RЦiISrSSKJrJrJrJrJr SSKrSSKJ r SSK J r J r SSK JrJrJrJrJrJrJrJr SSKJr SSKJr SS KJr SS KJrJr S /r"S S \ R@5r!"SS\ RD5r#"SS\ RD5r$"SS \ RD5r%\&"\&"/SQ/SQ/SQS/SQSSSS9\&"/SQ/SQ/SQS/SQSSSS9\&"/SQ/SQ/SQS/S QS!S!S"S9\&"/SQ/SQ/SQS/S QS!S!S"S9\&"/SQ/SQ/SQS/S QS!SS"S9\&"/SQ/SQ/SQS/S QS!SS"S9\&"/SQ/SQ/SQS/SQS!SS"S9\&"/SQ/SQ/SQS/SQS!SS"S9\&"/SQ/SQ/SQS/S#QS!SS"S9\&"/SQ/SQ/SQS/SQS!SS$S9S%9 r'\'S&\'S''SAS(jr(SBS)jr)\"\)"SS*9\)"SS*9\)"SS*9\)"S+S,S-S.9\)"SS*9\)"S+S,S-S.9\)"S+S/S/S-S0S1S2S3S49\)"SS*9\)"SS*9\)"SS*9S5. 5r*\SAS6\%4S7jj5r+\SAS6\%4S8jj5r,\SAS6\%4S9jj5r-\SAS6\%4S:jj5r.\SAS6\%4S;jj5r/\SAS6\%4S<jj5r0\SAS6\%4S=jj5r1\SAS6\%4S>jj5r2\SAS6\%4S?jj5r3\SAS6\%4S@jj5r4g)CaVoVNet (V1 & V2) Papers: * `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730 * `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667 Looked at https://github.com/youngwanLEE/vovnet-detectron2 & https://github.com/stigma0617/VoVNet.pytorch/blob/master/models_vovnet/vovnet.py for some reference, rewrote most of the code. Hacked together by / Copyright 2020 Ross Wightman )ListOptionalTupleUnionTypeNIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD) ConvNormActSeparableConvNormActBatchNormAct2dClassifierHeadDropPath create_attncreate_norm_act_layercalculate_drop_path_rates)build_model_with_cfg)feature_take_indices)checkpoint_seq)register_modelgenerate_default_cfgsVovNetc^\rSrSrU4SjrS\R S\\R S\R 4SjrSr U=r $)SequentialAppendListc >[TU]"U6 gN)super__init__)selfargskwargs __class__s Q/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/models/vovnet.pyr SequentialAppendList.__init__s $x concat_listreturnc[U5H>up4US:XaURU"U55 M$URU"US55 M@ [R"USS9nU$)Nrr)dim) enumerateappendtorchcat)r!r(r)imodules r%forwardSequentialAppendList.forward"sY"4IAAv""6!9-""6+b/#:; ) IIkq )r') __name__ __module__ __qualname____firstlineno__r r0Tensorrr4__static_attributes__ __classcell__r$s@r%rrs8 D4F5<<r'rc^\rSrSrSSS\\R SSS4S\S\S\S\S \S \S \ S \ \RS \ \RS\ \R4U4Sjjjr SrSrU=r$)OsaBlock,FNin_chsmid_chsout_chslayer_per_blockresidual depthwiseattn norm_layer act_layer drop_pathc >XS.n [TU]5 XPlX`l[ SXS.U D6nUnUR(a!X:waU(ae[ XS40UD6UlOSUl/n[U5HAnUR(a [X"40UD6nO [ XS40UD6nUnURU5 MC [U6Ul XU--n[ X40UD6Ul U(a [Xs40U D6OSUlXlg)NdevicedtyperJrKrr6)rr rGrHdictr conv_reductionranger r/rconv_mid conv_concatrrIrL)r!rCrDrErFrGrHrIrJrKrLrOrPdd conv_kwargs next_in_chs mid_convsr2convr$s r%r OsaBlock.__init__.s /  "LjLL  >>k4 <"-kA"U"UD "&D  'A~~+GL L";JkJ!K   T " (-i8 88 &{K{K8> %q!A ==1I Ar')rIrWrVrTrHrLrG)r7r8r9r:r nnReLUintboolstrrModulerr r4r<r=r>s@r%r@r@,s##*8)+-1-#-#-# -# ! -#  -#-#-#RYY-#BII-#  *-#-#^  r'r@c^\rSrSrSSSS\\R SSS4 S\S\S\S \S \S \S \S \S\ S\ \RS\ \RS\ \ \4U4SjjjrSrSrU=r$)OsaStagelTFeseNrCrDrEblock_per_stagerF downsamplerGrHrIrJrKdrop_path_ratesc >XS.n[TU]5 SUlU(a[R"SSSS9UlOSUl/n[ U5HYnUUS- :HnU bU US:a[U U5nOSnU[UUUU4U=(a US :UU(aU OS U U US .UD6/- nUnM[ [R"U6Ul g) NrNFrRT) kernel_sizestride ceil_moderrrB)rGrHrIrJrKrL) rr grad_checkpointingra MaxPool2dpoolrUrr@ Sequentialblocks)r!rCrDrErkrFrlrGrHrIrJrKrmrOrPrXrxr2 last_blockrLr$s r%r OsaStage.__init__ns" / "'  1MDIDI'Ao11J*q/AB/F$_Q%78  x  "+a!e#'TR%##    FF'((mmV, r'cURbURU5nUR(a;[RR 5(d[ UR U5nU$UR U5nU$r)rvrtr0jit is_scriptingrrxr!r(s r%r4OsaStage.forwards_ 99  ! A  " "599+A+A+C+Ct{{A.A AAr')rxrtrv)r7r8r9r:r rarbrcrdrerrfrrfloatr r4r<r=r>s@r%rhrhls $!#*8)+59/-/-/- /- ! /- ! /-/-/-/-/-RYY/-BII/-&d5k2/-/-br'rhc^\rSrSrSSSS\\R SSSS4 S\S \S \S \ S \S \ \RS\ \RS\ S\ 4U4Sjjjr \RR S&Sj5r\RR S'Sj5r\RR S\R4Sj5rS(S \\ 4SjjrS)S\R,S\\\\\4S\S\S\ S\S\\\R,\\R,\\R,444SjjrS*S\\\\4S\S\4S jjrS!rS&S"\4S#jjrS$rS%r U=r!$)+rrRavg rsNcfgin_chans num_classes global_pool output_striderJrK drop_ratedrop_path_ratec \>[T U]5 XS.n X0lX lXlUS:Xde[ U40U D6nUR SS5nUSnUSnUSnUSnUS n[ S XgS .U D6nUS -nUS (a[O[n[R"[X/S S4SS 0UD6U"US USS4SS0UD6U"USUS S4SU0UD6/6Ul [ USS SUS:XaSOS 3S9/Ul Un[U USS9nUSSUSS-n[ S USUS USS.UD6n/n[S5H~nUS :H=(d US :nU[UUUUUUUUU4UUUS.UD6/- nUUUlUU(aS OS-nU=R[ UR USU3S9/- sl M [R"U6UlUR Ul['UR U4XHS.U D6UlUR+5Hunn[-U[R.5(a+[R0R3UR4SSS9 MP[-U[R65(dMq[R0R9UR:5 M g)!ai Args: cfg (dict): Model architecture configuration in_chans (int): Number of input channels (default: 3) num_classes (int): Number of classifier classes (default: 1000) global_pool (str): Global pooling type (default: 'avg') output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) norm_layer (Union[str, nn.Module]): normalization layer act_layer (Union[str, nn.Module]): activation layer drop_rate (float): Dropout rate (default: 0.) drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) kwargs (dict): Extra kwargs overlayed onto cfg rNr stem_stridestem_chsstage_conv_chs stage_out_chsrkrFrQrorHrrRrqrzstem.)num_chs reductionr3T) stagewiser,NrGrI)rGrHrI)rlrmzstages.) pool_typerfan_outrelu)mode nonlinearityr6)rr rrrrSgetr r rarwstem feature_inforrUrh num_featuresstageshead_hidden_sizerhead named_modules isinstanceConv2dinitkaiming_normal_weightLinearzeros_bias)!r!rrrrrrJrKrrrOrPr#rXrrrrrkrFrYlast_stem_stride conv_typecurrent_stride stage_dpr in_ch_list stage_argsrr2rlnmr$s! r%r VovNet.__init__s8  /& """"3!