o hZ@s.UddlZddlZddlmZddlmZddlmZddlm Z m Z m Z m Z ddl Z ddl mZmZddlmZdd lmZmZdd lmZmZdd lmZd d lmZmZmZd dlmZd dl m!Z!m"Z"m#Z#gdZ$eGdddZ%Gddde%Z&Gddde%Z'Gdddej(Z)Gdddej(Z*Gdddej(Z+dee e&e'fde,de e-d e ed!e.d"e d#e+fd$d%Z/d&e0d"e d#e1ee e&e'fe e-ffd'd(Z2d)eiZ3e4e0e fe5d*<ie3d+d,d-Z6ie3d.d/d-Z7Gd0d1d1eZ8Gd2d3d3eZ9Gd4d5d5eZ:Gd6d7d7eZ;Gd8d9d9eZGd>d?d?eZ?Gd@dAdAeZ@GdBdCdCeZAGdDdEdEeZBee#dFe8jCfdGddHdId e e8d!e.d"e d#e+fdJdKZDee#dFe9jCfdGddHdId e e9d!e.d"e d#e+fdLdMZEee#dFe:jCfdGddHdId e e:d!e.d"e d#e+fdNdOZFee#dFe;jCfdGddHdId e e;d!e.d"e d#e+fdPdQZGee#dFejCfdGddHdId e e>d!e.d"e d#e+fdVdWZJee#dFe?jCfdGddHdId e e?d!e.d"e d#e+fdXdYZKee#dFe@jCfdGddHdId e e@d!e.d"e d#e+fdZd[ZLee#dFeAjCfdGddHdId e eAd!e.d"e d#e+fd\d]ZMee#dFeBjCfdGddHdId e eBd!e.d"e d#e+fd^d_ZNdS)`N)Sequence) dataclass)partial)AnyCallableOptionalUnion)nnTensor)StochasticDepth)Conv2dNormActivationSqueezeExcitation)ImageClassificationInterpolationMode)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_make_divisible_ovewrite_named_paramhandle_legacy_interface) EfficientNetEfficientNet_B0_WeightsEfficientNet_B1_WeightsEfficientNet_B2_WeightsEfficientNet_B3_WeightsEfficientNet_B4_WeightsEfficientNet_B5_WeightsEfficientNet_B6_WeightsEfficientNet_B7_WeightsEfficientNet_V2_S_WeightsEfficientNet_V2_M_WeightsEfficientNet_V2_L_Weightsefficientnet_b0efficientnet_b1efficientnet_b2efficientnet_b3efficientnet_b4efficientnet_b5efficientnet_b6efficientnet_b7efficientnet_v2_sefficientnet_v2_mefficientnet_v2_lc @steZdZUeed<eed<eed<eed<eed<eed<edejfed<e dd ed ed e ed efddZ d S) _MBConvConfig expand_ratiokernelstrideinput_channels out_channels num_layers.blockNchannels width_mult min_valuereturncCst||d|S)N)r)r9r:r;r>e/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torchvision/models/efficientnet.pyadjust_channels9sz_MBConvConfig.adjust_channelsN) __name__ __module__ __qualname__float__annotations__intrr Module staticmethodrr@r>r>r>r?r1/s &r1csreZdZ   ddedededededed ed ed eed ejfd dffdd Z e ded efddZ Z S) MBConvConfig?Nr2r3r4r5r6r7r: depth_multr8.r<c sL|||}|||}|||}| durt} t||||||| dSrA)r@ adjust_depthMBConvsuper__init__) selfr2r3r4r5r6r7r:rLr8 __class__r>r?rP@s  zMBConvConfig.__init__cCstt||SrA)rGmathceil)r7rLr>r>r?rMSszMBConvConfig.adjust_depth)rKrKN) rBrCrDrErGrrr rHrPrIrM __classcell__r>r>rRr?rJ>s6    rJcsPeZdZ d dededededededeed ejfd dffd d Z Z S)FusedMBConvConfigNr2r3r4r5r6r7r8.r<c s(|durt}t|||||||dSrA) FusedMBConvrOrP)rQr2r3r4r5r6r7r8rRr>r?rPZs zFusedMBConvConfig.__init__rA) rBrCrDrErGrrr rHrPrVr>r>rRr?rWXs&  rWc s\eZdZefdedededejfdedejfddf fdd Z d e de fd d Z Z S) rNcnfstochastic_depth_prob norm_layer.se_layerr<Nc std|jkrdkstdtd|jdko"|j|jk|_g}tj}| |j|j }||jkrC| t |j|d||d| t |||j |j|||dtd|jd}| |||ttjddd | t ||jd|ddtj||_t|d |_|j|_dS) Nrr illegal stride value kernel_sizer[activation_layer)r_r4groupsr[r`T)inplace)Z activationrow)rOrPr4 ValueErrorr5r6use_res_connectr SiLUr@r2appendr r3maxr Sequentialr8r stochastic_depth) rQrYrZr[r\layersr`expanded_channelsZsqueeze_channelsrRr>r?rPjsP        zMBConv.__init__inputcC&||}|jr||}||7}|SrAr8rfrkrQrnresultr>r>r?forward  zMBConv.forward) rBrCrDrrJrErr rHrPr rsrVr>r>rRr?rNis  :rNcsJeZdZdedededejfddffdd Zd e de fd d Z Z S) rXrYrZr[.r<Nc std|jkrdkstdtd|jdko"|j|jk|_g}tj}| |j|j }||jkrT| t |j||j |j||d| t ||jd|ddn| t |j|j|j |j||dtj||_t|d|_|j|_dS)Nrr r]r_r4r[r`r^rd)rOrPr4rer5r6rfr rgr@r2rhr r3rjr8r rk)rQrYrZr[rlr`rmrRr>r?rPsL      zFusedMBConv.