Merge branch 'master' into oneclick_nn_training

pom.xml

@@ -16,7 +16,7 @@
 
     <!-- .. SE-Libraries .................................................. -->
       <CNNArch.version>0.2.8</CNNArch.version>
-      <CNNTrain.version>0.2.5</CNNTrain.version>
+      <CNNTrain.version>0.2.6</CNNTrain.version>
       <embedded-montiarc-math-generator>0.1.2-SNAPSHOT</embedded-montiarc-math-generator>
     
         <!-- .. Libraries .................................................. -->

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/ArchitectureElementData.java

@@ -20,14 +20,12 @@
  */
 package de.monticore.lang.monticar.cnnarch.caffe2generator;
 
-import de.monticore.lang.monticar.cnnarch._symboltable.ArchTypeSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureElementSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.LayerSymbol;
 import de.monticore.lang.monticar.cnnarch.predefined.AllPredefinedLayers;
 import de.se_rwth.commons.logging.Log;
 
 import javax.annotation.Nullable;
-import java.util.Arrays;
 import java.util.List;
 
 public class ArchitectureElementData {
@@ -165,31 +163,21 @@ public class ArchitectureElementData {
     }
 
     @Nullable
-    public List<Integer> getPadding(){
+    public Integer getPadding(){
         return getPadding((LayerSymbol) getElement());
     }
 
     @Nullable
-    public List<Integer> getPadding(LayerSymbol layer){
-        List<Integer> kernel = layer.getIntTupleValue(AllPredefinedLayers.KERNEL_NAME).get();
-        List<Integer> stride = layer.getIntTupleValue(AllPredefinedLayers.STRIDE_NAME).get();
-        ArchTypeSymbol inputType = layer.getInputTypes().get(0);
-        ArchTypeSymbol outputType = layer.getOutputTypes().get(0);
-
-        int heightWithPad = kernel.get(0) + stride.get(0)*(outputType.getHeight() - 1);
-        int widthWithPad = kernel.get(1) + stride.get(1)*(outputType.getWidth() - 1);
-        int heightPad = Math.max(0, heightWithPad - inputType.getHeight());
-        int widthPad = Math.max(0, widthWithPad - inputType.getWidth());
-
-        int topPad = (int)Math.ceil(heightPad / 2.0);
-        int bottomPad = (int)Math.floor(heightPad / 2.0);
-        int leftPad = (int)Math.ceil(widthPad / 2.0);
-        int rightPad = (int)Math.floor(widthPad / 2.0);
-
-        if (topPad == 0 && bottomPad == 0 && leftPad == 0 && rightPad == 0){
-            return null;
+    public Integer getPadding(LayerSymbol layer){
+        String padding_type = ((LayerSymbol) getElement()).getStringValue(AllPredefinedLayers.PADDING_NAME).get();
+        Integer pad=0;
+
+        if (padding_type.equals(AllPredefinedLayers.PADDING_VALID)){
+            pad = 0;
+        } else if (padding_type.equals(AllPredefinedLayers.PADDING_SAME)){
+            pad = 1;
         }
 
-        return Arrays.asList(0,0,0,0,topPad,bottomPad,leftPad,rightPad);
+        return pad;
     }
 }

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/CNNArch2Caffe2.java

@@ -26,7 +26,6 @@ import de.monticore.lang.monticar.cnnarch._cocos.CNNArchCocos;
 import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureElementSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.CompositeElementSymbol;
-import de.monticore.lang.monticar.cnnarch._symboltable.IOSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchCompilationUnitSymbol;
 import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchLanguage;
 import de.monticore.lang.monticar.cnnarch.DataPathConfigParser;
@@ -53,18 +52,18 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
     private boolean isSupportedLayer(ArchitectureElementSymbol element, LayerSupportChecker layerChecker){
         List<ArchitectureElementSymbol> constructLayerElemList;
 
-        if (!(element instanceof IOSymbol) && (element.getResolvedThis().get() instanceof CompositeElementSymbol))
-        {
+        if (element.getResolvedThis().get() instanceof CompositeElementSymbol) {
             constructLayerElemList = ((CompositeElementSymbol)element.getResolvedThis().get()).getElements();
             for (ArchitectureElementSymbol constructedLayerElement : constructLayerElemList) {
-                if (!isSupportedLayer(constructedLayerElement, layerChecker)) return false;
+                if (!isSupportedLayer(constructedLayerElement, layerChecker)) {
+                    return false;
+                }
             }
         }
         if (!layerChecker.isSupported(element.toString())) {
             Log.error("Unsupported layer " + "'" + element.getName() + "'" + " for the backend CAFFE2.");
             return false;
-        }
-        else {
+        } else {
             return true;
         }
     }
@@ -72,7 +71,9 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
     private boolean supportCheck(ArchitectureSymbol architecture){
         LayerSupportChecker layerChecker = new LayerSupportChecker();
         for (ArchitectureElementSymbol element : ((CompositeElementSymbol)architecture.getBody()).getElements()){
-            if(!isSupportedLayer(element, layerChecker)) return false;
+            if(!isSupportedLayer(element, layerChecker)) {
+                return false;
+            }
         }
         return true;
     }
@@ -84,6 +85,11 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
     public void setModelPath(Path modelPath){
         this.modelPath = modelPath.toString();
     }
+    
+    private static void quitGeneration(){
+        Log.error("Code generation is aborted");
+        System.exit(1);
+    }
 
     public CNNArch2Caffe2() {
         setGenerationTargetPath("./target/generated-sources-cnnarch/");
@@ -116,13 +122,12 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
         Optional<CNNArchCompilationUnitSymbol> compilationUnit = scope.resolve(rootModelName, CNNArchCompilationUnitSymbol.KIND);
         if (!compilationUnit.isPresent()){
             Log.error("could not resolve architecture " + rootModelName);
-            System.exit(1);
+            quitGeneration();
         }
 
         CNNArchCocos.checkAll(compilationUnit.get());
         if (!supportCheck(compilationUnit.get().getArchitecture())){
-            Log.error("Code generation aborted.");
-            System.exit(1);
+            quitGeneration();
         }
 
         try{
@@ -132,8 +137,7 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
             compilationUnit.get().getArchitecture().setDataPath(dataPath);
             compilationUnit.get().getArchitecture().setComponentName(rootModelName);
             generateFiles(compilationUnit.get().getArchitecture());
-        }
-        catch (IOException e){
+        } catch (IOException e){
             Log.error(e.toString());
         }
     }
@@ -188,7 +192,7 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
         try {
             generateFromFilecontentsMap(fileContentMap);
         } catch (IOException e) {
-            e.printStackTrace();
+            Log.error("CMake file could not be generated" + e.getMessage());
         }
     }
 
@@ -211,8 +215,8 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
         cMakeConfig.addCMakeCommand("set(LIBS ${LIBS} -lprotobuf -lglog -lgflags)");
         cMakeConfig.addCMakeCommand("find_package(CUDA)" + "\n"
                                         + "set(INCLUDE_DIRS ${INCLUDE_DIRS} ${CUDA_INCLUDE_DIRS})" + "\n"
-                                        + "set(LIBS ${LIBS} ${CUDA_LIBRARIES} ${CUDA_curand_LIBRARY})" + "\n"); //Needed since CUDA cannot be found correctly (including CUDA_curand_LIBRARY) and as optional using CMakeFindModule
-
+                                        + "set(LIBS ${LIBS} ${CUDA_LIBRARIES} ${CUDA_curand_LIBRARY})" + "\n");
+                                        //Needed since CUDA cannot be found correctly (including CUDA_curand_LIBRARY)
 
         cMakeConfig.addCMakeCommand("if(CUDA_FOUND)" + "\n" + "  set(LIBS ${LIBS} caffe2 caffe2_gpu)"
                                         + "\n" + "else()" + "\n" + "  set(LIBS ${LIBS} caffe2)" + "\n" + "endif()");

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/CNNArch2Caffe2Cli.java

@@ -19,6 +19,7 @@
  * *******************************************************************************
  */
 package de.monticore.lang.monticar.cnnarch.caffe2generator;
+import de.se_rwth.commons.logging.Log;
 
 import org.apache.commons.cli.*;
 
@@ -73,13 +74,18 @@ public class CNNArch2Caffe2Cli {
         try {
             cliArgs = parser.parse(options, args);
         } catch (ParseException e) {
-            System.err.println("argument parsing exception: " + e.getMessage());
-            System.exit(1);
+            Log.error("argument parsing exception: " + e.getMessage());
+            quitGeneration();
             return null;
         }
         return cliArgs;
     }
 
+    private static void quitGeneration(){
+        Log.error("Code generation is aborted");
+        System.exit(1);
+    }
+
     private static void runGenerator(CommandLine cliArgs) {
         Path modelsDirPath = Paths.get(cliArgs.getOptionValue(OPTION_MODELS_PATH.getOpt()));
         String rootModelName = cliArgs.getOptionValue(OPTION_ROOT_MODEL.getOpt());

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/CNNArchTemplateController.java

@@ -37,6 +37,7 @@ public class CNNArchTemplateController {
 
     private LayerNameCreator nameManager;
     private ArchitectureSymbol architecture;
+    private String loss;
 
     //temporary attributes. They are set after calling process()
     private Writer writer;
@@ -44,6 +45,8 @@ public class CNNArchTemplateController {
     private Target targetLanguage;
     private ArchitectureElementData dataElement;
 
+    public static final String CROSS_ENTROPY = "cross_entropy";
+    public static final String EUCLIDEAN = "euclidean";
 
     public CNNArchTemplateController(ArchitectureSymbol architecture) {
         setArchitecture(architecture);
@@ -96,8 +99,7 @@ public class CNNArchTemplateController {
 
         if (isSoftmaxOutput(layer) || isLogisticRegressionOutput(layer)){
             inputNames = getLayerInputs(layer.getInputElement().get());
-        }
-        else {
+        } else {
             for (ArchitectureElementSymbol input : layer.getPrevious()) {
                 if (input.getOutputTypes().size() == 1) {
                     inputNames.add(getName(input));
@@ -132,6 +134,9 @@ public class CNNArchTemplateController {
         return getArchitecture().getComponentName();
     }
 
+    public String getArchitectureLoss(){
+        return this.loss;
+    }
 
     public void include(String relativePath, String templateWithoutFileEnding, Writer writer){
         String templatePath = relativePath + templateWithoutFileEnding + FTL_FILE_ENDING;
@@ -148,12 +153,10 @@ public class CNNArchTemplateController {
         if (ioElement.isAtomic()){
             if (ioElement.isInput()){
                 include(TEMPLATE_ELEMENTS_DIR_PATH, "Input", writer);
-            }
-            else {
+            } else {
                 include(TEMPLATE_ELEMENTS_DIR_PATH, "Output", writer);
             }
-        }
-        else {
+        } else {
             include(ioElement.getResolvedThis().get(), writer);
         }
 
@@ -170,8 +173,7 @@ public class CNNArchTemplateController {
                 String templateName = layer.getDeclaration().getName();
                 include(TEMPLATE_ELEMENTS_DIR_PATH, templateName, writer);
             }
-        }
-        else {
+        } else {
             include(layer.getResolvedThis().get(), writer);
         }
 
@@ -192,11 +194,9 @@ public class CNNArchTemplateController {
     public void include(ArchitectureElementSymbol architectureElement, Writer writer){
         if (architectureElement instanceof CompositeElementSymbol){
             include((CompositeElementSymbol) architectureElement, writer);
-        }
-        else if (architectureElement instanceof LayerSymbol){
+        } else if (architectureElement instanceof LayerSymbol){
             include((LayerSymbol) architectureElement, writer);
-        }
-        else {
+        } else {
             include((IOSymbol) architectureElement, writer);
         }
     }
@@ -209,15 +209,15 @@ public class CNNArchTemplateController {
     }
 
     public Map.Entry<String,String> process(String templateNameWithoutEnding, Target targetLanguage){
-        StringWriter writer = new StringWriter();
+        StringWriter newWriter = new StringWriter();
         this.mainTemplateNameWithoutEnding = templateNameWithoutEnding;
         this.targetLanguage = targetLanguage;
-        this.writer = writer;
+        this.writer = newWriter;
 
-        include("", templateNameWithoutEnding, writer);
+        include("", templateNameWithoutEnding, newWriter);
         String fileEnding = targetLanguage.toString();
         String fileName = getFileNameWithoutEnding() + fileEnding;
-        Map.Entry<String,String> fileContent = new AbstractMap.SimpleEntry<>(fileName, writer.toString());
+        Map.Entry<String,String> fileContent = new AbstractMap.SimpleEntry<>(fileName, newWriter.toString());
 
         this.mainTemplateNameWithoutEnding = null;
         this.targetLanguage = null;
@@ -246,27 +246,39 @@ public class CNNArchTemplateController {
 
 
     public boolean isLogisticRegressionOutput(ArchitectureElementSymbol architectureElement){
-        return isTOutput(Sigmoid.class, architectureElement);
+        if (isTOutput(Sigmoid.class, architectureElement)){
+            this.loss = CROSS_ENTROPY;
+            return true;
+        }
+        return false;
     }
 
     public boolean isLinearRegressionOutput(ArchitectureElementSymbol architectureElement){
-        return architectureElement.isOutput()
+        if (architectureElement.isOutput()
                 && !isLogisticRegressionOutput(architectureElement)
-                && !isSoftmaxOutput(architectureElement);
+                && !isSoftmaxOutput(architectureElement)){
+            this.loss = EUCLIDEAN;
+            return true;
+        }
+        return false;
     }
 
 
     public boolean isSoftmaxOutput(ArchitectureElementSymbol architectureElement){
-        return isTOutput(Softmax.class, architectureElement);
+        if (isTOutput(Softmax.class, architectureElement)){
+            this.loss = CROSS_ENTROPY;
+            return true;
+        }
+        return false;
     }
 
     private boolean isTOutput(Class inputPredefinedLayerClass, ArchitectureElementSymbol architectureElement){
-        if (architectureElement.isOutput()){
-            if (architectureElement.getInputElement().isPresent() && architectureElement.getInputElement().get() instanceof LayerSymbol){
-                LayerSymbol inputLayer = (LayerSymbol) architectureElement.getInputElement().get();
-                if (inputPredefinedLayerClass.isInstance(inputLayer.getDeclaration())){
-                    return true;
-                }
+        if (architectureElement.isOutput()
+                && architectureElement.getInputElement().isPresent()
+                && architectureElement.getInputElement().get() instanceof LayerSymbol){
+            LayerSymbol inputLayer = (LayerSymbol) architectureElement.getInputElement().get();
+            if (inputPredefinedLayerClass.isInstance(inputLayer.getDeclaration())){
+                return true;
             }
         }
         return false;

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/CNNTrain2Caffe2.java

@@ -8,6 +8,7 @@ import de.monticore.lang.monticar.cnntrain._cocos.CNNTrainCocos;
 import de.monticore.lang.monticar.cnntrain._symboltable.CNNTrainCompilationUnitSymbol;
 import de.monticore.lang.monticar.cnntrain._symboltable.CNNTrainLanguage;
 import de.monticore.lang.monticar.cnntrain._symboltable.ConfigurationSymbol;
+import de.monticore.lang.monticar.cnntrain._symboltable.OptimizerSymbol;
 import de.monticore.lang.monticar.generator.FileContent;
 import de.monticore.lang.monticar.generator.cpp.GeneratorCPP;
 import de.monticore.symboltable.GlobalScope;
@@ -37,7 +38,9 @@ public class CNNTrain2Caffe2 implements CNNTrainGenerator {
         it = configuration.getEntryMap().keySet().iterator();
         while (it.hasNext()) {
             String key = it.next().toString();
-            if (funcChecker.getUnsupportedElemList().contains(key)) it.remove();
+            if (funcChecker.getUnsupportedElemList().contains(key)) {
+                it.remove();
+            }
         }
     }
 
@@ -47,17 +50,25 @@ public class CNNTrain2Caffe2 implements CNNTrainGenerator {
             ASTOptimizerEntry astOptimizer = (ASTOptimizerEntry) configuration.getOptimizer().getAstNode().get();
             astOptimizer.accept(funcChecker);
             if (funcChecker.getUnsupportedElemList().contains(funcChecker.unsupportedOptFlag)) {
-                configuration.setOptimizer(null);
+                OptimizerSymbol adamOptimizer = new OptimizerSymbol("adam");
+                configuration.setOptimizer(adamOptimizer); //Set default as adam optimizer
             }else {
                 Iterator it = configuration.getOptimizer().getOptimizerParamMap().keySet().iterator();
                 while (it.hasNext()) {
                     String key = it.next().toString();
-                    if (funcChecker.getUnsupportedElemList().contains(key)) it.remove();
+                    if (funcChecker.getUnsupportedElemList().contains(key)) {
+                        it.remove();
+                    }
                 }
             }
         }
     }
 
