RЦiUSSKrSSKrSSKrSSKJr SSKJr SSKJrJ r J r J r J r J r JrJrJr SSKJr SSKJr SSKJrJrJrJr SSKJr SS KJr SS KJrJ r J!r!J"r"J#r#J$r$ SS K%J&r& SS K'J(r( SS K)J*r* SSK+J,r, \RZ"\.5r/Sq0Sq1\2"\RfRiSS55S:r5/SQr6\"S\RnS9r8S\ \9\4S\ \9\944Sjr:S4S\;SS4SjjrS6S\RnS\ \ \9\4S\2S\2S \ \ S!\;S\ \\9\4SS4S"jjr?S\ \9\4S\ \9\44S#jr@S$\ \9\4S%\ \9SS4S&jrAS7S'jrBS8S(\9S\ \\9\ \9\44S)\ \ \9\4S\(4S*jjrCS9S+\\\8\ S,\844S(\9S-\;S\ \ S)\ \ S.\ \S/\ \ S0\;S1\ \ S\ \\9\4S2\ \ \9S\84S3jjrDg):N)deepcopy)Path) AnyCallableDictListOptionalTupleTypeTypeVarUnion)nn)load_state_dict_from_url)FeatureListNetFeatureDictNetFeatureHookNetFeatureGetterNet)FeatureGraphNet)load_state_dict) has_hf_hubdownload_cached_filecheck_cached_fileload_state_dict_from_hfload_state_dict_from_pathload_custom_from_hf)adapt_input_conv) PretrainedCfg)adapt_model_from_file)get_pretrained_cfgFTIMM_USE_OLD_CACHE) set_pretrained_download_progressset_pretrained_check_hashload_custom_pretrainedload_pretrainedpretrained_cfg_for_featuresresolve_pretrained_cfgbuild_model_with_cfgModelT)boundpretrained_cfgreturnc\URSS5nURSS5nURSS5nURSS5nURSS5nSnSnUS:Xa[SS 9(aSnU(deUnOUS :XaS nUnO|U(aSnUn[U[5(deOYU(aSnUnOMS n[(aU(a [ U5OS nU(dU(a[SS 9(aSnUnO U(aSnUnUS:XaURS S5(aXpS 4nXg4$) Nsourceurlfile state_dict hf_hub_idhf-hubT) necessary local-dirFhf_hub_filename)getr isinstancedict_USE_OLD_CACHEr) r* cfg_sourcepretrained_urlpretrained_file pretrained_sdr2 load_frompretrained_locold_cache_valids S/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/models/_builder.py_resolve_pretrained_sourcerC+s9##Hb1J#''t4N$((6O"&&|T:M"";5IINX*t"< y" { " ( $I*Nnd33 33 I,N#O~GU"3N"C[`"yZ$5O$ !*! !/H!3!34Et!L!L'8I)JJ  $$enablecUqg)z@Set download progress for pretrained weights on/off (globally). N)_DOWNLOAD_PROGRESSrEs rBr!r![s  rDcUqg)z/checkpoints`` where `hub_dir` is the directory returned by :func:`~torch.hub.get_dir`. Args: model: The instantiated model to load weights into pretrained_cfg: Default pretrained model cfg load_fn: An external standalone fn that loads weights into provided model, otherwise a fn named 'load_pretrained' on the model will be called if it exists cache_dir: Override model checkpoint cache dir for this load r*Nz/Invalid pretrained config, cannot load weights.zHNo pretrained weights exist for this model. Using random initialization.r3zKHugging Face hub not currently supported for custom load pretrained models.r/) check_hashprogressrMr$zXValid function to load pretrained weights is not available, using random initialization.) getattr_loggerwarningrCrrJrGhasattrr$)rKr*rLrMr?r@s rBr#r#gs,$Mwu6F'MN IJ :> JI bcHef e - "'  & ) * * n-rsrD num_classesin_chans filter_fnstrictc U=(d [USS5nU(d [S5e[U5upxUS:Xa[R S5 Un GOUS:XaO[R SUS35 UR S S 5(aUR U5 g[U5n GOUS :Xao[R S US35 UR S S 5(a&[U[[US 9nUR U5 g[US[[SUS9n GO US:Xa[R SUS35 [U[[45(aJUR S S 5n [U [ 5(aU S:Xa[#/UQUP7SU06 g[%USU06n O[%USUS9n OzUS:XaS[R SUS35 ['U5n U R)5(a [+U 5n O/[SU35eUR SS5n [SU S35eUb U"X5n UR SS5nUbZUS:waT[U[ 5(aU4nUH6nUS -n[-X9U5U U'[R S!US"US#35 M8 UR S&S5nUR S'S(5nUb[U[ 5(aU4nX!S):wa6UH-nU R3US -S5 U R3US*-S5 M/ S nO5US(:a/UH)nU US -nUUSU US -'U US*-nUUSU US*-'M+ URXS+9nUR4(a2[R S,S-R7UR45S.35 UR8(a3[R1S/S-R7UR85S035 gg![a [US[[US9n GNf=f![an U"U 5n Sn A GNSn A ff=f![.a*n U U S n[R1S$US%35 Sn A GMSn A ff=f)1aLoad pretrained checkpoint Args: model: PyTorch module pretrained_cfg: Configuration for pretrained weights / target dataset num_classes: Number of classes for target model. Will adapt pretrained if different. in_chans: Number of input chans for target model. Will adapt pretrained if different. filter_fn: state_dict filter fn for load (takes state_dict, model as args) strict: Strict load of checkpoint cache_dir: Override model checkpoint cache dir for this load r*NzWInvalid pretrained config, cannot load weights. Use `pretrained=False` for random init.r1z*Loading pretrained weights from state dictr0z&Loading pretrained weights from file () custom_loadFr/z%Loading pretrained weights from url ()rPrOrMcpuT) map_locationrPrO weights_only model_dir)r]rPrOr_r3z2Loading pretrained weights from Hugging Face hub (hfrM)r^rMr5z1Loading pretrained weights from local directory (z#Specified path is not a directory: architecturez this modelz No pretrained weights exist for z). 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This may be expected if model is being adapted.)rQ RuntimeErrorrCrRinfor7r$rrrGrJr TypeErrorr8listtuplestrrrris_dirrrNotImplementedErrorrSpop missing_keysjoinunexpected_keys)rKr*rUrVrWrXrMr?r@r1r[pretrained_path model_namee input_convsinput_conv_name weight_name classifiersreclassifier_nameclassifier_weightclassifier_bias load_results rBr$r$s($Mwu6F'MN tuu :> JIL  AC# f  =n=MQOP   mU 3 3  ! !. 1 (8J e  <^URSS5nUb'[U5S:XdeURXES S5 MEMGUS :Xa>URSS5nUb'[U5S:XdeURXES 5 MMUS :Xa5URUS5nUbUS :aURX@U5 MMMURUS5nUcMURX@U5 M [XS 9 g)aUpdate the default_cfg and kwargs before passing to model Args: pretrained_cfg: input pretrained cfg (updated in-place) kwargs: keyword args passed to model build fn (updated in-place) kwargs_filter: keyword arg keys that must be removed before model __init__ )rUr~rVfixed_input_sizeF)img_sizer input_sizeNrcrVrrU)r)r7len setdefaultr)r*r kwargs_filterdefault_kwarg_namesrr default_vals rB_update_default_model_kwargsr3sEE,e44},  ?'++L$?J%:!+++!!!_5&*_'++L$?J%:!+++!!!]3&- (,,Q5K&;!+;!!!A%67,<&),,Q5K&!!!A%67+!06/rDvariantpretrained_cfg_overlaycUnSnU(a:[U[5(a [S0UD6nO[U[5(aUnSnU(d$U(aSR X/5n[ U5nU(d#[ RSUS35 [5nU=(d 0nUR(dURSU5 [R"U40UD6nU$)z6Resolve pretrained configuration from various sources.N.z*No pretrained configuration specified for ze model. 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Please add a config to the model pretrained_cfg registry or pass explicitly.ra) r8r9rrkrprrRrSrar dataclassesreplace)rr*rmodel_with_tagpretrained_tags rBr&r&\s NN nd + +*<^lUR UlU (a [X5nU (aS O[US U RS S 55nU(a[UUUU RSS5UUU S9 U (aSnSU;aURS5n[U[5(auUR5nUS;aURSS 5 SU;a[nOGUS:Xa[nO:US:Xa[ nO-US:Xa["nO US:Xa Sn[$nOSU35eO[n[USS 5nUbU(dUR SU5 U"U40UD6n['U5UlUR UlU$)aBuild model with specified default_cfg and optional model_cfg This helper fn aids in the construction of a model including: * handling default_cfg and associated pretrained weight loading * passing through optional model_cfg for models with config based arch spec * features_only model adaptation * pruning config / model adaptation Args: model_cls: Model class variant: Model variant name pretrained: Load the pretrained weights pretrained_cfg: Model's pretrained weight/task config pretrained_cfg_overlay: Entries that will override those in pretrained_cfg model_cfg: Model's architecture config feature_cfg: Feature extraction adapter config pretrained_strict: Load pretrained weights strictly pretrained_filter_fn: Filter callable for pretrained weights cache_dir: Override model cache dir for Hugging Face Hub and Torch checkpoints kwargs_filter: Kwargs keys to filter (remove) before passing to model **kwargs: Model args passed through to model __init__ prunedF)r*r features_onlyT out_indices)rrc feature_clsNcfgrrUrVrc)r*rUrVrWrXrM)r9rihookflatten_sequentialrrir9fxgetterzUnknown feature class output_fmtr)rnr&to_dictrrr* default_cfgrrQr7r$r8rklowerrrrrrr%)rrrr*rrrrrrMrrrfeaturesrKnum_classes_pretrained use_getterrrs rBr'r'sTHZZ% (FH#K,%5N $++-N Gzz/5))}o> F ")/M)BK & F ")/M)BK &#F#2i262),,E %e5#+Q}fjjYfhlNm0n ).ZZ A.*$  K '%//-8K+s++)//1 &>>OO$8$?[("0K F*"0K F*"0K D("1K H,!%J"2KH$:;-"HH5',*)KUL$7  !*  " "< <E1[1:>J!00 LrD)T)NNN)NrrcNTN)r+N)NN)NNNNTNNN)Erloggingoscopyrpathlibrtypingrrrrr r r r r torchr torch.hubrtimm.models._featuresrrrrtimm.models._features_fxrtimm.models._helpersrtimm.models._hubrrrrrrtimm.models._manipulatertimm.models._pretrainedrtimm.models._prunertimm.models._registryr getLogger__name__rRrGrJintenvironr7r:__all__Moduler(rkrCboolr!r"r#r$r%rrr&r'rrDrBrss SSS.bb40334144   H % RZZ^^$8!<=A   +-%tCH~-%%S/-%` T T dd48&*04 .tyy.t c3h0.t(#.tE#t),- .t  .tf48(,04HAyyHA c3h0HAHA HA H% HA  HAE#t),-HA HAVS#X4S>4S>$s)&0V@D;?!! sDcN':!;<!!)c3h 8! !P*.15#'&*"&3704.2wfxV '<<=www! w !) w C= wd^w w'x0wE#t),-w c +w wrD