Spaces:
Runtime error
Runtime error
File size: 6,966 Bytes
f549064 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
from mmengine.structures import InstanceData
from mmdet.registry import TASK_UTILS
from mmdet.utils import ConfigType
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
INF = 100000000
@TASK_UTILS.register_module()
class TaskAlignedAssigner(BaseAssigner):
"""Task aligned assigner used in the paper:
`TOOD: Task-aligned One-stage Object Detection.
<https://arxiv.org/abs/2108.07755>`_.
Assign a corresponding gt bbox or background to each predicted bbox.
Each bbox 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:
topk (int): number of bbox selected in each level
iou_calculator (:obj:`ConfigDict` or dict): Config dict for iou
calculator. Defaults to ``dict(type='BboxOverlaps2D')``
"""
def __init__(self,
topk: int,
iou_calculator: ConfigType = dict(type='BboxOverlaps2D')):
assert topk >= 1
self.topk = topk
self.iou_calculator = TASK_UTILS.build(iou_calculator)
def assign(self,
pred_instances: InstanceData,
gt_instances: InstanceData,
gt_instances_ignore: Optional[InstanceData] = None,
alpha: int = 1,
beta: int = 6) -> AssignResult:
"""Assign gt to bboxes.
The assignment is done in following steps
1. compute alignment metric between all bbox (bbox of all pyramid
levels) and gt
2. select top-k bbox as candidates for each gt
3. limit the positive sample's center in gt (because the anchor-free
detector only can predict positive distance)
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).
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.
alpha (int): Hyper-parameters related to alignment_metrics.
Defaults to 1.
beta (int): Hyper-parameters related to alignment_metrics.
Defaults to 6.
Returns:
:obj:`TaskAlignedAssignResult`: The assign result.
"""
priors = pred_instances.priors
decode_bboxes = pred_instances.bboxes
pred_scores = pred_instances.scores
gt_bboxes = gt_instances.bboxes
gt_labels = gt_instances.labels
priors = priors[:, :4]
num_gt, num_bboxes = gt_bboxes.size(0), priors.size(0)
# compute alignment metric between all bbox and gt
overlaps = self.iou_calculator(decode_bboxes, gt_bboxes).detach()
bbox_scores = pred_scores[:, gt_labels].detach()
# assign 0 by default
assigned_gt_inds = priors.new_full((num_bboxes, ), 0, dtype=torch.long)
assign_metrics = priors.new_zeros((num_bboxes, ))
if num_gt == 0 or num_bboxes == 0:
# No ground truth or boxes, return empty assignment
max_overlaps = priors.new_zeros((num_bboxes, ))
if num_gt == 0:
# No gt boxes, assign everything to background
assigned_gt_inds[:] = 0
assigned_labels = priors.new_full((num_bboxes, ),
-1,
dtype=torch.long)
assign_result = AssignResult(
num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
assign_result.assign_metrics = assign_metrics
return assign_result
# select top-k bboxes as candidates for each gt
alignment_metrics = bbox_scores**alpha * overlaps**beta
topk = min(self.topk, alignment_metrics.size(0))
_, candidate_idxs = alignment_metrics.topk(topk, dim=0, largest=True)
candidate_metrics = alignment_metrics[candidate_idxs,
torch.arange(num_gt)]
is_pos = candidate_metrics > 0
# limit the positive sample's center in gt
priors_cx = (priors[:, 0] + priors[:, 2]) / 2.0
priors_cy = (priors[:, 1] + priors[:, 3]) / 2.0
for gt_idx in range(num_gt):
candidate_idxs[:, gt_idx] += gt_idx * num_bboxes
ep_priors_cx = priors_cx.view(1, -1).expand(
num_gt, num_bboxes).contiguous().view(-1)
ep_priors_cy = priors_cy.view(1, -1).expand(
num_gt, num_bboxes).contiguous().view(-1)
candidate_idxs = candidate_idxs.view(-1)
# calculate the left, top, right, bottom distance between positive
# bbox 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
assign_metrics[max_overlaps != -INF] = alignment_metrics[
max_overlaps != -INF, argmax_overlaps[max_overlaps != -INF]]
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]
assign_result = AssignResult(
num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
assign_result.assign_metrics = assign_metrics
return assign_result
|