update API.spec, test=develop

paddle/fluid/operators/shuffle_channel_op.cc

@@ -27,8 +27,6 @@ class ShuffleChannelOp : public framework::OperatorWithKernel {
     auto input_dims = ctx->GetInputDim("X");
     PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
 
-    // ENFORCE group
-
     ctx->SetOutputDim("Out", input_dims);
   }
   /*
@@ -60,11 +58,11 @@ class ShuffleChannelOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 		Shuffle Channel operator
 		This operator obtains the group convolutional layer with channels shuffled.
-		First, divide the input channels in each group into several subgroups,
+		Firstly, divide the input channels in each group into several subgroups,
 		then, feed each group in the next layer with different subgroups.
 
-		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),
+		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
@@ -89,8 +87,6 @@ class ShuffleChannelGradOp : public framework::OperatorWithKernel {
     auto input_dims = ctx->GetInputDim("X");
     PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
 
-    // ENFORCE group
-
     ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
   }
   /*
@@ -112,7 +108,6 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(shuffle_channel, ops::ShuffleChannelOp,
                   ops::ShuffleChannelOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-//     paddle::framework::EmptyGradOpMaker);
 
 REGISTER_OPERATOR(shuffle_channel_grad, ops::ShuffleChannelGradOp);
 

paddle/fluid/operators/shuffle_channel_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -26,7 +26,6 @@ static inline int NumBlocks(const int N) {
 }
 
 template <typename T>
-
 __global__ void ShuffleChannel(const int nthreads, const int feature_map_size,
                                T* output, const T* input, int group_row,
                                int group_column, int len) {

paddle/fluid/operators/shuffle_channel_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -76,7 +76,6 @@ class ShuffleChannelGradOpKernel : public framework::OpKernel<T> {
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
     auto* input_grad =
         ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-
     T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
     const T* output_grad_data = output_grad->data<T>();
     for (int n = 0; n < num; ++n) {