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monticore
EmbeddedMontiArc
generators
CNNArch2Gluon
Commits
0e01c717
Commit
0e01c717
authored
Nov 27, 2019
by
Christian Fuß
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fixed test
parent
303f6e12
Changes
1
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1 changed file
with
58 additions
and
48 deletions
+58
-48
src/test/resources/target_code/CNNNet_Alexnet.py
src/test/resources/target_code/CNNNet_Alexnet.py
+58
-48
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src/test/resources/target_code/CNNNet_Alexnet.py
View file @
0e01c717
...
...
@@ -8,9 +8,9 @@ class ZScoreNormalization(gluon.HybridBlock):
super
(
ZScoreNormalization
,
self
).
__init__
(
**
kwargs
)
with
self
.
name_scope
():
self
.
data_mean
=
self
.
params
.
get
(
'data_mean'
,
shape
=
data_mean
.
shape
,
init
=
mx
.
init
.
Constant
(
data_mean
.
asnumpy
().
tolist
()),
differentiable
=
False
)
init
=
mx
.
init
.
Constant
(
data_mean
.
asnumpy
().
tolist
()),
differentiable
=
False
)
self
.
data_std
=
self
.
params
.
get
(
'data_std'
,
shape
=
data_mean
.
shape
,
init
=
mx
.
init
.
Constant
(
data_std
.
asnumpy
().
tolist
()),
differentiable
=
False
)
init
=
mx
.
init
.
Constant
(
data_std
.
asnumpy
().
tolist
()),
differentiable
=
False
)
def
hybrid_forward
(
self
,
F
,
x
,
data_mean
,
data_std
):
x
=
F
.
broadcast_sub
(
x
,
data_mean
)
...
...
@@ -26,9 +26,9 @@ class Padding(gluon.HybridBlock):
def
hybrid_forward
(
self
,
F
,
x
):
x
=
F
.
pad
(
data
=
x
,
mode
=
'constant'
,
pad_width
=
self
.
pad_width
,
constant_value
=
0
)
mode
=
'constant'
,
pad_width
=
self
.
pad_width
,
constant_value
=
0
)
return
x
...
...
@@ -93,17 +93,18 @@ class Net_0(gluon.HybridBlock):
if
data_mean
:
assert
(
data_std
)
self
.
input_normalization_data_
=
ZScoreNormalization
(
data_mean
=
data_mean
[
'data_'
],
data_std
=
data_std
[
'data_'
])
data_std
=
data_std
[
'data_'
])
else
:
self
.
input_normalization_data_
=
NoNormalization
()
self
.
conv1_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
2
,
1
,
2
,
1
))
self
.
conv1_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
conv1_
=
gluon
.
nn
.
Conv2D
(
channels
=
96
,
kernel_size
=
(
11
,
11
),
strides
=
(
4
,
4
),
use_bias
=
True
)
kernel_size
=
(
11
,
11
),
strides
=
(
4
,
4
),
use_bias
=
True
)
# conv1_, output shape: {[96,55,55]}
self
.
pool1_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
pool1_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
3
,
3
),
strides
=
(
2
,
2
))
...
...
@@ -113,11 +114,12 @@ class Net_0(gluon.HybridBlock):
self
.
conv2_1_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
2
,
2
,
2
,
2
))
self
.
conv2_1_
=
gluon
.
nn
.
Conv2D
(
channels
=
128
,
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv2_1_, output shape: {[128,27,27]}
self
.
pool2_1_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
pool2_1_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
3
,
3
),
strides
=
(
2
,
2
))
...
...
@@ -126,11 +128,12 @@ class Net_0(gluon.HybridBlock):
self
.
relu2_1_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv2_2_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
2
,
2
,
2
,
2
))
self
.
conv2_2_
=
gluon
.
nn
.
Conv2D
(
channels
=
128
,
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
5
,
5
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv2_2_, output shape: {[128,27,27]}
self
.
pool2_2_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
pool2_2_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
3
,
3
),
strides
=
(
2
,
2
))
...
...
@@ -139,28 +142,29 @@ class Net_0(gluon.HybridBlock):
self
.
relu2_2_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv3_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
1
,
1
,
1
,
1
))
self
.
conv3_
=
gluon
.
nn
.
Conv2D
(
channels
=
384
,
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv3_, output shape: {[384,13,13]}
self
.
relu3_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv4_1_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
1
,
1
,
1
,
1
))
self
.
conv4_1_
=
gluon
.
nn
.
Conv2D
(
channels
=
192
,
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv4_1_, output shape: {[192,13,13]}
self
.
relu4_1_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv5_1_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
1
,
1
,
1
,
1
))
self
.
conv5_1_
=
gluon
.
nn
.
Conv2D
(
channels
=
128
,
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv5_1_, output shape: {[128,13,13]}
self
.
pool5_1_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
pool5_1_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
3
,
3
),
strides
=
(
2
,
2
))
...
...
@@ -169,19 +173,20 @@ class Net_0(gluon.HybridBlock):
self
.
relu5_1_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv4_2_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
1
,
1
,
1
,
1
))
self
.
conv4_2_
=
gluon
.
nn
.
