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CNNArch2Caffe2
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monticore
EmbeddedMontiArc
generators
CNNArch2Caffe2
Commits
7bb6a3da
Commit
7bb6a3da
authored
Jan 29, 2019
by
Carlos Alfredo Yeverino Rodriguez
Browse files
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Plain Diff
Updated convolution and pooling layer templates
parent
c995f497
Changes
5
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Showing
5 changed files
with
65 additions
and
63 deletions
+65
-63
src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/ArchitectureElementData.java
...icar/cnnarch/caffe2generator/ArchitectureElementData.java
+11
-20
src/main/resources/templates/caffe2/elements/Convolution.ftl
src/main/resources/templates/caffe2/elements/Convolution.ftl
+9
-4
src/main/resources/templates/caffe2/elements/Pooling.ftl
src/main/resources/templates/caffe2/elements/Pooling.ftl
+8
-2
src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
...esources/target_code/CNNCreator_CifarClassifierNetwork.py
+24
-24
src/test/resources/target_code/CNNCreator_VGG16.py
src/test/resources/target_code/CNNCreator_VGG16.py
+13
-13
No files found.
src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/ArchitectureElementData.java
View file @
7bb6a3da
...
...
@@ -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
);
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/resources/templates/caffe2/elements/Convolution.ftl
View file @
7bb6a3da
...
...
@@ -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/Pooling.ftl
View file @
7bb6a3da
...
...
@@ -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/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
View file @
7bb6a3da
...
...
@@ -64,18 +64,18 @@ class CNNCreator_CifarClassifierNetwork:
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
,
...
...
@@ -83,18 +83,18 @@ class CNNCreator_CifarClassifierNetwork:
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
,
...
...
@@ -102,13 +102,13 @@ class CNNCreator_CifarClassifierNetwork:
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
,
...
...
@@ -116,13 +116,13 @@ class CNNCreator_CifarClassifierNetwork:
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
,
...
...
@@ -130,18 +130,18 @@ class CNNCreator_CifarClassifierNetwork:
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_
)
conv15_1_
=
brew
.
conv
(
model
,
relu14_1_
,
'conv15_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
)
conv15_1_
=
brew
.
conv
(
model
,
relu14_1_
,
'conv15_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv15_1_, output shape: {[32,8,8]}
batchnorm15_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv15_1_
,
fix_gamma
=
True
,
name
=
"batchnorm15_1_"
)
conv14_2_
=
brew
.
conv
(
model
,
relu13_
,
'conv14_2_'
,
dim_in
=
16
,
dim_out
=
32
,
kernel
=
1
,
stride
=
2
)
conv14_2_
=
brew
.
conv
(
model
,
relu13_
,
'conv14_2_'
,
dim_in
=
16
,
dim_out
=
32
,
kernel
=
1
,
stride
=
2
,
pad
=
1
)
# conv14_2_, output shape: {[32,8,8]}
batchnorm14_2_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv14_2_
,
fix_gamma
=
True
,
...
...
@@ -149,13 +149,13 @@ class CNNCreator_CifarClassifierNetwork:
add16_
=
batchnorm15_1_
+
batchnorm14_2_
# add16_, output shape: {[32,8,8]}
relu16_
=
brew
.
relu
(
model
,
add16_
,
add16_
)
conv17_1_
=
brew
.
conv
(
model
,
relu16_
,
'conv17_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
)
conv17_1_
=
brew
.
conv
(
model
,
relu16_
,
'conv17_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv17_1_, output shape: {[32,8,8]}
batchnorm17_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv17_1_
,
fix_gamma
=
True
,
name
=
"batchnorm17_1_"
)
relu17_1_
=
brew
.
relu
(
model
,
batchnorm17_1_
,
batchnorm17_1_
)
conv18_1_
=
brew
.
conv
(
model
,
relu17_1_
,
'conv18_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
)
conv18_1_
=
brew
.
conv
(
model
,
relu17_1_
,
'conv18_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv18_1_, output shape: {[32,8,8]}
batchnorm18_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv18_1_
,
fix_gamma
=
True
,
...
...
@@ -163,13 +163,13 @@ class CNNCreator_CifarClassifierNetwork:
add19_
=
batchnorm18_1_
+
relu16_
# add19_, output shape: {[32,8,8]}
relu19_
=
brew
.
relu
(
model
,
add19_
,
add19_
)
conv20_1_
=
brew
.
conv
(
model
,
relu19_
,
'conv20_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
)
conv20_1_
=
brew
.
conv
(
model
,
relu19_
,
'conv20_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv20_1_, output shape: {[32,8,8]}
batchnorm20_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv20_1_
,
fix_gamma
=
True
,
name
=
"batchnorm20_1_"
)
relu20_1_
=
brew
.
relu
(
model
,
batchnorm20_1_
,
batchnorm20_1_
)
conv21_1_
=
brew
.
conv
(
model
,
relu20_1_
,
'conv21_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
)
conv21_1_
=
brew
.
conv
(
model
,
relu20_1_
,
'conv21_1_'
,
dim_in
=
32
,
dim_out
=
32
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv21_1_, output shape: {[32,8,8]}
batchnorm21_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv21_1_
,
fix_gamma
=
True
,
...
