EiSSKrSSKJr SSKJr SSKJr SSKJrJ r J r SSK r SSK r SSK Jr SSKJrJr SSKJr SS KJr S S KJrJrJr S S KJr S S KJrJr /SQr\"SS55r S\\!S\!4Sjr"S\ RFS\!S\!S\$\ RF\!44Sjr%S\ RFS\!S\!S\!S\ RF4 Sjr&\ RNRQS5 \ RNRQS5 "SS\RR5r*S\ RFS\!S\ RF4S jr+S!\ RFS"\ RFS#\$\!\!\!4S$\$\!\!\!4S%\ RFS&\ RFS'\ RFS\ RF4S(jr,S\ RFS)\ RFS*\-4S+jr.\ RNRQS,5 \ RNRQS-5 "S.S/\RR5r/"S0S1\RR5r0"S2S3\RR5r1"S4S5\RR5r2S6\3\ S7\4S8\ \S9\-S:\S\24 S;jr5"S<S=\5r6"S>S?\5r7\"5\"S@\6Rp4SA9SSBSC.S8\ \6S9\-S:\S\24SDjj55r9\"5\"S@\7Rp4SA9SSBSC.S8\ \7S9\-S:\S\24SEjj55r:g)FN)Sequence) dataclass)partial)AnyCallableOptional)MLPStochasticDepth)VideoClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_KINETICS400_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)MViTMViT_V1_B_Weights mvit_v1_bMViT_V2_S_Weights mvit_v2_sct\rSrSr%\\S'\\S'\\S'\\\S'\\\S'\\\S'\\\S'S rg ) MSBlockConfig num_headsinput_channelsoutput_channelskernel_q kernel_kvstride_q stride_kvN)__name__ __module__ __qualname____firstlineno__int__annotations__list__static_attributes__r$Z/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torchvision/models/video/mvit.pyrrs;N3iCy3iCyr-rsreturnc$SnUHnX-nM U$Nr$)r/productvs r._prodr6'sG   Nr-x target_dim expand_dimcUR5nX1S- :XaURU5nX4$X1:wa[SUR35eX4$)Nr3zUnsupported input dimension )dim unsqueeze ValueErrorshaper7r8r9 tensor_dims r. _unsqueezerA.sVJ!^# KK # =  !7yABB =r-r@c8X1S- :XaURU5nU$r2)squeezer?s r._squeezerD7s!^# IIj ! Hr-rArDc ^\rSrSrS S\R S\\R S\\R S\SS4 U4SjjjrS \ RS \ \ \ \ 4S\ \ R\ \ \ \ 444S jr S rU=r$)PoolANpoolnorm activationnorm_before_poolr0c>[TU]5 Xl/nUbURU5 UbURU5 U(a[R "U6OSUlX@lg)N)super__init__rHappendnn Sequentialnorm_actrK)selfrHrIrJrKlayers __class__s r.rN Pool.__init__BsX     MM$   ! MM* %28 v.d 0r-r7thwc~[USS5up[R"USSS9upAURSS5nURSSupVnUR XV-U4U-5R 5nUR(aURbURU5nURU5nURSSupn UR XVUS5RSS5n[R"XA4SS9nUR(dURbURU5n[USSU5nXX44$) Nr3)r3r)indicesr;r r;) rAtorch tensor_split transposer>reshape contiguousrKrRrHcatrD) rSr7rWr@ class_tokenBNCTHWs r.forward Pool.forwardSs!"1a+ ++AtC KK1 ''"1+a IIquaj3& ' 2 2 4  T]]%> a A IIaL''!"+a IIaAr " , ,Q 2 II{&A .$$)B a A Q1j )a)|r-)rRrKrH)NF)r%r&r'r(rPModulerboolrNr]Tensortupler)rjr, __classcell__rUs@r.rFrFAs +/!& 1ii1ryy!1RYY' 1  1  11"E#sC-,@U5<rP functional interpolatepermuter<rC)rrrss r. _interpolater{msnqQ !!   a # - -a 0 "  Ar-attnqq_thwk_thw rel_pos_h rel_pos_w rel_pos_tc Uupxn Uupn [S[X5-S- 5n [S[X5-S- 5n[S[Xz5-S- 5n[X- S5n[X- S5n[R"U5SS2S4U-[R"U 5SSS24SU - -U-- n[X- S5n[X- S5n[R"U 5SS2S4U-[R"U 5SSS24SU - -U-- n[X- S5n[Xz- S5n[R"U5SS2S4U-[R"U 5SSS24SU - -U-- n[ XM5n[ X^5n[ Xo5nUUR 5nUUR 5nUUR 5nUR unnnnUSS2SS2SS24RUUXxU U5n [R"SU U5n![R"SU U5n"U RSSSSSS 5RUUU-U-U -U5n [R"U URSS55RSS5n#U#RUUXXz5RSSSSSS 5n#U!SS2SS2SS2SS2SS2SSS2S4U"SS2SS2SS2SS2SS2SSSS24-U#SS2SS2SS2SS2SS2SS2SS4-RUUXx-U -X-U -5n$USS2SS2SS2SS24==U$- ss'U$) Nrr3?zbythwc,hkc->bythwkzbythwc,wkc->bythwkrr rY) r)maxr]aranger{longr>r`einsumrzmatmulr_view)%r|r}r~rrrrq_tq_hq_wk_tk_hk_wdhdwdt q_h_ratio k_h_ratiodist_h q_w_ratio k_w_ratiodist_w q_t_ratio k_t_ratiodist_tRhRwRtrdn_head_r;r_qrel_h_qrel_w_qrel_q_trel_poss% r. _add_rel_posr|sSMCcMCc QS  " #B QS  " #B QS  " #BCIs#ICIs#I \\# q$w ') 3u||C7Hq7QUX[^U^7_cl6l lFCIs#ICIs#I \\# q$w ') 3u||C7Hq7QUX[^U^7_cl6l lFCIs#ICIs#I \\# q$w ') 3u||C7Hq7QUX[^U^7_cl6l lFY+IY+IY+I 6;;= !B 6;;= !B 6;;= !BAvq# Aq!"H+  a3 [TU]5 X lX0lX@lX4-UlS[ R"UR 5- UlXl Xl [R"USU-5Ul [R"X35/nU S:a$UR[R"U SS95 [R "U6UlSUl['U5S:d['U5S:auUVs/sHn[)US-5PM nn[+[R,"UR UR UUUUR SS 9U "UR 55UlSUlSUl['U5S:d['U5S:aUVs/sHn[)US-5PM nn[+[R,"UR UR UUUUR SS 9U "UR 55Ul[+[R,"UR UR UUUUR SS 9U "UR 55UlSUlSUlSUlU (Ga[9USS5n[;U5S :aUUS-OUn[;U5S :aUUS-OUnS[9UU5-S- nSUS -S- n[R<"[>R@"UUR 55Ul[R<"[>R@"UUR 55Ul[R<"[>R@"UUR 55Ul[RBREUR2S S 9 [RBREUR4S S 9 [RBREUR6S S 9 ggs snfs snf) Nrr rTinplacer3rF)stridepaddinggroupsbiasr{Gz?std)#rMrNrrrhead_dimmathsqrtscalerrrrPLinearqkvrODropoutrQprojectpool_qr6r)rFConv3dpool_kpool_vrrrrlen Parameterr]zerosinit trunc_normal_)rSrrrrr r!r"r#rrrrrrTr} padding_qkv padding_kvrvq_sizekv_size spatial_dim temporal_dimrUs r.rNMultiscaleAttention.__init__s@ "$""/ DIIdmm44 *'>$99YJ7#%99Z#D"E S= MM"**Wd; <}}f- +/ ?Q %/A"5.67hQ!VhI7 MMMM#%==4==) DK,0 +/  a 5#3a#71:;2#bAg,J; MMMM$&==4==) DK MMMM$&==4==) DK261515 z!"~&D,/MA,=TXa[(4F.1)nq.@dil*dGc&'22Q6Kz!},q0L\\%++k4==*QRDN\\%++k4==*QRDN\\%++lDMM*RSDN GG ! !$..d ! ; GG ! !$..d ! ; GG ! !$..d ! ; ]8"rr`rrr_unbindrrrr]rrrrrrsoftmaxrrrrr) rSr7rWrdrerfr}kr5rr|s r.rjMultiscaleAttention.forward!s''a((1+%%aAt~~t}}MWWXY[\]ddijdka ;; "{{1*HAuE ;; " A#A&A ;; "[[(FA||DKK!OQ[[A->? >> %$..*DIcD|||# LL !    ! < < = KK1  % %aT__ = LLOv r-)rrrrrrrrrrrrrrr)r%r&r'r(rP LayerNormr+r)rmfloatrrlrNr]rnrorjr,rprqs@r.rrs!/1||Z<IZ<Z< Z<  Z< s) Z<9Z<s)Z<9Z<Z<"&Z<Z<Z<S"))^,Z< Z<Z[TU]5 X`lSUl[ UR 5S:arUR V s/sHoS:aU S-OU PM n n U V s/sHn [ U S-5PM n n [[R"XR U S9S5UlU(a URO URnU "UR5Ul U "U5Ul [UR[R5Ul[#UURUUR$UR&UR(UR UR*UUUUU S9 Ul[/USU-UR/[R0USS9Ul[5US5UlSUlURUR:wa1[R:"URUR5Ulggs sn fs sn f)Nr3r)rr) r r!r"r#rrrrrrY)activation_layerrrrow)rMrNr pool_skipr6r"r)rFrP MaxPool3drrnorm1norm2 isinstance BatchNorm1dneeds_transposalrrr r!r#r|r GELUmlpr stochastic_depthrr)rSrrrrrrrrrr/ kernel_skipr padding_skipattn_dimrUs r.rNMultiscaleBlock.__init__Es ..2   ":=,,G,QE1q5q0,KG1<=ACQKL=! [|TVZDN+:3&&s?Q?Q 2 23 ) *4::r~~ F'      MM\\mm\\mm''$;!    \3.. /WW  !00Eu M,0   !4!4 499S%7%79L9LMDL 5MH=s G= Hr7rWcUR(a1URURSS55RSS5OURU5nURX25upEURbUR (dUOUR U5nUR cUOUR X5SnX`RU5-nUR(a1URURSS55RSS5OURU5nURbUR (aUOUR U5nXRURU55-U4$)Nr3rr) rrr_r|rrrrrr) rSr7rWx_norm1x_attnthw_newx_skipx_norm2x_projs r.rjMultiscaleBlock.forwards#CGCXCX$**Q[[A./99!Q?^b^h^hij^k))G1%T-A-AAt||T[G\nn,$..2H2K **62 2CGCXCX$**Q[[A./99!Q?^b^h^hij^kll*d.B.B U\H]--dhhw.?@@'IIr-) r|rrrrrrrr)r%r&r'r(rPrr+r)rrmrrrlrNr]rnrorjr,rprqs@r.rrDs'*/1||8NI8N8N 8N "& 8N  8N8N8N %8NS"))^,8N 8N8Nt J JE#sC-,@ JU5<>[TU]5 X lX0l[R "[ R"U55UlSUl SUl SUl U(d[R "[ R"URSURS-U55Ul [R "[ R"URU55Ul [R "[ R"U55Ul gg)Nrr3) rMrNrrrPrr]rrc spatial_pos temporal_pos class_pos)rSrrrrrUs r.rNPositionalEncoding.__init__s (*<< J(?@374815!||EKK8I8I!8LtO`O`abOc8ceo,pqD  " U[[9K9KZ-X YD \\%++j*ABDNr-r7cURRURS5S5RS5n[R "X!4SS9nUR bURbURbUR Rup4[R"URUSS9nURUR RS5RURSS5RSU55 [R "URRS5U4SS9RS5nURU5 U$)Nrr[r3r\)rcexpandrvr<r]rbrrr r>repeat_interleaverrr`)rSr7rchw_sizer pos_embeddings r.rjPositionalEncoding.forwards&&--affQi<FFqI II{&A .    'D,=,=,IdnnNh"&"2"2"8"8 G!33D4E4EwTUVM   t//99!<CCDDVDVXZ\^_gghjlvw x!IIt~~'?'?'BM&RXYZddefgM FF= !r-)r rcrrrr) r%r&r'r(r)rormrNr]rnrjr,rprqs@r.rrs\ C3 CeCHo CVY Cjn Csw C  %,,  r-rc$<^\rSrSrSS\\\4S\S\\S\S\S\S \S \ S \ S \ S \S\ \ S\ R4S\ \ S\ R4S\\\\4S\\\\4S\\\\4SS4"U4SjjjrS\R S\R 4SjrSrU=r$)riNrr block_settingrrrrrattention_dropoutr num_classesblock.rpatch_embed_kernelpatch_embed_stridepatch_embed_paddingr0cP>[TU]5 [U5 [U5nUS:Xa [ S5eU c[ n U c[ [RSS9n [R"SUSRUUUS9Ul [U4U-URR5VVs/sH unnUU-PM nnn[USRUSUS 4USUS 9Ul[R "5Ul[%U5HunnU U-US - - nUR"R'U "UUUUUUU UU S 9 5 [UR(5S:dMS[UUR(5VVs/sH unnUU-PM nnnM U "US R*5Ul[R."[R0"USS9[R2"US R*U 55UlUR75GHn[9U[R25(a[R:R=UR>SS9 [9U[R25(a;UR@b,[R:RCUR@S5 MMM[9U[R5(atUR>b*[R:RCUR>S 5 UR@b-[R:RCUR@S5 GM7GM:[9U[5(dGMRURE5H!n[R:R=USS9 M# GM gs snnfs snnf)a$ MViT main class. Args: spatial_size (tuple of ints): The spacial size of the input as ``(H, W)``. temporal_size (int): The temporal size ``T`` of the input. block_setting (sequence of MSBlockConfig): The Network structure. residual_pool (bool): If True, use MViTv2 pooling residual connection. residual_with_cls_embed (bool): If True, the addition on the residual connection will include the class embedding. rel_pos_embed (bool): If True, use MViTv2's relative positional embeddings. proj_after_attn (bool): If True, apply the projection after the attention. dropout (float): Dropout rate. Default: 0.0. attention_dropout (float): Attention dropout rate. Default: 0.0. stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0. num_classes (int): The number of classes. block (callable, optional): Module specifying the layer which consists of the attention and mlp. norm_layer (callable, optional): Module specifying the normalization layer to use. patch_embed_kernel (tuple of ints): The kernel of the convolution that patchifies the input. patch_embed_stride (tuple of ints): The stride of the convolution that patchifies the input. patch_embed_padding (tuple of ints): The padding of the convolution that patchifies the input. rz+The configuration parameter can't be empty.Ngư>)epsr ) in_channels out_channels kernel_sizerrr3r)rrrrr) rrrrrrrrrr[Trrrr)#rMrNr rr=rrrPrrr conv_projziprr pos_encoding ModuleListblocks enumeraterOr"rrIrQrrheadmodulesrrrweightr constant_ parameters)rSrrrrrrrrrrrrrrrrtotal_stage_blocksrvrrstage_block_idrsd_probmweightsrUs r.rN MViT.__init__sR  D! /  "JK K =#E   48J&q)88*%'  :=m=MP\=\^b^l^l^s^s9tu9tvdfn9t u/$Q'66$Q-A7$Q-'  mmo #,]#; NC+n<@RUX@XYG KK  )"/,C"/$3-*1)  3<< 1$ADZQTQ]Q]A^_A^vdfnA^ _ '$<(}R0@@A MM JJw - IImB'77 E  A!RYY''%%ahhD%9a++0BGG%%affc21C+Ar||,,88'GG%%ahh466%GG%%affc2&A122 ||~GGG))'t)< . Qv>`s ,N;N"r7c[USS5SnURU5nURS5RSS5nUR U5nURR 4URR -nURH nU"X5upM URU5nUSS2S4nURU5nU$)Nrrrr3) rArflattenr_r rrr"rIr$)rSr7rWrs r.rj MViT.forward"s q!Q  " NN1  IIaL " "1a (   a   ..043D3D3Q3QQ[[E1]FAs! IIaL adG IIaLr-)r"rr$rIr ) g?rriNN)r r2)rrYrY)r3r r )r%r&r'r(ror)rrrmrrrrPrlrNr]rnrjr,rprqs@r.rrsX#&'*489=3<3<4=#v=CHov=v= . v=  v= "& v=v=v=v=!v= %v=v=bii01v=Xc299n56v="#sC-0v= "#sC-0!v="#3S=1#v=$ %v=v=p%,,r-rrrr-progresskwargsc >Ub[US[URS55 URSSURSS:Xde[USURS5 [USURS5 URSS 5nURSS 5n[ SUUUURS S 5URS S5URSS 5URSS 5US.UD6nUbUR UR USS95 U$)Nr categoriesmin_sizerr3rrmin_temporal_sizer:rFrTrr)rrrrrrrr)r3 check_hashr$)rrmetapoprload_state_dictget_state_dict)rrr-r3r4rrmodels r._mvitrB9s$fmSl9S5TU||J'*gll:.Fq.IIIIfngll:6NOfow||DW7XY::nj9LJJ3M  !##jj%8 & +Dd Kjj%8 #4e<3   E g44hSW4XY Lr-cV\rSrSr\"S\"\SSSSS9SS\S S S S S SS.0SSS. S9r\r Sr g)riZz:https://download.pytorch.org/models/mvit_v1_b-dbeb1030.pthr9?rGrG?rIrI crop_size resize_sizemeanrr;zShttps://github.com/facebookresearch/pytorchvideo/blob/main/docs/source/model_zoo.mdThe weights were ported from the paper. The accuracies are estimated on video-level with parameters `frame_rate=7.5`, `clips_per_video=5`, and `clip_len=16`ip. Kinetics-400gJ +S@gh|?eW@zacc@1zacc@5guVQ@g rxa@ r7r8r6recipe_docs num_params_metrics_ops _file_sizeurl transformsr=r$N r%r&r'r(rrr rKINETICS400_V1DEFAULTr,r$r-r.rrZsf H  #%  #!#1k[###! !# N:Gr-rcV\rSrSr\"S\"\SSSSS9SS\S S S S S SS.0SSS. S9r\r Sr g)ri{z:https://download.pytorch.org/models/mvit_v2_s-ae3be167.pthr9rDrFrHrJr;zChttps://github.com/facebookresearch/SlowFast/blob/main/MODEL_ZOO.mdrNirOg r0T@g(\W@rPguVP@g?5^I|`@rQrXr$Nr[r$r-r.rr{sf H  #%  #!#1[[###! !# N:Gr-r pretrained)r-T)r-r3c |[RU5n/SQ/SQ/SQ//SQ//SQ///////////SQ///SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ///SQ//SQ///////////SQ///SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/S .n/n[[US 55HKnUR [ US UUS UUS UUS UUSUUSUUSUS 95 MM [ SSSUSSURSS5UUS.UD6$)aw Constructs a base MViTV1 architecture from `Multiscale Vision Transformers `__. .. betastatus:: video module Args: weights (:class:`~torchvision.models.video.MViT_V1_B_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.MViT_V1_B_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.video.MViT`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.MViT_V1_B_Weights :members: r3rrrYrYrYrYrYrYrYrYrYrYrYrb`rerfrfrfrfrfrfrfrfrfrfrg)rererfrfrfrfrfrfrfrfrfrfrfrgrgrgr r r r3rrr3rbrbr3rYrYr3r3r3rrrr r!r"r#rrrr r!r"r#r9r;Fr皙?)rrrrrrr-r3r$)rverifyrangerrOrrBr>r-r3r4configris r.rrs2 &&w/GFikB 2r2r2r2rSUWY[dfhi                ! $B 2r2r2r2rSUWY[dfhi                ! 1*FXM 3vk*+ ,  -a0%&67: &'8 9! < +A. -a0 +A. -a0  -  # %$jj)@#F    r-c [RU5n/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ//SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ//SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ//SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/SQ/S .n/n[[US 55HKnUR [ US UUS UUS UUS UUSUUSUUSUS 95 MM [ SSSUSSSSURSS5UUS. UD6$)aConstructs a small MViTV2 architecture from `Multiscale Vision Transformers `__ and `MViTv2: Improved Multiscale Vision Transformers for Classification and Detection `__. .. betastatus:: video module Args: weights (:class:`~torchvision.models.video.MViT_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.MViT_V2_S_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.video.MViT`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.MViT_V2_S_Weights :members: ra)rdrdrererfrfrfrfrfrfrfrfrfrfrfrgrcrhrlrirjrkrmrrrr r!r"r#r9r;TFrrn) rrrrrrrrr-r3r$)rrorprrOrrBr>rqs r.rrs4 &&w/GFhj                ! &                ! &                ! &                ! uLF\M 3vk*+ ,  -a0%&67: &'8 9! < +A. -a0 +A. -a0  -  # %$jj)@#F    r-);rcollections.abcr dataclassesr functoolsrtypingrrrr]torch.fxtorch.nnrPopsr r transforms._presetsr utilsr _apirrr_metar_utilsrr__all__rr)r6rnrorArDfxwraprlrFr{rrmrrrrrr+rrBrrr\rrr$r-r.rs[ $!** '6(77+C   Xc]s%,,CSU5<