added new parameters for GAN training and CoCos

src/main/grammars/de/monticore/lang/monticar/CNNTrain.mc4

@@ -259,10 +259,17 @@ grammar CNNTrain extends de.monticore.lang.monticar.Common2, de.monticore.Number
     PolicyDelayEntry implements ConfigEntry = name:"policy_delay" ":" value:IntegerValue;
 
     // GANs Extensions
+
     KValueEntry implements ConfigEntry = name:"k_value" ":" value:IntegerValue;
-    GeneratorLossEntry implements ConfigEntry = name:"generator_loss" ":" value:StringValue;
-    ConditionalInputEntry implements ConfigEntry = name:"conditional_input" ":" value:StringValue;
+    GeneratorTargetNameEntry implements ConfigEntry = name:"generator_target_name" ":" value:StringValue;
+    GeneratorLossEntry implements ConfigEntry = name:"generator_loss" ":" value:GeneratorLossValue;
+    GeneratorLossValue implements ConfigValue = (l1: "l1" | l2: "l2");
     NoiseInputEntry implements ConfigEntry = name:"noise_input" ":" value:StringValue;
+    GeneratorLossWeightEntry implements ConfigEntry = name:"generator_loss_weight" ":" value:NumberValue;
+    DiscriminatorLossWeightEntry implements ConfigEntry = name:"discriminator_loss_weight" ":" value:NumberValue;
+
+    SpeedPeriodEntry implements ConfigEntry = name:"speed_period" ":" value:IntegerValue;
+    PrintImagesEntry implements ConfigEntry = name:"print_images" ":" value:BooleanValue;
 
     interface MultiParamValueMapConfigEntry extends ConfigEntry;
     interface MultiParamValueMapParamValue extends ConfigValue;
@@ -285,6 +292,8 @@ grammar CNNTrain extends de.monticore.lang.monticar.Common2, de.monticore.Number
     MeanValueEntry implements NoiseDistributionGaussianEntry = name:"mean_value" ":" value:IntegerValue;
     SpreadValueEntry implements NoiseDistributionGaussianEntry = name:"spread_value" ":" value:IntegerValue;
 
+    NoiseDistributionUniformValue implements NoiseDistributionValue = name:"uniform" ("{"  "}")?;
+
     // Constraint Distributions
     ConstraintDistributionEntry implements MultiParamValueMapConfigEntry  = name:"constraint_distributions" ":" value:ConstraintDistributionValue;
     ConstraintDistributionValue implements MultiParamValueMapParamValue = ("{" params:ConstraintDistributionParam* "}")?;

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/CNNTrainCocos.java

@@ -51,4 +51,11 @@ public class CNNTrainCocos {
                 .addCoCo(new CheckCriticNetworkInputs());
         checker.checkAll(configurationSymbol);
     }
+
+    public static void checkGANCocos(final ConfigurationSymbol configurationSymbol) {
+        CNNTrainConfigurationSymbolChecker checker = new CNNTrainConfigurationSymbolChecker()
+                .addCoCo(new CheckGANNetworkPorts())
+                .addCoCo(new CheckGANConfigurationDependencies());
+        checker.checkAll(configurationSymbol);
+    }
 }
\ No newline at end of file

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/CheckGANConfigurationDependencies.java

+/**
+ * (c) https://github.com/MontiCore/monticore
+ *
+ * The license generally applicable for this project
+ * can be found under https://github.com/MontiCore/monticore.
+ */
+package de.monticore.lang.monticar.cnntrain._cocos;
+
+import de.monticore.lang.monticar.cnntrain._ast.ASTEntry;
+import de.monticore.lang.monticar.cnntrain._ast.ASTLearningMethodEntry;
+import de.monticore.lang.monticar.cnntrain._symboltable.ConfigurationSymbol;
+import de.monticore.lang.monticar.cnntrain._symboltable.LearningMethod;
+import de.monticore.lang.monticar.cnntrain.helper.ConfigEntryNameConstants;
+import de.monticore.lang.monticar.cnntrain.helper.ErrorCodes;
+import de.se_rwth.commons.logging.Log;
+import sun.security.krb5.internal.ccache.CredentialsCache;
+
+public class CheckGANConfigurationDependencies implements CNNTrainConfigurationSymbolCoCo{
+
+    public CheckGANConfigurationDependencies() { }
+
+    @Override
+    public void check(ConfigurationSymbol configurationSymbol) {
+
+        if(configurationSymbol.getLearningMethod() == LearningMethod.GAN) {
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.GENERATOR_LOSS) != null)
+                if (configurationSymbol.getEntry(ConfigEntryNameConstants.GENERATOR_TARGET_NAME) == null)
+                    Log.error("0" + ErrorCodes.REQUIRED_PARAMETER_MISSING +
+                            " Generator loss specified but conditional input is missing");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.GENERATOR_TARGET_NAME) != null)
+                if (configurationSymbol.getEntry(ConfigEntryNameConstants.GENERATOR_LOSS) == null)
+                    Log.error("0" + ErrorCodes.REQUIRED_PARAMETER_MISSING +
+                            " Conditional input specified but generator loss is missing");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.LOSS) != null)
+                Log.error("0" + ErrorCodes.UNSUPPORTED_PARAMETER +
+                        " Loss parameter not valid for GAN learning");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.NOISE_INPUT) != null)
+                if (configurationSymbol.getEntry(ConfigEntryNameConstants.NOISE_DISTRIBUTION) == null)
+                    Log.error("0" + ErrorCodes.REQUIRED_PARAMETER_MISSING +
+                            " Noise input specified but noise distribution parameter is missing");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.CONSTRAINT_DISTRIBUTION) != null)
+                if (configurationSymbol.getEntry(ConfigEntryNameConstants.QNETWORK_NAME) == null)
+                    Log.error("0" + ErrorCodes.REQUIRED_PARAMETER_MISSING +
+                            " Constraint distributions are given but q-network is missing");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.CONSTRAINT_LOSS) != null)
+                if (configurationSymbol.getEntry(ConfigEntryNameConstants.QNETWORK_NAME) == null)
+                    Log.error("0" + ErrorCodes.REQUIRED_PARAMETER_MISSING +
+                            " Constraint losses are given but q-network is missing");
+
+            if (configurationSymbol.getEntry(ConfigEntryNameConstants.NOISE_INPUT) == null)
+                Log.warn(" No noise input specified. Are you sure this is correct?");
+        }
+    }
+}

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/CheckGANNetworkPorts.java

+/**
+ * (c) https://github.com/MontiCore/monticore
+ *
+ * The license generally applicable for this project
+ * can be found under https://github.com/MontiCore/monticore.
+ */
+package de.monticore.lang.monticar.cnntrain._cocos;
+
+import de.monticore.lang.monticar.cnntrain._symboltable.ConfigurationSymbol;
+import de.monticore.lang.monticar.cnntrain._symboltable.NNArchitectureSymbol;
+import de.monticore.lang.monticar.cnntrain.helper.ErrorCodes;
+import de.se_rwth.commons.logging.Log;
+
+import java.util.Optional;
+
+public class CheckGANNetworkPorts implements CNNTrainConfigurationSymbolCoCo {
+
+    public void CheckGANNetworkPorts() { }
+
+    @Override
+    public void check(ConfigurationSymbol configurationSymbol) {
+
+        NNArchitectureSymbol gen = configurationSymbol.getTrainedArchitecture().get();
+        NNArchitectureSymbol dis = configurationSymbol.getDiscriminatorNetwork().get();
+        Optional<NNArchitectureSymbol> qnet = configurationSymbol.getQNetwork();
+
+        if(gen.getOutputs().size() != 1)
+            Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Generator network has more then one output, " +
+                    "but is supposed to only have one");
+
+        if(qnet.isPresent() && qnet.get().getInputs().size() != 1)
+            Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Q-Network has more then one input, " +
+                    "but is supposed to only have one");
+
+        if(qnet.isPresent() && dis.getOutputs().size() != 2)
+            Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Discriminator needs exactly 2 output " +
+                    "ports when q-network is given");
+
+        if(!qnet.isPresent() && dis.getOutputs().size() != 1)
+            Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Discriminator needs exactly 1 output " +
+                    "port when no q-network is given");
+
+        if(qnet.isPresent() && dis.getOutputs().size() == 2)
+            if(!dis.getOutputs().get(1).equals("features"))
+                Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Second output of discriminator network " +
+                        "has to be named features when " +
+                        "q-network is given");
+
+        if(qnet.isPresent() && !qnet.get().getInputs().get(0).equals("features"))
+                Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Input to q-network needs to be named features");
+
+        if(!gen.getOutputs().get(0).equals(dis.getInputs().get(0)))
+            Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " The generator networks output name does not " +
+                    "fit the first discriminators input name");
+
+        if(qnet.isPresent())
+            if(gen.getInputs().contains(qnet.get().getOutputs()))
+                Log.error("0" + ErrorCodes.GAN_ARCHITECTURE_ERROR + " Generator input does not contain all " +
+                    "latent-codes outputted by q-network");
+    }
+}

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/CheckLearningParameterCombination.java

@@ -53,8 +53,10 @@ public class CheckLearningParameterCombination implements CNNTrainASTEntryCoCo {
             = parameterAlgorithmMapping.isSupervisedLearningParameter(node.getClass());
         final boolean reinforcementLearningParameter
             = parameterAlgorithmMapping.isReinforcementLearningParameter(node.getClass());
+        final boolean ganLearningParameter
+                = parameterAlgorithmMapping.isGANLearningParameter(node.getClass());
 
-        assert (supervisedLearningParameter  || reinforcementLearningParameter) :
+        assert (supervisedLearningParameter  || reinforcementLearningParameter || ganLearningParameter) :
                 "Parameter " + node.getName() + " is not checkable, because it is unknown to Condition";
 
         if (supervisedLearningParameter && !reinforcementLearningParameter) {
@@ -62,7 +64,7 @@ public class CheckLearningParameterCombination implements CNNTrainASTEntryCoCo {
         } else if(!supervisedLearningParameter && reinforcementLearningParameter) {
             setLearningMethodOrLogErrorIfActualLearningMethodIsNotReinforcement(node);
     }
-    }
+}
 
     private void setLearningMethodOrLogErrorIfActualLearningMethodIsNotReinforcement(ASTEntry node) {
         if (isLearningMethodKnown()) {
@@ -91,11 +93,10 @@ public class CheckLearningParameterCombination implements CNNTrainASTEntryCoCo {
     private void evaluateLearningMethodEntry(ASTEntry node) {
         ASTLearningMethodValue learningMethodValue = (ASTLearningMethodValue)node.getValue();
         LearningMethod evaluatedLearningMethod;
-        if(learningMethodValue.isPresentReinforcement()) {
+        if(learningMethodValue.isPresentReinforcement())
             evaluatedLearningMethod = LearningMethod.REINFORCEMENT;
-        } else {
+        else
             evaluatedLearningMethod = LearningMethod.SUPERVISED;
-        }
 
         if (isLearningMethodKnown()) {
             logErrorIfEvaluatedLearningMethoNotEqualToActual(node, evaluatedLearningMethod);
@@ -127,17 +128,17 @@ public class CheckLearningParameterCombination implements CNNTrainASTEntryCoCo {
         if (learningMethod.equals(LearningMethod.REINFORCEMENT)) {
             return parameterAlgorithmMapping.getAllReinforcementParameters();
         }
+
         return parameterAlgorithmMapping.getAllSupervisedParameters();
     }
 
 
     private void setLearningMethod(final LearningMethod learningMethod) {
-        if (learningMethod.equals(LearningMethod.REINFORCEMENT)) {
+        if (learningMethod.equals(LearningMethod.REINFORCEMENT))
             setLearningMethodToReinforcement();
-        } else {
+        else
             setLearningMethodToSupervised();
     }
-    }
 
     private void setLearningMethodToSupervised() {
         this.learningMethod = LearningMethod.SUPERVISED;

src/main/java/de/monticore/lang/monticar/cnntrain/_cocos/ParameterAlgorithmMapping.java

@@ -129,9 +129,12 @@ class ParameterAlgorithmMapping {
             ASTDiscriminatorOptimizerEntry.class,
             ASTKValueEntry.class,
             ASTGeneratorLossEntry.class,
-            ASTConditionalInputEntry.class,
-            ASTNoiseInputEntry.class
-
+            ASTGeneratorTargetNameEntry.class,
+            ASTNoiseInputEntry.class,
+            ASTGeneratorLossWeightEntry.class,
+            ASTDiscriminatorLossWeightEntry.class,
+            ASTSpeedPeriodEntry.class,
+            ASTPrintImagesEntry.class
     );
 
     ParameterAlgorithmMapping() {
@@ -156,6 +159,12 @@ class ParameterAlgorithmMapping {
     }
 
     boolean isSupervisedLearningParameter(Class<? extends ASTEntry> entryClazz) {
+        return GENERAL_PARAMETERS.contains(entryClazz)
+            || EXCLUSIVE_SUPERVISED_PARAMETERS.contains(entryClazz);
+
+    }
+
+    boolean isGANLearningParameter(Class<? extends ASTEntry> entryClazz) {
         return GENERAL_PARAMETERS.contains(entryClazz)
                 || EXCLUSIVE_SUPERVISED_PARAMETERS.contains(entryClazz)
                 || GENERAL_GAN_PARAMETERS.contains(entryClazz);
@@ -180,6 +189,14 @@ class ParameterAlgorithmMapping {
             || EXCLUSIVE_TD3_PARAMETERS.contains(entryClazz);
     }
 
+    List<Class> getAllGANParameters() {
+        return ImmutableList.<Class> builder()
+                .addAll(GENERAL_PARAMETERS)
+                .addAll(EXCLUSIVE_SUPERVISED_PARAMETERS)
+                .addAll(GENERAL_GAN_PARAMETERS)
+                .build();
+    }
+
     List<Class> getAllReinforcementParameters() {
         return ImmutableList.<Class> builder()
             .addAll(GENERAL_PARAMETERS)

src/main/java/de/monticore/lang/monticar/cnntrain/_symboltable/CNNTrainSymbolTableCreator.java

@@ -145,7 +145,7 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
     }
 
     @Override
-    public void endVisit(ASTGeneratorLossEntry node) {
+    public void endVisit(ASTNoiseInputEntry node) {
         EntrySymbol entry = new EntrySymbol(node.getName());
         entry.setValue(getValueSymbolForString(node.getValue()));
         addToScopeAndLinkWithNode(entry, node);
@@ -153,7 +153,39 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
     }
 
     @Override
-    public void endVisit(ASTConditionalInputEntry node) {
+    public void endVisit(ASTGeneratorLossWeightEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForDouble(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void endVisit(ASTDiscriminatorLossWeightEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForDouble(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void endVisit(ASTSpeedPeriodEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForInteger(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void endVisit(ASTPrintImagesEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        entry.setValue(getValueSymbolForBoolean(node.getValue()));
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
+    @Override
+    public void endVisit(ASTGeneratorTargetNameEntry node) {
         EntrySymbol entry = new EntrySymbol(node.getName());
         entry.setValue(getValueSymbolForString(node.getValue()));
         addToScopeAndLinkWithNode(entry, node);
@@ -442,6 +474,22 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
         configuration.getEntryMap().put(node.getName(), entry);
     }
 
+    @Override
+    public void visit(ASTGeneratorLossEntry node) {
+        EntrySymbol entry = new EntrySymbol(node.getName());
+        ValueSymbol value = new ValueSymbol();
+
+        if (node.getValue().isPresentL1()) {
+            value.setValue(GeneratorLoss.L1);
+        } else if (node.getValue().isPresentL2()) {
+            value.setValue(GeneratorLoss.L2);
+        }
+
+        entry.setValue(value);
+        addToScopeAndLinkWithNode(entry, node);
+        configuration.getEntryMap().put(node.getName(), entry);
+    }
+
     @Override
     public void visit(ASTRLAlgorithmEntry node) {
         EntrySymbol entry = new EntrySymbol(node.getName());
@@ -564,14 +612,6 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
         configuration.getEntryMap().put(node.getName(), entry);
     }
 
-    @Override
-    public void visit(ASTPreprocessingEntry node) {
-        EntrySymbol entry = new EntrySymbol(node.getName());
-        entry.setValue(getValueSymbolForComponentNameAsString(node.getValue()));
-        addToScopeAndLinkWithNode(entry, node);
-        configuration.getEntryMap().put(node.getName(), entry);
-    }
-
 
     @Override
     public void visit(ASTReplayMemoryEntry node) {
@@ -628,11 +668,13 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
     @Override
     public void visit(ASTNoiseDistributionEntry node) {
         NoiseDistribution noiseDistribution;
-        if(node.getValue().getName().equals("gaussian")) {
+        if(node.getValue().getName().equals("gaussian"))
             noiseDistribution = NoiseDistribution.GAUSSIAN;
-        } else {
+        else if (node.getValue().getName().equals("uniform"))
+            noiseDistribution = NoiseDistribution.UNIFORM;
+        else
             noiseDistribution = NoiseDistribution.GAUSSIAN;
-        }
+
         processMultiParamConfigVisit(node, noiseDistribution);
     }
 
@@ -650,6 +692,14 @@ public class CNNTrainSymbolTableCreator extends CNNTrainSymbolTableCreatorTOP {
         addToScopeAndLinkWithNode(symbol, node);
     }
 
+    @Override
+    public void visit(ASTPreprocessingEntry node) {
+        PreprocessingComponentSymbol symbol = new PreprocessingComponentSymbol(node.getName());
+        symbol.setPreprocessingComponentName(node.getValue().getNameList());
+        configuration.setPreprocessingComponent(symbol);
+        addToScopeAndLinkWithNode(symbol, node);
+    }
+
     @Override
     public void visit(ASTSoftTargetUpdateRateEntry node) {
         EntrySymbol entry = new EntrySymbol(node.getName());

src/main/java/de/monticore/lang/monticar/cnntrain/_symboltable/ConfigurationSymbol.java

@@ -20,6 +20,7 @@ public class ConfigurationSymbol extends CommonScopeSpanningSymbol {
     private OptimizerSymbol criticOptimizer;
     private LossSymbol loss;
     private RewardFunctionSymbol rlRewardFunctionSymbol;
+    private PreprocessingComponentSymbol preprocessingComponentSymbol;
     private NNArchitectureSymbol trainedArchitecture;
     private NNArchitectureSymbol criticNetwork;
     private NNArchitectureSymbol discriminatorNetwork;
@@ -30,6 +31,7 @@ public class ConfigurationSymbol extends CommonScopeSpanningSymbol {
     public ConfigurationSymbol()  {
         super("", KIND);
         rlRewardFunctionSymbol = null;
+        preprocessingComponentSymbol = null;
         trainedArchitecture = null;
     }
 
@@ -65,6 +67,18 @@ public class ConfigurationSymbol extends CommonScopeSpanningSymbol {
         return Optional.ofNullable(this.rlRewardFunctionSymbol);
     }
 
+    public void setPreprocessingComponent(PreprocessingComponentSymbol preprocessingComponentSymbol) {
+        this.preprocessingComponentSymbol = preprocessingComponentSymbol;
+    }
+
+    public Optional<PreprocessingComponentSymbol> getPreprocessingComponent() {
+        return Optional.ofNullable(this.preprocessingComponentSymbol);
+    }
+
+    public boolean hasPreprocessor() {
+        return this.preprocessingComponentSymbol != null;
+    }
+
     public Optional<NNArchitectureSymbol> getTrainedArchitecture() {
         return Optional.ofNullable(trainedArchitecture);
     }
@@ -118,10 +132,6 @@ public class ConfigurationSymbol extends CommonScopeSpanningSymbol {
         return getLearningMethod().equals(LearningMethod.GAN);
     }
 
-    public boolean hasPreprocessor() {
-        return getEntryMap().containsKey(PREPROCESSING_NAME);
-    }
-
     public boolean hasCritic() {
         return getEntryMap().containsKey(CRITIC);
     }
@@ -144,16 +154,6 @@ public class ConfigurationSymbol extends CommonScopeSpanningSymbol {
         return Optional.of((String)criticNameValue);
     }
 
-    public Optional<String> getPreprocessingName() {
-        if (!hasPreprocessor()) {
-            return Optional.empty();
-        }
-
-        final Object preprocessingNameValue = getEntry(PREPROCESSING_NAME).getValue().getValue();
-        assert preprocessingNameValue instanceof String;
-        return Optional.of((String)preprocessingNameValue);
-    }
-
     public Optional<String> getDiscriminatorName() {
         if (!hasDiscriminator()) {
             return Optional.empty();