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

utils/torch_utils.py

@@ -295,7 +295,7 @@ class ModelEMA:
 
     def __init__(self, model, decay=0.9999, updates=0):
         # Create EMA
-        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
         # if next(model.parameters()).device.type != 'cpu':
         #     self.ema.half()  # FP16 EMA
         self.updates = updates  # number of EMA updates
@@ -309,7 +309,7 @@ class ModelEMA:
             self.updates += 1
             d = self.decay(self.updates)
 
-            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
+            msd = de_parallel(model).state_dict()  # model state_dict
             for k, v in self.ema.state_dict().items():
                 if v.dtype.is_floating_point:
                     v *= d