hZUddlZddlZddlmZddlmZddlmZddlm 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$eGddZ%Gdde%Z&Gdde%Z'GddejPZ)GddejPZ*GddejPZ+dee e&e'fde,de e-d e ed!e.d"e d#e+fd$Z/d%e0d"e d#e1ee e&e'fe e-ffd&Z2d'eiZ3e4e0e fe5d(<ie3d)d*d+Z6ie3d,d-d+Z7Gd.d/eZ8Gd0d1eZ9Gd2d3eZ:Gd4d5eZ;Gd6d7eZ<Gd8d9eZ=Gd:d;eZ>Gd<d=eZ?Gd>d?eZ@Gd@dAeZAGdBdCeZBee#dDe8jfEddFdGd e e8d!e.d"e d#e+fdHZDee#dDe9jfEddFdGd e e9d!e.d"e d#e+fdIZEee#dDe:jfEddFdGd e e:d!e.d"e d#e+fdJZFee#dDe;jfEddFdGd e e;d!e.d"e d#e+fdKZGee#dDejfEddFdGd e e>d!e.d"e d#e+fdNZJee#dDe?jfEddFdGd e e?d!e.d"e d#e+fdOZKee#dDe@jfEddFdGd e e@d!e.d"e d#e+fdPZLee#dDeAjfEddFdGd e eAd!e.d"e d#e+fdQZMee#dDeBjfEddFdGd e eBd!e.d"e d#e+fdRZNy)SN)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 eZdZUeed<eed<eed<eed<eed<eed<edejfed<e dd ed ed e ed efdZ y ) _MBConvConfig expand_ratiokernelstrideinput_channels out_channels num_layers.blockNchannels width_mult min_valuereturnc"t||zd|S)N)r)r;r<r=s ]/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/torchvision/models/efficientnet.pyadjust_channelsz_MBConvConfig.adjust_channels9sx*4aCCN) __name__ __module__ __qualname__float__annotations__intrr Module staticmethodrrBrCrAr3r3/so K KO CN ##D#D5DXc]D^aDDrCr3ceZdZ ddededededededed ed eed ejfd dffd Z e ded efdZ xZ S) MBConvConfigNr4r5r6r7r8r9r< depth_multr:.r>c |j||}|j||}|j||}| t} t |||||||| yrD)rB adjust_depthMBConvsuper__init__) selfr4r5r6r7r8r9r<rPr: __class__s rArUzMBConvConfig.__init__@s`--njI++L*E &&z:> =E vv~|U_afgrCcDttj||zSrD)rJmathceil)r9rPs rArRzMBConvConfig.adjust_depthSs499Z*4566rC)?r[N) rErFrGrHrJrrr rKrUrLrR __classcell__rWs@rArOrO>s 48hhh h  h  hhhhbii01h h&77%77rCrOcheZdZ d dededededededeed ejfd dffd Z xZ S) FusedMBConvConfigNr4r5r6r7r8r9r:.r>c @|t}t| |||||||yrD) FusedMBConvrTrU) rVr4r5r6r7r8r9r:rWs rArUzFusedMBConvConfig.__init__Zs* =E vv~|U_afgrCrD) rErFrGrHrJrrr rKrUr\r]s@rAr_r_Xs|59 h h h h  h  h hbii01 h  h hrCr_c eZdZefdedededejfdedejfddf fd Z d e de fd Z xZ S) rScnfstochastic_depth_prob norm_layer.se_layerr>Nc ~t |d|jcxkrdkstdtd|jdk(xr|j|j k(|_g}tj}|j|j|j}||jk7r)|jt|j|d|||jt|||j|j|||td|jdz}|j|||ttjd |jt||j d|dtj ||_t%|d |_|j |_y) Nrr illegal stride value kernel_sizereactivation_layer)rjr6groupsrerkT)inplace) activationrow)rTrUr6 ValueErrorr7r8use_res_connectr SiLUrBr4appendrr5maxr Sequentialr:r stochastic_depth) rVrcrdrerflayersrkexpanded_channelssqueeze_channelsrWs rArUzMBConv.__init__js SZZ$1$34 4%34 4"zzQY33E3EIYIY3Y"$77 //0B0BCDTDTU  2 2 2 MM$&&% !)%5    !!JJzz(%!1  q#"4"4"9: h02BwWYW^W^hlOmno  !3#3#3zlp  ]]F+ /0Eu M,,rCinputcl|j|}|jr|j|}||z }|SrDr:rrrwrVr{results rAforwardzMBConv.forward7E"   **62F eOF rC) rErFrGrrOrHrr rKrUr rr\r]s@rArSrSisk .? 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Aq#sB / % ! 4 %l 223 , - aAr2q 1 aAr2q 1 aAr2q 1 Aq"c1 - Aq#sB / Aq#sB / Aq#sA .% !  %l 22 , - aAr2q 1 aAr2q 1 aAr2q 1 Aq"c2 . Aq#sB / Aq#sB / Aq#sA .% !  %l 2224&9::rCr _COMMON_META)rrzUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v1)min_sizerecipe)!rzUhttps://github.com/pytorch/vision/tree/main/references/classification#efficientnet-v2c leZdZedeeddejieddddd id d d d Z e Z y)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_docsurl transformsrN rErFrGrrrrBICUBIC_COMMON_META_V1 IMAGENET1K_V1DEFAULTrMrCrArra X 3CO`OhOh     !##   L  M(GrCrc eZdZedeeddejieddddd id d d d Z edeeddejiedddddd id dddZ e Z y)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) rErFrGrrrrrrrBILINEAR IMAGENET1K_V2r rMrCrArrs X 3CO`OhOh     !