[Feature] Support score_per_joint in group and nms

mmpose/apis/inference.py

@@ -525,6 +525,7 @@ def inference_bottom_up_pose_model(model,
 
     cfg = model.cfg
     device = next(model.parameters()).device
+    score_per_joint = cfg.model.test_cfg.get('score_per_joint', False)
 
     # build the data pipeline
     channel_order = cfg.test_pipeline[0].get('channel_order', 'rgb')
@@ -576,7 +577,11 @@ def inference_bottom_up_pose_model(model,
             })
 
         # pose nms
-        keep = oks_nms(pose_results, pose_nms_thr, sigmas)
+        keep = oks_nms(
+            pose_results,
+            pose_nms_thr,
+            sigmas,
+            score_per_joint=score_per_joint)
         pose_results = [pose_results[_keep] for _keep in keep]
 
     return pose_results, returned_outputs

mmpose/core/post_processing/group.py

@@ -150,6 +150,7 @@ def __init__(self, cfg):
         self.pool = torch.nn.MaxPool2d(cfg['nms_kernel'], 1,
                                        cfg['nms_padding'])
         self.use_udp = cfg.get('use_udp', False)
+        self.score_per_joint = cfg.get('score_per_joint', False)
 
     def nms(self, heatmaps):
         """Non-Maximum Suppression for heatmaps.
@@ -375,7 +376,7 @@ def parse(self, heatmaps, tags, adjust=True, refine=True):
             tuple: A tuple containing keypoint grouping results.
 
             - results (list(np.ndarray)): Pose results.
-            - scores (list): Score of people.
+            - scores (list/list(np.ndarray)): Score of people.
         """
         results = self.match(**self.top_k(heatmaps, tags))
 
@@ -388,7 +389,10 @@ def parse(self, heatmaps, tags, adjust=True, refine=True):
             else:
                 results = self.adjust(results, heatmaps)
 
-        scores = [i[:, 2].mean() for i in results[0]]
+        if self.score_per_joint:
+            scores = [i[:, 2] for i in results[0]]
+        else:
+            scores = [i[:, 2].mean() for i in results[0]]
 
         if refine:
             results = results[0]

mmpose/core/post_processing/nms.py

@@ -86,22 +86,27 @@ def oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None):
     return ious
 
 
-def oks_nms(kpts_db, thr, sigmas=None, vis_thr=None):
+def oks_nms(kpts_db, thr, sigmas=None, vis_thr=None, score_per_joint=False):
     """OKS NMS implementations.
 
     Args:
         kpts_db: keypoints.
         thr: Retain overlap < thr.
         sigmas: standard deviation of keypoint labelling.
         vis_thr: threshold of the keypoint visibility.
+        score_per_joint: the input scores (in kpts_db) are per joint scores
 
     Returns:
         np.ndarray: indexes to keep.
     """
     if len(kpts_db) == 0:
         return []
 
-    scores = np.array([k['score'] for k in kpts_db])
+    if score_per_joint:
+        scores = np.array([k['score'].mean() for k in kpts_db])
+    else:
+        scores = np.array([k['score'] for k in kpts_db])
+
     kpts = np.array([k['keypoints'].flatten() for k in kpts_db])
     areas = np.array([k['area'] for k in kpts_db])
 
@@ -147,22 +152,32 @@ def _rescore(overlap, scores, thr, type='gaussian'):
     return scores
 
 
-def soft_oks_nms(kpts_db, thr, max_dets=20, sigmas=None, vis_thr=None):
+def soft_oks_nms(kpts_db,
+                 thr,
+                 max_dets=20,
+                 sigmas=None,
+                 vis_thr=None,
+                 score_per_joint=False):
     """Soft OKS NMS implementations.
 
     Args:
         kpts_db
         thr: retain oks overlap < thr.
         max_dets: max number of detections to keep.
         sigmas: Keypoint labelling uncertainty.
+        score_per_joint: the input scores (in kpts_db) are per joint scores
 
     Returns:
         np.ndarray: indexes to keep.
     """
     if len(kpts_db) == 0:
         return []
 
-    scores = np.array([k['score'] for k in kpts_db])
+    if score_per_joint:
+        scores = np.array([k['score'].mean() for k in kpts_db])
+    else:
+        scores = np.array([k['score'] for k in kpts_db])
+
     kpts = np.array([k['keypoints'].flatten() for k in kpts_db])
     areas = np.array([k['area'] for k in kpts_db])
 

tests/test_post_processing/test_group.py

@@ -32,9 +32,41 @@ def test_group():
     fake_tag[0, 8, 6, 6] = 0.9
     grouped, scores = parser.parse(fake_heatmap, fake_tag, True, True)
     assert grouped[0][0, 0, 0] == 10.25
+    assert abs(scores[0] - 0.2) < 0.001
     cfg['tag_per_joint'] = False
     parser = HeatmapParser(cfg)
     grouped, scores = parser.parse(fake_heatmap, fake_tag, False, False)
     assert grouped[0][0, 0, 0] == 10.
     grouped, scores = parser.parse(fake_heatmap, fake_tag, False, True)
     assert grouped[0][0, 0, 0] == 10.
+
+
+def test_group_score_per_joint():
+    cfg = {}
+    cfg['num_joints'] = 17
+    cfg['detection_threshold'] = 0.1
+    cfg['tag_threshold'] = 1
+    cfg['use_detection_val'] = True
+    cfg['ignore_too_much'] = False
+    cfg['nms_kernel'] = 5
+    cfg['nms_padding'] = 2
+    cfg['tag_per_joint'] = True
+    cfg['max_num_people'] = 1
+    cfg['score_per_joint'] = True
+    parser = HeatmapParser(cfg)
+    fake_heatmap = torch.zeros(1, 1, 5, 5)
+    fake_heatmap[0, 0, 3, 3] = 1
+    fake_heatmap[0, 0, 3, 2] = 0.8
+    assert parser.nms(fake_heatmap)[0, 0, 3, 2] == 0
+    fake_heatmap = torch.zeros(1, 17, 32, 32)
+    fake_tag = torch.zeros(1, 17, 32, 32, 1)
+    fake_heatmap[0, 0, 10, 10] = 0.8
+    fake_heatmap[0, 1, 12, 12] = 0.9
+    fake_heatmap[0, 4, 8, 8] = 0.8
+    fake_heatmap[0, 8, 6, 6] = 0.9
+    fake_tag[0, 0, 10, 10] = 0.8
+    fake_tag[0, 1, 12, 12] = 0.9
+    fake_tag[0, 4, 8, 8] = 0.8
+    fake_tag[0, 8, 6, 6] = 0.9
+    grouped, scores = parser.parse(fake_heatmap, fake_tag, True, True)
+    assert len(scores[0]) == 17

tests/test_post_processing/test_nms.py

@@ -29,6 +29,45 @@ def test_soft_oks_nms():
     keep = oks_nms([kpts[i] for i in range(len(kpts))], oks_thr)
     assert (keep == np.array([0, 2])).all()
 
+    kpts_with_score_joints = []
+    kpts_with_score_joints.append({
+        'keypoints':
+        np.tile(np.array([10, 10, 0.9]), [17, 1]),
+        'area':
+        100,
+        'score':
+        np.tile(np.array([0.9]), 17)
+    })
+    kpts_with_score_joints.append({
+        'keypoints':
+        np.tile(np.array([10, 10, 0.9]), [17, 1]),
+        'area':
+        100,
+        'score':
+        np.tile(np.array([0.4]), 17)
+    })
+    kpts_with_score_joints.append({
+        'keypoints':
+        np.tile(np.array([100, 100, 0.9]), [17, 1]),
+        'area':
+        100,
+        'score':
+        np.tile(np.array([0.7]), 17)
+    })
+    keep = soft_oks_nms([
+        kpts_with_score_joints[i] for i in range(len(kpts_with_score_joints))
+    ],
+                        oks_thr,
+                        score_per_joint=True)
+    assert (keep == np.array([0, 2, 1])).all()
+
+    keep = oks_nms([
+        kpts_with_score_joints[i] for i in range(len(kpts_with_score_joints))
+    ],
+                   oks_thr,
+                   score_per_joint=True)
+    assert (keep == np.array([0, 2])).all()
+
 
 def test_func_nms():
     result = nms(np.array([[0, 0, 10, 10, 0.9], [0, 0, 10, 8, 0.8]]), 0.5)