+ G-jF^RIt^RIt^RIt^RIt^RIHt^RIHtH t ^RI H t ^RI H t ^RIHtHtHtHtHtHtHtHtHtHtHtHtHtHtHtHtHt^RI H!t!!RR ]4t"R#) N)onnx_pb) BaseQuantizerQuantizationParams) TensorData) ONNXModel)TENSOR_NAME_QUANT_SUFFIXQuantizationModeQuantizedValueQuantizedValueType __producer__ __version__add_infer_metadataattribute_to_kwargcompute_scale_zpcompute_scale_zp_float8 find_by_nameget_qmin_qmax_for_qTypeget_qrange_for_qType ms_domainquantize_onnx_initializer&save_and_reload_model_with_shape_infertensor_proto_to_array)CreateOpQuantizerca]tRt^&toR"RltRtRtRtRtRt Rt R t R#R lt R t R tR tRtR$RltR%RltRtR"RltV3RlRltV3RlRltR&RltRtR#RltR'RltR(RltR)RltR*RltRtRtR t R!t!Vt"R#)+ ONNXQuantizerNc r\P!VVVVVVVV V V V 4 V'EgVPP4\ VPP4pVP P U u/uFqPV bK up VnVPPVP PUu/uFqPVbK up4VPPVP PUu/uFqPVbK up4\V4VnW@n WPnVP^ 8VnRVP"9;'dVP"R,Vn.VnRVn/VnVP*PVP PUu/uFqP^bK up4VP*PVP PUu/uFqP^bK up4VPPP P,F<pVP*P\.P1VP^44K> VP\29d\5RVP 24hVP74VnRVnRVnRVnRVn /Vn!VPPE4Vn#R#uup iuupiuupiuupiuupi) MatMulConstBOnly/zunsupported quantization mode fixed_quantization_range_uint8fixed_quantization_range_int8 fixed_zero fixed_zero_zpN)$r__init__modelreplace_gemm_with_matmulrgraph value_infoname value_infosupdateoutputinputrmodestatic opset_versionfuse_dynamic_quant extra_optionsq_matmul_const_b_only new_nodes graph_scope tensor_namesnodedictfromkeysr ValueErrorcalculate_quantization_paramsquantization_paramsfixed_qrange_uint8_namefixed_qrange_int8_namefixed_zero_namefixed_zero_zp_namequantized_value_mapget_non_initializer_inputsgenerated_value_names)selfr% per_channel reduce_ranger.r/ weight_qTypeactivation_qType tensors_rangenodes_to_quantizenodes_to_excludeop_types_to_quantizer2viotitr7s&&&&&&&&&&&&& x/Volumes/fast/ai/experiments/nudenet-smoke/.venv/lib/python3.14/site-packages/onnxruntime/quantization/onnx_quantizer.pyr$ONNXQuantizer.__init__'s            v JJ / / 1:4::;K;KLE6;kk6L6LM6L 6LMD     # #5;;;M;M$N;MRWWb[;M$N O    # #5;;;L;L$M;LRWWb[;L$M N"5)DJ  "&"4"4r"9%74;M;M%M%x%xRVRdRdewRx"   u{{7I7I!J7I''1*7I!JK   u{{7H7H!I7H''1*7H!IJJJ$$**//D    $ $T]]4;;%B C0 99, ,=dii[IJ J#'#E#E#G (H$&E#+"1$& &*ZZ%J%J%L"K N$N$M"K!Is;L L%L* L/L4c 8\PPVRVPPPR7p\ V4\ VVPVPVPVPVPVPVPVPVPVP VP"4 pWnVP& V R2VnVP)4VPPP*#)z generate submodel for the subgraph, so that we re-utilize current quantization implementation. quantize the submodel update subgraph and set it back to node onnx-quantizer) producer_name opset_importsr)onnxhelper make_modelr% opset_importrrrErFr.r/rGrHrIrJrKrLr2parentr5quantize_modelr')rDsubgraph graph_key warped_model sub_quantizers&&& rPquantize_subgraphONNXQuantizer.quantize_subgraphps {{-- *****77. <(%        II KK     ! !     " "  ! !  % %      $'+'7'7&8 1$E !$$&""((...c hVPUu.uFYpVP\PP8Xg,VP\PP 8XgKWVNK[ pp\ V4^8XdV#VP'd VPM#VP R\ VP4 2p/pVPEF pVP\PP8Xd9VPVPVPV RVP 24/pMVP\PP 8Xdb.pVPF@pVPVPVV RVP R\ V4 24.4KB VPV/pM \V4pVPV4EK \P P"!VPVP$VP&3RVP/VB#uupi)zd Check subgraph, if any, quantize it and replace it. return new_nodes added for quantizing subgraph _node_count_:r)) attributetyperVAttributeProtoGRAPHGRAPHSlenr)op_typer4r`ggraphsextendrr+rW make_noder-r,) rDr7attr graph_attrs node_namekwargskvvaluer\s && rPquantize_node_with_sub_graph*ONNXQuantizer.quantize_node_with_sub_graphs &yyD//555dFYFYF`F`9` D&  { q K!%DII4<<. 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Note you don't need to quantize a QAT model. OnnxRuntime support to run QAT model directly.r7z0Invalid model with unknown initializers/tensors.z com.microsoft) rloggingwarningr%renable_subgraph_quantizationrwrkr4rquantizeranger,rCr_dequantize_outputsr' ClearFieldr7rorZclean_initializers RuntimeErrorstrr rTr producer_versionrYdomainrversion) rDr7number_of_existing_new_nodes op_quantizerir_initializers_not_foundopsetms_opsetms_nodess & rPr[ONNXQuantizer.quantize_models!      OOn  JJ$$&D00088>+.t~~+> (,T8L  ! ! #7T^^9LM#'>>!#4#;#;K..22;?$<N'   " %%f- &&t~~6 ;; (, (E(E(G %A)*Q."#UX[\rXs#stt)5 &,7 )'+zz'7'7'D'Db'De XaHaEE'Db)-Z;;/;YHZ ((5599; ! ( zzcZs1K' K'*K,K,cRVP9d=\P!RVVPR,4VPR,#\RV: R24h)DefaultTensorTypezDget_tensor_type returns DefaultTensorType for tensor name %r, use %dz)Unable to find data type for weight_name=a7. shape_inference failed to return a type probably this node is from a different domain or using an input produced by such an operator. 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You may use extra_options `DefaultTensorType` to indicate the default weight type, usually `onnx.TensorProto.FLOAT`.)r2rinforrD tensor_names&&rP_get_default_tensor_type&ONNXQuantizer._get_default_tensor_typesf $"4"4 4 LLV""#67  %%&9: :7 GI J  rbc$\WPP44pVe VP#WP9dVPV,pVP P R4'd_V'd7VP PP^8XdVPV4#VP PP#VP'dVPfV'dVPV4#R#VPPV4pVeV#VP'd4VP'd"VPPV4pVeV#V'dVPV4#R#)N tensor_type)rr%r data_typer*rgHasFieldr elem_typerrrZis_valid_quantize_weightget_tensor_type)rDr mandatoryweightrMotyperess&&& rPrONNXQuantizer.get_tensor_types1k::+A+A+CD  ## # ** *!!+.Bww ..!4!4!>!>!!C88EEww**444111t{{7J44[AA 44[A  L  , , ,++--k:C 00= =rbcVPV4'dVPV4#WP9dVPV,pVPP R4'dZVPP P \PP\PP39dR#\P!RV: RVP R24R#VP'd.VP'dVPPV4#\P!RV: R24R#)rTzrr%add_initializerr@)rDrrrrinput_scale_namereduce_min_namereduce_min_nodereduce_max_namereduce_max_nodereduce_min_abs_namereduce_min_abs_nodereduce_max_abs_namereduce_max_abs_node abs_max_name abs_max_nodeinitializer_divscale_div_namescale_div_nodeinitializer_zps&&&& rPr9ONNXQuantizer._get_dynamic_input_quantization_params_int8Es&&++&0$|3++//  L t # $  0  /*$|3++//  L t # $  0  /*.6"kk33   # #A & ' 4 ' (    -.-6"kk33   # #A & ' 4 ' (    -.!J. {{,,  ' ' *,?,F,Fq,I J D !   ,'++11  ' '   !% (3 . /   ""?3#k1..    #T%@%@ A      .)001H1H%QSVWUXY "">2!8!8"b@@rbc \PPpVR,pVR,pVR,p\PP RV.VR,.V^R7pVP V4VR,p \PP RV.V R,.V ^R7p VP V 4\PPVPV.\V4.4p VPPV 4\PPVPV.R .4p VPPV 4VR ,p \PP R V P^,VP^,.V R,.V 4pVP V4VR ,p\PP R VP^,VP.V.V4pVP V4VR,p\PP R VPVP^,.VR,.V4pVP V4VR,p\PP R VP^,V.VR,.V4pVP V4VR,p\PP RVPVR,.V4pVP V4VR,p\PP RVPV.VVR7pVP V4WV..3#)aK Create nodes for dynamic quantization of input to uint8 and add them to nodes_list parameter input_name: Name of the input. parameter nodes_list: new nodes are appended to this list. parameter initial_type: initial weight type (FLAOT or FLOAT16) return: scale_name, zero_point_name, scale_shape, zero_point_shape. r _zero_pointrrrrrr _scale_SubSub _scale_Divr_zero_point_Sub_zero_point_Div_zero_point_FloorFloor_zero_point_CastCast)to)rrrrVrWrprrr=rr%rr?r,)rDrrrrr input_zp_namerrrrinitializer_qrangeinitializer_qvaluescale_sub_namescale_sub_noderr zp_sub_name zp_sub_node zp_div_name zp_div_node zp_floor_name zp_floor_node zp_cast_name zp_cast_nodes&&&& rPr:ONNXQuantizer._get_dynamic_input_quantization_params_uint8s4&&,,%0"]2 $|3++//  L t # $  0  /*$|3++//  L t # $  0  /*"[[44  ( (   !% ( )   ""#56![[44T5I5I(>q(A B d " #    .)#l2..   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Specified values for output z: zero_pointscaledtypez and param_name= quant_typerrzUnexpected dtype=z for param_name=T)Frrr))rHr<rr isinstancer TypeErrorrgrkr:nparrayhasattrrfloat32float16float64astyperVrWrraveltolistr%rrrrrreshape)rD param_name use_scale use_zeropointzero_point_typeparamszero_point_values scale_valuesrzero_point_shapezero_point_name scale_shape scale_nameinit_zp scale_type init_scales&&&& rP_get_quantization_params&ONNXQuantizer._get_quantization_paramss//   5''/:E]E]3] CJ<_`,,--j9Ff&899"24<.j^ST UVV~V!1 33=,bJ !#&*>)? @ 6'?G44w8M8MVXV`V`bdblblUm8m #3D4I3JJZ[eZh!ijj88VG_$56L%%3 33$\2O "- 9 88YK0L--j9F& w--+2259 %%3 33$}4 (* ++)) .>@W@W@Y@`@`@b  ""7+    +#//55J   2:: -#//77J01C1C0DDTU_Tbcd d[[,,Z[RfRfglRmRtRtRvw  "":.Z+OOrbc  VPV,pVR8wgQR4hV\,pVR,p Ve VeRYErp MVPV4wrp p .pV 'd'\PP RW{V .V.V 4pMVP 'dR#VP'dXV\PP8Xd9VR,p VR,p \PP R V.WV .V 4pMUVfQR V: R V R V R V 24hVPW~W6R7wp p pp\PP RW{V .V.V 4p\WxWV4VPV&.VOVN#)ar Given an input for a node (which is not a initializer), this function - add nodes to compute zero point and scale for this input if they don't exist. - add new QuantizeLinear node to quantize the input. :param node: node being quantized in NodeProto format. :param input_index: index of input in node.input. :param qType: type to quantize to. :param given_scale_name: if those inputs need to be quanitzed using this scale tensor. :param given_zp_name: if those inputs to be quantized using this zeropoint tensor. :param initial_type: type of the weight to quantize :return: List of newly created nodes in NodeProto format. rz*Cannot access undefined variable in graph._QuantizeLinearNTr{rrDynamicQuantizeLinearzCCannot quantize input without knowing the initial type, input_name=z, input_index=z, qType=z, node=r)r-rr.rVrWrpr/r1rrrrr rA)rDr7 input_indexrgiven_scale_name given_zp_namerrr ql_node_name data_foundr*zp_namerr qlinear_noder)zp_shapes&&&&&&& rP_get_quantize_input_nodes'ONNXQuantizer._get_quantize_input_nodes2s"ZZ , RM!MM #;; !$55  (}/H/35EGJG484Q4QR\4] 1JGQ ;;00 1  L{{{&&&5J4J4J4P4P+P'(2 $}4#{{44+L g6   $/"",~k](SXRYY`ae`fh/?? SX?t #{{44$W5 M   0>jWalq/r  ,%% %%rbcWP9dVPV,#VPeVPPV4#R#r)rArZfind_quantized_value)rDrs&&rPr?"ONNXQuantizer.find_quantized_valuexsA 11 1++J7 7 ;; ";;33J? ?rbc  f\P!V4p VRV ,,V, p \P!VP4\PR7p \P!VP4\PR7p W,pW8dVR8dW, pW,pV f;\ P !RV RV RV R24R\P!VVR73#\ P !R V R V R V RV R2 4RVPV43#R V3#) zHAdjust a single weight scale to ensure the int32 bias does not overflow.rrrzIncreasing scale for weight `z` by the ratio z to ensure bias `z` has a valid scale.TzIncreased scale[z] for weight `z ` by ratio F)rabsritemrrrr)rDbias_val input_scale weight_scaleweight_scale_dtype weight_name bias_nameqrangemultiplicative_epsilonidxabsmaxbias_smallest_valid_scaleinput_scale_fp64weight_scale_fp64bias_candidate_scaleratio new_scales&&&&&&&&&& rP$adjust_single_weight_scale_if_needed2ONNXQuantizer.adjust_single_weight_scale_if_neededs/!$:cFl$Kf$T!88K$4$4$6bjjIHH\%6%6%8 K/C +kRWQXY''0k1EGY--.@AAAl""rbc<V^8dQhRS[PRS[PRS[RS[PRS[RS[S[S[PR,3,/#)rFrGrIbias_tpis_per_channelreturnN)rndarrayrrVrbooltuple)format __classdict__s"rP __annotate__ONNXQuantizer.__annotate__sh6%6%ZZ6%jj6% 6% !! 6%  6% tRZZ$&& '6%rbc  VP'gR#\V4p\P!\P4pRp\P !VP \PR7\P !VP^,\PR7, p VPp Rp V'Eg\P!VP4\P !^\PR74p \P!VP 4\P !^\PR74p \P!\P!V 4\P!V 44pVPVVVV VVPV V4wppV'dTpRp W3#VP'd\!VP4^8Xdj\#VP^,4FIpVPVV,VVV,V VVPV VVR7 wppV'gKBVVV&Rp KK W3#)zOChecks if the bias scale is too small and increases the weight scale if needed.Fgqh?rBT)rM)FN)sizerriinfoint32rmaxrminrminimummaximumrCrUr)shaperkr)rDrFrGrIrYrZbias_float_data int32_inforLrKrHupdatedrminrmaxrNchangedrTrs&&&&&& rP#_adjust_weight_scale_for_int32_bias1ONNXQuantizer._adjust_weight_scale_for_int32_biass    /8XXbhh' !'*.. ;bhhz~~XYGYacakak>ll)//~::o113RXXarzz5RSD::o113RXXarzz5RSDZZt bffTl;F!%!J!J" & " GY( $$$#   C (:(:$;q$@<--a01%)%N%N#A& O&LL*&O &"7&/LO"G2 $$rbc><V^8dQhRS[RS[PRR/#)rXrIrTr[N)rrr\)r_r`s"rPrarbs&$1$1c$1bjj$1T$1rbc  JWP9dR#VPV,p\WPP44p\VPVPP44p\VP VPP44p\VP VPP44pVe Ve VeVfR#VPPV4VPPV4\PPV4pVPp \P!V\PPVP 4R7p \PP#V P%VP&4VP4p VPP)V 4\+VVP,VV V VP R7p VPP)V 4R#)zCRe-quantizes the given weight initializer using the provided scale.NrB)quant_weight_name)rArr%rr*r9q_nameremove_initializerrV numpy_helperto_arrayaxisrasarrayrWtensor_dtype_to_np_dtyper from_arrayrdimsrrrG) rDrIrTqv weight_tp scale_initzp_init q_weight_initweight_zero_pointr{scale_npnew_scale_init new_q_weights &&& rP_requantize_weight ONNXQuantizer._requantize_weights 66 6   % %k 2 jj.D.D.FG !"--1G1G1IJ rzz4::+A+A+CD$RYY 0F0F0HI   2goI^  %%j1 %%m4 --66w?ww::it{{/S/ST]TgTg/hi**55h6F6Fz6WY[YfYfg "">21        ii   ""<0rbc  2WP9dVPV,P#VPV,Pp\WPPP 44p\ V4pW P9dVPV,PpM6W P9dVPV4wrp M\RV R24h\WPP 44p \ V 4p VPV,Pp \WPP 44p V e \PPV 4MRpVPpVeVP'dVP!4'gVP"\$P&P(39dX\WPP 44pVP+V VVVV4wppV'dVP-VV4TpVP/WWt4wppppppWP9gQh\1TTTT\2P4VP^8d^MRVVR7pVVPV&V#)zM Quantized the bias. Zero Point == 0 and Scale == Input_Scale * Weight_Scale z Expected z5 to be in quantized value map for static quantizationN) node_type node_qtype)rArwr*rr%rrr<r.r:r9rVryrzrErdrrGrrrrrrquantize_bias_static_implr r Initializer)rDrJrrIbetaweight_scale_nameweight_initializerrGrrinputscale_initializerrFweight_zp_nameweight_zp_initrrZbias_initializer did_updatenew_weight_scalequantized_bias_namequantized_bias_scale_namequantized_bias_zp_namebias_scale_datarrquantized_values&&&&& rPquantize_bias_static"ONNXQuantizer.quantize_bias_staticsb 00 0++I6== =!44[ALL)*;ZZ=S=S=UV,-?@  11 1#77 CNN  33 3+/+H+H+T (AAqy 4ijk k!-.> @V@V@X!Y+,BC 11+>FF%njj6L6L6NOJXJdD--66~Fjn))  )!&&&%))++!!j&<&<&A&A%CC+Izz7M7M7OP +/+S+S  , (J('' 5EF/   * *9< V  % "    8 8888(   % "  * * %%)At!  />  +""rbc vWP9;'g%WP9;'gWP9#)za only check for value info and newly generated tensor names, initializers are checked separately )r*r6rCrs&&rPcontains_tensorONNXQuantizer.contains_tensorJsA ,, , ; ;000 ; ;999 rbc 2VPVVRRRRVR7#)Fr7indicesinitializer_use_weight_qTyperFop_level_per_channelr{ from_subgraphr_ONNXQuantizer__quantize_inputs)rDr7rrs&&&&rPquantize_activation!ONNXQuantizer.quantize_activationTs/%%).!&'&  rbc 2VPVVRVVVVR7#)Trr)rDr7rrFrr{rs&&&&&&&rPquantize_weightONNXQuantizer.quantize_weightas1%%)-%!5'&  rbc  .p.p .p .p VEFp VPV ,p WP9dgVPV ,pVPVP4V PVP4V PVP 4KV 'g6V PR4VPR4V PR4K\ WPP44pVeVP'dJV'dBTPVPV'd VPM VPVV4wpppM5TPTV'd VPM VPV4wpppV PV4V PV4VPV4EKVPV 4'EdXVPP!V R,VP"VPP%44pVEf1VPV ,pVVP&9dVP&V,pVP)R4'g QRV R24hVP*P)R4'g QRV R24hVP*P,P.pM0VVP09g QRV: R 24hVP0V,pVP3WVPVR 7pVfRu#V'dVP5V4MV P7V4VR,pVP8R 8XdcV P7VP:4VPVP^,4V PVP^,4EKV PVP:^,4VPVP:^,4V PVP:^,4EK&VP<etVP<P?VV .VVVVR R 7wppppV PV^,4VPV^,4V PV^,4EK\ARV RVPB 24h WW3#)aC Given a node, this function quantizes the inputs as follows: - If input is an initializer, quantize the initializer data, replace old initializer with new initializer - Else, add QuantizeLinear nodes to perform quantization parameter node: node being quantized in NodeProto format. parameter indices: input indices to quantize. return: (List of quantized input names, List of zero point names used for input quantization, List of scale names used for input quantization, List of new QuantizeLinear nodes created) rr1rgz value_info=z has no type.rz is not a tensor.zshape inference failed for zF and attribute 'tensor_names' does not have any value for this tensor.r3r{T)rrFrr{rz!Invalid tensor name to quantize: z @graph scope)NNNNr)"r-rArr*r9rwrr%rrEquantize_weight_per_channelr)rGrHquantize_initializerrfind_node_by_namer4r'r*rrgrrr6r<rrorlr,rZrr:r5)rDr7rrrFrr{r scale_nameszero_point_namesquantized_input_namesrr4 node_inputrr q_weight_namer9r*r:rr(rquantize_input_nodesparent_quantized_input_namesparent_zero_point_namesparent_scale_namesrs&&&&&&&& rP__quantize_inputsONNXQuantizer.__quantize_inputsts_.  ""KK0J555"&":"::"F""?#=#=> ''(?(?@%,,_-C-CD%,,R0""2& ''+&z::3I3I3KLK&###(< 88#((-I))tOdOd$  %":>9R9R#-I))tOdOd$:6M7J &,,]; ''0"":.%%j11#zz;;!22DNNDJJDTDTDV   '!%K!8J!T%5%55%)%5%5j%A )226::ck*Ub->>9*H+,> (,'8'8'D +/+I+I4+@+@|,J,(,377$**+?@ %9:#7#;L''+;;)001D1DE&&|'9'9!'<=$++L,>,>q,AB)001D1DQ1GH&&|':':1'=>$++L,?,?,BC( KK11 M1M!-)="&2 0+&&,,-I!-LM""#5a#89 ''(?(BC!#DZLP]^b^n^n]o!pqqG#J% JJrbc jVPVP9dAVPVP,pVPVPVP3#VP WW44wrgp\ VPVVV\PR4pWPPVP&WgV3#)aj :param weight: TensorProto initializer :param qType: type to quantize to :param keep_float_weight: Whether to quantize the weight. In some cases, we only want to qunatize scale and zero point. If keep_float_weight is False, quantize the weight, or don't quantize the weight. :return: quantized weight name, zero point name, scale name N) r)rArwr9r*quantize_initializer_implr r r) rDrrrFkeep_float_weightrrr9r*s &&&&& rPr"ONNXQuantizer.quantize_initializers ;;$22 2"66v{{CO&&''**  .2-K-K <. * ) KK     * *   1@  -z11rbcWP9d7VPV,pVPVPVP3#VP WW4V4wrxp \ VVV V\ PR4pW`PV&WxV 3#r)rArwr9r* quantize_weight_per_channel_implr r r) rDrIrG channel_axisrFrrrr9r*s &&&&&& rPr)ONNXQuantizer.quantize_weight_per_channels 22 2"66{CO&&''**  .2-R-R |CT. * )      * *   1@  -z11rbc &WP9EdWP9EdoVPV,p\VPVPP 44pVPPP R8wg*VPPP R8Xd9Ve5Ve1\PPV4P^8XgQhVR,pVPPW@PVPP44pVfHVPVPVP.p\P P#RWa.V4pV#WP$^,8XgQhR#)a~ Given a value (input/output) which is quantized, add a DequantizeLinear node to dequantize it back to float32 or float16 parameter value_name: value to dequantize parameter new_nodes_list: List of new nodes created before processing current node return: None if there is already a DequantizeLinear node that dequantizes it A DequantizeLinear node otherwise rSN_DequantizeLinearr|)rArCrr*r%rrTrVryrzrdrr4r'rwr9rWrpr,)rD value_namerr dqlinear_name dqlinear_nodedqlinear_inputsdequantize_nodes&& rP_dequantize_valueONNXQuantizer._dequantize_value8sR 22 2KeKe9e"66zBO&o&@&@$**BXBXBZ[Jzz--1AA   ..2BBzG]")T->->-G-G -S-X-X\]-]]]&)<>!3DE&**62>>!3DE+// T=Y=YZ .22>5I 4Zfop .t4yReRef /ATku/v  ,/.2#"rb)r>r=r?r@r1rCr5r.r%r4r3r<rAr/r6r*r)F)NN)NNN)g?)FFrF)TFFrF)FF)TF)#__name__ __module__ __qualname____firstlineno__r$r`rwrrrr[rrrrrrr.r<r?rUrrrrrrrrrrrrr;__static_attributes____classdictcell__)r`s@rPrr&sFMR/> fD < + Z "22 ARAh\7|9PvD&L##J6%6%p$1$1LF#P    &AKF2B2@%N 7 # #rbr)#rnumpyrrVonnx.numpy_helperrrbase_quantizerrr calibrater onnx_modelr quant_utilsrr r r r r rrrrrrrrrrrregistryrrrbrPrsK  &=!!&(e#Me#rb