simpleCifar10 tests added

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

@@ -74,7 +74,7 @@ public class IntegrationCaffe2Test extends AbstractSymtabTest {
     public void testDontRetrain1() {
         // The training hash is stored during the first training, so the second one is skipped
         Log.getFindings().clear();
-        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "CAFFE2"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simplesimpleCifar10.Cifar10Classifier", "-b", "CAFFE2"};
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().isEmpty());
         
@@ -86,19 +86,42 @@ public class IntegrationCaffe2Test extends AbstractSymtabTest {
         deleteHashFile();
     }
 
-//    @Test
-//    public void testForceRetrain() {
-//        // The training hash is written manually, but training is forced
-//        Log.getFindings().clear();
-//        createHashFile();
-//
-//        String[] args = {"-m", "src/test/resources/models/", "-r", "cNNCalculator.Network", "-b", "CAFFE2", "-f", "y"};
-//        EMADLGeneratorCli.main(args);
-//        assertTrue(Log.getFindings().isEmpty());
-//
-//        deleteHashFile();
-//    }
+ @Test
+    public void testDontRetrain2() {
+        // The training hash is written manually, so even the first training should be skipped
+        Log.getFindings().clear();
+        createHashFile();
+
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "CAFFE2"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().size() == 1);
+        assertTrue(Log.getFindings().get(0).getMsg().contains("skipped"));
 
+        deleteHashFile();
+    }
+
+    @Test
+    public void testDontRetrain3() {
+        // Multiple instances of the first NN are used. Only the first one should cause a training
+        Log.getFindings().clear();
+        String[] args = {"-m", "src/test/resources/models/", "-r", "instanceTestCifar.MainC", "-b", "CAFFE2"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().size() == 1);
+        assertTrue(Log.getFindings().get(0).getMsg().contains("skipped"));
+    }
+
+    @Test
+    public void testForceRetrain() {
+        // The training hash is written manually, but training is forced
+        Log.getFindings().clear();
+        createHashFile();
+
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "CAFFE2", "-f", "y"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().isEmpty());
+
+        deleteHashFile();
+    }
 
     
     

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

@@ -41,7 +41,7 @@ import static org.junit.Assert.assertFalse;
 
 public class IntegrationMXNetTest extends AbstractSymtabTest {
 
-    private Path cifarTrainingHashFile = Paths.get("./target/generated-sources-emadl/cifar10/CifarNetwork.training_hash");
+    private Path cifarTrainingHashFile = Paths.get("./target/generated-sources-emadl/simpleCifar10/CifarNetwork.training_hash");
 
     private void createHashFile() {
         try {
@@ -76,7 +76,7 @@ public class IntegrationMXNetTest extends AbstractSymtabTest {
     public void testDontRetrain1() {
         // The training hash is stored during the first training, so the second one is skipped
         Log.getFindings().clear();
-        String[] args = {"-m", "src/test/resources/models/", "-r", "cifar10.Cifar10Classifier", "-b", "MXNET"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "MXNET"};
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().isEmpty());
         
@@ -94,7 +94,7 @@ public class IntegrationMXNetTest extends AbstractSymtabTest {
         Log.getFindings().clear();
         createHashFile();
 
-        String[] args = {"-m", "src/test/resources/models/", "-r", "cifar10.Cifar10Classifier", "-b", "MXNET"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "MXNET"};
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().size() == 1);
         assertTrue(Log.getFindings().get(0).getMsg().contains("skipped"));
@@ -118,7 +118,7 @@ public class IntegrationMXNetTest extends AbstractSymtabTest {
         Log.getFindings().clear();
         createHashFile();
 
-        String[] args = {"-m", "src/test/resources/models/", "-r", "cifar10.Cifar10Classifier", "-b", "MXNET", "-f", "y"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "simpleCifar10.Cifar10Classifier", "-b", "MXNET", "-f", "y"};
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().isEmpty());
 

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

 configuration CifarNetwork{
      num_epoch:10
      batch_size:5
-     normalize:true
      context:cpu
-     load_checkpoint:false
      optimizer:adam{
          learning_rate:0.01
          learning_rate_decay:0.8

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

@@ -5,31 +5,9 @@ component CifarNetwork<Z(2:oo) classes = 10>{
          out Q(0:1)^{classes} softmax;
 
     implementation CNN {
-        def conv(kernel, channels, stride=1, act=true){
-            Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
-            BatchNorm() ->
-            Relu(?=act)
-        }
-        def resLayer(channels, stride=1, addSkipConv=false){
-            (
-                conv(kernel=3, channels=channels, stride=stride) ->
-                conv(kernel=3, channels=channels, act=false)
-            |
-                conv(kernel=1, channels=channels, stride=stride, act=false, ? = addSkipConv)
-            ) ->
-            Add() ->
-            Relu()
-        }
-
         data ->
-        resLayer(channels=8, addSkipConv=true) ->
-        resLayer(channels=16, stride=2, addSkipConv=true) ->
-        resLayer(channels=16, ->=2) ->
-        resLayer(channels=32, stride=2, addSkipConv=true) ->
-        resLayer(channels=32, ->=2) ->
-        resLayer(channels=64, stride=2, addSkipConv=true) ->
-        resLayer(channels=64, ->=2) ->
-        GlobalPooling(pool_type="avg") ->
+        Convolution(kernel=(5,5), channels=8, padding="valid") ->
+        Convolution(kernel=(5,5), channels=8, padding="valid") ->
         FullyConnected(units=128) ->
         Dropout()->
         FullyConnected(units=classes) ->