Adapt new wrapper interface

pom.xml

@@ -19,7 +19,7 @@
       <CNNTrain.version>0.3.4-SNAPSHOT</CNNTrain.version>
       <CNNArch2X.version>0.0.2-SNAPSHOT</CNNArch2X.version>
       <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
-      <EMADL2PythonWrapper.version>0.0.1</EMADL2PythonWrapper.version>
+      <EMADL2PythonWrapper.version>0.0.2-SNAPSHOT</EMADL2PythonWrapper.version>
     
         <!-- .. Libraries .................................................. -->
         <guava.version>18.0</guava.version>

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNTrain2Gluon.java

 package de.monticore.lang.monticar.cnnarch.gluongenerator;
 
 import com.google.common.collect.Maps;
+import de.monticore.lang.embeddedmontiarc.embeddedmontiarc._symboltable.instanceStructure.EMAComponentInstanceSymbol;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.critic.CriticNetworkGenerationPair;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.critic.CriticNetworkGenerator;
 import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.FunctionParameterChecker;
@@ -164,7 +165,7 @@ public class CNNTrain2Gluon extends CNNTrainGenerator {
             setRootProjectModelsDir(modelsDirPath.toString());
         }
 
-        rewardFunctionSourceGenerator.generate(getRootProjectModelsDir().get(),
+        EMAComponentInstanceSymbol emaSymbol = rewardFunctionSourceGenerator.generate(getRootProjectModelsDir().get(),
                 rewardFunctionRootModel, rewardFunctionOutputPath);
         fixArmadilloEmamGenerationOfFile(Paths.get(rewardFunctionOutputPath, String.join("_", fullNameOfComponent) + ".h"));
 
@@ -175,12 +176,11 @@ public class CNNTrain2Gluon extends CNNTrainGenerator {
         if (pythonWrapperApi.checkIfPythonModuleBuildAvailable()) {
             final String rewardModuleOutput
                     = Paths.get(getGenerationTargetPath(), REINFORCEMENT_LEARNING_FRAMEWORK_MODULE).toString();
-            componentPortInformation = pythonWrapperApi.generateAndTryBuilding(getRootProjectModelsDir().get(),
-                    rewardFunctionRootModel, pythonWrapperOutputPath, rewardModuleOutput);
+            componentPortInformation = pythonWrapperApi.generateAndTryBuilding(emaSymbol,
+                    pythonWrapperOutputPath, rewardModuleOutput);
         } else {
             Log.warn("Cannot build wrapper automatically: OS not supported. Please build manually before starting training.");
-            componentPortInformation = pythonWrapperApi.generate(getRootProjectModelsDir().get(), rewardFunctionRootModel,
-                    pythonWrapperOutputPath);
+            componentPortInformation = pythonWrapperApi.generate(emaSymbol, pythonWrapperOutputPath);
         }
         RewardFunctionParameterAdapter functionParameter = new RewardFunctionParameterAdapter(componentPortInformation);
         new FunctionParameterChecker().check(functionParameter);

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/reinforcement/RewardFunctionSourceGenerator.java

 package de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement;
 
+import de.monticore.lang.embeddedmontiarc.embeddedmontiarc._symboltable.instanceStructure.EMAComponentInstanceSymbol;
+
 /**
  *
  */
 public interface RewardFunctionSourceGenerator {
-    void generate(String modelPath, String qualifiedName, String targetPath);
+    EMAComponentInstanceSymbol generate(String modelPath, String qualifiedName, String targetPath);
 }
\ No newline at end of file

src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/IntegrationPythonWrapperTest.java

-package de.monticore.lang.monticar.cnnarch.gluongenerator;
-
-import de.monticore.lang.monticar.cnnarch.gluongenerator.reinforcement.RewardFunctionSourceGenerator;
-import de.monticore.lang.monticar.cnnarch.gluongenerator.util.TrainedArchitectureMockFactory;
-import de.monticore.lang.monticar.cnntrain.annotations.TrainedArchitecture;
-import de.se_rwth.commons.logging.Finding;
-import de.se_rwth.commons.logging.Log;
-import org.junit.Before;
-import org.junit.Ignore;
-import org.junit.Test;
-
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-import static junit.framework.TestCase.assertTrue;
-import static org.mockito.Mockito.mock;
-
-public class IntegrationPythonWrapperTest extends AbstractSymtabTest{
-    private RewardFunctionSourceGenerator rewardFunctionSourceGenerator;
-
-    @Before
-    public void setUp() {
-        // ensure an empty log
-        Log.getFindings().clear();
-        Log.enableFailQuick(false);
-        rewardFunctionSourceGenerator = mock(RewardFunctionSourceGenerator.class);
-    }
-
-    @Test
-    public void testReinforcementConfigWithRewardGeneration() {
-        Log.getFindings().clear();
-        Path modelPath = Paths.get("src/test/resources/valid_tests");
-        CNNTrain2Gluon trainGenerator = new CNNTrain2Gluon(rewardFunctionSourceGenerator);
-
-        TrainedArchitecture trainedArchitecture = TrainedArchitectureMockFactory.createTrainedArchitectureMock();
-
-        trainGenerator.generate(modelPath, "ReinforcementConfig1", trainedArchitecture);
-
-        assertTrue(Log.getFindings().stream().filter(Finding::isError).collect(Collectors.toList()).isEmpty());
-        checkFilesAreEqual(
-                Paths.get("./target/generated-sources-cnnarch"),
-                Paths.get("./src/test/resources/target_code/ReinforcementConfig1"),
-                Arrays.asList(
-                        "CNNTrainer_reinforcementConfig1.py",
-                        "start_training.sh",
-                        "reinforcement_learning/__init__.py",
-                        "reinforcement_learning/strategy.py",
-                        "reinforcement_learning/agent.py",
-                        "reinforcement_learning/environment.py",
-                        "reinforcement_learning/replay_memory.py",
-                        "reinforcement_learning/util.py",
-                        "reinforcement_learning/cnnarch_logger.py")
-        );
-        assertTrue(Paths.get("./target/generated-sources-cnnarch/reward/pylib").toFile().isDirectory());
-    }
-
-}

src/test/resources/target_code/ros-ddpg/CNNTrainer_rosActorNetwork.py

@@ -49,6 +49,7 @@ if __name__ == "__main__":
         'state_topic': '/environment/state',
         'action_topic': '/environment/action',
         'reset_topic': '/environment/reset',
+        'reward_topic': '/environment/reward',
     }
     env = reinforcement_learning.environment.RosEnvironment(**env_params)
 

src/test/resources/target_code/ros-ddpg/reinforcement_learning/environment.py

@@ -2,29 +2,12 @@ import abc
 import logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
-import reward_rewardFunction_executor
-
-class RewardFunction(object):
-    def __init__(self):
-        self.__reward_wrapper = reward_rewardFunction_executor.reward_rewardFunction_executor()
-        self.__reward_wrapper.init()
-
-    def reward(self, state, terminal):
-        s = state.astype('double')
-        t = bool(terminal)
-        inp = reward_rewardFunction_executor.reward_rewardFunction_input()
-        inp.state = s
-        inp.isTerminal = t
-        output = self.__reward_wrapper.execute(inp)
-        return output.reward
-
-
 
 class Environment:
     __metaclass__ = abc.ABCMeta
 
     def __init__(self):
-        self._reward_function = RewardFunction()
+        pass
 
     @abc.abstractmethod
     def reset(self):
@@ -60,6 +43,8 @@ class RosEnvironment(Environment):
         self.__waiting_for_terminal_update = False
         self.__last_received_state = 0
         self.__last_received_terminal = True
+        self.__last_received_reward = 0.0
+        self.__waiting_for_reward_update = False
 
         rospy.loginfo("Initialize node {0}".format(ros_node_name))
 
@@ -77,6 +62,9 @@ class RosEnvironment(Environment):
         self.__terminal_state_subscriber = rospy.Subscriber(terminal_state_topic, Bool, self.__terminal_state_callback)
         rospy.loginfo('Terminal State Subscriber registered with topic {}'.format(terminal_state_topic))
 
+        self.__reward_subscriber = rospy.Subscriber(reward_topic, Float32, self.__reward_callback)
+        rospy.loginfo('Reward Subscriber registered with topic {}'.format(reward_topic))
+
         rate = rospy.Rate(10)
 
         thread.start_new_thread(rospy.spin, ())
@@ -110,11 +98,12 @@ class RosEnvironment(Environment):
 
         self.__waiting_for_state_update = True
         self.__waiting_for_terminal_update = True
+        self.__waiting_for_reward_update = True
         self.__step_publisher.publish(action_rospy)
         self.__wait_for_new_state(self.__step_publisher, action_rospy)
         next_state = self.__last_received_state
         terminal = self.__last_received_terminal
-        reward = self.__calc_reward(next_state, terminal)
+        reward = self.__last_received_reward
         rospy.logdebug('Calculated reward: {}'.format(reward))
 
         return next_state, reward, terminal, 0
@@ -123,7 +112,7 @@ class RosEnvironment(Environment):
         time_of_timeout = time.time() + self.__timeout_in_s
         timeout_counter = 0
         while(self.__waiting_for_state_update
-              or self.__waiting_for_terminal_update):
+              or self.__waiting_for_terminal_update or self.__waiting_for_reward_update):
             is_timeout = (time.time() > time_of_timeout)
             if (is_timeout):
                 if timeout_counter < 3:
@@ -150,6 +139,7 @@ class RosEnvironment(Environment):
         logger.debug('Received terminal: {}'.format(self.__last_received_terminal))
         self.__waiting_for_terminal_update = False
 
-    def __calc_reward(self, state, terminal):
-        # C++ Wrapper call
-        return self._reward_function.reward(state, terminal)
+    def __reward_callback(self, data):
+        self.__last_received_reward = float(data.data)
+        logger.debug('Received reward: {}'.format(self.__last_received_reward))
+        self.__waiting_for_reward_update = False

src/test/resources/valid_tests/ddpg-ros/RosActorNetwork.cnnt

@@ -9,10 +9,9 @@ configuration RosActorNetwork {
         state_topic : "/environment/state"
         action_topic : "/environment/action"
         reset_topic : "/environment/reset"
+        reward_topic: "/environment/reward"
     }
 
-    reward_function : reward.rewardFunction
-
     agent_name : "ddpg-agent"
 
     num_episodes : 2500

src/test/resources/valid_tests/ddpg-ros/reward/RewardFunction.emadl

-package reward;
-
-component RewardFunction {
-    ports
-        in Q^{16} state,
-        in B isTerminal,
-        out Q reward;
-
-    implementation Math {
-        Q speed = state(15);
-        Q angle = state(1);
-
-        reward = speed * cos(angle);
-    }
-}
\ No newline at end of file