Reformat with google coding style

mace/core/common.h

@@ -26,8 +26,8 @@ typedef int64_t index_t;
 #ifndef DISABLE_COPY_AND_ASSIGN
 #define DISABLE_COPY_AND_ASSIGN(classname) \
  private:                                  \
-  classname(const classname&) = delete;    \
-  classname& operator=(const classname&) = delete
+  classname(const classname &) = delete;   \
+  classname &operator=(const classname &) = delete
 #endif
 
 #define MACE_NOT_IMPLEMENTED MACE_CHECK(false, "not implemented")

mace/core/logging.cc

@@ -14,7 +14,7 @@
 namespace mace {
 namespace internal {
 
-LogMessage::LogMessage(const char* fname, int line, int severity)
+LogMessage::LogMessage(const char *fname, int line, int severity)
     : fname_(fname), line_(line), severity_(severity) {}
 
 #if defined(PLATFORM_POSIX_ANDROID)
@@ -43,7 +43,7 @@ void LogMessage::GenerateLogMessage() {
   }
 
   std::stringstream ss;
-  const char* const partial_name = strrchr(fname_, '/');
+  const char *const partial_name = strrchr(fname_, '/');
   ss << (partial_name != nullptr ? partial_name + 1 : fname_) << ":" << line_
      << " " << str();
   __android_log_write(android_log_level, "native", ss.str().c_str());
@@ -69,7 +69,7 @@ void LogMessage::GenerateLogMessage() {
 namespace {
 
 // Parse log level (int64_t) from environment variable (char*)
-int64_t LogLevelStrToInt(const char* mace_env_var_val) {
+int64_t LogLevelStrToInt(const char *mace_env_var_val) {
   if (mace_env_var_val == nullptr) {
     return 0;
   }
@@ -89,12 +89,12 @@ int64_t LogLevelStrToInt(const char* mace_env_var_val) {
 }
 
 int64_t MinLogLevelFromEnv() {
-  const char* mace_env_var_val = getenv("MACE_CPP_MIN_LOG_LEVEL");
+  const char *mace_env_var_val = getenv("MACE_CPP_MIN_LOG_LEVEL");
   return LogLevelStrToInt(mace_env_var_val);
 }
 
 int64_t MinVLogLevelFromEnv() {
-  const char* mace_env_var_val = getenv("MACE_CPP_MIN_VLOG_LEVEL");
+  const char *mace_env_var_val = getenv("MACE_CPP_MIN_VLOG_LEVEL");
   return LogLevelStrToInt(mace_env_var_val);
 }
 
@@ -111,7 +111,7 @@ int64_t LogMessage::MinVLogLevel() {
   return min_vlog_level;
 }
 
-LogMessageFatal::LogMessageFatal(const char* file, int line)
+LogMessageFatal::LogMessageFatal(const char *file, int line)
     : LogMessage(file, line, FATAL) {}
 LogMessageFatal::~LogMessageFatal() {
   // abort() ensures we don't return (we promised we would not via

mace/core/logging.h

@@ -23,23 +23,23 @@ namespace internal {
 
 using std::string;
 
-inline void MakeStringInternal(std::stringstream& /*ss*/) {}
+inline void MakeStringInternal(std::stringstream & /*ss*/) {}
 
 template <typename T>
-inline void MakeStringInternal(std::stringstream& ss, const T& t) {
+inline void MakeStringInternal(std::stringstream &ss, const T &t) {
   ss << t;
 }
 
 template <typename T, typename... Args>
-inline void MakeStringInternal(std::stringstream& ss,
-                               const T& t,
-                               const Args&... args) {
+inline void MakeStringInternal(std::stringstream &ss,
+                               const T &t,
+                               const Args &... args) {
   MakeStringInternal(ss, t);
   MakeStringInternal(ss, args...);
 }
 
 template <typename... Args>
-string MakeString(const Args&... args) {
+string MakeString(const Args &... args) {
   std::stringstream ss;
   MakeStringInternal(ss, args...);
   return ss.str();
@@ -48,7 +48,7 @@ string MakeString(const Args&... args) {
 template <typename T>
 string MakeString(const std::vector<T> &args) {
   std::stringstream ss;
-  for (const T& arg: args) {
+  for (const T &arg : args) {
     ss << arg << ", ";
   }
   return ss.str();
@@ -56,14 +56,14 @@ string MakeString(const std::vector<T> &args) {
 
 // Specializations for already-a-string types.
 template <>
-inline string MakeString(const string& str) {
+inline string MakeString(const string &str) {
   return str;
 }
-inline string MakeString(const char* c_str) { return string(c_str); }
+inline string MakeString(const char *c_str) { return string(c_str); }
 
 class LogMessage : public std::basic_ostringstream<char> {
  public:
-  LogMessage(const char* fname, int line, int severity);
+  LogMessage(const char *fname, int line, int severity);
   ~LogMessage();
 
   // Returns the minimum log level for VLOG statements.
@@ -75,7 +75,7 @@ class LogMessage : public std::basic_ostringstream<char> {
   void GenerateLogMessage();
 
  private:
-  const char* fname_;
+  const char *fname_;
   int line_;
   int severity_;
 };
@@ -84,7 +84,7 @@ class LogMessage : public std::basic_ostringstream<char> {
 // logging this message.
 class LogMessageFatal : public LogMessage {
  public:
-  LogMessageFatal(const char* file, int line);
+  LogMessageFatal(const char *file, int line);
   ~LogMessageFatal();
 };
 
@@ -136,7 +136,7 @@ class LogMessageFatal : public LogMessage {
 #endif
 
 template <typename T>
-T&& CheckNotNull(const char* file, int line, const char* exprtext, T&& t) {
+T &&CheckNotNull(const char *file, int line, const char *exprtext, T &&t) {
   if (t == nullptr) {
     LogMessageFatal(file, line) << string(exprtext);
   }

mace/core/net.cc

@@ -7,18 +7,18 @@
 
 namespace mace {
 
-NetBase::NetBase(const std::shared_ptr<const NetDef>& net_def,
-                 Workspace* ws,
+NetBase::NetBase(const std::shared_ptr<const NetDef> &net_def,
+                 Workspace *ws,
                  DeviceType type)
     : name_(net_def->name()) {}
 
-SimpleNet::SimpleNet(const std::shared_ptr<const NetDef>& net_def,
-                     Workspace* ws,
+SimpleNet::SimpleNet(const std::shared_ptr<const NetDef> &net_def,
+                     Workspace *ws,
                      DeviceType type)
     : NetBase(net_def, ws, type) {
   VLOG(1) << "Constructing SimpleNet " << net_def->name();
   for (int idx = 0; idx < net_def->op_size(); ++idx) {
-    const auto& operator_def = net_def->op(idx);
+    const auto &operator_def = net_def->op(idx);
     VLOG(1) << "Creating operator " << operator_def.name() << ":"
             << operator_def.type();
     std::unique_ptr<OperatorBase> op{nullptr};
@@ -29,26 +29,29 @@ SimpleNet::SimpleNet(const std::shared_ptr<const NetDef>& net_def,
     }
   }
 }
-bool SimpleNet::Run(RunMetadata* run_metadata) {
+bool SimpleNet::Run(RunMetadata *run_metadata) {
   VLOG(1) << "Running net " << name_;
-  for (auto& op : operators_) {
+  for (auto &op : operators_) {
     VLOG(1) << "Running operator " << op->debug_def().name() << "("
             << op->debug_def().type() << ").";
-    OperatorStats* op_stats = nullptr;
+    OperatorStats *op_stats = nullptr;
     if (run_metadata) {
       op_stats = run_metadata->add_op_stats();
       op_stats->set_operator_name(op->debug_def().name());
       op_stats->set_type(op->debug_def().type());
       op_stats->set_all_start_micros(NowInMicroSec());
-      op_stats->set_op_start_rel_micros(NowInMicroSec() - op_stats->all_start_micros());
+      op_stats->set_op_start_rel_micros(NowInMicroSec() -
+                                        op_stats->all_start_micros());
     }
     if (!op->Run()) {
       LOG(ERROR) << "Operator failed: " << ProtoDebugString(op->debug_def());
       return false;
     }
     if (op_stats) {
-      op_stats->set_op_end_rel_micros(NowInMicroSec() - op_stats->all_start_micros());
-      op_stats->set_all_end_rel_micros(NowInMicroSec() - op_stats->all_start_micros());
+      op_stats->set_op_end_rel_micros(NowInMicroSec() -
+                                      op_stats->all_start_micros());
+      op_stats->set_all_end_rel_micros(NowInMicroSec() -
+                                       op_stats->all_start_micros());
     }
     VLOG(1) << "Op " << op->debug_def().name()
             << " has shape: " << internal::MakeString(op->Output(0)->shape());
@@ -56,15 +59,15 @@ bool SimpleNet::Run(RunMetadata* run_metadata) {
   return true;
 }
 
-unique_ptr<NetBase> CreateNet(const NetDef& net_def,
-                              Workspace* ws,
+unique_ptr<NetBase> CreateNet(const NetDef &net_def,
+                              Workspace *ws,
                               DeviceType type) {
   std::shared_ptr<NetDef> tmp_net_def(new NetDef(net_def));
   return CreateNet(tmp_net_def, ws, type);
 }
 
-unique_ptr<NetBase> CreateNet(const std::shared_ptr<const NetDef>& net_def,
-                              Workspace* ws,
+unique_ptr<NetBase> CreateNet(const std::shared_ptr<const NetDef> &net_def,
+                              Workspace *ws,
                               DeviceType type) {
   unique_ptr<NetBase> net(new SimpleNet(net_def, ws, type));
   return net;

mace/core/net.h

@@ -15,14 +15,14 @@ namespace mace {
 
 class NetBase {
  public:
-  NetBase(const std::shared_ptr<const NetDef>& net_def,
-          Workspace* ws,
+  NetBase(const std::shared_ptr<const NetDef> &net_def,
+          Workspace *ws,
           DeviceType type);
   virtual ~NetBase() noexcept {}
 
-  virtual bool Run(RunMetadata* run_metadata = nullptr) = 0;
+  virtual bool Run(RunMetadata *run_metadata = nullptr) = 0;
 
-  const string& Name() const { return name_; }
+  const string &Name() const { return name_; }
 
  protected:
   string name_;
@@ -32,11 +32,11 @@ class NetBase {
 
 class SimpleNet : public NetBase {
  public:
-  SimpleNet(const std::shared_ptr<const NetDef>& net_def,
-            Workspace* ws,
+  SimpleNet(const std::shared_ptr<const NetDef> &net_def,
+            Workspace *ws,
             DeviceType type);
 
-  bool Run(RunMetadata* run_metadata = nullptr) override;
+  bool Run(RunMetadata *run_metadata = nullptr) override;
 
  protected:
   vector<unique_ptr<OperatorBase> > operators_;
@@ -44,11 +44,11 @@ class SimpleNet : public NetBase {
   DISABLE_COPY_AND_ASSIGN(SimpleNet);
 };
 
-unique_ptr<NetBase> CreateNet(const NetDef& net_def,
-                              Workspace* ws,
+unique_ptr<NetBase> CreateNet(const NetDef &net_def,
+                              Workspace *ws,
                               DeviceType type);
-unique_ptr<NetBase> CreateNet(const std::shared_ptr<const NetDef>& net_def,
-                              Workspace* ws,
+unique_ptr<NetBase> CreateNet(const std::shared_ptr<const NetDef> &net_def,
+                              Workspace *ws,
                               DeviceType type);
 
 }  //  namespace mace

mace/core/proto_utils.cc

@@ -18,7 +18,7 @@
 
 namespace mace {
 
-bool ReadStringFromFile(const char* filename, string* str) {
+bool ReadStringFromFile(const char *filename, string *str) {
   std::ifstream ifs(filename, std::ios::in);
   if (!ifs) {
     VLOG(1) << "File cannot be opened: " << filename
@@ -33,7 +33,7 @@ bool ReadStringFromFile(const char* filename, string* str) {
   return true;
 }
 
-bool WriteStringToFile(const string& str, const char* filename) {
+bool WriteStringToFile(const string &str, const char *filename) {
   std::ofstream ofs(filename, std::ios::out | std::ios::trunc);
   if (!ofs.is_open()) {
     VLOG(1) << "File cannot be created: " << filename
@@ -54,15 +54,15 @@ bool WriteStringToFile(const string& str, const char* filename) {
 namespace {
 class IfstreamInputStream : public ::google::protobuf::io::CopyingInputStream {
  public:
-  explicit IfstreamInputStream(const string& filename)
+  explicit IfstreamInputStream(const string &filename)
       : ifs_(filename.c_str(), std::ios::in | std::ios::binary) {}
   ~IfstreamInputStream() { ifs_.close(); }
 
-  int Read(void* buffer, int size) {
+  int Read(void *buffer, int size) {
     if (!ifs_) {
       return -1;
     }
-    ifs_.read(static_cast<char*>(buffer), size);
+    ifs_.read(static_cast<char *>(buffer), size);
     return ifs_.gcount();
   }
 
@@ -71,7 +71,7 @@ class IfstreamInputStream : public ::google::protobuf::io::CopyingInputStream {
 };
 }  // namespace
 
-bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto) {
+bool ReadProtoFromBinaryFile(const char *filename, MessageLite *proto) {
   ::google::protobuf::io::CopyingInputStreamAdaptor stream(
       new IfstreamInputStream(filename));
   stream.SetOwnsCopyingStream(true);
@@ -82,8 +82,8 @@ bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto) {
   return proto->ParseFromCodedStream(&coded_stream);
 }
 
-void WriteProtoToBinaryFile(const MessageLite& /*proto*/,
-                            const char* /*filename*/) {
+void WriteProtoToBinaryFile(const MessageLite & /*proto*/,
+                            const char * /*filename*/) {
   LOG(FATAL) << "Not implemented yet.";
 }
 
@@ -98,25 +98,25 @@ using ::google::protobuf::io::CodedInputStream;
 using ::google::protobuf::io::ZeroCopyOutputStream;
 using ::google::protobuf::io::CodedOutputStream;
 
-bool ReadProtoFromTextFile(const char* filename, Message* proto) {
+bool ReadProtoFromTextFile(const char *filename, Message *proto) {
   int fd = open(filename, O_RDONLY);
   MACE_CHECK(fd != -1, "File not found: ", filename);
-  FileInputStream* input = new FileInputStream(fd);
+  FileInputStream *input = new FileInputStream(fd);
   bool success = google::protobuf::TextFormat::Parse(input, proto);
   delete input;
   close(fd);
   return success;
 }
 
-void WriteProtoToTextFile(const Message& proto, const char* filename) {
+void WriteProtoToTextFile(const Message &proto, const char *filename) {
   int fd = open(filename, O_WRONLY | O_CREAT | O_TRUNC, 0644);
-  FileOutputStream* output = new FileOutputStream(fd);
+  FileOutputStream *output = new FileOutputStream(fd);
   MACE_CHECK(google::protobuf::TextFormat::Print(proto, output));
   delete output;
   close(fd);
 }
 
-bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto) {
+bool ReadProtoFromBinaryFile(const char *filename, MessageLite *proto) {
 #if defined(_MSC_VER)  // for MSC compiler binary flag needs to be specified
   int fd = open(filename, O_RDONLY | O_BINARY);
 #else
@@ -135,7 +135,7 @@ bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto) {
   return success;
 }
 
-void WriteProtoToBinaryFile(const MessageLite& proto, const char* filename) {
+void WriteProtoToBinaryFile(const MessageLite &proto, const char *filename) {
   int fd = open(filename, O_WRONLY | O_CREAT | O_TRUNC, 0644);
   MACE_CHECK(fd != -1, "File cannot be created: ", filename, " error number: ",
              errno);
@@ -150,8 +150,8 @@ void WriteProtoToBinaryFile(const MessageLite& proto, const char* filename) {
 
 #endif  // MACE_USE_LITE_PROTO
 
-ArgumentHelper::ArgumentHelper(const OperatorDef& def) {
-  for (auto& arg : def.arg()) {
+ArgumentHelper::ArgumentHelper(const OperatorDef &def) {
+  for (auto &arg : def.arg()) {
     if (arg_map_.find(arg.name()) != arg_map_.end()) {
       MACE_CHECK(
           arg.SerializeAsString() == arg_map_[arg.name()].SerializeAsString(),
@@ -167,8 +167,8 @@ ArgumentHelper::ArgumentHelper(const OperatorDef& def) {
   }
 }
 
-ArgumentHelper::ArgumentHelper(const NetDef& netdef) {
-  for (auto& arg : netdef.arg()) {
+ArgumentHelper::ArgumentHelper(const NetDef &netdef) {
+  for (auto &arg : netdef.arg()) {
     MACE_CHECK(arg_map_.count(arg.name()) == 0,
                "Duplicated argument name found in net def: ",
                ProtoDebugString(netdef));
@@ -176,7 +176,7 @@ ArgumentHelper::ArgumentHelper(const NetDef& netdef) {
   }
 }
 
-bool ArgumentHelper::HasArgument(const string& name) const {
+bool ArgumentHelper::HasArgument(const string &name) const {
   return arg_map_.count(name);
 }
 
@@ -184,7 +184,7 @@ namespace {
 // Helper function to verify that conversion between types won't loose any
 // significant bit.
 template <typename InputType, typename TargetType>
-bool SupportsLosslessConversion(const InputType& value) {
+bool SupportsLosslessConversion(const InputType &value) {
   return static_cast<InputType>(static_cast<TargetType>(value)) == value;
 }
 }
@@ -192,8 +192,8 @@ bool SupportsLosslessConversion(const InputType& value) {
 #define INSTANTIATE_GET_SINGLE_ARGUMENT(T, fieldname,                         \
                                         enforce_lossless_conversion)          \
   template <>                                                                 \
-  T ArgumentHelper::GetSingleArgument<T>(const string& name,                  \
-                                         const T& default_value) const {      \
+  T ArgumentHelper::GetSingleArgument<T>(const string &name,                  \
+                                         const T &default_value) const {      \
     if (arg_map_.count(name) == 0) {                                          \
       VLOG(1) << "Using default parameter value " << default_value            \
               << " for parameter " << name;                                   \
@@ -211,7 +211,7 @@ bool SupportsLosslessConversion(const InputType& value) {
     return value;                                                             \
   }                                                                           \
   template <>                                                                 \
-  bool ArgumentHelper::HasSingleArgumentOfType<T>(const string& name) const { \
+  bool ArgumentHelper::HasSingleArgumentOfType<T>(const string &name) const { \
     if (arg_map_.count(name) == 0) {                                          \
       return false;                                                           \
     }                                                                         \
@@ -235,12 +235,12 @@ INSTANTIATE_GET_SINGLE_ARGUMENT(string, s, false)
                                           enforce_lossless_conversion)    \
   template <>                                                             \
   vector<T> ArgumentHelper::GetRepeatedArgument<T>(                       \
-      const string& name, const std::vector<T>& default_value) const {    \
+      const string &name, const std::vector<T> &default_value) const {    \
     if (arg_map_.count(name) == 0) {                                      \
       return default_value;                                               \
     }                                                                     \
     vector<T> values;                                                     \
-    for (const auto& v : arg_map_.at(name).fieldname()) {                 \
+    for (const auto &v : arg_map_.at(name).fieldname()) {                 \
       if (enforce_lossless_conversion) {                                  \
         auto supportsConversion =                                         \
             SupportsLosslessConversion<decltype(v), T>(v);                \
@@ -267,7 +267,7 @@ INSTANTIATE_GET_REPEATED_ARGUMENT(string, strings, false)
 
 #define MACE_MAKE_SINGULAR_ARGUMENT(T, fieldname)             \
   template <>                                                 \
-  Argument MakeArgument(const string& name, const T& value) { \
+  Argument MakeArgument(const string &name, const T &value) { \
     Argument arg;                                             \
     arg.set_name(name);                                       \
     arg.set_##fieldname(value);                               \
@@ -282,7 +282,7 @@ MACE_MAKE_SINGULAR_ARGUMENT(string, s)
 #undef MACE_MAKE_SINGULAR_ARGUMENT
 
 template <>
-Argument MakeArgument(const string& name, const MessageLite& value) {
+Argument MakeArgument(const string &name, const MessageLite &value) {
   Argument arg;
   arg.set_name(name);
   arg.set_s(value.SerializeAsString());
@@ -291,10 +291,10 @@ Argument MakeArgument(const string& name, const MessageLite& value) {
 
 #define MACE_MAKE_REPEATED_ARGUMENT(T, fieldname)                     \
   template <>                                                         \
-  Argument MakeArgument(const string& name, const vector<T>& value) { \
+  Argument MakeArgument(const string &name, const vector<T> &value) { \
     Argument arg;                                                     \
     arg.set_name(name);                                               \
-    for (const auto& v : value) {                                     \
+    for (const auto &v : value) {                                     \
       arg.add_##fieldname(v);                                         \
     }                                                                 \
     return arg;                                                       \
@@ -306,8 +306,8 @@ MACE_MAKE_REPEATED_ARGUMENT(int64_t, ints)
 MACE_MAKE_REPEATED_ARGUMENT(string, strings)
 #undef MACE_MAKE_REPEATED_ARGUMENT
 
-const Argument& GetArgument(const OperatorDef& def, const string& name) {
-  for (const Argument& arg : def.arg()) {
+const Argument &GetArgument(const OperatorDef &def, const string &name) {
+  for (const Argument &arg : def.arg()) {
     if (arg.name() == name) {
       return arg;
     }
@@ -318,10 +318,10 @@ const Argument& GetArgument(const OperatorDef& def, const string& name) {
   return std::move(Argument());
 }
 
-bool GetFlagArgument(const OperatorDef& def,
-                     const string& name,
+bool GetFlagArgument(const OperatorDef &def,
+                     const string &name,
                      bool def_value) {
-  for (const Argument& arg : def.arg()) {
+  for (const Argument &arg : def.arg()) {
     if (arg.name() == name) {
       MACE_CHECK(arg.has_i(), "Can't parse argument as bool: ",
                  ProtoDebugString(arg));
@@ -331,9 +331,9 @@ bool GetFlagArgument(const OperatorDef& def,
   return def_value;
 }
 
-Argument* GetMutableArgument(const string& name,
+Argument *GetMutableArgument(const string &name,
                              const bool create_if_missing,
-                             OperatorDef* def) {
+                             OperatorDef *def) {
   for (int i = 0; i < def->arg_size(); ++i) {
     if (def->arg(i).name() == name) {
       return def->mutable_arg(i);
@@ -341,7 +341,7 @@ Argument* GetMutableArgument(const string& name,
   }
   // If no argument of the right name is found...
   if (create_if_missing) {
-    Argument* arg = def->add_arg();
+    Argument *arg = def->add_arg();
     arg->set_name(name);
     return arg;
   } else {

mace/core/proto_utils.h

@@ -21,56 +21,56 @@ using std::string;
 using ::google::protobuf::MessageLite;
 
 // Common interfaces that reads file contents into a string.
-bool ReadStringFromFile(const char* filename, string* str);
-bool WriteStringToFile(const string& str, const char* filename);
+bool ReadStringFromFile(const char *filename, string *str);
+bool WriteStringToFile(const string &str, const char *filename);
 
 // Common interfaces that are supported by both lite and full protobuf.
-bool ReadProtoFromBinaryFile(const char* filename, MessageLite* proto);
-inline bool ReadProtoFromBinaryFile(const string filename, MessageLite* proto) {
+bool ReadProtoFromBinaryFile(const char *filename, MessageLite *proto);
+inline bool ReadProtoFromBinaryFile(const string filename, MessageLite *proto) {
   return ReadProtoFromBinaryFile(filename.c_str(), proto);
 }
 
-void WriteProtoToBinaryFile(const MessageLite& proto, const char* filename);
-inline void WriteProtoToBinaryFile(const MessageLite& proto,
-                                   const string& filename) {
+void WriteProtoToBinaryFile(const MessageLite &proto, const char *filename);
+inline void WriteProtoToBinaryFile(const MessageLite &proto,
+                                   const string &filename) {
   return WriteProtoToBinaryFile(proto, filename.c_str());
 }
 
 #ifdef MACE_USE_LITE_PROTO
 
-inline string ProtoDebugString(const MessageLite& proto) {
+inline string ProtoDebugString(const MessageLite &proto) {
   return proto.SerializeAsString();
 }
 
 // Text format MessageLite wrappers: these functions do nothing but just
 // allowing things to compile. It will produce a runtime error if you are using
 // MessageLite but still want text support.
-inline bool ReadProtoFromTextFile(const char* /*filename*/,
-                                  MessageLite* /*proto*/) {
+inline bool ReadProtoFromTextFile(const char * /*filename*/,
+                                  MessageLite * /*proto*/) {
   LOG(FATAL) << "If you are running lite version, you should not be "
              << "calling any text-format protobuffers.";
   return false;  // Just to suppress compiler warning.
 }
-inline bool ReadProtoFromTextFile(const string filename, MessageLite* proto) {
+inline bool ReadProtoFromTextFile(const string filename, MessageLite *proto) {
   return ReadProtoFromTextFile(filename.c_str(), proto);
 }
 
-inline void WriteProtoToTextFile(const MessageLite& /*proto*/,
-                                 const char* /*filename*/) {
+inline void WriteProtoToTextFile(const MessageLite & /*proto*/,
+                                 const char * /*filename*/) {
   LOG(FATAL) << "If you are running lite version, you should not be "
              << "calling any text-format protobuffers.";
 }
-inline void WriteProtoToTextFile(const MessageLite& proto,
-                                 const string& filename) {
+inline void WriteProtoToTextFile(const MessageLite &proto,
+                                 const string &filename) {
   return WriteProtoToTextFile(proto, filename.c_str());
 }
 
-inline bool ReadProtoFromFile(const char* filename, MessageLite* proto) {
+inline bool ReadProtoFromFile(const char *filename, MessageLite *proto) {
   return (ReadProtoFromBinaryFile(filename, proto) ||
           ReadProtoFromTextFile(filename, proto));
 }
 
-inline bool ReadProtoFromFile(const string& filename, MessageLite* proto) {
+inline bool ReadProtoFromFile(const string &filename, MessageLite *proto) {
   return ReadProtoFromFile(filename.c_str(), proto);
 }
 
@@ -78,27 +78,27 @@ inline bool ReadProtoFromFile(const string& filename, MessageLite* proto) {
 
 using ::google::protobuf::Message;
 
-inline string ProtoDebugString(const Message& proto) {
+inline string ProtoDebugString(const Message &proto) {
   return proto.ShortDebugString();
 }
 
-bool ReadProtoFromTextFile(const char* filename, Message* proto);
-inline bool ReadProtoFromTextFile(const string filename, Message* proto) {
+bool ReadProtoFromTextFile(const char *filename, Message *proto);
+inline bool ReadProtoFromTextFile(const string filename, Message *proto) {
   return ReadProtoFromTextFile(filename.c_str(), proto);
 }
 
-void WriteProtoToTextFile(const Message& proto, const char* filename);
-inline void WriteProtoToTextFile(const Message& proto, const string& filename) {
+void WriteProtoToTextFile(const Message &proto, const char *filename);
+inline void WriteProtoToTextFile(const Message &proto, const string &filename) {
   return WriteProtoToTextFile(proto, filename.c_str());
 }
 
 // Read Proto from a file, letting the code figure out if it is text or binary.
-inline bool ReadProtoFromFile(const char* filename, Message* proto) {
+inline bool ReadProtoFromFile(const char *filename, Message *proto) {
   return (ReadProtoFromBinaryFile(filename, proto) ||
           ReadProtoFromTextFile(filename, proto));
 }
 
-inline bool ReadProtoFromFile(const string& filename, Message* proto) {
+inline bool ReadProtoFromFile(const string &filename, Message *proto) {
   return ReadProtoFromFile(filename.c_str(), proto);
 }
 
@@ -107,21 +107,21 @@ inline bool ReadProtoFromFile(const string& filename, Message* proto) {
 template <class IterableInputs = std::initializer_list<string>,
           class IterableOutputs = std::initializer_list<string>,
           class IterableArgs = std::initializer_list<Argument>>
-OperatorDef CreateOperatorDef(const string& type,
-                              const string& name,
-                              const IterableInputs& inputs,
-                              const IterableOutputs& outputs,
-                              const IterableArgs& args) {
+OperatorDef CreateOperatorDef(const string &type,
+                              const string &name,
+                              const IterableInputs &inputs,
+                              const IterableOutputs &outputs,
+                              const IterableArgs &args) {
   OperatorDef def;
   def.set_type(type);
   def.set_name(name);
-  for (const string& in : inputs) {
+  for (const string &in : inputs) {
     def.add_input(in);
   }
-  for (const string& out : outputs) {
+  for (const string &out : outputs) {
     def.add_output(out);
   }
-  for (const Argument& arg : args) {
+  for (const Argument &arg : args) {
     def.add_arg()->CopyFrom(arg);
   }
   return def;
@@ -131,10 +131,10 @@ OperatorDef CreateOperatorDef(const string& type,
 // to specify args.
 template <class IterableInputs = std::initializer_list<string>,
           class IterableOutputs = std::initializer_list<string>>
-inline OperatorDef CreateOperatorDef(const string& type,
-                                     const string& name,
-                                     const IterableInputs& inputs,
-                                     const IterableOutputs& outputs) {
+inline OperatorDef CreateOperatorDef(const string &type,
+                                     const string &name,
+                                     const IterableInputs &inputs,
+                                     const IterableOutputs &outputs) {
   return CreateOperatorDef(type, name, inputs, outputs,
                            std::vector<Argument>());
 }
@@ -150,56 +150,56 @@ inline OperatorDef CreateOperatorDef(const string& type,
 class ArgumentHelper {
  public:
   template <typename Def>
-  static bool HasArgument(const Def& def, const string& name) {
+  static bool HasArgument(const Def &def, const string &name) {
     return ArgumentHelper(def).HasArgument(name);
   }
 
   template <typename Def, typename T>
-  static T GetSingleArgument(const Def& def,
-                             const string& name,
-                             const T& default_value) {
+  static T GetSingleArgument(const Def &def,
+                             const string &name,
+                             const T &default_value) {
     return ArgumentHelper(def).GetSingleArgument<T>(name, default_value);
   }
 
   template <typename Def, typename T>
-  static bool HasSingleArgumentOfType(const Def& def, const string& name) {
+  static bool HasSingleArgumentOfType(const Def &def, const string &name) {
     return ArgumentHelper(def).HasSingleArgumentOfType<T>(name);
   }
 
   template <typename Def, typename T>
   static vector<T> GetRepeatedArgument(
-      const Def& def,
-      const string& name,
-      const std::vector<T>& default_value = std::vector<T>()) {
+      const Def &def,
+      const string &name,
+      const std::vector<T> &default_value = std::vector<T>()) {
     return ArgumentHelper(def).GetRepeatedArgument<T>(name, default_value);
   }
 
   template <typename Def, typename MessageType>
-  static MessageType GetMessageArgument(const Def& def, const string& name) {
+  static MessageType GetMessageArgument(const Def &def, const string &name) {
     return ArgumentHelper(def).GetMessageArgument<MessageType>(name);
   }
 
   template <typename Def, typename MessageType>
-  static vector<MessageType> GetRepeatedMessageArgument(const Def& def,
-                                                        const string& name) {
+  static vector<MessageType> GetRepeatedMessageArgument(const Def &def,
+                                                        const string &name) {
     return ArgumentHelper(def).GetRepeatedMessageArgument<MessageType>(name);
   }
 
-  explicit ArgumentHelper(const OperatorDef& def);
-  explicit ArgumentHelper(const NetDef& netdef);
-  bool HasArgument(const string& name) const;
+  explicit ArgumentHelper(const OperatorDef &def);
+  explicit ArgumentHelper(const NetDef &netdef);
+  bool HasArgument(const string &name) const;
 
   template <typename T>
-  T GetSingleArgument(const string& name, const T& default_value) const;
+  T GetSingleArgument(const string &name, const T &default_value) const;
   template <typename T>
-  bool HasSingleArgumentOfType(const string& name) const;
+  bool HasSingleArgumentOfType(const string &name) const;
   template <typename T>
   vector<T> GetRepeatedArgument(
-      const string& name,
-      const std::vector<T>& default_value = std::vector<T>()) const;
+      const string &name,
+      const std::vector<T> &default_value = std::vector<T>()) const;
 
   template <typename MessageType>
-  MessageType GetMessageArgument(const string& name) const {
+  MessageType GetMessageArgument(const string &name) const {
     MACE_CHECK(arg_map_.count(name), "Cannot find parameter named " + name);
     MessageType message;
     if (arg_map_.at(name).has_s()) {
@@ -212,7 +212,7 @@ class ArgumentHelper {
   }
 
   template <typename MessageType>
-  vector<MessageType> GetRepeatedMessageArgument(const string& name) const {
+  vector<MessageType> GetRepeatedMessageArgument(const string &name) const {
     MACE_CHECK(arg_map_.count(name), "Cannot find parameter named " + name);
     vector<MessageType> messages(arg_map_.at(name).strings_size());
     for (int i = 0; i < messages.size(); ++i) {
@@ -226,20 +226,20 @@ class ArgumentHelper {
   std::map<string, Argument> arg_map_;
 };
 
-const Argument& GetArgument(const OperatorDef& def, const string& name);
-bool GetFlagArgument(const OperatorDef& def,
-                     const string& name,
+const Argument &GetArgument(const OperatorDef &def, const string &name);
+bool GetFlagArgument(const OperatorDef &def,
+                     const string &name,
                      bool def_value = false);
 
-Argument* GetMutableArgument(const string& name,
+Argument *GetMutableArgument(const string &name,
                              const bool create_if_missing,
-                             OperatorDef* def);
+                             OperatorDef *def);
 
 template <typename T>
-Argument MakeArgument(const string& name, const T& value);
+Argument MakeArgument(const string &name, const T &value);
 
 template <typename T>
-inline void AddArgument(const string& name, const T& value, OperatorDef* def) {
+inline void AddArgument(const string &name, const T &value, OperatorDef *def) {
   GetMutableArgument(name, true, def)->CopyFrom(MakeArgument(name, value));
 }
 

mace/core/registry.h

@@ -16,15 +16,15 @@ class Registry {
 
   Registry() : registry_() {}
 
-  void Register(const SrcType& key, Creator creator) {
+  void Register(const SrcType &key, Creator creator) {
     std::lock_guard<std::mutex> lock(register_mutex_);
     MACE_CHECK(registry_.count(key) == 0, "Key already registered.");
     registry_[key] = creator;
   }
 
-  inline bool Has(const SrcType& key) { return registry_.count(key) != 0; }
+  inline bool Has(const SrcType &key) { return registry_.count(key) != 0; }
 
-  unique_ptr<ObjectType> Create(const SrcType& key, Args... args) {
+  unique_ptr<ObjectType> Create(const SrcType &key, Args... args) {
     if (registry_.count(key) == 0) {
       LOG(FATAL) << "Key not registered: " << key;
     }
@@ -36,7 +36,7 @@ class Registry {
    */
   vector<SrcType> Keys() {
     vector<SrcType> keys;
-    for (const auto& it : registry_) {
+    for (const auto &it : registry_) {
       keys.push_back(it.first);
     }
     return keys;
@@ -52,8 +52,8 @@ class Registry {
 template <class SrcType, class ObjectType, class... Args>
 class Registerer {
  public:
-  Registerer(const SrcType& key,
-             Registry<SrcType, ObjectType, Args...>* registry,
+  Registerer(const SrcType &key,
+             Registry<SrcType, ObjectType, Args...> *registry,
              typename Registry<SrcType, ObjectType, Args...>::Creator creator) {
     registry->Register(key, creator);
   }
@@ -73,13 +73,13 @@ class Registerer {
 #endif
 
 #define MACE_DECLARE_TYPED_REGISTRY(RegistryName, SrcType, ObjectType, ...) \
-  Registry<SrcType, ObjectType, ##__VA_ARGS__>* RegistryName();             \
+  Registry<SrcType, ObjectType, ##__VA_ARGS__> *RegistryName();             \
   typedef Registerer<SrcType, ObjectType, ##__VA_ARGS__>                    \
       Registerer##RegistryName;
 
 #define MACE_DEFINE_TYPED_REGISTRY(RegistryName, SrcType, ObjectType, ...) \
-  Registry<SrcType, ObjectType, ##__VA_ARGS__>* RegistryName() {           \
-    static Registry<SrcType, ObjectType, ##__VA_ARGS__>* registry =        \
+  Registry<SrcType, ObjectType, ##__VA_ARGS__> *RegistryName() {           \
+    static Registry<SrcType, ObjectType, ##__VA_ARGS__> *registry =        \
         new Registry<SrcType, ObjectType, ##__VA_ARGS__>();                \
     return registry;                                                       \
   }

mace/core/runtime/opencl/opencl_allocator.cc

@@ -3,8 +3,8 @@
 //
 
 #include "mace/core/runtime/opencl/opencl_allocator.h"
-#include "mace/core/runtime/opencl/opencl_runtime.h"
 #include "mace/core/runtime/opencl/cl2.hpp"
+#include "mace/core/runtime/opencl/opencl_runtime.h"
 
 namespace mace {
 

mace/core/runtime/opencl/opencl_runtime.cc

@@ -30,7 +30,9 @@ bool ReadSourceFile(const char *filename, std::string *content) {
   return true;
 }
 
-bool BuildProgram(OpenCLRuntime *runtime, const char *filename, cl::Program *program) {
+bool BuildProgram(OpenCLRuntime *runtime,
+                  const char *filename,
+                  cl::Program *program) {
   MACE_CHECK_NOTNULL(filename);
   MACE_CHECK_NOTNULL(program);
 

mace/core/serializer.h

@@ -16,9 +16,9 @@ class Serializer {
   Serializer() {}
   ~Serializer() {}
 
-  unique_ptr<TensorProto> Serialize(const Tensor& tensor, const string& name);
+  unique_ptr<TensorProto> Serialize(const Tensor &tensor, const string &name);
 
-  unique_ptr<Tensor> Deserialize(const TensorProto& proto, DeviceType type);
+  unique_ptr<Tensor> Deserialize(const TensorProto &proto, DeviceType type);
 
   DISABLE_COPY_AND_ASSIGN(Serializer);
 };

mace/core/tensor.h

@@ -202,15 +202,15 @@ class Tensor {
   }
 
   class MappingGuard {
-    public:
-      MappingGuard(Tensor *tensor) : tensor_(tensor) {
-        MACE_ASSERT(tensor_ != nullptr);
-        tensor_->Map();
-      }
-      ~MappingGuard() { tensor_->Unmap(); }
-
-    private:
-      Tensor *tensor_;
+   public:
+    MappingGuard(Tensor *tensor) : tensor_(tensor) {
+      MACE_ASSERT(tensor_ != nullptr);
+      tensor_->Map();
+    }
+    ~MappingGuard() { tensor_->Unmap(); }
+
+   private:
+    Tensor *tensor_;
   };
 
  private:

mace/core/testing/test_benchmark.cc

@@ -16,36 +16,36 @@
 namespace mace {
 namespace testing {
 
-static std::vector<Benchmark*>* all_benchmarks = nullptr;
+static std::vector<Benchmark *> *all_benchmarks = nullptr;
 static std::string label;
 static int64_t bytes_processed;
 static int64_t items_processed;
 static int64_t accum_time = 0;
 static int64_t start_time = 0;
 
-Benchmark::Benchmark(const char* name, void (*fn)(int))
+Benchmark::Benchmark(const char *name, void (*fn)(int))
     : name_(name), num_args_(0), fn0_(fn) {
   args_.push_back(std::make_pair(-1, -1));
   Register();
 }
 
-Benchmark::Benchmark(const char* name, void (*fn)(int, int))
+Benchmark::Benchmark(const char *name, void (*fn)(int, int))
     : name_(name), num_args_(1), fn1_(fn) {
   Register();
 }
 
-Benchmark::Benchmark(const char* name, void (*fn)(int, int, int))
+Benchmark::Benchmark(const char *name, void (*fn)(int, int, int))
     : name_(name), num_args_(2), fn2_(fn) {
   Register();
 }
 
-Benchmark* Benchmark::Arg(int x) {
+Benchmark *Benchmark::Arg(int x) {
   MACE_CHECK(num_args_ == 1);
   args_.push_back(std::make_pair(x, -1));
   return this;
 }
 
-Benchmark* Benchmark::ArgPair(int x, int y) {
+Benchmark *Benchmark::ArgPair(int x, int y) {
   MACE_CHECK(num_args_ == 2);
   args_.push_back(std::make_pair(x, y));
   return this;
@@ -54,7 +54,7 @@ Benchmark* Benchmark::ArgPair(int x, int y) {
 // Run all benchmarks
 void Benchmark::Run() { Run("all"); }
 
-void Benchmark::Run(const char* pattern) {
+void Benchmark::Run(const char *pattern) {
   if (!all_benchmarks) return;
 
   if (std::string(pattern) == "all") {
@@ -117,11 +117,11 @@ void Benchmark::Run(const char* pattern) {
 }
 
 void Benchmark::Register() {
-  if (!all_benchmarks) all_benchmarks = new std::vector<Benchmark*>;
+  if (!all_benchmarks) all_benchmarks = new std::vector<Benchmark *>;
   all_benchmarks->push_back(this);
 }
 
-void Benchmark::Run(int arg1, int arg2, int* run_count, double* run_seconds) {
+void Benchmark::Run(int arg1, int arg2, int *run_count, double *run_seconds) {
   static const int64_t kMinIters = 10;
   static const int64_t kMaxIters = 1000000000;
   static const double kMinTime = 0.5;

mace/core/testing/test_benchmark.h

@@ -13,7 +13,7 @@
 
 #define MACE_BENCHMARK_CONCAT(a, b, c) a##b##c
 #define BENCHMARK(n)                                        \
-  static ::mace::testing::Benchmark* MACE_BENCHMARK_CONCAT( \
+  static ::mace::testing::Benchmark *MACE_BENCHMARK_CONCAT( \
       __benchmark_, n, __LINE__) = (new ::mace::testing::Benchmark(#n, (n)))
 
 namespace mace {
@@ -21,14 +21,14 @@ namespace testing {
 
 class Benchmark {
  public:
-  Benchmark(const char* name, void (*fn)(int));
-  Benchmark(const char* name, void (*fn)(int, int));
-  Benchmark(const char* name, void (*fn)(int, int, int));
-  Benchmark* Arg(int x);
-  Benchmark* ArgPair(int x, int y);
+  Benchmark(const char *name, void (*fn)(int));
+  Benchmark(const char *name, void (*fn)(int, int));
+  Benchmark(const char *name, void (*fn)(int, int, int));
+  Benchmark *Arg(int x);
+  Benchmark *ArgPair(int x, int y);
 
   static void Run();
-  static void Run(const char* pattern);
+  static void Run(const char *pattern);
 
  private:
   string name_;
@@ -39,7 +39,7 @@ class Benchmark {
   void (*fn2_)(int, int, int) = nullptr;
 
   void Register();
-  void Run(int arg1, int arg2, int* run_count, double* run_seconds);
+  void Run(int arg1, int arg2, int *run_count, double *run_seconds);
 };
 
 void RunBenchmarks();

mace/core/testing/test_benchmark_main.cc

@@ -6,7 +6,7 @@
 
 #include "mace/core/testing/test_benchmark.h"
 
-int main(int argc, char** argv) {
+int main(int argc, char **argv) {
   std::cout << "Running main() from test_main.cc\n";
 
   // TODO Use gflags

mace/core/types.cc

@@ -23,4 +23,4 @@ bool DataTypeCanUseMemcpy(DataType dt) {
   }
 }
 
-} //  namespace mace
\ No newline at end of file
+}  //  namespace mace
\ No newline at end of file

mace/core/workspace.cc

@@ -10,14 +10,14 @@ namespace mace {
 
 vector<string> Workspace::Tensors() const {
   vector<string> names;
-  for (auto& entry : tensor_map_) {
+  for (auto &entry : tensor_map_) {
     names.push_back(entry.first);
   }
   return names;
 }
 
-Tensor* Workspace::CreateTensor(const string& name,
-                                Allocator* alloc,
+Tensor *Workspace::CreateTensor(const string &name,
+                                Allocator *alloc,
                                 DataType type) {
   if (HasTensor(name)) {
     VLOG(1) << "Tensor " << name << " already exists. Skipping.";
@@ -28,7 +28,7 @@ Tensor* Workspace::CreateTensor(const string& name,
   return GetTensor(name);
 }
 
-bool Workspace::RemoveTensor(const string& name) {
+bool Workspace::RemoveTensor(const string &name) {
   auto it = tensor_map_.find(name);
   if (it != tensor_map_.end()) {
     VLOG(1) << "Removing blob " << name << " from this workspace.";
@@ -38,7 +38,7 @@ bool Workspace::RemoveTensor(const string& name) {
   return false;
 }
 
-const Tensor* Workspace::GetTensor(const string& name) const {
+const Tensor *Workspace::GetTensor(const string &name) const {
   if (tensor_map_.count(name)) {
     return tensor_map_.at(name).get();
   } else {
@@ -47,18 +47,17 @@ const Tensor* Workspace::GetTensor(const string& name) const {
   return nullptr;
 }
 
-Tensor* Workspace::GetTensor(const string& name) {
-  return const_cast<Tensor*>(
-      static_cast<const Workspace*>(this)->GetTensor(name));
+Tensor *Workspace::GetTensor(const string &name) {
+  return const_cast<Tensor *>(
+      static_cast<const Workspace *>(this)->GetTensor(name));
 }
 
-void Workspace::LoadModelTensor(const NetDef& net_def, DeviceType type) {
+void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
   Serializer serializer;
-  for (auto& tensor_proto : net_def.tensors()) {
-
-    VLOG(1) << "Load tensor: " << tensor_proto.name()
-            << " has shape: " << internal::MakeString(vector<index_t>(
-          tensor_proto.dims().begin(), tensor_proto.dims().end()));
+  for (auto &tensor_proto : net_def.tensors()) {
+    VLOG(1) << "Load tensor: " << tensor_proto.name() << " has shape: "
+            << internal::MakeString(vector<index_t>(tensor_proto.dims().begin(),
+                                                    tensor_proto.dims().end()));
     tensor_map_[tensor_proto.name()] =
         serializer.Deserialize(tensor_proto, type);
   }

mace/core/workspace.h

@@ -19,19 +19,19 @@ class Workspace {
 
   vector<string> Tensors() const;
 
-  Tensor* CreateTensor(const string& name, Allocator* alloc, DataType type);
+  Tensor *CreateTensor(const string &name, Allocator *alloc, DataType type);
 
-  bool RemoveTensor(const string& name);
+  bool RemoveTensor(const string &name);
 
-  inline bool HasTensor(const string& name) const {
+  inline bool HasTensor(const string &name) const {
     return tensor_map_.count(name);
   }
 
-  const Tensor* GetTensor(const string& name) const;
+  const Tensor *GetTensor(const string &name) const;
 
-  Tensor* GetTensor(const string& name);
+  Tensor *GetTensor(const string &name);
 
-  void LoadModelTensor(const NetDef& net_def, DeviceType type);
+  void LoadModelTensor(const NetDef &net_def, DeviceType type);
 
  private:
   TensorMap tensor_map_;

mace/examples/benchmark_example.cc

@@ -10,8 +10,8 @@ static void foo(int iters) {
   mace::testing::ItemsProcessed(tot);
   mace::testing::BytesProcessed(tot * (sizeof(float)));
 
-  float* inp = new float[N];
-  float* out = new float[N];
+  float *inp = new float[N];
+  float *out = new float[N];
 
   while (iters--) {
     for (int i = 0; i < N; i++) {
@@ -29,8 +29,8 @@ static void bar(int iters, int n) {
   mace::testing::ItemsProcessed(tot);
   mace::testing::BytesProcessed(tot * (sizeof(float)));
 
-  float* inp = new float[n];
-  float* out = new float[n];
+  float *inp = new float[n];
+  float *out = new float[n];
 
   while (iters--) {
     for (int i = 0; i < n; i++) {

mace/examples/mace_run.cc

@@ -12,8 +12,8 @@
  *          --output_file=mace.out  \
  *          --device=NEON
  */
-#include <fstream>
 #include <sys/time.h>
+#include <fstream>
 #include "mace/core/net.h"
 #include "mace/utils/command_line_flags.h"
 
@@ -83,12 +83,11 @@ int main(int argc, char **argv) {
 
   Workspace ws;
   ws.LoadModelTensor(net_def, DeviceType::CPU);
-  Tensor *input_tensor = ws.CreateTensor(input_node + ":0",
-                                         cpu_allocator(), DT_FLOAT);
+  Tensor *input_tensor =
+      ws.CreateTensor(input_node + ":0", cpu_allocator(), DT_FLOAT);
   input_tensor->Resize(shape);
   float *input_data = input_tensor->mutable_data<float>();
 
-
   // load input
   ifstream in_file(input_file, ios::in | ios::binary);
   in_file.read(reinterpret_cast<char *>(input_data),
@@ -112,14 +111,17 @@ int main(int argc, char **argv) {
     net->Run();
   }
   gettimeofday(&tv2, NULL);
-  cout << "avg duration: " << ((tv2.tv_sec - tv1.tv_sec) * 1000
-      + (tv2.tv_usec - tv1.tv_usec) / 1000) / round << endl;
+  cout << "avg duration: "
+       << ((tv2.tv_sec - tv1.tv_sec) * 1000 +
+           (tv2.tv_usec - tv1.tv_usec) / 1000) /
+              round
+       << endl;
 
   // save output
   const Tensor *output = ws.GetTensor(output_node + ":0");
 
   ofstream out_file(output_file, ios::binary);
-  out_file.write((const char *) (output->data<float>()),
+  out_file.write((const char *)(output->data<float>()),
                  output->size() * sizeof(float));
   out_file.flush();
   out_file.close();

mace/kernels/BUILD

@@ -20,7 +20,7 @@ cc_library(
     linkopts = if_android(["-lm"]),
     deps = [
         "//mace/core",
-        "//mace/utils:utils",
+        "//mace/utils",
     ],
 )
 

mace/kernels/addn.h

@@ -12,7 +12,7 @@ namespace kernels {
 
 template <DeviceType D, typename T>
 struct AddNFunctor {
-  void operator()(const vector<const T*>& inputs, T* output, index_t size) {
+  void operator()(const vector<const T *> &inputs, T *output, index_t size) {
     memset(output, 0, size * sizeof(T));
     int n = inputs.size();
     for (int i = 0; i < n; ++i) {
@@ -25,7 +25,7 @@ struct AddNFunctor {
 
 template <>
 void AddNFunctor<DeviceType::NEON, float>::operator()(
-    const vector<const float*>& inputs, float* output, index_t size);
+    const vector<const float *> &inputs, float *output, index_t size);
 
 }  //  namespace kernels
 }  //  namespace mace

mace/kernels/batch_norm.h

@@ -13,17 +13,16 @@ namespace kernels {
 
 template <DeviceType D, typename T>
 struct BatchNormFunctor {
-
-  void operator()(const T* input,
-                  const T* scale,
-                  const T* offset,
-                  const T* mean,
-                  const T* var,
+  void operator()(const T *input,
+                  const T *scale,
+                  const T *offset,
+                  const T *mean,
+                  const T *var,
                   const float variance_epsilon,
                   const index_t n,
                   const index_t channel,
                   const index_t sample_size,
-                  T* output) {
+                  T *output) {
     // Batch normalization in the paper https://arxiv.org/abs/1502.03167 .
     // The calculation formula for inference is
     // Y = \frac{ \scale } { \sqrt{var+\variance_epsilon} } * X +
@@ -40,8 +39,8 @@ struct BatchNormFunctor {
       index_t pos = c * sample_size;
 
       for (index_t i = 0; i < n; ++i) {
-        const T* input_sample_ptr = input + pos;
-        T* output_sample_ptr = output + pos;
+        const T *input_sample_ptr = input + pos;
+        T *output_sample_ptr = output + pos;
         for (index_t j = 0; j < sample_size; ++j) {
           output_sample_ptr[j] = new_scale * input_sample_ptr[j] + new_offset;
         }
@@ -53,16 +52,16 @@ struct BatchNormFunctor {
 
 template <>
 void BatchNormFunctor<DeviceType::NEON, float>::operator()(
-    const float* input,
-    const float* scale,
-    const float* offset,
-    const float* mean,
-    const float* var,
+    const float *input,
+    const float *scale,
+    const float *offset,
+    const float *mean,
+    const float *var,
     const float variance_epsilon,
     const index_t n,
     const index_t channel,
     const index_t sample_size,
-    float* output);
+    float *output);
 
 }  //  namepsace kernels
 }  //  namespace mace

mace/kernels/channel_shuffle.h

@@ -10,11 +10,10 @@
 namespace mace {
 namespace kernels {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 class ChannelShuffleFunctor {
  public:
-  ChannelShuffleFunctor(const int group)
-      : group_(group) {}
+  ChannelShuffleFunctor(const int group) : group_(group) {}
 
   void operator()(const T *input, const index_t *input_shape, T *output) {
     index_t batch = input_shape[0];
@@ -28,8 +27,8 @@ class ChannelShuffleFunctor {
     for (int b = 0; b < batch; ++b) {
       for (int c = 0; c < channels_of_group; ++c) {
         for (int g = 0; g < group_; ++g) {
-          index_t input_offset = (b * channels + g * channels_of_group + c) *
-                                 image_size;
+          index_t input_offset =
+              (b * channels + g * channels_of_group + c) * image_size;
           index_t output_offset = (b * channels + c * group_ + g) * image_size;
           memcpy(output + output_offset, input + input_offset,
                  image_size * sizeof(T));

mace/kernels/concat.h

@@ -5,13 +5,13 @@
 #ifndef MACE_KERNELS_CONCAT_H_
 #define MACE_KERNELS_CONCAT_H_
 
-#include "mace/proto/mace.pb.h"
 #include "mace/core/common.h"
 #include "mace/core/types.h"
+#include "mace/proto/mace.pb.h"
 namespace mace {
 namespace kernels {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 struct ConcatFunctor {
   void operator()(std::vector<const T *> &input_list,
                   const index_t inner_dim,
@@ -35,6 +35,6 @@ struct ConcatFunctor {
 };
 
 }  //  namepsace kernels
-} //  namespace mace
+}  //  namespace mace
 
-#endif //  MACE_KERNELS_CONCAT_H_
+#endif  //  MACE_KERNELS_CONCAT_H_

mace/kernels/conv_2d.h

@@ -11,15 +11,13 @@
 namespace mace {
 namespace kernels {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 struct Conv2dFunctor {
   Conv2dFunctor() {}
   Conv2dFunctor(const int *strides,
                 const std::vector<int> &paddings,
-                const int *dilations) :
-      strides_(strides),
-      paddings_(paddings),
-      dilations_(dilations) {}
+                const int *dilations)
+      : strides_(strides), paddings_(paddings), dilations_(dilations) {}
 
   void operator()(const T *input,  // NCHW
                   const index_t *input_shape,
@@ -66,7 +64,7 @@ struct Conv2dFunctor {
         for (int h = 0; h < height; ++h) {
           for (int w = 0; w < width; ++w) {
             index_t offset = n * channels * height * width +
-                c * height * width + h * width + w;
+                             c * height * width + h * width + w;
             output[offset] = bias_channel;
             T sum = 0;
             const T *filter_ptr = filter + c * kernel_size;
@@ -78,7 +76,7 @@ struct Conv2dFunctor {
                   if (inh < 0 || inh >= input_height || inw < 0 ||
                       inw >= input_width) {
                     MACE_CHECK(inh >= padded_h_start && inh < padded_h_stop &&
-                        inw >= padded_w_start && inw < padded_w_stop,
+                                   inw >= padded_w_start && inw < padded_w_stop,
                                "Out of range read from input: ", inh, ", ",
                                inw);
                     // else padding with 0:
@@ -86,8 +84,8 @@ struct Conv2dFunctor {
                   } else {
                     index_t input_offset =
                         n * input_channels * input_height * input_width +
-                            inc * input_height * input_width + inh * input_width +
-                            inw;
+                        inc * input_height * input_width + inh * input_width +
+                        inw;
                     sum += input[input_offset] * *filter_ptr;
                   }
                   ++filter_ptr;
@@ -101,12 +99,12 @@ struct Conv2dFunctor {
     }
   }
 
-  const int *strides_;    // [stride_h, stride_w]
-  std::vector<int> paddings_;   // [padding_h, padding_w]
-  const int *dilations_;  // [dilation_h, dilation_w]
+  const int *strides_;         // [stride_h, stride_w]
+  std::vector<int> paddings_;  // [padding_h, padding_w]
+  const int *dilations_;       // [dilation_h, dilation_w]
 };
 
-template<>
+template <>
 void Conv2dFunctor<DeviceType::NEON, float>::operator()(
     const float *input,
     const index_t *input_shape,

mace/kernels/conv_pool_2d_util.cc

@@ -72,16 +72,15 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
 }
 
 void CalPaddingSize(const index_t *input_shape,   // NCHW
-                     const index_t *filter_shape,  // OIHW
-                     const int *dilations,
-                     const int *strides,
-                     Padding padding,
-                     int *padding_size) {
-
+                    const index_t *filter_shape,  // OIHW
+                    const int *dilations,
+                    const int *strides,
+                    Padding padding,
+                    int *padding_size) {
   MACE_CHECK(dilations[0] > 0 && dilations[1] > 0,
              "Invalid dilations, must >= 1");
   MACE_CHECK((dilations[0] == 1 || strides[0] == 1) &&
-      (dilations[1] == 1 || strides[1] == 1),
+                 (dilations[1] == 1 || strides[1] == 1),
              "If dilations > 1, strides should be 1");
   MACE_CHECK_NOTNULL(padding_size);
 

mace/kernels/conv_pool_2d_util.h

@@ -26,11 +26,11 @@ void CalcPaddingAndOutputSize(const index_t *input_shape,   // NCHW
                               int *padding_size);
 
 void CalPaddingSize(const index_t *input_shape,   // NCHW
-                     const index_t *filter_shape,  // OIHW
-                     const int *dilations,
-                     const int *strides,
-                     Padding padding,
-                     int *padding_size);
+                    const index_t *filter_shape,  // OIHW
+                    const int *dilations,
+                    const int *strides,
+                    Padding padding,
+                    int *padding_size);
 
 void ConstructInputWithPadding(const float *input,
                                const index_t *input_shape,

mace/kernels/depthwise_conv2d.h

@@ -5,29 +5,27 @@
 #ifndef MACE_KERNELS_DEPTHWISE_CONV_H_
 #define MACE_KERNELS_DEPTHWISE_CONV_H_
 
-#include "mace/proto/mace.pb.h"
 #include "mace/core/common.h"
 #include "mace/kernels/conv_pool_2d_util.h"
+#include "mace/proto/mace.pb.h"
 
 namespace mace {
 namespace kernels {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 struct DepthwiseConv2dFunctor {
   DepthwiseConv2dFunctor() {}
   DepthwiseConv2dFunctor(const int *strides,
                          const std::vector<int> &paddings,
-                         const int *dilations) :
-      strides_(strides),
-      paddings_(paddings),
-      dilations_(dilations) {}
+                         const int *dilations)
+      : strides_(strides), paddings_(paddings), dilations_(dilations) {}
 
-  void operator()(const T *input, // NCHW
+  void operator()(const T *input,  // NCHW
                   const index_t *input_shape,
-                  const T *filter, // c_out, c_in, kernel_h, kernel_w
+                  const T *filter,  // c_out, c_in, kernel_h, kernel_w
                   const index_t *filter_shape,
-                  const T *bias, // c_out
-                  T *output, // NCHW
+                  const T *bias,  // c_out
+                  T *output,      // NCHW
                   const index_t *output_shape) {
     MACE_CHECK_NOTNULL(output);
 
@@ -68,7 +66,7 @@ struct DepthwiseConv2dFunctor {
         for (int h = 0; h < height; ++h) {
           for (int w = 0; w < width; ++w) {
             index_t offset = n * channels * height * width +
-                c * height * width + h * width + w;
+                             c * height * width + h * width + w;
             output[offset] = bias_channel;
             T sum = 0;
             const T *filter_ptr = filter + c * kernel_size;
@@ -79,16 +77,15 @@ struct DepthwiseConv2dFunctor {
                 if (inh < 0 || inh >= input_height || inw < 0 ||
                     inw >= input_width) {
                   MACE_CHECK(inh >= padded_h_start && inh < padded_h_stop &&
-                      inw >= padded_w_start && inw < padded_w_stop,
-                             "Out of range read from input: ", inh, ", ",
-                             inw);
+                                 inw >= padded_w_start && inw < padded_w_stop,
+                             "Out of range read from input: ", inh, ", ", inw);
                   // else padding with 0:
                   // sum += 0;
                 } else {
                   index_t input_offset =
                       n * input_channels * input_height * input_width +
-                          (c / multiplier) * input_height * input_width + inh * input_width +
-                          inw;
+                      (c / multiplier) * input_height * input_width +
+                      inh * input_width + inw;
                   sum += input[input_offset] * *filter_ptr;
                 }
                 ++filter_ptr;
@@ -101,20 +98,21 @@ struct DepthwiseConv2dFunctor {
     }
   }
 
-  const int *strides_; // [stride_h, stride_w]
-  std::vector<int> paddings_;   // [padding_h, padding_w]
-  const int *dilations_; // [dilation_h, dilation_w]
+  const int *strides_;         // [stride_h, stride_w]
+  std::vector<int> paddings_;  // [padding_h, padding_w]
+  const int *dilations_;       // [dilation_h, dilation_w]
 };
 
-template<>
-void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(const float *input,
-                                                                 const index_t *input_shape,
-                                                                 const float *filter,
-                                                                 const index_t *filter_shape,
-                                                                 const float *bias,
-                                                                 float *output,
-                                                                 const index_t *output_shape);
-} //  namespace kernels
-} //  namespace mace
-
-#endif //  MACE_KERNELS_DEPTHWISE_CONV_H_
+template <>
+void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(
+    const float *input,
+    const index_t *input_shape,
+    const float *filter,
+    const index_t *filter_shape,
+    const float *bias,
+    float *output,
+    const index_t *output_shape);
+}  //  namespace kernels
+}  //  namespace mace
+
+#endif  //  MACE_KERNELS_DEPTHWISE_CONV_H_

mace/kernels/global_avg_pooling.h

@@ -35,9 +35,7 @@ struct GlobalAvgPoolingFunctor {
 
 template <>
 void GlobalAvgPoolingFunctor<DeviceType::NEON, float>::operator()(
-    const float *input,
-    const index_t *input_shape,
-    float *output);
+    const float *input, const index_t *input_shape, float *output);
 
 }  //  namespace kernels
 }  //  namespace mace

mace/kernels/neon/avg_pooling_neon_2x2.cc

@@ -45,7 +45,7 @@ void PoolingAvgNeonK2x2S2x2(const float *input,
         int w = 0;
         int num_vectors = 0;
         if (!((h == 0 && padding_top > 0) ||
-            (h == out_height - 1 && padding_bottom > 0))) {
+              (h == out_height - 1 && padding_bottom > 0))) {
           r0 = input + input_offset + (h * 2 - padding_top) * in_width;
           r1 = r0 + in_width;
           if (padding_left > 0) {

mace/kernels/neon/avg_pooling_neon_3x3.cc

@@ -33,7 +33,7 @@ void PoolingAvgNeonK3x3S2x2(const float *input,
   int out_image_size = out_height * out_width;
   index_t input_offset = 0;
   index_t output_offset = 0;
-  float avg_factors[4] = {1.0/9.0, 1.0/9.0, 1.0/9.0, 1.0/9.0};
+  float avg_factors[4] = {1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0};
 
 #pragma omp parallel for collapse(2)
   for (int b = 0; b < batch; ++b) {
@@ -45,7 +45,7 @@ void PoolingAvgNeonK3x3S2x2(const float *input,
         int num_vectors = 0;
         const float *r0, *r1, *r2;
         if (!((h == 0 && padding_top > 0) ||
-            (h == out_height - 1 && padding_bottom > 0))) {
+              (h == out_height - 1 && padding_bottom > 0))) {
           r0 = input + input_offset + (h * 2 - padding_top) * in_width;
           r1 = r0 + in_width;
           r2 = r1 + in_width;
@@ -147,7 +147,7 @@ void PoolingAvgNeonK3x3S2x2Padded(const float *input,
   int out_image_size = out_height * out_width;
   index_t input_offset = 0;
   index_t output_offset = 0;
-  float avg_factors[4] = {1.0/9.0, 1.0/9.0, 1.0/9.0, 1.0/9.0};
+  float avg_factors[4] = {1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0};
 
 #pragma omp parallel for collapse(2)
   for (int b = 0; b < batch; ++b) {
@@ -200,8 +200,9 @@ void PoolingAvgNeonK3x3S2x2Padded(const float *input,
         }
 
         for (; remain > 0; remain--) {
-          *outptr = (r0[0] + r0[1] + r0[2] + r1[0] + r1[1] + r1[2] +
-                     r2[0] + r2[1] + r2[2]) / 9.0;
+          *outptr = (r0[0] + r0[1] + r0[2] + r1[0] + r1[1] + r1[2] + r2[0] +
+                     r2[1] + r2[2]) /
+                    9.0;
 
           r0 += 2;
           r1 += 2;

mace/kernels/neon/batch_norm_neon.cc

@@ -10,16 +10,16 @@ namespace kernels {
 
 template <>
 void BatchNormFunctor<DeviceType::NEON, float>::operator()(
-    const float* input,
-    const float* scale,
-    const float* offset,
-    const float* mean,
-    const float* var,
+    const float *input,
+    const float *scale,
+    const float *offset,
+    const float *mean,
+    const float *var,
     const float variance_epsilon,
     const index_t n,
     const index_t channel,
     const index_t sample_size,
-    float* output) {
+    float *output) {
   // Batch normalization in the paper https://arxiv.org/abs/1502.03167 .
   // The calculation formula for inference is
   // Y = \frac{ \scale } { \sqrt{var+\variance_epsilon} } * X +
@@ -40,8 +40,8 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
     float32x4_t new_scale_f = vdupq_n_f32(new_scale);
     float32x4_t new_offset_f = vdupq_n_f32(new_offset);
     for (index_t i = 0; i < n; ++i) {
-      const float* input_sample_ptr = input + pos;
-      float* output_sample_ptr = output + pos;
+      const float *input_sample_ptr = input + pos;
+      float *output_sample_ptr = output + pos;
 
       for (index_t j = 0; j < count; ++j) {
         float32x4_t input_f = vld1q_f32(input_sample_ptr);

mace/kernels/neon/conv_2d_neon.cc

@@ -41,20 +41,17 @@ extern void Conv2dNeonK5x5S1(const float *input,
                              const index_t *output_shape);
 
 template <>
-void Conv2dFunctor<DeviceType::NEON, float>::operator()(const float *input,
-                                                        const index_t *input_shape,
-                                                        const float *filter,
-                                                        const index_t *filter_shape,
-                                                        const float *bias,
-                                                        float *output,
-                                                        const index_t *output_shape) {
+void Conv2dFunctor<DeviceType::NEON, float>::operator()(
+    const float *input,
+    const index_t *input_shape,
+    const float *filter,
+    const index_t *filter_shape,
+    const float *bias,
+    float *output,
+    const index_t *output_shape) {
   typedef void (*Conv2dNeonFunction)(
-      const float *input,
-      const index_t *input_shape,
-      const float *filter,
-      const index_t *filter_shape,
-      const float *bias,
-      float *output,
+      const float *input, const index_t *input_shape, const float *filter,
+      const index_t *filter_shape, const float *bias, float *output,
       const index_t *output_shape);
   // Selection matrix: kernel_size x stride_size
   static const Conv2dNeonFunction selector[5][2] = {
@@ -81,12 +78,14 @@ void Conv2dFunctor<DeviceType::NEON, float>::operator()(const float *input,
   // Keep this alive during kernel execution
   Tensor padded_input;
   if (paddings_[0] > 0 || paddings_[1] > 0) {
-    ConstructInputWithPadding(input, input_shape, paddings_.data(), &padded_input);
+    ConstructInputWithPadding(input, input_shape, paddings_.data(),
+                              &padded_input);
     input = padded_input.data<float>();
     input_shape = padded_input.shape().data();
   }
   auto conv2d_neon_func = selector[kernel_h - 1][strides_[0] - 1];
-  conv2d_neon_func(input, input_shape, filter, nullptr, bias, output, output_shape);
+  conv2d_neon_func(input, input_shape, filter, nullptr, bias, output,
+                   output_shape);
 }
 
 }  //  namespace kernels

mace/kernels/neon/conv_2d_neon_1x1.cc

@@ -10,9 +10,8 @@ namespace mace {
 namespace kernels {
 static constexpr index_t kInputChannelBlockSize = 2;
 static constexpr index_t kOutputChannelBlockSize = 4;
-static __attribute__((__aligned__(64))) int32_t mask_array[8] = {
-    0, 0, 0, 0, -1, -1, -1, -1
-};
+static __attribute__((__aligned__(64)))
+int32_t mask_array[8] = {0, 0, 0, 0, -1, -1, -1, -1};
 
 static inline void NeonConv2x4Kernel(index_t input_channels,
                                      index_t pixel_size,
@@ -77,15 +76,15 @@ static inline void NeonConv2x4Kernel(index_t input_channels,
     output3 = output3 + pixel_size - 4;
     float32x4_t voutput3 = vld1q_f32(output3);
 
-    const float32x4_t vinput0 = vreinterpretq_f32_s32(
-        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
+    const float32x4_t vinput0 = vreinterpretq_f32_s32(vandq_s32(
+        vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
     voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
     voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
     voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
     voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
 
-    const float32x4_t vinput1 = vreinterpretq_f32_s32(
-        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
+    const float32x4_t vinput1 = vreinterpretq_f32_s32(vandq_s32(
+        vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
     voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
     voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
     voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
@@ -98,13 +97,14 @@ static inline void NeonConv2x4Kernel(index_t input_channels,
   }
 }
 
-static inline void NeonConv2x4SubBlockKernel(index_t input_channels_subblock_size,
-                                             index_t output_channels_subblock_size,
-                                             index_t input_channels,
-                                             index_t pixel_size,
-                                             const float *input,
-                                             const float *filter,
-                                             float *output) {
+static inline void NeonConv2x4SubBlockKernel(
+    index_t input_channels_subblock_size,
+    index_t output_channels_subblock_size,
+    index_t input_channels,
+    index_t pixel_size,
+    const float *input,
+    const float *filter,
+    float *output) {
   const float *input0 = input;
   const float *input1 = input + pixel_size;
 
@@ -204,16 +204,16 @@ static inline void NeonConv2x4SubBlockKernel(index_t input_channels_subblock_siz
       }
     }
 
-    const float32x4_t vinput0 = vreinterpretq_f32_s32(
-        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
+    const float32x4_t vinput0 = vreinterpretq_f32_s32(vandq_s32(
+        vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
     voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
     voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
     voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
     voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
 
     if (input_channels_subblock_size > 1) {
-      const float32x4_t vinput1 = vreinterpretq_f32_s32(
-          vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
+      const float32x4_t vinput1 = vreinterpretq_f32_s32(vandq_s32(
+          vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
       voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
       voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
       voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
@@ -237,8 +237,8 @@ void Conv2dNeonK1x1S1(const float *input,  // NCHW
                       const index_t *input_shape,
                       const float *filter,  // c_out, c_in, filter_h, filter_w
                       const index_t *filter_shape,
-                      const float *bias,    // c_out
-                      float *output,        // NCHW
+                      const float *bias,  // c_out
+                      float *output,      // NCHW
                       const index_t *output_shape) {
   const index_t batch = output_shape[0];
   const index_t channels = output_shape[1];
@@ -251,7 +251,7 @@ void Conv2dNeonK1x1S1(const float *input,  // NCHW
   const index_t input_width = input_shape[3];
 
   MACE_CHECK(input_batch == batch && input_height == height &&
-      input_width == width);
+             input_width == width);
 
   const index_t total_pixels = height * width;
   const index_t round_up_channels = RoundUp(channels, kOutputChannelBlockSize);
@@ -259,22 +259,27 @@ void Conv2dNeonK1x1S1(const float *input,  // NCHW
 #pragma omp parallel for collapse(2)
   for (index_t n = 0; n < batch; ++n) {
     for (int i = 0; i < channels; ++i) {
-      float *output_ptr_base = output + n * channels * total_pixels + i * total_pixels;
-      std::fill(output_ptr_base, output_ptr_base + total_pixels, bias ? bias[i] : 0);
+      float *output_ptr_base =
+          output + n * channels * total_pixels + i * total_pixels;
+      std::fill(output_ptr_base, output_ptr_base + total_pixels,
+                bias ? bias[i] : 0);
     }
   }
-  // benchmark omp collapsed(2)
+// benchmark omp collapsed(2)
 #pragma omp parallel for collapse(2)
   for (index_t n = 0; n < batch; ++n) {
     for (index_t c = 0; c < round_up_channels; c += kOutputChannelBlockSize) {
       const float *input_ptr = input + n * input_channels * total_pixels;
       const float *filter_ptr = filter + c * input_channels;
-      float *output_ptr = output + n * channels * total_pixels + c * total_pixels;
-      const index_t output_channel_block_size = std::min(channels - c, kOutputChannelBlockSize);
+      float *output_ptr =
+          output + n * channels * total_pixels + c * total_pixels;
+      const index_t output_channel_block_size =
+          std::min(channels - c, kOutputChannelBlockSize);
       index_t remain_input_channels = input_channels;
       if (c + kOutputChannelBlockSize <= channels) {
         while (remain_input_channels >= kInputChannelBlockSize) {
-          NeonConv2x4Kernel(input_channels, total_pixels, input_ptr, filter_ptr, output_ptr);
+          NeonConv2x4Kernel(input_channels, total_pixels, input_ptr, filter_ptr,
+                            output_ptr);
 
           input_ptr += kInputChannelBlockSize * total_pixels;
           filter_ptr += kInputChannelBlockSize;
@@ -282,25 +287,27 @@ void Conv2dNeonK1x1S1(const float *input,  // NCHW
         }
       }
       while (remain_input_channels != 0) {
-        const index_t input_channel_block_size = std::min(remain_input_channels, kInputChannelBlockSize);
-        NeonConv2x4SubBlockKernel(input_channel_block_size, output_channel_block_size,
-                                  input_channels, total_pixels, input_ptr, filter_ptr, output_ptr);
+        const index_t input_channel_block_size =
+            std::min(remain_input_channels, kInputChannelBlockSize);
+        NeonConv2x4SubBlockKernel(
+            input_channel_block_size, output_channel_block_size, input_channels,
+            total_pixels, input_ptr, filter_ptr, output_ptr);
         input_ptr += kInputChannelBlockSize * total_pixels;
         filter_ptr += kInputChannelBlockSize;
         remain_input_channels -= input_channel_block_size;
       }
-
     }
   }
 };
 
-void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
-                      const index_t *input_shape,
-                      const float *filter,  // c_out, c_in, kernel_h, kernel_w
-                      const index_t *filter_shape,
-                      const float *bias,    // c_out
-                      float *output,        // NCHW
-                      const index_t *output_shape) {
+void Conv2dNeonPixelK1x1S1(
+    const float *input,  // NCHW
+    const index_t *input_shape,
+    const float *filter,  // c_out, c_in, kernel_h, kernel_w
+    const index_t *filter_shape,
+    const float *bias,  // c_out
+    float *output,      // NCHW
+    const index_t *output_shape) {
   const index_t batch = output_shape[0];
   const index_t channels = output_shape[1];
   const index_t height = output_shape[2];
@@ -312,7 +319,7 @@ void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
   const index_t input_width = input_shape[3];
 
   MACE_CHECK(input_batch == batch && input_height == height &&
-      input_width == width);
+             input_width == width);
 
   const index_t total_pixels = height * width;
   // Process 4 * 2 = 8 pixels for each innermost loop
@@ -320,7 +327,7 @@ void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
   const index_t total_loops = total_pixels >> 3;
   const index_t loop_remaining = total_pixels & 7;
 
-  // benchmark omp collapsed(2)
+// benchmark omp collapsed(2)
 #pragma omp parallel for collapse(2)
   for (index_t n = 0; n < batch; ++n) {
     for (index_t c = 0; c < channels; ++c) {
@@ -341,8 +348,8 @@ void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
         float *output_ptr = channel_output_start;
         // The begining of each input feature map channel
         MACE_ASSERT(input_ptr ==
-            input + n * input_channels * input_height * input_width +
-                inc * input_height * input_width);
+                    input + n * input_channels * input_height * input_width +
+                        inc * input_height * input_width);
 
         const float *input_ptr1 = input_ptr + total_pixels;
         const float *input_ptr2 = input_ptr1 + total_pixels;
@@ -426,8 +433,8 @@ void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
       for (; inc < input_channels; ++inc) {
         float *output_ptr = channel_output_start;
         MACE_ASSERT(input_ptr ==
-            input + n * input_channels * input_height * input_width +
-                inc * input_height * input_width);
+                    input + n * input_channels * input_height * input_width +
+                        inc * input_height * input_width);
         MACE_ASSERT(filter_ptr == filter + c * input_channels + inc);
 
         const float k0 = filter_ptr[0];

mace/kernels/neon/conv_2d_neon_3x3.cc

@@ -11,44 +11,49 @@ namespace kernels {
 static const int kRegisterSize = 4;
 static const int kFilterSize = 9;
 
-void Conv2dNeonK3x3S1(const float *input, // NCHW
+void Conv2dNeonK3x3S1(const float *input,  // NCHW
                       const index_t *input_shape,
-                      const float *filter, // c_out, c_in, kernel_h, kernel_w
+                      const float *filter,  // c_out, c_in, kernel_h, kernel_w
                       const index_t *filter_shape,
-                      const float *bias, // c_out
-                      float *output, // NCHW
+                      const float *bias,  // c_out
+                      float *output,      // NCHW
                       const index_t *output_shape) {
-
   int height_count = (output_shape[2] >> 1) << 1;
 
-  int output_batch    = output_shape[0];
+  int output_batch = output_shape[0];
   int output_channels = output_shape[1];
-  int output_height   = output_shape[2];
-  int output_width    = output_shape[3];
-  int input_batch    = input_shape[0];
+  int output_height = output_shape[2];
+  int output_width = output_shape[3];
+  int input_batch = input_shape[0];
   int input_channels = input_shape[1];
-  int input_height   = input_shape[2];
-  int input_width    = input_shape[3];
-  int multiplier     = filter_shape == nullptr ? 0 : (filter_shape[0] / input_channels);
-  int filter_in_channels = filter_shape == nullptr ? input_channels : filter_shape[1];
+  int input_height = input_shape[2];
+  int input_width = input_shape[3];
+  int multiplier =
+      filter_shape == nullptr ? 0 : (filter_shape[0] / input_channels);
+  int filter_in_channels =
+      filter_shape == nullptr ? input_channels : filter_shape[1];
 #pragma omp parallel for collapse(2)
   for (int b = 0; b < output_batch; ++b) {
     for (int oc = 0; oc < output_channels; ++oc) {
-      float *output_ptr_base = output + b * output_channels * output_height * output_width;
+      float *output_ptr_base =
+          output + b * output_channels * output_height * output_width;
       const float *filter_ptr = filter + oc * filter_in_channels * kFilterSize;
-      const float *input_ptr = input + b * input_channels * input_height * input_width;
+      const float *input_ptr =
+          input + b * input_channels * input_height * input_width;
       if (filter_shape != nullptr) {
         input_ptr += (oc / multiplier) * input_height * input_width;
       }
       float *output_ptr = output_ptr_base + oc * output_height * output_width;
-      std::fill(output_ptr, output_ptr + output_height * output_width, bias ? bias[oc] : 0);
+      std::fill(output_ptr, output_ptr + output_height * output_width,
+                bias ? bias[oc] : 0);
       for (int ic = 0; ic < filter_in_channels; ++ic) {
-        float32x4_t n_filter_v[3] = {vld1q_f32(filter_ptr), vld1q_f32(filter_ptr+3), vld1q_f32(filter_ptr+6)};
+        float32x4_t n_filter_v[3] = {vld1q_f32(filter_ptr),
+                                     vld1q_f32(filter_ptr + 3),
+                                     vld1q_f32(filter_ptr + 6)};
 
         const float *row_ptr_v[kRegisterSize] = {
-            input_ptr, input_ptr + input_width,
-            input_ptr + 2 * input_width, input_ptr + 3 * input_width
-        };
+            input_ptr, input_ptr + input_width, input_ptr + 2 * input_width,
+            input_ptr + 3 * input_width};
 
         float *output_ptr_v[] = {output_ptr, output_ptr + output_width};
 
@@ -69,8 +74,10 @@ void Conv2dNeonK3x3S1(const float *input, // NCHW
 
             float32x4_t n_row1_former = vld1q_f32(row_ptr_v[1]);
             float32x4_t n_row1_latter = vld1q_f32(row_ptr_v[1] + kRegisterSize);
-            float32x4_t n_row1_ext0 = vextq_f32(n_row1_former, n_row1_latter, 1);
-            float32x4_t n_row1_ext1 = vextq_f32(n_row1_former, n_row1_latter, 2);
+            float32x4_t n_row1_ext0 =
+                vextq_f32(n_row1_former, n_row1_latter, 1);
+            float32x4_t n_row1_ext1 =
+                vextq_f32(n_row1_former, n_row1_latter, 2);
             n_sum0 = vfmaq_laneq_f32(n_sum0, n_row1_former, n_filter_v[1], 0);
             n_sum0 = vfmaq_laneq_f32(n_sum0, n_row1_ext0, n_filter_v[1], 1);
             n_sum0 = vfmaq_laneq_f32(n_sum0, n_row1_ext1, n_filter_v[1], 2);
@@ -115,11 +122,9 @@ void Conv2dNeonK3x3S1(const float *input, // NCHW
             }
           }
           for (; remain_count > 0; --remain_count) {
-            float32x4_t n_row_v[] = {
-                vld1q_f32(row_ptr_v[0]),
-                vld1q_f32(row_ptr_v[1]),
-                vld1q_f32(row_ptr_v[2])
-            };
+            float32x4_t n_row_v[] = {vld1q_f32(row_ptr_v[0]),
+                                     vld1q_f32(row_ptr_v[1]),
+                                     vld1q_f32(row_ptr_v[2])};
             float32x4_t n_sum0 = vmulq_f32(n_row_v[0], n_filter_v[0]);
             n_sum0 = vmlaq_f32(n_sum0, n_row_v[1], n_filter_v[1]);
             n_sum0 = vmlaq_f32(n_sum0, n_row_v[2], n_filter_v[2]);
@@ -185,8 +190,7 @@ void Conv2dNeonK3x3S1(const float *input, // NCHW
           }
           for (; remain_count > 0; --remain_count) {
             float32x4_t n_row_v[] = {
-                vld1q_f32(row_ptr_v[0]),
-                vld1q_f32(row_ptr_v[1]),
+                vld1q_f32(row_ptr_v[0]), vld1q_f32(row_ptr_v[1]),
                 vld1q_f32(row_ptr_v[2]),
             };
 
@@ -210,43 +214,49 @@ void Conv2dNeonK3x3S1(const float *input, // NCHW
   }
 }
 
-void Conv2dNeonK3x3S2(const float *input, // NCHW
+void Conv2dNeonK3x3S2(const float *input,  // NCHW
                       const index_t *input_shape,
-                      const float *filter, // c_out, c_in, kernel_h, kernel_w
+                      const float *filter,  // c_out, c_in, kernel_h, kernel_w
                       const index_t *filter_shape,
-                      const float *bias, // c_out
-                      float *output, // NCHW
+                      const float *bias,  // c_out
+                      float *output,      // NCHW
                       const index_t *output_shape) {
   int tail_step = 2 * (input_shape[3] - output_shape[3]);
 
-  int output_batch    = output_shape[0];
+  int output_batch = output_shape[0];
   int output_channels = output_shape[1];
-  int output_height   = output_shape[2];
-  int output_width    = output_shape[3];
-  int input_batch    = input_shape[0];
+  int output_height = output_shape[2];
+  int output_width = output_shape[3];
+  int input_batch = input_shape[0];
   int input_channels = input_shape[1];
-  int input_height   = input_shape[2];
-  int input_width    = input_shape[3];
-  int multiplier     = filter_shape == nullptr ? 0 : (filter_shape[0] / input_channels);
-  int filter_in_channels = filter_shape == nullptr ? input_channels : filter_shape[1];
+  int input_height = input_shape[2];
+  int input_width = input_shape[3];
+  int multiplier =
+      filter_shape == nullptr ? 0 : (filter_shape[0] / input_channels);
+  int filter_in_channels =
+      filter_shape == nullptr ? input_channels : filter_shape[1];
 
 #pragma omp parallel for collapse(2)
   for (int b = 0; b < output_batch; ++b) {
     for (int oc = 0; oc < output_channels; ++oc) {
-      float *output_ptr_base = output + b * output_channels * output_height * output_width;
+      float *output_ptr_base =
+          output + b * output_channels * output_height * output_width;
       const float *filter_ptr = filter + oc * filter_in_channels * kFilterSize;
-      const float *input_ptr = input + b * input_channels * input_height * input_width;
+      const float *input_ptr =
+          input + b * input_channels * input_height * input_width;
       if (filter_shape != nullptr) {
         input_ptr += (oc / multiplier) * input_height * input_width;
       }
       float *output_ptr = output_ptr_base + oc * output_height * output_width;
-      std::fill(output_ptr, output_ptr + output_height * output_width, bias ? bias[oc] : 0);
+      std::fill(output_ptr, output_ptr + output_height * output_width,
+                bias ? bias[oc] : 0);
       for (int ic = 0; ic < filter_in_channels; ++ic) {
-        float32x4_t n_filter_v[3] = {vld1q_f32(filter_ptr), vld1q_f32(filter_ptr+3), vld1q_f32(filter_ptr+6)};
+        float32x4_t n_filter_v[3] = {vld1q_f32(filter_ptr),
+                                     vld1q_f32(filter_ptr + 3),
+                                     vld1q_f32(filter_ptr + 6)};
 
-        const float *row_ptr_v[3] = {
-            input_ptr, input_ptr + input_width, input_ptr + 2 * input_width
-        };
+        const float *row_ptr_v[3] = {input_ptr, input_ptr + input_width,
+                                     input_ptr + 2 * input_width};
 
         float *output_ptr_inner = output_ptr;
 
@@ -259,24 +269,33 @@ void Conv2dNeonK3x3S2(const float *input, // NCHW
 
             float32x4x2_t n_row_former = vld2q_f32(row_ptr_v[0]);
             float32x4_t n_row_latter = vld1q_f32(row_ptr_v[0] + 8);
-            float32x4_t n_row_ext = vextq_f32(n_row_former.val[0], n_row_latter, 1);
+            float32x4_t n_row_ext =
+                vextq_f32(n_row_former.val[0], n_row_latter, 1);
 
-            n_sum = vfmaq_laneq_f32(n_sum, n_row_former.val[0], n_filter_v[0], 0);
-            n_sum = vfmaq_laneq_f32(n_sum, n_row_former.val[1], n_filter_v[0], 1);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row_former.val[0], n_filter_v[0], 0);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row_former.val[1], n_filter_v[0], 1);
             n_sum = vfmaq_laneq_f32(n_sum, n_row_ext, n_filter_v[0], 2);
 
             float32x4x2_t n_row1_former = vld2q_f32(row_ptr_v[1]);
             float32x4_t n_row1_latter = vld1q_f32(row_ptr_v[1] + 8);
-            float32x4_t n_row1_ext = vextq_f32(n_row1_former.val[0], n_row1_latter, 1);
-            n_sum = vfmaq_laneq_f32(n_sum, n_row1_former.val[0], n_filter_v[1], 0);
-            n_sum = vfmaq_laneq_f32(n_sum, n_row1_former.val[1], n_filter_v[1], 1);
+            float32x4_t n_row1_ext =
+                vextq_f32(n_row1_former.val[0], n_row1_latter, 1);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row1_former.val[0], n_filter_v[1], 0);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row1_former.val[1], n_filter_v[1], 1);
             n_sum = vfmaq_laneq_f32(n_sum, n_row1_ext, n_filter_v[1], 2);
 
             float32x4x2_t n_row2_former = vld2q_f32(row_ptr_v[2]);
             float32x4_t n_row2_latter = vld1q_f32(row_ptr_v[2] + 8);
-            float32x4_t n_row2_ext = vextq_f32(n_row2_former.val[0], n_row2_latter, 1);
-            n_sum = vfmaq_laneq_f32(n_sum, n_row2_former.val[0], n_filter_v[2], 0);
-            n_sum = vfmaq_laneq_f32(n_sum, n_row2_former.val[1], n_filter_v[2], 1);
+            float32x4_t n_row2_ext =
+                vextq_f32(n_row2_former.val[0], n_row2_latter, 1);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row2_former.val[0], n_filter_v[2], 0);
+            n_sum =
+                vfmaq_laneq_f32(n_sum, n_row2_former.val[1], n_filter_v[2], 1);
             n_sum = vfmaq_laneq_f32(n_sum, n_row2_ext, n_filter_v[2], 2);
 
             float32x4_t n_output_row = vld1q_f32(output_ptr_inner);
@@ -288,11 +307,9 @@ void Conv2dNeonK3x3S2(const float *input, // NCHW
             }
           }
           for (; remain_count > 0; --remain_count) {
-            float32x4_t n_row_v[] = {
-                vld1q_f32(row_ptr_v[0]),
-                vld1q_f32(row_ptr_v[1]),
-                vld1q_f32(row_ptr_v[2])
-            };
+            float32x4_t n_row_v[] = {vld1q_f32(row_ptr_v[0]),
+                                     vld1q_f32(row_ptr_v[1]),
+                                     vld1q_f32(row_ptr_v[2])};
             float32x4_t n_sum = vmulq_f32(n_row_v[0], n_filter_v[0]);
             n_sum = vmlaq_f32(n_sum, n_row_v[1], n_filter_v[1]);
             n_sum = vmlaq_f32(n_sum, n_row_v[2], n_filter_v[2]);
@@ -315,5 +332,5 @@ void Conv2dNeonK3x3S2(const float *input, // NCHW
     }
   }
 }
-} //  namespace kernels
-} //  namespace mace
+}  //  namespace kernels
+}  //  namespace mace

mace/kernels/neon/conv_2d_neon_5x5.cc

@@ -14,8 +14,8 @@ void Conv2dNeonK5x5S1(const float *input,  // NCHW
                       const index_t *input_shape,
                       const float *filter,  // c_out, c_in, kernel_h, kernel_w
                       const index_t *filter_shape,
-                      const float *bias,    // c_out
-                      float *output,        // NCHW
+                      const float *bias,  // c_out
+                      float *output,      // NCHW
                       const index_t *output_shape) {
   const index_t batch = output_shape[0];
   const index_t channels = output_shape[1];
@@ -41,7 +41,7 @@ void Conv2dNeonK5x5S1(const float *input,  // NCHW
   for (index_t n = 0; n < batch; ++n) {
     for (index_t c = 0; c < channels; ++c) {
       float *output_ptr = output + n * output_total_pixels_per_batch +
-          c * output_total_pixels_per_channel;
+                          c * output_total_pixels_per_channel;
       const float *input_ptr = input + n * input_total_pixels_per_batch;
 
       // Fill with bias

mace/kernels/neon/depthwise_conv_neon.cc

@@ -2,8 +2,8 @@
 // Copyright (c) 2017 XiaoMi All rights reserved.
 //
 
-#include "mace/kernels/depthwise_conv2d.h"
 #include "mace/kernels/conv_2d.h"
+#include "mace/kernels/depthwise_conv2d.h"
 
 namespace mace {
 namespace kernels {
@@ -24,21 +24,18 @@ extern void Conv2dNeonK3x3S2(const float *input,
                              float *output,
                              const index_t *output_shape);
 
-template<>
-void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(const float *input, // NCHW
-                                                                 const index_t *input_shape,
-                                                                 const float *filter, // c_out, c_in, kernel_h, kernel_w
-                                                                 const index_t *filter_shape,
-                                                                 const float *bias, // c_out
-                                                                 float *output, // NCHW
-                                                                 const index_t *output_shape) {
+template <>
+void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(
+    const float *input,  // NCHW
+    const index_t *input_shape,
+    const float *filter,  // c_out, c_in, kernel_h, kernel_w
+    const index_t *filter_shape,
+    const float *bias,  // c_out
+    float *output,      // NCHW
+    const index_t *output_shape) {
   typedef void (*Conv2dNeonFunction)(
-      const float *input,
-      const index_t *input_shape,
-      const float *filter,
-      const index_t *filter_shape,
-      const float *bias,
-      float *output,
+      const float *input, const index_t *input_shape, const float *filter,
+      const index_t *filter_shape, const float *bias, float *output,
       const index_t *output_shape);
   // Selection matrix: kernel_size x stride_size
   static const Conv2dNeonFunction selector[5][2] = {
@@ -57,7 +54,8 @@ void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(const float *in
                  << "filter" << kernel_h << "x" << kernel_w << ","
                  << " stride " << strides_[0] << "x" << strides_[1]
                  << " is not implemented yet, using slow version";
-    DepthwiseConv2dFunctor<DeviceType::CPU, float>(strides_, paddings_, dilations_)(
+    DepthwiseConv2dFunctor<DeviceType::CPU, float>(strides_, paddings_,
+                                                   dilations_)(
         input, input_shape, filter, filter_shape, bias, output, output_shape);
     return;
   }
@@ -65,13 +63,15 @@ void DepthwiseConv2dFunctor<DeviceType::NEON, float>::operator()(const float *in
   // Keep this alive during kernel execution
   Tensor padded_input;
   if (paddings_[0] > 0 || paddings_[1] > 0) {
-    ConstructInputWithPadding(input, input_shape, paddings_.data(), &padded_input);
+    ConstructInputWithPadding(input, input_shape, paddings_.data(),
+                              &padded_input);
     input = padded_input.data<float>();
     input_shape = padded_input.shape().data();
   }
   auto conv2d_neon_func = selector[kernel_h - 1][strides_[0] - 1];
-  conv2d_neon_func(input, input_shape, filter, filter_shape, bias, output, output_shape);
+  conv2d_neon_func(input, input_shape, filter, filter_shape, bias, output,
+                   output_shape);
 }
 
-} //  namespace kernels
-} //  namespace mace
\ No newline at end of file
+}  //  namespace kernels
+}  //  namespace mace
\ No newline at end of file

mace/kernels/neon/global_avg_pooling_neon.cc

@@ -8,11 +8,9 @@
 namespace mace {
 namespace kernels {
 
-template<>
+template <>
 void GlobalAvgPoolingFunctor<DeviceType::NEON, float>::operator()(
-    const float *input,
-    const index_t *input_shape,
-    float *output) {
+    const float *input, const index_t *input_shape, float *output) {
   index_t batch = input_shape[0];
   index_t channels = input_shape[1];
   index_t height = input_shape[2];

mace/kernels/neon/pooling_neon.cc

@@ -55,7 +55,7 @@ extern void PoolingAvgNeonK3x3S2x2Padded(const float *input,
                                          const index_t *out_shape);
 #endif
 
-template<>
+template <>
 void PoolingFunctor<DeviceType::NEON, float>::operator()(
     const float *input,
     const index_t *input_shape,
@@ -71,14 +71,14 @@ void PoolingFunctor<DeviceType::NEON, float>::operator()(
   if (kernels_[0] == 2 && kernels_[1] == 2 && strides_[0] == 2 &&
       strides_[1] == 2) {
     // kernel_size: 2x2, strides: 2x2
-    if (pooling_type_ == MAX) { // MAX_POOL_2x2s2x2
+    if (pooling_type_ == MAX) {  // MAX_POOL_2x2s2x2
 #ifdef __COPY_MAKE_PADDING
       PoolingMaxNeonK2x2S2x2Padded(input, input_shape, output, output_shape);
 #else
       PoolingMaxNeonK2x2S2x2(input, input_shape, output, output_shape,
                              paddings_);
 #endif
-    } else { // AVG_POOL_2x2s2x2
+    } else {  // AVG_POOL_2x2s2x2
 #ifdef __COPY_MAKE_PADDING
       PoolingAvgNeonK2x2S2x2Padded(input, input_shape, output, output_shape);
 #else
@@ -87,16 +87,16 @@ void PoolingFunctor<DeviceType::NEON, float>::operator()(
 #endif
     }
   } else if (kernels_[0] == 3 && kernels_[1] == 3 && strides_[0] == 2 &&
-      strides_[1] == 2) {
+             strides_[1] == 2) {
     // kernel_size: 3x3, strides: 2x2
-    if (pooling_type_ == MAX) { // MAX_POOL_3x3s2x2
+    if (pooling_type_ == MAX) {  // MAX_POOL_3x3s2x2
 #ifdef __COPY_MAKE_PADDING
       PoolingMaxNeonK3x3S2x2Padded(input, input_shape, output, output_shape);
 #else
       PoolingMaxNeonK3x3S2x2(input, input_shape, output, output_shape,
                              paddings_);
 #endif
-    } else { // AVG_POOL_3x3s2x2
+    } else {  // AVG_POOL_3x3s2x2
 #ifdef __COPY_MAKE_PADDING
       PoolingAvgNeonK3x3S2x2Padded(input, input_shape, output, output_shape);
 #else

mace/ops/addn.h

@@ -13,18 +13,18 @@ namespace mace {
 template <DeviceType D, class T>
 class AddNOp : public Operator<D, T> {
  public:
-  AddNOp(const OperatorDef& operator_def, Workspace* ws)
+  AddNOp(const OperatorDef &operator_def, Workspace *ws)
       : Operator<D, T>(operator_def, ws) {}
 
   bool Run() override {
-    Tensor* output_tensor = this->outputs_[0];
+    Tensor *output_tensor = this->outputs_[0];
     output_tensor->ResizeLike(this->inputs_[0]);
-    T* output = output_tensor->mutable_data<T>();
+    T *output = output_tensor->mutable_data<T>();
     index_t size = this->inputs_[0]->size();
     int n = this->inputs_.size();
-    vector<const T*> inputs(n);
+    vector<const T *> inputs(n);
     for (int i = 0; i < n; ++i) {
-      const Tensor* input_tensor = this->inputs_[i];
+      const Tensor *input_tensor = this->inputs_[i];
       inputs[i] = input_tensor->data<T>();
     }
 

mace/ops/addn_benchmark.cc

@@ -39,7 +39,7 @@ static void AddNBenchmark(int iters, int n, int size) {
   static void BM_ADDN_##N##_##SIZE##_##TYPE##_##DEVICE(int iters) { \
     const int64_t tot = static_cast<int64_t>(iters) * N * SIZE;     \
     mace::testing::ItemsProcessed(tot);                             \
-    mace::testing::BytesProcessed(tot*(sizeof(TYPE)));              \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));             \
     AddNBenchmark<DEVICE, TYPE>(iters, N, SIZE);                    \
   }                                                                 \
   BENCHMARK(BM_ADDN_##N##_##SIZE##_##TYPE##_##DEVICE)

mace/ops/addn_test.cc

@@ -11,7 +11,7 @@ class AddnOpTest : public OpsTestBase {};
 
 TEST_F(AddnOpTest, AddnOp) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("AddN", "AddNTest")
       .Input("Input1")
       .Input("Input2")

mace/ops/batch_norm.h

@@ -13,17 +13,16 @@ namespace mace {
 template <DeviceType D, class T>
 class BatchNormOp : public Operator<D, T> {
  public:
-  BatchNormOp(const OperatorDef& operator_def, Workspace* ws)
-      : Operator<D, T>(operator_def, ws),
-        functor_() {}
+  BatchNormOp(const OperatorDef &operator_def, Workspace *ws)
+      : Operator<D, T>(operator_def, ws), functor_() {}
 
   bool Run() override {
-    const Tensor* input = this->Input(0);
-    const Tensor* scale = this->Input(1);
-    const Tensor* offset = this->Input(2);
-    const Tensor* mean = this->Input(3);
-    const Tensor* var = this->Input(4);
-    const Tensor* epsilon = this->Input(5);
+    const Tensor *input = this->Input(0);
+    const Tensor *scale = this->Input(1);
+    const Tensor *offset = this->Input(2);
+    const Tensor *mean = this->Input(3);
+    const Tensor *var = this->Input(4);
+    const Tensor *epsilon = this->Input(5);
 
     MACE_CHECK(input->dim_size() == 4, "input must be 4-dimensional. ",
                input->dim_size());
@@ -38,23 +37,23 @@ class BatchNormOp : public Operator<D, T> {
     MACE_CHECK(epsilon->dim_size() == 0, "epsilon must be 0-dimensional. ",
                epsilon->dim_size());
 
-    Tensor* output = this->Output(0);
+    Tensor *output = this->Output(0);
     output->ResizeLike(input);
 
     const index_t n = input->dim(0);
     const index_t channel = input->dim(1);
     const index_t sample_size = input->dim(2) * input->dim(3);
 
-    const T* input_ptr = input->data<T>();
-    const T* scale_ptr = scale->data<T>();
-    const T* offset_ptr = offset->data<T>();
-    const T* mean_ptr = mean->data<T>();
-    const T* var_ptr = var->data<T>();
-    const T* epsilon_ptr = epsilon->data<T>();
-    T* output_ptr = output->mutable_data<T>();
+    const T *input_ptr = input->data<T>();
+    const T *scale_ptr = scale->data<T>();
+    const T *offset_ptr = offset->data<T>();
+    const T *mean_ptr = mean->data<T>();
+    const T *var_ptr = var->data<T>();
+    const T *epsilon_ptr = epsilon->data<T>();
+    T *output_ptr = output->mutable_data<T>();
 
-    functor_(input_ptr, scale_ptr, offset_ptr, mean_ptr, var_ptr, *epsilon_ptr, n, channel,
-             sample_size, output_ptr);
+    functor_(input_ptr, scale_ptr, offset_ptr, mean_ptr, var_ptr, *epsilon_ptr,
+             n, channel, sample_size, output_ptr);
     return true;
   }
 

mace/ops/batch_norm_benchmark.cc

@@ -47,7 +47,7 @@ static void BatchNorm(
       int iters) {                                                     \
     const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;   \
     mace::testing::ItemsProcessed(tot);                                \
-    mace::testing::BytesProcessed(tot*(sizeof(TYPE)));                 \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                \
     BatchNorm<DEVICE, TYPE>(iters, N, C, H, W);                        \
   }                                                                    \
   BENCHMARK(BM_BATCH_NORM_##N##_##C##_##H##_##W##_##TYPE##_##DEVICE)

mace/ops/batch_norm_test.cc

@@ -11,7 +11,7 @@ class BatchNormOpTest : public OpsTestBase {};
 
 TEST_F(BatchNormOpTest, SimpleCPU) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("BatchNorm", "BatchNormTest")
       .Input("Input")
       .Input("Scale")
@@ -51,7 +51,7 @@ TEST_F(BatchNormOpTest, SimpleNeon) {
   index_t height = 103;
   index_t width = 113;
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("BatchNorm", "BatchNormTest")
       .Input("Input")
       .Input("Scale")
@@ -74,7 +74,7 @@ TEST_F(BatchNormOpTest, SimpleNeon) {
   net.RunOp();
 
   // Check
-  Tensor* expected = net.GetOutput("Output");
+  Tensor *expected = net.GetOutput("Output");
 
   // Run NEON
   net.RunOp(DeviceType::NEON);

mace/ops/channel_shuffle.h

@@ -12,10 +12,10 @@
 
 namespace mace {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 class ChannelShuffleOp : public Operator<D, T> {
  public:
-  ChannelShuffleOp(const OperatorDef& operator_def, Workspace* ws)
+  ChannelShuffleOp(const OperatorDef &operator_def, Workspace *ws)
       : Operator<D, T>(operator_def, ws),
         group_(OperatorBase::GetSingleArgument<int>("group", 1)),
         functor_(this->group_) {}

mace/ops/channel_shuffle_benchmark.cc

@@ -11,12 +11,8 @@ using namespace mace;
 using namespace mace::kernels;
 
 template <DeviceType D>
-static void ChannelShuffle(int iters,
-                           int batch,
-                           int channels,
-                           int height,
-                           int width,
-                           int group) {
+static void ChannelShuffle(
+    int iters, int batch, int channels, int height, int width, int group) {
   mace::testing::StopTiming();
 
   OpsTestNet net;
@@ -40,18 +36,17 @@ static void ChannelShuffle(int iters,
   }
 }
 
-#define BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, DEVICE)                    \
-  static void                                                                       \
-      BM_CHANNEL_SHUFFLE_##N##_##C##_##H##_##W##_##G##_##DEVICE( \
-          int iters) {                                                              \
-    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                \
-    mace::testing::ItemsProcessed(tot);                                             \
-    mace::testing::BytesProcessed(tot*(sizeof(float)));                             \
-    ChannelShuffle<DEVICE>(iters, N, C, H, W, G);                                               \
-  }                                                                                 \
+#define BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, DEVICE)                  \
+  static void BM_CHANNEL_SHUFFLE_##N##_##C##_##H##_##W##_##G##_##DEVICE( \
+      int iters) {                                                       \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;     \
+    mace::testing::ItemsProcessed(tot);                                  \
+    mace::testing::BytesProcessed(tot *(sizeof(float)));                 \
+    ChannelShuffle<DEVICE>(iters, N, C, H, W, G);                        \
+  }                                                                      \
   BENCHMARK(BM_CHANNEL_SHUFFLE_##N##_##C##_##H##_##W##_##G##_##DEVICE)
 
-#define BM_CHANNEL_SHUFFLE(N, C, H, W, G)       \
+#define BM_CHANNEL_SHUFFLE(N, C, H, W, G) \
   BM_CHANNEL_SHUFFLE_MACRO(N, C, H, W, G, CPU);
 
 BM_CHANNEL_SHUFFLE(1, 64, 64, 64, 8);

mace/ops/channel_shuffle_test.cc

@@ -10,7 +10,7 @@ class ChannelShuffleOpTest : public OpsTestBase {};
 
 TEST_F(ChannelShuffleOpTest, C8G4) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("ChannelShuffle", "ChannelShuffleTest")
       .Input("Input")
       .Output("Output")
@@ -20,18 +20,15 @@ TEST_F(ChannelShuffleOpTest, C8G4) {
 
   // Add input data
   net.AddInputFromArray<float>(
-      "Input", {1, 8, 1, 2}, 
-      {0, 1, 2, 3, 4, 5, 6, 7, 
-       8, 9, 10, 11, 12, 13, 14, 15});
+      "Input", {1, 8, 1, 2},
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});
 
   // Run
   net.RunOp();
 
   // Check
-  auto expected =
-      CreateTensor<float>({1, 8, 1, 2}, 
-                          {0, 1, 4, 5, 8, 9, 12, 13, 
-                           2, 3, 6, 7, 10, 11, 14, 15});
+  auto expected = CreateTensor<float>(
+      {1, 8, 1, 2}, {0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15});
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
 }

mace/ops/concat.h

@@ -5,12 +5,12 @@
 #ifndef MACE_OPS_CONCAT_H_
 #define MACE_OPS_CONCAT_H_
 
-#include "mace/proto/mace.pb.h"
 #include "mace/core/operator.h"
 #include "mace/kernels/concat.h"
+#include "mace/proto/mace.pb.h"
 namespace mace {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 class ConcatOp : public Operator<D, T> {
  public:
   ConcatOp(const OperatorDef &op_def, Workspace *ws)
@@ -25,9 +25,11 @@ class ConcatOp : public Operator<D, T> {
                axis_tensor->dim_size());
     const int32_t concat_axis = *(axis_tensor->data<int32_t>());
     const int32_t input_dims = input0->dim_size();
-    const int32_t axis = concat_axis < 0 ? concat_axis + input_dims : concat_axis;
-    MACE_CHECK((0 <= axis && axis < input_dims), "Expected concatenating axis in the range [",
-               -input_dims, ", ", input_dims, "], but got", concat_axis);
+    const int32_t axis =
+        concat_axis < 0 ? concat_axis + input_dims : concat_axis;
+    MACE_CHECK((0 <= axis && axis < input_dims),
+               "Expected concatenating axis in the range [", -input_dims, ", ",
+               input_dims, "], but got", concat_axis);
     std::vector<index_t> output_shape(input0->shape());
     index_t inner_size = 1;
     for (int i = 0; i < axis; ++i) {
@@ -40,10 +42,14 @@ class ConcatOp : public Operator<D, T> {
     const Tensor *input = nullptr;
     for (int i = 1; i < values_count; ++i) {
       input = this->Input(i);
-      MACE_CHECK(input->dim_size() == input0->dim_size(), "Ranks of all input tensors must be same.");
+      MACE_CHECK(input->dim_size() == input0->dim_size(),
+                 "Ranks of all input tensors must be same.");
       for (int j = 0; j < axis_tensor->dim_size(); ++j) {
-        if (j == axis) { continue; }
-        MACE_CHECK(input->dim(j) == input0->dim(j), "Dimensions of inputs should equal except axis.");
+        if (j == axis) {
+          continue;
+        }
+        MACE_CHECK(input->dim(j) == input0->dim(j),
+                   "Dimensions of inputs should equal except axis.");
       }
       input_list[i] = input->data<T>();
       outer_sizes[i] = input->size() / inner_size;
@@ -53,9 +59,11 @@ class ConcatOp : public Operator<D, T> {
     Tensor *output = this->Output(OUTPUT);
     output->Resize(output_shape);
 
-    functor_(input_list, inner_size, outer_sizes.data(), output->mutable_data<T>());
+    functor_(input_list, inner_size, outer_sizes.data(),
+             output->mutable_data<T>());
     return true;
   }
+
  private:
   kernels::ConcatFunctor<D, T> functor_;
 
@@ -63,6 +71,6 @@ class ConcatOp : public Operator<D, T> {
   OP_OUTPUT_TAGS(OUTPUT);
 };
 
-} //  namespace mace
+}  //  namespace mace
 
-#endif //  MACE_OPS_CONCAT_H_
+#endif  //  MACE_OPS_CONCAT_H_

mace/ops/concat_benchmark.cc

@@ -7,9 +7,8 @@
 #include "mace/ops/ops_test_util.h"
 
 namespace mace {
-template<DeviceType D, typename T>
-static void ConcatHelper(
-    int iters, int concat_dim, int dim1) {
+template <DeviceType D, typename T>
+static void ConcatHelper(int iters, int concat_dim, int dim1) {
   mace::testing::StopTiming();
 
   OpsTestNet net;

mace/ops/concat_test.cc

@@ -3,8 +3,8 @@
 //
 
 #include "mace/ops/concat.h"
-#include "mace/ops/ops_test_util.h"
 #include "gmock/gmock.h"
+#include "mace/ops/ops_test_util.h"
 
 using namespace mace;
 
@@ -99,9 +99,7 @@ TEST_F(ConcatOpTest, Random) {
   for (int i = 0; i < num_inputs; ++i) {
     builder = builder.Input(("Input" + ToString(i)).c_str());
   }
-  builder.Input("Axis")
-      .Output("Output")
-      .Finalize(net.operator_def());
+  builder.Input("Axis").Output("Output").Finalize(net.operator_def());
 
   std::vector<index_t> shape_data;
   GenerateRandomIntTypeData<index_t>({dim}, shape_data, 1, dim);
@@ -114,7 +112,8 @@ TEST_F(ConcatOpTest, Random) {
     concat_axis_size += input_shapes[i][axis];
     GenerateRandomRealTypeData(input_shapes[i], inputs[i]);
     input_ptrs[i] = inputs[i].data();
-    net.AddInputFromArray<float>(("Input" + ToString(i)).c_str(), input_shapes[i], inputs[i]);
+    net.AddInputFromArray<float>(("Input" + ToString(i)).c_str(),
+                                 input_shapes[i], inputs[i]);
   }
   net.AddInputFromArray<int>("Axis", {}, {axis});
 
@@ -131,9 +130,9 @@ TEST_F(ConcatOpTest, Random) {
   const float *output_ptr = output->data<float>();
   while (output_ptr != (output->data<float>() + output->size())) {
     for (int i = 0; i < num_inputs; ++i) {
-      index_t num_elements = std::accumulate(input_shapes[i].begin() + axis,
-                                             input_shapes[i].end(), 1,
-                                             std::multiplies<index_t>());
+      index_t num_elements =
+          std::accumulate(input_shapes[i].begin() + axis, input_shapes[i].end(),
+                          1, std::multiplies<index_t>());
       for (int j = 0; j < num_elements; ++j) {
         EXPECT_EQ(*input_ptrs[i]++, *output_ptr++);
       }

mace/ops/conv_2d.h

@@ -13,7 +13,7 @@
 
 namespace mace {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 class Conv2dOp : public ConvPool2dOpBase<D, T> {
  public:
   Conv2dOp(const OperatorDef &op_def, Workspace *ws)
@@ -35,11 +35,10 @@ class Conv2dOp : public ConvPool2dOpBase<D, T> {
 
     std::vector<index_t> output_shape(4);
     std::vector<int> paddings(2);
-    kernels::CalcPaddingAndOutputSize(input->shape().data(),
-                                      filter->shape().data(),
-                                      this->dilations_.data(),
-                                      this->strides_.data(), this->padding_,
-                                      output_shape.data(), paddings.data());
+    kernels::CalcPaddingAndOutputSize(
+        input->shape().data(), filter->shape().data(), this->dilations_.data(),
+        this->strides_.data(), this->padding_, output_shape.data(),
+        paddings.data());
     output->Resize(output_shape);
     functor_.paddings_ = paddings;
 

mace/ops/conv_2d_benchmark.cc

@@ -54,16 +54,17 @@ static void Conv2d(int iters,
   }
 }
 
-#define BM_CONV_2D_MACRO(N, C, H, W, KH, KW, STRIDE, P, OC, TYPE, DEVICE)                        \
-  static void                                                                                    \
+#define BM_CONV_2D_MACRO(N, C, H, W, KH, KW, STRIDE, P, OC, TYPE, DEVICE)                          \
+  static void                                                                                      \
       BM_CONV_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE( \
-          int iters) {                                                                           \
-    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                             \
-    mace::testing::ItemsProcessed(tot);                                                          \
-    mace::testing::BytesProcessed(tot*(sizeof(TYPE)));                                           \
-    Conv2d<DEVICE, TYPE>(iters, N, C, H, W, KH, KW, STRIDE, mace::Padding::P, OC);               \
-  }                                                                                              \
-  BENCHMARK(                                                                                     \
+          int iters) {                                                                             \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                               \
+    mace::testing::ItemsProcessed(tot);                                                            \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                                            \
+    Conv2d<DEVICE, TYPE>(iters, N, C, H, W, KH, KW, STRIDE, mace::Padding::P,                      \
+                         OC);                                                                      \
+  }                                                                                                \
+  BENCHMARK(                                                                                       \
       BM_CONV_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE)
 
 #define BM_CONV_2D(N, C, H, W, KH, KW, S, P, OC, TYPE)       \

mace/ops/conv_2d_test.cc

@@ -12,7 +12,7 @@ class Conv2dOpTest : public OpsTestBase {};
 
 TEST_F(Conv2dOpTest, Simple_VALID) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Conv2D", "Conv2dTest")
       .Input("Input")
       .Input("Filter")
@@ -46,7 +46,7 @@ TEST_F(Conv2dOpTest, Simple_VALID) {
 
 TEST_F(Conv2dOpTest, Simple_SAME) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Conv2D", "Conv2dTest")
       .Input("Input")
       .Input("Filter")
@@ -82,7 +82,7 @@ TEST_F(Conv2dOpTest, Simple_SAME) {
 
 TEST_F(Conv2dOpTest, Combined) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Conv2D", "Conv2DTest")
       .Input("Input")
       .Input("Filter")
@@ -120,7 +120,7 @@ TEST_F(Conv2dOpTest, Combined) {
 
 TEST_F(Conv2dOpTest, Conv1x1) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Conv2D", "Conv2DTest")
       .Input("Input")
       .Input("Filter")
@@ -172,13 +172,13 @@ TEST_F(Conv2dOpTest, IdleConvNxNS12) {
     srand(time(NULL));
 
     // generate random input
-    index_t batch = 3 ;
+    index_t batch = 3;
     index_t input_channels = 64;
     index_t height = 32;
     index_t width = 32;
     index_t output_channels = 128;
     // Construct graph
-    auto& net = test_net();
+    auto &net = test_net();
     OpDefBuilder("Conv2D", "Conv2dTest")
         .Input("Input")
         .Input("Filter")
@@ -229,7 +229,7 @@ TEST_F(Conv2dOpTest, DisgustConvNxNS12) {
     index_t width = 113;
     index_t output_channels = 3 + rand() % 10;
     // Construct graph
-    auto& net = test_net();
+    auto &net = test_net();
     OpDefBuilder("Conv2D", "Conv2dTest")
         .Input("Input")
         .Input("Filter")

mace/ops/conv_pool_2d_base.h

@@ -13,13 +13,14 @@ namespace mace {
 template <DeviceType D, class T>
 class ConvPool2dOpBase : public Operator<D, T> {
  public:
-  ConvPool2dOpBase(const OperatorDef& op_def, Workspace* ws)
+  ConvPool2dOpBase(const OperatorDef &op_def, Workspace *ws)
       : Operator<D, T>(op_def, ws),
         strides_(OperatorBase::GetRepeatedArgument<int>("strides")),
         padding_(static_cast<Padding>(OperatorBase::GetSingleArgument<int>(
             "padding", static_cast<int>(SAME)))),
-        dilations_(OperatorBase::GetRepeatedArgument<int>("dilations", {1, 1})) {}
-  
+        dilations_(
+            OperatorBase::GetRepeatedArgument<int>("dilations", {1, 1})) {}
+
  protected:
   std::vector<int> strides_;
   Padding padding_;

mace/ops/depthwise_conv2d.cc

@@ -6,10 +6,12 @@
 
 namespace mace {
 
-REGISTER_CPU_OPERATOR(DepthwiseConv2d, DepthwiseConv2dOp<DeviceType::CPU, float>);
+REGISTER_CPU_OPERATOR(DepthwiseConv2d,
+                      DepthwiseConv2dOp<DeviceType::CPU, float>);
 
 #if __ARM_NEON
-REGISTER_NEON_OPERATOR(DepthwiseConv2d, DepthwiseConv2dOp<DeviceType::NEON, float>);
+REGISTER_NEON_OPERATOR(DepthwiseConv2d,
+                       DepthwiseConv2dOp<DeviceType::NEON, float>);
 #endif  // __ARM_NEON
 
 }  // namespace mace

mace/ops/depthwise_conv2d.h

@@ -9,12 +9,12 @@
 
 #include "mace/core/operator.h"
 #include "mace/kernels/conv_2d.h"
-#include "mace/ops/conv_pool_2d_base.h"
 #include "mace/kernels/depthwise_conv2d.h"
+#include "mace/ops/conv_pool_2d_base.h"
 
 namespace mace {
 
-template<DeviceType D, typename T>
+template <DeviceType D, typename T>
 class DepthwiseConv2dOp : public ConvPool2dOpBase<D, T> {
  public:
   DepthwiseConv2dOp(const OperatorDef &op_def, Workspace *ws)
@@ -34,16 +34,16 @@ class DepthwiseConv2dOp : public ConvPool2dOpBase<D, T> {
     Tensor *output = this->Output(OUTPUT);
 
     // resize filter shape.
-    std::vector<index_t> filter_shape(filter->shape().begin(), filter->shape().end());
+    std::vector<index_t> filter_shape(filter->shape().begin(),
+                                      filter->shape().end());
     filter_shape[0] *= filter_shape[1];
     filter_shape[1] = 1;
     std::vector<index_t> output_shape(4);
     std::vector<int> paddings(2);
-    kernels::CalcPaddingAndOutputSize(input->shape().data(),
-                                      filter_shape.data(),
-                                      this->dilations_.data(),
-                                      this->strides_.data(), this->padding_,
-                                      output_shape.data(), paddings.data());
+    kernels::CalcPaddingAndOutputSize(
+        input->shape().data(), filter_shape.data(), this->dilations_.data(),
+        this->strides_.data(), this->padding_, output_shape.data(),
+        paddings.data());
     output->Resize(output_shape);
     functor_.paddings_ = paddings;
 
@@ -62,6 +62,6 @@ class DepthwiseConv2dOp : public ConvPool2dOpBase<D, T> {
   OP_OUTPUT_TAGS(OUTPUT);
 };
 
-} //  namespace mace
+}  //  namespace mace
 
-#endif //  MACE_OPS_DEPTHWISE_CONV_H_
+#endif  //  MACE_OPS_DEPTHWISE_CONV_H_

mace/ops/depthwise_conv2d_test.cc

@@ -12,7 +12,7 @@ class DepthwiseConv2dOpTest : public OpsTestBase {};
 TEST_F(DepthwiseConv2dOpTest, Simple_VALID) {
   testing::internal::LogToStderr();
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
       .Input("Input")
       .Input("Filter")
@@ -26,23 +26,20 @@ TEST_F(DepthwiseConv2dOpTest, Simple_VALID) {
   net.AddIntsArg("dilations", {1, 1});
 
   // Add input data
-  net.AddInputFromArray<float>(
-      "Input", {1, 2, 2, 3},
-      {1, 3, 5, 7, 9, 11, 2, 4, 6, 8, 10, 12});
+  net.AddInputFromArray<float>("Input", {1, 2, 2, 3},
+                               {1, 3, 5, 7, 9, 11, 2, 4, 6, 8, 10, 12});
   net.AddInputFromArray<float>(
       "Filter", {2, 2, 2, 2},
-      {1.0f, 5.0f, 9.0f, 13.0f,
-       2.0f, 6.0f, 10.0f, 14.0f,
-       3.0f, 7.0f, 11.0f, 15.0f,
-       4.0f, 8.0f, 12.0f, 16.0f});
+      {1.0f, 5.0f, 9.0f, 13.0f, 2.0f, 6.0f, 10.0f, 14.0f, 3.0f, 7.0f, 11.0f,
+       15.0f, 4.0f, 8.0f, 12.0f, 16.0f});
   net.AddInputFromArray<float>("Bias", {4}, {.1f, .2f, .3f, .4f});
   // Run
   net.RunOp();
 
   // Check
-  auto expected = CreateTensor<float>({1, 4, 1, 2},
-                                      {196.1f, 252.1f, 216.2f, 280.2f,
-                                      272.3f, 344.3f, 296.4f, 376.4f});
+  auto expected = CreateTensor<float>(
+      {1, 4, 1, 2},
+      {196.1f, 252.1f, 216.2f, 280.2f, 272.3f, 344.3f, 296.4f, 376.4f});
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
 }
@@ -60,7 +57,7 @@ TEST_F(DepthwiseConv2dOpTest, ConvNxNS12) {
     index_t width = 113;
     index_t multiplier = 3 + rand() % 10;
     // Construct graph
-    auto& net = test_net();
+    auto &net = test_net();
     OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
         .Input("Input")
         .Input("Filter")
@@ -75,8 +72,8 @@ TEST_F(DepthwiseConv2dOpTest, ConvNxNS12) {
 
     // Add input data
     net.AddRandomInput<float>("Input", {batch, input_channels, height, width});
-    net.AddRandomInput<float>(
-        "Filter", {multiplier, input_channels, kernel_h, kernel_w});
+    net.AddRandomInput<float>("Filter",
+                              {multiplier, input_channels, kernel_h, kernel_w});
     net.AddRandomInput<float>("Bias", {multiplier * input_channels});
     // run cpu
     net.RunOp();

mace/ops/depthwise_conv_2d_benchmark.cc

@@ -13,15 +13,15 @@ namespace mace {
 
 template <DeviceType D, typename T>
 static void DepthwiseConv2d(int iters,
-                   int batch,
-                   int channels,
-                   int height,
-                   int width,
-                   int kernel_h,
-                   int kernel_w,
-                   int stride,
-                   Padding padding,
-                   int output_channels) {
+                            int batch,
+                            int channels,
+                            int height,
+                            int width,
+                            int kernel_h,
+                            int kernel_w,
+                            int stride,
+                            Padding padding,
+                            int output_channels) {
   mace::testing::StopTiming();
 
   OpsTestNet net;
@@ -54,16 +54,18 @@ static void DepthwiseConv2d(int iters,
   }
 }
 
-#define BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, STRIDE, P, OC, TYPE, DEVICE)                        \
-  static void                                                                                    \
+#define BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, STRIDE, P, OC, TYPE,                                  \
+                                   DEVICE)                                                                   \
+  static void                                                                                                \
       BM_DEPTHWISE_CONV_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE( \
-          int iters) {                                                                           \
-    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                             \
-    mace::testing::ItemsProcessed(tot);                                                          \
-    mace::testing::BytesProcessed(tot*(sizeof(TYPE)));                                           \
-    DepthwiseConv2d<DEVICE, TYPE>(iters, N, C, H, W, KH, KW, STRIDE, mace::Padding::P, OC);               \
-  }                                                                                              \
-  BENCHMARK(                                                                                     \
+          int iters) {                                                                                       \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                                         \
+    mace::testing::ItemsProcessed(tot);                                                                      \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                                                      \
+    DepthwiseConv2d<DEVICE, TYPE>(iters, N, C, H, W, KH, KW, STRIDE,                                         \
+                                  mace::Padding::P, OC);                                                     \
+  }                                                                                                          \
+  BENCHMARK(                                                                                                 \
       BM_DEPTHWISE_CONV_2D_##N##_##C##_##H##_##W##_K##KH##x##KW##S##STRIDE##_##P##_##OC##_##TYPE##_##DEVICE)
 
 #define BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, OC, TYPE)       \

mace/ops/global_avg_pooling.h

@@ -10,7 +10,7 @@
 
 namespace mace {
 
-template<DeviceType D, class T>
+template <DeviceType D, class T>
 class GlobalAvgPoolingOp : public Operator<D, T> {
  public:
   GlobalAvgPoolingOp(const OperatorDef &operator_def, Workspace *ws)

mace/ops/global_avg_pooling_benchmark.cc

@@ -11,11 +11,8 @@ using namespace mace;
 using namespace mace::kernels;
 
 template <DeviceType D>
-static void GlobalAvgPooling(int iters,
-                             int batch,
-                             int channels,
-                             int height,
-                             int width) {
+static void GlobalAvgPooling(
+    int iters, int batch, int channels, int height, int width) {
   mace::testing::StopTiming();
 
