Source code for mmrotate.core.bbox.assigners.sas_assigner
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops import convex_iou, points_in_polygons
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox.assigners.base_assigner import BaseAssigner
from ..builder import ROTATED_BBOX_ASSIGNERS
def convex_overlaps(gt_rbboxes, points):
"""Compute overlaps between polygons and points.
Args:
gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).
points (torch.Tensor): Points to be assigned, shape(n, 18).
Returns:
overlaps (torch.Tensor): Overlaps between k gt_bboxes and n bboxes,
shape(k, n).
"""
if gt_rbboxes.shape[0] == 0:
return gt_rbboxes.new_zeros((0, points.shape[0]))
overlaps = convex_iou(points, gt_rbboxes)
return overlaps
def get_horizontal_bboxes(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
def AspectRatio(gt_rbboxes):
"""Compute the aspect ratio of all gts.
Args:
gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).
Returns:
ratios (torch.Tensor): The aspect ratio of gt_rbboxes, shape (k, 1).
"""
pt1, pt2, pt3, pt4 = gt_rbboxes[..., :8].chunk(4, 1)
edge1 = torch.sqrt(
torch.pow(pt1[..., 0] - pt2[..., 0], 2) +
torch.pow(pt1[..., 1] - pt2[..., 1], 2))
edge2 = torch.sqrt(
torch.pow(pt2[..., 0] - pt3[..., 0], 2) +
torch.pow(pt2[..., 1] - pt3[..., 1], 2))
edges = torch.stack([edge1, edge2], dim=1)
width, _ = torch.max(edges, 1)
height, _ = torch.min(edges, 1)
ratios = (width / height)
return ratios
[docs]@ROTATED_BBOX_ASSIGNERS.register_module()
class SASAssigner(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, topk):
self.topk = topk
[docs] def assign(self,
bboxes,
num_level_bboxes,
gt_bboxes,
gt_bboxes_ignore=None,
gt_labels=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:
bboxes (torch.Tensor): Bounding boxes to be assigned, shape(n, 4).
num_level_bboxes (List): num of bboxes in each level
gt_bboxes (torch.Tensor): Groundtruth boxes, shape (k, 4).
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes 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.
"""
INF = 100000000
num_gt, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
overlaps = convex_overlaps(gt_bboxes, bboxes)
assigned_gt_inds = overlaps.new_full((num_bboxes, ),
0,
dtype=torch.long)
if num_gt == 0 or num_bboxes == 0:
max_overlaps = overlaps.new_zeros((num_bboxes, ))
if num_gt == 0:
assigned_gt_inds[:] = 0
if gt_labels is None:
assigned_labels = None
else:
assigned_labels = overlaps.new_full((num_bboxes, ),
-1,
dtype=torch.long)
return AssignResult(
num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
# compute center distance between all bbox and gt
# the center of poly
gt_bboxes_hbb = get_horizontal_bboxes(gt_bboxes)
gt_cx = (gt_bboxes_hbb[:, 0] + gt_bboxes_hbb[:, 2]) / 2.0
gt_cy = (gt_bboxes_hbb[:, 1] + gt_bboxes_hbb[:, 3]) / 2.0
gt_points = torch.stack((gt_cx, gt_cy), dim=1)
bboxes = bboxes.reshape(-1, 9, 2)
pts_x = bboxes[:, :, 0::2]
pts_y = bboxes[:, :, 1::2]
pts_x_mean = pts_x.mean(dim=1).squeeze()
pts_y_mean = pts_y.mean(dim=1).squeeze()
bboxes_points = torch.stack((pts_x_mean, pts_y_mean), dim=1)
distances = (bboxes_points[:, None, :] -
gt_points[None, :, :]).pow(2).sum(-1).sqrt()
# Selecting candidates based on the center distance
candidate_idxs = []
start_idx = 0
for level, bboxes_per_level in enumerate(num_level_bboxes):
end_idx = start_idx + bboxes_per_level
distances_per_level = distances[start_idx:end_idx, :]
_, topk_idxs_per_level = distances_per_level.topk(
self.topk, dim=0, largest=False)
candidate_idxs.append(topk_idxs_per_level + start_idx)
start_idx = end_idx
candidate_idxs = torch.cat(candidate_idxs, dim=0)
gt_bboxes_ratios = AspectRatio(gt_bboxes)
gt_bboxes_ratios_per_gt = gt_bboxes_ratios.mean(0)
candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)]
overlaps_mean_per_gt = candidate_overlaps.mean(0)
overlaps_std_per_gt = candidate_overlaps.std(0)
overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt
# new assign
iou_thr_weight = torch.exp((-1 / 4) * gt_bboxes_ratios_per_gt)
overlaps_thr_per_gt = overlaps_thr_per_gt * iou_thr_weight
is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :]
# limit the positive sample's center in gt
# inside_flag = torch.full([num_bboxes, num_gt],
# 0.).to(gt_bboxes.device).float()
inside_flag = points_in_polygons(bboxes_points, gt_bboxes)
# pointsJf(bboxes_points, gt_bboxes, inside_flag)
is_in_gts = inside_flag[candidate_idxs,
torch.arange(num_gt)].to(is_pos.dtype)
is_pos = is_pos & is_in_gts
for gt_idx in range(num_gt):
candidate_idxs[:, gt_idx] += gt_idx * num_bboxes
candidate_idxs = candidate_idxs.view(-1)
# if an anchor box is assigned to multiple gts,
# the one with the highest IoU will be selected.
overlaps_inf = torch.full_like(overlaps,
-INF).t().contiguous().view(-1)
index = candidate_idxs.view(-1)[is_pos.view(-1)]
overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index]
overlaps_inf = overlaps_inf.view(num_gt, -1).t()
max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1)
assigned_gt_inds[
max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1
if gt_labels is not None:
assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
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_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)