Merge pull request #12989 from velconia/fix_last_of_py3

Fix random diff between python2 and python3

python/paddle/dataset/movielens.py

@@ -24,6 +24,7 @@ set and test set into paddle reader creators.
 
 from __future__ import print_function
 
+import numpy as np
 import zipfile
 import paddle.dataset.common
 import re
@@ -150,12 +151,12 @@ def __initialize_meta_info__():
 
 def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
     fn = __initialize_meta_info__()
-    rand = random.Random(x=rand_seed)
+    np.random.seed(rand_seed)
     with zipfile.ZipFile(file=fn) as package:
         with package.open('ml-1m/ratings.dat') as rating:
             for line in rating:
                 line = cpt.to_text(line, encoding='latin')
-                if (rand.random() < test_ratio) == is_test:
+                if (np.random.random() < test_ratio) == is_test:
                     uid, mov_id, rating, _ = line.strip().split("::")
                     uid = int(uid)
                     mov_id = int(mov_id)

python/paddle/fluid/layers/nn.py

@@ -17,6 +17,7 @@ All layers just related to the neural network.
 
 from __future__ import print_function
 
+import numpy as np
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
@@ -24,7 +25,6 @@ from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc
 from .tensor import concat
 from . import utils
-import random
 from .. import unique_name
 from functools import reduce
 
@@ -5102,7 +5102,7 @@ def random_crop(x, shape, seed=None):
     dtype = x.dtype
     out = helper.create_tmp_variable(dtype)
     if seed is None:
-        seed = random.randint(-65536, 65535)
+        seed = np.random.randint(-65536, 65536)
     op_attrs = {"shape": shape}
     if isinstance(seed, int):
         op_attrs["startup_seed"] = seed

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -31,7 +31,6 @@ Steps to transpile pserver:
 """
 
 import math
-import random
 import numpy as np
 import collections
 import six
@@ -239,8 +238,8 @@ class DistributeTranspiler(object):
         grad_var_mapping_items = list(six.iteritems(self.grad_var_mapping))
 
         if not self.config.slice_var_up:
-            random.seed(self.origin_program.random_seed)
-            random.shuffle(grad_var_mapping_items)
+            np.random.seed(self.origin_program.random_seed)
+            np.random.shuffle(grad_var_mapping_items)
 
         grad_name_to_send_dummy_out = dict()
         for grad_varname, splited_vars in grad_var_mapping_items: