Merge branch 'fix_convolutionlayer_bug' into adapt_cnnpredictor_template

# Conflicts:
#	src/test/resources/target_code/CNNCreator_Alexnet.py
#	src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
#	src/test/resources/target_code/CNNCreator_VGG16.py

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

@@ -16,10 +16,6 @@
 <#else>
 	<#assign kernelParameter = "kernel=[${kernelHeight},${kernelWidth}]">
 </#if>
-<#if input = tc.architectureInputs[0]>	<#-- TODO: CHECK COMPARISON -->
-    		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=1, dim_out=${element.channels?c}, ${kernelParameter}, ${strideParameter})
-<#else>
     		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=${element.element.inputTypes[0].channels?c}, dim_out=${element.channels?c}, ${kernelParameter}, ${strideParameter})
-</#if>
     		<#-- TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")} -->
 <#include "OutputShape.ftl">
\ No newline at end of file

src/test/resources/target_code/CNNCreator_Alexnet.py

@@ -52,8 +52,7 @@ class CNNCreator_Alexnet:
 
     		data = data
     		# data, output shape: {[3,224,224]}
-
-    		conv1_ = brew.conv(model, data, 'conv1_', dim_in=1, dim_out=96, kernel=11, stride=4)
+      		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,

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

@@ -52,8 +52,7 @@ class CNNCreator_CifarClassifierNetwork:
 
     		data = data
     		# data, output shape: {[3,32,32]}
-
-    		conv2_1_ = brew.conv(model, data, 'conv2_1_', dim_in=1, 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)
     		# conv2_1_, output shape: {[8,32,32]}
     		batchnorm2_1_ = mx.symbol.BatchNorm(data=conv2_1_,
     		    fix_gamma=True,
@@ -64,8 +63,7 @@ class CNNCreator_CifarClassifierNetwork:
     		batchnorm3_1_ = mx.symbol.BatchNorm(data=conv3_1_,
     		    fix_gamma=True,
     		    name="batchnorm3_1_")
-
-    		conv2_2_ = brew.conv(model, data, 'conv2_2_', dim_in=1, 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)
     		# conv2_2_, output shape: {[8,32,32]}
     		batchnorm2_2_ = mx.symbol.BatchNorm(data=conv2_2_,
     		    fix_gamma=True,

src/test/resources/target_code/CNNCreator_VGG16.py

@@ -52,8 +52,7 @@ class CNNCreator_VGG16:
 
     		data = data
     		# data, output shape: {[3,224,224]}
-
-    		conv1_ = brew.conv(model, data, 'conv1_', dim_in=1, dim_out=64, kernel=3, stride=1)
+      		conv1_ = brew.conv(model, data, 'conv1_', dim_in=3, dim_out=64, kernel=3, stride=1)
     		# conv1_, output shape: {[64,224,224]}
     		relu1_ = brew.relu(model, conv1_, conv1_)
       		conv2_ = brew.conv(model, relu1_, 'conv2_', dim_in=64, dim_out=64, kernel=3, stride=1)

src/test/resources/valid_tests/LeNet.cnna

-architecture LeNet(img_height=28, img_width=28, img_channels=3, classes=10){
+architecture LeNet(img_height=28, img_width=28, img_channels=1, classes=10){
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions