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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
18993f09
Commit
18993f09
authored
Apr 25, 2019
by
Nicola Gatto
Committed by
Evgeny Kusmenko
Apr 25, 2019
Browse files
Integrate gluon backend
parent
9953bcd8
Changes
21
Hide whitespace changes
Inline
Side-by-side
.gitlab-ci.yml
View file @
18993f09
...
...
@@ -47,6 +47,12 @@ integrationCaffe2JobLinux:
script
:
-
mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B clean install --settings settings.xml -Dtest=IntegrationCaffe2Test
integrationGluonJobLinux
:
stage
:
linux
image
:
registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/mxnet:v0.0.3
script
:
-
mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B clean install --settings settings.xml -Dtest=IntegrationGluonTest
masterJobWindows
:
stage
:
windows
...
...
pom.xml
View file @
18993f09
...
...
@@ -8,7 +8,7 @@
<groupId>
de.monticore.lang.monticar
</groupId>
<artifactId>
embedded-montiarc-emadl-generator
</artifactId>
<version>
0.
2.13
</version>
<version>
0.
3.0
</version>
<!-- == PROJECT DEPENDENCIES ============================================= -->
...
...
@@ -19,6 +19,7 @@
<CNNTrain.version>
0.2.6
</CNNTrain.version>
<cnnarch-mxnet-generator.version>
0.2.14-SNAPSHOT
</cnnarch-mxnet-generator.version>
<cnnarch-caffe2-generator.version>
0.2.11-SNAPSHOT
</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>
0.1.6
</cnnarch-gluon-generator.version>
<embedded-montiarc-math-opt-generator>
0.1.4
</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
...
...
@@ -73,6 +74,12 @@
<version>
${cnnarch-mxnet-generator.version}
</version>
</dependency>
<dependency>
<groupId>
de.monticore.lang.monticar
</groupId>
<artifactId>
cnnarch-gluon-generator
</artifactId>
<version>
${cnnarch-gluon-generator.version}
</version>
</dependency>
<dependency>
<groupId>
de.monticore.lang.monticar
</groupId>
<artifactId>
cnnarch-caffe2-generator
</artifactId>
...
...
src/main/java/de/monticore/lang/monticar/emadl/generator/Backend.java
View file @
18993f09
...
...
@@ -2,6 +2,8 @@ package de.monticore.lang.monticar.emadl.generator;
import
de.monticore.lang.monticar.cnnarch.CNNArchGenerator
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.CNNArch2Gluon
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.CNNTrain2Gluon
;
import
de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNArch2MxNet
;
import
de.monticore.lang.monticar.cnnarch.caffe2generator.CNNArch2Caffe2
;
import
de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNTrain2MxNet
;
...
...
@@ -30,6 +32,16 @@ public enum Backend {
public
CNNTrainGenerator
getCNNTrainGenerator
()
{
return
new
CNNTrain2Caffe2
();
}
},
GLUON
{
@Override
public
CNNArchGenerator
getCNNArchGenerator
()
{
return
new
CNNArch2Gluon
();
}
@Override
public
CNNTrainGenerator
getCNNTrainGenerator
()
{
return
new
CNNTrain2Gluon
();
}
};
public
abstract
CNNArchGenerator
getCNNArchGenerator
();
...
...
@@ -43,6 +55,9 @@ public enum Backend {
case
"CAFFE2"
:
return
Optional
.
of
(
CAFFE2
);
case
"GLUON"
:
return
Optional
.
of
(
GLUON
);
default
:
return
Optional
.
empty
();
}
...
...
@@ -52,8 +67,10 @@ public enum Backend {
switch
(
backend
){
case
CAFFE2:
return
"CAFFE2"
;
case
GLUON:
return
"GLUON"
;
default
:
return
"MXNET"
;
}
}
}
}
\ No newline at end of file
src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGenerator.java
View file @
18993f09
...
...
@@ -160,7 +160,7 @@ public class EMADLGenerator {
printWriter
.
println
(
"#!/bin/bash"
);
printWriter
.
println
(
"cd "
+
getGenerationTargetPath
());
printWriter
.
println
(
"mkdir -
-parents
build"
);
printWriter
.
println
(
"mkdir -
p
build"
);
printWriter
.
println
(
"cd build"
);
printWriter
.
println
(
"cmake .."
);
printWriter
.
println
(
"make"
);
...
...
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
View file @
18993f09
...
...
