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ml: apply CV_OVERRIDE/CV_FINAL

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This commit is contained in:
Alexander Alekhin
2018-03-15 16:16:58 +03:00
parent 3314966acb
commit 4d0dd3e509
13 changed files with 370 additions and 301 deletions
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modules/ml/src/ann_mlp.cpp
+43 -32
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@@ -213,7 +213,7 @@ void ANN_MLP::setAnnealEnergyRNG(const RNG& rng)
this_->setAnnealEnergyRNG(rng);
}
class ANN_MLPImpl : public ANN_MLP_ANNEAL
class ANN_MLPImpl CV_FINAL : public ANN_MLP_ANNEAL
{
public:
ANN_MLPImpl()
@@ -226,23 +226,34 @@ public:
virtual ~ANN_MLPImpl() {}
CV_IMPL_PROPERTY(TermCriteria, TermCriteria, params.termCrit)
CV_IMPL_PROPERTY(double, BackpropWeightScale, params.bpDWScale)
CV_IMPL_PROPERTY(double, BackpropMomentumScale, params.bpMomentScale)
CV_IMPL_PROPERTY(double, RpropDW0, params.rpDW0)
CV_IMPL_PROPERTY(double, RpropDWPlus, params.rpDWPlus)
CV_IMPL_PROPERTY(double, RpropDWMinus, params.rpDWMinus)
CV_IMPL_PROPERTY(double, RpropDWMin, params.rpDWMin)
CV_IMPL_PROPERTY(double, RpropDWMax, params.rpDWMax)
CV_IMPL_PROPERTY(double, AnnealInitialT, params.initialT)
CV_IMPL_PROPERTY(double, AnnealFinalT, params.finalT)
CV_IMPL_PROPERTY(double, AnnealCoolingRatio, params.coolingRatio)
CV_IMPL_PROPERTY(int, AnnealItePerStep, params.itePerStep)
inline TermCriteria getTermCriteria() const CV_OVERRIDE { return params.termCrit; }
inline void setTermCriteria(TermCriteria val) CV_OVERRIDE { params.termCrit = val; }
inline double getBackpropWeightScale() const CV_OVERRIDE { return params.bpDWScale; }
inline void setBackpropWeightScale(double val) CV_OVERRIDE { params.bpDWScale = val; }
inline double getBackpropMomentumScale() const CV_OVERRIDE { return params.bpMomentScale; }
inline void setBackpropMomentumScale(double val) CV_OVERRIDE { params.bpMomentScale = val; }
inline double getRpropDW0() const CV_OVERRIDE { return params.rpDW0; }
inline void setRpropDW0(double val) CV_OVERRIDE { params.rpDW0 = val; }
inline double getRpropDWPlus() const CV_OVERRIDE { return params.rpDWPlus; }
inline void setRpropDWPlus(double val) CV_OVERRIDE { params.rpDWPlus = val; }
inline double getRpropDWMinus() const CV_OVERRIDE { return params.rpDWMinus; }
inline void setRpropDWMinus(double val) CV_OVERRIDE { params.rpDWMinus = val; }
inline double getRpropDWMin() const CV_OVERRIDE { return params.rpDWMin; }
inline void setRpropDWMin(double val) CV_OVERRIDE { params.rpDWMin = val; }
inline double getRpropDWMax() const CV_OVERRIDE { return params.rpDWMax; }
inline void setRpropDWMax(double val) CV_OVERRIDE { params.rpDWMax = val; }
inline double getAnnealInitialT() const CV_OVERRIDE { return params.initialT; }
inline void setAnnealInitialT(double val) CV_OVERRIDE { params.initialT = val; }
inline double getAnnealFinalT() const CV_OVERRIDE { return params.finalT; }
inline void setAnnealFinalT(double val) CV_OVERRIDE { params.finalT = val; }
inline double getAnnealCoolingRatio() const CV_OVERRIDE { return params.coolingRatio; }
inline void setAnnealCoolingRatio(double val) CV_OVERRIDE { params.coolingRatio = val; }
inline int getAnnealItePerStep() const CV_OVERRIDE { return params.itePerStep; }
inline void setAnnealItePerStep(int val) CV_OVERRIDE { params.itePerStep = val; }
// disabled getAnnealEnergyRNG()
inline void setAnnealEnergyRNG(const RNG& val) CV_OVERRIDE { params.rEnergy = val; }
//CV_IMPL_PROPERTY(RNG, AnnealEnergyRNG, params.rEnergy)
inline void setAnnealEnergyRNG(const RNG& val) { params.rEnergy = val; }
void clear()
void clear() CV_OVERRIDE
{
min_val = max_val = min_val1 = max_val1 = 0.;
rng = RNG((uint64)-1);
@@ -253,7 +264,7 @@ public:
int layer_count() const { return (int)layer_sizes.size(); }
void setTrainMethod(int method, double param1, double param2)
void setTrainMethod(int method, double param1, double param2) CV_OVERRIDE
{
if (method != ANN_MLP::RPROP && method != ANN_MLP::BACKPROP && method != ANN_MLP::ANNEAL)
method = ANN_MLP::RPROP;
@@ -276,12 +287,12 @@ public:
}
}
int getTrainMethod() const
int getTrainMethod() const CV_OVERRIDE
{
return params.trainMethod;
}
void setActivationFunction(int _activ_func, double _f_param1, double _f_param2)
void setActivationFunction(int _activ_func, double _f_param1, double _f_param2) CV_OVERRIDE
{
if( _activ_func < 0 || _activ_func > LEAKYRELU)
CV_Error( CV_StsOutOfRange, "Unknown activation function" );
@@ -362,12 +373,12 @@ public:
}
}
Mat getLayerSizes() const
Mat getLayerSizes() const CV_OVERRIDE
{
return Mat_<int>(layer_sizes, true);
}
void setLayerSizes( InputArray _layer_sizes )
void setLayerSizes( InputArray _layer_sizes ) CV_OVERRIDE
{
clear();
@@ -399,7 +410,7 @@ public:
}
}
float predict( InputArray _inputs, OutputArray _outputs, int ) const
float predict( InputArray _inputs, OutputArray _outputs, int ) const CV_OVERRIDE
{
if( !trained )
CV_Error( CV_StsError, "The network has not been trained or loaded" );
@@ -907,7 +918,7 @@ public:
calc_output_scale( outputs, flags );
}
bool train( const Ptr<TrainData>& trainData, int flags )
bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
const int MAX_ITER = 1000;
const double DEFAULT_EPSILON = FLT_EPSILON;
@@ -1108,7 +1119,7 @@ public:
int dcount0;
double* pE;
void operator()( const Range& range ) const
void operator()(const Range& range) const CV_OVERRIDE
{
double inv_count = 1./inputs.rows;
int ivcount = ann->layer_sizes.front();
@@ -1392,7 +1403,7 @@ public:
fs << "}" << "}";
}
void write( FileStorage& fs ) const
void write( FileStorage& fs ) const CV_OVERRIDE
{
if( layer_sizes.empty() )
return;
@@ -1502,7 +1513,7 @@ public:
}
}
void read( const FileNode& fn )
void read( const FileNode& fn ) CV_OVERRIDE
{
clear();
@@ -1531,28 +1542,28 @@ public:
trained = true;
}
Mat getWeights(int layerIdx) const
Mat getWeights(int layerIdx) const CV_OVERRIDE
{
CV_Assert( 0 <= layerIdx && layerIdx < (int)weights.size() );
return weights[layerIdx];
}
bool isTrained() const
bool isTrained() const CV_OVERRIDE
{
return trained;
}
bool isClassifier() const
bool isClassifier() const CV_OVERRIDE
{
return false;
}
int getVarCount() const
int getVarCount() const CV_OVERRIDE
{
return layer_sizes.empty() ? 0 : layer_sizes[0];
}
String getDefaultName() const
String getDefaultName() const CV_OVERRIDE
{
return "opencv_ml_ann_mlp";
}