# Source code for torchvision.ops.ps_roi_align

```
import torch
import torch.fx
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
from ._utils import check_roi_boxes_shape, convert_boxes_to_roi_format
[docs]@torch.fx.wrap
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.
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
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(ps_roi_align)
_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 :func:`ps_roi_align`.
"""
def __init__(
self,
output_size: int,
spatial_scale: float,
sampling_ratio: int,
):
super().__init__()
_log_api_usage_once(self)
self.output_size = output_size
self.spatial_scale = spatial_scale
self.sampling_ratio = sampling_ratio
[docs] 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:
s = (
f"{self.__class__.__name__}("
f"output_size={self.output_size}"
f", spatial_scale={self.spatial_scale}"
f", sampling_ratio={self.sampling_ratio}"
f")"
)
return s
```