fix

core/trainers/single_trainer.py

@@ -110,11 +110,13 @@ class SingleTrainer(TranspileTrainer):
         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)
+            reader = dataloader_instance.dataloader_by_name(
+                reader_class, dataset_name, self._config_yaml)
             reader_class = envs.lazy_instance_by_fliename(reader_class, "TrainReader")
             reader_ins = reader_class(self._config_yaml)
         else:
-            reader = dataloader_instance.slotdataloader_by_name("", dataset_name, self._config_yaml)
+            reader = dataloader_instance.slotdataloader_by_name(
+                "", dataset_name, self._config_yaml)
             reader_ins = SlotReader(self._config_yaml)
         if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
             dataloader.set_sample_list_generator(reader)
@@ -122,7 +124,6 @@ class SingleTrainer(TranspileTrainer):
             dataloader.set_sample_generator(reader, batch_size)
         return dataloader
 
-
     def _create_dataset(self, dataset_name):
         name = "dataset." + dataset_name + "."
         sparse_slots = envs.get_global_env(name + "sparse_slots")
@@ -131,7 +132,8 @@ class SingleTrainer(TranspileTrainer):
         batch_size = envs.get_global_env(name + "batch_size")
         type_name = envs.get_global_env(name + "type")
         if envs.get_platform() != "LINUX":
-            print("platform ", envs.get_platform(), " change reader to DataLoader")
+            print("platform ", envs.get_platform(),
+                  " change reader to DataLoader")
             type_name = "DataLoader"
         padding = 0
 
@@ -140,7 +142,6 @@ class SingleTrainer(TranspileTrainer):
         else:
             return self._get_dataset(dataset_name)
 
-
     def init(self, context):
         for model_dict in self._env["executor"]:
             self._model[model_dict["name"]] = [None] * 5
@@ -187,7 +188,6 @@ class SingleTrainer(TranspileTrainer):
             self._model[model_dict["name"]][3] = model
             self._model[model_dict["name"]][4] = train_program.clone()
 
-
         for dataset in self._env["dataset"]:
             if dataset["type"] != "DataLoader":
                 self._dataset[dataset["name"]] = self._create_dataset(dataset[
@@ -268,7 +268,8 @@ class SingleTrainer(TranspileTrainer):
         program = self._model[model_name][0].clone()
         if not model_dict["is_infer"]:
             program = fluid.compiler.CompiledProgram(
-                program).with_data_parallel(loss_name=model_class.get_avg_cost().name)
+                program).with_data_parallel(
+                    loss_name=model_class.get_avg_cost().name)
         fetch_vars = []
         fetch_alias = []
         fetch_period = 20
@@ -333,8 +334,10 @@ class SingleTrainer(TranspileTrainer):
                 envs.get_global_env("epoch.save_inference_interval", -1))
             if not need_save(epoch_id, save_interval, False):
                 return
-            feed_varnames = envs.get_global_env("epoch.save_inference_feed_varnames", None)
-            fetch_varnames = envs.get_global_env("epoch.save_inference_fetch_varnames", None)
+            feed_varnames = envs.get_global_env(
+                "epoch.save_inference_feed_varnames", None)
+            fetch_varnames = envs.get_global_env(
+                "epoch.save_inference_fetch_varnames", None)
             if feed_varnames is None or fetch_varnames is None or feed_varnames == "":
                 return
             fetch_vars = [