From 148bd4d0b3240d31c1c96ddac89ffd4935f71b03 Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Mon, 19 Dec 2016 15:04:48 +0800
Subject: [PATCH] add Layer::createFunction

---
 paddle/gserver/layers/Layer.h                 | 24 +++++++++++--
 paddle/gserver/layers/NormProjectionLayer.cpp | 34 +++++++------------
 2 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h
index 16f66a2205f..6dfd48fb966 100644
--- a/paddle/gserver/layers/Layer.h
+++ b/paddle/gserver/layers/Layer.h
@@ -102,9 +102,9 @@ protected:
   std::vector<bool> markInBackward_;
 
   /// Layer forward function
-  FunctionBase* forward_;
+  std::vector<std::shared_ptr<FunctionBase>> forward_;
   /// Layer backward function
-  FunctionBase* backward_;
+  std::vector<std::shared_ptr<FunctionBase>> backward_;
 
 public:
   /**
@@ -132,6 +132,26 @@ public:
   virtual void markAllInputGrad();
 
 protected:
+  /**
+   * Create layer function. Function is called in forward or backward.
+   * \param function, Layer::forward_ or Layer::backward_
+   * \param name, function name
+   * \param config, initialization configuration for the function
+   */
+  void createFunction(std::vector<std::shared_ptr<FunctionBase>>& function,
+                      const std::string& name,
+                      const FuncConfig& config) {
+    if (useGpu_) {
+      function.emplace_back(
+          FunctionBase::funcRegistrar_.createByType(name + "-GPU"));
+    } else {
+      function.emplace_back(
+          FunctionBase::funcRegistrar_.createByType(name + "-CPU"));
+    }
+    auto& func = function.back();
+    func->init(config);
+  }
+
   /**
    * Notify specified layer the output grad ready.
    * Called in the backward function.
diff --git a/paddle/gserver/layers/NormProjectionLayer.cpp b/paddle/gserver/layers/NormProjectionLayer.cpp
index 0f6f9b91d05..262d757c67e 100644
--- a/paddle/gserver/layers/NormProjectionLayer.cpp
+++ b/paddle/gserver/layers/NormProjectionLayer.cpp
@@ -45,21 +45,13 @@ bool CMRProjectionNormLayer::init(const LayerMap& layerMap,
   /* the size of inputs for norm-layer is 1 */
   CHECK_EQ(config_.inputs_size(), 1);
 
-  if (useGpu_) {
-    forward_ = FunctionBase::funcRegistrar_.createByType(
-        FUNC_NAME(CrossMapNormal, GPU));
-    backward_ = FunctionBase::funcRegistrar_.createByType(
-        FUNC_NAME(CrossMapNormalGrad, GPU));
-  } else {
-    forward_ = FunctionBase::funcRegistrar_.createByType(
-        FUNC_NAME(CrossMapNormal, CPU));
-    backward_ = FunctionBase::funcRegistrar_.createByType(
-        FUNC_NAME(CrossMapNormalGrad, CPU));
-  }
-  forward_->init(
+  createFunction(
+      forward_,
+      "CrossMapNormal",
       FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
-
-  backward_->init(
+  createFunction(
+      backward_,
+      "CrossMapNormalGrad",
       FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
 
   return true;
@@ -80,7 +72,7 @@ void CMRProjectionNormLayer::forward(PassType passType) {
   Matrix::resizeOrCreate(denoms_, batchSize, size, /* trans */ false, useGpu_);
 
   dims_ = {batchSize, channels_, imgSizeH_, imgSizeW_};
-  forward_->calc(
+  forward_[0]->calc(
       {Tensor(input->getData(), dims_)},
       {Tensor(outV->getData(), dims_), Tensor(denoms_->getData(), dims_)},
       {});
@@ -98,11 +90,11 @@ void CMRProjectionNormLayer::backward(const UpdateCallback& callback) {
   MatrixPtr localOutV = getOutputValue();
   MatrixPtr preOutV = inputLayers_[0]->getOutputValue();
 
-  backward_->calc({Tensor(preOutV->getData(), dims_),
-                   Tensor(localOutV->getData(), dims_),
-                   Tensor(localGrad->getData(), dims_),
-                   Tensor(denoms_->getData(), dims_)},
-                  {Tensor(preOutGrad->getData(), dims_)},
-                  {});
+  backward_[0]->calc({Tensor(preOutV->getData(), dims_),
+                      Tensor(localOutV->getData(), dims_),
+                      Tensor(localGrad->getData(), dims_),
+                      Tensor(denoms_->getData(), dims_)},
+                     {Tensor(preOutGrad->getData(), dims_)},
+                     {});
 }
 }  // namespace paddle
-- 
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