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

Performs Region of Interest (RoI) Pool operator described in Fast R-CNN

  • 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


The pooled RoIs.

Return type

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


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources