Fix CI issues

python/paddle/dataset/movielens.py

@@ -27,6 +27,8 @@ import paddle.dataset.common
 import re
 import random
 import functools
+import six
+import paddle.fluid.compat as cpt
 
 __all__ = [
     'train', 'test', 'get_movie_title_dict', 'max_movie_id', 'max_user_id',
@@ -112,6 +114,7 @@ def __initialize_meta_info__():
                 categories_set = set()
                 with package.open('ml-1m/movies.dat') as movie_file:
                     for i, line in enumerate(movie_file):
+                        line = cpt.to_literal_str(line, encoding='latin')
                         movie_id, title, categories = line.strip().split('::')
                         categories = categories.split('|')
                         for c in categories:
@@ -136,6 +139,7 @@ def __initialize_meta_info__():
                 USER_INFO = dict()
                 with package.open('ml-1m/users.dat') as user_file:
                     for line in user_file:
+                        line = cpt.to_literal_str(line, encoding='latin')
                         uid, gender, age, job, _ = line.strip().split("::")
                         USER_INFO[int(uid)] = UserInfo(
                             index=uid, gender=gender, age=age, job_id=job)
@@ -148,6 +152,7 @@ def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
     with zipfile.ZipFile(file=fn) as package:
         with package.open('ml-1m/ratings.dat') as rating:
             for line in rating:
+                line = cpt.to_literal_str(line, encoding='latin')
                 if (rand.random() < test_ratio) == is_test:
                     uid, mov_id, rating, _ = line.strip().split("::")
                     uid = int(uid)
@@ -187,7 +192,7 @@ def max_movie_id():
     Get the maximum value of movie id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, list(MOVIE_INFO.values())).index
+    return six.moves.reduce(__max_index_info__, list(MOVIE_INFO.values())).index
 
 
 def max_user_id():
@@ -195,7 +200,7 @@ def max_user_id():
     Get the maximum value of user id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, list(USER_INFO.values())).index
+    return six.moves.reduce(__max_index_info__, list(USER_INFO.values())).index
 
 
 def __max_job_id_impl__(a, b):
@@ -210,7 +215,7 @@ def max_job_id():
     Get the maximum value of job id.
     """
     __initialize_meta_info__()
-    return reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
+    return six.moves.reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
 
 
 def movie_categories():

python/paddle/dataset/wmt16.py

@@ -62,7 +62,7 @@ def __build_dict(tar_file, dict_size, save_path, lang):
     word_dict = defaultdict(int)
     with tarfile.open(tar_file, mode="r") as f:
         for line in f.extractfile("wmt16/train"):
-            line_split = line.strip().split("\t")
+            line_split = line.strip().split(six.b("\t"))
             if len(line_split) != 2: continue
             sen = line_split[0] if lang == "en" else line_split[1]
             for w in sen.split():

python/paddle/fluid/compat.py

@@ -13,39 +13,40 @@
 # limitations under the License.
 
 import six
+import math
 
 
 #  str and bytes related functions
-def to_literal_str(obj):
+def to_literal_str(obj, encoding='utf-8'):
     if isinstance(obj, list):
-        return [_to_literal_str(item) for item in obj]
+        return [_to_literal_str(item, encoding) for item in obj]
     elif isinstance(obj, set):
-        return set([_to_literal_str(item) for item in obj])
+        return set([_to_literal_str(item, encoding) for item in obj])
     else:
-        return _to_literal_str(obj)
+        return _to_literal_str(obj, encoding)
 
 
-def _to_literal_str(obj):
+def _to_literal_str(obj, encoding):
     if isinstance(obj, six.binary_type):
-        return obj.decode('utf-8')
+        return obj.decode(encoding)
     elif isinstance(obj, six.text_type):
         return obj
     else:
         return six.u(obj)
 
 
-def to_bytes(obj):
+def to_bytes(obj, encoding='utf-8'):
     if isinstance(obj, list):
-        return [_to_bytes(item) for item in obj]
+        return [_to_bytes(item, encoding) for item in obj]
     elif isinstance(obj, set):
-        return set([_to_bytes(item) for item in obj])
+        return set([_to_bytes(item, encoding) for item in obj])
     else:
-        return _to_bytes(obj)
+        return _to_bytes(obj, encoding)
 
 
-def _to_bytes(obj):
+def _to_bytes(obj, encoding):
     if isinstance(obj, six.text_type):
-        return obj.encode('utf-8')
+        return obj.encode(encoding)
     elif isinstance(obj, six.binary_type):
         return obj
     else:
@@ -53,9 +54,6 @@ def _to_bytes(obj):
 
 
 # math related functions
-import math
-
-
 def round(x, d=0):
     """
     Compatible round which act the same behaviour in Python3.

python/paddle/fluid/op.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import numpy as np
 import six
 
 import paddle.fluid.core as core
@@ -99,6 +100,8 @@ class OpDescCreationMethod(object):
                 new_attr = op_desc.attrs.add()
                 new_attr.name = attr.name
                 new_attr.type = attr.type
+                if isinstance(user_defined_attr, np.ndarray):
+                    user_defined_attr = user_defined_attr.tolist()
                 if attr.type == framework_pb2.INT:
                     new_attr.i = user_defined_attr
                 elif attr.type == framework_pb2.FLOAT:

python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py

@@ -25,7 +25,7 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
     groups = attrs['groups']
     assert in_c == f_c
     out_c = f_out_c * groups
-    sub_in_c = in_c / groups
+    sub_in_c = in_c // groups
 
     stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
         'dilations']

python/paddle/fluid/tests/unittests/test_maxout_op.py

@@ -19,7 +19,7 @@ from op_test import OpTest
 
 def maxout_forward_naive(input, groups):
     s0, s1, s2, s3 = input.shape
-    return np.ndarray([s0, s1 / groups, groups, s2, s3], \
+    return np.ndarray([s0, s1 // groups, groups, s2, s3], \
         buffer = input, dtype=input.dtype).max(axis=(2))