^j"9 2UdZddlmZmZmZmZmZmZmZddl Z ddl m Z ddl m cm ZddlmZmZddlmZddlmZddlmZdd lmZmZd gZgd gd gd gddZeeeeeeffed<eGdde j@Z!Gdd e j@Z"de#deee jHffdZ%dede&dede"fdZ'd(dedeeeffdZ(ee(de(de(de(de(de(de(de(ddZ)ed)de&dede"fd Z*ed)de&dede"fd!Z+ed)de&dede"fd"Z,ed)de&dede"fd#Z-ed)de&dede"fd$Z.ed)de&dede"fd%Z/ed)de&dede"fd&Z0ed)de&dede"fd'Z1y)*zVGG Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for timm functionality. Copyright 2021 Ross Wightman )AnyDictListOptionalTypeUnioncastNIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)ClassifierHead)build_model_with_cfg)register_notrace_module)register_modelgenerate_default_cfgsVGG) @Mrrrrrrrr)rrrrrrrrrrrrrrr)rrrrrrrrrrrrrrrrrr)rrrrrrrrrrrrrrrrrrrrr)vgg11vgg13vgg16vgg19cfgsceZdZdZdddddej ej ddf ded ed ed ed ed e ejde ejddffd Z de jde jfdZxZS)ConvMlpzxConvolutional MLP block for VGG head. Replaces traditional Linear layers with Conv2d layers in the classifier. r?g?N in_features out_features kernel_size mlp_ratio drop_rate act_layer conv_layerreturnc || d} t |||_t||z} ||| |fddi| |_|d|_t j||_|| |dfddi| |_ |d|_ y)aInitialize ConvMlp. Args: in_features: Number of input features. out_features: Number of output features. kernel_size: Kernel size for first conv layer. mlp_ratio: Ratio for hidden layer size. drop_rate: Dropout rate. act_layer: Activation layer type. conv_layer: Convolution layer type. devicedtypebiasTrN) super__init__input_kernel_sizeintfc1act1nnDropoutdropfc2act2) selfr#r$r%r&r'r(r)r-r.dd mid_features __class__s Z/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/models/vgg.pyr1zConvMlp.__init__&s. / !,<)34 k<V4VSUVdO JJy) lL!M$M"MdO xc|jd|jks|jd|jkr\t|j|jdt|j|jdf}tj||}|j |}|j |}|j|}|j|}|j|}|S)zoForward pass. Args: x: Input tensor. Returns: Output tensor. ) shaper2maxFadaptive_avg_pool2dr4r5r8r9r:)r;rA output_sizes r?forwardzConvMlp.forwardGs 772;// /1772;AWAW3Wt55qwwr{CSI_I_abahahikalEmnK%%a5A HHQK IIaL IIaL HHQK IIaLr@)__name__ __module__ __qualname____doc__r6ReLUConv2dr3floatrModuler1torchTensorrJ __classcell__r>s@r?rrs # $ "")+*,))$$$ $  $  $BII$RYY$ $B%,,r@rceZdZdZddddej ej dddddf d eed e d e d e d e de ejde ejde e ejdede ddffd Zej"j$d!dedeeeffdZej"j$d"deddfdZej"j$dejfdZd#d e de eddfdZdej2dej2fdZd!dej2dedej2fdZdej2dej2fdZd$d ZxZS)%rzVGG model architecture. Based on `Very Deep Convolutional Networks for Large-Scale Image Recognition` - https://arxiv.org/abs/1409.1556  r"Navggcfg num_classesin_chans output_strider&r(r) norm_layer global_poolr'r*c Rt|| | d} |dk(sJ||_||_| |_d|_|du|_g|_|}d}tj}g}|D]}t|dz }|dk(r>|jjt||d|||d d gz }|d z}Ttt|}|||fd dd | }|||||fi| |d gz }n|||d gz }|}tj||_|jjt||dt|dz ||_d|_t'||j$df|| ||d| |_t+|j$|f| | d| |_|j/y)a/Initialize VGG model. Args: cfg: Configuration list defining network architecture. num_classes: Number of classes for classification. in_chans: Number of input channels. output_stride: Output stride of network. mlp_ratio: Ratio for MLP hidden layer size. act_layer: Activation layer type. conv_layer: Convolution layer type. norm_layer: Normalization layer type. global_pool: Global pooling type. drop_rate: Dropout rate. r,rZFNrrz features.)num_chs reductionmodule)r%striderY)r%paddingT)inplacer r!)r&r'r(r)) pool_typer')r0r1r]r^r'grad_checkpointinguse_norm feature_infor6 MaxPool2dlenappenddictr r3 Sequentialfeatures num_featureshead_hidden_sizer pre_logitsr head_initialize_weights)r;r\r]r^r_r&r(r)r`rar'r-r.r<prev_chs net_stride pool_layerlayersvlast_idxconv2dr>s r?r1z VGG.__init__cs:  /"""&  ""'"$.  \\ "$ A6{QHCx!!((h*_hiqhr]s)tu:!A>??a aL#HaPQPRP)vz!':r':Id??F  v.    h*W`adekalopap`qUr!st$ $!   ! !    !    #  ! !  "        "r@coarsectddS)zGroup matcher for parameter groups. Args: coarse: Whether to use coarse grouping. Returns: Dictionary of grouped parameters. z ^features\.0z^features\.(\d+))stemblocks)rq)r;rs r? group_matcherzVGG.group_matchers1DEEr@enablec|rJdy)zEnable or disable gradient checkpointing. Args: enable: Whether to enable gradient checkpointing. z$gradient checkpointing not supportedN)r;rs r?set_grad_checkpointingzVGG.set_grad_checkpointingsAAAz6r@c.|jjS)zTGet the classifier module. Returns: Classifier module. )rwfc)r;s r?get_classifierzVGG.get_classifiersyy||r@cJ||_|jj||y)zReset the classifier. Args: num_classes: Number of classes for new classifier. global_pool: Global pooling type. N)r]rwreset)r;r]ras r?reset_classifierzVGG.reset_classifiers'  [1r@rAc(|j|}|S)zForward pass through feature extraction layers. Args: x: Input tensor. Returns: Feature tensor. )rsr;rAs r?forward_featureszVGG.forward_featuress MM! r@rvcp|j|}|r|j||S|j|S)zForward pass through head. Args: x: Input features. pre_logits: Return features before final linear layer. Returns: Classification logits or features. )rv)rvrw)r;rArvs r? forward_headzVGG.forward_heads5 OOA 6@tyyzy2RdiiPQlRr@cJ|j|}|j|}|S)zoForward pass. Args: x: Input tensor. Returns: Output logits. )rrrs r?rJz VGG.forwards)  ! !! $   a r@c|jD]a}t|tjrdtjj |j dd|jWtjj|jdt|tjrUtjj|j dtjj|jdt|tjs tjj|j ddtjj|jddy)zInitialize model weights.fan_outrelu)mode nonlinearityNrrg{Gz?) modules isinstancer6rPinitkaiming_normal_weightr/ constant_ BatchNorm2dLinearnormal_)r;ms r?rxzVGG._initialize_weightss -A!RYY'''yv'V66%GG%%affa0Ar~~.!!!((A.!!!&&!,Aryy)!T2!!!&&!, -r@F)T)N)r*N)rKrLrMrNr6rOrPrrr3rQrrRrstrr1rSjitignoreboolrrrrrrTrrrJrxrUrVs@r?rr\s $!#")+*,))48$!P#cP#P# P#  P#  P#BIIP#RYYP#!bii1P#P#P# P#d YY FD FT#s(^ F F YYBTBTBB YY 2C2hsm2W[2 %,, 5<<  Sell S S S  %,,  -r@ state_dictr*ci}|jD]r\}}|}|jdd}|jdd}|jdd}d|vr|jdd d d }d |vr|jdd d d }|||<t|S)zConvert patch embedding weight from manual patchify + linear proj to conv. Args: state_dict: State dictionary to filter. Returns: Filtered state dictionary. z classifier.0zpre_logits.fc1z classifier.3zpre_logits.fc2z classifier.6head.fczclassifier.0.weightrDrr!zclassifier.3.weightr r)itemsreplacereshape)rout_dictkr}k_rs r? _filter_fnrsH  " 1kk.*:;kk.*:;kk.)4 A % "c1a(A A % "dAq)A   Or@variant pretrainedkwargsc |jdd}|jdd}tt||ft|t d|t d|}|S)zCreate a VGG model. Args: variant: Model variant name. pretrained: Load pretrained weights. **kwargs: Additional model arguments. Returns: VGG model instance. _r out_indices)rrrfrYT)flatten_sequentialr) model_cfg feature_cfgpretrained_filter_fn)splitpoprrrrqr)rrrr\rmodels r? _create_vggr*sg -- Q C**],>?K   s)DkJ'    E Lr@urlc 2|dddddttdddd |S) zCreate default configuration dictionary. Args: url: Model weight URL. **kwargs: Additional configuration options. Returns: Configuration dictionary. rX)rYr)r!r!g?bilinearz features.0rz bsd-3-clause) rr] input_size pool_sizecrop_pct interpolationmeanstd first_conv classifierlicenser )rrs r?_cfgrDs5=vJ%.B")!    r@ztimm/) hf_hub_id)z vgg11.tv_in1kz vgg13.tv_in1kz vgg16.tv_in1kz vgg19.tv_in1kzvgg11_bn.tv_in1kzvgg13_bn.tv_in1kzvgg16_bn.tv_in1kzvgg19_bn.tv_in1kc 2tdi|}tdd|i|S)zVGG 11-layer model (configuration "A") from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ rr)rrqrrr model_argss r?rre" J  D: D DDr@c Rtddtji|}tdd|i|S)zVGG 11-layer model (configuration "A") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ r`rr)vgg11_bnrqr6rrrs r?rrn- ::6:J  Gj GJ GGr@c 2tdi|}tdd|i|S)zVGG 13-layer model (configuration "B") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ rr)rrrs r?rrwrr@c Rtddtji|}tdd|i|S)zVGG 13-layer model (configuration "B") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ r`rr)vgg13_bnrrs r?rrrr@c 2tdi|}tdd|i|S)zVGG 16-layer model (configuration "D") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ rr)rrrs r?rrrr@c Rtddtji|}tdd|i|S)zVGG 16-layer model (configuration "D") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ r`rr)vgg16_bnrrs r?rrrr@c 2tdi|}tdd|i|S)zVGG 19-layer model (configuration "E") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ rr)rrrs r?rrrr@c Rtddtji|}tdd|i|S)zVGG 19-layer model (configuration 'E') with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `._ r`rr)vgg19_bnrrs r?rrrr@)r)2rNtypingrrrrrrr rStorch.nnr6torch.nn.functional functionalrG timm.datar r timm.layersr _builderr _features_fxr _registryrr__all__rrr3__annotations__rRrrrqrTrrrr default_cfgsrrrrrrrrrr@r?rs@?? A&*1< 'N V e t *d3U38_% %&9bii99xt-"))t-n4Dell):$;.$##4cT#s(^*%G,G,G,G,w/w/w/w/ &  EdEcEcEEHHHHHEdEcEcEEHHHHHEdEcEcEEHHHHHEdEcEcEEHHHHHr@