuantizeLSTMg_pi,SampleOph_r,,,info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'weights' dimension 1 should be 3 times of dimension 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' of 3d shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1, 2 or 3 dimensions, got Expect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentonnxruntime::contrib::Attention::Computeh_P,,,0*i_д ,onnxruntime::contrib::EmbedLayerNorm::EmbedLayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangei_0,0,P,С,g0*j_,An axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsExpandDimsj_ABonnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\fused_conv.cck_,ponnxruntime::contrib::FusedGemm::FusedGemmD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\fused_gemm.cck_`P,onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.ccInput X must have 3 dimensions only. Actual:}. Actual:Input W must have shape {Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {}, actural: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape { found!], while Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:Attention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{}. Got: Attention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsInput initial_c must have shape {Input P must have shape {onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImpl l_--07-Tokenizermarkattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valuemincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedl_0d-embedding_sizeconv_window_sizechar_embedding_size conv filter size: embedding_size attribute: 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 . Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Char embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbedding m_д -positiveMurmurHash3keysonnxruntime::contrib::MurmurHash3::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0m_@S-onnxruntime::contrib::MaxpoolWithMask::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMask(n_---0w0*n_-У--`w0*(o_@---pw0*o_@-Input tensor to Unique op should be 1D(p_ }-ConvTransposeWithDynamicPadsp_`--CropAndResizebilinear specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResizeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/crop_and_resize.hNull crop_size_ptrNumber of dimensions for crop size should be exactly 10q_--- -g0*q_д -CDist0r_д p-metricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist::CDistThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:r_-0s_-onnxruntime::contrib::BiasGelu::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::Computes_..P.p.g0*0t_.. ..g0*t_0.0...g0*y"=5?y"=y"=y"=y"=*BL?*BL?*BL?*BL?0u_ .u_ .0v_.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\activations.hv_..` .  0*0w_..` .  0*w_..0..g0*@0x_д %.x_д &.Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale::ScaleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("beta", &beta_).IsOK()borderAttribute border needs to be specified with four border elements, got input dimensions insteadInput is expected to have four dimensions corresponding to [N,C,H,W], got ) + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder () needs to be greater than or equal to the leftBorder (Input's width () + scale_[0] () + scale_[1] (0y_-.-.Croponnxruntime::contrib::Crop::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\crop.hDynamicSlice0z_0C.C.ImageScaler) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler::ImageScalerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()z_ .0{_ }.{_ .0|_ P.op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Compute|_..`..g0*0}_..P..g0*}_..0.p.g0*0~_О.О.@..g0*~_д .0_д .onnxruntime::contrib::SkipLayerNorm::SkipLayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccinput is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNorm_... .g0*0_...0.g0*_y/Inverseonnxruntime::utils::MLTypeCallDispatcher::Invoke0_z/z/ {/0{/0*_д  K0upperinfo.GetAttr("upper", &temp).IsOK()onnxruntime::contrib::Trilu::TriluD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\trilu.hTriluk should be a 1-D or 0-D tensor.IsScalarOr1ElementVector(k)onnxruntime::contrib::Trilu::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\trilu.ccSoftmaxCPU inputs N, D and N * D must be < ;affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccA + (M * K) <= A_endweights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName%%L> AA %%%%%%%%7.7.7.7. A A A AAAAA onnxruntime::LSTMBase::ComputeImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\lstm_base.cc inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'valid did not.Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::Initialize0_p,8Failed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *_^1^1 ^1P^1g0*Einsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\ml-builds\ml-builds\onnxruntime\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]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()onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Data types of the inputs must match for MatMulinput_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]Einsum op: Exception during MatMul operation: onnxruntime::EinsumComputePreprocessor::RunD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber 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 . 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 incompatibledim_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 providedOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the input 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::PairwiseOperandProcessD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Right 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 alignEinsum 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::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputunimplemented activation: activation_params count mismatchInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of (_2Ю22 222@not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemory_p2 [seqno= row[.. rows[Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0p_33P330"0*_0303330"0*Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1dims[d_i] < d_maxonnxruntime::math::NextPositionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\util\math_cpu.ccGemv found an unexpected CBLAS_TRANSPOSE input ofonnxruntime::math::Gemv DeviceId: MemoryType:DeviceType:Device:[ OrtAllocatorType: OrtMemType: id:name:OrtMemoryInfo:[CudaCudaPinnedonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\allocator.ccSpecified device is not supported.OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled already is not supportedNot implementedOrtValue is not a Tensortype != nullptrOrtApis::GetTensorTypeAndShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_type_and_shape.ccArgument is not a tensortype_proto is not of type map!type_proto is not of type sequence!_ 38_P3_3(_3_3onnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dll_P3 303@3_3 3333_3333_3 3@3333_@33_333`333H_33333ОPQ5PQ5PQ5PQ50J50J503_33P3p30*p_@3@3`333P3`x}33`3333 3@3333@333333333@3P3p33303@3P3`33p3333303@333303333333333 3@3333333 3P33`3p33333333@33@3@303@3P33333 303@3333303333 303333@3P333 3P3`333 303@3P33`33333P33@333333333333@3`333@333`33 33333333P33333 3@3 3333333P33`3333P3`3p3333333 303P3`3p33333333333 3P3333`33303 303onnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::EnsureD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\provider_bridge_ort.ccProvider_SetHostonnxruntime::ProviderLibrary::GetGetProviderp_ 4`4SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library_#@" 4_`h4py44u4`@@p*v4onnxruntime::TensorTypeBase::GetElementType@_g40x44u4@@`p*@v4onnxruntime::SparseTensorTypeBase::GetElementType@_g4@@@p*`r44_g4v44t4@`@p*v4onnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerh_`h4py44u4`@@p*`u4_`h4py44u4`@@p*u4x_`h4py44u4`@@p*u4_`h4py44u4`@@p*u4_`h4py44u4`@@p* u4_`h4py44u4`@@p*0u4_`h4py44u4`@@p*Pu4 _`h4py44u4`@@p*@u4_`h4py44u4`@@p*u40_`h4py44u4`@@p*u4_`h4py44u4`@@p*pu4@_`h4py44u4`@@p*u4Ȗ_`h4py44u4`@@p*u4P_`h4py44u4`@@p*u4ؗ_g40x44u4@@`p*`u4`_g40x44u4@@`p*u4_g40x44u4@@`p*u4p_g40x44u4@@`p* u4_g40x44u4@@`p*0u4_g40x44u4@@`p*Pu4_g40x44u4@@`p*@u4_g40x44u4@@`p*u4_g40x44u4@@`p*u4_g40x44u4@@`p*pu4(_g40x44u4@@`p*u4_g40x44u4@@`p*u48_g40x44u4@@`p*u4_g4v4@4Pt4@@@p*r4`r44_g4v4@4@t4@@@p*r4`r44_g4v4@4@t4@@@p*r4`r44_g4v4@4@t4@@@p*r4`r44_g4v4@4pt4@@@p*r4`r44p_g4v4@4`t4@@@p*s4`r44`_g4v4@4`t4@@@p*s4`r44P_g4v4@4`t4@@@p*s4`r44@_g4v44t4@`@p*u4Ȧ_g4v44t4@`@p*u4P_g4v44t4@`@p* u4ا_g4v44t4@`@p*0u4`_g4v44t4@`@p*@u4_g4v44t4@`@p*Pu4p_g4v44t4@`@p*`u4_g4v44t4@`@p*pu4_g4v44t4@`@p*u4_g4v44t4@`@p*u4_g4v44t4@`@p*u4_g4v44t4@`@p*u4_g4v44t4@`@p*u4(_g4v44t4@`@p*u4_g4v4P4t4@@@p*s4`r44_g4v4P4t4@@@p*s4`r44(null)tensor type is not currently registered or supportedMLDataType for: _Ѓ @p4t4@@@p*_Ѓ @p4t4@@@p*_Ѓ @t4@@@p*(_Ѓ @4t4@@@p*_Ѓ @t4@@@p*8_Ѓ @t4@@@p*_Ѓ @`4t4@@@p*H_Ѓ @`4t4@@@p*г_Ѓ @4t4@@@p*X_Ѓ @4t4@@@p*_Ѓ @p4t4@@@p*h_Ѓ @4t4@@@p*_Ѓ @`4t4@@@p*x_Ѓ @`4t4@@@p*Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker expected to be a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::SetD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::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::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::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,class std::allocator > > >::Set_4444 "0* dimensions. for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Slice{}p_type != nullptronnxruntime::Tensor::TensorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor.ccshape.Size() must >=0tensor failed memory size calculationtensor size overflowonnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrConfig 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::AddConfigEntryD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\session_options.ccReceived nullptr for name.Received nullptr for OrtValue.Received OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.An OrtValue for this name has already been added.DnnlExecutionProviderOpenVINOExecutionProviderNupharExecutionProviderNnapiExecutionProviderRknpuExecutionProviderInternalTestingExecutionProviderз_0j?8invalid allocatorFailed to find allocator for device allocator != nullptronnxruntime::utils::BatchOrCopyMLValueD:\a\ml-builds\ml-builds\onnxruntime\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::ExecuteGraphcannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\op_kernel_info.ccP_ "5`node_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/node_index_info.hExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextD:\a\ml-builds\ml-builds\onnxruntime\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::GetOrCreateOutputMLValueAttibute name and type don't matchNo attribute with this name is defined. is defined.No attribute with name: but is of type: expected to be of type: Attribute: is expected to have type: Requested attribute: duplicated allocatoronnxruntime::IExecutionProvider::InsertAllocatorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\execution_provider.cc in KernelRegistryManagerfound duplicated provider ). (node The node is not placed on any Execution Provider. Failed to find kernel for data_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: Error mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksCaught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while loading custom ops with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLib_3&6@6Could not find OrtValue with idx 'session.disable_prepacking. Ignoring allocator from Allocator already registered for onnxruntime::SessionState::SetupAllocatorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\session_state.ccSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfoDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfo. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:onnxruntime::SessionState::PrepackConstantInitializedTensors) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMapping for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::SaveToOrtFormatMain Graph instance should have populated all subgraphs when being resolved.subgraphonnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::LoadFromOrtFormat::::operator ()Kernel create info is null. Invalid ORT format model.onnxruntime::SessionState::LoadFromOrtFormatSatisfied, but should not be: nullptr == fbs_kcis Kernel create info node indices are null. Invalid ORT format model.Satisfied, but should not be: nullptr == node_indices Kernel create info hashes are null. Invalid ORT format model.Satisfied, but should not be: nullptr == kernel_def_hashes Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Not satisfied: node_indices->size() == kernel_def_hashes->size() . Invalid ORT format model.Can't find node with index Satisfied, but should not be: compiled_kernel_hashes.empty() '.Unable to find compiled kernel hash for node 'Satisfied, but should not be: hash_info == compiled_kernel_hashes.cend() SessionState for subgraphs is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states is missing. Invalid ORT format model.Subgraph SessionState entry for Satisfied, but should not be: nullptr == fbs_sub_graph_ss is null. Invalid ORT format model.Subgraph SessionState for Satisfied, but should not be: nullptr == fbs_sub_session_state onnxruntime::SessionState::FinalizeSessionStateonnxruntime::SessionState::FinalizeSessionStateImpl Attribute: Index:' OpType:Missing session state for subgraph. Node:'entry != node_to_subgraph_ss.second.cend()p_op_kernel_Pi6Pi6i6k6 "0*_i6i6j6k6 "0*_pi6pi6i6k6 "0*Received invalid value of arena_extend_strategy onnxruntime::CreateAllocatorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\allocatormgr.cc_q6@"0jq6_q6@"0jq6_~6~6onnxruntime::FunctionKernel::FunctionKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/func_kernel.hcompute_info_->create_state_func(&context, &func_state_) == 01 == capability.nodes.size()onnxruntime::PlaceNodeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PartitionOnnxFormatModelImplMust use Function based fusion when exporting compiled nodes to dll.fusion_style == IExecutionProvider::FusionStyle::Function did not return correct number of compiled functionsonnxruntime::InlineNodesonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::PartitionOrtFormatModelImpl. Execution Provider must generate unique names across the entire model.Existing entry in compiled kernel hashes for onnxruntime::GraphPartitioner::PartitionOrtFormatModelNo provider specified.onnxruntime::GraphPartitioner::PartitionCompiled kernel hashes must be providedcompiled_kernel_hashes != nullptrnullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\mldata_type_utils.cc) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protodata overflowonnxruntime::utils::GetFileContentD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensorprotoutils.ccstring tensor can not have raw dataonnxruntime::utils::TensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedInitialized tensor with unexpected type: onnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Unsupported attribute value type of Unsupported sparse tensor data type of onnxruntime::utils::SparseTensorProtoToDenseTensorProtoBUG! Report to onnxruntime team.Must have a valid data typeHasDataType(dense_proto)onnxruntime::utils::DenseTensorToSparseTensorProtoUnsupported type: UnpackTensor: the pre-allocated size does not match the raw data size, expected `anonymous-namespace'::UnpackTensorWithRawDataIndicies expected to be INT64onnxruntime::utils::CopySparseDataNot satisfied: indices.data_type() == ONNX_NAMESPACE ::TensorProto_DataType_INT64 Sparse Indicies raw data size does not match expected.Not satisfied: indices.raw_data().size() == (elements * sizeof(int64_t)) Invalid SparseTensor indices. Should either have raw or int64 datarank == dims.size() && rank > 0cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:Invalid TensorProtoonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Getsource and destination buffer size mismatchonnxruntime::utils::ReadLittleEndianD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/endian_utils.hNot satisfied: source_bytes.size_bytes() == destination.size_bytes() ؽ_P7P7P7Q7g0*X_P7P7P7 Q7g0*offset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexؾ_s7@7ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/execution_frame.h_r77@7@7А77node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\execution_frame.cc Requested shape:OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains failed. Error:Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame should only appear once.Memory type static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behavior but the actually size is: , block in memory pattern size is: For 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.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffermlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.model format error! Need a key for the external data infomodel format error! Need a value for the external data infolocation failedparsing lengthchecksummodel format error!model format error! Missing 'location'onnxruntime::IDataTransfer::CopyTensorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensor kernel_end_version: kernel start version: node_version: Version mismatch. and type (Op with name ( This op has been implemented only for the following types ( However the types are incompatible. (node_version: in the supported version rangeFound kernel for Op with name (), but the node in the model has the following type (onnxruntime::KernelRegistry::TryCreateKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\kernel_registry.cc No matching hash for kernel not found in Encountered following errors: ( kernel is not supported in Kernel not foundkernel def can't be NULL: Conflicting with a registered kernel with op versions.Failed to add kernel for len <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoonnxruntime::NodeIndexInfo::Init::::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()п_777 7 "0*P_ 7 7707g0*_@7@77@7 "0*P_77`77g0*Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer::CreateD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated(). Shape:Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iterator _88_ 88 does not.All implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.onnxruntime::ParallelExecutor::ExecuteD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::ExecuteBegin executiononnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.Got nullptr from GetKernel for node: op_name_fence_before' Status Message: node. Name:'Non-zero status code returned while running provider_kernel_time_fence_afterUnknown exception was caught by catch-all handler.'. Exception running nodes starting at _888888880*onnxruntime::SequentialExecutor::ExecuteD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\sequential_executor.ccgraph_indexexec_plan_indexactivation_sizeparameter_sizeoutput_sizeSequentialExecutor::Execute bytes for [Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates onnxruntime::ReleaseNodeMLValues already exist.func info for node: Can't use func with null ptr not found.onnxruntime::FuncManager::GetFuncsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\fuse_nodes_funcs.cc_8888 "0*_8888 "0*_Є8Є8p88 "0*Previous entry was not terminated.starts_.size() == ends_.size()onnxruntime::AllocPlanPerValue::ProgramCounter::AddStartD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/sequential_execution_plan.hInvalid 'start'. Value is smaller than previous 'end'.starts_.empty() || start > ends_.back()No matching 'start' entry.starts_.size() == ends_.size() + 1onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndInvalid 'end'. Value is larger than 'start'.end >= starts_.back()`_88p88 : ) D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoonnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reusenullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeCan not find the node Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defonnxruntime::PlannerImpl::ComputeUseCountsCan not find the execution provider allocatoronnxruntime::PlannerImpl::GetLocationForNodeInputonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeReusePlanInvalid program_counter entries at index entry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::VerifyMemoryTimeScheduleAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::CreatePlan_88880*`_8888 "0*_0808880*_99p9 8Failed to get allocator for location: duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error. is not foundMem pattern for initializer ' is not foundWeight buffer for initializer '' failedGet preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::Trace0_@9998string tensor is not supported for copying between allocatorsonnxruntime::session_state_utils::DeserializeTensorProtoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\session_state_utils.cc, Got Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to ) is different from what is supplied () because the ORT planned memory location device Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end()[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( failed.Deserialize tensor Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node. input with name SubgraphGraph_PP9Q9R9)0*0_ P9 P9P9Q9)0*MaxAllocSize: NumAllocs: MaxInUse: TotalAllocated: InUse: Limit: _`n9o9`9v990909 | in_use: | Requested Size: Size: , prev: , next: 0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/bfc_arena.hp_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForp_int < base_int + memory_size_Could not find Region for onnxruntime::BFCArena::RegionManager::RegionFor arena_extend_strategy memory limit: max_dead_bytes_per_chunk: with following configs: initial_chunk_size_bytes: Creating BFCArena for onnxruntime::BFCArena::BFCArenaD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\bfc_arena.cc bins of max chunk size Creating BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandlecudaMallochipMallocIncorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator () is smaller than requested bytes of Available memory of Failed to allocate memory for requested buffer of size bytes.Extended allocation by onnxruntime::BFCArena::ExtendTotal allocated bytes: Allocated memory at reserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserveh != kInvalidChunkHandletried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternal rounded_bytes:. bin_num:Extending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes=. Current allocation summary follows.BFC Arena ran out of memory trying to allocate !chunk->in_use()onnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalescebin->free_chunks.count(h) == 1onnxruntime::BFCArena::get_bin_debug_infoc->bin_num == bin_numAllocator:onnxruntime::BFCArena::DumpMemoryLogBin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_useRequested . . Bytes : Chunks Diff between in-use and requested bytes is , Chunk State: bytes has max bytes of Bin for Overall chunks summary: of size at Chunk Free Summary of in-use chunks by size: . Total chunks of size Sum Total of in-use chunks: Stats: fff?Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZero!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/mem_pattern_planner.hcurrent <= buffer_size_invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\simple_tensor_allocator.cc', location: Failed to get allocator for initializer '0 ZALLOW_RELEASED_ONNX_OPSET_ONLYai.onnx.trainingai.onnx.preview.trainingX_:[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=_:H_P<T<T<:NoOpConstant_P: are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/graph/model_load_utils.h_@:0j< n<Output:. Falling back to lenient merge. target:' source:Error merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\graph.cc Input=Type mismatch. Current=Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. ] op_type [Node [Serialization of fused function body is not currently supported, does not have the graph for key onnxruntime::Node::SaveToOrtFormatNot 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 node_arg_name cannot be nullSatisfied, but should not be: nullptr == node_arg_name ], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [Satisfied, but should not be: nullptr == node_arg onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSatisfied, but should not be: nullptr == fbs_attr 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 Node::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:input index: onnxruntime::Node::LoadEdgesFromOrtFormatSatisfied, but should not be: fbs_node_edges.node_index() != index_ input edgesoutput 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 != nullptronnxruntime::Graph::Graph' Model is invalid.Duplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: '. Please, fix your model.' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'This 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 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 Graph 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:node_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 (This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeInvalid 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.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.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=Some nodes are not included in the topological sort, graph have a cycle.onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.P_ L<Graph attribute inferencing failed: _pQ<0T<`S<0S<PT<T<R<: in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) Op (Node () does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.) in node () of operator (' of input parameter (This is an invalid model. Type Error: Type ' in node () bound to different types (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) type inference failed) output arg () does not match expected type () of node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. Type Error: Shape of initializer This is an invalid model. Error in Node:onnxruntime::Graph::VerifyNodeAndOpMatch is not a registered function/opFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorsparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!found_token_onnxruntime::Graph::SaveToOrtFormat as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodeFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProto'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.Removing initializer 'onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != func_meta_defonnxruntime::Graph::CreateFusedSubGraphNodeonnxruntime::Graph::FinalizeFuseSubGraph'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::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::SetInputsonnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()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 Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sparse_tensor NodeArg is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_value_info Node is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node Node index is out of rangeSatisfied, but should not be: node->Index() >= fbs_graph.max_node_index() NodeEdge is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_edge Satisfied, but should not be: fbs_node_edge->node_index() >= fbs_graph.max_node_index() index < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/common/const_pointer_container.h_#@"`:p_1<1<3<8< "0*_2<2<4<8< "0*p_2<2<3<8< "0*_1<1<3<8< "0*Domain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker.than the operator set version , but it its version is higher0 Z@ 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_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.onnxruntime::Model::ModelD:\a\ml-builds\ml-builds\onnxruntime\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. 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 Failed to load model with error: No graph was found in the protobuf.onnxruntime::Model::LoadProtobuf parsing failed. less than 0. is less than 0.onnxruntime::Model::SaveProtobuf serialization failed.onnxruntime::Model::SaveToOrtFormatonnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_graph onnxruntime::SaveModelp_`<@"<_0<@"0j failed. File doesn't existLoad model system error number ̼+̼+CAttribute should specify a shapeNegative values are not allowed in a shape specificationIncompatible dimensions'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensionsInputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)weight1D input tensor with shape (3 * hidden_size)MAttention mask with shape (batch_size, past_sequence_length + sequence_length) or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).past3D output tensor with shape (batch_size, append_length, hidden_size)present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)presentConstrain input and output types to float tensors.Constrain mask index to integer typesscale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.input_scalescale of weight scale. It's a scalar, which means a per-tensor/layer quantization.weight_scaleAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.input_zero_pointzero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization.weight_zero_point3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.LongformerAttentionOne sided attention windows length W, or half of total window lengthwindowAttention mask with shape (batch_size, sequence_length)maskglobal_weightglobal_biasGGlobal attention flags with shape (batch_size, sequence_length)globalConstrain to integer typesThe epsilon value to use to avoid division by zero.2D words IDs with shape (batch_size, sequence_length)input_ids2D segment IDs with shape (batch_size, sequence_length)segment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embedding1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size)skip1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeUSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.inv_std_varConstrain mean and inv_std_var to float tensors.input_as_shapeextra_shapeInput'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 () 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] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionstash_typeInput rank must be >= 2. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Value of alphaValue of beta1D input tensor1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.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.Sliced data tensor.Constrain input and output types to all tensor types.Constrain indices to integer typesGivenTensorFillThe shape of filled tensorThe filled tensorvaluesInput data to be scaledOutput data after scalingGRUUnitBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesThe previous GRU hidden state.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesArray 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.hiddenIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain input0 and output types to float tensorsRfftsignal_ndimnormalizedonesidedIrfftComplexMulinput_0input_1ComplexMulConjWPadsConstrain input and output types to float tensorsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionStrings to tokenizeTokenized stringsInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredN-dimensional matrix AN-dimensional matrix BMatrix 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).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 multiplicationMatrix multiply resultsAn 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.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.Tensor of rank q >= 1.indicesTensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embeddingSequenceSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).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).Tensor after padding.A 1-D input tensor that is to be processed.xA 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'idxA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'countsInput can be of any tensor type.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)2D 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'.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.rois1-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_sizeRoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput data tensor from the previous layer.Scale tensor.Bias tensor.Output 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 output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.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.Constrain input and output types to all numeric tensors and bool tensors.apply softmax to elements for dimensions softmax_axis or higherbroadcast bias across input for dimensions broadcast_axis to softmax_axis-1The bias (or mask) as Tensor.p_@=@=0== "0*_П=П==0= "0*p_==== "0*_ = === "0*p_=== = "0*_P=P=@== "0*p_p=p=Р=Т= "0*_== == "0*p_0=0=== "0*_@=@=== "0*p_===@= "0*_`=`=== "0*p_===p= "0*_ = === "0*p_0=0= == "0*_===`= "0*p_`=`=P== "0*_P=P=== "0*p_===P= "0*_===p= "0*p_==p== "0*_==p=`= "0*p_==`== "0*IndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptronnxruntime::GraphViewer::GraphViewerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Input not found:nodeargMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: nodeInvalid ExecutionOrderonnxruntime::GraphViewer::GetNodesInTopologicalOrderNot supported with filtered graph.filter_info_ == nullptronnxruntime::GraphViewer::GetRootNodes_0=0=@== "0*_P=P=== "0*_==p== "0*_==== "0*_==== "0*std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\graph_utils.cc in one of the subgraphs. cannot be safely updated to 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::RemoveNodeWithSingleNodeInSingleUsedOutputAttempting to get index for an input which does not exist.itr != node.InputDefs().end()onnxruntime::graph_utils::GetNodeInputIndexFromInputNameAttempting 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.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::RemoveNodeInitializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializer ImplicitInputs: ExplicitInputs:. Index:Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputCan only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)onnxruntime::graph_utils::AddNodeInput->Failed since multiple edges matched:onnxruntime::graph_utils::FindPathTENSOR0Zname().empty() Invalid ORT format model.Missing indicies for sparse initializer: Satisfied, but should not be: nullptr == fbs_indicies_tensor Missing dims for sparse initializer: Null string attribute. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatSatisfied, but should not be: nullptr == fbs_str 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.Satisfied, 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.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 Null tensor in tensors attribute. Invalid ORT format model.`_:>_:а>Not supportedonnxruntime::ViewerFunctionImpl::BodyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/graph/function_impl.houtA node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::update_subgraphs_within_function_bodyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\function.ccinput_arg->Type() != nullptronnxruntime::CreateSchemafused_function_subgraphonnxruntime::FunctionImpl::FunctionImplResolve subgraph failed:`_>>>> "0*A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.RThe bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` sequence_lensOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_cThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.PThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` QWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` MWThe attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` memory_seq_lensThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` AWA tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. Y_hThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_ctensor rank too smallinvalid scales dimensioninvalid scales valueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cc0_>> >> "0*_>>`>> "0*0_>>p>> "0*_>> => "0*0_>>P> "0*_>>>> "0*0_ ? ? ? ? ?>Input tensor must have at least 2 dimensionsOutput tensor must have at least 2 dimensionsinputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccx_scalex_zero_pointww_scalew_zero_pointy_scaley_zero_pointInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) 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), or (N x D1 X D2 ... Dn x C) where N is the batch size.Scale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'. It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to singed/unsigned int8 tensors._@ ?@ ?` ? ? "0*0_0 ?0 ?P ?p ? "0*Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}First operand.Input A's scale. It's a scalar, which means a per-tensor/layer quantization.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.A_zero_pointSecond operand.Input B's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput 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_scaleOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_zero_pointResult, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors.axis must be in [-rank, rank-1]. input rank was Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.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.D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\quantization_defs.ccThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.ReduceSumIntegerAn input 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).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.MulIntegerConstrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.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_point1D 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'.a_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.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationQLinearReduceMeanInput 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_pointreduced_scalereduced_zero_pointQLinearAveragePoolWhether 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.Whether 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.Coefficient of leakage.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleInput 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_scaleOutput 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_pointNumber of neurons in the hidden layerCouple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors._D?D?E?PF? "0*0_E?E?E?F?0"0*_D?D?0E?F? "0*0_D?D?E?`F? "0*_D?D?@E?F? "0*0_D?D?PE? F? "0*_D?D?E?@F? "0*0_D?D?E?0F? "0*_E?E?E?pF? "0*Can not get shape initializer data!Unsupported type:Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.startTensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.limitTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.delta1-D Tensor of the range.0_@S?@S?PS?`S? "0*Unsupported non-raw-data data type!ExceptionReturnHrLogHrFailFast%hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %ws Msg:[%ws] CallContext:[%hs] [%hs(%hs)] [%hs] kernelbase.dllRaiseFailFastException_U?pS?%wsstd::exception: %hs_`?po?D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\platform\windows\env.ccSetThreadDescriptionkernel32.dllonnxruntime_0o?@t??? ?@??p??p??pz?z?p?r?px?x?py?y?u? x?{??0?P??А?@{??5p?Fatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation fail, errcode = open file GetFileSizeEx Invalid fd was supplied: Received negative size from stat callonnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satisfied: file_path Not satisfied: offset >= 0 Not satisfied: length <= buffer.size() SetFilePointerEx ReadFile fail: unexpected endMapFileIntoMemory is not implemented on Windows.\/, error code: DeleteFile() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathNot satisfied: result_length > 0 UNC\Failed to load library, error code: Failed to unload library, error code: Failed to find symbol in library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameonnxruntime::LoopDirD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/platform/path_lib.hSUCCESSFAILNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\status.ccOKSystemError[ONNXRuntimeError]length overflowonnxruntime::ToMBStringD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\str_helper.cconnxruntime::ToWideStringonnxruntimeSystemISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLoggerN@ZZSessionNodeenabled_onnxruntime::profiling::Profiler::StartTimeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ ",{"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} }, ] _ @?_@ @p?????`@? @`@Nested parallelism not supported!current_parallel_sectiononnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\threadpool.ccn >= 0onnxruntime::concurrency::ThreadPool::ParallelFor_(@(@@+@,@0"0*H_0(@0(@)@,@ "0*_0)@0)@0,@-@ "0*H_p(@*@,@P)@0*_P(@P(@ *@,@g0*H_)@)@ ,@-@ "0*{Gz???@j@A_:@iRiR?@@@P@@@@@@VIWEF [ is not a valid dateampm is not a valid day is not a valid yearpVtV\/\Failed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\path.ccNot satisfied: has_trailing_separator onnxruntime::Path::Parseunexpected failureillegal input path:X_Ѓ  ?@?f@%g_h@ i@i@{@l@pz@ j@0i@r@ n@`q@k@api-ms-win-eventing-provider-l1-1-0advapi32EventSetInformationP_{@|@`}@GetSystemTimePreciseAsFileTime _u `}@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??++Q5@.{1N36;A8?:X} <>=2?~>4?~?A++++++++Q5@.Q5@.Q5@.Q5@.{1{1{1{1N3N3N3N36666;A8;A8;A8;A8?:?:?:?:X} =>=>=>=2?~>2?~>2?~>2?~>4?~?4?~?4?~?4?~?AAAAA%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;AAAAA33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K57f9A :mL:~;;e9 <>*>p>>P"?r1?l?;?33{@@K7777mL:mL:mL:mL:~;~;~;~;;;;;e9 u>u>u>>>>>*>*>*>*>>>>>P"?P"?P"?P"?l?l?l?l?33{@33{@33{@33{@fȹfȹfȹfȹA A A A ƼƼƼƼEۼEۼEۼEۼppppr1r1r1r1rB¥B@K;?r1:n/ <*=(*>>??::::n/ (*>(*>(*>rBrBrBrBµµµ?@@@AA A@A0A@A0A0#A@"A@"A AA AA AAbad dimensionsH_)A)A)A*Ag0*@@@@||||88888888    packing unavailable_?QElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!/C??P_@A@A`AA0"0*xBX0Z@ZPZtensor_typesequence_typemap_typeonnxruntime::experimental::utils::SaveSequenceTypeOrtFormatD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::experimental::utils::SaveMapTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::experimental::utils::SaveTypeInfoOrtFormat] for nowWe do not support type [onnxruntime::experimental::utils::SaveValueInfoOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for dim_param value with no name. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatSatisfied, but should not be: nullptr == fbs_dim_param Null entry in dimensions. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatNull 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.onnxruntime::experimental::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadTypeInfoOrtFormatSatisfied, but should not be: nullptr == fbs_tensor_type 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 is not supported currentlyType:Null type info for onnxruntime::experimental::utils::LoadValueInfoOrtFormatSatisfied, but should not be: !value_info_proto.name().empty() Model must have opset imports. Invalid ORT format model.onnxruntime::experimental::utils::LoadOpsetImportOrtFormatSatisfied, but should not be: nullptr == fbs_op_set_ids opset id is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_op_set_id opset import domain is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_domain _u >P_P(E0,, has unsupported type: typestr: has inconsistent type has unsupported type ' has been deprecated since version Operator ', max= not in range [min=) has input size not in allowed input sizes.) has output size not in allowed output sizes.has output size ) in op definition.) than declared () has more inputs ( is marked single but has an empty string in the graph)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute ' for operator Unrecognized attribute: Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' has unknown expected type' is missing.Required attribute 'Attribute specification type mismatch.Duplicate type constraint name(inputs_.size() - 1) == i: failed validating the check: ONNX Schema (outputs_.size() - 1) == i!(it.GetName().empty())_}EE@E@E0*Type 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. 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. 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. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Constrain input and output types to high-precision numeric tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\math\old.cc]. Its actual value is: 'axis' must be in [Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Output tensor.ZConstrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.List of tensors for data_0Constrain input and output types to numeric tensors.sumInput tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsOptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).'shape' input must be 1D tensor of type INT64A 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.const_zeroconst_onetargetexpanded_targetignore_indexinput_gather_elementloss_NCddloss_N1ddnonereductionlossloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_ignore_indexconst_zero_target_typedexpanded_target_int64transform_targetsconst_zero_floatinput_gather_element_transformsqueeze_maskconst_one_floatweight_gather_tempweight_gather_temp_1Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType 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.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossscoresX_MaxX_SubX_ExpX_RSX_DivX_Loglog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLossDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizePass 1 to enable broadcastingbroadcastlegacy optimization attribute.consumed_inputsIf set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInput tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Output tensor (same size as X)Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsslopeSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CInvalid value for attribute axisInvalid value for attribute kTensor of shape [a_1, a_2, ..., a_n, r]Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.IndicesConstrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.K input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveK_pFpF@FF "0*(_FFFF "0*_FFFF "0*(_ F FFPF "0*_FF@FF "0*(_FF@F F "0*_FFFF "0*(_FFFF "0*_FFFF "0*(_FFPE?F "0*_FFF@F "0*(`PFPFFF "0*`FFFF "0*(`FFF0F "0*`FFFF "0*(`FFpFF "0*`FFPFF "0*(`FF@F F "0*`0F0FPF`F "0*(`FFFF "0*`pFpFFF "0*(`FFFF "0*``F`FpFF "0*(`FFFF "0*`FFFF "0*(`@F@F`FpF "0*V}?k+?First input operand for the logical operator.Second input operand for the logical operator.Result tensor.Constrains input types to all numeric tensors.Constrains output to boolean tensor.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\logical\old.ccEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Constrains input to boolean tensor.Constrains input to float tensors.Constrains input to integral tensors.`GGG"G "0*(`GG G"G "0*`GG!G"G "0*( `GGG"G "0* `GGGp"G "0*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).Constrain input and output types to high-precision and 8 bit numeric tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\reduction\old.cc'axis' must be in [-rank(indices), rank(indices)-1]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).Reduced output tensor with integer data type.The axis in which to compute the arg indices. 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.( `bGbGgGkG "0* ` cG cGiG lG "0*( `bGbGgGlG "0* `bGbG`cGkG "0*( `bGbGgGkG "0* `cGcGgGlG "0*( `@cG@cGkG0lG "0* `bGbGeGkG "0*(`bGbGgGkG "0*`bGbGPcGkG "0*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.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.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.(Optional) Seed to the random generator, if not specified we will auto generate one.The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.ratioIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.training_modeThe output mask.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\nn\old.cc) for attribute 'axis'Invalid value(A tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.EpsilonX_RMEX_squaredX_squaredE_XsquaredVarianceSTDX_varianceProcessed_STDInput tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionStride along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Dilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.p value of the Lp norm used to pool over the input data.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.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.number of groups input channels and output channels are divided into.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.momentumThe input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.varThe output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.saved_meanSaved variance used during training to speed up gradient computation. Should not be used for testing.saved_varThe input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.(` G G`G@G "0*`GGGG "0*(`GGGG "0*`GG`GG "0*(``G`GGG "0*`GGGG "0*(`@G@GG`G "0*`GGР=0G "0*(`GG@GG "0*`PGPG =pG "0*(`0G0GpGPG "0*`GG GG "0*(`GG0GG "0*`GGPGG "0*(`GGGG "0*`GGPGG "0*(`GGPG G "0*`GGGG "0*_p08@? 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). not specifiedValue of attribute should be of integer type and specify a type. does not specify a valid type.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInput tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\tensor\old.ccTarget shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesSpecified shape for output.Reshaped data.reshapedShape of the input tensorInput tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Required attribute axis is missingaxis must be in [-rank, rank-1].All 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)..List of tensors for concatenationinputsConcatenated tensorconcat_resultConstrain output types to any tensor type. Value=Invalid value of attribute 'axis'. Rank=) and outputs (Mismatch between number of splits () and the split dimension of the input (Mismatch between the sum of 'split' (The input is not evenly splittableThe tensor to splitOne or more outputs forming list of tensors after splittingoutputsWhich 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.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'step' cannot be 01-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.stepsInvalid attribute perm {}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Transposed output.transposedTensor of rank q + r - indices_shape[-1] - 1.updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.data tensor must have rank >= 1axis 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).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.Reshaped tensor with same data as input.squeezed'axes' attribute must not contain any duplicatesvalues 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).Original tensorexpandedBlocksize must be positiveInput tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCRDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].'Repeats' input 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.Input tensor of any shape.1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.repeatsOutput tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.N-D tensor1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"roiThe scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.sizesN-D tensor after resizingConstrain input 'X' and output 'Y' to all tensor types.Constrain roi type to float or double.Tensor to copy input into.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrain to all tensor types.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` tensorThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).constant_valueConstrains input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Constrain output types to float tensors.rank must be greater than axisWhich axis to concat onOptional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputList 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`.paddingsThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapeNumber of repeated copies to make of the input tensor.tilesAxis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scaleTwo interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.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)The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be insertedOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.depthRank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain to any tensor type.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.conditionTensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Invalid value of attribute 'axis'Which axis to split on. Attribute value for pads is requiredAttribute pads has incorrect lengthList of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.(`0I0I3I5I "0*`01I01I3I6I "0*(`@0I@0I2I 5I "0*``/I`/I1I@4I "0*(``0I`0I2I@5I "0*`0I0I 3I5I "0*(`0I0I2I5I "0*`/I/I1I`4I "0*(`/I/IG4I "0*`.I.IG3I "0*(`0/I0/I1I4I "0*`1I1I03I5I "0*(`/I/IP2I4I "0*`@1I@1I3I 6I "0*(` /I /I1I4I "0*`0I0I2I4I "0*( ` 1I 1I3I6I "0* `p/Ip/I1IP4I "0*(!`/I/I2I4I "0*!`0I0Ip2I4I "0*("`/I/I1I4I "0*"`00I00I2I5I "0*(#`0I0I3I5I "0*#`P/IP/I1I04I "0*($`0I0I2Ip5I "0*$`.I.IP1I3I "0*(%`@/I@/I1I 4I "0*%` 0I 0I1I5I "0*(&`0I0I3I5I "0*&`0I0I2I5I "0*('`/I/IGp4I "0*'`/I/I2I4I "0*((`0I0I1I`5I "0*(`/I/I`2I4I "0*()`1I1I3I5I "0*)`p0Ip0I2IP5I "0*(*`P0IP0I2I05I "0**`/I/Ip1I3I "0*(+`/I/I`1I3I "0*sparse_valuevalue_intvalue_intsvalue_floatvalue_floatsvalue_stringvalue_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.+`TITIUI@UI "0*(,`TITITI0UI "0*,`TITITI UI "0*(-`TITITIUI "0*fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.output_sequenceA tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\rnn\old.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.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.-``pI`pIpIpI "0*(.`ppIppIpIpI "0* 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. 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. Constrain input and output types to float and 8 bit tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\nn\defs.ccRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).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.Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Input shape must have either [C] or [1,C] dimensions where C > 0UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"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.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..`IIG0I "0*(/`PIPIII "0*/`IIGI "0*(0`pIpIР=I "0*0`III@I "0*(1`0I0II`I "0*1``I`I0II "0*(2` I I =PI "0*2`II II "0*(3`II II "0*3`IIIPI "0*(4`IIPII "0*4`II0II "0*(5`pIpI`>I "0*5`III0I "0*(6`II`>@I "0*6`II`I I "0*(7`IIGI "0*7`@I@IGpI "0*(8` I IG`I "0*8`@I@III "0*(9`II@II "0*9`PIPI II "0*(:`0I0IIpI "0*Input type was nullInput was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input element type of Output was expected to have tensor type. Got Input was expected to have either tensor or sequence type. Got was not a tensor.Scan input outputs. Expected Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output is invalid for a tensor of rank axis value ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified (Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).Initial values of the loop's N state variables followed by M scan_inputsinitial_state_and_scan_inputsFinal values of the loop's N state variables followed by K scan_outputsfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. 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.Int64 tensorAll Tensor typesD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\controlflow\old.cc was Loop 'body' subgraph outputs should all be tensors but output A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.condThe initial values of any loop-carried dependencies (values that change across loop iterations)v_initialFinal N loop carried dependency values then K scan_outputsv_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.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.then_branch and else_branch produce different number of outputs. but subgraphs produce If node has else= then=Mismatched type for output Mismatched tensor element type for output Condition for the ifValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).LessOrEqualGreaterOrEqual:`JJGЋJ "0*(;`JJJJ "0*;`JJ@FJ "0*Attribute expected to have tensor typeAttribute expected to have a one-dim tensorD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\generator\defs.ccShape input must be a one-dimensional tensor.Invalid shape value: (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Output type must be int32 or int64Input tensor must have rank 2Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.loop_body_attributeiprevcond_outcurrentrangesub_resultsub_result_casteddelta_casteddiv_resultceil_resultceil_result_reluceil_result_relu_intceil_result_relu_boolvariadic_outputAll inputs to 'Range' op must be of the same typeScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)(<`JJJJ "0*<`pJpJJpJ "0*(=``J`JJ`J "0*=`JJJJ "0*(>`JJJJ "0*>`PJPJJPJ "0*(?` J JJ J "0*?`0J0JJ0J "0*(@`@J@JJ@J "0*D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\math\defs.ccCoefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.X_alphaElu_ResultThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.CeluValue of alpha.Value of beta.X_ReduceMaxX_ReduceSumThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseinput and zero_point pair is expected to have same type.N-dimensional quantized matrix aascale of quantized input azero point of quantized input aN-dimensional quantized matrix bbscale of quantized input bzero point of quantized input bscale of quantized output yzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.Rank of input Einsum expression string.OperandsInputsOutputConstrain input and output types to all numerical tensor types.@`)L)L@F/L "0*(A`*L*L0+L0L "0*A`)L)L*L/L "0*(B`0*L0*L-L00L "0*B`*L*L@F0L "0*(C`P*LP*L@FP0L "0*C``*L`*L.L`0L "0*(D`)L)L*L/L "0*D`*L*L +L0L "0*(E`*L*L.L0L "0*E`*L*L.L0L "0*(F` *L *LPE? 0L "0*F`)L)L*L/L "0*(G`)L)LF/L "0*G`)L)L+L/L "0*(H`)L)L+L/L "0*H`@*L@*L.L@0L "0*(I`*L*LF0L "0*I`p*Lp*L.Lp0L "0*(J`*L*LF0L "0*_}?}-?First input tensor must have rank 3D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\rnn\defs.ccJ`pSLpSLSLSL "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_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).D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\tensor\defs.ccOptional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.'input' must have rank >= 2'sequence_lens' must have rank of 1(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. inverse_indicesA 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'(K`LLLPL "0*K`LL3IL "0*(L``L`L2IL "0*L`LL1IL "0*(M`LL2IpL "0*M`@L@LLL "0*(N`0L0L2IL "0*N`0L0LLL "0*(O`LLG`L "0*O`LLG@L "0*(P`pLpLGL "0*P`LL1I`L "0*(Q`LL03I0L "0*Q`LLLL "0*(R`LL LL "0*R`pLpL1IL "0*(S`PLPL2IL "0*S` L L3IL "0*(T`LL1IPL "0*T`@L@L1IL "0*(U`LLL@L "0*U`LLLL "0*(V`LL2I0L "0*V`LL2IpL "0*(W`LLL L "0*W``L`Lp2IL "0*(X`PLPLLL "0*X`LLLL "0*(Y`LL1IL "0*Y` L L2IL "0*Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\quantization\old.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.