Merge rnn into develop

.gitignore

@@ -5,7 +5,6 @@ nppBackup
 .classpath
 .idea
 .git
-
-*.iml
-
 .vscode
+*.iml
+train.log

.vscode/launch.json

-{
-    "configurations": [
-        {
-            "type": "java",
-            "name": "CodeLens (Launch) - EMADLGeneratorCli",
-            "request": "launch",
-            "mainClass": "de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli",
-            "projectName": "embedded-montiarc-emadl-generator"
-        }
-    ]
-}
\ No newline at end of file

pom.xml

@@ -15,10 +15,10 @@
   <properties>
 
     <!-- .. SE-Libraries .................................................. -->
-    <emadl.version>0.2.6</emadl.version>
+    <emadl.version>0.2.8-SNAPSHOT</emadl.version>
     <CNNTrain.version>0.3.2-SNAPSHOT</CNNTrain.version>
-    <cnnarch-mxnet-generator.version>0.2.15-SNAPSHOT</cnnarch-mxnet-generator.version>
-    <cnnarch-caffe2-generator.version>0.2.11-SNAPSHOT</cnnarch-caffe2-generator.version>
+    <cnnarch-mxnet-generator.version>0.2.16-SNAPSHOT</cnnarch-mxnet-generator.version>
+    <cnnarch-caffe2-generator.version>0.2.12-SNAPSHOT</cnnarch-caffe2-generator.version>
     <cnnarch-gluon-generator.version>0.2.1-SNAPSHOT</cnnarch-gluon-generator.version>
     <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
 

src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGenerator.java

@@ -370,6 +370,8 @@ public class EMADLGenerator {
         EMADLCocos.checkAll(componentInstanceSymbol);
 
         if (architecture.isPresent()){
+            cnnArchGenerator.check(architecture.get());
+
             String dPath = null;
             Path dataPathDefinition = Paths.get(getModelsPath(), "data_paths.txt");
             if (dataPathDefinition.toFile().exists()) {

src/main/java/de/monticore/lang/monticar/emadl/generator/RewardFunctionCppGenerator.java

@@ -9,9 +9,6 @@ import de.se_rwth.commons.logging.Log;
 import java.io.IOException;
 import java.util.Optional;
 
-/**
- *
- */
 public class RewardFunctionCppGenerator implements RewardFunctionSourceGenerator {
     public RewardFunctionCppGenerator() {
     }

src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java

@@ -43,7 +43,6 @@ import static junit.framework.TestCase.assertTrue;
 import static org.junit.Assert.assertFalse;
 
 public class GenerationTest extends AbstractSymtabTest {
-
     @Before
     public void setUp() {
         // ensure an empty log
@@ -143,7 +142,7 @@ public class GenerationTest extends AbstractSymtabTest {
     }
 
     @Test
-    public void testMnistClassifier() throws IOException, TemplateException {
+    public void testMnistClassifierForCaffe2() throws IOException, TemplateException {
         Log.getFindings().clear();
         String[] args = {"-m", "src/test/resources/models/", "-r", "mnist.MnistClassifier", "-b", "CAFFE2", "-f", "n", "-c", "n"};
         EMADLGeneratorCli.main(args);
@@ -181,7 +180,7 @@ public class GenerationTest extends AbstractSymtabTest {
                         "CNNCreator_mnist_mnistClassifier_net.py",
                         "CNNPredictor_mnist_mnistClassifier_net.h",
                         "CNNDataLoader_mnist_mnistClassifier_net.py",
-                        "supervised_trainer.py",
+                        "CNNSupervisedTrainer_mnist_mnistClassifier_net.py",
                         "mnist_mnistClassifier_net.h",
                         "HelperA.h",
                         "CNNTranslator.h",

src/test/java/de/monticore/lang/monticar/emadl/IntegrationCaffe2Test.java

@@ -20,28 +20,8 @@
  */
 package de.monticore.lang.monticar.emadl;
 
-import de.monticore.lang.monticar.emadl.generator.Backend;
-import de.monticore.lang.monticar.emadl.generator.EMADLGenerator;
-import de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli;
-import de.se_rwth.commons.logging.Log;
-import freemarker.template.TemplateException;
-import org.junit.Before;
-import org.junit.Ignore;
-import org.junit.Test;
-
-import java.io.IOException;
-import java.nio.charset.Charset;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.util.Arrays;
-import java.util.List;
-
-import static junit.framework.TestCase.assertTrue;
-import static org.junit.Assert.assertFalse;
-
 public class IntegrationCaffe2Test extends IntegrationTest {
     public IntegrationCaffe2Test() {
         super("CAFFE2", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#13D139510DC5681639AA91D7250288D3#1A42D4842D0664937A9F6B727BD60CEF");
     }
-}
+}
\ No newline at end of file

src/test/java/de/monticore/lang/monticar/emadl/IntegrationGluonTest.java

@@ -20,8 +20,60 @@
  */
 package de.monticore.lang.monticar.emadl;
 
+import de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli;
+import de.se_rwth.commons.logging.Log;
+import org.junit.Test;
+
+import java.nio.file.Files;
+import java.nio.file.NoSuchFileException;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+
+import static junit.framework.TestCase.assertTrue;
+import static org.junit.Assert.assertFalse;
+
 public class IntegrationGluonTest extends IntegrationTest {
+
+    private Path multipleInputsHashFile = Paths.get("./target/generated-sources-emadl/MultipleInputs.training_hash");
+    private Path multipleOutputsHashFile = Paths.get("./target/generated-sources-emadl/MultipleOutputs.training_hash");
+
     public IntegrationGluonTest() {
         super("GLUON", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A");
     }
+
+    @Test
+    public void testMultipleInputs() {
+        Log.getFindings().clear();
+
+        deleteHashFile(multipleInputsHashFile);
+
+        String[] args = {"-m", "src/test/resources/models/", "-r", "MultipleInputs", "-b", "GLUON"};
+        EMADLGeneratorCli.main(args);
+
+        assertTrue(Log.getFindings().isEmpty());
+    }
+
+    @Test
+    public void testMultipleOutputs() {
+        Log.getFindings().clear();
+
+        deleteHashFile(multipleOutputsHashFile);
+
+        String[] args = {"-m", "src/test/resources/models/", "-r", "MultipleOutputs", "-b", "GLUON"};
+        EMADLGeneratorCli.main(args);
+
+        assertTrue(Log.getFindings().isEmpty());
+    }
+
+    private void deleteHashFile(Path hashFile) {
+        try {
+            Files.delete(hashFile);
+        }
+        catch (NoSuchFileException e) {
+
+        }
+        catch(Exception e) {
+            assertFalse("Could not delete hash file", true);
+        }
+    }
 }

src/test/java/de/monticore/lang/monticar/emadl/IntegrationMXNetTest.java

@@ -20,25 +20,6 @@
  */
 package de.monticore.lang.monticar.emadl;
 
-import de.monticore.lang.monticar.emadl.generator.Backend;
-import de.monticore.lang.monticar.emadl.generator.EMADLGenerator;
-import de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli;
-import de.se_rwth.commons.logging.Log;
-import freemarker.template.TemplateException;
-import org.junit.Before;
-import org.junit.Test;
-
-import java.io.IOException;
-import java.nio.charset.Charset;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.util.Arrays;
-import java.util.List;
-
-import static junit.framework.TestCase.assertTrue;
-import static org.junit.Assert.assertFalse;
-
 public class IntegrationMXNetTest extends IntegrationTest {
     public IntegrationMXNetTest() {
         super("MXNET", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A");

src/test/resources/models/Alexnet.emadl

@@ -41,7 +41,7 @@ component Alexnet{
         fc(->=2) ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
 
     }
 }
\ No newline at end of file

src/test/resources/models/InstanceTest/NetworkB.emadl

@@ -18,7 +18,7 @@ component NetworkB<Z classes = 10> (Z convChannels){
         Dropout() ->
         FullyConnected(units=classes) ->
         Softmax() ->
-        predictions
+        predictions;
 
     }
 }
\ No newline at end of file

src/test/resources/models/InvalidLayerInput.emadl

@@ -27,6 +27,6 @@ component InvalidLayerInput{
         fc() ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
     }
 }
\ No newline at end of file

src/test/resources/models/MultipleInputs.cnnt

+configuration MultipleInputs{
+     num_epoch:10
+     batch_size:5
+     context:cpu
+     optimizer:adam{
+         learning_rate:0.01
+         learning_rate_decay:0.8
+         step_size:1000
+         weight_decay:0.0001
+     }
+}

src/test/resources/models/MultipleInputs.emadl

+component MultipleInputs{
+    ports in Z(0:255)^{3, 32, 32} data[2],
+         out Q(0:1)^{10} softmax;
+
+    implementation CNN {
+        (
+            data[0] ->
+            Convolution(kernel=(5,5), channels=8, padding="valid") ->
+            Convolution(kernel=(5,5), channels=8, padding="valid") ->
+            FullyConnected(units=128) ->
+            Dropout()
+        |
+            data[1] ->
+            Convolution(kernel=(5,5), channels=8, padding="valid") ->
+            Convolution(kernel=(5,5), channels=8, padding="valid") ->
+            FullyConnected(units=128) ->
+            Dropout()
+        ) ->
+        Concatenate() ->
+        FullyConnected(units=10) ->
+        Softmax() ->
+        softmax;
+    }
+}
\ No newline at end of file

src/test/resources/models/MultipleOutputs.cnnt

 configuration MultipleOutputs{
-    num_epoch:10
-    batch_size:64
-    normalize:true
-    load_checkpoint:false
-    optimizer:adam{
-        learning_rate:0.01
-        learning_rate_decay:0.8
-        step_size:1000
-    }
+     num_epoch:10
+     batch_size:5
+     context:cpu
+     optimizer:adam{
+         learning_rate:0.01
+         learning_rate_decay:0.8
+         step_size:1000
+         weight_decay:0.0001
+     }
 }

src/test/resources/models/MultipleOutputs.emadl

 component MultipleOutputs{
-    ports in Q(-oo:+oo)^{10,1,1} data,
-         out Q(0:1)^{4} pred[2];
+    ports in Z(0:255)^{3, 32, 32} data,
+         out Q(0:1)^{10} pred[2];
 
     implementation CNN {
-
         data ->
-        FullyConnected(units=128, no_bias=true) ->
-        Tanh() ->
-        (
-            FullyConnected(units=16, no_bias=true) ->
-            Tanh() ->
-            FullyConnected(units=4, no_bias=true)
-        |
-            FullyConnected(units=16, no_bias=true) ->
-            Tanh() ->
-            FullyConnected(units=4, no_bias=true)
-        ) ->
+        Convolution(kernel=(5,5), channels=8, padding="valid") ->
+        Convolution(kernel=(5,5), channels=8, padding="valid") ->
+        FullyConnected(units=128) ->
+        Dropout() ->
+        FullyConnected(units=10) ->
         (
-            [0] ->
             Softmax() ->
             pred[0]
         |
-            [1] ->
             Softmax() ->
             pred[1]
-        )
+        );
     }
 }
\ No newline at end of file

src/test/resources/models/MultipleOutputsOld.cnnt

+configuration MultipleOutputs{
+    num_epoch:10
+    batch_size:64
+    normalize:true
+    load_checkpoint:false
+    optimizer:adam{
+        learning_rate:0.01
+        learning_rate_decay:0.8
+        step_size:1000
+    }
+}

src/test/resources/models/MultipleOutputsOld.emadl

+component MultipleOutputs{
+    ports in Q(-oo:+oo)^{10,1,1} data,
+         out Q(0:1)^{4} pred[2];
+
+    implementation CNN {
+
+        data ->
+        FullyConnected(units=128, no_bias=true) ->
+        Tanh() ->
+        (
+            FullyConnected(units=16, no_bias=true) ->
+            Tanh() ->
+            FullyConnected(units=4, no_bias=true)
+        |
+            FullyConnected(units=16, no_bias=true) ->
+            Tanh() ->
+            FullyConnected(units=4, no_bias=true)
+        ) ->
+        (
+            [0] ->
+            Softmax() ->
+            pred[0]
+        |
+            [1] ->
+            Softmax() ->
+            pred[1]
+        );
+    }
+}
\ No newline at end of file

src/test/resources/models/ResNeXt50.emadl

@@ -40,6 +40,6 @@ component ResNeXt50{
         GlobalPooling(pool_type="avg") ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
     }
 }
\ No newline at end of file

src/test/resources/models/ResNet152.emadl

@@ -34,6 +34,6 @@ component ResNet152{
         GlobalPooling(pool_type="avg") ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
     }
 }
\ No newline at end of file

src/test/resources/models/ResNet34.emadl

@@ -32,6 +32,6 @@ component ResNet34{
         GlobalPooling(pool_type="avg") ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
     }
 }
\ No newline at end of file

src/test/resources/models/ThreeInputCNN_M14.emadl

@@ -24,7 +24,7 @@ component ThreeInputCNN_M14{
         Relu() ->
         FullyConnected(units=10) ->
         Softmax() ->
-        predictions
+        predictions;
 
