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D! j-24\]$$$ !2 &#6 $  (}}HM-67H-I))###A3'($1%;%H%H$:$F$F5<<#9#D#D)?)P)P  !7 C C .J   RYY=ghi )1NYYJWaDN!.14 << Aq*(EFa            %4?M  &#%99Z#EL )+:)SL &"$'')L #%99-@#NL ]]<0 dnnbii 0 0^^//$..2L2LQ2OORVR`R`RlRlmnRooF GG ! !$.."7"7TYYq6z=R ! S~~"".t~~223 ^^ % % 1jAYAY[][d[d6e6e GGOO((//ctyyt~~OgOgOtOtIt?u  ~~'',,8t~~77<<= 4::| , ,DJJ">DIIaRXjDY ! Z GGNN4::0055 6 djjooryy 1 1 GGNN4::??11 2 GGNN4::??// 0 2r5rreturncURup#pEURn[R"X@R:HSURSUS35 [R"XPR:HSURSUS35 XF-nXV-nUR U5nUR X RXx-5nURSSS5nU$)NzWrong image height! Expected z but got !zWrong image width! Expected rr r) rrrrrrreshaperqpermute) rMrnchwpn_hn_ws r6_process_input VisionTransformer._process_input sWW a OO a??*.KDOOK\\efgehhi,jk a??*.J4??J[[defdggh,ijff NN1  IIa#) 4 IIaA r5cURU5nURSnURRUSS5n[R "X1/SS9nUR U5nUSS2S4nURU5nU$)Nrrr)rrrexpandrcatrr)rMrrbatch_class_tokens r6rVisionTransformer.forward!s~    " GGAJ!,,33Ar2> II(,! 4 LLO adG JJqMr5)rrrrr<rrrqrr;r'rrrr)r*r+r,r-rfrr0rr.rhrrrr2listr"rCrrrr4rirjs@r6rrsE#&-15`_'s training recipe.  num_paramsrr_metrics_ops _file_size_docsurl transformsrz>https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pthr resize_size interpolationi^-)rrg~jtSU@giX@gˡEK@g|?5^t@ These weights are learnt via transfer learning by end-to-end fine-tuning the original `SWAG `_ weights on ImageNet-1K data. rrrrrrzAhttps://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth+https://github.com/pytorch/vision/pull/5793gbX9xT@gQ X@ These weights are composed of the original frozen `SWAG `_ trunk weights and a linear classifier learnt on top of them trained on ImageNet-1K data. rrrrrrrr)Nr*r+r,r-rrr r IMAGENET1K_V1r BICUBIC_COMMON_SWAG_METAIMAGENET1K_SWAG_E2E_V1IMAGENET1K_SWAG_LINEAR_V1DEFAULTr4r)r5r6rr_s# G.#>  ""f##  ! M,% L +33    ""##  ! 4!( O +33    C""##  ! !6Gr5rc T\rSrSr\"S\"\SS90\ESSSSS S S .0S S SS.ES9r\r Sr g)riz9https://download.pytorch.org/models/vit_b_32-d86f8d99.pthrri1BrzNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_b_32rg|?5^R@gW@rgA`Т@gl u@rrrr)N r*r+r,r-rrr rrrr4r)r5r6rrs\ G.#>  ""f##  ! M,Gr5rc \rSrSr\"S\"\SSS90\ESSSS S S S .0S SSS.ES9r\"S\"\SS\ RS90\ ESSS SSS .0SSSS.ES9r \"S\"\SS\ RS90\ ESSSS SS S .0S SS!S".ES9r \rS#rg$)%riz9https://download.pytorch.org/models/vit_l_16-852ce7e3.pthr)rri#rzNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_l_16rg|?5^S@gFԨW@rgףp= N@g;O$@a These weights were trained from scratch by using a modified version of TorchVision's `new training recipe `_. rrz>https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pthri0)r r gjtV@gT㥛ĠX@gƟv@gy&11@rrzAhttps://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pthrgMbXIU@g^I [X@rrr)Nrr)r5r6rrs% G.#3O  #"f##  " M.% L +33    #"##  " 4!( O +33    C#"##  " !6Gr5rc T\rSrSr\"S\"\SS90\ESSSSS S S .0S S SS.ES9r\r Sr g)riz9https://download.pytorch.org/models/vit_l_32-c7638314.pthrri[ErzNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_l_32rg|?5>S@gGzDW@rgK7.@gE@rrrr)Nr r)r5r6rrs\ G.#>  #"f#"  " M,Gr5rc \rSrSr\"S\"\SS\RS90\ ESSSSS S .0S S S S.ES9r \"S\"\SS\RS90\ ESSSSSSS .0SSSS.ES9r \ r Sr g)ri2z>https://download.pytorch.org/models/vit_h_14_swag-80465313.pthri%)rrrgS#V@g#~jX@rg~jŏ@gK7I@rrrzAhttps://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pthrri@%rgZd;OmU@gQnX@g= ףpd@gI k֢@rrr)N)r*r+r,r-rrr r rrrrrr4r)r5r6rr2s$ L +33    #"##  " 4!( O +33    C#"##  " !6%Gr5r pretrained)rT)rrc R[RU5n[SSSSSSUUS.UD6$)au Constructs a vit_b_16 architecture from `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. Args: weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ViT_B_16_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ViT_B_16_Weights :members:   rrrprqr;rrr))rverifyrrrrs r6rrkE(%%g.G      r5c R[RU5n[SSSSSSUUS.UD6$)au Constructs a vit_b_32 architecture from `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. Args: weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ViT_B_32_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.vision_transformer.VisionTransformer`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ViT_B_32_Weights :members: r#rrrrr))rrrrs r6rrrr5c R[RU5n[SSSSSSUUS.UD6$)au Constructs a vit_l_16 architecture from `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. Args: weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ViT_L_16_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ViT_L_16_Weights :members: rrr))rrrrs r6rrE(%%g.G      r5c R[RU5n[SSSSSSUUS.UD6$)au Constructs a vit_l_32 architecture from `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. Args: weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ViT_L_32_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.vision_transformer.VisionTransformer`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ViT_L_32_Weights :members: r#rrrrrr))rrrrs r6rrrr5)rNc R[RU5n[SSSSSSUUS.UD6$)au Constructs a vit_h_14 architecture from `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale `_. Args: weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ViT_H_14_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ViT_H_14_Weights :members: r#riirr))rrrrs r6r r rr5r model_statezOrderedDict[str, torch.Tensor]interpolation_mode reset_headscUSnURupgnUS:wa[SUR35eX-S-S-n X:wGa3US-nU S-n USS2SS2SS24n USS2SS2SS24n U RSSS5n [[R "U55n X-U:wa[SX-SU35eU R SXU 5n X-n [RRU U US S 9nUR SX5nURSSS5n[R"X/SS 9nXS'U(aC[5nUR5H#unnURS 5(aMUUU'M% UnU$) awThis function helps interpolate positional embeddings during checkpoint loading, especially when you want to apply a pre-trained model on images with different resolution. Args: image_size (int): Image size of the new model. patch_size (int): Patch size of the new model. model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model. interpolation_mode (str): The algorithm used for upsampling. Default: bicubic. reset_heads (bool): If true, not copying the state of heads. Default: False. Returns: OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model. zencoder.pos_embeddingrz%Unexpected position embedding shape: r NrzPseq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = z and seq_length = T)sizemode align_cornersrr)r ValueErrorrr.rrrr0 functional interpolaterrritems startswith)rrr#r$r%rrrrqnew_seq_lengthpos_embedding_tokenpos_embedding_img seq_length_1dnew_seq_length_1dnew_pos_embedding_imgnew_pos_embeddingmodel_state_copykvs r6interpolate_embeddingsr9s* 78M - 3 3A:Av@ATAT@UVWW .14q8N #a !+Arr1H5)!QR(3.55aA>DIIj12  (J 6bcpdAcCCUV`Uab  .55aTab&4!# 9 9 "# !:! !6 = =a \!6 = =aA F!II':&RXYZ/@+, AL #))+1||G,,*+$Q',+K r5)bicubicF)9r collectionsr functoolsrtypingrrrrrtorch.nnr0ops.miscr r transforms._presetsr r utilsr_apirrr_metar_utilsrr__all__r"r8r2rlrrr.boolrrdictstrr/rrrrrrrrrrrr r9r)r5r6rIsh #66 0H'66'B 9Z9+ s+ \!299!H#9bii#9LQ Qh    k " B&  d38n8KL{L^{4M{M`{46%{6%r,0@0N0N!OP6:T"23d]`evQ@,0@0N0N!OP6:T"23d]`evQ@,0@0N0N!OP6:T"23d]`evQ@,0@0N0N!OP6:T"23d]`evQ@!566:T"23d]`ev7H( KKK2K K  K & Kr5