...
@@ -177,18 +177,18 @@ class CNNCreator_CifarClassifierNetwork:
add22_
=
batchnorm21_1_
+
relu19_
# add22_, output shape: {[32,8,8]}
relu22_
=
brew
.
relu
(
model
,
add22_
,
add22_
)
conv23_1_
=
brew
.
conv
(
model
,
relu22_
,
'conv23_1_'
,
dim_in
=
32
,
dim_out
=
64
,
kernel
=
3
,
stride
=
2
)
conv23_1_
=
brew
.
conv
(
model
,
relu22_
,
'conv23_1_'
,
dim_in
=
32
,
dim_out
=
64
,
kernel
=
3
,
stride
=
2
,
pad
=
1
)
# conv23_1_, output shape: {[64,4,4]}
batchnorm23_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv23_1_
,
fix_gamma
=
True
,
name
=
"batchnorm23_1_"
)
relu23_1_
=
brew
.
relu
(
model
,
batchnorm23_1_
,
batchnorm23_1_
)
conv24_1_
=
brew
.
conv
(
model
,
relu23_1_
,
'conv24_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv24_1_
=
brew
.
conv
(
model
,
relu23_1_
,
'conv24_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv24_1_, output shape: {[64,4,4]}
batchnorm24_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv24_1_
,
fix_gamma
=
True
,
name
=
"batchnorm24_1_"
)
conv23_2_
=
brew
.
conv
(
model
,
relu22_
,
'conv23_2_'
,
dim_in
=
32
,
dim_out
=
64
,
kernel
=
1
,
stride
=
2
)
conv23_2_
=
brew
.
conv
(
model
,
relu22_
,
'conv23_2_'
,
dim_in
=
32
,
dim_out
=
64
,
kernel
=
1
,
stride
=
2
,
pad
=
1
)
# conv23_2_, output shape: {[64,4,4]}
batchnorm23_2_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv23_2_
,
fix_gamma
=
True
,
...
...
@@ -196,13 +196,13 @@ class CNNCreator_CifarClassifierNetwork:
add25_
=
batchnorm24_1_
+
batchnorm23_2_
# add25_, output shape: {[64,4,4]}
relu25_
=
brew
.
relu
(
model
,
add25_
,
add25_
)
conv26_1_
=
brew
.
conv
(
model
,
relu25_
,
'conv26_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv26_1_
=
brew
.
conv
(
model
,
relu25_
,
'conv26_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv26_1_, output shape: {[64,4,4]}
batchnorm26_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv26_1_
,
fix_gamma
=
True
,
name
=
"batchnorm26_1_"
)
relu26_1_
=
brew
.
relu
(
model
,
batchnorm26_1_
,
batchnorm26_1_
)
conv27_1_
=
brew
.
conv
(
model
,
relu26_1_
,
'conv27_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv27_1_
=
brew
.
conv
(
model
,
relu26_1_
,
'conv27_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv27_1_, output shape: {[64,4,4]}
batchnorm27_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv27_1_
,
fix_gamma
=
True
,
...
...
@@ -210,13 +210,13 @@ class CNNCreator_CifarClassifierNetwork:
add28_
=
batchnorm27_1_
+
relu25_
# add28_, output shape: {[64,4,4]}
relu28_
=
brew
.
relu
(
model
,
add28_
,
add28_
)
conv29_1_
=
brew
.
conv
(
model
,
relu28_
,
'conv29_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv29_1_
=
brew
.
conv
(
model
,
relu28_
,
'conv29_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv29_1_, output shape: {[64,4,4]}
batchnorm29_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv29_1_
,
fix_gamma
=
True
,
name
=
"batchnorm29_1_"
)
relu29_1_
=
brew
.
relu
(
model
,
batchnorm29_1_
,
batchnorm29_1_
)
conv30_1_
=
brew
.
conv
(
model
,
relu29_1_
,
'conv30_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv30_1_
=
brew
.
conv
(
model
,
relu29_1_
,
'conv30_1_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv30_1_, output shape: {[64,4,4]}
batchnorm30_1_
=
mx
.
symbol
.
BatchNorm
(
data
=
conv30_1_
,
fix_gamma
=
True
,
...
...
src/test/resources/target_code/CNNCreator_VGG16.py
View file @
7bb6a3da
...
...
@@ -64,51 +64,51 @@ class CNNCreator_VGG16:
data
=
data
# data, output shape: {[3,224,224]}
conv1_
=
brew
.
conv
(
model
,
data
,
'conv1_'
,
dim_in
=
3
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
)
conv1_
=
brew
.
conv
(
model
,
data
,
'conv1_'
,
dim_in
=
3
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
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
)
conv2_
=
brew
.
conv
(
model
,
relu1_
,
'conv2_'
,
dim_in
=
64
,
dim_out
=
64
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv2_, output shape: {[64,224,224]}
relu2_
=
brew
.
relu
(
model
,
conv2_
,
conv2_
)
pool2_
=
brew
.
max_pool
(
model
,
relu2_
,
'pool2_'
,
kernel
=
2
,
stride
=
2
)
# pool2_, output shape: {[64,112,112]}
conv3_
=
brew
.
conv
(
model
,
pool2_
,
'conv3_'
,
dim_in
=
64
,
dim_out
=
128
,
kernel
=
3
,
stride
=
1
)
conv3_
=
brew
.
conv
(
model
,
pool2_
,
'conv3_'
,
dim_in
=
64
,
dim_out
=
128
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv3_, output shape: {[128,112,112]}
relu3_
=
brew
.
relu
(
model
,
conv3_
,
conv3_
)
conv4_
=
brew
.
conv
(
model
,
relu3_
,
'conv4_'
,
dim_in
=
128
,
dim_out
=
128
,
kernel
=
3
,
stride
=
1
)
conv4_
=
brew
.
conv
(
model
,
relu3_
,
'conv4_'
,
dim_in
=
128
,
dim_out
=
128
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv4_, output shape: {[128,112,112]}
relu4_
=
brew
.
relu
(
model
,
conv4_
,
conv4_
)
pool4_
=
brew
.
max_pool
(
model
,
relu4_
,
'pool4_'
,
kernel
=
2
,
stride
=
2
)
# pool4_, output shape: {[128,56,56]}
conv5_
=
brew
.
conv
(
model
,
pool4_
,
'conv5_'
,
dim_in
=
128
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
)
conv5_
=
brew
.
conv
(
model
,
pool4_
,
'conv5_'
,
dim_in
=
128
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv5_, output shape: {[256,56,56]}
relu5_
=
brew
.
relu
(
model
,
conv5_
,
conv5_
)
conv6_
=
brew
.
conv
(
model
,
relu5_
,
'conv6_'
,
dim_in
=
256
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
)
conv6_
=
brew
.
conv
(
model
,
relu5_
,
'conv6_'
,
dim_in
=
256
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv6_, output shape: {[256,56,56]}
relu6_
=
brew
.
relu
(
model
,
conv6_
,
conv6_
)
conv7_
=
brew
.
conv
(
model
,
relu6_
,
'conv7_'
,
dim_in
=
256
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
)
conv7_
=
brew
.
conv
(
model
,
relu6_
,
'conv7_'
,
dim_in
=
256
,
dim_out
=
256
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv7_, output shape: {[256,56,56]}
relu7_
=
brew
.
relu
(
model
,
conv7_
,
conv7_
)
pool7_
=
brew
.
max_pool
(
model
,
relu7_
,
'pool7_'
,
kernel
=
2
,
stride
=
2
)
# pool7_, output shape: {[256,28,28]}
conv8_
=
brew
.
conv
(
model
,
pool7_
,
'conv8_'
,
dim_in
=
256
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv8_
=
brew
.
conv
(
model
,
pool7_
,
'conv8_'
,
dim_in
=
256
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv8_, output shape: {[512,28,28]}
relu8_
=
brew
.
relu
(
model
,
conv8_
,
conv8_
)
conv9_
=
brew
.
conv
(
model
,
relu8_
,
'conv9_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv9_
=
brew
.
conv
(
model
,
relu8_
,
'conv9_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv9_, output shape: {[512,28,28]}
relu9_
=
brew
.
relu
(
model
,
conv9_
,
conv9_
)
conv10_
=
brew
.
conv
(
model
,
relu9_
,
'conv10_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv10_
=
brew
.
conv
(
model
,
relu9_
,
'conv10_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv10_, output shape: {[512,28,28]}
relu10_
=
brew
.
relu
(
model
,
conv10_
,
conv10_
)
pool10_
=
brew
.
max_pool
(
model
,
relu10_
,
'pool10_'
,
kernel
=
2
,
stride
=
2
)
# pool10_, output shape: {[512,14,14]}
conv11_
=
brew
.
conv
(
model
,
pool10_
,
'conv11_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv11_
=
brew
.
conv
(
model
,
pool10_
,
'conv11_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv11_, output shape: {[512,14,14]}
relu11_
=
brew
.
relu
(
model
,
conv11_
,
conv11_
)
conv12_
=
brew
.
conv
(
model
,
relu11_
,
'conv12_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv12_
=
brew
.
conv
(
model
,
relu11_
,
'conv12_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv12_, output shape: {[512,14,14]}
relu12_
=
brew
.
relu
(
model
,
conv12_
,
conv12_
)
conv13_
=
brew
.
conv
(
model
,
relu12_
,
'conv13_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
)
conv13_
=
brew
.
conv
(
model
,
relu12_
,
'conv13_'
,
dim_in
=
512
,
dim_out
=
512
,
kernel
=
3
,
stride
=
1
,
pad
=
1
)
# conv13_, output shape: {[512,14,14]}
relu13_
=
brew
.
relu
(
model
,
conv13_
,
conv13_
)
pool13_
=
brew
.
max_pool
(
model
,
relu13_
,
'pool13_'
,
kernel
=
2
,
stride
=
2
)
...
...
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