+ jORt^RIt^RIt^RIHt^RIHt^RIHt^RI H t H t H t ^RI Ht^RIHtHt^RIHt]P(!]4tR R ltR%R R llt!R R]P24t!RR]P64t!RR]P24t!RR]P24t!RR]P24t!RR]P24t !RR]P24t!!RR]P24t"]!RR]44t#]!RR ]#44t$]!R!R"7!R#R$]#44t%.R&Ot&R#)'zPyTorch EfficientNet model.N)nn)initialization)ACT2FN)BaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringloggingEfficientNetConfigc0V^8dQhR\R\/#)config num_channelsr int)formats"w/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/efficientnet/modeling_efficientnet.py __annotate__r$s  , C cVPpWP,p\V\W^, ,4V,V,4pVRV,8d W2, p\V4#)z4 Round number of filters based on depth multiplier. g?) depth_divisorwidth_coefficientmaxr)rrdivisornew_dims&& r round_filtersr$s`""G,,,L'3|k9:gEOPG|## w<rcFV^8dQhR\\,R\/#)r kernel_sizeadjust)rtuplebool)rs"rrr3s"@@S5[@$@rc(\V\4'dW3pV^,^,V^,^,3pV'd1V^,^, V^,V^,^, V^,3#V^,V^,V^,V^,3#)a. Utility function to get the tuple padding value for the depthwise convolution. Args: kernel_size (`int` or `tuple`): Kernel size of the convolution layers. adjust (`bool`, *optional*, defaults to `True`): Adjusts padding value to apply to right and bottom sides of the input. ) isinstancer)rr corrects&& r correct_padr&3s~+s##"0 1~"KNa$78G  Q GAJNGAJGG GAJ GAJ??rcTaa]tRt^GtoRtV3RlV3RlltV3RlRltRtVtV;t #)EfficientNetEmbeddingszD A module that corresponds to the stem module of the original work. c <V^8dQhRS[/#rrr )r __classdict__s"rr#EfficientNetEmbeddings.__annotate__Ls 4 41 4rc <\SV`4\V^ 4Vn\P !RR7Vn\P!VPVP^^RRR7Vn \P!VPVPVPR7Vn \VP,VnR#) paddingvalidFrstrider0bias)epsmomentumN)r7r8)super__init__rout_dimr ZeroPad2dr0Conv2dr convolution BatchNorm2dbatch_norm_epsbatch_norm_momentum batchnormr hidden_act activationselfr __class__s&&rr:EfficientNetEmbeddings.__init__Ls $VR0 ||L9 99   1QPW^c  &:O:OZ`ZtZtu !2!23rcN<V^8dQhRS[PRS[P/#)r pixel_valuesreturn)torchTensor)rr+s"rrr,Ws#ELLU\\rcVPV4pVPV4pVPV4pVPV4pV#N)r0r>rBrD)rFrJfeaturess&& rforwardEfficientNetEmbeddings.forwardWsA<< -##H->>(+??8,r)rDrBr>r;r0 __name__ __module__ __qualname____firstlineno____doc__r:rQ__static_attributes____classdictcell__ __classcell__rGr+s@@rr(r(Gs# 4 4rr(c6aa]tRt^`toRV3RlltRtVtV;t#)EfficientNetDepthwiseConv2dc H<W,p \S V`VV VVVVVVVR7 R#)) in_channels out_channelsrr3r0dilationgroupsr4 padding_modeN)r9r:) rFr`depth_multiplierrr3r0rbr4rdrarGs &&&&&&&&& rr:$EfficientNetDepthwiseConv2d.__init__as:#5  #%#%  r)r8r8r7r8Tzeros)rTrUrVrWr:rYrZr[r\s@@rr^r^`s  rr^cTaa]tRt^ztoRtV3RlV3RlltV3RlRltRtVtV;t #)EfficientNetExpansionLayerzW This corresponds to the expansion phase of each block in the original implementation. c2<V^8dQhRS[RS[RS[RS[/#)rrin_dimr;r3r)rr+s"rr'EfficientNetExpansionLayer.__annotate__s+ 4 41 43 4 4VY 4rc<\SV`4\P!VV^RRR7Vn\P !W1P R7Vn\VP,Vn R#)r8sameFr`rarr0r4) num_featuresr5N) r9r:rr= expand_convr?r@ expand_bnrrC expand_act)rFrrmr;r3rGs&&&&&rr:#EfficientNetExpansionLayer.__init__sX 99   WBWBWX !2!23rcN<V^8dQhRS[PRS[P/#r hidden_statesrKrL FloatTensorrM)rr+s"rrrns%U%6%65<<rclVPV4pVPV4pVPV4pV#rO)rsrtrurFrys&&rrQ"EfficientNetExpansionLayer.forwards4((7 }5  6 r)rurtrsrSr\s@@rrkrkzs# 4 4rrkcTaa]tRt^toRtV3RlV3RlltV3RlRltRtVtV;t #)EfficientNetDepthwiseLayerzc This corresponds to the depthwise convolution phase of each block in the original implementation. c 8<V^8dQhRS[RS[RS[RS[RS[/#)rrrmr3radjust_paddingr rr")rr+s"rr'EfficientNetDepthwiseLayer.__annotate__s=77"77 7  7  7rcj<\SV`4W0nVP^8XdRMRp\WER7p\P !VR7Vn\W$W6RR7Vn\P!W!PVPR7Vn \VP,VnR#) rr1rp)r r/Fr2rrr5r6N)r9r:r3r&rr<depthwise_conv_padr^depthwise_convr?r@rAdepthwise_normrrC depthwise_act) rFrrmr3rrconv_padr0rGs &&&&&& rr:#EfficientNetDepthwiseLayer.__init__s  "kkQ.7FkA"$,,w"?9 FSX !nn%:%:VE_E_ $F$5$56rcN<V^8dQhRS[PRS[P/#rxrz)rr+s"rrrs%  U%6%6 5<< rcVP^8XdVPV4pVPV4pVPV4pVP V4pV#)r)r3rrrrr}s&&rrQ"EfficientNetDepthwiseLayer.forwardsT ;;!  33MBM++M: ++M: **=9 r)rrrrr3rSr\s@@rrrs#77,   rrcXaa]tRt^toRtRV3RlV3RllltV3RlRltRtVtV;t #)EfficientNetSqueezeExciteLayerzd This corresponds to the Squeeze and Excitement phase of each block in the original implementation. c2<V^8dQhRS[RS[RS[RS[/#)rrrm expand_dimexpandr)rr+s"rr+EfficientNetSqueezeExciteLayer.__annotate__s+''1'3'C'Y]'rc <\SV`4V'dTMTVn\^\ W!P ,44Vn\P!^R7Vn \P!VPVP ^RR7Vn \P!VP VP^RR7Vn \VP,Vn\P !4VnR#)r8) output_sizerp)r`rarr0N)r9r:dimrrsqueeze_expansion_ratiodim_serAdaptiveAvgPool2dsqueezer=reducerrrC act_reduceSigmoid act_expand)rFrrmrrrGs&&&&&rr:'EfficientNetSqueezeExciteLayer.__init__s !':V!S*H*H!HIJ ++: ii   ii    !!2!23**,rcN<V^8dQhRS[PRS[P/#rxrz)rr+s"rrr%  U%6%6 5<< rcTpVPV4pVPV4pVPV4pVPV4pVP V4p\ P !W!4pV#rO)rrrrrrLmul)rFryinputss&& rrQ&EfficientNetSqueezeExciteLayer.forwardsa ]3  M2  6  M2  6  &8 r)rrrrrrr)FrSr\s@@rrrs#''*   rrcTaa]tRt^toRtV3RlV3RlltV3RlRltRtVtV;t #)EfficientNetFinalBlockLayerzS This corresponds to the final phase of each block in the original implementation. c ><V^8dQhRS[RS[RS[RS[RS[RS[/#)rrrmr;r3 drop_rateid_skipr rfloatr")rr+s"rr(EfficientNetFinalBlockLayer.__annotate__s=//(/25/@C/MP/]b/mq/rc0<\SV`4V^8H;'dV'*Vn\P!VV^RRR7Vn\P !W1PVPR7Vn \P!VR7Vn R#)r8rpFrqrpN) r9r: apply_dropoutrr= project_convr?r@rA project_bnDropoutdropout)rFrrmr;r3rrrGs&&&&&&&rr:$EfficientNetFinalBlockLayer.__init__s{ #q[88[II   .. &;&;fF`F` zzI. rch<V^8dQhRS[PRS[PRS[P/#)r embeddingsryrKrz)rr+s"rrrs5%"3"3EDUDUZ_ZfZfrcVPV4pVPV4pVP'dVPV4pW!,pV#rO)rrrr)rFrrys&&&rrQ#EfficientNetFinalBlockLayer.forwardsH))-8  6     LL7M)6Mr)rrrrrSr\s@@rrrs#//"rrcTaa]tRtRtoRtV3RlV3RlltV3RlRltRtVtV;t #)EfficientNetBlockiaA This corresponds to the expansion and depthwise convolution phase of each block in the original implementation. Args: config ([`EfficientNetConfig`]): Model configuration class. in_dim (`int`): Number of input channels. out_dim (`int`): Number of output channels. stride (`int`): Stride size to be used in convolution layers. expand_ratio (`int`): Expand ratio to set the output dimensions for the expansion and squeeze-excite layers. kernel_size (`int`): Kernel size for the depthwise convolution layer. drop_rate (`float`): Dropout rate to be used in the final phase of each block. id_skip (`bool`): Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase of each block. Set to `True` for the first block of each stage. adjust_padding (`bool`): Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution operation, set to `True` for inputs with odd input sizes. cP<V^8dQhRS[RS[RS[RS[RS[RS[RS[RS[R S[/ #) rrrmr;r3 expand_ratiorrrrr)rr+s"rrEfficientNetBlock.__annotate__se' ' "' '  '  '  ' ' ' ' ' rc <\S V`4WPnVP^8gVnW%,p VP'd\ WWR7Vn\ TVP'dT MTVVV R7Vn\WWPR7Vn \TVP'dT MTVVVVR7Vn R#)r8)rrmr;r3)rrmr3rr)rrmrr)rrmr;r3rrN) r9r:rrrk expansionrrrsqueeze_exciter projection) rFrrmr;r3rrrrr expand_in_dimrGs &&&&&&&&&& rr:EfficientNetBlock.__init__s (''1, - ;;;7mDN9$(KKK=V#)  =];; 6$(KKK=V  rcN<V^8dQhRS[PRS[P/#rxrz)rr+s"rrrGrrcTpVP^8wdVPV4pVPV4pVPV4pVP W!4pV#)r8)rrrrr)rFryrs&& rrQEfficientNetBlock.forwardGsY"    ! NN=9M++M: ++M:  B r)rrrrrrrSr\s@@rrrs$4' ' R   rrcXaa]tRtRtoRtV3RlV3RlltRV3RlRlltRtVtV;t #) EfficientNetEncoderiTz Forward propagates the embeddings through each EfficientNet block. Args: config ([`EfficientNetConfig`]): Model configuration class. c <V^8dQhRS[/#r*r )rr+s"rr EfficientNetEncoder.__annotate__]s5858158rcL<aa\SS`4VSnVPSnV3Rlo\ VP 4p\ V3RlVP44p^p.p\V4EFp\WP V,4p\WPV,4pVPV,p VPV,p VPV,p \S!VPV,44Ftp V ^8Hp V ^8d^MT p V ^8dTMTpWAP9pVPV,V, p\!VVVV V V VV VR7 pVP#V4V^, pKv EK \$P&!V4Sn\$P*!X\VR4^RRR7Sn\$P.!VP0VP2VP4R7Sn\8VP:,SnR#) cd<\\P!SPV,44#rO)rmathceildepth_coefficient)repeatsrFs&r round_repeats3EfficientNetEncoder.__init__..round_repeatsbs"tyy!7!7'!ABC Crc34<"TF pS!V4xK R#5irOrg).0nrs& r /EfficientNetEncoder.__init__..gsL3Kaq))3Ks) rrmr;r3rrrrrirpFrqrN)r9r:rrlenr`sumnum_block_repeatsrangerrastrides kernel_sizes expand_ratiosdepthwise_paddingdrop_connect_raterappendr ModuleListblocksr=top_convr? hidden_dimr@rAtop_bnrrCtop_activation)rFrnum_base_blocks num_blockscurr_block_numrirmr;r3rrjrrrblockrrGsf& @rr:EfficientNetEncoder.__init__]s  !'!9!9 Df001L63K3KLL 'A"6+=+=a+@AF#F,?,?,BCG^^A&F --a0K!//2L=)A)A!)DEFq&!e$%Ev!/7O7O!O"44~E R )!!#! +!-'##1  e$!#'G(8mmF+  &vt4   nn**0E0EPVPjPj  %V%6%67rcb<V^8dQhRS[PRS[R,RS[R,RS[/#)rryoutput_hidden_statesN return_dictrK)rLr{r"r)rr+s"rrrsA  (( #Tk D[  (  rcdV'dV3MRpVPFpV!V4pV'gKWA3, pK VPV4pVPV4pVPV4pV'g/\;QJd.RW34FNK 5#!RW344#\ VVR7#)Nc30"TF qfKVxK R#5irOrg)rvs& rr.EfficientNetEncoder.forward..sX$Fq$Fs )last_hidden_statery)rrrrr!r)rFryrrall_hidden_statesrs&&&& rrQEfficientNetEncoder.forwards 1E],$[[E!-0M##!%55!!  m4  M2 ++M: 5X]$FX5 X5X]$FXX X-++  r)rrrrrr)FTrSr\s@@rrrTs$5858n   rrcaa]tRtRtoRtRtR tR.t]P!4V3RlV3Rll4t V3Rlt Rt Vt V;t#) EfficientNetPreTrainedModeli efficientnetrJrc4<V^8dQhRS[P/#)rmodule)rModule)rr+s"rr(EfficientNetPreTrainedModel.__annotate__s 8 8BII 8rc T<\SV`V4\V\P\P \P 34'd\P!VPRVPPR7VPe!\P!VP4\VRR4ec\P!VP4\P !VP"4\P!VP$4R#R#R#)zInitialize the weightsg)meanstdN running_mean)r9 _init_weightsr$rLinearr=r?initnormal_weightrinitializer_ranger4zeros_getattrrones_ running_varnum_batches_tracked)rFrrGs&&rr)EfficientNetPreTrainedModel._init_weightss f% fryy"))R^^D E E LLSdkk6S6S T{{& FKK(v~t4@ F//0 6--. F667A Frc&<V^8dQh/S[;R&#r*r )rr+s"rrrs rrg)image)rTrUrVrWbase_model_prefixmain_input_nameinput_modalities_no_split_modulesrLno_gradr__annotate_func__rYrZr[r\s@@rrrsE'$O!,- ]]_ 8 8 8rrc^aa]tRtRtoV3RlV3Rllt]RV3RlRll4tRtVtV;t #)EfficientNetModelic <V^8dQhRS[/#r*r )rr+s"rrEfficientNetModel.__annotate__s1rc<\SV`V4Wn\V4Vn\ V4VnVPR8Xd)\P!VPRR7Vn MQVPR8Xd)\P!VPRR7Vn M\RVP 24hVP4R#)rT) ceil_moderz2config.pooling must be one of ['mean', 'max'] got N)r9r:rr(rrencoder pooling_typer AvgPool2drpooler MaxPool2d ValueErrorpooling post_initrEs&&rr:EfficientNetModel.__init__s   08*62    & (,,v'8'8DIDK  E ),,v'8'8DIDKQRXR`R`Qabc c rc <V^8dQhRS[PR,RS[R,RS[R,RS[S[,/#)rrJNrrrK)rLr{r"r!r)rr+s"rrrsL# # ''$.# #Tk# D[ # 9 9 # rc VeTMVPPpVeTMVPPpVf \R4hVP V4pVP VVVR7pV^,pVP V4pVPVPR,4pV'gWx3VR,,#\VVVPR7#)Nz You have to specify pixel_valuesrr:NrN:r8NN)r pooler_outputry) rrrr'rr"r%reshapeshaperry) rFrJrrkwargsembedding_outputencoder_outputsr pooled_outputs &&&&, rrQEfficientNetModel.forwards%9$D $++JjJj &1% 1` a classification loss is computed (Cross-Entropy). Nr-:rNN)losslogitsry) rrrr.rr> loss_functionrry) rFrJrArrr1outputsr4rFrEoutputs &&&&&, rrQ*EfficientNetForImageClassification.forwards&1%rrrr;)NNNNr6r\s@@rr9r9s( " " " " rr9)T)r9rr)'rXrrLrrr  activationsrmodeling_outputsrrrmodeling_utilsrutilsr r configuration_efficientnetr get_loggerrTloggerrr&rr(r=r^rkrrrrrrrr9__all__rgrrrTs;" &! .,:   H % @(RYY2 ")) 44$$N$RYY$N"))BN NbW "))W t8/88*6 36 6 r 0 )D0  0 f er