RЦiNSrSSKrSSKJr SSKJrJrJr SSKrSSK J r SSK J s J r SSKJrJr SSKJr SSKJr SS KJrJr S /rS r"S S \ R45r"SS\ R45r"SS\5r"SS\5r"SS\5r"SS\ R45r "SS \ R45r!S2Sjr"S3Sjr#\"\#"SS9\#"SSS9\#"S S9\#"S!S9\#"S"S9\#"S#S9\#"S$SS9\#"S%S9\#"S&S9S'. 5r$\S2S(\!4S)jj5r%\S2S(\!4S*jj5r&\S2S(\!4S+jj5r'\S2S(\!4S,jj5r(\S2S(\!4S-jj5r)\S2S(\!4S.jj5r*\S2S(\!4S/jj5r+\S2S(\!4S0jj5r,\S2S(\!4S1jj5r-g)4a: SEResNet implementation from Cadene's pretrained models https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py Additional credit to https://github.com/creafz Original model: https://github.com/hujie-frank/SENet ResNet code gently borrowed from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py FIXME I'm deprecating this model and moving them to ResNet as I don't want to maintain duplicate support for extras like dilation, switchable BN/activations, feature extraction, etc that don't exist here. N) OrderedDict)TypeOptionalTupleIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)create_classifier)build_model_with_cfg)register_modelgenerate_default_cfgsSENetc~[U[R5(a*[RR UR SSS9 g[U[R 5(aU[RRUR S5 [RRURS5 gg)Nfan_outrelu)mode nonlinearityg?) isinstancennConv2dinitkaiming_normal_weight BatchNorm2d constant_bias)ms P/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/models/senet.py _weight_initr!sv!RYY yvN Ar~~ & & !((B' !&&"% 'c>^\rSrSrSS\S\4U4SjjjrSrSrU=r$)SEModule&channels reductionc>X4S.n[TU]5 [R"XU-4SS0UD6Ul[R "SS9Ul[R"X-U4SS0UD6Ul[R"5Ul g)Ndevicedtype kernel_sizer Tinplace) super__init__rrfc1ReLUrfc2Sigmoidsigmoid)selfr&r'r*r+dd __class__s r r0SEModule.__init__(sq / 99X9'<R!RrRGGD) 99X2HR!RrRzz| r"cUnURSSS9nURU5nURU5nURU5nUR U5nX!-$)N)T)keepdim)meanr1rr3r5)r6x module_inputs r forwardSEModule.forward0sV FF64F ( HHQK IIaL HHQK LLOr")r1r3rr5)NN) __name__ __module__ __qualname____firstlineno__intr0rA__static_attributes__ __classcell__r8s@r r$r$&s%$$$$  r"r$c\rSrSrSrSrSrg) Bottleneck:z@ Base class for bottlenecks that implements `forward()` method. cUnURU5nURU5nURU5nURU5nUR U5nURU5nUR U5nUR U5nURbURU5nURU5U-nURU5nU$N) conv1bn1rconv2bn2conv3bn3 downsample se_moduler6r?shortcutouts r rABottleneck.forward?sjjmhhsmiinjjohhsmiinjjohhsm ?? &q)HnnS!H,iin r"N)rCrDrErF__doc__rArHr\r"r rLrL:s r"rLcr^\rSrSrSrSrS S\S\S\S\S\S \\R4 U4S jjjr S r U=r $) SEBottleneckVz Bottleneck for SENet154. inplanesplanesgroupsr'striderVc <>XxS.n [T U]5 [R"XS-4SSS.U D6Ul[R "US-40U D6Ul[R"US-US-4SUSUSS.U D6Ul[R "US-40U D6Ul[R"US-US-4SSS.U D6Ul [R "US-40U D6Ul [R"S S 9Ul [US-4S U0U D6UlX`lXPlg) Nr)r;r Fr,rrar<r,repaddingrdrTr-r'r/r0rrrPrrQrRrSrTrUr2rr$rWrVre r6rbrcrdr'rerVr*r+r7r8s r r0SEBottleneck.__init__\s / YYx!UURTU >>&1*33YY QJ QJ       >>&1*33YYvz6A:W15WTVW >>&1*33GGD) !&1*H HRH$ r" rQrSrUrPrRrTrVrrWrer NNN rCrDrErFr] expansionrGrrModuler0rHrIrJs@r r_r_VsjI.2     !+r"r_cr^\rSrSrSrSrS S\S\S\S\S\S \\R4 U4S jjjr S r U=r $) SEResNetBottleneck~z ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe implementation and uses `stride=stride` in `conv1` and not in `conv2` (the latter is used in the torchvision implementation of ResNet). rarbrcrdr'rerVc >XxS.n [T U]5 [R"X4SSUS.U D6Ul[R "U40U D6Ul[R"X"4SSUSS.U D6Ul[R "U40U D6Ul[R"X"S-4SSS.U D6Ul [R "US-40U D6Ul [R"S S 9Ul [US-4S U0U D6UlX`lXPlg) Nr)r Fr,rrer<r,rirdrrargTr-r'rjrks r r0SEResNetBottleneck.__init__s / YYx`QUSY`]_` >>&/B/YYvi1aPV]bifhi >>&/B/YYvzSquSPRS >>&1*33GGD) !&1*H HRH$ r"rmrnrorJs@r rsrs~sj I.2     !+r"rscx^\rSrSrSrSrS S\S\S\S\S\S \\RS \4U4S jjjr S r U=r $)SEResNeXtBottleneckzA ResNeXt bottleneck type C with a Squeeze-and-Excitation module. rarbrcrdr'rerV base_widthc R>XS.n [T U]5 [R"X'S- -5U-n [R "X4SSSS.U D6Ul[R"U 40U D6Ul[R "X4SUSUSS.U D6Ul [R"U 40U D6Ul [R "XS-4SSS .U D6Ul [R"US-40U D6Ul [R"S S 9Ul[US-4S U0U D6UlX`lXPlg) Nr)@r Frvr<rhrargTr-r')r/r0mathfloorrrrPrrQrRrSrTrUr2rr$rWrVre) r6rbrcrdr'rerVr|r*r+r7widthr8s r r0SEResNeXtBottleneck.__init__s /  6"_56?YYxZAERSZWYZ >>%.2.YYuv6ST]cjovsuv >>%.2.YYuqjRaeRrR >>&1*33GGD) !&1*H HRH$ r"rm)r NraNNrorJs@r rzrzswI.2     !+r"rzct^\rSrSrSrS S\S\S\S\S\S\\R4 U4S jjjr S r S r U=r $) SEResNetBlockr rbrcrdr'rerVc >XxS.n [T U]5 [R"X4SSUSS.U D6Ul[R "U40U D6Ul[R"X"4SSUSS.U D6Ul[R "U40U D6Ul[R"SS9Ul [U4S U0U D6Ul X`l XPlg) Nr)r<r F)r,rirerrwTr-r')r/r0rrrPrrQrRrSr2rr$rWrVrerks r r0SEResNetBlock.__init__s / YYxkQRX_dkhjk >>&/B/YYvi1aPV]bifhi >>&/B/GGD) !&DIDD$ r"c\UnURU5nURU5nURU5nURU5nUR U5nURU5nUR bUR U5nUR U5U-nURU5nU$rO)rPrQrrRrSrVrWrXs r rASEResNetBlock.forwardsjjmhhsmiinjjohhsmiin ?? &q)HnnS!H,iin r")rQrSrPrRrVrrWrern) rCrDrErFrprGrrrqr0rArHrIrJs@r rrsmI.2     !+,r"rc^\rSrSrSS\\R S\\S4S\S\S\ S\S \S \ S \S \S \S\ 4U4Sjjjr SSjr \RR SSj5r\RR SSj5r\RR S\R 4Sj5rSS \S\ 4SjjrSrSS\ 4SjjrSrSrU=r$) rblocklayers.rdr' drop_ratein_chansrb input_3x3downsample_kernel_sizedownsample_padding num_classes global_poolc>[TU]5 XS.nXplXlX`lXPlU(aS[ R"USS4SSSS.UD64S [ R"S)0UD64S [ R"S S 94S [ R"SSSSS.UD64S[ R"S)0UD64S[ R"S S 94S[ R"SUS4SSSS.UD64S[ R"U40UD64S[ R"S S 94/ nOLS[ R"Xg4SSSSS.UD64S [ R"U40UD64S [ R"S S 94/n[ R"[U55Ul [ R"SSS S9Ul[USSS9/UlUR""U4SUSUUSSS.UD6UlU=R [SUR&-SSS9/- slUR""U4SUSSUUU U S.UD6UlU=R [SUR&-SS S9/- slUR""U4S!USSUUU U S.UD6UlU=R [S!UR&-S"S#S9/- slUR""U4S$USSUUU U S.UD6UlU=R [S$UR&-S%S&S9/- slS$UR&-=UlUl[3UR.UR4S'U 0UD6uUlUlUR95Hn[;U5 M g()*a Parameters ---------- block (nn.Module): Bottleneck class. - For SENet154: SEBottleneck - For SE-ResNet models: SEResNetBottleneck - For SE-ResNeXt models: SEResNeXtBottleneck layers (list of ints): Number of residual blocks for 4 layers of the network (layer1...layer4). groups (int): Number of groups for the 3x3 convolution in each bottleneck block. - For SENet154: 64 - For SE-ResNet models: 1 - For SE-ResNeXt models: 32 reduction (int): Reduction ratio for Squeeze-and-Excitation modules. - For all models: 16 dropout_p (float or None): Drop probability for the Dropout layer. If `None` the Dropout layer is not used. - For SENet154: 0.2 - For SE-ResNet models: None - For SE-ResNeXt models: None inplanes (int): Number of input channels for layer1. - For SENet154: 128 - For SE-ResNet models: 64 - For SE-ResNeXt models: 64 input_3x3 (bool): If `True`, use three 3x3 convolutions instead of a single 7x7 convolution in layer0. - For SENet154: True - For SE-ResNet models: False - For SE-ResNeXt models: False downsample_kernel_size (int): Kernel size for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 3 - For SE-ResNet models: 1 - For SE-ResNeXt models: 1 downsample_padding (int): Padding for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 1 - For SE-ResNet models: 0 - For SE-ResNeXt models: 0 num_classes (int): Number of outputs in `last_linear` layer. - For all models: 1000 r)rPr~r<r;r F)rerirrQrelu1Tr-rR)r~r~r<rSrelu2rTrUrelu3r,rerir)re ceil_modelayer0)num_chsr'moduler)rcblocksrdr'rrralayer1)rcrrerdr'rrlayer2layer3i layer4 pool_typeN)r~)r/r0rbrrrrrrr2 Sequentialrr MaxPool2dpool0dict feature_info _make_layerrrprrr num_featureshead_hidden_sizer r last_linearmodulesr!)r6rrrdr'rrrbrrrrrr*r+r7layer0_modulesrr8s r r0SENet.__init__sx  / & " "))Hb![Aqu[XZ[\0R01"''$/0"))UaURTUV0R01"''$/0"))B![Aqu[XZ[\x6267"''$/0 N"))HmAaYZafmjlmnx6267"''$/0N mmK$?@ \\!A> !(aQR&&   !9#$       d2+?1U]^__&&   !9#91      d3+@AV^_``&&   !9#91      d3+@BW_`aa&&   !9#91      d3+@BW_`aa47%//4IID1->      . ".  . *$*A O r"c  XS.n Sn US:wdURX!R-:wah[R"[R"URX!R-4UXhSS.U D6[R "X!R-40U D65n U"URX$XVU 40U D6/n X!R-Ul[ SU5H'nU RU"URX$U40U D65 M) [R"U 6$)Nr)r Fr)rbrprrrrrangeappend)r6rrcrrdr'rerrr*r+r7rVris r rSENet._make_layers / Q;$--6OO+CC MM6OO#;QI_!EQMOQv7>2> J vy*[XZ[\0 q&!A MM% vyOBO P"}}f%%r"c,[SU(aSOSS9nU$)Nz^layer0z ^layer(\d+)z^layer(\d+)\.(\d+))stemr)r)r6coarsematchers r group_matcherSENet.group_matchersJ~Mbcr"c U(aS5eg)Nz$gradient checkpointing not supportedr\)r6enables r set_grad_checkpointingSENet.set_grad_checkpointingsAAAz6r"returncUR$rO)r)r6s r get_classifierSENet.get_classifiersr"chXl[URURUS9uUlUlg)N)r)rr rrr)r6rrs r reset_classifierSENet.reset_classifiers1&->   t//;.H*$*r"cURU5nURU5nURU5nURU5nUR U5nUR U5nU$rO)rrrrrrr6r?s r forward_featuresSENet.forward_featuressU KKN JJqM KKN KKN KKN KKNr" pre_logitscURU5nURS:a)[R"XRURS9nU(aU$UR U5$)Nr)ptraining)rrFdropoutrr)r6r?rs r forward_headSENet.forward_headsN   Q  >>B  !~~ FAq7D$4$4Q$77r"cJURU5nURU5nU$rO)rrrs r rA SENet.forwards'  ! !! $   a r")rrrrrrbrrrrrrrrr) g?r<r~Fr ravgNN)r r rNNF)T)r)rCrDrErFrrrqrrGfloatboolstrr0rtorchjitignorerrrrrrrArHrIrJs@r rrsi ##*+&'#$P ?P#s(OP P  P  PPPP%(P!$PPPPdLMW[&& YY YYBB YY    HCHcH 8$8 r"c $[[X40UD6$rO)r r)variant pretrainedkwargss r _create_senetrs w Ef EEr"c 2USSSSS[[SSSS . UE$) Nr)r<r)rrg?bilinearz layer0.conv1rz apache-2.0) urlr input_size pool_sizecrop_pct interpolationr>std first_conv classifierlicenser)rrs r _cfgrs24}SYJ%.B$Ml     r"zmhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_senet154-e9eb9fe6.pth)rzhhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pthbicubic)rrzhhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pthzhhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pthzihttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pthzihttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pthzphttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pthzwhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext50_32x4d-f3651bad.pthzxhttps://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext101_32x4d-37725eac.pth) zlegacy_senet154.in1kzlegacy_seresnet18.in1kzlegacy_seresnet34.in1kzlegacy_seresnet50.in1kzlegacy_seresnet101.in1kzlegacy_seresnet152.in1kzlegacy_seresnext26_32x4d.in1kzlegacy_seresnext50_32x4d.in1kzlegacy_seresnext101_32x4d.in1krc T[[/SQSSS9n[SU40[U40UD6D6$)Nr;r;r;r;r rrrrdr'legacy_seresnet18rrrrr model_argss r rr3LbJJ ,j WDr$sK  #(( A)*< )& ryy (8%:%PB*B*BII*ZKBIIK\F% {}" v!# vx" vx# w y# w y%) ~&!&* F&G&* G'H'& 0XUXX XUXX XUXX YeYY YeYY V5VV_E__ _E__ `U``r"