:j:XddlmZddlmZddlmZmZddlZddlmZddl m Z ddl m Z d d lmZmZmZd d lmZd d lmZmZmZd d lmZmZmZd dlmZmZmZm Z m!Z!ddlm"Z"ddl#m$Z$gdZ%Gdde"Z&GddejNZ(GddejNZ)GddejNZ*GddejVZ,dede-dee.d e&fd!Z/ed"d#d$Z0Gd%d&eZ1Gd'd(eZ2Gd)d*eZ3dede-dee.d e&fd+Z4eed,e1jjfd-e!jlf.dd/dde!jld0d1ee1d2e.dee-d3ee.d4ee!d5ed e&fd6Z7eed,e2jjfd-ejlf.dd/ddejld0d1ee2d2e.dee-d3ee.d4eed5ed e&fd7Z8eed,e3jjfd-ejlf.dd/ddejld0d1ee3d2e.dee-d3ee.d4eed5ed e&fd8Z9y)9)Sequence)partial)AnyOptionalN)nn) functional)SemanticSegmentation)register_modelWeights WeightsEnum)_VOC_CATEGORIES)_ovewrite_value_paramhandle_legacy_interfaceIntermediateLayerGetter)mobilenet_v3_largeMobileNet_V3_Large_Weights MobileNetV3)ResNet resnet101ResNet101_Weightsresnet50ResNet50_Weights)_SimpleSegmentationModel)FCNHead) DeepLabV3DeepLabV3_ResNet50_WeightsDeepLabV3_ResNet101_Weights$DeepLabV3_MobileNet_V3_Large_Weightsdeeplabv3_mobilenet_v3_largedeeplabv3_resnet50deeplabv3_resnet101ceZdZdZy)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__~/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchvision/models/segmentation/deeplabv3.pyrrs   r+rc 8eZdZddededeeddffd ZxZS) DeepLabHead in_channels num_classes atrous_ratesreturnNc t|t||tjdddddtj dtj tjd|dy)Nr rF)paddingbias)super__init__ASPPrConv2d BatchNorm2dReLU)selfr/r0r1 __class__s r,r8zDeepLabHead.__init__2sS  l + IIc315 9 NN3  GGI IIc; *  r+)) $)r&r'r(intrr8 __classcell__r>s@r,r.r.1s/ C c RU jn  r+r.c0eZdZdedededdffd ZxZS)ASPPConvr/ out_channelsdilationr2Nctj||d||dtj|tjg}t ||y)Nr F)r5rHr6)rr:r;r<r7r8)r=r/rGrHmodulesr>s r,r8zASPPConv.__init__=sE IIk<Hx^c d NN< ( GGI  '"r+)r&r'r(rBr8rCrDs@r,rFrF<s)#C#s#c#d##r+rFcdeZdZdededdffd Zdej dej fdZxZS) ASPPPoolingr/rGr2Nc t|tjdtj||ddtj |tj y)NrFr6)r7r8rAdaptiveAvgPool2dr:r;r<)r=r/rGr>s r,r8zASPPPooling.__init__GsE    # IIk< ? NN< ( GGI  r+xcp|jdd}|D] }||} tj||ddS)NbilinearF)sizemode align_corners)shapeF interpolate)r=rPrTmods r,forwardzASPPPooling.forwardOs>wwrs| CAA }}QT %PPr+) r&r'r(rBr8torchTensorr[rCrDs@r,rLrLFs; C s t QQ%,,Qr+rLc peZdZd dedeededdffd ZdejdejfdZxZ S) r9r/r1rGr2Nc t|g}|jtjtj ||ddtj |tjt|}|D]}|jt||| |jt||tj||_ tjtj t|j|z|ddtj |tjtjd|_y)NrFrNg?)r7r8appendr Sequentialr:r;r<tuplerFrL ModuleListconvslenDropoutproject)r=r/r1rGrJratesrater>s r,r8z ASPP.__init__Ws  MM"))KquMr~~^jOkmomtmtmv w l# FD NN8KtD E F {; =>]]7+ }} IIc$**o 4lAE R NN< ( GGI JJsO   r+rPcg}|jD]}|j||tj|d}|j |S)Nr)dim)rdr`r\catrg)r=rP_resconvress r,r[z ASPP.forwardmsIJJ !D KKQ  !ii!$||C  r+)r4) r&r'r(rBrr8r\r]r[rCrDs@r,r9r9VsE C x} TW bf ,!!%,,!r+r9backboner0auxr2cddi}|rd|d<t||}|r td|nd}td|}t|||S)Nlayer4outrqlayer3 return_layersii)rrr.r)rpr0rqrwaux_classifier classifiers r,_deeplabv3_resnetrzusR u%M "' h&x}MH36WT;/DNT;/J Xz> ::r+)rrz These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. ) categoriesmin_size_docsc TeZdZedeediedddddd id d d  ZeZy)rzHhttps://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth resize_sizeijzVhttps://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50COCO-val2017-VOC-labelsgP@皙W@miou pixel_accgvWf@gGzd@ num_paramsrecipe_metrics_ops _file_sizeurl transformsmetaN r&r'r(r rr _COMMON_METACOCO_WITH_VOC_LABELS_V1DEFAULTr*r+r,rrsU% V/SA    "n) !%, !  "&Gr+rc TeZdZedeediedddddd id d d  ZeZy)r zIhttps://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pthrrijzQhttps://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101rgP@rrgS+p@gm&m@rrNrr*r+r,r r sU% W/SA    "i) !%, !  "&Gr+r c TeZdZedeediedddddd id d d  ZeZy)r!zMhttps://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pthrriPKz`https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_largergfffff&N@gV@rgCl$@gJ +&E@rrNrr*r+r,r!r!sU% [/SA    "x) !%,    "&Gr+r!c |j}dgt|Dcgc]\}}t|dds|c}}zt|dz gz}|d}||j}|d}||j} t |di} |rd| t |<t || }|r t| |nd} t||} t|| | Scc}}w) Nr_is_cnFrrtrqrv) features enumerategetattrrerGstrrrr.r) rpr0rqib stage_indicesout_pos out_inplanesaux_pos aux_inplanesrwrxrys r,_deeplabv3_mobilenetv3rs   HC8)<\A8UZ@[1\\`cdl`mpq`q_rrMBGG$11LBGG$11L\5)M &+ c'l#&x}MH;>W\;7DN\;7J Xz> ::]s CC pretrainedpretrained_backbone)weightsweights_backboneT)rprogressr0aux_lossrrrrrkwargsc Ltj|}tj|}|3d}td|t |j d}td|d}n|d}t |gd}t|||}|"|j|j|d |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: Nr0r{rTFTTrreplace_stride_with_dilationr check_hash) rverifyrrrerrrzload_state_dictget_state_dictrrr0rrrrpmodels r,r#r#sJ)//8G'../?@+M;GLLYeLfHgh (XtD    0ObcH h X >E g44hSW4XY Lr+c Ltj|}tj|}|3d}td|t |j d}td|d}n|d}t |gd}t|||}|"|j|j|d |S) 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: Nr0r{rTrrrr) r rrrrerrrzrrrs r,r$r$sJ*009G(//0@A+M;GLLYeLfHgh (XtD   !1PcdH h X >E g44hSW4XY Lr+c Htj|}tj|}|3d}td|t |j d}td|d}n|d}t |d}t|||}|"|j|j|d|S) akConstructs a DeepLabV3 model with a MobileNetV3-Large backbone. Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation `__. Args: weights (:class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. num_classes (int, optional): number of output classes of the model (including the background) aux_loss (bool, optional): If True, it uses an auxiliary loss weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained weights for the backbone **kwargs: unused .. autoclass:: torchvision.models.segmentation.DeepLabV3_MobileNet_V3_Large_Weights :members: Nr0r{rTr)rdilatedr) r!rrrrerrrrrrs r,r"r"TsF399'BG1889IJ+M;GLLYeLfHgh (XtD   !*:DIH "8[( CE g44hSW4XY Lr+):collections.abcr functoolsrtypingrrr\rtorch.nnrrXtransforms._presetsr _apir r r_metar_utilsrrr mobilenetv3rrrresnetrrrrrrfcnr__all__rrar.rFrLModuler9rBboolrzrrr r!rr IMAGENET1K_V1r#r$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+