- 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.
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. 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 < 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.
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 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
The pooled RoIs.
- Return type:
Tensor[K, C, output_size, output_size]