a PSic? @snddlmZddlmZmZmZmZmZmZm Z m Z ddl m Z ddl mZddlmZddlmZdd lmZmZdd lmZdd lmZmZgd ZGd dde jZGddde jZGddde jZ Gddde j!Z"Gddde j!Z#Gddde jZ$Gddde jZ%Gddde j!Z&ee e"e#feee ee efee'ede j!feee(e e&ddd Z)d!ed"d#d$Z*Gd%d&d&eZ+Gd'd(d(eZ,Gd)d*d*eZ-ed+e+j.fd,dd-d.ee+e(e e&d/d0d1Z/ed+e,j.fd,dd-d.ee,e(e e&d/d2d3Z0ed+e-j.fd,dd-d.ee-e(e e&d/d4d5Z1dd6lm2Z2e2e+j.j3e,j.j3e-j.j3d7Z4dS)8)partial)TupleOptionalCallableListSequenceTypeAnyUnionN)Tensor)VideoClassification)_log_api_usage_once) WeightsEnumWeights)_KINETICS400_CATEGORIES)handle_legacy_interface_ovewrite_named_param) VideoResNetR3D_18_WeightsMC3_18_WeightsR2Plus1D_18_Weightsr3d_18mc3_18 r2plus1d_18csPeZdZd eeeeeeddfdd ZeeeeeefdddZZ S) Conv3DSimpleN in_planes out_planes midplanesstridepaddingreturncstj||d||dddS)N)r r r F in_channels out_channels kernel_sizer"r#biassuper__init__selfrr r!r"r# __class__[/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/video/resnet.pyr,szConv3DSimple.__init__r"r$cCs |||fSNr1r"r1r1r2get_downsample_stride'sz"Conv3DSimple.get_downsample_stride)Nrr __name__ __module__ __qualname__intrr, staticmethodrr6 __classcell__r1r1r/r2rs rcsLeZdZd eeeeeddfdd ZeeeeeefdddZZS) Conv2Plus1DrNrc s`ttj||dd||fd||fddt|tjddtj||d|ddf|ddfdddS) Nrr r rrFr(r"r#r)Tinplacer rrr+r,nnConv3d BatchNorm3dReLUr-r/r1r2r,-s zConv2Plus1D.__init__r3cCs |||fSr4r1r5r1r1r2r6>sz!Conv2Plus1D.get_downsample_stride)rr) r8r9r:r;r,r<rr6r=r1r1r/r2r>,sr>csPeZdZd eeeeeeddfdd ZeeeeeefdddZZ S) Conv3DNoTemporalNrrcs(tj||dd||fd||fdddS)Nr?rrFr%r*r-r/r1r2r,DszConv3DNoTemporal.__init__r3cCs d||fSNrr1r5r1r1r2r6Qsz&Conv3DNoTemporal.get_downsample_stride)Nrrr7r1r1r/r2rICs rIcsReZdZdZd eeedejfeeejddfdd Z e e ddd Z Z S) BasicBlockrN.inplanesplanes conv_builderr" downsampler$cs||ddd|ddd|}tt|||||t|tjdd|_t||||t||_tjdd|_||_ ||_ dS)Nr TrA) r+r,rE SequentialrGrHconv1conv2relurPr"r.rMrNrOr"rPr!r/r1r2r,Zs( zBasicBlock.__init__xr$cCsB|}||}||}|jdur,||}||7}||}|Sr4)rRrSrPrTr.rWresidualoutr1r1r2forwardms     zBasicBlock.forward)rN r8r9r: expansionr;rrEModulerr,r r[r=r1r1r/r2rKVs rKcsReZdZdZd eeedejfeeejddfdd Z e e dd d Z Z S) BottleneckrN.rLcst||ddd|ddd|}ttj||dddt|tjdd|_t|||||t|tjdd|_ttj|||j dddt||j |_ tjdd|_ ||_ ||_ dS)Nr rF)r(r)TrA)r+r,rErQrFrGrHrRrSr]conv3rTrPr"rUr/r1r2r,~s ("zBottleneck.__init__rVcCsL|}||}||}||}|jdur6||}||7}||}|Sr4)rRrSrarPrTrXr1r1r2r[s      zBottleneck.forward)rNr\r1r1r/r2r_{s r_cs&eZdZdZddfdd ZZS) BasicStemz$The default conv-batchnorm-relu stemNr$c s4ttjdddddddtdtjdd dS) Nr @)r rerrrr?Fr@TrArDr.r/r1r2r,s  zBasicStem.__init__r8r9r:__doc__r,r=r1r1r/r2rbsrbcs&eZdZdZddfdd ZZS) R2Plus1dStemzRR(2+1)D stem is different than the default one as it uses separated 3D convolutionNrcc sZttjdddddddtdtjdd tjdd d d d ddtd tjdd dS)Nr -)rrererf)rr r Fr@TrArdrCrrr)rrrrDrgr/r1r2r,s  zR2Plus1dStem.