&!ggmQ/ z?-.O, /0/0LjLL '!+,/ ,<(+ MM 1+q J Jk J hqk8A; K! K{ K hqk8A; Z:J Zk Z$   "QK1u+QRBRQXYNUXY_U`pdop qA$)2QUJ x1 q!a "  & )!     F!.a 0D  :a1 4N   $t/@/@Nelmnlocp"q!r r  mmV, $ 1 1"4#4#4kt[tqst &&(DAq!RYY''''yv'VAryy))qvv& )r'c,[SU(aSS9$SS9$)Nz^stemz^stages\.(\d+)z^stages\.(\d+).blocks\.(\d+))rrx)rS)r!coarses r% group_matcherVovNet.group_matchers%(.$  4S  r'c6URH nXlM gr)rrt)r!enabless r%set_grad_checkpointingVovNet.set_grad_checkpointing sA#) r'r*c.URR$r)rfc)r!s r%get_classifierVovNet.get_classifiersyy||r'cFXlURRX5 gr)rrreset)r!rrs r%reset_classifierVovNet.reset_classifiers&  1r'r(indicesnorm stop_early output_fmtintermediates_onlycUS;dS5e/n[SU5upSn URSS"U5nX;aURU5 URS"U5n[RR 5(dU(d UR n OUR SU n [U SS9H%upU "U5nX;dMURU5 M' U(aU$X4$) aForward features that returns intermediates. Args: x: Input image tensor indices: Take last n blocks if int, all if None, select matching indices if sequence norm: Apply norm layer to compatible intermediates stop_early: Stop iterating over blocks when last desired intermediate hit output_fmt: Shape of intermediate feature outputs intermediates_only: Only return intermediate features Returns: )NCHWzOutput shape must be NCHW.rNr,r)start)rrr/r0r|r}rr.) r!r(rrrrr intermediates take_indices max_indexfeat_idxrstages r%forward_intermediatesVovNet.forward_intermediatess*Y&D(DD& "6q'"B  IIcrN1   #   # IIbM!  99 ! ! # #:[[F[[),F(q9OHaA'$$Q' :  r' prune_norm prune_headc|[SU5upEURSUUlU(aURSS5 U$)z?Prune layers not required for specified intermediates. rNrrB)rrr)r!rrrrrs r%prune_intermediate_layers VovNet.prune_intermediate_layersFs>#7q'"B kk*9-   ! !!R (r'cFURU5nURU5$r)rrr~s r%forward_featuresVovNet.forward_featuresTs IIaL{{1~r' pre_logitscPU(aURXS9$URU5$)N)r)r)r!r(rs r% forward_headVovNet.forward_headXs#6@tyyy2RdiiPQlRr'cJURU5nURU5nU$r)rrr~s r%r4VovNet.forward[s'  ! !! $   a r') rrrrrrrrrF)Tr)NFFrF)rFT)"r7r8r9r:r rarbrSrcrerrfrr r0r|ignorerrrrrr;rrrdrrrrrr4r<r=r>s@r%rrs  #$!#*8)+!$&V'V'V' V'  V'  V'RYYV'BIIV'V'"V'V'p YY   YY** YY 2#28<$$',- ||- eCcN34-  -  -  - !%-  tELL!5tELL7I)I#JJ K- b./$# 3S >*   S$Sr')@r)r)iir)rrroroFrB)rrrrFrkrGrHrI)rrrrR)rrr)rP`p)rrirrR)rrrrTrj)rrR rReca) vovnet39a vovnet57aese_vovnet19b_slim_dwese_vovnet19b_dwese_vovnet19b_slim ese_vovnet19b ese_vovnet39b ese_vovnet57b ese_vovnet99b eca_vovnet39brese_vovnet39b_evosc H[[UU4[U[SS9S.UD6$)NT)flatten_sequential) model_cfg feature_cfg)rr model_cfgsrS)variant pretrainedr#s r%_create_vovnetrs9  W%D1     r'c 2USSSSS[[SSSS . 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