
    ڧg6                         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 ddlm	Z	 ddl
mZmZ e j        j        	 dd
ededededef
d            Z G d dej                  ZdS )    N)nnTensor)_pair)_assert_has_ops   )_log_api_usage_once   )check_roi_boxes_shapeconvert_boxes_to_roi_format      ?inputboxesoutput_sizespatial_scalereturnc                    t           j                                        s2t           j                                        st	          t
                     t                       t          |           |}t          |          }t          |t           j
                  st          |          }t           j        j                            | |||d         |d                   \  }}|S )a  
    Performs Position-Sensitive Region of Interest (RoI) Pool operator
    described in R-FCN

    Args:
        input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element
            contains ``C`` feature maps of dimensions ``H x W``.
        boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
            format where the regions will be taken from.
            The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
            If a single Tensor is passed, then the first column should
            contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``.
            If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i
            in the batch.
        output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling
            is performed, as (height, width).
        spatial_scale (float): a scaling factor that maps the box coordinates to
            the input coordinates. For example, if your boxes are defined on the scale
            of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of
            the original image), you'll want to set this to 0.5. Default: 1.0

    Returns:
        Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs.
    r   r	   )torchjitis_scripting
is_tracingr   ps_roi_poolr   r
   r   
isinstancer   r   opstorchvision)r   r   r   r   roisoutput_s          W/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/ops/ps_roi_pool.pyr   r      s    > 9!!## )EI,@,@,B,B )K(((%   D$$KdEL)) 1*400	%11%}kZ[n^ijk^lmmIFAM    c                   L     e Zd ZdZdedef fdZdededefdZde	fd	Z
 xZS )
	PSRoIPoolz"
    See :func:`ps_roi_pool`.
    r   r   c                     t                                                       t          |            || _        || _        d S N)super__init__r   r   r   )selfr   r   	__class__s      r   r%   zPSRoIPool.__init__;   s=    D!!!&*r   r   r   r   c                 :    t          ||| j        | j                  S r#   )r   r   r   )r&   r   r   s      r   forwardzPSRoIPool.forwardA   s    5$(8$:LMMMr   c                 D    | j         j         d| j         d| j         d}|S )Nz(output_size=z, spatial_scale=))r'   __name__r   r   )r&   ss     r   __repr__zPSRoIPool.__repr__D   s1    ~&llT5EllW[Willlr   )r,   
__module____qualname____doc__intfloatr%   r   r)   strr.   __classcell__)r'   s   @r   r!   r!   6   s         +C + + + + + + +NV N6 Nf N N N N#        r   r!   )r   )r   torch.fxr   r   torch.nn.modules.utilsr   torchvision.extensionr   utilsr   _utilsr
   r   fxwrapr2   r3   r   Moduler!    r   r   <module>r?      s             ( ( ( ( ( ( 1 1 1 1 1 1 ' ' ' ' ' ' F F F F F F F F 
 	' ''' ' 	'
 ' ' ' 'T    	     r   