`iOdZddlZddlmZmZddlZddlmZddlmZddl m Z m Z m Z ddl mZdd lmZmZd d lmZejeZd ed efdZd,deeefdefdZGddejZGddejZGddejZ GddejZ!GddejZ"GddejZ#Gdd ejZ$Gd!d"ejZ%eGd#d$eZ&eGd%d&e&Z'ed'(Gd)d*e&Z(gd+Z)dS)-zPyTorch EfficientNet model.N)OptionalUnion)nn)ACT2FN)BaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)EfficientNetConfigconfig num_channelsc|j}||jz}t|t||dz z|z|z}|d|zkr||z }t|S)z< Round number of filters based on depth multiplier. g?) depth_divisorwidth_coefficientmaxint)rrdivisornew_dims /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/efficientnet/modeling_efficientnet.py round_filtersr%sk"GF,,L'3|gk9::gEOPPG|###7 w<<T kernel_sizeadjustct|tr||f}|ddz|ddzf}|r$|ddz |d|ddz |dfS|d|d|d|dfS)aJ 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"4s+s##1"K0 1~"KNa$78G @ Q GAJNGAJGG GAJ GAJ??rcLeZdZdZdeffd ZdejdejfdZxZ S)EfficientNetEmbeddingszL A module that corresponds to the stem module of the original work. rc|tt|d|_t jd|_t j|j|jdddd|_ t j |j|j |j |_ t|j|_dS) N )rrrrpaddingrrvalidFrstrider(bias)epsmomentum)super__init__rout_dimr ZeroPad2dr(Conv2dr convolution BatchNorm2dbatch_norm_epsbatch_norm_momentum batchnormr hidden_act activationselfr __class__s rr0zEfficientNetEmbeddings.__init__Ms $VR00 |L999 9  1QPW^c    &:OZ`Ztuuu !23r pixel_valuesreturnc||}||}||}||}|SN)r(r4r8r:)r<r>featuress rforwardzEfficientNetEmbeddings.forwardXsM<< --##H-->>(++??8,,r) __name__ __module__ __qualname____doc__rr0torchTensorrC __classcell__r=s@rr$r$Hsu 41 4 4 4 4 4 4ELU\rr$c.eZdZ dfd ZxZS)EfficientNetDepthwiseConv2drrrTzerosc f||z} t|| ||||||| dS)N) in_channels out_channelsrr+r(dilationgroupsr, padding_mode)r/r0) r<rPdepth_multiplierrr+r(rRr,rTrQr=s rr0z$EfficientNetDepthwiseConv2d.__init__bsV#%55  #%#%  r)rrrrrTrN)rDrErFr0rJrKs@rrMrMasT           rrMcXeZdZdZdedededeffd Zdejdej fd Z xZ S) EfficientNetExpansionLayerz_ This corresponds to the expansion phase of each block in the original implementation. rin_dimr1r+cttj||ddd|_tj||j|_t|j |_ dS)NrsameFrPrQrr(r,) num_featuresr-) r/r0rr3 expand_convr5r6 expand_bnrr9 expand_act)r<rrXr1r+r=s rr0z#EfficientNetExpansionLayer.__init__so 9     W&BWXXX !23r hidden_statesr?c||}||}||}|SrA)r]r^r_r<r`s rrCz"EfficientNetExpansionLayer.forwards=((77 }55  66 r) rDrErFrGrrr0rH FloatTensorrIrCrJrKs@rrWrW{s 41 43 4 4VY 4 4 4 4 4 4U%65<rrWc \eZdZdZdededededef fd Zdej d ej fd Z xZ S) EfficientNetDepthwiseLayerzk This corresponds to the depthwise convolution phase of each block in the original implementation. rrXr+radjust_paddingcvt||_|jdkrdnd}t||}t j||_t||||d|_t j ||j |j |_ t|j|_dS) Nrr)rZ)rr'Fr*r\r-r.)r/r0r+r"rr2depthwise_conv_padrMdepthwise_convr5r6r7depthwise_normrr9 depthwise_act) r<rrXr+rrfconv_padr(r=s rr0z#EfficientNetDepthwiseLayer.__init__s  "kQ..77Fk.AAA"$,w"?"?"?9  FHSX   !nV%:VE_   $F$56rr`r?c|jdkr||}||}||}||}|S)Nr)r+rirjrkrlrbs rrCz"EfficientNetDepthwiseLayer.forwardsa ;!   33MBBM++M:: ++M:: **=99 r rDrErFrGrrboolr0rHrcrIrCrJrKs@rreres7"77 7  7  777777, U%6 5<        rrec ZeZdZdZd dedededeffd Zdej d ej fd Z xZ S) EfficientNetSqueezeExciteLayerzl This corresponds to the Squeeze and Excitement phase of each block in the original implementation. FrrX expand_dimexpandct|r|n||_tdt ||jz|_tjd|_ tj |j|jdd|_ tj |j|jdd|_ t|j|_tj|_dS)Nr) output_sizerZ)rPrQrr()r/r0dimrrsqueeze_expansion_ratiodim_serAdaptiveAvgPool2dsqueezer3reducertrr9 act_reduceSigmoid act_expand)r<rrXrsrtr=s rr0z'EfficientNetSqueezeExciteLayer.__init__s !'3::V!S&*H!HIIJJ +::: i     i      !!23*,,rr`r?c|}||}||}||}||}||}t j||}|SrA)r{r|r}rtrrHmul)r<r`inputss rrCz&EfficientNetSqueezeExciteLayer.forwardss ]33  M22  66  M22  66  &-88 r)FrorKs@rrrrrs''1'3'C'Y]''''''* U%6 5<        rrrc neZdZdZdedededededef fd Zd e j d e j d e j fd Z xZ S) EfficientNetFinalBlockLayerz[ This corresponds to the final phase of each block in the original implementation. rrXr1r+ drop_rateid_skipc t|dko| |_tj||ddd|_tj||j|j|_ tj ||_ dS)NrrZFr[rhp) r/r0 apply_dropoutrr3 project_convr5r6r7 project_bnDropoutdropout)r<rrXr1r+rrr=s rr0z$EfficientNetFinalBlockLayer.__init__s #q[8[I     . f&;fF`   zI... r embeddingsr`r?c||}||}|jr||}||z}|SrA)rrrr)r<rr`s rrCz#EfficientNetFinalBlockLayer.forwardsR))-88  66   7 LL77M)J6MrrDrErFrGrrfloatrpr0rHrcrIrCrJrKs@rrrs/(/25/@C/MP/]b/mq//////"%"3EDUZ_ZfrrcleZdZdZdededededededed ed effd Zd e j d e j fdZ xZ S)EfficientNetBlocka 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. rrXr1r+ expand_ratiorrrrfc t||_|jdk|_||z} |jrt ||| ||_t ||jr| n|||| |_t||| |j|_ t||jr| n||||||_ dS)Nr)rrXr1r+)rrXr+rrf)rrXrsrt)rrXr1r+rr) r/r0rrtrW expansionrerjrrsqueeze_exciter projection) r<rrXr1r+rrrrrf expand_in_dimr=s rr0zEfficientNetBlock.__init__s ('1, - ; 7fmFDN9$(K;==V#)    =&]4;   6$(K;==V    rr`r?c|}|jdkr||}||}||}|||}|S)Nr)rrrjrr)r<r`rs rrCzEfficientNetBlock.forwardHsg"   ! ! NN=99M++M:: ++M::  MBB rrrKs@rrrs4' "' '  '  '  ' ' ' ' ' ' ' ' ' ' R U%6 5<        rrc heZdZdZdeffd Z d dejdee dee d e fd Z xZ S) EfficientNetEncoderz Forward propagates the embeddings through each EfficientNet block. Args: config ([`EfficientNetConfig`]): Model configuration class. rc t|_|j_fdt |j}t fd|jD}d}g}t|D]}t||j|}t||j |}|j |} |j |} |j |} t|j|D]d} | dk} | dkrdn| } | dkr|n|}||jv}|j|z|z }t!|||| | | || | }|||dz }et%j|_t%j|t|dddd _t%j|j|j|j _t8|j_dS) NcVttjj|zSrA)rmathceildepth_coefficient)repeatsr<s r round_repeatsz3EfficientNetEncoder.__init__..round_repeatscs#ty!7'!ABBCC Crc3.K|]}|VdSrA).0nrs r z/EfficientNetEncoder.__init__..hs-LLaq))LLLLLLrrr) rrXr1r+rrrrrfirZFr[rh)r/r0rrlenrPsumnum_block_repeatsrangerrQstrides kernel_sizes expand_ratiosdepthwise_paddingdrop_connect_raterappendr ModuleListblocksr3top_convr5 hidden_dimr6r7top_bnrr9top_activation)r<rnum_base_blocks num_blockscurr_block_numrirXr1r+rrjrrfrblockrr=s` @rr0zEfficientNetEncoder.__init__^sE  !'!9 D D D D Df011LLLL63KLLLLL '' $ $A"66+=a+@AAF#FF,?,BCCG^A&F -a0K!/2L==)A!)DEEFF $ $q&!ee$%EEv!/v7O!O"4~E R )!!#! +!-'##1    e$$$!#' $*mF++  &vt44     n*0EPVPj    %V%67rFTr`output_hidden_states return_dictr?c$|r|fnd}|jD]}||}|r||fz }||}||}||}|st d||fDSt ||S)Nc3K|]}||V dSrAr)rvs rrz.EfficientNetEncoder.forward..s"XXq!-----XXr)last_hidden_stater`)rrrrtupler)r<r`rrall_hidden_statesrs rrCzEfficientNetEncoder.forwards 1EN],,$[ 6 6E!E-00M# 6!m%55! m44  M22 ++M::  YXX]4E$FXXXXX X-++    r)FT) rDrErFrGrr0rHrcrrprrCrJrKs@rrrUs581585858585858t05&*   ( 'tn d^  (         rrc<eZdZUeed<dZdZgZdej fdZ dS)EfficientNetPreTrainedModelr efficientnetr>modulect|tjtjtjfrR|jjd|jj |j "|j j dSdSdS)zInitialize the weightsg)meanstdN) r rLinearr3r5weightdatanormal_rinitializer_ranger,zero_)r<rs r _init_weightsz)EfficientNetPreTrainedModel._init_weightss} fry")R^D E E ) M  & &CT[5R & S S S{&  &&(((((  ) )'&rN) rDrErFr__annotations__base_model_prefixmain_input_name_no_split_modulesrModulerrrrrrsP &$O)BI))))))rrc eZdZdeffd Ze d deejdee dee de e e ffdZ xZS) EfficientNetModelrct|||_t||_t ||_|jdkr!tj |j d|_ nC|jdkr!tj |j d|_ ntd|j|dS)NrT) ceil_moderz2config.pooling must be one of ['mean', 'max'] got )r/r0rr$rrencoder pooling_typer AvgPool2drpooler MaxPool2d ValueErrorpooling post_initr;s rr0zEfficientNetModel.__init__s     088*622   & ( (,v'8DIIIDKK  E ) ),v'8DIIIDKKbRXR`bbcc c rNr>rrr?c||n |jj}||n |jj}|td||}||||}|d}||}||jdd}|s||f|ddzSt|||j S)Nz You have to specify pixel_valuesrrrrr)r pooler_outputr`) rruse_return_dictrrrrreshapeshaper r`)r<r>rrembedding_outputencoder_outputsr pooled_outputs rrCzEfficientNetModel.forwards%9$D $+Jj &1%> ? " " " ^" " " " " rrz EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. ) custom_introceZdZfdZe d deejdeejdee dee de e e ff dZ xZS) "EfficientNetForImageClassificationct||j|_||_t ||_t j|j|_ |jdkrt j |j |jnt j |_ |dS)Nrr)r/r0 num_labelsrrrrr dropout_raterrrIdentity classifierrr;s rr0z+EfficientNetForImageClassification.__init__s     + -f55zF$7888 KO?]^K^K^")F$5tGGGdfdodqdq rNr>labelsrrr?cj||n |jj}||||}|r|jn|d}||}||}d}|||||j}|s|f|ddz} ||f| zn| St|||jS)a 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). Nrrr)losslogitsr`) rrrrrr loss_functionr r`) r<r>rrroutputsrrroutputs rrCz*EfficientNetForImageClassification.forward s&1%r s"! """""""" !!!!!! .-----,,,,,,,,::::::  H % % , C    @@U3:.@@@@@(RY2     ")   44$$$$$$$$N$$$$$RY$$$N")BNNNNN NNNbW W W W W ")W W W t ) ) ) ) )/ ) ) ) 5 5 5 5 5 35 5 5 p / / / / / )D/ /  / d e d dr