U (d @s|ddlZddlmZmZddlmZddlmZddlmZddl m Z m Z deee e e ed d d ZGdddejZdS)N)nnTensor)_pair)_assert_has_ops)_log_api_usage_once)convert_boxes_to_roi_formatcheck_roi_boxes_shape?)inputboxes output_size spatial_scalesampling_ratioreturncCsrtjstjstttt||}t|}t |tj sJt |}tj j ||||d|d|\}}|S)aT 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)torchZjitZ is_scripting is_tracingr ps_roi_alignrr r isinstancerr opsZ torchvision)r rrrrroisoutput_r@/tmp/pip-unpacked-wheel-vx7f76es/torchvision/ops/ps_roi_align.pyr s"$ rcsJeZdZdZeeedfdd ZeeedddZe dd d Z Z S) PSRoIAlignz# See :func:`ps_roi_align`. )rrrcs(tt|||_||_||_dSN)super__init__rrrr)selfrrr __class__rrr As  zPSRoIAlign.__init__)r rrcCst|||j|j|jSr)rrrr)r!r rrrrforwardMszPSRoIAlign.forward)rcCs*|jjd|jd|jd|jd}|S)Nz (output_size=z, spatial_scale=z, sampling_ratio=))r#__name__rrr)r!srrr__repr__Ps$zPSRoIAlign.__repr__) r& __module__ __qualname____doc__intfloatr rr$strr( __classcell__rrr"rr<s r)r r )rrrZtorch.nn.modules.utilsrZtorchvision.extensionrutilsr_utilsr r r,r-rModulerrrrrs     2