ڧgDddlZddlmZddlmZmZmZmZddlZddl m Z ddlm Z ddl m Z ddlmZdd lmZmZmZdd lmZdd lmZmZgd Zd ZGdde jZdededededededede jfdZ d9dedededefdZ!dedeefdZ"Gd d!ej jZ#d"ed#d$Z$Gd%d&eZ%Gd'd(eZ&Gd)d*eZ'Gd+d,eZ(ded-eed.e)d/ede#f d0Z*eed1e%j+f2dd3d4d-ee%d.e)d/ede#fd5Z,eed1e&j+f2dd3d4d-ee&d.e)d/ede#fd6Z-eed1e'j+f2dd3d4d-ee'd.e)d/ede#fd7Z.eed1e(j+f2dd3d4d-ee(d.e)d/ede#fd8Z/dS):N)partial)AnyDictListOptional)Tensor)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) MNASNetMNASNet0_5_WeightsMNASNet0_75_WeightsMNASNet1_0_WeightsMNASNet1_3_Weights mnasnet0_5 mnasnet0_75 mnasnet1_0 mnasnet1_3g0U0*3?cPeZdZ d dededededededd ffd Zd edefd ZxZS)_InvertedResidual皙?in_chout_ch kernel_sizestrideexpansion_factor bn_momentumreturnNc`t|dvrtd||dvrtd|||z}||ko|dk|_t jt j||ddt j||t jd t j||||d z||d t j||t jd t j||ddt j|||_ dS) Nr r z#stride should be 1 or 2 instead of )z(kernel_size should be 3 or 5 instead of r F)biasmomentumTinplacer paddingr"groupsr*) super__init__ ValueErrorapply_residualnn SequentialConv2d BatchNorm2dReLUlayers) selfrr r!r"r#r$mid_ch __class__s V/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/models/mnasnet.pyr3z_InvertedResidual.__init__#s<    K6KKLL L f $ $U UUVV V))#vo=&A+m IeVQU 3 3 3 N6K 8 8 8 GD ! ! ! Iffk;!;KTZcipu v v v N6K 8 8 8 GD ! ! ! Iffae 4 4 4 N6K 8 8 8    inputcj|jr|||zS||S)N)r5r;)r<rAs r?forwardz_InvertedResidual.forward;s6   &;;u%%- -;;u%% %r@)r) __name__ __module__ __qualname__intfloatr3rrC __classcell__r>s@r?rr"sru   "% 47 AD X[ jo       0&V&&&&&&&&&r@rrr r!r" exp_factorrepeatsr$r%c |dkrtd|t||||||}g}td|D]*} |t|||d||+t j|g|RS)z&Creates a stack of inverted residuals.r z$repeats should be >= 1, instead got )r$)r4rrangeappendr6r7) rr r!r"rKrLr$first remaining_s r?_stackrSBs{{IIIJJJ eV[&*Ze f f fEI 1g  qq*66;:cnooopppp = + + + ++r@?valdivisor round_up_biascd|cxkrdksntd|t|t||dz z|z|z}|||zkr|n||zS)zAsymmetric rounding to make `val` divisible by `divisor`. With default bias, will round up, unless the number is no more than 10% greater than the smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88.?zIround_up_bias should be greater than 0.0 and smaller than 1.0 instead of r )r4maxrG)rUrVrWnew_vals r?_round_to_multiple_ofr]Ps  $ $ $ $ $ $ $ $terttuuu'3sWq[011Wz_get_depths..^s( H H H !%%- 3 3 H H Hr@)r^depthss` r? _get_depthsrnZs*0 / /F H H H H H H HHr@ceZdZdZdZddedededd ffd Zd edefd Z d e de de de de e de e de e dd ffd ZxZS)raMNASNet, as described in https://arxiv.org/abs/1807.11626. This implements the B1 variant of the model. >>> model = MNASNet(1.0, num_classes=1000) >>> x = torch.rand(1, 3, 224, 224) >>> y = model(x) >>> y.dim() 2 >>> y.nelement() 1000 r 皙?r^ num_classesdropoutr%Nc^tt||dkrtd|||_||_t |}tjd|dddddtj |dt tj d tj|d|dddd|dd tj |dt tj d tj|d|dddddtj |dt t|d|dddddtt|d|dd dddtt|d|dd dddtt|d|d ddddtt|d |dd dddtt|d|dddddttj|ddddddtj dt tj d g}tj ||_tj tj|d tjd||_|D]8}t'|tjrStj|jdd|j$tj|jpt'|tj rItj|jtj|jt'|tjrKtj|jddtj|j:dS)NrYz,alpha should be greater than 0.0 instead of r(rr r Fr0r"r*r+Tr-r/r)i)pr.fan_outrelu)mode nonlinearitysigmoid)r2r3r r4r^rrrnr6r8r9 _BN_MOMENTUMr:rSr7r;DropoutLinear classifiermodules isinstanceinitkaiming_normal_weightr*zeros_ones_kaiming_uniform_)r<r^rrrsrmr;mr>s r?r3zMNASNet.__init__pss D!!! C<<SESSTT T &U## IaAq G G G N6!9| < < < GD ! ! ! IfQiAq6RS9[` a a a N6!9| < < < GD ! ! ! IfQiAq O O O N6!9| < < < 6!9fQiAq!\ B B 6!9fQiAq!\ B B 6!9fQiAq!\ B B 6!9fQiAq!\ B B 6!9fQiAq!\ B B 6!9fQiAq!\ B B IfQiq!AE J J J N4, 7 7 7 GD ! ! !+ .mV, - Wd(K(K(KRYW[]hMiMijj ' 'A!RY'' '''yv'VVV6%GNN16***Ar~.. ' ah'''qv&&&&Ary)) '(( PY(ZZZqv&&& ' 'r@xc||}|ddg}||S)Nr r()r;meanr)r<rs r?rCzMNASNet.forwards6 KKNN FFAq6NNq!!!r@ state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgscB|dd}|dvrtd||dkrC|jdks7t|j} t jdddddd t jdt t jd t jddddddd t jdt t jd t jdddddd t jdt td| dddddtg } t| D]\} } | |j | <d|_ tjdtt!