add log print module

.clang-format

+---
+Language:        Cpp
+BasedOnStyle:  LLVM
+Standard:  Cpp11 
+IndentWidth: 4
+NamespaceIndentation: All
+...

.pre-commit-config.yaml

@@ -6,6 +6,7 @@ repos:
         files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
     -   id: remove-tabs
         files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
+
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     sha: 5bf6c09bfa1297d3692cadd621ef95f1284e33c0
     hooks:
@@ -18,11 +19,21 @@ repos:
         files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
     -   id: trailing-whitespace
         files: (src).*\.(md|py|mm|swift|java|c|cc|cxx|cpp|cu|h|hpp|hxx)$
+
 -   repo: local
     hooks:
     -   id: clang-format-with-version-check
         name: clang-format
         description: Format files with ClangFormat.
-        entry: bash .clang_format.hook -i
+        entry: bash ./tools/pre-commit.hooks/.clang_format.hook -i
         language: system
         files: (src).*\.(c|cc|cxx|cpp|h|hpp|hxx)$
+
+#-   repo: local
+#    hooks:
+#    -   id: copyright_checker
+#        name: copyright_checker
+#        entry: python ./tools/pre-commit.hooks/.copyright.hook
+#        language: system
+#        files: (src).*\.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
+#        exclude: (?!.*third_party)^.*$ | (?!.*book)^.*$

CMakeLists.txt

@@ -46,7 +46,8 @@ target_link_libraries(paddle-mobile-static protobuf-lite openblas)
 add_dependencies(paddle-mobile openblas_proj)
 
 # gen test
-ADD_EXECUTABLE(paddle-mobile-test test/main.cpp test/test_helper.h)
+ADD_EXECUTABLE(paddle-mobile-test test/main.cpp test/test_helper.h
+        test/elementwise_add_op_test.h test/test_include.h)
 target_link_libraries(paddle-mobile-test paddle-mobile)
 
 # gen test log

README.md

 # Paddle-Mobile 
 
+![License MIT](https://img.shields.io/github/license/mashape/apistatus.svg)  [![Build Status](https://travis-ci.org/PaddlePaddle/paddle-mobile.svg?branch=develop&longCache=true&style=flat-square)](https://travis-ci.org/PaddlePaddle/paddle-mobile)
+
+
+
 This project is used to develop the next version deep learning freamwork for mobile device.
 
 

src/common/type_define.h

@@ -23,30 +23,31 @@ SOFTWARE.
 
 namespace paddle_mobile {
 
-namespace framework {
-template <typename Dtype> class OperatorBase;
-class OpDesc;
-class BlockDesc;
-class InferShapeContext;
-}
+    namespace framework {
+        template <typename Dtype> class OperatorBase;
+        class OpDesc;
+        class BlockDesc;
+        class InferShapeContext;
+    }
 
-using VariableNameMap = std::map<std::string, std::vector<std::string>>;
+    using VariableNameMap = std::map<std::string, std::vector<std::string>>;
 
-template <typename Dtype>
-using OpCreator = std::function<framework::OperatorBase<Dtype> *(
+    template <typename Dtype>
+    using OpCreator = std::function<framework::OperatorBase<Dtype> *(
         const std::string & /*type*/, const VariableNameMap & /*inputs*/,
         const VariableNameMap & /*outputs*/,
         const framework::AttributeMap & /*attrs*/)>;
 
-using GradOpMakerFN =
+    using GradOpMakerFN =
         std::function<std::vector<std::unique_ptr<framework::OpDesc>>(
             const framework::OpDesc &,
             const std::unordered_set<std::string> & /*no_grad_set*/,
             std::unordered_map<std::string, std::string> * /*grad_to_var*/,
             const std::vector<framework::BlockDesc *> &grad_block)>;
 
-using InferVarTypeFN = std::function<void(const framework::OpDesc & /*op_desc*/,
+    using InferVarTypeFN =
+        std::function<void(const framework::OpDesc & /*op_desc*/,
                            framework::BlockDesc * /*block*/)>;
 
-using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
+    using InferShapeFN = std::function<void(framework::InferShapeContext *)>;
 };

src/common/types.h

@@ -19,19 +19,19 @@ SOFTWARE.
 #pragma once;
 
 namespace paddle_mobile {
-enum class Precision : int { FP32 = 0 };
+    enum class Precision : int { FP32 = 0 };
 
-//! device type
-enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 };
+    //! device type
+    enum DeviceTypeEnum { kINVALID = -1, kCPU = 0, kFPGA = 1, kGPU_MALI = 2 };
 
-template <DeviceTypeEnum T> struct DeviceType {};
+    template <DeviceTypeEnum T> struct DeviceType {};
 
-typedef DeviceType<kCPU> CPU;
-typedef DeviceType<kFPGA> FPGA;
-typedef DeviceType<kGPU_MALI> GPU_MALI;
+    typedef DeviceType<kCPU> CPU;
+    typedef DeviceType<kFPGA> FPGA;
+    typedef DeviceType<kGPU_MALI> GPU_MALI;
 
-//! data type
-enum DataType {
+    //! data type
+    enum DataType {
         PM_INVALID = -1,
         PM_HALF = 0,
         PM_FLOAT = 1,
@@ -47,9 +47,9 @@ enum DataType {
         PM_BOOL = 11,
         PM_SHAPE = 12,
         PM_TENSOR = 13
-};
-//!
-enum PMStatus {
+    };
+    //!
+    enum PMStatus {
         PMSuccess = 0xFF,        /*!< No errors */
         PMNotInitialized = 0x01, /*!< Data not initialized. */
         PMInvalidValue = 0x02,   /*!< Incorrect variable value. */
@@ -59,5 +59,5 @@ enum PMStatus {
         PMOutOfMem = 0x06,       /*!< OOM error*/
         PMUnImplError = 0x07,    /*!< Unimplement error. */
         PMWrongDevice = 0x08     /*!< un-correct device. */
-};
+    };
 }

src/common/variant.h

@@ -21,9 +21,9 @@ SOFTWARE.
 #pragma once
 
 namespace paddle_mobile {
-template <int ID, typename Type> struct IDToType { typedef Type type_t; };
+    template <int ID, typename Type> struct IDToType { typedef Type type_t; };
 
-template <typename F, typename... Ts> struct VariantHelper {
+    template <typename F, typename... Ts> struct VariantHelper {
         static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
                                        ? sizeof(F)
                                        : VariantHelper<Ts...>::size;
@@ -35,9 +35,9 @@ template <typename F, typename... Ts> struct VariantHelper {
                 VariantHelper<Ts...>::Destroy(id, data);
             }
         }
-};
+    };
 
-template <typename F> struct VariantHelper<F> {
+    template <typename F> struct VariantHelper<F> {
         static const size_t size = sizeof(F);
         inline static void Destroy(size_t id, void *data) {
             if (id == typeid(F).hash_code()) {
@@ -46,19 +46,19 @@ template <typename F> struct VariantHelper<F> {
                 //              std::cout << "未匹配到 " << std::endl;
             }
         }
-};
+    };
 
-template <size_t size> class RawData {
-public:
+    template <size_t size> class RawData {
+      public:
         char data[size];
         RawData() {}
         RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
         //      void operator=(const RawData &raw_data){
         //        strcpy(data, raw_data.data);
         //      }
-};
+    };
 
-template <typename... Ts> struct Variant {
+    template <typename... Ts> struct Variant {
         Variant(const Variant &variant) {
             //        std::cout << " 赋值构造函数 " << std::endl;
             type_id = variant.type_id;
@@ -87,13 +87,13 @@ template <typename... Ts> struct Variant {
 
         size_t TypeId() const { return type_id; }
 
-private:
+      private:
         static inline size_t invalid_type() { return typeid(void).hash_code(); }
         typedef VariantHelper<Ts...> helper;
         size_t type_id;
         RawData<helper::size> data;
-};
+    };
 
-template <typename T> struct Vistor { typedef T type_t; };
+    template <typename T> struct Vistor { typedef T type_t; };
 
 } // namespace paddle_mobile

src/framework/attribute.cpp

@@ -19,5 +19,5 @@ SOFTWARE.
 #include "attribute.h"
 
 namespace paddle_mobile {
-namespace framework {}
+    namespace framework {}
 } // namespace paddle_mobile

src/framework/attribute.h

@@ -22,13 +22,14 @@ SOFTWARE.
 #include "framework.pb.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class BlockDesc;
+        class BlockDesc;
 
-class Attribute {
-public:
-  static Attribute GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
+        class Attribute {
+          public:
+            static Attribute
+            GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
                 //    std::cout << "begin get attr value" << std::endl;
                 Attribute attr;
                 switch (attr_desc.type()) {
@@ -93,36 +94,38 @@ public:
             }
 
             Attribute() {}
-  template <typename T, typename... Args> Attribute &Set(Args &&... args) {
+            template <typename T, typename... Args>
+            Attribute &Set(Args &&... args) {
                 variant_.Set<T>(args...);
                 return *this;
             }
 
             template <typename T> T &Get() const { return variant_.Get<T>(); }
 
-private:
-  Variant<int, float, std::string, std::vector<int>, std::vector<float>,
-          std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
-          int64_t>
+          private:
+            Variant<int, float, std::string, std::vector<int>,
+                    std::vector<float>, std::vector<std::string>, bool,
+                    std::vector<bool>, BlockDesc *, int64_t>
                 variant_;
-};
+        };
 
-using AttributeMap = std::unordered_map<std::string, Attribute>;
+        using AttributeMap = std::unordered_map<std::string, Attribute>;
 
-class AttrReader {
-public:
+        class AttrReader {
+          public:
             explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
 
             template <typename T> inline T Get(const std::string &name) const {
-    //          PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in
+                //          PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should
+                //          be in
                 //          AttributeMap",
                 //                         name);
                 return ((Attribute)attrs_.at(name)).Get<T>();
             }
 
-private:
+          private:
             const AttributeMap &attrs_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/block_desc.cpp

@@ -19,32 +19,32 @@ SOFTWARE.
 #include "block_desc.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
+        std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
             std::vector<std::shared_ptr<VarDesc>> res;
             for (const auto &p : vars_) {
                 res.push_back(p.second);
             }
             return res;
-}
+        }
 
-std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
+        std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
             std::vector<std::shared_ptr<OpDesc>> res;
             for (const auto &op : ops_) {
                 res.push_back(op);
             }
             return res;
-}
+        }
 
-BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
+        BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
             for (const proto::VarDesc &var_desc : desc_.vars()) {
                 vars_[var_desc.name()].reset(new VarDesc(var_desc));
             }
             for (const proto::OpDesc &op_desc : desc_.ops()) {
                 ops_.emplace_back(new framework::OpDesc(op_desc));
             }
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/block_desc.h

@@ -24,39 +24,43 @@ SOFTWARE.
 #include "var_desc.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class BlockDesc : PaddleMobileObject {
-public:
+        class BlockDesc : PaddleMobileObject {
+          public:
             BlockDesc(const proto::BlockDesc &desc);
 
             const int &ID() const { return desc_.idx(); }
 
             const int &Parent() const { return desc_.parent_idx(); }
 
-  bool operator==(const paddle_mobile::framework::BlockDesc &in_block) const {
-    return this->ID() == in_block.ID() && this->Parent() == in_block.Parent();
+            bool operator==(
+                const paddle_mobile::framework::BlockDesc &in_block) const {
+                return this->ID() == in_block.ID() &&
+                       this->Parent() == in_block.Parent();
             }
 
-  bool operator<(const paddle_mobile::framework::BlockDesc &in_block) const {
-    return this->ID() < in_block.ID() && this->Parent() < in_block.Parent();
+            bool operator<(
+                const paddle_mobile::framework::BlockDesc &in_block) const {
+                return this->ID() < in_block.ID() &&
+                       this->Parent() < in_block.Parent();
             }
 
             std::vector<std::shared_ptr<VarDesc>> Vars() const;
             std::vector<std::shared_ptr<OpDesc>> Ops() const;
 
-private:
+          private:
             proto::BlockDesc desc_;
             std::vector<std::shared_ptr<OpDesc>> ops_;
             std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile
 
 namespace std {
 
-template <> struct hash<paddle_mobile::framework::BlockDesc> {
+    template <> struct hash<paddle_mobile::framework::BlockDesc> {
         typedef paddle_mobile::framework::BlockDesc argument_type;
         typedef std::size_t result_type;
         result_type operator()(argument_type const &s) const noexcept {
@@ -64,6 +68,6 @@ template <> struct hash<paddle_mobile::framework::BlockDesc> {
             result_type const h2(std::hash<int>{}(s.ID()));
             return h1 ^ (h2 << 1);
         }
-};
+    };
 
 } // namespace std

src/framework/data_layout.h

@@ -19,15 +19,15 @@ limitations under the License. */
 #include <string>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-enum class DataLayout {
+        enum class DataLayout {
             kNHWC = 0,
             kNCHW = 1,
             kAnyLayout = 2,
-};
+        };
 
-inline DataLayout StringToDataLayout(const std::string &str) {
+        inline DataLayout StringToDataLayout(const std::string &str) {
             std::string s(str);
             for (size_t i = 0; i < s.size(); ++i) {
                 s[i] = toupper(s[i]);
@@ -42,9 +42,9 @@ inline DataLayout StringToDataLayout(const std::string &str) {
             } else {
                 //    std::cout << "Unknown storage order string: %s", s;
             }
-}
+        }
 
-inline std::string DataLayoutToString(const DataLayout &data_layout) {
+        inline std::string DataLayoutToString(const DataLayout &data_layout) {
             switch (data_layout) {
             case DataLayout::kNHWC:
                 return "NHWC";
@@ -56,12 +56,13 @@ inline std::string DataLayoutToString(const DataLayout &data_layout) {
                 break;
                 //      std::cout << "unknown DataLayou %d", data_layout;
             }
-}
+        }
 
-inline std::ostream &operator<<(std::ostream &out, const DataLayout &l) {
+        inline std::ostream &operator<<(std::ostream &out,
+                                        const DataLayout &l) {
             out << DataLayoutToString(l);
             return out;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/data_transform.cpp

@@ -21,14 +21,14 @@ SOFTWARE.
 #include "data_transform.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-static void PassTensorData(Tensor *from, Tensor *to) {
+        static void PassTensorData(Tensor *from, Tensor *to) {
             to->ShareDataWith(*from);
             *from = Tensor();
-}
+        }
 
-void DataTransform(const OpKernelType &expected_kernel_type,
+        void DataTransform(const OpKernelType &expected_kernel_type,
                            const OpKernelType &kernel_type_for_var,
                            const Tensor &input_tensor, Tensor *output_tensor) {
             bool transformed = false;
@@ -39,14 +39,17 @@ void DataTransform(const OpKernelType &expected_kernel_type,
             //  // do layout transform
             //  if (NeedTransformLayout(expected_kernel_type.data_layout_,
             //                          kernel_type_for_var.data_layout_)) {
-  //    TransDataLayout(kernel_type_for_var, expected_kernel_type, in, &out);
+            //    TransDataLayout(kernel_type_for_var, expected_kernel_type, in,
+            //    &out);
             //    transformed = true;
             //    PassTensorData(&out, &in);
             //  }
             //
             //  // do data type transform
-  //  if (expected_kernel_type.data_type_ != kernel_type_for_var.data_type_) {
-  //    TransDataType(kernel_type_for_var, expected_kernel_type, in, &out);
+            //  if (expected_kernel_type.data_type_ !=
+            //  kernel_type_for_var.data_type_) {
+            //    TransDataType(kernel_type_for_var, expected_kernel_type, in,
+            //    &out);
             //    transformed = true;
             //    PassTensorData(&out, &in);
             //  }
@@ -59,13 +62,14 @@ void DataTransform(const OpKernelType &expected_kernel_type,
             //    PassTensorData(&out, &in);
             //  }
             //
-  //  PADDLE_ENFORCE(transformed, "No transform is applied, please check!");
+            //  PADDLE_ENFORCE(transformed, "No transform is applied, please
+            //  check!");
             // get output data
             output_tensor->ShareDataWith(in);
-}
+        }
 
-void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
-                            Variable &out_var) {
+        void CopyVariableWithTensor(const Variable &in_var,
+                                    const Tensor &tensor, Variable &out_var) {
             //  if (in_var.IsType<LoDTensor>()) {
             //    auto& in_lod_tensor = in_var.Get<LoDTensor>();
             //    auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
@@ -74,14 +78,15 @@ void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
             //    tran_lod_tensor->ShareDataWith(tensor);
             //  } else if (in_var.IsType<SelectedRows>()) {
             //    auto& in_selected_rows = in_var.Get<SelectedRows>();
-  //    auto* trans_selected_rows = out_var.GetMutable<SelectedRows>();
+            //    auto* trans_selected_rows =
+            //    out_var.GetMutable<SelectedRows>();
             //    trans_selected_rows->set_height(in_selected_rows.height());
             //    trans_selected_rows->set_rows(in_selected_rows.rows());
             //    trans_selected_rows->mutable_value()->ShareDataWith(tensor);
             //  } else {
             //    PADDLE_THROW("unknown var type");
             //  }
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/data_transform.h

@@ -28,14 +28,14 @@ SOFTWARE.
 #include "variable.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-void DataTransform(const OpKernelType &expected_kernel_type,
+        void DataTransform(const OpKernelType &expected_kernel_type,
                            const OpKernelType &kernel_type_for_var,
                            const Tensor &input_tensor, Tensor *out);
 
-void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
-                            Variable &out_var);
+        void CopyVariableWithTensor(const Variable &in_var,
+                                    const Tensor &tensor, Variable &out_var);
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/data_type.h

@@ -21,23 +21,23 @@ SOFTWARE.
 #include "framework.pb.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-//    inline proto::VarType::Type ToDataType(std::type_index type) {
-//        using namespace paddle_mobile::framework::proto;
-//        if (typeid(float).hash_code() == type.hash_code()) {
-//            return proto::VarType::FP32;
-//        } else if (typeid(double).hash_code() == type.hash_code()) {
-//            return proto::VarType::FP64;
-//        } else if (typeid(int).hash_code() == type.hash_code()) {
-//            return proto::VarType::INT32;
-//        } else if (typeid(int64_t).hash_code() == type.hash_code()) {
-//            return proto::VarType::INT64;
-//        } else if (typeid(bool).hash_code() == type.hash_code()) {
-//            return proto::VarType::BOOL;
-//        } else {
-////            PADDLE_THROW("Not supported");
-//        }
-//    }
-}
+        //    inline proto::VarType::Type ToDataType(std::type_index type) {
+        //        using namespace paddle_mobile::framework::proto;
+        //        if (typeid(float).hash_code() == type.hash_code()) {
+        //            return proto::VarType::FP32;
+        //        } else if (typeid(double).hash_code() == type.hash_code()) {
+        //            return proto::VarType::FP64;
+        //        } else if (typeid(int).hash_code() == type.hash_code()) {
+        //            return proto::VarType::INT32;
+        //        } else if (typeid(int64_t).hash_code() == type.hash_code()) {
+        //            return proto::VarType::INT64;
+        //        } else if (typeid(bool).hash_code() == type.hash_code()) {
+        //            return proto::VarType::BOOL;
+        //        } else {
+        ////            PADDLE_THROW("Not supported");
+        //        }
+        //    }
+    }
 } // namespace paddle_mobile

src/framework/ddim.cc

@@ -15,17 +15,17 @@ limitations under the License. */
 #include "ddim.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-/// @cond HIDDEN
+        /// @cond HIDDEN
 
-template <int i> Dim<i> make_dim(const int64_t *d) {
+        template <int i> Dim<i> make_dim(const int64_t *d) {
             return Dim<i>(*d, make_dim<i - 1>(d + 1));
-}
+        }
 
-template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
+        template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
 
-void make_ddim(DDim &ddim, const int64_t *dims, int n) {
+        void make_ddim(DDim &ddim, const int64_t *dims, int n) {
             switch (n) {
             case 0:
                 ddim = make_dim<0>(dims);
@@ -58,70 +58,74 @@ void make_ddim(DDim &ddim, const int64_t *dims, int n) {
                 ddim = make_dim<9>(dims);
                 break;
             default:
-    //      std::cout << "Dynamic dimensions must have between [1, 9]
+                //      std::cout << "Dynamic dimensions must have between [1,
+                //      9]
                 //      dimensions.";
                 break;
             }
-}
+        }
 
-/// @endcond
+        /// @endcond
 
-DDim make_ddim(std::initializer_list<int64_t> dims) {
+        DDim make_ddim(std::initializer_list<int64_t> dims) {
             DDim result(make_dim(0));
             make_ddim(result, dims.begin(), dims.size());
             return result;
-}
+        }
 
-DDim make_ddim(const std::vector<int64_t> &dims) {
+        DDim make_ddim(const std::vector<int64_t> &dims) {
             DDim result(make_dim(0));
             make_ddim(result, &dims[0], dims.size());
             return result;
-}
+        }
 
-DDim make_ddim(const std::vector<int> &dims) {
+        DDim make_ddim(const std::vector<int> &dims) {
             std::vector<int64_t> res(dims.size());
             std::transform(dims.begin(), dims.end(), res.begin(),
                            [](int d) { return static_cast<int64_t>(d); });
             return make_ddim(res);
-}
+        }
 
-/// @cond HIDDEN
-// XXX For some reason, putting this in an anonymous namespace causes errors
-struct DynamicMutableIndexer : Vistor<int64_t &> {
-public:
+        /// @cond HIDDEN
+        // XXX For some reason, putting this in an anonymous namespace causes
+        // errors
+        struct DynamicMutableIndexer : Vistor<int64_t &> {
+          public:
             explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
 
-  template <int D> int64_t &operator()(Dim<D> &dim) const { return dim[idx_]; }
+            template <int D> int64_t &operator()(Dim<D> &dim) const {
+                return dim[idx_];
+            }
 
-private:
+          private:
             int idx_;
-};
+        };
 
-struct DynamicConstIndexer : public Vistor<int64_t> {
-public:
+        struct DynamicConstIndexer : public Vistor<int64_t> {
+          public:
             explicit DynamicConstIndexer(int idx) : idx_(idx) {}
 
             template <int D> int64_t operator()(const Dim<D> &dim) const {
                 return dim[idx_];
             }
 
-private:
+          private:
             int idx_;
-};
+        };
 
-/// @endcond
+        /// @endcond
 
-int64_t &DDim::operator[](int idx) {
+        int64_t &DDim::operator[](int idx) {
             return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
-}
+        }
 
-int64_t DDim::operator[](int idx) const {
+        int64_t DDim::operator[](int idx) const {
             return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
-}
+        }
 
-int DDim::size() const { return arity(*this); }
+        int DDim::size() const { return arity(*this); }
 
-bool DDim::operator==(DDim d) const {
+        bool DDim::operator==(DDim d) const {
             //  if (var.which() != d.getVar().which()) {
             //    return false;
             //  } else {
@@ -136,11 +140,11 @@ bool DDim::operator==(DDim d) const {
 
             return true;
             //  }
-}
+        }
 
-bool DDim::operator!=(DDim d) const { return !(*this == d); }
+        bool DDim::operator!=(DDim d) const { return !(*this == d); }
 
-DDim DDim::operator+(DDim d) const {
+        DDim DDim::operator+(DDim d) const {
             std::vector<int64_t> v1 = vectorize(*this);
             std::vector<int64_t> v2 = vectorize(d);
 
@@ -153,9 +157,9 @@ DDim DDim::operator+(DDim d) const {
             }
 
             return make_ddim(v3);
-}
+        }
 
-DDim DDim::operator*(DDim d) const {
+        DDim DDim::operator*(DDim d) const {
             std::vector<int64_t> v1 = vectorize(*this);
             std::vector<int64_t> v2 = vectorize(d);
 
@@ -168,14 +172,14 @@ DDim DDim::operator*(DDim d) const {
             }
 
             return make_ddim(v3);
-}
+        }
 
-int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
+        int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
 
-void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
+        void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
 
-/// @cond HIDDEN
-struct VectorizeVisitor : Vistor<void> {
+        /// @cond HIDDEN
+        struct VectorizeVisitor : Vistor<void> {
             std::vector<int64_t> &vector;
 
             explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
@@ -186,36 +190,36 @@ struct VectorizeVisitor : Vistor<void> {
             }
 
             void operator()(const Dim<0> &t) {}
-};
-/// @endcond
+        };
+        /// @endcond
 
-std::vector<int64_t> vectorize(const DDim &ddim) {
+        std::vector<int64_t> vectorize(const DDim &ddim) {
             std::vector<int64_t> result;
             VectorizeVisitor visitor(result);
             DDim::ApplyVistor(visitor, ddim);
             return result;
-}
+        }
 
-// NOTE: framework::vectorize converts to type int64_t
-//       which does not fit cudnn inputs.
-std::vector<int> vectorize2int(const DDim &ddim) {
+        // NOTE: framework::vectorize converts to type int64_t
+        //       which does not fit cudnn inputs.
+        std::vector<int> vectorize2int(const DDim &ddim) {
             std::vector<int64_t> temp = vectorize(ddim);
             std::vector<int> result(temp.begin(), temp.end());
             return result;
-}
+        }
 
-struct ProductVisitor : Vistor<int64_t> {
+        struct ProductVisitor : Vistor<int64_t> {
             template <int D> int64_t operator()(const Dim<D> &dim) {
                 return product(dim);
             }
-};
+        };
 
-int64_t product(const DDim &ddim) {
+        int64_t product(const DDim &ddim) {
             ProductVisitor visitor;
             return DDim::ApplyVistor(visitor, ddim);
-}
+        }
 
-struct SliceVectorizeVisitor : Vistor<void> {
+        struct SliceVectorizeVisitor : Vistor<void> {
             std::vector<int64_t> &vector;
             int begin;
             int end;
@@ -223,10 +227,12 @@ struct SliceVectorizeVisitor : Vistor<void> {
             SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
                 : vector(v), begin(b), end(e) {
                 //    PADDLE_ENFORCE(begin < end,
-    //                   "Begin index must be less than end index in ddim
+                //                   "Begin index must be less than end index in
+                //                   ddim
                 //                   slice.");
                 //    PADDLE_ENFORCE(begin >= 0,
-    //                   "Begin index can't be less than zero in ddim slice.");
+                //                   "Begin index can't be less than zero in
+                //                   ddim slice.");
             }
 
             template <int S> void operator()(const Dim<S> &dim) {
@@ -242,11 +248,12 @@ struct SliceVectorizeVisitor : Vistor<void> {
             }
 
             void operator()(const Dim<0> &dim) {
-    //    PADDLE_ENFORCE(end == 0, "End index in ddim slice is out of bound.");
+                //    PADDLE_ENFORCE(end == 0, "End index in ddim slice is out
+                //    of bound.");
             }
-};
+        };
 
-DDim slice_ddim(const DDim &ddim, int begin, int end) {
+        DDim slice_ddim(const DDim &ddim, int begin, int end) {
             std::vector<int64_t> vec;
             vec.reserve(end - begin);
             SliceVectorizeVisitor visitor(vec, begin, end);
@@ -254,72 +261,74 @@ DDim slice_ddim(const DDim &ddim, int begin, int end) {
             DDim::ApplyVistor(visitor, ddim);
             //  visitor(ddim.var.Get<Dim<4>>());
             return make_ddim(vec);
-}
+        }
 
-/// \cond HIDDEN
+        /// \cond HIDDEN
 
-struct ArityVisitor : Vistor<int> {
+        struct ArityVisitor : Vistor<int> {
             template <int D> int operator()(Dim<D>) const { return D; }
-};
+        };
 
-/// \endcond
+        /// \endcond
 
-int arity(const DDim &d) {
+        int arity(const DDim &d) {
             ArityVisitor arityVisitor = ArityVisitor();
             return DDim::ApplyVistor(arityVisitor, d);
             //  return arityVisitor(d.var.Get<Dim<4>>());
             //  return boost::apply_visitor(ArityVisitor(), d); }
-}
-/// \cond HIDDEN
+        }
+        /// \cond HIDDEN
 
-/// \endcond
+        /// \endcond
 
-struct OSVistor : Vistor<std::ostream &> {
+        struct OSVistor : Vistor<std::ostream &> {
             OSVistor(std::ostream &os) : os_(os) {}
 
             template <int D> std::ostream &operator()(Dim<D> dim) const {
                 return os_ << dim;
             }
 
-private:
+          private:
             std::ostream &os_;
-};
+        };
 
-std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
+        std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
             auto vistor = OSVistor(os);
             DDim::ApplyVistor(vistor, ddim);
             return os;
-}
+        }
 
-DDim::DDim(std::initializer_list<int64_t> init_list) {
+        DDim::DDim(std::initializer_list<int64_t> init_list) {
             *this = make_ddim(init_list);
-}
+        }
 
-DDim flatten_to_2d(const DDim &src, int num_col_dims) {
+        DDim flatten_to_2d(const DDim &src, int num_col_dims) {
             int rank = src.size();
             return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
                               product(slice_ddim(src, num_col_dims, rank))});
-}
+        }
 
-DDim flatten_to_1d(const DDim &src) { return make_ddim({product(src)}); }
+        DDim flatten_to_1d(const DDim &src) {
+            return make_ddim({product(src)});
+        }
 
-DDim stride(const DDim &ddim) {
+        DDim stride(const DDim &ddim) {
             std::vector<int64_t> strides(ddim.size());
             strides[ddim.size() - 1] = 1;
             for (int i = ddim.size() - 2; i >= 0; --i) {
                 strides[i] = strides[i + 1] * ddim[i + 1];
             }
             return framework::make_ddim(strides);
-}
+        }
 
-DDim stride_numel(const framework::DDim &ddim) {
+        DDim stride_numel(const framework::DDim &ddim) {
             std::vector<int64_t> strides(ddim.size());
             strides[ddim.size() - 1] = ddim[ddim.size() - 1];
             for (int i = ddim.size() - 2; i >= 0; --i) {
                 strides[i] = strides[i + 1] * ddim[i];
             }
             return framework::make_ddim(strides);
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/ddim.h

@@ -22,21 +22,22 @@ limitations under the License. */
 #include <vector>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-/**
+        /**
          * \brief A dynamically sized dimension.
          *
          * The number of dimensions must be between [1, 9].
          */
-struct DDim {
-  typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
-                  Dim<7>, Dim<8>, Dim<9>>
+        struct DDim {
+            typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>,
+                            Dim<6>, Dim<7>, Dim<8>, Dim<9>>
                 DDimVar;
             DDimVar var;
 
             template <typename Vistor>
-  static typename Vistor::type_t ApplyVistor(Vistor vistor, const DDim &d) {
+            static typename Vistor::type_t ApplyVistor(Vistor vistor,
+                                                       const DDim &d) {
                 if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
                     return vistor(d.var.Get<Dim<0>>());
                 } else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
@@ -66,7 +67,9 @@ struct DDim {
 
             DDim() { var.Set<Dim<1>>(Dim<1>()); }
 
-  template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
+            template <int D> explicit DDim(const Dim<D> &in) {
+                var.Set<Dim<D>>(in);
+            }
 
             /*implicit*/ DDim(std::initializer_list<int64_t> init_list);
 
@@ -85,7 +88,8 @@ struct DDim {
             //  }
             //
             //  template <typename Visitor>
-  //  typename Visitor::result_type apply_visitor(Visitor& visitor) const {
+            //  typename Visitor::result_type apply_visitor(Visitor& visitor)
+            //  const {
             //    return var.apply_visitor(visitor);
             //  }
 
@@ -100,62 +104,63 @@ struct DDim {
             DDim operator*(DDim d) const;
 
             int size() const;
-};
+        };
 
-/**
+        /**
          * \brief Make a DDim from std::vector<int64_t>
          *
          * \param dims An vector of ints. Must be sized between [1, 9]
          */
-DDim make_ddim(const std::vector<int64_t> &dims);
+        DDim make_ddim(const std::vector<int64_t> &dims);
 
-DDim make_ddim(const std::vector<int> &dims);
+        DDim make_ddim(const std::vector<int> &dims);
 
-/**
+        /**
          * \brief Make a DDim from an initializer list
          *
          * \param dims An initializer list of ints. Must be sized between [1, 9]
          *
          */
-DDim make_ddim(std::initializer_list<int64_t> dims);
+        DDim make_ddim(std::initializer_list<int64_t> dims);
 
-int64_t get(const DDim &dim, int idx);
+        int64_t get(const DDim &dim, int idx);
 
-void set(DDim &dim, int idx, int val);
+        void set(DDim &dim, int idx, int val);
 
-std::vector<int64_t> vectorize(const DDim &ddim);
+        std::vector<int64_t> vectorize(const DDim &ddim);
 
-std::vector<int> vectorize2int(const DDim &ddim);
+        std::vector<int> vectorize2int(const DDim &ddim);
 
-int64_t product(const DDim &ddim);
+        int64_t product(const DDim &ddim);
 
-/**
+        /**
          * \brief Slice a ddim
          *
          * Slice dim with [begin, end).
          * e.g.  DDim d = make_ddim({1,2,3,4,5});
          *       slice_ddim(d, 1, 3); ====> {2,3}
          */
-DDim slice_ddim(const DDim &dim, int begin, int end);
+        DDim slice_ddim(const DDim &dim, int begin, int end);
 
-/**
+        /**
          * \brief What is the length of this dimension?
          *
          * \param Dynamic dimension to inspect
          */
 
-int arity(const DDim &ddim);
+        int arity(const DDim &ddim);
 
-std::ostream &operator<<(std::ostream &, const DDim &);
+        std::ostream &operator<<(std::ostream &, const DDim &);
 
-// Reshape a tensor to a matrix. The matrix's first dimension(column length)
-// will be the product of tensor's first `num_col_dims` dimensions.
-DDim flatten_to_2d(const DDim &src, int num_col_dims);
+        // Reshape a tensor to a matrix. The matrix's first dimension(column
+        // length)
+        // will be the product of tensor's first `num_col_dims` dimensions.
+        DDim flatten_to_2d(const DDim &src, int num_col_dims);
 
-DDim flatten_to_1d(const DDim &src);
+        DDim flatten_to_1d(const DDim &src);
 
-DDim stride(const DDim &ddim);
+        DDim stride(const DDim &ddim);
 
-DDim stride_numel(const DDim &ddim);
-} // namespace framework
+        DDim stride_numel(const DDim &ddim);
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/dim.h

@@ -21,25 +21,29 @@
 #include "platform/hostdevice.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-// Statically sized, statically indexed dimension
-template <int i> struct Dim {
+        // Statically sized, statically indexed dimension
+        template <int i> struct Dim {
             static constexpr int dimensions = i;
 
             template <typename... Args>
-  HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
-    static_assert(sizeof...(_tail) == i - 1,
+            HOSTDEVICE Dim(int64_t _head, Args... _tail)
+                : head(_head), tail(_tail...) {
+                static_assert(
+                    sizeof...(_tail) == i - 1,
                     "Dim initialized with the wrong number of parameters");
             }
 
             HOSTDEVICE
-  Dim(int64_t _head, const Dim<i - 1> &_tail) : head(_head), tail(_tail) {}
+            Dim(int64_t _head, const Dim<i - 1> &_tail)
+                : head(_head), tail(_tail) {}
 
             HOSTDEVICE
             Dim() : head(0), tail() {}
 
-  /** Construct a Dim from a linear index and size.  Uses Fortran order
+            /** Construct a Dim from a linear index and size.  Uses Fortran
+             * order
              * indexing. */
             HOSTDEVICE
             Dim(int64_t idx, const Dim<i> &size)
@@ -66,10 +70,10 @@ template <int i> struct Dim {
 
             int64_t head;
             Dim<i - 1> tail;
-};
+        };
 
-// Base case specialization
-template <> struct Dim<0> {
+        // Base case specialization
+        template <> struct Dim<0> {
             static constexpr int dimensions = 0;
 
             HOSTDEVICE
@@ -99,36 +103,41 @@ template <> struct Dim<0> {
             int64_t &operator[](int idx);
             HOSTDEVICE
             int64_t operator[](int idx) const;
-};
+        };
 
-namespace {
+        namespace {
 
-// Helper for accessing Dim classes
-template <int i> struct DimGetter {
+            // Helper for accessing Dim classes
+            template <int i> struct DimGetter {
                 // Return a copy if Dim is const
-  template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
+                template <typename D>
+                HOSTDEVICE static int64_t impl(const D &d) {
                     return DimGetter<i - 1>::impl(d.tail);
                 }
                 // Return a reference if Dim is mutable
                 template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
                     return DimGetter<i - 1>::impl(d.tail);
                 }
-};
+            };
 
-// Eureka! We found the element!
-template <> struct DimGetter<0> {
+            // Eureka! We found the element!
+            template <> struct DimGetter<0> {
                 // Return a copy if Dim is const
-  template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
+                template <typename D>
+                HOSTDEVICE static int64_t impl(const D &d) {
                     return d.head;
                 }
                 // Return a reference if Dim is mutable
-  template <typename D> HOSTDEVICE static int64_t &impl(D &d) { return d.head; }
-};
+                template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
+                    return d.head;
+                }
+            };
 
-template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
+            template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
 #ifndef __CUDA_ARCH__
                 if (idx < 0) {
-    throw std::invalid_argument("Tried to access a negative dimension");
+                    throw std::invalid_argument(
+                        "Tried to access a negative dimension");
                 }
 #else
                 PADDLE_ASSERT(idx >= 0);
@@ -137,9 +146,9 @@ template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
                     return dim.head;
                 }
                 return indexer(dim.tail, idx - 1);
-}
+            }
 
-template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
+            template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
 #ifndef __CUDA_ARCH__
                 throw std::invalid_argument("Invalid index");
 #else
@@ -153,12 +162,14 @@ template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
 #endif
                 return head;
 #endif
-}
+            }
 
-template <int D> HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
+            template <int D>
+            HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
 #ifndef __CUDA_ARCH__
                 if (idx < 0) {
-    throw std::invalid_argument("Tried to access a negative dimension");
+                    throw std::invalid_argument(
+                        "Tried to access a negative dimension");
                 }
 #else
                 PADDLE_ASSERT(idx >= 0);
@@ -167,9 +178,10 @@ template <int D> HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
                     return dim.head;
                 }
                 return indexer(dim.tail, idx - 1);
-}
+            }
 
-template <> HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
+            template <>
+            HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
 #ifndef __CUDA_ARCH__
                 throw std::invalid_argument("Invalid index");
 #else
@@ -183,146 +195,152 @@ template <> HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
 #endif
                 return head;
 #endif
-}
+            }
 
-} // namespace
-// Static access to constant Dim
-template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
+        } // namespace
+        // Static access to constant Dim
+        template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
             return DimGetter<i>::impl(d);
-}
+        }
 
-// Static access to mutable Dim
-template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
+        // Static access to mutable Dim
+        template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
             return DimGetter<i>::impl(d);
-}
+        }
 
-// Dynamic access to constant Dim
-template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
+        // Dynamic access to constant Dim
+        template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
             //  std::cout << "l: " << l << std::endl;
             return indexer(*this, i);
-}
+        }
 
-// Dynamic access to mutable Dim
-template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
+        // Dynamic access to mutable Dim
+        template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
             return indexer(*this, i);
-}
+        }
 
-// Dynamic access to constant Dim
-inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
+        // Dynamic access to constant Dim
+        inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
             return indexer(*this, i);
-}
+        }
 
-// Dynamic access to mutable Dim
-inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
+        // Dynamic access to mutable Dim
+        inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
             return indexer(*this, i);
-}
+        }
 
-// Dynamic access to constant Dim
-// without std::enable_if will try to instantiate this on get<0>(d)
-template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l> &d,
-                                                               int i) {
+        // Dynamic access to constant Dim
+        // without std::enable_if will try to instantiate this on get<0>(d)
+        template <int l>
+        HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type
+        get(const Dim<l> &d, int i) {
             return d[i];
-}
+        }
 
-// Dynamic access to mutable Dim
-template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type get(Dim<l> &d,
-                                                                 int i) {
+        // Dynamic access to mutable Dim
+        template <int l>
+        HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type
+        get(Dim<l> &d, int i) {
             return d[i];
-}
+        }
 
-// Dot product of two dims
-template <int i>
-HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
+        // Dot product of two dims
+        template <int i>
+        HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
             return a.head * b.head + linearize(a.tail, b.tail);
-}
+        }
 
-// Base case dot product of two Dims
-// Notice it is inline because it is no longer a template
-template <>
-HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
+        // Base case dot product of two Dims
+        // Notice it is inline because it is no longer a template
+        template <>
+        HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
             return 0;
-}
+        }
 
-// Product of a Dim
-template <int i> HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
+        // Product of a Dim
+        template <int i>
+        HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
             return prod * a.head * product(a.tail);
-}
+        }
 
-// Base case product of a Dim
-// Notice it is inline because it is no longer a template
-template <> HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
+        // Base case product of a Dim
+        // Notice it is inline because it is no longer a template
+        template <>
+        HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
             return prod;
-}
+        }
 
-// Is 0 <= idx_i < size_i for all i?
-template <int i>
-HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
+        // Is 0 <= idx_i < size_i for all i?
+        template <int i>
+        HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
             return ((0 <= idx.head) && (idx.head < size.head) &&
                     contained(idx.tail, size.tail));
-}
+        }
 
-// Base case of is 0 <= idx_i < size_i ?
-// Notice it is inline because it is no longer a template
-template <>
-HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
+        // Base case of is 0 <= idx_i < size_i ?
+        // Notice it is inline because it is no longer a template
+        template <>
+        HOSTDEVICE inline bool contained(const Dim<0> &idx,
+                                         const Dim<0> &size) {
             return true;
-}
+        }
 
-/**
+        /**
          * \brief Compute exclusive prefix-multiply of a Dim.
          */
-template <int i>
-HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
+        template <int i>
+        HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
             return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
-}
+        }
 
-///\cond HIDDEN
-// Base case of ex_prefix_mul
-// Notice it is inline because it is no longer a template
-template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
+        ///\cond HIDDEN
+        // Base case of ex_prefix_mul
+        // Notice it is inline because it is no longer a template
+        template <>
+        HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
             return Dim<0>();
-}
-///\endcond
+        }
+        ///\endcond
 
-/**
+        /**
          * Add two dimensions together
          */
-template <int i> HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
+        template <int i>
+        HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
             return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
-}
+        }
 
-// Base case
-template <>
-HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
+        // Base case
+        template <>
+        HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
             return Dim<0>();
-}
+        }
 
-template <int i>
-HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
+        template <int i>
+        HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
             return dim_plus(lhs, rhs);
-}
+        }
 
-/**
+        /**
          * Multiply two dimensions together
          */
-template <int i> HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
+        template <int i>
+        HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
             return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
-}
+        }
 
-// Base case
-template <>
-HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
+        // Base case
+        template <>
+        HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
             return Dim<0>();
-}
+        }
 
-template <int i>
-HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
+        template <int i>
+        HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
             return dim_mult(lhs, rhs);
-}
+        }
 
-/**
+        /**
          * \brief Normalize strides to ensure any dimension with extent 1
          * has stride 0.
          *
@@ -332,66 +350,70 @@ HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
          *
          */
 
-template <int i>
-HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
+        template <int i>
+        HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size,
+                                            const Dim<i> &stride) {
             int norm_stride = size.head == 1 ? 0 : stride.head;
-  return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
-}
+            return Dim<i>(norm_stride,
+                          normalize_strides(size.tail, stride.tail));
+        }
 
-///\cond HIDDEN
+        ///\cond HIDDEN
 
-template <>
-HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
+        template <>
+        HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
                                                    const Dim<0> &stride) {
             return Dim<0>();
-}
+        }
 
-///\endcond
+        ///\endcond
 
-/**
+        /**
          * Helper function to create a Dim
          *
- * \param idxes The type of Dim constructed depends on the number of params
+         * \param idxes The type of Dim constructed depends on the number of
+         * params
          *
          */
 
-template <typename... Args>
-HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
+        template <typename... Args>
+        HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
             return Dim<sizeof...(Args)>(idxes...);
-}
+        }
 
-// Allows us to output a Dim
-// XXX For some reason, overloading fails to resolve this correctly
-template <int i>
-typename std::enable_if<(i > 1), std::ostream &>::type
-operator<<(std::ostream &os, const Dim<i> &d) {
+        // Allows us to output a Dim
+        // XXX For some reason, overloading fails to resolve this correctly
+        template <int i>
+        typename std::enable_if<(i > 1), std::ostream &>::type
+        operator<<(std::ostream &os, const Dim<i> &d) {
             os << d.head << ", " << d.tail;
             return os;
-}
+        }
 
-// Base case that allows us to output a Dim
-// XXX I wish this could be an overload instead of a template
-template <int i>
-typename std::enable_if<(i == 1), std::ostream &>::type
-operator<<(std::ostream &os, const Dim<i> &d) {
+        // Base case that allows us to output a Dim
+        // XXX I wish this could be an overload instead of a template
+        template <int i>
+        typename std::enable_if<(i == 1), std::ostream &>::type
+        operator<<(std::ostream &os, const Dim<i> &d) {
             os << d.head;
             return os;
-}
+        }
 
-inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
+        inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
             return os;
-}
+        }
 
-template <int i> HOST std::string Dim<i>::to_string() const {
+        template <int i> HOST std::string Dim<i>::to_string() const {
             std::stringstream stream;
 
             stream << *this;
 
             return stream.str();
-}
+        }
 
-template <int D>
-HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
+        template <int D>
+        HOSTDEVICE Dim<D> linear_to_dimension(int linear_index,
+                                              Dim<D> extents) {
             Dim<D> result;
 
             for (int i = 0; i < D - 1; ++i) {
@@ -402,7 +424,7 @@ HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
             result[D - 1] = linear_index;
 
             return result;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/executor.cpp

@@ -23,10 +23,10 @@ SOFTWARE.
 #include "variable.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype>
-Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
+        template <typename Dtype>
+        Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
             if (use_optimize_) {
                 to_predict_program_ = program_.optimizeProgram;
             } else {
@@ -40,24 +40,25 @@ Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
                 std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
                 for (int j = 0; j < ops.size(); ++j) {
                     std::shared_ptr<OpDesc> op = ops[j];
-      if (op->Type() == "conv2d" && op->Input("Input")[0] == "pixel") {
+                    if (op->Type() == "conv2d" &&
+                        op->Input("Input")[0] == "pixel") {
                         Attribute strides_attr = op->GetAttrMap().at("strides");
-        std::vector<int> stride = strides_attr.Get<std::vector<int>>();
+                        std::vector<int> stride =
+                            strides_attr.Get<std::vector<int>>();
                         for (int k = 0; k < stride.size(); ++k) {
                         }
-
                         std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
                             std::make_shared<operators::ConvOp<Dtype, float>>(
-                op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
-                program_.scope);
+                                op->Type(), op->GetInputs(), op->GetOutputs(),
+                                op->GetAttrMap(), program_.scope);
                         ops_of_block_[*block_desc.get()].push_back(conv);
                     }
                 }
             }
-}
+        }
 
-template <typename Dtype>
-std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
+        template <typename Dtype>
+        std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
             // feed
             auto scope = program_.scope;
             Variable *g_feed_value = scope->Var("pixel");
@@ -68,27 +69,29 @@ std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
             Tensor *output_tensor = con_output->GetMutable<Tensor>();
             output_tensor->mutable_data<float>({1, 16, 32, 32});
             //  std::cout << typeid(output_tensor).name() << std::endl;
-  //  std::cout << "output_tensor dims: " << output_tensor->dims() << std::endl;
+            //  std::cout << "output_tensor dims: " << output_tensor->dims() <<
+            //  std::endl;
 
             std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
             out_tensor.reset(output_tensor);
 
             predict(t, 0);
             return out_tensor;
-}
+        }
 
-template <typename Dtype>
-void Executor<Dtype>::predict(const Tensor &t, int block_id) {
+        template <typename Dtype>
+        void Executor<Dtype>::predict(const Tensor &t, int block_id) {
             std::shared_ptr<BlockDesc> to_predict_block =
                 to_predict_program_->Block(block_id);
-  for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
+            for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
+                 ++j) {
                 auto op = ops_of_block_[*to_predict_block.get()][j];
                 //    std::cout << "开始run" << std::endl;
                 op->Run();
             }
-}
+        }
 
-template class Executor<CPU>;
+        template class Executor<CPU>;
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/executor.h

@@ -32,14 +32,14 @@ SOFTWARE.
 #include "variable.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype> class Executor {
-public:
+        template <typename Dtype> class Executor {
+          public:
             Executor(const Program<Dtype> p);
             std::shared_ptr<Tensor> predict(Tensor &t);
 
-private:
+          private:
             const framework::Program<Dtype> program_;
             std::shared_ptr<ProgramDesc> to_predict_program_;
             void predict(const Tensor &t, int block_id);
@@ -47,7 +47,7 @@ private:
                      std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
                 ops_of_block_;
             bool use_optimize_ = false;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/lod_tensor.cc

@@ -19,9 +19,9 @@ limitations under the License. */
 #include <string.h>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-std::ostream &operator<<(std::ostream &os, const LoD &lod) {
+        std::ostream &operator<<(std::ostream &os, const LoD &lod) {
             os << "{";
             for (auto &v : lod) {
                 os << "{";
@@ -39,10 +39,11 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
             os << "}";
 
             return os;
-}
+        }
 
-std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
-  //  PADDLE_ENFORCE(t.type().hash_code() == typeid(float).hash_code());
+        std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
+            //  PADDLE_ENFORCE(t.type().hash_code() ==
+            //  typeid(float).hash_code());
 
             //  if (!platform::is_cpu_place(t.place())) {
             //    LoDTensor tt;
@@ -65,15 +66,15 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
             }
 
             return os;
-}
+        }
 
-std::string LoDToString(const LoD &lod) {
+        std::string LoDToString(const LoD &lod) {
             std::ostringstream stream;
             stream << lod;
             return stream.str();
-}
+        }
 
-LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
+        LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                          size_t elem_end) {
             //  PADDLE_ENFORCE_LT(level, in.size());
             //  PADDLE_ENFORCE_LT(elem_end, in[level].size());
@@ -91,7 +92,8 @@ LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                                  in_level.begin() + above_level.back() + 1);
             }
             for (size_t lvl = 0; lvl < res.size(); lvl++) {
-    // to make the first offset equals 0, all the elements minus the first
+                // to make the first offset equals 0, all the elements minus the
+                // first
                 // element
                 size_t front = res[lvl].front();
                 for (auto &ele : res[lvl]) {
@@ -99,23 +101,24 @@ LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                 }
             }
             return res;
-}
+        }
 
-LoD ToAbsOffset(const LoD &in) {
+        LoD ToAbsOffset(const LoD &in) {
             // the lowest level stores relative offsets
             if (in.empty() || in.size() == 1)
                 return in;
             LoD result = in;
-  for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
+            for (auto level = static_cast<int>(in.size() - 2); level >= 0;
+                 level--) {
                 for (size_t i = 0; i < in[level].size(); ++i) {
                     size_t index = in[level][i];
                     result[level][i] = result[level + 1][index];
                 }
             }
             return result;
-}
+        }
 
-bool operator==(const LoD &a, const LoD &b) {
+        bool operator==(const LoD &a, const LoD &b) {
             if (a.size() != b.size()) {
                 return false;
             }
@@ -133,21 +136,25 @@ bool operator==(const LoD &a, const LoD &b) {
                 }
             }
             return true;
-}
+        }
 
-bool CheckLoD(const LoD &in, int tensor_height) {
+        bool CheckLoD(const LoD &in, int tensor_height) {
             if (in.empty())
                 return true;
             for (const auto &level : in) {
-    // check: there should be more than 2 offsets existing in each level.
+                // check: there should be more than 2 offsets existing in each
+                // level.
                 if (level.size() < 2)
                     return false;
-    // check: the first offset(the begin offset) of each level should be 0.
+                // check: the first offset(the begin offset) of each level
+                // should be 0.
                 if (level.front() != 0)
                     return false;
-    // check: all the offsets in a level should be ascending(no same items
+                // check: all the offsets in a level should be ascending(no same
+                // items
                 // allows).
-    if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
+                if (!std::is_sorted(level.begin(), level.begin(),
+                                    [](size_t a, size_t b) {
                                         if (a < b)
                                             return true;
                                         return false;
@@ -156,29 +163,34 @@ bool CheckLoD(const LoD &in, int tensor_height) {
                     return false;
                 }
             }
-  // check: the lowest level's last offset should equals `tensor_height` if
+            // check: the lowest level's last offset should equals
+            // `tensor_height` if
             //        tensor_height>0.
             if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
                 return false;
 
-  // check: the higher level's last offset should equals the lower level's
+            // check: the higher level's last offset should equals the lower
+            // level's
             // size-1.
-  // NOTE LoD store the levels from top to bottom, so the higher level goes
+            // NOTE LoD store the levels from top to bottom, so the higher level
+            // goes
             // first.
             for (size_t level = 0; level < in.size() - 1; level++) {
                 if (in[level].back() != in[level + 1].size() - 1)
                     return false;
             }
             return true;
-}
+        }
 
-bool CheckAbsLoD(const LoD &in, int tensor_height) {
+        bool CheckAbsLoD(const LoD &in, int tensor_height) {
             if (in.empty())
                 return true;
             for (const auto &level : in) {
-    // check: all the offsets in a level should be ascending(no same items
+                // check: all the offsets in a level should be ascending(no same
+                // items
                 // allows).
-    if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
+                if (!std::is_sorted(level.begin(), level.begin(),
+                                    [](size_t a, size_t b) {
                                         if (a < b)
                                             return true;
                                         return false;
@@ -186,11 +198,13 @@ bool CheckAbsLoD(const LoD &in, int tensor_height) {
                     return false;
                 }
 
-    // check: there should be more than 2 offsets existing in each level.
+                // check: there should be more than 2 offsets existing in each
+                // level.
                 if (level.size() < 2)
                     return false;
 
-    // check: the first offset of each level should be 0, and the last should be
+                // check: the first offset of each level should be 0, and the
+                // last should be
                 // the same(the height of underlying tensor).
                 if (level.front() != 0)
                     return false;
@@ -201,20 +215,24 @@ bool CheckAbsLoD(const LoD &in, int tensor_height) {
                 }
             }
             return true;
-}
+        }
 
-using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
+        using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
 
-LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
-                                        size_t end_idx, size_t start_level) {
+        LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod,
+                                                size_t start_idx,
+                                                size_t end_idx,
+                                                size_t start_level) {
             LoD sub_lod;
 
-  for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
+            for (size_t level_idx = start_level; level_idx < lod.size();
+                 ++level_idx) {
                 //    PADDLE_ENFORCE_LE(start_idx, end_idx);
                 //    PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
                 std::vector<size_t> level_lens;
                 for (size_t i = start_idx; i < end_idx; ++i) {
-      level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
+                    level_lens.push_back(lod[level_idx][i + 1] -
+                                         lod[level_idx][i]);
                 }
                 sub_lod.emplace_back(level_lens);
                 start_idx = lod[level_idx][start_idx];
@@ -222,12 +240,13 @@ LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
             }
 
             return LoDAndOffset{sub_lod, {start_idx, end_idx}};
-}
+        }
 
-void AppendLoD(LoD *lod, const LoD &lod_length) {
+        void AppendLoD(LoD *lod, const LoD &lod_length) {
             //  PADDLE_ENFORCE(
             //      lod->empty() || lod->size() == lod_length.size(),
-  //      "The lod_length should has the same size with the appended lod.");
+            //      "The lod_length should has the same size with the appended
+            //      lod.");
             if (lod->empty()) {
                 for (size_t i = 0; i < lod_length.size(); ++i) {
                     lod->emplace_back(1, 0); // size = 1, value = 0;
@@ -240,12 +259,13 @@ void AppendLoD(LoD *lod, const LoD &lod_length) {
                     level.push_back(level.back() + len);
                 }
             }
-}
+        }
 
-void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
+        void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
             { // the 1st field, uint32_t version for LoDTensor
                 constexpr uint32_t version = 0;
-    os.write(reinterpret_cast<const char *>(&version), sizeof(version));
+                os.write(reinterpret_cast<const char *>(&version),
+                         sizeof(version));
             }
             {
                 // the 2st field, LoD information
@@ -258,27 +278,31 @@ void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
                 os.write(reinterpret_cast<const char *>(&size), sizeof(size));
 
                 for (auto &each : lod) {
-      size = each.size() * sizeof(framework::LoD::value_type::value_type);
-      os.write(reinterpret_cast<const char *>(&size), sizeof(size));
+                    size = each.size() *
+                           sizeof(framework::LoD::value_type::value_type);
+                    os.write(reinterpret_cast<const char *>(&size),
+                             sizeof(size));
                     os.write(reinterpret_cast<const char *>(each.data()),
                              static_cast<std::streamsize>(size));
                 }
             }
             // the 3st field, Tensor
             TensorToStream(os, static_cast<Tensor>(tensor));
-}
+        }
 
-void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
+        void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
             {
                 // the 1st field, unit32_t version for LoDTensor
                 uint32_t version;
                 is.read(reinterpret_cast<char *>(&version), sizeof(version));
-    //    PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
+                //    PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
+                //    supported");
             }
             {
                 // the 2st field, LoD information
                 uint64_t lod_level;
-    is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
+                is.read(reinterpret_cast<char *>(&lod_level),
+                        sizeof(lod_level));
                 auto &lod = *tensor->mutable_lod();
                 lod.resize(lod_level);
                 for (uint64_t i = 0; i < lod_level; ++i) {
@@ -292,7 +316,7 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
             }
             // the 3st filed, Tensor
             TensorFromStream(is, static_cast<Tensor *>(tensor));
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/lod_tensor.h

@@ -23,13 +23,15 @@ limitations under the License. */
 
 namespace paddle_mobile {
 
-namespace framework {
+    namespace framework {
 
-/*
+        /*
          * LoD is short for Level of Details.
          *
- * - in a level, each element indicates relative offset of the lower level
- * - the first element should be 0 and that indicates that this sequence start
+         * - in a level, each element indicates relative offset of the lower
+         * level
+         * - the first element should be 0 and that indicates that this sequence
+         * start
          * from 0
          * - each sequence's begin and end(no-inclusive) is level[id, id+1]
          *
@@ -40,25 +42,25 @@ namespace framework {
          *    0 2 4 7
          *    0 2 5 7 10 12 15 20
          */
-using LoD = std::vector<std::vector<size_t>>;
+        using LoD = std::vector<std::vector<size_t>>;
 
-std::ostream &operator<<(std::ostream &os, const LoD &lod);
+        std::ostream &operator<<(std::ostream &os, const LoD &lod);
 
-std::ostream &operator<<(std::ostream &os, const LoDTensor &t);
+        std::ostream &operator<<(std::ostream &os, const LoDTensor &t);
 
-std::string LoDToString(const LoD &lod);
+        std::string LoDToString(const LoD &lod);
 
-LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
+        LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                          size_t elem_end);
 
-/*
+        /*
          * Transform an LoD from relative offsets to absolute offsets.
          */
-LoD ToAbsOffset(const LoD &in);
+        LoD ToAbsOffset(const LoD &in);
 
-bool operator==(const LoD &a, const LoD &b);
+        bool operator==(const LoD &a, const LoD &b);
 
-/*
+        /*
          * Check whether this lod's format is valid.
          *
          * ATTENTION:
@@ -66,37 +68,41 @@ bool operator==(const LoD &a, const LoD &b);
          *
          * It will check two things:
          *
- *  1. all the offsets in a level should be ascending(no same items allows).
+         *  1. all the offsets in a level should be ascending(no same items
+         * allows).
          *  2. there should be more than 2 offsets existing in each level.
- *  3. the higher level's last offset should equals the lower level's size-1.
+         *  3. the higher level's last offset should equals the lower level's
+         * size-1.
          *  4. the first offset(the begin offset) of each level should be 0.
          *  5. the lowest level's last offset should equals `tensor_height` if
          * tensor_height>0.
          */
 
-bool CheckLoD(const LoD &in, int tensor_height = -1);
+        bool CheckLoD(const LoD &in, int tensor_height = -1);
 
-/*
+        /*
          * Check whether this absolute lod's format is valid.
          *
          * ATTENTION:
          *   - Empty lod is treated as valid.
          *
          * It will check two things:
- *  1. all the offsets in a level should be ascending(no same items allows)
+         *  1. all the offsets in a level should be ascending(no same items
+         * allows)
          *  2. there should be more than 2 offsets existing in each level.
- *  3. the first offset of each level should be 0, and the last should be the
+         *  3. the first offset of each level should be 0, and the last should
+         * be the
          *     same(the height of underlying tensor) or `tensor_height` if
          *     tensor_height>0.
          */
-bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
+        bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
 
-/*
+        /*
          * LoDTensor (Level of details Tensor)
          * see https://en.wikipedia.org/wiki/Level_of_details for reference.
          */
-class LoDTensor : public Tensor {
-public:
+        class LoDTensor : public Tensor {
+          public:
             LoDTensor() : Tensor() {}
 
             explicit LoDTensor(const LoD &lod) : lod_(lod) {}
@@ -110,15 +116,19 @@ public:
             /*
              * Get the start offset and end offset of an  element from LoD.
              */
-  std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
+            std::pair<size_t, size_t> lod_element(size_t level,
+                                                  size_t elem) const {
                 //    PADDLE_ENFORCE_LT(level, NumLevels());
                 //    PADDLE_ENFORCE_LT(elem, NumElements(level));
-    return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
+                return std::make_pair((lod_)[level][elem],
+                                      (lod_)[level][elem + 1]);
             }
 
             /*
-   * Number of LoDTensor's levels, each level has units of data, for example,
-   * in the sentence's view, article, paragraph, sentence are 3 levels.
+             * Number of LoDTensor's levels, each level has units of data, for
+             * example,
+             * in the sentence's view, article, paragraph, sentence are 3
+             * levels.
              */
             size_t NumLevels() const { return lod_.size(); }
 
@@ -131,11 +141,11 @@ public:
                 return (lod_)[level].size() - 1;
             }
 
-private:
+          private:
             LoD lod_;
-};
+        };
 
-/*
+        /*
          * Expand the `source` to fit the LoD of `lod`. For example, a `source`
          * LoDTensor is
          *  - LoD: [0, 2]
@@ -145,8 +155,9 @@ private:
          * returns a new LoDTensor
          *  - [a0 a0 a0 a1 a1]
          */
-template <typename T>
-LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, size_t level) {
+        template <typename T>
+        LoDTensor LodExpand(const LoDTensor &source, const LoD &lod,
+                            size_t level) {
             LoD abs_lod = ToAbsOffset(lod);
             const auto &lod_level = lod[level];
             size_t num_instances = source.dims()[0];
@@ -161,40 +172,41 @@ LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, size_t level) {
 
             //  PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
             for (size_t ins = 0; ins < num_instances; ins++) {
-    for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1]; elem++) {
+                for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1];
+                     elem++) {
                     auto slice = tensor.Slice(elem, elem + 1);
                     TensorCopy(source.Slice(ins, ins + 1), &slice);
                 }
             }
             return tensor;
-}
-
-// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
-// relative length of details for every levels(i.e., [start_level: ]).
-//
-// For example,
-//   lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
-//   start_level = 0
-//   start_idx = 1
-//   end_idx = 3
-//
-// Returns:
-//  LoD = [[1, 4], [2, 4, 2, 3, 2]]
-//  pair<size_t, size_t> = {11, 24}
-std::pair<LoD, std::pair<size_t, size_t>>
-GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx, size_t end_idx,
-                           size_t start_level);
-
-void AppendLoD(LoD *lod, const LoD &lod_length);
-
-/*
+        }
+
+        // Get the absolute offset of a lod[start_level][start_idx:end_idx] and
+        // relative length of details for every levels(i.e., [start_level: ]).
+        //
+        // For example,
+        //   lod = [[0, 3, 4, 8], [0, 9, 10, 11, 13, 17, 19, 22, 24]]
+        //   start_level = 0
+        //   start_idx = 1
+        //   end_idx = 3
+        //
+        // Returns:
+        //  LoD = [[1, 4], [2, 4, 2, 3, 2]]
+        //  pair<size_t, size_t> = {11, 24}
+        std::pair<LoD, std::pair<size_t, size_t>>
+        GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
+                                   size_t end_idx, size_t start_level);
+
+        void AppendLoD(LoD *lod, const LoD &lod_length);
+
+        /*
          * Serialize/Desiralize LoDTensor to std::ostream
          * You can pass ofstream or ostringstream to serilize to file
          * or to a in memory string. GPU tensor will be copied to CPU.
          */
-void SerializeToStream(std::ostream &os, const LoDTensor &tensor);
+        void SerializeToStream(std::ostream &os, const LoDTensor &tensor);
 
-void DeserializeFromStream(std::istream &is, LoDTensor *tensor);
+        void DeserializeFromStream(std::istream &is, LoDTensor *tensor);
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/op_desc.cpp

@@ -5,9 +5,9 @@
 #include "op_desc.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
+        OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
             for (int i = 0; i < desc_.inputs_size(); ++i) {
                 const proto::OpDesc::Var &var = desc_.inputs(i);
                 std::vector<std::string> &args = inputs_[var.parameter()];
@@ -36,24 +36,27 @@ OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
                     //      }
                 }
             }
-}
+        }
 
-const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
+        const std::vector<std::string> &
+        OpDesc::Input(const std::string &name) const {
             return inputs_.find(name)->second;
-}
+        }
 
-const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
+        const std::vector<std::string> &
+        OpDesc::Output(const std::string &name) const {
             return outputs_.find(name)->second;
-}
+        }
 
-Attribute OpDesc::GetAttr(const std::string &name) const {
+        Attribute OpDesc::GetAttr(const std::string &name) const {
             auto it = attrs_.find(name);
             return it->second;
-}
+        }
 
-const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
+        const std::unordered_map<std::string, Attribute> &
+        OpDesc::GetAttrMap() const {
             return attrs_;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/op_desc.h

@@ -23,13 +23,15 @@ SOFTWARE.
 #include "paddle_mobile_object.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class OpDesc : PaddleMobileObject {
-public:
+        class OpDesc : PaddleMobileObject {
+          public:
             OpDesc(const proto::OpDesc &desc);
-  const std::vector<std::string> &Input(const std::string &name) const;
-  const std::vector<std::string> &Output(const std::string &name) const;
+            const std::vector<std::string> &
+            Input(const std::string &name) const;
+            const std::vector<std::string> &
+            Output(const std::string &name) const;
             Attribute GetAttr(const std::string &name) const;
 
             const VariableNameMap &GetInputs() { return inputs_; }
@@ -40,12 +42,12 @@ public:
 
             const std::string &Type() { return desc_.type(); };
 
-private:
+          private:
             proto::OpDesc desc_;
             VariableNameMap inputs_;
             VariableNameMap outputs_;
             AttributeMap attrs_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/op_info.h

@@ -22,23 +22,25 @@ SOFTWARE.
 #include "framework.pb.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype> struct OpInfo {
+        template <typename Dtype> struct OpInfo {
             OpCreator<Dtype> creator_;
             const OpCreator<Dtype> &Creator() const {
                 //    PADDLE_ENFORCE_NOT_NULL(creator_,
-    //                            "Operator Creator has not been registered");
+                //                            "Operator Creator has not been
+                //                            registered");
                 return creator_;
             }
-};
+        };
 
-template <typename Dtype> class OpInfoMap;
+        template <typename Dtype> class OpInfoMap;
 
-template <typename Dtype> static OpInfoMap<Dtype> *g_op_info_map = nullptr;
+        template <typename Dtype>
+        static OpInfoMap<Dtype> *g_op_info_map = nullptr;
 
-template <typename Dtype> class OpInfoMap {
-public:
+        template <typename Dtype> class OpInfoMap {
+          public:
             static OpInfoMap &Instance() {
                 if (g_op_info_map<Dtype> == nullptr) {
                     g_op_info_map<Dtype> = new OpInfoMap();
@@ -51,13 +53,15 @@ public:
             }
 
             void Insert(const std::string &type, const OpInfo<Dtype> &info) {
-    //    PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type);
+                //    PADDLE_ENFORCE(!Has(type), "Operator %s has been
+                //    registered", type);
                 map_.insert({type, info});
             }
 
             const OpInfo<Dtype> &Get(const std::string &type) const {
                 auto op_info_ptr = GetNullable(type);
-    //    PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not been
+                //    PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not
+                //    been
                 //    registered",
                 //                            type);
                 return *op_info_ptr;
@@ -80,12 +84,12 @@ public:
                 return &map_;
             }
 
-private:
+          private:
             OpInfoMap() = default;
             std::unordered_map<std::string, OpInfo<Dtype>> map_;
 
             //  DISABLE_COPY_AND_ASSIGN(OpInfoMap);
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/op_kernel_type.h

@@ -22,12 +22,14 @@ SOFTWARE.
 #include "framework.pb.h"
 
 namespace paddle_mobile {
-namespace framework {
-struct OpKernelType {
+    namespace framework {
+        struct OpKernelType {
             struct Hash {
                 size_t operator()(const OpKernelType &key) const {
-      int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
-      int data_layout = static_cast<int>(key.data_layout_) << (LEFT_SHIFT * 2);
+                    int data_type = static_cast<int>(key.data_type_)
+                                    << LEFT_SHIFT;
+                    int data_layout = static_cast<int>(key.data_layout_)
+                                      << (LEFT_SHIFT * 2);
 
                     std::hash<int> hasher;
                     return hasher(data_type + data_layout);
@@ -45,20 +47,26 @@ struct OpKernelType {
                 : data_type_(data_type), data_layout_(data_layout) {}
 
             bool operator==(const OpKernelType &o) const {
-    return data_type_ == o.data_type_ && data_layout_ == o.data_layout_;
+                return data_type_ == o.data_type_ &&
+                       data_layout_ == o.data_layout_;
             }
 
-  bool operator!=(const OpKernelType &o) const { return !(*this == o); }
-};
+            bool operator!=(const OpKernelType &o) const {
+                return !(*this == o);
+            }
+        };
 
-inline bool NeedTransformLayout(const DataLayout &l, const DataLayout &r) {
-  return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
-}
+        inline bool NeedTransformLayout(const DataLayout &l,
+                                        const DataLayout &r) {
+            return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout &&
+                   l != r;
+        }
 
-inline bool TransFromNeeded(const OpKernelType &l, const OpKernelType &r) {
+        inline bool TransFromNeeded(const OpKernelType &l,
+                                    const OpKernelType &r) {
             return (l.data_type_ != r.data_type_) ||
                    NeedTransformLayout(l.data_layout_, r.data_layout_);
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/op_proto_maker.h

@@ -19,8 +19,8 @@ SOFTWARE.
 #pragma once
 
 namespace paddle_mobile {
-namespace framework {
-// this class not only make proto but also init attribute checkers.
-class OpProtoAndCheckerMaker {};
-} // namespace framework
+    namespace framework {
+        // this class not only make proto but also init attribute checkers.
+        class OpProtoAndCheckerMaker {};
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/operator.cpp

@@ -20,10 +20,10 @@ SOFTWARE.
 #include "op_info.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype>
-OperatorBase<Dtype>::OperatorBase(const std::string &type,
+        template <typename Dtype>
+        OperatorBase<Dtype>::OperatorBase(const std::string &type,
                                           const VariableNameMap &inputs,
                                           const VariableNameMap &outputs,
                                           const AttributeMap &attrs,
@@ -31,15 +31,12 @@ OperatorBase<Dtype>::OperatorBase(const std::string &type,
             : type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
               scope_(scope) {
             CheckAllInputOutputSet();
-}
+        }
+        template <typename Dtype>
+        void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
 
-template <typename Dtype> void OperatorBase<Dtype>::Run() { RunImpl(); }
+        template class OperatorBase<CPU>;
+        template class OperatorWithKernel<CPU>;
 
-template <typename Dtype>
-void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
-
-template class OperatorBase<CPU>;
-template class OperatorWithKernel<CPU>;
-
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/operator.h

@@ -18,8 +18,6 @@ SOFTWARE.
 
 #pragma once
 
-#include <map>
-
 #include "attribute.h"
 #include "block_desc.h"
 #include "common/type_define.h"
@@ -31,62 +29,62 @@ SOFTWARE.
 #include "scope.h"
 #include "tensor.h"
 #include "variable.h"
+#include <map>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype> class OperatorBase : PaddleMobileObject {
-public:
+        template <typename Dtype> class OperatorBase : PaddleMobileObject {
+          public:
             OperatorBase(const std::string &type, const VariableNameMap &inputs,
-               const VariableNameMap &outputs, const AttributeMap &attrs,
+                         const VariableNameMap &outputs,
+                         const AttributeMap &attrs,
                          std::shared_ptr<Scope> scope);
             virtual ~OperatorBase() {}
-  virtual void Run();
+            virtual void Run() const = 0;
+
             const VariableNameMap &Inputs() const { return inputs_; }
             const VariableNameMap &Outputs() const { return outputs_; }
             const std::string &Type() const { return type_; }
             const AttributeMap &Attrs() const { return attrs_; }
+            void ClearVariables() const {
+                if (this->scope_) {
+                    this->scope_->EraseVars(this->inputs_.at("Filter"));
+                    this->scope_->EraseVars(this->inputs_.at("Input"));
+                }
+            }
 
-protected:
+          protected:
             std::shared_ptr<Scope> scope_;
             std::string type_;
             VariableNameMap inputs_;
             VariableNameMap outputs_;
             AttributeMap attrs_;
 
-private:
+          private:
             void CheckAllInputOutputSet() const;
-  virtual void RunImpl() const = 0;
-};
+        };
 
-template <typename Dtype>
-class OperatorWithKernel : public OperatorBase<Dtype> {
-public:
-  OperatorWithKernel(const std::string &type, const VariableNameMap &inputs,
-                     const VariableNameMap &outputs, const AttributeMap &attrs,
+        template <typename Dtype>
+        class OperatorWithKernel : public OperatorBase<Dtype> {
+          public:
+            OperatorWithKernel(const std::string &type,
+                               const VariableNameMap &inputs,
+                               const VariableNameMap &outputs,
+                               const AttributeMap &attrs,
                                std::shared_ptr<Scope> scope)
                 : OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
             virtual void InferShape() const = 0;
+            virtual void Run() const = 0;
+        };
 
-  void ClearVariables() const {
-    if (this->scope_) {
-      this->scope_->EraseVars(this->inputs_.at("Filter"));
-      this->scope_->EraseVars(this->inputs_.at("Input"));
-    }
-  }
-
-protected:
-  virtual void RunImpl() const = 0;
-
-private:
-};
-
-template <typename Dtype, typename P> class OpKernelBase : PaddleMobileObject {
-public:
+        template <typename Dtype, typename P>
+        class OpKernelBase : PaddleMobileObject {
+          public:
             virtual void Compute(const P &para) const = 0;
 
             virtual ~OpKernelBase() = default;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/paddle_mobile_object.h

@@ -23,14 +23,14 @@ SOFTWARE.
 
 namespace paddle_mobile {
 
-class PaddleMobileObject {
-public:
+    class PaddleMobileObject {
+      public:
         virtual inline const std::string &ToString() {
             char address[128] = {0};
             sprintf(address, "%p", this);
             return std::string(address);
         }
 
-private:
-};
+      private:
+    };
 } // namespace paddle_mobile

src/framework/program.cpp

@@ -17,5 +17,5 @@ SOFTWARE.
 ==============================================================================*/
 
 namespace paddle_mobile {
-namespace framework {}
+    namespace framework {}
 } // namespace paddle_mobile

src/framework/program.h

@@ -24,17 +24,17 @@ SOFTWARE.
 #include "scope.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-template <typename Dtype, Precision P = Precision::FP32>
-class Program : PaddleMobileObject {
-public:
+        template <typename Dtype, Precision P = Precision::FP32>
+        class Program : PaddleMobileObject {
+          public:
             std::shared_ptr<ProgramDesc> originProgram;
             std::shared_ptr<ProgramDesc> optimizeProgram;
             std::shared_ptr<Scope> scope;
 
-private:
-};
+          private:
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/program_desc.cpp

@@ -5,18 +5,18 @@
 #include "program_desc.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
+        ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
             for (auto &block_desc : *desc_.mutable_blocks()) {
                 // new framework::BlockDesc(block_desc)
                 blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
             }
-}
+        }
 
-std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
+        std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
             return blocks_[idx];
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/program_desc.h

@@ -25,18 +25,20 @@ SOFTWARE.
 #include "paddle_mobile_object.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class ProgramDesc : PaddleMobileObject {
-public:
+        class ProgramDesc : PaddleMobileObject {
+          public:
             ProgramDesc(const proto::ProgramDesc &desc);
             std::shared_ptr<BlockDesc> Block(size_t idx);
-  const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { return blocks_; };
+            const std::vector<std::shared_ptr<BlockDesc>> &Blocks() {
+                return blocks_;
+            };
 
-private:
+          private:
             std::vector<std::shared_ptr<BlockDesc>> blocks_;
             proto::ProgramDesc desc_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/scope.cc

@@ -4,15 +4,15 @@
 #include <vector>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-Scope &Scope::NewScope() const {
+        Scope &Scope::NewScope() const {
             std::unique_lock<std::mutex> lock(mutex_);
             kids_.push_back(new Scope(this));
             return *kids_.back();
-}
+        }
 
-Variable *Scope::Var(const std::string &name) {
+        Variable *Scope::Var(const std::string &name) {
             auto *pvar = FindVarLocally(name);
             if (pvar != nullptr) {
                 return pvar;
@@ -21,58 +21,59 @@ Variable *Scope::Var(const std::string &name) {
             vars_[name] = pvar;
             pvar->name_ = &(vars_.find(name)->first);
             return pvar;
-}
+        }
 
-//            Variable* Scope::Var(std::string* name) {
-//                auto var_name = string::Sprintf("%p.%d", this, vars_.size());
-//                if (name != nullptr) {
-//                    *name = var_name;
-//                }
-//                return Var(var_name);
-//            }
+        //            Variable* Scope::Var(std::string* name) {
+        //                auto var_name = string::Sprintf("%p.%d", this,
+        //                vars_.size());
+        //                if (name != nullptr) {
+        //                    *name = var_name;
+        //                }
+        //                return Var(var_name);
+        //            }
 
-Variable *Scope::FindVar(const std::string &name) const {
+        Variable *Scope::FindVar(const std::string &name) const {
             auto *pvar = FindVarLocally(name);
             if (pvar != nullptr) {
                 return pvar;
             }
             return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
-}
+        }
 
-const Scope *Scope::FindScope(const Variable *var) const {
+        const Scope *Scope::FindScope(const Variable *var) const {
             for (auto &name_var : vars_) {
                 if (name_var.second == var) {
                     return this;
                 }
             }
             return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
-}
+        }
 
-void Scope::DropKids() {
+        void Scope::DropKids() {
             for (Scope *s : kids_) {
                 delete s;
             }
             kids_.clear();
-}
+        }
 
-std::vector<std::string> Scope::LocalVarNames() const {
+        std::vector<std::string> Scope::LocalVarNames() const {
             std::vector<std::string> known_vars;
             known_vars.reserve(vars_.size());
             for (auto &name_var : vars_) {
                 known_vars.emplace_back(name_var.first);
             }
             return known_vars;
-}
+        }
 
-void Scope::DeleteScope(Scope *scope) const {
+        void Scope::DeleteScope(Scope *scope) const {
             std::unique_lock<std::mutex> lock(mutex_);
             auto it = std::find(kids_.begin(), kids_.end(), scope);
             kids_.erase(it);
             delete scope;
             // deferent
-}
+        }
 
-void Scope::EraseVars(const std::vector<std::string> &var_names) {
+        void Scope::EraseVars(const std::vector<std::string> &var_names) {
             std::set<std::string> var_set(var_names.begin(), var_names.end());
             for (auto it = vars_.begin(); it != vars_.end();) {
                 if (var_set.find(it->first) != var_set.end()) {
@@ -82,9 +83,9 @@ void Scope::EraseVars(const std::vector<std::string> &var_names) {
                     ++it;
                 }
             }
-}
+        }
 
-void Scope::Rename(const std::string &origin_name,
+        void Scope::Rename(const std::string &origin_name,
                            const std::string &new_name) const {
             auto origin_it = vars_.find(origin_name);
             if (origin_it == vars_.end()) {
@@ -96,21 +97,23 @@ void Scope::Rename(const std::string &origin_name,
             }
             vars_[new_name] = origin_it->second;
             vars_.erase(origin_it);
-}
-//
-//            std::string Scope::Rename(const std::string& origin_name) const {
-//                auto var_name = string::Sprintf("%p.%d", this, vars_.size());
-//                Rename(origin_name, var_name);
-//                return var_name;
-//            }
+        }
+        //
+        //            std::string Scope::Rename(const std::string& origin_name)
+        //            const {
+        //                auto var_name = string::Sprintf("%p.%d", this,
+        //                vars_.size());
+        //                Rename(origin_name, var_name);
+        //                return var_name;
+        //            }
 
-Variable *Scope::FindVarLocally(const std::string &name) const {
+        Variable *Scope::FindVarLocally(const std::string &name) const {
             auto it = vars_.find(name);
             if (it != vars_.end()) {
                 return it->second;
             }
             return nullptr;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/scope.h

@@ -24,9 +24,9 @@ SOFTWARE.
 #include <unordered_map> //std::unordered_map
 
 namespace paddle_mobile {
-namespace framework {
-class Scope {
-public:
+    namespace framework {
+        class Scope {
+          public:
             Scope() {}
             ~Scope() {}
 
@@ -46,7 +46,8 @@ public:
 
             const Scope *parent() const { return parent_; }
 
-  /// Find the scope or an ancestor scope that contains the given variable.
+            /// Find the scope or an ancestor scope that contains the given
+            /// variable.
             const Scope *FindScope(const Variable *var) const;
 
             void DeleteScope(Scope *scope) const;
@@ -66,7 +67,7 @@ public:
 
             Variable *FindVarLocally(const std::string &name) const;
 
-private:
+          private:
             // Call Scope::NewScope for a sub-scope.
             explicit Scope(Scope const *parent) : parent_(parent) {}
 
@@ -75,6 +76,6 @@ private:
             Scope const *parent_{nullptr};
 
             mutable std::mutex mutex_;
-};
-} // namespace framework
+        };
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/selected_rows.h

@@ -24,11 +24,12 @@ SOFTWARE.
 #include "tensor.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class SelectedRows {
-public:
-  SelectedRows(const std::vector<int64_t> &rows, const int64_t &height)
+        class SelectedRows {
+          public:
+            SelectedRows(const std::vector<int64_t> &rows,
+                         const int64_t &height)
                 : rows_(rows), height_(height) {
                 value_.reset(new Tensor());
             }
@@ -67,14 +68,15 @@ public:
                 return make_ddim(dims);
             }
 
-private:
-  // Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
+          private:
+            // Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9}
+            // here.
             // SelectedRows are simply concated when adding together. Until a
             // SelectedRows add a Tensor, will the duplicate rows be handled.
             std::vector<int64_t> rows_;
             std::unique_ptr<Tensor> value_{nullptr};
             int64_t height_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/tensor.h

@@ -25,10 +25,10 @@ limitations under the License. */
 #include "memory/t_malloc.h"
 
 namespace paddle_mobile {
-namespace framework {
-template <typename... T> struct SizeOfTypeFunctor;
+    namespace framework {
+        template <typename... T> struct SizeOfTypeFunctor;
 
-template <typename T> struct SizeOfTypeFunctor<T> {
+        template <typename T> struct SizeOfTypeFunctor<T> {
             size_t operator()(std::type_index type) const {
                 if (typeid(T).hash_code() == type.hash_code()) {
                     return sizeof(T);
@@ -36,14 +36,14 @@ template <typename T> struct SizeOfTypeFunctor<T> {
                     return 0UL;
                 }
             }
-};
+        };
 
-template <> struct SizeOfTypeFunctor<> {
+        template <> struct SizeOfTypeFunctor<> {
             size_t operator()(std::type_index type) const { return 0UL; }
-};
+        };
 
-template <typename HEAD, typename... TAIL>
-struct SizeOfTypeFunctor<HEAD, TAIL...> {
+        template <typename HEAD, typename... TAIL>
+        struct SizeOfTypeFunctor<HEAD, TAIL...> {
             size_t operator()(std::type_index type) const {
                 SizeOfTypeFunctor<HEAD> head;
                 size_t head_size = head(type);
@@ -53,37 +53,42 @@ struct SizeOfTypeFunctor<HEAD, TAIL...> {
                 SizeOfTypeFunctor<TAIL...> tail;
                 return tail(type);
             }
-};
+        };
 
-static inline size_t SizeOfType(std::type_index type) {
-  SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t> functor;
+        static inline size_t SizeOfType(std::type_index type) {
+            SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool,
+                              size_t>
+                functor;
             size_t size = functor(type);
-  //  PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s", type.name());
+            //  PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
+            //  type.name());
             return size;
-}
+        }
 
-class LoDTensor;
+        class LoDTensor;
 
-class Tensor {
-public:
+        class Tensor {
+          public:
             Tensor() : offset_(0) {}
 
             /*! Return a pointer to mutable memory block. */
             template <typename T> inline T *data() {
                 check_memory_size();
                 //  PADDLE_ENFORCE(std::is_same<T, void>::value ||
-    //                     holder_->type().hash_code() == typeid(T).hash_code(),
+                //                     holder_->type().hash_code() ==
+                //                     typeid(T).hash_code(),
                 //                 "Tensor holds the wrong type, it holds %s",
                 //                 this->holder_->type().name());
-    return reinterpret_cast<T *>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
-                                 offset_);
+                return reinterpret_cast<T *>(
+                    reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
             }
 
             /*! Return a pointer to constant memory block. */
             template <typename T> inline const T *data() const {
                 check_memory_size();
                 //  PADDLE_ENFORCE(std::is_same<T, void>::value ||
-    //                     holder_->type().hash_code() == typeid(T).hash_code(),
+                //                     holder_->type().hash_code() ==
+                //                     typeid(T).hash_code(),
                 //                 "Tensor holds the wrong type, it holds %s",
                 //                 this->holder_->type().name());
 
@@ -107,8 +112,10 @@ public:
                     holder_->set_type(type);
                 }
                 //  PADDLE_ENFORCE_GE(numel(), 0,
-    //                    "When calling this method, the Tensor's numel must be
-    //                    " "equal or larger than zero. " "Please check
+                //                    "When calling this method, the Tensor's
+                //                    numel must be
+                //                    " "equal or larger than zero. " "Please
+                //                    check
                 //                    Tensor::Resize has been called first.");
                 int64_t size = numel() * SizeOfType(type);
                 /* some versions of boost::variant don't have operator!= */
@@ -123,7 +130,8 @@ public:
 
             inline void *mutable_data() {
                 //  PADDLE_ENFORCE(this->holder_ != nullptr,
-    //                 "Cannot invoke mutable data if current hold nothing.");
+                //                 "Cannot invoke mutable data if current hold
+                //                 nothing.");
                 return mutable_data(holder_->type());
             }
 
@@ -163,19 +171,24 @@ public:
             /**
              * @brief  Return a sub-tensor of the given tensor.
              *
-   * @param[in] begin_idx   The index of the start row(inclusive) to slice.
+             * @param[in] begin_idx   The index of the start row(inclusive) to
+             * slice.
              *                        The index number begins from 0.
-   * @param[in] end_idx     The index of the end row(exclusive) to slice.
+             * @param[in] end_idx     The index of the end row(exclusive) to
+             * slice.
              *                        The index number begins from 0.
              */
             inline Tensor Slice(int begin_idx, int end_idx) const {
                 check_memory_size();
                 //  PADDLE_ENFORCE_GE(begin_idx, 0,
-    //                    "The start row index must be greater than 0.");
-    //  PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is out of
+                //                    "The start row index must be greater than
+                //                    0.");
+                //  PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
+                //  out of
                 //  bound."); PADDLE_ENFORCE_LT(
                 //      begin_idx, end_idx,
-    //      "The start row index must be lesser than the end row index.");
+                //      "The start row index must be lesser than the end row
+                //      index.");
 
                 if (dims_[0] == 1) {
                     return *this;
@@ -187,14 +200,16 @@ public:
                     DDim dst_dims = dims_;
                     dst_dims[0] = end_idx - begin_idx;
                     dst.Resize(dst_dims);
-      dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
+                    dst.offset_ =
+                        offset_ + begin_idx * base * SizeOfType(type());
                     return dst;
                 }
             }
 
             std::type_index type() const {
                 //                PADDLE_ENFORCE_NOT_NULL(
-    //                        holder_, "Tensor not initialized yet when
+                //                        holder_, "Tensor not initialized yet
+                //                        when
                 //                        Tensor::type() is called.");
                 return holder_->type();
             }
@@ -206,23 +221,29 @@ public:
 
             inline void check_memory_size() const {
                 //  PADDLE_ENFORCE_NOT_NULL(
-    //      holder_, "Tensor holds no memory. Call Tensor::mutable_data
+                //      holder_, "Tensor holds no memory. Call
+                //      Tensor::mutable_data
                 //      first.");
                 //  PADDLE_ENFORCE_LE(
                 //      numel() * SizeOfType(type()), memory_size(),
-    //      "Tensor's dims_ is out of bound. Call Tensor::mutable_data "
+                //      "Tensor's dims_ is out of bound. Call
+                //      Tensor::mutable_data "
                 //      "first to re-allocate memory.\n"
-    //      "or maybe the required data-type mismatches the data already
+                //      "or maybe the required data-type mismatches the data
+                //      already
                 //      stored.");
             }
 
             inline DataLayout layout() const { return layout_; }
 
-  inline void set_layout(const DataLayout layout) { layout_ = layout; }
+            inline void set_layout(const DataLayout layout) {
+                layout_ = layout;
+            }
 
-private:
+          private:
             /**
-   * @note    Placeholder hides type T, so it doesn't appear as a template
+             * @note    Placeholder hides type T, so it doesn't appear as a
+             * template
              *          parameter of Variable.
              */
             struct Placeholder {
@@ -242,15 +263,20 @@ private:
                     : ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
                            memory::PODDeleter<uint8_t>()),
                       size_(size), type_(type) {
-      //                    PADDLE_ENFORCE_NOT_NULL(ptr_, "Insufficient %s
+                    //                    PADDLE_ENFORCE_NOT_NULL(ptr_,
+                    //                    "Insufficient %s
                     //                    memory to allocation.",
-      //                                            (is_cpu_place(place_) ?
-      //                                            "CPU" : "GPU"));
+                    //                                            (is_cpu_place(place_)
+                    //                                            ?
+                    //                                            "CPU" :
+                    //                                            "GPU"));
                 }
 
                 virtual size_t size() const { return size_; }
 
-    virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
+                virtual void *ptr() const {
+                    return static_cast<void *>(ptr_.get());
+                }
 
                 virtual std::type_index type() const { return type_; }
 
@@ -280,8 +306,10 @@ private:
             /**
              * @brief the layout of memory block, default is NHWC.
              *
-   * @note the memory allocation order, describe how weight/data is stored
-   *       For example, in 4-D Tensor(rank=4), there are three commonly
+             * @note the memory allocation order, describe how weight/data is
+             * stored
+             *       For example, in 4-D Tensor(rank=4), there are three
+             * commonly
              *       used layout. They are
              *            NCHW, NHWC, CHWN.
              *       N,C,H,W for respectively the batch size, the number of
@@ -295,17 +323,18 @@ private:
              *
              * @note    Some of them may be slices of the others. So the offset_
              *          is introduced here to indicate the byte offset between
-   *          PlaceHolder::ptr_ and where the tensor data really begins.
+             *          PlaceHolder::ptr_ and where the tensor data really
+             * begins.
              */
             size_t offset_;
-};
+        };
 
-inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
+        inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
             Tensor res;
             res.ShareDataWith(src);
             res.Resize(flatten_to_2d(src.dims(), num_col_dims));
             return res;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/tensor_util.cc

@@ -18,10 +18,11 @@
 #include <vector>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-void TensorCopy(const Tensor &src, Tensor *dst) {
-  //  VLOG(3) << "TensorCopy " << src.dims() << " from " << src.place() << " to
+        void TensorCopy(const Tensor &src, Tensor *dst) {
+            //  VLOG(3) << "TensorCopy " << src.dims() << " from " <<
+            //  src.place() << " to
             //  "
             //          << dst_place;
             src.check_memory_size();
@@ -35,10 +36,11 @@ void TensorCopy(const Tensor &src, Tensor *dst) {
             auto size = src.numel() * SizeOfType(src.type());
 
             memory::Copy(dst_ptr, src_ptr, size);
-}
+        }
 
-void TensorCopySync(const Tensor &src, Tensor *dst) {
-  //  VLOG(3) << "TensorCopySync " << src.dims() << " from " << src.place()
+        void TensorCopySync(const Tensor &src, Tensor *dst) {
+            //  VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
+            //  src.place()
             //          << " to " << dst_place;
             src.check_memory_size();
             dst->Resize(src.dims());
@@ -47,14 +49,15 @@ void TensorCopySync(const Tensor &src, Tensor *dst) {
             auto dst_ptr = dst->mutable_data(src.type());
             auto size = src.numel() * SizeOfType(src.type());
             memory::Copy(dst_ptr, src_ptr, size);
-}
+        }
 
-template <typename Predicate> struct AnyDTypeVisitor {
+        template <typename Predicate> struct AnyDTypeVisitor {
             Predicate predicate_;
             const Tensor &tensor_;
             Tensor *out_;
 
-  AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, Tensor *out)
+            AnyDTypeVisitor(Predicate predicate, const Tensor &tensor,
+                            Tensor *out)
                 : predicate_(predicate), tensor_(tensor), out_(out) {}
 
             template <typename T> void operator()() const {
@@ -63,16 +66,16 @@ template <typename Predicate> struct AnyDTypeVisitor {
                 // return any of predicate_(t) is true.
                 //    o.device(*ctx_.eigen_device()) = predicate_(t).any();
             }
-};
+        };
 
-template <typename Predicate>
-inline void AnyImpl(Predicate predicate, const Tensor &tensor,
+        template <typename Predicate>
+        inline void AnyImpl(Predicate predicate, const Tensor &tensor,
                             framework::Tensor *out) {
             VisitDataType(ToDataType(tensor.type()),
                           AnyDTypeVisitor<Predicate>(predicate, tensor, out));
-}
+        }
 
-template <typename Predicate> struct AnyVisitor {
+        template <typename Predicate> struct AnyVisitor {
             const framework::Tensor &tensor_;
             Predicate predicate_;
 
@@ -90,47 +93,48 @@ template <typename Predicate> struct AnyVisitor {
             bool GetResult(const framework::Tensor &out) const {
                 return *out.data<bool>();
             }
-};
+        };
 
-template <typename Predicate>
-inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
+        template <typename Predicate>
+        inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
             AnyVisitor<Predicate> visitor(tensor, predicate);
             //  return platform::VisitPlace(visitor);
             return visitor();
-}
+        }
 
-struct ContainsNANPredicate {
+        struct ContainsNANPredicate {
             template <typename T>
             auto operator()(const T &eigen_vec) const
                 -> decltype(std::declval<T>().isnan()) {
                 // Cast eigen_vector to vector of bool. true if is inf.
                 return eigen_vec.isnan();
             }
-};
+        };
 
-bool TensorContainsNAN(const framework::Tensor &tensor) {
+        bool TensorContainsNAN(const framework::Tensor &tensor) {
             ContainsNANPredicate predicate;
             return Any(tensor, predicate);
-}
+        }
 
-struct ContainsInfPredicate {
+        struct ContainsInfPredicate {
             template <typename T>
             auto operator()(const T &eigen_vec) const
                 -> decltype(std::declval<T>().isinf()) {
                 // Cast eigen_vector to vector of bool. true if is inf.
                 return eigen_vec.isinf();
             }
-};
+        };
 
-bool TensorContainsInf(const framework::Tensor &tensor) {
+        bool TensorContainsInf(const framework::Tensor &tensor) {
             ContainsInfPredicate predicate;
             return Any(tensor, predicate);
-}
+        }
 
-void TensorToStream(std::ostream &os, const Tensor &tensor) {
+        void TensorToStream(std::ostream &os, const Tensor &tensor) {
             { // the 1st field, uint32_t version
                 constexpr uint32_t version = 0;
-    os.write(reinterpret_cast<const char *>(&version), sizeof(version));
+                os.write(reinterpret_cast<const char *>(&version),
+                         sizeof(version));
             }
             { // the 2nd field, tensor description
                 // int32_t  size
@@ -149,15 +153,16 @@ void TensorToStream(std::ostream &os, const Tensor &tensor) {
             { // the 3rd field, tensor data
                 uint64_t size = tensor.memory_size();
                 auto *data_ptr = tensor.data<void>();
-    //    PADDLE_ENFORCE(size < std::numeric_limits<std::streamsize>::max(),
+                //    PADDLE_ENFORCE(size <
+                //    std::numeric_limits<std::streamsize>::max(),
                 //                   "Index overflow when writing tensor");
 
                 os.write(static_cast<const char *>(data_ptr),
                          static_cast<std::streamsize>(size));
             }
-}
+        }
 
-struct DeserializedDataFunctor {
+        struct DeserializedDataFunctor {
             DeserializedDataFunctor(void **buf, Tensor *tensor)
                 : buf_(buf), tensor_(tensor) {}
 
@@ -167,9 +172,9 @@ struct DeserializedDataFunctor {
 
             void **buf_;
             Tensor *tensor_;
-};
+        };
 
-void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
+        void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
             uint32_t version;
             is.read(reinterpret_cast<char *>(&version), sizeof(version));
             //  PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
@@ -186,7 +191,8 @@ void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
             { // read tensor
                 std::vector<int64_t> dims;
                 dims.reserve(static_cast<size_t>(desc.dims().size()));
-    std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
+                std::copy(desc.dims().begin(), desc.dims().end(),
+                          std::back_inserter(dims));
                 tensor->Resize(framework::make_ddim(dims));
                 void *buf;
 
@@ -194,7 +200,7 @@ void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
                                          DeserializedDataFunctor(&buf, tensor));
                 is.read(static_cast<char *>(buf), tensor->memory_size());
             }
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/tensor_util.h

@@ -20,39 +20,39 @@ limitations under the License. */
 #include <vector>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-void TensorCopy(const Tensor &src, Tensor *dst);
-void TensorCopySync(const Tensor &src, Tensor *dst);
+        void TensorCopy(const Tensor &src, Tensor *dst);
+        void TensorCopySync(const Tensor &src, Tensor *dst);
 
-template <typename T>
-void TensorFromVector(const std::vector<T> &src, Tensor *dst);
+        template <typename T>
+        void TensorFromVector(const std::vector<T> &src, Tensor *dst);
 
-template <typename T>
-void TesnorToVector(const Tensor &src, std::vector<T> *dst);
+        template <typename T>
+        void TesnorToVector(const Tensor &src, std::vector<T> *dst);
 
-bool TensorContainsNAN(const framework::Tensor &tensor);
-bool TensorContainsInf(const framework::Tensor &tensor);
+        bool TensorContainsNAN(const framework::Tensor &tensor);
+        bool TensorContainsInf(const framework::Tensor &tensor);
 
-void TensorToStream(std::ostream &os, const Tensor &tensor);
-void TensorFromStream(std::istream &is, Tensor *tensor);
+        void TensorToStream(std::ostream &os, const Tensor &tensor);
+        void TensorFromStream(std::istream &is, Tensor *tensor);
 
-//
-// The implementation of template functions.
-//
+        //
+        // The implementation of template functions.
+        //
 
-template <typename T>
-void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
+        template <typename T>
+        void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
             auto src_ptr = static_cast<const void *>(src.data());
             dst->Resize({static_cast<int64_t>(src.size())});
             auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
             auto size = src.size() * sizeof(T);
 
             memory::Copy(dst_ptr, src_ptr, size);
-}
+        }
 
-template <typename T>
-void TensorToVector(const Tensor &src, std::vector<T> *dst) {
+        template <typename T>
+        void TensorToVector(const Tensor &src, std::vector<T> *dst) {
             auto src_ptr = static_cast<const void *>(src.data<T>());
             auto size = src.numel() * sizeof(T);
 
@@ -60,7 +60,7 @@ void TensorToVector(const Tensor &src, std::vector<T> *dst) {
             auto dst_ptr = static_cast<void *>(dst->data());
 
             memory::Copy(dst_ptr, src_ptr, size);
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/var_desc.cpp

@@ -20,9 +20,9 @@ SOFTWARE.
 
 namespace paddle_mobile {
 
-namespace framework {
+    namespace framework {
 
-VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
+        VarDesc::VarDesc(const proto::VarDesc &desc) : desc_(desc) {}
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/var_desc.h

@@ -22,10 +22,10 @@ SOFTWARE.
 #include "paddle_mobile_object.h"
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-class VarDesc {
-public:
+        class VarDesc {
+          public:
             VarDesc(const proto::VarDesc &desc);
 
             std::string Name() const { return desc_.name(); }
@@ -68,7 +68,8 @@ public:
 
             template <typename T>
             std::vector<T> RepeatedToVector(
-      const google::protobuf::RepeatedField<T> &repeated_field) const {
+                const google::protobuf::RepeatedField<T> &repeated_field)
+                const {
                 std::vector<T> ret;
                 ret.reserve(repeated_field.size());
                 std::copy(repeated_field.begin(), repeated_field.end(),
@@ -80,9 +81,9 @@ public:
                 return this->RepeatedToVector(tensor_desc().dims());
             }
 
-private:
+          private:
             proto::VarDesc desc_;
-};
+        };
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/var_type.h

@@ -23,16 +23,17 @@ SOFTWARE.
 #include "variable.h"
 
 namespace paddle_mobile {
-namespace framework {
-inline proto::VarType::Type ToVarType(std::type_index type) {
+    namespace framework {
+        inline proto::VarType::Type ToVarType(std::type_index type) {
             if (type.hash_code() == typeid(LoDTensor).hash_code()) {
                 return proto::VarType_Type_LOD_TENSOR;
             } else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
                 return proto::VarType_Type_SELECTED_ROWS;
             } else {
-    //    PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
+                //    PADDLE_THROW("ToVarType:Unsupported type %s",
+                //    type.name());
+            }
         }
-}
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/framework/variable.h

@@ -26,12 +26,9 @@ SOFTWARE.
 #include <typeinfo>
 
 namespace paddle_mobile {
-namespace framework {
-class Variable : public PaddleMobileObject {
-public:
-  Variable() {}
-  ~Variable() {}
-
+    namespace framework {
+        class Variable : public PaddleMobileObject {
+          public:
             template <typename T> const T *Get() const {
                 return static_cast<const T *>(holder_->Ptr());
             }
@@ -43,7 +40,8 @@ public:
             template <typename T> T *GetMutable() {
                 if (!IsType<T>()) {
                     if (*Name() == "pixel") {
-        //        std::cout << " reset " << *Name() << std::endl;
+                        //        std::cout << " reset " << *Name() <<
+                        //        std::endl;
                     }
                     holder_.reset(new PlaceholderImp<T>(new T()));
                 }
@@ -53,11 +51,12 @@ public:
             template <typename T> bool IsType() const {
                 if (holder_) {
                     //                printf("not null \n");
-      printf(" holder type : %s, this type %s \n", holder_->Type().name(),
-             typeid(T).name());
+                    printf(" holder type : %s, this type %s \n",
+                           holder_->Type().name(), typeid(T).name());
                 }
 
-    //              std::cout << " " << holder_->Type() << " " <<  typeid(T) <<
+                //              std::cout << " " << holder_->Type() << " " <<
+                //              typeid(T) <<
                 //              std::endl;
                 return holder_ != nullptr && holder_->Type() == typeid(T);
             }
@@ -68,7 +67,7 @@ public:
 
             void SetName(const std::string *name) { name_ = name; }
 
-private:
+          private:
             struct Placeholder {
                 Placeholder() = default;
                 virtual ~Placeholder() = default;
@@ -92,6 +91,6 @@ private:
             std::unique_ptr<Placeholder> holder_;
             friend class Scope;
             const std::string *name_;
-};
-} // namespace framework
+        };
+    } // namespace framework
 } // namespace paddle_mobile

src/io.cpp

@@ -29,7 +29,7 @@ SOFTWARE.
 
 namespace paddle_mobile {
 
-void ReadBinaryFile(const std::string &filename, std::string *contents) {
+    void ReadBinaryFile(const std::string &filename, std::string *contents) {
         std::ifstream fin(filename, std::ios::in | std::ios::binary);
         fin.seekg(0, std::ios::end);
         contents->clear();
@@ -37,10 +37,10 @@ void ReadBinaryFile(const std::string &filename, std::string *contents) {
         fin.seekg(0, std::ios::beg);
         fin.read(&(contents->at(0)), contents->size());
         fin.close();
-}
+    }
 
-template <typename Dtype, Precision P>
-void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
+    template <typename Dtype, Precision P>
+    void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
                                    const std::string &file_path) {
 
         LOG(kLOG_DEBUG) << "  to load " << file_path;
@@ -79,7 +79,8 @@ void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
 
         // 3. tensor version
         uint32_t tensor_version;
-  is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
+        is.read(reinterpret_cast<char *>(&tensor_version),
+                sizeof(tensor_version));
         //  std::cout << "   tensor_version: " << tensor_version << std::endl;
 
         // 4. tensor desc
@@ -92,17 +93,20 @@ void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
         framework::proto::VarType::TensorDesc desc;
         desc.ParseFromArray(buf.get(), size);
 
-  //  std::cout << "   desc dims size " << desc.dims().size() << std::endl;
+        //  std::cout << "   desc dims size " << desc.dims().size() <<
+        //  std::endl;
         int memory_size = 1;
         for (int l = 0; l < desc.dims().size(); ++l) {
-    //    std::cout << "    dim " << l << " value: " << desc.dims()[l] <<
+            //    std::cout << "    dim " << l << " value: " << desc.dims()[l]
+            //    <<
             //    std::endl;
             memory_size *= desc.dims()[l];
         }
 
         std::vector<int64_t> dims;
         dims.reserve(static_cast<size_t>(desc.dims().size()));
-  std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
+        std::copy(desc.dims().begin(), desc.dims().end(),
+                  std::back_inserter(dims));
         tensor->Resize(framework::make_ddim(dims));
 
         void *memory;
@@ -139,15 +143,16 @@ void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
             //      std::cout << "    not support" << std::endl;
         }
 
-  //  std::cout << "    malloc size: " << memory_size * type_size << std::endl;
+        //  std::cout << "    malloc size: " << memory_size * type_size <<
+        //  std::endl;
         is.read(static_cast<char *>(memory), memory_size * type_size);
         //  std::cout << "    memory: " << memory << std::endl;
         is.close();
-};
+    };
 
-template <typename Dtype, Precision P>
-const framework::Program<Dtype, P>
-Loader<Dtype, P>::Load(const std::string &dirname) {
+    template <typename Dtype, Precision P>
+    const framework::Program<Dtype, P>
+    Loader<Dtype, P>::Load(const std::string &dirname) {
         std::string model_filename = dirname + "/__model__";
         std::string program_desc_str;
         ReadBinaryFile(model_filename, &program_desc_str);
@@ -171,10 +176,13 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
             for (int i = 0; i < block->Vars().size(); ++i) {
                 std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
                 auto var = scope->Var(var_desc->Name());
-      if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
+                if (var_desc->GetType() ==
+                    framework::proto::VarType::LOD_TENSOR) {
                     if (var_desc->Persistable() &&
-            var_desc->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
-            var_desc->GetType() != framework::proto::VarType::FETCH_LIST) {
+                        var_desc->GetType() !=
+                            framework::proto::VarType::FEED_MINIBATCH &&
+                        var_desc->GetType() !=
+                            framework::proto::VarType::FETCH_LIST) {
                         framework::LoDTensor *tensor =
                             var->GetMutable<framework::LoDTensor>();
                         // to load
@@ -196,64 +204,79 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
                 //      std::cout << " op: " << op.type() << std::endl;
                 for (int m = 0; m < op.inputs_size(); ++m) {
                     const framework::proto::OpDesc::Var &var = op.inputs(m);
-        //        std::cout << "  input parameter: " << var.parameter() <<
+                    //        std::cout << "  input parameter: " <<
+                    //        var.parameter() <<
                     //        std::endl;
                     for (int n = 0; n < var.arguments().size(); ++n) {
-          //          std::cout << "   argument - " << var.arguments()[n] <<
+                        //          std::cout << "   argument - " <<
+                        //          var.arguments()[n] <<
                         //          std::endl;
                     }
                 }
 
                 for (int y = 0; y < op.outputs_size(); ++y) {
                     const framework::proto::OpDesc::Var &var = op.outputs(y);
-        //        std::cout << "  output parameter: " << var.parameter() <<
+                    //        std::cout << "  output parameter: " <<
+                    //        var.parameter() <<
                     //        std::endl;
                     for (int z = 0; z < var.arguments().size(); ++z) {
-          //          std::cout << "   argument - " << var.arguments()[z] <<
+                        //          std::cout << "   argument - " <<
+                        //          var.arguments()[z] <<
                         //          std::endl;
                     }
                 }
 
                 for (int x = 0; x < op.attrs().size(); ++x) {
                     const framework::proto::OpDesc_Attr attr = op.attrs()[x];
-        //        std::cout << "  attr name: " << attr.name() << std::endl;
-        //        std::cout << "  attr type: " << attr.type() << std::endl;
+                    //        std::cout << "  attr name: " << attr.name() <<
+                    //        std::endl;
+                    //        std::cout << "  attr type: " << attr.type() <<
+                    //        std::endl;
 
                     switch (attr.type()) {
                     case framework::proto::AttrType::BOOLEAN:
-          //            std::cout << "   boolen: " << attr.b() << std::endl;
+                        //            std::cout << "   boolen: " << attr.b() <<
+                        //            std::endl;
                         break;
                     case framework::proto::AttrType::INT:
-          //            std::cout << "   int: " << attr.i() << std::endl;
+                        //            std::cout << "   int: " << attr.i() <<
+                        //            std::endl;
                         break;
                     case framework::proto::AttrType::FLOAT:
-        //            std::cout << "   float: " << attr.f() << std::endl;
+                    //            std::cout << "   float: " << attr.f() <<
+                    //            std::endl;
                     case framework::proto::AttrType::STRING:
-        //            std::cout << "   string: " << attr.s() << std::endl;
+                    //            std::cout << "   string: " << attr.s() <<
+                    //            std::endl;
                     case framework::proto::AttrType::BOOLEANS:
                         //                            std::vector<bool>
                         //                            bools(attr.bools_size());
                         for (int y = 0; y < attr.bools_size(); ++y) {
-            //              std::cout << "   bool - " << attr.bools(y) <<
+                            //              std::cout << "   bool - " <<
+                            //              attr.bools(y) <<
                             //              std::endl;
                         }
                     case framework::proto::AttrType::LONG:
-        //            std::cout << "   long: " << attr.l() << std::endl;
+                    //            std::cout << "   long: " << attr.l() <<
+                    //            std::endl;
                     case framework::proto::AttrType::FLOATS:
                         for (int y = 0; y < attr.floats_size(); ++y) {
-            //              std::cout << "   float - " << y << ": " <<
+                            //              std::cout << "   float - " << y <<
+                            //              ": " <<
                             //              attr.floats(y)
                             //                        << std::endl;
                         }
                     case framework::proto::AttrType::INTS:
                         for (int y = 0; y < attr.ints_size(); ++y) {
-            //              std::cout << "   int - " << y << ": " <<
+                            //              std::cout << "   int - " << y << ":
+                            //              " <<
                             //              attr.ints(y)
                             //                        << std::endl;
                         }
                     case framework::proto::AttrType::STRINGS:
                         for (int y = 0; y < attr.strings_size(); ++y) {
-            //              std::cout << "   string - " << y << ": " <<
+                            //              std::cout << "   string - " << y <<
+                            //              ": " <<
                             //              attr.strings(y)
                             //                        << std::endl;
                         }
@@ -263,40 +286,53 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
 
             for (int k = 0; k < block.vars().size(); ++k) {
                 framework::proto::VarDesc var = block.vars()[k];
-      if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
-        //        std::cout << " var name: " << var.name() << std::endl;
+                if (var.type().type() ==
+                    framework::proto::VarType::LOD_TENSOR) {
+                    //        std::cout << " var name: " << var.name() <<
+                    //        std::endl;
                     const framework::proto::VarType::TensorDesc &tensor_desc =
                         var.type().lod_tensor().tensor();
                     //        std::cout << "  in var tensor desc dims size "
-        //                  << tensor_desc.dims().size() << std::endl;
+                    //                  << tensor_desc.dims().size() <<
+                    //                  std::endl;
                     int memory_size = 1;
                     for (int l = 0; l < tensor_desc.dims().size(); ++l) {
                         //          std::cout << " var tensor desc dim " << l
-          //                    << " value: " << tensor_desc.dims()[l] <<
+                        //                    << " value: " <<
+                        //                    tensor_desc.dims()[l] <<
                         //                    std::endl;
                     }
                 }
 
                 if (var.persistable() &&
-          var.type().type() != framework::proto::VarType::FEED_MINIBATCH &&
-          var.type().type() != framework::proto::VarType::FETCH_LIST) {
-        //        std::cout << "  to load " << var.name() << std::endl;
+                    var.type().type() !=
+                        framework::proto::VarType::FEED_MINIBATCH &&
+                    var.type().type() !=
+                        framework::proto::VarType::FETCH_LIST) {
+                    //        std::cout << "  to load " << var.name() <<
+                    //        std::endl;
                     std::string file_path = dirname + "/" + var.name();
                     std::ifstream is(file_path);
-        std::streampos pos = is.tellg(); //   save   current   position
+                    std::streampos pos =
+                        is.tellg(); //   save   current   position
                     is.seekg(0, std::ios::end);
-        //        std::cout << "  file length = " << is.tellg() << std::endl;
+                    //        std::cout << "  file length = " << is.tellg() <<
+                    //        std::endl;
                     is.seekg(pos); //   restore   saved   position
 
                     // 1. version
                     uint32_t version;
-        is.read(reinterpret_cast<char *>(&version), sizeof(version));
-        //        std::cout << "   version: " << version << std::endl;
+                    is.read(reinterpret_cast<char *>(&version),
+                            sizeof(version));
+                    //        std::cout << "   version: " << version <<
+                    //        std::endl;
 
                     // 2 Lod information
                     uint64_t lod_level;
-        is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
-        //        std::cout << "   load level: " << lod_level << std::endl;
+                    is.read(reinterpret_cast<char *>(&lod_level),
+                            sizeof(lod_level));
+                    //        std::cout << "   load level: " << lod_level <<
+                    //        std::endl;
                     //        std::cout << "   lod info: " << std::endl;
                     for (uint64_t i = 0; i < lod_level; ++i) {
                         uint64_t size;
@@ -305,29 +341,35 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
                         is.read(reinterpret_cast<char *>(tmp.data()),
                                 static_cast<std::streamsize>(size));
                         for (int j = 0; j < tmp.size(); ++j) {
-            //            std::cout << "    lod - " << tmp[j] << std::endl;
+                            //            std::cout << "    lod - " << tmp[j] <<
+                            //            std::endl;
                         }
                     }
 
                     uint32_t tensor_version;
-        is.read(reinterpret_cast<char *>(&version), sizeof(version));
-        //        std::cout << "   tensor_version: " << tensor_version <<
+                    is.read(reinterpret_cast<char *>(&version),
+                            sizeof(version));
+                    //        std::cout << "   tensor_version: " <<
+                    //        tensor_version <<
                     //        std::endl;
 
                     int32_t size;
                     is.read(reinterpret_cast<char *>(&size), sizeof(size));
-        //        std::cout << "   tensor desc size: " << size << std::endl;
+                    //        std::cout << "   tensor desc size: " << size <<
+                    //        std::endl;
                     std::unique_ptr<char[]> buf(new char[size]);
                     is.read(reinterpret_cast<char *>(buf.get()), size);
 
                     framework::proto::VarType::TensorDesc desc;
                     desc.ParseFromArray(buf.get(), size);
 
-        //        std::cout << "   desc dims size " << desc.dims().size() <<
+                    //        std::cout << "   desc dims size " <<
+                    //        desc.dims().size() <<
                     //        std::endl;
                     int memory_size = 1;
                     for (int l = 0; l < desc.dims().size(); ++l) {
-          //          std::cout << "    dim " << l << " value: " <<
+                        //          std::cout << "    dim " << l << " value: "
+                        //          <<
                         //          desc.dims()[l]
                         //                    << std::endl;
                         memory_size *= desc.dims()[l];
@@ -362,14 +404,18 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
                         break;
                     default:
                         break;
-          //            std::cout << "    not support" << std::endl;
+                        //            std::cout << "    not support" <<
+                        //            std::endl;
                     }
 
-        //        std::cout << "    malloc size: " << memory_size * type_size
+                    //        std::cout << "    malloc size: " << memory_size *
+                    //        type_size
                     //                  << std::endl;
                     void *memory = malloc(memory_size * type_size);
-        is.read(static_cast<char *>(memory), memory_size * type_size);
-        //        std::cout << "    memory: " << memory << std::endl;
+                    is.read(static_cast<char *>(memory),
+                            memory_size * type_size);
+                    //        std::cout << "    memory: " << memory <<
+                    //        std::endl;
                     is.close();
                 } else {
                     //        std::cout << "  *not load "
@@ -380,8 +426,8 @@ Loader<Dtype, P>::Load(const std::string &dirname) {
 
 #endif
         return program;
-}
+    }
 
-template class Loader<CPU, Precision::FP32>;
+    template class Loader<CPU, Precision::FP32>;
 
 } // namespace paddle_mobile

src/io.h

@@ -27,13 +27,14 @@ SOFTWARE.
 
 namespace paddle_mobile {
 
-template <typename Dtype, Precision P = Precision::FP32>
-class Loader : PaddleMobileObject {
-public:
+    template <typename Dtype, Precision P = Precision::FP32>
+    class Loader : PaddleMobileObject {
+      public:
         const framework::Program<Dtype, P> Load(const std::string &dirname);
 
-private:
-  void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
-};
+      private:
+        void LoadVar(framework::LoDTensor *tensor,
+                     const std::string &file_path);
+    };
 
 } // namespace paddle_mobile

src/memory/t_malloc.cc

@@ -22,30 +22,30 @@ SOFTWARE.
 #include <cstring>
 
 namespace paddle_mobile {
-namespace memory {
-const int MALLOC_ALIGN = 16;
+    namespace memory {
+        const int MALLOC_ALIGN = 16;
 
-void Copy(void *dst, const void *src, size_t num) {
+        void Copy(void *dst, const void *src, size_t num) {
             std::memcpy(dst, src, num);
-};
+        };
 
-void *Alloc(size_t size) {
+        void *Alloc(size_t size) {
             size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
             char *p = static_cast<char *>(malloc(offset + size));
             if (!p) {
                 return nullptr;
             }
-  void *r = reinterpret_cast<void *>(reinterpret_cast<size_t>(p + offset) &
-                                     (~(MALLOC_ALIGN - 1)));
+            void *r = reinterpret_cast<void *>(
+                reinterpret_cast<size_t>(p + offset) & (~(MALLOC_ALIGN - 1)));
             static_cast<void **>(r)[-1] = p;
             return r;
-}
+        }
 
-void Free(void *ptr) {
+        void Free(void *ptr) {
             if (ptr) {
                 free(static_cast<void **>(ptr)[-1]);
             }
-}
+        }
 
-} // namespace memory
+    } // namespace memory
 } // namespace paddle_mobile

src/memory/t_malloc.h

@@ -21,15 +21,15 @@ SOFTWARE.
 #include <type_traits>
 
 namespace paddle_mobile {
-namespace memory {
+    namespace memory {
 
-void Copy(void *dst, const void *src, size_t num);
+        void Copy(void *dst, const void *src, size_t num);
 
-void *Alloc(size_t size);
+        void *Alloc(size_t size);
 
-void Free(void *ptr);
+        void Free(void *ptr);
 
-/**
+        /**
          * \brief   Free memory block in one place.
          *
          * \note    In some cases, custom deleter is used to
@@ -37,16 +37,16 @@ void Free(void *ptr);
          *          std::unique_ptr<T> in tensor.h.
          *          static_cast
          */
-template <typename T> class PODDeleter {
+        template <typename T> class PODDeleter {
             static_assert(std::is_pod<T>::value, "T must be POD");
 
-public:
+          public:
             explicit PODDeleter(){};
 
             void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
-};
+        };
 
-/**
+        /**
          * \brief   Free memory block in one place does not meet POD
          *
          * \note    In some cases, custom deleter is used to
@@ -54,11 +54,11 @@ public:
          *          std::unique_ptr<T> in tensor.h.
          *          reinterpret_cast
          */
-template <typename T> class PlainDeleter {
-public:
+        template <typename T> class PlainDeleter {
+          public:
             explicit PlainDeleter(){};
 
             void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
-};
-} // namespace memory
+        };
+    } // namespace memory
 } // namespace paddle_mobile

src/operators/conv_op.cpp

@@ -22,17 +22,17 @@ SOFTWARE.
 #include "framework/operator.h"
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-int ConvOutputSize(int input_size, int filter_size, int dilation, int padding,
-                   int stride) {
+        int ConvOutputSize(int input_size, int filter_size, int dilation,
+                           int padding, int stride) {
             const int dkernel = dilation * (filter_size - 1) + 1;
             int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
             return output_size;
-}
+        }
 
-template <typename Dtype, typename T>
-void ConvOp<Dtype, T>::InferShape() const {
+        template <typename Dtype, typename T>
+        void ConvOp<Dtype, T>::InferShape() const {
             //  std::cout << " begin get dims: " << std::endl;
 
             auto in_dims = param_.Input()->dims();
@@ -61,16 +61,16 @@ void ConvOp<Dtype, T>::InferShape() const {
 
             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(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
-                                          dilations[i], paddings[i],
-                                          strides[i]));
+                output_shape.push_back(
+                    ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
+                                   dilations[i], paddings[i], strides[i]));
             }
 
             framework::DDim ddim = framework::make_ddim(output_shape);
             param_.Output()->Resize(ddim);
-}
+        }
 
-template class ConvOp<CPU, float>;
+        template class ConvOp<CPU, float>;
 
-} // namespace operators
+    } // namespace operators
 } // namespace paddle_mobile

src/operators/conv_op.h

@@ -22,32 +22,33 @@ SOFTWARE.
 #include "operators/kernel/conv_kernel.h"
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-using namespace framework;
+        using namespace framework;
 
-template <typename DeviceType, typename T>
-class ConvOp : public framework::OperatorWithKernel<DeviceType> {
-public:
+        template <typename DeviceType, typename T>
+        class ConvOp : public framework::OperatorWithKernel<DeviceType> {
+          public:
             ConvOp(const std::string &type, const VariableNameMap &inputs,
-         const VariableNameMap &outputs, const framework::AttributeMap &attrs,
+                   const VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs,
                    std::shared_ptr<framework::Scope> scope)
-      : framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
-                                                  scope),
+                : framework::OperatorWithKernel<DeviceType>(
+                      type, inputs, outputs, attrs, scope),
                   param_(inputs, outputs, attrs, *scope) {}
 
             using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
             void InferShape() const override;
 
-protected:
-  void RunImpl() const {
+            void Run() const {
                 operators::ConvKernel<DeviceType, T, ConvParam> kernel;
                 kernel.Compute(param_);
                 this->ClearVariables();
             }
 
+          private:
             ConvParam param_;
-};
+        };
 
-} // operators
+    } // operators
 } // paddle_mobile

src/operators/elementwise_add_op.cpp

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+
+#include "elementwise_add_op.h"
+
+namespace paddle_mobile {
+    namespace operators {
+
+        template <typename Dtype, typename T>
+        void ElementwiseAddOp<Dtype, T>::InferShape() const {
+            auto x_dim = param_.InputX()->dims();
+            param_.Out()->Resize(x_dim);
+        }
+        template class ElementwiseAddOp<CPU, float>;
+    }
+}

src/operators/elementwise_add_op.h

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+
+#include "framework/operator.h"
+#include "kernel/elementwise_add_kernel.h"
+#include "op_param.h"
+
+namespace paddle_mobile {
+    namespace operators {
+
+        using namespace framework;
+
+        template <typename DeviceType, typename T>
+        class ElementwiseAddOp
+            : public framework::OperatorWithKernel<DeviceType> {
+          public:
+            ElementwiseAddOp(const std::string &type,
+                             const VariableNameMap &inputs,
+                             const VariableNameMap &outputs,
+                             const framework::AttributeMap attrs,
+                             std::shared_ptr<framework::Scope> scope)
+                : framework::OperatorWithKernel<DeviceType>(
+                      type, inputs, outputs, attrs, scope),
+                  param_(inputs, outputs, attrs, *scope) {}
+
+            void Run() const {
+                operators::ElementwiseAddKernel<DeviceType, T,
+                                                ElementwiseAddParam>
+                    kernel;
+                kernel.Compute(param_);
+            }
+
+            using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
+            void InferShape() const override;
+
+          protected:
+            ElementwiseAddParam param_;
+        };
+    }
+}

src/operators/kernel/arm/conv_kernel.cpp

@@ -19,27 +19,31 @@ SOFTWARE.
 #include "operators/kernel/conv_kernel.h"
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-bool IsExpand(const std::vector<int64_t> &filter_dim,
-              const std::vector<int> &strides, const std::vector<int> &paddings,
+        bool IsExpand(const std::vector<int64_t> &filter_dim,
+                      const std::vector<int> &strides,
+                      const std::vector<int> &paddings,
                       const std::vector<int> &dilations) {
-  bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
+            bool filter_1 = true, strides_1 = true, padding_0 = true,
+                 dilation_1 = true;
             for (size_t j = 0; j < strides.size(); ++j) {
-    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
+                filter_1 =
+                    filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
                 strides_1 = strides_1 && (strides[j] == 1);
                 padding_0 = padding_0 && (paddings[j] == 0);
                 dilation_1 = dilation_1 && (dilations[j] == 1);
             }
             return !(filter_1 && strides_1 && padding_0 && dilation_1);
-}
-
-template <>
-void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
-  const Tensor *input = param.Input();
+        }
 
+        template <>
+        void ConvKernel<CPU, float, ConvParam>::Compute(
+            const ConvParam &param) const {
             LOG(kLOG_DEBUG) << param;
 
+            const Tensor *input = param.Input();
+
             // The filter will be reshaped in the calculations,
             // so here use an assignment operation,
             // that avoids modifying the variable in the Scope.
@@ -57,13 +61,18 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
 
             const int batch_size = static_cast<int>(input->dims()[0]);
 
-  // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
-  std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
-  // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, o_w}
-  std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
+            // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
+            // k_w}
+            std::vector<int64_t> filter_shape_vec(
+                framework::vectorize(filter.dims()));
+            // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
+            // o_w}
+            std::vector<int64_t> output_shape_vec(
+                framework::vectorize(output->dims()));
 
             // use col_shape in the im2col calculation
-  // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
+            // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
+            // k_w, o_d,
             // o_h, o_w}
             size_t data_dim = filter_shape_vec.size() - 2;
             std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
@@ -75,12 +84,14 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
             framework::DDim col_shape(framework::make_ddim(col_shape_vec));
 
             // use col_matrix_shape in the gemm calculation
-  // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, o_d *
+            // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
+            // o_d *
             // o_h * o_w)
             framework::DDim col_matrix_shape =
                 framework::flatten_to_2d(col_shape, data_dim + 1);
 
-  bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
+            bool is_expand =
+                IsExpand(filter_shape_vec, strides, paddings, dilations);
             Tensor col;
             // col_matrix shares the same piece of data with col,
             // but will be reshaped into a two-dimensional matrix shape
@@ -95,14 +106,10 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
             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]};
+            framework::DDim filter_matrix_shape = {
+                filter.dims()[0], filter.numel() / filter.dims()[0]};
             filter.Resize(filter_matrix_shape);
 
-  DLOG << " input dim " << input->dims();
-
-  DLOG << " output dim " << output->dims();
-
             framework::DDim output_matrix_shape = {
                 output->dims()[1],
                 output->numel() / (output->dims()[0] * output->dims()[1])};
@@ -118,10 +125,12 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
             //            device_context<DeviceContext>();
             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);
+                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);
+                    Tensor in_slice =
+                        in_batch.Slice(g * in_step, (g + 1) * in_step);
 
                     if (!is_expand) {
                         col.ShareDataWith(in_slice);
@@ -130,8 +139,8 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
                     } else if (data_dim == 2U) {
                         // im2col
                         im2col(in_slice, dilations, strides,
-               std::vector<int>{paddings[0], paddings[1], paddings[0],
-                                paddings[1]},
+                               std::vector<int>{paddings[0], paddings[1],
+                                                paddings[0], paddings[1]},
                                &col);
                     } else if (data_dim == 3U) {
                         // vol2col
@@ -139,15 +148,17 @@ void ConvKernel<CPU, float, ConvParam>::Compute(const ConvParam &param) const {
                     }
 
                     // 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);
-      math::matmul<float>(filter_slice, false, col_matrix, false, float(1.0),
-                          &out_slice, float(0.0));
+                    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);
+                    math::matmul<float>(filter_slice, false, col_matrix, false,
+                                        float(1.0), &out_slice, float(0.0));
+                }
             }
         }
-}
 
-template class ConvKernel<CPU, float, ConvParam>;
+        template class ConvKernel<CPU, float, ConvParam>;
 
-} // namespace operators
+    } // namespace operators
 } // namespace paddle_mobile

src/operators/kernel/arm/elementwise_add_kernel.cpp

+/* Copyright (c) 2016 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
+
+#include "operators/kernel/elementwise_add_kernel.h"
+
+namespace paddle_mobile {
+    namespace operators {
+
+        template <typename T> struct AddFunctor {
+            inline T operator()(T a, T b) const { return a + b; }
+        };
+
+        template <>
+        void ElementwiseAddKernel<CPU, float, ElementwiseAddParam>::Compute(
+            const ElementwiseAddParam &param) const {
+            const Tensor *input_x = param.InputX();
+            const Tensor *input_y = param.InputY();
+            Tensor *Out = param.Out();
+            Out->mutable_data<float>();
+            const int axis = param.Axis();
+            ElementwiseComputeEx<AddFunctor<float>, float>(
+                input_x, input_y, axis, AddFunctor<float>(), Out);
+        }
+
+        template class ElementwiseAddKernel<CPU, float, ElementwiseAddParam>;
+
+    } // namespace operators
+} // namespace paddle

src/operators/kernel/conv_kernel.h

@@ -25,14 +25,15 @@ SOFTWARE.
 #pragma once;
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-using namespace framework;
+        using namespace framework;
 
-template <typename DeviceType, typename T, typename P>
-class ConvKernel : public framework::OpKernelBase<DeviceType, ConvParam> {
-public:
+        template <typename DeviceType, typename T, typename P>
+        class ConvKernel
+            : public framework::OpKernelBase<DeviceType, ConvParam> {
+          public:
             void Compute(const ConvParam &param) const;
-};
-}
+        };
+    }
 }

src/operators/kernel/elementwise_add_kernel.h

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+#pragma once;
+
+#include "framework/operator.h"
+#include "operators/math/elementwise_op_function.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+    namespace operators {
+
+        using namespace framework;
+
+        template <typename DeviceType, typename T, typename P>
+        class ElementwiseAddKernel
+            : public framework::OpKernelBase<DeviceType, ElementwiseAddParam> {
+          public:
+            void Compute(const ElementwiseAddParam &param) const;
+        };
+    }
+}

src/operators/kernel/fpga/conv_kernel.cpp

@@ -19,11 +19,12 @@ SOFTWARE.
 #include "operators/kernel/conv_kernel.h"
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-// template<>
-// void ConvKernel<FPGA, float>::Compute(const ConvParam &param) const {}
-//
-// template class ConvKernel<FPGA, float>;
-}
+        // template<>
+        // void ConvKernel<FPGA, float>::Compute(const ConvParam &param) const
+        // {}
+        //
+        // template class ConvKernel<FPGA, float>;
+    }
 }

src/operators/math/elementwise_op_function.h

+/* Copyright (c) 2016 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
+#include "transform.h"
+
+#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
+
+namespace paddle_mobile {
+    namespace operators {
+
+        /*
+         * Out = X ⊙ Y
+         * If Y's shape does not match X' shape, they will be reshaped.
+         * For example:
+         * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+         *    pre=2, n=3*4, post=5
+         *    x.shape(2, 12, 5) * y.shape(1, 12, 1).broadcast(2, 12, 5)
+         * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
+         *    pre=2*3, n=4*5, post=1
+         *    x.shape(6, 20, 1) * y.shape(1, 20, 1).broadcast(6, 20, 1)
+         */
+        inline void get_mid_dims(const framework::DDim &x_dims,
+                                 const framework::DDim &y_dims, const int axis,
+                                 int *pre, int *n, int *post) {
+            *pre = 1;
+            *n = 1;
+            *post = 1;
+            // compute pre
+            for (int i = 0; i < axis; ++i) {
+                (*pre) *= x_dims[i];
+            }
+
+            for (int i = 0; i < y_dims.size(); ++i) {
+                assert(x_dims[i + axis] == y_dims[i]);
+                /// "Broadcast dimension mismatch.");
+                (*n) *= y_dims[i];
+            }
+
+            for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
+                (*post) *= x_dims[i];
+            }
+        }
+
+        /// remove dims tail 1. (4,20,1,1) -> (4,20)
+        inline void trim_trailing_singular_dims(framework::DDim *dims) {
+            // Remove trailing dimensions of size 1 for y
+            auto actual_dims_size = dims->size();
+            for (; actual_dims_size != 0; --actual_dims_size) {
+                if ((*dims)[actual_dims_size - 1] != 1)
+                    break;
+            }
+            if (actual_dims_size != dims->size()) {
+                auto actual_dims = framework::vectorize(*dims);
+                actual_dims.resize(actual_dims_size);
+                *dims = framework::make_ddim(actual_dims);
+            }
+        }
+
+        template <typename T> class RowwiseTransformIterator {
+          public:
+            RowwiseTransformIterator(const T *ptr, int n)
+                : ptr_(ptr), i_(0), n_(n) {}
+
+            RowwiseTransformIterator<T> &operator++() {
+                ++i_;
+                if (UNLIKELY(i_ == n_)) {
+                    i_ = 0;
+                }
+                return *this;
+            }
+
+            bool operator==(const RowwiseTransformIterator<T> &rhs) const {
+                return (ptr_ + i_) == &(*rhs);
+            }
+
+            bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
+                return (ptr_ + i_) != &(*rhs);
+            }
+
+            const T &operator*() { return ptr_[i_]; }
+
+          private:
+            const T *ptr_;
+            int i_;
+            int64_t n_;
+        };
+
+        /// (4,20,2)+(20,): (20,) just as (20,1), when move 2 strides in last
+        /// dimension
+        /// in (4,20,2) is 2 ,
+        /// (20,1) move 1 stride , to fill(add) 2 element with the same number.
+        template <typename T> class MidWiseTransformIterator {
+          public:
+            MidWiseTransformIterator(const T *ptr, int n, int post)
+                : ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
+
+            MidWiseTransformIterator<T> &operator++() {
+                ++j_;
+                if (UNLIKELY(j_ == post_)) {
+                    ++i_;
+                    j_ = 0;
+                    if (UNLIKELY(i_ == n_)) {
+                        i_ = 0;
+                    }
+                }
+                return *this;
+            }
+
+            bool operator==(const MidWiseTransformIterator<T> &rhs) const {
+                return (ptr_ + i_) == &(*rhs);
+            }
+
+            bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
+                return (ptr_ + i_) != &(*rhs);
+            }
+
+            const T &operator*() { return ptr_[i_]; }
+
+          private:
+            const T *ptr_;
+            int64_t i_;
+            int64_t j_;
+            int64_t n_;
+            int64_t post_;
+        };
+
+        template <typename Functor, typename T, typename OutType = T>
+        class TransformFunctor {
+          public:
+            TransformFunctor(const framework::Tensor *x,
+                             const framework::Tensor *y, framework::Tensor *z,
+                             Functor func)
+                : x_(x->data<T>()), y_(y->data<T>()),
+                  z_(z->mutable_data<OutType>()), nx_(x->numel()), func_(func) {
+            }
+
+            inline void Run() const {
+                math::Transform trans;
+                // 同时执行func(x_, y_)传入z_。
+                trans(x_, x_ + nx_, y_, z_, func_);
+            }
+
+            inline void RunRowWise(int n, int pre) const {
+                math::Transform trans;
+                trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_,
+                      func_);
+            }
+
+            inline void RunMidWise(int n, int pre, int post) const {
+                math::Transform trans;
+                trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post),
+                      z_, func_);
+            }
+
+          private:
+            const T *x_;
+            const T *y_;
+            OutType *z_;
+            int64_t nx_;
+            Functor func_;
+        };
+
+        template <typename Functor, typename T, typename OutType = T>
+        void ElementwiseComputeEx(const framework::Tensor *x,
+                                  const framework::Tensor *y, int axis,
+                                  Functor func, framework::Tensor *z) {
+            TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
+
+            auto x_dims = x->dims();
+            auto y_dims = y->dims();
+            // PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+            //                  "Rank of first input must >= rank of second
+            //                  input.");
+
+            if (x_dims == y_dims) {
+                functor.Run();
+                return;
+            }
+
+            /// axis = -1 represent the last dimension.
+            axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+            // PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+            //               "Axis should be in range [0, x_dims)");
+            trim_trailing_singular_dims(&y_dims);
+            axis = (y_dims.size() == 0) ? x_dims.size() : axis;
+
+            int pre, n, post;
+            get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
+            if (post == 1) {
+                functor.RunRowWise(n, pre);
+                return;
+            } else {
+                functor.RunMidWise(n, pre, post);
+                return;
+            }
+        }
+
+    } // namespace operators
+} // namespace paddle

src/operators/math/im2col.cc

@@ -16,19 +16,22 @@ limitations under the License. */
 #include "common/types.h"
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
+    namespace operators {
+        namespace math {
 
-/*
+            /*
              * im = [input_channels, input_height, input_width]
              * col =
- *   [input_channels, filter_height, filter_width, output_height, output_width]
+             *   [input_channels, filter_height, filter_width, output_height,
+             * output_width]
              */
-template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
-public:
-  void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
+            template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
+              public:
+                void operator()(const framework::Tensor &im,
+                                const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *col) {
+                                const std::vector<int> &padding,
+                                framework::Tensor *col) {
                     //    PADDLE_ENFORCE(im.dims().size() == 3);
                     //    PADDLE_ENFORCE(col->dims().size() == 5);
 
@@ -40,22 +43,29 @@ public:
                     int col_height = col->dims()[3];
                     int col_width = col->dims()[4];
 
-    //    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
-    //                       ((dilation[0] * (filter_height - 1) + 1))) /
+                    //    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
+                    //    -
+                    //                       ((dilation[0] * (filter_height - 1)
+                    //                       + 1))) /
                     //                              stride[0] +
                     //                          1,
                     //                      col_height,
-    //                      "Output_height and padding(padding_up, padding_down)
+                    //                      "Output_height and
+                    //                      padding(padding_up, padding_down)
                     //                      are " "inconsistent.");
-    //    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
-    //                       ((dilation[1] * (filter_width - 1) + 1))) /
+                    //    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
+                    //    -
+                    //                       ((dilation[1] * (filter_width - 1)
+                    //                       + 1))) /
                     //                              stride[1] +
                     //                          1,
                     //                      col_width,
-    //                      "Output_height and padding(padding_up, padding_down)
+                    //                      "Output_height and
+                    //                      padding(padding_up, padding_down)
                     //                      are " "inconsistent.");
 
-    int channels_col = im_channels * filter_height * filter_width;
+                    int channels_col =
+                        im_channels * filter_height * filter_width;
 
                     const T *im_data = im.data<T>();
                     T *col_data = col->data<T>();
@@ -64,13 +74,20 @@ public:
                         int h_offset = (c / filter_width) % filter_height;
                         int c_im = c / (filter_width * filter_height);
                         for (int h = 0; h < col_height; ++h) {
-        int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
+                            int im_row_idx = h * stride[0] - padding[0] +
+                                             h_offset * dilation[0];
                             for (int w = 0; w < col_width; ++w) {
-          int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
-          int col_idx = (c * col_height + h) * col_width + w;
-          int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
+                                int im_col_idx = w * stride[1] - padding[1] +
+                                                 w_offset * dilation[1];
+                                int col_idx =
+                                    (c * col_height + h) * col_width + w;
+                                int im_idx =
+                                    (im_row_idx + c_im * im_height) * im_width +
+                                    im_col_idx;
 
-          col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
+                                col_data[col_idx] =
+                                    (im_row_idx < 0 ||
+                                     im_row_idx >= im_height ||
                                      im_col_idx < 0 || im_col_idx >= im_width)
                                         ? static_cast<T>(0)
                                         : im_data[im_idx];
@@ -78,19 +95,21 @@ public:
                         }
                     }
                 }
-};
+            };
 
-/*
+            /*
              * im = [input_channels, input_height, input_width]
              * col =
- *   [input_channels, filter_height, filter_width, output_height, output_width]
+             *   [input_channels, filter_height, filter_width, output_height,
+             * output_width]
              */
-template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
-public:
+            template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
+              public:
                 void operator()(const framework::Tensor &col,
                                 const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *im) {
+                                const std::vector<int> &padding,
+                                framework::Tensor *im) {
                     //    PADDLE_ENFORCE(im->dims().size() == 3);
                     //    PADDLE_ENFORCE(col.dims().size() == 5);
                     int im_channels = im->dims()[0];
@@ -101,22 +120,29 @@ public:
                     int col_height = col.dims()[3];
                     int col_width = col.dims()[4];
 
-    //    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
-    //                       ((dilation[0] * (filter_height - 1) + 1))) /
+                    //    PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
+                    //    -
+                    //                       ((dilation[0] * (filter_height - 1)
+                    //                       + 1))) /
                     //                              stride[0] +
                     //                          1,
                     //                      col_height,
-    //                      "Output_height and padding(padding_up, padding_down)
+                    //                      "Output_height and
+                    //                      padding(padding_up, padding_down)
                     //                      are " "inconsistent.");
-    //    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
-    //                       ((dilation[1] * (filter_width - 1) + 1))) /
+                    //    PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
+                    //    -
+                    //                       ((dilation[1] * (filter_width - 1)
+                    //                       + 1))) /
                     //                              stride[1] +
                     //                          1,
                     //                      col_width,
-    //                      "Output_height and padding(padding_up, padding_down)
+                    //                      "Output_height and
+                    //                      padding(padding_up, padding_down)
                     //                      are " "inconsistent.");
 
-    int channels_col = im_channels * filter_height * filter_width;
+                    int channels_col =
+                        im_channels * filter_height * filter_width;
 
                     T *im_data = im->data<T>();
                     const T *col_data = col.data<T>();
@@ -126,35 +152,46 @@ public:
                         int h_offset = (c / filter_width) % filter_height;
                         int c_im = c / (filter_width * filter_height);
                         for (int h = 0; h < col_height; ++h) {
-        int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
+                            int im_row_idx = h * stride[0] - padding[0] +
+                                             h_offset * dilation[0];
                             for (int w = 0; w < col_width; ++w) {
-          int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
-          if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
-              (im_col_idx) >= 0 && (im_col_idx) < im_width) {
-            im_data[(im_row_idx + c_im * im_height) * im_width + im_col_idx] +=
-                col_data[(c * col_height + h) * col_width + w];
+                                int im_col_idx = w * stride[1] - padding[1] +
+                                                 w_offset * dilation[1];
+                                if ((im_row_idx) >= 0 &&
+                                    (im_row_idx) < im_height &&
+                                    (im_col_idx) >= 0 &&
+                                    (im_col_idx) < im_width) {
+                                    im_data[(im_row_idx + c_im * im_height) *
+                                                im_width +
+                                            im_col_idx] +=
+                                        col_data[(c * col_height + h) *
+                                                     col_width +
+                                                 w];
                                 }
                             }
                         }
                     }
                 }
-};
+            };
 
-template class Im2ColFunctor<ColFormat::kCFO, CPU, float>;
-template class Im2ColFunctor<ColFormat::kCFO, CPU, double>;
-template class Col2ImFunctor<ColFormat::kCFO, CPU, float>;
-template class Col2ImFunctor<ColFormat::kCFO, CPU, double>;
+            template class Im2ColFunctor<ColFormat::kCFO, CPU, float>;
+            template class Im2ColFunctor<ColFormat::kCFO, CPU, double>;
+            template class Col2ImFunctor<ColFormat::kCFO, CPU, float>;
+            template class Col2ImFunctor<ColFormat::kCFO, CPU, double>;
 
-/*
+            /*
              * im = [input_channels, input_height, input_width]
              * col =
- *   [output_height, output_width, input_channels, filter_height, filter_width]
+             *   [output_height, output_width, input_channels, filter_height,
+             * filter_width]
              */
-template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
-public:
-  void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
+            template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
+              public:
+                void operator()(const framework::Tensor &im,
+                                const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *col) {
+                                const std::vector<int> &padding,
+                                framework::Tensor *col) {
                     //    PADDLE_ENFORCE(im.dims().size() == 3);
                     //    PADDLE_ENFORCE(col->dims().size() == 5);
                     int im_channels = im.dims()[0];
@@ -166,42 +203,59 @@ public:
                     int col_width = col->dims()[1];
 
                     //    PADDLE_ENFORCE_EQ(
-    //        (im_height + padding[0] + padding[2] - filter_height) / stride[0]
-    //        + 1, col_height, "Output_height and padding(padding_up,
+                    //        (im_height + padding[0] + padding[2] -
+                    //        filter_height) / stride[0]
+                    //        + 1, col_height, "Output_height and
+                    //        padding(padding_up,
                     //        padding_down) are " "inconsistent.");
                     //    PADDLE_ENFORCE_EQ(
-    //        (im_width + padding[1] + padding[3] - filter_width) / stride[1] +
-    //        1, col_width, "col_width and padding(padding_left, padding_right)
+                    //        (im_width + padding[1] + padding[3] -
+                    //        filter_width) / stride[1] +
+                    //        1, col_width, "col_width and padding(padding_left,
+                    //        padding_right)
                     //        are " "inconsistent.");
 
                     const T *im_data = im.data<T>();
                     T *col_data = col->data<T>();
 
-    for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
-      for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
-        for (int channel = 0; channel < im_channels; ++channel) {
-          for (int filter_row_idx = 0; filter_row_idx < filter_height;
+                    for (int col_row_idx = 0; col_row_idx < col_height;
+                         ++col_row_idx) {
+                        for (int col_col_idx = 0; col_col_idx < col_width;
+                             ++col_col_idx) {
+                            for (int channel = 0; channel < im_channels;
+                                 ++channel) {
+                                for (int filter_row_idx = 0;
+                                     filter_row_idx < filter_height;
                                      ++filter_row_idx) {
                                     int im_row_offset =
-                col_row_idx * stride[0] + filter_row_idx - padding[0];
-            for (int filter_col_idx = 0; filter_col_idx < filter_width;
+                                        col_row_idx * stride[0] +
+                                        filter_row_idx - padding[0];
+                                    for (int filter_col_idx = 0;
+                                         filter_col_idx < filter_width;
                                          ++filter_col_idx) {
                                         int im_col_offset =
-                  col_col_idx * stride[1] + filter_col_idx - padding[1];
+                                            col_col_idx * stride[1] +
+                                            filter_col_idx - padding[1];
 
                                         int col_offset =
-                  ((((col_row_idx)*col_width + col_col_idx) * im_channels +
+                                            ((((col_row_idx)*col_width +
+                                               col_col_idx) *
+                                                  im_channels +
                                               channel) *
                                                  filter_height +
                                              filter_row_idx) *
                                                 filter_width +
                                             filter_col_idx;
 
-              int im_offset = (channel * im_height + im_row_offset) * im_width +
+                                        int im_offset = (channel * im_height +
+                                                         im_row_offset) *
+                                                            im_width +
                                                         im_col_offset;
                                         col_data[col_offset] =
-                  (im_row_offset < 0 || im_row_offset >= im_height ||
-                   im_col_offset < 0 || im_col_offset >= im_width)
+                                            (im_row_offset < 0 ||
+                                             im_row_offset >= im_height ||
+                                             im_col_offset < 0 ||
+                                             im_col_offset >= im_width)
                                                 ? static_cast<T>(0)
                                                 : im_data[im_offset];
                                     }
@@ -210,19 +264,21 @@ public:
                         }
                     }
                 }
-};
+            };
 
-/*
+            /*
              * im = [input_channels, input_height, input_width]
              * col =
- *   [output_height, output_width, input_channels, filter_height, filter_width]
+             *   [output_height, output_width, input_channels, filter_height,
+             * filter_width]
              */
-template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
-public:
+            template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
+              public:
                 void operator()(const framework::Tensor &col,
                                 const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *im) {
+                                const std::vector<int> &padding,
+                                framework::Tensor *im) {
                     //    PADDLE_ENFORCE(im->dims().size() == 3);
                     //    PADDLE_ENFORCE(col.dims().size() == 5);
                     int im_channels = im->dims()[0];
@@ -234,43 +290,61 @@ public:
                     int col_width = col.dims()[1];
 
                     //    PADDLE_ENFORCE_EQ(
-    //        (im_height + padding[0] + padding[2] - filter_height) / stride[0]
-    //        + 1, col_height, "Output_height and padding(padding_up,
+                    //        (im_height + padding[0] + padding[2] -
+                    //        filter_height) / stride[0]
+                    //        + 1, col_height, "Output_height and
+                    //        padding(padding_up,
                     //        padding_down) are " "inconsistent.");
                     //    PADDLE_ENFORCE_EQ(
-    //        (im_width + padding[1] + padding[3] - filter_width) / stride[1] +
-    //        1, col_width, "col_width and padding(padding_left, padding_right)
+                    //        (im_width + padding[1] + padding[3] -
+                    //        filter_width) / stride[1] +
+                    //        1, col_width, "col_width and padding(padding_left,
+                    //        padding_right)
                     //        are " "inconsistent.");
 
                     T *im_data = im->data<T>();
                     const T *col_data = col.data<T>();
 
-    for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
-      for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
-        for (int channel = 0; channel < im_channels; ++channel) {
-          for (int filter_row_idx = 0; filter_row_idx < filter_height;
+                    for (int col_row_idx = 0; col_row_idx < col_height;
+                         ++col_row_idx) {
+                        for (int col_col_idx = 0; col_col_idx < col_width;
+                             ++col_col_idx) {
+                            for (int channel = 0; channel < im_channels;
+                                 ++channel) {
+                                for (int filter_row_idx = 0;
+                                     filter_row_idx < filter_height;
                                      ++filter_row_idx) {
                                     int im_row_offset =
-                col_row_idx * stride[0] + filter_row_idx - padding[0];
-            for (int filter_col_idx = 0; filter_col_idx < filter_width;
+                                        col_row_idx * stride[0] +
+                                        filter_row_idx - padding[0];
+                                    for (int filter_col_idx = 0;
+                                         filter_col_idx < filter_width;
                                          ++filter_col_idx) {
                                         int im_col_offset =
-                  col_col_idx * stride[1] + filter_col_idx - padding[1];
+                                            col_col_idx * stride[1] +
+                                            filter_col_idx - padding[1];
 
                                         int col_offset =
-                  (((col_row_idx * col_width + col_col_idx) * im_channels +
+                                            (((col_row_idx * col_width +
+                                               col_col_idx) *
+                                                  im_channels +
                                               channel) *
                                                  filter_height +
                                              filter_row_idx) *
                                                 filter_width +
                                             filter_col_idx;
 
-              if (im_row_offset >= 0 && im_row_offset < im_height &&
-                  im_col_offset >= 0 && im_col_offset < im_width) {
+                                        if (im_row_offset >= 0 &&
+                                            im_row_offset < im_height &&
+                                            im_col_offset >= 0 &&
+                                            im_col_offset < im_width) {
                                             int im_offset =
-                    (channel * im_height + im_row_offset) * im_width +
+                                                (channel * im_height +
+                                                 im_row_offset) *
+                                                    im_width +
                                                 im_col_offset;
-                im_data[im_offset] += col_data[col_offset];
+                                            im_data[im_offset] +=
+                                                col_data[col_offset];
                                         }
                                     }
                                 }
@@ -278,13 +352,13 @@ public:
                         }
                     }
                 }
-};
+            };
 
-template class Im2ColFunctor<ColFormat::kOCF, CPU, float>;
-template class Im2ColFunctor<ColFormat::kOCF, CPU, double>;
-template class Col2ImFunctor<ColFormat::kOCF, CPU, float>;
-template class Col2ImFunctor<ColFormat::kOCF, CPU, double>;
+            template class Im2ColFunctor<ColFormat::kOCF, CPU, float>;
+            template class Im2ColFunctor<ColFormat::kOCF, CPU, double>;
+            template class Col2ImFunctor<ColFormat::kOCF, CPU, float>;
+            template class Col2ImFunctor<ColFormat::kOCF, CPU, double>;
 
-} // namespace math
-} // namespace operators
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/math/im2col.h

@@ -17,14 +17,16 @@ limitations under the License. */
 #include "framework/tensor.h"
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
+    namespace operators {
+        namespace math {
 
-/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
-enum class ColFormat { kCFO = 0, kOCF = 1 };
+            /* The storage format of the coldata in the Im2ColFunctor and
+             * Col2ImFunctor. */
+            enum class ColFormat { kCFO = 0, kOCF = 1 };
 
-/*
- * \brief Converts the image data of three dimensions(CHW) into a colData of
+            /*
+             * \brief Converts the image data of three dimensions(CHW) into a
+             * colData of
              *        five dimensions in the Im2ColFunctor calculation,
              *        And in the Col2ImFunctor calculation, it is reversed.
              *
@@ -44,11 +46,15 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
              * \param 4-dimension  [up_pad, left_pad, down_pad, right_pad].
              *
              * If the template argument Format is kCFO, the shape of colData is:
- * [input_channels, filter_height, filter_width, output_height, output_width]
- * So, it is easy to reshape into a convolution matrix for convolution
+             * [input_channels, filter_height, filter_width, output_height,
+             * output_width]
+             * So, it is easy to reshape into a convolution matrix for
+             * convolution
              * calculation based on matrix multiplication.
- * The shape of convolution matrix is [height, width], where the height is equal
- * input_channels * filter_height * filter_width, and the width is equal
+             * The shape of convolution matrix is [height, width], where the
+             * height is equal
+             * input_channels * filter_height * filter_width, and the width is
+             * equal
              * output_height * output_width.
              *
              * Reshape:
@@ -60,9 +66,12 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
              *      output_width]
              *
              * If the template argument Format is kOCF, the shape of colData is:
- * [output_height, output_width, input_channels, filter_height, filter_width]
- * So, it is easy to reshape into a sequence matrix for rnn calculation.
- * The shape of sequence matrix is [seq_length, step_size], where the seq_length
+             * [output_height, output_width, input_channels, filter_height,
+             * filter_width]
+             * So, it is easy to reshape into a sequence matrix for rnn
+             * calculation.
+             * The shape of sequence matrix is [seq_length, step_size], where
+             * the seq_length
              * is equal output_height * output_width, and the step_size is equal
              * input_channels * filter_height * filter_width.
              *
@@ -74,26 +83,30 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
              *      filter_height,
              *      filter_width]
              *
- * \note The caller needs to ensure that imShape.inputChannels is equal to
+             * \note The caller needs to ensure that imShape.inputChannels is
+             * equal to
              *       colShape.inputChannels.
              */
-template <ColFormat Format, typename DeviceType, typename T>
-class Im2ColFunctor {
-public:
-  void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
+            template <ColFormat Format, typename DeviceType, typename T>
+            class Im2ColFunctor {
+              public:
+                void operator()(const framework::Tensor &im,
+                                const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *col);
-};
+                                const std::vector<int> &padding,
+                                framework::Tensor *col);
+            };
 
-template <ColFormat Format, typename DeviceType, typename T>
-class Col2ImFunctor {
-public:
+            template <ColFormat Format, typename DeviceType, typename T>
+            class Col2ImFunctor {
+              public:
                 void operator()(const framework::Tensor &col,
                                 const std::vector<int> &dilation,
                                 const std::vector<int> &stride,
-                  const std::vector<int> &padding, framework::Tensor *im);
-};
+                                const std::vector<int> &padding,
+                                framework::Tensor *im);
+            };
 
-} // namespace math
-} // namespace operators
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/math/math_function.cc

@@ -15,65 +15,75 @@ limitations under the License. */
 #include "math_function.h"
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
-
-template <>
-void gemm<float>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
-                 const int M, const int N, const int K, const float alpha,
-                 const float *A, const float *B, const float beta, float *C) {
+    namespace operators {
+        namespace math {
+
+            template <>
+            void gemm<float>(const CBLAS_TRANSPOSE transA,
+                             const CBLAS_TRANSPOSE transB, const int M,
+                             const int N, const int K, const float alpha,
+                             const float *A, const float *B, const float beta,
+                             float *C) {
                 int lda = (transA == CblasNoTrans) ? K : M;
                 int ldb = (transB == CblasNoTrans) ? N : K;
                 int ldc = N;
-  cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
-              beta, C, ldc);
-}
-
-template <>
-void gemm<double>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
-                  const int M, const int N, const int K, const double alpha,
-                  const double *A, const double *B, const double beta,
-                  double *C) {
+                cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A,
+                            lda, B, ldb, beta, C, ldc);
+            }
+
+            template <>
+            void gemm<double>(const CBLAS_TRANSPOSE transA,
+                              const CBLAS_TRANSPOSE transB, const int M,
+                              const int N, const int K, const double alpha,
+                              const double *A, const double *B,
+                              const double beta, double *C) {
                 int lda = (transA == CblasNoTrans) ? K : M;
                 int ldb = (transB == CblasNoTrans) ? N : K;
                 int ldc = N;
-  cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
-              beta, C, ldc);
-}
-
-template <>
-void gemm<float>(const bool transA, const bool transB, const int M, const int N,
-                 const int K, const float alpha, const float *A, const int lda,
-                 const float *B, const int ldb, const float beta, float *C,
-                 const int ldc) {
-  cblas_sgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
-              transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
-              lda, B, ldb, beta, C, ldc);
-}
-
-template <>
-void gemm<double>(const bool transA, const bool transB, const int M,
-                  const int N, const int K, const double alpha, const double *A,
-                  const int lda, const double *B, const int ldb,
-                  const double beta, double *C, const int ldc) {
-  cblas_dgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
-              transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
+                cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A,
                             lda, B, ldb, beta, C, ldc);
-}
+            }
 
-template <>
-void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
-                   const framework::Tensor &matrix_b, bool trans_b, float alpha,
-                   framework::Tensor *matrix_out, float beta) {
+            template <>
+            void gemm<float>(const bool transA, const bool transB, const int M,
+                             const int N, const int K, const float alpha,
+                             const float *A, const int lda, const float *B,
+                             const int ldb, const float beta, float *C,
+                             const int ldc) {
+                cblas_sgemm(CblasRowMajor,
+                            transA == false ? CblasNoTrans : CblasTrans,
+                            transB == false ? CblasNoTrans : CblasTrans, M, N,
+                            K, alpha, A, lda, B, ldb, beta, C, ldc);
+            }
+
+            template <>
+            void gemm<double>(const bool transA, const bool transB, const int M,
+                              const int N, const int K, const double alpha,
+                              const double *A, const int lda, const double *B,
+                              const int ldb, const double beta, double *C,
+                              const int ldc) {
+                cblas_dgemm(CblasRowMajor,
+                            transA == false ? CblasNoTrans : CblasTrans,
+                            transB == false ? CblasNoTrans : CblasTrans, M, N,
+                            K, alpha, A, lda, B, ldb, beta, C, ldc);
+            }
+
+            template <>
+            void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
+                               const framework::Tensor &matrix_b, bool trans_b,
+                               float alpha, framework::Tensor *matrix_out,
+                               float beta) {
                 auto dim_a = matrix_a.dims();
                 auto dim_b = matrix_b.dims();
                 auto dim_out = matrix_out->dims();
-  //  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() ==
+                //  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
+                //  dim_out.size() ==
                 //  2,
                 //                 "The input and output of matmul be matrix");
                 //
                 //  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
-  //                     platform::is_cpu_place(matrix_b.place()) &&
+                //                     platform::is_cpu_place(matrix_b.place())
+                //                     &&
                 //                     platform::is_cpu_place(matrix_out->place()),
                 //                 "Matrix must all be in CPUPlace");
 
@@ -81,26 +91,32 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
                 int N = dim_out[1];
                 int K = (trans_a == false) ? dim_a[1] : dim_a[0];
 
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+                CBLAS_TRANSPOSE transA =
+                    (trans_a == false) ? CblasNoTrans : CblasTrans;
+                CBLAS_TRANSPOSE transB =
+                    (trans_b == false) ? CblasNoTrans : CblasTrans;
 
-  gemm<float>(transA, transB, M, N, K, alpha, matrix_a.data<float>(),
-              matrix_b.data<float>(), beta, matrix_out->data<float>());
-}
+                gemm<float>(transA, transB, M, N, K, alpha,
+                            matrix_a.data<float>(), matrix_b.data<float>(),
+                            beta, matrix_out->data<float>());
+            }
 
-template <>
-void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
+            template <>
+            void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
                                 const framework::Tensor &matrix_b, bool trans_b,
-                    double alpha, framework::Tensor *matrix_out, double beta) {
+                                double alpha, framework::Tensor *matrix_out,
+                                double beta) {
                 auto dim_a = matrix_a.dims();
                 auto dim_b = matrix_b.dims();
                 auto dim_out = matrix_out->dims();
-  //  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() ==
+                //  PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
+                //  dim_out.size() ==
                 //  2,
                 //                 "The input and output of matmul be matrix");
                 //
                 //  PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
-  //                     platform::is_cpu_place(matrix_b.place()) &&
+                //                     platform::is_cpu_place(matrix_b.place())
+                //                     &&
                 //                     platform::is_cpu_place(matrix_out->place()),
                 //                 "Matrix must all be in CPUPlace");
 
@@ -108,13 +124,16 @@ void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
                 int N = dim_out[1];
                 int K = (trans_a == false) ? dim_a[1] : dim_a[0];
 
-  CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
-  CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
+                CBLAS_TRANSPOSE transA =
+                    (trans_a == false) ? CblasNoTrans : CblasTrans;
+                CBLAS_TRANSPOSE transB =
+                    (trans_b == false) ? CblasNoTrans : CblasTrans;
 
-  gemm<double>(transA, transB, M, N, K, alpha, matrix_a.data<double>(),
-               matrix_b.data<double>(), beta, matrix_out->data<double>());
-}
+                gemm<double>(transA, transB, M, N, K, alpha,
+                             matrix_a.data<double>(), matrix_b.data<double>(),
+                             beta, matrix_out->data<double>());
+            }
 
-} // namespace math
-} // namespace operators
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/math/math_function.h

@@ -19,24 +19,26 @@ limitations under the License. */
 #include <cmath>
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
-
-template <typename T>
-void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
-          const int M, const int N, const int K, const T alpha, const T *A,
-          const T *B, const T beta, T *C);
-
-template <typename T>
-void gemm(const bool transA, const bool transB, const int M, const int N,
-          const int K, const T alpha, const T *A, const int lda, const T *B,
-          const int ldb, const T beta, T *C, const int ldc);
-
-// matrix multiply with continuous memory
-template <typename T>
-void matmul(const framework::Tensor &matrix_a, bool trans_a,
-            const framework::Tensor &matrix_b, bool trans_b, T alpha,
-            framework::Tensor *matrix_out, T beta);
-} // namespace math
-} // namespace operators
+    namespace operators {
+        namespace math {
+
+            template <typename T>
+            void gemm(const CBLAS_TRANSPOSE transA,
+                      const CBLAS_TRANSPOSE transB, const int M, const int N,
+                      const int K, const T alpha, const T *A, const T *B,
+                      const T beta, T *C);
+
+            template <typename T>
+            void gemm(const bool transA, const bool transB, const int M,
+                      const int N, const int K, const T alpha, const T *A,
+                      const int lda, const T *B, const int ldb, const T beta,
+                      T *C, const int ldc);
+
+            // matrix multiply with continuous memory
+            template <typename T>
+            void matmul(const framework::Tensor &matrix_a, bool trans_a,
+                        const framework::Tensor &matrix_b, bool trans_b,
+                        T alpha, framework::Tensor *matrix_out, T beta);
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/math/transform.h

+/* Copyright (c) 2016 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
+
+#include <algorithm>
+
+namespace paddle_mobile {
+    namespace operators {
+        namespace math {
+
+            // Transform applys a unary or a binary functor on each element in a
+            // range defined by a pair of iterators.
+            //
+            // - The specialization for CPU calls std::transform.
+            // - The specialization for CUDA calls thrust::tranform.
+            //
+            // NOTE: We need to define InputIter and OutputIter defined as
+            //       different types, because the InputIter points op's inputs
+            //       and
+            //       OutputIter pints to op's outputs.
+            //
+            // NOTE: We don't assume that InputIter to be const InputType* and
+            //       OutputIter to be OutputType*, because we might use a
+            //       iterator
+            //       class, paddle::fluid::operators::RowwiseTRansformIterator.
+
+            struct Transform {
+                template <typename InputIter, typename OutputIter,
+                          typename UnaryOperation>
+                void operator()(InputIter first, InputIter last,
+                                OutputIter result, UnaryOperation op) {
+                    std::transform(first, last, result, op);
+                }
+
+                template <typename InputIter1, typename InputIter2,
+                          typename OutputIter, typename BinaryOperation>
+                void operator()(InputIter1 first1, InputIter1 last1,
+                                InputIter2 first2, OutputIter result,
+                                BinaryOperation op) {
+                    std::transform(first1, last1, first2, result, op);
+                }
+            };
+        }
+    } // namespace platform
+} // namespace paddle

src/operators/math/vol2col.cc

@@ -15,21 +15,23 @@ limitations under the License. */
 #include "vol2col.h"
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
+    namespace operators {
+        namespace math {
 
-using Tensor = paddle_mobile::framework::Tensor;
-/*
+            using Tensor = paddle_mobile::framework::Tensor;
+            /*
              * vol = [input_channels, input_depth, input_height, input_width]
              * col =
              *   [input_channels, filter_depth, filter_height, filter_width,
              *                    output_depth, output_height, output_width]
              */
-template <typename T> class Vol2ColFunctor<CPU, T> {
-public:
-  void operator()(const Tensor &vol, const std::vector<int> &dilations,
+            template <typename T> class Vol2ColFunctor<CPU, T> {
+              public:
+                void operator()(const Tensor &vol,
+                                const std::vector<int> &dilations,
                                 const std::vector<int> &strides,
-                  const std::vector<int> &paddings, Tensor *col) const {
+                                const std::vector<int> &paddings,
+                                Tensor *col) const {
                     //    PADDLE_ENFORCE(vol.dims().size() == 4);
                     //    PADDLE_ENFORCE(col->dims().size() == 7);
 
@@ -43,25 +45,29 @@ public:
                     int output_depth = col->dims()[4];
                     int output_height = col->dims()[5];
                     int output_width = col->dims()[6];
-    int channels_col =
-        input_channels * filter_depth * filter_height * filter_width;
+                    int channels_col = input_channels * filter_depth *
+                                       filter_height * filter_width;
 
                     //    PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
-    //                       ((dilations[0] * (filter_depth - 1) + 1))) /
+                    //                       ((dilations[0] * (filter_depth - 1)
+                    //                       + 1))) /
                     //                              strides[0] +
                     //                          1,
                     //                      output_depth,
                     //                      "input_depth and output_depth are "
                     //                      "mismatching.");
                     //    PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
-    //                       ((dilations[1] * (filter_height - 1) + 1))) /
+                    //                       ((dilations[1] * (filter_height -
+                    //                       1) + 1))) /
                     //                              strides[1] +
                     //                          1,
                     //                      output_height,
-    //                      "input_height and output_height are "
+                    //                      "input_height and output_height are
+                    //                      "
                     //                      "mismatching.");
                     //    PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
-    //                       ((dilations[2] * (filter_width - 1) + 1))) /
+                    //                       ((dilations[2] * (filter_width - 1)
+                    //                       + 1))) /
                     //                              strides[2] +
                     //                          1,
                     //                      output_width,
@@ -74,24 +80,35 @@ public:
                     for (int c = 0; c < channels_col; ++c) {
                         int w_offset = c % filter_width;
                         int h_offset = (c / filter_width) % filter_height;
-      int d_offset = (c / filter_width / filter_height) % filter_depth;
-      int c_in = c / filter_width / filter_height / filter_depth;
+                        int d_offset =
+                            (c / filter_width / filter_height) % filter_depth;
+                        int c_in =
+                            c / filter_width / filter_height / filter_depth;
                         for (int d = 0; d < output_depth; ++d) {
-        int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
+                            int d_pad = d * strides[0] - paddings[0] +
+                                        d_offset * dilations[0];
                             for (int h = 0; h < output_height; ++h) {
-          int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
+                                int h_pad = h * strides[1] - paddings[1] +
+                                            h_offset * dilations[1];
                                 for (int w = 0; w < output_width; ++w) {
-            int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
+                                    int w_pad = w * strides[2] - paddings[2] +
+                                                w_offset * dilations[2];
 
-            int col_idx =
-                ((c * output_depth + d) * output_height + h) * output_width + w;
+                                    int col_idx = ((c * output_depth + d) *
+                                                       output_height +
+                                                   h) *
+                                                      output_width +
+                                                  w;
                                     int vol_idx =
-                ((c_in * input_depth + d_pad) * input_height + h_pad) *
+                                        ((c_in * input_depth + d_pad) *
+                                             input_height +
+                                         h_pad) *
                                             input_width +
                                         w_pad;
                                     col_data[col_idx] =
-                (h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
-                 w_pad >= input_width || d_pad < 0 || d_pad >= input_depth)
+                                        (h_pad < 0 || h_pad >= input_height ||
+                                         w_pad < 0 || w_pad >= input_width ||
+                                         d_pad < 0 || d_pad >= input_depth)
                                             ? static_cast<T>(0)
                                             : vol_data[vol_idx];
                                 }
@@ -99,19 +116,21 @@ public:
                         }
                     }
                 }
-};
+            };
 
-/*
+            /*
              * vol = [input_channels,input_depth, input_height, input_width]
              * col =
              *   [input_channels, filter_depth, filter_height, filter_width,
              *                    output_depth, output_height, output_width]
              */
-template <typename T> class Col2VolFunctor<CPU, T> {
-public:
-  void operator()(const Tensor &col, const std::vector<int> &dilations,
+            template <typename T> class Col2VolFunctor<CPU, T> {
+              public:
+                void operator()(const Tensor &col,
+                                const std::vector<int> &dilations,
                                 const std::vector<int> &strides,
-                  const std::vector<int> &paddings, Tensor *vol) const {
+                                const std::vector<int> &paddings,
+                                Tensor *vol) const {
                     //    PADDLE_ENFORCE(vol->dims().size() == 4);
                     //    PADDLE_ENFORCE(col.dims().size() == 7);
 
@@ -125,25 +144,29 @@ public:
                     int output_depth = col.dims()[4];
                     int output_height = col.dims()[5];
                     int output_width = col.dims()[6];
-    int channels_col =
-        input_channels * filter_depth * filter_height * filter_width;
+                    int channels_col = input_channels * filter_depth *
+                                       filter_height * filter_width;
 
                     //    PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
-    //                       ((dilations[0] * (filter_depth - 1) + 1))) /
+                    //                       ((dilations[0] * (filter_depth - 1)
+                    //                       + 1))) /
                     //                              strides[0] +
                     //                          1,
                     //                      output_depth,
                     //                      "input_depth and output_depth are "
                     //                      "mismatching.");
                     //    PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
-    //                       ((dilations[1] * (filter_height - 1) + 1))) /
+                    //                       ((dilations[1] * (filter_height -
+                    //                       1) + 1))) /
                     //                              strides[1] +
                     //                          1,
                     //                      output_height,
-    //                      "input_height and output_height are "
+                    //                      "input_height and output_height are
+                    //                      "
                     //                      "mismatching.");
                     //    PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
-    //                       ((dilations[2] * (filter_width - 1) + 1))) /
+                    //                       ((dilations[2] * (filter_width - 1)
+                    //                       + 1))) /
                     //                              strides[2] +
                     //                          1,
                     //                      output_width,
@@ -155,24 +178,34 @@ public:
                     for (int c = 0; c < channels_col; ++c) {
                         int w_offset = c % filter_width;
                         int h_offset = (c / filter_width) % filter_height;
-      int d_offset = (c / filter_width / filter_height) % filter_depth;
-      int cIm = c / filter_width / filter_height / filter_depth;
+                        int d_offset =
+                            (c / filter_width / filter_height) % filter_depth;
+                        int cIm =
+                            c / filter_width / filter_height / filter_depth;
                         for (int d = 0; d < output_depth; ++d) {
-        int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
+                            int d_pad = d * strides[0] - paddings[0] +
+                                        d_offset * dilations[0];
                             for (int h = 0; h < output_height; ++h) {
-          int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
+                                int h_pad = h * strides[1] - paddings[1] +
+                                            h_offset * dilations[1];
                                 for (int w = 0; w < output_width; ++w) {
-            int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
+                                    int w_pad = w * strides[2] - paddings[2] +
+                                                w_offset * dilations[2];
 
-            if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
-                w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {
+                                    if (h_pad >= 0 && h_pad < input_height &&
+                                        w_pad >= 0 && w_pad < input_width &&
+                                        d_pad >= 0 && d_pad < input_depth) {
                                         int vol_idx =
-                  ((cIm * input_depth + d_pad) * input_height + h_pad) *
+                                            ((cIm * input_depth + d_pad) *
+                                                 input_height +
+                                             h_pad) *
                                                 input_width +
                                             w_pad;
 
-              int col_idx =
-                  ((c * output_depth + d) * output_height + h) * output_width +
+                                        int col_idx = ((c * output_depth + d) *
+                                                           output_height +
+                                                       h) *
+                                                          output_width +
                                                       w;
                                         vol_data[vol_idx] += col_data[col_idx];
                                     }
@@ -181,13 +214,13 @@ public:
                         }
                     }
                 }
-};
+            };
 
-template class Vol2ColFunctor<CPU, float>;
-template class Vol2ColFunctor<CPU, double>;
-template class Col2VolFunctor<CPU, float>;
-template class Col2VolFunctor<CPU, double>;
+            template class Vol2ColFunctor<CPU, float>;
+            template class Vol2ColFunctor<CPU, double>;
+            template class Col2VolFunctor<CPU, float>;
+            template class Col2VolFunctor<CPU, double>;
 
-} // namespace math
-} // namespace operators
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/math/vol2col.h

@@ -18,21 +18,24 @@ limitations under the License. */
 #include "framework/tensor.h"
 
 namespace paddle_mobile {
-namespace operators {
-namespace math {
-/*
- * \brief Converts the feature data of four dimensions(CDHW) into a colData of
+    namespace operators {
+        namespace math {
+            /*
+             * \brief Converts the feature data of four dimensions(CDHW) into a
+             * colData of
              *        seven dimensions in the Vol2ColFunctor calculation,
              *        And in the Col2VolFunctor calculation, it is reversed.
              *
              * \param volData   Vol data.
              * \param volShape  The shape of volData,
- *                 [input_channels, input_depth, input_height, input_width].
+             *                 [input_channels, input_depth, input_height,
+             * input_width].
              * \param colData  Column data.
              * \param colShape The shape of colData.
              *
              * \param dilations    dilation data.
- * \param 3-dimension  [dilation_depth, dilation_height, dilation_width].
+             * \param 3-dimension  [dilation_depth, dilation_height,
+             * dilation_width].
              *
              * \param strides      stride data.
              * \param 3-dimension  [stride_depth, stride_height, stride_width].
@@ -41,12 +44,16 @@ namespace math {
              * \param 3-dimension  [d_pad, h_pad, w_pad].
              *
              * The shape of colData is:
- * [input_channels, filter_depth, filter_height, filter_width, output_depth,
+             * [input_channels, filter_depth, filter_height, filter_width,
+             * output_depth,
              * output_height, output_width]
- * So, it is easy to reshape into a convolution matrix for convolution
+             * So, it is easy to reshape into a convolution matrix for
+             * convolution
              * calculation based on matrix multiplication.
- * The shape of convolution matrix is [height, width], where the height is equal
- * input_channels * filter_depth * filter_height * filter_width, and the width
+             * The shape of convolution matrix is [height, width], where the
+             * height is equal
+             * input_channels * filter_depth * filter_height * filter_width, and
+             * the width
              * is equal output_depth * output_height * output_width.
              *
              * Reshape:
@@ -59,25 +66,30 @@ namespace math {
              *      output_height,
              *      output_width]
              *
- * \note The caller needs to ensure that volShape.inputChannels is equal to
+             * \note The caller needs to ensure that volShape.inputChannels is
+             * equal to
              *       colShape.inputChannels.
              */
-using Tensor = paddle_mobile::framework::Tensor;
+            using Tensor = paddle_mobile::framework::Tensor;
 
-template <typename DeviceType, typename T> class Vol2ColFunctor {
-public:
-  void operator()(const Tensor &vol, const std::vector<int> &dilations,
+            template <typename DeviceType, typename T> class Vol2ColFunctor {
+              public:
+                void operator()(const Tensor &vol,
+                                const std::vector<int> &dilations,
                                 const std::vector<int> &strides,
-                  const std::vector<int> &paddings, Tensor *col) const;
-};
+                                const std::vector<int> &paddings,
+                                Tensor *col) const;
+            };
 
-template <typename DeviceType, typename T> class Col2VolFunctor {
-public:
-  void operator()(const Tensor &col, const std::vector<int> &dilations,
+            template <typename DeviceType, typename T> class Col2VolFunctor {
+              public:
+                void operator()(const Tensor &col,
+                                const std::vector<int> &dilations,
                                 const std::vector<int> &strides,
-                  const std::vector<int> &paddings, Tensor *vol) const;
-};
+                                const std::vector<int> &paddings,
+                                Tensor *vol) const;
+            };
 
-} // namespace math
-} // namespace operators
+        } // namespace math
+    }     // namespace operators
 } // namespace paddle_mobile

src/operators/op_param.cpp

@@ -19,28 +19,27 @@ SOFTWARE.
 #include "op_param.h"
 
 namespace paddle_mobile {
-namespace operators {
-
-Print &operator<<(Print &printer, const ConvParam &conv_param) {
+    namespace operators {
+        Print &operator<<(Print &printer, const ConvParam &conv_param) {
             printer << "parameter of conv: "
                     << "\n";
             printer << "  stride: "
-          << " (" << conv_param.Strides()[0] << conv_param.Strides()[1] << ") "
+                    << " (" << conv_param.Strides()[0]
+                    << conv_param.Strides()[1] << ") "
                     << "\n";
             printer << "  paddings: "
-          << " (" << conv_param.Paddings()[0] << conv_param.Paddings()[1]
-          << ") "
+                    << " (" << conv_param.Paddings()[0]
+                    << conv_param.Paddings()[1] << ") "
                     << "\n";
             printer << "  dilations: "
-          << " (" << conv_param.Dilations()[0] << conv_param.Dilations()[1]
-          << ") "
+                    << " (" << conv_param.Dilations()[0]
+                    << conv_param.Dilations()[1] << ") "
                     << "\n";
             printer << "  groups: " << conv_param.Groups() << "\n";
             printer << "  input  dims: " << conv_param.Input()->dims() << "\n";
             printer << "  filter dims: " << conv_param.Filter()->dims() << "\n";
             printer << "  output dims: " << conv_param.Output()->dims();
             return printer;
-}
-
-} // namespace operators
+        }
+    } // namespace operators
 } // namespace paddle_mobile

src/operators/op_param.h

@@ -26,25 +26,52 @@ SOFTWARE.
 #include "framework/variable.h"
 
 namespace paddle_mobile {
-namespace operators {
+    namespace operators {
 
-using namespace framework;
+        using namespace framework;
 
-class OpParam : PaddleMobileObject {
-public:
-protected:
+        class OpParam : PaddleMobileObject {
+          public:
+          protected:
             template <typename T>
-  static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
+            static T *InputFrom(const VariableNameMap &inputs,
+                                const Scope &scope) {
                 return GetVarValue<T>("Input", inputs, scope);
             }
 
             template <typename T>
-  static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
+            static T *InputXFrom(const VariableNameMap &inputs,
+                                 const Scope &scope) {
+                return GetVarValue<T>("X", inputs, scope);
+            }
+
+            template <typename T>
+            static T *InputYFrom(const VariableNameMap &inputs,
+                                 const Scope &scope) {
+                return GetVarValue<T>("Y", inputs, scope);
+            }
+
+            template <typename T>
+            static std::vector<T *>
+            InputMultiFrom(const VariableNameMap &inputs, const Scope &scope) {
+                return GetMultiVarValue<T>("Input", inputs, scope);
+            }
+
+            template <typename T>
+            static T *OutputFrom(const VariableNameMap &outputs,
+                                 const Scope &scope) {
                 return GetVarValue<T>("Output", outputs, scope);
             }
 
             template <typename T>
-  static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
+            static T *OutFrom(const VariableNameMap &outputs,
+                              const Scope &scope) {
+                return GetVarValue<T>("Out", outputs, scope);
+            }
+
+            template <typename T>
+            static T *FilterFrom(const VariableNameMap &inputs,
+                                 const Scope &scope) {
                 return GetVarValue<T>("Filter", inputs, scope);
             }
 
@@ -54,22 +81,39 @@ protected:
             }
 
             template <typename T>
-  static T *GetVarValue(std::string key, const VariableNameMap &var_map,
+            static T *GetVarValue(std::string key,
+                                  const VariableNameMap &var_map,
                                   const Scope &scope) {
                 auto var_vec = var_map.at(key);
                 if (var_vec.size()) {
-      //      std::cout << " get var value -- " << var_vec[0] << std::endl;
+                    //      std::cout << " get var value -- " << var_vec[0] <<
+                    //      std::endl;
                     auto var = scope.FindVar(var_vec[0]);
                     return var->GetMutable<T>();
                 } else {
                     return nullptr;
                 }
             }
-};
 
-class ConvParam : OpParam {
-public:
-  ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
+            template <typename T>
+            static std::vector<T *>
+            GetMultiVarValue(std::string key, const VariableNameMap &var_map,
+                             const Scope &scope) {
+                auto var_vecs = var_map.at(key);
+                assert(var_vecs.size() > 1);
+                std::vector<T *> var_res;
+                for (auto &var_vec : var_vecs) {
+                    auto var = scope.FindVar(var_vec);
+                    var_res.push_back(var->GetMutable<T>());
+                }
+                return var_res;
+            }
+        };
+
+        class ConvParam : OpParam {
+          public:
+            ConvParam(const VariableNameMap &inputs,
+                      const VariableNameMap &outputs,
                       const framework::AttributeMap &attrs,
                       const framework::Scope &scope) {
                 filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
@@ -95,7 +139,7 @@ public:
 
             const int &Groups() const { return groups; }
 
-private:
+          private:
             Tensor *input_;
             Tensor *output_;
             LoDTensor *filter_;
@@ -103,9 +147,90 @@ private:
             std::vector<int> paddings_;
             std::vector<int> dilations_;
             int groups;
-};
+        };
+
+        Print &operator<<(Print &printer, const ConvParam &conv_param);
+
+        class ElementwiseAddParam : OpParam {
+          public:
+            ElementwiseAddParam(const VariableNameMap &inputs,
+                                const VariableNameMap &outputs,
+                                const framework::AttributeMap &attrs,
+                                const framework::Scope &scope) {
+                input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
+                input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
+                out_ = OutFrom<framework::Tensor>(outputs, scope);
+                axis_ = GetAttr<int>("axis", attrs);
+            }
+
+            const Tensor *InputX() const { return input_x_; }
+
+            const Tensor *InputY() const { return input_y_; }
+
+            Tensor *Out() const { return out_; }
+
+            const int &Axis() const { return axis_; }
+
+          private:
+            Tensor *input_x_;
+            Tensor *input_y_;
+            Tensor *out_;
+            int axis_;
+        };
+
+        class MulParam : OpParam {
+          public:
+            MulParam(const VariableNameMap &inputs,
+                     const VariableNameMap &outputs,
+                     const framework::AttributeMap &attrs,
+                     const framework::Scope &scope) {
+                input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
+                input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
+                out_ = OutFrom<framework::Tensor>(outputs, scope);
+                x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
+                y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
+            }
+
+            const Tensor *InputX() const { return input_x_; }
+
+            const Tensor *InputY() const { return input_y_; }
+
+            Tensor *Out() const { return out_; }
+
+            const int &XNumColDims() const { return x_num_col_dims_; }
+
+            const int &YNumColDims() const { return y_num_col_dims_; }
+
+          private:
+            Tensor *input_x_;
+            Tensor *input_y_;
+            Tensor *out_;
+            int x_num_col_dims_;
+            int y_num_col_dims_;
+        };
+
+        class ConcatParam : public OpParam {
+          public:
+            ConcatParam(const VariableNameMap &inputs,
+                        const VariableNameMap &outputs,
+                        const framework::AttributeMap &attrs,
+                        const framework::Scope &scope) {
+                inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
+                out_ = OutFrom<framework::Tensor>(outputs, scope);
+                axis_ = GetAttr<int>("axis", attrs);
+            }
+
+            std::vector<Tensor *> Inputs() const { return inputs_; }
+
+            Tensor *Out() const { return out_; }
+
+            const int &Axis() const { return axis_; }
 
-Print &operator<<(Print &printer, const ConvParam &conv_param);
+          private:
+            std::vector<Tensor *> inputs_;
+            Tensor *out_;
+            int axis_;
+        };
 
-} // namespace operators
+    } // namespace operators
 } // namespace paddle_mobile

src/platform/data_type.h

@@ -19,14 +19,15 @@ limitations under the License. */
 #include <typeindex>
 
 namespace paddle_mobile {
-namespace framework {
+    namespace framework {
 
-inline proto::VarType::Type ToDataType(std::type_index type) {
+        inline proto::VarType::Type ToDataType(std::type_index type) {
             /*if (typeid(platform::float16).hash_code() == type.hash_code()) {
               return proto::VarType::FP16;
             } else */
             if (typeid(const float).hash_code() == type.hash_code()) {
-    // CPPLint complains Using C-style cast.  Use static_cast<float>() instead
+                // CPPLint complains Using C-style cast.  Use
+                // static_cast<float>() instead
                 // One fix to this is to replace float with const float because
                 // typeid(T) == typeid(const T)
                 // http://en.cppreference.com/w/cpp/language/typeid
@@ -43,9 +44,9 @@ inline proto::VarType::Type ToDataType(std::type_index type) {
                 //    PADDLE_THROW("Not supported");
                 //    std::cout << "Not supported";
             }
-}
+        }
 
-inline std::type_index ToTypeIndex(proto::VarType::Type type) {
+        inline std::type_index ToTypeIndex(proto::VarType::Type type) {
             switch (type) {
             //    case proto::VarType::FP16:
             //      return typeid(platform::float16);
@@ -63,10 +64,10 @@ inline std::type_index ToTypeIndex(proto::VarType::Type type) {
                 //      PADDLE_THROW("Not support type %d", type);
                 printf("Not support type %d", type);
             }
-}
+        }
 
-template <typename Visitor>
-inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
+        template <typename Visitor>
+        inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
             switch (type) {
             //    case proto::VarType::FP16:
             //      visitor.template operator()<platform::float16>();
@@ -90,9 +91,9 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
                 //      PADDLE_THROW("Not supported");
                 printf("Not supported");
             }
-}
+        }
 
-inline std::string DataTypeToString(const proto::VarType::Type type) {
+        inline std::string DataTypeToString(const proto::VarType::Type type) {
             switch (type) {
             case proto::VarType::FP16:
                 return "float16";
@@ -112,13 +113,13 @@ inline std::string DataTypeToString(const proto::VarType::Type type) {
                 //      PADDLE_THROW("Not support type %d", type);
                 printf("Not support type %d", type);
             }
-}
+        }
 
-inline std::ostream &operator<<(std::ostream &out,
+        inline std::ostream &operator<<(std::ostream &out,
                                         const proto::VarType::Type &type) {
             out << DataTypeToString(type);
             return out;
-}
+        }
 
-} // namespace framework
+    } // namespace framework
 } // namespace paddle_mobile

src/platform/macros.h

@@ -17,7 +17,7 @@ limitations under the License. */
 // Disable the copy and assignment operator for a class.
 #ifndef DISABLE_COPY_AND_ASSIGN
 #define DISABLE_COPY_AND_ASSIGN(classname)                                     \
-private:                                                                       \
+  private:                                                                     \
     classname(const classname &) = delete;                                     \
     classname(classname &&) = delete;                                          \
     classname &operator=(const classname &) = delete;                          \

test/elementwise_add_op_test.h

+
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+#pragma once
+#include "operators/elementwise_add_op.h"
+#include "test_include.h"
+
+namespace paddle_mobile {
+    namespace framework {
+
+        template <typename Dtype> class TestElementwiseAddOp {
+          public:
+            TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
+                if (use_optimize_) {
+                    to_predict_program_ = program_.optimizeProgram;
+                } else {
+                    to_predict_program_ = program_.originProgram;
+                }
+
+                const std::vector<std::shared_ptr<BlockDesc>> blocks =
+                    to_predict_program_->Blocks();
+                //  std::cout << " **block size " << blocks.size() << std::endl;
+                for (int i = 0; i < blocks.size(); ++i) {
+                    std::shared_ptr<BlockDesc> block_desc = blocks[i];
+                    std::vector<std::shared_ptr<OpDesc>> ops =
+                        block_desc->Ops();
+                    //    std::cout << " ops " << ops.size() << std::endl;
+                    for (int j = 0; j < ops.size(); ++j) {
+                        std::shared_ptr<OpDesc> op = ops[j];
+                        if (op->Type() == "elementwise_add") {
+                            if (op->GetAttrMap().at("axis").Get<int>() != -1) {
+                                std::cout
+                                    << "attr: axis = "
+                                    << op->GetAttrMap().at("axis").Get<int>()
+                                    << std::endl;
+                            }
+                        }
+                        std::cout << "op:" << op->Type() << std::endl;
+                        if (op->Type() == "elementwise_add" &&
+                            op->Input("X")[0] == "batch_norm_2.tmp_2") {
+                            std::cout << " elementwise_add attr size: "
+                                      << op->GetAttrMap().size() << std::endl;
+                            std::cout
+                                << " inputs size: " << op->GetInputs().size()
+                                << std::endl;
+                            std::cout
+                                << " outputs size: " << op->GetOutputs().size()
+                                << std::endl;
+                            std::cout << " Input X is : " << op->Input("X")[0]
+                                      << std::endl;
+                            std::cout << " Input Y is : " << op->Input("Y")[0]
+                                      << std::endl;
+                            std::cout
+                                << " Output Out is : " << op->Output("Out")[0]
+                                << std::endl;
+                            Attribute axis_attr = op->GetAttrMap().at("axis");
+                            int axis = axis_attr.Get<int>();
+                            std::cout << " Attr axis is : " << axis
+                                      << std::endl;
+
+                            std::shared_ptr<
+                                operators::ElementwiseAddOp<Dtype, float>>
+                                add = std::make_shared<
+                                    operators::ElementwiseAddOp<Dtype, float>>(
+                                    op->Type(), op->GetInputs(),
+                                    op->GetOutputs(), op->GetAttrMap(),
+                                    program_.scope);
+                            ops_of_block_[*block_desc.get()].push_back(add);
+                        }
+                    }
+                }
+            }
+
+            std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
+                // feed
+                auto scope = program_.scope;
+                Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
+                auto tensor_x = x_feed_value->GetMutable<Tensor>();
+                tensor_x->ShareDataWith(t1);
+
+                Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
+                auto tensor_y = y_feed_value->GetMutable<Tensor>();
+                tensor_y->ShareDataWith(t2);
+
+                Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
+                Tensor *output_tensor = con_output->GetMutable<Tensor>();
+                output_tensor->mutable_data<float>({1, 3, 224, 224});
+                //  std::cout << typeid(output_tensor).name() << std::endl;
+                //  std::cout << "output_tensor dims: " << output_tensor->dims()
+                //  <<
+                //  std::endl;
+
+                std::shared_ptr<Tensor> out_tensor =
+                    std::make_shared<LoDTensor>();
+                out_tensor.reset(output_tensor);
+
+                predict_add(t1, t2, 0);
+                return out_tensor;
+            }
+
+          private:
+            const framework::Program<Dtype> program_;
+            std::shared_ptr<ProgramDesc> to_predict_program_;
+            std::map<framework::BlockDesc,
+                     std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
+                ops_of_block_;
+            bool use_optimize_ = false;
+
+            void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
+                std::shared_ptr<BlockDesc> to_predict_block =
+                    to_predict_program_->Block(block_id);
+                for (int j = 0;
+                     j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
+                    auto op = ops_of_block_[*to_predict_block.get()][j];
+                    std::cout << "op -> run()" << std::endl;
+                    op->Run();
+                }
+            }
+        };
+
+        template class TestElementwiseAddOp<CPU>;
+    } // namespace framework
+
+    namespace test {
+        void testElementwiseAdd() {
+            paddle_mobile::Loader<paddle_mobile::CPU> loader;
+            auto program = loader.Load(
+                std::string("../../test/models/"
+                            "image_classification_resnet.inference.model"));
+
+            /// input x (1,3,224,224)
+            paddle_mobile::framework::Tensor inputx;
+            SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
+                               static_cast<float>(1));
+            float *inputx_ptr = inputx.data<float>();
+            /// input y (224,)
+            paddle_mobile::framework::Tensor inputy;
+            SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
+                               static_cast<float>(1));
+            float *inputy_ptr = inputy.data<float>();
+
+            paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
+                testElementwiseAddOp(program);
+
+            auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
+            float *output_add_ptr = output_add->data<float>();
+            for (int j = 0; j < output_add->numel(); ++j) {
+                // std::cout << "value of output: " << output_add_ptr[j] <<
+                // std::endl;
+            }
+
+            /// output (1,3,224,224)
+            std::cout << "output memory size : " << output_add->memory_size()
+                      << std::endl;
+            std::cout << "output numel : " << output_add->numel() << std::endl;
+
+            std::cout << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
+                      << output_add_ptr[226] << std::endl;
+        }
+    } // namespace test
+} // namespace paddle_mobile

test/main.cpp

@@ -16,6 +16,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 ==============================================================================*/
 
+#include "elementwise_add_op_test.h"
 #include "framework/executor.h"
 #include "io.h"
 #include "test_helper.h"
@@ -49,7 +50,6 @@ int main() {
     //    }
 
     paddle_mobile::Loader<paddle_mobile::CPU> loader;
-  //../../test/models/image_classification_resnet.inference.model
     auto program = loader.Load(std::string(
         "../../test/models/image_classification_resnet.inference.model"));
 
@@ -74,7 +74,7 @@ int main() {
     //  float* output_ptr = output->data<float>();
     //  for (int j = 0; j < output->numel(); ++j) {
     //    std::cout << " value of output: " << output_ptr[j] << std::endl;
-  //  }
-
+    //
+    paddle_mobile::test::testElementwiseAdd();
     return 0;
 }

test/test_helper.h

@@ -15,20 +15,21 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 ==============================================================================*/
-
-#include <random>
+#pragma once
 #include "framework/ddim.h"
 #include "framework/tensor.h"
+#include <random>
 
 template <typename T>
-void SetupTensor(paddle_mobile::framework::Tensor* input,
+void SetupTensor(paddle_mobile::framework::Tensor *input,
                  paddle_mobile::framework::DDim dims, T lower, T upper) {
     static unsigned int seed = 100;
     std::mt19937 rng(seed++);
     std::uniform_real_distribution<double> uniform_dist(0, 1);
 
-  T* input_ptr = input->mutable_data<T>(dims);
+    T *input_ptr = input->mutable_data<T>(dims);
     for (int i = 0; i < input->numel(); ++i) {
-    input_ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
+        input_ptr[i] =
+            static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
     }
 }

test/test_include.h

+#include "framework/block_desc.h"
+#include "framework/framework.pb.h"
+#include "framework/lod_tensor.h"
+#include "framework/operator.h"
+#include "framework/program.h"
+#include "framework/program_desc.h"
+#include "framework/scope.h"
+#include "framework/tensor.h"
+#include "framework/variable.h"
+#include "framework/variable.h"
+#include "io.h"
+#include "test_helper.h"
+#include <map>
+#include <string>
+#include <vector>
\ No newline at end of file

tools/pre-commit.hooks/.copyright.hook

+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import io, re
+import sys, os
+import subprocess
+import platform
+
+COPYRIGHT = '''
+Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+'''
+
+LANG_COMMENT_MARK = None
+
+NEW_LINE_MARK = None
+
+COPYRIGHT_HEADER = None
+
+if platform.system() == "Windows":
+    NEW_LINE_MARK = "\r\n"
+else:
+    NEW_LINE_MARK = '\n'
+    COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
+    p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
+    process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
+    date, err = process.communicate()
+    date = date.decode("utf-8").rstrip("\n")
+    COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
+
+
+def generate_copyright(template, lang='C'):
+    if lang == 'Python':
+        LANG_COMMENT_MARK = '#'
+    else:
+        LANG_COMMENT_MARK = "//"
+
+    lines = template.split(NEW_LINE_MARK)
+    BLANK = " "
+    ans = LANG_COMMENT_MARK + BLANK + COPYRIGHT_HEADER + NEW_LINE_MARK
+    for lino, line in enumerate(lines):
+        if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
+        if len(line)  == 0:
+            BLANK = ""
+        else:
+            BLANK = " "
+        ans += LANG_COMMENT_MARK + BLANK + line + NEW_LINE_MARK
+
+    return ans + "\n"
+
+
+def lang_type(filename):
+    if filename.endswith(".py"):
+        return "Python"
+    elif filename.endswith(".h"):
+        return "C"
+    elif filename.endswith(".c"):
+        return "C"
+    elif filename.endswith(".hpp"):
+        return "C"
+    elif filename.endswith(".cc"):
+        return "C"
+    elif filename.endswith(".cpp"):
+        return "C"
+    elif filename.endswith(".cu"):
+        return "C"
+    elif filename.endswith(".cuh"):
+        return "C"
+    elif filename.endswith(".go"):
+        return "C"
+    elif filename.endswith(".proto"):
+        return "C"
+    else:
+        print("Unsupported filetype %s", filename)
+        exit(0)
+
+
+PYTHON_ENCODE = re.compile("^[ \t\v]*#.*?coding[:=][ \t]*([-_.a-zA-Z0-9]+)")
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(
+        description='Checker for copyright declaration.')
+    parser.add_argument('filenames', nargs='*', help='Filenames to check')
+    args = parser.parse_args(argv)
+
+    retv = 0
+    for filename in args.filenames:
+        fd = io.open(filename, encoding="utf-8")
+        first_line = fd.readline()
+        second_line = fd.readline()
+        if "COPYRIGHT (C)" in first_line.upper(): continue
+        if first_line.startswith("#!") or PYTHON_ENCODE.match(
+                second_line) != None or PYTHON_ENCODE.match(first_line) != None:
+            continue
+        original_contents = io.open(filename, encoding="utf-8").read()
+        new_contents = generate_copyright(
+            COPYRIGHT, lang_type(filename)) + original_contents
+        print('Auto Insert Copyright Header {}'.format(filename))
+        retv = 1
+        with io.open(filename, 'w') as output_file:
+            output_file.write(new_contents)
+
+    return retv
+
+
+if __name__ == '__main__':
+    exit(main())

tools/pre-commit.hooks/clang-format.bash

+#!/bin/bash
+set -e
+
+readonly VERSION="version 3."
+
+version=$(clang-format -version)
+
+if ! [[ $version == *"$VERSION"* ]]; then
+    echo "clang-format version check failed."
+    echo "a version contains '$VERSION' is needed, but get '$version'"
+    echo "you can install the right version, and make an soft-link to '\$PATH' env"
+    exit -1
+fi
+
+clang-format $@

tools/pre-commit.hooks/copyright.py

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import io, re
+import sys, os
+import subprocess
+import platform
+
+COPYRIGHT = '''
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+'''
+
+LANG_COMMENT_MARK = None
+
+NEW_LINE_MARK = None
+
+COPYRIGHT_HEADER = None
+
+if platform.system() == "Windows":
+    NEW_LINE_MARK = "\r\n"
+else:
+    NEW_LINE_MARK = '\n'
+    COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
+    p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
+    process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
+    date, err = process.communicate()
+    date = date.decode("utf-8").rstrip("\n")
+    COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
+
+
+def generate_copyright(template, lang='C'):
+    if lang == 'Python':
+        LANG_COMMENT_MARK = '#'
+    else:
+        LANG_COMMENT_MARK = "//"
+
+    lines = template.split(NEW_LINE_MARK)
+    BLANK = " "
+    ans = LANG_COMMENT_MARK + BLANK + COPYRIGHT_HEADER + NEW_LINE_MARK
+    for lino, line in enumerate(lines):
+        if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
+        if len(line) == 0:
+            BLANK = ""
+        else:
+            BLANK = " "
+        ans += LANG_COMMENT_MARK + BLANK + line + NEW_LINE_MARK
+
+    return ans + "\n"
+
+
+def lang_type(filename):
+    if filename.endswith(".py"):
+        return "Python"
+    elif filename.endswith(".h"):
+        return "C"
+    elif filename.endswith(".c"):
+        return "C"
+    elif filename.endswith(".hpp"):
+        return "C"
+    elif filename.endswith(".cc"):
+        return "C"
+    elif filename.endswith(".cpp"):
+        return "C"
+    elif filename.endswith(".cu"):
+        return "C"
+    elif filename.endswith(".cuh"):
+        return "C"
+    elif filename.endswith(".go"):
+        return "C"
+    elif filename.endswith(".proto"):
+        return "C"
+    else:
+        print("Unsupported filetype %s", filename)
+        exit(0)
+
+
+PYTHON_ENCODE = re.compile("^[ \t\v]*#.*?coding[:=][ \t]*([-_.a-zA-Z0-9]+)")
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(
+        description='Checker for copyright declaration.')
+    parser.add_argument('filenames', nargs='*', help='Filenames to check')
+    args = parser.parse_args(argv)
+
+    retv = 0
+    for filename in args.filenames:
+        fd = io.open(filename, encoding="utf-8")
+        first_line = fd.readline()
+        second_line = fd.readline()
+        if "COPYRIGHT " in first_line.upper(): continue
+        if first_line.startswith("#!") or PYTHON_ENCODE.match(
+                second_line) != None or PYTHON_ENCODE.match(
+                    first_line) != None:
+            continue
+        original_contents = io.open(filename, encoding="utf-8").read()
+        new_contents = generate_copyright(
+            COPYRIGHT, lang_type(filename)) + original_contents
+        print('Auto Insert Copyright Header {}'.format(filename))
+        retv = 1
+        with io.open(filename, 'w') as output_file:
+            output_file.write(new_contents)
+
+    return retv
+
+
+if __name__ == '__main__':
+    exit(main())

tools/pre-commit.hooks/cpplint.bash

+#!/bin/bash
+
+TOTAL_ERRORS=0
+
+#iclang-tidy *.[ch]pp -checks=* 
+# The trick to remove deleted files: https://stackoverflow.com/a/2413151
+for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'|grep -v ".pb." | grep -v "third-party/"); do
+    cpplint $file
+    TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
+done
+
+exit $TOTAL_ERRORS
+