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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``. If the tensor is quantized, we expect a batch size of ``N == 1``. 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 aligned (bool): If False, use the legacy implementation. If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two neighboring pixel indices. This version is used in Detectron2 Returns: Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. rr)rjit is_scripting is_tracingr roi_alignrr rfrrr $are_deterministic_algorithms_enabledrUis_mpsrr,typerr ops torchvision)r2rrrhrkrlrgs r rrsKR 9 ! ! # #'EI,@,@,B,B'I&&&%   D $$K dEL ) )1*400 9 ! ! # #s  s$IKK sQVQ^ sbgbn s"5<#455 seT=+a.+VW.Zhjqrr r 9 * * t]KNKNNT[  r"c eZdZdZ d deedededeffd Zde d e e e e fd e fd Z d e fd ZxZS)RoIAlignz See :func:`roi_align`. Frrhrkrlctt|||_||_||_||_dSr)super__init__r rrhrkrl)selfrrhrkrl __class__s r rzRoIAlign.__init__ sL D!!!&*, r"r2rgrcRt|||j|j|j|jSr)rrrhrkrl)rr2rgs r forwardzRoIAlign.forwards'd&68JDL_aeamnnnr"c d|jjd|jd|jd|jd|jd }|S)Nz (output_size=z, spatial_scale=z, sampling_ratio=z , aligned=))rrrrhrkrl)rss r __repr__zRoIAlign.__repr__sh~&  +  #1  !% 3       r")F)r __module__ __qualname____doc__rr;rWboolrrrrrstrr __classcell__)rs@r rrs  &s+          oVo5f1E+Fo6oooo #        r"r)r)raF)"rtypingrrrtorch.fxrrtorch._dynamo.utilsrtorch.jit.annotationsrtorch.nn.modules.utilsr torchvision.extensionr r utilsr _utilsrrr%rRrYrfxwrapr;rWrrModulerr$r"r rs 444444333333((((((;;;;;;;;''''''FFFFFFFF&===DdUUUp  77 7 f% &7#3'7 7  7  7 7777tryr"