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modify description of fuzzing parameter mutate_config

tags/v1.8.0
ZhidanLiu 3 years ago
parent
eb0881d3d4
commit
f8fd121454
1 changed files with 39 additions and 43 deletions
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  1. +39
    -43
      mindarmour/fuzz_testing/fuzzing.py

+ 39
- 43
mindarmour/fuzz_testing/fuzzing.py View File

@@ -105,49 +105,44 @@ class Fuzzer:
target_model (Model): Target fuzz model.

Examples:
>>> import numpy as np
>>> from mindspore import context
>>> from mindspore import nn
>>> from mindspore.common.initializer import TruncatedNormal
>>> from mindspore.ops import operations as P
>>> from mindspore.train import Model
>>> from mindspore.ops import TensorSummary
>>> from mindarmour.fuzz_testing import Fuzzer
>>> from mindarmour.fuzz_testing import KMultisectionNeuronCoverage
>>>
>>> class Net(nn.Cell):
>>> def __init__(self):
>>> super(Net, self).__init__()
>>> self.conv1 = nn.Conv2d(1, 6, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
>>> self.conv2 = nn.Conv2d(6, 16, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
>>> self.fc1 = nn.Dense(16 * 5 * 5, 120, TruncatedNormal(0.02), TruncatedNormal(0.02))
>>> self.fc2 = nn.Dense(120, 84, TruncatedNormal(0.02), TruncatedNormal(0.02))
>>> self.fc3 = nn.Dense(84, 10, TruncatedNormal(0.02), TruncatedNormal(0.02))
>>> self.relu = nn.ReLU()
>>> self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
>>> self.reshape = P.Reshape()
>>> self.summary = TensorSummary()
>>>
>>> def construct(self, x):
>>> x = self.conv1(x)
>>> x = self.relu(x)
>>> self.summary('conv1', x)
>>> x = self.max_pool2d(x)
>>> x = self.conv2(x)
>>> x = self.relu(x)
>>> self.summary('conv2', x)
>>> x = self.max_pool2d(x)
>>> x = self.reshape(x, (-1, 16 * 5 * 5))
>>> x = self.fc1(x)
>>> x = self.relu(x)
>>> self.summary('fc1', x)
>>> x = self.fc2(x)
>>> x = self.relu(x)
>>> self.summary('fc2', x)
>>> x = self.fc3(x)
>>> self.summary('fc3', x)
>>> return x
>>>
... def __init__(self):
... super(Net, self).__init__()
... self.conv1 = nn.Conv2d(1, 6, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
... self.conv2 = nn.Conv2d(6, 16, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
... self.fc1 = nn.Dense(16 * 5 * 5, 120, TruncatedNormal(0.02), TruncatedNormal(0.02))
... self.fc2 = nn.Dense(120, 84, TruncatedNormal(0.02), TruncatedNormal(0.02))
... self.fc3 = nn.Dense(84, 10, TruncatedNormal(0.02), TruncatedNormal(0.02))
... self.relu = nn.ReLU()
... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
... self.reshape = P.Reshape()
... self.summary = TensorSummary()
...
... def construct(self, x):
... x = self.conv1(x)
... x = self.relu(x)
... self.summary('conv1', x)
... x = self.max_pool2d(x)
... x = self.conv2(x)
... x = self.relu(x)
... self.summary('conv2', x)
... x = self.max_pool2d(x)
... x = self.reshape(x, (-1, 16 * 5 * 5))
... x = self.fc1(x)
... x = self.relu(x)
... self.summary('fc1', x)
... x = self.fc2(x)
... x = self.relu(x)
... self.summary('fc2', x)
... x = self.fc3(x)
... self.summary('fc3', x)
... return x
>>> net = Net()
>>> model = Model(net)
>>> mutate_config = [{'method': 'GaussianBlur',
@@ -175,7 +170,6 @@ class Fuzzer:
>>> # make initial seeds
>>> for img, label in zip(test_images, test_labels):
>>> initial_seeds.append([img, label])

>>> initial_seeds = initial_seeds[:10]
>>> nc = KMultisectionNeuronCoverage(model, train_images, segmented_num=100, incremental=True)
>>> model_fuzz_test = Fuzzer(model)
@@ -243,15 +237,17 @@ class Fuzzer:
'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}]
...].
The supported methods list is in `self._strategies`, and the params of each method must within the
range of optional parameters. Supported methods are grouped in three types: Firstly, pixel value based
transform methods include: 'Contrast', 'Brightness', 'Blur' and 'Noise'. Secondly, affine transform
methods include: 'Translate', 'Scale', 'Shear' and 'Rotate'. Thirdly, attack methods include: 'FGSM',
range of optional parameters. Supported methods are grouped in two types: Firstly, natural robustness
methods include: 'Translate', 'Scale', 'Shear', 'Rotate', 'Perspective', 'Curve', 'GaussianBlur',
'MotionBlur', 'GradientBlur', 'Contrast', 'GradientLuminance', 'UniformNoise', 'GaussianNoise',
'SaltAndPepperNoise', 'NaturalNoise'. Secondly, attack methods include: 'FGSM',
'PGD' and 'MDIIM'. 'FGSM', 'PGD' and 'MDIIM'. are abbreviations of FastGradientSignMethod,
ProjectedGradientDescent and MomentumDiverseInputIterativeMethod.
`mutate_config` must have method in the type of pixel value based transform methods.
`mutate_config` must have method in ['Contrast', 'GradientLuminance', 'GaussianBlur', 'MotionBlur',
'GradientBlur', 'UniformNoise', 'GaussianNoise', 'SaltAndPepperNoise', 'NaturalNoise'].
The way of setting parameters for first and second type methods can be seen in
'mindarmour/fuzz_testing/image_transform.py'. For third type methods, the optional parameters refer to
`self._attack_param_checklists`.
'mindarmour/natural_robustness/transform/image'. For third type methods, the optional parameters refer
to `self._attack_param_checklists`.
initial_seeds (list[list]): Initial seeds used to generate mutated samples. The format of initial seeds is
[[image_data, label], [...], ...] and the label must be one-hot.
coverage (CoverageMetrics): Class of neuron coverage metrics.


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