U h # @sddlmZddlmZmZddlmZddlmZddl m Z m Z m Z ddl mZdd lmZmZmZdd lmZmZmZmZmZd d lmZd ddddgZGdd d eZGdddejZedddZGddde ZGddde Z ee!ee"edddZ#e edej$fdej%fdd d!d d ej%d"eee"ee!ee"eeeed#d$dZ&e ede j$fdej%fdd d!d d ej%d"ee e"ee!ee"eeeed#d%dZ'd S)&)partial)AnyOptional)nn)SemanticSegmentation)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)ResNet resnet101ResNet101_Weightsresnet50ResNet50_Weights)_SimpleSegmentationModelFCNFCN_ResNet50_WeightsFCN_ResNet101_Weights fcn_resnet50 fcn_resnet101c@seZdZdZdS)ra Implements FCN model from `"Fully Convolutional Networks for Semantic Segmentation" `_. Args: backbone (nn.Module): the network used to compute the features for the model. The backbone should return an OrderedDict[Tensor], with the key being "out" for the last feature map used, and "aux" if an auxiliary classifier is used. classifier (nn.Module): module that takes the "out" element returned from the backbone and returns a dense prediction. aux_classifier (nn.Module, optional): auxiliary classifier used during training N)__name__ __module__ __qualname____doc__r r U/var/www/html/venv/lib/python3.8/site-packages/torchvision/models/segmentation/fcn.pyrscs&eZdZeeddfdd ZZS)FCNHeadN) in_channelschannelsreturnc sP|d}tj||ddddt|ttdt||dg}tj|dS)NrrF)paddingZbiasg?)rZConv2dZ BatchNorm2dZReLUZDropoutsuper__init__)selfr#r$Zinter_channelsZlayers __class__r r!r)%s zFCNHead.__init__)rrrintr) __classcell__r r r+r!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. ) categoriesZmin_sizeZ_docsc @s@eZdZedeeddedddddd id d d d ZeZdS)rzBhttps://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pthZ resize_sizeijzPhttps://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet50COCO-val2017-VOC-labelsg@N@gV@ZmiouZ pixel_accgmc@g?5^I`@Z num_paramsZrecipeZ_metricsZ_ops _file_sizeurlZ transformsmetaN rrrr rr _COMMON_METACOCO_WITH_VOC_LABELS_V1DEFAULTr r r r!r<s  c @s@eZdZedeeddedddddd id d d d ZeZdS)rzChttps://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pthr0r1ij<zWhttps://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet101r2gO@gV@r3gV-m@gni@r4r6Nr9r r r r!rQs  )backbone num_classesauxr%cCsHddi}|rd|d<t||d}|r.td|nd}td|}t|||S)NZlayer4outr?Zlayer3) return_layersii)rr"r)r=r>r?rAZaux_classifier classifierr r r! _fcn_resnetfs  rCZ pretrainedZpretrained_backbone)weightsweights_backboneNT)rDprogressr>aux_lossrE)rDrFr>rGrEkwargsr%cKst|}t|}|dk rDd}td|t|jd}td|d}n |dkrPd}t|dddgd}t|||}|dk r||j |dd |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: Nr>r/rGTFrDZreplace_stride_with_dilationrFZ check_hash) rverifyrr lenr8rrCload_state_dictget_state_dictrDrFr>rGrErHr=modelr r r!rus(   cKst|}t|}|dk rDd}td|t|jd}td|d}n |dkrPd}t|dddgd}t|||}|dk r||j |dd |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: Nr>r/rGTrIFrJrK) rrLrr rMr8rrCrNrOrPr r r!rs(   )( functoolsrtypingrrZtorchrZtransforms._presetsrZ_apir r r _metar Z_utilsr rrZresnetrrrrrr__all__rZ Sequentialr"r:rrr-boolrCr;Z IMAGENET1K_V1rrr r r r!sx       6