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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
0cc00c55
Commit
0cc00c55
authored
Jul 09, 2019
by
Sebastian Nickels
Browse files
Updated version numbers and fixed some tests
parent
6fbb6934
Pipeline
#158802
failed with stages
in 1 minute and 9 seconds
Changes
4
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
pom.xml
View file @
0cc00c55
...
...
@@ -16,11 +16,11 @@
<!-- .. SE-Libraries .................................................. -->
<emadl.version>
0.2.8-SNAPSHOT
</emadl.version>
<CNNTrain.version>
0.3.
2
-SNAPSHOT
</CNNTrain.version>
<cnnarch-generator.version>
0.0.
1
-SNAPSHOT
</cnnarch-generator.version>
<CNNTrain.version>
0.3.
4
-SNAPSHOT
</CNNTrain.version>
<cnnarch-generator.version>
0.0.
2
-SNAPSHOT
</cnnarch-generator.version>
<cnnarch-mxnet-generator.version>
0.2.16-SNAPSHOT
</cnnarch-mxnet-generator.version>
<cnnarch-caffe2-generator.version>
0.2.12-SNAPSHOT
</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>
0.2.
1
-SNAPSHOT
</cnnarch-gluon-generator.version>
<cnnarch-gluon-generator.version>
0.2.
2
-SNAPSHOT
</cnnarch-gluon-generator.version>
<embedded-montiarc-math-opt-generator>
0.1.4
</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
...
...
src/test/resources/target_code/CNNCreator_cifar10_cifar10Classifier_net.py
View file @
0cc00c55
...
...
@@ -104,10 +104,83 @@ class CNNCreator_cifar10_cifar10Classifier_net:
logging
.
error
(
"Data loading failure. File '"
+
os
.
path
.
abspath
(
train_path
)
+
"' does not exist."
)
sys
.
exit
(
1
)
def
loss_function
(
self
,
loss
,
params
):
label
=
mx
.
symbol
.
var
(
name
=
self
.
_output_names_
[
0
],
)
prediction
=
self
.
module
.
symbol
.
get_children
()[
0
]
margin
=
params
[
'margin'
]
if
'margin'
in
params
else
1.0
sparseLabel
=
params
[
'sparse_label'
]
if
'sparse_label'
in
params
else
True
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
params
[
'from_logits'
]
if
'from_logits'
in
params
else
False
if
not
fromLogits
:
prediction
=
mx
.
symbol
.
log_softmax
(
data
=
prediction
,
axis
=
1
)
if
sparseLabel
:
loss_func
=
mx
.
symbol
.
mean
(
-
mx
.
symbol
.
pick
(
prediction
,
label
,
axis
=-
1
,
keepdims
=
True
),
axis
=
0
,
exclude
=
True
)
else
:
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
mean
(
-
mx
.
symbol
.
sum
(
prediction
*
label
,
axis
=-
1
,
keepdims
=
True
),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"softmax_cross_entropy"
)
elif
loss
==
'cross_entropy'
:
prediction
=
mx
.
symbol
.
log
(
prediction
)
if
sparseLabel
:
loss_func
=
mx
.
symbol
.
mean
(
-
mx
.
symbol
.
pick
(
prediction
,
label
,
axis
=-
1
,
keepdims
=
True
),
axis
=
0
,
exclude
=
True
)
else
:
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
mean
(
-
mx
.
symbol
.
sum
(
prediction
*
label
,
axis
=-
1
,
keepdims
=
True
),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"cross_entropy"
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_func
=
mx
.
symbol
.
LogisticRegressionOutput
(
data
=
prediction
,
name
=
self
.
module
.
symbol
.
name
)
elif
loss
==
'l1'
:
loss_func
=
mx
.
symbol
.
MAERegressionOutput
(
data
=
prediction
,
name
=
self
.
module
.
symbol
.
name
)
elif
loss
==
'l2'
:
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
mean
(
mx
.
symbol
.
square
((
label
-
prediction
)
/
2
),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"L2"
)
elif
loss
==
'huber'
:
rho
=
params
[
'rho'
]
if
'rho'
in
params
else
1
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
abs
(
label
-
prediction
)
loss_func
=
mx
.
symbol
.
where
(
loss_func
>
rho
,
loss_func
-
0.5
*
rho
,
(
0.5
/
rho
)
*
mx
.
symbol
.
square
(
loss_func
))
loss_func
=
mx
.
symbol
.
mean
(
loss_func
,
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"huber"
)
elif
loss
==
'hinge'
:
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
mean
(
mx
.
symbol
.
relu
(
margin
-
prediction
*
label
),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"hinge"
)
elif
loss
==
'squared_hinge'
:
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
loss_func
=
mx
.
symbol
.
mean
(
mx
.
symbol
.
square
(
mx
.
symbol
.
relu
(
margin
-
prediction
*
label
)),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"squared_hinge"
)
elif
loss
==
'logistic'
:
labelFormat
=
params
[
'label_format'
]
if
'label_format'
in
params
else
'signed'
if
labelFormat
not
in
[
"binary"
,
"signed"
]:
logging
.
error
(
"label_format can only be signed or binary"
)
label
=
mx
.
symbol
.
reshape_like
(
label
,
prediction
)
if
labelFormat
==
'signed'
:
label
=
(
label
+
1.0
)
/
2.0
loss_func
=
mx
.
symbol
.
relu
(
prediction
)
-
prediction
*
label
loss_func
=
loss_func
+
mx
.
symbol
.
Activation
(
-
mx
.
symbol
.
abs
(
prediction
),
act_type
=
"softrelu"
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
mx
.
symbol
.
mean
(
loss_func
,
0
,
exclude
=
True
),
name
=
"logistic"
)
elif
loss
==
'kullback_leibler'
:
fromLogits
=
params
[
'from_logits'
]
if
'from_logits'
in
params
else
True
if
not
fromLogits
:
prediction
=
mx
.
symbol
.
log_softmax
(
prediction
,
axis
=
1
)
loss_func
=
mx
.
symbol
.
mean
(
label
*
(
mx
.
symbol
.
log
(
label
)
-
prediction
),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"kullback_leibler"
)
elif
loss
==
'log_cosh'
:
loss_func
=
mx
.
symbol
.
mean
(
mx
.
symbol
.
log
(
mx
.
symbol
.
cosh
(
prediction
-
label
)),
axis
=
0
,
exclude
=
True
)
loss_func
=
mx
.
symbol
.
MakeLoss
(
loss_func
,
name
=
"log_cosh"
)
else
:
logging
.
error
(
"Invalid loss parameter."
)
return
loss_func
def
train
(
self
,
batch_size
=
64
,
num_epoch
=
10
,
eval_metric
=
'acc'
,
loss
=
'softmax_cross_entropy'
,
loss_params
=
{},
optimizer
=
'adam'
,
optimizer_params
=
((
'learning_rate'
,
0.001
),),
load_checkpoint
=
True
,
...
...
@@ -136,7 +209,6 @@ class CNNCreator_cifar10_cifar10Classifier_net:
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
.
module
==
None
:
if
normalize
:
...
...
@@ -144,6 +216,14 @@ class CNNCreator_cifar10_cifar10Classifier_net:
else
:
self
.
construct
(
mx_context
)
loss_func
=
self
.
loss_function
(
loss
=
loss
,
params
=
loss_params
)
self
.
module
=
mx
.
mod
.
Module
(
symbol
=
mx
.
symbol
.
Group
([
loss_func
,
mx
.
symbol
.
BlockGrad
(
self
.
module
.
symbol
.
get_children
()[
0
],
name
=
"pred"
)]),
data_names
=
self
.
_input_names_
,
label_names
=
self
.
_output_names_
,
context
=
mx_context
)
begin_epoch
=
0
if
load_checkpoint
:
begin_epoch
=
self
.
load
(
mx_context
)
...
...
@@ -157,9 +237,11 @@ class CNNCreator_cifar10_cifar10Classifier_net:
if
not
os
.
path
.
isdir
(
self
.
_model_dir_
):
raise
metric
=
mx
.
metric
.
create
(
eval_metric
,
output_names
=
[
'pred_output'
])
self
.
module
.
fit
(
train_data
=
train_iter
,
eval_metric
=
eval_
metric
,
eval_metric
=
metric
,
eval_data
=
test_iter
,
optimizer
=
optimizer
,
optimizer_params
=
optimizer_params
,
...
...
@@ -656,8 +738,10 @@ class CNNCreator_cifar10_cifar10Classifier_net:
num_hidden
=
10
,
no_bias
=
False
,
name
=
"fc32_"
)
softmax
=
mx
.
symbol
.
SoftmaxOutput
(
data
=
fc32_
,
softmax32_
=
mx
.
symbol
.
softmax
(
data
=
fc32_
,
axis
=
1
,
name
=
"softmax32_"
)
softmax
=
mx
.
symbol
.
SoftmaxOutput
(
data
=
softmax32_
,
name
=
"softmax"
)
self
.
module
=
mx
.
mod
.
Module
(
symbol
=
mx
.
symbol
.
Group
([
softmax
]),
...
...
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py
View file @
0cc00c55
...
...
@@ -120,7 +120,7 @@ class Net_0(gluon.HybridBlock):
self
.
fc3_
=
gluon
.
nn
.
Dense
(
units
=
10
,
use_bias
=
True
)
# fc3_, output shape: {[10,1,1]}
self
.
last_layers
[
'predictions'
]
=
's
oftmax
'
self
.
softmax3_
=
S
oftmax
()
def
hybrid_forward
(
self
,
F
,
image
):
...
...
@@ -134,6 +134,7 @@ class Net_0(gluon.HybridBlock):
fc2_
=
self
.
fc2_
(
fc2_flatten_
)
relu2_
=
self
.
relu2_
(
fc2_
)
fc3_
=
self
.
fc3_
(
relu2_
)
outputs
.
append
(
fc3_
)
softmax3_
=
self
.
softmax3_
(
fc3_
)
outputs
.
append
(
softmax3_
)
return
outputs
[
0
]
src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py
View file @
0cc00c55
...
...
@@ -6,6 +6,31 @@ import os
import
shutil
from
mxnet
import
gluon
,
autograd
,
nd
class
CrossEntropyLoss
(
gluon
.
loss
.
Loss
):
def
__init__
(
self
,
axis
=-
1
,
sparse_label
=
True
,
weight
=
None
,
batch_axis
=
0
,
**
kwargs
):
super
(
CrossEntropyLoss
,
self
).
__init__
(
weight
,
batch_axis
,
**
kwargs
)
self
.
_axis
=
axis
self
.
_sparse_label
=
sparse_label
def
hybrid_forward
(
self
,
F
,
pred
,
label
,
sample_weight
=
None
):
pred
=
F
.
log
(
pred
)
if
self
.
_sparse_label
:
loss
=
-
F
.
pick
(
pred
,
label
,
axis
=
self
.
_axis
,
keepdims
=
True
)
else
:
label
=
gluon
.
loss
.
_reshape_like
(
F
,
label
,
pred
)
loss
=
-
F
.
sum
(
pred
*
label
,
axis
=
self
.
_axis
,
keepdims
=
True
)
loss
=
gluon
.
loss
.
_apply_weighting
(
F
,
loss
,
self
.
_weight
,
sample_weight
)
return
F
.
mean
(
loss
,
axis
=
self
.
_batch_axis
,
exclude
=
True
)
class
LogCoshLoss
(
gluon
.
loss
.
