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
@@ -5416,7 +5416,7 @@ def prelu(x, mode, param_attr=None, name=None):
  		       channel:elements in a channel share same weight
  		       element:each element has a weight
 	  name(str|None): A name for this layer(optional). If set None, the layer
-                        will be named automatically. 
+                        will be named automatically.
 
     Returns:
         Variable: The output tensor with the same shape as input.
@@ -5530,23 +5530,23 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
 
     Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
     :code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
-    
+
     .. math::
-     
+
         y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))
 
     Args:
-        x (Variable): Input tensor of sequence_mask layer, 
+        x (Variable): Input tensor of sequence_mask layer,
                       whose elements are integers less than :code:`maxlen`.
         maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
                            is None, it would be replace with :math:`max(x)`.
         dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
-        name (str|None): A name for this layer(optional). If set None, the 
-                         layer will be named automatically.  
-    
+        name (str|None): A name for this layer(optional). If set None, the
+                         layer will be named automatically.
+
     Returns:
         Variable: The output sequence mask.
-    
+
     """
 
     helper = LayerHelper('sequence_mask', **locals())
@@ -5571,23 +5571,23 @@ def stack(x, axis=0):
     **Stack Layer**
 
     This layer stacks all of the input :code:`x` along axis.
-   
-    Input :code:`x` can be a single variable, a :code:`list` of variables, 
-    or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or 
-    :code:`tuple`, the shapes of all these variables must be the same.  
-    Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`, 
-    the shape of the output variable would be 
-    :math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`. 
+
+    Input :code:`x` can be a single variable, a :code:`list` of variables,
+    or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or
+    :code:`tuple`, the shapes of all these variables must be the same.
+    Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`,
+    the shape of the output variable would be
+    :math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`.
     If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
-    If :code:`axis` is None, it would be replaced with 0. 
+    If :code:`axis` is None, it would be replaced with 0.
 
     Args:
-        x (Variable|list(Variable)|tuple(Variable)): Input variables. 
+        x (Variable|list(Variable)|tuple(Variable)): Input variables.
         axis (int|None): The axis along which all inputs are stacked.
-    
+
     Returns:
         Variable: The stacked variable.
-    
+
     """
 
     helper = LayerHelper('stack', **locals())

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: