Skip to content
GitLab
Projects
Groups
Snippets
/
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Sign in
Toggle navigation
Menu
Open sidebar
monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
eb892398
Commit
eb892398
authored
Apr 08, 2019
by
Nicola Gatto
Browse files
Adapt tests to new version
parent
7c707a98
Changes
8
Hide whitespace changes
Inline
Side-by-side
pom.xml
View file @
eb892398
...
...
@@ -19,7 +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>
<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 .................................................. -->
...
...
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
View file @
eb892398
...
...
@@ -177,6 +177,8 @@ public class GenerationTest extends AbstractSymtabTest {
"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"
,
...
...
src/test/java/de/monticore/lang/monticar/emadl/IntegrationTest.java
View file @
eb892398
...
...
@@ -160,7 +160,4 @@ public abstract class IntegrationTest extends AbstractSymtabTest {
deleteHashFile
();
}
}
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
View file @
eb892398
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
()
...
...
@@ -60,176 +41,6 @@ class CNNCreator_mnist_mnistClassifier_net:
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
)
...
...
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.pyc
deleted
100644 → 0
View file @
7c707a98
File deleted
src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
0 → 100644
View file @
eb892398
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/CNNTrainer_mnist_mnistClassifier_net.py
View file @
eb892398
import
logging
import
mxnet
as
mx
import
supervised_trainer
import
CNNCreator_mnist_mnistClassifier_net
import
CNNDataLoader_mnist_mnistClassifier_net
if
__name__
==
"__main__"
:
logging
.
basicConfig
(
level
=
logging
.
DEBUG
)
...
...
@@ -8,8 +10,12 @@ if __name__ == "__main__":
handler
=
logging
.
FileHandler
(
"train.log"
,
"w"
,
encoding
=
None
,
delay
=
"true"
)
logger
.
addHandler
(
handler
)
mnist_mnistClassifier_net
=
CNNCreator_mnist_mnistClassifier_net
.
CNNCreator_mnist_mnistClassifier_net
()
mnist_mnistClassifier_net
.
train
(
mnist_mnistClassifier_net_creator
=
CNNCreator_mnist_mnistClassifier_net
.
CNNCreator_mnist_mnistClassifier_net
()
mnist_mnistClassifier_net_loader
=
CNNDataLoader_mnist_mnistClassifier_net
.
mnist_mnistClassifier_netDataLoader
()
mnist_mnistClassifier_net_trainer
=
supervised_trainer
.
CNNSupervisedTrainer
(
mnist_mnistClassifier_net_loader
,
mnist_mnistClassifier_net_creator
)
mnist_mnistClassifier_net_trainer
.
train
(
batch_size
=
64
,
num_epoch
=
11
,
context
=
'gpu'
,
...
...
src/test/resources/target_code/gluon/supervised_trainer.py
0 → 100644
View file @
eb892398
import
mxnet
as
mx
import
logging
import
numpy
as
np
import
time
import
os
import
shutil
from
mxnet
import
gluon
,
autograd
,
nd
class
CNNSupervisedTrainer
(
object
):
def
__init__
(
self
,
data_loader
,
net_constructor
,
net
=
None
):
self
.
_data_loader
=
data_loader
self
.
_net_creator
=
net_constructor
self
.
_net
=
net
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
.
_data_loader
.
load_data
(
batch_size
)
if
self
.
_net
is
None
:
if
normalize
:
self
.
_net_creator
.
construct
(
context
=
mx_context
,
data_mean
=
nd
.
array
(
data_mean
),
data_std
=
nd
.
array
(
data_std
))
else
:
self
.
_net_creator
.
construct
(
context
=
mx_context
)
begin_epoch
=
0
if
load_checkpoint
:
begin_epoch
=
self
.
_net_creator
.
load
(
mx_context
)
else
:
if
os
.
path
.
isdir
(
self
.
_net_creator
.
_model_dir_
):
shutil
.
rmtree
(
self
.
_net_creator
.
_model_dir_
)
self
.
_net
=
self
.
_net_creator
.
net
try
:
os
.
makedirs
(
self
.
_net_creator
.
_model_dir_
)
except
OSError
:
if
not
os
.
path
.
isdir
(
self
.
_net_creator
.
_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
.
parameter_path
()
+
'-'
+
str
(
epoch
).
zfill
(
4
)
+
'.params'
)
self
.
_net
.
save_parameters
(
self
.
parameter_path
()
+
'-'
+
str
(
num_epoch
+
begin_epoch
).
zfill
(
4
)
+
'.params'
)
self
.
_net
.
export
(
self
.
parameter_path
()
+
'_newest'
,
epoch
=
0
)
def
parameter_path
(
self
):
return
self
.
_net_creator
.
_model_dir_
+
self
.
_net_creator
.
_model_prefix_
\ No newline at end of file
Write
Preview
Supports
Markdown
0%
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment