Merge branch 'adapt_pending_predefined_layers' into 'master'

Adapt pending predefined layers

See merge request !21

pom.xml

@@ -8,7 +8,7 @@
 
   <groupId>de.monticore.lang.monticar</groupId>
   <artifactId>cnnarch-caffe2-generator</artifactId>
-  <version>0.2.7-SNAPSHOT</version>
+  <version>0.2.8-SNAPSHOT</version>
 
   <!-- == PROJECT DEPENDENCIES ============================================= -->
 

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

@@ -165,31 +165,22 @@ 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);
+    public Integer getPadding(LayerSymbol layer){
+        String padding_type = ((LayerSymbol) getElement()).getStringValue(AllPredefinedLayers.PADDING_NAME).get();
+        Integer pad=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;
+        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/TrainParamSupportChecker.java

@@ -76,10 +76,7 @@ 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

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class ${tc.fileNameWithoutEnding}:
 
     module = None
@@ -58,7 +59,7 @@ 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)}
@@ -118,7 +119,7 @@ ${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)
+    	${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, 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)
     	with core.DeviceScope(device_opts):
@@ -141,7 +142,7 @@ ${tc.include(tc.architecture.body)}
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts)
+    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -159,7 +160,7 @@ ${tc.include(tc.architecture.body)}
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

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

@@ -41,6 +41,8 @@ if __name__ == "__main__":
     <#elseif param == "step_size">
         <#assign paramName = "stepsize">
     <#elseif param == "gamma1">
+        <#assign paramName = "gamma1">
+    <#elseif param == "learning_rate_decay">
         <#assign paramName = "gamma">
     </#if>
         ${paramName}=${config.optimizerParams[param]}<#sep>,

src/main/resources/templates/caffe2/elements/Add.ftl

-    		${element.name} = ${tc.join(element.inputs, " + ")}
-<#include "OutputShape.ftl">
\ No newline at end of file
+<#-- This layer is currently not supported -->

src/main/resources/templates/caffe2/elements/BatchNorm.ftl

-    		${element.name} = mx.symbol.BatchNorm(data=${element.inputs[0]},
-    		    fix_gamma=${element.fixGamma?string("True","False")},
-    		    name="${element.name}")
+<#-- This layer is currently not supported -->

src/main/resources/templates/caffe2/elements/Concatenate.ftl

-    		${element.name} = mx.symbol.concat(${tc.join(element.inputs, ", ")},
-    		    dim=1,
-    		    name="${element.name}")
-<#include "OutputShape.ftl">
\ No newline at end of file
+<#-- This layer is currently not supported -->

src/main/resources/templates/caffe2/elements/Convolution.ftl

@@ -3,8 +3,14 @@
 <#assign strideWidth = element.stride[1]>
 <#assign kernelHeight = element.kernel[0]>
 <#assign kernelWidth = element.kernel[1]>
-<#if element.padding??>  <#-- Check wheather padding null is. -->
-    		<#-- TODO: check how to adapt CNNArchLang argument pad_width=${element.padding[0]} -->
+<#if element.padding??>
+	<#if element.padding == 0>
+		<#assign padParameter = ""><#--Don't add anything since "valid" is the default padding of Caffe2-->
+	<#elseif element.padding == 1>
+		<#assign padParameter = ", pad=1">
+	</#if>
+<#else>
+	<#assign padParameter = ", pad=1">
 </#if>
 <#if strideHeight == strideWidth>
 	<#assign strideParameter = "stride=${strideHeight}">
@@ -16,6 +22,5 @@
 <#else>
 	<#assign kernelParameter = "kernel=[${kernelHeight},${kernelWidth}]">
 </#if>
-    		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=${element.element.inputTypes[0].channels?c}, dim_out=${element.channels?c}, ${kernelParameter}, ${strideParameter})
-    		<#-- TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")} -->
+    		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=${element.element.inputTypes[0].channels?c}, dim_out=${element.channels?c}, ${kernelParameter}, ${strideParameter}${padParameter})
 <#include "OutputShape.ftl">
\ No newline at end of file

