format

src/common/types.cpp

@@ -60,8 +60,7 @@ std::unordered_map<
         {G_OP_TYPE_TRANSPOSE, {{"X"}, {"Out"}}},
         {G_OP_TYPE_BOX_CODER,
          {{"PriorBox", "PriorBoxVar", "TargetBox"}, {"OutputBox"}}},
-        {G_OP_TYPE_FUSION_CONV_ADD_BN_RELU,
-                {{"Input"}, {"Out"}}},
+        {G_OP_TYPE_FUSION_CONV_ADD_BN_RELU, {{"Input"}, {"Out"}}},
         {G_OP_TYPE_PRIOR_BOX, {{"Image", "Input"}, {"Boxes", "Variances"}}},
         {G_OP_TYPE_MULTICLASS_NMS, {{"BBoxes", "Scores"}, {"Out"}}},
         {G_OP_TYPE_FC, {{"X", "Y", "Z"}, {"Out"}}},

src/operators/fusion_conv_add_bn_relu_op.h

@@ -42,12 +42,14 @@ class FusionConvAddBNReluMatcher : public framework::FusionOpMatcher {
       std::vector<std::shared_ptr<framework::Node>> *removed_nodes) {
     vector<std::shared_ptr<framework::OpDesc>> origin_descs =
         node->OpDescs(node_.Depth());
-      node->Folder(node_.Depth(), Type(),
-                   { {G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}},
-                     {G_OP_TYPE_BATCHNORM, {{"Scale", "Scale"},
-                                                         {"Mean", "Mean"},
-                                                         {"Bias", "Bias"},
-                                                         {"Variance", "Variance"}}}}, removed_nodes);
+    node->Folder(node_.Depth(), Type(),
+                 {{G_OP_TYPE_ELEMENTWISE_ADD, {{"Y", "Y"}}},
+                  {G_OP_TYPE_BATCHNORM,
+                   {{"Scale", "Scale"},
+                    {"Mean", "Mean"},
+                    {"Bias", "Bias"},
+                    {"Variance", "Variance"}}}},
+                 removed_nodes);
   }
 
   std::string Type() { return G_OP_TYPE_FUSION_CONV_ADD_BN_RELU; }
@@ -78,7 +80,7 @@ class FusionConvAddBNReluOp
 
 #ifdef PADDLE_MOBILE_CPU
 //#ifndef FUSION_CONV_ADD_BN_RELU_REGISTER
-//static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
+// static framework::FusionOpRegistrar fusion_conv_add_bn_relu_registrar(
 //    new FusionConvAddBNReluMatcher());
 //#define FUSION_CONV_ADD_BN_RELU_REGISTER
 //#endif

src/operators/kernel/central-arm-func/batchnorm_arm_func.h

@@ -53,7 +53,7 @@ void BatchnormCompute(const BatchNormParam &param) {
                         "C must equal to variance.numel()");
 
   int HXW = H * W;
-  if (0&&HXW > 32) {
+  if (0 && HXW > 32) {
     int NXC = N * C;
     float *inv_std_ptr = new float[NXC * 4];
     float *volatile new_scale_ptr = new float[NXC * 4];
@@ -222,15 +222,14 @@ void BatchnormCompute(const BatchNormParam &param) {
             }
           }
         }
-
       }
     }
-//      for(int i = 0; i < new_scale.numel(); i++){
-//          std::cout << "new_scale " << new_scale_ptr[i] <<std::endl;
-//      }
-//      for(int i = 0; i < new_bias.numel(); i++){
-//          std::cout << "new_bias " << new_bias_ptr[i] <<std::endl;
-//      }
+    //      for(int i = 0; i < new_scale.numel(); i++){
+    //          std::cout << "new_scale " << new_scale_ptr[i] <<std::endl;
+    //      }
+    //      for(int i = 0; i < new_bias.numel(); i++){
+    //          std::cout << "new_bias " << new_bias_ptr[i] <<std::endl;
+    //      }
 
     delete[] inv_std_ptr;
   }

src/operators/kernel/central-arm-func/conv_add_bn_relu_func.h

@@ -15,8 +15,8 @@ limitations under the License. */
 #ifdef FUSION_CONVADDBNRELU_OP
 
 #pragma once
-#include "operators/math/depthwiseconv3x3s1p1.h"
 #include "operators/kernel/conv_add_bn_relu_kernel.h"
+#include "operators/math/depthwiseconv3x3s1p1.h"
 #include "operators/op_param.h"
 namespace paddle_mobile {
 namespace operators {
@@ -130,11 +130,12 @@ void ConvAddBNReluCompute(const FusionConvAddBNReluParam &param) {
     }
 
     auto output_ptr = output->data<float>();
-    for (int c = 0; c < output_matrix_shape[0]; c++){
+    for (int c = 0; c < output_matrix_shape[0]; c++) {
       // int start = c * output_matrix_shape[1];
-      for (int j = 0; j < output_matrix_shape[1]; j++){
-       //  output_ptr[start + j] = output_ptr[start +j]*new_scale_ptr[c]+new_bias_ptr[c];
-        //  output_ptr[start + j] = output_ptr[start+j]< 0 ? 0 : output_ptr[start +j];
+      for (int j = 0; j < output_matrix_shape[1]; j++) {
+        //  output_ptr[start + j] = output_ptr[start
+        //  +j]*new_scale_ptr[c]+new_bias_ptr[c]; output_ptr[start + j] =
+        //  output_ptr[start+j]< 0 ? 0 : output_ptr[start +j];
       }
     }
   }

src/operators/kernel/mali/batchnorm_kernel.cpp

@@ -128,7 +128,7 @@ class AclBatchNormOp : public acl::ACLOperator {
 };
 
 template <>
-bool BatchNormKernel<GPU_MALI, float>::Init(BatchNormParam *param) const {
+bool BatchNormKernel<GPU_MALI, float>::Init(BatchNormParam* param) const {
   AclBatchNormOp<GPU_MALI, float>* acl_op =
       reinterpret_cast<AclBatchNormOp<GPU_MALI, float>*>(this->GetAclOp());
   if (acl_op == nullptr) {

src/operators/kernel/mali/conv_kernel.cpp

@@ -195,7 +195,7 @@ class AclConvOp : public acl::ACLOperator {
 };
 
 template <>
-bool ConvKernel<GPU_MALI, float>::Init(ConvParam *param) const {
+bool ConvKernel<GPU_MALI, float>::Init(ConvParam* param) const {
   AclConvOp<GPU_MALI, float>* acl_op =
       reinterpret_cast<AclConvOp<GPU_MALI, float>*>(this->GetAclOp());
   if (acl_op == nullptr) {

src/operators/kernel/mali/elementwise_add_kernel.cpp

@@ -27,7 +27,8 @@ struct AddFunctor {
 };
 
 template <>
-bool ElementwiseAddKernel<GPU_MALI, float>::Init(ElementwiseAddParam *param) const {
+bool ElementwiseAddKernel<GPU_MALI, float>::Init(
+    ElementwiseAddParam *param) const {
   return true;
 }
 

src/operators/math/depthwise_conv_3x3.cpp

@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 #include "operators/math/depthwise_conv_3x3.h"
-#include <vector>
 #include <arm_neon.h>
+#include <vector>
 
 namespace paddle_mobile {
 namespace operators {

src/operators/math/depthwiseconv3x3s1p1.cpp

@@ -66,10 +66,11 @@ void DepthwiseConv3x3s1p1(const Tensor *input, Tensor filter, Tensor *output,
       float w21 = filter_data_tmp[7];
       float w22 = filter_data_tmp[8];
 
-      output_data[0] =(w11 * input_data[0] + w12 * input_data[1] + w21 * input_data[l] +
-                                w22 * input_data[l + 1] + bias_data[j]) *
-                               newscale_data[j] +
-                               newbias_data[j];
+      output_data[0] =
+          (w11 * input_data[0] + w12 * input_data[1] + w21 * input_data[l] +
+           w22 * input_data[l + 1] + bias_data[j]) *
+              newscale_data[j] +
+          newbias_data[j];
       output_data[l - 1] = (w10 * input_data[l - 2] + w11 * input_data[l - 1] +
                             w20 * input_data[2 * l - 2] +
                             w21 * input_data[2 * l - 1] + bias_data[j]) *
@@ -88,11 +89,13 @@ void DepthwiseConv3x3s1p1(const Tensor *input, Tensor filter, Tensor *output,
                                 w11 * input_data[l * l - 1] + bias_data[j]) *
                                    newscale_data[j] +
                                newbias_data[j];
-      if(if_relu){
+      if (if_relu) {
         output_data[0] = output_data[0] < 0 ? 0 : output_data[0];
-        output_data[l-1] = output_data[l-1] < 0 ? 0 : output_data[l-1];
-        output_data[(l-1)*l] = output_data[(l-1)*l] < 0 ? 0 : output_data[(l-1)*l];
-        output_data[l * l - 1] = output_data[l * l - 1] < 0 ? 0 : output_data[l * l - 1];
+        output_data[l - 1] = output_data[l - 1] < 0 ? 0 : output_data[l - 1];
+        output_data[(l - 1) * l] =
+            output_data[(l - 1) * l] < 0 ? 0 : output_data[(l - 1) * l];
+        output_data[l * l - 1] =
+            output_data[l * l - 1] < 0 ? 0 : output_data[l * l - 1];
       }
       for (int i = 1; i < l - 1; ++i) {
         output_data[i * l] =
@@ -111,9 +114,10 @@ void DepthwiseConv3x3s1p1(const Tensor *input, Tensor filter, Tensor *output,
              w21 * input_data[i * l + l - 1 + l] + bias_data[j]) *
                 newscale_data[j] +
             newbias_data[j];
-        if(if_relu){
+        if (if_relu) {
           output_data[i * l] = output_data[i * l] < 0 ? 0 : output_data[i * l];
-          output_data[i * l + l - 1] = output_data[i * l + l - 1] < 0 ? 0 : output_data[i * l + l - 1];
+          output_data[i * l + l - 1] =
+              output_data[i * l + l - 1] < 0 ? 0 : output_data[i * l + l - 1];
         }
       }
 
@@ -332,7 +336,6 @@ void DepthwiseConv3x3s1p1(const Tensor *input, Tensor filter, Tensor *output,
       filter_data_tmp += 9;
     }
   }
-
 }
 }  // namespace math
 }  // namespace operators

src/operators/op_param.h

@@ -823,9 +823,9 @@ class FusionConvAddParam : public OpParam {
 
   const int &Groups() const { return groups; }
 
-    void Set(Tensor *t) {t_ = t;}
+  void Set(Tensor *t) { t_ = t; }
 
-    const Tensor *Get() const {return t_;}
+  const Tensor *Get() const { return t_; }
 
  protected:
   Tensor *bias_;
@@ -837,7 +837,7 @@ class FusionConvAddParam : public OpParam {
   vector<int> paddings_;
   vector<int> dilations_;
   int groups;
-    Tensor *t_;
+  Tensor *t_;
 };
 
 Print &operator<<(Print &printer, const FusionConvAddParam &conv_param);