kh7 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$Z.ed)de&dede"fd%Z/ed)de&dede"fd&Z0ed)de&dede"fd'Z1y)*zVGG Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for timm functionality. Copyright 2021 Ross Wightman )AnyDictListOptionalTypeUnioncastNIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)ClassifierHead)build_model_with_cfg)register_notrace_module)register_modelgenerate_default_cfgsVGG) @Mrrrrrrrr)rrrrrrrrrrrrrrr)rrrrrrrrrrrrrrrrrr)rrrrrrrrrrrrrrrrrrrrr)vgg11vgg13vgg16vgg19cfgsceZdZdZdddddej ej fdeded ed ed ed e ejd e ejffd Z de jde jfdZxZS)ConvMlpzxConvolutional MLP block for VGG head. Replaces traditional Linear layers with Conv2d layers in the classifier. r?g? in_features out_features kernel_size mlp_ratio drop_rate act_layer conv_layerctt| ||_t ||z}||||d|_|d|_tj||_ |||dd|_ |d|_ y)aInitialize ConvMlp. Args: in_features: Number of input features. out_features: Number of output features. kernel_size: Kernel size for first conv layer. mlp_ratio: Ratio for hidden layer size. drop_rate: Dropout rate. act_layer: Activation layer type. conv_layer: Convolution layer type. T)biasrN) superr__init__input_kernel_sizeintfc1act1nnDropoutdropfc2act2) selfr#r$r%r&r'r(r) mid_features __class__s K/var/www/teggl/fontify/venv/lib/python3.12/site-packages/timm/models/vgg.pyr-zConvMlp.__init__&ss* gt%'!,<)34 k<4PdO JJy) lL!$GdO xreturnc|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. ) shaper.maxFadaptive_avg_pool2dr0r1r4r5r6)r7r< output_sizes r:forwardzConvMlp.forwardDs 772;// /1772;AWAW3Wt55qwwr{CSI_I_abahahikalEmnK%%a5A HHQK IIaL IIaL HHQK IIaLr;)__name__ __module__ __qualname____doc__r2ReLUConv2dr/floatrModuler-torchTensorrF __classcell__r9s@r:rrs # $ "")+*,))$$$ $  $  $BII$RYY$<%,,r;rceZdZdZddddej ej dddf d eed e d e d e d e de ejde ejde e ejdede ddffd Zej"j$d!dedeeeffdZej"j$d"deddfdZej"j$dejfdZd#d e de eddfdZdej2dej2fdZd!dej2dedej2fdZdej2dej2fdZd$d ZxZS)%rzVGG model architecture. Based on `Very Deep Convolutional Networks for Large-Scale Image Recognition` - https://arxiv.org/abs/1409.1556  r"Navggcfg num_classesin_chans output_strider&r(r) norm_layer global_poolr'r=c :tt| |dk(sJ||_| |_d|_|du|_g|_|} d} tj} g}|D]}t|dz }|dk(r>|jjt| | d||| dd gz }| dz} Ttt|}|| |d d }||||||d gz }n|||d gz }|} tj||_|jjt| | dt|dz | |_d|_t'| |j$d|| |||_t+|j$|| | |_|j/y)a/Initialize VGG model. Args: cfg: Configuration list defining network architecture. num_classes: Number of classes for classification. in_chans: Number of input channels. output_stride: Output stride of network. mlp_ratio: Ratio for MLP hidden layer size. act_layer: Activation layer type. conv_layer: Convolution layer type. norm_layer: Normalization layer type. global_pool: Global pooling type. drop_rate: Dropout rate. rVFNrrz features.)num_chs reductionmodule)r%striderU)r%paddingT)inplacer r!)r&r'r(r)) pool_typer')r,rr-rYr'grad_checkpointinguse_norm feature_infor2 MaxPool2dlenappenddictr r/ Sequentialfeatures num_featureshead_hidden_sizer pre_logitsr head_initialize_weights)r7rXrYrZr[r&r(r)r\r]r'prev_chs net_stride pool_layerlayersvlast_idxconv2dr9s r:r-z VGG.__init__`s6 c4!#"""&""'"$.  \\ "$ A6{QHCx!!((h*_hiqhr]s)tu:!A>??a aL#HaQJ)vz!}i6MNNFvy'>??F  v.    h*W`adekalopap`qUr!st$ $!   ! ! ! #  ! ! !      "r;coarsectddS)zGroup matcher for parameter groups. Args: coarse: Whether to use coarse grouping. Returns: Dictionary of grouped parameters. z ^features\.0z^features\.(\d+))stemblocks)rm)r7r|s r: group_matcherzVGG.group_matchers1DEEr;enablec|rJdy)zEnable or disable gradient checkpointing. Args: enable: Whether to enable gradient checkpointing. z$gradient checkpointing not supportedN)r7rs r:set_grad_checkpointingzVGG.set_grad_checkpointingsAAAz6r;c.|jjS)zTGet the classifier module. Returns: Classifier module. )rsfc)r7s 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)rYrsreset)r7rYr]s r:reset_classifierzVGG.reset_classifiers'  [1r;r<c(|j|}|S)zForward pass through feature extraction layers. Args: x: Input tensor. Returns: Feature tensor. )ror7r<s r:forward_featureszVGG.forward_featuress MM! r;rrcp|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. )rr)rrrs)r7r<rrs r: forward_headzVGG.forward_heads5 OOA 6@tyyzy2RdiiPQlRr;cJ|j|}|j|}|S)zoForward pass. Args: x: Input tensor. Returns: Output logits. )rrrs r:rFz 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 isinstancer2rLinitkaiming_normal_weightr+ constant_ BatchNorm2dLinearnormal_)r7ms r:rtzVGG._initialize_weightss -A!RYY'''yv'V66%GG%%affa0Ar~~.!!!((A.!!!&&!,Aryy)!T2!!!&&!, -r;F)T)N)r=N)rGrHrIrJr2rKrLrrr/rMrrNrstrr-rOjitignoreboolrrrrrrPrrrFrtrQrRs@r:rrYs $!#")+*,))48$!J#cJ#J# J#  J#  J#BIIJ#RYYJ#!bii1J#J#J# J#X 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.weightr@rr!zclassifier.3.weightr r)itemsreplacereshape)rout_dictkryk_rs r: _filter_fnr sH  " 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)rrrbrUT)flatten_sequentialr) model_cfg feature_cfgpretrained_filter_fn)splitpoprrrrmr)rrrrXrmodels r: _create_vggr!sg -- Q C**],>?K   s)DkJ'    E Lr;urlc 0|dddddttddd |S) zCreate default configuration dictionary. Args: url: Model weight URL. **kwargs: Additional configuration options. Returns: Configuration dictionary. rT)rUr)r!r!g?bilinearz features.0r) rrY input_size pool_sizecrop_pct interpolationmeanstd first_conv classifierr )rrs r:_cfgr;s2=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)rrmrrr model_argss r:rr[" 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_bnrmr2rrrs r:rrd- ::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:rrmrr;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:rrvrr;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)2rJtypingrrrrrrr rOtorch.nnr2torch.nn.functional functionalrC timm.datar r timm.layersr _builderr _features_fxr _registryrr__all__rrr/__annotations__rNrrrmrPrrrr default_cfgsrrrrrrrrrr;r:rs@?? A&*1< 'N V e t *d3U38_% %&6bii66rn-"))n-b4Dell):$;.$##4cT#s(^(%G,G,G,G,w/w/w/w/ &  EdEcEcEEHHHHHEdEcEcEEHHHHHEdEcEcEEHHHHHEdEcEcEEHHHHHr;