__init__rhr1r1r/r2rjsrjc seZdZdeeeefeeeee e fe e e dejfe eddfdd Zeedd d Zdeeeefeeee e fe e e ejd d dZZS)rF.N)block conv_makerslayersstem num_classeszero_init_residualr$cstt|d|_||_|j||dd|ddd|_|j||dd|ddd|_|j||dd|ddd|_|j||dd |ddd|_ t d |_ t d |j||_|D]}t|t jrt jj|jd d d |jdurbt j|jdqt|t jr4t j|jdt j|jdqt|t j rt j|jddt j|jdq|r|D]$}t|trrt j|jjdqrdS)a^Generic resnet video generator. Args: block (Type[Union[BasicBlock, Bottleneck]]): resnet building block conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator function for each layer layers (List[int]): number of blocks per layer stem (Callable[..., nn.Module]): module specifying the ResNet stem. num_classes (int, optional): Dimension of the final FC layer. Defaults to 400. zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False. rdrrr5rr irlfan_outrT)mode nonlinearityNg{Gz?)r+r,rrMrq _make_layerlayer1layer2layer3layer4rEAdaptiveAvgPool3davgpoolLinearr]fcmodules isinstancerFinitkaiming_normal_weightr) constant_rGnormal_r_bn3)r.rnrorprqrrrsmr/r1r2r,s2       zVideoResNet.__init__rVcCsT||}||}||}||}||}||}|d}||}|SrJ)rqrzr{r|r}rflattenr)r.rWr1r1r2r[s        zVideoResNet.forwardr)rnrOrNblocksr"r$c Csd}|dks|j||jkrV||}ttj|j||jd|ddt||j}g}|||j||||||j|_td|D]} |||j||qtj|S)NrF)r(r"r)) rMr]r6rErQrFrGappendrange) r.rnrOrNrr"rPZ ds_striderpir1r1r2ry s  zVideoResNet._make_layer)rmF)r)r8r9r:rr rKr_rrrIr>rr;rrEr^boolr,r r[rQryr=r1r1r/r2rs* 4r.)rnrorprqweightsprogresskwargsr$cKsR|durt|dt|jdt||||fi|}|durN||j|d|S)Nrr categories)r)rlenmetarload_state_dictget_state_dict)rnrorprqrrrmodelr1r1r2 _video_resnet#s r)rrzKhttps://github.com/pytorch/vision/tree/main/references/video_classificationzSThese weights reproduce closely the accuracy of the paper for 16-frame clip inputs.)min_sizerrecipe_docsc @s@eZdZedeedddieddddd id d ZeZd S) rz7https://download.pytorch.org/models/r3d_18-b3b3357e.pthprrt crop_size resize_sizeiP5 Kinetics-400g`J@gR@zacc@1zacc@5 num_params_metricsurl transformsrN r8r9r:rrr _COMMON_METAKINETICS400_V1DEFAULTr1r1r1r2r?s rc @s@eZdZedeedddieddddd id d ZeZd S) rz7https://download.pytorch.org/models/mc3_18-a90a0ba3.pthrrriPurg33333J@g(\S@rrrNrr1r1r1r2rQs rc @s@eZdZedeedddieddddd id d ZeZd S) rz`__. Args: weights (:class:`~torchvision.models.video.R3D_18_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.R3D_18_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.resnet.VideoResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.R3D_18_Weights :members: r`rrrr)rverifyrrKrrbrrrr1r1r2rus rcKs4t|}tttgtgdgdt||fi|S)aConstruct 18 layer Mixed Convolution network as in .. betastatus:: video module Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition `__. Args: weights (:class:`~torchvision.models.video.MC3_18_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.MC3_18_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.resnet.VideoResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.MC3_18_Weights :members: r r)rrrrKrrIrbrr1r1r2rs rcKs.t|}tttgdgdt||fi|S)aConstruct 18 layer deep R(2+1)D network as in .. betastatus:: video module Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition `__. Args: weights (:class:`~torchvision.models.video.R2Plus1D_18_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.video.R2Plus1D_18_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.resnet.VideoResNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.video.R2Plus1D_18_Weights :members: r`r)rrrrKr>rjrr1r1r2rs r) _ModelURLs)rrr)5 functoolsrtypingrrrrrrr r torch.nnrEtorchr Ztransforms._presetsr utilsr_apirr_metar_utilsrr__all__rFrrQr>rIr^rKr_rbrjrr;rrrrrrrrrrrr model_urlsr1r1r1r2s^ (      %1 _  "#"#"$