     }
 }
\ No newline at end of file

src/test/resources/models/VGG16.emadl

@@ -29,6 +29,6 @@ component VGG16{
         fc() ->
         FullyConnected(units=1000) ->
         Softmax() ->
-        predictions
+        predictions;
     }
 }
\ No newline at end of file

src/test/resources/models/cNNCalculator/Network.emadl

@@ -14,7 +14,7 @@ component Network{
         Relu() ->
         FullyConnected(units=10) ->
         Softmax() ->
-        predictions
+        predictions;
 
     }
 }

src/test/resources/models/cifar10/CifarNetwork.emadl

@@ -34,7 +34,7 @@ component CifarNetwork<Z(2:oo) classes = 10>{
         Dropout()->
         FullyConnected(units=classes) ->
         Softmax() ->
-        softmax
+        softmax;
 
     }
 }
\ No newline at end of file

src/test/resources/models/data_paths.txt

-
-cifar10.CifarNetwork src/test/resources/training_data
-simpleCifar10.CifarNetwork src/test/resources/training_data
-cNNCalculator.Network src/test/resources/training_data
-MultipleOutputs data/MultipleOutputs
+cifar10.CifarNetwork src/test/resources/training_data/Cifar
+simpleCifar10.CifarNetwork src/test/resources/training_data/Cifar
+cNNCalculator.Network src/test/resources/training_data/Cifar
 InstanceTest.NetworkB data/InstanceTest.NetworkB
 Alexnet data/Alexnet
 ThreeInputCNN_M14 data/ThreeInputCNN_M14
 VGG16 data/VGG16
 ResNeXt50 data/ResNeXt50
-instanceTestCifar.CifarNetwork src/test/resources/training_data
+instanceTestCifar.CifarNetwork src/test/resources/training_data/Cifar
 mnist.LeNetNetwork data/mnist.LeNetNetwork
+MultipleInputs src/test/resources/training_data/MultipleInputs
+MultipleOutputs src/test/resources/training_data/MultipleOutputs
\ No newline at end of file

src/test/resources/models/instanceTestCifar/CifarNetwork.emadl

@@ -12,7 +12,7 @@ component CifarNetwork<Z(2:oo) classes = 10>{
         Dropout()->
         FullyConnected(units=classes) ->
         Softmax() ->
-        softmax
+        softmax;
 
     }
 }
\ No newline at end of file

src/test/resources/models/mnist/LeNetNetwork.emadl

@@ -14,7 +14,7 @@ component LeNetNetwork<Z(2:oo) classes = 10>{
         Relu() ->
         FullyConnected(units=classes) ->
         Softmax() ->
-        predictions
+        predictions;
 
     }
 }

src/test/resources/models/reinforcementModel/cartpole/agent/CartPoleDQN.emadl

@@ -12,6 +12,6 @@ component CartPoleDQN {
         FullyConnected(units=256) ->
         Tanh() ->
         FullyConnected(units=2) ->
-        qvalues
+        qvalues;
     }
 }
\ No newline at end of file

src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarActor.emadl

@@ -13,6 +13,6 @@ component MountaincarActor {
         Relu() ->
         FullyConnected(units=1) ->
         Tanh() ->
-        action
+        action;
     }
 }
\ No newline at end of file

src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarCritic.cnna

@@ -8,5 +8,5 @@ implementation Critic(state, action) {
         FullyConnected(units=300)
     ) ->
     Add() ->
-    Relu()
+    Relu();
 }
\ No newline at end of file

src/test/resources/models/reinforcementModel/torcs/agent/dqn/TorcsDQN.emadl

@@ -12,6 +12,6 @@ component TorcsDQN<Z(1:oo) discrete_actions = 2> {
         FullyConnected(units=256) ->
         Tanh() ->
         FullyConnected(units=discrete_actions) ->
-        qvalues
+        qvalues;
     }
 }
\ No newline at end of file

src/test/resources/models/simpleCifar10/CifarNetwork.emadl

@@ -12,7 +12,7 @@ component CifarNetwork<Z(2:oo) classes = 10>{
         Dropout()->
         FullyConnected(units=classes) ->
         Softmax() ->
-        softmax
+        softmax;
 
