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CNNArch2Gluon
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monticore
EmbeddedMontiArc
generators
CNNArch2Gluon
Commits
0cc51c81
Commit
0cc51c81
authored
Nov 26, 2019
by
Christian Fuß
Browse files
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Browse Files
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Plain Diff
adjusted save_attention_image scripts to Beamsearch
parent
d10e51a6
Pipeline
#211283
failed with stages
in 1 minute and 3 seconds
Changes
6
Pipelines
1
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6 changed files
with
62 additions
and
22 deletions
+62
-22
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
+1
-1
src/main/resources/templates/gluon/saveAttentionImageTest.ftl
...main/resources/templates/gluon/saveAttentionImageTest.ftl
+7
-2
src/main/resources/templates/gluon/saveAttentionImageTrain.ftl
...ain/resources/templates/gluon/saveAttentionImageTrain.ftl
+6
-1
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
...est/resources/target_code/CNNSupervisedTrainer_Alexnet.py
+16
-6
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
...arget_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
+16
-6
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
+16
-6
No files found.
src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
View file @
0cc51c81
...
...
@@ -247,7 +247,7 @@ class ${tc.fileNameWithoutEnding}:
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)
if loss == 'softmax_cross_entropy_ignore_indices':
el
if 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':
...
...
src/main/resources/templates/gluon/saveAttentionImageTest.ftl
View file @
0cc51c81
...
...
@@ -13,13 +13,18 @@
attention = mx.nd.squeeze(attention)
attention_resized = np.resize(attention.asnumpy(), (8, 8))
ax = fig.add_subplot(max_length//3, max_length//4, l+2)
if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
ax.set_title("<unk>")
img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
break
elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
ax.set_title(".")
img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
break
else:
ax.set_title(dict[int(mx.nd.slice_axis(
mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1
).asscalar())])
ax.set_title(dict[int(mx.nd.slice_axis(
outputs[l+1], axis=0, begin=0, end=1).squeeze(
).asscalar())])
img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
...
...
src/main/resources/templates/gluon/saveAttentionImageTrain.ftl
View file @
0cc51c81
...
...
@@ -20,7 +20,12 @@
attention = mx.nd.squeeze(attention)
attention_resized = np.resize(attention.asnumpy(), (8, 8))
ax = fig.add_subplot(max_length//3, max_length//4, l+2)
if dict[int(labels[l+1][0].asscalar())] == "<end>":
if int(labels[l+1][0].asscalar()) > len(dict):
ax.set_title("<unk>")
img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
break
elif dict[int(labels[l+1][0].asscalar())] == "<end>":
ax.set_title(".")
img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
...
...
src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
View file @
0cc51c81
...
...
@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_Alexnet:
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
)
if
loss
==
'softmax_cross_entropy_ignore_indices'
:
el
if
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'
:
...
...
@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_Alexnet:
train_test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
train_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
)
...
...
@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_Alexnet:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
if
int
(
labels
[
l
+
1
][
0
].
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_Alexnet:
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
)
...
...
@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_Alexnet:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())]
==
"<end>"
:
if
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
else
:
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())])
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
(
).
asscalar
())])
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
View file @
0cc51c81
...
...
@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
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
)
if
loss
==
'softmax_cross_entropy_ignore_indices'
:
el
if
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'
:
...
...
@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
train_test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
train_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
)
...
...
@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
if
int
(
labels
[
l
+
1
][
0
].
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
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
)
...
...
@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())]
==
"<end>"
:
if
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
else
:
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())])
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
(
).
asscalar
())])
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
View file @
0cc51c81
...
...
@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_VGG16:
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
)
if
loss
==
'softmax_cross_entropy_ignore_indices'
:
el
if
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'
:
...
...
@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_VGG16:
train_test_iter
.
reset
()
metric
=
mx
.
metric
.
create
(
eval_metric
,
**
eval_metric_params
)
for
batch_i
,
batch
in
enumerate
(
train_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
)
...
...
@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_VGG16:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
if
int
(
labels
[
l
+
1
][
0
].
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
labels
[
l
+
1
][
0
].
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
train_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_VGG16:
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
)
...
...
@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_VGG16:
attention
=
mx
.
nd
.
squeeze
(
attention
)
attention_resized
=
np
.
resize
(
attention
.
asnumpy
(),
(
8
,
8
))
ax
=
fig
.
add_subplot
(
max_length
//
3
,
max_length
//
4
,
l
+
2
)
if
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())]
==
"<end>"
:
if
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())
>
len
(
dict
):
ax
.
set_title
(
"<unk>"
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
elif
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
().
asscalar
())]
==
"<end>"
:
ax
.
set_title
(
"."
)
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
break
else
:
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
mx
.
nd
.
argmax
(
outputs
[
l
+
1
],
axis
=
1
),
axis
=
0
,
begin
=
0
,
end
=
1
).
asscalar
())])
ax
.
set_title
(
dict
[
int
(
mx
.
nd
.
slice_axis
(
outputs
[
l
+
1
],
axis
=
0
,
begin
=
0
,
end
=
1
).
squeeze
(
).
asscalar
())])
img
=
ax
.
imshow
(
test_images
[
0
+
test_batch_size
*
(
batch_i
)].
transpose
(
1
,
2
,
0
))
ax
.
imshow
(
attention_resized
,
cmap
=
'gray'
,
alpha
=
0.6
,
extent
=
img
.
get_extent
())
...
...
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