roi_align

torchvision.ops.roi_align(input: Tensor, boxes: Union[Tensor, List[Tensor]], output_size: None, spatial_scale: float = 1.0, sampling_ratio: int = - 1, aligned: bool = False) → Tensor

Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask 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. If the tensor is quantized, we expect a batch size of N == 1.

• 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

• aligned (bool) – If False, use the legacy implementation. If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two neighboring pixel indices. This version is used in Detectron2

Returns:

The pooled RoIs.

Return type:

Tensor[K, C, output_size[0], output_size[1]]