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ultralytics 8.0.107 (#2778)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Peter van Lunteren <contact@pvanlunteren.com>
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Glenn Jocher 2023-05-23 13:50:24 +02:00 • committed by GitHub
parent 4db686a315
commit dada5b73c4
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23 changed files with 236 additions and 73 deletions
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16
ultralytics/yolo/utils/tal.py
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@ -41,7 +41,7 @@ def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
# (b, n_max_boxes, h*w) -> (b, h*w)
fg_mask = mask_pos.sum(-2)
if fg_mask.max() > 1: # one anchor is assigned to multiple gt_bboxes
mask_multi_gts = (fg_mask.unsqueeze(1) > 1).repeat([1, n_max_boxes, 1]) # (b, n_max_boxes, h*w)
mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1) # (b, n_max_boxes, h*w)
max_overlaps_idx = overlaps.argmax(1) # (b, h*w)
is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
@ -132,7 +132,7 @@ class TaskAlignedAssigner(nn.Module):
# Get anchor_align metric, (b, max_num_obj, h*w)
align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
# Get topk_metric mask, (b, max_num_obj, h*w)
mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.repeat([1, 1, self.topk]).bool())
mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
# Merge all mask to a final mask, (b, max_num_obj, h*w)
mask_pos = mask_topk * mask_in_gts * mask_gt
@ -146,15 +146,15 @@ class TaskAlignedAssigner(nn.Module):
bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # 2, b, max_num_obj
ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes) # b, max_num_obj
ind[1] = gt_labels.long().squeeze(-1) # b, max_num_obj
ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes) # b, max_num_obj
ind[1] = gt_labels.squeeze(-1) # b, max_num_obj
# Get the scores of each grid for each gt cls
bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt] # b, max_num_obj, h*w
# (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
pd_boxes = pd_bboxes.unsqueeze(1).repeat(1, self.n_max_boxes, 1, 1)[mask_gt]
gt_boxes = gt_bboxes.unsqueeze(2).repeat(1, 1, na, 1)[mask_gt]
overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp(0)
pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
return align_metric, overlaps
@ -273,4 +273,4 @@ def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
def bbox2dist(anchor_points, bbox, reg_max):
"""Transform bbox(xyxy) to dist(ltrb)."""
x1y1, x2y2 = bbox.chunk(2, -1)
return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01) # dist (lt, rb)
return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp_(0, reg_max - 0.01) # dist (lt, rb)