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# ps_roi_align

torchvision.ops.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.

  Parameters:

    - 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:

    The pooled RoIs

  Return type:

    Tensor[K, C / (output_size[0] * output_size[1]), output_size[0], output_size[1]]

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