Conv2D
(
channels
=
192
,
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv4_2_, output shape: {[192,13,13]}
self
.
relu4_2_
=
gluon
.
nn
.
Activation
(
activation
=
'relu'
)
self
.
conv5_2_padding
=
Padding
(
padding
=
(
0
,
0
,
0
,
0
,
1
,
1
,
1
,
1
))
self
.
conv5_2_
=
gluon
.
nn
.
Conv2D
(
channels
=
128
,
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
kernel_size
=
(
3
,
3
),
strides
=
(
1
,
1
),
use_bias
=
True
)
# conv5_2_, output shape: {[128,13,13]}
self
.
pool5_2_padding
=
Padding
(
padding
=
(
0
,
0
,
-
1
,
0
,
0
,
0
,
0
,
0
))
self
.
pool5_2_
=
gluon
.
nn
.
MaxPool2D
(
pool_size
=
(
3
,
3
),
strides
=
(
2
,
2
))
...
...
@@ -209,11 +214,12 @@ class Net_0(gluon.HybridBlock):
conv1_padding
=
self
.
conv1_padding
(
data_
)
conv1_
=
self
.
conv1_
(
conv1_padding
)
lrn1_
=
F
.
LRN
(
data
=
conv1_
,
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool1_
=
self
.
pool1_
(
lrn1_
)
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool1_padding
=
self
.
pool1_padding
(
lrn1_
)
pool1_
=
self
.
pool1_
(
pool1_padding
)
relu1_
=
self
.
relu1_
(
pool1_
)
split1_
=
F
.
split
(
relu1_
,
axis
=
1
,
num_outputs
=
2
)
...
...
@@ -221,21 +227,23 @@ class Net_0(gluon.HybridBlock):
conv2_1_padding
=
self
.
conv2_1_padding
(
get2_1_
)
conv2_1_
=
self
.
conv2_1_
(
conv2_1_padding
)
lrn2_1_
=
F
.
LRN
(
data
=
conv2_1_
,
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool2_1_
=
self
.
pool2_1_
(
lrn2_1_
)
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool2_1_padding
=
self
.
pool2_1_padding
(
lrn2_1_
)
pool2_1_
=
self
.
pool2_1_
(
pool2_1_padding
)
relu2_1_
=
self
.
relu2_1_
(
pool2_1_
)
get2_2_
=
split1_
[
1
]
conv2_2_padding
=
self
.
conv2_2_padding
(
get2_2_
)
conv2_2_
=
self
.
conv2_2_
(
conv2_2_padding
)
lrn2_2_
=
F
.
LRN
(
data
=
conv2_2_
,
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool2_2_
=
self
.
pool2_2_
(
lrn2_2_
)
alpha
=
0.0001
,
beta
=
0.75
,
knorm
=
2
,
nsize
=
5
)
pool2_2_padding
=
self
.
pool2_2_padding
(
lrn2_2_
)
pool2_2_
=
self
.
pool2_2_
(
pool2_2_padding
)
relu2_2_
=
self
.
relu2_2_
(
pool2_2_
)
concatenate3_
=
F
.
concat
(
relu2_1_
,
relu2_2_
,
dim
=
1
)
conv3_padding
=
self
.
conv3_padding
(
concatenate3_
)
...
...
@@ -249,7 +257,8 @@ class Net_0(gluon.HybridBlock):
relu4_1_
=
self
.
relu4_1_
(
conv4_1_
)
conv5_1_padding
=
self
.
conv5_1_padding
(
relu4_1_
)
conv5_1_
=
self
.
conv5_1_
(
conv5_1_padding
)
pool5_1_
=
self
.
pool5_1_
(
conv5_1_
)
pool5_1_padding
=
self
.
pool5_1_padding
(
conv5_1_
)
pool5_1_
=
self
.
pool5_1_
(
pool5_1_padding
)
relu5_1_
=
self
.
relu5_1_
(
pool5_1_
)
get4_2_
=
split3_
[
1
]
conv4_2_padding
=
self
.
conv4_2_padding
(
get4_2_
)
...
...
@@ -257,7 +266,8 @@ class Net_0(gluon.HybridBlock):
relu4_2_
=
self
.
relu4_2_
(
conv4_2_
)
conv5_2_padding
=
self
.
conv5_2_padding
(
relu4_2_
)
conv5_2_
=
self
.
conv5_2_
(
conv5_2_padding
)
pool5_2_
=
self
.
pool5_2_
(
conv5_2_
)
pool5_2_padding
=
self
.
pool5_2_padding
(
conv5_2_
)
pool5_2_
=
self
.
pool5_2_
(
pool5_2_padding
)
relu5_2_
=
self
.
relu5_2_
(
pool5_2_
)
concatenate6_
=
F
.
concat
(
relu5_1_
,
relu5_2_
,
dim
=
1
)
fc6_
=
self
.
fc6_
(
concatenate6_
)
...
...
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