Merge branch 'refactoring' into develop

app/otbImageClassifierFromDeepFeatures.cxx

@@ -92,9 +92,11 @@ private:
     ShareParameter("deepmodel",  "tfmodel.model",
         "Deep net model parameters",      "Deep net model parameters");
     ShareParameter("output",     "tfmodel.output",
-        "Deep net outputs parameters",    "Deep net outputs parameters");
-    ShareParameter("finetuning", "tfmodel.finetuning",
-        "Deep net fine tuning parameters","Deep net fine tuning parameters");
+        "Deep net outputs parameters",
+        "Deep net outputs parameters");
+    ShareParameter("optim", "tfmodel.optim",
+        "This group of parameters allows optimization of processing time",
+        "This group of parameters allows optimization of processing time");
 
     // Classify shared parameters
     ShareParameter("model"      , "classif.model"      , "Model file"          , "Model file"          );

app/otbTensorflowModelServe.cxx

@@ -106,14 +106,14 @@ public:
 
     // Parameter group keys
     ss_key_in      << ss_key_group.str() << ".il";
-    ss_key_dims_x  << ss_key_group.str() << ".fovx";
-    ss_key_dims_y  << ss_key_group.str() << ".fovy";
+    ss_key_dims_x  << ss_key_group.str() << ".rfieldx";
+    ss_key_dims_y  << ss_key_group.str() << ".rfieldy";
     ss_key_ph      << ss_key_group.str() << ".placeholder";
 
     // Parameter group descriptions
     ss_desc_in     << "Input image (or list to stack) for source #" << inputNumber;
-    ss_desc_dims_x << "Field of view width for source #"            << inputNumber;
-    ss_desc_dims_y << "Field of view height for source #"           << inputNumber;
+    ss_desc_dims_x << "Input receptive field (width) for source #"  << inputNumber;
+    ss_desc_dims_y << "Input receptive field (height) for source #" << inputNumber;
     ss_desc_ph     << "Name of the input placeholder for source #"  << inputNumber;
 
     // Populate group
@@ -182,22 +182,22 @@ public:
     MandatoryOn                              ("output.names");
 
     // Output Field of Expression
-    AddParameter(ParameterType_Int,           "output.foex", "The output field of expression (x)");
-    SetMinimumParameterIntValue              ("output.foex", 1);
-    SetDefaultParameterInt                   ("output.foex", 1);
-    MandatoryOn                              ("output.foex");
-    AddParameter(ParameterType_Int,           "output.foey", "The output field of expression (y)");
-    SetMinimumParameterIntValue              ("output.foey", 1);
-    SetDefaultParameterInt                   ("output.foey", 1);
-    MandatoryOn                              ("output.foey");
+    AddParameter(ParameterType_Int,           "output.efieldx", "The output expression field (width)");
+    SetMinimumParameterIntValue              ("output.efieldx", 1);
+    SetDefaultParameterInt                   ("output.efieldx", 1);
+    MandatoryOn                              ("output.efieldx");
+    AddParameter(ParameterType_Int,           "output.efieldy", "The output expression field (height)");
+    SetMinimumParameterIntValue              ("output.efieldy", 1);
+    SetDefaultParameterInt                   ("output.efieldy", 1);
+    MandatoryOn                              ("output.efieldy");
 
     // Fine tuning
-    AddParameter(ParameterType_Group,         "finetuning" , "Fine tuning performance or consistency parameters");
-    AddParameter(ParameterType_Bool,          "finetuning.disabletiling", "Disable tiling");
-    MandatoryOff                             ("finetuning.disabletiling");
-    AddParameter(ParameterType_Int,           "finetuning.tilesize", "Tile width used to stream the filter output");
-    SetMinimumParameterIntValue              ("finetuning.tilesize", 1);
-    SetDefaultParameterInt                   ("finetuning.tilesize", 16);
+    AddParameter(ParameterType_Group,         "optim" , "This group of parameters allows optimization of processing time");
+    AddParameter(ParameterType_Bool,          "optim.disabletiling", "Disable tiling");
+    MandatoryOff                             ("optim.disabletiling");
+    AddParameter(ParameterType_Int,           "optim.tilesize", "Tile width used to stream the filter output");
+    SetMinimumParameterIntValue              ("optim.tilesize", 1);
+    SetDefaultParameterInt                   ("optim.tilesize", 16);
 
     // Output image
     AddParameter(ParameterType_OutputImage, "out", "output image");
@@ -205,8 +205,8 @@ public:
     // Example
     SetDocExampleParameterValue("source1.il",             "spot6pms.tif");
     SetDocExampleParameterValue("source1.placeholder",    "x1");
-    SetDocExampleParameterValue("source1.fovx",           "16");
-    SetDocExampleParameterValue("source1.fovy",           "16");
+    SetDocExampleParameterValue("source1.rfieldx",        "16");
+    SetDocExampleParameterValue("source1.rfieldy",        "16");
     SetDocExampleParameterValue("model.dir",              "/tmp/my_saved_model/");
     SetDocExampleParameterValue("model.userplaceholders", "is_training=false dropout=0.0");
     SetDocExampleParameterValue("output.names",           "out_predict1 out_proba1");
@@ -248,16 +248,16 @@ public:
     m_TFFilter = TFModelFilterType::New();
     m_TFFilter->SetGraph(m_SavedModel.meta_graph_def.graph_def());
     m_TFFilter->SetSession(m_SavedModel.session.get());
-    m_TFFilter->SetOutputTensorsNames(GetParameterStringList("output.names"));
+    m_TFFilter->SetOutputTensors(GetParameterStringList("output.names"));
     m_TFFilter->SetOutputSpacingScale(GetParameterFloat("output.spcscale"));
     otbAppLogINFO("Output spacing ratio: " << m_TFFilter->GetOutputSpacingScale());
 
     // Get user placeholders
-    TFModelFilterType::DictListType dict;
     TFModelFilterType::StringList expressions = GetParameterStringList("model.userplaceholders");
+    TFModelFilterType::DictType dict;
     for (auto& exp: expressions)
     {
-      TFModelFilterType::DictType entry = tf::ExpressionToTensor(exp);
+      TFModelFilterType::DictElementType entry = tf::ExpressionToTensor(exp);
       dict.push_back(entry);
 
       otbAppLogINFO("Using placeholder " << entry.first << " with " << tf::PrintTensorInfos(entry.second));
@@ -267,7 +267,7 @@ public:
     // Input sources
     for (auto& bundle: m_Bundles)
     {
-      m_TFFilter->PushBackInputBundle(bundle.m_Placeholder, bundle.m_PatchSize, bundle.m_ImageSource.Get());
+      m_TFFilter->PushBackInputTensorBundle(bundle.m_Placeholder, bundle.m_PatchSize, bundle.m_ImageSource.Get());
     }
 
     // Fully convolutional mode on/off
@@ -281,15 +281,15 @@ public:
     FloatVectorImageType::SizeType foe;
     foe[0] = GetParameterInt("output.foex");
     foe[1] = GetParameterInt("output.foey");
-    m_TFFilter->SetOutputFOESize(foe);
+    m_TFFilter->SetOutputExpressionFields({foe});
 
-    otbAppLogINFO("Output field of expression: " << m_TFFilter->GetOutputFOESize());
+    otbAppLogINFO("Output field of expression: " << m_TFFilter->GetOutputExpressionFields()[0]);
 
     // Streaming
-    if (GetParameterInt("finetuning.disabletiling")!=1)
+    if (GetParameterInt("optim.disabletiling")!=1)
     {
       // Get the tile size
-      const unsigned int tileSize = GetParameterInt("finetuning.tilesize");
+      const unsigned int tileSize = GetParameterInt("optim.tilesize");
       otbAppLogINFO("Force tiling with squared tiles of " << tileSize)
 
       // Update the TF filter to get the output image size

app/otbTensorflowModelTrain.cxx

@@ -53,7 +53,7 @@ public:
   itkNewMacro(Self);
   itkTypeMacro(TensorflowModelTrain, Application);
 
-  /** Typedefs for tensorflow */
+  /** Typedefs for TensorFlow */
   typedef otb::TensorflowMultisourceModelTrain<FloatVectorImageType>    TrainModelFilterType;
   typedef otb::TensorflowMultisourceModelValidate<FloatVectorImageType> ValidateModelFilterType;
   typedef otb::TensorflowSource<FloatVectorImageType>                   TFSource;
@@ -75,8 +75,8 @@ public:
     // Parameters keys
     std::string m_KeyInForTrain;     // Key of input image list (training)
     std::string m_KeyInForValid;     // Key of input image list (validation)
-    std::string m_KeyPHNameForTrain; // Key for placeholder name in the tensorflow model (training)
-    std::string m_KeyPHNameForValid; // Key for placeholder name in the tensorflow model (validation)
+    std::string m_KeyPHNameForTrain; // Key for placeholder name in the TensorFlow model (training)
+    std::string m_KeyPHNameForValid; // Key for placeholder name in the TensorFlow model (validation)
     std::string m_KeyPszX;   // Key for samples sizes X
     std::string m_KeyPszY;   // Key for samples sizes Y
   };
@@ -122,18 +122,19 @@ public:
     // Parameter group keys
     ss_key_tr_in   << ss_key_tr_group.str()  << ".il";
     ss_key_val_in  << ss_key_val_group.str() << ".il";
-    ss_key_dims_x  << ss_key_tr_group.str()  << ".fovx";
-    ss_key_dims_y  << ss_key_tr_group.str()  << ".fovy";
+    ss_key_dims_x  << ss_key_tr_group.str()  << ".patchsizex";
+    ss_key_dims_y  << ss_key_tr_group.str()  << ".patchsizey";
     ss_key_tr_ph   << ss_key_tr_group.str()  << ".placeholder";
-    ss_key_val_ph  << ss_key_val_group.str() << ".placeholder";
+    ss_key_val_ph  << ss_key_val_group.str() << ".name";
 
     // Parameter group descriptions
     ss_desc_tr_in  << "Input image (or list to stack) for source #" << inputNumber << " (training)";
     ss_desc_val_in << "Input image (or list to stack) for source #" << inputNumber << " (validation)";
-    ss_desc_dims_x << "Field of view width for source #"            << inputNumber;
-    ss_desc_dims_y << "Field of view height for source #"           << inputNumber;
+    ss_desc_dims_x << "Patch size (x) for source #"                 << inputNumber;
+    ss_desc_dims_y << "Patch size (y) for source #"                 << inputNumber;
     ss_desc_tr_ph  << "Name of the input placeholder for source #"  << inputNumber << " (training)";
-    ss_desc_val_ph << "Name of the input placeholder for source #"  << inputNumber << " (validation)";
+    ss_desc_val_ph << "Name of the input placeholder "
+        "or output tensor for source #"                             << inputNumber << " (validation)";
 
     // Populate group
     AddParameter(ParameterType_Group,          ss_key_tr_group.str(),  ss_desc_tr_group.str());
@@ -194,21 +195,25 @@ public:
     AddParameter(ParameterType_StringList,  "training.userplaceholders",
                  "Additional single-valued placeholders for training. Supported types: int, float, bool.");
     MandatoryOff                           ("training.userplaceholders");
-    AddParameter(ParameterType_StringList,  "training.targetnodesnames",    "Names of the target nodes");
-    MandatoryOn                            ("training.targetnodesnames");
-    AddParameter(ParameterType_StringList,  "training.outputtensorsnames",  "Names of the output tensors to display");
-    MandatoryOff                           ("training.outputtensorsnames");
+    AddParameter(ParameterType_StringList,  "training.targetnodes",    "Names of the target nodes");
+    MandatoryOn                            ("training.targetnodes");
+    AddParameter(ParameterType_StringList,  "training.outputtensors",  "Names of the output tensors to display");
+    MandatoryOff                           ("training.outputtensors");
+    AddParameter(ParameterType_Bool,        "training.usestreaming",   "Use the streaming through patches (slower but can process big dataset)");
+    MandatoryOff                           ("training.usestreaming");
 
     // Metrics
-    AddParameter(ParameterType_Group,       "validation",            "Validation parameters");
+    AddParameter(ParameterType_Group,       "validation",              "Validation parameters");
     MandatoryOff                           ("validation");
-    AddParameter(ParameterType_Choice,      "validation.mode",       "Metrics to compute");
-    AddChoice                              ("validation.mode.none",  "No validation step");
-    AddChoice                              ("validation.mode.class", "Classification metrics");
-    AddChoice                              ("validation.mode.rmse",  "Root mean square error");
+    AddParameter(ParameterType_Choice,      "validation.mode",         "Metrics to compute");
+    AddChoice                              ("validation.mode.none",    "No validation step");
+    AddChoice                              ("validation.mode.class",   "Classification metrics");
+    AddChoice                              ("validation.mode.rmse",    "Root mean square error");
     AddParameter(ParameterType_StringList,  "validation.userplaceholders",
                  "Additional single-valued placeholders for validation. Supported types: int, float, bool.");
     MandatoryOff                           ("validation.userplaceholders");
+    AddParameter(ParameterType_Bool,        "validation.usestreaming", "Use the streaming through patches (slower but can process big dataset)");
+    MandatoryOff                           ("validation.usestreaming");
 
     // Input/output images
     AddAnInputImage();
@@ -220,15 +225,15 @@ public:
     // Example
     SetDocExampleParameterValue("source1.il",                "spot6pms.tif");
     SetDocExampleParameterValue("source1.placeholder",       "x1");
-    SetDocExampleParameterValue("source1.fovx",              "16");
-    SetDocExampleParameterValue("source1.fovy",              "16");
+    SetDocExampleParameterValue("source1.patchsizex",        "16");
+    SetDocExampleParameterValue("source1.patchsizey",        "16");
     SetDocExampleParameterValue("source2.il",                "labels.tif");
     SetDocExampleParameterValue("source2.placeholder",       "y1");
-    SetDocExampleParameterValue("source2.fovx",              "1");
-    SetDocExampleParameterValue("source2.fovy",              "1");
+    SetDocExampleParameterValue("source2.patchsizex",        "1");
+    SetDocExampleParameterValue("source2.patchsizex",        "1");
     SetDocExampleParameterValue("model.dir",                 "/tmp/my_saved_model/");
     SetDocExampleParameterValue("training.userplaceholders", "is_training=true dropout=0.2");
-    SetDocExampleParameterValue("training.targetnodenames",  "optimizer");
+    SetDocExampleParameterValue("training.targetnodes",      "optimizer");
     SetDocExampleParameterValue("model.saveto",              "/tmp/my_saved_model_vars1");
 
   }
@@ -289,7 +294,7 @@ public:
       m_InputPatchesSizeForTraining.push_back(patchSize);
 
       otbAppLogINFO("New source:");
-      otbAppLogINFO("Field of view            : "<< patchSize);
+      otbAppLogINFO("Patch size               : "<< patchSize);
       otbAppLogINFO("Placeholder (training)   : "<< placeholderForTraining);
 
       // Prepare validation sources
@@ -350,13 +355,13 @@ public:
   //
   // Get user placeholders
   //
-  TrainModelFilterType::DictListType GetUserPlaceholders(const std::string key)
+  TrainModelFilterType::DictType GetUserPlaceholders(const std::string key)
   {
-    TrainModelFilterType::DictListType dict;
+    TrainModelFilterType::DictType dict;
     TrainModelFilterType::StringList expressions = GetParameterStringList(key);
     for (auto& exp: expressions)
       {
-      TrainModelFilterType::DictType entry = tf::ExpressionToTensor(exp);
+      TrainModelFilterType::DictElementType entry = tf::ExpressionToTensor(exp);
       dict.push_back(entry);
 
       otbAppLogINFO("Using placeholder " << entry.first << " with " << tf::PrintTensorInfos(entry.second));
@@ -414,15 +419,16 @@ public:
     m_TrainModelFilter = TrainModelFilterType::New();
     m_TrainModelFilter->SetGraph(m_SavedModel.meta_graph_def.graph_def());
     m_TrainModelFilter->SetSession(m_SavedModel.session.get());
-    m_TrainModelFilter->SetOutputTensorsNames(GetParameterStringList("training.outputtensorsnames"));
-    m_TrainModelFilter->SetTargetNodesNames(GetParameterStringList("training.targetnodesnames"));
+    m_TrainModelFilter->SetOutputTensors(GetParameterStringList("training.outputtensors"));
+    m_TrainModelFilter->SetTargetNodesNames(GetParameterStringList("training.targetnodes"));
     m_TrainModelFilter->SetBatchSize(GetParameterInt("training.batchsize"));
     m_TrainModelFilter->SetUserPlaceholders(GetUserPlaceholders("training.userplaceholders"));
+    m_TrainModelFilter->SetUseStreaming(GetParameterInt("training.usestreaming"));
 
     // Set inputs
     for (unsigned int i = 0 ; i < m_InputSourcesForTraining.size() ; i++)
       {
-      m_TrainModelFilter->PushBackInputBundle(
+      m_TrainModelFilter->PushBackInputTensorBundle(
           m_InputPlaceholdersForTraining[i],
           m_InputPatchesSizeForTraining[i],
           m_InputSourcesForTraining[i]);
@@ -454,18 +460,21 @@ public:
       m_ValidateModelFilter->SetBatchSize(GetParameterInt("training.batchsize"));
       m_ValidateModelFilter->SetUserPlaceholders(GetUserPlaceholders("validation.userplaceholders"));
 
+      // AS we use the learning data here, it's rational to use the same option as streaming during training
+      m_ValidateModelFilter->SetUseStreaming(GetParameterInt("training.usestreaming"));
+
       // 1. Evaluate the metrics against the learning data
 
       for (unsigned int i = 0 ; i < m_InputSourcesForEvaluationAgainstLearningData.size() ; i++)
         {
-        m_ValidateModelFilter->PushBackInputBundle(
+        m_ValidateModelFilter->PushBackInputTensorBundle(
             m_InputPlaceholdersForValidation[i],
             m_InputPatchesSizeForValidation[i],
             m_InputSourcesForEvaluationAgainstLearningData[i]);
         }
-      m_ValidateModelFilter->SetOutputTensorsNames(m_TargetTensorsNames);
+      m_ValidateModelFilter->SetOutputTensors(m_TargetTensorsNames);
       m_ValidateModelFilter->SetInputReferences(m_InputTargetsForEvaluationAgainstLearningData);
-      m_ValidateModelFilter->SetOutputFOESizes(m_TargetPatchesSize);
+      m_ValidateModelFilter->SetOutputExpressionFields(m_TargetPatchesSize);
 
       // Update
       AddProcess(m_ValidateModelFilter, "Evaluate model (Learning data)");
@@ -485,6 +494,7 @@ public:
         m_ValidateModelFilter->SetInput(i, m_InputSourcesForEvaluationAgainstValidationData[i]);
         }
       m_ValidateModelFilter->SetInputReferences(m_InputTargetsForEvaluationAgainstValidationData);
+      m_ValidateModelFilter->SetUseStreaming(GetParameterInt("validation.usestreaming"));
 
       // Update
       AddProcess(m_ValidateModelFilter, "Evaluate model (Validation data)");
@@ -509,10 +519,13 @@ public:
 
 private:
 
+  tensorflow::SavedModelBundle     m_SavedModel; // must be alive during all the execution of the application !
+
+  // Filters
   TrainModelFilterType::Pointer    m_TrainModelFilter;
   ValidateModelFilterType::Pointer m_ValidateModelFilter;
-  tensorflow::SavedModelBundle     m_SavedModel; // must be alive during all the execution of the application !
 
+  // Inputs
   BundleList m_Bundles;
 
   // Patches size

app/otbTrainClassifierFromDeepFeatures.cxx

@@ -63,7 +63,6 @@ private:
 
   }
 
-
   void DoInit()
   {
 
@@ -91,7 +90,7 @@ private:
   }
   ShareParameter("model", "tfmodel.model", "Deep net model parameters", "Deep net model parameters");
   ShareParameter("output", "tfmodel.output", "Deep net outputs parameters", "Deep net outputs parameters");
-  ShareParameter("finetuning", "tfmodel.finetuning", "Deep net fine tuning parameters", "Deep net fine tuning parameters");
+  ShareParameter("optim", "tfmodel.optim", "This group of parameters allows optimization of processing time", "This group of parameters allows optimization of processing time");
 
   // Train shared parameters
   ShareParameter("vd"  , "train.io.vd"      , "Input vector data list"      , "Input vector data list" );

include/otbTensorflowGraphOperations.cxx

@@ -105,7 +105,9 @@ void GetTensorAttributes(const tensorflow::GraphDef & graph, std::vector<std::st
       if (node.name().compare((*nameIt)) == 0)
       {
         found = true;
-        tensorflow::DataType ts_dt;
+
+        // Set default to DT_FLOAT
+        tensorflow::DataType ts_dt = tensorflow::DT_FLOAT;
 
