format ..

paddle/operators/math/unpooling.cu

@@ -21,8 +21,7 @@ namespace math {
 template <typename T>
 __global__ void KernelUnpool2dMax(const int nthreads, const T* input_data,
                                   const int* indices_data,
-                                  const int input_height,
-                                  const int input_width,
+                                  const int input_height, const int input_width,
                                   const int channels, T* output_data,
                                   const int output_height,
                                   const int output_width) {
@@ -71,8 +70,8 @@ template <typename T>
 class Unpool2dMaxFunctor<platform::GPUPlace, T> {
  public:
   void operator()(const platform::DeviceContext& context,
-              const framework::Tensor& input,
-              const framework::Tensor& indices, framework::Tensor* output) {
+                  const framework::Tensor& input,
+                  const framework::Tensor& indices, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_height = input.dims()[2];
     const int input_width = input.dims()[3];
@@ -86,10 +85,10 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T> {
     int grid = (input.numel() + threads - 1) / threads;
     KernelUnpool2dMax<
         T><<<grid, threads, 0,
-            reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                .stream()>>>(input.numel(), input_data, indices_data,
-                             input_height, input_width, output_channels,
-                             output_data, output_height, output_width);
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(input.numel(), input_data, indices_data,
+                              input_height, input_width, output_channels,
+                              output_data, output_height, output_width);
   }
 };
 /*
@@ -119,11 +118,11 @@ class Unpool2dMaxGradFunctor<platform::GPUPlace, T> {
     int grid = (input.numel() + threads - 1) / threads;
     KernelUnpool2dMaxGrad<
         T><<<grid, threads, 0,
-            reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                .stream()>>>(input.numel(), input_data, indices_data,
-                             input_height, input_width, output_channels,
-                             output_data, output_grad_data, output_height,
-                             output_width, input_grad_data);
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(input.numel(), input_data, indices_data,
+                              input_height, input_width, output_channels,
+                              output_data, output_grad_data, output_height,
+                              output_width, input_grad_data);
   }
 };
 template class Unpool2dMaxGradFunctor<platform::GPUPlace, float>;

paddle/operators/unpool_op.cc

@@ -94,12 +94,11 @@ class UnpoolOp : public framework::OperatorWithKernel {
                    "Output(Out) of UnpoolOp should not be null.");
     auto in_x_dims = ctx->GetInputDim("X");
     auto in_y_dims = ctx->GetInputDim("Indices");
-    std::string unpooling_type = ctx->Attrs()
-                                 .Get<std::string>("unpooling_type");
+    std::string unpooling_type =
+        ctx->Attrs().Get<std::string>("unpooling_type");
     std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
     std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
-    std::vector<int> paddings =
-        ctx->Attrs().Get<std::vector<int>>("paddings");
+    std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
     PADDLE_ENFORCE(in_x_dims.size() == 4,
                    "Unpooling intput must be of 4-dimensional.");
     PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
@@ -142,4 +141,3 @@ REGISTER_OP_CPU_KERNEL(unpool,
 REGISTER_OP_CPU_KERNEL(
     unpool_grad, ops::UnpoolGradKernel<paddle::platform::CPUPlace, float>,
     ops::UnpoolGradKernel<paddle::platform::CPUPlace, double>);
-