Merge pull request #1534 from UniQP/override

Add missing override specifiers

src/lstm/fullyconnected.h

@@ -32,9 +32,9 @@ class FullyConnected : public Network {
 
   // Returns the shape output from the network given an input shape (which may
   // be partially unknown ie zero).
-  virtual StaticShape OutputShape(const StaticShape& input_shape) const;
+  StaticShape OutputShape(const StaticShape& input_shape) const override;
 
-  virtual STRING spec() const {
+  STRING spec() const override {
     STRING spec;
     if (type_ == NT_TANH)
       spec.add_str_int("Ft", no_);
@@ -63,31 +63,31 @@ class FullyConnected : public Network {
 
   // Suspends/Enables training by setting the training_ flag. Serialize and
   // DeSerialize only operate on the run-time data if state is false.
-  virtual void SetEnableTraining(TrainingState state);
+  void SetEnableTraining(TrainingState state) override;
 
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
-  virtual int InitWeights(float range, TRand* randomizer);
+  int InitWeights(float range, TRand* randomizer) override;
   // Recursively searches the network for softmaxes with old_no outputs,
   // and remaps their outputs according to code_map. See network.h for details.
   int RemapOutputs(int old_no, const std::vector<int>& code_map) override;
 
   // Converts a float network to an int network.
-  virtual void ConvertToInt();
+  void ConvertToInt() override;
 
   // Provides debug output on the weights.
-  virtual void DebugWeights();
+  void DebugWeights() override;
 
   // Writes to the given file. Returns false in case of error.
-  virtual bool Serialize(TFile* fp) const;
+  bool Serialize(TFile* fp) const override;
   // Reads from the given file. Returns false in case of error.
-  virtual bool DeSerialize(TFile* fp);
+  bool DeSerialize(TFile* fp) override;
 
   // Runs forward propagation of activations on the input line.
   // See Network for a detailed discussion of the arguments.
-  virtual void Forward(bool debug, const NetworkIO& input,
-                       const TransposedArray* input_transpose,
-                       NetworkScratch* scratch, NetworkIO* output);
+  void Forward(bool debug, const NetworkIO& input,
+               const TransposedArray* input_transpose, NetworkScratch* scratch,
+               NetworkIO* output) override;
   // Components of Forward so FullyConnected can be reused inside LSTM.
   void SetupForward(const NetworkIO& input,
                     const TransposedArray* input_transpose);
@@ -97,9 +97,8 @@ class FullyConnected : public Network {
 
   // Runs backward propagation of errors on the deltas line.
   // See Network for a detailed discussion of the arguments.
-  virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
-                        NetworkScratch* scratch,
-                        NetworkIO* back_deltas);
+  bool Backward(bool debug, const NetworkIO& fwd_deltas,
+                NetworkScratch* scratch, NetworkIO* back_deltas) override;
   // Components of Backward so FullyConnected can be reused inside LSTM.
   void BackwardTimeStep(const NetworkIO& fwd_deltas, int t, double* curr_errors,
                         TransposedArray* errors_t, double* backprop);
@@ -112,8 +111,8 @@ class FullyConnected : public Network {
   // Sums the products of weight updates in *this and other, splitting into
   // positive (same direction) in *same and negative (different direction) in
   // *changed.
-  virtual void CountAlternators(const Network& other, double* same,
-                                double* changed) const;
+  void CountAlternators(const Network& other, double* same,
+                        double* changed) const override;
 
  protected:
   // Weight arrays of size [no, ni + 1].

src/lstm/lstm.h

@@ -53,9 +53,9 @@ class LSTM : public Network {
 
   // Returns the shape output from the network given an input shape (which may
   // be partially unknown ie zero).
-  virtual StaticShape OutputShape(const StaticShape& input_shape) const;
+  StaticShape OutputShape(const StaticShape& input_shape) const override;
 
-  virtual STRING spec() const {
+  STRING spec() const override {
     STRING spec;
     if (type_ == NT_LSTM)
       spec.add_str_int("Lfx", ns_);
@@ -71,46 +71,45 @@ class LSTM : public Network {
 
   // Suspends/Enables training by setting the training_ flag. Serialize and
   // DeSerialize only operate on the run-time data if state is false.
-  virtual void SetEnableTraining(TrainingState state);
+  void SetEnableTraining(TrainingState state) override;
 
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
-  virtual int InitWeights(float range, TRand* randomizer);
+  int InitWeights(float range, TRand* randomizer) override;
   // Recursively searches the network for softmaxes with old_no outputs,
   // and remaps their outputs according to code_map. See network.h for details.
   int RemapOutputs(int old_no, const std::vector<int>& code_map) override;
 
   // Converts a float network to an int network.
-  virtual void ConvertToInt();
+  void ConvertToInt() override;
 
   // Provides debug output on the weights.
-  virtual void DebugWeights();
+  void DebugWeights() override;
 
   // Writes to the given file. Returns false in case of error.
-  virtual bool Serialize(TFile* fp) const;
+  bool Serialize(TFile* fp) const override;
   // Reads from the given file. Returns false in case of error.
-  virtual bool DeSerialize(TFile* fp);
+  bool DeSerialize(TFile* fp) override;
 
   // Runs forward propagation of activations on the input line.
   // See Network for a detailed discussion of the arguments.
-  virtual void Forward(bool debug, const NetworkIO& input,
-                       const TransposedArray* input_transpose,
-                       NetworkScratch* scratch, NetworkIO* output);
+  void Forward(bool debug, const NetworkIO& input,
+               const TransposedArray* input_transpose, NetworkScratch* scratch,
+               NetworkIO* output) override;
 
   // Runs backward propagation of errors on the deltas line.
   // See Network for a detailed discussion of the arguments.
-  virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
-                        NetworkScratch* scratch,
-                        NetworkIO* back_deltas);
+  bool Backward(bool debug, const NetworkIO& fwd_deltas,
+                NetworkScratch* scratch, NetworkIO* back_deltas) override;
   // Updates the weights using the given learning rate, momentum and adam_beta.
   // num_samples is used in the adam computation iff use_adam_ is true.
   void Update(float learning_rate, float momentum, float adam_beta,
               int num_samples) override;
   // Sums the products of weight updates in *this and other, splitting into
   // positive (same direction) in *same and negative (different direction) in
   // *changed.
-  virtual void CountAlternators(const Network& other, double* same,
-                                double* changed) const;
+  void CountAlternators(const Network& other, double* same,
+                        double* changed) const override;
   // Prints the weights for debug purposes.
   void PrintW();
   // Prints the weight deltas for debug purposes.

src/lstm/plumbing.h

@@ -34,63 +34,63 @@ class Plumbing : public Network {
   virtual ~Plumbing();
 
   // Returns the required shape input to the network.
-  virtual StaticShape InputShape() const { return stack_[0]->InputShape(); }
-  virtual STRING spec() const {
+  StaticShape InputShape() const override { return stack_[0]->InputShape(); }
+  STRING spec() const override {
     return "Sub-classes of Plumbing must implement spec()!";
   }
 
   // Returns true if the given type is derived from Plumbing, and thus contains
   // multiple sub-networks that can have their own learning rate.
-  virtual bool IsPlumbingType() const { return true; }
+  bool IsPlumbingType() const override { return true; }
 
   // Suspends/Enables training by setting the training_ flag. Serialize and
   // DeSerialize only operate on the run-time data if state is false.
-  virtual void SetEnableTraining(TrainingState state);
+  void SetEnableTraining(TrainingState state) override;
 
   // Sets flags that control the action of the network. See NetworkFlags enum
   // for bit values.
-  virtual void SetNetworkFlags(uint32_t flags);
+  void SetNetworkFlags(uint32_t flags) override;
 
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
   // Note that randomizer is a borrowed pointer that should outlive the network
   // and should not be deleted by any of the networks.
   // Returns the number of weights initialized.
-  virtual int InitWeights(float range, TRand* randomizer);
+  int InitWeights(float range, TRand* randomizer) override;
   // Recursively searches the network for softmaxes with old_no outputs,
   // and remaps their outputs according to code_map. See network.h for details.
   int RemapOutputs(int old_no, const std::vector<int>& code_map) override;
 
   // Converts a float network to an int network.
-  virtual void ConvertToInt();
+  void ConvertToInt() override;
 
   // Provides a pointer to a TRand for any networks that care to use it.
   // Note that randomizer is a borrowed pointer that should outlive the network
   // and should not be deleted by any of the networks.
-  virtual void SetRandomizer(TRand* randomizer);
+  void SetRandomizer(TRand* randomizer) override;
 
   // Adds the given network to the stack.
   virtual void AddToStack(Network* network);
 
   // Sets needs_to_backprop_ to needs_backprop and returns true if
   // needs_backprop || any weights in this network so the next layer forward
   // can be told to produce backprop for this layer if needed.
-  virtual bool SetupNeedsBackprop(bool needs_backprop);
+  bool SetupNeedsBackprop(bool needs_backprop) override;
 
   // Returns an integer reduction factor that the network applies to the
   // time sequence. Assumes that any 2-d is already eliminated. Used for
   // scaling bounding boxes of truth data.
   // WARNING: if GlobalMinimax is used to vary the scale, this will return
   // the last used scale factor. Call it before any forward, and it will return
   // the minimum scale factor of the paths through the GlobalMinimax.
-  virtual int XScaleFactor() const;
+  int XScaleFactor() const override;
 
   // Provides the (minimum) x scale factor to the network (of interest only to
   // input units) so they can determine how to scale bounding boxes.
-  virtual void CacheXScaleFactor(int factor);
+  void CacheXScaleFactor(int factor) override;
 
   // Provides debug output on the weights.
-  virtual void DebugWeights();
+  void DebugWeights() override;
 
   // Returns the current stack.
   const PointerVector<Network>& stack() const {
@@ -117,9 +117,9 @@ class Plumbing : public Network {
   float* LayerLearningRatePtr(const char* id) const;
 
   // Writes to the given file. Returns false in case of error.
-  virtual bool Serialize(TFile* fp) const;
+  bool Serialize(TFile* fp) const override;
   // Reads from the given file. Returns false in case of error.
-  virtual bool DeSerialize(TFile* fp);
+  bool DeSerialize(TFile* fp) override;
 
   // Updates the weights using the given learning rate, momentum and adam_beta.
   // num_samples is used in the adam computation iff use_adam_ is true.
@@ -128,8 +128,8 @@ class Plumbing : public Network {
   // Sums the products of weight updates in *this and other, splitting into
   // positive (same direction) in *same and negative (different direction) in
   // *changed.
-  virtual void CountAlternators(const Network& other, double* same,
-                                double* changed) const;
+  void CountAlternators(const Network& other, double* same,
+                        double* changed) const override;
 
  protected:
   // The networks.

src/lstm/series.h

@@ -32,9 +32,9 @@ class Series : public Plumbing {
 
   // Returns the shape output from the network given an input shape (which may
   // be partially unknown ie zero).
-  virtual StaticShape OutputShape(const StaticShape& input_shape) const;
+  StaticShape OutputShape(const StaticShape& input_shape) const override;
 
-  virtual STRING spec() const {
+  STRING spec() const override {
     STRING spec("[");
     for (int i = 0; i < stack_.size(); ++i)
       spec += stack_[i]->spec();
@@ -45,39 +45,38 @@ class Series : public Plumbing {
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
   // Returns the number of weights initialized.
-  virtual int InitWeights(float range, TRand* randomizer);
+  int InitWeights(float range, TRand* randomizer) override;
   // Recursively searches the network for softmaxes with old_no outputs,
   // and remaps their outputs according to code_map. See network.h for details.
   int RemapOutputs(int old_no, const std::vector<int>& code_map) override;
 
   // Sets needs_to_backprop_ to needs_backprop and returns true if
   // needs_backprop || any weights in this network so the next layer forward
   // can be told to produce backprop for this layer if needed.
-  virtual bool SetupNeedsBackprop(bool needs_backprop);
+  bool SetupNeedsBackprop(bool needs_backprop) override;
 
   // Returns an integer reduction factor that the network applies to the
   // time sequence. Assumes that any 2-d is already eliminated. Used for
   // scaling bounding boxes of truth data.
   // WARNING: if GlobalMinimax is used to vary the scale, this will return
   // the last used scale factor. Call it before any forward, and it will return
   // the minimum scale factor of the paths through the GlobalMinimax.
-  virtual int XScaleFactor() const;
+  int XScaleFactor() const override;
 
   // Provides the (minimum) x scale factor to the network (of interest only to
   // input units) so they can determine how to scale bounding boxes.
-  virtual void CacheXScaleFactor(int factor);
+  void CacheXScaleFactor(int factor) override;
 
   // Runs forward propagation of activations on the input line.
   // See Network for a detailed discussion of the arguments.
-  virtual void Forward(bool debug, const NetworkIO& input,
-                       const TransposedArray* input_transpose,
-                       NetworkScratch* scratch, NetworkIO* output);
+  void Forward(bool debug, const NetworkIO& input,
+               const TransposedArray* input_transpose, NetworkScratch* scratch,
+               NetworkIO* output) override;
 
   // Runs backward propagation of errors on the deltas line.
   // See Network for a detailed discussion of the arguments.
-  virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
-                        NetworkScratch* scratch,
-                        NetworkIO* back_deltas);
+  bool Backward(bool debug, const NetworkIO& fwd_deltas,
+                NetworkScratch* scratch, NetworkIO* back_deltas) override;
 
   // Splits the series after the given index, returning the two parts and
   // deletes itself. The first part, up to network with index last_start, goes