+ j: ^RIHt^RIHt^RIHtHt^RIt^RIHt^RI H t ^RI H t ^RIHtHtHt^R IHt^R IHtHtHt^R IHtHtHt^R IHtHtHtH t H!t!^R IH"t"^RI#H$t$.R6Ot%!RR]"4t&!RR]PN4t(!RR]PN4t)!RR]PN4t*!RR]PV4t,RRlt-R]RR7R R!/t.!R"R]4t/!R#R]4t0!R$R]4t1R%R&lt2]!4]!R']/Pf3R(]!Ph3R)7R*RR+R,R-RR.RR/]!Ph/R0R1ll44t5]!4]!R']0Pf3R(]Ph3R)7R*RR+R,R-RR.RR/]Ph/R2R3ll44t6]!4]!R']1Pf3R(]Ph3R)7R*RR+R,R-RR.RR/]Ph/R4R5ll44t7R#)8)Sequence)partial)AnyOptionalN)nn) functional)SemanticSegmentation)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3)ResNet resnet101ResNet101_Weightsresnet50ResNet50_Weights)_SimpleSegmentationModel)FCNHead DeepLabV3DeepLabV3_ResNet50_WeightsDeepLabV3_ResNet101_Weights$DeepLabV3_MobileNet_V3_Large_Weightsc]tRt^tRtRtR#)rai 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____firstlineno____doc____static_attributes__r j/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/models/segmentation/deeplabv3.pyrrs   r'cBaa]tRt^1toRV3RlV3RllltRtVtV;t#) DeepLabHeadc@<V^8dQhRS[RS[RS[S[,RR/#) in_channels num_classes atrous_ratesreturnNintr)format __classdict__s"r( __annotate__DeepLabHead.__annotate__2s/  C c RU jn r'c <\SV`\W4\P!RR^^RR7\P !R4\P !4\P!RV^44R#)F)paddingbiasN)super__init__ASPPrConv2d BatchNorm2dReLU)selfr-r.r/ __class__s&&&&r(r<DeepLabHead.__init__2sQ   + IIc315 9 NN3  GGI IIc; *  r'r )) $r!r"r#r$r<r&__classdictcell__ __classcell__rBr4s@@r(r*r*1s   r'r*c>aa]tRt^r?r@r;r<)rAr-rNrOmodulesrBs&&&& r(r<ASPPConv.__init__=sA IIkH^c d NN< ( GGI  '"r'r rGrJs@@r(rLrL<s###r'rLcPaa]tRt^FtoV3RlV3RlltV3RlRltRtVtV;t#) ASPPPoolingc*<V^8dQhRS[RS[RR/#)r,r-rNr0NrP)r3r4s"r(r5ASPPPooling.__annotate__Gs"  C s t r'c <\SV`\P!^4\P!W^RR7\P !V4\P !44R#)Fr:N)r;r<rAdaptiveAvgPool2dr>r?r@)rAr-rNrBs&&&r(r<ASPPPooling.__init__GsC    # IIk ? NN< ( GGI  r'cN<V^8dQhRS[PRS[P/#r,xr0torchTensor)r3r4s"r(r5rYOs'QQQ%,,Qr'ctVPRRpVF pV!V4pK \P!WRRR7#)r,NbilinearF)sizemode align_corners)shapeF interpolate)rArargmods&& r(forwardASPPPooling.forwardOs7wwrs|CAA}}Q %PPr'r r!r"r#r$r<ror&rHrIrJs@@r(rWrWFs!  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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: Nr.rrTrreplace_stride_with_dilationr check_hashFTT) rverifyrrr{rrrload_state_dictget_state_dictrrr.rrrrmodels$$$$$, r(deeplabv3_resnet50rsJ)//8G'../?@+M;GLLYeLfHgh (XtD    0ObcH hX >E g44hSW4XY Lr'cV^8dQhR\\,R\R\\,R\\,R\\,R\ R\ /#r)rrrr2rrr)r3s"r(r5r5!sd00 1 200# 0 tn 0 01 000r'c `\PV4p\P!V4pVe8Rp\RV\ VP R,44p\RVR4pMVf^p\ V.ROR7p\WbV4pVe#VPVPVRR74V#) a!Constructs a DeepLabV3 model with a ResNet-101 backbone. .. betastatus:: segmentation module Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. num_classes (int, optional): number of output classes of the model (including the background) aux_loss (bool, optional): If True, it uses an auxiliary loss weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights for the backbone **kwargs: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet101_Weights :members: Nr.rrTrrr) rrrrr{rrrrrrs$$$$$, r(deeplabv3_resnet101rsJ*009G(//0@A+M;GLLYeLfHgh (XtD   !1PcdH hX >E g44hSW4XY Lr'cV^8dQhR\\,R\R\\,R\\,R\\,R\ R\ /#r)rrrr2rrr)r3s"r(r5r5Ysd.. : ;..# . tn . 9: ...r'c \\PV4p\P!V4pVe8Rp\RV\ VP R,44p\RVR4pMVf^p\ VRR7p\WbV4pVe#VPVPVRR74V#)a'Constructs a DeepLabV3 model with a MobileNetV3-Large backbone. Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. num_classes (int, optional): number of output classes of the model (including the background) aux_loss (bool, optional): If True, it uses an auxiliary loss weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone **kwargs: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights :members: Nr.rrT)rdilatedr) rrrrr{rrrrrrs$$$$$, r(deeplabv3_mobilenet_v3_largerTsF399'BG1889IJ+M;GLLYeLfHgh (XtD   !*:DIH "8( CE g44hSW4XY Lr')rrrrrrr)r[r[)8collections.abcr functoolsrtypingrrrcrtorch.nnrrltransforms._presetsr _apir r r _metar _utilsrrr mobilenetv3rrrresnetrrrrrrfcnr__all__rrwr*rLrWModuler=rrrrrrr IMAGENET1K_V1rrrr r'r(rsG$ $777#\\UUUU,  ( & "-- #r}}# Q"-- Q !299!> ; /  &&*&+&*&;&*;. 5MM N+-=-K-KL04800"& 0 $ 0 4D3Q3Q 0  0f 6NN O+->-L-LM05900"& 0 $ 0 5F4S4S 0  0f ?WW X+-G-U-UV.>B.."& . $ . >X=e=e .  .r'