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CNNArch2Gluon
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monticore
EmbeddedMontiArc
generators
CNNArch2Gluon
Commits
418a354b
Commit
418a354b
authored
Jan 31, 2020
by
Julian Treiber
Browse files
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Plain Diff
refactoring: lossAxis to loss_axis
parent
01425f2d
Changes
12
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Showing
12 changed files
with
36 additions
and
36 deletions
+36
-36
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
...est/resources/target_code/CNNSupervisedTrainer_Alexnet.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
...arget_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py
...t/resources/target_code/CNNSupervisedTrainer_Invariant.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py
...urces/target_code/CNNSupervisedTrainer_MultipleStreams.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
...t/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
...t/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py
...est/resources/target_code/CNNSupervisedTrainer_RNNtest.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
...t/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py
...rces/target_code/CNNSupervisedTrainer_Show_attend_tell.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
...ces/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
+3
-3
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
+3
-3
No files found.
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
View file @
418a354b
...
...
@@ -254,17 +254,17 @@ class ${tc.fileNameWithoutEnding}:
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
ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
loss
Axis = loss_params['lossAxis'] if 'lossA
xis' in loss_params else -1
loss
_axis = loss_params['loss_axis'] if 'loss_a
xis' in loss_params else -1
if loss == 'softmax_cross_entropy':
fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss
A
xis, from_logits=fromLogits, sparse_label=sparseLabel)
loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss
_a
xis, from_logits=fromLogits, sparse_label=sparseLabel)
elif loss == 'softmax_cross_entropy_ignore_indices':
fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, 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(axis=loss
A
xis, sparse_label=sparseLabel)
loss_function = CrossEntropyLoss(axis=loss
_a
xis, sparse_label=sparseLabel)
elif loss == 'l2':
loss_function = mx.gluon.loss.L2Loss()
elif loss == 'l1':
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
View file @
418a354b
...
...
@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_Alexnet:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
View file @
418a354b
...
...
@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_Invariant:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_MultipleStreams:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNencdec:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNsearch:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNtest:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_ResNeXt50:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_Show_attend_tell:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
View file @
418a354b
...
...
@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
View file @
418a354b
...
...
@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_VGG16:
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
ignore_indices
=
[
loss_params
[
'ignore_indices'
]]
if
'ignore_indices'
in
loss_params
else
[]
loss
Axis
=
loss_params
[
'lossAxis'
]
if
'lossA
xis'
in
loss_params
else
-
1
loss
_axis
=
loss_params
[
'loss_axis'
]
if
'loss_a
xis'
in
loss_params
else
-
1
if
loss
==
'softmax_cross_entropy'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
A
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss
_a
xis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
elif
loss
==
'softmax_cross_entropy_ignore_indices'
:
fromLogits
=
loss_params
[
'from_logits'
]
if
'from_logits'
in
loss_params
else
False
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
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
(
axis
=
loss
A
xis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss
_a
xis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
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