diff --git a/src/operators/kernel/fpga/V1/feed_kernel.cpp b/src/operators/kernel/fpga/V1/feed_kernel.cpp
index 161d8c9f0cf22ac79d1367e07b8ba3318a7a7123..9c6468404e334a5a3002f8702d4f3b9818028f77 100644
--- a/src/operators/kernel/fpga/V1/feed_kernel.cpp
+++ b/src/operators/kernel/fpga/V1/feed_kernel.cpp
@@ -28,8 +28,8 @@ template <>
 void FeedKernel<FPGA, float>::Compute(const FeedParam<FPGA> &param) {
   auto input =
       reinterpret_cast<Tensor *>(const_cast<LoDTensor *>(param.InputX()));
-  auto input_ptr = input->data<float>();
   fpga::format_image(input);
+  auto input_ptr = input->data<float>();
   Tensor *output = param.Out();
   auto output_ptr = output->data<float>();
 
diff --git a/src/operators/kernel/fpga/V2/feed_kernel.cpp b/src/operators/kernel/fpga/V2/feed_kernel.cpp
index 4092307083bd38346b03857b8e9ec858795f3941..d1a721b5eb02a775498c17adcae84ce560ac1135 100644
--- a/src/operators/kernel/fpga/V2/feed_kernel.cpp
+++ b/src/operators/kernel/fpga/V2/feed_kernel.cpp
@@ -29,8 +29,8 @@ template <>
 void FeedKernel<FPGA, float>::Compute(const FeedParam<FPGA> &param) {
   auto input =
       reinterpret_cast<Tensor *>(const_cast<LoDTensor *>(param.InputX()));
-  auto input_ptr = input->data<float>();
   fpga::format_image(input);
+  auto input_ptr = input->data<float>();
   Tensor *output = param.Out();
   auto output_ptr = output->data<float>();
 
diff --git a/test/operators/test_quantize_op.cpp b/test/operators/test_quantize_op.cpp
index 1d3b570818b994b755555bae574c75080150d390..9988661bcb898daa5e79b6d22d65d90cfa03c668 100644
--- a/test/operators/test_quantize_op.cpp
+++ b/test/operators/test_quantize_op.cpp
@@ -27,34 +27,38 @@ enum RoundType {
 }
 
 template <round::RoundType T>
-static int8_t Round(float x);
+struct Round {
+  int8_t operator()(float x);
+};
 
 template <>
-static int8_t Round<round::RoundAwayZero>(float x) {
-  return std::round(x);
-}
+struct Round<round::RoundAwayZero> {
+  int8_t operator()(float x) { return std::round(x); }
+};
 
 template <>
-static int8_t Round<round::RoundTowardsZero>(float x) {
-  return int8_t(x);
-}
+struct Round<round::RoundTowardsZero> {
+  int8_t operator()(float x) { return int8_t(x); }
+};
 
 template <>
-static int8_t Round<round::RoundToEven>(float x) {
-  int8_t ret = 0;
-  float v = std::round(x);
-  int32_t q = (int32_t)v;
-  if (abs(abs(q - x) - 0.5) > 0) {
-    ret = q;
-  } else {
-    if (abs(q) % 2 == 0) {
+struct Round<round::RoundToEven> {
+  int8_t operator()(float x) {
+    int8_t ret = 0;
+    float v = std::round(x);
+    int32_t q = (int32_t)v;
+    if (abs(abs(q - x) - 0.5) > 0) {
       ret = q;
     } else {
-      ret = q + ((q > 0) ? -1 : 1);
+      if (abs(q) % 2 == 0) {
+        ret = q;
+      } else {
+        ret = q + ((q > 0) ? -1 : 1);
+      }
     }
+    return ret;
   }
-  return ret;
-}
+};
 
 template <round::RoundType T>
 static void quantize(const Tensor *input, const float scale, const int pad,
@@ -70,7 +74,6 @@ static void quantize(const Tensor *input, const float scale, const int pad,
   const float *x = input->data<const float>();
   int8_t *y = output->mutable_data<int8_t>();
 
-  std::cout << "pad: " << pad << ", pad_val: " << int(pad_val) << std::endl;
   for (int nc = 0; nc < batch_size * channels; ++nc) {
     const float *xh = x + nc * input_spatial;
     int8_t *yh = y + nc * output_spatial;
@@ -86,7 +89,7 @@ static void quantize(const Tensor *input, const float scale, const int pad,
         yh[w] = pad_val;
       }
       for (int w = 0; w < input_w; ++w) {
-        yh[w + pad] = Round<T>(xh[w] * scale);
+        yh[w + pad] = Round<T>()(xh[w] * scale);
       }
       // pad right
       for (int w = 0; w < pad; ++w) {