Loss
):
def
__init__
(
self
,
weight
=
None
,
batch_axis
=
0
,
**
kwargs
):
super
(
LogCoshLoss
,
self
).
__init__
(
weight
,
batch_axis
,
**
kwargs
)
def
hybrid_forward
(
self
,
F
,
pred
,
label
,
sample_weight
=
None
):
loss
=
F
.
log
(
F
.
cosh
(
pred
-
label
))
loss
=
gluon
.
loss
.
_apply_weighting
(
F
,
loss
,
self
.
_weight
,
sample_weight
)
return
F
.
mean
(
loss
,
axis
=
self
.
_batch_axis
,
exclude
=
True
)
class
CNNSupervisedTrainer_mnist_mnistClassifier_net
:
def
__init__
(
self
,
data_loader
,
net_constructor
):
self
.
_data_loader
=
data_loader
...
...
@@ -15,6 +40,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
def
train
(
self
,
batch_size
=
64
,
num_epoch
=
10
,
eval_metric
=
'acc'
,
loss
=
'softmax_cross_entropy'
,
loss_params
=
{},
optimizer
=
'adam'
,
optimizer_params
=
((
'learning_rate'
,
0.001
),),
load_checkpoint
=
True
,
...
...
@@ -68,19 +95,36 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
trainers
=
[
mx
.
gluon
.
Trainer
(
network
.
collect_params
(),
optimizer
,
optimizer_params
)
for
network
in
self
.
_networks
.
values
()]
loss_functions
=
{}
for
network
in
self
.
_networks
.
values
():
for
output_name
,
last_layer
in
network
.
last_layers
.
items
():
if
last_layer
==
'softmax'
:
loss_functions
[
output_name
]
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
()
elif
last_layer
==
'sigmoid'
:
loss_functions
[
output_name
]
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
last_layer
==
'linear'
:
loss_functions
[
output_name
]
=
mx
.
gluon
.
loss
.
L2Loss
()
else
:
loss_functions
[
output_name
]
=
mx
.
gluon
.
loss
.
L2Loss
()
logging
.
warning
(
"Invalid last layer, defaulting to L2 loss"
)
margin
=
loss_params
[
'margin'
]
if
'margin'
in
loss_params
else
1.0
sparseLabel
=
loss_params
[
'sparse_label'
]
if
'sparse_label'
in
loss_params
else
True
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'huber'
:
rho
=
loss_params
[
'rho'
]
if
'rho'
in
loss_params
else
1
loss_function
=
mx
.
gluon
.
loss
.
HuberLoss
(
rho
=
rho
)
elif
loss
==
'hinge'
:
loss_function
=
mx
.
gluon
.
loss
.
HingeLoss
(
margin
=
margin
)
elif
loss
==
'squared_hinge'
:
loss_function
=
mx
.
gluon
.
loss
.
SquaredHingeLoss
(
margin
=
margin
)
elif
loss
==
'logistic'
:
labelFormat
=
loss_params
[
'label_format'
]
if
'label_format'
in
loss_params
else
'signed'
loss_function
=
mx
.
gluon
.
loss
.
LogisticLoss
(
label_format
=
labelFormat
)
elif
loss
==
'kullback_leibler'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
True
loss_function
=
mx
.
gluon
.
loss
.
KLDivLoss
(
from_logits
=
fromLogits
)
elif
loss
==
'log_cosh'
:
loss_function
=
LogCoshLoss
()
else
:
logging
.
error
(
"Invalid loss parameter."
)
speed_period
=
50
tic
=
None
...
...
@@ -95,7 +139,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
predictions_output
=
self
.
_networks
[
0
](
image_data
)
loss
=
\
loss_function
s
[
'predictions'
]
(
predictions_output
,
predictions_label
)
loss_function
(
predictions_output
,
predictions_label
)
loss
.
backward
()
...
...
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