Added invalid type test

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

@@ -208,4 +208,18 @@ public class GenerationTest extends AbstractSymtabTest {
 
         assertTrue(Log.getErrorCount() == 0);
     }
+
+    @Test
+    public void testInvalidTypeCocos() {
+        Log.getFindings().clear();
+        String[] args = { "-m", "src/test/resources/models/", "-r", "tagging.AlexnetInvalidType", "-b", "MXNET", "-f",
+                "n", "-c", "n" };
+        EMADLGeneratorCli.main(args);
+        assertEquals(Log.getFindings().size(), 2);
+        assertEquals(Log.getFindings().get(0).toString(), "DatapathType is incorrect, must be of Type: HDF5 or LMDB");
+        assertEquals(Log.getFindings().get(1).toString(),
+                "Tagfile was found, ignoring data_paths.txt: src/test/resources/models");
+
+        assertTrue(Log.getErrorCount() == 0);
+    }
 }

src/test/resources/models/tagging/AlexNet.tag

@@ -4,6 +4,7 @@ conforms to dltag.DataPathTagSchema;
 tags Alexnet {
 tag Alexnet with DataPath = {path = src/test/resources/models, type = LMDB};
 tag AlexnetInvalid with DataPath = {path = test/resources/models, type = random};
+tag AlexnetInvalidType with DataPath = {path = src/test/resources/models, type = LMBD};
 tag Parent.a1 with DataPath = {path = instanceA1, type = random};
 }
 

src/test/resources/models/tagging/AlexnetInvalidType.cnnt

+configuration AlexnetInvalidType{
+     num_epoch : 100
+     batch_size : 500
+     optimizer : adam{
+         learning_rate : 0.001
+     }
+}

src/test/resources/models/tagging/AlexnetInvalidType.emadl

+package tagging;
+
+component AlexnetInvalidType{
+    ports in Z(0:255)^{3, 224, 224} image,
+         out Q(0:1)^{1000} predictions;
+
+    implementation CNN {
+
+        def split1(i){
+            [i] ->
+            Convolution(kernel=(5,5), channels=128) ->
+            Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
+            Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
+            Relu()
+        }
+        def split2(i){
+            [i] ->
+            Convolution(kernel=(3,3), channels=192) ->
+            Relu() ->
+            Convolution(kernel=(3,3), channels=128) ->
+            Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
+            Relu()
+        }
+        def fc(){
+            FullyConnected(units=4096) ->
+            Relu() ->
+            Dropout()
+        }
+
+        image ->
+        Convolution(kernel=(11,11), channels=96, stride=(4,4), padding="no_loss") ->
+        Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
+        Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
+        Relu() ->
+        Split(n=2) ->
+        split1(i=[0|1]) ->
+        Concatenate() ->
+        Convolution(kernel=(3,3), channels=384) ->
+        Relu() ->
+        Split(n=2) ->
+        split2(i=[0|1]) ->
+        Concatenate() ->
+        fc(->=2) ->
+        FullyConnected(units=1000) ->
+        Softmax() ->
+        predictions
+
+    }
+}
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