o V hA@sddlmZddlmZmZmZmZmZmZm Z m Z ddl m Z ddl mZddlmZddlmZdd lmZmZmZdd lmZdd lmZmZgd ZGd dde jZGddde jZ Gddde jZ!Gddde j"Z#Gddde j"Z$Gddde jZ%Gddde jZ&Gddde j"Z'de e e#e$fdee e ee!e fdee(d ed!e j"fd"eed#e)d$ed%e'fd&d'Z*d(ed)d*d+Z+Gd,d-d-eZ,Gd.d/d/eZ-Gd0d1d1eZ.eed2e,j/fd3dd4d5d"ee,d#e)d$ed%e'fd6d7Z0eed2e-j/fd3dd4d5d"ee-d#e)d$ed%e'fd8d9Z1eed2e.j/fd3dd4d5d"ee.d#e)d$ed%e'fd:d;Z2dd)partial)AnyCallableListOptionalSequenceTupleTypeUnionN)Tensor)VideoClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_KINETICS400_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) VideoResNetR3D_18_WeightsMC3_18_WeightsR2Plus1D_18_Weightsr3d_18mc3_18 r2plus1d_18c ^eZdZ d dededeedededdf fd d Zededeeeeffd d ZZ S) Conv3DSimpleN in_planes out_planes midplanesstridepaddingreturncstj||d||dddS)N)r r r F in_channels out_channels kernel_sizer#r$biassuper__init__selfr r!r"r#r$ __class__S/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/video/resnet.pyr-s zConv3DSimple.__init__cC |||fSNr2r#r2r2r3get_downsample_stride' z"Conv3DSimple.get_downsample_strideNrr __name__ __module__ __qualname__intrr- staticmethodrr7 __classcell__r2r2r0r3r" &rc sXeZdZd dedededededdf fd d Zededeeeeffd d ZZS) Conv2Plus1Drr r!r"r#r$r%Nc s`ttj||dd||fd||fddt|tjddtj||d|ddf|ddfdddS) Nrr r rrFr)r#r$r*Tinplacer rrr,r-nnConv3d BatchNorm3dReLUr.r0r2r3r--s zConv2Plus1D.__init__cCr4r5r2r6r2r2r3r7>r8z!Conv2Plus1D.get_downsample_striderr) r;r<r=r>r-r?rr7r@r2r2r0r3rB,s(&rBc r)Conv3DNoTemporalNrr r!r"r#r$r%cs(tj||dd||fd||fdddS)NrCrrFr&r+r.r0r2r3r-Ds zConv3DNoTemporal.__init__cCs d||fSNrr2r6r2r2r3r7Qr8z&Conv3DNoTemporal.get_downsample_strider9r:r2r2r0r3rNCrArNc sbeZdZdZ  ddedededejfdedeejd df fd d Z d e d e fd dZ Z S) BasicBlockrNinplanesplanes conv_builder.r# downsampler%cs||ddd|ddd|}tt|||||t|tjdd|_t||||t||_tjdd|_||_ ||_ dS)Nr TrE) r,r-rI SequentialrKrLconv1conv2relurTr#r/rQrRrSr#rTr"r0r2r3r-Zs(  zBasicBlock.__init__xcCsB|}||}||}|jdur||}||7}||}|Sr5)rVrWrTrXr/rZresidualoutr2r2r3forwardms     zBasicBlock.forwardrN r;r<r= expansionr>rrIModulerr-r r^r@r2r2r0r3rPVs$ rPc sbeZdZdZ  ddedededejfded eejd df fd d Z d e d e fddZ Z S) BottleneckrNrQrRrS.r#rTr%cst||ddd|ddd|}ttj||dddt|tjdd|_t|||||t|tjdd|_ttj|||j dddt||j |_ tjdd|_ ||_ ||_ dS)Nr rF)r)r*TrE)r,r-rIrUrJrKrLrVrWraconv3rXrTr#rYr0r2r3r-~s (" zBottleneck.__init__rZcCsL|}||}||}||}|jdur||}||7}||}|Sr5)rVrWrerTrXr[r2r2r3r^s      zBottleneck.forwardr_r`r2r2r0r3rc{s$ rcc"eZdZdZdfdd ZZS) BasicStemz$The default conv-batchnorm-relu stemr%Nc s4ttjdddddddtdtjdd dS) Nr @)r rirrrrCFrDTrErHr/r0r2r3r-s  zBasicStem.