   OpsTestNet net;
@@ -38,15 +35,14 @@ static void GlobalAvgPooling(int iters,
   }
 }
 
-#define BM_GLOBAL_AVG_POOLING_MACRO(N, C, H, W, DEVICE)                    \
-  static void                                                                       \
-      BM_GLOBAL_AVG_POOLING_##N##_##C##_##H##_##W##_##DEVICE( \
-          int iters) {                                                              \
-    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                \
-    mace::testing::ItemsProcessed(tot);                                             \
-    mace::testing::BytesProcessed(tot*(sizeof(float)));                             \
-    GlobalAvgPooling<DEVICE>(iters, N, C, H, W);                                               \
-  }                                                                                 \
+#define BM_GLOBAL_AVG_POOLING_MACRO(N, C, H, W, DEVICE)               \
+  static void BM_GLOBAL_AVG_POOLING_##N##_##C##_##H##_##W##_##DEVICE( \
+      int iters) {                                                    \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;  \
+    mace::testing::ItemsProcessed(tot);                               \
+    mace::testing::BytesProcessed(tot *(sizeof(float)));              \
+    GlobalAvgPooling<DEVICE>(iters, N, C, H, W);                      \
+  }                                                                   \
   BENCHMARK(BM_GLOBAL_AVG_POOLING_##N##_##C##_##H##_##W##_##DEVICE)
 
 #define BM_GLOBAL_AVG_POOLING(N, C, H, W)       \

mace/ops/global_avg_pooling_test.cc

@@ -10,7 +10,7 @@ class GlobalAvgPoolingOpTest : public OpsTestBase {};
 
 TEST_F(GlobalAvgPoolingOpTest, 3x7x7_CPU) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("GlobalAvgPooling", "GlobalAvgPoolingTest")
       .Input("Input")
       .Output("Output")
@@ -19,24 +19,22 @@ TEST_F(GlobalAvgPoolingOpTest, 3x7x7_CPU) {
   // Add input data
   std::vector<float> input(147);
   for (int i = 0; i < 147; ++i) {
-    input[i] = i/49 + 1;
+    input[i] = i / 49 + 1;
   }
-  net.AddInputFromArray<float>(
-      "Input", {1, 3, 7, 7}, input);
+  net.AddInputFromArray<float>("Input", {1, 3, 7, 7}, input);
 
   // Run
   net.RunOp();
 
   // Check
-  auto expected =
-      CreateTensor<float>({1, 3, 1, 1}, {1, 2, 3});
+  auto expected = CreateTensor<float>({1, 3, 1, 1}, {1, 2, 3});
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
 }
 
 TEST_F(GlobalAvgPoolingOpTest, 3x7x7_NEON) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("GlobalAvgPooling", "GlobalAvgPoolingTest")
       .Input("Input")
       .Output("Output")
@@ -45,17 +43,15 @@ TEST_F(GlobalAvgPoolingOpTest, 3x7x7_NEON) {
   // Add input data
   std::vector<float> input(147);
   for (int i = 0; i < 147; ++i) {
-    input[i] = i/49 + 1;
+    input[i] = i / 49 + 1;
   }
-  net.AddInputFromArray<float>(
-      "Input", {1, 3, 7, 7}, input);
+  net.AddInputFromArray<float>("Input", {1, 3, 7, 7}, input);
 
   // Run
   net.RunOp(DeviceType::NEON);
 
   // Check
-  auto expected =
-      CreateTensor<float>({1, 3, 1, 1}, {1, 2, 3});
+  auto expected = CreateTensor<float>({1, 3, 1, 1}, {1, 2, 3});
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
 }

mace/ops/ops_test_util.h

@@ -43,7 +43,7 @@ class OpsTestNet {
  public:
   OpsTestNet() {}
 
-  template<typename T>
+  template <typename T>
   void AddInputFromArray(const char *name,
                          const std::vector<index_t> &shape,
                          const std::vector<T> &data) {
@@ -55,7 +55,7 @@ class OpsTestNet {
     memcpy(input_data, data.data(), data.size() * sizeof(T));
   }
 
-  template<typename T>
+  template <typename T>
   void AddRepeatedInput(const char *name,
                         const std::vector<index_t> &shape,
                         const T data) {
@@ -66,7 +66,7 @@ class OpsTestNet {
     std::fill(input_data, input_data + input->size(), data);
   }
 
-  template<typename T>
+  template <typename T>
   void AddRandomInput(const char *name,
                       const std::vector<index_t> &shape,
                       bool positive = false) {
@@ -173,38 +173,37 @@ class OpsTestBase : public ::testing::Test {
   OpsTestNet test_net_;
 };
 
-template<typename T>
-void GenerateRandomRealTypeData(const std::vector<index_t> &shape, std::vector<T> &res) {
+template <typename T>
+void GenerateRandomRealTypeData(const std::vector<index_t> &shape,
+                                std::vector<T> &res) {
   std::random_device rd;
   std::mt19937 gen(rd());
   std::normal_distribution<T> nd(0, 1);
 
-  index_t size = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<index_t>());
+  index_t size = std::accumulate(shape.begin(), shape.end(), 1,
+                                 std::multiplies<index_t>());
   res.resize(size);
 
-  std::generate(res.begin(), res.end(),
-                [&gen, &nd] {
-                  return nd(gen);
-                });
+  std::generate(res.begin(), res.end(), [&gen, &nd] { return nd(gen); });
 }
 
-template<typename T>
-void GenerateRandomIntTypeData(const std::vector<index_t> &shape, std::vector<T> &res,
-                               const T a = 0, const T b = std::numeric_limits<T>::max()) {
+template <typename T>
+void GenerateRandomIntTypeData(const std::vector<index_t> &shape,
+                               std::vector<T> &res,
+                               const T a = 0,
+                               const T b = std::numeric_limits<T>::max()) {
   std::random_device rd;
   std::mt19937 gen(rd());
   std::uniform_int_distribution<> nd(a, b);
 
-  index_t size = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<index_t>());
+  index_t size = std::accumulate(shape.begin(), shape.end(), 1,
+                                 std::multiplies<index_t>());
   res.resize(size);
 
-  std::generate(res.begin(), res.end(),
-                [&gen, &nd] {
-                  return nd(gen);
-                });
+  std::generate(res.begin(), res.end(), [&gen, &nd] { return nd(gen); });
 }
 
-template<typename T>
+template <typename T>
 unique_ptr<Tensor> CreateTensor(const std::vector<index_t> &shape,
                                 const std::vector<T> &data) {
   unique_ptr<Tensor> res(new Tensor(cpu_allocator(), DataTypeToEnum<T>::v()));
@@ -237,23 +236,23 @@ inline std::string ShapeToString(const Tensor &x) {
   return std::string(stream.str());
 }
 
-template<typename T>
+template <typename T>
 struct is_floating_point_type {
   static const bool value =
       std::is_same<T, float>::value || std::is_same<T, double>::value;
 };
 
-template<typename T>
+template <typename T>
 inline void ExpectEqual(const T &a, const T &b) {
   EXPECT_EQ(a, b);
 }
 
-template<>
+template <>
 inline void ExpectEqual<float>(const float &a, const float &b) {
   EXPECT_FLOAT_EQ(a, b);
 }
 
-template<>
+template <>
 inline void ExpectEqual<double>(const double &a, const double &b) {
   EXPECT_DOUBLE_EQ(a, b);
 }
@@ -264,11 +263,11 @@ inline void AssertSameTypeDims(const Tensor &x, const Tensor &y) {
                                 << "y.shape [ " << ShapeToString(y) << "]";
 }
 
-template<typename T, bool is_fp = is_floating_point_type<T>::value>
+template <typename T, bool is_fp = is_floating_point_type<T>::value>
 struct Expector;
 
 // Partial specialization for float and double.
-template<typename T>
+template <typename T>
 struct Expector<T, true> {
   static void Equal(const T &a, const T &b) { ExpectEqual(a, b); }
 
@@ -294,17 +293,17 @@ struct Expector<T, true> {
   }
 };
 
-template<typename T>
+template <typename T>
 void ExpectTensorNear(const Tensor &x, const Tensor &y, const double abs_err) {
   static_assert(is_floating_point_type<T>::value,
                 "T is not a floating point type");
   Expector<T>::Near(x, y, abs_err);
 }
 
-template<typename T>
-std::string ToString(const T& input) {
+template <typename T>
+std::string ToString(const T &input) {
   std::stringstream ss;
-  ss<<input;
+  ss << input;
   return ss.str();
 }
 

mace/ops/pooling.h

@@ -14,7 +14,7 @@ namespace mace {
 template <DeviceType D, class T>
 class PoolingOp : public ConvPool2dOpBase<D, T> {
  public:
-  PoolingOp(const OperatorDef& op_def, Workspace* ws)
+  PoolingOp(const OperatorDef &op_def, Workspace *ws)
       : ConvPool2dOpBase<D, T>(op_def, ws),
         kernels_(OperatorBase::GetRepeatedArgument<int>("kernels")),
         pooling_type_(
@@ -22,8 +22,8 @@ class PoolingOp : public ConvPool2dOpBase<D, T> {
                 "pooling_type", static_cast<int>(AVG)))){};
 
   bool Run() override {
-    const Tensor* input = this->Input(INPUT);
-    Tensor* output = this->Output(OUTPUT);
+    const Tensor *input = this->Input(INPUT);
+    Tensor *output = this->Output(OUTPUT);
 
     std::vector<index_t> output_shape(4);
     std::vector<int> paddings(2);
@@ -34,11 +34,10 @@ class PoolingOp : public ConvPool2dOpBase<D, T> {
     filter_shape[2] = kernels_[0];
     filter_shape[3] = kernels_[1];
 
-    kernels::CalcPaddingAndOutputSize(input->shape().data(),
-                                      filter_shape.data(),
-                                      this->dilations_.data(),
-                                      this->strides_.data(), this->padding_,
-                                      output_shape.data(), paddings.data());
+    kernels::CalcPaddingAndOutputSize(
+        input->shape().data(), filter_shape.data(), this->dilations_.data(),
+        this->strides_.data(), this->padding_, output_shape.data(),
+        paddings.data());
     output->Resize(output_shape);
 
     auto pooling_func = kernels::PoolingFunctor<D, T>(

mace/ops/pooling_benchmark.cc

@@ -56,7 +56,7 @@ static void Pooling(int iters,
           int iters) {                                                              \
     const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W;                \
     mace::testing::ItemsProcessed(tot);                                             \
-    mace::testing::BytesProcessed(tot*(sizeof(float)));                             \
+    mace::testing::BytesProcessed(tot *(sizeof(float)));                            \
     Pooling<DEVICE>(iters, N, C, H, W, KE, STRIDE, Padding::PA,                     \
                     PoolingType::PO);                                               \
   }                                                                                 \

mace/ops/pooling_test.cc

@@ -15,7 +15,7 @@ class PoolingOpTest : public OpsTestBase {};
 
 TEST_F(PoolingOpTest, MAX_VALID) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -46,7 +46,7 @@ TEST_F(PoolingOpTest, MAX_VALID) {
 
 TEST_F(PoolingOpTest, AVG_VALID) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -77,7 +77,7 @@ TEST_F(PoolingOpTest, AVG_VALID) {
 
 TEST_F(PoolingOpTest, MAX_SAME) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -105,7 +105,7 @@ TEST_F(PoolingOpTest, MAX_SAME) {
 
 TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -134,7 +134,7 @@ TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
 
 TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -148,9 +148,9 @@ TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
   net.AddIntsArg("dilations", {1, 1});
 
   // Add input data
-  net.AddInputFromArray<float>("Input", {1, 1, 2, 9},
-                               {0, 1, 2, 3, 4, 5, 6, 7, 8,
-                                9, 10, 11, 12, 13, 14, 15, 16, 17});
+  net.AddInputFromArray<float>(
+      "Input", {1, 1, 2, 9},
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17});
   // Run
   net.RunOp(DeviceType::NEON);
 
@@ -162,7 +162,7 @@ TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
 
 TEST_F(PoolingOpTest, MAX_k3x3s2x2) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -176,10 +176,10 @@ TEST_F(PoolingOpTest, MAX_k3x3s2x2) {
   net.AddIntsArg("dilations", {1, 1});
 
   // Add input data
-  net.AddInputFromArray<float>("Input", {1, 1, 3, 9},
-                               {0, 1, 2, 3, 4, 5, 6, 7, 8,
-                                9, 10, 11, 12, 13, 14, 15, 16, 17,
-                                18, 19, 20, 21, 22, 23, 24, 25, 26});
+  net.AddInputFromArray<float>(
+      "Input", {1, 1, 3, 9},
+      {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13,
+       14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26});
   // Run
   net.RunOp(DeviceType::NEON);
 
@@ -191,7 +191,7 @@ TEST_F(PoolingOpTest, MAX_k3x3s2x2) {
 
 TEST_F(PoolingOpTest, AVG_k2x2s2x2) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Pooling", "PoolingTest")
       .Input("Input")
       .Output("Output")
@@ -207,15 +207,12 @@ TEST_F(PoolingOpTest, AVG_k2x2s2x2) {
   // Add input data
   net.AddInputFromArray<float>(
       "Input", {1, 1, 2, 8},
-      {0, 1, 2, 3, 4, 5, 6, 7,
-       8, 9, 10, 11, 12, 13, 14, 15});
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});
   // Run
   net.RunOp(DeviceType::NEON);
 
   // Check
-  auto expected = CreateTensor<float>({1, 1, 1, 4},
-                                      {4.5, 6.5, 8.5, 10.5});
+  auto expected = CreateTensor<float>({1, 1, 1, 4}, {4.5, 6.5, 8.5, 10.5});
 
   ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 0.001);
 }
-

mace/ops/relu.h

@@ -13,17 +13,17 @@ namespace mace {
 template <DeviceType D, class T>
 class ReluOp : public Operator<D, T> {
  public:
-  ReluOp(const OperatorDef& operator_def, Workspace* ws)
+  ReluOp(const OperatorDef &operator_def, Workspace *ws)
       : Operator<D, T>(operator_def, ws) {
-      functor_.max_limit_ =
-          OperatorBase::GetSingleArgument<T>("max_limit", static_cast<T>(-1));
+    functor_.max_limit_ =
+        OperatorBase::GetSingleArgument<T>("max_limit", static_cast<T>(-1));
   }
   bool Run() override {
-    const Tensor* input_tensor = this->inputs_[0];
-    Tensor* output_tensor = this->outputs_[0];
+    const Tensor *input_tensor = this->inputs_[0];
+    Tensor *output_tensor = this->outputs_[0];
     output_tensor->ResizeLike(input_tensor);
-    const T* input = input_tensor->data<T>();
-    T* output = output_tensor->mutable_data<T>();
+    const T *input = input_tensor->data<T>();
+    T *output = output_tensor->mutable_data<T>();
     index_t size = input_tensor->size();
 
     functor_(input, output, size);

mace/ops/relu_benchmark.cc

@@ -36,7 +36,7 @@ static void ReluBenchmark(int iters, int size) {
   static void BM_RELU_##SIZE##_##TYPE##_##DEVICE(int iters) { \
     const int64_t tot = static_cast<int64_t>(iters) * SIZE;   \
     mace::testing::ItemsProcessed(tot);                       \
-    mace::testing::BytesProcessed(tot*(sizeof(TYPE)));        \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));       \
     ReluBenchmark<DEVICE, TYPE>(iters, SIZE);                 \
   }                                                           \
   BENCHMARK(BM_RELU_##SIZE##_##TYPE##_##DEVICE)

mace/ops/relu_test.cc

@@ -11,7 +11,7 @@ class ReluOpTest : public OpsTestBase {};
 
 TEST_F(ReluOpTest, ReluOp) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Relu", "ReluTest")
       .Input("Input")
       .Output("Output")
@@ -34,7 +34,7 @@ TEST_F(ReluOpTest, ReluOp) {
 
 TEST_F(ReluOpTest, ReluOpWithMax) {
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("Relu", "ReluTestWithMax")
       .Input("Input")
       .Output("Output")
@@ -56,5 +56,4 @@ TEST_F(ReluOpTest, ReluOpWithMax) {
   ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 0.01);
 }
 
-
 }  // namespace mace

mace/ops/resize_bilinear.h

@@ -13,21 +13,21 @@ namespace mace {
 template <DeviceType D, class T>
 class ResizeBilinearOp : public Operator<D, T> {
  public:
-  ResizeBilinearOp(const OperatorDef& operator_def, Workspace* ws)
+  ResizeBilinearOp(const OperatorDef &operator_def, Workspace *ws)
       : Operator<D, T>(operator_def, ws),
         functor_(
             OperatorBase::GetSingleArgument<bool>("align_corners", false)) {}
 
   bool Run() override {
-    const Tensor* input = this->Input(0);
-    const Tensor* resize_dims = this->Input(1);
+    const Tensor *input = this->Input(0);
+    const Tensor *resize_dims = this->Input(1);
 
     MACE_CHECK(input->dim_size() == 4, "input must be 4-dimensional.",
                input->dim_size());
     MACE_CHECK(resize_dims->dim_size() == 1,
                "resize dim must be 2-dimensional.", resize_dims->dim_size());
 
-    Tensor* output = this->Output(0);
+    Tensor *output = this->Output(0);
 
     index_t n = input->dim(0);
     index_t channels = input->dim(1);
@@ -38,8 +38,8 @@ class ResizeBilinearOp : public Operator<D, T> {
     vector<index_t> out_shape{n, channels, out_height, out_width};
     output->Resize(out_shape);
 
-    const T* input_ptr = input->data<T>();
-    T* output_ptr = output->mutable_data<T>();
+    const T *input_ptr = input->data<T>();
+    T *output_ptr = output->mutable_data<T>();
 
     functor_(input_ptr, output_ptr, n, channels, in_height, in_width,
              out_height, out_width);

mace/ops/resize_bilinear_test.cc

@@ -13,7 +13,7 @@ class ResizeBilinearTest : public OpsTestBase {};
 TEST_F(ResizeBilinearTest, ResizeBilinearWOAlignCorners) {
   testing::internal::LogToStderr();
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
       .Input("Input")
       .Input("OutSize")
@@ -38,7 +38,7 @@ TEST_F(ResizeBilinearTest, ResizeBilinearWOAlignCorners) {
 TEST_F(ResizeBilinearTest, ResizeBilinearWAlignCorners) {
   testing::internal::LogToStderr();
   // Construct graph
-  auto& net = test_net();
+  auto &net = test_net();
   OpDefBuilder("ResizeBilinear", "ResizeBilinearTest")
       .Input("Input")
       .Input("OutSize")

mace/proto/BUILD

@@ -33,8 +33,8 @@ cc_proto_library(
 py_proto_library(
     name = "mace_py",
     srcs = ["mace.proto"],
+    default_runtime = "@com_google_protobuf//:protobuf_python",
+    protoc = "@com_google_protobuf//:protoc",
     srcs_version = "PY2AND3",
     deps = ["@com_google_protobuf//:protobuf_python"],
-    protoc = "@com_google_protobuf//:protoc",
-    default_runtime = "@com_google_protobuf//:protobuf_python",
-)
\ No newline at end of file
+)

mace/python/tools/BUILD

@@ -16,4 +16,3 @@ py_binary(
         "@six_archive//:six",
     ],
 )
-

mace/tools/benchmark/benchmark_model.cc

@@ -3,14 +3,13 @@
 //
 
 #include "mace/core/net.h"
-#include "mace/utils/command_line_flags.h"
 #include "mace/tools/benchmark/stat_summarizer.h"
+#include "mace/utils/command_line_flags.h"
 #include "mace/utils/utils.h"
 
 #include <fstream>
 #include <thread>
 
-
 namespace mace {
 namespace str_util {
 
@@ -29,8 +28,9 @@ std::vector<std::string> Split(const string &str, char delims) {
   return result;
 }
 
-
-bool SplitAndParseToInts(const string &str, char delims, std::vector<index_t>* result) {
+bool SplitAndParseToInts(const string &str,
+                         char delims,
+                         std::vector<index_t> *result) {
   string tmp = str;
   while (!tmp.empty()) {
     index_t dim = atoi(tmp.data());
@@ -44,13 +44,15 @@ bool SplitAndParseToInts(const string &str, char delims, std::vector<index_t>* r
   }
 }
 
-} //  namespace str_util
+}  //  namespace str_util
 
 namespace benchmark {
 
-bool RunInference(NetBase* net, StatSummarizer* summarizer, int64_t* inference_time_us) {
+bool RunInference(NetBase *net,
+                  StatSummarizer *summarizer,
+                  int64_t *inference_time_us) {
   RunMetadata run_metadata;
-  RunMetadata* run_metadata_ptr = nullptr;
+  RunMetadata *run_metadata_ptr = nullptr;
   if (summarizer) {
     run_metadata_ptr = &run_metadata;
   }
@@ -71,9 +73,13 @@ bool RunInference(NetBase* net, StatSummarizer* summarizer, int64_t* inference_t
   return true;
 }
 
