jyCFddlZddlmZddlmZddlmZmZmZm Z ddl Z ddl m Z ddl m cmZddlmcmZddl mZddlmZddlmZd d lmZmZmZd d lmZd d lmZm Z gd Z!Gdde j"Z#Gdde j$Z%Gdde j&Z'Gdde j"Z(de j"dede)ddfdZ*de+de e+e+e+e+fde+deede)dede(fdZ,d ed!d"d#Z-Gd$d%eZ.Gd&d'eZ/Gd(d)eZ0Gd*d+eZ1ee d,e.j2f-dd.d/dee.de)dede(fd0Z3ee d,e/j2f-dd.d/dee/de)dede(fd1Z4ee d,e0j2f-dd.d/dee0de)dede(fd2Z5ee d,e1j2f-dd.d/dee1de)dede(fd3Z6dS)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 8eZdZ ddedededededdf fd Zd eedefd Z d eedefd Z e j j d eedefdZe j jd eedefdZe j jd edefdZd edefdZxZS) _DenseLayerFnum_input_features growth_ratebn_size drop_ratememory_efficientreturnNcttj||_tjd|_tj|||zddd|_tj||z|_ tjd|_ tj||z|dddd|_ t||_ ||_dS)NTinplacer F kernel_sizestridebiasr)r*paddingr+)super__init__nn BatchNorm2dnorm1ReLUrelu1Conv2dconv1norm2relu2conv2floatr"r#)selfrr r!r"r# __class__s LC:\Projects\Historical\venv\Lib\site-packages\torchvision/models/densenet.pyr0z_DenseLayer.__init__ s ^$677 WT*** Y17[3HVW`ahmnnn ^Gk$9:: WT*** Yw4kqYZdelqrrr y)) 0inputsctj|d}||||}|SNr )torchcatr7r5r3)r<r@concated_featuresbottleneck_outputs r> bn_functionz_DenseLayer.bn_function/sE!Ifa00 JJtzz$**=N2O2O'P'PQQ  r?inputc$|D] }|jrdS dS)NTF) requires_grad)r<rHtensors r>any_requires_gradz_DenseLayer.any_requires_grad5s-  F# tt ur?c8fd}tj|g|RddiS)Nc.|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> , , , , ,}WBuBBBEBBBr?cdSrOr<rHs r>forwardz_DenseLayer.forwardB r?cdSrOrVrWs r>rXz_DenseLayer.forwardFrYr?ct|tr|g}n|}|jrX||rCtjrtd||}n| |}| | | |}|j dkr!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 $ $ ""GMM!M   @T%;%;M%J%J @y%%'' I GHHH $ ? ? N N   $ 0 0 ? ? zz$**TZZ8I-J-J"K"KLL >A  9\T^dm\\\Lr?F)__name__ __module__ __qualname__intr;boolr0rr rGrLrCr_unusedrT_overload_methodrX __classcell__r=s@r>rrsrw 1 1"% 147 1BE 1RW 1ko 1  1 1 1 1 1 1!$v,!6!!!! tF|  YCV CCCCC  Y T&\ f      Y V      Vr?rcTeZdZdZ ddedededededed d ffd Zd ed efd Z xZ S) _DenseBlockr F num_layersrr!r r"r#r$Nctt|D]8}t|||zz||||}|d|dzz|9dS)N)r r!r"r#z denselayer%dr )r/r0ranger add_module) r<rrrr!r r"r#ilayerr=s r>r0z_DenseBlock.__init__cs z"" = =A"Q_4'#!1 E OONa!e4e < < < < = =r? init_featuresc|g}|D]%\}}||}||&tj|dSrB)itemsappendrCrD)r<rxfeaturesnamerwres r>rXz_DenseBlock.forwardwsX!?::<< * *KD% 5??L OOL ) ) ) )y1%%%r?rf) rgrhri_versionrjr;rkr0r rXrnros@r>rqrq`sH"'=== = =  =  == ======(&V&&&&&&&&&r?rqc,eZdZdededdffd ZxZS) _Transitionrnum_output_featuresr$Nc ttj||_tjd|_tj||ddd|_tj dd|_ dS)NTr&r Fr(r )r)r*) r/r0r1r2normr4relur6conv AvgPool2dpool)r<rrr=s r>r0z_Transition.__init__sz N#566 GD))) I02EST]^ejkkk LQq999 r?)rgrhrirjr0rnros@r>rrsR:3:S:T::::::::::r?rcxeZdZdZ dd ed eeeeefd ed ed edededdffd Zde de fdZ 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" `_.  @rFr block_confignum_init_featuresr!r" num_classesr#r$Nc :tt|tjt dtjd|ddddfdtj|fdtjd fd tj ddd fg|_ |}t|D]\} } t| |||||} |j d| d zz| || |zz}| t|d z kr:t||dz} |j d| d zz| |dz}|j dtj|tj|||_|D]} t'| tjr%tj| jAt'| tjrKtj| jd tj| jdt'| tjr%tj| jddS)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!!! bi+??@bgd3334blqANNNO       ) &|44 1 1MAz%#/'#!1 E M $ $^q1u%=u E E E'*{*BBLC %%)))#|YeijYjkkk ((1q5)A5III+q0    ".*F*FGGG)L+>> - -A!RY'' -''1111Ar~.. -!!!(A...!!!&!,,,,Ary)) -!!!&!,,, - -r?xc||}tj|d}tj|d}t j|d}||}|S)NTr&)r r r )r|rbradaptive_avg_pool2drCflattenr)r<rr|outs r>rXzDenseNet.forwards`==##fXt,,,#C00mC##ooc"" r?)rrrrrrF) rgrhri__doc__rjrr;rkr0r rXrnros@r>rrs  "2A!#!&:-:-:-Cc3./:- :-  :-  :-:-:- :-:-:-:-:-:-xFr?rmodelweightsprogressr$cntjd}||d}t|D]R}||}|r9|d|dz}||||<||=S||dS)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 jhG''d'KKJJOO%%&&  mmC    iillSYYq\\1G",S/Jw 3 *%%%%%r?r rrkwargsc |)t|dt|jdt|||fi|}|t ||||S)Nr categories)rrr)rrmetarr)r rrrrrrs r> _densenetrsefmSl9S5T5TUUU [,0A L LV L LEugIIII Lr?)rz*https://github.com/pytorch/vision/pull/116z'These weights are ported from LuaTorch.)min_sizerrecipe_docsc `eZdZedeediedddddid d d  ZeZd S)rz@ num_params_metrics_ops _file_sizeurl transformsrN rgrhrirrr _COMMON_META IMAGENET1K_V1DEFAULTrVr?r>rr sG J7.#>>>    !##        M GGGr?rc `eZdZedeediedddddid d d  ZeZd S)rzrrsG J7.#>>>    "##   !    M GGGr?rc `eZdZedeediedddddid d d  ZeZd S)rzrr3sG J7.#>>>    "##        M GGGr?rc `eZdZedeediedddddid d d  ZeZd S)rzrrGsG J7.#>>>    "##        M GGGr?r pretrained)rT)rrc Xt|}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[5*"((11G R"gx J J6 J JJr?c Xt|}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 Xt|}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 Xt|}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 ######------------ #########555555''''''6666666666''''''BBBBBBBB   >>>>>")>>>B&&&&&"-&&&>:::::"-:::RRRRRryRRRj&BI& &t&PT&&&&&S#s*+k "     (&: :  +(+(+(+(,0C0Q!RSSS<@SWKKKH%89KDKcfKksKKKTSK0,0C0Q!RSSS<@SWKKKH%89KDKcfKksKKKTSK0,0C0Q!RSSS<@SWKKKH%89KDKcfKksKKKTSK0,0C0Q!RSSS<@SWKKKH%89KDKcfKksKKKTSKKKr?