+ G-j ^RIt^RIt^RIt^RIt^RIHt^RIHt^RIH t ^RI H t H t H t HtHtHtHtHtHt^RIHtHt^RIHt]P2!]4t!RR 4tR#) N)deque)Path)convert_float_to_float16) AttributeProto GraphProto ModelProto NodeProto TensorProtoValueInfoProtohelper numpy_helper save_model)load_external_data_for_tensoruses_external_data)SymbolicShapeInferenceHelpercza]tRt^toRtRtRt/R3RltR`RltR`Rlt Rt R t R t R t R tR tRtRtRtRtRtRaRltRaRltRaRltRaRlt]R4tRt]R4tRtRtRtRbRlt RaRlt!RaR lt".3R!lt#RR.R3R"lt$R#t%R$t&RcR%lt'RdR&lt(RR.3V3R'lR(llt)RdR)lt*R*t+R+t,ReR,lt-R-t.ReR.lt/RaR/lt0R0t1RaV3R1lR2llt2RaV3R3lR4llt3]V3R5lR6l4t4R7t5R8t6RfR9lt7RfR:lt8RaR;lt9R<t:R=t;RaR>ltRAt?RBt@RCtARdRDltBRgREltCRFtD]R`RGl4tERgRHltF]RhRIl4tGRhRJltHRKtIRLtJR`RMltK]RiV3RNlROll4tL]RjV3RPlRQll4tMRRtNV3RSlRTltOV3RUlRVltPRWtQRXtRV3RYlRZltSV3R[lR\ltTV3R]lR^ltUR_tVVtWR#)k OnnxModelc(VPV4R#N) initializeselfmodels&&t/Volumes/fast/ai/experiments/nudenet-smoke/.venv/lib/python3.14/site-packages/onnxruntime/transformers/onnx_model.py__init__OnnxModel.__init__ s cfWn/VnRVnRVnRVnRVnRVnR#NT)r_node_name_suffixshape_infer_helperenable_shape_infer all_graphs _dtype_dict _shape_dictrs&&rrOnnxModel.initialize#s8!& 13@D(,37 3737rcRVnR#)FN)r"rs&rdisable_shape_inference!OnnxModel.disable_shape_inference0s "'rFcjVP'dbVPe V'd\VP4VnVPP V4'd VP#R#R# \ d2RTn\ R\P!4^,4R#i;i)NFzfailed in shape inference) r"r!rrinfer Exceptionprintsysexc_info)rdynamic_axis_mappingupdates&&&rinfer_runtime_shapeOnnxModel.infer_runtime_shape3s  " " "&&.&*Ftzz*R' F**001EFF222G   F*/'13<<>!3DE  Fs A6% A668B21B2c/pV'gVP4MVPPPpVFDpVPF1pV'gK WR9dV.W%&KW%,P V4K3 KF V#r)nodesrgraphnodeinputappend)rexclude_subgraphsinput_name_to_nodesnodes_to_searchr8 input_names&& rr<OnnxModel.input_name_to_nodesAsn .?$**,TZZEUEUEZEZ#D"jj :!<;?&+7+7>>tD )$#"rc/pV'gVP4MVPPPpVF$pVPFpV'gK WBV&K K& V#r)r6rr7r8output)rr;output_name_to_noder=r8 output_names&& rrBOnnxModel.output_name_to_nodeMsT .?$**,TZZEUEUEZEZ#D#{{ ;7; 4 +$#"rcF\VPP4.pV#r)listr functions)r all_functionss& rrGOnnxModel.functionsVsdjj2234 rc.pVP4F'pVPFpVPV4K K) V#r)graphsr8r:)r all_nodesr7r8s& rr6OnnxModel.nodesZs; [[]E   &##rc.VPP#r)rr7r(s&rr7OnnxModel.graphaszzrcVPe VP#.VnVPP.pV'Ed#VP^4pVPP V4VP FpVP FpVP\PP8Xd>\VP\4'gQhVP VP4VP\PP8XgKVPF,p\V\4'gQhVP V4K. K K EK+VP#r)r#rr7popr:r8 attributetyper AttributeTypeGRAPH isinstancegrGRAPHSrK)r graph_queuer7r8attrrWs& rrKOnnxModel.graphsds ?? &?? "zz''( kOOA&E OO " "5 )  NNDyyN$@$@$F$FF)$&&*====#**4662yyN$@$@$G$GG!%A#-a#<#<<#<'..q1"- +#rc.pVP4F1pVPFpVPVP4K K3 V#r)rKr9r:name)r input_namesr7r9s& rget_graphs_input_names OnnxModel.get_graphs_input_namesws? [[]E""5::.%#rc.pVP4F1pVPFpVPVP4K K3 V#r)rKrAr:r])r output_namesr7rAs& rget_graphs_output_names!OnnxModel.get_graphs_output_names~s? [[]E,,##FKK0'#rc\VP4FpWP9gKVu# R#r)rKr8rr8r7s&& rget_graph_by_nodeOnnxModel.get_graph_by_nodes&[[]Ezz! #rc\VP4FpWP8XgKVu# R#r)rKr])r graph_namer7s&& rget_graph_by_nameOnnxModel.get_graph_by_names&[[]EZZ' #rc\VP4F$wr4VPFpWR9gK Vuu# K& \VP4#r) enumerater8r9len)rr7outputsidxr8r9s&&& rget_topological_insert_id#OnnxModel.get_topological_insert_ids@"5::.IC#J$/5::rcVP4F1pWP9gKVPPV4R# \P RV4R#)NzFailed to remove node %s)rKr8removeloggerwarningrfs&& r remove_nodeOnnxModel.remove_nodesB[[]Ezz! !!$'# 148rc:VFpVPV4K R#r)rx)rnodes_to_remover8s&& r remove_nodesOnnxModel.remove_nodess#D   T "$rNcHVe%W PPP8Xd3VPPPP V.4R#VP V4pVP W1P4pVPPWA4R#r) rr7r]r8extendrkrrrAinsert)rr8rjr7 insert_idxs&&& radd_nodeOnnxModel.add_nodess  zz/?/?/D/D!D JJ   ! ! ( ($ 0**:6E77{{KJ JJ  j /rcVf2VPPPPV4R#VF&pW#P,pVP W44K( R#r)rr7r8rr]r)r nodes_to_addnode_name_to_graph_namer8rjs&&& r add_nodesOnnxModel.add_nodessH " * JJ   ! ! ( ( 6$4YY?  d/%rcVe%W PPP8Xd3VPPPP V.4R#VP V4pVPP V.4R#r)rr7r] initializerrrk)rtensorrjr7s&&& radd_initializerOnnxModel.add_initializersc  zz/?/?/D/D!D JJ   ( ( / / 9**:6E    $ $fX .rcVe%W PPP8Xd3VPPPP V.4R#VP V4pVPP V.4R#r)rr7r]r9rrk)rr9rjr7s&&& r add_inputOnnxModel.add_inputsa  zz/?/?/D/D!D JJ   " " ) )5' 2**:6E KK  w 'rc\V\4'd\V\4'gQh\\VP44F+pVPV,V8XgKW PV&K- R#r)rVstrrangeror9)r8old_input_namenew_input_namejs&&& rreplace_node_inputOnnxModel.replace_node_inputsS.#..:nc3R3RRRs4::'Azz!}. . 1 (rc`VP4Fp\PW1V4K R#r)r6rr)rrrr8s&&& rreplace_input_of_all_nodes$OnnxModel.replace_input_of_all_nodess"JJLD  ( (~ N!rc\V\4'd\V\4'gQh\\VP44F+pVPV,V8XgKW PV&K- R#r)rVrrrorA)r8old_output_namenew_output_namers&&& rreplace_node_outputOnnxModel.replace_node_outputsU/3//JPS4T4TTTs4;;'(A{{1~0!0 A)rcVPPPFp\P W1V4K R#r)rr7r8rr)rrrr8s&&& rreplace_output_of_all_nodes%OnnxModel.replace_output_of_all_nodess.JJ$$))D  ) )$ Q*rcVP4F-pVPFpVPV8XgKVuu# K/ R#r)rKrr])rr]r7rs&& rget_initializerOnnxModel.get_initializers8[[]E++;;$&!M,#rc.pVP4F'pVPV8XgKVPV4K) V#r)r6op_typer:)rrr6r8s&& rget_nodes_by_op_typeOnnxModel.get_nodes_by_op_types6JJLD||w& T"! rc*VfVP4p.pVeGV\VP48d+VPV,pWR9d\W%,4pV#VPF"pWR9gK VP W%,4K$ V#r)r<rorArFr)rr8r< output_indexchildrenrAs&&&& r get_childrenOnnxModel.get_childrens  &"&":":"<   #c$++..\20#$7$?@H  ++0OO$7$?@&rcVfVP4p.pVPF"pWB9gK VPW$,4K$ V#r)rBr9r:)rr8rBparentsr9s&&& r get_parentsOnnxModel.get_parentssG  &"&":":"< ZZE+29: rcVfVP4p\VP4V8:dR#VPV,pWC9dR#W4,#r)rBror9)rr8irBr9s&&&& r get_parentOnnxModel.get_parentsI  &"&":":"<  tzz?a  1   +"))rc \VP4FVwrVWc9gK W6,pVPV8Xd Wt9dWu3u#\P RV RVP 24KX R#)a Find parent node based on constraints on op_type. Args: node (str): current node name. parent_op_type (str): constraint of parent node op_type. output_name_to_node (dict): dictionary with output name as key, and node as value. exclude (list): list of nodes that are excluded (not allowed to match as parent). Returns: parent: The matched parent node. None if not found. index: The input index of matched parent node. None if not found. zTo find first z , current NN)rnr9rrvdebug)rr8parent_op_typerBexcluderr9parents&&&&& rmatch_first_parentOnnxModel.match_first_parentsg"$**-HA+,3>>^38M!9$LL>.1AFNNK[!\] .rc VfQhVe V^8gQhVfVP4pVf,VPWWE4wrxVeVPV4V#V\VP48d1\ P RV R\VP4 24R#VPWV4pVeVPV8Xd Wu9dV#Ve&\ P RV RVP 24R#)a Find parent node based on constraints on op_type and index. When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index. Args: node (str): current node name. parent_op_type (str): constraint of parent node op_type. input_index (int or None): only check the parent given input index of current node. output_name_to_node (dict): dictionary with output name as key, and node as value. exclude (list): list of nodes that are excluded (not allowed to match as parent). return_indice (list): a list to append the input index when input_index is None. Returns: parent: The matched parent node. Nz input_index z >= node inputs zExpect z, Got ) rBrr:ror9rvrrr) rr8r input_indexrBr return_indicerindexs &&&&&&& r match_parentOnnxModel.match_parent*s2"kQ&666  &"&":":"<    33DJ]gMF($$U+M #djj/ ) LL< }4DS_DUV W4GH  &..N"BvG\M   LL7>"2&8HI Jrc\V4FTwrE\V\\34'gQh.pVP W^,V^,W64pV'gKPWGV3u# R#)r)NN)rnrVrFtuplematch_parent_path)rr8pathsrBrpathrmatcheds&&&& rmatch_parent_pathsOnnxModel.match_parent_paths\sd 'GAdT5M22 22M,,T7DGEXhGw=00 ( rc8...rep\V4Fwrx\V\\34'gQh.p VP W^,V^,W94p V 'gKPVP V4VP V 4VP V 4K WEV3#r)rnrVrFrrr:) rr8rrBmatch_imatchesreturn_indicesrrrrs &&&& rmatch_parent_paths_all OnnxModel.match_parent_paths_alles+-r2. 