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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
7c707a98
Commit
7c707a98
authored
Apr 05, 2019
by
Nicola Gatto
Browse files
Integrate Gluon generator
parent
f13e436a
Changes
18
Hide whitespace changes
Inline
Side-by-side
pom.xml
View file @
7c707a98
...
...
@@ -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>
0.1.5
</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 @
7c707a98
...
...
@@ -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 @
7c707a98
...
...
@@ -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 @
7c707a98
...
...
@@ -160,6 +160,31 @@ 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"
,
"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 @
7c707a98
/**
*
* ******************************************************************************
* 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/resources/target_code/gluon/CNNBufferFile.h
0 → 100644
View file @
7c707a98
#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 @
7c707a98
import
mxnet
as
mx
import
logging
import
os
import
errno
import
shutil
import
h5py
import
sys
import
numpy
as
np
import
time
from
mxnet
import
gluon
,
autograd
,
nd
from
CNNNet_mnist_mnistClassifier_net
import
Net
@
mx
.
init
.
register
class
MyConstant
(
mx
.
init
.
Initializer
):
def
__init__
(
self
,
value
):
super
(
MyConstant
,
self
).
__init__
(
value
=
value
)
self
.
value
=
value
def
_init_weight
(
self
,
_
,
arr
):
arr
[:]
=
mx
.
nd
.
array
(
self
.
value
)
class
CNNCreator_mnist_mnistClassifier_net
:
_data_dir_
=
"data/mnist.LeNetNetwork/"
_model_dir_
=
"model/mnist.LeNetNetwork/"
_model_prefix_
=
"model"
_input_names_
=
[
'image'
]
_input_shapes_
=
[(
1
,
28
,
28
)]
_output_names_
=
[
'predictions_label'
]
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
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
)
def
train
(
self
,
batch_size
=
64
,
num_epoch
=
10
,
eval_metric
=
'acc'
,
optimizer
=
'adam'
,
optimizer_params
=
((
'learning_rate'
,
0.001
),),
load_checkpoint
=
True
,
context
=
'gpu'
,
checkpoint_period
=
5
,
normalize
=
True
):
if
context
==
'gpu'
:
mx_context
=
mx
.
gpu
()
elif
context
==
'cpu'
:
mx_context
=
mx
.
cpu
()
else
:
logging
.
error
(
"Context argument is '"
+
context
+
"'. Only 'cpu' and 'gpu are valid arguments'."
)
if
'weight_decay'
in
optimizer_params
:
optimizer_params
[
'wd'
]
=
optimizer_params
[
'weight_decay'
]
del
optimizer_params
[
'weight_decay'
]
if
'learning_rate_decay'
in
optimizer_params
:
min_learning_rate
=
1e-08
if
'learning_rate_minimum'
in
optimizer_params
:
min_learning_rate
=
optimizer_params
[
'learning_rate_minimum'
]
del
optimizer_params
[
'learning_rate_minimum'
]
optimizer_params
[
'lr_scheduler'
]
=
mx
.
lr_scheduler
.
FactorScheduler
(
optimizer_params
[
'step_size'
],
factor
=
optimizer_params
[
'learning_rate_decay'
],
stop_factor_lr
=
min_learning_rate
)
del
optimizer_params
[
'step_size'
]
del
optimizer_params
[
'learning_rate_decay'
]
train_iter
,
test_iter
,
data_mean
,
data_std
=
self
.
load_data
(
batch_size
)
if
self
.
net
==
None
:
if
normalize
:
self
.
construct
(
context
=
mx_context
,
data_mean
=
nd
.
array
(
data_mean
),
data_std
=
nd
.
array
(
data_std
))
else
:
self
.
construct
(
context
=
mx_context
)
begin_epoch
=
0
if
load_checkpoint
:
begin_epoch
=
self
.
load
(
mx_context
)
else
:
if
os
.
path
.
isdir
(
self
.
_model_dir_
):
shutil
.
rmtree
(
self
.
_model_dir_
)
try
:
os
.
makedirs
(
self
.
_model_dir_
)
except
OSError
:
if
not
os
.
path
.
isdir
(
self
.
_model_dir_
):
raise
trainer
=
mx
.
gluon
.
Trainer
(
self
.
net
.
collect_params
(),
optimizer
,
optimizer_params
)
if
self
.
net
.
last_layer
==
'softmax'
:
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
()
elif
self
.
net
.
last_layer
==
'sigmoid'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
self
.
net
.
last_layer
==
'linear'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
else
:
# TODO: Change default?
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
logging
.
warning
(
"Invalid last_layer, defaulting to L2 loss"
)
speed_period
=
50
tic
=
None
for
epoch
in
range
(
begin_epoch
,
begin_epoch
+
num_epoch
):
train_iter
.
reset
()
for
batch_i
,
batch
in
enumerate
(
train_iter
):
data
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
label
=
batch
.
label
[
0
].
as_in_context
(
mx_context
)
with
autograd
.
record
():
output
=
self
.
net
(
data
)
loss
=
loss_function
(
output
,
label
)
loss
.
backward
()
trainer
.
step
(
batch_size
)
if
tic
is
None
:
tic
=
time
.
time
()
else
:
if
batch_i
%
speed_period
==
0
:
try
:
speed
=
speed_period
*
batch_size
/
(
time
.
time
()
-
tic
)
except
ZeroDivisionError
:
speed
=
float
(
"inf"
)
logging
.
info
(
"Epoch[%d] Batch[%d] Speed: %.2f samples/sec"
%
(
epoch
,
batch_i
,
speed
))
tic
=
time
.
time
()
tic
=
None
train_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
)
for
batch_i
,
batch
in
enumerate
(
train_iter
):
data
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
label
=
batch
.
label
[
0
].
as_in_context
(
mx_context
)
output
=
self
.
net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
train_metric_score
=
metric
.
get
()[
1
]
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
data
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
label
=
batch
.
label
[
0
].
as_in_context
(
mx_context
)
output
=
self
.
net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
test_metric_score
=
metric
.
get
()[
1
]
logging
.
info
(
"Epoch[%d] Train: %f, Test: %f"
%
(
epoch
,
train_metric_score
,
test_metric_score
))
if
(
epoch
-
begin_epoch
)
%
checkpoint_period
==
0
:
self
.
net
.
save_parameters
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'-'
+
str
(
epoch
).
zfill
(
4
)
+
'.params'
)
self
.
net
.
save_parameters
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'-'
+
str
(
num_epoch
+
begin_epoch
).
zfill
(
4
)
+
'.params'
)
self
.
net
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
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/CNNCreator_mnist_mnistClassifier_net.pyc
0 → 100644
View file @
7c707a98
File added
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py
0 → 100644
View file @
7c707a98
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 @
7c707a98
File added