Merge branch 'feature/add_const_in_gradient_machine_eval' into feature/mnist_train_api

paddle/gserver/gradientmachines/GradientMachine.h

@@ -181,12 +181,12 @@ public:
   /**
    * Create an evaluator which can be used for eval()
    */
-  virtual Evaluator* makeEvaluator() = 0;
+  virtual Evaluator* makeEvaluator() const = 0;
 
   /**
    * evaluate using the given evaluator
    */
-  virtual void eval(Evaluator* evaluator) = 0;
+  virtual void eval(Evaluator* evaluator) const = 0;
 
   std::vector<ParameterPtr>& getParameters() { return parameters_; }
 

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -327,11 +327,11 @@ void MultiGradientMachine::finish() {
   }
 }
 
-Evaluator* MultiGradientMachine::makeEvaluator() {
+Evaluator* MultiGradientMachine::makeEvaluator() const {
   return threads_[0]->getGradientMachine()->makeEvaluator();
 }
 
-void MultiGradientMachine::eval(Evaluator* evaluator) {
+void MultiGradientMachine::eval(Evaluator* evaluator) const {
   for (auto& thread : threads_) {
     SetDevice device(thread->getDeviceId());
     thread->getGradientMachine()->eval(evaluator);

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -193,9 +193,9 @@ public:
 
   virtual void finish();
 
-  virtual Evaluator* makeEvaluator();
+  virtual Evaluator* makeEvaluator() const;
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   bool useGpu() const { return useGpu_; }
 

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -171,7 +171,7 @@ protected:
   std::vector<std::unique_ptr<Evaluator>> evaluators_;
 };
 
-Evaluator* MultiNetwork::makeEvaluator() {
+Evaluator* MultiNetwork::makeEvaluator() const {
   MultiCombinedEvaluator* multiCombinedEvaluator = new MultiCombinedEvaluator();
   for (size_t i = 0; i < subNetworks_.size(); i++) {
     std::unique_ptr<Evaluator> evaluator(subNetworks_[i]->makeEvaluator());
@@ -180,6 +180,6 @@ Evaluator* MultiNetwork::makeEvaluator() {
   return multiCombinedEvaluator;
 }
 
-void MultiNetwork::eval(Evaluator* evaluator) { evaluator->eval(*this); }
+void MultiNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); }
 
 }  // namespace paddle

paddle/gserver/gradientmachines/MultiNetwork.h

@@ -46,9 +46,9 @@ public:
 
   virtual void onPassEnd();
 
-  virtual Evaluator* makeEvaluator();
+  virtual Evaluator* makeEvaluator() const;
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   const std::vector<std::unique_ptr<NeuralNetwork>>& getSubNetworks() const {
     return subNetworks_;

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -348,7 +348,7 @@ protected:
   std::vector<std::unique_ptr<Evaluator>> evaluators_;
 };
 
-Evaluator* NeuralNetwork::makeEvaluator() {
+Evaluator* NeuralNetwork::makeEvaluator() const {
   CombinedEvaluator* combinedEvaluator = new CombinedEvaluator();
   auto subModelConfig = std::find_if(config_.sub_models().begin(),
                                      config_.sub_models().end(),
@@ -383,7 +383,7 @@ Evaluator* NeuralNetwork::makeEvaluator() {
   return combinedEvaluator;
 }
 
-void NeuralNetwork::eval(Evaluator* evaluator) { evaluator->eval(*this); }
+void NeuralNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); }
 
 void NeuralNetwork::setOutputGrad(const std::vector<Argument>& args) {
   CHECK_GE(outputLayers_.size(), args.size());

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -96,9 +96,9 @@ public:
 
   virtual void onPassEnd();
 
-  virtual Evaluator* makeEvaluator();
+  virtual Evaluator* makeEvaluator() const;
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
   virtual void resetState();
   virtual void setOutputGrad(const std::vector<Argument>& args);
 

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -593,7 +593,7 @@ void RecurrentGradientMachine::forwardBackward(
   LOG(FATAL) << "should not use this function";
 }
 
-void RecurrentGradientMachine::eval(Evaluator* evaluator) {
+void RecurrentGradientMachine::eval(Evaluator* evaluator) const {
   // call printers frame by frame
   for (int i = 0; i < maxSequenceLength_; ++i) {
     LOG(INFO) << "Recurrent Layer Group eval frame " << i << " begin";

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -63,7 +63,7 @@ public:
                        const UpdateCallback& callback);
 
   virtual void resetState() {}
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   const std::vector<int>& getParameterIds() { return parameterIds_; }