ADD: streaming option for training/validation

app/otbTensorflowModelTrain.cxx

@@ -198,17 +198,21 @@ public:
     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();
@@ -418,6 +422,7 @@ public:
     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++)
@@ -454,6 +459,9 @@ 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++)
@@ -485,6 +493,7 @@ 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)");