1.优化Trainer中对exception的处理；2.修改static_embedding贴合seq2seq

fastNLP/core/trainer.py

@@ -617,6 +617,14 @@ class Trainer(object):
                 elif on_exception == 'raise':
                     raise e
 
+            if self.dev_data is not None and self.best_dev_perf is not None and load_best_model:
+                model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
+                load_succeed = self._load_model(self.model, model_name)
+                if load_succeed:
+                    self.logger.info("Reloaded the best model.")
+                else:
+                    self.logger.info("Fail to reload best model.")
+        finally:
             if self.dev_data is not None and self.best_dev_perf is not None:
                 self.logger.info(
                     "\nIn Epoch:{}/Step:{}, got best dev performance:".format(self.best_dev_epoch, self.best_dev_step))
@@ -624,15 +632,7 @@ class Trainer(object):
                 results['best_eval'] = self.best_dev_perf
                 results['best_epoch'] = self.best_dev_epoch
                 results['best_step'] = self.best_dev_step
-                if load_best_model:
-                    model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
-                    load_succeed = self._load_model(self.model, model_name)
-                    if load_succeed:
-                        self.logger.info("Reloaded the best model.")
-                    else:
-                        self.logger.info("Fail to reload best model.")
-        finally:
-            pass
+
         results['seconds'] = round(time.time() - start_time, 2)
 
         return results

fastNLP/embeddings/static_embedding.py

@@ -76,6 +76,10 @@ class StaticEmbedding(TokenEmbedding):
         """
         super(StaticEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
         if embedding_dim > 0:
+            if model_dir_or_name is not None:
+                warnings.warn(f"StaticEmbedding will ignore `model_dir_or_name`, and randomly initialize embedding with"
+                              f" dimension {embedding_dim}. If you want to use pre-trained embedding, "
+                              f"set `embedding_dim` to 0.")
             model_dir_or_name = None
         
         # 得到cache_path