- class mmcv.ops.BorderAlign(pool_size: int)¶
Border align pooling layer.
Applies border_align over the input feature based on predicted bboxes. The details were described in the paper BorderDet: Border Feature for Dense Object Detection.
For each border line (e.g. top, left, bottom or right) of each box, border_align does the following:
pool_size+1 positions on this line, involving the start and end points.
the corresponding features on these points are computed by bilinear interpolation.
max pooling over all the
pool_size+1 positions are used for computing pooled feature.
pool_size (int) – number of positions sampled over the boxes’ borders (e.g. top, bottom, left, right).
- forward(input: torch.Tensor, boxes: torch.Tensor) → torch.Tensor¶
input – Features with shape [N,4C,H,W]. Channels ranged in [0,C), [C,2C), [2C,3C), [3C,4C) represent the top, left, bottom, right features respectively.
boxes – Boxes with shape [N,H*W,4]. Coordinate format (x1,y1,x2,y2).
Pooled features with shape [N,C,H*W,4]. The order is (top,left,bottom,right) for the last dimension.
- Return type