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EMADL2CPP
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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
df882e6e
Commit
df882e6e
authored
Oct 07, 2019
by
Nicola Gatto
Committed by
Evgeny Kusmenko
Oct 07, 2019
Browse files
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Update Gluon Generator and CNNTrain and minor test changes
parent
9e4a0b62
Changes
52
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Showing
52 changed files
with
9458 additions
and
27 deletions
+9458
-27
pom.xml
pom.xml
+3
-3
src/test/java/de/monticore/lang/monticar/emadl/IntegrationPythonWrapperTest.java
...ore/lang/monticar/emadl/IntegrationPythonWrapperTest.java
+49
-1
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.emadl
...reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.emadl
+15
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.tag
...s/reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.tag
+8
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/Reward.emadl
...forcementModel/torcs_td3/torcs/agent/network/Reward.emadl
+31
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor.cnnt
...cementModel/torcs_td3/torcs/agent/network/TorcsActor.cnnt
+61
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor.emadl
...ementModel/torcs_td3/torcs/agent/network/TorcsActor.emadl
+19
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor_ddpg.cnnt
...tModel/torcs_td3/torcs/agent/network/TorcsActor_ddpg.cnnt
+56
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsCritic.emadl
...mentModel/torcs_td3/torcs/agent/network/TorcsCritic.emadl
+20
-0
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsCritic_ddpg.emadl
...odel/torcs_td3/torcs/agent/network/TorcsCritic_ddpg.emadl
+26
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/reward/pylib/armanpy/armanpy.hpp
...reinforcementModel/torcs/reward/pylib/armanpy/armanpy.hpp
+0
-1
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CMakeLists.txt
...et_code/gluon/reinforcementModel/torcs_td3/CMakeLists.txt
+27
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNBufferFile.h
...t_code/gluon/reinforcementModel/torcs_td3/CNNBufferFile.h
+51
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNCreator_torcs_agent_torcsAgent_actor.py
...odel/torcs_td3/CNNCreator_torcs_agent_torcsAgent_actor.py
+59
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNDataLoader_torcs_agent_torcsAgent_actor.py
...l/torcs_td3/CNNDataLoader_torcs_agent_torcsAgent_actor.py
+93
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNNet_torcs_agent_torcsAgent_actor.py
...entModel/torcs_td3/CNNNet_torcs_agent_torcsAgent_actor.py
+118
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNPredictor_torcs_agent_torcsAgent_actor.h
...del/torcs_td3/CNNPredictor_torcs_agent_torcsAgent_actor.h
+107
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNSupervisedTrainer_torcs_agent_torcsAgent_actor.py
..._td3/CNNSupervisedTrainer_torcs_agent_torcsAgent_actor.py
+213
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNTrainer_torcs_agent_torcsAgent_actor.py
...odel/torcs_td3/CNNTrainer_torcs_agent_torcsAgent_actor.py
+134
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNTranslator.h
...t_code/gluon/reinforcementModel/torcs_td3/CNNTranslator.h
+128
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/HelperA.h
.../target_code/gluon/reinforcementModel/torcs_td3/HelperA.h
+141
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/cmake/FindArmadillo.cmake
...on/reinforcementModel/torcs_td3/cmake/FindArmadillo.cmake
+38
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/CNNCreator_torcs_agent_network_torcsCritic.py
...nt_learning/CNNCreator_torcs_agent_network_torcsCritic.py
+59
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/CNNNet_torcs_agent_network_torcsCritic.py
...cement_learning/CNNNet_torcs_agent_network_torcsCritic.py
+128
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/__init__.py
...rcementModel/torcs_td3/reinforcement_learning/__init__.py
+0
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/agent.py
...nforcementModel/torcs_td3/reinforcement_learning/agent.py
+1269
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/cnnarch_logger.py
...tModel/torcs_td3/reinforcement_learning/cnnarch_logger.py
+95
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/environment.py
...mentModel/torcs_td3/reinforcement_learning/environment.py
+149
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/replay_memory.py
...ntModel/torcs_td3/reinforcement_learning/replay_memory.py
+208
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/strategy.py
...rcementModel/torcs_td3/reinforcement_learning/strategy.py
+218
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/torcs_agent_network_reward_executor.py
...forcement_learning/torcs_agent_network_reward_executor.py
+167
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/util.py
...inforcementModel/torcs_td3/reinforcement_learning/util.py
+300
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/CMakeLists.txt
.../gluon/reinforcementModel/torcs_td3/reward/CMakeLists.txt
+26
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/HelperA.h
..._code/gluon/reinforcementModel/torcs_td3/reward/HelperA.h
+141
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/cmake/FindArmadillo.cmake
...forcementModel/torcs_td3/reward/cmake/FindArmadillo.cmake
+38
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/CMakeLists.txt
.../reinforcementModel/torcs_td3/reward/pylib/CMakeLists.txt
+24
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/armanpy.hpp
...forcementModel/torcs_td3/reward/pylib/armanpy/armanpy.hpp
+232
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/armanpy.i
...inforcementModel/torcs_td3/reward/pylib/armanpy/armanpy.i
+233
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/armanpy_1d.i
...orcementModel/torcs_td3/reward/pylib/armanpy/armanpy_1d.i
+569
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/armanpy_2d.i
...orcementModel/torcs_td3/reward/pylib/armanpy/armanpy_2d.i
+452
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/armanpy_3d.i
...orcementModel/torcs_td3/reward/pylib/armanpy/armanpy_3d.i
+444
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/armanpy/numpy.i
...reinforcementModel/torcs_td3/reward/pylib/armanpy/numpy.i
+3161
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/torcs_agent_network_reward_executor.cpp
..._td3/reward/pylib/torcs_agent_network_reward_executor.cpp
+17
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/torcs_agent_network_reward_executor.h
...cs_td3/reward/pylib/torcs_agent_network_reward_executor.h
+22
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/pylib/torcs_agent_network_reward_executor.i
...cs_td3/reward/pylib/torcs_agent_network_reward_executor.i
+9
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/torcs_agent_network_reward.cpp
...mentModel/torcs_td3/reward/torcs_agent_network_reward.cpp
+1
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reward/torcs_agent_network_reward.h
...cementModel/torcs_td3/reward/torcs_agent_network_reward.h
+37
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/start_training.sh
...code/gluon/reinforcementModel/torcs_td3/start_training.sh
+2
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/torcs_agent_torcsAgent.cpp
...n/reinforcementModel/torcs_td3/torcs_agent_torcsAgent.cpp
+1
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/torcs_agent_torcsAgent.h
...uon/reinforcementModel/torcs_td3/torcs_agent_torcsAgent.h
+28
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/torcs_agent_torcsAgent_actor.h
...inforcementModel/torcs_td3/torcs_agent_torcsAgent_actor.h
+31
-0
train.log
train.log
+0
-22
No files found.
pom.xml
View file @
df882e6e
...
...
@@ -9,7 +9,7 @@
<groupId>
de.monticore.lang.monticar
</groupId>
<artifactId>
embedded-montiarc-emadl-generator
</artifactId>
<version>
0.3.
6
-SNAPSHOT
</version>
<version>
0.3.
7
-SNAPSHOT
</version>
<!-- == PROJECT DEPENDENCIES ============================================= -->
...
...
@@ -17,11 +17,11 @@
<!-- .. SE-Libraries .................................................. -->
<emadl.version>
0.2.10-SNAPSHOT
</emadl.version>
<CNNTrain.version>
0.3.
6
-SNAPSHOT
</CNNTrain.version>
<CNNTrain.version>
0.3.
7
-SNAPSHOT
</CNNTrain.version>
<cnnarch-generator.version>
0.0.4-SNAPSHOT
</cnnarch-generator.version>
<cnnarch-mxnet-generator.version>
0.2.17-SNAPSHOT
</cnnarch-mxnet-generator.version>
<cnnarch-caffe2-generator.version>
0.2.13-SNAPSHOT
</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>
0.2.
8
-SNAPSHOT
</cnnarch-gluon-generator.version>
<cnnarch-gluon-generator.version>
0.2.
9
-SNAPSHOT
</cnnarch-gluon-generator.version>
<cnnarch-tensorflow-generator.version>
0.1.0-SNAPSHOT
</cnnarch-tensorflow-generator.version>
<embedded-montiarc-math-opt-generator>
0.1.4
</embedded-montiarc-math-opt-generator>
...
...
src/test/java/de/monticore/lang/monticar/emadl/IntegrationPythonWrapperTest.java
View file @
df882e6e
...
...
@@ -4,7 +4,6 @@ package de.monticore.lang.monticar.emadl;
import
de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli
;
import
de.se_rwth.commons.logging.Finding
;
import
de.se_rwth.commons.logging.Log
;
import
org.junit.Ignore
;
import
org.junit.Test
;
import
java.nio.file.Paths
;
...
...
@@ -12,6 +11,7 @@ import java.util.Arrays;
import
java.util.stream.Collectors
;
import
static
junit
.
framework
.
TestCase
.
assertTrue
;
import
static
org
.
junit
.
Assume
.
assumeFalse
;
/**
*
...
...
@@ -19,6 +19,7 @@ import static junit.framework.TestCase.assertTrue;
public
class
IntegrationPythonWrapperTest
extends
AbstractSymtabTest
{
@Test
public
void
testGluonReinforcementModelRosEnvironment
()
{
assumeFalse
(
System
.
getProperty
(
"os.name"
).
toLowerCase
().
startsWith
(
"win"
));
Log
.
getFindings
().
clear
();
String
[]
args
=
{
"-m"
,
"src/test/resources/models/reinforcementModel"
,
"-r"
,
"torcs.agent.TorcsAgent"
,
"-b"
,
"GLUON"
,
"-f"
,
"n"
,
"-c"
,
"n"
};
EMADLGeneratorCli
.
main
(
args
);
...
...
@@ -71,4 +72,51 @@ public class IntegrationPythonWrapperTest extends AbstractSymtabTest {
"./target/generated-sources-emadl/reinforcement_learning/torcs_agent_dqn_reward_executor.py"
)
.
toFile
().
exists
());
}
@Test
public
void
testTorcsTD3
()
{
Log
.
getFindings
().
clear
();
String
[]
args
=
{
"-m"
,
"src/test/resources/models/reinforcementModel/torcs_td3"
,
"-r"
,
"torcs.agent.TorcsAgent"
,
"-b"
,
"GLUON"
,
"-f"
,
"n"
,
"-c"
,
"n"
};
EMADLGeneratorCli
.
main
(
args
);
assertTrue
(
Log
.
getFindings
().
stream
().
filter
(
Finding:
:
isError
).
collect
(
Collectors
.
toList
()).
isEmpty
());
checkFilesAreEqual
(
Paths
.
get
(
"./target/generated-sources-emadl"
),
Paths
.
get
(
"./src/test/resources/target_code/gluon/reinforcementModel/torcs_td3"
),
Arrays
.
asList
(
"CMakeLists.txt"
,
"CNNBufferFile.h"
,
"torcs_agent_torcsAgent.cpp"
,
"torcs_agent_torcsAgent.h"
,
"torcs_agent_torcsAgent_actor.h"
,
"CNNCreator_torcs_agent_torcsAgent_actor.py"
,
"CNNNet_torcs_agent_torcsAgent_actor.py"
,
"CNNPredictor_torcs_agent_torcsAgent_actor.h"
,
"CNNTrainer_torcs_agent_torcsAgent_actor.py"
,
"start_training.sh"
,
"reward/CMakeLists.txt"
,
"reward/torcs_agent_network_reward.cpp"
,
"reward/torcs_agent_network_reward.h"
,
"reward/pylib/CMakeLists.txt"
,
"reward/pylib/torcs_agent_network_reward_executor.cpp"
,
"reward/pylib/torcs_agent_network_reward_executor.h"
,
"reward/pylib/torcs_agent_network_reward_executor.i"
,
"reward/pylib/armanpy/armanpy.hpp"
,
"reward/pylib/armanpy/armanpy.i"
,
"reward/pylib/armanpy/armanpy_1d.i"
,
"reward/pylib/armanpy/armanpy_2d.i"
,
"reward/pylib/armanpy/armanpy_3d.i"
,
"reward/pylib/armanpy/numpy.i"
,
"reinforcement_learning/__init__.py"
,
"reinforcement_learning/strategy.py"
,
"reinforcement_learning/agent.py"
,
"reinforcement_learning/environment.py"
,
"reinforcement_learning/replay_memory.py"
,
"reinforcement_learning/util.py"
,
"reinforcement_learning/cnnarch_logger.py"
,
"reinforcement_learning/CNNCreator_torcs_agent_network_torcsCritic.py"
,
"reinforcement_learning/CNNNet_torcs_agent_network_torcsCritic.py"
)
);
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.emadl
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
;
import
torcs
.
agent
.
network
.
TorcsActor
;
component
TorcsAgent
{
ports
in
Q
^{
29
}
state
,
out
Q
(-
1
:
1
)^{
3
}
action
;
instance
TorcsActor
actor
;
connect
state
->
actor
.
state
;
connect
actor
.
commands
->
action
;
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/TorcsAgent.tag
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
;
conforms
to
de
.
monticore
.
lang
.
monticar
.
generator
.
roscpp
.
RosToEmamTagSchema
;
tags
TorcsAgent
{
tag
torcsAgent
.
state
with
RosConnection
=
{
topic
=(/
torcs
/
state
,
std_msgs
/
Float32MultiArray
)};
tag
torcsAgent
.
action
with
RosConnection
=
{
topic
=(/
torcs
/
step
,
std_msgs
/
Float32MultiArray
)};
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/Reward.emadl
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
.
network
;
component
Reward
{
ports
in
Q
^{
29
}
state
,
in
B
isTerminal
,
out
Q
reward
;
implementation
Math
{
Q
speedX
=
state
(
22
)
*
300
;
Q
angle
=
state
(
1
)
*
3.1416
;
Q
trackPos
=
state
(
21
);
if
speedX
<
0
speedX
=
0
;
end
reward
=
(
speedX
*
cos
(
angle
))
-
(
speedX
*
sin
(
angle
))
-
(
speedX
*
abs
(
trackPos
));
if
abs
(
trackPos
)
>
1.0
reward
=
-
200
;
end
for
i
=
2
:
20
if
abs
(
state
(
i
))
>
1.0
reward
=
-
200
;
end
end
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor.cnnt
0 → 100644
View file @
df882e6e
/* (c) https://github.com/MontiCore/monticore */
configuration TorcsActor {
context : gpu
learning_method : reinforcement
agent_name: "TorcsAgent"
rl_algorithm: td3-algorithm
policy_noise: 0.2
noise_clip: 0.5
policy_delay: 2
critic: torcs.agent.network.torcsCritic
environment : ros_interface {
state_topic: "/torcs/state"
terminal_state_topic: "/torcs/terminal"
action_topic: "/torcs/step"
reset_topic: "/torcs/reset"
}
reward_function: torcs.agent.network.reward
num_episodes : 3500
discount_factor : 0.99
num_max_steps : 900000
training_interval : 1
start_training_at: 0
evaluation_samples: 1
soft_target_update_rate: 0.005
snapshot_interval : 150
replay_memory : buffer{
memory_size : 120000
sample_size : 100
}
strategy : ornstein_uhlenbeck{
epsilon : 1.0
min_epsilon : 0.0001
epsilon_decay_method: linear
epsilon_decay : 0.000008
epsilon_decay_start: 10
epsilon_decay_per_step: true
theta: (0.6, 1.0, 1.0)
mu: (0.0, 0.0, -1.2)
sigma: (0.3, 0.2, 0.05)
}
actor_optimizer: adam {
learning_rate: 0.001
}
critic_optimizer: adam {
learning_rate: 0.001
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor.emadl
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
.
network
;
component
TorcsActor
{
ports
in
Q
^{
29
}
state
,
out
Q
(-
1
:
1
)^{
3
}
commands
;
implementation
CNN
{
state
->
FullyConnected
(
units
=
300
)
->
Relu
()
->
FullyConnected
(
units
=
600
)
->
Relu
()
->
FullyConnected
(
units
=
3
)
->
Tanh
()
->
commands
;
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsActor_ddpg.cnnt
0 → 100644
View file @
df882e6e
/* (c) https://github.com/MontiCore/monticore */
configuration TorcsActor {
context : gpu
learning_method : reinforcement
agent_name: "TorcsAgent"
rl_algorithm: ddpg-algorithm
critic: torcs.agent.network.torcsCritic
environment : ros_interface {
state_topic: "/torcs/state"
terminal_state_topic: "/torcs/terminal"
action_topic: "/torcs/step"
reset_topic: "/torcs/reset"
}
reward_function: torcs.agent.network.reward
num_episodes : 3500
discount_factor : 0.99
num_max_steps : 900000
training_interval : 1
start_training_at: 0
evaluation_samples: 1
soft_target_update_rate: 0.001
snapshot_interval : 150
replay_memory : buffer{
memory_size : 120000
sample_size : 32
}
strategy : ornstein_uhlenbeck{
epsilon : 1.0
min_epsilon : 0.0001
epsilon_decay_method: linear
epsilon_decay : 0.000008
epsilon_decay_start: 10
epsilon_decay_per_step: true
theta: (0.6, 1.0, 1.0)
mu: (0.0, 0.0, -1.2)
sigma: (0.3, 0.2, 0.05)
}
actor_optimizer: adam {
learning_rate: 0.0001
}
critic_optimizer: adam {
learning_rate: 0.001
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsCritic.emadl
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
.
