Yh; ddlmZddlmZddlmZmZmZddlZddlm Z m Z ddl m Z ddlmZmZdd lmZdd lmZdd lmZd d lmZmZmZd dlmZd dlmZmZgdZ Gdde j!Z"Gdde j#Z$GddZ%Gdde j#Z&de'e%de(deede)dede&f dZ*d ed!d"d#Z+Gd$d%eZ,Gd&d'eZ-Gd(d)eZ.Gd*d+eZ/eed,e,j0f-dd.d/dee,de)dede&fd0Z1eed,e-j0f-dd.d/dee-de)dede&fd1Z2eed,e.j0f-dd.d/dee.de)dede&fd2Z3eed,e/j0f-dd.d/dee/de)dede&fd3Z4dS)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_largeceZdZdedefdZdS) LayerNorm2dxreturnc|dddd}tj||j|j|j|j}|dddd}|S)Nrr r)permuteF layer_normnormalized_shapeweightbiasepsselfr#s m/var/www/tools.fuzzalab.pt/emblema-extractor/venv/lib/python3.11/site-packages/torchvision/models/convnext.pyforwardzLayerNorm2d.forward sU IIaAq ! ! LD14; 48 T T IIaAq ! !N)__name__ __module__ __qualname__r r1r2r0r"r"s6Fr2r"c jeZdZ d dededeedejfddffd Zde de fd Z xZ S) CNBlockN layer_scalestochastic_depth_prob norm_layer.r$c Jt|ttjd}tjtj||dd|dtgd||tj|d|zd tj tjd|z|d tgd |_ tj tj |d d |z|_t|d |_dS) Nư>r-r&T) kernel_sizepaddinggroupsr,)rr r&r) in_features out_featuresr,)rr&rr rrow)super__init__rr LayerNorm SequentialConv2dr LinearGELUblock Parametertorchonesr9rstochastic_depth)r/dimr9r:r; __class__s r0rHzCNBlock.__init__(s    4888J] Ic3Aq4 P P P LLL ! ! JsOO I#AG$ G G G GII I!c'$ G G G LLL ! !   < 31(=(= (KLL /0Eu M Mr2inputct|j||z}||}||z }|SN)r9rNrR)r/rUresults r0r1zCNBlock.forward?s<!DJJu$5$55&&v..% r2rW) r3r4r5floatrrrModulerHr r1 __classcell__rTs@r0r8r8's :> NNN % N Xc29n56 N  NNNNNN.Vr2r8c>eZdZdedeededdfdZdefdZdS) CNBlockConfiginput_channels out_channels num_layersr$Nc0||_||_||_dSrW)r_r`ra)r/r_r`ras r0rHzCNBlockConfig.__init__Hs -($r2cl|jjdz}|dz }|dz }|dz }|dz }|jdi|jS)N(zinput_channels={input_channels}z, out_channels={out_channels}z, num_layers={num_layers})r6)rTr3format__dict__)r/ss r0__repr__zCNBlockConfig.__repr__RsU N #c ) .. ,, (( Sqx(($-(((r2)r3r4r5intrrHstrrir6r2r0r^r^Fsm%%sm% %  %%%%)#))))))r2r^ceZdZ ddeedededed eed e j fd eed e j fd e d dffd Z de d e fdZde d e fdZxZS)rr=N block_settingr:r9 num_classesrN.r;kwargsr$c tt||stdt |t rt d|Dstd|t}|ttd}g}|dj } | td| ddd|dd td |D} d} |D]} g} t| jD]7}|| z| d z z }| || j ||| d z } 8| t#j| | jV| t#j|| j t#j| j | jddt#j||_t#jd |_|d}|j|jn|j }t#j||t#jd t#j|||_|D]y}t |t"jt"jfrQt"j|jd|j$t"j |jzdS)Nz%The block_setting should not be emptyc8g|]}t|tSr6) isinstancer^).0rhs r0 z%ConvNeXt.__init__..ks#>s>s>s`az!]?[?[>s>s>sr2z/The block_setting should be List[CNBlockConfig]r=r>rr&rCT)r@striderAr;activation_layerr,c3$K|] }|jV dSrW)ra)rucnfs r0 z$ConvNeXt.__init__..s$ I IC I I I I I Ir2g?rr )r@rwg{Gz?)std)!rGrHr ValueErrorrtrall TypeErrorr8rr"r_appendr sumrangerarrJr`rKfeaturesAdaptiveAvgPool2davgpoolFlattenrL classifiermodulesinit trunc_normal_r+r,zeros_)r/ror:r9rprNr;rqlayersfirstconv_output_channelstotal_stage_blocksstage_block_idrzstage_sd_prob lastblocklastconv_output_channelsmrTs r0rHzConvNeXt.