'GAdT5M22 22M,,T7DGEXhGwq!w'%%m4(//rc  Ve\V4\V48XgQhVfVP4pTp.p\V4FwrTPTT Ve W8,MRV.VR7p V fNVe*\P RV RW8, RV 2RR7R#\P RV RV 2RR7R#VP V 4T pK V#)a Find a sequence of input edges based on constraints on parent op_type and index. When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index. Args: node (str): current node name. parent_op_types (str): constraint of parent node op_type of each input edge. parent_input_index (list): constraint of input index of each input edge. None means no constraint. output_name_to_node (dict): dictionary with output name as key, and node as value. return_indice (list): a list to append the input index When there is no constraint on input index of an edge. Returns: parents: a list of matched parent node. N)rrzFailed to match index=z parent_input_index= op_type=T stack_info)rorBrnrrvrr:) rr8parent_op_typesparent_input_indexrBr current_nodematched_parentsrrmatched_parents &&&&&& rrOnnxModel.match_parent_pathqs 0  ))*c/.BB BB  &"&":":"<  #O4JA!..);)G"%T#+ /N%%1LL03GHZH]G^^ghogpq#'! LL#9!IgY!O\`La  " "> 2)L)5,rcVPW4p\V4p\V4^8d[VP4pVPV8XdV#V'gK=VPWs4pVFpVP V4K KjR#rN)rrrorQr appendleft) rr8 child_typer< recursiverdqrchilds &&&&& rfind_first_child_by_type"OnnxModel.find_first_child_by_typesu$$T? 8_"gk668L##z1##y,,\O%EMM%(&rcT<V^8dQhRS[S[S[S[3,,R,/#)edgesN)rFrint)format __classdict__s"r __annotate__OnnxModel.__annotate__s)= = E#s(O$t+ = rc 0Ve\V4\V48XgQhVFep\V\4'dM\V4^8Xd=\V^,\4'd \V^,\4'dKeQh VfVP 4pTp.p\ V4FwrRp VfVP WtR7p M VP WtW9,^,R7p V Fip V PV 8XgKW9gKVeFV PW9,^,,VPW9,^,,8wdKgT p M V f!\PRV RV 2RR7R#VPV 4T pK V#)af Find a sequence of input edges based on constraints on parent op_type and index. Note that we use greedy approach and only consider the first matched child, so it has chance to miss matching. Args: node (str): current node name. child_op_types (str): constraint of child node op_type of each input edge. edges (list): each edge is represented by two integers: output index of parent node, input index of child node. None means no constraint. exclude(list): list of nodes that are excluded (not allowed to match as child). Returns: children: a list of matched children node. N)r<)r<rzFailed to match child rTr) rorVrrr<rnrrr9rArvrr:)rr8child_op_typesrr<redgermatched_childrenrr matched_childchildren_nodesrs&&&&&& rmatch_child_pathOnnxModel.match_child_paths,  u:^!44 44tU++D Q:dSTgWZC[C[`jkopqkrtw`x`xx  &"&":":"<  #N3JA M}!%!2!2>Cxx7*+44SUU;;*:**;7  "((5 5rcv\VP4Fwr#VPV4pVfKW$3u# R#)Nr)rnr9r)rr8rr9rs&& rget_constant_inputOnnxModel.get_constant_inputs<!$**-HA++E2E x. rcVPV4wrEVe*VP^8Xd\WR, 4V8dV#R#)Nr)rsizeabs)rr8expected_valuedeltarrs&&&& rfind_constant_inputOnnxModel.find_constant_inputs>**40  qS9O5PSX5XH rc VPV4pVf\PV RV R24R#\VP4V8wd-\PV RV RV RVP 24R#R#)N z is not initializer.Fz shall have z dimensions. Got shape T)rrvrroshape)rrC dimensions descriptionrs&&&& r$is_constant_with_specified_dimension.OnnxModel.is_constant_with_specified_dimension&sw'' 4 = LLK=+6JK L u{{ z ) LLK=+l:,Nefkfqfqers trc,VPWV4^8#r)r )rr8r r s&&&&rhas_constant_inputOnnxModel.has_constant_input2s''eDIIrczVfVP4pW1P^,,p.p\V4p\V4^8dqVP 4pWr9dK(Wu9gK0VP V4VPF-pW9gK W8,pVFp VP V 4K K/ KV#r)r<rArrorQr:r) r root_node stop_nodesr<r unique_nodesrrrArs &&&& rget_children_subgraph_nodes%OnnxModel.get_children_subgraph_nodes5s  &"&":":"< &'7'7':; 8_"gk668L)/##L1*11F4#6#>%-EMM%0&.2 rc 6.pVPPF|pVPR4'dVPVP4K7VPR4'dVPVP 4KkVPR4K~ V#)zConvert tensor shape to list dim_value dim_param?)rdimHasFieldr:r r!)r tensor_type shape_listds&& rtensor_shape_to_listOnnxModel.tensor_shape_to_listNsu ""&&Azz+&&!!!++.K((!!!++.!!