# Copyright (c) OpenMMLab. All rights reserved. from typing import Optional, Tuple, Union 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 @TASK_UTILS.register_module() class GridAssigner(BaseAssigner): """Assign a corresponding gt bbox or background to each bbox. Each proposals will be assigned with `-1`, `0`, or a positive integer indicating the ground truth index. - -1: don't care - 0: negative sample, no assigned gt - positive integer: positive sample, index (1-based) of assigned gt Args: pos_iou_thr (float): IoU threshold for positive bboxes. neg_iou_thr (float or tuple[float, float]): 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). Defaults to 0. gt_max_assign_all (bool): Whether to assign all bboxes with the same highest overlap with some gt to that gt. iou_calculator (:obj:`ConfigDict` or dict): Config of overlaps Calculator. """ def __init__( self, pos_iou_thr: float, neg_iou_thr: Union[float, Tuple[float, float]], min_pos_iou: float = .0, gt_max_assign_all: bool = True, iou_calculator: ConfigType = 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.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. The process is very much like the max iou assigner, except that positive samples are constrained within the cell that the gt boxes fell in. This method assign a gt bbox to every bbox (proposal/anchor), each bbox will be assigned with -1, 0, or a positive number. -1 means don't care, 0 means negative sample, positive number is the index (1-based) of assigned gt. The assignment is done in following steps, the order matters. 1. assign every bbox to -1 2. assign proposals whose iou with all gts <= neg_iou_thr to 0 3. for each bbox within a cell, if the iou with its nearest gt > pos_iou_thr and the center of that gt falls inside the cell, assign it to that bbox 4. for each gt bbox, assign its nearest proposals within the cell the gt bbox falls in 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. """ gt_bboxes = gt_instances.bboxes gt_labels = gt_instances.labels priors = pred_instances.priors responsible_flags = pred_instances.responsible_flags num_gts, num_priors = gt_bboxes.size(0), priors.size(0) # compute iou between all gt and priors overlaps = self.iou_calculator(gt_bboxes, priors) # 1. assign -1 by default assigned_gt_inds = overlaps.new_full((num_priors, ), -1, dtype=torch.long) if num_gts == 0 or num_priors == 0: # No ground truth or priors, return empty assignment max_overlaps = overlaps.new_zeros((num_priors, )) if num_gts == 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_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) # 2. assign negative: below # for each anchor, which gt best overlaps with it # for each anchor, the max iou of all gts # shape of max_overlaps == argmax_overlaps == num_priors max_overlaps, argmax_overlaps = overlaps.max(dim=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, list)): 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: falls into responsible cell and above # positive IOU threshold, the order matters. # the prior condition of comparison is to filter out all # unrelated anchors, i.e. not responsible_flags overlaps[:, ~responsible_flags.type(torch.bool)] = -1. # calculate max_overlaps again, but this time we only consider IOUs # for anchors responsible for prediction 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 # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) pos_inds = (max_overlaps > self.pos_iou_thr) & responsible_flags.type( torch.bool) assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 # 4. assign positive to max overlapped anchors within responsible cell 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]) & \ responsible_flags.type(torch.bool) assigned_gt_inds[max_iou_inds] = i + 1 elif responsible_flags[gt_argmax_overlaps[i]]: assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1 # assign labels of positive anchors 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_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)