a PSic{:@s*ddlmZddlmZmZmZmZmZddlZddlm Z m Z ddl m Z ddlmZmZddlmZdd lmZdd lmZd d lmZmZd d lmZd dlmZmZgdZGddde j Z!Gddde j"Z#GdddZ$Gddde j"Z%ee$e&eee'ee%dddZ(dedddZ)Gdd d eZ*Gd!d"d"eZ+Gd#d$d$eZ,Gd%d&d&eZ-ed'e*j.fd(dd)d*ee*e'ee%d+d,d-Z/ed'e+j.fd(dd)d*ee+e'ee%d+d.d/Z0ed'e,j.fd(dd)d*ee,e'ee%d+d0d1Z1ed'e-j.fd(dd)d*ee-e'ee%d+d2d3Z2dS)4)partial)AnyCallableListOptionalSequenceN)nnTensor) functional)Conv2dNormActivationPermute)StochasticDepth)ImageClassification)_log_api_usage_once) WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param) ConvNeXtConvNeXt_Tiny_WeightsConvNeXt_Small_WeightsConvNeXt_Base_WeightsConvNeXt_Large_Weights convnext_tinyconvnext_small convnext_baseconvnext_largec@seZdZeedddZdS) LayerNorm2dxreturncCs>|dddd}t||j|j|j|j}|dddd}|S)Nrr r)permuteF layer_normnormalized_shapeweightbiasepsselfr"r.W/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/models/convnext.pyforwardszLayerNorm2d.forwardN)__name__ __module__ __qualname__r r0r.r.r.r/r sr csHeZdZd eeeedejfddfdd Ze e dddZ Z S) CNBlockN.) layer_scalestochastic_depth_prob norm_layerr#c st|dur ttjdd}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$rr rrow)super__init__rr LayerNorm SequentialConv2dr LinearGELUblock Parametertorchonesr5rstochastic_depth)r-dimr5r6r7 __class__r.r/rC's    zCNBlock.__init__)inputr#cCs&|j||}||}||7}|SN)r5rIrM)r-rQresultr.r.r/r0>s zCNBlock.forward)N) r1r2r3floatrrrModulerCr r0 __classcell__r.r.rOr/r4&sr4c@s2eZdZeeeeddddZedddZdS) CNBlockConfigN)input_channels out_channels num_layersr#cCs||_||_||_dSrR)rXrYrZ)r-rXrYrZr.r.r/rCGszCNBlockConfig.__init__)r#cCs>|jjd}|d7}|d7}|d7}|d7}|jfi|jS)N(zinput_channels={input_channels}z, out_channels={out_channels}z, num_layers={num_layers}))rPr1format__dict__)r-sr.r.r/__repr__Qs  zCNBlockConfig.__repr__)r1r2r3intrrCstrr`r.r.r.r/rWEs  rWc sreZdZdeeeeeeede j feede j fe ddfdd Z e e d d d Ze e d d d ZZS)rr8N.) block_settingr6r5 num_classesrIr7kwargsr#c stt||s tdn$t|trjz%ConvNeXt.__init__..z/The block_setting should be List[CNBlockConfig]r8r9rr$r>T)r;strider<r7Zactivation_layerr*css|] }|jVqdSrR)rZ)ricnfr.r.r/ rkz$ConvNeXt.__init__..g?rr )r;rlg{Gz?)std)!rBrCr ValueErrorrhrall TypeErrorr4rr rXappendr sumrangerZrrErYrFfeaturesAdaptiveAvgPool2davgpoolFlattenrG classifiermodulesinit trunc_normal_r)r*zeros_)r-rer6r5rfrIr7rglayersZfirstconv_output_channelsZtotal_stage_blocksZstage_block_idrmstage_Zsd_probZ lastblockZlastconv_output_channelsmrOr.r/rC[sj           zConvNeXt.__init__r!cCs"||}||}||}|SrR)rwryr{r,r.r.r/ _forward_impls   zConvNeXt._forward_implcCs ||SrR)rr,r.r.r/r0szConvNeXt.forward)rcr8rdNN)r1r2r3rrWrTrarrrrUrrCr rr0rVr.r.rOr/rZs"Nr)rer6weightsprogressrgr#cKsP|durt|dt|jdt|fd|i|}|durL||j|d|S)Nrf categoriesr6)r)rlenmetarload_state_dictget_state_dict)rer6rrrgmodelr.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 @s@eZdZedeedddieddddd id d ZeZd S) rz>https://download.pytorch.org/models/convnext_tiny-983f1562.pth crop_size resize_sizeiH< ImageNet-1KgzGT@gMbX X@zacc@1zacc@5 num_params_metricsurl transformsrN r1r2r3rrr _COMMON_META IMAGENET1K_V1DEFAULTr.r.r.r/rs rc @s@eZdZedeedddieddddd id d ZeZd S) rz?https://download.pytorch.org/models/convnext_small-0c510722.pthrriHZrgClT@g)X@rrrNrr.r.r.r/rs rc @s@eZdZedeedddieddddd id d ZeZd S) rz>https://download.pytorch.org/models/convnext_base-6075fbad.pthrrihGrgU@gHz7X@rrrNrr.r.r.r/rs rc @s@eZdZedeedddieddddd id d ZeZd S) rz?https://download.pytorch.org/models/convnext_large-ea097f82.pthrrri rg"~U@gX9v>X@rrrNrr.r.r.r/rs r pretrained)rT)rr)rrrgr#cKsXt|}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$ Nr6g?)rverifyrWpoprrrrgrer6r.r.r/rs      rcKsXt|}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$rrNr6g?)rrrWrrrr.r.r/r7s      rcKsXt|}tdddtdddtdddtdddg}|dd }t||||fi|S) aConvNeXt Base model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Base_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Base_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Base_Weights :members: r$iirNr6?)rrrWrrrr.r.r/rWs      rcKsXt|}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$rirNr6r)rrrWrrrr.r.r/rus      r)3 functoolsrtypingrrrrrrKrr torch.nnr r&Zops.miscr r Zops.stochastic_depthrZtransforms._presetsrutilsr_apirr_metar_utilsrr__all__rDr rUr4rWrrTboolrrrrrrrrrrrr.r.r.r/s`       Z  " "