maxout code review 2nd

paddle/operators/math/maxouting.cc

@@ -42,11 +42,11 @@ class MaxOutFunctor<platform::CPUPlace, MaxOutProcess, T> {
     const T* input_data = input.data<T>();
     T* output_data = output->mutable_data<T>(context.GetPlace());
 
-    for (int i = 0; i < batch_size; i++) {
+    for (int i = 0; i < batch_size; ++i) {
       int new_bindex =  c_size * i;
       for (int c = 0; c < output_channels; ++c) {
         int new_cindex = fea_size * c;
-        for (int f = 0; f < fea_size; f++) {
+        for (int f = 0; f < fea_size; ++f) {
           T ele = maxout_process.initial();
           for (int ph = 0; ph < groups; ++ph) {
             maxout_process.compute(ele,
@@ -82,15 +82,15 @@ public:
     const T* output_grad_data = output_grad.data<T>();
     T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
 
-    for (int i = 0; i < batch_size; i++) {
+    for (int i = 0; i < batch_size; ++i) {
       int blen = fea_size * output_channels * i;
       for (int c = 0; c < output_channels; ++c) {
         int clen = fea_size * c;
-        for (int f = 0; f < fea_size; f++) {
+        for (int f = 0; f < fea_size; ++f) {
           int input_idx = 0;
           bool stop = false;
           int output_idx = blen + clen + f;
-          for (int g = 0; g < groups && !stop; g++) {
+          for (int g = 0; g < groups && !stop; ++g) {
               input_idx = (blen + clen) * groups + fea_size * g + f;
               input_grad_data[input_idx] = 0;
               if (input_data[input_idx] == output_data[output_idx]) {

paddle/operators/math/maxouting.cu

@@ -21,9 +21,10 @@ namespace math {
 
 template <typename MaxOutProcess, typename T>
 __global__ void KernelMaxOut(const int nthreads, const T* input_data,
-                             T* output_data, const int channels,
+                            const int channels,
                              const int input_height, const int input_width,
-                             int groups, MaxOutProcess maxout_process) {
+                             int groups, T* output_data,
+                             MaxOutProcess maxout_process) {
   const int size = input_height * input_width * channels / groups;
   const int feat_len = input_height * input_width;
   for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
@@ -58,7 +59,7 @@ __global__ void KernelMaxoutGrad(
         (batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
       int maxIndex = -1;
       bool stop = false;
-      for (int g = 0; g < groups && !stop; g++) {
+      for (int g = 0; g < groups && !stop; ++g) {
         if (input_data[data_idx + g * feat_len] == output_data[index]) {
           maxIndex = data_idx + g * feat_len;
           stop = true;
@@ -99,9 +100,9 @@ class MaxOutFunctor<platform::GPUPlace, MaxOutProcess, T> {
         MaxOutProcess,
         T><<<grid, threads, 0,
              reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                 .stream()>>>(nthreads, input_data, output_data, input_channels,
+                 .stream()>>>(nthreads, input_data, input_channels,
                               input_height, input_width, groups,
-                              maxout_process);
+                              output_data, maxout_process);
   }
 };
 /*

paddle/operators/maxout_op.h

@@ -54,13 +54,11 @@ class MaxOutGradKernel : public framework::OpKernel<T> {
 
     int groups = context.template Attr<int>("groups");
 
-
-
+    auto& device_ctx = context.device_context();
+    math::SetConstant<Place, T> zero;
     if (in_x_grad) {
       in_x_grad->mutable_data<T>(context.GetPlace());
-      auto temp = framework::EigenVector<T>::Flatten(*in_x_grad);
-      temp.device(context.GetEigenDevice<Place>()) =
-      temp.constant(static_cast<T>(0));
+      zero(device_ctx, in_x_grad, static_cast<T>(0.0));
 
       paddle::operators::math::MaxOutGradFunctor<Place, T>
       maxout_backward;

python/paddle/v2/framework/tests/test_maxout_op.py

@@ -26,8 +26,6 @@ class TestMaxOutOp(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        print self.inputs
-        print self.outputs
         self.check_grad(['X'], 'Out')
 
     def init_test_case(self):