updates

train.py

@@ -288,7 +288,7 @@ def train():
             else:
                 loss.backward()
 
-            # Accumulate gradient for x batches before optimizing
+            # Optimize accumulated gradient
             if ni % accumulate == 0:
                 optimizer.step()
                 optimizer.zero_grad()
@@ -301,6 +301,9 @@ def train():
 
             # end batch ------------------------------------------------------------------------------------------------
 
+        # Update scheduler
+        scheduler.step()
+
         # Process epoch results
         final_epoch = epoch + 1 == epochs
         if not opt.notest or final_epoch:  # Calculate mAP
@@ -316,9 +319,6 @@ def train():
                                       single_cls=opt.single_cls,
                                       dataloader=testloader)
 
-        # Update scheduler
-        scheduler.step()
-
         # Write epoch results
         with open(results_file, 'a') as f:
             f.write(s + '%10.3g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)