import torch import torch.nn.functional as F from utils.box_ops import box_iou class AlignedOTAMatcher(object): """ This code referenced to https://github.com/open-mmlab/mmyolo/models/task_modules/assigners/batch_dsl_assigner.py """ def __init__(self, num_classes, soft_center_radius=3.0, topk_candidates=13, ): self.num_classes = num_classes self.soft_center_radius = soft_center_radius self.topk_candidates = topk_candidates @torch.no_grad() def __call__(self, fpn_strides, anchors, pred_cls, pred_box, gt_labels, gt_bboxes): # [M,] strides = torch.cat([torch.ones_like(anchor_i[:, 0]) * stride_i for stride_i, anchor_i in zip(fpn_strides, anchors)], dim=-1) # List[F, M, 2] -> [M, 2] num_gt = len(gt_labels) anchors = torch.cat(anchors, dim=0) # check gt if num_gt == 0 or gt_bboxes.max().item() == 0.: return { 'assigned_labels': gt_labels.new_full(pred_cls[..., 0].shape, self.num_classes).long(), 'assigned_bboxes': gt_bboxes.new_full(pred_box.shape, 0), 'assign_metrics': gt_bboxes.new_full(pred_cls[..., 0].shape, 0), 'assigned_pss': gt_labels.new_full(pred_cls[..., 0].shape, 0).float(), } # get inside points: [N, M] is_in_gt = self.find_inside_points(gt_bboxes, anchors) valid_mask = is_in_gt.sum(dim=0) > 0 # [M,] # ----------------------- Soft center prior ----------------------- gt_center = (gt_bboxes[..., :2] + gt_bboxes[..., 2:]) / 2.0 distance = (anchors.unsqueeze(0) - gt_center.unsqueeze(1) ).pow(2).sum(-1).sqrt() / strides.unsqueeze(0) # [N, M] distance = distance * valid_mask.unsqueeze(0) soft_center_prior = torch.pow(10, distance - self.soft_center_radius) # ----------------------- Regression cost ----------------------- pair_wise_ious, _ = box_iou(gt_bboxes, pred_box) # [N, M] pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) * 3.0 # ----------------------- Classification cost ----------------------- ## select the predicted scores corresponded to the gt_labels pairwise_pred_scores = pred_cls.permute(1, 0) # [M, C] -> [C, M] pairwise_pred_scores = pairwise_pred_scores[gt_labels.long(), :].float() # [N, M] ## scale factor scale_factor = (pair_wise_ious - pairwise_pred_scores.sigmoid()).abs().pow(2.0) ## cls cost pair_wise_cls_loss = F.binary_cross_entropy_with_logits( pairwise_pred_scores, pair_wise_ious, reduction="none") * scale_factor # [N, M] del pairwise_pred_scores ## foreground cost matrix cost_matrix = pair_wise_cls_loss + pair_wise_ious_loss + soft_center_prior max_pad_value = torch.ones_like(cost_matrix) * 1e9 cost_matrix = torch.where(valid_mask[None].repeat(num_gt, 1), # [N, M] cost_matrix, max_pad_value) # ----------------------- Dynamic label assignment ----------------------- matched_pred_ious, matched_gt_inds, fg_mask_inboxes = self.dynamic_k_matching( cost_matrix, pair_wise_ious, num_gt) matched_pss_inds, fg_mask_top1 = self.topk_one_matching(cost_matrix, pair_wise_ious, num_gt) del pair_wise_cls_loss, cost_matrix, pair_wise_ious, pair_wise_ious_loss # ----------------------- Process assigned labels ----------------------- assigned_labels = gt_labels.new_full(pred_cls[..., 0].shape, self.num_classes) # [M,] assigned_labels[fg_mask_inboxes] = gt_labels[matched_gt_inds].squeeze(-1) assigned_labels = assigned_labels.long() # [M,] assigned_bboxes = gt_bboxes.new_full(pred_box.shape, 0) # [M, 4] assigned_bboxes[fg_mask_inboxes] = gt_bboxes[matched_gt_inds] # [M, 