#& 'rc4<V^8dQhRS[RS[R,/#rr]symbolic_shape_helperNrr)rrs"rrrZs"c:VY]:]rc 4VPEf/Vn\P!VPPP VPPP VPPP4F:pVPPPVPVP&K< VPPPFCpVPVP9gK VPVPVP&KE WP9dVPV,#VeDWP9d4VPV,pVPPP#R#)zXTry get data type given a name (could be initializer, input or output of graph or node).N)r$ itertoolschainrr7 value_infor9rArSr% elem_typer]r data_type known_vi_)rr]r,r1rs&&& r get_dtypeOnnxModel.get_dtypeZs/    #!D 'oo   ++   &&   '' 5?OO4O4O4Y4Y  1  $zz//;; ##4+;+;;9D9N9ND$$[%5%56 < ## ###D) ) ,9X9X1X.88>J??..88 8rc4<V^8dQhRS[RS[R,/#r+r-)rrs"rrrss"c:VY]:]rc JVPEf/Vn\P!VPPP VPPP VPPP4FpVPPPR4'gK0.pVPPPPFMpVP'dVPVP4K2VPVP4KO W@PVP &K VPPP"FCpVP VP9gK VP$VPVP &KE WP9dVPV,#VeDWP&9d4VP&V,pVPPP(#R#)zTTry get shape given a name (could be initializer, input or output of graph or node).Nr)r%r/r0rr7r1r9rArSr%r$rr#r!r:r r]rdimsr4r2)rr]r,r1rr#rs&&& r get_shapeOnnxModel.get_shapess    #!D 'oo   ++   &&   '' ??..77@@E)::@@DD===!LL7!LL7 E 9>$$Z__5 $zz//;; ##4+;+;;9D9I9ID$$[%5%56 < ## ###D) ) ,9X9X1X.88>J??..88 8rc&<V^8dQhRS[RS[/#)rr8attribute_name)r r)rrs"rrrsCrcVPF.pVPV8XgK\P!V4pVu# R#r)rRr]r get_attribute_value)r8r=rZrs&& rget_node_attributeOnnxModel.get_node_attributes6NNDyyN*2248 #rc VP4p^pVP4FppVPR8XgKVPV^VR7pV'gK4VPR8XgKGVP^,VP^&V^, pKr V^8d)\ P RV4VP4R#R#)aVRemove Cast node that are followed by another Cast node like --> Cast --> Cast --> Note that this shall be used carefully since it might introduce semantic change. For example, float -> int -> float could get different value than the original float value. So, it is recommended to used only in post-processing of mixed precision conversion. Cast)rBzRemoved %d cascaded Cast nodesN)rBr6rrr9rvinfo prune_graph)rrB removed_countr8rs& rremove_cascaded_cast_nodes$OnnxModel.remove_cascaded_cast_nodess #668 JJLD||v%qFYZ6fnn6$*LLODJJqM!Q&M ! 1  KK8- H     rc VPRR7pVP'dVf\PR4.pVP 4FpVP R8XgKVP VP^,V4pVP VP^,V4pV'gKfWE8XgKnVPV4K V'Edg\VP44p\VP44pVEF p\\VP4V,4'd\\VP4V,4'gl\VP4VP^,,4^8Xd6VP!VP^,VP^,4M6KVP#VP^,VP^,4VP%V4EK \P'R\V44R#R#)zKRemove cast nodes that are not needed: input and output has same data type.T)r2NzFshape inference failed which might impact useless cast node detection.rCz4Removed %d Cast nodes with output type same as input)r3r"rvrwr6rr5r9rAr:setr_rcboolror<rrrxrD)r shape_inferr{r8 input_dtype output_dtypegraph_input_namesgraph_output_namess& rremove_useless_cast_nodes#OnnxModel.remove_useless_cast_nodess..d.;  " " "{': NNc dJJLD||v%"nnTZZ]KH #~~dkk!nkJ ;;#>#**40 ! ? #D$?$?$A B !$T%A%A%C!D 'DKK(+==>> TZZ3D!DEE30024::a=ALL88A TUW 33DKKNDJJqMR  &( KKFO$  rcV\PR4VPRVR7R#)zbThe function convert_model_float32_to_float16 is deprecated. Use convert_float_to_float16 instead!T)use_symbolic_shape_infer keep_io_typesN)rvrwr)rcast_input_outputs&&r convert_model_float32_to_float16*OnnxModel.convert_model_float32_to_float16s' p  %%tSd%erc RV9dRVR&VPpV'Ed\V4pVPVRRR7pVEe/pVPPEFp\ VP R4'gK"\ VP PR4'gKJVP PP\P8wgKVP'gK\4pVPV4\ VP PR4'd&VP PPR4WVP&EK VPPF"pVPV9gKWgPK$ VP4F(pVPPP!V4K* R V/p T P)R U u/uFp W9gK WV ,bK up 4\+V3/V Bp VP-V 4VP/4VP14R# \"d\$P'R 4Li;iuup i) aConvert a model to half (default) or mixed precision. To use mixed precision, user need specify which graph inputs, outputs, operator type or list of nodes shall keep in float32. Note that the conversion might not proceed without type information for the whole graph. By default, we use symbolic shape inference to get type information. The benefit of symbolic shape inference is that it could handle fused operators in com.microsoft domain. Those operators cannot be handled in onnx shape inference so symbolic shape inference is recommended for optimized model. When symbolic shape inference is used (even if it failed), ONNX shape inference will be disabled. Note that onnx shape inference will fail for model larger than 2GB. For large model, you have to enable symbolic shape inference. If your model is not optimized, you can also use model path to call convert_float_to_float16 in float16.py (see https://github.com/microsoft/onnxruntime/pull/15067) to avoid the 2GB limit. Args: use_symbolic_shape_infer (bool, optional): use symbolic shape inference instead of onnx shape inference. Defaults to True. keep_io_types (Union[bool, List[str]], optional): boolean or a list of float32 input/output names. If True, model inputs/outputs should be left as float32. Defaults to True. op_block_list (List[str], optional): List of operator types to leave as float32. Defaults to None, which will use `float16.DEFAULT_OP_BLOCK_LIST`. node_block_list (List[str], optional): List of node names to leave as float32. Defaults to None. force_fp16_initializers(bool): force converting all float initializers to float16. Default to false. min_positive_val (float, optional): minimal positive value. Defaults to 1e-7. max_finite_val (float, optional): maximal finite value. Defaults to 1e4. force_fp16_inputs(Dict[str, List[int]]): Force the conversion of the inputs of some operators to float16, even if this script's preference it to keep them in float32. rUTF) auto_mergeguess_output_rankNr%r2rziFailed to run symbolic shape inference. Please file an issue in https://github.com/microsoft/onnxruntime.disable_shape_infer)rUmin_positive_valmax_finite_val op_block_listnode_block_listforce_fp16_initializersforce_fp16_inputs#use_bfloat16_as_blocked_nodes_dtype)rr infer_shapesr7r1hasattrrSr%r2r UNDEFINEDr]r CopyFrom ClearFieldvaluesr:r-rvrwr2rrrGrQ) rrTkwargsrr!model_with_shapename_vivivi_copy parameterskey fp16_models &&, rr"OnnxModel.convert_float_to_float16sD & (&*F? #  # #">e!D  #5#B#B5UYmr#B#s $/ G.44??#BGG];; '(;(;[ I I " 3 3 = =AVAV V "&4&6G#,,R0&w||'?'?II ' 8 8 C CG L/6BGG,@$kk4477g- ' 05&nn. ..55b9/,-EF    C=!C[    ".eBzB   # '') &&(9   s>AI#I)0II2B IA I6I< I< I98I9c .V'd$VPR4'dTMVR,pM VR,p^pW0P9dVPV,^,pMVP4FopVP'gKVPP V4'gK:\ VP\ V4R4p\V^,V4pKq W@PV&V\V4,# \dKi;i)a:Create a unique node name that starts with a prefix (default is operator type). The name will not be duplicated with any name that generated or existed in current graphs. Args: op_type (str): operator type name_prefix (str, optional): prefix of node name. Defaults to None. Returns: str: node name _N) endswithr r6r] startswithrromax ValueErrorr)rr name_prefixprefixsuffixr8rs&&& rcreate_node_nameOnnxModel.create_node_name7s $/$8$8$=$=[KRUDUFs]F ++ +++F3a7F 999!5!5f!=!=! #DIIc&km$< =!$UQY!7 %*0v&F ## &! !s-4D DDc~VPPPFpVPV8XgKVu# R#r)rr7r9r])rr>r9s&& rfind_graph_inputOnnxModel.find_graph_inputZs2ZZ%%++EzzZ' ,rc~VPPPFpVPV8XgKVu# R#r)rr7rAr])rrCrAs&& rfind_graph_outputOnnxModel.find_graph_output`s2jj&&--F{{k) .rcTVfVP4p.pVPW4p\V4p\V4^8dfVP 4pWr9dK(Wt9gK0VP V4VP F"pW9gK VPW8,4K$ KuV#r)rBrrrorQr:r9r) rr8rrBrrrrr9s &&&& rget_parent_subgraph_nodes#OnnxModel.get_parent_subgraph_nodesfs  &"&":":"<  ""4= 7^"gk668L)/##L1)//E3 &9&@A0rc d.pVPF5pVPV4'gKWC9gK$VPV4K7 V'daVPV.4pVFHpVPF5pVPV4'gKWC9gK$VPV4K7 KJ V#)z0 Find graph inputs that linked to current node. )r9rr:r)rrr graph_inputsr9 parent_nodesr8s&&& rget_graph_inputsOnnxModel.get_graph_inputs|s !''E$$U++0I##E*( 99,KL$!ZZE,,U338Q$++E2(%rcV\VP4Fwr#W08XgK Vu# R#)r)rnr9) node_output child_noderr9s&& rrOnnxModel.input_indexs+%j&6&67LE# 8 rc`VP4p.pVP4pVFApVPR8XgKVP^,V9gK0VP V4KC VP V4\ V4^8d$\PR\ V4 24R#R#)rzRemoved unused constant nodes: N) r<r6rrAr:r|rorvr)rr< unused_nodesr6r8s& rremove_unused_constant OnnxModel.remove_unused_constants"668  D||z)dkk!nDW.W##D) ,' | q LL:3|;L:MN O !rc \4pVPF^pVP\P8XgK$VP P pVFpVPVP4K K` V#)z4 Get inputs to all nodes in all subgraphs of a node ) rJrRrSrrUrWr8r2r9)rr8subgraph_nodes_inputsrZ child_nodesrs&& r_get_subgraph_inputs_of_node&OnnxModel._get_subgraph_inputs_of_nodes_ !