diff --git a/core/trainers/single_trainer.py b/core/trainers/single_trainer.py
index 3ddc48e25a049a101a7f4431d7a18573c5d620b3..c4a5fed2ad7eebe505580452c86907f6951bcba7 100755
--- a/core/trainers/single_trainer.py
+++ b/core/trainers/single_trainer.py
@@ -64,12 +64,12 @@ class SingleTrainer(TranspileTrainer):
         dense_slots = envs.get_global_env(name + "dense_slots")
         thread_num = envs.get_global_env(name + "thread_num")
         batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env("data_convertor")
+        reader_class = envs.get_global_env(name + "data_converter")
         abs_dir = os.path.dirname(os.path.abspath(__file__))
         reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
 
         if sparse_slots is None and dense_slots is None:
-            pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "fake", self._config_yaml)
+            pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config_yaml)
         else:
             if sparse_slots is None:
                 sparse_slots = "#"
@@ -103,7 +103,7 @@ class SingleTrainer(TranspileTrainer):
         dense_slots = envs.get_global_env(name + "dense_slots")
         thread_num = envs.get_global_env(name + "thread_num")
         batch_size = envs.get_global_env(name + "batch_size")
-        reader_class = envs.get_global_env("data_convertor")
+        reader_class = envs.get_global_env(name + "data_converter")
         abs_dir = os.path.dirname(os.path.abspath(__file__))
         if sparse_slots is None and dense_slots is None:
             reader = dataloader_instance.dataloader_by_name(reader_class, dataset_name, self._config_yaml)
@@ -156,7 +156,7 @@ class SingleTrainer(TranspileTrainer):
                         if envs.get_global_env("dataset." + dataset_name + ".type") == "DataLoader":
                             model._init_dataloader()
                             self._get_dataloader(dataset_name, model._data_loader)
-                        model.net(model._data_var, is_infer=model_dict["is_infer"])
+                        model.net(model._data_var, is_infer=model_dict.get("is_infer", False))
                         optimizer = model._build_optimizer(opt_name, opt_lr, opt_strategy)
                         optimizer.minimize(model._cost)
             self._model[model_dict["name"]][0] = train_program