Merge branch 'develop' into layer

paddle/api/Evaluator.cpp

@@ -27,3 +27,18 @@ std::string Evaluator::toString() {
   m->rawPtr->printStats(sout);
   return sout.str();
 }
+
+std::vector<std::string> Evaluator::getNames() const {
+  std::vector<std::string> retv;
+  m->rawPtr->getNames(&retv);
+  return retv;
+}
+
+double Evaluator::getValue(const std::string name) const {
+  paddle::Error err;
+  double v = m->rawPtr->getValue(name, &err);
+  if (err) {
+    throw std::runtime_error(err.msg());
+  }
+  return v;
+}

paddle/api/PaddleAPI.h

@@ -900,6 +900,10 @@ public:
    */
   std::string toString();
 
+  std::vector<std::string> getNames() const;
+
+  double getValue(const std::string name) const;
+
 private:
   EvaluatorPrivate* m;
 

paddle/api/test/testTrain.py

@@ -89,9 +89,14 @@ def main():
             except Exception as e:
                 print e
 
+        ev = m.makeEvaluator()
+        ev.start()
         m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN,
                           update_callback)
-
+        m.eval(ev)
+        ev.finish()
+        for name in ev.getNames():
+            print name, ev.getValue(name)
         for optimizer in optimizers:
             optimizer.finishBatch()
 

paddle/gserver/evaluators/CTCErrorEvaluator.cpp

@@ -20,7 +20,7 @@ namespace paddle {
 /**
  * calculate sequence-to-sequence edit distance
  */
-class CTCErrorEvaluator : public Evaluator {
+class CTCErrorEvaluator : public NotGetableEvaluator {
 private:
   MatrixPtr outActivations_;
   int numTimes_, numClasses_, numSequences_, blank_;

paddle/gserver/evaluators/Evaluator.cpp

@@ -13,9 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/gserver/evaluators/Evaluator.h"
-#include "paddle/utils/Stat.h"
-
 #include "paddle/gserver/gradientmachines/NeuralNetwork.h"
+#include "paddle/utils/Stat.h"
+#include "paddle/utils/StringUtil.h"
 
 DECLARE_int32(trainer_id);
 
@@ -122,6 +122,10 @@ public:
   virtual void distributeEval(ParameterClient2* client) {
     mergeResultsOfAllClients(client);
   }
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const { return "classification_error"; }
 };
 
 /**
@@ -160,6 +164,10 @@ public:
   virtual void distributeEval(ParameterClient2* client) {
     mergeResultsOfAllClients(client);
   }
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const { return "seq_classification_error"; }
 };
 REGISTER_EVALUATOR(seq_classification_error,
                    SequenceClassificationErrorEvaluator);
@@ -250,6 +258,10 @@ public:
 private:
   IVectorPtr cpuLabel_;
   MatrixPtr cpuWeight_;
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const { return "sum"; }
 };
 /**
  * @brief column sum Evaluator
@@ -357,10 +369,18 @@ public:
   }
 
 private:
-  ColumnSumEvaluator() {}
   int32_t colIdx_;
   size_t colNum_;
   MatrixPtr sum_; /* cpu matrix */
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const {
+    if (colIdx_ == -1)
+      return "last-column-sum";
+    else
+      return "column-sum";
+  }
 };
 
 void AucEvaluator::start() {
@@ -469,6 +489,16 @@ double AucEvaluator::calcAuc() const {
   }
 }
 
+real AucEvaluator::getValueImpl() const { return calcAuc(); }
+
+std::string AucEvaluator::getTypeImpl() const {
+  if (colIdx_ == -1) {
+    return "last-column-auc";
+  } else {
+    return "auc";
+  }
+}
+
 // class RankAucEvaluator
 REGISTER_EVALUATOR(rankauc, RankAucEvaluator);
 
@@ -548,12 +578,15 @@ double RankAucEvaluator::calcRankAuc(real* outputData,
                                         : aucTmp / (clickSum * noClickSum);
 }
 
+std::string RankAucEvaluator::getTypeImpl() const { return "rankauc"; }
+
 // class PrecisionRecallEvaluator
 REGISTER_EVALUATOR(precision_recall, PrecisionRecallEvaluator);
 
 void PrecisionRecallEvaluator::start() {
   Evaluator::start();
   statsInfo_.clear();
+  values_.clear();
 }
 
 real PrecisionRecallEvaluator::evalImp(std::vector<Argument>& arguments) {
@@ -614,52 +647,23 @@ real PrecisionRecallEvaluator::evalImp(std::vector<Argument>& arguments) {
 }
 
 void PrecisionRecallEvaluator::printStats(std::ostream& os) const {
-  int label = config_.positive_label();
-  if (label != -1) {
-    CHECK(label >= 0 && label < (int)statsInfo_.size())
-        << "positive_label [" << label << "] should be in range [0, "
-        << statsInfo_.size() << ")";
-    double precision =
-        calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP);
-    double recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN);
-    os << "positive_label=" << label << " precision=" << precision
-       << " recall=" << recall
-       << " F1-score=" << calcF1Score(precision, recall);
-    return;
-  }
-
-  // micro average method: precision = (TP1+TP2)/(TP1+FP1+TP2+FP2)
-  // macro average method: precision = (precision1+precision2)/2
-  double microTotalTP = 0;
-  double microTotalFP = 0;
-  double microTotalFN = 0;
-  double macroAvgPrecision = 0;
-  double macroAvgRecall = 0;
-  size_t numLabels = statsInfo_.size();
-  for (size_t i = 0; i < numLabels; ++i) {
-    microTotalTP += statsInfo_[i].TP;
-    microTotalFP += statsInfo_[i].FP;
-    microTotalFN += statsInfo_[i].FN;
-    macroAvgPrecision += calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP);
-    macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN);
-  }
-  macroAvgPrecision /= numLabels;
-  macroAvgRecall /= numLabels;
-  double macroAvgF1Score = calcF1Score(macroAvgPrecision, macroAvgRecall);
-  os << "macro-average-precision=" << macroAvgPrecision
-     << " macro-average-recall=" << macroAvgRecall
-     << " macro-average-F1-score=" << macroAvgF1Score;
-
-  double microAvgPrecision = calcPrecision(microTotalTP, microTotalFP);
-  double microAvgRecall = calcPrecision(microTotalTP, microTotalFN);
-  double microAvgF1Score = calcF1Score(microAvgPrecision, microAvgRecall);
-  if (!isMultiBinaryLabel_) {
-    // precision and recall are equal in this case
-    os << " micro-average-precision=" << microAvgPrecision;
-  } else {
-    os << " micro-average-precision=" << microAvgPrecision
-       << " micro-average-recall=" << microAvgRecall
-       << " micro-average-F1-score=" << microAvgF1Score;
+  PrintStatsInfo info;
+  bool containMacroMicroInfo = getStatsInfo(&info);
+  os << "positive_label=" << config_.positive_label()
+     << " precision=" << info.precision << " recall=" << info.recall
+     << " F1-score=" << info.f1;
+  if (containMacroMicroInfo) {
+    os << "macro-average-precision=" << info.macroAvgPrecision
+       << " macro-average-recall=" << info.macroAvgRecall
+       << " macro-average-F1-score=" << info.macroAvgF1Score;
+    if (!isMultiBinaryLabel_) {
+      // precision and recall are equal in this case
+      os << " micro-average-precision=" << info.microAvgPrecision;
+    } else {
+      os << " micro-average-precision=" << info.microAvgPrecision
+         << " micro-average-recall=" << info.microAvgRecall
+         << " micro-average-F1-score=" << info.microAvgF1Score;
+    }
   }
 }
 
@@ -741,6 +745,60 @@ void PrecisionRecallEvaluator::calcStatsInfoMulti(const MatrixPtr& output,
   }
 }
 
+void PrecisionRecallEvaluator::storeLocalValues() const {
+  if (this->values_.size() == 0) {
+    PrintStatsInfo info;
+    bool containMacroMicroInfo = getStatsInfo(&info);
+    values_["precision"] = info.precision;
+    values_["recal"] = info.recall;
+    values_["F1-score"] = info.f1;
+    if (containMacroMicroInfo) {
+      values_["macro-average-precision"] = info.macroAvgPrecision;
+      values_["macro-average-recall"] = info.macroAvgRecall;
+      values_["macro-average-F1-score"] = info.macroAvgF1Score;
+      if (!isMultiBinaryLabel_) {
+        // precision and recall are equal in this case
+        values_["micro-average-precision"] = info.microAvgPrecision;
+      } else {
+        values_["micro-average-precision"] = info.microAvgPrecision;
+        values_["micro-average-recall"] = info.microAvgRecall;
+        values_["micro-average-F1-score"] = info.microAvgF1Score;
+      }
+    }
+  }
+}
+
+void PrecisionRecallEvaluator::getNames(std::vector<std::string>* names) {
+  this->storeLocalValues();
+  names->reserve(this->values_.size());
+  for (auto it = this->values_.begin(); it != this->values_.end(); ++it) {
+    names->push_back(this->config_.name() + "." + it->first);
+  }
+}
+
+real PrecisionRecallEvaluator::getValue(const std::string& name,
+                                        Error* err) const {
+  this->storeLocalValues();
+  std::vector<std::string> buffers;
+  paddle::str::split(name, '.', &buffers);
+  auto it = this->values_.find(buffers[buffers.size() - 1]);
+  if (it == this->values_.end()) {  // not found
+    *err = Error("No such key %s", name.c_str());
+    return .0f;
+  }
+
+  return it->second;
+}
+
+std::string PrecisionRecallEvaluator::getType(const std::string& name,
+                                              Error* err) const {
+  this->getValue(name, err);
+  if (!err->isOK()) {
+    return "";
+  }
+  return "precision_recall";
+}
+
 void PrecisionRecallEvaluator::distributeEval(ParameterClient2* client) {
   size_t size = 4 * statsInfo_.size();
   double* buf = new double[size];
@@ -760,6 +818,47 @@ void PrecisionRecallEvaluator::distributeEval(ParameterClient2* client) {
   delete[] buf;
 }
 
