Scipy kmeans-robust autoanchor update (#2470)

Fix for ultralytics/yolov5#2394

utils/autoanchor.py

@@ -37,17 +37,21 @@ def metric(k):  # compute metric
         bpr = (best > 1. / thr).float().mean()  # best possible recall
         return bpr, aat
 
-    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
+    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
+    bpr, aat = metric(anchors)
     print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
     if bpr < 0.98:  # threshold to recompute
         print('. Attempting to improve anchors, please wait...')
         na = m.anchor_grid.numel() // 2  # number of anchors
-        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
+        try:
+            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        except Exception as e:
+            print(f'{prefix}ERROR: {e}')
+        new_bpr = metric(anchors)[0]
         if new_bpr > bpr:  # replace anchors
-            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
-            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
             check_anchor_order(m)
             print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
         else:
@@ -119,6 +123,7 @@ def print_results(k):
     print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
     s = wh.std(0)  # sigmas for whitening
     k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
     k *= s
     wh = torch.tensor(wh, dtype=torch.float32)  # filtered
     wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered