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CNNArch2Caffe2
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monticore
EmbeddedMontiArc
generators
CNNArch2Caffe2
Commits
f0801126
Commit
f0801126
authored
Feb 01, 2019
by
Evgeny Kusmenko
Browse files
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Browse Files
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Plain Diff
Merge branch 'adapt_pending_predefined_layers' into 'master'
Adapt pending predefined layers See merge request
!21
parents
59c74918
4ec7308a
Pipeline
#101955
passed with stages
in 7 minutes and 44 seconds
Changes
26
Pipelines
1
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26 changed files
with
144 additions
and
295 deletions
+144
-295
pom.xml
pom.xml
+1
-1
src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/ArchitectureElementData.java
...icar/cnnarch/caffe2generator/ArchitectureElementData.java
+11
-20
src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/TrainParamSupportChecker.java
...car/cnnarch/caffe2generator/TrainParamSupportChecker.java
+1
-4
src/main/resources/templates/caffe2/CNNCreator.ftl
src/main/resources/templates/caffe2/CNNCreator.ftl
+5
-4
src/main/resources/templates/caffe2/CNNTrainer.ftl
src/main/resources/templates/caffe2/CNNTrainer.ftl
+2
-0
src/main/resources/templates/caffe2/elements/Add.ftl
src/main/resources/templates/caffe2/elements/Add.ftl
+1
-2
src/main/resources/templates/caffe2/elements/BatchNorm.ftl
src/main/resources/templates/caffe2/elements/BatchNorm.ftl
+1
-3
src/main/resources/templates/caffe2/elements/Concatenate.ftl
src/main/resources/templates/caffe2/elements/Concatenate.ftl
+1
-4
src/main/resources/templates/caffe2/elements/Convolution.ftl
src/main/resources/templates/caffe2/elements/Convolution.ftl
+9
-4
src/main/resources/templates/caffe2/elements/Dropout.ftl
src/main/resources/templates/caffe2/elements/Dropout.ftl
+3
-3
src/main/resources/templates/caffe2/elements/Flatten.ftl
src/main/resources/templates/caffe2/elements/Flatten.ftl
+1
-2
src/main/resources/templates/caffe2/elements/FullyConnected.ftl
...in/resources/templates/caffe2/elements/FullyConnected.ftl
+1
-4
src/main/resources/templates/caffe2/elements/Get.ftl
src/main/resources/templates/caffe2/elements/Get.ftl
+1
-1
src/main/resources/templates/caffe2/elements/GlobalPooling.ftl
...ain/resources/templates/caffe2/elements/GlobalPooling.ftl
+6
-5
src/main/resources/templates/caffe2/elements/Input.ftl
src/main/resources/templates/caffe2/elements/Input.ftl
+2
-3
src/main/resources/templates/caffe2/elements/Lrn.ftl
src/main/resources/templates/caffe2/elements/Lrn.ftl
+7
-6
src/main/resources/templates/caffe2/elements/Output.ftl
src/main/resources/templates/caffe2/elements/Output.ftl
+3
-4
src/main/resources/templates/caffe2/elements/Pooling.ftl
src/main/resources/templates/caffe2/elements/Pooling.ftl
+8
-2
src/main/resources/templates/caffe2/elements/Softmax.ftl
src/main/resources/templates/caffe2/elements/Softmax.ftl
+2
-3
src/main/resources/templates/caffe2/elements/Split.ftl
src/main/resources/templates/caffe2/elements/Split.ftl
+1
-5
src/test/java/de/monticore/lang/monticar/cnnarch/caffe2generator/GenerationTest.java
...lang/monticar/cnnarch/caffe2generator/GenerationTest.java
+1
-1
src/test/resources/target_code/CNNCreator_Alexnet.py
src/test/resources/target_code/CNNCreator_Alexnet.py
+18
-58
src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
...esources/target_code/CNNCreator_CifarClassifierNetwork.py
+31
-128
src/test/resources/target_code/CNNCreator_LeNet.py
src/test/resources/target_code/CNNCreator_LeNet.py
+5
-4
src/test/resources/target_code/CNNCreator_VGG16.py
src/test/resources/target_code/CNNCreator_VGG16.py
+20
-23
src/test/resources/target_code/CNNTrainer_fullConfig.py
src/test/resources/target_code/CNNTrainer_fullConfig.py
+2
-1
No files found.
pom.xml
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
${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
View file @
f0801126
${element.name} = mx.symbol.BatchNorm(data=${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
View file @
f0801126
${element.name} = mx.symbol.concat(${tc.join(element.inputs, ", ")},
dim=1,
name="${element.name}")
<#include "OutputShape.ftl">
\ No newline at end of file
<#-- This layer is currently not supported -->
src/main/resources/templates/caffe2/elements/Convolution.ftl
View file @
f0801126
...
...
@@ -3,8 +3,14 @@
<#assign strideWidth = element.stride[1]>
<#assign kernelHeight = element.kernel[0]>
<#assign kernelWidth = element.kernel[1]>
<#if element.padding??> <#-- Check wheather padding null is. -->
<#-- TODO: check how to adapt CNNArchLang argument pad_width=${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
View file @
f0801126
${element.name} = mx.symbol.Dropout(data=${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
View file @
f0801126
${element.name} = mx.symbol.Flatten(data=${element.inputs[0]},
name="${element.name}")
\ No newline at end of file
${element.name} = model.net.Flatten(${element.inputs[0]}, "${element.name}")
\ No newline at end of file
src/main/resources/templates/caffe2/elements/FullyConnected.ftl
View file @
f0801126
...
...
@@ -4,13 +4,10 @@
<#assign inputChannels = element.element.inputTypes[0].channels?c>
<#assign inputHeight = element.element.inputTypes[0].height>
<#assign inputWidth = element.element.inputTypes[0].width>
<#if flatten>
<#-- TODO: check how to adapt CNNArchLang flatten #${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
View file @
f0801126
${element.name} = ${element.inputs[element.index]}
<#-- This layer is currently not supported -->
src/main/resources/templates/caffe2/elements/GlobalPooling.ftl
View file @
f0801126
${element.name} = mx.symbol.Pooling(data=${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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
${element.name} = mx.symbol.LRN(data=${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
View file @
f0801126
...
...
@@ -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 ${element.name}
\ No newline at end of file
src/main/resources/templates/caffe2/elements/Pooling.ftl
View file @
f0801126
...
...
@@ -4,7 +4,13 @@
<#assign kernelHeight = element.kernel[0]>
<#assign kernelWidth = element.kernel[1]>
<#if element.padding??>
<#-- TODO: check how to adapt CNNArchLang argument pad_width=${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
View file @
f0801126
<#-- 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
View file @
f0801126
${element.name} = mx.symbol.split(data=${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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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
View file @
f0801126
...
...
@@ -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_
)
<