Corrected Convolution, Pooling and FullyConnected layer templates: Adapted...

Corrected Convolution, Pooling and FullyConnected layer templates: Adapted stride with same and different height and width in convolution and pooling layers. Corrected dim_in value in convolution and fullyconnected layers.

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

 <#assign input = element.inputs[0]>
+<#assign strideHeight = element.stride[0]>
+<#assign strideWidth = element.stride[1]>
 <#if element.padding??>  <#-- Check wheather padding null is. -->
-<#assign input = element.name>
-        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
+		<#-- TODO: check how to adapt CNNArchLang argument pad_width=${element.padding[0]} -->
 </#if>
-        ${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=1, dim_out=${element.channels?c}, kernel=${element.kernel}, stride=${element.stride})
-        #TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")}
-        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
+<#if input = tc.architectureInputs[0]>	<#-- TODO: CHECK COMPARISON -->
+	<#if strideHeight == strideWidth>
+		${element.name} = brew.conv(model, '${input}', '${element.name}', dim_in=1, dim_out=${element.channels?c}, kernel=${element.kernel[0]}, stride=${strideHeight})
+	<#else>
+		${element.name} = brew.conv(model, '${input}', '${element.name}', dim_in=1, dim_out=${element.channels?c}, kernel=${element.kernel[0]}, stride_h=${strideHeight}, stride_w=${strideWidth})
+	</#if>
+<#else>
+	<#if strideHeight == strideWidth>
+		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=${element.element.inputTypes[0].channels?c}, dim_out=${element.channels?c}, kernel=${element.kernel[0]}, stride=${strideHeight})
+	<#else>
+		${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=${element.element.inputTypes[0].channels?c}, dim_out=${element.channels?c}, kernel=${element.kernel[0]}, stride_h=${strideHeight}, stride_w=${strideWidth})
+	</#if>
+</#if>
+		<#-- TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")} -->
 <#include "OutputShape.ftl">
+		# Yeverino input layer name: ${element.inputs[0]}
+		# Yeverino input/previous layer dim_out: ${element.element.inputTypes[0].channels?c}
+		# Yeverino current layer dim_out: ${element.element.outputTypes[0].channels?c}

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

 <#assign flatten = element.element.inputTypes[0].height != 1 || element.element.inputTypes[0].width != 1>
 <#assign input = element.inputs[0]>
+<#assign inputLayerType = element.element.getInputElement().get()?string>
+<#assign inputChannels = element.element.inputTypes[0].channels?c>
+<#assign inputHeight = element.element.inputTypes[0].height>
+<#assign inputWidth = element.element.inputTypes[0].width>
 <#if flatten>
-        ${element.name} = mx.symbol.flatten(data=${input}) #TODO: check how to adapt CNNArchLang flatten
-<#assign input = element.name>
+		<#-- TODO: check how to adapt CNNArchLang flatten #${element.name} = mx.symbol.flatten(data=${input}) -->
 </#if>
-        ${element.name} = brew.fc(model, ${input}, '${element.name}', dim_in=50 * 4 * 4, dim_out=${element.units?c})
-
-        #TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")}
+<#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")} -->
+		# Yeverino input layer type: ${inputLayerType}
+		# Yeverino current layer type: ${element.element}
+<#include "OutputShape.ftl">
\ No newline at end of file

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

 <#assign input = element.inputs[0]>
+<#assign strideHeight = element.stride[0]>
+<#assign strideWidth = element.stride[1]>
 <#if element.padding??>
-<#assign input = element.name>
-        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
+		<#-- TODO: check how to adapt CNNArchLang argument pad_width=${element.padding[0]} -->
 </#if>
-
 <#if element.poolType == "max">
-        ${element.name} = brew.max_pool(model, ${input}, '${element.name}', kernel=${element.kernel}, stride=${element.stride})
+	<#if strideHeight == strideWidth>
+		${element.name} = brew.max_pool(model, ${input}, '${element.name}', kernel=${element.kernel[0]}, stride=${strideHeight})
+	<#else>
+		${element.name} = brew.max_pool(model, ${input}, '${element.name}', kernel=${element.kernel[0]}, stride_h=${strideHeight}, stride_w=${strideWidth})
+	</#if>
 <#elseif element.poolType == "avg">
-        ${element.name} = brew.average_pool(model, ${input}, '${element.name}', kernel=${element.kernel}, stride=${element.stride})
+	<#if strideHeight == strideWidth>
+		${element.name} = brew.average_pool(model, ${input}, '${element.name}', kernel=${element.kernel[0]}, stride=${strideHeight})
+	<#else>
+		${element.name} = brew.average_pool(model, ${input}, '${element.name}', kernel=${element.kernel[0]}, stride_h=${strideHeight}, stride_w=${strideWidth})
+	</#if>
 </#if>
-
-
 <#include "OutputShape.ftl">
\ No newline at end of file