fix develop build issue (#10978)

* fix develop build issue

* fix google style

* cpplint check only fluid

.clang-format

@@ -19,7 +19,7 @@ BasedOnStyle:  Google
 IndentWidth:     2
 TabWidth:        2
 ContinuationIndentWidth: 4
-AccessModifierOffset: -2  # The private/protected/public has no indent in class
+AccessModifierOffset: -1  # The private/protected/public has no indent in class
 Standard:  Cpp11 
 AllowAllParametersOfDeclarationOnNextLine: true
 BinPackParameters: false

paddle/api/GradientMachine.cpp

@@ -94,7 +94,7 @@ void UpdateCallback::apply(Parameter* p) {
 }
 
 class UpdateCallbackWrapper {
-public:
+ public:
   explicit UpdateCallbackWrapper(const UpdateCallback& callback)
       : callback(const_cast<UpdateCallback&>(callback)) {}
 
@@ -105,7 +105,7 @@ public:
     delete p;
   }
 
-private:
+ private:
   UpdateCallback& callback;
 };
 

paddle/api/PaddleAPI.h

@@ -59,9 +59,10 @@ class RangeError {};
 
 /// Not support Error, such as access GPU memory directly, etc.
 class UnsupportError : public std::runtime_error {
-public:
-  UnsupportError() : std::runtime_error(" "){};
-  UnsupportError(const std::string& message) : std::runtime_error(message){};
+ public:
+  UnsupportError() : std::runtime_error(" ") {}
+  explicit UnsupportError(const std::string& message)
+      : std::runtime_error(message) {}
 };
 
 /// This type will map to python's list of float.
@@ -105,7 +106,7 @@ class Matrix {
   DISABLE_COPY(Matrix);
   static Matrix* createByPaddleMatrixPtr(void* sharedPtr);
 
-public:
+ public:
   virtual ~Matrix();
 
   /**
@@ -231,7 +232,7 @@ public:
 
   bool isGpu() const;
 
-private:
+ private:
   void* getSharedPtr() const;
 
   MatrixPrivate* m;
@@ -248,7 +249,7 @@ class Vector {
 
   void* getSharedPtr();
 
-public:
+ public:
   ~Vector();
 
   /// Create Vector filled with zero.
@@ -310,10 +311,10 @@ public:
   /// __len__ in python
   size_t getSize() const;
 
-private:
+ private:
   VectorPrivate* m;
 
-private:
+ private:
   friend class Parameter;
   friend class ParameterOptimizer;
   friend struct ParameterTraverseCallbackPrivate;
@@ -325,7 +326,7 @@ class IVector {
   DISABLE_COPY(IVector);
   static IVector* createByPaddleVectorPtr(void* ptr);
 
-public:
+ public:
   /// Create IVector filled with zero
   static IVector* createZero(size_t sz, bool useGpu = isUsingGpu());
 
@@ -389,7 +390,7 @@ public:
   /// This method will map to python __len__();
   size_t getSize() const;
 
-private:
+ private:
   void* getSharedPtr() const;
 
   friend class Arguments;
@@ -400,11 +401,11 @@ struct ArgumentsPrivate;
 
 /// The Arguments is actual a std::vector<paddle::Argument> in paddle.
 class Arguments {
-private:
+ private:
   Arguments();  // Internal Create.
   DISABLE_COPY(Arguments);
 
-public:
+ public:
   /**
    * Create a arguments with size.
    * Note that it can be zero.
@@ -475,12 +476,12 @@ public:
 
   float sum() const;
 
-private:
+ private:
   static Arguments* createByPaddleArgumentVector(void* ptr);
   static Arguments* createByPaddleArgument(const void* ptr);
   void* getInternalArgumentsPtr() const;
 
-private:
+ private:
   ArgumentsPrivate* m;
   friend class Trainer;
   friend class GradientMachine;
@@ -507,7 +508,7 @@ class ParameterConfig {
   static ParameterConfig* createParameterConfigFromParameterPtr(void* ptr);
   void* getRawPtr();
 
-public:
+ public:
   ~ParameterConfig();
 
   /**
@@ -515,10 +516,10 @@ public:
    */
   std::string toProtoString() const;
 
-private:
+ private:
   ParameterConfigPrivate* m;
 
-private:
+ private:
   friend class Parameter;
   friend class ParameterOptimizer;
   friend struct ParameterTraverseCallbackPrivate;
@@ -529,7 +530,7 @@ class OptimizationConfig {
   DISABLE_COPY(OptimizationConfig);
   OptimizationConfig();
 
-public:
+ public:
   static OptimizationConfig* createFromProtoString(const std::string& str);
   ~OptimizationConfig();
 
@@ -538,7 +539,7 @@ public:
    */
   std::string toProtoString();
 
-private:
+ private:
   OptimizationConfigPrivate* m;
 
   friend class TrainerConfig;
@@ -549,11 +550,11 @@ private:
 
 struct ParameterPrivate;
 class Parameter {
-private:
+ private:
   Parameter();
   DISABLE_COPY(Parameter);
 
-public:
+ public:
   virtual ~Parameter();
 
   /**
@@ -580,11 +581,11 @@ public:
 
   size_t getSize() const;
 
-private:
+ private:
   static Parameter* createFromRawPtr(void* ptr);
   static Parameter* createFromSharedPtr(void* ptr);
 
-private:
+ private:
   ParameterPrivate* m;
   friend class UpdateCallbackWrapper;
   friend class GradientMachine;
@@ -598,14 +599,14 @@ struct ModelConfigPrivate;
  * It is used by GradientMachine.
  */
 class ModelConfig {
-private:
+ private:
   ModelConfig();
   DISABLE_COPY(ModelConfig);
 
-public:
+ public:
   virtual ~ModelConfig();
 
-private:
+ private:
   ModelConfigPrivate* m;
   friend class TrainerConfig;
   friend struct TrainerConfigPrivate;
@@ -619,11 +620,11 @@ struct TrainerConfigPrivate;
  * It is used by GradientMachine.
  */
 class TrainerConfig {
-private:
+ private:
   TrainerConfig();
   DISABLE_COPY(TrainerConfig);
 
-public:
+ public:
   virtual ~TrainerConfig();
 
   static TrainerConfig* createFromTrainerConfigFile(
@@ -634,7 +635,7 @@ public:
 
   OptimizationConfig* getOptimizationConfig() const;
 
-private:
+ private:
   TrainerConfigPrivate* m;
   friend class Trainer;
 };
@@ -654,7 +655,7 @@ private:
  * @endcode
  */
 class UpdateCallback {
-public:
+ public:
   virtual ~UpdateCallback();
   virtual void apply(Parameter* p);
 };
@@ -664,14 +665,14 @@ class ParameterTraverseCallback {
   DISABLE_COPY(ParameterTraverseCallback);
   ParameterTraverseCallback();
 
-public:
+ public:
   ~ParameterTraverseCallback();
 
   void apply(const std::vector<Vector*>& vecs,
              const ParameterConfig& config,
              size_t sparseId);
 
-private:
+ private:
   ParameterTraverseCallbackPrivate* m;
   friend class ParameterOptimizer;
 };
@@ -686,7 +687,7 @@ class ParameterOptimizer {
   DISABLE_COPY(ParameterOptimizer);
   ParameterOptimizer();
 
-public:
+ public:
   static ParameterOptimizer* create(OptimizationConfig* config);
 
   ~ParameterOptimizer();
@@ -710,7 +711,7 @@ public:
   ParameterTraverseCallback* needSpecialTraversal(
       const ParameterConfig& config) const;
 
-private:
+ private:
   ParameterOptimizerPrivate* m;
 };
 
@@ -718,11 +719,11 @@ class SequenceGenerator;
 class Evaluator;
 struct GradientMachinePrivate;
 class GradientMachine {
-private:
+ private:
   GradientMachine();
   DISABLE_COPY(GradientMachine);
 
-public:
+ public:
   virtual ~GradientMachine();
 
   /**
@@ -817,7 +818,7 @@ public:
 
   void eval(Evaluator* evaluator);
 
-private:
+ private:
   GradientMachinePrivate* m;
 
   static GradientMachine* createFromPaddleModelPtr(
@@ -833,10 +834,10 @@ private:
 
 struct ParameterUpdaterPrivate;
 class ParameterUpdater {
-private:
+ private:
   ParameterUpdater();
 
-public:
+ public:
   static ParameterUpdater* createLocalUpdater(OptimizationConfig* config);
   static ParameterUpdater* createRemoteUpdater(OptimizationConfig* config,
                                                int passCount,
@@ -911,17 +912,17 @@ public:
    */
   void catchUpWith();
 
-private:
+ private:
   ParameterUpdaterPrivate* m;
 };
 
 struct EvaluatorPrivate;
 class Evaluator {
-private:
+ private:
   Evaluator();
   DISABLE_COPY(Evaluator);
 
-public:
+ public:
   ~Evaluator();
 
   /**
@@ -945,7 +946,7 @@ public:
 
   double getValue(const std::string name) const;
 
-private:
+ private:
   EvaluatorPrivate* m;
 
   friend class GradientMachine;
@@ -953,13 +954,13 @@ private:
 
 struct TrainerPrivate;
 class Trainer {
-private:
+ private:
   TrainerPrivate* m;
   Trainer();
   Trainer(TrainerConfig* optConfig, GradientMachine* gm);
   DISABLE_COPY(Trainer);
 
-public:
+ public:
   virtual ~Trainer();
 
   /// Create A Trainer By TrainerConfig. using paddle command line.
@@ -1002,7 +1003,7 @@ public:
 
 /// the N-Best results generated from one input sequence.
 class ISequenceResults {
-public:
+ public:
   virtual ~ISequenceResults();
 
   /// Number of result.
@@ -1026,7 +1027,7 @@ class SequenceGenerator {
   DISABLE_COPY(SequenceGenerator);
   SequenceGenerator();
 
-public:
+ public:
   virtual ~SequenceGenerator();
 
   /**
@@ -1044,10 +1045,10 @@ public:
   void setMaxLength(size_t maxlength);
   void setBeamSize(size_t beamSize);
 
-private:
+ private:
   static SequenceGenerator* createByGradientMachineSharedPtr(void* ptr);
   friend class GradientMachine;
 
-private:
+ private:
   SequenceGeneratorPrivate* m;
 };

paddle/api/SequenceGenerator.cpp

@@ -138,7 +138,7 @@ struct SequenceGeneratorPrivate {
         maxLength(0UL),
         feedback(__create_feedback__()) {}
 
-private:
+ private:
   static paddle::Argument __create_feedback__() {
     paddle::Argument feedback;
     feedback.ids = paddle::IVector::create(/* size= */ 1, FLAGS_use_gpu);
@@ -157,7 +157,7 @@ SequenceGenerator::~SequenceGenerator() { delete m; }
 
 class PathSequenceResults : public ISequenceResults {
   // ISequenceResults interface
-public:
+ public:
   PathSequenceResults(const std::shared_ptr<std::vector<Path>>& path,
                       const std::shared_ptr<std::vector<std::string>>& dict)
       : path_(path), dict_(dict) {}
@@ -196,7 +196,7 @@ public:
     }
   }
 
-private:
+ private:
   std::shared_ptr<std::vector<Path>> path_;
   std::shared_ptr<std::vector<std::string>> dict_;
 };

paddle/capi/gradient_machine.cpp

@@ -26,7 +26,7 @@ enum GradientMatchineCreateMode {
 namespace paddle {
 
 class MyNeuralNetwork : public NeuralNetwork {
-public:
+ public:
   MyNeuralNetwork(const std::string& name, NeuralNetwork* network)
       : NeuralNetwork(name, network) {}
 };

paddle/contrib/inference/paddle_inference_api.h

@@ -50,7 +50,7 @@ struct PaddleTensor {
 * TODO(Superjomn) Prepare another API for NLP-related usages.
 */
 class PaddlePredictor {
-public:
+ public:
   struct Config;
   PaddlePredictor() = default;
   PaddlePredictor(const PaddlePredictor&) = delete;
@@ -66,6 +66,7 @@ public:
   // be thread-safe.
   virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
 
+  virtual bool InitShared() { return false; }
   // Destroy the Predictor.
   virtual ~PaddlePredictor() {}
 

paddle/contrib/inference/paddle_inference_api_impl.cc

@@ -28,7 +28,7 @@ namespace {
 
 // Timer for timer
 class Timer {
-public:
+ public:
   double start;
   double startu;
   void tic() {
@@ -135,8 +135,8 @@ bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
 
 std::unique_ptr<PaddlePredictor> PaddlePredictorImpl::Clone() {
   VLOG(3) << "Predictor::clone";
-  std::unique_ptr<PaddlePredictorImpl> cls(new PaddlePredictorImpl(config_));
-  if (!cls->InitShared(this)) {
+  std::unique_ptr<PaddlePredictor> cls(new PaddlePredictorImpl(config_));
+  if (!cls->InitShared()) {
     LOG(ERROR) << "fail to call InitShared";
     return nullptr;
   }
@@ -144,7 +144,7 @@ std::unique_ptr<PaddlePredictor> PaddlePredictorImpl::Clone() {
 }
 
 // TODO(panyx0718): Consider merge with Init()?
-bool PaddlePredictorImpl::InitShared(PaddlePredictorImpl *cls) {
+bool PaddlePredictorImpl::InitShared() {
   VLOG(3) << "Predictor::init_shared";
   // 1. Define place, executor, scope
   if (this->config_.device >= 0) {

paddle/contrib/inference/paddle_inference_api_impl.h

@@ -41,7 +41,7 @@ struct VisConfig : public PaddlePredictor::Config {
  * Do not use this, just a demo indicating how to customize a Predictor.
  */
 class PaddlePredictorImpl : public PaddlePredictor {
-public:
+ public:
   explicit PaddlePredictorImpl(const VisConfig &config) : config_(config) {}
 
   bool Init();
@@ -53,8 +53,8 @@ public:
 
   ~PaddlePredictorImpl() override{};
 
-private:
-  bool InitShared(PaddlePredictorImpl *cls);
+ private:
+  bool InitShared();
   bool SetFeed(const std::vector<PaddleTensor> &input_datas,
                std::vector<paddle::framework::LoDTensor> *feeds);
   bool GetFetch(const std::vector<paddle::framework::LoDTensor> &fetchs,

paddle/contrib/inference/test_paddle_inference_api.cc

@@ -31,7 +31,7 @@ struct DemoConfig : public PaddlePredictor::Config {
  * Do not use this, just a demo indicating how to customize a Predictor.
  */
 class DemoPredictor : public PaddlePredictor {
-public:
+ public:
   explicit DemoPredictor(const DemoConfig &config) {
     LOG(INFO) << "I get other_config " << config.other_config;
   }

paddle/cuda/include/hl_activation_functions.h

@@ -31,7 +31,7 @@ namespace hppl {
  */
 template <class T>
 class Active {
-public:
+ public:
   typedef T (*forward)(T);
   typedef T (*backward)(T, T);
 };

paddle/cuda/include/hl_tensor_ops.h

@@ -23,128 +23,128 @@ namespace unary {
 
 template <class T>
 class add_scale {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE add_scale(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a + p; }
 };
 
 template <class T>
 class sub_scale {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE sub_scale(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a - p; }
 };
 
 template <class T>
 class mul_scale {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE mul_scale(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a * p; }
 };
 
 template <class T>
 class div_scale {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE div_scale(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a / p; }
 };
 
 template <class T>
 class neg {
-public:
+ public:
   INLINE T operator()(const T a) const { return -a; }
 };
 
 template <class T>
 class exp_op {
-public:
+ public:
   INLINE T operator()(const T a) const { return std::exp(a); }
 };
 
 template <class T>
 class log_op {
-public:
+ public:
   INLINE T operator()(const T a) const { return std::log(a); }
 };
 
 template <class T>
 class sqrt_op {
-public:
+ public:
   INLINE T operator()(const T a) const { return std::sqrt(a); }
 };
 
 template <class T>
 class square {
-public:
+ public:
   INLINE T operator()(const T a) const { return a * a; }
 };
 
 template <class T>
 class reciprocal {
-public:
+ public:
   INLINE T operator()(const T a) const { return T(1) / a; }
 };
 
 template <class T>
 class abs {
-public:
+ public:
   INLINE T operator()(const T a) const { return a > 0 ? a : -a; }
 };
 
 template <class T>
 class sign {
-public:
+ public:
   INLINE T operator()(const T a) const { return (a > 0) - (a < 0); }
 };
 
 template <class T>
 class min {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE min(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a > p ? p : a; }
 };
 
 template <class T>
 class max {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE max(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return a < p ? p : a; }
 };
 
 template <class T>
 class pow_op {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE pow_op(const T s) : p(s) {}
   INLINE T operator()(const T a) const { return std::pow(a, p); }
 };
 
 template <class T>
 class constant {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE constant(const T s) : p(s) {}
   INLINE T operator()(int i) const { return p; }
   INLINE T operator()(int i, int j) const { return p; }
@@ -152,80 +152,80 @@ public:
 
 template <class T>
 class cmp_eq {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_eq(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a == p; }
 };
 
 template <class T>
 class cmp_ne {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_ne(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a != p; }
 };
 
 template <class T>
 class cmp_le {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_le(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a <= p; }
 };
 
 template <class T>
 class cmp_lt {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_lt(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a < p; }
 };
 
 template <class T>
 class cmp_ge {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_ge(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a >= p; }
 };
 
 template <class T>
 class cmp_gt {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE cmp_gt(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a > p; }
 };
 
 template <class T>
 class and_op {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE and_op(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a && p; }
 };
 
 template <class T>
 class or_op {
-private:
+ private:
   const T p;
 
-public:
+ public:
   INLINE or_op(const T s) : p(s) {}
   INLINE bool operator()(const T a) const { return a || p; }
 };
@@ -235,96 +235,96 @@ public:
 namespace binary {
 template <class T>
 class add {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a + b; }
 };
 
 template <class T>
 class add_scale {
-private:
+ private:
   const T p1;
   const T p2;
 
-public:
+ public:
   INLINE add_scale(const T s1, const T s2) : p1(s1), p2(s2) {}
   INLINE T operator()(const T a, const T b) const { return p1 * a + p2 * b; }
 };
 
 template <class T>
 class sub {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a - b; }
 };
 
 template <class T>
 class mul {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a * b; }
 };
 
 template <class T>
 class div {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a / b; }
 };
 
 template <class T>
 class cmp_eq {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a == b; }
 };
 
 template <class T>
 class cmp_ne {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a != b; }
 };
 
 template <class T>
 class cmp_le {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a <= b; }
 };
 
 template <class T>
 class cmp_lt {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a < b; }
 };
 
 template <class T>
 class cmp_ge {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a >= b; }
 };
 
 template <class T>
 class cmp_gt {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a > b; }
 };
 
 template <class T>
 class and_op {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a && b; }
 };
 
 template <class T>
 class or_op {
-public:
+ public:
   INLINE bool operator()(const T a, const T b) const { return a || b; }
 };
 
 template <class T>
 class min {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a > b ? b : a; }
 };
 
 template <class T>
 class max {
-public:
+ public:
   INLINE T operator()(const T a, const T b) const { return a < b ? b : a; }
 };
 
@@ -332,7 +332,7 @@ public:
 #ifndef PADDLE_TYPE_DOUBLE
 template <>
 class add<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_add_ps(a, b);
   }
@@ -340,11 +340,11 @@ public:
 
 template <>
 class add_scale<__m128> {
-private:
+ private:
   const __m128 p1;
   const __m128 p2;
 
-public:
+ public:
   INLINE add_scale(const __m128 s1, const __m128 s2) : p1(s1), p2(s2) {}
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_add_ps(_mm_mul_ps(p1, a), _mm_mul_ps(p2, b));
@@ -353,7 +353,7 @@ public:
 
 template <>
 class sub<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_sub_ps(a, b);
   }
@@ -361,7 +361,7 @@ public:
 
 template <>
 class mul<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_mul_ps(a, b);
   }
@@ -369,7 +369,7 @@ public:
 
 template <>
 class div<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_div_ps(a, b);
   }
@@ -377,7 +377,7 @@ public:
 
 template <>
 class min<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_min_ps(a, b);
   }
@@ -385,7 +385,7 @@ public:
 
 template <>
 class max<__m128> {
-public:
+ public:
   INLINE __m128 operator()(const __m128 a, const __m128 b) const {
     return _mm_max_ps(a, b);
   }
@@ -393,7 +393,7 @@ public:
 #else
 template <>
 class add<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_add_pd(a, b);
   }
@@ -401,11 +401,11 @@ public:
 
 template <>
 class add_scale<__m128d> {
-private:
+ private:
   const __m128d p1;
   const __m128d p2;
 
-public:
+ public:
   INLINE add_scale(const __m128d s1, const __m128d s2) : p1(s1), p2(s2) {}
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_add_pd(_mm_mul_pd(p1, a), _mm_mul_pd(p2, b));
@@ -414,7 +414,7 @@ public:
 
 template <>
 class sub<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_sub_pd(a, b);
   }
@@ -422,7 +422,7 @@ public:
 
 template <>
 class mul<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_mul_pd(a, b);
   }
@@ -430,7 +430,7 @@ public:
 
 template <>
 class div<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_div_pd(a, b);
   }
@@ -438,7 +438,7 @@ public:
 
 template <>
 class min<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_min_pd(a, b);
   }
@@ -446,7 +446,7 @@ public:
 
 template <>
 class max<__m128d> {
-public:
+ public:
   INLINE __m128d operator()(const __m128d a, const __m128d b) const {
     return _mm_max_pd(a, b);
   }
@@ -458,7 +458,7 @@ public:
 #ifndef PADDLE_TYPE_DOUBLE
 template <>
 class add<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     return vaddq_f32(a, b);
@@ -467,11 +467,11 @@ public:
 
 template <>
 class add_scale<float32x4_t> {
-private:
+ private:
   const float32x4_t p1;
   const float32x4_t p2;
 
-public:
+ public:
   INLINE add_scale(const float32x4_t s1, const float32x4_t s2)
       : p1(s1), p2(s2) {}
   INLINE float32x4_t operator()(const float32x4_t a,
@@ -482,7 +482,7 @@ public:
 
 template <>
 class sub<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     return vsubq_f32(a, b);
@@ -491,7 +491,7 @@ public:
 
 template <>
 class mul<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     return vmulq_f32(a, b);
@@ -500,7 +500,7 @@ public:
 
 template <>
 class div<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     float32x4_t tmp = vrecpeq_f32(b);
@@ -510,7 +510,7 @@ public:
 
 template <>
 class min<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     return vminq_f32(a, b);
@@ -519,7 +519,7 @@ public:
 
 template <>
 class max<float32x4_t> {
-public:
+ public:
   INLINE float32x4_t operator()(const float32x4_t a,
                                 const float32x4_t b) const {
     return vmaxq_f32(a, b);

paddle/cuda/src/hl_cuda_lstm.cu

@@ -30,7 +30,7 @@ bool hl_lstm_sequence_parallel(int frameSize) {
 }
 
 class frameValue {
-public:
+ public:
   real *value_;
   __device__ frameValue(real *value) : value_(value) {}
   template <int reversed, int frameSize>

paddle/function/BlockExpandOp.cpp

@@ -33,7 +33,7 @@ namespace paddle {
  * \param outputs[0] Image data of NCHW format.
  */
 class BlockExpandFunction : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     // function arguments
     strides_ = config.get<std::vector<size_t>>("strides");
@@ -81,7 +81,7 @@ public:
                         (size_t)blockW()});
   }
 
-protected:
+ protected:
   std::vector<size_t> strides_;
   std::vector<size_t> paddings_;
   std::vector<size_t> blocks_;
@@ -101,7 +101,7 @@ protected:
 
 template <DeviceType Device>
 class BlockExpandForward : public BlockExpandFunction {
-public:
+ public:
   void init(const FuncConfig& config) override {
     BlockExpandFunction::init(config);
   }
@@ -149,7 +149,7 @@ public:
 
 template <DeviceType Device>
 class BlockExpandBackward : public BlockExpandFunction {
-public:
+ public:
   void init(const FuncConfig& config) override {
     BlockExpandFunction::init(config);
   }

paddle/function/BufferArg.h

@@ -63,12 +63,12 @@ enum ArgType {
   ADD_TO = 2,
 };
 class BufferArg {
-public:
+ public:
   void setArgType(ArgType argType) { argType_ = argType; }
 
   ArgType getArgType() const { return argType_; }
 
-public:
+ public:
   BufferArg(ValueType valueType,
             const TensorShape& shape,
             ArgType argType = UNSPECIFIED)
@@ -169,7 +169,7 @@ public:
   const SequenceArg& sequence() const;
   const SparseMatrixArg& sparse() const;
 
-protected:
+ protected:
   void* buf_;
   ValueType valueType_;
   TensorShape shape_;
@@ -185,7 +185,7 @@ protected:
 // valueType_ = int32
 // if a < b then value_.buf_[a] < value_.buf_[b]
 class SequenceIdArg : public BufferArg {
-public:
+ public:
   SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED)
       : BufferArg(VALUE_TYPE_INT32, shape, argType) {
     bufferType_ = TENSOR_SEQUENCE_ID;
@@ -212,7 +212,7 @@ public:
 
   size_t numSeqs() const { return numSeqs_; }
 
-private:
+ private:
   size_t numSeqs_;
 };
 
@@ -222,7 +222,7 @@ private:
 // SequenceArg can be used to represent sequences that contain multiple
 // unequal lengths.
 class SequenceArg : public BufferArg {
-public:
+ public:
   SequenceArg(ValueType valueType,
               const TensorShape& shape,
               ArgType argType = UNSPECIFIED)
@@ -255,7 +255,7 @@ public:
   SequenceIdArg& getSequenceId() { return startPositions_; }
   const SequenceIdArg& getSequenceId() const { return startPositions_; }
 
-private:
+ private:
   SequenceIdArg startPositions_;
 };
 
@@ -263,7 +263,7 @@ private:
 // valueType_ == float or double
 // shape_.ndims() == 2
 class SparseMatrixArg : public BufferArg {
-public:
+ public:
   SparseMatrixArg(void* buf,
                   ValueType valueType,
                   const TensorShape& shape,
@@ -353,7 +353,7 @@ public:
 
   SparseDataType dataType() const { return type_; }
 
-private:
+ private:
   BufferArg row_;
   BufferArg col_;
   size_t nnz_;

paddle/function/ContextProjectionOp.cpp

@@ -100,7 +100,7 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
  */
 template <DeviceType Device>
 class ContextProjectionForwardFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     context_length_ = config.get<size_t>("context_length");
     context_start_ = config.get<int>("context_start");
@@ -146,7 +146,7 @@ public:
                                      begin_pad_);
   }
 
-private:
+ private:
   size_t context_length_;
   int context_start_;
   size_t begin_pad_;
@@ -223,7 +223,7 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(const CpuMatrix& out_grad_mat,
  */
 template <DeviceType Device>
 class ContextProjectionBackwardFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     context_length_ = config.get<size_t>("context_length");
     context_start_ = config.get<int>("context_start");
@@ -278,7 +278,7 @@ public:
                                       total_pad_);
   }
 
-private:
+ private:
   size_t context_length_;
   int context_start_;
   size_t begin_pad_;
@@ -299,7 +299,7 @@ private:
  */
 template <DeviceType Device>
 class ContextProjectionBackwardDataFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     context_length_ = config.get<size_t>("context_length");
     context_start_ = config.get<int>("context_start");
@@ -331,7 +331,7 @@ public:
         out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_);
   }
 
-private:
+ private:
   size_t context_length_;
   int context_start_;
 };
@@ -348,7 +348,7 @@ private:
  */
 template <DeviceType Device>
 class ContextProjectionBackwardWeightFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     context_length_ = config.get<size_t>("context_length");
     context_start_ = config.get<int>("context_start");
@@ -382,7 +382,7 @@ public:
                                             begin_pad_);
   }
 
-private:
+ private:
   size_t context_length_;
   int context_start_;
   size_t begin_pad_;

paddle/function/ConvOp.h

@@ -56,7 +56,7 @@ namespace paddle {
  *      H and W is height and width of filter.
  */
 class ConvFunctionBase : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     // function arguments
     strides_ = config.get<std::vector<size_t>>("strides");
@@ -101,7 +101,7 @@ public:
     }
   }
 
-protected:
+ protected:
   size_t getFilterHeight(const TensorShape& filter) const {
     return filter[filter.ndims() - 2];
   }

paddle/function/CosSimOp.cpp

@@ -97,7 +97,7 @@ class CosSimForwardFunc : public FunctionBase {
     CosSimForward<Device>(out_mat, in1_mat, in2_mat, scale_);
   }
 
-private:
+ private:
   real scale_;
 };
 
@@ -227,7 +227,7 @@ class CosSimBackwardFunc : public FunctionBase {
         out_grad, out_val, in1_val, in2_val, in1_grad, in2_grad, scale_);
   }
 
-private:
+ private:
   real scale_;
 };
 

paddle/function/CropOp.cpp

@@ -112,7 +112,7 @@ void CropGrad<DEVICE_TYPE_CPU>(const real* inGrad,
  */
 template <DeviceType Device>
 class CropFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { conf_ = config; }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -130,7 +130,7 @@ public:
                  conf_);
   }
 
-private:
+ private:
   FuncConfig conf_;
 };
 
@@ -145,7 +145,7 @@ private:
 
 template <DeviceType Device>
 class CropGradFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { conf_ = config; }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -163,7 +163,7 @@ public:
                      conf_);
   }
 
-private:
+ private:
   FuncConfig conf_;
 };
 

paddle/function/CrossMapNormalOp.cpp

@@ -160,7 +160,7 @@ void CrossMapNormalGrad<DEVICE_TYPE_CPU>(real* inputsGrad,
  */
 template <DeviceType Device>
 class CrossMapNormalFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     // function arguments
     size_ = config.get<size_t>("size");
@@ -220,7 +220,7 @@ public:
     return ops;
   }
 
-private:
+ private:
   size_t size_;
   real scale_;
   real pow_;
@@ -260,7 +260,7 @@ private:
  */
 template <DeviceType Device>
 class CrossMapNormalGradFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     // function arguments
     size_ = config.get<size_t>("size");
@@ -328,7 +328,7 @@ public:
     return ops;
   }
 
-private:
+ private:
   size_t size_;
   real scale_;
   real pow_;

paddle/function/DepthwiseConvOp.cpp

@@ -19,7 +19,7 @@ namespace paddle {
 
 template <class T>
 class DepthwiseConvFunctor<DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(const T* inputData,
                   const T* filterData,
                   int batchSize,
@@ -43,7 +43,7 @@ public:
 
 template <class T>
 class DepthwiseConvGradInputFunctor<DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* filterData,
                   int batchSize,
@@ -66,7 +66,7 @@ public:
 
 template <class T>
 class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* inputData,
                   int batchSize,
@@ -93,7 +93,7 @@ public:
  */
 template <DeviceType Device>
 class DepthwiseConvFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }
@@ -156,7 +156,7 @@ public:
  */
 template <DeviceType Device>
 class DepthwiseConvGradInputFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }
@@ -220,7 +220,7 @@ public:
  */
 template <DeviceType Device>
 class DepthwiseConvGradFilterFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }

paddle/function/DepthwiseConvOp.h

@@ -44,7 +44,7 @@ namespace paddle {
  */
 template <DeviceType Device, class T>
 class DepthwiseConvFunctor {
-public:
+ public:
   void operator()(const T* inputData,
                   const T* filterData,
                   int batchSize,
@@ -89,7 +89,7 @@ public:
  */
 template <DeviceType Device, class T>
 class DepthwiseConvGradInputFunctor {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* filterData,
                   int batchSize,
@@ -135,7 +135,7 @@ public:
  */
 template <DeviceType Device, class T>
 class DepthwiseConvGradFilterFunctor {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* inputData,
                   int batchSize,

paddle/function/DepthwiseConvOpGpu.cu

@@ -199,7 +199,7 @@ __global__ void ConvolutionDepthwiseFilterBackward(const int num_i,
 
 template <class T>
 class DepthwiseConvFunctor<DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(const T* inputData,
                   const T* filterData,
                   int batchSize,
@@ -249,7 +249,7 @@ public:
 
 template <class T>
 class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* filterData,
                   int batchSize,
@@ -300,7 +300,7 @@ public:
 
 template <class T>
 class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(const T* outputGrad,
                   const T* inputData,
                   int batchSize,

paddle/function/EigenThreadDevice.h

@@ -46,7 +46,7 @@ int GetCpuCount() { return 1; }
 #endif
 
 class EigenDeviceWarpper {
-public:  // NOLINT
+ public:  // NOLINT
 #if EIGEN_USE_THREADS
   static Eigen::ThreadPoolDevice* device() {
     const int num_cpus = GetCpuCount();

paddle/function/Function.h

@@ -29,7 +29,7 @@ namespace paddle {
  * The argument type of Function::init.
  */
 class FuncConfig {
-public:
+ public:
   template <typename T>
   T get(const std::string& key, Error* err = nullptr) const {
     try {
@@ -59,7 +59,7 @@ public:
     return *this;
   }
 
-protected:
+ protected:
   mutable std::unordered_map<std::string, any> valueMap_;
 };
 
@@ -77,7 +77,7 @@ protected:
  * in the BufferArgs life time.
  */
 class BufferArgs {
-public:
+ public:
   BufferArgs() {}
 
   ~BufferArgs() {
@@ -137,7 +137,7 @@ public:
 
   void addArg(SparseMatrixArg& arg) { args_.push_back(&arg); }
 
-private:
+ private:
   std::vector<BufferArg*> args_;
   // The BufferArg object is constructed and freed by BufferArgs.
   std::vector<BufferArg*> _args_;
@@ -163,7 +163,7 @@ private:
  * If Function has more than one output, each output can have different modes.
  */
 class FunctionBase {
-public:
+ public:
   virtual ~FunctionBase() {}
 
   virtual void init(const FuncConfig& config) {}
@@ -192,7 +192,7 @@ public:
 
   static ClassRegistrar<FunctionBase> funcRegistrar_;
 
-protected:
+ protected:
   // numInputs_ and numOutputs_ represents the maximum
   // input and output supported by Function.
   // Some functions are optimized for input and output,

paddle/function/FunctionTest.h

@@ -39,7 +39,7 @@ struct Allocator<DEVICE_TYPE_GPU> {
 // Copy argument1 to argument2
 template <DeviceType DType1, DeviceType DType2>
 class CopyArgument {
-public:
+ public:
   void operator()(const BufferArg& arg1, BufferArg& arg2) {
     CHECK_EQ(arg1.valueType(), arg2.valueType());
     CHECK_LE(arg1.shape().getElements(), arg2.shape().getElements());
@@ -95,7 +95,7 @@ public:
  */
 template <DeviceType DType1, DeviceType DType2>
 class Compare2Function {
-public:
+ public:
   typedef typename test::Allocator<DType1>::type Allocator1;
   typedef typename test::Allocator<DType2>::type Allocator2;
   typedef typename Tensor<real, DType1>::Vector Vector1;
@@ -305,7 +305,7 @@ public:
 
   std::shared_ptr<FunctionBase> getFunction2() const { return function2_; }
 
-protected:
+ protected:
   // only init cpu argument, gpu argument copy from cpu argument.
   void initArg(BufferArg& arg) {
     Vector1 vector(arg.shape().getElements(), (real*)arg.data());
@@ -381,7 +381,7 @@ protected:
     }
   }
 
-protected:
+ protected:
   std::shared_ptr<FunctionBase> function1_;
   std::shared_ptr<FunctionBase> function2_;
   std::vector<std::shared_ptr<Allocator1>> func1Memory_;
@@ -400,7 +400,7 @@ protected:
 
 class CpuGpuFuncCompare
     : public Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> {
-public:
+ public:
   CpuGpuFuncCompare(const std::string& name, const FuncConfig& config)
       : Compare2Function(name + "-CPU", name + "-GPU", config) {}
 

paddle/function/GemmConvOp.cpp

@@ -24,7 +24,7 @@ namespace paddle {
  */
 template <DeviceType Device>
 class GemmConvFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }
@@ -136,7 +136,7 @@ public:
  */
 template <DeviceType Device>
 class GemmConvMobileFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }
@@ -297,7 +297,7 @@ public:
  */
 template <DeviceType Device>
 class GemmConvGradInputFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }
@@ -404,7 +404,7 @@ public:
  */
 template <DeviceType Device>
 class GemmConvGradFilterFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }

paddle/function/Im2Col.h

@@ -70,7 +70,7 @@ enum ColFormat { kCFO = 0, kOCF = 1 };
  */
 template <ColFormat Format, DeviceType Device, class T>
 class Im2ColFunctor {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,
@@ -85,7 +85,7 @@ public:
 
 template <ColFormat Format, DeviceType Device, class T>
 class Col2ImFunctor {
-public:
+ public:
   void operator()(T* imData,
                   const TensorShape& imShape,
                   const T* colData,
@@ -100,7 +100,7 @@ public:
 
 template <class T>
 class Im2ColMobileFunctor {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,

paddle/function/Im2ColOp.cpp

@@ -23,7 +23,7 @@ namespace paddle {
  */
 template <class T>
 class Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,
@@ -75,7 +75,7 @@ public:
  */
 template <class T>
 class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(T* imData,
                   const TensorShape& imShape,
                   const T* colData,
@@ -130,7 +130,7 @@ template class Col2ImFunctor<kCFO, DEVICE_TYPE_CPU, double>;
  */
 template <class T>
 class Im2ColFunctor<kOCF, DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,
@@ -188,7 +188,7 @@ public:
  */
 template <class T>
 class Col2ImFunctor<kOCF, DEVICE_TYPE_CPU, T> {
-public:
+ public:
   void operator()(T* imData,
                   const TensorShape& imShape,
                   const T* colData,

paddle/function/Im2ColOpGpu.cu

@@ -71,7 +71,7 @@ __global__ void im2col(const T* data_im,
  */
 template <class T>
 class Im2ColFunctor<kCFO, DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,
@@ -184,7 +184,7 @@ __global__ void col2im(size_t n,
  */
 template <class T>
 class Col2ImFunctor<kCFO, DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(T* imData,
                   const TensorShape& imShape,
                   const T* colData,
@@ -292,7 +292,7 @@ __global__ void im2colOCF(const T* imData,
  */
 template <class T>
 class Im2ColFunctor<kOCF, DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(const T* imData,
                   const TensorShape& imShape,
                   T* colData,
@@ -399,7 +399,7 @@ __global__ void col2imOCF(T* imData,
  */
 template <class T>
 class Col2ImFunctor<kOCF, DEVICE_TYPE_GPU, T> {
-public:
+ public:
   void operator()(T* imData,
                   const TensorShape& imShape,
                   const T* colData,

paddle/function/MulOp.cpp

@@ -240,7 +240,7 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
  */
 template <DeviceType Device>
 class MulFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     aTrans_ = config.get<bool>("aTrans");
     bTrans_ = config.get<bool>("bTrans");
@@ -335,7 +335,7 @@ public:
     }
   }
 
-private:
+ private:
   bool aTrans_;
   bool bTrans_;
 };

paddle/function/NaiveConvOp.cpp

@@ -24,7 +24,7 @@ namespace paddle {
  */
 template <class T>
 class NaiveConvFunctor {
-public:
+ public:
   void operator()(const T* inputData,
                   size_t batchSize,
                   size_t inputChannels,
@@ -85,7 +85,7 @@ public:
 
 template <DeviceType Device>
 class NaiveConvFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }

paddle/function/PadOp.cpp

@@ -132,7 +132,7 @@ static inline PadConf castToPadConf(const FuncConfig& conf) {
 
 template <DeviceType Device>
 class PadFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { pad_ = castToPadConf(config); }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -157,7 +157,7 @@ public:
                 pad_);
   }
 
-private:
+ private:
   PadConf pad_;
 };
 
