:j; ddlmZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddlmZmZdd lmZdd lmZdd lmZd d lmZmZmZd dlmZd dlmZmZgdZ Gdde jBZ"Gdde jFZ$GddZ%Gdde jFZ&de'e%de(deede)dede&f dZ*d ed!d"d#Z+Gd$d%eZ,Gd&d'eZ-Gd(d)eZ.Gd*d+eZ/eed,e,j`f-dd.d/dee,de)dede&fd0Z1eed,e-j`f-dd.d/dee-de)dede&fd1Z2eed,e.j`f-dd.d/dee.de)dede&fd2Z3eed,e/j`f-dd.d/dee/de)dede&fd3Z4y)4)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_largeceZdZdedefdZy) LayerNorm2dxreturnc|jdddd}tj||j|j|j |j }|jdddd}|S)Nrr r)permuteF layer_normnormalized_shapeweightbiasepsselfr#s p/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchvision/models/convnext.pyforwardzLayerNorm2d.forward sW IIaAq ! LLD114;; 488 T IIaAq !N)__name__ __module__ __qualname__r r1r2r0r"r"sFr2r"c heZdZ d dededeedejfddffd Zde de fd Z xZ S) CNBlockN layer_scalestochastic_depth_prob norm_layer.r$c t||ttjd}tj tj ||dd|dtgd||tj|d|zd tjtjd|z|d tgd |_ tjtj|d d |z|_t|d |_y) Nư>r-r&T) kernel_sizepaddinggroupsr,)rr r&r) in_features out_featuresr,)rr&rr rrow)super__init__rr LayerNorm SequentialConv2dr LinearGELUblock Parametertorchonesr9rstochastic_depth)r/dimr9r:r; __class__s r0rHzCNBlock.__init__(s    48J]] IIc3Aq4 P L ! sO II#AG$ G GGI II!c'$ G L !  << 31(= (KL /0Eu Mr2inputcn|j|j|z}|j|}||z }|SN)r9rNrR)r/rUresults r0r1zCNBlock.forward?s9!!DJJu$55&&v.% r2rW) r3r4r5floatrrrModulerHr r1 __classcell__rTs@r0r8r8's_ :> NN % N Xc299n56 N  N.Vr2r8c6eZdZdedeededdfdZdefdZy) CNBlockConfiginput_channels out_channels num_layersr$Nc.||_||_||_yrW)r_r`ra)r/r_r`ras r0rHzCNBlockConfig.__init__Hs -($r2c|jjdz}|dz }|dz }|dz }|dz }|jdi|jS)N(zinput_channels={input_channels}z, out_channels={out_channels}z, num_layers={num_layers})r6)rTr3format__dict__)r/ss r0__repr__zCNBlockConfig.__repr__RsX NN # #c ) .. ,, (( Sqxx($--((r2)r3r4r5intrrHstrrir6r2r0r^r^Fs=%%sm% %  %)#)r2r^ceZdZ ddeededededeede jfdeede jfd e d dffd Z d e d e fd Zd e d e fdZxZS)rN block_settingr:r9 num_classesrN.r;kwargsr$c t|t||s tdt |t r't |Dcgc]}t |tc}s td|t}|ttd}g} |dj} | jtd| ddd|dd td |D} d} |D]} g}t!| j"D]5}|| z| d z z }|j|| j||| d z } 7| jt%j&|| j(| jt%j&|| jt%j*| j| j(d d t%j&| |_t%j.d |_|d}|j( |j(n |j}t%j&||t%j2d t%j4|||_|j9D]}t |t$j*t$j4fs.t$j:j=|j>d|j@ft$j:jC|j@ycc}w)Nz%The block_setting should not be emptyz/The block_setting should be List[CNBlockConfig]r=r>rr&rCT)r@striderAr;activation_layerr,c34K|]}|jywrW)ra).0cnfs r0 z$ConvNeXt.__init__..s IC Isg?rr )r@rqg{Gz?)std)"rGrHr ValueError isinstancerallr^ TypeErrorr8rr"r_appendr sumrangerarrJr`rKfeaturesAdaptiveAvgPool2davgpoolFlattenrL classifiermodulesinit trunc_normal_r+r,zeros_)r/rmr:r9rnrNr;rorhlayersfirstconv_output_channelstotal_stage_blocksstage_block_idrustage_sd_prob lastblocklastconv_output_channelsmrTs r0rHzConvNeXt.__init__\s D!DE E]H5#er>s`az!]?[>s:tMN N =E   $7J"$%2!$4$C$C! )%!%  ! I= II  C%'E3>>* $/.@DVY\D\] U3#5#5{GLM!#  $ MM"--/ 0+ MM"3#5#56 #"4"4c6F6FTU^_` $ v. ++A. !"% &/&<&<&HI " "iNfNf !-- / 0"**Q-KcepAq  +A!bii34%%ahhD%966%GGNN166*  +s?tsK5r#cl|j|}|j|}|j|}|SrW)rrrr.s r0 _forward_implzConvNeXt._forward_impls0 MM!  LLO OOA r2c$|j|SrW)rr.s r0r1zConvNeXt.forwards!!!$$r2)gr=iNN)r3r4r5listr^rYrjrrrrZrrHr rr1r[r\s@r0rr[s(+!489=L+M*L+ %L+ L+  L+ bii01 L+Xc299n56L+L+ L+\v& %%F%r2rrmr:weightsprogressror$c |#t|dt|jdt|fd|i|}|"|j |j |d|S)Nrn categoriesr:T)r check_hash)rlenmetarload_state_dictget_state_dict)rmr:rrromodels r0 _convnextrsdfmSl9S5TU ] Z:O ZSY ZE g44hSW4XY Lr2) rzNhttps://github.com/pytorch/vision/tree/main/references/classification#convnexta These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. )min_sizerrecipe_docsc TeZdZedeeddieddddd id d d  ZeZy)rz>https://download.pytorch.org/models/convnext_tiny-983f1562.pth crop_size resize_sizeiH< ImageNet-1KgzGT@gMbX X@zacc@1zacc@5gm@gV-G[@ num_params_metrics_ops _file_sizeurl transformsrN r3r4r5rrr _COMMON_META IMAGENET1K_V1DEFAULTr6r2r0rrsS L.#3O    "##  !  M Gr2rc TeZdZedeeddieddddd id d d  ZeZy)rz?https://download.pytorch.org/models/convnext_small-0c510722.pthrriHZrgClT@g)X@rg|?5^!@g"~g@rrNrr6r2r0rrsS M.#3O    "##  !  M Gr2rc TeZdZedeeddieddddd id d d  ZeZy)rz>https://download.pytorch.org/models/convnext_base-6075fbad.pthrrihGrgU@gHz7X@rg(\µ.@g/$!u@rrNrr6r2r0rrsS L.#3O    "##  !  M Gr2rc TeZdZedeeddieddddd id d d  ZeZy)rz?https://download.pytorch.org/models/convnext_large-ea097f82.pthrrri rg"~U@gX9v>X@rg|?5.A@gK@rrNrr6r2r0rrsS M.#3O    ###  !  M Gr2r pretrained)rT)rrc tj|}tdddtdddtdddtdddg}|jdd }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& Nr:g?)rverifyr^poprrrrormr:s r0rr"sy&$**73G b#q!c3"c3"c4# M #JJ'>D ]$97H WPV WWr2c tj|}tdddtdddtdddtdddg}|jdd }t ||||fi|S) 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&rrNr:g?)rrr^rrrs r0rrAsy*%++G4G b#q!c3"c3#c4# M #JJ'>D ]$97H WPV WWr2c tj|}tdddtdddtdddtdddg}|jdd }t ||||fi|S) aConvNeXt Base model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Base_Weights :members: r&iirNr:?)rrr^rrrs r0rrbsy&$**73G c3"c3"c4$dD!$ M #JJ'>D ]$97H WPV WWr2c tj|}tdddtdddtdddtdddg}|jdd }t ||||fi|S) aConvNeXt Large model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Large_Weights :members: rrr&rirNr:r)rrr^rrrs r0r r sy*%++G4G c3"c3"c4$dD!$ M #JJ'>D ]$97H WPV WWr2)5collections.abcr functoolsrtypingrrrrPrr torch.nnr r(ops.miscr r ops.stochastic_depthrtransforms._presetsrutilsr_apirrr_metar_utilsrr__all__rIr"rZr8r^rrrYboolrrrrrrrrrrr r6r2r0rs$** $425'66'B ",,bii>))*V%ryyV%r & k "     &&^  K([(K([(,0E0S0S!TU@DW[Xh'<=XPTXgjXowXVX:,0F0T0T!UV37$X/0XCGXZ]X XWX>,0E0S0S!TU@DW[Xh'<=XPTXgjXowXVX:,0F0T0T!UV37$X/0XCGXZ]X XWXr2