add grad norm

yolo/engine.py

@@ -214,13 +214,13 @@ class YoloTrainer(object):
 
             # Backward
             self.scaler.scale(losses).backward()
-            grad_norm = None
+            gnorm = None
 
             # Optimize
             if (iter_i + 1) % self.grad_accumulate == 0:
                 if self.cfg.clip_max_norm > 0:
                     self.scaler.unscale_(self.optimizer)
-                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.cfg.clip_max_norm)
+                    gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.cfg.clip_max_norm)
                 self.scaler.step(self.optimizer)
                 self.scaler.update()
                 self.optimizer.zero_grad()
@@ -233,7 +233,7 @@ class YoloTrainer(object):
             metric_logger.update(**loss_dict_reduced)
             metric_logger.update(lr=self.optimizer.param_groups[2]["lr"])
             metric_logger.update(size=img_size)
-            metric_logger.update(grad_norm=grad_norm)
+            metric_logger.update(gnorm=gnorm)
 
             if self.args.debug:
                 print("For debug mode, we only train 1 iteration")