modified Network, tests, data

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

@@ -41,13 +41,13 @@ import static org.junit.Assert.assertFalse;
 
 public class IntegrationCaffe2Test extends AbstractSymtabTest {
 
-    private Path vggTrainingHashFile = Paths.get("./target/generated-sources-emadl/cNNCalculator/VGG16.training_hash");
+    private Path netTrainingHashFile = Paths.get("./target/generated-sources-emadl/cNNCalculator/Network.training_hash");
 
     private void createHashFile() {
         try {
-            vggTrainingHashFile.toFile().getParentFile().mkdirs();
+            netTrainingHashFile.toFile().getParentFile().mkdirs();
             List<String> lines = Arrays.asList("7A7FBAC4E0AD84993C1C5F8B4F431055#D85A46E95F839BBEE22D9AC3E6A4BC5C#6BE4AED3D0DA1940B750FEA8088A7D21#6BE4AED3D0DA1940B750FEA8088A7D21");
-            Files.write(vggTrainingHashFile, lines, Charset.forName("UTF-8"));
+            Files.write(netTrainingHashFile, lines, Charset.forName("UTF-8"));
         }
         catch(Exception e) {
             assertFalse("Hash file could not be created", true);
@@ -56,7 +56,7 @@ public class IntegrationCaffe2Test extends AbstractSymtabTest {
 
     private void deleteHashFile() {
         try {
-            Files.delete(vggTrainingHashFile);
+            Files.delete(netTrainingHashFile);
         }
         catch(Exception e) {
             assertFalse("Could not delete hash file", true);
@@ -74,13 +74,13 @@ 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", "cNNCalculator.VGG16", "-b", "CAFFE2"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "cNNCalculator.Network", "-b", "CAFFE2"};
         EMADLGeneratorCli.main(args);
-        //assertTrue(!Log.getFindings().isEmpty());
+        assertTrue(Log.getFindings().isEmpty());
         
         Log.getFindings().clear();
         EMADLGeneratorCli.main(args);
-        //assertTrue(Log.getFindings().size() == 1);
+        assertTrue(Log.getFindings().size() == 1);
         assertTrue(Log.getFindings().get(0).getMsg().contains("skipped"));
 
         deleteHashFile();
@@ -92,7 +92,7 @@ public class IntegrationCaffe2Test extends AbstractSymtabTest {
         Log.getFindings().clear();
         createHashFile();
 
-        String[] args = {"-m", "src/test/resources/models/", "-r", "cNNCalculator.VGG16", "-b", "CAFFE2", "-f", "y"};
+        String[] args = {"-m", "src/test/resources/models/", "-r", "cNNCalculator.Network", "-b", "CAFFE2", "-f", "y"};
         EMADLGeneratorCli.main(args);
         assertTrue(Log.getFindings().isEmpty());
 

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

-package cNNCalculator;
-
-component Add{
-    ports
-            in Z(0:999) num1,
-            in Z(0:999) num2,
-            out Z(0:1998) sum;
-
-    implementation Math{
-        sum = num1 + num2;
-    }
-}

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

-package cNNCalculator;
-
-component ArgMax<Z(1:oo) n = 2>{
-    ports in Q^{n} inputVector,
-          out Z(0:oo) maxIndex,
-          out Q maxValue;
-
-    implementation Math{
-         maxIndex = 0;
-         maxValue = inputVector(1);
-
-         for i = 2:n
-            if inputVector(i) > maxValue
-                maxIndex = i - 1;
-                maxValue = inputVector(i);
-            end
-         end
-    }
-}

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

-package cNNCalculator;
-
-component Calculator {
-        ports  
-                in Q(0:1)^10 in1_1,
-                in Q(0:1)^10 in1_2,
-                in Q(0:1)^10 in1_3,
-                in Q(0:1)^10 in2_1,
-                in Q(0:1)^10 in2_2,
-                in Q(0:1)^10 in2_3,
-
-                out Z(0:1998) out1;
-        
-        instance ArgMax<10> number1_ones;
-        instance ArgMax<10> number1_tens;
-        instance ArgMax<10> number1_hundreds;
-        instance ArgMax<10> number2_ones;
-        instance ArgMax<10> number2_tens;
-        instance ArgMax<10> number2_hundreds;
-        instance DigitCombiner number1;
-        instance DigitCombiner number2;
-        instance Add add;
-        
-
-        connect in1_1 -> number1_hundreds.inputVector;
-        connect in1_2 -> number1_tens.inputVector;
-        connect in1_3 -> number1_ones.inputVector;
-        connect in2_1 -> number2_hundreds.inputVector;
-        connect in2_2 -> number2_tens.inputVector;
-        connect in2_3 -> number2_ones.inputVector;
-
-        connect number1_ones.maxIndex -> number1.ones;
-        connect number1_tens.maxIndex -> number1.tens;
-        connect number1_hundreds.maxIndex -> number1.hundreds;
-        connect number2_ones.maxIndex -> number2.ones;
-        connect number2_tens.maxIndex -> number2.tens;
-        connect number2_hundreds.maxIndex -> number2.hundreds;
-
-        connect number1.number -> add.num1;
-        connect number2.number -> add.num2;
-
-        connect add.sum -> out1;
-
-}

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

-package cNNCalculator;
-
-component Connector {
-    ports in Z(0:255)^{1, 28, 28} image1,
-          in Z(0:255)^{1, 28, 28} image2,
-          in Z(0:255)^{1, 28, 28} image3,
-          in Z(0:255)^{1, 28, 28} image4,
-          in Z(0:255)^{1, 28, 28} image5,
-          in Z(0:255)^{1, 28, 28} image6,
-          out Z(0:1998) res;
-
-    instance Network<10> predictor1;
-    instance Network<10> predictor2;
-    instance Network<10> predictor3;
-    instance Network<10> predictor4;
-    instance Network<10> predictor5;
-    instance Network<10> predictor6;
-    instance Calculator cal;
-
-    instance ArgMax<10> maxi;
-
-    connect image1 -> predictor1.image;
-    connect image2 -> predictor2.image;
-    connect image3 -> predictor3.image;
-    connect image4 -> predictor4.image;
-    connect image5 -> predictor5.image;
-    connect image6 -> predictor6.image;
-
-    connect predictor1.predictions -> cal.in1_1;
-    connect predictor2.predictions -> cal.in1_2;
-    connect predictor3.predictions -> cal.in1_3;
-    connect predictor4.predictions -> cal.in2_1;
-    connect predictor5.predictions -> cal.in2_2;
-    connect predictor6.predictions -> cal.in2_3;
-
-    connect cal.out1 -> res;
-
-}

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

-package cNNCalculator;
-
-component DigitCombiner{
-    ports
-            in Z(0:9) hundreds,
-            in Z(0:9) tens,
-            in Z(0:9) ones,
-            out Z(0:999) number;
-
-    implementation Math{
-        number = ones + 10 * tens + 100 * hundreds;
-    }
-}

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

 package cNNCalculator;
 
-component Network<Z(2:oo) classes = 10>{
+component Network{
     ports in Z(0:255)^{1, 28, 28} image,
-         out Q(0:1)^{classes} predictions;
+         out Q(0:1)^{10} predictions;
 
     implementation CNN {
         image ->
@@ -12,7 +12,7 @@ component Network<Z(2:oo) classes = 10>{
         Pooling(pool_type="max", kernel=(2,2), stride=(2,2), padding="valid") ->
         FullyConnected(units=500) ->
         Relu() ->
-        FullyConnected(units=classes) ->
+        FullyConnected(units=10) ->
         Softmax() ->
         predictions
 

src/test/resources/models/cNNCalculator/VGG16.cnnt

-configuration VGG16{
-    num_epoch:1
-    batch_size:64
-    normalize:true
-    load_checkpoint:false
-    optimizer:adam{
-        learning_rate:0.01
-        learning_rate_decay:0.8
-        step_size:1000
-        weight_decay: 0.01
-    }
-}
-

src/test/resources/models/cNNCalculator/VGG16.emadl

-package cNNCalculator;
-
-component VGG16{
-    ports in Z(0:255)^{1, 28, 28} image,
-         out Q(0:1)^{10} predictions;
-
-         implementation CNN {
-
-             def conv(filter, channels){
-                 Convolution(kernel=(filter,filter), channels=channels) ->
-                 Relu()
-             }
-             def fc(){
-                 FullyConnected(units=100) ->
-                 Relu() ->
-                 Dropout(p=0.5)
-             }
-
-             image ->
-             conv(filter=2, channels=64, ->=2) ->
-             Pooling(pool_type="max", kernel=(2,2), stride=(1,1)) ->
-             fc() ->
-             FullyConnected(units=10) ->
-             Softmax() ->
-             predictions
-         }
-  }

src/test/resources/models/data_paths.txt

 
 cifar10.CifarNetwork src/test/resources/training_data
-cNNCalculator.VGG16 src/test/resources/training_data
+cNNCalculator.Network src/test/resources/training_data
 MultipleOutputs data/MultipleOutputs
 InstanceTest.NetworkB data/InstanceTest.NetworkB
 Alexnet data/Alexnet