U
    (dM                     @   s   d Z ddlmZmZ ddlZddlmZmZ ddlmZ	m
Z
 ddd	d
dg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TupleN)Tensornn   )
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   C/tmp/pip-unpacked-wheel-vx7f76es/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   c                       sv   e Zd Zdddejdee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
      g
ףp=
?gv/?gCl?gZd;O?gy&1?g?)resize_sizemeanstdinterpolation.N	crop_sizer+   r,   r-   r.   r   c                   s8   t    |g| _|g| _t|| _t|| _|| _d S r   )super__init__r0   r+   listr,   r-   r.   r   r0   r+   r,   r-   r.   r    r   r   r2   '   s    	


zImageClassification.__init__r   c                 C   s\   t j|| j| jd}t || j}t|ts6t |}t 	|t
j}t j|| j| jd}|S Nr.   r,   r-   )r   resizer+   r.   center_cropr0   r   r   r   r   r   r   	normalizer,   r-   r   r   r   r   r   7   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!   r0   r+   r,   r-   r.   r   format_stringr   r   r   r#   @   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+   r.   r0   r,   r-   r"   r   r   r   r$   J   s    ,zImageClassification.describe)r!   r%   r&   r   BILINEARintr   r   r2   r   r   r'   r#   r$   __classcell__r   r   r5   r   r
   &   s   

	
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-   r.   .Nr/   c                   s<   t    t|| _t|| _t|| _t|| _|| _d S r   )r1   r2   r3   r0   r+   r,   r-   r.   r4   r5   r   r   r2   T   s    	




zVideoClassification.__init__)vidr   c                 C   s   d}|j dk r|jdd}d}|j\}}}}}|d|||}tj|| j| j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dimTr7   r8      r         )ndimZ	unsqueezeshapeviewr   r9   r+   r.   r:   r0   r   r   r   r;   r,   r-   ZpermuteZsqueeze)r   rQ   Zneed_squeezeNTCHWr   r   r   r   d   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<   rC   rD   r   r   r   r#   x   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=rG   rH   rI   rJ   zP``. Finally the output dimensions are permuted to ``(..., C, T, H, W)`` tensors.rL   r"   r   r   r   r$      s    ,zVideoClassification.describe)r!   r%   r&   r   rM   r   rN   r   r2   r   r   r'   r#   r$   rO   r   r   r5   r   r   S   s   




c                       sv   e Zd Zddejdee 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   r)   r*   rP   .N)r+   r,   r-   r.   r   c                   s<   t    |d k	r|gnd | _t|| _t|| _|| _d S r   )r1   r2   r+   r3   r,   r-   r.   )r   r+   r,   r-   r.   r5   r   r   r2      s
    


zSemanticSegmentation.__init__r   c                 C   sZ   t | jtr tj|| j| jd}t |ts4t|}t|t	j
}tj|| j| jd}|S r6   )r   r+   r3   r   r9   r.   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?   r@   rA   rB   )r    r!   r+   r,   r-   r.   rD   r   r   r   r#      s    zSemanticSegmentation.__repr__c              	   C   s&   d| j  d| j d| j d| j d	S )NrF   rG   rI   rJ   rK   )r+   r.   r,   r-   r"   r   r   r   r$      s    $zSemanticSegmentation.describe)r!   r%   r&   r   rM   r   rN   r   r   r2   r   r   r'   r#   r$   rO   r   r   r5   r   r      s   

		c                   @   sB   e Zd Zeeeeef dddZedddZedddZd	S )
r   )img1img2r   c                 C   s   t |tst|}t |ts(t|}t|tj}t|tj}tj|dddgdddgd}tj|dddgdddgd}| }| }||fS )Ng      ?r8   )	r   r   r   r   r   r   r   r;   
contiguous)r   r_   r`   r   r   r   r      s    



zOpticalFlow.forwardr   c                 C   s   | j jd S r   r   r"   r   r   r   r#      s    zOpticalFlow.__repr__c                 C   s   dS )NzAccepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. The images are rescaled to ``[-1.0, 1.0]``.r   r"   r   r   r   r$      s    zOpticalFlow.describeN)	r!   r%   r&   r   r   r   r'   r#   r$   r   r   r   r   r      s   )__doc__typingr   r   r   r   r    r   r   r   __all__Moduler	   r
   r   r   r   r   r   r   r   <module>   s   	-9*