ZjOSrSSKrSSKrSSKJr SSKJr SSKJr SSK J r J r J r SSK Jr SS KJrJr S S KJr \R("\5rS \S \4SjrS+S\\-S\4Sjjr"SS\R85r"SS\R<5r"SS\R85r "SS\R85r!"SS\R85r""SS\R85r#"SS\R85r$"S S!\R85r%\"S"S#\55r&\"S$S%\&55r'\"S&S'9"S(S)\&55r(/S*Qr)g),zPyTorch EfficientNet model.N)nn)initialization)ACT2FN)BaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)EfficientNetConfigconfig num_channelscURnXR-n[U[XS- -5U-U-5nUSU-:aX2- n[U5$)z4 Round number of filters based on depth multiplier. g?) depth_divisorwidth_coefficientmaxint)rrdivisornew_dims ڇ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/efficientnet/modeling_efficientnet.py round_filtersr$s`""G,,,L'3|k9:gEOPG|## w< kernel_sizeadjustc[U[5(aX4nUSS-USS-4nU(aUSS- USUSS- US4$USUSUSUS4$)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. rrr ) isinstancer)rrcorrects r correct_padr!3s~+s##"0 1~"KNa$78G  Q GAJNGAJGG GAJ GAJ??rcn^\rSrSrSrS\4U4SjjrS\RS\R4Sjr Sr U=r $) EfficientNetEmbeddingsGzD A module that corresponds to the stem module of the original work. rc |>[TU]5 [US5Ul[R "SS9Ul[R"URURSSSSS9Ul [R"URURURS 9Ul [URUlg) N )rr rr paddingrrvalidFrstrider(bias)epsmomentum)super__init__rout_dimr ZeroPad2dr(Conv2dr convolution BatchNorm2dbatch_norm_epsbatch_norm_momentum batchnormr hidden_act activationselfr __class__s rr0EfficientNetEmbeddings.__init__Ls $VR0 ||L9 99   1QPW^c  &:O:OZ`ZtZtu !2!23r pixel_valuesreturncURU5nURU5nURU5nURU5nU$N)r(r4r8r:)r<r?featuress rforwardEfficientNetEmbeddings.forwardWsA<< -##H->>(+??8,r)r:r8r4r1r() __name__ __module__ __qualname____firstlineno____doc__rr0torchTensorrD__static_attributes__ __classcell__r=s@rr#r#Gs5 41 4ELLU\\rr#c:^\rSrSrSU4SjjrSrU=r$)EfficientNetDepthwiseConv2d`c 8>X-n [T U]UU UUUUUUUS9 g)N) in_channels out_channelsrr+r(dilationgroupsr, padding_mode)r/r0) r<rTdepth_multiplierrr+r(rVr,rXrUr=s rr0$EfficientNetDepthwiseConv2d.__init__as:#5  #%#%  r)r rr rr Tzeros)rFrGrHrIr0rMrNrOs@rrQrQ`s$   rrQcz^\rSrSrSrS\S\S\S\4U4SjjrS\RS \R4S jr S r U=r $) EfficientNetExpansionLayerzzW This corresponds to the expansion phase of each block in the original implementation. rin_dimr1r+c>[TU]5 [R"UUSSSS9Ul[R "X1R S9Ul[URUl g)Nr sameFrTrUrr(r,) num_featuresr-) r/r0rr3 expand_convr5r6 expand_bnrr9 expand_act)r<rr`r1r+r=s rr0#EfficientNetExpansionLayer.__init__sX 99   WBWBWX !2!23r hidden_statesr@clURU5nURU5nURU5nU$rB)rerfrgr<ris rrD"EfficientNetExpansionLayer.forwards4((7 }5  6 r)rgrfre)rFrGrHrIrJrrr0rK FloatTensorrLrDrMrNrOs@rr^r^zsM 41 43 4 4VY 4U%6%65<<rr^c ~^\rSrSrSrS\S\S\S\S\4 U4SjjrS \ RS \ R4S jr S r U=r$) EfficientNetDepthwiseLayerzc This corresponds to the depthwise convolution phase of each block in the original implementation. rr`r+radjust_paddingcF>[TU]5 X0lURS:XaSOSn[XES9n[R "US9Ul[X$X6SS9Ul[R"X!RURS9Ul [URUlg) Nrr)rb)rr'Fr*rdr-r.)r/r0r+r!rr2depthwise_conv_padrQdepthwise_convr5r6r7depthwise_normrr9 depthwise_act) r<rr`r+rrqconv_padr(r=s rr0#EfficientNetDepthwiseLayer.__init__s  "kkQ.7FkA"$,,w"?9 FSX !nn%:%:VE_E_ $F$5$56rrir@cURS:XaURU5nURU5nURU5nUR U5nU$)Nr)r+rtrurvrwrks rrD"EfficientNetDepthwiseLayer.forwardsT ;;!  33MBM++M: ++M: **=9 r)rwrurtrvr+rFrGrHrIrJrrboolr0rKrmrLrDrMrNrOs@rroros_7"77 7  7  7, U%6%6 5<<  rroc ~^\rSrSrSrS S\S\S\S\4U4SjjjrS\ RS \ R4S jr S r U=r$) EfficientNetSqueezeExciteLayerzd This corresponds to the Squeeze and Excitement phase of each block in the original implementation. rr` expand_dimexpandc>[TU]5 U(aUOUUl[S[ X!R -55Ul[R"SS9Ul [R"URUR SSS9Ul [R"UR URSSS9Ul [URUl[R "5Ulg)Nr ) output_sizerb)rTrUrr()r/r0dimrrsqueeze_expansion_ratiodim_serAdaptiveAvgPool2dsqueezer3reducerrr9 act_reduceSigmoid act_expand)r<rr`rrr=s rr0'EfficientNetSqueezeExciteLayer.__init__s !':V!S*H*H!HIJ ++: ii   ii    !!2!23**,rrir@cUnURU5nURU5nURU5nURU5nUR U5n[ R "X!5nU$rB)rrrrrrKmul)r<riinputss rrD&EfficientNetSqueezeExciteLayer.forwardsa ]3  M2  6  M2  6  &8 r)rrrrrrr)Fr|rOs@rrrsR'1'3'C'Y]''* U%6%6 5<<  rrc ^\rSrSrSrS\S\S\S\S\S\4 U4S jjr S \ RS \ RS \ R4S jr SrU=r$)EfficientNetFinalBlockLayerzS This corresponds to the final phase of each block in the original implementation. rr`r1r+ drop_rateid_skipc>[TU]5 US:H=(a U(+Ul[R"UUSSSS9Ul[R "X1RURS9Ul [R"US9Ul g)Nr rbFrcrsp) r/r0 apply_dropoutrr3 project_convr5r6r7 project_bnDropoutdropout)r<rr`r1r+rrr=s rr0$EfficientNetFinalBlockLayer.__init__sx #q[8[II   .. &;&;fF`F` zzI. r embeddingsrir@cURU5nURU5nUR(aURU5nX!-nU$rB)rrrr)r<rris rrD#EfficientNetFinalBlockLayer.forwardsE))-8  6    LL7M)6Mr)rrrrrFrGrHrIrJrrfloatr}r0rKrmrLrDrMrNrOs@rrrso/(/25/@C/MP/]b/mq/"%"3"3EDUDUZ_ZfZfrrc^\rSrSrSrS\S\S\S\S\S\S \S \S \4U4S jjr S \ RS\ R4Sjr SrU=r$)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. rr`r1r+ expand_ratiorrrrqc f>[T U]5 XPlURS:gUlX%-n UR(a[ XXS9Ul[ UUR(aU OUUUU S9Ul[XXRS9Ul [UUR(aU OUUUUUS9Ul g)Nr )rr`r1r+)rr`r+rrq)rr`rr)rr`r1r+rr) r/r0rrr^ expansionrorursqueeze_exciter projection) r<rr`r1r+rrrrrq expand_in_dimr=s rr0EfficientNetBlock.__init__s (''1, - ;;7mDN9$(KK=V#)  =];; 6$(KK=V  rrir@cUnURS:waURU5nURU5nURU5nUR X!5nU$)Nr )rrrurr)r<rirs rrDEfficientNetBlock.forwardGsY"    ! NN=9M++M: ++M:  B r)rurrrrrrrOs@rrrs4' "' '  '  '  ' ' ' ' ' R U%6%6 5<<  rrc v^\rSrSrSrS\4U4SjjrS S\RS\ S-S\ S-S \ 4S jjr S r U=r $) EfficientNetEncoderiTz Forward propagates the embeddings through each EfficientNet block. Args: config ([`EfficientNetConfig`]): Model configuration class. rc>^^[TT]5 UTlURTlU4Sjm[ UR 5n[ U4SjUR55nSn/n[U5Hn[XR U5n[XRU5nURUn URUn URUn [T"URU55Hcn U S:Hn U S:aSOU n U S:aUOUnXAR;nURU-U- n[!UUUU U U UU US9 nUR#U5 US- nMe M [$R&"U5Tl[$R*"W[US5SSSS 9Tl[$R."UR0UR2UR4S 9TlR:Tlg) Nc\>[[R"TRU-55$rB)rmathceildepth_coefficient)repeatsr<s r round_repeats3EfficientNetEncoder.