U (d? @sxddlmZddlmZmZmZmZmZmZm Z m Z ddl m Z ddl mZddlmZddlmZdd lmZmZdd lmZdd lmZmZd d dddddgZGddde jZGddde jZGddde jZ Gddde j!Z"Gddde j!Z#Gddde jZ$Gdd d e jZ%Gd!d d e j!Z&ee e"e#feee ee efee'ed"e j!feee(e e&d#d$d%Z)d&ed'd(d)Z*Gd*d d eZ+Gd+ddeZ,Gd,ddeZ-ed-e+j.fd.dd/d0ee+e(e e&d1d2dZ/ed-e,j.fd.dd/d0ee,e(e e&d1d3dZ0ed-e-j.fd.dd/d0ee-e(e e&d1d4dZ1dd5lm2Z2e2e+j.j3e,j.j3e-j.j3d6Z4dS)7)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 FZ in_channelsZ out_channels kernel_sizer"r#biassuper__init__selfrr r!r"r# __class__C/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/video/resnet.pyr*szConv3DSimple.__init__r"r$cCs |||fSNr/r"r/r/r0get_downsample_stride'sz"Conv3DSimple.get_downsample_stride)Nrr __name__ __module__ __qualname__intrr* staticmethodrr4 __classcell__r/r/r-r0rs rcsLeZdZd eeeeeddfdd ZeeeeeefdddZZS) Conv2Plus1DrNrc s`ttj||dd||fd||fddt|tjddtj||d|ddf|ddfdddS) Nrr r rrFr&r"r#r'TZinplacer rrr)r*nnConv3d BatchNorm3dReLUr+r-r/r0r*-s( zConv2Plus1D.__init__r1cCs |||fSr2r/r3r/r/r0r4>sz!Conv2Plus1D.get_downsample_stride)rr) r6r7r8r9r*r:rr4r;r/r/r-r0r<,sr<csPeZdZd eeeeeeddfdd ZeeeeeefdddZZ S) Conv3DNoTemporalNrrcs(tj||dd||fd||fdddS)Nr=rrFr%r(r+r-r/r0r*DszConv3DNoTemporal.__init__r1cCs d||fSNrr/r3r/r/r0r4Qsz&Conv3DNoTemporal.get_downsample_stride)Nrrr5r/r/r-r0rFCs rFcsReZdZdZd eeedejfeeejddfdd Z e e ddd Z Z S) BasicBlockrN.inplanesplanes conv_builderr" downsampler$cs||ddd|ddd|}tt|||||t|tjdd|_t||||t||_tjdd|_||_ ||_ dS)Nr Tr?) r)r*rB SequentialrDrEconv1conv2relurMr"r,rJrKrLr"rMr!r-r/r0r*Zs(   zBasicBlock.__init__xr$cCsB|}||}||}|jdk r,||}||7}||}|Sr2)rOrPrMrQr,rTZresidualoutr/r/r0forwardms     zBasicBlock.forward)rN r6r7r8 expansionr9rrBModulerr*r rWr;r/r/r-r0rHVs rHcsReZdZdZd eeedejfeeejddfdd Z e e dd d Z Z S) BottleneckrN.rIcst||ddd|ddd|}ttj||dddt|tjdd|_t|||||t|tjdd|_ttj|||j dddt||j |_ tjdd|_ ||_ ||_ dS)Nr rF)r&r'Tr?)r)r*rBrNrCrDrErOrPrYconv3rQrMr"rRr-r/r0r*~s& (   zBottleneck.__init__rScCsL|}||}||}||}|jdk r6||}||7}||}|Sr2)rOrPr]rMrQrUr/r/r0rWs      zBottleneck.forward)rNrXr/r/r-r0r[{s r[cs&eZdZdZddfdd ZZS) BasicStemz$The default conv-batchnorm-relu stemNr$c s4ttjdddddddtdtjdd dS) Nr @)r rarrrr=Fr>Tr?rAr,r-r/r0r*s  zBasicStem.__init__r6r7r8__doc__r*r;r/r/r-r0r^sr^cs&eZdZdZddfdd ZZS) R2Plus1dStemzRR(2+1)D stem is different than the default one as it uses separated 3D convolutionNr_c sZttjdddddddtdtjdd tjdd d d d ddtd tjdd dS)Nr -)rrararb)rr r Fr>Tr?r`r@rrr)rrrrArcr-r/r0r*s  zR2Plus1dStem.__init__rdr/r/r-r0rfsrfc seZdZdeeeefeeeee e fe e e dejfe eddfdd Zeedd d Zdeeeefeeee e fe e e ejd d dZZS)rF.N)block conv_makerslayersstem num_classeszero_init_residualr$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 |jdk rbt 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. 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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\r)rverifyrrHrr^rrrr/r/r0rus  cKs4t|}tttgtgdddddgt||f|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)rrrrHrrFr^rr/r/r0rs  cKs.t|}tttgdddddgt||f|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)rrrrHr<rfrr/r/r0rs  ) _ModelURLs)rrr)5 functoolsrtypingrrrrrrr r Ztorch.nnrBZtorchr Ztransforms._presetsr utilsrZ_apirrZ_metar_utilsrr__all__rCrrNr<rFrZrHr[r^rfrr9rrrrrrrrrrrrZ model_urlsr/r/r/r0sl (      %1 _  "#"#"$