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CNNArch2Gluon
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monticore
EmbeddedMontiArc
generators
CNNArch2Gluon
Commits
01425f2d
Commit
01425f2d
authored
Jan 30, 2020
by
Julian Treiber
Browse files
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updated tests
parent
536b80fa
Changes
13
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13 changed files
with
43 additions
and
30 deletions
+43
-30
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
...est/resources/target_code/CNNSupervisedTrainer_Alexnet.py
+3
-2
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
...arget_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
+3
-2
src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py
...t/resources/target_code/CNNSupervisedTrainer_Invariant.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py
...urces/target_code/CNNSupervisedTrainer_MultipleStreams.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
...t/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
...t/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py
...est/resources/target_code/CNNSupervisedTrainer_RNNtest.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
...t/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py
...rces/target_code/CNNSupervisedTrainer_Show_attend_tell.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
...ces/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
+4
-3
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
+3
-2
src/test/resources/target_code/CNNTrainer_fullConfig.py
src/test/resources/target_code/CNNTrainer_fullConfig.py
+1
-0
src/test/resources/valid_tests/FullConfig.cnnt
src/test/resources/valid_tests/FullConfig.cnnt
+1
-0
No files found.
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
View file @
01425f2d
...
...
@@ -253,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
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 @
01425f2d
...
...
@@ -253,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
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 @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -425,7 +426,7 @@ class CNNSupervisedTrainer_Invariant:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
3
)]
data_0_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -417,7 +418,7 @@ class CNNSupervisedTrainer_MultipleStreams:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
2
)]
data_0_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -481,7 +482,7 @@ class CNNSupervisedTrainer_RNNencdec:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
30
)]
source_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -479,7 +480,7 @@ class CNNSupervisedTrainer_RNNsearch:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
30
)]
source_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -458,7 +459,7 @@ class CNNSupervisedTrainer_RNNtest:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
5
)]
source_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -407,7 +408,7 @@ class CNNSupervisedTrainer_ResNeXt50:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
1
)]
data_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -473,7 +474,7 @@ class CNNSupervisedTrainer_Show_attend_tell:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
25
)]
images_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py
View file @
01425f2d
...
...
@@ -246,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
@@ -411,7 +412,7 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
test_iter
):
if
True
:
if
True
:
labels
=
[
batch
.
label
[
i
].
as_in_context
(
mx_context
)
for
i
in
range
(
1
)]
data_0_
=
batch
.
data
[
0
].
as_in_context
(
mx_context
)
...
...
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
View file @
01425f2d
...
...
@@ -253,16 +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
[]
lossAxis
=
loss_params
[
'lossAxis'
]
if
'lossAxis'
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
(
from_logits
=
fromLogits
,
sparse_label
=
sparseLabel
)
loss_function
=
mx
.
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(
axis
=
lossAxis
,
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
(
sparse_label
=
sparseLabel
)
loss_function
=
CrossEntropyLoss
(
axis
=
lossAxis
,
sparse_label
=
sparseLabel
)
elif
loss
==
'l2'
:
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
elif
loss
==
'l1'
:
...
...
src/test/resources/target_code/CNNTrainer_fullConfig.py
View file @
01425f2d
...
...
@@ -30,6 +30,7 @@ if __name__ == "__main__":
loss
=
'softmax_cross_entropy'
,
loss_params
=
{
'sparse_label'
:
True
,
'loss_axis'
:
-
1
,
'from_logits'
:
False
},
optimizer
=
'rmsprop'
,
optimizer_params
=
{
...
...
src/test/resources/valid_tests/FullConfig.cnnt
View file @
01425f2d
...
...
@@ -5,6 +5,7 @@ configuration FullConfig{
load_checkpoint : true
eval_metric : mse
loss: softmax_cross_entropy{
loss_axis: -1
sparse_label: true
from_logits: false
}
...
...
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