(Z`pLpL0E?L "0*Z``L`LE?L "0*An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.boxesAn input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.max_output_boxes_per_classFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.iou_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.score_thresholdselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].selected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\object_detection\old.cc([`LLLL "0* but has rank expected to have rank Dimension mismatch in unification between expected to have rank >Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.avgThe pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\object_detection\defs.cc[`@L@L`LL "0*(\`PLPLpLL "0*Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified.(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)D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\quantization\defs.ccScale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified.(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)Quantized output tensorOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinQ_MaxX_MinX_Min_AdjustedX_Max_AdjustedX_RangeMin_ScaledInitial_ZeroPoint_FPClipped_ZeroPoint_FPRounded_ZeroPoint_FPZeropoint\`0"M0"M0E?P"M "0*(]`@"M@"ME?`"M "0* target= source=Mismatched type:Mismatched tensor element type:source sequence type missing element type.target sequence type missing element type.seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output All Tensor and Sequence typesD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\controlflow\defs.ccFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Attribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\sequence\defs.ccSequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index Element type of inputs are expected to be the same.Tensors.Sequence enclosing the input tensors.Constrain input types to any tensor type.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=Input sequence.input_sequenceInput tensor to be inserted into the input sequence.tensorPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).positionOutput sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Input type for input at index 0 is null. Type info is expected.Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain input types to all tensor types.Constrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[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]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenation]`MM MM "0*(^`MMMM "0*^`MM0MM "0*(_`MM@MM "0*_``M`MM`M "0*(``MMMM "0*``pMpMMpM "0*(a`PMPMДMPM "0*axes as an input and attribute cannot be specified at the same time.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\reduction\defs.cca`0M0MMкM "0*(b`@M@MMM "0*b`pMpMMM "0*(c``M`MMM "0*Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\traditionalml\defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.map(int64, string)map(int64, float)The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, int64)map(int64, double)map(string, float)map(string, double)The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input data. It can be either tensor or scalar.Output data.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Input's shape should be 1D or 2DData to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataMAXOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapThe output will be a sequence of string or integer maps to float.seq(map(string, float))seq(map(int64, float))The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.c`NNNN "0*(d`NN NN "0*d`NN0NN "0*(e`NNpNN "0*e`NN`NN "0*(f`NNPNN "0*f`NNPNN "0*(g`NN@NN "0*Input data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\traditionalml\old.ccg` N N N0N "0*tensor(map(sparse_tensor(seqmapopaquesparse_tensorfloatfloat16doubleint16int32int64uint8uint16uint32uint64complex64complex128boolConstantFill ==> Context: is required to be non-empty.value_info' of nameField ' is required but missing.typeelem_typekey_typevalue_type): Unrecognized type value case (value_info name: data_type) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_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!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: values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (] 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 () 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.Attribute (name: ) should refer to attribute in parent node.op_type) has zero input and zero output., type: NodeProto (name: Warning: Checker does not support models with experimental ops: 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 'initTensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers 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.(h`N0jh`NNPNN NPN`NN is out of boundsinput output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.opaque_typesparse_tensor_typeNOT_SET) vs (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (: (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.Warning: Unsupported operator Shape inference error(s): were provided inputs but Graph has , node name: (op_type:) 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.D:\a\ml-builds\ml-builds\onnxruntime\build\external\onnx\onnx\onnx-ml.pb.ccFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINED   DEFAULTEXTERNALBFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16INT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINED   IR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19_START_VERSION(i`jOO0O@OПO`0C0O`{OO O"Ponnx.AttributeProtor`yOpOOO O`0C@O`OO !P@CPonnx.ValueInfoProtoj`lOOOOO`0C`O`OOO@-Ponnx.NodeProto(o`tOOOOO`0COOOPP=Ponnx.TrainingInfoProto(j`lOpOpOOO`0CPOOOO`*Ponnx.ModelProto(l`nOOOOpO`0COOOPp2Ponnx.StringStringEntryProtol`@pOpOO OO`0CO@OOP@3Ponnx.TensorAnnotationi`kO OPO`OO`0C@OpOOO0'Ponnx.GraphProtom`rO0OPO`OO`0CO0OOP:Ponnx.TensorProto.Segment(m`@qOO0O@O@O`0COOOP04Ponnx.TensorProtok`nOOOOO`0COOOP0Ponnx.SparseTensorProton`sOOOOO`0COДOOP@Ponnx.TypeProto(k` mOOOOO`0CpOpOOP/Ponnx.OperatorSetIdProtoD:\a\ml-builds\ml-builds\onnxruntime\build\external\onnx\onnx\onnx-operators-ml.pb.ccEXPERIMENTALSTABLE(s`RP_P hP0hP[P`0C[PTPOjP0xPonnx.FunctionProtos`RP_P@hPPhP ]P`0C[PWPOpPP|Ponnx.OperatorProto(t`PTP@`P`hPphP]P`0C[PXPOrPp}Ponnx.OperatorSetProtoD:\a\ml-builds\ml-builds\onnxruntime\build\external\onnx\onnx\onnx-data.pb.ccMAPSEQUENCESPARSE_TENSORTENSORUNDEFINED(u`PpP@PPPP`0C@PPO`P Ponnx.SequenceProtot`pP P P0PPP`0C0PPO`PPonnx.MapProtoUnknown encoding D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\re2.cc': Error parsing 'Error compiling 'pattern too large - compile failedError reverse compiling 'pattern too large - reverse compile failedInvalid RE2: text size: endpos: startpos: RE2: invalid startpos, endpos pair. [list count bytemap range DFA out of memory: size SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyUnexpected opcode in IsMatch: D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\prog.ccunhandled opcode: in AddToThreadqunhandled D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\nfa.cc in stepUnhandled context does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte|D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetFailed to analyze start state.Cannot use SearchOnePass for unanchored matches.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\onepass.ccjob_.size() = njob_ = GrowStack() failed: D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\bitstate.cc arg Unexpected opcode: Bad arg in kInstAltMatch: Bad arg in kInstCapture: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupRegexp not destroyed.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: xv`vQQQQu`wQQQQQNumCapturesWalker::ShortVisit calledStack not empty.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2/walker-inl.hWalk NULLAnyAddFoldedRange recurses too much.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\parse.ccv`vQ@QQQRRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad hex digit Bad call to ParseState::ParsePerlFlagsw`R9Rp.R$R w`R9R.R $R:Rshould never happenD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classMissing case in Compiler: x`wQNR0IRQ [truncated](?:kRegexpCapture cap() == 0D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\tostring.cc?P<>(){}[]*+?|.^$\[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)[]^-\\\r\t\n\f\x{%x}D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\simplify.ccCase not handled in ComputeSimple: y`QR_RXR3x`QR_RXRUR_Rhy`QR_R0\RUR `RCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat AZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&&*&'%EETpsvw{}t&%@? 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