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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from mmdet.registry import TASK_UTILS
from .assign_result import AssignResult
from .max_iou_assigner import MaxIoUAssigner
@TASK_UTILS.register_module()
class ApproxMaxIoUAssigner(MaxIoUAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with an integer indicating the ground truth
index. (semi-positive index: gt label (0-based), -1: background)
- -1: negative sample, no assigned gt
- semi-positive integer: positive sample, index (0-based) of assigned gt
Args:
pos_iou_thr (float): IoU threshold for positive bboxes.
neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
min_pos_iou (float): Minimum iou for a bbox to be considered as a
positive bbox. Positive samples can have smaller IoU than
pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
gt_max_assign_all (bool): Whether to assign all bboxes with the same
highest overlap with some gt to that gt.
ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
`gt_bboxes_ignore` is specified). Negative values mean not
ignoring any bboxes.
ignore_wrt_candidates (bool): Whether to compute the iof between
`bboxes` and `gt_bboxes_ignore`, or the contrary.
match_low_quality (bool): Whether to allow quality matches. This is
usually allowed for RPN and single stage detectors, but not allowed
in the second stage.
gpu_assign_thr (int): The upper bound of the number of GT for GPU
assign. When the number of gt is above this threshold, will assign
on CPU device. Negative values mean not assign on CPU.
iou_calculator (:obj:`ConfigDict` or dict): Config of overlaps
Calculator.
"""
def __init__(
self,
pos_iou_thr: float,
neg_iou_thr: Union[float, tuple],
min_pos_iou: float = .0,
gt_max_assign_all: bool = True,
ignore_iof_thr: float = -1,
ignore_wrt_candidates: bool = True,
match_low_quality: bool = True,
gpu_assign_thr: int = -1,
iou_calculator: Union[ConfigDict, dict] = dict(type='BboxOverlaps2D')
) -> None:
self.pos_iou_thr = pos_iou_thr
self.neg_iou_thr = neg_iou_thr
self.min_pos_iou = min_pos_iou
self.gt_max_assign_all = gt_max_assign_all
self.ignore_iof_thr = ignore_iof_thr
self.ignore_wrt_candidates = ignore_wrt_candidates
self.gpu_assign_thr = gpu_assign_thr
self.match_low_quality = match_low_quality
self.iou_calculator = TASK_UTILS.build(iou_calculator)
def assign(self,
pred_instances: InstanceData,
gt_instances: InstanceData,
gt_instances_ignore: Optional[InstanceData] = None,
**kwargs) -> AssignResult:
"""Assign gt to approxs.
This method assign a gt bbox to each group of approxs (bboxes),
each group of approxs is represent by a base approx (bbox) and
will be assigned with -1, or a semi-positive number.
background_label (-1) means negative sample,
semi-positive number is the index (0-based) of assigned gt.
The assignment is done in following steps, the order matters.
1. assign every bbox to background_label (-1)
2. use the max IoU of each group of approxs to assign
2. assign proposals whose iou with all gts < neg_iou_thr to background
3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
assign it to that bbox
4. for each gt bbox, assign its nearest proposals (may be more than
one) to itself
Args:
pred_instances (:obj:`InstanceData`): Instances of model
predictions. It includes ``priors``, and the priors can
be anchors or points, or the bboxes predicted by the
previous stage, has shape (n, 4). ``approxs`` means the
group of approxs aligned with ``priors``, has shape
(n, num_approxs, 4).
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It usually includes ``bboxes``, with shape (k, 4),
and ``labels``, with shape (k, ).
gt_instances_ignore (:obj:`InstanceData`, 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.
"""
squares = pred_instances.priors
approxs = pred_instances.approxs
gt_bboxes = gt_instances.bboxes
gt_labels = gt_instances.labels
gt_bboxes_ignore = None if gt_instances_ignore is None else \
gt_instances_ignore.get('bboxes', None)
approxs_per_octave = approxs.size(1)
num_squares = squares.size(0)
num_gts = gt_bboxes.size(0)
if num_squares == 0 or num_gts == 0:
# No predictions and/or truth, return empty assignment
overlaps = approxs.new(num_gts, num_squares)
assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
return assign_result
# re-organize anchors by approxs_per_octave x num_squares
approxs = torch.transpose(approxs, 0, 1).contiguous().view(-1, 4)
assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
num_gts > self.gpu_assign_thr) else False
# compute overlap and assign gt on CPU when number of GT is large
if assign_on_cpu:
device = approxs.device
approxs = approxs.cpu()
gt_bboxes = gt_bboxes.cpu()
if gt_bboxes_ignore is not None:
gt_bboxes_ignore = gt_bboxes_ignore.cpu()
if gt_labels is not None:
gt_labels = gt_labels.cpu()
all_overlaps = self.iou_calculator(approxs, gt_bboxes)
overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares,
num_gts).max(dim=0)
overlaps = torch.transpose(overlaps, 0, 1)
if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0):
if self.ignore_wrt_candidates:
ignore_overlaps = self.iou_calculator(
squares, gt_bboxes_ignore, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
ignore_overlaps = self.iou_calculator(
gt_bboxes_ignore, squares, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
if assign_on_cpu:
assign_result.gt_inds = assign_result.gt_inds.to(device)
assign_result.max_overlaps = assign_result.max_overlaps.to(device)
if assign_result.labels is not None:
assign_result.labels = assign_result.labels.to(device)
return assign_result
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