change return type Argument

paddle/api/GradientMachine.cpp

@@ -148,7 +148,8 @@ Arguments* GradientMachine::getLayerOutput(const std::string& layerName) const
     throw(UnsupportError) {
   auto nn = m->machine;
   if (nn) {
-    return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName));
+    auto arg = nn->getLayerOutput(layerName);
+    return Arguments::createByPaddleArgument(&arg);
   } else {
     throw UnsupportError();
   }

paddle/api/Trainer.cpp

@@ -134,7 +134,8 @@ void Trainer::finishTestPeriod() { m->finishTestPeriod(); }
 Arguments* Trainer::getLayerOutput(const std::string& layerName) const {
   auto nn = this->m->getGradientMachine();
   CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork";
-  return Arguments::createByPaddleArgument(&nn->getLayerOutput(layerName));
+  auto arg = nn->getLayerOutput(layerName);
+  return Arguments::createByPaddleArgument(&arg);
 }
 
 void Trainer::forwardOneBatch(size_t batchSize) {

paddle/gserver/gradientmachines/GradientMachine.h

@@ -134,7 +134,7 @@ public:
     backward(callback);
   }
 
-  virtual const Argument& getLayerOutput(const std::string& layerName) {
+  virtual Argument getLayerOutput(const std::string& layerName) {
     return *((Argument*)nullptr);
   }
 

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -282,8 +282,7 @@ void MultiGradientMachine::forwardBackward(const std::vector<Argument>& inArgs,
   backwardImp(callback);
 }
 
-const Argument& MultiGradientMachine::getLayerOutput(
-    const std::string& layerName) {
+Argument MultiGradientMachine::getLayerOutput(const std::string& layerName) {
   std::vector<Argument> args;
   args.reserve(threads_.size());
 

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -189,7 +189,7 @@ public:
                        PassType passType,
                        const UpdateCallback& callback);
 
-  virtual const Argument& getLayerOutput(const std::string& layerName);
+  virtual Argument getLayerOutput(const std::string& layerName);
 
   virtual void onPassEnd();
 

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -293,7 +293,7 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
   }
 }
 
-const Argument& NeuralNetwork::getLayerOutput(const std::string& layerName) {
+Argument NeuralNetwork::getLayerOutput(const std::string& layerName) {
   return getLayer(layerName)->getOutput();
 }
 

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -87,7 +87,7 @@ public:
 
   virtual void backward(const UpdateCallback& callback = nullptr);
 
-  virtual const Argument& getLayerOutput(const std::string& layerName);
+  virtual Argument getLayerOutput(const std::string& layerName);
 
   const LayerPtr& getLayer(const std::string& layerName) const {
     auto it = layerMap_.find(layerName);

paddle/gserver/layers/CosSimVecMatLayer.cpp

@@ -112,7 +112,7 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap,
 
 void CosSimVecMatLayer::forward(PassType passType) {
   Layer::forward(passType);
-  CHECK_EQ(forward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);
@@ -145,7 +145,7 @@ void CosSimVecMatLayer::forward(PassType passType) {
 }
 
 void CosSimVecMatLayer::backward(const UpdateCallback& callback) {
-  CHECK_EQ(backward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(backward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);