roi_pool

torchvision.ops.roi_pool(input: Tensor, boxes: Union[Tensor, List[Tensor]], output_size: None, spatial_scale: float = 1.0) → Tensor

Performs Region of Interest (RoI) Pool operator described in Fast 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 after the cropping 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

Returns:

The pooled RoIs.

Return type:

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