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A-155B [[__""59b"X1#=#=#=>> r.rrs@r/rrWs}$!<[<[<[<[<[<[|r.rc8eZdZdZdddddedffd ZdZxZS) DarkStagezDarkNet stage.rFrNFc  tt|| p|} t| d| d}| dd}| rVt j|dkrt jdnt j t||fdd|d||_ nt||fd|| ||d ||_ |}tt||z}t j|_t|D]C}|jt#|| d |||||| | |nd d | |}DdS) Nr_rarrbr rrr"rrrr-)rrrrrrrrrrrrr*rrrrr,)rUrrr!rr?rGrHrErrIrrrrrbrrrrs r/rzDarkStage.__init__s i'')))'38\%5%5k%B%B|O_O_`lOmOmnnn ##J55  2]#)Q;; QBKMMFGc1VccWbccDNN )2-.v_e!22%022DNE'K"78899 moo u % %A K " "3q6688,%!)*3*?)A,,R ,,,,   %HH % %r.cZ||}||}|SrT)rrrUr6s r/rzDarkStage.forwards' NN1   KKNNr.rrs@r/rrsj$,%,%,%,%,%,%\r.rr"rr r$c tj} g} t|ttfs|g}t |} | sJ|dvsJd} |} | dz }d}t |D]\}}d|dz}|dkr|dks||kr |dkr|sdnd}|dkr| | | | |t| ||||dkr|nd||||z}|} t| |d d |g } |r|dksJ| | | |O| d tj d dd | d|| dd}n-| d tj d dd d }|dz}t| |d d |g } | | | | fS)N)rr rconvrr r$)r!r%r_ra.r[num_chs reductionmoduler&r")r#r!r%aa)channelsr!) rrr2r3r4r5 enumerateappendrrrjoin MaxPool2d)in_chansrr#r!r&r%r_rarbr[ feature_info stem_depth prev_featrlast_idx stem_striderchs conv_name conv_stride pool_names r/create_csp_stemr sr =??DL gt} - -)WJ  Y    IHA~HKG$$hh3"1q5NN FFvzzqH}}RS\`aagh ??y4    * * *  ; c; AvvGG2! $ $ $     {" [SY[dReIfIfggg  hzzzz     * * *   OOFBLQqRS$T$T$T U U U OOD((HQ"G"G"G H H HII OOFBLQqRS$T$T$T U U UIq [SY[dReIfIfggg  """  r.c |d}|dvsJ|dkrJ|dd|dd|ddt}n|dkrt}nt}||fS)NrM)rdrLrwrdrPrQrRrL)rrrr) stage_argsrMstage_fns r/ _get_stage_fnr s --J / / / / /V~t,,,~t,,,}d+++ u   Z r.c|d}|dvsJ|dkr t|fS|dkr t|fSt|fS)NrO)rdedgerNrdr)rrrr})r rOs r/ _get_block_fnrs\ --J 3 3 3 3 3V*$$ v  *$$ **r.c|d}|ddpi}|t|}|r t|fi|}||fS)NrJrK)rrr)r rJrKs r/ _get_attn_fnr)sf --J..55;Kj))  < ;;{;;J z !!r.cfgdrop_path_rate output_stride stem_featc t|jt|jj}|sdg|znSdt jd|t |jj|jjDd<fdt D}t|j |j }d}|d}|d} |} g} g} t|D]\} }t|\}}t|\}}t!|\}}|d }|dkr| r| | ||kr |dkr||z}d}||z}|d vrdnd }| || fi||||||j|d |gz } |d } t| |d| } | | t)j| | fS)Nc6g|]}|Sr-)tolist)rkr6s r/rnz%create_csp_stages..<s nnnnnnr.rrcng|]1}tt|2Sr-)rzipkeys)rkvaluescfg_dicts r/rnz%create_csp_stages..=s3[[[$s8==??F3344[[[r.rrrrr!rr r )r!rrrrbrJrzstages.r)rr\r5r?rlinspacesumrrrrr_rarr rrrrrbrr)rrrr num_stagesr rr net_striderrrr\ stage_idxr rattn_fnr!rrs @r/create_csp_stagesr&3si cj!!HSZ%&&J7EoTFZ//nnU^A~s3:CS?T?TUU[[\_\f\lmmnnn [[[[[3HYHYCZ[[[J->L H;'J#HIL F!*:!6!6__ :,Z88*,Z88**:66)) Q;;9;    * * *  & &6A::  HFf &&00a88      )\        i(ZH]R[H]H]^^^  """ =& !< //r.ceZdZdZ ddeffd Zejjdd Z ejjdd Z ejjd e j fdZ ddedeefdZdZddefdZdZxZS)raCross Stage Partial base model. Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929 Ref Impl: https://github.com/WongKinYiu/CrossStagePartialNetworks NOTE: There are differences in the way I handle the 1x1 'expansion' conv in this impl vs the darknet impl. I did it this way for simplicity and less special cases. r"ravgrTrc  t||_||_|dvsJt |fi| }t |j|j|j} g|_ t|fit|j | \|_ } |j | ddt|||| d\|_} | dd} |j | | x|_|_t%| ||||_t)t+t,||dS) a Args: cfg (CspModelCfg): Model architecture configuration in_chans (int): Number of input channels (default: 3) num_classes (int): Number of classifier classes (default: 1000) output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) global_pool (str): Global pooling type (default: 'avg') drop_rate (float): Dropout rate (default: 0.) drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) zero_init_last (bool): Zero-init last weight of residual path kwargs (dict): Extra kwargs overlayed onto cfg )r)r_rarbNr1)rrrr) in_features num_classes pool_type drop_rate)r])rrr.r0rrr_rarbrr rr[extendr&r\ num_featureshead_hidden_sizerheadrr _init_weights)rUrrr.r global_poolr0rr]kwargs layer_argsstem_feat_infostage_feat_inforrs r/rzCspNet.__init__rsz0 &" ++++c$$V$$m~\   %4H$_$_sx@P@P$_T^$_$_! >   !4555(9 )'$R( ( ( ( $ _ #2&y1   1115=<D1" #!      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