a PSicg @s\ddlmZddlmZddlmZmZmZddlm Z m Z ddl m Z ddlmZddlmZd d lmZmZd d lmZd d lmZmZmZd d lmZmZmZgdZGddde j Z!Gddde j Z"ee#e!dddZ$GdddeZ%ede%j&fdej'fddddej'dee%e(ee#eeee!ddd Z)d d!lm*Z*e*d"e%j&j+iZ,dS)#) OrderedDict)partial)AnyDictOptional)nnTensor) functional)SemanticSegmentation)_log_api_usage_once) WeightsEnumWeights)_VOC_CATEGORIES)IntermediateLayerGetterhandle_legacy_interface_ovewrite_value_param) MobileNetV3MobileNet_V3_Large_Weightsmobilenet_v3_large)LRASPP!LRASPP_MobileNet_V3_Large_Weightslraspp_mobilenet_v3_largecsLeZdZdZd ejeeeeddfdd Zee e efddd Z Z S) ra Implements a Lite R-ASPP Network for semantic segmentation from `"Searching for MobileNetV3" `_. Args: backbone (nn.Module): the network used to compute the features for the model. The backbone should return an OrderedDict[Tensor], with the key being "high" for the high level feature map and "low" for the low level feature map. low_channels (int): the number of channels of the low level features. high_channels (int): the number of channels of the high level features. num_classes (int, optional): number of output classes of the model (including the background). inter_channels (int, optional): the number of channels for intermediate computations. N)backbone low_channels high_channels num_classesinter_channelsreturncs,tt|||_t|||||_dS)N)super__init__r r LRASPPHead classifier)selfrrrrr __class__b/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/segmentation/lraspp.pyr"#s zLRASPP.__init__inputr cCsB||}||}tj||jddddd}t}||d<|S)NbilinearFsizemode align_cornersout)rr$F interpolateshaper)r%r+featuresr2resultr(r(r)forward+s   zLRASPP.forward)r) __name__ __module__ __qualname____doc__rModuleintr"rrstrr8 __classcell__r(r(r&r)rs rcsBeZdZeeeeddfdd ZeeefedddZZ S)r#N)rrrrr c stttj||dddt|tjdd|_ttdtj||dddt |_ t||d|_ t||d|_ dS)NF)biasT)inplace) r!r"r SequentialConv2d BatchNorm2dReLUcbrAdaptiveAvgPool2dSigmoidscalelow_classifierhigh_classifier)r%rrrrr&r(r)r"7s  zLRASPPHead.__init__r*cCs\|d}|d}||}||}||}tj||jddddd}||||S)Nlowhighr,r-Fr.)rHrKr3r4r5rLrM)r%r+rNrOxsr(r(r)r8Fs  zLRASPPHead.forward) r9r:r;r>r"rr?rr8r@r(r(r&r)r#6sr#)rrr cCsz|j}dgddt|Dt|dg}|d}|d}||j}||j}t|t|dt|did }t||||S) NrcSs g|]\}}t|ddr|qS)_is_cnF)getattr).0ibr(r(r) Vz'_lraspp_mobilenetv3..rArNrO) return_layers)r6 enumeratelen out_channelsrr?r)rr stage_indiceslow_poshigh_posrrr(r(r)_lraspp_mobilenetv3Rs&  rbc @s>eZdZedeedddeddddd d id d d ZeZdS)rzJhttps://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pthi) resize_sizei"(1)rArAz]https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_largezCOCO-val2017-VOC-labelsg33333L@gV@)miou pixel_accz These weights were trained on a subset of COCO, using only the 20 categories that are present in the Pascal VOC dataset. ) num_params categoriesmin_sizerecipe_metrics_docs)url transformsmetaN) r9r:r;rrr rCOCO_WITH_VOC_LABELS_V1DEFAULTr(r(r(r)r`s  r pretrainedpretrained_backbone)weightsweights_backboneNT)rsprogressrrt)rsrurrtkwargsr cKs|ddrtdt|}t|}|durJd}t|t|jd}n |durVd}t|dd}t ||}|dur| |j |d |S) a|Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from `Searching for MobileNetV3 `_ paper. .. betastatus:: segmentation module Args: weights (:class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.segmentation.LRASPP_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: parameters passed to the ``torchvision.models.segmentation.LRASPP`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights :members: aux_lossFz&This model does not use auxiliary lossNrgT)rsdilated)ru) popNotImplementedErrorrverifyrrr]rnrrbload_state_dictget_state_dict)rsrurrtrvrmodelr(r(r)rxs%     r) _ModelURLsZlraspp_mobilenet_v3_large_coco)- collectionsr functoolsrtypingrrrtorchrrtorch.nnr r3Ztransforms._presetsr utilsr _apirr_metar_utilsrrr mobilenetv3rrr__all__r=rr#r>rbrro IMAGENET1K_V1boolrrrl model_urlsr(r(r(r)sH      #7