o
    h;                     @   sl  d dl mZ d dlmZ d dlmZmZmZ d dlZd dlm	Z	m
Z
 d dlmZ ddlmZmZ dd	lmZ dd
lmZ ddlmZ ddlmZmZmZ ddlmZ ddlmZmZ g dZ G dd de	j!Z"G dd de	j#Z$G dd dZ%G dd de	j#Z&de'e% de(dee de)dede&fdd Z*d!ed"d#d$Z+G d%d& d&eZ,G d'd( d(eZ-G d)d* d*eZ.G d+d, d,eZ/e ed-e,j0fd.dd/d0dee, de)dede&fd1d2Z1e ed-e-j0fd.dd/d0dee- de)dede&fd3d4Z2e ed-e.j0fd.dd/d0dee. de)dede&fd5d6Z3e ed-e/j0fd.dd/d0dee/ de)dede&fd7d8Z4dS )9    )Sequence)partial)AnyCallableOptionalN)nnTensor)
functional   )Conv2dNormActivationPermute)StochasticDepth)ImageClassification)_log_api_usage_once   )register_modelWeightsWeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)	ConvNeXtConvNeXt_Tiny_WeightsConvNeXt_Small_WeightsConvNeXt_Base_WeightsConvNeXt_Large_Weightsconvnext_tinyconvnext_smallconvnext_baseconvnext_largec                   @   s   e Zd ZdedefddZdS )LayerNorm2dxreturnc                 C   s>   | dddd}t|| j| j| j| j}| dddd}|S )Nr   r
      r   )ZpermuteFZ
layer_normZnormalized_shapeweightbiasepsselfr!    r*   a/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torchvision/models/convnext.pyforward    s   zLayerNorm2d.forwardN)__name__
__module____qualname__r   r,   r*   r*   r*   r+   r       s    r    c                
       sR   e Zd Z	ddededeedejf  ddf fddZd	e	de	fd
dZ
  ZS )CNBlockNlayer_scalestochastic_depth_prob
norm_layer.r"   c                    s   t    |d u rttjdd}ttj||dd|ddtg d||tj|d| dd	t	 tjd| |dd	tg d
| _
tt|dd| | _t|d| _d S )Nư>r'      r#   T)kernel_sizepaddinggroupsr&   )r   r
   r#   r      )Zin_featuresZout_featuresr&   )r   r#   r   r
   r   row)super__init__r   r   	LayerNorm
SequentialConv2dr   LinearZGELUblock	ParametertorchZonesr1   r   stochastic_depth)r)   dimr1   r2   r3   	__class__r*   r+   r=   (   s   


	zCNBlock.__init__inputc                 C   s&   | j | | }| |}||7 }|S N)r1   rB   rE   )r)   rI   resultr*   r*   r+   r,   ?   s   
zCNBlock.forwardrJ   )r-   r.   r/   floatr   r   r   Moduler=   r   r,   __classcell__r*   r*   rG   r+   r0   '   s    r0   c                   @   s8   e Zd Zdedee deddfddZdefdd	ZdS )
CNBlockConfiginput_channelsout_channels
num_layersr"   Nc                 C   s   || _ || _|| _d S rJ   )rP   rQ   rR   )r)   rP   rQ   rR   r*   r*   r+   r=   H   s   
zCNBlockConfig.__init__c                 C   s>   | j jd }|d7 }|d7 }|d7 }|d7 }|jdi | jS )N(zinput_channels={input_channels}z, out_channels={out_channels}z, num_layers={num_layers})r*   )rH   r-   format__dict__)r)   sr*   r*   r+   __repr__R   s   zCNBlockConfig.__repr__)r-   r.   r/   intr   r=   strrX   r*   r*   r*   r+   rO   F   s    

rO   c                       s   e Zd Z					ddee dededed	eed
e	j
f  deed
e	j
f  deddf fddZdedefddZdedefddZ  ZS )r           r4     Nblock_settingr2   r1   num_classesrB   .r3   kwargsr"   c                    s  t    t|  |stdt|trtdd |D s!td|d u r't}|d u r1t	t
dd}g }|d j}	|td|	d	d	d|d d
d tdd |D }
d}|D ]D}g }t|jD ]}|| |
d  }|||j|| |d7 }q]|tj|  |jd ur|t||jtj|j|jddd qTtj| | _td| _|d }|jd ur|jn|j}t||tdt||| _|  D ] }t|tjtjfrtjj|jdd |jd urtj |j qd S )Nz%The block_setting should not be emptyc                 S   s   g | ]}t |tqS r*   )
isinstancerO   ).0rW   r*   r*   r+   
<listcomp>k   s    z%ConvNeXt.__init__.<locals>.<listcomp>z/The block_setting should be List[CNBlockConfig]r4   r5   r   r#   r:   T)r7   strider8   r3   Zactivation_layerr&   c                 s   s    | ]}|j V  qd S rJ   )rR   )ra   cnfr*   r*   r+   	<genexpr>   s    z$ConvNeXt.__init__.<locals>.<genexpr>g      ?r   r
   )r7   rc   g{Gz?)Zstd)!r<   r=   r   
ValueErrorr`   r   all	TypeErrorr0   r   r    rP   appendr   sumrangerR   r   r?   rQ   r@   featuresZAdaptiveAvgPool2davgpoolZFlattenrA   
classifiermodulesinitZtrunc_normal_r%   r&   Zzeros_)r)   r]   r2   r1   r^   rB   r3   r_   ZlayersZfirstconv_output_channelsZtotal_stage_blocksZstage_block_idrd   Zstage_Zsd_probZ	lastblockZlastconv_output_channelsmrG   r*   r+   r=   \   sp   





zConvNeXt.__init__r!   c                 C   s"   |  |}| |}| |}|S rJ   )rm   rn   ro   r(   r*   r*   r+   _forward_impl   s   


zConvNeXt._forward_implc                 C   s
   |  |S rJ   )rt   r(   r*   r*   r+   r,      s   
zConvNeXt.forward)r[   r4   r\   NN)r-   r.   r/   listrO   rL   rY   r   r   r   rM   r   r=   r   rt   r,   rN   r*   r*   rG   r+   r   [   s2    	Nr   r]   r2   weightsprogressr_   r"   c                 K   sR   |d urt |dt|jd  t| fd|i|}|d ur'||j|dd |S )Nr^   
categoriesr2   T)rw   Z
check_hash)r   lenmetar   Zload_state_dictZget_state_dict)r]   r2   rv   rw   r_   modelr*   r*   r+   	_convnext   s   r|   )    r}   zNhttps://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
        <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
    )Zmin_sizerx   ZrecipeZ_docsc                	   @   D   e Zd Zedeedddi eddddd	id
dddZeZdS )r   z>https://download.pytorch.org/models/convnext_tiny-983f1562.pth      Z	crop_sizeZresize_sizeiH<ImageNet-1KgzGT@gMbX	X@zacc@1zacc@5gm@gV-G[@Z
num_paramsZ_metricsZ_ops
_file_sizeurlZ
transformsrz   N	r-   r.   r/   r   r   r   _COMMON_METAIMAGENET1K_V1DEFAULTr*   r*   r*   r+   r      $    r   c                	   @   r~   )r   z?https://download.pytorch.org/models/convnext_small-0c510722.pthr      r   iHZr   gClT@g)X@r   g|?5^!@g"~g@r   r   Nr   r*   r*   r*   r+   r      r   r   c                	   @   r~   )r   z>https://download.pytorch.org/models/convnext_base-6075fbad.pthr      r   ihGr   gU@gHz7X@r   g(\µ.@g/$!u@r   r   Nr   r*   r*   r*   r+   r      r   r   c                	   @   r~   )r   z?https://download.pytorch.org/models/convnext_large-ea097f82.pthr   r   r   ir   g"~U@gX9v>X@r   g|?5.A@gK@r   r   Nr   r*   r*   r*   r+   r     r   r   Z
pretrained)rv   T)rv   rw   c                 K   X   t | } tdddtdddtdddtdddg}|dd	}t||| |fi |S )
a  ConvNeXt Tiny model architecture from the
    `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`_ 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
            <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ConvNeXt_Tiny_Weights
        :members:
    `      r#        	   Nr2   g?)r   verifyrO   popr|   rv   rw   r_   r]   r2   r*   r*   r+   r   "     




r   c                 K   r   )
a  ConvNeXt Small model architecture from the
    `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`_ 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
            <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ConvNeXt_Small_Weights
        :members:
    r   r   r#   r   r      Nr2   g?)r   r   rO   r   r|   r   r*   r*   r+   r   A     




r   c                 K   r   )
a  ConvNeXt Base model architecture from the
    `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`_ 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
            <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ConvNeXt_Base_Weights
        :members:
          r#   i   i   r   Nr2         ?)r   r   rO   r   r|   r   r*   r*   r+   r   b  r   r   c                 K   r   )
a  ConvNeXt Large model architecture from the
    `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`_ 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
            <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ConvNeXt_Large_Weights
        :members:
    r   r   r#   r   i   r   Nr2   r   )r   r   rO   r   r|   r   r*   r*   r+   r     r   r   )5collections.abcr   	functoolsr   typingr   r   r   rD   r   r   Ztorch.nnr	   r$   Zops.miscr   r   Zops.stochastic_depthr   Ztransforms._presetsr   utilsr   Z_apir   r   r   _metar   Z_utilsr   r   __all__r>   r    rM   r0   rO   r   ru   rL   boolr|   r   r   r   r   r   r   r   r   r   r   r*   r*   r*   r+   <module>   s    Y
**