@@ -173,7 +173,7 @@ private:
 
 template <DeviceType Device>
 class PadGradFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { pad_ = castToPadConf(config); }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -201,7 +201,7 @@ public:
                     pad_);
   }
 
-private:
+ private:
   PadConf pad_;
 };
 

paddle/function/RowConvOp.cpp

@@ -129,7 +129,7 @@ void RowConvGrad<DEVICE_TYPE_CPU>(const CpuMatrix& outG,
 
 template <DeviceType Device>
 class RowConvFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {}
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -176,7 +176,7 @@ public:
 template <DeviceType Device>
 class RowConvGradFunc : public FunctionBase {
   // TODO(qingqing): split into RowConvDataFunc and RowConvWeightFunc
-public:
+ public:
   void init(const FuncConfig& config) override {}
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {

paddle/function/ScaleSubRegionOp.cpp

@@ -92,7 +92,7 @@ void ScaleSubRegionGrad<DEVICE_TYPE_CPU>(const real* inGrad,
  */
 template <DeviceType Device>
 class ScaleSubRegionFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { conf_ = config; }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -109,7 +109,7 @@ public:
                            conf_);
   }
 
-private:
+ private:
   FuncConfig conf_;
 };
 
@@ -124,7 +124,7 @@ private:
 
 template <DeviceType Device>
 class ScaleSubRegionGradFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override { conf_ = config; }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -141,7 +141,7 @@ public:
                                conf_);
   }
 
-private:
+ private:
   FuncConfig conf_;
 };
 

paddle/function/SwitchOp.cpp

@@ -75,7 +75,7 @@ void NHWC2NCHW<DEVICE_TYPE_CPU>(real* outputs,
  */
 template <DeviceType Device>
 class NCHW2NHWCFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {}
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
@@ -108,7 +108,7 @@ public:
  */
 template <DeviceType Device>
 class NHWC2NCHWFunc : public FunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {}
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {

paddle/function/TensorShape.h

@@ -22,7 +22,7 @@ namespace paddle {
  * TensorShape used to represent shape of normal tensor.
  */
 class TensorShape {
-public:
+ public:
   TensorShape() : ndims_(0), nelements_(0) { initDims(0); }
 
   TensorShape(size_t ndims) : ndims_(ndims), nelements_(1) { initDims(ndims); };
@@ -80,7 +80,7 @@ public:
 
   bool operator!=(const TensorShape& t) const { return !(*this == t); }
 
-private:
+ private:
   // compute number of elements
   void numElements() {
     nelements_ = 1;

paddle/function/neon/NeonDepthwiseConv.cpp

@@ -21,7 +21,7 @@ namespace paddle {
 
 template <DeviceType Device>
 class NeonDepthwiseConvFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }

paddle/function/neon/NeonDepthwiseConvTranspose.cpp

@@ -21,7 +21,7 @@ namespace paddle {
 
 template <DeviceType Device>
 class NeonDepthwiseConvTransposeFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
   }

paddle/function/nnpack/NNPACKConvOp.cpp

@@ -46,7 +46,7 @@ nnp_convolution_algorithm get_nnp_convolution_algorithm(
 
 template <DeviceType Device>
 class NNPACKConvFunction : public ConvFunctionBase {
-public:
+ public:
   void init(const FuncConfig& config) override {
     ConvFunctionBase::init(config);
     algorithm_ = get_nnp_convolution_algorithm(config.get<std::string>("algo"));
@@ -231,7 +231,7 @@ public:
     }
   }
 
-private:
+ private:
   nnp_convolution_algorithm algorithm_;
   nnp_convolution_transform_strategy transform_strategy_;
   void* workspaceBuffer_;

paddle/gserver/activations/ActivationFunction.cpp

@@ -44,10 +44,10 @@ static ClassRegistrar<ActivationFunction> gActivationRegistrar;
  */
 #define BEGIN_DEFINE_ACTIVATION(ACTIVATION_NAME)                             \
   class ACTIVATION_CLASS_NAME(ACTIVATION_NAME) : public ActivationFunction { \
-  private:                                                                   \
+   private:                                                                  \
     static const std::string name;                                           \
                                                                              \
-  public:                                                                    \
+   public:                                                                   \
     const std::string& getName() const { return name; }
 /**
  * @def END_DEFINE_ACTIVATION
@@ -70,7 +70,7 @@ static ClassRegistrar<ActivationFunction> gActivationRegistrar;
  * Do nothing when forward/backward.
  */
 class IdentityActivation : public ActivationFunction {
-public:
+ public:
   static const std::string name;
   Error __must_check forward(Argument& act) {
     (void)act;

paddle/gserver/activations/ActivationFunction.h

@@ -31,7 +31,7 @@ struct Argument;
  *
  */
 class ActivationFunction {
-public:
+ public:
   static ActivationFunction* create(const std::string& type);
   static std::vector<std::string> getAllRegisteredTypes();
 

paddle/gserver/activations/MKLDNNActivation.cpp

@@ -35,10 +35,10 @@ static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
  * @def END_MKLDNN_ACTIVATION
  */
 #define END_MKLDNN_ACTIVATION(ACT_TYPE)                            \
-private:                                                           \
+ private:                                                          \
   static const std::string name;                                   \
                                                                    \
-public:                                                            \
+ public:                                                           \
   const std::string& getName() const { return name; }              \
   }                                                                \
   ;                                                                \
@@ -63,11 +63,11 @@ public:                                                            \
 #define DEFINE_MKLDNN_ELTWISE_ACTIVATION(                            \
     ACT_TYPE, BASE_CLASS, ALPHA, BWD_ALPHA)                          \
   BEGIN_MKLDNN_ACTIVATION(ACT_TYPE, BASE_CLASS)                      \
-private:                                                             \
+ private:                                                            \
   static const float alpha;                                          \
   static const float bwdAlpha;                                       \
                                                                      \
-public:                                                              \
+ public:                                                             \
   float getAlpha() const { return alpha; }                           \
   float getBwdAlpha() const { return bwdAlpha; }                     \
   END_MKLDNN_ACTIVATION(ACT_TYPE)                                    \

paddle/gserver/activations/MKLDNNActivation.h

@@ -27,7 +27,7 @@ namespace paddle {
  * including mkldnn_relu, mkldnn_elu, mkldnn_tanh, mkldnn_softmax
  */
 class MKLDNNActivation : public ActivationFunction {
-protected:
+ protected:
   // input value element count
   size_t cnt_;
   // should not merge the resetBwd into resetFwd,
@@ -43,7 +43,7 @@ protected:
   std::vector<mkldnn::primitive> pipelineFwd_;
   std::vector<mkldnn::primitive> pipelineBwd_;
 
-public:
+ public:
   MKLDNNActivation() : cnt_(0), needResetBwd_(true) {}
   ~MKLDNNActivation() {}
   static ActivationFunction* create(const std::string& type);
@@ -72,7 +72,7 @@ class MKLDNNEltwiseActivation : public MKLDNNActivation {
   typedef mkldnn::eltwise_backward eltwise_bwd;
   typedef mkldnn::algorithm algorithm;
 
-protected:
+ protected:
   // save the forward primitive desc, which can be used backward
   std::shared_ptr<eltwise_fwd::primitive_desc> fwdPD_;
   // eltwise_bwd need src input value
@@ -80,7 +80,7 @@ protected:
   // use for copy data
   std::shared_ptr<mkldnn::reorder> copyInVal_;
 
-public:
+ public:
   MKLDNNEltwiseActivation() {}
   ~MKLDNNEltwiseActivation() {}
   virtual const std::string& getName() const = 0;
@@ -102,12 +102,12 @@ public:
 class MKLDNNSoftmaxActivation : public MKLDNNActivation {
   typedef mkldnn::softmax_forward softmax_fwd;
 
-private:
+ private:
   // for backward
   MatrixPtr sftMaxSum_;
   MatrixPtr sftMaxDot_;
 
-public:
+ public:
   MKLDNNSoftmaxActivation() {}
   ~MKLDNNSoftmaxActivation() {}
   virtual const std::string& getName() const = 0;

paddle/gserver/dataproviders/DataProvider.h

@@ -71,7 +71,7 @@ typedef std::shared_ptr<BufferBatch> BufferBatchPtr;
  * @brief Data for batch training a neural network
  */
 class DataBatch {
-public:
+ public:
   DataBatch() : size_(0) { data_.clear(); }
   /**
    * @brief Get batch size
@@ -181,7 +181,7 @@ public:
     }
   }
 
-protected:
+ protected:
   /**
    * @brief batch size
    */
@@ -194,7 +194,7 @@ protected:
 };
 
 class BufferBatch {
-public:
+ public:
   BufferBatch() {
     hlStream_ = HPPL_STREAM_DEFAULT;
     hlEvent_ = NULL;
@@ -235,7 +235,7 @@ public:
   void swap(BufferBatch* bufBatch);
   void clone(DataBatch* srcBatch, bool useGpu);
 
-protected:
+ protected:
   DataBatch* batchData_;
   hl_stream_t hlStream_;
   hl_event_t hlEvent_;
@@ -247,7 +247,7 @@ typedef std::shared_ptr<DataProvider> DataProviderPtr;
 typedef Queue<BufferBatch*> BufferBatchQueue;
 
 class DoubleBuffer {
-public:
+ public:
   DoubleBuffer(DataProvider* dataPool, bool useGpu, int64_t batchSize = 0);
   virtual ~DoubleBuffer();
   void removeOneBatch(DataBatch* dataBatch);
@@ -267,7 +267,7 @@ public:
 
   void setPending(bool pending) { pending_ = pending; }
 
-protected:
+ protected:
   virtual void asyncLoadBatch();
   void insertOneBatch(DataBatch* batch);
 
@@ -290,7 +290,7 @@ protected:
  * one is for input, one is for label.
  */
 class DataProvider {
-public:
+ public:
   static ClassRegistrar<DataProvider, DataConfig, ModelConfig, bool> registrar_;
   static DataProvider* create(const DataConfig& config,
                               const ModelConfig& modelConfig,
@@ -359,7 +359,7 @@ public:
    */
   virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch) = 0;
 
-protected:
+ protected:
   DataConfig config_;
   bool skipShuffle_;
   float usageRatio_;
@@ -382,7 +382,7 @@ protected:
  * necessary configurations such as stream_names
  */
 class DummyDataProvider : public DataProvider {
-public:
+ public:
   DummyDataProvider(const DataConfig& config, bool useGpu)
       : DataProvider(config, useGpu) {}
   virtual void shuffle() {}
@@ -399,7 +399,7 @@ public:
  * Data provider for one input and one integer label.
  */
 class SimpleDataProviderBase : public DataProvider {
-protected:
+ protected:
   /// sample feature dimension
   int64_t sampleDim_;
   /// the number of samples
@@ -425,7 +425,7 @@ protected:
 
   RWLock lock_;
 
-public:
+ public:
   SimpleDataProviderBase(const DataConfig& config, bool useGpu, bool withInfo);
   ~SimpleDataProviderBase() {}
 
@@ -440,7 +440,7 @@ public:
   /// return the number of samples in the buffer
   int64_t fillBuffer();
 
-protected:
+ protected:
   /**
    * @brief Fill at most size samples into data and label.
    *
@@ -458,12 +458,12 @@ protected:
 };
 
 class SimpleDataProvider : public SimpleDataProviderBase {
-public:
+ public:
   SimpleDataProvider(const DataConfig& config, bool useGpu);
   ~SimpleDataProvider();
   virtual void reset();
 
-protected:
+ protected:
   void loadData(const std::string& fileName);
   void loadDataFile(const std::string& fileName);
   virtual int64_t fillBufferImp(real* data,
@@ -471,7 +471,7 @@ protected:
                                 int* info,
                                 int64_t size);
 
-protected:
+ protected:
   size_t currentSampleIndex_;
   std::vector<int> labels_;
   std::vector<real> data_;

paddle/gserver/dataproviders/DataProviderGroup.h

@@ -20,7 +20,7 @@ namespace paddle {
 
 template <class T>
 class DataProviderGroup : public DataProvider {
-protected:
+ protected:
   typedef T ProviderType;
   typedef std::shared_ptr<ProviderType> ProviderPtrType;
   ProviderPtrType provider_;
@@ -29,7 +29,7 @@ protected:
   std::mutex lock_;
   std::unique_ptr<MultiThreadWorker<ProviderType>> loader_;
 
-public:
+ public:
   DataProviderGroup(const DataConfig& config, bool useGpu);
   ~DataProviderGroup() {}
 
@@ -38,7 +38,7 @@ public:
   virtual int64_t getSize() { return -1; }
   virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
 
-private:
+ private:
   void startLoader();
   void stopLoader();
   void forceStopLoader();

paddle/gserver/dataproviders/MultiDataProvider.h

@@ -19,10 +19,10 @@ limitations under the License. */
 namespace paddle {
 
 class MultiDataProvider : public DataProvider {
-protected:
+ protected:
   std::vector<std::unique_ptr<DataProvider>> subDataProviders_;
 
-public:
+ public:
   MultiDataProvider(const DataConfig& config,
                     const ModelConfig& modelConfig,
                     bool useGpu);
@@ -33,7 +33,7 @@ public:
   virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
   bool isTestMode() const { return isTestMode_; }
 
-private:
+ private:
   int totalDataRatio_;
   bool isTestMode_;
 };

paddle/gserver/dataproviders/ProtoReader.h

@@ -28,7 +28,7 @@ namespace paddle {
  * messages from/to i/ostream.
  */
 class ProtoReader {
-public:
+ public:
   explicit ProtoReader(std::istream* s, bool dataCompression = false) {
     CHECK(s) << "istream pointer is nullptr";
     istreamInput_.reset(new google::protobuf::io::IstreamInputStream(s));
@@ -109,7 +109,7 @@ public:
     return true;
   }
 
-protected:
+ protected:
   std::unique_ptr<google::protobuf::io::ZeroCopyInputStream> istreamInput_;
   std::unique_ptr<google::protobuf::io::GzipInputStream> gzipInput_;
   std::unique_ptr<google::protobuf::io::CodedInputStream> codedInput_;
@@ -144,7 +144,7 @@ protected:
 };
 
 class ProtoWriter {
-public:
+ public:
   explicit ProtoWriter(std::ostream* s, bool dataCompression = false) {
     CHECK(s) << "ostream pointer is nullptr";
     ostreamOutput_.reset(new google::protobuf::io::OstreamOutputStream(s));
@@ -168,7 +168,7 @@ public:
     return ret;
   }
 
-protected:
+ protected:
   std::unique_ptr<google::protobuf::io::ZeroCopyOutputStream> ostreamOutput_;
   std::unique_ptr<google::protobuf::io::GzipOutputStream> gzipOutput_;
   std::unique_ptr<google::protobuf::io::CodedOutputStream> codedOutput_;

paddle/gserver/dataproviders/PyDataProvider.h

@@ -23,7 +23,7 @@ limitations under the License. */
 namespace paddle {
 
 class PyDataProvider : public DataProvider {
-public:
+ public:
   PyDataProvider(const DataConfig& config,
                  bool useGpu,
                  bool loadDataAll = true);
@@ -40,7 +40,7 @@ public:
 
   virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
 
-protected:
+ protected:
   struct ProtoSlot;
   // return false if each each sample is one sequence, i.e., independent
   // of other samples.
@@ -73,7 +73,7 @@ protected:
   void resetSlots();
   void loadData(const std::vector<std::string>& fileList);
 
-protected:
+ protected:
   struct ProtoSlot {
     SlotDef::SlotType type;
     int dim;

paddle/gserver/dataproviders/PyDataProvider2.cpp

@@ -93,7 +93,7 @@ inline std::ostream& operator<<(std::ostream& os, const SlotHeader& header) {
  * prepare step, fill data into argument during fill step.
  */
 class IFieldScanner {
-public:
+ public:
   DISABLE_COPY(IFieldScanner);
   /**
    * Ctor.
@@ -146,7 +146,7 @@ public:
    */
   static IFieldScanner* create(SlotHeader* header);
 
-protected:
+ protected:
   SlotHeader* headerPtr_;
 };
 
@@ -154,7 +154,7 @@ protected:
  * Py Data Provider Cache Interface.
  */
 class IPyDataProviderCache {
-public:
+ public:
   virtual ~IPyDataProviderCache() {}
 
   /**
@@ -193,7 +193,7 @@ public:
  * data. And it support cache strategies.
  */
 class PyDataProvider2 : public DataProvider {
-public:
+ public:
   /**
    * Ctor
    */
@@ -234,7 +234,7 @@ public:
    */
   virtual ~PyDataProvider2() { resetImpl(false); }
 
-private:
+ private:
   void createPyDataObj(const std::string& model,
                        const std::string& className,
                        const std::string& fileListName,
@@ -435,7 +435,7 @@ private:
     exit_ = false;
   }
 
-private:
+ private:
   std::unique_ptr<std::thread> loadThread_;
   std::atomic<bool> exit_;
   std::deque<PyObjectPtr> callingContexts_;
@@ -461,7 +461,7 @@ private:
   static PyObjectPtr zeroTuple_;
 
   class PositionRandom {
-  public:
+   public:
     inline explicit PositionRandom(bool skipRand)
         : eng_(ThreadLocalRandomEngine::get()), skipRand_(skipRand) {}
 
@@ -476,14 +476,14 @@ private:
       }
     }
 
-  private:
+   private:
     std::default_random_engine& eng_;
     std::unique_ptr<std::uniform_int_distribution<size_t>> dist_;
     bool skipRand_;
   };
 
   // DataProvider interface
-public:
+ public:
   /**
    * Resetting the PyDataProvider. May start reading thread here.
    */
@@ -666,7 +666,7 @@ REGISTER_DATA_PROVIDER_EX(py2, PyDataProvider2);
  * Scanner for dense slot.
  */
 class DenseScanner : public IFieldScanner {
-public:
+ public:
   explicit DenseScanner(SlotHeader* ptr) : IFieldScanner(ptr), height_(0) {}
 
   /**
@@ -708,7 +708,7 @@ public:
     ++height_;
   }
 
-private:
+ private:
   size_t height_;
 };
 
@@ -716,7 +716,7 @@ private:
  * Scanner for index slot
  */
 class IndexScanner : public IFieldScanner {
-public:
+ public:
   explicit IndexScanner(SlotHeader* ptr) : IFieldScanner(ptr), cnt_(0) {}
 
   /**
@@ -740,12 +740,12 @@ public:
     CHECK(ok) << "Cannot cast int " << py::repr(obj);
   }
 
-private:
+ private:
   size_t cnt_;
 };
 
 class SparseNonValueScanner : public IFieldScanner {
-public:
+ public:
   explicit SparseNonValueScanner(SlotHeader* ptr)
       : IFieldScanner(ptr), nnz_(0), height_(0) {}
 
@@ -790,7 +790,7 @@ public:
     ++height_;
   }
 
-protected:
+ protected:
   /**
    * Set a single sparse index and value.
    * @param [out] col sparse index
@@ -809,7 +809,7 @@ protected:
 };
 
 class SparseValueScanner : public SparseNonValueScanner {
-public:
+ public:
   explicit SparseValueScanner(SlotHeader* ptr) : SparseNonValueScanner(ptr) {}
 
   virtual void finishPrepare(Argument& argument) {
@@ -817,7 +817,7 @@ public:
         argument.value, height_, headerPtr_->dim, nnz_, FLOAT_VALUE);
   }
 
-protected:
+ protected:
   virtual void setData(int* col, real* dat, PyObject* obj) {
     py::SequenceHelper s(obj);
     SparseNonValueScanner::setData(col, dat, s[0]);
@@ -829,7 +829,7 @@ protected:
  * Sequence Scanner. Scanner for sequence or sub-sequence.
  */
 class SequenceScanner : public IFieldScanner {
-public:
+ public:
   /**
    * Ctor
    * @param innerScanner inner scanner for each timestep or sub-sequence.
@@ -902,7 +902,7 @@ public:
    */
   virtual void finishFill(Argument& argument) { inner_->finishFill(argument); }
 
-protected:
+ protected:
   size_t getSize(PyObject* obj) {
     py::SequenceHelper s(obj);
     auto sc = dynamic_cast<SequenceScanner*>(inner_.get());
@@ -917,7 +917,7 @@ protected:
     }
   }
 
-private:
+ private:
   std::unique_ptr<IFieldScanner> inner_;
   size_t cnt_;
   std::function<ICpuGpuVectorPtr&(Argument&)> getSeqStartPos_;
@@ -969,7 +969,7 @@ IFieldScanner* IFieldScanner::create(SlotHeader* header) {
  * python every pass.
  */
 class NoCacheStrategy : public IPyDataProviderCache {
-public:
+ public:
   virtual bool reset() { return true; }
 
   virtual void drop(std::deque<PyObjectPtr>* data) { data->clear(); }
@@ -984,7 +984,7 @@ public:
  * The rest passes, will load data from memory.
  */
 class CacheOnePassInMemory : public IPyDataProviderCache {
-public:
+ public:
   CacheOnePassInMemory()
       : objPool_(new std::deque<PyObjectPtr>()),
         droppedPool_(new std::deque<PyObjectPtr>()) {}
@@ -1011,7 +1011,7 @@ public:
 
   virtual std::deque<PyObjectPtr>* load() { return objPool_.get(); }
 
-private:
+ private:
   std::unique_ptr<std::deque<PyObjectPtr>> objPool_;
   std::unique_ptr<std::deque<PyObjectPtr>> droppedPool_;
 };

paddle/gserver/evaluators/CTCErrorEvaluator.cpp

@@ -22,7 +22,7 @@ namespace paddle {
  * calculate sequence-to-sequence edit distance
  */
 class CTCErrorEvaluator : public Evaluator {
-private:
+ private:
   MatrixPtr outActivations_;
   int numTimes_, numClasses_, numSequences_, blank_;
   real deletions_, insertions_, substitutions_;
@@ -197,7 +197,7 @@ private:
         (real)seqClassficationError_ / numSequences_;
   }
 
-public:
+ public:
   CTCErrorEvaluator()
       : numTimes_(0),
         numClasses_(0),

paddle/gserver/evaluators/ChunkEvaluator.cpp

@@ -77,7 +77,7 @@ class ChunkEvaluator : public Evaluator {
   std::set<int> excludedChunkTypes_;
   mutable std::unordered_map<std::string, real> values_;
 
-public:
+ public:
   virtual void init(const EvaluatorConfig& config) {
     Evaluator::init(config);
     if (config.chunk_scheme() == "IOB") {
@@ -276,7 +276,7 @@ public:
     return "chunk";
   }
 
-private:
+ private:
   void storeLocalValues() const {
     CHECK_GE(numOutputSegments_, 0);
     CHECK_GE(numLabelSegments_, 0);

paddle/gserver/evaluators/DetectionMAPEvaluator.cpp

@@ -28,7 +28,7 @@ namespace paddle {
  * The config file api is detection_map_evaluator.
  */
 class DetectionMAPEvaluator : public Evaluator {
-public:
+ public:
   DetectionMAPEvaluator()
       : evaluateDifficult_(false), cpuOutput_(nullptr), cpuLabel_(nullptr) {}
 
@@ -132,7 +132,7 @@ public:
     LOG(FATAL) << "Distribute detection evaluation not implemented.";
   }
 
-protected:
+ protected:
   void calcTFPos(const size_t batchSize,
                  const vector<map<size_t, vector<NormalizedBBox>>>& allGTBBoxes,
                  const vector<map<size_t, vector<pair<real, NormalizedBBox>>>>&
@@ -287,7 +287,7 @@ protected:
 
   real getValueImpl() const { return calcMAP(); }
 
-private:
+ private:
   real overlapThreshold_;  // overlap threshold when determining whether matched
   bool evaluateDifficult_;  // whether evaluate difficult ground truth
   size_t backgroundId_;     // class index of background

paddle/gserver/evaluators/Evaluator.cpp

@@ -38,7 +38,7 @@ void Evaluator::eval(const NeuralNetwork& nn) {
  * The config file api is classification_error_evaluator.
  */
 class ClassificationErrorEvaluator : public Evaluator {
-public:
+ public:
   /*
   ClassificationErrorEvaluator() : totalScore2_(0) {}
 
@@ -124,7 +124,7 @@ public:
   }
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const { return "classification_error"; }
 };
 
@@ -135,7 +135,7 @@ protected:
  */
 class SequenceClassificationErrorEvaluator
     : public ClassificationErrorEvaluator {
-public:
+ public:
   virtual void updateSamplesNum(const std::vector<Argument>& arguments) {
     numSamples_ += arguments[0].getNumSequences();
   }
@@ -166,7 +166,7 @@ public:
   }
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const { return "seq_classification_error"; }
 };
 REGISTER_EVALUATOR(seq_classification_error,
@@ -178,7 +178,7 @@ REGISTER_EVALUATOR(seq_classification_error,
  * The config file api is sum_evaluator.
  */
 class SumEvaluator : public Evaluator {
-public:
+ public:
   SumEvaluator() : cpuLabel_(nullptr), cpuWeight_(nullptr) {}
 
   virtual void updateSamplesNum(const std::vector<Argument>& arguments) {
@@ -255,12 +255,12 @@ public:
     mergeResultsOfAllClients(client);
   }
 
-private:
+ private:
   IVectorPtr cpuLabel_;
   MatrixPtr cpuWeight_;
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const { return "sum"; }
 };
 /**
@@ -274,7 +274,7 @@ protected:
  *
  */
 class ColumnSumEvaluator : public Evaluator {
-public:
+ public:
   explicit ColumnSumEvaluator(int32_t colIdx)
       : colIdx_(colIdx), colNum_(0), sum_(nullptr) {}
 
@@ -368,13 +368,13 @@ public:
     client->reduce(&numSamples_, &numSamples_, 1, FLAGS_trainer_id, 0);
   }
 
-private:
+ private:
   int32_t colIdx_;
   size_t colNum_;
   MatrixPtr sum_; /* cpu matrix */
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const {
     if (colIdx_ == -1)
       return "last-column-sum";
@@ -1018,7 +1018,7 @@ static InitFunction __reg_type_auc_sum__([]() {
  * The config file api is value_printer_evaluator.
  */
 class ValuePrinter : public NotGetableEvaluator {
-public:
+ public:
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
       nn.getLayer(name)->getOutput().printValueString(LOG(INFO),
@@ -1038,7 +1038,7 @@ REGISTER_EVALUATOR(value_printer, ValuePrinter);
  * The config file api is gradient_printer_evaluator.
  */
 class GradientPrinter : public NotGetableEvaluator {
-public:
+ public:
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
       const Argument& argu = nn.getLayer(name)->getOutput();
@@ -1061,11 +1061,11 @@ REGISTER_EVALUATOR(gradient_printer, GradientPrinter);
  * The config file api is maxid_printer_evaluator.
  */
 class MaxIdPrinter : public NotGetableEvaluator {
-private:
+ private:
   IVectorPtr maxIds_;
   MatrixPtr maxValues_;
 
-public:
+ public:
   MaxIdPrinter() {}
 
   virtual void eval(const NeuralNetwork& nn) {
@@ -1103,12 +1103,12 @@ REGISTER_EVALUATOR(max_id_printer, MaxIdPrinter);
  * The config file api is maxframe_printer_evaluator.
  */
 class MaxFramePrinter : public NotGetableEvaluator {
-private:
+ private:
   IVectorPtr maxIds_;
   MatrixPtr maxValues_;
   MatrixPtr value_;
 
-public:
+ public:
   MaxFramePrinter() {
     value_ =
         Matrix::create(nullptr, /* height= */ 1, 1, /* trans= */ false, false);
@@ -1190,7 +1190,7 @@ REGISTER_EVALUATOR(max_frame_printer, MaxFramePrinter);
  *
  */
 class SequenceTextPrinter : public NotGetableEvaluator {
-private:
+ private:
   /// dict_file, which contains a list of tokens
   std::vector<std::string> dict_;
   /// result_file, which is the output file
@@ -1203,7 +1203,7 @@ private:
   /// store the probability associated with each sequence
   std::vector<MatrixPtr> cpuIn_;
 
-public:
+ public:
   SequenceTextPrinter() {}
 
   virtual void init(const EvaluatorConfig& config) {
@@ -1334,7 +1334,7 @@ REGISTER_EVALUATOR(seq_text_printer, SequenceTextPrinter);
  * The config file api is classification_error_printer_evaluator.
  */
 class ClassificationErrorPrinter : public ClassificationErrorEvaluator {
-public:
+ public:
   virtual void updateSamplesNum(const std::vector<Argument>& arguments) {}
 
   virtual real evalImp(std::vector<Argument>& arguments) {

paddle/gserver/evaluators/Evaluator.h

@@ -40,7 +40,7 @@ class NeuralNetwork;
  * has been by a trained model.
  */
 class Evaluator {
-public:
+ public:
   static Evaluator* create(const EvaluatorConfig& config);
 
   Evaluator() : numSamples_(0), totalScore_(0) {}
@@ -172,7 +172,7 @@ public:
     return this->getTypeImpl();
   }
 
-protected:
+ protected:
   /**
    * @brief getValueImpl The simplest way to define getValue result. If this
    * evaluator doesn't contain multiple fields, and do not throw any error, just
@@ -191,7 +191,7 @@ protected:
    */
   virtual std::string getTypeImpl() const { return "base"; }
 
-protected:
+ protected:
   EvaluatorConfig config_;
   double numSamples_;
   double totalScore_;
@@ -204,7 +204,7 @@ protected:
  */
 class NotGetableEvaluator : public Evaluator {
   // Evaluator interface
-public:
+ public:
   void getNames(std::vector<std::string>* names) {}
 
   real getValue(const std::string& name, Error* err) const {
@@ -219,7 +219,7 @@ public:
 };
 
 class DummyEvaluator : public Evaluator {
-public:
+ public:
   DummyEvaluator() {}
   virtual void init(const EvaluatorConfig&) {}
   virtual void start() {}
@@ -232,7 +232,7 @@ public:
   virtual void printStats(std::ostream&) const {}
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const;
 };
 /**
@@ -251,7 +251,7 @@ protected:
  *
  */
 class AucEvaluator : public Evaluator {
-public:
+ public:
   AucEvaluator(int32_t colIdx)
       : colIdx_(colIdx),
         realColumnIdx_(0),
@@ -269,7 +269,7 @@ public:
 
   virtual void distributeEval(ParameterClient2* client);
 
-private:
+ private:
   static const uint32_t kBinNum_ = (1 << 24) - 1;
   static const int kNegativeLabel_ = 0;
   double statPos_[kBinNum_ + 1];
@@ -292,7 +292,7 @@ private:
   double calcAuc() const;
 
   // Evaluator interface
-protected:
+ protected:
   real getValueImpl() const;
   std::string getTypeImpl() const;
 };
@@ -305,7 +305,7 @@ protected:
  * dense value.
  */
 class RankAucEvaluator : public Evaluator {
-public:
+ public:
   // evaluate ranking AUC
   virtual void start();
 
@@ -317,7 +317,7 @@ public:
     mergeResultsOfAllClients(client);
   }
 
-private:
+ private:
   MatrixPtr output_;
   MatrixPtr click_;
   MatrixPtr pv_;
@@ -329,7 +329,7 @@ private:
                      size_t size);
 
   // Evaluator interface
-protected:
+ protected:
   std::string getTypeImpl() const;
 };
 
@@ -344,7 +344,7 @@ protected:
  * The config file api is precision_recall_evaluator.
  */
 class PrecisionRecallEvaluator : public Evaluator {
-public:
+ public:
   // Evaluate precision, recall and F1 score
   PrecisionRecallEvaluator()
       : isMultiBinaryLabel_(false),
@@ -379,7 +379,7 @@ public:
     StatsInfo() : TP(0.0), TN(0.0), FP(0.0), FN(0.0) {}
   };
 
-private:
+ private:
   bool isMultiBinaryLabel_;
   std::vector<StatsInfo> statsInfo_;
 
@@ -444,7 +444,7 @@ private:
  * The config file api is pnpair_evaluator.
  */
 class PnpairEvaluator : public Evaluator {
-public:
+ public:
   PnpairEvaluator()
       : cpuOutput_(nullptr),
         cpuLabel_(nullptr),
@@ -491,7 +491,7 @@ public:
               << " calc total neg pair: " << pairArray_[1];
   }
 
-private:
+ private:
   static const uint32_t kPairArrayNum_ = 2;
   double pairArray_[kPairArrayNum_];
   MatrixPtr cpuOutput_;
@@ -500,7 +500,7 @@ private:
   MatrixPtr cpuWeight_;
 
   // Evaluator interface
-protected:
+ protected:
   real getValueImpl() const {
     return pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]);
   }

paddle/gserver/gradientmachines/GradientMachine.h

@@ -73,7 +73,7 @@ class GradientMachine;
 typedef std::shared_ptr<GradientMachine> GradientMachinePtr;
 
 class GradientMachine {
-public:
+ public:
   enum CreateMode {
     kNormal = 0,
     kSgdSparseCpuTraining = 3,
@@ -240,7 +240,7 @@ public:
    */
   virtual void releaseOutput() {}
 
-protected:
+ protected:
   virtual void onLoadParameter() {}
 
   std::vector<ParameterPtr> parameters_;

paddle/gserver/gradientmachines/GradientMachineMode.h

@@ -19,14 +19,14 @@ limitations under the License. */
 namespace paddle {
 
 class IGradientMachineMode {
-public:
+ public:
   virtual ~IGradientMachineMode() {}
 
-public:  // interfaces
-         /**
-          * @brief create current mode's gradient machine by model config.
-          * @param config model config
-          */
+ public:  // interfaces
+          /**
+           * @brief create current mode's gradient machine by model config.
+           * @param config model config
+           */
   virtual GradientMachine* create(const ModelConfig& config) = 0;
 
   /**
@@ -55,14 +55,14 @@ public:  // interfaces
    */
   virtual bool needTrainWholeDataInOneBatch() const = 0;
 
-public:  // static methods.
-         /**
-          * @brief register a custom gradient machine mode.
-          * @note For user to register a custom gradient machine mode, id should >=
-          * kCustom.
-          * @param mode mode id.
-          * @param ptr mode description object.
-          */
+ public:  // static methods.
+          /**
+           * @brief register a custom gradient machine mode.
+           * @note For user to register a custom gradient machine mode, id should >=
+           * kCustom.
+           * @param mode mode id.
+           * @param ptr mode description object.
+           */
   static void regGradientMachineMode(
       int32_t mode, std::unique_ptr<IGradientMachineMode>&& ptr) {
     modes_.insert(std::make_pair(mode, std::move(ptr)));
@@ -141,7 +141,7 @@ public:  // static methods.
     }
   }
 
-private:
+ private:
   static std::unordered_map<int32_t, std::unique_ptr<IGradientMachineMode>>
       modes_;
 };

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -166,7 +166,7 @@ struct GradBuffer {
  *     the merged gradient to parameter server.
  */
 class MultiGradientMachine : public GradientMachine {
-public:
+ public:
   enum TaskType {
     TASK_FORWARD_BACKWARD = 0,
     TASK_FORWARD = 1,
@@ -213,7 +213,7 @@ public:
   /// The gradietns will be copied to each thread in the computing threads.
   virtual void setOutputGrad(const std::vector<Argument>& args);
 
-protected:
+ protected:
   friend class TrainerThread;
 
   std::vector<TrainerThreadPtr>& getAllThreads() { return threads_; }
@@ -281,7 +281,7 @@ protected:
 
   int paraMainThread(int pid) const { return paraMainThread_[pid]; }
 
-protected:
+ protected:
   virtual void forwardImp(const std::vector<Argument>& inArgs,
                           std::vector<Argument>* outArgs,
                           PassType passType,
@@ -298,7 +298,7 @@ protected:
 
   void allocGradBufs();
 
-protected:
+ protected:
   bool useGpu_;
 
   bool hasNonstaticCpuParamters_;
@@ -342,7 +342,7 @@ protected:
 };
 
 class TrainerThread {
-public:
+ public:
   TrainerThread(const ModelConfig& config,
                 int threadId,
                 MultiGradientMachine* multiMachine);
@@ -392,7 +392,7 @@ public:
   /// Whether the thread has input data.
   bool hasInputData() { return batchSize_ != 0; }
 
-protected:
+ protected:
   void mergeCpuGradients();
 
   void mergeGradSparse(
@@ -421,7 +421,7 @@ protected:
   /// GradientMachine::backward
   void doCallback(int pid);
 
-protected:
+ protected:
   MultiGradientMachine* multiMachine_;
   ModelConfig config_;
   /// whether the thread should stop

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -122,7 +122,7 @@ void MultiNetwork::finish() {
 }
 
 class MultiCombinedEvaluator : public Evaluator {
-public:
+ public:
   MultiCombinedEvaluator() {}
   void addEvaluator(std::unique_ptr<Evaluator>&& evaluator) {
     evaluators_.emplace_back(std::move(evaluator));
@@ -167,7 +167,7 @@ public:
     }
   }
 
-protected:
+ protected:
   std::vector<std::unique_ptr<Evaluator>> evaluators_;
 };
 

paddle/gserver/gradientmachines/MultiNetwork.h

@@ -22,7 +22,7 @@ limitations under the License. */
 namespace paddle {
 
 class MultiNetwork : public NeuralNetwork {
-public:
+ public:
   explicit MultiNetwork(std::string subModelName = "")
       : NeuralNetwork(subModelName) {}
 
@@ -58,7 +58,7 @@ public:
 
   virtual void finish();
 
-protected:
+ protected:
   std::vector<std::unique_ptr<NeuralNetwork>> subNetworks_;
 };
 }  // namespace paddle

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -362,7 +362,7 @@ void NeuralNetwork::releaseOutput() {
 #ifndef PADDLE_MOBILE_INFERENCE
 
 class CombinedEvaluator : public Evaluator {
-public:
+ public:
   void addEvaluator(std::unique_ptr<Evaluator>&& evaluator) {
     evaluators_.emplace_back(std::move(evaluator));
   }
@@ -400,11 +400,11 @@ public:
     }
   }
 
-protected:
+ protected:
   std::vector<std::unique_ptr<Evaluator>> evaluators_;
 
   // Evaluator interface
-public:
+ public:
   /**
    * @brief getNames will return all inside evaluators' names.
    * @param names [out]: return names.
@@ -435,7 +435,7 @@ public:
         });
   }
 
-private:
+ private:
   template <typename T>
   T getMethodHelper(const std::string& name,
                     Error* err,
@@ -454,7 +454,7 @@ private:
 };
 
 class SubnetEvaluator : public CombinedEvaluator {
-public:
+ public:
   SubnetEvaluator(const std::string& layerName,
                   std::unique_ptr<Evaluator>&& evaluator)
       : layerName_(layerName) {
@@ -473,7 +473,7 @@ public:
                     << " in submodel " << nn.getName();
   }
 
-protected:
+ protected:
   std::string layerName_;
 };
 

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -56,7 +56,7 @@ void parameterInitNN(int paramId,
                      std::vector<ParameterPtr>* sharedParams);
 
 class NeuralNetwork : public GradientMachine {
-public:
+ public:
   virtual void init(const ModelConfig& config,
                     ParamInitCallback callback = nullptr,
                     const std::vector<ParameterType>& parameterTypes =
@@ -144,7 +144,7 @@ public:
    */
   void releaseOutput();
 
-protected:
+ protected:
   /**
    * The constructor of NeuralNetwork.
    * The sub networks can get parameters_ and parameterMap_

paddle/gserver/gradientmachines/ParallelNeuralNetwork.h

@@ -32,7 +32,7 @@ enum TaskType {
  * multiple threads in parallel.
  */
 class ParallelNeuralNetwork : public NeuralNetwork {
-public:
+ public:
   ParallelNeuralNetwork(std::string subModelName = "",
                         NeuralNetwork *rootNetwork = nullptr)
       : NeuralNetwork(subModelName, rootNetwork) {}
@@ -66,7 +66,7 @@ public:
 
   // virtual void eval(Evaluator* evaluator);
 