network
;
component
TorcsCritic
{
ports
in
Q
^{
29
}
state
,
in
Q
(-
1
:
1
)^{
3
}
action
,
out
Q
(-
oo
:
oo
)^{
1
}
qvalues
;
implementation
CNN
{
(
state
|
action
)->
Concatenate
()
->
FullyConnected
(
units
=
300
)
->
Relu
()
->
FullyConnected
(
units
=
600
)
->
Relu
()
->
FullyConnected
(
units
=
1
)
->
qvalues
;
}
}
src/test/resources/models/reinforcementModel/torcs_td3/torcs/agent/network/TorcsCritic_ddpg.emadl
0 → 100644
View file @
df882e6e
/*
(
c
)
https
://
github
.
com
/
MontiCore
/
monticore
*/
package
torcs
.
agent
.
network
;
component
TorcsCritic
{
ports
in
Q
^{
29
}
state
,
in
Q
(-
1
:
1
)^{
3
}
action
,
out
Q
(-
oo
:
oo
)^{
1
}
qvalues
;
implementation
CNN
{
(
state
->
FullyConnected
(
units
=
300
)
->
Relu
()
->
FullyConnected
(
units
=
600
)
|
action
->
FullyConnected
(
units
=
600
)
)->
Add
()
->
FullyConnected
(
units
=
600
)
->
Relu
()
->
FullyConnected
(
units
=
1
)
->
qvalues
;
}
}
src/test/resources/target_code/gluon/reinforcementModel/torcs/reward/pylib/armanpy/armanpy.hpp
View file @
df882e6e
/* (c) https://github.com/MontiCore/monticore */
// Copyright (C) 2012 thomas.natschlaeger@gmail.com
//
// This file is part of the ArmaNpy library.
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CMakeLists.txt
0 → 100644
View file @
df882e6e
cmake_minimum_required
(
VERSION 3.5
)
set
(
CMAKE_CXX_STANDARD 14
)
project
(
torcs_agent_torcsAgent LANGUAGES CXX
)
#set cmake module path
set
(
CMAKE_MODULE_PATH
${
CMAKE_MODULE_PATH
}
${
CMAKE_CURRENT_SOURCE_DIR
}
/cmake
)
# add dependencies
find_package
(
Armadillo REQUIRED
)
set
(
INCLUDE_DIRS
${
INCLUDE_DIRS
}
${
Armadillo_INCLUDE_DIRS
}
)
set
(
LIBS
${
LIBS
}
${
Armadillo_LIBRARIES
}
)
# additional commands
set
(
LIBS
${
LIBS
}
mxnet
)
# create static library
include_directories
(
${
INCLUDE_DIRS
}
)
add_library
(
torcs_agent_torcsAgent torcs_agent_torcsAgent.cpp
)
target_include_directories
(
torcs_agent_torcsAgent PUBLIC
${
CMAKE_CURRENT_SOURCE_DIR
}
${
INCLUDE_DIRS
}
)
target_link_libraries
(
torcs_agent_torcsAgent PUBLIC
${
LIBS
}
)
set_target_properties
(
torcs_agent_torcsAgent PROPERTIES LINKER_LANGUAGE CXX
)
# export cmake project
export
(
TARGETS torcs_agent_torcsAgent FILE torcs_agent_torcsAgent.cmake
)
# additional commands end
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNBufferFile.h
0 → 100644
View file @
df882e6e
#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/reinforcementModel/torcs_td3/CNNCreator_torcs_agent_torcsAgent_actor.py
0 → 100644
View file @
df882e6e
import
mxnet
as
mx
import
logging
import
os
from
CNNNet_torcs_agent_torcsAgent_actor
import
Net_0
class
CNNCreator_torcs_agent_torcsAgent_actor
:
_model_dir_
=
"model/torcs.agent.network.TorcsActor/"
_model_prefix_
=
"model"
def
__init__
(
self
):
self
.
weight_initializer
=
mx
.
init
.
