point_assigner.py
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@BBOX_ASSIGNERS.register_module()
class PointAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each point.
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
"""
def __init__(self, scale=4, pos_num=3):
self.scale = scale
self.pos_num = pos_num
def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
"""Assign gt to points.
This method assign a gt bbox to every points set, each points set
will be assigned with the background_label (-1), or a label number.
-1 is background, and semi-positive number is the index (0-based) of
assigned gt.
The assignment is done in following steps, the order matters.
1. assign every points to the background_label (-1)
2. A point is assigned to some gt bbox if
(i) the point is within the k closest points to the gt bbox
(ii) the distance between this point and the gt is smaller than
other gt bboxes
Args:
points (Tensor): points to be assigned, shape(n, 3) while last
dimension stands for (x, y, stride).
gt_bboxes (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.
NOTE: currently unused.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
num_points = points.shape[0]
num_gts = gt_bboxes.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() # [3...,4...,5...,6...,7...]
lvl_min, lvl_max = points_lvl.min(), points_lvl.max()
# assign gt box
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)
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)