RЦi(SrSSKJrJr SSKJr SSKJrJr SSK J r SSK J r SSK JrJr SS KJr "S S \R$5rS'S jrS(S jr\"\"SS9\"SS9\"SS9\"SSSS9\"SSSSSS9\"SSSSSS9\"SSS9\"SSS9S.5r\S'S\4Sjj5r\S'S\4Sjj5r\S'S\4S jj5r\S'S\4S!jj5r\S'S\4S"jj5r\S'S\4S#jj5r\S'S\4S$jj5r\S'S\4S%jj5rg&))aResNeSt Models Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955 Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang Modified for torchscript compat, and consistency with timm by Ross Wightman )OptionalType)nnIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD) SplitAttn)build_model_with_cfg)register_modelgenerate_default_cfgs)ResNetc(^\rSrSrSrSrSSSSSSSSSSS\R\RSSSSSS4S\ S \ S \ S \ \RS \ S \ S\ S\ S\ S\ S\ S\ S\ \ S\ \RS\ \RS\ \ \RS\ \ \RS\ \ \RS\ \R4&U4SjjjrSrSrSrU=r$)ResNestBottleneckzResNet Bottleneck r N@Finplanesplanesstride downsampleradix cardinality base_widthavd avd_firstis_first reduce_firstdilationfirst_dilation act_layer norm_layer attn_layeraa_layer drop_block drop_pathc J>UUS.n[TU]5 U S:XdeUbS5eUbS5e[X'S- -5U-nU =(d U n U(aUS:dU (aUnSnOSnXPl[R "UU4SSS.UD6UlU"U40UD6UlU"S S 9UlUS:aU (a[R"S USS 9OSUl URS:aj[UU4S UU U UUUUS .UD6Ul [R"5Ul[R"5Ul[R"5UlOa[R "UU4S UU U USS.UD6Ul U"U40UD6UlUbU"5O[R"5UlU"S S 9UlUS:aU (d[R"S USS 9OSUl[R "UUS-4SSS.UD6UlU"US-40UD6UlU"S S 9UlX@lUUlg)N)devicedtyper zattn_layer is not supportedzaa_layer is not supportedgP@rF) kernel_sizebiasT)inplace)padding)r*rr.rgroupsrr" drop_layer)r*rr.rr/r+r)super__init__intrrConv2dconv1bn1act1 AvgPool2drr conv2Identitybn2r%act2avd_lastconv3bn3act3rr&)selfrrrrrrrrrrrrr r!r"r#r$r%r&r(r)dd group_width avd_stride __class__s R/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/models/resnest.pyr2ResNestBottleneck.__init__s60 / q   !@#@@!V_aQ?ei ::?" &'"%%  DJ{{}DH kkmDO DI &'"  DJ"+44DH.8.Djl"++-DO!$/DIBLq.Yb Q A>hl YY{FQJXAEXUWX fqj/B/d+ $"c[URSS5b4[RR URR 5 gg)Nweight)getattrr?rinitzeros_rJ)rAs rFzero_init_last ResNestBottleneck.zero_init_lastns2 488Xt , 8 GGNN488?? + 9rHcVUnURU5nURU5nURU5nURbURU5nUR U5nUR U5nUR U5nURU5nURbURU5nURU5nURU5nURbURU5nURbURU5nX2- nURU5nU$N)r5r6r7rr9r;r%r<r=r>r?r&rr@)rAxshortcutouts rFforwardResNestBottleneck.forwardrsjjmhhsmiin >> %..%Cjjohhsmooc"iin == $--$Cjjohhsm >> %q!A ?? &q)H iin rH)r7r<r@rr=r6r;r?r5r9r>rr%r&r)__name__ __module__ __qualname____firstlineno____doc__ expansionrReLU BatchNorm2dr3rModuleboolrr2rNrU__static_attributes__ __classcell__)rEs@rFrrsI .2  #" !,0)+*,..482648-1-R#R#R# R# !+ R#  R#R#R#R#R#R#R#R#%SMR#BIIR# RYY!R#"!bii1#R#$tBII/%R#&!bii1'R#(  *)R#R#h,rHrc &[[UU40UD6$rQ)r r)variant pretrainedkwargss rF_create_resnestrgs"     rHc 2USSSSS[[SSSS . UE$) Ni)r-ri)rjg?bilinearzconv1.0fcz apache-2.0) url num_classes input_size pool_sizecrop_pct interpolationmeanstd first_conv classifierlicenser)rmrfs rF_cfgrxs3=vJ%.Bt    rHztimm/) hf_hub_id)r-rz)r{)ryrorp)r-@r|) r}gJ +?bicubic)ryrorprqrr)r-r) rgV-?)ryrr)zresnest14d.gluon_in1kzresnest26d.gluon_in1kzresnest50d.in1kzresnest101e.in1kzresnest200e.in1kzresnest269e.in1kzresnest50d_4s2x40d.in1kzresnest50d_1s4x24d.in1kreturnc n[[/SQSSSSS[SSSS 9S 9n[S S U0[U40UD6D6$) z4ResNeSt-14d model. Weights ported from GluonCV. )r r r r deep Trr Frrrblocklayers stem_type stem_widthavg_downrr block_argsre) resnest14ddictrrgrerf model_kwargss rFrrK R$2STaTU;=L  _J _$|B^W]B^ __rHc n[[/SQSSSSS[SSSS 9S 9n[S S U0[U40UD6D6$) z4ResNeSt-26d model. Weights ported from GluonCV. )rrrrrrTrr rFrrre) resnest26drrs rFrrrrHc n[[/SQSSSSS[SSSS 9S 9n[S S U0[U40UD6D6$) zResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955 Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample. r-rr-rrTrr rFrrre) resnest50drrs rFrrsK  R$2STaTU;=L  _J _$|B^W]B^ __rHc n[[/SQSSSSS[SSSS9S 9n[S S U0[U40UD6D6$) zResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955 Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. )r-rr-rrTr rFrrre) resnest101errs rFrrsK  R$2STaTU;=L  `Z `4 C_X^C_ ``rHc n[[/SQSSSSS[SSSS9S 9n[S S U0[U40UD6D6$) zResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955 Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. )r-$r-rrTr rFrrre) resnest200errs rFrrK R$2STaTU;=L  `Z `4 C_X^C_ ``rHc n[[/SQSSSSS[SSSS9S 9n[S S U0[U40UD6D6$) zResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955 Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. )r-0r{rrTr rFrrre) resnest269errs rFrrrrHc n[[/SQSSSSS[SSSS9S 9n[S S U0[U40UD6D6$) z]ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md rrrT(rrrrre)resnest50d_4s2x40drrs rFrrK R$2STaTT:rs"A!*<{ {| %!G4!G4g. F4 HuT]_ HuT]_ $ ! $ !!& ,`f```f```f``avaaavaaavaahfhhhfhhrH