-bool Run(NetBase* net, StatSummarizer* summarizer,
-         int num_runs, double max_time_sec, int64_t sleep_sec,
-         int64_t* total_time_us, int64_t* actual_num_runs) {
+bool Run(NetBase *net,
+         StatSummarizer *summarizer,
+         int num_runs,
+         double max_time_sec,
+         int64_t sleep_sec,
+         int64_t *total_time_us,
+         int64_t *actual_num_runs) {
   *total_time_us = 0;
 
   LOG(INFO) << "Running benchmark for max " << num_runs << " iterators, max "
@@ -85,7 +91,7 @@ bool Run(NetBase* net, StatSummarizer* summarizer,
   Stat<int64_t> stat;
 
   bool util_max_time = (num_runs <= 0);
-  for (int i = 0; util_max_time || i < num_runs ; ++i) {
+  for (int i = 0; util_max_time || i < num_runs; ++i) {
     int64_t inference_time_us = 0;
     bool s = RunInference(net, summarizer, &inference_time_us);
     stat.UpdateStat(inference_time_us);
@@ -113,7 +119,7 @@ bool Run(NetBase* net, StatSummarizer* summarizer,
   return true;
 }
 
-int Main(int argc, char** argv) {
+int Main(int argc, char **argv) {
   std::string model_file = "/data/local/tmp/mobi_mace.pb";
   std::string device = "CPU";
   std::string input_layer_string = "input:0";
@@ -182,8 +188,10 @@ int Main(int argc, char** argv) {
     return -1;
   }
 
-  std::vector<std::string> input_layers = str_util::Split(input_layer_string, ',');
-  std::vector<std::string> input_layer_shapes = str_util::Split(input_layer_shape_string, ':');
+  std::vector<std::string> input_layers =
+      str_util::Split(input_layer_string, ',');
+  std::vector<std::string> input_layer_shapes =
+      str_util::Split(input_layer_shape_string, ':');
   std::vector<string> input_layer_types =
       str_util::Split(input_layer_type_string, ',');
   std::vector<string> input_layer_files =
@@ -260,17 +268,17 @@ int Main(int argc, char** argv) {
   ws.LoadModelTensor(net_def, DeviceType::CPU);
   // Load inputs
   for (size_t i = 0; i < inputs_count; ++i) {
-    Tensor *input_tensor = ws.CreateTensor(input_layers[i],
-                                           cpu_allocator(), DT_FLOAT);
+    Tensor *input_tensor =
+        ws.CreateTensor(input_layers[i], cpu_allocator(), DT_FLOAT);
     vector<index_t> shapes;
     str_util::SplitAndParseToInts(input_layer_shapes[i], ',', &shapes);
     input_tensor->Resize(shapes);
     float *input_data = input_tensor->mutable_data<float>();
 
-
     // load input
     if (i < input_layer_files.size()) {
-      std::ifstream in_file(input_layer_files[i], std::ios::in | std::ios::binary);
+      std::ifstream in_file(input_layer_files[i],
+                            std::ios::in | std::ios::binary);
       in_file.read(reinterpret_cast<char *>(input_data),
                    input_tensor->size() * sizeof(float));
       in_file.close();
@@ -285,31 +293,31 @@ int Main(int argc, char** argv) {
   int64_t warmup_time_us = 0;
   int64_t num_warmup_runs = 0;
   if (warmup_runs > 0) {
-    bool status = Run(net.get(), nullptr,
-                      warmup_runs, -1.0, inter_inference_sleep_seconds,
-                      &warmup_time_us, &num_warmup_runs);
+    bool status =
+        Run(net.get(), nullptr, warmup_runs, -1.0,
+            inter_inference_sleep_seconds, &warmup_time_us, &num_warmup_runs);
     if (!status) {
       LOG(ERROR) << "Failed at warm up run";
     }
   }
 
   if (inter_benchmark_sleep_seconds > 0) {
-    std::this_thread::sleep_for(std::chrono::seconds(inter_benchmark_sleep_seconds));
+    std::this_thread::sleep_for(
+        std::chrono::seconds(inter_benchmark_sleep_seconds));
   }
   int64_t no_stat_time_us = 0;
   int64_t no_stat_runs = 0;
-  bool status = Run(net.get(), nullptr,
-                    max_num_runs, max_benchmark_time_seconds, inter_inference_sleep_seconds,
-                    &no_stat_time_us, &no_stat_runs);
+  bool status =
+      Run(net.get(), nullptr, max_num_runs, max_benchmark_time_seconds,
+          inter_inference_sleep_seconds, &no_stat_time_us, &no_stat_runs);
   if (!status) {
     LOG(ERROR) << "Failed at normal no-stat run";
   }
 
   int64_t stat_time_us = 0;
   int64_t stat_runs = 0;
-  status = Run(net.get(), stats.get(),
-               max_num_runs, max_benchmark_time_seconds, inter_inference_sleep_seconds,
-               &stat_time_us, &stat_runs);
+  status = Run(net.get(), stats.get(), max_num_runs, max_benchmark_time_seconds,
+               inter_inference_sleep_seconds, &stat_time_us, &stat_runs);
   if (!status) {
     LOG(ERROR) << "Failed at normal stat run";
   }
@@ -325,9 +333,7 @@ int Main(int argc, char** argv) {
   return 0;
 }
 
-} //  namespace benchmark
-} //  namespace mace
+}  //  namespace benchmark
+}  //  namespace mace
 
-int main (int argc, char** argv) {
-  mace::benchmark::Main(argc, argv);
-}
+int main(int argc, char **argv) { mace::benchmark::Main(argc, argv); }

mace/tools/benchmark/stat_summarizer.cc

@@ -2,17 +2,16 @@
 // Copyright (c) 2017 XiaoMi All rights reserved.
 //
 
-#include "mace/core/common.h"
 #include "mace/tools/benchmark/stat_summarizer.h"
+#include "mace/core/common.h"
 #include "mace/proto/stats.pb.h"
 
 #include <iomanip>
 #include <queue>
 
-
 namespace mace {
 
-StatSummarizer::StatSummarizer(const StatSummarizerOptions& options)
+StatSummarizer::StatSummarizer(const StatSummarizerOptions &options)
     : options_(options) {}
 
 StatSummarizer::~StatSummarizer() {}
@@ -23,17 +22,14 @@ void StatSummarizer::Reset() {
   details_.clear();
 }
 
-
 void StatSummarizer::ProcessMetadata(const RunMetadata &run_metadata) {
   int64_t curr_total_us = 0;
   int64_t mem_total = 0;
 
-  int64_t first_node_start_us =
-      run_metadata.op_stats(0).all_start_micros();
+  int64_t first_node_start_us = run_metadata.op_stats(0).all_start_micros();
 
   int node_num = 0;
-  for (const auto& ops : run_metadata.op_stats()) {
-
+  for (const auto &ops : run_metadata.op_stats()) {
     std::string name = ops.operator_name();
     std::string op_type = ops.type();
 
@@ -41,7 +37,7 @@ void StatSummarizer::ProcessMetadata(const RunMetadata &run_metadata) {
     const int64_t curr_time = ops.all_end_rel_micros();
     curr_total_us += curr_time;
     auto result = details_.emplace(name, Detail());
-    Detail* detail = &(result.first->second);
+    Detail *detail = &(result.first->second);
 
     detail->start_us.UpdateStat(ops.all_start_micros() - first_node_start_us);
     detail->rel_end_us.UpdateStat(curr_time);
@@ -77,13 +73,13 @@ std::string StatSummarizer::ShortSummary() const {
   return stream.str();
 }
 
-std::ostream& InitField(std::ostream& stream, int width) {
+std::ostream &InitField(std::ostream &stream, int width) {
   stream << "\t" << std::right << std::setw(width) << std::fixed
          << std::setprecision(3);
   return stream;
 }
 
-std::string StatSummarizer::HeaderString(const std::string& title) const {
+std::string StatSummarizer::HeaderString(const std::string &title) const {
   std::stringstream stream;
 
   stream << "============================== " << title
@@ -102,9 +98,9 @@ std::string StatSummarizer::HeaderString(const std::string& title) const {
   return stream.str();
 }
 
-std::string StatSummarizer::ColumnString(const StatSummarizer::Detail& detail,
+std::string StatSummarizer::ColumnString(const StatSummarizer::Detail &detail,
                                          const int64_t cumulative_stat_on_node,
-                                         const Stat<int64_t>& stat) const {
+                                         const Stat<int64_t> &stat) const {
   const double start_ms = detail.start_us.avg() / 1000.0;
   const double first_time_ms = detail.rel_end_us.first() / 1000.0;
   const double avg_time_ms = detail.rel_end_us.avg() / 1000.0;
@@ -127,12 +123,12 @@ std::string StatSummarizer::ColumnString(const StatSummarizer::Detail& detail,
 }
 
 void StatSummarizer::OrderNodesByMetric(
-    SortingMetric metric, std::vector<const Detail*>* details) const {
-  std::priority_queue<std::pair<std::string, const Detail*>> sorted_list;
+    SortingMetric metric, std::vector<const Detail *> *details) const {
+  std::priority_queue<std::pair<std::string, const Detail *>> sorted_list;
   const int num_nodes = details_.size();
 
-  for (const auto& det : details_) {
-    const Detail* detail = &(det.second);
+  for (const auto &det : details_) {
+    const Detail *detail = &(det.second);
     std::stringstream stream;
     stream << std::setw(20) << std::right << std::setprecision(10)
            << std::fixed;
@@ -169,16 +165,16 @@ void StatSummarizer::OrderNodesByMetric(
 }
 
 void StatSummarizer::ComputeStatsByType(
-    std::map<std::string, int64_t>* node_type_map_count,
-    std::map<std::string, int64_t>* node_type_map_time,
-    std::map<std::string, int64_t>* node_type_map_memory,
-    std::map<std::string, int64_t>* node_type_map_times_called,
-    int64_t* accumulated_us) const {
+    std::map<std::string, int64_t> *node_type_map_count,
+    std::map<std::string, int64_t> *node_type_map_time,
+    std::map<std::string, int64_t> *node_type_map_memory,
+    std::map<std::string, int64_t> *node_type_map_times_called,
+    int64_t *accumulated_us) const {
   int64_t run_count = run_total_us_.count();
 
-  for (const auto& det : details_) {
+  for (const auto &det : details_) {
     const std::string node_name = det.first;
-    const Detail& detail = det.second;
+    const Detail &detail = det.second;
 
     int64_t curr_time_val =
         static_cast<int64_t>(detail.rel_end_us.sum() / run_count);
@@ -186,7 +182,7 @@ void StatSummarizer::ComputeStatsByType(
 
     int64_t curr_memory_val = detail.mem_used.newest();
 
-    const std::string& node_type = detail.type;
+    const std::string &node_type = detail.type;
 
     (*node_type_map_count)[node_type] += 1;
     (*node_type_map_time)[node_type] += curr_time_val;
@@ -215,8 +211,9 @@ std::string StatSummarizer::GetStatsByNodeType() const {
                      &accumulated_us);
 
   // Sort them.
-  std::priority_queue<std::pair<int64_t, std::pair<std::string, int64_t>>> timings;
-  for (const auto& node_type : node_type_map_time) {
+  std::priority_queue<std::pair<int64_t, std::pair<std::string, int64_t>>>
+      timings;
+  for (const auto &node_type : node_type_map_time) {
     const int64_t mem_used = node_type_map_memory[node_type.first];
     timings.emplace(node_type.second,
                     std::pair<std::string, int64_t>(node_type.first, mem_used));
@@ -259,10 +256,10 @@ std::string StatSummarizer::GetStatsByNodeType() const {
   return stream.str();
 }
 
-std::string StatSummarizer::GetStatsByMetric(const std::string& title,
+std::string StatSummarizer::GetStatsByMetric(const std::string &title,
                                              SortingMetric sorting_metric,
                                              int num_stats) const {
-  std::vector<const Detail*> details;
+  std::vector<const Detail *> details;
   OrderNodesByMetric(sorting_metric, &details);
 
   double cumulative_stat_on_node = 0;

mace/tools/benchmark/stat_summarizer.h

@@ -12,7 +12,6 @@
 #include <sstream>
 #include <string>
 
-
 namespace mace {
 
 class RunMetadata;
@@ -62,7 +61,7 @@ class Stat {
     return all_same() ? 0 : std::sqrt(squared_sum_ / count_ - avg() * avg());
   }
 
-  void OutputToStream(std::ostream* stream) const {
+  void OutputToStream(std::ostream *stream) const {
     if (empty()) {
       *stream << "count=0";
     } else if (all_same()) {
@@ -75,8 +74,8 @@ class Stat {
     }
   }
 
-  friend std::ostream& operator<<(std::ostream& stream,
-                                  const Stat<ValueType>& stat) {
+  friend std::ostream &operator<<(std::ostream &stream,
+                                  const Stat<ValueType> &stat) {
     stat.OutputToStream(&stream);
     return stream;
   }
@@ -131,12 +130,12 @@ class StatSummarizer {
     BY_TYPE,
   };
 
-  explicit StatSummarizer(const StatSummarizerOptions& options);
+  explicit StatSummarizer(const StatSummarizerOptions &options);
 
   ~StatSummarizer();
 
   // Adds another run's StepStats output to the aggregate counts.
-  void ProcessMetadata(const RunMetadata& run_metadata);
+  void ProcessMetadata(const RunMetadata &run_metadata);
 
   // Returns a string detailing the accumulated runtime stats in a tab-separated
   // format which can be pasted into a spreadsheet for further analysis.
@@ -147,15 +146,16 @@ class StatSummarizer {
   // Prints the string returned by GetOutputString().
   void PrintOperatorStats() const;
 
-  void ComputeStatsByType(std::map<std::string, int64_t>* node_type_map_count,
-                          std::map<std::string, int64_t>* node_type_map_time,
-                          std::map<std::string, int64_t>* node_type_map_memory,
-                          std::map<std::string, int64_t>* node_type_map_times_called,
-                          int64_t* accumulated_us) const;
+  void ComputeStatsByType(
+      std::map<std::string, int64_t> *node_type_map_count,
+      std::map<std::string, int64_t> *node_type_map_time,
+      std::map<std::string, int64_t> *node_type_map_memory,
+      std::map<std::string, int64_t> *node_type_map_times_called,
+      int64_t *accumulated_us) const;
 
   std::string GetStatsByNodeType() const;
 
-  std::string GetStatsByMetric(const std::string& title,
+  std::string GetStatsByMetric(const std::string &title,
                                SortingMetric sorting_metric,
                                int num_stats) const;
 
@@ -165,7 +165,7 @@ class StatSummarizer {
   int num_runs() const { return run_total_us_.count(); }
 
   // Returns stats of total microseconds spent by all nodes in each run.
-  const Stat<int64_t>& run_total_us() const { return run_total_us_; }
+  const Stat<int64_t> &run_total_us() const { return run_total_us_; }
 
  private:
   struct Detail {
@@ -179,12 +179,12 @@ class StatSummarizer {
   };
 
   void OrderNodesByMetric(SortingMetric sorting_metric,
-                          std::vector<const Detail*>* details) const;
+                          std::vector<const Detail *> *details) const;
 
-  std::string HeaderString(const std::string& title) const;
-  std::string ColumnString(const Detail& detail,
+  std::string HeaderString(const std::string &title) const;
+  std::string ColumnString(const Detail &detail,
                            const int64_t cumulative_stat_on_node,
-                           const Stat<int64_t>& stat) const;
+                           const Stat<int64_t> &stat) const;
 
   Stat<int64_t> run_total_us_;
   Stat<int64_t> memory_;
@@ -193,6 +193,6 @@ class StatSummarizer {
   StatSummarizerOptions options_;
 };
 
-} //  namespace mace
+}  //  namespace mace
 
 #endif  // MACE_TOOLS_BENCHMARK_STAT_SUMMARIZER_H_

mace/utils/command_line_flags.cc

@@ -10,19 +10,21 @@ namespace mace {
 namespace {
 
 bool StringConsume(string &arg, const string &x) {
-  if ((arg.size() >= x.size())
-      && (memcmp(arg.data(), x.data(), x.size()) == 0)) {
+  if ((arg.size() >= x.size()) &&
+      (memcmp(arg.data(), x.data(), x.size()) == 0)) {
     arg = arg.substr(x.size());
     return true;
   }
   return false;
 }
 
-bool ParseStringFlag(string arg, string flag,
-                     string *dst, bool *value_parsing_ok) {
+bool ParseStringFlag(string arg,
+                     string flag,
+                     string *dst,
+                     bool *value_parsing_ok) {
   *value_parsing_ok = true;
-  if (StringConsume(arg, "--") && StringConsume(arg, flag)
-      && StringConsume(arg, "=")) {
+  if (StringConsume(arg, "--") && StringConsume(arg, flag) &&
+      StringConsume(arg, "=")) {
     *dst = arg;
     return true;
   }
@@ -30,11 +32,13 @@ bool ParseStringFlag(string arg, string flag,
   return false;
 }
 
-bool ParseInt32Flag(string arg, string flag,
-                    int32_t *dst, bool *value_parsing_ok) {
+bool ParseInt32Flag(string arg,
+                    string flag,
+                    int32_t *dst,
+                    bool *value_parsing_ok) {
   *value_parsing_ok = true;
-  if (StringConsume(arg, "--") && StringConsume(arg, flag)
-      && StringConsume(arg, "=")) {
+  if (StringConsume(arg, "--") && StringConsume(arg, flag) &&
+      StringConsume(arg, "=")) {
     char extra;
     if (sscanf(arg.data(), "%d%c", dst, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
@@ -47,11 +51,13 @@ bool ParseInt32Flag(string arg, string flag,
   return false;
 }
 
-bool ParseInt64Flag(string arg, string flag,
-                    long long *dst, bool *value_parsing_ok) {
+bool ParseInt64Flag(string arg,
+                    string flag,
+                    long long *dst,
+                    bool *value_parsing_ok) {
   *value_parsing_ok = true;
-  if (StringConsume(arg, "--") && StringConsume(arg, flag)
-      && StringConsume(arg, "=")) {
+  if (StringConsume(arg, "--") && StringConsume(arg, flag) &&
+      StringConsume(arg, "=")) {
     char extra;
     if (sscanf(arg.data(), "%lld%c", dst, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
@@ -64,8 +70,7 @@ bool ParseInt64Flag(string arg, string flag,
   return false;
 }
 
-bool ParseBoolFlag(string arg, string flag,
-                   bool *dst, bool *value_parsing_ok) {
+bool ParseBoolFlag(string arg, string flag, bool *dst, bool *value_parsing_ok) {
   *value_parsing_ok = true;
   if (StringConsume(arg, "--") && StringConsume(arg, flag)) {
     if (arg.empty()) {
@@ -90,11 +95,13 @@ bool ParseBoolFlag(string arg, string flag,
   return false;
 }
 
-bool ParseFloatFlag(string arg, string flag,
-                    float *dst, bool *value_parsing_ok) {
+bool ParseFloatFlag(string arg,
+                    string flag,
+                    float *dst,
+                    bool *value_parsing_ok) {
   *value_parsing_ok = true;
-  if (StringConsume(arg, "--") && StringConsume(arg, flag)
-      && StringConsume(arg, "=")) {
+  if (StringConsume(arg, "--") && StringConsume(arg, flag) &&
+      StringConsume(arg, "=")) {
     char extra;
     if (sscanf(arg.data(), "%f%c", dst, &extra) != 1) {
       LOG(ERROR) << "Couldn't interpret value " << arg << " for flag " << flag
@@ -152,7 +159,8 @@ bool Flag::Parse(string arg, bool *value_parsing_ok) const {
   return result;
 }
 
-/*static*/ bool Flags::Parse(int *argc, char **argv,
+/*static*/ bool Flags::Parse(int *argc,
+                             char **argv,
                              const std::vector<Flag> &flag_list) {
   bool result = true;
   std::vector<char *> unknown_flags;

mace/utils/command_line_flags.h

@@ -39,16 +39,14 @@ class Flags {
   // with matching flags, and remove the matching arguments from (*argc, argv).
   // Return true iff all recognized flag values were parsed correctly, and the
   // first remaining argument is not "--help".
-  static bool Parse(int *argc,
-                    char **argv,
-                    const std::vector<Flag> &flag_list);
+  static bool Parse(int *argc, char **argv, const std::vector<Flag> &flag_list);
 
   // Return a usage message with command line cmdline, and the
   // usage_text strings in flag_list[].
   static string Usage(const string &cmdline,
-                      const std::vector <Flag> &flag_list);
+                      const std::vector<Flag> &flag_list);
 };
 
-} // namespace mace
+}  // namespace mace
 
-#endif // MACE_CORE_COMMAND_LINE_FLAGS_H
+#endif  // MACE_CORE_COMMAND_LINE_FLAGS_H

mace/utils/utils.h

@@ -24,5 +24,5 @@ inline int64_t NowInMicroSec() {
   return static_cast<int64_t>(tv.tv_sec * 1000000 + tv.tv_usec);
 }
 
-} //  namespace mace
-#endif //  MACE_UTILS_UTILS_H_
+}  //  namespace mace
+#endif  //  MACE_UTILS_UTILS_H_