o ,h; @slddlmZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddlmZmZdd lmZdd lmZdd lmZd d lmZmZmZd dlmZd dlmZmZgdZ Gddde j!Z"Gddde j#Z$GdddZ%Gddde j#Z&de'e%de(deede)dede&f dd Z*d!ed"d#d$Z+Gd%d&d&eZ,Gd'd(d(eZ-Gd)d*d*eZ.Gd+d,d,eZ/eed-e,j0fd.dd/d0dee,de)dede&fd1d2Z1eed-e-j0fd.dd/d0dee-de)dede&fd3d4Z2eed-e.j0fd.dd/d0dee.de)dede&fd5d6Z3eed-e/j0fd.dd/d0dee/de)dede&fd7d8Z4dS)9)Sequence)partial)AnyCallableOptionalN)nnTensor) functional)Conv2dNormActivationPermute)StochasticDepth)ImageClassification)_log_api_usage_once)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) ConvNeXtConvNeXt_Tiny_WeightsConvNeXt_Small_WeightsConvNeXt_Base_WeightsConvNeXt_Large_Weights convnext_tinyconvnext_small convnext_baseconvnext_largec@seZdZdedefddZdS) LayerNorm2dxreturncCs>|dddd}t||j|j|j|j}|dddd}|S)Nrr r)permuteF layer_normnormalized_shapeweightbiasepsselfr!r-V/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/models/convnext.pyforward szLayerNorm2d.forwardN)__name__ __module__ __qualname__rr/r-r-r-r.r sr c sReZdZ d dededeedejfddffdd Zd e de fd d Z Z S) CNBlockN layer_scalestochastic_depth_prob norm_layer.r"c st|durttjdd}ttj||dd|ddtgd||tj|d|dd t tjd||dd tgd |_ t t |d d ||_t|d |_dS) Nư>r*r#T) kernel_sizepaddinggroupsr))rr r#r) in_features out_featuresr))rr#rr rrow)super__init__rr LayerNorm SequentialConv2dr LinearGELUblock Parametertorchonesr4r stochastic_depth)r,dimr4r5r6 __class__r-r.rB(s    zCNBlock.__init__inputcCs&|j||}||}||7}|SN)r4rHrL)r,rPresultr-r-r.r/?s zCNBlock.forwardrQ) r0r1r2floatrrrModulerBrr/ __classcell__r-r-rNr.r3'sr3c@s8eZdZdedeededdfddZdefdd ZdS) CNBlockConfiginput_channels out_channels num_layersr"NcCs||_||_||_dSrQ)rWrXrY)r,rWrXrYr-r-r.rBHs zCNBlockConfig.__init__cCs>|jjd}|d7}|d7}|d7}|d7}|jdi|jS)N(zinput_channels={input_channels}z, out_channels={out_channels}z, num_layers={num_layers})r-)rOr0format__dict__)r,sr-r-r.__repr__Rs zCNBlockConfig.__repr__)r0r1r2intrrBstrr_r-r-r-r.rVFs  rVcseZdZ     ddeedededed eed e j fd eed e j fd e d dffdd Z de d e fddZde d e fddZZS)rr7N block_settingr5r4 num_classesrH.r6kwargsr"c stt||stdt|trtdd|Ds!td|dur't}|dur1t t dd}g}|dj } | t d| d d d|dd d td d |D} d} |D]D} g} t| jD]}|| | d}| || j ||| d7} q]| tj| | jdur| t|| j tj| j | jdddqTtj||_td|_|d}|jdur|jn|j }t||tdt|||_|D] }t|tjtjfrtjj|jdd|jdurtj |jqdS)Nz%The block_setting should not be emptycSsg|]}t|tqSr-) isinstancerV).0r^r-r-r. ksz%ConvNeXt.__init__..z/The block_setting should be List[CNBlockConfig]r7r8rr#r=T)r:strider;r6activation_layerr)css|]}|jVqdSrQ)rY)rhcnfr-r-r. sz$ConvNeXt.__init__..g?rr )r:rjg{Gz?)std)!rArBr ValueErrorrgrall TypeErrorr3rr rWappendr sumrangerYrrDrXrEfeaturesAdaptiveAvgPool2davgpoolFlattenrF classifiermodulesinit trunc_normal_r(r)zeros_)r,rdr5r4rerHr6rflayersfirstconv_output_channelstotal_stage_blocksstage_block_idrlstage_sd_prob lastblocklastconv_output_channelsmrNr-r.rB\sp          zConvNeXt.__init__r!cCs"||}||}||}|SrQ)rvrxrzr+r-r-r. _forward_impls   zConvNeXt._forward_implcCs ||SrQ)rr+r-r-r.r/s zConvNeXt.forward)rbr7rcNN)r0r1r2listrVrSr`rrrrTrrBrrr/rUr-r-rNr.r[s2 Nrrdr5weightsprogressrfr"cKsR|durt|dt|jdt|fd|i|}|dur'||j|dd|S)Nre categoriesr5T)r check_hash)rlenmetarload_state_dictget_state_dict)rdr5rrrfmodelr-r-r. _convnexts r) rzNhttps://github.com/pytorch/vision/tree/main/references/classification#convnexta These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. )min_sizerrecipe_docsc @DeZdZedeedddieddddd id d d d ZeZdS)rz>https://download.pytorch.org/models/convnext_tiny-983f1562.pth crop_size resize_sizeiH< ImageNet-1KgzGT@gMbX X@zacc@1zacc@5gm@gV-G[@ num_params_metrics_ops _file_sizeurl transformsrN r0r1r2rrr _COMMON_META IMAGENET1K_V1DEFAULTr-r-r-r.r$ rc @r)rz?https://download.pytorch.org/models/convnext_small-0c510722.pthrriHZrgClT@g)X@rg|?5^!@g"~g@rrNrr-r-r-r.rrrc @r)rz>https://download.pytorch.org/models/convnext_base-6075fbad.pthrrihGrgU@gHz7X@rg(\µ.@g/$!u@rrNrr-r-r-r.rrrc @r)rz?https://download.pytorch.org/models/convnext_large-ea097f82.pthrrri rg"~U@gX9v>X@rg|?5.A@gK@rrNrr-r-r-r.rrr pretrained)rT)rrcKXt|}tdddtdddtdddtdddg}|dd }t||||fi|S) aConvNeXt Tiny model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_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. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights :members: `r# Nr5g?)rverifyrVpoprrrrfrdr5r-r-r.r"      rcKr) aConvNeXt Small model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_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. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Small_Weights :members: rrr#rrNr5g?)rrrVrrrr-r-r.rA      rcKr) aConvNeXt Base model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_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. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Base_Weights :members: r#iirNr5?)rrrVrrrr-r-r.rbrrcKr) aConvNeXt Large model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_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. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Large_Weights :members: rrr#rirNr5r)rrrVrrrr-r-r.rrr)5collections.abcr functoolsrtypingrrrrJrrtorch.nnr r%ops.miscr r ops.stochastic_depthr transforms._presetsrutilsr_apirrr_metar_utilsrr__all__rCr rTr3rVrrrSboolrrrrrrrrrrrr-r-r-r.s       Y  **