o ,hN< @s,ddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z dd lmZmZmZdd lmZdd lmZmZgd Zd e dede fddZGdddejZGdddejZdeededededef ddZdeddZGdd d eZ Gd!d"d"eZ!Gd#d$d$eZ"Gd%d&d&eZ#eed'e j$fd(dd)d*dee dededefd+d,Z%eed'e!j$fd(dd)d*dee!dededefd-d.Z&eed'e"j$fd(dd)d*dee"dededefd/d0Z'eed'e#j$fd(dd)d*dee#dededefd1d2Z(dS)3)partial)AnyCallableOptionalN)Tensor)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) ShuffleNetV2ShuffleNet_V2_X0_5_WeightsShuffleNet_V2_X1_0_WeightsShuffleNet_V2_X1_5_WeightsShuffleNet_V2_X2_0_Weightsshufflenet_v2_x0_5shufflenet_v2_x1_0shufflenet_v2_x1_5shufflenet_v2_x2_0xgroupsreturncCsP|\}}}}||}||||||}t|dd}|||||}|S)Nr r)sizeviewtorch transpose contiguous)rr batchsize num_channelsheightwidthchannels_per_groupr'Z/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/shufflenetv2.pychannel_shuffles r)cspeZdZdedededdffdd Ze dd ed ed ededededejfddZ de de fddZ Z S)InvertedResidualinpoupstriderNcsTtd|krdkstdtd||_|d}|jdkr7||d>kr7td|d|d|d|jdkrct|j||d|jdd t|tj||ddd d d t|tj d d|_ nt|_ ttj|jdkrs|n||ddd d d t|tj d d|j||d|jdd t|tj||ddd d d t|tj d d|_ dS)Nr zillegal stride valuerzInvalid combination of stride z, inp z and oup zB values. If stride == 1 then inp should be equal to oup // 2 << 1. kernel_sizer-paddingrF)r0r-r1biasTinplace) super__init__ ValueErrorr-nn Sequentialdepthwise_conv BatchNorm2dConv2dReLUbranch1branch2)selfr+r,r-branch_features __class__r'r(r6,sJ       zInvertedResidual.__init__r rFior0r1r2c Cstj|||||||dS)N)r2r)r8r<)rDrEr0r-r1r2r'r'r(r:VszInvertedResidual.depthwise_convrcCsb|jdkr|jddd\}}tj|||fdd}ntj||||fdd}t|d}|S)Nr r)dim)r-chunkrcatr?r>r))r@rx1x2outr'r'r(forward\s  zInvertedResidual.forward)r rF) __name__ __module__ __qualname__intr6 staticmethodboolr8r<r:rrL __classcell__r'r'rBr(r*+s(* r*c sneZdZdefdeedeedededejfddf fd d Z d e de fd d Z d e de fddZ Z S)ristages_repeatsstages_out_channels num_classesinverted_residual.rNc sdtt|t|dkrtdt|dkrtd||_d}|jd}ttj||ddddd t |tj d d |_ |}tj dddd |_ |||d ddD}t|||jddD])\}} }|||dg} t| dD] } | |||dqtt||tj| |}qb|jd}ttj||ddddd t |tj d d |_t|||_dS)Nr.z2expected stages_repeats as list of 3 positive intsz7expected stages_out_channels as list of 5 positive intsrrr F)r2Tr3r/cSsg|]}d|qS)stager').0rDr'r'r( sz)ShuffleNetV2.__init__..)rr.)r5r6r lenr7_stage_out_channelsr8r9r<r;r=conv1 MaxPool2dmaxpoolziprangeappendsetattrconv5Linearfc) r@rTrUrVrWinput_channelsoutput_channels stage_namesnamerepeatsseqrDrBr'r(r6isB        zShuffleNetV2.__init__rcCsX||}||}||}||}||}||}|ddg}||}|S)Nrr.)r`rbstage2stage3stage4rgmeanrir@rr'r'r( _forward_impls       zShuffleNetV2._forward_implcCs ||S)N)rurtr'r'r(rLs zShuffleNetV2.forward)rMrNrOr*listrPrr8Moduler6rrurLrSr'r'rBr(rhs  0 rweightsprogressargskwargscOsL|durt|dt|jdt|i|}|dur$||j|dd|S)NrV categoriesT)ry check_hash)rr^metarload_state_dictget_state_dict)rxryrzr{modelr'r'r( _shufflenetv2s r)r r z2https://github.com/ericsun99/Shufflenet-v2-Pytorch)min_sizer|recipec @DeZdZedeeddiedddddid d d d d ZeZdS)rzDhttps://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth crop_sizei ImageNet-1Kg-FN@g9voT@zacc@1zacc@5g{Gz?gT㥛 @VThese weights were trained from scratch to reproduce closely the results of the paper. num_params_metrics_ops _file_size_docsurl transformsr~N rMrNrOr rr _COMMON_META IMAGENET1K_V1DEFAULTr'r'r'r(r& rc @r)rzBhttps://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pthrri"rgI +WQ@gNbX9V@rg(\?gE!@rrrNrr'r'r'r(rrrc @HeZdZedeedddieddddd d id d d ddZeZdS)rzBhttps://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pthrr resize_size+https://github.com/pytorch/vision/pull/5906iv5rg9v?R@g/$V@rgl?gw/+@ These weights were trained from scratch by using TorchVision's `new training recipe `_. rrrrrrrNrr'r'r'r(r( rc @r)rzBhttps://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pthrrrriprgQS@gMb@W@rg-?g+n<@rrrNrr'r'r'r(rrr pretrained)rxT)rxrycK(t|}t||gdgdfi|S)a  Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights :members: r\r\)0`)rverifyrrxryr{r'r'r(r rcKr)a  Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights :members: r)rtrir)rrrrr'r'r(r>rrcKr)a  Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights :members: r)ri`ir)rrrrr'r'r(r]rrcKr)a  Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `__. Args: weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights :members: r)riii)rrrrr'r'r(r|rr)) functoolsrtypingrrrrtorch.nnr8rtransforms._presetsrutilsr _apir r r _metar_utilsrr__all__rPr)rwr*rrRrrrrrrrrrrrr'r'r'r(s       =A