src/main/resources/templates/caffe2/elements/Dropout.ftl

-    		${element.name} = mx.symbol.Dropout(data=${element.inputs[0]},
-    		    p=${element.p?c},
-    		    name="${element.name}")
+<#assign input = element.inputs[0]>
+<#assign ratio = element.p?c?string>
+    		${element.name} = brew.dropout(model, ${input}, '${element.name}', ratio=${ratio}, is_test=False)

src/main/resources/templates/caffe2/elements/Flatten.ftl

-    		${element.name} = mx.symbol.Flatten(data=${element.inputs[0]},
-    		    name="${element.name}")
\ No newline at end of file
+            ${element.name} = model.net.Flatten(${element.inputs[0]}, "${element.name}")
\ No newline at end of file

src/main/resources/templates/caffe2/elements/FullyConnected.ftl

@@ -4,13 +4,10 @@
 <#assign inputChannels = element.element.inputTypes[0].channels?c>
 <#assign inputHeight = element.element.inputTypes[0].height>
 <#assign inputWidth = element.element.inputTypes[0].width>
-<#if flatten>
-    		<#-- TODO: check how to adapt CNNArchLang flatten #${element.name} = mx.symbol.flatten(data=${input}) -->
-</#if>
+<#--flatten is not needed since the fc layer applies it automatically-->
 <#if inputLayerType?matches("FullyConnected") || (inputHeight == 1 && inputWidth == 1)>
     		${element.name} = brew.fc(model, ${input}, '${element.name}', dim_in=${inputChannels}, dim_out=${element.units?c})
 <#else>
     		${element.name} = brew.fc(model, ${input}, '${element.name}', dim_in=${inputChannels} * ${inputHeight} * ${inputWidth}, dim_out=${element.units?c})
 </#if>
-    		<#-- TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")} -->
 <#include "OutputShape.ftl">
\ No newline at end of file

src/main/resources/templates/caffe2/elements/Get.ftl

-    		${element.name} = ${element.inputs[element.index]}
+<#-- This layer is currently not supported -->

src/main/resources/templates/caffe2/elements/GlobalPooling.ftl

-    		${element.name} = mx.symbol.Pooling(data=${element.inputs[0]},
-    		    global_pool=True,
-    		    kernel=(1,1),
-    		    pool_type="${element.poolType}",
-    		    name="${element.name}")
+<#assign input = element.inputs[0]>
+<#if element.poolType == "max">
+    		${element.name} = brew.max_pool(model, ${input}, '${element.name}', global_pooling=True)
+<#elseif element.poolType == "avg">
+    		${element.name} = brew.average_pool(model, ${input}, '${element.name}', global_pooling=True)
+</#if>
 <#include "OutputShape.ftl">
\ No newline at end of file

src/main/resources/templates/caffe2/elements/Input.ftl

@@ -9,11 +9,10 @@
     		${element.name} = data
 <#include "OutputShape.ftl">
 <#if heightIndex != channelIndex + 1 || widthIndex != heightIndex + 1>
-    		${element.name} = mx.symbol.transpose(data=${element.name},mx.sym.var    <#-- TODO: check how to adapt CNNArchLang transpose case -->
-    		    axes=(0,${tc.join(indexList, ",")}))
+    		${element.name} = model.net.Transpose(${element.name}, '${element.name}', axes=[0,${tc.join(indexList, ",")}])
 
 </#if>
 <#if indexList?size != 3>
-    		${element.name} = mx.symbol.reshape(data=${element.name},    <#-- TODO: check how to adapt CNNArchLang transpose case -->
+    		${element.name}, _ = model.net.Reshape('${element.name}', ['${element.name}', '${element.name}_old_shape'],
     		    shape=(0,${element.element.outputTypes[0].channels?c},${element.element.outputTypes[0].height?c},${element.element.outputTypes[0].width?c}))
 </#if>

src/main/resources/templates/caffe2/elements/Lrn.ftl

-    		${element.name} = mx.symbol.LRN(data=${element.inputs[0]},
-    		    alpha=${element.alpha?c},
-    		    beta=${element.beta?c},
-    		    knorm=${element.knorm?c},
-    		    nsize=${element.nsize?c},
-    		    name="${element.name}")
+<#assign input = element.inputs[0]>
+<#if !element.knorm?string?contains(".")>
+	<#assign bias = element.knorm?string["0.0"]>
+<#else>
+	<#assign bias = element.knorm?c>
+</#if>
+    		${element.name} = brew.lrn(model, ${input}, '${element.name}', size=${element.nsize?c}, alpha=${element.alpha?c}, beta=${element.beta?c}, bias=${bias})

src/main/resources/templates/caffe2/elements/Output.ftl

@@ -2,11 +2,10 @@
 <#if element.softmaxOutput>
     		${element.name} = brew.softmax(model, ${input}, '${element.name}')
 <#elseif element.logisticRegressionOutput>
-    		${element.name} = mx.symbol.LogisticRegressionOutput(data=${element.inputs[0]}, <#-- TODO: check how to adapt LogisticRegressionOutput -->
-    		    name="${element.name}")
+    		${element.name} = model.net.Sigmoid(${input}, '${element.name}')
 <#elseif element.linearRegressionOutput>
-    		${element.name} = mx.symbol.LinearRegressionOutput(data=${element.inputs[0]}, <#-- TODO: check how to adapt linearRegressionOutput -->
-    		    name="${element.name}")
+    		<#--Don't add L2 loss here but within the function "add_training_operators" from CNNCreator.ftl-->
+    		${element.name} = ${input}
 </#if>
 
     		return ${element.name}
\ No newline at end of file

src/main/resources/templates/caffe2/elements/Pooling.ftl

@@ -4,7 +4,13 @@
 <#assign kernelHeight = element.kernel[0]>
 <#assign kernelWidth = element.kernel[1]>
 <#if element.padding??>
-    		<#-- TODO: check how to adapt CNNArchLang argument pad_width=${element.padding[0]} -->
+	<#if element.padding == 0>
+		<#assign padParameter = ""><#--Don't add anything since "valid" is the default padding of Caffe2-->
+	<#elseif element.padding == 1>
+		<#assign padParameter = ", pad=1">
+	</#if>
+<#else>
+	<#assign padParameter = ", pad=1">
 </#if>
 <#if strideHeight == strideWidth>
 	<#assign strideParameter = "stride=${strideHeight}">
@@ -19,6 +25,6 @@
 <#if element.poolType == "max">
     		${element.name} = brew.max_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter})
 <#elseif element.poolType == "avg">
-    		${element.name} = brew.average_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter})
+    		${element.name} = brew.average_pool(model, ${input}, '${element.name}', ${kernelParameter}, ${strideParameter}${padParameter})
 </#if>
 <#include "OutputShape.ftl">
\ No newline at end of file

src/main/resources/templates/caffe2/elements/Softmax.ftl

 <#-- This template is not used if the followiing architecture element is an output. See Output.ftl -->
-    		${element.name} = mx.symbol.softmax(data=${element.inputs[0]},
-    		    axis=1,
-    		    name="${element.name}")
+<#assign input = element.inputs[0]>
+    		${element.name} = brew.softmax(model, ${input}, '${element.name}')

src/main/resources/templates/caffe2/elements/Split.ftl

-    		${element.name} = mx.symbol.split(data=${element.inputs[0]},
-    		    num_outputs=${element.numOutputs?c},
-    		    axis=1,
-    		    name="${element.name}")
-<#include "OutputShape.ftl">
\ No newline at end of file
+<#-- This layer is currently not supported -->

src/test/java/de/monticore/lang/monticar/cnnarch/caffe2generator/GenerationTest.java

