dbg

ppcls/engine/trainer.py

@@ -96,15 +96,14 @@ class Trainer(object):
     def train(self):
         # build train loss and metric info
         if self.train_loss_func is None:
-            loss_info = self.config.get("Loss", None)
-            if loss_info is not None:
-                loss_info = loss_info["Train"]
+            loss_info = self.config["Loss"]["Train"]
             self.train_loss_func = build_loss(loss_info)
         if self.train_metric_func is None:
-            metric_config = self.config.get("Metric", None)
+            metric_config = self.config.get("Metric")
             if metric_config is not None:
-                metric_config = metric_config["Train"]
-                self.train_metric_func = build_metrics(metric_config)
+                metric_config = metric_config.get("Train")
+                if metric_config is not None:
+                    self.train_metric_func = build_metrics(metric_config)
 
         if self.train_dataloader is None:
             self.train_dataloader = build_dataloader(self.config["DataLoader"],
@@ -223,20 +222,22 @@ class Trainer(object):
     def eval(self, epoch_id=0):
         self.model.eval()
         if self.eval_loss_func is None:
-            loss_info = self.config.get("Loss", None)
-            if loss_info is not None:
-                loss_info = loss_info["Eval"]
-                self.eval_loss_func = build_loss(loss_info)
+            loss_config = self.config.get("Loss", None)
+            if loss_config is not None:
+                loss_config = loss_config.get("Eval")
+                if loss_config is not None:
+                    self.eval_loss_func = build_loss(loss_config)
         if self.eval_mode == "classification":
             if self.eval_dataloader is None:
                 self.eval_dataloader = build_dataloader(
                     self.config["DataLoader"], "Eval", self.device)
 
             if self.eval_metric_func is None:
-                metric_config = self.config.get("Metric", None)
+                metric_config = self.config.get("Metric")
                 if metric_config is not None:
-                    metric_config = metric_config["Eval"]
-                    self.eval_metric_func = build_metrics(metric_config)
+                    metric_config = metric_config.get("Eval")
+                    if metric_config is not None:
+                        self.eval_metric_func = build_metrics(metric_config)
 
             eval_result = self.eval_cls(epoch_id)