Normal
()
self
.
networks
=
{}
def
load
(
self
,
context
):
earliestLastEpoch
=
None
for
i
,
network
in
self
.
networks
.
items
():
lastEpoch
=
0
param_file
=
None
try
:
os
.
remove
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
)
+
"_newest-0000.params"
)
except
OSError
:
pass
try
:
os
.
remove
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
)
+
"_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_
+
"_"
+
str
(
i
)
in
file
:
epochStr
=
file
.
replace
(
".params"
,
""
).
replace
(
self
.
_model_prefix_
+
"_"
+
str
(
i
)
+
"-"
,
""
)
epoch
=
int
(
epochStr
)
if
epoch
>
lastEpoch
:
lastEpoch
=
epoch
param_file
=
file
if
param_file
is
None
:
earliestLastEpoch
=
0
else
:
logging
.
info
(
"Loading checkpoint: "
+
param_file
)
network
.
load_parameters
(
self
.
_model_dir_
+
param_file
)
if
earliestLastEpoch
==
None
or
lastEpoch
<
earliestLastEpoch
:
earliestLastEpoch
=
lastEpoch
return
earliestLastEpoch
def
construct
(
self
,
context
,
data_mean
=
None
,
data_std
=
None
):
self
.
networks
[
0
]
=
Net_0
(
data_mean
=
data_mean
,
data_std
=
data_std
)
self
.
networks
[
0
].
collect_params
().
initialize
(
self
.
weight_initializer
,
ctx
=
context
)
self
.
networks
[
0
].
hybridize
()
self
.
networks
[
0
](
mx
.
nd
.
zeros
((
1
,
29
,),
ctx
=
context
))
if
not
os
.
path
.
exists
(
self
.
_model_dir_
):
os
.
makedirs
(
self
.
_model_dir_
)
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNDataLoader_torcs_agent_torcsAgent_actor.py
0 → 100644
View file @
df882e6e
import
os
import
h5py
import
mxnet
as
mx
import
logging
import
sys
from
mxnet
import
nd
class
CNNDataLoader_torcs_agent_torcsAgent_actor
:
_input_names_
=
[
'state'
]
_output_names_
=
[
'commands_label'
]
def
__init__
(
self
):
self
.
_data_dir
=
"data/"
def
load_data
(
self
,
batch_size
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
train_data
=
{}
data_mean
=
{}
data_std
=
{}
for
input_name
in
self
.
_input_names_
:
train_data
[
input_name
]
=
train_h5
[
input_name
]
data_mean
[
input_name
]
=
nd
.
array
(
train_h5
[
input_name
][:].
mean
(
axis
=
0
))
data_std
[
input_name
]
=
nd
.
array
(
train_h5
[
input_name
][:].
std
(
axis
=
0
)
+
1e-5
)
train_label
=
{}
for
output_name
in
self
.
_output_names_
:
train_label
[
output_name
]
=
train_h5
[
output_name
]
train_iter
=
mx
.
io
.
NDArrayIter
(
data
=
train_data
,
label
=
train_label
,
batch_size
=
batch_size
)
test_iter
=
None
if
test_h5
!=
None
:
test_data
=
{}
for
input_name
in
self
.
_input_names_
:
test_data
[
input_name
]
=
test_h5
[
input_name
]
test_label
=
{}
for
output_name
in
self
.
_output_names_
:
test_label
[
output_name
]
=
test_h5
[
output_name
]
test_iter
=
mx
.
io
.
NDArrayIter
(
data
=
test_data
,
label
=
test_label
,
batch_size
=
batch_size
)
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'
)
for
input_name
in
self
.
_input_names_
:
if
not
input_name
in
train_h5
:
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the dataset "
+
"'"
+
input_name
+
"'"
)
sys
.
exit
(
1
)
for
output_name
in
self
.
_output_names_
:
if
not
output_name
in
train_h5
:
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the dataset "
+
"'"
+
output_name
+
"'"
)
sys
.
exit
(
1
)
if
os
.
path
.
isfile
(
test_path
):
test_h5
=
h5py
.
File
(
test_path
,
'r'
)