[docs]def roi_pool(
input: Tensor,
boxes: Tensor,
output_size: BroadcastingList2[int],
spatial_scale: float = 1.0,
) -> Tensor:
"""
Performs Region of Interest (RoI) Pool operator described in Fast 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
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.roi_pool(input, rois, spatial_scale,
output_size[0], output_size[1])
return output
here is the implementation of ROI_Pool in torchvision. I can’t understand how it works because I didn’t find the to torch.ops.torchvision.roi_pool. please help me and explain how does this function work.