be compatible with Python3

ppcls/modeling/utils.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
 #    http://www.apache.org/licenses/LICENSE-2.0
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
+import six
 import types
 from difflib import SequenceMatcher
 
@@ -24,7 +25,7 @@ def get_architectures():
     """
     names = []
     for k, v in architectures.__dict__.items():
-        if isinstance(v, (types.FunctionType, types.ClassType)):
+        if isinstance(v, (types.FunctionType, six.class_types)):
             names.append(k)
     return names
 
@@ -35,9 +36,11 @@ def similar_architectures(name='', names=[], thresh=0.1, topk=10):
     """
     scores = []
     for idx, n in enumerate(names):
-        if n[:2] == '__': continue
+        if n.startswith('__'):
+            continue
         score = SequenceMatcher(None, n.lower(), name.lower()).quick_ratio()
-        if score > thresh: scores.append((idx, score))
+        if score > thresh:
+            scores.append((idx, score))
     scores.sort(key=lambda x: x[1], reverse=True)
     similar_names = [names[s[0]] for s in scores[:min(topk, len(scores))]]
     return similar_names