     }
 }
\ No newline at end of file

src/test/resources/target_code/CNNCreator_cifar10_cifar10Classifier_net.py

@@ -18,7 +18,7 @@ class MyConstant(mx.init.Initializer):
 class CNNCreator_cifar10_cifar10Classifier_net:
 
     module = None
-    _data_dir_ = "src/test/resources/training_data/"
+    _data_dir_ = "src/test/resources/training_data/Cifar/"
     _model_dir_ = "model/cifar10.CifarNetwork/"
     _model_prefix_ = "model"
     _input_names_ = ['data']

src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py

@@ -43,12 +43,11 @@ class CNNCreator_mnist_mnistClassifier_net:
             self.net.load_parameters(self._model_dir_ + param_file)
             return lastEpoch
 
-
     def construct(self, context, data_mean=None, data_std=None):
         self.net = Net(data_mean=data_mean, data_std=data_std)
         self.net.collect_params().initialize(self.weight_initializer, ctx=context)
         self.net.hybridize()
-        self.net(mx.nd.zeros((1,)+self._input_shapes_[0], ctx=context))
+        self.net(mx.nd.zeros((1,) + self._input_shapes_[0], ctx=context))
 
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)

src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py

@@ -3,8 +3,9 @@ import h5py
 import mxnet as mx
 import logging
 import sys
+from mxnet import nd
 
-class mnist_mnistClassifier_netDataLoader:
+class CNNDataLoader_mnist_mnistClassifier_net:
     _input_names_ = ['image']
     _output_names_ = ['predictions_label']
 
@@ -14,21 +15,38 @@ class mnist_mnistClassifier_netDataLoader:
     def load_data(self, batch_size):
         train_h5, test_h5 = self.load_h5_files()
 
-        data_mean = train_h5[self._input_names_[0]][:].mean(axis=0)
-        data_std = train_h5[self._input_names_[0]][:].std(axis=0) + 1e-5
+        train_data = {}
+        data_mean = {}
+        data_std = {}
+
+        for input_name in self._input_names_:
+            train_data[input_name] = train_h5[input_name]
+            data_mean[input_name] = nd.array(train_h5[input_name][:].mean(axis=0))
+            data_std[input_name] = nd.array(train_h5[input_name][:].std(axis=0) + 1e-5)
+
+        train_label = {}
+        for output_name in self._output_names_:
+            train_label[output_name] = train_h5[output_name]
+
+        train_iter = mx.io.NDArrayIter(data=train_data,
+                                       label=train_label,
+                                       batch_size=batch_size)
 
-        train_iter = mx.io.NDArrayIter(train_h5[self._input_names_[0]],
-                                       train_h5[self._output_names_[0]],
-                                       batch_size=batch_size,
-                                       data_name=self._input_names_[0],
-                                       label_name=self._output_names_[0])
         test_iter = None
+
         if test_h5 != None:
-            test_iter = mx.io.NDArrayIter(test_h5[self._input_names_[0]],
-                                          test_h5[self._output_names_[0]],
-                                          batch_size=batch_size,
-                                          data_name=self._input_names_[0],
-                                          label_name=self._output_names_[0])
+            test_data = {}
+            for input_name in self._input_names_:
+                test_data[input_name] = test_h5[input_name]
+
+            test_label = {}
+            for output_name in self._output_names_:
+                test_label[output_name] = test_h5[output_name]
+
+            test_iter = mx.io.NDArrayIter(data=test_data,
+                                          label=test_label,
+                                          batch_size=batch_size)
+
         return train_iter, test_iter, data_mean, data_std
 
     def load_h5_files(self):
@@ -36,21 +54,39 @@ class mnist_mnistClassifier_netDataLoader:
         test_h5 = None
         train_path = self._data_dir + "train.h5"
         test_path = self._data_dir + "test.h5"
+
         if os.path.isfile(train_path):