         // Default (input?) tensor type
         auto test_is_output = node.attr().find("T");

include/otbTensorflowMultisourceModelBase.h

@@ -30,15 +30,36 @@ namespace otb
 
 /**
  * \class TensorflowMultisourceModelBase
- * \brief This filter is base for TensorFlow model over multiple input images.
+ * \brief This filter is the base class for all TensorFlow model filters.
  *
- * The filter takes N input images and feed the TensorFlow model.
- * Names of input placeholders must be specified using the
- * SetInputPlaceholdersNames method
+ * This abstract class implements a number of generic methods that are used in
+ * filters that use the TensorFlow engine.
  *
- * TODO:
- *   Replace FOV (Field Of View) --> RF (Receptive Field)
- *   Replace FEO (Field Of Expr) --> EF (Expression Field)
+ * The filter has N input images (Input), each one corresponding to a placeholder
+ * that will fed the TensorFlow model. For each input, the name of the
+ * placeholder (InputPlaceholders, a std::vector of std::string) and the
+ * receptive field (InputReceptiveFields, a std::vector of SizeType) i.e. the
+ * input space that the model will "see", must be provided. Hence the number of
+ * input images, and the size of InputPlaceholders and InputReceptiveFields must
+ * be the same. If not, an exception will be thrown during the method
+ * GenerateOutputInformation().
+ *
+ * The TensorFlow graph and session must be set using the SetGraph() and
+ * SetSession() methods.
+ *
+ * Target nodes names of the TensorFlow graph that must be triggered can be set
+ * with the SetTargetNodesNames.
+ *
+ * The OutputTensorNames consists in a strd::vector of std::string, and
+ * corresponds to the names of tensors that will be computed during the session.
+ * As for input placeholders, output tensors field of expression
+ * (OutputExpressionFields, a std::vector of SizeType), i.e. the output
+ * space that the TensorFlow model will "generate", must be provided.
+ *
+ * Finally, a list of scalar placeholders can be fed in the form of std::vector
+ * of std::string, each one expressing the assigment of a signle valued
+ * placeholder, e.g. "drop_rate=0.5 learning_rate=0.002 toto=true".
+ * See otb::tf::ExpressionToTensor() to know more about syntax.
  *
  * \ingroup OTBTensorflow
  */
@@ -72,10 +93,10 @@ public:
   typedef typename TInputImage::RegionType           RegionType;
 
   /** Typedefs for parameters */
-  typedef std::pair<std::string, tensorflow::Tensor> DictType;
+  typedef std::pair<std::string, tensorflow::Tensor> DictElementType;
   typedef std::vector<std::string>                   StringList;
   typedef std::vector<SizeType>                      SizeListType;
-  typedef std::vector<DictType>                      DictListType;
+  typedef std::vector<DictElementType>               DictType;
   typedef std::vector<tensorflow::DataType>          DataTypeListType;
   typedef std::vector<tensorflow::TensorShapeProto>  TensorShapeProtoList;
   typedef std::vector<tensorflow::Tensor>            TensorListType;
@@ -87,27 +108,28 @@ public:
   tensorflow::Session * GetSession()             { return m_Session;    }
 
   /** Model parameters */
-  void PushBackInputBundle(std::string placeholder, SizeType receptiveField, ImagePointerType image);
+  void PushBackInputTensorBundle(std::string name, SizeType receptiveField, ImagePointerType image);
+  void PushBackOuputTensorBundle(std::string name, SizeType expressionField);
+
+  /** Input placeholders names */
+  itkSetMacro(InputPlaceholders, StringList);
+  itkGetMacro(InputPlaceholders, StringList);
 
-//  /** Input placeholders names */
-//  itkSetMacro(InputPlaceholdersNames, StringList);
-  itkGetMacro(InputPlaceholdersNames, StringList);
-//
-//  /** Receptive field */
-//  itkSetMacro(InputFOVSizes, SizeListType);
-  itkGetMacro(InputFOVSizes, SizeListType);
+  /** Receptive field */
+  itkSetMacro(InputReceptiveFields, SizeListType);
+  itkGetMacro(InputReceptiveFields, SizeListType);
 
   /** Output tensors names */
-  itkSetMacro(OutputTensorsNames, StringList);
-  itkGetMacro(OutputTensorsNames, StringList);
+  itkSetMacro(OutputTensors, StringList);
+  itkGetMacro(OutputTensors, StringList);
 
   /** Expression field */
-  itkSetMacro(OutputFOESizes, SizeListType);
-  itkGetMacro(OutputFOESizes, SizeListType);
+  itkSetMacro(OutputExpressionFields, SizeListType);
+  itkGetMacro(OutputExpressionFields, SizeListType);