$NNDyyN000"ffkk "-J)001A1AB#.# %$rc a\\V3RlVPPP44p\ 4pVF%pVP V4pVPV4K' W#3#)zp Get input names to all nodes in all subgraphs where subgraphs are graph attributes of a node in the main graph c"<VPS9#r)r)r8ops_with_graph_attrss&r:OnnxModel._get_subgraph_nodes_and_inputs..s$,,BV2Vr)rFfilterrr7r8rJrr2)rrsubgraph_nodesr parent_nodesubgraph_inputs_of_parent_nodes&f r_get_subgraph_nodes_and_inputs(OnnxModel._get_subgraph_nodes_and_inputssg f%VX\XbXbXhXhXmXmno #)K-1-N-N{-[ * ! ( ()G H*44rc 8 Vf:VPPPUu.uFq3PNK upMTpVP RR7pVP 4pRp\ VP44^8dVP0R mR7wr\ V4^8Xd\PR4R#VPPPF7p W9dK V PFpW99gK W59gKWC., pK K9 /p \4p VF"pW69gK V PWc,4K$ \ V 4^8d}V P4p V!V 4p V 'gK2W9gK:WV &V PF<p\ V4^8gKW9gKW9gK%V P!Wn,4K> K.p^pVPPPFbp V!V 4p V P#V 4pV'd6VP$V P$8XdVV 8XdVPV 4KYV^, pKd RVnVPPP)R4VPPPP+V4.pVeVPPPF'pVPV9gKVPV4K) VF2pVPPPP-V4K4 .pV'dVP 4pVPPPUu.uFpVPV9gKVNK ppVF2pVPPPP-V4K4 V'gV'gV^8d.pV'dVP\ V4 R24V'dVP\ V4 R 24V^8dVPV R 24\P/R R P1V44VP34R#uupiuupi)a Prune graph to keep only required outputs. It removes unnecessary nodes that are not linked (directly or indirectly) to any required output. There is also an option to remove graph inputs that are not used to generate any required output. Args: outputs (list): a list of graph outputs to retain. If it is None, all graph outputs will be kept. allow_remove_graph_inputs (bool): allow remove graph inputs. NT)r;cVP^,'dVP^,#\\VPUu.uFq'gK VNK up4R4#uupir)rAnextiter)r8os& rget_first_output/OnnxModel.prune_graph..get_first_outputsI{{1~~{{1~%:Aaa:;TB B:s  A% A% )rz)Skip prune_graph since graph has subgraphr8z inputsz outputsz nodesz Removed %sz, >IfLoopScan)rr7rAr]r<rBrorKrrvrr8rr:rQr9rgetrr#rhrrurDjoin update_graph)rrpallow_remove_graph_inputsrA keep_outputs"input_name_to_nodes_for_main_graphrBrrrr8output_to_noder first_outputr] nodes_to_keepnum_nodes_removed kept_nodeoutput_to_removeinput_to_remover<r9removeds&&& rrEOnnxModel.prune_graphsOVo$**2B2B2I2IJ2I 2IJcj -1-E-EX\-E-]*"668 C t{{}  !484W4W%;5X5 1N>"a' HI ((--)#kkF66;k$0 *.W"F, -56#"gk668D+D1L|!C/3|, JJD4y1}$*EDLf &9&?@'  JJ$$))D+D1L&**<8IY..$,,>9PTCT$$T*!Q&!*   ##F+ $$]3  ****11;;g-$++F32+   ''..v6+ $"&":":"< 26**2B2B2H2Hr2HEJJ^qLquu2HOr'   &&--d3( .2Ca2GG#o"6!7w?@#&6"7!8AB 1$"3!4F;< KK dii&8 9 CKfssRRRcVPPp\4pVPFdpVPR9d#VP V4pVP V4VPR8wgKIVP VP4Kf V'd\PRV 24.pV'd\VPF'pVPV9gKVPV4K) VFpVPPV4K VUu.uFqPNK p p\PR\V4 RV 24.p .p VPFcp V PV9d5VPV P4'gV PV 4KHV PV P4Ke V Fp VPPV 4K V U u.uFqPNK p p \PR\V 4 RV 24V'd\PRV 24VP!4R#uupiuup i) rrzremaining input names: zremove z unused inputs: z unused initializers: zremaining initializers:N)rrr)rr7rJr8rrr2r9rvrr]r:rurorrr) rverboserr7remaining_input_namesr8subgraph_inputs_of_nodeinputs_to_remover9names_to_removeweights_to_removeweights_to_keeprs &&& rrOnnxModel.update_graph+s    #JJD||55*.*K*KD*Q'%,,-DE||z)%,,TZZ8  LL23H2IJ K $::%::$++E2%* ""5)*4DD3C%::3CD ws#3455EoEVWX ,,K'<.zs _`_e_errpNc38"TFq'gK ^xK R#5i)rN).0rts& r 3OnnxModel.graph_topological_sort..s!=Z1!!Zs  FTz)Graph is not a DAG: len(sorted_node_set)=z, len(graph.node)=z, failed at node r8r)rJrr]r9sortaddr8sortedrornsumr:rA RuntimeErrorrhr)r7is_deterministicdeps_setsorted_node_set sorted_nodesinitinitializer_namesr9rOr^r>sorted_node_set_len graph_nodeslast_node_namenode_idxr8 input_countrAfaileds&& rgraph_topological_sort OnnxModel.graph_topological_sortisC5% 383D3DE3D4YY3DE5:[[A[EZZ[A';     %J LL $&!(8ejjfUZZUe>f /"c+&66?#'::"%o"6 "+K"8.!!=TZZ!== !# ''-#''1"&++!6$LL0#."&**J!zj&@!%)-#- ''-#''1"&++!6$LL0#./#92  3uzz? 2;C>"455KK"JK]J^ _`II01::j))&);J."s2a5&RTUVRWrr)r6rrrvrDritems)rinclude_domainop_countr8ops&& rget_operator_statistics!OnnxModel.get_operator_statisticss~JJLD'5$+++$++#2QUQ]Q]]B " 219IHL!  j(8>X!Y Z[\rc,<V^8dQhRS[RS[RS[/#)rrbase_dirreturn)r rr)rrs"rrr's"++[+C++rc VPR4'd \R4hVP\P8Xd \ R4hVPp\ P!V4pVP\P8XdE\W4p\\;QJd.RV4FNK 54#!RV444#\V4'd \W4VPR4'd\VP4#\P !V4p\VP#44#)zConverts a tensor def object to a hash for data comparison purposes. Args: tensor: a TensorProto object. base_dir: if external tensor exists, base_dir can help to find the path to it Returns: hash: a hash of the data. segmentz*Currently not supporting loading segments.z4The element type in the input tensor is not defined.c3B"TFqPR4xK R#5i)zutf-8N)decode)rss& rr)OnnxModel.to_data_hash..8sFAhhw//sraw_data)r$rxr3r rf TypeErrorr tensor_dtype_to_fieldSTRINGgetattrhashrrrr#r rtobytes)rr tensor_dtype storage_field utf8_stringsnp_datas&& r to_data_hashOnnxModel.to_data_hash&s ??9 % %IJ J   {44 4RS S'' 44\B   {11 1"69LFFG GFFFG G f % % )& ; ??: & &( ("++F3G)* *rc`<V^8dQhRS[RS[RS[R,RS[R,RS[RS[RS[/#) rtensor1tensor2signature_cache1Nsignature_cache2rtolatolr)r dictfloatrK)rrs"rrrCsW////+/+ /  /  / /rc bV'd$VPV9dW P,M\PV4pV'd$VPV9dW1P,M\PV4pVeWbVP&VeWsVP&VPVP8XdpVPVP8XdU\ P !V4p\ P !V4p Wg8XdW8HP4#^RIH p V !WWER7#R#)aReturns True when two tensors have same value. Note that name can be different. Args: tensor1 (TensorProto): initializer 1 tensor2 (TensorProto): initializer 2 signature_cache1 (dict): Optional dictionary to store data signatures of tensor1 in order to speed up comparison. signature_cache2 (dict): Optional dictionary to store data signatures of tensor2 in order to speed up comparison. rtol (float): Optional relative difference threshold for minor precision differences atol (float): Optional absolute difference threshold for minor precision differences Returns: bool: True when two initializers has same value. )allclose)r5r6F) r]rr.r3r9r rallnumpyr:) r1r2r3r4r5r6sig1sig2n1n2r:s &&&&&& rhas_same_valueOnnxModel.has_same_valueBs0 GLL4D$D \\ *''0  GLL4D$D \\ *''0  '-1W\\ *  '-1W\\ *    1 1 1gllgll6R&&w/B&&w/B|~~''+T==rcVP4F1pWP9gKVPPV4R# \P RV4R#)NzFailed to remove initializer %s)rKrrurvrw)rrr7s&& rremove_initializerOnnxModel.remove_initializertsF[[]E***!!((0# 8&Arc.<V^8dQhRS[R,/#)rcacheN)r7)rrs"rrr{s#I#I4$;#Irc \VP44^8d\PR4\VPP P 4pR.V,p\V^, 4FpW4,^8dK\V^,V4Fqp\PVPP P V,VPP P V,VV4'gKmWCV&Ks K ^p\V4FpW4,^8gKV^, pVPVPP P V,PVPP P W4,,P4K V^8d"VP4\RV R24R#R#)aRemove initializers with duplicated values, and only keep the first one. It could help reduce size of models (like ALBert) with shared weights. If require_raw_data passed, method will only compare raw_data initializers to speed runtime Note: this function does not process subgraph. z9remove_duplicated_initializer does not process subgraphs.zRemoved z# initializers with duplicated valueNr)rorKrvrwrr7rrrrArr]rr.)rrGinitializer_countsamerrcounts&& rremove_duplicated_initializer'OnnxModel.remove_duplicated_initializer{sv t{{}  ! NNV W 0 0 < <=t''(1,-Aw!|1q5"34++JJ$$003JJ$$003   G5.()Aw!| //JJ$$00388JJ$$009>>* 19     HUG#FG H rc <V^8dQhRS[/#)rrzr)rrs"rrrs ; ;# ;rc Z\VP44^8d\PR4VPP P Uu.uFq"PNK upVPP PUu.uFq3PNK up,R.,pVPP PFFpVPV9gKWP,V9gK/WP,VnKH VPP PEFp\\VP 44FcpVP V,V9gKWP V,,V9gK=WP V,,VP V&Ke \\VP44FcpVPV,V9gKWPV,,V9gK=WPV,,VPV&Ke EK VPP PF-pVPV9gKWP,VnK/ R#uupiuupi)zAdd prefix to initializer or intermediate outputs in graph. Main graph inputs and outputs are excluded. It could help avoid conflicting in name of node_args when merging two graphs. Note: this function does not process subgraph. z/add_prefix_to_names does not process subgraphs.r N) rorKrvrwrr7r9r]rArr8rr1) rrzrrexcludedrr8rr1s && radd_prefix_to_namesOnnxModel.add_prefix_to_namess t{{}  ! NNL M%)JJ$4$4$:$:;$:qFF$:;tzzO_O_OfOf>gOf!vvOf>ggkmjnn::++77Kx/,,,H<'-0@0@'@K$8 JJ$$))D3tzz?+::a=0 1 -X=(.A(> 1 , 3t{{+,;;q>1 A.h>)/++a.)@ A-*****55Jh."(??": 6)<>gs J#J(cPVPPPR4R#)r1N)rr7rhr(s&rclean_shape_inferOnnxModel.clean_shape_infers ##L1rc .pVPVPP4V'Ed.pVEFp\V\4'gK\ P !VPVPVP4FpVPPP\P8XdR#VPPR4'gK]VPP PPP\P8XgKR# VP"F&pVP$\P8XgK$R# VP&EFppVP(R8XdKVP*F:pVP,R8XgKVP.\P8XgK7R# VP*EFpVP\0P28XdVPVP44VP6FpVPV4K \VP8\4'd/VP8P$\P8XdR#VP:F@p\V\4'gKVP$\P8XgK<R# EK EKs EK TpEKR#)z$Check whether the model uses float16T sequence_typerCtoF)r:rr7rVrr/r0r9rAr1rSr%r2r FLOAT16r$rXrr3r8rrRr]rrrUrWrKrtensors) rqueue sub_graphsr7vrr8rZrWs & r use_float16OnnxModel.use_float16s TZZ%%&eJ!%44"ellEDTDTUAvv))33{7J7JJ#vv7766//99EEOOS^SfSff#' V**A{{k&9&99#+"JJD||v-$(NND#yyD0TVV{?R?R5R'+%3!%99(<(<<&--dff5!%A&--a0"-&dffk::tvv?O?OS^SfSf?f#'!%A)![99akk[M`M`>`'+".!/ 'FErc&<V^8dQhRS[RS[/#)r graph_inputnew_typer r)rrs"rrrs=.=.#=.=.rc \V\4'gQhVPVP4'gQhVPP P \V48XdR.3#VP4pRp.pVP4pVPV9EdWaP,pVUu.uFqPR8wgKVNK p pV 'dVPR4p V R,VP,p VPP4p V PV4Wn\P !RVP.V .\VPP P 4V R7pVP"P%V.4V F#p\&P)WPV 4K% VUu.uFqPR8XgKVNK p pV Fp\&P+VR4\V48Xd.VP-VP.^,VP4VP1VP.^,4'dK~VP3V4K V'dVP5V4\V4VPP nWE3#uupiuupi)a9Change graph input type, and add Cast node if needed. Args: graph_input (ValueInfoProto): input of the graph new_type (int): new data type like TensorProto.INT32. Returns: NodeProto: a new Cast node that added. None if Cast node is not added. List[NodeProto]: Cast nodes that have been removed. NrCrtrYr]rY)rVr rr]rSr%r2rr7r<rr|r1rrgr make_noder8rrrr@rrArr:r|)rrbrcr7 new_cast_noder{r<r6r8nodes_not_cast node_namerCnew_value_info nodes_casts&&& rchange_graph_input_type!OnnxModel.change_graph_input_typesL+~6666$$[%5%56666    ' ' 1 1S] B8O  "668   2 2'(8(89E05Out 8NdduNO 11&9 '#o 0@0@@ !&!1!1!5!5!7'' 4&1# & 0 0 %%& M;++77AAB" !  !!=/2*D007G7GU+ ,1K54LLF4J$$5JK"//d;s8}L33DKKNKDTDTU--dkk!n==#**40 # !!/214X $$.--?P*Ls:KKK +K c&<V^8dQhRS[RS[/#)r graph_outputrcrd)rrs"rrr1s''$''rc \V\4'gQhVPVP4'gQhVPP P \V48XdR#RpVP4pVPR4pVR,VP,pVPVPV4VPP4pVPV4Wgn\P!RV.VP.\V4VR7pVP P#V.4\V4VPP nV#)zChange graph input type, and add Cast node if needed. Args: graph_input (str | ValueInfoProto): output of the graph new_type (int): new data type. Returns: NodeProto: a new Cast node that added. None if Cast node is not added. NrCrtrf)rVr rr]rSr%r2rr7r|rr1rrgr rgr8r)rrprc cast_noder7rjr>rks&&& rchange_graph_output_type"OnnxModel.change_graph_output_type1s+,7777%%l&7&78888    ( ( 2 2c(m C  ))&1 _|'8'88  '' (9(9:F))--/ -($$  L    8}    9+&25h- %%/rc&<V^8dQhRS[RS[/#)rold_namenew_namerO)rrs"rrrZs ' 'C '3 'rc,W P49d \R4hVP4pVPFTpVPV8XgK\ P RW4VPW4VPW4W$nKV R#)z{new_name} exists in graphz!replace output name from %s to %sN) rBrr7rAr]rvrrr)rrvrwr7rAs&&& rrename_graph_outputOnnxModel.rename_graph_outputZsr //1 1;< < llF{{h& @(U//C00D& #r)r$r r%r#r"rr!)Frr)NNNr)gư>)T)FF)FTiF)r )NNgh㈵>g:0yE>)X__name__ __module__ __qualname____firstlineno__rrr)r3r<rBrGr6r7rKr_rcrgrkrrrxr|rrrr staticmethodrrrrrrrrrrrrrrrrrrrr rrrr(r5r:r@rGrQrWrr|rrrrrrrrrErrrrrrr r rr.rArDrLrRrUr_rmrsry__static_attributes____classdictcell__)rs@rrrs 8(8:%  ## &  9#00/(// O11 R" *UW6 0d 06p &/3  = = ~$  J2 2@& Df _)B!$F  ," P % 5m^+&Z"6(6(p 2+2+h52 I +++6///bB#I#IJ ; ;D2+Z=.=.~''R ' 'rr)r/loggingrr/ collectionsrpathlibrfloat16ronnxrrrr r r r r ronnx.external_data_helperrrr!r getLoggerr{rvrrrrrsP  ,   X;   8 $E'E'r