__init__rncCrorArprqr>r>r?rsrtzFusedMBConv.forward) rBrCrDrWrErr rHrPr rsrVr>r>rRr?rXs 4rXcseZdZ    ddeeeefdededede e d e j fd e ed dffd d Z ded efddZded efddZZS)r皙?Ninverted_residual_settingdropoutrZ num_classesr[. last_channelr<c s0tt||stdt|trtdd|Ds!td|dur(tj }g}|dj }| t d|dd|tj d td d |D} d} |D]8} g} t| jD]&} t| }| rc|j|_ d |_|t| | }| ||||| d 7} qS| tj| qJ|d j}|dur|nd|}| t ||d |tj dtj||_td |_ttj|ddt|||_|D]V}t|tjrtjj |j!dd|j"durtj#|j"qt|tj tj$frtj%|j!tj#|j"qt|tjrdt&'|j(}tj)|j!| |tj#|j"qdS)a EfficientNet V1 and V2 main class Args: inverted_residual_setting (Sequence[Union[MBConvConfig, FusedMBConvConfig]]): Network structure dropout (float): The droupout probability stochastic_depth_prob (float): The stochastic depth probability num_classes (int): Number of classes norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use last_channel (int): The number of channels on the penultimate layer z1The inverted_residual_setting should not be emptycSsg|]}t|tqSr>) isinstancer1).0sr>r>r? sz)EfficientNet.__init__..z:The inverted_residual_setting should be List[MBConvConfig]Nrr rucss|]}|jVqdSrA)r7)r}rYr>r>r? sz(EfficientNet.__init__..rrbr^T)prcZfan_out)moderK)*rOrPrrer|rall TypeErrorr BatchNorm2dr5rhr rgsumranger7copyr6r4rEr8rjfeaturesZAdaptiveAvgPool2davgpoolZDropoutZLinear classifiermodulesZConv2dinitZkaiming_normal_weightZbiasZzeros_Z GroupNormZones_rTsqrtZ out_featuresZuniform_)rQrxryrZrzr[r{rlZfirstconv_output_channelsZtotal_stage_blocksZstage_block_idrYZstage_Z block_cnfZsd_probZlastconv_input_channelsZlastconv_output_channelsmZ init_rangerRr>r?rPs~           zEfficientNet.__init__xcCs.||}||}t|d}||}|S)Nr)rrtorchflattenrrQrr>r>r? _forward_implMs    zEfficientNet._forward_implcCs ||SrA)rrr>r>r?rsWs zEfficientNet.forward)rvrwNN)rBrCrDrrrJrWrErGrrr rHrPr rrsrVr>r>rRr?rs,c rrxryr{weightsprogresskwargsr<cKsT|durt|dt|jdt||fd|i|}|dur(||j|dd|S)Nrz categoriesr{T)rZ check_hash)rlenmetarZload_state_dictZget_state_dict)rxryr{rrrmodelr>r>r? _efficientnet[s rarchc KsB|drRtt|d|dd}|dddddd|d dd dd d |d d d d d d |d dd d dd|d d dddd|d d d ddd|d dddddg}d}||fS|drtdddd d d tddd d ddtddd dddtddd ddd td dddddtd dd dddg}d}||fS|drtdddd d dtddd d dd tddd ddd tddd dddtd dddddtd dd dd d!td ddd d"d g}d}||fS|d#rtddddddtddd dddtddd dd$dtddd d$dd%td dddd&d'td dd d&d(d)td ddd(d*dg}d}||fStd+|),NZefficientnet_br:rLr:rLrr r (Pprb@r.0@ ir/i0ir0` izUnsupported model type ) startswithrrJpoprWre)rrZ bneck_confrxr{r>r>r?_efficientnet_confnsZ  $     rr _COMMON_META)rrzUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1)Zmin_sizerecipe)!rzUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2c @JeZdZedeeddejdieddddd id d d d dZ e Z dS)rzJhttps://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pthrr crop_size resize_size interpolationidP ImageNet-1Kg?5^IlS@g5^I bW@zacc@1zacc@5gNbX9?g~jts4@1These weights are ported from the original paper. num_params_metrics_ops _file_size_docsurlZ transformsrN rBrCrDrrrrBICUBIC_COMMON_META_V1 IMAGENET1K_V1DEFAULTr>r>r>r?r* rc @seZdZedeeddejdieddddd id d d d dZ edeeddej diedddddd id ddddZ e Z dS)rzJhttps://download.pytorch.org/models/efficientnet_b1_rwightman-bac287d4.pthrrivrg +S@gClW@rgCl?gM">@rrrz@https://download.pytorch.org/models/efficientnet_b1-c27df63c.pthzOhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuninggʡS@gƻW@gA`">@$ These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. )rrrrrrN) rBrCrDrrrrrrrBILINEARZ IMAGENET1K_V2rr>r>r>r?rsR  rc @JeZdZedeeddejdiedddddid d d d d Z e Z dS)rzJhttps://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pthi rirgx&T@gp= W@rg rh?gʡEA@rrrNrr>r>r>r?rrrc @r)rzJhttps://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pthi,rrirgnT@g~jtX@rgZd;?gd;OG@rrrNrr>r>r>r?rrrc @r)rzJhttps://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pthi|rri0!'rgjtT@gt&X@rg~jt@gKR@rrrNrr>r>r>r?r0rrc @r)r zJhttps://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pthirirg#~jT@gx&1(X@rgx&1$@gK7]@rrrNrr>r>r>r?r Hrr c @r)r!zJhttps://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pthirirgnU@gv:X@rg rh3@g$d@rrrNrr>r>r>r?r!`rr!c @r)r"zJhttps://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pthiXricrg+U@g'1:X@rgsh|B@go@rrrNrr>r>r>r?r"xrr"c @r)r#zBhttps://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pthrri8nGrg;OU@gx&18X@rgZd @gVT@rrrN rBrCrDrrrrr_COMMON_META_V2rrr>r>r>r?r#0r#c @r)r$zBhttps://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pthri:rgI +GU@gDlIX@rgE8@gQj@rrrNrr>r>r>r?r$rr$c @sNeZdZedeeddejdddieddddd id d d d dZ e Z dS)r%zBhttps://download.pytorch.org/models/efficientnet_v2_l-59c71312.pthr)?rr)rrrmeanZstdiHfrgʡEsU@gOnrX@rg ףp= L@gI +i|@rrrN) rBrCrDrrrrrrrrr>r>r>r?r%s4r%Z pretrained)rT)rrcKs<t|}tdddd\}}t||dd|||fi|S)aEfficientNet B0 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B0_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B0_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B0_Weights :members: r&rKrryrv)rverifyrrrrrrrxr{r>r>r?r& r&cK<t|}tdddd\}}t||dd|||fi|S)aEfficientNet B1 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B1_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B1_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B1_Weights :members: r'rK皙?rryrv)rrrrrrr>r>r?r'rr'cKr)aEfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B2_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B2_Weights :members: r(r333333?rry333333?)rrrrrrr>r>r?r('rr(cKr)aEfficientNet B3 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B3_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B3_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B3_Weights :members: r)rffffff?rryr)rrrrrrr>r>r?r)F  r)cKr)aEfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B4_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B4_Weights :members: r*r?rry皙?)rrrrrrr>r>r?r*jrr*cKLt|}tdddd\}}t||dd|||fdttjdd d i|S) aEfficientNet B5 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B5_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B5_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B5_Weights :members: r+g?g@rryrr[MbP?{Gz?epsZmomentum)r rrrrrr rrr>r>r?r+  r+cKr) aEfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B6_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B6_Weights :members: r,rg@rryrr[rrr)r!rrrrrr rrr>r>r?r,rr,cKr) aEfficientNet B7 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B7_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B7_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B7_Weights :members: r-g@g@rryrr[rrr)r"rrrrrr rrr>r>r?r-rr-cKDt|}td\}}t||dd|||fdttjddi|S)a Constructs an EfficientNetV2-S architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_S_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights :members: r.ryrvr[rr)r#rrrrrr rrr>r>r?r.    r.cKr)a Constructs an EfficientNetV2-M architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_M_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights :members: r/ryrr[rr)r$rrrrrr rrr>r>r?r/#rr/cKr)a Constructs an EfficientNetV2-L architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_L_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_L_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.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_L_Weights :members: r0ryrr[rr)r%rrrrrr rrr>r>r?r0Irr0)OrrTcollections.abcr dataclassesr functoolsrtypingrrrrrr r Ztorchvision.opsr Zops.miscr rZtransforms._presetsrrutilsrZ_apirrr_metarZ_utilsrrr__all__r1rJrWrHrNrXrrErGboolrstrtuplerrdictrFrrrrrrrr r!r"r#r$r%rr&r'r(r)r*r+r,r-r.r/r0r>r>r>r?s       C=r  80""###$$