RЦiSrSSKrSSKJrJr SSKJr SSKJrJ r J r J r J r J r JrJr SSKrSSKJr SSKJrJr SSKJrJrJrJrJrJrJr SSKJrJrJ r J!r! S S K"J#r# S S K$J%r% S S K&J'r'J(r( S S K)J*r*J+r+J,r, SS/r-\"SS55r.S\/S\0S\04Sjr1GSsS\ \0S\ \/S\ \0S\/S\\ \0\ \044 Sjjr2GStS\/S\0S\/S\0S\0S \0S\\ \0\0\ \044S!jjr3GSuS"\0S#\0S$\0S%\0S&\0S'\ \\RhS(\5S\Rh4S)jjr6GSuS"\0S#\0S$\0S%\0S&\0S'\ \\RhS(\5S\Rn4S*jjr8GSvS+\ \9S"\0S#\0S$\0S%\0S&\\0\04S'\ \\RhS(\5S\ \Rh4S,jjr:"S-S.\Rh5r;"S/S0\Rh5r<"S1S2\Rh5r="S3S\Rh5r>GSwS5\RhS6\9S7\5SS4S8jjr?S9\ \9\4S\ \9\44S:jr@\A"GSx0S;\."SSS?S@9_SA\."SSS?SySz9_S{\."SJS|S}SSDSySz9_S~\."SJSSSDSSySz9_S\."SNSSSDSSySz9_S\."SNSSSDSSySGS9_S\."SJSSS4SjrCGSyS\9S\ \9\44SjjrDGSyS\9S\ \9\44SjjrEGSyS\9S\ \9\44SjjrF\*"0S\D"SSS9_S\D"SSS9_S\D"SSS9_S\D"SSS9_S\D"SS9_S\D"SS9_S\D"SS9_S\D"SSS9_S\D"SSS9_S\D"S4S9_S\D"SSSS9_S\D"SSSS9_S\D"S4S9_S\D"SSSSSSS9_S\D"SSSSSSS9_S\D"SSS9_S\F"SSS9_0S\F"SSS9_S\F"SSS9_S\F"SSS9_S\F"SGSS9_GS\F"SGSS9_GS\F"SGSS9_GS\F"SGSS9_GS\F"SGSS9_GS \F"SGS S9_GS \F"SGS S9_GS \F"SGSS9_GS\F"SGSS9_GS\F"SGSS9_GS\F"SGSGSGSGSSGS9_GS\F"SGSGSGSGSSGS9_GS\F"SGSGSGSGSSGS9_GS\F"SGSGSGS9_E0GS \F"SGS!GSGS9_GS"\F"SGS#GSGS9_GS$\F"SGS%GS&GS'GSGSSGS(9_GS)\F"SGS%GS&GS*GSGSSGS(9_GS+\F"SGS%GS&GS,GSGSSGS(9_GS-\F"SGS%GS&GS.GSGSSGS(9_GS/\F"SGS0SGS%GS&GS19_GS2\F"SGS3SGS%GS&GS19_GS4\F"SGS5SGS%GS&GS19_GS6\E"SS9_GS7\E"SS9_GS8\E"SS9_GS9\E"SS9_GS:\E"SS9_GS;\E"SS9_GS<\E"SS9_GS=\E"SS9_E0GS>\E"SS9_GS?\E"SS9_GS@\E"SS9_GSA\E"SS9_GSB\E"SS9_GSC\E"SS9_GSD\E"SS9_GSE\E"SS9_GSF\E"SS9_GSG\E"SS9_GSH\E"SS9_GSI\E"SS9_GSJ\E"SS9_GSK\E"SS9_GSL\E"SS9_GSM\E"SS9_E5rG\+GSzS\5S\>4GSNjj5rH\+GSzS\5S\>4GSOjj5rI\+GSzS\5S\>4GSPjj5rJ\+GSzS\5S\>4GSQjj5rK\+GSzS\5S\>4GSRjj5rL\+GSzS\5S\>4GSSjj5rM\+GSzS\5S\>4GSTjj5rN\+GSzS\5S\>4GSUjj5rO\+GSzS\5S\>4GSVjj5rP\+GSzS\5S\>4GSWjj5rQ\+GSzS\5S\>4GSXjj5rR\+GSzS\5S\>4GSYjj5rS\+GSzS\5S\>4GSZjj5rT\+GSzS\5S\>4GS[jj5rU\+GSzS\5S\>4GS\jj5rV\+GSzS\5S\>4GS]jj5rW\+GSzS\5S\>4GS^jj5rX\+GSzS\5S\>4GS_jj5rY\+GSzS\5S\>4GS`jj5rZ\+GSzS\5S\>4GSajj5r[\+GSzS\5S\>4GSbjj5r\\+GSzS\5S\>4GScjj5r]\+GSzS\5S\>4GSdjj5r^\+GSzS\5S\>4GSejj5r_\+GSzS\5S\>4GSfjj5r`\+GSzS\5S\>4GSgjj5ra\+GSzS\5S\>4GShjj5rb\+GSzS\5S\>4GSijj5rc\+GSzS\5S\>4GSjjj5rd\+GSzS\5S\>4GSkjj5re\+GSzS\5S\>4GSljj5rf\+GSzS\5S\>4GSmjj5rg\+GSzS\5S\>4GSnjj5rh\+GSzS\5S\>4GSojj5ri\+GSzS\5S\>4GSpjj5rj\+GSzS\5S\>4GSqjj5rk\,"\lGSrS05 g({aRegNet X, Y, Z, and more Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678 Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877 Original Impl: None Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here) and