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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import List, Optional
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
def bbox_center_distance(bboxes: Tensor, priors: Tensor) -> Tensor:
"""Compute the center distance between bboxes and priors.
Args:
bboxes (Tensor): Shape (n, 4) for , "xyxy" format.
priors (Tensor): Shape (n, 4) for priors, "xyxy" format.
Returns:
Tensor: Center distances between bboxes and priors.
"""
bbox_cx = (bboxes[:, 0] + bboxes[:, 2]) / 2.0
bbox_cy = (bboxes[:, 1] + bboxes[:, 3]) / 2.0
bbox_points = torch.stack((bbox_cx, bbox_cy), dim=1)
priors_cx = (priors[:, 0] + priors[:, 2]) / 2.0
priors_cy = (priors[:, 1] + priors[:, 3]) / 2.0
priors_points = torch.stack((priors_cx, priors_cy), dim=1)
distances = (priors_points[:, None, :] -
bbox_points[None, :, :]).pow(2).sum(-1).sqrt()
return distances
@TASK_UTILS.register_module()
class ATSSAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each prior.
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
If ``alpha`` is not None, it means that the dynamic cost
ATSSAssigner is adopted, which is currently only used in the DDOD.
Args:
topk (int): number of priors selected in each level
alpha (float, optional): param of cost rate for each proposal only
in DDOD. Defaults to None.
iou_calculator (:obj:`ConfigDict` or dict): Config dict for iou
calculator. Defaults to ``dict(type='BboxOverlaps2D')``
ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
`gt_bboxes_ignore` is specified). Negative values mean not
ignoring any bboxes. Defaults to -1.
"""
def __init__(self,
topk: int,
alpha: Optional[float] = None,
iou_calculator: ConfigType = dict(type='BboxOverlaps2D'),
ignore_iof_thr: float = -1) -> None:
self.topk = topk
self.alpha = alpha
self.iou_calculator = TASK_UTILS.build(iou_calculator)
self.ignore_iof_thr = ignore_iof_thr
# https://github.com/sfzhang15/ATSS/blob/master/atss_core/modeling/rpn/atss/loss.py
def assign(
self,
pred_instances: InstanceData,
num_level_priors: List[int],
gt_instances: InstanceData,
gt_instances_ignore: Optional[InstanceData] = None
) -> AssignResult:
"""Assign gt to priors.
The assignment is done in following steps
1. compute iou between all prior (prior of all pyramid levels) and gt
2. compute center distance between all prior and gt
3. on each pyramid level, for each gt, select k prior whose center
are closest to the gt center, so we total select k*l prior 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
If ``alpha`` is not None, and ``cls_scores`` and `bbox_preds`
are not None, the overlaps calculation in the first step
will also include dynamic cost, which is currently only used in
the DDOD.
Args:
pred_instances (:obj:`InstaceData`): Instances of model
predictions. It includes ``priors``, and the priors can
be anchors, points, or bboxes predicted by the model,
shape(n, 4).
num_level_priors (List): Number of bboxes in each level
gt_instances (:obj:`InstaceData`): Ground truth of instance
annotations. It usually includes ``bboxes`` and ``labels``
attributes.
gt_instances_ignore (:obj:`InstaceData`, optional): Instances
to be ignored during training. It includes ``bboxes``
attribute data that is ignored during training and testing.
Defaults to None.
Returns:
:obj:`AssignResult`: The assign result.
"""
gt_bboxes = gt_instances.bboxes
priors = pred_instances.priors
gt_labels = gt_instances.labels
if gt_instances_ignore is not None:
gt_bboxes_ignore = gt_instances_ignore.bboxes
else:
gt_bboxes_ignore = None
INF = 100000000
priors = priors[:, :4]
num_gt, num_priors = gt_bboxes.size(0), priors.size(0)
message = 'Invalid alpha parameter because cls_scores or ' \
'bbox_preds are None. If you want to use the ' \
'cost-based ATSSAssigner, please set cls_scores, ' \
'bbox_preds and self.alpha at the same time. '
# compute iou between all bbox and gt
if self.alpha is None:
# ATSSAssigner
overlaps = self.iou_calculator(priors, gt_bboxes)
if ('scores' in pred_instances or 'bboxes' in pred_instances):
warnings.warn(message)
else:
# Dynamic cost ATSSAssigner in DDOD
assert ('scores' in pred_instances
and 'bboxes' in pred_instances), message
cls_scores = pred_instances.scores
bbox_preds = pred_instances.bboxes
# compute cls cost for bbox and GT
cls_cost = torch.sigmoid(cls_scores[:, gt_labels])
# compute iou between all bbox and gt
overlaps = self.iou_calculator(bbox_preds, gt_bboxes)
# make sure that we are in element-wise multiplication
assert cls_cost.shape == overlaps.shape
# overlaps is actually a cost matrix
overlaps = cls_cost**(1 - self.alpha) * overlaps**self.alpha
# assign 0 by default
assigned_gt_inds = overlaps.new_full((num_priors, ),
0,
dtype=torch.long)
if num_gt == 0 or num_priors == 0:
# No ground truth or boxes, return empty assignment
max_overlaps = overlaps.new_zeros((num_priors, ))
if num_gt == 0:
# No truth, assign everything to background
assigned_gt_inds[:] = 0
assigned_labels = overlaps.new_full((num_priors, ),
-1,
dtype=torch.long)
return AssignResult(
num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
# compute center distance between all bbox and gt
distances = bbox_center_distance(gt_bboxes, priors)
if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
and gt_bboxes_ignore.numel() > 0 and priors.numel() > 0):
ignore_overlaps = self.iou_calculator(
priors, gt_bboxes_ignore, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
ignore_idxs = ignore_max_overlaps > self.ignore_iof_thr
distances[ignore_idxs, :] = INF
assigned_gt_inds[ignore_idxs] = -1
# Selecting candidates based on the center distance
candidate_idxs = []
start_idx = 0
for level, priors_per_level in enumerate(num_level_priors):
# on each pyramid level, for each gt,
# select k bbox whose center are closest to the gt center
end_idx = start_idx + priors_per_level
distances_per_level = distances[start_idx:end_idx, :]
selectable_k = min(self.topk, priors_per_level)
_, topk_idxs_per_level = distances_per_level.topk(
selectable_k, 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)
# get corresponding iou for the these candidates, and compute the
# mean and std, set mean + std as the iou threshold
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
is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :]
# limit the positive sample's center in gt
for gt_idx in range(num_gt):
candidate_idxs[:, gt_idx] += gt_idx * num_priors
priors_cx = (priors[:, 0] + priors[:, 2]) / 2.0
priors_cy = (priors[:, 1] + priors[:, 3]) / 2.0
ep_priors_cx = priors_cx.view(1, -1).expand(
num_gt, num_priors).contiguous().view(-1)
ep_priors_cy = priors_cy.view(1, -1).expand(
num_gt, num_priors).contiguous().view(-1)
candidate_idxs = candidate_idxs.view(-1)
# calculate the left, top, right, bottom distance between positive
# prior center and gt side
l_ = ep_priors_cx[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 0]
t_ = ep_priors_cy[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 1]
r_ = gt_bboxes[:, 2] - ep_priors_cx[candidate_idxs].view(-1, num_gt)
b_ = gt_bboxes[:, 3] - ep_priors_cy[candidate_idxs].view(-1, num_gt)
is_in_gts = torch.stack([l_, t_, r_, b_], dim=1).min(dim=1)[0] > 0.01
is_pos = is_pos & is_in_gts
# 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
assigned_labels = assigned_gt_inds.new_full((num_priors, ), -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]
return AssignResult(
num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
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