+bool PrecisionRecallEvaluator::getStatsInfo(
+    PrecisionRecallEvaluator::PrintStatsInfo* info) const {
+  int label = config_.positive_label();
+  if (label != -1) {
+    CHECK(label >= 0 && label < (int)statsInfo_.size())
+        << "positive_label [" << label << "] should be in range [0, "
+        << statsInfo_.size() << ")";
+    info->precision = calcPrecision(statsInfo_[label].TP, statsInfo_[label].FP);
+    info->recall = calcRecall(statsInfo_[label].TP, statsInfo_[label].FN);
+    info->f1 = calcF1Score(info->precision, info->recall);
+    return false;
+  }
+
+  // micro average method: precision = (TP1+TP2)/(TP1+FP1+TP2+FP2)
+  // macro average method: precision = (precision1+precision2)/2
+  double microTotalTP = 0;
+  double microTotalFP = 0;
+  double microTotalFN = 0;
+  info->macroAvgPrecision = 0;
+  info->macroAvgRecall = 0;
+  size_t numLabels = statsInfo_.size();
+  for (size_t i = 0; i < numLabels; ++i) {
+    microTotalTP += statsInfo_[i].TP;
+    microTotalFP += statsInfo_[i].FP;
+    microTotalFN += statsInfo_[i].FN;
+    info->macroAvgPrecision +=
+        calcPrecision(statsInfo_[i].TP, statsInfo_[i].FP);
+    info->macroAvgRecall += calcRecall(statsInfo_[i].TP, statsInfo_[i].FN);
+  }
+  info->macroAvgPrecision /= numLabels;
+  info->macroAvgRecall /= numLabels;
+  info->macroAvgF1Score =
+      calcF1Score(info->macroAvgPrecision, info->macroAvgRecall);
+
+  info->microAvgPrecision = calcPrecision(microTotalTP, microTotalFP);
+  info->microAvgRecall = calcPrecision(microTotalTP, microTotalFN);
+  info->microAvgF1Score =
+      calcF1Score(info->microAvgPrecision, info->microAvgRecall);
+  return true;
+}
+
 REGISTER_EVALUATOR(pnpair, PnpairEvaluator);
 void PnpairEvaluator::start() {
   Evaluator::start();
@@ -884,6 +983,8 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
             << " calc total special pair: " << special;
 }
 
+std::string PnpairEvaluator::getTypeImpl() const { return "pnpair"; }
+
 ClassRegistrar<Evaluator> Evaluator::registrar_;
 Evaluator* Evaluator::create(const EvaluatorConfig& config) {
   Evaluator* evaluator = registrar_.createByType(config.type());
@@ -905,7 +1006,7 @@ static InitFunction __reg_type_auc_sum__([]() {
  *
  * The config file api is value_printer_evaluator.
  */
-class ValuePrinter : public Evaluator {
+class ValuePrinter : public NotGetableEvaluator {
 public:
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
@@ -919,12 +1020,13 @@ public:
   virtual real evalImp(std::vector<Argument>& arguments) { return 0; }
 };
 REGISTER_EVALUATOR(value_printer, ValuePrinter);
+
 /**
  * @brief print gradient of each layer.
  *
  * The config file api is gradient_printer_evaluator.
  */
-class GradientPrinter : public Evaluator {
+class GradientPrinter : public NotGetableEvaluator {
 public:
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
@@ -947,7 +1049,7 @@ REGISTER_EVALUATOR(gradient_printer, GradientPrinter);
  *
  * The config file api is maxid_printer_evaluator.
  */
-class MaxIdPrinter : public Evaluator {
+class MaxIdPrinter : public NotGetableEvaluator {
 private:
   IVectorPtr maxIds_;
   MatrixPtr maxValues_;
@@ -989,7 +1091,7 @@ REGISTER_EVALUATOR(max_id_printer, MaxIdPrinter);
  *
  * The config file api is maxframe_printer_evaluator.
  */
-class MaxFramePrinter : public Evaluator {
+class MaxFramePrinter : public NotGetableEvaluator {
 private:
   IVectorPtr maxIds_;
   MatrixPtr maxValues_;
@@ -1076,7 +1178,7 @@ REGISTER_EVALUATOR(max_frame_printer, MaxFramePrinter);
  * The config file api is seqtext_printer_evaluator.
  *
  */
-class SequenceTextPrinter : public Evaluator {
+class SequenceTextPrinter : public NotGetableEvaluator {
 private:
   /// dict_file, which contains a list of tokens
   std::vector<std::string> dict_;
@@ -1243,4 +1345,6 @@ public:
 };
 REGISTER_EVALUATOR(classification_error_printer, ClassificationErrorPrinter);
 
+std::string DummyEvaluator::getTypeImpl() const { return "dummy"; }
+
 }  // namespace paddle

paddle/gserver/evaluators/Evaluator.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/parameter/Argument.h"
 #include "paddle/pserver/ParameterClient2.h"
 #include "paddle/utils/ClassRegistrar.h"
+#include "paddle/utils/Error.h"
 
 namespace paddle {
 
@@ -117,12 +118,105 @@ public:
 
   static ClassRegistrar<Evaluator> registrar_;
 
+  /**
+   * @brief getNames will return all field names of current evaluator.
+   *
+   * The format of name is `evaluator_name.evaluator_fields`. If the evaluator
+   * has multiple field, the name could be `evaluator_name.field1`. For example
+   * the PrecisionRecallEvaluator contains `precision`, `recall` fields. The get
+   * names will return `precision_recall_evaluator.precision`,
+   * `precision_recall_evaluator.recal`, etc.
+   *
+   * Also, if current Evaluator is a combined evaluator. getNames will return
+   * all names of all evaluators inside the combined evaluator.
+   *
+   * @param names [out]: the field names of current evaluator.
+   * @note Never clear the names parameter inside getNames.
+   */
+  virtual void getNames(std::vector<std::string>* names) {
+    names->push_back(config_.name());
+  }
+
+  /**
+   * @brief getValue will return the current evaluate value of one field.
+   *
+   * @param name: The field name of current evaluator.
+   * @param err [out]: The error state.
+   *
+   * @return The evaluate value(metric).
+   */
+  virtual real getValue(const std::string& name, Error* err) const {
+    if (name != config_.name()) {
+      *err = Error("no such name of evaluator %s", name.c_str());
+      return .0f;
+    }
+    return this->getValueImpl();
+  }
+
+  /**
+   * @brief getType will return the evaluator type by field name.
+   *
+   * Evaluate Type is the current type of evaluator in string. Such as 'auc',
+   * 'precision_recall'. In combined evaluator, different name may get different
+   * evaluate type because it could be evaluated by different evaluator inside.
+   *
+   * @param name: The field name of current Evaluator.
+   * @param err: The error state. nullptr means don't care.
+   * @return the evaluator type string.
+   */
+  virtual std::string getType(const std::string& name, Error* err) const {
+    if (name != config_.name()) {
+      *err = Error("no such name of evaluator %s", name.c_str());
+      return std::string();
+    }
+    return this->getTypeImpl();
+  }
+
+protected:
+  /**
+   * @brief getValueImpl The simplest way to define getValue result. If this
+   * evaluator doesn't contain multiple fields, and do not throw any error, just
+   * implemented this method to get the evaluate result(metric).
+   * @return Evaluate result(metric).
+   */
+  virtual real getValueImpl() const {
+    return numSamples_ != .0 ? totalScore_ / numSamples_ : .0;
+  }
+
+  /**
+   * @brief getTypeImpl The simplest way to define getType result. If this
+   * evaluator doesn't combine many evaluators, the get type should only return
+   * itself type.
+   * @return Evaluator type.
+   */
+  virtual std::string getTypeImpl() const { return "base"; }
+
 protected:
   EvaluatorConfig config_;
   double numSamples_;
   double totalScore_;
 };
 
+/**
+ * @brief The NotGetableEvaluator class is the base class of evaluator that
+ * cannot get value in runtime. The most NotGetableEvaluator is Printer
+ * Evaluator, which is only used to debug network configuration.
+ */
+class NotGetableEvaluator : public Evaluator {
+  // Evaluator interface
+public:
+  void getNames(std::vector<std::string>* names) {}
+
+  real getValue(const std::string& name, Error* err) const {
+    *err = Error("Not implemented");
+    return .0f;
+  }
+  std::string getType(const std::string& name, Error* err) const {
+    *err = Error("Not implemented");
+    return "";
+  }
+};
+
 class DummyEvaluator : public Evaluator {
 public:
   DummyEvaluator() {}
@@ -135,6 +229,10 @@ public:
   }
   virtual void finish() {}
   virtual void printStats(std::ostream&) const {}
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const;
 };
 /**
  * @brief evaluate AUC using colIdx-th column as prediction.
@@ -191,6 +289,11 @@ private:
   }
 
   double calcAuc() const;
+
+  // Evaluator interface
+protected:
+  real getValueImpl() const;
+  std::string getTypeImpl() const;
 };
 
 /**
@@ -223,6 +326,10 @@ private:
                      real* clickData,
                      real* pvData,
                      size_t size);
+
+  // Evaluator interface
+protected:
+  std::string getTypeImpl() const;
 };
 /**
  * @brief precision, recall and f1 score Evaluator
@@ -272,6 +379,20 @@ private:
   IVectorPtr cpuLabel_;
   MatrixPtr cpuWeight_;
 
+  struct PrintStatsInfo {
+    double precision;
+    double recall;
+    double f1;
+    double macroAvgPrecision;
+    double macroAvgRecall;
+    double macroAvgF1Score;
+    double microAvgPrecision;
+    double microAvgRecall;
+    double microAvgF1Score;
+  };
+
+  bool getStatsInfo(PrintStatsInfo* info) const;
+
   void calcStatsInfo(const MatrixPtr& output,
                      const IVectorPtr& label,
                      const MatrixPtr& weight);
@@ -303,6 +424,15 @@ private:
       return 0;
     }
   }
+
+  mutable std::unordered_map<std::string, real> values_;
+
+  void storeLocalValues() const;
+  // Evaluator interface
+public:
+  void getNames(std::vector<std::string>* names);
+  real getValue(const std::string& name, Error* err) const;
+  std::string getType(const std::string& name, Error* err) const;
 };
 
 /*
@@ -349,8 +479,7 @@ public:
   virtual void finish() { calc(predictArray_); }
 
   virtual void printStats(std::ostream& os) const {
-    os << " pos/neg"
-       << "=" << pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]);
+    os << " pos/neg=" << this->getValueImpl();
   }
 
   virtual void distributeEval(ParameterClient2* client) {
@@ -366,6 +495,13 @@ private:
   IVectorPtr cpuLabel_;
   IVectorPtr cpuInfo_;
   MatrixPtr cpuWeight_;
+
+  // Evaluator interface
+protected:
+  real getValueImpl() const {
+    return pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]);
+  }
+  std::string getTypeImpl() const;
 };
 
 }  // namespace paddle

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -306,7 +306,6 @@ void NeuralNetwork::onPassEnd() {
 
 class CombinedEvaluator : public Evaluator {
 public:
-  CombinedEvaluator() {}
   void addEvaluator(std::unique_ptr<Evaluator>&& evaluator) {
     evaluators_.emplace_back(std::move(evaluator));
   }
@@ -346,6 +345,55 @@ public:
 
 protected:
   std::vector<std::unique_ptr<Evaluator>> evaluators_;
+
+  // Evaluator interface
+public:
+  /**
+   * @brief getNames will return all inside evaluators' names.
+   * @param names [out]: return names.
+   */
+  void getNames(std::vector<std::string>* names) {
+    for (auto& eval : evaluators_) {
+      eval->getNames(names);
+    }
+  }
+
+  /**
+   * @brief getValue could get all inside evaluators' value.
+   */
+  real getValue(const std::string& name, Error* err) const {
+    return this->getMethodHelper<real>(
+        name, err, [&name, err](const std::unique_ptr<Evaluator>& eval) {
+          return eval->getValue(name, err);
+        });
+  }
+
+  /**
+   * @brief getType could get all inside evaluators' type.
+   */
+  std::string getType(const std::string& name, Error* err) const {
+    return this->getMethodHelper<std::string>(
+        name, err, [&name, err](const std::unique_ptr<Evaluator>& eval) {
+          return eval->getType(name, err);
+        });
+  }
+
+private:
+  template <typename T>
+  T getMethodHelper(const std::string& name,
+                    Error* err,
+                    const std::function<T(const std::unique_ptr<Evaluator>&)>&
+                        callback) const {
+    for (auto& eval : evaluators_) {
+      std::vector<std::string> names;
+      eval->getNames(&names);
+      if (std::find(names.begin(), names.end(), name) != names.end()) {
+        return callback(eval);
+      }
+    }
+    *err = Error("No such key %s", name.c_str());
+    return T();
+  }
 };
 
 Evaluator* NeuralNetwork::makeEvaluator() const {

paddle/gserver/tests/test_Evaluator.cpp

@@ -110,6 +110,18 @@ void testEvaluator(TestConfig testConf,
   testEvaluator->finish();
   LOG(INFO) << *testEvaluator;
 
+  std::vector<std::string> names;
+  testEvaluator->getNames(&names);
+  paddle::Error err;
+  for (auto& name : names) {
+    auto value = testEvaluator->getValue(name, &err);
+    ASSERT_TRUE(err.isOK());
+    LOG(INFO) << name << " " << value;
+    auto tp = testEvaluator->getType(name, &err);
+    ASSERT_TRUE(err.isOK());
+    ASSERT_EQ(testConf.evaluatorConfig.type(), tp);
+  }
+
   double totalScore2 = 0.0;
   if (testConf.testAccumulate) {
     testEvaluator->start();

paddle/scripts/docker/Dockerfile

@@ -10,28 +10,30 @@ RUN apt-get update && \
     apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \
     apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \
     apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \
-    apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \
+    apt-get install -y automake && \
     apt-get clean -y
 
 RUN pip install --upgrade pip && \
-    pip install -U protobuf && \
+    pip install -U "protobuf==3.1.0" && \
     pip install -U wheel pillow BeautifulSoup && \
     pip install -U docopt PyYAML sphinx && \
     pip install -U sphinx_rtd_theme recommonmark jupyter
 
 RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
-    cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \
+    cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \
     cd .. && rm -rf cmake-3.4.1
 
+ARG BUILD_WOBOQ
 ARG BUILD_AND_INSTALL
 ARG WITH_AVX
 ARG WITH_DOC
 ARG WITH_STYLE_CHECK
 
+ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF}
 ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF}
 ENV WITH_GPU=OFF
 ENV WITH_AVX=${WITH_AVX:-ON}
-ENV WITH_DOC=${WITH_DOC:-ON}
+ENV WITH_DOC=${WITH_DOC:-OFF}
 ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF}
 
 RUN mkdir /paddle

paddle/scripts/docker/Dockerfile.gpu

@@ -10,28 +10,30 @@ RUN apt-get update && \
     apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \
     apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \
     apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \
-    apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \
+    apt-get install -y automake && \
     apt-get clean -y
 
 RUN pip install --upgrade pip && \
-    pip install -U protobuf && \
+    pip install -U "protobuf==3.1.0" && \
     pip install -U wheel pillow BeautifulSoup && \
     pip install -U docopt PyYAML sphinx && \
     pip install -U sphinx_rtd_theme recommonmark jupyter
 
 RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
-    cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \
+    cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \
     cd .. && rm -rf cmake-3.4.1
 
+ARG BUILD_WOBOQ
 ARG BUILD_AND_INSTALL
 ARG WITH_AVX
 ARG WITH_DOC
 ARG WITH_STYLE_CHECK
 
+ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF}
 ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF}
 ENV WITH_GPU=ON
 ENV WITH_AVX=${WITH_AVX:-ON}
-ENV WITH_DOC=${WITH_DOC:-ON}
+ENV WITH_DOC=${WITH_DOC:-OFF}
 ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF}
 
 RUN mkdir /paddle

paddle/scripts/docker/build.sh

@@ -11,7 +11,7 @@ set -e
 # If Dockerfile.* sets BUILD_AND_INSTALL to 'ON', it would have copied
 # source tree to /paddle, and this scripts should build it into
 # /paddle/build.
-if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
+if [[ ${BUILD_AND_INSTALL:-OFF} == 'ON' ]]; then
     if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
 	ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so
     fi
@@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
     mkdir -p /paddle/build # -p means no error if exists
     cd /paddle/build
     cmake .. \
-	  -DWITH_DOC=ON \
+	  -DWITH_DOC=${WITH_DOC:-OFF} \
 	  -DWITH_GPU=${WITH_GPU:-OFF} \
 	  -DWITH_AVX=${WITH_AVX:-OFF} \
 	  -DWITH_SWIG_PY=ON \
@@ -29,28 +29,32 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then
     make -j `nproc`
     make install
 
-    # Install woboq_codebrowser.
-    git clone https://github.com/woboq/woboq_codebrowser /woboq
-    cd /woboq
-    cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
-	  -DCMAKE_BUILD_TYPE=Release \
-	  .
-    make
-
-    export WOBOQ_OUT=/usr/share/nginx/html/paddle
-    export BUILD_DIR=/paddle/build
-    mkdir -p $WOBOQ_OUT
-    cp -rv /woboq/data $WOBOQ_OUT/../data
-    /woboq/generator/codebrowser_generator \
-	-b /paddle/build \
-	-a \
-	-o $WOBOQ_OUT \
-	-p paddle:/paddle
-    /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
-    cd /woboq
-    make clean
-
-    pip install /usr/local/opt/paddle/share/wheels/*.whl
+    if [[ ${BUILD_WOBOQ:-OFF} == 'ON' ]]; then
+        apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev 
+        # Install woboq_codebrowser.
+        git clone https://github.com/woboq/woboq_codebrowser /woboq
+        cd /woboq
+        cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \
+        -DCMAKE_BUILD_TYPE=Release \
+        .
+        make
+
+        export WOBOQ_OUT=/usr/share/nginx/html/paddle
+        export BUILD_DIR=/paddle/build
+        mkdir -p $WOBOQ_OUT
+        cp -rv /woboq/data $WOBOQ_OUT/../data
+        /woboq/generator/codebrowser_generator \
+        -b /paddle/build \
+        -a \
+        -o $WOBOQ_OUT \
+        -p paddle:/paddle
+        /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT
+        cd /woboq
+        make clean
+    fi
+
+    pip install /usr/local/opt/paddle/share/wheels/py_paddle*linux*.whl
+    pip install /usr/local/opt/paddle/share/wheels/paddle*.whl
     paddle version
 fi
 

paddle/utils/Error.h

@@ -37,10 +37,10 @@ namespace paddle {
  *
  * Error __must_check bar() {
  *   // do something.
- *   Status s = foo();  // invoke other method return status.
- *   if (!s) return s;
+ *   Error err = foo();  // invoke other method return status.
+ *   if (err) return err;
  *   // do something else.
- *   return Status();
+ *   return Error();
  * }
  * @endcode{cpp}
  *
@@ -53,8 +53,8 @@ namespace paddle {
  *
  * int foo(Error* error) {
  *   // Do something.
- *   Error s = bar();
- *   if (!s) {
+ *   Error err = bar();
+ *   if (err) {
  *     *error = s;
  *     return 0;
  *   }
@@ -68,10 +68,10 @@ namespace paddle {
  * }
  *
  * Error foobar() {
- *   Error s;
+ *   Error err;
  *   // do something.
- *   foo(&s);
- *   if (!s) return s;
+ *   foo(&err);
+ *   if (err) return err;
  * }
  * @endcode{cpp}
  *
@@ -112,16 +112,22 @@ public:
   }
 
   /**
-   * @brief operator bool, return True if there is no error.
+   * @brief operator bool, return True if there is something error.
    */
-  operator bool() const { return msg_ == nullptr; }
+  operator bool() const { return !this->isOK(); }
+
+  /**
+   * @brief isOK return True if there is no error.
+   * @return True if no error.
+   */
+  bool isOK() const { return msg_ == nullptr; }
 
   /**
    * @brief check this status by glog.
    * @note It is a temp method used during cleaning Paddle code. It will be
    *       removed later.
    */
-  void check() const { CHECK(*this) << msg(); }
+  void check() const { CHECK(this->isOK()) << msg(); }
 
 private:
   std::shared_ptr<std::string> msg_;

paddle/utils/tests/test_Error.cpp

@@ -18,17 +18,17 @@ limitations under the License. */
 
 TEST(Error, testAll) {
   paddle::Error error;
-  ASSERT_TRUE(error);
-  error = paddle::Error("I'm the error");
   ASSERT_FALSE(error);
+  error = paddle::Error("I'm the error");
+  ASSERT_TRUE(error);
   ASSERT_STREQ("I'm the error", error.msg());
 
   error = paddle::Error("error2");
-  ASSERT_FALSE(error);
+  ASSERT_TRUE(error);
   ASSERT_STREQ("error2", error.msg());
 
   int i = 3;
   auto error3 = paddle::Error("error%d", i);
-  ASSERT_FALSE(error3);
+  ASSERT_TRUE(error3);
   ASSERT_STREQ("error3", error3.msg());
 }

python/CMakeLists.txt

@@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS
 
 add_subdirectory(paddle/trainer_config_helpers/tests)
 add_subdirectory(paddle/reader/tests)
+add_subdirectory(paddle/v2/tests)
 
 install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
     DESTINATION opt/paddle/share/wheels

python/paddle/reader/__init__.py

@@ -21,3 +21,5 @@
 #
 #     r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
 from decorator import *
+
+import creator

python/paddle/reader/creator.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__all__ = ['np_array', 'text_file']
+
+
+def np_array(x):
+    """
+    Creates a reader that yields elements of x, if it is a
+    numpy vector. Or rows of x, if it is a numpy matrix.
+    Or any sub-hyperplane indexed by the highest dimension.
+
+    :param x: the numpy array to create reader from.
+    :returns: data reader created from x.
+    """
+
+    def reader():
+        if x.ndim < 1:
+            yield x
+
+        for e in x:
+            yield e
+
+    return reader
+
+
+def text_file(path):
+    """
+    Creates a data reader that outputs text line by line from given text file.
+    Trailing new line ('\n') of each line will be removed.
+
+    :path: path of the text file.
+    :returns: data reader of text file
+    """
+
+    def reader():
+        f = open(path, "r")
+        for l in f:
+            yield l.rstrip('\n')
+        f.close()
+
+    return reader

python/paddle/reader/tests/CMakeLists.txt

@@ -2,3 +2,8 @@ add_test(NAME reader_decorator_test
   COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
         ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py
     WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
+
+add_test(NAME reader_creator_test
+  COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py
+    WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/reader/tests/creator_test.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+import paddle.reader.creator
+import numpy as np
+import os
+
+
+class TestNumpyArray(unittest.TestCase):
+    def test_numpy_array(self):
+        l = [[1, 2, 3], [4, 5, 6]]
+        x = np.array(l, np.int32)
+        reader = paddle.reader.creator.np_array(x)
+        for idx, e in enumerate(reader()):
+            self.assertItemsEqual(e, l[idx])
+
+
+class TestTextFile(unittest.TestCase):
+    def test_text_file(self):
+        path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
+        reader = paddle.reader.creator.text_file(path)
+        for idx, e in enumerate(reader()):
+            self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/reader/tests/test_data_creator.txt

+0 1
+2 3
+4 5

python/paddle/v2/optimizer.py

@@ -3,7 +3,10 @@ import paddle.trainer_config_helpers.optimizers as v1_optimizers
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 import paddle.v2
 
-__all__ = ['Adam', 'Adamax']
+__all__ = [
+    'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
+    'RMSProp', 'ModelAverage', 'L2Regularization'
+]
 
 
 class Optimizer(object):
@@ -38,6 +41,14 @@ class Optimizer(object):
                                                              pass_num)
 
 
+class Momentum(Optimizer):
+    def __init__(self, momentum=None, sparse=False, **kwargs):
+        learning_method = v1_optimizers.MomentumOptimizer(
+            momentum=None, sparse=False)
+        super(Momentum, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
 class Adam(Optimizer):
     def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8, **kwargs):
         learning_method = v1_optimizers.AdamOptimizer(
@@ -52,7 +63,45 @@ class Adamax(Optimizer):
         super(Adamax, self).__init__(learning_method=learning_method, **kwargs)
 
 
+class AdaGrad(Optimizer):
+    def __init__(self, **kwargs):
+        learning_method = v1_optimizers.AdaGradOptimizer()
+        super(AdaGrad, self).__init__(learning_method=learning_method, **kwargs)
+
+
+class DecayedAdaGrad(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.DecayedAdaGradOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(DecayedAdaGrad, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class AdaDelta(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.AdaDeltaOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(AdaDelta, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class RMSProp(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-6, **kwargs):
+        learning_method = v1_optimizers.RMSPropOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(RMSProp, self).__init__(learning_method=learning_method, **kwargs)
+
+
+ModelAverage = v1_optimizers.ModelAverage
+L2Regularization = v1_optimizers.L2Regularization
+
 if __name__ == '__main__':
     swig_api.initPaddle('--use_gpu=false')
-    opt = paddle.v2.optimizer.Adam()
-    print opt.enable_types()
+    for opt in [
+            Momentum(), Adam(), Adamax(), AdaGrad(), DecayedAdaGrad(),
+            AdaDelta(), RMSProp(), Adam(
+                model_average=ModelAverage(average_window=0.5),
+                regularization=L2Regularization(rate=0.5),
+                gradient_clipping_threshold=25)
+    ]:
+        print opt, opt.enable_types()

python/paddle/v2/tests/CMakeLists.txt

+add_test(NAME test_v2_layer
+        COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/test_layer.py
+        WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/v2/tests/test_layer.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import difflib
+import unittest
+
+import paddle.trainer_config_helpers as conf_helps
+import paddle.v2.activation as activation
+import paddle.v2.attr as attr
+import paddle.v2.data_type as data_type
+import paddle.v2.layer as layer
+from paddle.trainer_config_helpers.config_parser_utils import \
+    parse_network_config as parse_network
+
+pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
+label = layer.data(name='label', type=data_type.integer_value(10))
+weight = layer.data(name='weight', type=data_type.dense_vector(10))
+score = layer.data(name='score', type=data_type.dense_vector(1))
+hidden = layer.fc(input=pixel,
+                  size=100,
+                  act=activation.Sigmoid(),
+                  param_attr=attr.Param(name='hidden'))
+inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
+
+
+class CostLayerTest(unittest.TestCase):
+    def test_cost_layer(self):
+        cost1 = layer.classification_cost(input=inference, label=label)
+        cost2 = layer.classification_cost(
+            input=inference, label=label, weight=weight)
+        cost3 = layer.cross_entropy_cost(input=inference, label=label)
+        cost4 = layer.cross_entropy_with_selfnorm_cost(
+            input=inference, label=label)
+        cost5 = layer.regression_cost(input=inference, label=label)
+        cost6 = layer.regression_cost(
+            input=inference, label=label, weight=weight)
+        cost7 = layer.multi_binary_label_cross_entropy_cost(
+            input=inference, label=label)
+        cost8 = layer.rank_cost(left=score, right=score, label=score)
+        cost9 = layer.lambda_cost(input=inference, score=score)
+        cost10 = layer.sum_cost(input=inference)
+        cost11 = layer.huber_cost(input=score, label=label)
+
+        print dir(layer)
+        layer.parse_network(cost1, cost2)
+        print dir(layer)
+        #print layer.parse_network(cost3, cost4)
+        #print layer.parse_network(cost5, cost6)
+        #print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
+
+
+if __name__ == '__main__':
+    unittest.main()