kh ddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z d d lmZmZmZd d lmZd d lmZmZgd ZGddej0ZGddej4ZGddej4ZGddeZeedej<fddddeedededefdZ y))partial)AnyCallableOptionalN)nn)Conv3dNormActivation)VideoClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_KINETICS400_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)S3D S3D_Weightss3dcVeZdZdededededededej ff fd ZxZS) TemporalSeparableConv in_planes out_planes kernel_sizestridepadding norm_layer.c t|t||d||fd||fd||fd|t|||ddf|ddf|ddfd|y)NrF)rrrbiasr)super__init__r)selfrrrrrr __class__s X/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/video/s3d.pyr"zTemporalSeparableConv.__init__sv   [966*GW-%  !(!Q/1~ !Q%  ) __name__ __module__ __qualname__intrrModuler" __classcell__r$s@r%rrsT         S"))^,  r&rcdeZdZdedededededededed ej fffd Zd ZxZ S) SepInceptionBlock3Drb0_outb1_midb1_outb2_midb2_outb3_outr.c t |t||dd||_t j t||dd|t ||ddd||_t j t||dd|t ||ddd||_t j t jdddt||dd||_ y)Nrrrrr )rrrrr r r rrr) r!r"rbranch0r Sequentialrbranch1branch2 MaxPool3dbranch3) r#rr0r1r2r3r4r5rr$s r%r"zSepInceptionBlock3D.__init__7s +Iv1UVcmn }} F!Xb c !&&aSTak l  }} F!Xb c !&&aSTak l  }} LLYq! D F!Xb c  r&c|j|}|j|}|j|}|j|}t j ||||fd}|S)Nr)r:r<r=r?torchcat)r#xx0x1x2x3outs r%forwardzSepInceptionBlock3D.forwardRsT \\!_ \\!_ \\!_ \\!_iiRR(!, r&) r'r(r)r*rrr+r"rIr,r-s@r%r/r/6sh           S"))^, 6r&r/c zeZdZdZ d dededeedejjfddffd Z d Z xZ S) raWS3D main class. Args: num_class (int): number of classes for the classification task. dropout (float): dropout probability. norm_layer (Optional[Callable]): Module specifying the normalization layer to use. Inputs: x (Tensor): batch of videos with dimensions (batch, channel, time, height, width) N num_classesdropoutr.returnct|t||ttj dd}t j tddddd|t jddd tddd d | tdd dd d |t jddd td dddddd|tdddd ddd|t jddd tdd ddddd|tddddddd|tddddddd|tddddddd|td ddd!ddd|t jddd" td#ddd!ddd|td#d$d d$ddd||_ t jd%d &|_ t j t j|'t jd(|d d d)*|_y)+NgMbP?)epsmomentumr @r )rr r )rr r )rrrr9rr7` r8)r r r )rrri0ipi ii@)rrri@i)r rRrR)rr)piT)rrr )r!r"r rr BatchNorm3dr;rr>rr/features AvgPool3davgpoolDropoutConv3d classifier)r#rKrLrr$s r%r"z S3D.__init__hs D!   UUKJ !!RAq* = LLYy) T %   ""c1aJ ? LLYy) T RS"b"j I S#sBB K LLYy) T S"c2r2z J S#sBB K S#sBB K S#sBB K S#sBS* M LLYy) T S#sBS* M S#sBS* M-  0|| !D -- JJ ! IIdKQqt L r&c|j|}|j|}|j|}tj|d}|S)N)r r )dim)rbrdrgrAmean)r#rCs r%rIz S3D.forwards@ MM!  LLO OOA  JJqi (r&)ig?N)r'r(r)__doc__r*floatrrrArr+r"rIr,r-s@r%rr\s[ ?C ( ( ( Xc588??&:;< (  ( Tr&rcVeZdZedeeddddedddd d d d id dd ZeZy)rz4https://download.pytorch.org/models/s3d-d76dad2f.pth)r]r])rXrX) crop_size resize_sizezOhttps://github.com/pytorch/vision/tree/main/references/video_classification#s3dzThe weights aim to approximate the accuracy of the paper. The accuracies are estimated on clip-level with parameters `frame_rate=15`, `clips_per_video=1`, and `clip_len=128`.i0~z Kinetics-400gd;OQ@g33333V@)zacc@1zacc@5gv1@gF?@) min_sizemin_temporal_size categoriesrecipe_docs num_params_metrics_ops _file_size)url transformsmetaN) r'r(r)rrr rKINETICS400_V1DEFAULTr&r%rrs` B  " #!#1g\"##!  # N6Gr&r pretrained)weightsT)rprogressrrkwargsrMc tj|}|#t|dt|jdt di|}|"|j |j|d|S)aConstruct Separable 3D CNN model. Reference: `Rethinking Spatiotemporal Feature Learning `__. .. betastatus:: video module Args: weights (:class:`~torchvision.models.video.S3D_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.S3D_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.video.S3D`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.S3D_Weights :members: rKrtT)r check_hashr)rverifyrlenr}rload_state_dictget_state_dict)rrrmodels r%rrsi0  )GfmSl9S5TU M&ME g44hSW4XY Lr&)! functoolsrtypingrrrrArtorchvision.ops.miscrtransforms._presetsr utilsr _apir rr_metar_utilsrr__all__r;rr+r/rrr~boolrrr&r%rs** 56(77+C  BMM @#"))#L;"));|+>, 0J0J!KL,04 H[) D SV [^ M r&