Reuse de_parallel() rather than is_parallel() (#6354)

utils/loss.py

@@ -7,7 +7,7 @@ import torch
 import torch.nn as nn
 
 from utils.metrics import bbox_iou
-from utils.torch_utils import is_parallel
+from utils.torch_utils import de_parallel
 
 
 def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
@@ -107,7 +107,7 @@ class ComputeLoss:
         if g > 0:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
+        det = de_parallel(model).model[-1]  # Detect() module
         self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
         self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
         self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance