[fix]: fix softnms (#1019)

* fix basemodule

* fix typo

* fix unitest

mmcv/ops/nms.py

@@ -282,18 +282,21 @@ def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
         # This assumes `dets` has 5 dimensions where
         # the last dimension is score.
         # TODO: more elegant way to handle the dimension issue.
+        # Some type of nms would reweight the score, such as SoftNMS
         scores = dets[:, 4]
     else:
         total_mask = scores.new_zeros(scores.size(), dtype=torch.bool)
+        # Some type of nms would reweight the score, such as SoftNMS
+        scores_after_nms = scores.new_zeros(scores.size())
         for id in torch.unique(idxs):
             mask = (idxs == id).nonzero(as_tuple=False).view(-1)
             dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_)
             total_mask[mask[keep]] = True
-
+            scores_after_nms[mask[keep]] = dets[:, -1]
         keep = total_mask.nonzero(as_tuple=False).view(-1)
-        keep = keep[scores[keep].argsort(descending=True)]
+        scores, inds = scores_after_nms[keep].sort(descending=True)
+        keep = keep[inds]
         boxes = boxes[keep]
-        scores = scores[keep]
 
     return torch.cat([boxes, scores[:, None]], -1), keep
 

tests/test_ops/test_nms.py

@@ -157,3 +157,22 @@ class Testnms(object):
         assert torch.equal(keep, seq_keep)
         assert torch.equal(boxes, seq_boxes)
         assert torch.equal(keep, torch.from_numpy(results['keep']))
+
+        nms_cfg = dict(type='soft_nms', iou_threshold=0.7)
+        boxes, keep = batched_nms(
+            torch.from_numpy(results['boxes']),
+            torch.from_numpy(results['scores']),
+            torch.from_numpy(results['idxs']),
+            nms_cfg,
+            class_agnostic=False)
+
+        nms_cfg.update(split_thr=100)
+        seq_boxes, seq_keep = batched_nms(
+            torch.from_numpy(results['boxes']),
+            torch.from_numpy(results['scores']),
+            torch.from_numpy(results['idxs']),
+            nms_cfg,
+            class_agnostic=False)
+
+        assert torch.equal(keep, seq_keep)
+        assert torch.equal(boxes, seq_boxes)