QЦiSSKrSSKJr SSKJr SSKJrJrJrJ r J r J r J r SSK r SSKr SSKJr SSKJrJr SSKJr SSKJr S S KJrJrJr S S KJr S S KJrJ r /S Qr!\"SS55r"S\ \#S\#4Sjr$S\ RJS\#S\#S\ \ RJ\#44Sjr&S\ RJS\#S\#S\#S\ RJ4 Sjr'\ RPRSS5 \ RPRSS5 "SS\RT5r+S\ RJS\#S\ RJ4Sjr,S \ RJS!\ RJS"\ \#\#\#4S#\ \#\#\#4S$\ RJS%\ RJS&\ RJS\ RJ4S'jr-S\ RJS(\ RJS)\.4S*jr/\ RPRSS+5 \ RPRSS,5 "S-S.\RT5r0"S/S0\RT5r1"S1S2\RT5r2"S3S4\RT5r3S5\ \"S6\4S7\ \S8\.S9\S\34 S:jr5"S;S<\5r6"S=S>\5r7\"5\ "S?\6Rp4S@9SSASB.S7\ \6S8\.S9\S\34SCjj55r9\"5\ "S?\7Rp4S@9SSASB.S7\ \7S8\.S9\S\34SDjj55r:g)EN) dataclass)partial)AnyCallableDictListOptionalSequenceTuple)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__r__static_attributes__r'\/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torchvision/models/video/mvit.pyrrs;N3iCy3iCyr/rsreturnc$SnUHnX-nM U$Nr')r1productvs r0_prodr8&sG   Nr/x target_dim expand_dimcUR5nX1S- :XaURU5nX4$X1:wa[SUR35eX4$)Nr5zUnsupported input dimension )dim unsqueeze ValueErrorshaper9r:r; tensor_dims r0 _unsqueezerC-sVJ!^# KK # =  !7yABB =r/rBc8X1S- :XaURU5nU$r4)squeezerAs r0_squeezerF6s!^# IIj ! 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[|TVZDN+:3&&s?Q?Q 2 23 ) *4::r~~ F'      MM\\mm\\mm''$;!    \3.. /WW  !00Eu M,0   !4!4 499S%7%79L9LMDL 5MH=s G= Hr9rYcUR(a1URURSS55RSS5OURU5nURX25upEURbUR (dUOUR U5nUR cUOUR X5SnX`RU5-nUR(a1URURSS55RSS5OURU5nURbUR (aUOUR U5nXRURU55-U4$)Nr5rr) rrrar}rrrrrr) rUr9rYx_norm1x_attnthw_newx_skipx_norm2x_projs r0rlMultiscaleBlock.forward~s#CGCXCX$**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+rRrrr,rrorrrnrPr_rpr rlr.rqrrs@r0rrCs'*/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)Nrr5) rOrPrrrRrr_rre spatial_pos temporal_pos class_pos)rUrrrrrWs r0rPPositionalEncoding.__init__s (*<< J(?@374815!||EKK8I8I!8LtO`O`abOc8ceo,pqD  " U[[9K9KZ-X YD \\%++j*ABDNr/r9cURRURS5S5RS5n[R "X!4SS9nUR bURbURbUR Rup4[R"URUSS9nURUR RS5RURSS5RSU55 [R "URRS5U4SS9RS5nURU5 U$)Nrr]r5r^)reexpandrwr>r_rdrr r r@repeat_interleaverrrb)rUr9rehw_sizer pos_embeddings r0rlPositionalEncoding.forwards&&--affQi<FFqI II{&A .    'D,=,=,IdnnNh"&"2"2"8"8 G!33D4E4EwTUVM   t//99!<CCDDVDVXZ\^_gghjlvw x!IIt~~'?'?'BM&RXYZddefgM FF= !r/)r rerrr r) r(r)r*r+r,r rorPr_rprlr.rqrrs@r0rrs\ C3 CeCHo CVY Cjn Csw C  %,,  r/rc$<^\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$)riNrr block_settingrrrrrattention_dropoutr num_classesblock.rpatch_embed_kernelpatch_embed_stridepatch_embed_paddingr2cP>[TU]5 [U5 [U5nUS:Xa [ S5eU c[ n U c[ [RSS9n [R"SUSRUUUS9Ul [U4U-URR5VVs/sH unnUU-PM nnn[USRUSUS 4USUS 9Ul[R "5Ul[%U5HunnU U-US - - nUR"R'U "UUUUUUU UU S 9 5 [UR(5S:dMS[UUR(5VVs/sH unnUU-PM nnnM U "US R*5Ul[R."