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36c25d9f
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mindarmour
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体验新版 GitCode,发现更多精彩内容 >>
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36c25d9f
编写于
8月 29, 2020
作者:
P
pkuliuliu
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Avoid error of graph topological order
上级
135e7a82
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
33 addition
and
25 deletion
+33
-25
example/mnist_demo/mnist_defense_nad.py
example/mnist_demo/mnist_defense_nad.py
+29
-25
mindarmour/defenses/adversarial_defense.py
mindarmour/defenses/adversarial_defense.py
+4
-0
未找到文件。
example/mnist_demo/mnist_defense_nad.py
浏览文件 @
36c25d9f
...
@@ -12,6 +12,7 @@
...
@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
"""defense example using nad"""
"""defense example using nad"""
import
os
import
sys
import
sys
import
numpy
as
np
import
numpy
as
np
...
@@ -19,41 +20,43 @@ from mindspore import Tensor
...
@@ -19,41 +20,43 @@ from mindspore import Tensor
from
mindspore
import
context
from
mindspore
import
context
from
mindspore
import
nn
from
mindspore
import
nn
from
mindspore.nn
import
SoftmaxCrossEntropyWithLogits
from
mindspore.nn
import
SoftmaxCrossEntropyWithLogits
from
mindspore.train.serialization
import
load_checkpoint
,
load_param_into_net
from
mindspore.train
import
Model
from
mindspore.train.callback
import
LossMonitor
from
lenet5_net
import
LeNet5
from
mindarmour.attacks
import
FastGradientSignMethod
from
mindarmour.attacks
import
FastGradientSignMethod
from
mindarmour.defenses
import
NaturalAdversarialDefense
from
mindarmour.defenses
import
NaturalAdversarialDefense
from
mindarmour.utils.logger
import
LogUtil
from
mindarmour.utils.logger
import
LogUtil
from
lenet5_net
import
LeNet5
sys
.
path
.
append
(
".."
)
sys
.
path
.
append
(
".."
)
from
data_processing
import
generate_mnist_dataset
from
data_processing
import
generate_mnist_dataset
LOGGER
=
LogUtil
.
get_instance
()
LOGGER
=
LogUtil
.
get_instance
()
LOGGER
.
set_level
(
"INFO"
)
TAG
=
'Nad_Example'
TAG
=
'Nad_Example'
def
test_nad_method
():
def
test_nad_method
():
"""
"""
NAD-Defense test
for CPU device
.
NAD-Defense test.
"""
"""
# 1. load trained network
mnist_path
=
"./MNIST_unzip/"
ckpt_name
=
'./trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
batch_size
=
32
# 1. train original model
ds_train
=
generate_mnist_dataset
(
os
.
path
.
join
(
mnist_path
,
"train"
),
batch_size
=
batch_size
,
repeat_size
=
1
)
net
=
LeNet5
()
net
=
LeNet5
()
load_dict
=
load_checkpoint
(
ckpt_name
)
load_param_into_net
(
net
,
load_dict
)
loss
=
SoftmaxCrossEntropyWithLogits
(
is_grad
=
False
,
sparse
=
True
)
loss
=
SoftmaxCrossEntropyWithLogits
(
is_grad
=
False
,
sparse
=
True
)
opt
=
nn
.
Momentum
(
net
.
trainable_params
(),
0.01
,
0.09
)
opt
=
nn
.
Momentum
(
net
.
trainable_params
(),
0.01
,
0.09
)
model
=
Model
(
net
,
loss
,
opt
,
metrics
=
None
)
nad
=
NaturalAdversarialDefense
(
net
,
loss_fn
=
loss
,
optimizer
=
opt
,
model
.
train
(
10
,
ds_train
,
callbacks
=
[
LossMonitor
()]
,
bounds
=
(
0.0
,
1.0
),
eps
=
0.3
)
dataset_sink_mode
=
False
)
# 2. get test data
# 2. get test data
data_list
=
"./MNIST_unzip/test"
ds_test
=
generate_mnist_dataset
(
os
.
path
.
join
(
mnist_path
,
"test"
),
batch_size
=
32
batch_size
=
batch_size
,
repeat_size
=
1
)
ds_test
=
generate_mnist_dataset
(
data_list
,
batch_size
=
batch_size
)
inputs
=
[]
inputs
=
[]
labels
=
[]
labels
=
[]
for
data
in
ds_test
.
create_tuple_iterator
():
for
data
in
ds_test
.
create_tuple_iterator
():
...
@@ -73,16 +76,15 @@ def test_nad_method():
...
@@ -73,16 +76,15 @@ def test_nad_method():
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
LOGGER
.
debug
(
TAG
,
'accuracy of TEST data on original model is : %s'
,
LOGGER
.
info
(
TAG
,
'accuracy of TEST data on original model is : %s'
,
np
.
mean
(
acc_list
))
np
.
mean
(
acc_list
))
# 4. get adv of test data
# 4. get adv of test data
attack
=
FastGradientSignMethod
(
net
,
eps
=
0.3
,
loss_fn
=
loss
)
attack
=
FastGradientSignMethod
(
net
,
eps
=
0.3
,
loss_fn
=
loss
)
adv_data
=
attack
.
batch_generate
(
inputs
,
labels
)
adv_data
=
attack
.
batch_generate
(
inputs
,
labels
)
LOGGER
.
debug
(
TAG
,
'adv_data.shape is : %s'
,
adv_data
.
shape
)
LOGGER
.
info
(
TAG
,
'adv_data.shape is : %s'
,
adv_data
.
shape
)
# 5. get accuracy of adv data on original model
# 5. get accuracy of adv data on original model
net
.
set_train
(
False
)
acc_list
=
[]
acc_list
=
[]
batchs
=
adv_data
.
shape
[
0
]
//
batch_size
batchs
=
adv_data
.
shape
[
0
]
//
batch_size
for
i
in
range
(
batchs
):
for
i
in
range
(
batchs
):
...
@@ -92,11 +94,13 @@ def test_nad_method():
...
@@ -92,11 +94,13 @@ def test_nad_method():
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
LOGGER
.
debug
(
TAG
,
'accuracy of adv data on original model is : %s'
,
LOGGER
.
info
(
TAG
,
'accuracy of adv data on original model is : %s'
,
np
.
mean
(
acc_list
))
np
.
mean
(
acc_list
))
# 6. defense
# 6. defense
net
.
set_train
()
net
.
set_train
()
nad
=
NaturalAdversarialDefense
(
net
,
loss_fn
=
loss
,
optimizer
=
opt
,
bounds
=
(
0.0
,
1.0
),
eps
=
0.3
)
nad
.
batch_defense
(
inputs
,
labels
,
batch_size
=
32
,
epochs
=
10
)
nad
.
batch_defense
(
inputs
,
labels
,
batch_size
=
32
,
epochs
=
10
)
# 7. get accuracy of test data on defensed model
# 7. get accuracy of test data on defensed model
...
@@ -110,8 +114,8 @@ def test_nad_method():
...
@@ -110,8 +114,8 @@ def test_nad_method():
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
LOGGER
.
debug
(
TAG
,
'accuracy of TEST data on defensed model is : %s'
,
LOGGER
.
info
(
TAG
,
'accuracy of TEST data on defensed model is : %s'
,
np
.
mean
(
acc_list
))
np
.
mean
(
acc_list
))
# 8. get accuracy of adv data on defensed model
# 8. get accuracy of adv data on defensed model
acc_list
=
[]
acc_list
=
[]
...
@@ -123,11 +127,11 @@ def test_nad_method():
...
@@ -123,11 +127,11 @@ def test_nad_method():
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
label_pred
=
np
.
argmax
(
logits
,
axis
=
1
)
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
acc_list
.
append
(
np
.
mean
(
batch_labels
==
label_pred
))
LOGGER
.
debug
(
TAG
,
'accuracy of adv data on defensed model is : %s'
,
LOGGER
.
info
(
TAG
,
'accuracy of adv data on defensed model is : %s'
,
np
.
mean
(
acc_list
))
np
.
mean
(
acc_list
))
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
# device_target can be "CPU", "GPU" or "Ascend"
# device_target can be "CPU", "GPU" or "Ascend"
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
"
CPU
"
)
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
"
Ascend
"
)
test_nad_method
()
test_nad_method
()
mindarmour/defenses/adversarial_defense.py
浏览文件 @
36c25d9f
...
@@ -136,6 +136,7 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
...
@@ -136,6 +136,7 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
self
.
_replace_ratio
=
check_param_in_range
(
'replace_ratio'
,
self
.
_replace_ratio
=
check_param_in_range
(
'replace_ratio'
,
replace_ratio
,
replace_ratio
,
0
,
1
)
0
,
1
)
self
.
_graph_initialized
=
False
def
defense
(
self
,
inputs
,
labels
):
def
defense
(
self
,
inputs
,
labels
):
"""
"""
...
@@ -150,6 +151,9 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
...
@@ -150,6 +151,9 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
"""
"""
inputs
,
labels
=
check_pair_numpy_param
(
'inputs'
,
inputs
,
'labels'
,
inputs
,
labels
=
check_pair_numpy_param
(
'inputs'
,
inputs
,
'labels'
,
labels
)
labels
)
if
not
self
.
_graph_initialized
:
self
.
_train_net
(
Tensor
(
inputs
),
Tensor
(
labels
))
self
.
_graph_initialized
=
True
x_len
=
inputs
.
shape
[
0
]
x_len
=
inputs
.
shape
[
0
]
n_adv
=
int
(
np
.
ceil
(
self
.
_replace_ratio
*
x_len
))
n_adv
=
int
(
np
.
ceil
(
self
.
_replace_ratio
*
x_len
))
...
...
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