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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import TASK_UTILS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
@TASK_UTILS.register_module()
class MaxIoUAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, or a semi-positive integer
indicating the ground truth index.
- -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).
`min_pos_iou` is set to avoid assigning bboxes that have extremely
small iou with GT as positive samples. It brings about 0.3 mAP
improvements in 1x schedule but does not affect the performance of
3x schedule. More comparisons can be found in
`PR #7464 <https://github.com/open-mmlab/mmdetection/pull/7464>`_.
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 low quality matches. This is
usually allowed for RPN and single stage detectors, but not allowed
in the second stage. Details are demonstrated in Step 4.
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 (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: float = -1,
iou_calculator: dict = dict(type='BboxOverlaps2D')):
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 bboxes.
This method assign a gt bbox to every bbox (proposal/anchor), each bbox
will be assigned with -1, or a semi-positive number. -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 the background
2. assign proposals whose iou with all gts < neg_iou_thr to 0
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). The bboxes predicted by
the current model or stage will be named ``bboxes``,
``labels``, and ``scores``, the same as the ``InstanceData``
in other places.
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.
Example:
>>> from mmengine.structures import InstanceData
>>> self = MaxIoUAssigner(0.5, 0.5)
>>> pred_instances = InstanceData()
>>> pred_instances.priors = torch.Tensor([[0, 0, 10, 10],
... [10, 10, 20, 20]])
>>> gt_instances = InstanceData()
>>> gt_instances.bboxes = torch.Tensor([[0, 0, 10, 9]])
>>> gt_instances.labels = torch.Tensor([0])
>>> assign_result = self.assign(pred_instances, gt_instances)
>>> expected_gt_inds = torch.LongTensor([1, 0])
>>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
"""
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
assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
gt_bboxes.shape[0] > self.gpu_assign_thr) else False
# compute overlap and assign gt on CPU when number of GT is large
if assign_on_cpu:
device = priors.device
priors = priors.cpu()
gt_bboxes = gt_bboxes.cpu()
gt_labels = gt_labels.cpu()
if gt_bboxes_ignore is not None:
gt_bboxes_ignore = gt_bboxes_ignore.cpu()
overlaps = self.iou_calculator(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):
if self.ignore_wrt_candidates:
ignore_overlaps = self.iou_calculator(
priors, gt_bboxes_ignore, mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
ignore_overlaps = self.iou_calculator(
gt_bboxes_ignore, priors, 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
def assign_wrt_overlaps(self, overlaps: Tensor,
gt_labels: Tensor) -> AssignResult:
"""Assign w.r.t. the overlaps of priors with gts.
Args:
overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
shape(k, n).
gt_labels (Tensor): Labels of k gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
# 1. assign -1 by default
assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-1,
dtype=torch.long)
if num_gts == 0 or num_bboxes == 0:
# No ground truth or boxes, return empty assignment
max_overlaps = overlaps.new_zeros((num_bboxes, ))
assigned_labels = overlaps.new_full((num_bboxes, ),
-1,
dtype=torch.long)
if num_gts == 0:
# No truth, assign everything to background
assigned_gt_inds[:] = 0
return AssignResult(
num_gts=num_gts,
gt_inds=assigned_gt_inds,
max_overlaps=max_overlaps,
labels=assigned_labels)
# for each anchor, which gt best overlaps with it
# for each anchor, the max iou of all gts
max_overlaps, argmax_overlaps = overlaps.max(dim=0)
# for each gt, which anchor best overlaps with it
# for each gt, the max iou of all proposals
gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
# 2. assign negative: below
# the negative inds are set to be 0
if isinstance(self.neg_iou_thr, float):
assigned_gt_inds[(max_overlaps >= 0)
& (max_overlaps < self.neg_iou_thr)] = 0
elif isinstance(self.neg_iou_thr, tuple):
assert len(self.neg_iou_thr) == 2
assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0])
& (max_overlaps < self.neg_iou_thr[1])] = 0
# 3. assign positive: above positive IoU threshold
pos_inds = max_overlaps >= self.pos_iou_thr
assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
if self.match_low_quality:
# Low-quality matching will overwrite the assigned_gt_inds assigned
# in Step 3. Thus, the assigned gt might not be the best one for
# prediction.
# For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2,
# bbox 1 will be assigned as the best target for bbox A in step 3.
# However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's
# assigned_gt_inds will be overwritten to be bbox 2.
# This might be the reason that it is not used in ROI Heads.
for i in range(num_gts):
if gt_max_overlaps[i] >= self.min_pos_iou:
if self.gt_max_assign_all:
max_iou_inds = overlaps[i, :] == gt_max_overlaps[i]
assigned_gt_inds[max_iou_inds] = i + 1
else:
assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
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]
return AssignResult(
num_gts=num_gts,
gt_inds=assigned_gt_inds,
max_overlaps=max_overlaps,
labels=assigned_labels)
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