U (d @sddlmZmZddlZddlmZmZddlmZddlm Z ddl m Z ddl m Z d d lmZmZdeeeeefeeeeeedddZGdddejZdS))ListUnionN)nnTensor)BroadcastingList2)_pair)_assert_has_ops)_log_api_usage_once)convert_boxes_to_roi_formatcheck_roi_boxes_shape?F)inputboxes output_size spatial_scalesampling_ratioalignedreturnc Csltjstjstttt||}t|}t |tj sJt |}tj j ||||d|d||S)aj Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask 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``. 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 )torchZjitZ is_scripting is_tracingr roi_alignrr r isinstancerr opsZ torchvision)rrrrrrroisr=/tmp/pip-unpacked-wheel-vx7f76es/torchvision/ops/roi_align.pyr s"( rcsReZdZdZd eeeeedfdd Ze e e dddZ e d d d Z Z S) RoIAlignz See :func:`roi_align`. F)rrrrcs.tt|||_||_||_||_dSN)super__init__r rrrr)selfrrrr __class__rrr"Gs  zRoIAlign.__init__)rrrcCst|||j|j|j|jSr )rrrrr)r#rrrrrforwardUszRoIAlign.forward)rc Cs2|jjd|jd|jd|jd|jd }|S)Nz (output_size=z, spatial_scale=z, sampling_ratio=z , aligned=))r%__name__rrrr)r#srrr__repr__Xs,zRoIAlign.__repr__)F)r( __module__ __qualname____doc__rintfloatboolr"rr&strr* __classcell__rrr$rrBs r)rrF)typingrrrrrZtorch.jit.annotationsrZtorch.nn.modules.utilsrZtorchvision.extensionrutilsr _utilsr r r.r/r0rModulerrrrrs(     5