o V h:@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z dd l mZmZmZdd lmZdd lmZmZmZdd lmZmZmZdd lmZmZmZmZm Z ddlm!Z!ddl"m#Z#gdZ$Gddde!Z%Gdddej&Z'Gdddej&Z(Gdddej&Z)Gdddej*Z+dede,dee-de%fd d!Z.ed"d#d$Z/Gd%d&d&eZ0Gd'd(d(eZ1Gd)d*d*eZ2dede,dee-de%fd+d,Z3eed-e0j4fd.e j5fd/dd0dde j5d1d2ee0d3e-dee,d4ee-d5ee d6ede%fd7d8Z6eed-e1j4fd.ej5fd/dd0ddej5d1d2ee1d3e-dee,d4ee-d5eed6ede%fd9d:Z7eed-e2j4fd.ej5fd/dd0ddej5d1d2ee2d3e-dee,d4ee-d5eed6ede%fd;d<Z8dS)=)partial)AnyOptionalSequenceN)nn) functional)SemanticSegmentation)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3)ResNet resnet101ResNet101_Weightsresnet50ResNet50_Weights)_SimpleSegmentationModel)FCNHead) DeepLabV3DeepLabV3_ResNet50_WeightsDeepLabV3_ResNet101_Weights$DeepLabV3_MobileNet_V3_Large_Weightsdeeplabv3_mobilenet_v3_largedeeplabv3_resnet50deeplabv3_resnet101c@seZdZdZdS)ra Implements DeepLabV3 model from `"Rethinking Atrous Convolution for Semantic Image 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(]/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/models/segmentation/deeplabv3.pyrsrc s4eZdZd dededeeddffdd ZZS) DeepLabHead $ in_channels num_classes atrous_ratesreturnNc sBtt||tjddddddtdttd|ddS)NrrF)paddingbias)super__init__ASPPrConv2d BatchNorm2dReLU)selfr/r0r1 __class__r(r)r71s zDeepLabHead.__init__)r+)r$r%r&intrr7 __classcell__r(r(r=r)r*0s,r*cs.eZdZdedededdffdd ZZS)ASPPConvr/ out_channelsdilationr2Ncs6tj||d||ddt|tg}tj|dS)NrF)r4rCr5)rr9r:r;r6r7)r<r/rBrCmodulesr=r(r)r7<s zASPPConv.__init__)r$r%r&r?r7r@r(r(r=r)rA;s&rAcs@eZdZdededdffdd Zdejdejfdd ZZS) ASPPPoolingr/rBr2Nc s4ttdtj||dddt|tdS)NrFr5)r6r7rAdaptiveAvgPool2dr9r:r;)r<r/rBr=r(r)r7Fs zASPPPooling.__init__xcCs2|jdd}|D]}||}q tj||dddS)NbilinearF)sizemode align_corners)shapeF interpolate)r<rHrKmodr(r(r)forwardNs zASPPPooling.forward) r$r%r&r?r7torchTensorrRr@r(r(r=r)rEEsrEc sJeZdZd dedeededdffdd Zd ejdejfd d ZZ S) r8r3r/r1rBr2Nc stg}|ttj||dddt|tt|}|D] }|t |||q#|t ||t ||_ ttjt |j ||dddt|ttd|_dS)NrFrFg?)r6r7appendr Sequentialr9r:r;tuplerArE ModuleListconvslenDropoutproject)r<r/r1rBrDratesrater=r(r)r7Vs $  z ASPP.__init__rHcCs6g}|jD] }|||qtj|dd}||S)Nr)dim)rYrUrScatr\)r<rH_resconvresr(r(r)rRls   z ASPP.forward)r3) r$r%r&r?rr7rSrTrRr@r(r(r=r)r8Us$r8backboner0auxr2cCsHddi}|r d|d<t||d}|rtd|nd}td|}t|||S)Nlayer4outrelayer3 return_layersii)rrr*r)rdr0rerjaux_classifier classifierr(r(r)_deeplabv3_resnetts   rm)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 @DeZdZedeeddiedddddd id d d d ZeZdS)rzHhttps://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth resize_sizeijzVhttps://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50COCO-val2017-VOC-labelsgP@皙W@miou pixel_accgvWf@gGzd@ num_paramsrecipe_metrics_ops _file_sizeurl transformsmetaN r$r%r&r rr _COMMON_METACOCO_WITH_VOC_LABELS_V1DEFAULTr(r(r(r)r& rc @rq)rzIhttps://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pthrrrsijzQhttps://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101rugP@rvrwgS+p@gm&m@rzrNrr(r(r(r)rrrc @rq)r zMhttps://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pthrrrsiPKz`https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_largerugfffff&N@gV@rwgCl$@gJ +&E@rzrNrr(r(r(r)r rr c Cs|j}dgddt|Dt|dg}|d}||j}|d}||j}t|di}|r6d|t|<t||d }|rCt||nd} t||} t|| | S) NrcSs g|] \}}t|ddr|qS)_is_cnF)getattr).0ibr(r(r) s z*_deeplabv3_mobilenetv3..rrgreri) features enumeraterZrBstrrrr*r) rdr0re stage_indicesout_pos out_inplanesaux_pos aux_inplanesrjrkrlr(r(r)_deeplabv3_mobilenetv3s&       r pretrainedpretrained_backbone)weightsweights_backboneT)rprogressr0aux_lossrrrrrkwargscKt|}t|}|dur"d}td|t|jd}td|d}n|dur(d}t|gdd}t|||}|durD||j |dd |S) adConstructs a DeepLabV3 model with a ResNet-50 backbone. .. betastatus:: segmentation module Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet50_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_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: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet50_Weights :members: Nr0rnrTFTTrreplace_stride_with_dilationr check_hash) rverifyrrrZrrrmload_state_dictget_state_dictrrr0rrrrdmodelr(r(r)r" %  r"cKr) aiConstructs a DeepLabV3 model with a ResNet-101 backbone. .. betastatus:: segmentation module Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_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: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet101_Weights :members: Nr0rnrTrrrr) rrrrrZrrrmrrrr(r(r)r#rr#cKst|}t|}|dur"d}td|t|jd}td|d}n|dur(d}t|dd}t|||}|durB||j |dd|S) akConstructs a DeepLabV3 model with a MobileNetV3-Large backbone. Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_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.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone **kwargs: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights :members: Nr0rnrTr)rdilatedr) r rrrrZrrrrrrr(r(r)r!Ss #   r!)9 functoolsrtypingrrrrSrtorch.nnrrOtransforms._presetsr _apir r r _metar_utilsrrr mobilenetv3rrrresnetrrrrrrfcnr__all__rrVr*rArEModuler8r?boolrmrrrr rr IMAGENET1K_V1r"r#r!r(r(r(r)s             33