a
    dG!                     @   s   d 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
mZ g dZG dd	 d	ejZG d
d dejZG dd dejZG dd dejZG dd dejZdS )z
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
    )OptionalTupleUnionN)nnTensor   )
functionalInterpolationMode)ObjectDetectionImageClassificationVideoClassificationSemanticSegmentationOpticalFlowc                   @   s8   e Zd ZeedddZedddZedddZd	S )
r
   imgreturnc                 C   s"   t |tst|}t|tjS N)
isinstancer   Fpil_to_tensorconvert_image_dtypetorchfloatselfr    r   h/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchvision/transforms/_presets.pyforward   s    

zObjectDetection.forwardr   c                 C   s   | j jd S Nz()	__class____name__r   r   r   r   __repr__   s    zObjectDetection.__repr__c                 C   s   dS )NzAccepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. The images are rescaled to ``[0.0, 1.0]``.r   r#   r   r   r   describe   s    zObjectDetection.describeN)r"   
__module____qualname__r   r   strr$   r%   r   r   r   r   r
      s   r
   c                
       s   e Zd Zdddejddeeeedf eedf eee	e
ef  dd fd	d
ZeedddZe
dddZe
dddZ  ZS )r      g
ףp=
?gv/?gCl?gZd;O?gy&1?g?warn)resize_sizemeanstdinterpolation	antialias.N)	crop_sizer-   r.   r/   r0   r1   r   c                   s>   t    |g| _|g| _t|| _t|| _|| _|| _d S r   )	super__init__r2   r-   listr.   r/   r0   r1   )r   r2   r-   r.   r/   r0   r1   r!   r   r   r4   '   s    



zImageClassification.__init__r   c                 C   s`   t j|| j| j| jd}t || j}t|ts:t 	|}t 
|tj}t j|| j| jd}|S Nr0   r1   r.   r/   )r   resizer-   r0   r1   center_cropr2   r   r   r   r   r   r   	normalizer.   r/   r   r   r   r   r   9   s    

zImageClassification.forwardr   c                 C   sh   | j jd }|d| j 7 }|d| j 7 }|d| j 7 }|d| j 7 }|d| j 7 }|d7 }|S N(z
    crop_size=
    resize_size=

    mean=	
    std=
    interpolation=
)r!   r"   r2   r-   r.   r/   r0   r   format_stringr   r   r   r$   B   s    zImageClassification.__repr__c                 C   s.   d| j  d| j d| j d| j d| j dS )NAccepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. The images are resized to ``resize_size=`` using ``interpolation=.``, followed by a central crop of ``crop_size=]``. Finally the values are first rescaled to ``[0.0, 1.0]`` and then normalized using ``mean=`` and ``std=``.r-   r0   r2   r.   r/   r#   r   r   r   r%   L   s    zImageClassification.describe)r"   r&   r'   r	   BILINEARintr   r   r   r   r(   boolr4   r   r   r$   r%   __classcell__r   r   r6   r   r   &   s"   

	
r   c                       s   e Zd Zddejdeeef eeef eedf eedf edd fddZe	e	d	d
dZ
edddZedddZ  ZS )r   )gFj?g.5B?g?)gr@H0?gc=yX?gDKK?)r.   r/   r0   .N)r2   r-   r.   r/   r0   r   c                   s<   t    t|| _t|| _t|| _t|| _|| _d S r   )r3   r4   r5   r2   r-   r.   r/   r0   )r   r2   r-   r.   r/   r0   r6   r   r   r4   V   s    	




zVideoClassification.__init__)vidr   c                 C   s   d}|j dk r|jdd}d}|j\}}}}}|d|||}tj|| j| jdd}t|| j	}t
|tj}tj|| j| jd}| j	\}}||||||}|dd	d
dd}|r|jdd}|S )NF   r   )ZdimTr8   r9      r         )ndimZ	unsqueezeshapeviewr   r:   r-   r0   r;   r2   r   r   r   r<   r.   r/   ZpermuteZsqueeze)r   rR   Zneed_squeezeNTCHWr   r   r   r   f   s     

zVideoClassification.forwardr   c                 C   sh   | j jd }|d| j 7 }|d| j 7 }|d| j 7 }|d| j 7 }|d| j 7 }|d7 }|S r=   rD   rE   r   r   r   r$   ~   s    zVideoClassification.__repr__c                 C   s.   d| j  d| j d| j d| j d| j dS )NzAccepts batched ``(B, T, C, H, W)`` and single ``(T, C, H, W)`` video frame ``torch.Tensor`` objects. The frames are resized to ``resize_size=rH   rI   rJ   rK   zP``. Finally the output dimensions are permuted to ``(..., C, T, H, W)`` tensors.rM   r#   r   r   r   r%      s    zVideoClassification.describe)r"   r&   r'   r	   rN   r   rO   r   r4   r   r   r(   r$   r%   rQ   r   r   r6   r   r   U   s   




r   c                	       s   e Zd Zddejddee eedf eedf eee	e
ef  dd fdd	Zeed
ddZe
dddZe
dddZ  ZS )r   r*   r+   r,   )r.   r/   r0   r1   .N)r-   r.   r/   r0   r1   r   c                   sB   t    |d ur|gnd | _t|| _t|| _|| _|| _d S r   )r3   r4   r-   r5   r.   r/   r0   r1   )r   r-   r.   r/   r0   r1   r6   r   r   r4      s    	


zSemanticSegmentation.__init__r   c                 C   s^   t | jtr$tj|| j| j| jd}t |ts8t|}t	|t
j}tj|| j| jd}|S r7   )r   r-   r5   r   r:   r0   r1   r   r   r   r   r   r<   r.   r/   r   r   r   r   r      s    

zSemanticSegmentation.forwardr   c                 C   sX   | j jd }|d| j 7 }|d| j 7 }|d| j 7 }|d| j 7 }|d7 }|S )Nr>   r?   r@   rA   rB   rC   )r!   r"   r-   r.   r/   r0   rE   r   r   r   r$      s    zSemanticSegmentation.__repr__c              	   C   s&   d| j  d| j d| j d| j d	S )NrG   rH   rJ   rK   rL   )r-   r0   r.   r/   r#   r   r   r   r%      s    zSemanticSegmentation.describe)r"   r&   r'   r	   rN   r   rO   r   r   r   r(   rP   r4   r   r   r$   r%   rQ   r   r   r6   r   r      s   

		r   c                   @   sB   e Zd Zeeeeef dddZedddZedddZd	S )
r   )img1img2r   c                 C   s   t |tst|}t |ts(t|}t|tj}t|tj}tj|g dg dd}tj|g dg dd}| }| }||fS )N)      ?rb   rb   r9   )	r   r   r   r   r   r   r   r<   
contiguous)r   r`   ra   r   r   r   r      s    



zOpticalFlow.forwardr   c                 C   s   | j jd S r   r    r#   r   r   r   r$      s    zOpticalFlow.__repr__c                 C   s   dS )NzAccepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. The images are rescaled to ``[-1.0, 1.0]``.r   r#   r   r   r   r%      s    zOpticalFlow.describeN)	r"   r&   r'   r   r   r   r(   r$   r%   r   r   r   r   r      s   r   )__doc__typingr   r   r   r   r   r    r   r   r	   __all__Moduler
   r   r   r   r   r   r   r   r   <module>   s   	/=,