- torchvision.ops.ps_roi_pool(input: Tensor, boxes: Tensor, output_size: int, spatial_scale: float = 1.0) Tensor [source]¶
Performs Position-Sensitive Region of Interest (RoI) Pool operator described in R-FCN
input (Tensor[N, C, H, W]) – The input tensor, i.e. a batch with
Nelements. Each element contains
Cfeature 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 < x2and
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
The pooled RoIs.
- Return type:
Tensor[K, C / (output_size * output_size), output_size, output_size]