+ jRt^RIt^RIt^RIHt^RIt^RIHt^RIH t ^RI H t ^RI HtHt^RIHt^RIHt^R IHt^R IHtHtHt^R IHt^R IHt]P<!]4t ]!R R7]!RR]444t!]!RR7]!RR]444t"]!RR7]!RR]444t#]!RR7]!RR]444t$!RR]PJ4t&!RR]PJ4t'!RR]PJ4t(!R R!]PJ4t)!R"R#]PJ4t*!R$R%]PJ4t+!R&R']4t,!R(R)]PJ4t-]!R*R+]44t.]!R,R-].44t/]!R.R7!R/R0].44t0]!R1R7!R2R3].44t1]!R4R7!R5R6]].44t2.R7Ot3R#)8zPyTorch FocalNet model.N) dataclass)nn)initialization)ACT2FN) BackboneMixinfilter_output_hidden_states)GradientCheckpointingLayer)BackboneOutput)PreTrainedModel) ModelOutputauto_docstringlogging)can_return_tupleFocalNetConfigzC FocalNet encoder's outputs, with potential hidden states. ) custom_introc<a]tRt^%toRtRtRtRtV3RltRt Vt R#)FocalNetEncoderOutputa reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<V^8dQh/S[PR,;R&S[S[P,R,;R&S[S[P,R,;R&#)Nlast_hidden_state hidden_statesreshaped_hidden_statestorch FloatTensortuple)format __classdict__s"o/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/focalnet/modeling_focalnet.py __annotate__"FocalNetEncoderOutput.__annotate__%sW ((4/6!"**+d29#$"%"3"34t;B%) __name__ __module__ __qualname____firstlineno____doc__rrr__annotate_func____static_attributes____classdictcell__rs@rrr%s$3759M>B%r"rzZ FocalNet model's outputs that also contains a pooling of the last hidden states. c@a]tRt^:toRtRtRtRtRtV3Rlt Rt Vt R#)FocalNetModelOutputa pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): Average pooling of the last layer hidden-state. reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<V^8dQh/S[PR,;R&S[PR,;R&S[S[P,R,;R&S[S[P,R,;R&#)rNr pooler_outputrrr)rrs"rr FocalNetModelOutput.__annotate__:so$((4/6%&$$t+2'(**+d29)*"%"3"34t;B+r"r#) r$r%r&r'r(rr0rrr)r*r+r,s@rr.r.:s) 37.2M59M>B+r"r.z. FocalNet masked image model outputs. c@a]tRt^RtoRtRtRtRtRtV3Rlt Rt Vt R#)!FocalNetMaskedImageModelingOutputa loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided): Masked image modeling (MLM) loss. reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Reconstructed pixel values. reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<V^8dQh/S[PR,;R&S[PR,;R&S[S[P,R,;R&S[S[P,R,;R&#)rNlossreconstructionrrr)rrs"rr .FocalNetMaskedImageModelingOutput.__annotate__Rso(   d "))*%%,3+,**+d29-."%"3"34t;B/r"r#) r$r%r&r'r(r5r6rrr)r*r+r,s@rr3r3Rs( &*D/3N59M>B/r"r3z4 FocalNet outputs for image classification. c@a]tRt^ltoRtRtRtRtRtV3Rlt Rt Vt R#)FocalNetImageClassifierOutputa loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<V^8dQh/S[PR,;R&S[PR,;R&S[S[P,R,;R&S[S[P,R,;R&#)rNr5logitsrrr)rrs"rr *FocalNetImageClassifierOutput.__annotate__lso(   d "))*    $++,**+d29-."%"3"34t;B/r"r#) r$r%r&r'r(r5r;rrr)r*r+r,s@rr9r9ls( &*D'+F59M>B/r"r9cPaa]tRt^toRtRV3RlltRV3RlRlltRtVtV;t #)FocalNetEmbeddingszP Construct the patch embeddings and layernorm. Optionally, also the mask token. c "<\SV`4\VVPVPVP VP VPRR7VnVPPVn V'd7\P!\P!^^VP 44MRVn\P !VP VP"R7Vn\P&!VP(4VnR#)T)config image_size patch_size num_channels embed_dimuse_conv_embedis_stemNeps)super__init__FocalNetPatchEmbeddingsrArBrCrDrEpatch_embeddings grid_size patch_gridr Parameterrzeros mask_token LayerNormlayer_norm_epsnormDropouthidden_dropout_probdropout)selfr@use_mask_token __class__s&&&rrJFocalNetEmbeddings.__init__s  7((((,,&&!00! //99O]",,u{{1a9I9I'JKcgLL!1!1v7L7LM zz&"<"<= r"c<V^8dQhRS[PR,RS[PR,RS[S[P,/#)r pixel_valuesNbool_masked_posreturn)rr BoolTensorrTensor)rrs"rr FocalNetEmbeddings.__annotate__sE--!--4-GLGWGWZ^G^- u|| -r"cRVPV4wr4VPV4pVP4wrVpVeZVPP WVR4pVP R4P V4p VRV , ,W,,pVPV4pW43#)N?)rLrTsizerQexpand unsqueezetype_asrW) rXr]r^ embeddingsoutput_dimensions batch_sizeseq_len_ mask_tokensmasks &&& rforwardFocalNetEmbeddings.forwards)-(=(=l(K% YYz* !+!2 Q  &//00bIK",,R088ED#sTz2[5GGJ\\*- ,,r")rWrQrTrLrN)FN r$r%r&r'r(rJrqr*r+ __classcell__rZrs@@rr>r>s>$---r"r>cNaa]tRt^toRV3RlltRtV3RlRltRtVtV;t #)rKc <\S V`4\V\PP 4'dTMW"3p\V\PP 4'dTMW33pV^,V^,,V^,V^,,,p W nW0nW@nWn V^,V^,,V^,V^,,3Vn V'd5V'd^p ^p ^p M^p ^p ^p \P!WEWV R7Vn M\P!WEW3R7Vn V'd)\P!WQPR7VnR#RVnR#)) kernel_sizestridepadding)rzr{rGN)rIrJ isinstance collectionsabcIterablerArBrC num_patchesrMrConv2d projectionrRrSrT)rXr@rArBrCrDadd_normrErFrrzr|r{rZs&&&&&&&&& rrJ FocalNetPatchEmbeddings.__init__s$ #-j+//:R:R#S#SZZdYq #-j+//:R:R#S#SZZdYq !!} 1 5*Q-:VW=:XY $$(&$Q-:a=8*Q-:VW=:XY    ii[Y`DO!ii ZkDO  Y4I4IJDIDIr"cW0P^,,^8wdS^VP^,W0P^,,, 3p\PPW4pW P^,,^8wdU^^^VP^,W P^,,, 3p\PPW4pV#ry)rBr functionalpad)rXr]heightwidth pad_valuess&&&& r maybe_pad!FocalNetPatchEmbeddings.maybe_pads ??1% % *T__Q/%//!:L2LLMJ==,,\FL OOA& &! +Q4??1#5QRAS8S#STJ==,,\FLr"c<V^8dQhRS[PR,RS[S[PS[S[,3,/#)rr]Nr_)rrrraint)rrs"rr $FocalNetPatchEmbeddings.__annotate__s;--E$5$5$<-u||UZ[^U_G_A`-r"cHVPwr#rEW0P8wd \R4hVPWV4pVP V4pVPwr$pWE3pVP ^4P ^^4pVPeVPV4pWg3#)zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaperC ValueErrorrrflatten transposerT)rXr]rnrCrrrjrks&& rrqFocalNetPatchEmbeddings.forwards)5););& ,, ,w ~~lEB __\2 (..1e#O''*44Q: 99 :.J,,r")rMrArTrCrrBr)FFF) r$r%r&r'rJrrqr*r+rurvs@@rrKrKs(T---r"rKc<aa]tRt^toRV3RlltRtRtVtV;t#)FocalNetModulationcj<\S V`4W0nVPV,VnVP V,VnW@nVPVnVPVn \P!V^V,VP ^,,VR7Vn \P!W3^^VR7Vn\P!4Vn\P!W34Vn\P$!V4Vn\P(!4Vn.Vn\/VP 4FpVPV,VP,pVP*P1\P2!