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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!bii344%%ahhD%966%GGNN166*  s?ts Lr$clURU5nURU5nURU5nU$r])rrrr/s r1 _forward_implConvNeXt._forward_impls0 MM!  LLO OOA r4c$URU5$r])rr/s r1r2ConvNeXt.forwards!!!$$r4)rrr)grBiNN)r6r7r8r9listrer`rtrrrrarrMr rr2r:rbrcs@r1rr[s(+!489=L+M*L+ %L+ L+  L+ bii01 L+Xc299n56L+L+ L+L+\v& %%F%%r4rrxr?weightsprogressrzr%c Ub#[US[URS55 [U4SU0UD6nUbUR UR USS95 U$)Nry categoriesr?T)r check_hash)rlenmetarload_state_dictget_state_dict)rxr?rrrzmodels r1 _convnextrsdfmSl9S5TU ] Z:O ZSY ZE g44hSW4XY Lr4) rzNhttps://github.com/pytorch/vision/tree/main/references/classification#convnexta These weights improve upon the results of the original paper by using a modified version of TorchVision's `new training recipe `_. )min_sizerrecipe_docsc P\rSrSr\"S\"\SSS90\ESSSS S .0S S S .ES9r\r Sr g)rz>https://download.pytorch.org/models/convnext_tiny-983f1562.pth crop_size resize_sizeiH< ImageNet-1KgzGT@gMbX X@zacc@1zacc@5gm@gV-G[@ num_params_metrics_ops _file_sizeurl transformsrr5N r6r7r8r9rrr _COMMON_META IMAGENET1K_V1DEFAULTr:r5r4r1rrsS L.#3O    "##  !  M Gr4rc P\rSrSr\"S\"\SSS90\ESSSSS .0S S S .ES 9r\r Sr g)rz?https://download.pytorch.org/models/convnext_small-0c510722.pthrriHZrgClT@g)X@rg|?5^!@g"~g@rrr5Nrr5r4r1rrsS M.#3O    "##  !  M Gr4rc P\rSrSr\"S\"\SSS90\ESSSS S .0S S S .ES9r\r Sr g)rz>https://download.pytorch.org/models/convnext_base-6075fbad.pthrrihGrgU@gHz7X@rg(\µ.@g/$!u@rrr5Nrr5r4r1rrsS L.#3O    "##  !  M Gr4rc P\rSrSr\"S\"\SSS90\ESSSS S .0S S S .ES9r\r Sr g)riz?https://download.pytorch.org/models/convnext_large-ea097f82.pthrrri rg"~U@gX9v>X@rg|?5.A@gK@rrr5Nrr5r4r1rrsS M.#3O    ###  !  M Gr4r pretrained)rT)rrc [RU5n[SSS5[SSS5[SSS5[SSS5/nURSS 5n[ X4X40UD6$) aMConvNeXt Tiny model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Tiny_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights :members: `r' Nr?g?)rverifyrepoprrrrzrxr?s r1rr"st&$**73G b#q!c3"c3"c4# M #JJ'>D ]7 WPV WWr4c [RU5n[SSS5[SSS5[SSS5[SSS5/nURSS 5n[ X4X40UD6$) aQConvNeXt Small model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Small_Weights :members: rrr'rrNr?g?)rrrerrrs r1rrAst*%++G4G b#q!c3"c3#c4# M #JJ'>D ]7 WPV WWr4c [RU5n[SSS5[SSS5[SSS5[SSS5/nURSS 5n[ X4X40UD6$) aMConvNeXt 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??)rrrerrrs r1rrbst&$**73G c3"c3"c4$dD!$ M #JJ'>D ]7 WPV WWr4c [RU5n[SSS5[SSS5[SSS5[SSS5/nURSS 5n[ X4X40UD6$) aQConvNeXt Large model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Large_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Large_Weights` below for more details and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.ConvNeXt_Large_Weights :members: rrr'rirNr?r)rrrerrrs r1r r st*%++G4G c3"c3"c4$dD!$ M #JJ'>D ]7 WPV WWr4)5collections.abcr functoolsrtypingrrrrUrr torch.nnr r)ops.miscr r ops.stochastic_depthrtransforms._presetsrutilsr_apirrr_metar_utilsrr__all__rNr"rar<rerrr`boolrrrrrrrrrrr r5r4r1rs$** $425'66'B ",,bii>))*V%ryyV%r & k "     &&^  K([(K([(,0E0S0S!TU@DW[Xh'<=XPTXgjXowXVX:,0F0T0T!UV37$X/0XCGXZ]X XWX>,0E0S0S!TU@DW[Xh'<=XPTXgjXowXVX:,0F0T0T!UV37$X/0XCGXZ]X XWXr4