[Feature]Support skip nms (#1552)

* skip nms

* judge at beginning

* add test

* remove else

* add more details in docstr including version not

* fix unitest

* fix doc

* fix doc

* fix typo

* resove conversation

* fix link

mmcv/ops/nms.py

@@ -260,20 +260,25 @@ def soft_nms(boxes,
 def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
     r"""Performs non-maximum suppression in a batched fashion.
 
-    Modified from https://github.com/pytorch/vision/blob\
-    /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
+    Modified from
+    https://github.com/pytorch/vision/blob/505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
     In order to perform NMS independently per class, we add an offset to all
     the boxes. The offset is dependent only on the class idx, and is large
     enough so that boxes from different classes do not overlap.
 
+    Note:
+        In v1.4.1 and later, ``batched_nms`` supports skipping the NMS and
+        returns sorted raw results when `nms_cfg` is None.
+
     Args:
         boxes (torch.Tensor): boxes in shape (N, 4).
         scores (torch.Tensor): scores in shape (N, ).
         idxs (torch.Tensor): each index value correspond to a bbox cluster,
             and NMS will not be applied between elements of different idxs,
             shape (N, ).
-        nms_cfg (dict): specify nms type and other parameters like iou_thr.
-            Possible keys includes the following.
+        nms_cfg (dict | None): Supports skipping the nms when `nms_cfg`
+            is None, otherwise it should specify nms type and other
+            parameters like `iou_thr`. Possible keys includes the following.
 
             - iou_thr (float): IoU threshold used for NMS.
             - split_thr (float): threshold number of boxes. In some cases the
@@ -288,7 +293,19 @@ def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
 
     Returns:
         tuple: kept dets and indice.
+
+        - boxes (Tensor): Bboxes with score after nms, has shape
+          (num_bboxes, 5). last dimension 5 arrange as
+          (x1, y1, x2, y2, score)
+        - keep (Tensor): The indices of remaining boxes in input
+          boxes.
     """
+    # skip nms when nms_cfg is None
+    if nms_cfg is None:
+        scores, inds = scores.sort(descending=True)
+        boxes = boxes[inds]
+        return torch.cat([boxes, scores[:, None]], -1), inds
+
     nms_cfg_ = nms_cfg.copy()
     class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic)
     if class_agnostic:
@@ -333,7 +350,8 @@ def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
             boxes = boxes[:max_num]
             scores = scores[:max_num]
 
-    return torch.cat([boxes, scores[:, None]], -1), keep
+    boxes = torch.cat([boxes, scores[:, None]], -1)
+    return boxes, keep
 
 
 def nms_match(dets, iou_threshold):

tests/test_ops/test_nms.py

@@ -182,3 +182,14 @@ class Testnms(object):
 
         assert torch.equal(keep, seq_keep)
         assert torch.equal(boxes, seq_boxes)
+
+        # test skip nms when `nms_cfg` is None
+        seq_boxes, seq_keep = batched_nms(
+            torch.from_numpy(results['boxes']),
+            torch.from_numpy(results['scores']),
+            torch.from_numpy(results['idxs']),
+            None,
+            class_agnostic=False)
+        assert len(seq_keep) == len(results['boxes'])
+        # assert score is descending order
+        assert ((seq_boxes[:, -1][1:] - seq_boxes[:, -1][:-1]) < 0).all()