rewrite the comments, test=develop

paddle/fluid/operators/shuffle_channel_op.cc

@@ -55,17 +55,12 @@ class ShuffleChannelOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddComment(R"DOC(
 		Shuffle Channel operator
-		This operator obtains the group convolutional layer with channels shuffled.
+		This opearator shuffles the channels of input x.
-		Firstly, divide the input channels in each group into several subgroups,
+		It  divide the input channels in each group into several subgroups,
-		then, feed each group in the next layer with different subgroups.
+		and obtain a new order by selecting element from every subgroup one by one.
-
-		According to the paper, "Suppose a convolution layer with G groups
-		whose output has (G * N) channels, first reshape the output channel dimension into(G,N),
-		transposing and then flattening it back as the input of next layer. "
 
 		Shuffle channel operation makes it possible to build more powerful structures
 		with multiple group convolutional layers.
-
 		please get more information from the following paper:
 		https://arxiv.org/pdf/1707.01083.pdf
         )DOC");

python/paddle/fluid/layers/nn.py

@@ -9338,27 +9338,57 @@ def get_tensor_from_selected_rows(x, name=None):
 def shuffle_channel(x, group, name=None):
     """
     **Shuffle Channel Operator**
-    This operator obtains the group convolutional layer with channels shuffled.
+    This operator shuffles the channels of input x.
-    First, divide the input channels in each group into several subgroups,
+    It divide the input channels in each group into :attr:`group` subgroups,
-    then, feed each group in the next layer with different subgroups.
+    and obtain a new order by selecting element from every subgroup one by one.
-    Channel shuffling operation makes it possible to build more powerful structures
+
-    with multiple group convolutional layers.
+    Please refer to the paper
+    https://arxiv.org/pdf/1707.01083.pdf
     
+    .. code-block:: text
+        Given a 4-D tensor input with the shape (N, C, H, W):
+            input.shape = (1, 4, 2, 2)
+            input.data =[[[[0.1, 0.2],
+                           [0.2, 0.3]],
+
+                          [[0.3, 0.4],
+                           [0.4, 0.5]],
+
+                          [[0.5, 0.6],
+                           [0.6, 0.7]],
+
+                          [[0.7, 0.8],
+                           [0.8, 0.9]]]]
+            Given group: 2
+            then we get a 4-D tensor out whth the same shape of input:
+            out.shape = (1, 4, 2, 2)
+            out.data = [[[[0.1, 0.2],
+                          [0.2, 0.3]],
+                          
+                         [[0.5, 0.6],
+                          [0.6, 0.7]],
+                          
+                         [[0.3, 0.4],
+                          [0.4, 0.5]],
+                          
+                         [[0.7, 0.8],
+                          [0.8, 0.9]]]]
+                        
     Args: 
-        x(Variable): The input tensor variable.
+        x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W]
-        group(Integer): The num of group.
+        group(int): Indicating the conuts of subgroups, It should divide the number of channels.
 
     Returns:
-        Variable: channels shuffled tensor variable.
+        out(Variable): the channels shuffling result is a tensor variable with the 
+        same shape and same type as the input.
 
     Raises:
         ValueError: If group is not an int type variable.
 
     Examples:
         .. code-block:: python
-
+            input = fluid.layers.data(name='input', shape=[1,4,2,2], dtype='float32')
-        out = fluid.layers.shuffle_channel(x=group_conv,group=4)
+            out = fluid.layers.shuffle_channel(x=input, group=2)
-
     """
     helper = LayerHelper("shuffle_channel", **locals())