Source code for mmrotate.core.bbox.assigners.convex_assigner
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox.assigners.base_assigner import BaseAssigner
from ..builder import ROTATED_BBOX_ASSIGNERS
[docs]@ROTATED_BBOX_ASSIGNERS.register_module()
class ConvexAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox. Each
proposals will be assigned with `0` or a positive integer indicating the
ground truth index.
- 0: negative sample, no assigned gt
- positive integer: positive sample, index (1-based) of assigned gt
Args:
scale (float): IoU threshold for positive bboxes.
pos_num (float): find the nearest pos_num points to gt center in this
level.
"""
def __init__(self, scale=4, pos_num=3):
self.scale = scale
self.pos_num = pos_num
[docs] def get_horizontal_bboxes(self, gt_rbboxes):
"""get_horizontal_bboxes from polygons.
Args:
gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).
Returns:
gt_rect_bboxes (torch.Tensor): The horizontal bboxes, shape (k, 4).
"""
gt_xs, gt_ys = gt_rbboxes[:, 0::2], gt_rbboxes[:, 1::2]
gt_xmin, _ = gt_xs.min(1)
gt_ymin, _ = gt_ys.min(1)
gt_xmax, _ = gt_xs.max(1)
gt_ymax, _ = gt_ys.max(1)
gt_rect_bboxes = torch.cat([
gt_xmin[:, None], gt_ymin[:, None], gt_xmax[:, None], gt_ymax[:,
None]
],
dim=1)
return gt_rect_bboxes
[docs] def assign(self,
points,
gt_rbboxes,
gt_rbboxes_ignore=None,
gt_labels=None,
overlaps=None):
"""Assign gt to bboxes.
The assignment is done in following steps
1. compute iou between all bbox (bbox of all pyramid levels) and gt
2. compute center distance between all bbox and gt
3. on each pyramid level, for each gt, select k bbox whose center
are closest to the gt center, so we total select k*l bbox as
candidates for each gt
4. get corresponding iou for the these candidates, and compute the
mean and std, set mean + std as the iou threshold
5. select these candidates whose iou are greater than or equal to
the threshold as positive
6. limit the positive sample's center in gt
Args:
points (torch.Tensor): Points to be assigned, shape(n, 18).
gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).
gt_rbboxes_ignore (Tensor, optional): Ground truth polygons that
are labelled as `ignored`, e.g., crowd boxes in COCO.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
num_points = points.shape[0]
num_gts = gt_rbboxes.shape[0]
if num_gts == 0 or num_points == 0:
# If no truth assign everything to the background
assigned_gt_inds = points.new_full((num_points, ),
0,
dtype=torch.long)
if gt_labels is None:
assigned_labels = None
else:
assigned_labels = points.new_full((num_points, ),
-1,
dtype=torch.long)
return AssignResult(
num_gts, assigned_gt_inds, None, labels=assigned_labels)
points_xy = points[:, :2]
points_stride = points[:, 2]
points_lvl = torch.log2(points_stride).int()
lvl_min, lvl_max = points_lvl.min(), points_lvl.max()
assert gt_rbboxes.size(1) == 8, 'gt_rbboxes should be (N * 8)'
gt_bboxes = self.get_horizontal_bboxes(gt_rbboxes)
# assign gt rbox
gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2
gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6)
scale = self.scale
gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) +
torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int()
gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max)
# stores the assigned gt index of each point
assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long)
# stores the assigned gt dist (to this point) of each point
assigned_gt_dist = points.new_full((num_points, ), float('inf'))
points_range = torch.arange(points.shape[0])
for idx in range(num_gts):
gt_lvl = gt_bboxes_lvl[idx]
# get the index of points in this level
lvl_idx = gt_lvl == points_lvl
points_index = points_range[lvl_idx]
# get the points in this level
lvl_points = points_xy[lvl_idx, :]
# get the center point of gt
gt_point = gt_bboxes_xy[[idx], :]
# get width and height of gt
gt_wh = gt_bboxes_wh[[idx], :]
# compute the distance between gt center and
# all points in this level
points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1)
# find the nearest k points to gt center in this level
min_dist, min_dist_index = torch.topk(
points_gt_dist, self.pos_num, largest=False)
# the index of nearest k points to gt center in this level
min_dist_points_index = points_index[min_dist_index]
# The less_than_recorded_index stores the index
# of min_dist that is less then the assigned_gt_dist. Where
# assigned_gt_dist stores the dist from previous assigned gt
# (if exist) to each point.
less_than_recorded_index = min_dist < assigned_gt_dist[
min_dist_points_index]
# The min_dist_points_index stores the index of points satisfy:
# (1) it is k nearest to current gt center in this level.
# (2) it is closer to current gt center than other gt center.
min_dist_points_index = min_dist_points_index[
less_than_recorded_index]
# assign the result
assigned_gt_inds[min_dist_points_index] = idx + 1
assigned_gt_dist[min_dist_points_index] = min_dist[
less_than_recorded_index]
if gt_labels is not None:
assigned_labels = assigned_gt_inds.new_full((num_points, ),
-1,
dtype=torch.long)
pos_inds = torch.nonzero(
assigned_gt_inds > 0, as_tuple=False).squeeze()
if pos_inds.numel() > 0:
assigned_labels[pos_inds] = gt_labels[
assigned_gt_inds[pos_inds] - 1]
else:
assigned_labels = None
return AssignResult(
num_gts, assigned_gt_inds, None, labels=assigned_labels)