+ jZaRTtI0t^RIt^RIt^RIHt^RIHt^RIHt^RI H t H t H t H t ^RIt^RIHtHt^RIHt^RIHtHt^R IHtHt^R IHt^R IHtHtHt^R IH t ^R I!H"t"H#t#H$t$.RUOt%]!RR44t&!RR]&4t'!RR]&4t(!R R!]PR4t*!R"R#]PR4t+!R$R]PR4t,R%R&lt-R'R(lt.R)] /t/]^k/]/CR*RVR+R,/Ct0/]/CR*RWR+R-/Ct1!R.R]4t2!R/R]4t3!R0R]4t4!R1R]4t5!R2R]4t6!R3R]4t7!R4R]4t8!R5R]4t9!R6R]4t:!R7R]4t;!R8R]4t<]!4]$!R9]2Pz3R:7R;RRR?ll44t>]!4]$!R9]3Pz3R:7R;RRtoRV3RlV3Rlllt]V3RlRl4tRtVtV;t #) MBConvConfigc<V^8dQhRS[RS[RS[RS[RS[RS[RS[RS[R S[S[R S[P 3,,R R / #) r(r:r;r<r=r>r?r* depth_multr@.r,Nr.r-rrr rA)r/r0s"r1r2MBConvConfig.__annotate__@shhhh h  h  hhhhbii01h hr4c <VPWG4pVPWW4pVPWh4pV f\p \S V`WW4WVV 4R#rC)r7 adjust_depthMBConvsuper__init__) selfr:r;r<r=r>r?r*rOr@ __class__s &&&&&&&&&&r1rVMBConvConfig.__init__@sS--nI++LE &&z> =E v|afgr4c&<V^8dQhRS[RS[/#)r(r?rO)r-r.)r/r0s"r1r2rQTs777%7r4cL\\P!W,44#rC)r-mathceil)r?rOs&&r1rSMBConvConfig.adjust_depthSs499Z4566r4rB)?r_N) rDrErFrGrVrHrSrJrK __classcell__rXr0s@@r1rMrM>s*hh&777r4rMcBaa]tRt^XtoRV3RlV3RllltRtVtV;t#)FusedMBConvConfigc<V^8dQhRS[RS[RS[RS[RS[RS[RS[S[RS[P 3,,R R /#) r(r:r;r<r=r>r?r@.r,NrP)r/r0s"r1r2FusedMBConvConfig.__annotate__Zsw h h h h h  h  h hbii01 h  hr4c @<Vf\p\SV` WW4WVV4R#rC) FusedMBConvrUrV) rWr:r;r<r=r>r?r@rXs &&&&&&&&r1rVFusedMBConvConfig.__init__Zs# =E v|afgr4rBrC)rDrErFrGrVrJrKr`ras@@r1rcrcXs h h hr4rccVaa]tRt^ito]3V3RlV3RllltV3RlRltRtVtV;t #)rTc <V^8dQhRS[RS[RS[RS[P3,RS[RS[P3,RR/#)r(cnfstochastic_depth_prob norm_layer.se_layerr,N)rMr.rr rA)r/r0s"r1r2MBConv.__annotate__jsW8-8- 8- %8-S"))^, 8- 3 >* 8-  8-r4c <\S V`4^VPu;8:d^8:gM\R4hVP^8H;'dVPVP 8HVn.p\PpVPVPVP4pWqP8wd*VP\VPV^VVR74VP\VVVPVPVVVR74\^VP^,4pVPV!Wx\\PRR7R74VP\WqP ^VRR74\P !V!Vn\%VR4VnVP VnR#) illegal stride value kernel_sizermactivation_layer)rtr<groupsrmruT)inplace) activationNrow)rUrVr< ValueErrorr=r>use_res_connectr SiLUr7r:appendr r;maxr Sequentialr@r stochastic_depth) rWrkrlrmrnlayersruexpanded_channelssqueeze_channelsrXs &&&&& r1rVMBConv.__init__js SZZ$1$34 4"zzQYY33E3EIYIY3Y"$77 //0B0BCDTDTU  2 2 2 MM$&&% !)%5    !!JJzz(%!1  q#"4"4"9: h0wWYW^W^hlOmno  !#3#3zlp  ]]F+ /0Eu M,,r4c&<V^8dQhRS[RS[/#r(inputr,r )r/r0s"r1r2roVr4c~VPV4pVP'dVPV4pW!, pV#rCr@r{rrWrresults&& r1forwardMBConv.forward8E"    **62F OF r4r@r>rr{) rDrErFrGrrVrrJrKr`ras@@r1rTrTis$ .? 8-8-tr4rTcPaa]tRt^toV3RlV3RlltV3RlRltRtVtV;t#)rgcX<V^8dQhRS[RS[RS[RS[P3,RR/#)r(rkrlrm.r,N)rcr.rr rA)r/r0s"r1r2FusedMBConv.__annotate__s@2-2- 2- %2-S"))^, 2-  2-r4c ><\SV`4^VPu;8:d^8:gM\R4hVP^8H;'dVPVP 8HVn.p\PpVPVPVP4pWaP8wdhVP\VPVVPVPVVR74VP\WaP ^VRR74MHVP\VPVP VPVPVVR74\P!V!Vn\!VR4VnVP VnR#)rqrrrtr<rmruNrsry)rUrVr<rzr=r>r{r r|r7r:r}r r;rr@r r)rWrkrlrmrrurrXs&&&& r1rVFusedMBConv.__init__sW SZZ$1$34 4"zzQYY33E3EIYIY3Y"$77//0B0BCDTDTU  2 2 2 MM$&&% # ::)%5   MM$%'7'7QS]pt  MM$&&$$ # ::)%5  ]]F+ /0Eu M,,r4c&<V^8dQhRS[RS[/#rr)r/r0s"r1r2rrr4c~VPV4pVP'dVPV4pW!, pV#rCrrs&& r1rFusedMBConv.forwardrr4r) rDrErFrGrVrrJrKr`ras@@r1rgrgs2-2-hr4rgcfaa]tRt^toRV3RlV3RllltV3RlRltV3RlRltRtVtV;t #) rc<V^8dQhRS[S[S[S[3,,RS[RS[RS[RS[S[RS[P3,,RS[S[,RR /#) r(inverted_residual_settingdropoutrl num_classesrm. last_channelr,N) rr rMrcr.r-rrr rA)r/r0s"r1r2EfficientNet.__annotate__sa'a'#+E,@Q2Q,R#Sa'a' % a'  a' Xc299n56 a'sma' a'r4c  <\SV`4\V4V'g \R4h\ V\ 4'd0\ VUu.uFp\ V\4NK up4'g \R4hVf\Pp.pV^,Pp VP\^V ^^V\PR74\RV44p ^p VFp .p \!V P"4F}p\$P$!V 4pV 'dVP&Vn ^VnV\+V 4,V , pV PVP-VVV44V ^, p K VP\P.!V !4K VR ,P&pVeTM^V,pVP\VV^V\PR74\P.!V!Vn\P2!^4Vn\P.!\P6!VRR7\P8!VV44VnVP=4EFp\ V\P>4'dg\P@PCVPDR R 7VPFe,\P@PIVPF4KK\ V\P\PJ34'dV\P@PMVPD4\P@PIVPF4EK\ V\P84'gEK2R \NPP!VPR4, p\P@PUVPDV)V4\P@PIVPF4EK R#uupi) a EfficientNet V1 and V2 main class Args: inverted_residual_setting (Sequence[Union[MBConvConfig, FusedMBConvConfig]]): Network structure dropout (float): The droupout probability stochastic_depth_prob (float): The stochastic depth probability num_classes (int): Number of classes norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use last_channel (int): The number of channels on the penultimate layer z1The inverted_residual_setting should not be emptyz:The inverted_residual_setting should be List[MBConvConfig]Nrc38"TFqPxK R#5irC)r?).0rks& r1 (EfficientNet.__init__..s U;TC;TsrsT)prwfan_out)moder_)+rUrVrrz isinstancerallr& TypeErrorr BatchNorm2dr=r}r r|sumranger?copyr>r<r.r@rfeaturesAdaptiveAvgPool2davgpoolDropoutLinear classifiermodulesConv2dinitkaiming_normal_weightbiaszeros_ GroupNormones_r\sqrt out_featuresuniform_)rWrrrlrrmrsrfirstconv_output_channelstotal_stage_blocksstage_block_idrkstage_ block_cnfsd_problastconv_input_channelslastconv_output_channelsm init_rangerXs&&&&&&& r1rVEfficientNet.__init__s ( D!(PQ Q 0( ; ;;TU;TaZ=1;TUVVXY Y  J"$%>a$@$O$O! ,!AR\oqovov  ! U;T UU,C%'E3>>* IIcN /8/E/EI,'(I$0%2GGJ\\ Y__YLM!#+ MM"--/ 0#-(#> ahh'qvv&Aryy)) 499Q^^#<<   J; Cqvv& wVs P<c&<V^8dQhRS[RS[/#r(xr,r)r/r0s"r1r2rMsv&r4cVPV4pVPV4p\P!V^4pVP V4pV#)rq)rrtorchflattenrrWrs&&r1 _forward_implEfficientNet._forward_implMs@ MM!  LLO MM!Q  OOA r4c&<V^8dQhRS[RS[/#rr)r/r0s"r1r2rWs%%%F%r4c$VPV4#rC)rrs&&r1rEfficientNet.forwardWs!!!$$r4)rrr)皙?iNN) rDrErFrGrVrrrJrKr`ras@@r1rrs+a'a'F%%%r4cV^8dQhR\\\\3,,R\R\ \ ,R\ \,R\R\R\/#)r(rrrweightsprogresskwargsr,) rr rMrcr.rr-rboolrr)r/s"r1r2r2[sg'l@r r rz@https://download.pytorch.org/models/efficientnet_b1-c27df63c.pthrzOhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuninggʡS@gƻW@gA`">@$ These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. rBN)rDrErFrGrrrrrrrBILINEAR IMAGENET1K_V2rrJrBr4r1rrs X 3CO`OhOh     ' VV  E & L  M( N 3CO`OiOi   ' g VV  E &  M0Gr4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)rrzJhttps://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pthi rrirrrgx&T@r gp= W@r g rh?r gʡEA@r r rrBNrrBr4r1rrrr4c]tRtRt]!R]!]RR]PR7/] CRRRR R R R R //RRRRRR/CR7t ] t Rt R#)rizJhttps://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pthi,rrrirrrgnT@r g~jtX@r gZd;?r gd;OG@r r rrBNrrBr4r1rrq X 3CO`OhOh     ( VV  E & L  M(Gr4c]tRtRt]!R]!]RR]PR7/] CRRRR R R R R //RRRRRR/CR7t ] t Rt R#)ri0zJhttps://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pthi|rrri0!'rrrgjtT@r gt&X@r g~jt@r gKR@r r rrBNrrBr4r1rr0rr4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)riHzJhttps://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pthirrirrrg#~jT@r gx&1(X@r gx&1$@r gK7]@r r rrBNrrBr4r1rrHq X 3CO`OhOh     ( VV  F ' L  M(Gr4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)r i`zJhttps://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pthirrirrrgnU@r gv:X@r g rh3@r g$d@r r rrBNrrBr4r1r r `r r4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)r!ixzJhttps://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pthiXrricrrrg+U@r g'1:X@r gsh|B@r go@r r rrBNrrBr4r1r!r!xr r4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)r"izBhttps://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pthrrri8nGrrrg;OU@r gx&18X@r gZd @r gVT@r rrrBN rDrErFrGrrrrr_COMMON_META_V2rrrJrBr4r1r"r"su P +44    ( VV  E &  M4Gr4c]tRtRt]!R]!]RR]PR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)r#izBhttps://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pthrri:rrrgI +GU@r gDlIX@r gE8@r gQj@r rrrBNr$rBr4r1r#r#su P +44    ( VV  F &  M4Gr4c]tRtRt]!R]!]RR]PRRR7/] CRRRRR R R R //R RRRRR/CR7t ] t Rt R#)r$izBhttps://download.pytorch.org/models/efficientnet_v2_l-59c71312.pthr')rrrmeanstdriHfrrrgʡEsU@r gOnrX@r g ףp= L@r gI +i|@r r rrBN)?r+r+) rDrErFrGrrrrrr%rrrJrBr4r1r$r$sy P +33       ) VV  E ' L  M0Gr4 pretrained)rrrTc^V^8dQhR\\,R\R\R\/#r(rrrr,)rrrrr)r/s"r1r2r2201DH[^r4c \PV4p\RRRR7wr4\W2P RR4W@V3/VB#)aEfficientNet B0 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B0_Weights :members: efficientnet_b0r_rrr)rverifyrrrrrrrrs$$, r1r1r1Q.&,,W5G.@AR_bor.s+ !::i#=|V^ bh r4c^V^8dQhR\\,R\R\R\/#r.)rrrrr)r/s"r1r2r2 r/r4c \PV4p\RRRR7wr4\W2P RR4W@V3/VB#)aEfficientNet B1 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B1_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B1_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B1_Weights :members: efficientnet_b1r_皙?rrr)rr2rrrr3s$$, r1r7r7r4r4c^V^8dQhR\\,R\R\R\/#r.)rrrrr)r/s"r1r2r2)r/r4c \PV4p\RRRR7wr4\W2P RR4W@V3/VB#)aEfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B2_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B2_Weights :members: efficientnet_b2r8333333?rr333333?)rr2rrrr3s$$, r1r;r;'r4r4c^V^8dQhR\\,R\R\R\/#r.)rrrrr)r/s"r1r2r2H201DH[^r4c \PV4p\RRRR7wr4\VVP RR4VVV3/VB#)aEfficientNet B3 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B3_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B3_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B3_Weights :members: efficientnet_b3r<ffffff?rrr=)rr2rrrr3s$$, r1rArAFZ.