[Enhance] Extract update loss

mmengine/runner/loops.py

@@ -401,21 +401,7 @@ def run_iter(self, idx, data_batch: Sequence[dict]):
         with autocast(enabled=self.fp16):
             outputs = self.runner.model.val_step(data_batch)
 
-        if isinstance(outputs[-1],
-                      BaseDataElement) and outputs[-1].keys() == ['loss']:
-            loss = outputs[-1].loss  # type: ignore
-            outputs = outputs[:-1]
-        else:
-            loss = dict()
-        # get val loss and avoid breaking change
-        for loss_name, loss_value in loss.items():
-            if loss_name not in self.val_loss:
-                self.val_loss[loss_name] = HistoryBuffer()
-            if isinstance(loss_value, torch.Tensor):
-                self.val_loss[loss_name].update(loss_value.item())
-            elif is_list_of(loss_value, torch.Tensor):
-                for loss_value_i in loss_value:
-                    self.val_loss[loss_name].update(loss_value_i.item())
+        outputs, self.val_loss = _update_losses(outputs, self.val_loss)
 
         self.evaluator.process(data_samples=outputs, data_batch=data_batch)
         self.runner.call_hook(
@@ -498,21 +484,7 @@ def run_iter(self, idx, data_batch: Sequence[dict]) -> None:
         with autocast(enabled=self.fp16):
             outputs = self.runner.model.test_step(data_batch)
 
-        if isinstance(outputs[-1],
-                      BaseDataElement) and outputs[-1].keys() == ['loss']:
-            loss = outputs[-1].loss  # type: ignore
-            outputs = outputs[:-1]
-        else:
-            loss = dict()
-        # get val loss and avoid breaking change
-        for loss_name, loss_value in loss.items():
-            if loss_name not in self.test_loss:
-                self.test_loss[loss_name] = HistoryBuffer()
-            if isinstance(loss_value, torch.Tensor):
-                self.test_loss[loss_name].update(loss_value.item())
-            elif is_list_of(loss_value, torch.Tensor):
-                for loss_value_i in loss_value:
-                    self.test_loss[loss_name].update(loss_value_i.item())
+        outputs, self.test_loss = _update_losses(outputs, self.test_loss)
 
         self.evaluator.process(data_samples=outputs, data_batch=data_batch)
         self.runner.call_hook(
@@ -545,3 +517,32 @@ def _parse_losses(losses: Dict[str, HistoryBuffer],
 
     loss_dict[f'{stage}_loss'] = all_loss
     return loss_dict
+
+
+def _update_losses(outputs: list, losses: dict) -> Tuple[list, dict]:
+    """Update and record the losses of the network.
+
+    Args:
+        outputs (list): The outputs of the network.
+        losses (dict): The losses of the network.
+
+    Returns:
+        list: The updated outputs of the network.
+        dict: The updated losses of the network.
+    """
+    if isinstance(outputs[-1],
+                  BaseDataElement) and outputs[-1].keys() == ['loss']:
+        loss = outputs[-1].loss  # type: ignore
+        outputs = outputs[:-1]
+    else:
+        loss = dict()
+
+    for loss_name, loss_value in loss.items():
+        if loss_name not in losses:
+            losses[loss_name] = HistoryBuffer()
+        if isinstance(loss_value, torch.Tensor):
+            losses[loss_name].update(loss_value.item())
+        elif is_list_of(loss_value, torch.Tensor):
+            for loss_value_i in loss_value:
+                losses[loss_name].update(loss_value_i.item())
+    return outputs, losses