__init__\s D!!! ODEE E]H55 O#>s>ser>s>s>s:t:t OMNN N =E   $777J"$%2!$4$C! )%!%    ! I I= I I III   C%'E3>** $ $/.@DVY\D\] UU3#5{GLLMMM!# MM"-/ 0 0 0+ M" 3#566 #"4c6FTU^_``` v. +A.. !"% &/&<&HI " "iNf !- J/ 0 0"*Q--KcepAqAq   + +A!bi344 +%%ahD%9996%GNN16***  + +r2r#c||}||}||}|SrW)rrrr.s r0 _forward_implzConvNeXt._forward_impls8 MM!   LLOO OOA  r2c,||SrW)rr.s r0r1zConvNeXt.forwards!!!$$$r2)rmr=rnNN)r3r4r5listr^rYrjrrrrZrrHr rr1r[r\s@r0rr[s(+!489=L+L+M*L+ %L+ L+  L+ bi01 L+Xc29n56L+L+ L+L+L+L+L+L+\v& %%F%%%%%%%%r2rror:weightsprogressrqr$c |)t|dt|jdt|fd|i|}|*|||d|S)Nrp categoriesr:T)r check_hash)rlenmetarload_state_dictget_state_dict)ror:rrrqmodels r0 _convnextrszfmSl9S5T5TUUU ] Z Z:O ZSY Z ZE g44hSW4XXYYY 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 beZdZedeeddieddddd id 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 r3r4r5rrr _COMMON_META IMAGENET1K_V1DEFAULTr6r2r0rrsG L7.#3OOO    "##   !    M GGGr2rc beZdZedeeddieddddd id d d  ZeZdS)rz?https://download.pytorch.org/models/convnext_small-0c510722.pthrriHZrgClT@g)X@rg|?5^!@g"~g@rrNrr6r2r0rrsG M7.#3OOO    "##   !    M GGGr2rc beZdZedeeddieddddd id d d  ZeZdS)rz>https://download.pytorch.org/models/convnext_base-6075fbad.pthrrihGrgU@gHz7X@rg(\µ.@g/$!u@rrNrr6r2r0rrsG L7.#3OOO    "##   !    M GGGr2rc beZdZedeeddieddddd id d d  ZeZdS)rz?https://download.pytorch.org/models/convnext_large-ea097f82.pthrrri rg"~U@gX9v>X@rg|?5.A@gK@rrNrr6r2r0rrsG M7.#3OOO    ###   !    M GGGr2r pretrained)rT)rrc t|}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& Nr:g?)rverifyr^poprrrrqror:s r0rr"s&$**733G b#q!!c3""c3""c4## M #JJ'>DD ]$97H W WPV W WWr2c t|}tdddtdddtdddtdddg}|dd }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 r0rrAs*%++G44G b#q!!c3""c3##c4## M #JJ'>DD ]$97H W WPV W WWr2c t|}tdddtdddtdddtdddg}|dd }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 r0rrbs&$**733G c3""c3""c4$$dD!$$ M #JJ'>DD ]$97H W WPV W WWr2c t|}tdddtdddtdddtdddg}|dd }t ||||fi|S) aConvNeXt Large model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Large_Weights :members: rrr&rirNr:r)rrr^rrrs r0r r s*%++G44G c3""c3""c4$$dD!$$ M #JJ'>DD ]$97H W WPV W WWr2)5collections.abcr functoolsrtypingrrrrPrr torch.nnr r(ops.miscr r ops.stochastic_depthrtransforms._presetsrutilsr_apirrr_metar_utilsrr__all__rIr"rZr8r^rrrYboolrrrrrrrrrrr r6r2r0rs($$$$$$********** $$$$$$44444444222222555555''''''6666666666''''''BBBBBBBB   ",bi>))))))))*V%V%V%V%V%ryV%V%V%r & k "     &&^   K([(K([(,0E0S!TUUU@DW[XXXh'<=XPTXgjXowXXXVUX:,0F0T!UVVV37$XXX/0XCGXZ]X XXXWVX>,0E0S!TUUU@DW[XXXh'<=XPTXgjXowXXXVUX:,0F0T!UVVV37$XXX/0XCGXZ]X XXXWVXXXr2