@@ -130,7 +130,7 @@ public class GenerationTest extends AbstractSymtabTest{
         CNNTrain2Caffe2 trainGenerator = new CNNTrain2Caffe2();
         trainGenerator.generate(Paths.get(sourcePath), "FullConfig");
 
-        assertTrue(Log.getFindings().size() == 9);
+        assertTrue(Log.getFindings().size() == 8);
         checkFilesAreEqual(
                 Paths.get("./target/generated-sources-cnnarch"),
                 Paths.get("./src/test/resources/target_code"),

src/test/resources/target_code/CNNCreator_Alexnet.py

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class CNNCreator_Alexnet:
 
     module = None
@@ -58,97 +59,56 @@ class CNNCreator_Alexnet:
 
             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):
 
     		data = data
     		# data, output shape: {[3,224,224]}
     		conv1_ = brew.conv(model, data, 'conv1_', dim_in=3, dim_out=96, kernel=11, stride=4)
     		# conv1_, output shape: {[96,55,55]}
-    		lrn1_ = mx.symbol.LRN(data=conv1_,
-    		    alpha=0.0001,
-    		    beta=0.75,
-    		    knorm=2,
-    		    nsize=5,
-    		    name="lrn1_")
+    		lrn1_ = brew.lrn(model, conv1_, 'lrn1_', size=5, alpha=0.0001, beta=0.75, bias=2.0)
     		pool1_ = brew.max_pool(model, lrn1_, 'pool1_', kernel=3, stride=2)
     		# pool1_, output shape: {[96,27,27]}
     		relu1_ = brew.relu(model, pool1_, pool1_)
-    		split1_ = mx.symbol.split(data=relu1_,
-    		    num_outputs=2,
-    		    axis=1,
-    		    name="split1_")
-    		# split1_, output shape: {[48,27,27][48,27,27]}
-    		get2_1_ = split1_[0]
-      		conv2_1_ = brew.conv(model, get2_1_, 'conv2_1_', dim_in=48, dim_out=128, kernel=5, stride=1)
+    		conv2_1_ = brew.conv(model, get2_1_, 'conv2_1_', dim_in=48, dim_out=128, kernel=5, stride=1, pad=1)
     		# conv2_1_, output shape: {[128,27,27]}
-    		lrn2_1_ = mx.symbol.LRN(data=conv2_1_,
-    		    alpha=0.0001,
-    		    beta=0.75,
-    		    knorm=2,
-    		    nsize=5,
-    		    name="lrn2_1_")
+    		lrn2_1_ = brew.lrn(model, conv2_1_, 'lrn2_1_', size=5, alpha=0.0001, beta=0.75, bias=2.0)
     		pool2_1_ = brew.max_pool(model, lrn2_1_, 'pool2_1_', kernel=3, stride=2)
     		# pool2_1_, output shape: {[128,13,13]}
     		relu2_1_ = brew.relu(model, pool2_1_, pool2_1_)
-    		get2_2_ = split1_[1]
-      		conv2_2_ = brew.conv(model, get2_2_, 'conv2_2_', dim_in=48, dim_out=128, kernel=5, stride=1)
+    		conv2_2_ = brew.conv(model, get2_2_, 'conv2_2_', dim_in=48, dim_out=128, kernel=5, stride=1, pad=1)
     		# conv2_2_, output shape: {[128,27,27]}
-    		lrn2_2_ = mx.symbol.LRN(data=conv2_2_,
-    		    alpha=0.0001,
-    		    beta=0.75,
-    		    knorm=2,
-    		    nsize=5,
-    		    name="lrn2_2_")
+    		lrn2_2_ = brew.lrn(model, conv2_2_, 'lrn2_2_', size=5, alpha=0.0001, beta=0.75, bias=2.0)
     		pool2_2_ = brew.max_pool(model, lrn2_2_, 'pool2_2_', kernel=3, stride=2)
     		# pool2_2_, output shape: {[128,13,13]}
     		relu2_2_ = brew.relu(model, pool2_2_, pool2_2_)
-    		concatenate3_ = mx.symbol.concat(relu2_1_, relu2_2_,
-    		    dim=1,
-    		    name="concatenate3_")
-    		# concatenate3_, output shape: {[256,13,13]}
-      		conv3_ = brew.conv(model, concatenate3_, 'conv3_', dim_in=256, dim_out=384, kernel=3, stride=1)
+    		conv3_ = brew.conv(model, concatenate3_, 'conv3_', dim_in=256, dim_out=384, kernel=3, stride=1, pad=1)
     		# conv3_, output shape: {[384,13,13]}
     		relu3_ = brew.relu(model, conv3_, conv3_)