__init__..round_repeatsbs"tyy!7!7'!ABC Crc34># UH nT"U5v M g7frBr[).0nrs r /EfficientNetEncoder.__init__..gsL3Kaq))3Ksrr ) rr`r1r+rrrrrqirbFrcrs)r/r0rrlenrTsumnum_block_repeatsrangerrUstrides kernel_sizes expand_ratiosdepthwise_paddingdrop_connect_raterappendr ModuleListblocksr3top_convr5 hidden_dimr6r7top_bnrr9top_activation)r<rnum_base_blocks num_blockscurr_block_numrir`r1r+rrjrrqrblockrr=s` @rr0EfficientNetEncoder.__init__]s  !'!9!9 Df001L63K3KLL 'A"6+=+=a+@AF#F,?,?,BCG^^A&F --a0K!//2L=)A)A!)DEFq&!e$%Ev!/7O7O!O"44~E R )!!#! +!-'##1  e$!#'G(8mmF+  &vt4   nn**0E0EPVPjPj  %V%6%67rrioutput_hidden_statesN return_dictr@cU(aU4OSnURHnU"U5nU(dMXA4- nM URU5nURU5nURU5nU(d[ SX455$[ UUS9$)Nc3.# UH ocMUv M g7frBr[)rvs rr.EfficientNetEncoder.forward..sX$Fq$Fs )last_hidden_stateri)rrrrtupler)r<rirrall_hidden_statesrs rrDEfficientNetEncoder.forwards 1E],$[[E!-0M##!%55!!  m4  M2 ++M: X]$FXX X-++  r)rrrrrr)FT)rFrGrHrIrJrr0rKrmr}rrDrMrNrOs@rrrTs\58158t-2#'  (( #Tk D[  (   rrc|\rSrSr%\\S'SrSrSrS/r \ R"5S\ R4Sj5rS rg ) EfficientNetPreTrainedModelir efficientnetr?)imagermodulec"[U[R[R[R45(a[ R "URSURRS9 URb [ R"UR5 [USS5ba[ R"UR5 [ R"UR5 [ R"UR 5 ggg)zInitialize the weightsg)meanstdN running_mean)rrLinearr3r5initnormal_weightrinitializer_ranger,zeros_getattrrones_ running_varnum_batches_tracked)r<rs r _init_weights)EfficientNetPreTrainedModel._init_weightss fryy"))R^^D E E LLSdkk6S6S T{{& FKK(v~t4@ F//0 6--. F667A Frr[N)rFrGrHrIr__annotations__base_model_prefixmain_input_nameinput_modalities_no_split_modulesrKno_gradrModulerrMr[rrrrsE &$O!,- ]]_ 8BII 8 8rrc ^\rSrSrS\4U4Sjjr\S S\RS-S\ S-S\ S-S\ \ -4S jj5r S r U=r$) EfficientNetModelirc>[TU]U5 Xl[U5Ul[ U5UlURS:Xa%[R"URSS9Ul OMURS:Xa%[R"URSS9Ul O[SUR35eUR5 g)NrT) ceil_moderz2config.pooling must be one of ['mean', 'max'] got )r/r0rr#rrencoder pooling_typer AvgPool2drpooler MaxPool2d ValueErrorpooling post_initr;s rr0EfficientNetModel.__init__s   08*62    & (,,v'8'8DIDK  E ),,v'8'8DIDKQRXR`R`Qabc c rNr?rrr@c UbUOURRnUbUOURRnUc [S5eUR U5nUR UUUS9nUSnUR U5nURURSS5nU(d Xx4USS-$[UUURS9$)Nz You have to specify pixel_valuesrrrrr )r pooler_outputri) rrrrrrrreshapeshaperri) r<r?rrkwargsembedding_outputencoder_outputsr pooled_outputs rrDEfficientNetModel.forwards%9$D $++JjJj &1%[TU]U5 URUlXl[ U5Ul[ R"URS9Ul URS:a+[ R"URUR5O[ R"5Ul UR5 g)Nrr)r/r0 num_labelsrrrrr dropout_raterrrIdentity classifierrr;s rr0+EfficientNetForImageClassification.__init__s   ++ -f5zzF$7$78 KO??]^K^"))F$5$5tGdfdododq rNr?labelsrrr@c nUbUOURRnURXUS9nU(a UROUSnUR U5nUR U5nSn UbUR X(UR5n U(dU4USS-n U bU 4U -$U $[U UURS9$)ab labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nr r r)losslogitsri) rrrr rr loss_functionr ri) r<r?rrrroutputsrr routputs rrD*EfficientNetForImageClassification.forwards&1%r/s^" &! .,:   H % , C @S5[@$@(RYY2 ")) 44$$N$RYY$N"))BN NbW "))W t8/88(6 36 6 r 0 )D0  0 f er