ps_roi_align¶
-
torchvision.ops.ps_roi_align(input: torch.Tensor, boxes: torch.Tensor, output_size: int, spatial_scale: float = 1.0, sampling_ratio: int = - 1) → torch.Tensor[source]¶ 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
Nelements. Each element containsCfeature maps of dimensionsH 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 < x2and0 <= 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_ratiosampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed asceil(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]]