cleaned up with more descriptive variable names. Weights from original pycls impl have been modified: * first layer from BGR -> RGB as most PyTorch models are * removed training specific dict entries from checkpoints and keep model state_dict only * remap names to match the ones here Supports weight loading from torchvision and classy-vision (incl VISSL SEER) A number of custom timm model definitions additions including: * stochastic depth, gradient checkpointing, layer-decay, configurable dilation * a pre-activation 'V' variant * only known RegNet-Z model definitions with pretrained weights Hacked together by / Copyright 2020 Ross Wightman N) dataclassreplace)partial)AnyCallableDictListOptionalUnionTupleTypeIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)ClassifierHead AvgPool2dSame ConvNormActSEModuleDropPath GroupNormActcalculate_drop_path_rates) get_act_layerget_norm_act_layer create_conv2dmake_divisible)build_model_with_cfg)feature_take_indices)checkpoint_seq named_apply)generate_default_cfgsregister_modelregister_model_deprecationsRegNet RegNetCfgc\rSrSr%SrSr\\S'Sr\\S'Sr \ \S'S r \ \S 'S r \\S 'S r \ \S'Sr\ \S'Sr\ \S'Sr\\S'Sr\\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\\4\S'Sr\\\4\S'Srg )!r%-z"RegNet architecture configuration.depthPw0q= ףPE@waHzG@wm group_size? bottle_ratiose_ratiogroup_min_ratio stem_widthconv1x1 downsampleF linear_outpreactr num_featuresrelu act_layer batchnorm norm_layerN)__name__ __module__ __qualname____firstlineno____doc__r)int__annotations__r+r-floatr/r1r3r5r6r8r:r strr;boolr<r=r?r rrA__static_attributes__rBQ/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/models/regnet.pyr%r%-s,E3OBLBBJL%HeOUJ )J )JFDL#&,IuS(]#,'2Jc8m$2rNfqreturnc4[[X- 5U-5$)zConverts a float to the closest non-zero int divisible by q. Args: f: Input float value. q: Quantization divisor. Returns: Quantized integer value. )rHround)rPrQs rOquantize_floatrUAs uQU|a  rNwidths bottle_ratiosgroups min_ratioc [X5VVs/sHupE[XE-5PM nnn[X&5VVs/sHupx[Xx5PM nnnU(a*[Xb5VVs/sHup[XU5PM nnnO([Xb5VVs/sHup[ X5PM nnn[Xa5VVs/sHup[X- 5PM nnnX4$s snnfs snnfs snnfs snnfs snnf)a Adjusts the compatibility of widths and groups. Args: widths: List of channel widths. bottle_ratios: List of bottleneck ratios. groups: List of group sizes. min_ratio: Minimum ratio for divisibility. Returns: Tuple of adjusted widths and groups. )ziprHminrrU) rVrWrXrYwbbottleneck_widthsgw_bots rOadjust_widths_groups_comprbNs"14F0JK0JQU0JK,/,J