-    		split3_ = mx.symbol.split(data=relu3_,
-    		    num_outputs=2,
-    		    axis=1,
-    		    name="split3_")
-    		# split3_, output shape: {[192,13,13][192,13,13]}
-    		get4_1_ = split3_[0]
-      		conv4_1_ = brew.conv(model, get4_1_, 'conv4_1_', dim_in=192, dim_out=192, kernel=3, stride=1)
+    		conv4_1_ = brew.conv(model, get4_1_, 'conv4_1_', dim_in=192, dim_out=192, kernel=3, stride=1, pad=1)
     		# conv4_1_, output shape: {[192,13,13]}
     		relu4_1_ = brew.relu(model, conv4_1_, conv4_1_)
-      		conv5_1_ = brew.conv(model, relu4_1_, 'conv5_1_', dim_in=192, dim_out=128, kernel=3, stride=1)
+    		conv5_1_ = brew.conv(model, relu4_1_, 'conv5_1_', dim_in=192, dim_out=128, kernel=3, stride=1, pad=1)
     		# conv5_1_, output shape: {[128,13,13]}
     		pool5_1_ = brew.max_pool(model, conv5_1_, 'pool5_1_', kernel=3, stride=2)
     		# pool5_1_, output shape: {[128,6,6]}
     		relu5_1_ = brew.relu(model, pool5_1_, pool5_1_)
-    		get4_2_ = split3_[1]
-      		conv4_2_ = brew.conv(model, get4_2_, 'conv4_2_', dim_in=192, dim_out=192, kernel=3, stride=1)
+    		conv4_2_ = brew.conv(model, get4_2_, 'conv4_2_', dim_in=192, dim_out=192, kernel=3, stride=1, pad=1)
     		# conv4_2_, output shape: {[192,13,13]}
     		relu4_2_ = brew.relu(model, conv4_2_, conv4_2_)
-      		conv5_2_ = brew.conv(model, relu4_2_, 'conv5_2_', dim_in=192, dim_out=128, kernel=3, stride=1)
+    		conv5_2_ = brew.conv(model, relu4_2_, 'conv5_2_', dim_in=192, dim_out=128, kernel=3, stride=1, pad=1)
     		# conv5_2_, output shape: {[128,13,13]}
     		pool5_2_ = brew.max_pool(model, conv5_2_, 'pool5_2_', kernel=3, stride=2)
     		# pool5_2_, output shape: {[128,6,6]}
     		relu5_2_ = brew.relu(model, pool5_2_, pool5_2_)
-    		concatenate6_ = mx.symbol.concat(relu5_1_, relu5_2_,
-    		    dim=1,
-    		    name="concatenate6_")
-    		# concatenate6_, output shape: {[256,6,6]}
     		fc6_ = brew.fc(model, concatenate6_, 'fc6_', dim_in=256 * 6 * 6, dim_out=4096)
     		# fc6_, output shape: {[4096,1,1]}
     		relu6_ = brew.relu(model, fc6_, fc6_)
-    		dropout6_ = mx.symbol.Dropout(data=relu6_,
-    		    p=0.5,
-    		    name="dropout6_")
+    		dropout6_ = brew.dropout(model, relu6_, 'dropout6_', ratio=0.5, is_test=False)
     		fc7_ = brew.fc(model, dropout6_, 'fc7_', dim_in=4096, dim_out=4096)
     		# fc7_, output shape: {[4096,1,1]}
     		relu7_ = brew.relu(model, fc7_, fc7_)
-    		dropout7_ = mx.symbol.Dropout(data=relu7_,
-    		    p=0.5,
-    		    name="dropout7_")
+    		dropout7_ = brew.dropout(model, relu7_, 'dropout7_', ratio=0.5, is_test=False)
     		fc8_ = brew.fc(model, dropout7_, 'fc8_', dim_in=4096, dim_out=10)
     		# fc8_, output shape: {[10,1,1]}
     		predictions = brew.softmax(model, fc8_, 'predictions')
@@ -210,7 +170,7 @@ class CNNCreator_Alexnet:
     	# == 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)
-    	predictions = self.create_model(train_model, data, device_opts=device_opts)
+    	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, predictions, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
@@ -233,7 +193,7 @@ class CNNCreator_Alexnet:
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(test_model, data, device_opts=device_opts)
+    	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -251,7 +211,7 @@ class CNNCreator_Alexnet:
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class CNNCreator_CifarClassifierNetwork:
 
