# Copyright (c) OpenMMLab. All rights reserved. import warnings from typing import List, Optional import torch from mmengine.structures import InstanceData from torch import Tensor from mmdet.registry import TASK_UTILS from mmdet.utils import ConfigType from .assign_result import AssignResult from .base_assigner import BaseAssigner def bbox_center_distance(bboxes: Tensor, priors: Tensor) -> Tensor: """Compute the center distance between bboxes and priors. Args: bboxes (Tensor): Shape (n, 4) for , "xyxy" format. priors (Tensor): Shape (n, 4) for priors, "xyxy" format. Returns: Tensor: Center distances between bboxes and priors. """ bbox_cx = (bboxes[:, 0] + bboxes[:, 2]) / 2.0 bbox_cy = (bboxes[:, 1] + bboxes[:, 3]) / 2.0 bbox_points = torch.stack((bbox_cx, bbox_cy), dim=1) priors_cx = (priors[:, 0] + priors[:, 2]) / 2.0 priors_cy = (priors[:, 1] + priors[:, 3]) / 2.0 priors_points = torch.stack((priors_cx, priors_cy), dim=1) distances = (priors_points[:, None, :] - bbox_points[None, :, :]).pow(2).sum(-1).sqrt() return distances @TASK_UTILS.register_module() class ATSSAssigner(BaseAssigner): """Assign a corresponding gt bbox or background to each prior. Each proposals 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 If ``alpha`` is not None, it means that the dynamic cost ATSSAssigner is adopted, which is currently only used in the DDOD. Args: topk (int): number of priors selected in each level alpha (float, optional): param of cost rate for each proposal only in DDOD. Defaults to None. iou_calculator (:obj:`ConfigDict` or dict): Config dict for iou calculator. Defaults to ``dict(type='BboxOverlaps2D')`` ignore_iof_thr (float): IoF threshold for ignoring bboxes (if `gt_bboxes_ignore` is specified). Negative values mean not ignoring any bboxes. Defaults to -1. """ def __init__(self, topk: int, alpha: Optional[float] = None, iou_calculator: ConfigType = dict(type='BboxOverlaps2D'), ignore_iof_thr: float = -1) -> None: self.topk = topk self.alpha = alpha self.iou_calculator = TASK_UTILS.build(iou_calculator) self.ignore_iof_thr = ignore_iof_thr # https://github.com/sfzhang15/ATSS/blob/master/atss_core/modeling/rpn/atss/loss.py def assign( self, pred_instances: InstanceData, num_level_priors: List[int], gt_instances: InstanceData, gt_instances_ignore: Optional[InstanceData] = None ) -> AssignResult: """Assign gt to priors. The assignment is done in following steps 1. compute iou between all prior (prior of all pyramid levels) and gt 2. compute center distance between all prior and gt 3. on each pyramid level, for each gt, select k prior whose center are closest to the gt center, so we total select k*l prior as candidates for each gt 4. get corresponding iou for the these candidates, and compute the mean and std, set mean + std as the iou threshold 5. select these candidates whose iou are greater than or equal to the threshold as positive 6. limit the positive sample's center in gt If ``alpha`` is not None, and ``cls_scores`` and `bbox_preds` are not None, the overlaps calculation in the first step will also include dynamic cost, which is currently only used in the DDOD. 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). num_level_priors (List): Number of bboxes in each level 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. Returns: :obj:`AssignResult`: The assign result. """ 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 INF = 100000000 priors = priors[:, :4] num_gt, num_priors = gt_bboxes.size(0), priors.size(0) message = 'Invalid alpha parameter because cls_scores or ' \ 'bbox_preds are None. If you want to use the ' \ 'cost-based ATSSAssigner, please set cls_scores, ' \ 'bbox_preds and self.alpha at the same time. ' # compute iou between all bbox and gt if self.alpha is None: # ATSSAssigner overlaps = self.iou_calculator(priors, gt_bboxes) if ('scores' in pred_instances or 'bboxes' in pred_instances): warnings.warn(message) else: # Dynamic cost ATSSAssigner in DDOD assert ('scores' in pred_instances and 'bboxes' in pred_instances), message cls_scores = pred_instances.scores bbox_preds = pred_instances.bboxes # compute cls cost for bbox and GT cls_cost = torch.sigmoid(cls_scores[:, gt_labels]) # compute iou between all bbox and gt overlaps = self.iou_calculator(bbox_preds, gt_bboxes) # make sure that we are in element-wise multiplication assert cls_cost.shape == overlaps.shape # overlaps is actually a cost matrix overlaps = cls_cost**(1 - self.alpha) * overlaps**self.alpha # assign 0 by default assigned_gt_inds = overlaps.new_full((num_priors, ), 0, dtype=torch.long) if num_gt == 0 or num_priors == 0: # No ground truth or boxes, return empty assignment max_overlaps = overlaps.new_zeros((num_priors, )) if num_gt == 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_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels) # compute center distance between all bbox and gt distances = bbox_center_distance(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): ignore_overlaps = self.iou_calculator( priors, gt_bboxes_ignore, mode='iof') ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) ignore_idxs = ignore_max_overlaps > self.ignore_iof_thr distances[ignore_idxs, :] = INF assigned_gt_inds[ignore_idxs] = -1 # Selecting candidates based on the center distance candidate_idxs = [] start_idx = 0 for level, priors_per_level in enumerate(num_level_priors): # on each pyramid level, for each gt, # select k bbox whose center are closest to the gt center end_idx = start_idx + priors_per_level distances_per_level = distances[start_idx:end_idx, :] selectable_k = min(self.topk, priors_per_level) _, topk_idxs_per_level = distances_per_level.topk( selectable_k, dim=0, largest=False) candidate_idxs.append(topk_idxs_per_level + start_idx) start_idx = end_idx candidate_idxs = torch.cat(candidate_idxs, dim=0) # get corresponding iou for the these candidates, and compute the # mean and std, set mean + std as the iou threshold candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)] overlaps_mean_per_gt = candidate_overlaps.mean(0) overlaps_std_per_gt = candidate_overlaps.std(0) overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :] # limit the positive sample's center in gt for gt_idx in range(num_gt): candidate_idxs[:, gt_idx] += gt_idx * num_priors priors_cx = (priors[:, 0] + priors[:, 2]) / 2.0 priors_cy = (priors[:, 1] + priors[:, 3]) / 2.0 ep_priors_cx = priors_cx.view(1, -1).expand( num_gt, num_priors).contiguous().view(-1) ep_priors_cy = priors_cy.view(1, -1).expand( num_gt, num_priors).contiguous().view(-1) candidate_idxs = candidate_idxs.view(-1) # calculate the left, top, right, bottom distance between positive # prior 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 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_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)