__init__r%Nr;r<r=__doc__r-r@r2r2r0r3rgrgcrf) R2Plus1dStemzRR(2+1)D stem is different than the default one as it uses separated 3D convolutionr%Nc sZttjdddddddtdtjdd tjdd d d d ddtd tjdd dS)Nr -)rririrj)rr r FrDTrErhrGrrr)rrrrHrkr0r2r3r-s  zR2Plus1dStem.__init__rlrmr2r2r0r3rprorpcseZdZ  ddeeeefdeeeee e fde e de dejfde d ed d ffd d Zded efddZ ddeeeefdeeee e fde de de d ejf ddZZS)rFblock conv_makerslayersstem. num_classeszero_init_residualr%Ncstt|d|_||_|j||dd|ddd|_|j||dd|ddd|_|j||dd|ddd|_|j||dd |ddd|_ t d |_ t d |j||_|D]N}t|t jrt jj|jd d d |jdurt j|jdq`t|t jrt j|jdt j|jdq`t|t j rt j|jddt j|jdq`|r|D]}t|trt j|jjdqdSdS)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. rhrrr6rr irrfan_outrX)mode nonlinearityNg{Gz?)r,r-rrQrw _make_layerlayer1layer2layer3layer4rIAdaptiveAvgPool3davgpoolLinearrafcmodules isinstancerJinitkaiming_normal_weightr* constant_rKnormal_rcbn3)r/rtrurvrwrxrymr0r2r3r-s<         zVideoResNet.__init__rZcCsT||}||}||}||}||}||}|d}||}|SrO)rwrrrrrflattenr)r/rZr2r2r3r^s        zVideoResNet.forwardrrSrRblocksr#c Csd}|dks|j||jkr+||}ttj|j||jd|ddt||j}g}|||j||||||j|_td|D] } |||j||qDtj|S)NrF)r)r#r*) rQrar7rIrUrJrKappendrange) r/rtrSrRrr#rT ds_stridervir2r2r3r s   zVideoResNet._make_layer)rsF)r)r;r<r=r r rPrcrrrNrBrr>rrIrbboolr-r r^rUrr@r2r2r0r3rsB 4rrtrurvrw.weightsprogresskwargsr%cKsT|durt|dt|jdt||||fi|}|dur(||j|dd|S)Nrx categoriesT)r check_hash)rlenmetarload_state_dictget_state_dict)rtrurvrwrrrmodelr2r2r3 _video_resnet#s  rrMzKhttps://github.com/pytorch/vision/tree/main/references/video_classificationzThe weights reproduce closely the accuracy of the paper. The accuracies are estimated on video-level with parameters `frame_rate=15`, `clips_per_video=5`, and `clip_len=16`.)min_sizerrecipe_docsc @DeZdZedeedddieddddd id d d d ZeZdS)rz7https://download.pytorch.org/models/r3d_18-b3b3357e.pthprrz crop_size resize_sizeiP5 Kinetics-400gO@g-T@zacc@1zacc@5gK7YD@g"_@ num_params_metrics_ops _file_sizeurl transformsrN r;r<r=rrr _COMMON_METAKINETICS400_V1DEFAULTr2r2r2r3rB$ rc @r)rz7https://download.pytorch.org/models/mc3_18-a90a0ba3.pthrrriPurg{GO@gQU@rgClE@gtVF@rrNrr2r2r2r3rVrrc @r)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: rdrrrr)rverifyrrPrrgrrrr2r2r3r~ 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)rrrrPrrNrgrr2r2r3rs rcKr)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: rdr)rrrrPrBrprr2r2r3rrr) _ModelURLs)rrr)6 functoolsrtypingrrrrrrr r torch.nnrItorchr transforms._presetsr utilsr_apirrr_metar_utilsrr__all__rJrrUrBrNrbrPrcrgrprr>rrrrrrrrrrrr model_urlsr2r2r2r3sv (      %1 ^   *#*#* $