:jDddlZddlmZddlmZmZddlZddlmZddlm Z ddl m Z ddl m Z dd lmZmZmZdd lmZdd lmZmZgd Zd ZGddej2Zdedededededededej:fdZd8dedededefdZdede efdZ!Gdd ejj2Z"d!ed"d#Z#Gd$d%eZ$Gd&d'eZ%Gd(d)eZ&Gd*d+eZ'ded,eed-e(d.ede"f d/Z)eed0e$jTf1dd2d3d,ee$d-e(d.ede"fd4Z+eed0e%jTf1dd2d3d,ee%d-e(d.ede"fd5Z,eed0e&jTf1dd2d3d,ee&d-e(d.ede"fd6Z-eed0e'jTf1dd2d3d,ee'd-e(d.ede"fd7Z.y)9N)partial)AnyOptional)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 dededededededdffd Zd edefd ZxZS) _InvertedResidualin_chout_ch kernel_sizestrideexpansion_factor bn_momentumreturnNc<t||dvrtd||dvrtd|||z}||k(xr|dk(|_t j t j ||ddt j||t jd t j ||||d z||d t j||t jd t j ||ddt j|||_ y) Nr rz#stride should be 1 or 2 instead of )z(kernel_size should be 3 or 5 instead of r F)biasmomentumTinplacerpaddingrgroupsr') super__init__ ValueErrorapply_residualnn SequentialConv2d BatchNorm2dReLUlayers) selfrrrrr r!mid_ch __class__s o/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchvision/models/mnasnet.pyr0z_InvertedResidual.__init__#s   B6(KL L f $G }UV V))#vo=&A+mm IIeVQU 3 NN6K 8 GGD ! IIffk;!;KTZcipu v NN6K 8 GGD ! IIffae 4 NN6K 8   inputcd|jr|j||zS|j|S)N)r2r8)r9r>s r<forwardz_InvertedResidual.forward;s.   ;;u%- -;;u% %r=)g?) __name__ __module__ __qualname__intfloatr0rr@ __classcell__r;s@r<rr"sVru  "% 47 AD X[ jo  0&V&&r=rrrrr exp_factorrepeatsr!r"c |dkrtd|t||||||}g}td|D]"} |jt|||d||$t j |g|S)z&Creates a stack of inverted residuals.r z$repeats should be >= 1, instead got )r!)r1rrangeappendr3r4) rrrrrHrIr!first remaining_s r<_stackrPBs{?yIJJ eV[&*Ze fEI 1g q*66;:cnopq == + ++r=valdivisor round_up_biascd|cxkrdksntd|t|t||dz z|z|z}|||zk\r|S||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)r1maxrD)rQrRrSnew_vals r<_round_to_multiple_ofrYPsf  $ $derdstuu'3sWq[01W>> model = MNASNet(1.0, num_classes=1000) >>> x = torch.rand(1, 3, 224, 224) >>> y = model(x) >>> y.dim() 2 >>> y.nelement() 1000 rrZ num_classesdropoutr"Nct|t||dkrtd|||_||_t |}tjd|dddddtj|dt tjd tj|d|dddd|dd tj|dt tjd tj|d|dddddtj|dt t|d|dddddtt|d|dd dddtt|d|dd dddtt|d|d ddddtt|d |dd dddtt|d|dddddttj|ddddddtjdt tjd g}tj||_tjtj|d tj d||_|j%D]^}t'|tjrctj(j+|j,dd|j.Wtj(j1|j.t'|tjrStj(j3|j,tj(j1|j.t'|tj s tj(j5|j,ddtj(j1|j.ay)NrUz,alpha should be greater than 0.0 instead of r%rr rFr-rr'r(Tr*r,r&i)pr+fan_outrelu)mode nonlinearitysigmoid)r/r0r r1rZrhrfr3r5r6 _BN_MOMENTUMr7rPr4r8DropoutLinear classifiermodules isinstanceinitkaiming_normal_weightr'zeros_ones_kaiming_uniform_)r9rZrhrirdr8mr;s r<r0zMNASNet.__init__ps D! C<KE7ST T &U# IIaAq G NN6!9| < GGD ! IIfQiAq6RS9[` a NN6!9| < GGD ! IIfQiAq O NN6!9| < 6!9fQiAq!