Ei)SSKrSSKrSSKJrJr SSKJr SSKJr SSKJ r SSK J r J r \RRSS \S \S \S \S \S\4 Sjj5r"SS\R$5rg)N)nnTensor)_pair)_assert_has_ops)_log_api_usage_once)check_roi_boxes_shapeconvert_boxes_to_roi_formatinputboxes output_size spatial_scalesampling_ratioreturnc[RR5(d2[RR5(d[ [ 5 [ 5 [U5 Un[U5n[U[R5(d [U5n[RRR XX2SUSU5upgU$)a Performs Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head 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 (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 sampling_ratio (int): number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed as ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 Returns: Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]: The pooled RoIs rr )torchjit is_scripting is_tracingr ps_roi_alignrr r isinstancerr ops torchvision)r r rrrroisoutput_s Y/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torchvision/ops/ps_roi_align.pyrr sJ 99 ! ! # #EII,@,@,B,BL)% D $K dELL ) )*40 %%22 ]NKNNIF Mc`^\rSrSrSrS\S\S\4U4SjjrS\S\S \4S jr S \ 4S jr S r U=r $) PSRoIAlign>z See :func:`ps_roi_align`. rrrc\>[TU]5 [U5 XlX lX0lgN)super__init__rrrr)selfrrr __class__s rr&PSRoIAlign.__init__Cs* D!&*,rr rrcZ[XURURUR5$r$)rrrr)r'r rs rforwardPSRoIAlign.forwardOs%E)9)94;M;MtObObccrcURRSURSURSURS3nU$)Nz (output_size=z, spatial_scale=z, sampling_ratio=))r(__name__rrr)r'ss r__repr__PSRoIAlign.__repr__RsS~~&&'(++,t112 3 34   r)rrr)r/ __module__ __qualname____firstlineno____doc__intfloatr&rr+strr1__static_attributes__ __classcell__)r(s@rr!r!>sX - - - -dVd6dfd#rr!)g?)rtorch.fxrrtorch.nn.modules.utilsrtorchvision.extensionrutilsr_utilsr r fxwrapr7r8rModuler!rrrFs (1'F  / / // /  /  //dr