ڧg? ddlmZddlmZmZmZmZmZddlZddlm Z m Z ddl m Z m ZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZmZgd ZGddZGdde jZ Gdde jZ! d.de"de#de$de$def dZ%deede&deede$ded e!f d!Z'd"ed#Z(Gd$d%eZ)Gd&d'eZ*eed(e)j+f)dd*d+dee)de$ded e!fd,Z,eed(e*j+f)dd*d+dee*de$ded e!fd-Z-dS)/)partial)AnyCallableListOptionalSequenceN)nnTensor)Conv2dNormActivationSqueezeExcitation)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_make_divisible_ovewrite_named_paramhandle_legacy_interface) MobileNetV3MobileNet_V3_Large_WeightsMobileNet_V3_Small_Weightsmobilenet_v3_largemobilenet_v3_smallcZeZdZdedededededededed efd Zed ed efd Z d S)InvertedResidualConfiginput_channelskernelexpanded_channels out_channelsuse_se activationstridedilation width_multc ||| |_||_||| |_||| |_||_|dk|_||_||_dS)NHS) adjust_channelsrr r!r"r#use_hsr%r&) selfrr r!r"r#r$r%r&r's Z/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/models/mobilenetv3.py__init__zInvertedResidualConfig.__init__sy#22>:NN !%!5!56G!T!T 00zJJ  D(    channelsc(t||zdS)N)r)r0r's r-r*z&InvertedResidualConfig.adjust_channels/sx*4a888r/N) __name__ __module__ __qualname__intboolstrfloatr. staticmethodr*r/r-rrs!!! !  !  !!!!!!!!*9#95999\999r/rc eZdZeeejfdededej fdedej fffd Z de de fd Z xZ S) InvertedResidual)scale_activationcnf norm_layer.se_layerc td|jcxkrdksntd|jdko|j|jk|_g}|jr tj n tj }|j |jkr1| t|j|j d|||jdkrdn|j}| t|j |j |j||j|j |||jr* 6%6%6%6%6%6%pVr/r=ceZdZ ddeedededeedej fd eedej fd e d e d dffd Z de d e fdZde d e fdZxZS)rN皙?inverted_residual_setting last_channel num_classesrO.r@dropoutkwargsrUc ztt||stdt |t rt d|Dstd|t}|ttj dd}g}|dj } | td | d d |tj |D]!} | || |"|d j} d | z} | t| | d|tjtj||_tjd|_tjtj| |tjdtj|dtj|||_|D]B} t | tjrRtj| jd| j$tj| jot | tj tjfrItj | jtj| jt | tjrJtj!| jddtj| jDdS)a. MobileNet V3 main class Args: inverted_residual_setting (List[InvertedResidualConfig]): Network structure last_channel (int): The number of channels on the penultimate layer num_classes (int): Number of classes block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use dropout (float): The droupout probability z1The inverted_residual_setting should not be emptyc8g|]}t|tSr;) isinstancer).0ss r- z(MobileNetV3.__init__..s#^^^qZ#9::^^^r/zDThe inverted_residual_setting should be List[InvertedResidualConfig]NgMbP?g{Gz?)epsmomentumrr )rDr%r@rErrCT)inplace)prrfan_out)mode)"rHr.rrIrirall TypeErrorr=rr BatchNorm2drrMr rKr"rNfeaturesAdaptiveAvgPool2davgpoolLinearDropout classifiermodulesConv2dinitkaiming_normal_weightbiaszeros_ GroupNormones_normal_)r,rbrcrdrOr@rerfrQfirstconv_output_channelsr?lastconv_input_channelslastconv_output_channelsmrSs r-r.zMobileNetV3.__init__vs* D!!!( dPQQ Q 0( ; ; d^^D]^^^__ dbcc c =$E   UTJJJJ"$%>a$@$O! )%!#     - 2 2C MM%%Z00 1 1 1 1##>  '(%!#        v. +A.. - I. = = L & & & J$ / / / IlK 0 0    ' 'A!RY'' '''y'AAA6%GNN16***A =>> ' ah'''qv&&&&Ary)) '!T222qv&&& ' 'r/xc||}||}tj|d}||}|S)Nr)ryr{torchflattenr~r,rs r- _forward_implzMobileNetV3._forward_implsI MM!   LLOO M!Q   OOA  r/c,||SrW)rrs r-rYzMobileNetV3.forwards!!!$$$r/)r`NNra)r3r4r5rrr6rrr r\r9rr.r rrYr]r^s@r-rrus  489=Y'Y'#'(>#?Y'Y' Y' bi01 Y' Xc29n56 Y'Y'Y' Y'Y'Y'Y'Y'Y'vv&%%F%%%%%%%%r/r?Farchr' reduced_taildilatedrfc |rdnd}|rdnd}tt|}ttj|}|dkr%|dddddddd|ddd d dddd|d dd d dddd|d d d d dddd|d d dd dddd|d d dd dddd|d ddddddd|dddddddd|dddddddd|dddddddd|dddddddd|dddddddd|dd dd|zddd||d|zd d|zd|zddd||d|zd d|zd|zddd|g} |d|z} n|dkr|dddddddd|ddd d dddd|d ddd dddd|d d dd dddd|d d dd dddd|d d dd dddd|d d dddddd|dd dddddd|dd d d|zddd||d|zd d!