@@ -160,6 +160,33 @@ public class GenerationTest extends AbstractSymtabTest {
"CNNTrainer_mnist_mnistClassifier_net.py"
));
}
@Test
public
void
testMnistClassifierForGluon
()
throws
IOException
,
TemplateException
{
Log
.
getFindings
().
clear
();
String
[]
args
=
{
"-m"
,
"src/test/resources/models/"
,
"-r"
,
"mnist.MnistClassifier"
,
"-b"
,
"GLUON"
,
"-f"
,
"n"
,
"-c"
,
"n"
};
EMADLGeneratorCli
.
main
(
args
);
assertTrue
(
Log
.
getFindings
().
isEmpty
());
checkFilesAreEqual
(
Paths
.
get
(
"./target/generated-sources-emadl"
),
Paths
.
get
(
"./src/test/resources/target_code/gluon"
),
Arrays
.
asList
(
"CNNBufferFile.h"
,
"CNNNet_mnist_mnistClassifier_net.py"
,
"mnist_mnistClassifier.cpp"
,
"mnist_mnistClassifier.h"
,
"CNNCreator_mnist_mnistClassifier_net.py"
,
"CNNPredictor_mnist_mnistClassifier_net.h"
,
"CNNDataLoader_mnist_mnistClassifier_net.py"
,
"supervised_trainer.py"
,
"mnist_mnistClassifier_net.h"
,
"HelperA.h"
,
"CNNTranslator.h"
,
"mnist_mnistClassifier_calculateClass.h"
,
"CNNTrainer_mnist_mnistClassifier_net.py"
,
"mnist_mnistClassifier_net.h"
));
}
@Test
public
void
testHashFunction
()
{
EMADLGenerator
tester
=
new
EMADLGenerator
(
Backend
.
MXNET
);
...
...
src/test/java/de/monticore/lang/monticar/emadl/IntegrationGluonTest.java
0 → 100644
View file @
18993f09
/**
*
* ******************************************************************************
* MontiCAR Modeling Family, www.se-rwth.de
* Copyright (c) 2017, Software Engineering Group at RWTH Aachen,
* All rights reserved.
*
* This project is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 3.0 of the License, or (at your option) any later version.
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this project. If not, see <http://www.gnu.org/licenses/>.
* *******************************************************************************
*/
package
de.monticore.lang.monticar.emadl
;
public
class
IntegrationGluonTest
extends
IntegrationTest
{
public
IntegrationGluonTest
()
{
super
(
"GLUON"
,
"39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A"
);
}
}
src/test/java/de/monticore/lang/monticar/emadl/IntegrationTest.java
View file @
18993f09
...
...
@@ -25,9 +25,13 @@ import de.monticore.lang.monticar.emadl.generator.EMADLGenerator;
import
de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli
;
import
de.se_rwth.commons.logging.Log
;
import
freemarker.template.TemplateException
;
import
org.apache.commons.io.FileUtils
;
import
org.junit.AfterClass
;
import
org.junit.Before
;
import
org.junit.BeforeClass
;
import
org.junit.Test
;
import
java.io.File
;
import
java.io.IOException
;
import
java.nio.charset.Charset
;
import
java.nio.file.Files
;
...
...
@@ -44,6 +48,20 @@ public abstract class IntegrationTest extends AbstractSymtabTest {
private
String
backend
;
private
String
trainingHash
;
@BeforeClass
public
static
void
setupClass
()
throws
IOException
{
if
(
new
File
(
"model"
).
exists
())
{
FileUtils
.
deleteDirectory
(
new
File
(
"model"
));
}
}
@AfterClass
public
static
void
tearDown
()
throws
IOException
{
if
(
new
File
(
"model"
).
exists
())
{
FileUtils
.
deleteDirectory
(
new
File
(
"model"
));
}
}
public
IntegrationTest
(
String
backend
,
String
trainingHash
)
{
this
.
backend
=
backend
;
this
.
trainingHash
=
trainingHash
;
...
...
@@ -116,6 +134,18 @@ public abstract class IntegrationTest extends AbstractSymtabTest {
EMADLGeneratorCli
.
main
(
args
);
assertTrue
(
Log
.
getFindings
().
size
()
==
1
);
assertTrue
(
Log
.
getFindings
().
get
(
0
).
getMsg
().
contains
(
"skipped"
));
deleteInstanceTestCifarHashFile
();
}
private
void
deleteInstanceTestCifarHashFile
()
{
final
Path
instanceTestCifarHasFile
=
Paths
.
get
(
"./target/generated-sources-emadl/instanceTestCifar/CifarNetwork.training_hash"
);
try
{
Files
.
delete
(
instanceTestCifarHasFile
);
}
catch
(
Exception
e
)
{
assertFalse
(
"Could not delete hash file"
,
true
);
}
}
@Test
...
