h:VSSKJr SSKJr SSKJrJr SSKrSSKJr SSK J r SSK J r S S KJrJrJr S S KJr S S KJrJrJr S S KJrJrJr S SKJrJrJrJ r J!r! SSKJ"r" SSK#J$r$ /SQr%"SS\"5r&"SS\RN5r("SS\RN5r)"SS\RN5r*"SS\RV5r,S\S\-S\\.S \&4S!jr/\S"S#S$.r0"S%S&\5r1"S'S(\5r2"S)S*\5r3S\S\-S\\.S \&4S+jr4\"5\"S,\1Rj4S-\!Rl4S.9SS/SS\!RlS0.S1\\1S2\.S\\-S3\\.S4\\!S5\S \&4S6jj55r7\"5\"S,\2Rj4S-\Rl4S.9SS/SS\RlS0.S1\\2S2\.S\\-S3\\.S4\\S5\S \&4S7jj55r8\"5\"S,\3Rj4S-\Rl4S.9SS/SS\RlS0.S1\\3S2\.S\\-S3\\.S4\\S5\S \&4S8jj55r9g)9)Sequence)partial)AnyOptionalN)nn) functional)SemanticSegmentation)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3)ResNet resnet101ResNet101_Weightsresnet50ResNet50_Weights)_SimpleSegmentationModel)FCNHead) DeepLabV3DeepLabV3_ResNet50_WeightsDeepLabV3_ResNet101_Weights$DeepLabV3_MobileNet_V3_Large_Weightsdeeplabv3_mobilenet_v3_largedeeplabv3_resnet50deeplabv3_resnet101c\rSrSrSrSrg)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'a/var/www/auris/envauris/lib/python3.13/site-packages/torchvision/models/segmentation/deeplabv3.pyrrs   r.rc F^\rSrSrS S\S\S\\SS4U4SjjjrSrU=r$) DeepLabHead1 in_channels num_classes atrous_ratesreturnNc >[TU][X5[R"SSSSSS9[R "S5[R "5[R"SUS55 g)Nr rF)paddingbias)super__init__ASPPrConv2d BatchNorm2dReLU)selfr3r4r5 __class__s r/r<DeepLabHead.__init__2sQ   + IIc315 9 NN3  GGI IIc; *  r.r')) $) r(r)r*r+intrr<r- __classcell__rBs@r/r1r11s/ C c RU jn  r.r1c<^\rSrSrS\S\S\SS4U4SjjrSrU=r$) ASPPConv<r3 out_channelsdilationr6Nc >[R"XSX3SS9[R"U5[R"5/n[TU]"U6 g)Nr F)r9rNr:)rr>r?r@r;r<)rAr3rMrNmodulesrBs r/r<ASPPConv.__init__=sA IIkH^c d NN< ( GGI  '"r.r')r(r)r*r+rGr<r-rHrIs@r/rKrK<s)#C#s#c#d##r.rKcr^\rSrSrS\S\SS4U4SjjrS\RS\R4SjrS r U=r $) ASPPPoolingFr3rMr6Nc >[TU][R"S5[R"XSSS9[R "U5[R "55 g)NrFr:)r;r<rAdaptiveAvgPool2dr>r?r@)rAr3rMrBs r/r<ASPPPooling.__init__GsC    # IIk ? NN< ( GGI  r.xcnURSSnUH nU"U5nM [R"XSSS9$)NbilinearF)sizemode align_corners)shapeF interpolate)rArYr]mods r/forwardASPPPooling.forwardOs7wwrs|CAA}}Q %PPr.r') r(r)r*r+rGr<torchTensorrdr-rHrIs@r/rSrSFsA C s t QQ%,,QQr.rSc ^\rSrSrS S\S\\S\SS4U4SjjjrS\RS\R4S jr S r U=r $) r=Vr3r5rMr6Nc >[TU]5 /nUR[R"[R "XSSS9[R "U5[R"555 [U5nUHnUR[XU55 M UR[X55 [R"U5Ul [R"[R "[UR5U-USSS9[R "U5[R"5[R"S55Ulg)NrFrVg?)r;r<appendr Sequentialr>r?r@tuplerKrS ModuleListconvslenDropoutproject)rAr3r5rMrPratesraterBs r/r< ASPP.__init__Ws  MM"))KquMr~~^jOkmomtmtmv w l#D NN8KtD E {;=>]]7+ }} IIc$**o 4lAE R NN< ( GGI JJsO   r.rYc/nURHnURU"U55 M [R"USS9nUR U5$)Nr)dim)rorkrfcatrr)rArY_resconvress r/rd ASPP.forwardmsDJJD KKQ ii!$||C  r.)rorr)r8) r(r)r*r+rGrr<rfrgrdr-rHrIs@r/r=r=VsO C x} TW bf  ,!!%,,!!r.r=backboner4auxr6cSS0nU(aSUS'[XS9nU(a [SU5OSn[SU5n[XU5$)Nlayer4outr~layer3 return_layersii)rrr1r)r}r4r~raux_classifier classifiers r/_deeplabv3_resnetrusN u%M "' h&xMH36WT;/DNT;/J X> ::r.)rrz These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. ) categoriesmin_size_docsc P\rSrSr\"S\"\SS90\ESSSSS S .0S S S .ES9r\r Sr g)rzHhttps://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth resize_sizeijzVhttps://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50COCO-val2017-VOC-labelsgP@皙W@miou pixel_accgvWf@gGzd@ num_paramsrecipe_metrics_ops _file_sizeurl transformsmetar'N r(r)r*r+r rr _COMMON_METACOCO_WITH_VOC_LABELS_V1DEFAULTr-r'r.r/rrsU% V/SA    "n) !%, !  "&Gr.rc P\rSrSr\"S\"\SS90\ESSSSS S .0S S S .ES9r\r Sr g)r zIhttps://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pthrrijzQhttps://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101rgP@rrgS+p@gm&m@rrr'Nrr'r.r/r r sU% W/SA    "i) !%, !  "&Gr.r c P\rSrSr\"S\"\SS90\ESSSSS S .0S S S .ES9r\r Sr g)r!zMhttps://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pthrriPKz`https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_largergfffff&N@gV@rgCl$@gJ +&E@rrr'Nrr'r.r/r!r!sU% [/SA    "x) !%,    "&Gr.r!c URnS/[U5VVs/sHup4[USS5(dMUPM snn-[U5S- /-nUSnXRnUSnXRn [ U5S0n U(aSU [ U5'[ X S 9nU(a [X5OSn [Xq5n [X U 5$s snnf) Nr_is_cnFrrr~r) features enumerategetattrrprMstrrrr1r) r}r4r~ib stage_indicesout_pos out_inplanesaux_pos aux_inplanesrrrs r/_deeplabv3_mobilenetv3rs   HC8)<\)<8UZ@[1)<\\`cdl`mpq`q_rrMBG$11LBG$11L\5)M &+ c'l#&xMH;>W\7DN\7J X> ::]s CC pretrainedpretrained_backbone)weightsweights_backboneT)rprogressr4aux_lossrrrrrkwargsc F[RU5n[R"U5nUb3Sn[SU[ UR S55n[SUS5nOUcSn[ U/SQS9n[XbU5nUbURURUSS 95 U$) aConstructs 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: Nr4rrTFTTrreplace_stride_with_dilationr check_hash) rverifyrrrprrrload_state_dictget_state_dictrrr4rrrr}models r/r#r#sJ)//8G'../?@+M;GLLYeLfHgh (XtD    0ObcH hX >E g44hSW4XY Lr.c F[RU5n[R"U5nUb3Sn[SU[ UR S55n[SUS5nOUcSn[ U/SQS9n[XbU5nUbURURUSS 95 U$) a!Constructs a DeepLabV3 model with a ResNet-101 backbone. .. betastatus:: segmentation module Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. num_classes (int, optional): number of output classes of the model (including the background) aux_loss (bool, optional): If True, it uses an auxiliary loss weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights for the backbone **kwargs: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet101_Weights :members: Nr4rrTrrrr) r rrrrprrrrrrs r/r$r$sJ*009G(//0@A+M;GLLYeLfHgh (XtD   !1PcdH hX >E g44hSW4XY Lr.c B[RU5n[R"U5nUb3Sn[SU[ UR S55n[SUS5nOUcSn[ USS9n[XbU5nUbURURUSS95 U$) 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: Nr4rrTr)rdilatedr) r!rrrrprrrrrrs r/r"r"TsF399'BG1889IJ+M;GLLYeLfHgh (XtD   !*:DIH "8( CE g44hSW4XY Lr.):collections.abcr functoolsrtypingrrrfrtorch.nnrratransforms._presetsr _apir r r_metar_utilsrrr mobilenetv3rrrresnetrrrrrrfcnr__all__rrlr1rKrSModuler=rGboolrrrr r!rr IMAGENET1K_V1r#r$r"r'r.r/rs9$ $777#\\UUUU,  ( & "-- #r}}# Q"-- Q !299!> ; ; ; $ ; ; " &&*&+&*&;&*;;; $; ;. 5MM N+-=-K-KL 59!%#3C3Q3Q 0 0 100# 0 tn 0 /0 000  0f 6NN O+->-L-LM 6:!%#4E4S4S 0 1 200# 0 tn 0 01 000  0f ?WW X+-G-U-UV ?C!%#=W=e=e . : ;..# . tn . 9: ...  .r.