code style

models/recall/fasttext/model.py

@@ -130,8 +130,8 @@ class Model(ModelBase):
 
         loss = fluid.layers.log_loss(fluid.layers.sigmoid(logits), label)
         avg_cost = fluid.layers.reduce_sum(loss)
-        
-	global_right_cnt = fluid.layers.create_global_var(
+
+        global_right_cnt = fluid.layers.create_global_var(
             name="global_right_cnt",
             persistable=True,
             dtype='float32',
@@ -144,7 +144,7 @@ class Model(ModelBase):
             shape=[1],
             value=0)
         global_right_cnt.stop_gradient = True
-        global_total_cnt.stop_gradient = True 
+        global_total_cnt.stop_gradient = True
         self._cost = avg_cost
         self._metrics["LOSS"] = avg_cost
 
@@ -211,7 +211,7 @@ class Model(ModelBase):
 
         tmp1 = fluid.layers.elementwise_add(right_cnt, global_right_cnt)
         fluid.layers.assign(tmp1, global_right_cnt)
-        
+
         tmp2 = fluid.layers.elementwise_add(total_cnt, global_total_cnt)
         fluid.layers.assign(tmp2, global_total_cnt)
 

models/recall/fasttext/preprocess.py

@@ -50,8 +50,8 @@ def parse_args():
         default='./word_ngrams',
         help="The path of word_ngrams ")
     parser.add_argument(
-	'--ngrams_id_path',
-	type=str,
+        '--ngrams_id_path',
+        type=str,
         default='./word_ngrams_id',
         help="The path of word_ngrams_id ")
     parser.add_argument(
@@ -157,8 +157,7 @@ def filter_corpus(args):
 
     # write word2id file
     print("write word2id file to : " + args.dict_path + "_word_to_id_")
-    with io.open(
-            args.word_id_path, 'w+', encoding='utf-8') as fid:
+    with io.open(args.word_id_path, 'w+', encoding='utf-8') as fid:
         for k, v in word_to_id_.items():
             fid.write(k + " " + str(v) + '\n')
     # filter corpus and convert id