4] assign_metrics = gt_bboxes.new_full(pred_cls[..., 0].shape, 0) # [M,] assign_metrics[fg_mask_inboxes] = matched_pred_ious # [M,] assigned_pss = gt_labels.new_zeros(pred_cls[..., 0].shape) # [M,] assigned_pss[fg_mask_top1] = torch.ones_like(gt_labels)[matched_pss_inds].squeeze(-1) assigned_pss = assigned_pss.float() assigned_dict = dict( assigned_labels = assigned_labels, assigned_bboxes = assigned_bboxes, assign_metrics = assign_metrics, assigned_pss = assigned_pss, ) return assigned_dict def find_inside_points(self, gt_bboxes, anchors): """ gt_bboxes: Tensor -> [N, 2] anchors: Tensor -> [M, 2] """ num_anchors = anchors.shape[0] num_gt = gt_bboxes.shape[0] anchors_expand = anchors.unsqueeze(0).repeat(num_gt, 1, 1) # [N, M, 2] gt_bboxes_expand = gt_bboxes.unsqueeze(1).repeat(1, num_anchors, 1) # [N, M, 4] # offset lt = anchors_expand - gt_bboxes_expand[..., :2] rb = gt_bboxes_expand[..., 2:] - anchors_expand bbox_deltas = torch.cat([lt, rb], dim=-1) is_in_gts = bbox_deltas.min(dim=-1).values > 0 return is_in_gts def dynamic_k_matching(self, cost_matrix, pairwise_ious, num_gt): matching_matrix = torch.zeros_like(cost_matrix, dtype=torch.uint8) # select candidate topk ious for dynamic-k calculation candidate_topk = min(self.topk_candidates, pairwise_ious.size(1)) topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=1) # calculate dynamic k for each gt dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1) # sorting the batch cost matirx is faster than topk _, sorted_indices = torch.sort(cost_matrix, dim=1) for gt_idx in range(num_gt): topk_ids = sorted_indices[gt_idx, :dynamic_ks[gt_idx]] matching_matrix[gt_idx, :][topk_ids] = 1 del topk_ious, dynamic_ks, topk_ids prior_match_gt_mask = matching_matrix.sum(0) > 1 if prior_match_gt_mask.sum() > 0: cost_min, cost_argmin = torch.min( cost_matrix[:, prior_match_gt_mask], dim=0) matching_matrix[:, prior_match_gt_mask] *= 0 matching_matrix[cost_argmin, prior_match_gt_mask] = 1 # get foreground mask inside box and center prior fg_mask_inboxes = matching_matrix.sum(0) > 0 matched_pred_ious = (matching_matrix * pairwise_ious).sum(0)[fg_mask_inboxes] matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) return matched_pred_ious, matched_gt_inds, fg_mask_inboxes def topk_one_matching(self, cost_matrix, pairwise_ious, num_gt): matching_matrix = torch.zeros_like(cost_matrix, dtype=torch.uint8) # select candidate topk ious for dynamic-k calculation candidate_topk = min(1, pairwise_ious.size(1)) topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=1) # calculate dynamic k for each gt dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1) # sorting the batch cost matirx is faster than topk _, sorted_indices = torch.sort(cost_matrix, dim=1) for gt_idx in range(num_gt): topk_ids = sorted_indices[gt_idx, :dynamic_ks[gt_idx]] matching_matrix[gt_idx, :][topk_ids] = 1 del topk_ious, dynamic_ks, topk_ids prior_match_gt_mask = matching_matrix.sum(0) > 1 if prior_match_gt_mask.sum() > 0: cost_min, cost_argmin = torch.min( cost_matrix[:, prior_match_gt_mask], dim=0) matching_matrix[:, prior_match_gt_mask] *= 0 matching_matrix[cost_argmin, prior_match_gt_mask] = 1 # get foreground mask inside box and center prior fg_mask_inboxes = matching_matrix.sum(0) > 0 matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) return matched_gt_inds, fg_mask_inboxes