Fix some issues.

Author: wanghaoshuang

python/paddle/fluid/framework.py

@@ -1155,7 +1155,7 @@ def __init__(self, block, shape, dtype, **kwargs):
 
         self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
 
-        self.average = kwargs.get('average', True)
+        self.do_model_average = kwargs.get('do_model_average', None)
 
     def __str__(self):
         return self.to_string(True)
@@ -1177,7 +1177,7 @@ def to_string(self, throw_on_error, with_details=False):
         if with_details:
             res_str = Variable.to_string(self, throw_on_error, True)
             additional_attr = ("trainable", "optimize_attr", "regularizer",
-                               "gradient_clip_attr", "average")
+                               "gradient_clip_attr", "do_model_average")
             for attr_name in additional_attr:
                 res_str += "%s: %s\n" % (attr_name,
                                          str(getattr(self, attr_name)))

python/paddle/fluid/layers/nn.py

@@ -1489,8 +1489,7 @@ def batch_norm(input,
                name=None,
                moving_mean_name=None,
                moving_variance_name=None,
-               average_mean=True,
-               average_variance=True):
+               do_model_average_for_mean_and_var=False):
     """
     This function helps create an operator to implement
     the BatchNorm layer using the configurations from the input parameters.
@@ -1519,12 +1518,15 @@ def batch_norm(input,
     bias = helper.create_parameter(
         attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
 
+    if do_model_average_for_mean_and_var:
+        do_model_average_for_mean_and_var = None
+
     mean = helper.create_parameter(
         attr=ParamAttr(
             name=moving_mean_name,
             initializer=Constant(0.0),
             trainable=False,
-            average=average_variance),
+            do_model_average=do_model_average_for_mean_and_var),
         shape=param_shape,
         dtype=input.dtype)
     mean.stop_gradient = True
@@ -1534,7 +1536,7 @@ def batch_norm(input,
             name=moving_variance_name,
             initializer=Constant(1.0),
             trainable=False,
-            average=average_mean),
+            do_model_average=do_model_average_for_mean_and_var),
         shape=param_shape,
         dtype=input.dtype)
     variance.stop_gradient = True
@@ -3352,14 +3354,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
     Here are some examples to explain it.
 
     1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
-    is [6, 8], the reshape operator will transform x into a 2-D tensor with 
+    is [6, 8], the reshape operator will transform x into a 2-D tensor with
     shape [6, 8] and leaving x's data unchanged.
 
     2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
     specified is [2, 3, -1, 2], the reshape operator will transform x into a
     4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
-    case, one dimension of the target shape is set to -1, the value of this 
-    dimension is inferred from the total element number of x and remaining 
+    case, one dimension of the target shape is set to -1, the value of this
+    dimension is inferred from the total element number of x and remaining
     dimensions.
 
     3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
@@ -3593,7 +3595,7 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
 def pad(x, paddings, pad_value=0., name=None):
     """
     Pads a tensor with a constant value given by :attr:`pad_value`, and the
-    padded width is specified by :attr:`paddings`. 
+    padded width is specified by :attr:`paddings`.
 
     Specifically, the number of values padded before the contents of :attr:`x`
     in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
@@ -3621,7 +3623,7 @@ def pad(x, paddings, pad_value=0., name=None):
         x (Variable): The input tensor variable.
         paddings (list): A list of integers. Its elements specify the padded
                          width before and after for each dimension in turn.
-                         The length of :attr:paddings must be 
+                         The length of :attr:paddings must be
                          :math:`rank(x) \\times 2`.
         pad_value (float): The constant value used to pad.
         name(str|None): A name for this layer(optional). If set None, the layer

python/paddle/fluid/optimizer.py

@@ -840,7 +840,7 @@ class ModelAverage(Optimizer):
     """
 
     def __init__(self,
-                 average_window_rate=0.15,
+                 average_window_rate,
                  params_grads=None,
                  min_average_window=10000,
                  max_average_window=10000,
@@ -856,7 +856,7 @@ def __init__(self,
             params[param.name] = (param, grad)
         for param in framework.default_main_program().global_block(
         ).all_parameters():
-            if param.name not in params and param.average:
+            if param.name not in params and param.do_model_average != False:
                 grad = param.block.create_var(
                     name=unique_name.generate(".".join([param.name, 'tmp'])),
                     dtype=param.dtype,

python/paddle/fluid/param_attr.py

@@ -29,14 +29,14 @@ def __init__(self,
                  regularizer=None,
                  trainable=True,
                  gradient_clip=None,
-                 average=True):
+                 do_model_average=None):
         self.name = name
         self.initializer = initializer
         self.learning_rate = learning_rate
         self.regularizer = regularizer
         self.trainable = trainable
         self.gradient_clip = gradient_clip
-        self.average = average
+        self.model_average = do_model_average
 
     def set_default_initializer(self, initializer):
         if initializer is None:
@@ -83,7 +83,7 @@ def to_kwargs(self, with_initializer=False):
             'regularizer': self.regularizer,
             'trainable': self.trainable,
             'gradient_clip_attr': self.gradient_clip,
-            'average': self.average
+            'model_average': self.model_average
         }
         if with_initializer:
             kwargs['initializer'] = self.initializer