[R0"USS9[R2"US R*U 55UlUR75GHn[9U[R25(a[R:R=UR>SS9 [9U[R25(a;UR@b,[R:RCUR@S5 MMM[9U[R5(atUR>b*[R:RCUR>S 5 UR@b-[R:RCUR@S5 GM7GM:[9U[5(dGMRURE5H!n[R:R=USS9 M# GM gs snnfs snnf)a$ 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_sizerrr5r)rrrrr) rrrrrrrrrr]Trrrr)#rOrPrrr?rrrRrrr! conv_projziprr pos_encoding ModuleListblocks enumeraterQr%r"rKrSrrheadmodulesrrrweightr constant_ parameters)rUrrrrrrrrrrrrrrrrtotal_stage_blocksrwrrstage_block_idrsd_probmweightsrWs r0rP 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^ _ '$<(}R0@@A MM JJw - IImB'77 E  A!RYY''%%ahhD%9a++0BGG%%affc21C+Ar||,,88'GG%%ahh466%GG%%affc2&A122 ||~GGG))'t)< . Qv>`s ,N;N"r9c[USS5SnURU5nURS5RSS5nUR U5nURR 4URR -nURH nU"X5upM URU5nUSS2S4nURU5nU$)Nrrrr5) rCrflattenrar!rrr#rKr%)rUr9rYrs r0rl MViT.forward!s q!Q  " NN1  IIaL " "1a (   a   ..043D3D3Q3QQ[[E1]FAs! IIaL adG IIaLr/)r#rr%rKr!) g?rriNN)r r3)rr[r[)r5r r )r(r)r*r+r r,r rrorr rrRrnrPr_rprlr.rqrrs@r0rrsX#&'*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$)Nr categoriesmin_sizerr5rrmin_temporal_sizer;rFrTrr)rrrrrrrr)r4 check_hashr')rrmetapoprload_state_dictget_state_dict)rrr.r4r5rrmodels r0_mvitrC8s$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)riYz:https://download.pytorch.org/models/mvit_v1_b-dbeb1030.pthr:?rHrH?rJrJ 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@ r8r9r7recipe_docs num_params_metrics_ops _file_sizeurl transformsr>r'N r(r)r*r+rrrrKINETICS400_V1DEFAULTr.r'r/r0rrYsf H  #%  #!#1k[###! !# N:Gr/rcV\rSrSr\"S\"\SSSSS9SS\S S S S S SS.0SSS. S9r\r Sr g)rizz:https://download.pytorch.org/models/mvit_v2_s-ae3be167.pthr:rErGrIrKr<zChttps://github.com/facebookresearch/SlowFast/blob/main/MODEL_ZOO.mdrOirPg r0T@g(\W@rQguVP@g?5^I|`@rRrYr'Nr\r'r/r0rrzsf H  #%  #!#1[[###! !# N:Gr/r pretrained)r.T)r.r4c |[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$)aw Constructs a base MViTV1 architecture from `Multiscale Vision Transformers `__. .. betastatus:: video module Args: weights (:class:`~torchvision.models.video.MViT_V1_B_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.MViT_V1_B_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.video.MViT`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.MViT_V1_B_Weights :members: r5rrr[r[r[r[r[r[r[r[r[r[r[rc`rfrgrgrgrgrgrgrgrgrgrgrh)rfrfrgrgrgrgrgrgrgrgrgrgrgrhrhrhr r r r5rrr5rcrcr5r[r[r5r5r5r r!r"r#r$r%r&r r!r"r#r$r%r&r:r<Fr皙?)rrrrrrr.r4r')rverifyrangerrQrrCr?r.r4r5configris r0rrs2 &&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$)aConstructs a small MViTV2 architecture from `Multiscale Vision Transformers `__ and `MViTv2: Improved Multiscale Vision Transformers for Classification and Detection `__. .. betastatus:: video module Args: weights (:class:`~torchvision.models.video.MViT_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.MViT_V2_S_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.video.MViT`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.MViT_V2_S_Weights :members: rb)rererfrfrgrgrgrgrgrgrgrgrgrgrgrhrdrirmrjrkrlrnr r!r"r#r$r%r&r:r<TFrro) rrrrrrrrr.r4r')rrprqrrQrrCr?rrs r0rrs4 &&w/GFhj                ! &                ! &                ! &                ! uLF\M 3vk*+ ,  -a0%&67: &'8 9! < +A. -a0 +A. -a0  -  # %$jj)@#F    r/);r dataclassesr functoolsrtypingrrrrr r r r_torch.fxtorch.nnrRopsr rtransforms._presetsrutilsr_apirrr_metar_utilsrr__all__rr,r8rprCrFfxwraprnrHr|rrorrrrrrrCrrr]rrr'r/r0rs[ !GGG '6(77+C   Xc]s%,,CSU5<