...
@@ -130,7 +160,4 @@ public abstract class IntegrationTest extends AbstractSymtabTest {
deleteHashFile
();
}
}
src/test/resources/target_code/gluon/CNNBufferFile.h
0 → 100644
View file @
18993f09
#ifndef CNNBUFFERFILE_H
#define CNNBUFFERFILE_H
#include
<stdio.h>
#include
<iostream>
#include
<fstream>
// Read file to buffer
class
BufferFile
{
public
:
std
::
string
file_path_
;
int
length_
;
char
*
buffer_
;
explicit
BufferFile
(
std
::
string
file_path
)
:
file_path_
(
file_path
)
{
std
::
ifstream
ifs
(
file_path
.
c_str
(),
std
::
ios
::
in
|
std
::
ios
::
binary
);
if
(
!
ifs
)
{
std
::
cerr
<<
"Can't open the file. Please check "
<<
file_path
<<
".
\n
"
;
length_
=
0
;
buffer_
=
NULL
;
return
;
}
ifs
.
seekg
(
0
,
std
::
ios
::
end
);
length_
=
ifs
.
tellg
();
ifs
.
seekg
(
0
,
std
::
ios
::
beg
);
std
::
cout
<<
file_path
.
c_str
()
<<
" ... "
<<
length_
<<
" bytes
\n
"
;
buffer_
=
new
char
[
sizeof
(
char
)
*
length_
];
ifs
.
read
(
buffer_
,
length_
);
ifs
.
close
();
}
int
GetLength
()
{
return
length_
;
}
char
*
GetBuffer
()
{
return
buffer_
;
}
~
BufferFile
()
{
if
(
buffer_
)
{
delete
[]
buffer_
;
buffer_
=
NULL
;
}
}
};
#endif // CNNBUFFERFILE_H
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
0 → 100644
View file @
18993f09
import
mxnet
as
mx
import
logging
import
os
from
CNNNet_mnist_mnistClassifier_net
import
Net
class
CNNCreator_mnist_mnistClassifier_net
:
_model_dir_
=
"model/mnist.LeNetNetwork/"
_model_prefix_
=
"model"
_input_shapes_
=
[(
1
,
28
,
28
)]
def
__init__
(
self
):
self
.
weight_initializer
=
mx
.
init
.
Normal
()
self
.
net
=
None
def
load
(
self
,
context
):
lastEpoch
=
0
param_file
=
None
try
:
os
.
remove
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_newest-0000.params"
)
except
OSError
:
pass
try
:
os
.
remove
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_newest-symbol.json"
)
except
OSError
:
pass
if
os
.
path
.
isdir
(
self
.
_model_dir_
):
for
file
in
os
.
listdir
(
self
.
_model_dir_
):
if
".params"
in
file
and
self
.
_model_prefix_
in
file
:
epochStr
=
file
.
replace
(
".params"
,
""
).
replace
(
self
.
_model_prefix_
+
"-"
,
""
)
epoch
=
int
(
epochStr
)
if
epoch
>
lastEpoch
:
lastEpoch
=
epoch
param_file
=
file
if
param_file
is
None
:
return
0
else
:
logging
.
info
(
"Loading checkpoint: "
+
param_file
)
self
.
net
.
load_parameters
(
self
.
_model_dir_
+
param_file
)
return
lastEpoch
def
construct
(
self
,
context
,
data_mean
=
None
,
data_std
=
None
):
self
.
net
=
Net
(
data_mean
=
data_mean
,
data_std
=
data_std
)
self
.
net
.
collect_params
().
initialize
(
self
.
weight_initializer
,
ctx
=
context
)
self
.
net
.
hybridize
()
self
.
net
(
mx
.
nd
.
zeros
((
1
,)
+
self
.
_input_shapes_
[
0
],
ctx
=
context
))
if
not
os
.
path
.
exists
(
self
.
_model_dir_
):
os
.
makedirs
(
self
.
_model_dir_
)
self
.
net
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
,
epoch
=
0
)
src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
0 → 100644
View file @
18993f09
import
os
import
h5py
import
mxnet
as
mx
import
logging
import
sys
class
mnist_mnistClassifier_netDataLoader
:
_input_names_
=
[
'image'
]
_output_names_
=
[
'predictions_label'
]
def
__init__
(
self
):
self
.
