conv shift op: change to CamelCase

paddle/operators/conv_shift_op.cu

@@ -22,7 +22,7 @@ using framework::Tensor;
 
 namespace {
 
-inline int div_up(int x, int y) { return (x + y - 1) / y; }
+inline int DivUp(int x, int y) { return (x + y - 1) / y; }
 
 // Some notes on the design:
 //
@@ -33,7 +33,7 @@ inline int div_up(int x, int y) { return (x + y - 1) / y; }
 // y is fairly small. For large y, it would probably be more efficient
 // to also tile across y.
 template <typename T>
-__global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
+__global__ void ConvShiftForward(const T *x, const T *y, T *out, int x_width,
                                  int y_width, int y_half_width,
                                  int batch_size) {
   extern __shared__ T mem[];
@@ -79,7 +79,7 @@ __global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
 
 // Compute x gradient - initial naive implementation with atomic add.
 template <typename T>
-__global__ void conv_shift_dx(const T *dout, const T *y, T *dx, int x_width,
+__global__ void ConvShiftGradX(const T *dout, const T *y, T *dx, int x_width,
                                int y_width, int y_half_width, int batch_size) {
   int i = blockIdx.x * blockDim.x + threadIdx.x;  // x index
   int j = blockIdx.y;                             // y index
@@ -94,7 +94,7 @@ __global__ void conv_shift_dx(const T *dout, const T *y, T *dx, int x_width,
 
 // Compute y gradient - initial naive implementation with atomic add.
 template <typename T>
-__global__ void conv_shift_dy(const T *x, const T *dout, T *dy, int x_width,
+__global__ void ConvShiftDy(const T *x, const T *dout, T *dy, int x_width,
                             int y_width, int y_half_width, int batch_size) {
   int i = blockIdx.x * blockDim.x + threadIdx.x;  // x index
   int j = blockIdx.y;                             // y index
@@ -125,14 +125,14 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
     int y_half_width = (y_width - 1) / 2;
 
     const int x_per_block = 256;
-    int num_x_blocks = div_up(x_width, x_per_block);
+    int num_x_blocks = DivUp(x_width, x_per_block);
     int mem_per_block = (x_per_block + 2 * y_width) * sizeof(T);
 
     dim3 grid_dim(num_x_blocks, batch_size);
 
     auto stream = context.cuda_device_context().stream();
 
-    conv_shift_forward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
+    ConvShiftForward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
         x_data, y_data, out_data, x_width, y_width, y_half_width, batch_size);
   }
 };
@@ -160,20 +160,20 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
     auto stream = context.cuda_device_context().stream();
 
     const int x_per_block = 256;
-    int num_x_blocks = div_up(x_width, x_per_block);
+    int num_x_blocks = DivUp(x_width, x_per_block);
     dim3 grid_dim(num_x_blocks, y_width, batch_size);
 
     if (dX) {
       T *dx_data = dX->mutable_data<T>(context.GetPlace());
       cudaMemsetAsync(dx_data, 0, dX->numel() * sizeof(T), stream);
-      conv_shift_dx<T><<<grid_dim, x_per_block, 0, stream>>>(
+      ConvShiftGradX<T><<<grid_dim, x_per_block, 0, stream>>>(
           dout_data, y_data, dx_data, x_width, y_width, y_half_width,
           batch_size);
     }
     if (dY) {
       T *dy_data = dY->mutable_data<T>(context.GetPlace());
       cudaMemsetAsync(dy_data, 0, dY->numel() * sizeof(T), stream);
-      conv_shift_dy<T><<<grid_dim, x_per_block, 0, stream>>>(
+      ConvShiftDy<T><<<grid_dim, x_per_block, 0, stream>>>(
           x_data, dout_data, dy_data, x_width, y_width, y_half_width,
           batch_size);
     }