+    private static void quitGeneration(){
+        Log.error("Code generation is aborted");
+        System.exit(1);
+    }
+
     public CNNTrain2Caffe2() {
         setGenerationTargetPath("./target/generated-sources-cnnarch/");
     }
@@ -89,7 +100,7 @@ public class CNNTrain2Caffe2 implements CNNTrainGenerator {
         Optional<CNNTrainCompilationUnitSymbol> compilationUnit = scope.resolve(rootModelName, CNNTrainCompilationUnitSymbol.KIND);
         if (!compilationUnit.isPresent()) {
             Log.error("could not resolve training configuration " + rootModelName);
-            System.exit(1);
+            quitGeneration();
         }
         setInstanceName(compilationUnit.get().getFullName());
         CNNTrainCocos.checkAll(compilationUnit.get());
@@ -107,7 +118,7 @@ public class CNNTrain2Caffe2 implements CNNTrainGenerator {
                 genCPP.generateFile(new FileContent(fileContents.get(fileName), fileName));
             }
         } catch (IOException e) {
-            e.printStackTrace();
+            Log.error("CNNTrainer file could not be generated" + e.getMessage());
         }
     }
 

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/ConfigurationData.java

@@ -67,6 +67,13 @@ public class ConfigurationData {
         return getConfiguration().getEntry("eval_metric").getValue().toString();
     }
 
+    public String getLoss() {
+        if (!getConfiguration().getEntryMap().containsKey("loss")) {
+            return null;
+        }
+        return getConfiguration().getEntry("loss").getValue().toString();
+    }
+
     public String getOptimizerName() {
         if (getConfiguration().getOptimizer() == null) {
             return null;
@@ -89,8 +96,7 @@ public class ConfigurationData {
             Class realClass = entry.getValue().getValue().getValue().getClass();
             if (realClass == Boolean.class) {
                 valueAsString = (Boolean) entry.getValue().getValue().getValue() ? "True" : "False";
-            }
-            else if (lrPolicyClasses.contains(realClass)) {
+            } else if (lrPolicyClasses.contains(realClass)) {
                 valueAsString = "'" + valueAsString + "'";
             }
             mapToStrings.put(paramName, valueAsString);

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/LayerNameCreator.java

@@ -47,17 +47,14 @@ public class LayerNameCreator {
     protected int name(ArchitectureElementSymbol architectureElement, int stage, List<Integer> streamIndices){
         if (architectureElement instanceof CompositeElementSymbol){
             return nameComposite((CompositeElementSymbol) architectureElement, stage, streamIndices);
-        }
-        else{
+        } else{
             if (architectureElement.isAtomic()){
                 if (architectureElement.getMaxSerialLength().get() > 0){
                     return add(architectureElement, stage, streamIndices);
-                }
-                else {
+                } else {
                     return stage;
                 }
-            }
-            else {
+            } else {
                 ArchitectureElementSymbol resolvedElement = architectureElement.getResolvedThis().get();
                 return name(resolvedElement, stage, streamIndices);
             }
@@ -78,8 +75,7 @@ public class LayerNameCreator {
 
             streamIndices.remove(lastIndex);
             return Collections.max(endStages) + 1;
-        }
-        else {
+        } else {
             int endStage = stage;
             for (ArchitectureElementSymbol subElement : compositeElement.getElements()){
                 endStage = name(subElement, endStage, streamIndices);
@@ -113,8 +109,7 @@ public class LayerNameCreator {
                 name = name + "_" + arrayAccess + "_";
             }
             return name;
-        }
-        else {
+        } else {
             return createBaseName(architectureElement) + stage + createStreamPostfix(streamIndices) + "_";
         }
     }
@@ -132,11 +127,9 @@ public class LayerNameCreator {
             } else {
                 return layerDeclaration.getName().toLowerCase();
             }
-        }
-        else if (architectureElement instanceof CompositeElementSymbol){
+        } else if (architectureElement instanceof CompositeElementSymbol){
             return "group";
-        }
-        else {
+        } else {
             return architectureElement.getName();
         }
     }

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/TemplateConfiguration.java

@@ -43,6 +43,11 @@ public class TemplateConfiguration {
         configuration.setTemplateExceptionHandler(TemplateExceptionHandler.RETHROW_HANDLER);
     }
 
+    private static void quitGeneration(){
+        Log.error("Code generation is aborted");
+        System.exit(1);
+    }
+
     public Configuration getConfiguration() {
         return configuration;
     }
@@ -58,14 +63,12 @@ public class TemplateConfiguration {
         try{
             Template template = TemplateConfiguration.get().getTemplate(templatePath);
             template.process(ftlContext, writer);
-        }
-        catch (IOException e) {
+        } catch (IOException e) {
             Log.error("Freemarker could not find template " + templatePath + " :\n" + e.getMessage());
-            System.exit(1);
-        }
-        catch (TemplateException e){
+            quitGeneration();
+        } catch (TemplateException e){
             Log.error("An exception occured in template " + templatePath + " :\n" + e.getMessage());
-            System.exit(1);
+            quitGeneration();
         }
     }
 

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/TrainParamSupportChecker.java

@@ -25,12 +25,14 @@ public class TrainParamSupportChecker implements CNNTrainVisitor {
     public TrainParamSupportChecker() {
     }
 
-    public String unsupportedOptFlag = "unsupported_optimizer";
+    public static final String unsupportedOptFlag = "unsupported_optimizer";
 
     public List getUnsupportedElemList(){
         return this.unsupportedElemList;
     }
 
+    //Empty visit method denotes that the corresponding training parameter is supported.
+    //To set a training parameter as unsupported, add the corresponding node to the unsupportedElemList
     public void visit(ASTNumEpochEntry node){}
 
     public void visit(ASTBatchSizeEntry node){}
@@ -76,10 +78,7 @@ public class TrainParamSupportChecker implements CNNTrainVisitor {
 
     public void visit(ASTWeightDecayEntry node){}
 
-    public void visit(ASTLRDecayEntry node){
-        printUnsupportedOptimizerParam(node.getName());
-        this.unsupportedElemList.add(node.getName());
-    }
+    public void visit(ASTLRDecayEntry node){}
 
     public void visit(ASTLRPolicyEntry node){}
 

src/main/resources/templates/caffe2/CNNCreator.ftl

@@ -3,10 +3,12 @@ from caffe2.python.predictor import mobile_exporter
 from caffe2.proto import caffe2_pb2
 import numpy as np
 import math
+import datetime
 import logging
 import os
 import sys
 import lmdb
+
 class ${tc.fileNameWithoutEnding}:
 
     module = None
@@ -27,6 +29,15 @@ class ${tc.fileNameWithoutEnding}:
 
         return iterations_int
 
+    def get_epoch_as_iter(self, num_epoch, batch_size, dataset_size):   #To print metric durint training process
+        #Force floating point calculation
+        batch_size_float = float(batch_size)
+        dataset_size_float = float(dataset_size)
+
+        epoch_float = math.ceil(dataset_size_float/batch_size_float)
+        epoch_int = int(epoch_float)
+
+        return epoch_int
 
     def add_input(self, model, batch_size, db, db_type, device_opts):
         with core.DeviceScope(device_opts):
@@ -58,16 +69,20 @@ class ${tc.fileNameWithoutEnding}:
 
             return data, label, dataset_size
 
-    def create_model(self, model, data, device_opts):
+    def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
 ${tc.include(tc.architecture.body)}
 
     # this adds the loss and optimizer
-    def add_training_operators(self, model, output, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
+    def add_training_operators(self, model, output, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
     	with core.DeviceScope(device_opts):
-    		xent = model.LabelCrossEntropy([output, label], 'xent')
-    		loss = model.AveragedLoss(xent, "loss")
+    		if loss == 'cross_entropy':
+    		    xent = model.LabelCrossEntropy([output, label], 'xent')
+    		    loss = model.AveragedLoss(xent, "loss")
+    		elif loss == 'euclidean':
+    		    dist = model.net.SquaredL2Distance([label, output], 'dist')
+    		    loss = dist.AveragedLoss([], ['loss'])
 
     		model.AddGradientOperators([loss])
 
@@ -104,7 +119,7 @@ ${tc.include(tc.architecture.body)}
                 accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
             return accuracy
 
-    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
+    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', loss='${tc.architectureLoss}', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
         if context == 'cpu':
             device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
             print("CPU mode selected")
@@ -118,9 +133,10 @@ ${tc.include(tc.architecture.body)}
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts)
-    	self.add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
-    	self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
+    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
+    	self.add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, loss, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
+    	if not loss == 'euclidean':
+    		self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
     		brew.add_weight_decay(train_model, weight_decay)
 
@@ -130,38 +146,62 @@ ${tc.include(tc.architecture.body)}
 
     	# Main Training Loop
     	iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
-        print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")