diff --git a/ppocr/data/det/dataset_traversal.py b/ppocr/data/det/dataset_traversal.py
index 92b9aac4f541a754fc218761e28439ea2134a502..c4ad5ced4f0240fa776afce8b7bf3e34310bd186 100644
--- a/ppocr/data/det/dataset_traversal.py
+++ b/ppocr/data/det/dataset_traversal.py
@@ -36,17 +36,17 @@ class TrainReader(object):
             "absence process_function in Reader"
         self.process = create_module(params['process_function'])(params)
 
-    def __call__(self, process_id):
+    def __call__(self, process_id):     
+        with open(self.label_file_path, "rb") as fin:
+            label_infor_list = fin.readlines()
+        img_num = len(label_infor_list)
+        img_id_list = list(range(img_num))
+        if sys.platform == "win32" and self.num_workers != 1:
+            print("multiprocess is not fully compatible with Windows."
+                  "num_workers will be 1.")
+            self.num_workers = 1
         def sample_iter_reader():
-            with open(self.label_file_path, "rb") as fin:
-                label_infor_list = fin.readlines()
-            img_num = len(label_infor_list)
-            img_id_list = list(range(img_num))
             random.shuffle(img_id_list)
-            if sys.platform == "win32" and self.num_workers != 1:
-                print("multiprocess is not fully compatible with Windows."
-                      "num_workers will be 1.")
-                self.num_workers = 1
             for img_id in range(process_id, img_num, self.num_workers):
                 label_infor = label_infor_list[img_id_list[img_id]]
                 outs = self.process(label_infor)
diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py
index 4d23f62656f5561dd93f40fa97a3f7874e4b2040..74a200f5e5661ecfe1409290871a931bdf18e99d 100755
--- a/ppocr/utils/save_load.py
+++ b/ppocr/utils/save_load.py
@@ -21,7 +21,6 @@ import os
 import shutil
 import tempfile
 
-import paddle
 import paddle.fluid as fluid
 
 from .utility import initial_logger
@@ -113,14 +112,12 @@ def init_model(config, program, exe):
         path = checkpoints
         fluid.load(program, path, exe)
         logger.info("Finish initing model from {}".format(path))
-        return
 
     pretrain_weights = config['Global'].get('pretrain_weights')
     if pretrain_weights:
         path = pretrain_weights
         load_params(exe, program, path)
         logger.info("Finish initing model from {}".format(path))
-    return
 
 
 def save_model(program, model_path):