##   L  M( N 3CO`OiOi   !g##    M0GrCrc leZdZedeeddejiedddddid d d d  Z e Z y)rzJhttps://download.pytorch.org/models/efficientnet_b2_rwightman-c35c1473.pthi rirgx&T@gp= W@rg rh?gʡEA@rrrNrrMrCrArrr rCrc leZdZedeeddejieddddd id d d d Z e Z y)rzJhttps://download.pytorch.org/models/efficientnet_b3_rwightman-b3899882.pthi,rrirgnT@g~jtX@rgZd;?gd;OG@rrrNrrMrCrArra X 3CO`OhOh     "##   L  M(GrCrc leZdZedeeddejieddddd id d d d Z e Z y)r zJhttps://download.pytorch.org/models/efficientnet_b4_rwightman-23ab8bcd.pthi|rri0!'rgjtT@gt&X@rg~jt@gKR@rrrNrrMrCrAr r 0rrCr c leZdZedeeddejiedddddid d d d  Z e Z y)r!zJhttps://download.pytorch.org/models/efficientnet_b5_lukemelas-1a07897c.pthirirg#~jT@gx&1(X@rgx&1$@gK7]@rrrNrrMrCrAr!r!Ha X 3CO`OhOh     "##  !L  M(GrCr!c leZdZedeeddejiedddddid d d d  Z e Z y)r"zJhttps://download.pytorch.org/models/efficientnet_b6_lukemelas-24a108a5.pthirirgnU@gv:X@rg rh3@g$d@rrrNrrMrCrAr"r"`rrCr"c leZdZedeeddejiedddddid d d d  Z e Z y)r#zJhttps://download.pytorch.org/models/efficientnet_b7_lukemelas-c5b4e57e.pthiXricrg+U@g'1:X@rgsh|B@go@rrrNrrMrCrAr#r#xrrCr#c leZdZedeeddejiedddddid d d d  Z e Z y)r$zBhttps://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pthrri8nGrg;OU@gx&18X@rgZd @gVT@rrrN rErFrGrrrrr_COMMON_META_V2rr rMrCrAr$r$se P +44    "##    M4GrCr$c leZdZedeeddejiedddddid d d d  Z e Z y)r%zBhttps://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pthri:rgI +GU@gDlIX@rgE8@gQj@rrrNrrMrCrAr%r%se P +44    "##    M4GrCr%c peZdZedeeddejddieddddd id d d d Z e Z y)r&zBhttps://download.pytorch.org/models/efficientnet_v2_l-59c71312.pthr)?rr)rrrmeanstdiHfrgʡEsU@gOnrX@rg ףp= L@gI +i|@rrrN) rErFrGrrrrrrrr rMrCrAr&r&si P +33       ###  !L  M0GrCr& pretrained)rT)rrc tj|}tddd\}}t||j 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'r[rrr)rverifyrrrrrrrrs rAr'r'W.&,,W5G.@AR_bor.s+| !6::i#=|WV^ bh rCc tj|}tddd\}}t||j 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(r[皙?rrr)rr$rrrr%s rAr(r(r&rCc tj|}tddd\}}t||j dd|||fi|S)aEfficientNet B2 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B2_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B2_Weights :members: r)r(333333?rr333333?)rr$rrrr%s rAr)r)'r&rCc tj|}tddd\}}t||j dd|||fi|S)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*r*ffffff?rrr+)rr$rrrr%s rAr*r*F\.&,,W5G.@AR_bor.s+| ! 9c"     rCc tj|}tddd\}}t||j dd|||fi|S)aEfficientNet B4 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B4_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B4_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B4_Weights :members: r+r-?rr皙?)r r$rrrr%s rAr+r+jr.rCc tj|}tddd\}}t||j dd|||fdt t jdd 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@rrr1reMbP?{Gz?epsmomentum)r!r$rrrrr rr%s rAr,r,s.&,,W5G.@AR_bor.s+| ! 9c"   2>>utD    rCc tj|}tddd\}}t||j dd|||fdt t jdd i|S) aEfficientNet B6 model architecture from the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks `_ paper. Args: weights (:class:`~torchvision.models.EfficientNet_B6_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_B6_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_B6_Weights :members: r-r0g@rrrrer3r4r5)r"r$rrrrr rr%s rAr-r-r8rCc tj|}tddd\}}t||j dd|||fdt t jdd i|S) 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@rrrrer3r4r5)r#r$rrrrr rr%s rAr.r.r8rCc tj|}td\}}t||j dd|||fdt t jdi|S)a Constructs an EfficientNetV2-S architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_S_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_S_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_S_Weights :members: r/rrrer3r6)r$r$rrrrr rr%s rAr/r/k0(..w7G.@AT.U+| ! 9c"   2>>u5    rCc tj|}td\}}t||j dd|||fdt t jdi|S)a Constructs an EfficientNetV2-M architecture from `EfficientNetV2: Smaller Models and Faster Training `_. Args: weights (:class:`~torchvision.models.EfficientNet_V2_M_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.EfficientNet_V2_M_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.EfficientNet_V2_M_Weights :members: r0rr+rer3r<)r%r$rrrrr rr%s rAr0r0#r=rCc tj|}td\}}t||j dd|||fdt t jdi|S)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: r1rr1rer3r<)r&r$rrrrr rr%s rAr1r1Ir=rC)OrrYcollections.abcr dataclassesr functoolsrtypingrrrr rr r torchvision.opsr ops.miscrrtransforms._presetsrrutilsr_apirrr_metar_utilsrrr__all__r3rOr_rKrSrarrHrJboolrstrtuplerrdictrIrrrrrrr r!r"r#r$r%r&rr'r(r)r*r+r,r-r.r/r0r1rMrCrArPs $!11 +>H'66'SS 6  D D  D7=74h h"@RYY@F:")):zo%299o%d'l