K,Jc!m,JF KQTUfQopQoXU^Ei@QopFIJ[FdeFd(%^E5Fde-01B-R S-Rc%)n-RF S >L Kqe SsCC(C%C+:C1 width_slope width_initial width_multr)r1quantcUS:aUS:aUS:aX-S:Xde[R"U[RS9U-U-n[R"[R"Xa- 5[ R"U5- 5n[R"U[R "X'5-U- 5U-n[[R"U55[UR5R55S-p[R"[U 5V s/sHoPM sn [RS9n UR5R5XR54$s sn f)aMGenerates per block widths from RegNet parameters. Args: width_slope: Slope parameter for width progression. width_initial: Initial width. width_mult: Width multiplier. depth: Network depth. group_size: Group convolution size. quant: Quantization factor. Returns: Tuple of (widths, num_stages, groups). rr)dtype)torcharangefloat32rTlogmathpowlenuniquerHmaxitemtensorrangeint32tolist) rdrerfr)r1rg widths_cont width_expsrV num_stages max_stage_rXs rOgenerate_regnetr}js* !   1j1nI^bcIcc c,,uEMM:[H=XKUYY{'BCdhhzFZZ[J [[-%))J*KKuT UX] ]F V 45s:>>;K;P;P;R7SVW7W \\uZ/@A/@!:/@A UF ::<   *mmo ==Bs! E.in_chsout_chs kernel_sizestridedilationrAr<c XxS.n U=(d [RnUS:XaUS:XaSOUnUS:aUOSnU(a[UUU4UUS.U D6$[UUU4UUUSS.U D6$)aACreate convolutional downsampling module. Args: in_chs: Input channels. out_chs: Output channels. kernel_size: Convolution kernel size. stride: Convolution stride. dilation: Convolution dilation. norm_layer: Normalization layer. preact: Use pre-activation. Returns: Downsampling module. devicerir)rrF)rrrA apply_act)nn BatchNorm2drr) r~rrrrrAr<rridds rOdownsample_convrs2 +B-r~~J{x1}!+K&?xH               !    rNc `XxS.n U=(d [RnUS:XaUOSn [R"5n US:dUS:a+U S:Xa US:a[O[Rn U "SU SSS9n U(a[ XS4SS0U D6n O[ XS4SUSS.U D6n [R"X/6$) aCreate average pool downsampling module. AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment. Args: in_chs: Input channels. out_chs: Output channels. kernel_size: Convolution kernel size. stride: Convolution stride. dilation: Convolution dilation. norm_layer: Normalization layer. preact: Use pre-activation. Returns: Sequential downsampling module. rrTF) ceil_modecount_include_padr)rrAr)rrIdentityr AvgPool2drr Sequential)r~rrrrrAr<rrir avg_stridepool avg_pool_fnconvs rOdownsample_avgrs6 +B-r~~J#q=aJ ;;=D zX\'1Q8aXS.n[TU]5 [U 5n [[ X%-55nUU-n[ XS9n[ UU4SS0UDUD6Ul[ UU4SUUSUU S.UDUD6UlU(a+[[ X-55n[U4UU S.UD6Ul O[R"5Ul [ UU4SS S .UDUD6Ul U (a[R"5OU "5Ul[UUU4SUUU S .UD6UlU S:a[#U 5Ulg [R"5Ulg ) a;Initialize RegNet Bottleneck block. Args: in_chs: Input channels. out_chs: Output channels. stride: Convolution stride. dilation: Dilation rates for conv2 and shortcut. bottle_ratio: Bottleneck ratio (reduction factor). group_size: Group convolution size. se_ratio: Squeeze-and-excitation ratio. downsample: Shortcut downsampling type. linear_out: Use linear activation for output. act_layer: Activation layer. norm_layer: Normalization layer. drop_block: Drop block layer. drop_path_rate: Stochastic depth drop rate. rr?rArrr)rrrrX drop_layer rd_channelsr?F)rr)rrrrAN)super__init__rrHrTrrconv1conv2rserrconv3act3rr:r drop_path)selfr~rrrr3r1r5r:r;r?rArrrrirbottleneck_chsrXcargs se_channels __class__s rOrBottleneck.__init__snF / !), U7#9:;:-y@ VQV%VSUV     a[!        eF$567K~b;R[b_abDGkkmDG haSXh\ahegh %/BKKMY[ )     !    6Da5G.1R[[]rNrRc~[RRURRR 5 g)z1Zero-initialize the last batch norm in the block.N)rinitzeros_rbnweightrs rOzero_init_lastBottleneck.zero_init_last`s  tzz}}++,rNxcUnURU5nURU5nURU5nURU5nURb#UR U5UR U5-nUR U5nU$zGForward pass. Args: x: Input tensor. Returns: Output tensor. )rrrrr:rrrrshortcuts rOforwardBottleneck.forwarddsw JJqM JJqM GGAJ JJqM ?? &q!DOOH$==A IIaLrN)rrrrr:rrrRNrCrDrErFrGrReLUrrHr rJrKrLr Moduler rrrjTensorrrM __classcell__rs@rOrrs,(."#"'$)+*,..48$&!G[G[G[ G[ CHo G[  G[G[G[G[G[BIIG[RYYG[!bii1G["G[G[R-%,,rNrcL^\rSrSrSrSSSSSSS\R \RSS SS4 S \S \S \S \ \\4S\ S\S\ S\ S\ S\ \RS\ \RS\\ \RS\ 4U4SjjjrSSjrS\R&S\R&4SjrSrU=r$) PreBottleneckizzfPre-activation RegNet Bottleneck block. Similar to Bottleneck but with pre-activation normalization. rrrr9FNr4r~rrrr3r1r5r:r;r?rArrcx>XS.n[TU]5 [X5n[[ X%-55nUU-nU"U40UD6Ul[ UU4SS0UD6UlU"U40UD6Ul[ UU4SUUSUS.UD6Ul U(a+[[ X-55n[U4UU S.UD6Ul O[R"5Ul U"U40UD6Ul[ UU4SS0UD6Ul[!UUU4SUUSS .UD6UlU S:a[%U 5Ulg [R"5Ulg ) aJInitialize pre-activation RegNet Bottleneck block. Args: in_chs: Input channels. out_chs: Output channels. stride: Convolution stride. dilation: Dilation rates for conv2 and shortcut. bottle_ratio: Bottleneck ratio (reduction factor). group_size: Group convolution size. se_ratio: Squeeze-and-excitation ratio. downsample: Shortcut downsampling type. linear_out: Use linear activation for output. act_layer: Activation layer. norm_layer: Normalization layer. drop_block: Drop block layer. drop_path_rate: Stochastic depth drop rate. rrrrr)rrrrXrT)rrrr<N)rrrrHrTnorm1rrnorm2rrrrrnorm3rrr:rr)rr~rrrr3r1r5r:r;r?rArrrrirnorm_act_layerrrXrrs rOrPreBottleneck.__init__sgF / +JBU7#9:;:-#F1b1 "6>OqOBO #N9b9 "   a[     eF$567K~b;R[b_abDGkkmDG#N9b9 ">7PPRP )         6Da5G.1R[[]rNrRcg)z?Zero-initialize the last batch norm (no-op for pre-activation).NrBrs rOrPreBottleneck.zero_init_lasts rNrcXURU5nUnURU5nURU5nURU5nUR U5nUR U5nUR U5nURb#URU5URU5-nU$r) rrrrrrrr:rrs rOrPreBottleneck.forwards JJqM JJqM JJqM JJqM GGAJ JJqM JJqM ?? &q!DOOH$==ArN) rrrr:rrrrrrrrs@rOrrzs,(."#"'$)+*,..48$&!F[F[F[ F[ CHo F[  F[F[F[F[F[BIIF[RYYF[!bii1F["F[F[P %,,rNrc^\rSrSrSrS\4S\S\S\S\S\S \\\ S \ \ R4U4S jjjr S \RS \R4SjrSrU=r$)RegStageizRegNet stage (sequence of blocks with the same output shape). A stage consists of multiple bottleneck blocks with the same output dimensions. Nr)r~rrrdrop_path_ratesblock_fnc >[TU]5 SUlUS;aSOSn [U5HWn U S:XaUOSn U S:XaUOUn X4n UbXjOSnSR U S-5nUR UU"U U4U U US .UD65 Un MY g) aHInitialize RegNet stage. Args: depth: Number of blocks in stage. in_chs: Input channels. out_chs: Output channels. stride: Stride for first block. dilation: Dilation rate. drop_path_rates: Drop path rates for each block. block_fn: Block class to use. **block_kwargs: Additional block arguments. F)rrrrrNr4zb{})rrr)rrgrad_checkpointingruformat add_module)rr)r~rrrrr block_kwargsfirst_dilationi block_stride block_in_chsblock_dilationdprnamers rOrRegStage.__init__s. "'&&0auA%&!V6L%&!V6L,7N(7(C/$C<<A&D OO (+#&  #  &N#rNrrRcUR(a?[RR5(d[ UR 5U5nU$UR 5H nU"U5nM U$)zgForward pass through all blocks in the stage. Args: x: Input tensor. Returns: Output tensor. )rrjjit is_scriptingrchildren)rrblocks rOrRegStage.forwardsX  " "599+A+A+C+Ct}}2A!H)rN)r)rCrDrErFrGrrHr r rJr rrrrjrrrMrrs@rOrrs6:(2,&,&,& ,&  ,&  ,&&d5k2,&299o,&,&\%,,rNrcj^\rSrSrSrS%S\S\S\S\S\S \S \S \ 4U4S jjjr S&S\S \S\S \S\ \ \ \\4\ \\444 Sjjr\R"R$S'S\ S\ \\44Sjj5r\R"R$S(S\ SS4Sjj5r\R"R$S\R,4Sj5rS)S\S\\SS4SjjrS*S\R4S\\\\ \4S\ S\ S\S\ S\\ \R4\ \R4\ \R4444SjjrS+S\\\ \4S\ S\ S\ \4SjjrS\R4S\R44S jrS'S\R4S!\ S\R44S"jjrS\R4S\R44S#jr S$r!U=r"$),r$i)zRegNet-X, Y, and Z Models. Paper: https://arxiv.org/abs/2003.13678 Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py Ncfgin_chans num_classes output_stride global_pool drop_raterrc >[TU]5 XS.n X0lX lX`lUS;de[ U40U D6nUR n [URURS9nUR(a[X-S4SS0U D6Ul O[X-S4SS0UDU D6Ul [U SSS9/UlU nSnURUUUS 9unn[!U5S :XdeUR(a["O[$n['U5HhunnS R)US -5nUR+U[-SUUS .UDUDU D65 USnUUS-nU=R[UUUS9/- slMj UR.(a1[XR.4SS 0UDU D6UlUR.UlO`UR2=(d URnU(a[5UR5"5O[6R8"5UlXlUR.Ul[=SUR.UUUS.U D6Ul[A[C[DUS9U5 g)aInitialize RegNet model. Args: cfg: Model architecture configuration. in_chans: Number of input channels. num_classes: Number of classifier classes. output_stride: Output stride of network, one of (8, 16, 32). global_pool: Global pooling type. drop_rate: Dropout rate. drop_path_rate: Stochastic depth drop-path rate. zero_init_last: Zero-init last weight of residual path. kwargs: Extra kwargs overlayed onto cfg. r)rcr7rrrrstem)num_chs reductionmodule)rrzs{}r)r~rrr) in_featuresr pool_typer)rNrB)#rrrrrrr8rr?rAr<rrr feature_info_get_stage_argsrprr enumeraterrrr= final_convr;rrrhead_hidden_sizerheadr r _init_weights)rrrrrrrrrrrikwargsrr8na_args prev_width curr_strideper_stage_args common_argsrr stage_args stage_name final_actrs rOrRegNet.__init__0sd6  /& " +++c$V$^^ 3>>J ::%hANaN2NDI#H!WAWWTVWDI!*&QR  &*&:&: ')';' # >"a'''$'JJ=J&~6MAza!e,J OO%%!"    $I.J :h/ /K   $z[Yc"d!e e 7"   )*6F6FgTUgY`gdfgDO # 0 0D 4#**I@ImCMM:Nrrr)rsetattrrrrr4)rr6rLrMrErFrHrIs rOprune_intermediate_layers RegNet.prune_intermediate_layerssi #7q'"B . !*- A DR[[] + q= kkmDO   ! !!R (rNcURU5nURU5nURU5nURU5nUR U5nUR U5nU$)zjForward pass through feature extraction layers. Args: x: Input tensor. Returns: Feature tensor. )rr?r@rArBrrrs rOforward_featuresRegNet.forward_features)sX IIaL GGAJ GGAJ GGAJ GGAJ OOA rN pre_logitscPU(aURXS9$URU5$)zForward pass through classifier head. Args: x: Input features. pre_logits: Return features before final linear layer. Returns: Classification logits or features. )rV)r)rrrVs rO forward_headRegNet.forward_head:s&7Atyyy2RdiiPQlRrNcJURU5nURU5nU$)zGForward pass. Args: x: Input tensor. Returns: Output logits. )rTrXrSs rOrRegNet.forwardFs)  ! !! $   a rN) rrrrrrrr=r) rr7rr4r4TNN)rr7r4F)T)N)NFFr<F)rFT)#rCrDrErFrGr%rHrKrJrLrr r rrrrjrignorer%r,rrr0r r4rr rJrPrTrXrrMrrs@rOr$r$)s#!#$!$&#'WQWQWQ WQ  WQ  WQWQ"WQ!WQWQx#$!#$& 6+6+ 6+ 6+ " 6+ tDcN#T#s(^3 4 6+p YY D T#s(^   YY*T*T**  YY *  c 6%,,5<<" Sell S S S  %,,  rNrrrrc[U[R5(aURSURS-UR-nX0R -nUR RRS[R"SU- 55 URb%URRR5 gg[U[R5(aa[RRUR SSS9 URb*[RRUR5 ggU(a#[!US5(aUR#5 ggg) zInitialize module weights. Args: module: PyTorch module to initialize. name: Module name. zero_init_last: Zero-initialize last layer weights. rr@Nr4g{Gz?)meanstdr) isinstancerConv2dr out_channelsrXrdatanormal_rnsqrtbiaszero_Linearrrhasattrr)rrrfan_outs rOrrTs &"))$$$$Q'&*<*? ;; " KK   " " $ # FBII & &  CT: ;; " GGNN6;; ' # GF,<==>rN state_dictcURSU5n/SQnSU;aSSKnUSSSn0nUSR5HpupEURSS 5nURS S 5nUR S S U5nUR SSU5nUHupgURXg5nM XSU'Mr USR5H)upESU;dSU;aMURSS5nXSU'M+ U$SU;aSSKn0nUR5HoupEURSS 5nURSS 5nUR SSU5nUHupgURXg5nM URSS5nXSU'Mq U$U$)zFilter and remap state dict keys for compatibility. Args: state_dict: Raw state dictionary. 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