a PSicB# @sddlmZddlmZmZddlmZddlmZddl m Z m Z ddl m Z dd lmZmZmZdd lmZmZmZmZmZd d lmZgd ZGdddeZGdddejZe dddZGddde ZGddde Zee ee!edddZ"edej#fdej$fddd ddej$d!eee!ee ee!eeeed"d#d$Z%edej#fdej$fddd ddej$d!eee!ee ee!eeeed"d%d&Z&dd'lm'Z'e'ej#j(ej#j(d(Z)dS)))partial)AnyOptional)nn)SemanticSegmentation) WeightsEnumWeights)_VOC_CATEGORIES)IntermediateLayerGetterhandle_legacy_interface_ovewrite_value_param)ResNetResNet50_WeightsResNet101_Weightsresnet50 resnet101)_SimpleSegmentationModel)FCNFCN_ResNet50_WeightsFCN_ResNet101_Weights fcn_resnet50 fcn_resnet101c@seZdZdZdS)ra Implements FCN model from `"Fully Convolutional Networks for Semantic Segmentation" `_. Args: backbone (nn.Module): the network used to compute the features for the model. The backbone should return an OrderedDict[Tensor], with the key being "out" for the last feature map used, and "aux" if an auxiliary classifier is used. classifier (nn.Module): module that takes the "out" element returned from the backbone and returns a dense prediction. aux_classifier (nn.Module, optional): auxiliary classifier used during training N)__name__ __module__ __qualname____doc__rr_/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/segmentation/fcn.pyrsrcs&eZdZeeddfdd ZZS)FCNHeadN) in_channelschannelsreturnc sP|d}tj||ddddt|ttdt||dg}tj|dS)NrrF)paddingbiasg?)rConv2d BatchNorm2dReLUDropoutsuper__init__)selfr"r#Zinter_channelslayers __class__rr r-%s zFCNHead.__init__)rrrintr- __classcell__rrr0r r!$sr!)rrz These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. ) categoriesmin_size_docsc @s@eZdZedeeddiedddddd id d ZeZd S) rzBhttps://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth resize_sizeijzPhttps://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet50COCO-val2017-VOC-labelsg@N@gV@miou pixel_acc num_paramsrecipe_metricsurl transformsmetaN rrrr rr _COMMON_METACOCO_WITH_VOC_LABELS_V1DEFAULTrrrr r<s  rc @s@eZdZedeeddiedddddd id d ZeZd S) rzChttps://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pthr7r8ij<zWhttps://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet101r:gO@gV@r;r>rBNrFrrrr rOs  r)backbone num_classesauxr$cCsHddi}|rd|d<t||d}|r.td|nd}td|}t|||S)Nlayer4outrLlayer3) return_layersii)r r!r)rJrKrLrPaux_classifier classifierrrr _fcn_resnetbs  rS pretrainedpretrained_backbone)weightsweights_backboneNT)rVprogressrKaux_lossrW)rVrXrKrYrWkwargsr$cKst|}t|}|dur@d}t|t|jd}t|d}n |durLd}t|gdd}t|||}|dur||j |d|S)a\Fully-Convolutional Network model with a ResNet-50 backbone from the `Fully Convolutional Networks for Semantic Segmentation `_ paper. .. betastatus:: segmentation module Args: weights (:class:`~torchvision.models.segmentation.FCN_ResNet50_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.FCN_ResNet50_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. num_classes (int, optional): number of output classes of the model (including the background). aux_loss (bool, optional): If True, it uses an auxiliary loss. weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for the backbone. **kwargs: parameters passed to the ``torchvision.models.segmentation.fcn.FCN`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.segmentation.FCN_ResNet50_Weights :members: Nr4TFTTrVreplace_stride_with_dilationrX) rverifyrrlenrErrSload_state_dictget_state_dictrVrXrKrYrWrZrJmodelrrr rqs'    rcKst|}t|}|dur@d}t|t|jd}t|d}n |durLd}t|gdd}t|||}|dur||j |d|S)aaFully-Convolutional Network model with a ResNet-101 backbone from the `Fully Convolutional Networks for Semantic Segmentation `_ paper. .. betastatus:: segmentation module Args: weights (:class:`~torchvision.models.segmentation.FCN_ResNet101_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.FCN_ResNet101_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. num_classes (int, optional): number of output classes of the model (including the background). aux_loss (bool, optional): If True, it uses an auxiliary loss. weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights for the backbone. **kwargs: parameters passed to the ``torchvision.models.segmentation.fcn.FCN`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.segmentation.FCN_ResNet101_Weights :members: Nr4Tr[r\r]r_) rr`rrrarErrSrbrcrdrrr rs'    r) _ModelURLs)Zfcn_resnet50_cocoZfcn_resnet101_coco)* functoolsrtypingrrtorchrtransforms._presetsr_apir r _metar _utilsr r rresnetrrrrrr__all__r Sequentialr!rGrrr2boolrSrH IMAGENET1K_V1rrrfrC model_urlsrrrr s       67