o ,hA@sddlmZddlmZddlmZmZmZmZddl m Z ddl m Z ddl mZddlmZd d lmZmZmZd d lmZd d lmZmZgd ZGddde jZGddde jZGddde jZGddde j Z!Gddde j Z"Gddde jZ#Gddde jZ$Gddde j Z%de&ee!e"fdee&eeeefd e'e(d!ed"e j fd#eed$e)d%ed&e%fd'd(Z*d)ed*d+d,Z+Gd-d.d.eZ,Gd/d0d0eZ-Gd1d2d2eZ.eed3e,j/fd4dd5d6d#ee,d$e)d%ed&e%fd7d8Z0eed3e-j/fd4dd5d6d#ee-d$e)d%ed&e%fd9d:Z1eed3e.j/fd4dd5d6d#ee.d$e)d%ed&e%fd;d<Z2d d=lm3Z3e3e,j/j4e-j/j4e.j/j4d>Z5dS)?)Sequence)partial)AnyCallableOptionalUnionN)Tensor)VideoClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_KINETICS400_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) VideoResNetR3D_18_WeightsMC3_18_WeightsR2Plus1D_18_Weightsr3d_18mc3_18 r2plus1d_18c ^eZdZ d dededeedededdf fd d Zededeeeeffd d ZZ S) Conv3DSimpleN in_planes out_planes midplanesstridepaddingreturncstj||d||dddS)N)r r r F in_channels out_channels kernel_sizer r!biassuper__init__selfrrrr r! __class__Z/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/video/resnet.pyr*s zConv3DSimple.__init__cC |||fSNr/r r/r/r0get_downsample_stride( z"Conv3DSimple.get_downsample_strideNrr __name__ __module__ __qualname__intrr* staticmethodtupler4 __classcell__r/r/r-r0r" &rc sXeZdZd dedededededdf fd d Zededeeeeffd d ZZS) Conv2Plus1Drrrrr r!r"Nc s`ttj||dd||fd||fddt|tjddtj||d|ddf|ddfdddS) Nrr r rrFr&r r!r'Tinplacer rrr)r*nnConv3d BatchNorm3dReLUr+r-r/r0r*.s zConv2Plus1D.__init__cCr1r2r/r3r/r/r0r4?r5z!Conv2Plus1D.get_downsample_striderr) r8r9r:r;r*r<r=r4r>r/r/r-r0r@-s(&r@c r)Conv3DNoTemporalNrrrrr r!r"cs(tj||dd||fd||fdddS)NrArrFr#r(r+r-r/r0r*Es zConv3DNoTemporal.__init__cCs d||fSNrr/r3r/r/r0r4Rr5z&Conv3DNoTemporal.get_downsample_strider6r7r/r/r-r0rLDr?rLc sbeZdZdZ  ddedededejfdedeejd df fd d Z d e d e fd dZ Z S) BasicBlockrNinplanesplanes conv_builder.r downsampler"cs||ddd|ddd|}tt|||||t|tjdd|_t||||t||_tjdd|_||_ ||_ dS)Nr TrC) r)r*rG SequentialrIrJconv1conv2relurRr r,rOrPrQr rRrr-r/r0r*[s(  zBasicBlock.__init__xcCsB|}||}||}|jdur||}||7}||}|Sr2)rTrUrRrVr,rXresidualoutr/r/r0forwardns     zBasicBlock.forwardrN r8r9r: expansionr;rrGModulerr*rr\r>r/r/r-r0rNWs$ rNc sbeZdZdZ  ddedededejfded eejd df fd d Z d e d e fddZ Z S) BottleneckrNrOrPrQ.r rRr"cst||ddd|ddd|}ttj||dddt|tjdd|_t|||||t|tjdd|_ttj|||j dddt||j |_ tjdd|_ ||_ ||_ dS)Nr rF)r&r'TrC)r)r*rGrSrHrIrJrTrUr_conv3rVrRr rWr-r/r0r*s (" zBottleneck.__init__rXcCsL|}||}||}||}|jdur||}||7}||}|Sr2)rTrUrcrRrVrYr/r/r0r\s      zBottleneck.forwardr]r^r/r/r-r0ra|s$ rac"eZdZdZdfdd ZZS) BasicStemz$The default conv-batchnorm-relu stemr"Nc s4ttjdddddddtdtjdd dS) Nr @)r rgrr r rAFrBTrCrFr,r-r/r0r*s  zBasicStem.__init__r"Nr8r9r:__doc__r*r>r/r/r-r0rerecrd) 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 -)rrgrgrh)rr r FrBTrCrfrErrr)rrrrFrir-r/r0r*s  zR2Plus1dStem.__init__rjrkr/r/r-r0rnrmrncseZdZ  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. rfrrr3r r irpfan_outrV)mode nonlinearityNg{Gz?)r)r*r rOru _make_layerlayer1layer2layer3layer4rGAdaptiveAvgPool3davgpoolLinearr_fcmodules isinstancerHinitkaiming_normal_weightr' constant_rInormal_rabn3)r,rrrsrtrurvrwmr-r/r0r*s<         zVideoResNet.__init__rXcCsT||}||}||}||}||}||}|d}||}|SrM)rur~rrrrflattenr)r,rXr/r/r0r\s        zVideoResNet.forwardrrQrPblocksr c Csd}|dks|j||jkr+||}ttj|j||jd|ddt||j}g}|||j||||||j|_td|D] } |||j||qDtj|S)NrF)r&r r') rOr_r4rGrSrHrIappendrange) r,rrrQrPrr rR ds_stridertir/r/r0r} s   zVideoResNet._make_layer)rqF)r)r8r9r:typerrNrarrrLr@listr;rrGr`boolr*rr\rSr}r>r/r/r-r0rsB 4rrrrsrtru.weightsprogresskwargsr"cKsT|durt|dt|jdt||||fi|}|dur(||j|dd|S)Nrv categoriesT)r check_hash)rlenmetarload_state_dictget_state_dict)rrrsrtrurrrmodelr/r/r0 _video_resnet$s  rrKzKhttps://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.pthprrx crop_size resize_sizeiP5 Kinetics-400gO@g-T@zacc@1zacc@5gK7YD@g"_@ num_params_metrics_ops _file_sizeurl transformsrN r8r9r:rrr _COMMON_METAKINETICS400_V1DEFAULTr/r/r/r0rC$ rc @r)rz7https://download.pytorch.org/models/mc3_18-a90a0ba3.pthrrriPurg{GO@gQU@rgClE@gtVF@rrNrr/r/r/r0rWrrc @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: rbr r r r )rverifyrrNrrerrrr/r/r0r 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)rrrrNrrLrerr/r/r0rs 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: rbr)rrrrNr@rnrr/r/r0rrr) _ModelURLs)rrr)6collections.abcr functoolsrtypingrrrrtorch.nnrGtorchrtransforms._presetsr utilsr _apir rr_metar_utilsrr__all__rHrrSr@rLr`rNrarernrrrr;rrrrrrrrrrrr model_urlsr/r/r/r0sx       %1 ^   *#*#* $