\P!W3V^W8^,RR7\P!444VP,P1V4K VP'd)\P4!W1P6R7VnR#R#)r)bias)rzr{rF)rzr{groupsr|rrGN)rIrJdim focal_windows focal_window focal_levels focal_level focal_factor use_post_layernorm_in_modulationnormalize_modulatorrLinear projection_inrprojection_contextGELU activationprojection_outrUprojection_dropout ModuleList focal_layers kernel_sizesrangeappend SequentialrRrS layernorm) rXr@indexrrrrkrzrZs &&&&&&& rrJFocalNetModulation.__init__s "007!..u5(060W0W-#)#=#= YYsAGt7G7G!7K,LSWX"$))C!ATX"Y'') ii1"$**-?"@MMOt''(A++a/$2C2CCK    $ $ IIk!ChiYipuGGI      $ $[ 1)  0 0 0\\#3H3HIDN 1r"c VPR,pVPV4P^^^^4P4p\P !W2W P ^,3^4wrEp^p\VP 4F<pVPV,!V4pWuVRW^,13,,,pK> VPVP^RR7P^RR74p WyVRVP R13,,,pVP'dWpP ^,, pVPV4p WJ,p V P^^^^4P4p VP'dVPV 4p VPV 4p VP!V 4p V #)zh Args: hidden_state: Input features with shape of (batch_size, height, width, num_channels) :NNNT)keepdimNre)rrpermute contiguousrsplitrrrrmeanrrrrrr) rX hidden_staterCxqctxgatesctx_alllevel ctx_global modulatorx_outs && rrqFocalNetModulation.forwards $))"-    | , 4 4Q1a @ K K M AlDTDTWXDX'Y[\] 4++,E##E*3/CeAuqy/@,@&A AAG-__SXXaX%>%C%CAt%C%TU q$2B2B2D/D)EEE  # # #!1!1A!56G++G4   aAq)446  0 0 0NN5)E##E*''. r")rrrrrrrrrrrrrr)rT r$r%r&r'rJrqr*r+rurvs@@rrrsJB""r"rc<aa]tRtRtoRV3RlltRtRtVtV;t#) FocalNetMlpi<c.<\SV`4T;'gTpT;'gTp\P!W#4Vn\ VP ,Vn\P!W44Vn\P!V4Vn R#rs) rIrJrrfc1r hidden_actrfc2rUdrop)rXr@ in_featureshidden_features out_featuresrrZs&&&&&&rrJFocalNetMlp.__init__=sj #22{ )88[99[: !2!2399_;JJt$ r"cVPV4pVPV4pVPV4pVPV4pVPV4pV#rs)rrrr)rXrs&&rrqFocalNetMlp.forwardFsNxx - |4 yy. xx - yy. r")rrrr)NNrrrvs@@rrr<s%r"rcjaa]tRtRtoRtR V3RlV3RllltV3RlRltV3RlRltR tVt V;t #) FocalNetDropPathiPzStochastic depth (DropPath) per sample, for residual blocks. Identity when ``drop_prob`` is 0 or outside training. See `Deep Networks with Stochastic Depth `_. c$<V^8dQhRS[RR/#)r drop_probr_N)float)rrs"rr FocalNetDropPath.__annotate__Ws##%#$#r"c0<\SV`4WnR#rs)rIrJr)rXrrZs&&rrJFocalNetDropPath.__init__Ws "r"cN<V^8dQhRS[PRS[P/#)rrr_)rra)rrs"rr r[s#<>S   & $$Q')DM4F4F4J,KK 50C0CML`L`a  M$=>   +m;;r"c <V^8dQhRS[/#)rr_)str)rrs"rr rds%%C%r"c RVP 2#)zp=rrXs&r extra_reprFocalNetDropPath.extra_reprdsDNN#$$r"rr) r$r%r&r'r(rJrqrr*r+rurvs@@rrrPs- ##<<%%%r"rc@aa]tRtRtoRtRV3RlltRtRtVtV;t #) FocalNetLayerihaSFocal Modulation Network layer (block). Args: config (`FocalNetConfig`): Model config. index (`int`): Layer index. dim (`int`): Number of input channels. input_resolution (`tuple[int]`): Input resolution. drop_path (`float`, *optional*, defaults to 0.0): Stochastic depth rate. cr<\SV`4WnW0nW@nVP VnVPVn\P!W1PR7Vn \VVVVP R7Vn VR8d \V4M\P!4Vn\P!W1PR7Vn\%W1P&,4p\)WW`P R7VnRVnRVnVP0'd\P2!VP4\6P8!V4,RR7Vn\P2!VP4\6P8!V4,RR7VnR#R#)rG)r@rrrr)r@rrrrdT) requires_gradN)rIrJr@rinput_resolutionrVruse_post_layernormrrRrSnorm1r modulationrIdentity drop_pathnorm2r mlp_ratiormlpgamma_1gamma_2use_layerscalerOlayerscale_valuerones)rXr@rrrrmlp_hidden_dimrZs&&&&&& rrJFocalNetLayer.__init__xs5   0.. "(";";\\#+@+@A ,#yy  9BC))4R[[]\\#+@+@A S#3#334f~dmdmn   <<(?(?%**S/(QaefDL<<(?(?%**S/(QaefDL !r"c xVwr4VPwrVpTpVP'dTMVPV4pVPWSWG4pVP V4PWSV,V4pVP'gTMVPV4pWP VP V,4,pYP VPVP'd!VPVPV44MVPVPV44,4,pV#rs) rrrviewrrrrrr) rXrinput_dimensionsrrrlrnrCshortcuts &&& rrqFocalNetLayer.forwards( &2&8&8# |(,'>'>'>|DJJ|D\ #((UQ |499*unVbc +/+B+B+B| S_H`  .. 1L"MM #nn LL595L5L5Ltzz$((<01RVRZRZ[_[e[efr[sRt v'  r") r@rrrrrrrrrrrrrtrvs@@rrrhs g@r"rcDaa]tRtRtoV3RltV3RlRltRtVtV;t#) FocalNetStageic<\S V`4Wn\VP4Vn\ VP 4Uu.uFqAP^V,,NK ppWR,pW P ^, 8dWR^,,MRpW P ^, 8d\MRp\P!^VP\VP4RR7U u.uFqP4NK p p V \VPRV4\VPRV^,4p \P!\ VPV,4Uu.uF1p\!TTTT\#V \$4'd W,MT R7NK3 up4VnVe!V!VV^VVRVP(RR7VnMRVnRVnR#uupiuup iuupi)rNcpu)r)r@rrrrTF)r@rArBrCrDrrErF)rIrJr@lendepths num_stagesrrDrKrlinspacedrop_path_ratesumitemrrrr}listlayersrE downsamplepointing) rXr@rrirDrout_dimrrdprrrZs &&&& rrJFocalNetStage.__init__s  fmm,8=doo8NO8N1%%A..8N O+0??Q3F+F)AI&T1619L1L,SW "'63H3H#fmmJ\ej!kl!kAvvx!klFMM&512S{QR9S5TU mmv}}U34 5A!%5.8D.I.Iily 5      !(+ !%44 DO#DO IP m s"G*3G/37G4cz<V^8dQhRS[PRS[S[S[3,RS[S[P,/#)rrrr_)rrarr)rrs"rr FocalNetStage.__annotate__s<U\\U3PS8_Y^_d_k_kYlr"c Vwr4VPF pV!W4pK TpVPeMVwr4VP^^4PVP^,RW44pVPV4wrMW4W43pWV3pV#)Nre)rrrreshaper) rXrrrr layer_module!hidden_states_before_downsamplingrk stage_outputss &&& rrqFocalNetStage.forwards(  KKL(IM(-:) ?? &,MF)33Aq9AA177:BM04}/M ,M,"( > &K\] r")r@rrrrrrvs@@rr r s*Xr"r cHaa]tRtRtoV3RltRV3RlRlltRtVtV;t#)FocalNetEncoderic h<\SV`4\VP4VnWn\ P!\VP4Uu.uF=p\VVV^,^V,,V^,^V,,3R7NK? up4Vn RVn R#uupi))r@rrFN) rIrJrrrr@rrrr stagesgradient_checkpointing)rXr@rMi_layerrZs&&& rrJFocalNetEncoder.__init__s fmm, mm %T__5   6G !!&/lq'z&BIaLUVX_U_D`%a 6     ',# sAB/c <V^8dQhRS[PRS[S[S[3,RS[R,RS[R,RS[R,RS[S[,/#)rrroutput_hidden_statesN(output_hidden_states_before_downsampling return_dictr_)rrarrboolr)rrs"rr FocalNetEncoder.__annotate__se5 5 ||5  S/5 #Tk 5 37+ 5 D[ 5  & &5 r"cV'dRMRpV'dRMRpV'dLVPwrp VP!V.VOV N5!p V P^^^^4p Wa3, pW{3, p\VP4EFwrV !W4pV^,pV^,pV^,pVR,VR,3pV'dfV'd^VPwrp VP!V.V^,V^,3OV N5!p V P^^^^4p Wo3, pW{3, pKV'gKV'dKVPwrp VP!V.VOV N5!p V P^^^^4p Wa3, pW{3, pEK V'g/\ ;QJd.RW34FNK 5#!RW344#\ VVVR7#)Nc30"TF qfKVxK R#5irsr#).0vs& r *FocalNetEncoder.forward..1sX$Fq$Fs )rrrr#re)rrr enumerater*rr)rXrrr/r0r1all_hidden_statesall_reshaped_hidden_statesrlrn hidden_sizereshaped_hidden_stater stage_moduler$r#rks&&&&&& rrqFocalNetEncoder.forwards#7BD+?RT" )6)<)< &J;$1$6$6z$bDT$bVa$b !$9$A$A!Q1$M ! !1 1  &*B B &(5OA(IM)!,M0=a0@ - -a 0  1" 57H7LM #(P-N-T-T* {)J(N(N)"3A"68I!8L!M)OZ)%)>(E(EaAq(Q%!%II!*.FF*%%.V.V-:-@-@* {(5(:(::(fHX(fZe(f%(=(E(EaAq(Q%!%55!*.FF*3 665X]$FX5 X5X]$FXX X$++#=  r")r@r+rr*)FFTrrvs@@rr'r's,$5 5 5 r"r'cxaa]tRtRtoRtRtRtR.t]P!4V3Rl4t V3Rlt Rt Vt V;t#) FocalNetPreTrainedModeli:focalnetr]Tr c <\SV`V4\V\4'd3VPe#\ P !VP4R#R#\V\4'dVPP'dm\ P!VPVPP4\ P!VPVPP4R#R#R#)zInitialize the weightsN)rI _init_weightsr}r>rQinitzeros_rr@r constant_rrr)rXmodulerZs&&rrF%FocalNetPreTrainedModel._init_weightsBs f% f0 1 1  , F--.-  . .{{)))v~~t{{/K/KLv~~t{{/K/KL*/r"c&<V^8dQh/S[;R&#rr@r)rrs"rr $FocalNetPreTrainedModel.__annotate__:s r"r#)r$r%r&r'base_model_prefixmain_input_namesupports_gradient_checkpointing_no_split_modulesrno_gradrFr)r*r+rurvs@@rrCrC:sB#$O&*#() ]]_ M Mr"rCc\aa]tRtRtoRV3RlltRt]RV3RlRll4tRtVt V;t #) FocalNetModeliOc <\SV`V4Wn\VP4Vn\ VP^VP ^, ,,4Vn\WR7Vn \WPP4Vn \P!VPVP R7VnV'd\P$!^4MRVnVP)4R#)z add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer use_mask_token (`bool`, *optional*, defaults to `False`): Whether to use a mask token for masked image modeling. )rYrGN)rIrJr@rrrrrD num_featuresr>rjr'rNencoderrrRrSrAdaptiveAvgPool1dpooler post_init)rXr@add_pooling_layerrYrZs&&&&rrJFocalNetModel.__init__Qs   fmm, 0 0119L3M MN,VS&v/I/IJ d&7&7V=R=RS1Bb**1-  r"c.VPP#rs)rjrLrs&rget_input_embeddings"FocalNetModel.get_input_embeddingsfs///r"c <V^8dQhRS[PR,RS[PR,RS[R,RS[R,RS[S[,/#rr]Nr^r/r1r_)rrr`r2rr.)rrs"rr FocalNetModel.__annotate__jsa/ / ''$./ ))D0/ #Tk / D[ /  $ $/ r"c "VeTMVPPpVeTMVPPpVf \R4hVP WR7wrgVP VVVVR7pV^,p VP V 4p Rp VPe9VPV P^^44p \P!V ^4p V'gW3VR,,p V #\V V VPVPR7#)z bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Nz You have to specify pixel_values)r^r/r1:ryNN)rr0rr)r@r/r1rrjrXrrZrrrr.rr) rXr]r^r/r1kwargsembedding_outputrencoder_outputssequence_output pooled_outputoutputs &&&&&, rrqFocalNetModel.forwardis%9$D $++JjJj &1% Note that we provide a script to pre-train this model on custom data in our [examples directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). cRaa]tRtRtoV3Rlt]RV3RlRll4tRtVtV;t #)FocalNetForMaskedImageModelingic<\SV`V4\VRRR7Vn\ VP 4Vn\VP^VP ^, ,,4p\P!\P!W!P^,VP,^R7\P!VP44VnVP!4R#)FT)r\rY) in_channels out_channelsrzN)rIrJrUrDrrrrrDrrrencoder_striderC PixelShuffledecoderr[)rXr@rWrZs&& rrJ'FocalNetForMaskedImageModeling.__init__s  %fVZ[ fmm,6++aDOOa4G.HHI }} II(7L7La7ORXReRe7est  OOF11 2    r"c <V^8dQhRS[PR,RS[PR,RS[R,RS[R,RS[S[,/#rb)rrr`r2rr3)rrs"rr +FocalNetForMaskedImageModeling.__annotate__shO O ''$.O ))D0O #Tk O D[ O  2 2O r"c VeTMVPPpVPVVVVR7pV^,pVP^^4pVPwrp \ P !V R,4;rVPWW4pVPV4p RpVEeVPPVPP,pVPR W4pVPVPP^4PVPP^4P^4P4p\PP!WRR7pVV,P#4VP#4R,, VPP$, pV'g"V 3VR,,pVe V3V,#T#\'VV VP(VP*R7#) a bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Examples: ```python >>> from transformers import AutoImageProcessor, FocalNetConfig, FocalNetForMaskedImageModeling >>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/focalnet-base-simmim-window6-192") >>> config = FocalNetConfig() >>> model = FocalNetForMaskedImageModeling(config) >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.logits >>> list(reconstructed_pixel_values.shape) [1, 3, 192, 192] ```N)r^r/r1g?none) reductiongh㈵>rNN)r5r6rrre)r@r1rDrrmathrr!rurArBrepeat_interleaverhrrrl1_lossrrCr3rr)rXr]r^r/r1rfoutputsrirlrCsequence_lengthrrreconstructed_pixel_valuesmasked_im_lossrfrpreconstruction_lossrks&&&&&, rrq&FocalNetForMaskedImageModeling.forwardsN&1% 1` a classification loss is computed (Cross-Entropy). Nrer|)r5r;rr)r@r1rDr loss_functionr9rr) rXr]rr/r1rfrrjr;r5rks &&&&&, rrq&FocalNetForImageClassification.forward%s&1%>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> processor = AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny-lrf") >>> model = AutoBackbone.from_pretrained("microsoft/focalnet-tiny-lrf") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```NTre) feature_mapsr attentionsr#) r@r1r/rDrr; stage_namesrrr ) rXr]r/r1rfrrridxstagerks &&&&, rrqFocalNetBackbone.forwardas<&1%rKrrrrr r'rCrUrorr__all__r#r"rrs^ ! &!H9.-99-2   H %   CK C  C  C+C  C$  C C  C(  CKC  C(%-%-PD-biiD-NDDN"))(%ryy%0BBIIBJ?.?DH biiH VMoMM(I +I I X  b %<b  b J 8 %<8  8 v B }&=B  B J r"