Adjust NMS time limit warning to batch size (#7156)

utils/general.py

@@ -709,6 +709,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
          list of detections, on (n,6) tensor per image [xyxy, conf, cls]
     """
 
+    bs = prediction.shape[0]  # batch size
     nc = prediction.shape[2] - 5  # number of classes
     xc = prediction[..., 4] > conf_thres  # candidates
 
@@ -719,13 +720,13 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
     # Settings
     min_wh, max_wh = 2, 7680  # (pixels) minimum and maximum box width and height
     max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 10.0  # seconds to quit after
+    time_limit = 0.030 * bs  # seconds to quit after
     redundant = True  # require redundant detections
     multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
     merge = False  # use merge-NMS
 
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
+    t, warn_time = time.time(), True
+    output = [torch.zeros((0, 6), device=prediction.device)] * bs
     for xi, x in enumerate(prediction):  # image index, image inference
         # Apply constraints
         x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
@@ -789,7 +790,9 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
 
         output[xi] = x[i]
         if (time.time() - t) > time_limit:
-            LOGGER.warning(f'WARNING: NMS time limit {time_limit}s exceeded')
+            if warn_time:
+                LOGGER.warning(f'WARNING: NMS time limit {time_limit:3f}s exceeded')
+                warn_time = False
             break  # time limit exceeded
 
     return output