-protected:
+ protected:
   bool useGpu_;
   /// number of gpu devices
   int numDevices_;
@@ -74,7 +74,7 @@ protected:
 };
 
 class ParallelThread {
-public:
+ public:
   ParallelThread(int threadId, int deviceId, bool useGpu);
   ~ParallelThread();
   void jobEnqueue(LayerPtr layer, TaskType task);
@@ -87,10 +87,10 @@ public:
   }
   void setForwardPassType(PassType passType) { passType_ = passType; }
 
-protected:
+ protected:
   void computeThread();
 
-public:
+ public:
   struct Job {
     LayerPtr layer_;
     TaskType task_;
@@ -98,7 +98,7 @@ public:
   typedef Queue<Job> JobQueue;
   JobQueue queue_;
 
-protected:
+ protected:
   /// from 0 to threads-1
   int threadId_;
   /// the GPU device Id which the computeThread_ used

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -96,7 +96,7 @@ static InitFunction __init__diy_prob_method(
     std::numeric_limits<int>::max());
 
 class BeamSearchControlCallbacks {
-public:
+ public:
   RecurrentGradientMachine::BeamSearchCandidatesAdjustCallback
       beamSearchCandidateAdjust;
   RecurrentGradientMachine::NormOrDropNodeCallback normOrDropNode;
@@ -115,7 +115,7 @@ public:
 };
 
 class BeamSearchStatisticsCallbacks {
-public:
+ public:
   RecurrentGradientMachine::EachStepCallback onEachStepStarted;
   RecurrentGradientMachine::EachStepCallback onEachStepStoped;
 
@@ -148,11 +148,11 @@ RecurrentGradientMachine::RecurrentGradientMachine(
  *    so it's should not be placed in root network.
  */
 class BootBiasLayer : public Layer {
-protected:
+ protected:
   std::unique_ptr<Weight> biases_;
   IVectorPtr cpuIds_;
 
-public:
+ public:
   explicit BootBiasLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -30,7 +30,7 @@ class BeamSearchControlCallbacks;
 class BeamSearchStatisticsCallbacks;
 
 class RecurrentGradientMachine : public NeuralNetwork {
-public:
+ public:
   RecurrentGradientMachine(const std::string& subModelName,
                            NeuralNetwork* rootNetwork);
 
@@ -290,7 +290,7 @@ public:
     return this->finalPaths_;
   }
 
-protected:
+ protected:
   std::vector<Argument::SeqInfo> commonSeqInfo_;
   ICpuGpuVectorPtr sequenceStartPositions_;
   void calcSequenceStartPositions();
@@ -447,7 +447,7 @@ protected:
   MatrixPtr cpuProb_;
   IVectorPtr cpuEos_;
 
-private:
+ private:
   /*
    * @return beam size in beam search
    */

paddle/gserver/layers/AddtoLayer.h

@@ -33,10 +33,10 @@ namespace paddle {
  * The config file api is addto_layer.
  */
 class AddtoLayer : public Layer {
-protected:
+ protected:
   std::unique_ptr<Weight> biases_;
 
-public:
+ public:
   explicit AddtoLayer(const LayerConfig& config) : Layer(config) {}
 
   ~AddtoLayer() {}

paddle/gserver/layers/AgentLayer.h

@@ -26,11 +26,11 @@ namespace paddle {
  * called to set one and only one real layer
  */
 class AgentLayer : public Layer {
-protected:
+ protected:
   LayerPtr realLayer_;
   int numSamples_;
 
-public:
+ public:
   explicit AgentLayer(const LayerConfig& config) : Layer(config) {}
 
   ~AgentLayer() {}
@@ -55,14 +55,14 @@ public:
  * GatherAgentLayer collect a complete sequence.
  */
 class GatherAgentLayer : public Layer {
-protected:
+ protected:
   std::vector<LayerPtr> realLayers_;
   std::vector<IVectorPtr> idsVec_;
   // we don't clear idsVec_ vector to aviod IVector alloc/free
   IVectorPtr allIds_;
   std::vector<int> idIndex_;
 
-public:
+ public:
   explicit GatherAgentLayer(const LayerConfig& config) : Layer(config) {}
 
   virtual ~GatherAgentLayer() {}
@@ -95,7 +95,7 @@ public:
  * if it is, the agent will select a few ids in real layer.
  */
 class ScatterAgentLayer : public Layer {
-protected:
+ protected:
   LayerPtr realLayer_;
   IVectorPtr ids_;
   IVectorPtr cpuIds_;
@@ -113,7 +113,7 @@ protected:
   // true for setRealLayer, false for setRealLayerAndOutput
   bool selectionMode_;
 
-public:
+ public:
   explicit ScatterAgentLayer(const LayerConfig& config) : Layer(config) {}
 
   virtual ~ScatterAgentLayer() {}

paddle/gserver/layers/AverageLayer.h

@@ -37,7 +37,7 @@ namespace paddle {
  * The config file api is pooling_layer.
  */
 class AverageLayer : public SequencePoolLayer {
-public:
+ public:
   enum AverageStrategy { kAverage = 0, kSum = 1, kAverageSquareRootN = 2 };
   explicit AverageLayer(const LayerConfig& config)
       : SequencePoolLayer(config) {}
@@ -48,7 +48,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   int mode_;
 };
 }  // namespace paddle

paddle/gserver/layers/BatchNormBaseLayer.h

@@ -40,7 +40,7 @@ namespace paddle {
  */
 
 class BatchNormBaseLayer : public Layer {
-public:
+ public:
   explicit BatchNormBaseLayer(const LayerConfig& config) : Layer(config) {}
 
   ~BatchNormBaseLayer() {}
@@ -61,7 +61,7 @@ public:
    */
   void calFeatureMapSize();
 
-protected:
+ protected:
   /// Batch normalization scale parameter, which is referred to as gamma in
   /// in original paper.
   std::unique_ptr<Weight> weight_;

paddle/gserver/layers/BatchNormalizationLayer.h

@@ -27,7 +27,7 @@ namespace paddle {
  */
 
 class BatchNormalizationLayer : public BatchNormBaseLayer {
-public:
+ public:
   explicit BatchNormalizationLayer(const LayerConfig& config)
       : BatchNormBaseLayer(config), firstTest_(true) {}
 
@@ -38,7 +38,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   /// Load pre-calculated mean and std.
   void setMeanAndStd();
 

paddle/gserver/layers/BilinearInterpLayer.h

@@ -26,13 +26,13 @@ namespace paddle {
  * @note  The config file api is bilinear_interp_layer.
  */
 class BilinearInterpLayer : public Layer {
-protected:
+ protected:
   size_t outImgH_, outImgW_;
   size_t inImgH_, inImgW_;
   real ratioH_, ratioW_;
   size_t numChannels_;
 
-public:
+ public:
   explicit BilinearInterpLayer(const LayerConfig& config) : Layer(config) {}
 
   virtual ~BilinearInterpLayer() {}

paddle/gserver/layers/BlockExpandLayer.h

@@ -40,7 +40,7 @@ namespace paddle {
  * The config file api is block_expand_layer.
  */
 class BlockExpandLayer : public Layer {
-protected:
+ protected:
   /**
    * @brief Calculate outputH_ and outputW_ and return block number which
    * actually is time steps.
@@ -53,7 +53,7 @@ protected:
   TensorShape inputShape_;
   TensorShape outputShape_;
 
-public:
+ public:
   explicit BlockExpandLayer(const LayerConfig& config) : Layer(config) {}
 
   ~BlockExpandLayer() {}

paddle/gserver/layers/CRFDecodingLayer.h

@@ -30,14 +30,14 @@ namespace paddle {
  * See LinearChainCRF.h for the detail of the CRF formulation.
  */
 class CRFDecodingLayer : public CRFLayer {
-public:
+ public:
   explicit CRFDecodingLayer(const LayerConfig& config) : CRFLayer(config) {}
   bool init(const LayerMap& layerMap,
             const ParameterMap& parameterMap) override;
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback) override;
 
-protected:
+ protected:
   std::unique_ptr<LinearChainCRF> crf_;
 };
 

paddle/gserver/layers/CRFLayer.h

@@ -27,14 +27,14 @@ namespace paddle {
  * See class LinearChainCRF for the detail of the CRF formulation.
  */
 class CRFLayer : public Layer {
-public:
+ public:
   explicit CRFLayer(const LayerConfig& config) : Layer(config) {}
   bool init(const LayerMap& layerMap,
             const ParameterMap& parameterMap) override;
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback) override;
 
-protected:
+ protected:
   size_t numClasses_;
   ParameterPtr parameter_;
   std::vector<LinearChainCRF> crfs_;

paddle/gserver/layers/CTCLayer.h

@@ -20,7 +20,7 @@ limitations under the License. */
 namespace paddle {
 
 class CTCLayer : public Layer {
-public:
+ public:
   explicit CTCLayer(const LayerConfig& config) : Layer(config) {}
   bool init(const LayerMap& layerMap,
             const ParameterMap& parameterMap) override;
@@ -31,7 +31,7 @@ public:
                    const Argument& softmaxSeqs,
                    const Argument& labelSeqs);
 
-protected:
+ protected:
   size_t numClasses_;
   bool normByTimes_;
   std::vector<LinearChainCTC> ctcs_;

paddle/gserver/layers/ClipLayer.cpp

@@ -24,11 +24,11 @@ namespace paddle {
  */
 
 class ClipLayer : public Layer {
-protected:
+ protected:
   double min_;
   double max_;
 
-public:
+ public:
   explicit ClipLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,

paddle/gserver/layers/ConcatenateLayer.cpp

@@ -23,7 +23,7 @@ namespace paddle {
  * each input as one row for the output of this layer and apply activation.
  */
 class ConcatenateLayer : public Layer {
-public:
+ public:
   explicit ConcatenateLayer(const LayerConfig& config) : Layer(config) {}
 
   ~ConcatenateLayer() {}
@@ -97,7 +97,7 @@ void ConcatenateLayer::backward(const UpdateCallback& callback) {
  * processed by a Projection.
  */
 class ConcatenateLayer2 : public Layer {
-public:
+ public:
   explicit ConcatenateLayer2(const LayerConfig& config) : Layer(config) {}
 
   ~ConcatenateLayer2() {}
@@ -108,7 +108,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   std::vector<std::unique_ptr<Projection>> projections_;
   std::vector<Argument> projOutput_;
   std::vector<std::pair<size_t, size_t>> projCol_;

paddle/gserver/layers/ContextProjection.h

@@ -42,7 +42,7 @@ namespace paddle {
  * The config file api is context_projection.
  */
 class ContextProjection : public Projection {
-public:
+ public:
   /**
    * Constructor. If context_start is zero and context_lenth is one, it will
    * set trainable_padding false. trainable_padding is an optional arguments
@@ -63,7 +63,7 @@ public:
 
   virtual bool init();
 
-protected:
+ protected:
   std::unique_ptr<Weight> weight_;
   /// number of extra timesteps added at the beginning
   size_t beginPad_;

paddle/gserver/layers/Conv3DLayer.h

@@ -26,7 +26,7 @@ namespace paddle {
  * calculate convolution operation.
  */
 class Conv3DLayer : public ConvBaseLayer {
-public:
+ public:
   explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
   ~Conv3DLayer() {}
 
@@ -40,7 +40,7 @@ public:
   void bpropWeights(int i);
   size_t getSize();
 
-protected:
+ protected:
   // Figure out the dimensions for individual gemms.
   IntV M_;  /// numFilters_ / filter_group_;
   IntV N_;  /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_

paddle/gserver/layers/ConvBaseLayer.h

@@ -24,7 +24,7 @@ namespace paddle {
  */
 
 class ConvBaseLayer : public Layer {
-protected:
+ protected:
   typedef std::vector<int> IntV;
 
   /// True if it's deconv layer, false if it's convolution layer
@@ -88,7 +88,7 @@ protected:
   /// of output size.
   bool caffeMode_;
 
-public:
+ public:
   explicit ConvBaseLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,

paddle/gserver/layers/ConvBaseOperator.h

@@ -29,7 +29,7 @@ namespace paddle {
  */
 
 class ConvBaseOperator : public Operator {
-public:
+ public:
   ConvBaseOperator(const OperatorConfig &config, bool useGpu);
   /**
    * Free workspace in device and destroy cudnn tensor descriptor.
@@ -46,7 +46,7 @@ public:
     hl_destroy_convolution_descriptor(convDesc_);
   }
 
-protected:
+ protected:
   /**
    * Get convolution parameters from layer config and
    * initialize member variables.

paddle/gserver/layers/ConvBaseProjection.h

@@ -23,7 +23,7 @@ namespace paddle {
  * @brief Base class for ConvProjection and ConvTransProjection.
  */
 class ConvBaseProjection : public Projection {
-public:
+ public:
   /**
    * Constructor.
    */
@@ -33,7 +33,7 @@ public:
 
   ~ConvBaseProjection();
 
-protected:
+ protected:
   void getConvParams();
   void initCudnn();
 

paddle/gserver/layers/ConvOperator.h

@@ -29,7 +29,7 @@ namespace paddle {
  */
 
 class ConvOperator : public ConvBaseOperator {
-public:
+ public:
   ConvOperator(const OperatorConfig &config, bool useGpu)
       : ConvBaseOperator(config, useGpu) {}
   /**

paddle/gserver/layers/ConvProjection.h

@@ -23,7 +23,7 @@ namespace paddle {
  * @brief Convolution projection do the same calculation with CudnnConvLayer.
  */
 class ConvProjection : public ConvBaseProjection {
-public:
+ public:
   /**
    * Constructor.
    */

paddle/gserver/layers/ConvShiftLayer.cpp

@@ -42,7 +42,7 @@ namespace paddle {
  */
 
 class ConvShiftLayer : public Layer {
-public:
+ public:
   explicit ConvShiftLayer(const LayerConfig& config) : Layer(config) {}
 
   ~ConvShiftLayer() {}

paddle/gserver/layers/ConvTransOperator.h

@@ -29,7 +29,7 @@ namespace paddle {
  */
 
 class ConvTransOperator : public ConvBaseOperator {
-public:
+ public:
   ConvTransOperator(const OperatorConfig &config, bool useGpu)
       : ConvBaseOperator(config, useGpu) {}
   /**

paddle/gserver/layers/ConvTransProjection.h

@@ -23,7 +23,7 @@ namespace paddle {
  * @brief Convolution projection do the same calculation with CudnnConvLayer.
  */
 class ConvTransProjection : public ConvBaseProjection {
-public:
+ public:
   /**
    * Constructor.
    */

paddle/gserver/layers/ConvexCombinationLayer.cpp

@@ -36,7 +36,7 @@ namespace paddle {
  * The config file api is linear_comb_layer.
  */
 class ConvexCombinationLayer : public Layer {
-protected:
+ protected:
   /// A matrix pointer pointing to second input.
   MatrixPtr tmpMtx0;
   /// A matrix pointer pointing to first input.
@@ -44,7 +44,7 @@ protected:
   /// A matrix pointer pointing to output.
   MatrixPtr tmpRow1;
 
-public:
+ public:
   explicit ConvexCombinationLayer(const LayerConfig& config) : Layer(config) {}
 
   ~ConvexCombinationLayer() {}

paddle/gserver/layers/CosSimLayer.h

@@ -33,7 +33,7 @@ namespace paddle {
  * The config file api is cos_sim.
  */
 class CosSimLayer : public Layer {
-public:
+ public:
   explicit CosSimLayer(const LayerConfig& config) : Layer(config) {}
 
   ~CosSimLayer() {}

paddle/gserver/layers/CosSimVecMatLayer.cpp

@@ -32,7 +32,7 @@ namespace paddle {
  */
 
 class CosSimVecMatLayer : public Layer {
-protected:
+ protected:
   MatrixPtr tmpMtx0;
   MatrixPtr tmpMtx1;
   MatrixPtr tmpRow0;
@@ -40,7 +40,7 @@ protected:
   MatrixPtr tmpRow2;
   MatrixPtr tmpRow3;
 
-public:
+ public:
   explicit CosSimVecMatLayer(const LayerConfig& config) : Layer(config) {}
 
   ~CosSimVecMatLayer() {}

paddle/gserver/layers/CostLayer.cpp

@@ -716,7 +716,7 @@ void HuberTwoClassification::backwardImp(Matrix& output,
  * \f]
  */
 class SumCostLayer : public Layer {
-public:
+ public:
   explicit SumCostLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,

paddle/gserver/layers/CostLayer.h

@@ -29,7 +29,7 @@ namespace paddle {
  * handled by the base class.
  */
 class CostLayer : public Layer {
-public:
+ public:
   explicit CostLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,
@@ -51,7 +51,7 @@ public:
                            Argument& label,
                            Matrix& outputGrad) = 0;
 
-protected:
+ protected:
   LayerPtr weightLayer_;
   real coeff_;
 };
@@ -65,7 +65,7 @@ protected:
  * \f]
  */
 class MultiClassCrossEntropy : public CostLayer {
-public:
+ public:
   explicit MultiClassCrossEntropy(const LayerConfig& config)
       : CostLayer(config) {}
 
@@ -95,7 +95,7 @@ public:
  *     In Proceedings of the ACL 2014 Conference.
  */
 class MultiClassCrossEntropyWithSelfNorm : public CostLayer {
-public:
+ public:
   explicit MultiClassCrossEntropyWithSelfNorm(const LayerConfig& config)
       : CostLayer(config) {}
 
@@ -108,7 +108,7 @@ public:
                    Argument& label,
                    Matrix& outputGrad) override;
 
-protected:
+ protected:
   MatrixPtr sftMaxSum_;
   MatrixPtr sumInv_;
 };
@@ -120,7 +120,7 @@ protected:
  * \f]
  */
 class SoftBinaryClassCrossEntropy : public CostLayer {
-public:
+ public:
   explicit SoftBinaryClassCrossEntropy(const LayerConfig& config)
       : CostLayer(config) {}
 
@@ -133,7 +133,7 @@ public:
                    Argument& label,
                    Matrix& outputGrad) override;
 
-protected:
+ protected:
   MatrixPtr targetPerDim_;
 };
 
@@ -145,7 +145,7 @@ protected:
  * \f]
  */
 class SumOfSquaresCostLayer : public CostLayer {
-public:
+ public:
   explicit SumOfSquaresCostLayer(const LayerConfig& config)
       : CostLayer(config) {}
 
@@ -171,7 +171,7 @@ public:
  * x = output - label
  */
 class SmoothL1CostLayer : public CostLayer {
-public:
+ public:
   explicit SmoothL1CostLayer(const LayerConfig& config) : CostLayer(config) {}
 
   bool init(const LayerMap& layerMap,
@@ -197,7 +197,7 @@ public:
  *      Rank useing Gradient Descent.
  */
 class RankingCost : public Layer {
-public:
+ public:
   explicit RankingCost(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,
@@ -225,7 +225,7 @@ public:
     (void)outputGrad;
   }
 
-private:
+ private:
   double posPairCount_;
   double negPairCount_;
   MatrixPtr margin_;
@@ -250,7 +250,7 @@ private:
  *     with Nonsmooth Cost Functions.
  */
 class LambdaCost : public Layer {
-public:
+ public:
   explicit LambdaCost(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap,
@@ -270,7 +270,7 @@ public:
                 real* gradData,
                 int size);
 
-private:
+ private:
   MatrixPtr marginGrad_;
   int truncationSize_;
   int maxSortSize_;
@@ -287,10 +287,10 @@ private:
  * \f]
  */
 class MultiBinaryLabelCrossEntropy : public CostLayer {
-protected:
+ protected:
   MatrixPtr targetPerDim_;
 
-public:
+ public:
   explicit MultiBinaryLabelCrossEntropy(const LayerConfig& config)
       : CostLayer(config) {}
 
@@ -308,7 +308,7 @@ public:
  * A base layer for HuberRegressionLoss and HuberTwoClassification.
  */
 class HuberCost : public CostLayer {
-public:
+ public:
   std::vector<Argument> tmpCpuInput_;
 
   explicit HuberCost(const LayerConfig& config) : CostLayer(config) {}
@@ -331,7 +331,7 @@ public:
  * Loss = delta * abs(y - f) - 0.5 * delta^2, otherwise
  */
 class HuberRegressionLoss : public HuberCost {
-public:
+ public:
   explicit HuberRegressionLoss(const LayerConfig& config) : HuberCost(config) {}
 
   bool init(const LayerMap& layerMap,
@@ -343,7 +343,7 @@ public:
                    Argument& label,
                    Matrix& outputGrad) override;
 
-protected:
+ protected:
   real delta_;
 };
 
@@ -356,7 +356,7 @@ protected:
  * Loss = 0, otherwise
  */
 class HuberTwoClassification : public HuberCost {
-public:
+ public:
   explicit HuberTwoClassification(const LayerConfig& config)
       : HuberCost(config) {}
 

paddle/gserver/layers/CropLayer.h

@@ -28,7 +28,7 @@ namespace paddle {
  *                  crop input as this shape conf
  */
 class CropLayer : public Layer {
-public:
+ public:
   explicit CropLayer(const LayerConfig& config) : Layer(config) {}
 
   ~CropLayer() {}
@@ -38,7 +38,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   void setOutDims();
   void setInDims();
 

paddle/gserver/layers/CrossEntropyOverBeam.h

@@ -44,7 +44,7 @@ struct BeamExpansion {
 typedef std::shared_ptr<BeamExpansion> BeamExpansionPtr;
 
 class CostForOneSequence {
-public:
+ public:
   CostForOneSequence()
       : beamSize_(0), validExpansionCount_(0), goldAsExtraPath_(false) {}
   void setData(const BeamExpansionPtr bPtr, size_t beamSize) {
@@ -64,7 +64,7 @@ public:
   real forward();
   void backward();
 
-private:
+ private:
   void calValidExpandStep();
   void constructTotalExpansion();
   size_t initLastExpansion();
@@ -93,14 +93,14 @@ private:
 };
 
 class CrossEntropyOverBeam : public Layer {
-public:
+ public:
   explicit CrossEntropyOverBeam(const LayerConfig& config) : Layer(config) {}
   bool init(const LayerMap& layerMap,
             const ParameterMap& parameterMap) override;
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback) override;
 
-private:
+ private:
   void checkInputs();
   void copyInputsToCpu();
   void resizeOutput();

paddle/gserver/layers/CudnnBatchNormLayer.h

@@ -30,7 +30,7 @@ namespace paddle {
  */
 
 class CudnnBatchNormLayer : public BatchNormBaseLayer {
-public:
+ public:
   explicit CudnnBatchNormLayer(const LayerConfig& config)
       : BatchNormBaseLayer(config) {}
 
@@ -46,7 +46,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   /// Epsilon value used in the batch normalization formula.
   /// Same epsilon value should be used in forward and backward functions.
   double eps_;

paddle/gserver/layers/CudnnConvBaseLayer.h

@@ -31,14 +31,14 @@ namespace paddle {
  * The config file api is img_conv_layer.
  */
 class CudnnConvBaseLayer : public ConvBaseLayer {
-protected:
+ protected:
   std::vector<std::unique_ptr<ProjectionConfig>> projConf_;
   std::vector<std::unique_ptr<Projection>> projections_;
 
   hl_tensor_descriptor biasDesc_;
   hl_tensor_descriptor outputDesc_;
 
-public:
+ public:
   explicit CudnnConvBaseLayer(const LayerConfig& config)
       : ConvBaseLayer(config) {}
 

paddle/gserver/layers/CudnnPoolLayer.h

@@ -26,7 +26,7 @@ namespace paddle {
  */
 
 class CudnnPoolLayer : public PoolLayer {
-protected:
+ protected:
   int windowHeight, windowWidth;
   int heightPadding, widthPadding, strideHeight, strideWidth;
   int imageH_, imageW_, outputH_, outputW_;
@@ -40,7 +40,7 @@ protected:
   /// A description of a pooling operation.
   hl_pooling_descriptor poolingDesc_;
 
-public:
+ public:
   static bool typeCheck(const std::string& poolType,
                         hl_pooling_mode_t* mode = nullptr);
   explicit CudnnPoolLayer(const LayerConfig& config);

paddle/gserver/layers/DataLayer.h

@@ -25,7 +25,7 @@ namespace paddle {
  * The config file api is data_layer.
  */
 class DataLayer : public Layer {
-public:
+ public:
   explicit DataLayer(const LayerConfig& config) : Layer(config) {}
 
   virtual void setData(const Argument& data) { data_ = data; }
@@ -58,10 +58,10 @@ public:
     }
   }
 
-private:
+ private:
   void copyDataToOutput(Argument& output);
 
-protected:
+ protected:
   Argument data_;
 };
 

paddle/gserver/layers/DataNormLayer.h

@@ -37,7 +37,7 @@ namespace paddle {
  */
 
 class DataNormLayer : public Layer {
-public:
+ public:
   enum NormalizationStrategy { kZScore = 0, kMinMax = 1, kDecimalScaling = 2 };
 
   explicit DataNormLayer(const LayerConfig& config) : Layer(config) {}
@@ -50,7 +50,7 @@ public:
   void forward(PassType passType) override;
   void backward(const UpdateCallback& callback = nullptr) override;
 
-protected:
+ protected:
   int mode_;
   std::unique_ptr<Weight> weight_;
   MatrixPtr min_;

paddle/gserver/layers/DeConv3DLayer.h

@@ -27,7 +27,7 @@ namespace paddle {
  * calculate deconvolution3D operation.
  */
 class DeConv3DLayer : public ConvBaseLayer {
-public:
+ public:
   explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
   ~DeConv3DLayer() {}
   bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
@@ -40,7 +40,7 @@ public:
   void bpropWeights(int i);
   size_t getSize();
 
-protected:
+ protected:
   // Figure out the dimensions for individual gemms.
   IntV M_;  /// numFilters_ / filter_group_;
   IntV N_;  /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_

paddle/gserver/layers/DetectionOutputLayer.h

@@ -33,7 +33,7 @@ namespace paddle {
  */
 
 class DetectionOutputLayer : public Layer {
-public:
+ public:
   explicit DetectionOutputLayer(const LayerConfig& config) : Layer(config) {}
 
   bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
@@ -42,7 +42,7 @@ public:
 
   void backward(const UpdateCallback& callback = nullptr) {}
 
-protected:
+ protected:
   inline LayerPtr getPriorBoxLayer() { return inputLayers_[0]; }
 
   inline LayerPtr getLocInputLayer(size_t index) {
@@ -53,7 +53,7 @@ protected:
     return inputLayers_[1 + inputNum_ + index];
   }
 
-private:
+ private:
   size_t numClasses_;  // number of classes
   size_t inputNum_;    // number of input layers
   real nmsThreshold_;