|||||||dS)Nversionr'z+version shluld be set to 1 or 2 instead of r rZr(r`r Frur+Tr-r/raraA new version of MNASNet model has been implemented. Your checkpoint was saved using the previous version. This checkpoint will load and work as before, but you may want to upgrade by training a newer model or transfer learning from an updated ImageNet checkpoint.)getr4r^rnr6r8r9rr:rS enumerater;_versionwarningswarn UserWarningr2_load_from_state_dict)r<rrrrrrrrrmv1_stemidxlayerr>s r?rzMNASNet._load_from_state_dicts!$$Y55 & T7TTUU U a<< c 1 1 !,,F !RAaeDDDrL999%%% "b!Qq%PPPrL999%%% "b!QquEEErL999r6!9aAq,?? G(00 ) ) U#( C  DM MI      %%  oWa     r@)rprq)rDrErF__doc__rrHrGr3rrCrstrboolrrrIrJs@r?rras   H,','e,'#,'u,'W[,',',',',','\""F"""" / / /  /  / 3i / c/ I/  / / / / / / / / / / r@r)r r z(https://github.com/1e100/mnasnet_trainer)min_size categoriesrecipec beZdZedeediedddddid d d d  ZeZdS)rzIhttps://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth crop_sizei! ImageNet-1Kg"P@g(\U@zacc@1zacc@5g9v?g;O.!@9These weights reproduce closely the results of the paper. num_params_metrics_ops _file_size_docsurl transformsmetaN rDrErFrrr _COMMON_META IMAGENET1K_V1DEFAULTrlr@r?rrsG W7.#>>>    !##   T    M"GGGr@rc feZdZedeeddieddddd d id d d dZeZdS)rz`_. rrrrrrrNrrlr@r?rrsG J7.#3OOO  C!##       M*GGGr@rc beZdZedeediedddddid d d d  ZeZdS)rzIhttps://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pthrriPBrgw/]R@gq= ףV@rgjt?g ףp=0@rrrNrrlr@r?rr sG W7.#>>>    !##    T    M"GGGr@rc feZdZedeeddieddddd d id d d dZeZdS)rz;https://download.pytorch.org/models/mnasnet1_3-a4c69d6f.pthrrrri_rgMb S@g rhaW@rgE?g">8@rrrNrrlr@r?rr sG I7.#3OOO  C!##       M*GGGr@rweightsprogresskwargsc |)t|dt|jdt|fi|}|r*|||d|S)NrrrT)r check_hash)rlenrrload_state_dictget_state_dict)r^rrrmodels r?_mnasnetr9stfmSl9S5T5TUUU E $ $V $ $EZ g44hSW4XXYYY Lr@ pretrained)rT)rrc Tt|}td||fi|S)aMNASNet with depth multiplier of 0.5 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet0_5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet0_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.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet0_5_Weights :members: g?)rverifyrrrrs r?rrE1.!''00G C( 5 5f 5 55r@c Tt|}td||fi|S)aMNASNet with depth multiplier of 0.75 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet0_75_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet0_75_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.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet0_75_Weights :members: g?)rrrrs r?rras1."((11G D'8 6 6v 6 66r@c Tt|}td||fi|S)aMNASNet with depth multiplier of 1.0 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet1_0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet1_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.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet1_0_Weights :members: rZ)rrrrs r?rr}rr@c Tt|}td||fi|S)aMNASNet with depth multiplier of 1.3 from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ paper. Args: weights (:class:`~torchvision.models.MNASNet1_3_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.MNASNet1_3_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.mnasnet.MNASNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.MNASNet1_3_Weights :members: g?)rrrrs r?rrrr@)rT)0r functoolsrtypingrrrrtorchtorch.nnr6rtransforms._presetsr utilsr _apir rr_metar_utilsrr__all__rModulerrGrHr7rSr]rnrrrrrrrrrrrrrrlr@r?rs!,,,,,,,,,,,, 555555''''''6666666666''''''BBBBBBBB    &&&&& &&&@ ,  , ,*- ,7: ,HK ,VY ,hm ,] , , , ,LLuLsL5LSVLLLLIuIcIIIIr r r r r ehor r r l&8 *+2*2 E H[$9 T UX ]d    ,0B0P!QRRR:>QU6668$676$6ad6ip666SR64,0C0Q!RSSS<@SW777H%897D7cf7kr777TS74,0B0P!QRRR:>QU6668$676$6ad6ip666SR64,0B0P!QRRR:>QU6668$676$6ad6ip666SR666r@