&,,W5G.@AR_bor.s+ ! 9c"     r4c^V^8dQhR\\,R\R\R\/#r.)rrrrr)r/s"r1r2r2lr?r4c \PV4p\RRRR7wr4\VVP RR4VVV3/VB#)aEfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B4_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B4_Weights :members: efficientnet_b4rB?rr皙?)rr2rrrr3s$$, r1rFrFjrCr4c^V^8dQhR\\,R\R\R\/#r.)rrrrr)r/s"r1r2r22  01 DH [^  r4c \PV4p\RRRR7wr4\VVP RR4VVV3R\ \ PRR R 7/VB#) aEfficientNet B5 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B5_Weights :members: efficientnet_b5g?g@rrrHrmMbP?{Gz?epsmomentum)rr2rrrrr rr3s$$, r1rLrLq.&,,W5G.@AR_bor.s+ ! 9c"   2>>utD    r4c^V^8dQhR\\,R\R\R\/#r.)rr rrr)r/s"r1r2r2rJr4c \PV4p\RRRR7wr4\VVP RR4VVV3R\ \ PRR R 7/VB#) aEfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B6_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B6_Weights :members: efficientnet_b6rGg@rrr+rmrMrNrO)r r2rrrrr rr3s$$, r1rUrUrRr4c^V^8dQhR\\,R\R\R\/#r.)rr!rrr)r/s"r1r2r2rJr4c \PV4p\RRRR7wr4\VVP RR4VVV3R\ \ PRR R 7/VB#) aEfficientNet B7 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B7_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B7_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B7_Weights :members: efficientnet_b7g@g@rrr+rmrMrNrO)r!r2rrrrr rr3s$$, r1rXrXrRr4c^V^8dQhR\\,R\R\R\/#r.)rr"rrr)r/s"r1r2r22!!23!FJ!]`!!r4c \PV4p\R4wr4\VVP RR4VVV3R\ \ PRR7/VB#)a Constructs an EfficientNetV2-S architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_S_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights :members: rrrrmrMrP)r"r2rrrrr rr3s$$, r1rri0(..w7G.@AT.U+ ! 9c"   2>>u5    r4c^V^8dQhR\\,R\R\R\/#r.)rr#rrr)r/s"r1r2r2%rZr4c \PV4p\R4wr4\VVP RR4VVV3R\ \ PRR7/VB#)a Constructs an EfficientNetV2-M architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_M_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights :members: rrr=rmrMr\)r#r2rrrrr rr3s$$, r1rr#r]r4c^V^8dQhR\\,R\R\R\/#r.)rr$rrr)r/s"r1r2r2KrZr4c \PV4p\R4wr4\VVP RR4VVV3R\ \ PRR7/VB#)a Constructs an EfficientNetV2-L architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_L_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_L_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_L_Weights :members: rrrHrmrMr\)r$r2rrrrr rr3s$$, r1rrIr]r4cbV^8dQh/^\9d\\\3,;R&#)r( _COMMON_META)__conditional_annotations__dictrr)r/s"r1r2r2s& H d38nI  r4)rrrrrrrr r!r"r#r$r1r7r;rArFrLrUrXrrr)rqrq)!rf)Jrdrr\collections.abcr dataclassesr functoolsrtypingrrrr rr r torchvision.opsr ops.miscr rtransforms._presetsrrutilsr_apirrr_metar_utilsrrr__all__r&rMrcrArTrgrrrrcrr%rrrrrrr r!r"r#r$rr1r7r;rArFrLrUrXrrrr2)rds@r1rssf $!11 +>H'66'SS 6  D D  D7=74h h"@RYY@F:")):zo%299o%d&43p&    e ek0-k-`k0k0k0k0k0k0 < < :,0G0U0U!VW48KOX:,0G0U0U!VW48KOX:,0G0U0U!VW48KOX:,0G0U0U!VW48KOXD,0G0U0U!VW48KOXD,0G0U0U!VW 48 KO X F,0G0U0U!VW 48 KO X F,0G0U0U!VW 48 KO X F,0I0W0W!XY!6:!MQ!Z!H,0I0W0W!XY!6:!MQ!Z!H,0I0W0W!XY!6:!MQ!Z!r4