Merge branch 'feature/unify_print_layer_evaluator' into feature/EvaluatorValue

paddle/gserver/evaluators/Evaluator.cpp

@@ -993,12 +993,14 @@ public:
 
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
-      std::vector<std::tuple<std::string, std::string>> out;
-      auto err = nn.getLayerOutputValue(name, &out);
-      err.check();
-      for (auto& each : out) {
-        LOG(INFO) << "layer=" << name << std::get<0>(each) << ":\n"
-                  << std::get<1>(each);
+      auto& argu = nn.getLayer(name)->getOutput();
+      std::unordered_map<std::string, std::string> out;
+      argu.getValueString(&out);
+      for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) {
+        auto it = out.find(field);
+        if (it != out.end()) {
+          LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second;
+        }
       }
     }
   }

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -405,42 +405,4 @@ NeuralNetwork* NeuralNetwork::newNeuralNetwork(const std::string& name,
   }
 }
 
-Error NeuralNetwork::getLayerOutputValue(
-    const std::string& layerName,
-    std::vector<std::tuple<std::string, std::string>>* out) const {
-  auto& layers = this->config_.layers();
-  auto it = std::find_if(
-      layers.begin(), layers.end(), [&layerName](const LayerConfig& conf) {
-        return conf.name() == layerName;
-      });
-  if (it == layers.end()) {
-    return Error("Cannot find layer %s", layerName.c_str());
-  }
-  auto& layer = this->getLayer(layerName);
-  out->reserve(4);
-  auto& argu = layer->getOutput();
-
-  if (argu.value) {
-    std::ostringstream os;
-    argu.value->print(os);
-    out->push_back({"value", os.str()});
-  }
-  if (argu.ids) {
-    std::ostringstream os;
-    argu.ids->print(os, argu.ids->getSize());
-    out->push_back({"ids", os.str()});
-  }
-  if (auto startPos = argu.sequenceStartPositions) {
-    std::ostringstream os;
-    startPos->getVector(false)->print(os, startPos->getSize());
-    out->push_back({"sequence pos", os.str()});
-  }
-  if (auto subStartPos = argu.subSequenceStartPositions) {
-    std::ostringstream os;
-    subStartPos->getVector(false)->print(os, subStartPos->getSize());
-    out->push_back({"sub-sequence pos", os.str()});
-  }
-  return Error();
-}
-
 }  // namespace paddle

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -128,10 +128,6 @@ public:
   static NeuralNetwork* newNeuralNetwork(const std::string& name = "",
                                          NeuralNetwork* rootNetwork = nullptr);
 
-  inline Error __must_check getLayerOutputValue(
-      const std::string& layerName,
-      std::vector<std::tuple<std::string, std::string>>* out) const;
-
 protected:
   /**
    * The constructor of NeuralNetwork.

paddle/gserver/layers/PrintLayer.cpp

@@ -26,28 +26,15 @@ public:
 void PrintLayer::forward(PassType passType) {
   Layer::forward(passType);
   for (size_t i = 0; i != inputLayers_.size(); ++i) {
-    const auto& argu = getInput(i);
+    auto& argu = getInput(i);
     const std::string& name = inputLayers_[i]->getName();
-    if (argu.value) {
-      std::ostringstream os;
-      argu.value->print(os);
-      LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str();
+    std::unordered_map<std::string, std::string> out;
+    argu.getValueString(&out);
+    for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) {
+      auto it = out.find(field);
+      if (it != out.end()) {
+        LOG(INFO) << "layer=" << name << " " << field << ":\n" << it->second;
       }
-    if (argu.ids) {
-      std::ostringstream os;
-      argu.ids->print(os, argu.ids->getSize());
-      LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str();
-    }
-    if (auto startPos = argu.sequenceStartPositions) {
-      std::ostringstream os;
-      startPos->getVector(false)->print(os, startPos->getSize());
-      LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str();
-    }
-    if (auto subStartPos = argu.subSequenceStartPositions) {
-      std::ostringstream os;
-      subStartPos->getVector(false)->print(os, subStartPos->getSize());
-      LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n"
-                << os.str();
     }
   }
 }

paddle/parameter/Argument.cpp

@@ -602,6 +602,32 @@ void Argument::degradeSequence(const Argument& input, bool useGpu) {
   tgtBuf[numSequences] = numSubSequences;
 }
 
+void Argument::getValueString(
+    std::unordered_map<std::string, std::string>* out) const {
+  if (value) {
+    std::ostringstream os;
+    value->print(os);
+    out->insert({"value", os.str()});
+  }
+  if (ids) {
+    std::ostringstream os;
+    ids->print(os, ids->getSize());
+    out->insert({"ids", os.str()});
+  }
+  if (sequenceStartPositions) {
+    std::ostringstream os;
+    sequenceStartPositions->getVector(false)->print(
+        os, sequenceStartPositions->getSize());
+    out->insert({"sequence pos", os.str()});
+  }
+  if (subSequenceStartPositions) {
+    std::ostringstream os;
+    subSequenceStartPositions->getVector(false)->print(
+        os, subSequenceStartPositions->getSize());
+    out->insert({"sub-sequence pos", os.str()});
+  }
+}
+
 void Argument::subArgFrom(const Argument& input,
                           size_t offset,
                           size_t height,

paddle/parameter/Argument.h

@@ -297,6 +297,14 @@ struct Argument {
    sequence has sub-sequence degrades to a sequence.
    */
   void degradeSequence(const Argument& input, bool useGpu);
+
+  /**
+   * @brief getValueString will return the argument's output in string. There
+   * are several kinds of output. The keys of output dictionary are 'value',
+   * 'id', 'sequence pos', 'sub-sequence pos'.
+   * @param out [out]: the return values.
+   */
+  void getValueString(std::unordered_map<std::string, std::string>* out) const;
 };
 
 }  // namespace paddle