
    hy                        d dl 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	 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eee   f   dee   dedef
d       Z G d dej.                        Zy)    )UnionN)nnTensor)BroadcastingList2)_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                  j                         s-t         j                  j                         st	        t
               t                t        |       |}t        |      }t        |t         j                        st        |      }t         j                  j                  j                  | |||d   |d         \  }}|S )aU  
    Performs Region of Interest (RoI) Pool operator described in Fast R-CNN

    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 after the cropping
            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]]: The pooled RoIs.
    r   r   )torchjitis_scripting
is_tracingr
   roi_poolr   r   r   
isinstancer   r   opstorchvision)r   r   r   r   roisoutput_s          V/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/torchvision/ops/roi_pool.pyr   r      s    < 99!!#EII,@,@,BH%% D$KdELL)*40		%%..udM;WX>[fgh[ijIFAM    c                   b     e Zd ZdZdee   def fdZdede	ee
e   f   defdZdefd	Z xZS )
RoIPoolz
    See :func:`roi_pool`.
    r   r   c                 T    t         |           t        |        || _        || _        y N)super__init__r
   r   r   )selfr   r   	__class__s      r   r&   zRoIPool.__init__=   s&    D!&*r    r   r   r   c                 F    t        ||| j                  | j                        S r$   )r   r   r   )r'   r   r   s      r   forwardzRoIPool.forwardC   s    tT%5%5t7I7IJJr    c                 l    | j                   j                   d| j                   d| j                   d}|S )Nz(output_size=z, spatial_scale=))r(   __name__r   r   )r'   ss     r   __repr__zRoIPool.__repr__F   s;    ~~&&'}T5E5E4FFVW[WiWiVjjklr    )r-   
__module____qualname____doc__r   intfloatr&   r   r   listr*   strr/   __classcell__)r(   s   @r   r"   r"   8   sY    +$5c$: +5 +KV K5f1E+F K6 K# r    r"   )g      ?)typingr   r   torch.fxr   r   torch.jit.annotationsr   torch.nn.modules.utilsr   torchvision.extensionr   utilsr
   _utilsr   r   fxwrapr5   r3   r4   r   Moduler"    r    r   <module>rC      s        3 ( 1 ' F 
 	&&f%&& #3'& 	&
 & &Rbii r    