khAddlZddlmZddlmZddlmZmZmZm Z ddl Z ddl m Z ddl m cmZddlmcmZddl mZddlmZddlmZd d lmZmZmZd d lmZd d lmZm Z gd Z!Gdde jDZ#Gdde jHZ%Gdde jLZ'Gdde jDZ(de jDdede)ddfdZ*de+de e+e+e+e+fde+deede)dede(fdZ,d ed!d"d#Z-Gd$d%eZ.Gd&d'eZ/Gd(d)eZ0Gd*d+eZ1ee d,e.jdf-dd.d/dee.de)dede(fd0Z3ee d,e/jdf-dd.d/dee/de)dede(fd1Z4ee d,e0jdf-dd.d/dee0de)dede(fd2Z5ee d,e1jdf-dd.d/dee1de)dede(fd3Z6y)4N) OrderedDict)partial)AnyListOptionalTuple)Tensor)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) DenseNetDenseNet121_WeightsDenseNet161_WeightsDenseNet169_WeightsDenseNet201_Weights densenet121 densenet161 densenet169 densenet201c JeZdZ ddedededededdf fd Zd eedefd Z d eedefd Z e jjd eedefd Ze jjd eedefdZe jjd edefdZd edefdZxZS) _DenseLayernum_input_features growth_ratebn_size drop_ratememory_efficientreturnNct|tj||_tj d|_tj|||zddd|_tj||z|_ tj d|_ tj||z|dddd|_ t||_ ||_y)NTinplacer F kernel_sizestridebiasr)r*paddingr+)super__init__nn BatchNorm2dnorm1ReLUrelu1Conv2dconv1norm2relu2conv2floatr"r#)selfrr r!r"r# __class__s W/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/densenet.pyr0z_DenseLayer.__init__ s ^^$67 WWT* YY17[3HVW`ahmn ^^Gk$9: WWT* YYw4kqYZdelqr y) 0inputsctj|d}|j|j|j |}|SNr )torchcatr7r5r3)r<r@concated_featuresbottleneck_outputs r> bn_functionz_DenseLayer.bn_function/s;!IIfa0 JJtzz$**=N2O'PQ  r?inputc.|D]}|jsyy)NTF) requires_grad)r<rHtensors r>any_requires_gradz_DenseLayer.any_requires_grad5s" F## r?c@fd}tj|g|ddiS)Nc&j|SN)rG)r@r<s r>closurez7_DenseLayer.call_checkpoint_bottleneck..closure=s##F+ +r? use_reentrantF)cp checkpoint)r<rHrPs` r>call_checkpoint_bottleneckz&_DenseLayer.call_checkpoint_bottleneck;s# ,}}WBuBEBBr?cyrOr<rHs r>forwardz_DenseLayer.forwardB r?cyrOrVrWs r>rXz_DenseLayer.forwardFrYr?ct|tr|g}n|}|jrL|j|r;tj j r td|j|}n|j|}|j|j|j|}|jdkDr,tj||j|j }|S)Nz%Memory Efficient not supported in JITr)ptraining) isinstancer r#rLrCjit is_scripting ExceptionrTrGr:r9r8r"Fdropoutr])r<rH prev_featuresrF new_featuress r>rXz_DenseLayer.forwardLs eV $"GM!M  T%;%;M%Jyy%%' GHH $ ? ? N  $ 0 0 ? zz$**TZZ8I-J"KL >>A 99\T^^dmm\Lr?F)__name__ __module__ __qualname__intr;boolr0rr rGrLrCr_unusedrT_overload_methodrX __classcell__r=s@r>rrs rw 1"% 147 1BE 1RW 1ko 1  1!$v,!6! tF|  YYCV CCC  YY T&\ f    YY V    Vr?rcTeZdZdZ d dededededededd ffd Zd edefd Z xZ S) _DenseBlockr num_layersrr!r r"r#r$Nct |t|D]0}t|||zz||||}|j d|dzz|2y)N)r r!r"r#z denselayer%dr )r/r0ranger add_module) r<rrrr!r r"r#ilayerr=s r>r0z_DenseBlock.__init__cs_ z" =A"Q_4'#!1 E OONa!e4e < =r? init_featuresc|g}|jD]\}}||}|j| tj|dSrB)itemsappendrCrD)r<rxfeaturesnamerwres r>rXz_DenseBlock.forwardwsJ!?::< *KD% ?L OOL ) *yy1%%r?rf) rgrhri_versionrjr;rkr0r rXrnros@r>rqrq`siH"'== = =  =  == =(&V&&r?rqc,eZdZdededdffd ZxZS) _Transitionrnum_output_featuresr$Nct|tj||_tj d|_tj||ddd|_tjdd|_ y)NTr&r Fr(r )r)r*) r/r0r1r2normr4relur6conv AvgPool2dpool)r<rrr=s r>r0z_Transition.__init__s^ NN#56 GGD) II02EST]^ejk LLQq9 r?)rgrhrirjr0rnros@r>rrs":3:S:T::r?rcreZdZdZ ddedeeeeefdededededed d ffd Zd e d e fd Z xZ S)raKDensenet-BC model class, based on `"Densely Connected Convolutional Networks" `_. Args: growth_rate (int) - how many filters to add each layer (`k` in paper) block_config (list of 4 ints) - how many layers in each pooling block num_init_features (int) - the number of filters to learn in the first convolution layer bn_size (int) - multiplicative factor for number of bottle neck layers (i.e. bn_size * k features in the bottleneck layer) drop_rate (float) - dropout rate after each dense layer num_classes (int) - number of classification classes memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_. r block_confignum_init_featuresr!r" num_classesr#r$Nc "t|t|tjt dtj d|ddddfdtj|fdtjd fd tjddd fg|_ |}t|D]\} } t| |||||} |jjd| d zz| || |zz}| t|d z k7sSt||dz} |jjd| d zz| |dz}|jjdtj|tj |||_|j%D]} t'| tj r*tj(j+| j,Gt'| tjrUtj(j/| j,d tj(j/| j0dt'| tj stj(j/| j0dy)Nconv0r,r Fr-norm0relu0Tr&pool0r )r)r*r.)rrrr!r r"r#z denseblock%d)rrz transition%dnorm5r)r/r0r r1 Sequentialrr6r2r4 MaxPool2dr| enumeraterqrulenrLinear classifiermodulesr^initkaiming_normal_weight constant_r+)r<r rrr!r"rr# num_featuresrvrrblocktransmr=s r>r0zDenseNet.__init__s D! bii+?@bggd34bllqANO     ) &|4 1MAz%#/'#!1 E MM $ $^q1u%=u E'*{*BBLC %))#|YeijYjk ((1q5)A5I+q0  1"   "..*FG))L+> -A!RYY'''1Ar~~.!!!((A.!!!&&!,Aryy)!!!&&!, -r?xc|j|}tj|d}tj|d}t j |d}|j |}|S)NTr&)r r r )r|rbradaptive_avg_pool2drCflattenr)r<rr|outs r>rXzDenseNet.forwardsU==#ffXt,##C0mmC#ooc" r?)  @riF) rgrhri__doc__rjrr;rkr0r rXrnros@r>rrs "2A!#!&:-:-Cc3./:- :-  :-  :-:-:- :-xFr?rmodelweightsprogressr$c6tjd}|j|d}t|j D]D}|j |}|s|j d|j dz}||||<||=F|j|y)Nz]^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$T)r check_hashr r )recompileget_state_dictlistkeysmatchgroupload_state_dict)rrrpattern state_dictkeyresnew_keys r>_load_state_dictrs jjhG''d'KJJOO%& mmC  iilSYYq\1G",S/Jw 3   *%r?r rrkwargsc |#t|dt|jdt|||fi|}|t ||||S)Nr categories)rrr)rrmetarr)r rrrrrrs r> _densenetrsOfmSl9S5TU [,0A LV LEugI Lr?)rz*https://github.com/pytorch/vision/pull/116z'These weights are ported from LuaTorch.)min_sizerrecipe_docsc ReZdZedeediedddddid d d  ZeZy )rz@ num_params_metrics_ops _file_sizeurl transformsrN rgrhrirrr _COMMON_META IMAGENET1K_V1DEFAULTrVr?r>rr sQ J.#>    !##     M Gr?rc ReZdZedeediedddddid d d  ZeZy )rzrrsQ J.#>    "##  !  M Gr?rc ReZdZedeediedddddid d d  ZeZy )rzrr3sQ J.#>    "##     M Gr?rc ReZdZedeediedddddid d d  ZeZy )rzrrGsQ J.#>    "##     M Gr?r pretrained)rT)rrc Ltj|}tddd||fi|S)a{Densenet-121 model from `Densely Connected Convolutional Networks `_. Args: weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.DenseNet121_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.densenet.DenseNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet121_Weights :members: rrr)rverifyrrrrs r>rr[,*"((1G R"gx J6 JJr?c Ltj|}tddd||fi|S)a{Densenet-161 model from `Densely Connected Convolutional Networks `_. Args: weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.DenseNet161_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.densenet.DenseNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet161_Weights :members: 0)rr$r`)rrrrs r>rrurr?c Ltj|}tddd||fi|S)a{Densenet-169 model from `Densely Connected Convolutional Networks `_. Args: weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.DenseNet169_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.densenet.DenseNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet169_Weights :members: r)rrrrr)rrrrs r>rrrr?c Ltj|}tddd||fi|S)a{Densenet-201 model from `Densely Connected Convolutional Networks `_. Args: weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.DenseNet201_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.densenet.DenseNet`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet201_Weights :members: r)rrrrr)rrrrs r>rrrr?)7r collectionsr functoolsrtypingrrrrrCtorch.nnr1torch.nn.functional functionalrbtorch.utils.checkpointutilsrSrRr transforms._presetsr r _apirrr_metar_utilsrr__all__Moduler ModuleDictrqrrrrkrrjrrrrrrrrrrrrVr?r>rs #-- ##5'66'B >"))>B&"--&>:"--:RryyRj&BII& &t&PT&&S#s*+k "     (&: :  +(+(+(+(,0C0Q0Q!RS<@SWKH%89KDKcfKksKTK0,0C0Q0Q!RS<@SWKH%89KDKcfKksKTK0,0C0Q0Q!RS<@SWKH%89KDKcfKksKTK0,0C0Q0Q!RS<@SWKH%89KDKcfKksKTKr?