ڧg ddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z d d lmZmZmZd d lmZd d lmZmZgd ZGddejZGddejZGddejZGddeZeedejfddddeedededefdZ dS))partial)AnyCallableOptionalN)nn)Conv3dNormActivation)VideoClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_KINETICS400_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)S3D S3D_Weightss3dcReZdZdededededededejff fd ZxZS) TemporalSeparableConv in_planes out_planes kernel_sizestridepadding norm_layer.c tt||d||fd||fd||fd|t|||ddf|ddf|ddfd|dS)NrF)rrrbiasr)super__init__r)selfrrrrrr __class__s X/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/models/video/s3d.pyr"zTemporalSeparableConv.__init__s   [966*GW-%    !(!Q/1~ !Q%        ) __name__ __module__ __qualname__intrrModuler" __classcell__r$s@r%rrs         S")^,          r&rc`eZdZdedededededededed ejfffd Zd ZxZ S) SepInceptionBlock3Drb0_outb1_midb1_outb2_midb2_outb3_outr.c tt||dd||_t jt||dd|t ||ddd||_t jt||dd|t ||ddd||_t jt j dddt||dd||_ dS)Nrrrrr )rrrrr r r rrr) r!r"rbranch0r Sequentialrbranch1branch2 MaxPool3dbranch3) r#rr0r1r2r3r4r5rr$s r%r"zSepInceptionBlock3D.__init__7s +Iv1UVcmnnn } F!Xb c c c !&&aSTak l l l   } F!Xb c c c !&&aSTak l l l   } LYq! D D D F!Xb c c c   r&c||}||}||}||}t j||||fd}|S)Nr)r:r<r=r?torchcat)r#xx0x1x2x3outs r%forwardzSepInceptionBlock3D.forwardRs] \\!__ \\!__ \\!__ \\!__iRR(!,, r&) r'r(r)r*rrr+r"rIr,r-s@r%r/r/6s           S")^,      6r&r/c reZdZdZ d dededeedej j fd dffd 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) 皙?N num_classesdropoutr.returnc,tt||ttjdd}t jtddddd|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 j d%d &|_ t jt j|'t jd(|d d d)*|_dS)+NgMbP?)epsmomentumr @r )rr r )rr r )rrrr9rr7` r8)r r r )rrri0ipi ii@)rrri@i)r rTrT)rr)piT)rrr )r!r"r rr BatchNorm3dr;rr>rr/features AvgPool3davgpoolDropoutConv3d classifier)r#rMrNrr$s r%r"z S3D.__init__hs- D!!!   UUKKKJ !!RAq* = = LYy) T T T %     ""c1aJ ? ? LYy) T T T RS"b"j I I S#sBB K K LYy) T T T S"c2r2z J J S#sBB K K S#sBB K K S#sBB K K S#sBS* M M LYy) T T T S#sBS* M M S#sBS* M M-   0| !DDD - J ! ! ! IdKQqt L L L  r&c||}||}||}tj|d}|S)N)r r )dim)rdrfrirAmean)r#rCs r%rIz S3D.forwardsL MM!   LLOO OOA   Jqi ( ( (r&)rKrLN)r'r(r)__doc__r*floatrrrArr+r"rIr,r-s@r%rr\s  ?C ( ( ( ( Xc58?&:;< (  ( ( ( ( ( ( Tr&rcdeZdZedeeddddedddd d d d id dd ZeZdS)rz4https://download.pytorch.org/models/s3d-d76dad2f.pth)r_r_)rZrZ) 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%rrsW B7  "   #!#1g\"##!!  #  N6GGGr&r pretrained)weightsT)rprogressrrkwargsrOc t|}|)t|dt|jdt di|}|*|||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: NrMrvT)r check_hashr)rverifyrlenrrload_state_dictget_state_dict)rrrmodels r%rrs0  ))GfmSl9S5T5TUUU MM&MME g44hSW4XXYYY Lr&)! functoolsrtypingrrrrArtorchvision.ops.miscrtransforms._presetsr utilsr _apir rr_metar_utilsrr__all__r;rr+r/rrrboolrrr&r%rs********** 555555666666((((((7777777777++++++CCCCCCCC        BM   @#####")###L;;;;;");;;|+>, 0J!KLLL,04   H[) D SV [^   ML   r&