_data_dir
=
"data/mnist.LeNetNetwork/"
def
load_data
(
self
,
batch_size
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
data_mean
=
train_h5
[
self
.
_input_names_
[
0
]][:].
mean
(
axis
=
0
)
data_std
=
train_h5
[
self
.
_input_names_
[
0
]][:].
std
(
axis
=
0
)
+
1e-5
train_iter
=
mx
.
io
.
NDArrayIter
(
train_h5
[
self
.
_input_names_
[
0
]],
train_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
test_iter
=
None
if
test_h5
!=
None
:
test_iter
=
mx
.
io
.
NDArrayIter
(
test_h5
[
self
.
_input_names_
[
0
]],
test_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
return
train_iter
,
test_iter
,
data_mean
,
data_std
def
load_h5_files
(
self
):
train_h5
=
None
test_h5
=
None
train_path
=
self
.
_data_dir
+
"train.h5"
test_path
=
self
.
_data_dir
+
"test.h5"
if
os
.
path
.
isfile
(
train_path
):
train_h5
=
h5py
.
File
(
train_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
train_h5
and
self
.
_output_names_
[
0
]
in
train_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
test_iter
=
None
if
os
.
path
.
isfile
(
test_path
):
test_h5
=
h5py
.
File
(
test_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
test_h5
and
self
.
_output_names_
[
0
]
in
test_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
test_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
else
:
logging
.
warning
(
"Couldn't load test set. File '"
+
os
.
path
.
abspath
(
test_path
)
+
"' does not exist."
)
return
train_h5
,
test_h5
else
:
logging
.
error
(
"Data loading failure. File '"
+
os
.
path
.
abspath
(
train_path
)
+
"' does not exist."
)
sys
.
exit
(
1
)
\ No newline at end of file
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py
0 → 100644
View file @
18993f09
import
mxnet
as
mx
import
numpy
as
np
from
mxnet
import
gluon
class
Softmax
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
**
kwargs
):
super
(
Softmax
,
self
).
__init__
(
**
kwargs
)
def
hybrid_forward
(
self
,
F
,
x
):
return
F
.
softmax
(
x
)
class
Split
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
num_outputs
,
axis
=
1
,
**
kwargs
):
super
(
Split
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
self
.
axis
=
axis
self
.
num_outputs
=
num_outputs
def
hybrid_forward
(
self
,
F
,
x
):
return
F
.
split
(
data
=
x
,
axis
=
self
.
axis
,
num_outputs
=
self
.
num_outputs
)
class
Concatenate
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
dim
=
1
,
**
kwargs
):
super
(
Concatenate
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
self
.
dim
=
dim
def
hybrid_forward
(
self
,
F
,
*
x
):
return
F
.
concat
(
*
x
,
dim
=
self
.
dim
)
class
ZScoreNormalization
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
data_mean
,
data_std
,
**
kwargs
):
super
(
ZScoreNormalization
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
self
.
data_mean
=
self
.
params
.
get
(
'data_mean'
,
shape
=
data_mean
.
shape
,
init
=
mx
.
init
.
Constant
(
data_mean
.
asnumpy
().
tolist
()),
differentiable
=
False
)
self
.
data_std
=
self
.
params
.
get
(
'data_std'
,
shape
=
data_mean
.
shape
,
init
=
mx
.
init
.
Constant
(
data_std
.
asnumpy
().
tolist
()),
differentiable
=
False
)
def
hybrid_forward
(
self
,
F
,
x
,
data_mean
,
data_std
):
x
=
F
.
broadcast_sub
(
x
,
data_mean
)
x
=
F
.
broadcast_div
(
x
,
data_std
)
return
x
class
Padding
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
padding
,
**
kwargs
):
super
(
Padding
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
self
.
pad_width
=
padding
def
hybrid_forward
(
self
,
F
,
x
):
x
=
F
.
pad
(
data
=
x
,
mode
=
'constant'
,
pad_width
=
self
.
pad_width
,
constant_value
=
0
)
return
x
class
NoNormalization
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
**
kwargs
):
super
(
NoNormalization
,
self
).
__init__
(
**
kwargs
)
def
hybrid_forward
(
self
,
F
,
x
):
return
x
class
Net
(
gluon
.
HybridBlock
):
def
__init__
(
self
,
data_mean
=
None
,
data_std
=
None
,
**
kwargs
):
super
(
Net
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
if
not
data_mean
is
None
:
assert
(
not
data_std
is
None
)
self
.
input_normalization
=
ZScoreNormalization
(
data_mean
=
data_mean
,
data_std
=
data_std
)
else
:
self
.
input_normalization
=
NoNormalization
()
self
.
conv1_
=
gluon
.
nn
.
Conv2D
(
channels
=
20
,
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv1_, output shape: {[20,24,24]}
self
.
pool1_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
2
,
2
),
strides
=
(
2
,
2
))
# pool1_, output shape: {[20,12,12]}
self
.
conv2_
=
gluon
.
nn
.
Conv2D
(
channels
=
50
,
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv2_, output shape: {[50,8,8]}
self
.
pool2_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
2
,
2
),
strides
=
(
2
,
2
))
# pool2_, output shape: {[50,4,4]}
self
.
fc2_flatten
=
gluon
.
nn
.
Flatten
()
self
.
fc2_
=
gluon
.
nn
.
Dense
(
units
=
500
,
use_bias
=
True
)
# fc2_, output shape: {[500,1,1]}
self
.
relu2_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
fc3_
=
gluon
.
nn
.
Dense
(
units
=
10
,
use_bias
=
True
)
# fc3_, output shape: {[10,1,1]}
self
.
last_layer
=
'softmax'
def
hybrid_forward
(
self
,
F
,
x
):
image
=
self
.
input_normalization
(
x
)
conv1_
=
self
.
conv1_
(
image
)
pool1_
=
self
.
pool1_
(
conv1_
)
conv2_
=
self
.
conv2_
(
pool1_
)
pool2_
=
self
.
pool2_
(
conv2_
)
fc2_flatten_
=
self
.
fc2_flatten
(
pool2_
)
fc2_
=
self
.
fc2_
(
fc2_flatten_
)
relu2_
=
self
.
relu2_
(
fc2_
)
fc3_
=
self
.
fc3_
(
relu2_
)
return
fc3_
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.pyc
0 → 100644
View file @
18993f09
File added
src/test/resources/target_code/gluon/CNNPredictor_mnist_mnistClassifier_net.h
0 → 100644
View file @
18993f09
#ifndef CNNPREDICTOR_MNIST_MNISTCLASSIFIER_NET
#define CNNPREDICTOR_MNIST_MNISTCLASSIFIER_NET
#include
<mxnet/c_predict_api.h>
#include
<cassert>
#include
<string>
#include
<vector>
#include
<CNNBufferFile.h>
class
CNNPredictor_mnist_mnistClassifier_net
{
public:
const
std
::
string
json_file
=
"model/mnist.LeNetNetwork/model_newest-symbol.json"
;
const
std
::
string
param_file
=
"model/mnist.LeNetNetwork/model_newest-0000.params"
;
//const std::vector<std::string> input_keys = {"data"};
const
std
::
vector
<
std
::
string
>
input_keys
=
{
"image"
};
const
std
::
vector
<
std
::
vector
<
mx_uint
>>
input_shapes
=
{{
1
,
1
,
28
,
28
}};
const
bool
use_gpu
=
false
;
PredictorHandle
handle
;
explicit
CNNPredictor_mnist_mnistClassifier_net
(){
init
(
json_file
,
param_file
,
input_keys
,
input_shapes
,
use_gpu
);
}
~
CNNPredictor_mnist_mnistClassifier_net
(){
if
(
handle
)
MXPredFree
(
handle
);
}
void
predict
(
const
std
::
vector
<
float
>
&
image
,
std
::
vector
<
float
>
&
predictions
){
MXPredSetInput
(
handle
,
"data"
,
image
.
data
(),
image
.
size
());
//MXPredSetInput(handle, "image", image.data(), image.size());
MXPredForward
(
handle
);
mx_uint
output_index
;
mx_uint
*
shape
=
0
;
mx_uint
shape_len
;
size_t
size
;
output_index
=
0
;
MXPredGetOutputShape
(
handle
,
output_index
,
&
shape
,
&
shape_len
);
size
=
1
;
for
(
mx_uint
i
=
0
;
i
<
shape_len
;
++
i
)
size
*=
shape
[
i
];
assert
(
size
==
predictions
.
size
());
MXPredGetOutput
(
handle
,
0
,
&
(
predictions
[
0
]),
predictions
.
size
());
}
void
init
(
const
std
::
string
&
json_file
,
const
std
::
string
&
param_file
,
const
std
::
vector
<
std
::
string
>
&
input_keys
,