add dwconv+bn+relu

src/common/types.cpp

@@ -24,6 +24,8 @@ const std::string G_OP_TYPE_CONCAT = "concat";
 const std::string G_OP_TYPE_ELEMENTWISE_ADD = "elementwise_add";
 const std::string G_OP_TYPE_FUSION_CONV_ADD_RELU = "fusion_conv_add_relu";
 const std::string G_OP_TYPE_FUSION_CONV_ADD_BN_RELU = "fusion_conv_add_bn_relu";
+const std::string G_OP_TYPE_FUSION_DWCONV_BN_RELU = "fusion_dwconv_bn_relu";
+
 const std::string G_OP_TYPE_FC = "fusion_fc";
 const std::string G_OP_TYPE_FUSION_CONV_ADD = "fusion_conv_add";
 const std::string G_OP_TYPE_LRN = "lrn";

src/common/types.h

@@ -81,6 +81,7 @@ extern const std::string G_OP_TYPE_FUSION_CONV_ADD_RELU;
 extern const std::string G_OP_TYPE_FC;
 extern const std::string G_OP_TYPE_FUSION_CONV_ADD;
 extern const std::string G_OP_TYPE_FUSION_CONV_ADD_BN_RELU;
+extern const std::string G_OP_TYPE_FUSION_DWCONV_BN_RELU;
 
 extern const std::string G_OP_TYPE_LRN;
 extern const std::string G_OP_TYPE_MUL;

src/framework/program/program_desc.cpp

@@ -52,10 +52,10 @@ void ProgramDesc::Description(std::string header) {
           LOG(kLOG_DEBUG3) << "argument - " << n;
         }
       }
-      for (auto &attr : op->GetAttrMap()) {
-        LOG(kLOG_DEBUG2) << "attr name:: " << attr.first;
-        LOG(kLOG_DEBUG3) << "argument - " << attr.second;
-      }
+      //      for (auto &attr : op->GetAttrMap()) {
+      //        LOG(kLOG_DEBUG2) << "attr name:: " << attr.first;
+      //        LOG(kLOG_DEBUG3) << "argument - " << attr.second;
+      //      }
     }
 
     for (const auto &var_desc : block->Vars()) {

src/operators/fusion_conv_add.h

@@ -68,11 +68,11 @@ class FusionConvAddOp : public framework::OperatorWithKernel<
 
 #ifdef PADDLE_MOBILE_CPU
 
-#ifndef CONV_ADD_REGISTER
-static framework::FusionOpRegistrar convadd_registrar(
-    new FusionConvAddMatcher());
-#define CONV_ADD_REGISTER
-#endif
+//#ifndef CONV_ADD_REGISTER
+// static framework::FusionOpRegistrar convadd_registrar(
+//    new FusionConvAddMatcher());
+//#define CONV_ADD_REGISTER
+//#endif
 
 #endif
 

src/operators/fusion_dwconv_bn_relu_op.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef FUSION_DWCONVBNRELU_OP
+
+#include "operators/fusion_dwconv_bn_relu_op.h"
+#include "operators/math/conv_func.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <typename Dtype, typename T>
+void FusionDWConvBNReluOp<Dtype, T>::InferShape() const {
+  auto in_dims = this->param_.Input()->dims();
+  auto filter_dims = this->param_.Filter()->dims();
+  const std::vector<int> &strides = this->param_.Strides();
+  std::vector<int> paddings = this->param_.Paddings();
+  int groups = this->param_.Groups();
+  std::vector<int> dilations = this->param_.Dilations();
+
+  PADDLE_MOBILE_ENFORCE((in_dims.size() == filter_dims.size() &&
+                         dilations.size() == paddings.size() &&
+                         paddings.size() == strides.size()),
+                        "ConvParam is not suitable");
+
+  std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
+  for (size_t i = 0; i < strides.size(); ++i) {
+    output_shape.push_back(
+        math::ConvOutputSize(in_dims[i + 2], filter_dims[i + 2], dilations[i],
+                             paddings[i], strides[i]));
+  }
+
+  framework::DDim ddim = framework::make_ddim(output_shape);
+  this->param_.Output()->Resize(ddim);
+}
+template class FusionDWConvBNReluOp<CPU, float>;
+}  // namespace operators
+}  // namespace paddle_mobile
+
+namespace ops = paddle_mobile::operators;
+#ifdef PADDLE_MOBILE_CPU
+REGISTER_OPERATOR_CPU(fusion_dwconv_bn_relu, ops::FusionDWConvBNReluOp);
+#endif
+#ifdef PADDLE_MOBILE_MALI_GPU
+#endif
+#ifdef PADDLE_MOBILE_FPGA
+#endif
+
+#endif

src/operators/fusion_dwconv_bn_relu_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef FUSION_DWCONVBNRELU_OP
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "framework/operator.h"
+#include "framework/program/program-optimize/fusion_op_register.h"
+#include "op_param.h"
+#include "operators/kernel/dwconv_bn_relu_kernel.h"
+
+namespace paddle_mobile {
+namespace operators {
+using std::string;
+using std::vector;
+class FusionDWConvBNReluMatcher : public framework::FusionOpMatcher {
+ public:
+  FusionDWConvBNReluMatcher() {
+    node_ = framework::Node(G_OP_TYPE_DEPTHWISE_CONV);
+    node_ > std::make_shared<framework::Node>(G_OP_TYPE_BATCHNORM) >
+        std::make_shared<framework::Node>(G_OP_TYPE_RELU);
+  }
+
+  void FolderNodes(
+      framework::Node *node,
+      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_BATCHNORM,
+                   {{"Scale", "Scale"},
+                    {"Mean", "Mean"},
+                    {"Bias", "Bias"},
+                    {"Variance", "Variance"}}}},
+                 removed_nodes);
+  }
+
+  std::string Type() { return G_OP_TYPE_FUSION_DWCONV_BN_RELU; }
+};
+
+template <typename DeviceType, typename T>
+class FusionDWConvBNReluOp : public framework::OperatorWithKernel<
+                                 DeviceType, FusionDWConvBNReluParam,
+                                 operators::DWConvBNReluKernel<DeviceType, T>> {
+ public:
+  FusionDWConvBNReluOp(const string &type, const VariableNameMap &inputs,
+                       const VariableNameMap &outputs,
+                       const framework::AttributeMap &attrs,
+                       std::shared_ptr<framework::Scope> scope)
+      : framework::OperatorWithKernel<
+            DeviceType, FusionDWConvBNReluParam,
+            operators::DWConvBNReluKernel<DeviceType, T>>(type, inputs, outputs,
+                                                          attrs, scope) {}
+
+  using framework::OperatorWithKernel<
+      DeviceType, FusionDWConvBNReluParam,
+      operators::DWConvBNReluKernel<DeviceType, T>>::OperatorWithKernel;
+  void InferShape() const override;
+
+ protected:
+};
+
+#ifdef PADDLE_MOBILE_CPU
+
+#ifndef FUSION_DWCONV_BN_RELU_REGISTER
+static framework::FusionOpRegistrar fusion_dwconv_bn_relu_registrar(
+    new FusionDWConvBNReluMatcher());
+#define FUSION_DWCONV_BN_RELU_REGISTER
+#endif
+
+#endif
+
+#ifdef PADDLE_MOBILE_MALI_GPU
+
+#ifndef FUSION_DWCONV_BN_RELU_REGISTER
+static framework::FusionOpRegistrar fusion_dwconv_bn_relu_registrar(
+    new FusionDWConvBNReluMatcher());
+#define FUSION_DWCONV_BN_RELU_REGISTER
+#endif
+
+#endif
+
+#ifdef PADDLE_MOBILE_FPGA
+#endif
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#ifdef PADDLE_MOBILE_CPU
+USE_OP_CPU(fusion_dwconv_bn_relu);
+#endif
+#ifdef PADDLE_MOBILE_MALI_GPU
+#endif
+#ifdef PADDLE_MOBILE_FPGA
+#endif
+
+#endif

src/operators/kernel/arm/conv_add_bn_relu_kernel.cpp

@@ -15,7 +15,7 @@ limitations under the License. */
 #ifdef FUSION_CONVADDBNRELU_OP
 
 #include "operators/kernel/conv_add_bn_relu_kernel.h"
-#include "operators/kernel/central-arm-func/conv_add_bn_relu_func.h"
+#include "operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h"
 
 namespace paddle_mobile {
 namespace operators {

src/operators/kernel/arm/dwconv_bn_relu_kernel.cpp

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef FUSION_DWCONVBNRELU_OP
+
+#include "operators/kernel/dwconv_bn_relu_kernel.h"
+#include "operators/kernel/central-arm-func/dwconv_bn_relu_arm_func.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+bool DWConvBNReluKernel<CPU, float>::Init(FusionDWConvBNReluParam *param) {
+  const Tensor *mean = param->InputMean();
+  const Tensor *variance = param->InputVariance();
+  const Tensor *scale = param->InputScale();
+  const Tensor *bias = param->InputBias();
+  const float epsilon = param->Epsilon();
+
+  auto mean_ptr = mean->data<float>();
+  auto variance_ptr = variance->data<float>();
+  auto scale_ptr = scale->data<float>();
+  auto bias_ptr = bias->data<float>();
+
+  const int C = mean->numel();
+  float inv_std_ptr[C];
+  for (int i = 0; i < C; i++) {
+    inv_std_ptr[i] =
+        1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
+  }
+  Tensor *new_scale = new Tensor();
+  Tensor *new_bias = new Tensor();
+  auto new_scale_ptr = new_scale->mutable_data<float>({C});
+  auto new_bias_ptr = new_bias->mutable_data<float>({C});
+  for (int i = 0; i < C; i++) {
+    new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
+    new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
+  }
+  param->SetNewScale(new_scale);
+  param->SetNewBias(new_bias);
+  return true;
+}
+
+template <>
+void DWConvBNReluKernel<CPU, float>::Compute(
+    const FusionDWConvBNReluParam &param) const {
+  DWConvBNReluCompute<float>(param);
+}
+template class DWConvBNReluKernel<CPU, float>;
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif

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

@@ -15,6 +15,8 @@ limitations under the License. */
 #ifdef FUSION_CONVADDBNRELU_OP
 
 #pragma once
+
+#include <vector>
 #include "operators/math/depthwise_conv_3x3.h"
 #include "operators/op_param.h"
 
@@ -23,14 +25,9 @@ namespace operators {
 void ConvAddBNReluBasic(const FusionConvAddBNReluParam &param) {
   const Tensor *input = param.Input();
   Tensor filter = *param.Filter();
-  Tensor bias = *param.Bias();
   Tensor new_bias = *param.NewBias();
   Tensor new_scale = *param.NewScale();
-  int axis = param.Axis();
   Tensor *output = param.Output();
-  math::expand_bias(bias, axis, output->dims());
-  output->ShareDataWith(bias);
-
   int groups = param.Groups();
   std::vector<int> strides = param.Strides();
   std::vector<int> paddings = param.Paddings();
@@ -121,7 +118,7 @@ void ConvAddBNReluCompute(const FusionConvAddBNReluParam &param) {
       param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
     math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
                                         param.Output(), param.NewScale(),
-                                        param.NewBias(), 1);
+                                        param.NewBias(), true);
   } else if (param.Groups() == param.Input()->dims()[1] &&
              param.Input()->dims()[1] == param.Output()->dims()[1] &&
              param.Filter()->dims()[2] == param.Filter()->dims()[3] &&

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

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#ifdef FUSION_DWCONVBNRELU_OP
+
+#pragma once
+#include <vector>
+#include "operators/math/depthwise_conv_3x3.h"
+#include "operators/op_param.h"
+namespace paddle_mobile {
+namespace operators {
+void DWConvBNReluBasic(const FusionDWConvBNReluParam &param) {
+  const Tensor *input = param.Input();
+  Tensor filter = *param.Filter();
+  Tensor new_bias = *param.NewBias();
+  Tensor new_scale = *param.NewScale();
+
+  Tensor *output = param.Output();
+
+  int groups = param.Groups();
+  std::vector<int> strides = param.Strides();
+  std::vector<int> paddings = param.Paddings();
+  std::vector<int> dilations = param.Dilations();
+
+  const int batch_size = static_cast<int>(input->dims()[0]);
+
+  std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
+
+  std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
+  size_t data_dim = filter_shape_vec.size() - 2;
+  std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+  col_shape_vec[0] = input->dims()[1] / groups;
+  for (size_t j = 0; j < data_dim; ++j) {
+    col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+    col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
+  }
+  framework::DDim col_shape(framework::make_ddim(col_shape_vec));
+
+  framework::DDim col_matrix_shape =
+      framework::flatten_to_2d(col_shape, data_dim + 1);
+
+  bool is_expand =
+      math::IsExpand(filter_shape_vec, strides, paddings, dilations);
+  Tensor col;
+  Tensor col_matrix;
+  if (is_expand) {
+    col.mutable_data<float>(col_shape);
+    col_matrix.ShareDataWith(col);
+    col_matrix.Resize(col_matrix_shape);
+  }
+
+  framework::DDim input_shape = framework::slice_ddim(
+      input->dims(), 1, static_cast<int>(input->dims().size()));
+
+  framework::DDim filter_matrix_shape = {filter.dims()[0],
+                                         filter.numel() / filter.dims()[0]};
+  filter.Resize(filter_matrix_shape);
+  framework::DDim output_matrix_shape = {
+      output->dims()[1],
+      output->numel() / (output->dims()[0] * output->dims()[1])};
+
+  // convolution operator: im2col(or vol2col) + gemm
+  int in_step = static_cast<int>(input->dims()[1]) / groups;
+  int out_step = static_cast<int>(output->dims()[1]) / groups;
+
+  math::Vol2ColFunctor<CPU, float> vol2col;
+  math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
+
+  for (int i = 0; i < batch_size; i++) {
+    Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
+    Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
+
+    for (int g = 0; g < groups; g++) {
+      Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
+
+      if (!is_expand) {
+        col.ShareDataWith(in_slice);
+        col_matrix.ShareDataWith(col);
+        col_matrix.Resize(col_matrix_shape);
+      } else if (data_dim == 2U) {
+        // im2col
+        im2col(in_slice, dilations, strides,
+               std::vector<int>{paddings[0], paddings[1], paddings[0],
+                                paddings[1]},
+               &col);
+      } else if (data_dim == 3U) {
+        // vol2col
+        vol2col(in_slice, dilations, strides, paddings, &col);
+      }
+      // gemm
+      Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
+      Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
+      std::cout << "***************" << std::endl;
+      math::matmulWithBn<float>(
+          filter_slice, false, col_matrix, false, static_cast<float>(1),
+          &out_slice, static_cast<float>(0), false, &new_scale, &new_bias);
+    }
+  }
+}
+template <typename P>
+void DWConvBNReluCompute(const FusionDWConvBNReluParam &param) {
+  if (param.Groups() == param.Input()->dims()[1] &&
+      param.Input()->dims()[1] == param.Output()->dims()[1] &&
+      param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
+      param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
+    math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
+                                        param.Output(), param.NewScale(),
+                                        param.NewBias(), true);
+  } else if (param.Groups() == param.Input()->dims()[1] &&
+             param.Input()->dims()[1] == param.Output()->dims()[1] &&
+             param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
+             param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
+    //    math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
+    //                                        param.Output(), param.NewScale(),
+    //                                        param.NewBias(), 1);
+    math::DepthwiseConvAddBNRelu3x3s2p1v2(param.Input(), param.Filter(),
+                                          param.Output(), param.NewScale(),
+                                          param.NewBias(), true);
+  } else {
+    DWConvBNReluBasic(param);
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif

src/operators/kernel/dwconv_bn_relu_kernel.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#pragma once
+
+#ifdef FUSION_DWCONVBNRELU_OP
+
+#include <vector>
+#include "framework/ddim.h"
+#include "framework/operator.h"
+#include "operators/math/conv_func.h"
+#include "operators/math/im2col.h"
+#include "operators/math/math_function.h"
+#include "operators/math/vol2col.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+using framework::DDim;
+using framework::OpKernelBase;
+
+template <typename DeviceType, typename T>
+class DWConvBNReluKernel
+    : public OpKernelBase<DeviceType, FusionDWConvBNReluParam> {
+ public:
+  void Compute(const FusionDWConvBNReluParam &param) const;
+  bool Init(FusionDWConvBNReluParam *param);
+};
+
+}  // namespace operators
+}  // namespace paddle_mobile
+
+#endif

src/operators/op_param.h

@@ -1059,6 +1059,86 @@ class FusionConvAddBNReluParam : public OpParam {
 Print &operator<<(Print &printer, const FusionConvAddParam &conv_param);
 #endif
 
+#ifdef FUSION_DWCONVBNRELU_OP
+class FusionDWConvBNReluParam : public OpParam {
+ public:
+  FusionDWConvBNReluParam(const VariableNameMap &inputs,
+                          const VariableNameMap &outputs,
+                          const AttributeMap &attrs, const Scope &scope) {
+    filter_ = FilterFrom<LoDTensor>(inputs, scope);
+    input_ = InputFrom<LoDTensor>(inputs, scope);
+    output_ = OutFrom<LoDTensor>(outputs, scope);
+    strides_ = GetAttr<vector<int>>("strides", attrs);
+    paddings_ = GetAttr<vector<int>>("paddings", attrs);
+    dilations_ = GetAttr<vector<int>>("dilations", attrs);
+    groups = GetAttr<int>("groups", attrs);
+    input_bias_ = InputBiasFrom<LoDTensor>(inputs, scope);
+    input_mean_ = InputMeanFrom<LoDTensor>(inputs, scope);
+    input_scale_ = InputScaleFrom<LoDTensor>(inputs, scope);
+    input_variance_ = InputVarianceFrom<LoDTensor>(inputs, scope);
+    epsilon_ = GetAttr<float>("epsilon", attrs);
+    momentum_ = GetAttr<float>("momentum", attrs);
+    is_test_ = GetAttr<bool>("is_test", attrs);
+  }
+
+  const Tensor *Input() const { return input_; }
+
+  const Tensor *Filter() const { return filter_; }
+
+  Tensor *Output() const { return output_; }
+
+  const vector<int> &Strides() const { return strides_; }
+
+  const vector<int> &Paddings() const { return paddings_; }
+
+  const vector<int> &Dilations() const { return dilations_; }
+
+  const int &Groups() const { return groups; }
+
+  const Tensor *InputBias() const { return input_bias_; }
+
+  const Tensor *InputMean() const { return input_mean_; }
+
+  const Tensor *InputScale() const { return input_scale_; }
+
+  const Tensor *InputVariance() const { return input_variance_; }
+
+  const float &Epsilon() const { return epsilon_; }
+
+  const float &Momentum() const { return momentum_; }
+
+  const bool &IsTest() const { return is_test_; }
+
+  void SetNewScale(Tensor *new_scale) { new_scale_ = new_scale; }
+
+  void SetNewBias(Tensor *new_bias) { new_bias_ = new_bias; }
+
+  const Tensor *NewScale() const { return new_scale_; }
+
+  const Tensor *NewBias() const { return new_bias_; }
+
+ protected:
+  Tensor *input_;
+  Tensor *output_;
+  Tensor *filter_;
+  vector<int> strides_;
+  vector<int> paddings_;
+  vector<int> dilations_;
+  int groups;
+  Tensor *input_bias_;
+  Tensor *input_mean_;
+  Tensor *input_scale_;
+  Tensor *input_variance_;
+  float epsilon_;
+  float momentum_;
+  bool is_test_;
+  Tensor *new_bias_;
+  Tensor *new_scale_;
+};
+
+Print &operator<<(Print &printer, const FusionConvAddParam &conv_param);
+#endif
+
 #ifdef IM2SEQUENCE_OP
 class Im2SequenceParam : public OpParam {
  public:

tools/op.cmake

@@ -64,6 +64,7 @@ else ()
   set(TRANSPOSE_OP ON)
   set(FUSION_CONVADD_RELU_OP ON)
   set(FUSION_CONVADDBNRELU_OP ON)
+  set(FUSION_DWCONVBNRELU_OP ON)
   set(PRELU_OP ON)
   set(RESIZE_OP ON)
   set(SCALE_OP ON)
@@ -155,6 +156,9 @@ endif()
 if (FUSION_CONVADDBNRELU_OP)
   add_definitions(-DFUSION_CONVADDBNRELU_OP)
 endif()
+if (FUSION_DWCONVBNRELU_OP)
+  add_definitions(-DFUSION_DWCONVBNRELU_OP)
+endif()
 if (PRELU_OP)
   add_definitions(-DPRELU_OP)
 endif()