\ B 6!9fQiAq!\ B 6!9fQiAq!\ B 6!9fQiAq!\ B 6!9fQiAq!\ B 6!9fQiAq!\ B IIfQiq!AE J NN4, 7 GGD !+ .mmV, -- Wd(KRYYW[]hMij 'A!RYY'''yv'V66%GGNN166*Ar~~. ahh'qvv&Aryy)(( PY(Zqvv& 'r=xcl|j|}|jddg}|j|S)Nrr%)r8meanrx)r9rs r<r@zMNASNet.forwards/ KKN FFAq6Nq!!r= state_dictprefixlocal_metadatastrict missing_keysunexpected_keys error_msgsc|jdd}|dvrtd||dk(rH|jdk(s8t|j} t j dddddd t j dt t jd t j ddddddd t j dt t jd t j dddddd t j dt td| dddddtg } t| D]\} } | |j| <d|_ tjdtt |E|||||||y)Nversionr$z+version shluld be set to 1 or 2 instead of r rVr%r\rFrkr(Tr*r,r]raA 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.)getr1rZrfr3r5r6rur7rP enumerater8_versionwarningswarn UserWarningr/_load_from_state_dict)r9rrrrrrrrrdv1_stemidxlayerr;s r<rzMNASNet._load_from_state_dicts`!$$Y5 & J7)TU U a< c 1 !,F !RAaeDrL9% "b!Qq%PrL9% "b!QquErL9r6!9aAq,? G(0 ) U#( C  )DM MMI    %  oWa r=)ig?)rArBrC__doc__rrErDr0rr@dictstrboollistrrFrGs@r<rras H,'e,'#,'u,'W[,'\""F" / / /  /  / 3i / c/ I/  / / r=r)r r z(https://github.com/1e100/mnasnet_trainer)min_size categoriesrecipec TeZdZedeediedddddid d d d  ZeZy)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 rArBrCr rr _COMMON_META IMAGENET1K_V1DEFAULTr=r<rrsT W.#>    !##  T  M"Gr=rc XeZdZedeeddieddddd d id d d dZeZy)rz`_. rrrrrrrNrrr=r<rrs[ J.#3O  C!##    M*Gr=rc TeZdZedeediedddddid d d d  ZeZy)rzIhttps://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pthrriPBrgw/]R@gq= ףV@rgjt?g ףp=0@rrrNrrr=r<rr sT W.#>    !##   T  M"Gr=rc XeZdZedeeddieddddd d id d d dZeZy)rz;https://download.pytorch.org/models/mnasnet1_3-a4c69d6f.pthrrrri_rgMb S@g rhaW@rgE?g">8@rrrNrrr=r<rr s[ I.#3O  C!##    M*Gr=rweightsprogresskwargsc |#t|dt|jdt|fi|}|r"|j |j |d|S)NrhrT)r check_hash)rlenrrload_state_dictget_state_dict)rZrrrmodels r<_mnasnetr9sXfmSl9S5TU E $V $E g44hSW4XY Lr= pretrained)rT)rrc Htj|}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<rrE(.!''0G C( 5f 55r=c Htj|}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<rras(."((1G D'8 6v 66r=c Htj|}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: rV)rrrrs r<rr}rr=c Htj|}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=)g?)/r functoolsrtypingrrtorchtorch.nnr3rtransforms._presetsrutilsr _apir r r _metar_utilsrr__all__ruModulerrDrEr4rPrYrrfrrrrrrrrrrrrrrr=r<rs 5'66'B  & &@ ,  , ,*- ,7: ,HK ,VY ,hm ,]] ,LuLsL5LSVLIuIcIr ehhoor l&8 *+2*2 E H[$9 T UX ]d ,0B0P0P!QR:>QU68$676$6ad6ip6S64,0C0Q0Q!RS<@SW7H%897D7cf7kr7T74,0B0P0P!QR:>QU68$676$6ad6ip6S64,0B0P0P!QR:>QU68$676$6ad6ip6S6r=