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monticore
EmbeddedMontiArc
generators
CNNArch2Caffe2
Commits
e9adcb57
Commit
e9adcb57
authored
Aug 22, 2018
by
Carlos Alfredo Yeverino Rodriguez
Browse files
Adapted Templates Convolution, FullyConnected and Pooling. Added LeNet.cnna model
parent
abeb3c80
Changes
5
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src/main/resources/templates/caffe2/elements/Convolution.ftl
View file @
e9adcb57
<#
assign
input = element.inputs[0]>
<#
if
element.padding??>
<#
if
element.padding??>
<#-- Check wheather padding null is. -->
<#
assign
input = element.name>
$
{
element
.name
}
= mx.symbol.pad(data=$
{
element
.inputs
[
0
]}
, #TODO: pending to adapt
mode='constant',
pad_width=($
{
tc
.join
(
element
.padding
,
","
)}
),
constant_value=0)
#TODO: check how to adapt CNNArchLang argument pad_width=$
{
element
.padding
}
</#
if
>
$
{
element
.name
}
= brew.conv(model, $
{
input
}
, '$
{
element
.name
}
', dim_in=1, dim_out=20, kernel=5)
$
{
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
}
<#
include
"OutputShape.ftl">
src/main/resources/templates/caffe2/elements/FullyConnected.ftl
View file @
e9adcb57
<#
assign
flatten = element.element.inputTypes[0].height != 1 || element.element.inputTypes[0].width != 1>
<#
assign
input = element.inputs[0]>
<#
if
flatten>
$
{
element
.name
}
= mx.symbol.flatten(data=$
{
input
}
) #TODO:
Pending to adapt
$
{
element
.name
}
= mx.symbol.flatten(data=$
{
input
}
) #TODO:
check how to adapt CNNArchLang flatten
<#
assign
input = element.name>
</#
if
>
$
{
element
.name
}
= brew.fc(model, $
{
input
}
, '$
{
element
.name
}
', dim_in=50
*
4
*
4, dim_out=500)
$
{
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"
)}
src/main/resources/templates/caffe2/elements/MaxPooling.ftl
deleted
100644 → 0
View file @
abeb3c80
<#
assign
input = element.inputs[0]>
<#
if
element.padding??>
<#
assign
input = element.name>
$
{
element
.name
}
= mx.symbol.pad(data=$
{
element
.inputs
[
0
]}
, #TODO: Pending to adapt o eliminate
mode='constant',
pad_width=($
{
tc
.join
(
element
.padding
,
","
)}
),
constant_value=0)
</#
if
>
$
{
element
.name
}
= brew.max_pool(model, $
{
input
}
, '$
{
element
.name
}
', kernel=2, stride=2)
src/main/resources/templates/caffe2/elements/Pooling.ftl
View file @
e9adcb57
<#
assign
input = element.inputs[0]>
<#
if
element.padding??>
<#
assign
input = element.name>
$
{
element
.name
}
= mx.symbol.pad(data=$
{
element
.inputs
[
0
]}
,
mode='constant',
pad_width=($
{
tc
.join
(
element
.padding
,
","
)}
),
constant_value=0)
#TODO: check how to adapt CNNArchLang argument pad_width=$
{
element
.padding
}
</#
if
>
$
{
element
.name
}
= mx.symbol.Pooling(data=$
{
input
}
,
kernel=($
{
tc
.join
(
element
.kernel
,
","
)}
),
pool_type=$
{
element
.poolType
}
,
stride=($
{
tc
.join
(
element
.stride
,
","
)}
),
name="$
{
element
.name
}
")
<#
if
element.poolType == "max">
$
{
element
.name
}
= brew.max_pool(model, $
{
input
}
, '$
{
element
.name
}
', kernel=$
{
element
.kernel
}
, stride=$
{
element
.stride
}
)
<#
elseif
element.poolType == "avg">
$
{
element
.name
}
= brew.average_pool(model, $
{
input
}
, '$
{
element
.name
}
', kernel=$
{
element
.kernel
}
, stride=$
{
element
.stride
}
)
</#
if
>
<#
include
"OutputShape.ftl">
\ No newline at end of file
src/test/resources/valid_tests/LeNet.cnna
View file @
e9adcb57
architecture LeNet(img_height=2
8
, img_width=2
8
, img_channels=3, classes=10){
architecture LeNet(img_height=
3
2, img_width=
3
2, img_channels=3, classes=10){
def input Z(0:255)^{img_channels, img_height, img_width} image
def output Q(0:1)^{classes} predictions
image ->
Convolution(kernel=(5,5), channels=20, stride=(1,1), padding="no_loss") ->
MaxPooling(kernel=(3,3), stride=(2,2), padding="no_loss") ->
FullyConnected(units=64, no_bias=true) ->
Tanh() ->
FullyConnected(units=classes, no_bias=true) ->
Convolution(kernel=(5,5), channels=20) ->
Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
Convolution(kernel=(5,5), channels=50) ->
Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
FullyConnected(units=500) ->
Relu() ->
FullyConnected(units=classes) ->
Softmax() ->
predictions
}
\ No newline at end of file
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