Resolve precommit utils/segment/general

utils/segment/general.py

@@ -5,7 +5,7 @@
 import torch.nn.functional as F
 import torchvision
 
-from ..general import xywh2xyxy
+from ..general import LOGGER, xywh2xyxy
 from ..metrics import box_iou
 
 
@@ -53,11 +53,11 @@ def non_max_suppression_masks(
 
         # Cat apriori labels if autolabelling
         if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
+            lb = labels[xi]
+            v = torch.zeros((len(lb), nc + 5), device=x.device)
+            v[:, :4] = lb[:, 1:5]  # box
             v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
             x = torch.cat((x, v), 0)
 
         # If none remain process next image
@@ -101,7 +101,7 @@ def non_max_suppression_masks(
         i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
         if i.shape[0] > max_det:  # limit detections
             i = i[:max_det]
-        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
+        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
             # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
             iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
             weights = iou * scores[None]  # box weights
@@ -111,7 +111,7 @@ def non_max_suppression_masks(
 
         output[xi] = x[i]
         if (time.time() - t) > time_limit:
-            print(f"WARNING: NMS time limit {time_limit}s exceeded")
+            LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
             break  # time limit exceeded
 
     return output