a
    h/                     @   s  d dl Z d dlmZmZ d dlZd dlZd dlmZmZ ddlm	Z	 ddl
mZ ejjeeddd	Zejjeed
ddZdeeeeeeef  eeeef  eeeeeef  f dddZG dd dejZeee ee edddZeee ee edddZdS )    N)AnyOptional)nnTensor   )	ImageList)paste_masks_in_imageimagereturnc                 C   s   ddl m} || dd  S )Nr   )	operators)Z
torch.onnxr   Zshape_as_tensor)r
   r    r   T/var/www/auris/lib/python3.9/site-packages/torchvision/models/detection/transform.py_get_shape_onnx   s    r   )vr   c                 C   s   | S )Nr   )r   r   r   r   _fake_cast_onnx   s    r   )r
   self_min_sizeself_max_sizetarget
fixed_sizer   c                 C   s  t  rt| }n.tj r2t| jdd  }n| jdd  }d }d }d }|d urf|d |d g}ntj sxt  rt|j	tj
d}	t|j	tj
d}
t|}t|}t||	 ||
 }t  rt|}n| }n"t|}	t|}
t||	 ||
 }d}tjjj| d  ||d|ddd } |d u r:| |fS d	|v r|d	 }tjjj|d d d f  |||d
d d df  }||d	< | |fS )Nr   r   r   )dtypeTZbilinearF)sizescale_factormoderecompute_scale_factorZalign_cornersmasks)r   r   r   )torchvision_is_tracingr   torchjitZis_scriptingtensorshapemintofloat32maxfloatr   itemr   
functionalZinterpolatebyte)r
   r   r   r   r   Zim_shaper   r   r   min_sizemax_sizeZself_min_size_fZself_max_size_fZscalemaskr   r   r   _resize_image_and_masks   sX    



	


r.   c                
       s  e Zd ZdZd!eeee ee eeeeef  e	d fddZ
d"ee eeeeef   eeeeeeef   f ddd	Zeed
ddZee edddZd#eeeeef  eeeeeef  f dddZejjd$ee eedddZeee  ee dddZd%ee eedddZeeeef  eeeef  eeeef  eeeef  dddZeddd Z  ZS )&GeneralizedRCNNTransformah  
    Performs input / target transformation before feeding the data to a GeneralizedRCNN
    model.

    The transformations it performs are:
        - input normalization (mean subtraction and std division)
        - input / target resizing to match min_size / max_size

    It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
        N)r+   r,   
image_mean	image_stdsize_divisibler   kwargsc                    sT   t    t|ttfs|f}|| _|| _|| _|| _|| _	|| _
|dd| _d S )N_skip_resizeF)super__init__
isinstancelisttupler+   r,   r1   r2   r3   r   popr5   )selfr+   r,   r1   r2   r3   r   r4   	__class__r   r   r7   b   s    

z!GeneralizedRCNNTransform.__init__)imagestargetsr   c                 C   sB  dd |D }|d urPg }|D ],}i }|  D ]\}}|||< q.|| q|}tt|D ]v}|| }	|d urx|| nd }
|	 dkrtd|	j | |	}	| |	|
\}	}
|	||< |d ur\|
d ur\|
||< q\dd |D }| j	|| j
d}g }|D ]4}tt|dkd|  ||d	 |d
 f qt||}||fS )Nc                 S   s   g | ]}|qS r   r   .0imgr   r   r   
<listcomp>z       z4GeneralizedRCNNTransform.forward.<locals>.<listcomp>   zFimages is expected to be a list of 3d tensors of shape [C, H, W], got c                 S   s   g | ]}|j d d qS )r   Nr"   rA   r   r   r   rD      rE   )r3      zMInput tensors expected to have in the last two elements H and W, instead got r   r   )itemsappendrangelendim
ValueErrorr"   	normalizeresizebatch_imagesr3   r   Z_assertr   )r<   r?   r@   Ztargets_copytdatakr   ir
   Ztarget_indexZimage_sizesZimage_sizes_listZ
image_size
image_listr   r   r   forwardw   s<    




z GeneralizedRCNNTransform.forwardr	   c                 C   st   |  std|j d|j|j }}tj| j||d}tj| j||d}||d d d d f  |d d d d f  S )NzOExpected input images to be of floating type (in range [0, 1]), but found type z insteadr   device)Zis_floating_point	TypeErrorr   rY   r   Z	as_tensorr1   r2   )r<   r
   r   rY   meanZstdr   r   r   rO      s    z"GeneralizedRCNNTransform.normalize)rT   r   c                 C   s*   t tddtt| }|| S )z
        Implements `random.choice` via torch ops, so it can be compiled with
        TorchScript and we use PyTorch's RNG (not native RNG)
        r   g        )intr   emptyZuniform_r'   rL   r(   )r<   rT   indexr   r   r   torch_choice   s    "z%GeneralizedRCNNTransform.torch_choice)r
   r   r   c                 C   s   |j dd  \}}| jr4| jr&||fS | | j}n
| jd }t||| j|| j\}}|d u rf||fS |d }t|||f|j dd  }||d< d|v r|d }t	|||f|j dd  }||d< ||fS )Nr   boxes	keypoints)
r"   trainingr5   r_   r+   r.   r,   r   resize_boxesresize_keypoints)r<   r
   r   hwr   Zbboxrb   r   r   r   rP      s"    
zGeneralizedRCNNTransform.resize)r?   r3   r   c           
         s  g }t |d  D ]< tt fdd|D tjtj}|| q|}t	|d tj| | tj|d< t	|d tj| | tj|d< t
|}g }|D ]P}dd t|t
|jD }tjj|d|d d|d d|d f}	||	 qt|S )Nr   c                    s   g | ]}|j   qS r   rG   rA   rU   r   r   rD      rE   z?GeneralizedRCNNTransform._onnx_batch_images.<locals>.<listcomp>r   rH   c                 S   s   g | ]\}}|| qS r   r   )rB   s1s2r   r   r   rD      rE   )rK   rM   r   r&   stackr$   r%   int64rJ   ceilr:   zipr"   r   r)   pad)
r<   r?   r3   r,   Z
max_size_istrideZpadded_imgsrC   paddingZ
padded_imgr   rh   r   _onnx_batch_images   s    .**(z+GeneralizedRCNNTransform._onnx_batch_images)the_listr   c                 C   sB   |d }|dd  D ](}t |D ]\}}t|| |||< q q|S )Nr   r   )	enumerater&   )r<   rs   ZmaxesZsublistr^   r(   r   r   r   max_by_axis   s
    z$GeneralizedRCNNTransform.max_by_axisc           	      C   s   t  r| ||S | dd |D }t|}t|}ttt|d | | |d< ttt|d | | |d< t	|g| }|d 
|d}t|jd D ]@}|| }||d |jd d |jd d |jd f | q|S )Nc                 S   s   g | ]}t |jqS r   )r9   r"   rA   r   r   r   rD      rE   z9GeneralizedRCNNTransform.batch_images.<locals>.<listcomp>r   rH   r   )r   r   rr   ru   r'   r9   r\   mathrm   rL   Znew_fullrK   r"   Zcopy_)	r<   r?   r3   r,   rp   Zbatch_shapeZbatched_imgsrU   rC   r   r   r   rQ      s    ""6z%GeneralizedRCNNTransform.batch_images)resultimage_shapesoriginal_image_sizesr   c                 C   s   | j r
|S tt|||D ]~\}\}}}|d }t|||}||| d< d|v rp|d }	t|	||}	|	|| d< d|v r|d }
t|
||}
|
|| d< q|S )Nra   r   rb   )rc   rt   rn   rd   r   re   )r<   rw   rx   ry   rU   predZim_sZo_im_sra   r   rb   r   r   r   postprocess  s    z$GeneralizedRCNNTransform.postprocess)r   c                 C   sZ   | j j d}d}|| d| j d| j d7 }|| d| j d| j d7 }|d	7 }|S )
N(z
    zNormalize(mean=z, std=)zResize(min_size=z, max_size=z, mode='bilinear')z
))r>   __name__r1   r2   r+   r,   )r<   format_string_indentr   r   r   __repr__  s    z!GeneralizedRCNNTransform.__repr__)r0   N)N)N)r0   )r0   )r~   
__module____qualname____doc__r\   r9   r'   r   r:   r   r7   r   dictstrr   rW   rO   r_   rP   r   r    unusedrr   ru   rQ   r{   r   __classcell__r   r   r=   r   r/   V   sF      ) r/   )rb   original_sizenew_sizer   c           	         s    fddt ||D }|\}}  }tj r|d d d d df | }|d d d d df | }tj|||d d d d df fdd}n |d  |9  < |d  |9  < |S )	Nc                    s8   g | ]0\}}t j|t j jd t j|t j jd  qS rX   r   r!   r%   rY   rB   sZs_origrb   r   r   rD   !  s   z$resize_keypoints.<locals>.<listcomp>r   r   rH   rM   ).r   ).r   )rn   cloner   Z_CZ_get_tracing_staterk   )	rb   r   r   ratiosZratio_hZratio_wZresized_dataZresized_data_0Zresized_data_1r   r   r   re      s    

(re   )ra   r   r   r   c           
         sh    fddt ||D }|\}} d\}}}}	|| }|| }|| }|	| }	tj||||	fddS )Nc                    s8   g | ]0\}}t j|t j jd t j|t j jd  qS r   r   r   ra   r   r   rD   3  s   z resize_boxes.<locals>.<listcomp>r   r   )rn   Zunbindr   rk   )
ra   r   r   r   Zratio_heightZratio_widthZxminZyminZxmaxZymaxr   r   r   rd   2  s    
rd   )NN)rv   typingr   r   r   r   r   r   rV   r   Z	roi_headsr   r    r   r   r'   r   r\   r   r   r:   r.   Moduler/   r9   re   rd   r   r   r   r   <module>   s0   	  = K