o
    )i;                     @   sh  d dl mZ d dlmZmZ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+j/fd-dd.d/dee+ de(dede&fd0d1Z0e ed,e,j/fd-dd.d/dee, de(dede&fd2d3Z1e ed,e-j/fd-dd.d/dee- de(dede&fd4d5Z2e ed,e.j/fd-dd.d/dee. de(dede&fd6d7Z3dS )8    )partial)AnyCallableListOptionalSequenceN)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+   j/var/www/html/eduruby.in/lip-sync/lip-sync-env/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onesr2   r   stochastic_depth)r*   dimr2   r3   r4   	__class__r+   r,   r>   '   s   


	zCNBlock.__init__inputc                 C   s&   | j | | }| |}||7 }|S N)r2   rC   rF   )r*   rJ   resultr+   r+   r,   r-   >   s   
zCNBlock.forwardrK   )r.   r/   r0   floatr   r   r   Moduler>   r	   r-   __classcell__r+   r+   rH   r,   r1   &   s    r1   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 rK   )rQ   rR   rS   )r*   rQ   rR   rS   r+   r+   r,   r>   G   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+   )rI   r.   format__dict__)r*   sr+   r+   r,   __repr__Q   s   zCNBlockConfig.__repr__)r.   r/   r0   intr   r>   strrY   r+   r+   r+   r,   rP   E   s    

rP   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           r5     Nblock_settingr3   r2   num_classesrC   .r4   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+   )
isinstancerP   ).0rX   r+   r+   r,   
<listcomp>j   s    z%ConvNeXt.__init__.<locals>.<listcomp>z/The block_setting should be List[CNBlockConfig]r5   r6   r   r$   r;   T)r8   strider9   r4   Zactivation_layerr'   c                 s   s    | ]}|j V  qd S rK   )rS   )rb   cnfr+   r+   r,   	<genexpr>   s    z$ConvNeXt.__init__.<locals>.<genexpr>g      ?r   r   )r8   rd   g{Gz?)Zstd)!r=   r>   r   
ValueErrorra   r   all	TypeErrorr1   r   r!   rQ   appendr   sumrangerS   r   r@   rR   rA   featuresZAdaptiveAvgPool2davgpoolZFlattenrB   
classifiermodulesinitZtrunc_normal_r&   r'   Zzeros_)r*   r^   r3   r2   r_   rC   r4   r`   ZlayersZfirstconv_output_channelsZtotal_stage_blocksZstage_block_idre   Zstage_Zsd_probZ	lastblockZlastconv_output_channelsmrH   r+   r,   r>   [   sp   





zConvNeXt.__init__r"   c                 C   s"   |  |}| |}| |}|S rK   )rn   ro   rp   r)   r+   r+   r,   _forward_impl   s   


zConvNeXt._forward_implc                 C   s
   |  |S rK   )ru   r)   r+   r+   r,   r-      s   
zConvNeXt.forward)r\   r5   r]   NN)r.   r/   r0   r   rP   rM   rZ   r   r   r   rN   r   r>   r	   ru   r-   rO   r+   r+   rH   r,   r   Z   s2    	Nr   r^   r3   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_   
categoriesr3   T)rw   Z
check_hash)r   lenmetar   Zload_state_dictZget_state_dict)r^   r3   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/   r0   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$        	   Nr3   g?)r   verifyrP   popr|   rv   rw   r`   r^   r3   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      Nr3   g?)r   r   rP   r   r|   r   r+   r+   r,   r   @     




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   Nr3         ?)r   r   rP   r   r|   r   r+   r+   r,   r   a  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   Nr3   r   )r   r   rP   r   r|   r   r+   r+   r,   r      r   r    )4	functoolsr   typingr   r   r   r   r   rE   r   r	   Ztorch.nnr
   r%   Zops.miscr   r   Zops.stochastic_depthr   Ztransforms._presetsr   utilsr   Z_apir   r   r   _metar   _utilsr   r   __all__r?   r!   rN   r1   rP   r   rM   boolr|   r   r   r   r   r   r   r   r   r   r    r+   r+   r+   r,   <module>   s    Y
**