o )iA@sddlZddlmZddlmZddlmZmZmZm Z ddl Z ddl m Z ddl m mZddlmmZddl mZddlmZddlmZd d lmZmZmZd d lmZd d lmZm Z gd Z!Gddde j"Z#Gddde j$Z%Gddde j&Z'Gddde j"Z(de j"dede)ddfddZ*de+de e+e+e+e+fde+deede)dede(fd d!Z,d"ed#d$d%Z-Gd&d'd'eZ.Gd(d)d)eZ/Gd*d+d+eZ0Gd,d-d-eZ1ee d.e.j2fd/dd0d1dee.de)dede(fd2d3Z3ee d.e/j2fd/dd0d1dee/de)dede(fd4d5Z4ee d.e0j2fd/dd0d1dee0de)dede(fd6d7Z5ee d.e1j2fd/dd0d1dee1de)dede(fd8d9Z6dS):N) 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 seZdZ ddedededededdf fd d Zd eedefd d Z deedefddZ e j j deedefddZe j jdeedefddZe j jdedefddZdedefddZZS) _DenseLayerFnum_input_features growth_ratebn_size drop_ratememory_efficientreturnNcstt||_tjdd|_tj|||dddd|_t|||_ tjdd|_ tj|||ddddd|_ t ||_ ||_dS)NTZinplacer F kernel_sizestridebiasr%r&paddingr')super__init__nn BatchNorm2dnorm1ReLUrelu1Conv2dconv1norm2relu2conv2floatr r!)selfrrrr r! __class__j/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/torchvision/models/densenet.pyr, s    z_DenseLayer.__init__inputscCs&t|d}||||}|SNr )torchcatr3r1r/)r8r=Zconcated_featuresbottleneck_outputr;r;r< bn_function/s z_DenseLayer.bn_functioninputcCs|D]}|jr dSqdS)NTF)Z requires_grad)r8rCZtensorr;r;r<any_requires_grad5s z_DenseLayer.any_requires_gradcsfdd}tj|g|RS)Ncs |SN)rB)r=r8r;r<closure=s z7_DenseLayer.call_checkpoint_bottleneck..closure)cp checkpoint)r8rCrGr;rFr<call_checkpoint_bottleneck;s z&_DenseLayer.call_checkpoint_bottleneckcCdSrEr;r8rCr;r;r<forwardBz_DenseLayer.forwardcCrKrEr;rLr;r;r<rMFrNcCst|tr |g}n|}|jr"||r"tjrtd||}n| |}| | | |}|j dkrAtj||j |jd}|S)Nz%Memory Efficient not supported in JITr)ptraining) isinstancerr!rDr?jitZ is_scripting ExceptionrJrBr6r5r4r FZdropoutrP)r8rCZ prev_featuresrA new_featuresr;r;r<rMLs     F)__name__ __module__ __qualname__intr7boolr,rrrBrDr?rRZunusedrJZ_overload_methodrM __classcell__r;r;r9r<rs0rcsTeZdZdZ ddedededededed d ffd d Zd ed efddZ Z S) _DenseBlockr F num_layersrrrr r!r"Nc sJtt|D]}t|||||||d}|d|d|q dS)N)rrr r!z denselayer%dr )r+r,ranger add_module) r8r^rrrr r!ilayerr9r;r<r,cs  z_DenseBlock.__init__ init_featurescCs6|g}|D] \}}||}||qt|dSr>)itemsappendr?r@)r8rcfeaturesnamerbrUr;r;r<rMws   z_DenseBlock.forwardrV) rWrXrY_versionrZr7r[r,rrMr\r;r;r9r<r]`s& r]cs*eZdZdededdffdd ZZS) _Transitionrnum_output_featuresr"NcsNtt||_tjdd|_tj||dddd|_tj ddd|_ dS)NTr#r Fr$r )r%r&) r+r,r-r.Znormr0relur2convZ AvgPool2dpool)r8rrjr9r;r<r,s  z_Transition.__init__)rWrXrYrZr,r\r;r;r9r<ris"ricspeZdZdZ       dd ed eeeeefd ed ed edededdffdd Zde de fddZ Z 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_featuresrr num_classesr!r"Nc stt|ttdtjd|dddddfdt|fdtjd d fd tj ddd d fg|_ |}t |D]>\} } t | |||||d} |j d| d | || |}| t|d krwt||dd} |j d| d | |d}q9|j dt|t|||_|D]5} t| tjrtj| jqt| tjrtj| jd tj| jdqt| tjrtj| jdqdS)NZconv0r(r Fr)Znorm0Zrelu0Tr#Zpool0r )r%r&r*)r^rrrr r!z denseblock%d)rrjz transition%dZnorm5r)r+r,r r- Sequentialrr2r.r0Z MaxPool2drf enumerater]r`lenriZLinear classifiermodulesrQinitZkaiming_normal_weightZ constant_r')r8rrwrxrr ryr!Z num_featuresrar^blockZtransmr9r;r<r,sP        zDenseNet.__init__xcCs>||}tj|dd}t|d}t|d}||}|S)NTr#)r r r )rfrTrkZadaptive_avg_pool2dr?flattenr~)r8rrfoutr;r;r<rMs    zDenseNet.forward)rnrortrurrvF) rWrXrY__doc__rZrr7r[r,rrMr\r;r;r9r<rs6 @Z num_paramsZ_metricsZ_ops _file_sizeurlZ transformsrN rWrXrYrrr _COMMON_META IMAGENET1K_V1DEFAULTr;r;r;r<r $ rc @r)rz`_. 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: rnrort)rverifyrrrrr;r;r<r[ rcKr)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)rprq$rr`)rrrrr;r;r<rurrcKr)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: rn)rprqrnrnrt)rrrrr;r;r<rrrcKr)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: rn)rprqrrnrt)rrrrr;r;r<rrr)7r collectionsr functoolsrtypingrrrrr?Ztorch.nnr-Ztorch.nn.functionalZ functionalrTZtorch.utils.checkpointutilsrIrHrZtransforms._presetsr r Z_apir rr_metar_utilsrr__all__ModulerZ ModuleDictr]r{rirr[rrZrrrrrrrrrrrr;r;r;r<sp        A U ***.