|zd|zddd||d|zd d!|zd|zddd|g } |d"|z} ntd#|| | fS)$Nr r)r'rroFRE@H(TxPr)ipiiirX`0i i@izUnsupported model type )rrr*rI) rr'rrrfreduce_dividerr& bneck_confr*rbrcs r-_mobilenet_v3_confrs'-QQAN"qqH/JGGGJ4DQ[\\\O ### Jr1b"eT1a 8 8 Jr1b"eT1a 8 8 Jr1b"eT1a 8 8 Jr1b"dD!Q 7 7 Jr1c2tT1a 8 8 Jr1c2tT1a 8 8 Jr1c2udAq 9 9 Jr1c2udAq 9 9 Jr1c2udAq 9 9 Jr1c2udAq 9 9 Jr1c3dAq 9 9 JsAsCtQ : : JsAsC>$94q( S S Jsn,a1F~H]_ceiklnv w w Jsn,a1F~H]_ceiklnv w w% !"'t~'=>> % % % Jr1b"dD!Q 7 7 Jr1b"eT1a 8 8 Jr1b"eT1a 8 8 Jr1b"dD!Q 7 7 Jr1c2tT1a 8 8 Jr1c2tT1a 8 8 Jr1c2tT1a 8 8 Jr1c2tT1a 8 8 Jr1c2#7tQ Q Q Jr^+Q~0Er^G[]acgijlt u u Jr^+Q~0Er^G[]acgijlt u u % !'t~'=>> 9499::: $l 22r/rbrcweightsprogressrUc |)t|dt|jdt||fi|}|*|||d|S)Nrd categoriesT)r check_hash)rlenmetarload_state_dictget_state_dict)rbrcrrrfmodels r- _mobilenet_v3rsvfmSl9S5T5TUUU 1< J J6 J JE g44hSW4XXYYY Lr/)rr)min_sizerc eZdZedeediedddddd id d d d Zedeeddiedddddd id ddd ZeZ dS)rzChttps://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth crop_sizeiS^https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small ImageNet-1Kg R@g(\V@zacc@1zacc@5g-?gw/5@zJThese weights were trained from scratch by using a simple training recipe. num_paramsrecipe_metrics_ops _file_size_docsurl transformsrzChttps://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth)r resize_sizezHhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuninggK7R@gNbX9$W@gZd5@a/ These weights improve marginally upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. N) r3r4r5rrr _COMMON_META IMAGENET1K_V1 IMAGENET1K_V2DEFAULTr;r/r-rr(sG Q7.#>>>    !v##    e    M$G Q7.#3OOO  !`##       M,GGGr/rc deZdZedeediedddddd id d d d ZeZdS)rzChttps://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pthrri&rrgnP@g}?5^U@rgv/?g r#@z} These weights improve upon the results of the original paper by using a simple training recipe. rrN) r3r4r5rrrrrrr;r/r-rrTsG Q7.#>>>  !v##      M(GGGr/r pretrained)rT)rrc tt|}tdi|\}}t||||fi|S)a Constructs a large MobileNetV3 architecture from `Searching for MobileNetV3 `__. Args: weights (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MobileNet_V3_Large_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.mobilenet.MobileNetV3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights :members: r)r)rverifyrrrrrfrbrcs r-rrlO2)//88G.@.`.`Y_.`.`+| 2L'8 ^ ^W] ^ ^^r/c tt|}tdi|\}}t||||fi|S)a Constructs a small MobileNetV3 architecture from `Searching for MobileNetV3 `__. Args: weights (:class:`~torchvision.models.MobileNet_V3_Small_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MobileNet_V3_Small_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.mobilenet.MobileNetV3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MobileNet_V3_Small_Weights :members: r)r)rrrrrs r-rrrr/)rFF). functoolsrtypingrrrrrrr r ops.miscr r rZtransforms._presetsrutilsr_apirrr_metar_utilsrrr__all__rr\r=rr8r9r7rr6rrrrrrrr;r/r-rs:::::::::::::: IIIIIIII555555''''''6666666666''''''SSSSSSSSSS   999999998>>>>>ry>>>Bg%g%g%g%g%")g%g%g%VUZ.3.3 .3 .36:.3MQ.3eh.3.3.3.3b#$:;k "     && ))))))))X0,0J0X!YZZZ7;d___34_GK_^a____[Z_:,0J0X!YZZZ7;d___34_GK_^a____[Z___r/