     module = None
@@ -58,182 +59,84 @@ class CNNCreator_CifarClassifierNetwork:
 
             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):
 
     		data = data
     		# data, output shape: {[3,32,32]}
-      		conv2_1_ = brew.conv(model, data, 'conv2_1_', dim_in=3, dim_out=8, kernel=3, stride=1)
+    		conv2_1_ = brew.conv(model, data, 'conv2_1_', dim_in=3, dim_out=8, kernel=3, stride=1, pad=1)
     		# conv2_1_, output shape: {[8,32,32]}
-    		batchnorm2_1_ = mx.symbol.BatchNorm(data=conv2_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm2_1_")
     		relu2_1_ = brew.relu(model, batchnorm2_1_, batchnorm2_1_)
-      		conv3_1_ = brew.conv(model, relu2_1_, 'conv3_1_', dim_in=8, dim_out=8, kernel=3, stride=1)
+    		conv3_1_ = brew.conv(model, relu2_1_, 'conv3_1_', dim_in=8, dim_out=8, kernel=3, stride=1, pad=1)
     		# conv3_1_, output shape: {[8,32,32]}
-    		batchnorm3_1_ = mx.symbol.BatchNorm(data=conv3_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm3_1_")
-    		conv2_2_ = brew.conv(model, data, 'conv2_2_', dim_in=3, dim_out=8, kernel=1, stride=1)
+    		conv2_2_ = brew.conv(model, data, 'conv2_2_', dim_in=3, dim_out=8, kernel=1, stride=1, pad=1)
     		# conv2_2_, output shape: {[8,32,32]}
-    		batchnorm2_2_ = mx.symbol.BatchNorm(data=conv2_2_,
-    		    fix_gamma=True,
-    		    name="batchnorm2_2_")
-    		add4_ = batchnorm3_1_ + batchnorm2_2_
-    		# add4_, output shape: {[8,32,32]}
     		relu4_ = brew.relu(model, add4_, add4_)
-      		conv5_1_ = brew.conv(model, relu4_, 'conv5_1_', dim_in=8, dim_out=16, kernel=3, stride=2)
+    		conv5_1_ = brew.conv(model, relu4_, 'conv5_1_', dim_in=8, dim_out=16, kernel=3, stride=2, pad=1)
     		# conv5_1_, output shape: {[16,16,16]}
-    		batchnorm5_1_ = mx.symbol.BatchNorm(data=conv5_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm5_1_")
     		relu5_1_ = brew.relu(model, batchnorm5_1_, batchnorm5_1_)
-      		conv6_1_ = brew.conv(model, relu5_1_, 'conv6_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
+    		conv6_1_ = brew.conv(model, relu5_1_, 'conv6_1_', dim_in=16, dim_out=16, kernel=3, stride=1, pad=1)
     		# conv6_1_, output shape: {[16,16,16]}
-    		batchnorm6_1_ = mx.symbol.BatchNorm(data=conv6_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm6_1_")
-    		conv5_2_ = brew.conv(model, relu4_, 'conv5_2_', dim_in=8, dim_out=16, kernel=1, stride=2)
+    		conv5_2_ = brew.conv(model, relu4_, 'conv5_2_', dim_in=8, dim_out=16, kernel=1, stride=2, pad=1)
     		# conv5_2_, output shape: {[16,16,16]}
-    		batchnorm5_2_ = mx.symbol.BatchNorm(data=conv5_2_,
-    		    fix_gamma=True,
-    		    name="batchnorm5_2_")
-    		add7_ = batchnorm6_1_ + batchnorm5_2_
-    		# add7_, output shape: {[16,16,16]}
     		relu7_ = brew.relu(model, add7_, add7_)
-      		conv8_1_ = brew.conv(model, relu7_, 'conv8_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
+    		conv8_1_ = brew.conv(model, relu7_, 'conv8_1_', dim_in=16, dim_out=16, kernel=3, stride=1, pad=1)
     		# conv8_1_, output shape: {[16,16,16]}
-    		batchnorm8_1_ = mx.symbol.BatchNorm(data=conv8_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm8_1_")
     		relu8_1_ = brew.relu(model, batchnorm8_1_, batchnorm8_1_)
-      		conv9_1_ = brew.conv(model, relu8_1_, 'conv9_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
+    		conv9_1_ = brew.conv(model, relu8_1_, 'conv9_1_', dim_in=16, dim_out=16, kernel=3, stride=1, pad=1)
     		# conv9_1_, output shape: {[16,16,16]}
-    		batchnorm9_1_ = mx.symbol.BatchNorm(data=conv9_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm9_1_")
-    		add10_ = batchnorm9_1_ + relu7_
-    		# add10_, output shape: {[16,16,16]}
     		relu10_ = brew.relu(model, add10_, add10_)
-      		conv11_1_ = brew.conv(model, relu10_, 'conv11_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
+    		conv11_1_ = brew.conv(model, relu10_, 'conv11_1_', dim_in=16, dim_out=16, kernel=3, stride=1, pad=1)
     		# conv11_1_, output shape: {[16,16,16]}
-    		batchnorm11_1_ = mx.symbol.BatchNorm(data=conv11_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm11_1_")
     		relu11_1_ = brew.relu(model, batchnorm11_1_, batchnorm11_1_)
-      		conv12_1_ = brew.conv(model, relu11_1_, 'conv12_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
+    		conv12_1_ = brew.conv(model, relu11_1_, 'conv12_1_', dim_in=16, dim_out=16, kernel=3, stride=1, pad=1)
     		# conv12_1_, output shape: {[16,16,16]}
-    		batchnorm12_1_ = mx.symbol.BatchNorm(data=conv12_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm12_1_")
-    		add13_ = batchnorm12_1_ + relu10_
-    		# add13_, output shape: {[16,16,16]}
     		relu13_ = brew.relu(model, add13_, add13_)
-      		conv14_1_ = brew.conv(model, relu13_, 'conv14_1_', dim_in=16, dim_out=32, kernel=3, stride=2)
+    		conv14_1_ = brew.conv(model, relu13_, 'conv14_1_', dim_in=16, dim_out=32, kernel=3, stride=2, pad=1)
     		# conv14_1_, output shape: {[32,8,8]}
-    		batchnorm14_1_ = mx.symbol.BatchNorm(data=conv14_1_,
-    		    fix_gamma=True,
-    		    name="batchnorm14_1_")
     		relu14_1_ = brew.relu(model, batchnorm14_1_, batchnorm14_1_)