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monticore
EmbeddedMontiArc
generators
CNNArch2Gluon
Commits
d6eb626f
Commit
d6eb626f
authored
Feb 01, 2020
by
Julian Treiber
Browse files
added batch_loss to CNNSupervisedTrainer template and tests
parent
418a354b
Changes
12
Hide whitespace changes
Inline
Side-by-side
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
View file @
d6eb626f
...
...
@@ -255,16 +255,17 @@ class ${tc.fileNameWithoutEnding}:
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['loss_axis'] if 'loss_axis' in loss_params else -1
batch_axis = loss_params['batch_axis'] if 'batch_axis' in loss_params else 0
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_axis, from_logits=fromLogits, sparse_label=sparseLabel)
loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel
, batch_axis=batch_axis
)
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)
loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel
, batch_axis=batch_axis
)
elif loss == 'sigmoid_binary_cross_entropy':
loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
elif loss == 'cross_entropy':
loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel
, batch_axis=batch_axis
)
elif loss == 'l2':
loss_function = mx.gluon.loss.L2Loss()
elif loss == 'l1':
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
View file @
d6eb626f
...
...
@@ -254,16 +254,17 @@ class CNNSupervisedTrainer_Alexnet:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
View file @
d6eb626f
...
...
@@ -254,16 +254,17 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_Invariant:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_MultipleStreams:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_RNNencdec:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_RNNsearch:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_RNNtest:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_ResNeXt50:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_Show_attend_tell:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
View file @
d6eb626f
...
...
@@ -247,16 +247,17 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
View file @
d6eb626f
...
...
@@ -254,16 +254,17 @@ class CNNSupervisedTrainer_VGG16:
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
[
'loss_axis'
]
if
'loss_axis'
in
loss_params
else
-
1
batch_axis
=
loss_params
[
'batch_axis'
]
if
'batch_axis'
in
loss_params
else
0
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_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
loss_axis
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
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
)
loss_function
=
SoftmaxCrossEntropyLossIgnoreIndices
(
ignore_indices
=
ignore_indices
,
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'sigmoid_binary_cross_entropy'
:
loss_function
=
mx
.
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()
elif
loss
==
'cross_entropy'
:
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
loss_axis
,
sparse_label
=
sparseLabel
,
batch_axis
=
batch_axis
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
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