added OneHotLayer

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonLayerSupportChecker.java

@@ -22,6 +22,7 @@ public class CNNArch2GluonLayerSupportChecker extends LayerSupportChecker {
         supportedLayerList.add(AllPredefinedLayers.ADD_NAME);
         supportedLayerList.add(AllPredefinedLayers.CONCATENATE_NAME);
         supportedLayerList.add(AllPredefinedLayers.FLATTEN_NAME);
+        supportedLayerList.add(AllPredefinedLayers.ONE_HOT_NAME);
     }
 
 }

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonTemplateController.java

@@ -71,7 +71,7 @@ public class CNNArch2GluonTemplateController extends CNNArchTemplateController {
 
         if (layer.isAtomic()){
             ArchitectureElementSymbol nextElement = layer.getOutputElement().get();
-            if (!isSoftmaxOutput(nextElement) && !isLogisticRegressionOutput(nextElement)){
+            if (!isSoftmaxOutput(nextElement) && !isLogisticRegressionOutput(nextElement) && !isOneHotOutput(nextElement)){
                 String templateName = layer.getDeclaration().getName();
                 include(TEMPLATE_ELEMENTS_DIR_PATH, templateName, writer, netDefinitionMode);
             }

src/main/resources/templates/gluon/CNNNet.ftl

@@ -2,6 +2,16 @@ import mxnet as mx
 import numpy as np
 from mxnet import gluon
 
+class OneHot(gluon.HybridBlock):
+    def __init__(self, size, **kwargs):
+        super(OneHot, self).__init__(**kwargs)
+        with self.name_scope():
+            self.size = size
+
+    def hybrid_forward(self, F, x):
+        return F.one_hot(indices=F.argmax(data=x, axis=1), depth=self.size)
+
+
 class Softmax(gluon.HybridBlock):
     def __init__(self, **kwargs):
         super(Softmax, self).__init__(**kwargs)

src/main/resources/templates/gluon/elements/OneHot.ftl

+<#assign input = element.inputs[0]>
+<#assign mode = definition_mode.toString()>
+<#assign size = element.size>
+<#if mode == "ARCHITECTURE_DEFINITION">
+            self.${element.name} = OneHot(size=${size})
+            <#include "OutputShape.ftl">
+</#if>
+<#if mode == "FORWARD_FUNCTION">
+        ${element.name} = self.${element.name}(${input})
+</#if>

src/main/resources/templates/gluon/elements/Output.ftl

@@ -7,6 +7,8 @@
         self.last_layers['${element.name}'] = 'sigmoid'
     <#elseif element.linearRegressionOutput>
         self.last_layers['${element.name}'] = 'linear'
+    <#elseif element.oneHotOutput>
+        self.last_layers['${element.name}'] = 'softmax'
     </#if>
 </#if>
 <#if mode == "FORWARD_FUNCTION">

src/test/resources/target_code/CNNNet_Alexnet.py

@@ -2,6 +2,16 @@ import mxnet as mx
 import numpy as np
 from mxnet import gluon
 
+class OneHot(gluon.HybridBlock):
+    def __init__(self, size, **kwargs):
+        super(OneHot, self).__init__(**kwargs)
+        with self.name_scope():
+            self.size = size
+
+    def hybrid_forward(self, F, x):
+        return F.one_hot(indices=F.argmax(data=x, axis=1), depth=self.size)
+
+
 class Softmax(gluon.HybridBlock):
     def __init__(self, **kwargs):
         super(Softmax, self).__init__(**kwargs)

src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py

@@ -2,6 +2,16 @@ import mxnet as mx
 import numpy as np
 from mxnet import gluon
 
+class OneHot(gluon.HybridBlock):
+    def __init__(self, size, **kwargs):
+        super(OneHot, self).__init__(**kwargs)
+        with self.name_scope():
+            self.size = size
+
+    def hybrid_forward(self, F, x):
+        return F.one_hot(indices=F.argmax(data=x, axis=1), depth=self.size)
+
+
 class Softmax(gluon.HybridBlock):
     def __init__(self, **kwargs):
         super(Softmax, self).__init__(**kwargs)

src/test/resources/target_code/CNNNet_VGG16.py

@@ -2,6 +2,16 @@ import mxnet as mx
 import numpy as np
 from mxnet import gluon
 
+class OneHot(gluon.HybridBlock):
+    def __init__(self, size, **kwargs):
+        super(OneHot, self).__init__(**kwargs)
+        with self.name_scope():
+            self.size = size
+
+    def hybrid_forward(self, F, x):
+        return F.one_hot(indices=F.argmax(data=x, axis=1), depth=self.size)
+
+
 class Softmax(gluon.HybridBlock):
     def __init__(self, **kwargs):
         super(Softmax, self).__init__(**kwargs)