Source code for torchvision.ops.ps_roi_align

import torch
from torch import nn, Tensor

from torch.nn.modules.utils import _pair
from torch.jit.annotations import List, Tuple

from torchvision.extension import _assert_has_ops
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape

[docs]def ps_roi_align( input: Tensor, boxes: Tensor, output_size: int, spatial_scale: float = 1.0, sampling_ratio: int = -1, ) -> Tensor: """ Performs Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head R-CNN. Arguments: input (Tensor[N, C, H, W]): input tensor 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. If a single Tensor is passed, then the first column should contain the batch index. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in a 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 input coordinates to the box coordinates. 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 grid points are used. If <= 0, then an adaptive number of grid points are used (computed as ceil(roi_width / pooled_w), and likewise for height). Default: -1 Returns: output (Tensor[K, C, output_size[0], output_size[1]]) """ _assert_has_ops() check_roi_boxes_shape(boxes) rois = boxes output_size = _pair(output_size) if not isinstance(rois, torch.Tensor): rois = convert_boxes_to_roi_format(rois) output, _ = torch.ops.torchvision.ps_roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio) return output
[docs]class PSRoIAlign(nn.Module): """ See ps_roi_align """ def __init__( self, output_size: int, spatial_scale: float, sampling_ratio: int, ): super(PSRoIAlign, self).__init__() self.output_size = output_size self.spatial_scale = spatial_scale self.sampling_ratio = sampling_ratio def forward(self, input: Tensor, rois: Tensor) -> Tensor: return ps_roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio) def __repr__(self) -> str: tmpstr = self.__class__.__name__ + '(' tmpstr += 'output_size=' + str(self.output_size) tmpstr += ', spatial_scale=' + str(self.spatial_scale) tmpstr += ', sampling_ratio=' + str(self.sampling_ratio) tmpstr += ')' return tmpstr


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