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| # 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 | |
| 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) | |