Sherwood lib:

parallel forest trainer: bug fixing parallel forest trainer: enabling with PPL
Project-10 · Aug 23, 2016 · 4fde247 · 4fde247
1 parent fff0936
commit 4fde247
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Showing 10 changed files with 633 additions and 697 deletions.
diff --git a/DGM/DGM.vcxproj b/DGM/DGM.vcxproj
@@ -58,6 +58,9 @@
     <ClInclude Include="PriorNode.h" />
     <ClInclude Include="Random.h" />
     <ClInclude Include="RForest.h" />
+    <ClInclude Include="sherwood\Forest.h" />
+    <ClInclude Include="sherwood\ForestTrainer.h" />
+    <ClInclude Include="sherwood\ParallelForestTrainer.h" />
     <ClInclude Include="sherwood\utilities\DataPointCollection.h" />
     <ClInclude Include="sherwood\utilities\FeatureResponseFunctions.h" />
     <ClInclude Include="sherwood\utilities\StatisticsAggregators.h" />

diff --git a/DGM/DGM.vcxproj.filters b/DGM/DGM.vcxproj.filters
@@ -310,6 +310,15 @@
     <ClInclude Include="..\include\parallel.h">
       <Filter>Include</Filter>
     </ClInclude>
+    <ClInclude Include="sherwood\ForestTrainer.h">
+      <Filter>External\Sherwood Utilities</Filter>
+    </ClInclude>
+    <ClInclude Include="sherwood\ParallelForestTrainer.h">
+      <Filter>External\Sherwood Utilities</Filter>
+    </ClInclude>
+    <ClInclude Include="sherwood\Forest.h">
+      <Filter>External\Sherwood Utilities</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="sherwood\utilities\FeatureResponseFunctions.cpp">

diff --git a/DGM/TrainNodeMsRF.cpp b/DGM/TrainNodeMsRF.cpp
@@ -1,7 +1,13 @@
 #include "TrainNodeMsRF.h"
 #include "macroses.h"
 
-//#include "sherwood\ParallelForestTrainer.h"				// for parallle computing
+#ifdef USE_SHERWOOD
+
+#include "sherwood\Sherwood.h"
+
+#ifdef USE_PPL
+#include "sherwood\ParallelForestTrainer.h"				// for parallle computing
+#endif
 
 #include "sherwood\utilities\FeatureResponseFunctions.h"
 #include "sherwood\utilities\StatisticsAggregators.h"
@@ -27,11 +33,12 @@ CTrainNodeMsRF::CTrainNodeMsRF(byte nStates, word nFeatures, int maxSamples) : C
 void CTrainNodeMsRF::init(TrainNodeMsRFParams params)
 {
 	// Some default parameters
-	m_params.MaxDecisionLevels						= params.max_decision_levels - 1;
-	m_params.NumberOfCandidateFeatures				= params.num_of_candidate_features;
-	m_params.NumberOfCandidateThresholdsPerFeature	= params.num_of_candidate_thresholds_per_feature;
-	m_params.NumberOfTrees							= params.num_ot_trees;
-	m_params.Verbose								= params.verbose;
+	m_pParams = std::auto_ptr<sw::TrainingParameters>(new sw::TrainingParameters());
+	m_pParams->MaxDecisionLevels						= params.max_decision_levels - 1;
+	m_pParams->NumberOfCandidateFeatures				= params.num_of_candidate_features;
+	m_pParams->NumberOfCandidateThresholdsPerFeature	= params.num_of_candidate_thresholds_per_feature;
+	m_pParams->NumberOfTrees							= params.num_ot_trees;
+	m_pParams->Verbose									= params.verbose;
 
 	m_pData = std::auto_ptr<sw::DataPointCollection>(new sw::DataPointCollection());
 	m_pData->m_dimension = m_nFeatures;
@@ -100,8 +107,12 @@ void CTrainNodeMsRF::train(void)
 
 	sw::Random random;
 	sw::ClassificationTrainingContext classificationContext(m_nStates, m_nFeatures);
-	m_pForest = sw::ForestTrainer<sw::LinearFeatureResponse, sw::HistogramAggregator>::TrainForest(random, m_params, classificationContext, *m_pData);
-	//m_pForest = sw::ParallelForestTrainer<sw::LinearFeatureResponse, sw::HistogramAggregator>::TrainForest(random, m_params, classificationContext, *m_pData);
+#ifdef USE_PPL
+	// Use this function with cautions - it is not verifiied!
+	m_pForest = sw::ParallelForestTrainer<sw::LinearFeatureResponse, sw::HistogramAggregator>::TrainForest(random, *m_pParams, classificationContext, *m_pData);
+#else
+	m_pForest = sw::ForestTrainer<sw::LinearFeatureResponse, sw::HistogramAggregator>::TrainForest(random, *m_pParams, classificationContext, *m_pData);
+#endif
 }	
 
 void CTrainNodeMsRF::calculateNodePotentials(const Mat &featureVector, Mat &potential, Mat &mask) const
@@ -128,4 +139,5 @@ void CTrainNodeMsRF::calculateNodePotentials(const Mat &featureVector, Mat &pote
 	for (byte s = 0; s < m_nStates; s++) 
 		potential.at<float>(s, 0) = (1.0f - mudiness) * h.GetProbability(s);
 }
-}
+}
+#endif
diff --git a/DGM/TrainNodeMsRF.h b/DGM/TrainNodeMsRF.h
@@ -3,17 +3,19 @@
 #pragma once
 
 #include "TrainNode.h"
-#include "sherwood\Sherwood.h"
 
-
-namespace sw = MicrosoftResearch::Cambridge::Sherwood;
+#ifdef USE_SHERWOOD
 
 namespace MicrosoftResearch { namespace Cambridge { namespace Sherwood {
 	class LinearFeatureResponse;
 	class HistogramAggregator;
 	class DataPointCollection;
+	template<class F, class S> class Forest;
+	struct TrainingParameters;
 }}}
 
+namespace sw = MicrosoftResearch::Cambridge::Sherwood;
+
 namespace DirectGraphicalModels
 {
 	///@brief Microsoft Research Random Forest parameters
@@ -43,6 +45,7 @@ namespace DirectGraphicalModels
 	* @ingroup moduleTrainNode
 	* @brief Microsoft Sherwood Random Forest training class
 	* @details This class is based on the <a href="http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/">Sherwood C++ code library for decision forests</a> v.1.0.0
+	* > In order to use the Sherwood library, DGM must be build with the \b USE_SHERWOOD flag
 	* @author Sergey G. Kosov, [email protected]
 	*/
 	class CTrainNodeMsRF : public CTrainNode
@@ -100,7 +103,8 @@ namespace DirectGraphicalModels
 
 
 	private:
-		sw::TrainingParameters															m_params;
+		std::auto_ptr<sw::TrainingParameters>											m_pParams;
 		size_t																			m_maxSamples;
 	};
 }
+#endif